Lappeenranta University of Technology
Tel. 05- 621 7238, 040-541 9831 kaisu.puumalainen@lut.fi

 critically evaluate the research design and results of empirical studies
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 design an international large-scale survey use databases to collect literature and data develop valid and reliable measures for abstract constructs
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 recognize the main problems in cross-cultural studies understand the applicability of the most typical quantitative analysis methods
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 use SAS software for analysing data write a master’s thesis based on quantitative empirical data

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15.2. introduction, research process, reporting
26.2. databases
15.3. research design
22.3. research design
29.3. international issues
9.4. assignment 1 DL
12.4. analysis methods
14.4. exam
19.4. introduction to SAS
26.4. analysis with SAS
7.5. assignment 2 DL
14.5. exam resit, if needed

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Part I: review the two research proposals
– Write down a report of 2-5 pages
– You can do the report together with another student
–
A list of issues to be covered is on the following slide
– Structure the report e.g. as follows:
1.
Description and evaluation of proposal I
2.
Description and evaluation of proposal II
3.
Comparison of the two proposals
Part II: evaluation of the two theses
– Give grades 1-5 for each area and complement with max 1 page description of the strengths and weaknesses
DL 9.4.2010, return to kaisu.puumalainen@lut.fi

 Overall structure, are all relevant issues covered?
 Problem specification
 Empirical context of the study (country, industry, firm size), fit with problem?
 Research approach and data collection (method, sampling, informant)
 Operationalization of key concepts
 Analysis methods (choice, reporting)
 Biases, reliability and validity
 Formalities (references, writing, etc.)

4.
5.
1.
2.
3.
6.
7.
8.
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12.
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Title
Background
The research problem and research objective(s)/question(s) (which further can be divided into sub objectives/questions)
Literature overview (What literature and studies are available of the subject? How this study is positioned to these research streams, and whether a research gap exists?)
Preliminary theoretical framework (What area(s) of business theory does the research topic belong to.)
Definitions (of special terminology used in the thesis)
Limitations and scope (what issues will be excluded and for what reason)
Method of research
Structure of the research
Tentative table of contents of the final report
Available source material
Tentative time table http://www.des.emory.edu/mfp/proposal.html
http://www.statpac.com/research-papers/research-proposal.htm

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Definition of the research problem
Positioning to existing research
Concepts, models, hypotheses& frameworks
Data collection
Analysis
Discussion, interpretation of results
Balanced structure of the report
Systematic and logic of the report
Thoroughness
Independence, criticality and effort
Reporting style
Readability

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 DL 7.5.
 Graded 0-5, forms 25% of final grade
 Pairwork
 Data collection starts on 26.2. and more detailed instructions will be given

 Structure of the report/article:
– Introduction
– Theoretical part (including framework + hypotheses)
– Methodology (sampling + data collection + measures + analysis)
– Results (descriptive + testing)
– Discussion (evaluation + implications)
– Conclusion (limitations + further research)

 Relevance of the topic
– Practical reasons
– Academic interest
 Research gap and research questions
– Overall literature review
– It has not been done yet, why should it be done
 How are we going to fill the gap in this study
 Clearly articulate the study’s contributions

 Article databases
– ABI, EBSCO, Elsevier, Emerald, JSTOR, Springer,
Wiley
– http://www.lut.fi/fi/library/databases works through
VPN
 Citation information
– ISI Web of Science, ISI JCR
– http://www.lut.fi/fi/library/databases works through
VPN
 Google Scholar
– http://scholar.google.fi/

 Stand-alone and embedded reviews
 Literature search (leading journals, databases, reference lists, web of science for forward citations, conference proceedings, working papers, books, managerial journals)
 Start reading (find key articles, reviews, metaanalyses, date order, key author order)
 Create a concept matrix, tables

 Analyze the literature
– History and origins of the topic
– Main concepts
– Key relationships of the concepts
– Research methods and applications
 Identify key contributions, strengths and deficiencies or inconsistencies
 Synthesize
– A research agenda
– A taxonomy
– An alternative model or conceptual framework

 Torraco, R.J. (2005) Writing integrative literature reviews: Guidelines and examples, Human Resource
Development Review , 4 (3):356-367
 Webster, J. & Watson, R.T. (2002) Analyzing the past to prepare for the future: Writing a literature review, MIS
Quarterly , 26 (2):13-23
 Rowley, J.& Slack, F. (2004) Conducting a literature review, Management Research News , 27 (6):31-39
 Gabbott, M. (2004) Undertaking a literature review in marketing, The Marketing Review , 4:411-429

 Three sources:
– Theoretical explanation for ”why?” (must always be there)
– Past empirical findings (optional, from same or related fields)
– Practice or experience (optional)

 sample:
– Population specifications, sampling frame, size
– Informant(s), method, process
 Data collection:
– Choice of data collection method, process, instrument development, pre-testing
– Response rate, representativeness

The empirical data used in this study is drawn from a dataset collected using a structured mail questionnaire. The survey was carried out in spring 2004. The initial population consisted of Finnish companies engaged in R&D from eight different industry categories: food, forestry, furniture, chemicals, metals, electronics, information and communications technology (ICT), and services.
The questionnaire was developed partly by using extant measurement scales, which were translated into
Finnish. The use of a back-translation procedure involving a native English speaker ensured that the meanings of the item statements were not altered. Seven-point Likert scales were mainly used to minimize executive response time and effort (Knight & Cavusgil 2004). Pretests for getting feedback regarding the clarity of the survey items were conducted with ten companies of varying size in different sectors.
Like numerous other researchers, we chose to rely on single key informants in our data collection. In order to maximize the data accuracy and reliability, we followed Huber and Power’s (1985) guidelines on how to get quality data from single informants. Entrepreneurial orientation is normally operationalized from the perspective of the CEO (Covin & Slevin 1989; Wiklund & Shepherd 2003), and CEOs are typically the most knowledgeable persons regarding their companies’ strategies and overall business situations (Zahra &
Covin 1995). Most of our respondents had titles such as chief executive officer, managing director, chief technology officer and R&D director, indicating a senior position in the firm.

A total of 1140 companies were identified from the Blue Book Database.
Of those, 881 were reached by telephone and were found eligible to answer questionnaire. Other firms were not reached in spite of numerous telephone calls, or were considered ineligible. Eligibility and the identity of the most suitable key informants were ascertained during the telephone conversation. Participation in the survey was solicited by means of incentives such as the offer of a summary report of the results, and by assuring confidentiality of the responses. Of the firms contacted by telephone, 200 refused to participate. The survey questionnaire, along with a preaddressed postage-paid return envelope and a cover letter describing the purpose of the research, was mailed to the 681 firms that agreed to participate. A reminder e-mail was sent to those who had not answered within two weeks.

A total of 299 responses were received, yielding a satisfactory effective response rate of
33.9% (299/881). Non-response bias was assessed on a number of variables (e.g., size, profitability, time of latest new product launch, international operation mode) by comparing early and late respondents, following the suggestions of Armstrong and Overton (1977).
There was no evidence of non-response bias, with the exception that the firm size of the early and late respondents differed slightly: it was larger in the late-respondent group when measured against the number of employees (the sample means for the early and late respondents were 140 and 205 employees, t= -2.50, d.f.=121, sig.=.014). We also compared the distribution of the number of employees in our data with the corresponding distribution of all Finnish companies with more than 50 employees, and found that in the categories between
100 and 999 employees, the proportions were equal. Four per cent of firms have more than
1000 employees (Statistics Finland 2004), as did 13% of our sample. This suggests that very large companies may be over-represented, and is in contrast with the comparison of early and late respondents implying that companies with large numbers of employees might be underrepresented. Furthermore, as there was no significant difference between the early and late respondents in terms of turnover, we concluded that our sample was not biased.

In order to minimize social desirability bias in the measurement of constructs, it was emphasized in the cover letter that there were no right or wrong answers, and that the responses would remain strictly confidential (Zahra & Covin 1995). The respondents were asked to recall the situation in their companies during the most recent three year period to avoid recollection errors.
The sample used in this paper includes 217 firms from manufacturing and service segments.
Seven different industry sectors were selected in aim to obtain a heterogeneous sample so as to increase the generalizability of the findings. Since we want to make distinction between individual and firm-level factors and in this study we aspire for capturing firm-level entrepreneurship and rather formal organizational renewal capabilities, the size class was restricted to firms with 50 employees or more. The upper cut-off 1000 employees was used to filter the largest firms out.
This was done because the measures used to assess hypothesized relationship between independent and dependent variables include questions concerning organizational changes and international performance during the last three years. It is presumable that due to the organizational inertia in very large firms the lag between organizational changes and enhanced performance is longer than in small firms. Thus, it is possible that to capture the impact of organizational changes on performance of very large firms, the time period should be longer than used in this survey. To avoid the possible bias in results, the largest firms were omitted from this study.

 measures:
– Measure development, control variables
– validity and reliability
 analyses:
– What analysis methods were applied for testing the hypotheses
– Validation and generalizability?
– The choices and statistics to be reported vary by analysis method

Dependent variables: international performance
We agree with many other authors (e.g., Cavusgil & Zou 1994; Katsikeas et al. 2000) that international performance is a multidimensional construct that should be measured using a variety of indicators (for a thorough review of the measures used, see e.g., Zou & Stan 1998; Leonidou et al. 2002; Manolova &
Manev 2004). These indicators could be objective or subjective, absolute or relative, reflecting either the scale of international operations or success in them.
We measured the scale of international operations on two objective indicators: 1) international sales as a percentage of total sales, and 2) the number of countries in which the company operates. These are both among the most commonly used proxies in this context (Walters & Samiee 1990; Sullivan 1994; Robertson
& Chetty 2000; Autio et al 2000). In their review of 31 performance studies, Walters and Samiee (1990) found that 68% of them used the first and 13% the second measure. We also computed objective relative measures of the degree of internationalization by standardizing the international sales percentage and number of countries within each industry. These relative measures gave results that were identical to the absolute measures, and are thus not reported separately. We acknowledge that growth measures would be useful objective indicators of international performance as well. Autio et al. (2000) examined change in international sales as a percentage of total sales and growth in total sales, in order to understand the overall impact of growth in international sales.
The success of international operations was assessed in a subjective manner. The respondents were asked to indicate their level of satisfaction with their international activities during the previous three years on six different dimensions of performance, and as a whole. The average of these seven items was also used as an overall indicator (Cronbach alpha = .91).

Our reliance on self-reported data from single informants introduces the risk of common method variance. In order to obviate this risk, we followed the procedure suggested by Wiklund and Shepherd (2003) and computed the correlation coefficient with a self-reported profitability measure and an externally obtained one. We were able to find the return on investment
(ROI) figures of 68 respondent companies from Talouselämä and
Tietoviikko magazines, which are Finnish business magazines that collect and publish annual financial data from several industries. The correlation between the measures was .40 (p<.01). In fact, the results of previous research suggest that subjective measures of performance can accurately reflect objective measures (Lumpkin & Dess 2001).

Independent variables
Entrepreneurial orientation was conceptualized as consisting of the dimensions of innovativeness, proactiveness and risk-taking. The measure was adapted from Naman and Slevin (1993), and Wiklund (1998), which were based on measures developed in Covin and Slevin (1988) and Miller and Friesen (1982). Pretests were conducted, after which some original items were dropped and new ones generated on the basis of previous studies on firm-level entrepreneurship. The measure included nine items, which were assessed on a scale from one to seven (see Appendix). The three dimensions are closely related, so a composite measure was constructed as an average of all nine items, resulting in a reliability coefficient of .74, which is satisfactory according to the guidelines presented in Nunnally (1978).

Control variables
There are firm-specific and external factors that may affect a firm’s international performance, regardless of its strategic orientation (Lumpkin
& Dess 1996) or its renewal capability. We therefore controlled for firm size, experience in international operations, and environmental dynamism.
Firm size is normally operationalized as the number of employees and/or amount of annual sales. It is assumed to affect international performance positively, as a larger firm has a larger pool of resources to exploit and the possibility to achieve advantages of scale in its international operations. In order to avoid problems of multicollinearity in the hypothesis testing, we only used annual sales turnover (reported in million €) as an indicator of firm size. The sales were log-transformed to correct for positive skewness.

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Graphics
– Bar, histogram
– pie
– Line and area
– scatter
Frequency tables
Descriptive statistics (in a table)
– N
– Mean, median
– Standard deviation, min, max
– Above statistics for non-transformed variables
– (Skewness, kurtosis)
– Correlation matrix (for transformed variables)

N of firms
% of firms
Food Forest Chem. Metal Electronics Service ICT Total
20 21 18 79 23 17 39 217
9 10 8 36 11 8 18 100
% international 55 85 83 75 91 53 35 68
Start year Mean 1938 1957 1957 1967 1971 1953 1970 1962 in industry S.D. 43.2 26.8 28.0 24.7
Sales M€
Mean 162.3 37.9 173.1 21.4 in 2003 S.D. 344.5 52.1 258.3 25.2
25.7
40.4
40.4
43.0
125.5
152.3
36.8
30.3
33.1
32.4
59.4
143.9
Employees Mean 231.4 183.0 333.6 122.8 200.2 247.6 189.3 185.8 in 2003 S.D. 224.6 175.1 254.4 75.8 167.3 224.3 132.2 166.6

Variable
1. Sales M€
2. Intnl. experience
3. Env. dynamism
4. Entr. orientation
5. Rec. cap. number
6. Rec. cap. success
7. Intnl. performance
8. % of sales intnl
9. # of countries
Minimum
Maximum
Mean
Std. Deviation
Cronbach α
1 2
.29
b
3
-.19
a
4
-.06 -.01 -.01 -.05 .02 .25
b
.08
5
-.04
6
.02
7
.19
a
8
.34
b
.18
b
.22
b
.28
b
.03
.19
b
.13
.26
b
.02
.04
9
.25
.24
b b
.03
.13
-.08 .03 -.01 .08
.21
a
.00 -.03
.45
b
.24
b
.50
b
2 1 1,14 2,33 0 1,86 2 0 0
1177 204 6,57 6,06 7 5 9,43 100 140
59 28 4,14 4,14 3,98 3,57 5,91 52,05 12,23
144 25 0,99 0,74 2,14 0,61 1,68 32,6 17,61 n.a. n.a. .75 .74 n.a. .79 .91 n.a. n.a.
Significance a p < .05, b p < .01

 Varies by analysis method
 Model fit statistics
 Test statistic (+ standard error) and significance level or confidence interval
 Mention that basic assumptions were checked for
 (Power of the tests)
 No software output as such
 Use tables!!

The hypotheses were tested by hierarchical linear regression analysis. In the base model, only the control variables (ln-transformed sales, lntransformed years of international experience and environmental dynamism) were entered into the regression model. The hypothesized independent variables (entrepreneurial orientation, number of reconfiguring activities, and success in reconfiguring activities) were then added in the second phase. The hypothesized effects would then be significant only if the increase in the coefficient of determination after the base model was large enough and the regression coefficients of the hypothesized variables in the effect model were statistically significant. The use of the hierarchical model thus directly shows the increase in predictive power that can be attributed to the hypothesized variables over and above the effects of the control variables. The results of the regression analyses are presented in Table 3.

Std. regression coefficients of independent variables
Dependent Model
Env. dyn.
Size
% of sales intnl
Base
Effect
.09
.09
.17
.17
.20
a
# of countries
Intnl. perf.,
Base .08
Effect .05 .20
b
Base .17
a mean of items Effect
.13
.05
.07
Satisfaction as a whole
Base .18
a
-.04
Capability development
Effect .15 -.02
Base .18
a
.10
Image development
Effect .12
Base .20
a
Effect .18
a
.12
.11
.12
Intnl exp.
.31
b
.20
a
RC number
RC succ.
.19
b
.10 .05 -.04 .07
a
.01
.21
.18
a
.24
b a
.21
a
.15 -.01 .22
a
.11
b
.11
EO
.21
a
-.03 .16 .10
Model fit
Adj.
R
2
.13
b
.31
b
.01 -.02 -.01 .11
b
.08
.05
a b
.05
a b
.03
.08 .23
a
.01 .16 .10
b
.04
a
.12
.09 .16 -.04 .11 .06
a
R
.08
2 change
.00
.08
b
.09
a b
.04
Market access
Profitability
Market share
Sales volume
Base .08
Effect .04
Base .25
b
Effect .22
a
Base .05
Effect .02
Base .05
Effect .02
.10
.11
-.11
-.10
.12
.13
.04
.05
.06
.03 .19
.22
a
.19
.21
.18
a a a
.23
a
.21
a
.14 -.04 .17
a
.21
a a
.00
-.03 .09 .02 .05
.07
b
.11
b
.07
a
-.07 .08 .12
b
.03
.12 .02 .11 .05
.06
a
.05
.03

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Avoid numbers here, state clearly what the results mean
Bring up the results that were surprising, new or important
 Compare with earlier empirical studies, it is good to get some similar results, and something new
 If your results conflict with earlier ones, try to explain why
 Comment on the stability, generalizability and accuracy of the results
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Limitations (e.g. Research design, sample, measures)
Further research (often arise from the limitations)

5.
6.
7.
8.
1.
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4.
9.
10.
Select topic
Literature review
Theoretical framework
Research questions
Theory and hypotheses
Research methodology
Conduct empirical data collection
Analysis and results
Discussion
Conclusions (limitations and further research)

 Define objectives, research questions and type of study
 Research approach
 Data collection methods (desk, field)
 Sampling
 Measurement and questionnaire design
 Analysis methods
 Timetables and costs
 What can go wrong?

phenomenon conceptualization concepts operationalization
Population definition variables measurement population sample Data matrix sampling Data collection analysis results

Phenomenon, concept: company innovativeness
Dimensions:
(1)
New product introductions, ”generation”
(2)
Implementation of new processes, ”adoption”
Variables:
(1)
(2)
(a) % of sales from products that were launched during the past three years, (b) how many new products were launched last year
(a) investments on new manufacturing technologies during the past three years, (b) number of process improvements implemented last year

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Operational indicator of a concept numeric
Discrete or continuous
Levels of measurement
– Nominal
– Ordinal
–
Interval
–
Ratio scale

5 variables 6 observations
5
6
3
4 obs
1
2 name
Anne
Berit
Clas M
Daniel M
Emil
Frida
M
F sex
F
F
30
21
35
50 age
22
15
2
4
1
5
LikertA
3
4

 All have the same basic elements
– variable j (k is the number of variables) COLUMN
– Observation or case i (n is the number of cases) ROW
– The value of variable j for case i (k x n is the number of values)
CELL
 But there are three types of k x n data matrices
– Cross-sectional: the observations (rows) are independent
– Time series: the observations (rows) are consequtive time periods with equal intervals
– Panel: combination of cross-sectional and time series data. The cases are independent but the same variables are measured at several time periods, can be presented as wide or long

obs
3
4
1
2
5
6
Firm name
Industry
Nokia Telec
Age
50
Lukoil Ener 25
Valio
Shell
Food
Ener
GM Car
Motorola telec
80
45
100
30
Empl
60
90
10
100
150
20

5
6
3
4 obs
1
2
Day Nokia OMX
1.1.2010 10.11
7900
2.1.2010 10.25
8000
3.1.2010 9.96
7550
4.1.2010 10.00
8011
5.1.2010 11.00
8321
8.1.2010 10.74
8205

obs
3
4
1
2
5
6
Firm name
Emp
2008
Nokia 60
Lukoil 90
Valio
Shell
10
100
GM 150
Motorola 20
9
99
130
22
Emp
2009
57
95
10
98
110
23
Emp
2010
55
95

obs
3
4
1
2
5
6
Firm name
Year
Nokia 2008
Nokia 2009
Nokia 2010
Lukoil 2008
Lukoil 2009
Lukoil 2010
Emp
60
57
55
90
95
95

 Exploratory, Descriptive, Explanatory, correlational, causal
 Predictive, Optimization
 Experimental, observational, ex post facto
 Desk, field, laboratory, simulation
 Cross-sectional, longitudinal, panel
 Business vs. academic
 Description usually not enough in thesis

WHY (NOT) A QUANTITATIVE STUDY?
 Philosophical background
– positivism, empiricism, attempt to explain phenomena
– objectivity, rationality, cumulative nature
– hypotheses, deductive approach
– If you cannot measure it, it isn’t there
 ”Anglo-american” way of thinking about scientific research
 Possibilities to get published (and cited)
 Theory testing and theory development
– no theory development without empirical testing
– an empirical study is not scientific without a theoretical basis

WHY (NOT) A QUANTITATIVE STUDY?
 Theory is built from concepts and their relationships
 A researcher has to identify, define, and operationalize the concepts
 Deductive approach: concept – measurement
– empirical results – feedback to theory
 Empirical studies are needed to test theories in varying contexts

1. Conceptualization ( innovativeness )
2. Theoretical hypothesis= proposed relationship between concepts
( innovativeness and cosmopoliteness are positively related )
3. Empirical hypothesis= proposed relationship between operational measures of the concepts ( early adoption of a product, travelling )

4.
5.
6.
Analysis -> support or rejection of empirical hypothesis
Cumulative evidence from empirical studies
-> generalizations, principles, laws
Theory develops or becomes more specific as cumulative empirical support is gained from varying contexts or anomalies are found

1. Empirical observations from several contexts: diffusion of an innovation has an S-shaped pattern
2. Theoretical explanation: it is a communication process within a social system
3. Adopters can be classified based on timing of adoption
4. Theoretical hypothesis states a relationship between concepts: cosmopoliteness has a positive effect on innovativeness
5. Empirical hypothesis states a relationship between the operational indicators (measures) of the concepts: those who travel more outside the system, adopt the innovation earlier
6. Testing with data from different contexts (innovations, social systems) by different scholars strengthens the theory and reveals the limits -> replications are important
7. Extension of the theory to other levels of analysis: organization, country

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Act as a guide to research design and report
Hypothesis vs. the null (H0)
Testable? (eg. networks hard to measure, TCE)
Simple? (2-3 concepts in one hypothesis)
Exact? (has an effect /positive effect/ U-shaped effect)
Trivial? New?
Well-reasoned? (analytical reasoning based on theory + earlier empirical results)
Descriptive or causal
Max 5-10 hypotheses in an article
Of which 1-2 are new hypotheses
About 50% are supported by the data

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There is a positive relationship between a firm’s export sales and the amount of R&D expenditures
Customer focus is a key driver of product quality in born global firms
In environments that are characterized by high market turbulence, TMT risk taking behavior does moderate the relationship between market orientation and performance

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Independent/predictor/explanatory/exogenous/cause variable/ x / IV
Dependent/criterion/endogenous/effect variable/ y /DV
Moderating variable z / MoV
– “environment variable” or “contingency variable”
– the relationship between x and y differs at different levels of z
–
Sharma et al (1981) Journal of Marketing Research 18(3):291-
300
Control variable /CV
– variable that is controlled for, not hypothesized but known to affect y
Mediating variable /MeV
– The effect of x on y is mediated by MeV
–
Baron & Kenny (1986) Journal Of Personality and Social Psych.,
51, 1173-1182

IV
IV
MoV
DV
DV
IV
CV
DV
IV MeV DV

 x and y are correlated
 x occurs before y
 the correlation between x and y is not spurious
(caused by some extraneous variable z)
 x and y can be observed indepedently from each other (common method bias)
 the relationship can be explained /deduced from a theory
 -> survey is not the best way to detect causality

 Stimulus-response
– A price increase results in fewer unit sales
 Property-disposition
– Company’s age and management’s attitudes about exporting
 Disposition-behavior
– Job satisfaction and work output
 Property-behavior
– Social class and sports participation

COUNTRY EFFECT
TIME EFFECT
H2e
Country’s wealth
H3
Adoption year of country H5
H4
Diffusion patterns (m, p, q)
CULTURAL EFFECT
H1
H2a H2b H2c H2d
Cultural distance from innovation center
Uncertainty avoidance
Individualism Power distance Masculinity

 Identification of phenomenon X, conceptualization, dimensionality and measure development
 consequences, so what? X -> Y
 determinants A -> X
 Contextual dependencies and moderators
 E.g. market orientation

You are working at the HR department of a large company.
Your boss tells you that the IT department performs poorly due to its high employee turnover. He suggests that you should conduct a survey among other large companies to find out how they deal with problems due to employee turnover.
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What are the hypotheses of your boss?
What is the research problem?
What would be your research questions and hypotheses?
What kinds of data matrices could you use?

The phenomenon of interest is Growth strategy of the firm
1.
2.
Which dimensions does this concept have?
Which variables could be used for measuring the dimensions?
Write a hypothesis where growth strategy (or one of its dimensions) is a
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
Dependent variable
Independent variable
Moderating variable
Dependent variable, but the effect is moderated by another variable
 Mediator variable

 Specify the population and informant(s)
 Specify what is to be measured
 Choose the sampling frame
 Choose the sampling method
 Specify the sample size
 Conduct the sampling
 Collect the data from the sample
 Assess non-response bias
– Contact again, get the distribution of basic variables from another source and compare with the data, compare early and late respondents

 Population = group to which we wish to generalize the results
 Census = collect data from whole population
 Larger samples yield more generalizable results, smaller std errors, better power of tests
 Sample must be representative
 Sample size n> 30, e.g. Finns n= 1000-2500
 Generalization from random samples

 Unit of analysis
– Person, household
– Team, SBU, firm, venture
– Dyad, network
– Industry, country
 Basic characteristics (size, age,..)
 Must be relevant to the theoretical problem
 Informant(s) must have the ability and willingness to respond

 = a list of units ín the population


Statistics Finland and others
Population Register Centre


Telephone directories
Kompass, Dun&Bradstreet


Thomson, Amadeus, Ruslan
Patentti- ja rekisterihallitus

 www
Company databases
 Russian sampling frames?

 Random (or probability)
– simple
– systematic
– clustered
– stratified
 non-random (non-probability)
– convenience
– snowball
– judgement
– quota

 Systematic
– Choose starting point randomly between 1-k, and take every k:th
– Sampling frame must be in no particular order
 stratified
– To ensure that subpopulations are adequately represented
– Determine strata and their shares of population
– Sample proportionally (or not) from each strata

 clustered
– Divide the population into many small clusters, and choose randomly which clusters are to be studied
– Within-cluster variation is desirable, but between-cluster is not
– Economical but not statistically efficient
 sequential
– Use various methods sequentially

 Theoretically inferior, but sometimes practical
 If statistical generalization is not required
 Ok in exploratory research
 Convenience or judgment sampling
 Quota sampling
 Snowball, when respondents are hard to reach

 Can be determined if error margin is set z
1
 
2 z
1
 
2
2  Mean & proportion n

(
N

)
2 n n

4 E
2
5% of the population
E
 Error margin can be adjusted if sample >

 larger sample is needed when…
– More variation in the population
– Smaller significance levels are required
– More subgroups to be compared




 min 30 cases per subgroup

 min 5-10 per variable in multivariate analyses
 larger sample yields better statistical power and generalizability (see e.g. Cohen 1988 for power analysis) e.g. Finnish people -> 2000 not a given % of the population usually 100-500 should be enough but do not forget that….

 these apply for the real sample size, i.e. the usable responses you get
 x= number of units taken from the sampling frame
 .80*x will be contacted and eligible
 .80*(.80*x) will agree to participate
 .40*(.80*.80*x) will respond
 if you need 100 responses, x=100/.256=390

 Desk research
– Company internal databases
– Statistical databases
– Commercial databases
– Standard research products
• Consumer panels
• Monitor, RISC, etc.
– Meta-analysis
 Field research
– Survey
– Observation
– Experiment

causal exploratory
Secondary sources internal IS external databanks services
Primary sources qualitative survey experiment good good good good ok descriptive ok ok ok ok good ok ok ok good

 advantages
– Economical, fast
– Suitable for studying the past
– Longitudinal
 limitations
– Not specific to the research problem
– Reliability?
– Mostly directly observable simple indicators, no measures for abstract constructs

 Thomson One Banker, DataStream (global, financials of large companies)


SDC Platinum (global, M&A and alliances)
ETLA company database (Finland, financials of top 600 companies) + Internet –database (Finland, statistics)
 Amadeus (Europe, financials & ownership of all companies)


Voitto Plus (financials of Finnish companies)
ITU World Telecommunications /ICT Indicators (global, country data)


RISI (global, country and company data on pulp & paper)
MarketLine (global, country data)

– General statistics about countries:
– http://www.undp.org/hdr2001/ http://globaledge.msu.edu/ibrd/ibrd.asp
(very good!)
– www.ibrc.bschool.ukans.edu
(very good!)
– www.GlobalBusinessWeb.com
– http://faculty.insead.edu/parker/resume/person al.htm
(very good!)
– www.cia.org
(world factbook)

– Business magazines:
– http://economist.com
(financial)
– www.businessweek.com
(general)
– www.ft-se.co.uk
(Financial Times)
– www.forbes.com
(general)
– www.pathfinder.com/fortune (Fortune)
– www.wsj.com
(Wall Street Journal)

 Analyzes data from existing published quantitative empirical studies
 Provides a synthesis of earlier studies by describing and explaining the means and variances of effect size across studies
 What is the generalizability of findings
 Can identify moderator effects
 Guidelines in Hunter & Schmidt (2004). Methods of meta-analysis . Sage

 The data is collected by asking the respondents
 Good for measuring abstract concepts
 E.g. Attitudes, values, opinions, intentions, expectations, feelings
 Ok for measuring events that occured earlier
 The respondent needs to cooperate with the researcher
 The most common method in business research

 Normally 10-95%
– Depends on data collection method and procedure, target population/ informant
– Higher in interviews, internal company surveys
– Aim at 30-40%, do not accept less than 15%
 Effective response rate =
Responses obtained / eligible sample size
 The lower the response rate, the more you have to examine the possibility of non-response bias

 Your target population is Finnish exporting SMEs
 From the Amadeus database you find 45 000 firms satisfying these criteria
 You take a random sample of 1 000 firms and phone them
– 50 cannot be reached at all
– 40 are not SMEs any more
– 200 are SMEs but not exporting
– 60 are eligible but refuse to participate
– You get back 200 questionnaires, of which 10 are returned empty with a message saying that the firm has no exports
 Eligible sample size?

 Net individual benefit (appeals, personalization, incentives)
 Societal outcome /norm (source, anonymity)
 Commitment /involvement (prenotification, DL, follow-up)
 Novelty (envelope, cover letter, questionnaire)
 Convenience (postage paid)
 Expertise (informant choice)

(Cavusgil & Elvey-Kirk,1998)
 Social utility: Your assistance is needed! The information you provide can (1) contribute to an understanding of consumers’ views on auto care, and how they can be better served by retailers of maintenance service and supplies, as well as auto manufacturers, and (2) serve as inputs for auto repair legislation at state and federal levels. Your cooperation is truly appreciated.

 Egoistic: Your opinions are important! It is very important for you to express your opinions so various retailers of maintenance services and supplies, as well as the auto manufacturers, will know the type of products and service facilities you would like to have available. Thanks for expressing your opinions.

 Help the sponsor: We need your assistance!
Your preferences and opinions are very important to our successful completion of this study. The accuracy of our findings depends wholly on the responses from each individual, like yourself, in the sample group. We truly appreciate your help.

 Combined: Your opinions are important and useful! Your preferences and opinions are important for three reasons:
(1) they can provide information that leads to an understanding of consumers’ views on auto care, as well as serving as inputs for auto repair legislation, (2) they can enable the retailers of maintenance services and supplies and aut manufacturers to know the types of products and service facilities you would like to have available, and (3) they will help us successfully complete this study. The accuracy of our findings depends wholly on the responses from each individual, like yourself, in the sample group. Thank you for your cooperation.

 Which of the previous appeals works best in the U.S.?
 How about Russia or Finland?
 Which appeal works best in an academic / commercial study?
 Which appeal works best in a sample of consumers / professionals?

Cavusgil & Elvey-Kirk,1998

 Personal interview
 Telephone interview
 Mail survey /fax/ e-mail
 Web survey
 On-site terminal or questionnaires
 Data collection methods are often combined

+Response rate
+Aids can be used
+Interviewer can ask for more specific information
+Flexible ordering of questions
+Sampling frame not always necessary
+Control over who responds
+Can include a lot of questions
– Time-consuming
– Expensive
– Effect of the interviewer on the responses

+Response rate
+Fast
+Interviewer can ask for more specific information
+Flexible ordering of questions
+Not very costly
+Control over who responds
+Can be easily repeated
+Good for prenotification and follow-up
– No aids
– Not many questions
(5-10 min.)
– Easy and short questions only
– Representativeness of the sample?
– Effect of the interviewer on the responses

 Selection of interviewers
 Briefing of interviewers
 Motivating the interviewee
 Introduction of study
– Why me?
– Why these questions?
– How will the information be used?
 Data collection
– Coding of responses
– How much to help the interviewee?

 Design includes
– Sampling frame
– Cover letter
– Questionnaire
– Pre-testing
– Return arrangements
– Pre-notification
– 2nd round
– Incentives to solicit responses
– follow-up

 advantages
– Fast for achieving large samples
– Cost- efficient
– Exact information, the respondent can take time to find the answer
– Impersonal, good for asking delicate issues
 limitations
– No control over who responds
– Question ordering not very flexible
– Length max 5-10 (20) pages
– Does the respondent understand the question?
– Low response rate, 10-50%

 design
– With sampling frame or available to everyone
– Accompanying message
– Questionnaire + pre-test
– Compatibility of data with analysis program
– Incentives
– E.g. SPSS Data Entry, Webropol

 advantages
– Same as mail survey, but even cheaper and faster
– Flexible ordering of questions
– Elimination of inconsistent responses
 limitations
– Who responds
– Are the population net users
– Technical problems (different browsers, misclicks, save without submitting and continue later)

 What is to be asked (research framework!)
– Is the question really needed
– For what purpose /analysis
 How to ask
– Format of questions: open, closed, other,________
– Direct or projective
– Wording of questions
 Order and layout of questions
 Pre-testing




A study targeted at people living in a new housing area.
What kind of people are they? Why they moved into this area? Are they satisfied with the area?
A study targeted at LUT students. Which one of the three candidates are they going to vote for the president of the
Student Union? Why?
A study targeted at those responsible for R&D in large companies in Finland. How do they protect their innovations? What kind of R&D cooperation do they have?









Personal, name of respondent
Purpose of the research
Importance of each response
Confidentiality
No right or wrong answers
Incentives
How long it takes to answer
Instructions for returning (by which date, return envelope)



Contact information of researcher + signature
Source of address (sampling frame)
Thanks for responding










Clarity and brevity
Non-ambiguity
No double-barreled questions
– Have you ever felt guilty for being unfaithful to your spouse?
– Have you already stopped mugging your wife?
No leading questions
Consistent use of pronouns (sinä/te)
Behavior, attitude, opinion, intention
Include negatively worded items (balanced scales)
Variance!!!
Response categories (exclusive, amount, order, open?)



Did you happen to have murdered your wife?
As you know, many people kill their wives nowadays.
Did you happen to have killed yours?
 Do you know about other people who have killed their wives? How about yourself?
 Thank you for completing this survey, and by the way, did you kill your wife?
 Three cards are attached to this survey. One says your wife died of natural causes; one says you killed her; and the third says Other (explain). Please tear off the cards that do not apply, leaving the one that best describes your situation.

 Easy ones first
 Logic and headings
 General -> detailed
 Difficult and delicate ones near the end
 Basic background information first or last
 Open comments to the end
 Thank you for your response

 Rating (evaluate each item separately)
 Ranking (compare to other items, pairwise comparison, put in rank order, max 7 items)
 Categorization
 Open ended

 Likert- summated scale (usually 5 or 7-point, totally agree – neither agree or disagree - totally disagree)
 Semantic differential, Osgood scale (anchored by opposite alternatives, good-bad)
 Numerical scale (only anchors are labeled)
 Fixed sum (max 4-5 items, ipsative)
 Graphic (Visual Analogy Scale)



Time is a limited resource
All the senior executives of our company visit regularly our most important customers


A view exists, that all things are interrelated
Selffulfillment can be deduced from each person’s place in a social process
 Contracts are unnecessary, because they are not needed after they have been signed


Innovativeness has a crucial impact on our competitiveness
When I evaluate my partner’s trustworthiness I pay attention to open, fast and sufficient communication

 Representative of the real sample
 Similar situation (or personal interview)
 Ensure comprehension
 Ensure variance
 Ensure that questions can be answered
 Are the respondents interested
 How long does it take to answer

RESEARCH SCHEDULE research problem specification ideas of what to ask formulation of questions:how to ask questionnaire design cover page design cover letter /message design reminder letter /message design translation and back pretesting the questionnaire modifying the questionnaire copying the questionnaire mailing arrangements prenotifications mailing mailing the reminder coding the responses preparing the data file analysing data writing the report
YEAR
Month1 Month 2 month 3 month 4 month 5 month 6 month 7 responsibility
COL all
COL
COL
COL
COL
COL all all all all all all all
COL
COL
COL
All









Sampling frame
Research assistant
– 1 week for preparing the sample
– Can send about 10-20 questionnaires per day
– Coding time 2-10 minutes per response
– Data transformations, basic analysis and report 2-4 weeks
Mailing
– Number of agreed participants *2
– Reminders .8*the above
Copying, envelopes
Translators
Incentives
Telephone costs
Totals up to 15-20 000 €

 Research design
– Population specification
– Selection bias
– Sampling frame
– measurement
 Data collection
– Question incorrectly presented
– Coding
– Interference during responding
 Response errors
– Intentional and unintentional (response styles ARS/DARS,
ExtremeRS, RRange, MidPointR)
 Non-response error

 Leniency
– Skewed distributions
– Explanation of anchors may help
– E.g. How important are the following factors…
 Central tendency
– Respondents tend to avoid margin alternatives, especially if the topic is not familiar
– Explanation of anchors, add scale points
 Halo effect
– Bias due to the respondent having a general attitude towards the topic
– Question order may help

 Common method variance (bias)
– Campbell & Fiske 1959
– Correlation of two or more self-reported measures may be due to the common source rather than true effect
– Harman’s one factor test
– Different respondents for different variables (if unit of analysis e.g.
Team)
– Respond at different times
 Consistency motif
– Respondent has a lay theory and tends to confirm it
– Reorder scales (x then y rather than y then x)
 Social desirability
– May cause the other problems mentioned above
– Include scale by Crowne&Marlowe 1964, and partial out in analysis

A large corporation is sponsoring a study about sexual harrassment in the workplace. The research will be conducted because some female employees have expressed their concern about the issue.
 What is the real purpose of the study?
– Finding out the facts
– Raising the employees’ awareness
– Imposing change of behavior
 How would you do the sampling?


How would you collect the data?
How would you minimize response and nonresponse errors?







Determinants of Industrial Mail Survey Response: A Survey-on-Surveys Analysis of
Researchers' and Managers' Views.
By: Diamantopoulos , Adamantios ; Schlegelmich,
Bodo B.. Journal of Marketing Management, Aug96, Vol. 12 Issue 6, p505, 27p; ( AN
5480001 )
The effect of pretest method on error detection rates.
By: Reynolds, Nina;
Diamantopoulos , Adamantios . European Journal of Marketing, 1998, Vol. 32 Issue
5/6, p480, 19p, 5 charts; ( AN 921930 )
An Analysis of Response Bias in Executives' Self-Reports.
By: Mathews, Brian P.;
Diamantopoulos , Adamantios . Journal of Marketing Management, Nov95, Vol. 11
Issue 8, p835, 12p; ( AN 4969428
Mail survey response behavior.
By: Cavusgil , S. Tamer ; Elvey-Kirk, Lisa A..
European Journal of Marketing, 1998, Vol. 32 Issue 11/12, p1165, 28p, 7 charts, 1 diagram; ( AN 1401765 )
Methodological Issues in Empirical Cross-cultural Research: A Survey of the
Management Literature and a Framework.
By: Cavusgil , S. Tamer ; Das, Ajay.
Management International Review (MIR), 1997 1st Quarter, Vol. 37 Issue 1, p71, 26p;
( AN 12243002 )
Response Styles in Marketing Research: A Cross-National Investigation.
(cover story)
By: Baumgartner, Hans; Steenkamp, Jan-Benedict E.M.. Journal of Marketing
Research (JMR), May2001, Vol. 38 Issue 2, p143, 14p; ( AN 4628360 )





Armstrong, J.S. and T.S. Overton (1977) Estimating non-response bias in mail surveys. Journal of Marketing Research , 14 (3): 396-402.
Huber, George P. and Daniel J. Power (1985) Retrospective reports of strategic-level managers: Guidelines for increasing their accuracy.
Strategic Management Journal , 6 (2): 171-180.
Podsakoff, P.M., & Organ, D.W. (1986). Self-reports in organizational research: Problems and prospects. Journal of
Management , 12, 531-544.
Reynolds, N.L., Simintiras, A.C., Diamantopoulos, A. (2003)
Theoretical justification of sampling choices in international marketing research: key issues and guidelines for researchers.
Journal of International Business Studies , 34 (1):80-89










Ghauri, P., Gronhaug, K., Kristianslund, I. (1995) Research methods in business studies: A Practical guide . Prentice Hall, Englewood Cliffs.
Cooper, Schindler (2001) Business Research methods .
Hair, Anderson, Tatham, Black (1998) Multivariate data analysis , 5 th ed.
Upper Saddle River, NJ: Prentice Hall
Cohen, J., Cohen, P., West, S.G. & Aiken, L.S. (2003), Applied Multiple
Regression/ Correlation Analysis for the Behavioral Sciences, (3rd ed.).
Mahwah, NJ.:Lawrence Erlbaum Associates.
Cohen, J. (1988) Statistical Power analysis for the behavioral sciences , 2nd edn, Hillsdale: Lawrence Erlbaum Associates.
Aaker, Kumar, Day (2002) Marketing research
Diamantopoulos & Schlegelmilch (1997) Taking the fear out of Data analysis
Hunter, Schmidt (2004) Methods of meta-analysis. Thousand Oaks: Sage
Hofstede, Geert (2001) Culture’s Consequences: Comparing Values,
Behaviors, Institutions and Organizations Across Nations , 2nd edn,
Thousand Oaks: Sage

 If the concept is abstract, not readily observable or multi-faceted, a multi-item measure is always better than a single-item measure
 Psychology good, management ok, marketing fair, strategic
Multi-item Measures
TRUE
Actual
Cf. photographing an object from different angles
Actual-1 Actual-2
TRUE
Actual-3

 Reflective measurement
– The latent construct causes variance on the observed indicators
(items)
– The item is a function of the construct
– If the construct changes, all the items change
– The traditional and most common approach
– 96% of constructs in top 4 mkt journals, only 69% should be
 Formative measurement
– The latent construct is a function of the indicators
– If one of the items change, the construct changes
– e.g. SES, HDI, country risk and other indices
– (Diamantopoulos, Jarvis et al)
 Assessment of validity and reliability differs











Definition of the concept to be measured
Item generation
Item reduction
Data collection
Item reduction
Computing the scale
Unidimensionality assessment
Reliability assessment
Validity assessment
Generalizability assessment (replication, stability across samples)

 Literature review!
 Also look at other fields of study /disciplines
 Think about various points of view and units of analysis
 Operationalization in earlier empirical studies
 Qualitative field research
 Own work experience
 Distinguish from nearby concepts

 Earlier empirical studies
 Handbooks of scales
 Qualitative methods (critical incident)
 Delphi, brainstorming, GDSS, etc.
 Focus groups, company interviews
 As many as possible, will be later reduced
 Positively and negatively worded
 Clear and unequivocal
 Min 10 per concept

An inductive approach
1.
managers described a person they trust and another they do not trust
2.
3.
They described critical indicents that led to the emergence or loss of trust altogether 280 + 174 antecedents were found
4.
5.
6.
They were classified by students into 10 groups
A definition was written for each group
4 items were generated for each group

 Expert opinion
 Grouping
– The concept definitions are presented to the experts and they combine each item with the corresponding concept
 Only those items that experts agree on, are retained
 Pilot study / pre-test sample
– Distribution of each item
– Inter- item correlations (min .30)
– Exploratory factor analysis

 Sum of items (SPSS: compute, sum)
 Mean of items (SPSS:compute, mean)
– Generally better than the sum, you may want to compare scales with different number of items
 Factor score
 Weighted mean of items (weights from the factor loadings)

 Factor analysis
– exploratory
– confirmatory
– remove items that load less than .40 or have high loadings on wrong dimensions
– split-sample validation of the factor structure
– see article by Gerbing and Anderson (JMR)

Scale
Evaluation
Validity
Reliability
Test-Retest
Alternative
Forms
Internal
Consistency
Content
Criterion
Construct
Convergent
Validity
Discriminant
Validity
Nomological
Validity

 Absence of random error
 types:
– Stability (“test-retest reliability”)
– Equivalence (“parallel forms reliability”)
– Consistency (“split-half reliability”)
– Homogeneity (“internal consistency reliability”)
– Inter-rater reliability (concordance)

 Cronbach alpha
– Measures the internal consistency of a scale
– More items -> higher alpha
– Is based on inter-item correlations (min .30)
– Alpha should exceed 0.60 in exploratory research,
0.70 in theory testing (Nunnally)
– Remove items with item-total correlation less than
.50

N of items
3
3
5
2
3
2
2
7
9
5
5
Average interitem correlation Alpha
0,3 0,461538
0,5 0,666667
0,7 0,823529
0,3 0,5625
0,5
0,7
0,75
0,875
0,3 0,681818
0,5 0,833333
0,7 0,921053
0,3 0,75
0,2 0,692308
 
N
( 1

( N
* r

1 ) * r
N=number of items r= average inter-item correlation

a) External v of findings = generalizability b) Internal v of findings = if x actually causes y c) V of measurement scales
 Are we measuring what we purport to measure
 Absence of systematic error (bias) in measurement
– Content / face validity
– Criterion validity (predictive validity)
– Construct validity (convergent, discriminant, nomological)

 Can the measure yield answers to the research problem
 Can the measure capture the domain of the construct
 No matemathical methods to assess
 Also known as face validity
 Assessment based on quality of concept definition and content of the items

 Do the measures provide a good model fit or a good predictive accuracy
 Concurrent or predictive validity
 Is the criterion itself measured in a valid way
– relevance (e.g. performance)
– unbiased
– reliable (stability)
– availability

 Is the measure theoretically valid
 captures the whole concept but nothing but the concept
(deficiency, contamination)
 convergent validity (yields similar results as other measures of the same construct)
– correlation, MTMM
 discriminant validity (differs from other constructs)
– Factor analysis, MTMM
 nomological validity (is related to other constructs as predicted by theory)

(Campbell & Fiske, 1959)
Method 1 Method 2
Trait a Trait b Trait a Trait b
Method 1
Trait a b
1
Trait b m
1 b
1
d
Method 2
Trait a v
a
Trait b d b
2 m
2 v b b
2
Correlation coefficients
{ b
1 v a
= reliability for method 1
= convergent validity for both methods wrt trait a m
1
= discriminant validity d = “nonsense”-correlation for method 1
Requirements:
• v > 0 and "high enough"
• v > d
• v > m
• d low

3 traits
– Guilt feelings about sex
– Hostile guilt
–
Guilt concerning morality
3 methods
–
Incomplete sentences
"When I dream about sex …"
– Forced choice
• " When I dream about sex …" a) I don't remember a thing in the morning b) I feel happy when I get up
– True/False
• "When I dream about sex I wake up feeling happy"

MTMM matrix for the Mosher Forced
Choice Guilt Scale
Sexual
Hostile
Morality
TF
FC
IS
TF
(true/false)
FC
(forced choice)
IS
(incompl. sent.)
SG HG MC SG HG MC SG HG MC
SG .91
HG .52
.84
FC very reliable,
TF too, IS not
MC .68 .50
.84
SG .86
.56 .73
.97
HG .53
.83
.53
.61
.96
MC .63
.
54 .83
.70 .58
.92
Good convergent validity
Discriminant validity OK
SG .78
.51 .63
.79
.54 .57
.72
HG .24
.67
.23 .33
.73
.37
.32
.65
MC .47 .40
.66
.48 .49
.70
.49 .28
.55

 The performance of a measure should always be evaluated in a separate sample
 Replications help to set the limits to the applicability of theories in different contexts
 Cross-cultural validation
 LISREL group comparisons

 Each group will receive a concept definition and scales for market orientation
– A: Kohli, Jaworski & Kumar (1993): MARKOR- a measure of market orientation, JMR, 30 (4):467-477
– B: Narver & Slater (1990): The effect of a market orientation on business profitability, JM, 54 (4):20-35
 Read it and discuss:
– Content validity
– Clarity of the items
– Overlap of the items
– Use of reverse coded items
– Generalizability across contexts

http://www.socialsciencesweb.com/ a lot of books there!



Nunnally & Bernstein (1994) Psychometric Theory. McGraw Hill
DeVellis (1991) Scale Development: Theory and Applications. Sage
Marketing Scales Handbook: A Compilation of Multi-Item Measures, Vol. I-III
Authors: G. Bruner , K. James , P. Hensel
 Handbook of Marketing Scales: Multi-Item Measures for Marketing for Marketing and Consumer Behavior Research by W.O. Bearden, R.G. Netemeyer
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Measures of Personality and Social Psychological Attitudes : Volume 1: Measures of Social Psychological Attitudes. Authors: J. Robinson , P. Shaver , L.
Wrightsman
Metsämuuronen (2004): Tutkimuksen tekemisen perusteet ihmistieteissä
Price JL and Mueller CW. (1986).
Marshfield,Mass.: Pitman.
Handbook of organizational measurement.
 Rubin RB, Palmgreen P & Sypher HE. (1994). Communication research measures: A sourcebook.
New York: Guilford Pr.
 Psychoogy measures: http://www.ull.ac.uk/subjects/guides/psycscales.shtml

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Churchill (1979) A paradigm for developing better measures of marketing constructs. J Mark Res, 16(1):64-73
Campbell et al (1973) The development and evaluation of behaviorally based rating scales. J Appl Psych, 57:15-22
Mullen (1995) Diagnosing measurement equivalence in cross-national research.
J Int Bus Stud, 26(3):573-96
Campbell & Fiske (1959) Convergent and discriminant validity by the multitraitmultimethod matrix. Psych Bulletin 56(March):81-105
Gerbing & Anderson (1988) An updated paradigm for scale development incorporating unidimensionality and its assessment. J Mktng Res 25(May):186-
192
Hinkin (1995) A review of scale development practices in the study of organizations. Journal of management, 21(5)
Jarvis, Mackenzie & Podsakoff (2003) A Critical review of construct indicators and measurement model misspecification in marketing and consumer research.
Journal of Consumer Research, 30 (Sep):199-218
Boyd, Gove & Hitt (2004) Construct measurement in strategic management research: illusion or reality. Strategic Management Journal
Diamantopoulos & Winklhofer (2001) Index construction with formative indicators: an alternative to scale development, Journal of Marketing Reseach, 38(May):269-
277

 Survey
– Personal interview /CAPI
– Telephone interview /CATI
– Mail survey /fax
– Web survey /e-mail
 observation
 experiment

 Nonbehavioral
– Historical or financial records (=secondary data)
– Physical condition analysis like store audits
– Process or activity analysis like traffic flows
 Behavioral
– Nonverbal like movements
– Linguistic
– Extralinguistic (loudness, rate, interruption..)
– Spatial

 Real time information on overt behavior or environment
 Must be easily codable
 In a natural environment
 Should the object know? (Hawthorne)
 Should the observer participate?
 If the purpose of the study needs to be disguised
(e.g. Phantom shoppers in service quality studies)

 True and field experiment
 Good for detecting causality
 The researcher manipulates the independent variable
 Test group and control group
 Blind and double-blind treatment
 Easy to replicate
 Hard to generalize from
 Best for easily measurable concepts
 Ethics of manipulation? (plasebo-knee surgery)

 Selection of variables
 Decide how to manipulate the treatment levels
 Controlling the experiment environment
 Design of the experiment
 Selection of subjects and assignment to experiment and control groups (random or matched)
 Pilot experiment, revision, experiment
 Analysis

 pre-experiment (statistically weak)
– X-O
– O-X-O
– Test group X-O and control group O (non-random assignment)
 true experiment (random or matched assignment)
– Test group O-X-O and control group O-O
– Test group X-O and control group O
– Many test groups, O-X-O, but each group has a different level of
X
– Randomized block, Latin square, factorial design
 field experiment, quasi- experiment
– Assignment to groups non-controllable

 Internal validity (is there really causality)
– O-X-O other factors that may cause a change in O
– Changes in the subject
– Subject learns from the first measurement
– Researcher or measurement instrument changes
– Assignment to groups, stability of groups
– Extremes tend towards the mean
 External validity (generalizability)
– Voluntary subjects

 Preliminary examination and classification of open ended responses
 Coding and input
 transformations
 description, checking of normality
 Testing the hypotheses
 Discussion and conclusions
 Software: Excel, SPSS, SAS, Statgraphics,
DataFit, E-Views, Stata, etc.

 Numerical variables if at all possible
 Exact first, you can classify later
 Define informative variable and value labels
 What to do with missing data (NA)
 What is a missing value (checklists)
 Identification variables (ID number, dates, interviewer, etc.)

 Classification of open-ended responses
 Classifying continuous variables
 Reversing items
 Computing multi-item scales
 Computing lags, logs or other new variables
 Checking for inconsistent responses
 Removing outliers?

 Univariate or bivariate tests based on measurement level and normality of distribution
 5% significance level normally
 Remember also practical significance
 You hope to reject the H0 -> support for your research hypothesis
 Tests of means and independence
 Correlations
 Multivariate analysis

Phase of research dependent
Reliab,val concepts na independents
Na
FA
CA concepts na typologies na
Na
Na
LinReg effects
ANOVA effects
LogReg effects continuous continuous continuous categorical categorical continuous

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Interdependence of continuous variables
Reduce variables, detect underlying dimensions
Used in measure development
Cavusgil, S.T. (1985) Factor congruency analysis..Journal of the market research society, 27(2):147-155
Report:
– Extraction method (PC, PAF, ML)
– (rotated) factor loadings
– Communalities
– Eigenvalues + % of variance explained
– KMO and Bartlett’s test
– How the number of factors was chosen

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The most common method of hypotheses testing
Dependent variable continuous
Independent variables continuous or dummies
Can incorporate interactions, mediating or moderating effects
Report:
– Model fit (R square and significance, increase in R square if hierarchical model)
– Regression coefficients (beta), std. errors or t-values, significance
– Estimation method
– Violations of assumptions (residual analyses, multicollinearity, influence statistics)
– Validation

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Dependent variable nominal, a priori groups
Independent variables continuous or dummy
How the independents can separate between the groups: understanding or prediction
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LR less sensitive to violation of assumptions
Report:
– model fit (Wilks lambda, pseudo R squares)
– Effects and significance of independents (DF coefficients or loadings, exp(B))
– Classification results (hit ratio)
– Validation

 Oneway ANOVA, ANCOVA, MANOVA,
MANCOVA
 Continuous dependent variable /variate
 Independent variables can be factors (nominal) or covariates (continuous)
 Interactions among the independents can be modeled
 Suitable for testing hypotheses
 Report: estimated group means and the significance of each effect, + the full model, post hoc differences or contrasts

 Grouping the cases into homogeneous subsets
 Grouping is based on several variables
 Not for hypotheses testing
 Report:
– proximity measure,
– clustering algorithm,
– clustering method,
– criteria used for selecting the number of clusters,
– cluster description: centroids, n of cases
– validation

 Confirmatory analysis
 Multiple interrelated dependence relationships simultaneously
 Accounts for measurement error
 Can incorporate latent variables
 Path models, group comparisons, moderating effects, etc..
 Other programs: AMOS, EQS

 Chi square (should not be significant)
 Goodness of fit-index GFI, AGFI (>.90)
 Incremental fit NFI (>.90)
 Residual statistics RMSEA, RMSR (<.08)
 Critical N

 Majority of the studies in IB are
– Ethnocentric /replications (Adler, 1983)
– Static, cross-sectional surveys
– Manufacturing sector
– Micro-level unit of analysis
– Single informant
– Judgement sampling
 Equivalence
– Sample (why these countries?)
– Construct (”urban” or ”soft drink”)
– Instrument (back-translation, Osgood is internationally most consistent response-format)
– Data collection

Yang et al. (2006) IBR, 15:601-617
 All empirical studies from JIBS, MIR, JWB,
IMR, JIM, IBR 1992-2003
 1296 studies, 67.3% of all articles were empirical
 60% surveys, 33% secondary data, 2% experiment
 61% one country, 17% two countries, 22% more countries
 89% Europe, 66% Asia, 52% North America, 2%
Africa
 39% USA, 16% UK, 14% Japan, 11% China

Yang et al. (2006) IBR, 15:601-617
 50% managers/CEOs, 11% consumers,
10% financial data, 10% government data.
4% students
 Median sample size around 200
 Mean response rate in mail surveys 27%
 Very few studies using multiple informants

(source:ESOMAR, 1990)
FRA NED SWE SUI UK
Mail
Phone
4 33 23 8 9
15 18 44 21 16
Street
Home/office -
52 37 -
8
Groups 13 5
-
44
6
-
54
11
In-depth 12 12 2
Secondary 4 4
Other 0 0 14 5
8
8 -
-
10

 Parochial (single culture, assumes universality, 80% in
1970-80)
 Ethnocentric (second culture replications, questions universality, standardized research design, often interprets differences as design defects)
 Polycentric (many individual domestic studies, denies universality, mostly inductive, anthropology)
 Comparative (many cultures but none dominant, looks for universality and culture specificity of elements)
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Geocentric (MNOs, search for similarity across cultures)
Synergistic (intercultural interaction, action research)

 What is culture?
 Can country be used as a surrogate for it?
 Is culture x or y or contingency variable?
 Does cultural perspective of the researcher affect the interpretation of findings?
 Identical topic vs. equivalent research design?

 Topic should be
– Conceptually equivalent
– Equally important and appropriate
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Size of sample (cultures & within)
Representative or matched samples
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Translation and back to ensure equivalence in meaning
Scaling procedures equivalent, similar pattern of correlations
Administration (interviewer, data collection, timing)

Laurent (1983) The cultural diversity of western conceptions of management. Intnl studies of mgmt and organization, 13(1-2):75-96
It is important for a manager to have at hand precise answers to most of the questions that his subordinates may raise about their work
90
80
70
60
50
40
30
20
10
0
Sw ed en
N et he rla nd s
U
.S
.
D en m ar k
U
.K
.
Sw itze rla nd
Be lg iu m
G er m an y
Fr an ce
Ita ly
In do ne si a
C hi na
Ja pa n
Problem-solvers – experts – loss of face
Chinese described an ideal picture (not real) and kept the questionnaires

 Criterion problem
– Definition of culture
– Country as a surrogate for culture
– When is culture a contingency?
– Cultural biases of researchers
– Cultural biases of national theories
 Methodological simplicity
– Difficulties of rigorous designs
– Cross-sectional case studies
– One-shot static studies
– Static group comparisons
– Functional equivalence
– Time problems
– Single-discipline studies
– No synergy

 Sampling issues
– Selection of cultures and subjects (convenience)
– Student samples
– Sample size and representativeness
– Matched samples
– Independence of samples (Galton’s problem)
– Description of the characteristics of the samples
 Instrumentation
– Equivalence of language (transalation)
– Equivalence of variables
– Equivalence of scaling (response formats, PRC,JPN central tendency unless even number of alternatives)

(Kumar, 2000)
Four men, a Saudi, a Russian, A North Korean, and a New Yorker are walking down the street. A researcher says to them: ”Excuse me, what is your opinion on the meat shortage?”
The Saudi says: ”What’s a shortage?”
The Russian says: ”What’s meat?”
The Korean says: ”What’s an opinion?” and the New Yorker says: ”What’s excuse me?”

 Data collection
– Equivalence of administration
– Respose equivalence
– Timing of data collection
– Status and other psychological issues
– Cross-sectional versus longitudinal data collection
 Data analysis
– Qualitative vs. quantitative data
– Non-parametric vs. parametric statistics
– Univariate vs. multivariate analyses
 Level of analysis
– Data collection and analysis at one level, inferences at another
– Individual/organizational/societal
– Ecological fallacy (Hofstede) or aggregation problem

(Reynolds et al. (2003)
JIBS, Vol.34(1):80-89)
Type of research
Objective Sampling objective Sample attributes Sampling method
Descriptive Examine attitudes and behavior within specific countries
Contextual Examine attributes of a cross-national group
Comparative Examine similarities or differences between countries
Theoretical Examine the crossnational generalizability of a theory or model
Within-country representativeness
Representativeness of the cross-national population
Cross-national comparability
Cross-national comparability
Estimate sampling error
Estimate sampling error
Homogeneity to control for extraneous factors
Homogeneity or deliberate heterogeneity
Random within each country
Random within the population
Non-random acceptable, matched
Non-random acceptable

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 Hofstede’s dimensions (Culture’s consequences, 1980,
2001)
– Individualism / collectivism
– Power distance
– Masculinity / femininity
– Uncertainty avoidance
– (Long-term orientation)
High vs. low context cultures (Hall, 1959)
Cultural orientations (Kluckhohn & Strodtbeck, 1961)
Individualism – collectivism (Triandis, 1983)

Country PDI UAI IDV
Finland 33 59 63
Russia 93
Germany 35
95
65
39
67
USA
Japan
40
54
46
92
91
46
Denmark 18 23 74 http://spectrum.troy.edu/~vorism/hofstede.htm
MAS
26
36
66
62
95
16

 Discuss how the differences in cultural dimensions may affect data collection, e.g.
– Choice of data collection method
– Choice of informants
– Choice of objective vs. subjective measures
– Choice of direct vs. projective measurement
– Use of appeals to solicit responses

 Adler (1983) A typology of management studies involving culture, JIBS, 14(2):29-47
 Adler et al. (1989) In search of appropriate methodology…JIBS, 20:61-74.
 Buckley & Chapman (1996) Theory and method in IB research. IBR, 5(3):233-245
 Cavusgil & Das (1997) Methodological issues in empirical cross-cultural research…MIR, 37(1):71-96
 Nasif et al. (1991) Methodological problems in crosscultural research…MIR, 31(1):79-91
 Coviello & Jones (2004) methodological issues in international entrepreneurship research. JBV, 19:485-508
 Kumar (2000) International marketing research. Book