RELATIONSHIP BETWEEN DEMOGRAPHIC, PSYCHOLOGICAL,
TECHNICAL, SOCIAL DOMAIN FACTORS AND SUCCESS OF
GRASSROOTS LEVEL INVENTORS IN SRI LANKA
By
CHAMINDA NALAKA WICKRAMASINGHE
Thesis Submitted to the School of Graduate Studies, Universiti Putra
Malaysia, in Fulfilment of the Requirements for the Degree of
Doctor of Philosophy
April 2012
DEDICATION
This thesis is dedicated to
My
Late father, Ananda Wickramasinghe
Mother, Else Wickramasinghe
Wife, Devika Kodithuwakku
Daughter, Navithma
and
All the Sri Lankan inventors those who are trying to be different from the odds
ii
Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment
of the requirement for the degree of Doctor of Philosophy
RELATIONSHIP BETWEEN DEMOGRAPHIC, PSYCHOLOGICAL,
TECHNICAL, SOCIAL DOMAIN FACTORS AND SUCCESS OF
GRASSROOTS LEVEL INVENTORS IN SRI LANKA
By
CHAMINDA NALAKA WICKRAMASINGHE
April 2012
Chair: Nobaya Ahmad, PhD
Faculty: Human Ecology
Owing to the poorer explicit successful performances, the grassroots level inventors
in developing countries were not given needed attention and acceptable level
recognition. Therefore, the question of why these inventors continuously involved in
inventive activities where surroundings are becoming hostile to independent
inventing remained unanswered in the literature.
The study has four main and two sub-objectives. First, the study explains the
comprehensive nature of the grassroots level inventive community of Sri Lanka.
Secondly, it explains what their objective and subjective success levels are. Thirdly,
it explores the factors influence on their objective and subjective level success and
finally, it explores how the happiness and satisfaction of life influence on their
inventive lives. The study was designed as an exploratory correlational research. Out
of 640 patent applied grassroots level inventors in Sri Lanka between the year 2000
and 2008, 200 were randomly selected as the sample of the study. The sample
iii
represented 31% of the target population and it has provided the acceptable statistical
power 0.80 at 0.05 confidence level.
According to the results, average grassroots level inventor in Sri Lanka is a middleaged married male who involves in radical product inventions as part-time inventor.
When comparing the demographic, psychological and technical profiles, the Sri
Lankan grassroots level inventors are having similar characteristics as the inventors
in industrial countries. However, social capital and external linkages were relatively
weaker among the inventors in Sri Lanka. Further , the grassroots level inventors of
Sri Lanka have shown fairly higher success level in front-end inventive activities, but
they have achieved only moderate and lower success in back-end inventive activities
related to commercialization and profit earnings. However, the majority of grassroots
level inventors have achieved moderate and high-level subjective happiness and
moderate level satisfaction with life. Further, the objective success and subjective
success significantly contribute to each other.
According to the bottom-up path model, income, engagement in inventive activities
and external linkages has positive influence on the objective success of grassroots
level inventors. On the other hand, grassroots level inventors’ marital status, internet
usage, life orientation and social capital have significant positive influence on the
subjective success of inventors. More importantly, selected inventive life inputs:
income, engagement in inventive activities and external linkages as well as the
outcomes: objective success, inventive career satisfaction and community
connectedness have significant indirect and direct positive influences on subjective
success respectively.
iv
According to the top-down path model, subjective success of the grassroots level
inventors has significant positive effects on every aspects of the inventive lives along
with other psychological and social life domains of grassroots level inventors. The
happiness and satisfaction of the life have been the central powerful drive that makes
the grassroots level inventors continually involve in inventive activities and expect to
achieve higher objective success in the future. The study highlights the importance of
the use of subjective success as a measurement of the success of the grassroots level
inventors than the use of pure objective measurements. The study also suggests some
recommendations for the future studies.
v
Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai
memenuhi keperluan untuk ijazah Doktor Falsafah
PERKAITAN FAKTOR DOMAIN DEMOGRAFI, PSIKOLOGI, TEKNIKAL
DAN SOSIAL DENGAN KEJAYAAN PENCIPTA PERINGKAT AKAR
UMBI DI SRI LANKA
Oleh
CHAMINDA NALAKA WICKRAMASINGHE
April 2012
Pengerusi
: Nobaya Ahmad, PhD
Fakulti
: Ekologi Manusia
Berdasarkan kepada pencapaian kejayaan yang kurang berkesan secara nyata,
pencipta peringkat akar umbi di Negara membangun tidak diberikan perhatian dan
pengiktirafan yang sewajarnya. Oleh itu, persoalan mengapa pencipta ini berterusan
melibatkan diri dalam aktiviti rekacipta sedangkan persekitaran sedia ada tidak
membantu dalam usaha untuk mereka merekacipta belum terjawab dalam manamana penulisan.
Kajian ini mepunyai enam objektif khusus.Pertama, kajian ini menjelaskan secara
menyeluruh latarbelakang pencipta peringkat akar umbi di Sri Lanka. Kedua, ia
menerangkan tahap kejayaan objektif dan subjektif pencipta. Ketiga, menkaji faktor
yang mempengaruhi kejayaan objektif dan subjektif dan yang terkahir ia mengkaji
bagaimana kebahagiaan dan kepuasan hidup mempengaruhi kehidupan untuk
merekacipta. Kajian ini mengambil pendekatan perkaitan eksploratori. Dari 640
pemohon paten dari kalangan perekacipta peringkat akar umbi di Sri Lanka antara
vi
tahun 2000 hingga 2008, 200 terlah dipilih secara rawak sebagai sampel kajian.
Sampel ini mewakili 31% dari populasi dan memberi kekuatan kuasa statistik pada
0.80 di tahap keyakinan 0.05.
Berdasarkan dapatan kajian, secara purata pencipta peringkat akar umbi di Sri Lanka
adalah pada usia pertengahan dan berkahwin yang terlibat sebagai pencipta produk
radikal sebagai pencipta separuh masa. Apabila dibandingkan profil demografi,
psikologi dan teknikal, mereke mempunyai ciri yang hampir sama dengan pencipta
di negera maju. Kajian juga menunjukkan bahawa pencipta peringkat akar umbi di
Sri Lanka mempunyai tahap kejayaan aktiviti rekacipta permulaan (front-end) yang
tinggi, tetapi, menunjukkan tahap yang sederhana dan rendah bagi aktiviti hiliran
(back-end) yang dikaitkan dengan pengkomersilan dan keuntungan dari hasil produk.
Namun begitu, majoriti pencipta peringkat akar umbi mencapai tahap kejayaan
subjektif yang sederhana tinggi dan tahap kepuasan hidup pada peringkat sederhana.
Selanjutnya, berdasarkan kajian, kejayaan subjektif dan objektif menyumbang secara
signifikan ke atas satu sama lain.
Berdasarkan model ‘bottom up path’, pendapatan, penglibatan dalam aktiviti
merekacipta, dan hubungan luaran mempunyai pengaruh positif ke atas kejayaan
objektif pencipta peringkat akar umbi. Manakala tahap perkahwinan, penggunaan
internet, orientasi hidup dan modal sosial mempunyai pengaruh positif ke atas
kejayaan subjektif. Yang lebih penting adalah beberapa input kehidupan merekacipta
terpilih seperti pendapatan, penglibatan dalam aktiviti merekacipta dan hubungan
luar serta hasil seperti kejayaan objektif, kepuasan kerjaya merekacipta dan
hubungan dengan komuniti mempunyai pengaruh positif secara langsung dan tidak
langsung dengan kejyaan subjektif.
vii
Beradasrkan model ‘top-down path’ pula, kejayaan subjektif pencipta peringkat akar
umbi pula mempunyai kesan positif yang signifikan dalam segenap aspek kehidupan
merekacipta termasuk domain lain seperti psikologi dan sosial. Kebahagiaan dan
kepuasan kehidupan menjadi kuasa pendorong utama pencipta peringkat akar umbi
terlibat dalam aktiviti merekacipta dan berharap mendapat keyaan objektif yang lebih
baik di masa akan datang. Kajian ini menekankan kepentingan penggunaan kejayaan
subjektif sebagai pengukur kejayaan pencipta peringkat akar umbi dari hanya
mengukur kejayaan berdasarkan ukuran objektif semata-mata. Kajian ini juga
mencadangkan beberapa kajian lanjutan di masa akan datang.
viii
ACKNOWLEDGEMENTS
Praise and thanks be first to my parents, school teachers, lecturers who taught me at
undergraduate and post graduate level without whom this thesis would have not
come to a reality.
I am grateful to my supervisor, Professor Dr. Nobaya Ahmad, Faculty of Human
Ecology, for her invaluable guidance, supervision, advice encouragement and
constructive criticisms throughout the study. She devoted much of her time to guide
me during my work with patience giving enormous encouragement. I am very
thankful to Professor Dr. Sharifah Norazizan bte Syed Abd Rashid, a member of my
supervisory committee, for her valuable discussions and help. I am also thankful to
Dr. Zahid Emby, member of supervisory committee, for his guidance, supervision,
encouragement, and support in all steps of this study. I like to extend my thanks to all
staff and colleagues at the Faculty of Human Ecology, Universiti Putra Malaysia,
Malaysia.
I also want to thank all the staff members of the Sri Lanka National Intellectual
Property Office for kind support given me to collect contact details of the inventors.
Further, I want to extend my gratitude to the grassroots level inventors for their
fullest commitment and support given on this study. Without their commitment, this
study would not come to a reality.
I would like to be grateful to the Commonwealth Scholarship and Fellowship Plan
(CSFP), Secretary General and all the staff members of the Scholarship division of
the Ministry of Higher Education Malaysia for giving me all the financial support to
carryout Ph.D. studies in Malaysia.
ix
I like to thank Dr. Shantha Abeysinghe, Head, Department of Social Science, Open
University of Sri Lanka, for his invaluable assistance throughout this study. Further, I
extend my gratitude to all the course coordinators and staff members of the
Kurunagala, Galle and Kandy branches of Open University of Sri Lanka for
providing me all the required facilities to conduct data collection panel interviews in
those branches.
I am also grateful to Mrs. Sunethrani Amarathunga, Head, Department of Commerce
and Financial Management, University of Kelaniya, Sri Lanka for giving me her
fullest support to complete my study. I thankful to all the academic, non-academic
staff members and students of the Department of Commerce and Financial
Management, for their fullest support given me during the data collection in Sri
Lanka.
Further, I like to dedicate me gratitude to Dammika Pieris, Ph.D. student at Monash
University, Malaysia, Samanthi, Mohan, Priyanthi, Somachandra, Piraha, Karu and
Geethani, Ph.D. and Master students in University Putra Malaysia for being together
and helping me throughout my studies. I also like to thank my sister, Thanuja,
brother, Udayanga and Danushka for their support and encouragements given me to
make my study a success.
Finally yet importantly, I am grateful to my wife, Devika, my daughter, Navithma for
their invaluable support, sacrifices, patience, and love given to me during my studies
in Malaysia as in all stages of my life.
x
I certified that a Thesis Examination Committee has met on 30th April 2012 to
conduct the final examination of Chaminda Nalaka Wickramasinghe on his thesis
entitled “Relationship between demographic, psychological, technical, social
domain factors and success of grassroots level inventors in Sri Lanka” in
accordance with the Universities and University Colleges Act 1971 and the
constitution of the Unversiti Putra Malaysia [P.U. (A) 106] 15 March 1998. The
Committee recommends that the student be awarded the Doctor of Philosophy
Members of the Thesis Examination Committee were as follows:
Ahmad Tarmizi Talib, PhD
Head of the Department of Governement and Civilization Studies
Faculty of Human Ecology
University Putra Malaysia
(Chairman)
Bahaman Abu Samah, PhD
Professor
Institute for Social Science Studies
Universiti Putra Malaysia
(Internal Examiner)
Mohammad Shatar Sabran, PhD
Professor
Faculty of Human Ecology
Univeriti Putra Malaysia
(Internal Examiner)
Awang Hasmadi Awang Mois, PhD
Professor
Department of Sociology-Antropology
Faculty of Arts and Social Science
University of Brunei Darussalam
(External Examiner)
APROVAL
________________________
SEOW HENG FONG, PhD
Professor and Deputy Dean
School of Graduate Studies
Universiti Putra Malaysia
Date:
xi
This thesis was submitted to the Senate of Universiti Putra Malaysia and has been
accepted as fulfillment of the requirement fro the degree of Doctor of Philosophy.
The members of the Supervisory Committee were as follows:
Nobaya Ahmad, PhD
Professor
Faculty of Human Ecology
University Putra Malaysia
(Chairman)
Sharifah Norazizan Syed Abdul Rashid, PhD
Professor
Faculty of Human Ecology
Universiti Putra Malaysia
(Member)
Zahid Emby, Ph.D
Senior Lecturer
Faculty of Human Ecology
Univeriti Putra Malaysia
(Member)
________________________________
BUJANG BIN KIM HUAT, Ph.D
Professor and Dean
School of Graduate Studies
Universiti Putra Malaysia
Date:
xii
DECLARATION
I declare that the thesis is my original work except for quotations and citations,
which have been duly acknowledged. I also declare that it has not been previously,
and is not concurrently, submitted for any other degree at Universiti Putra Malaysia
or at any other institution.
…………………………………………………………
Chaminda Nalaka Wickramasinghe
Date : 19 May 2012
xiii
TABLE OF CONTENTS
Page
iii
vi
ix
xi
xiii
xviii
xx
xxii
xxiii
ABSTRACT
ABSTRAK
ACKNOWLEDGEMENTS
APROVAL
DECLARATION
LIST OF TABLES
LIST OF FIGURES
LIST OF APPENDICES
LIST OF ABREVIATIONS
CHAPTER
1. INTRODUCTION
Background of the Study
Context of Grass-roots Level Inventive Community
Grassroots Level Inventions in Sri Lanka
Moving From Objective to Subjective Success
In search of Subjective Phase for Community Development of Inventors
Statement of Problem
Research Questions
Research Objectives
Significance of the Study
Scope and Limitations of the Study
Focus
Data collection
Coverage
Research design
Definition of Key Terms
Summary
2. LITERATURE REVIEW
Introduction
Background of the Grassroots Level Inventive Community
Who are the grassroots level inventors?
Past studies on grassroots level inventors in the world
Significance of grassroots level inventors in developing countries
Grassroots level Inventors and Community Development
Grassroots level inventors as a community of interest
Thinking beyond the need and assets based community development
Objective Perspective of the Success of Inventors
Evolution of the scope of inventors’ objective success measurements
Hauschildt’s classification of innovation success measurements
Approaches of Subjective Perspectives of the Success
xiv
1
2
3
5
8
10
12
15
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16
19
19
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20
20
21
23
24
24
24
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27
29
31
32
35
37
37
40
42
What is the meaning of ‘success’ for the great inventors?
Aristotle’s philosophical perspective of success
Positive psychological perspective of ‘subjective success’
Intrinsic motives vs. subjective success
Measures of Subjective Success: Life Satisfaction and Happiness
Theoretical Framework of the Study
Correlates of Subjective Success
Demographic factors
Technical factors
Psychological factors
Social and community factors
Correlates of Objective Success
Demographic factors
Technical factors
Psychological Factors
Social factors
Conceptual Framework of the Study
Alternative Top-Down Model: Consequences of Subjective Success
Summary
3. METHODOLOGY
Introduction
Research Design
Operationalization and Measurements of Variables
Profiling variables
Exogenous variables in the conceptual model
Endogenous variables in the conceptual model
Pilot Studies
Validity and Reliability Evidences
Validity evidences
Reliability evidences
Sampling Design
Target population and sampling frame of the study
Power analysis and sample size determination
Sampling method
Sample selection process
Data Collection Process of Study
Secondary data and expert advices
Preliminary data collection
Cross sectional survey data collection
Statistical Analysis Design
Statistical methods and tools
Exploratory data analysis (EDA) on statistical assumptions
Summary
xv
42
45
47
49
51
54
59
59
62
64
67
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72
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137
144
147
4. RESULTS
148
Introduction
148
Exploratory Data Analysis (EDA)
148
Demographic, Psychological, Technical and Social (D.P.T.S.) Profiles of Sri
Lankan Grassroots Level Inventors
155
Demographic profile of the grassroots level inventors
156
Psychological factor profile of the grassroots level inventors
164
Technical profile of the grassroots level inventors
166
Social factor profile of the grassroots level inventors
170
Objective and Subjective Success of Sri Lankan Grassroots Level Inventors 172
Objective success of Sri Lankan grassroots level inventors
173
Subjective success of Sri Lankan grassroots level inventors
180
Association between level of objective and subjective success
183
Influences of DPTS Factors on Success of Grassroots Level Inventors in Sri Lanka 184
Categorical profiling variables and level of success
Bottom-up conceptual path model of the study
Correlation analysis of variables in path model of the study
Path Analysis of the Bottom-Up Model of the Study
Model specification
Model identification and estimation
Model testing
Model modification
Model fit
Test for mediation
Path Analysis of the Top-Down Model of the Study
Model identification
Model testing
Comparison of casual directions
Model modification
Model fit
Test for mediation
Comparison of Bottom-up and Top-down Models
Summary
5. DISCUSSION
Who are the Grassroots Level Inventors?
Demographic profile of grassroots level inventors
Psychological profile of grassroots level inventors
Technical profile of grassroots level inventors
Social profiles of grassroots level inventors
Objective Success of Grassroots Level Inventors in Sri Lanka
Subjective Success of Grassroots Level Inventors in Sri Lanka
Relationship between Objective and Subjective Success
Factors Influencing the Objective Success of Grassroots Level Inventors
xvi
185
214
215
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250
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258
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265
269
Factors Influencing the Subjective Success of Grassroots Level Inventors
Impact of Objective Success on D. P. T.S Factors of Grassroots Level Inventors
Impact of Subjective Success on D. P.T.S Factors of Grassroots Level Inventors
Summary
6. SUMMARY, GENERAL CONCLUSION AND
RECOMMENDATIONS FOR FUTURE RESEARCH
Introduction
Summary of the Study
Summary of Findings of the Study
General Conclusions and Recommendations
Implications of the Study
Implications for the body of knowledge
Implication for the policy development
Implication to the practice
Suggestions for the Future Research
REFERENCES
APPENDICES
BIO DATA OF THE STUDENT
LIST OF PUBLICATIONS
276
280
285
287
289
289
289
291
299
306
307
310
315
318
320
348
421
422
xvii
LIST OF TABLES
Table
Page
1: Resident Patent Applications in Sri Lanka -2000-2008
7
2: PCT patent applications by type of applicants in selected developing countries 30
3: Cronbach’s Alpha statistics of scales: pilot and the real study
119
4: PASS 2008 output of required Sample size (N) at small to medium effect sizes 127
5: Grassroots level inventors’ Sample selection process
129
6: Response rates and distribution of sample
134
7: Summary of statistical method and tools of the study
144
8: Univaraite Normality Test Results after data transformation
151
9: Testing for linear relationships between endogenous and exogenous variables 152
10: Multivariate Normality Test Results of the variables in the model
153
11: Multicollinearity test of exogenous variables of conceptual model
154
12: Age profile of Grassroots level inventors
156
13: Location of the respondent grassroots level inventors in Sri Lanka
158
14: Living Districts of respondents by population density
159
15: Respondent by Highest Educational Qualifications
161
16: Respondent Grassroots level inventors by Employee Status
162
17: Respondent Grassroots level inventors by Employed Sector
163
18: Respondent Grassroots level inventors by Job Mobility
163
19: Respondent Grassroots level inventors by Income Level
164
20: Respondent Grassroots level inventors by ICS
165
21: Respondent Grassroots level inventors by Maximizing Tendency
165
22: Respondent Grassroots level inventors by Life Orientation
166
23: Respondent Grassroots level inventors by type of inventions
166
24: Respondent Grassroots level inventors by Field of inventions
167
25: Respondent Grassroots level inventors by Inventive life Span
168
26: Respondent Grassroots level inventors by daily inventive hours
168
27: Respondent Grassroots level inventors by No. of working prototypes
169
28: Respondent Grassroots level inventors by commercialization method
169
29: Respondent Grassroots level inventors by internet usage
170
30: Respondent Grassroots level inventors by External Linkages
170
31: Respondent Grassroots level inventors by Social Capital
171
32: Respondent Grassroots level inventors by Community Connectedness
172
33: Level of Objective success of the respondent inventors
173
34: Respondent inventors by patent grants
175
35: Respondents by number of Awards winning inventions
175
36: Respondents by number of launched inventions
176
37: Respondents by number of inventions still in the market
177
38: Respondents by number profitable inventions
178
39: Subjective Success levels of Respondent Grassroots level inventors
180
xviii
40: Subjective Happiness levels of Respondent Grassroots level inventors
41: Satisfaction with life levels of Respondent Grassroots level inventors
42: Cross tabulation between level of objective and subjective success
43: Level of Objective success by respondents’ age categories
44: Level of Objective success by respondents’ location
45: Level of Objective success by Education Level
46: Level of Objective success by Employment Level
47: Level of Objective Success by Job Mobility
48: Level of Objective success by Type of inventor
49: Level of Objective success by Field of inventions
50: Level of Objective success by Commercialization effort
51: Level of Objective success by Inventive Life Span
52: Level of Subjective Success by Age
53: Level of Subjective Success by Location
54: Level of Subjective Success by Education Level
55: Level of Subjective Success by Employment Level
56: Level of Subjective Success by Job Mobility
57: Level of Subjective Success by Type of inventors
58: Level of Subjective Success by Field of Invention
59: Level of Subjective Success by Commercialization Effort
60: Level of Subjective Success by Inventive Life Span
61: Pearson Product Movement Correlation of variables in conceptual model
62: Model Fit indices, Cutoff criteria and Modified bottom up model values
63: Bootstrapping results of the mediation effects-Bottom-up Model
64: Boostrap significance of full, partial, no mediation and indirect effects
65: Model fit indices of initial top-down path model
66: Comparison of paths in bottom-up and top-down models
67: Model fit indices of modified top-down path model
68: Bootstrapping results of the indirect effects-Top-down model
69: Boostrapping significance for full, partial, no mediation and indirect effects
70: Bottom-up and top-Down Model Comparison
71: Demographic profile and objective success measures of informants
72: The feel of happiness and reasoning of the informants.
73: The feel of satisfaction and reasoning of Independent inventors
74: Demographic profile of the respondents of pilot test
75: Testing for Missing Values
76: Descriptive Statistics of Variables
xix
181
182
183
187
188
190
190
192
193
195
197
198
201
202
203
204
205
207
209
210
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216
226
226
227
231
232
236
239
239
242
350
351
352
355
373
373
LIST OF FIGURES
Figure
page
1: Relationship between Innovation attributes and success measures
41
2: Stages and issues of measurement of success of innovation process
42
3: Focus and Scope of subjective well-being global measures
52
4 : Broaden-and-Build Model of Positive Emotions
56
5: Bottom up and top down theories of Subjective success
58
6: Bottom-Up conceptual model of the present study
79
7: Alternative reversal top-down conceptual model
82
8: Distribution of Grassroots Level Inventors across Districts
128
9: Map of Grassroots inventors Population and Sample distribution
132
10: Gender composition of the respondents Grassroots Level Inventors
157
11: Marital status among the respondent Grassroots Level Inventors
158
12: Geographical spatial pattern of distribution of GLI in Sri Lanka
160
13: Respondent grassroots level inventors by objective success levels
174
14: Respondent Inventors’ success rates at innovation process stages
179
15: Respondents’ subjective happiness, satisfaction and success levels
182
16: Mean differences of objective success by age group
186
17: Mean differences of objective success by Location
188
18: Mean differences of objective success by Education Level
189
19: Mean differences of objective success by Level of Employment
191
20: Mean differences of objective success by Level of Job Mobility
192
21: Mean differences of objective success by type of inventor
194
22: Mean differences of objective success by Field of inventions
196
23: Mean differences of Objective Success by Commercialization Effort
197
24: Mean differences of Objective Success by Inventive Life Span
199
25: Mean differences of Subjective Success by Age Range
200
26: Mean differences of Subjective Success by Location
202
27: Mean differences of Subjective Success by Level of Education
204
28: Mean differences of Subjective Success by Employment Status
205
29: Mean differences of Subjective Success by Job Mobility
206
30: Mean differences of Subjective Success by Invention Type
208
31: Mean differences of Subjective Success by Field of Inventions
208
xx
32: Mean differences of Subjective Success by Commercialization Effort
211
33: Mean differences of Subjective Success by Inventive Life Span
212
34: The Operationalized Conceptual Path Model
214
35: Standardized Estimates of initial Bottom-up Path Model
221
36: Standardized Estimates of Modified Bottom-up Path Model
224
37: Standardized Estimates of initial Top-Down Path Model
230
38: Standardized Estimates of Modified Top-Down Path Model
237
39 : Exploratory Analysis Plots of Age
377
40 : Exploratory Analysis Plots of Income
378
41: Exploratory Analysis Plots of Engagement in Invention
379
42: Exploratory Analysis Plots of Internet Usage
380
43: Exploratory Analysis Plots of Social Capital
381
44: Exploratory Analysis Plots of Maximizing Tendency
382
45: Exploratory Analysis Plots of Life Orientation
383
46: Exploratory Analysis Plots of Inventive Life Satisfaction
384
47: Exploratory Analysis Plots of Community Connectedness
385
48: Exploratory Analysis Plots of External Linkages
386
49: Exploratory Analysis Plots of Objective Success
387
50: Exploratory Analysis Plots of Subjective Success
388
51: Scatter plots of exogenous variables Vs. Subjective Success
390
52: Original AMOS 18 Path diagram of Initial Conceptual Model
392
53: Original AMOS 18 Path diagram of Final Modified Conceptual Model
393
54: Original AMOS 18-path diagram of Initial Top-Down Model
401
55: Original AMOS 18 Output of final Modified Top-Down Model
403
xxi
LIST OF APPENDICES
Appendix
Page
A: Data Collection and Results of the Pilot Studies
351
B: Data Collection Instrument
358
C: List of Expert Advisors
361
D: Personal Communication with Advisors
363
E: Power Analysis and Sample size Calculation
370
F: Exploratory Data Analysis
373
G: Path Analysis Equation Model
391
H: AMOS 18 Bottom-up model original result outputs
392
I: AMOS 18 Top-down model original result outputs
401
J: Model Comparison by assuming Partial, Indirect Effect and No mediation effects
410
K: Factor Analysis Results for Convergent and Divergent Evidences
416
xxii
LIST OF ABREVIATIONS
ABCD - Assets Based Community Development
AGFI – Adjusted Goodness-of-fit Index
AIC – Akaike’s Information Criterion
CFI – Comparative Fit Index
DPTS - Demographic, Psychological, Technical and Social
GFI – Goodness-of-fit- Index
GLI- Grassroots Level Inventors
ICT – Information and Communication Technology
IFI – Incremental Index of Fit
LDCs - Least Developed Countries
MLE – Maximum Likelihood Estimates
NFI – Normed Fit Index
RMSEA – Root Mean Square Approximation
ROI- Return on Investment
SEM - Structural Equation Modeling
SLNIPO – Sri Lanka Intellectual Property Office
SPMR – Standardized Root Mean Square
TLI – Tucker- Lewis Index
WIPO - World Intellectual Property Organization
xxiii
CHAPTER 1
INTRODUCTION
‘Most of us believe the independent inventors are dead and buried, but they will
never stop and will continue in inventive activities’
(Schmookler, 1957)
Inventors are the people who initiate the ideas of all products and processes that
make life easier. Modern hallmark inventive community comprises of Ph.D holders
who work in research laboratories, multinational companies and research
universities. Hence, the independent inventors have rapidly become the grassroots
level of the inventive community (Scotchmer, 2004). These grassroots level
inventors involved in inventive activities in their garages with limited resources,
while industrial, cooperate and academic inventors are working in sophisticated
research laboratories with large resource budgets and return on investments. In recent
years, innovation systems and technology development have changed in favor of
these organizational inventors. However, even with the unfriendly environmental
changes, grassroots level inventors are continuously involved in inventive activities
while they are not gaining much material benefits out from their inventions (IFIA,
2006). Especially they are the major players in invention systems in developing
countries (WIPO, 2009(a)). This behavior raises the question; whether these
inventors value their success on some unseen factors, which subjectively drive them
than the material outcomes they gain. Even though the intuitive knowledge suggests
the existence of such relationship, there is hardly any empirical study conducted to
explore the causes and consequences of the subjective aspect of success within the
grassroots level inventive community. The present study aims to fill the said
knowledge gap by exploring the demographic, psychological, technical and social
causes and consequences of objective and subjective success of grassroots level
inventors in Sri Lanka. This chapter will explain the overview, context and
significance of the research problem. It will also explain the intended research
questions and specific objectives of the study. Then it will discuss the significance,
scope and limitation of the study. Finally, it will explain the definitions of the key
terms used in the study.
Background of the Study
Local innovations have been identified as a pathway to bring change to the
developing countries to catch-up with the technological development of the
developed world (World Bank Organization, 1999). Unlike in the industrialized
countries, the independent inventors developed a majority of the technological
inventions in developing countries without having structural support (Weick &
Eakin, 2005; Moussa, 2001). Therefore, the achievement of technological
development in developing countries mainly depends on the grassroots level
inventors. However, the attention given to these inventions and inventors in
developing countries is not acceptable (Gupta A. , 2000; The Lemelson-MIT
Program, 2003). Previous studies on grassroots level inventors have indicated that
local inventors have not achieved higher level of success in patenting and
commercializing their inventions (Astebro T. , 1998). Owing to the lower level of
objective success such as number of patents, patent citations, commercialization and
profit, grassroots level inventors have been marginalized in modern industrial
economy (Lamoreaux & Sokoloff, 2005). Hence, very limited number of published
studies has discussed explicit characteristics of the independent inventors and their
lower objective success in industrial countries. However, in general, the questions of
who are the grassroots level inventors, what is their level of “success” and possible
2
causes and consequences of their continuing inventive behavior in developing
countries have not adequately answered by the existing literature (Mahmood &
Singh, 2003; WIPO, 2009(a)). According to the Lundvall (2008), the most successful
economies in the world are those that engage ordinary people in progresses of
creative thinking, doing and using (Lundvall, 2008). Owing to that, the less
separation of the inventive community from the society is actually an opportunity
rather than a problem for developing countries to be successful in knowledge
economy. Hence, the positivistic understanding of grassroots level inventive
community from inside out becomes an important issue in the developing countries.
Context of Grass-roots Level Inventive Community
In literature, the grassroots level inventors have been identified by different names as
lone inventors, independent inventors, leisure time inventors and garage inventors.
The great inventors like Edison, Tesla and Graham Bell who had dominated the
golden era of innovations in the early twentieth century at least started their careers
as independent inventors in their garage laboratories (Bessen, 2004). According to
past studies in the industrial world, even though grassroots level inventors had
worked alone, they mostly share common characteristics, strengths, interest and
capacities. The independent inventors work on inventions in their leisure time, using
their own resources, with their own imaginations and expectations. In the invention
process, from idea generation to the creation of a profitable product, they have to
make use of their own resources, time and effort for their own set of rewards.
Owing to the drastic takeover of the competitive capitalistic economic model soon
after the World War II, most of the industrial countries have encouraged more
3
organizational innovations than the independent inventions (Schumpeter, 1942;
Schmookler, 1957). The growing number of commercial and organizational
inventions has reduced the demand for independent inventions in the industrial
world. Since then the independent invention has been defined as a commercial
activity that is driven by commercial needs rather than the curiosity of inventors.
Owing to the emergence of knowledge and market driven society, success of the
inventor has been measured based on the external achievements of the invention such
as patent citations, commercial success and profitability (Hauschildt, 1991).
However, these measures have worked against the independent inventors.
Independent Inventors have not achieved the expected objective success that has
been defined in the innovation literature (Astebro, 1998; Invention statistics, 2008;
Amesse & Desranleau, 1991; Weick & Eakin, 2005). It has further discounted the
importance and respect of the independent inventors in modern societies. The
discouraging objective results were expected to reduce the enthusiasm and decrease
the inventive activities of grassroots level inventors. Hence, some experts had
predicted that modern hallmark innovation become out of reach to the garage
inventors (Scotchmer, 2004). However, the grassroots level inventors have not
diminished from the developing world and even in the most industrialized countries
(Macdonald, 1986; Amesse & Desranleau, 1991; Meyer, 2005; Weick & Eakin,
2005). Grassroots level inventors in Sri Lanka are such a community who have been
engaged in inventive activities even when they are discriminated by the modern
market driven knowledge economy.
4
Grassroots Level Inventions in Sri Lanka
Sri Lanka is a multi-ethnic, lower middle-income island nation in South Asia with 20
million mid-year population in year 2009. Sri Lanka has comparatively higher
human development index than rest of the South Asian countries, but she has fallen
behind the technological development compared to neighboring countries in Asia
(Dissanayake, 2003). Exports of garments and textiles, worker remittance, tea and
tourism have been the major sources of export income and 81 % of the imports
consist of intermediate and investment imports (Central bank of Sri Lanka, 2008).
The import dependency on industrial and technological products of Sri Lanka has
been drastically shifted from western countries to Asian countries during the last five
years. In year 2008, 73 % of total imports of Sri Lanka were originated from India
(24%), Singapore (9%), China (8%), Hong Kong (5%) and other Asian and Middle
East countries (51%) (Central bank of Sri Lanka, 2008). This growing trend of
importing relatively cheap products from Asian countries has reduced the demand for
local products and that has negatively affected for local inventors to achieve higher
commercial success. Owing to the comparative economic advantages of importing
cheap technological products from other countries, Sri Lankan large-scale corporate
sector is hardly involved in inventive activities. Lack of corporate inventions is a
significant factor that dramatically influences the technological stagnation of Sri
Lanka. Small private companies rather than public-quoted companies forwarded the
only limited number of business affiliated patent applications. This scenario has
weakened the private sector funding opportunities for large-scale research and
development activities, public awareness about the importance of local inventions,
and especially the government’s intention to provide facilities to improve the local
innovations. Hence, citizens tend to be addicted to imported products and demand for
5
locally invented products has rapidly weakened. Because of this trend, neither the
universities nor the research institutes have been encouraged to be involved in
inventive activities in Sri Lanka. Therefore, the over-cautiousness on the objective
economic disadvantages of the inventions has negatively influenced the
technological development of the country. Eventually, it has seriously hurt the
independent inventors who sacrificed their time, money, and other resources to
invent something new and useful to the world. Therefore, blindly following the
universal objective measurements to measure the feasibility of the local inventions
has become a serious trap to Sri Lanka, which has increased her technological
dependency on other countries and the ignorance of the local independent inventors.
Hence, the overall environment in Sri Lanka has not favored the grassroots level
inventions.
The technological environment in Sri Lanka is not encouraging for the grassroots
level inventors; however, they have not been discouraged. They have been the
driving force of the Sri Lankan innovation system. Recent patent statistics show that
significant percentage of independent inventions represent the national patent system
in Sri Lanka (Table 01). On average, the independent inventors in Sri Lanka have
forwarded 77% of the applications. It has increased up to 80% and 85% in years
2007 and 2008 respectively.
6
Table 1: Resident Patent Applications in Sri Lanka -2000-2008
% of
Independent
Independent
Inventors Total inventions
Year
Research
Institutes
University
Affiliated
Business
Affiliated
Non-Resident
Affiliated
2000
2001
2002
2003
2004
2005
2006
2007
2008
Total
5
7
11
13
4
10
13
7
9
79
2
1
6
5
6
6
12
7
6
51
9
12
12
11
14
16
14
15
14
117
1
1
1
1
0
0
0
1
2
7
52
92
69
50
82
113
121
123
170
872
69
113
99
80
106
145
160
153
201
1126
7.0
4.5
10.4
0.6
77%
100.0
%
75%
81%
70%
62.5
77%
78%
76%
80%
85%
77%
Source: Sri Lanka National Intellectual Property Office
Apart from the significant proportion of independent inventions, grassroots level
inventions in Sri Lanka have showed high technical merits as well. Some of the
inventions have been recognized as the best inventions in the world. In the 37th
International Exhibition of Inventions of Geneva 2009, Sri Lankan independent
inventors have won the prestigious World Intellectual Property (WIPO) award and
the International Press Award. Sri Lankan invention of “safety kerosene lamp” also
won the BBC World Challenge 2009 award as the best invention of the competition.
Further, all the best inventor awards of the Sri Lankan annual presidential awards for
inventors in 2006 and 2007 have been won by independent inventors. Unfortunately,
a majority of the award-winning inventors were unable to achieve high level
commercial success for their inventions. Owing to the emerging hostile technological
environment in the world, the growth of grassroots level inventions in Sri Lanka is
not a generally expected behavior. Hence, the controversial growth of the grassroots
level inventors in Sri Lanka has raised a question of why the grassroots level
inventors are kept involved in inventive activities in an environment that has rapidly
become hostile for their survival and growth.
7
Moving From Objective to Subjective Success
Historically, the success of the inventors has been measured by their objective
performances as inventors (Scotchmer, 2004). Inventors have to achieve large
number of patents, patent citations, commercialized inventions and profits to be
considered as successful. However, controversial growth of grassroots level
invention in developing countries has questioned the validity of such pure objective
success measures. According to Dubina et al.(2011), the objective criteria only
represents a stereotyped convention to consider one inventor to be successful than
others (Dubina, Carayannis, & Campbell, 2011). Hence, objective criterions do not
measure the actual success of an inventor.
Success is defined as an accomplishment of aims or purpose (Oxford University
Press, 2010 ). Almost all human actions and reactions are directed towards success in
what they do. Everyone wants to achieve higher success; however, there is no
general agreement about what success is and what exactly predicts the success
(Wrosch & Scheier, 2003). Especially there is a large number of arguments over the
definition of success, its causes and consequences on human life. Different fields of
studies, such as social science, health science, economics, psychology and
community development define the concept of success and its predictors from the
perspectives of their particular field. In spite of the multidisciplinary nature of the
concept, success has been generally defined as a composite concept that has at least
two broad facets. First, the objective success that comprises wealth, physical
conditions and physical standard of life. Second, the subjective success that
comprises the feeling of happiness and satisfaction with life
Gough, 2003).
8
(Campbell, 1976;
Stanley (1904) wrote a poem called “success” and it begins with, “He has achieved
success; that has lived well, laughed often, and loved much…” (Stanley, 1904). If a
person is able to live the life happily and satisfactorily, he/she eventually achieves
the success of life. Likewise, in positive psychology literature, happiness and
satisfaction with life have been defined as the ultimate aim of living (Conceição &
Bandura, 2008; Kenny, 2002). Even though, Stanley quoted living well and lauging
often as signs of success, in modern positive psychology literature happiness and
satisfaction with life is defined as ‘subjective well being’ (Snyder & Lopez, 2007).
Jankovic and Dittmar (2006), had found that, even though materialism is growing in
the modern world, strong commitment to materialism is detrimental to individual’s
happiness. According to them, if ‘success’ encapsulates only materialistic outcomes,
it will be harmful and counter-productive to the happiness of individuals. Hence, the
term “success” needs to be encapsulated as an amalgamation of contrasting
materialistic outcomes and subjective well-being. Parallel to that, previous literature
on measures of success has criticized the measures of success purely based on
financial and objective outcomes. It has suggested the importance of measuring the
subjective aspect of success (Gill & Feinstein, 1994; Rogerson, Findlay, Morris, &
Coombes, 1989; Diener & Suh, 1997 a). Following this argument, many
commentators have described subjective well-being as an indicator of success of
human life (Gough & McGregor, 2007; Diener & Suh, 2003). Hence, in recent socioeconomic literature, the subjective well-being has been identified as a better social
indicator of quality of life than the traditional objective indicators (White, 2007;
Heylighen & Bernheim, 2001). With the emergence of subjective well-being as an
indicator of the subjective aspect of success (Heylighen & Bernheim, 2001) and
9
quality of life (Wrosch & Scheier, 2003), it could be utilized to measure the
subjective aspect of success of different social groups. Owing to the conceptual
agreement of the subjective well-being as the indicator of subjective success, the
present study operationally defined subjective success as the persons’ own
assessment of subjective happiness and satisfaction of life. Therefore, hereafter in
this study the term subjective success will be used as a synonymous with the
subjective well-being.
In search of Subjective Phase for Community Development of Inventors
Even though the grassroots level inventors represent the lowest layer of the inventive
community, they are significantly different from the rest of the inventive community.
Owing to their unique nature, they are segmented as its own community with its own
community capacities. Easterling (Cited in Simpson, Wood, & Daws, 2003) had
defined a community capacity as the ‘set of assets or strengths residents individually
and collectively in a community that brings to the cause of improving local quality of
life’. Even though it has been overlooked in community development, underlyning
meaning of the quality of life in community also have the two aspects of
achievements. Firstly, achievement of objective success or physical well-being that
comprises of good health, good economic condition, good education, political
freedom and better social recognition. Secondly, achieving subjective success that
contains self-assessment of how people achieve happiness and satisfaction of their
lives as a whole. Subjective success has influenced the social behavior both as an
outcome and as a factor of functioning of social behavior (Veenhoven, 2008).
Historically subjective success has not been a great issue in community development;
it has only focused on the objective aspect of community behavior. However, there
10
are both social causes and consequences of subjective success that need to be studied
to understand the actual nature of success (Veenhoven, 2008).
Large number of studies conducted on different groups of people has suggested a
number of demographic, psychological, technical and socio-cultural factors influence
subjective success (Diener E. , 2009 a). However, very limited numbers of studies
have measured all the factors in a single study (Rogatko, 2010). In addition, the
sociological attention on the causes and consequences of subjective success is still
being far from the satisfactory level (Veenhoven, 2008). Owing to the low
sociological interest on subjective success, the existing community development
literature has not identified subjective success as an asset, strength or outcome of the
communities. Therefore, the behavioral nature of subjective success and its
predictors in specific communities has not been thoroughly explained. However, the
subjective success is expected to have a significant influence community
development (Kusago & Kiya, 2009; Kingdon & Knight, 2007).
In knowledge-based society, conventional communities are becoming scattered and
not being strongly attached to the geographical locations. Therefore, building the
strong communities would be difficult, unless community development pay attention
to the needs, resources and achievement of subjective success of the emerging
communities of interest and practice (Hughes, Black, Kaldor, Bellamy, & Castle,
2007). Therefore, Community Development needs to give attention to positivistic
psychological factors and have to use subjective well-being as a measurement of
subjective success and subjective empowerment of the communities (Diener &
Diener, 2003). Owing to the natural formation of grassroots level inventors as their
own community in developing countries like Sri Lanka, it is timely to explore the
11
grassroots level inventors in the context they are empowered to increase the local
technological development.
Statement of Problem
Past studies on grassroots level inventors (independent inventors) have measured the
success of inventors based on pure objective measurements such as patents, patent
citations, commercialization and profit earnings. The majority of the studies have
concluded that grassroots level inventors are not achieving higher success in modern
world and therefore, they will diminish from the world. However, recent patent
statistics in the country like Sri Lanka have shown continuous increase of the patent
applications from grassroots level inventors. Hence, findings of the past studies on
grassroots level inventors contradict with the continuous increase of grassroots level
inventive activities in Sri Lanka. The existing literature has not thoroughly explained
the reasons why grassroots level inventors keep engaging in inventive activities in
unfavorable environments, especially, the question of why there is an increasing
trend of grassroots level inventions in developing country like Sri Lanka has never
been explained. Therefore, existing measures and literature on inventors’ success
have not been able to explain true nature of the success perceived by the grassroots
level inventors. Because of that, the researchers have been unable to explain the
causes and consequences of success that might influence the controversial behavior
of the grassroots level inventors in the middle-income country like Sri Lanka.
With the emergence of positive psychology, the importance of understanding
positive strengths of the people rather than only negatives/weaknesses has been
broadly accepted. In positive psychology, subjective well-being has been recognized
12
as an indicator to measure the subjective aspect of the success of life, which had
never been able to measure by the traditional objective measurements. Even though
the concept of subjective success is relatively new, there are number of emerging
theoretical arguments in the field of subjective success (Diener E. , 2009 b). Bottomup theoretical perspective of subjective success is an approach that proposes
subjective success as an ultimate goal of life. It suggests that different life domains
have positively or negatively influenced the achievement of subjective success of
life. As far as the bottom-up tradition had evolved from the Aristotle’s pioneering
thought of good life, the majority of the initial studies on subjective success have
focused on determining this bottom-up relation of subjective success. However, there
are recent studies that have investigated the opposing top-down theoretical
arguments of the consequences of subjective success. Top down theories have
discussed the long-term consequences of being happy and satisfied with life. One of
the major top-down theories of subjective success, the Fredrickson’s Broaden-and–
build theory suggested the importance of studying subjective success as a relatively
static trait by discussing how the happy emotions increase the social resources,
knowledge and skills of people. Further, the emergence of Veenhoven’s sociological
theory on subjective success indicates that there are personal and social causes as
well as consequences of subjective success within the society. By considering all the
theoretical arguments, Headey et al. (2005) suggested that majority of the existing
studies on subjective success are not comprehensively evaluated which personal,
psychological and social factors are the predictors (causes) or which personal,
psychological and social factors are the consequences of the subjective success.
Hence, the level of understanding of possible predictors and influences of the
13
subjective success is not absolute. However, so far adequate empirical attention has
not been given to investigate the validity of these opposing theoretical arguments.
Stated contextual and theoretical knowledge deficiency of behavior of the grassroots
level inventors and subjective success suggest the importance of searching for
answers to the under studied problem of why grassroots level inventors are
continually engaged in their inventive activities, while they are not achieving much
objective success defined by the society. If the inventors are not achieving the
objective success of their inventions, inventive life might be a significant life domain
that influences the inventors to be happy and satisfied with their lives. Therefore, as
other life domains, there might be positive contributions from the factors of inventive
life on the subjective success of grassroots level inventors. Otherwise, the inventors’
general tendency to be happy and satisfied with life might encourage them being
keeping involved in inventive life. However, existing studies on grassroots level
inventors were unable to explain coexistence of objective and subjective success and
their personal, psychological and social causes and consequences of the grassroots
level inventors. This limited contextual and theoretical knowledge of the problem
have driven the researcher to investigate, how the demographic, psychological,
technical and social life factors relate to the subjective and objective perspectives of
success of grassroots level inventors in Sri Lanka. Therefore, the main purpose of the
present study is to explore the demographic, psychological, technical and social
causes and consequences of objective and subjective success of grassroots level
inventors in Sri Lanka to explain who are and why these inventors are continually
involved in inventive activities where surroundings are becoming hostile to
independent inventing.
14
Research Questions
The aim of this study is to explore the demographic, psychological, technical and
social causes and consequences of objective and subjective success of grassroots
level inventors in Sri Lanka. To achieve the aim of the study, the researcher expected
to answer four research questions.
1. Who are the grassroots level inventors in Sri Lanka?
2. What are the levels of objective success and subjective success achieved by the
grassroots level inventors in Sri Lanka?
3. How the selected demographic, psychological, technical, social life domain factors
can influence the objective and subjective success of grassroots level inventors in Sri
Lanka?
4. How the subjective success can influence the objective success and selected
demographic, psychological, technical and social life domain factors of grassroots
level inventors in Sri Lanka?
Research Objectives
In order to answer the said research problem and the research questions, four (04)
main and two (02) sub-research objectives of the study were set. By conducting the
research, researcher expected to :1. explain the selected demographic, psychological, technical and social factor profiles
of Sri Lankan grassroots level inventors.
15
2. explore the objective and subjective success of Sri Lankan grassroots level inventors.
3. determine the influences of selected demographic, psychological, technical and
social domain factors on objective and subjective success of grassroots level
inventors in Sri Lanka.
4. determine the influences of subjective success on objective success and selected
demographic, psychological, technical and social domain factors of grassroots level
inventors in Sri Lanka.
5. test the mediation effect of objective success on the life domain factors and
subjective success
6. dertermine which theoretical proposition of subjective success (bottom-up or topdown) is more appropriate to explain the relationship between domain factors and
success of grassroots level inventors in Sri Lanka
Significance of the Study
Recent technological development in developing countries like Sri Lanka has been
marginal. Their local innovation systems heavily depend on independent grassroots
level inventors, but grassroots level inventors have not been given required attention
(Weick & Eakin, 2005). However, by facing all the discomforts, grassroots level
inventors have been continuously involved in inventive activities. Hence, they are the
unsung heroes in developing countries who can take their countries towards
achieving higher technological development. The present study addresses this
important but understudied community phenomenon in developing countries.
Therefore, the study gives an exclusive knowledge about the grassroots level
inventors in Sri Lanka and contributes the pioneering knowledge about the tacit
dimension of the grassroots level inventive activities in developing countries.
16
Further, the findings of the study explain structural information of the grassroots
level inventors in Sri Lanka that is hard to find in most of the developing countries.
The policy makers can use the findings of the study to identify the areas for
improvement of grassroots level inventive activities. This study suggests integrating
intellectual property system, empowerment and capacity building concepts of
community development to the local innovation system. Hence, the finding of the
study gives opportunity to policy makers to rethink the innovation promotion policies
of the country.
In addition, the majority of earlier studies that measured inventors’ success have
utilized single objective success measures to assess the success of inventors. Number
of patents, patent citations, number of commercialized inventions and profitability
have been widely used measures to assess the success; however, the measurement
used in the present study evaluate the inventor’s objective success at different stages
of the innovation process. Hence, it considered the entire innovation process to
measure the objective success of inventors.
The findings of the study add new knowledge about the inventors’ subjective success
and the causes and consequences of the subjective success of them. Findings of the
present study explain which factors follow bottom-up casual direction and which
factors that follow the top-down casual direction of subjective success. It will
contribute to the positive psychology by explaining how selected demographic;
technical, psychological and social factors behave in relation to the subjective
success, which is identical to the concept of subjective well-being.
17
In order to identify and empower the grassroots inventive communities, the
methodology and the results of the study can be used by other developing countries,
especially those that are marginalized and considered as Least Developed Countries
(LDCs). It would be helpful to those countries to localize their innovation systems
and gaining economical and social development that is expected in the era of
knowledge and IT revolution. This study provide an opportunity for Asian
developing countries to identify grassroots inventive communities as integral section
in their innovation and technological development agenda and as important part as
industrial development and institutional research and development efforts. Hence,
the present study has unique contribution to knowledge, policy and practice of the
grassroots level inventors’ community and broadly to the innovation development in
developing countries.
Finally yet importantly, the present study will contribute to the community
development beyond the objective and physical boundaries of the existing
community development approaches by introducing subjective approach that focuses
on overall and ultimate subjective success of grassroots level inventive communities.
Traditional need based community development and emerging Asset Based
Community Development (ABCD) require to understand the communities from
inside out; however, both these approaches have mainly focused on the objective
problems, needs, and assets. The present study has considered both internal
(subjective) and external (objective) dimensions of grassroots level inventors and
their success. Therefore, the findings of the study would give comprehensive
understanding and knowledge about the positive strengths of the grassroots level
inventive community and their success.
18
Scope and Limitations of the Study
Focus
This study focused only on the Sri Lankan residential individual inventors those who
received the final decision for patent applications during the period of 2000-2008.
Inventors come under following categories would not be covered by the findings of
this study.
1. Not applied for the Sri Lankan patents,
2. Applied for patents only before the 1st January 2000 and after the 31st December
2008 period,
3. Even though applied for first and only patent within the 2000-2008, but application
was still in examination process by the 31st December 2009 and
4. Institutional affiliated or non-residents patent applicants
Apart from above mentioned inventors, the scope of the study will not cover
inventors of any of the other means of intellectual property rights such as copyrights,
industrial design, trademarks and business processes other than patents.
Data collection
Self-reporting questionnaire is the major research instrument of this study. During
the data collection, the researcher physically contacted the respondents to fill up the
questionnaires. Apart from the expectation of high response rate, it was expected to
give significant interaction between the researcher and respondent to clarify the
complex issues of the questionnaire. However, there are unavoidable inherent
limitations in the self-reporting data collection methods. Therefore, such limitations
19
of self-reporting survey questionnaire method might influence the findings of the
study. To minimize the such influences, the researcher followup the data collection
with panel discussion with the entire respondent to get qualitative validation for their
responses.
Coverage
The present study was conducted based on selected significant demographic,
technical, psychological and social factor variables of grassroots level inventors in
Sri Lanka. Those factors are not necessarily the only factors that influence the
objective or subjective success of grassroots level inventors. There might be other
life domains factors that influence objective and subjective success, which are not
covered by the present study.
Research design
The present study was designed as a cross sectional correlational research and hence,
it is not able to determine the true cause and effect relationship between the
exogenous and endogenous variables. However, Klien (2010) suggested an
alternative analysis of two competing models to explore the bottom up and top down
directional relationship with sample data to determine the relative strengths of the
casual directions of relationships between variables (Kline, 2011). The researcher
adopted Kline’s alternative method to identify the causes and consequences of the
subjective success. Hence, the findings of the study should be interpreted subject to
any inherent limitations persist in this alternative model comparison technique over
the experimental or longitudinal studies.
20
Definition of Key Terms
The researcher defined the concepts and terms of the study after comprehensive
literature
review.
Descriptive
explanation
of
the
conceptualization
and
operationalization of the concepts and variables of the study is presented in Chapter
3. However, in order to avoid any misconceptions when reading Chapter 2, in this
section the researcher stated the specific operational meanings of the concepts and
key terms of the study. Unless specifically mentioned otherwise, throughout this
thesis the usage of these terms would follow the specific meanings and definitions
described in this section.
Invention
Invention means an idea of an inventor that is new, involving an inventive step and is
industrially applicable which permits in practice the solution to a specific problem in
the field of technology. It may relate to either product or process.
Patent
A patent is an exclusive right granted for an invention, which is a product or a
process that provides, in general, a new way of doing something, or offers a new
technical solution to a problem.
Grassroots level inventor
Local individual of a country, who is involved in patentable inventive activities and
trying to obtain patents for himself, for his own reasons and own rewards out of the
formal organizational structures such as firms, universities and research labs.
21
Objective success
Objective success is defined as the measurable and observable monetary and nonmonitory achievements of the innovation process. That includes the patent received,
awards and rewards, commercialization, commercial survival and profit earnings.
Subjective success
The Subjective success is defined as the persons’ own assessment of the overall
happiness and satisfaction with their lives. It describes how the person perceive their
lives.
Community Connectedness
Community connectedness or Sense of community is a convergence of individuals’
desires to belong to a community, establish a mutually influential relationship with
that community, satisfy their individual needs and be rewarded through their
collective affiliation, and a shared emotional connection.
External Linkages
External Linkage is the positive relationship between a person (inventor),
organizations, and structures that has influenced on his/her inventive activities.
Life Orientation
Life orientation is an individual difference variable that reflects the extent to which
people hold generalized favorable or unfavorable expectancies for their future.
Maximizing Tendency
Maximizing tendency is tendency of individual decision making to achieve the
highest possible outcome of an activity or behavior.
22
Satisfaction with life
Life satisfaction is the sense of pleasure and peace stemming from small gaps
between wants and needs. It describes the feeling of balancing what persons’ have
achieved and what he/she has not been achieved in his or her life.
Subjective Happiness
Happiness is the positive emotional state that is subjectively defined by each person
and it is related to the achievements and gains that a person perceived as positive.
Social Capital
Social capital is assets of a person that result from their social relationships, network
of contacts, and friends.
Summary
Grassroots level inventive community has been discriminated in the world. However,
in middle-income developing country like Sri Lanka they have shown controversial
growth in engagement in inventive activities. This chapter explained the synopsis of
the research problem, the research questions, objectives, significance and scope of
the study. The next chapter further explains the context, existing theoretical and
empirical literature. Finally, based on the literature and theoretical explanations, it
will explain the theoretical framework and conceptual models of the present study.
23
CHAPTER 2
LITERATURE REVIEW
“Happiness is the meaning and the purpose of life, the whole aim and end of human
existence, hence Happiness means success.”
Aristotle (384 BC-322 BC)
Introduction
In order to provide context and background that support to the study, search for
related literature is recommended to conduct throughout the study (Ary, Jacobs, &
Sorensen, 2006). According to Randolph (2009), the stages for reporting a literature
review should be paralleled with the process for conducting the primary research
(Randolph, 2009, p. 4). The aim of this chapter is to explain the theoretical and
empirical background literature of the context of the research problem and
development of the conceptual framework of the study.
Background of the Grassroots Level Inventive Community
Who are the grassroots level inventors?
Drastic social, economical and technological changes in the world have increased the
debate regarding the gaps between the ‘haves’ and ‘have-nots’ using a deficiency
approach (Kellner, 2002; Dollar & Kraay, 2002). The majority of developing
countries have utilized the deficiency approach to bridging the gaps between “haves”
and “have-nots” in everything. Present innovation promotion movements in
developing countries also focus more on the grassroots level inventors who “have
not” applied for patents. There have been arguments to bring the grassroots invention
into patent system, by changing the existing policies and procedures of patent system
(Gupta A. , 2000). It believed that the improvement of innovations in one community
could be used to benefit other community members (Wettansinha, Wongtschowski,
& Waters-Bayers, 2008). Nevertheless, owing to the deficiency approach, the patent
applied independent inventors have been ignored in the discussions of grassroots
inventors and existing patent applied independent inventors have been understudies
in developing countries (Weick & Eakin, 2005).
In developing countries, the term “grassroots” defined as the ordinary people in
lowest layers of the society (Sen, 2005). In those countries, “grassroots inventors”
have been recognized as ‘inventors in rural communities’ rather than the ‘community
of independent inventors’ (Gupta A. , 2000; Prolinnova, 2009; Gupta, et al., 2003).
Further, the grassroots innovation has been defined as need-based, simple, costeffective, and sustainable technologies developed by someone in a community who
has first-hand experience of the issues involved (Chinzah, 2005). Hence, the studies
on grassroots inventors in developing countries mostly focus on the utilization of
indigenous knowledge and problem solving ability of the lower level community
members rather than the novelty and originality of the inventive outcomes (Sen,
2005). According to the innovation development programs in eastern world, the
grassroots innovations are not necessarily novel to the world, novel utilization of
existing
products
also
consider
as
grassroots
innovation
(Wettansinha,
Wongtschowski, & Waters-Bayers, 2008). This practice is against the basic
definition of invention that defines invention as an outcome that is new and
introduces first time to the world. Owing to the conceptual difference of grassroots
inventors in developing countries, grassroots inventors have not been searched in the
patent system. Therefore, locating a grassroots inventor has been a very difficult,
25
complicated and informal fieldwork in developing countries (Wettansinha,
Wongtschowski, & Waters-Bayers, 2008).
Conversely, in the western innovation literature, grassroots level inventors have been
recognized as the independent inventors who are involved in mainstream patentable
inventive activities. McDonalds (1986) has defined the efforts of the person who
invents himself, for his own reasons and his own rewards as an independent inventor
(Macdonald, 1986). As far as the patent system is the most reliable standard mean to
recognize the technical products and processes developed first time to the world,
independent inventors were also defined as someone who personally owns the
invention registered at patent office (Amesse & Desranleau, 1991). Hence, the
available literature on independent inventors have defined either patent granted or
patent applied inventors as their respondents (Amesse & Desranleau, 1991; Astebro,
1998; Sirilli, 1987; Macdonald, 1986; Weick & Eakin, 2005; Georgia Tech
Enterprise innovation Institute, 2008). The authors considered patent applied
independent inventors as grassroots level inventive community within the
mainstream innovation system.
Owing to the narrow focus of the developing world definition of grassroots
inventors, the inventors those who voluntarily engage in mainstream innovation
system by applying patent rights for their inventions have recognized as “haves” and
they have been ignored as the members of grassroots innovation development
movements. Even though, not every independent inventor applies for the patent
rights, the patent has been the only standard method to recognize and reward the
inventions and inventors (Schmookler, 1957; Macdonald, 1986; Amesse &
26
Desranleau, 1991). All the western-based studies identified the patents and patent
applications as the most suitable and convenient source to locate the independent
inventors. Then again, as far as the patent system is a systematic process of
evaluating the inventions, every invention needs to fulfill certain criterion and
requirements to grant a patent. From inventor’s point of view, it is an evaluation of
the originality and industrial applicability of his/her invention. Therefore, by
avoiding the limitation of available definitions of “grassroots inventors”, the present
study defines local individuals of a country as “grassroots level inventors”, who
involved in patentable inventive activities and trying to obtain patents for himself, for
his own reasons and own rewards out of the formal organizational structures such as
firms, universities and research labs. Unless in discussions of literature or
specifically said otherwise, in the present study the “grassroots level inventors” are
defined based on the proposed definition. Owing to a majority of the literature are
western-based, in the literature discussions, the term independent inventors and
grassroots level inventors are used interchangeably with same meaning to define the
inventors who are involved in inventive activities out of the organizational rights and
obligations.
Past studies on grassroots level inventors in the world
Most of the studies carried out so far on grassroots level inventors had tried to
explain the socio-demographic characteristics and technical factors of the inventors
(Macdonald, 1986; Sirilli, 1987; Amesse & Desranleau, 1991; Weick & Eakin,
2005). However, some of the studies further analyzed the informational and
intermediation assistant they have received (Georgia Tech Enterprise innovation
Institute, 2008; Macdonald, 1986; Amesse & Desranleau, 1991). A number of studies
27
have extended to investigate the nature of the inventions and their invention process
(Macdonald S. , 1986; Sirilli, 1987; Amesse & Desranleau, 1991; Weick & Eakin,
2005; Davis, Davis, & Hosil, 2009). Several studies gave attention to
commercialization related factors (Amesse & Desranleau, 1991; Weick & Eakin,
2005; Georgia Tech Enterprise innovation Institute, 2008). Only few studies have
investigated the psychological motives of the success of the inventors (Wolf & Mieg,
2009; Henderson, 2002).
In past studies, the success of an inventor was largely measured by the number of
patents, commercialized inventions, income and net income generated by those
inventions (Amesse & Desranleau, 1991; Weick & Eakin, 2005). Owing to the nature
of the commonly used indicators of success, inventors have at least three roles to
play: inventor, entrepreneur and investor. Success in patent applications means an
inventor is successful in inventive role, successful commercialization means the
inventor is successful in entrepreneur’s role and finally financial success means the
inventor is successful in the investor’s role. Therefore, failure in one of these roles
expected to be affected the overall objective achievements of the grassroots level
inventors and might be the subjective feeling of success as well. A large number of
the studies indicated the low success rate of grassroots level inventors in each role
(Invention statistics, 2008). However, none of these studies went into an indepth
empirical reasoning to find why the grassroots level inventors have continued in
inventive activities, when the world is saying that they are not successful.
Unlike industrial countries, studies on grassroots level inventors in developing
countries are very rare (WIPO, 2009(a); Mahmood & Singh, 2003; Weick & Eakin,
28
2005). However, in the transition countries like India, grass root level innovations are
identified as major field of discussion among the academics and policy makers
(Bahaduri & Kumar, 2010). According to Kumar and Bahaduri, education, age,
income level and area of living have effected on Indian grassroots level inventors’
involvement in inventive activities. Kumar and Bahaduri also identified that 48% of
the inventors were intrinsically motivated and only 10% motivated purely with
extrinsic factors. Their priorities seemed to be different, and they innovate without
having external pressures or commercial motivations. Therefore, generating marketoriented inventions from them would not be straightforward and would require a
transformation process to utilize grassroots level inventors in commercialization.
Hence, country needs to identify its unique characteristics and develop localized
innovation systems based on the objective and subjective requirements of the
inventors. (Commission on Interlectual Property Rights, 2002). Due to the different
characteristics of grassroots inventive communities in different countries, the results
of available studies in world would not be able to generalize the requirements and
issues of grassroots level inventive community in the developing countries.
Significance of grassroots level inventors in developing countries
Organizational and employed inventors are becoming the driving force of modern
technological development in the west (Scotchmer, 2004). As said by the literature,
some have argued the diminishing importance of independent inventors in the
modern industrial world (Schumpeter, 1942; Scotchmer, 2004), However even in
industrialized countries the independent inventors are still important (Macdonald,
1986; Sirilli, 1987; Weick & Eakin, 2005; Georgia Tech Enterprise innovation
29
Institute, 2008). The number of inventions created by the grassroots level inventors
still represents the significant proportions in industrialized countries (IFIA, 2006).
However, As long as the grassroots level inventors do not receive resources from
formal organizations, the employed inventors will always overshadow them in the
market driven knowledge economies. According to the past studies in industrialized
countries, the success rates of the grassroots level inventors in patenting and
commercializing their products have been very modest (Astebro, 1998).
Meanwhile, recent patent statistics have shown that, in developing countries the
higher proportion of international patent applications has been forwarded by
grassroots level inventors (Table 02).
Table 2: PCT patent applications by type of applicants in selected developing
countries
Country
Individual Inventors
China
India
Southern Africa
Singapore
Brazil
Mexico
Colombia
Philippines
Cuba
Others
Developing Countries
Academic Institutions
Appli.
%
Appli.
%
507
73
308
84
76
106
28
25
27
2119
45.1
15.2
73.7
26.1
37.3
82.8
84.8
96.2
0
45.8
39.5
63
6
8
57
13
1
4
173
5.6
1.3
1.9
17.7
6.4
0.8
0
0
0
6.3
3.2
Public
Institutions R & D
Appli.
%
38
199
7
30
12
6
1
1
13
8
393
3.4
41.5
1.7
9.3
5.9
4.7
3
3.8
100
13.6
7.3
Companies
Appli.
%
516
202
95
151
103
15
4
20
2674
45.9
42.1
22.7
46.9
50.5
11.7
12.1
0
0
33.9
49.9
Total
1124
480
418
322
204
128
33
26
13
59
5359
Source: The International Patent System in 2004 Yearly Review of PCT
As indicated by Table 02, independent inventors have forwarded almost 40% of the
international patent applications of developing countries. The grassroots level
inventors in countries like Philippines, Colombia, Mexico, and Southern Africa have
forwarded well over 70 % of the PCT applications. In Sri Lanka also, grassroots
30
level inventors forwarded more than 77 % of the residential patent applications and
in 2008, it has increased up to 85% (National Intellectual Property Office, 2008).
Even though the grassroots level inventors are important in technological
development in developing countries, studies on independent inventors in developing
countries are extremely rare to get insight about them and their inventions. Lack of
empirical studies about the grassroots level inventors in developing countries
indicates that they are not receiving sufficient attention by the authorities,
organizations and academics (Weick & Eakin, 2005). As far as the globalization and
market-driven economy has influenced every aspect of the developing countries, it is
important to realize why the grassroots level inventors are involved in inventive
activities and how they perceive their success as inventors.
Grassroots level Inventors and Community Development
Recently there is an emerging trend towards promoting community level local
innovations in the developing countries (Deka, Qutub, Barbaruah, Omore, Staal, &
Grace, 2009; Wettansinha, Wongtschowski, & Waters-Bayers, 2008; Prolinnova,
2009). However, the current trend in developing countries ignores the patent applied
grassroots level inventors from their attention. The community level innovation
promotion programs narrowly define the community inventors as local inventors
within the communities (Prolinnova, 2009). The locals, those who develop newer
and better ways of doing things using their own resources and materials as their own
initiatives without having an influence from formal organizational structures are
generally defined as local inventors and grassroots scientists (Wettansinha,
Wongtschowski, & Waters-Bayers, 2008; Martin, 2005). Their inventive outcomes
are known as the grassroots innovations (Martin, 2005). Hence, both the market
31
driven innovation promotions and community driven innovation promotions have
ignored the patent applied grassroots level inventors. Therefore, a majority of the
patent applied grassroots level inventors in developing countries have been isolated
and ignored.
Grassroots level inventors as a community of interest
The term ‘Community’ is defined differently in different phenomenon. However, in
Community Development literature, the term community has been defined at least
from two perspectives, namely, geographical perspective, such as neighborhood or
town (communities of place) and social perspective, such as people sharing common
interest (communities of interest)(Phillips & Pittman, 2009). The geographical
perspective defined the community boundary by precise physical location and the
social perspective defined the community by similarities of interest. Traditionally,
communities have been defined in terms of geographical location. However,
geographical definition of the community is only one way of looking at the
community (Frank & Smith, 1999).
As a result of extensive industrialization and modernization, the shared common
interests have not attached the people who live in the same geographical area, Hence,
people in the same geographical area do not include in the same community because,
sometimes they do not share common interest. Fast growing liberal thinking on
occupations, religions, races, cultures and economic status have changed the
geographical and social bonds of modern families and communities (Faunce &
Smucker, 1966; Kashyap, 2004; Hughes, Black, Kaldor, Bellamy, & Castle, 2007).
32
Even though the people live in same geographical location, they might not interact
with each other to share common interest. Then again, in the society, some
communities are not living in the same geographical location, but they share
common culture, language, beliefs, practices, interests and sense as communities.
Therefore, the definition of the community can also be drawn from these
perspectives and geographical location is not superior to the common interest (Frank
& Smith, 1999). What is most important is the people who are working as a social
unit to conduct the specific activities. As said by Warren, 1963, a community is a
combination of social units and systems that perform major social functions and the
organization of social activities (Warren, 1963 cited in Phillips & Pittman, 2009).
Even though Warren’s definition is quite old, it has highlighted the common interest
perspective of the community rather than the relatively modern definitions. It
indicates that common interest and shared values of people are more powerful
elements than just geographical or physical elements of the community (Phillips &
Pittman, 2009, p. 6). This aspect of the community makes the second perspective on
community, as a community of common interest.
According to the common interest perspective of the community, grassroots level
inventors can be defined as a community with common interest on technical
inventions, patents, and commercialization. Inventions are the products or processes
that can be industrially applicable to solve the technical problems of the society and
inventors are the people who create inventions (Freeman, 1979). Therefore, in
principle, all independent inventors share common interest through their inventions.
Even though inventors might be dispersed geographically, their independent,
informal, and self-driven interest for inventions permit them to be defined as a
33
‘community of common interest’. They are involving in social function that provides
novel, inventive and usable technical solutions to the society. Therefore, in this study
Grassroots Level Inventors are defined as a community of interest.
Even though the grassroots level inventors have the capacity to invent novel solution
to the technical problems of the society, the majority of the grassroots level inventors
in the world are not achieving the commercial success and having problems in their
inventive activities (Amesse & Desranleau, 1991; Macdonald, 1986; Weick & Eakin,
2005). By identifying, assessing, and empowering grassroots inventive community,
they can be encouraged to invent the technical solutions to the requirements of other
communities, societies, and countries. Therefore, grassroots level inventive
community of the society actually provides solutions to the existing social and
technical problems of other communities. As far as they supply solutions, tools and
techniques to the society, they need to be identified as “Supply side community”.
The Capacity building and empowerment of supply side communities will not only
give them chance to solve their problems by their own, but would solve macro-level
socio-economic and technical problems of the whole society as well. The Grassroots
level inventors have the capabilities to provide technical innovations to industrial
problems of a country. Therefore, they need to be identified as important innovation
niche of the less innovative economies. Hence, there is a need to build the grassroots
level inventive community to increase the success of grassroots level inventors.
However, the existing body of knowledge on inventors in the developing countries is
insufficient.
34
Thinking beyond the need and assets based community development
Until recently, community development has focused empowerment and capacity
building of marginalized geographically based communities to overcome their
problems and achieve objective quality of life such as health, education, and
economic status. This traditional thinking of Community Development has defined
the major deficient areas of objective aspect of a community life as needs for
improvement or the problems. Thus, traditional community development has been
defined as need based or problem driven community development (Kretzmann &
McKnight, 1993). Due to the need-based approach of the traditional community
development, outsiders have influenced the community life and hence, the anatomy
of subjective thinking of communities. Community anatomy is the assets and
resource structure of the community that development needs to be building upon.
The overriding and undercutting of community autonomy by the external parties is a
serious problem in traditional community development (Ellerman, 2006). According
to the Kretzmann and McKnight (1993) overriding and undercutting of community
autonomy is a problem of lack of understanding of community anatomy. Hence,
community development needs to understand the anatomy of the community from
inside out.
In consequence of the inherent limitations of the need-based approach to community
development, in 1993, Kretzmann and McKnight suggested modified approach to the
community development as assets based community development. Assets Based
Community Development (ABCD) is a relatively new approach that says assessment
of existing undiscovered community assets and makes use of them for their fullest
capacity (Mathie & Cunningham, 2003). ABCD is the first approach of evaluating
35
the existing individual, physical and social assets and resources of communities
before identification of problems, solutions for problems and needs of the
communities. Even though, it has been a better approach than the traditional
community development, the objective aspect drives it. Hence, it also ignores the
subjective well-being and other psychological factors as assets or resources of a
community. However, recent literature of community psychology signifies the
universal importance of understanding of the psychology of the communities
(Perkins, 2009).
According to Diener and Diener (2003), objective well being is not sufficient to
achieve successful community empowerment. The empowerment has both objective
and subjective perspectives and psychological empowerment is related with the
happy and satisfactory life (Linley, Bhaduri, & Shar, 2011). Hence, the community
development has to be going beyond the objective aspect of community assets.
Traditional community development approaches have ignored the fact that objective
deficits in life become problems, only when they affect the happiness and satisfaction
of life of the community members. If the community members are happy and
satisfied with the way they are living, deficit of objective resources such as health,
education and income will not be the burning problems of such communities (Diener
& Diener, 2001). On the other hand, if the community members could not achieve
the happiness and satisfaction of life, providing community-assistance to achieve
their objective needs and wants will not affect positive change within the community
(Diener & Diener, 2003). The importance of achieving happiness and satisfaction of
life has been the ignored assets in community development, and until recently there
were no serious effort taken to measure the subjective well-being of marginalized
36
communities. The understanding of community assets needs to be increased up to the
clear understanding of the level and functionality of psychological assets and
subjective success of the community, because that is the ultimate success of any
community (Diener & Diener, 2003). Therefore, the researcher expects to take the
assessment of community capacities to a level that has not been thoroughly described
in existing community development literature. The present study utilized the
demographic, technical, psychological and social domain factors as the pillars of
success of the grassroots level inventive community in Sri Lanka.
Objective Perspective of the Success of Inventors
Evolution of the scope of inventors’ objective success measurements
Historically, the success of the inventors has been measured based on the explicit
significance of their inventions. The first known inventor Imhotep was a government
employed architect who built the first steep pyramid in 2650BC. At that time, he was
rewarded the chief minister’s post by the second king of Egypt’s third dynasty as a
reward for his intellectual capabilities (Curley, 2009, p. 20). That was the first known
external involvement in assessing and rewarding the success of inventor. Since then
throughout history, the success of the inventors has been evaluated by third parties
based on the merits of the inventions as the solutions for critical problems faced by
the society (Scotchmer, 2004). In that era, inventor’s success had depended on the
emerging problems of the society. Hence, not all the inventions were considered as
successful, unless they solved the published technical problems. When an inventor
invented a satisfactory solution for a challenging issue announced by the governing
authority, it was considered as successful invention and inventor was granted awards
37
and rewards for that invention. Since then number of rewards, recognition from the
state and amount of research grants received have been used to measure the success
of an inventor (Scotchmer, 2004). Winning awards, recognition and benefits from the
rulers had been explained as the ultimate aim of the inventors. Therefore, most of the
inventions were kept as secrets without fuly disclosure to the public until the
challenge was announced. To avoid the secrecy of inventions, patent system was
introduced in the year 1474 (WIPO, 1997).
With the introduction of the patent system in 1474 in Italy, 1623 in the UK and 1790
in the USA, technical details of inventions become a public knowledge with limited
monopolistic rights given to the inventors by the governing body of the patent
system. Patent system examines the novelty, inventive step and industrial
applicability of the invention when granting a patent. In order to grant a patent, an
invention has to be properly disclosed (WIPO, 2009 (b)). Therefore, the introduction
of the patent system has drastically increased the disclosure of inventions, and it has
increased the numbers of close incremental inventions developed based on earlier
inventions. Hence, the importance of number of patent grants and citations has
increased (Trajtenberg, 1990). According to Scotchmer (2004), unless there is a
patent system, the technological development could have been seen very slow
progress.
Even though the patent system was introduced in the fifteenth century, the real
impact of the patent system occurred in the early twentieth century in the USA. It has
been the golden era of innovation and number of patent applications had
outperformed the total number of application in earlier era (Scotchmer, 2004).
38
Especially in the post World War II, external evaluation had dominated the
innovation measurements than the inventors’ self-assessment of the success
(Hauschildt, 1991; Schumpeter, 1942; Scotchmer, 2004). During this era, inventors
who had higher number of patents and higher number of patent citations have been
considered as successful prolific inventors. This growing enthusiasm in getting
patent rights for inventions had created serious financial burden on inventors. The
patent by itself does not give any immediate financial rewards or benefit for the
inventor, it only provides monopolistic rights for future exploitation of financial
benefits from the patent (WIPO, 1997). Owing to these ex-post benefits of the
patents, inventors had to finance their research and development costs, patent
application and administrative costs. Hence, the inventors wanted to commercialize
their inventions and develop marketable products based on their inventions to raise
finances for their future invention activities.
Prolific inventors like Thomas Edison created his own research laboratory and
recruited some engineers to carryout inventive experiments and commercialization.
His small garage laboratory has become the renowned company called “General
Electric” in the USA. After the creation of General Electric, Edison used to say,
“Anything that won’t sell, I don’t want to invent. Its sale is proof of utility, and
utility is success” (eQuotes.com, 2008). This change of attitude of inventors has
created an opportunity to financial institutes, financial suppliers and business entities
to provide finances to the inventors to receive the ex-post benefits of patents in
return. This inventor- investor collaboration has expanded the demand for marketable
inventions. This transition has been accelerated by the engagement of formal
cooperate institutions in technological research and development and they have
39
drastically outperformed the grassroots level innovations in indeustrial countries
(Schumpeter, 1942). With the increase in the commercial importance of the
inventions, the success of the inventor has been measured purely based on the
external achievements of the inventions such as successful inventive ideas (number
of patents, awards, and patent citations), successful commercialization and successful
moneymaking
(Invention statistics, 2008). This trend has continued to flourish
throughout the twentieth century and commercial success of innovations has become
a most important measure of success in twenty first century. Even in modern
knowledge society, invention has been defined as a commercial activity that is driven
by commercial needs rather than the curiosity of inventors.
Hauschildt’s classification of innovation success measurements
Hauschildt (1991) has investigated the past thirty years’ empirical studies on
innovation and tried to find the measurements and causes of innovation success.
According to his analysis, there was no single standard measurement to measure the
innovation success. Based on his analysis he defined three dimensions of innovation
attributes: technical effects, economical effects and other effects. He subdivided each
dimension into direct effect and indirect effect. Finally, the direct and indirect
measures of each dimension create the scope of the success measures as technical
utility, economical utility and other utility (Figure 01). In general, technical utility
measures the scientific and technological merits of the inventive idea such as patent
grant. Economic utility measures the financial outcomes of the invention such as
commercialization and profit. Finally, the other utility measures any other objective
benefits achieved by the inventors through his/her inventions. According to
40
Hauschildt, each utility have both direct and indirect achievements that contributes to
create the total success level of an invention.
Attributes of
innovation
Techniocal Dimension
Direct effects
Indirect effects
Technical utility (success)
Financial Dimension
Direct effects
Indirect effects
Economical utility (success)
Other Dimension
Direct effects
Indirect effects
Other utility (Success)
Total utility (Success)
Adapted from Hauschildt (1991)
Figure 1: Relationship between Innovation attributes and success measures
In his study, Hauschildt concluded that since the innovation is a process rather than a
static activity, success should be measured at different milestones of the life cycle of
the innovation process:
“Measuring the “overall success” or “overall commercial success” is
naturally out of the question as long as the innovation has not yet
been introduced to the market. It is not legitimate to evaluate the
“market success” without taking into account the innovation’s life
cycle. It does not make sense to try to measure the “technical success”
if the innovation process has not yet gone beyond the research stage”
(Hauschildt, 1991, p. 607)
Even though the invention has not achieved the total utility, it is important to
measure the innovation process stages to determine where the inventor was
successful and where he failed. It gives an opportunity to gain experience for future
innovations to strengthen the weaker stages of the innovation process (Figure 02).
41
Front-End activities
Idea generation
reports/
elaborated models
Reserach and
Development
prototypes
Back-End activities
Invention
Production
Commercialization
patents and
publications
product
development
sales and profits
Adapted from Hauschildt, 1991
Figure 2: Stages and issues of measurement of success of innovation process
In this process, later stages’ performances depend on the accomplishment of early
successive tasks. Hence the technical, economical and other dimensions need to be
measured relative to the respective stages of the innovation process. In this study the
measurement of objective success developed based on the Hauschildt’s classification
of technical, economical and other dimentions of success through the invention
process stages (discussed in detail in Chapter 3).
Approaches of Subjective Perspectives of the Success
What is the meaning of ‘success’ for the great inventors?
Most of the inventors in the golden era of innovations like Thomas Edison, Nikola
Tesla and Graham Bell at least started their careers as independent inventors without
having any obligation to third parties over rights of their inventions. From idea
generation to creation of workable prototype, they had to make use of their own
resources, time and effort for their own set of rewards from inventing. Sometimes
inventors had to sacrifice their lives to bring their ideas into reality (Collings, 2008).
The independent inventors worked in their leisure time, using their own resources,
42
with their own imaginations and expectations. Even though, the rest of the world
thinks one’s invention is failure and useless, inventors believe that they are
successful. They believe they live to feel the happiness of being successful in their
inventions.
Nikola
Tesla,
the
inventor
of
groundbreaking
invention
of
electromagnetism once said that,
"I do not think there is any thrill that can go through the human heart
like that felt by the inventor as he sees some creation of the brain
unfolding to success. Such emotions make a man forgets food, sleep,
friends, love, everything."
Source: Center for the Advancement of Science and Technology, (2005).
According to the Nikola Tesla, successful design of a workable invention is the
upmost reward of the inventor. Great inventors had involved in inventive activities to
achieve emotional success and keep challenging themselves to overcome all the
barriers coming against their effort to achieve it. Hence, the commercialization and
monetary values of the invention had not been the leading drive of the great
inventors. Thomas Edison, one of the highly admired inventors in all-time said that,
"One might think that the money value of an invention constitutes its
reward to the man who loves his work. However, I continue to find my
greatest pleasure, and so my reward, in the work that precedes what
the world calls success."
Source: Center for the Advancement of Science and Technology, (2005).
His statement also indicates that inventors feel greatest pleasure and reward within
the work of inventions rather than the outcome of it. When the pleasure and reward
found within the work, inventor’s success needs to be a subjective psychological
emotion rather than an objective external outcome. Great inventors like Nikola
Tesla, Thomas Edison and Charles Kettering had confidence, patience and self
43
believe of their inventions to be succeeded. Charles Kettering, the inventor who
holds 140 US patents including electric starter and ignition system said that
“Inventor fails 1000 times, and if he succeeds once, he’s in, he treats his failures
simply as practice shots” (Oech, 1986). They had felt every step they moved forward
and every error they detected as the successful steps. The successful psychological
feelings further drove them to be involved in inventive activities and finally achieved
the recognition and wealth as world greatest inventors (Khan & Sokoloff, 2004).
Not all the inventors achieve the objective success and the greatness that describe by
the world, but still they are not willing to give up their efforts because inventive
activities give positive emotional feeling to them (Arthur C. , 1991).
Although new science and technology doubled every year, today it is difficult to find
“successful” inventors like James Watt or Thomas Edison (Bessen, 2004). Even
though this is the era that technology development achieves its upmost height, why
are there no great inventors like Watt, Edison, and Graham bell?
It raises the
question of where have the great inventors gone and demand to investigate the inside
stories of present day inventors. Inventors’ subjective meaning of success worked as
their upmost incentive to be involved in inventive activities and this trend is evitable
even in modern inventors as well. Their subjective feeling of success discounted all
the external advices, comments and warning.
Recent research on inventors in
western countries found that inventors have continued to expand resources on their
inventions even though they were asked to stop by the experts (Amodza, 2004;
Astebro, Jeffrey, & Adomdza, 2004). Some studies have indicated that there are
socio-demographic, psychological and technical factors that have influenced on such
inflexible behavior of the grassroots level inventors (Audia & Goncalo, 2007; Arthur,
44
1991; Astebro T. , 2003; Dahlin, Taylor, & Fichman, 2004; Wolf & Mieg, 2009).
However, these studies had not designed to investigate all these factors thoroughly to
get conclusive evidences on what is subjective success and what are the factors
affecting the subjective success of inventors. During the first pilot study of the
present study, the researcher asked the selected inventors to explain whether they are
happy and satisfied with their lives in detail. Their responses are mostly identical
with the explanations given by the great inventors. They also gain positive
reinforcements from the existing outcomes or expected outcomes of their inventive
activities to be happy and satisfied with their lives (Wickramasinghe, Qualitative
Pilot Study Interviews, 2009).
Aristotle’s philosophical perspective of success
Aristotle is one of the philosophers who had introduced pioneering explanations to
“subjective meaning of success” and what people need to do to achieve it. Success in
this context has translated from the Greek word eudemonia, which is often translated
as “A state of pleasant well-being”. (Hutchinson, 1995, p. 201). Living a successful
life means a person consistently participates in happy activities throughout his or her
life. In his book, Ethics Aristotle had explained the importance of living one’s life
with goals and objectives to achieve happiness and satisfaction in the mind. In his
explanation of success, he has defined success as not just an acquisition of wealth.
He said that one should not be confused with being successful only by being a
powerful and worthy person (Hutchinson, 1995). According to him, the main factor
that determines the success or failure of human life is acquisition of certain powers to
use them to achieve happiness in life. All other things are secondary and subordinate
45
(Aristotle, Reprint 2004). Human life consists with complicated life domains such as
family, economy, work life, children, social and politics. All of these domains
connect to the mind and feeling of successful life comes from the positive or
negative influences of the domains of the life. According to Aristotle, one can be
involved in activities that relate with gaining wealth, power and worthiness, but its
physical outcome do not bring the feeling of success, unless the outcome create
happiness in life. On the other hand, a person may not have the wealth and power
even for routine survival, yet can feel success through happiness in life.
As mentioned in the philosophical literature of Aristotle, objective indicators do not
necessarily mean the individual’s success. Therefore, the extensive use of objective
measures to measure the success and satisfaction of human beings is worthless. In
modern capitalistic societies, success has been explained from the objective
perspective that measured the success based on predefined measures of the external
outcomes that can be verified and observed by the third parties (Gunz & Heslin,
2005). The high levels of economic wealth and social status have been the criteria to
define a successful person. Descriptions of successful people are often based on their
physical wealth and assets; however, it is well known that what some people see as
success in the sense of a good outcome, can be quite opposite to others (Tracy,
1989). People’s behavior is often guided by their beliefs about the types of things
that make them happy (Gilbert, 2006). Even though the external world set standards
and criteria to measure the successful behaviors and outcomes (objective success),
the real feeling of success is determined by a happy and satisfied feeling that the
person’s self-regulation system perceived from the objective outcomes. Not all the
46
people in the world have the same thought about happiness; therefore, it is
meaningless to measure the success using pure objective measures.
Positive psychological perspective of ‘subjective success’
Historically, conventional psychology is concerned about the negative psychological
status of the mind of people. However, positive psychology is concerned with the
positive individual traits and scientific evaluation of the implicit positive feelings and
strengths of the people (Seligman & Csikszentmihalyi, 2000). The objective success
indicators are verifiable, observable and can be measured easily. However, the
subjective success is a psychological construct, it is not observable and verifiable
(Hall & Chandler, 2005). It is very difficult to measure the level of subjective
success of an individual. Recent growth of the subjective measures of happiness,
satisfaction and well-being have developed a new psychological discipline called
positive psychology that has questioned the validity of using only objective measures
to explain the success. According to Diener, money and all the other material
outcomes are means of human satisfaction but not the end, the end is the well-being
of the person, and it is subjective (Diener E. , 2009 b). One person can feel success
for an outcome, but the same outcome can be an unpleasant experience to another
(Bartolome & Evans, 1990). Therefore, subjective well being has become a major
concern of positive psychology. Positive psychological concept of subjective well
being very much relates with the Aristotle’s thoughts of the state of pleasant wellbeing (Schwartz & Sharpe, 2006; Csikszentmihalyi, 2009). Even though the
commentators of positive psychology use the term subjective well-being instead of
subjective success, the inherent meanings of both the terms are related to happiness
and satisfaction (Snyder & Lopez, 2007). In literature subjective well-being, morale,
47
positive effects, happiness and life satisfaction have been used interchangeably to
define the subjective aspect of the individual assessment of success of life as a whole
(Prieto, Diener, Tamir, Scollon, & Diener, 2005). It has been defined from different
facets such as positive effects, worrying and personal coping (Diener E. , 2009 a;
Snyder & Lopez, 2007). However, as per Diener (2009 c) subjective happiness and
life satisfaction has been the most popular facets of the subjective well-being studies.
In the history of well-being research, happiness and satisfaction have been defined at
least three different ways (Rogatko, 2010). Some authors defined happiness and
satisfaction as interchangeable concepts. Veenhoven (2003) stated that ‘happiness is
the overall appreciation of one’s life as-a-whole’ (Veenhoven, 2003). The term
‘overall appreciation’ integrated both the emotional aspect (Happiness) and the
cognitive aspect (Satisfaction). Some commentators suggest that even though
happiness and satisfaction seem to be overlapped, they have some degree of
independence (Diener, Oishi, & Lucas, 2003; Ormel, Lindenberg, Steverink, &
Verbrugge, 1999). Ormel et al. (1999) stated that “there is current consensus that two
components comprise subjective well-being: people’s average pleasantness level
over the long-run (called hedonic tone) and overall life satisfaction” (Ormel,
Lindenberg, Steverink, & Verbrugge, 1999, pp. 75-76). Hedonic tone is the balance
between pleasant and unpleasant affects. It is an emotional reaction and that
generally termed as happiness by other commentators. Life satisfaction is more
overtly accepted cognitive judgment of life. Third argument is even though the
happiness and satisfaction are independent facets; the subjective well-being should
be measured as composed of happiness and life satisfaction (Pichler, 2006). Instead
of using the term subjective well-being, Pichler (2006) defined it as quality of life,
48
but he used both terms interchangeably. This accumulative approach is more
justifiable than other approaches, because it measures the two major facets of
subjective well-being together and gives indicator that include both emotional aspect
and cognitive aspect of subjective well-being. In his dissertation, Rogatko has used
individual happiness and life satisfaction items to make up the measurement for
subjective well-being (Rogatko, 2010). Following Pichler (2006), Rogatko (2010)
and Diener E. , (2009 c), present study is defined subjective success as
comprehensive assessment of emotional evaluation (Happiness) and the cognitive
evaluation of life (Life Satisfaction) as a whole. As far as it is a psychological wealth
needs to be achieved by the individuals (Diener & Biswas-Diener, 2008), the present
study defines subjective well-being as subjective success. Therefore, throughout this
study, subjective well-being and subjective success are used with synonymous
meanings.
Intrinsic motives vs. subjective success
In literature, there are some of the psychological elements identified as indicators of
intrinsic motives; pride in achievement,
intrinsic satisfaction, self-worth,
commitment to work, fulfilling relationships and moral satisfaction (Nicholson &
Andrews, 2005, pp. 141-142). However, according to the Ed Diener’s explanations,
these indicators measure the intermediate status of mind that lead to ultimate
subjective success of life satisfaction and happiness. Satisfaction of life and
happiness are the only measures of the “bottom line” impact of the consequences of
life events and incidences (Andrew & Robinson, 1991). Individual’s self-assessment
of subjective life satisfaction and happiness developed as a self-regulatory rational of
positive and negative intrinsic feeling of their life activities and incidences. In
49
positive psychology literature, these subsections of life activities and incidences
defined as life domains and individuals have different life domains that generate
positives and negatives of their overall life satisfaction and happiness (Rojas, 2006).
Persons’ family life, work life, economic status, social life and leisure life consists
with the different life domains and intrinsic motivation in specific domain provide
some weight to the subjective success of the life. Therefore, intrinsic psychological
motives need to be defined as the predictors of the subjective success of life that has
been measured as the overall or global subjective life satisfaction and happiness.
Sometimes people have wealth and the process of earning the wealth might be
challenging, bring pride in achievements, moral satisfaction, and even feel of
autonomy. However, they have to sacrifice certain aspects of happiness and
satisfaction of their other domains of lives to achieve it. They might not feel
happiness and hence, might not achieve the subjective success. On the other hand,
some people do not have wealth, not fighting for gaining wealth, but feel the
happiness of their lives and hence achieve the ultimate subjective success.
Differences in the subjective definitions of the individual goals, objectives and
desired outcomes, influence to the differences in subjective meaning of the success.
According to the available literature on grassroots level inventors, the majority of
them have been involved in inventive activities as either part time employee activity
(Wieck & Martin, 2006) or leisure time activity (Dahlin, Taylor, & Fichman, 2004).
It makes the inventors’ inventive activities and outcomes to be influential on
different life domains that have roots to their work life domain, leisure life domain
and economic life domain. Therefore, the objective outcomes of inventive activities
and perceived intrinsic motives within the inventive domain of the life of the
50
grassroots inventors need to be considered as the predictors of their subjective
success, but not as the end of the achievements. Hence, the other intrinsic
psychological status of mind should be generally considered as the predictors of the
subjective success.
Measures of Subjective Success: Life Satisfaction and Happiness
Even though the recent literature have given significant attention to the happiness
and satisfaction of life, first measurement scales of global life satisfaction and
happiness have been developed way back in 1961 (Neugarten, Havighurst, & Tobin,
1961). However, since then theories of subjective well being have developed a long
way. In recent literature, the cognitive component; relatively stable facet defined as
the satisfaction and the affective component; relatively dynamic facet defined as the
happiness have been the yardsticks to differentiate the measurements of subjective
success (Pavot & Diener, 2009). Measures of the affective component focus on
global happiness, positive and negative life effects. The measures of cognitive
components focus on the long term, relatively stable life satisfaction. Andrew and
Robinson (1991) comprehensively elaborated on the available measures of subjective
success and indicated that in recent years, there has been a remarkable increase in the
studies on subjective success. According to Andrew and Robinson, those studies
have used different scales to measure the different dimensions of subjective success.
According to their explanations, there are two broad categories of subjective success
scales; single item scales and multi item scales (Figure 3). The available measures of
subjective success either assess affective or cognitive components of positive feeling
using single item or multi item global measurements (Lyubomirsky & Lepper, 1997,
p. 139). Single item scales have used only one question to measure the happiness and
51
life satisfaction (Andrew & Robinson, 1991, p. 72; Lyubomirsky & Lepper, 1997, p.
139). In single item scales, respondents were asked single question that generally
starts with “Your life as a whole….” to measure the general happiness and
satisfaction or “how satisfied you with….” to measure the specific domain
satisfaction.
Affect Component
Happiness
Single Item Scale
Multi item Sacle
Subjective Success
Cognitive component
Satisfaction with life
Single Item Scale
Multi Item Scale
Figure 3: Focus and Scope of subjective well-being global measures
The expected response to the question could be long or short depending on the
research approach and data collection method. Owing to the economies of the
administration, a majority of the pioneering studies of subjective success have used
the single item scales. Bradburn’s Affect balance scale (1969), Andrew and Withey’s
Delighted-terrible scale (1976), Cantril’s Self-anchoring scale (1960) and Bradburn’s
Global happiness item (1969) are the popular single item scales that have measured
the different aspects of subjective success. Even though intense usage of single item
measures of happiness and satisfaction in the early days of subjective success studies,
single item scales are not the suitable alternative for the multi item scales. Generally,
compared to single item measures, multi item scales have higher validity and
reliability because it reduces the random and measurement errors, therefore it is
recommended to be use in future studies (Diener E. , 2009 c).
52
The present study intends to measure the global subjective success of the grassroots
level inventors. A conceptual definition of the subjective success has considered both
affective and cognitive components of the subjective well-being and therefore, it
demands to utilize composite global measurements of happiness and satisfaction with
life to measure the subjective success. As per Diener et al. (1985), majority of the
available measurements have measured the domain satisfactions rather than the
global satisfaction of life. Therefore, Diener et al. (1985) developed the Satisfaction
with Life Scale (SWLS) to measure the overall satisfaction of life. Owing to the high
reliability, validity, small number of items and open access, it has been the most
popular measurement of the cognitive component of the subjective well-being
studies conducted in various countries in different populations (Pavot & Diener,
2009). What was missing in the literature for a long time the measurement of global
“subjective happiness” to assess the affective aspect of whether the person is a happy
or an unhappy person (Lyubomirsky & Lepper, 1997). As per Diener, such measure
reflects a broad and more complex category of well-being and tap into more global
psychological phenomena (Diener E. , 2009 c). Lyubomirsky & Lepper, (1997)
developed, tested and validated a multi-item instrument to measure global happiness;
the Subjective Happiness Scale (SHS). Subjective Happiness Scale has been
translated to different languages and identified as most popular and accurate
measurement of affective component of subjective success (Swami, 2008; Shimai,
Otake, Park, & Seligman, 2006; Chen & Davey, 2008). Therefore, in this study
SWLS and SHS were used to measure the subjective success of the grassroots level
inventors in Sri Lanka.
53
Theoretical Framework of the Study
Veenhoven (2008) has attempted to develop sociological theory of subjective
success. Subjective success has inductive theoretical link with sociological theory of
the nature and behavior of good society. According to the Veenhoven’s attempt to
develop sociological theory, he explained subjective success as an outcome of social
system, as well as a factor of its functioning. Therefore, subjective success belongs to
the core aspect of sociology. Veenhoven has suggested four questions to be answered
to establish a sociological theory of subjective success. First, what is subjective
success, specifically, how to distinguish subjective success from its determinants?
Second, how people appraise how successful they are. Third, what are the conditions
that increase the subjective success? It is closely linked to how subjective success
can be raised. The last question is what are the consequences of subjective success?
(Veenhoven, 2008). In his theory, Veenhoven has explained that the sociological
literature on the subjective success do not sufficiently answer these four questions to
establish the sociological theory of subjective success. However, he suggested the
importance of understanding the subjective success as one of the key determinants of
social behavior. Therefore, the fundamental theoretical basis of the present study was
Veenhoven’s theoretical discussion on subjective success. The present study attempts
to answer Veenhven’s four principal questions to develop a theoretical argument of
subjective success by examining how the subjective success, its predictors and
consequences work within the grass-roots level inventors in Sri Lanka. Further, the
development of conceptual model is supported by the bottom-up and top-down
theories of subjective success.
54
The Bottom up theories explain the subjective success as sum of many small
pleasures of different life domains (Diener E. , 2009 a). Following this theoretical
tradition, Emmons’s goal achievement theory stated that the presence of challenging
positive goals and making progress towards achieving them would influence to
positive effect and global life satisfaction (Emmons & Diener, 1986). Kilinger (1977)
said that being preoccupied with trying to avoid negative outcomes and incentives
(unsatisfied needs and problems) produced negative effects than the positive effects
on people (Klinger, 1977). Therefore preparing for the elimination or avoidance of
the negative outcomes (problems and unsatisfied needs) would not generate positive
psychological effects on people. The validity of Klinger’s argument has been
empirically proven by Emmons’s goal achievement theory (Emmons & Diener,
1986). People who have challenging positive goals and who are progress towards
achieving those positive goals have shown higher subjective success. According to
Emmons’s theory, positive or negative events, circumstances, and even demographic
factors can have impact on subjective success, because they influence on the one’s
ability to achieve goals. Therefore, the objective goals (objective success) works as
mediators in a relationship between different life domains and the subjective success.
Therefore, according to Emmons’s goal-achievement theoretical proposition,
demographic, psychological, technical and social factors of particular community can
be influenced directly or indirectly on the subjective success of them through the
mediation of objective success.
The top down theories of subjective success explains how subjective success can
influence other life domains (Diener E. , 2009 a). Barbara Fredrickson’s Broadenand-Build theory of positive emotion proposes that positive emotions are evolved
adaptations that to build lasting resources (Fredrickson, 1998; Cohn, Fredrickson,
55
Brown, Mikels, & Conway, 2009). This theory discusses how the positive emotions
(happiness, interest, contentment and love) and outcome of positive emotions
(subjective success) influence on broadening and building of new knowledge, skills
and resources of an individual. According to Fredrickson’s theory, positive emotions
create new and broad range of thoughts and actions that might not critical to
immediate need satisfaction and problem solving. However, overtime, these new
experiences aggregate into consequential knowledge and resources that can change
the people’s lives. People can use these new resources long after the happy
experience to meet life’s challenges and opportunities (Fredrickson, 1998, p. 305).
According to the Broaden-and-Build theory, subjective success is more than just the
summation of good or bad feelings over time. Therefore, subjective success has
effects on broadening and developing durable individual intellectual, physical and
social skills (Figure 04).
Scope of
Attention
Social
Resources
Scope of
cognition
Positive
Emotions
Intellectual
Resources
Scope of
action
Physical
resources
Broaden
Build
(Adapted from: Fredrickson, 1998)
Figure 4 : Broaden-and-Build Model of Positive Emotions
56
Level of Broaden-and-Build of resources by the positive emotions depends on the
growth of the multi facet ego-resilience skill that involves emotion regulation,
problem solving and the ability to change (Cohn, Fredrickson, Brown, Mikels, &
Conway, 2009). Therefore, subjective success is influenced by the ego-resilience and
it depends on individual level factors of the people, which can perceive the positive
emotions differently (Letzring, Block, & Funder, 2005). This might be the reason,
why people who have faced similar experiences have reacted differently. Therefore,
level of happiness and satisfaction of members of a group will broaden and build
their resources at different levels. According to Fredrickson’s theory, people who
show higher happiness and satisfaction tend to have higher capacity and resources to
face future challenges and opportunities. When it considers a community, members
with higher level of happiness and satisfaction (Subjective success) should have
higher level of resources compared to members with lower level of subjective
success. Therefore, understanding the subjective success of community members
would provide new knowledge and resources depository that can be used to build
communities inside out.
The bottom-up theoretical perspective of the subjective success explains how the
different life domains influence on the subjective success as a dynamic state of mind
while Fredrickson’s broaden-and-build theory from the top-down theoretical
perspective of the subjective success explains how the subjective success as a static
trait of the mind that paybacks to different life domains (Figure 5).
57
Factors of
life domains
Objective
Success
Subjective
Success
Bottom-up theory
Objective
Success
Factors of
life domains
Top-down theory
Figure 5: Bottom up and top down theories of Subjective success
These two theoretical propositions collectively signify the importance and motivate
the researcher to explore the behavior of the subjective success of grassroots level
inventive community to empower them to achieve successful community
development. So far, majority of the subjective success studies have followed either
bottom-up or top-down theoretical traditions (Headey, Veenhoven, & Weari, 2005).
Even though the pure bottom-up or top-down studies are common, they have not
adequately summarized the relation between different life domains and subjective
success (Headey, Veenhoven, & Weari, 2005).
In their study, Headey et al. (2005) suggested that there might be factors that have
bottom-up causation, top-down causation or two-way causation with the subjective
success. Therefore, true nature of the subjective success could not be studied with
pure bottom-up view or pure top-down view. Owing to the undeniable validity of
these two propositions, recent studies of subjective success have tried to test the
casual directions of the two propositions using longitudinal studies (Brief, Butcher,
George, & Link, 1993). Kline (2010) suggested the possibility of using two opposing
correlation models with the variables of cross sectional study to get the statistically
acceptable approximation of the causal direction of the relationship between
58
variables. Owing to the inherent time constraints, the present study was designed as a
cross-sectional study. Therefore, the study has to follow the two-model comparison
approach to determine the causes of subjective success and to analyze the reversal
model to determine the consequences of subjective success as the practice suggested
by Kline (2010). This approach was expected to answer the Veenhoven’s four basic
questions for developing sociological theory of subjective success. In order to select
the variables to construct the research model, the researcher critically analyzed the
significant variables in the previous studies on inventors with the studies on
subjective success and its two facets.
Correlates of Subjective Success
Analogous to the different theoretical arguments, there are at least two broad
perspectives on placing the locus of subjective success in internal and external
conditions. Psychological theorists argued that individual’s attitude and personality
matters on the subjective success, whereas, sociological theorists believed the sociodemographic and economic factors as the dominant forces in producing subjective
success (Diener E. , 2009 a).
However, according to the history of research
conducted on subjective success in different segments of the society, there is a mix of
both internal and external domain factors that influence subjective success.
Demographic factors
Gender: In literature there are so many factors that have been identified as the
predictors of subjective success. Many studies have found that the demographic
factors such as gender and age are not the consistent predictors of subjective success
59
(Rogatko, 2010; Diener E. , 2009 b; Jian, Qingyue, Yip, Qiang, Jiangbin, & Liying,
2010). As per the Diener’s Meta analysis of research conducted on subjective
success, there is little difference in global happiness and satisfaction usually found
between sexes (Diener E. , 2009 a). Therefore, in multi-factor well-being studies that
had more predictor variables, gender has not been a popular predictor variable
(Rogatko, 2010). Owing to the lack of literature evidences of the causal relationship
between gender of subjective success and lower female representation in the
grassroots level inventive community, present study ignored the gender as a predictor
variable of the subjective success.
Age: Age is also an inconclusive predictor of subjective success. The findings of the
correlation between age and subjective success have not been conclusive. As per
Diener (2009), there are three types of findings in earlier studies, which had
considered age as a predictor variable of subjective success. Some of the initial
studies found that young people were happier than old (Bradburn and Caplovitz,
1965; Gurin et al. 1960; Wessman, 1957 in Diener, 2009). There are some studies
found positive correlation between age and subjective success (Medley, 1980;
NehRke, Hulicka, & Morganti, 1980). Some of the studies found that there is no
impact of age on subjective success. According to Diener (2009), Alston et al. 1974;
Cameron, 1975; Spreitzer and Snyder, 1974 had found that there is no significant
relationship between age and subjective success. Most of the recent findings indicate
that subjective success does not correlate with age. Diener and Suh (1997) conducted
an international meta analysis of subjective well-being and age with large sample
surveys drawn from many nations. According to the findings, subjective success is
not correrelated with age (Diener & Suh, 1997 b). Horley and Lavery (1995) also
studied the relation between age and subjective success. They have concluded that
60
even though there is positive relationship between age and subjective well-being, it
is equivocal (Horley & Lavery, 1995). Study conducted by Ehrlich and Isaacowitz
(2002) had concluded that there is no significant difference of subjective success
among different age groups (Ehrlich & Isaacowitz, 2002). Clark and Osward (2006)
revealed that the relationship between age and subjective success is non-linear
relationship (Clark & Oswarld, 2006). Therefore, predicting a linear relationship
between subjective success and age of grassroots inventors is not justified in the
recent literature because age was considered as a doubtful predictor variable of
subjective success. Hence, decision to exclude age from the path models was taken
based on the findings of the exploratory data analysis of the present study. However,
the researcher explored the association of age and subjective success using crosstabulation chi-square test.
Marriage: Even though there are inconsistent results of gender and age, marital
status and income have consistently shown low and moderate level positive
correlation with well-being (Kim & McKenry, 2002; Diener E. , 2009 a). Marriage is
not just a relationship between two people. Generally, it links with the other
important life domains such as children, economy, social status and relatives. These
newly connected strings of relationships bring both positive and negative effects to
the people. Therefore, marriage is one of the main demographic factors that predict
the subjective success of a person. Diener et al. (2000) found that impact of marriage
on subjective success is constant around the world (Diener, Gohm, Suh, & Oishi,
2000). A study comprising 59,169 respondents from 42 nations has shown that
married people are happier than the unmarried ones. In literature, other than few
studies in 1970s and 1980s, virtually all the studies have shown that marriage is
positively correlated with subjective success (Diener E. , 2009 b). Therefore, the
61
present study also assumed significant positive influence of marital status on
subjective success.
Income: Even though income is correlated with subjective success, it has
consistently shown very low positive correlation with subjective success (Headey &
Wooden, 2004; Cummins, 2000; Conceição & Bandura, 2008). Unlike other factors,
money has a capability to capture the required resources that can bring happiness to
people (Cummins, 2000). However, a study very wealthy people in USA revealed
that money is not a factor of their happiness (Diener, Horwitz, & Emmos, 1985).
When the people have very poor income, it will become important predictor of
happiness. Nevertheless, for middle and high income earning people, income will not
be a significant factor of their well-being (Diener & Diener, 2002). Therefore, even
if the strength of the relationship between income and subjective success is lower
than the expected, it is still considered as important predictor variable in subjective
success studies (Headley & Wooden, 2004). Therefore, the present study also
expects a significant positive influence of income on the subjective success of the
grassroots level inventors in Sri Lanka.
Technical factors
Engagement in invention: A number of studies have investigated the impact of
technical factors such as involvement in interesting effective work and leisure
activities on subjective well being of the people. Snyder and Lopez (2007) stated that
people who are participating in exciting activities that match or challenge their skills
in daily life tend to be very happy and tend to continue these activities in their life
(Snyder & Lopez, 2007, p. 138). A study conducted by Reis et al, (2000) found that
62
every day activities and events that contribute to autonomy, competence and
relatedness have influenced the level of subjective success (Reis, Sheldon, Gable,
Roscoe, & Ryan, 2000). Another study has shown that carrying out activities, which
provides a sense of competence, sense of autonomy and personal freedom highly
contribute to the subjective success (Miller, Marks, & Michaelson, 2008). On the
other hand, in the literature on subjective success, leisure activities have identified as
a positive contributor for the happiness and satisfaction (Lu & Argyle, 1994;
Steinkamp & Kelly, 1987; Argyle & Martin, 1991; Nimrod, 2007; Pressman, et al.,
2009). Most of the people feel happy and satisfied with their leisure time activities.
People spend serious amount of money in leisure activities and some of the activities
are even dangerous life threatening physical activities (Celsi, Rose, & Leigh, 1993;
Schnohr, 2009). According to the series of publications of Davis and Davis and
others, grassroots level inventors have been defined as ‘leisure time’ inventors who
are involved in inventive activities during their leisure time as a hobby activity
(Davis & Davis, 2007; Davis & Davis, 2007 (b); Davis, Davis, & Hoisl, 2008; Davis,
Davis, & Hoisl, 2009; Dahlin, Taylor, & Fichman, 2004). Therefore, the present
study hypothesized that the time spends on engaging in inventive activities is a
significant positive predictor of subjective success of the grassroots level inventors.
Internet Usage: Internet usage has been identified as an influential factor of
knowledge development, social thinking and subjective success (Kraut, Kiesler,
Boneva, Cummings, Helgeson, & Crawford, 2002; Contarello & Sarrica, 2007;
Weiser, 2004). Internet has redefined the way social relationships are progressing
(Kraut, Kiesler, Boneva, Cummings, Helgeson, & Crawford, 2002). In 1990s Internet
is thoughht to be negative to the social and psychological success. However, recent
empirical studies have found that the Internet either have no impact (Jackson, Eye,
63
Barbatis, Biocca, Fitzgerald, & Zhao, 2004) or have positive impact on the happiness
and satisfaction of people who use Internet resources for their advantage (Kiesler,
Kraut, Cummings, Boneva, Helgeson, & Crawford, 2002).
Recently concluded
World Values Survey found that there is positive relationship between internet usage
and happiness (Kelly, 2010). Studies on independent inventors have found that
Internet usage is one of the main resource providers that is becoming very popular
among the inventors (Georgia Tech Enterprise innovation Institute, 2008). Therefore,
Internet usage of grassroots level inventors was expected to have significant positive
influence on their subjective success.
Psychological factors
Inventive Career Satisfaction (ICS): Owing to the moderate impact of
demographic factors on subjective success, most of the studies have attempted to
investigate the impact of psychological factors on subjective success (Diener, Oishi,
& Lucas, 2003). According to literature, work domain satisfaction has been
significantly correlate with the subjective success (Diener E. , 2009 b; Diener, Oishi,
& Lucas, 2003). Hence, work life has been identified as significant life domain that
gives both positive and negative impact on subjective success. Studies have found
that inability to engage in satisfying employment is negatively correlated with
subjective success (Korpi, 1997). When people engage in a challenging and
interesting work in their life, successful achievements of interesting work have also
influenced subjective success (Argyle & Martin, 1991). Elliot, Sheldon and Church
(1997) revealed that students who had avoid their personal goals (feel negatively
about the outcome) were having lower subjective success and students who had
achieved their personal goal ( feel positive about the outcome) had shown higher
64
subjective success (Elliot, Sheldon, & Church, 1997). As a inventor, Thomas Edison
had quoted that the success of inventor creates within the positive experiences of
inventive work (eQuotes.com, 2008). Therefore, the present study hypothesized a
significant positive influence of inventive career satisfaction on the subjective
success of grassroots level inventors.
Maximizing tendency: There are a number of studies that argued that individual
differences play an important role to determine how a person would react in life
circumstances (Snyder & Lopez, 2007; Argyle & Martin, 1991; Carver, Smith,
Antoni, Petronis, Weiss, & Derhagopian, 2005; Cohn, Fredrickson, Brown, Mikels,
& Conway, 2009; Wrosch & Scheier, 2003). Maximizing tendency is defined as
seeking only for the best option and not settling for anything less (Schwartz, Ward,
Monterosso, Lyubomirsky, White, & Lehman, 2002). Schwartz et al. (2002) have
attracted considerable attention to the concept of maximizing tendency by proposing
that individuals differ in their global disposition to maximize versus satisfies in
decision-making (Lai, 2010). As Diener suggested, apart from the Big-five traits,
personality constructs such as maximization tendency and optimization can be used
to measure the different dimensions of relatively stable psychological traits. A
majority of the studies found that maximizing tendency have negative effect on the
subjective success (Schwartz, Ward, Monterosso, Lyubomirsky, White, & Lehman,
2002; Lyenger, Wells, & Schwartz, 2006). Schwartz (2004) mentioned that
maximizing is a receipt of unhappiness (Schwartz B. , 2004). Even though the
maximizers can achieve better decisions and choices, they become negative
evaluators of the outcomes of the decisions and therefore not make better decisions
(Lyenger, Wells, & Schwartz, 2006; Parker, Bruin, & Fischhoff, 2007). Sometimes
maximizing tendency leads to negative objective outcomes and therefore, it increases
65
unhappiness (Polman, 2010). Hence, most people who are looking for maximum
results are not happy and satisfied with their lives. On the other hand, satisfiers
positively evaluate whatever outcome they received and therefore subjectively be
happy and satisfied. The grassroots level inventors are single-handed decision
makers. Therefore, the present study hypothesizes that maximizing tendency might
be a significant negatively related predictor variable of the subjective success of
grassroots level inventors as well.
Life orientation: Life orientation comprises of two propositions, optimism and
pessimism. Optimism is a personality variable that reflects the extent to which people
hold generalized favorable expectancies for their future (Carver, Scheier, &
Segerstrom, 2010). Optimists expect good outcomes, even when things are hard and
hostile. This generates relatively positive mix of feelings and generally, higher level
of optimism generates higher subjective success. Pessimistic expect bad outcomes
and this yield feelings like anxiety, stress and anger, which leads to lower subjective
success (Carver & Scheier, 1998). A number of recent studies have shown that
optimistic personality have positive correlation with the subjective success (Carver,
Smith, Antoni, Petronis, Weiss, & Derhagopian, 2005; Carver, Scheier, &
Segerstrom, Optimism, 2010; Cha, 2003). Based on the findings of the past studies,
present study considered the life orientation as a possible predictor of subjective
success of inventors. The study hypothesized a significant positive influence of life
orientation on the subjective success of grassroots level inventors.
66
Social and community factors
Social Capital: Phillips and Pittman ( 2009) defined social capital or capacity as the
extent to which members of a community can work together effectively to develop
and sustain strong relationships to solve problems, make group decisions and
collaborate effectively to plan, set goals, and get things done (Phillips & Pittman,
2009, p. 6). Recent literature in social sciences and community development
highlighted the importance of individual social capital as significant contributor of
subjective success (Yip, Subramanian, Mitchell, Lee, Wang, & Kawachi, 2007;
Cheung & Chan, 2008; Helliwell & Putnam, 2004). Social capital improves the
subjective success by giving opportunities to the community members to share
knowledge, resources and feelings (Winkelmann, 2009). Hence, social capital is one
of the primary features of socially organized communities and it allows citizens to
resolve collective problems more easily (Wiesinger, 2007). Social capital has been
defined at both individual level and collectivities (Portes, 2000). Grassroots level
inventors are work alone. Hence, the individual social capital is more appropriate for
them. Past studies have confirmed that the individual level relationships with family,
friends, neighbors and other social organizations positively contributed to the
subjective success (Helliwell & Putnam, 2004; Hooghe & Vanhoutte, 2009). Hence,
the present study expects the individual social capital as a significant positive
predictor of subjective success of grassroots level inventors.
Community Connectedness (Sense of Community): Community connectedness or
sense of community is a feeling that members have belonging, a feeling that
members matter to one another and to the group, and a shared faith that members’
needs will be met through their commitment to be together (McMillan & Chavis,
67
1986). Apart from social impact of individual social capital, there are number of
studies, which examined the impact of community connectedness on subjective
success. Past studies have found that social connectedness or sense of community
positively correlate with the subjective success (Helliwell J. F., 2003; Helliwell &
Putnam, 2004; Winkelmann, 2009; Helliwell J. F., 2007). On the other hand, lack of
social contacts has negatively correlated with the subjective success (Dolan,
Peasgood, & White, 2008). Davidson and Cotter (1991) found that a strong sense of
community has positively correlated with the happiness of the people (Davidson &
Cotter, 1991).
In their study, Yoon, Lee and Goh also had found a positive
relationship between social connectedness and subjective success (Yoon, Lee, &
Goh, 2008). Owing to the majority of studies on sense of community or community
connectedness indicate positive correlation with subjective success, the present study
hypothesized the community connectedness as a significant positive predictor of the
subjective success of grassroots level inventors.
External Linkages: Social capital and community connectedness relate more to the
physical and cognitive relationships within a community. However, community
strength involves strong relationship between community members, external
organizations, and structures that provide goods and services to the community. The
behavior of the general economy and its institutions have impact on the well-being of
the people (Frey & Stutzer, 2002). Hence, the success of community members partly
depends on the linkages between community and the external organizations that
provide services and products to the community (Hughes, Black, Kaldor, Bellamy, &
Castle, 2007, p. 83). According to Huges et al. (2007), this influence of external
linkages has been excluded from the social capital and therefore need to consider
separately. In grassroots level inventive community, external Linkages can be any
68
advices, opinions, services and technical encouragements received from the
specialized organizations, knowledgeable persons and sources relating to the
inventions that considerably helped the inventor to get their inventions up to the
current standard. A study on Georgia’s independent inventors has revealed that
successful inventors have positive attitude on their external linkages, which were
helped them during their invention process (Georgia Tech Enterprise innovation
Institute, 2008). Therefore, individual assessment of quality of the external linkages
is expected to have positive influence on subjective success. However, if the people
perceived as external linkages do not provide adequate service to them, this would
reduce their confidence. According to the Huges et al. (2007), confidence on the
organization and their services depend on how the organization considers the public
interest. Therefore, the impact of linkages on subjective success depends on the
confidence of the relationship and it can be either negative or positive depend on the
experiences of the people. Therefore, the present study hypothesized external
linkages as a significant predictor variable of subjective success.
69
Correlates of Objective Success
Different theories and previous empirical studies on different groups in the society
revealed that the majority of the internal and external factors that influence
subjective success also contributed to their objective achievements, performances
and success.
Demographic factors
Age:
Large body of evidences supports the notion that cognitive abilities,
productivity and performances decline from some stage in adulthood (Skirbekk,
2003). According to the Lehman (2006), innovation activities are greater at young
ages and tend to be lower in late ages. Hence, inventor’s research and inventive
output rise steeply in young ages, reach the peak at middle ages and then reduce at
older ages (Lehman, 2006). Page (1962) mentioned that a young scientist’s mind is
not filled with many information and facts. Therefore, they are ignorant enough to
undertake unreasonable and unlikely inventive projects that would not achieve
success (Page, 1962). Even though the number of inventions has been high with
younger inventors, successful achievements have been high with older inventors.
Recent empirical study on age and great inventions revealed that successful
achievements of inventors are achieved in late years (Jones, 2010). However, the
literature evidences shows that the relationship between age and objective success is
non-linear, which cannot be explained in linear model. Therefore, the researcher
excluded the age from the linear path model based on the confirmation of non-linear
relationship by the exploratory data analysis results of the present study.
70
Nevertheless, the researcher was planning to explore the impact of age on objective
success using categorical level data in cross-tabulation chi square test.
Income: Past studies have shown significant positive influence of income on general
physical life such as health and education (Ettner, 1996; Fritzell, Nermo, &
Lundberg, 2004; Dahl & Lochner, 2005). Most of the studies have revealed income
is a significant predictor of objective success or achievement. Dahl and Lochner
(2005) had revealed the impact of family income on child achievement (Dahl &
Lochner, 2005). Another study on the influence of parent education and family
income on child achievement had revealed indirect impact of income on achievement
(Kean, 2005). Past studies on independent inventors also indicated that, majority of
the successful inventors were having relatively higher income (Amesse &
Desranleau, 1991; Georgia Tech Enterprise innovation Institute, 2008). Owing the
fact that the past studies indicate positive relationship between income and objective
outcomes, the present study assumed income as a significant positive predictor
variable of the objective success of the grassroots level inventors.
Marriage: According to the surveys on independent inventors, the majority of
inventors are married (Georgia Tech Enterprise innovation Institute, 2008; Whalley,
1992; Rossman, 1931). Winston (1937) found that 98% of American inventors had
married and there was high re-marriage rate among American inventors (Winston,
1937). The previous research findings indicated that there is a relationship between
marriage and objective achievements. Pfeffer and Ross (1982) found that being
married has positive effects on the occupational status and wage attainments (Pfeffer
& Ross, 1982). In their study, Ginther and Kahn (2009) concluded that married men
have higher success rate than married women in science and engineering fields.
71
Therefore, marriage has been a predictor variable of the objective career success.
Based on the findings of previous studies, the present study hypothesized there
would be a significant positive effect of marital status on the objective success of Sri
Lankan grassroots level inventors.
Technical factors
Engagement in invention:
Inventing is a process that consists of a series of
activities (Hauschildt, 1991). Independent inventors need to execute all the activities
in the process on their own. Therefore, independent inventors need to spend more
time on inventions related activities to achieve success in their inventions (Dahlin,
Taylor, & Fichman, 2004). Hence, the time spent on inventive activities becomes a
significant factor in the success of grassroots level inventors. Past study on perceived
chances of success of entrepreneurs revealed the long working hours as a factor of
success (Cooper, Woo, & Dunkelberg, 1988). Then again Weick and Eakin (2005)
and Weick and Martin (2006) hinted that full time commitment required to achieve
commercial success of the independent inventions. Therefore, it was realistic to the
researcher to assume a significant positive influence of time spends on invention on
the objective success of grassroots level inventors.
Internet Usage:
Internet has changed the nature and aspect of information and
communication technology. Hence, it has identified as world largest knowledge
depository and efficient communication channel. Generally, Internet has recognized
as a tool that can increase technology transfer across the developing countries to
make them succeed in technological development (UNDP, 2001). NESTA research
report on the new inventors indicates that internet is rapidly creating product users as
72
grassroots level inventors (NESTA, 2008). Not only Internet created new inventors,
internet has identified as a critical success factor of modern small businesses (Sparks
& Thomas, 2001). The Internet usage is considered as one of the major contributors
of the improvements of the performance of R&D activities and innovation
(Kafouros, 2006). As far as number of inventions has grown with the expansion of
internet, there are evidences that internet usage have influence on inventors. Findings
of the 2007 Georgia’s independent inventors indicated that internet is among the top
three resources of commercially successful inventors (Georgia Tech Enterprise
innovation Institute, 2008). Therefore, in present study, the researcher hypothesized
that internet usage is a significant positive predictor of the objective success of
grassroots level inventors.
Psychological Factors
Inventive Career Satisfaction (ICS): Even though the intuitive appeal may suggest
an unconditional positive relationship between career satisfaction and career success,
literature evidences have not suggested such unidirectional strong relationship
between career satisfaction and success. Some studies revealed that career
satisfaction (job satisfaction) is not a strong predictor of the performance (Petty,
Mcgee, & Cavender, 1984; Iaffaldano & Muchinsky, 1985). However, recent
literature has shown positive relationship between work satisfaction and objective
career success. The study by Abele and Spunk (2009) indicated that over the time,
job satisfaction has more influence on the growth of objective success (Abele &
Spunk, 2009). A study on managers and US professionals also revealed that
expectancy disconfirmation, contradictory role demand, sense of external control and
loss of afflictive as significant factors of personal failures (Koman, Berman, & Lang,
73
1981). Further, study on impact of inventor’s perceived past success on their future
performances revealed that inventors who achieve past success gain knowledge
through experience and are satisfied with their inventive careers. Hence, they tend to
generate increasing incremental inventions (Audia & Goncalo, 2007). Therefore,
inventors who have high inventive career satisfaction are expected to achieve higher
objective success. Therefore, in the present study the researcher hypothesized a
significant positive impact of inventive career satisfaction on the objective success.
Maximization Tendency: Personality traits have been identified as a significant
determinant of successful decision making of a person (Saunders & Stanton, 1976).
According to Schwartz (2004), maximizing represents a way for unhappiness due to
overly high expectations and self-fulfilling fears of regret (Schwartz B. , 2004). As
study by Lyenger et al. (2006) revealed that students with high maximizing tendency
gain highly paid jobs than students with low maximizing tendency (Lyenger, Wells,
& Schwartz, 2006). However, Polman (2010) propose that maximizing tendency not
only relates to positive objective outcomes, but also the maximizing tendency has
positively related to the negative objective outcomes as well. His empirical findings
have shown that people with maximizing tendency often reject the objective
outcomes by evaluating them as not optimum (Polman, 2010). Therefore,
maximizing tendency can also be negatively correlated with the objective
achievements. Therefore, the correlational direction of the maximizing tendency and
objective success is vague to some extent. Therefore, the researcher only
hypothesized that there is a significant influence of maximizing tendency on the
objective success of grassroots level inventors.
74
Life Orientation: Previous studies have shown a number of evidences to believe
that optimism has positive effect on objective success. Most of the studies have
indicated positive relationship between optimism and the performances (Anzi &
Owayed, 2005; Hoy, Tarter, & Hoy, 2006). Shulman (1999) has indicated that
optimism has capability to increase the motivation, superior achievement in work
and better health (Schulman, 1999). According to the literature, optimism had
positively influenced to the physical and mental health of people (Schacter & Addis,
2007). Optimistic individuals were found to have more successful treatment
outcomes for heart disease, cancer, and general surgery (Scheier & Carver, 1985).
Study on school students’ mathematic performance has shown that student who are
pessimistic about their lives are not performing well in mathematics (Yates, 2002).
As per Schacter and Addis (2007), even though, be an optimistic is good thing for
life, some time people may be optimistic to fault predictions. Some studies have
revealed that over optimism or optimistic bias may have negative effects on the
objective outcomes (Chapin, 2001). Over optimism make people set unrealistic
targets by ignoring the realistic constraints and believe that they can achieve them
(Weinstein, 1980). Optimism not always promotes the adaptive behavior, but
sometimes it can even be detrimental. According to Sholey et al. (2002), unrealistic
optimism about the future, sometimes negatively correlates with achievement and
lead people to live with risk behavior (Shorey, Snyder, Rand, & Hockemeyer, 2002).
In such situation, pessimism can facilitate preparedness and the use of strategies to
reduce the occurrence of negative outcomes (Schacter & Addis, 2007). A study on
inventor’s behavior indicates that optimistic independent inventors continue with
their invention developments even though experts suggest to stop it and finally
receive negative results (Astebro, Jeffrey, & Adomdza, 2004). Owing to a large
75
number of studies have been divided into for and against the impact of optimism on
objective achievements, in the present study the researcher has to explore the
directional impact of optimism on the objective success of grassroots level inventors
in Sri Lanka.
Social factors
Social Capital: There are large number of empirical studies, which measured the
strengths of the influence of social capital on objective success. The majority of
studies on social capital and success have suggested positive relationship between the
two variables. A study has shown that social capital has influenced to the careers
success (Seibert, Kraimer, & Linden, 2001). Then again, Stachowicz and Somka
(2004) identified social capital as a critical success factor of the innovation
development projects (Stachowicz & Somka, 2004). Tymon and Stumpf (1996)
explain how social capital can influence the success of the knowledge workers
(Tymon & Stumpf, 2003). Hoing (1998) found that social capital as an influencing
factor of the success of Jamaican micro entrepreneurs (Hoing, 1998). In his book,
Baker (2000) explains the importance of social capital to achieve the business
success (Baker, 2000). Owing to the large number of studies that indicated the
positive influence of social capital on the success, the present study hypothesized a
significant positive influence of social capital on the objective success of grassroots
level inventors.
Community Connectedness: Fleming et al. (2004) recognized the regional network
of inventors enhance the innovation capabilities, which leads to the success of
Silicon Valley in the USA (Fleming, Colfer, Marin, & McPhie, 2004). Sorenson and
76
Singh (2007) mentioned that diffusion of tacit knowledge stem from the social
connectedness between the scientists (Sorenson & Singh, 2007). Further, empirical
studies on community connectedness and success have also shown the positive
correlation between connectedness and success. The study by Bain et al. (2010)
found that community connectedness as one of the key success factors of the
performance of graduate students (Bain, Fedynich, & Knight, 2010). Therefore, the
present study hypothesized that there is a significant positive relationship between
inventors’ connectedness and their objective success.
External Linkages: External linkages can be any advice, opinion, service, financial
assistance and technical encouragement received from the specialized organizations,
knowledgeable persons and sources relating to the invention that considerably helped
the inventor to get their invention up to the current standard. Linkages are attached to
all the sub processes of the innovation process. Hence, the level of assistance
received at each stage of the process contributes to the overall success of the
inventor. Owing to that, previous researchers have given special attention to the
external linkages by examining the role of intermediates in the innovation process
(Howells, 2006). There are upstream intermediates, which are helping the inventors
in information searching, idea generation and downstream intermediates, which are
helping the inventors to get patents and commercialize their inventions. In literature,
intermediation role at commercialization has overlooked the role of helping inventors
at inventing and patenting efforts (Hoppe & Ozdenoren, 2005). Especially
independent inventors are not getting formal organized support, and they desperately
need government support to succeed (Rines, 2003; Svensson, 2007). Many studies
highlighted the lack of financial support as the major barrier for the independent
inventors to commercialize their inventions (Dahlin, Taylor, & Fichman, 2007;
77
Svensson, 2007). That makes the external linkages to become one of the most
significant factors of the success of independent inventors. Therefore, the present
study hypothesized a significant positive relationship between external linkages and
the objective success of grassroots level inventors in Sri Lanka.
Conceptual Framework of the Study
Since Aristotle had described happiness as the ultimate aim of the human existence,
the majority of the theoretical models in subjective well-being/success studies have
been designed as bottom-up models that considered the subjective success as the
ultimate effect that has many causes (Headey, Veenhoven, & Weari, 2005). Diener,
Oishi and Lucas (2003) have hypothesized that the subjective success as the ultimate
effect that people search for and the different life domains and objective success as
the causes for it (Diener, Oishi, & Lucas, 2003, p. 420). As discussed in the previous
section, large number of demographic, technical, psychological and social factors
have been tested as exogenous variables in number of different bottom-up studies on
subjective success (Diener E. , 2009 b). However, there were hardly any
comprehensive studies that have measured a large number of factors in a single
model (Rogatko, 2010). Therefore, collective effect of the correlates or predictors on
the subjective success has not been understood. To understand the real picture of
how subjective success functioned among people, majority of the possible predictor
variables need to be included within an exploratory sturdy framework. Therefore,
based on the theoretical framework, literature on correlates of success and the
qualitative pilot study, the present study has defined the bottom-up conceptual model
using selected demographic, technical, psychological and social factors as the
78
exogenous variables, objective success as mediator variable and subjective success as
2. Income
3. Inventive Career
Satisfaction
4. Maximizing
Tendency
5. Life orientation
6. Engagement on
invention
7. Internet Usage
Psychological
Factors
1. Marriage
11.
Objective
success
Technical
Factors
0. Profile Factors
Demographic
Factors
the ultimate endogenous variable (Figure 6).
12.
Subjective
success
8. Social Capital
9. Community
Connectedness
10. External
Linkages
Social Factors
(Subjective Well
Being)
Figure 6: Bottom-Up conceptual model of the present study
79
Alternative Top-Down Model: Consequences of Subjective Success
Why is the enhancement and understanding of the subjective success important?
Veenhoven, (2008) suggested a theoretical question on what are the consequences of
subjective success (Veenhoven, 2008). In his theory, Veenhoven suggested
subjective success as both an outcome and factor of the social function. Further, in
Fredrickson’s broaden-and–build theory, she has explained how postive feelings can
broaden and build the individual resources to achive high objective success
(Fredrickson, 1998; Fredrickson, 2004). Therefore, according to the theoretical point
of view, it is important to have an emperical understanding of the impact of
subjective success on different domains of life. Recent literature have indicated that
subjective success leads to achieve success in other life domains including higher
ncome and higher work performances (Lyubomirsky, King, & Diener, 2005; Achor,
2010). Therfore, impact of subjective success on objective acheivments has been
discussed in the previous studies. As indicated in the discussion of Heady et al.
(2005), either bottom up theories or top-down theories on subjective success have not
explicitly explained whether all domain variables should follow either bottom-up or
top-down direction (Headey, Veenhoven, & Weari, 2005). Headey et al. (2005)
argues that most of the past studies only showed the correlates of subjective success
and they did not show the actual casual directions of the relationships. When the
researcher start the study with the bottom-up theoretical framework, the results of the
study explains the bottom-up correlations. Where as, when the researcher approached
the study by assuming top-down theoretical framework, then the findings of the
study indicates top-down correlations (Lyubomirsky, King, & Diener, 2005).
80
According to the discussions of correlates of subjective success, variables like
marriage, income, social relationships, maximizing tendency and optimization have
been identified as predictors of the subjective success of the different groups of
people. However, a number of isolated top down theoretical studies have found
subjective success as a determining factor of different life domains. The subjective
success had influenced marriage decisions (Stutzer & Frey, 2006; Veenhoven, 1989).
Then again, happy people are successful in social relationships (Oishi, Diener, &
Lucas, 2007; Diener & Seligman, 2002) and happiness increase the community
connectedness (Saguaro Seminar, 2001). Studies also have found that the subjective
success as a minor predictor of the income (Kenny C. , I999). Even psychological
variables such as maximizing tendency have determined by the subjective success
(Lewer, Gerlich, & Gretz, 2009). Dunavold (1996) mentioned that happiness, hope
and optimism contribute to each other as cycle (Dunavold, 1996).
Headey and Veehoven (1989) suggested that comparison of two models of the same
set of variables with subjective success would indicate which variables have topdown, bottom-up or two-way causality with the subjective success (Headey &
Veenhoven, 1989). They define these relationships as causes (Bottom-up) and
consequences (Top-down) of the subjective success. Kline (2011) mentioned the
statistical validity of the casual directions, which are drawn by comparing two
opposing correlational models using cross-sectional data. Hence, in order to identify
the possible consequences of subjective success on other life domains, top down
reverse model was developed. As far as the present study was planning to use the
cross-sectional data in path analysis to test the model, the procedure was too complex
to measure the impact of subjective success and objective success on categorical and
81
dichotomous variables. Therefore, the categorical profiling variables and marital
Psychological
Factors
Demographic
Factors
status were omitted from this model (Figure 07).
3. Income
4. Inventive Career
Satisfaction
5. Maximizing
Tendency
6. Life orientation
2. Objective success
Technical
Factors
1.
Subjective success
7. Engagement on
invention
Social Factors
8. Internet Usage
Figure 7: Alternative reversal top-down conceptual model
82
9. Social Capital
10. Community
Connectedness
11. External
Linkages
Summary
This chapter discussed the literature evidences on the research problem, theoretical
and conceptual frameworks of the study. Further, it explained the existing knowledge
and the way the present study might contribute to the knowledge of the field of
study. Based on the conceptual framework developed in this chapter, the next chapter
will discuss the methodology adopted in the study.
83
CHAPTER 03
METHODOLOGY
Art and science have their meeting point in Method
- Lytton E.B
Introduction
The aim of this study was to explore the demographic, psychological, technical and
social causes and consequences of objective and subjective success of grassroots
level inventors in Sri Lanka. This chapter first, explains the research design of the
study. Secondly, explains the operationalization of variables and instrument design.
Thirdly, explains the pilot studies, validity and reliability evidences, and finally it
explains the sample design and data collection process adopted in the study.
Research Design
Appropriate research design is essential for accurate determination of data, data
collection techniques, data analysis techniques and sampling procedures (Hair,
Black, Babin, & Anderson, 2009; Neuman, 2006). Ary, Jacobs and Sorensen (2006)
had mentioned that the researcher's choice of a research design should be based on
the context of the study and the research objectives. Hence, the research design
should follow the research questions in a way that offers the best chance to obtain
useful answers (Ary, Jacobs, & Sorensen, 2006, p. 25). Ary et al, (2006) have
suggested that the studies aim to learn the relationships, influences, causes and
concequences in a single group of respondents at non-experimental conditions should
follow the quantitative approach with correlational research design. The correlational
research can evaluate the direction and strengths of the relationships and patterns of
the relationships among variables in a single group of subjects without controlling
the variables. It has wide range of designs to detect simple relationship between
variables to complex casual directional designs (Ary, Jacobs, & Sorensen, 2006).
Correlational research can be either exploratory or confirmatary research.
Exploratory research is usually done when the alternative options have not been
clearly defined or their scope is unclear (Singh, 2007). In exploratory research
design, researchers investigate the possible relationships, causes and effects.
Advanced statistical methods such as path analysis and structural equation modeling
facilitate the researchers to statistically confirm the findings of the exploratory
studies (Hair, Black, Babin, & Anderson, 2009).
The aim of this study was to explore the demographic, psychological, technical and
social causes and influences of objective and subjective success of grassroots level
inventors in Sri Lanka. The demographic, psychological, technical, social domain
variables, objective success and subjective success might have been influenced by
the external factors that are beyond the scope of the study. The researcher conducted
the study in open social domains, therefore was unable to control the influences of
those extraneous factors. On the other hand, existing bottom-up and top-down
theoretical arguments have made the scope and direction of the subjective success
indecisive, similar to a ‘chicken and egg story’. Therefore, the current study was
designed as exploratory correlational research. Use of path analysis models allow the
reasercher to statistically investigate the suggested bottom-up and top-down
directional models of the present study. The operationalization of variables,
instrumentation, sample selection, data collection and data analysis of the present
study were designed by the researcher adhering to the exploratory correlational
research design.
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Operationalization and Measurements of Variables
Operationalization is the development of specific research procedures (operations)
that ensures the results of empirical observations represent the concepts and objects
in the real world (Babbie, 2005, pp. 132-133). Generally, the concepts and variables
in the social studies are ambiguous and have different meanings, measures and
scales. Operationalization describes the operations that need to be performed to
measure the concepts and hence, the operational definitions are akin to the specific
rules of measurements in the study (Viswanathan, 2005). Therefore, the researcher
needs to define the specific meanings of the concepts, their operational definitions
and ways to measure the variables in the study. This section of the thesis explains the
operational definitions of the variables and their specific scales of measurements
used in the present study.
Profiling variables
Profiling is the act or process of extrapolating information about a person or group of
people based on known traits or tendencies (Encyclopædia Britannica, 2010). Most
of the available studies on independent inventors thoroughly explore the
characteristics of the inventors. Age, location, education level, employment status
gender, income levels (Macdonald, 1986; Sirilli, 1987; Amesse & Desranleau, 1991),
type of inventions (Schoenmakers & Duysters, 2010; Georgia Tech Enterprise
innovation Institute, 2008) and types of commercialization (Weick & Eakin, 2005)
have been explained using categorical data and descriptive statistics such as
frequencies, percentages, mean, median and mode. Owing to the lack of background
information of grassroots level inventors in Sri Lanka, exploration of profiling
86
variables is identified as one of specific objectives of present study. Therefore, to
explain the nature grassroots inventive community, following specific profiling
variables were collected and analyzed in the present study.
i. Age
Age is defined as the inventor’s age in years as at 31 December 2009. For profiling
purpose, age is categorized based on the modified version of Erickson’s
psychological age categorization (Erickson, 1993). Respondents were asked to state
their age in years as at 31 December 2009. Then the researcher categorized the age
and gave the numerical coding values as follows.
12-18 – Adolescent
19-30 – Young age
31-40 – Late Young age
41-55 – Middle age
56-65 - Late Middle age
66 or high – Old
1
2
3
4
5
6
Even though the age was used as categorical variable in univariate and bivariate
profile analysis, for multivariate analysis, age is defined as metric variable as the
inventor’s age in years as at 31 December 2009.
ii. Gender
Gender is the biological differentiation of the inventors as male or female.
Respondents were asked to tick their gender. Coding for the variable was,
Male
-0
Female – 1
iii. Marital Status:
Marital Status is defined as the social state of the inventor’s life regarding the
marriage at the time of the survey. Even though some of the studies have indicated
87
divorced, separated and widow/widowers as categories, this categorization had
created both intentional and unintentional misreporting (Weaver, 2000). According
to the Weaver, when respondents are given more options for marital status, most of
the time they get confused about their legitimate marital status. Especially in eastern
cultures, they tend to be sensitive and emotional when they are trying to respond to
options such as divorced, separated and widow/widowers. Therefore, in the present
study, marital status was operationalized as dichotomous variable that has measured
using binary scale.
Unmarried
-
0
Married
-
1
iv. Location
Location is defined as the urban or rural status of the inventor’s permanent residence
for last five years from the time of survey. Different countries define rural areas
based on their own definitions (United Nations, 2007). Sri Lanka has been divided
into the urban, semi-urban and rural areas based on the type of local governing
authorities. The areas, which are controlled by the municipal councils and the urban
councils, are categorized as the urban areas. The areas controlled by the ‘Pradeshiya
Saba’ are defined as the rural areas (Gunasekara, 2007; United Nations, 2007).
Respondents were asked to state the local authority type of their living area. Type of
the local authority inventor’s living area are given the following coding.
Pradeshiya Saba - Rural
-1
Urban council – Semi-Urban
-2
Municipal Council – Urban
-3
Rural 1
Urban 2
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v. Highest Education Qualification
Highest Education Qualification is defined as the highest formal education
qualification completed by the inventor at the time of the survey. Respondents were
asked to select one of the qualifications from the educational qualifications suggested
by the researcher that was adopted from the UNESCO International Standard
Classification of Education (UNESCO, 1997). Coding for the variable responses and
its sub categories were,
Primary education
Secondary Education
Professional exams
Vocational training
Diploma
First degree
Postgraduate Degree (non PhD)
PhD
01
02
03
04
05
06
07
08
School
1
Professional/Vocational 2
Tertiary Education
3
Post Graduate
4
vi. Employment Status
Marshal (1998) defined the employee status as the legal classification of someone’s
employment as an employee, worker and working their own account (Marshal,
1998). However, in the context of the present study, employment status is
operationally defined as the legal status of the inventor’s involvement in significant
economic or income earning activities at the time of the survey. Respondents were
asked to select the category that closely describes their employment status. Inventors
who were not involving any income earning activity other than invention and those
who are involved in inventions after their retirement from an employment or other
income earning activities at the time of the survey were defined as full-time
inventors.
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Coding for the variable responses was as follows.
Employer
Employee
Self-employee
Student
Fulltime Inventor
Retired
01
02
03
04
05
06
Part time inventors
1
Full-time inventors
2
vii. Employed Sector
Employed Sector is defined as the legitimate organizational structure of the
employment of the inventor at the time of the survey. Respondents were asked to
select the category that closely describes their employment sector and the coding for
the variable responses were as follows.
Government
Semi-government
University/Research
Private sector
NGO
Self-employee
Full-time Student
Full-time inventor
Retired
01
02
03
04
05
06
07
08
09
Public sector
1
Private sector
2 sector
Non government
3
1
Freelance Sector
4
viii. Income
In this study, income is defined as the average monthly income earned by the
inventor from all sources at the time of survey rounded to the nearest thousand of Sri
Lankan rupees. Respondents were asked to state their income in Sri Lankan rupees
on the given space of the questionnaire. In order to maintain the numerical
simplicity, the researcher converted the income to thousands by dividing the stated
income by thousand. Owing to the numerical value of the income, it has been defined
as metric variable that can be used in multivariate data analysis.
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For the profiling purposes, income has been categorized and coded as follows.
Less than
11(000)
10(000)
20(000)
1
2
21(1000)
- 30(000)
3
31(000)
- 40(000)
4
41(000)
- 50(000)
5
51(000)
- 60(000)
6
61(000)
- 70(000)
7
71(000)
- 80(000)
8
81(000)
- or higher
9
-
Low income
(1)
Middle Income (2)
High Income (3)
ix. Job Mobility
Generally, job mobility is defined as the ability of an individual to change his/her
position, rank or occupation. This definition includes both intra-firm job mobility
(within the same organization or employer) and inter-firm job mobility (among
different organizations and employers)
(Cole, 1979). In the present study, job
mobility is defined from the inter-firm mobility perspective. Therefore, job mobility
is defined as the degree that an inventor changes his/her work place in his life. This
variable measures the number of previous working places that the inventor worked as
at 31 December 2009. Respondents were asked to select the number of places that
they had worked as suggested by the questionnaire. Coding for the variable responses
was as follows.
Not employed anywhere
One place
Two places
Three places
Four or More
0
1
2
3
4
Low
1
Moderate
2
High
3
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x. Type of invention:
The type of invention is defined as the generally preferred invention type of the
inventor. It can be a technical product that can be applied in specific task, or a
technical process that can improve the way of doing specific task. According to
Dahlin, Taylor and Fichman (2007), there are another two basic competing invention
types; radical and incremental inventions that inventors can preferred to be inventing.
Radical inventions are the original inventions that invented from the scratch without
having any prior evidences of similar product or process. Incremental inventions are
the inventions that are further developments or new additions to the existing product
and processes available anywhere in the world (Dahlin, Taylor, & Fichman, 2007).
This variable measures the general categories of the majority of the inventor’s
inventions. Respondents were asked to select the category that closely describes the
general type of their inventions. Coding for the variable responses were,
Radical product inventions
01
Radical process inventions
02
Product improvements
03
Process improvements
04
Radical Inventor
1
1
Incremental Inventor
2
2
xi. Field of invention
Field of invention is defined as the industrial product category of the inventor’s most
significant inventions at the time of survey. This variable measures the inventive
field of interest of the inventors. Respondents were asked to select the category that
closely describes their major field of inventions from the list, which was adapted
from the World Intellectual Property Report (WIPO, 2007). Coding for the variable
responses was as follows.
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Environment and Energy
Automotive
Sports and Leisure
Agriculture
Medical and Health
01
02
03
04
05
Tools
House hold and consumables
High tech equipments
Security and Safety
Industrial equipments
06
07
08
09
10
xii. Commercialization method
Even though, the non-patented inventions also can be commercialized by the
inventor, there is no legal protection over licensing the invention to others. However,
patented invention can be commercialized through the legitimate options granted by
the patent protection. Therefore, inventors have to select their choice over the
commercialization method. This variable measured the methods selected by the
inventors to commercialize their inventions. Coding for the variable responses was as
follows.
Produce and sell by own
01
Licensing to others
02
Outright sale of patent
03
Consultancy and teaching
04
Not tried to commercialize
05
xiii. Inventive lifespan:
Inventive lifespan is defined as the number of years involved in inventive activities
since the inventor’s first patent application. Respondents were asked to state the year
of their first patent application. The researcher calculated the number of years by
subtracting the stated year by the year 2009. As per the SLNIPO, average patent
application pending time in Sri Lanka is ranged from one to two years. Therefore, the
inventors have to wait for more or less two years to receive the final decision of their
patent application. Considering the pending time for the patent applications, the
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researcher categorized the inventive lifespan of the respondents using the following
classification and coding.
=<3
Immature inventors
1
4-7
Growing inventors
2
7 or higher
Matured inventors
3
Exogenous variables in the conceptual model
Variables in the conceptual model are selected based on the discussion of theoretical
and literature evidences of the correlates of subjective success and objective success
described in the literature review. This section of the chapter describes the
operationalization of the measurements of variables in the multivariate conceptual
models of the study.
i. Demographic Factors
Demographic factors generally define as the statistically measured biological, social
and economical characteristics of a specific social group or a population. During the
literature review, the researcher recognized marital status and income as potential
influential predictor variables of both objective and subjective success of grassroots
level inventors in Sri Lanka. Therefore, the present study considered these two
demographic variables as exogenous variables of the bottom-up conceptual model.
Operationalization of marital status and income has been discussed earlier under the
profiling variables.
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ii. Psychological factors
Psychological factors are generally defined as the mental and emotional states of a
person that influence on determine his/her behavior. As discussed in the literature
review, success is influenced by both mental and emotional factors. Hence, based on
the literature, the present study identified three psychological factors; Inventive
Career Satisfaction, Maximizing Tendency and Life-Orientation as possible
predictors of subjective and objective success of grassroots level inventors.
a. Inventive Career Satisfaction (ICS):
Career satisfaction is one of the most enduring psychological constructs in the
studies of industrial relations. Many commentators have explained the concept with
different names as career satisfaction, job satisfaction and work satisfaction. Job
satisfaction is generally defined as the extent to which people like (satisfaction) or
dislike (dissatisfaction) their jobs (Spector, 1997, p. 2). Traditional job satisfaction
facet includes; co-workers, pay, job conditions, supervision, nature of work and
benefits (Williams, 2004). Even though the available definitions applicable to the
independent inventors, the freelance nature of their inventive work requires most of
the job satisfaction facets to be modified. Even though career satisfaction can be
measured by single item scale, Wanous, Reichers and Hudy (1997) have
recommended to use multi items scales when possible (Wanous, Reichers, & Hudy,
1997). Greenhaus, Parasuraman and Warmley (1990) defined the career satisfaction
as the satisfaction of a worker towards the successful outcomes of his work life
(Greenhaus, Parasuraman, & Warmley, 1990). They developed five items scale to
measure the career satisfaction of workers covering the satisfaction of achievements,
career goals, income, advancement and skill development. Then again the definition
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of job satisfaction is determined by the psychological reactions to the characteristics
of the job, Macdonald and MacIntyre (1997) developed ten-item scale (Macdonald
& MacIntyre, 1997). The ten-item scale covers the feelings of social recognition,
supervision, job security, benefits, skill utilization and overall interest of the job.
Both of these scales have shown higher validity, reliability and applicability in
different work situations without modifications. However, like most of the career
satisfaction scales, these two scales targeted to measure the career satisfaction of
employed workers, rather than freelance workers. Independent inventing is a
freelance career activity and therefore neither of these two scales are directly
applicable to measure the grassroots level inventors career satisfaction. Thr present
study defines the inventive career satisfaction as the inventor’s psychological
assessment of the overall characteristics and outcomes of the inventive activities of
his or her inventive life. Owing to the specific focus of the context of the present
study, at least five items of Macdonald and MacIntyres’ scale had to be removed
(item number 1, 6, 7, 8, and 10). Other five items were also needed to be modified to
measure the career satisfaction of grassroots level inventors. According to the factors
analysis of the Macdonald and MacIntyres’ original scale items, “I feel good about
my job”, “ I receive recognition for a job well done”, “ I feel good about working at
this company” and “ I feel secure of about my job” have achieved higher factor
loadings (.77, .70, .61 and .64 respectively). Therefore, the researcher selected these
four items to be modified. Modification for these four items was done by concerning
the core satisfactions suggested by the Greenhaus et al. (1990) career satisfaction
scale. After the modifications, the researcher developed four item scale to measure
the career satisfaction; covering satisfaction with achievements, satisfaction with
recognition, satisfaction with inventing, and willingness to continue the inventing
96
activities. Each item was measured using five point likert like scale, ranging from 5highly satisfied/very high to 1- highly dissatisfied/very low. Summated value of the
inventive career satisfaction was determined by adding individual scores of the four
items. Owing to the suggested scoring method, in principle, inventor’s inventive
career satisfaction can obtain any value between 4 (1Χ 4) and 20 (4Χ 5). For
multivariate statistical analysis, summated value has been used as metric variable.
For the univarite analysis, summated value categorized as low, medium and high by
dividing the maximum possible range of score by three using following coding.
4- 9 10-15 16-20 -
Low
Medium
High
-1
-2
-3
b. Maximizing Tendency (MT):
Work of Schwartz et al. (2002) has attracted considerable attention to the
maximizing tendency by proposing that individuals differ in their global disposition
to maximize versus satisfied in decision making (Lai, 2010). Maximizing is
conceptualized as the tendency for seeking only the best option not settling anything
less (Schwartz, Ward, Monterosso, Lyubomirsky, White, & Lehman, 2002). Hence,
the maximizing tendency is the psychological drive of spending resources on aiming
to achieve the optimal results rather than satisfying with less optimal results.
Schwartz, et al. (2002) proposed that this difference may represent a general
behavioral tendency, and they developed a scale to capture the distinction between
decision makers who tend to be “maximizers” and those who tend to be “satisfiers”.
Most of the prior studies have used Schwartz et al. (2002) 13 item maximization
tendency scale (Parker, Bruin, & Fischhoff, 2007; Lyenger, Wells, & Schwartz,
2006). However, emerging interest of complex multivariate studies demanded to
97
have shorter scale that can be administrated within large number of other variables.
Therefore, instrument developers have developed shorter maximization tendency
scales based on Schwartz’s maximization tendency scale (Diab, Gillespie, &
Highhouse, 2008; Lai, 2010; Nenkov, Morrin, Ward, Schwartz, & Hulland, 2008).
Among them, Nenkov et al. (2008) tried to refine the original 13-item maximizing
tendency scale to nine-item, six-item and three-item scales. Based on the empirical
evidences on validity and reliability of the shortened scales, they recommended sixitem scale as a best shorter version for future studies. Owing to this six-item scale
developed direct items from the original maximizing tendency scale that had higher
academic interest, the present study used the Nenkov et al. (2008) six-item
maximization scale to measure the maximizing tendency. Each item has seven-option
likert like scale from completely agree to completely disagree. As per the scale
values, in principle summated value of the maximizing tendency can be ranged from
six to 42. For univariate explanatory purposes maximizing tendency was defined as
low, medium and high by dividing the maximum possible range of score by three
using following coding.
6-17 - Low
18-30 - Medium
31-42 - High
-
1
2
3
c. Life Orientation (LOT)
Operational definition of the Life orientation in present study is akin to the definition
proposed by the Carver et al. (2010). They defined Life orientation as an individual
difference variable that reflects the extent to which people hold generalized favorable
(Optimistic) or unfavorable (Pessimistic) expectancies for their future (Carver,
Scheier, & Segerstrom, 2010). Scheiver and Carver (1985) also used the term
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dispositional optimism to explain the concept and proposed that dispositional
optimism as a personality variable. Literature review of the study explained how the
optimism be a possible correlate of the subjective and objective success of grassroots
level inventors.
Scheier and Carver (1985) developed the Life Orientation Test (LOT) to assess
individual differences in generalized optimism versus pessimism (Scheier & Carver,
1985). The LOT and its successor the LOT-R (Scheier, Carver, & Bridges, 1994)
have been used in a number of studies on the behavioral, affective, and health
consequences. Its shortness makes it ideal for use in studies with many variables like
the present study. Compared to LOT, LOT-R is also a short scale consists with 10
items with only six scoring items (Three items for positive direction and three items
for negative direction). They used four items as filler items that has no numerical
impact to the summated value. In original LOT-R respondents were ask to indicate
their extent of agreement or disagreement with each of the items using five point
likert like scale; 0= Strongly Disagree,1= disagree, 2=neutral and 3= agree and 4=
Strongly agree. Owing to this scoring, in principle original LOT-R summated value
rages between 0 and 24. However, in present study LOT-R responses were slightly
modified by using the likert scales as 1=Strongly Disagree, 2=Disagree, 3=neutral,
4= Agree and 5= Strongly Agree. Therefore, in principle, summated value expected
to be ranged from 6 to 30. For univariate explanatory purposes, Life orientation is
defined as low, medium and high by dividing the maximum possible range of score
by three using following coding,
6-13
-
Low
-
1
14-21 -
Medium
-
2
22-30 -
High
-
3
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iii. Technical Factors
In this study, technical factors are defined as factors that are essential to provide
technical resources in innovation process. The present study selected two technical
factors that have potential influence on both objective and subjective success of
inventors.
a. Internet Usage (IU)
Internet has been identified as the most popular medium of transferring information,
knowledge, and communication with others (Becker, 1998; Kraut, Lundmark,
Kiesler, Mukhopadhyay, & Scherlis, 1997). According to the literature, timely
updated, complete, and interpretable information contributes to the successful
innovation process (Al-Hakam, 2007). However, the technical information and
knowledge required by the inventors are not readily available in common mass
media. Hence, Internet usage has been identified as one of the most influencing
technical factor in modern innovation development (Lansiti & MacCormack, 1997).
Even though the common people use the Internet for various general and casual
purposes, in present study, Internet usage is operationally defined as the grassroots
level inventors’ intensity to use internet for knowledge and information collection,
sharing and communication.
Korgaonkar, Silverblatt, & O’Leary, (1999) have devloped seven factors scale to
measure the motivations and concerns towards using the internet. However, during
exploratory study, they found that only informational, social and economical motives
are significant factors that influenced on internet usage. Adhere to that findings,
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Rodgers and Sheldon (2002) have developed the Web Motivation Inventory (WMI)
scale by using four factors; researching, communicating, surfing, and shopping. They
developed 12 items as 5-point likert scale, which included three items for each factor
(Rodgers, Jin, Rettie, Alpert, & Yoon, 2005). Items in WMI’s surf factor consist of
three items asking the motivation to explore new sites, surf for fun and find
interesting web pages. In broader sense, these items measure the usage of internet for
general information purposes. The Researching facet in WMI consists of three items:
do the research, get the information I need, and findout things I need to know. These
items focus on internet usage for purposive knowledge and information search.
Communication factor consisted of three items asking on sending e-mails to people,
contact with friends, and communicate with others.
In this study, the researcher wanted to measure the grassroots level inventors’
internet usage for their information, knowledge, and communication needs.
Therefore, items in shopping motive were considered as irrelevant. The researcher
modified the WMI scale items to develop a shorter scale by reducing items through
combining similar items together. To the extent that surf and research factors
measures the general and purposive informational usage of internet, item number 4
and 6 of WMI were combined together as (you use internet) “ to collect information”
and item 7 and 9 combined as “to get knowledge”. Item 8 and 12 of WMI was
combined as “to share information” and item 10 and 11 as “to communicate with
others”. Hence, in the present study Internet usage scale consisted with the four item
likert anchors as 1=Strongly Disagree, 2=Disagree, 3=neutral, 4= Agree and 5=
Strongly Agree. Therefore, in principle summated value expected to be ranged from
4 to 20. For univariate explanatory purposes internet usage defined as low, medium
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and high by dividing the maximum possible range of score by three and using
following coding.
4- 9
-
Low IU
-1
10-15 -
Medium IU
-2
16-20 -
High IU
-3
b. Engagement in invention (Daily Inventive Hours)
According to previous studies, majority of independent inventors are part time
inventors and time has been the most scare resource for them. Therefore, time spend
on inventive activities is a primary technical scare resource of grassroots level
inventors (Whalley, 1992).
Past studies on independent inventors used time spent
on invention as an indicator of their intensity to inventive activities (Sirilli, 1987;
Whalley, 1992). Not only the infrastructure, material, patent application and
maintenance cost, time allocation for inventing has also been considered as serious
investment for the independent inventors (Amesse & Desranleau, 1991; Macdonald,
1986). Even though other resources are changing from invention to invention, time
spent on inventive activities is a relatively consistence measure of the engagement on
inventions of grassroots level inventors. Therefore, the present study measured the
engagement in invention by average hours spend in inventive activities per day by
the grassroots level inventor.
In the present study inventors were asked to state the average hours per day they
have involved in their inventive activities. For multivariate data analysis, stated
number of hours has been taken as the metric variable to measure the engagement in
inventions. For univariate explanatory purposes, stated number of hours spends on
inventing activities per day categorized in to three categories. In principle, the
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number of hours could range from zero to any value. Therefore, upper level of the
higher category had to be decided by the researcher. Generally, Sri Lanka has eighthour working day. Therefore, if the inventor works eight or more hours in inventive
activities, it was considered as higher engagement in inventive activities. For
univariate explanatory purposes, the range (0-8) was sub-divided in three as follows.
0-3
4-7
8 or more
Low engagement
-1
Medium engagement - 2
High engagement
-3
iv. Social Factors
Generally, social factors defined as the characteristics of interaction between
individuals and groups of the society (Encyclopædia Britannica, 2010). Hughes, et al.
(2007) defined the social interaction in community as bonds (the close relationships
between members of the community), bridges (the relationship between associates
and acquaintances of the community) and linkages (the relationship between
community, influencing organizations and structures). Akin to the Huges et al.
(2007), in the present study social factors were defined as the existing behavior and
relationship between different social domain groups and the grassroots level
inventors in Sri Lanka. Three types of social domains have been identified as
influential social factors of the present study; External linkages, Social Capital and
Community Connectedness.
a. External Linkages
In this study, external linkages are defined as the positive relationship between a
grassroots level inventor and the third party (expert personnel, organizations and
structures) that influence inventing, patenting and commercializing activities. In
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innovation literature, the third parties who are involved in innovation process known
as intermediates. Their assistance has been recognized as an important aspect in the
innovation process (Svensson, 2007; Howells, 2006; Hoppe & Ozdenoren, 2005).
The survey instrument of Georgia’s independent inventors 2007, suggested a list of
external resources that inventor could access during the innovation process (Georgia
Tech Enterprise innovation Institute, 2008). They suggested 24 types of external
linkages with expert personnel, organizations and entities. However, not all the items
were available and applicable in the context of Sri Lanka. Therefore, the researcher
selected 13 items from the list that can be applicable in the Sri Lankan context. In
expert linkages scale of the pilot study the respondents were asked to mark the level
of assistance they received from each of the external link at inventing, patenting and
commercializing stages of the innovation process using likert like scales as 1=Very
Low, 2=Low, 3=Average, 4= High 5= Very High. Then the researcher calculated the
average value of each item to take the summated score of the external linkages.
However, at the quantitative pilot test, the researcher revealed that respondents could
answer better, when they were asked to assess the overall contribution from the
external linkages. Therefore the researcher modified the External linkages scale by
asking to select the overall support they received from the external links using likert
like scales as 1=Very Low, 2=Low, 3=Average, 4= High 5= Very High. External
Linkages were measured by the summated value of the 13 items of the scale. In
principle, summated value could ranged from 13 to 65. For univariate explanatory
purposes external linkages defined as low, medium and high by dividing the
maximum possible range of the score by three and using following coding,
13-29 – Low -
1
30-46 – Medium -
2
47-65 – High -
3
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b. Social Capital
Literature on social capital has identified it as a positive contributor of the subjective
well-being (Helliwell & Putnam, 2004; Helliwell J. F., 2003; Winkelmann, 2009).
There are at least two broad approaches to identify social capital: collective social
capital of a community or individual social capital of an individual (Phillips &
Pittman, 2009). Individual social capital defined as the collection of resources owned
by members of the individual personal social network, which may become available
for use because of the investments in personal relationships (Gaag, 2005). According
to Gaag(2005), the personal relationships of an individual provide most of the
resources required for their daily life. Individual social capital primarily concerned
the individual benefits resulting from the inclusion of the individual within his social
environment (Franke, 2005, p. 11). As far as the grassroots level inventors involved
in inventive activities as individuals, measure of their individual social capital
considered to be more meaningful than collective social capital. Therefore, the
present study measured the social capital from the individual perspective to identify
how the grassroots level inventors received required resources from their social
relationships.
Social relationships can be identified as, strong or weak ties that might influence the
strength of the social capital (Granovetter M. S., 1973). The strength of the tie
depends on a combination of factors including the amount of time, the emotional
intensity, the intimacy and reciprocal services characterized within the tie. Based on
the criteria mentioned social ties can be strong, weak or absent. The relationships
satisfy all the four criteria defined as strong ties and the relationships that not satisfy
these criteria defined as weak ties. Generally, strong ties consist of the relationships
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of family members, relatives and close friends. Weak ties consist with official or
indirect relationships between social groups. Relationships with distance such as
relatives’ friends or friends of friends considered as relatively weak ties. Absent
means there is no strong or weak tie between two parties.
The present study
measured the individual social capital based on the Gaag’s (2005) 17 item resource
generator scale by integrating the response structure of Granovetter’s (1973) strong,
weak and absent of social ties.
Resource generator scale was developed based on the data collected by a sample of
1007 individuals from Dutch population in 1999-2000. Original scales had 35 items
and factor analysis results reduced the validated scale to 17 item four dimentional
scale (Gaag & Snijders, 2004). This instrument asks about access to a fixed list of
specific social resources, that each represent a vivid, concrete sub-collection of social
capital, together covering several domains of life. This instrument can be
administered quickly, and results are easily interpretable as the representations of
social capital, with more possibilities for use in goal speficity research (Gaag &
Snijders, 2003). Even though the original 17-item social capital resource generator
scale used acquaintance, friend and family as the response scale, the scoring was
dichotomous “Yes” and “No” scale. Therefore, some authors complained about the
high average positive responses of Gaag’s resource generator scale (Lannoo, 2009).
However, Granovetter’s (1973) discussions of strong, weak and absent of social ties
had provided a better framework to assess the strength of social capital with higher
diversity. Therfore in this study, 17 items of Gaag’s individual social capital resource
generator scale were translated to Sinhala language by changing only the currency of
the item number 4 to Sri Lankan rupees. But, the reasercher modified the response
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options of the resources generator scale as 1= No, 2=official level, 3= Friend’s
friend, 4=friend, 5= relative and 6= family member. Higher summated scores
represent the strong social capital and lower summated scores represent the weak
social capital. In principle, summated score of social capital can range from 17 to
102. For univariate analysis social capital was categorized in to three categories low,
medium and high by dividing the maximum possible range of the score by three and
using following score ranges.
17- 45 -
Low
-
1
46-73 -
Medium-
2
74- 102 -
High -
3
c. Community Connectedness
External linkages and social capital measures the tangible participative social factors
of the grassroots inventors. However, community connectedness conceptualized as
cognitive construct synonymous to the sense of community (Davidson & Cotter,
1991). According to the definition proposed by the McMillan and Chavis (1986), a
sense of community (community connectedness) is a feeling that members have of
belonging, a feeling that members matter to one another and to the group, and a
shared faith that members’ needs will be met through their commitment to be
together (McMillan & Chavis, 1986). In present study, community connectedness is
defined as the convergence of individuals’ desires to belong to a community,
establish a mutually influential relationship with that community, satisfy their
individual needs and be rewarded through their collective affiliation, and construct a
shared emotional connection (Whitelock, 2007; Frost & Meyer, 2009).
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Even though there are a number of established instruments to measure the sense of
community (community connectedness), they are very long instruments (Doolittle &
MacDonald, 1978; Davidson & Cotter, 1986). However, Frost and Meyer (2009)
have measured the community connectedness of Lesbian, Gay and Bisexual (LGB)
community using relatively shorter scale. Owing to the LGB community being a
community of interest rather than a neighborhood community, the scale was able to
adapt to measure the connectedness of grassroots level inventive community. Frost
and Meyer’s Community connectedness scale consists of 8-items that were adapted
from a 7-item community cohesion scale used in the Urban Men’s Health Study
(UMHS). They added one item “You feel a bond with other [men who are gay or
bisexual]” taken from Herek & Glunt (1995) community consciousness scale. This
scale has shown high validity and Cronbach alpha internal consistent value (Frost &
Meyer, 2009). In the present study, Frost and Meyer’s community connectedness
scale was modified by replacing the specific words related to LGB community by
words related to grassroots level inventive community. Even though the original
Frost and Meyer’s scale have only four likert like responses (1= strongly Disagree to
4= strongly agree), to increase the scale sensitivity, the present study used 7 point
likert like scale (1= Strongly Disagree to 7= strongly agree). Hence, in principle
summated value of community connectedness is ranged from 8-56. For the univariate
statistical analysis, the range of community connectedness scale summated value
categorized in to three as low, medium and high by dividing the maximum possible
range of the score by three. Category ranges are shown below:8-23
– Low -
1
24- 39 – Medium -
2
40-56 - High -
3
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Endogenous variables in the conceptual model
i. Objective Success:
As discussed in the literature review, the present study adopted the Hauschildt’s
innovation process approach (Hauschildt, 1991) to measure the objective success of
the inventors. Objective success defined as the measurable and observable monetary
and non-monitory achievements of the innovation process. That includes the patent
received, awards and rewards, commercialization, commercial survival and profit
earnings. The researcher initially developed the objective success measurement as
ten-item likert like scale and asked the selected panel of experts to validate the scale.
When the researcher consulted Professor Chinta Weick, she advised the researcher to
use limited number of items with dichotomous response, because it is straightforward
to measure and avoid complex comparisons (Weick C, Personal Communication, 12
August 2008). Weick & Eakin (2005) also measured the commercial success of
inventors using multi-item dichotomous (0, 1) scale. Therefore, objective success
was measured as the summation of five items measured using dichotomous scale (0,
1) on the patent grants, award and rewards, commercial startup, commercial
continuation and profitable inventions. In the questionnaire, the researcher asked the
respondents to state how many patents they received, how many inventions have won
either awards or rewards, how many inventions started to commercialized, how many
inventions have been commercialized and how many inventions have earned profits
at the time of survey. Respondents who reported values 1 or more were considered as
one (1) and others considered as zero (0). By calculating the summation of
dichotomous responses, the researcher has generated the continuous objective
success variable ranging from zero to five. That is higher than the four scale values,
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which is the minimum recommended range of scales in structural equation and path
modeling (Hair, Black, Babin, & Anderson, 2009).
The objectives of the study required the researcher to explain the univariate and
bivariate behavior of the objective success. In order to achieve this, the objective
success is divided into three broad categories low (0 and 1), medium (2 and 3) and
high (4 and 5). Low, medium and high categories of objective success were analyzed
with selected variables using cross tabulations. Then again, owing to the innovation
activity considered as a process, the researcher is required to understand the strong
and weak stages of the innovation process of the grassroots level inventors. The
measurement strategy adapted in the objective success facilitated the researcher to
explain the overall objective success, as well as the success of different stages of
innovation process using descriptive statistics such as frequencies, percentages, and
graphs.
ii. Subjective Success:
As per the operational definition adapted in the present study, the subjective success
is synonymous with the definition of the subjective well-being. According to the
literature, definitions of the subjective well-being consist of emotional (mostly
measured by the happiness) and cognitive aspects (mostly measured by satisfaction
with life). Subjective Happiness Scale (SHS) and Satisfaction with Life Scale
(SWLS) are the most administrated scales to measure subjective success (Snyder &
Lopez, 2007; Diener E. , 2009 a). The Satisfaction with Life Scale has been tested for
its reliability and validity by the authors and test has shown high level of consistency,
validity and reliability to measure the satisfaction of life of different type of domains
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(Diener, Emmons, Larsen, & Graiffin, 1985; Pavot & Diener, 1993). The Subjective
Happiness Scale is also widely used validated instrument in 14 different studies with
2,732 participants (University of Pennsylvania, 2007). The results of the study have
signified that the Subjective Happiness Scale has the high internal consistency, and
validity. In order to measure both emotional and cognitive aspects of subjective
success, integration of the Subjective Happiness Scale and Satisfaction with Life
Scale was already practiced by the Pichler (2006) and Lyubomirsky (2008).
Therefore, Professor Lyubomirsky recommended the researcher to use integrated
scale in the present study (Lyubomirsky S, Personal Communication, 21st February
2010). Both the SHS and SWLS are available for free usage with copy left policy.
Therefore, in the present study, Subjective success was measured using summation
of original Subjective Happiness Scale-4 items (Lyubomirsky & Lepper, 1997) and
Satisfaction with Life Scale – 5 items (Diener, Emmons, Larsen, & Graiffin, 1985).
Both the scales have seven point likert like responses from (1) strongly disagree to
strongly agree (7). Therefore, in principle summated value for subjective success can
range from 9 to 63. For univariate and bivariate analytical purposes, summated value
of subjective success is sub-divided in to three categories: Low (9-27), Medium (2845) and High (46-63).
Pilot Studies
Apart from the literature review, researchers need to conduct pilot studies to explore
the context of the study to design, and test the validity and reliability of the
instruments. They are the small-scale studies to be done in preparation for the major
study. Pilot studies can be based on quantitative and/or qualitative methods and large
studies might employ both methods along with a number of pilot studies before the
111
major study. Therefore, the pilot studies are important aspect of the empirical studies
(Kazer, 2000). When the researcher is conducting a research in an unexplored or
under explored topic or a context, the researcher is recommended to conduct
qualitative pilot study as a basis for designing and conceptualizing the subsequent
quantitative phase of the study (Tashakkori & Teddlie, 1998). Nevertheless, unlike
literature review, pilot test results are under-reported in research reports. Still it is
important to report the details of the pilot studies in study reports (Tejilingen &
Hundley, 2001). According to the literature review of the present study, grassroots
level inventors are under-explored research area in developing countries. Especially
in Asia and Sri Lanka, they have been an unexplored community.
Therefore,
adhering to the recommendations of the Tejilingen and Hundley (2001) and the
Tashakkori and Teddlie (1998), the first phase of the pilot study was conducted as
qualitative inquiry using telephone interviews with small number of respondents. It
strengthened the researcher’s understanding of grassroots level inventors, and the
way they assess their success. It allowed the researcher to determine how objective
and subjective success should be conceptualized and operationalized in the main
study. Even though the researcher can depend on the factor validity of the standard
scales, it is important to test the reliability of the ranslated version of the instrument
and how it works in the culture and the context of the study (Sewell, 1943; Marnet,
2009). Therefore, after the conceptualization, operationalization and instrumentation,
the next phase of the pilot study was conducted as a quantitative pilot study to test
the reliability and validity of the questionnaire instrument. Data collection process
and basic results of the two pilot studies are presented in Appendix A.
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Validity and Reliability Evidences
According to the Standards for Educational and Psychological Testing 1999,
sufficient information about the measurement error is essential to the proper
evaluation and use of a test instrument (APA, NCME, AERA, 1999). Viswanathan
(2005) categorized the measurement error as random and systematic. According to
Viswanathan (2005), Random error can be detected and corrected by reliability
analysis. Further, both the random error and systematic error can be detected by
validity tests (Viswanathan, 2005). Hence, the researcher is required to provide
validity and reliability evidences of the data collection instruments.
Validity evidences
According to the Standards for Educational and Psychological Testing (1999),
validity refers to the degree to which evidence and theory support the interpretations
of test scores entailed by proposed uses of tests (APA, NCME, AERA, 1999).
Therefore, the validity is the most fundamental consideration in developing and
evaluating tests. Professional judgment guides the decisions regarding the specific
forms of evidence that can be the best support for the intended interpretation and use
of instruments (APA, NCME, AERA, 1999, p. 9). Traditionally, validity of tests
categorized into three types; criterion, content and construct (Trochim & Donnely,
2007; Dooley, 2004). However, the 1999 version of Standards for Educational and
Psychological Testing describes the validity based on the sources of validity
evidences. Namely the evidences are based on test content; response processes;
internal structure; relation to other variables and consequences of testing (Goodwin
& Leech, 2003). The standard has stated that the sources of validity evidences do not
113
represent distinct types of validity. It is a degree to which all of the accumulated
evidences support the intended interpretation of the test score for intended purpose
(APA, NCME, AERA, 1999, p. 11). Goodwin and Leech (2003) mentioned that
factor analysis results have been over used in validation of the instruments and it is
not the only validity measure available. Moreover, they suggested a number of
methods that could be used to provide evidences of the validity of the instruments.
The present study tests the validity of the scales adhering to the Standards for
Educational and Psychological Testing 1999.
Development of valid and reliable instruments takes time, patience and specialized
knowledge and it is not the focus of most of the researchers (Pett, Lackey, &
Sullivan, 2003). According to Pett et al. (2003), researchers need to focus on
instrument development only when valid and reliable instruments are not available to
measure the intended latent constructs of the study.
If there are standardized
instruments available to measure, the latent constructs of the study, Pett, Lackey and
Sullivan have recommended to use those instruments instead of developing new
instruments. Standard 1.4 of the standards for educational and psychological test
stated, “If a test is used in a way that has not been validated, it is incumbent on the
user to justify the new use, collecting new evidence if necessary” (APA, NCME,
AERA, 1999). In present study, the researcher carefully selected and slightly
modified the available tests to measure the originally intended constructs of the tests.
Hence, new validity evidences, especially the new evidences based on internal
structure and consequences of testing, are not necessary to provide for the present
study. Evdeinces that show the convergent and discrinant validity of the scales can
be used to statisfy the requirenet of evidences based on relation to other variables
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(Goodwin and Leech, 2003; Pg.187). The researcher conducted the factor analysis
and calculated the interitem correlation to test the convergent and discriminant
validty of the scales (DeVon, Block, Moyle-Wright, Ernst, & Hayden, 2007).
According to Devon et al. (2007) to achieve convergent validity, the items in a scale
that suppose to measure a construct, should show relatively high positive correlation
within the items in the scale. Similarly, in order to achieve the discriminant validity
interintem correlation between the items in different scales should be lower than the
items within the scales. According to the factor analysis results and the interitem
correlations values of the study, the items in the same scale have shown high factor
loadings and relatively high correlation among the items in the same scale while
items in the dirrefent scales have shown relatively low factor loading and interitem
correlation (Interitem correlation metrix have shown in appendix K).
All the sub-scales that have been used to measure the variables in the study were
published standardized scales or modified versions of them that have been used in
prior studies as valid and reliable measures to measure the similar constructs. The
measurements and their sub-scales to measure the variables in the present study were
selected through the rigorous literature review of existing related theories and
empirical studies. By conducting a comprehensive literature review, the researcher
has selected standardized instruments that can be able to apply directly or as adapted
versions to measure the constructs of the present study. Even though the researcher
was planning to use standardized or adapted scales to measure the variables, experts
in the field were consulted for their advices on using and modification of
standardized scales. The present study was conducted under the guidance and
supervision of supervisory committee consisted of three well-experienced faculty
115
members of the Faculty of Human Ecology, University Putra Malaysia. They were
involved in the process of selecting constructs, variables and measurements of the
present study. Apart from that, to get the expert advices, evidences and additional
literature, the researcher personally contacted some of the instruments developers,
experts and the researchers who were involved in related studies. Then again, even
though the researcher used standardized scales or modified versions of the
standardized scales, wordings and meanings of the translated version were corrected
by the researcher and two-language experts in Open University Sri Lanka and
University of Kelaniya, Sri Lanka. Owing to this process, the researcher was able to
ensure the instrument measures the intended constructs of original scales. This
process ensured the translated instrument measures the same constructs intended by
the original sub scales.
In order to gather evidences based on the response processes, the researcher
conducted two pilot tests before and after the instrument development. The first pilot
test was a qualitative telephone interviews with president award winning grassroots
level inventors. It was conducted to get to know about the nature and understand the
significant constructs and variables of the target population. The second pilot test
was conducted to test the instrument and get feedback from the respondents to
improve the instrument. Based on the comments and suggestions of the pilot test
respondents, the researcher further modified the subscales to ensure the accurate
measurement of the intended constructs. Owing to the adoption of already validated
standardized scales and slightly modified versions of standardized scales under the
approval of panel of experts, the researcher adhere with the standard by avoiding the
116
statistical validation of instrument using number of pilot studies and factor analysis
(Pett, Lackey, & Sullivan, 2003).
Reliability evidences
Reliability refers to the consistency of measurements when the testing procedure is
repeated on a population of individuals or groups (APA, NCME, AERA, 1999).
Therefore, the researcher needs to ensure that the instrument is having minimum
level of random error to be considered reliable. As stated in the Viswanathan (2005),
in order to measure the random error, the researcher needs to examine the
consistency of the measurement scales. Statistical tests of reliability can show the
consistency level of the scales only after data collection. Therefore, the researchers
should conduct reliability tests in pilot studies and need to employ systematic
procedures to minimize practical issues that may have an effect on random error
(Trochim & Donnely, 2007).
Generally, the inconsistent data collection methods also have an effect on the random
error (Chirchill, 1979). The present study designed the data collection procedure in
the systematic way that reduced the data collection administrative influences and
emotional influences on the responses. The researcher consistently contacted the
respondents through mails and telephone conversations to clarify and make the
respondents aware of the data collection procedures prior to data collection. Then the
researcher physically contacted the respondents during the data collection. Therefore,
random error that could occur owing to the administrative process has been
sufficiently secured throughout the data collection process.
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Cronbatch (1951) had suggested a method of evaluating the internal consistency of a
scale based on the number of items and variances of the scores (Cronbatch, 1951).
Higher Cronbach alpha values indicate the higher internal consistency of the items in
the scale. George and Mallery (2006) provide the following rules of thumb: > .9 –
Excellent, > .8 – Good, > .7 – Acceptable, > .6 – Questionable, > .5 – Poor, and <
.5 – Unacceptable. According to the Yurdugul (2008), Cronbach’s alpha values of
very small sample sizes can be used as robust estimators of population coefficient
alpha. Therefore, use of small number of respondents has been recommended in pilot
studies. Adhering to the Yurdugul (2008) pilot test requirement, the researcher
conducted a pilot test with 25 respondents in the month of February 2009 (Details of
the pilot study explained in Appendix A.2). The researcher modified the scale items
that have shown low internal correlation values in Cronbatch alpha test. Comparison
of the Cronbach’s alpha values of pilot test and real test of present study is shown in
Table 3. Table 3 shows that in the pilot test External linkages scale achieved only
.662 Cronbach alpha value and make the scale questionable. Original External
linkages scale of the present study consisted of complex response categories
containing three likert-like responses for each item. Therefore, the respondents were
asked to write three values for each item based on their inventing, patenting and
commercializing stages. According to the comments of the pilot test respondents, this
external linkages scale was very hard to answer. Hence, they suggested a single
response on overall assistance received from the suggested external parties. Based on
the suggestion of the respondents, the researcher revised the response scale to single
response likert like scale.
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Table 3: Cronbach’s Alpha statistics of scales: pilot and the real study
Scale
#
Items
Internet Usage
External Linkages
Individual Social
Capital
Maximizing
Tendency
LOT-R
ICS
Community
Connectedness
Subjective
Success
Subjective
Happiness Scale
SWLS
Pilot Test
Cronbach’s Cronbach's
Alpha
Alpha Based
on
Standardized
Items
4
13
.835
.662
.843
.644
17
.781
.767
6
.574
.574
6
4
.748
.728
8
Actions
Taken
#
Items
Real Test
Cronbach’s
Alpha
Cronbach's
Alpha Based
on
Standardized
Items
4
13
.867
.713
.868
.712
17
.739
.737
6
.705
.706
.752
.736
6
4
.707
.701
.714
.702
.768
.789
8
.813
.822
9
.838
.844
9
.776
.777
4
.820
.826
4
.758
.759
5
.809
.818
5
.766
.767
1
2
Actions taken
1.
2.
Change the response structure of the scale
Item number 1,3 and 4 of the original scale were modified
Table 3 also shows that the maximization tendency scale has a poor Cronbach alpha
value at the pilot test (.574). According to the pilot study respondents’ comments,
maximizing tendency scale’s item number 1, 3 and 4 were not appropriate for study
domain context. During the discussions after the test, the respondents told the
researcher, that they are not listening to the radio very often instead of watching TV.
They were not often buying gifts for friends, other than relatives and they are not
normally renting videos from video renting shops. Therefore in the final study these
items were modified using ‘television channel’ instead of ‘radio station’ in item 1,
‘buy a gift for someone’ rather than ‘buy a gift for a friend’ in item 2 and ‘purchase
something’ rather than ‘renting a video’ in item 4 to explain the domain context in
Sri Lanka. The remedial actions taken had paid off in the real study and Cronbach
alpha values for maximizing tendency increased to .705 and external linkages scale
increased up to .713 at the actual study. When compared to the pilot test reliability
119
scores with the real test, it indicates that Cronbach’s coefficient alpha values were
relatively consistence and not showing any significant deviation of the values beyond
the acceptable Cronbach’s coefficient alpha values recommended by George and
Mallery (2006). Based on the relaibility test of the measurement scales of the latent
constructs, The researcher concluded that all the sub-scales of the present study were
adhered to the minimum acceptable internal consistency and random error.
Sampling Design
For most research, it is hard to conduct a study on the whole population; actually it is
unnecessary and waste of resources (Ary, Jacobs, & Sorensen, 2006; Singh, 2007).
Therefore, the researcher has to identify representative cross section of the accessible
population as the sample of the study. The sample must be representative to
generalize the conclusions on the population with reasonable confidence. Based on
the literature, there are two systematic steps in sampling. First, target population have
to be clearly defined and once the population is defined, sample should be selected
from that population based on appropriate sampling procedure (Ary, Jacobs, &
Sorensen, 2006; Singh, 2007).
Therefore, the researcher must have a good
understanding of the target population and should have a systematic procedure to
derive a representative sample from that population.
Target population and sampling frame of the study
Population is a group of individuals, objects or items among which samples are taken
for measurement (Singh, 2007, p. 87). It includes all the members of any welldefined class of people, events or objects (Ary, Jacobs, & Sorensen, 2006, p. 148).
120
The present study operationally defines the “grassroots level inventor” as a local
individual of Sri Lanka, who is involved in patentable inventive activities and trying
to obtain patents for himself/herself, for his/her own reasons and own rewards out of
the formal organizational structures such as firms, universities and research labs.
Based on the operational definition, the population of the study is defined according
to the following operational criteria.
1. Inventor should be a Sri Lankan citizen.
2. Inventor must apply for the patent for his/her invention. Inventor should be the
applicant of the patent.
3. Patent applicant should be the inventor of the invention that mentioned in the patent
application presented to the Sri Lanka National Intellectual Property Office
(SLNIPO).
4. Inventor should not indicate any institutional interest or involvement in the patent
application (should not mention Institutional name, official designation and official
addresses as contact details).
Apart from the operational criteria, there was unavoidable inherent limitation to be
considered when defining the accessible population. Even though the SLNIPO
functioned since 1982, they have started to allow public access to patent data since
the year 2008. They introduced the electronic patent search in 2008, and it has given
access to patent applicants’ contact data only from the year 2000. Owing to scam
protection rules and regulations, access to unpublished patent data has been strictly
prohibited in SLNIPO. Therefore, the available descriptive data of the patent
applicants who applied for the patent in Sri Lanka limited to the applications made
121
after 1 January 2000. Hence, the inventors those who have not applied a patent after
1 January 2000 had not included in the target population of the study. Further,
according to the patent application process of SLNIPO there is at least eighteenmonth gap between the date of application and the date of the final decision of the
application. The number of patent grants at the point of the survey has influenced on
the objective success of the inventors. Therefore, the upper limit of the cross section
of study is limited to 31 December 2008. It allowed the inventors to receive the final
decision on their latest patent application, and it avoided the negative influence on
the objective success of the inventors, especially those who applied for their first and
the only patent. Hence, the population that was defined to draw the sample of the
study consisted of the local grassroots level inventors who have applied for the
patents during 1 January 2000 to 31 December 2008. The researcher collected the
entire resident patent applicants list for the period of year 2000 to 2008 from the
SLNIPO in May 2009. Owing to the mix of grassroots level and institutional
investors in the patent applications list, the researcher had to identify the grassroots
level inventors manually by analyzing the name of the inventor, name of the patent
applicant and addresses of the inventors. According to the analysis of SLNIPO patent
registry, from 1 January 2000 to 31 December 2008 there were 872 patent
applications forwarded by the Sri Lankan grassroots inventors who named as both
the inventor and applicant of the patent. However, only 640 inventors have
forwarded these 872 patent applications (Some of the inventors apply for more than
one patent during the period). Therefore, 640 grassroots level inventors were
identified as the target population size of the study.
Identified independent
inventors’ patent application numbers, names and postal addresses were entered to
Microsoft Excel 2007 worksheet and sorted in ascending order based on their patent
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application numbers. This list consisted the details of 640 inventors and considered
as the sampling frame of the target population.
Power analysis and sample size determination
Different statistical tests require different sample sizes and sample size of a study
needs to satisfy the minimum requirements of the statistical tools intended to use in
the study (Isreal, 1992 ). According to Cohen (1988), different statistical tests require
different sample sizes to ensure that the inferences of the statistical tests to achieve
the required power. The power of a statistical test is the probability that it leads to the
rejection of the false null hypothesis (Cohen, 1988, p. 4). Power analysis must be at
the core of any rational basis for deciding on the sample size to be used in an
investigation (Cohen, 1988, p. 14). Different multivariate, bivariate and categorical
level data analysis of the present study was planning to be done primarily based on
the path analysis, Pearson product movement correlation and Chi-Square statistic.
Therefore, the researcher had to select sample size that satisfies the minimum
requirements of all the statistical tools.
In principle, path analysis and structural equation modeling are large sample
statistical tools that require minimum of 150-200 sample sizes (Ullman & Bentler,
2004; Hair, Black, Babin, & Anderson, 2009). Generally, the traditional accepted
norm of the structural equation modeling is to select a sample with at least 5-10
observations per free parameter to be estimated (Kline, 2005). Owing to this norm,
models with large number of variables require very large sample sizes. Therefore, the
researchers have a tendency to use less optimal models using lesser number of
variables and relationships to ensure their models satisfy the required number of
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observation per parameter (Hall, Snell, & Foust, 1999). However, there was very
limited empirical support received for this recommendation of number of
observations per free parameter. Monte Carlo Investigation conducted by Jackson
(2001) was unable to detect practically significant effect on number of observation
per estimated parameter (Jackson, 2001). Jackson (2003) revisited the same issue and
found a mediocre support for the hypothesis of observation per parameter. However,
the overall effect was small relative to the absolute sample size (Jackson, 2003).
Therefore, the number of observation per parameter is no more valid in structural
equation modeling (McQuitty, 2004). Then the study of Kenny & McCoach (2003),
revealed that the number of variables (Degree of freedom based on the variables) in
the model have an impact on the measures of model fit, but is not serious enough to
demand to develop less optimal models with limited number of variables. In Kenny
& McCoachs’ conclusion, they have suggested researchers to use optimal models
with required number of variables that clearly explain the actual scenario of the
study.
Then again, Fan et al (1999) have explained that the model estimation method has
significant influence on the model fit indices (Fan, Thompson, & Wang, 1999).
Therefore, the model estimation method which is based on the rule of thumb has
been suggested to use in estimations of the minimum sample size. Maximum
Likelihood Estimation (MLE) is the most common estimation procedure in path
analysis and structural equation modeling. Even though the path analysis and
structural equation modeling require bigger samples, MLE become more sensitive
with sample sizes lager than 400. According to the literature evidences on path
analysis and structural equation modeling sample size discussions, minimum sample
124
size of 200 gives sound basis for MLE (Hair, Black, Babin, & Anderson, 2009;
Sclove, 1998; Blunch, 2008). Barret (2007) recommended to use 200 as a sample
size, when the population is small and restricted in size (Barret, 2007). Hoe (2008)
concluded that as a rule of thumb any number on or above 200 provide sufficient
statistical power for data analysis (Hoe, 2008).
McQuitty (2004) had suggested that it is important to determine the minimum
sample size required in order to achieve the desired level of statistical power in path
analysis and structural equation modeling (McQuitty, 2004). Cohen (1988) did not
explain the power analysis of path analysis and structural equation modeling.
Therefore, unlike other statistical methods, sample size in structural equation
modeling and path analysis has been a debatable issue in statistics since a long time.
The power analysis of structural equation models has never been in the active
discussion until the MacCallum et al. (1996).
MacCallum et al. (1996) have introduced a method to calculate power in structural
equation modeling based on the Root Means Square Error Approximation (RMSEA).
According to them, power can be calculated either assuming perfect model fit (null
hypothesis RMSEA =.00 and Alternative hypothesis =.05) or assuming close model
fit (null hypothesis RMSEA = 0.05 and Alternative hypothesis =.08 (mostly.09)).
When the researcher assumes perfect to close model fit (accept any RMSEA value
less than .90), the researcher can assume null hypothesis RMSEA as .00 and
alternative hypothesis RMSEA as .09.
Based on the MacCallum et al. (1996) RMSEA power analysis, Dudgeon (2003) has
developed open source MS DOS based software called “NIESEM” to conduct the
125
power analysis in Structural Equation Modeling (Dudgeon, 2003). This software is
widely used in recent structural equation modeling studies (Dattalo, 2009; Tomiuk &
Pinsonneault, 2008; Oort, 2009; Jak, Oort, & Dolan, 2010). When the researcher
enters the sample size = 200, null hypothesis RMSEA =.00, Alternative RMSEA=.09
(.01 higher than the acceptable close fit), Alpha=.05, DF= 9 (the expected minimum
DF in present study), number of group=1, NIESEM software calculated the power of
the RMSEA estimation as .7621. Murphy & Myors (2004) stated that mimimum
required power level in social sciences must be atleast over and above the .50 and .80
to be the genrally acceptable level. The power of .7621 is very close approximation
of 0.80 power level (Kline, 2011, p. 275). Therefore, the researcher statistically
confirmed that the decided minimum sample size 200 was adequate to get the
acceptable results in path analysis as suggested by Hoyle, (1995).
Unlike in path analysis and structural equation modeling there are standard
commercial software programs available to conduct the power analysis on Pearson
product movement correlation. Hintz (2009) developed a new version of PASS
software based on Cohen’s power analysis explanations (Hintze, 2009). This
software is highly rated as one of the best available software to conduct the power
analysis (Thomas & Creb, 1997). In the study, PASS 2008 version 8.0.12 has been
used to perform the power analysis for Pearson product movement correlation.
Cohen suggests using small to medium effect size in social researches. According to
Cohen, expected power value in social sciences is .80. When the required values set
as alpha = .05, R0; the r value of the null hypothesis to zero and R 1 to be ranged as
0.01 (small effect), .02 (small to medium), 0.3 (medium effect) PASS 2008
calculated the required sample sizes (N) as depicted in Table 4.
126
Table 4: PASS 2008 output of required Sample size (N) at small to medium
effect sizes
Power
0.80
0.80
0.80
N
782
193
84
Alpha
0.05
0.05
0.05
Beta
0.19982
0.19992
0.19966
R0
0.0
0.0
0.0
R1
0.10
0.20
0.30
Since the required sample size (782) at the small effect size is higher than the target
population of the study, that was impossible to be achieved. However, the sample
size required to small to medium effect size (193) was possible to achieve within the
target population. Therefore, the researcher wanted to select at least 193 respondents
to conduct Pearson product movement correlation to achieve .80-power level. Then
the researcher calculated the minimum sample size requirement for Chi-square test
by assuming .80 power level (1-β), .05 alpha level (α), 10 degree of freedom
(maximum expected Contingency Table is 6 by 3) and medium effect size (W) .30.
According to the PASS 2008 output, minimum required sample size for the Chi
square test for the given criteria was 181.
The minimum sample requirement for path analysis (N=200) is higher than the
required sample size for person product movement correlation and the Chi-square.
Therefore, the researcher selected the 200 respondents as the required sample size of
the present study. This sample size represented almost 1/3 (31.25%) of the target
population and this coverage ratio was much higher than the past studies on
grassroots level inventors. Power calculation software outputs are available in
Appendix E.
127
Sampling method
In order to achieve the objectives of the study quantitative research approach was
used and data were collected by a sample survey. Hence, the non-probability
sampling procedure was inappropriate to select the sample of the study. The
researcher derived the sample based on the stratified proportional random sampling
method. In this method, first, the total population has to be sub-divided into small
segments. Then the required number of respondents needs to be drawn from each
segment based on the proportion of the segment. Selection of the respondents within
the segments needs to be done on random basis (Singh, 2007).
Even though the target population of the present study was relatively small, it was
distributed throughout 24 districts covering the size of 65,610 Km2 in Sri Lanka
(Figure8).
250
229
200
150
100
75
50
35 35
51
42 48
Figure 8: Distribution of Grassroots Level Inventors across Districts
128
Colombo
Gampaha
Kandy
kalutara
Kurunagala
Matara
Galle
Kegalle
Ratnapura
Badulla
Polonnaruwa
Putalam
Anuradapura
Nuwara eliya
matale
Hambantota
Unknown
Ampara
Monaragala
Jafna
Trinco
baticallo
Mannar
0
Vauniya
15 18
8 9 9 11 12 13 14
1 1 1 2 2 3 3 3
According to Figure 8, there is a significant disparity of distribution of inventors
between districts. Based on the number of inventors there were large (N1>50),
medium (N1>20) and small (N1<20) inventive districts in Sri Lanka. Therefore, in
order to give all the inventors equal chance to be selected in the sample, the
researcher considered this geographical distribution of the inventors and employed
stratified proportional random sampling method to select the 200 respondents as the
sample of the study.
Sample selection process
In order to select representative random sample from the distributed population, the
researcher arranged the number of inventors in all the districts in ascending order.
Then, 24 districts were divided in to four sub-divisions containing six districts in
each division. After that, the proportion of the inventors in each division was
calculated. Then required sample, 200 inventors were calculated according to the
proportionate value of the each segment as illustrated in Table 5.
After the calculation of the required sample sizes from each segment, the researcher
generated 400 random numbers representing at least 50% of the each proportionate
segment of the population. These 400 inventors were contacted through mail to get
their telephone contacts and approval for their participation in the study. By selecting
400 respondents, the researcher was able to provide sufficient protection to not
reduce the sample size to less than 200 due to non-responses. When the selected
respondent was unable to contact, the researcher selected next immediate random
numbered inventor for the sample. Even though the number of inventors was highest
in the capital city of Colombo, they were the least contactable inventors in the study.
129
Owing to poor response to the letters sent by the researcher in Colombo, the actual
number of inventors selected to the sample from forth segment was marginally less
than the required amount. However, the researcher was able to select 85% of the
required number of respondents from the segment four to the actual sample.
Therefore, the researcher assumed that the sample represents the population of
grassroots level inventors in Sri Lanka.
Table 5: Grassroots level inventors’ Sample selection process
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Population
District
#
%
Mannar
Vauniya
Batticallo
Trinco
Jafna
Ampara
Monaragala
Unknown
Hambantota
matale
Nuwara eliya
Putalam
Anuradhapura
Polonnaruwa
Badulla
Ratnapura
Kegalle
Galle
Matara
Kurunagala
Kalutara
Kandy
Gampaha
Colombo
Total
1
1
1
2
2
3
3
3
8
9
9
11
12
13
14
15
18
35
35
42
48
51
75
229
640
0.2
0.2
0.2
0.3
0.3
0.5
0.5
0.5
1.3
1.4
1.4
1.7
1.9
2.0
2.2
2.3
2.8
5.5
5.5
6.6
7.5
8.0
11.7
35.8
100.0
Cumulative
summation
ID
1
2
3
5
7
10
13
16
24
33
42
53
65
78
92
107
125
160
195
237
285
336
411
640
640
Segments %
1Q
10
Sample size: 200
Actual
Required
Sample
Sample size
size from
from each
each
segment
segment
2%
4
4
7%
14
24
17%
34
45
74%
148
127
100%
200
2Q
43
3Q
107
4Q
480
640
130
200
Figure 9 shows the population and sample distribution across Sri Lanka. It displays
the researcher’s base location during the study and shows how far he had gone to
contact grassroots level inventors as sample. The data collection of the study was
conducted at the conclusion of thirty years long civil war in the Northern and Eastern
parts of Sri Lanka. Even though there were five patent applicants from Jaffna,
Vaunia, Mannar and Batticallo, they were unable to contact owing to the internal
displacements and re-settlement process in those parts of the country. A majority of
the data collection panels were conducted in Gampha district, while Kurunagala,
Kandy and Galle districts had one each.
131
Location
North
N= 2
n= 0
N= 1
n= 0
N= 1
n= 0
N= 2
n= 2
N= 12
n= 10
N= 11
n= 4
N= 42
n= 18
N= 9
n= 9
N= 1
n= 0
N= 51
n= 14 N= 14
n= 0
N= 9
n= 2
N= 18
n= 1
N= 75
n= 27
N= 13
n= 4
N= 3
n= 3
N= 229
n= 58
N= 48
n= 7
N= 15
n= 7
Researcher’s
Base Location
N= 35
n= 23
N = Population size
N= 3
n= 2
N= 35
n= 3
N= 8
n= 6
n = Sample size
Figure 9: Map of Grassroots inventors Population and Sample distribution
132
Data Collection Process of Study
Secondary data and expert advices
Most of the literature and secondary data were collected from the library materials
and electronic databases of the libraries of University Putra Malaysia and Sri Lanka
National Intellectual Property Office. If the literature were not available or the access
restricted the access, the researcher personally contacted the authors of the literature
through e-mail to request and collect the articles directly from them. During the
research process, the researcher came across theoretical and methodical issues those
were not clearly explained in the existing literature. Apart from the advices and
guidance received from the supervisory committee, the researcher contacted the
expert scholars in the field through e-mail to get their advices and opinions on such
complicated issues during the research process. The list of contacted experts and
copies of their personal communication are attached in the Appendices C and D
respectively.
Preliminary data collection
In order to select 200 respondents for the main survey, the researcher contacted the
majority of the inventors in the target population to get their updated contact details.
The researcher requested the list of contact details of the resident patent applicants
from the Sri Lanka National Intellectual Property Office.
Since the electronic
database included only their postal addresses as contact details, the researcher only
received the mailing addresses of the target population as the contact method.
Beacuse to that, the researcher had to encountered serious difficulty to locate the
grassroots inventors selected for the sample. Therefore, in last week of February
133
2010, the researcher sent letters to randomly selected 400 inventors explaining the
objectives, and the importance of the research and requesting their cooperation in the
survey with post paid printed post cards. The respondents were requested to send the
post cards back with their latest contact details including telephone numbers, e-mail
addresses and how they would like to participate in the survey. By the end of March
2010, the researcher received 14 letters returned as undelivered by saying errors in
the addresses and person not available in the given address. Meanwhile, 228
inventors responded to the letters through the post cards with their latest contact
details and decision on participate in the survey. The relatives of the five inventors
sent back the post cards mentioning that the inventors were no longer living in Sri
Lanka for various reasons including foreign employment and education. Three
respondents had already passed away at the time the researcher contacted them. All
the other 220 inventors who responded to the letters were willing to participate in the
survey. Therefore, out of 400 letters sent 220 inventors were available as respondents
for the study. The positive respond rate was 55% and it was 34% of the target
population. After receiving telephone contact details, the researcher called these 220
inventors to select 200 respondents for the survey. When a selected inventor cannot
be contacted, the immediate next higher random numbered inventor was contacted.
The details of response rates and coverage of population by the sample is presented
in Table 6.
Table 6: Response rates and distribution of sample
Segment
1Q
2Q
3Q
4Q
Total
Population
10
43
107
480
640
Contacted
by mail
5
40
64
291
400
Responded
4
30
49
145
228
134
Response
Rate %
80.0
75.0
76.6
49.8
57
Final
Sample
4
24
45
127
200
% Final sample
from population
40.0
55.8
42.1
26.5
31.3
Cross sectional survey data collection
This study was designed to collect data using self-administrated questionnaires at a
single point in time. According to the literature on research, this method is defined as
the cross sectional survey method (Babbie, 1990). In the survey, data collection
method, the researcher can use either mail survey or interview method to collect data.
Babbie (1990) stated that even though the mail survey is cost-effective way of
collecting data from large and dispersed sample, the low response rate and the time
taken has been a great obstacle in mail surveys. The geographical disproportion of
the respondents of the study depicts in the Figure 9. Therefore, researcher was unable
to conduct one-to-one personal interviews for entire sample in a cost and time
effective manner. However, owing to the overwhelming enthusiastic welcome for the
survey and generosity of the selected respondents, the researcher was able to collect
the data by physically contacting the respondents by avoiding the inherent
disadvantages of mail survey. The researcher received remarkable positive responses
from the responded inventors to participate in the survey in selected common
location. A sum of 117 inventors mentioned that they would like to participate in the
survey in any place, 67 inventors mentioned that they would like to participate to the
survey at any common place in their living district. At the time of the survey,
majority of these 67 respondents were based around the Kurunagala, Kandy and
Galle districts. Further, nine inventors mentioned that they would like to participate
in the survey at their homes and seven inventors mentioned that they would like to
participate in the survey by mail. After the explanation given by the telephone to
seven respondents who asked for the mail survey, four inventors who requested mail
questionnaires method earlier, agreed to participate for data collection at University
of Kelaniya, Sri Lanka. Other three respondents allowed the researcher to contact
135
them at their homes or working places. Therefore, the researcher was able to gather
majority of the respondents into small panels or groups and collect the data using
self-administrative questionnaire.
Selected inventors were asked to give appointments for the data collection of the
survey from 1 April to 31 July 2010. Based on the dates given, the researcher
grouped the respondents in to small panels of 10-20 inventors per group and asked
them to come for data collection at specific location and time. After confirming the
date and time the researcher sent invitation letters to the panel respondents three days
before as the reminders. The researcher conducted majority of the panel discussions
at the Business Knowledge Centre of Department of Commerce and Financial
Management, University of Kelaniya in Gampaha district. Other than the main
centre, the researcher conducted panel data collections at Kurunagala, Galle and
Kandy branches of the Open University of Sri Lanka. The respondents who were
unable to participate in the panel data collection were contacted either at their homes
or offices. At the beginning of the each panel data collection, the researcher
introduced himself, aims and objectives of the study and potential benefits to the
inventors in Sri Lanka. After the introduction, the researcher asked the respondents
to answer questionnaires that were given to them in two separate parts for profiling
variables and psychological measurements. While the respondents were the
answering the questionnaire, the researcher helped to explain to the respondents
certain questions and issues of the questionnaire. After all the respondents had filled
up the questionnaires, the researcher collected the questionnaires after surface scan
for unanswered or missing values. If there were unanswered or missing values, the
researcher pointed the mistake to the relevant respondents, fclarified the meaning of
136
the question further and kindly asked the respondents to complete it with his/her
response.
After filling the questionnaires, to get some qualitative inputs for the study, each
panel respondents were given 5-10 minutes to explain how they feel their inventive
activities, achievements, hardships and required help. All the panels were
enthusiastic during 3-4 hours interactive sessions that all the panel members actively
shared their thoughts and experiences. Participants of the survey were thankful to the
researcher for conducting the study and the panel interviews in a way that allowed
the inventors to get to know each other and share their experiences. Respondents
who were involved in the study at their homes and offices were also given the almost
identical data collection treatment even though the conditions of the environments
were different. This practice allowed the researcher to collect more data at a time
and closely monitor the answering process to minimize confusions and missing data
in the questionnaires. On the other hand, the researcher was able to conduct
discussions with the respondents after the data collection to get more in-depth
qualitative information regarding the inventors’ inventive activities.
Statistical Analysis Design
Statistical methods and tools
Owing to the exploratory correlational research design of the study, the researcher
had to select statistical methods to achieve the stated research objectives through the
quantitative data of the study. According to the literature, statistical data analysis can
be categorized as univariate analysis that examines the distribution of value
137
categories or values for a single variable, bivariate analysis that examine the
relationship between two variables and multivariate analysis that examine the
relationship between three or more variables (Weinbach & Grinnel, 2007). On the
other hand, based on the purpose of the analysis, statistical methods have been
divided in to two broad categories as descriptive and inferential. Descriptive statistics
are numerical and graphical methods that enable the researcher to explore, organize,
summarize and describe the quantitative data. The inferential statistics enables the
researcher to employ inductive reasoning to make conclusion of the population based
on the sample results (Ary, Jacobs, & Sorensen, 2006). In order to answer the
research questions, the researcher developed four specific stated objectives and
systematically selected the appropriate descriptive and inferential statistical methods
to achieve each of the stated objectives of the study.
Exploration of sample characteristics is important to understand the nature of the
grassroots level inventive community. However, the previous studies on independent
and grassroots level inventors have given higher emphasis on profiling of inventors.
Owing to the importance of understanding the community from inside out, the
researcher stated profiling as a specific objective of the present study. Therefore, the
first objective of the study was set to explain the selected demographic,
psychological, technical and social domain factor profiles of Sri Lankan grassroots
level inventors. By achieving this objective, the researcher expectes to understand the
significant attributes of the grassroots level inventive community through the sample
data. Profiling of the grassroots inventive community could be achieved by
explaining the univariate attributes of the variables of the study. Therefore, the
present study selected descriptive statistics; frequencies, percentages and pie charts
138
to explain the demographic, psychological, technical and social pillar factors of the
grassroots level inventive community in Sri Lanka.
The second objective of the study was to explore the objective and subjective success
of Sri Lankan grassroots level inventors. In this regrad, the researcher intended first,
to explore the objective success and subjective success of the respondents using
univeraite analysis. Secondly, to explore the bivariate behavior of the frequency
levels of objective and subjective success using cross tabulation technique. The cross
tabulation is a joint frequency distribution of cases based on two or more categorical
variables. The joint frequency distribution can be analyzed with the chi square
statistic or Fisher’s exact test to determine, whether the variables are statistically
independent or associated. If a dependency between variables does exist, then the
indicators of association, such as Cramer’s V and Cohen Effect size (W) can be used
to describe the strength of the values of one variable predict or vary with those of the
other variable (George & Mallery, 2006).
The third and fourth objectives of the study focused on determining the causes (third
Objective) and consequences (fourth objective) of subjective success of the
grassroots level inventors. The main endogenous variables; the objective and
subjective success of the grassroots level inventors were planed to calculate as the
summated values of the subscales used to measure the each construct. Therefore,
both objective and subjective success are considered as metric variables. Further,
according to past studies, the true nature of the majority of demographic and
technical domain factors of the inventors can be nominal, categorical or continuous.
Therefore categorical data analysis methods are also needed to be employed to
139
achieve the third objective. Low, medium and high categorization of objective and
subjective success was adopted to analyze the selected non-metric profiling variables
using cross tabulation and descriptive statistic tools. Therefore, to analyze the
association between non-metric variables, the level of objective and subjective
success, the researcher planned to use Pearson Chi-square test (and Fisher’s exact
test) alone with the graphical analysis of means to determine whether the variances
of mean between groups are equal or not. When the 80% of the cells in the
contingency tables have not met the minimum number of expected value 5, Fisher’s
test and Monte Carlo p-value were used as an alternative for Chi square test (George
& Mallery, 2006). The bivariate relationship between metric predictor variables and
metric dependent variables were expected to be measured using Pearson product
movement correlation analysis (Hair, Black, Babin, & Anderson, 2009). However,
casual direction of the relationship cannot be determined by the correlation
coefficient (Ary, Jacobs, & Sorensen, 2006). Therefore, conceptual models of the
present study needed to be analyzed in multivariate models.
Directional correlational design such as multiple regression analysis is able to
determine the relationship between one or more independent variables and one
dependent variable. However, regression analysis is not directly applicable in
complex research designs with multiple dependent variables (Hair, Black, Babin, &
Anderson, 2009; Ahn, 2002). Even though the hierarchical regression analysis is able
to achieve the said objectives, more robust statistical methods such as path analysis
and structural equation modeling can be used to understand the direct and indirect
relationships of complex theoritical models with multiple endogenous variables in
the correlational research (Schumacker & Lomex, 2004). Majority of the recent
140
studies on subjective well being have used structural equation models (Headey,
Veenhoven, & Weari, 2005; Rogatko, 2010; Roderiguiz, 2006). However, a number
of latent constructs in the present study made the measurement structure of a
structural equation model (SEM) seriously complex. It questioned the ability to
satisfy the multivariate normality assumption and .80 statistical power for the data
anal ysis with only 200 respondents. The structural equation models with only
observed variables have been defined as path analysis and it is the statistical
technique that used to examine the causal relationship between two or more variables
based on the linear equation system (Olobatuyi, 2006). Path analysis has been
developed as a method of studying direct and indirect effects of variables while
regression analysis remains as the method of discovering casual relationships (Ahn,
2002). Path analysis is essential technique in confirmatory purposes. However, by
using a series of model fit indices researchers can test the validity of the models and
further developed the models for best fit (Todman & Dugard, 2007). Path analysis
allows the researcher to determine the best-fit model by trimming the model using
model-generating approach. Using saturated path models researchers can implement
data analysis akin to the hierarchical regression to determine the significant
regression weights. Saturated path models gives regression weights of the allpossible relationships in the model and the researcher can modify the model by
trimming the insignificant relationships and develop an alternative model that fit with
the data and theoretical model (Schumacker & Lomex, 2004). Then the researcher
can determine the model fit using the model fit indices. Owing to the overwhelming
advantages of path analysis over the regression analysis, the researcher selected the
path analysis model generating and comparison approaches to achieve the third and
fourth objectives of the study.
141
According to the statistical literature, single multiple regression, path or structural
equation models are unable to detect the actual casual directions other than
hypothesized correlation between variables in a cross-sectional study (Kline, 2011).
Even if the true casuality can only be dictected with longitudanal or experiment data,
different top down and bottom-up models can be tested with cross sectional data to
get meaningful idea of the casual detecteds in contradicting theoretical grounds (Hox
& Bechger, 1998; Norman & Streiner, 2003; Kline, 2011). More complex nonrecursive path analysis and Structural Equation Modeling (SEM) techniques allow
the researchers to use double-headed arrows to both directions in the models, but it
was not highly recommended in the literature (Norman & Streiner, 2003; Kenny D.
A., 2003). Norman and Streiner (2003) have strongly recommended using two
opposing models instead of using non-recursive model to test the possible casual
directions. Therefore, by developing bottom-up path model and reverse top-down
path model, the researcher expected to explore the casual (bottom-up), consequential
(top-down) and two-way (reciprocal) relationships between selected domain
variables, objective and subjective success of grassroots level inventors.
Path Analysis models also provide a way to analyze comprehensive relations among
variables including direct and indirect effects. These indirect effects are useful in
defining the mediator effects. Baron and Kenny (1986) had introduced the four-step
method to identify the mediator effect. According to both writers, a variable may be
considered as a mediator to the extent it carries influence of a given independent
variable to a given dependent variable. Generally, mediation occurs when,
independent variable significant affects the mediator, mediator significantly affects
the dependent variable and when mediator variable is available, independent variable
should not have significant effect on the dependent variable (in total mediation
142
situation, correlation between independent and dependent variables =0). Sobel (1982)
has introduced a manual method to calculate the critical ratio to explain the
significant non-zero relationship between independent variables and the dependent
variable via mediator variable (Sobel, 1982). However, owing to the development of
computer intensive methods for mediation analysis, recent literature recommended to
use computer based bootstrapping sampling method to detect the mediation effect
(Mackinnon, 2008; Preacher & Leonardelli, 2007). In bootstrapping, computer
programs such as AMOS 18 run the re-sampling of the actual sample. The mediation
effects estimated in each bootstrap sample are used to form a distribution of the
bootstrap mediated effect estimates and confidence limits are obtained from this
bootstrap distribution (Mackinnon, 2008, p. 335). In present study, the significance
of the indirect effect and mediation effects was analyzed using AMOS 18
bootstrapping technique using 2000 bootstrapping samples at 95% Bias-Corrected
confidence interval.
This section of the chapter explained how the four stated research objectives of the
study required different statistical tools. Summary of selected statistical tools and
methods of the present study illustrate in Table 7.
143
Table 7: Summary of statistical method and tools of the study
Objective
First Objective
Method
Descriptive Statistics
Tools
Frequency distribution,
Central tendency, Pie
charts
Second Objective
Descriptive Statistics
Frequency distribution,
Central tendency, Cross
tabulation, Radar
diagrams
Third and Fourth Objectives Categorical level data
Analysis
Continuous level and
Multivariate data
Analysis
Cross tabulation, Pearson
Chi-square, Fisher’s exact
test, Mean plots
Pearson product
movement correlation,
Path Analysis, Model fit
indices
Exploratory data analysis (EDA) on statistical assumptions
In general, bivariate and multivariate statistical methods have to be adhered to
statistical assumptions. Especially Path analysis and Structural Equation Modeling
are highly sensitive to these assumptions. Generally, to conduct path analysis, data
have to be adhered to the assumptions on univariate, multivariate normality,
univariate, multivariate outliers and missing values. The researcher also needs to
satisfy the multicollinearity, linearity and homoscedasticity assumptions before
analyzing the data
(Kline, 2005). Therefore, before conducting inferential data
analysis, the researcher has to conduct the Exploratory Data Analysis (EDA) to test
the data for the required statistical assumptions using recommended techniques.
144
In the present study EDA, objective oriented data analysis and sample size power
analysis were done using PASW Statistics 18.0 (SPSS version 18), AMOS Graphics
Version 18.0, PASS 2008 and NISEM Software packages.
i. Outliers
Outliers are the cases, which are falling at the unacceptable outer ranges (high or
low) of the data distribution. Hair et al. (2009) stated that the researcher should
utilize as many methods to detect univariate and multivariate outliers of the data set.
In the present study, the researcher tested the univariate outliers using PASW
Statistics 18 Stem and Leaf diagrams and Box-Plots. Multivariate outliers were
tested using Mahalanobis distance P1-value and P2 Values.
ii. Univeraite and Multivariate Normality
Normality is one of the fundamental assumptions in parametric statistic procedures.
According to Hair et al. (2009), when the variables in the data set are multivariate
normal, it is also considered as univariately normal; however the reverse is not
necessarily true (Hair, Black, Babin, & Anderson, 2009). Therefore, the researcher
first tests the univariate normality of variables using Skewness and kurtosis statistics.
The AMOS 18 normality test output was extracted to test the normality of the
variables. Kline (2005) had recommended a cut off criteria for assuming nomality
using absolute skew index less than 3.00 and absolute kurtosis index less than 10.
Recently published structural equation modeling studies have recommended to use
the Mardia (1970) kurtosis critical ratio (=Mrdia’ kurtosis coefficient/ Standard
Error) 1.96 as a cutoff value to check multivariate normallity (Gao, Mokhtarian, &
Jonston, 2008; Ni & Yang, 2010; Lane, Harrington, Donohew, & Zimmerman, 2006;
Mardia, 1970).
145
iii. Linearity
Linearity is the implicit assumption of all multivariate techniques based on
correlational measures of association, including path analysis and structural equation
modeling (Hair, Black, Babin, & Anderson, 2009). Owing to the fact that non-linear
effects will not be represented in correlation value, it is important to ensure the linear
relationship between endgenous (dependent) and exogenous (independent) variables
in the model. Even though scatterplots can be used to determine the non-linear
relationships, linearity is very hard to judge, when the relatiosnship between
variables are low or moderate. Therefore, explicit alternative models of linear and
non linear realtionship was recommended to detect the non linear relationships (Hair,
Black, Babin, & Anderson, 2009). When the correlation value (R) of linear
relationship is significantly lower than the correlation value (R) of quadatic nonlinear relatiosnship, then the relationship between two varaibles need to be
considered as non-linear relationship.
iv. Homescedasticity
Homoscedasticity refers to the assumption that endogenous (dependent) variables
exhibit equal level of variance across the exogenous (predictor) variables (Hair,
Black, Babin, & Anderson, 2009). Therefore in homoscedastic relationships,
scatterplots of dependent and independent variables shows roughly a same width all
over with bulging towards the middle.
v. Multicollinearity
Multicollinearity is one of the cause of singular covarance metrices, which occurs
when intercorrelation among some varaibles are significantly high (>.85) (Kline,
2011). PASW Statistics 18 detects the multicollinearity by two indices; tollarence: 1146
R2 and it should be greater than .10. VIF index: 1/(1-R2) and that should be less
than 10 to meet the assumption of multicollinearity.
Summary
This chapter descriptively discussed the research design, statistical design and the
sample design employed in the study. It also discussed the operationalization,
instrument design, and validity and reliability evidences of the instruments of the
study. In order to provide the background preoperational information and instrument
reliability testing, the researcher had also discussed about the two earlier pilot studies
of the present study. Finally, the researcher proceeded to explain the data collection
procedures adapted at each stage of the study to collect data from different sources
for different purposes. The next Chapter will elaborate the detail data analysis and
results of the study.
147
CHAPTER 4
RESULTS
No great discovery made without bold guess;
The purpose of research is to discover, not to prove the guess
–Sir Isaac Newton
Introduction
The aim of this study was to explore the demographic, psychological, technical and
social causes and consequences of objective and subjective success of grassroots
level inventors in Sri Lanka. In order to achieve the aim of the study, four specific
research objectives were set in the Chapter 1. To achieve the specific research
objectives and answer the research questions, the researcher needed to analyze the
collected data using appropriate statistical tools and methods (Lewis-Beck, 1995). In
Chapter 3, the researcher explained the statistical tools selected for the data analysis
of the present study. The key results of the two pilot studies conducted before the
main study is explained in appendix A. This chapter describes the data analysis of the
final study and it will start with presenting the exploratory data analysis of variables
for the common assumptions of statistical methods used to achieve the objectives of
the study. Then it will proceed to detail statistical analysis of data and the results of
the study. Finally, it will discuss the findings of the study to answer the stated
research questions.
Exploratory Data Analysis (EDA)
As discussed in chapter 3, statistical procedures adapted in the present study are
required to test the data for preliminary assumptions and make remedial actions,
when there is violation of the assumptions. Parametric statistics such as Pearson
product movement correlation, mean comparison and path analysis have to be
adhered to the assumptions of outliers, multicollinearity, normality, linearity and
homoscedasticity (Kline, 2011; Hair, Black, Babin, & Anderson, 2009; George &
Mallery, 2006).
The researcher conducted EDA using descriptive explorer of the PASW Statistics 18
to test the data for outliers, univariate normality, linearity and multicollinearity.
AMOS 18 was used to detect multivariate outliers and multivariate normality.
Univariate normality of continuous variables were tested using critical ratios of
skewness and kurtosis rule of thumb 3 and the Normal probability plot of residuals
(Normal Q-Q Plot). Multivariate normality was tested by using Mardia’s kurtosis
calculated in the AMOS 18. The critical value of the Mardia’s kurtosis is below the
1.96 indicating there is no significant violation of multivariate normality (George &
Mallery, 2006). Univariate outliers were detected using stem and leaf diagram and
multivariate outliers were tested using Mahalanobis Distance cutoff p-value .001.
Muliticollinearity was tested using tolerance (should be > 0.10) and VIF values
(should be < 10). Linearity and homoscedensticity were tested using bivariate scatter
plots and R values of the linear and non-linear curve estimations. Results of the EDA
process of the study present in following sub sections,
i. Testing for Missing values and Univariate Outliers:
Owing to the data collection procedure adopted in this study, the researcher ensured
that there were no missing values for any of the variables in the data set. However, as
per the preliminary descriptive exploration of the continuous variables in the study,
some of the variables had outlier values. Once the outliers found, the researcher
149
rechecked the data for reporting and data entering errors. When the researcher
ensures that there were no reporting errors, descriptive statistics of the means and 5%
trimmed means were compared to detect any significant impact of the outliers (Table
73). According to the analysis, there were no significant differences detected
between mean and 5% trimmed means. Therefore, the outliers available in the
variables did not have serious influence on the means of the variables. Therefore,
other than conducting robust data transformation, the researcher reduced the impact
of the univariate outliers by assigning one unit larger or smaller values to the outlier
values as recommended by the many authors (Field, 2005; Tabachnick & Fidell,
2007). Stem and leaf diagrams and Box-plots of the continuous variables after the
remedial actions depict in the Appendix F3.
ii. Testing for Univariate Normality:
Apart from the external linkages, all the other variables had met with the rule of
thumb Skewness and kurtosis critical ratios recommended by the Kline. According to
the Tabachnick & Fidell ( 2007) dichotonomus variables need not to be tested for
normality, but the ratio between two groups need to be at least 9:1. In present study
the ratio between married and unmarried was 2:1. Therefore marital status satisfied
the required ratio.
Critical ratio of external linkages Skewness was 4.38, that was significantly deviated
from the minimum requrement of 3.00. Therefore, the reseracher transformed the
variable using log transformation to aviod the significant positive skewness. After
the transformation of external linkages, the researcher run the normality test again to
check the changes of normality of the data set. Table 8 depicts the normality test
150
results after the data transformation. Absolute values of the Skewness and kurtosis
critical ratios of the external linkages were 1.94 and 2.22 and within the
recommended limit of 3.00.
Table 8: Univaraite Normality Test Results after data transformation
Variable
External Linkages
Income
Community Connectedness
Inventive Career Satisfaction
Life Orientation
Maximizing Tendency
Social Capital
Internet usage
Objective Success
Subjective Success
Engagement on Inventions
Age
min
max
skew
c.r.
kurtosis
c.r.
1.114
1.531
.337
1.944
-.770
-2.223
5.000
28.000
11.000
16.000
15.000
31.000
4.000
.000
24.000
1.000
14.000
84.000
56.000
20.000
30.000
38.000
76.000
20.000
5.000
58.000
8.000
74.000
.310
-.341
-.314
-.405
-.231
-.105
-.190
.263
-.097
.221
.119
1.789
-1.969
-1.815
-2.341
-1.335
-.609
-1.096
1.516
-.557
1.278
.689
-.595
-.433
-.361
-.172
-.721
-.174
-.898
-1.016
-.010
-.449
-.910
-1.719
-1.249
-1.041
-.495
-2.080
-.502
-2.592
-2.932
-.028
-1.297
-2.628
When the sample size is large as 200, Tabachnick & Fidell recommended to check
the distribution of the data using graphical methods too (Tabachnick & Fidell, 2007,
p. 80). Therefore, normal Q-Q plot has been used to detect the deviation from normal
distribution. In principle, significant upward or downward deviation of the Q-Q plot
from the dignal line, especially at the middle of the plot indicates serious violation of
normality and indicates serious skewnesss and kurtosis. As far as all the variables
have shown the Q-Q plots within this guideline, the variables in present study have
not shown any significant deviation from normality (depicts in the Appendix F3 ).
iii. Testing for Linearity:
Table 9 depicts the PASW 18 linear and non-linear model estimations for the
variables in the present study. Table 9 clearly shows that age is seriously deviated
from the linear relationship with subjective success. R value 0.036 indicates very
151
negligible relationship and the difference between linear and non-linear correlation
coefficients was very high (94.4%).
Table 9: Testing for linear relationships between endogenous and exogenous
variables
Subjective Success
Linear
Objective Success
Non-Linear
Linear
Non-Linear
R
R2
R
R2
%
Change R
R
R2
R
R2
%
change R
Age
0.036
0.001
0.070
0.005
94.4
0.155
0.024
0.210
0.044
35.5
MS
0.134
0.018
0.134
0.018
0.0
0.142
0.02
0.142
0.02
0.0
E on I
0.310
0.096
0.322
0.103
3.9
0.363
0.132
0.367
0.134
1.1
IU
0.348
0.121
0.358
0.128
2.9
0.161
0.026
0.161
0.026
0.0
SC
0.314
0.098
0.386
0.149
22.9
0.192
0.037
0.260
0.067
35.4
MT
0.195
0.038
0.196
0.038
0.5
0.049
0.002
0.052
0.003
6.1
LOT
0.365
0.133
0.372
0.139
1.9
0.089
0.008
0.096
0.009
7.9
ICS
0.438
0.192
0.441
0.194
0.7
0.188
0.035
0.227
0.052
20.7
CC
0.414
0.172
0.431
0.186
4.1
0.129
0.017
0.146
0.021
13.2
Income
0.230
0.053
0.232
0.054
0.9
0.272
0.074
0.304
0.093
11.8
ExLinks
0.225
0.05
0.249
0.062
10.7
0.354
0.125
0.356
0.127
0.6
MS-Marital Status, EonI-Engagement on invention, IU-Internet Usage, SC-Social Capital,
MT-Maximizing Tendency, LOT-Life Orientation, ICS-Inventive Career Satisfaction, CCCommunity Connectedness, LogExlinks-Transformed Expertlinkages
Consequently the relationship between age and objective success also indicated
higher strength (R) in non-linear realationship than in linear relationship. Further
more, in the literature review of the study, the researcher found that age has not
shown significant relationship with subjective success. Also, even the past literature
on achievement and success of inventors have shown the relationship between age
and the achievement as non linear relationship. The achievement increases with the
young to certain level of middle age and in older ages it decreases. Therefore, the
researcher concluded that age inherently was no linear relationship with population
subjective or objective success. Therefore age was restricted to descriptive analysis
of the present study and not included in the path model.
152
iv. Testing for Multivariate Normality and Outliers:
In the present study, multivariate normality was tested using Mardia’s multivariate
kurtosis.
Table 10: Multivariate Normality Test Results of the variables in the model
Variable
Log External Linkages
Income
Community Connectedness
Inventive C Satisfaction
Life Orientation
Maximizing Tendency
Social Capital
Internet Usage
Objective Success
Subjective Success
Engagement on Inventions
min
max
skew
c.r.
kurtosis
c.r.
1.114
5.000
28.000
11.000
16.000
15.000
31.000
4.000
.000
24.000
1.000
1.531
84.000
56.000
20.000
30.000
38.000
76.000
20.000
5.000
58.000
8.000
.337
.310
-.341
-.314
-.405
-.231
-.105
-.190
.263
-.097
.221
1.944
1.789
-1.969
-1.815
-2.341
-1.335
-.609
-1.096
1.516
-.557
1.278
-.770
-.595
-.433
-.361
-.172
-.721
-.174
-.898
-1.016
-.010
-.449
-2.223
-1.719
-1.249
-1.041
-.495
-2.080
-.502
-2.592
-2.932
-.028
-1.297
-4.265
-1.645
Multivariate
Table 10 shows the multivariate noramality test performed by the AMOS 18. After
removing the age from the multivariate analysis, Mardia’s multivariate kurtosis
critical value was 1.654 and based on the 1.96 cutoff criteria the variables in the
model showed acceptable multivariate normality (CR=1.654<1.96). Futher, the
minimum Mahalanobis distance P1-value was 0.008 and its parallel P2 value was
.792. Therefore, none of the P2 values was less than the P1 values and P1 values
always higher than the 0.001. Hence, there was no violation of the multivariate
outlier criteria (Bynrne, 2009). Therefore, there was no threat of multivariate outliers
detected in the data set.
v. Testing for Homoscedasticity:
Tabachnick & Fidell stated that when the variables achieved the multivariate
normality, the relationship between variables are considered to be homoscedastic
153
(Tabachnick & Fidell, 2007, p. 85). Apendix F3 shows the scatter plots of the
variables of the study and they dipect reasonable homoscedasticity.
vi. Testing for Multicollinearity:
Table 11 shows the tolarence and VIF values of the exogenous variables of the
conceptual models. All the variables’ tolerance values are higher than the 0.10 and
the VIF values are well bellow the 10. Therefore, there was no serious
multicollinearity between the exogenous variables in the study.
Table 11: Multicollinearity test of exogenous variables of conceptual model
Model
Collinearity Statistics
Tolerance
VIF
.743
1.346
.803
1.246
.694
1.441
.747
1.340
.867
1.153
.910
1.098
.868
1.152
.818
1.223
.806
1.241
.658
1.521
.803
1.246
Marital Status
Engagement on Invention
Objective Success
Internet Usage
Social Capital
Maximizing Tendency
Life Orientation
Inventive Career Satisfaction
Community Connectedness
Income
Log External Linkages
During the validation and reliability evidences analysis that presented in chapter 3,
the researcher tested the internal consistency of the data collection instrument and
therfore ensures the reliability of the collected data. During the EDA, the researcher
tested the data set of the present study
for missing values, outliers, linearity,
normality, homoscedasticity and multicollinearity. After implementing remedial
actions to outliers and violation of linearity assumption the researcher ensures that
varaibles of the present study adhere to the fundamental assumptions of parametric
data analysis. Therefore, proceeded to the objective based depth data anlysis of the
study.
154
Demographic, Psychological, Technical and Social (D.P.T.S.) Profiles of Sri
Lankan Grassroots Level Inventors
Discusion of the demographic profile of the respondents has been a common
preliminary analysis in quantitaive studies. However, past studies on independent or
grassroots level inventors have given a serious attention on the respondent profiles.
Majority of the past studies on inventors have thoroughly examined the profiles of
the respondents (Macdonald, 1986; Amesse & Desranleau, 1991; Sirilli, 1987;
Whalley, 1992; Weick & Eakin, 2005; Georgia Tech Enterprise innovation Institute,
2008). Amesse and Desranleau (1991) have summarized the purposes and variables
of past studies of individual inventors. It indicates that the main purpose of the
majority of past studies was to describe, evaluate and measure the profiles of the
individual inventors (Amesse & Desranleau, 1991, p. 14). Whalley (1992) mentioned
that the little research attention given to the importance of the independent inventors
have demanded to provide a comprehensive profile of the independent inventors.
Unlike industrial countries, studies on inventors in the developing countries are very
rare (Weick & Eakin, 2005). Owing to that, the researcher was unable to locate any
published study on Asian or Sri Lankan grassroots level inventors during the
literature review. Further more, the respondents of the study informed the researcher
that, other than mass media correspondents, they were never been contacted by any
researchers before. Therefore, following the past studies on grassroots level
inventors, the present study conducted comprehensive profiles analysis on selected
variables of the respondents as the first objective of the study.
155
Demographic profile of the grassroots level inventors
i. Age
According to the previous studies average grassroots level inventor was middle aged
male. Table 12 depicts the frequency distribution of age of the respondent grassroots
level inventors of present study. The average age of the respondent grassroots level
inventors was 42 years. The youngest inventor was 14 years old and the oldest
inventor was 74 years old. However, both the old and adolescent inventors
represented only 8% of the grassroots level inventors. It indicates that inventions are
not very popular among the very young and old age groups. The majority of the
respondent grassroots level inventors are distributed between young to middle ages.
Whereas, 74% of the inventors were in middle or young ages (19- 55 years) and 30%
were in the middle age (41-55 years).
Table 12: Age profile of Grassroots level inventors
Adolescent
Young
Late Young
Middle
Late Middle
Old
Total
Category
Value
12-18
19-30
31-40
41-55
56-65
65+
Frequency
10
43
45
60
36
6
200
%
5.0
21.5
22.5
30.0
18.0
3.0
100.0
Cumulative %
5.0
26.5
49.0
79.0
97.0
100.0
Mean Age: 42 Years, Minimum age: 14 Years, Maximum age: 74 Years
ii. Gender
According to the Whittington and Smith-Doerr (2008) and Giuri et al.(2007),
generally number of female inventors are very modest compared to male inventors
in many parts of the world. Therefore, historically the grassroots invention was
considered as male activity than the unisex activity. Figure 10 represents the gender
composition of the respondents of present study. According to the Figure, 95% of
156
the respodents were male and only 10 female respondents were included in the
sample. Therefore, it suggests that the female representaion in inventive activities in
Sri Lanka is negligible and it is at par with the existing literature evidences in
industrial countries as well.
Female
N=10
5%
Male
N=190
95%
Figure 10: Gender composition of the respondents Grassroots Level Inventors
iii. Marital Status
Winston, (1937) is one of the oldest studies identified that majority of the inventors
were married. The most recent study of Åstebro & Thompson, (2007) also found
similar trend that has shown 89 % of the independent inventors in Canada were
married. Figure 11 depicts the marital status of the respondents of the present study.
Figure indicates that 67% of the respondent grassroots level inventors were married.
Out of 200 inventors, only 65 respondents were unmarried at the time of survey.
Hence, the marital status of Sri Lankan inventors largely similar to the trend that
identified in other studies.
157
Married
N=135
67%
Unmarried
N=65
33%
Figure 11: Marital status among the respondent Grassroots Level Inventors
iv. Location
In industrial countries a majority of the grassroots level inventors were located in
urban, semi urban or metropolitan areas (Georgia Tech Enterprise innovation
Institute, 2008; Bettencourt, Lobo, & Strumsky, 2007; Whalley, 1992). However,
according to the Table 13, 64% of the respondents of the grassroots level inventors of
Sri Lanka located in areas that controlled by Pradeshiya Saba, the lowest level
governing council of Sri Lanka, which are also recognized as the rural areas of Sri
Lanka. According to the governing council of the location, majority of the
respondent grassroots level inventors in Sri Lanka are living in rural areas of the
country. However, district wise analysis indicated that, majority of the respondent
inventors were living in district with high population density and urbanization.
Table 13: Location of the respondent grassroots level inventors in Sri Lanka
Governing Council
Pradashiya Saba
Urban Council
Municipal Council
Total
Rural: 128 (64%) Urban:
Frequency
128
57
15
200
72 (36%)
158
%
Cumulative %
64.0
28.5
7.5
100.0
64.0
92.5
100.0
According to Table 14, 66% of the respondents were living in districts that have
more than 500 persons per KM2. Only 10% of the Respondents were coming from
the districts with less than 200 persons per KM2. This indicates that even though 64%
of the respondent inventors were coming from administrative rural areas, population
density wise, most of them were living in districts with high or middle population
density. Grassroots level inventors’ population data and the distribution of the
respondents presented in Table 14 indicate that more than 87 % of the grassroots
level inventors were living in places where located inside or adjacent to the lower left
quadrant of the country.
Table 14: Living Districts of respondents by population density
Colombo
Sample
n
58
Gampaha
Kandy
27
14
14
7
43
50
1539
667
7
23
3
1
2
18
7
4
9
6
2
4
12
2
1
1
9
4
2
5
3
1
53
65
66
67
68
77
80
82
87
90
91
667
613
600
466
412
316
314
246
226
211
140
2
4
10
3
200
1
2
5
2
100
92
94
99
100
135
117
112
72
District
Kalutara
Galle
Matara
Kegalle
Nuwaraeliya
Kurunagala
Rathnapura
Puttalam
Matale
Hambantota
Ampara
Trincomalee
Pollonnaruwa
Anuradhapura
Moneragala
Total
Population
N
473 (74%)
112 (18%)
33 (5%)
29
Cumulative
%
29
Density
Persons per KM2
3330
%
GLI: Grassroots Level Inventors
The lower left quadrant of the country owns the administrative capital of Sri Lanka;
Sri Jayewardenepura, which is located in Colombo, the main commercial district of
159
Sri Lanka (Figure 12). This quadrant also claimed the higher population density in
Sri Lanka. Therefore, the findings of the location of the grassroots level inventors in
Sri Lanka suggest that urban districts with high population density have been the
locations where majority of the inventors reside.
Upper Left
Upper Right
Quadrant
Quadrant
Lower Left
Lower Right
Quadrant
Quadrant
Figure 12: Geographical spatial pattern of distribution of GLI in Sri Lanka
160
v. Highest Educational Qualifications
Past studies have revealed that independent inventors are relatively well educated
(Georgia Tech Enterprise innovation Institute, 2008; Åstebro & Thompson, 2007).
According to the Table 15, 40% of the respondents have completed the formal school
education and from that 32.5% completed secondary school education. One third of
the respondent inventors had lower tertiary education that includes 20.5% diploma
holders and 12% university first-degree holders. Only 10.5% of the respondents had
postgraduate degrees and only six inventors had doctoral degrees. Overall, 92.5% of
the respondents had secondary school or higher education qualification.
Table 15: Respondent by Highest Educational Qualifications
Category
Primary
Secondary
Professional Exam
Vocational Training
Diploma
First Degree
Post Graduate (other than PhD
PhD
Total
School
:
Professional/ Vocational:
Lower Tertiary
:
Post graduate
:
Frequency
%
15
65
15
19
41
24
15
6
200
7.5
32.5
7.5
9.5
20.5
12.0
7.5
3.0
100.0
Cumulative
%
7.5
40.0
47.5
57.0
77.5
89.5
97.0
100.0
80 (40.0%)
34 (17.0%)
65 (32.5%)
21 (10.5%)
vi. Employment Status
According to Table 16, 39% of the respondents were working as employees and 17%
of inventors were running their own business as employers. Then again, 34 (17%)
inventors have employed as self-employees and 25 (11.5%) inventors were the full
time university, professional and vocational training students. Hence, the majority of
the respondents were part-time inventors, where 85.5% of the respondent inventors
161
involved in other economic or study activities as their primary employment. Only
14.5% inventors can be considered as full time inventors at the time of survey.
Table 16: Respondent Grassroots level inventors by Employee Status
Category
Employer
Employee
Self Employee
Student
Full time Inventor
Retired
Total
Part Time Inventor : 171 (85.5%)
Frequency
%
34
17.0
78
39.0
34
17.0
25
12.5
23
11.5
6
3.0
200
100.0
Full Time Inventor :
Cumulative
%
17.0
56.0
73.0
85.5
97.0
100.0
29 (14.5%)
vii. Employed Sector
According to the Table 17, the majority of the respondent grassroots level inventors
(44%) have employed in freelance sector and the next highest employed sector is
private sector (38.5%). Among all the respondents, 17% of the inventors employed in
public sector and only 4% employed in research sector including the universities.
Meanwhile, 25 of the respondents engaged in full time studies while engaged in
inventive activities. Even though the non-government sector has largely engaged in
community development and poverty reduction activities in Sri Lanka, only one
respondent inventor was working in a NGO.
162
Table 17: Respondent Grassroots level inventors by Employed Sector
Government Sector
Semi Government
University/Research
Private Sector
NGO
Self Employed
Full time Students
Full time Inventors
Retired
Total
Public Sector
Private Sector
Non Government Sector
Freelance Sector
Frequency
19
7
8
77
1
34
25
23
6
200
: 34 (17.0%)
: 77 (38.5%)
: 01(0.5%)
: 88(44.0%)
%
9.5
3.5
4.0
38.5
.5
17.0
12.5
11.5
3.0
100.0
Cumulative %
9.5
13.0
17.0
55.5
56.0
73.0
85.5
97.0
100.0
viii. Job Mobility
Job mobility of the respondents presents in the Table 18. According to the table,
18.5% of the inventors previously worked at more than four places and had shown
high-level job mobility. Then again, 29% of the inventors have not worked
anywhere. Hence, the majority of the respondent inventors have shown low and
moderate job mobility.
Table 18: Respondent Grassroots level inventors by Job Mobility
Frequency
Never Worked before
58
One Place
30
Two Places
39
Three Places
36
Four or More places
37
Total
200
Low: 88 (44.0%)
Moderate: 75 (37.5%)
%
Cumulative %
29.0
29.0
15.0
44.0
19.5
63.5
18.0
81.5
18.5
100.0
100.0
High: 37 (18.5%)
ix. Income
According to the monthly income levels of the respondent inventors that illustrate in
Table 19, 47.5% of the inventors belongs to middle income category and 39.5% of
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the inventors were belongs to low income category with less than Rs. 30,000 per
month. 8.5% of the inventors have earned less than Rs. 10,000 (Approx. less than US
$ 90) per month. Only 13% of the inventors have received income higher than Rs.
61,000 (Approx. higher than US $ 550). Respondents’ mean monthly income was
Rs. 38,260 (Approx US $ 347) and majority of the respondents received Rs. 30,000
(Approx US $ 272). Hence, the average respondent inventor belonged to the middleincome level. In general the mean income level of inventors is relatively higher than
the national household mean income level Rs. 26, 286 (Approx US $ 232)
(Department of Census & Statistics Sri Lanka, 2008).
Table 19: Respondent Grassroots level inventors by Income Level
Income level in
SLRs. 000s
Less than 10
11-20
21-30
31-40
41-50
51-60
61-70
71-80
81 and above
Total
Low: 79 (39.5%)
Frequency
17
23
39
39
36
20
15
9
2
200
Medium: 95 (47.5%)
%
Cumulative %
8.5
11.5
19.5
19.5
18.0
10.0
7.5
4.5
1.0
100.0
8.5
20.0
39.5
59.0
77.0
87.0
94.5
99.0
100.0
High: 26 (13.0%)
Mean : Rs. 38.26 (000’s) Median: Rs. 37 (000’s) Mode : Rs. 30 (000’s) SD=19.13
Psychological factor profile of the grassroots level inventors
i. Inventive Career Satisfaction (ICS)
The researcher used the ICS to measure the respondents’ satisfaction of their
inventive careers. According to the Table 20, 65.5% of the respondents had high
level of ICS and 34.5% were having medium level of ICS. There were no
respondents, who had low-level ICS. The results indicate that the central tendency of
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the ICS score is at the high end of the scale. Hence, the majority of the respondents
highly satisfied with their inventive careers (M=16.23, SD=2.11).
Table 20: Respondent Grassroots level inventors by ICS
Level
Score Range
Frequency
Low
4-9
0
Medium
10-15
69
High
16-20
131
Total
200
Mean: 16.23 Median: 16 Mode: 17 SD: 2.11
%
0.0
34.5
65.5
100.0
Cumulative %
0.0
34.5
100.0
ii. Maximizing Tendency
Maximizing tendency scale measures the respondents’ general tendency to look for
maximum results than the satisfying results. Data illustrated in Table 21 shows that
82.5% of the respondents had medium level of maximizing tendency. Only 17.5% of
the respondents scored low level of maximizing tendency and interesting fact is none
of the respondents scored high level of maximizing tendency. Further, all central
tendency measures, mean (27.49), median (28.00) and mode (28.00) pooled in the
medium level of the scale. Hence, the majority of respondents are moderate level
maximizers (M=27.49, SD= 5.20)
Table 21: Respondent Grassroots level inventors by Maximizing Tendency
Level
Low
Medium
High
Total
Mean: 27.49
Score Range
6-17
18-30
31-42
Frequency
%
Cumulative %
35
17.5
17.5
165
82.5
100.0
0
0.0
100.0
200
100.0
Median: 28.00
Mode: 28.00 SD: 5.20
iii. Life Orientation
Life orientation measured the optimism (or pessimism) of the respondent inventors.
Table 22 illustrates that 75.5% of the respondents scored high level of optimism.
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Forty-nine (49) respondents (24.5%) scored medium level of optimism and none of
the respondents scored low level of optimism. Hence, in general, majority of the
respondents are highly optimistic inventors (M=23.47, SD= 3.02).
Table 22: Respondent Grassroots level inventors by Life Orientation
Level
Low
Medium
High
Total
Mean: 23.47
Score Range
6-13
14-21
22-30
Median: 24.00
Frequency
0
49
151
200
Mode: 24.00
%
0.0
24.5
75.5
100.0
SD= 3.02
Cumulative %
0.0
24.5
100.0
Technical profile of the grassroots level inventors
i. Type of invention
According to the Table 23, 63% of the respondents were product inventors and only
37% inventors were process inventors. Then again, 63% of the respondent grassroot
level inventors were radical inventors and 37% were incremental inventors. 45%
involved in radical product inventions and 18% involved in radical process
inventions. Equal number of inventors (37) have involved in incremental product
development and incremental process development. Hence, high percentage of the
respondent inventors are radical product inventors.
Table 23: Respondent Grassroots level inventors by type of inventions
Type of invention
New Products
New process
Product Development
Process Development
Total
Radical Inventor: 126 (63.0%)
Frequency
89
37
37
37
200
Incremental Inventor:
166
%
44.5
18.5
18.5
18.5
100.0
74 (37.0%)
Cumulative %
44.5
63.0
81.5
100.0
ii. Field of invention
Table 24 depicts the main patent fields that respondent inventors’ have majored in
inventions. Highest number of inventors involved in inventions in agriculture (17%),
environmental and Energy (16%) and house hold equipments (15.5%). Only one
inventor involved in education instrument invention. Along with that, sports and
entertainment (2.5%) and tools (2.5%) were least preferred fields of the respondent
inventors. However, compared to sports, entertainment and tools fields, inventors’
involvement in industrial equipment (12%), medical equipment (10%) and
automotive (9%) fields were relatively high.
Table 24: Respondent Grassroots level inventors by Field of inventions
Environmental and Energy
Automotive
Sports and Entertainment
Agriculture related
Medical Equipments
Tools
Household Equipments
High tech products
Security and safety
Industrial Equipments
Educational Instruments
Total
Frequency
32
18
5
34
20
5
31
19
11
24
1
200
%
16.0
9.0
2.5
17.0
10.0
2.5
15.5
9.5
5.5
12.0
.5
100.0
Cumulative %
16.0
25.0
27.5
44.5
54.5
57.0
72.5
82.0
87.5
99.5
100.0
iii. Inventive Life Span
According to the Table 25, among the 200 respondents, 53% of the inventors were
immature inventors those who forwarded their first patent application within last
three years. Owing to the lagging time of patent evaluation, actually these inventors
can have maximum of one and half years post patent experiences. Only 21.5% of the
respondents were matured inventors who had more than five years of post patent
experiences. Other 51 (25.5%) respondents had experience between 4 to 7 years
since their first patent applications. The mean life span was 4.67 years and compared
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with mean, the standard deviation was very high (M=4.67, SD=4.35). However, the
median inventive life of the respondents was 3 years and mode of the inventors’ life
span is one year. That indicates majority of the respondent inventors had relativly
lower post-patent experiences.
Table 25: Respondent Grassroots level inventors by Inventive life Span
Category
Immature Inventors
Growing Inventors
Matured Inventors
Total
Mean: 4.67 Years
Years
=< 3
4-7
>=7
Median: 3 Years
Frequency
%
Cumulative %
106
53.0
53.0
51
25.5
78.5
43
21.5
100.0
200
100.0
Mode: 1 Year SD: 4.35
iv. Engagement in invention (Daily Inventive Hours)
According to the Table 26, 98% of the respondent inventors have worked less than 8
hours per day in their inventions. Among all the respondents, 44% inventors have
worked for their inventive activities less than 3 hours per day. Meanwhile only four
inventors (2%) engaged in more than 8 hours in inventive activities. This finding
further indicates that the majority of the respondent inventors are part time inventors
(M=3.80, SD=1.7).
Table 26: Respondent Grassroots level inventors by daily inventive hours
Level
# Hours
Frequency
Low
<3
88
Medium
4-7
108
High
>=8
4
Total
200
Mean : 3.80 hours
Median : 4.00 hours
%
Cumulative %
44.0
44.0
54.0
98.0
2.0
100.0
100.0
Mode : 4.00 hours SD: 1.7
v. Number of Working Prototype (WP)
As the Table 27, at the time of survey, 51% of the inventors had less than two
working prototypes. Then the 31% had 3 to 4 working prototypes and only 18% had
more than five working prototypes. According to the table, on average grassroots
level inventors had 3-4 working prototypes (M=3.23, SD =2.95).
168
Table 27: Respondent Grassroots level inventors by No. of working prototypes
Level
#WP
Frequency
Low
Less than 3
102
Medium
3-4
62
High
5 or more
36
Total
200
Mean: 3.23 WPs
Median: 2 WPs Mode: 1 WP
%
51.0
31.0
18.0
100.0
SD: 2.95
Cumulative %
51.0
82.0
100.0
vi. Commercialization method
Commercialization method examined the respondents’ ways and efforts used to
commercialize their inventions. Results in the Table 28 depicts that high percentage
of respondent inventors (46.5%) tried to produce and sell products by their own.
Then again, large number of inventors had not tried to commercialize their
inventions. According to the Table 28, 34% of the respondent inventors never tried to
commercialize their inventions. Licensing (8%), outright sales (8%) and consultancy
(3.5%) were not very popular commercialization methods of the inventors. Hence,
there are two major dominant types of respondent inventors: inventors those who
tried to commercialize their inventions by their own and inventors those who never
tried to commercialize their inventions.
Table 28: Respondent Grassroots level inventors by commercialization method
Produce and sell by inventor
Licensing to others
Outright sales of patent
Teaching and consultation
not try to sell
Total
Frequency
93
16
16
7
68
200
%
46.5
8.0
8.0
3.5
34.0
100.0
Cumulative %
46.5
54.5
62.5
66.0
100.0
vii. Internet Usage
Internet has become the significant information depository that increases the
assistance and help for inventors in the developing world (WIPO, 2006). Therefore,
169
the inventors in developing countries are expected to have high internet usage to get
state of the art knowledge about the inventions and patents. According to the
responses of the respondent inventors of the study that depicted in the Table 29, only
32% of the inventors had high internet usage. Larger number of them (45%) had
medium internet usage and 23% had low internet usage. Both mean and median
positioned in the medium level internet usage. Finding indicates that there is
relatively moderate level internet usage among the respondent grassroots level
inventors in Sri Lanka (M=12.85, SD= 4.39).
Table 29: Respondent Grassroots level inventors by internet usage
Level
Score Range
Frequency
%
Cumulative %
4-9
46
23.0
23.0
Medium
10-15
90
45.0
68.0
High
16-20
64
32.0
100.0
200
100.0
Low
Total
Mean: 12.85
Median: 13.00
Mode: 18.00
SD: 4.39
Social factor profile of the grassroots level inventors
i. External Linkages
External linkages shows respondent inventors’ level of assistance and support
received from the external experts, organizations and entities during their inventing,
patenting and commercializing activities.
Table 30: Respondent Grassroots level inventors by External Linkages
Level
Low
Score Range
13-29
Frequency
191
%
95.5
Cumulative %
95.5
Medium
30-46
9
4.5
100.0
High
47-65
0
0.0
100.0
Total
Mean
200
: 19.52
Median : 18.00
Mode
170
100.0
: 16.00
SD: 5.07
According to the Table 30, 95.5% of the respondents had low-level support from
external linkages and none of the respondents had high-level support from external
linkages. Only nine respondents received medium level support from external
linkages. Therefore, the results indicate low external support/assistance level
received by the grassroots level inventors in Sri Lanka (M= 19.52, SD= 5.07).
ii. Social Capital
Levels of Social capital indicate the strengths of the respondents’ individual resource
generating social relationships. According to the Table 31, 84% of the respondents
had medium level, 15% of the respondents had low-level social capital. There were
only two respondents (1%) who scored high level of social capital. Hence, the
respondent inventors had received medium level social support from relatively weak
ties of their social relationships than the strong ties (M=54.20, SD=9.41).
Table 31: Respondent Grassroots level inventors by Social Capital
Level
Score Range
Frequency
%
Cumulative %
Low
17-45
30
15.0
15.0
Medium
46-73
168
84.0
99.0
High
74-102
2
1.0
100.0
200
100.0
Total
Mean
iii.
: 54.20
Median : 54
Mode
: 54 SD: 9.41
Community Connectedness
Community Connectedness shows the level of respondents’ cognitive sense and
belonging to the grassroots level inventive community. According to the results
presented in Table 32, 75% of the respondent inventors had high and 25% had
medium level community connectedness. None of the respondents had low-level
171
community connectedness. Hence, there was a high sense of cognitive and emotional
community connectedness among the respondent inventors (M=43.27, SD=6.26).
Table 32: Respondent Grassroots level inventors by Community Connectedness
Level
Score Range
Frequency
%
Cumulative %
Low
8-23
0
0.0
0.0
Medium
24-39
50
25.0
25.0
High
40-56
150
75.0
100.0
200
100.0
Total
Mean : 43.27
Median : 44
Mode : 43
SD: 6.26
During the univariate analysis of the demographic, psychological, technical and
social factors, the researcher was able to draw the basis to answer the first research
question; who are the grassroots level inventors in Sri Lanka. The results will be
discussed in detail at the discussion section of this chapter.
Objective and Subjective Success of Sri Lankan Grassroots Level Inventors
The second objective of the study is intended to explore the nature of the objective
and subjective success of the grassroots level inventors in Sri Lanka. The researcher
was able to achieve this objective by analyzing the objective and subjective success
and their internal facets separately with the descriptive statistics. By achieving the
objective, the researcher was able to answer the second research question, what are
the levels of objective and subjective success achieved by the Sri Lankan grassroots
level inventors? The following sections of this chapter explain the results and
findings of the data analysis of the second objective of the study.
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Objective success of Sri Lankan grassroots level inventors
The objective success of the present study was measured by the summated value of
the dichotonomous items on inventions, which were received patent, received award
and reward, start to commercialized, continued to be commercialized and earned
profits. According to the Table 33, 42% of the respondents achieved medium level
objective success. Further, 30.5% of the respondents achieved only low-level
objective success. Only 27.5% of the respondent inventors achieved high-level of
objective success. Mean objective success score was 2.52 and the median objective
success score of the respondents were two.
Table 33: Level of Objective success of the respondent inventors
Level
Score Range
Frequency
Low
0-1
61
Medium
2-3
84
High
4-5
55
Total
200
Mean: 2.52 Median: 2 Mode: 1 SD: 1.49
%
30.5
42.0
27.5
100.0
Cumulative %
30.5
72.5
100.0
The results indicate that the average respondent has achieved only medium level
objective success (M= 2.52, SD= 1.49).
Figure 13 graphically illustrates the percentage distribution of the respondents based
on their level of objective success. It clearly visualizes the unsatisfactory level of
objective success and the tendency of the objective success towards the moderate and
low levels.
173
Low
100
80
60
40
20
Objective Success
0
Mediumm
Middle
High
Figure 13: Respondent grassroots level inventors by objective success levels
In order to explore the respondents’ achievements of the objective success at each
stage of the innovation process, the researcher conducted descriptive data analysis of
each sub indicator of the objective success.
i. Patent success of the grassroots level inventors
Patent is the universally accepted legitimate measure to determine the novelty of the
technological invention. Therefore, patent grant indicates the inventors’ viability of
succeed in creating novel technical products or processes that can be industrially
applicable. According to the Table 34, 79.5% respondents had received at least one
patent. The remaining 20.5% inventors have not had any patents at the time of
survey. Meanwhile, there were 22 inventors (11%) who had more than three patents
at the time of survey and 68.5% of the respondent inventors had only one or two
patents. Mean value of the number of patent was 1.52 and both median and mode
was one patent. The frequency analysis and central tendency measures indicate that
majority of the inventors received only one or two patent at the time of the survey
(M=1.52, SD=2.47).
174
Table 34: Respondent inventors by patent grants
Number of Patents
Frequency
%
Cumulative %
None
41
20.5
20.5
1-2
137
68.5
89.0
3-4
14
7.0
96.0
5 or more
8
4.0
100.0
Total
200
100.0
Mean : 1.52 Median: 1 Mode: 1 SD: 2.47
Do have received any local patent? Yes: 159 (79.5%) No: 41 (20.5%)
ii. Awards winning success of the grassroots level inventors
Table 35 illustrates the number of inventions that have won local or international
awards/ rewards. According to the Table 35, 60.5% of the respondent inventors have
never won any award or reward for their inventions. Then 34.5% inventors have only
one or two awards winning inventions and only ten inventors (5%) have more than
three awards winning inventions.
Table 35: Respondents by number of Awards winning inventions
# Awards
Frequency
None
121
1-2
69
3-4
9
5 or more
1
Total
200
Mean : 0.60 Median : 0 Mode : 0 SD :
Do have won any Award or reward? Yes : 79
%
Cumulative %
60.5
60.5
34.5
95.0
4.5
99.5
.5
100.0
100.0
.925
(39.5%) No : 121 (60.5%)
Further, the mean number of award winning inventions was less than one and median
and mode was zero. Aon the basis of the survey conducted on awards/rewards
winning inventions, majority of the respondents are not successful in achieving
awards/rewards (M=.60, SD=.93).
iii. Product launch success of the grassroots level inventors
According to the innovation process in modern society, success of invention or
inventor is measured by its commercial success. However, there is a significant
175
distance between making an invention and taking it to the market. Therefore, the
number of inventions they have started to commercialize shows a significant aspect
of inventors’ objective success. According to the Table 36, 41% of the respondents
never launch their inventive products to the market by any mean. Then again, 46%
of the inventors launched only one or two inventions to the market.
Table 36: Respondents by number of launched inventions
# Inventions
Frequency
%
None
82
41.0
1-2
92
46.0
3-4
26
13.0
5 or more
0
0.0
Total
200
100.0
Mean : 1.03 Median: 1 Mode: 0 SD : 1.158
Do have at least one product launched? Yes: 118 (59.0%)
Cumulative %
41.0
87.0
100.0
100.0
No:
82 (41.0%)
Only 13% of the inventors had three or four product launches and none of the
respondent inventors had more than five product launches. The average product
launch among the respondent was 1.03 and median product launch was one. In
general, 59% of the respondents have at least a single product launched and 41%
never launched any of their inventions in the market. Table 28 also indicated that
more than one –third of the inventors have kept their inventions commercially
inactive conceptual inventions and have stopped in the middle of innovation process.
These results indicate that significant proportion of inventors were unable to
commercialize their inventions and hence, have never achieved the high-end
objective achievement through the commercialization.
iv. Product Survival Success of the grassroots level inventors
Survival in the market is harder than the product launch. Therefore, survival in the
market for long time is an indication of the commercial success of the inventors
176
(Wieck & Martin, 2006). Table 37 shows the descriptive statistical analysis of the
commercial continuation success of the respondent grassroots level inventors in Sri
Lanka. According to the results depicted in the Table, 63% of the inventors had not
any commercialized inventions at the time of the survey. Only 30.5% of the
respondents had one or two commercialized inventions. Thirteen respondents (6.5%)
had three or four active commercialized inventions and none of the inventors had
more than five commercialized inventions in the market at the time of the survey.
Table 37: Respondents by number of inventions still in the market
# Inventions
Frequency
%
Cumulative %
None
126
63.0
63.0
1-2
61
30.5
93.5
3-4
13
6.5
100.0
5 or more
0
0
100.0
Total
200
100.0
Mean : .61 Median: 0 Mode: 0 SD: .996
Do have any inventions still in the market? Yes: 74 (37%) No: 126 (63%)
Central tendency measures also indicate the very low continued commercialization
of inventors with zero absolute value mean (0.60), median (0) and mode (0). Hence,
in general the majority of inventors had not inventions in the market at the time of
survey.
v. Profitability success of the grassroots level inventors
According to the existing standard measurements of innovation success, any
invention has to give acceptable return on investment (Astebro, 2003). Therefore, in
the present study, respondents were asked to state the number of inventions that had
achieved any amount of profit that is defined as any excess of income over the cost
spend on the product. According to the Table 38, 63% of the respondents never
earned profits from any of their inventions.
177
Table 38: Respondents by number profitable inventions
# Invention
Frequency
None
126
1-2
59
3-4
15
5 or More
0
Total
200
Mean : 0.59 Median : 0 Mode : 0
%
63.0
29.5
7.5
0.0
100.0
SD: 0.963
Had profitable inventions? Yes: 74 (37%)
Cumulative %
63.0
92.5
100.0
100.0
No: 126 (63%)
Meanwhile, 37% of the respondents had one to four profitable inventions. None of
them had more than five profitable inventions. Mean (0.59), median (0) and mode (0)
shows that central tendency of the profitable invention was zero. Overall, 126
respondents (63%) had no profitable inventions and 74 respondents (37%) had at
least one profitable invention during their inventive life.
iv. Strong and Weak areas of Grassroots level inventors Objective Success
Figure 14 summarizes the percentage frequency values of the minimal success rates
of the respondent grassroots level inventors at each stage of innovation process. If
the inventor had at least one patented invention, one awarded invention, one
commercialized invention, one survived (continue commercialization) invention and
one profitable invention, they were considered as successful inventors at the
respective stages of the innovation process.
178
Profit
Patent
100
80
60
40
20
0
Award
Success
Servival
commercilization
Figure 14: Respondent Inventors’ success rates at innovation process stages
According to the Figure 14, approximately 80% of the inventors achieved the patent
success. Therefore, there is a high patent success rate among respondent grassroots
level inventors. It indicates the strength that grassroots level inventors have to create
original industrially applicable technical inventions. However, only 40% of the
inventors received local or international award or reward for at least one of their
inventions. This relatively low success rate suggests that the inventions created by
the grassroots level inventors were not attractive. However, according to the
respondents’ comments there were limited opportunities for them to participate for
international competitions and sometimes in local competitions too. Then again,
approximately 60% of the respondents were able to launch (commercialize) at least
one of their inventions. However, the survival in commercialization was less than
40%. That indicates even though, respondents had commercialized their inventions,
they were unable to survive and unable to continue the commercialization of their
inventions. Then again, profitability also has shown less than 40% success rate.
Employed inventors in research institutes, business organizations and even in
universities need not to worry about commercialization stages of innovation process,
because these are business, marketing and entrepreneurial stages rather than
179
invention stages. However, grassroots level inventors need to go through entire
innovation process by their own with limited external support. Therefore, surviving
in commercialization and profit earnings are the major concerns of the objective
success of grassroots level inventors in Sri Lanka.
Subjective success of Sri Lankan grassroots level inventors
In the present study, integrated scale of subjective happiness and satisfaction with
life was used to measure the subjective aspect of success of the grassroots level
inventors. Following section of the chapter will explore the subjective success and its
sub components; happiness and satisfaction among the respondent grassroots level
inventors.
According to the Table 39, 69.5 % of the respondents achieved medium level
subjective success. Then again, more than 1/4 of the respondents achieved high level
of subjective success. Overall, 96% of the respondent grassroots level inventors
achieved medium or high level of subjective success. Only 4% of the respondents
have shown low-level subjective success. Average subjective success score of the
respondents was 41.1 and highest number of respondents achieved subjective success
value 39. Therefore, the results show that in general, respondent grassroots level
inventors have achieved upper medium and high level of subject success (M=41.1,
SD=7.05).
Table 39: Subjective Success levels of Respondent Grassroots level inventors
Level
Score Range
Frequency
%
Cumulative %
Low
9-27
8
4.0
4.0
Medium
28-45
139
69.5
73.5
High
46-63
53
26.5
100.0
Total
200
100.0
Mean: 41.1 Median: 41
Mode: 39 SD: 7.05
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According to the operationalization of the subjective success, it has both emotional
aspect (happiness) and cognitive aspect (satisfaction) of subjective success.
Therefore, the researcher further investigated the emotional aspect and cognitive
aspect of subjective success.
i. Subjective happiness of respondent grassroots level inventors
According to the results presented in the Table 40, majority of the respondents
(60.5%) achieved medium level of happiness. Meanwhile, 34.5% of the respondent
inventors achieved high level of happiness. Therefore, in overall, 95% of the
respondents achieved medium or high level of happiness. Only 10 respondents (5%)
had low level of happiness. The results indicates that, in general, the average
inventor had achieved upper medium level happiness (M=19.05, SD=3.79).
Table 40: Subjective Happiness levels of Respondent Grassroots level inventors
Level
Low
Medium
High
Total
Mean: 19.05
Score Range
Frequency
%
Cumulative %
10
121
69
200
5.0
60.5
34.5
100.0
5.0
65.5
100.0
4-12
13-20
21-28
Median: 19
Mode: 19
SD: 3.79
ii. Satisfaction with life of respondent grassroots level inventors
According to the Table 41, 93% of the respondents had medium level satisfaction
with their lives. Only five respondents (2.5%) achieved high-level satisfaction with
life and only nine respondents (4.5%) had low-level satisfaction with life. Therefore,
in general, average grassroots level inventor moderately satisfied with their life
(M=20.5, SD=2.88).
181
Table 41: Satisfaction with life levels of Respondent Grassroots level inventors
Level
Low
Medium
High
Total
Mean: 20.50
Score range
Frequency
%
Cumulative %
5-15
16-25
26-35
9
186
5
4.5
93.0
2.5
4.5
97.5
100.0
Median: 21
200
Mode: 21
100.0
SD: 2.88
iii. Levels of respondent grassroots level inventors’ subjective happiness,
satisfaction and success
Figure 15 illustrates the distribution of respondents between low, medium and high
levels of subjective success and its two facets; subjective happiness (SHS) and
satisfaction with life (SWLS). It clearly indicates that majority of the respondent
grassroots level inventors were having medium level happiness and satisfaction.
Hence, they also achieved the medium level subjective success. The subjective
success and subjective happiness shows that there were 20 to 40 percent respondents
who had achieved high level of success and happiness. However, compared to
percentage of respondents who achieved high-level happiness, there was relatively
very small number of respondents who achieved high-level satisfaction with life.
Low
100
80
60
40
Subjective Success
20
SHS
0
SWLS
High
Medium
Figure 15: Respondents’ subjective happiness, satisfaction and success levels
182
Association between level of objective and subjective success
After analyzing the levels of objective and subjective success and their facets, the
researcher examined the association (or independence) between the levels of
objective and subjective success. Table 42 presents the cross tabulation results of the
analysis. According to the Table, 85.2% of the respondents who had achieved low
level of objective success had shown medium level subjective success. Then again,
the respondents (96.4%) who had achieved medium level objective success had
shown medium (63.1%) or high (33.3%) subjective success. Further, all the
respondents who achieved high level of objective success achieved medium (61.8%)
and high (38.2%) level subjective success. Hence, the results indicate that the
respondents who achieved high objective success have a tendency to achieve high
level of the subjective success than the respondents who achieved low or medium
level objective success.
Level of Objective Success
Table 42: Cross tabulation between level of objective and subjective success
Low
Medium
High
Level of Subjective Success
Low
Medium
High
5
52
4
2.4
42.4
16.2
8.2%
85.2%
6.6%
3
53
28
3.4
58.4
22.3
3.6%
63.1%
33.3%
0
34
21
2.2
38.2
14.6
.0%
61.8%
38.2%
Count
Expected Count
% within
Count
Expected Count
% within
Count
Expected Count
% within
χ2 = 21.531, df = 4, Fisher’s exact test = 23.823, P-value = .000, CV= .232, Effect size
(W)= 0.328 Cells with expected count less than 5 = 33%
Owing to the fact that 33% of the cells in the cross tabulation have expected count
less than 5 (higher than the rule of thumb of 20%), Chi square statistics could not be
used to detect the statistical significance of the association between level of objective
183
success and subjective success. Fisher’s exact test is an alternative test that can be
utilized to test the association when the minimum cells with expected value 5 are
more than 20% (Field, 2005; George & Mallery, 2006). According the Fisher’s
exact test, there is a significant association between the level of objective success and
subjective success (Fisher’s exact test= 23.823, p-value =.000). Cramer’s-V is the
statistic used to detect the effect size or the strength of the association (George &
Mallery, 2006). According to rule of thumb suggested by the Cohen (1988), effect
size (W) can be calculated based on Cramer’s ɸ (Cohen, 1988, p. 223). PASS 2008
provides facility to calculate effect size index (W) directly from the contingency
Table. The researcher calculated the effect size index (W) using the PASS 2008
software program. According to Cohen’s recommendations on effect size (W=.10
small, W=.30 medium and W=.5 large), the strength of the relationship between level
of objective success and level of subjective success has shown medium to high effect
size (W=.328).
Influences of D.P.T.S Factors on Success of Grassroots Level Inventors in Sri
Lanka
In the literature review and theoretical framework of the study, the researcher
explained two theoretical approaches to examine the relationship between the
success (objective and subjective) and factors influence on them: the bottom up
approach and top down approach. The third objective of the study was intended to
examine the bottom up theoretical proposition of the success. In this objective, both
objective and subjective success were assumed as the endogenous variables that are
determined by the selected demographic, psychological, technical and social domain
factors of the study.
184
Categorical profiling variables and level of success
According to the operationalization of the variables of the study, some profiling
variables could only be measured in nominal or ordinal scales. Therefore, the
influence of these variables could not be determined by the traditional path analysis
method that required the interval level variables or complex estimation methods.
Hence, in order to examine the nature of the variances of means between groups and
dependence among nominal/ordinal-scaled profiling variables and the levels of
objective and subjective success of grassroots level inventors, the researcher utilized
mean plots and Contingency Table analysis using PSAW statistics 18.
a. Categorical profiling variables and objective success
i. Age range and objective success
Figure 16 depicts the mean differences of objective success of the respondents’ age
categories (F=2.562, p-value =.029). Even though it is not exactly “n” shaped, mean
distribution across the age groups shows approximately quadratic relationship. The
sub-division of young age group is the major disturbance that made the mean
distribution deviate from the smooth “n” shape. Even though the young age group
(19-40 years) is sub-divided as young (19-30) and late young (31-40), the mean
difference between these two groups was not significant. The mean plot of age
categories and objective success indicates that level of objective success is increasing
from low level to high level through the adolescent age (12-18) to middle age (4155). Then the level of objective success begins to decrease and at the old age (66 and
above) it reaches the lower level compared to the middle age.
185
Figure 16: Mean differences of objective success by age group
The researcher used the cross tabulation Chi-square test to check the association
between level of objective success and age categories. The obtained results are
illustrated in Table 43. Contingency Table clearly depicts that large number of
respondents pooled in young to middle age categories at each level of objective
success. Further, high percentage of inventors who achieved high-level objective
success was middle aged (45.5%) and late middle-aged (23.6%) inventors.
In
Contingency Table, there was 33% of cells contained expected value less than 5.
Therefore, the association between age range and the level of objective success was
unable to measure using Chi-square values.
186
Level of Objective Success
Table 43: Level of Objective success by respondents’ age categories
Count (N=61)
Expected Count
Low
% within
Count (N=84)
Medium Expected Count
% within
Count (N=55)
Expected Count
High
% within
Age Category
Late
Adolescent Young Young Middle
3
14
19
15
3.1
13.1
13.7
18.3
4.9%
23.0% 31.1% 24.6%
7
21
17
20
4.2
18.1
18.9
25.2
8.3%
25.0% 20.2% 23.8%
0
8
9
25
2.8
11.8
12.4
16.5
.0%
14.5% 16.4% 45.5%
Late
Middle Old
9
1
11.0
1.8
14.8% 1.6%
14
5
15.1
2.5
16.7% 6.0%
13
0
9.9
1.7
23.6% .0%
χ2 = 21.457, df = 10, Fisher’s exact test =19.995, Monte Carlo p-value =0.020
CV= .232/ Monte Carlo p-value =0.016, Effect size (W)= 0.328/p-value= .002
Cells with expected count less than 5 = 33%
Hence, the researcher tried to run Fisher’s exact test as alternative test, but the
PSAW statistics 18 gave an error message “Computer memory not enough!”
According to the literature, this error message is common when the Contingency
Table has more columns and rows. Therefore, Monte Carlo sample p-value was
recommended to use as a close approximation in such situations (Hitchin, 2005). In
the 1000 samples Monte Carlo test, Fisher’s exact test was 19.995 with p-value=
0.020. As far as p-value smaller than the 0.05, it indicated that there is a significant
association between age range and level of objective success. Effect size index (W)
was 0.328 and that shows medium to high effect size.
ii. Location and objective success
Table 44 indicates the levels of objective success by the residential location of the
respondents. According to the Table, more than 77% of the rural respondents
achieved only low and medium level objective success, while nearly 74% of the
urban respondents achieved medium and high objective success. However, Chi-
187
square test, Cramer’s V test and effects size had p-values greater than 0.05 indicating
non-significant association between location and objective success.
Location
Table 44: Level of Objective success by respondents’ location
Rural
Urban
Level of Objective Success
Low
Medium
High
42
57
29
39.0
53.8
35.2
32.8%
44.5%
22.7%
19
27
26
22.0
30.2
19.8
26.4%
37.5%
36.1%
Count (N=128)
Expected Count
% within Location
Count (N= 72)
Expected Count
% within Location
χ2 = 4.199, df = 2, p-value =.124, CV= .145, Effect size (W)= 0.144
Cells with expected count less than 5 = 0%
According to the result, the association between location and objective success has
not shown statistically significant at 0.05 level. Further, the means plot depicts in
Figure 17 indicates that there is no significant mean deference between the rural and
urban respondents (F=2.083, p-value= .151).
Figure 17: Mean differences of objective success by Location
188
iii.
Education level and objective success
Figure 18: Mean differences of objective success by Education Level
Mean plot of objective success and educational level depicted in Figure 18 shows
that compared to the respondents who had school education and postgraduate
qualifications, the respondents who had professional/vocational and lower tertiary
education have achieved higher objective success. However, the mean difference
between the educational groups was not statistically significant (F= .549, p-value=
.650). Cross-tabulation results in Table 45 also show that there is no significant
association between respondents’ education level and objective success (χ2 = 5.432,
df = 6, p-value =.495, CV=.117).
189
Level of Objective Success
Table 45: Level of Objective success by Education Level
Count (n= 61)
Expected Count
% within
Count (n=84)
Medium Expected Count
% within
Count (n= 55)
Expected Count
High
% within
Low
School
20
24.4
32.8%
39
33.6
46.4%
21
22.0
38.2%
Education Level
Professional/
Vocational
Lower Tertiary Post graduate
9
23
9
10.4
19.8
6.4
14.8%
37.7%
14.8%
15
22
8
14.3
27.3
8.8
17.9%
26.2%
9.5%
10
20
4
9.4
17.9
5.8
18.2%
36.4%
7.3%
χ2 = 5.432, df = 6, p-value =.495, CV= .117, Effect size (W)= 0.164
Cells with expected count less than 5 = 0%
iv.
Employment level and objective success
According to the Table 46, two-thirds of the part time inventors achieved medium
(72) or low (55) level of success. Meanwhile, majority of the full time inventors
achieved medium (12) or high (11) level of success. Compared to 9.8% of low
objective success, full time inventors represent 20% of the highly successful
inventors. However, the association between levels of objective success and
employment is not statistically significant (χ2 = 2.415, df = 2, p-value =.299, CV=
.110).
Level of Objective Success
Table 46: Level of Objective success by Employment Level
Low
Medium
High
Employment Level
Part Time
Inventor
Full Time Inventor
55
6
52.2
8.8
90.2%
9.8%
72
12
71.8
12.2
85.7%
14.3%
44
11
47.0
8.0
80.0%
20.0%
Count (n=61)
Expected Count
% within
Count (n=84)
Expected Count
% within
Count (n=55)
Expected Count
% within
χ2 = 2.415, df = 2, p-value =.299, CV= .110, Effect size (W)= 0.110
Cells with expected count less than 5 = 0%
190
Figure 19 depicts the means plot of part time and full time inventors. The mean
value of objective success of part time inventors, and full time inventors are 2.44 and
2.97 respectively. The mean difference between part time and full time inventors is
not statistically significant at .05 level (F=3.063, p-value =.082).
Figure 19: Mean differences of objective success by Level of Employment
v.
Job mobility and objective success`
According to the analysis, majority of the highly successful inventors had shown low
job mobility and the inventors who had high job mobility, have achieved relatively
low objective success. Figure 20 illustrates the means plot and it indicates the
significant mean difference between level of job mobility (F= 3.505, p-value= .032).
191
.
Figure 20: Mean differences of objective success by Level of Job Mobility
The frequency distribution in Table 47 indicates that the inventors who had low job
mobility represent the higher percentages in medium (47.6%) and high (52.7%)
levels of objective success. The percentage of the low objectively successful
inventors with high job mobility (27.9%) is higher than the percentage of the
inventors with medium (16.7%) and high level (10.9%) of objective success. This
finding indicates that higher job mobility might distract the inventors’ focus on the
inventive activities at certain level. However, according to the Table 47, the
association between the level of job mobility and objective success is statistically
significant only at .10 level (χ2 = 8.540, df = 4, p-value =.074, CV=.146).
Level of Objective Success
Table 47: Level of Objective Success by Job Mobility
Low
Medium
High
Level of Job Mobility
Low
Moderate
High
19
25
17
26.8
22.9
11.3
31.1%
41.0%
27.9%
40
30
14
37.0
31.5
15.5
47.6%
35.7%
16.7%
29
20
6
24.2
20.6
10.2
52.7%
36.4%
10.9%
Count (n=61)
Expected Count
% within
Count (n=84)
Expected Count
% within
Count (n= 55)
Expected Count
% within
χ2 = 8.540, df = 4, p-value =.074, CV= .146, Effect size (W)= 0.207
Cells with expected count less than 5 = 0%
192
vi.
Invention type and objective success
According to the Contingency Table presented as Table 48, the percentage of low
successful radical inventors (29.4%) is slightly lower than the percentage of
incremental inventors (32.4%). Then again, compared to the incremental inventors,
higher percentage of radical inventors had achieved high objective success (20.3% to
31.7%). However, there was no significant relationship between level of objective
success and the type of the inventors (χ2 = 3.161, df = 2, p-value =.206, CV=.126).
Table 48: Level of Objective success by Type of Inventor
Level of Objective Success
Type of Inventor
Radical
Incremental
Inventor
Inventor
Count (n = 61)
37
24
Expected Count
38.4
22.6
Low
% within
60.7%/29.4%*
39.3%/ 32.4%*
Count (n=84)
49
35
Expected Count
52.9
31.1
Medium
% within
58.3%/38.9%*
41.7%/ 47.3%*
Count (n= 55)
40
15
Expected Count
34.7
20.4
High
% within
72.7%/31.7%*
27.3%/20.3%*
χ2 = 3.161, df = 2, p-value =.206, CV= .126, Effect size (W)= 0.125
Cells with expected count less than 5 = 0% * Column wise percentage value
The mean plot that is depicted in Figure 21 also shows no significant objective
success mean difference between radical and incremental inventors (F= 1.061, pvalue = .304). The mean value of objective success of the radical inventors (2.60)
was only marginally higher than the mean value of incremental inventors (2.38).
193
Figure 21: Mean differences of objective success by type of inventor
vii.
Field of invention and objective success
Table 49 presents the level of objective success achieved by the inventors who have
majored in different field of inventions. More than 25% of the inventors who are
involved in environmental and energy, sport and leisure, agriculture, medical, high
tech and industrial inventions achieved high level of objective success. Then again,
more than 30% of the educational, industrial, security and safety, household, tools,
sports and leisure, automotive and environmental and energy inventors achieved only
low level of objective success. The dispersed distribution of success of inventors by
different fields has made the association between field of inventions and level of
objective success statistically insignificant (χ2 = 16.518, df = 20, Fisher’s exact test
=16.772, Monte Carlo p-value =0.667, CV=.203).
194
Table 49: Level of Objective success by Field of Inventions
Field of Invention
Level of Objective Success
Low
Medium
High
Environmental/
Count (n = 32)
11
11
10
Energy
Expected Count
9.8
13.4
8.8
% within
34.4%
34.4%
31.3%
Automotive
Count (n = 18)
7
7
4
Expected Count
5.5
7.6
5.0
% within
38.9%
38.9%
22.2%
Sports/leisure
Count (n= 5)
2
1
2
Expected Count
1.5
2.1
1.4
% within
40.0%
20.0%
40.0%
Agriculture
Count (n = 34)
5
20
9
Expected Count
10.4
14.3
9.4
% within
14.7%
58.8%
26.5%
Medical
Count ( n = 20)
4
9
7
Expected Count
6.1
8.4
5.5
% within
20.0%
45.0%
35.0%
Tools
Count (n= 5)
2
3
0
Expected Count
1.5
2.1
1.4
% within
40.0%
60.0%
.0%
Household
Count (n= 31)
10
14
7
Expected Count
9.5
13.0
8.5
% within
32.3%
45.2%
22.6%
High tech
Count (n = 19)
5
7
7
Expected Count
5.8
8.0
5.2
% within
26.3%
36.8%
36.8%
Security/Safety
Count (n = 11)
4
5
2
Expected Count
3.4
4.6
3.0
% within
36.4%
45.5%
18.2%
Industrial
Count (n = 24)
10
7
7
Expected Count
7.3
10.1
6.6
% within
41.7%
29.2%
29.2%
Educational
Count (n = 1)
1
0
0
Expected Count
.3
.4
.3
% within
100.0%
.0%
.0%
χ2 = 16.518, df = 20, Fisher’s exact test =16.772, Monte Carlo p-value =0.667
CV= .203/ Monte Carlo p-value =0.884, Cells with expected count less than 5 = 39.4%
The means values of the objective success of each field of invention is depicted in
Figure 22. It clearly illustrates that the success level of educational invention and
tools is very low, while high tech inventions had shown higher success. However,
there was no significant mean difference between different fields of inventions (F =
.691, p-value = .733).
195
Figure 22: Mean differences of objective success by Field of inventions
viii. Commercialization effort and objective success
Table 50 illustrates the relationship between commercialization effort and level of
objective success of the respondents. A large number of inventors (93) tried to
produce and market their inventions on their own. More than 4/5 of them achieved
medium or high objective success. The second largest number of inventors (68) had
never tried to commercialize their inventions and therefore achieved only low or
medium level success through patent and awards winnings. Whereas large proportion
of inventors those who tried to commercialize their inventions by other means also
achieved medium or high level of objective success. Hence, the relationship between
commercialization effort and the level of objective success was significant (Fisher’s
exact test = 64.743, Monte Carlo p-value = 0.000, CV = .367). The effect size of the
relationship is large (Effect size (W) = 0.519).
196
Table 50: Level of Objective Success by Commercialization Effort
Commercialization effort
Level of Objective Success
Low
Medium
High
Produce and sell by inventor
Count (n = 93)
15
33
45
Expected Count
28.4
39.1
25.6
% within
16.1%
35.5%
48.4%
Licensing to others
Count (n = 16)
4
7
5
Expected Count
4.9
6.7
4.4
% within
25.0%
43.8%
31.3%
Outright sales of patent
Count (n = 16)
4
8
4
Expected Count
4.9
6.7
4.4
% within
25.0%
50.0%
25.0%
Teaching and consultation
Count (n = 7)
2
4
1
Expected Count
2.1
2.9
1.9
% within
28.6%
57.1%
14.3%
not try to sell
Count (n = 68)
36
32
0
Expected Count
20.7
28.6
18.7
% within
52.9%
47.1%
.0%
χ2 = 53.860, df = 8, Fisher’s exact test =64.743, Monte Carlo p-value = 0.000
CV= .367/ Monte Carlo p-value = 0.000, Effect size (W) = 0.519
Cells with expected count less than 5 = 46.7%
Figure 23 clearly depicts that inventors who have produced and marketed their
inventions on their own have higher average objective success and other means of
commercializing showed relatively lower average objective success.
Figure 23: Mean differences of Objective Success by Commercialization Effort
197
Inventors who had not tried to commercialize their inventions obviously have the
lowest average objective success. These findings indicate that the lower commercial
success of the grassroots level inventors is partially a result of lower
commercialization efforts of them. Figure 23 indicates significant difference of
means between respondents who had different commercialization efforts (F= 18.9, pvalue = 0.000)
ix.
Inventive life span and objective success
The majority of the respondents who were immature (77.4%) and at a growing
(66.7%) stage of their inventive life achieved only low or medium level objective
success. Further, the majority of matured inventors (72.1%) achieved moderate or
high level objective success. However, according to the cross tabulation results
presented in Table 51, there was no significant association between inventive life
span and level of objective success (χ2 = 6.210, df = 4, p-value =.184, CV= .125).
Inventive Life Span
Table 51: Level of Objective success by Inventive Life Span
Immature Inventors
Growing Inventors
Matured Inventors
Levels of Objective Success
Low
Medium
High
30
52
24
32.3
44.5
29.2
22.6%
28.3%
49.1%
19
15
17
15.6
21.4
14.0
33.3%
37.3%
29.4%
12
17
14
13.1
18.1
11.8
27.9%
39.5%
32.6%
Count (n= 106)
Expected Count
% within
Count (n = 51)
Expected Count
% within
Count (n = 43)
Expected Count
% within
χ2 = 6.210, df = 4, p-value =.184, CV= .125, Effect size (W) = 0.176
Cells with expected count less than 5 = 0%
The mean plot of the inventive life span and mean of objective success in Figure 24
also indicate that matured inventors have slightly higher mean objective success than
198
immature and growing inventors do. However, there is no significant difference
between objective success among immature, growing and matured inventors (F=.58,
p-value=.579).
Figure 24: Mean differences of Objective Success by Inventive Life Span
x.
Findings on categorical profiling variables and objective success
According to the analysis of categorical profiling variables and objective success,

Only age range (medium effect size) and commercialization effort (Large effect size)
have statistically significant association with level of objective success at 0.05.

There are statistically significant objective success mean differences between age
range, job mobility and commercialization effort at 0.05 level.

Middle-aged inventors have achieved higher objective success compared to the
younger and older age groups.

Inventors with higher level of job mobility have shown relatively lower average
objective success level compared to the inventors with low job mobility.
199

Inventors who had tried to commercialize their inventions on their own have
achieved higher objective success than the inventors who have tried to
commercialize their invention through the third party.

Factors such as location, education level, employment level, type of inventions, field
of invention and inventive life span have not shown either significant association or
significant variances of mean with the objective success of the grassroots level
inventors in Sri Lanka.
b. Categorical profiling variables and subjective success
i. Age range and subjective success
The mean plot depicted in Figure 25 indicates that mean score of subjective success
of the respondents have ranged between 36 and 43 in all age groups. That shows
there is no significant difference between age groups and their mean subjective
success (F= 1. 035, p-value = .398).
Figure 25: Mean differences of Subjective Success by Age Range
200
Chi-square test results of the Table 52 also indicates that excluding the old aged
respondents, more than 94% of respondents in all other age categories achieved
medium level or high level subjective success. However, 50% of the old inventors
achieved high level of objectives success. Among the other inventors, 33.3% of
middle aged and 28.9% of late young inventors achieved high subjective success.
However, there is no significant association between age range and subjective
success at .05 level (Fisher’s exact test =15.795, df= 10, p-value = 0.061, CV=.236).
Table 52: Level of Subjective Success by Age
Age Categories Age Range
Adolescent
Young
Late Young
Middle
Late Middle
Old
12-18
Count (n= 10)
Expected Count
% within
19-30
Count (n = 43)
Expected Count
% within
31-40
Count (n= 45)
Expected Count
% within
41-55
Count (n=60)
Expected Count
% within
56-65
Count (n=36)
Expected Count
% within
66 or Higher Count (n=6)
Expected Count
% within
Level of Subjective Success
Low
Medium
High
0
8
2
.4
7.0
2.7
.0%
80.0%
20.0%
2
33
8
1.7
29.9
11.4
4.7%
76.7%
18.6%
1
31
13
1.8
31.3
11.9
2.2%
68.9%
28.9%
1
39
20
2.4
41.7
15.9
1.7%
65.0%
33.3%
2
27
7
1.4
25.0
9.5
5.6%
75.0%
19.4%
2
1
3
.2
4.2
1.6
33.3%
16.7%
50.0%
χ2 = 22.221, df = 10, Fisher’s exact test =15.795, p-value = 0.061
CV= .236, Effect size (W) = 0.333, Cells with expected count less than 5 = 50%
ii. Location and subjective success
According to Table 53, more than 65% of both rural and urban respondent inventors
achieved medium level subjective success. Further, more than 25% respondents from
both locations achieved high level of subjective success. Hence, according to the
201
results presented in Table 53, there is no significant association between the location
and level of subjective success (χ2 = 1.271, df = 2, p-value = 0.510, CV= .080, Effect
size (W) = 0.080).
Table 53: Level of Subjective Success by Location
Level of Subjective Success
Low
Medium
High
Rural
Count (n = 128)
4
92
32
Expected Count
5.1
89.0
33.9
% within
3.1%
71.9%
25.0%
Urban
Count (n = 72)
4
47
21
Expected Count
2.9
50.0
19.1
% within
5.6%
65.3%
29.2%
2
χ = 1.271, df = 2, p-value = 0.510, CV= .080, Effect size (W) = 0.080
Cells with expected count less than 5 = 16.7%
The mean plot illustrated in Figure 26 also clearly depicts that there is no significant
mean difference between rural and urban respondent inventors (F=1.513, p-value =
.220).
Figure 26: Mean differences of Subjective Success by Location
202
iii. Education level and subjective success
According to the Table 54, 21.3% to 32.3% of the respondents at each education
level achieved high level of subjective success. Then again, more than 63% of the
respondents at each education level have achieved medium level subjective success.
This indicates irrespective of the education level, majority of responded inventors
achieved medium or high-level subjective success. Hence, the association between
education levels and subjective success is not significant (χ2 = 6.057, df = 6, Fisher’s
Exact test = 5.502, p-value = 0.447, CV= .123).
Table 54: Level of Subjective Success by Education Level
Level of Subjective Success
Low
Medium
High
School
Count (n = 80)
2
61
17
Expected Count
3.2
55.6
21.2
% within
2.5%
76.3%
21.3%
Professional/Vocational
Count (n = 34)
3
22
9
Expected Count
1.4
23.6
9.0
% within
8.8%
64.7%
26.5%
Lower Tertiary
Count (n = 65)
3
41
21
Expected Count
2.6
45.2
17.2
% within
4.6%
63.1%
32.3%
Post graduate
Count (n = 21)
0
15
6
Expected Count
.8
14.6
5.6
% within
.0%
71.4%
28.6%
2
χ = 6.057, df = 6, Fisher’s Exact test = 5.502, p-value = 0.447, CV= .123,
Effect size (W) = .174, Cells with expected count less than 5 = 33.3%
Figure 27 depicts that there is negligible mean difference at each education level.
According to the Figure, the respondents with tertiary and postgraduate qualifications
indicate slightly higher subjective success, followed by the respondents with school
and professional/vocational education. However, the researcher reveals that there is
no significant subjective success mean difference among the respondents with
different levels of education (F= .639, p-value =.591).
203
Figure 27: Mean differences of Subjective Success by Level of Education
iv. Employment level and subjective success
According to the cross-tabulation analysis presented in Table 55, more than 93% of
both part time and full time inventors achieved medium or high-level subjective
success. Compared with part time inventors (26%), marginally higher percentage of
full time inventors (31%) achieved high level of subjective success. At the medium
subjective success level, part-time inventors have shown slightly higher percentage
(71%) than the full-time inventors have (62%). However, there was no statistically
significant association between employment level and the subjective success (χ2 =
1.244, df = 2, p-value = 0.537, CV= .079).
Table 55: Level of Subjective Success by Employment Level
Level of Subjective Success
Low
Medium
High
Part Time Inventors
Count (n = 171)
6
121
44
Expected Count
6.8
118.8
45.3
% within
3.5%
70.8%
25.7%
Full Time Inventors
Count (n = 29 )
2
18
9
Expected Count
1.2
20.2
7.7
% within
6.9%
62.1%
31.0%
χ2 = 1.244, df = 2, p-value = 0.537, CV= .079, Effect size (W) = 0.079,
Cells with expected count less than 5 = 16.7%
204
According to the Figure 28, the mean scores of both the groups are almost identical.
Hence there is no significant mean difference between the mean values of subjective
success of part-time and full-time inventors (F= .041, p-value = .840).
Figure 28: Mean differences of Subjective Success by Employment Status
v. Job mobility and subjective success
According to Table 56, more than 62% of the inventors in each level of job mobility
achieved medium level subjective success.
Table 56: Level of Subjective Success by Job Mobility
Level of Subjective Success
Low
Medium
High
Count ( n = 88)
3
65
20
Expected Count
3.5
61.2
23.3
% within
3.4%
73.9%
22.7%
Moderate
Count ( n = 75)
3
51
21
Expected Count
3.0
52.1
19.9
% within
4.0%
68.0%
28.0%
High
Count (n = 37 )
2
23
12
Expected Count
1.5
25.7
9.8
% within
5.4%
62.2%
32.4%
χ2 = 1.839, df = 4, Fisher’s Exact test = 2.197, p-value = 0.717, CV= .068,
Effect size (W) = .096, Cells with expected count less than 5 = 33.3%
Level of
Job Mobility
Low
205
Within medium level subjective success, the percentage of low job mobility group is
higher (73.9%), than the moderate (68%) and high (62.2%) groups. Nevertheless,
compared to low (23%) and moderate (28%) level job mobility, higher percentage of
respondents with high job mobility (32%) have achieved high subjective success.
Therefore, there are no evidences for the significant dependence among job mobility
and subjective success. Further, the Figure 29 clearly illustrates the uniqueness of
mean scores of subjective success at each job mobility levels.
Figure 29: Mean differences of Subjective Success by Job Mobility
Cross-tabulation result, which illustrated in Table 56 and mean comparison results
depicted in Figure 29 indicate that there is neither any significant association (χ2 =
1.839, df = 4, Fisher’s Exact test = 2.197, p-value = 0.717, CV= .068) nor significant
mean difference (F = .072, p-value = .931) between respondent grassroots level
inventors’ job mobility levels and their subjective success.
206
vi. Type of inventors and subjective success
Table 57 illustrates that more than 95% of both radical and incremental inventors
achieved at least medium level subjective success. Compared to incremental
inventors (68%), marginally high percentage of radical inventors (71%) achieved
medium level subjective success. However, relatively higher percentage of the
incremental inventors (29.7%) achieved high-level subjective success than the
radical inventors (24.6%). Overall, the percentage differences of radical inventors
and incremental inventors who achieved the low, medium and high levels of
subjective success were relatively low. Hence, according to the results obtained from
the cross-tabulation analysis of Table 57, there was no significant association
between type of inventors and subjective success (χ2 = 1.020, df = 2, p-value = 0.601,
CV= .071).
Table 57: Level of Subjective Success by Type of inventors
Type of
Level of Subjective Success
Inventor
Low
Medium
High
Count (n = 126)
6
89
31
Expected Count
5.0
87.6
33.4
4.8%
70.6%
24.6%
Count (n = 74)
2
50
22
Expected Count
3.0
51.4
19.6
2.7%
67.6%
29.7%
Radical Inventor
% within
Incremental Inventor
% within
2
χ = 1.020, df = 2, p-value = 0.601, CV= .071,
Effect size (W) = .071, Cells with expected count less than 5 = 33.3%
Figure 30 depicts the mean scores of the subjective success of the radical inventors
and the incremental inventors. According to the Figure, there was no significant
mean difference of subjective success between two types of inventors (F = .642, pvalue = .424).
207
Figure 30: Mean differences of Subjective Success by Invention Type
vii. Field of invention and subjective success
Figure 31 shows the mean differences of subjective success by the respondents’ field
of invention. It indicates that there were no significant subjective success mean
differences among the fields of inventions (F = .342, p-value =.968).
Figure 31: Mean differences of Subjective Success by Field of Inventions
208
Table 58 depicts the association between field of invention and level of subjective
success. According to the table, majority of each category had achieved medium
level subjective success. Fisher’s exact test results indicates that there was no
significant association between respondents’ field of inventions and their level of
subjective success (χ2 = 20.131, df = 20, Fisher’s exact test =18.229, Monte Carlo pvalue = 0.529, CV= .224).
Table 58: Level of Subjective Success by Field of Invention
Level of Subjective Success
Low
Medium
High
Field of
Invention
Environmental/ Energy
Automotive
Sports/ Leisure
Agriculture
Medical
Tools
Household
High-tech
Security/safety
Industrial
Educational
Count ( n = 32)
Expected Count
% within
Count ( n = 18)
Expected Count
% within
Count (n = 5)
Expected Count
% within
Count ( n = 34)
Expected Count
% within
Count ( n = 20)
Expected Count
% within
Count (n = 5)
Expected Count
% within
Count (n = 31)
Expected Count
% within
Count (n = 19)
Expected Count
% within
Count (n = 11)
Expected Count
% within
Count (n = 24)
Expected Count
% within
Count ( n = 1)
Expected Count
% within
0
1.3
.0%
2
.7
11.1%
0
.2
.0%
4
1.4
11.8%
0
.8
.0%
0
.2
.0%
0
1.2
.0%
2
.8
10.5%
0
.4
.0%
0
1.0
.0%
0
.0
.0%
23
22.2
71.9%
12
12.5
66.7%
4
3.5
80.0%
20
23.6
58.8%
14
13.9
70.0%
4
3.5
80.0%
21
21.5
67.7%
11
13.2
57.9%
8
7.6
72.7%
21
16.7
87.5%
1
.7
100.0%
9
8.5
28.1%
4
4.8
22.2%
1
1.3
20.0%
10
9.0
29.4%
6
5.3
30.0%
1
1.3
20.0%
10
8.2
32.3%
6
5.0
31.6%
3
2.9
27.3%
3
6.4
12.5%
0
.3
.0%
χ2 = 20.131, df = 20, Fisher’s exact test =18.229, Monte Carlo p-value = 0.529
CV= .224/ Monte Carlo p-value = 0.392, Cells with expected count less than 5 = 57.6%
209
viii.
Commercialization effort and subjective success
The cross-tabulation result in Table 59 indicates that more than 93% of the
respondents in each commercialization category achieved at least medium level of
subjective success. Even though the licensing to other categories indicated 37.5% of
high-level subjective success, the researcher was unable to detect special relationship
pattern between commercialization effort and the subjective success. None of the
categories indicate large number of inventors with low subjective success.
Consequently, the Fisher’s exact test indicates that there was no significant
association between commercialization effort and the level of subjective success (χ2
= 3.992, df = 8, Fisher’s Exact test = 3.889, p-value = 0.851, CV= .100).
.
Table 59: Level of Subjective Success by Commercialization Effort
Level of Subjective Success
Low
Medium
High
Produce and sell by inventor Count ( n = 93)
3
63
27
Expected Count
3.7
64.6
24.6
% within
3.2%
67.7%
29.0%
Licensing to others
Count ( n = 16)
0
10
6
Expected Count
.6
11.1
4.2
% within
.0%
62.5%
37.5%
Outright sales of patent
Count (n = 16)
1
12
3
Expected Count
.6
11.1
4.2
% within
6.3%
75.0%
18.8%
Teaching and consultation
Count (n = 7)
0
5
2
Expected Count
.3
4.9
1.9
% within
.0%
71.4%
28.6%
not try to sell
Count ( n = 68)
4
49
15
Expected Count
2.7
47.3
18.0
% within
5.9%
72.1%
22.1%
χ2 = 3.992, df = 8, Fisher’s Exact test = 3.889, p-value = 0.851, CV= .100,
Effect size (W) = .141, Cells with expected count less than 5 = 60%
The mean plot presented in Figure 32 indicates that inventors who were trying to
commercialize their inventions on their own and giving license to others have
210
achieved marginally higher subjective success. Meanwhile, compared to outright
sale, teaching and consultation categories, majority of the respondents who had not
tried to commercialize their inventions also have achieved relatively high subjective
success mean score. However, the statistical test of the mean differences indicates
that there were no significant subjective success mean differences among the
respondent inventors, who used different levels of effort for commercialization (F =
1.225, p-value = .301).
Figure 32: Mean differences of Subjective Success by Commercialization Effort
ix. Inventive life span and subjective success
According to the Table 60, more than 94% of the respondents at each inventive life
stage achieved medium or high subjective success. Hence, as the pattern of the
majority of profiling variables, there was no significant association between
inventive life span and the level of subjective success (χ2 = 1.234, df = 4, Fisher’s
Exact test = 1.529, p-value = 0.836, CV= .056).
211
Table 60: Level of Subjective Success by Inventive Life Span
Level of Subjective Success
Low
Immature Inventors
Medium
Count ( n = 106 )
3
73
Expected Count
4.2
73.7
% within
2.8%
68.9%
Growing Inventors
Count ( n= 51 )
3
35
Expected Count
2.0
35.4
% within
5.9%
68.6%
Matured Inventors
Count (n = 43)
2
31
Expected Count
1.7
29.9
% within
4.7%
72.1%
χ2 = 1.234, df = 4, Fisher’s Exact test = 1.529, p-value = 0.836, CV= .056,
Effect size (W) = .079, Cells with expected count less than 5 = 33.3%
High
30
28.1
28.3%
13
13.5
25.5%
10
11.4
23.3%
The mean plot in Figure 33 also clearly illustrates that there was no significant
difference of average subjective success between respondents’ inventive life span (F
= .023, p-value = .977).
Figure 33: Mean differences of Subjective Success by Inventive Life Span
212
x. Findings on categorical profiling variables and subjective success
According to the analysis of categorical profiling variables and subjective success,
 None of the categorical profiling variables: age, location, education,
employment level, job mobility, type of invention, field of invention,
commercialization effort and inventive life span has shown statistically
significant association with level of subjective success at 0.05 significant
level.

None of the categorical profiling variables: age, location, education,
employment level, job mobility, type of invention, field of invention,
commercialization effort and inventive life span have shown statistically
significant means difference at 0.05 significant level.

Hence, the demographic factors: age, location, education, employment level,
job mobility and technical factors: type of invention, field of invention,
commercialization effort and inventive life span have no influence on and
make no difference of the respondents’ subjective success.
213
Bottom-up conceptual path model of the study
Based on the theoretical and empirical evidences of the correlates of objective and
subjective success, the researcher developed the conceptual framework of the study.
Figure 34 illustrates the condensed version of conceptual framework indicating
operationalization of the bottom-up conceptual model for the statistical path analysis.
Marital Status
Income
Internet Usage
Daily invent Hours
Inventive Career
Satisfaction
Objective Success
Subjective Success
Life Orientation
External Linkages
Maximizing
Tendency
Social Capital
Community
Connectedness
Figure 34: The Operationalized Conceptual Path Model
During the cross tabulation and mean comparison analysis, the researcher tried to
determine the association between categorical profiling variables and the objective
and subjective success of the respondents. Apart from the age range, all the other
profiling variables were measured as nominal and ordinal variables. Even though the
214
age was measured as a continuous variable, during the exploratory data analysis age
has shown a non-linear relationship with subjective and objective success. Further,
according to the literature review, the recent studies have found there was no
relationship between age and subjective success. Therefore, the researcher did not
include age in the path model and just analyzed the age as converted categorical
variable.
Correlation analysis of variables in path model of the study
In this section, the researcher presents the bivariate analysis of the relationship
between variables that are included in the bottom-up model of the study. Marital
status was measured as dichotomous scale variable and in Pearson correlation
analysis, researchers are allowed to use single dichotomous variable along with
continuous variables (Meyers, Gamst, & Guarino, 2006, p. 118). In Pearson product
movement correlation matrix, r-value indicates the strength of the relationship
(correlation) between two variables and the p-value indicates the statistical
significance of the correlation. Guildford (1977) suggested a rule of thumb to
interpret the correlation coefficients. According to him, absolute values (+/-) of the
correlation coefficients, which range from 0 to .2 indicates negligible relationship,
0.2 to 0.4 indicates low relationship, .4 to.7 indicates moderate relationship, .7 to .9
high relationship and .9 to 1 indicates very high relationship (Guilford, 1977).
Pearson product movement correlation matrix of selected demographic, technical,
psychological and social variables in the bottom-up path model is shown in Table 61.
It shows that there is a statistically significant relationship between objective success
and subjective success at 0.01. The positive relationship indicated that both objective
success and subjective success are moving the same direction.
215
Table 61: Pearson Product Movement Correlation of variables in conceptual model
Mean
SD
Subjective Success
41.10
7.051
1
Objective Success
2.52
1.490
.341**
1
.68
.470
.134
.142*
1
38.26
19.135
.230**
.272**
.429**
1
3.80
1.672
.310**
.363**
.128
.215**
1
Internet Usage
12.85
4.393
.348**
.161*
-.095
.278**
.148*
1
Inventive career Satisfaction
16.24
2.110
.438**
.188**
.027
.016
.194**
.111
1
Maximization Tendency
27.49
5.204
.195**
-.049
-.098
-.086
-.059
.155*
.142*
1
Life Orientation
23.47
3.024
.365**
.089
.015
.098
.114
.222**
.186**
.179*
1
1.28
.108
.225**
.354**
-.047
-.019
.025
.157*
.163*
.026
.151*
1
Social Capital
54.20
9.405
.314**
.192**
.068
.216**
.180*
.303**
.075
.040
.089
.067
Community Connectedness
43.28
6.265
.414**
.129
-.118
.037
.129
.161*
.348**
.125
Marital Status
Income
Daily Invent Hours
External Linkages (Log)
* * P<0.01
* P<0.05
Y1
SD= Standard Deviation
Y2
X1
X2
N= 200
216
X3
X4
X5
X6
X7
X8
.244** .184**
X9
X10
1
.098
1
However, the correlation coefficient (r) was .341 and it indicates the low magnitude
of the relationship. According to the Table 61, other than marital status (X1), all the
other variables have shown statistically significant positive correlation with the
subjective success (Y1), even at the more stringent 0.01 alpha level. Only
maximizing tendency has shown negligible level relationship, but r-value .195 is
very close approximation to .2. Inventive career satisfaction and community
connectedness had moderate relationship with subjective success (r=.438 and .414
respectively). Therefore, other than marital status, all the other variables in the
conceptual model indicate low to moderate level significant positive relationship
with subjective success.
Unlike subjective success, marital status indicates statistically significant negligible
positive relationship (r = .142) with objective success at 0.05 level. Along with that,
internet usage (r= .161), inventive career satisfaction (r = .188) and social capital
(.192) have shown significant but negligible positive correlation with objective
success. Meanwhile, maximizing tendency has shown negative relationship with
objective success, however the strength of the relationship was negligible (r= -.049)
and not significant at 0.05 level. Daily inventive hours (r = .363), External linkages
(r = .354), and income (r = .272), have shown the highest strengths of the statistically
significant correlations. However, according to Guilford rule of thumb, still these
values indicate low relationship with objective success. Apart from maximizing
tendency (r=-.049), life orientation(r=.089) and community connectedness (r = .129)
also have not shown significant relationship with objective success. Multicollinearity
among exogenous variables occurs only when the correlation coefficient become
higher than .8 (Katz, 2006, p. 69). In the correlation matrix, only marital status and
income has moderate (r = .429) relationship among the exogenous variables. All
217
other bivariate relationships between exogenous variables (independent variables) of
the suggested conceptual model, indicates either negligible (r <.2) or low (r <.4)
relationship. Therefore, among exogenous variables, there is no threat of
multicollinearity and each variable is approximately independent or at least only just
marginally correlate with each other.
In general, correlation analysis indicates that as hypothesized, there is a significant
relationship between objective and subjective success. Then again, other than marital
status, all other exogenous variables have significant relationship with subjective
success. Apart from maximizing tendency, life orientation and community
connectedness, all the other exogenous variables are significantly correlated with
objective success.
Path Analysis of the Bottom-Up Model of the Study
Process of path analysis consists of multiple stages namely model specification,
identification, estimation, testing and modification (Schumacker & Lomex, 2004;
Ullman & Bentler, 2004). The researcher followed all four steps during the path
analysis and extracts of the analysis present in this section of the chapter.
Model specification
According to Schumacker and Lomex (2004), model specification involves, finding
relevant theories and prior researches to formulate the theoretical path model
(Schumacker & Lomex, 2004, p. 129). In chapter 2, the researcher explained the
theoretical framework, correlates of subjective and objective success and
development of the conceptual framework of the study. In chapter 3, the researcher
218
operationalized the concepts into measurable variables and developed the
operationalized conceptual path model illustrated in Figure 34.
There were
arguments in literature over the possibility of using dichotomous exogenous
variables in path models. Therefore, the researcher contacted the Kenneth Bollen and
Rex Kline, the authors of best selling structural equation modeling books for their
advice on the possibility of using marital status as exogenous variable in path model.
According to their opinions, the researcher was advised to use dichotomous variable
as an exogenous variable of the present path model without having any problems
(Bollen K, Personal communication, 23 September 2010 and Kline, Personal
communication, 10 December 2010). Therefore, marital status, income, internet
usage, daily inventive hours, incentive career satisfaction (ICS), life orientation,
external linkages, maximizing tendency, social capital and community connectedness
were defined as exogenous variables. Objective success and subjective success were
defined as endogenous variables and the researcher hypothesized positive influence
of objective success on the subjective success. Based on the operationalised
conceptual model, the researcher developed the path diagram of the present study.
During the EDA, all the variables in the model were tested for univarite, multivariate
normality and outliers, linearity, multi-colinearity and all the other required
multivariate assumptions. Sample size and power analysis indicated that the sample
of present study was achieved the minimum required sample size for required power
level .80. In order to do the path analysis, the researcher selected SPSS AMOS
version 18 and using Amos graphics, the researcher developed the initial path model
of the study. In order to avoid the influences of unmeasured external variables over
the exogenous variables, covariance of each of the exogenous variables were
correlated using double-headed arrows. Circles that stated as e1 and e2 measures the
219
errors of objective success and subjective success respectively. Each single headed
arrow from exogenous variables to endogenous variable indicates the hypothesized
directional relationship between two variables identified in the literature review of
the study.
Model identification and estimation
The researcher interested to explore how the exogenous variables influence the
endogenous factors using model-trimming approach (Bynrne, 2009). Hence, the
initial path model was defined as just identified recursive saturated model, which had
equal number of free parameters (78) and the data points (78) with zero degree of
freedom. Maximum Likelihood (ML) is the usual default estimation method in most
structural equation models ( (Ullman & Bentler, 2004; Schumacker & Lomex, 2004;
Hair, Black, Babin, & Anderson, 2009; Kline, 2011). Owing to the scale free
estimate of the ML method, the researcher was able to use transformed variables
within the model with non-transformed variables. As far as the variables in the model
satisfied the multivariate normality, outliers’ assumptions and minimum sample size
requirements, the researcher adapted the maximum likelihood (ML) method to
estimate the parameters of the path analysis.
Model testing
In path analysis, hypothesis of the model check based on the significance and the
strength of the standardized regression estimates of the individual paths of the model
(Schumacker & Lomex, 2004). Figure 35 shows the re-produced initial path model
and standardized estimates of the individual paths of the model based on the original
AMOS 18 output presented in the Appendix H-Figure 52.
220
All Exogenous Variables were correlated using double-headed arrows in AMOS model
Marital Status
.03
Income
Internet Usage
.19**
.12**
.00
.03
Daily invent
Hours
.15**
.28*
Inventive Career
Satisfaction
.10***
.23*
.08
Objective Success
2
R =.306
e2
-.03
Life Orientation
External Linkages
e1
.13**
Subjective Success
2
R =.481
.17**
.04
.34*
.10***
.15**
-.03
.22*
Maximizing
Tendency
.07
Social Capital
Kline’s effect size criteria of
path coefficients
<.10 - Small effect
<.30 - Medium effect
>.50 - large effect
.00
Community
Connectedness
*significant at p<.01
** significant at p<.05
***significant at p< .1
Figure 35: Standardized Estimates of initial Bottom-up Path Model
According to the estimated path diagram in Figure 35, Objective success was a
significant predictor of the subjective success (β = .13) at 0.05 significant level.
However, some of the hypothesized relationships were not significant at 0.05 level.
Especially the hypothesized predictors of objective success; Marital Status (β=.03),
221
Internet Usage (β=.00), Inventive career satisfaction (β=.08), Life orientation (β=.03), Maximizing tendency (β=-.03), social capital (β=.07) and community
connectedness (β=.00) have not shown significant regression coefficient at the 0.05
level. However, the income (β=.19), daily inventive hours (β=.28) and external
linkages (β=.34) were significant at 0.05 levels.
According to the standardized regression estimates, influences of inventive career
satisfaction (β =.23) and community connectedness (β=.22) on subjective success
were significant at .01 level. The influences of marital status (β=.12), internet usage
(β=.15), life orientation (β =.17) and social capital (β=.15) on subjective success
were significant at .05 level. However, daily inventive hours (β=.10) and maximizing
tendency (β=.10) were significant only at .10 level. Income (β =.03) and external
linkages (β=.04) have not shown significant influence even at the .1 level.
Squared multiple correlation coefficient (R2) estimates the relative amount of
variance of the endogenous variable (Y) explained or accounted for by the
exogenous variables (x1, x2, x3…) (Joreskog, 2000). In initial path model squared
multiple correlation (R2) of objective success and subjective success were .306 and
.481 respectively. It indicates that exogenous variables in the initial model were able
to explain 31% of the variance of objective success and exogenous variables in the
model were able to explain 48% of variation of subjective success.
Cohen (1988) suggested Effect size as an indicator of the degree of which the tested
phenomenon is present in the population. He suggested the calculation of Effects
size (f2) based on the R2 as,
f2 = R2 / [1- R2] where
222
f2= Effects size
R2 = Squared Multiple Correlation coefficient
Hence the effect size of objective success,
f2OS = R2OS / [1- R2OS]
f2OS = .306 / [1- .306]
= .306 / .694
= .440
In addition, effect size of subjective success,
f2SS = R2SS / [1- R2SS]
f2SS = .481 / [1-.481]
= .481 / .542
= .926
According to the Cohen (1988) general recommendations on effect size (small= .02,
medium =.15 and Large=.35), both objective success and subjective success models
have shown large effect sizes (N= 200).
As far as the number of data points of the model equals to free parameters, the model
was just identified saturated model. As far as in saturated model χ2 and degree of
freedom equals to zero, model fit indices or modification indices were unable to
calculate for the initial model.
Model modification
According to initial model, there were some non-significant relationships between
exogenous variables and endogenous variables at .05 level. The researcher modified
the path model by eliminating the relationships that were not significant at least at
0.05 levels and re-test the modified model using ML method. All the paths in the
modified path model depicted in Figure 36 were significant at 0.05 level. (Original
Amos output is available in Appendix H-Figure 53).
223
Chi Square= 6.337
Df= 9
P= .706
GFI= .994
RMSEA=.000
CFI=1.000
IFI=1.007
TLI=1.049
All Exogenous Variables were correlated using double-headed arrows in AMOS model
Marital Status
Income
Internet Usage
.21*
.13**
Daily invent Hours
.18**
.31*
Inventive Career
Satisfaction
e1
.26*
e2
Objective Success
2
R =.294
.17**
Subjective Success
2
R =.458
.19*
Life Orientation
.35*
.16**
External Linkages
.23*
Kline’s effect size criteria of
path coefficients
<.10 - Small effect
<.30 - Medium effect
>.50 - large effect
Social Capital
Community
Connectedness
*significant at p<.001
** significant at .001<p<.05
Figure 36: Standardized Estimates of Modified Bottom-up Path Model
In the modified model marital status (β=.13, p=.013), internet usage (β=.18, p=.002),
inventive career satisfaction (β=.26, p=.000), life orientation (β=.19, p=.000), social
capital (β=.16, p= .004), community connectedness (β=.23, p=.000), and objective
224
success (β=.17, p=.002) were the significant predictors of subjective success.
Meanwhile income (β=.21, p=000), daily inventive hours (β=.31, p=.000) and
external linkages (β=.35, p=.000) were significant predictors of the objective success
at 0.05 level. According to the Kline (2011) effect size criteria of path coefficients,
all the paths in the modified model have shown small to medium level effect size.
Compared to initial model R2 for objective success decrease slightly to .294 (.306 in
initial model) and subjective success decrease to .458 (.481 in initial model).
Therefore, effect size of objective success has dropped to .416 and .845 respectively,
but still has indicated large effect sizes. In structural equation modeling Goodness of
fit index (GFI) roughly analogous to the multiple R2 that represents the overall
amount of the covariation among the observed variables that can be accounted for by
the hypothesized model (Stevens, 2002, p. 431). In modified model, GFI was .992
and indicated satisfactory amount of the covariation among the observed variables
that can be accounted for by the hypothesized model.
Model fit
Owing to the reduction of free parameters to be estimated in the modified model,
degree of freedom increased to nine and therefore, χ2 and other model fit indices
were able calculated. Hu and Bentler (1999) had introduced the cut of criteria for fit
indices of Structural Equation Models (Hu & Bentler, 1999). Table 62 shows the
major model fit indices and their cutoff values recommended by the Hu and Bentler
(1999), Bynrne, (2009) and Kline, (2010) along with the estimated values of the
modified model of the present study. Modified model satisfactorily achieved the
values over and above the cut-off criteria of all indices. Hoelter’s CN (0.05) is higher
than 200 shows the adequacy of the sample size. Then again, standardized residual
225
covariance matrix had no values higher than the cutoff value of 2.00 (Bynrne, 2009).
The maximum standardized residual covariance value was 1.040. Therefore, the
modified bottom-up model of the study satisfactorily fitted with the sample data in
the variance and covariance matrix. Standardized residual covariance matrix of the
modified path model is presented in appendix H.
Table 62: Model Fit indices, Cutoff criteria and Modified bottom up model
values
Index
Absolute
Fit Indices
Non
centralitybased indices
Relative Fit
Indices
χ2(df, N), P
χ2/df
GFI
AGFI
SRMR
Hoelter’s CN (.05)
AIC
CFI
RMSEA(LO90, HI90)
PCLOSE
IFI
TLI
NFI
Recommended
cut off value
p>0.05
<3.00
>.90
>.90
<.05
>200
Lower the Better
>.95
<.08
>.50
>.90
>.95
>.90
Value in the
model
6.3(9, 200)P= .706
χ2/df =.704
GFI = .994
AGFI=.958
SRMR= .017
Hoelter’ CN= 532
AIC=120.34
CFI=1.00
RMSEA= 0.00(.00,.06)
PCLOSE=.908
IFI=1.00
TLI=1.00
NFI= .984
Decision
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Test for mediation
In the modified final model of the present study, income, daily inventive hours and
external linkages have indirect effect on subjective success. In order to detect
whether these indirect effects were significantly different from zero, the researcher
selected 2000 bootstrap samples and bias-correlated confidence intervals 95 percent
using AMOS 18 bootstrapping.
Table 63: Bootstrapping results of the mediation effects-Bottom-up Model
Variable
Income
External Linkages
Daily invent Hours
Standardized
Indirect
effect
.036
.059
.052
S.E.
Bootstrapping Estimates
Lower Bound
Upper Bound
(2.5%percentile) (97.5%percentile)
.016
.023
.021
.013
.023
.019
226
.078
.118
.103
Sig. P
(twotailed)
.001
.001
.001
According to Table 63, the standardized indirect effects of income, external linkages
and daily inventive hours are significantly different from zero at .01 level (P =.001,
two tailed). Hence, income, external linkages, daily inventive hours has significant
indirect effect on the subjective success. However, none of these variables indicates
significant direct effect on subjective success in the bottom-up path model.
Therefore, objective success possibly be a full mediator variable between income,
daily inventive hours, external linkages and subjective success. However, to assume
objective success as a full mediator, the researcher had to check whether there is
significant relationship between income, daily inventive hours and external linkages
on the subjective success without having the effect mediation variable. When the
researcher assumed the objective success is not an intervening variable, income
(p=.548) and external linkages (EL) (p=.118) have not shown significant direct effect
on the Subjective success (Table 64). However, the engagement in invention (EI) has
shown significant direct influence on the subjective success (p=.024).
Table 64: Boostrap significance of full, partial, no mediation and indirect effects
No mediation
Income
EL
EI
IU
CC
ICS
MS
SC
LO
OS
mediation
X SS
XOS
OSSS
XSS
p
p
p
p
.548
.118
.024*
.004*
.001*
.001*
.037*
.008*
.005*
…
.001*
.001*
.001*
…
…
…
…
…
…
…
.014*
.014*
.015*
…
…
…
…
…
…
…
.855
.482
.083
.005
.001
.001
.034
.011
.005
.018
Decision
Indirect
Indirect
Full mediation
OS=objective Success, SC=Subjective Success, CC=Community Connectedness, MT=Maximizing
Tendency, EL=External linkages, LO=Life Orientation, ICS=Inventive Career Satisfaction,
EI=Engagement in inventions, IU=Internet Usage, *= sig. at .o5
227
Therefore, objective success was a candidate to be a full mediator between the
engagement in invention and the subjective success. To test the actual nature of the
relationship between these variables, the researcher assumed two possible effects: the
indirect effect and partial mediation effect. The researcher entered the objective
success as intervening variable in both models. In the indirect effect path model,
income, external linkages and engagement in invention showed significant indirect
effect on the subjective success (Table 64). In partial mediation model the direct
effects of income (p=.855), external linkages (p=.482) and engagement in invention
(p=.083) on subjective success were not significant. As far as without intervening
variable engagement in invention had shown significant influence on the subjective
success in no mediation model, the objective success was confired as a full mediator
variable between engagement in invention and the subjective success. However,
either income or external linkages have not shown significant direct influence on the
subjective success in both no mediation and partial mediation models. Hence, the
income and external linkages have only the indirect effects on the subjective success.
Fianally, the researcher tested the indirect effect, partial, and no mediation models
using nested model approach in AMOS. Chi-squre difference test indicated that
when assuming partial mediation model to be correct, the chi square difference
between
no
mediation
and
partial
mediation
is
statistically
significant
(CMIN=73.165, Df=4, p=.000). However, the chi-squre difference between partial
mediation and indirect effect models was not significant (CMIN=3.055, Df=3,
p=.383). Terefore, there were no concluding evidences to belive partial meadition
model is better than the indirect effect model (AMOS out of nested model
comparison depicted in Appendix ) .
228
Path Analysis of the Top-Down Model of the Study
Model identification
During the discussions of literature review, theoretical and conceptual framework in
Chapter 2, researcher explained the importance of studying the top down relationship
of subjective success. Veenhovan (2006) and Fredrickson (2004) theoretical
arguments discussed under the theoretical framework of Chapter 2 raise the
importance of examining how the subjective success can influence the objective
success and the selected demographic, technical, psychological and social factors of
grassroots level inventors in Sri Lanka. As far as Marital Status is a dichotomous
variable, the cross sectional study was unable to detect the impact of subjective
success on marriage decisions. Hence, it was omitted from the reverse path model
analysis. Initial top-down model was defined as over identified model with excess
data points than the free parameters and analyzed using ML Method.
Model testing
Figure 37 indicates the reproduced extraction of the top-down path model that was
analyzed using AMOS 18 (original AMOS 18 path diagram attached as Appendix I –
Figure 54).
229
Chi Square= 67.663
Df= 36
P= .001
GFI= .939
RMSEA=.066
CFI=.892
IFI=.898
TLI=.835
.22**
.05
.16**
.33*
.29*
.21**
.42*
Subjective Success
.341*
.04
Objective Success
2
R =.12
Income
2
R =.10
e2
Internet Usage
2
R =.12
e3
Daily invent
Hours
2
R =.17
e4
Inventive Career
Satisfaction
2
R =.19
e5
Life Orientation
2
R =.13
e6
External Linkages
2
R =.14
e7
Maximizing
Tendency
2
R =.05
e8
Social Capital
2
R =.11
e9
e1
.-.04
.38*
.12***
.31*
.24**
.28*
.-.13***
.42*
.10
Kline’s effect size criteria of
path coefficients
<.10 - Small effect
<.30 - Medium effcct
>.50 - large effect
.-02
Community
Connectedness
R2=.17
*significant at p<.01
** significant at p<.05
e10
***significant at P <.1
Figure 37: Standardized Estimates of initial Top-Down Path Model
According to the standardized regression coefficients (β) in Figure 37, subjective
success significantly contributed to all the variables at least at .05 level, except the
external linkages (β=.12, p=.092). Influence of subjective success on objective
success (β=.34) was significant at .01 level. Then again, influence of subjective
success on internet usage (β=.33), inventive career satisfaction (β=.42), life
230
orientation (β=.38), social capital (β=.28) and community connectedness (β=,42)
were also significant at .01 level. Influence of subjective success on income (β =.16),
daily inventive activities (β=.21), maximizing tendency (β=.24) were significant at
.05 level. However, objective success only had significant influence on income
(β=.22, p=.002), daily inventive hours (β=.29, p=.000) and external linkages (β=,31,
p=.000) at .05 level. Meanwhile, influence of objective success on maximizing
tendency (β= -.13) was significant only at .1 level. Influence of objective success on
internet usage (β=.05), inventive career satisfaction (β=.04), life orientation (β= -.04),
social capital (β=.10) and community connectedness (β= -.02) were not statistically
significant at .05 or even .1 level. According to the model fit indices depicted in the
Table 65, majority of the major model fit indices were not satisfied in the initial
model.
Table 65: Model fit indices of initial top-down path model
Index
Absolute
Fit Indices
Non
centrality-based
indices
Relative
Fit Indices
χ2 (df, N) P
χ2/df
GFI
AGFI
SRMR
Hoelter’s CN (.05)
CFI
RMSEA(LO90, HI90)
PCLOSE
IFI
TLI
NFI
Recommended
cut off value
p>0.05
<3.00
>.90
>.90
<.05
>200
>.95
<.08
>.50
>. 90
>.95
>.90
Value in the
model
67. 663(36, 200) P= .001
χ2/df =1.880
GFI = .939
AGFI=.888
SRMR= .064
Hoelter’ CN= 150
CFI=.892
RMSEA= 0.066
PCLOSE=.128
IFI=.898
TLI=.835
NFI= .805
Decision
Not Satisfied
Satisfied
Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Not Satisfied
Satisfied
Not Satisfied
Very Close
Not Satisfied
Not Satisfied
Before conducting the model modification, the researcher conducted a comparison of
standardized regression estimates and p-values of initial conceptual model that
followed the bottom up approach of success and alternative reversal conceptual
231
model that followed the top down approach of success. The researcher expected to
explore the strengths of the competing bottom-up and top-down casual directions.
Comparison of casual directions
Table 66 shows the comparison of standardized regression coefficients of bottom-up
and top-down path models.
Bottom Up
Top Down
XY
Y X
Standard
Standard
P
P
β Estimate
β Estimate
OS
< > SS
.128
.037
.341
***
Engagement
< > OS
.283
***
.291
***
Maximization
< > OS
-.030
.624
-.131
.075ɸ
External Links
< > OS
.340
***
.314
***
Income
< > OS
.188
.009
.219
.002
Social Capital
< > OS
.150
.239
.096
.177
Life Orientation
< > OS
-.029
.642
-.040
.566
Inventive Satisfaction < > OS
.077
.236
.044
.519
Internet Usage
< > OS
-.005
.946
.048
.501
Com Connectedness
< > OS
.004
.950
-.014
.839
Marital Status
< > OS
.032
.644
ɸ
Engagement
< > SS
.105
.067
.210
.002
Maximization
< > SS
.101
.060 ɸ
.240
.001
External Links
< > SS
.043
.450
.118
.093 ɸ
Income
< > SS
.026
.677
.155
.031
Social Capital
< > SS
.150
.006
.281
***
Internet Usage
< > SS
.150
.011
.332
***
Life Orientation
< > SS
.170
.002
.379
***
Inventive Satisfaction < > SS
.234
***
.423
***
Com Connectedness
< > SS
.216
***
.419
***
Marital Status
< > SS
.134
.013
OS = Objective Success SS = Subjective success
D = Dual/Two way causality, T = Top down Causality, B- Bottom up causality,
NR=No significant relation even at .1 level, ɸ significant only at .1 level, *** =.000
X
<>
Y
Causal
Direction
Table 66: Comparison of paths in bottom-up and top-down models
D
D
T
D
D
NR
NR
NR
NR
NR
NR
T
T
T
T
D
D
D
D
D
B
According to the Table 66, the influence of objective success on subjective success
(bottom up causality) and influence of subjective success on objective success (top
down causality), both were significant at .05 level. Therefore, it indicates dual/ two232
way causality between two variables. That means both objective success and
subjective success mutually contributing to each other. However, the strength of the
top down causality is relatively higher than the bottom up contribution.
Similarly, the relationship between objective success and engagement in invention
(label as daily inventive hours), external linkages and income has shown dual casual
relationships. Even though the Maximization tendency was not significant at .05
level in both models, in reversal model p-value improved substantially and at .1 level
it was significant. Therefore, the contribution of objective success to maximizing
tendency is substantially significant (β= -.131, p = .075) than the contribution of
maximizing tendency to objective success (β= -.30, p = .624). Whereas, the
relationship between objective success and other social factors (social capital,
community connectedness), psychological factors (life orientation, inventive career
satisfaction) and internet usage have not shown significant relationship for either
directions. Marital status was tested only at the bottom up model (β=.032, p= .644)
and it had no significant contribution to objective success even at .1 level.
The relationship between subjective success and internet usage, community
connectedness, social capital, life orientation and inventive career satisfaction has
significant two-way causality at 0.05 level. The bottom up influence of income
(β=.026), engagement in invention (β=.105), and maximization tendency (β=.10) on
subjective success were not significant at 0.05 level. However, influence of
subjective success on these variables was significant at .05 level and indicated the
existence of only top down causal relationship. Marital status was tested only at
bottom up model and it had indicated statistically significant contribution to
subjective success (β=.134, p=.013). Even though both bottom up and top down
233
relationships between subjective success and external linkages not significant at .05
level, compared to influence of external linkages on subjective success (β=.043, p
=.450), the influence of subjective success on external linkages (β =.118, p=.093)
was significant at .1 level. Therefore, the relationship between subjective success and
external linkages indicated tendency towards the top down casual relationship.
Remarkably none of the relationships has showed a pure bottom-up relationship
other than dual-causality. According to the standardized estimate values, only
external linkages have shown relatively high strength in bottom-up relationship
(β=.340) than the top-down relationship (β=.314) with objective success. Then again,
every single top-down relationships between subjective success and the other
variables have shown relatively higher estimation strength than the bottom-up
relationship.
Model modification
According to the model fit indices presented in Table 65, initial top-down model
was not satisfactorily fit with the data. Therefore, the researcher considered the
possibility to modify the model to achieve the best-fitted model. As a first step, the
researcher deleted the non-significant paths at .05 one by one. The paths from
objective success to maximizing tendency and subjective success to external linkages
were not significant at .05 level. However, owing to the relatively significance of top
down causality (Table 66), the researcher did not deleted these paths. During the
second stage, the researcher modified the model based on the standardized residual
covariances. Large standardized residual values (greater than 1.96 or 2.00) indicate
that a particular relationship is not well accounted by the model (Schumacker &
Lomex, 2004). Any standardized residual value greater than 2.00 indicate that there
234
is covariance between the two variables and it indicates the possible new
relationships between the variables that the residuals are belonging. Owing to the
inherent nature of the model, the number of endogenous variables was predicted by
only two exogenous variables. Hence, the influence of common third factor/factors
on the endogenous variables has been omitted and that might influence the high
residual matrix values of the model (Kline, 2011). Using AMOS modification indices
on regression weights, the researcher was able to draw new paths among the
endogenous variables. (Modification Indices on regression weights present in the
Appendix H). However, the researcher wanted to compare the exact reversal model
of the original bottom-up model. Therefore, owing to this scope limitation of the
theoretical argument of the present study to enforce such new relationships, the
possible new paths between the endogenous variables have been purposely
neglected. According to the Kline (2010) and Hoyle (1995), when the researcher can
theoretically justify there are possible relationships between endogenous variables
due to other common causes that cannot be accounted by the model, the residuals
between those variables can be correlated in the path model. Recently published
empirical studies have provided evidences for the existence of the possible common
causes, which contribute to the relationships between income and internet usage
(Zhou, Singh, & Kaushik, 2011; Talukdar & Gauri, 2011), income and social capital
(Robison & Ritchie, 2010; Akcomak & Weel, 2009), satisfaction and connectedness
(Gaughan, 2011; White, Vanc, & Stafford, 2010), internet and social capital
(Hamburger & Hayat, 2010; Stern, 2010). These empirical justifications of the
possible common causes grant the permission to the researcher to omit the
restrictions in path analysis to correlate the residuals, which had high-standardized
residual values (Streiner, 2005). The researcher correlated the residuals of
235
endogenous variables, which had standardized residual covariance values higher than
2.00 one at a time as suggested by Bynrne (2009) and Schumacker and Lomex
(2004). However, the researcher suggests future researchers to include the
relationships between these endogenous variables in future extensive studies.
Model fit
After series of iterations, the researcher developed the model with standard residual
matrixes with less than 2.00 cell values and that satisfied all the model fit indices
(Table 67).
Table 67: Model fit indices of modified top-down path model
Index
Absolute
Fit Indices
Non
centrality-based
indices
Relative
Fit Indices
χ2 (df, N)P
χ2/df
GFI
AGFI
SRMR
Hoelter’s CN (.05)
AIC
CFI
RMSEA (LO90,HI90)
PCLOSE
IFI
TLI
NFI
Recommended
cut off value
p>0.05
<3.00
>.90
>.90
<.05
>200
Lower the better
>.95
<.08
>.50
>.90
>.95
>.90
Value in the
model
41. 508 (37, 200)P= .281
χ2/df =1.122
GFI = .962
AGFI=.932
SRMR=.048
Hoelter’ CN= 251
AIC-= 99.508
CFI=.985
RMSEA= .025(.00,.58)
PCLOSE=.881
IFI=.985
TLI=.977
NFI= .880
Decision
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
Satisfied
close
The modified model of the study was fit with the sample data in the variance and
covariance matrix. Hoelter’s CN (0.05) is higher than 200 and it further indicates the
adequacy of the sample size for the top down model estimations (Bynrne, 2009).
Extract of AMOS 18 final modified top-down path model with standardized
Regression estimates re-produced in Figure 38 (full AMOS 18-output path diagram
is in Appendix H-Figure 55).
236
Income
2
R =.10
Chi Square= 41.508
Df= 37
P= .281
GFI= .962
RMSEA=.025
CFI=.985
IFI=.985
TLI=.977
e2
.21**
Internet Usage
2
R =.12
e3
.14**
.20**
Daily invent Hours
2
R =.17
.16**
e4
.22**
.35*
.29*
.21**
.44*
.341*
Objective Success
2
R =.12
Subjective Success
Inventive Career
Satisfaction
2
R =.19
e5
Life Orientation
2
R =.13
e6
External Linkages
2
R =.14
e7
Maximizing
Tendency
2
R =.05
e8
Social Capital
2
R =.10
e9
e1
.37*
.12***
.24**
.31*
.31*
.41*
.-.13***
Kline’s effect size criteria of
path coefficients
<.10 - Small effect
< 30 - Medium effcct
>.50 - large effect
Community
Connectedness
R2=.17
*significant at P < .01 ** significant at P < .05
.20**
e10
***significant at P < .1
Figure 38: Standardized Estimates of Modified Top-Down Path Model
Compared to initial model standardized regression estimates, in optimal top-down
model estimates between subjective success and internet usage (β=.35), Inventive
Career Satisfaction (β=.44), Social Capital (β=.31) are slightly increased. While, the
estimates between subjective success and life orientation (β=.37), community
237
connectedness (β=.41) and estimate between objective success and income (β=.20)
are marginally reduced. In the optimal top-down path model, each endogenous
variable is predicted by subjective success or/and objective success. Hence, the
researcher interested to examine the variance of endogenous variables explains by its
predictors. According to the Cohen (1988) general recommendations on effect size
of squared multiple correlation coefficients (small= .02, medium =.15 and
Large=.35), R2 were ranged from small to large effect sizes among the endogenous
variables in the top-down model. More precisely, social capital (R2 = .098, ES=.108),
maximizing tendency (R2 = .053, ES=.056) and income (R2 = .089, ES=.098),
objective success (R2 = .116, ES=.131), internet usage (R2 = .121, ES=.137) had
small to medium effect size. Then the external linkages (R2 = .138, ES=.160), life
orientation (R2 = .133, ES=.153), community connectedness (R2 = .172, ES=.207),
inventive career satisfaction (R2 = .192, ES=. 237) and engagement in invention (R2
= .171, ES=.206) had medium to large effect sizes.
Test for mediation
Similar to the bottom-up model, objective success was the mediator variable in the
top-down model. Therefore, there were indirect impacts of subjective success on
income, daily inventive hours, external linkages and maximizing tendency.
Following the bootstrapping procedure adopted in the bottom-up model, the
researcher tested the significance of the indirect effects. Table 68 shows the 2000 resampling bootstrapping results of the reversal conceptual model at 95 level biascorrelated confidence intervals. According to the Table 68, the standardized indirect
effects of income (β=.068, p=.003), external linkages (β=.107, p=.001) and daily
inventive hours (β=.099, p=.001) are significantly different from zero at .05 level.
238
However the indirect effect of maximization tendency is not significantly different
from zero at .05 level (β= -.044, p=.064).
Table 68: Bootstrapping results of the indirect effects-Top-down model
Variable
Standardized
Indirect
effect
.068
.107
.099
-.044
Income
External Linkages
Dailyinventive Hours
Maximizing tendency
S.E.
Bootstrapping Estimates
Lower Bound
Upper Bound
(2.5%percentile) (97.5%percentile)
.027
.031
.028
.026
.024
.056
.053
-.105
.130
.183
.160
.003
Sig. P
(twotailed)
.003
.001
.001
.064
As far as boostrapping results provides on the significance of indirect effect, to test
the full, partial and no mediation the researcher tested the nested models assuming no
mediation, indirect effect and partial mediation. In the no-mediation model, the
researcher test the path model by assuming there is no mediation effect of objective
success. Bootstrapping two tailed significance (BC) result of the model depicts in the
Table 69 and it indicates all the direct effects of subjective success on intervening
and endogenous variables.
Table 69: Boostrapping significance for full, partial, no mediation and indirect effects
No mediation
SS X
OS
SC
CC
MT
EL
LO
ICS
EI
IU
Income
Mediation
SSOS
OSX
SSX
p
p
p
p
.001*
.001*
.007*
.001*
.001*
.001*
.001*
.001*
.001*
.001*
-
.093
.001*
-.
.001*
.006*
.004
.073*
.001*
.035*
Decision
No Mediation
Full mediation
Partial mediatiom
Partial Mediation
OS=objective Success, SC=Subjective Success, CC=Community Connectedness, MT=Maximizing
Tendency, EL=External linkages, LO=Life Orientation, ICS=Inventive Career Satisfaction,
EI=Engagement in inventions, IU=Internet Usage, *=significant at .05
239
Owing to the significance of direct effect of subjective success on external linkages,
engagement in invention and income in no mediation model, the researcher wanted
to test whether the indirect effect represent a partial mediation or full mediation. The
researcher then tested the second model by assuming partial mediation effect of
subjective success on the four endogenous variables in the model. According to the
bootstrapping results, indirect effect of maximizing tendency in the partial mediation
model is not significant (p=.064). Hence, the objective success was not a significant
mediator variable on the maximizing tendency. However, the indirect effect on
external linkages (p=.001), engagement in inventions (p=.001) and income (p=.003)
were significant. Further the direct influence of subjective success on engagement in
inventions (p=.001) and income (p=.035) in partial mediation model were also
significant. Whereas all indirect, direct and total effects of subjective success on
engagement in inventions and income become significant, the objective success was
a partial mediator between the subjective success and engagement in invention and
the Income. Even though the indirect effect on external linkages was significant
(=.001), the direct effect become non-significant (p=.073) in the partial mediation
model. Whereas the direct effect on external linkages was significant in no mediation
model, influence of the subjective success on the external linkages fully mediated by
the objective success. Hence, according to the results of the bootstrapping test of
models, the objective success was a full mediator variable between the relationship
between the subjective success and external linkages. Further, it has been a partial
mediator between the subjective success and engagement in inventions and income.
The researcher tested Partial mediation, no mediation and indirect effect-nested
models by assuming partial mediation model to be correct. Chi-squre difference
between partial meadition model and indirect effect model (CMIN=150.878, df=9,
240
p=.000) and difference between partial meditaion model with no meditaion mdel
(CMIN=72,067, df=5, p=.000) was statistically significant (AMOS different model
comparison out put depicts as an Appendix). Therefore, partial mediation model was
the more acceptable model than the indirect effect and no mediation models.
Comparison of Bottom-up and Top-down Models
Apart from the model fit indices, Akaike Information Criterion (AIC) and Bayes
Information Criterion (BIC) index can be used to compare non-hireachical models
and non-nested models with different number of parameters to estimate with same
set of data (Schumacker & Lomex, 2004; Kline, 2011). In AMOS, AIC is calculated
by χ2 + 2q, where q is the number of estimated parameters in the model. However,
according to Kline (2011), AIC can be calculated using χ2 – 2df. When comparing
the two models, the model with lower AIC value is considered as the better-fit model
(Hu & Bentler, 1999).
The researcher omitted the marital status the top-down model, due to the model
estimation limitations in AMOS with the dichtonomus exogenous variable.
Therefore, comparison of bottom-up model with marital status with the top-down
model without marital status can be influenced by the number of free parameters
(and degree of freedom) of that model and hence, on the AIC. In order to avoid the
influence of extra variable in the bottom-up model on the AIC, apart from the
original modified bottom-up model, the researcher tested a new bottom-up model
excluding the marital status. Selected model indices for the three models are
presented in Table 70.
241
Table 70: Bottom-up and top-Down Model Comparison
Model
NPAR
CMIN
DF
P
RMSEA
AIC(q)
BIC AIC (df)
Bottom Up –
without MS
47
8.117
8
.422
.009
102.117 257.138
-7.887
Bottom up
With MS
57
6.337
9
.706
.000
123.281 321.181
-11.663
Top Down
29
41.508
37
.281
.025
99.508 195.159
-16.492
According to the model fit indices presented in Table 70, bottom-up model without
marital status (χ2=8.117, df=8, p=.422, RMSEA=.009), bottom-up model with MS
(χ2=6.337, df=9, p=.706, RMSEA=.000) and top-down model (χ2=41.508, df=37,
p=.281, RMSEA=.025) satisfy with the minimum cut-off criteria of each model fit
indices.
However, these indices cannot be used to compare the non-nested
competing models. Therefore, the models need to be compared using AIC (and BIC)
indices (Bandalos D. Personal Communication. 14th May 2012). In modified bottomup model with out MS, AIC index was 102.117 (BIC=257.238, AIC (Df) = -7.887).
In modified bottom-up model with marital status, AIC index was 123.281
(BIC=321.181, AIC(Df) = -11.663). Whereas in modified top-down model AIC
index was 99.508 (BIC=195.159, AIC (Df) = -16.492). Therefore, top-down model is
the relatively better-fit model than the bottom-up models with and without marital
status.
Summary
This chapter explained the data analysis and statistical findings of the study. First, the
descriptiove data analysis of the profiling variables of the study explained the
demographic, psychological, technical and social profiles of the grassroots level
inventors in Sri Lanka. Sencondly, descriptive and correlational analaysis of the
objective and subjective success explain the nature and the positive relationship
242
between the objective success and subjective success of grassroots level inventors in
Sri Lanka. Finally, the analysis of bottom-up and top-down path models explained
the factors that have bottom-up, top-down or two-way influence on the objective
success and the subjective success of the grassroots level inventors in Sri Lanka.
Chapter 5 will interpret and discuss the findings of the present study.
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CHAPTER 5
DISCUSSION
The specific objectives of the present study were to answer the research questions,
which were based on the research problem of the study. The researcher successfully
achieved all the stated objectives of the study. Subsequent to the data analysis and
obtaining the results, the researcher is required to explain and discuss the meanings
of the results of study to answer the research questions (Hess, 2004). This section of
the study discusses the major findings of the study based on the following subsections that are aligned with the chronological order of the main research objectives
of the study.
1. Explanation of the Sri Lankan grassroots level inventors through the selected
demographic, psychological, technical and social factor profiles.
2. Exploration of the nature of the objective and subjective success of Sri Lankan
grassroots level inventors.
3. Determination of the influences of selected demographic, psychological, technical
and social domain factors on the objective success and subjective success of
grassroots level inventors in Sri Lanka.
4. Determination of the influences of the subjective success on objective success and
selected demographic, psychological, technical and social domain factors of
grassroots level inventors in Sri Lanka.
Who are the Grassroots Level Inventors?
The present study provides pioneering explanations and framework of the grassroots
level inventors in developing country like Sri Lanka towards think beyond the
narrow definitions of inventors as the novel utilization of indigenous knowledge and
grassroots inventors in rural communities. Therefore, in the first place, present study
244
has ended the long-standing drought of empirical studies on patent applied grassroots
level inventors in a developing country.
According to the Wieck and Eakin (2005), there were hardly any studies on
technological inventors in developing countries and therefore, the recognizing
grassroots level inventors in developing countries has not been easier as the inventors
in industrial countries. Wieck and Eaken (2005) mentioned that owing to the
significance of the independent inventions in developing countries, they need to be
identified as a major element of the technological development efforts in developing
countries. However, there was a conceptual disagreement between developed and
developing country literature and practice on defining the grassroots level inventive
community. There were no clear indicators to identify either the grassroots invention
or grassroots level inventors. The majority of the available studies in developing
countries have focused on novel utilization of indigenous knowledge (Sen, 2005),
community inventors in rural and marginalized communities who are trying to
overcome their day-today problems with primitive inventions of their own (Gupta, et
al., 2003) and users as the inventors (Lettl, 2005). Therefore, the process of
identifying grassroots inventions and inventors in communities has been the most
exhausting process in the innovation development process in developing countries
(Wettansinha, Wongtschowski, & Waters-Bayers, 2008). According to the Diyamett
& Mabala, (2007) poor attention given to the informal inventors who invent
patentable inventions in developing countries is one of the reason for their lower
techchnological development (Diyamett & Mabala, 2007).
In the literature review of the present study, the researcher explained the conceptual
differences between grassroots inventors that have been defined in developing
245
countries and independent inventors defined in western literature (Ch. 2, Pages 2225). The present study provided a formal definition to recognize the grassroots level
inventors in developing countries and comprehensively explored the demographic,
psychological, technical and social domain factors of grassroots level inventors in Sri
Lanka. In the present study, grassroots level inventor is defined as a “local individual
of a country, who involves in patentable inventive activities and trying to obtain
patents for himself, for his own reasons and own rewards out of the formal
organizational structures such as firms, universities and research laboratories”. This
definition provides the universal benchmark characteristics for defining the
grassroots level inventors. Furthermore, it provides patent application register as
central location to find grassroots level inventors. It would avoid the exhausting
fieldwork for searching grassroots level inventors. Therefore, the definition of
grassroots level inventors suggested by the study provides more focused and
systematic way of identifying and locating the grassroots level inventive community
in a developing country like Sri Lanka. Whereas, this definition links the grassroots
level inventors to the mainstream technological innovation of a country as its lower
layer of the inventive community. It gives national level importance and recognition
to the grassroots level inventors as the technological knowledge creators.
Demographic profile of grassroots level inventors
The study revealed that the average grassroots level inventor in Sri Lanka is an
educated, married, middle-aged male who lived in rural area of an urban district.
Then again, four out of five inventors were part time inventors and majority has
received medium level income. These findings are similar to the findings of the
previous studies on independent inventors conducted in industrial countries. Winston
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(1937) in USA, MacDonald (1986) in Australia, Sirilli (1987) in Italy, Amesse et al.
(1990) in Canada, Whalley (1992) in USA, Wieck and Eakin (2005) again in USA,
Giuri, et al.(2007) in Europe and Georgia Tech Enterprise Innovation Institute (2007)
in Georgia have revealed that, average independent inventor is middle aged,
educated, employed, married male who has middle level income. Hence, grassroots
level inventors in Sri Lanka are also showing similar demographic profile of the
western independent inventors.
The consistency of the results of the present study and past western studies has
indicated the universally similar demographic profiles among the grassroots level
inventors. It suggests that there are common demographic characteristics among the
inventors that would stimulate their creative and inventive skills in any favorable or
unfavorable conditions. Therefore, in general, the grassroots level inventors belong
to a common demographic profile, no matter where they reside whether in developed
or developing countries. Hence, the grassroots level inventors in Sri Lanka should
also be treated as equal members of the global grassroots level inventive community.
The major difference between the demographic profiles of past studies and the
present study was the geographical locations of the inventors. According to past
studies, average inventors live in metropolitan areas in the industrial countries. Most
of the western countries have defined the urban and rural areas basis on the
population density, but Sri Lanka has defined the rural and urban areas based on the
nature of the governing council than the population density (United Nations, 2007).
Therefore, in political sense the majority of the grassroots inventors in Sri Lanka are
rural based inventors. However, the results of the present study indicate that the
majority of the grassroots level inventors have pooled in districts that have high
247
population density. Therefore, in principle, the finding on geographical location of
grassroots level inventors in Sri Lanka still aligns with the western studies that had
indicated higher number of independent inventions in metropolitan areas (Carlino,
Charttergee, & Hunt, 2007; Sonn & Storper, 2008). This finding also indicates that
the urbanization and urban issues might have higher influence on people to be
involved the inventions than the rural problems in rural areas. This might be owing to
the higher level of access to the information and availability of other basic
ingredients for technological inventions in urban areas of the country.
Psychological profile of grassroots level inventors
In the present study, grassroots level inventors’ inventive career satisfaction (ICS),
maximizing tendency and life orientation were measured to understand their
psychological characteristics. The results of the study revealed that a majority of the
grassroots level inventors in Sri Lanka are highly optimistic and highly satisfied
inventors who have moderate level maximizing-tendency. Owing to these
characteristics, grassroots level inventors were able to tolerate the negative or
unsatisfactory objective outcomes of their inventive lives. Their high level of
satisfaction with inventive life, optimism about the future and a moderate need for
maximum results guided them to see something good in their existing status of
inventive life and make them feel something good will happen in the future. This
seems to be a significant factor in the continuous increase of independent patent
applications in Sri Lanka during the last decade.
Unlike demographic profile surveys, there was a very limited number of past studies
that had examined the inventors’ psychological characteristics. However, available
248
past studies have indicated the high level of inventive career satisfaction, optimism
and maximizing tendency among the inventors in the western countries. Rossman
(1931) have found that the top two motives of the inventors as love of inventing and
desire to improve. Therefore, there was a natural tendency among the inventors to be
maximizers, optimistics and satisfied with their inventive careers even in the early
twentith century. Futhermore, Astebro (2003) and Amodza, (2004) had revealed that
even experts say stop, majority of the independent inventors continually improve
their inventions, owing to their extreme optimism and risk seeking nature. The term
risk seeking indicates the desire to achieve higher results at serious risk. Therefore, it
shows high maximizing tendency towards inventive outcomes among the Canadian
inventors. Extension of their study has further identified that inventors are over
confident and optimistic than the general population in Canada (Astebro, Jeffrey, &
Adomdza, 2007). Wieck & Martin (2006) also revealed that modern independent
inventors in USA also show high optimism on the future. On the whole, it can be
concluded that the Sri Lankan grassroots level inventors are also satisfied, optimistic
risk seekers. Therefore, the finding of the present study suggest very close
psychological similarity between the grassroots inventors in Sri Lanka and the
independent inventors in industrial countries such as Canada.
Even though past studies suggest high level of maximizing tendency, a majority of
the grassroots level inventors in the present study have shown moderate maximizing
tendency. The difference between the finding of present and past studies on
maximizing tendency might be a result of the differences in measurements between
the studies. Astebro et al. (2007) measured the invention specific risk taking and
therefore, measured specific type of maximizing tendency. The present study
249
measured the global or general maximizing tendency of the inventors. Then again,
during the post-survey panel discussions of the present study, a majority of the
inventors said that they are satisfied with what they have achieved from their
inventions. They have not expected to earn huge money from their inventions, but
they like to see their inventions give benefit to the society (Wickramasinghe, 2010).
Their commercial objectives were much more socialistic than the inventors’
objectives in the industrial countries and therefore that might have impact on their
medium level maximizing tendency. Further, specific social and cultural background
of Sri Lanka might have some influence on these differences.
Technical profile of grassroots level inventors
In the present study, grassroots inventors’ technical profile was examined by the type
of invention, field of invention, inventive life span, engagement in invention, internet
usage, number of working prototypes and commercialization efforts. The study
found that the average Sri Lankan grassroot level inventor is an immature radical
product inventor mainly in the fields of agricultural, environmental, medical,
industrial equipments and household equipments. A majority of the inventors have
only one or two significant inventions and mostly tried to commercialize their
invention on their own. However, one-third of the inventors have not even tried to
commercialize their inventions. Then again, even though the internet is a very good
medium of gaining knowledge and sharing information, one-forth of the inventors
has shown low-level internet usage, where the majority showed only moderate level
internet usage.
250
According to the results of the study, more than one-half of the respondents had only
less than three years of post-patent application experience as inventors. It clearly
indicates the immaturity and the growing nature of the grassroots level inventive
community in recent years. Further, the findings indicated that a significant number
of the grassroots level inventors have engaged in inventing activities as part-time
inventors and in expanded fields of inventions. This indicates the tendency of the
continuous expansions of both depth and the breadth of grassroots level inventive
community in Sri Lanka.
Several past studies that had investigated the demographic profiles of inventors also
examined the technical profiles of the inventors in industrial countries. However,
none had examined all the aspects of technical profile of inventors in a single study.
Meyer (2005) and Dahlin et al. (2004) found that majority of the independent
inventors were radical inventors. Studies of Astebro (2003), Georgia Tech (2007)
and Weick and Eakin (2005) have examined the field of invention and found that
household and consumer equipments, environment and energy, automotive and
medical devices were among the top inventive fields among independent inventors.
However, agricultural inventions had not among the most popular fields. This may
be the result of the influence of the industrial development of those countries.
Historically, Sri Lanka has been an agriculture-based economy. Hence, the grassroots
level inventions have natural tendency towards finding solutions to the technical
issues in agro industry.
The findings of inventive lifespan of the grassroots level inventors in Sri Lanka align
with the Wieck and Eakin (2005) on full time and part time independent inventors.
251
They found that majority of the independent inventors had less than five years of
post patent inventive life. Meanwhile, Whalley (1992), Weick and Eakin (2005)
have examined the number of working prototypes developed by the independent
inventors. Whalley (1992) had found that there were very limited number of
inventors that have developed final level prototypes of their inventions. However,
Weick and Eakin have revealed that more than one half of the inventors have at least
one working prototypes. Compared to earlier studies, Sri Lankan Grassroots level
inventors have higher number of working prototypes. Sri Lanka Intellectual Property
Office normally requests the inventors to provide working prototypes during the
patent examination process. Therefore, inventors have a tendency to develop
working skretches of their inventions at the eairly stage of their invention process.
Commercialization methods among the Sri Lankan Grassroots level inventors have
shown the similar trend as the industrial country inventors. Studies of Whalley
(1992), Parker, Udell, & Blades (1996) and Wieck and Eakin (2005) revealed that
nearly one-third of the inventors have never tried to take any of their invention to the
market. Then again, according to their studies, a majority of the inventors wanted to
commercailize their inventions by their own. The present study also revealed the
similar pattern, where the 34% of the inventors never tried any of the
commercialization method and the majority who wanted to commercailize their
inventions have tried to do it by their own. A subtantial number of intentional noncommercialized inventions indicate that unlike employeed inventors, grassroots level
inventors might not going after the commercialization full heatedly.
252
Ibrahim & Fallah (2005) mentioned that the internet is one of the major sources for
the inventors to get inventive ideas. Georgia Tech (2007) identified that internet is
one of the top three resource source among the georgian inventors. However,
compared to those studies, internet usage among Sri Lankan inventors was lower
than the expected level. According to the explanation of the inventors, even though
they use public internet access to communicate with others through e-mails, more
than one-half were not using internet for patent search, information or knowledge
search in their inventive activities. This is mainly, owing to the lack of awareness
and limited internet access available to the inventors. Owing to the lack of usage of
internet,
their
awareness
and
knowledge
about
existing
inventions
and
commercialized innovations were subtantially lower. This might be a reason for their
high involvement in radical inventions than the incremental inventions, because they
are mostly trying to start from scratch than using available knowledge and
information of existing products or processes as part of their inventions. McDermott
(1999) have said that ICT is not good enough for knowledge management. Even the
knowledge workers of world largest companies who invested large sums of money
on ICT have used their ICT platforms mainly to e-mail to others (McDermott, 1999).
Hence, internet based ICT have not been able to actively contribute to the knowledge
creation rather than information sharing among the known people. According to the
Internet prenatration data, Sri Lanka have very low rating compared to industrial
countries. Only 8.3% of the population have internet access ( Miniwatts Marketing
Group, 2010). Further, most of the inventors mentioned that they do not have internet
connection at home, but very keen to have and learn how to use it in their inventive
activities (Wickramasinghe, 2010). Therefore limited internet access of the general
population is also reflected among the grassroots level inventors in Sri Lanka.
253
As the conclusion of the technical profile, it can be said that similar to the
demographic and psychological profiles, grassroots level inventors in Sri Lanka are
also having technological profile that is largely similar to the industrial independent
inventors. Significant differences between the two communities have based on the
fields of inventions and internet usages, which are fundamentally rooted in the
inherent technological divide between the developing and industrial countries, rather
than the differences among inventors.
Social profiles of grassroots level inventors
Historically, invention has been identified as a part of a psychological process.
However, inventors and their inventions have been largely influenced by the
assistance and demands of different stakeholders in the society (Carlson, 1991;
Merton, 1938; LeFevre, 1987; Meyer, 2005). The present study investigated the
nature of social factors that can affect the grassroots inventors from three aspects:
external linkages, social sapital and community connectedness. The overall findings
on social factors suggest that the average grassroots level inventor in Sri Lanka has
received only marginal level of positive external support and has received social
resources from relatively weak ties of their social relationships. Then again, even
though grassroots level inventors have not physically attached to each other, they
have emotionally attached to the inventive community.
According to the results of the study, external support received by the grassroots
inventors in Sri Lanka was very low. Nine out of ten inventors received low-level
external support for their inventing, patenting and commercialization activities.
Therefore, the existing external organizational, structural, institutional and expert
254
systems have not encouraged and hence, have neglected the grassroots level
inventors. Inventors in Sri Lanka have been exhausted with the struggle to get the
required support from the external expert entities and personnel in Sri Lanka.
However, according to the literature, the strength of the external support received by
the independent inventors in industrial countries was relatively higher. In Georgia,
there were relatively high external linkages and external support received by the
independent inventors (Georgia Tech Enterprise innovation Institute, 2008). The
increasing numbers, importance and easier access to intermediate inventor support
organizations have further strengthen the external support received by the
independent inventors in industrial countries (Hoppe & Ozdenoren, 2005; Svensson,
2007). However, there is no such urge in Sri Lanka to support grassroots level
inventors. The small number of inventors, high level of risk of outcomes of the
inventions, lack of public awareness about local inventors and lack of published
inventors’ success stories might be the factors that made external entities not to be
very cooperative with grassroots level inventors in Sri Lanka.
The negative impact of unsatisfactory external linkages has been further degenerated
by the lack of explicit collaboration or association between the grassroots inventors
in Sri Lanka. According to the results of the study, grassroots level inventors in Sri
Lanka have high emotional and cognitive community connectedness.
However,
according to the comments of the respondents, there were no signs of explicitly
active collaboration among the inventors in Sri Lanka. Even though there are
effective independent inventors associations, clubs and collaborations in the
industrial countries such as Inventors Association of Australia, Inventors’
Association of Georgia, and Inventors’ Association of New England, there was no
255
such national level association for the grassroots level inventors in Sri Lanka. Hence,
the grassroots level inventors in Sri Lanka do not have common platform or
association among themselves. Therefore, they are working as scattered lone
inventors. According to the comments of the respondents, in Sri Lanka, there was no
explicit collaboration between the inventors and they are desperate to having such
collaboration (Wickramasinghe, 2010). This desperate need is explicitly seen by the
findings of the community connectedness among the grassroots level inventors.
Therefore, this high community connectedness can be a stepping-stone to build a
stronger inventive community association in Sri Lanka to help inventors by
themselves.
Granovetter’s work on social relationships has explained the importance of weak ties
of the social relationships (Granovetter M. S., 1973; Granovetter M. , 1983). His
study explained the potential of the weak ties to enable the reach to inaccessible
populations and audiences that are unable to access by strong ties. A Study by Levin
and Cross (2004) on knowledge transfer also confirmed the power of the weak ties of
social relationships on knowledge transfer (Levin & Cross, 2004). Medium and low
level scores of social capital indicates that the majority of grassroots level inventors
in Sri Lanka do not have strong social capital to receive the required social resources
and they received them only through the weak ties of their social relationships.
Therefore, the importance of weak ties such as unknown officials and distanced
nodes of social relationships (Example: friend’s friends) is relatively higher than the
strong ties such as family members, relatives and even friends. Hence, the mutual
trust within the society have playing key role in individual social capital of the
inventors. This finding is complies with the Granovetter (1983) and Levin and Cross
256
(2004) findings. Therefore, it can be concluded that in general, weakly tied social
relationships are more important and more powerful than the strong ties among the
grassroots level inventors in Sri Lanka.
As a conclusion to the discussion on social factors, it can be said that among the four
factor profiles studied, social factors seem to be the most unfavorable factors which
have wided the gap between inventors in Sri Lanka and other industrial countries.
Lower external support and lack of explicit collaboration and resource generation
through weak ties have forced the grassroots level inventors to be isolated in
inventive activities.
As an overall conclusion on the discussion of the profiling factors of the grassroots
level inventors, it can be said that demographic, technical, psychological profiles of
grassroots level inventors in Sri Lanka are largely look-alike with independent
inventors in the industrial countries. Only the technical and social factors such as
higher internet usage, higher external linkages through intermediates and explicit
community connectedness achieved through inventors’ associations and clubs make
the industrial country independent inventors more socially supported, socially strong
and competitive than the grassroots level inventors in Sri Lanka. Furthermore, the
findings of profiling factors supports the researcher’s criticism over the narrow scope
of the definitions of grassroots inventors that generally defines the grassroots
inventors as members of rural communities those who have utilized primitive or
traditional knowledge in their daily lives in developing countries (Hanna, 2010;
Gupta, et al., 2003). These narrow definitions have ignored the real inventors in
developing countries those who share the similar characteristics of the inventors in
257
most industrially advanced countries. This ignorance of the local inventors might be
one of the significant factors for the existing technological disadvantage of
developing countries. Most of the industrial countries have developed their
technological competences on the shoulders of grassroots level inventors such as
Edison, Tesla or Graham bell who had started their inventive careers in their garage
laboratories. Owing to the grassroots level inventors in Sri Lanka and inventors in
industrial countries are look-alike, they might also have the capability to flying high
in the emerging knowledge economy. Therefore, by removing the unfavorable
discrimination of grassroots level inventors and enhancing the external social and
structural conditions that support the grassroots level inventing, Sri Lanka would be
able to strengthen the technological competitiveness of the grassroots level inventors.
Objective Success of Grassroots Level Inventors in Sri Lanka
Objective
success
of
grassroots
level
inventors
was
measured
by
the
cumulative index of the explicit achievements of grassroots level inventors at
different stages of innovation process. As far as the index considers at least one
outcome as an indication of success at each stage of the invention process, it actually
measured the minimum threshold level of the objective success. According to the
findings of the study, a majority of the respondents have achieved medium or lower
levels of overall objective success. If the success of invention considers as the
profitable commercialization of patented inventions, to be successful, inventor need
to achieve high level of objective success. However, out of 200 respondents only 55
respondents (27%) have achieved the high-level objective success. The finding
indicates the difficulties faced by the majority of grassroots level inventors in Sri
258
Lanka to take at least one of the inventions through the invention process towards the
higher ends of the objective success.
Meanwhile, owing to the novelty of the composite index, the researcher is unable to
compare the present findings with the previous studies. However, the studies in
western countries have analyzed most of the sub-components of the objective success
index separately. According to the Rossman (1931) and Meyer (2005), inventors not
always look for financial benefits from their inventions and they want to seek the
non-financial benefits of inventing as well. According to the Rossman, this need was
at the top of the independent inventors’ priority list than the financial benefits. In the
present study, patent grants and awards or reward winnings measured the nonfinancial objective success of grassroots level inventors. According to the results of
present study, every eight-out of-ten respondent grassroots level inventors in Sri
Lanka have at least one patent at the time of survey. The average number of patents
per respondent was 1.52. Whalley (1992) revealed that the average number of patents
among the US independent inventors was 3.4 per inventor, but only one-third of
inventors have at least one patent. MacDonald (1986) found in Australia, average
number of patents was 1.96 per inventor and only 37% have at least single patent. In
Canada, average number of patents was 2.2 per inventor and 55.4% inventors had at
least one patent (Amesse & Desranleau, 1991). Compared to the findings of present
study, previous studies have shown higher average patents among the inventors in
the USA, Canada and Australia. However, compared to those countries, higher
percentage of grassroots level inventors in Sri Lanka have at least single patent. Even
though, that might suggest a superior technical merit of Sri Lankan Grassroots level
inventors, differences in respondents, number of inventors, patent applications and
259
patent examine procedures between these countries might be the significant factors
that made much of difference between these countries. Evaluations of such factors
are beyond the scope of this study and future research can explore the impact of such
factors on objective success of grassroots level inventors in the developing and
industrial countries.
Historically, awards and rewards received by the inventors have been one of the
ways of assessing their success. Before the introduction of patent system, inventors
had been acknowledged by the imperial awards and rewards (Scotchmer, 2004). This
tradition is continuing even in present societies at a lesser extent. Apart from the
Nobel price, there are large numbers of inventors’ exhibitions and competitions
taking place in both industrial and developing countries. In Sri Lanka there are
annual national, regional, district based inventors’ competitions to evaluate the best
inventions
and
inventors.
However,
none
of
the
previous
studies
had
comprehensively investigated the number of awards and rewards won by the
independent inventors in industrial countries. Western studies are concerned more on
Nobel Prize winners than the independent inventors (Stephan & Levin, 1993).
Therefore, the researcher was unable to compare the real technical merits of the
grassroots level inventors in Sri Lanka with other countries.
According to the
findings of the present study, 3/5 of the respondents did not have any award or
reward winnings inventions. That indicates the majority of grassroots level
inventions are not technically or industrially significant. However, this conclusion
needs to be discounted for the respondents’ comments on lower participation and
barriers for participation in local and especially foreign inventors’ competitions.
According to the findings of the present study, majority of the inventors are
260
immature inventors who had less experience as inventors. However, other than junior
level inventors’ competitions, most of the invention competitions are open to all
immature, growing and mature inventors. According to the comments made by the
majority of immature inventors, they were always falling behind the matured
inventors those who have experience on how the competitions evaluate the
inventions (Wickramasinghe, 2010).
Commercialization and profitability indices of inventions have been the popular
bottom line inventive success measures in the past studies. According to the findings,
of the present study, every two out of three grassroots level inventors in Sri Lanka
took at least one of their inventions to the market by any means. Even though they
have more than one invention, on average, grassroots level inventor has taken only
one invention to the market. However, only one out of three inventors has at least
one invention that survived in the market and the same number of inventors has at
least single invention that earned profits. According to the Whalley (1992), in USA,
only 29% of the inventors had taken their inventions to the market. Astebro (2003)
found that only 7% to 9% of independent inventions reach to the market and from
that more than 60% suffered loss and median invention has earned only negative
income. A study on Georgia’s inventors revealed that only 40% of the inventions
have been taken to the market and only 31.4% has achieved profits (Georgia Tech
Enterprise innovation Institute, 2008). Amesse et al. (1991) revealed that 48.3% of
the inventions have taken to the market and only 28.6% has earned profits. Weick
and Eakin (2005) revealed that even though 39% of the inventors have
commercialized their inventions and only 22% of the inventors have earned profits.
However, most recent statistics on independent inventions in industrial countries
261
revealed that generally, only one out of ten inventions entered to the market and from
that less than 7% actually earn profits (Invention statistics, 2008). Compared to the
previous studies, Sri Lankan grassroots level inventors have shown higher market
entering success than industrial counterparts. However, survival and profitability
wise, the inventors in industrial countries were marginally ahead than the Sri Lankan
inventors. Even though, there are inventors’ protection and assistance programs and
policies in industrial countries, Sri Lanka does not have active inventor protection
and supporting programs or policies to help the inventors at the commercialization
stage. According to the inventors’ comments at the post survey panel discussions,
owing to uncontrolled cheep imported products, financial entry barriers and lack of
financial assistance, they are facing problems in marketing their inventions
(Wickramasinghe, 2010).
In general, grassroots level inventors in Sri Lanka have acceptable level of technical
merits and technical success. In addition, they have entered the market more often
than inventors in other countries have. However, they were unable to earn profit and
hence, survive in the market for a long time. Therefore, Sri Lankan grassroots level
inventors are not successful especially in the back-end innovation activities. Backend innovation activities consists of activities that are relate to the development of
marketable products, commercialization and marketing the products (Coates, 2009).
That indicates, even though Sri Lankan grassroots level inventors are capable in
inventing and entering to the market, they might not have specialized entrepreneurial
and commercialization resources, knowledge, skills and support that required to be
succeeded in marketing their invention to achieve higher objective success. In recent
literature, the internet has been recognized as supporting platform of the back end
262
innovation activities (Sawhney, Verona, & Prandelli, 2005). However, the grassroots
level inventors have only moderate level Internet usage. This moderate level Internet
usage might also contributed to the low knowledge, information and skills at the
back-end innovation activities of the grassroots level inventors in Sri Lanka.
Subjective Success of Grassroots Level Inventors in Sri Lanka
The present study substantially deviates from the previous studies on inventors by
measuring the subjective aspect of success of the grassroots level inventors. Even
though way back in 1931, Rossman had revealed that majority of inventors are
motivated by the subjective incentives of inventions (Rossman, 1931), none of the
past studies including Rossman have tried to empircally measure the subjective
success of the grassroots level inventors. To the extent that there was no published
literature on global subjective success of the inventors, the present study provides
some pioneering findings on subjective success and their two facets subjective
happiness and subjective satisfaction of life of the grassroots level inventors.
A study conducted by Lynne & Steel (2006) using general national level samples
found that the subjective happiness among Sri Lankans was very high (7.34) as
developed countries such as the USA (7.75), Canada (7.25), Australia (7.57) and
England (7.14). Therefore, Sri Lanka is generally considered as a country with high
subjective happiness. However, Diener and Seligman (2009) have explained that
different social groups in the society have their own structure of subjective life. Even
though the indices used in Lynne and Steel (2006) and the present study are not
identical, path analysis results of the present study suggest that grassroots level
inventors’ inventive life activities and outcomes have influenced on their subjective
263
assessments of life. According to the findings of the study, every one out of four
respondents has shown a high-level of subjective success. Further, every nine out of
ten respondents have shown a medium or high level of subjective success. Hence, the
majority of the grassroots level inventors have perceived upper moderate and high
level of subjective success (M=41.1, SD=7.05). Even though they were going
through hardships in their objective inventive lives, they are generally happy and
satisfied with their overall lives. Consequently, the level of frustration, anxiety and
regret of the grassroots level inventors was lower than the general expectation.
Specific positive and negative effects of their inventive lives have influenced on their
level of subjective success. Hence, the findings of the present study indicate that the
level of subjective success of grassroots level inventors in Sri Lanka was slightly
lesser. The results of the present study highlight the importance of studying the niche
communities like grassroots level inventors those who have different sources of
subjective success at micro level than the generalized macro level of the society as
suggested by the Diener & Seligman (2009).
In previous studies on subjective success, happiness and satisfaction, there had been
debates on how to define the concepts of subjective well-being, happiness and
satisfaction (Diener, 2009 b). Some authors argued that these are similar concepts
and some said they are related but independent concepts. When examining how the
two facets; happiness and satisfaction determine the subjective success, the
researcher revealed that the subjective happiness has almost identical trend as the
composite value of subjective success. The results of the present study has also
shown that even though emotional (Happiness) and cognitive (Satisfaction)
evaluations shows the similar pattern, at specific level, happiness level is not exactly
same as the satisfaction level. The subjective satisfaction with life of the respondents
264
was highly concentrated at medium level. Only 3% of the respondents had high
subjective satisfaction of their lives. This indicates that even though the respondents
were emotionally at upper moderate to high level of success, at cognitive level they
just achieve only moderate level satisfaction with their lives. Compared with the
sixty-nine respondents who have shown high level of subjective happiness, only five
respondents have achieved high level of satisfaction with life. Therefore, the density
of the subjective success has been discounted by the different levels of the
respondents’ subjective happiness and satisfaction with life. According to the
theories of happiness, happiness represents the relatively short-term emotional
evaluation of most recent events and incidences of the life. The results indicate the
majority of the grassroots level inventors in Sri Lanka were having moderate and
high level of emotional success of their lives. Therefore, the findings of the present
study suggest that the subjective satisfaction with life need not to demonstrate the
similar level as either subjective happiness or the subjective success. However, they
have analogous trends towards identical direction. As far as the subjective success
shows the average value of subjective happiness and satisfaction with life, it is the
better indicator of the general subjective success or well-being of life.
Relationship between Objective and Subjective Success
There have been arguments over the coexistence of objective and subjective success;
whether objective success influences to achieve the subjective success or subjective
success influences to achieve the objective success? It was like a “Chicken and egg”
story (Hall & Chandler, 2005; Nicholson & Andrews, 2005; Achor, 2010; Diener E. ,
2009 a). During the qualitative pilot study, the respondents were asked to explain,
how they feel about their success. Majority of the informants have stressed their
265
critical objective achievements or failures when describing the assessment on their
success. Most of the inventors, who were happy and satisfied, have been thinking
about further improvements of their inventions and new inventions planning to do in
the future. Then again, most of the inventors who were not happy, tend to gave up
their inventive activities and did not like to be involved in inventive activities in the
future (Wickramasinghe, 2009). Therefore, the researcher found that, there is
possible coexistence between the objective and subjective success, and they have
influenced each other. Inventors who have achieved high objective success tend to
achieve happiness and satisfaction in life. Then again, inventors who are happy and
satisfied with their lives, tend to achieve higher objective success as well.
Owing to the theoretical and empirical contradictions of the relationship between
objective and subjective success, the researcher analyzed the relationship between
objective and subjective success at three different levels. At categorical levels (low,
medium and high) by using Chi-square estimations in the cross tabulation, at
bivariate level with Pearson product movement correlation and finally at multivariate
level using the path analysis. The association between the frequencies of respondents
at low, medium and high levels of objective and subjective success is significant and
it has shown medium to high effect size (Fisher’s χ2 = 23.823, Df=4, p=.000,
CV=.232, W=.328). Meanwhile, the correlation between objective and subjective
success scores has shown significant positive relationship (R=.341, P<.01). These
two results ensured that there is a relationship between the objective success and
subjective success among the respondent grassroots level inventors. However, those
findings are not good enough to detect the causal directions of the relationship.
266
The path analysis results revealed that there is a dual-causal directional effect
between objective and subjective success. From the casual direction point of view,
findings of the study indicate the validity of both the Emmons’s goal attainment
theory and Fredrickson’s Broaden-and-Build theory among the grassroots level
inventors in Sri Lanka. Therefore, combined discussion of the validity of bottom up
theories and top down theories is the better approach to address the subjective
success. However, the findings show that the strength of the influence of subjective
success on objective success (β =.341, p<.05) was higher than the influence of
objective success on subjective success (β =.170, p<.05). This effect size differences
of the influences of the bottom-up and top-down relationships of the present study
agrees with the Diener and Seligman (2009) argument that happiness and satisfaction
of life lead to higher productivity and performance in life than the performance leads
to happiness and satisfaction (Diener & Seligman, 2009). Hence, the subjective
success of the grassroots level inventors is a significant factor that makes them to be
involved in inventive activities to achieve better objective success in the future.
Even though the findings of the study support the existing literature on the subjective
success having higher influence on achieving objective success, the arguments made
by the recently published studies such as Anchor (2010) “Happiness causes success
and achievement, not the opposite” is questioned by the findings of the present study.
Anchor’s study was conducted with sample of employees from high profiled
multinational companies in USA and the argument was based on the findings of
Lyubomirsky, King, & Diener (2005) Meta analysis study and Staw, Sutton, &
Pelled (1994) study on positive emotions and outcomes of work place. However,
Anchor’s study originally conceptualized the reversal causation as happiness or
267
positive emotions leads to objective success, but it had not studied both the top-down
causality and bottom-up causality. In oder to determine the actual causaulity,
different top-down and bottom-up models need to be tested and compared to get
meaningful casual deirections (Hox & Bechger, 1998; Norman & Streiner, 2003).
Finding of present study partially agrees with the results of the Meta analysis of cross
sectional, longitudinal and experiments studies on reversal causation conducted by
the Lyubomirsky et al (2005), but still there are significant evidences that show
objective success also leads to subjective success. This can be mainly due to the
grassroots level inventors’ individual involvement in resource generation, planning,
organizing, implementing and controlling of the innovation process, which require
significant amount of financial and physical resources to make their inventive ideas
to reality. Therefore, objective achievements might give them explicit positive
reinforcement and confidence over their inventive activities to be happy and satisfied
with their efforts.
Furthermore, according to the comments of the inventors during the panel
discussions, they were not getting the anticipated governmental and social support to
gain the required resources. Some of the inventors had devoted their entire careers to
invention and sacrificed all the things they had, but had not achieved significant
objective outcomes. Therefore, their inventive activities have continued with the
constraints, frustrations and de-motivations. Because of that, even minor level of
objective success has significant influence on their subjective success. Based on the
Diener and Seligman (2009) general explanations on objective achievements and
their influences on subjective success, whatever objective achievement of inventions
might give the grassroots level inventors the means of achieving respect, source of
268
engagement, challenge and meaning of the inventions. This may lead them to
achieve higher subjective success and engage in inventive activities with higher
commitment and enthusiasm. Eventually it might also lead them to achieve high
objective success. In conclusion, it is viable to say that, both objective and subjective
success are integral aspects of success of the grassroots level inventors in Sri Lanka.
Inventors who achieve higher objective success can achieve high subjective success
and inventors who achieve higher subjective success can achieve much higher
objective success. These interchangeable effects of subjective and objective success
drive the grassroots level inventors to continue the inventive activities, even the
environment become hostile.
Factors Influencing the Objective Success of Grassroots Level Inventors
The study analyzed four categories of life domain factors, namely demographic,
psychological, technical and social factors of grassroots level inventors in Sri Lanka.
Among the profiling variables, age has shown significant relationship with objective
success (Fisher’s χ2 = 19.995, Df=10, p=.002, CV= 232, W= .323). Finding indicates
that compared to adolescents and old aged inventors, middle-aged inventors have
achieved high level of objective success. Then again, among the young to late
middle-aged inventors, average success level was highest among the middle-aged
inventors. This finding agrees with the general theories and past studies on age and
achievements, those had said that people achieve greater success in their middle ages
and positive relationship between age and achievements (Simonton, 1988; Jones,
2010; Lehman, 2006). According to the respondents’ responses, majority of the
adolescents were full time students and older inventors were retirees. Both groups are
269
actually the financial dependents, who have been lacking financial resources.
Therefore, naturally, the financial dependence limits the high cost of improvements
of the grassroots level inventions and the reluctance of the inventors to take their
inventions to the commercial level (Whalley, 1992). Then again, the adolescents
have to give priority to their education than searching for the commercial success of
their inventions. A majority of the late young, middle and late middle-aged inventors
are part-time employed inventors, they have higher financial independence, have
more resources to improve their inventions and take their inventions to marketable
levels that leads to the achievement of back-end inventive success.
Commercialization effort also has shown significant relationship with objective
success. (Fisher’s χ2 = 64.743, Df=8, p=.000, CV= .367, W= .519). According to the
findings of the previous studies, the grassroots level inventors naturally favor to
commercialize their inventions through their own ventures (Weick & Eakin, 2005;
Georgia Tech Enterprise innovation Institute, 2008). Significant association between
commercialization effort and objective success with large effect size found in the
present study also provides some evidences to justify this relationship. The results
indicate the high level of objective success among the inventors who tried to produce
and sell their inventions by their own. Just as among the entrepreneurs, strong selfefficacy, need for autonomy and independence in decision-making
(Markman,
Balkin, & Baron, 2002; Licht & Siegel, 2006) might be the reasons for the natural
tendency among the grassroots level inventors to establish their own ventures to
commercialize their inventions. Owing to the majority of inventors are part time
inventors, their primary employments might also expose the inventors to different
270
experiences and situations (Hellmann, 2002). It might also have increased their
knowledge of running their own business.
The job mobility showed significant mean difference of objective success (F=3.505,
p=.032). However, the association between two was significant only at .1 level (χ2 =
8.540, Df=4, p=.074). The results indicate that inventors those who had high job
mobility achieved less objective success than the inventors with low job mobility.
The experience and knowledge gained through different work situations could have
influenced the inventors to be more proactive in their inventive activities and achieve
higher objective success. However, higher job mobility might not give chance to the
inventors to gain enough exposure in single work situation to gain the required
experience and knowledge that might help to be successful inventors (Stair & Stair,
2006). Then again, higher job mobility can cause stress or dissatisfaction (Mats &
Kerstin, 2009). Hence, the pre-and-post job mobility mental situations might require
the inventors to stay away from their part time inventive activities or not to give
significant attention to their inventions. These might be the reasons why those
inventors with higher job mobility had achieved relatively lower objective success as
inventors.
The profiling factors like location, employment status, educational qualifications,
invention types, field of inventions, inventive life span have not shown significant
relationship with the objective success. Therefore, other than age, commercialization
effort and job mobility all the other profile factors of the study did not make any
impact or difference of the objective success of the grassroots level inventors.
271
Path analysis results revealed that only income (β=.21, p=.000), engagement in
invention (β=.31, p=.000) and external linkages (β=.35, p=.000) have statistically
significant positive influence on the objective success. None of the other
demographic, psychological, technical and social factors have influence on the
objective success. Meanwhile, engagement in invention (daily inventive hours) and
external linkages had higher effect sizes than the income. The findings are very much
at par with the findings of the previous studies on independent inventors. Arthur
(1991) stated that academic qualifications or computer usage do not guarantee the
commercial success of inventors, but fulltime involvement (time spend on inventive
activities) is necessary to achieve success (Arthur, 1991). According to Whalley
(1992), time, external support and income were the major resources that independent
inventors really want and unfortunately, they were really lacking these resources. In
Georgia, there was a sizable relationship between inventors’ income, external
resources usage and their commercial success (Georgia Tech Enterprise innovation
Institute, 2008). The findings of the study confirm the Whalley’s, Georgia tech’s and
Arthur’s comments on the factors effect on the success of independent inventors.
Hence, the findings suggest the universal importance of these three factors for the
objective success of the grassroots level inventors.
How to make ICT as a mean of disseminating knowledge is a concern of the
knowledge economy (McDermott, 1999). Even though there was a excitement on the
impact of Internet on technology development in developing countries, the findings
of the present study do not support that argument. According to the path analysis
results, Internet usage has not significantly influenced the objective success of
grassroots level inventors in Sri Lanka. It seems that even though the Internet has
272
provided large array of information, there is deficiencies in utilizing these
information to create knowledge and help to achieve the success. This finding agrees
with the explanations of McDermott (1991) on the inadequacy of considering ICT as
a knowledge management tool. According to him, apart from the access, rational
utilization of ICT in knowledge creation needs to be improved. The findings of the
present study suggest that even though there is moderate level Internet usage among
the grassroots level inventors, still there is significant gap between inventors’
Internet usage as information and communication medium to create knowledge. The
comments made by the respondents at the discussions also suggested that the
majority of them have no internet connections at their homes and they have no
knowledge on how to search the patent and innovation information on the Internet
(Wickramasinghe, 2010). They used Internet largely to communicate with others
through e-mails. The results indicate the impact of internet on inventive success
artificially inflated hype than the real situation in the developing country like Sri
Lanka. Therefore, the lack of internet access, information searching and knowledge
divide in Sri Lanka might still be the valid reasons for the low impact of internet
usage on the objective success of grassroots level inventors.
Although the career satisfaction, life orientation and maximizing tendency have
shown impact on objective achievements of various groups in the previous studies,
the present study identified none of these factors have significant influence on the
objective success of grassroots level inventors in Sri Lanka. Audia and Goncalo
(2007) have found that past successes and experiences have significant infleunce on
the present decision making of the inventors. Optimism does not always promote
adaptive behavior, but sometimes it can even be detrimental. As per Sholey et al.
273
(2002), unrealistic optimism about the future, sometimes negatively correlated with
achievement leads people to live with risk behavior (Shorey, Snyder, Rand, &
Hockemeyer, 2002). According to the comments made by the majority of
respondents, in general grassroots level inventors in Sri Lanka have not experienced
substantial level technological development and commercial success to generate over
estimated positive expectations on objective success. Hence, the inventing process
rather than the outcome of the process drive their inventive career satisfaction. Even
though the history of the intellectual property protection had began in 1860, more
localized national intellectual property office in Sri Lanka was established in 1982,
after the introduction of open economic policy in 1979. Therefore, even the current
generation of grassroots level inventors in Sri Lanka is still going through the stage
where they are just looking to establish as technological inventors by removing the
contextual barriers that made their inventions not valuable and marketable in Sri
Lanka. Therefore, uncontrollable hostile commercial environment, immaturity and
not exposed to substantial level of past success might have diluted the influence of
psychological factors on their objective success.
Community connectedness and social capital also not showed significant influence
on objective success. Unlike industrial countries, there was no platform for
collaboration among the grassroots level inventors in Sri Lanka. The finding of the
community connectedness indicates need to overcome the physically scattered and
individualistic nature of the grassroots level inventors community in Sri Lanka. Even
though they are emotionally attached to each other, in practical sense there was no
commitment among inventors to support each other. Whereas, the findings of the
study indicate that, the majority of the inventors receive social resources from the
274
weak ties rather than the strong ties of their social relationships outside the inventive
community. Even though, the inventors received social resources from relatively
weak ties, the general lackness of mutual trust within the society on individual than a
community may have influenced the insignificant influence of social capital on the
objective success of grassroots level inventors. Therefore, existing community
connectedness and social relationships were unable to generate sufficient resources
they require to achieve the objective success. Hence, the existing level of social
capital has no strength to contribute to the grassroots level inventors’ objective
success. This seems to be the reason why the path analysis does not show significant
relationship between community connectedness, social capital and objective success.
According to the comments made by the grassroots level inventors at the panel
discussions, there is a desperate need for forming a common platform that would
allow the convergence of grassroots level inventors in Sri Lanka to build stronger
ties. If they can improve the opportunities for active community connectedness and
stronger social capital, that would influence access to more resources and higher
level of success.
Inventors’ demographic and technical profiles have been extensively studied in the
western countries (Amesse & Desranleau, 1991; Macdonald S. , 1986; Wieck &
Martin, 2006; Weick & Eakin, 2005; Georgia Tech Enterprise innovation Institute,
2008). These studies have provided insights to understand what grassroots level
community looks like; what are the commonalities or differences between the
inventors in different countries and continents. However, the findings of the present
study have questioned the value of overemphasis on profiling factors of inventors in
the past studies. As a conclusion to the factors influencing objective success, the
275
researcher argues that even though past studies on independent inventors had
extensively studied demographic and technical profiles of inventors, a majority of
these factors have not significantly influenced the objective success of the Sri
Lankan grassroots level inventors. Even though they are valuable to explain the
nature of the grassroots level inventive community, those factors have not influenced
the success and achievement of inventors. As far as the inventors are not successful
in back-end inventive processes, objective success might also be influenced by
entrepreneurial, managerial skills of the inventors and market related contextual
factors in Sri Lanka. However, measuring those influences are beyond the scope of
the present study.
Factors Influencing the Subjective Success of Grassroots Level Inventors
The researcher already discussed the significant influence of the objective success on
the subjective success. According to the results, objective success has statistically
significant influence on the subjective success of the inventors (β=.17,p<.05).
However the effect size or the density of the effect is only ranged between low to
medium (β =.17). This indicates that the objective success have somewhat lesser
influence on the subjective success of life of the grassroots level inventors in Sri
Lanka.
Further, the profile variables such as age, location, education, employment status, job
mobility, type and field of invention, commercialization effort and inventive life
have not shown significant influence on the subjective success. Hence, the inherent
profiling factors do not make any difference to the subjective success of inventors.
However, the path analysis results found that marital status (β =.13, p<.05), internet
276
usage (β=.18, p<.05), inventive career satisfaction (β=.26, p<.01), life orientation
(β=.19, p=.01), social capital (β=.16, p<.05) and community connectedness (β=.23,
p<.01) had significant positive direct influences on the subjective success. These
findings are on par with the findings of past studies. Analysis of marital status across
the different cultures by Diener et al. (2000) and Meta analysis of studies by Hidore
et al. (1985) have shown that marital status is a influential factor on subjective
success. Kraut et al. (2002), Contarello & Sarrica (2007) and Jackson et al. (2004)
indicated the emerging impact of internet on subjective well-being of the people.
Sparks et al (2005), Diener (2009 a), Argyle and Martin (1991) revealed that there is
positive correlation with work domain, job satisfaction and subjective success.
Furthermore, a series of studies found that there is a positive influnce of life
orientation (optimism) on the subjective success (well-being) of people (Carver C.
S., 2004; Carver, Smith, Antoni, Petronis, Weiss, & Derhagopian, 2005; Carver,
Scheier, & Segerstrom, Optimism, 2010). A number of prior studies supported the
findings by declaring social capital as a significant contributor of the subjective
success (Yip, Subramanian, Mitchell, Lee, Wang, & Kawachi, 2007; Cheung &
Chan, 2008; Helliwell & Putnam, 2004; Kroll, 2010). Then again, community
connectedness has also been identified as a predictor of subjective success (Helliwell
J. F., 2003; Helliwell & Putnam, 2004; Winkelmann, 2009; Helliwell J. F., 2007).
Hence, marital status, internet usage, inventive career satisfaction, life orientation,
social capital and community connectedness have influence on subjective success
among different populations. Therefore, they can be considered as the significant
general factors that influence on the subjective success of the grassroots level
inventive community in Sri Lanka.
277
Following the recently emerged arguments over the diminishing influence of income
on subjective success (Diener E. , 2009 a), the present study was also unable to
explain significant direct influence of income on subjective success (β=.03, p≥.05).
Meanwhile, the external linkages also had no significant direct influence on the
subjective success of the grassroots level inventors (β=.04, p≥.05). That means the
inventors who had satisfactory external linkages and inventors who had no
satisfactory external linkages share almost similar or mixed level of subjective
success. Therefore, the external linkages do not significantly matter to the subjective
success. The result was the same with the direct influence of engagement in
inventions on the subjective success (β=.10, p≥.05). However, analysis on indirect
effects and mediation effect of income (β=.036, p =.001), engagement in inventions
(β=.059, p=.001) and external linkages (β=.52, p=.001) had shown statistically
significant non-zero influence on subjective success through the mediation effect of
the objective success. Cummins (2000) stated that factors such as income could give
access to factors that make people happy. When income can provide finance to
develop the inventions, external linkages and engagement in inventive activities can
provide other resources that have significant direct influence on the objective success
of inventors. Hence, as the income, these two factors also behave as the resourcegenerating factors in invention process, which make inventors happy and satisfied as
subsequent to the objective achievements.
Then again, inventors’ maximizing or satisfying personality traits was not the
significant factor that matters to determine the subjective success of grassroots level
inventors at 0.05 significant level (β=.10, p=.060). Therefore, individual personality
differences do not have significant effects on the inventors’ subjective success. This
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finding contradicts the past study results that suggested a significant negative
influence on the subjective success. Majority of the studies found that maximizing
tendency have negative effects on the subjective success (Schwartz, Ward,
Monterosso, Lyubomirsky, White, & Lehman, 2002; Lyenger, Wells, & Schwartz,
2006). However, according to the frequency analysis of the present study, the
majority of the respondents had moderate level maximizing tendency. Therefore,
they are not belongs to either extreme maximizing or extreme satisfying categories.
Hence, majority of the respondents are in the middle-of-the road, which might make
them to be neutral about the outcomes of their life events and activities. According to
the comments made by the inventors, they are not much concern about the outcome
of their inventive activities until they reach to the status they believe they can
achieve that. The inventors who are engaged in inventions have thrilled by the
progressive development of their inventions, and therefore becoming more and more
attached to the inventive activities and feel happy (Wickramasinghe, 2009;
Wickramasinghe, 2010). On the other hand, the grassroots level inventors are
largely engaged in developing radical inventions (Dahlin, Taylor, & Fichman, 2004).
Therefore, knowledge about what would be the maximum outcome and what would
be the optimal decision might not be predictable. They need to keep on developing
their inventions until their desired invention come out. As Edison stated, inventors
consider failures as just target practices and part of learning how to do it and not to
do it. Therefore, moderate level maximizing tendency and uncertainty about the
future might have neutralized the extreme attachment of desired outcomes of their
inventions.
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Veenhoven (2008) predicted the possible individual and societal influences on
subjective success and therefore defined the subjective success as part of social
process. According to the findings of the study, there are individual, psychological,
technical and social influences on the subjective success of the grassroots level
inventors in Sri Lanka. Subjective success is not a pure static trait of mind. The states
of individual, personality/psychological, work related/technical and social factors
have undeniable influence on decides it. As the finale of the discussion of the factors
influencing the subjective success it can be summarized that among the demographic
factors only marital status, among the technical factors only the internet usage,
among the psychological factors inventive career satisfaction, life-orientation and
among social factors, social capital and community connectedness have significant
direct influence on subjective success. Even though, the maximizing tendency not
statistically significant, the nature of its influence has shown the unique
characteristics of inventors. Then again, the income, engagement in inventions and
external linkages have significant indirect effects through the mediation effect on
objective success. Therefore, all the exogenous variables in the final model directly
or indirectly influence to the subjective success of grassroots level inventors in Sri
Lanka. Therefore, the findings of the study validated the existence of bottom-up
causation of subjective success.
Impact of Objective Success on D. P. T.S Factors of Grassroots Level Inventors
The results of the top-down path analysis indicated that the inventors’ achievements
of high-level objective success positively influenced on them to earn higher income
(β=.20, p<.05), extra engagement in their inventive activities (β=.29, p<.01) and
having better external linkages (β=.31, p<.01). According to Arthur, inventors can
280
achieve optimum objective success only by successful commercialization of their
inventions (Arthur, 1991). Therefore, the commercialization naturally increases their
income levels, even though they might not achieve net profits. Not only income,
commercialization can bring the fame and social recognition to the inventors. As
Nikola Tesla quoted, inventors feel unimaginable feeling when they have seen their
inventions becoming successful and that thrill encourage them to be more engaged in
inventive activities. Past studies on inventors also revealed that inventors past
success have an impact on their present and future inventive activities (Audia &
Goncalo, 2007; Davis & Davis, 2007). A majority of the inventors in present study
also commented that when they contacted the external entities to get financial and
other resources to develop their inventions, external parties especially entities like
banks, requested evidences to prove their inventive success (Wickramasinghe, 2010).
Therefore, inventors who achieved past success tend to have higher acceptance by
the external parties. When combined the top down influence to the bottom up impact
of the income, engagement in inventions and external linkages on objective success,
it gives the impression that there is a cycling relationship between income,
engagement in inventions, external linkages and objective success. Higher income,
engagement in invention and external linkages lead to the achievement of higher
objective success and then the higher objective success leads to increase in income,
engagement and external linkages of inventors.
Unlike the statistically insignificant influence of maximizing tendency on the
objective success (β=-.03, p=.624), the results of the top-down model indicated
somewhat significant negative influence of objective success on maximizing
tendency (β= -.13, p=.075). This indicated that the inventors’ objective success
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achievements have made the inventors to be more realistic and slightly degraded
their unrealistic maximizing tendencies. During the panel discussions, immature
inventors those who were not commercialized their inventions tend to be more
critical about the government support, bank loans and public attitude over the local
products. However, matured inventors who had gone through the troubles of
commercializing their inventions were concentrated on only one or two inventions
and trying to move forward systematically. One inventor who had eight Sri Lankan
patents had tried to commercialize seven of his inventions. However, at the time of
survey, he concentrated on only the “high efficient paddy processing system”, which
has high market potential in agro industry (Wickramasinghe, 2010). Therefore, the
process that leads to the achievement of the objective success has slightly negative
effects on the maximizing tendency of inventors. However, owing to comparatively
lower effect sizes of both bottom-up and top-down models, in general in can
conclude that maximizing tendency appeared as relatively stable trait of the
inventors.
Then again, level objective success had only statistically insignificant negligible
negative impact on life-orientation (β=-.040,p=.566). The past studies have shown
inconclusive results on the influence of objective success on life orientation.
According to the literature, life-orientation (optimism) is relatively stable personality
characteristic of a person (Carver, Scheier, & Segerstrom, 2010). Therefore, it is not
significantly volatile to the external stimulus. When facing negative outcomes (low
objective success), life-orientation just regulates the optimistic person (inventor) to
strengthen him with expecting optimistic results in the future (Carver C. S., 2004).
Therefore, in most situations life-orientation would not be increased or decreased
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with external events and outcomes. As such, life-orientation neither can be
drastically increased nor decreased by the present external outcomes and events.
According to the results, objective success has not significant influence on the
internet usage, career satisfaction, social capital and community connectedness. Even
though, there is drastic Internet infrastructure development going on in Sri Lanka, in
general, the internet penetration rate is very low and majority of the citizens have no
access to the internet. Therefore, even where inventors wanted to get internet access
it has been difficult and costly project. Even they have access, their awareness and
usage of internet to gain knowledge and information of their inventive activities has
been lower. Therefore, potential impact of inventors’ achievements on internet usage
might not evitable among the grassroots level inventors in Sri Lanka.
In industrial relation literature, performance has been expected to influence job
satisfaction. However, grassroots level inventors are challenged themselves to make
breakthroughs rather than achieve the external outcomes (Dahlin, Taylor, &
Fichman, 2007). Therefore, they perceived that satisfaction with their work rather
than in the outcome. The greatest inventor of all time Thomas Edison once quoted
that “One might think that the money value of an invention constitutes its reward to
the man who loves his work. However, I continue to find my greatest pleasure, and
so my reward, in the work that precedes what the world calls success”. Insignificant
impact of objective success on inventive career satisfaction among grassroots level
inventors indicates the universal validity of Edison’s explanation of the inventors.
Most of the grassroots innovation promotion movements in developing countries
have ignored the patents applied inventors by assuming patent would provide
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monopolistic rights to the inventors to achieve commercial success that might work
against the other members of the community (Wettansinha, Wongtschowski, &
Waters-Bayers, 2008). The results of the present study indicates that objective
success of the grassroots level inventors in Sri Lanka have no significant impact on
the social capital and community connectedness. Therefore, the increase (or
decrease) of objective success have not negatively affected the inventors social
capital or his connectedness to the inventive community. This finding suggests that
even though the patent applied grassroots level inventors are getting monopolistic
rights for commercial exploitation of their inventions, their objective success has not
negatively influence on their connectedness towards the inventive community.
Hence, the ignorance of patent applied inventors from the innovation promotion
movements in developing countries seems to be based on false assumptions, which
were based on wrong contextual interpretations. Therefore, the findings of present
study suggest the importance of supporting patent applied grassroots inventors in
developing countries such as Sri Lanka.
In conclusion, the findings of the study indicate that only the factors that
providesignificant physical resources to the innovation process such as income, time
and external linkages are getting the significant paybacks from the objective success.
The achievement of objective success slightly makes the inventors more rational and
conservative towards what actually can be achieved.
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Impact of Subjective Success on D. P.T.S Factors of Grassroots Level Inventors
The findings of the top down impact of the subjective success on objective success of
the present study had been explained in earlier section of this chapter. As far as the
present study is a cross sectional study, it was not possible to determine the impact of
subjective success on marital status of the inventors. Findings of the top down model
indicates that, apart from the external linkages, subjective success has significantly
influenced to all the other selected demographic, technical, psychological and social
factors of the grassroots level inventors.
According to the results, the impact of the subjective success on inventive career
satisfaction (β=.44, p<.01) and community connectedness (β=.41,p<.01) have
relatively high effect sizes. While internet usage (β=.35, p<.01), life-orienation
(β=.37, p<.01) and social capital (β=.31, p<.01) have medium to high effect.
Compared to other factors, impact of subjective success on income (β=.16, p<.05),
engagement in inventive activities (β=.21, p<.05) and maxmizing tendency
(β=.24,p<.05) have shown relatively low level impact. Even though the significance
of the direct impact of subjective success on external linkages (β=.12, p<.1) is not as
higher as the other variables in the model, compared to bottom up influence, impact
of top down influence of subjective success on external linkages can be considered as
significant. Then again, the findings of the indirect impact of subjective success on
external linkages indicated significant indirect effect through the mediation effect of
objective success. Along with the external linkages (β=.107, p=.001), income
(β=.068,p=.003) and engagement in inventive activities (β =.099, p=.001) also had
significant indirect influence from the subjective success. Even though the indirect
impact of subjective success on maximizing tendency was not significant (β= -.044,
285
p=.064), it indicates the negative indirect impact of subjective success on
maximizing tendency.
The overall findings of the reversal model of this study implies the importance and
the power of the subjective success of the grassroots level inventors in Sri Lanka. It
has been a significant driving force of the every aspect of their lives and especially
the acievement of objective success of inventive activities. Findings revealed that
happiness and satisfaction with life largely influenced to satisfaction of being an
inventors, social capital and connectedness to the inventive community. Then again
happiness and satisfaction with life drives the grassroots level inventors to be
mentally strong self-belivers and positive about their future. Further subjective
success indirectly leads the inventors to establish strong external linkages.
Findings of the influence of subjective success on D.T.P.S factors of grassroots level
inventors in Sri Lanka indicates the sub-optimal status of the existing inventor
success measures that doninated by patents, commercialization and profits. The
impact of the subjective happiness and staifaction with life of the inventors have
much higher impact on all the life doimans of the inventors than the objective
success. Then subjective success has been more significant predictor of objective
success that contained patents, awards, commercialization and profits. Therefore,
continuous progress of grassroots level inventions in difficult environment seems to
be driven the subjective success rather than the objective success. Therefore
evaluating the grassroots level inventors by pure objective measures would not be the
optimal way of doing it.
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The findings of the impact of subjective success on the D.T.P.S factors are aligned
with the major theoretical groundings of the top down arguments of subjective
success. Both the attempt of the development of sociological theory of subjective
well being (Veenhoven, 2008) and Broaden-and-Build theory of positive emotions
(Fredrickson, 2004) argued that there are social and psychological consequences of
subjective success. Meta analysis of number of cross-sectional, longitudanal and
experiment top down causuality studies also indicated significant impact of
subjective success on selected life domains (Lyubomirsky, King, & Diener, 2005).
Therefore, the findings of the present study further confirm the existance of
significant consequnces of the happiness and satisfaction on different domains of
life. This finding suggest that the subjective success wich comprises subjective
happiness and statisfaction with life as a significant asset of the grassroots level
inventive community that influence all aspects of respondents lives. In the setting
traditional community development practice ignored the subjective success totally,
Assets Based Community Development (ABCD) has also not considered subjective
success as an asset of a community. The finding of the top-down model suggest new
insights to community development to think beyond the material or objective
deficiencies and efficiencies of communities, towards the subjective deficiencies and
efficiencies.
Summary
This chapter explained the results of the statistical findings of the present study.
First, it explored the demographic, technical, psychological and social profiles of
grassroots level inventors in Sri Lanka. According to the results, grassroots level
inventors in Sri Lanka largely shared the common characteristics of independent
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inventors explored in previous studies. The chapter also discussed the nature of
objective and subjective success of the grassroots level inventors. According to the
findings, even though the inventors achieved only moderate and low levels objective
success, they have achieved moderate and high levels of subjective success. The
results indicated that objective success and subjective success contribute to each
other, but influence of subjective success on objective success is relatively high. The
influences of demographic, technological, psychological and social factors on
objective and subjective success of grassroots level inventors and the findings of
reversal consequences of subjective success and objective success on demographic,
technical, psychological and social factors were presented and discussed in the
chapter.
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CHAPTER 6
SUMMARY, GENERAL CONCLUSION AND
RECOMMENDATIONS FOR FUTURE RESEARCH
Introduction
This chapter aims to conclude the study by summarizing the aim, research process
and findings of the study. This chapter will also explain the general conclusions and
prospective implications of the findings of the study. Finally, it will suggest
recommendations for the future studies based on the findings and conclusions of the
present study.
Summary of the Study
In this study grassroots level inventors are defined as the independent inventors who
applied patents for their own inventions. Even though, there is no significant increase
of success stories, in recent years there was steady increase of grassroots level
inventive activities in developing countries like Sri Lanka, while in industrial
countries they are shrinking (Weick & Eakin, 2005). Available innovation,
development and community development theories and approaches were unable to
explain the rationale for such unexpected growth of the grassroots level inventors in
developing countries. Recently emerged positive psychological bottom-up and topdown theories on subjective happiness and satisfaction with life have explained the
existence of hidden subjective aspect of success. This subjective success could have
influenced the grassroots level inventors in two ways to be involved in inventive
activities while they are not achieving objective success. However, the relationship
between objective and subjective success of inventors and their bottom-up or topdown influences on inventors’ life domains have never been explored in the previous
289
studies. The aim of the present study was to explore the demographic, psychological,
technical and social domain causes and consequences of objective and subjective
success of grassroots level inventors in Sri Lanka to understand, why these inventors
continually involve in inventive activities where the surroundings are becoming
hostile to achieve success in independent inventing. In order to achieve the aim of the
study, the researcher expected to answer four major research questions through the
six stated research objectives to
1. explain the selected demographic, psychological, technical and social factor profiles
of Sri Lankan grassroots level inventors.
2. explore the objective and subjective success of Sri Lankan grassroots level inventors.
3. determine the influences of selected demographic, psychological, technical and
social domain factors on objective and subjective success of grassroots level
inventors in Sri Lanka.
4. determine the influences of subjective success on objective success and selected
demographic, psychological, technical and social domain factors of grassroots level
inventors in Sri Lanka.
5. test the mediation effect of objective success on the life domain factors and
subjective success
6. dertermine which theoretical proposition of subjective success (bottom-up or topdown) is more appropriate to explain the relationship between domain factors and
success of grassroots level inventors in Sri Lanka
The study was primarily designed as exploratory correlational research and the data
collection instrument of the study was developed based on adapted scales, which
were developed, tested and validated in the previous empirical studies. After
290
developing the instrument, it was tested with a small sample of grassroots level
inventors in Sri Lanka for its reliability and validity evidences. After making the
modifications for the instrument based on the expert opinions, reliability analysis and
respondents’ comments, final survey was conducted with randomly selected 200
grassroots level inventors from the patent application register of Sri Lanka. The
researcher was able to achieve all the research objectives by analyzing the screened
data through several statistical tools: frequency analysis, mean comparison and path
analysis.
Summary of Findings of the Study
By achieving the stated research objectives, the researcher was able to answer the
specified research questions of the study. Findings of the statistical analysis of the
study have depicted and discussed thoroughly in the chapter 4. The following are the
summary of the key findings of the present study that answers the specific research
questions:-
Who are the grassroots level inventors?
In order to answer the research question, the researcher stated the first research
objective to explain the selected demographic, psychological, technical and social
factor profiles of Sri Lankan grassroots level inventors.
1. Demographic profile of the grassroots level inventors in Sri Lanka
i.
Grassroots level inventors in Sri Lanka largely belongs to the middle-aged group
with the average age of 42 years.
291
ii.
Grassroots level inventors in Sri Lanka are predominantly the males, where only
5% were female.
iii. Large number of Grassroots level inventors in Sri Lanka are married, where twothird of the inventors were married.
iv.
According to the political and administrative definition, majority of the grassroots
level inventors resides in rural areas; however, Population density wise majority of
inventors are living in metropolitan districts.
v.
Generally, the Grassroots level inventors in Sri Lanka are well-educated group.
vi.
Grassroots level inventors in Sri Lanka are largely the part time inventors.
vii.
Grassroots level inventors in Sri Lanka largely represent the middle-income group
of the country.
2. Psychological profile of the grassroots level inventors in Sri Lanka
i.
The grassroots level inventors in Sri Lanka are generally highly satisfied with
their inventive achievements, social recognition and inventive life.
ii.
The grassroots level inventors in Sri Lanka are generally moderate level
maximizers.
iii.
The grassroots level inventors in Sri Lanka are generally very optimistic about
their orientation towards the future.
292
3. Technical profile of the grassroots level inventors in Sri Lanka
i.
The grassroots level inventors in Sri Lanka are mostly the radical product
inventors.
ii.
Grassroots level inventors in Sri Lanka have mainly involved in agriculture,
environment or energy and household equipments inventions.
iii.
The grassroots level inventors in Sri Lanka are generally the immature inventors
with three years or less experience in inventing.
iv.
The grassroots level inventors in Sri Lanka generally develop at least one
working prototype of their inventions.
v.
Grassroots level inventors in Sri Lanka generally prefer to commercialize their
inventions by their own.
vi.
Grassroots level inventors in Sri Lanka are generally moderate level Internet
users.
4. Social profile of the grassroots level inventors in Sri Lanka
i.
Majority of the grassroots level inventors in Sri Lanka have received only lowlevel support and linkages from external entities and experts who could help
them in inventing process.
ii.
Grassroots level inventors in Sri Lanka generally have moderate level social
capital.
iii.
Grassroots level inventors’ social capital is largely dominated by the weak ties of
the social relationships.
iv.
Majority of the grassroots level inventors in Sri Lanka have high emotional
connection to the inventive community.
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What is the level of objective success achieved by the Sri Lankan
grassroots level inventors?
i.
Majority of the grassroots level inventors in Sri Lanka have achieved only
moderate or lower level of objective success.
ii.
Majority of the grassroots level inventors in Sri Lanka have received at least one
Sri Lankan patents. Average number of patents per grassroots level inventor in
Sri Lanka is 1.52
iii. Only one-third of the grassroots level inventors in Sri Lanka have received
either local or international award or reward for their inventions.
iv. More than one-half of the grassroots level inventors have started to
commercialize at least one of their inventions.
v.
Majority of the grassroots level inventors’ inventive products were unable to
survive in the market and earn positive net income.
vi. One-third of grassroots level inventors have never tried to commercialize their
inventions at all.
What is the level of subjective success achieved by the Sri Lankan
grassroots level inventors?
i.
Majority of the grassroots level inventors in Sri Lanka have achieved moderate or
high level of subjective success.
ii.
Majority of the grassroots level inventors in Sri Lanka have achieved moderate or
high level of subjective happiness.
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iii.
Majority of the grassroots level inventors in Sri Lanka have moderately satisfied
with their lives.
iv.
There is a statistically significant association between level of objective success
and subjective success among the grassroots level inventors in Sri Lanka.
How the demographic, psychological, technical and social life domain
factors can influence on the objective success of the Sri Lankan
grassroots level inventors?
i.
Middle-aged grassroots level inventors have achieved relatively higher objective
success than the young and older aged inventors have.
ii.
Income has significant positive influence on the objective success of the grassroots
level inventors.
iii. Type of the commercialization effort have influenced on the objective success of
the grassroots level inventors in Sri Lanka, where the respondents who tried to
commercialize their inventions by themselves have achieved higher objective
success.
iv.
Engagement in inventive activities or time allocation for the inventive activities
have positive influence on the objective success of grassroots level inventors in Sri
Lanka, where the inventors who had high daily inventive hours have achieved
higher objective success.
v.
External linkages have significant positive influence on the objective success of
the grassroots level inventors in Sri Lanka, where increase of the external linkages
have positive influence to the objective success.
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vi.
Demographic variables such as marital status, geographical location, education
level, and employment status have no significant influence on the objective
success of the grassroots level inventors in Sri Lanka.
vii.
Psychological factors such as inventive career satisfaction (ICS), Life-orientation
and maximizing tendency have no statistically significant influence on the
objective success of the grassroots level inventors in Sri Lanka.
viii. Technical factors such as type of invention, field of invention and inventive life
span or experience level as inventors and internet usage do not have significant
influence on the objective success of the grassroots level inventors in Sri Lanka.
ix. Social factors such as social capital and community connectedness do not have
significant influence on the objective success of the grassroots level inventors in
Sri Lanka.
How the demographic, psychological, technical, social life domain
factors and the objective success can influence on the subjective
success of the Sri Lankan grassroots level inventors?
i.
Marital status has significant positive influence on the subjective success of the
grassroots level inventors in Sri Lanka, where the married grassroots level
inventors have achieved higher subjective success.
ii.
Inventive career satisfaction has significant positive influence on the subjective
success of the grassroots level inventors in Sri Lanka.
iii.
Life orientation has significant positive influence on the subjective success of the
grassroots level inventors in Sri Lanka.
iv.
Internet usage has significant positive influence on the subjective success of the
grassroots level inventors in Sri Lanka.
296
v.
Social capital has significant positive influence on the subjective success of the
grassroots level inventors in Sri Lanka.
vi.
Community connectedness has significant positive influence on the subjective
success of the grassroots level inventors in Sri Lanka.
vii.
Objective success has significant positive influence on the subjective success of
the grassroots level inventors in Sri Lanka.
viii.
Even though the income, engagement inventive activities and external linkages
do not have significant direct influence on the subjective success, through the
objective success it has significant indirect positive influence on the subjective
success of the grassroots level inventors in Sri Lanka.
ix.
Influence of income, daily engagement in invention activities and external
linkages on the subjective success of the grassroots level inventors in Sri Lanka
has been significantly mediated by the objective success.
x.
Demographic factors such as age, location, educational qualifications,
employment status, and job mobility do not have significant influence on the
subjective success of the grassroots level inventors in Sri Lanka.
xi.
Maximizing tendency does not have significant direct or indirect influence on the
subjective success of the grassroots level inventors in Sri Lanka.
xii.
Technical
factors such as
types
of inventions,
field
of inventions,
commercialization effort and inventive life span do not have significant influence
on the subjective success of the grassroots level inventors in Sri Lanka.
297
How the subjective success can influence on the objective success and
demographic, psychological, technical and social life domain factors of
the grassroots level inventors?
i.
Subjective success has significant positive influence on objective success of the
grassroots level inventors in Sri Lanka. The strength of the influence is higher
than the bottom up influence of the objective success on the subjective success.
ii.
Subjective success has significant positive influence on the income of grassroots
level inventors.
iii.
Subjective success has significant positive influence on the inventive career
satisfaction of the grassroots level inventors in Sri Lanka.
iv.
Subjective success has significant positive influence on the life orientation of the
grassroots level inventors in Sri Lanka.
v.
Subjective success has significant positive influence on maximizing tendency.
vi.
Subjective success has significant positive influence on the Internet usage of the
grassroots level inventors in Sri Lanka.
vii.
Subjective success has significant positive influence on the daily engagement in
inventive activities.
viii.
Subjective success has significant positive influence on the external linkages of
the grassroots level inventors in Sri Lanka.
ix.
Subjective success has significant positive influence on the social capital of the
grassroots level inventors in Sri Lanka.
x.
Subjective success has significant positive influence on the community
connectedness of the grassroots level inventors in Sri Lanka.
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xi.
Influence of the subjective success on income, daily engagement in inventive
activities and external linkages has been significantly mediated by the objective
success.
xii.
The strengths of the coefficients of the significant influences of subjective
success on demographic, psychological, technical and social life domain
variables (top down model relationships) are higher than the influence of those
life domain factors on the subjective success (bottom up model relationships).
General Conclusions and Recommendations
Even though the technology innovations have significantly contributed to the recent
emergence of the developing countries, the local inventors in developing countries
have never been comprehensively studied
(Mahmood & Singh, 2003; Weick &
Eakin, 2005). Further, in modern world inventor’s success is defined based on the
explicit objective achievements. There was no considerable attention given to the
subjective nature of the success. Present study has ended the long-standing
deficiency of the empirical studies on the grassroots level inventive community in
the low and middle-income developing countries. First, the present study was able to
explain the grassroots level inventors in Sri Lanka by means of general explicit
factors such as demographic and technical profiles, as well as the complex implicit
factors such as psychological and social profiles. Second, the study was able to
explore the mutual influences between these factors and the underexplored twodimensions of the success of grassroots level inventive community in Sri Lanka. It
indicates that objective success and subjective success are interrelated but two
different facets of success and that are influenced by different set of variables.
Therefore, findings of the study explain a way to understand how the success been
299
achieved. To explain the overall success, both objective success and subjective
success need to be examine together, but as separate facets.
The present study was able to comprehensively explain the unexplored objective and
subjective community capacity and anatomy of the grassroots level inventive
community in Sri Lanka. According to the findings of the study, the average
grassroots level inventor in Sri Lanka is a middle-aged, married, male with middle
income and higher educational levels, who lives in rurally administrated area of a
highly populated metropolitan district in Sri Lanka. Psychologically, the average
grassroots level inventor in Sri Lanka is a highly optimistic, moderate level
maximizer who is highly satisfied with their inventive life. Technically, the average
inventor in Sri Lanka is radical product inventor those who engaged in inventive
activities as a part time career. Even though the Internet has promised knowledge
transfer among inventors, the Sri Lankan inventors are moderate internet users.
Socially, there is a high community connectedness and sense of community among
the grassroots level inventors, but owing to lower external linkages and weakly tied
moderate level social capital, they do not receive acceptable level assistance and
social support for their inventive activities. Hence, the grassroots level inventors are
socially marginalized members of inventive community in Sri Lanka.
Most of the demographic, psychological and technical characteristics of the
grassroots level inventors in Sri Lanka are identical with the characteristics of
independent inventors in the western industrial countries. Owing to the inherent
demographic, psychological and technical factor anatomy that are identical to
grassroots level inventors identified by previous studies, the finding suggests that the
grassroots level or independent inventors are typically look-alike wherever they
300
reside. Hence, the majority of the demographic, psychological and technical factors
explained in the present study have been the common universal anatomy of the
community of grassroots level inventors. This common anatomy of the inventors
suggests that the basic inherent characteristics within the Sri Lankan grassroots level
inventors have drives them to be continually involved in inventive activities. Even
though, the social and environmental factors are not identical and supporting as the
industrial countries, owing to their inherent traits, the grassroots level inventors will
not stop their inventive activities even in the unfavorable and unsupportive social
environment like Sri Lanka.
In existing innovation literature, objective achievements of inventors have been
recognized as the only measurements to evaluate the success of inventors
(Hauschildt, 1991). According to the findings, the majority of grassroots level
inventors in Sri Lanka have achieved only moderate and low level objective success.
Analysis of the sub-indicators of the objective success indicated that front-end
inventive process achievements such as idea generation, inventing, patenting and
developing prototypes have been high among the grassroots level inventors in Sri
Lanka. These indicators have shown the high inventive and industrial value of the
inventions made by the grassroots level inventors in Sri Lanka. Therefore, in frontend inventive activities, majority of the grassroots level inventors are successful.
The grassroots level inventors in Sri Lanka are not successful in the back-end
innovation activities, that mainly deal with activities relates to commercialization of
inventions. According to the findings, even though the majority of inventors have
taken their inventive product to the market, they were unable to be successful in long
301
run. Actually, the commercialization and marketing aspect of inventions are backend product development activities than the real inventive activities. In the
innovative organizations and institutions, these functions are executed by the
different departments who have expertise in commercialization, financing and
marketing. Owing to grassroots level inventors being independent inventors, they
need to perform the back-end innovation processes by them selves. However, owing
to the lack in specialized knowledge, skill and other external resources, they are not
doing well in back end invention process activities. According to the findings of the
study, the grassroots level inventors in Sri Lanka have to generate required
knowledge, skills and external resources through the weak ties of their social
relationships, weaker external linkages, and their moderate level Internet usage,
which are not very strong. Therefore, they are unable to receive essential knowledge,
skills, resources and committed external support for these back-end innovation
processes. The situation demands the government and policy level attention to help
the grassroots level inventors’ community for their back-end innovation activities.
Further, the findings of the present study have provided strong evidences on the
importance of understanding the financial, social support and time constraints as the
significant reasons, why the grassroots level inventors are not objectively successful.
The income, external linkages and engagement in inventions have been significant
predictors of the objective success. Hence, these factors actually provides
intermediatory resources to achieve objective success of inventive activities among
the grassroots level inventors in Sri Lanka. However, these are not higher level
factors among the grassroots level inventors in Sri Lanka. Therefore, by increasing
income levels, external linkages and time spending on inventive activities, inventors
302
would be able to increase the mass of required resources for their back-end inventive
activities to achieve higher objective success.
The findings on internet usage of the grassroots level inventors contribute to the
internet paradox. The Internet has been recognized as world largest information and
knowledge depository that can transfer knowledge from developed to developing
countries to succeeding in their technology development. The findings of the study
were unable to explain significant relationship between internet usage and objective
success of the grassroots level inventors. Even though, the
Internet has been
expected to be negative influence on the happiness and satisfaction of lives of the
societies, findings of the present study have support the emerging counter arguments
that says internet usage improve the social and psychological quality of life.
According to the findings, the subjective success of the inventors aligns with ex-post
rationalization of individuals that have been explained in positive psychology
theories. Even though the majority of grassroots level inventors in Sri Lanka have
achieved moderate and low level objective success, generally they have achieved
moderate and high level subjective success. In bottom up theoritical model, the lower
objective achievements could have contributed to the lower subjective success of the
grassroots level inventors. However, the majority of inventors have achieved at least
front-end inventive success. The findings indicate that the inventors have compared
their predefined desired outcomes with the actual outcomes and then they have
rationalized their subjective success based on the actual outcome rather than the
expected outcomes. Even though they are not commercially successful, independent
inventors have not felt that they are unsuccessful. Happiness and satisfaction of life
303
have been the positive experiences that were not influenced by the missed
opportunities of inventions. Even though inventors have achieved lower level
objective success, they are satisfied with their achievements and sensed the
happiness from available objective achievements. They positively rationalize their
actual outcome as “half-full” rather than negatively sensing it as “half-empty”. As
explained in the Emmons’s goal attainment theory, this optimistic approach to assess
the objective outcome has worked as a positive contributor of the overall subjective
success of the inventors.
According to the model fit indices of modified path model of the bottom-up
theoretical framework, which tested the causes of objective and subjective success of
the grassroots level inventors in Sri Lanka is statistically significant. Therefore, the
bottom up theoretical argument that consider the subjective success as the ultimate
aim of life is significantly supported by the findings of the present study. The model
fit indices of modified path model of the top-down theoretical model that has tested
the consequences of subjective success of the grassroots level inventors in Sri Lanka
is also statistically significant. The findings of the study indicate the validity of
Frederickson's’s broaden and build theory of positive feelings. Therefore, findings of
the study also agrees with the top down theoretical argument of happiness and
satisfaction have positive effects on every aspect of life. It suggests that subjective
success influence on the grassroots level inventors as outcome as well as the
powerful asset. The top down theoretical model of the study suggests that the
subjective success is also a powerful direct cause that is able to regulate the
demographic, psychological, technical and social life domain factors of the
grassroots level inventors in Sri Lanka. Generally the effect sizes of the top down
304
impact of the subjective success on objective success and all the domain factors are
relatively higher than the bottom-up impact of objective success and other domain
factors on the subjective success. It indicates that happy and satisfied inventors have
gain high capacity and internal resources to achieve higher success of their life.
The statistical significance of both bottom-up and top-down path models indicates
that, the subjective success has been a consequence as well as the cause of achieving
objective success and better life of the grassroots level inventors in Sri Lanka.
Consequently, bottom-up theories of subjective success and top-down theories of
subjective success are not opposing to each other. They are actually explaining the
two approaches of examining the stages of the process that explain how the
subjective success influence on life; as a cause and as a consequence. Therefore,
focus of one theoretical tradition would not explain the true nature of the subjective
success. For that reason, to understand the real functionality of subjective success on
different life domains factors need to be examined from both these approaches at the
same time. Hence, the results of the present study confirm the social causes and
consequnces of subjective success that suggested by the Veenhoven’s sociological
theory of subjective well-being.
Finally the researcher concludes that the commonality of demographic,
psychological and technical charasteristics, along with the significant relationship
between inventor’s inventive life elements and the subjective success are the two
most significant factors that can explain the continuous increase of grassroots level
inventors in Sri Lanka. Invention has been one of the significant life domains that
contribute to the happiness and satisfaction of the lives of the grassroots level
305
inventors. Inventive career satisfaction, engagement in inventive activities, external
linkages, sense of inventive community and objective success of inventive actvities
directly or indirectly contribute to their happiness and satisfaction of life. Then again,
the happiness and staisfaction with life drive them to be increasingly involved in
inventive activities, achieve higher objective success, external linkages, inventive
career satisfaction and sense of community. As far as inventive activities contribute
to their happiness and satisfaction of life they will continuely involve in inventive
activities, even the external environment become hostile than the present. It doesn't
matter what objective achievements are expected by the external world to be
considered as successful inventors, the bottom-up and top-down contributions of
their inventive life domains on their subjective happiness and satisfaction of life
would psychologically drive the grassroots level inventors to be involved in
inventive activities in future. Owing to the existing trend, the future value and
utilization of grassroots level innovation in emerging knowledge economies is
uncertain. However, as far as the existence of their inherent characteristics and
mutual influences between invetive life domain factors and happiness and
satisfaction of life, the grassroots level inventive community will continue inventive
activities in the world for a certain.
Implications of the Study
The present study aims to explore the demographic, psychological, technical and
social causes and consequences of objective and subjective success of grassroots
level inventors in Sri Lanka and to understand, why these inventors continually
involved in inventive activities where surroundings are becoming hostile to
independent inventing. Owing to the multi-disciplinary nature of the study, findings
306
can be deducted to the body of knowledge, policies and practices of different
disciplines.
Implications for the body of knowledge
The present study contributes to the body of knowledge of the community
development by providing novel approach to understand communities from inside
out. The existing community development approaches never recognized the
subjective success that comprises of happiness and satisfaction of life of the
community members as either an ultimate need or a powerful internal asset of the
communities. Existing approaches of community development have evaluated the
communities either based on the objective/material problems and unsatisfied needs
of a community (Need based approach) or available explicit skills, capacities,
resources and assets of a community (Assets based approach). Even though the assets
based approach have been identified as better approach of the two, both approaches
mainly focus on the external or explicit structures of the communities. None of these
approaches considers the significance of subjective success as a need (consequence)
or asset (cause) of a successful community. Findings of the present study have
revealed the significance of psychological assets such as subjective success as the
powerful internal asset (cause) and ultimate internal need (consequence) of the
grassroots level inventive community in Sri Lanka. Therefore, the subjective success
can be utilized as an approach to community development to help materially
marginalized communities to achieve, what they internally want from their
communities to live happy and satisfied lives.
307
The present study contributes to the body of knowledge of the emerging discipline of
knowledge management by introducing subjective happiness and satisfaction of life
as the measurements of tacit dimension of success of the grassroots level inventors.
In recent literature, readiness of a country to become a knowledge economy has been
evaluated based on the technological development that is largely measured by the
pure explicit objective measures in inventing, patenting and technological
commercialization. However, owing to the historical disadvantages of the lower and
middle-income countries on technological development, their innovations systems
have not been appreciated as the industrial countries. Especially, the countries with
high proportion of grassroots level inventors are severely penalized within the
existing explicit measurement models. The findings of the study have explained the
tacit dimension of the grassroots level inventive activities and have explored the
factors of controversial behavior of grassroots level inventors. The findings of the
present study have shown that the grassroots level inventors in Sri Lanka are largely
driven by the subjective success than the objective success. Therefore, evaluating
their success based on explicit objective success measures such as number of patents,
patent citations, commercialization and profitability is counterproductive. Findings
of the study suggest that subjective success as a measurement of the tacit dimension
of the success of the technological knowledge creators in modern knowledge
societies.
The present study contributes to the body of knowledge of the Innovation
Management by comprehensively explaining the demographic, psychological,
technical and social anatomy and subjective behavior of the grassroots level
inventors in a developing country.
So far, the studies on the grassroots level
308
inventors have focused on studying one or two domain factors such as demographic
and technical profiles of the inventors. Recent literature has shown that such narrow
and restricted research approaches do not explain the real nature of the phenomenon
in scientific research (Ioannidis, 2005; Kenny & McCoach, 2003). Therefore,
findings of one domain in a one sample may not comparable with the findings of
another domain in another sample. Therefore, available knowledge about the
comprehensive appearance of grassroots level or independent inventors is not very
extensive. This study has examined the grassroots level inventors from all four major
life domains. This approach gives an opportunity to understand the complete
anatomy of the grassroots level community in Sri Lanka. As far as none of the
published studies has examined all these aspect in single sample, the research design
adopted in the study provides comprehensive framework for studying the grassroots
level inventors in other countries as well.
In a broader sense, the present study contributes to the body of knowledge of the
sociological theory of subjective well-being by answering all the questions prompted
by the Veenhoven, (2008) as the four building blocks of sociological theory of
subjective well-being (instead of using the common term subjective well-being,
present study defined the same concept as subjective success). The present study
have explained the answers to the questions raised by Veehoven to develop
sociological theory of subjective success: what is subjective success, how people do
appraise how well they are, how subjective success to raised (what are the causes)
and whether subjective success should be raised (what are the consequnces). The
study found that even though the subjective success stems from the positive
psychology, it has remarkable impact on the specific behavior of the people as the
grassroots level inventors. Therefore, the impact of subjective success or commonly
309
known subjective well-being might be a siginificant concept in understanding the
behavior of the members of the society.
Last but not least, the present study contributes to the body of knowledge of positive
psychology by comprehensively explaining, how the bottom-up and top down
casuality of subjective success works among the grassroots level inventors. There are
bottom up theoritical school explains the subjective success as ultimate end, which is
influenced by other life domains and top down theoritical school which explains
subjective success as main contributor of other life domains. However, none of these
schools clearly explain which factors follows the bottom up argument and which
factors follows the top down argument. Therefore, there was a theoretical
requirements to understand which factors follows the bottom-up tradition and which
factors follows the top down and are there dual casual factors (Headey, Veenhoven,
& Weari, 2005). Comparison of bottom-up conceptual model and top-down reversal
models of present study was able to explain the which factors follows top down or
bottom up direction and what are follows the two way casuality.
Implication for the policy development
The findings of the study reveale the holistic understanding of the grassroots level
inventors that indicate the association between the policy issues in the fields of
community development, innovation management, information technology, and
patent systems. All these fields are the major players that are essential to develop
more localized successful innovation community in the developing countries.
310
In consequence of the rapid expansion of modernization and urbanization, traditional
geographically based communities have been diminishing from the societies and new
communities of professions, common interest and practices have been emerging
beyond the geographical locality (Hughes, Black, Kaldor, Bellamy, & Castle, 2007).
Therefore, building stronger communities can be achieved only by defining new
boundaries of the communities based on the emerging commonalities. The present
study provides evidences of the demographic, psychological and technical
similarities of the people who involved in the inventive activities in Sri Lanka.
Further, it shows that even though the inventors are physically disconnected from
each other, there is strong community connectedness among the inventors. It shows
that even though they are dispersed throughout the country, there is a high
willingness to develop them as a stronger inventive community in Sri Lanka.
Therefore, community development policy makers can use the findings of the present
study to develop policies to define new communities based on the hidden
commonalities in the modern society.
The present study comprehensively explained the nature of the objective and
subjective aspects of success and their interrelationship with each other. Hence, more
focused mechanism needs to be identified to empower the grassroots inventors to
invent what community needs and help them to commercialize their inventions based
on the established understanding of who are the grassroots level inventors and what
they want from inventing. It would give opportunity to the less innovative countries
to overcome lack of inventions and bridge the cognitive divide in the knowledge
economy. Understanding of grassroots sciences would be the appropriate macrolevel Knowledge Management practice that can give benefits to the less innovative
311
countries to re-establish the technical knowledge of grassroots inventors in national
innovation system to interrupt the continuity of the deepen in marginalization. The
findings of the study give opportunity to the policy makers to compare and contrast
the causes and consequences of inventive activities and success of grassroots level
inventors. It would allow them to develop policies to improve the standard of the
local inventors and inventions.
Unavailability of acceptable mechanism to identify the grassroots level inventors in
developing countries has been a serious problem for their local innovation
development efforts (Wettansinha, Wongtschowski, & Waters-Bayers, 2008). Hence,
the demographic, psychological, technical and social profile of the grassroots level
inventors, which were explained by the study would be one of the most
comprehensive frameworks to explain the grassroots level inventors in a low and
middle-income country. Even though the findings of the study explains the
grassroots level inventive community of Sri Lanka, in macro sense, the theoretical
framework adapted in the present study would be useful to explain the structures and
behaviors of local inventive communities in similar countries. By conducting an
annual survey on profiling the inventors as suggested by the study, policy makers
would be able to understand the short-term changes and long-term trend patterns of
grassroots inventions in Sri Lanka. It will provide wider information base for the
long-term technology development policies.
Internet has been identified as a tool that has made technological knowledge transfer
from developed to developing countries. Hence, most of the developing countries
have given serious attention to develop internet based information and
communication technologies to bridge the digital divide without concerning “for
312
what”. However, the present study found that internet usage among the grassroots
level inventors in Sri Lanka is at moderate level and there is no significant influence
of internet usage on objective success of the inventors. Hence, current Internet usage
might not have influence on the innovation development in Sri Lanka. Therefore,
technological knowledge transfer has not evitable in Sri Lanka as expected.
However, the findings of the study revealed that internet usage has been a significant
predictor of the happiness and satisfaction of life among the inventors. Therefore,
internet has been a significant contributor of the subjective quality of life of the
inventors. This suggests that inventors use internet as social communication medium
rather than technological knowledge source. The findings of the study suggest that
apart from the infrastructure development and improving the access, technical
awareness, technical and language skills need to use the Internet as a knowledge
source need to be improved among the inventors.
The present study found that the majority of the grassroots level inventors are
subjectively driven inventors. Majority of them are weak in back-end innovation
activities and external linkages are not satisfactory. Owing to these reasons, one-third
of the grassroots level inventors never try to commercialize at least one of their
inventions. Therefore, there might be high proportion of patents have become only
the concept patents and lapsed patents. These patents might be significantly useful,
could be improved and might leads to the future innovations. Owing to the restricted
disclosure and access to the patent information by the Sri Lanka intellectual property
office in Sri Lanka, potential investors, business community and general public who
might be interested in contributing to the back-end activities of these invention are
unable to get information about the significance of local inventions and inventors.
313
Therefore, policies need to be introduced to improve the dissemination of patent
information and local inventor to enhance the interaction between inventive
communities, business community, potential user community and public to establish
the external linkages to help the grassroots level inventors, especially for their backend inventive activities.
In general, the policies of community development need to have more holistic scope
than the current practice. It should not narrow down to the demand side issues of
rural villages and specific capacity building and empowerment effort in rural
communities. It should search for new common interests to build stronger
communities as suggested grassroots level inventive community by the present study.
Then again, the patent system needs to be more flexible, affordable and encouraging
the local requirements and the independent inventors. It should improve the
dissemination of local inventions. Further, ICT policy of the country should not be
just for the need to bridge the digital divide of grassroots inventors only, but it should
include the transfer of knowledge and awareness to utilize the available resources
and links to access the external resources for grassroots level innovation activities.
All these policy issues are inter-related and achievement of individual element does
not guarantee the achievement of desired objective or any other element. Therefore,
the entire system needs to be identified holistically rather than in isolation.
Finally, according to the existing development policies, subjective success has been
used as an underutilized indicator of quality of life. So far, objective measures such
as income level, education and health indicators have dominated as indicators of the
social development. The importance of subjective happiness and satisfaction of life is
314
progressively emerging as better indicator of social development. Findings of the
present study provide evidences on the influence of subjective success as a cause as
well as the consequence of the major life domains of people. Therefore, in
development policies, subjective success should be given higher priority than the
existing attention given on subjective aspects of life.
Implication to the practice
Findings of the study indicated that Sri Lankan independent inventors are welleducated, middle-aged male who live in urban and semi urban districts. These
findings suggested the demographical similarities among independent inventors in
both industrial countries and Sri Lanka. Even though, this finding is expected to be
further verified by using large sample surveys in other developing countries, expose
of the demographic similarity between western and eastern inventors would be a
significant starting point to convince the value of the independent inventors in
countries like Sri Lanka. It would increase their self-respect, value and social status
to be happy and satisfied about themselves as natives of the competitive inventive
community.
The study found that the subjective happiness and satisfaction largely depend on the
self-evaluation of the existing outcomes and future anticipations. Therefore, inventor
assessment programs in developing countries should not overemphasize on assessing
inventors based on pure objective measures such as number of patents, patent
citations, awards and rewards, commercialized inventions or profitability.
Overemphasis on these factors would create pessimistic thinking and uncertainty
among the inventors about their inventive lives and it would create extra burden on
315
the inventors. This might be counter-productive when the inventors give up inventive
activities or find much easier ways to achieve subjective success of life than been an
inventor. Therefore, independent inventors in developing countries should be
considered as national assets and should be evaluated in a more constructive way
than the destructive straightforward “Pass” or “Fail” binary type of evaluations.
There should be comprehensive reward system that can give psychological rewards
to the inventors at each stage of the innovation process. There should be different
layers of the rewards and awards system that can maximize the opportunities for
inventors to feel success. Sri Lankan presidential award is a good initiative, but it
need to increase the number of awards to encourage many inventors. Inventors need
to be given the recognition as significant contributors to the country. These
implications would provide positive seeds to the self-regulation process of the
independent inventors to be happy and satisfied with their inventive lives and
continually involved in technological inventions.
Even though, the community connectedness and social capital have not influenced on
the objective success of the grassroots level inventors, they have influenced on the
subjective success of them. Owing to the facts that subjective success have
significant influence on the objective achievements, building of strongly connected
inventive community would positively contribute to the success of grassroots level
inventors. Therefore, there should be a formal mechanism to get together the
dispersed inventors to common forum. National level inventors’ association,
grassroots level inventor’s forums and conferences might be possible options to
allow the grassroots level inventors to know each other and share their resources,
316
knowledge and experiences among themselves to be stronger self-sufficient inventive
community in Sri Lanka.
Past studies have indicated that there are high level of irrational expectations about
the inventions among grassroots level inventors and that leads them not to accept
opportunities that come to them. Present study found that, increase of objective
achievements somewhat reduced the maximizing tendency of the inventors.
Therefore, to make them rational about their inventions and market mechanisms,
they should be given the opportunities in commercialization effort. Inventor’s
competitions, trade fairs and investor forums would help their chances to be
successful in back-end inventive activities and it would be a learning process to
overcome their over optimism and irrational maximizing tendency to avoid the
irrational expectations from the inventions.
Finally yet importantly, there should be a formal mechanism to help inventors to take
their inventions to the local or foreign market. Even though the expansion of foreign
trade had promised technology transfer from developed to developing countries,
developing countries should carefully design their foreign trade policies.
Uncontrolled imports create uncertainty about the future of local inventors and
discourage the independent inventors. Even though the technological development
in Asia create an opportunity for South-South trade agreements, uncontrolled low
cost technical imports from countries like China, India and Taiwan need to be
regulated in less developed countries to encourage local inventions. Once the
independent inventors feel success and satisfy, it will allow less innovative
317
developing countries to develop their local innovations and become competitive
knowledge economies in the world.
Suggestions for the Future Research
Owing to the limited background information on independent inventors in
developing countries, the current study may be considered as one of the pioneering
attempts to study independent inventors and their subjective success in Asia. Future
studies are expected to conduct similar inquiries about the inventors to develop
comprehensive knowledge and understanding of the independent inventors and their
inventive activities in developing countries. Further, to generalize the findings of the
independent inventors and their subjective success, large-scale quantitative studies
on different samples need to be conducted by using same variables and also the other
possible demographic, psychological, technical and social domain factors other than
the variables identified by the present study. It is expected to increase the studies on
the subjective success of inventors in different level of developing countries in Asia,
and different continents using different types of samples of independent inventors. In
addition to inventive community, there is a possibility of studying other communities
using the theoretical framework developed by the present study to enhance the
understanding of how subjective happiness and satisfaction with life drive the
different communities for certain explicit behavioral patterns. Positive psychology
studies have already developed novel quantitative measurements to measure the
subjective well-being of people. Recent improvements in statistical methods such as
structural equation modeling, path analysis have made it possible to analyze complex
relationships with large number of variables. Most of the inherent restrictions over
sample size, variable types, stringent model fit indices and complexity of the
318
modeling have been significantly relaxed with the new developments of SEM
software packages. The present study never tested the possible influence among the
exogenous variables of the models and therefore, the models developed in the present
study are not necessarily the ultimate models that could be explained the reality at it
best. Therefore, by using new instruments and complex statistical models, future
researchers can explore more complexities in subjective success of independent
inventors in Sri Lanka and other communities around the world. It would increase
the understanding of relationship between objective success and subjective success
of independent inventors beyond the findings of the present study. Further, the
attention given to study the happiness and satisfaction of employees and workers in
organizational setting is not very satisfactory. In order to understand how subjective
success influence the performance of the employees, the researchers are expected to
extend the subjective success studies on them too.
“All humans seek one goal: Success or Happiness. The only way to
achieve true success is to express your-self completely in service to
society. First, have a definite, clear, practical ideal - a goal, an
objective. Second, have the necessary means to achieve it. Third,
adjust all your means to that end”
Aristotle (384-322 BC)
319
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APPENDICES
A: Data Collections and Results of the Pilot Studies
B: Data Collection Instrument
C: List of Expert Advisors
D: Personal Communication with Advisors
E: Power Analysis and Sample size Calculation
F: Exploratory Data Analysis
G: Path Analytic Equation Model
H: AMOS 18 Bottom-up model original result outputs
I: AMOS 18 Top-down model original result outputs
A : Data Collection and Results of the Pilot sudies
A.1 . Qualitative pilot study
Most of the available studies on grassroots level inventors are western studies and
they have focused only on objective aspects of inventive success. The researcher
was unable to locate any comprehensive study on grassroots level inventors in
developing country, Asia or in Sri Lankan context. Therefore, the prior knowledge of
the objective and subjective success of grassroots level inventors in developing
country have not been adequate to employ objectivist approach directly to measure
the objective and subjective success of independent inventors in Sri Lanka. Hence,
the researcher designed qualitative pilot study to get the initial understanding of the
objective and subjective success and their relationships within grassroots level
inventors in Sri Lanka.
i. Informants and Structure of the pilot study
The informants of the pilot study had received at least one patent in Sri Lanka and
received the President’s Award for their inventions at the annual Sri Lanka
Inventors’ Commission President Awards competition for local inventions. During
the latest award ceremony held in 2008, eight inventors were granted the President’s
Awards for their inventions invented in the years 2006 and 2007. The researcher
tried to contact all the award winners using the telephone numbers given in the
president awards 2008 report. However, two of the targeted informants were unable
to contact and six inventors were finally contacted for a semi-structured telephone
interviews conducted in the first week of May 2009. Each telephone interview was
designed for 20-30 minutes covering three basic segments. First segment of the
interview asked the basic demographic profile; age, educational qualification,
employment and living area of the informants. The second segment asked about the
status of their award winning inventions; patent status, commercialization and
reasons for its status. The third segment was designed as an open-ended question
asking about their perceived success as inventors. To assess the subjective success,
the respondents were asked twp questions: (1) Are you a happy person?; (2) Are you
satisfied with your life? Respondents were allowed to answer the questions freely to
explain their feelings, thoughts and reasons for their assessments for both questions.
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To avoid the chances of reporting errors and misinterpretation of the thoughts of the
respondents, the interviewer filled semi-structured questionnaire soon after the each
interview and ask the respondents to recheck the written answer with their
explanations given at the interview.
ii. Demographic profile of informants
Demographic profile and the objective success measures of the informants as shown
in the Table 71. The informants’ average age was 46 years, and the ages were ranged
between 29 to 57 years. Three informants were aged over fifty years and only one
informant was under thirty years. Only one informant lived in an area that fall under
the lowest local government that classified as a rural area. Others lived in urban and
semi urban areas of the country. All the inventors were well educated and at least
completed the upper secondary school education. Four of them had either university
diploma or degree and two of the inventors worked as professionals in the field of
science. All the inventors were part time inventors who were employed in other
primary careers. Three inventors were self employed, but did not consider inventing
as their primary profession, invention has been their secondary career. Primary
careers of all the respondents had higher autonomy and freedom to work as
independent inventors.
Table 71: Demographic profile and objective success measures of informants
Interview Section
Inv001
Inv002
Inv003
Inv004
Inv005
Inv006
Section 01
Age-Years
Living Area
Education Qualification
Employment
56
Urban
Dip
Enter
53
Urban
P.grad
Consultant
45
S.Ur
Sec.Sch
Mechanic
36
S.Ur
Degree
Lecturer
29
Rural
Degree
Landscaper
57
S.Ur
Sec. Sch
Farmer
12
7
0
No
1
1
1
No
1
0
0
No
1
1
1
No
1
0
0
No
1
0
0
No
Section 02
Patent received
Started to commercialized
Still commercialized
Profitable
S.Ur- Semi Urban, Dip- Diploma, P.grad- Post Graduate degree, Sec. Sch- secondary school, Enter –
Entrepreneur,
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iii. Objective success of informants
Section 2 of the Table 71 indicates the summaries of the objective success of the
informants. According to the findings, there was only one experienced inventor, who
received the president awards. All the other inventors had received the president
awards for their first invention. Only three informants had started to commercialize
their inventions. The inventor who was granted 12 patents started to commercialize
seven of the patented products through his company, but had not received the
expected profits. One inventor licensed his patent to government organization but
was not satisfied with the way the government organization promotes the product.
Other one was involved in on-demand manufacturing; if someone orders the product,
he was willing to make the product. All the informants said that they were not
receiving enough profits from their inventions and therefore, did not achieve the
higher commercial success. This suggests that even if the inventions received
patents, awards and rewards, it does not guarantee successful commercialization.
iv. Subjective success: Happiness and Satisfaction of informants
Third segment of the interview asked about the ‘happiness’ and ‘satisfaction’ of the
respondents. The interviewer allowed the informants to explain the happiness and
satisfaction they have perceived and give reasons for their assessment. Table 72
present the answers were given to the question of ‘Are you a happy person?’ The
respondents assessed their success and happiness based on whatever they have
objectively achieved so far from the inventions. Each respondent highlighted his or
her highest objective achievement as the critical incident or event when describing
himself as a happy inventor.
Table 72: The feel of happiness and reasoning of the informants.
Respondent ID
Are you happy?
Explained rationale
Inv001
Yes of course
I invented so many products and started to
commercialize them. Even though they are not
commercially and financially successful, I am happy
with what I achieved so far.
Inv 002
Yes.. I am
I have won 2006 best commercial invention
president award and I went Geneva Inventors’
competition as well. So I am successful inventor.
Isn’t it?
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Inv003
Yes.. surely
As I said earlier I am an inventor,
commercialization is not my part. I won Gold medal
at the Geneva inventor competition for my
invention..so..I invented world-wining invention
that is what inventors should do.
Inv004
Yes..yes..I am
I invented workable and marketable innovation and
I won award for that. My only concern is I am not
getting the intended profits from it because of
inefficiency of the licensee.
Inv005
Hmmm. Yes… I think
I won president award and I think my concept is
widely accepted. I hope that I would be able to
commercialize it soon
Inv006
Yes..I feel it
I invented multifunctional water pump that can be
used to put fertilizers to crops, bring water to small
farms. I am not in a position to commercialize it, but
I feel success when I see it
Unlike the question on happiness, informants gave different thoughts when they were
asked, “Are you satisfied?” Four of the informants said they are satisfied and two of
them were not satisfied (Table 73). Inventors who were satisfied with themselves
explained their assessment based on existing inventions and enthusiasm for future
inventive activities. The Inventors who were not satisfied complained about the
current policies and procedures that discouraged inventors and inventive products.
They said they are discouraged from being involved in inventive activities because of
the unfavorable environment. This finding suggests that inventor’s subjective
satisfaction or dissatisfaction is mainly driven by their anticipated future inventive
activities and events. When the inventor perceives the future events will going
against them, they are expected to be not satisfied.
Table 73: The feel of satisfaction and reasoning of Independent inventors
Respondent ID
Are you satisfied
Rationale
Inv001
Yes. I am
I am still inventing, I have new ideas to be
implemented but the problem is I do not have
much time
Inv 002
No not at all
In Sri Lanka, there is not respect or demand for
inventors. As far as price is low, they try to import
everything. I don’t know when Sri Lanka realized
the importance of local inventions
Inv003
Yes I am
I am still inventing. I achieved most of the things
as inventor but as marketing wise, it is not success
yet. Actually, we do not know marketing, we are
inventors. Someone should help us on this
Inv004
Yes surely
I am enjoying inventions. I have another idea to
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implement
Inv005
Yes
I am satisfied with my product but I would be
more satisfied when I commercialize it
Inv006
No I am not
There is no help for inventors in Sri Lanka, they
were not respected
The subjective success of inventors aligns with ex-post rationalization of individuals
that was explained in self-regulation theory. Informants have not felt them as
unsuccessful even though they were not commercially successful. Even though
inventors have not achieved commercial success, they assess their success based on
the existing highest-level achievements of their inventive life. It has been found that
the ex-post rationalization process, optimism and hope regulate their feeling of
success. The findings of the qualitative pilot study strengthen the rationale of the
hypothesized relationship between objective and subjective success of present study.
It was suggested to conceptualize the subjective success as the ultimate endogenous
variable and objective success as predictor of the subjective success.
v.
Informants of the qualitative pilot study
1. Interview 01:
Mr. M.R. Wimal Jayaratne – Winner- Best Commercial Invention 2007
2. Interview 02
Dr. Kapila weeratunga Arachchi- Winner- Best Commercial Invention 2006
3. Interview 03
Mr. Prince Chandrasena –Winner - Best local invention 2007
4. Interview 04
Mr. E.M. Ranatunga - Winner- Best local Invention 2006
5. Interview 05
Mr. K.T.G. Janaka – 1st Runner-up- Best Local Invention 2006
6. Interview 06
Mr. H.C. kulathunga –1st Runner-up – Best Local Inventions-2007
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A.2. Quantitative pilot study
i.
Sample selection for pilot test
Target population of the present study consisted with the 640 patent applied
inventors and their patent application numbers and mail addresses included in the
sampling frame. Cronbach’s coefficient Alpha is the popular tool to measure the
internal consistency (Yurdugul, 2008). Therefore researcher was planning to measure
the reliability of the instrument using Cronbach’s alpha. Peterson (1994) conducted
a Meta analysis of studies on Cronbach’s coefficient alpha and found that there is no
substantive relationship between sample size and Cronbach alpha. According to the
Yurdugul (2008), Cronbach’s alpha values of very small sample sizes can be used as
robust estimators of population coefficient alpha. Therefore, according to the
Petersons (1994) and Yurdugul (2008) researcher considered 25 respondents as
adequate sample size for the pilot study. The researcher selected 25 respondents (4%
of the target population) for pilot study using 25 random numbers to draw from the
ID numbers given to the sampling frame.
ii.
Data collection in pilot study
The researcher was planning to do the data collection in actual study by physically
contacting the respondents. In order to test the applicability of the method the
researcher was planning to employ the same strategy to collect data at the pilot study.
The researcher contacted the respondents through mail and requested the respondents
to send their telephone contact numbers. After receiving the respondents’ contact
numbers, the researcher asked from the selected respondents whether they prefer to
participate in the study. There was overwhelming welcome for the study among the
contacted inventors. Therefore, majority of the respondent were willing to participate
for the pilot study in common location. Therefore, the researcher conducted three
panel data collection meetings during the month of February 2010 at the Knowledge
Centre of the Commerce and Financial Management studies, University of Kelaniya.
Those respondents those who were unable to come for panel data collection were
contacted at their residencies.
354
iii.
Demographic profile of the respondents of pilot test
The pilot test for the survey questionnaires were done with the respondents who were
representatives of the target population. Therefore, the researcher explored the
selected demographic factors of the respondents of the pilot study. Table 74 shows
the distribution of the respondents’ age, living district, local authority, gender,
marital status and education level and type of invention the invented.
Table 74: Demographic profile of the respondents of pilot test
Variable
N
%
Variable
Age
Yong (19-40)
Middle (40-65)
Old (over 65)
Total
District
Matale
Puttalam
Rathnapura
Galle
Kurunagala
Colombo
Anuradhapura
Total
%
Gender
8
15
2
25
2
1
1
2
1
13
5
25
32.0
60.0
8.0
100.0
8.0
4.0
4.0
8.0
4.0
52.0
20.0
100.0
Location
Pradashiya Saba
Urban Council
Municipal Council
Total
N
16
2
7
25
64.0
8.0
28.0
100.0
Male
Female
Total
23
2
25
92.0
8.0
100.0
Marital Status
Unmarried
Married
Total
5
20
25
20.0
80.0
100.0
Education
School
Professional/Vocational
Tertiary
Post Graduate
Total
8
6
7
4
25
32.0
24.0
28.0
16.0
100.0
Type of Invention
New Products
new process
Product Development
Process Development
Total
7
6
5
7
25
28.0
24.0
20.0
28.0
100.0
According to the Table 74 majority of the respondents were middle aged, educated,
married males who are living in Colombo and in rural areas of the Sri Lanka. These
characteristics were common among the grassroots level inventors who were studied
in earlier studies in industrial countries (Macdonald, 1986; Amesse & Desranleau,
1991; Whalley, 1992; Georgia Tech Enterprise innovation Institute, 2008).
Therefore, the researcher ensured that the instrument was tested with the respondents
belonged to the grassroots level inventive community.
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B : Data Collection Instrument
ශ්රීs ලංකා නව නිපැයුම්කරැවන්ගේ සමීක්ෂණය- 2010
Sri Lanka Inventors’ Survey – 2010
Random Number: ________
Serial Number: ________
Sri Lanka Inventors’ Survey SLIS-2010
You sacrifice your valuable time, money and knowledge to contribute something
356
special to the world. This survey is the first step of trying to empower you to deliver
better inventions to the nation, while achieving your personal goals.
This survey conduct to identify Sri Lankan Independent inventors, their lives, and
efforts they are taking to invent something that no one invented so far. You have been
selected to be part of first ever survey conducted on inventors in Sri Lanka. You are
requested to contribute it from best of you can by spending little time on this
questionnaire to provide true and correct information as possible.
All the information will be confidential and use considering your privacy and
confidentiality. So please fill this questionnaire by giving correct information as
possible.
C. Nalaka Wickramasinghe
Lecturer,
Department of Commerce & Financial Management,
Faculty of Commerce and Management studies,
University of Kelaniya, Sri Lanka.
Office: 094-11-2914485
Home: 094-11-5023041
E-Mail: nalakacw@yahoo.com
357
ශ්රීs ලංකා නව නිපැයුම්කරැවන්ගේ සමීක්ෂණය
SLIS - 2010
ල ෝකයට විල ේෂිත යමක් දායාද කිරීම ලෙනුලෙන් ඔබ ඔබලේ ෙටිනා කා ය‚ මුදල් සහ දැනුම
කැපකර ඇත. ජාතියට ෙඩා ෙැඩදායක නෙනිපයුම් බිහිකරමින් ඔබලේ පුද්ගලික අරමුණු ඉටුකර
ගැනීමට ඔබලේ හැකියාෙ ෙර්ධනය කිරීලම් පළමු පියෙර ල ස ලමම සමීක්ෂණය ඔබ අතට පත්ලේ.
ශ්රීම ාාංකික නෙ නිපයුම්කරැෙන්‚ ඔවුන්ලේ ජීවිත සහ කිසිෙකු ලමලතක් බිහිකර ලනොමැති යමක්
බිහිකිරීමට ඔවුන් කරන කැපකිරීම හඳුනාගැනීලම් අරමුණින් ලමම සමීක්ෂණය දියත් ලකලර්. ශ්රීම
ාංකාලේ නෙ නිපයුම්කරුෙන් සම්බන්ධලයන් පළමුෙරට පෙත්ෙන ලමම සමීක්ෂණයට දායකවීම
සඳහා ඔබෙ ලතෝරාලගන ඇත. ඔබලේ ෙටිනා කා ලයන් බිඳක් ලමම ප්ර ්නාෙලියට ඔබට හැකි
උපරිමලයන් සත්ය හා නිෙැරදි පිළිතුරු බා දීමට කැප කරනලමන් ඉල් ා සිටිමි.
ඔබ විසින් සපයන සියඵ ලතොරතුරුෙ රහස්යභාෙය සහ ඔබලේ ලපෞද්ගලිකත්ෙය ආරක්ෂාෙන
අයුරින් ලයොදා ගැනීම තහවුරු කර ඇති නිසා කරැණාකර හැකිතාක් දුරට සත්ය හා නිෙැරදි ලතොරතුරු
සපයන්න
සී. නා ක වික්රමසිාංහ
කථිකාචාර්ය
ොණිජ්ය හා මුල්ය කළමණාකරණ අධ්ය නාාං ය,
ොණිජ්ය හා කළමණාකරණ අධ්යයන පීඨය,
කැළණිය වි ්ෙ විද්යා ය.
කාර්යා ය: 094-11-2914485
නිෙස: 094-11-5023041
විද්යුත් තැපෑ : nalakacw@yahoo.com
358

උපගෙස්
ඔබලේ පහසුෙ පිණිස ලමම ප්ර ්නාෙලිය සිාංහ සහ ඉාංේරිසි භෂා ලදලකන්ම ඉදිරිපත් කරඇත.
පිලිතුරු සැපයීලම්දී ඔබට කැමති භාෂාෙක් ලතෝරාගත හැක.

කරුණාකර පිලිතුරු සැපයීමට ලපර දී ඇති සියඵ උපලදස් කියෙන්න
Instructions

For your convenience, this questionnaire is presented both in Sinhala and English
Languages. You can choose either the language to answer the questionnaire.

Please read the question and guidance provided for each question before answering
SECTION I
1 ගකොටස
1.1.1
Part 1
2009 ලදසැමබර් 31 දිනට ඔබලේ ෙයස අවුරුදු, (කරුණාකර දී ඇති ලකොටුෙ තුළ ෙයස ලියන්න).
Your age as at 31st of December 2009 in years (Please write the age in the cage given)
අවුරුදු / years
1.1.2
ෙසර 2000 සිට ෙැඩි කා යක් ඔබ ජීෙත්වූ දිස්ත්රික්කය කුමක්ද?
(කරුණාකර දී ඇති ලකොටුෙ තුළ දිස්ත්රික්කලේ නම ලියන්න)
What is your living district for majority of time since 2000?
(Please write the name of the district in the box given)
1.1.3
……………………………………………..
ඔබ ජීෙත්ෙන ප්රාලද් ය පා නය කරණු බන්ලන්
(කරුණාකර ගැ ලපන කාණ්ඩය “X” කුණු කරන්න).
Your living place is governed by (Please check (×) the category that matches)
ප්රාලද්ශීය සභාෙකිනි
Pradeshiya saba
1.1.4
නගර සභාෙකිනි
Urban council
මහ නගර සභාෙකිනි
Municipal council
ඔබලේ ස්ත්රී/පුරුෂ භාෙය, (කරුණාකර ගැ ලපන කාණ්ඩයට ඉදිරිලයන් “X” කුණු කරන්න).
Your gender (Please check (×) the category that matches.)
පුරුෂ
Male
1.1.5
ස්ත්රීa
Female
ඔබලේ විොහක/ අවිොහකභාෙය.
(කරුණාකර ගැ ලපන කාණ්ඩයට ඉදිරිලයන් “X” කුණු කරන්න).
Your marital status (Please check (×) the category that matches.)
විොහක
Married
අවිොහක
Unmarried
359
1.1.6
ඔබ විසින් සම්පූර්ණ කර ඇති ඉහළම අධාාපන සුදුසුකම කුමක්ද?
(කරුණාකර එකකට පමණක් ඉදිරිලයන් “X” කුණු කරන්න).
What is the highest education qualification you completed? (Please check (×) only one)
ඩිප්ල ෝමා
Diploma
පළමු උපාධිය
First degree
ප ්චාත් උපාධි
Postgraduate
ආචාර්ය උපාධිය
PhD
ප්රාථමික අධ්යාපනය
Primary school
ද්විතීක අධ්යාපනය
Secondary Education
ෙෘත්තීය විභාග
Professional Exams
ෙෘත්තීය පුහුණු
Vocational training
1.1.7
ඔබලේ ෙර්තමාන රැකියා තත්ෙය කුමක්ද?
(කරුණාකර එකකට පමණක් ඉදිරිලයන් “X” කුණු
කරන්න).
What is your current employee status? (Please check (×) only one .If other, please mention.).
1.
2.
3.
4.
5.
6.
1.1.8
ලසේොලයෝජකලයකි
Employer
ලසේොදායකලයකි
Employee
ස්ෙයාං රැකියාෙක ලයලදන්ලනකි
Self- Employed
ශිෂ්යලයකි
Student
පූර්ණ කාළීන නෙ නිපැයුම්කරුලෙකි
Full time inventor
වි ාමික
Retiree
ඔබලේ ෙර්තමාන රැකියා අාං ය කුමක්ද? (කරුණාකර එකකට පමණක් ඉදිරිලයන් “X” කුණු කරන්න).
What is your current employment sector? (Please check (×) only one).
රාජ්ය අාං ය
Government sector
අර්ධ රාජ්ය (වි ්ෙ විද්යා /පර්ලේෂණ ආයතන හැර)
Semi-government (other than university/research institutes)
වි ්ෙ විද්යා /පර්ලේෂණ ආයතන
University/ Research institutes
පුද්ගලික අාං ය
Private sector
1.1.9
වර්තමාන රැකියාවට ගපර ඔබ ගවනත් ස්ථානයක/වල රැකියාවල නියුක්ත වී තිගේෙ?
(කරුණාකර එකකට පමණක් ඉදිරිලයන් “X” කුණු කරන්න)
Have you employed in any other work place(s) before the current employment?
(Please check (×) only
one).
ඔේ
Yes
1.1.10
රාජ්ය ලනොෙන සාංවිධාන
Non-Government Organization
ස්ෙයාං රැකියා
Self-employee
Full time student
පූර්ණ කාළීන ශිෂ්යය
පූර්ණ කාළීන නෙ නිපැයුම්කරුලෙකි
Full time inventor
නැත
No
2009 ගෙසැම්බර් 31 දිනට ඔබ ගකොපමණෙ රැකියා ස්ථාන ගණනක ගසේවය කරතිගේෙ?
(කරුණාකර එකක් ඉදිරිලයන් පමණක් “X”
කුණු කරන්න)
As at 31st December 2009, how many work places you worked in? (Please check (×) only one)
.
ස්ථාන හතරක ලහෝ ඊට ෙැඩි/ Four or more places
ස්ථාන ලදකක / Two places
ලසේෙය කර ලනොමැත/Not worked
ස්ථාන තුනක / Three places
එක් ස්ථානයක / One place
348
1.1.11
වැටුප් හා ගවනත් ආොයම් සියල්ගලහිම එකතුව ගත්කල ඔබගේ වර්තමාන සාමාන්ය මාසික ආොයම ශ්රී ලංකා රුපියල්
වලින් ගකොපමණෙ?
(කරුණාකර නිෙැරදි ආදායම ආසන්න රුපියල් 1000 ෙටයා දීඇති ලකොටුලේ ලියන්න)
Including salary any other sources of income, what is your current average monthly income in Sri Lankan
rupees?
(Please round to the actual income to nearest 1000 and write in the given cage)
රුපියල් / Rupees
2ගකොටස Part 2
1.2.1.
්රී ලංකා බුද්ධිමය ගෙපල කාර්යාලයට ඔබගේ ප්රථම ගප්ටන්ට් අයදුම්පත ඉදිරිපත් කළ වර්ෂය කුමක්ෙ?
When you forward your first patent application to the Sri Lanka Intellectual Property Office
Year/ ෙර්ෂය: ………………………….
1.2.2.
සාමාන්යය දිනයක ඔබ පැය කීයක් වැඩ කරන්ගන්ෙ ? (කරුණාකර දී ඇති ගකොටුව තුළ ලියන්න)
In a normal day, how many hours you are working? (Please write in the cage given)
පැය / hours
1.2.3.
සාමාන්යය දිනයක ඔබ පැය කීයක් ඔබගේ නවනිපැයුමි සම්බන්ධ වැඩ කරන්ගන්ෙ ? ?
(කරුණාකර දී ඇති ගකොටුව තුළ ලියන්න)
In a normal day, how many hours you are working on your inventions?
(Please write in the cage given)
පැය / hours
1.2.4.
ඔබ වැඩි වශගයන් ගපළගෙන්ගන්,
(කරුණාකර එකක් පමණක් “X” ලකුණු කරන්න)
You are mostly prefer to (Please check (×) which is applicable)
නෙ භාණ්ඩ නිපැයුම්ෙ ට
New product invention
නෙ ක්රියාෙලි නිපැයුම්ෙ ට
New process invention
පෙතින භාණ්ඩ ෙැඩිදියුණු කරන නිපැයුම්ෙ ට
Existing Product improvement invention
පෙතින ක්රියාෙලි ෙැඩිදියුණු කරන නිපැයුම්ෙ ට
Existing Process improvement inventions
349
1.2.5.
ඔබලේ නෙ නිපැයුම් බහුල වශගයන් අයත්ෙන්ලන් කුමණ ක්ලෂේත්රයටද?
(කරුණාකර අොල ක්ගෂේත්රය “X” ලකුණු කරන්න; ගවනත් නම්, කරැණාකර සඳහන් කරන්න)
Which of the following industries your inventions mostly apply to:
(Please check industry (×) which is applicable; if other, please mention).
පරිසරය හා බ ක්ති
Environmental and Energy
රථොහන ආ ්රිත Automotives
ක්රීඩා හා විලනෝදා ්ොද Toys,
Sports and Leisure
කෘෂිකාර්මික
Agriculture
වෙද්ය ලහෝ ලසෞඛ්යය Medical
or Health
ලමෙ ම්
Tools
ගෘහා ්රිත හා පාරිලභෝගික
Household and consumable
ඉහළ තාක්ෂණික උපකරණ
High tech equipment
ආරක්ෂක හා ආරක්ෂාෙ
Security and safety
කාර්මික ලයදවුම්
Industrial applications
3 ගකොටස Part 3
1.2.6.
ඔබ කිසියම් භාණ්ඩයක් සංවර්ධනය කිරීම ආරම්භ කිරීමට ගපර, එම නගවෝත්පාෙනය හා සම්බන්ධ ෙැනට පවතින
ගතොරතුරු ගසවීමක් සිදුකරන්ගන්ෙ? (කරුණාකර අොල ප්රකාශය ඉදිරිගයන් “X” ලකුණු
කරන්න).
Before starting to develop a product, how often you search for the available information relating to the idea of
your invention. (Please check (×) which is applicable)
Always / නිරන්තරලයන්
Regular / ලබොලහෝ අෙස්ථාෙ
Sometimes / ඇතැම් අෙස්ථාෙ
Rarely / කළාතුරකින්
Very rarely / ඉතා කළාතුරකින්
1.2.7.
ඔබගේ නව නිපැයුම් වලට අොල ගතොරතුරු ගසවීම සඳහා බහුල වශගයන් ගයොොගන්නා මුලාශ්ර ගමොනවාෙ?
(දී ඇති එක් එක් මුලාශ්ර සම්බන්ධගයන් ඔබගේ භාවිතගේ මට්ටම සළකුණු (×) කරන්න)
From what sources you use to search the relevant patent and invention information. Please mark (×) your
usage level for each statement given
Always / නිරන්තරලයන්
Regular / ලබොලහෝ අෙස්ථාෙ
Sometimes / ඇතැම් අෙස්ථාෙ
Rarely / කළාතුරකින්
Very rarely / ඉතා කළාතුරකින්
=5
=4
=3
=2
=1
5
1.
2.
3.
4.
5.
6.
7.
8.
9.
්රී ාංකා ජාතික බුද්ධිමය ලද්පළ කාර්යා ය
SLNIPO
්රී ාංකා නෙ නිපැයුම්කරුෙන්ලේ ලකොමිසම
Sri Lanka Inventors’ Commission
අන්තර්ජා ය
Internet
රපොහීනී ෙැඩසටහන්
TV programs
ලර්ඩිලයෝ ෙැඩසටහන්
Radio programs
ලපොත්පත් හා සඟරා
books and magazines
ක්ලෂේත්රලේ සිටින විද්ෙතුන්
Educated persons of the field
අලනකුත් නෙ නිපැයුම්කරුෙන්
other inventors
යහළුෙන් හා පවුලල් සාමාජිකයන්
Friends and family members
350
4
3
2
1
1.2.8.
පහත ෙක්වා ඇති කාර්යන් සඳහා ඔබ අන්තර්ජාලය භාවිත කරන්ගන්
(දී ඇති එක් එක් මුලාශ්ර සම්බන්ධගයන් ඔබගේ භාවිතගේ මට්ටම සළකුණු (×) කරන්න)
For the following activities you use the Internet
(Please mark (×) your usage level for each statement given)
Always / නිරන්තරලයන්
Regular / ලබොලහෝ අෙස්ථාෙ
Sometimes / ඇතැම් අෙස්ථාෙ
Rarely / කළාතුරකින්
Very rarely / ඉතාම කළාතුරකින්
=5
=4
=3
=2
=1
5
1.
2.
3.
4.
1.2.9.
4
3
2
1
ලතොරතුරු බාගැනීමට
Information collection
දැනුම බාගැනීමට
Gain knowledge
ලතොරතුරු හුෙමාරු කරගැනීමට
Information sharing
අන්අය සමඟ සන්නිලේදනය කිරීමට
Communicate with others
පහත දැක්ලෙන ක්රියාෙන් සඳහා ලකොපමණ ොරයක් අන්තර්ජාලය භාවිත කරන්ලන්ද?
(කරුණාකර අොල ඔබගේ මතය “X” ලකුණු කරන්න)
How often you use Internet for following activities
(Please check (×) your opinion for each statement given)
5.
5.
Always
නිරන්තරලයන්
4- Regular
4 - ලබොලහෝ අෙස්ථාෙ
3- Sometimes
3- ඇතැම් අෙස්ථාෙ
5
1.
\
2.
3.
4.
5.
6.
7.
8.
ප්රෙෘත්ති හා ොර්තා කියවීමට
Read news on the web
විද්යාත්මක ලිපි හා ොර්තා කියවීමට
Read scientific articles and reports
නෙ නිෂ්පාදන පිළිබඳ ලතොරතුරු ලසවීමට Search for
new product information
ලප්ටන්ට් සම්බන්ධ ලතොරතුරු ලසවීමට
Search for patent information
විද්යුත් ලිපි යැවීමට හා ැබීමට
Send and receive E-mails
යහළුෙන් සමඟ “චැට්” කිරීමට
Chat with your friends
ේයාපාරික සාකච්ඡා කිරීමට
For business Conferencing
විලනෝදාස්ොදය සඳහා
For entertainment
351
2- Rarely
2 - කළාතුරකින්
4
3
2
1- Very Rarely
1 - ඉතා කළාතුරකින්
1
4 ගකොටස Part 4
1.4.1
ඔබලේ නෙ නිපැයුම් ජීවිතය තුළ දැනට සම්පූර්ණ වශගයන් ක්රියාත්මක තත්වගේ පවතින නිර්මාණ කීයක් බිහිලකොට
තිලේද? (කරුණාකර සාංඛ්යාෙ ලියන්න)
How many completed working prototypes you invented during your inventive career? (Please write the
number)
නව නිපැයුම් /Inventions
1.4.2
ඔබලේ නෙ නිපැයුම් ජීවිතය තුළ දැනට ගද්ශිය ගප්ටන්ට් අයැදුම්පත් කීයක් ඉදිරිපත් ලකොට තිලේද? (කරුණාකර සාංඛ්යාෙ
ලියන්න)
How many local patent applications you forwarded during your inventive career?
(Please write the number)
අයැදුම්පත් /Applications
1.4.3
2009 ගෙසැම්බර් 31 දින ෙනවිට ඔබට ගද්ශීය ගප්ටන්ට් කීයක් ප්රදානය ලකොට තිලේද?
(කරුණාකර සාංඛ්යාෙ ලියන්න)
How many Local patents granted to you as at 31st December 2009? (Please write the number)
ගප්ටන්ට් / Patents
1.4.4
්රී ාංකා බුද්ධිමය ලද්පළ කාර්යා ය විසින් 2009 ගෙසැම්බර් 31 දිනට ලප්ටන්ට් අයදුම්පත් කීයක් අෙසාන ෙ ලයන්
ප්රතික්ලෂේප කර තිලේද? (කරුණාකර සාංඛ්යාෙ ලියන්න)
As at 31st December 2009 how many patent applications were finally rejected by the Sri Lanka Intellectual
property Office? (Please write the number)
අයදුම්පත් / Applications
1.4.6
2009 ගෙසැම්බර් 31 දින වනවිට ඔබගේ ගප්ටන්ට් අයැදුම්පත් කීයක තීන්දු බලාගපොරත්තු වන්ගන්ෙ?
How many patent applications are pending as at 31st December 2009?
අයදුම්පත් / Applications
1.4.7
ඔබලේ නෙ නිපැයුම් කීයක් ගද්ශිය ත්යාග හා ප්රොන දිනා ඇත්ද?
How many of your inventions have won local prizes and awards?
නව නිපැයුම් /Inventions
1.4.8
ඔබලේ නෙ නිපැයුම් කීයක් ජාත්යන්තර ත්යාග හා ප්රොන දිනා ඇත්ද?
How many of your inventions have won local or international prizes and awards?
නව නිපැයුම් /Inventions
1.4.9
2009 ලදසැම්බර් 31 දින ෙනවිට ඔබ නෙ නිෂ්පාදන ලකොපමණ සාංඛ්යාෙක් විකිණීම ආරම්භ කගළේෙ?
(ඔබ විසින් විකිණීම ලහෝ ලෙනත් පාර් ්ෙයක් සමඟ විකිණීමට වනතික එකඟතාෙයක් ඇතිකර ගැනීම, ලප්ටන්ට්
අයිතිය විකිණීම ලහෝ බ පත්රය දීම)
As at 31st December 2009 how many inventive products you started to commercialize?
(Sell by you or engage in legal agreement to sell the product, sell the patent, or give licenses)
නව නිපැයුම් /Inventions
352
1.4.10
ඔබලේ නෙ නිපැයුම් අලළවිකිරීම සඳහා ලතොරාගත් ප්රධාන ක්රමය කුමක්ද?
(කරුණාකර අොල එක් වගන්තියකට ඉදිරිගයන් පමණක් “X” ලකුණු කරන්න)
What was the method you chose to commercialize your inventions?
(Please check (×) only one statement)
1.
2.
3.
4.
5.
ඔබ විසින්ම නිෂ්පාදනය හා අලළවිය සිදුකිරීම
Manufacturing and selling by your own
අන් අයට බ පත්ර බාදීම
Licensing to others
ලප්ටන්ට් අයිතිය විකිණීම
Selling the patent rights
නෙ ක්රමය පිළිබඳෙ උපලද් ක ලසේො හා ඉගැන්වීලම් කටයුතු සිදුකිරීම
Consultancy and teaching the methods
අලළවි කිරීමට උත්සාහ කලළේ නැත
Not tried to commercialize
1.4.11
ඔබලේ නෙ නිර්මාණ කීයක නිෂ්පාදන 2009 ගෙසැම්බර් 31 දින වනවිට අගළවි කරමින් පවතින්ගන්ෙ?
How many inventive products continue to commercialize as at 31 December 2009?
නව නිපැයුම් /Inventions
1.4.12
ඔබලේ නෙ නිර්මාණ කීයක් ශුද්ධ ලාබ (ආදායම - සියඑ වියදමි)උපයා ඇද්ද ?
How many your inventions have earned net profits (income – all expenses) ?
නව නිපැයුම් /Inventions
353
5 ගකොටස Part 5
1.5.1
ඔබලේ නෙ නිර්මාණ කටයුතු, ලප්ටන්ට් බ පත්ර බාගැනීම හා අලළවි කිරීලම් ක්රියාෙලියට පහත සඳහන්
පාර් ්ෙ දායකවූ ආකාරය(අදා අාංකය දී ඇති ලකොටුෙ තුළ අදා අාංකය ලියන්න,)
How you linked with following parties during the process of inventing, patenting, and
commercializing your inventions? (Write the relevant number in the cage)
1- ඉතාමඅඩුයි
1-Very Low
1
2
3
4
5
6
7
8
9
10
11
12
13
2- අඩුයි
2- Low
3- සාමන්යයයි
3 – Average
නෙ නිපැයුම්කරුෙන්ලේ සම්ලම් න
Inventor Associations
ක්ලෂේත්රලේ ලද්ශීය විල ේෂඥයන්
Local Experts in the field
නීති උපලද් කයන් හා නිලයෝජිතයන්
Patent agents & Legal Advises
සමාගම් හා ේයාපාර ඒකක
Companies & Business entities
ජන මාධ්ය
Mass media
ලද් පා නඥයන්
Politicians
මහජන පුස්තකා
Public libraries
වි ්ෙ විද්යා
Universities
පර්ලේෂණ ආයතන
Research institutes
බැාංකු හා මුල්ය ආයතන
Banks & Financial Institutes
ලෙළඳ සාංවිධාන
Trade organizations
රජලේ අමාත්යාං හා ලදපාර්තලම්න්තු
Government ministries/agencies
විලද්ශීය විල ේෂඥයන්
Foreign experts contacted
354
4- ඉහ යි
4- High
5- ඉතා ඉහ යි
Very high
1.5.2
ලමම ප්රeකා ය ඔබ ගපෞද්ගලිකව හඳුනන පුද්ගලයන් සම්බන්ධෙය.ලපෞද්ගලිකෙ හැඳීනීම ල ස අදහස් ලකලරන්ලන්
ඔබලේ නමකියු පමණින් ඔබෙ හඳුනාගත හැකි පුද්ග යන්ය. ඔබ එක් පුද්ග යකුට ෙඩා හඳුනන්ලන් නම් ඔබට වඩාත්ම
වැෙගත්පුද්ගලයා කුණු කරන්න.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
ජනමාධ්ය සමග ලහොඳ සම්බන්ධතා ඇති
Has a good contacts with mass media
නිොඩු නිලක්තනයක් අයිති
Owns a holiday home
සාහිත්ය පිලිබඳ දැනුමක් ඇති
Has a knowledge of literature
රැපියල් 200,000 ෙඩා මාසික ආදායමක් උපයන
Earns more than Rs. 200,000 monthly
වි ්ෙවිද්යා උපාධිධාරී
Has graduated from a university
ඉහ ෙෘත්තීය අධ්යාපනයක් ඇති
Have higher vocational education
ලද් පා න පක්ෂයක ක්රියාකාරී
Is active in political party
රජලේ ලරගු ාසි පිළිබඳ වි ා දැනුමක් ඇති
Knows lot of about government regulations
මූල්ය කටයුතු පිළිබඳ දැනුම ඇති
Has a knowledge about financial matters
ෙෘත්තිය සඟරා කියෙන
Reads a professional journal
කාර් එකක් අයිති
Owns a car
විලද් භාෂාෙක් කථා කිරීමට හා ලිවීමට හැකි
Can speak and write foreign language
පුද්ග පරිගණකයක ෙැඩ කිරීමට හැකි
Can work with personal computer
රැකියාෙකට ඉල්ලුම් කිරීලම්දී ලහොඳ චරිත සහතිකයක් දියහැකි
Can give good reference when applying for job
රාජකාරී කටයුතු සම්බන්ධ ආරවුල්ෙ දී උපලදස් දියහැකි
Can give advice about conflict at work
පවුලල් සමාජිකයන් සම්බන්ධ ආරවුල්ෙ දී උපලදස් දියහැකි
Can give advice about conflict with family members
ලගෙල් මාරැකිරීලම්දී උදේ වියහැකි
Can help when moving home
355
ඥාතිෙරයකු
Relative
පවුලල්සමාජිකයකු
Family member
(2)
ඔබලේ යහඵෙකු
Your Friend
ෙෘත්තීය මට්ටමින්
Officially known
(1)
ඔබ …… කවුරැන් ලහෝ අඳුරන්ලන්ද?
Do you know anyone who ……
යහඵෙකුලේ යහඵෙකු
Friend’s Friend
නැත No
This question is on the people you personally known. Personally known means the person known you by
hearing your name. If you have more than one, please tick the most influential person for you
(3)
(4)
(5)
(6)
SECTION II
1 ලකොටස Part 1
2.1
ප්රeකා හයක් පහත දක්ො ඇත. එක් එක් ප්රeකා ය ලහොඳින් කියො එම එක් එක් ප්රeකා ය ඔබෙ විස්තර කරන
ආකාරය දක්ෙන්න. කරැණාකර එක් එක් ප්රeකා යට අදා හිස්තැලන් ඔබලේ මතයට ගැ ලපන අාංකය ලියන්න. හරි ලහෝ
ෙැරදි පිළිතුරැ ලනොමැත.
There are six statements given bellow. Read the sentence carefully and state at how each statement
describe you. .Please write the number of your opinion for each statement given. There is no right or
wrong answer
1= සම්පූර්ණලයන්ම එකඟ ලනොලේ / completely Disagree
2= එකඟ ලනොලේ / Disagree
3= යම්තාක් දුරකට එකඟ ලනොලේ / Slightly Disagree
4= එකඟවීමක් ලහෝ එකඟ ලනොවීමක් ලනොමැත / Neither agree or disagree
5= යම්තාක් දුරකට එකඟ ලේ / Slightly Agree
6= එකඟ ලේ / Agree
7= සම්පූර්ණලයන්ම එකඟ ලේ / completely Agree
2.1.1
රෑපොහිනී නරඹනවිට, දැන් බ න ෙැඩසටහනට මම කැමති වුෙත් මම චැනල් මාරැකරමින් ඊටත්
ෙඩා ලහොඳ ෙැඩසටහනක් ප්රචාරය ෙන්ලන්දැයි පරීක්ෂා කරමි.
When I watch television, I often check other channels to see if something better is
playing, even if I am satisfied with what I am watching
2.1.2.
මම මාලේ ෙර්තමාන රැකියාෙ ගැන ලකතරම් තෘප්තිමත් වුෙත් ඊට ෙඩා ලහොඳ රැකියා අෙස්ථා
පිළිබඳෙ අෙධානලයන් සිටීමට කැමැතිය.
No matter how satisfied I am with my job, it’s only right for me to be on lookout for
better opportunities
2.1.3.
ලබොලහෝවිට යලමකුට දීමට තෑේගක් ලතෝරා ගැනීමට මට අපහසුය.
I often find it difficult to shop for a gift for someone
2.1.4.
කුමක් ලතෝරා ගතයුතුදැයි තීරණයක් ගැනීමට ලනොහැකි නිසා මට යමක් මි දී ගැනීමට ෙැඩි
කා යක් ගතලේ.
Buying something is difficult for me. I am always struggling to pick the best one
2.1.5.
මම කුමක් කළත්, මා ලකලරහි ඇති තත්ෙය ඉහලින් පෙත්ො ගනිමි.
No matter what I do, I have the highest standards for myself
2.1.6.
මම කිසිවිටක ලදෙනියාවීලමන් සෑහීමකට පත් ලනොලෙමි.
I never settle for second best
356
2 ගකොටස Part 2
2.2
ප්රeකා හයක් පහත දක්ො ඇත. එක් එක් ප්රeකා ය ලහොඳින් කියො එම එක් එක් ප්රeකා ය ඔබෙ විස්තර කරන
ආකාරය දක්ෙන්න. කරැණාකර එක් එක් ප්රeකා යට අදා හිස්තැලන් ඔබලේ මතයට ගැ ලපන අාංකය ලියන්න. හරි ලහෝ
ෙැරදි පිළිතුරැ ලනොමැත.
There are six statements given bellow. Read the sentence carefully and state at how each statement
describe you. .Please writes the number of your opinion for each statement given. There is no right or
wrong answer
1= දැඩිලසේ එකඟ ලනොලේ / Strongly Disagree
2= එකඟ ලනොලේ / Disagree
3= එකඟවීමක් ලහෝ එකඟ ලනොවීමක් ලනොමැත / Neither agree or disagree
4= එකඟ ලේ / Agree
5= දැඩිලසේ එකඟ ලේ / Strongly Agree
2.2.1
අවිනි ්චිත කා ෙ දී වුෙත් මම සාමාන්යලයන් ඉහ ප්රeතිඵ අලප්ක්ෂා කරමි.
In uncertain times, I usually expect the best.
1.2.2
කිසිෙක් මට එලරහිෙ ක්රි යාත්මක විමටඉඩ ඇත්නම්, එය අනිොර්ලයන්ම එලසේ සිදුලේ.
If something can go wrong for me, it will.
1.2.3
මම සෑමවිටම මාලේ අනාගතය පිළිබඳෙ සුභොදී ලෙමි.
I am always optimistic about my future.
1.2.4
මම ලකදිනකෙත් මාලේ කැමැත්ත අනුෙ කටයුතු සිදුවිය යුතුයැයි බ ාලපොලරොත්තු ලනොලෙමි.
I hardly ever expect things to go my way.
1.2.5
මම ලකදිනකෙත් මට ලහොඳ ලදයක් සිදුලේයැයි බ ාලපොලරොත්තු තබා ලනොගනිමි.
I rarely count on good things happening to me.
2.2.6
සමස්ථයක් ල ස, මම නරක ලද්ෙල්ෙ ට ෙඩා ලහොඳ ලද්ෙල් සිදුලේයැයි බ ාලපොලරොත්තු ෙන්ලනමි.
Overall, I expect more good things to happen to me than bad.
3 ගකොටස Part 3
2.3.1
ඔබලේ නෙ නිපැයුම් ජීවිතලේදී ැබූ ජයේරහණයන් පිළිබඳෙ ඔබලේ හැඟීම
(කරුණාකර එකක් පමණක් “X” ලකුණු කරන්න)
Your feeling of the achievements of your inventive career is (Please mark(×) only one)
ඉතාමත් තෘප්තිමත් / Very Satisfied
තෘප්තිමත් /Satisfied
සාමාන්යයි /Average
අතෘප්තිමත් / Dissatisfied
ඉතා අතෘප්තිමත් / Very Dissatisfied
2.3.2
අන් අයලගන් ඔබට ැබුණු පිළිගැනීලම් මට්ටම (කරුණාකර එකක් පමණක් “X” ලකුණු කරන්න)
The level of recognition you received from others is (Please mark (×) only one)
ඉතාමත් තෘප්තිමත් / Very Satisfied
තෘප්තිමත් /Satisfied
සාමාන්යයි /Average
අතෘප්තිමත් / Dissatisfied
ඉතා අතෘප්තිමත් / Very Dissatisfied
357
2.3.3
නෙ නිපැයුම්කරුෙකු වීලමන් ඔබ බන ස්ෙයාං තෘප්තිමත් භාෙය
(කරුණාකර එකක් පමණක් “X” ලකුණු කරන්න)
Your self-satisfaction of being an inventor (Please mark (×) only one)
ඉතාමත් තෘප්තිමත් / Very Satisfied
තෘප්තිමත් /Satisfied
සාමාන්යයි /Average
අතෘප්තිමත් / Dissatisfied
ඉතා අතෘප්තිමත් / Very Dissatisfied
2.3.4.
අනාගතලේදී නෙ නිපැයුම් කටයුතුෙ නිරතවීමට ඔබ තුළ ඇති කැමැත්ත
(කරුණාකර එකක් පමණක් “X” ලකුණු කරන්න)
Your interest to engage in inventive activities in the future (Please mark (×) only one)
ඉතාමත් තෘප්තිමත් / Very Satisfied
තෘප්තිමත් /Satisfied
සාමාන්යයි /Average
අතෘප්තිමත් / Dissatisfied
ඉතා අතෘප්තිමත් / Very Dissatisfied
4 ලකොටස Part 4
2.4.
2.5.
ඔබ එකඟෙන ලහෝ එකඟලනොෙන ප්රeකා පහක් පහත දක්ො ඇත. පහත දැක්ලෙන 1 සිට 7 දක්ො ඇති
දර් කයන්ට අදා අාංකය දී ඇති හිස් ලකොටුලේ ලිවීම මඟින් එක් එක් ප්රeකා ය සම්බන්ධලයන් ඔබලේ
එකඟතාෙය ලපන්නුම් කරන්න. නිෂ්චිත හරි ගහෝ වැරදි පිළිතුරක් ගනොමැත
There are five statements given bellow, with which you may agree or disagree. Using the 1-7
scale
below indicate your agreement with each statement by writing appropriate
number in the blank
cell provided for that item. Please be open and honest in your
response. There is no any right or wrong answers.
1= දැඩිලසේ එකඟ ලනොලේ / Strongly Disagree
2= එකඟ ලනොලේ / Disagree
3= යම්තාක් දුරකට එකඟ ලනොලේ / Slightly Disagree
4= එකඟවීමක් ලහෝ එකඟ ලනොවීමක් ලනොමැත / Neither agree or disagree
5= යම්තාක් දුරකට ලේ
/ Slightly Agree
6= එකඟ ලේ / Agree
7= දැඩිලසේ එකඟ ලේ / Strongly Agree
2.4.1
ලබොලහෝ අාං ෙලින් මාලේ ජීවිතය උපරිමයට ආසන්නය
In most ways my life is close to my ideal
2.4.2
මාලේ ජීවිතලේ තත්ෙයන් ඉතා විශිෂ්ඨය
The conditions of my life are excellent
2.4.3
මා ජීවිතය පිළිබඳ තෘප්තිමත්ය
I am satisfied with life
2.4.4
ලම්තාක් ජීවිතලේ බාගත යුතු ෙැදගත් ලදෙල් මා බා ඇත
So far I have gotten the important things I want in life
2.4.5
මා හට ෙැඩි කා යක් ජීෙත්වීමට හැකි නම්, මට ලෙනස් කිරීමට කිසිම ලදයක් ලනොමැත
If I could live my life over, I would change almost nothing
358
5 ගකොටස Part 5
2.5.
කරුණාකර ඔබට හැලගන ආකාරටය පහත දී ඇති එක් එක් ප්රeකා ය ඔබව වඩාත්ම ගයෝේය ගලස විස්තර
කිරීමට ඇති හැකියාව අනුෙ දී ඇති 1 සිට 7 දක්ො අාංක දර් කලේ අදා අාංකය කුණු (×) කරන්න. අංක 1 න්
අවම මට්ටමෙ, අංක 7 උපරිම මට්ටමෙ ගපන්නුම්ගකගර්. 2 සිට 6 අංක අවමය හා උපරිමය අතර මට්ටම්
ගපන්නුම් කරයි.
For each of the following statements and/or questions, please mark (×) the point on the 1 to 7
number scale that you feel is most appropriate in describing you. Number 1 represent the minimum
level and number 7 represent the maximum level. Numbers from 2 to 6 represent the levels
between minimum and maximum.
1.
මා
සමස්ථයක් ල ස ගත්කළ මට හැලගන ආකාරයට,
In general, I consider myself
2.
ලබොලහොමයක් පුද්ග යන්ට සාලප්ක්ෂෙ බ න කළ,
මට හැලගන ආකාරයට, මා
ඉතාමත් ප්රීeතිමත්
පුද්ග ලයකු
ලනොලේ
Not a very
happy person
1
අඩුලෙන්
ප්රීeතිමත් ය
Less happy
ඉතාමත්
ප්රීeතිමත්
පුද්ග ලයකි
2
3
4
5
2
3
4
5
A very
happy person
6
7
ෙැඩිලයන්
ප්රීeතිමත් ය
More happy
Compared to most of other people, I consider my self
1
3.
“ඇතැම් පුද්ග යන් සාමාන්යලයන් ප්රීeතිමත්ය;
ඔවුන් කුමක් සිදුෙන්ලන්දැයි ලනොසළකමින් ජීවිතය විඳිමින්
ැලබන සෑම ලදයකින්ම ොලේ සතුටක් බාගනී.” ලමම
ප්රකා ය ලකතරම් දුරට ඔබෙ විස්තර කරයිද?
‘Some people are generally happy. They enjoy life
regardless of what is going on, getting the most out of
everything’. To what extent this statement describes you
4.
“ඇතැම් පුද්ග යන් සාමාන්යලයන් ඉතාමත්
ප්රීeතිමත් නැත; ඔවුන් දුක්ලෙමින් ලනොසිටියත්, ඔවුන්ට සිටිය
හැකි ප්රීතිමත්භාෙය ලපන්නුම් ලනොකරයි .” ලමම ප්රකා ය
ලකතරම් දුරට ඔබෙ විස්තර කරයිද?
‘Some people are generally not very happy. Although
they are not depressed, they never seem as happy as
they might be’. To what extent does this statement
describe you
359
6
කිසිලසේත්ම නැත
7
ඉතා වි ා
ල ස
Not at all
1
කිසිලසේත්ම නැත
2
3
4
5
6
A great deal
7
ඉතා වි ා
ල ස
Not at all
A great deal
6 ගකොටස Part 6
2.6
පහත ෙක්වා ඇති එක් එක් ප්රeකා ය සම්බන්ධගයන් ඔබගේ අෙහසට අොල පිලිතුර ලකුණු කරන්න
Indicate your agreement with each statement by writing appropriate number in the blank cell
provided for that item.
1= දැඩිලසේ එකඟ ලනොලේ / Strongly Disagree
2= එකඟ ලනොලේ / Disagree
3= යම්තාක් දුරකට එකඟ ලනොලේ / Slightly Disagree
4= එකඟවීමක් ලහෝ එකඟ ලනොවීමක් ලනොමැත / Neither agree or disagree
5= යම්තාක් දුරකට ලේ
/ Slightly Agree
6= එකඟ ලේ / Agree
7= දැඩිලසේ එකඟ ලේ / Strongly Agree
1.
1. ඔබට ඔබ ශ්රීම ාාංකික නෙ නිපයුම්කාර ප්රජාලේ ලකොටස්කරැෙකුයැයි හැලේ
You feel you're a part of Sri Lankan Inventive community.
2.
2.ශ්රීම
3.
3. ශ්රීම ාාංකික නෙ නිපයුම්කාර ප්රජාෙ සමඟ බැඳීමක් ඇති බෙ ඔබට හැලේ
ාාංකික නෙ නිපයුම්කාර ප්රජාෙට දායකවීම සතුටුදායක කරැණකි
Participating in Sri Lankan Inventive community is a positive thing for you.
You feel a bond with Sri Lankan Inventive community
4.
4. ශ්රීම
5.
5. ශ්රීම
6.
6. සියඑ ලදනා එක්ෙ ක්රියාක ලහොත්, ශ්රීම ාාංකික නෙ නිපයුම්කාර ප්රජාලේ සියඑ සාමාජිකයන්ට
ඔවුන්ලේ ගැටඑ විසඳාගත හැක
If we work together, every inventors can solve their problems in Sri Lankan Inventive
community
7.
7. ශ්රීම
8.
8. අලනකුත් නෙ නිපයුම්කරැෙන් සමඟ බැඳීමක් ඇතැයි ඔබට හැලේ
You feel a bond with other inventors
ාාංකික නෙ නිපයුම්කාර ප්රජාෙ ගැන ඔබ ඉතා ආඩම්බරය
You are proud of Sri Lankan Inventive community
ාාංකික නෙ නිපයුම්කාර ප්රජාලේ ක්රියාකාරී සාමාජිකයකුවීම ඔබට ෙැදගත්ය
It is important for you to be politically active in Sri Lankan Inventive community
ාාංකික නෙ නිපයුම්කාර ප්රජාෙ මුහුණ ලදන ඕනෑම ගැටඑෙක්, ඔබලේ ගැටඑෙක් ල ස දැඩිලසේ හැලේ
You really feel that any problems faced by Sri Lankan Inventive community
are also your own problems.
ඔලේ ලනොමසුරැ දායකත්ෙය ලෙනුලෙන් කෘතඥ පුර්ෙක ස්තුතිය
Thank you very much for your kind cooperation
360
C: List of Expert Advisors
1. Prof. Nobaya Ahmad, PhD -Chairman-Supervisory Committee
Deputy Dean (International Affairs)
Faculty of Human Ecology
University Putra Malaysia
2. Prof. Sharifa Rashid, PhD- Member of the Supervisory Committee
Senior Lecturer
Faculty of Human Ecology
University Putra Malaysia
3. Dr. Zahid Emby, PhD - Member of the Supervisory Committee
Senior Lecturer
Faculty of Human Ecology
University Putra Malaysia
4. Lynne C. Levesque, Ed.D – Expert Advisor for Instrument Development
Leadership through breakthrough creativity
Boston,
Massachusetts,
USA.
Author: Breakthrough Creativity: Achieving Top Performance Using the
Eight Creative Talents (Davies Black, 2001) and the "Breakthrough
Creativity Profile" (HRDQ, 2003).
5. Prof. Cynthia Wagner Weick, PhD – Expert Advisor for Instrument Development
Eberhardt School of Business
University of Pacific,
California, USA.
Author :
Weick, C.W. and J.D. Martin. 2006. Part time and Full time Inventors: Rising
with the Creative Class. International Journal of Entrepreneurship and
Innovation. Vol 7(1), pp 5-12.
Weick, C.W. and C.F. Eakin. 2005. Independent Inventors and Innovation:
An Empirical Study, International Journal of Entrepreneurship and Innovation,
Vol 6 (1), pp. 5-15.
6. Prof. Lynn K. Mytelka, PhD – Expert Advisor for Instrument Development
Professorial fellow
United Nations University
Netherlands.
Author : Mytelka, Lynn K. (ed.), 2007, Innovation and Economic Development,
Edward Elgar.
361
7. Prof. Sonja Lyubomirsky, PhD – Expert Advisor for Instrument Development
Professor
Department of Psychology,
University of California Riverside,
California, USA.
Author : Lyubomirsky, S. (2008). The how of happiness: A scientific
approach to getting the life you want. New York: Penguin Press.
8. Prof. Kenneth Bollen, PhD – Expert Advisor for Statistical Analysis
Director,
Odum Institute for Research in Social Sciences,
University of North Carolina,
USA.
Author: Bollen, K.A. and J.S. Long (eds). 1993. Testing Structural Equation
Models. Newbury Park,CA: Sage, 320 pages.
9. Prof. Rex Kline, PhD – Expert Advisor for Statistical Analysis
Professor
Psychology, Concordia University
Montreal, Quebec, Canada.
Author: Kline R. B. (2010) Principles and Practice of Structural Equation
Modeling, 3rd Edition, New York, Guilford press
10. Prof. Deborah L. Bandalos – Expert Advisor for Model Comparison
Assessment and Measurement Program Director
Department of Graduate Psychology
James Madison University
Editor of the Journal of Structural Equation Modeling; Multideciplinary Journal
362
D: Personal Communication with Advisors
1. Lynne C. Levesque
From: "Lynne Levesque" <l_levesque@comcast.net>
To: "Nalaka Wickramasinghe" nalakacw@yahoo.com
Subject: Re: requesting a comments and opinion about inventors' success measures
Date: Monday, July 6, 2009 7:46 AM
Message contains attachments
2 Files (567KB) | Download All

TRIZ patent research.doc

Success of inventor.docx
Nalaka...
I have to say I am very impressed with the instrument as well as your approach to getting feedback on it. I am
attaching my comments and edits although I realize I was editing a lot of your rationale statements, which may
or may not have been needed. However, there were some cases where I was confused by what you were asking
so I
noted those areas. Also, there were points where I challenge your assumptions, but then I hope it is
safe to assume that these are your hypotheses not necessarily proven statements at this point. I was not able to
touch your mind map nor did I want to -- since any changes in wording to line up with any changes in the text
would be up to you?
Some other suggestions in addition to those made with each item:
1. What about defining "invention?" Does anything that has been awarded a patent qualify? Are there levels
of patent quality or applicability or originality that you might need to capture? I am not sure it's totally relevant
but a Russian by the name of Altschuler worked in the Soviet patent office and classified thousands and
thousands of patents by their level of inventiveness and then developed the TRIZ methodology based on it to
help others be more creative in their problem solving. I am also attaching a brief overview of this work for your
information.
2. You appear to be limited your inventions to products and processes, those that have been granted a patent.
Am I correct? That's fair, since it's a measure you can find. I assume however, that this line of research would
ignore social inventions such as Grameen Bank, unless it received a patent?
Let me know if you have any questions?
Cheers and good luck with your research. Before you start using the Eight Creative Talents instrument, please
check in with me so we can be sure you have the right edition, etc.!
Thanks, Lynne
Lynne C. Levesque, Ed.D.
363
2.
Prof. Cynthia Wagner Weick
From: Cynthia Weick <cwagner@PACIFIC.EDU>
Subject: RE: requesting guidence
To: nalakacw@yahoo.com
Date: Tuesday, August 12, 2008, 8:28 PM
Dear Nalaka:
Thank you for your email.
It is very difficult to measure innovation, as you know. There is no
complete nor perfect measure. We have to do the best we can.
Here are my thoughts.
(1) Inventive success= (no. of patents received/no of inventions made)*100
Okay, but here are your limitations. You will be challenged to measure
the denominator. I
assume this will be selfreported. Will "patents" be in your country? WIPO? US patents? Are all
inventions that reach the marketplace patented? No. Some inventions, as you know, are comprised of
many patented inventions. Will this be accounted for?
(2) Innovation success= (no. commercialized innovations/No. patents
received)*100
Again, your limitation is that not all inventions that go to market are
patented; and some innovations are comprised of many patents.
(3) Financial success= (monthly net income received from
commercialization/monthly R&D cost)*100
Why "monthly" net income and R and D cost versus annual? How will you tie "R and D cost" to the
above variables? Will you be able to isolate the R&D associated with particular
inventions/innovations?
If I were you, I would consider doing the following:
(1) select a recognizable "sector" of patented inventions, ideally
one in which one patented invention typically leads to a single product (and simply exclude inventions
that are not patented, though recognize these exist). Perhaps you can even confine your study to small
cos focused on one product.
(2) define "success in innovation" as generation of sales (binary).
This is relatively easy to measure.
(3) request the "sales level" achieved over a period of time (e.g,
sales over the first three years - or other time period), though this will not
be as accurately reported as (2).
(4) also request the " profit level" achieved (e.g, profit over the
first three years), though this is very hard to achieve accuracy with, unless
you are focusing on one product companies based on one or a few patents.
(Imagine if you were in a company with several products and R&D directions
and you had to separate out the specific R&D associated with a single
product - very difficult).
Since the measures in (2), (3) and (4) have differing levels of accuracy. I would treat them in separate
equations. Experience suggests that you may end up defaulting to (2) given that it is clearest and less
subject to inaccuracy either a patented invention reaches the market or it does not. You do not detail
what independent variables you will be testing, so I cannot comment on this.
In summary, I personally believe it is best to start out with a focused study using data that is well
defined. While this approach is artificial in a sense, it does provide a measurable picture. And it may
allow you to broaden your studies in the future.
I hope this helps.
Best regards,
Cynthia
364
3.
Prof. Lynn Mytelka
From: "Lynn Mytelka" <lmytelka@gmail.com>
To: "Nalaka Wickramasinghe" nalakacw@yahoo.com
Subject: Re: requesting kind advice for developing an instrument to measure "success of inventor"
Date: Wednesday, July 1, 2009 1:52 AM
Dear Nalaka,
I like your tree diagram and your dimension are certainly the important ones. I have no specific comments to
make on these. I would, however, question your decision to rate success in 5 categories which are not ordinal
because category 3 would only apply if the respondent had exactly 50%. This distorts the perception of that
category. It might be better if you were to divide the answers up among 5 equal categories (20% each).This
would give a more intuitively meaningful pattern.
I am also concerned about how you will find the data to test these propositions? Are you planning to interview
the firms/send a mailed questionnaire? If the latter you risk having too small a response rate and a not very
representative sample.
There is also the issue of the distribution of patenting across sectors/technological families - for example, there
is far more patenting, as you well know, in pharma/biopharma than in renewable energies, yet the later is
certainly growing. Success would reflect these differences across sectors and overtime. Please see the EU
studies below for a recent reference on this.
good luck with your study.
All the Best
Lynn K. Mytelka
Professorial Fellow, UNU-MERIT, Maastricht, NL
Distinguished Research Professor, Carleton University, Ottawa, Canada
EU, JRC working paper series
16/06/2009
European Union. The JRC's Institute for Prospective Technological Studies has published 12 working
papers on Research and Development
The JRC recently published a series of working papers on Research and Development (R&D) elaborated by the
JRC's Institute for Prospective Technological Studies.
The papers cover a number of issues, including "Corporate R&D: A policy target looking for instruments",
"EU-US differences in the size of R&D intensive firms", "R&D and Productivity: Testing Sectoral Peculiarities
Using Micro Data", "EU-R&D in Services Industries and the EU-US R&D Investment Gap" or "The
public/private nexus of R&D".
According to the JRC's press release, the papers show that R&D intensity (the ratio of investment in R&D
against sales) by European companies is comparable to or higher than the same ratio for companies in other
parts of the world. The difference between the EU and the rest of the world is shown by the weight that different
sectors have in their respective economies. The heaviest European R&D investors are in the car sector while in
the U.S. and other commercial partners the lead is taken by companies the Information and Communication
Technology (ICT) sector.
Related Links:
Related JRC press release
IPTS Working Papers on Corporate R&D and Innovation
365
4.
Prof . Sonja Lyubomirsky
From: "Sonja Lyubomirsky" <sonja.lyubomirsky@ucr.edu>
To: "Nalaka Wickramasinghe" nalakacw@yahoo.com
Subject: Re: Requesting Opinion on developing composite index of subjective well-being
Date: Sunday, February 21, 2010 5:43 PM
Message contains attachments
Lyubomirsky & Lepper, 1999.pdf
Hi Nalaka – the attached SHS scale validation paper should be helpful.
I have some papers that have combined these two scales with no problem. See the “papers and publications”
link on my academic website (URL below) for examples.
Good luck,
-_SL
________________________
Sonja Lyubomirsky, Ph.D.
Professor and Graduate Advisor
Department of Psychology
University of California
Riverside, CA 92521
My academic web site: www.faculty.ucr.edu/~sonja/
The How of Happiness: A Scientific Approach to Getting the Life You Want (Penguin Press, 2008)
site: www.thehowofhappiness.com
Book web
My blog at Psychology Today: blogs.psychologytoday.com/blog/the-how-happiness
On 2/21/10 5:39 PM, "Nalaka Wickramasinghe" <nalakacw@yahoo.com> wrote:
Dear Professor Lyubomirsky,
I am Nalaka Wickramasinghe from Sri Lanka reading for PhD in UPM, Malaysia.
My study is on identifying the individual level factors influencing the objective and subjective success of
grassroots level inventors in Sri Lanka. Here I operationally define subjective success as the subjective
well-being that comprise with subjective happiness (emotional aspect) and life satisfaction (cognitive
aspect). I am planning to use your Subjective Happiness Scale (SHS) and Satisfaction with life scale(SWLS)
to measure the subjective success.
My question is,
is there any validity issues need to be considered of creating composite index using the items os SHS and
SWLS to measure Subjective success (SS).
SS = SHS+ SWLS
SS = Emotional aspect + Cognitive aspect
or is there less items scales (items 12 or less ) that capture the both aspects of subjective well-being?
Your constructive opinion in this regard is highly appreciated and I promise to cite your work in my thesis and
all the publications that I will make based on this study.
Thank you very much
C.N. Wickramasinghe
{MBA in IT(Moratuwa),PGDiP in IT(Kelaniya), B.Com(SP) Hons(S'Japura), MAAT}
366
5.
Prof. Kenneth Bollen
From: Kenneth Bollen <bollen@email.unc.edu>
Subject: Re: Requesting opinion on using binary exogenous variable in Path Model
To: "Nalaka Wickramasinghe" <nalakacw@yahoo.com>
Date: Thursday, September 23, 2010, 4:25 AM
Dichotomous exogenous variables like marital status cause no problem.
If all of your exogenous variables are observed variables like marital
status, then you can test the normality of the residuals from your
model. Or you can always use bootstrapping procedures or robust
standard errors and chi square to compare to the usual estimates.
________________________________________________________________________________________
On 9/23/10 5:39 AM, Nalaka Wickramasinghe wrote:
> Dear prof. Bollen,
>
> I am Nalaka Wickramasinghe from Sri Lanka reading for PhD in social
> science. I am very novice to the SEM and Path analysis. In my model i
> have to use marital status as exogenous variable with other 12
> continuous variables.
> Please kindly advice me how this binary variable should treat in path analysis especially in
normality test and model estimations.
>
> Your kind response would be highly appreciated
>
> Thank you very much
> C.N. Wickramasinghe
> {MBA in IT(Moratuwa),PGDiP in IT(Kelaniya), B.Com(SP) Hons(S'Japura), MAAT}
From: Kenneth Bollen <bollen@email.unc.edu>
Subject: Re: Requesting opinion on using binary exogenous variable in Path Model
To: "Nalaka Wickramasinghe" <nalakacw@yahoo.com>
Date: Thursday, September 23, 2010 6:22 A
Amos does not have a test for the residuals. If your model is just a 2-equation system with observed
variables, you'll not need to worry about a normal distribution for the residuals provided your sample
size is large.
________________________________________________________________________________________________
On 9/23/10 9:09 AM, Nalaka Wickramasinghe wrote:
Dear prof. Bollen,
Thank you very much for the prompt response.
Yes. My model have 10 observed exogenous variables. Marital status is the only one dichotomous variable, all
others are continuous variables. I have two endogenous continuous variables. Is that possible to test the
normality using AMOS normality test with skewness and Kurtosis?
Thank you very much for the advice and I will follow your instructions to measure the normality. I will promise
to cite your contribution in my thesis and publications based on the study.
Cheers
C.N. Wickramasinghe
{MBA in IT(Moratuwa),PGDiP in IT(Kelaniya), B.Com(SP) Hons(S'Japura), MAAT}
367
6.
Prof. Rex Kline
From: "Rex B Kline" <rbkline@alcor.concordia.ca>
Subject: Re: Requesting advice on Path Analysis (ML method)
To: "Nalaka Wickramasinghe" <nalakacw@yahoo.com>
Date: Friday, December 10, 2010 4:38 AM
Nalaka:
Yes you can. There is no special problem using ML estimation when you
have categorical exogenous variables. For example, you can include a
dichotomous variable as an exogenous variable in a path model with no
special coding. It is when the endogenous variables are categorical that
a different estimation method is needed.
Regards,
Rex Kline
-Professor
Psychology, Concordia University
Montreal, Quebec, Canada
http://tinyurl.com/rexkline
________________________________________________________________________
On Fri, December 10, 2010 12:55 am, Nalaka Wickramasinghe wrote:
> Dear Prof. Kline,
>
> I am Nalaka Wickramasinghe, from Sri Lanka
> reading for PhD at UPM, Malaysia. I am novice to the SEM and Path
> Analysis.
>
> I am doing a study on determining the
> factors effecting the happiness of inventors. There I have to consider
> marital status (yes / No) as exogenous variables in a Path model using
> Maximum likely-hood estimates. There are other 9 exogenous variables
> those are continuous and two continuous endogenous variables.
>
> Please
> kindly advice me can I use the marital status (yes/No) in path model
> (ML method). Your kind advice would be highly appreciated.
>
> Thank you very much
>
> C.N. Wickramasinghe
> {MBA in IT(Moratuwa),PGDiP in IT(Kelaniya), B.Com(SP) Hons(S'Japura),
> MAAT}
368
7. Profesor Deborah Bandalos
Subject:RE: comparison of Two path models
From: Bandalos, Deborah Louise - bandaldl (bandaldl@jmu.edu)
To:
nalakacw@yahoo.com;
Date: Monday, May 14, 2012 11:09 PM
Dear Nalaka,
These models are not nested because they do not have the same variables.
Therefore, indexes such as the chi-square difference or other chi-square based fit indexes
are not appropriate. However, information criterion based indexes such as the AIC, BIC, etc.
can be used to compare non-nested models such as these.
All the best,
Debbi Bandalos
From: Nalaka Wickramasinghe [nalakacw@yahoo.com]
Sent: Monday, May 14, 2012 8:14 AM
To: Bandalos, Deborah Louise - bandaldl; rbkline@alcor.concordia.ca
Subject: comparison of Two path models
Dear Professor.,
I have two competing path models
A: bottom up model:
Exo( x1: x1:x3:x4:x5:x6:x7:x8:x9) --> M---> Y
in this model x9 is marital status measured as dichotomous variable: all other variable are continuous
variables
B : top down model
Exo(Y)-->M-->( x1: x1:x3:x4:x5:x6:x7:x8:)
in second model I omit marital status as far as it is dichotomous variable (I used AMOS it does not
provide facility for Dichotomous Endogenous variables). all other variables are same from same data
set that used in model A.
Please advice me on
1.what is the best way to compare these two model.
2.I there any problem to compare these two two models using AIC, because the marital status was
omitted from the second model.
3. can we use RMSEA, Chi Square, P or other indexes (other than AIC) to do the model comparison
in this nature.
Your advice on this would be highly appreciated.
Thank you
Nalaka
369
E: Power Analysis and Sample size Calculation
NIESEM Power Analysis of Structural Equation Modeling
SEM Power Analysis Submenu:
============================
[a] Estimate power for given N
[b] Estimate N for given power
[z] Return to main menu
Select a menu item letter: a
Enter the following information about the model when prompted...
...the chosen sample size:
200
...the null hypothesized RMSEA value:
.00
...the alternative hypothesized RMSEA value: .09
...the chosen alpha significance level (e.g., 0.05): .05
...the degrees of freedom of the model:
9
...the number of groups in the model:
1
------- CSM Power Analysis -----RMSEA Null Value
=
RMSEA Alternative Value =
Alpha significance level =
Degrees of freedom
=
Number of groups
=
Proposed sample size =
0.000
0.090
0.050
9
1
200
--------------------------------Estimated power =
0.7621
--------------------------------Press the <ENTER> key to continue...
370
PASS 2008: One Correlation Power Analysis
Page/Date/Time
1
11/26/2010 6:59:04 AM
Numeric Results when Ha: R0<>R1
Power
N
Alpha
Beta
0.80018
782
0.05000
0.19982
0.80008
193
0.05000
0.19992
0.80034
84
0.05000
0.19966
R0
0.00000
0.00000
0.00000
R1
0.10000
0.20000
0.30000
References
Graybill, Franklin. 1961. An Introduction to Linear Statistical Models. McGraw-Hill. New York,
New York.
Guenther, William C. 1977. 'Desk Calculation of Probabilities for the Distribution of the Sample
Correlation
Coefficient', The American Statistician, Volume 31, Number 1, pages 45-48.
Zar, Jerrold H. 1984. Biostatistical Analysis. Second Edition. Prentice-Hall. Englewood Cliffs,
New Jersey.
Report Definitions
Power is the probability of rejecting a false null hypothesis. It should be close to one.
N is the size of the sample drawn from the population. To conserve resources, it should be small.
Alpha is the probability of rejecting a true null hypothesis. It should be small.
Beta is the probability of accepting a false null hypothesis. It should be small.
R0 is the value of the population correlation under the null hypothesis.
R1 is the value of the population correlation under the alternative hypothesis.
Summary Statements
A sample size of 782 achieves 80% power to detect a difference of -0.10000 between the null
hypothesis correlation of 0.00000 and the alternative hypothesis correlation of 0.10000 using a
two-sided hypothesis test with a significance level of 0.05000.
Chart Section
N vs R1 with R0=0.00 Alpha=0.05 Power=0.80 Corr Test
800
N
600
400
200
0
0.10
0.15
0.20
0.25
R1
371
0.30
0.35
PASS 2008: Chi-Square Test Power Analysis
Page/Date/Time
1
11/26/2010 6:56:52 AM
Numeric Results for Chi-Square Test
Power
N
W
0.80147
181
0.3000
Chi-Square
16.2900
DF
10
Alpha
0.05000
Beta
0.19853
References
Cohen, Jacob. 1988. Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum
Associates,
Hillsdale, New Jersey.
Report Definitions
Power is the probability of rejecting a false null hypothesis. It should be close to one.
N is the size of the sample drawn from the population. To conserve resources, it should be small.
W is the effect size--a measure of the magnitude of the Chi-Square that is to be detected.
DF is the degrees of freedom of the Chi-Square distribution.
Alpha is the probability of rejecting a true null hypothesis.
Beta is the probability of accepting a false null hypothesis.
Summary Statements
A sample size of 181 achieves 80% power to detect an effect size (W) of 0.3000 using a 10
degrees of freedom Chi-Square Test with a significance level (alpha) of 0.05000.
Chart Section
N vs W with DF=10 Alpha=0.05 Power=0.80 Chi2 Test
200
N
150
100
50
0
0.0
0.1
0.2
W
372
0.3
0.4
F: Exploratory Data Analysis
Table 75: Testing for Missing Values
Cases
Valid
N
Missing
Percent
N
Total
Percent
N
Percent
Age
200
100.0%
0
.0%
200
100.0%
MaritialSta
200
100.0%
0
.0%
200
100.0%
Incomein
200
100.0%
0
.0%
200
100.0%
DailyInvenHours
200
100.0%
0
.0%
200
100.0%
InternetShort
200
100.0%
0
.0%
200
100.0%
SocialCap
200
100.0%
0
.0%
200
100.0%
Maximization
200
100.0%
0
.0%
200
100.0%
LifeOrientation
200
100.0%
0
.0%
200
100.0%
InventiveSatisfaction
200
100.0%
0
.0%
200
100.0%
comConnectedness
200
100.0%
0
.0%
200
100.0%
LogExtLinks
200
100.0%
0
.0%
200
100.0%
ObjectiveSuccess
200
100.0%
0
.0%
200
100.0%
Subjective Success
200
100.0%
0
.0%
200
100.0%
Table 76: Descriptive Statistics of Variables
Age
MaritialSta
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
373
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Statistic
Std. Error
41.77
1.031
39.73
43.80
41.67
41.00
212.542
14.579
14
74
60
23
.120
.172
-.903
.342
.68
.033
.61
.74
.69
1.00
.220
.470
0
1
1
1
-.753
.172
-1.448
.342
IncomeinThou
DailyInvenHours
InternetShort
SocialCap
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
374
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Lower Bound
Upper Bound
38.2600
35.5919
40.9281
37.7556
37.0000
366.143
19.13487
5.00
84.00
79.00
25.00
.312
-.580
3.80
3.57
4.03
3.76
4.00
2.794
1.672
1
8
7
2
.223
-.430
12.8450
12.2325
13.4575
12.9222
13.0000
19.297
4.39289
4.00
20.00
16.00
6.00
-.191
-.890
54.2000
52.8885
55.5115
54.2722
54.0000
88.462
9.40544
31.00
76.00
45.00
12.75
-.106
-.148
1.35304
.172
.342
.118
.172
.342
.31062
.172
.342
.66507
.172
.342
Maximization
LifeOrientation
InventiveSatisfaction
comConnectedness
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
375
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Lower Bound
Upper Bound
27.4850
26.7593
28.2107
27.5778
28.0000
27.085
5.20434
15.00
38.00
23.00
7.75
-.233
-.708
23.4650
23.0433
23.8867
23.5500
24.0000
9.144
3.02399
16.00
30.00
14.00
4.00
-.409
-.145
16.2350
15.9408
16.5292
16.2833
16.0000
4.452
2.10998
11.00
20.00
9.00
3.00
-.317
-.339
43.2750
42.4014
44.1486
43.3778
44.0000
39.256
6.26543
28.00
56.00
28.00
8.75
-.344
-.413
.36800
.172
.342
.21383
.172
.342
.14920
.172
.342
.44303
.172
.342
LogExtLinks
ObjectiveSuccess
Subjective Success
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
Mean
95% Confidence Interval for
Mean
5% Trimmed Mean
Median
Variance
Std. Deviation
Minimum
Maximum
Range
Interquartile Range
Skewness
Kurtosis
376
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Lower Bound
Upper Bound
1.2767
1.2616
1.2918
1.2739
1.2553
.012
.10831
1.11
1.53
.42
.14
.339
-.759
2.5200
2.3122
2.7278
2.5222
2.0000
2.221
1.49020
.00
5.00
5.00
3.00
.265
-1.011
41.1000
40.1168
42.0832
41.1389
41.0000
49.719
7.05114
24.00
58.00
34.00
9.00
-.097
.021
.00766
.172
.342
.10537
.172
.342
.49859
.172
.342
F3: Exploratory Data Plots
F3.1 Age
Frequency Stem & Leaf
2.00
9.00
14.00
16.00
25.00
30.00
21.00
21.00
19.00
18.00
12.00
10.00
3.00
1 . 44
1 . 777788889
2 . 00000002222333
2 . 5555666666667779
3 . 0000000000001112233333344
3 . 555555666666667777778888889999
4 . 001111222223334444444
4 . 555566667888888999999
5 . 0112222333333444444
5 . 566677788889999999
6 . 122334444444
6 . 5555555999
7 . 224
Stem width: 10
Each leaf:
1 case(s)
(a)
(b)
(c )
(d)
Figure 39 : Exploratory Analysis Plots of Age
377
F3.2 Income
Frequency
.00
7.00
12.00
15.00
15.00
14.00
21.00
21.00
19.00
25.00
6.00
12.00
10.00
8.00
6.00
3.00
6.00
Stem & Leaf
0.
0 . 5555577
1 . 000000000014
1 . 555555555666666
2 . 000000111122223
2 . 55555555555568
3 . 000000000000000011122
3 . 555555555555556777889
4 . 0000000000000122344
4 . 5555555555666777788899999
5 . 000004
5 . 555555789999
6 . 0000000123
6 . 55555589
7 . 000034
7 . 555
8 . 000044
Stem width: 10.00
Each leaf:
1 case(s)
(a)
(b)
(c)
(d)
Figure 40 : Exploratory Analysis Plots of Income
378
F3.3 Engagement in Invention
Frequency Stem & Leaf
18.00
30.00
40.00
43.00
39.00
19.00
7.00
4.00
1 . 000000000000000000
2 . 000000000000000000000000000000
3 . 0000000000000000000000000000000000000000
4 . 0000000000000000000000000000000000000000000
5 . 000000000000000000000000000000000000000
6 . 0000000000000000000
7 . 0000000
8 . 0000
Stem width: 1
Each leaf:
1 case(s)
(a)
(b)
(c)
(d)
Figure 41: Exploratory Analysis Plots of Engagement in Invention
379
F3.4 Internet Usage
Frequency Stem & Leaf
7.00
3.00
11.00
6.00
12.00
7.00
15.00
19.00
13.00
14.00
14.00
15.00
16.00
10.00
20.00
5.00
13.00
4 . 0000000
5 . 000
6 . 00000000000
7 . 000000
8 . 000000000000
9 . 0000000
10 . 000000000000000
11 . 0000000000000000000
12 . 0000000000000
13 . 00000000000000
14 . 00000000000000
15 . 000000000000000
16 . 0000000000000000
17 . 0000000000
18 . 00000000000000000000
19 . 00000
20 . 0000000000000
Stem width: 1.00
Each leaf:
1 case(s)
(a)
(b)
(c)
(d)
Figure 42: Exploratory Analysis Plots of Internet Usage
380
F3.4 Social Capital
Frequency Stem & Leaf
6.00
7.00
15.00
35.00
42.00
40.00
26.00
19.00
8.00
2.00
3.
3.
4.
4.
5.
5.
6.
6.
7.
7.
111244
6678999
000000113344444
55666666677777888888888899999999999
000000001111112222223333333333444444444444
5555555556666677777777788888888999999999
00000111112233333333344444
5555555666677788999
11333333
56
Stem width: 10.00
Each leaf:
1 case(s)
( a)
(b)
(c)
(d )
Figure 43: Exploratory Analysis Plots of Social Capital
381
F3.5 Maximizing Tendency
Frequency Stem & Leaf
1.00
1. 5
5.00
1 . 66777
11.00
1 . 88889999999
13.00
2 . 0000001111111
18.00
2 . 222223333333333333
24.00
2 . 444444444445555555555555
22.00
2 . 6666666666777777777777
30.00
2 . 888888888888888888899999999999
26.00
3 . 00000000111111111111111111
22.00
3 . 2222222222333333333333
21.00
3 . 444444444444455555555
5.00
3 . 66677
2.00
3 . 88
Stem width:
Each leaf:
10.00
1 case(s)
(a )
(b)
( c)
(d)
Figure 44: Exploratory Analysis Plots of Maximizing Tendency
382
F3.6 Life Orientation
Frequency Stem & Leaf
5.00
16 . 00000
4.00
17 . 0000
5.00
18 . 00000
7.00
19 . 0000000
13.00
20 . 0000000000000
15.00
21 . 000000000000000
18.00
22 . 000000000000000000
24.00
23 . 000000000000000000000000
32.00
24 . 00000000000000000000000000000000
25.00
25 . 0000000000000000000000000
20.00
26 . 00000000000000000000
18.00
27 . 000000000000000000
8.00
28 . 00000000
4.00
29 . 0000
2.00
30 . 00
Stem width:
Each leaf:
1.00
1 case(s)
(a)
(b)
(b)
( c)
(d)
Figure 45: Exploratory Analysis Plots of Life Orientation
383
F3.7 Inventive Life Satisfaction
InventiveSatisfaction Stem-and-Leaf Plot
Frequency Stem & Leaf
4.00
11 . 0000
6.00
12 . 000000
13.00
13 . 0000000000000
17.00
14 . 00000000000000000
29.00
15 . 00000000000000000000000000000
35.00
16 . 00000000000000000000000000000000000
38.00
17 . 00000000000000000000000000000000000000
30.00
18 . 000000000000000000000000000000
17.00
19 . 00000000000000000
11.00
20 . 00000000000
Stem width:
Each leaf:
1.00
1 case(s)
(a)
(b)
( c)
(d)
Figure 46: Exploratory Analysis Plots of Inventive Life Satisfaction
384
F3.8 Community Connectedness
Frequency Stem & Leaf
.00
3.00
4.00
10.00
11.00
9.00
13.00
19.00
27.00
25.00
27.00
19.00
17.00
9.00
4.00
3.00
2.
2.
3.
3.
3.
3.
3.
4.
4.
4.
4.
4.
5.
5.
5.
5.
888
0111
2222222223
44444445555
666777777
8888888888999
0000000000011111111
222222222233333333333333333
4444444444444555555555555
666666666666677777777777777
8888888888889999999
00000001111111111
222222333
4445
666
Stem width: 10.00
Each leaf:
1 case(s)
(a)
(b)
( c)
(d)
Figure 47: Exploratory Analysis Plots of Community Connectedness
385
F3.9 External Linkages
Frequency Stem & Leaf
35.00
12.00
34.00
34.00
37.00
18.00
12.00
15.00
3.00
11 . 11111111111111111144444444444444444
11 . 777777777777
12 . 0000000000000000000003333333333333
12 . 5555555555555555555557777777777777
13 . 0000000000000222222222222222444444444
13 . 666666668888999999
14 . 111333334444
14 . 666666666777999
15 . 003
Stem width:
.10
Each leaf:
1 case(s)
(a)
(b)
( c)
(d)
Figure 48: Exploratory Analysis Plots of External Linkages
386
F3.10 Objective Success
Frequency Stem & Leaf
11.00
50.00
47.00
37.00
26.00
29.00
0.
1.
2.
3.
4.
5.
00000000000
00000000000000000000000000000000000000000000000000
00000000000000000000000000000000000000000000000
0000000000000000000000000000000000000
00000000000000000000000000
00000000000000000000000000000
Stem width: 1.00
Each leaf:
1 case(s)
( a)
(b)
( c)
(d)
Figure 49: Exploratory Analysis Plots of Objective Success
387
F3.11 Subjective Success
Frequency Stem & Leaf
.00
4.00
4.00
6.00
8.00
5.00
7.00
19.00
28.00
26.00
22.00
18.00
17.00
14.00
8.00
6.00
2.00
4.00
2.00
2.
2 . 4455
2 . 6667
2 . 889999
3 . 00001111
3 . 23333
3 . 4444555
3 . 6666666677777777777
3 . 8888888888889999999999999999
4 . 00000000000001111111111111
4 . 2222222222333333333333
4 . 444444444444455555
4 . 66666666666666777
4 . 88888889999999
5 . 00011111
5 . 223333
5 . 55
5 . 6666
5 . 88
Stem width: 10.00
Each leaf:
1 case(s)
( a)
(b)
( c)
(d)
Figure 50: Exploratory Analysis Plots of Subjective Success
388
F4 Scatter plots: exogenous variables vs. Subjective Success
(a)
(b)
( c)
(d)
(e)
(f)
389
(g)
(i)
(h)
(j)
(k)
Figure 51: Scatter plots of exogenous variables Vs. Subjective Success
390
G: Path Analysis Equation Model
Path Analytic Equation Model
Variables in the Model
Subjective success = Y1
Objective Success = Y2
Marital Status = X1
Income = X2
Internet Usage = X3
Engagement in Invention (Daily inventive Hours) = X4
Inventive Career Satisfaction = X5
Life Orientation = X6
External Linkages = X7
Maximizing Tendency = X8
Social Capital = X9
Community Connectedness = X10
βn = Standardized regression coefficient of n th relationship between variable
Path Analytic Predictor Equations of Initial conceptual model
Y2 =
βY2.X1X1+ βY2.X2X2+ β Y2.X3X3+ β Y2.X4X4+ β Y2.X5X5+ β Y2.X6X6+ β Y2.X7X7+ β Y2.X8X8+ β Y2.X9X9 +
β Y2.X10X10+ error(Y2)
Y1 =
β Y1.Y2Y2+ βY1.X1X1+ βY1.X2X2+ β Y1.X3X3+ β Y1.X4X4+ β Y1.X5X5+ β Y1.X6X6+ β Y1.X7X7+ β Y1.X8X8+
β Y1.X9X9+ β Y1.X10X10+ error(Y1)
Path Analytic Equations of Initial Reversal Conceptual Model
Y2= βY1 + Error
X2 = βY1.X2Y1+ βY2.X2Y2+ Error (X2)
X3 = βY1.X3Y1+ βY2.X3Y2+ Error (X3)
X4 = βY1.X4Y1+ βY2.X4Y2+ Error (X4)
X5 = βY1.X5Y1+ βY2.X5Y2+ Error (X5)
X6 = βY1.X6Y1+ βY2.X6Y2+ Error (X6)
X7 = βY1.X7Y1+ βY2.X7Y2+ Error (X7)
X8 = βY1.X8Y1+ βY2.X8Y2+ Error (X8)
X9 = βY1.X9Y1+ βY2.X9Y2+ Error (X9)
X10= βY1.X10Y1+ βY2.X10Y2+ Error (X10)
391
H: AMOS 18 Bottom-up model original result outputs
Figure 52: Original AMOS 18 Path diagram of Initial Conceptual Model
392
Figure 53: Original AMOS 18 Path diagram of Final Modified Conceptual Model
393
Full AMOS 18-text output of the final modified Bottom-Up path model
Notes for Model (Default model)
Computation of degrees of freedom (Default model)
Number of distinct sample moments:
Number of distinct parameters to be estimated:
Degrees of freedom (66 - 57):
66
57
9
Result (Default model)
Minimum was achieved
Chi-square = 6.337
Degrees of freedom = 9
Probability level = .706
Estimates (Group number 1 - Default model)
Scalar Estimates (Group number 1 - Default model)
Maximum Likelihood Estimates
Regression Weights: (Group number 1 - Default model)
ObjectiveSuccess
ObjectiveSuccess
ObjectiveSuccess
SWB
SWB
SWB
SWB
SWB
SWB
SWB
<--<--<--<--<--<--<--<--<--<---
Dailyinvethours
LogExtLinks
IncomeinThou
InternetShort
ObjectiveSuccess
InventiveSatisfaction
LifeOrientation
SocialCap
MaritalStatus
ComConnectedness
Estimate
.275
4.822
.017
.285
.795
.857
.445
.120
2.007
.257
S.E.
.054
.820
.005
.091
.259
.189
.128
.041
.805
.065
C.R.
5.050
5.878
3.480
3.132
3.074
4.543
3.477
2.891
2.494
3.976
Standardized Regression Weights: (Group number 1 - Default model)
ObjectiveSuccess
ObjectiveSuccess
ObjectiveSuccess
SWB
SWB
SWB
SWB
SWB
SWB
SWB
<--<--<--<--<--<--<--<--<--<---
Dailyinvethours
LogExtLinks
IncomeinThou
InternetShort
ObjectiveSuccess
InventiveSatisfaction
LifeOrientation
SocialCap
MaritalStatus
ComConnectedness
394
Estimate
.308
.350
.212
.178
.169
.258
.192
.161
.134
.229
P
***
***
***
.002
.002
***
***
.004
.013
***
Label
par_35
par_37
par_39
par_27
par_41
par_42
par_43
par_44
par_45
par_46
Covariances: (Group number 1 - Default model)
LifeOrientation
SocialCap
SocialCap
MaritalStatus
LifeOrientation
SocialCap
MaritalStatus
InventiveSatisfaction
SocialCap
LifeOrientation
InventiveSatisfaction
ComConnectedness
SocialCap
MaritalStatus
LifeOrientation
InventiveSatisfaction
ComConnectedness
InternetShort
ComConnectedness
SocialCap
LifeOrientation
InventiveSatisfaction
Dailyinvethours
InternetShort
MaritalStatus
MaritalStatus
ComConnectedness
SocialCap
LogExtLinks
LifeOrientation
Dailyinvethours
InternetShort
MaritalStatus
LifeOrientation
LifeOrientation
InventiveSatisfaction
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
<-->
SocialCap
MaritalStatus
InventiveSatisfaction
InventiveSatisfaction
InventiveSatisfaction
ComConnectedness
ComConnectedness
ComConnectedness
InternetShort
InternetShort
InternetShort
InternetShort
Dailyinvethours
Dailyinvethours
Dailyinvethours
Dailyinvethours
Dailyinvethours
Dailyinvethours
LogExtLinks
LogExtLinks
LogExtLinks
LogExtLinks
LogExtLinks
LogExtLinks
LogExtLinks
InternetShort
IncomeinThou
IncomeinThou
IncomeinThou
IncomeinThou
IncomeinThou
IncomeinThou
IncomeinThou
ComConnectedness
MaritalStatus
IncomeinThou
395
Estimate
S.E.
C.R.
P
Label
2.532 2.014 1.257 .209 par_1
.300
.312
.961 .337 par_2
1.473 1.404 1.049 .294 par_3
.026
.070
.377 .706 par_4
1.181
.458 2.579 .010 par_5
5.720 4.176 1.370 .171 par_6
-.346
.209 -1.654 .098 par_7
4.580
.987 4.639 *** par_8
12.456 3.045 4.091 *** par_9
2.937
.960 3.060 .002 par_10
1.026
.658 1.560 .119 par_11
4.413 1.966 2.244 .025 par_12
2.815 1.127 2.498 .012 par_13
.100
.056 1.792 .073 par_14
.573
.359 1.597 .110 par_15
.682
.253 2.691 .007 par_16
1.345
.745 1.806 .071 par_17
1.084
.524 2.070 .038 par_18
.125
.049 2.558 .011 par_19
.068
.072
.945 .344 par_20
.049
.023 2.113 .035 par_21
.037
.016 2.270 .023 par_22
.005
.013
.359 .719 par_23
.074
.034 2.189 .029 par_24
-.002
.004 -.663 .507 par_25
-.195
.146 -1.337 .181 par_26
4.398 8.462
.520 .603 par_28
38.753 12.988 2.984 .003 par_29
-.040
.146 -.273 .785 par_30
5.669 4.101 1.382 .167 par_31
6.832 2.307 2.961 .003 par_32
23.230 6.153 3.775 *** par_33
3.834
.690 5.561 *** par_34
4.602 1.376 3.345 *** par_36
.021
.100
.211 .833 par_38
.624 2.848
.219 .827 par_40
Variances: (Group number 1 - Default model)
LifeOrientation
SocialCap
MaritalStatus
InventiveSatisfaction
ComConnectedness
InternetShort
Dailyinvethours
LogExtLinks
IncomeinThou
e1
e2
Estimate
S.E.
9.099
.912
88.020 8.824
.219
.022
4.430
.444
39.059 3.916
19.201 1.925
2.780
.279
.012
.001
364.312 36.523
1.561
.156
26.532 2.660
C.R.
9.975
9.975
9.975
9.975
9.975
9.975
9.975
9.975
9.975
9.975
Squared Multiple Correlations: (Group number 1 - Default model)
ObjectiveSuccess
SWB
396
Estimate
.294
.458
P
***
***
***
***
***
***
***
***
***
***
Label
par_47
par_48
par_49
par_50
par_51
par_52
par_53
par_54
par_55
par_56
Residual Covariances (Group number 1 - Default model)
IncomeinThou
LogExtLinks
Dailyinvethours
InternetShort
ComConnectedness
InventiveSatisfaction
Marital Status
SocialCap
LifeOrientation
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
1.548
.000
.000
.000
.000
.000
.000
.000
.000
.000
.015
.000
.000
.000
.000
.000
.000
.000
.000
.883
.000
.000
.000
.000
.000
.000
.005
.004
Com
Connectedness
Inventive
Satisfaction
Marital
Status
.000
.000
.000
.000
.000
.154
.123
.000
.000
.000
.000
.211
.168
.000
.000
.000
.020
.016
Com
Connectedness
Inventive
Satisfaction
Marital
Status
.000
.000
.000
.000
.000
.233
.037
.000
.000
.000
.000
.946
.148
.000
.000
.000
.395
.067
SocialCap
.000
.000
.933
.742
Life
Orientation
Objective
Success
Subjective
Success
.000
-.091
-.072
.000
.333
.530
Life
Orientation
Objective
Success
Subjective
Success
.000
-.285
-.045
.000
.431
.108
Standardized Residual Covariances (Group number 1 - Default model)
IncomeinThou
LogExtLinks
Dailyinvethours
InternetShort
ComConnectedness
InventiveSatisfaction
Marital Status
SocialCap
LifeOrientation
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.160
.000
.000
.000
.000
.000
.000
.000
.000
.000
.278
.000
.000
.000
.000
.000
.000
.000
.000
1.040
.000
.000
.000
.000
.000
.000
.011
.002
397
SocialCap
.000
.000
.937
.153
Model Fit Summary
CMIN
Model
Default model
Saturated model
Independence model
NPAR
57
66
11
CMIN
6.337
.000
388.610
RMR
.278
.000
8.220
GFI
.994
1.000
.680
DF
9
0
55
P
.706
CMIN/DF
.704
.000
7.066
AGFI
.958
PGFI
.136
.616
.567
RMR, GFI
Model
Default model
Saturated model
Independence model
Baseline Comparisons
Model
Default model
Saturated model
Independence model
NFI
Delta1
.984
1.000
.000
RFI
rho1
.900
.000
IFI
Delta2
1.007
1.000
.000
TLI
rho2
1.049
CFI
1.000
1.000
.000
.000
Parsimony-Adjusted Measures
Model
Default model
Saturated model
Independence model
PRATIO
.164
.000
1.000
PNFI
.161
.000
.000
PCFI
.164
.000
.000
NCP
.000
.000
333.610
LO 90
.000
.000
274.795
NCP
Model
Default model
Saturated model
Independence model
HI 90
6.638
.000
399.913
FMIN
Model
Default model
Saturated model
Independence model
FMIN
.032
.000
1.953
F0
.000
.000
1.676
LO 90
.000
.000
1.381
HI 90
.033
.000
2.010
RMSEA
Model
Default model
Independence model
RMSEA
.000
.175
LO 90
.000
.158
HI 90
.061
.191
PCLOSE
.908
.000
AIC
Model
Default model
Saturated model
Independence model
AIC
120.337
132.000
410.610
BCC
127.652
140.471
412.022
BIC
308.341
349.689
446.892
CAIC
365.341
415.689
457.892
398
ECVI
Model
Default model
Saturated model
Independence model
ECVI
.605
.663
2.063
LO 90
.618
.663
1.768
HI 90
.651
.663
2.397
MECVI
.641
.706
2.070
HOELTER
Model
Default model
Independence model
HOELTER
.05
532
38
HOELTER
.01
681
43
399
Total Effects (Group number 1 - Default model)
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.017
.013
4.822
3.835
.275
.219
.000
.285
Com
Connectedness
.000
.257
Inventive
Satisfaction
.000
.857
Com
Connectedness
.000
.229
Inventive
Satisfaction
.000
.258
Com
Connectedness
.000
.257
Inventive
Satisfaction
.000
.857
Com
Connectedness
.000
.229
Inventive
Satisfaction
.000
.258
Com
Connectedness
.000
.000
Inventive
Satisfaction
.000
.000
Com
Connectedness
.000
.000
Inventive
Satisfaction
.000
.000
Marital Status
SocialCap
.000
2.007
.000
.120
Marital Status
SocialCap
.000
.134
.000
.161
Marital Status
SocialCap
.000
2.007
.000
.120
Marital Status
SocialCap
.000
.134
.000
.161
Marital Status
SocialCap
.000
.000
.000
.000
Marital Status
SocialCap
.000
.000
.000
.000
Life
Orientation
.000
.445
Objective
Success
.000
.795
Life
Orientation
.000
.192
Objective
Success
.000
.169
Life
Orientation
.000
.445
Objective
Success
.000
.795
Life
Orientation
.000
.192
Objective
Success
.000
.169
Life
Orientation
.000
.000
Objective
Success
.000
.000
Life
Orientation
.000
.000
Objective
Success
.000
.000
Standardized Total Effects (Group number 1 - Default model)
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.212
.036
.350
.059
.308
.052
.000
.178
Direct Effects (Group number 1 - Default model)
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.017
.000
4.822
.000
.275
.000
.000
.285
Standardized Direct Effects (Group number 1 - Default model)
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.212
.000
.350
.000
.308
.000
.000
.178
Indirect Effects (Group number 1 - Default model)
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.000
.013
.000
3.835
.000
.219
.000
.000
Standardized Indirect Effects (Group number 1 - Default model)
ObjectiveSuccess
Subjective Success
Income
LogExtLinks
Dailyinvethours
Internet
.000
.036
.000
.059
.000
.052
.000
.000
400
I: AMOS 18 Top-down model original result outputs
Figure 54: Original AMOS 18-path diagram of Initial Top-Down Model
401
Residual Covariance, Standardized Residual Covariance Matrices and Modification
Indices of initial Reversal Model
Modification Indices- Regression weights
SocialCap
SocialCap
ComConnectedness
ComConnectedness
ComConnectedness
Maximization
Maximization
LogExtLinks
LogExtLinks
LogExtLinks
LifeOrientation
LifeOrientation
InventiveSatisfaction
InventiveSatisfaction
F1.2.3
InternetShort
InternetShort
InternetShort
IncomeinThou
IncomeinThou
IncomeinThou
IncomeinThou
<--<--<--<--<--<--<--<--<--<--<--<--<--<--<--<--<--<--<--<--<--<---
InternetShort
IncomeinThou
LogExtLinks
LifeOrientation
InventiveSatisfaction
LifeOrientation
IncomeinThou
ComConnectedness
F1.2.3
IncomeinThou
Maximization
InternetShort
ComConnectedness
IncomeinThou
LogExtLinks
SocialCap
LifeOrientation
IncomeinThou
SocialCap
Maximization
LogExtLinks
InternetShort
M.I.
8.029
3.524
2.178
2.053
6.721
2.241
2.320
2.092
3.598
4.003
2.452
2.161
6.904
2.158
3.741
8.179
2.132
8.079
3.480
2.428
3.814
7.830
Par Change
.407
.062
5.507
.192
.497
.178
-.029
.002
-.008
-.001
.060
.067
.056
-.010
-1.927
.089
.141
.043
.256
-.386
-23.268
.822
402
Figure 55: Original AMOS 18 Output of final Modified Top-Down Model
403
Full AMOS 18 text output of the Final modified Top Down Model
Notes for Model (Default model)
Computation of degrees of freedom (Default model)
Number of distinct sample moments: 66
Number of distinct parameters to be estimated: 29
Degrees of freedom (66 - 29): 37
Result (Default model)
Minimum was achieved
Chi-square = 41.508
Degrees of freedom = 37
Probability level = .281
Estimates (Group number 1 - Default model)
Scalar Estimates (Group number 1 - Default model)
Maximum Likelihood Estimates
Regression Weights: (Group number 1 - Default model)
ObjectiveSuccess
F1.2.3
Maximization
LogExtLinks
IncomeinThou
F1.2.3
ComConnectedness
SocialCap
Maximization
LifeOrientation
InventiveSatisfaction
LogExtLinks
IncomeinThou
InternetShort
<--<--<--<--<--<--<--<--<--<--<--<--<--<---
SWB
ObjectiveSuccess
ObjectiveSuccess
ObjectiveSuccess
ObjectiveSuccess
SWB
SWB
SWB
SWB
SWB
SWB
SWB
SWB
SWB
Estimate
.072
.326
-.456
.023
2.570
.050
.368
.418
.177
.157
.131
.002
.438
.217
S.E.
C.R.
P
Label
.014 5.114 *** par_11
.077 4.238 *** par_1
.256 -1.779 .075 par_2
.005 4.486 *** par_3
.900 2.855 .004 par_4
.016 3.063 .002 par_5
.057 6.420 *** par_6
.090 4.659 *** par_7
.054 3.264 .001 par_8
.028 5.530 *** par_9
.019 6.877 *** par_10
.001 1.680 .093 par_12
.194 2.256 .024 par_16
.041 5.234 *** par_17
Standardized Regression Weights: (Group number 1 - Default model)
Estimate
ObjectiveSuccess
<--- SWB
.341
F1.2.3
<--- ObjectiveSuccess
.291
Maximization
<--- ObjectiveSuccess
-.131
LogExtLinks
<--- ObjectiveSuccess
.314
IncomeinThou
<--- ObjectiveSuccess
.201
F1.2.3
<--- SWB
.210
ComConnectedness <--- SWB
.414
SocialCap
<--- SWB
.314
Maximization
<--- SWB
.240
LifeOrientation
<--- SWB
.365
InventiveSatisfaction <--- SWB
.438
LogExtLinks
<--- SWB
.118
IncomeinThou
<--- SWB
.162
InternetShort
<--- SWB
.348
404
Covariances: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P
Label
e3 <--> e9
7.971 2.655 3.002 .003 par_13
e5 <--> e10
2.193
.779 2.817 .005 par_14
e2 <--> e3
15.848 5.412 2.928 .003 par_15
e2 <--> e9
22.812 11.631 1.961 .050 par_18
Correlations: (Group number 1 - Default model)
Estimate
e3 <--> e9
.218
e5 <--> e10
.204
e2 <--> e3
.212
e2 <--> e9
.141
Variances: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P
Label
SWB
49.470 4.959 9.975 *** par_19
e1
1.953
.196 9.975 *** par_20
e2
329.789 33.072 9.972 *** par_21
e3
16.878 1.692 9.975 *** par_22
e4
2.306
.231 9.975 *** par_23
e5
3.579
.359 9.975 *** par_24
e6
7.887
.791 9.975 *** par_25
e7
.010
.001 9.975 *** par_26
e8
25.519 2.558 9.975 *** par_27
e10
32.358 3.244 9.975 *** par_28
e9
79.363 7.956 9.975 *** par_29
Squared Multiple Correlations: (Group number 1 - Default model)
Estimate
ObjectiveSuccess
.116
SocialCap
.098
ComConnectedness
.172
Maximization
.053
LogExtLinks
.138
LifeOrientation
.133
InventiveSatisfaction
.192
F1.2.3
.171
InternetShort
.121
IncomeinThou
.089
405
Matrices (Group number 1 - Default model)
Residual Covariances (Group number 1 - Default model)
Objective
Com
SWB
SocialCap
Success
Connectedness
Subjective Success
.000
ObjectiveSuccess
.000
.000
SocialCap
.000
1.185
.000
ComConnectedness
.000
-.114
-1.897
.000
Maximization
.000
.000
-1.051
1.436
LogExtLinks
.000
.000
-.003
.062
LifeOrientation
.000
-.160
-.707
1.752
ICS
.000
.121
-1.241
.000
DailyInventHours
.000
.000
1.296
.009
InternetShort
.000
.273
.000
.467
IncomeinThou
.000
.471
3.046
-6.946
Maximization
LogExtLinks
.000
.010
1.690
.622
-.745
1.992
-10.626
.000
.023
.015
-.022
.037
-.261
Maximization
LogExtLinks
.000
.264
1.518
.800
-1.214
1.232
-1.517
.000
.977
.907
-1.742
1.110
-1.780
Life
Orientation
.000
.165
.005
1.259
.844
ICS
.000
.206
-.379
-3.418
Daily
InventHours
.000
.297
2.932
Internet
Income
.000
.703
2.420
Standardized Residual Covariances (Group number 1 - Default model)
SWB
Subjective Success
ObjectiveSuccess
SocialCap
ComConnectedness
Maximization
LogExtLinks
LifeOrientation
InventiveSatisfaction
DailyInventHours
InternetShort
IncomeinThou
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
Objective
Success
.000
1.192
-.172
.000
.000
-.498
.538
.000
.588
.228
SocialCap
.000
-.453
-.304
-.046
-.350
-.878
1.164
.000
.236
Com
Connectedness
.000
.622
1.283
1.296
.000
.012
.238
-.820
406
Life
Orientation
.000
.363
.013
1.333
.207
ICS
.000
.821
-.573
-1.198
Daily
InventHours
.000
.571
1.294
Internet
Income
.000
.115
.067
Total Effects (Group number 1 - Default model)
SWB
ObjectiveSuccess
ObjectiveSuccess
.072
.000
SocialCap
.418
.000
ComConnectedness
.368
.000
Maximization
.144
-.456
LogExtLinks
.003
.023
LifeOrientation
.157
.000
InventiveSatisfaction
.131
.000
F1.2.3
.073
.326
InternetShort
.217
.000
IncomeinThou
.623
2.570
Standardized Total Effects (Group number 1 - Default model)
SWB
ObjectiveSuccess
ObjectiveSuccess
.341
.000
SocialCap
.314
.000
ComConnectedness
.414
.000
Maximization
.195
-.131
LogExtLinks
.225
.314
LifeOrientation
.365
.000
InventiveSatisfaction
.438
.000
F1.2.3
.310
.291
InternetShort
.348
.000
IncomeinThou
.230
.201
Direct Effects (Group number 1 - Default model)
SWB
ObjectiveSuccess
ObjectiveSuccess
.072
.000
SocialCap
.418
.000
ComConnectedness
.368
.000
Maximization
.177
-.456
LogExtLinks
.002
.023
LifeOrientation
.157
.000
InventiveSatisfaction
.131
.000
F1.2.3
.050
.326
InternetShort
.217
.000
IncomeinThou
.438
2.570
Standardized Direct Effects (Group number 1 - Default model)
SWB
ObjectiveSuccess
ObjectiveSuccess
.341
.000
SocialCap
.314
.000
ComConnectedness
.414
.000
Maximization
.240
-.131
LogExtLinks
.118
.314
LifeOrientation
.365
.000
InventiveSatisfaction
.438
.000
F1.2.3
.210
.291
InternetShort
.348
.000
IncomeinThou
.162
.201
Indirect Effects (Group number 1 - Default model)
SWB
ObjectiveSuccess
ObjectiveSuccess
.000
.000
SocialCap
.000
.000
ComConnectedness
.000
.000
Maximization
-.033
.000
407
LogExtLinks
LifeOrientation
InventiveSatisfaction
F1.2.3
InternetShort
IncomeinThou
SWB
.002
.000
.000
.024
.000
.185
ObjectiveSuccess
.000
.000
.000
.000
.000
.000
Standardized Indirect Effects (Group number 1 - Default model)
SWB
ObjectiveSuccess
ObjectiveSuccess
.000
.000
SocialCap
.000
.000
ComConnectedness
.000
.000
Maximization
-.044
.000
LogExtLinks
.107
.000
LifeOrientation
.000
.000
InventiveSatisfaction
.000
.000
F1.2.3
.099
.000
InternetShort
.000
.000
IncomeinThou
.068
.000
Model Fit Summary
CMIN
Model
Default model
Saturated model
Independence model
NPAR
29
66
11
RMR, GFI
Model
Default model
Saturated model
Independence model
RMR
1.838
.000
8.358
GFI
.962
1.000
.691
AGFI
.932
PGFI
.539
.629
.575
NFI
Delta1
.880
1.000
.000
RFI
rho1
.822
IFI
Delta2
.985
1.000
.000
TLI
rho2
.977
CMIN
41.508
.000
347.337
DF
37
0
55
P
.281
CMIN/DF
1.122
.000
6.315
Baseline Comparisons
Model
Default model
Saturated model
Independence model
Parsimony-Adjusted Measures
Model
PRATIO
Default model
.673
Saturated model
.000
Independence model
1.000
NCP
Model
Default model
Saturated model
Independence model
NCP
4.508
.000
292.337
.000
PNFI
.592
.000
.000
LO 90
.000
.000
237.208
.000
PCFI
.662
.000
.000
HI 90
24.529
.000
354.967
408
CFI
.985
1.000
.000
FMIN
Model
Default model
Saturated model
Independence model
FMIN
.209
.000
1.745
RMSEA
Model
Default model
Independence model
RMSEA
.025
.163
LO 90
.000
.147
AIC
Model
Default model
Saturated model
Independence model
AIC
99.508
132.000
369.337
BCC
103.230
140.471
370.749
ECVI
Model
Default model
Saturated model
Independence model
ECVI
.500
.663
1.856
F0
.023
.000
1.469
LO 90
.477
.663
1.579
LO 90
.000
.000
1.192
HI 90
.123
.000
1.784
HI 90
.058
.180
PCLOSE
.881
.000
BIC
195.159
349.689
405.619
HI 90
.601
.663
2.171
MECVI
.519
.706
1.863
HOELTER
Model
Default model
Independence model
HOELTER
.05
251
43
CAIC
224.159
415.689
416.619
HOELTER
.01
288
48
409
J: Model Comparison assuming Partial, Indirect Effect and No mediation
effects
Bottom-up model assuming Partial Mediation effect
410
Bottom-up model assuming Indirect- effect
411
Bottom-up model assuming No Mediation effect
Nested Model Comparisons
Assuming model PartialMediation to be correct:
Model
Indirect effect
Nomediation
DF
CMIN
P
3
4
3.055
73.165
.383
.000
NFI
Delta-1
.008
.188
412
IFI
Delta-2
.008
.191
RFI
rho-1
.022
1.005
TLI
rho2
.026
1.170
Top-Down model assuming Partial Mediation effect
413
Top-Down model assuming Full Mediation effect
414
Top-Down model assuming No Mediation Effect
Nested Model Comparisons
Assuming model Partial Mediation to be correct:
NFI
IFI
Model
DF
CMIN
P
Delta-1 Delta-2
Indirect effect
9 150.878 .000
.434
.486
No Mediation
5
72.065 .000
.207
.232
415
RFI
rho-1
.485
.251
TLI
rho2
.576
.298
K: Factor Analysis Results for Convergent and Divergent Evidences
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Approx. Chi-Square
Bartlett's Test of Sphericity
.654
5182.676
df
2211
Sig.
.000
416
Total Variance Explained
Extraction Sums of
Squared Loadings
Total
% of Var
Cum %
Total
% of Var
Cum %
1
7.814
11.662
11.662
7.814
11.662
11.662
2
3.723
5.557
17.219
3.723
5.557
17.219
3
3.222
4.809
22.028
3.222
4.809
22.028
4
3.034
4.528
26.556
3.034
4.528
26.556
5
2.586
3.860
30.416
2.586
3.860
30.416
6
2.324
3.469
33.884
2.324
3.469
33.884
7
2.288
3.415
37.299
2.288
3.415
37.299
8
2.002
2.988
40.287
2.002
2.988
40.287
9
1.889
2.820
43.107
1.889
2.820
43.107
10
1.829
2.730
45.837
1.829
2.730
45.837
11
1.660
2.478
48.315
1.660
2.478
48.315
12
1.621
2.420
50.735
13
1.475
2.201
52.936
14
1.426
2.128
55.065
15
1.337
1.995
57.060
16
1.289
1.924
58.984
17
1.287
1.920
60.904
18
1.149
1.715
62.619
19
1.106
1.650
64.270
20
1.090
1.627
65.897
21
1.046
1.561
67.458
22
1.010
1.508
68.966
23
.982
1.466
70.432
24
.953
1.422
71.854
25
.928
1.385
73.238
26
.908
1.356
74.594
27
.820
1.224
75.818
28
.809
1.208
77.026
29
.783
1.168
78.194
30
.762
1.137
79.331
31
.722
1.077
80.409
32
.710
1.059
81.468
33
.682
1.017
82.485
34
.633
.944
83.430
35
.616
.919
84.349
36
.604
.902
85.251
37
.566
.845
86.096
38
.553
.826
86.922
39
.523
.780
87.702
40
.510
.761
88.463
41
.505
.754
89.217
42
.471
.703
89.920
43
.455
.679
90.599
44
.437
.653
91.252
45
.416
.621
91.873
46
.402
.600
92.474
47
.397
.593
93.066
48
.390
.583
93.649
49
.372
.555
94.204
50
.344
.514
94.717
51
.327
.488
95.205
52
.306
.457
95.661
53
.292
.437
96.098
54
.273
.408
96.505
55
.265
.395
96.900
56
.251
.375
97.276
57
.225
.335
97.611
58
.221
.330
97.941
59
.211
.314
98.256
60
.187
.279
98.535
61
.181
.269
98.804
62
.163
.243
99.048
63
.155
.231
99.278
64
.144
.215
99.493
65
.131
.195
99.688
66
.122
.182
99.870
67
.087
.130
100.000
Extraction Method: Principal Component Analysis.
Compo
Initial Eigenvalues
417
Rotation Sums of
Squared Loadings
Total
% of Var
Cume %
3.780
5.642
5.642
3.700
5.522
11.164
3.266
4.875
16.039
3.255
4.858
20.897
3.001
4.479
25.376
2.976
4.442
29.817
2.883
4.304
34.121
2.869
4.282
38.403
2.542
3.794
42.197
2.102
3.137
45.333
1.998
2.982
48.315
Rotated Component Matrixa
1
2
.759
.748
.755
.754
3
InternetInfo
InternetKnow
InternetInfoShare
InternetCom
InventClubs
InventLocalEx
InventLaw
InventBusiness
-.222
InventMedia
InventNGO
InventLibrary
.201
InventUniver
InventResearch
InventBanks
InventChamber
-.206
InventMinistry
InventFoExpert
.202
Radio
Job
Present
Dress
HigherStatus
NoSecond
HighExpectation
.241
EventAgienst
Optimism
Workmyliking
Goodhappen
BadOverGood
.214
Acheivement
Recognition
Selfsatisfaction
FutureInterest
MaxLife
ExcellentLife
SatisfiedLife
GotImportant
.333
NothingChange
HappyPerson
.205
.202
UnhappyPerson
.222
FeelHappy
FeelUnhappy
PartofComm
.473
HappyComm
.748
BondComm
.691
ProudComm
.443
ActiveMemCom
.656
ProSolComm
.644
ComprobMyProb
.644
BandwithOthers
.700
SocialMedia
SocialRest
SocialLitera
SocialHighIncome
SocialGraduate
.290
SocialHighProf
SocialPolitic
SocialGovReg
SocialFinance
SocialMagazine
SocialCar
.596
SocialFLanguage
.679
SocialComputer
.749
SocialCharaCer
SocialOffConflict
.310
SocialDomConflict
SocialMoving
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 11 iterations.
4
5
Component
6
7
8
9
10
11
.263
.370
.596
.570
.351
.598
.514
.232
.559
.428
.410
.536
.495
.349
.218
-.291
.252
-.320
-.207
.213
.341
.611
.653
.592
.676
.565
.506
.274
.257
.361
.269
.267
.665
.651
.717
.505
.700
.696
.551
.535
.623
.392
.409
.433
.435
.250
-.273
-.305
-.252
-.236
.424
.672
.543
.680
.669
.547
.247
.202
.242
.295
.206
.306
.384
.220
-.230
.311
.233
.241
.422
.226
.226
-.281
.350
.252
.217
.340
-.204
.371
.383
.363
.479
.655
.672
.650
.203
.220
.261
.298
.472
.598
.679
.483
.235
.328
.293
418
.286
.232
.219
.493
.418
.682
.639
InterItem Correlations
Scale
Internet Usage
InternetInfo
InternetKnow
InternetInfoShare
InternetCom
1
.834**
.855**
.851**
.844**
2
.207**
.222**
.023
.066
3
.195**
.259**
.301**
.269**
4
.184**
.173*
.100
.075
5
.243**
.268**
.135
.116
6
.129
.125
.105
.024
7
.277**
.297**
.315**
.289**
8
.108
.142*
.143*
.152*
External Linkages
InventClubs
InventLocalEx
InventLaw
InventBusiness
InventMedia
InventNGO
InventLibrary
InventUniver
InventResearch
InventBanks
InventChamber
InventMinistry
InventFoExpert
.012
.076
.149*
.058
.133
.090
.026
.138
.080
.000
-.054
-.046
.227**
.413**
.616**
.525**
.409**
.599**
.508**
.320**
.610**
.481**
.362**
.453**
.499**
.330**
-.064
.030
.096
-.095
.083
-.054
.026
.014
.108
.081
.012
.027
.125
.068
.046
-.029
.027
.082
-.020
.002
.011
-.025
-.061
-.047
-.057
.049
.057
.101
.082
-.056
.141*
.064
.113
.120
.006
.059
.053
.045
.061
.182**
-.014
.048
.038
.096
.068
.069
.110
.072
.063
.044
.157*
.043
.244**
.126
.126
.056
.117
.072
.066
.108
.156*
.077
-.002
.056
.022
.112
.139*
.065
.068
.108
.105
.135
.167*
.151*
-.001
-.029
-.015
-.005
Social Capital
SocialMedia
SocialRest
SocialLitera
SocialHighIncome
SocialGraduate
SocialHighProf
SocialPolitic
SocialGovReg
SocialFinance
SocialMagazine
SocialCar
SocialFLanguage
SocialComputer
SocialCharaCer
SocialOffConflict
SocialDomConflict
SocialMoving
.102
.170*
.137
.230**
.117
.105
.171*
.114
.196**
.165*
.165*
.127
.166*
.174*
.124
-.023
-.066
.069
.045
.045
-.039
.029
.035
.108
-.003
.119
.020
-.022
-.005
.100
-.027
-.051
.039
-.063
.318**
.306**
.493**
.488**
.509**
.406**
.399**
.397**
.440**
.368**
.625**
.514**
.572**
.442**
.440**
.406**
.336**
-.053
.020
.087
.131
.030
-.035
-.111
-.145*
.011
.009
.045
.094
.167*
.008
-.013
-.124
.063
-.056
.037
.068
.115
.044
.094
.046
-.078
.021
.016
.129
.097
.133
.052
-.025
-.146*
-.004
-.038
-.071
.087
.025
.085
.076
.005
-.030
.091
.120
-.027
.052
.121
-.011
.068
-.031
.012
.076
.190**
.159*
.176*
.131
.149*
.149*
.063
.116
.164*
.258**
.237**
.257**
.060
.066
-.027
.015
.072
-.003
.120
.095
.031
.141*
.045
-.085
-.026
.099
.023
.123
.136
.016
.048
-.014
-.057
Maximizing Tendency
Radio
Job
Present
Dress
HigherStatus
NoSecond
.125
.118
.014
.051
.141*
.144*
.015
.010
-.014
-.057
.026
.092
-.025
-.010
-.059
-.012
.121
.133
.672**
.617**
.613**
.675**
.641**
.598**
.145*
.166*
-.085
.006
.201**
.229**
.094
.004
.063
.076
.208**
.090
.143*
.000
.051
.001
.249**
.280**
.073
.069
.083
.018
.158*
.069
Optimization
HighExpectation
EventAgienst
Optimism
Workmyliking
Goodhappen
BadOverGood
.263**
.028
.092
.124
.158*
.174*
.042
.103
-.005
.141*
.131
.145*
.059
.047
-.021
.013
.092
.155*
.279**
-.063
.224**
.078
-.012
.169*
.616**
.649**
.593**
.673**
.671**
.634**
.077
.063
.166*
.097
.176*
.182**
.280**
.268**
.196**
.242**
.176*
.236**
.094
.123
.127
.162*
.238**
.199**
Inventive Career Succes
Acheivement
Recognition
Selfsatisfaction
FutureInterest
.018
-.009
.130
.213**
.050
.061
.101
.259**
.044
.052
.050
.092
.049
.067
.086
.229**
.113
.000
.164*
.273**
.769**
.712**
.752**
.666**
.334**
.269**
.412**
.275**
.304**
.237**
.307**
.170*
419
1
2
3
4
5
6
7
8
Subjective Success
MaxLife
ExcellentLife
SatisfiedLife
GotImportant
NothingChange
HappyPerson
UnhappyPerson
FeelHappy
FeelUnhappy
.104
.200**
.236**
.324**
.056
.230**
.263**
.270**
.235**
.068
.028
.193**
.169*
-.002
.223**
.199**
.083
.186**
.154*
.210**
.251**
.101
.085
.264**
.282**
.192**
.200**
.081
.113
.120
.192**
.182**
.135
.068
.094
.079
.205**
.235**
.293**
.200**
.073
.256**
.299**
.238**
.190**
.236**
.225**
.379**
.285**
.220**
.307**
.357**
.260**
.121
.600**
.645**
.692**
.653**
.514**
.637**
.644**
.545**
.455**
.227**
.243**
.326**
.307**
.131
.317**
.294**
.193**
.211**
Com. Connectedness
PartofComm
HappyComm
BondComm
ProudComm
ActiveMemCom
ProSolComm
ComprobMyProb
BandwithOthers
.206**
.158*
-.007
.034
.154*
.131
.108
.089
.125
.061
.109
.052
.145*
.190**
.157*
.093
.189**
.124
-.019
-.030
.069
.125
.111
-.032
.181*
.171*
-.084
.069
.138
.099
.085
.026
.166*
.205**
.131
.041
.232**
.225**
.103
.227**
.389**
.285**
.187**
.301**
.358**
.117
.116
.141*
.302**
.304**
.271**
.177*
.343**
.303**
.304**
.221**
.603**
.757**
.705**
.559**
.721**
.664**
.634**
.687**
InternetShort
ExternLinks
SocialCap
Maximization
LifeOrientation
InventiveSatisfaction
Subjective Success
comConnectedness
1
.150*
.303**
.155*
.222**
.111
.348**
.161*
1
.057
.018
.140*
.163*
.209**
.176*
1
.040
.089
.075
.314**
.098
1
.179*
.142*
.195**
.125
1
.186**
.365**
.244**
1
.438**
.348**
1
.414**
1
420
BIO DATA OF THE STUDENT
Chaminda Nalaka Wickramasinghe was born in 1976 in Colombo, Sri Lanka as the youngest
of his family. He completed his school education at St. Johns’ College, Nugegoda, Sri Lanka
in 1995. He obtained his Bachelor of Commerce (Special) Degree with first class honors in
year 2001 from the University of Sri Jayewardenepura, Sri Lanka. After completion of the
first degree, in year 2001 he joined the Department of Commerce and Financial
Management, University of Kelaniya, Sri Lanka as an assistant lecturer. He completed his
postgraduate diploma in Information Technology at University of Kelaniya, Sri Lanka in
year 2003. After that, he obtained the Master of Business Administration (Information
Technology) from the University of Moratuwa, Sri Lanka in Year 2005. After the
completion of the master degree, he was promoted as a lecturer at the Department of
Commerce and Financial Management since year 2007. Currently he is teaching Knowledge
Management, Innovation Management and Information Technology to the Bachelor of
Commerce undergraduates. Apart from conducting lecturers, he conducted number of policy
studies on Sri Lankan technological and knowledge issues. He has participated number of
international conferences as a presenter in Thailand, Malaysia, Hong Kong and Sri Lanka.
421
LIST OF PUBLICATIONS
1. Wickramasinghe, C. N., Ahmad, N., Rashid, S., & Emby, Z. (In press). Impact of
Subjective Well Being on Success of Technological Knowledge Creation among
Independent Inventors in Developing Countries: a first look at Sri Lanka. Journal of
the Knowledge Economy . Spinger Link. Abstracted and Indexed in Expanded
Academic, OCLC, SCOPUS, Summon by Serial Solutions
2. Wickramasinghe,C.N.and Nobaya Ahmad (2009). Revolution of Digital Communication
and the Asian Competitive Creativity Chasm, Asian Journal of Technological
Innovations (AJTI), Vol. 17 (1). Taylar and Francis Group. Indexed in Thomson
Reuters Social Science Citation Index (SSCI). 2010 Impact Factor – 0.556
3. Wickramasinghe, C.N., Ahmad N., Rashid, S and Emby, Z. (2010). Reestablishing
grassroots level inventors in national innovation systems in less innovative countries.
In Chu, S., Ritter, W. and Hawamdeh, S. (2010). Series on innovation and
Knowledge Management-Vol.8 : Managing Knowledge for Global and
Collaborative Innovations. Singapore. World Scientific Publications. Indexed in
British Library cataloging-in- publication data.
4. Wickramasinghe, C.N., Ahmad N., Rashid, S. and Emby, Z. (2010). Does motivation
make happy employees? Integrating the supply chain of happiness with employee
motivation. Proceedings of the International Conference on Business and
Information, 2010. Sri Lanka. University of Kelaniya. Held at 4th June 2010. ISBN978-955-8044-91-8.
5. Wickramasinghe, C.N., Ahmad N., Rashid, S and Emby, Z. (2010). Success, Happiness
and Subjective Satisfaction: How Objective and Subjective Success Drive the
Independent Inventors in Sri Lanka. Book of Abstracts of 5th European Conference
on Positive Psychology, 2010 (ECPP, 2010). P-123. Available at
http://www.ecpp2010.dk/media/ECPP_-_Book_of_Abstracts_-_24_06_2010.pdf.
6. Wickramasinghe, C.N., Ahmad N., Rashid, S and Emby, Z. (2009). Reestablishing
grassroots level inventors in national innovation systems in less innovative countries.
Proceedings of sixth International Conference on Knowledge Management (ICKM).
Hong Kong. University of Hong Kong. Held at 3-4 December 2009. ISBN- 978-98818659-1-5. Was categorized among the 30 best papers at the conference.
7. Wickramasinghe, C.N. and Nobaya Ahmad (2009). Digital communication Revolution
and Creative Asia in the Next Decade: Possibilities of ICT becoming a silver bullet,
Proceedings of International Conference of Communication & Sustainable
Development in the Next Decade, Bangkok, Chulalongkorn University. Held at 1113th February 2009.
8. Wickramasinghe, C. N. and Ahmad, N. (2009). Determining a New Model For Utilize the
heroics of Grassroots Inventors in National Level Development of Less Developed
Countries. Proceeding of the International Conference on Development- CICD2009. Kuala Lumpur: International Islamic University Malaysia (IIUM).
9. Wickramasinghe, C. N. and Ahmad, N. (2008). Empowering Grassroots Inventors in
Digital Age. E- Gov Asia 2008. Kuala Lumpur. Centre for Science, Development
and Media Studies (CSDMS). Held at 11-13th November 2008. Available at:
http://www.e-asia.org/2008/KeySpeakers.asp
422
10. Wickramasinghe, C.N., Ahmad N., Rashid, S and Emby, Z.(2010), Exploration of the
objective and subjective Success of Knowledge Creators; First look at the
subjective wellbeing of Sri Lankan grassroots level inventors, West Lake
International Conference on Small and Medium Business, 2010 (WLICSMB2010), Hangzhou, China, Held at 24-26 October 2010- Full Paper Accepted
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