A STUDY INVESTIGATING THE DETERMINANTS
OF CONSUMER BUYER BEHAVIOUR RELATING
TO THE PURCHASE OF ORGANIC FOOD PRODUCTS
IN URBAN CHINA
Jue Chen
Thesis submitted in fulfillment of the requirements of the degree
of Doctor of Philosophy
Faculty of Business and Enterprise
Swinburne University of Technology
2012
ABSTRACT
In the last two decades, organic food has developed as one of the fastest growing areas
in the world and has assumed greater global awareness. Organic food is perceived as
being more nutritious, healthier, safer and environmentally friendly. China has
experienced dramatic economic growth, and is developing an affluent urban middle
class which is attracted to the organic food market. As it is a highly populated country
with great potential in production and consumption of organic food, the understanding
of Chinese organic food consumers’ behaviour is critical for the global organic food
market. Current literature examining consumers’ awareness of organic food has been
well developed in North America and Western Europe. Hence the aim of this research is
to investigate the various determinants and elements which influence consumers’
purchase behaviour of organic food products in urban China.
A five stages conceptual framework was proposed for analysing consumer purchase
decision making towards organic food in urban China. The five stages are influencing
stage, cognitive/affective stage, evaluation of alternatives stage, behavioural intentions
stage and purchase stage. The emphasis was placed on the influencing aspects and
dimensions of this process in the Chinese context. The four constructs – namely,
product, regulatory, lifestyle and ethnocentrism – were positioned in the influencing
stage. The research design is predominantly quantitative in nature. The data was
collected in China in two stages. Stage one involved 204 valid online surveys which
were collected for the purpose of the pilot study. Stage two, the main study, involved
964 paper-based surveys which were collected in four major cities. The data analyses
were performed using exploratory factor analysis, confirmatory factor analysis and
structural equation modelling.
The findings of this study have revealed that the influencing stage’s product-related,
regulatory and lifestyle constructs directly or indirectly influence urban Chinese
consumers’ beliefs/attitudes, pre-purchase evaluation and behavioural/purchase
intention. The cognitive/affective stage of which beliefs and attitudes were the main
component was found to be a significant predictor of pre-purchase evaluation. In the
i
evaluation of alternatives stage, pre-purchase evaluation was found to have a highly
significant effect on behavioural/purchase intentions.
This study is one of the very few associated with consumer buyer behaviour of organic
food in urban China, and it was the first national organic food consumption survey in
China. It has provided valuable insights into current studies of global and Chinese
consumer behaviour towards organic food. Beneficiaries of this research study include
stakeholders in China and globally such as consumers, vendors both local and
international, and government agencies.
ii
ACKNOWLEDGEMENTS
Thank you
To those people who have encouraged me and inspired me during my PhD journey…
From my homeland China, and now to my new home Australia. It has been a
migrant’s dream…
I can never thank enough what life has given to me…
iii
As a Chinese migrant to Australia, it has always been my dream to connect my
homeland – China where I was born, where I grew up and where I worked for many
years, to my new home – Australia. This study intends to make my dream come true.
First of all, I would like to take this opportunity to thank my principal supervisor
Associate Professor Antonio Lobo who provided me with tremendous support and
invaluable guidance. Tony’s rigorous scholarship toward research influenced me
throughout this study and will continually influence my future academic life. I must say
I am very lucky to have had the best supervisor who always gave me whatever support
whenever I needed it. Thanks also go to my associate supervisor Dr Bruno Mascitelli
whose hard-working and unwavering passion for the academic world has inspired me
during my academic journey. Without his continued courage and support, I would not
have finished this study. Both Tony and Bruno are not only my supervisors during this
thesis, they are my academic mentors who provided invaluable advice and support and
also encouraged me to pursue, continue and complete this long PhD journey. Thanks
also go to my third supervisor Professor Chongguang Li (from China), who gave me
tremendous support and guidance during the data collection in China.
Secondly, thanks go to people working in the organic industry who provided invaluable
advice and inspiration during this study – German Organic Services GmbH director Mr
Udo Censkowsky, Chinese National Professional Committee of Organic Health
Industry Deputy Secretary-General Mr Mika Yuan (袁晓东), China ‘Organic farm’
Deputy General Manager Ms XiaoXue Li (李晓雪) and Australia Pure Harvest manager
Ms Linda Liu – and to colleagues from the Victoria Organic Industry Committee
Women’s Leadership group and especially to my industry mentor, VOICe chair Ms Liz
Clay. I’d love to borrow her motto: live and breathe organic.
Thirdly, I would like to acknowledge Swinburne University of Technology for
awarding me with a scholarship (Swinburne University Postgraduate Research Award,
SUPRA) that enabled me to complete this thesis. English is my second language and Ms
Nancy Moncrieff provided invaluable assistance in improving my writing skills.
Associate Professor Denny Meyer and Ms Natalia Rajendran assisted me with the
statistical side of this research. I also wish to express my appreciation to Mr David
iv
Hudson for his proofreading of this thesis during the final stages of the manuscript.
Many of my PhD colleagues in BA 701 inspired and energised me to continue the long
journey. We had a great time, had so much fun and shared so much emotion and joy.
Finally, I’d like to acknowledge the love, support and understanding of my family, my
husband Frank and my son Mark. Particular thanks go to my parents, Shuzhen and
Weimin who came to Australia to help me look after the family during the final year of
the completion of this thesis to enable me to fully concentrate on finishing this study.
I cannot believe that I have finished this journey. I still enjoy doing research and I feel
as though it has just begun …
v
DECLARATION
This thesis contains no material which has been accepted for the award to the candidate
of any other degree or diploma in any university or other institution. To the best of my
knowledge, this thesis contains no material previously published or written by another
person, except where due reference is made in the text.
Jue Chen
Melbourne, Australia
vi
TABLE OF CONTENTS
CHAPTER ONE: INTRODUCTION AND BACKGROUND ............................................. 1
1.1 Introduction .................................................................................................................... 1
1.2 Background of Chinese economic development ............................................................ 3
1.3 Evolution of the global organic food scenario ............................................................... 5
1.3.1 A brief history of the organic concept ................................................................... 5
1.3.2 A contemporary overview of the global organic food market ............................... 7
1.3.3 The nature and meaning of the term ‘organic food’ .............................................. 8
1.4 Changes in the Chinese economy ................................................................................ 10
1.4.1 China’s new urban consumers ............................................................................. 10
1.4.2 The organic industry in China .............................................................................. 12
1.5 Research problem and questions .................................................................................. 16
1.6 Academic and practical justification for this research ................................................. 18
1.6.1 Understanding Chinese culture ............................................................................ 18
1.6.2 Theoretical academic contribution ....................................................................... 18
1.6.3 Business implications and beneficiaries of this research ..................................... 19
1.7 Research methodology ................................................................................................. 20
1.8 Outline of the research ................................................................................................. 21
1.9 Chapter summary ......................................................................................................... 24
CHAPTER TWO: LITERATURE REVIEW ...................................................................... 25
2.1 Introduction .................................................................................................................. 25
2.2 Understanding organic food consumers ....................................................................... 26
2.2.1 Is organic food healthier? ..................................................................................... 26
2.2.2 Consumer awareness and knowledge of organic food ......................................... 27
2.2.3 Differences between buyers and non-buyers of organic food .............................. 28
2.3 Demographic profiles of organic food consumers ....................................................... 29
2.3.1 Gender .................................................................................................................. 30
2.3.2 Age ....................................................................................................................... 31
2.3.3 Education level ..................................................................................................... 31
2.3.4 Income level ......................................................................................................... 32
2.3.5 Role of the family................................................................................................. 32
2.4 Purchase motivations ................................................................................................... 33
2.4.1 Food attributes ...................................................................................................... 34
2.4.2 Trends and fashion ............................................................................................... 35
2.4.3 Health consciousness ........................................................................................... 35
2.4.4 Organic food information and knowledge ........................................................... 36
2.4.5 Impact on food safety issues ................................................................................ 37
2.4.6 Environmental concerns ....................................................................................... 38
2.4.7 Animal rights and social justice ........................................................................... 38
2.4.8 Country of origin .................................................................................................. 39
2.5 Barriers to organic food consumption .......................................................................... 40
2.5.1 Price...................................................................................................................... 40
2.5.2 Willingness to pay ................................................................................................ 41
2.5.3 Labelling and certification ................................................................................... 42
2.5.4 Lack of availability .............................................................................................. 43
2.6 Pre-purchase intention .................................................................................................. 44
vii
2.6.1 Attitudes ............................................................................................................... 44
2.6.2 Values................................................................................................................... 45
2.6.3 Beliefs .................................................................................................................. 46
2.6.4 Purchase intention ................................................................................................ 47
2.7 Theories associated with previous studies ................................................................... 48
2.7.1 The Consumer Decision Process (CDP) model applied to food consumption .... 50
2.7.2 Attitude theory and measurement ........................................................................ 62
2.7.3 Integration of three models: CDP, TPB and Hierarchy of Effects ....................... 67
2.8 Summary of previous studies ....................................................................................... 70
2.9 Limitation of previous studies...................................................................................... 71
2.10 Chapter summary ..................................................................................................... 73
CHAPTER THREE: DEVELOPMENT OF THE CONCEPTUAL FRAMEWORK
AND RELATED HYPOTHESES ....................................................................................... 74
3.1 Introduction .................................................................................................................. 74
3.2 Development of the proposed conceptual framework ................................................. 75
3.3 Influencing stage .......................................................................................................... 80
3.3.1 The product construct ........................................................................................... 80
3.3.2 The regulatory construct ...................................................................................... 83
3.3.3 The lifestyle construct .......................................................................................... 87
3.3.4 The ethnocentrism construct ................................................................................ 90
3.4 Cognitive/affective stage .............................................................................................. 93
3.5 Evaluation of alternatives stage ................................................................................... 95
3.6 Behavioural/purchase intention stage .......................................................................... 96
3.7 Demographic variables................................................................................................. 97
3.8 Chapter summary ....................................................................................................... 102
CHAPTER FOUR: METHODOLOGY ............................................................................. 103
4.1 Introduction ................................................................................................................ 103
4.2 Research design .......................................................................................................... 104
4.2.1 Research paradigm ............................................................................................. 104
4.2.2 Research context ................................................................................................ 105
4.2.3 Unit of analysis .................................................................................................. 106
4.2.4 Refining the unit of analysis .............................................................................. 106
4.3 Design of the survey instrument ................................................................................ 107
4.3.1 Focus groups ...................................................................................................... 107
4.3.2 Operationalising the constructs .......................................................................... 108
4.3.3 Description of the survey instrument ................................................................. 116
4.3.4 Scaling and measurement................................................................................... 117
4.3.5 Validity and reliability of survey instrument ..................................................... 118
4.4 Data Collection........................................................................................................... 121
4.4.1 Ethics approval ................................................................................................... 121
4.4.2 Sampling ............................................................................................................ 122
4.4.3 Stage one: pilot study (online survey) ................................................................ 123
4.4.4 Stage two: main data collection (paper-based survey) ....................................... 124
4.4.5 Selection of cities in China for stage two: paper-based data collection ............. 125
4.5 Chapter summary ....................................................................................................... 128
viii
CHAPTER FIVE: ANALYSES AND RESULTS OF THE PILOT STUDY
(STUDY ONE) .................................................................................................................. 129
5.1 Introduction ................................................................................................................ 129
5.2 The objectives of the data analysis in the pilot study ................................................ 129
5.3 Choosing the right statistical method ......................................................................... 130
5.4 Identifying factors and latent variables ...................................................................... 132
5.4.1 Results of the EFA for the product-related construct......................................... 133
5.4.2 EFA results for the regulatory construct ............................................................ 135
5.4.3 EFA results for the lifestyle construct ................................................................ 136
5.4.4 EFA results for the ethnocentrism construct ...................................................... 137
5.4.5 EFA results for the beliefs and attitudes construct............................................. 138
5.4.6 EFA results for the pre-purchase evaluation construct ...................................... 138
5.4.7 EFA results for the behavioural/purchase intention construct ........................... 139
5.4.8 Consolidated details of the ten factors ............................................................... 140
5.5 Chapter summary ........................................................................................................ 140
CHAPTER SIX: ANALYSES AND RESULTS OF THE MAIN STUDY
(STUDY TWO).................................................................................................................. 142
6.1 Chapter overview ....................................................................................................... 142
6.2 Data screening ............................................................................................................ 143
6.2.1 Screening and cleaning missing data ................................................................. 143
6.2.2 Assessing reliability and normality .................................................................... 144
6.2.3 Outliers ............................................................................................................... 145
6.2.4 Assessing normality ........................................................................................... 146
6.3 Profile of respondents ............................................................................................... 146
6.4 Confirmatory factor analysis and measurement models ............................................ 153
6.4.1 Confirmatory factor analysis .............................................................................. 153
6.4.2 Maximum Likelihood ........................................................................................ 153
6.4.3 Measurement model fit indices .......................................................................... 154
6.4.4 Results of CFA ................................................................................................... 157
6.4.5 Summary of the results of CFA ......................................................................... 172
6.5 Full structural equation model ................................................................................... 172
6.5.1 Convergent validity ............................................................................................ 173
6.5.2 Discriminant validity.......................................................................................... 173
6.5.3 Establishing a full structural model.................................................................... 177
6.5.4 The output of the final model ............................................................................. 178
6.5.5 Assessing the reliability of the final model ........................................................ 182
6.5.6 Hypotheses tests ................................................................................................. 182
6.5.7 Discussion relating to the hypotheses tests of SEM........................................... 185
6.6 Analysis of additional data ......................................................................................... 189
6.6.1 Analysis of demographic control variables as shown in the conceptual
framework .. ……………………………………………………………………………189
6.6.2 Important attributes relating to purchase of organic food .................................. 193
6.6.3 Usage pattern of organic food ............................................................................ 195
6.6.4 Important attributes versus usage pattern ........................................................... 196
6.6.5 Comparative analysis of responses from the four selected cities ....................... 199
6.7 Chapter summary ....................................................................................................... 206
ix
CHAPTER SEVEN: DISCUSSION OF FINDINGS, RECOMMENDATIONS AND
CONCLUSION .................................................................................................................. 207
7.1 Introduction ................................................................................................................ 207
7.2 Implications of the best-fit SEM model ..................................................................... 208
7.3 Discussion of findings relating to research question one ........................................... 212
7.3.1 Product ............................................................................................................... 212
7.3.2 Regulatory .......................................................................................................... 216
7.3.3 Lifestyle.............................................................................................................. 218
7.3.4 Ethnocentrism .................................................................................................... 221
7.3.5 Pre-purchase evaluation and behavioural/purchase intention ............................ 224
7.4 Discussion related to research question two .............................................................. 225
7.4.1 Gender ................................................................................................................ 225
7.4.2 Age ..................................................................................................................... 226
7.4.3 Education and Income levels ............................................................................. 227
7.4.4 Usage patterns .................................................................................................... 228
7.5 Contribution of this study .......................................................................................... 230
7.5.1 Theoretical contributions ................................................................................... 231
7.5.2 Business implications ......................................................................................... 233
7.6 Limitations of this study ............................................................................................ 241
7.7 Future research directions .......................................................................................... 242
7.8 Concluding remarks ................................................................................................... 244
REFERENCES................................................................................................................... 246
APPENDICES ................................................................................................................... 283
Appendix 1: Questionnaires ............................................................................................... 283
Appendix 2: Summary of key authors from selected studies in different continents and
countries ............................................................................................................................. 298
Appendix 3: Ethics approval .............................................................................................. 300
Appendix 4: Detailed analysis of the Pilot study(study 1) ................................................. 302
Appendix 5: Detailed analysis of the main study (study 2) ............................................... 317
Appendix 6: Discriminant validity ..................................................................................... 328
Appendix 7: The output of the final model ........................................................................ 329
Appendix 8: Hypotheses seven .......................................................................................... 334
Appendix 9: Referred Journal, Book and Conference Publications .................................. 349
x
LIST OF FIGURES
Figure 1.1 Labels of food categories in China
Figure 1.2 Research design and sequence
Figure 1.3 Roadmap to the structure of the thesis
Figure 2.1 Structure of Chapter Two
Figure 2.2 The Consumer Decision Process Model
Figure 2.3 Conceptual framework for analysing consumer decisionmaking towards the purchase of meat
Figure 2.4 Framework of factors which affect organic consumers’
attitudes and purchase decisions
Figure 2.5 The Theory of Planned Behaviour
Figure 3.1 Structure of Chapter Three
Figure 3.2 Proposed conceptual framework for the purchase intention of
organic foods in urban China
Figure 4.1 Structure of Chapter Four
Figure 4.2 Map showing the geographic dispersion of the four selected
cities
Figure 5.1 Structure of Chapter Five
Figure 6.1 Structure of Chapter Six
Figure 6.2 Recognition of organic food logos
Figure 6.3 First thing in mind when thinking about organic food
Figure 6.4 Types of organic food recently purchased
Figure 6.5 Usage of the most recently purchased organic food
Figure 6.6 Willing to pay extra for organic food
Figure 6.7 Country of origin of imported organic food
Figure 6.8 Choice of distribution channel
Figure 6.9 Frequency of purchase of organic food products
Figure 6.10 Monthly family income levels
Figure 6.11a A two factor CFA model of the product-related construct
Figure 6.11b A re-specified one factor CFA model for the productrelated construct
Figure 6.12a A one factor CFA model of the regulatory construct
Figure 6.12b A re-specified one factor CFA model for the regulatory
construct
Figure 6.13a Initial three factor model for the lifestyle construct
Figure 6.13b A re-specified three factor model for the lifestyle construct
Figure 6.13c Higher-order CFA for the lifestyle construct
Figure 6.14 CFA for ethnocentrism measures
Figure 6.15a One factor CFA model for the beliefs/attitudes construct
xi
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Figure 6.15b A re-specified CFA for the beliefs/attitudes construct
Figure 6.16a One factor CFA model for the pre-purchase evaluation
construct
Figure 6.16b A re-specified CFA for the pre-purchase evaluation
construct
Figure 6.17 CFA for behavioural/purchase intention construct
Figure 6.18 Endogenous constructs of the influencing stage of the
conceptual model
Figure 6.19 Exogenous construct of the cognitive/affective, evaluation
of alternatives and behavioural/purchase intention stages of the
conceptual model
Figure 6.20 Final best-fit model of consumer purchase intention of
organic food in urban China
Figure 6.21 Simplified final best-fit model
Figure 7.1 Structure of Chapter Seven
Figure 7.2 Final best-fit model for the purchase intention of organic
foods in Urban China
xii
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183
208
232
LIST OF TABLES
Table 2.1 Summary of key attributes measured with associated theory in
studies relating to organic products
Table 3.1 List of all research hypotheses
Table 4.1 Components of product-related construct and associated
measures
Table 4.2 Components of regulatory construct and associated measures
48-49
Table 4.3 Components of lifestyle construct and associated measures
Table 4.4 Components of ethnocentrism construct and associated
measures
Table 4.5 Attributes of beliefs/attitudes construct and associated
measures
Table 4.6 Attributes of pre-purchase evaluation and behavioural/
purchase intention constructs and associated measures
Table 4.7 Important attributes when purchasing organic food
Table 5.1 Pattern and structure matrix for EFA of the product-related
construct and associated measures
Table 5.2 Factor loadings for EFA of the regulatory construct and
associated measures
Table 5.3 Pattern and structure matrix for EFA of the lifestyle construct
and associated measures
Table 5.4 Result for EFA of the ethnocentrism construct and associated
measures
Table 5.5 Result for EFA of the beliefs and attitudes construct and
associated measures
Table 5.6 Result for EFA of the pre-purchase evaluation construct and
associated measures
Table 5.7 Result for EFA of the behavioural/purchase intentions
construct and associated measures
Table 5.8 Summary of the ten factors
Table 6.1 Progressive reliability scores for the ten factors
Table 6.2 Model fit indicators adopted in this study
Table 6.3 Regression weights for the product-related construct
Table 6.4 Regression weights for regulatory measures
Table 6.5 Regression weights for lifestyle measures
Table 6.6 Regression weights for ethnocentrism measures
Table 6.7 Regression weights for beliefs/attitudes measures
Table 6.8 Regression weights for pre-purchase evaluation measures
Table 6.9: Regression weights for behavioural/purchase intention
Table 6.10 Summary of reliability of the measurement models
111
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xiii
101
109
110
113
114
115-116
134
135
136
137
138
139
140
140
145
156
159
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170
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Table 6.11 Factor pattern and structure coefficients for the endogenous
constructs of the influencing stage
Table 6.12 Factor pattern and structure coefficients for the exogenous
constructs of beliefs/attitudes, evaluation of alternatives and
behavioural/ purchase intention
Table 6.13 Summary of techniques used to test hypotheses
175
Table 6.14 Summary of reliability of the final model scales
Table 6.15 Summary of hypotheses tests (H1a- H6)
Table 6.16 Independent samples t test
Table 6.17 ANOVA (purchase intentions, age groups)
Table 6.18 ANOVA (purchase intentions, education levels)
Table 6.19 ANOVA (purchase intentions, income levels)
Table 6.20 Top five important attributes
Table 6.21 Five least important attributes
Table 6.22 Means of important attributes as applicable to the usage
pattern
Table 6.23 Summary of hypotheses testing
Table 6.24 Goodness-of-fit indices showing comparative analysis of
data from the four cities
Table 6.25 Goodness-of-fit indices for data from all four cities
Table 6.26 Standardised regression weights (four cities)
Table 6.27 Evaluated hypotheses test for Shanghai
Table 7.1 Action plans for the decision makers
182
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xiv
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LIST OF ABBREVIATIONS
ACO
ANOVA
CDP
CFA
CGFDC
CPC
DV
EFA
GM
GDP
IFOAM
IV
KMO
LOHAS
OFDC
SEM
SEZ
PRC
TPB
TRA
Australian Certificated Organic (logo)
analysis of variance
Consumer Decision Process
confirmatory factor analysis
China Green Food Development Center
Communist Party of China
dependent variable
exploratory factor analysis
genetically modified
gross domestic product
International Federation of Organic Agriculture Movements
independent variable
Kaiser-Meyer-Olkin
Lifestyle of Health and Sustainability
Organic Food Development Center
structural equation modelling
Special Economic Zone
People’s Republic of China
Theory of Planned Behaviour
Theory of Reasoned Action
xv
CHAPTER ONE:
INTRODUCTION AND BACKGROUND
1.1
Introduction
Over the last decade, there has been a significant increase in interest in organic foods. New
food technology, growing health awareness, busier lifestyle and global environmental
issues have triggered an awareness of organic foods in many developed countries.
Consumers have started to question what they eat and the food production processes, as
well as question whether food is safe (IFOAM 2012). Organic food has developed as one of
the fastest growing areas of the world food market. From being a niche market, organics
have evolved as mainstream products. Organic food studies have drawn the attention of
scholars and government policy makers, and many studies have been undertaken in
developed countries. In some developing markets, such as China, as a result of its dramatic
and recent economic development, a series of food scandals have damaged the food
industry and confidence in some sectors of the industry (Euromonitor International 2010).
This in turn has raised awareness and sensitivity towards organic food products. China’s
economic development has created a large middle class attuned to western trends and
lifestyle adaptation. Organics have emerged as an important sector of food and, while
predominantly understood and consumed in western countries, it is relatively new in
countries such as China (Yin et al. 2010). It is evident from this research that few studies
related to Chinese consumers buying organic food have been carried out. The primary
objective of this study is to investigate the determinants of consumer purchase behaviour
relating to organic food in urban cities of mainland China.
The purpose of this first chapter is to introduce the topic and to provide an overview of this
thesis as well as the rationale for undertaking this study. It provides the background to the
research and explains the nature and use of the term ‘organic food’. As will become clearer,
the term is not understood or agreed upon by most people in China. The research problem
and associated research questions have been elicited from gaps in the extant literature. A
conceptual model is developed and hypotheses related to its various constructs are
1
formulated, then the justification for the research is presented. The later sections of this
chapter briefly describe the research methodology employed and the chapter concludes
with an outline of the structure of the thesis.
Research into organic food products has failed to provide convincing scientific evidence
demonstrating their health merits (Brennan, Gallagher & McEachern 2003; Magkos,
Arvaniti & Zampelas 2006). Conventional food outlets such as supermarket chains are
adamant that the differences between conventional and organic foods are either nonexistent or so small as to be insignificant, which has created confusion among consumers
when it comes to understanding the health benefits of organic food. This research does not
aim at making any judgement on this debate; however, it investigates related perceptions of
existing and potential Chinese consumers of organic foods.
Previous studies have shown that consumers’ concerns about health and the environment
are the most important factors that influence their purchase of organic food (Chen 2007;
Chryssochoidis & Krystallis 2005; Millock, Wier & Andersen 2004; Padel & Foster 2005;
Radman 2005; Tsakiridou, Mattas & Tzimitra-Kalogianni 2006). Organic foods are
perceived as being more nutritious and healthier, safer, environmentally friendly,
containing few chemical residues and tasting better than conventional food (Harper &
Makatouni 2002; Krystallis & Chryssohoidis 2005; Sanjuán et al. 2003). As a result,
consumers are willing to pay a premium price for them (Aguirre 2007; Botonaki et al. 2006;
Krystallis & Chryssohoidis 2005; Ureña, Bernabéu & Olmeda 2008). Even in newly
emerging markets, consumers are willing to pay more for higher quality and for food
certified as being ‘safe’ (Grannis, Hine & Thilmany 2001). The range of organic food
products not only includes fruits, vegetables and meat, but also extends to grains, nuts,
dairy produce and many other processed products (Aarset et al. 2004; International Trade
Centre 2011). Reasons for the non-purchase of organic food include consumers’
perceptions of it being expensive, its lack of perceived value, limited availability, lack of
choice, low profile distribution channels and even unsatisfactory appearance (Bhaskaran et
al. 2006; Chang, Zepeda & Griffith 2005; Essoussi & Zahaf 2008; Radman 2005;
Stobbelaar et al. 2007). Misleading labelling and certification have also been identified as
2
barriers to purchase (Aarset et al. 2004; Brennan, Gallagher & McEachern 2003; Essoussi
& Zahaf 2008; Krystallis, Fotopoulos & Zotos 2006).
China, the geographical focus of this study, has experienced dramatic economic growth in
the last 20 years. Its organic food sector has grown faster than the worldwide average
(IFOAM 2006) and the market has been developing at an annual double digit rate over the
past decade (US Department of Agriculture 2008). Official figures for the exact sales of
organic food products are unavailable, although they had an estimated market value of
US$1.1 billion in 2008 (International Trade Centre 2011). According to one source, China
was the fourth largest organic market in the world (Zhou 2008b). It is developing an
affluent urban middle class and also an expatriate community who are interested in
purchasing organic food (Euromonitor International 2008b). Large international retailers
such as Wal-Mart (US) and Carrefour (France) have significantly increased their sales of
organic food products in China, even though these are relatively more expensive for
consumers (Baer 2007; Sanders 2006).
1.2
Background of Chinese economic development
Before discussing the nature of the organic industry in China, it is desirable to provide a
background of the evolution of modern China and especially the economic reforms which
have defined it in its modern context. China’s history is a long and complex one. Modern
China – the People’s Republic of China (PRC) – was founded by the Communist Party of
China (CPC) in 1949. From 1949 to 1979, the PRC’s development was slow and quite
unbalanced. The programs under the banner of the Great Leap Forward of the late 1950s
failed miserably as an economic and social campaign by the CPC. The economic decisions
between 1958 to 1961, to rapidly transform the country from an agrarian economy into a
modern communist society through agriculturisation, industrialisation and collectivisation,
were patently unsuccessful. Private farming was prohibited, and those engaged in it were
labelled as counter-revolutionaries and persecuted. The Great Leap Forward ended in
catastrophe, resulting in millions of deaths, and was deemed a very expensive disaster in
modern Chinese history (Chesneaux 1979; Tian & Dong 2011).
3
Further complicating Chinese economic development was the Cultural Revolution (19661976). According to the then leadership of the CPC, this was a political movement designed
to further advance ‘socialism in the country’ by removing capitalist elements from society
and imposing Maoist orthodoxy within the party. The movement brought chaos and largescale damage to China economically and socially. Millions of people were persecuted,
suffering a wide range of abuses in the violent factional struggles that ensued across the
country. A large segment of the population was forcibly displaced, most notably the
transfer of urban youth to rural regions during the Down to the Countryside Movement
(Chesneaux 1979; Slavicek 2010; Tian & Dong 2011). Food was scarce and many people
were at starvation point.
In 1978 the new CPC leader Deng Xiao Ping introduced and shaped the economic reforms
soon after Mao’s death.1 These reforms have been a transitionary path to a market based
economy, under Deng’s famous slogan ‘As long as we increase production, we can even
revert to individual enterprise; it hardly matters whether a good cat is black or white, as
long as it catches mice, it is a good cat’ (Chesneaux 1979, p. 115). China started to adapt an
open door policy, coinciding with a shift in thinking from a centralised planned economy to
a so-called ‘socialist market economy with Chinese characteristics’. Over time, China
started to reintegrate within the global economy (Fairbank & Goldman 1998). One of the
first and most conspicuous signs of change in the early 1980s was the welcomed array of
new food and everyday items of consumption. The expansion of the free market, relaxed
price controls and proliferation of retail outlets boosted the availability and range of highquality foods (Croll 2006). By the 1980s, economic reform had fundamentally changed
Chinese consumers’ experience, shopping and the dynamics of food retailing. Food
coupons and ration books to purchase food and many other consumer goods eventually
were phased out from people’s daily lives (Veeck 2000). In 1997, the party also announced
the phasing out of state-owned industries and, except for sensitive and key industries, most
state enterprises were to be sold off (Fairbank & Goldman 1998). Consumer products,
including fast food outlets, gradually opened up and the Chinese retail market began
1
Mao Zedong (Mao Tse-tung), commonly referred to as Chairman Mao (26 Dec. 1893 – 9 Sept. 1976), was
the founder of the PRC in 1949 and ruled until his death in 1976.
4
opening its doors to products manufactured by foreign companies. Consumers were
becoming more aware of foreign products (Kahal 2001), and now had a wider selection of
foreign and domestic products.
China’s inclusion in the World Trade Organization (WTO) in 2001 allowed it to take the
next step to invigorate its economic reforms. It was a major milestone in its economic
transition, and led the country to embrace global standards of manufacturing in many areas
(Croll 2006; Keane 2007). There has been a sense of expectation that China would play by
the market rules and its emergence would create unprecedented opportunities for the middle
classes, a social demographic segment comprising between 48 million and 90-100 million
people (Keane 2007). By staging the 2008 Beijing Olympic Games, China displayed its
economic and political prowess to the world (Xu 2008). In the last decade China has
experienced dramatic economic growth with an average GDP growth of about 10%, and is
now the single largest contributor to global economic growth and the second largest
economy after the US (DFAT 2012). In 2010, the value of foreign imports reached
US$1,398 billion, up by 38.7% on the previous year (National Bureau of Statistics of China
2011). By 2015, China is envisaged to become the third biggest consumer market after the
US and Japan (Atsmon et al. 2009). The chances of it becoming the biggest economy in the
world, once thought impossible, are within reach. Most importantly, China has the potential
to offer increased purchasing power to the largest population in the world (Wu 1999).
As this study involves investigation of organic food consumers’ perceptions, both within
China and the rest of the world, it is fitting to introduce the related concept and industry of
organic foods. As organic food products result from the use of organic agriculture (Guido et
al. 2010), a brief history of organic agriculture will bring rich context to this study.
1.3
Evolution of the global organic food scenario
1.3.1 A brief history of the organic concept
As noted in previous studies, ‘The origins of modern organic agriculture are intertwined
with the birth of today’s industrially based agriculture’ (Kristiansen 2006, p. 4). Organic
agriculture was practised initially in response to the industrialisation of agriculture
5
(Torjusen et al. 2001). It was developed as a form of farming compatible with natural
systems and codified in the work of Rudolf Steiner and Sir Albert Howard in the 1920s
(Courville 2006), evolving as an alternative to the major initiatives taking place in
agricultural development. In 1943 Howard published a forward thinking endorsement of
organic agriculture, An Agricultural Testament (Heckman 2005; Howard 1943), advocating
that the UK preserve the cycle of life and adopt permanent agriculture systems using urban
food waste and sewage to enhance fertile soil. The term ‘organic farming’ was first used by
Lord Northbourne in his 1940 book, Look to the Land. It was later championed by US
businessman and publisher Jerome Rodale who was the first to use the term ‘organic’ in
food production in his publication Organic Gardening and Farming. This sought to
promote a healthy and active lifestyle that emphasised organically grown foods (Heckman
2005; US Department of Agriculture 2012c). The first organic farming society was
established in Australia in October 1944: the Australian Organic Farming and Gardening
Society (Paull 2008b). In 1946, Lady Eve Balfour was inspired by Howard to set up the
Soil Association of the UK (Institute of Food Science and Technology 2009). The
publication of Silent Spring (Carson 1962) in the US saw a time of significant change and
upheaval, which witnessed the start of both modern organic and environmental movements.
The Soil Association established the UK’s first set of Organic Food Standards in 1974
(Institute of Food Science and Technology 2009). Prior to industrialisation, fertilisers and
chemicals were not used and all food was grown ‘organically’. However, today there is a
high degree of difficulty in determining whether a farm’s production is or is not organic
without organic certification (Halpin 2004). Scientists became increasingly aware of
organic agriculture in the 1980s, including those who did not support alternative
agricultural systems (Kristiansen 2006). Organic products are now a rapidly growing sector
in most developed agricultural economies, becoming one of the most convincing answers to
the increasing environmental and food quality issues (Sottoni et al. 2002).
In the 1960s, organic production was very much outside the mainstream of the agrifood
business, and few took it seriously. This brought various organic stakeholders together to
form the International Federation of Organic Agriculture Movements (IFOAM) in 1972
(Courville 2006). This stands out today as the only global organic non-government
6
organisation (IFOAM 2012; Kristiansen 2006), with more than 750 member organisations
in 116 countries. The IFOAM organic guarantee system accredits national organic food
certification bodies to endorse genuine organic food products with the IFOAM seal
(IFOAM 2012, 2011a). However, as organic food labels are designed by certification
bodies within each country, there are no standard global labels and this obviously creates
ambiguity and problems with international scrutiny and consistency (Aarset et al. 2004).
For example, since 1993 the European Union (EU) has regulated standards for organic food
products. In the UK, all organic food producers are required to be registered on the UK
Register of Organic Food Standards (UKROF) or one of its approved certification bodies
(Byng 1993). As organic food production has increased in volume and value, governments
have increasingly taken an interest in regulating the industry. Tensions continually arise
between private accreditation bodies accredited by IFOAM and public bodies controlled by
regional and national governments, creating significant threats to the development of
organic agriculture and trade (Courville 2006).
1.3.2 A contemporary overview of the global organic food market
Organic food and beverages, from being an insignificant niche market, have become part of
the mainstream since the mid-1990s (Lockie, Halpin & Pearson 2006), with global sales
reaching $US27.8 billion in 2004. An estimated annual growth rate of organic product sales
reached 16% to 20% in the European market between 2002 and 2005 (Willer & Yussefi
2006). Rising disposable income, growth of health awareness and busier lifestyles have
driven a burgeoning world demand for organic and dietetic food and beverages (2005).
Although the organic food market is comparatively small, as little as 1% of farming in most
OECD countries, it is regarded as one of the biggest growth markets in the food industry
(Hughner et al. 2007), as well as in some developing countries (Kortbech-Olesen 1998).
Consumers especially in industrialised countries have an increased awareness in organic
food (Wier & Calverley 2002), with consumption increasing significantly across the world
(Chen 2007; Makatouni 2002; Sottoni et al. 2002; Wier & Calverley 2002). According to
the latest report from the Research Institute of Organic Agriculture in Switzerland,
Germany and Austria, organic agriculture is practised in 160 countries and 37.2 million
7
hectares of agricultural land are managed organically by 1.8 million farmers. These figures
show that global organic products continue to grow even in the face of the global economic
crisis. The total sales of organic products reached nearly $US55 billion in 2010 (IFOAM
2011b). The countries with the greatest levels of allocated organic farmland areas are
Australia 11.8 million hectares, Argentina 3.1 million hectares, China 2.3 million hectares
and the US 1.6 million hectares (Willer & Yussefi 2006).
1.3.3 The nature and meaning of the term ‘organic food’
The term ‘organic’ is derived from the Greek word bios, meaning life or way of living
(Essoussi & Zahaf 2008). It has many different meanings, interpretations and definitions
(Aarset et al. 2004; Brennan, Gallagher & McEachern 2003; Chryssochoidis 2000). In 1995
the US National Organic Standards Board adopted the following definition:
‘Organic’ is a labeling term that denotes products produced under the authority of
the Organic Foods Production Act. The principal guidelines for organic production
are to use materials and practices that enhance the ecological balance of natural
systems and that integrate the parts of the farming system into an ecological whole
(US Department of Agriculture 2012b).
Australia’s National Standard for Organic and Bio-Dynamic Produce defines ‘organic’ as:
the application of practices that emphasise the: use of renewable resources; and
conservation of energy, soil and water; and recognition of livestock welfare needs;
and environmental maintenance and enhancement, while producing optimum
quantities of produce without the use of artificial fertiliser or synthetic chemicals
(Organic Federation of Australia 2005, p. 8).
The Food and Agriculture Organization of the United Nations (FAO) (2002, p. 1) defines
organic products as those which are ‘certified as having been produced through clearly
defined organic production methods’. In support of this definition, ‘organic foods are
minimally processed to maintain the integrity of the food without artificial ingredients,
preservatives or irradiation’ (Essoussi & Zahaf 2008, p. 96).
8
Equally as significant is the link between organic food production and organic farming. EU
regulations on organic farming entail ‘significant restrictions on the use of fertilisers and
pesticides which may have detrimental effects on the environment or result in the presence
of residues in agricultural produce’ (Roddy, Cowan & Hutchinson 1994, p. 2). Organic
agriculture has been developed as an alternative form of farming compatible with natural
systems, and the concept of organic farming is based on a holistic viewpoint (KortbechOlesen 1998). Organic agriculture is different from other techniques of farming, such as
conventional agriculture which is an industrialised system using chemical synthetic
substances for fertilisers and insecticide to maximise the volume of production and profit
(Guido et al. 2010). Vindigni, Janssen and Jager (2002) argue that ‘organic’ often refers to
a claim associated with the process, not a claim associated with the product. According to
IFOAM (2009b):
Organic agriculture is a production system that sustains the health of soils,
ecosystems and people. It relies on ecological processes, biodiversity and cycles
adapted to local conditions, rather than the use of inputs with adverse effects.
Organic agriculture combines tradition, innovation and science to benefit the
shared environment and promote fair relationships and a good quality of life for all
involved.
Global consumers require a clearer understanding of what organic food actually means, as
confusion prevails amongst regulatory bodies. Fotopoulos and Krystallis (2002b) report
that consumers are confused by the use of different terminologies. Even if they have heard
of the term ‘organic’, nearly half could not give an accurate definition of it (Krystallis,
Petrovici & Arvanitoyannis 2006). Various contrasting ‘organic’ definitions are often
coupled and confused with terms such as ‘biological’, ‘ecological’, ‘natural’, ‘alternative’,
‘unsprayed’, ‘free of pesticides’, ‘without artificial additives’, ‘without chemicals’,
‘environmentally produced’, ‘green’ and ‘sustainable’ (Aarset et al. 2004; Essoussi &
Zahaf 2008; Hutchins & Greenhalgh 1995; McEachern & McClean 2002; Schifferstein &
Oude Ophuis 1998). Others argue that organic food should never be defined as pesticidefree (Institute of Food Science and Technology 2009). It can be seen that the dilemma
9
associated with the term ‘organic’ has caused worldwide confusion in the minds of
consumers and researchers alike.
For the purposes of this study, the definition adopted is taken from the Australian organic
market report 2010: ‘Organic is a labelling term that denotes products that have been
produced in accordance with organic production standards and certified by a duly
constituted certification body or authority’ (Kristiansen et al. 2010, p. 8).
1.4
Changes in the Chinese economy
1.4.1 China’s new urban consumers
For more than two decades, economic reforms have lifted hundreds of millions of people
out of poverty, giving many the opportunities to benefit from a rise in income levels and
living standards. The growth and reforms have brought on fundamental structural shifts in
China’s economy. The country has become a manufacturing powerhouse, combining
technologically sophisticated factories with energetic, intelligent and low-cost labour
(Austrade 2011; McGregor 2005). According to the Chinese Academy of Social Sciences
2011 report, urbanisation is at the centre of Chinese economic reform and development and
will be one of the most important driving forces for future economic growth. China will
also accelerate the pace of its urbanisation in the next 20 years, from the current 47% to
more than 70% by 2030 (Xing 2011). The global consulting firm McKinsey predicts that
urban households in China will make up one of the largest consumer markets in the world
by 2025 (Farrell, Gersch & Stephenson 2006). The number of wealthy households
continually grows, and in 2010 China held the world’s fourth-largest concentration of
wealthy people. Chinese consumers account for more than 20% of the global luxury market
(Atsmon & Dixit 2009). As former Australian Prime Minister Kevin Rudd commented,
‘within 30 years, China has transformed itself from an impoverished, isolated and mostly
agrarian economy to the increasingly wealthy, internationalised and urban economy we see
today’ (Callick 2011, p. 1).
The steady growth in income levels and shrinking family size (Farndon 2008) has led to
newer lifestyles emerging in the reformed China (Wei 1997). Consumers’ product choices
10
in its large cities match those in most developed economies (Piron 2006). A recent trend of
lifestyle emanating from the US is called LOHAS (Lifestyle of Health and Sustainability).
There has been an increase in the number of people seeking to emulate this lifestyle trend in
Shanghai. In China’s large cities, consuming organic food, growing fruits and vegetables,
and touring the local farms has become popular. In Chinese this trend is called ‘Nong Jia
Le’ which means ‘vacation in the countryside’. Since June 2008, the government has
banned free plastic bags in supermarkets, and the media has raised awareness about
environmental and sustainability issues, forcing more consumers to bring their own
shopping bags to supermarkets (US Department of Agriculture 2008).
The rising sense of ‘enjoying life’ and ‘quality of life’ is beginning to drive up spending,
not only among consumers in key cities but also in the massive second and third tier cities.
China also demonstrates big differences between its cities and regions in terms of wealth
and size. Although the richer cities such as Beijing, Shanghai and Guangzhou are the
logical first point of entry for companies targeting high-end consumers, there are several
second tier cities such as Dalian and Qiangdao in the north, Ningbo, Hangzhou and Nanjing
in the Yangtze River Delta and Xiamen in the south (Smith 2007). Certainly in big cities
like Beijing, Shanghai, Guangzhou and Shenzhen, the large middle class considerably
influences the rest of the population with their lifestyle adaptations (Zhou 2008a).
With the increase in the pace of home and working life, health issues are becoming
important, especially among office workers who lack fresh air and facilities for exercise.
Healthy products and supplements are popular among these people, being middle-aged and
young adults with high incomes and a taste for the good life. They pay more attention to the
enjoyment of cultural products and a sense of wellbeing. These groups make up the elites in
society, and are part of a big consumer group (Euromonitor International 2008b). In some
Chinese cities, the general population’s obesity level is approximately 20%, similar to that
in the US and Europe (Kluger 2010).
The recent increases in income levels have raised issues regarding food safety and food
nutrition in China (Peng 2006). Concerns about the safety of food following recent food
safety scandals as well as the rising purchasing power of consumers have driven growth in
11
sales of organic food (Euromonitor International 2010). It has been reported that Chinese
consumers are losing confidence in locally produced milk after a series of scandals
involving food safety (Euromonitor International 2011). The 2008 incident of baby milk
formula contamination involving melamine-tainted dairy products has increased concerns
about domestically produced food products. This scandal prompted well-off consumers to
turn to imported milk as the alternative (US Department of Agriculture 2008). The
melamine crisis was a wake-up call to the entire Chinese society, causing issues relating to
food safety to become top priority on the agenda of the state councils.
1.4.2 The organic industry in China
Chinese organic agriculture originally came into being over a thousand years ago, based on
traditional sustainable farming practices (Gao et al. 2009). The record of sustainable
farming dates back 4,000 years, involving organic practices such as crop rotation,
composting and diversified production. The ‘green revolution’ in China occurred in the
1980s, much later than the rest of world, due to the economic reform being introduced at
that time (International Trade Centre 2011).
1.4.2.1 Debunking the organic food myth in China
The term ‘organic food’ has many different connotations and interpretations, and in China
is sometimes used interchangeably with the term ‘green food’. Organic products are often
referred to as being ‘eco-products’, suitable for ‘green’ consumers who are ecologically or
environmentally aware and concerned with health and quality of life issues (Fotopoulos &
Krystallis 2002b). Generally speaking, in many western countries, ‘green’ connotes a sense
of environment associated with fears of global warming, and saving the planet by reducing
carbon emissions (Bonini & Oppenheim 2008; Kalafatis et al. 1999; Okun 2007). Concerns
about climate change are another reason to go ‘green’ (Bonini & Oppenheim 2008): ‘The
green consumer is generally defined as one who adopts environmentally-friendly behaviour
and/or who purchases green products over the standard alternatives’ (Shamdasani, ChonLin & Richmond 1993, p. 488). Other stereotypical images of organic consumers are
‘greenies’, ‘health nuts’ or ‘yuppies’, more interested in fashion than anything else, hence
12
consumption of organic food reflects a ‘greening lifestyle’ (Lockie et al. 2002). According
to Tanner and Kast (2003, p. 885):
Green food products are domestically cultivated rather than imported from foreign
countries; they are organically rather than conventionally grown; they are seasonal
and fresh rather than frozen; they are not wrapped and support fair trade.
Many developed countries have set stringent standards to ensure quality of their organic
food products. A number of developing countries including China have started to establish
national organic product standards and regulations (Food and Agriculture Organization of
the United Nations 2002).
In China three categories of food are deemed to be safe, ecological and environmentally
friendly: ‘non-polluted food’ (or hazard-free food), ‘green food’ and ‘organic food’. These
three categories can be explained by means of a pyramid where the lowest level is nonpolluted food, and the highest is organic food (Gao et al. 2009; Luo 2007; Paull 2008a;
Zhang 2007). The term ‘organic food’ has been influenced by the demand of the
international market, while ‘green food’ and ‘non-polluted food’ labels have been
introduced for domestic consumption (Gao et al. 2009). Figure 1.1 illustrates the labels for
the three categories.
Figure 1.1 Labels of food categories in China
Non-Polluted Food
This logo has only Chinese
script.
Green Food
This logo has both Chinese
and English scripts. Most
Chinese consumers are aware
of this logo.
Organic Food
This logo has both Chinese and
English scripts. Only a few
Chinese consumers are aware of
this logo.
13
Source: Gao et al. 2009; Luo 2007; Paull 2008a; Yin et al. 2010; Zhang 2007
Although only a very small and almost insignificant percentage of the population consume
organic food products, China has gone well beyond being an adopter of the global organic
concepts, and is now an active organic food innovator by promoting the concept of ‘green
food’ (Gao et al. 2009; Paull 2008a). Most Chinese consumers are familiar with the
concepts of ‘green’ and ‘non-polluted’ food, but many are not as familiar with the term
‘organic food’ (Paull 2008a; Zhang 2007). A survey by Li, Cheng and Ren (2005) suggests
that 92.2% of respondents were aware of ‘green food’, 69.6% knew about ‘non-polluted
food’, but only 37.2% were aware of ‘organic food’. The ‘green food’ concept appears to
be well understood and recognised in China (Paull 2008a). However, the concept’s
interpretation in China is different from that understood by consumers in western countries,
and will be further elaborated in later chapters.
1.4.2.2 Organic food standards in China
With increasing affluence derived from the rapid economic growth, certain sections of
Chinese society and government have begun to realise the hazardous impact of
environmental deterioration on national wellbeing (Chan 2001; Peng 2006). The Chinese
market for green and organic food is still in its early stage. Standards are low, inspections
are weak, and regulations are not adequately policed. In recent years, the government has
increased warnings against sub-standard food products, and consumers have become
cautious about food safety issues (Ho, Vermeer & Zhao 2006). The General Standard for
the Labelling of Food (GB7718-94) (Chinese new food labelling law) has been in effect
since February 1995 and closely follows standards recommended by the Food and
Agriculture Organization of the United Nations/World Health Organization’s Food Law
Committee. It requires that imported food labels are primarily in English or other foreign
languages, and the required content in simplified Chinese characters (Zhao et al. 2000). The
Chinese national organic logo has been mandatory for all Chinese organic products since
2005, and this has made understanding organic labels more straightforward for consumers
(Euromonitor International 2010).
14
The China Green Food Development Center (CGFDC) was founded in 1992 to meet
domestic consumers’ demand and the level of development of China’s agricultural
production and international competition. It is a specialised agency for the development and
management of green food under the supervision of the Ministry of Agriculture. This
agency advocates:
Green food products include edible produce and processed products produced in a
sustainable environment using quality standards governing quality control,
pollution, safety, quality and certification (CGFDC 2009).
The CGFDC has instituted two standards, ‘A’ and ‘AA’. ‘A’ represents a transitional level
between conventional and organic food, allowing restricted use of chemical fertilisers and
pesticides, while the ‘AA’ symbolises full organic status and matches all the international
organic food standards. A total of 98% of green food produced in China belongs to standard
‘A’ (Baer 2007; Sanders 2006). However, the ‘AA’ standard is not accepted by
international markets as organic food (Zhou et al. 2004).
The Organic Food Development Center (OFDC), founded in 1994, is the oldest and largest
research, inspection and certification organisation. It is the only organic food certification
body in China which has affiliations to IFOAM. Its mission is to ensure human health and
protect the ecological environment through promoting sustainable agriculture (Organic
Food Development Center 2009).
The Ministry of Agriculture oversees certification schemes in relation to non-polluted and
green food. Organic food certification is jointly supervised by the ministry and the State
Environment Protection Agency (Paull 2008a; Zhang 2007). In July 2002, premier Wen Jia
Bao emphasised that the government must regulate the certification and accreditation of
non-polluted, green and organic food, and accelerate support for modern production
techniques (Gao et al. 2009).
In recent years, the government has established a legislative framework in line with
international organic institutions, with administrative measures for organic product
certification and rules for implementing the certification of organic food. The Certification
15
and Accreditation Administration of the PRC is a peak body set up by the State Council to
perform the functions of administrative operation, unified management, supervision and
comprehensive coordination of certifications and accreditations (Wang 2010).
1.5
Research problem and questions
Research investigating consumers’ awareness and perceptions of organic food is well
developed in North America and Western Europe (Bonti-Ankomah & Yiridoe 2006). On
the other hand, studies of the Chinese market indicate that little is known or understood
about consumers’ attitudes towards organic produce or about their related purchasing
behaviour (Yin, Wu & Chen 2008; Yin et al. 2010), and the general awareness of organic
food products is relatively low (Li, Cheng & Ren 2005; Yin, Wu & Chen 2008; Yin et al.
2010). As a heavily populated country with great potential in production and consumption
of organic food, the development of China’s organic food market is likely to affect the
global market of organic food (Yin et al. 2010).
The overarching research problem and objective for this study is to ascertain the framework,
conditioning and new understanding of consumers’ purchase behaviour in relation to
organic food in China. To date, there is no theoretical framework or model that attempts to
evaluate the overall organic food purchasing behaviour of mainland Chinese consumers.
Organic food in China is a relatively new development both in its consumption and related
marketing strategies. Recent food scandals have produced scepticism towards both the food
industry and the government. It has not passed without notice that much Chinese consumer
behaviour is influenced by western fashion and lifestyle trends. This has also influenced
organic food consumption, being a sector which is influenced by health, food safety,
lifestyle and government regulations. These, on examination, appear to be similar to the
consumer motivation for purchase of organic foods in western societies.
Factors affecting consumer choice of organic food in China have not been analysed on a
national basis. To further investigate this topic, the following research questions are posed
as result of gaps in the literature:
16
1. What are the factors that influence urban Chinese consumers’ beliefs and attitudes,
pre-purchase evaluation, and behavioural/purchase intention towards the purchase
of organic foods?
2. Evaluate the role of demographic variables and usage patterns in the behavioural
/purchase intentions of urban Chinese consumers towards organic food.
Hence the primary research aim is to investigate the various determinants and elements
which influence consumers’ purchase behaviour of organic food products in urban China.
The main research objective details are as follows:
The first objective is to investigate the dimensions that influence the personal attitudes of
urban Chinese consumers towards the purchase of organic food.
The second objective is to examine the impact of urban Chinese consumers’ personal
attitudes on their pre-purchase evaluation and purchase behavioural intentions associated
with organic food.
The third objective is to examine the influence of demographic segmentation variables on
the behaviour/purchase intention of organic food.
The fourth objective is to develop and empirically test a conceptual model which
incorporates all the elements of the above three objectives.
This research study also intends to develop a theoretical model which would explain the
purchase intention and decision-making processes about organic food in this respect. The
following hypotheses have been elicited from the conceptual framework:
H1: Influencing dimensions have positive and direct relationships with consumers beliefs/
attitudes, pre-purchase evaluation and behavioural/purchase intention of organic food.
H2: Consumers’ beliefs/attitudes have direct and positive relationships with pre-purchase
evaluation.
H3: Pre-purchase evaluation has a direct and positive relationship with behavioural/
purchase intention.
H4: Demographic variables influence behavioural/purchase intention of organic food.
A comprehensive country-wide survey was conducted in order to quantify and explicate the
impact of various determinants influencing consumers’ purchase intention relating to
organic food in urban China. While the aim of this study is to investigate the buyer
17
behaviour of urban Chinese consumers, its findings can be used by various stakeholders in
China for the marketing and promotion of organic food products.
1.6
Academic and practical justification for this research
1.6.1 Understanding Chinese culture
The author of this study is of Chinese origin and has conducted research in both English
and Chinese. Coming originally from mainland China, possessing a bilingual capability and
having both Australian and Chinese industry background as well as a deep understanding of
the Chinese people and their culture is of value for this study. These advantages enable the
researcher to scrutinise and analyse both Chinese and English secondary and primary data,
and to address issues arising during data collection.
This benefit of having access to both languages is acknowledged by many scholars. Sun
(2003) emphasises that there are distinct advantages if the researcher is a native Chinese
with a Chinese cultural background who is also fluent in both Chinese and English. Bednall
and Kanuk (in Sun 2003) claim that there are nine basic differences in terms of crosscultural consumer behaviour research that could undermine the research qualities, if the
researcher does not belong to the culture and cannot communicate appropriately in its
language.
1.6.2 Theoretical academic contribution
The unique theoretical model used in this study is a combination and integration of three
theoretical models: the theory of Consumer Decision Process (CDP) (Blackwell, Miniard &
Engel 2006), the Hierarchy of Effects model (Lavidge & Steiner 1961; Barry & Howard
1990) and the Theory of Planned Behaviour (TPB) (Ajzen 1991). These models have been
widely utilised within food and organic food studies (Bredahl, Grunert & Frewer 1998;
Drichoutis, Lazaridis & Nayga Jr 2007).
Organic foods are perceived as being more nutritious and healthier, safer, environmentally
friendly, containing fewer chemical residues and tasting better than normal food (Krystallis,
18
Fotopoulos & Zotos 2006). Consumers’ purchase of organic food is based on subjective
experiences and perception (Hughner et al. 2007). Those with positive attitudes towards
organic food are more likely to form positive intentions to purchase it (Chen 2007;
Honkanen, Verplanken & Olsen 2006). Demographic and psychographic variables
demonstrate significant influences on consumers’ decision-making in the purchase of
organic foods (Chang, Zepeda & Griffith 2005; Lea & Worsley 2005).
This research is designed to provide a better understanding of Chinese consumers’ purchase
intention towards organic food. It extends and integrates theory and findings from several
related areas, and empirically validates theory and practice.
1.6.3 Business implications and beneficiaries of this research
This study is primarily designed for an Australian audience and stakeholders. Australia is
an active participant in organic farming and has one of the largest outlays of land in the
world for organic production. Its organic food industry has been strongly influenced by the
rapid growth of overseas demand (Willer & Yussefi 2006). With China being a potential
huge market, the study’s findings would certainly interest both public and private
enterprises in Australia. Investigating Chinese consumers’ attitudes towards locally grown
and imported organic food are essential for Australian and international players who are
interested in entering the Chinese market. Given the background and research questions
delineated in the previous sections, this study provides valuable insights into the global
consumer behaviour related to organic foods. It also provides a better understanding of
Chinese consumers’ attitudes towards the purchase of organic food products and builds a
new body of knowledge necessary to market such products in China. The findings would be
useful to public policy makers interested in identifying strategies aimed at increasing the
demand for organic food. They will also have important implications for marketers of
organic food in China. Beneficiaries of this research include stakeholders in China and
globally, such as consumers, vendors both local and international and government agencies.
19
1.7
Research methodology
In order to effectively address the research problem and questions stated in Section 1.5, it
was decided to employ a quantitative research design. This was done in two stages (Study
One and Study Two) as shown in Figure 1.2.
Figure 1.2 Research design and sequence
Source: structure adapted and modified from Mathews (2009)
Data was collected in China in two stages. Firstly, online data was collected for the purpose
of the pilot study (Study One) in December 2009. Subsequently, paper-based data (Study
Two) was obtained from four major Chinese cities in May 2010. The survey instrument
was developed using validated measures from previous literature. It was then pre-tested
using two focus groups in China. The English version of the survey instrument was
translated into Chinese by the author. All items of the survey were translated back into
English to verify the accuracy of translation. The back-translation technique is the most
widely employed method in cross-cultural studies (Usunier 2000). The translated versions
were also cross-checked by three other bilingual researchers, thus ensuring content and face
validity of the survey instrument.
The online survey in Study One was made available via the university’s Opinio platform.
This Opinio software enables data collection, production and reporting of a survey ensuring
anonymity, confidentiality and privacy to the participant. The web link to the online survey
20
was sent to selected Chinese food outlets for onward submission to their customers. The
sampling frame was drawn up using retail food outlets’ customer databases. Participants
were randomly selected from the sampling frame. Care was taken to include a proportionate
number of prospective customers. A series of exploratory factor analyses (EFA) were
performed on the pilot study data to identify the factors and associated latent variables.
After the online data was analysed, the survey instrument was slightly modified for use in
the paper-based survey, which comprised Study Two. Previous studies in the organic food
area indicated that data collected in food outlets of retail chains is an efficient method
(Drichoutis, Lazaridis & Nayga Jr 2007). The paper-based survey questionnaires were
administrated at major supermarkets in four selected first and second tier cities, namely,
Beijing, Shanghai, Shenzhen and Chengdu. These cities are geographically dispersed being
located in the north, south, east and west respectively of China, and are economically and
politically prominent and the main engines of China’s phenomenal economic development.
Hence these consumers are more affluent, and are more likely to be aware of organic foods.
All the participating supermarkets had dedicated organic food sections. A series of
confirmatory factor analyses (CFA) were then conducted on data from the paper-based
surveys to validate the findings from the EFA of the pilot study. The structural model was
tested and presented as the final stage, and then a number of hypotheses were tested.
Further analysis using invariance testing was performed to investigate whether there were
significant differences in the data obtained from the four cities.
1.8
Outline of the research
This thesis comprises a total of seven chapters. Figure 1.3 depicts the roadmap to the
structure of the thesis as suggested by Perry (1995).
21
Figure 1.3 Roadmap to the structure of the thesis
Source: developed from Perry (1995) for this research
Chapter One provides an overview of the thesis, introduces the research background, and
explains the nature and meaning of the term ‘organic food’. It further identifies the research
22
problem and teases out the research questions in this study. Additionally, justification and a
brief research methodology have been developed. Chapter Two provides a context of
previous studies and literature. It examines previous studies relating to organic food
products in the global sense, discusses and identifies the gaps in the literature, noting the
very few studies undertaken within a Chinese context. The chapter also explores the
extensive literature surrounding related theories. CDP, Hierarchy of Effects model and TPB
are the most important theories used to investigate consumers’ buyer behaviour of organic
food.
Chapter Three develops a unique conceptual framework and theoretical model. Various
constructs have been introduced which influence consumers’ purchase behaviour of organic
food products. This chapter elicits a series of relevant hypotheses intended to address the
research questions. Chapter Four develops an appropriate research design and describes
details of the methodology utilised in the study. Two stages of data collection are
employed, online and paper-based surveys. The survey instrument originally designed in
English was translated into Chinese.
Chapter Five presents the analysis and findings of Study One, the pilot study, which is
based on 200 valid online responses. A series of EFAs were performed on the data of the
pilot study to identify the factors and associated latent variables. Various reliability and
validity tests were conducted. Chapter Six presents the analyses and results of the main
study (Study Two), which is based on 1000 paper-based responses. The analysis of this data
is completed using a series of CFAs to build measurement models, which in turn are used
in a structural equation model, and finally hypotheses tests are conducted. A comparative
study of the four selected cities which are geographically representative was conducted by
the use of invariance tests. Chapter Seven discusses the findings in relation to the
theoretical model. Results of individual hypotheses tests are discussed to provide
meaningful insights and answers to the research questions. Both practical and theoretical
findings and recommendations are suggested to various stakeholders. Finally, limitations
are articulated and future research directions are proposed.
23
1.9
Chapter summary
This chapter has provided a brief background of the global organic market. Organic food
products are perceived as being more nutritious, healthier, safer and environmentally
friendly. China has experienced dramatic economic growth in the last 20 years, and in more
recent times the Chinese organic food sector has grown faster than the worldwide average.
China has gone well beyond being an adopter of the world organic concepts, and is now an
active organic innovator. Current research examining consumers’ awareness of organic
food has been well developed in North America and Western Europe, but very few studies
of this nature have been undertaken in China. Little is known or understood about Chinese
consumers’ attitudes towards organic food products or their associated purchasing
behaviour. Thus, this research seeks to explore the determinants of Chinese consumers’
buyer behaviour towards organic food. In order to evaluate previous research in this area, a
thorough literature review is performed which is explained in the next chapter.
24
CHAPTER TWO:
LITERATURE REVIEW
2.1
Introduction
The purpose of this chapter is to document and analyse previous studies related to organic
food and to provide an overview of consumers’ purchase behaviour of organic food in the
global sense. It also seeks to review relevant literature of theories associated with this
theme. Given that this study focuses on organic food, the chapter addresses relevant
literature from well-developed and mature markets such as Europe, North America and
Australia. This is undertaken by unpackaging the elements and determinants which
influence consumers’ purchase behaviour of organic food (Section 2.2). Categories which
play a role in the decision-making include demographic profiles of consumers (Section 2.3),
purchasing motivations (Section 2.4), barriers to organic food consumption (Section 2.5)
and purchase intention (Section 2.6). This chapter also seeks to identify themes that reflect
the various rationales used by consumers when deciding to purchase organic food and
explore possible theoretical approaches to explain their buyer behaviour (Section 2.7). It
further evaluates and summarises previous studies (Section 2.8) and finally examines the
limitation of previous studies (Section 2.9). Figure 2.1 diagrammatically illustrates the
structure of Chapter Two.
25
Figure 2.1 Structure of Chapter Two
Source: developed from Perry (1995) for this research
2.2
Understanding organic food consumers
2.2.1 Is organic food healthier?
Although this study references industry experts who strongly argue that there are many
scientific studies demonstrating the benefits of organic food (Leu 2009), this is not
universally agreed on by researchers in the field. Even though organic food is perceived as
healthier and safer, scientific evidence for such claims is scarce and, as is later
26
demonstrated, ‘organic’ does not automatically equal ‘safe’. It is debatable whether organic
food production and its marketing is an opportunity or just hype (Bhaskaran et al. 2006).
There have been very few scientific studies which compare the differences between organic
food and conventional food in terms of their nutrient composition or their biological effects
on animal or human subjects. Even small variations of levels of certain micronutrients have
been reputed to have unlikely health implications for consumers (Williams 2002).
A critical review (Brennan, Gallagher & McEachern 2003) of literature on UK consumers’
interest in organic food makes the claim that although consumers believe that organic food
is healthier, more nutritious and tastes better, these beliefs are not scientifically
demonstrated. In addition, food watchdogs warn that organic products may not be quite as
healthy and environmentally friendly as consumers perceive. According to Magkos,
Arvaniti and Zampelas (2003, 2006), there is a popular view that organic food is pesticidefree, though it does contains lower levels of agro-chemical products than conventional
products. This research concurs with findings of a 2009 study in the American Journal of
Clinical Nutrition (cited in Kluger 2010) that there is no significant nutritional difference
between organic and conventional products with regard to all but three of the vitamins and
other food components examined. So is buying organic food more for food safety or just
peace of mind?
One US study (Zhao et al. 2007) demonstrates that there are no significant differences
between organic and conventional vegetables in terms of ‘consumer perceived’ sensory
quality. It indicates that the demand for foods that are produced under environmentally
sustainable standards has been slow to take off. Consumers do not perceive these products
as offering any special benefits. They distrust the claims made by organisations and are
confused by the use of different terminologies such as organic, green and environmentally
friendly in promotion of food products (Zhao et al. 2007).
2.2.2 Consumer awareness and knowledge of organic food
Though there is some level of confusion amongst consumers in relation to organic food, at
the same time there is growing consumer awareness of organic food around the world and
27
especially in Western Europe (Aarset et al. 2004; Bonti-Ankomah & Yiridoe 2006). As
introduced in the previous chapter, the terms ‘organic food’ and ‘green food’ have many
different connotations and interpretations. There is little doubt that this has caused
confusion in the minds of consumers worldwide (Aarset et al. 2004; Krystallis, Fotopoulos
& Zotos 2006). This leads many to be sceptical about organic food products (Aarset et al.
2004; Bonti-Ankomah & Yiridoe 2006; Hill & Lynchehaun 2002). Barnes, Vergunst and
Topp (2009) suggest that changes in consumer knowledge have been significant enough to
affect organic consumption decisions. Consumers who are more aware of organic products
tend to accept paying a premium for them (Chang & Zepeda 2005; Makatouni 2002).
In contrast, an Austrian study (Gotschi et al. 2010) highlights that knowledge about organic
food products does not necessarily lead to positive attitudes or higher levels of self-reported
purchase behaviour of organic products. This conclusion is consistent with findings from a
Belgian study (Verdurme, Gellynck & Viaene 2002) which states that knowledge about
organic food does not correspond with negative attitudes towards genetically modified (GM)
food, and therefore does not influence the consumers’ purchasing decision of organic food.
2.2.3 Differences between buyers and non-buyers of organic food
In a Greek study which focuses on wine purchasing, Fotopoulos, Krystallis and Ness (2003)
identify organic wine buyers’ motives in comparison to non-buyers. Healthiness, quality,
information, attractiveness and good taste are the five main motivations for organic wine
purchasers. The differences between organic and non-organic wine buyers derive from their
distinctive purchasing behaviour, meaning an evaluation of consumers’ cognitive structures
and the consumption motives with which wine’s organic character is associated.
In a further study, Krystallis, Fotopoulos and Zotos (2006) investigated the differences
between buyers and non-buyers of organic food in Greece. It found that organic consumers
are less price sensitive and more interested in signs of food quality, evidenced by organic
labelling and food production methods. However, both organic and non-organic buyers
share the same perceptions, particularly in relation to food safety, as they both give
importance to country of origin, physical structure, freshness, vitamin content and
28
nutritional value. Some of these views are supported by a US study (Williams & Hammitt
2000) which indicates that approximately 91% of organic buyers reported that they would
be willing to pay higher prices for organically produced food. Only 46% of organic buyers
rated price as an important attribute, compared with 64% of conventional buyers. This
study also revealed that only 45% of organic buyers believed that the current US food
supply is safe, compared to 79% of conventional buyers.
Chang and Zepeda (2005) suggest that Australian organic food buyers tend to consider the
effect of food on personal health, the environment, animal welfare and other social issues,
while non-organic food buyers tend to focus more on products’ sensory appeal, such as
taste and appearance. It also suggests that increasing consumers’ awareness of organic
products and proper certification may be effective in increasing the organic food market. In
their research on Indian consumers, Chakrabarti and Baisya (2007) observe that both
regular and occasional organic food buyers tend to be motivated by health and nutritional
concerns. However, regular organic buyers tend to attach more importance to taste whereas
occasional purchasers tend to be just curious. Shepherd, Magnusson and Sjödén (2005)
suggest that health consciousness in Sweden appears to be more important than
environmental concerns in the purchase of organic foods. However, this trend differs
between heavy and light consumers of organic products.
2.3
Demographic profiles of organic food consumers
Past studies suggest that demographic profiles of consumers significantly impact their
purchase behaviour of organic food (Lea & Worsley 2005; Tsakiridou et al. 2008). Socioeconomic variables such as age, education and income are important in the decisionmaking and purchase of organic food. Consumers with higher income and education levels
display a strong correlation to food safety and environment concerns (Tsakiridou et al.
2008). Generally, consumers of organic food are from higher socio-economic segments
(Connor & Douglas 2001; O’Donovan & McCarthy 2002).
Another study reveals that Japanese organic consumers are from better-educated, more
affluent urban centres and are more concerned about environmental and food safety issues
29
(Gendall, Betteridge & Bailey 1999). Similarly in Ireland, organic food consumers tend to
be highly educated and from a high income category (Roddy, Cowan & Hutchinson 1996).
A demographic profile shows them to be female, aged between 30-45, with children and
having higher levels of disposable income (Davies, Titterington & Cochrane 1995).
In Greece, socio-economic and demographic variables also seem to influence organic food
purchasing. Studies (Fotopoulos & Krystallis 2002a, b; Tsakiridou, Mattas & TzimitraKalogianni 2006) reveal that higher educational and higher income groups display a high
awareness and high purchase intention for organic food, while the non-organic users exhibit
lower education and income levels.
2.3.1 Gender
One of the key demographic variables for the purchase of organic food is gender. Studies
(Lea & Worsley 2005; Tsakiridou, Mattas & Tzimitra-Kalogianni 2006) suggest that
women have more positive attitudes towards organic food and are more likely to purchase it
regularly. This could be explained partly by the fact that they do the household shopping,
and take a higher level of responsibility for the family, hence are more likely to be aware of
organic food (Lockie et al. 2004; Onyango, Hallman and Bellows 2007). Another study
(Ureña, Bernabéu & Olmeda 2008) acknowledges that although women have more
favourable attitudes towards the purchase and consumption of organic food than men, they
are less willing to pay a premium for it. Women are more concerned about health,
nourishment and environmental information, but they also demand more information about
organic food products and seek to take advantage of lower costs where available.
Conversely, Roitner-Schobesberger et al.’s (2008) study curiously reveals that men in
Bangkok seem more likely than women to purchase organic food. It suggests that men in
Thailand have higher educational qualifications and earn higher income.
Dahm, Samonte & Shows (2009) investigated students’ attitudes in the south-eastern US.
Equal numbers of males and females knew the correct definition of the term organic,
recognised the organic labels and expressed positive attitudes towards organic foods,
underscoring the fact that gender is not important in the decision to purchase organic food.
30
This finding varies somewhat from other studies which have identified females as having
the stronger opinion and attitude towards organic food. Thus, gender influence on organic
food purchase behaviour could be considered to be debatable.
2.3.2 Age
There are many studies which advance the notion that younger consumers are more likely
to purchase organic food compared to older consumers (Onyango, Hallman & Bellows
2007). Budget constraints may indirectly influence purchase as many older consumers have
limited disposable income (Davies, Titterington & Cochrane 1995; Rimal, Moon &
Balasubramanian 2005). By contrast, Roitner-Schobesberger et al. (2008) reveal that
organic food consumers in Thailand tend to be older. Older consumers (aged over 51) have
more positive attitudes towards organic food than younger age groups. The older consumer
in Thailand tends to have health issues, partly due to perceived health vulnerability and an
awareness that they are generally at a higher health risk than younger consumers
(Bhaskaran & Hardley 2002). In a Swedish study, Magnusson et al. (2001; 2003) suggest
that there are no significant differences in the age groups in terms of their intention to buy
organic food. As such it could be inferred that the importance of age might not be as
clearcut as initially understood or perceived.
2.3.3 Education level
There is a strong correlation between increasing consumption of organic food and
increasing levels of formal education. In Australia, consumers with a general science
education background are more inclined to be consumers of organic products (Lockie et al.
2002). A European study by Margetts et al. (1997) suggested that the level of education is
the strongest factor influencing perceptions of a healthy diet. In Greece, education appears
to have turned the unaware consumer into a highly motivated organic supporter, and on the
whole educated respondents appeared to be more likely to be aware of nutrition information
(Krystallis, Fotopoulos & Zotos 2006).
Several studies (Arvanitoyannis, Krystallis & Kapirti 2003; Fotopoulos & Krystallis 2002a;
Tsakiridou et al. 2008) assert that consumers with higher education are more willing to pay
31
a premium price for organic food. By contrast, an earlier study in the US (Thompson &
Kidwell 1998) found a negative relationship between education and willingness to pay;
shoppers with graduate or professional degrees were less likely to purchase organic produce
as they were less trustful of the retail outlet, the certification process and the organic value.
Lea and Worsley (2005) claim that educational level produced minimal differences in terms
of propensity to purchase organic food, a view that is supported by other studies (Lin,
Smith & Huang 2008; Rimal, Moon & Balasubramanian 2005). As a result, it is unclear to
what extent level of education can be an influencing factor in the purchase of organic food.
2.3.4 Income level
Higher income and education levels have a strong correlation with food safety and
environment concerns (Tsakiridou et al. 2008). According to UK studies (Rimal, Moon &
Balasubramanian 2005; Tregear, Dent & McGregor 1994), the level of disposable income
is positively correlated with the purchasing of organic food. As was discovered in a Greek
study, most organic food consumers were higher income earners, often professionals with
good education levels (Chryssochoidis & Krystallis 2005). These findings contradict an
earlier Greek study (Fotopoulos & Chryssochoidis 2000) which revealed that higher
income in a household was not an essential issue in the purchase of organic food.
Nevertheless, Lockie et al. (2002) in their study, based on Australian data, highlight that
low income earners are not necessarily less interested in consuming organic products. Here
too we are confronted with contrasting data and conclusions and as such need to maintain
some reservations on the importance of the level of income of households as important
demographic variables for organic food purchase behaviour.
2.3.5 Role of the family
The role of the family is another potential influence on the choice of organic food (Margetts
et al. 1997). Hill and Lynchehaun (2002) suggest that children have a major influence on
decisions to purchase of organic products. Families are often introduced to organic food
with the arrival of newly born babies, given the extra concern that families have for baby
safety. Organic food purchasers are more likely to be married couples aged between 35-55
32
with children (McEachern & Willock 2004). According to one study, households with
young children are willing to pay a higher price for organic foods than other households
(Soler, Gil & Sánchez 2002). These studies concur with previous studies which testify to
the presence of children in the family and to their encouraging role in the choice of organic
food consumption (Huang 1996; Thompson & Kidwell 1998). By contrast, a UK study
(Padel & Foster 2005) indicates that individual health was a more motivating reason than
family health, even for parents with young children. ‘Frequent’ organic shoppers generally
have fewer children than average. Households without children made a clear connection
with organic food as a source of ‘enjoyment’. Surprisingly, a Swedish study (Magnusson et
al. 2001) downplays the effect of children, indicating a minimal difference in organic food
purchase intention between families with children and those without.
2.4
Purchase motivations
Some studies have highlighted the significance of country focused organic products
consumption. An early study by Beharrell and MacFie (1991) observed that differences in
the ranking and intensity of reasons to buy organic food appear to be country specific. A
cross-cultural study (Squires, Juric & Cornwell 2001) echoes that consumers from different
countries might have different attitudes towards organic food consumption. Keegan (cited
in Squires, Juric & Cornwell 2001) suggests that consumers’ needs and attitudes may differ
depending on the state of development of the market and/or stage of the product’s life cycle.
Another comparative study (Baker, Thompson & Engelken 2004) of German and UK
consumers’ purchase behaviour of organic food found great similarity in the ‘health’ and
‘enjoyment’ value of organic food. The major difference was the absence among the UK
group of any connection between organic foods and the ‘environment’. Several other
studies have confirmed these findings.
Some studies indicate that organic food purchasing motivations can be triggered by a series
of concerns such as environmental and ethical considerations, organic food sensory appeal
and health concerns (Arvanitoyannis, Krystallis & Kapirti 2003; Tsakiridou, Mattas &
Tzimitra-Kalogianni 2006). Most organic food consumers believe that it is better quality
33
since it is free of pesticides and chemical residues (Botonaki et al. 2006; Tsakiridou et al.
2008). Added to health considerations, buying organic food according to another study is
also motivated by animal welfare and support for the local economy (Tsakiridou et al.
2008). McEachern and McClean (2002) suggest that purchasing motivations can vary from
individual to individual, industry to industry and country to country. These motivations are
identified as both self-interest-centred (better tasting and safer) and altruistic
(environmental and ethical). The details of these motivations are discussed as follows.
2.4.1 Food attributes
A study from Norway (Torjusen et al. 2001) found that the traditional food quality aspects
such as appearance, freshness and taste, named ‘observation traits’, were important to all
respondents. Most were also concerned about aspects related to production and processing,
meaning foods with no harmful substances and the least possible additives. Those who
purchased organic foods frequently were more concerned about ethical, environmental and
health issues, named ‘reflection traits’.
A US study (Onyango, Hallman & Bellows 2007) suggests that organic food preferences
were related to its naturalness, such as non-artificial flavours or colourings. Organic food
attribute variables were found significant and positively related to its purchases, and the
regularity of purchases was influenced by the particular food attributes deemed important in
a consumption decision. Those respondents who viewed food attributes (naturalness) to be
extremely important were 35% more likely to buy organic food. A more recent study
(Wirth, Stanton & Wiley 2011) reveals that organic apple attributes like flavour, texture
and price are more important than any other attributes in terms of the preference for organic
food by consumers. Taste is an equally important item as quality, followed in order by price,
appearance and availability (Dahm, Samonte & Shows 2009). The above studies are
consistent in their findings that appearance, taste and quality are the key issues involved.
2.4.2 Trends and fashion
Studies have highlighted the influence of ‘trends’ and ‘fashion’ as aspects of organic food
consumption behaviour (Hughner et al. 2007; Lockie et al. 2002; Roitner-Schobesberger et
34
al. 2008). In part this has been a product of the media marketing and consumer perceptions
of the fashionable nature of organic food (Hill & Lynchehaun 2002). An Australian study
(Lockie et al. 2002) indicates that some consumers are interested in organic food for
reasons related to fashion and green lifestyle and consider it to be ‘trendy’. A later study
(Lockie et al. 2004) shows that the major determinants of increasing organic consumption
are concerns with the naturalness of food, the sensory and emotional experience of eating
organic foods, and those who consider themselves as ‘green consumers’. This is reflected in
the growing trend of recycling and in lower levels of concern with convenience in the
purchase and preparation of food.
According to Wier and Calverley (2002), consumers in industrialised countries are
changing their lifestyle and habits and becoming increasingly interested in nutrition and
health. At the same time, life is getting busier and they tend to spend less time preparing
meals. Also the availability of processed organic food is limited. For regular buyers,
consumption of organic food is part of their lifestyle and they have related interests in
nature, society and the environment. Consumers who attempt to pursue a healthy diet and
balanced lifestyle have stronger intentions of purchasing organic food products (de
Magistris & Gracia 2008; Schifferstein & Oude Ophuis 1998).
2.4.3 Health consciousness
There is a prevalent belief that organic food is healthier, safer, more nutritious and of
higher quality (Chen 2007; Chryssochoidis & Krystallis 2005; Millock, Wier & Andersen
2004; Padel & Foster 2005; Radman 2005; Tsakiridou, Mattas & Tzimitra-Kalogianni
2006). Health consciousness is considered to be a major motivation for purchase and
consumption (Chakrabarti & Baisya 2007; Schifferstein & Oude Ophuis 1998; Shepherd,
Magnusson & Sjödén 2005; Squires, Juric & Cornwell 2001). Health concern is directly
and positively associated with the buying decision-making of organic food products (Chen
2007; Chinnici, D’Amico & Peorino 2002; Lea & Worsley 2005; Lockie et al. 2002).
Consumers’ interest in health seems to correspond to their interest in and desire to use
organic food (Connor & Douglas 2001; Schifferstein & Oude Ophuis 1998; Verhoef 2005).
35
These studies provide evidence that consumers consider health concerns as one of the most
critical reasons that influence their purchase behaviour of organic food.
Consumers are willing to pay a premium price for pesticide eliminated organic food
(Aguirre 2007; Botonaki et al. 2006; Krystallis & Chryssohoidis 2005; Ureña, Bernabéu &
Olmeda 2008). Even in newly emerging markets such as Macedonia, consumers are willing
to pay a premium price for higher quality, taste, and ‘safety’ certified foods (Grannis, Hine
& Thilmany 2001). These studies evidence the fact that consumers consider health concern
as one of the most important motives to influence their purchase behaviour.
2.4.4 Organic food information and knowledge
An Italian study (de Magistris & Gracia 2008) suggests that consumers with knowledge
that organic food is healthier and higher quality show positive attitudes towards organic
food, and this influences their purchasing decision. Obviously, enhancing consumers’
organic knowledge is vital to develop the market. This study also found that more
information in the organic food market certainly improves consumers’ knowledge. This is
supported by an earlier Swedish study in which Tanner and Kast (2003) suggest that
organic food consumers have adequate knowledge to distinguish between environmental
friendly and environmental harmful products.
A study in the Netherlands (Stobbelaar et al. 2007) suggests that as adolescents are
tomorrow’s consumers of organic food, understanding their attitudes can create potential
business opportunities. This study found that most children are aware of organic food and
consider it healthy and environmentally friendly. However, their knowledge about organic
food is low and their willingness to buy it is also low.
The importance of knowledge of organic foods is an issue affecting purchasing decisions,
since consumers without information cannot differentiate the positive attributes of organic
food from conventional alternatives (Yiridoe, Bonti-Ankomah & Martin 2005). Greater
information on the organic food market builds higher consumer knowledge and also
positively influences their attitudes towards organic food products.
36
2.4.5 Impact on food safety issues
The ‘mad cow’ disease scare in 1996 impacting UK beef and the dioxin crisis in Belgium in
1999 resulted in consumers in those countries looking for alternative healthier, safer food,
with organic and natural products (McDonald 2001). According to a number of studies
(McEachern & McClean 2002; Rimal, Moon & Balasubramanian 2005; Tsakiridou, Mattas
& Tzimitra-Kalogianni 2006), food safety is the one of essential criteria to affect consumer
purchase of organic food. The increase of food safety awareness can also impact on
attitudes towards specific food products.
As economic development and income increases across many parts of the world, consumers
demand higher levels of safety and quality in their food (Botonaki et al. 2006). Food safety
is receiving more attention from governments and policy makers, health professionals, the
food industry and the public. Recent Chinese food scandals have increased consumers’
demand for ‘safer food’. As the Ministry of Agriculture points out: ‘The food security in
China will have an important bearing on world food security’ (in Paull 2008a, p. 11). The
Chinese Ministry of Health (in Peng 2006) reported in 2005 that there were 256 food
poisoning incidents, 9021 people fell ill from food poisoning, and 235 people died. Fu (in
Peng 2006) states that the figures are much higher, with approximately 200,000 to 300,000
people being poisoned annually due to food safety issues in China. The government faces
the challenge of maintaining credibility of food safety and nutrition in the world’s most
populated country.
Studies in Sweden (Magnusson et al. 2001; Magnusson et al. 2003) found that egotistical
motives (health benefits for the individual or the family) rather than altruistic motives
(benefits to society, such as environment and animal welfare) were the drivers of
consumption of organic foods. A Scottish study (McEachern & McClean 2002) also
portrays organic food consumers as being more self-interest-centred than altruistic.
2.4.6 Environmental concerns
Organic agriculture initially developed in response to agricultural industrialisation
(Torjusen et al. 2001). In general, materials and methods that enhance the ecological
37
balance of natural systems are used in organic production. Organic food is believed to be
produced without pesticides, herbicides, inorganic fertilisers, antibiotics and growth
hormones. Certain consumers are influenced by feelings of moral obligation and
responsibility towards the environment. Thus, one motivation for buying organic food is
driven by growing interest in ecological values and concern about environmental
sustainability and threat of conventional agricultural production (McDonald 2001; Sanjuán
et al. 2003; Tsakiridou et al. 2008). A few studies (Honkanen, Verplanken & Olsen 2006;
Magnusson et al. 2003; Makatouni 2002; McEachern & Willock 2004) also demonstrate
that ecological motives have the strongest impact on attitudes towards organic food, which
highlights the important role of environmental issues influencing organic food consumption.
However, a Greek study (Chryssochoidis & Krystallis 2005) argues that public related
issues such as care for nature and the environment may not be relevant to organic food
purchase in some societies. In an Indian study, the environment was not a major motivation
for organic buyers (Chakrabarti & Baisya 2007). Further findings from an earlier study
(Jain and Kaur 2004) indicate that consumers in India were less aware of environmental
and ecological issues than those in developed countries. It would appear that, in a
developing country like India, true green consumerism is in its infancy. Developing
countries face other serious problems, such as overpopulation, poverty, unemployment,
high level of illiteracy and disparity of wealth.
2.4.7 Animal rights and social justice
Harper and Macaroni (2002) indicate that ethical concerns, especially in relation to
standards of animal welfare, play a significant role in the decision to purchase organic food
in UK. Moreover, such standards are perceived as a sign of the safety and healthiness of
food. Another UK study found that values related to animal welfare were the second most
important attributes which influenced consumers to purchase organic food. This study
suggests that the lives of animals have an impact on human beings. Hence the animals’
living condition can affect human health, which then suggests that happy animals produce
healthy meat (Makatouni 2002). A further study by McEachern and Willock (2004) asserts
that animal welfare, especially related to animal transport and housing, influences organic
38
dairy and meat consumption. Buying organic food is seen as supporting the implementation
of higher standards of animal welfare.
Another study conducted in the US suggests that consumers purchasing organic food are
motivated by issues of animal rights and social justice. Standards for the humane treatment
of animals had the highest support among respondents who were mainly women, EuropeanAmericans, young and frequent organic food purchasers (Howard & Allen 2006). These
views are seen primarily in developed countries and contrast with the attitude in developing
countries where there is a greater need to contend with matters more directly related to day
to day survival.
2.4.8 Country of origin
Consumers seem to consider quality as being related to the geographical origin of
production (Cicia, Giudice & Scarpa 2002). Studies demonstrate that country of origin is an
important issue for organic food consumers. Norwegian consumers, for example, trust
locally produced organic food as being safer than imported cheaper organic food. This then
influences purchasing locally produced organic food and supporting local businesses
(Storstad & Bjørkhaug 2003; Torjusen et al. 2001). Locally grown products were the
second most popular choice by California respondents, after their first choice was standards
for the humane treatment of animals (Howard & Allen 2006). Botonaki et al. (2006)
propose that Greek consumers’ willingness to buy organic products is affected by their
attitudes towards country of origin, and this also reflects the perceived organic food quality
attributes. Greek consumers, though they might be aware of organic food, will still remain
‘traditional’ in terms of their cuisine and restaurant selection. Country of origin of a food
product seems a powerful component of the promotional mix, since Greek organic buyers
exhibit a very strong ethnocentric tendency (Fotopoulos & Krystallis 2002a, b).
A Japanese study confirms that most respondents prefer to purchase locally produced
organic food. However, if they were to purchase imported organic produce, they would
consider choosing it from Australia, New Zealand, Canada, the US, Germany, England and
China (Gendall, Betteridge & Bailey 1999). These exporting countries are seen as having
39
clean environments, clean water, air and soil. McEachern and Willock (2004) note that one
of the obstacles preventing UK consumers from purchasing organic meat was the
appearance of mainly imported products. Consumers are not keen to purchase organic meat
originating outside the UK. The concept of meat safety was also central to attitudes,
particularly within the context of country of origin.
2.5
Barriers to organic food consumption
A range of studies have underscored the barriers which exist to purchasing organic food.
An Italian study undertaken in 2002 (Chinnici, D’Amico & Peorino 2002) revealed that
these ranged from purchasing location, purchasing difficulties (lack of choice), percentage
of food expenditure devoted to organic products, perception of organic price, price
differential and willingness to pay. Another study noted that people who live in an urban
centre are more likely to purchase organic food, since they have easier access to diverse
organic food products (Lockie et al. 2004). Greek studies (Krystallis, Fotopoulos & Zotos
2006; Tsakiridou et al. 2008) concur that the lack of product availability and higher prices
are barriers to purchasing organic products. It was noted that there was a low level of
consumer awareness and low knowledge of organic certification systems (Botonaki et al.
2006; Krystallis, Fotopoulos & Zotos 2006).
2.5.1 Price
It is widely understood that organic farming processes cost more than conventional farming.
One of the reasons is a greater loss of organic production to insects and disease if artificial
pesticides and fertilisers are not used (Connor & Douglas 2001). Organic products tend to
be more expensive than non-organic products. Several studies (O’Donovan & McCarthy
2002; Roddy, Cowan & Hutchinson 1996; Shepherd, Magnusson & Sjödén 2005; Tregear,
Dent & McGregor 1994) indicate that price premiums of organic food tend to negatively
affect purchase. If the organic food price was as low as the non-organic one, consumers
would be willing to purchase more organic products. A study conducted by McEachern and
Willock (2004) showed that barriers to purchasing organic meat were its high price and
lack of perceived difference in taste. Padel and Foster (2005) show that price is not, in all
40
circumstances, an absolute barrier but only one factor in a complex decision-making
process. Consumers consider price in the context of disposable income, but also as a factor
of ‘value for money’ and the need to justify a premium through other gains. Less wealthy
consumers are generally not interested in purchasing organic foods. It also could be argued
that price was not always the main reason for the reluctance to purchase organic food
(related to food choice). Conner (in Onyango, Hallman & Bellows 2007) found a
connection between paying premium price and beliefs in the superiority of organic foods
and their health benefits.
While small communities appear to favour the local economy and thus support local
organic producers, price as a factor did not seem to influence their purchase decisions. This
is contradicted by Anders and Moeser (2008) who argue that organic consumers show a
significant level of price sensitivity, with organic beef cuts perceived as luxury goods.
Chang, Zepeda and Griffith (2005) in their overview of the Australian organic sector,
highlighted that previously income and price had been important factors in the
determination of purchasing of organic food but more recently this has declined in its
importance. Willingness to pay for organic food is influenced by factors including food
quality, safety and trust in the certification (Krystallis & Chryssohoidis 2005).
2.5.2 Willingness to pay
Several studies (Botonaki et al. 2006; Cicia, Giudice & Scarpa 2002; Krystallis &
Chryssohoidis 2005) indicate that consumers consider price as a signal of quality, or a form
of proxy for quality. In a Canadian study, consumers were willing to pay premium prices
for the products they considered ‘healthier’. It it was argued that the organic food industry
could improve profitability from the introduction of organic functional foods (Larue et al.
2004). Another study (Thompson & Kidwell 1998) indicates that price premiums for
organic products ranged from 40% to 174% in comparison to conventional products in the
US market. Lockie, Halpin and Pearson (2006) summarised previous studies on the average
national retail price premiums for certified organic food from 10% to 15% in Germany,
20% to 30% in Austria, 80% in Australia, and 10% to 100% in the US and the UK. Retail
premiums of 25% to 30% are acceptable for most European consumers.
41
A Greek study (Krystallis & Chryssohoidis 2005) points out that consumers’ willingness to
pay a premium on organic food across product categories was higher in relation to those
foods that were purchased more frequently. Lin, Smith and Huang (2008) acknowledge that
the organic price premium payment by US consumers can vary from 20% to 60% according
to the product variation. The price and cost of organic food is important in the final
purchasing decision. An Italian study suggests that consumers are willing to pay up to 30%
more on organic foods than on corresponding conventional ones (Chinnici, D’Amico &
Peorino 2002). A Spanish study (Ureña, Bernabéu & Olmeda 2008) explained that
occasional consumers of organic food were willing to pay approximately 10% higher
premium, while regular consumers would pay a premium of approximately 15% extra. An
Irish study (O’Donovan & McCarthy 2002) demonstrates that 70% of consumers were not
willing to pay more than 10% extra. Not surprisingly, consumers who enjoy the benefits of
consumption and perceived value of organic food are willing to pay premium prices.
Overall, willingness to pay premium prices for organic food varies in different markets.
This poses a challenge to marketers involved in setting price levels.
2.5.3 Labelling and certification
Consumers seek clear, accurate and reliable information about organic food, especially in
labelling. Usually they will have seen a label or logo defining its organic credentials, and
they express a need for more information about organic food (Giannakas 2002; Lin, Smith
& Huang 2008). There is a relatively poor understanding of the legal use of the term
‘organic’ on food products, including inspection and certification systems. This can often
have adverse effects on the consumption of organic food. However, trustworthy labels
guarantee the authenticity of organic food products (Padel & Foster 2005; Wier &
Calverley 2002). Both eco and fair trade labelling in Sweden make products more attractive
(Grankvist, Lekedal & Marmendal 2007).
A US study (Bellows et al. 2008) indicates that more people value organic production
methods as shown through labelling. The claims of buying organics and therefore clear
labelling, and placing importance on organic production systems when deciding what to eat,
42
are highly correlated. However, only about 27% of all of those who value organic farming
production methods are regular organic purchasers.
An earlier study in Greece found that organic food buyers who were the ‘consumption
leaders’ believed that differences between organic and non-organic food products were
through their trust in the ‘organic’ labels (Fotopoulos & Chryssochoidis 2000). A similar
study in Greece in 2006 found that food labels appeared to have a significant influence on
consumers’ purchases (Tsakiridou, Mattas & Tzimitra-Kalogianni 2006). However, almost
half of organic food buyers are confused as to the differences between organic and
conventional foods (Krystallis, Fotopoulos & Zotos 2006). It is believed that organic
labelling and certification may play a larger role in purchase decisions.
2.5.4 Lack of availability
One of the reasons for consumers not buying organic food is perceived lack of availability.
A study in Italy in 2002 (Chinnici, D’Amico & Peorino 2002) suggests that location and
purchasing difficulties (i.e. lack of choice) are amongst the most important components. A
Danish study refers to people living in capital cities who are more likely to purchase
organic food, since they have easier access to diverse products (Millock, Wier & Andersen
2004). Similarly, a study in the UK (Padel & Foster 2005) discovered that 26% of
consumers in Wales did not know where to find organic products, and a further 30% found
it difficult to source organic food.
Likewise, O’Donovan and McCarthy’s study (2002) reports that among Irish respondents
who did not purchase organic food, 28% cited lack of availability, and also 85% of nonpurchasers suggested that they would purchase organic food if it became available at their
regular purchasing place. Even in the emerging market of Slovenia, the purchase of organic
food is most influenced by availability in retail outlets. Clearly, the importance of organic
food availability favours effective distribution activities, and it also indicates the emerging
initiative of promoting local farmers markets and retail chain distribution of organic food
(Kuhar & Juvancic 2010).
43
2.6
Pre-purchase intention
Literature has demonstrated that consumers’ attitudes, values, beliefs and purchase
intention may influence the purchase of organic food. Key findings from selected studies
are introduced in the following sections.
2.6.1 Attitudes
An early Danish study (Grunert & Juhl 1995) found that school teachers who had
environmentally sensitive attitudes were more likely to purchase organic foods, and
positive attitudes towards environmental issues were found to be positively correlated with
frequency of organic food purchase. A Norwegian study (Honkanen, Verplanken and Olsen
2006) determined a strong correlation between ecological motives and attitudes, which in
turn were statistically significant in explaining purchase behaviour of organic food. Wong
(2004), in examining organic food purchase behaviour in New Zealand, concluded that key
factors were personal based attitudes and values derived from a range of influences
including attitudes towards health, ecology, society, family and politics. Attitudes are also
derived from sensory effects such as taste and appearance. This supports the view which
identifies value systems and their influence on consumers’ organic food purchase and
consumption. These findings are in line with an Italian study (Zanoli & Naspetti 2002) in
which most respondents held positive attitudes towards organic foods, even though these
were expensive and not always readily available. Consumers’ purchase of organic food is
based on subjective experiences and perceptions (Hughner et al. 2007). Kirk et al. (cited in
Lodorfos & Dennis 2008) found that respondents had positive attitudes towards organic
food, which resulted from their perception of it being better quality than conventional food.
Similarly, Chen (2007) investigated consumers’ attitudes and purchase intention in relation
to organic food in Taiwan. She discovered that it was perceived as healthier, natural,
nutritious and environmentally friendly. The respondents’ attitudes towards organic foods
purchase was positively related to the attitude towards such types of food. Chen’s (2009)
second study indicates that health consciousness and environmental attitudes influence
consumers’ attitudes towards organic foods. Positive attitudes can be determined by health
44
consciousness and environmental attitudes if they are keen to undertake healthy activities,
such as organic food consumption (Chen 2009). This has profound implications for
understanding mainland Chinese consumers’ purchase behaviour since consumers in
Taiwan and in China share a similar cultural heritage and are heavily influenced by
Confucianism and traditional values (Tai & Tam 1997).
Another important study (Shepherd, Magnusson & Sjödén 2005) demonstrates a
discrepancy between attitudes and behaviour of consumers having positive attitudes
towards organic foods who, despite this, may not purchase or value the superiority of
organic food. Magnusson at al. (2001) found that although between 46% and 67% of
respondents held positive attitudes, only 4% to 10% of them indicated an intention to
purchase different types of organic food.
On the other hand, Roddy, Cowan & Hutchinson (1994) argue that organic food attributes
such as higher price, unclear promotion and packaging can generate negative attitudes
towards their purchase. However, another study (Tsakiridou, Mattas & Tzimitra-Kalogianni
2006) disputes these findings about attitudes and values, denying their effect on the
consumption of organic food. Consumer attitudes towards organic products were not found
to be significant determinants in the purchase of organic food.
2.6.2 Values
A study in the Netherlands (Grunert and Juhl 1995) defined values as cognitive patterns by
which individuals orientate themselves in society. They drive much consumer behaviour,
and while people may possess the same values, they may have different value systems.
Values can be viewed as representing motivations, since they are used by individuals to
select and justify actions (Grunert & Juhl 1995). A Greek study (Chryssochoidis &
Krystallis 2005) contends that a better understanding of consumers’ value structures is
important in determining their motivation to purchase organic food. Based on their values,
Greek consumers of organic foods can be categorised into three clusters, in order of
importance: health-consciousness, pursuit of hedonism and environmental consciousness.
Values have been determined to have a direct relation to organic food consumers’
45
behaviour (Baker, Thompson & Engelken 2004; Chryssochoidis & Krystallis 2005;
Essoussi & Zahaf 2008; Makatouni 2002; Zanoli & Naspetti 2002).
Millock, Wier, and Andersen (2004) supported by a further study (Lea & Worsley 2005),
indicate that organic food consumers have both public and private sets of values.
Consumers who assign personal values such as personal health, taste and freshness are
more likely to purchase organic food, while those who perceive public values such as
environmental and animal welfare attributes are less likely to do so.
2.6.3 Beliefs
An early Northern Ireland study (Davies, Titterington & Cochrane 1995) reveals that
organic food is perceived as food without ‘chemicals’ and ‘hormones’. Consumers
purchase organic food as they perceive it to contain lower pesticide and fertiliser residues.
A UK study (Harper and Makatouni 2002) suggests that both buyers and non-buyers have
similar perceptions of what organic food means. Perceptions are affected by beliefs about
the safety and quality of conventional food production, and subsequent attitudes to
conventional versus organic products.
Similarly, a Belgian survey (Verdurme, Gellynck & Viaene 2002) focused on consumer
beliefs (perceptions) about food production in general, organic food and GM food. 2
Respondents believed that food had become less safe, less tasty, less healthy and a bit more
expensive than 20 years ago, while organic food was perceived to be healthy, safe and
environmentally friendly compared to the negative attitudes harboured by respondents
towards GM food.
One of the first studies which examined the relationship between personal values and
beliefs associated with organic food reveals that self-transcendence values are related to the
purchase of organic food. Personal values related to nature, environment and equality were
discovered to be the dominant predictor of positive beliefs associated with organic food
2
GM (genetically modified) food is derived from genetically modified organisms which have had specific
changes introduced into their DNA by genetic engineering techniques.
46
(Lea and Worsley 2005). These studies suggest that consumers’ beliefs and perceptions
towards organic food could be interesting to investigate.
2.6.4 Purchase intention
Purchase intention is influenced by a number of elements. Attitudes, for instance, were
found to be significant predictors of intention to purchase organic fruit and vegetables
(Saba & Messina 2003). Consumers perceive organic food to possess better quality
attributes than ordinary food and obviously these positive attitudes are likely to transform
into positive intentions for its purchase (Honkanen, Verplanken & Olsen 2006).
An Indian study argued that consumers’ intentions to buy organic food are influenced by
positive attitudes towards the importance of personal health (Chakrabarti & Baisya 2007).
Several studies have reaffirmed that factors such as health consciousness, subjective norms,
familiarity and quality of organic food all have some bearing on purchasing intentions
(Magnusson et al. 2001; Magnusson et al. 2003; Smith & Paladino 2010).
According to an exhaustive Taiwanese study (Chen 2007), purchase intention are strongly
influenced by six key food choice motives: mood, natural content, animal welfare,
environmental protection, political values and religion. Shaharudin et al. (2010a; 2010c)
claim that Malaysian consumers’ purchase intention of organic food is influenced by their
perceived value and health consciousness. Food safety is considered to be of less
importance in this respect. These findings concur with some western studies in that the
perceived value has significant impact on consumers’ desire to purchase organic food and
their willingness to pay the extra cost.
Many positive attitudes towards organic food emerged on the basis of perceptions of it as
being of better quality. Consumers with positive attitudes towards organic food are
invariably more likely to form positive intentions to purchase it (Honkanen, Verplanken &
Olsen 2006; Saba & Messina 2003; Sparks & Shepherd 1992). A positive attitude is one of
the more important motivations, and very likely to influence the purchase intention.
47
However, although consumers may have positive attitudes towards organic food, this may
not be translated into greater levels of purchase on a regular basis (Fotopoulos & Krystallis
2002a; Roddy, Cowan & Hutchinson 1996). Verdurme, Gellynck and Viaene (2002) also
question the relationship between organic food buying intention and attitudes/beliefs.
Organic food has been perceived as healthy, safe and nutritious, and consumers purchase
organic food products mainly because of the belief in their beneficial effects.
2.7
Theories associated with previous studies
Previous research has largely used the Consumer Decision Process (CDP) model
(Blackwell, Miniard & Engel 2006) and the Theory of Planned Behaviour (TPB) to
understand issues relating to organic food consumption. These have provided theoretical
support and explored the motivations and barriers associated with the consumption of
organic food. They also analysed consumers’ attitudes, purchase intention and purchase
behaviour towards organic products, and investigated various determinants which
influenced organic food consumers in making their purchasing decision. Table 2.1
summarises previous studies in alphabetical order and the respective use of these theories.
The TPB was the model most encountered in the literature which primarily examined
consumer attitudes in the psychological context. On the other hand, the CDP model
examined the interaction between environmental and individual factors with respect to
consumer purchase behaviour. These will be discussed in greater detail later in this study.
Table 2.1 Summary of key attributes measured with associated theory in studies
relating to organic products
Author and year
Key attributes measured
Associated theory
Barnes,Vergunst & Topp
2009
Knowledge of organic product
Consumer Decision Process
Chakrabarti and Baisya
2007
Attitudes
Theory of Planned Behaviour
Chen 2007
Attitudes, purchase intention
Theory of Panned Behaviour
Chinnici, D’Amico &
Peorino 2002
Price, lifestyle
Consumer Decision Process
Chryssochoidis 2000
Information search
Consumer Decision Process
Guido et al. 2010
Purchase intention
Theory of Planned Behaviour
48
Essoussi & Zahaf 2008
Knowledge, labelling,
certification, trust
Consumer Decision Process
Gotschi et al. 2010
Knowledge of organic product
Theory of Reasoned Action
Hill & Lynchehaun 2002
Internal and external factors
Consumer Decision Process
Honkanen, Verplanken, &
Olsen 2006
Motives, attitudes, intention
Theory of Planned Behaviour
Krystallis, Arvanitoyannis
& Chryssohoidis 2006,
Pre-purchase evaluation, product Consumer Decision Process
attributes
Lodorfos & Dennis 2008
Purchase intention
Theory of Planned Behaviour
de Magistris & Gracia
2008
Lifestyle, attitudes, purchase
intention
Consumer Decision Process
Theory of Planned Behaviour
Magnusson et al. 2001;
Magnusson et al. 2003
Sensory characteristics,
purchasing intention
Theory of Reasoned Action
McEachern & Willock
2004
Attitudes, behaviour
Theory of Reasoned Action
Theory of Planned Behaviour
McEachern, & McClean
2002
Perception, attitudes,
knowledge, purchasing
behaviour
Consumer Decision Process
Padel & Foster 2005
Motivation/barrier to purchasing
Consumer Decision Process
Rimal, Moon &
Balasubramanian 2005
Food safety
Consumer Decision Process
Saba & Messina 2003
Attitudes, intention
Theory of reasoned action
Theory of Planned Behaviour
Schifferstein & Oude
Ophuis 1998
Lifestyle, behaviour
Consumer Decision Process
Salleh et al. 2010
Purchase intention
Theory of Planned Behaviour
Shaharudin et al. 2010a;
2010b
Purchase intention
Theory of reasoned action
Theory of Planned Behaviour
Shepherd, Magnusson &
Sjödén 2005
Attitudes
Theory of Planned Behaviour
Smith & Paladino 2010
Attitudes, intention, behaviour
Theory of Reasoned Action
Theory of Planned Behaviour
Thøgersen 2009
Attitudes, intention
Theory of Planned Behaviour
Vindigni, Janssen & Jager
2002
Behaviour modelling
Consumer Decision Process
Source: Author, 2011
49
2.7.1 The Consumer Decision Process (CDP) model applied to food
consumption
There are many models related to consumer behaviour in the marketing discipline. One of
the classical models utilised in the measurement of consumer buyer behaviour is the
Consumer Decision Process (CDP) model (Blackwell, Miniard & Engel 2006). In recent
years, there has been growing interest in the study of consumer behaviour for organic food
along with the way in which consumers make their purchasing decisions (Vindigni, Janssen
& Jager 2002).
The CDP model was first proposed in 1968 by Engel, Kollatt and Blackwell. It has been
developed and modified to a more advanced and sophisticated model comprising seven
stages. Figure 2.2 portrays the model as ‘a road map of consumers’ mind that marketers and
managers can use to help guide product mix, communication and sales strategies’
(Blackwell, Miniard & Engel 2006, p. 70).
Consumer decision-making is influenced and shaped by many factors and determinants can
be categorised into three sectors: individual differences, environmental influences and
psychological processes. A full description of each level of the process is given after Figure
2.2.
50
Figure 2.2 The Consumer Decision Process
51
Stage One: Need recognition
This stage is the start of the consumer decision-making process – the recognition of a
need – in which consumers locate a product ‘to satisfy a need or want’ (Kotler et al.
2001, p. 8). Need recognition depends on how much discrepancy exists between a
consumer’s current situation and the situation they want to be in. It can be triggered by
discontent with existing products. Wier and Calverley (2002) suggest that product
specific characteristics that benefit consumers provide the major motivation for them to
purchase organic foods. However, this type of motivation varies between different
consumer segments. Consumers could be motivated by organic food that appears fresh
in appearance, which signifies quality, or by reliable nutritional content and absence of
chemical residuals.
This stage demonstrates that consumers have desires, and believe in products that have
the ability to resolve their problems, hence they are worth purchasing sooner or later.
Marketers examine consumers’ desires, including ability and authority to purchase.
Hence this stage provides the platform in identifying the motivations of consumers
intending to purchase or not to purchase organic food products.
Blackwell, Miniard, and Engel (2006) also identified environmental influences and
individual differences that impact consumers’ level of need. The questions one might
ask in the context of organic foods are: What categories of Chinese consumers have the
need to purchase organic food? Are they motivated by health consciousness,
environmental issues or perceptions of organic products’ attributes?
Stage Two: Search for information
The second stage involves search for information about available purchase options.
Once consumers have experienced the need for products or services, they have to search
for the ‘motivated activation of knowledge stored in memory or acquisition of
information from the environment about potential need satisfiers’ (Blackwell, Miniard
& Engel 2006, p. 109). Information search may be determined by internal factors,
scrutinising knowledge stored in memory, and by external information from the
marketplace (Kotler et al. 2001).
52
Steenkamp (1997) claims that previous experience with food products is the most
important information source. For example, the search for information about fresh meat
might depend on consumers’ consumption experiences, information provided by
retailers such as supermarkets or butchers, and information provided on the packaging.
The questions one might ask in the context of organic food purchase are: Do consumers
in their search for information rely on their existing knowledge about organic food?
What is the level of consumers’ confidence in their existing knowledge? What is the
level of satisfaction with their previous experience of consumption of organic food?
When internal search proves inadequate, additional information might be collected.
Stage Three: Pre-purchase evaluation of alternatives
During this stage, consumers reflect on new or pre-existing evaluations stored in their
memory and seek to get answers when evaluating products’ attributes, brands, personal
values and lifestyle. They utilise different evaluative criteria, and the choice is
influenced by both individual and environmental differences (Blackwell, Miniard &
Engel 2006). Purchase behaviour is affected by many elements including demographics,
psychographics, values, personality, consumer resources, motivation, knowledge and
attitudes. It is also influenced by culture, social class, family, personal influence and
situational behaviour (Steenkamp 1997). Consumers either like or dislike particular
products based on attitudes towards available alternatives (Fishbein & Ajzen 1975).
Stage Four: Purchase
After the pre-purchase evaluation of alternatives stage, consumers move to the next
stage which is that of purchase. Consumers first choose the place of purchase, which
might be a general store, retail or speciality store, supermarket or online purchase, and
then evaluate the alternatives available in-store. They may decide not to proceed further
because of their experiences during the purchasing stage. Purchase behaviour is also
influenced by shopping location preferences, frequency of purchase and advertising
(Blackwell, Miniard & Engel 2006). In the context of consumers purchasing organic
food in China, they might purchase particular types like imported or domestically
produced. Other factors influencing their purchase might be branding, type of sales
outlets and assistance provided during sales. They might also examine features of
53
organic food products such as appearance, quality, freshness, availability, presentation
and pricing level.
Stage Five: Consumption
After the purchase is made, the consumption of the products occurs. It is important to
identify the products’ end user. Sloof et al. (in Sun & Collins 2006) contend that the
consumption of perishable products such as fruit might depend on the intended uses of
the product, whether it is meant for personal consumption or as a gift to others.
Consumers place more importance on the quality of the product if it is meant for the
family or for children; whereas they would also look for attractive packaging if it is
meant to be a gift.
Stage Six: Post-consumption evaluation
Following the purchase and consumption of the product, satisfaction or dissatisfaction
occurs. If the products’ attributes do not match the buyer’s purchase intention, or if the
consumer does not use the product properly, dissatisfaction may occur (Blackwell,
Miniard & Engel 2006). When the consumer’s expectation matches the product’s
perceived performance, satisfaction occurs. The consumer may re-purchase the product
and also recommend it to others. Emotions play an important role in the postconsumption evaluation stage.
Stage Seven: Divestment
This is the final stage of the CDP model. Consumers might react differently during this
stage. For example, they might dispose, recycle or re-sell the product (Blackwell,
Miniard & Engel 2006). Post-purchase behaviour is based on satisfaction or
dissatisfaction with the consumption (Kotler et al. 2001).
Later in this chapter, other relevant variables which influence consumers’ organic food
consumption are examined. The first three stages of this model – need recognition,
information search and pre-purchase evaluation of alternatives – form the first phase of
the CDP model. These three stages, and not all the seven stages of the model, are used
in this study to evaluate consumers’ purchase intention towards organic food in China.
54
2.7.1.1 Applicability of the CDP model and Hierarchy of Effects
model in the context of food
A significant Belgian study was conducted by Verbeke (1999, 2000), built on a fourstage conceptual framework (Engel, Miniard & Blackwell 1993; Engel, Kollat &
Blackwell 1968) which was designed for consumer decision-making for fresh meat, as
seen in Figure 2.3. The stages of this process include problem recognition, information
search, alternative evaluation and choice/behaviour.
The model is linked and integrated, firstly, with a Hierarchy of Effects model as
proposed by Lavidge and Steiner (1961) and later adapted by Barry and Howard (1990).
This model indicates the different mental stages that consumers go through when
making purchasing decisions and responding to messages in sequence. It is generally
agreed that the structure includes cognitive (learning, knowing), affective (thinking,
feeling) and conative (intending, doing) components, although there is no clear-cut
evidence about the sequence and inter-distance of these hierarchical stages (Verbeke
2000). Dubé, Cervellon and Han (2003) demontrate that a hierarchical model performed
better than any alternative representation in capturing consumers’ attitudes and also
proved to have superior ability to predict purchase behaviour of food.
Secondly, concepts related to information-processing as presented by McGuire (1978)
and re-elaborated by Scholten (1996) are supplemented in Verbeke’s (1999, 2000)
model. There is a linkage to potential influences on consumer decision-making from the
impact of communication and marketing. The information-process concept is
underpinned in the study of persuasion in social psychology, and focuses on the impact
from persuasive communication (Verbeke 2000).
Finally, a classification of factors or variables that potentially influence consumer
decision-making is adopted (Pilgrim 1957; Steenkamp 1997). The Pilgrim model
suggested that the acceptance of food behaviour can be grouped into attitudes, sensory
tests and consumption. Steenkamp echoed this in that generally there are three groups of
factors influencing consumption of food: properties of the food, personal related factors
and environmental factors.
55
In the main, Verbeke’s model is entwinned in the CDP model. These include need
recognition (or problem recognition), information search and alternative evaluation as
essential stages before a choice of product purchase can be affected (Steenkamp 1997;
Verbeke 1999, 2000).
Verbeke’s research is valuable for analysing consumer decision-making, in this case,
towards fresh meat. The results conclude that consumers go through relatively extended
problem solving and intensive evaluation of food attributes: ‘The rather intensive
decision-making process, with extended evaluation of product alternatives on a large
number of attributes, points towards relatively high involvement with respect to fresh
meat consumption decision and includes periods of continuous attention to food and
meat safety issues’ (Verbeke 2000, p. 530). Figure 2.3 depicts Verbeke’s conceptual
framework for analysing consumer decision-making towards the purchase of meat.
56
Source: Verbeke 1999, 2000
Figure 2.3 Conceptual framework for analysing consumer decision-making towards the purchase of meat
57
The CDP model is also used in a study by Bareham (1995) which examines various
factors that influenced European consumers’ purchase of food and beverages. Bareham
uses both Engel, Blackwell and Miniard’s (1990) consumer behaviour approach and
Fishbein & Ajzen’s (1975) attitudes and behaviour measurement theories. His study
covers political, economic and technical influences, cultural and social influences,
psychological and marketing influences. Non-economic factors include health and diet
concerns, the growth of vegetarianism, the convenience of buying food, the life cycle of
the households and advertising impact on consumers’ food choice. Bareham (1995, p.
98) also argues that ‘taboos, religious doctrines, cults, scares and the status of different
foods can all have a considerable impact on food consumption’. (Another study
undertaken by Wheelock (in Mitsostergios & Skiadas 1994) suggests that food
consumption can be influenced by economic and non-economic factors. Food price and
personal income have been two dominant factors post-1945 which have determined
consumer food purchase. However, the influence of prices on the choice of food is
diminishing. In addition, Cronin, Brady and Hult (2000) suggest that the consumer
decision-making process is the best model that incorporates both direct and indirect
influences on consumer behavioural intentions.
The study by Furst et al. (1996) identifies value factors in the food choice process,
including sensory perceptions, monetary considerations, health and nutrition, managing
relationships and quality. Similarly, Lennernas et al. (1997) suggest that taste, health,
long shelf-life, quality or freshness, price, family preference and habits are the most
important factors which impact the choice of food. These studies concur with an organic
food study by Wier and Calverley (2002) in eliciting personal user values, such as
products’ health benefits, taste and freshness, which are more important than public
values, such as environmental and animal welfare.
Hansen (2001) found many similarities in the motives of Chinese and Western buyers
regarding the purchase decision behaviour of seafood, with the most important factors
being high product quality, suppliers’ reliability and service level, product availability,
product certification by an authorised organisation and competitive prices.
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2.7.1.2 Applicability of the CDP model to organic food
An earlier study in the Netherlands (Schifferstein & Oude Ophuis 1998) investigated
various determinants of organic food consumption and their interrelationship using the
CDP model. They suggested that in the problem recognition stage, the primary reason
for buying organic food was some manifested or potential health problem. Furthermore,
findings from the evaluation of alternatives stage suggest that health food buyers are
attaching more importance to absence of food additives, health value, naturalness and
biodynamic influences, and giving less importance to freshness and appearance of food.
Vindigni, Janssen and Jager (2002, p. 634) remark that they have witnessed in recent
years a large amount of interest in the study of consumers’ behaviour towards organic
food: ‘Various approaches have been developed aiming at describing, modelling and
predicting the various ways that consumers make purchase decisions’.
Sanjuán et al. (2003) suggest that it could be valuable to develop a model to analyse the
different influences of independent variables on the consumer decision-making process
in future organic studies. An exploratory Canadian study (Essoussi & Zahaf 2008)
provides another theoretical contribution to understand decision-making processes with
regard to organic food. This study has an integrative and complex structure, and
explains not only how, what and why consumers buy or don’t buy in terms of organic
food, but also examines macro-environmental factors (in Canada). Five themes relating
to the consumer decision process of community organic food consumers were identified:
organic food definition and recognition, consumer’ motivation, trust with regards to
organic food, labelling and certification process, and organic food distribution channels.
Similarly, de Magistris & Gracia (2008) investigated the consumer decision process of
organically produced food products. It found consumers’ attitudes towards health
attributes, such as healthy diet and balanced lifestyle, and towards the environment are
the most important factors which explain consumers’ decision-making processes for
organic food products for Italian consumers.
On the contrary, Padel and Foster (2005), in their UK study, underline that the decisionmaking process about organic food is complex, and the importance of motivations and
barriers may differ between categories. They demonstrate a need to differentiate diverse
59
reasons to buy organic food according to product categories, and provide insights into
trade-offs that consumers make between competing and conflicting values and needs.
Bonti-Ankomah and Yiridoe (2006) developed a conceptual framework which affected
organic attitudes and purchase decisions, based on reviewing a large amount of
literature. As depicted in Figure 2.4, this suggests that knowledge and awareness have a
direct and indirect effect on attitudes towards organic products and the willingness to
pay a premium, and are critical in the consumer purchasing decision process since
organic food is a certified product. Consumers cannot identify whether a product is
produced using organic or conventional methods unless they have been told or can see
organic logos or labels attached to the products. Several studies (Chang & Zepeda 2005;
de Magistris & Gracia 2008; Radman 2005; Tanner & Kast 2003; Zanoli & Naspetti
2002) have found that available information in the market creates higher organic food
knowledge and positively influences attitudes towards organic food products.
Consumers who are more knowledgeable about organic food are more tolerant of higher
prices and inaccessibility and are more likely to purchase organic foods. On the other
hand, knowledge and awareness may not directly translate into purchase, as barriers
might limit the ability of consumers to transform such knowledge and perceived
demand into actual demand (Bonti-Ankomah and Yiridoe 2006).
60
Figure 2.4 Framework of factors which affect organic consumers’ attitudes and
purchase decisions
Source: Bonti-Ankomah and Yiridoe 2006, p. 18
61
2.7.2 Attitude theory and measurement
Grønhøj (2006) suggests that the tendency of using individualist approaches is
widespread in the growing environmentally oriented study of organic products.
Therefore, predicting the individual consumer’s behaviour towards buying organic food
is often used in the attitude or value based model evidenced in the Theory of Planned
Behaviour model. Attitude information and decision-making is more complex and
closely related to personal values.
2.7.2.1 Introduction to the Theory of Planned Behaviour (TPB)
The Theory of Planned Behaviour (TPB) has been widely used as a model of social
cognition in psychology (or social behaviour). It is an extension of the Theory of
Reasoned Action (TRA) but differs from this by its addition of perceived behavioural
control (Ajzen 1991). Psychologist and marketing researchers have used both TRA and
TPB to explain consumers’ food choice behaviour (Chen 2007; Eves & Cheng 2007;
Petrovici, Ritson & Ness 2004; Verdurme, Gellynck & Viaene 2002; Verdurme &
Viaene 2003). These models have also been used extensively in applied food decisionmaking, food consumption attitudes and predicting food choices, as they are good
predictors of food consumption behaviour as well as they explain the cognitive
processes that determine consumer behaviour. According to Vindigni, Janssen and Jager
(2002), they explain the art of the process that determines consumer behaviour.
The TRA was first developed by Fishbein and Ajzen (Ajzen & Fishbein 1980; Fishbein
& Ajzen 1975) and was introduced in 1967 as Fishbein’s multi-attribute attitudes model
(Blackwell, Miniard & Engel 2006; Fishbein 1967). It sets out a method of explaining
and exploring the underlying determinants of intentions and behaviour (Fishbein &
Ajzen 1975). The TRA is one of the most developed ‘knowledge-attitudes-behaviour’
(KAB) models and is extensively used in social psychology and consumer decisionmaking. It also has been applied to a number of health and environmental behaviour
studies (Donovan & Henley 2003). It suggests that a person’s behaviour is determined
by his or her intention to perform the behaviour, and states that ‘a person’s intention is a
function of two basic determinants, one personal in nature and other reflecting social
influences’ (Ajzen & Fishbein 1980, p. 6). These are termed normative beliefs. The
62
subjective norm is a measure of the perceptions held by the person and may not reflect
the importance of what others actually think they should do. It is defined as an
individual’s perception of behaviour influenced by peers and family (Ajzen & Fishbein
1980; Donovan & Henley 2003). Fishbein and Ajzen (1975, p. 14) state that ‘The
totality of a person’s beliefs serves as the informational base that ultimately determines
one’s attitudes, intentions and behaviours’. According to these authors it is necessary to
distinguish between beliefs, attitudes, intentions and behaviour.
Over the years, the TPB has been developed, tested and refined. Ajzen (1991, p. 206)
states it ‘traces attitudes, subjective norms, and perceived behaviour control to an
underlying foundation of beliefs about the behaviour’. According to the Planned
Behaviour model (see Figure 2.5), intention is the best predictor of behaviour and thus
behaviour can be deliberately planned. There are three major determinants embraced in
the TPB: attitudes towards the behaviour, subjective norms and perceptions of
behavioural control (Ajzen 1991; Ajzen 2005; Ajzen & Albarracin 2007). Demographic
variables become external in social psychological studies because they are external to
the cognitive structure associated with making a specific decision (Ajzen 2005). Figure
2.5 illustrates the role of background factors in the TPB. It describes a multitude of
variables which may be related to or influence the beliefs consumers embrace. These
background factors are divided into three categories: dispositions, demographics and
information. The effects of background factors are assumed to indirectly influence
intention and behaviour by affecting behavioural, normative and control beliefs (Ajzen
& Albarracin 2007).
63
Source: Ajzen & Albarracin 2007, p. 6
Figure 2.5 The Theory of Planned Behaviour
64
The TPB has been successfully applied to explain aspects of consumer behaviour (Bredahl,
Grunert & Frewer 1998). It offers an excellent starting point for the examination of organic
food purchase and consumption (Shepherd, Magnusson & Sjödén 2005). Ajzen and
Albarracin (2007) as well as Blackwell, Miniard and Engel (2006) suggest that the TRA
model has some limitations. These include a significant risk of confounding attitudes and
norms, since attitudes can be reframed as norms and vice versa. A further study (Schütte &
Ciarlante 1998) suggests that the TRA is not valid in the Asian context, particularly in those
cultures influenced by Confucianism, where the purchase intention is more influenced by
the face-saving pressures and social influences of group conformity.
Attitudes towards consuming a product have been found to be one of the most important
antecedents of predicting and explaining consumers’ choice of food (Bredahl 2001; Conner
et al. 2003). This was evidenced by an earlier UK study (Bredahl, Grunert & Frewer 1998)
which used two models to explain consumer attitudes and decision-making towards
genetically modified food: (1) Fishbein’s multi-attribute attitude model explained consumer
attitudes towards GM food, which are determined by both consumers’ beliefs regarding the
product process and their beliefs regarding products, and (2) consumer purchase behaviour
with regard to GM food is explained by Ajzen’s TPB. The purchase (or avoidance)
intention is summarised in the attitude to behaviour, subjective norm, perceived control,
perceived difficulty and perceived moral obligation similar to that described in the original
TPB model as shown in Figure 2.5.
2.7.2.2 Applicability of attitude theories in the context of organic food
Previous studies (see Table 2.1) suggest that the Theory of Planned Behaviour can be
applied to food choices, and this view is supported by the efficacy of its application towards
organic food choice and intention. French et al. (in Hansen 2005) claim that food
consumption is not just an issue of obtaining nutrients, but is also psychologically related.
Maslow’s Theory of Motivation explains why people are driven by particular needs at a
particular time (in Kotler et al. 2001). Food consumption can satisfy physiological needs,
such as hunger and thirst, and also meets other higher level of needs.
65
A study in the UK (Lodorfos & Dennis 2008) uses the TPB as the conceptual framework to
examine consumers’ intention to buy organic food. The findings offer considerable support
for the model in explaining organic food consumption intentions. This study also suggests
that organic food product attributes and the subjective opinions of others are important
determinants of consumers’ intention. The result echoes previous studies (Kalafatis et al.
1999; McEachern & Willock 2004), which suggests that the TPB represents a reliable
predictive model of intention to purchase environmental products.
A study in Taiwan (Chen 2007) examined consumer attitudes and purchase intention in
relation to organic food, and adopted the TPB. This study showed that the model explained
quite effectively the results of the main effects of Taiwan consumers’ organic food
intentions. Consumers’ intention in purchasing organic food is supported by their attitude to
organic food purchase, subjective norms, perceived behavioural control and perceived
difficulty. Furthermore, it proposed six food motive determinants which contribute towards
consumers’ positive attitudes towards organic food: consumer mood, natural content,
animal welfare, environmental protection, political values and religion. Inconvenience in
purchasing organic foods negatively affected attitudes towards purchase. This study was
one of the first attempts to examine the moderating effects of intention and attitudes on the
purchase of organic food in the TPB model.
Smith and Paladino’s (2010) Australian study utilised the TPB to form a causal model
which established the relationship between organic knowledge, subjective norms and
familiarity, revealing that intentions were mediated by attitudes towards organic knowledge,
subjective norms and environmental concerns. This study has contributed to the growing
organic food research and provides theoretical implications in an Australian context.
In addition, an Indian study applied the TPB to examine both regular and occasional
organic food buyers’ motivations and attitudes, showing that Indian consumers’ intention to
buy organic food is relatively strong as they have positive attitudes towards organic food
(Chakrabarti & Baisya 2007). Consumers across the board with positive attitudes towards
organic food are more likely to form intentions to purchase it (Honkanen, Verplanken &
Olsen 2006; Saba & Messina 2003; Sparks & Shepherd 1992).
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2.7.3 Integration of three models: CDP, TPB and Hierarchy of Effects
2.7.3.1 Limitation of organic food studies
The TPB argues that behaviour, such as choice of organic food, is predicted by intention,
which is in turn is predicted by attitudes, perceived social pressure and degree of personal
control (Ajzen 1991). Criticism related to the TRA and the TPB suggests that these theories
can only partly explain food consumption (Petrovici, Ritson & Ness 2004). They fail to
effectively incorporate some key features that are important in the choice of organic food.
There are affective responses to food and moral concerns about the way in which it is
produced (Shepherd, Magnusson & Sjödén 2005). They also only explain parts of the
processes that determine consumer behaviour (Vindigni, Janssen & Jager 2002). Armitage,
Conner and Norman (1999) address some of the criticisms, and argue that the models are
too reliant on rational decision-making and the influence of individualistic behaviour.
The TRA and TPB are multi-attribute attitude measurements which emphasise consumers’
beliefs, feelings, attitudes and intentions. However, they are not the only important beliefs
in terms of how consumers behave. Others range from what consumers believe about future
economic conditions to what they believe about the trustworthiness of alternative
information sources (Blackwell, Miniard & Engel 2006): ‘For a complete insight into
attitude formation, the multi-attribute model should incorporate both the importance of the
evaluative criteria as well as their evaluation’ (Steenkamp 1997, p. 151).
Criticism has also been raised about the CDP model in that it may be a generalised model
applied to the purchase of any product or service. Various authors have focused on the sets
of variables which have impacted on food choices (Bareham 1995). An Italian study (de
Magistris & Gracia 2008, p. 931) suggests that ‘consumers’ attitudes towards different
organic food attributes and towards the environment are the most important factors that
explain consumers’ decision-making process for organic food products’. The CDP model
fails to explore how beliefs and attitudes influence purchase decision made in advance of
product evaluation (Shaw & Clarke 1999).
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Ajzen (2001) suggests that attitudes contain both cognitive (thinking) and affective (feeling)
components. The multi-components view of attitudes assumes that evaluations are
influenced by cognition as well as affect. This is supported by Dean et al. (cited in Aertsens
2009) who claim that both cognition and affect are relevant to predict purchase intention of
organic food in a compensatory way. Consumers evaluate behaviour not only in terms of
cost and benefits, but also by the positive and negative feelings generated. These findings
highlight the importance of including both cognitive and affective components of attitudes
in food purchase intention models.
The current study has identified the importance of integrating the three models: CDP, TPB
and Hierarchy of Effects.
2.7.3.2 Integrating the theories
Need recognition/information search
The CDP model (Blackwell, Miniard & Engel 2006) suggests that no-one buys a product
unless they have a need or a want. Barriers that significantly influence organic food
purchase are lack of trust in the control mechanisms, lack of interest in organic foods and
lack of trust that the food is healthy. Hence teasing out the most important factor in
influencing consumers in their decision to purchase or consume organic foods can be a
critical issue in the CDP model.
From a marketing perspective, it is important to understand why consumers consume a
certain quantity of organic food, their changing consumption patterns and their motives
(Vindigni, Janssen & Jager 2002). The phase of need recognition arises when discrepancy
exists between the consumer’s current circumstance and the situation they want to be in. On
the other hand, need recognition does not happen if the difference is below the threshold
level (Blackwell, Miniard & Engel 2006). Previous studies identify concerns for personal
health, food safety and environmental issues which seem to be the most commonly stated
triggers to motivate purchase of organic food products. Consumers may desire to get
quality food, since they have heard of or purchased organic food for a while. According to
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Ajzen and Fishbein (1980), intentions are assumed to capture the motivational factors that
influence behaviour and are indications of how people would try and how much effort they
would make to purchase organic foods.
Search for information is the most important factor related to the previous purchase
experience. How knowledgeable are consumers? A study based on food with therapeutic
claims suggests that most consumers read nutritional facts and then, based on this
information, make their decision (Bhaskaran & Hardley 2002). Although they do not trust
claims made by the producers, and may not believe that the product would help with an
existing health problem, their purchase decisions are based on the hope that the product has
a therapeutic feature.
Pre-purchase evaluation
Steenkamp (1997) points out in relation to the CDP model that studies from seven
European countries investigating the importance of a large set of evaluation criteria for the
choice of generic food products found that there were five important criteria: product
quality, price, brand name, freshness and guarantee. Guarantee was implied based on brand
name, quality labelling and geographic origin which, in turn, allowed for consumers to
easily process this information. However, the criteria used by consumers in the evaluation
of food choice depended on the type of food products involved. For instance, taste intensity,
taste evaluation and fatness were key criteria for meat products.
An Australian study of consumer perception and demand for organic food (Chang &
Zepeda 2005, p. 164) suggests that ‘consumers preferences and food choices appear to be
influenced by socio-demographic factors such as income, family structure, lifestyle, dietary
restrictions and some social values and beliefs’. Larger studies (Chen 2007; Wong 2004)
show that the correlation between organic food purchasing decisions associated with
people’s attitudes is positive and significant. In the TPB, consumers with positive attitudes
are more likely to display purchase intention behaviours. On the contrary, those with
negative attitudes towards organic food will be inclined not to purchase. Barriers towards
purchase of organic foods can be investigated in this phase as indicated in the TPB and the
69
model of Hierarchy of Effects. For example, price may elicit both positive and negative
attitudes. It is positive when a higher price of organic food denotes a symbol of quality, and
negative when it signifies burden of purchase.
At this stage, consumers decide whether they will purchase or not. They evaluate product
attributes, brands, personal values and lifestyle. It is important to recognise that ‘attitudes
influence consumers’ purchase and consumption intentions’ (Blackwell, Miniard & Engel
2006, p. 392). Consumers are reluctant to pay higher prices for organic food even if they
show positive attitudes towards them. Such attitudes do not necessarily translate into a final
decision to purchase.
2.8
Summary of previous studies
Consumers perceive that organic food has a healthier image attached to it. They believe it is
better for the environment, promotes animal welfare and has a better taste. It has a more
trustworthy quality and consumers are willing to pay more for it. On the other hand, some
of the reasons consumers have indicated for not purchasing organic food include
perceptions of it being expensive and limited in availability and choices (Bhaskaran et al.
2006; Chang, Zepeda & Griffith 2005; Connor & Douglas 2001; Lea & Worsley 2005;
Radman 2005; Sanjuán et al. 2003). Non-purchase is also influenced by unsatisfactory
appearance, low profile distribution channels, misleading labelling and certification, and
lack of perceived value (Brennan, Gallagher & McEachern 2003; Chang, Zepeda & Griffith
2005; Essoussi & Zahaf 2008; Krystallis, Fotopoulos & Zotos 2006; Stobbelaar et al. 2007).
In recent years, studies of consumers’ purchase behaviour related to organic food have been
carried out in many countries, including the US, UK, Netherlands, Ireland, Germany,
Denmark, Sweden and Italy. They have explored consumer characteristics, awareness,
knowledge, attitudes, values and even risk perceptions. Various approaches with the aim of
describing, modelling and predicting consumers decision-making process in purchasing
organic foods have been investigated (Vindigni, Janssen & Jager 2002).
70
Studies in Asian countries such as Japan, Taiwan, India and Thailand confirm that the most
important reasons and motivation for consumers to purchase organic food products are
health, food safety and concern for lifestyle. Furthermore, these studies examined whether
consumers are willing or not to buy organic food. What is evident from this chapter is the
inconclusive nature of the literature on this topic. The review has uncovered related extant
literature on a global and regional basis and highlights important yet partial findings.
There is an extensive and well developed European literature on the topic. Curiously, there
was significant literature in countries such as Greece, yet much less in countries such as
Germany. Some of this imbalance might seem in part due to language accessibility. The
literature in the Americas was also extensive as it was in other localities such as in
Australia. The key authors from selected studies conducted in different continents and
countries are summarised in Appendix 2.
2.9
Limitation of previous studies
Various studies relating to consumers purchase’ behaviour towards organic products have
been carried out in Europe and internationally (Krystallis, Fotopoulos & Zotos 2006). Only
a handful of studies relating to organic food consumption have been conducted in Asian
countries such as Japan, Taiwan, India and Thailand (Chakrabarti & Baisya 2007; Chen
2007; Roitner-Schobesberger et al. 2008). These reveal that purchase behaviour in Asian
countries is different from that in the west. Issues such as environment, for example, are not
a major motivation for Indian organic buyers (Chakrabarti & Baisya 2007).
Furthermore, very few studies have been conducted in China (Dai, Zhu & Ying 2006; Yin
et al. 2010). Some studies in the Chinese language (including Dai, Zhu & Ying 2006)
examined consumers in Nanjing on their propensity to purchase organic vegetable products,
and their purchase behaviour based on age, education, knowledge of organic vegetables and
concern about food safety and environmental protection issues. A similar study (Wang, liu
& Tian 2008) in Beijing revealed that the older the consumer, the greater the intention to
purchase organic food. This was due to their own health concerns about food. On the other
hand, the greater the educational levels, the greater the levels of scepticism about organic
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food production and certification. These two studies focused mostly on demographic
factors influencing Chinese consumers and the data was collected in a single city.
Yin, Wu and Chen (2008) investigated consumers in Shandong province and highlighted
the greater level of confidence which consumers had in organic food and in the certification
process. The study also found that age, education level and environmental issues are not so
important to Chinese consumers’ organic food purchase behaviour. In a more recent study,
Yin et al. (2010) claim that none of the previous studies had analysed factors that affect
consumers’ choice of organic food in the mainland Chinese market. This survey of 432
consumers was conducted in three southern cities: Shenzhen, Guangzhou and Zhuhai. The
results concur with Yin, Wu and Chen (2008) that Chinese consumers’ intention to
purchase organic food is strongly influenced by income level, health, degree of trust in
organic food and degree of acceptance of organic food price (Yin et al. 2010). A weakness
with this study is that the survey data was conducted in the same region, the Zhujiang
Triangle Zone of Guangdong province in southern China. China constitutes a
heterogeneous consumer market, with distinct differences in buyer behaviour in inland and
coastal cities, and many disparities across north, south, east and west (Cui & Liu 2000; Dou,
Wang & Zhou 2006; Sun & Collins 2006; Uncles & Kwok 2008; Zhou et al. 2010).
Therefore this study fails to provide a consistent overview of the entire Chinese market.
The most recent study by Nees (2011) faced a similar problem. Her study only collected
data from Guangzhou over a period of three days and only focuses on the purchase of
organic vegetables. However, the range of organic food products in the Chinese market
now not only includes fruit, vegetables and meat, but also extends to rice, green tea, dairy
produce and other processed food (International Trade Centre 2011).
There is scant empirical research on consumer behaviour towards organic food across
China, and the market suffers from significant misunderstanding of the terms ‘organic’ and
‘green’ types of food which are used interchangeably by consumers. Due to some
significant omissions in previous studies, it is the intention of this study to empirically
investigate the determinants related to Chinese consumers’ pre-purchase evaluation of
organic food in a way which fills the void left by numerous but incomplete studies.
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2.10 Chapter summary
This chapter has examined previous organic food studies across numerous years and
countries. The research undertaken is both significant and in part inconclusive. This
literature review has sought to investigate and define the most significant findings of
previous studies on organic food and consumer behaviour towards it. The chapter began by
investigating the stereotype and myths around organic food. It examined the importance in
the literature of demographic and other profile factors which may have played a role in
understanding consumer behaviour towards organic food across the globe. Consumer
attitudes towards organic food and decision-making in choosing organic food have been
well documented across much of the developed world, but only a handful of studies have
been conducted in Asian countries. These studies, especially in western markets, are
important so as to better understand the issues and whether there are any patterns or trends
in consumer behaviour which may have been pertinent for the Chinese context within this
study. Consumer purchase of organic food might be dependent on the perception that it is
safer, healthier and more environmental friendly than conventional food. There are several
important factors and motivations why consumers are motivated to purchase organic food
products, and barriers to non-purchase.
This chapter has identified that the Consumer Decision Process model, the Hierarchy of
Effects and the Theory of Planned Behaviour are widely utilised within food and organic
food studies. They have provided significant theoretical underpinning for research in the
areas of consumer buying behaviour. These theories analyse consumers’ attitudes,
purchasing intentions and behaviour towards organic products, and examine various
determinants that influence organic food consumers to make purchase decisions.
Chapter Three, which follows, develops an applicable conceptual framework that
incorporates the Consumer Decision Process in choice of consumption of organic food in
China. The conceptual framework developed is based on integration of these theories and
also based on the assessment of consumer perception, belief, attitude, behaviour and other
related issues towards organic food.
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CHAPTER THREE:
DEVELOPMENT OF THE CONCEPTUAL FRAMEWORK
AND RELATED HYPOTHESES
3.1
Introduction
Chapter Two evaluated related extant literature including the three key models of consumer
purchase behaviour: Consumer Decision (making) Process (CDP), Hierarchy of Effects and
the Theory of Planned Behaviour (TPB). These are central to most studies in the food and
organic products area. This chapter aims to formalise the conceptual framework (Section
3.2). It discusses the influencing stage (Section 3.3), cognitive/affective stage (Section 3.4),
evaluation of alternatives stage (Section 3.5) and behavioural intentions stage (Section 3.6).
In addition, demographic influences are analysed as control variables (Section 3.7) and a
chapter summary is provided (Section 3.8). The hypotheses are developed as result of the
interrelationship between the different constructs of the conceptual framework. Figure 3.1
diagrammatically illustrates the structure of Chapter Three.
74
Figure 3.1 Structure of Chapter Three
Source: developed for this research
3.2
Development of the proposed conceptual framework
As identified by Cavana, Delahaye and Sekaran (2001), literature reviews play an important
role in identifying and scoping models which can then become the basis for a new
theoretical framework. The proposed conceptual framework is developed on an extensive
review of the relevant body of literature and is designed to address the research questions.
Chapter Two explored the key characteristics of three models: the CDP, Hierarchy of
Effects and TPB. These have been widely utilised within studies of food consumption
behaviour (Drichoutis, Lazaridis & Nayga Jr 2007; Tanner & Kast 2003; Verbeke 2000) as
75
well as more specific organic food studies (Chen 2007; Essoussi & Zahaf 2008; de
Magistris & Gracia 2008; Verdurme, Gellynck & Viaene 2002). Despite their importance,
the proposed conceptual framework for the current research is unique and incorporates the
idea developed by Verbeke (1999, 2000). For the purposes of explaining consumer
purchase of organic food in urban China, this study has established that the CDP model is
far too general and lacks specificity for examining specific indicators influencing consumer
purchase behaviour.
A five stage conceptual framework for analysing consumer decision-making towards
organic food in urban China is proposed (Figure 3.2). This is embedded in the three
underpinning models mentioned above. Essentially it is a staged model which assumes that
consumers move through a rational problem solving process in ultimately purchasing
organic food. The five stages are influencing, cognitive/affective, evaluation of alternatives,
behavioural intentions and purchase. The problem solving process in the minds of
consumers (cognitive/affective stage) involves the search and processing of information as
suggested by the CDP model and also by the TPB (beliefs, attitudes and norms). The
Hierarchy of Effects model (Barry & Howard 1990) is in line with this thinking as it
advocates the different mental stages of cognitive, affective and conative stages that
consumers go through when making buying decisions.
Pilgrim (1957) and Steenkemp (1997) suggest a classification of factors or variables that
potentially influence consumer decision-making. This forms the first stage of the proposed
conceptual framework. As a result of the extensive literature review of organic food studies
in Chapter Two, it was decided to include other relevant constructs which influence
consumer decision-making towards the purchase of organic food in urban China, that is,
product, regulatory, lifestyle and ethnocentrism. The product construct includes physical
appearance, smell and taste, which are part of sensory appeal. This construct also covers
product nutritional value and price perception. The regulatory construct includes items
related to certification and labelling, government regulation and policy, and also
environmental influences as suggested by Steenkamp (1997). The lifestyle construct
includes three types of lifestyles: self-indulgence, variety seeking and opinion leadership.
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Finally, the ethnocentrism construct includes aspects of country of origin and the level of
ethnocentrism.
The attitudes and beliefs of consumers which form the second stage of the proposed
conceptual framework are akin to the need recognition and search for information stages of
the CDP model. It involves consumers’ awareness and understanding of the product, and is
embedded in the cognitive and affective domains of the Hierarchy of Effects model. This
model suggests that three mental stages – cognitive, affective and conative – are involved
when consumers make purchase decisions. The cognitive stage is the mental activity as
reflected in knowledge and beliefs, the affective stage is related to feelings and emotions,
and the conative stage refers to purchasing intention or behaviour (Barry & Howard 1990).
Essentially, attitudes are consumers’ feelings or affective responses (Verbeke 1999, 2000).
The third stage of the conceptual framework addresses consumers’ pre-purchase evaluation.
This stage is exactly the same as that proposed by the CDP model. Consumers seek the best
options when they evaluate and select various products (Blackwell, Miniard & Engel 2006).
The prospective purchaser weighs up the pros and cons of each product, which naturally
depends on personal judgement. Evaluative criteria are derived from beliefs, attitudes and
intentions (Bareham 1995). At this stage, urban Chinese consumers would compare what
they know about different safe and healthy foods in the Chinese market with what they
consider to be the most important attributes. They could use new or pre-existing knowledge
stored in memory to select organic food products that would most likely result in their
overall satisfaction. Blackwell, Miniard and Engel (2006) suggest that pre-purchase
evaluation is influenced by both individual and environmental influences.
The fourth stage of the conceptual framework is related to behavioural and purchase
intention. This stage is suggested by the TPB, which then leads to actual purchase. As a
rule of thumb, the more favourable the attitude, subjective norm and perceived control, the
stronger should be the person’s intention to perform the behaviour (Ajzen 1991).
Finally, the fifth stage is the actual purchase, which is what marketers are really interested
in and is articulated in all the three associated theories of CDP, TPB and Hierarchy of
77
Effects. Most studies relating to consumer buyer behaviour only investigate their
behavioural or purchase intention as the actual purchase involves collection of data from
the sample at some future time (longitudinal studies). Hence, this stage is depicted by
dotted lines in the conceptual framework and was not investigated in this research.
Generally, positive attitudes are likely to lead to positive purchasing intentions, which may
eventually translate to actual purchase (Tarkiainen & Sundqvist 2005). The proposed
conceptual framework as shown in Figure 3.2 is developed to illustrate and explain Chinese
consumers’ purchase behaviour and includes key determinants which influence their
decision-making process for the purchase of organic food. Details of the individual stages
of this conceptual framework and the development of hypotheses are explained from
Section 3.3 onwards.
78
79
3.3
Influencing stage
The term ‘influencing’ was adapted by Verbeke (1999, 2000) based on one of the earliest
models of consumer buyer behaviour of food (Pilgrim 1957) and also on the influencing
factors suggested by Steenkamp (1997) who asserts that there are three types of
influencing factors on consumers: environmental factors, personal related factors and
properties of the food.
Based on this idea and on factors identified in the literature related to organic food
consumption as discussed in previous chapters, four significant constructs are elicited:
product, regulatory, lifestyle and ethnocentrism, as discussed in the sections that follow.
3.3.1 The product construct
More than 40 years ago, Lancaster published his now famous product characteristic
theory which reflects the importance of product characteristic or attributes (Drichoutis,
Lazaridis, & Nayga Jr 2007). This construct is categorised as ‘properties of food’
(Steenkamp 1997; Verbeke 1999). It includes two key factors: sensory appeal and price
perception. Product attributes of food vary in intensity, favourability and the degree of
conviction which individuals have (Ophuis et al., cited in Chryssochoidis 2000). Pilgrim
(1957, p. 171) suggests that the criterion of food acceptance may be described as
‘consumption with pleasure, the nutrition of body and soul’. Although current studies
suggest little consistency across countries, they are nonetheless similar in terms of
consumers’ perceptions towards organic product attributes (Bonti-Ankomah & Yiridoe
2006).
Consumers consider sensory characteristics to be one of the key factors in the choice of
organic food (Magnusson et al. 2001; Magnusson et al. 2003; Torjusen et al. 2001). For
most, organic food purchase motivations arise largely from product-specific
characteristics directly benefiting the consumers (Wier & Calverley 2002). Obviously,
taste is one of the important elements which influence consumers’ choice of organic food,
followed by appearance and price (Stobbelaar et al. 2007). However, non-sensory
80
attributes such as absence of food additives and residuals and nutritional value (Torjusen
et al. 2001) are increasingly important.
Steptoe, Pollard, and Wardle (1995) found that concern for health, convenience of food
preparation and sensory appeal, price and convenience are mechanisms in the selection of
food at the individual level. The statements related to appearance, smell and taste
contribute towards sensory appeal. Food attributes such as price, taste and nutrition are
referred to as measures of product involved factors when making food purchasing
decision. Accordingly, different consumers can be associated with different food
attributes (Drichoutis, Lazaridis & Nayga Jr 2007; Steptoe, Pollard & Wardle 1995).
A Greek study reveals that organic food purchase is motivated by consumers’ sensory
appeal and concern for health (Arvanitoyannis, Krystallis & Kapirti 2003), and a New
Zealand study (Wong 2004) discloses that participants considered the taste of organic
food to be better and to possess superior flavour as compared to conventional food.
However, organic food is not always aesthetically appealing due to its shorter shelf life.
Its physical appearance is sometimes considered as a hindering element to its
consumption (Roddy, Cowan & Hutchinson 1996).
A Belgian study (Verdurme, Gellynck & Viaene 2002) found that consumers of organic
food regarded organic food as being, in order of importance, ‘healthy’, ‘tasty’ and
‘environmentally friendly’. Another interesting Danish study by Millock, Wier and
Andersen (2004) suggests that attitudes towards taste, freshness and aspects of health
associated with organic food are more important drivers than the environment and animal
welfare attributes in its actual purchase.
In contrast, a Greek study by Chryssochoidis (2000) questions whether organic food
attributes such as physical appearance do actually influence the purchase intention
decision. It indicates that physical appearance can in fact be non-appealing to some
consumers, which in turn impacts their purchase intention. As the standardised coefficient
between physical appearance of organic food and purchase intention in this study was
weak (0.08), the finding needs further investigation.
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The price of organic food is controversial and may be associated with other variables
such as gender and socio-economic positioning. Steptoe, Pollard and Wardle (1995) refer
to cost as being an important issue for low income earners. It is also seen as more crucial
amongst female rather than male consumers. However, this study observes that those
female consumers concerned about dietary issues are less influenced by price. This is
consistent with a Belgian study (Verdurme, Gellynck & Viaene 2002) which noted that
existing consumers consider organic food as healthy, environmentally friendly and less
expensive.
On a different note, one reason for the non-purchase of organic food is perceptions of it
as being expensive. Organic production means more work and lower yields; organic food
is also overpriced by unscrupulous middlemen, according to Chang & Zepeda’s (2005)
Australian study. However, an Italian study (Cicia, Giudice & Scarpa 2002) demonstrates
that, in terms of consumers’ perception of quality in organic food, price is often perceived
as a proxy for quality. In China, its price is perceived to be relatively higher in line with
its perception of excellent quality and reliability (Lu 2008).
Development of hypotheses associated with the product construct
Based on the above discussion, the following relationships between the product construct
and other stages of the proposed conceptual framework are hypothesised.
The first hypothesis deals with the product construct and the second stage of the
conceptual framework, which is the cognitive/affective stage, and is postulated as follows:
H1a:
The product-related construct has a positive influence on the beliefs and
attitudes of urban Chinese consumers towards the purchase of organic food products
The next hypothesis deals with the product construct and the third stage of the conceptual
framework, which is pre-purchase evaluation. The importance of product attributes is
supported by a Greek organic meat study (Krystallis, Arvanitoyannis & Chryssohoidis
2006) which suggests that pre-purchase evaluation involves visual judgement like colour,
leanness and similar elements. Accordingly the following has been hypothesised:
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H1b:
Organic food product-related construct is positively correlated to urban Chinese
consumers’ pre-purchase evaluation
Finally, a hypothesis between the product construct and the fourth stage of the conceptual
framework which is behavioural and purchase intention is elicited. Product-related
attributes are vital to Chinese consumers. Historically, they place high expectations on
the original flavour, taste and freshness of food products. Many Chinese, particularly the
elderly, prefer to purchase fresh foods on a daily basis. The freshness, flavour and texture
add value to the food products (Zhao et al. 2000). It is evident from the literature that the
motivation of European consumers for buying organic food is product specific
characteristics which directly benefit them. However, this motivation appears to vary
between different consumer segments (Wier & Calverley 2002). A study in Taiwan
(Chen 2007) acknowledges that the characteristics of specific food and the relevance that
consumers assign to them have both a direct and an indirect effect on the intention to buy
organic products. Based on the above discussion, the following hypothesis is formulated:
H1c:
Organic food product-related construct is positively correlated to urban Chinese
consumers’ behavioural/purchase intention
3.3.2 The regulatory construct
The regulatory construct relates to two areas: government regulation and the complex
area of labelling. There is a relatively poor understanding of the legal use of the term
‘organic’ on the labelling of food products including the inspection and certification
systems (Padel & Foster 2005). Some studies have highlighted the seemingly stringent
regulatory process (Courville 2006; Wier & Calverley 2002). Others suggest the
confusing nature of labelling and regulatory processes (Aarset et al. 2004), and still other
studies strongly question the validity of the labelling and certification and highlight the
distrust felt by consumers in relation to what they are told about organic food (Williams
& Hammitt 2000).
From a purely material and technical point of view, organic food certification ensures for
consumers the reliability of the claims that the labels represent (Getz & Shreck 2006).
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Consumers generally do not know whether a product is organically produced unless they
have been told or have seen the evidence of certification or the label of the product
concerned (Lin, Smith & Huang 2008). As Ennis (2007, p. 104) noted: ‘A credible good
is one whose quality cannot be identified with consumption, but whose history affects
some consumers’ attitudes towards the good’. Consumers generally avoid purchasing
food products which do not have signs of the products’ quality.
As a credible product, organic food quality can influence positive attitudes on consumers
who have greater knowledge and experience with the product. Food safety concerns
appear as the most important influence on purchase decision-making. A European study
highlighted the lack of organic product knowledge and confusion among European Union
consumers. Many were unsure, even dubious, about the concept of organically farmed
salmon and were distrustful of the regulatory process (Aarset et al. 2004).
Labelling seems to be one of the important issues in relation to the purchase of organic
food. Consumers perceive organic food labels as providing a guarantee of the product
(Magnusson et al. 2001; Torjusen et al. 2001). Despite the general awareness of organic
food labels in Greece, almost half of the buyers were confused by the differentiation
between organic and conventional food products (Krystallis, Fotopoulos & Zotos 2006).
Not surprisingly, organic food consumers are more likely to rate food labels as more
important than conventional food consumers, but ‘organic’ or ‘pesticide-free’ labels can
be misleading (Williams & Hammitt 2000). There is also a difficulty for consumers
awareness due to an absence of a well-known organic brand, unlike conventional
products where brand recognition is well understood. As such there is a lack of variety of
branded organic products. Organic labelling maximises the effectiveness of organic
products which thus reveals a direct relationship between price and brand awareness.
There are also many organic certification bodies in the market, and it is debatable as to
which certification stamps or logos are genuine and can be trusted by consumers. For
example, in Taiwan, there are more than 14 kinds of certificates and labels (Chen 2007),
and there are 27 domestic and six overseas (including US, Europe, Australia and Japan)
organic certification bodies for the Chinese market (Yuan & Liu 2009). Euromonitor
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International (2010) reports that many Chinese consumers do not believe the descriptions
on the packaging and have no confidence in the manufacturers’ claims. This report
confirms Taylor’s (2008) findings regarding Chinese consumers’ distrust of labelling of
organic food.
The regulatory regime associated with organic food can be a compelling factor across
numerous markets. For example, Denmark has the highest consumption of organic food
per capita in the world, and consumers are not unduly concerned about related health and
food safety issues. This is because Denmark has a relatively well functioning processing
and distribution system in the organic food industry and also boasts a reliable certification
and labelling system. Organic food is offered in a variety of forms and is available
through mass retailing and supermarkets (Wier & Calverley 2002).
On the other hand, perceptions about government regulations can attract ongoing feelings
of mistrust towards the government in relation to organic food and the environment. A
study undertaken in Croatia shows that only 11% of respondents thought that organic
agriculture is properly regulated by law, 61% did not agree, and more than 25% did not
know anything about organic agriculture legislation. This is further evidence of the low
awareness of organic systems and mistrust of organic products regulation (Radman 2005).
The recent food scandals in China which involved melamine-tainted dairy products have
increased consumers’ concerns about domestically produced food products and,
according to US sources, have led them to doubt the food regulatory system (US
Department of Agriculture 2008). A survey (Euromonitor International 2011) conducted
by the Beijing Times during 2011 found that 70% of consumers would not purchase milk
powder produced in China. The main concern for many is that toxic substances are
illegally added to milk products to boost their apparent protein content. Chinese
consumers prefer to buy imported products with foreign organic certification as this is
viewed as being more trustworthy. The government has sought to restore consumer
confidence by introducing stricter organic rules and regulations (International Trade
Centre 2011).
85
A recent study (Xie, Li & Yi 2011) indicates that China has had voluntary national
organic standards since 2005, and attempts have been made to provide protection for
consumers against deceptive and misleading claims on organic products and to strengthen
the organic industry’s capacity to respond to the domestic market. Lack of equivalency
and non-harmonisation of standards and regulation are some of the impediments for
trading in the Chinese organic food market.
Development of hypotheses associated with the regulatory construct
Based on the above discussion, the following relationship between the regulatory
construct and attitudes of urban Chinese consumers is hypothesised:
H2a:The regulatory construct associated with organic foods has a positive influence on
beliefs and attitudes of urban Chinese consumers
A US study (Bellows et al. 2008) suggests that the use of organic labelling information
normally increases the organic label familiarity. A certification stamp or logo provides a
guarantee to the consumer that the product has been produced organically (McDonald
2001). Some food industry stakeholders argue that labels are often confusing and open to
misinterpretation by the public. However, if a product is certified as organic by an
accredited organisation and its logo is displayed on the label, it is a guarantee to
consumers that it is the closest thing to what nature intended (Abbey 2005). Lea and
Worsley’s (2005) Australian study concedes that about half of the participants did not
find organic food labelling as being trustworthy. Other studies (Aarset et al. 2004;
Hughner et al. 2007) further contend that there is lack of trust in the organic product
certification process.
With the increasing affluence derived from rapid economic growth, certain sections of
Chinese society and also the Chinese government have begun to realise the hazardous
impact of environmental deterioration on national wellbeing (Chan 2001). It is the
government’s desire to implement anti-pollution laws with stronger punishment for
offences against the environment (Chan 2001). The concern for the environment by
consumers is also evidenced in developed markets and green behaviour, and perceived
86
consumer effectiveness can positively affect the choice of organic food (Verhoef 2005).
In light of the above discussion, the following relationship between regulations and prepurchase evaluation is hypothesised:
H2b: The regulatory construct associated with organic foods is positively correlated to
urban Chinese consumers’ pre-purchase evaluation
The regulatory construct has an impact on organic food safety. An Irish study
(O’Donovan & McCarthy 2002) reports that respondents who purchased or had intention
to purchase organic meat placed higher levels of importance on food safety, compared to
those with no such intention. A US study (Williams & Hammitt 2000) found that organic
food buyers value safe food. They do not trust conventional food supply and believe that
the federal food safety agencies are not trustworthy. Organic meat consumers believe that
it is superior in terms of quality, safety, labelling, production methods and value
(O’Donovan & McCarthy 2002). With these points in mind, the following hypothesis is
developed:
H2c: The regulatory construct associated with organic food is positively correlated to
urban Chinese consumers’ behavioural/purchase intention
3.3.3 The lifestyle construct
Lifestyle segmentation is often used to predict consumer behaviour. The concept of
lifestyle was introduced in marketing research from the psychology area in 1964 by
William Lazer who described it as ‘a distinctive or characteristic mode of living, in its
aggregative and broadest sense, of a whole society or segment thereof’’(cited in Zhu et al.
2009, p. 300). To further elaborate its importance, Kotler et al. (2001, p. 208) provide
another definition. They say that a person’s lifestyle profiles their whole pattern of acting
and interacting in the world and can help the marketer to understand changing consumer
values and how they affect purchase behaviour’. Brunsø and Grunert (1995, p. 475)
define food-related lifestyle as ‘the system of cognitive categories, scripts and their
associations, which relate a set of products to a set of values’. Lifestyle has a multidimensional basis for explaining consumer behaviour (Reid et al. 2001) and has been
87
widely utilised in marketing management for designing advertising strategy,
segmentation of populations, new product development and market surveillance (Brunsø
& Grunert 1995).
A study conducted in the Netherlands suggests that the consumption of organic food is
part of lifestyle of people who may have related interests regarding nature, society and
the environment. According to this study: ‘Lifestyle results from an ideology connected
to a particular value system that affects personality measures, attitudes and consumption
behaviour’ (Schifferstein & Oude Ophuis 1998, p. 119). Health and environmental
consciousness which are de facto lifestyle expressions have become some of the
motivations for Dutch consumers to buy organic food. A US study based in Boston
reinforces the findings that organic food consumers (compared to purchasers of
conventional foods) saw this as a lifestyle choice (Williams & Hammitt 2000).
For some consumers, hedonic consumption is linked to a search for sensorial pleasure,
and organic food could well be one such example (Piron 2006). Chen (2009) supports
previous findings that health consciousness and environmental attitudes towards lifestyle
are the most common motives for purchasing organic food in Taiwan. It also shows that a
healthy lifestyle mediates the relationship between health consciousness and
environmental attitudes and the consumer’s attitude towards organic food. In Malaysia,
increasing consumption of organic food resulted from changes in lifestyles owing to an
awareness of food safety issues (Shaharudin et al. 2010b). On the contrary, Wier and
Calverley (2002) suggest that changes in lifestyle can have adverse effects on the
consumption of organic food. This is because life is becoming busier, and consumers may
either frequently eat outside the home or prepare meals from convenience products.
One Australian study depicts current stereotypical images of organic consumers as being
‘greenies’, ‘health nuts’ or ‘yuppies’, with consumption of organic food reflecting their
‘green’ lifestyle and interest in these products as an element of trend (Lockie et al. 2002).
Organic foods are perceived in some cases as being trendy symbols, as suggested by a
Thai study (Roitner-Schobesberger et al. 2008). For regular Italian organic consumers,
health and wellbeing are the most transcendental principles combined with expressions of
hedonism, pleasure and achievement (Zanoli & Naspetti 2002). A Greek study
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(Chryssochoidis & Krystallis 2005) indicates that there is almost no difference between
consumers who wish to enjoy life and those who are ‘health conscious’ when sourcing
organic food.
Yang (2004) categorises consumers into three distinct lifestyle groups on the basis of
their
lifestyle
characteristics:
experiencers,
traditionalists
and
self-indulgents.
Experiencers seek variety and novelty, and prefer trendy and fashionable products.
Typical traditionalist shoppers conform to norms and are less experimental. Selfindulgents are risk takers and self-centred individuals.
The goal of a ‘well-off’ society in China is to translate economic growth into a better
quality of life. Hence there is optimism surrounding hyper-consumption from the
perspective of good health and a long life (Zhang et al. 2008). The LOHAS (Lifestyle of
Health and Sustainability) lifestyle which evolved in the US is seen as a trend for some
consumers to follow (Essoussi & Zahaf 2008; Kristiansen et al. 2010). A lifestyle related
study (Tai & Tam 1997) suggests that women in China consider imported ‘green’
products to be fashionable.
Gil, Gracia & Sanchez (2000) introduced lifestyle factors to investigate Spanish
consumers’ willingness to pay for organic products. They found that those whose lifestyle
was based on healthy diets and who were concerned about environmental degradation
were more likely to buy organic food. Spanish consumers who are aware about health,
balanced lifestyle, natural diet and environmental issues showed positive attitudes
towards organic food and were willing to pay higher prices (Sanjuán et al. 2003).
However, some consumers who believed that organic products are merely fashion
statements had negative attitudes towards their purchase (Gil, Gracia & Sanchez 2000).
Development of hypotheses associated with the lifestyle construct
Based on the above discussion, the following relationships between the lifestyle construct
and the following three stages of the proposed conceptual framework are hypothesised:
H3a: The lifestyle construct has a positive influence on the beliefs/attitudes of urban
Chinese consumers in relation to the purchase of organic food products
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H3b: The lifestyle construct is positively correlated to consumers’ pre-purchase
evaluation of organic food products in urban China
H3c: The lifestyle construct is positively correlated to consumers’ behavioural/purchase
intention of organic food products in urban China
3.3.4 The ethnocentrism construct
Wild et al. (2007, p. 59) propose that ‘ethnocentricity is the belief that one’s own ethnic
group or culture is superior to that of others’. Shimp & Sharma (1987, p. 280) define
consumer ethnocentrism as:
Beliefs held by consumers about the appropriateness, indeed morality, of
purchasing foreign made products. Consumer ethnocentrism gives the individual
a sense of identity, feelings of belongingness, and an understanding of what
purchase behaviour is acceptable or unacceptable to the in-group.
In some societies this is translated as being nationalistic or patriotic. In the context of
consumer behaviour it impacts in two ways: it defines one’s own country as being
superior, and it views the products of others as being potentially inferior.
Preferences for foreign products can ruin home produced goods and hurt the domestic
economy. On the other hand, discrimination against imports from certain countries also
creates invisible barriers to globalisation (Agbonifoh & Elimimian 1999; Wild et al.
2007). In a Greek study (Fotopoulos & Krystallis 2002b), consumers generally refrained
from purchasing foreign organic products because of their ‘patriotic’ stance. The country
of origin seems to have had a powerful influence on Greek consumers. They also seem to
be indifferent towards advertising, and seek information about organic products from
their family and friends. Generally Greek consumers displayed strong ethnocentric
tendencies.
Some consumers may be willing to purchase organic food to support local producers,
hence they tend to prefer shipments from locally grown organic produce compared to
products shipped from other areas (Bonti-Ankomah & Yiridoe 2006). Locally produced
90
goods involve short food miles, implying lower fuel costs. This concept motivates
consumers who are concerned for the environment (Wirth, Stanton & Wiley 2011). A US
study (Williams & Hammitt 2000) found that organic food buyers were twice as likely as
conventional buyers to rate locally grown produce as an important factor influencing their
purchase decision. Howard and Allen (2006) suggest that locally grown products are
preferred by older consumers and by households with children.
From ancient times, the Chinese people have valued patriotism. This is widely reflected
among people of all ages and areas, and in turn influences attitudes towards foreign-made
products. A recent study () indicates that Chinese consumers’ ethnocentrism tendencies
play an important role in shaping their purchase attitudes towards domestic fresh fruit. In
the last 20 years this patriotism has become somewhat diluted although there are signs of
a return to higher level of ethnocentrism, especially among older and less educated
people with less foreign contact and influence (Hsu & Nien 2008). Chinese consumers’
attitudes towards western luxury goods are generally positive owing to the fact that there
are currently no viable Chinese alternatives (Lu 2008). The McKinsey survey (StMaurice, Süssmuth-Dyckerhoff & Tsai 2008) finds that as Chinese consumers become
more sophisticated, nationalistic tendencies play a smaller role when they shop. However,
Chinese nationalism surfaces in various way, particularly as the country increases its
economic development and international visibility, such as hosting the 2008 Olympic
Games. Kwok, Uncles and Huang (2006) suggest that Chinese consumers generally
prefer local branded products with regards to fast moving consumer goods. There is a
growing tension between consumers’ pride and their interest in imitating and integrating
into the western way of life (Huliyeti, Marchesini & Canavari 2008).
As organic food advocates environmental and local produce, ethnocentrism comprises
items related to country of origin. Previous studies demonstrate that country of origin is
an important issue for organic food consumers. Pilgrim (1957) indicates that it is related
to preferences for many foods. Studies on country of origin conducted in developed
countries have revealed that ethnocentric consumers tend to perceive domestic products
as being of better quality than imported products. However, there is a variance in the
level of ethnocentric tendencies measured across different type of products (Herche 1992).
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Country of origin of organic food is important to most Danish consumers: 72% of
respondents preferred to buy domestically produced conventional fruit and vegetables
rather than foreign produced organic food; 80% had confidence in the quality of food
produced in Denmark when they read the package description of where and how it was
produced (Millock, Wier & Andersen 2004). Irish consumers prefer local, fresh, seasonal
and ‘chemical-free’ fruit and food over imported certified organic products (Moore 2006).
In developing countries, purchasing of foreign products is generally seen as a social
symbol, and one associated with status searching. This is particularly the case with those
products which originate from prestigious countries (Batra et al. 2000). However, Anic’s
(2010) study in Croatia (a developing country) found that consumers displayed a
relatively high level of ethnocentrism towards domestically made retail food products.
Another study by Ozretic-Dosen, Skare and Krupka (2007) concurs that almost half of
young Croatian respondents would purchase more expensive domestically produced
chocolate rather than western European brands if the national brand was of similar quality.
There are signs that after more than a decade of embracing all things Western, the
Chinese are turning to things Chinese (Prasso, Ting & Dan 2007). A contrasting report
portrays Chinese consumers as being no longer interested in foreign brands or products,
while domestic products are seen to provide better value with good and matching quality
at lower prices (Croll 2006). Watson and Wright (2000) suggest that cultural similarity is
an important consideration for highly ethnocentric consumers in evaluation of foreign
products.
Another Chinese study (Liu et al. 2006) shows that the differing attitudes of consumers
towards goods imported from western countries and from Japan. This has been attributed
to the historical animosity between China and Japan (Klein, Ettenson & Morris 1998).
Country of origin impact on Chinese consumers’ ethnocentric beliefs and attitudes
relating to foreign brands can be country specific, for example, it might impact more on
US than Australian brands.
Development of hypotheses associated with the ethnocentrism construct
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Based on the above discussion, the following relationships between the ethnocentrism
construct and the following three stages of the proposed conceptual framework are
hypothesised:
H4a: The ethnocentrism construct has a significant influence on the beliefs/attitudes of
urban Chinese consumers towards the purchase of organic food products
H4b: The ethnocentrism construct is significantly correlated to the pre-purchase
evaluation of Chinese consumers towards the purchase of organic food products
H4c: The ethnocentrism construct is significantly correlated to consumers’
behavioural/purchase intention of organic food products in urban China
3.4
Cognitive/affective stage
Consumers’ attitudes and beliefs
The second stage of the conceptual framework is termed cognitive/affective. The most
significant components are consumers’ beliefs and attitudes. Fishbein and Ajzen (1975)
propose that attitudes are composed of several aspects of beliefs. These are called
normative beliefs and are developed from other factors such as peers and surrounding
environment. The present study does not differentiate between attitudes and beliefs in
relation to organic food consumption intentions. According to the Theory of Planned
Behaviour (Ajzen 1991), human action is guided by three kinds of considerations:
attitude towards the behaviour, subjective norm and perceived behaviour control.
Perceived control refers to the consumer’s perception about their ability to perform a
given behaviour. It is determined by beliefs about the presence of factors that may
facilitate or impede performance of the behaviour. In agreement with this view, Chen
(2007) claims that consumers may have positive attitudes towards organic food purchase,
but may not buy it when faced with a perceived impediment. Aertsens et al. (2009)
suggest that perceived behaviour control may cover ‘perceived barriers’ that may impede
and ‘perceived abilities’ that may facilitate behavioural intention and behaviour towards
consumption of organic food, for example, relatively higher price might be perceived as
negative attributes.
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Studies on personal beliefs and motivations indicate that food safety concern appears to
be the most important factor in influencing the decision-making and purchase of organic
food (Rimal, Moon & Balasubramanian 2005; Tsakiridou, Mattas & Tzimitra-Kalogianni
2006). Also consumers of organic food are motivated by their beliefs and concern for the
ecology and are more attuned to matters of personal health (Shepherd, Magnusson &
Sjödén 2005) and environmental consciousness (Honkanen, Verplanken & Olsen 2006).
Interestingly, other factors such as lifestyle and cultural influences create particular
personal beliefs for consumers of organic food and can also be a driver for its purchase
(de Magistris & Gracia 2008). The European Action Plan states that ‘increasing
consumers’ organic knowledge is of vital importance for the development of organic food
demand because organic knowledge influences attitudes towards organic food products
that directly determine the decision or intention to buy the product’ (de Magistris &
Gracia 2008, p. 943).
A Danish study examined the relationship between attitudes and the purchase of organic
foods. It describes eleven motivational values: universalism, benevolence, spirituality,
conformity, tradition, security, power, achievement, hedonism, stimulation and selfdirection. These values are both self-centred and socially based (Grunert & Juhl 1995).
The results suggest that Danish school teachers with ‘green’ attitudes are more likely to
buy organic foods. In a sense, they possessed more environmental protection attitudes
that unified them with nature. It is also evident in a Spanish study that consumers’
attitudes towards the environment and the agricultural system are highly significant in the
decision to pay a premium for organic olive oil (Soler et al. 2002).
On the other hand, a Swedish study (Shepherd, Magnusson & Sjödén 2005) has
demonstrated a discrepancy between attitudes and behaviour in that positive attitudes
towards organic food do not always guarantee its purchase. Generally consumers with
positive attitudes towards organic food are more likely to display positive behavioural
and purchase intention (Honkanen, Verplanken, and Olsen 2006). After all, behaviour is
determined by the intention of individuals’ motivation (Fishbein and Ajzen 1975).
Consumers who are more knowledgeable about organic food believe that it is healthier
and of a higher quality. They therefore present more positive attitudes towards organic
94
food (de Magistris & Gracia 2008). An Italian study (Saba & Messina 2003) suggests that
those consumers who hold positive attitudes towards eating organic fruit and vegetables
on average agree that organic food products are healthier, environmentally friendly, taste
better and are more nutritious than conventional food.
Tanner and Kast (2003) suggest that personal influential factors, such as attitudes towards
food, and personal norms play a significant role in the purchase of green food in Sweden.
Consumers who attempt to pursue a healthy diet and balanced lifestyle are more likely to
have positive attitudes towards organic food products and the environment, and are thus
more likely to purchase organic food (de Magistris & Gracia 2008).
3.5
Evaluation of alternatives stage
Pre-purchase evaluation
A Greek study (Drichoutis, Lazaridis & Nayga Jr 2007) related to purchase behaviour of
food suggests that nutritional knowledge is often regarded as a proxy for prior food
knowledge. The action of searching for nutritional information is as important as that of
searching for price information prior to the purchase of food. In line with this thinking, a
study conducted in the Netherlands (Schifferstein & Oude Ophuis 1998) suggests that the
conceptual model of consumers’ organic food choice decision process should embrace
problem recognition, search information and alternative evaluation stages. Problem
recognition might occur due to some obvious or latent health problem, which then
motivates consumers to seek information about different types of food, and finally
consider alternative evaluations prior to purchase.
An Australian study (Chang & Zepeda 2005) emphasises that knowledge of organic food
products appears to be an important attribute in influencing consumers’ attitudes and
consumption. Regular consumers have more knowledge, and are more tolerant of higher
prices and inaccessibility. Verbeke (2000) suggests that the consumer decision-making
process regarding purchase of meat involves stages such as problem recognition, search
and processing of information and evaluation of product alternatives.
Development of hypotheses associated with the pre-purchase evaluation
95
Based on the above discussion, the following relationship between consumers’ attitudes/
beliefs and pre-purchase evaluation of the proposed conceptual framework is
hypothesised:
H5: Chinese consumers’ beliefs/attitudes towards organic foods are directly and
positively correlated to their pre-purchase evaluation
3.6
Behavioural/purchase intention stage
Intentions are generally good predictors of behaviour (Honkanen, Verplanken & Olsen
2006). Consumers who attempt to pursue a healthy diet and balanced lifestyle have
stronger intentions of purchasing organic food products (de Magistris & Gracia 2008).
Those who have positive attitudes towards organic foods naturally have greater intention
to purchase them (Tarkiainen and Sundqvist 2005). Hansen (2005) developed a better
understanding of consumers’ perception of food quality. This quality perception process
framework was constructed using a number of variables which included stimuli, expected
quality attributes, feeling of pleasure and purchase intention.
Previous studies (Chen 2007; Honkanen, Verplanken & Olsen 2006) demonstrate that
consumers with positive attitudes towards organic food have stronger intentions to buy it:
‘Intentions are assumed to capture the motivational factors that influence behaviour. They
are indications of how hard people are willing to try, of how much of an effort they are
planning to exert, in order to perform the behaviour’ (Ajzen 1991, p. 181). This means
that the higher the intention to engage in purchase behaviour (of organic food), the more
likely they are to undertake the purchase. Relevant studies (de Magistris & Gracia 2008;
Saba & Messina 2003) show that consumers who believe that organic food is healthier
and of higher quality have higher intentions to purchase it. Consumers who have a more
positive attitude towards organic food have greater intention to purchase it (Tarkiainen &
Sundqvist 2005).
Development of hypotheses associated with the pre-purchase evaluation
96
Based on the above discussion, the following relationship between pre-purchase
evaluation and behavioural/purchase intention of the proposed conceptual framework is
hypothesised:
H6: The pre-purchase evaluation of Chinese consumers to organic food is directly and
positively related to their behavioural/purchase intention
3.7
Demographic variables
As discussed in the literature review, several studies exhibit the impact of demographic
variables on organic food consumption. The socio-demographic profile of consumers
which includes level of income, education, occupation, gender, marital status, age group
and social status significantly influences consumer attitudes towards the purchase of
organic food. A higher level of disposable household income and education significantly
increases the probability of being a heavy organic food consumer (Millock et al. 2004).
The influence of demographic variables is not explicitly mentioned in the Theory of
Planned Behaviour or Hierarchy of Effects model, both of which underpin the proposed
conceptual model for the current study. They are nonetheless present implicitly, as
consumer groups are divided along socio-demographic lines which may produce
differences in relation to attitude, intentions and behaviour (Aertsens et al. 2009): ‘The
influence of demographic factors is pervasive, affecting various stages of the consumer
decision process, but the level of theorising is not commensurate with their importance’
(Steenkamp 1997, p. 161). Since the current study only focuses on the impact of the
influencing stage (product/regulatory/lifestyle/ethnocentrism) on consumers’ beliefs/
attitudes, pre-purchase evaluation and purchase intention, the demographic variables are
explained as perceived abilities in the stages of consumption of organic food. Hence, they
are positioned as control variables during the purchase intention stage.
As organic food is more expensive than conventional food, numerous studies (Rimal,
Moon & Balasubramanian 2005; Tregear, Dent & McGregor 1994) suggest that higher
income households are more likely to purchase it. Such households have positive
attitudes towards purchase of health labelled products, as compared to those with lower
97
income levels. Other studies (Lockie et al. 2002; Squires, Juric & Cornwell 2001)
question the strong link between higher income and consumption of organic food.
A pan-European survey of consumer attitudes towards healthy eating highlights that
educational levels appeared to be the strongest influence on perceptions of healthy diet,
food choice, food quality and freshness (Margetts et al. 1997). Educational levels seem to
be higher in the organic consumption group than the conventional food group (Storstad
and Bjørkhaug 2003).
Gender can also be an influential demographic variable in the decision-making and
choice of organic food. Women are more concerned about health, nourishment and the
environment and more likely to demand more health information. As such, they are more
likely than men to purchase organic food (Davies, Titterington & Cochrane 1995;
McEachern & McClean 2002; Rimal, Moon & Balasubramanian 2005). In Lockie et al.’s
(2002) Australian study, 44% of women respondents claimed to have consumed certified
organic food in comparison with 34% of men. These figures concur with a previous study
undertaken in Northern Ireland by Davies, Titterington and Cochrane (1995) in which
46% of women consumed organic food as opposed to 34% of men. These studies
identified women as being more aware of organic food and having more positive attitudes
towards it. Women are more likely than men to hold positive attitudes towards the
purchase of organic foods (Lea & Worsley 2005; Lockie et al. 2002; McEachern &
McClean 2002).
On the other hand, because women are more price conscious than men, men are more
willing to pay a premium for organic food (Ureña, Bernabéu & Olmeda 2008). A
Canadian study claims that males in a single household holding a bachelor degree and
individuals older than 51 years are more likely to pay a premium price (Haghiri &
McNamara 2007). Do demographic variables influence Chinese consumer attitudes
towards organic food? Does gender affect Chinese consumers’ attitude? Do income,
professional and education levels positively influence organic food consumption in urban
China? The answers to these questions are explored later in this study.
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Sun and Collins (2006) suggest that there are five intended uses of imported fruit among
Chinese consumers: for gift giving, for aged parents, for their only child, for visiting
patients and for self-consumption. Generally in urban China, both parents and
grandparents may spend large amounts of money for children who are products of a onechild policy3 (Schütte & Ciarlante 1998). An Indian study (Chakrabarti and Baisya 2007)
explains that one intended use of organic food is for consumers seeking remedies in poor
health conditions.
Sun and Collins (2006) point out that Chinese consumers purchase imported fruit not
only to receive high quality products, but they also expect to gain some pleasure and
symbolic benefits, such as ‘superior appearance’, ‘expensive’ and ‘high prestige’. The
gift reflects both the status of the donor and respect towards the receiver. The Hofstede
cultural dimension suggests that status is important to show power, success and
achievement. National or global brands are regarded as revealing one’s status or being in
the ‘right place’ in society (Mooij & Hofstede 2002).
McEwen et al. (2006) argue that there is a misconception that Chinese consumers are all
well-off and have a lot of money to spend. Yet at the same time they spend more on basic
household goods and products like mobile phones, televisions and air conditioners, which
can be seen as status as well as concern about products’ reliability. Bao, Zhou and Su
(2003) demonstrate that purchasing certain kinds of products is a ‘face saving’ issue and
positively influences consumers’ brand-consciousness and price-equals-quality and
negatively affects the price-conscious and value-for-money perception.
Development of hypotheses associated with the behavioural/purchase intention
Based on the above discussion, the following relationships between demographic
variables and behavioural/purchase intention of the proposed conceptual framework are
hypothesised:
3
The Chinese government’s one-child policy refers to family bearing a single child applied to the Chinese
mainland. The policy was introduced in 1978 and officially restricts married and especially urban couples
to a one-child reproduction. It does allow some exemptions, primarily in rural areas and to ethnic minorities.
99
H7a:Women are more likely to purchase organic food than men in urban China
H7b:Younger consumers are more likely to purchase organic food than older
consumers
H7c:Highly educated consumers are more likely to purchase organic food as compared
to those who are less educated
H7d:Consumers earning higher income are more likely to purchase organic food as
compared to those earning less
H7e:The important attributes of organic food products have positive influences on their
intended usage pattern in urban China
Summary of research hypotheses
The development of hypotheses is based on the proposed conceptual framework in Figure
3.2 which illustrates and explains the purchase of organic food in urban China, and the
key determinants which influence consumers’ decision-making process. Seven series of
hypotheses have been developed because of the complex range of determinants in the
process of consumer decision-making. The H1, H2, H3 and H4 battery of hypotheses
address the relationship between product/regulatory/lifestyle/ethnocentrism and various
stages of the proposed conceptual framework. H5 addresses the correlation of consumers’
beliefs/attitudes to pre-purchase evaluation. H6 addresses the relationship between prepurchase evaluation and behavioural/purchase intention. H7 examines the impact of
control variables: gender, age, education, income and usage pattern, towards purchase
intention. A summary of all research hypotheses is provided in Table 3.1.
Table 3.1 List of all research hypotheses
No.
Hypotheses
H1a
The product-related construct has a positive influence on the beliefs/attitudes of urban
Chinese consumers towards the purchase of organic food products.
Organic food product-related construct is positively correlated to the Chinese
consumers’ pre-purchase evaluation.
Organic food product-related construct is positively correlated to Chinese consumers’
behavioural/purchase intention.
The regulatory construct associated with organic food has a positive influence on
beliefs/attitudes of urban Chinese consumers.
The regulatory construct associated with organic food is positively correlated to the
Chinese consumers’ pre-purchase evaluation.
The regulatory construct associated with organic food is positively correlated to Chinese
H1b
H1c
H2a
H2b
H2c
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H3a
H3b
H3c
H4a
H4b
H4c
H5
H6
H7a
H7b
H7c
H7d
H7e
3.8
consumers’ behavioural/purchase intention.
The lifestyle construct has a positive influence on the beliefs/attitudes of urban Chinese
consumers towards the purchase of organic food products
The lifestyle construct is positively correlated to consumers’ pre-purchase evaluation of
organic food products in urban China
The lifestyle construct is positively correlated to consumers’ behavioural/purchase
intention of organic food products in urban China
The ethnocentrism construct has a significant influence on the beliefs/attitudes of urban
Chinese consumers towards the purchase of organic food products
The ethnocentrism construct is significantly correlated to the pre-purchase evaluation of
organic food products in urban China
The ethnocentrism construct is significantly correlated to Chinese consumers’
behavioural/purchase intention of organic food products
Chinese consumers’ beliefs/attitudes are directly and positively correlated to their prepurchase evaluation.
The pre-purchase evaluation of Chinese consumers to organic food is directly and
positively related to their behavioural/purchase intention.
Women are more likely to purchase organic food than men in urban China
Younger consumers are more likely to purchase organic food than older consumers
Highly educated consumers are more likely to purchase organic food as compared to
those who are less educated
Consumers earning higher income are more likely to purchase organic food as compared
to those earning less
The important attributes of organic food products have positive influences on their
intended usage pattern in China
Chapter summary
This chapter describes the development of the proposed conceptual framework for the
purchase of organic food in urban China. The proposed conceptual framework was built
on and developed from the Consumer Decision Process (CDP) model along with the
Theory of Planned Behaviour model (TPB) and Hierarchy of Effects model. This
framework provides the theoretical foundations to examine the five stages of consumers’
decision-making process towards organic food, namely, influencing, cognitive/affective,
evaluation of alternatives, behavioural/purchase intention and finally purchase. The
current study only investigates consumers’ behavioural/purchase intention as the actual
purchase involves collection of data of the same sample at some future period of time
(longitudinal study). This chapter also puts greater emphasis on investigating the extent to
which consumers’ beliefs and attitudes pertaining to organic foods are influenced by
constructs such as product, regulatory, lifestyle and ethnocentrism. Along with the
proposed conceptual framework, a series of elaborate and comprehensive hypotheses are
developed to examine the relationship between the various constructs of the proposed
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conceptual framework. Table 3.1 provides a full listing of all these hypotheses. The
methodology used is explained in the following chapter.
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CHAPTER FOUR:
METHODOLOGY
4.1
Introduction
The purpose of this chapter is to introduce and justify the research design employed in
this study, which is a deductive approach, predominantly involving testing of the
hypotheses developed in Chapter Three. Figure 4.1 diagrammatically illustrates the
chapter structure
Figure 4.1 Structure of Chapter Four
Source: developed for this research
Section 4.2 follows up on the conceptual framework developed in Chapter Three, and
explains the research design. Section 4.3 establishes the development of the survey
instrument, which includes measurement of the constructs of the proposed conceptual
framework. It also deals with issues associated with reliability and validity of the data.
Section 4.4 explains the sampling and the data collection procedures. Two stages of data
collection were employed in this study. They included online and paper-based surveys.
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4.2
Research design
4.2.1 Research paradigm
A research paradigm is a philosophical framework that guides how scientific research
should be conducted (Creswell 2009). The two main traditional paradigms are categories
called positivism and interpretivism, which are associated with quantitative and
qualitative methods of analyses. Collis and Hussey (2009, p. 56) affirm:
Under positivism, theories provide the basis of explanation, permit the
anticipation of phenomena, predict their occurrence and therefore allow them to
be controlled. Explanation consists of establishing causal relationships between
the variables by establishing causal laws and linking them to a deductive or
integrated theory.
A set of interrelated variables and propositions that specify relationships among the
variables is a theory. Variables are attributes of the phenomenon which can be changed
and measured, indicating cause and effect relationships. On the other hand, interpretivism
attempts to understand subjective human perception by focusing on exploring, seeking
and describing the meaning rather than measurement of statistics (Collis & Hussey 2009).
Creswell (2009) summarises previous studies which demonstrate that there are five
philosophical assumptions – ontological, epistemological, axiological, rhetorical and
methodological – referred to as quantitative and qualitative paradigms.
The nature of this research paradigm is influenced by the literature and research issues
examined in the previous chapters. The main research problem is to understand the most
important factors that influence consumers’ purchase intention relating to organic food in
urban China. In the positivist paradigm, this research is predominantly deductive.
Individual researchers have the freedom to choose the best method to suit the research
purpose. A particular research paradigm may be more acceptable by personal choices and
follow a tradition in the research discipline although it is determined by the research
problem (Collis & Hussey 2009; Creswell 2009). Current literature studying consumers’
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awareness of organic food has been well developed in North America and Western
Europe (Bonti-Ankomah & Yiridoe 2006). Previous studies (Chen 2007; Fotopoulos &
Krystallis 2002b; Storstad & Bjørkhaug 2003) have provided highly specific and precise
measurements to examine consumers’ purchase behaviour in relation to organic food.
They emphasise that a survey instrument is most suitable to collect data in the organic
food studies, particularly when the population of an entire country needs to be presented.
A survey instrument provides more control over the research process, as described by
Babbie (in Creswell 2009, p. 12):
Survey provides a quantitative or numeric description of trends, attitudes, or
opinions of a population by studying a sample of that population. It includes
cross-sectional and longitudinal studies using questionnaires or structural
interviews for data collection, with the intent of generalising from a sample to a
population.
A survey instrument is a proper sampling technique which allows the collection of a large
amount of data from a sizeable population in a highly economical way, hence its
popularity (Saunders, Lewis & Thornhill 2007). Although the term ‘survey’ is often used
to describe the collecting of data using questionaries, it also includes structured
observation and structured interviews (Saunders, Lewis & Thornhill 2007). In this study,
survey instruments will be referred to as questionnaires.
4.2.2 Research context
This section discusses the setting of the research environment to achieve stated research
objectives. This study is conducted in the context of a developing and emerging market,
China. Like in many industrialised countries, there is a realisation amongst Chinese
consumers of the need for healthy and nutritious products such as organic food (Moon et
al. 1998). Although a small and almost insignificant percentage of the 1.3 billion Chinese
consume organic food products, China is developing an affluent urban middle class
market for domestic organic food. Willer and Yussefi (2006) estimate that the Chinese
domestic organic market increased by 30% compared to the previous year and this
growth predominantly came from eastern China. Demand is also increasing for imported
105
organic products. Large international retailers like Wal-Mart and Carrefour have
substantially increased sales of organic food products, even though they are more
expensive (Baer 2007). As more consumers become health-conscious, China will
potentially develop the world’s largest organic food market (CMP Information Ltd 2006).
4.2.3 Unit of analysis
‘The unit of analysis refers to the level of aggregation of the data collected during the
subsequent data analysis stage. The research objective determines the unit of analysis’
(Cavana, Delahaye & Sekaran 2001, p. 119). In China, consumers in major cities are
more affluent than in other cities, hence they are ideal targets for consumer good studies
(Eves & Cheng 2007). Urban consumers also differ from those in rural areas (Dickson et
al. 2004). In this study, the urban Chinese consumer is defined as the unit of analysis.
Consumers were sourced across major supermarkets in four selected Chinese cities where
the demand for organic food products is growing. Justification for choosing the four cities
is provided in Section 4.4.5. The unit of analysis may compose the primary empirical
research aim, individual or group under investigation (Davis 2005). The unit of analysis
has been selected in line with the research objectives stated in Chapter One.
4.2.4 Refining the unit of analysis
In a Chinese organic food study by Li, Cheng & Ren (2005), only 37.2% of respondents
were aware of ‘organic food’. The term ‘organic’ is little understood by Chinese
consumers, as organic food is only available in supermarkets in major cities (Paull 2008a).
Studies suggest that using a screening question about the awareness of organic food at the
commencement of a survey discriminates between buyers or potential buyers of organic
food and non-buyers (Fotopoulos & Krystallis 2002a, b; Krystallis, Fotopoulos & Zotos
2006; Roitner-Schobesberger et al. 2008). In this study, a screening question – ‘Have you
ever heard of the term “organic food”?’ – was asked at the beginning of the survey to
ensure that data was collected only from valid participants. In other words, it was a
requirement that respondents should have heard of the term and should have a basic
understanding about organic food. It was determined for this study the respondents
106
should be regular, occasional or even potential organic food consumers. Respondents
who answered ‘no’ were not included in this study. Therefore, this study only focuses on
urban Chinese consumers who are aware of organic food products.
4.3 Design of the survey instrument
The survey instrument comprised statements which were considered important in
relation to the purchase intention of organic food in urban China. These statements were
obtained from previous literature and three focus group discussions in China, and were
refined by the researcher on the premises that:
x
They were appropriate for all respondents of the survey;
x
They were able to generate sufficient data to test the research hypotheses
4.3.1 Focus groups
Focus groups are used for many purposes, including exploring issues, opinions and ideas
of the research, developing survey questionnaires, increasing the understanding of survey
findings and generating hypotheses that can be tested in the research (Boyce 2002). They
often provide insights that direct questioning cannot present (Malhotra 2008). Focus
groups are used to generate information to guide further quantitative research (Kotabe et
al. 2005) and enlighten the content of further surveys (Saunders, Lewis & Thornhill
2007). They have been widely used in organic food research (Aarset et al. 2004; Chang &
Zepeda 2005; Essoussi & Zahaf 2008; Lockie et al. 2004) and have been recognised as a
tool for aiding survey design, providing better knowledge of consumer perceptions and
demand for organic food and suggesting directions for further research. The objective of
these focus groups was to investigate Chinese consumers’ attitudes to and motivations in
purchasing organic food.
Three focus groups each consisting of eight participants were organised in China with
participants from different socio-demographic background. Structured questionnaires and
open-ended questions were employed to understand Chinese consumers’ attitudes and
beliefs towards the purchase of organic food. The focus groups’ processes were intended
to gain a better understanding of attitudes towards the purchase of organic food, hence
107
develop meaningful survey questions. All participants had heard of the term ‘organic’ and
purchased organic food products.
The major questions discussed in the focus groups were:
x
Are Chinese consumers confused about differences in the terms ‘green food’,
‘non-polluted food’ and ‘organic food’?
x
What do you know about the following factors which influence the consumption
of organic food:
a. Product-related factors including sensory attributes and price perception
b. Government policies and regulations, certification and labelling
c. Sociocultural factors and lifestyle
d. Country of origin and ethnocentric factors?
x
Why do Chinese consumers purchase organic food?
x
What are the salient demographic features of organic food consumers in China?
The researcher who originally hails from China and has a thorough understanding of the
nature of this study was the moderator for the three focus groups. After discussion with
each focus group, the researcher modified and re-phrased the initial questions which were
developed from previous literature. Additional items were identified, and comments and
suggestions were utilised to arrive at the pre-test version of the survey instrument (see
Section 4.3.5).
4.3.2 Operationalising the constructs
‘A strategy to develop scale measurement is to find a suitable existing instrument whose
results generalise to a broader class of measures that legitimately employ the same name’
(Nunnally & Bernstein 1994, p. 53). Most statements were adapted from published
research articles and validated measures for the seven key constructs of the proposed
conceptual
framework:
product,
regulatory,
lifestyle,
ethnocentrism,
personal
beliefs/attitudes, pre-purchase evaluation and behavioural/purchase intention.
Table 4.1 depicts the components of the product-related construct and associated
measures. Four statements relating to food product characteristics – texture, appearance,
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smell and taste – which are termed ‘sensory appeal’ were selected. Chen (2007) adopted
these measures to explain Taiwanese consumers’ attitudes and purchase intention in
relation to organic food, and the internal consistency results were positive (α=0.87). The
reasons for the non-purchase of organic food include consumers’ perception of it being
expensive and lacking in value. Price perception items were also chosen by previous
studies (Burton et al. 1998; Gil, Gracia & Sanchez 2000; Lichtenstein, Ridgway &
Netemeyer 1993; Yoo, Donthu & Lee 2000).
Table 4.1: Components of product-related construct and associated measures
Component
Author and year
Adapted
measure
Sensory appeal
Steptoe et al.
1995; Chen 2007
As above
As above
As above
Price perception Gil et al. 2000;
Chen 2007
Krystallis &
Chryssohoidis
2005
Lichtenstein et al.
1993; Burton et
al. 1998
Verhoef 2005;
Yoo et al. 2000
Steptoe et al.
1995; Chen 2007
Factor
loading
Cronbach’s
alpha
Instrument
item
Has a pleasant
texture
0.70
3.1
Looks nice
Smells nice
Tastes good
Consuming
organic food is
trendy
Organic food has
high nutritional
value
Generally
speaking, the
higher the price of
a product, the
higher the quality
The price of
organic food is
too high
Organic food is
good value for
money
0.72
0.80
0.53
0.71
Chen: 0.87;
Steptoe et al.:
0.70
As above
As above
As above
N/A
0.70
N/A
3.6
N/A
Lichtenstein
0.78; Burton
et al. 0.85
3.7
Yoo et
al. 0.94
Yoo et al.
0.88
3.8
Steptoe
et al.
0.76
Chen: 0.72
3.9
3.2
3.3
3.4
3.5
Food safety is one of the most important issues influencing the purchase of organic food
(Ho, Vermeer & Zhao 2006). There is a relatively poor understanding of the legal use of
the term ‘organic food products’, especially in relation to inspection and certification
systems (Krystallis & Chryssohoidis 2005). An organic food logo is the consumer’s
guarantee that the product has been produced organically (Botonaki et al. 2006).
However, owing to the huge number of labels and organic certification bodies, there is a
need for government regulations to address this (Özcelik & Uçar 2008; Walsh, Laczniak
109
& Carlson 1998). There is also doubt about which of those can be trusted by consumers.
Eight attributes associated with the regulatory construct were selected as shown in Table
4.2.
Table 4.2: Components of regulatory construct and associated measures
Component
Certification
and labelling
Government
regulation/
policy
Author and year
Botonaki et al.
2006
Krystallis &
Chryssohoidis
2005
As above
Focus group
Walsh 1998
Focus group
Özcelik & Uçar
2008
Ho et al. 2006
Adapted measure
When I buy a food product, I
always read the label
I don’t trust organic food
certification bodies
I trust the outlets which sell
certified organic food
Chinese government regulate food
marketing, and have developed
significant policies
Lack of adequate government
control of media allows
advertisers to take advantage of
consumers
Logos depicting types of organic
foods should be controlled
More land should be allocated for
organic farming
The market for ‘organic’ and
‘green’ is chaotic
Factor
loading
N/A
Instrument
item
3.10
0.76
3.11
0.81
3.12
N/A
3.13
N/A
3.14
N/A
3.15
0.43
3.16
N/A
3.17
Consumption of organic food is related to lifestyle. Previous studies (Chen 2009; Lockie
et al. 2002) demonstrate that lifestyle has an impact on consumers’ attitudes and
consumption of organic food. The lifestyle variables comprising 12 items shown in Table
4.3 were adapted from Yang (2004). These scales were used to identity which lifestyle
segments influence consumers’ attitudes towards purchase of organic food products in
China.
110
Table 4.3: Components of lifestyle construct and associated measures
Component
Adapted measure
Factor 1: selfWhen I like something, I will buy it without too
indulgence (a = 0.70) much deliberation
I always do whatever I feel like and whenever I
feel like it
The sole purpose of making money is to spend it
Sometimes I feel like spending money on
anything I lay my eyes on
I often make impulse purchases
Factor 2: variety
I always try something new and unique
seeking (a = 0.70)
I love fashionable and trendy products
It does not hurt to be trendy if I feel like it
I am often influenced by advertisements of new
products
Factor 3: opinion
I can easily influence people around me during
leadership (a = 0.52) conversation
My friends often consult me when they cannot
make up their own mind
I have a strong desire to be successful
Factor
loading
0.592
Instrument
item
3.28
0.512
3.29
0.609
0.727
3.30
3.31
0.760
0.587
3.32
3.33
0.639
0.723
0.706
3.34
3.35
3.36
0.707
3.37
0.628
3.38
0.554
3.39
Source: Yang 2004
A product’s country of origin is an important attribute to influence organic food
consumers’ willingness to pay. Consumers generally perceive locally produced products
to be of better quality than imported ones (Krystallis & Chryssohoidis 2005). In addition,
those with ‘local’ concern are more likely to purchase organic food (Torjusen et al. 2001).
Locally produced goods involve short food miles implying lower fuel costs. This concept
motivates consumers who are concerned for the environment (Wirth, Stanton & Wiley
2011). Organic food consumers show stronger ethnocentrism attitudes as compared to
non-organic food consumers (Fotopoulos & Krystallis 2002a, b). Components for the
ethnocentrism construct and associated measures were adapted from previous related
studies (Shimp & Sharma 1987; Klein et al. 1998; Lindeman & Vaananen 2000;
Chryssochoidis, Krystallis & Perreas 2007) and are shown in Table 4.4:
111
Table 4.4: Components of ethnocentrism construct and associated measures
Component
Ethnocentrism
Country of
origin
Author and year
Shimp & Sharma
1987;
Chryssochoidis et
al. 2007
As above
Klein, Ettenson &
Morris 1998
Lindeman &
Vaananen 2000
Ethnocentrism
Shimp & Sharma
1987;
Chryssochoidis et
al. 2007
Adapted measure
Chinese people should
buy domestic products
rather than imported
products
China should levy heavy
tariff on foreign products
to reduce their quantity
into China
If two organic food
products were the same in
quality, but one was
imported and the other
was Chinese, I would pay
more for the imported
product
I would purchase organic
food coming from a
country I approve of
politically
Chinese people should not
buy foreign products,
because it would hurt
domestic business, and
cause more
unemployment
Factor
loading
0.65
Instrument
item
3.40
0.58
3.41
N/A
3.42
0.85
3.43
0.67;
3.44
0.80
Consumers’ purchase of organic food is based on subjective experiences and perceptions
(Hughner et al. 2007). The strongest motivation is related to health (Kraft & Goodell 1993;
Squires, Juric & Cornwell 2001; Verhoef 2005). Another is driven by growing interest in
ecological values, which is related to environmental sustainability and the impact of
conventional agriculture on the environment (Honkanen, Verplanken & Olsen 2006).
Consumers’ positive motivations towards organic food products have a bearing on their
subsequent positive attitudes towards their purchase (Chen 2007). Ten attributes depicted
in Table 4.5 were elicited to determine Chinese consumers’ beliefs and attitudes towards
purchase of organic food.
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Table 4.5: Attributes of beliefs/attitudes construct and associated measures
Component
Author and year
Beliefs/
attitudes
RoitnerSchobesberger et
al. 2008;
Magnusson et al.
2003
Gil et al. 2000
Focus group
Kraft & Goodell
1993
RoitnerSchobesberger et
al. 2008
As above
Gil et al. 2000
Focus group
Botonaki et al.
2006
McEachern &
Willock 2004
Adapted measure
Organic food is good for
myself and my family’s
health
Organic food has no harmful
effects
I like the brands associated
with organic food
I worry about there being
harmful chemicals in my
food
Organic food does not
contain pesticides
Organic food is good for the
environment
I believe that organic food
has superior quality
I personally think I should
always buy organic food
To me, it is important that
the food I usually eat can be
easily found in the food
outlets near my house or
workplace
Organic food labels mean
high quality food products
Factor
loading
0.822
Instrument
item
3.18
0.74
3.19
N/A
3.20
0.759
3.21
0.696
3.22
0.612
3.23
0.74
3.24
N/A
3.25
N/A
3.26
0.646
3.27
There is a prevalent belief that organic food is healthier, safer, more nutritious and of
higher quality. Consumers with positive attitudes towards organic food are more likely to
form a stronger intention to purchase it. Hence Chinese consumers are willing to pay a
premium price (Botonaki et al. 2006). Four items used to identify their pre-evaluation of
organic food consumption were previously used by Bower (2001), and an additional item
was used by Bennett (1999). Although some of the wording has been slightly altered,
they are all related to pre-evaluation. Three behavioural intentional items were utilised by
Cronin Jr et al. (2000), and the wording has also been slightly modified to suit Chinese
consumers’ organic food purchase intention. Table 4.6 shows the measures for the prepurchase evaluation and behavioural intention constructs.
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Table 4.6: Attributes of pre-purchase evaluation and behavioural/purchase
intention constructs and associated measures
Component
Pre-purchase
evaluation
Author and year
Bower 2001
As above
As above
As above
Bennett 1999
Behavioural/
purchase
intention
Cronin Jr et al.
2000
As above
As above
Adapted measure
I am eager to check
out the product
because of
advertisements and
promotion
I intend to try out the
product
I am interested in
experiencing the
benefits of using the
product
It is likely that I will buy
the product when it
becomes available
I can recall the brand
names and labelling of
some of the organic
food products
I will probably use the
product in the future
I will recommend usage
of the product to my
friends and relatives
If I had to purchase the
product again, I would
make the same choice
Factor
loading
N/A
Cronbach’s
alpha
Reliability of
scale = alpha
of 0.80
Instrument
item
4.1
N/A
As above
4.2
N/A
As above
4.3
N/A
As above
4.4
N/A
N/A
4.5
N/A
4.6
N/A
Reliability of
scale = alpha
of 0.87
As above
N/A
As above
4.8
4.7
Twenty-three statements (Section Two of the Survey Instrument) were used to measure
the important attributes of the purchase intention of organic food. These statements were
utilised by previous studies, with some of the wording being slightly modified to suit the
Chinese context. Obviously, product-related measures such as taste, smell, appearance
and overall quality have been considered as important (Steptoe et al. 1995). In addition,
limited availability and choice of organic food has been regarded as one of the obstacles
to purchasing organic food (Steptoe, Pollard & Wardle 1995; Verhoef 2005). Food not
only satisfies hunger, but in the Chinese context it is also a form of social status and
prestige. Face saving (mianzi) is important for Chinese consumers. This means that
people are more concerned with the perception of others towards themselves and with
maintaining their own status. As such, ‘saving face’ is likely to influence Chinese
consumers’ purchasing decisions (Qian, Razzaque & Keng 2007). An organic study
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(Fotopoulos, Krystallis & Ness 2003) reveals that organic wine satisfies the expectations
of high social status consumers who search for novel forms and styles. Similarly, Chinese
consumers perceive certain types of imported fruit to be superior in appearance, and their
consumption reflects their wealth and social status (Sun & Collins 2006). Chinese
consumers are also becoming increasingly interested in grape wines (they traditionally
only drink rice wine) and this is contributing to lifestyle changes. Initially, grape wines
were considered to be symbols of social status, but they are now appreciated because of
their taste and pleasure (Euromonitor International 2011). Table 4.7 depicts the important
attributes when purchasing organic food.
Table 4.7 Important attributes when purchasing organic food
Author and year
Krystallis &
Chryssohoidis 2005
As above
As above
de Magistris & Gracia
2008
Krystallis &
Chryssohoidis 2005
Verhoef 2005; Steptoe
et al. 1995
Focus group
Fotopoulos &
Krystallis 2002 a, b
Krystallis &
Chryssohoidis 2005
Qian, Razzaque &
Keng 2007
Sun & Collins 2006;
Fotopoulos, Krystallis
& Ness 2003
de Magistris & Gracia
2008; Schifferstein &
Oude Ophuis 1998
Fotopoulos &
Krystallis 2002 a, b
Krystallis &
Chryssohoidis 2005
As above
As above
Focus group
Focus group
Adapted measure
The taste of organic food
Instrument
Items
2.1
The smell of organic food
The appearance of organic food
The overall quality of organic food
2.2
2.3
2.4
The price of organic food
2.5
The availability of organic food in convenience stores
and supermarkets
The promotion and advertising of organic food
The value of organic foods relative to its price
2.6
The environmental benefits of organic food
2.9
The idea of face saving (mianzi) when purchasing
organic food
The social status of people purchasing organic food
2.10
The knowledge of organic food products
2.12
The awareness of organic food products
2.13
The country of origin of the organic food
2.14
Organic food that is produced in China
The brand name of the organic food
Government regulations and policies relating to the
sale of organic food
Food safety in relation to organic food
2.15
2.16
2.17
2.7
2.8
2.11
2.18
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Krystallis &
Chryssohoidis 2005
Focus group
Krystallis &
Chryssohoidis 2005
Krystallis &
Chryssohoidis 2005
Krystallis &
Chryssohoidis 2005
Certification relating to the quality of organic food
2.19
Enforcement relating to the quality of organic food
Packaging of organic food
2.20
2.21
Correct labelling of organic food
2.22
Information about the nutritional value of organic food
2.23
4.3.3 Description of the survey instrument
The survey instrument shown in Appendix 1 comprises five sections, as below:
Section one: Investigated Chinese consumers’ general knowledge about organic food. It
required respondents to indicate whether they recognised any of the organic food labels
(Roitner-Schobesberger et al. 2008). Three logos used in the domestic market – ‘nonpolluted products’, ‘green food’ and ‘organic products’ – were selected and three logos
belonging to imported organic foods. This was followed by questions related to their
knowledge about organic food (Essoussi & Zahaf 2008).
Section two: Respondents were required to indicate the level of importance of the
attributes for the purchase of organic food. Twenty-three attributes were selected using a
five point Likert scale.
Section three: Examined perceptions, attitudes and beliefs towards organic food. The 44
statements
operationalised
constructs
related
to
product,
regulatory,
lifestyle,
ethnocentrism and beliefs/attitudes.
Section four: Contained attributes concerning pre-purchase evaluation and purchase
intention. The 8 statements operationalised constructs related to pre-purchase evaluation
and behavioural/purchase intention. All statements used a five point Likert scale.
Section five: Investigated socio-demographic variables of respondents, which included
age, gender, occupation, family income and education level. Additionally, it sought to
obtain their usage pattern. Five types of organic food were selected because these are the
most popular and widely available in Chinese organic food stores. They represent staple
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foods in the Chinese diet, and are significantly different from one another (US
Department of Agriculture 2008). The last part of the survey was open ended, inviting
respondents to comment on any aspect of organic food not covered in the survey
instrument.
4.3.4 Scaling and measurement
Attitudes and opinions are effectively evaluated and quantified by using Likert scales
(Leedy & Ormrod 2005). A Likert scale is often used to collect data relating to opinion,
and it is designed to examine how strongly the respondent agrees or disagrees with a
statement (Cavana, Delahaye & Sekaran 2001; Saunders, Lewis & Thornhill 2007). A
range of attitudes, guided by previous literature, were developed in relation to themes
surrounding organic food and were measured using rating scales. Previous studies (Eves
& Cheng 2007; Kim, Ma & Kim 2006) confirmed that five point Likert scales are
comprehensible by Chinese consumers, and widely applied in Chinese food consumption
studies (Eves & Cheng 2007; Sun & Collins 2002; Zhang 2002). Hence it was decided to
use such scales to evaluate the strength of respondents’ agreement to the statements in the
survey instrument. The efficacy of the five point Likert scales was also supported by the
three focus groups.
Chen (cited in Lee et al. 2002) suggests that collectivist and individualist 4 cultures
produce different response patterns. An individualist would present an extreme response,
whereas a collectivist would be happy to provide a moderate response to be in agreement
with the group (Wild et al. 2007). In order to minimise the chances of Chinese
respondents selecting neutral answers, both positive and negative statements were
employed to ensure that all statements were read carefully before being answered
(Saunders, Lewis & Thornhill 2007).
4
According to the Hofstede framework, people in collectivist cultures feel a strong association to groups,
including family and work units, and the goal of maintaining group harmony is probably most evident in
the family structure. Individualistic culture is opposite (Wild et al. 2007, p. 86).
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4.3.5 Validity and reliability of survey instrument
4.3.5.1
Validity
‘Validity is concerned with whether the findings are really about what they appear to be
about’ (Saunders et al. 2007 p. 150). Three validity tests were employed: face validity,
content validity and construct validity.
Face validity and pre-test
Face validity indicates that the statements being presented in the questionnaire are clear
and understandable (Cavana, Delahaye & Sekaran 2001). Prior to large-scale data
collection, a pre-test was employed for both hard copy and online questionnaires to assess
face validity. The purpose was to refine the questionnaires, so that respondents would not
have any difficulty in answering all questions (Malhotra 2008; Saunders, Lewis &
Thornhill 2007). The initial questionnaires were pre-tested using a set of 20 selected
Chinese organic food consumers.
Follow up emails or phone calls were made to the pre-test participants. Extra questions
were asked, including: How many minutes did it take to complete the questionnaire? Are
there any sentences difficult to understand? Is the layout of the survey clear and attractive
(Saunders, Lewis & Thornhill 2007)? How about grammar, structure and content of the
questions? Consequently, a number of changes were made to make the statements more
understood by Chinese respondents. In order to maintain the validity and reliability of the
final data set, respondents who were involved in the pre-test were not invited to
participate in the actual data collection process.
Content validity and translation into Chinese
The primary data originates for the specific purpose of dealing with the research problem
(Malhotra et al. 2006). In order to successfully interpret the meaning of the statements
and collect primary data, Chinese cultural and linguistic characteristics need to be
considered in this methodology (Ho, Vermeer & Zhao 2006).
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People who speak more than one language have an advantage of being able to understand
many different international points of view (McMurray, Pace & Scott 2004; Carroll 1974).
The researcher possesses a bilingual capability in the Chinese and English languages and
this was obviously an advantage. The original survey instrument was written in English,
and then translated into Chinese by the bilingual researcher. Significant misunderstanding
or confusion caused by a cross-cultural transformation can be detected through the backtranslation, and ensure the validity for a cross-cultural setting (La Ferle & Lee 2003; Phau
& Min 2009; Wang et al. 2010). The translated Chinese version was back-translated into
English to ensure the same meaning (Chan 2001). Discrepancies in the translation need to
be carefully inspected and corrected to ensure the measurement instruments mean the
same thing (Mullen 1995), and also contain no jargon and complex social judgements. To
eliminate any errors, the translated version should be confirmed by different individuals
(Kotabe et al. 2005; Zhuang et al. 2006). ‘Care must be taken to ensure that apparent
cross-cultural differences are real and actually do not stem from poor translation’ (Lee et
al. 2002, p. 298). Therefore, the translated versions were cross-checked by three other
bilingual researchers who were competent in both Chinese and English and were selected
from Australian and Chinese universities.
Construct validity
‘Construct validity testifies to how well the results obtained form the use of the measure
fit the theories around which the test is designed’ (Cavana, Delahaye & Sekaran 2001, p.
213). Two specific forms of construct validity, convergent validity and discriminant
validity were used in this study, as discussed in Chapter Six: Sections 6.5.1 and 6.5.2.
4.3.5.2
Reliability
‘Reliability refers to the consistency and stability of a score from a measurement scale’
(Davis 2005, p. 188). In other words, the valid scale must be a reliable scale. EasterbySmith, Thorpe and Lowe (2002, p. 53) suggest that three questions must be considered
before and whilst undertaking research:
x
Will the measures yield the same results on other occasions?
x
Will similar observation be reached by other observers?
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x
Is there transparency in how sense was made from the raw data?
Robson (in Saunders, Lewis & Thornhill 2007) argues that there are four circumstances
which invalidate the reliability: subject or participant error, subject or participant bias,
observer error and observer bias.
Subject or participant error may occur in a survey completed at different times of the
week. The web link to the online survey was sent to selected food outlets for onward
submission to customers. This enabled respondents to complete the survey at their
convenience during the survey period. The paper-based data was collected on weekdays
and weekends during supermarket trading hours (for details of data collection procedures,
see Sections 4.4.3 and 4.4.4). In order to reduce subject or participant bias, both the
online and paper-based responses were strictly anonymous and confidential, and
respondents were free to decline or withdraw at any time. This was made known to them
by a cover letter which accompanied the survey.
Observer error and observer bias might arise due to different personnel involved in
conducting data collection, with potentially different ways of asking questions and
different ways of interpreting the replies. To eliminate such bias, the paper-based survey
was administered by trained personnel who assisted the respondents in completing the
survey.
‘Reliability means that a measure should consistently reflect the construct that it is
measuring’ (Field 2009, p. 673). To assess the reliability of the statements, test-retest,
alternative form and internal consistency approaches can be employed (Malhotra et al.
2006). Test-retest: ‘the reliability coefficient obtained with a repetition of the same
measure on a second occasion is called “test-retest reliability”’ (Cavana, Delahaye &
Sekaran 2001, p. 211). To find a cohort of the same respondents to answer repeats of the
survey is quite challenging, therefore 20 Chinese consumers were selected using the
snowball technique during the pre-test. The survey was sent to them by email in word
document format and, after two weeks, it was sent again using a web link requesting them
to complete the online version. The test-retest technique was also applied to the data
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analysis in this study explained in Section 6.2.2, which split the data into different data
set and then tested the data reliability.
The alternative form approach ‘forms techniques of estimating reliability and assesses
the equivalency of the content of sets of items. This technique usually involves the
administration of equivalent scales to the same individuals, with or without a time
interval’ (Davis 2005, p. 191). Saunders, Lewis and Thornhill (2007) suggest that it is
often difficult to ensure the alternative form as it might involve increasing the length of
the questionnaire. ‘Considerable debate centers around which of several alternative
reliability estimates is best’ (Hair et al. 2006, p. 777).
A commonly used measure of reliability is internal consistency which involves
correlating the responses to each question in the survey with those of other questions
(Saunders, Lewis & Thornhill 2007). Cronbach’s alpha is widely used to measure internal
consistency of reliability (Hair et al. 2006). In order to main the internal consistency of
data in this study, Cronbach’s alpha was used in the analysis phase.
4.4
Data Collection
4.4.1 Ethics approval
Research ethical issues generally occur in the design and conduct of research on human
subjects (Veal 2005). Prior to undertaking this research, ethical approval was sought from
the Swinburne University of Technology, Human Research Ethics committee. A copy of
the approved ethics proposal is attached (Appendix 3).
This research was supported by the Chinese National Professional Committee of Organic
Health Industry, and permission was acquired from the organisation to assist in data
collection. An ‘informed consent’ was introduced at the beginning of the survey, which
read as follows:
x
All questions and discussion about the research have been answered to
respondents’ satisfaction;
121
x
Respondents’ participation in the research is voluntary and they may withdraw at
any time;
x
The survey is strictly anonymous. Data obtained is kept confidential, and only
aggregated results (not individual responses) are mentioned in the research
output.
4.4.2 Sampling
The population of this study can be defined as ‘urban Chinese persons aged 18 and above
who are familiar with the term organic food’. Survey participants were adults who were
either regular, occasional or potential organic food consumers.
The constructs and components of the conceptual framework were operationalised using
different scales from past studies. Hence it was decided to perform a comprehensive pilot
study. This would best be achieved by doing an online survey of respondents in China
who were aware of organic food products. The objectives of the online data survey were
to (i) perform an EFA to identify dimensions and latent variables of the proposed
conceptual model, hence eliminating unnecessary indicators, (ii) inform the refinement of
the survey instrument for further administration of the paper-based survey, (iii) initially
scrutinise the validity and reliability of the factors and (iv) examine interrelationships
between constructs and components of the conceptual framework in order to test research
hypotheses at a later stage.
Following the pilot study, it was decided to obtain a representative sample size using
paper-based surveys. The purpose of this survey was to (i) confirm interrelationships
between the constructs and components in the measurement model, (ii) assess the
convergent and discriminate validities of various items in the survey, (iii) modify and
finally confirm the measurement models, hence addressing the research hypotheses, (iv)
estimate the best fit model, including the goodness-of-fit and significance of loadings and
(v) use invariance tests to evaluate the impact of demographic and psychographic
variables on the behavioural and purchase intention of respondents.
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An explanation of the two stages of data collection, that is, stage one (pilot study using an
online survey) and stage two (main data collection using a paper-based survey) follows:
4.4.3 Stage one: pilot study (online survey)
The pilot study was conducted as an online experiment (Kim & Lennon 2010). Online
surveys are an economical method of collecting data in the organic food industry from a
national base (Rimal, Moon & Balasubramanian 2005). Completing the survey online has
the advantage that data is delivered in an electronic format which can be instantly
analysed (Saunders, Lewis & Thornhill 2007; Veal 2005) and respondents are
geographically dispersed (Saunders, Lewis & Thornhill 2007). As part of the Chinese
economic reforms, 1999 was declared ‘the year of getting on the internet’, with all
ministries ensuring that they had their organisational websites (Kahal 2001, p. 70).
According to the China Internet Network Information Centre (2009), there were over 338
million online users in China in 2009. A Chinese study (Cai et al. 2008) provided initial
evidence that internet surveys are as reliable as paper surveys. However, although online
data collection has its obvious advantages, respondents may not be representative of the
general population (Malhotra 2008), hence this methodology of data collection was used
only for the pilot study.
The online survey was made available via Swinburne University of Technology’s Opinio
platform. Opinio software enables production and reporting of a survey and ensures
anonymity, confidentiality and privacy to the participant. The web link to the online
survey was sent to the Chinese National Professional Committee of Organic Health
Industry, which in turn forwarded it to selected Chinese food outlets. These outlets sent
the web link to their customers. The sampling frames were drawn up using the
participating food outlets’ customer mailing lists and participants were randomly selected
from sampling frames.
In order to guarantee the survey’s accuracy, and to prevent multiple completion by the
same respondent, the default in Opinio was set as follows: ‘not allow multiple
submissions’, and ‘prevent with cookies and IP-address check’. This means that the
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Opinio software recognised every respondent’s IP address (computer ID), hence only one
completed survey was received from a particular computer.
Online surveys can be simply completed by respondents when screening questions are
involved (Veal 2005). ‘Organic’ is a relatively new term and many Chinese consumers
are still unaware of it. Hence the screening question used at the start of the survey was
‘Have you ever heard of organic food?’. A total of 405 responses (computer) IDs were
detected by the Opinio software and a final total of 204 valid online surveys with no
missing data were received. Hence the response rate was approximately 50%. The online
survey was active for the whole of December 2009.
4.4.4 Stage two: main data collection (paper-based survey)
After the online data was analysed, the survey instrument was slightly modified for use in
paper-based surveys. Organic foods are relatively new products, primarily available in
large supermarkets in major Chinese cities (Li, Cheng & Ren 2005). Previous organic
studies suggest that it is efficient to collect data through food outlets of retail chains
(Drichoutis, Lazaridis & Nayga Jr 2007; Krystallis, Arvanitoyannis & Chryssohoidis
2006; Krystallis & Chryssohoidis 2005).
The paper-based surveys were administered at major supermarkets in four selected first
and second tier5 cities: Beijing, Shanghai, Shenzhen and Chengdu, over a period of one
month. Details of selected survey locations are justified in the next section. These four
cities are economically and politically prominent and are the main engines of China’s
phenomenal economic development. Hence consumers are more affluent, and are more
likely to be aware of organic foods. All participating supermarkets had organic food
sections.
5
First tier cities are those granted approval by the central government to establish joint venture retail
enterprises. They include Beijing, Guangzhou, Shanghai and the Special Economic Zones (SEZs). Second
tier cities are those without such approval but with good potential due to their advantageous location of
being in close proximity to first tier cities (Wong and Yu 2003).
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Shoppers were approached randomly by trained personnel and asked to participate in
self-administered surveys at the main entrance of the supermarkets. The trained personnel
who had Bachelors Degrees in Commerce were staff members of the Chinese National
Professional Committee of the Organic Health Industry. They were instructed to seek out
demographic variation where possible and the surveys were presented to consumers
entering these supermarkets throughout the day during the stores’ trading hours.
Consumers were asked ‘Have you ever heard of the term organic food?’. If they answered
‘Yes’, they were then asked ‘Would you like to participate in the survey?’. If they
answered ‘No’, the next customer entering the store would be approached and so on.
Then, every tenth individual consumer who walked into the supermarket was asked the
same questions. A small gift of organic food (e.g. nuts, vegetables, milk) was offered in
appreciation of their time to answer the questions. The paper-based data was collected
over a period of four weeks in April 2010. The researcher was stationed in China
throughout the duration of the data collection. Previous studies (Fotopoulos & Krystallis
2002b; Kuhar & Juvancic 2010; Magnusson et al. 2001; McEachern & Willock 2004;
Roddy, Cowan & Hutchinson 1996) suggest that a sample size of more than 1000
respondents is sufficient to conduct a national survey. A total of 2000 surveys (500 in
each city) were distributed, and 964 valid questionnaires were obtained.
Both the online and paper-based survey response rates were as high as 50%. This is
consistent with extant literature which suggests that the response rates of consumers of
specialised goods like organic food products can be as high as 40% to 50% (Haghiri &
McNamara 2007; Honkanen, Verplanken & Olsen 2006; McEachern & Willock 2004;
Tregear, Dent & McGregor 1994).
4.4.5 Selection of cities in China for stage two: paper-based data
collection
China’s economic reforms have helped alleviate poverty and have been successful in
exponentially increasing per capita income. However, they have also led to increasing
disparity and inequality in income and wealth (Yeon 2008). It is important to recognise
that China is a country of a conglomeration of regional diversity and economic disparity
(Cui & Liu 2000; Ho & Tang 2006; Wong & Yu 2003), so market segments cannot be
125
oversimplified (Dickson et al. 2004). According to Euromonitor International (2008a),
there are 22 provinces, four municipalities and five autonomous regions. The two most
populous cities, Beijing and Shanghai, have the highest household expenditure. First tier
cities, such as Beijing, Guangzhou and Shanghai have propelled the Chinese economy
forward. Second-tier ones such as Chengdu, Dalian, Hangzhou and Xian also contribute
to economic growth, with consumption patterns comparable to those in the first tier cities.
Previous related studies chose four cities, i.e. Beijing, Shanghai, Chengdu and
Guangzhou, to draw sample of consumers, representing regions with different geographic
location, political and commercial backgrounds (Uncles & Kwok 2008; Wang et al. 2000;
Wang & Chen 2004). However, Shenzhen and Guangzhou have a lot in common, partly
because they are both located in the same province and just a few hours distant from each
other by car. Shenzhen is a city representing modern Chinese lifestyle where organic food
products are available in plenty (Yuan & Liu 2009). Hence this study has intentionally
selected Shenzhen in lieu of Guangzhou. Figure 4.2 depicts the four cities selected in this
study which are geographically dispersed throughout China.
Figure 4.2 Map showing the geographic dispersion of the four selected cities
Source: Google Image 2010
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Shanghai
Shanghai is the most populous city in China with 20 million people. It is the largest
commercial centre located in the eastern seaboard. It has been the main engine of China’s
phenomenal economic development. The per capita GDP in Shanghai in 2007 was
US$9900, notably higher than the national average of US$2490. Its growing middle class
has taken a lead role in purchasing organic food products in urban China. The first
organic food shop in China opened in Shanghai (US Department of Agriculture 2008).
Consumers in Shanghai are more adventurous than those in other cities and are willing to
accept new products (Zhao et al. 2000). They are also likely to be trendsetters as
compared to those in Beijing who are regarded as being more serious and sober in their
judgement (Cui & Liu 2000; Schütte & Ciarlante 1998).
Beijing
Beijing is the political and cultural capital of China. It is home to over 8600 diplomatic
missions, international organisations and Chinese provincial and city representative
offices, with a population of 15 million. It is the one of the largest and wealthiest cities in
China (US Department of Agriculture 2012a). With a history dating back more than 3000
years, Beijing is an amazing mix of old and new China (Beijing Municipal Government
2010).
Chengdu
Chengdu is the largest south-western city in China, and the capital of Sichuan province
with a substantial population of 11 million (National Bureau of Statistics of China 2009).
It is the wealthiest and most advanced city in the region, and symbolises the more
traditional part of China (Wang & Chen 2004). It opened up to foreign business much
later than Shanghai (Fram, Le & Reid 2005). Chinese economist Zhang Wuchang regards
Chengdu as the economic headquarters of western China (Yin 2010). Although per capita
incomes are lower than in some coastal cities, residents have a strong interest in food and
are prepared to pay a premium price for high quality products (Stewart & Bean 2006).
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Shenzhen
The rise of its coastal cities has played a critical role in China’s economic development.
The Special Economic Zones (SEZ) are looking to replicate the economies of Asian Tiger
countries. Shenzhen is a young city, built in the early 1980s. It was the first Chinese SEZ
resulting from the economic reforms and it attracts a migration population from the rest
of China (Ho & Tang 2006). According to a government report (Shenzhen government
online 2009), Shenzhen’s economic power is ranked fourth in China with its per capita
GDP the highest in the country at US$13,153 in 2008, and the per capita annual
disposable income reaching RMB26,726 (equivalent to US$3,818). There are 10.36
million permanent residents and its domestic migration makes up 86% of its total
population (Atsmon et al. 2009). It is a city more open and exposed to contemporary
lifestyles, and constitutes the most important future organic food market in mainland
China (Yin et al. 2010).
4.5
Chapter summary
This chapter introduced the research design, which is predominantly quantitative in
nature, and explained why surveys were the best method to use to address the research
objectives of this study. It detailed several stages in the development and design of the
survey instruments. Focus groups were conducted prior to establishment of survey
instruments. To ensure their validity and reliability, pre-testing was undertaken prior to
the final survey distribution. The data was collected in China in two stages: 204 valid
online questionnaires were collected for the purpose of the pilot study, and later 964
completed paper-based questionnaires were collected in four major cities. The next two
chapters discuss the analysis of data collected during the pilot and main stages.
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CHAPTER FIVE: ANALYSES AND RESULTS OF THE
PILOT STUDY (STUDY ONE)
5.1
Introduction
As explained in the previous chapter, online data was collected for the purpose of the
pilot study. This was analysed using the exploratory factor analysis technique to generate
relevant factors and associated latent variables in each of the constructs of the proposed
conceptual model. Statistical Software Package for Social Sciences (SPSS) version 17
was employed to compute and analyse the data. Section 5.2 introduces the objectives of
the data analysis of the pilot study. Section 5.3 describes the statistical methods employed.
Section 5.4 identifies the factors and latent variables in each construct. Section 5.5
concludes with the chapter summary. Figure 5.1 diagrammatically illustrates the structure
of Chapter Five.
Figure 5.1 Structure of Chapter Five
Source: developed for this research
5.2
The objectives of the data analysis in the pilot study
It is always desirable to conduct a pilot study before administering a self-completion
questionnaire to the sample (Bryman & Bell 2007). ‘A pilot study is a dress rehearsal of
129
the main study where you replicate the conditions as closely as possible to the main
study’ (McMurray, Pace & Scott 2004, p. 268). The survey should be piloted with a
reasonable sample size of respondents who come from or closely resemble the target
population (Cavana, Delahaye & Sekaran 2001). The sample of the pilot study was drawn
from adult Chinese consumers who had heard of the term ‘organic food’. The objectives
of the pilot study were:
x
To perform an EFA to identify factors and associated latent variables for the
constructs of the proposed conceptual model. During this process, variables which
did not load on the factors would be eliminated.
x
To develop initial scales which would be subsequently translated into
measurement models by confirmatory factor analysis (CFA) using data from the
main study;
x
5.3
To initially examine the validity and reliability of the online data.
Choosing the right statistical method
Data screening
The online data for the pilot study was generated using Swinburne’s Opinio platform. A
total of 405 respondents logged into the online web link, of whom 204 (50.4%)
completed the survey. There were no missing data as the online survey had a built-in
filter system. Each respondent could attempt to complete the survey only once on any one
computer. The final response rate is consistent with literature which suggests that it can
be high as 40% to 50% in organic food studies (Honkanen, Verplanken & Olsen 2006;
McEachern & Willock 2004). 48% of valid respondents were females and 52% were
males.
Generally statistical techniques are sensitive to outliers. ‘An outlier is a case with such an
extreme value on one variable (a univariate outlier) or such a strange combination of
scores on two or more variables (multivariate outliers) that it distorts statistics’
(Tabachnick & Fidell 2007, p. 72). The descriptive function in SPSS calculates means
and 5% trimmed means, which give an indication of the problem associated with outliers.
If the two mean values are closely similar, it is indicative that the outliers are not
130
problematic (Pallant 2007). The results (Appendix 4 Table A) of this type of analysis
showed that there were no significant differences between the means and the 5% trimmed
means (when 5% of highest and lowest values are deleted). Therefore, no outliers were
deleted from the data of the pilot study.
Exploratory factor analysis
Data was analysed using exploratory factor analysis (EFA) which is often carried out in
the early stages of research to consolidate variables and to produce hypotheses about
underlying processes (Tabachnick & Fidell 2007, p. 935). The main purpose is to identify
the smallest number of meaningful latent variables or factors that closely reproduce the
original correlations and covariances amongst a larger set of measured variables (Gorsuch
1997). In order to ascertain the constructs or factors and latent variables and also explore
the correlation of each factor in the proposed conceptual model, a series of EFAs were
performed on the data of the pilot study.
Rotation
Principal components factor extraction has been widely applied in food consumption
studies (Botonaki et al. 2006; Chryssochoidis, Krystallis & Perreas 2007; Sanjuán et al.
2003). This method is commonly used in the analysis of psychological data (Pallant 2007;
Todman & Dugard 2007). There are two general categories of rotation in factor analysis,
orthogonal and oblique. Factors are always rotated at right angle to each other in
orthogonal rotation, which means they are uncorrelated to each other. Oblique rotation
assumes that the factors are correlated, which allows more freedom in selecting the
position of factors in factor space than does orthogonal rotation (Gorsuch 1983; Kline
1994). In practice, orthogonal and oblique often result in very similar solutions
(Tabachnick & Fidell 2007). Many researchers conduct both approaches and then report
the result which is clearer and easier to interpret (Pallant 2007). Hence in this study
principal components factor extraction with direct oblimin (oblique) rotation was selected
to derive the factor loadings.
131
Factor loadings are ‘the correlations of a variable with a factor’ (Kline 1994, p. 5), which
explain weighted combination of the variables’ loadings on each factor, within the range
of 0 to 1 (Gorsuch 1983; Pallant 2007). They score the weight of impact of each variable
on the endogenous variables. There is no consensus about what constitutes a ‘high’ or
‘low’ factor loading; however, in the social sciences the threshold is 0.30, the higher the
factor loadings, the better the results (Hair et al. 2006; Peterson 2000). If more than one
factor is generated during the EFA, a structure matrix of correlations between factors and
variables and a pattern matrix of unique relationships between each factor and each
observed variable are presented (Tabachnick & Fidell 2007).
5.4
Identifying factors and latent variables
The analyses of the pilot study comprised a series of EFAs which were conducted to
eliminate variables with factor loading < 0.3 (Hair et al. 2006; Peterson 2000). In order to
be considered suitable for factor analysis, Bartlett’s Test of Sphericity value should be
statistically significant at p <0.056 and the Kaiser-Meyer-Olkin (KMO) value should be
0.6 or above (Pallant 2007). Bartlett’s Test of Sphericity indicates that the data is suitable
for factor analysis. A non-significant (p>0.05) Bartlett’s Test of Sphericity indicates that
the data is not suitable for factor analysis. KMO reports the amount of variance in the
data that can be explained by the factors and is a measure of sampling adequacy (Allen &
Bennett 2010).
Both Eigenvalues and Scree Plot estimate the number of factors that should be selected.
‘Eigenvalues represent variance. Because the variance that each standardised variable
contributes to a principal components extraction is 1, a component with an eigenvalue
less than 1 is not as important, from a variance perspective, as an observed variable’
(Tabachnick & Fidell 2007, p. 644). It is important to study the Scree Plot provided by
SPSS. Catell (in Pallant 2007) recommends retaining all factors above the elbow or break
in the plot as these contribute the most to the explanation of the variance in the data set.
6
P-value is the probability or chance that the null hypothesis is true. The lower the p-value, the less likely
the result is if the null hypothesis is true, and consequently the more ‘significant’ the result is, in the sense
of statistical significance (Cavana, Delahaye & Sekaran 2001).
132
To be considered suitable for factor analysis, the correlation matrix should also show at
least some correlation of r =0.3 or above (Pallant 2007). However, larger sample sizes
tend to produce smaller correlations (Tabachnick & Fidell 2007).
The internal consistency was examined by estimating the Cronbach’s alpha coefficients
in this study. ‘Internal consistency reliability is concerned with the homogeneity of the
statements within a scale. Scales based on classical measurement models are intended to
measure a single phenomenon which is typically equated with Cronbach’s coefficient
alpha, α’ (DeVellis 2003, p. 27). The reliability of the measurement scale was checked in
order to meet the reliability of the survey instrument. Cronbach’s alpha measures the
inter-statement consistency reliability (Cavana, Delahaye & Sekaran 2001). Cronbach’s
alpha values above 0.7 (Cronbach 1951) are considered acceptable, and values above 0.8
are preferable. This is a measurement of internal consistency (Pallant 2007). Following
the conceptual framework developed in Chapter Four, 52 statements were used to
examine consumers’ purchase intention relating to organic food in urban China. These
statements were subjected to EFA to identify underlying factor patterns, and in the
process eliminating unnecessary indicators. It was decided to conduct EFA for each
construct separately rather than for a combination of the 52 statements. This is because
most constructs predominately used items from previously validated scales as has been
explained in Chapter Four.
5.4.1 Results of the EFA for the product-related construct
Table 5.1 shows the results of the EFA for the product-related construct and associated
measures. The KMO value was 0.696 which exceeded the recommended value of 0.6.
Bartlett’s Test of Sphericity value was significant (p<0.05), therefore factor analysis was
appropriate for this construct. Only the first two components recorded eigenvalues above
1 (2.628, 1.226), and they explained a total of 55.10% of the variance. The Scree Plot
also suggested the existence of two components which were labelled ‘Sensory appeal’
and ‘price perception’ (details are shown in Appendix 4, Table B).
133
Table 5.1 Pattern and structure matrix for EFA of the product-related construct
and associated measures
Factor
Factor 1:
Sensory
appeal
Factor 2:
Survey
Content of statement
statement
3.2
Organic food looks nice
3.1
Organic food has a pleasant
texture
3.3
Organic food smells nice
3.9
Organic food is good value for
money
Price
perception
Pattern
matrix
.882
.865
Structure
matrix
.848
.878
.694
.779
.730
.743
.648
.693
.643
.697
.485
.470
Cronbach’s
alpha
.767
.547
3.5
Consuming organic food is
trendy
3.6
Organic food has high nutritional
value
3.7
Generally speaking, the higher
the price of a product, the higher
the quality
Total Variance Explained (%) 55.053
KMO: .696 Bartlett’s Test of Sphericity: .000
From the initial nine statements in this construct, two were eliminated: statement 3.4 was
deleted due to cross-loadings, and statement 3.8 was deleted as the loading was less than
0.4. The results (Appendix 4, Table B in the Component Correlation Matrix) showed that
the correlation matrix of the two factors was 0.298, which is close to 0.30. This is
considered to be moderately correlated as r =0.30 to 0.49 signifies a medium correlation
(Pallant 2007). As this was a pilot study with a reasonable final response of 204, this value
is acceptable.
Overall, the factor loadings were acceptable, with the highest loading of 0.882 (pattern)
and the lowest loading of 0.485 (pattern). The Cronbach’s alpha value for ‘sensory appeal’
was 0.767, which suggests good internal consistency of the scale. Nunnally (1967)
suggests that the reliability of Cronbach’s alpha ranging from 0.50 to 0.60 is sufficient for
early stages of basic research. Therefore, although the Cronbach’s alpha of ‘price
perception’ was 0.547, which is less than the desirable value of 0.70, it was retained for the
next stage of analysis.
134
5.4.2 EFA results for the regulatory construct
EFA was selected for analysing of the regulatory construct and associated measures, as
the KMO was 0.771 (greater than the recommended 0.6), and Bartlett’s Test of Sphericity
was significant. The initial EFA results suggested a two factor solution for the eight
statements. However, one of the factors comprised only two statements (statement 3.12 ‘I
trust the outlets which sell certified organic food’ and statement 3.13 ‘Chinese
government regulate food marketing, and have developed significant policies’ shown in
Appendix 1). Two statements are generally insufficient for factorial stability, unless
highly correlated (r>0.7) with the factor and relatively uncorrelated with other variables
(Worthington & Whittaker 2006). The two statements had a correlation value of r=0.389
which is lower than the bench mark. Hence these two statements were deleted from the
results of the EFA. The remaining six statements suggested one factor which had an
eigenvalue above 1 (2.558), and which explained 42.63% of the variance. The Scree Plot
also recommended a single factor for the regulatory construct (see Appendix 4, Table C).
Table 5.2 illustrates the EFA results for the regulatory construct and its associated
measures. Overall, the factor loadings are acceptable, with the highest loading of 0.783
and the lowest loading of 0.401. The Cronbach’s alpha value for this factor was 0.709.
This suggests good internal consistency of the sample data.
Table 5.2 Factor loadings for EFA of the regulatory construct
and associated measures
Survey
statement
3.15
Contents of statements
Logos depicting types of organic foods should be
controlled
3.17
The market for organic and green is chaotic
3.16
More land should be allocated for organic farming
3.14
Lack of adequate government control of media allows
advertisers to take advantage of consumers
3.10
When I buy a food product, I always read the label
3.11
I don’t trust organic food certification bodies
Total Variance Explained (%) 42.627
KMO: .771 Bartlett’s Test of Sphericity: .000
Loadings
Cronbach’s
alpha
.783
.750
.702
.709
.637
.567
.401
135
5.4.3 EFA results for the lifestyle construct
The KMO value was 0.748, and Bartlett’s Test of Sphericity value was significant, hence
EFA was employed to analyse the data. Only the first three components recorded
eigenvalues above 1 (3.624, 1.553, 1.330), which explained a total of 59.15% of the
variance. The Scree Plot also recommended a three factor solution for this data.
Statement 3.31 ‘Sometimes I feel like spending money on anything I lay my eyes on’ was
deleted, due to this statement’s significant cross-loadings on both variety seeking and
self-indulgence. The correlations between factors ranged between r=0.268, and r=0.340,
indicating that they were moderately correlated (details are shown in Appendix 4, Table
D). Table 5.3 shows the results of EFA for the lifestyle construct and associated measures.
Table 5.3 Pattern and structure matrix for EFA of the lifestyle construct and
associated measures
Survey
Contents of statements
statement
Factor 1:
3.34
I love fashionable and trendy
Variety
products
seeking
3.32
I often make impulse purchases
3.36
I am often influenced by
advertisements of new products
3.33
I always try something new and
unique
3.35
It does not hurt to be trendy if I
feel like it
Factor 2:
3.28
When I like something, I will
Selfbuy it without too much
indulgence
deliberation
3.29
I always do whatever I feel like
and whenever I feel like it
3.30
The sole purpose of making
money is to spend it
Factor 3:
3.38
My friends often consult me
when they cannot make up their
own mind
Opinion
3.37
I can easily influence people
leadership
around me during conversation
3.39
I have a strong desire to be
successful
Total Variance Explained (%) 59.149
KMO: .748 Bartlett’s Test of Sphericity: .000
Factor
Pattern
matrix
.812
Structure
matrix
.809
.736
.682
.745
.685
.679
.684
.673
.693
.916
.906
.898
.890
.672
.732
.829
.821
.734
.759
.590
.594
Cronbach’s
alpha
.773
.801
.570
136
The resultant factor loadings of between 0.590 and 0.916 are all acceptable. The twelve
statements which measure lifestyle were adapted from Young’s (2004) study, which
similarly reported a three factor solution. However, the internal consistencies for the
factors in this study were stronger than those reported in Young’s study, that is, the
Cronbach’s alpha values of the three factors in this study were 0.773, 0.801 and 0.570 as
compared to Young’s 0.700, 0.700 and 0.520. Similar to Young, the three factors
generated in this study have been named variety seeking, self-indulgence and opinion
leadership.
5.4.4 EFA results for the ethnocentrism construct
Table 5.4 shows the EFA results for the ethnocentrism construct and associated measures.
The KMO value was 0.689, and Bartlett’s Test of Sphericity value was significant, hence
it was appropriate to use EFA. Only one factor recorded an eigenvalue above 1 (2.07),
which explained a total of 69.12% of the variance. The Scree Plot also recommended a
one factor solution. Two of the five statements in this construct were deleted due to the
low correlation of the two statements (r =0.251) and low Cronbach’s alpha (α= 0.400).
These were 3.42 ‘If two organic food products were the same in quality, but one was
imported and the other was Chinese, I would pay more for the imported product’ and 3.43
‘I would purchase organic food come from a country I approve of politically’. The overall
results were good with all loadings above 0.8 (details are shown in Appendix 4, Table E).
The Cronbach’s alpha value was 0.776, which suggests good internal consistency
Table 5.4 Result for EFA of the ethnocentrism construct and associated measures
Survey
Contents of statements
statement
3.40
Chinese people should buy domestic products rather than
imported products
3.41
China should levy heavy tariff on foreign products to
reduce their quantity into China
3.44
Chinese people should not buy foreign products, because
it would hurt domestic
Loadings
Cronbach’s
alpha
.864
.821
.776
.808
Total Variance Explained (%) 69.115
KMO: . 689 Bartlett’s Test of Sphericity: .000
137
5.4.5 EFA results for the beliefs and attitudes construct
Table 5.5 depicts the EFA results for the beliefs and attitudes construct and its associated
measures. The KMO value was 0.900, and Bartlett’s Test of Sphericity value was
significant, hence it was appropriate to use EFA. Only one factor recorded an eigenvalue
above 1 (4.898), and explained a total of 48.99% of the variance. The Scree Plot also
recommended the retention of only one factor in this construct. Overall, the loadings were
acceptable, with the highest being 0.801 and the lowest 0.476 (details are shown in
Appendix 4, Table F). The Cronbach’s alpha value was 0.881, which suggests good
internal consistency of the items in this factor.
Table 5.5 Result for EFA of the beliefs and attitudes construct and associated
measures
Survey
statement
3.24
3.23
3.18
Contents of statements
I believe organic food has superior quality
Organic food is good for the environment
Organic food is good for myself and my family’s
health
3.27
Organic food labels mean high quality food
products
3.25
I personally think I should always buy organic food
3.20
I like the brands associated with organic food
3.19
Organic food has no harmful effects
3.22
Organic food does not contain pesticides
3.21
I worry about harmful chemicals in my food
3.26
To me, it is important that the food I usually eat can
be easily found in the food outlets near my house or
workplace
Total Variance Explained (%) 48.985
KMO: .900 Bartlett’s Test of Sphericity: .000
Loadings
Cronbach’s
alpha
.801
.778
.736
.729
.881
.711
.703
.699
.686
.627
.476
5.4.6 EFA results for the pre-purchase evaluation construct
Table 5.6 shows the EFA results for the pre-purchase evaluation construct and associated
measures. The KMO value was 0.711 and Bartlett’s Test of Sphericity value was
significant, hence it was appropriate to use EFA. Only one factor recorded an eigenvalue
above 1 (2.512), which explained a total of 50.24% of the variance. The Scree Plot also
recommended the retention of only one factor. Overall, the loadings were acceptable,
with the highest being 0.851 and the lowest 0.452 (details are shown in Appendix 4,
138
Table G). The Cronbach’s alpha value was 0.699, which suggests adequate internal
consistency of the items in this factor.
Table 5.6 Result for EFA of the pre-purchase evaluation construct and associated
measures
Survey
statement
4.3
Contents of statements
I am interested in experiencing the benefits of using
organic foods
4.2
I intend to try out organic food products
4.4
It is likely that I will buy organic food products
when they become available
4.5
I can recall the brand names and labelling of some
of the organic food products
4.1
I am eager to check out organic food products
because of advertisements and promotion
Total Variance Explained (%) 50.235
KMO: .711 Bartlett’s Test of Sphericity: .000
Loadings
Cronbach’s
alpha
.851
.839
.803
.699
.485
.452
5.4.7 EFA results for the behavioural/purchase intention construct
Table 5.7 shows the EFA results for the behavioural/purchase intention construct and
associated measures. The KMO value was 0.704, and Bartlett’s Test of Sphericity value
was significant, hence it was appropriate to use EFA. Only one factor recorded an
eigenvalue above 1 (2.233), which explained a total of 74.43% of the variance. The Scree
Plot suggested the retention of only one factor for items of this construct (details are
shown in Appendix 4, Table H). All loadings were above 0.80, indicating their strength.
The Cronbach’s alpha value was 0.827, which suggests good internal consistency of
items in this factor.
Table 5.7 Result for EFA of the behavioural/purchase intention construct
and associated measures
Survey
statement
4.7
Contents of statements
I will recommend usage of organic foods to my friends
and relatives
4.8
If I had to purchase organic foods again, I would make
the same choice
4.6
I will probably use organic food products in the future
Total Variance Explained (%) 74.431
KMO: .704 Bartlett’s Test of Sphericity: .000
Loadings
Cronbach’s
alpha
.890
.876
.827
.821
139
5.4.8 Consolidated details of the ten factors
Ten factors were retained from the process of a series of EFAs based on the proposed
framework for the purchase intention of organic foods in urban China (Figure 3.2). The
overall reliability showed good internal consistency of the items within the ten factors.
Hence 45 out of the 52 statements from the original survey were retained and were used
in the next stage of data collection, the paper-based survey. Table 5.8 summarises the ten
factors obtained from this analysis.
Table 5.8 Summary of the ten factors
Construct
Product
(two factors)
Regulatory
Lifestyle
(three factors)
Ethnocentrism
Beliefs/attitudes
Pre-purchase
evaluation
Behavioural/
purchase intention
5.5
9
Number of
statements
deleted
during EFA
2
Number of
statements
retained in
EFA
7
8
12
2
1
6
11
5
10
5
2
Nil
Nil
3
10
5
α 1=.767
α 2=.547
.709
α 1=.773
α 2=.801
α 3=.570
.776
.881
.699
3
Nil
3
.827
Original
number of
statements
Reliability
(Cronbach’s alpha)
Chapter summary
To summarise, a series of EFAs were performed on the data of the pilot study to identify
the factors and associated latent variables. The analysis was based on data collected from
a final sample of 204. Of the 52 statements, a total of seven were deleted during the
process of EFA. These results suggest that there are ten factors underpinning seven
constructs in the proposed conceptual framework: sensory appeal, price perception,
regulatory,
self-indulgence,
variety
seeking,
opinion-leadership,
ethnocentrism,
beliefs/attitudes, pre-purchase evaluation and behavioural/purchase intention. This pilot
study has provided strong evidence to support further analysis of the proposed conceptual
140
model. The next chapter discusses analysis of the main study’s data obtained using paperbased surveys.
141
CHAPTER SIX: ANALYSES AND RESULTS OF THE MAIN
STUDY (STUDY TWO)
6.1
Chapter overview
This chapter gives details of Study Two which aims to validate the measurement models,
test the hypotheses and address the research questions. Data screening and cleaning were
performed based on the paper-based data collected in four Chinese cities (Section 6.2).
Respondents’ profiles were analysed using demographic variables (Section 6.3). The
measurement models were assessed using CFA (Section 6.4), along with a
comprehensive measurement model evaluation and hypotheses testing using structural
equation modelling (SEM) (Section 6.5). Additionally, control variables and important
attributes of organic food purchase were examined, then a comparative study of the four
cities was conducted using invariance tests (Section 6.6). Computer software applications
PASW Statistics version 18 and AMOS (Analysis of Moment Structures) version18 were
used to perform the analyses. Figure 6.1 diagrammatically illustrates the structure of
Chapter Six.
142
Figure 6.1 Structure of Chapter Six
Source: developed for this research
6.2
Data screening
Prior to the analysis, the data was examined using various functions of SPSS version 18.
It was checked for accuracy, missing values, fit between their distributions and the
assumption of multivariate analysis.
6.2.1 Screening and cleaning missing data
A total of 1117 paper-based survey responses were collected. The data was screened and
cleaned using descriptive analysis for out-of-range values and missing values, and
143
frequency analyses were conducted for each variable to screen for out-of-range values.
The maximum and minimum statistics were examined and extreme values were revisited
and corrected using descriptive analysis. Questionnaires which had more than 25%
missing data were discarded (Byrne 2010). This brought the total number of valid
responses to 964.
Out of the 52 statements included in the initial online survey of the pilot study, seven
were eliminated during the process of EFA. Hence, only 45 statements were used in the
paper-based survey. Cohen and Cohan (1983) suggest that as a rule of thumb, if more
than 10% of the variables are missing, interpretation of the results could be problematic.
Hence any completed survey which was found to have more than four statements missing
was eliminated. There were 17 such cases, which reduced the number of usable surveys
to 947.
Since structural equation modelling (SEM) requires a complete data set (Kaplan 2009),
there are various methods to handle missing data. One of the most popular methods is
imputing missing data (Tabachnick & Fidell 2007). The statements associated with
consumers’ purchase intention relating to organic food (Sections three and four of the
survey) were imputed through the missing value function which uses the expectationmaximisation algorithm (EM). This process eliminated another ten cases, hence the final
number of usable responses was 937.
6.2.2
Assessing reliability and normality
As explained in Chapter Four, the reliability of measures is established by testing both
consistency and stability. One such stability of measures can be assessed through testretest reliability (Cavana, Delahaye & Sekaran 2001). Field (2009) suggests that the
simplest way to practise test-retest reliability is to use split-half reliability. This method
randomly splits the data into two data sets. Cronbach’s alpha was used to measure
internal consistencies of reliability for all constructs in this study. To ensure that all
subjects came from the same population before treating them differently, random
sampling techniques were used in SPSS (Tabachnick & Fidell 2007). Random sampling
reliability analysis was repeated every time when an additional batch of 200 random
144
responses was added within the 937 cases of the paper-based data file. This technique
was repeated to validate these measures based on factors obtained from the EFA in Study
One.
Table 6.1 illustrates the progressive reliability tests done for each batch of responses. The
results reveal that the overall internal consistencies of the various batches of responses
are acceptable, as the lowest Cronbach’s alpha obtained was 0.527 and the highest 0.835.
This is within the acceptable limits in the early stage of research (Nunnally 1967). The
results of these tests provide evidence that generalisation can be made to the population.
Table 6.1 Progressive reliability scores for the ten factors
Sample size
Sensory appeal
Value for money
Regulatory
Variety seeking
Self-indulgence
Opinion-leadership
Ethnocentrism
Personal beliefs/attitudes
Pre-purchase evaluation
Purchase intention
50
.557
.615
.529
.771
.746
.774
.814
.821
.571
.562
200
.649
.581
.624
.821
.788
.644
.840
.819
.708
.678
400
.680
.543
.586
.830
.746
.559
.835
.815
.700
.643
600
.680
.533
.577
.813
.758
.618
.817
.814
.692
.674
800
.683
.527
.561
.804
.758
.637
.810
.811
.691
.698
6.2.3 Outliers
Models may fail to fit the data due to the presence of influential outliers or extreme data
points (Cunningham 2008). Univariate outliers can be detected by visual checking of
histograms and plots of individual variables (Pallant 2007). Multivariate outliers can be
detected by visual inspection based on the ‘Mahalanobis distance (D) statistic, which
indicates the distance in standard deviation units between a set of scores for an individual
case and the same means for all variables’ (Kline 2005, p. 51). Cases with outliers can
generate very different results, therefore outliers found to influence the result were
deleted at every step of the CFA (see Section 6.4.1). Six cases with outliers were deleted;
this resulted in a final count of 931 cases which comprised the entire data set of Study
Two. Comrey and Lee (in Tabachnick and Fidell 2007) suggest that as a guide a sample
size of 100 is poor, 200 is fair, 500 is very good and 1000 is excellent.
145
6.2.4 Assessing normality
Screening continuous variable data for normality is important in the early stages of every
multivariate analysis, and can be assessed by either statistical or graphical methods
(Tabachnick & Fidell 2007). Normality means that the data is sampled from a normally
distributed population (Allen & Bennett 2010). To assess the degree of normality, values
of skewness and kurtosis can be examined. West, Finch and Curran (1995) suggest that
absolute values of skewness and kurtosis exceeding 2 and 7 respectively are indicative of
moderately non-normal distributions. Kline (2005) recommends that absolute kurtosis
values greater than 10.0 are indicative of problematic non-normality, and values greater
than 20.0 are indicative of serious deviations from multivariate normality. In addition, a
standardised residual exceeding an absolute of 2.58 for larger models suggests that the
model is unable to explain much of the covariation that exists between particular
statement pairs (Byrne 2010). In this study, distribution assumption was used to assess
normality.
6.3
Profile of respondents
This section examines the profile of the final 931 respondents. All respondents had heard
of the term ‘organic food’ and were at least 18 years old. Figure 6.2 illustrates recognition
of the six selected logos used in the survey.
Figure 6.2 Recognition of organic food logos
146
The results show that 89.8% of respondents recognised the ‘Chinese Green Food’ logo,
62% recognised the ‘Chinese Non-polluted Food’ logo, and only 58.3% recognised the
‘Chinese Organic Product’ logo. This is in line with Li et al.’s (2005) study which
revealed that only 37.2% of respondents recognised ‘organic food’. The understanding of
organic food in China seems to have improved in the last few years. With regards to
imported organic food products, 9.1% of respondents recognised the USDA organic logo,
which again confirms that Chinese consumers are more familiar with American products
in comparison with other countries’ imported products. 6.8% recognised the German Bio
organic logo. The lowest awareness rating was with the Australian Certificated Organic
logo (ACO) which represented 5.2% of respondents.
With respect to knowledge about organic food, 44.6% of respondents selected ‘I know a
little about what ‘organic’ means’, 39.7% selected ‘I have heard of organic food, but am
not sure what it means’, only 15.7% answered ‘I know a lot about organically produced
food’, which again confirms the low awareness and understanding of organic food
products.
Figure 6.3 illustrates responses to the question ‘What is the first thing which comes to
your mind when you think about organic food?’.
Figure 6.3 First thing in mind when thinking about organic food
147
Most respondents answered ‘no pesticide’ (30.3%), followed by ‘something natural’
(18.2%) and ‘green’ (16.1%), ‘no additives’ (13.5%), ‘environmentally produced’
(13.9%), ‘free of GM contents’ (7.2%); 0.8% selected ‘others’, which included ‘healthy’,
‘healthier than non-polluted and green food’, ‘hormone free’, ‘using organic fertiliser’
and ‘no chemical pesticides’. 77.5% of respondents had purchased organic food. With
respect to the last time of their purchase, 6.9% answered ‘more than a year ago’, 17.2%
half a year ago, 30.4% a month ago, and 43.2% of respondents a week ago.
Figure 6.4 illustrates the types of organic foods that respondents had recently purchased.
Figure 6.4 Types of organic food recently purchased
66.9% of respondents had purchased organic fruit and vegetables, followed by 24.1%
who had purchased grains, 21.3% organic dairy products, 15.4% organic meat and
seafood, and 14.2% had purchased ‘others’ which included tea, beverages, and eggs.
148
Figure 6.5 illustrates the usage pattern for their most recent purchase of organic food.
Figure 6.5 Usage of the most recently purchased organic food
42% of respondents purchased organic food for family use, 38% for own consumption,
14.6% for children, 6.7% elderly people, and 2.4% purchased organic food as gifts.
Finally10.6% selected ‘others’, which could possibly include things such as ‘employer
bonus’.
Figure 6.6 illustrates how much people were willing to pay extra for organic food.
Figure 6.6 Willing to pay extra for organic food
When asked ‘How much extra are you willing to pay for organic food as compared to
conventional food?’, 60.8% were willing to pay an extra 20% to 50%, and 9.4% an extra
149
51% to 100%. Only 0.8% were willing to pay in excess of an extra 100%. 29% said they
‘don’t want to pay extra’. The price of organic food seems to be an issue for Chinese
consumers.
Figure 6.7 illustrates the respondents’ preferred country of origin when they purchase
imported organic food.
Figure 6.7 Country of origin of imported organic food
Surprisingly, 17.8% preferred organic food from Australia/New Zealand. This could
possibly be owing to Australia being perceived as clean and green (Chang & Kristiansen
2006). 13.9% preferred European organic products, 7.9% preferred Japanese/Korean
organic food, 6.4% preferred US organic products and 6.1% preferred organic food from
Hongkong and Taiwan. Not surprising, nearly half (47.9%) did not mind which country
or region the organic food came from.
Figure 6.8 depicts respondents’ choice of distribution channels: 71.2% prefer to buy
organic food in a supermarket, 10.3% from speciality stores, 9.3% from organic farms,
4.3% from local market, 2.1% prefer to purchase online, and only 0.4% prefer to
purchase organic food through mail order catalogues. These findings confirm that
‘organic’ is still a new concept, and organic food products in China are mainly available
in supermarkets in the larger cities.
150
Figure 6.8 Choice of distribution channel
Figure 6.9 depicts the frequency of purchase of organic food products. 44.4% of
respondents purchased ‘occasionally’, 27% ‘at least once a week’, 16.4% ‘every
fortnight’ and 12.2% ‘monthly’.
Figure 6.9 Frequency of purchase of organic food products
151
Figure 6.10 shows the monthly family income level of respondents: 43% earned less than
RMB 5000, 33.8% earned between RMB 5,001 and RMB10,000, 16% earned between
RMB 10,001 and RMB 20,000, and only 7.2% earned more than RMB 20,000.
Figure 6.10 Monthly family income levels
Types of household
21% of respondents were single, 19.2% were couples without children, 23.5% lived with
parents, 22.4% lived with young children and 13.9 lived with adult children.
Gender
Although the administrators of the survey were instructed to seek out demographic
variation where possible, the gender breakdown of respondents was 59.7% females and
40.3% males. Perhaps this can be explained by women taking more responsibility for
purchasing food in the family, and going shopping more frequently than men.
Age group
48.7% of respondents were between 18 and 30 years old, 35.3% were between 31 and 45,
and 12.4% were between 46 and 60. Only 3.6% were older than 60, which indicates that
younger consumers prefer to shop in supermarkets where organic food products are
readily available.
152
Education level
10.1% of respondents had a postgraduate degree or above, 40.1% had a bachelor degree,
22% had a two years college or associate’s degree, 17.5% went to high school, and only
10.2% were educated less than high school. That is, more than half had higher education
degrees, implying that most affluent people in China are aware of organic food products.
Occupation
36% of respondents were white collar employees, 35.1% chose ‘others’, which could
possibly be ‘teacher’, ‘public servant’, ‘doctor’, ‘owner of small business’, 13.9% were
blue collar employees, 6.3% were not working and 8.6% were students.
The demographic mix of respondents appears to concur with previous studies in relation
to organic food consumers being both highly educated and high income earners
(Tsakiridou et al. 2008).
6.4
Confirmatory factor analysis and measurement models
6.4.1 Confirmatory factor analysis
A series of confirmatory factor analyses (CFA) were conducted on the data to validate the
EFA results from Study One. CFA is like creating a structure and testing an objective
against the structure to see how well it fits. In comparison to EFA, CFA is a more
complicated and sophisticated technique, used in the advanced states of the research
process to test the theory about latent processes or to test specific hypotheses usually
performed through SEM (Pallant 2007, Tabachnick & Fidell 2007). To evaluate the
measurement models for each construct, covariance structure analysis was carried out
using the software application AMOS version 18.
6.4.2 Maximum Likelihood
Maximum likelihood (ML) is the most widely used fitting function for general SEM
(Bollen 1989). Hu and colleagues (in Tabachnick & Fidell 2007) found that when the
153
normality assumption was reasonable, the ML performed well with sample sizes over 500.
In this study, the paper-based survey generated about 1000 cases, therefore ML
estimation was chosen to provide parameter estimates and standard errors that are
asymptotically unbiased, consistent and efficient (Bollen 1989). (Tarkiainen & Sundqvist
(2005) also adapted this technique to avoid the problem with missing values when
examining organic food consumers’ purchase intention, by using SEM. ML estimation
was employed to estimate all models.
6.4.3 Measurement model fit indices
The primary fit index used in measurement models is the Chi-square (χ2) test, which is
used to determine whether the data supports the model. A number of other fit indices are
also used to test the overall model. This section describes absolute goodness-of-fit
statistics used in this study. Some of the fit indices used for the CFA are described below:
x
Chi-square value
The model evaluation was assessed by χ2 and its accompanying significance test. If the
associated p value is not significant (p>0.05), which means there is no significant
difference between the sample variance/covariance matrix and the model-implied
variance/covariance matrix, it can be assumed the data fits the model well. However,
Kline (2005) states that χ2 is sensitive to sample size. The larger the sample size, the more
likely the p-value associated with the χ2 will result in a significant difference between the
model and the data, therefore this study also takes into account the normed chi-square
value. To reduce the sensitivity of χ2 to sample size, the values of χ2/ df was assessed.
According to Bollen (in Kline 2005), the values of χ2/df of two, three, even as high as
five have been recommended as indicative of reasonable fit. The column CMIN/DF is the
χ2 value divided by the df, and is referred to as the normed χ2.
Bollen-Stine bootstrap adjusts for the non-normality in the data, and can be used to assess
the model fit. The bootstrapping technique produces a new chi-square distribution from
which an adjusted p-value is computed (Cunningham 2008). 1000 bootstrapping samples
154
were generated in this study, producing the chi-square distribution that corrects for
departure from multivariate normality (pa = Bollen-Stine bootstrap p value).
x
Goodness-of-fit index (GFI) and adjusted goodness-of-fit index (AGFI)
According to Kline (1994, p. 97) ‘GFI is the ratio of the sum of the squared discrepancies
to the observed varices’. GFI=1.0 indicates perfect model fit, and GFI>0.90 indicates
good fit (Kline 2005). The AGFI is the GFI adjusted for the degree of freedom of the
model relative to the number of variables. Both the GFI and the AGFI with values
exceeding 0.95 are considered to be indications that the data fits the model well
(Cunningham 2008).
x
Tucker Lewis index (TLI)
There are two commonly used incremental fit indices widely used in SEM, known as TLI
and comparative fit index (CFI). The TLI was originally proposed by Tucker Lewis as
early as 1973. It indicates the improvement per degree of freedom of the target model
over an independent model. It has a range from 0 to 1, where values exceeding 0.95
indicate good model fit (Hu & Bentler 1998). However, the TLI can frequently exceed a
value of 1, especially for over-fitting models (Cunningham 2008).
x
Comparative fit index (CFI)
CFI is derived from the χ2, and it measures an improvement on the normed fit index
(NFI). The CFI is the most widely used indicator, and has a range from 0 to 1 (Hair et al.
2006; Kline 2005). Hu and Bentle (1998) suggest that as a rule of thumb, CFI values
greater than roughly 0.90 may indicate a reasonably good fit of the model. A value of
0.95 is preferable (Kline 2005) and was applied in this study.
x
Root-mean-square error of approximation (RMSEA)
RMSEA is strongly recommended by many statisticians since the availability of its
confidence interval provides important information about the precision of the estimate of
fit (Browne & Cudeck 1992; Hu & Bentler 1998; Kline 2005). It measures the
155
discrepancy per degree of freedom, with values of about 0.05 or less indicative of close
fit of the model to the data, and with values between 0.05 and 0.08 indicative of
reasonable fit (Browne & Cudeck 1992). Hair et al. (2006) claim that RMSEA values for
90% confidence interval (90% CI) which range between 0.03 and 0.08 are preferable.
x
Standardised root-mean-square residual (SRMR)
The SRMR is a measure of the average difference between corresponding elements of the
sample and model-implied correlation matrices. A value of less than 0.05 suggests good
fit. Large value of the SRMR indicates outliers in the data (Cunningham 2008).
Table 6.2 summarises all model fit indicators adopted in this study. The model fit
evaluates various ranges of model fit indices in AMOS version 18.
Table 6.2 Model fit indicators adopted in this study
Indicator
CMIN/df
P-value or pa (BollenStine bootstrap p value)
GFI/AGFI
TLI
CFI
RMSEA
SRMR
Definition
Value used in this study
Value of normed χ2
No significant difference
below 5.0
>0.05
Goodness-of-fit index,
adjusted goodness-of-fit index
Tucker Lewis index
Comparative fit index
Root-mean-square error of
approximation
Standardised root-meansquare residual
>0.95, or even >0.90
>0.95, sometimes >1.00
>0.95, sometimes only >0.90
<0.05, sometimes <0.08
<0.05
The re-specified structural equation model should not only rely on statistical information,
but also on the theoretical reasoning/judgements, as theory is at the centre of any respecification (Anderson & Gerbing 1988). The results of this study are based on both
theoretical and statistical indications. AMOS produces diagrams such as those displayed
in the figures that follow. Circles embody the error terms associated with each attribute or
statement. Rectangles symbolise the indicator variables or statements. Ovals represent the
latent or unobserved factors and arrows indicate the relationships between the variables.
156
6.4.4 Results of CFA
6.4.4.1 CFA for the product-related construct
The two components of the product-related construct, that is, ‘sensory appeal’ and ‘price
perception’, were initially linked to form a two factor CFA measurement model as shown
in Figure 6.11a.
Figure 6.11a A two factor CFA model of the product-related construct
Chi-square = 73.084, df=13, pa = .001, CMIN/DF=5.622, GFI=.978, AGFI=.954, TLI=.902,
CFI=.940, RMSEA=.070, 90%CI=(.055,.086), SRMR=.0403
As can be seen, the correlation between the two components was (0.83) and there was a
lack of discriminant validity (see Appendix 5, Table A). As can also be seen from the
results, the data did not fit the model well. This suggests that the two factors are strongly
co-related and are measuring a similar construct. Hence, the statements of the two
components were combined to form a one factor solution. Statements 3.7 and 3.9
(statements associated with price perception) were removed in order to improve the
model fit, and the final best fit congeneric measurement model is shown in Figure 6.11b.
157
Figure 6.11b A re-specified one factor CFA model for the product-related construct
Chi-square=16.690, df=5, pa=.160, CMIN/DF=3.338, GFI=.993, AGFI=.978, TLI=.971,
CFI=.985, RMSEA=.050, 90%CI= (.025, .077), SRMR=.0224
As shown in the results, pa >0.05 represents a non-significant difference between the
model and the data. Although it is desirable for the factor loadings to be higher, the
threshold factor loading in the CFA stage was based on the minimum level of .30 for
interpretation of structure (Hair et al. 2006; Peterson 2000). Hair et al. (2006) suggest that
unidimensionality means a set of measured items with only one underlying latent
construct. The Eignevalues (1.409 0.521 0.454 0.331 0.298) shows a unidimensional
characteristic with the re-specified measurement model.
The GFI and AGFI represent a good approximation of the data, with the RMSEA <0.08.
The value of the CFI is close to one, which suggests that it is a well-fitting model. The
construct reliability (α) for the product-related construct was 0.715. The regression
weights for the statements in the final product-related measurement model are shown in
Table 6.3.
158
Table 6.3 Regression weights for the product-related construct
Estimate
Product_
1
Product_
2
Product_
3
Product_
5
Product_
6
<-<-<-<-<--
PRODUCT_RELATED
PRODUCT_RELATED
PRODUCT_RELATED
PRODUCT_RELATED
PRODUCT_RELATED
SE
CR
P
1.000
Logo
Pleasant texture
1.011
.074
13.694
***
Looks nice
1.013
.074
13.766
***
Smells nice
.913
.075
12.181
***
Is trendy
.775
.073
10.568
***
High nutritional
value
Note: ***p<.001
There are five statements that indicate a significant relationship. One is the weight
assigned to statement 1 in order to obtain a solution. The weights for the last two
statements were less than one, but still significant at p< 0.001 level (details are shown in
Appendix 5, Table A).
6.4.4.2 CFA for the regulatory construct
The statements in this construct were used to form a one factor model as shown in Figure
6.12a.
159
Figure 6.12a A one factor CFA model of the regulatory construct
Chi-square=38.127, df=9, p =.000, CMIN/DF=4.236, GFI=.987, AGFI=.970, TLI=.895, CFI=.937,
RMSEA=.059, 90%CI=(.040, .079), SRMR=0.345
As can be seen from the results above, the data did not fit the model well as evidenced by
the results CMI/DF=4.236, TLI (0.895) and CFI (0.937) being less than 0.95. Hence
statement 3.11 (I don’t trust organic food certification bodies) was removed in order to
improve the model fit, and the final best fit congeneric measurement model is shown in
Figure 6.12b.
160
Figure 6.12b A re-specified one factor CFA model for the regulatory construct
Chi-square=8.065, df=5, p=.153, CMIN/DF=1.613, GFI=.997, AGFI=.990, TLI=.986,
CFI=.993, RMSEA=.026, 90%CI=(.000,.057), SRMR=.0183
As shown in the results above, both the GFI and AGFI represent a good approximation of
the data, with RMSEA <0.05. The value of CFI is close to one, which suggests that it is a
well-fitting model. The construct reliability (α) for the regulatory construct was 0.601.
The regression weights for the statements in the final regulatory measurement model are
shown in Table 6.4.
Table 6.4 Regression weights for regulatory measures
Regulatory_8 <--- REGULATION
Estimate
1.000
S.
CR
P
Regulatory_7 <--- REGULATION
.844
.098
8.649
***
Regulatory_6 <--- REGULATION
1.242
.122
10.182
***
Regulatory_5 <--- REGULATION
.976
.103
9.493
***
Regulatory_1 <--- REGULATION
.544
.078
6.990
***
Logo
Market is chaotic
More land should be
organic
Logo should be
control
Lack government
control
Read the label
Note: ***p<.001
There are five statements that indicate a significant relationship. One is the weight
assigned to statement 1 in order to obtain a solution. The weights for the other four
variables are significant at p< 0.001 level (details are shown in Appendix 5, Table B).
161
6.4.4.3 CFA for lifestyle construct
The three factors of this construct, i.e. variety seeking, self-indulgence and opinionleadership, were initially linked to form a three factor CFA measurement model as shown
in Figure 6.13a.
Figure 6.13a Initial three factor model for the lifestyle construct
Chi-square=221.323, df=41, pa= .001, CMIN/DF=5.398, GFI=.958, AGFI=.932, TLI=.923,
CFI=.943, RMSEA=.068, 90%CI=(.060,.077), SRMR=.0448
As can be seen from the results, the data did not fit the model well. Consequently
statement 3.35 ‘It does not hurt to be trendy if I feel like it’ was deleted in order to
improve the model fit, and the final best fit congeneric measurement model is shown in
Figure 6.13b.
162
Figure 6.13b A re-specified three factor model for the lifestyle construct
Chi-square=125.621, df=31, pa = .001, CMIN/DF=3.926, GFI=.973, AGFI=.954, TLI=.952,
CFI=.966, RMSEA=.056, 90%CI=(.046,.067), SRMR=.0424
In CFA when the resulting latent variables are moderately to highly correlated, higherorder factors might be hypothesised as an explanation of the correlations that exist
amongst the lower-order factors (Cunningham 2008). The three lower-order factors of
variety seeking, self-indulgence and opinion-leadership were moderately intercorrelated
with values ranging from a low of 0.33 to a high of 0.52. Related theory suggests that this
model might be a higher-order model. Kline (2005) recommends that that a higher-order
CFA model must have at least three first-order factors, and each first-order factor should
163
comprise at least two indicators. The model in Figure 6.13c satisfies all these
requirements, therefore a higher-order factor is suggested.
Figure 6.13c Higher-order CFA for the lifestyle construct
Chi-square=125.621, df=31, pa = .001, CMIN/DF=3.926, GFI=.973, AGFI=.954, TLI=.952,
CFI=.966, RMSEA=.056, 90%CI=(.046,.067), SRMR=.0424
Cunningham (2008) suggests that lower-order and higher-order models are equivalent;
however, the higher-order factors must be established by relevant theory, which in this
case is done using Yang’s (2004) study. The reliabilities of the three factors were: variety
seeking 0.782, self-indulgence 0.766 and opinion-leadership was 0.630. Figure 6.13c
depicts the results of the higher-order CFA. The two models have the same results and
produce the same fit statistics. The regression weights for the statements in the final
lifestyle measurement model are shown in Table 6.5.
164
Table 6.5 Regression weights for lifestyle measures
Lifestyle_9
Lifestyle_7
Lifestyle_6
Lifestyle_5
Lifestyle_3
Lifestyle_2
<--<--<--<--<--<---
VARIETY-_SEEKING
VARIETY-_SEEKING
VARIETY-_SEEKING
VARIETY-_SEEKING
SELF-_INDULGENCE
SELF-_INDULGENCE
Estimate
1.000
1.425
1.443
1.341
1.000
1.300
Lifestyle_1
<---
SELF-_INDULGENCE
1.220
.072
16.866
***
OPINION-_LEADERSHIP
OPINION-_LEADERSHIP
OPINION-_LEADERSHIP
1.000
1.158
.802
.092
.076
12.593
10.530
***
***
Lifestyle_11 <--Lifestyle_10 <--Lifestyle_12 <---
SE
CR
P
.093
.094
.095
15.312
15.301
14.144
***
***
***
.077
16.878
***
Logo
Influenced
Love trendy
Try new
Impulse purchase
Spend it
Do feel like it
Without
deliberation
Consult me
Influence people
Successful
Note: ***p<.001
There are ten statements which are significant. One was the weight assigned to three
statements (lifestyle 9/lifestyle 3/lifestyle 11) in order to obtain a solution. The weight for
the last statement was less than one, but still significant at p<0.001 level. Weights for the
rest of the statements were all significant at p<0.001 level (details are shown in Appendix
5, Table C).
6.4.4.4 CFA for the ethnocentrism construct
A one factor model should ideally have at least three or four indicators, with the
minimum requirement being three indicators (Bollen 1989; Kline 2010). Three indicators
(statements) were taken from the EFA results of Study One, and all these have been
retained as shown in Figure 6.14. The result χ2 (1) =0.132, p=0.717 indicates that the
data fits the model well. The model also generated other indicators, GFI=1.000,
AGFI=0.9999, TLI=1.000, CFI=1.000, all of which exceed 0.95, indicating that this is a
well-fitting model. The reliability of the ethnocentrism construct was 0.812 indicating
good internal consistency.
165
Figure 6.14 CFA for ethnocentrism measures
Chi-square =.132, df=1, p= .717, CMIN/DF=.132, GFI=1.000, AGFI=.9999, TLI=1.000,
CFI=1.000, RMSEA=.000, 90%CI=(.000,.062), SRMR=.0020
Table 6.6 shows the regression weights for the statements in the ethnocentrism
measurement model. There are three statements that are significant. One is the weight
assigned to statement 1 in order to obtain a solution. The weights for the last two
statements were greater than one, but still significant at p< 0.001 level (details are shown
in Appendix 5, Table D).
Table 6.6 Regression weights for ethnocentrism measures
Estimate
SE
CR
P
Logo
Should not
buy
Levy
tariff
Buy
domestic
Ethnocentric_5 <--- ETHNOCENTRISM
1.000
Ethnocentric_2 <--- ETHNOCENTRISM
1.021
.046
22.260
***
Ethnocentric_1 <--- ETHNOCENTRISM
1.021
.046
22.260
***
Note: ***p<.001
6.4.4.5 CFA for the beliefs/attitudes construct
The ten statements of this construct were initially linked to form a one factor CFA
measurement model as shown in Figure 6.15a. The beliefs and attitudes construct
166
included statements related to consumers’ personal beliefs and attitudes towards organic
food purchase.
Figure 6.15a: One factor CFA model for the beliefs/attitudes construct
Chi-square =282.557, df=35, pa = .001, CMIN/DF=8.073, GFI=.941, AGFI=.907, TLI=.853,
CFI=.886, RMSEA=.087, 90%CI=(.078, .097), SRMR=.0522
As can be seen from the results, the data did not fit the model well. Hence, five
statements were removed (there were large residuals in these five statements) in order to
improve the model fit, and the final best fit congeneric measurement model is shown in
Figure 6.15b.
167
Figure 6.15b: A re-specified CFA for the beliefs/attitudes construct
Chi-square =22.781, Df=5, pa= .041, CMIN/DF=4.556, GFI=.990, AGFI=.970, TLI=.963,
CFI=.981, RMSEA=.062, 90%CI=(.038,.089), SRMR=.0253
As shown in the results, the GFI and AGFI represent a good approximation of the data,
with RMSEA <0.08. The value of CFI is close to one, which suggests that it is a wellfitting model. The construct reliability (α) for this construct was 0.748. The regression
weights for the statements in the final measurement model are shown in Table 6.7.
Table 6.7 Regression weights for beliefs/attitudes measures
Personal_3
Personal_6
Personal_7
Personal_8
<--<--<--<---
BEL/ATT
BEL/ATT
BEL/ATT
BEL/ATT
Personal_10 <--- BEL/ATT
Estimate
1.000
1.165
1.589
1.491
S.E.
C.R.
P
.101
.123
.120
11.556
12.913
12.475
***
***
***
1.179
.104
11.362
***
Logo
Like organic
Good for environment
Believe the quality
Should always buy
Labels mean high
quality
Note: ***p<.001
There are five statements that are significant. One is the weight assigned to statement 3 in
order to obtain a solution. The weights for the other variables were significant at
p < 0.001 level (details are shown in Appendix 5, Table E).
168
6.4.4.6 CFA for the pre-purchase evaluation construct
The five statements of the pre-purchase evaluation construct were initially linked to form
a one factor CFA measurement model as shown in Figure 6.16a.
Figure 6.16a: One factor CFA model for the pre-purchase evaluation construct
Chi-square =48.233, df=5, pa= .001, CMIN/DF=9.647, GFI=.981, AGFI=.944,
TLI=.900, CFI=.950, RMSEA=.096, 90%CI=(.072,.122), SRMR=.043
As can be seen from the results, the data did not fit the model well. Statement 4.5 (I can
recall the brand names and labelling of some of the organic food products) was deleted in
order to improve the model fit, and the final best fit congeneric measurement model is
shown in Figure 6.16b.
169
Figure 6.16b A re-specified CFA for the pre-purchase evaluation construct
Chi-square =13.953, df=2, pa= .027, CMIN/DF=6.977, GFI=.992, AGFI=.962, TLI=.952,
CFI=.984, RMSEA=.080, 90%CI=(.044,.122), SRMR=.0255
As shown in the results, GFI and AGFI represent a good approximation of the data, with
RMSEA ≤0.08. The value of CFI is close to one, which suggests that it is a well-fitting
model. The construct reliability (α) for this construct is 0.695. The regression weights for
the statements in the final measurement model are shown in Table 6.8.
Table 6.8 Regression weights for pre-purchase evaluation measures
Note: ***p<.001
There are four statements that are significant. One is the weight assigned to statement Pre
_ purchase _1 in order to obtain a solution. The weights for the other three variables were
significant at p<0.001 level (details are shown in Appendix 5, Table F).
170
6.4.4.7 CFA for the behavioural/purchase intention construct
Three indicators (statements) were taken from the results of the EFA in Study One. As
can be seen, all the statements were retained and the data fitted the model well. The final
congeneric measurement model is shown in Figure 6.17.
Figure 6.17 CFA for behaviour/purchase intention construct
Chi-square =.343, df=1, p= .558, CMIN/DF=.343, GFI=1.000, AGFI=.999,
TLI=1.004, CFI=1.000, RMSEA=.0000, 90%CI=(.000,.072), SRMR=.0049
As shown in the results, GFI and AGFI represent a good approximation of the data, with
RMSEA <0.05. The value of CFI was one, which suggests that it is a well-fitting model.
The construct reliability (α) for this construct was 0.696 which indicates good internal
consistency. The regression weights for the statements in this measurement model are
shown in Table 6.9.
Table 6.9 Regression weights for behavioural/purchase intention
Be_intention_3 <--- INTENTION
Be_intention_2 <--- INTENTION
Be_intention_1 <--- INTENTION
Estimate SE
1.085
.078
1.085
.078
1.000
CR
P
13.966 ***
13.966 ***
Logo
Make same choice
Recommend
Probably use
Note: ***p<.001
171
There are three statements that are significant. One is the weight assigned to statement
Be_intention_1 in order to obtain a solution. The weights for the other statements were
significant at p < 0.001 level (details are shown in Appendix 5, Table G).
6.4.5 Summary of the results of CFA
The factors obtained from the EFA in Study One were validated using CFA, and a total of
ten statements were deleted due to model fit requirements. Thirty-five statements
comprising seven measurement models (or ten factors) were retained (see Appendix 5).
Most of the modifications contributed adequately towards model fit requirements in the
re-specified models. Since the seven measurement models would later form a structural
equation model, it was important to test their reliability. Cronbach’s alpha was adopted to
test their internal consistencies. Table 6.10 summarises the reliability scores of the seven
measurement models. The scores range from a high of 0.827 to a low of 0.570. These are
acceptable and will be further tested in the final SEM.
Table 6.10 Summary of reliability of the measurement models
Measurement model
Product-related
Regulatory
Lifestyle
(three factors)
Ethnocentrism
Beliefs/attitudes
Pre-purchase evaluation
Behavioural/purchase
intention
6.5
Number of statements
retained in CFA
5
5
10
3
5
4
3
Reliability (Cronbach’s
alpha)
.715
.601
α 1=.782
α 2=.766
α 3=.630
.809
.747
.693
.689
Full structural equation model
Before conducting the hypotheses tests, convergent validity and discriminant validity
were ascertained as follows.
172
6.5.1 Convergent validity
Convergent validity refers to the extent to which multiple attempts measuring the same
concept with different methods are in agreement (Hair et al. 2006). It refers to the degree
in which items strongly load on one single factor. In confirmatory factor analysis,
researchers can establish convergent validity within the context of the measurement
model (Steenkamp & van Trijp 1991). In this study, CFA was used to assess the
convergent validity of the constructs in two ways. Firstly, measurement models for each
construct were examined in terms of the significance and strength of the item loadings.
All items had significant factor loadings above 0.30. Secondly, modification indices
(which find where the fit in a model could be improved) were used to identify possible
cross-loading items (Straub, Boudreau & Gefen 2004). Convergent validity is confirmed
when items have significant factor loadings above 0.30, and load only on the a priori
theoretical construct
6.5.2 Discriminant validity
In order to confirm how well the results obtained from the use of measures fit the theories
around which this study was designed, discriminant validity tests were conducted based
on the measurement models (Cavana, Delahaye & Sekaran 2001). A set of variables
presumed to measure different constructs shows discriminant validity if their
intercorrelations are not too high (Kline 2010, p. 72). Pattern and structure coefficients
are used in determining whether constructs in measurement models are empirically
distinguishable (Cunningham 2008; Thompson 1997).
The discriminant validity was assessed in two sections of the proposed conceptual model:
Section one (Figure 6.18) examined the influencing stage which comprised endogenous
constructs and the results are shown in Table 6.11. This test clearly identifies that there
are five constructs, namely, product, regulatory, self-indulgence, variety seeking and
ethnocentrism. Essentially, discriminant validity was established by eliciting five
distinctly different constructs which are not highly correlated to each other (Cavana,
Delahaye & Sekaran 2001).
173
Figure 6.18 Endogenous constructs of the influencing stage of the conceptual model
174
Table 6.11 Factor pattern and structure coefficients for the endogenous constructs
of the influencing stage
Indicator
statements
Product_1
Product_2
Product_3
Product_5
Product_6
Regulatory_5
Regulatory _6
Regulatory_7
Regulatory_8
Lifestyle_1
Lifestyle_2
Lifestyle_3
Lifestyle_6
Lifestyle_7
Lifestyle_9
Ethnocentric_1
Ethnocentric_2
Ethnocentric_5
Product
P
.616
.659
.641
.532
.444
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
S
.616
.659
.641
.532
.444
.142
.173
.122
.146
.139
.145
.106
.182
.190
.115
.100
.099
.092
Regulatory
P
0*
0*
0*
0*
0*
.522
.635
.449
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
S
.168
.179
.174
.145
.121
.522
.635
.449
.534
.034
.036
.026
.100
.104
.063
.036
.036
.033
Self_
indulgence
P
S
0*
.108
0*
.116
0*
.113
0*
.094
0*
.078
0*
.023
0*
.028
0*
.020
0*
.023
.788 .788
.826 .826
.602 .602
0*
.365
0*
.381
0*
.230
0*
.196
0*
.194
0*
.180
Variety
seeking
P
S
0*
.142
0*
.152
0*
.148
0*
.123
0*
.103
0*
.066
0*
.080
0*
.057
0*
.068
0*
.365
0*
.383
0*
.279
.788
.788
.823
.823
.497
.497
0*
.140
0*
.139
0*
.128
Ethnocentrism
P
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
0*
.792
.782
.725
S
.078
.083
.081
.067
.056
.024
.029
.020
.024
.195
.205
.149
.140
.146
.088
.792
.782
.725
Note: P=Pattern coefficients. S=Structure coefficients. Tabled values are standardised parameter estimates.
Asterisked values are parameters fixed at reported levels to identify the model (Appendix 6: Table A)
From Table 6.11, it is evident that the items purported to compose each individual
construct have significantly higher loadings compared to the other items that are not
believed to make up that construct.
Section two (Figure 6.19) assessed the exogenous construct of the conceptual model, i.e.
cognitive/affective, evaluation of alternatives and behavioural Intentions, and the results
are shown in Table 6.12. The factor pattern and structure coefficients matrix demonstrate
that there are three distinctly different latent constructs, namely, beliefs/attitudes, prepurchase evaluation, behavioural/purchase intention which display discriminant validity.
Appendix 6 provides details of the results of discriminant validity for the entire structural
equation model.
175
Figure 6.19 Exogenous construct of the cognitive/affective, evaluation of alternatives
and behavioural/purchase intention stages of the conceptual model
A higher-order construct is suggested when the correlations between constructs are high
and when discriminate validity is lacking (Cunningham 2008). Although the correlations
between the three constructs are high, discriminate validity was evident as shown in
Table 6.12, hence it was deemed unnecessary to test for a higher-order construct.
176
Table 6.12 Factor pattern and structure coefficients for the exogenous constructs of
beliefs/attitudes, evaluation of alternatives and behavioural/purchase intention
Indicator
statements
Beliefs/attitudes
Pre-purchase
evaluation
Behavioural
intention
P
S
P
S
P
S
BEL/ATT_6
.606
.606
0*
.293
0*
.347
BEL/ATT_7
.760
.760
0*
.368
0*
.435
ATT/BEL_8
.634
.634
0*
.307
0*
.363
ATT/BEL_10
.554
.554
0*
.268
0*
.317
Prepurchase _2
0*
.316
.652
.652
0*
.503
Prepurchase _3
0*
.337
.696
.696
0*
.537
Prepurchase _4
0*
.371
.767
.767
0*
.591
Be_intention_1
0*
.383
0*
.515
.668
.668
Be_intention_2
0*
.374
0*
.503
.652
.652
Be_intention_3
0*
.366
0*
.493
.640
.640
Note: P=Pattern coefficients. S=Structure coefficients. Tabled values are standardised parameter estimates.
Asterisked values are parameters fixed at reported levels to identify the model (Appendix 6: Table B).
6.5.3 Establishing a full structural model
Some studies (Grunert & Juhl 1995; McEachern & Willock 2004) suggest that it would
be advantageous to adopt SEM in future research relating to organic consumers’ attitudes
and motivations, as it would create the capacity to scrutinise a series of dependent
relationships concurrently. Previous studies (Saba & Messina 2003; Tarkiainen &
Sundqvist 2005) have demonstrated that this is a relatively strong method to analyse
consumers’ purchase behaviour of organic food. It allows simultaneous analysis of the
relationships between dependent (DV) and independent variables (IV) in the organic food
behavioural intention model (de Magistris & Gracia 2008).
Tabachnick and Fidell (2007, p. 676) describe the process of SEM as:
Structural equation modelling (SEM) is a collection of statistical techniques that
allow a set of relationships between one or more IVs, either continuous or
discrete, and one or more DVs, either continuous or discrete, to be examined.
177
Confirmatory factor analysis is actually a special type of Structural equation
modelling.
SEM is a technique used to evaluate the goodness-of-fit of the hypothesised model to the
sample data in order to offer support for the theoretical model (Cunningham 2008). It
confirms the interrelationship between sets of observed indicator variables and factors of
the proposed theoretical model, assesses the convergent and discriminate validities of
various statements used in this study, and provides suitable indicators to test consumers’
purchase intention associated with organic food in urban China. Table 6.13 summarises
the techniques used to test the hypotheses developed in Chapter Three.
Table 6.13 Summary of techniques used to test hypotheses
Research questions
Research question one
Related hypotheses
Hypothesis 1a/b/c
What are the factors that influence
Hypothesis 2a/b/c
urban Chinese consumers’
Hypothesis 3a/b/c
beliefs/attitudes towards the
purchase of organic foods, and how
Hypothesis 4a/b/c
they impact on the pre-purchase
Hypothesis 5
Techniques used to test
Study One: Exploratory
factor analysis (EFA)
Study Two: Confirmatory
factor analysis (CFA) and
structural equation modelling
(SEM)
evaluation and behavioural/purchase
intention?
Hypothesis 6
Research question two
Hypothesis 7a
Evaluate the role of demographic
Hypothesis 7b
variables and usage patterns in the
Study Two : T-test and
analysis of variance
(ANOVA)
Hypothesis 7c
behavioural /purchase intentions of
urban Chinese consumers towards
Hypothesis 7d
organic food.
Hypothesis 7e
6.5.4 The output of the final model
A structural model aims to specify which latent variables directly or indirectly influence
the other latent variables in a model (Bollen 1989). The structural model was tested and
178
presented as the final stage of analysis. Post-hoc modifications were performed in an
attempt to develop a better fitting and possibly more parsimonious model.
The hypothesised structural model was tested based on validated measures derived from
CFA in Study Two, and guided by the proposed conceptual framework developed in
Chapter Three (Figure 3.2). Causal flows linked the influencing stage, namely, product,
regulatory, lifestyle, and ethnocentrism to consumers’ beliefs/attitudes, pre-purchase
evaluation and later to behavioural/purchase intention. Meanwhile a causal path linked
beliefs/attitudes to pre-purchase evaluation and later to behavioural intentions.
Furthermore, there was a direct path between beliefs/attitudes and behavioural intentions.
These paths occur with multiple regression relationships corresponding to the hypotheses
developed in the proposed conceptual framework (details are shown in Appendix 7,
Tables A to D). Three extra links were added to improve the model fit. Firstly, the
product-related construct significantly influenced the regulatory construct as evidenced
by the standardised direct effect of 0.282 (p value was significant). This essentially means
that when the product-related construct increased by 1 standardised deviation, the
regulatory construct increased by 0.282 standardised deviation. Secondly, the lifestyle
construct significantly influenced the product-related construct (p value was significant)
as evidenced by the standardised direct effect of 0.288. This essentially means that when
the lifestyle construct increased by 1 standardised deviation, the product-related construct
increased by 0.288 standardised deviation. Thirdly, the beliefs/attitudes construct
significantly influenced behavioural/purchase intention as evidenced by the standardised
direct effect of 0.232 (p value was significant).
One statement from the regulatory construct, i.e. ‘Regulatory_1’(loading =.32), and one
from pre-purchase evaluation construct, i.e. ‘Pre-purchase evaluation_1’(loading = .35)
were deleted due to low factor loadings. The overall factor loadings of the remaining
statements ranged from 0.480 to 0.825 (Appendix 7, Table E). Statement Lifestyle_5 and
beliefs/attitudes statement personal _3 were removed due to large residuals. The entire
factor of opinion-leadership (three statements) was removed as it was distorting the
whole model, and its Cronbach’s alpha was relatively low (α=0.633). Hence, in the final
best-fit model four statements and one factor (comprising three statements) were removed
179
from the original seven measurement models. Figure 6.20 illustrates the final best-fit
model. Latent variables are represented by circles and measured variables are represented
by squares.
In this study, all latent variables of the measurement models were validated and the
re-specified models fitted well. The output of the Squared Multiple Correlations
(Appendix 7, Table F) shows that the final model explains 41.3% of the variance in
‘attitudes’, 39.5% of the variance in ‘pre-purchase evaluation’ and 66.4% of the variance
in ‘behavioural intention’.
180
CMIN/DF=2.813, p= .001, GFI=.931, AGFI=.915, TLI=.905, CFI=.916, RMSEA=.044, 90%CI=(.041,.048), SRMR=.0497
Figure 6.20 Final best-fit model of consumer purchase intention of organic food in urban China
181
6.5.5 Assessing the reliability of the final model
Table 6.14 summarises the progression of how the 52 statements of the original survey
were reduced to 28 statements in the final best-fitting model. It demonstrates that the
highest Cronbach’s alpha was 0.809, and the lowest 0.611. Although ideally, the
Cronbach’s alpha should be above 0.7, they are quite sensitive to the number of items in
the scale. With scales of fewer than ten items, it is common to find low Cronbach values
(Pallant 2007). This is the case for the ‘regulatory’ construct which has a Cronbach’s
alpha of 0.611. Therefore, the internal consistencies of all scales in this study are
acceptable and adequate.
Table 6.14 Summary of reliability of the final model scales
Original
number of
statements
Number of
statements
deleted in
EFA
Number of
statements
deleted in
CFA
Number of
statements
deleted in
final model
Number of
statements
retained in
final model
Scale reliability
in final model
(Cronbach’s
alpha)
Productrelated
Regulatory
9
2
2
Nil
5
.710
8
2
1
1
4
.611
Lifestyle
(two factors)
12
1
1
4
6
α 1=.733
α 2=.775
Ethnocentrism
5
2
Nil
Nil
3
.809
Beliefs/
attitudes
Pre-purchase
evaluation
10
Nil
5
1
4
.730
5
Nil
1
1
3
.750
Behavioural/
purchase
intention
Total
statements
3
Nil
Nil
Nil
3
.689
52
7
10
7
28
Construct
6.5.6 Hypotheses tests
Figure 6.21 illustrates a simplified final validated best-fit model with relevant
hypotheses. Lines with arrows indicate hypothesised direct relationship between the
different constructs (latent variables). This simplified model depicts only the hypotheses
182
which were eventually accepted. Although the items associated with ‘Ethnocentrism’
were retained in the final best-fit model, the hypothesis associated with this construct
was not accepted, hence it is not included in the simplified model.
Figure 6.21 Simplified final best-fit model
Table 6.15 summarises the results (Appendix 7, Table A and Table D) of the hypotheses
tests. A version of ‘t’ test was employed which uses critical ratios from the SEM.
Standard errors are shown in the SE column, and the CR column stands for critical ratio
(magnitude > 2 indicates statistical significance at the .05 level). P value indicates
statistical significance at levels of 0.001, 0.01 and 0.05 respectively. The direction and
importance of the relationships is determined by the magnitude of Beta weights.
183
REGULATORY
<--<--<--<--<--<--<--<--<--<--<---
PRE_-PURCHASE
BEHAVIOUR_INTENTION
BEL/ATT
PRE_-PURCHASE
BEHAVIOUR_INTENTION
BEL/ATT
PRE_-PURCHASE
BEHAVIOUR_INTENTION
PRE_-PURCHASE
BEHAVIOUR_INTENTION
H2a
H2b
H2c
H3a
H3b
H3c
H4a
H4b
H4c
H5
H6
Note: ***p<.001; **p <.01; *p <.05
PRODUCT
<---
BEHAVIOUR_INTENTION
BEL/ATT
H1c
PRE_-PURCHASE
BELIEFS/ATTITUDES
ETHNOCENTRISM
ETHNOCENTRISM
ETHNOCENTRISM
LIFESTYLE
LIFESTYLE
LIFESTYLE
REGULATORY
REGULATORY
PRODUCT
<---
PRE_-PURCHASE
H1b
PRODUCT
<---
BEL/ATT
H1a
Hypothesis
No.
.566
.223
.011
.027
.024
.222
.119
.404
.075
.464
.294
-.085
.264
.450
Estimate
.058
.068
.026
.029
.026
.095
.105
.098
.061
.066
.053
.062
.071
.066
SE
9.729
3.257
.420
.922
.926
2.347
1.131
4.111
1.234
7.066
5.567
-1.363
3.695
6.820
CR
Table 6.15 Summary of hypotheses tests (H1a-H6)
***
**
.674
.356
.355
*
.258
***
.217
***
***
.173
***
***
P
Accepted
Accepted
Rejected
Rejected
Rejected
Accepted
Rejected
Accepted
Rejected
Accepted
Accepted
Rejected
Accepted
Accepted
Accepted/
rejected
.621
.196
.016
.037
.038
.127
.062
.239
.067
.379
.274
-.069
.197
.383
Beta
184
6.5.7 Discussion relating to the hypotheses tests of SEM
Hypothesis one
H1a
The product-related construct has a positive influence on the beliefs/attitudes of
urban Chinese consumers towards the purchase of organic food products
The results (refer to Table 6.15) revealed that the product-related construct significantly
influenced consumers’ beliefs/attitudes (p value was significant). The standardised
direct effect of the product-related construct on beliefs/attitudes was 0.383, which
means that, when this construct increases by 1 standardised deviation, beliefs/attitudes
increases by 0.383 standardised deviation (Cunningham 2008). Thus, hypothesis H1a is
accepted.
H1b
Organic food product-related construct is positively correlated to Chinese
consumers’ pre-purchase evaluation
The results (refer to Table 6.15) revealed that the product-related construct had a
significant influence on pre-purchase evaluation (p value was significant). The
standardised direct effect of this construct on pre-purchase evaluation was 0.197 which
means that when this construct increases by 1 standardised deviation, pre-purchase
evaluation increases by 0.197 standardised deviation. Again, hypothesis H1b is accepted.
H1c
Organic food product-related construct is positively correlated to Chinese
consumers’ behavioural/purchase intention
The results (refer to Table 6.15) revealed that the product-related construct did not have
a significant influence on behavioural/purchase intention (p value was not significant).
It was also negatively associated with the behavioural/purchase intention of urban
Chinese consumers. This means that it had no significant bearing on consumers’
purchase intention of organic food. Thus, hypothesis H1c is rejected.
However, the results (Appendix 7, Table B) also revealed that the product-related
construct had an indirect effect (0.371) on behavioural/purchase intention, which means
185
that the product-related construct was associated with behavioural/purchase intention
through the mediatory affect of beliefs/attitudes and pre-purchase evaluation.
Hypothesis two
H2a
The regulatory construct associated with organic food has a positive influence
on beliefs/attitudes of urban Chinese consumers
The results (refer to Table 6.15) revealed that the regulatory construct had a significant
influence on beliefs/attitudes (p value was significant). The standardised direct effect of
the regulatory construct on beliefs/attitudes was 0.274, which means that when this
construct increases by 1 standardised deviation, beliefs/attitudes increases by 0.274
standardised deviation. Thus, hypothesis H2a is accepted.
H2b
The regulatory construct associated with organic food is positively correlated to
Chinese consumers’ pre-purchase evaluation
The results (refer to Table 6.15) revealed that the regulatory construct had a significant
influence on beliefs/attitudes (p value was significant). The standardised direct effect of
this construct on pre-purchase evaluation was 0.379, which means that when this
construct increases by 1 standardised deviation, pre-purchase evaluation increases by
0.379 standardised deviation. Again, hypothesis H2b is accepted.
H2c
The regulatory construct associated with organic food is positively correlated to
Chinese consumers’ behavioural/purchase intention
The results (refer to Table 6.15) revealed that the regulatory construct did not have a
significant influence on behavioural/purchase intention (p value was not significant).
Thus, hypothesis H2c is rejected.
However, the results (Appendix 7, Table B) also revealed that the regulatory construct
had an indirect effect (0.332) on behavioural/purchase intention, which means that the
regulatory construct was associated with behavioural/purchase intention through the
mediatory affect of beliefs/attitudes and pre-purchase evaluation.
186
Hypothesis three
H3a
The lifestyle construct has a positive influence on the beliefs/attitudes of urban
Chinese consumers towards the purchase of organic food products
The results (refer to Table 6.15) revealed that the lifestyle construct had a significant
influence on beliefs/attitudes (p value was significant). The standardised direct effect of
the lifestyle construct on beliefs/attitudes was 0.239 which means that when this
construct increases by 1 standardised deviation, beliefs/attitudes increases by 0.239
standardised deviation. Thus, hypothesis H3a is accepted.
H3b
The lifestyle construct is positively correlated to consumers’ pre-purchase
evaluation of organic food products in urban China
The results (refer to Table 6.15) revealed that the lifestyle construct did not have a
significant influence on pre-purchase evaluation (p value was not significant). Thus,
hypothesis H3b is rejected. However, the results (Appendix 7, Table B) also revealed
that the lifestyle construct had an indirect effect (0.161) on pre-purchase evaluation,
which means the lifestyle construct was associated with pre-purchase evaluation
through the mediatory affect of beliefs/attitudes.
H3c
The lifestyle construct is positively correlated to Chinese consumers’
behavioural/purchase intention of organic food products
The results (refer to Table 6.15) revealed that the lifestyle construct had a significant
influence on behavioural/purchase intention (p value was significant). The standardised
direct effect of the lifestyle construct on behavioural/purchase intention was 0.127,
which means that when the lifestyle construct increases by 1 standardised deviation,
behavioural/purchase intention increases by 0.127 standardised deviation. Again,
hypothesis H3c is accepted.
187
Hypothesis four
H4a
The ethnocentrism construct has a significant influence on beliefs/attitudes of
urban Chinese consumers towards the purchase of organic food products
H4b
The ethnocentrism construct is significantly correlated to the pre-purchase
evaluation of organic food products in urban China
H4c
The ethnocentrism construct is significantly correlated to Chinese consumers’
behavioural/purchase intention of organic food products
The results (refer to Table 6.15) revealed that the ethnocentrism construct did not have a
significant influence on beliefs/attitudes, pre-purchase evaluation and behavioural/
purchase intention (p values were not significant). Thus, hypotheses H4a H4b and H4c are
all rejected.
Hypothesis five
H5:
Chinese consumers’ beliefs/attitudes are directly and positively correlated to
their pre-purchase evaluation
The results (refer to Table 6.15) revealed that the beliefs/attitudes related construct had
a significant influence on pre-purchase evaluation (p value was significant). The
standardised direct effect of the beliefs/attitudes on pre-purchase evaluation was 0.196,
which means that when beliefs/attitudes construct increases by 1 standardised deviation,
pre-purchase evaluation increases 0.196 standardised deviations. Thus, hypothesis H5 is
accepted.
Hypothesis six
H6:
The pre-purchase evaluation of Chinese consumers to organic food is directly
and positively related to their behavioural/purchase intention
The results (refer to Table 6.15) revealed that the pre-purchase evaluation construct had
a significant influence on behavioural/purchase intention (p value was significant). The
188
standardised direct effect of the pre-purchase evaluation on behavioural/purchase
intention was 0.621, which means that when pre-purchase evaluation increases by 1
standardised
deviation,
behavioural/purchases
intentions
increases
by
0.621
standardised deviations behavioural/purchase intention. Thus, hypothesis H6 is accepted.
6.6
Analysis of additional data
Besides the analysis of the 52 statements of the survey (EFA, CFA and SEM) reported
thus far, analysis of data obtained from other sections of the survey was also undertaken
and is reported in this section.
6.6.1 Analysis of demographic control variables as shown in the
conceptual framework
This section analyses the influences of demographic control variables, that is, gender,
age, education and income levels, towards the purchase intention of organic food in
China. The analyses were conducted using t-tests and ANOVA statistical techniques.
H7a: Women are more likely to purchase organic food than men in urban China
Independent samples t-tests are often used to compare the mean scores on continuous
variables for two different groups of participants, that is, male and female (Allen &
Bennett 2010; Pallant 2007). Hence an independent samples t-test was conducted, and
the results are shown in Table 6.16.
189
Table 6.16 Independent samples t test
Levene’s
test for
equality of
variances
t-test for equality of means
95% confidence
interval of the
difference
Total_
Equal
purchase_ variances
intention assumed
Equal
variances
not
assumed
F
Sig.
t
df
Sig. (2tailed)
Mean
Std. error
difference difference Lower
.210
.647
.726
921
.468
.028
.039
-.048
.104
.730
812.267
.466
.028
.038
-.047
.103
Upper
Table 6.16 illustrates that Levene’s test was non-significant (sig. =.647). This result was
greater than the cut-off of .05, thus equal variances can be assumed. The value in the sig.
(2-tailed) column was .486, which is also above the required cut-off of .05. Therefore,
there were no significant differences (p>.05) in the purchase intention of organic food
between females and males in urban China. Thus, hypothesis H7a is rejected.
H7b: Younger consumers are more likely to purchase organic food than older
consumers
ANOVA is used to test hypotheses about means when there are two or more groups of
one independent variable (Pallant 2007). In this case, age group was considered to be
the independent variable, which included four age groups: (a) between 18-30; (b)
between 31-45; (c) between 46-60; (d) greater than 60. A factorial between groups
ANOVA was used to compare the mean intention scores of different age groups.
A Levene’s test for homogeneity of the four age groups was conducted. The results
demonstrated that the differences of the variance between the groups was not significant
at p = .05 [F (3, 915) = 2.162, p=.091]. The results of the ANOVA are depicted in
Table 6.17.
190
Table 6.17 ANOVA (purchase intention, age groups)
Sum of squares
df
Mean square
F
Sig.
2.484
3
.828
2.511
.057
301.750
304.234
915
918
.330
Between groups
Within groups
Total
Figures in Table 6.17 reveals that a statistical value of sig (0.057) was greater than 0.05,
indicating that there were no significant differences in the mean scores for purchase
intention of organic food across the four age groups. Thus, hypothesis H7b is rejected.
H7c: Highly educated consumers are more likely to purchase organic food as compared
to those who are less educated
ANOVA is used to test hypotheses about means when there are two or more groups of
one independent variable. In this case, educational group was considered to be the
independent variable, which included five levels of educational groups: (a) below high
school; (b) high school; (c) two years college or associate degree; (d) bachelor degree;
(e) postgraduate or above.
The Levene’s test for homogeneity of the five different educational groups was
conducted. The results demonstrate that the differences of the variances between the
groups was not significant at p=0.05 [F (4, 913) =0.552, p=0.697]. The results of the
ANOVA depicted in Table 6.18 reveals that a statistical value of sig (.000) is less than
0.05. It indicates that there were significant differences at the p<0.05 level in the mean
scores for purchase intention of organic food across the five education groups [F (4, 913)
= 7.229, p=0.000].
Table 6.18 ANOVA (purchase intention, education levels)
Sum of squares
df
Mean square
F
Sig.
Between groups
9.310
4
2.328
7.229
.000
Within groups
293.965
913
.322
Total
303.275
917
191
Despite reaching statistical significance, the actual difference in mean scores between
the groups was quite small. The effect size calculated using eta squared7 was:
Eta squared = Sum of squares between groups/Total sum of squares
= 9.310/303.272
= 0.03
Post-hoc comparisons (Appendix 8, Tables A and B) of results indicate that the mean
score for group 1 ‘below high school’ (M = 3.47, SD =0.575) was significantly different
from group 2 ‘high school’ (M = 3.68, SD =0.533), group 3 ‘Two years college or
associate degree’ (M =3.84, SD =0.591), group 4 ‘bachelor degree’ (M = 3.73, SD =
0.568), and group 5 ‘postgraduate or above’ (M = 3.76, SD = 0.564). However, group 2
did not significantly differ from groups 3, 4 or 5. Therefore, hypothesis H7c was
partially accepted.
H7d: Consumers earning higher income are more likely to purchase organic food as
compared to those earning less
ANOVA was used to test whether the higher the Chinese consumers’ income level, the
more they intend to purchase organic food. The four different income level groups
included: (1) Less than RMB5,000 (2) Between RMB5,001-10,000 (3) Between
RMB10, 001-20,000 (4) Greater than RMB20, 000.
Levene’s test for homogeneity of the four different income groups was conducted. The
results demonstrated that the differences of the variances between the groups was not
significant at p=.05 [F (3, 913) =1.606, p=0.186]. The results of the ANOVA are
depicted in Table 6.19 which revealed that a statistical value of sig (0.000) was less than
0.05. It indicates that there were statistically significant differences at the p<0.05 level
in purchasing intentions scores across the four income groups [F (3, 913) = 6.870,
p=0.000].
7
Eta squared: a rough estimate of effect size is available for any ANOVA through eta squared. It reflects
the proportion variance in the dependent variable that is associated with the level of an independent
variable (Tabachnick & Fidell 2007).
192
Table 6.19 ANOVA (purchase intention, income levels)
Sum of squares
df
Mean square
F
Sig.
Between groups
6.703
3
2.234
6.870
.000
Within groups
Total
296.971
303.674
913
916
.325
Despite reaching statistical significance, the actual difference in mean scores between
the groups was quite small. The effect size calculated using eta squared was:
Eta squared = Sum of squares between groups/Total sum of squares
= 6.703/303.674
=0.02
Post-hoc comparisons (Appendix 8, Tables C and D) of results indicate that the mean
score for group 1 (consumers’ income ‘less than RMB5,000’) (M=3.63, SD=0.545) was
significantly different from groups 2 (‘Between RMB5,001-10,000’) (M=3.81,
SD=0.572) and group 4 (‘Greater than RMB20,000’) (M=3.84, SD=0.540), but did not
differ significantly from group 3 (‘Between RMB10,001-20,000’) (M=3.74, SD=0.643).
Group 2 did not differ significantly from either group 3 or 4. Hence, hypothesis H7d is
partially accepted.
6.6.2 Important attributes relating to purchase of organic food
This section analyses the important attributes associated with the purchase of organic
food in China. Twenty-one of the 23 statements of section two of the survey were given
mean ratings above ‘3’ (neutral rating) as shown in Appendix 8, Table E. This suggests
that on the whole, the ratings given by the respondents to the various statements of
important attributes for the purchase of organic food were generally significant. Table
6.20 illustrates the top five important attributes of these statements, ‘certification
relating to the quality of organic food’, and the ‘overall quality of organic food’ were
rated most important, followed by ‘enforcement relating to the quality of organic food’,
‘information about the nutritional value of organic food’, and ‘food safety in relation to
193
organic food’. This confirms the general concern and poor confidence that respondents
have about certification and quality of organic food in China.
Table 6.20 Top five important attributes
No.
2.19
2.4
2.20
2.23
2.18
Statements
Certification relating to the quality of organic food
The overall quality of organic food
Enforcement relating to the quality of organic food
Information about the nutritional value of organic food
Food safety in relation to organic food
Mean
4.24
4.24
4.22
4.15
4.14
Std.
deviation
.842
.829
.868
.851
.890
Table 6.21 shows the five least important attributes relating to the purchase of organic
food in China. These include ‘the idea of saving face when purchasing organic food’,
and ‘social status of people purchasing organic food’. Attributes relating to status are
least important in the purchase of organic food. This is in contrast to results obtained by
Sun and Collins (2006) which indicate that organic food consumption is not an
everyday activity practised in public, and organic food is used only for private
consumption. China is described as being a highly collectivist society with high power
distance, and status is important as an indicator of power and achievement. However,
face-saving is important only when purchasing a socially visible product, such as an
expensive watch or prestigious car, which can demonstrate the owner’s status. The
pragmatic Chinese tend to value function over form for private-use products and quality
is considered more important than appearance (Schütte & Ciarlante 1998).
Table 6.21 Five least important attributes
No.
Statements
Mean
2.3
2.14
2.7
2.11
2.10
The appearance of organic food
The country of origin of the organic food
The promotion and advertising of organic food
The social status of people purchasing organic food
The idea of face saving (mianzi) when purchasing organic food
3.34
3.30
3.27
2.35
2.30
Std.
deviation
.947
1.043
.977
1.103
1.084
As shown above, the least important attributes in respondents’ choice also include
‘promotion and advertising’, ‘country of origin’ and ‘appearance’. Attributes relating to
status are not so important in the purchase of organic food. This finding is in contrast to
194
that obtained by Sun and Collins (2006) who pointed out that Chinese consumers
purchase imported fruit not only to attain a high quality product, but also expect to gain
some pleasure and symbolic benefit such as ‘superior appearance’, ‘expensive’ and
‘high prestige’. Consumers in developing countries buy foreign products which are
generally considered social symbols and also very trendy, particularly those whose
origins are from prestigious countries (Batra et al. 2000). Another study argued that
consumers from developing countries, such as China, highlight the impact of
ethnocentrism by their willingness to buy domestic products. However, when these
consumers hold higher conspicuous consumption values, they tend to perceive domestic
products as being lower quality (Wang & Chen, 2004). These findings are consistent
with the results obtained after testing hypothesis four which was associated with
ethnocentrism.
6.6.3 Usage pattern of organic food
Essentially, there were five intended uses for respondents’ most recent purchase of
organic food: for their own consumption, for children, for elderly people, as a gift and
for family consumption. The importance of each attribute might be affected by its single
intended use as compared to multiple intended usages (Sun & Collins 2006). Since
many respondents in this survey indicated multiple intended usages, the results would
be dissimilar to those of consumers with a single intended use. Respondents who
selected more than one reason were eliminated in this analysis. Therefore, in this section,
the analysis is only based on examining responses relating to a single intended use and
their evaluation of important attributes of organic food.
A total of 710 respondents (Appendix 8, Table F) chose a single reason for their latest
organic food purchases. The main reasons were ‘for own consumption’ (n=254), ‘for
children’ (n=90), ‘for elderly people’ (n=27), ‘for gift’ (n=11) and ‘for family
consumption’ (n =328) (details are shown in Appendix 8, Table F).
195
6.6.4 Important attributes versus usage pattern
ANOVA between groups was used to investigate whether there were significant
differences between intended uses in consumers’ evaluations of important attributes for
the purchase of organic food. Table 6.22 reports that there were statistically significant
differences at p<0.05 level in (1) social status [F (4, 702) = 4.877, p=0.001]; (2) the
awareness of organic food products [F (4, 702) = 6.502, p=0.000]; (3) food safety [F (4,
700) = 3.948, p=0.004] (details are shown in Appendix 8, Table G).
Table 6.22 Means of important attributes as applicable to the usage pattern
Important
attributes
Taste
Smell
Appearance
Quality
Price
Availability
Promotion
Value
Environmental
Face saving
Social status
Knowledge
Awareness
Country of
origin
Produce in
China
Brand
Regulation
Food safety
Certification
Enforcement
Packaging
Labelling
Information
Own
consumption
3.83
3.86
3.25
4.32
3.88
3.73
3.20
3.94
4.01
2.16
2.14
3.66
3.38
3.17
Children
Elderly
Gift
Family
ANOVA(F)
3.82
3.58
3.40
4.09
4.04
3.81
3.27
3.84
3.69
2.44
2.58
3.71
3.81
3.36
3.63
3.67
3.48
4.39
3.81
4.00
3.37
3.59
3.96
2.19
2.48
3.70
3.85
3.30
3.45
3.64
3.36
4.27
3.82
3.80
3.64
3.73
3.91
2.64
3.18
3.73
4.09
3.36
3.83
3.71
3.41
4.24
3.93
3.79
3.27
3.88
3.94
2.30
2.30
3.74
3.63
3.37
.853
2.365
1.204
1.612
.765
.675
.748
1.088
1.922
1.675
4.877*
.248
6.502*
1.424
3.33
3.38
3.26
3.36
3.40
.246
3.35
3.87
4.27
4.30
4.28
3.36
3.99
4.20
3.51
3.72
3.87
4.08
4.16
3.50
4.14
4.08
3.52
3.93
3.93
4.22
4.04
3.56
3.96
3.81
3.55
4.18
4.27
4.27
4.36
3.36
4.18
4.18
3.56
3.88
4.10
4.27
4.22
3.52
4.10
4.15
1.994
.835
3.948*
1.208
.786
1.068
1.006
1.406
*p <0.05
196
Hypothesis 7e: tests whether the important attributes of organic food products have
positive influences on their intended usage pattern in China
In ANOVA, if the significance value is equal to or less than 0.05, it means that there is a
significant difference somewhere among the mean scores on the dependent variable for
the groups. The post-hoc test tells exactly where the differences among the groups occur.
The statistical significance of the differences between each pair of groups is examined
in multiple comparisons, which give the results of the post-hoc test (Pallant 2007). The
results show that social status, awareness and food safety were significantly different
from the rest of the twenty important attributes, in terms of the five intended usages. To
investigate where these differences existed among the five intended use groups, posthoc test were conducted on the data. Despite reaching statistical significance, the actual
differences in mean scores between groups were quite small.
The effect sizes, calculated using eta squared were:
Eta squared = Sum of squares between groups/Total sum of squares. Hence,
Eta squared of social status =22.929/848.028=.0027
Eta squared of awareness=20.527/574.549=0.035
Eta squared of food safety=12.555/569.016=0.022
According to Cohen’s term (in Pallant 2007), 0.01 is a small effect, 0.06 is a medium
effect and 0.14 is a large effect. Therefore, despite reaching statistical significance, the
actual difference in mean scores between the five different intended use groups was
quite small.
The results (Appendix 8, Tables G, H and I) of the post-hoc comparisons for the
evaluation of ‘the social status of people purchasing organic food’ revealed that
differences in the mean score for the group of ‘own consumption’ (M=2.14, SD=1.078)
was significantly different from the group ‘use for children’ (M=2.58, SD=1.085), and
‘gift’ (M=3.18, SD=1.079). Social status was more important for gift giving, and least
important for own consumption.
197
In terms of evaluation of ‘the awareness of organic food products’, the mean score for
own consumption (M=3.38, SD=0.975) was significantly different from that for
children (M=3.81, SD=0.748) and for families (M=3.86, SD=0.867). Awareness of
organic food was most important for gift giving (M=4.09, SD=0.701) and least
important for own consumption (M=3.38, SD=0.975), which seems logical in the
Chinese context.
In terms of evaluation of ‘food safety in relation to organic food’, the mean score for
own consumption (M=4.27, SD=0.859) was significantly different for children
(M=3.87, SD=.919). Food safety was important for own consumption (M=4.27,
SD=0.859), as well as for gift giving (M=4.27, SD=0.786). Hence, some support for
hypothesis H7e was found. The results suggest that the above statements and associated
factors are important to consumers when deciding about the purchase of organic food.
Table 6.23 summarises the acceptance/rejection of the hypotheses. The results revealed
that the ethnocentrism construct did not influence beliefs and attitudes, pre-purchase
evaluation or behavioural/purchase intention of urban Chinese consumers towards
organic food.
Table 6.23 Summary of hypotheses testing
No.
H1a
H1b
H1 c
H2 a
H2b
H2c
H3a
H3b
Hypothesis
Product-related construct has positive influence on the personal
beliefs/attitudes of urban Chinese consumers
Product-related construct is positively correlated to the prepurchase evaluation
Product-related construct is positively correlated to Chinese
consumers’ behavioural/purchase intention
Regulatory construct has positive influence on personal
beliefs/attitudes of urban Chinese consumers
Regulatory construct is positively correlated to the pre-purchase
evaluation
Regulatory construct is positively correlated to Chinese consumers’
behavioural/purchase intention
Lifestyle construct has positive influence on the personal
beliefs/attitudes of urban Chinese consumers
Lifestyle construct is positively correlated to the pre-purchase
evaluation
Accepted/
rejected
Accepted
Accepted
Rejected
Accepted
Accepted
Rejected
Accepted
Rejected
198
H3 c
H4 a
H4 b
H4 c
H5
H6
H7 a
H7 b
H7 c
H7 d
H7 e
Lifestyle construct is positively correlated to the Chinese
consumers’ behavioural/purchase intention
Ethnocentrism construct has a negative influence on the personal
beliefs/attitudes of urban Chinese consumers
Ethnocentrism construct is negatively correlated to the pre-purchase
evaluation
Ethnocentrism construct is negatively correlated to Chinese
consumers’ behavioural/purchase intention
Personal beliefs/attitudes is directly and positively correlated to
Pre-purchase evaluation
Pre-purchase evaluation construct is directly and positively related
to behavioural/purchase intention
Women are more likely to purchase organic food
The younger the consumers’ age, the more they intend to purchase
organic food
The higher the Chinese consumers’ level of education, the more
they intend to purchase organic food
The higher the Chinese consumers’ income, the more they intend to
purchase organic food
The important attributes of organic food products have positive
influences on their intended usage pattern
Accepted
Rejected
Rejected
Rejected
Accepted
Accepted
Rejected
Rejected
Partially
Accepted
Partially
Accepted
Partially
Accepted
6.6.5 Comparative analysis of responses from the four selected
cities
Previous studies suggest that urban China does not constitute a homogeneous market;
there are significant differences between various regions and cities (Fram, Le & Reid
2005; Yusuf & Brooks 2010). To determine whether there were differences in the final
structural model for responses from the four selected cities, invariance tests were
conducted.
6.6.5.1
Invariance tests
Multigroup comparisons are commonly used to test the invariance of measurement
instruments across groups (Jones-Farmer, Pitts & Rainer 2008). Members of different
populations who have the same standing on the construct being measured receive the
same observed score on the test. The measure can be tested invariantly (Schmitt &
Kuljanin 2008). Invariant testing is an assessment of the relationships between latent
199
variables and their indicator statements, across different groups, e.g. culture, age, gender,
location (Vandenberg & Lance 2000).
In the context of invariance tests, Byrne (2010, p. 209) suggests that:
Testing for multigroup invariance provides two important functions. First, it
allows for invariance tests to be conducted across the two groups
simultaneously. Second, the fit of this configural model provides the baseline
value against which all subsequently specified invariance models are compared.
Despite the multigroup structure of this and subsequent models, analyses yield
only one set of fit statistics for overall model fit.
The respondents from four cities (Beijing, Shanghai, Shenzhen and Chengdu) were used
to carry out these invariance tests. The aim was to check whether their views were
similar or different, and whether the final model was adequately valid for each city.
Byrne (2004) suggests that establishing a baseline model for each group separately is
often a prerequisite for testing multigroup invariance. Therefore the invariance analyses
were performed using a two-stage routine. The first step was to investigate the data
from each city to ascertain its fit with the model. The second step was to investigate the
data from all the four cities simultaneously in a single analysis.
6.6.5.2
Step one of the invariance tests
The initial step involved the application of the final structural model to the data from
each city. The sample size and fit indices for each group and the whole data set are
presented in Table 6.24, which shows goodness-of-fit indices for the four cities.
200
Table 6.24 Goodness-of-fit indices showing comparative analysis of data
from the four cities
Note: p= Bollen-Stein p values.
The model served as a baseline for comparison. The chi-square test statistics showed
that the data did not fit the model well for each of the four cities (p=0.001) and hence
did not support configural invariance across the four groups. Since all of SRMR values
were greater than 0.05, it may suggest that outliers were present in the four groups.
However, the results did not improve even after deleting the potential outliers.
Consequently, no outlying cases were removed. The components of influencing
dimensions (product, regulatory, lifestyle) would accommodate different statements.
For all these models, all parameter estimates were not significant, and the pattern of
relationships between influencing dimensions with personal attitudes, pre-purchase
evaluation and purchase intention were very different. It possibly suggests that
individual models would apply for each city. According to model fit indices used in this
study (Table 6.2), and based on value of CMIN/DF, the results suggested that in
comparative terms, the model fitted the data best for respondents from Chengdu
(CMIN/DF=1.576),
followed
by
Beijing
(CMIN/DF=1.771),
Shenzhen
(CMIN/DF=1.772) and Shanghai (CMIN/DF=2.912).
6.6.5.3
Step two of the invariance tests
The most efficient way of conducting measurement invariance tests in AMOS is to run
a number of simultaneous multiple-group testing in a single analysis (Byrne 2010;
Cunningham 2008). This involves invariance tests across the four groups
simultaneously. Structural covariances were chosen, which means equating the
variances and covariances of the measured variables across all groups (Cunningham
201
2008). Hence analysis was performed based on the final model by applying it to the data
obtained from respondents in the four cities simultaneously. The idea of invariance tests
is to progressively test increasingly constrained models that are nested in previously
estimated less constrained models. The difference of chi-square (∆ χ2) test is required
(Cunningham 2008), and can be calculated from results shown in Table 6.25. This result
suggests that the variance and covariances patterns were not consistent across four cities.
Table 6.25 Goodness-of-fit indices for data from all four cities
Model
Unconstrained
Measurement weights
Structural weights
Structural covariances
Structural residuals
Measurement residuals
Saturated model
Independence model
NPAR
296
239
185
182
158
74
1624
112
CMIN
2841.312
2953.846
3046.691
3059.356
3120.014
3730.999
.000
9354.366
DF
1328
1385
1439
1442
1466
1550
0
1512
P
.000
.000
.000
.000
.000
.000
CMIN/DF
2.140
2.133
2.117
2.122
2.128
2.407
.000
6.187
The difference in chi-square between the nested models is also distributed as a chisquare distribution with degrees of freedom equal to the difference in degrees of
freedom ∆ x 2/∆ df
=3059.36-2841.312/1442-1328
= 1.61E-08.
Obviously this is significant at p<0.001 level.
The above analysis indicates that the data was not a good fit to the structural equation
model as there were significant differences across the four cities. Therefore, the
invariance tests indicate that the structural equation model did not apply homogeneously
across data from all four cities. Although the model fits well with the entire data, it does
not work with data from each dividual city. Hence, we cannot assume that the data was
drawn from the same population. In other words, respondents from the north had
different opinions to those from the south, east and west.
202
6.6.5.4
Step three of the invariance tests
The final step was to locate where the differences occurred in the data from the four
cities. There are a number of ways to operationalise this. One way is to examine the
standardised factor coefficients (standardised regression weights) across data from the
four cities (Vandenberg & Lance 2000). Table 6.26 depicts the AMOS output of the
data from respondents in each city, showing significant differences in the standardised
regression weight.
Table: 6.26 Standardised regression weights (four cities)
Beijing Shenzhen Chengdu Shanghai
<---
PRODUCT_RELATED
REGULATORY
LIFESTYLE
ETHNOCENTRISM
BEL/ATT
REGULATORY
LIFESTYLE
PRODUCT_RELATED
ETHNOCENTRISM
<---
PRE_-PURCHASE
.740
.827
.582
.416
<---
LIFESTYLE
.417
-.047
.201
-.056
<---
PRODUCT_RELATED
-.213
-.164
.010
.035
<---
REGULATORY
-.005
-.137
-.042
.183
<---
ETHNOCENTRISM
-.160
.220
-.060
.088
BEL/ATT
<---
BEL/ATT
BEL/ATT
BEL/ATT
PRE_-PURCHASE
PRE_-PURCHASE
PRE_-PURCHASE
<--<--<--<--<--<---
PRE_-PURCHASE <--PRE_-PURCHASE
BEHAVIOUR_INTENTION
BEHAVIOUR_INTENTION
BEHAVIOUR_INTENTION
BEHAVIOUR_INTENTION
BEHAVIOUR_INTENTION
.284
.407
.319
.442
.422
.173
-.019
.077
.597
.267
.160
.096
.120
.372
.359
.054
.267
.383
.007
.188
.348
.304
.273
-.003
.032
.157
.114
.022
.009
.222
.049
.367
-.027
-.223
.013
.119
(Chi-squared= 2841.321, df =1328, p<0.001)
Data from respondents in Shanghai had the worst model fit (details are shown in Table
6.25), such as χ2/DF equivalent 2.912 in Shanghai compared to 1.576 in Chengdu. Thus,
six hypotheses were tested again based on data obtained from respondents of Shanghai.
Table 6.28 summarises the results of the hypotheses tests for Shanghai.
203
<--<--<--<--<--<--<--<---
BELIEFS/ATTITUDES
PRE_PURCHASE
BEHAVIOUR_INTENTION
BELIEFS/ATTITUDES
PRE_PURCHASE
BEHAVIOUR_INTENTION
BELIEFS/ATTITUDES
PRE_-PURCHASE
BEHAVIOUR_INTENTION
PRE_-PURCHASE
BEHAVIOUR_INTENTION
H2a
H2b
H2c
H3a
H3b
H3c
H4a
H4b
H4c
H5
H6
Note: ***p<.001; **p <.01; *p <.05
<---
BEHAVIOUR_INTENTION
<---
<---
<---
PRE_PURCHASE
BELIEFS/ATTITUDES
ETHNOCENTRISM
ETHNOCENTRISM
ETHNOCENTRISM
LIFESTYLE
LIFESTYLE
LIFESTYLE
REGULATORY
REGULATORY
REGULATORY
PRODUCT
.455
.160
.180
.222
.059
-.046
.017
-.002
.214
.122
.287
.051
.489
H1c
PRODUCT
PRE_PURCHASE
H1b
<---
.579
PRODUCT
BELIEFS/ATTITUDES
H1a
<---
Estimate
Hypothesis
no.
.092
.096
.144
.148
.137
.054
.054
.050
.092
.093
.089
.128
.138
.132
S.E.
Table 6.27 Evaluated hypotheses tests for Shanghai
4.929
1.668
1.251
1.501
.431
-.852
.312
-.044
2.336
1.304
3.237
.398
3.554
4.369
C.R.
***
.095
.211
.133
.667
.394
.755
.965
*
.192
**
.691
***
***
P
.416
.157
Rejected
Accepted
.088
.119
.032
-.056
.022
-.003
.183
.114
.273
.035
.367
.442
Beta
Rejected
Rejected
Rejected
Rejected
Rejected
Rejected
Accepted
Rejected
Accepted
Rejected
Accepted
Accepted
Accepted/
rejected
204
A comparison of Tables 6.25 and 6.27 reveals that the relationships between constructs
were different as compared to similar relationships in the whole data set applied to the
model, i.e. regulatory construct to pre-purchase and behavioural/purchase intention,
lifestyle construct to beliefs/attitudes and behavioural/purchase intention, beliefs/attitudes
to pre-purchase evaluation. The results also suggest that the hypotheses tests (H1–H6) were
quite different for data obtained from Shanghai and from four cities as a whole. There are a
total 14 series of hypotheses, five of them demonstrating different results. The details are as
follows:
(1) Regulatory construct had direct relationship with pre-purchase evaluation in the whole
data set, but did not show direct influence effect in the data obtained from Shanghai.
(2) Regulatory construct did not have direct relationship with behavioural/purchase
intention in the data obtained from the four cities as a whole, but had direct relationship
with behavioural/purchase intention in the data obtained from Shanghai.
(3) Lifestyle construct had direct relationship with beliefs and attitudes in the data obtained
from the four cities as a whole, but did not have direct relationship with beliefs and
attitudes in the data obtained from Shanghai.
(4) Lifestyle construct had direct relationship with behavioural/purchase intention in the
data obtained from the four cities as a whole, but did not have direct relationship with
behavioural/purchase intention in the data obtained from Shanghai.
(5) There was direct relationship between beliefs/attitudes and pre-purchase evaluation in
the data obtained from the four cities as a whole, but the results from data obtained from
Shanghai did not show the direct relationship.
These results therefore would suggest that the relationships between factors and associated
latent variables were not invariant across the data obtained from each of the four cities.
They reveal that China is not a homogeneous country, a finding which is similar to those in
205
previous studies (Cui & Liu 2000; Fram, Le & Reid 2005; Wong & Yu 2003; Yusuf &
Brooks 2010).
6.7
Chapter summary
This chapter has presented a detailed analysis of data obtained in Study Two. A series of
CFAs were performed to validate the findings from the EFA results of Study One. This
process confirmed and modified the factors and the associated constructs in the
measurement models, and assessed their validity and reliability. The re-specified models
indicated reasonable model fit for the data. Finally, the measurement models were linked
using a structural equation to obtain a final best-fit model. A series of hypotheses were
tested. Invariance tests were performed on the data obtained from the four selected cities.
206
CHAPTER SEVEN: DISCUSSION OF FINDINGS,
RECOMMENDATIONS AND CONCLUSION
7.1
Introduction
This study was conducted to explore and investigate the various determinants which
influence consumers’ purchase behaviour of organic food products in urban China. The
three underpinning theories used in developing the conceptual framework for this research
study are: the Consumer Decision Process model (CDP), the Hierarchy Effects model and
the Theory of Planned Behaviour (TPB). The five stages of the proposed conceptual
framework as explained in Chapter Three were operationalised and investigated by
analysing data obtained in the pilot study (Chapter Five) and the main study (Chapter Six).
This chapter engages in a discussion of the relationships between the various stages of the
conceptual framework as evidenced by the final best-fit model (Section 7.2). The two
research questions are addressed separately and answers are provided. Section 7.3 discusses
the implications of the findings regarding the first research question which relates to the
factors that influence urban Chinese consumers’ beliefs and attitudes, pre-purchase
evaluation and behavioural/purchase intention towards the purchase of organic food.
Section 7.4 addresses the second research question which relates to the role played by the
purchase behaviour of organic food. The seven hypotheses presented in this study were
empirically tested, and the resulting outcomes provide key findings as conclusive evidence
of consumer buyer behaviour of organic food products in urban China. Section 7.5 includes
contributions and recommendations both for the theory and practice of understanding
consumer buyer behaviour of organic food in urban China. It becomes evident that there are
implications for the various stakeholders involved in this industry. In addition this chapter
highlights the limitations of the study (Section 7.6) and suggests future research directions
(Section 7.7). Finally Section 7.8 provides concluding remarks to this research study.
Figure 7.1 diagrammatically illustrates the structure of Chapter Seven.
207
Figure 7.1 Structure of Chapter Seven
Source: developed for this research
7.2
Implications of the best-fit SEM model
The main aim of this research study was to investigate the determinants that influence
urban Chinese consumers’ decision-making towards organic food consumption. A five
stages conceptual framework was proposed to achieve this aim. The five stages were named
as influencing, cognitive/affective, evaluation of alternatives, behavioural/purchase
intention and purchase.
208
As the antecedent of purchase and consumption of organic food, the influencing stage plays
a vital role in the total consumer decision-making process. The emphasis was placed on the
influencing aspects and dimensions of this process in the Chinese context. This stage
comprised four constructs: product, regulatory, lifestyle and ethnocentrism. The final bestfit structural equation model (Figure 6.20) revealed that three influencing constructs,
namely, those related to product, regulatory and lifestyle, directly and significantly
influence urban Chinese consumers’ beliefs and attitudes towards the purchase of organic
food. They also influence stages two (cognitive/affective), three (evaluation of alternative)
and four (behavioural/purchase intention). The fourth construct of the influencing stage,
ethnocentrism, was not found to have a significant effect on consumers’ beliefs/attitudes,
pre-purchase evaluation and purchase intention.
The findings of this study reveal that the influencing dimension constructs (product and
regulatory) are associated with pre-purchase evaluation, and also with behavioural/purchase
intention through the mediatory affects of beliefs/attitudes and pre-purchase evaluation.
There is a positive direct influence of the lifestyle construct on organic food behavioural/
purchase intention. The results of the final best-fit model regarding beliefs/attitudes reveal
that Chinese consumers believe that organic food is good for the environment, and has
superior quality. Also organic food labels denote high quality of product, and consumers
indicated their preference for organic food as compared to conventional food. In addition,
three new linkages between (i) the product and lifestyle constructs, (ii) regulatory and
product constructs and (iii) personal attitudes and behavioural intentions were made in the
final best-fit model. These linkages indicate direct and significant relationships between
these factors and constructs in the context of this research study. These findings assist in the
accurate identification of determinants that influence Chinese consumers’ purchase
intentions with regard to organic food, and have significant implications for both the
theoretical study and management of the industry. A set of action plans is suggested, as
summarised in Table 7.1.
209
Research question
one:
What are the factors
that influence urban
Chinese consumers’
beliefs and attitudes,
pre-purchase
evaluation and
behavioural/purchase
intention towards the
purchase of organic
food?
Research questions
Lifestyle related
construct indirectly
and positively
influences on urban
Regulatory related
construct
influences on urban
Chinese consumers’
beliefs/attitudes
towards the purchase
of organic food
Product related
construct influences
on urban Chinese
consumers’
beliefs/attitudes
towards the purchase
of organic food.
Key research
findings
Consumption of organic food is part of
the lifestyle in urban China. The
findings suggest that consumers who are
more self-indulgent and more desirous
of variety are more likely to have
1. This study suggests that sensory
appeal (appearance, texture, smell) as
well as nutritional value are part of a
complex multitude of influences that
shape Chinese consumers' purchase
intentions with regard to organic food.
The freshness of food is a key dietary
consideration of Chinese consumers’
and a central element of Chinese
cuisine.
2. Price sensitivity seems less important
in the purchase of organic food.
1. Chinese urban consumers believe that
there is lack of sufficient government
involvement in scrutinising certifications
and labels on organic food.
2. The terms 'organic' and 'green' are
used interchangeably by many in the
industry and are, in consequence,
confusing for consumers.
Implication of the current study
210
1. The organic food producers and marketers should
develop technologies and methods of storage that
will keep the products fresh for longer periods
without resorting to the use of chemicals.
Marketing organic food according to this concept
means not only the method of production, but also
the whole process of distribution from the farm to
the table by means of organic methods.
2. Organic marketers may capitalise on the image of
organic food as fresh and environmentally friendly,
even though it is more expensive than conventional
food.
1. Government policy makers and the organic
industries should ensure the credibility of
inspection and labelling of organic food to enhance
Chinese consumers' trust in overall food safety.
2. There is a need for the industry to clearly explain
the meaning of the term ‘organic’ and all that it
implies with regard to foods. Educational
campaigns need to be organised to raise awareness
of, and clarify, the term 'organic'.
The organic marketers should endeavour to focus
on the ‘variety-seeking’ and ‘self-indulgent’
consumers. New marketing strategies should
promote the additional attributes of organic food
and how they meet the different concerns of these
Action plans for the decision makers
Table 7.1 Action plans for the decision makers
Research question
two:
Evaluate the role of
demographic variables
and usage patterns in
the behavioural
/purchase intentions of
urban Chinese
consumers towards
organic food
1. Organic food
consumptions in urban
China are partially
influenced by the
consumers' education
and income levels.
2. There are significant
differences in terms of
social status, product
awareness and food
safety issues related to
purchasing organic
food in urban China
Chinese consumers
beliefs/attitudes
towards organic food
1. This finding suggests the most
affluent consumers in urban China who
purchase organic food were also well
educated.
2. There are five intended uses for
organic food in urban China, indicating
that the important attributes of organic
food have exerted an influence on the
different usage patterns.
positive attitudes towards organic food.
211
1. The organic industry should anticipate targeting
consumers who are highly educated and are high
income earners. Providing additional knowledge of
the concept of 'organic' is a key to motivating
consumers in the higher demographic.
2. Organic marketers should improve the packaging
of their products, in order to emphasise the benefits
of consuming organic food, as well as provide
certified organic logos that are signs of quality and
prestige.
consumers. These include such things as
environmental and health benefits, and the need to
possess novelty icons or follow trends.
7.3
Discussion of findings relating to research question one
The first research question as previously articulated in Chapter One read as follows:
What are the factors that influence urban Chinese consumers’ beliefs and
attitudes, pre-purchase evaluation and behavioural/purchase intention towards the
purchase of organic food?
Research question one has been addressed through the influencing, cognitive/affective,
evaluation of alternatives and behavioural/purchase intention stages of the proposed
conceptual model. This model was tested and validated using a series of hypotheses (H1H6). The hypotheses tests performed on the constructs of the final best-fit model revealed
that product, regulatory and lifestyle influenced urban Chinese consumers’ beliefs and
attitudes towards the purchase of organic food. The subsequent sections address details of
how the various constructs of the influencing stage impact on pre-purchase evaluation and
purchase intention of urban Chinese consumers of organic food. A detailed discussion on
each of these significant constructs and hypotheses follows:
7.3.1 Product
H1a which hypothesises that the organic food product-related construct has a positive
influence on urban Chinese consumers’ beliefs/attitudes was accepted.
The results of the final best-fit structural equation model (Figure 6.20) demonstrate that the
product-related construct has a total effect of 0.468 on beliefs and attitudes. The total effect
comprises a direct effect of 0.38 plus an indirect effect of 0.08 (0.38+0.08=0.46). The
evidence of this outcome is clearly indicated in Appendix 7. Kline (2005) suggests that the
higher the standardised regression weights in the structural equation model, the more
important the construct in predicting the results. This indicates that the product-related
8
All statistical analysis results in this chapter will only provide a two decimal showing.
212
construct is the most important construct positively influencing urban Chinese consumers’
beliefs and attitudes towards the purchase of organic foods. The texture, appearance, smell,
nutritional value and trendiness are important attributes associated with this construct.
These findings are consistent with previous studies (Magnusson et al. 2001; Torjusen et al.
2001), which suggest that aspects such as sensory appeal (e.g. odour, texture) are important
to organic food consumers, and are part of the complex function of a multitude of
influences associated with the consumption of food. The items related to the product
construct are herewith discussed in a logical sequence as enumerated in the survey
instrument shown in Appendix 1.
Previous studies (Veeck 2000; Veeck & Burns 2005) suggested that serving fresh food to
Chinese consumers is important, and even more important is their mind-set that food is
‘fresh’ only when it has been purchased that day and has not been refrigerated. Frequent
shopping trips to purchase the freshest, tastiest and nutritious meals for family members
remain the custom in China. The results of this study further confirm that Chinese
consumers hold in high regard food sensory characteristics (Veeck 2000; Zhao et al. 2000).
The freshness and texture are of high value on decision-making of purchasing food
products (Zhao et al. 2000). As stated by Ennis (2007), consumers are possibly reluctant to
purchase products which have no visual signs of quality. Importantly, in the process of
obtaining the final best-fit structural equation model, the item relating to ‘taste’ was deleted.
Hence, it is likely that ‘taste’ is less important for organic consumers in China. Confirming
this view was a Taiwanese study (Chen 2007) which reported that taste evaluation (sensory
appeal) did not contribute in influencing consumers’ purchase attitudes towards organic
food. Zhao et al. (2007) also suggest that US customers perceive no significant taste
difference in conventional and organic produced vegetables.
The findings of this study suggest that organic food is perceived as being of high nutritional
value. This view is supported by several studies conducted in different countries (Botonaki
et al. 2006; Krystallis & Chryssohoidis 2005). Generally organic food consumers perceive
that organic food is more nutritious than conventional food, even though this perception is
213
sometimes overstated, especially as there is lack of extensive scientific evidence to support
such a claim (Kluger 2010).
Respondents in this study perceive organic food to be ‘trendy’. This finding is similar to
that reported by Lockie et al. (2002) who found that stereotype images of organic food
consumers seem to be more interested in fashion than anything else. A study conducted in
Thailand by Roitner-Schobesberger et al. (2008) revealed that the second most important
motive for the purchase of organic food was its attraction as new and ‘fashionable’. The
way organic food is marketed in the media reinforces its perception amongst some
consumers as being fashionable and trendy (Hill & Lynchehaun 2002).
As the data for this research study was obtained in early 2010, a period following the most
severe global economic downturn since the Great Depression, it was expected that the price
of organic food would be an important consideration to consumers. However, in the process
of obtaining the final best-fit structural equation model, items relating to price were
removed. It was expected that price sensitivity would be important for Chinese consumers,
nevertheless the findings of this study suggest that price has no significant influence on
their beliefs and attitudes or on their purchase intention.
Previous literature focusing on global studies suggests that the higher cost of organic food
is one of the barriers to its consumption. Moreover, price premiums tend to negatively
affect organic food purchasing (O’Donovan & McCarthy 2002; Shepherd, Magnusson &
Sjödén 2005; Tregear, Dent & McGregor 1994). On the other hand, Chang and Zepeda
(2005) observe that consumers who are more knowledgeable about organic foods are
willing to pay higher prices.
In contrast, the findings of this study provide a somewhat different outcome. Chinese
consumers do not have great understanding about organic food, and they seem to accept
and are willing to pay higher prices. Only 15.7% of respondents in this study believed that
they knew a lot about organically produced food, while the others were unsure as to what
‘organic’ meant and knew little about it. This indicates that organic food is still a new
214
concept in China, and its awareness is low. Chinese consumers are generally not
knowledgeable about organic foods, even though they tolerate higher prices and are willing
to pay extra.
Some other studies (Fotopoulos, Krystallis & Ness 2003; Tarkiainen & Sundqvist 2005)
also suggest the reduced importance of price. Consumers did not see that the higher price of
organic food would affect their intention to buy. As was evidenced in a Greek study
(Krystallis, Fotopoulos & Zotos 2006), organic consumers are less price sensitive and more
interested in the quality of the food. This may provide evidence that, in a relatively affluent
urban Chinese society, price is less important than food safety and health related attributes.
This may support the argument that food safety and not price is one of the more important
issues influencing consumers’ purchase of organic food products in urban China. In this
respect, Eves and Cheng (2007) report that product appearance and quality are more
important than price when evaluating factors driving Chinese consumers’ purchase
intention of new food products.
H1b which hypothesises that the product-related construct is positively correlated to the
pre-purchase evaluation was accepted.
The output of the final best-fit model (Appendix 7) demonstrates that the product-related
construct has a total effect of 0.40 on pre-purchase evaluation which comprises a direct
effect of 0.20 plus an indirect effect of 0.20 (0.20+0.20=0.40). This reveals that the
product-related construct is positively correlated to the pre-purchase evaluation.
H1c which hypothesises that the product-related construct is positively correlated to urban
Chinese consumers behavioural/purchase intention was not supported.
Furthermore, the results (Appendix 7) also demonstrate that the product-related construct
has a total effect of 0.30 on purchase intention which comprises a direct effect of -0.07 plus
an indirect effect of 0.37 (-0.07+0.37=0.30). The very weak (-0.07) direct effect suggests
that there is no significant relationship between the product-related construct and purchase
215
intention. However, the product-related construct is associated with purchase intention
through the mediatory construct of beliefs/attitudes and pre-purchase evaluation. These
findings are similar to those reported by Chryssochoidis (2000) where the standardised
coefficient between purchase intention and organic food physical product appearance is
weak (0.08).
7.3.2 Regulatory
H2a which hypothesises that the regulatory construct has a positive influence on urban
Chinese consumers’ beliefs/attitudes was accepted.
The results (Appendix 7) of the final best-fit model (Figure 6.20) demonstrate that the
regulatory construct in the influencing stage has a total direct effect of 0.27 on
beliefs/attitudes. This suggests that it is the second most important construct influencing
consumers’ beliefs/attitudes after the product construct. Although the relationship between
the regulatory construct and beliefs/attitudes is relatively weak (0.27), it is statistically
significant (p<0.05), which suggests that it plays an important role in directly and
positively influencing Chinese urban consumers’ beliefs and attitudes. Consumers perceive
that there is lack of adequate government control on organic production. Moreover, organic
logos and labels need to be controlled and monitored, including allocating more land for
organic production. In addition, the findings of the study in relation to certification of the
logos for organic food suggest that consumers believe that the organic food market is quite
chaotic, and the terms ‘organic’ and ‘green’ are confusing. The items related to the
regulatory construct are herewith discussed in a logical sequence enumerated in the survey
instrument shown in Appendix 1.
The findings reveal that, as in many western countries, Chinese consumers are motivated
by food safety issues related to personal health and also the environment. They are
suspicious regarding the quality of food purchased and have high quality expectations of
organic food certification, inspections and policy enforcement. They are also eager to
understand more about organic food and what this industry is all about. An organic food
216
logo is the consumer’s guarantee that the product has been produced organically. This
finding is similar to that obtained by a study conducted by the United States Department of
Agriculture (2008). It reveals that Chinese consumers may doubt whether labelled organic
products are really organic or not and whether they meet all declared standards. On the
matter of market regulation, the results support previous studies (Giannakas 2002; Radman
2005) which suggest that consumers believe that the organic market is not regulated by
appropriate laws. They demonstrate a low trust in the regulations of organic food and they
possess little awareness of organic systems. Similar results are reflected in Table 6.20
which lists the top five most important attributes relating to the purchase of organic food.
From the foregoing discussion it is evidenced that Chinese consumers regard certification,
quality, enforcement, information about nutritional value and food safety related to organic
food as the key issues when they consider their purchase.
H2b which hypothesises that the regulatory construct is positively correlated to prepurchase evaluation was accepted.
The results of the final best-fit SEM reveal that the regulatory construct has a total effect of
0.43 on pre-purchase evaluation (Appendix 7). This comprises a direct effect of 0.38 plus
an indirect effect of 0.05 (0.38+0.05=0.43). These findings indicate that the regulatory
construct is positively correlated with the pre-purchase evaluation.
H2c which hypothesises that the regulatory construct is positively correlated to Chinese
consumers’ behavioural/purchase intention was not supported.
The regulatory construct has a total effect of 0.40 on purchase intention which comprises a
direct effect of 0.07 plus an indirect effect of 0.33 (0.07+0.33=0.40). The direct effect is
very weak, showing its relative unimportance. This suggests that the regulatory construct is
associated with behavioural/purchase intention through the mediatory affect of beliefs/
attitudes and pre-purchase evaluation.
217
Depending on the pre-purchase evaluation, Chinese consumers would purchase or give up
purchasing organic food. Government policy or regulations may directly influence their
beliefs/attitudes and pre-purchase evaluation, and further indirectly influence purchase
intention. From a related regulatory standpoint, the results confirm that consumers
searching for information about organic food products during the pre-purchase phase are
interested to know where they are produced. They ascertain whether the organic logo can
be trusted, and where they can buy available products. Krystallis, Arvanitoyannis and
Chryssohoidis (2006) suggest that consumers’ purchase of meat is based on the evaluation
of the pre-purchase phase which includes visual quality. Organic buyers show more interest
in signs of quality guaranteed by the label. This implies that regulatory attributes
significantly influence consumers’ pre-purchase evaluation process.
The final best-fit SEM (Figure 6.20) also demonstrates that the product-related construct
has a direct effect of 0.28 (Appendix 7 Table B) on the regulatory construct. This
supplementary finding suggests that information on organic food products could make them
more attractive and influence consumers’ perceived evaluation. This is also found in a
European study (Wier & Calverley 2002) where product specific characteristics are one of
the major motivations for consumers to purchase organic food.
7.3.3 Lifestyle
H3a which hypothesises that the lifestyle related construct has a positive influence on the
urban Chinese consumers’ beliefs/attitudes was accepted.
The results (Appendix 7) of the final best-fit SEM (Figure 6.20) demonstrate that the
lifestyle construct has a total effect of 0.37 on beliefs and attitudes, which comprises a
direct effect of 0.24 plus an indirect effect of 0.13 (0.24+0.13=0.37). These findings reveal
that the lifestyle construct plays a relatively important role in directly and positively
influencing consumers’ personal beliefs and attitudes. The final best-fit SEM retained two
lifestyle factors: self-indulgence and variety seeking. The original proposed conceptual
framework (Figure 3.2) which contained the ‘opinion leadership’ factor was removed. This
218
category of consumers are opinion leaders among their friends and their community. They
do not seek novel and trendy products and are less affected by advertisement of new
products. They are also very traditional, less experimental and are not risk takers (Yang
2004). The findings of this study revealed that consumers’ beliefs and attitudes are
influenced partially by the two lifestyle segments: variety seeking and self-indulgence. The
‘self-indulgence’ category comprises those who make spontaneous decisions without
deliberate thinking, and are those who do what and when they feel like. This category also
considers the purpose of ‘making money is to spend it’. Variety seekers are those seeking
information, who want variety and the novelty of doing things. They always try something
new and unique and enjoy a challenge. They are prepared to take the risk to experience new
products and are quite influenced by advertisement. The findings suggest that organic food
consumers in urban China are less traditional and are looking for experimental experiences.
They are risk takers who are individually centred, who seek novelties and new lifestyles.
These findings are in contrast to those of Gil, Gracia and Sanchezs (2000) who suggest that
Spanish consumers who display negative attitudes towards the purchase of organic food
product generally perceive those products to be fashionable.
This study demonstrates that the two lifestyle segments of self-indulgent and variety
seeking influence consumers’ beliefs and attitudes towards organic food, suggesting that
people who are more self-indulgent and more desirous of variety are more likely to have a
positive attitude towards organic food. Previous studies suggest that consumption of
organic food reflects a ‘greening of consumer lifestyles’ (Lockie et al. 2002, p. 24). For
regular organic food buyers, consumption of organic food is part of a lifestyle, with related
interests in nature, society and the environment (Schifferstein & Oude Ophuis 1998).
Lockie et al. (2002) suggest stereotypical images of organic consumers as ‘greenies’,
‘health nuts’ or ‘yuppies’, who are more interested in fashion than anything else, hence
consumption of organic food reflects a ‘greening’ lifestyle. Organic food consumers in
China may belong to this kind of lifestyle.
219
H3b which hypothesises that the Lifestyle construct is positively correlated to the prepurchase evaluation was not supported.
The lifestyle-related construct has a total effect of 0.22 on pre-purchase evaluation which
comprises a direct effect of 0.06 plus an indirect effect of 0.16 (0.06+0.16=0.22). These
findings show a direct relationship between lifestyle and pre-purchase evaluation as being
weak, and relatively unimportant. It reveals that the lifestyle construct impacts on the
consumer pre-purchase evaluation of organic food through the mediating effects of
consumers’ attitudes.
However, H3c which hypothesises that the lifestyle construct is positively correlated to the
Chinese consumers behavioural/purchase intention was accepted.
The results (Appendix 7) demonstrates that the lifestyle construct has a total effect of 0.34
on purchase intention which comprises a direct effect of 0.13 plus an indirect effect of 0.21
(0.13+0.21=0.34). It reveals that the lifestyle construct impacts on the consumer purchase
intention of organic food. This is in agreement with studies of Gil, Gracia and Sanchez
(2000) and Sanjuán et al. (2003) who concur that the lifestyle attribute is a key factor in
explaining organic food consumption. Consumers who are concerned about balanced and
healthy lifestyles are more likely to have positive attitudes towards organic food. This then
enhances consumers’ purchase intention of organic food.
In addition, the results of the final best-fit SEM also demonstrate that the lifestyle construct
has a direct effect of 0.29 on the Product-related construct (Appendix 7 Table A). This
suggests that the lifestyle related attributes significantly influence the Product-related
construct. This result is in line with a Chinese lifestyle study (Zhu et al. 2009) which
suggests that there are significant interactive effects between lifestyle influences and
product attributes. As such product attributes are a critical factor for understanding
consumers with different lifestyles. People with certain lifestyles will be drawn particularly
to a set of product attributes because of the perceived functional or emotional relevance of
these product attributes to the consumers’ needs. Reid et al. (2001) also report that food
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related lifestyles are displayed when influenced by novelty and social events. Consumers
like to experiment with new flavours and new food styles.
7.3.4 Ethnocentrism
H4a which hypothesises that the ethnocentrism construct has a significant influence on the
beliefs and attitudes of urban Chinese consumers towards the purchase of organic food was
not supported.
Noticeably the results (Appendix 7) of the final SEM (Figure 6.20) reveal that there is no
significant relationship between the fourth influencing construct, ethnocentrism, and urban
Chinese consumers’ beliefs/attitudes. It has a total effect of 0.16 on consumers’
beliefs/attitudes which comprise a direct effect of 0.04 plus an indirect effect of 0.12
(0.04+0.12=0.16). It is evident that there is a very weak relationship (0.04) between
ethnocentrism and beliefs/attitude, meaning that it is relatively unimportant. The
ethnocentrism construct as such cannot impact urban Chinese consumers’ beliefs/attitudes
towards organic food.
H4b which hypothesises that the Ethnocentrism construct is significantly correlated to the
pre-purchase evaluation was not supported.
The results (Appendix 7) of the final best-fit SEM demonstrate that the ethnocentrism
construct has a total effect of 0.12 on pre-purchase evaluation which comprises a direct
effect of 0.04 plus an indirect effect of 0.08 (0.04+0.08=0.12). This therefore displays a
very weak relationship between ethnocentrism and pre-purchase evaluation which is not
supported by the hypothesis.
H4c which hypothesises that the Ethnocentrism construct is significantly correlated to
Chinese consumers’ behavioural/purchase intention was not supported.
Furthermore, the ethnocentrism construct has a total effect of 0.16 on purchase intention
which comprises a direct effect of 0.02 plus an indirect effect of 0.14 (0.02+0.14=0.16).
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The direct relationship between ethnocentrism and behavioural/purchase intention is still
very weak and hence relatively unimportant.
Overall, the foregoing results demonstrate that the ethnocentrism construct does not
significantly influence consumers’ beliefs/attitudes, pre-purchase evaluation or purchase
intention of organic food in urban China. In contrast, some previous studies suggest that
organic food is perceived as being environmentally friendly, hence consumers may prefer
to buy less travelled organic products from local sources rather than shipped from overseas
(Williams & Hammitt 2000; Bonti-Ankomah & Yiridoe 2006). This has a bearing on
support for the locally produced organic foods. Therefore ethnocentric attitudes may
develop in organic food consumers owing to their desire of supporting local farmers.
‘Ethnocentric’ category consumers prefer to purchase local and domestically produced
organic food products rather than imported ones (Fotopoulos & Krystallis 2002a).
Chinese consumers in this study tended to show weak ethnocentric beliefs and attitudes
towards organic food. These findings are supported by Hsu and Nien (2008) who suggest
that ethnocentrism attitudes towards imported products in big cities tend to be diluted,
particularly with those consumers who are more exposed to foreign products. In relation to
this, consumers from developing countries such as China sometimes tend to perceive
domestic products as being of lower quality (Wang & Chen 2004). It may also suggest that
Chinese consumers are not too concerned about the country of origin which features in the
five least important attributes of purchasing organic food (Table 6.21).
Noticeably, nearly half of the respondents did not mind which country or region the organic
food came from. Beyond this, interestingly Australia and New Zealand products had the
highest acceptance rates (17.8%) as preferred importing countries compared to the rest of
the world (Figure 6.7). This could possibly be due to them being perceived as clean and
green as well as the successful presence of New Zealand dairy products in the Chinese
market (Sedgman 2011). At the same time the survey results indicate a low awareness and
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recognition (Figure 6.2) of the Australian Certificated Organic logo (ACO), in comparison
to the better recognised US logo.
The findings of this study are supported by a recent McKinsey research survey (St-Maurice,
Süssmuth-Dyckerhoff & Tsai 2008) which reported that, in 2008, only 30% of respondents
trusted Chinese brands, down sharply from 44% in 2007. In the bigger cities, only 13%
expressed a clear preference for Chinese brands. This proves that in Chinese first and
second tier cities, consumers tend to show no ethnocentrism towards organic food.
Although there are alternative organic equivalent products produced in China, the recent
spate of food scandals has damaged consumers’ confidence in terms of domestically
produced food products.
The result from this study on the role of ethnocentrism is also supported by a recent report
from Euromonitor International (2011) which concludes that Chinese consumers are losing
confidence in locally produced milk after a series of scandals involving food safety. In
2008, melamine-tainted milk power killed six infants and saw over 300,000 children unwell
across the country. This food scandal involved one of China’s largest dairy firms SanLu
(Euromonitor International 2011; Shumei 2009). The scandal caused Chinese consumers to
lose faith in the country’s dairy industry. Another more recent incident involving China’s
largest meat processor Shuanghui provoked the government to suspend its activities due to
the presence of the illegal additive Clenbuterol9 which was found in the pork meat products
(Wu & Zhao 2011; Zhou 2011). The Clenbuterol pork meat scandal led consumers to
express concern and disappointment in the meat processor giant. Both SanLu and
Shuanghui were considered to be amongst the most reliable brands in China, but now
Chinese consumers are unsure which brand they can trust. These issues relating to food
safety seem to be paramount in the minds of consumers in the light of the above incidents
of food poisoning and food scandals.
9
Clenbuterol is a chemical that can be used to prevent pork meat from accumulating fat. It is banned as an
injected additive in pig feed in China as it can end up in the flesh of pigs and is poisonous to humans.
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7.3.5 Pre-purchase evaluation and behavioural/purchase intention
H5 which hypothesises that urban Chinese consumers’ beliefs/attitudes are directly and
positively correlated to pre-purchase evaluation was accepted.
H6 which hypothesises that the pre-purchase evaluation construct is directly and positively
related to behavioural/purchase intention was accepted.
The results (Appendix 7 Table A) of the final best-fit SEM (Figure 6.20) demonstrates that
(i) consumers beliefs and attitudes have a total effect of 0.20 on pre-purchase evaluation
which is both a direct effect and a positive relationship and (ii) that pre-purchase evaluation
has a total effect of 0.62 on purchase intention which demonstrates there is a direct effect
and strong relationship between pre-purchase evaluation and behavioural/purchase
intention.
These findings support expectations that consumers’ beliefs/attitudes play an important role
in the pre-purchase evaluation. Pre-purchase evaluation ultimately influences the purchase
intention. Consumers with a positive attitude to organic foods are more likely to have a
positive pre-purchase evaluation. Finally, potential consumers’ positive pre-purchase
evaluations will likely translate into purchase intention. After searching and obtaining
adequate information related to organic food, potential consumers evaluate this information
and then select the best option. This study confirms previous findings, for example,
Tarkiainen and Sundqvist (2005) in which attitudes were found to significantly contribute
to purchase intention among Finnish consumers.
In conclusion, the findings (Table 6.15) of this study reveal that consumers’ attitudes are
positively and directly influenced by product-related, regulatory and lifestyle constructs
(especially those in the category of variety seeking and self-indulgence). The productrelated construct (Beta=0.38) made the strongest contribution to explain beliefs/attitudes
towards organic food. This was followed by regulatory (Beta=0.27) and lifestyle
(Beta=0.24) effects. The results also reveal that both product-related and regulatory
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constructs have significant influences on pre-purchase evaluation. In addition these
constructs are associated with behavioural/purchase intention through the mediatory affect
of personal beliefs/attitudes and pre-purchase evaluation. Furthermore, personal beliefs and
attitudes partially mediated the relationship between lifestyle construct and pre-purchase
evaluation. However, the lifestyles construct directly influences behavioural/purchase
intention.
7.4
Discussion related to research question two
The second research question as previously articulated in Chapter One reads as follows:
Evaluate the role of demographic variables and usage patterns in the behavioural
/purchase intentions of urban Chinese consumers towards organic food.
This research question has been answered by analysis of the control variables of the
proposed conceptual model, which are addressed in hypotheses seven. Hypotheses tests
were performed on gender, age group, education and income levels, and usage patterns. A
detailed discussion on each of these control variables and hypotheses follows:
7.4.1 Gender
H7a which hypothesises that women are more likely to purchase organic food was not
supported.
The findings of this study reveal that consumer’ intentions to purchase organic food do not
seem to be influenced by gender. This means that there are no differences in purchase
intention towards organic food between women and men in urban China. This finding is
similar to previous findings from a Greek (Tsakiridou et al. 2008) and a US (Dahm,
Samonte & Shows 2009) study. Noticeably, even though this study sought out demographic
variations where possible, the gender percentage of final respondents was 59.7% females
and only 40.3% males. The greater participation of females reflecting the same results was
225
evidenced in other studies (Janssen, Heid & Hamm 2009; Krystallis & Chryssohoidis 2005),
in which the sample size indicated that more than 70% were female and only 30% male,
suggesting that women were predominantly responsible for the purchase of food products
in the family.
7.4.2 Age
H7b which hypothesises that younger consumers are more likely to purchase organic food
than older consumers was not accepted.
There is much debate on the importance of age as an influencing factor on consumers’
purchase intention. Young consumers may purchase organic food for reasons of
environmental concern, and older consumers due to personal health concerns (Magnusson
et al. 2001, 2003; Tsakiridou et al. 2008). The findings of this study indicate that there are
no significant differences between age groups in terms of purchase intention relating to
organic food. These findings concur with a Swedish study by Magnusson et al. (2001,
2003). Furthermore, an Indian study (Chakrabarti & Baisya 2007) suggests that consumers’
attitudes and motivation towards the purchase of organic food are not influenced by
different age groups.
In this study, a small proportion of respondents were older than 60 years of age, with the
overwhelming number in the younger age category. This may indicate a propensity of
younger consumers having money to spend and preferring to shop in supermarkets where
organic food products are readily available while older consumers may prefer to shop in
other traditional outlets. Almost half of the respondents belonged to the youngest age group
(18-30), followed by the 31-45 years old category accounting for 35%, with only 12% in
the 46-60 years old category. While recent economic reforms have been implemented and
the economic wellbeing of many Chinese has improved significantly, many older
consumers aged over 60 still largely depend on retirement pensions and subsidies which
places them in a lower income category compared to other age groups.
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7.4.3 Education and Income levels
H7c which hypothesises that the higher the Chinese consumers’ level of education, the more
likely they are to purchase organic food was partially accepted.
H7d which hypothesises that the higher the Chinese consumers’ income, the more likely are
to purchase organic food was partially accepted.
The findings of this study reveal that levels of education and income are partially correlated
with organic food purchase intention. Consumers with at least high school levels of
education tended to have greater intentions to purchase organic food. Meanwhile, higher
levels of income earners also indicated a stronger preference in purchase intention towards
organic food. These results are supported by previous studies (Roddy, Cowan &
Hutchinson 1996; Tsakiridou et al. 2008). Higher education and higher income are an
influence on organic consumers’ consumption. It also can be noted that higher educational
and higher income groups show high awareness and high purchase intention of organic
food. Equally, non-organic users exhibit low education levels and lower income levels as
compared to organic users (Fotopoulos & Krystallis 2002a, b; Tsakiridou, Mattas &
Tzimitra-Kalogianni 2006). The findings of this and most previous studies suggest that the
most affluent people who purchased organic food were well educated.
Previous studies (Connor & Douglas 2001; Lea & Worsley 2005; O’Donovan & McCarthy
2002; Tsakiridou et al. 2008) suggest that demographic variables such as gender, age, level
of education and income influence consumers’ organic food purchase intention. Partially in
agreement with these studies, the current study indicates that gender and age did not
influence Chinese consumers’ purchasing intentions towards organic food, but the level of
education and income did impact the purchase intention of organic food.
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7.4.4 Usage patterns
H7e which hypothesises that the important attributes of organic food products have positive
influences on their intended usage pattern was partially accepted.
The results (Table 6.22) of this study reveal that urban Chinese consumers have five
intended uses for organic food: own consumption, for children, elderly people, for gift
giving and for family consumption. Analysis of the intended usage patterns reveals that
there are significant differences in the attributes of social status, awareness and food safety
when urban Chinese consumers buy organic food products for their own consumption, for
children, for the elderly, for gifts or for their family.
Social status is considered to be the most important attribute for the purchase of organic
food for gift giving, while own consumption is the least important attribute. Previous
studies (Qian, Razzaque & Keng 2007) suggest that social status is important for Chinese
gift giving. Personal favours or gift giving can be used as a medium of social exchange for
people in reciprocal relationships. Sun and Collins (2006) also reveal that a gift not only
reflects the receiver’s social status, but also reflects the giver’s good-will and self-image.
An expensive gift offered by Chinese consumers seeks to also imitate superior attributes.
Sun and Collins suggest that Chinese consumers seek to benefit from expensive imported
fruit as gifts which convey symbolic meaning such as ‘clean’, ‘superior appearance’ and
‘expensive and high prestige’. These attributes project imported fruits as being consumed
by wealthy consumers who apparently have high social status. Food can be a symbol of
social status (Bareham 1995) and consumers with a high social status may obtain
satisfaction because of its novelty and style of food consumption (Fotopoulos, Krystallis &
Ness 2003).
The awareness of organic food is the most important attribute for gift giving and least
important attribute for own consumption as per Tables 6.22. Awareness of organic food
means purchasers and consumers understand the differences between organic and nonorganic foods, and appreciate their benefits. As Chinese consumers engage in gift giving as
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reciprocal favours, the awareness of organic food could improve the receiver’s
understanding of the product, and perceive organic food as an expensive product.
Obviously, food safety is considered an important attribute for both own consumption and
gift giving usage patterns (the means of own consumption and for gift giving are both 4.27
as shown in Table 6.22). This finding is consistent with the study by Krystallis, Fotopoulos
and Zotos (2006) who suggest that both organic and non-organic food buyers share the
same perception towards food safety issues.
The results of this study of intended usages pattern reveal that social status is more
important for gift giving and less important for own consumption in China. If organic food
purchase for gift giving is the intended usage, then the package appearance and description
of the product are essential. The importance of giving gifts in Chinese culture is quite
different from western countries. It is of utmost importance to offer gifts in a reciprocal
arrangement in undertaking a relationship (business or otherwise). The prestigious nature of
the gift can also represent the respect and financial strength of the donor. The gift reflects
both the status of the donor and respect towards the receiver (Huliyeti, Marchesini &
Canavari 2008). Sun and Collins (2006) report the high price of imported fruit, for gift
purposes as it is perceived as a luxury product. As gifts, the high price of products may
signify prestige (sign of respect) to the receiver and demonstrates the generosity of the
giver (Huliyeti, Marchesini & Canavari 2008). In this study it could be suggested that
respondents could not identify significant differences between organic and conventional or
even GM food from the appearance. It is recommended that marketers need to be aware
that consumers would only understand organic food appearance by improved packaging,
identification of the logos and the labels.
Most consumers who purchase organic food for their own consumption and family use are
not gift givers. Because of the private nature of consumption, quality thus assumes greater
importance than packaging. China is described as a highly collectivist society with high
power distance and status as important indicators of power and achievement (Hofstede
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1994). However, the face-saving exercise is important only when purchasing a socially
visible product, such as an expensive watch or a luxury car, all of which can demonstrate
the owner’s status. The pragmatic Chinese tend to value functionality over appearance for
private use of products. Quality in this context is considered as more important than
appearance (Schütte & Ciarlante 1998).
The presence of children in the household in this study does not have a significant impact
on the purchase of organic food products. Even the one child policy in China, which was
apparently directed at giving more attention to children from four grandparents and parents,
has not resulted in greater levels of consumption of organic food. This finding seems to
contradict the UK study (Hill & Lynchehaun 2002) which offered this nexus of organic
food with feeding children and with the perceived increase of organic food as a result of the
arrival of babies. In the UK study there were increasing numbers of new parents who were
buying organic baby food. However, this research study indicates that food safety concern
is not just for children, it is for families. Therefore the organic food purchase is not only for
the one child, but for the entire family.
7.5
Contribution of this study
China experienced dramatic economic development in the last twenty years and its entry
into the WTO in 2001 further opened up its huge retail markets to the rest of the world.
This study has provided a systematic and extensive analysis and findings of urban Chinese
consumers’ purchase behaviour of organic food. As best as can be ascertained, very few
studies on organic foods have been undertaken in China. The Chinese economy is
experiencing phenomenal growth in the organic food sector. Little is known or understood
about Chinese consumers’ attitudes towards organic food products or their associated
purchase behaviour. This study is one of the very few dealing with consumers’ buyer
behaviour of organic food in urban China. Moreover this study, the first of its kind in
obtaining data from the major cities of China, has made significant contributions to both
academics and practitioners.
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7.5.1 Theoretical contributions
This study has examined the main determinants which influence consumption of organic
food in mainland China, and has developed and tested a distinctive conceptual model of
organic food consumption intentions in the Chinese context. The unique conceptual model
(Figure 7.2) applied in this study is the integration of three theoretical models, namely,
Consumer Decision Process (CDP), Hierarchy of Effects and Theory of Planned Behaviour
(TPB). The results provide empirical evidence to verify the applicability of these three
theories for purchase intention of organic food in urban China. It examined the influencing
stage, which comprised constructs related to product, regulatory, lifestyle and
ethnocentrism. These four constructs influence consumers’ beliefs and attitudes, their prepurchase evaluation and finally their organic food purchase intention. The final best-fit
structural equation model obtained in this study suggests that: (i) urban Chinese consumers’
beliefs/attitudes towards organic food consumption are positively and directly influenced
by product-related, regulatory and lifestyle constructs (especially variety seeking and selfindulgence); (ii) both product-related and regulatory constructs had significant influences
on pre-purchase evaluation, and these constructs were associated with behavioural/purchase
intention through the mediatory affect of beliefs/attitudes and pre-purchase evaluation; and
(iii) some demographic variables such as level of income and education partially influence
organic food consumption. Also some important attributes of organic food product
influence consumers’ intended usage pattern.
231
232
This study has contributed a new body of knowledge to the growing research in the field of
organic food purchase behaviour and adds to the current literature, which is well developed
in Western European and North American countries. It has also provided valuable insights
into current studies of global and Chinese consumer behaviour towards organic food. Most
importantly, it has addressed consumers’ purchase intention towards organic food in the
Chinese context. It provides a new understanding of organic food consumers, and has
related implications for global and Chinese organic food studies.
China has developed urban districts over the decades and the move from the countryside
continues insistently. Understanding Chinese urban consumers is crucial to understanding
Chinese society. This study has examined organic food product-related attributes and
Chinese consumers’ lifestyle-related aspects. In addition it has examined the regulatory
factors related to government policies and their impact on consumers’ attitudes towards
organic food purchase. This study provides a political, economic, and social-cultural
understanding of Chinese urban consumers and their consumption behaviour, especially
towards food products. Hence, it provides a strong empirical contribution to research in the
Chinese context.
Theoretical contributions are often a primary objective of academic research, but
practitioners may develop or propose a set of relationships that are as complex and
interrelated as any academically based theory. Thus, researchers from both academia and
industry can benefit from the unique analytical tools provided by the Structural Equation
Model (SEM). A final best-fit model was developed which examined various factors and
their interrelationships and this may prove beneficial to both academics and practitioners.
7.5.2 Business implications
The findings of this research have important implications for marketers of organic food in
China. They should attempt to leverage on these findings by educating their target audience
(both existing and potential customers) and by promoting trials of organic food products.
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The rise of globalisation and the importance of foreign markets for the business
environment requires an understanding of purchase and consumption behaviour. This
applies to all sectors, including the organic food industry. Valuable insights into global
consumer behaviour have emerged in this study in terms of organic foods building on a
new body of knowledge with regard to the potential of organic food business in China.
Beneficiaries of this study include various stakeholders in China and globally such as
consumers, vendors both local and international and government agencies.
The results of this study provide some valuable marketing implications, revealing a number
of factors that affect the purchase intention of organic food consumers in China. Organic
food sectors, including food marketers who want to develop their market in China, will find
the analysis and findings of this study valuable. It has identified important factors and their
elements which influence consumers’ purchase behaviour of organic food in China. As
such these findings may give future directions to policy marketers in developing strategies
aimed at promoting such products.
It is evident from the results of this study that supermarkets in the larger Chinese cities are
the main retail distribution outlets of organic food. The convenience of shopping in a
supermarket is especially attractive for younger Chinese consumers and also for potential
new buyers. The future of increased organic food availability, of distribution channels,
particularly with speciality stores, as well as organic farms is becoming more prevalent in
China. China has developed one of the largest online shopping markets – known as
taobao.com – which is gaining importance. It is suggested that online shopping could be a
future direct market strategy. This study highlights the increasing awareness of organic
food products, and their availability through different types of retail outlets. There is also a
need to enhance the inspection and certification of organic food labelling as well as to
ensure that labelling and logos are a sign of actual quality.
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7.5.2.1
Importance of the product-related construct to marketers
One of the important business implications of this study is to provide information to
marketers about organic food product attributes. The results suggest that consumers
perceive organic food to appear and taste better than conventional food. Some organic food
products, as a result of less chemical use during production, transportation and storage,
seemingly do not appear as good as conventionally grown food products. Consumers may
not purchase organic food if it does not appear fresh. Finding ways of preserving organic
food for a longer shelf life is a challenge for growers and marketers. Organic food is
categorised as fresh or processed produce. It is suggested that with fresh organic food, such
as vegetables, fruit and meat, a fresher looking product is perceived as being of higher
quality in urban China.
This study reveals that the higher price of organic food as compared to conventional food
does not deter Chinese consumers from purchasing. Producers and marketers of organic
food products can use this information in establishing and setting their prices, generally in
the Chinese context the higher price signifies an image of safe, clean and high quality. The
price differentiator might be used to maintain organic products’ niche marketing strategy.
In China, prices of certified organic food and green food can be up to 700% higher than
those for conventional products (Kluger 2010). Overall price sensitivity seems less
important in terms of purchase of organic food as a result of food safety issues. On the
other hand, the results of this study indicate that retail price premiums remain higher than
what most consumers are willing to pay. More than half of the respondents (60.8%) were
willing to pay an extra 20% to 50%, nearly 10% were willing to pay an extra 51% to 100%,
and 1% were willing to pay an extra premium greater than 100%. Approximately one
quarter of respondents did not wish to pay extra for the organic products. Emerging from
the study was the consumers’ preferred premium payment of an extra 20% to 50%.
Therefore an organic food premium of an extra 20% to 50% is suggested as the acceptance
price range. These findings are supported by Yin et al. (2010) who suggest that Chinese
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consumers are willing to pay an extra 35.3% for organic food. The organic food consumers
in China have similar views on price as compared to those in Western Europe and North
America (Lockie, Halpin and Pearson (2006). Although Chinese consumers traditionally
seek value for money, they are prepared to be more tolerant of the higher cost of organic
food. The very public food scandals in China have made consumers tolerate paying a
higher price for organic food where it stands as a symbol of safety.
Organic food products are strategically important to some retailers, enabling them to
practice and implement a differentiation marketing strategy. This is due to consumers’
perceived view of organic food as being environmentally friendly and of high quality
(Aertsens, Mondelaers & Huylenbroeck 2009). The findings of this study are in line with
previous studies, which suggest that organic food is perceived as being safe and of higher
quality. Organic food marketers may well emphasise these beneficial attributes, such as
higher nutritional values, freshness and being environmentally friendly. The perception of
organic food being safe and of high quality might be reflected in packaging and promotion
material.
7.5.2.2
Importance of the regulatory construct to policy makers
The term ‘organic’ has many different interpretations, and previous studies indicate that
consumers seem to lack knowledge about organic standards. The ‘Chinese green food’
label had the highest rate of recognition by Chinese consumers, followed by ‘Chinese nonpolluted food’ and ‘Chinese organic food’ logo. Organic food logos in this study were
chosen from both China and overseas. The overseas organic food logos were less
recognised by Chinese consumers. Li, Cheng and Ren (2005) suggest that Chinese
consumers have less awareness of the term ‘organic’ as evidenced in this study. Organic
food is still a new concept and not very well- known in China (Thøgersen & Zhou 2012).
Rising personal income and a growing abundance of material goods in China has
significantly improved consumers’ standard of living and quality of life. A series of food
236
scandals has triggered higher consumer demand for ‘safe food’ (including organic food,
green food and non-polluted food). There is a clear need to educate consumers regarding
the differences between organic, conventional and GM types of food in the market place.
Overall there is a lack of trust in the quality credentials of organic food as well as doubt in
the enforcement of quality by Chinese authorities. Government agencies and industry need
to be aware of the necessity to continually regulate the organic food market. They also need
to enhance the inspection and certification of organic food labelling as well as ensure that
the labelling and logos guarantee quality. The findings of this study reveal that the
regulatory-related construct influences consumers’ attitudes and their pre-purchase
evaluation towards organic food. Organic producers and government agencies should make
a concerted effort towards improving the awareness and benefits of consuming organic
food. Relevant stakeholders of this industry need to implement market strategies aimed at
enhancing consumers’ confidence and trust in organic labelling. The findings of this
research may also be useful to public policy makers who are interested in identifying
strategies to increase awareness and demand for organic food.
The recent food scandals in China have made consumers more sceptical of manufacturers’
claims, and they doubt the reliability of certification bodies and government agencies. They
are more suspicious of organic food products in comparison to consumers from many other
countries. The Chinese government and producers should improve certification and
inspection systems to regain consumers’ trust and confidence. Certainly, there is a need for
government agencies to give clear and safe advice on food safety issues, as well as a need
for organic food certification bodies and marketers to ensure that the certification labelling
and explanations are clear and comprehensive.
7.5.2.3
Lifestyle
As regards lifestyle, the findings of this study reveal that Chinese organic food consumers
tend to be more self-indulgent and variety seeking and prefer product hedonic attributes.
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They love to try something new and trendy, and often show impulse buying behaviour.
Therefore they would be prepared to try more and enjoy the organic food product attributes,
such as fragrance, texture and nutrition. For regular organic food consumers, their purchase
and consumption are part of today’s modern lifestyle. The traditional Chinese diet which
primarily includes rice and vegetables and only moderate amounts of pork, poultry and
seafood provides for a relatively healthy lifestyle. However, many newly affluent urban
consumers are doing away with this traditional diet and are becoming Western adopters.
Looking at today’s China, there are more than 169 million overweight adults, and an
additional 26 million who are obese (Zhang et al. 2008). Lifestyle segmentation is a critical
step to better understand existing and potential organic food consumers, and also nonorganic food buyers. It is recommended that organic food marketers emphasise both
sensory and non-sensory organic food attributes to the different segments of the market
based on Chinese consumers’ lifestyle categories. Organic food marketers should
endeavour to focus on the variety seeking and self-indulgent consumers with enhanced
market strategies. Additionally they could possibly convert consumers of the opinionleadership lifestyle category to be buyers of organic food. This category is more traditional
and capable of influencing others. Marketing strategies such as word-of-mouth promotion
and direct marketing could be used to promote and distribute organic food products.
7.5.2.4
Country of origin and impact of ethnocentrism
Chinese consumers’ ethnocentric attitudes towards imported goods have undergone
changes in the recent past, especially in relation to goods originating from developed
western countries. When China opened its door to the world in 1978, imported goods were
scarce and unknown. Over time this reality has changed. Consumers expressed positive
attitudes towards imported goods after many decades in which products were unavailable in
China (Sun and Collins 2002). Over the decades, China’s economy has soared, with joining
the WTO in 2001 and hosting the 2008 Olympic Games. In turn consumers are gaining
back their national pride back. Today China is the second largest economy in the world
even though its per capita GDP is relatively low. Chinese consumers no longer perceive
238
imported products as symbols of high quality, as they may have done in the past. However,
in the last few years, owing to a series of food scandals and environmental disasters, they
have lost some confidence in purchasing domestically produced organic food.
This study reveals that ethnocentrism does not influence Chinese consumers’ organic food
purchase behaviour. This may suggest that locally produced organic food would not
necessarily have the upper hand. Hence the finding may provide significant export business
opportunities for international organic firms. Clearly, there is room for marketing
international organic food products in the Chinese market. The main exporters of organic
foods to China include the United States, Australia and the European Union countries
(International Trade Centre 2011). This research reveals that nearly half of the respondents
did not mind which country or region the organic foods came from. However, if they were
purchasing imported organic food, they indicated their preference for Australian and
European products rather than Japanese or American. This might reflect in part Chinese
consumers’ animosity towards Japanese products and possibly the rivalry which exists with
the United States (Klein, Ettenson & Morris 1998). The results of this study also reveal that
Chinese consumers are more familiar with American certified logos rather than European
and Australian ones. It seems to suggest that Australian and European organic food
producers need to increase the familiarity of their products and brandings within the
Chinese market. Australia is often regarded as a ‘lucky country’ with vast spaces, a cleaner
environment and has the image of being ‘green and clean’. Not surprisingly it has the
largest organic area under cultivation in the world and offers the ideal image for promoting
organic products to overseas markets. With this in mind, Australian organic producers
should give due importance to the burgeoning Chinese market. It may suggest that in order
to gain access to a larger share of this market, they must address the relatively low
understanding of the Australian certification procedures.
239
7.5.2.5
China is not a single market
The findings of this study concur with previous studies that China is not simply one big
uniform market. It is heterogeneous and diverse from north to south and from east to west.
Companies that attempt to develop the Chinese organic product market need to understand
the varying aspects of consumer behaviour across the country. Consumers’ knowledge and
awareness is crucial to expansion of organic marketing, but such awareness in China is
relatively low. There needs to be an increase in the availability of distribution channels and
there is a need to publicise the overall quality process of organic food related to food safety
issues. Also there is a need to educate consumers to understand the value of organic food
relative to its price.
This study concurs with the findings of another study by Cui and Liu (2000) which reports
that Chinese consumers from various regions differ significantly in terms of attitudes,
lifestyles and consumption patterns. Attitudes tend to be influenced more by productrelated attributes in Shanghai (eastern region) and Shenzhen (southern region) where
consumers place greater emphasis on organic food texture, fragrance, nutritional value as
well as its fashionable trend setting. On the other hand, consumers from Beijing tend to be
influenced more by regulatory related items. As the capital city in China, consumers with
access to key government agencies tend to be more aware of government regulations and
policies. These consumers generally show more concern regarding the certification and
regulatory authorisation of logos. On the other hand, consumers in Chengdu (western
region) tend to associate lifestyle with the purchase behaviour of organic food.
Therefore, it is suggested that international companies which attempt to enter the Chinese
market must understand the consumer purchase behaviour patterns in the different regions.
As Fairbank and Goldman (1998) observe, economic and social differences are deepened
by the accelerating geographic disparities between the coastal area involved in international
trade versus the poorer inland provinces. Cui and Liu (2000) suggest that regional markets
240
in the south and east represent China’s ‘growth markets’, as they are more advanced
economically and have more affluent consumers than the hinterland provinces.
7.6
Limitations of this study
There is no scientific evidence which clearly and unambiguously demonstrates that organic
food is scientifically healthier than the so-called conventional food. This is an inhibiting
element in all organic food studies, and one that this study could not address.
Some people in China have never heard of the term ‘organic’ although the respondents in
this study were generally aware of it. Respondents were divided into four consumer
segments according to their buying frequency: frequent buyers, medium buyers, occasional
buyers and non-buyers. Frequent buyers are those who purchase organic food weekly;
medium buyers are those who purchase every fortnight or monthly. Nearly half of the
respondents indicated they were occasional buyers. Noticeably, nearly 23% of the
respondents were in the category of ‘non-buyers but aware’ of organic food. This market
segment should be targeted in the future to investigate how they could be converted.
The results of this study indicate that 77.5% of the respondents had purchased organic food
at some point in their life, but it was unclear whether they had actually purchased ‘organic’
products or some other Chinese categorised safe food (such as green food or non-polluted
food). Some consumers did not recognise the logos of the organic foods and instead
indicated they had purchased food with logos indicating they were ‘green’ or ‘non-polluted’.
It is abundantly clear that confusion still exists among many Chinese consumers. A need
for further clarification between ‘organic’, ‘green’ and ‘non-polluted’ food has been
identified in the Chinese market.
Due to time and financial constraints, this study was only able to collect a limited amount
of responses (964 valid responses). The data was based primarily on that collected from
four selected cities. These included cities in the north, south, west and east which has
allowed insights into the understanding of Chinese consumers’ organic food purchase
241
behaviour as a cross-national phenomenon. The findings from the invariant tests applied to
data from the four cities confirm that China is not a homogenous market. This implies that
consumers living in the north, south, west and east of China show significantly different
influencing dimensions and attitudes towards purchasing organic food.
China has become the largest manufacturing hub in the world and as a result of its intense
economic growth and development has sought to limit the activities around environmental
controls. In the west there is a clearer concern for climate change, pollution levels and other
environmental issues. Another limitation for further studies in the Chinese market is the
different level of understanding of environmental concerns, as evidenced by the serious
pollution levels in the larger cities. This study does not take into account the environmental
concerns of the Chinese government and consumers and how these impact the purchase
behaviour of organic food.
7.7
Future research directions
As organic food has only been recently introduced in China, and mainly available in
supermarkets of the larger cities, its awareness among the general consumers is relatively
low. As economic development continues unabated in China, future research in this market
and this sector could result in consumers being ‘more aware’, thus enhancing the results
and findings of such a study.
Secondly, China is an organic innovator. The three categories which can be summarised as
organic, green and non-polluted food are generally perceived as being safe in China. As
awareness of these terms becomes more widespread, future research may focus on the
differences of those consumer groups and examine the switching behaviour of the Chinese
consumers between these three categories of food.
Thirdly, China has developed one of the largest consumer markets with a population of 1.3
billion and annual GDP growth of around 8% to 9% in an area covering 9,600,000 square
kilometres. Future research should endeavor to collect data from a greater number of
242
locations in order to enhance the geographical generalisations. It would be ideal to choose
more cities from each region as there are over 70 large and medium sized cities in China
(National Bureau of Statistics of China 2011). Moreover, China has demonstrated itself to
be a heterogeneous market, with both similarities and differences between north, south, east
and west. Future studies may develop different best-fit SEM to suit the diversification of
the Chinese market.
Fourthly, organic consumers believe that organic food is an environmentally related
product. As the awareness is increased and there is greater emphasis on environmental
issues in China, a future study may investigate how environmental factors impact
consumers’ purchase of organic food.
Fifthly, actual purchase behaviour was evaluated by the purchase frequency and usage
patterns through consumers’ past experience. Longitudinal studies to ascertain the changes
which occur in time with Chinese consumer preferences, attitudes and behaviour in relation
to organic food would be recommended.
Finally, this study has evidenced the dissimilarity between Chinese organic consumers
from the western literature. As Chinese consumers are becoming more affluent, their
buying behaviour is evolving alongside economic development. The future of research
direction should also include studying the cross-cultural comparison of organic food
purchasing behaviour. Data would be collected in China and other emerging markets as
well as the developed countries. It would be interesting to know what the differences
between those consumers’ attitudes towards food attributes, regulatory, lifestyle,
ethnocentrism factors are as well as their intentions related to purchase of organic foods.
This would elicit those factors that exert the strongest influence on their behaviour with
regard to purchasing organic food.
243
7.8
Concluding remarks
In the last two decades, organic food has assumed greater global awareness. Organic
products have been perceived as being more nutritious, healthier, safer and environmentally
friendly. They contain fewer chemical residues and are considered to taste better than
conventional food and as such consumers might consider paying a premium price for them.
In contrast to the above perceptions, other studies have revealed that there are also barriers
to consumers purchasing organic food.
China has experienced dramatic economic growth which has accelerated the pace of its
urbanisation with more than half a billion consumers living in urban areas. With the
increasing quality of life in urban China, consumers also desire to pursue the choice for
quality food. On the other hand, rapid economic development has seen food standards
suffer, thus triggering a series of food scandals with calls for ‘safer food’. Consumer
attitudes towards organic food and decision-making in choosing organic food have been
well documented across much of the developed world. Only a handful of studies have been
conducted in Asian countries, and even fewer in China.
A five stages conceptual framework was proposed to investigate consumer decision-making
towards organic food in urban China. This framework was underpinned in three models:
CDP, Hierarchy of Effects and TPB. The five stages of the conceptual framework were
influencing, cognitive/affective, evaluation of alternatives, behavioural intentions and
purchase. The emphasis was placed on the influencing aspects and dimensions of this
process in the Chinese context. The four constructs, namely product, regulatory, lifestyle
and ethnocentrism, were operationalised in the influencing stage. This is the first study of
its kind in the organic food consumption research, which has integrated three well known
models to investigate consumer buyer behaviour.
The findings of this study have revealed that Chinese consumers’ attitudes towards organic
food are influenced by the dimensions of product, regulatory and partially lifestyle (variety
244
seeking and self-indulgence). Additionally it was revealed that all the dimensions and their
components mentioned above significantly impacted the behavioural intentions for the
purchase of organic food in urban China through the mediating effects of consumers’
attitudes and their pre-purchase evaluation. The influence of demographic and
segmentation variables was also examined. Chinese urban consumers now contribute to
being one of the largest consumer markets in the world. Studying their buying behaviour is
critical for understanding the overall Chinese consumer profile.
Results obtained by the final best-fit model have demonstrated that Structural Equation
Modelling is an efficacious statistical technique to address various issues associated with
the purchase behaviour of organic food products. This model has the capacity to
simultaneously examine various factors and their interrelationships which influence
consumer decision making towards the purchase of organic food. The theoretical and
practical business implications significantly contributed to a further understanding of urban
Chinese consumers’ purchase behaviour of organic food products. This study has sought to
contribute to this understanding.
245
REFERENCES
Aarset, B, Beckmann, S, Bigne, E, Beveridge, M et al. 2004, ‘The European consumers’
understanding and perceptions of the “organic” food regime: the case of aquaculture’,
British Food Journal, vol. 106, pp. 93-105.
Abbey, K 2005, Going organic: your guide to a healthier life, revised edn, New Holland,
Sydney.
Abrams, K, Meyers, C & Irani, T 2010, ‘Naturally confused: consumers’ perceptions of allnatural and organic pork products’, Agriculture and Human Values, vol. 27, no. 3, pp.
365-374.
Aertsens, J, Mondelaers, K & Huylenbroeck, GV 2009, ‘Differences in retail strategies on
the emerging organic market’, British Food Journal, vol. 111, no. 2, pp. 138-154.
Aertsens, J, Verbeke, W, Mondelaers, K & Huylenbroeck, GV 2009, ‘Personal
determinants of organic food consumption: a review’, British Food Journal, vol. 111,
no. 10, pp. 1140-1167.
Agbonifoh, BA & Elimimian, JU 1999, ‘Attitudes of developing countries towards
‘country-of-origin’ products in an era of multiple brands’, Journal of International
Consumer Marketing, vol. 11, no. 4, pp. 97-116.
Aguirre, JA 2007, ‘The farmer’s market organic consumer of Costa Rica’, British Food
Journal, vol. 109, no. 2, pp. 145-154.
Ahmad, S & Juhdi, N 2010, ‘Organic food: a study on demographic characteristics and
factors influencing purchase intention among consumers in Klang Valley, Malaysia’,
International Journal of Business and Management, vol. 5, pp. 105-118.
Ajzen, I 1991, ‘The theory of planned behavior’, Organizational Behavior and Human
Decision Processes, vol. 50, no. 2, pp. 179-211.
Ajzen, I 2001, ‘Nature and operation of attitudes’, Annual Review of Psychology, vol. 52,
pp. 27-58.
Ajzen, I 2005, Attitudes, personality, and behavior, 2nd edn, Open University Press, New
York.
246
Ajzen, I & Albarracin, D 2007, ‘Predicting and changing behavior: a reasoned action
approach’ in I Ajzen, D Albarracίn and R Hornik (eds) Prediction and change of
health behavior: applying the reasoned action approach, Lawrence Erlbaum
Associates, Mahwah, NJ.
Ajzen, I & Fishbein, M 1969, ‘The prediction of behavioral intentions in a choice situation’,
Journal of Experimental Social Psychology vol. 5, no. 4, pp. 400-416.
Ajzen, I & Fishbein, M 1980, Understanding attitudes and predicting social behavior,
Prentice Hall, Englewood Cliffs, NJ.
Allen, P & Bennett, K 2010, PASW statistics by SPSS: a practical guide: version 18.0,
CENGAGE Learning, Melbourne.
Anders, S & Moeser, A 2008, ‘Assessing the demand for value-based organic meats in
Canada: a combined retail and household scanner-data approach’, International
Journal of Consumer Studies, vol. 32, no. 5, pp. 457-469.
Anderson, JC & Gerbing, DW 1988, ‘Structural equation modeling in practice: a review
and recommended two-step approach’, Psychological Bulletin, vol. 103, no. 3, pp. 411423.
Anić, I-D 2010, ‘Attitudes and purchasing behavior of consumers in domestic and foreign
food retailers in Croatia’, Preliminary communication, vol. 28, no. 1, pp. 113-133.
Armitage, CJ, Conner, M & Norman, P 1999, ‘Differential effects of mood on information
processing: evidence from the theories of reasoned action and planned behaviour’,
European Journal of Social Psychology, vol. 29, pp. 419-433.
Arvanitoyannis, IS, Krystallis, A & Kapirti, A 2003, ‘Health and environmental
consciousness: Greek consumers’ attitudes toward the organic, HACCPand ISO14000
certifications on food’, Journal of International Food & Agribusiness Marketing, vol.
15, no. 1/2, pp. 93-136.
Atsmon, Y, Ding, J, Dixit, V, Leibowitz, G, Magni, M & Zipser, D 2009, 2009 Annual
Chinese consumer study, McKinsey Asia, viewed 15 August 2010,
247
<http://www.mckinsey.com/locations/greaterchina/McKinsey_Annual_Consumer_Rep
ort_Downturn_part2.pdf>.
Atsmon, Y & Dixit, V 2009, Understanding China’s wealthy, viewed 15 August 2010,
<http://www.mckinseyquarterly.com/Understanding_Chinas_wealthy_2392>.
Austrade 2011, China country overview: economic climate, viewed 15 March 2011,
<http://www.austrade.gov.au/China-Market-Overview/default.aspx>.
Baer, N 2007, The spread of organic food in China, Woodrow Wilson International Center
for Scholars, Washington, DC.
Baker, S, Thompson, KE & Engelken, J 2004, ‘Mapping the values driving organic food
choice: Germany vs the UK’, European Journal of Marketing, vol. 38, no. 8, pp. 9951012.
Bao, Y, Zhou, KZ & Su, C 2003, ‘Face consciousness and risk aversion: do they affect
consumer decision-making?’, Psychology and Marketing, vol. 20, no. 8, pp. 733-755.
Bareham, J 1995, Consumer behaviour in the food industry: a European perspective,
Butterworth-Heinemann, Oxford.
Barnes, AP, Vergunst, P & Topp, K 2009, ‘Assessing the consumer perception of the term
“organic”: a citizens’ jury approach’, British Food Journal, vol. 111, no. 2, pp. 155164.
Barry, TF & Howard, DJ 1990, ‘A review and critique of the Hierarchy of Effects in
advertising’, International Journal of Advertising, vol. 9, no. 2, pp. 121-135.
Batra, R, Ramaswamy, V, Alden, DL, Steenkamp, J-BEM & Ramachander, S 2000,
‘Effects of brand local and nonlocal origin on consumer attitudes in developing
countries’, Journal of Consumer Psychology, vol. 9, pp. 83-95.
Beharrell, B & MacFie, JH 1991, ‘Consumer attitudes to organic foods’, British Food
Journal, vol. 93, no. 2, pp. 25-30.
Beijing Municipal Government 2010, Residing in Beijing, viewed 10 August 2010,
<http://www.ebeijing.gov.cn/feature_2/ResidinginBeijing/>.
248
Bellows, AC, Onyango, B, Hallman, WK & Diamond, A 2008, ‘Understanding consumer
interest in organics: production values vs. purchasing behavior’, Journal of
Agricultural & Food Industrial Organisation, vol. 6, no. 1, pp. 1-31.
Bennett, R 1999, ‘Sports sponsorship, spectator recall and false consensus’, European
Journal of Marketing, vol. 33, no. 3-4, pp. 291-313.
Bhaskaran, S & Hardley, F 2002, ‘Buyer beliefs, attitudes and behaviour: foods with
therapeutic claims’, Journal of Consumer Marketing, vol. 19, no. 7, pp. 591-606.
Bhaskaran, S, Polonsky, M, Gary, J & Fernandez, S 2006, ‘Environmentally sustainable
food production and marketing: opportunity or hype?’, British Food Journal, vol. 108,
no. 8, pp. 677-690.
Blackwell, RD, Miniard, PW & Engel, JF 2006, Consumer behavior, 10th edn, Thomson
South-Western, Boston.
Bollen, KA 1989, Structural equations with latent variables, John Wiley, Hoboken, NJ.
Bonini, SMJ & Oppenheim, JM 2008, ‘Helping “green” products grow’, McKinsey
Quarterly, Oct., pp. 1-7.
Bonti-Ankomah, S & Yiridoe, EK 2006, Organic and conventional food: a literature
review of the economics of consumer perceptions and preference, Organic Agriculture
Centre of Canada, Nova Scotia Agriculture College, Truro, NS.
Botonaki, A, Polymeros, K, Tsakiridou, E & Mattas, K 2006, ‘The role of food quality
certification on consumers’ food choices’, British Food Journal, vol. 108, no. 2/3, pp.
77-90.
Bower, AB 2001, ‘Highly attractive models in advertising and the women who loathe them:
the implications of negative affect for spokesperson effectiveness’, Journal of
Advertising, vol. 15, no. 3, pp. 51-63.
Boyce, J 2002, Market research in practice, McGraw-Hill, Sydney
Bredahl, L, Grunert, KG & Frewer, LJ 1998, ‘Consumer attitudes and decision-making
with regard to genetically engineered food products: a review of the literature and a
249
presentation of models for future research’, Journal of Consumer Policy, vol. 21, no. 3,
pp. 251-277.
Brennan, C, Gallagher, K & McEachern, M 2003, ‘A review of the “consumer interest” in
organic meat’, International Journal of Consumer Studies, vol. 27, no. 5, pp. 381-394.
Browne, MW & Cudeck, R 1992, ‘Alternative ways of assessing model fit’, Sociological
Methods & Research, vol. 21, no. 2, pp. 230-258.
Brunsø, K & Grunert, K 1995, ‘Development and testing of a cross-culturally valid
instrument: food-related life style’, Advances in Consumer Research, vol. 22, pp. 475480.
Bryman, A & Bell, E 2007, Business research methods, 2nd edn, Oxford University Press,
Oxford.
Burton, S, Lichtenstein, DR, Netemeyer, RG & Garretson, JA 1998, ‘A scale for measuring
attitude toward private label products and an examination of its psychological and
behavioral correlates’, Journal of the Academy of Marketing Science, vol. 26, no. 4, pp.
293-306.
Byng, J 1993, ‘EC organic food standards’, British Food Journal, vol. 95, no. 1, pp. 16-17.
Byrne, BM 2004, ‘Testing for multigroup invariance using AMOS graphics: a road less
traveled’, Structural Equation Modeling, vol. 11, no. 2, pp. 272-300.
Byrne, BM 2010, Structural equation modeling with AMOS: basic concepts, applications,
and programming, 2nd edn, Taylor & Francis, London.
Cai, H-J, Lin, Y-J, Wu, Q-P, Yan, L & Huang, X-F 2008, ‘Examining the measurement
invariance between paper-and-pencil and Internet-administered tests in China’, Acta
Psychologica Sinica, vol. 40, no. 2, pp. 228-239.
Callick, R 2011, ‘Special report: China: growing partnership’, Australian, 6 June.
Canavari, M, Bazzani, GM, Spadoni, R & Regazzi, D 2002, ‘Food safety and organic fruit
demand in Italy: a survey’, British Food Journal, vol. 104, no. 3-5, pp. 220-232.
Carroll, JB (ed.) 1974, Language, thought and reality: selected writings of Benjamin Lee
Whorf, MIT Press, Cambridge, Mass.
250
Carson, R 1962, Silent spring, Houghton Mifflin, Boston.
Cavana, RY, Delahaye, BL & Sekaran, U 2001, Applied business research qualitative and
quantitative methods, John Wiley, Brisbane.
CGFDC 2009, About China Green Food Development Center, viewed 30 March 2009,
<http://www.greenfood.org.cn/sites/GREENFOOD/List_3675_3811.html>.
Chakrabarti, S & Baisya, RK 2007, ‘Purchase motivations and attitudes of organic food
buyers’, Decision, vol. 34, no. 1, pp. 1-22.
Chan, RYK 2001, ‘Determinants of Chinese consumers’ green purchase behaviour’,
Psychology & Marketing, vol. 18, no. 4, pp. 389-413.
Chang, H-S & Kristiansen, P 2006, ‘Selling Australia as “clean and green”’, Australian
Journal of Agricultural & Resource Economics, vol. 50, no. 1, pp. 103-113.
Chang, H-S & Zepeda, L 2005, ‘Consumer perceptions and demand for organic food in
Australia: focus group discussions’, Renewable Agriculture and Food Systems, vol. 20,
no. 3, pp. 155-167.
Chang, H-S, Zepeda, L & Griffith, G 2005, ‘The Australian organic food products market:
overview, issues and research needs’, Australian Agribusiness Review, vol. 13, paper
no. 15.
Chen, M-F 2007, ‘Consumer attitudes and purchase intention in relation to organic foods in
Taiwan: moderating effects of food-related personality traits’, Food Quality &
Preference, vol. 18, pp. 1008-1021.
Chen, M-F 2009, ‘Attitude toward organic foods among Taiwanese as related to health
consciousness, environmental attitudes, and the mediating effects of a healthy lifestyle’,
British Food Journal, vol. 111, no. 2, pp. 165-178.
Chesneaux, J 1979, China: the People’s Republic, 1949-1976, Harvester, Brighton.
China Internet Network Information Center 2009, China Internet Statistics Report (in
Chinese), viewed 20 July 2010,
<http://www.cnnic.cn/uploadfiles/pdf/2009/7/16/125126.pdf>.
251
Chinnici, G, D’Amico, M & Peorino, B 2002, ‘A multivaritate statistical analysis on the
consumers of organic products’, British Food Journal, vol. 104, no. 3/4/5, pp. 187-199.
Chryssochoidis, G 2000, ‘Repercussions of consumer confusion for late introduced
differentiated products’, European Journal of Marketing, vol. 34, no. 5/6, pp. 705-722.
Chryssochoidis, G, Krystallis, A & Perreas, P 2007, ‘Ethnocentric beliefs and country-oforigin (COO) effect: impact of country, product and product attributes on Greek
consumers’ evaluation of food products’, European Journal of Marketing, vol. 41, no.
11/12, pp. 1518-1544.
Chryssochoidis, GM & Krystallis, A 2005, ‘Organic consumers’ personal values research:
testing and validating the list of values (LOV) scale and implementing a value-based
segmentation task’, Food Quality & Preference, vol. 16, pp. 585-599.
Cicia, G, Giudice, TD & Scarpa, R 2002, ‘Consumers’ perception of quality in organic
food: a random utility model under preference heterogeneity and choice correlation
from rank-orderings’, British Food Journal, vol. 104, no. 3/4/5, pp. 200-213.
CMP Information Ltd 2006, ‘Global view: Chinese “green food” revolution is growing’,
Arable Farming, 10 July, p. 40.
Cohen, J & Cohen, P 1983, Applied multiple regression/correlation analysis for the
behavioral sciences, 2nd edn, Lawrence Erlbaum Associates, Hillsdale, NJ.
Collis, J & Hussey, R 2009, Business research: a practical guide to undergraduate and
postgraduate students, 3rd edn, Palgrave Macmillan, Basingstoke, UK
Connor, R & Douglas, L 2001, ‘Consumer attitudes to organic foods’, Nutrition and Food
Science, vol. 31, no. 4/5, pp. 254-264.
Courville, S 2006, ‘Organic standards and certification’ in P Kristiansen, A Taji and J
Reganold (eds) Organic agriculture: a global perspective, CSIRO Publishing,
Melbourne.
Creswell, JW 2009, Research design: qualitative, quantitative, and mixed method
approaches, 3rd edn, Sage, Thousand Oaks, Calif.
252
Croll, E 2006, China’s new consumers: social development and domestic demand,
Routledge, London.
Cronbach, LJ 1951, ‘Coefficient alpha and the internal structure of tests’, Psychometrika,
vol. 16, no. 3, pp. 297-334.
Cronin Jr, JJ, Brady, MK & Hult, GTM 2000, ‘Assessing the effects of quality, value, and
customer satisfaction on consumer behavioral intentions in service environments’,
Journal of Retailing, vol. 76, no. 2, pp. 193-218.
Cui, G & Liu, Q 2000, ‘Regional market segments of China: opportunities and barriers in a
big emerging market’, Journal of Consumer Marketing, vol. 17, no. 1, pp. 55-72.
Cunningham, E 2008, A practical guide to structural equation modelling using Amos,
Statsline, Melbourne.
Dahm, MJ, Samonte, AV & Shows, AR 2009, ‘Organic foods: do eco-friendly attitudes
predict eco-friendly behaviors?’, Journal of American College Health, vol. 58, no. 3,
pp. 195-202.
Dai, Y-c, Zhu, B & Ying, R-y 2006, ‘Consumers’ choice on food safety: a case study of
organic vegetable purchasing behaviour in Nanjing’, Journal of Nanjing Agricultural
University (Social Science Edition), vol. 6, no. 1, pp. 47-52 (in Chinese).
Davies, A, Titterington, AJ & Cochrane, C 1995, ‘Who buys organic food ? A profile of the
purchasers of organic food in Northern Ireland’, British Food Journal, vol. 97, no. 10,
pp. 17-23.
Davis, D 2005, Business research for decision-making, 6th edn, Thomson/Brooks/Cole,
Belmont, Calif.
de Magistris, T & Gracia, A 2008, ‘The decision to buy organic food products in Southern
Italy’, British Food Journal, vol. 110, no. 9, pp. 929-947.
DeVellis, RF 2003, Scale development: theory and applications, Applied Social Research
Methods Series, vol. 26, Sage, Thousand Oaks, Calif.
DFAT 2012, People’s Republic of China country brief, viewed 25 March 2012
<http://www.dfat.gov.au/geo/china/china_brief.html>.
253
Denver, S, Christensen, T, Jensen, JD & Jensen, KOD 2012, 'The Stability and Instability
of Organic Expenditures in Denmark, Great Britain, and Italy', Journal of International
Food & Agribusiness Marketing, vol. 24, no. 1, pp. 47-65.
Dickson, MA, Lennon, SJ, Montalto, CP, Shen, D & ZhangˈLi 2004, ‘Chinese consumer
market segments for foreign apparel products’, Journal of Consumer Marketing, vol.
21, no. 5, pp. 301-317.
Donovan, R & Henley, N 2003, Social marketing: principles and practice, IP
Communications, Melbourne.
Dou, W, Wang, G & Zhou, N 2006, ‘Generational and regional differences in media
consumption patterns of Chinese generation X consumers’, Journal of Advertising, vol.
35, no. 2, pp. 101-110.
Drichoutis, AC, Lazaridis, P & Nayga Jr, RM 2007, ‘An assessment of product class
involvement in food-purchasing behavior’, European Journal of Marketing, vol. 41, pp.
888-914.
Dubé, L, Cervellon, M-C & Han, J 2003, ‘Should consumer attitudes be reduced to their
affective and cognitive bases? Validation of a hierarchical model’, International
Journal of Research in Marketing, vol. 20, no. 3, pp. 259-272.
Easterby-Smith, M, Thorpe, R & Lowe, A 2002, Management research: an introduction,
2nd edn, Sage, London.
Engel, J, Miniard, PW & Blackwell, R 1993, Consumer behavior, Dryden, Fort Worth, Tex.
Engel, JF, Kollat, DT & Blackwell, RD 1968, Consumer behavior, Holt, Rinehart and
Winston, New York.
Ennis, S 2007, ‘Competition and regulation in agriculture: background note’, OECD
Journal of Competition Law and Policy, vol. 9, no. 2, pp. 93-165.
Essoussi, LH & Zahaf, M 2008, ‘Decision-making process of community organic food
consumers: an exploratory study’, Journal of Consumer Marketing, vol. 25, no. 2, pp.
95-104.
254
Euromonitor International 2005, ‘Food, beverages and tobacco forecast’, Food & Drinks
Forecast World, June, pp. 1-486.
Euromonitor International 2008a, China income & expenditure, viewed 7 October 2009,
<http://www.portal.euromonitor.com/passport/DocumentView.aspx>.
Euromonitor International 2008b, Consumer lifestyles – China, Country Market Insight,
viewed 26 July 2010,
<http://www.portal.euromonitor.com/Portal/accessPDF.ashx?c=13\PDF\&f=F-10752114730713.pdf&code=mAXXAFKUOH%2fHUrT62gtynszoG%2fA%3d>.
Euromonitor International 2010, Organic packaged food in China, Country Market Insight,
viewed 3 February 2010,
<http://www.portal.euromonitor.com/Portal/Pages/Search/SearchResultsList.aspx>.
Euromonitor International 2011, China: country pulse, Country Market Insight, viewed 30
March 2011,
<http://www.portal.euromonitor.com/Portal/accessPDF.ashx?c=97\PDF\&f=F-4878419583197.pdf&code=1VdjTdtOG3UMlwZUR%2f7%2b2Gcj5vA%3d>.
Eves, A & Cheng, L 2007, ‘Cross-cultural evaluation of factors driving intention to
purchase new food products-Beijing, China and South-East England’, International
Journal of Consumer Studies, vol. 31, no. 4, pp. 410-417.
Fairbank, JK & Goldman, M 1998, China: a new history, Belknap, Cambridge, Mass.
Farndon, J 2008, China rises: how China’s astonishing growth will change the world,
Virgin, London.
Farrell, D, Gersch, UA & Stephenson, E 2006, ‘The value of China’s emerging middle
class’, McKinsey Quarterly, special edition, pp. 61-68.
Field, A 2009, Discovering statistics using SPSS, 3rd edn, Sage, London.
Finch, JE 2005, ‘The impact of personal consumption values and beliefs on organic food
purchase behavior’, Journal of Food Products Marketing, vol. 11, no. 4, pp. 63-76.
First, I & Brozin, S 2009, ‘Cultural influences on motives for organic food consumption’,
EuroMed Journal of Business, vol. 4, no. 2, pp. 185-199.
255
Fishbein, M (ed.) 1967, Attitude and the prediction of behavior, John Wiley, New York.
Fishbein, M & Ajzen, I 1975, Belief, attitude, intention, and behavior: an introduction to
theory and research, Addison-Wesley, Reading, Mass.
Food and Agriculture Organization of the United Nations 2002, Market developments for
organic meat and dairy products: implications for developing countries, Committee on
Commodity Problems, Intergovernmental Group on Meat and Dairy Products.
Fotopoulos, C & Chryssochoidis, G 2000, ‘Factors affecting the decision to purchase
organic food’, Journal of Euromarketing, vol. 9, no. 3, pp. 45-66.
Fotopoulos, C & Krystallis, A 2002a, ‘Organic product avoidance: reasons for rejection and
potential buyers’ identification in a countrywide survey’, British Food Journal, vol.
104, no. 3-5, pp. 233-260.
Fotopoulos, C & Krystallis, A 2002b, ‘Purchasing motives and profile of the Greek organic
consumer: a countrywide survey’, British Food Journal, vol. 104, no. 8/9, pp. 730-765.
Fotopoulos, C, Krystallis, A & Ness, M 2003, ‘Wine produced by organic grapes in Greece:
using means--end chains analysis to reveal organic buyers’ purchasing motives in
comparison to the non-buyers’, Food Quality and Preference, vol. 14, no. 7, pp. 549566.
Fram, EH, Le, L & Reid, DM 2005, ‘Consumer behavior in China: an exploratory study of
two cities’, Journal of Asia-Pacific Business, vol. 5, no. 4, pp. 25-42.
Furst, T, Connors, M, Bisogni, CA, Sobal, J & Falk, LW 1996, ‘Food choice: a conceptual
model of the process’, Appetite, vol. 26, no. 3, pp. 247-266.
Gao, Z, Zhao, K, xiao, X & Tai, c 2009, Organic agriculture and organic food, China
Environmental Science Publishing House, Beijing (in Chinese).
Gendall, P, Betteridge, K & Bailey, B 1999, ‘The Japanese market for organic fruit and
vegetables’, Marketing Bulletin, vol. 10, pp. 24-37.
Getz, C & Shreck, A 2006, ‘What organic and fair trade labels do not tell us: towards a
place-based understanding of certification’, International Journal of Consumer Studies,
vol. 30, no. 5, pp. 490-501.
256
Giannakas, K 2002, ‘Information asymmetries and consumption decisions in organic food
product markets’, Canadian Journal of Agricultural Economics, vol. 50, no. 1, pp. 3550.
Gil, JM, Gracia, A & Sanchez, M 2000, ‘Market segmentation and willingness to pay for
organic products in Spain’, International Food and Agribusiness Management Review,
vol. 3, no. 2, pp. 207-226.
González, JAA 2009, ‘Market trends and consumer profile at the organic farmers market in
Costa Rica’, British Food Journal, vol. 111, no. 5, pp. 498-510.
Google Image 2010, China map, viewed 8 March 2010,
<http://www.google.com.au/search?q=China+map&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a>.
Gorsuch, RL 1983, Factor analysis, 2nd edn, Lawrence Erlbaum Associates, Hillsdale, NJ.
Gorsuch, RL 1997, ‘Exploratory factor analysis: its role in item analysis’, Journal of
Personality Assessment, vol. 68, no. 3, pp. 532-560.
Gotschi, E, Vogel, S, Lindenthal, T & Larcher, M 2010, ‘The role of knowledge, social
norms, and attitudes toward organic products and shopping behavior: survey results
from high school students in Vienna’, Journal of Environmental Education, vol. 41, pp.
88-100.
Govindasamy, R, DeCongelio, M & Bhuyan, S 2005, ‘An evaluation of consumer
willingness to pay for organic produce in the Northeastern US’, Journal of Food
Products Marketing, vol. 11, no. 4, pp. 3-20.
Grankvist, G, Lekedal, H & Marmendal, M 2007, ‘Values and eco- and fair-trade labelled
products’, British Food Journal, vol. 109, no. 2, pp. 169-181.
Grannis, J, Hine, S & Thilmany, D 2001, ‘Marketing premium food products in emerging
economies: the case of Macedonian cheese’, Journal of International Food &
Agribusiness Marketing, vol. 13, no. 2/3, pp. 59-76.
257
Grønhøj, A 2006, ‘Communication about consumption: a family process perspective on
“green” consumer practices’, Journal of Consumer Behaviour, vol. 5, no. 6, pp. 491503.
Grunert, SC & Juhl, HJ 1995, ‘Values, environmental attitudes, and buying of organic
foods’, Journal of Economic Psychology, vol. 16, no. 1, pp. 39-62.
Guido, G, Prete, M, Peluso, A, Maloumby-Baka, R & Buffa, C 2010, ‘The role of ethics
and product personality in the intention to purchase organic food products: a structural
equation modeling approach’, International Review of Economics, vol. 57, no. 1, pp.
79-102.
Haghiri, M & McNamara, ML 2007, ‘Predicting consumers’ acceptability of pesticide-free
fresh produce in Canada’s maritime provinces: a probit analysis’, Journal of
International Food & Agribusiness Marketing, vol. 19, pp. 45-59.
Hair, JFJ, Black, WC, Babin, BJ, Anderson, RE & Tatham, RL 2006, Multivariate data
analysis, 6th edn, Pearson Prentice Hall, Upper Saddle River, NJ.
Halpin, D 2004, The Australian organic industry: a profile, Department of Agriculture,
Fisheries and Forestry, Canberra. <http://www.daff.gov.au/agriculturefood/food/organic-biodynamic/industry>.
Hamzaoui-Essoussi, L & Zahaf, M 2012, 'Canadian organic food consumers' profile and
their willingness to pay premium prices', Journal of International Food & Agribusiness
Marketing, vol. 24, no. 1, pp. 1-21.
Hansen, K 2001, ‘Purchasing decision behaviour by Chinese supermarkets’, International
Review of Retail, Distribution & Consumer Research, vol. 11, no. 2, pp. 159-175.
Hansen, T 2005, ‘Understanding consumer perception of food quality: the cases of shrimps
and cheese’, British Food Journal, vol. 107, no. 7, pp. 500-525.
Harper, GC & Makatouni, A 2002, ‘Consumer perception of organic food production and
farm animal welfare’, British Food Journal, vol. 104, no. 3, pp. 287-299.
258
Heckman, J 2005, ‘A history of organic farming: transitions from Sir Albert Howard’s war
in the soil to USDA national organic program’, Renewable Agriculture and Food
Systems, vol. 21, no. 3, pp. 143-150.
Herche, J 1992, ‘A note on the predictive validity of the CETSCALE’, Journal of the
Academy of Marketing Science, vol. 20, no. 3, pp. 261-264.
Hill, H & Lynchehaun, F 2002, ‘Organic milk: attitidues and consumption patterns’, British
Food Journal, vol. 104, no. 7, pp. 526-542.
Ho, P, Vermeer, EB & Zhao, JH 2006, ‘Biotechnology and food safety in China:
consumers’ acceptance or resistance?’, Development & Change, vol. 37, no. 1, pp.
227-254.
Ho, S-c & Tang, F 2006, ‘An exploratory investigation into the food-shopping attitudes and
behavior of Chinese shoppers’, Journal of International Consumer Marketing, vol. 19,
no. 2, pp. 53-74.
Hofstede, G 1994, ‘Management scientists are human’, Management Science, vol. 40, no. 1,
pp. 4-13.
Honkanen, P, Verplanken, B & Olsen, SO 2006, ‘Ethical values and motives driving
organic food choice’, Journal of Consumer Behaviour, vol. 5, no. 5, pp. 420-430.
Howard, A 1943, An agricultural testament, Oxford University Press, London.
Howard, PH & Allen, P 2006, ‘Beyond organic: consumer interest in new labelling
schemes in the Central Coast of California’, International Journal of Consumer Studies,
vol. 30, no. 5, pp. 439-451.
Hsu, JL & Nien, H-P 2008, ‘Who are ethnocentric? Examining consumer ethnocentrism in
Chinese societies’, Journal of Consumer Behaviour, vol. 7, pp. 436-447.
Hu, L-t & Bentler, PM 1998, ‘Fit indices in covariance structure modeling: sensitivity to
underparameterized model misspecification’, Psychological Methods, vol. 3, no. 4, pp.
424-453.
259
Hu, W, Chen, K & Yoshida, K 2006, ‘Japanese consumers’ perceptions on and willingness
to pay for credence attributes associated with canola oil’, Journal of Agricultural and
Applied Economics, vol. 38, no. 1, pp. 91-103.
Huang, CL 1996, ‘Consumer preferences and attitudes towards organically grown produce’,
European Review of Agricultural Economics, vol. 23, no. 3, pp. 331-342.
Hughner, RS, McDonagh, P, Prothero, A, Shultz, CJ, II & Stanton, J 2007, ‘Who are
organic food consumers? A compilation and review of why people purchase organic
food’, Journal of Consumer Behaviour, vol. 6, no. 2, pp. 94-110.
Huliyeti, H, Marchesini, S & Canavari, M 2008, ‘Chinese distribution practitioners’
attitudes towards Italian quality foods’, Journal of Chinese Economic and Foreign
Trade Studies, vol. 1, no. 3, pp. 214-231.
Hutchins, RK & Greenhalgh, LA 1995, ‘Organic confusion: sustaining competitive
advantage’, Nutrition and Food Science, vol. 95, no. 6, pp. 11-14.
IFOAM 2006, International Federation of Organic Agriculture Movements annual reports
2005 and 2006, viewed 30 March 2008,
<http://www.ifoam.org/about_ifoam/inside_ifoam/annual_reports.html>.
IFOAM 2012, The history of IFOAM, viewed 20 March 2012,
<http://www.ifoam.org/about_ifoam/inside_ifoam/history.html>.
IFOAM 2009, The IFOAM organic guarantee system, viewed 2 February 2009,
<http://www.ifoam.org/about_ifoam/standards/ogs.html>.
IFOAM 2011a, About the International Federation of Organic Agriculture Movements
(IFOAM), viewed 28 May 2011, <http://www.ifoam.org/about_ifoam/index.html>.
IFOAM 2011b, Growth in organic continues, viewed 14 April 2011,
<http://www.ifoam.org/IFOAMBioFachMediatext-e.pdf>.
Institute of Food Science and Technology 2009, Information statement organic food,
viewed 13 March 2009,
<http://www.ifst.org/site/cms/contentcategoryview.asp?category=126>.
260
International Trade Centre 2011, Organic food products in China: market overview,
International Trade Centre, Geneva, viewed 24 April 2011,
<http://www.intracen.org/uploadedFiles/intracenorg/Content/Publications/Organicfood-products-in-China-market-overview.pdf>.
Jain, S 2006, ‘Role of socio-demographics in segmenting and profiling green consumers’,
Journal of International Consumer Marketing, vol. 18, no. 3, pp. 107-146.
Jain, SK & Kaur, G 2004, ‘Green marketing: an attitudinal and behavioural analysis of
Indian consumers’, Global Business Review, vol. 5, no. 2, pp. 187-205.
Janssen, M, Heid, A & Hamm, U 2009, ‘Is there a promising market “in between” organic
and conventional food? Analysis of consumer preferences’, Renewable Agriculture and
Food Systems, vol. 24, no. 3, pp. 205-213.
Jones-Farmer, LA, Pitts, JP & Rainer, RK 2008, ‘A note on multigroup comparisons using
SAS PROC CALIS’, Structural Equation Modeling: A Multidisciplinary Journal, vol.
15, no. 1, pp. 154-173.
Kahal, SE 2001, Business in Asia Pacific: text and cases, Oxford University Press, Oxford.
Kalafatis, SP, Pollard, M, East, R & Tsogas, MH 1999, ‘Green marketing and Ajzen’s
Theory of Planned Behaviour: a cross-market examination’, Journal of Consumer
Marketing, vol. 16, no. 5, pp. 441-460.
Kaplan, D 2009, Structural equation modeling: foundations and extensions, 2nd edn, Sage,
Thousand Oaks, Calif.
Keane, M 2007, Created in China: the Great New Leap Forward, Routledge, Abingdon.
Kiesel, K & Villas-Boas, SB 2007, ‘Got organic milk? Consumer valuations of milk labels
after the implementation of the USDA organic seal’, Journal of Agricultural & Food
Industrial Organization, vol. 5, pp. 1-40.
Kim, J-H & Lennon, SJ 2010, ‘Information available on a web site: effects on consumers’
shopping outcomes’, Journal of Fashion Marketing and Management, vol. 14, no. 2,
pp. 247-262.
261
Kim, WG, Ma, X & Kim, DJ 2006, ‘Determinants of Chinese hotel customers’ esatisfaction and purchase intention’, Tourism Management, vol. 27, no. 5, pp. 890-900.
Klein, JG, Ettenson, R & Morris, MD 1998, ‘The animosity model of foreign product
purchase: an empirical test in the People’s Republic of China’, Journal of Marketing,
vol. 62, no. 1, pp. 89-100.
Kline, P 1994, An easy guide to factor analysis, Routledge, London.
Kline, RB 2005, Principles and practice of structural equation modeling, 2nd edn, Guilford,
New York.
Kline, RB 2010, Principles and practice of structural equation modeling, 3rd edn, Guilford,
New York.
Klonsky, K & Greene, C 2005, ‘Widespread adoption of organic agriculture in the US: are
market driven policies enough?’, American Agricultural Economics Association
Annual Meeting, Providence, RI, 23-27 July.
Kluger, J 2010, ‘What’s so great about organic food?’, Time, vol. 196, no. 10.
Kortbech-Olesen, R 1998, ‘Exporting organic foods’, International Trade Forum, no. 3, pp.
4-9.
Kotabe, M, peloso, A, Gregory, G, Noble, G, Macarthur, W, Neal, C, Riege, A & Helsen, K
2005, International marketing: an Asia Pacific focus, John Wiley, Brisbane.
Kotler, P, Brown, L, Adam, S & Armstrong, G 2001, Marketing, 5th edn, Pearson
Education, Sydney.
Kraft, FB & Goodell, PW 1993, ‘Identifying the health conscious consumer’, Journal of
Health Care Marketing, vol. 13, no. 3, pp. 18-25.
Kristiansen, P 2006, ‘Overview of organic agriculture’ in P Kristiansen, A Taji and J
Reganold (eds) Organic agriculture: a global perspective, CSIRO Publishing,
Melbourne.
Kristiansen, P, Mitchell, A, Bez, N & Monk, A 2010, Australian organic market report
2010, Biological Farmers of Australia, Brisbane.
262
Kristiansen, P & Smithson, A 2008, Australian organic market report 2008, Organic
Research Centre, University of New England, Armidale, viewed 26 July 2009,
<http://www.bfa.com.au/index.asp?Sec_ID=259>.
Krystallis, A, Arvanitoyannis, I & Chryssohoidis, G 2006, ‘Is there a real difference
between conventional and organic meat? Investigating consumers’ attitudes towards
both meat types as an indicator of organic meat’s market potential’, Journal of Food
Products Marketing, vol. 12, no. 2, pp. 47-78.
Krystallis, A & Chryssohoidis, G 2005, ‘Consumers’ willingness to pay for organic food:
factors that affect it and variation per organic product type’, British Food Journal, vol.
107, no. 5, pp. 320-343.
Krystallis, A, Fotopoulos, C & Zotos, Y 2006, ‘Organic consumers’ profile and their
willingness to pay (WTP) for selected organic food products in Greece’, Journal of
International Consumer Marketing, vol. 19, no. 1, pp. 81-106.
Krystallis, A, Petrovici, D & Arvanitoyannis, I 2006, ‘From commodities to the
consumption of quality foods in Eastern European context: an empirical examination
of the determinants of consumer behaviour towards honey’, Journal of East-West
Business, vol. 12, no. 4, pp. 5-37.
Kuhar, A & Juvancic, L 2010, ‘Determinants of purchasing behaviour for organic and
integrated fruits and vegetables in Slovenia’, Agricultural Economics Review, vol. 11,
no. 2, pp. 70-83.
Kwok, S, Uncles, M & Huang, Y 2006, ‘Brand preferences and brand choices among urban
Chinese consumers: an investigation of country-of-origin effects’, Asia Pacific Journal
of Marketing and Logistics, vol. 18, no. 3, pp. 163-172.
La Ferle, C & Lee, W-N 2003, ‘Attitudes toward advertising: a comparative study of
consumers in China, Taiwan, South Korea and the United States’, Journal of
International Consumer Marketing, vol. 15, no. 2, pp. 5-23.
Larue, B, West, GE, Gendron, C & Lambert, R 2004, ‘Consumer response to functional
foods produced by conventional, organic, or genetic manipulation’, Agribusiness, vol.
20, no. 2, pp. 155-166.
263
Lavidge, RJ & Steiner, GA 1961, ‘A model for predictive measurements of advertising
effectiveness’, Journal of Marketing, vol. 25, no. 6, pp. 59-62.
Lea, E & Worsley, T 2005, ‘Australians’ organic food beliefs, demographics and values’,
British Food Journal, vol. 107, no. 11, pp. 855-869.
Lee, JW, Jones, PS, Mineyama, Y & Zhang, XE 2002, ‘Cultural differences in responses to
a Likert scale’, Research in Nursing & Health, vol. 25, no. 4, pp. 295-306.
Leedy, PD & Ormrod, JE 2005, Practical research: planning and design, 8th edn, Merrill
Prentice Hall, Upper Saddle River, NJ.
Lennernas, M, Fjellstrom, C, Becker, W, Giachetti, I et al. 1997, ‘Influences on food choice
perceived to be important by nationally-representative samples of adults in the
European Union’, European Journal of Clinical Nutrition, vol. 51, pp. S8-15.
Leu, A 2009, The benefits of organic food, viewed 2 August 2009,
<http://www.ofa.org.au/papers//The%20Benefits%20of%20Organic%20Food%20pape
r.htm>.
Li, J, Zepeda, L & Gould, BW 2007, ‘The demand for organic food in the US: an empirical
assessment’, Journal of Food Distribution Research, vol. 38, no. 3, pp. 54-69.
Li, Y, Cheng, L & Ren, G 2005, ‘Product differences and purchasing decision-making on
non-environmental pollution agricultural products: an analysis of consumer survey in
Jiaxin’ (in Chinese), Zhejiang Statistics, vol. 2.
Lichtenstein, DR, Ridgway, NM & Netemeyer, RG 1993, ‘Price perceptions and consumer
shopping behavior: a field study’, Journal of Marketing Research, vol. 30, pp. 234-245.
Lin, B-H, Smith, TA & Huang, CL 2008, ‘Organic premiums of US fresh produce’,
Renewable Agriculture and Food Systems, vol. 23, no. 3, pp. 208-216.
Lindeman, M & Vaananen, M 2000, ‘Measurement of ethical food choice motives’,
Appetite, vol. 34, no. 1, pp. 55-59.
Liu, F, Murphy, J, Li, J & Liu, X 2006, ‘English and Chinese? The role of consumer
ethnocentrism and country of origin in Chinese attitudes towards store signs’,
Australasian Marketing Journal, vol. 14, no. 2, pp. 5-16.
264
Lockie, S, Halpin, D & Pearson, D 2006, ‘Understanding the market for organic food’ in P
Kristiansen, A Taji and J Reganold (eds) Organic agriculture: a global perspective,
CSIRO Publishing, Melbourne.
Lockie, S, Lyons, K, Lawrence, G & Grice, J 2004, ‘Choosing organics: a path analysis of
factors underlying the selection of organic food among Australian consumers’,
Appetite, vol. 43, no. 2, pp. 135-146.
Lockie, S, Lyons, K, Lawrence, G & Mummery, K 2002, ‘Eating “green”: motivations
behind organic food consumption in Australia’, Sociologia Ruralis, vol. 42, no. 1, pp.
23-40.
Lodorfos, GN & Dennis, J 2008, ‘Consumers’ intent: in the organic food market’, Journal
of Food Products Marketing, vol. 14, no. 2, pp. 17-38.
Loureiro, M, L. & Hine, S 2002, ‘Discovering niche markets: a comparison of consumer
willingness to pay for local (Colorado grown), organic, and GMO-free products’,
Journal of Agricultural and Applied Economics, vol. 34, no. 3, pp. 477-487.
Lu, PX 2008, Elite China: luxury consumer behavior in China, John Wiley, Singapore.
Luo, S 2007, ‘An analysis of organic food demand and slow development in China’ (in
Chinese), Rural Economy and Science-Technology, vol. 18, no. 7.
Magkos, F, Arvaniti, F & Zampelas, A 2003, ‘Putting the safety of organic food into
perspective’, Nutrition Research Reviews, vol. 16, pp. 211-221.
Magkos, F, Arvaniti, F & Zampelas, A 2006, ‘Organic food: buying more safety or just
peace of mind? A critical review of the literature’, Critical Reviews in Food Science
and Nutrition, vol. 46, no. 1, pp. 23-56.
Magnusson, M, K., Arvola, A, Koivisto Hursti, U-K, Aberg, L & Sjoden, P-O 2001,
‘Attitudes towards organic foods among Swedish consumers’, British Food Journal,
vol. 103, no. 3, pp. 209-227.
Magnusson, MK, Arvola, A, Hursti, U-KK, Åberg, L & Sjödén, P-O 2003, ‘Choice of
organic foods is related to perceived consequences for human health and to
environmentally friendly behaviour’, Appetite, vol. 40, no. 2, pp. 109-117.
265
Makatouni, A 2002, ‘What motivates consumers to buy organic food in the UK? Results
from a qualitative study’, British Food Journal, vol. 104, no. 3-5, pp. 345-352.
Malhotra, N 2008, Essentials of marketing research: an applied orientation, 2nd edn,
Pearson Education, Sydney.
Malhotra, N, Hall, J, Shaw, M & Oppenheim, P 2006, Marketing research: an applied
orientation, 3rd edn, Pearson Education, Sydney.
Margetts, BM, Martinez, JA, Saba, A, Holm, L & Kearney, M 1997, ‘Definitions of
“healthy” eating: a pan-EU survey of consumer attitudes to food, nutrition and health’,
European Journal of Clinical Nutrition, vol. 51, pp. S23-29.
Mather, D, Knight, J & Holdsworth, D 2005, ‘Pricing differentials for organic, ordinary and
genetically modified food’, Journal of Product & Brand Management, vol. 14, no. 6,
pp. 387-392.
Mathews, SW 2009, Internationalisation: the internet’s influence on international market
growth in the firm’s outward internationalisation process, PhD thesis, School of
Advertising, Marketing & Public Relations, Queensland University of Technology,
Brisbane.
McDonald, P 2001, The organic food market in Europe: an overview of Europe’s fastest
growing niche food market, Queensland Government Trade & Investment Office
Europe.
McEachern, MG & McClean, P 2002, ‘Organic purchasing motivations and attitudes: are
they ethical?’, International Journal of Consumer Studies, vol. 26, no. 2, pp. 85-92.
McEachern, MG & Willock, J 2004, ‘Producers and consumers of organic meat: a focus on
attitudes and motivations’, British Food Journal, vol. 106, pp. 534-552.
McEwen, W, Fang, X, Zhang, C & Burkholder, R 2006, ‘Inside the mind of the Chinese
consumer’, Harvard Business Review, vol. 84, no. 3, pp. 68-76.
McGregor, J 2005, One billion customers: lessons from the front lines of doing business in
China, Nicholas Brealey, London.
266
McGuire, W 1978, ‘An information processing model of advertising effectiveness’ in HL
Davis and AJ Silk (eds) Behavioral and management science in marketing, Ronald
Press, New York, pp. 156-180.
McMurray, AJ, Pace, RW & Scott, D 2004, Research: a commonsense approach, Thomson
Social Science Press, Melbourne.
Millock, K, Wier, M & Andersen, LM 2004, ‘Consumer demand for organic foods:
attitudes, values and purchasing behavior’, paper presented at Thirteenth Annual
Conference of European Association of Environmental and Resource Economics,
Budapest, 25-28 June.
Mitsostergios, KT & Skiadas, CH 1994, ‘Attitudes and perceptions of fresh pasteurised
milk consumer: a qualitative and quantitative survey’, British Food Journal, vol. 96, no.
7, pp. 4-10.
Mooij, Md & Hofstede, G 2002, ‘Convergence and divergence in consumer behavior:
implications for international retailing’, Journal of Retailing, vol. 78, no. 1, pp. 61-69.
Moon, W, Florkowski, W, Resurreccion, AV, Beuchat, LR, Jordanov, J, Chinnan, M &
Paraskova, P 1998, ‘Consumer concerns about nutritional attributes in a transition
economy’, Food Policy, vol. 23, no. 5, pp. 357-369.
Moore, O 2006, ‘Understanding postorganic fresh fruit and vegetable consumers at
participatory farmers’ markets in Ireland: reflexivity, trust and social movements’,
International Journal of Consumer Studies, vol. 30, no. 5, pp. 416-426.
Mullen, MR 1995, ‘Diagnosing measurement equivalence in cross-national research’,
Journal of International Business Studies, vol. 26, pp. 573-596.
National Bureau of Statistics of China 2009, China Statistical Yearbook 2009, viewed 1
August 2010,
<http://www.stats.gov.cn/english/whatsnew/t20090925_402590214.htm>.
National Bureau of Statistics of China 2011, Statistical communiqué of the People’s
Republic of China on the 2010 national economic and social development, viewed 30
267
March 2011,
<http://www.stats.gov.cn/english/newsandcomingevents/t20110228_402705764.htm>.
Nees, WP 2011, Chinese consumers and organic food, Master’s thesis, Department of
Marketing and Statistics, Aarhus University, Denmark.
Nunnally, JC 1967, Psychometric theory, McGraw-Hill, New York.
Nunnally, JC & Bernstein, IH 1994, Psychometric theory, 3rd edn, McGraw-Hill, New
York.
O’Donovan, P & McCarthy, M 2002, ‘Irish consumer preference for organic meat’, British
Food Journal, vol. 104, no. 34/55, pp. 353-370.
Okun, J 2007, ‘A growing movement toward green food’, Buffalo News, 18 July.
Onozaka, Y 2007, Three essays on consumers’ preferences for fresh organic produce, PhD
thesis, Agricultural and Resource Economics, University of California, Davis.
Onyango, BM, Hallman, WK & Bellows, AC 2007, ‘Purchasing organic food in US food
systems: a study of attitudes and practice’, British Food Journal, vol. 109, no. 5, pp.
399-411.
Oraman, Y & Unakitan, G 2010, ‘Analysis of factors influencing organic fruit and
vegetable purchasing in Istanbul, Turkey’, Ecology of Food and Nutrition, vol. 49, no.
6, pp. 452-466.
Organic Federation of Australia 2005, National standard for organic and bio-dynamic
produce, viewed 2 August 2009, <http://www.ofa.org.au/pages/BecomingCertified.html>.
Organic Food Development Center 2009, OFDC introduction, viewed 30 March 2009,
<http://www.ofdc.org.cn/about/about.asp>.
Özcelik, AÖ & Uçar, A 2008, ‘Turkish academic staffs’ perception of organic foods’,
British Food Journal, vol. 110, no. 9, pp. 948-960.
Ozretic-Dosen, D, Skare, V & Krupka, Z 2007, ‘Assessments of country of origin and
brand cues in evaluating a Croatian, western and eastern European food product’,
Journal of Business Research, vol. 60, no. 2, pp. 130-136.
268
Padel, S & Foster, C 2005, ‘Exploring the gap between attitudes and behaviour:
understanding why consumers buy or do not buy organic food’, British Food Journal,
vol. 107, no. 8, pp. 606-625.
Pallant, J 2007, SPSS survival manual: a step by step guide to data analysis using SPSS for
Windows (Version 15), 3rd edn, Allen & Unwin, Sydney.
Paull, J 2008a, The greening of China’s food-green food, organic food, and eco-labelling,
Sustainable Consumption and Alternative Agri-Food Systems Conference (SUSCONS),
Liege University, Belgium
Paull, J 2008b, ‘The lost history of organic farming in Australia’, Journal of Organic
Systems, vol. 3, no. 2, pp. 2-17.
Peng, H 2006, Consumer behaviour research in food safety management: an empirical
study from Beijing (in Chinese), PhD thesis, Department of Economy and Management,
China Agricultural University, Beijing.
Perry, C 1995, A structured approach to presenting PhD theses: notes for candidates and
their supervisors, paper presented to ANZ Doctoral Consortium, University of Sydney.
Peterson, RA 2000, ‘A meta-analysis of variance accounted for and factor loadings in
exploratory factor analysis’, Marketing Letters, vol. 11, no. 3, pp. 261-275.
Petrovici, DA, Ritson, C & Ness, M 2004, ‘The theory of reasoned action and food choice:
insights from a transitional economy’, Journal of International Food & Agribusiness
Marketing, vol. 16, no. 1, pp. 59-87.
Phau, I & Min, T 2009, ‘Devil wears (counterfeit) Prada: a study of antecedents and
outcomes of attitudes towards counterfeits of luxury brands’, Journal of Consumer
Marketing, vol. 26, pp. 15-27.
Pilgrim, FJ 1957, ‘The components of food acceptance and their measurement’, American
Journal of Clinical Nutrition, vol. 5, pp. 171-175.
Piron, F 2006, ‘China’s changing culture: rural and urban consumers’ favorite things’,
Journal of Consumer Marketing, vol. 23, pp. 327-334.
269
Prasso, S, Ting, W & Dan, Z 2007, ‘China’s new cultural revolution’, Fortune, vol. 155, no.
10, p. 28.
Qian, W, Razzaque, MA & Keng, KA 2007, ‘Chinese cultural values and gift-giving
behavior’, Journal of Consumer Marketing, vol. 24, no. 4, pp. 214-228.
Qing, P, Lobo, A & Li, C 2012, 'The impact of lifestyle and ethnocentrism on consumers'
purchase intentions of fresh fruit in China', Journal of Consumer Marketing, vol. 29,
no. 1, pp. 43-51.
Radman, M 2005, ‘Consumer consumption and perception of organic products in Croatia’,
British Food Journal, vol. 107, no. 4, pp. 263-273.
Reid, M, Li, E, Bruwer, J & Grunert, K 2001, ‘Food-related lifestyles in a cross-cultural
context’, Journal of Food Products Marketing, vol. 7, no. 4, pp. 57-75.
Rimal, A, Moon, W & Balasubramanian, SK 2006, ‘Perceived risks of agro-biotechnology
and organic food purchases in the United States’, Journal of Food Distribution
Research, vol. 37, no. 2, pp. 70-79.
Rimal, A, P., Moon, W & Balasubramanian, S 2005, ‘Agro-biotechnology and organic food
purchase in the United Kingdom’, British Food Journal, vol. 107, no. 2, pp. 84-97.
Roddy, G, Cowan, C & Hutchinson, G 1994, ‘Organic food: a description of the Irish
market’, British Food Journal, vol. 96, no. 4, pp. 3-10.
Roddy, G, Cowan, CA & Hutchinson, G 1996, ‘Consumer attitudes and behavior to organic
foods in Ireland’, Journal of International Consumer Marketing, vol. 9, no. 2, pp. 4163.
Roitner-Schobesberger, B, Darnhofer, I, Somsook, S & Vogl, CR 2008, ‘Consumer
perceptions of organic foods in Bangkok, Thailand’, Food Policy, vol. 33, no. 2, pp.
112-121.
Saba, A & Messina, F 2003, ‘Attitudes towards organic foods and risk/benefit perception
associated with pesticides’, Food Quality and Preference, vol. 14, no. 8, pp. 637-645.
270
Salleh, M, Ali, S, Harun, E, Jalil, M & Shaharudin, M 2010, ‘Consumers’ perception and
purchase intention towards organic food products: exploring attitude among
academician’, Canadian Social Science, vol. 6, no. 6, pp. 119-129.
Sanders, R 2006, ‘A market road to sustainable agriculture? Ecological agriculture, green
food and organic agriculture in China’, Development & Change, vol. 37, no. 1, pp.
201-226.
Sanjuán, AI, Sánchez, M, Gil, JM, Gracia, A & Soler, F 2003, ‘Brakes to organic market
enlargement in Spain: consumers’ and retailers’ attitudes and willingness to pay’,
International Journal of Consumer Studies vol. 27, no. 2, pp. 134-144.
Saunders, M, Lewis, P & Thornhill, A 2007, Research methods for business students, 4th
edn, Pearson Prentice Hall Financial Times, Harlow, UK.
Schifferstein, HNJ & Oude Ophuis, PAM 1998, ‘Health-related determinants of organic
food consumption in the Netherlands’, Food Quality and Preference, vol. 9, no. 3, pp.
119-133.
Schmitt, N & Kuljanin, G 2008, ‘Measurement invariance: review of practice and
implications’, Human Resource Management Review, vol. 18, no. 4, pp. 210-222.
Scholten, M 1996, ‘Lost and found: the information-processing model of advertising
effectiveness’, Journal of Business Research, vol. 37, no. 2, pp. 97-104.
Schütte, H & Ciarlante, D 1998, Consumer behavior in Asia, New York University Press,
New York.
Sedgman, P 2011, China imports of New Zealand milk jump five-fold, aiding quake-hit
nation, Bloomberg, 9 March, viewed 20 September 2011,
<http://www.bloomberg.com/news/2011-03-08/china-imports-of-new-zealand-milkjump-five-fold-aiding-quake-hit-nation.html>.
Shaharudin, M, Pani, J, Mansor, S & Elias, S 2010a, ‘Factors affecting purchase intention
of organic food in Malaysia’s Kedah State’, Cross-Cultural Communication, vol. 6, no.
2, pp. 105-116.
271
Shaharudin, M, Pani, J, Mansor, S, Elias, S & Sadek, D 2010b, ‘Purchase intention of
organic food in Kedah, Malaysia; a religious overview’, International Journal of
Marketing Studies vol. 2, no. 1, pp. 96-103.
Shaharudin, MR, Pani, JJ, Mansor, SW & Elias, SJ 2010c, ‘Purchase intention of organic
food: perceived value overview’, Canadian Social Science, vol. 6, no. 1, pp. 70-79.
Shamdasani, P, Chon-Lin, GO & Richmond, D 1993, ‘Exploring green consumers in an
oriental culture: role of personal and marketing mix factors’, Advances in Consumer
Research, vol. 20, no. 1, pp. 488-493.
Shaw, D & Clarke, I 1999, ‘Belief formation in ethical consumer groups: an exploratory
study’, Marketing Intelligence & Planning, vol. 17, no. 2, pp. 109-120.
Shenzhen government online 2009, Comprehensive economic strength, viewed 8 December
2009, <http://english.sz.gov.cn/lis/>.
Shepherd, R, Magnusson, M & Sjödén, P-O 2005, ‘Determinants of consumer behaviour
related to organic foods’, Ambio, vol. 34, no. 4/5, pp. 352-359.
Shimp, TA & Sharma, S 1987, ‘Consumer ethnocentrism: construction and validation of
the CETSCALE’, Journal of Marketing Research, vol. 24, pp. 280-289.
Shumei, C 2009, ‘Sham or shame: rethinking the China’s milk powder scandal from a legal
perspective’, Journal of Risk Research, vol. 12, no. 6, pp. 725-747.
Slavicek, LC 2010, The Chinese Cultural Revolution, Chelsea House, New York.
Smith, L 2007, ‘Think China, act local’, Australia China Connections, vol. 7, no. 4, p. 26.
Smith, S & Paladino, A 2010, ‘Eating clean and green? Investigating consumer motivations
towards the purchase of organic food’, Australasian Marketing Journal, vol. 18, no. 2,
pp. 93-104.
Smith, T, Huang, C & Lin, B 2009a, ‘Does price or income affect organic choice? Analysis
of US fresh produce users’, Journal of Agricultural and Applied Economics, vol. 41,
no. 3, pp. 731-744.
Smith, T, Huang, C & Lin, B 2009b, ‘Estimating organic premiums in the US fluid milk
market’, Renewable Agriculture and Food Systems, vol. 24, pp. 197-204.
272
Soler, F, Gil, JM & Sánchez, M 2002, ‘Consumers’ acceptability of organic food in Spain’,
British Food Journal, vol. 104, no. 8/9, pp. 670-687.
Sottoni, VA, Contini, C, Martini, A & Menghini, S 2002, ‘Possible development of organic
production in a mountain area of the Florence province in Tuscany’, British Food
Journal, vol. 104, no. 8/9, pp. 654-669.
Sparks, P, Hedderley, D & Shepherd, R 1992, ‘An investigation into the relationship
between perceived control, attitude variability and the consumption of two common
foods’, European Journal of Social Psychology, vol. 22, pp. 55-71.
Sparks, P & Shepherd, R 1992, ‘Self-identity and the theory of planned behavior: assessing
the role of identification with “green consumerism”’, Social Psychology Quarterly, vol.
55, no. 4, pp. 388-399.
Squires, L, Juric, B & Cornwell, TB 2001, ‘Level of market development and intensity of
organic food consumption: cross-cultural study of Danish and New Zealand
consumers’, Journal of Consumer Marketing, vol. 18, no. 4/5, pp. 392-409.
St-Maurice, I, Süssmuth-Dyckerhoff, C & Tsai, H 2008, ‘What’s new with the Chinese
consumer’, McKinsey Quarterly, Sept.
Steenkamp, J-BEM 1997, ‘Dynamics in consumer behavior with respect to agricultural and
food products’ in B Wierenga, Av Tilburg, K Grunert and J-BEM Steenkamp (eds)
Agricultural marketing and consumer behavior in a changing world, Kluwer
Academic Publishers, Boston, pp. 143-188.
Steenkamp, J-BEM & van Trijp, HCM 1991, ‘The use of LISREL in validating marketing
constructs’, International Journal of Research in Marketing, vol. 8, no. 4, pp. 283-299.
Steptoe, A, Pollard, TM & Wardle, J 1995, ‘Development of a measure of the motives
underlying the selection of food: the food choice questionnaire’, Appetite, vol. 25, no. 3,
pp. 267-284.
Stewart, B & Bean, R 2006, China, Peoples Republic of market development reports
Chengdu: an emerging market in China’s heartland, GAIN Report, USDA Foreign
273
Agricultural Service, viewed 10 August 2010,
<http://www.fas.usda.gov/gainfiles/200601/146176543.pdf>.
Stobbelaar, DJ, Casimir, G, Borghuis, J, Marks, I, Meijer, L & Zebeda, S 2007,
‘Adolescents’ attitudes towards organic food: a survey of 15- to 16-year old school
children’, International Journal of Consumer Studies, vol. 31, no. 4, pp. 349-356.
Storstad, O & Bjørkhaug, H 2003, ‘Foundations of production and consumption of organic
food in Norway: common attitudes among farmers and consumers?’, Agriculture and
Human Values, vol. 20, pp. 151-163.
Straub, D, Boudreau, M-C & Gefen, D 2004, ‘Validation guidelines for IS positivist
research’, Communications of the Association for Information Systems, vol. 13, no. 1,
pp. 380-427.
Sun, X 2003, The dynamics of Chinese consumer behaviour in relation to the purchase of
imported fruit, PhD thesis, School of Natural and Rural System Management,
University of Queensland, Brisbane.
Sun, X & Collins, R 2002, ‘Attitudes and consumption values of consumers of imported
fruit in Guangzhou, China’, International Journal of Consumer Studies, vol. 26, no. 1,
pp. 34-43.
Sun, X & Collins, R 2006, ‘Chinese consumer response to imported fruit: Intended uses
and their effect on perceived quality’, International Journal of Consumer Studies, vol.
30, no. 2, pp. 179-188.
Tabachnick, BG & Fidell, LS 2007, Using multivariate statistics, 5th edn, Pearson
Education, Boston
Tai, SHC & Tam, JLM 1997, ‘A lifestyle analysis of female consumers in greater China’,
Psychology and Marketing, vol. 14, no. 3, pp. 287-307.
Tanner, C & Kast, SW 2003, ‘Promoting sustainable consumption: determinants of green
purchases by Swiss consumers’, Psychology & Marketing, vol. 20, no. 10, pp. 883-902.
274
Tarkiainen, A & Sundqvist, S 2005, ‘Subjective norms, attitudes and intentions of Finnish
consumers in buying organic food’, British Food Journal, vol. 107, no. 10/11, pp. 808822.
Taylor, DA 2008, ‘Recovering the good earth: China’s growing organic market’,
Environmental Health Perspectives, vol. 116, no. 8, pp.346-349.
Thøgersen, J 2009, ‘Consumer decision-making with regard to organic food products’ in
TdeN Vaz, P Nijkamp and J-L Rastoin (eds) Traditional food production and rural
sustainable development: a European challenge, Ashgate, Farnham, UK.
Thøgersen, J & Zhou, Y 2012, 'Chinese consumers’ adoption of a ‘green’ innovation – The
case of organic food', Journal of Marketing Management, vol. 28, no. 3/4, pp. 313-333.
Thompson, B 1997, ‘The importance of structure coefficients in structural equation
modeling confirmatory factor analysis’, Educational and Psychological Measurement,
vol. 57, no.1, pp. 5-19.
Thompson, GD 1998, ‘Consumer demand for organic foods: what we know and what we
need to know’, American Journal of Agricultural Economics, vol. 80, no. 5, pp. 11131118.
Thompson, GD & Kidwell, J 1998, ‘Explaining the choice of organic produce: cosmetic
defects, prices and consumer preferences’, American Journal of Agricultural
Economics, vol. 80, no. 2, pp. 277-287.
Tian, K & Dong, L 2011, Consumer-citizens of China: the role of foreign brands in the
imagined future, Routledge, London.
Todman, JB & Dugard, P 2007, Approaching multivariate analysis: an introduction for
psychology, Psychology Press, Hove, UK.
Torjusen, H, Lieblein, G, Wandel, M & Francis, CA 2001, ‘Food system orientation and
quality perception among consumers and producers of organic food in Hedmark
County, Norway’, Food Quality and Preference, vol. 12, no. 3, pp. 207-216.
Tregear, A, Dent, JB & McGregor, MJ 1994, ‘The demand for organically-grown produce’,
British Food Journal, vol. 96, no. 4, pp. 21-25.
275
Tsakiridou, E, Boutsouki, C, Zotos, Y & Mattas, K 2008, ‘Attitudes and behaviour towards
organic products: an exploratory study’, International Journal of Retail & Distribution
Management, vol. 36, no. 2, pp. 158-175.
Tsakiridou, E, Mattas, K & Tzimitra-Kalogianni, I 2006, ‘The influence of consumer
characteristics and attitudes on the demand for organic olive oil’, Journal of
International Food & Agribusiness Marketing, vol. 18, no. 3/4, pp. 23-31.
Uncles, M & Kwok, S 2008, ‘Generalizing patterns of store-type patronage: an analysis
across major Chinese cities’, International Review of Retail, Distribution and
Consumer Research, vol. 18, no. 5, pp. 473-493.
Ureña, F, Bernabéu, R & Olmeda, M 2008, ‘Women, men and organic food: differences in
their attitudes and willingness to pay: a Spanish case study’, International Journal of
Consumer Studies, vol. 32, no. 1, pp. 18-26.
US Department of Agriculture 2008, The People’s Republic of China, organic products:
Shanghai organic retail market profile. Vol. CH8821, Gain report.
US Department of Agriculture 2012a, Regional coverage: Beijing, viewed 14 January 2012
<http://www.usdachina.org/en_pop.asp?id=66>.
US Department of Agriculture 2012b, Organic production and organic food: information
access tools, viewed 9 February 2012,
<http://www.nal.usda.gov/afsic/pubs/ofp/ofp.shtml>.
US Department of Agriculture 2012c, Sustainable agriculture: definitions and terms,
viewed 20 March 2012,
<http://www.nal.usda.gov/afsic/pubs/terms/srb9902terms.shtml#term23>.
Usunier, J-C 2000, Marketing across cultures, 3rd edn, Pearson Education, Harlow, UK.
Vandenberg, RJ & Lance, CE 2000, ‘A review and synthesis of the measurement
invariance literature: suggestions, ractices, and recommendations for organizational
research’, Organizational Research Methods, vol. 3, no. 1, pp. 4-69.
Veal, AJ 2005, Business research methods: a managerial approach, 2nd edn, Pearson
Addison Wesley, Melbourne.
276
Veeck, A 2000, ‘The revitalisaition of the market place: food market of Nanjing’ in DS
Davis (ed.) The consumer revolution in urban China, University of California Press,
Berkeley.
Veeck, A & Burns, AC 2005, ‘Changing tastes: the adoption of new food choices in postreform China’, Journal of Business Research, vol. 58, no. 5, pp. 644-652.
Verbeke, W 1999, Factors influencing the consumer decision-making process toward meat
PhD thesis, Department of Agricultural Economics, Ghent University, Belgium.
Verbeke, W 2000, ‘Influences on the consumer decision-making process towards fresh
meat: insights from Belgium and implications’, British Food Journal, vol. 102, no. 7,
pp. 522-538.
Verdurme, A, Gellynck, X & Viaene, J 2002, ‘Are organic food consumers opposed to GM
food consumers?’, British Food Journal, vol. 104, no. 8/9, pp. 610-623.
Verdurme, A & Viaene, J 2003, ‘Consumer beliefs and attitude towards genetically
modified food: basis for segmentation and implications for communication’,
Agribusiness, vol. 19, no. 1, pp. 91-113.
Verhoef, P, C. 2005, ‘Explaining purchases of organic meat by Dutch consumers’,
European Review of Agricultural Economics, vol. 32, no. 2, pp. 245-267.
Vindigni, G, Janssen, MA & Jager, W 2002, ‘Organic food consumption: a multitheoretical framework of consumer decision-making’, British Food Journal, vol. 104,
no. 8/9, pp. 624-642.
Walsh, AD, Laczniak, RN & Carlson, L 1998, ‘Mothers’ preferences for regulating
children’s television’, Journal of Advertising, vol. 27, no. 3, pp. 23-36.
Wang, C-L, Chen, Z-X, Chan, AKK & Zheng, Z-C 2000, ‘The influence of hedonic values
on consumer behaviors: an empirical investigation in China’, Journal of Global
Marketing, vol. 14, no. 1, pp. 169-186.
Wang, CL & Chen, ZX 2004, ‘Consumer ethnocentrism and willingness to buy domestic
products in a developing country setting: testing moderating effects’, Journal of
Consumer Marketing, vol. 21, no. 6, pp. 391-400.
277
Wang, M 2010, The supervision and international cooperation of China organic product
certification, BioFach Conference on International Organic Food Markets and
Development, China Green Food Development Center, Beijing.
Wang, Y, Liu, D & Tian, X 2008, ‘Analysis of consumer willingness to pay for organic
food’ (in Chinese), Journal of Anhui Agricultural Sciences, vol. 36, no. 33.
Wang, YJ, Doss, SK, Guo, C & Li, W 2010, ‘An investigation of Chinese consumers’
outshopping motives from a culture perspective: implications for retail and
distribution’, International Journal of Retail & Distribution Management, vol. 38, no.
6, pp. 423-442.
Wei, R 1997, ‘Emerging lifestyles in China and consequences for perception of advertising,
buying behaviour and consumption preferences’, International Journal of Advertising,
vol. 16, pp. 261-275.
West, S, G, Finch, JF & Curran, PJ 1995, ‘Structural equation models with nonnormal
variables’ in RH Hoyle (ed.) Structural equation modeling: concepts, issues, and
applications, Sage, Thousand Oaks, Calif.
Wier, M & Calverley, C 2002, ‘Market potential for organic foods in Europe’, British Food
Journal, vol. 104, no. 1, pp. 45-62.
Wild, JJ, Wild, KL, Han, JCY & Rammal, HG 2007, International business: the challenges
of globalisation, Pearson Education, Sydney.
Willer, H & Yussefi, M (eds) 2006, The world of organic agriculture: statistics &
emerging trends 2006, International Federation of Organic Agriculture Movements &
Research Institute of Organic Agriculture, Bonn.
Williams, CM 2002, ‘Nutritional quality of organic food: shades of grey or shades of
green?’, Proceedings of the Nutrition Society, vol. 61, no. 1, pp. 19-24.
Williams, PRD & Hammitt, JK 2000, ‘A comparison of organic and conventional fresh
produce buyers in the Boston area’, Risk Analysis, vol. 20, no. 5, pp. 735-746.
278
Wirth, F, Stanton, J & Wiley, J 2011, ‘The relative importance of search versus credence
product attributes: organic and locally grown’, Agricultural and Resource Economics
Review, vol. 40, no. 1, pp. 48-62.
Wong, GKM & Yu, L 2003, ‘Consumers’ perception of store image of joint venture
shopping centres: first-tier versus second tier cities in China’, Journal of Retailing and
Consumer Services, vol. 10, no. 2, pp. 61-70.
Wong, J 2004, ‘Organic food and the activist mother’, University of Auckland Business
Review, vol. 6, no. 1, pp. 2-11.
Worthington, RL & Whittaker, TA 2006, ‘Scale development research: a content analysis
and recommendations for best practices’, Counseling Psychologist, vol. 34, no. 6, pp.
806-838.
Wu, C & Zhao, R 2011, ‘Shuanghui involved in clenbuterol scandal’ (in Chinese), China
Youth Daily, 24 April.
Wu, Y 1999, China’s consumer revolution: the emerging patterns of wealth and
expenditure, Edward Elgar, Cheltenham, UK.
Xie, B, Li, T, & Yi, Q 2011, ‘Organic certification and the market: organic exports from
and imports to China’, British Food Journal, vol. 113, no. 10, pp. 1200-1216.
Xing, W 2011, ‘Urbanisation to exceed 70% by 2030’, China Daily, 7 April,
Xu, G 2008, Olympic dreams: China and sports, 1895-2008, Harvard University Press,
Cambridge, Mass.
Yaakob, MR & Zakaria, A 2011, ‘Customers’ awareness, perception and future prospects
of green products in Pahang, Malaysia’, Journal of Commerce, vol. 3, no. 2, pp. 1-10.
Yang, KCC 2004, ‘A comparison of attitudes towards internet advertising among lifestyle
segments in Taiwan’, Journal of Marketing Communications, vol. 10, no. 3, pp. 195212.
Yeon, EK-K (ed.) 2008, ‘Disparity in China: poverty, inequality and interregional
imbalance’ in EK-K Yeon (ed.) Facets of a transforming China: resource, trade and
equity, Institute of China Studies, University of Malaya, Kuala Lumpur.
279
Yin, S, Wu, L & Chen, M 2008, ‘The analysis of Chinese consumers’ willing to pay for
organic food’ (in Chinese), Journal of Agrotechnical Economics, no. 5, pp. 81-88.
Yin, S, Wu, L, Du, L & Chen, M 2010, ‘Consumers’ purchase intention of organic food in
China’, Journal of the Science of Food and Agriculture, vol. 90, no. 8, pp. 1361-1367.
Yin, T 2010, ‘Chengdu is the best location to develop headquarter economy in west China’
(in Chinese), Chengdu Daily, 16 Aug.
Yiridoe, E, Bonti-Ankomah, S & Martin, R 2005, ‘Comparison of consumer perceptions
and preference toward organic versus conventionally produced foods: a review and
update of the literature’, Renewable Agriculture and Food Systems, vol. 20, no. 4, pp.
193-205.
Yoo, B, Donthu, N & Lee, S 2000, ‘An examination of selected marketing mix elements
and brand equity’, Journal of the Academy of Marketing Science, vol. 28, no. 2, pp.
195-211.
Yuan, X & Liu, T (eds) 2009, 2009 China organic food development trend report 2009 (in
Chinese), Organic Service GmbH, Nanjing.
Yusuf, F & Brooks, G 2010, ‘Demographics and consumption patterns in urban China’,
Population Research and Policy Review, vol. 29, no. 1, pp. 5-17.
Zanoli, R & Naspetti, S 2002, ‘Consumer motivations in the purchase of organic food: a
means-end approach’, British Food Journal, vol. 104, pp. 643-653.
Zepeda, L, Chang, H-S & Leviten-Reid, C 2006, ‘Organic food demand: a focus group
study involving Caucasian and African-American shoppers’, Agriculture and Human
Values, vol. 23, pp. 385-394.
Zepeda, L & Deal, D 2009, ‘Organic and local food consumer behaviour: Alphabet Theory’,
International Journal of Consumer Studies, vol. 33, no. 6, pp. 697-705.
Zepeda, L & Li, J 2007, ‘Characteristics of organic food shoppers’, Journal of Agricultural
and Applied Economics, vol. 39, no. 1, pp. 17-28.
280
Zhang, X 2002, ‘The dynamics of Chinese consumers: a case of Shanghai food
consumption’, Journal of International Food & Agribusiness Marketing, vol. 14, no. 1,
pp. 47-66.
Zhang, X 2007, The high quality vegetable industry in China: an economic analysis and
policy studies (in Chinese), PhD thesis, College of Economics & Management,
Shandong Agricultural University, Shandong.
Zhang, X, Dagevos, H, He, Y, van der Lans, I & Zhai, F 2008, ‘Consumption and
corpulence in China: a consumer segmentation study based on the food perspective’,
Food Policy, vol. 33, no. 1, pp. 37-47.
Zhao, X, Chambers, E, Matta, Z, Loughin, T & Carey, E 2007, ‘Consumer sensory analysis
of organically and conventionally grown vegetables’, Journal of Food Science, vol. 72,
no. 2, pp. 87-91.
Zhao, Y, Wells, JH, Xie, J & Xu, S 2000, ‘The features of the Chinese food packaging’,
Foodservice Research International, vol. 12, no. 3, pp. 197-212.
Zhou, JX, Arnold, MJ, Pereira, A & Yu, J 2010, ‘Chinese consumer decision-making styles:
a comparison between the coastal and inland regions’, Journal of Business Research,
vol. 63, no. 1, pp. 45-51.
Zhou, X 2008a, ‘Eat, drink and sing, and be modern and global: food, karaoke and “middle
class” consumers in China’ in C Jaffrelot and P van der Veer (eds) Patterns of middle
class consumption in India and China, Sage, New Delhi, pp. 110-126.
Zhou, Z 2008b, The development of organic sector in China, ‘Guaranteeing integrity of
organic products from China’ workshop, International Federation of Organic
Agriculture Movements.
Zhou, Z 2011, ‘Shuanghui apologizes over additive scandal’, China Daily, 17 March.
Zhou, Z, Zong, L, Yang, Y & Xiao, X 2004, The theory and practice of Chinese ecological
agriculture and organic agriculture (in Chinese), China Environmental Science
Publishing House, Beijing.
281
Zhu, H, Wang, Q, Yan, L & Wu, G 2009, ‘Are consumers what they consume?-Linking
lifestyle segmentation to product attributes: an exploratory study of the Chinese mobile
phone market’, Journal of Marketing Management, vol. 25, no. 3-4, pp. 295-314.
Zhuang, G, Tsang, ASL, Zhou, N, Li, F & Nicholls, JAF 2006, ‘Impacts of situational
factors on buying decisions in shopping malls: an empirical study with multinational
data’, European Journal of Marketing, vol. 40, no. 1-2, pp. 17-43.
282
APPENDICES
Appendix 1: Questionnaires
Appendix 1A: Survey Questionnaire in English
Dear Sir / Madam,
This is a study conducted by Ms Jue Chen, who is a PhD student at the Swinburne University of
Technology, Australia. She is researching the factors associated with the consumption of organic
food products in China. The information you provide will help her to develop a better understanding
of the factors that influence the Chinese consumers’ buyer behaviour of organic food products.
Completion of this questionnaire is taken as your ‘Informed Consent’ to participate in this research.
‘Informed Consent’ means that:
ƒ
ƒ
ƒ
all questions about the research have been answered to your satisfaction
your participation in the research is voluntary and you may withdraw at any time
the survey is strictly anonymous and your responses will be kept confidential and only
aggregated results (not individual responses) will be mentioned in the research output.
This survey is a purely academic, and it is completely independent of any commercial interests.We
highly value your feedback, and would be appreciative if you could take about 15 minutes to
complete this survey.
We shall be grateful if you could please inform your friends and colleagues who are consumers of
organic food about this online questionnaire. If you have any questions or want more information
about this survey please contact myself
Jue Chen
Faculty of Business and Enterprise
Swinburne University of Technology
PO Box 218 Hawthorn
Victoria 3122, Australia
Email: juchen@swin.edu.au
Ph (W): +61 3 9214 5821
Or:
Jue Chen
Room1028, Building 8,
No 83, Guanglin 1st Road, Shanghai, China
Post code: 200083
Tel: 021-65171278 (China), Mobile: 135 1219 3808 (China)
Email: juchen@swin.edu.au
283
Or my PhD supervisor Dr. Antonio Lobo,
Faculty of Business and Enterprise
Swinburne University of Technology
PO Box 218 Hawthorn
Victoria 3122, Australia
Email: alobo@swin.edu.au
Ph (W): +61 3 9214 8535
Have you ever heard of the term ‘organic food’?
YES (please proceed to answer the questions below)
NO (thank you for your time, you need not continue this survey)
This questionnaire consists of a total of 5 sections.
284
SECTION 1: This section relates to your general knowledge about organic food
1.1
Please tick the logos you recognise.
1)
1.2
yes
2)
yes
3)
yes
4)
yes
5)
yes
6)
yes
How knowledgeable are you about organic food (please tick only one box)?
I have heard of it, but am not sure what it means
I know a little about what ‘organic’ means
I know a lot about organically produced food
1.3
What is the first thing which comes to your mind when you think about organic
food (please tick only one box)?
No additives
No pesticide
Free of GM contents
Environmentally produced
Something natural
Green
Other: …………………………… (please specify)
1.4
Have you ever purchased organic food?
YES (please proceed to question 1.5)
NO (please go straight to section 2)
1.5 When did you last purchase any organic food?
More than one year ago
Half a year ago
A week ago
A month ago
285
Not important
Neutral
Important
2.1
The taste of organic food
1
2
3
4
5
2.2
The smell of organic food
1
2
3
4
5
2.3
The appearance of organic food
1
2
3
4
5
2.4
The overall quality of organic food
1
2
3
4
5
2.5
The price of organic food
1
2
3
4
5
2.6
1
2
3
4
5
2.7
The availability of organic food in convenience
stores and supermarkets
The promotion and advertising of organic food
1
2
3
4
5
2.8
The value of organic food relative to its price
1
2
3
4
5
2.9
The environmental benefits of organic food
1
2
3
4
5
2.10
1
2
3
4
5
2.11
The idea of face saving (mianzi) when purchasing
organic food
The social status of people purchasing organic food
1
2
3
4
5
2.12
The knowledge of organic food products
1
2
3
4
5
2.13
The awareness of organic food products
1
2
3
4
5
2.14
The country of origin of the organic food
1
2
3
4
5
2.15
Organic food that is produced in China
1
2
3
4
5
2.16
The brand name of the organic food
1
2
3
4
5
2.17
1
2
3
4
5
2.18
Government regulations and policies relating to the
sale of organic food
Food safety in relation to organic food
1
2
3
4
5
2.19
Certification relating to the quality of organic food
1
2
3
4
5
2.20
Enforcement relating to the quality of organic food
1
2
3
4
5
2.21
Packaging of organic food
1
2
3
4
5
2.22
Correct labelling of organic food
1
2
3
4
5
2.23
Information about the nutritional value of organic
food
1
2
3
4
5
Extremely
important
Important attributes for the purchase of organic
food (Circle the preferred answer)
Not important
at all
SECTION 2: Important attributes of organic food
When responding to these statements focus on your knowledge and experience for the purchase of
organic foods. Please indicate by circling only one number the level of importance of each
statement. Selecting 1 means that it is not important at all and selecting 5 means that it is
extremely important. You may select any of the numbers in between 1 and 5 to indicate the level
of importance. There are no right or wrong answers – all we are interested in is a number that best
shows your experiences related to organic food.
286
SECTION 3: Your perceptions, attitudes and beliefs regarding organic food
Please indicate the extent to which you agree with the statements listed below. Selecting 1 means
that you strongly disagree with the statement and selecting 5 means that you strongly agree. You
may select any of the numbers in between 1 and 5 to indicate the strength of your agreement. There
are no right or wrong answers – all we are interested in is a number that best shows your
experiences related to organic food.
Disagree
Neutral
Agree
3.1
Organic food has a pleasant texture
1
2
3
4
5
3.2
Organic food looks nice
1
2
3
4
5
3.3
Organic food smells nice
1
2
3
4
5
3.4
Organic food tastes good
1
2
3
4
5
3.5
Consuming organic food is trendy
1
2
3
4
5
3.6
Organic food has high nutritional value
1
2
3
4
5
3.7
Generally speaking, the higher the price of a
1
2
3
4
5
Strongly
Agree
organic food (Circle your preferred answer)
Strongly
Disagree
Perception, attitudes and beliefs regarding
product, the higher the quality
3.8
The price of organic food is too high
1
2
3
4
5
3.9
Organic food is good value for money
1
2
3
4
5
3.10
When I buy a food product, I always read the label
1
2
3
4
5
3.11
I don’t trust organic food certification bodies
1
2
3
4
5
3.12
I trust the outlets which sell certified organic food
1
2
3
4
5
3.13
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
3.16
Chinese government regulate food marketing, and
have developed significant policies
Lack of adequate government control of media
allows advertisers to take advantage of consumers
Logos depicting types of organic foods should be
controlled
More land should be allocated for organic farming
1
2
3
4
5
3.17
The market for ‘organic’ and ‘green’ is chaotic
1
2
3
4
5
3.18
1
2
3
4
5
3.19
Organic food is good for myself and my family’s
health
Organic food has no harmful effects
1
2
3
4
5
3.20
I like the brands associated with organic food
1
2
3
4
5
3.21
1
2
3
4
5
3.22
I worry about there being harmful chemicals in my
food
Organic food does not contain pesticides
1
2
3
4
5
3.23
Organic food is good for the environment
1
2
3
4
5
3.14
3.15
287
3.24
I believe that organic food has superior quality
1
2
3
4
5
3.25
I personally think I should always buy organic food
1
2
3
4
5
3.26
To me, it is important that the food I usually eat can
be easily found in the food outlets near my house or
workplace
Organic food labels mean high quality food
products
When I like something, I will buy it without too
much deliberation
I always do whatever I feel like and whenever I feel
like it
The sole purpose of making money is to spend it
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
3.32
Sometimes I feel like spending money on anything
I lay my eyes on
I often make impulse purchases
1
2
3
4
5
3.33
I always try something new and unique
1
2
3
4
5
3.34
I love fashionable and trendy products
1
2
3
4
5
3.35
It does not hurt to be trendy if I feel like it
1
2
3
4
5
3.36
I am often influenced by advertisements of new
products
I can easily influence people around me during
conversation
My friends often consult me when they cannot
make up their own mind
I have a strong desire to be successful
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
Chinese people should buy domestic products
rather than imported products
China should levy heavy tariff on foreign products
to reduce their quantity into China
If two organic food products were the same in
quality, but one was imported and the other was
Chinese, I would pay more for the imported product
I would purchase organic food coming from a
country I approve of politically
Chinese people should not buy foreign products,
because it would hurt domestic business, and cause
more unemployment
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
3.27
3.28
3.29
3.30
3.31
3.37
3.38
3.39
3.40
3.41
3.42
3.43
3.44
288
4.4
4.5
4.6
4.7
4.8
I am eager to check out organic food products because of
advertisements and promotion
I intend to try out organic food products
1
2
3
4
5
1
2
3
4
5
I am interested in experiencing the benefits of using
organic foods
It is likely that I will buy organic food products when
they become available
I can recall the brand names and labelling of some of the
organic food products
I will probably use organic food products in the future
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
I will recommend usage of organic foods to my friends
and relatives
If I had to purchase organic foods again, I would make
the same choice
1
2
3
4
5
1
2
3
4
5
Neutral
Strongly
Agree
4.3
Agree
4.2
Disagree
4.1
Purchase intention regarding organic food
(Circle your preferred answer)
Strongly
Disagree
SECTION 4: Your purchase intention regarding organic food
Please indicate the extent to which you agree with the statements listed below. Selecting 1 means
that you strongly disagree with the statement and selecting 5 means that you strongly agree. You
may select any of the numbers in between 1 and 5 to indicate the strength of your agreement. There
are no right or wrong answers – all we are interested in is a number that best shows your
experiences related to organic food.
SECTION 5: May I ask a few questions about you and your family?
The following questions are for classification purposes only, and will be kept strictly
confidential.
5.1 What types of organic foods have you recently purchased?
Dairy
Fruit & Vegetable
Meat & Seafood
Grains
Other
5.2 What is your intended use for the most recent purchase of organic food?
Own consumption
for family
for children
for elderly people
Gift
Other
5.3 How much extra are you willing to pay for organic food as compared to conventional food? (only tick
one)
Between 20%-50%
Between 51%-100%
Great than 100%
No extra
5.4 If you were buying imported organic food, from which country or region would you prefer most? (only
tick one)
Europe
U.S.A
Australia / New Zealand
Japan / Korea
HK / Taiwan
Don’t mind
289
5.5 Where would you prefer to buy organic food from? (only tick one)
Supermarket
On line
Local market
Mail order catalogues
Organic farm
Speciality stores
Other
5.6 How often do you purchase organic food products? (only tick one)
At least once a week
Every fortnight
Monthly
Occasionally
5.7 Please indicate your monthly family income? (tick one)
Less than RMB5, 000
Between RMB5, 001-10,000
Between RMB10, 001-20,000
More than RMB20, 000
5.8
Your household includes? (tick one)
Yourself (single)
Couple without children
With young children
With parents
With adult children
5.9 Please indicate your gender (tick one)
Female
Male
5.10 Please indicate your age group: (tick one)
Between 18-30
5.11
Between 31-45
Bachelor degree
5.14
High school
2 years college or associate’s degree
Postgraduate or above
Please indicate your occupation: (tick one)
White collar
5.13
Greater than 60
Please specify the highest level of education you have completed: (tick one)
Below high school
5.12
Between 46-60
Not working
Student
Blue collar
Other
Please indicate which city are you located in? (only tick one)
Beijing
Shanghai
Chengdu
Other
Shenzhen
Please feel free to add any additional information you would like to provide with regard to the
purchase of organic food that has not previously been addressed
______________________________________________________________________________________
_______________________________________________________________________________________
THANK YOU FOR YOUR TIME. IT IS VERY MUCH APPRECIATED.
290
Appendix 1B: Survey Questionnaire translated into Chinese
Ё೑᳝ᴎ亳ક⍜䌍ᢑḋ䇗ᶹ
ᭀਃ㗙ˈ
▇໻߽Ѯᮃ࿕ᴀ໻ᄺ䆮䙔䇋ᙼখϢϔ乍݇Ѣ᳝ᴎ亳કЁ೑⍜䌍㗙ⱘ㘨ড়䇗ᶹDŽ៥Ӏᳳᳯ䖭乍
ⷨおৃҹࡴ⏅ᄺᴃ⬠ᇍ䆹乚ඳⱘ䖯ϔℹ䅸ⶹˈ᳝ࡽѢ᳈དഄњ㾷Ё೑⍜䌍㗙䌁ф᳝ᴎ亳કⱘ
‫އ‬ᅮᗻ಴㋴DŽ
ᙼᦤկⱘ᠔ֵ᳝ᙃᇚҹϡ䆄ৡᮍᓣ䆄ᔩ, ᠔᳝䯂ो㒧ᵰᇚҹ䲚ড়ᮍᓣ㸼⦄ˈ ӏԩҎ䛑᮴⊩䆚
߿ᙼⱘϾҎ䌘᭭DŽ
ᅠ៤ᴀ䇗ⷨⱘ䯂ोᇚ㾚Ў˖
x
ᙼৠᛣᇍ䯂ोЁⱘ᠔᳝䯂乬԰ߎㄨ໡
x
ᙼ㞾ᜓখϢᴀ䇗ⷨ ( ᙼгৃҹ䱣ᯊ⊼䫔ᙼⱘ䯂ो)
ᴀ䇗ⷨЎ㒃ᄺᴃᗻ⌏ࡼˈϢӏԩଚϮ⌏ࡼ᮴݇DŽ
བᵰ㛑ऴ⫼ᙼᅱ䌉ⱘ 15 ߚ䩳฿‫ݭ‬ᴀ䯂ो, ៥Ӏ㸼⼎कߚᛳ䇶DŽ
བ᳝ӏԩ⭥䯂ˈ ៪Ꮰᳯ㦋ᕫ᳝݇䇗ⷨⱘ䖯ϔℹ䌘᭭ˈ 䇋㘨㋏
Jue Chen (म຿ⷨお⫳˖䰜⦣)
Faculty of Business and Enterprise
Swinburne University of Technology
PO Box 218 Hawthorn
Victoria 3122, Australia
Email: juchen@swin.edu.au
Ph (W): +61 3 9214 5821˄▇໻߽Ѯ˅
13512193808 (Ё೑)
៪म຿⫳ᇐᏜ˖ Dr. Antonio Lobo, alobo@swin.edu.au
ᙼ਀䇈䖛᳝ᴎ亳ક৫˛
਀䇈䖛, 䇋㒻㓁ಲㄨҹϟ䯂乬
≵਀䇈䖛 ˄䇶䇶ᙼˈ ৃϡᖙ㒻㓁ℸ䯂ो˅
ᴀ䯂ो݅ЎѨϾ䚼ߚ˖
291
㄀ϔ䚼ߚ: ᳝ᴎ亳કⱘϔ㠀ᐌ䆚
1.1 䇋䗝ᢽᙼⶹ䘧ⱘᷛ䆚 (ᙼৃҹ䗝ᢽ໮Ͼˈгৃҹϡ䗝)
1)
ⶹ䘧
2) ⶹ䘧
3)
ⶹ䘧
4)
ⶹ䘧
5)
ⶹ䘧
6)
ⶹ䘧
1.2 ᳝ᴎ亳કⶹ䆚㞾៥䆘Ӌ(া㛑䗝ϔϾㄨḜ)?
៥਀䇈䖛 “᳝ᴎ亳ક” ˈԚϡ໾⹂ᅮᰃҔМᛣᗱ
៥ᇍ”᳝ᴎ亳ક”ᰃҔМ᳝ϔ⚍њ㾷
៥ᕜњ㾷ҔМᰃ “᳝ᴎ亳ક”
1.3
ᔧᙼᛇࠄ᳝ᴎ亳કˈᙼⱘ㄀ϔὖᗉᰃҔМ(া㛑䗝ϔϾㄨḜ)?
᮴⏏ࡴࠖ
᮴࣪ᄺ‫ݰ‬㥃
᮴䕀෎಴
⦃ֱѻક
໽✊ⱘ
㓓㡆ⱘ
݊Ҫ: …………………………….(䇋⊼ᯢ)
1.4
ᙼ䌁ф䖛᳝ᴎ亳ક৫˛
ф䖛 ˄䇋㒻㓁ಲㄨ䯂乬 1.5˅
≵᳝ф䖛 ( 䇋Ⳉ᥹ಲㄨ㄀Ѡ䚼ߚ)
1.5 ᙼϞϔ⃵ᰃҔМᯊ‫׭‬䌁фⱘ᳝ᴎ亳ક?
ϔᑈҹࠡ
ञᑈҹࠡ
ϔϾ᳜ҹࠡ
ϔϾ᯳ᳳҹࠡ
292
㄀Ѡ䚼ߚ˖᳝ᴎ亳કⱘ䞡㽕⡍ᗻ
2 . 1
᳝
ᴎ
亳
ક
ⱘ
ষ
ᛳ
2 . 2
᳝
ᴎ
亳
ક
ⱘ
⇨
ੇ
2 . 3
᳝
ᴎ
亳
ક
ⱘ
໪
㾖
2 . 4
᳝ ᴎ 亳 ક ⱘ ᭈ ԧ ક 䋼
2 . 5
᳝
2 . 6
ৃҹ೼߽֓ᑫ੠䍙Ꮦ䌁фࠄ᳝ᴎ亳ક
2 . 7
᳝ ᴎ 亳 ક ⱘ ֗ 䫔 ੠ ᑓ ਞ
2 . 8
᳝ᴎ亳કᗻӋ↨催ˈ⠽᳝᠔ؐ
2 . 9
᳝ ᴎ 亳 ક ᳝ Ⲟ Ѣ ⦃ ֱ
2.10
䌁 ф ᳝ ᴎ 亳 ક ᰃ ᳝ 䴶 ᄤ ⱘ џ
2.11
䌁ф᳝ᴎ亳કᯊⱘҎⱘ⼒Ӯഄԡ
2.12
᳝ ᴎ 亳 ક ѻ ક ⱘ ⶹ 䆚
2.13
᳝ ᴎ 亳 ક ѻ ક ⱘ ⶹ ৡ ᑺ
2.14
᳝
2.15
೑
ѻ
ⱘ
᳝
ᴎ
亳
ક
2.16
᳝
ᴎ
亳
ક
ⱘ
ક
⠠
2.17
ᬓᑰ݇Ѣ䫔ଂ᳝ᴎ亳કⱘ⊩㾘੠ᮍ䩜
2.18
亳 ક ᅝ ܼ ⍝ ঞ ࠄ ᳝ ᴎ 亳 ક
2.19
݇ Ѣ ᳝ ᴎ 亳 ક ⱘ ક 䋼 ⱘ 䅸 䆕
2.20
᳝ ᴎ 亳 ક ⱘ ક 䋼 ⱘ ᠻ 㸠
2.21
᳝
2.22
᳝ ᴎ 亳 ક ⱘ ℷ ⹂ ᷛ 䆚
2.23
݇Ѣ᳝ᴎ亳કⱘ㧹‫ݏ‬Ӌؐⱘֵᙃ
ᴎ
ᴎ
ᴎ
亳
亳
亳
ક
ક
ⱘ
ⱘ
ક
ॳ
ⱘ
Ӌ
ѻ
ࣙ
ᕜ䞡㽕
䞡㽕
Ёゟ
ϡ䞡㽕
䌁ф᳝ᴎ亳કⱘ䞡㽕ሲᗻ (䇋䗝ᢽⳌᑨ
ⱘ᭄ᄫ)
ḍᴀϡ
䞡㽕
෎ѢᙼϾҎᇍ᳝ᴎ亳કⱘԧ偠੠䅸䆚ˈ 䇋ᇍϟ߫↣ϔϾ㾖⚍㸼ᯢᙼৠᛣϢ৺ⱘ⿟ᑺDŽᙼৃҹ䗝ᢽ”ḍ
ᴀϡ䞡㽕”ˈ”ϡ䞡㽕”ˈ”Ёゟ”ˈ”䞡㽕”៪”ᕜ䞡㽕”ЁⱘӏԩϔϾ㾖⚍DŽ ᇍᴀ䇗ᶹ㗠㿔, ᙼⱘ㾖⚍≵᳝
ᇍ៪䫭ПߚDŽ䇋䗝ᢽⳌᑨⱘᮍḚDŽ
Ḑ
೑
㺙
293
㄀ϝ䚼ߚ: ݇Ѣ᳝ᴎ亳કⱘ 䅸ⶹ, ᗕᑺ ੠ֵӏ⿟ᑺ
䇋㸼ᯢᙼᇍϟ߫↣ϔϾ㾖⚍ⱘৠᛣ⿟ᑺDŽᙼৃҹ䗝ᢽ “ᔎ⚜ডᇍ”ˈ”ডᇍ”ˈ”Ёゟ”ˈ”ৠᛣ”៪”䴲ᐌ
ৠᛣ”ЁⱘӏԩϔϾDŽᙼⱘ㾖⚍≵᳝䫭੠ᇍПߚˈ៥ӀাᰃᏠᳯњ㾷ᙼᇍ᳝ᴎ亳કⱘ䅸ⶹ, ᗕᑺ੠ֵӏ
᳝ᴎ亳કᕜད䯏
3.4
᳝ᴎ亳કᕜདৗ
3.5
᳝ᴎ亳કᕜ亢㸠
3.6
᳝ᴎ亳ક‫݋‬᳝ᕜ催ⱘ㧹‫ݏ‬Ӌؐ
3.7
ϔ㠀㗠㿔ˈѻક䋼䞣䍞དˈӋḐህ䍞䌉
3.8
᳝ᴎ亳ક໾䌉њ
3.9
᳝ᴎ亳ક⠽᳝᠔ؐ
3.10
ᔧ៥䌁ф亳કᯊˈ៥ϔᅮӮⳟଚᷛ
3.11
៥ϡֵӏ᳝ᴎ亳કⱘ䅸䆕ᴎᵘ
3.12
3.15
䙷ѯߎଂ䅸䆕䖛ⱘ᳝ᴎ亳કⱘ䳊ଂଚ䅽៥
ֵᕫ䖛
ᬓᑰᏆ㒣ߎৄњ䞡㽕ⱘᬓㄪˈ㾘㣗њ亳ક
ⱘ䫔ଂ
೑ᆊ㔎Уᇍၦԧᑨ᳝ⱘⲥ᥻ˈ䅽ᑓਞଚ䗮
䖛䇃ᇐ⍜䌍㗙㗠㦋߽
⫼ᴹ㸼ᕕ᳝ᴎ亳કⱘଚᷛᑨ䆹ফࠄⲥㅵ
3.16
᳈໮ⱘೳഄᑨ䆹䕀࣪Ў᳝ᴎ‫⬄ݰ‬
3.17
Ꮦ䴶Ϟᇍ”᳝ᴎ”੠”㓓㡆”ⱘὖᗉᕜ⏋х
3.18
᳝ᴎ亳ક᳝ⲞѢ៥੠ᆊҎⱘ‫ع‬ᒋ
3.19
᳝ᴎ亳ક᮴᳝ᆇᬜᑨ
3.20
៥୰⃶Ϣ᳝ᴎ亳ક᳝݇ⱘક⠠
3.21
៥ᕜᢙᖗ៥ⱘ亳⠽䞠Ӯ᳝᳝ᆇⱘ࣪ড়⠽
3.22
᳝ᴎ亳કϡ৿‫ݰ‬㥃
3.23
᳝ᴎ亳ક᳝ⲞѢ⦃๗
3.24
៥മֵ᳝ᴎ亳ક䋼䞣ϞЬ
3.13
3.14
䴲ᐌ
ৠᛣ
3.3
ৠᛣ
᳝ᴎ亳કⳟϞএϡ䫭
Ёゟ
3.2
ডᇍ
3.1
᳝ᴎ亳કⱘ䅸ⶹ, ⳟ ⊩੠ֵӏ⿟ᑺ(೼
೼ 䗝ᢽ
ⱘ᭄ᄫϞ⬏೜)
᳝ᴎ亳ક‫݋‬᳝ҸҎᛝᙺⱘ䋼ᛳ
ᔎ⚜
ডᇍ
⿟ᑺ DŽᙼৃҹ䗝ᢽӏԩϔϾᮍḚ㸼ᯢᙼⱘৠᛣϢ৺ⱘ⿟ᑺDŽ
294
3.25
3.26
3.27
3.28
ህ៥ϾҎ㗠㿔ˈ៥䅸Ў៥ᑨ䆹ᘏᰃф᳝ᴎ
亳ક
ᇍ៥㗠㿔ˈ ೼៥ᆊ៪៥Ꮉ԰䰘䖥㛑фࠄ៥
ৗⱘ亳⠽ᕜ䞡㽕
᳝ᴎ亳કⱘᷛㅒᷛᖫⴔ亳⠽ⱘ催䋼䞣
3.30
៥୰⃶ҔМˈ ህӮ↿ϡ⢍䈿ഄфϟˈϡএ
⏅ᗱ❳㰥
៥ᘏᰃᛇфҔМህфҔМˈ ᛇҔМᯊ‫׭‬ф
ህҔМᯊ‫׭‬ф
䌮䪅ⱘଃϔⳂⱘህᰃ㢅䪅
3.31
᳝ᯊ៥ⳳᛇ㾕ҔМфҔМ
3.32
៥ᐌӮ᳝‫ⱘࡼކ‬䌁ф
3.33
៥ᘏᰃᇱ䆩ᮄ༛ⱘϰ㽓
3.34
៥୰⃶ᮄ╂ⱘϰ㽓
3.35
া㽕៥催݈ˈ䍊ᯊ傺≵ҔМ䯂乬
3.36
៥ᕜᆍᯧ㹿ᮄѻકⱘᑓਞ᠔ᕅડ
3.37
៥ᕜᆍᯧ೼Ѹ䇜ЁऴЏ㾦
3.38
៥ⱘ᳟ট೼ҪӀᣓϡᅮЏᛣᯊ㒣ᐌ䇋ᬭ៥
3.39
៥␈ᳯ៤ࡳ
3.40
Ё೑Ҏᑨ䆹ф೑ѻⱘˈϡᑨ䆹ф䖯ষⱘϰ
㽓
Ё೑ᬓᑰᑨ䆹ᇍ䖯ষѻક๲ࡴ݇⿢ˈ Ң㗠
‫ޣ‬ᇥ䖯ষѻક᭄䞣
བᵰϸ⾡᳝ᴎ亳ક‫݋‬᳝ৠḋⱘ䋼䞣, ϔ⾡ᰃ
䖯ষⱘˈϔ⾡ᰃ೑ѻⱘˈ៥ᚙᜓ໮Ҭ⚍䪅
䌁ф䖯ষⱘ᳝ᴎ亳ક
៥ᜓᛣ䌁ф᳝ᴎ亳કᴹ㞾Ѣ៥ᬓ⊏Ϟ䅸ৃ
ⱘ೑ᆊ
Ё೑Ҏϡᑨ䆹䌁ф೑໪ѻકˈ৺߭Ӯᕅડ
⇥ᮣѻϮˈᇐ㟈᳈໮ⱘЁ೑Ҏ༅ϮDŽ
3.29
3.41
3.42
3.43
3.44
295
㄀ಯ䚼ߚ˖᳝ᴎ亳કⱘ䌁фᛣ೒
䇋㸼ᯢᙼᇍϟ߫↣ϔϾ㾖⚍ⱘৠᛣ⿟ᑺDŽᙼৃҹ䗝ᢽ”ᔎ⚜ডᇍ”ˈ”ডᇍ”ˈ”Ёゟ”ˈ”䌲៤”ˈ”䴲ᐌ䌲
4.2
⬅Ѣᑓਞ੠֗䫔ⱘॳ಴ˈ៥䖿ߛᏠᳯ䌁ф᳝ᴎ
亳ક
៥ᠧㅫᇱ䆩᳝ᴎ亳ક
4.3
៥᳝݈䍷ԧ偠ৗ᳝ᴎ亳કⱘⲞ໘
4.4
བᵰ᳝ᴎ亳ક᳝पˈ៥ᕜৃ㛑Ӯф
4.5
៥㛑ᛇ䍋᳝ѯ᳝ᴎ亳કⱘક⠠੠ଚᷛ
4.6
ᇚᴹ៥ৃ㛑Ӯৗ᳝ᴎ亳ક
4.7
៥Ӯ৥៥ⱘ҆៮᳟ট᥼㤤᳝ᴎ亳કⱘ⫼䗨
4.8
བᵰ៥ϡᕫϡ‫ݡ‬ϔ⃵䌁ф᳝ᴎ亳કˈ៥Ӯ‫خ‬ৠ
ḋⱘ䗝ᢽ
4.1
㄀Ѩ䚼ߚ˖ৃҹ䯂ᙼ޴Ͼ᳝݇ᙼϾҎ੠ᆊᒁⱘ䯂乬৫˛
ϟ߫䯂乬ҙ⫼Ѣߚ㉏԰⫼ˈ ᇚӮϹḐֱᆚ
5.1
᳔䖥ᙼ䌁ф䖛ા⾡᳝ᴎ亳ક? ˄ৃҹ໮䗝˅
чࠊક
5.2
㒭ᄽᄤ
5.6
݊Ҫ______
㒭㗕Ҏ
䗕⼐
ܼᆊৗ
݊Ҫ_______
໮ᬃҬ 51%-100%
໮Ѣ 100%
ϡᜓ໮Ҭ
བᵰᙼ䳔㽕䗝ᢽ䖯ষ᳝ᴎ亳કˈ ᙼ᳈ᜓᛣ䌁фાϾ೑ᆊ៪ഄऎⱘѻક˛(া㛑䗝ϔϾㄨḜ)
⃻⌆
5.5
䈋⠽㊂亳
ⳌᇍѢϔ㠀亳કˈᙼᜓᛣ๲ࡴ໮ᇥ䞥乱䌁фৠ㉏ⱘ᳝ᴎ亳ક? (া㛑䗝ϔϾㄨḜ˅
໮ᬃҬ 20%-50%
5.4
㙝㉏੠⍋剰
ᙼ᳔䖥䌁ф᳝ᴎ亳કᰃЎњ ? ˄ৃҹ໮䗝˅
㞾Ꮕৗ
5.3
∈ᵰ੠㬀㦰
㕢⌆
᮹ᴀ/䶽೑
ৄ⑒/佭␃
▇໻߽Ѯ/ᮄ㽓݄
᮴᠔䇧
ᙼ୰⃶೼ԩ໘䌁ф᳝ᴎ亳ક˛(া㛑䗝ϔϾㄨḜ˅
䍙Ꮦ
‫ݰ‬䌌Ꮦഎ
᳝ᴎ‫ݰ‬എ
ϧपᑫ
㔥Ϟ
Ⳃᔩ䫔ଂ
݊Ҫ _______
ᙼ䌁ф᳝ᴎ亳કⱘ਼ᳳЎ? (া㛑䗝ϔϾㄨḜ˅
㟇ᇥ↣᯳ᳳϔ⃵
↣ϸ᯳ᳳ
↣᳜
‫ي‬ᇨ
296
䴲ᐌ
䌲៤
Ёゟ
ডᇍ
ᔎ⚜
᳝ᴎ亳કⱘ䌁фᛣ೒ (೼
೼䗝ᢽⱘ᭄ᄫϞ⬏೜)
ডᇍ
៤”ЁⱘӏԩϔϾ㾖⚍ˈ㸼ᯢᙼⱘৠᛣ៪ϡৠᛣ⿟ᑺˈᙼⱘ㾖⚍≵᳝䫭੠ᇍПߚDŽ
5.7 䇋䗝ᢽᙼⱘᆊᒁ᳜ᬊܹ? (া㛑䗝ϔϾㄨḜ˅
ԢѢҎ⇥Ꮥ 5ˈ000 ‫ܗ‬
Ҏ⇥Ꮥ 5, 001-10,000 П䯈
Ҏ⇥Ꮥ 10, 001-20,000 П䯈
催Ѣ Ҏ⇥Ꮥ 20, 000
5.8 ᙼᆊ䞠ԣⴔ䙷ѯҎ? (া㛑䗝ϔϾㄨḜ)
ϔϾҎԣ (ऩ䑿)
໿ཛ˄≵ᄽᄤ˅
Ϣ≵៤ᑈⱘᄽᄤϔ䍋ԣ
Ϣ㗕Ҏϔ䍋ԣ
Ϣ៤ᑈᄽᄤϔ䍋ԣ
5.9 䇋䗝ᢽᙼⱘᗻ߿: (া㛑䗝ϔϾㄨḜ˅
⬋
5.10
ཇ
䇋䗝ᢽᙼⱘᑈ啘↉: (া㛑䗝ϔϾㄨḜ˅
18 -30 П䯈
5.11
ᴀ⾥
催Ё/Ёϧ
໻ϧ
⸩຿ҹϞ˄ࣙᣀ⸩຿˅
㪱乚
ϡᎹ԰
ᄺ⫳
݊Ҫ _______
䇋䗝ᢽᙼⳂࠡሙԣජᏖ˖(া㛑䗝ϔϾㄨḜ˅
࣫Ҁ
៤䛑
5.14
60 ቕҹϞ
䇋䗝ᢽᙼⳂࠡ㘠Ϯ: (া㛑䗝ϔϾㄨḜ˅
ⱑ乚
5.13
46-60 П䯈
䇋䗝ᢽᙼⱘ᳔催ᄺग़: (া㛑䗝ϔϾㄨḜ˅
催Ёҹϟ
5.12
31-45 П䯈
Ϟ⍋
⏅ഇ
݊Ҫ ________
བᙼ䅸Ў᳝ӏԩ೼ᴀ䯂ोЁ᳾⍝ঞⱘ݇Ѣ᳝ᴎ亳ક⍜䌍ⱘֵᙃঞ䯂乬ˈ 䇋ᙼ䇈ᯢ
üüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüü
üüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüüü
䴲ᐌᛳ䇶ᙼⱘ໻࡯ᬃᣕ!
297
Appendix 2: Summary of key authors from selected studies in
different continents and countries
Country
Austria
Belgium
Croatia
Denmark
Finland
Germany
Greece
Ireland
Italy
Netherlands
Norway
Slovenia
Spain
Sweden
Turkey
United
Kingdom
Authors
Europe
Gotschi et al. 2010
Aertsens, Mondelaers & Huylenbroeck 2009; Verdurme, Gellynck &
Viaene 2002
First & Brozin 2009; Radman 2005
Grunert & Juhl 1995; Millock, Wier & Andersen 2004
Tarkiainen & Sundqvist 2005
Janssen, Heid & Hamm 2009
Arvanitoyannis, Krystallis & Kapirti 2003; Botonaki et al. 2006;
Chryssochoidis 2000; Chryssochoidis & Krystallis 2005; Fotopoulos &
Chryssochoidis 2000; Fotopoulos & Krystallis 2002a, b; Fotopoulos,
Krystallis & Ness 2003; Krystallis, Arvanitoyannis & Chryssohoidis
2006; Krystallis & Chryssohoidis 2005; Krystallis, Fotopoulos & Zotos
2006; Magkos, Arvaniti & Zampelas 2006; Tsakiridou et al. 2008;
Tsakiridou, Mattas & Tzimitra-Kalogianni 2006
Davies, Titterington & Cochrane 1995; Moore 2006; O’Donovan &
McCarthy 2002; Roddy, Cowan & Hutchinson 1994; Roddy, Cowan &
Hutchinson 1996
Canavari et al. 2002; Chinnici, D’Amico & Peorino 2002; Cicia,
Giudice & Scarpa 2002; de Magistris & Gracia 2008; Saba & Messina
2003; Vindigni, Janssen & Jager 2002; Zanoli & Naspetti 2002
Schifferstein & Oude Ophuis 1998; Stobbelaar et al. 2007; Verhoef
2005
Honkanen, Verplanken & Olsen 2006; Storstad & Bjørkhaug 2003;
Torjusen et al. 2001
Kuhar & Juvancic 2010
Gil, Gracia & Sanchez 2000; Sanjuán et al. 2003; Soler, Gil & Sánchez
2002; Ureña, Bernabéu & Olmeda 2008
Magnusson et al. 2001; Magnusson et al. 2003; Shepherd, Magnusson &
Sjödén 2005; Tanner & Kast 2003
Oraman & Unakitan 2010; Özcelik & Uçar 2008
Barnes, Vergunst & Topp 2009; Beharrell & MacFie 1991; Brennan,
Gallagher & McEachern 2003; Connor & Douglas 2001; Harper &
Makatouni 2002; Hill & Lynchehaun 2002; Lodorfos & Dennis 2008;
Makatouni 2002; McEachern & McClean 2002; McEachern & Willock
2004; Padel & Foster 2005; Rimal, Moon & Balasubramanian 2005;
Sparks & Shepherd 1992; Tregear, Dent & McGregor 1994
298
Overall
European
region
United States
Canada
Costa Rica
Australia
New Zealand
China
India
Japan
Malaysia
Taiwan
Thailand
DenmarkNew Zealand
Denmark,
Great Britain,
and Italy
France-Italy
Germany-UK
Aarset et al. 2004; Margetts et al. 1997; Thøgersen 2009; Wier &
Calverley 2002
Americas
Abrams, Meyers & Irani 2010; Bellows et al. 2008; Dahm, Samonte &
Shows 2009; Finch 2005; Govindasamy, DeCongelio & Bhuyan 2005;
Howard & Allen 2006; Huang 1996; Kiesel & Villas-Boas 2007;
Klonsky & Greene 2005; Li, Zepeda & Gould 2007; Lin, Smith &
Huang 2008; Loureiro & Hine 2002; Onozaka 2007; Onyango, Hallman
& Bellows 2007; Rimal, Moon & Balasubramanian 2006; Smith, Huang
& Lin 2009a, b; Thompson 1998; Thompson & Kidwell 1998; Williams
& Hammitt 2000; Wirth, Stanton & Wiley 2011; Zepeda, Chang &
Leviten-Reid 2006; Zepeda & Deal 2009; Zepeda & Li 2007; Zhao et al.
2007
Anders & Moeser 2008; Essoussi & Zahaf 2008; Haghiri & McNamara
2007; Hamzaoui-Essoussi & Zahaf 2012; Larue et al. 2004
Aguirre 2007; González 2009
Oceania
Bhaskaran et al. 2006; Chang & Zepeda 2005; Chang, Zepeda & Griffith
2005; Kristiansen et al. 2010; Kristiansen & Smithson 2008; Lea &
Worsley 2005; Lockie et al. 2004; Lockie et al. 2002; Kristiansen &
Smithson 2008; Paull 2008b; Smith & Paladino 2010
Mather, Knight & Holdsworth 2005; Wong 2004
Asia
Dai, Zhu & Ying 2006; International Trade Centre 2011; Nees 2011;
Thøgersen & Zhou 2012; Wang, Liu & Tian 2008; Xie, Li, & Yi 2011;
Yin, Wu & Chen 2008; Yin et al. 2010
Chakrabarti & Baisya 2007
Gendall, Betteridge & Bailey 1999; Hu, Chen & Yoshida 2006
Ahmad & Juhdi 2010; Yaakob & Zakaria 2011Salleh et al. 2010;
Shaharudin et al. 2010a, 2010b,. 2010c
Chen 2007, 2009
Roitner-Schobesberger et al. 2008
Cross-cultural studies
Squires, Juric & Cornwell 2001
et al. 2012
Guido et al. 2010
Baker, Thompson & Engelken 2004
Source: author’s research 2011
299
Appendix 3: Ethics approval
From:
“Kaye Goldenberg”
Wednesday-November 11, 2009 12:11
<KGOLDENBERG@groupwise.swin.
PM
edu.au>
To:
<alobo@swin.edu.au>, <juchen@swin.edu.au>
CC:
“Resethics” <Resethics@groupwise.swin.edu.au>
Subject:
SUHREC Project 2009/245 Ethics Clearance
[View] [Save As]
Attachments: Mime.822 (5 KB)
To: Dr Antonio Lobo, FBE/Ms Jue Chen
Dear Dr Lobo,
SUHREC Project 2009/245 A study investigating the determinants of consumer buyer
behaviour relating to purchase of organic food products in China
Dr Antonio Lobo, FBE/Ms Jue Chen
Approved Duration: 11/11/2009 To 11/11/2010 [Adjusted]
I refer to the ethical review of the above project protocol undertaken on behalf of
Swinburne’s Human Research Ethics Committee (SUHREC) by SUHREC Subcommittee
(SHESC2) at a meeting held on 15 October 2009. Your responses to the review, and
request for amendment to the protocol (by revising the questionnaire), as e-mailed on 4 and
5 November respectively, were put to a nominated SHESC2 delegate for consideration.
I am pleased to advise that, as submitted to date, the project has approval to proceed in line
with standard on-going ethics clearance conditions here outlined.
- All human research activity undertaken under Swinburne auspices must conform to
Swinburne and external regulatory standards, including the National Statement on Ethical
Conduct in Human Research and with respect to secure data use, retention and disposal.
- The named Swinburne Chief Investigator/Supervisor remains responsible for any
300
personnel appointed to or associated with the project being made aware of ethics clearance
conditions, including research and consent procedures or instruments approved. Any
change in chief investigator/supervisor requires timely notification and SUHREC
endorsement.
- The above project has been approved as submitted for ethical review by or on behalf of
SUHREC. Amendments to approved procedures or instruments ordinarily require prior
ethical appraisal/ clearance. SUHREC must be notified immediately or as soon as possible
thereafter of (a) any serious or unexpected adverse effects on participants and any redress
measures; (b) proposed changes in protocols; and (c) unforeseen events which might affect
continued ethical acceptability of the project.
- At a minimum, an annual report on the progress of the project is required as well as at the
conclusion (or abandonment) of the project.
- A duly authorised external or internal audit of the project may be undertaken at any time.
Please contact me if you have any queries about on-going ethics clearance. The SUHREC
project number should be quoted in communication. Chief Investigators/Supervisors and
Student Researchers should retain a copy of this e-mail as part of project record-keeping.
Best wishes for the project.
Kaye Goldenberg
Secretary, SHESC2
*******************************************
Kaye Goldenberg
Administrative Officer (Research Ethics)
Swinburne Research (H68)
Swinburne University of Technology
P O Box 218
HAWTHORN VIC 3122
Tel +61 3 9214 8468
Fax +61 3 9214 5267
301
Appendix 4: Detailed analysis of the Pilot study(study 1)
Table A: Descriptive information
3.1 Organic food has a pleasant texture
Mean
5% Trimmed Mean
3.2 Organic food looks nice
Mean
5% Trimmed Mean
3.3 Organic food smells nice
Mean
5% Trimmed Mean
3.4 Organic food tastes good
Mean
5% Trimmed Mean
3.5 Organic products are in fashion
Mean
5% Trimmed Mean
3.6 Organic food has high nutritional value
Mean
5% Trimmed Mean
3.7 Generally speaking, the higher the price of a product, the
Mean
higher the quality
5% Trimmed Mean
3.8 The price of organic food is too high
Mean
5% Trimmed Mean
3.9 Organic food is good value for money
Mean
5% Trimmed Mean
3.10 When I buy a food product, I always read the label
Mean
5% Trimmed Mean
3.11 I don’t trust organic food certification bodies
Mean
5% Trimmed Mean
3.12 I trust the outlets which sell certified organic food
Mean
5% Trimmed Mean
3.13 Chinese government regulate food marketing, and have
Mean
developed significant policies
5% Trimmed Mean
3.14 Lack of adequate government control of media allows
Mean
advertisers to take advantage of consumers
5% Trimmed Mean
3.15 Logos depicting types of organic foods should be controlled Mean
5% Trimmed Mean
3.16 More land should be allocated for organic farming
Mean
5% Trimmed Mean
3.17 The market for organic and green is chaotic
Mean
5% Trimmed Mean
3.54
3.57
3.63
3.66
3.29
3.31
3.49
3.49
3.11
3.13
3.64
3.66
3.56
3.62
3.99
4.02
3.28
3.28
3.81
3.84
3.17
3.16
3.27
3.29
3.00
2.99
3.87
3.94
4.31
4.38
3.96
4.02
4.32
4.38
302
3.18 Organic food is good for myself and my family’s health
Mean
5% Trimmed Mean
3.19 Organic food has no harmful effects
Mean
5% Trimmed Mean
3.20 I like the brands associated with organic food
Mean
5% Trimmed Mean
3.21 I worry about harmful chemicals in my food
Mean
5% Trimmed Mean
3.22 Organic food does not contain pesticides
Mean
5% Trimmed Mean
3.23 Organic food is good for the environment
Mean
5% Trimmed Mean
3.24 I believe organic food has superior quality
Mean
5% Trimmed Mean
3.25 I personally think I should always buy organic food
Mean
5% Trimmed Mean
3.26 To me, it is important that the food I usually eat can be
Mean
easily found in the food outlets near my house or work place
5% Trimmed Mean
3.27 Organic food labels mean high quality food products
Mean
5% Trimmed Mean
3.28 When I like something, I will buy it without too much
Mean
deliberation
5% Trimmed Mean
3.29 I always do whatever I feel like and whenever I feel like it Mean
5% Trimmed Mean
3.30 The sole purpose of making money is to spend it
Mean
5% Trimmed Mean
3.31 Sometimes I feel like spending money on anything I lay my Mean
eyes on
5% Trimmed Mean
3.32 I often make impulse purchases
Mean
5% Trimmed Mean
3.33 I always try something new and unique
Mean
5% Trimmed Mean
3.34 I love fashionable and trendy products
Mean
5% Trimmed Mean
3.35 It does not hurt to be trendy if I feel like it
Mean
5% Trimmed Mean
3.36 I am often influenced by advertisements of new products
Mean
4.03
4.07
3.56
3.58
3.44
3.46
4.17
4.24
3.84
3.90
3.97
4.01
3.55
3.57
3.18
3.16
3.93
3.98
3.48
3.50
3.02
3.05
2.91
2.93
2.71
2.70
3.11
3.12
3.20
3.22
3.54
3.57
3.21
3.23
3.03
3.06
3.02
303
5% Trimmed Mean
3.37 I can easily influence people around me during
Mean
conversation
5% Trimmed Mean
3.38 My friends often consult me when they cannot make up
Mean
their own mind
5% Trimmed Mean
3.39 I have a strong desire to be successful
Mean
5% Trimmed Mean
3.40 Chinese people should buy domestic products rather than
Mean
imported products
5% Trimmed Mean
3.41 China should levy heavy tariff on foreign products to
Mean
reduce their quantity into China
5% Trimmed Mean
3.42 If two organic food products were the same in quality, but Mean
one was imported and the other was Chinese, I would pay
5% Trimmed Mean
3.43 I would purchase organic food come from a country I
Mean
approve of politically
5% Trimmed Mean
3.44 Chinese people should not buy foreign products, because it Mean
would hurt domestic business, and causes more unemployment 5% Trimmed Mean
4.1 I am eager to check out organic food products because of
Mean
advertisements and promotion
5% Trimmed Mean
4.2 I intend to try out organic food products
Mean
5% Trimmed Mean
4.3 I am interested in experiencing the benefits of using organic Mean
foods
5% Trimmed Mean
4.4 It is likely that I will buy organic food products when they
Mean
become available
5% Trimmed Mean
4.5 I can recall the brand names and labelling of some of the
Mean
organic food products
5% Trimmed Mean
4.6 I will probably use organic food products in the future
Mean
5% Trimmed Mean
4.7 I will recommend usage of organic foods to my friends and Mean
relatives
5% Trimmed Mean
4.8 If I had to purchase organic foods again, I would make the
Mean
same choice
5% Trimmed Mean
3.04
3.23
3.26
3.47
3.50
4.01
4.06
3.02
3.03
2.87
2.88
2.87
2.85
2.99
2.99
2.67
2.69
2.67
2.69
3.84
3.86
3.98
4.01
3.93
3.96
3.23
3.25
3.97
3.99
3.75
3.79
3.69
3.72
304
Table B: Results of the EFA for the product-related construct
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
Bartlett’s Test of Sphericity
.696
Approx. chi-square
298.716
df
21
Sig.
.000
Total variance explained
Initial Eigenvalues
Extraction sums of squared loadings
Rotation sums
of squared
loadingsa
Comp
-onent
Total
% of
Variance
Cumulative %
Total
% of
Variance
Cumulative %
Total
1
2.628
37.545
37.545
2.628
37.545
37.545
2.300
2
1.226
17.508
55.053
1.226
17.508
55.053
1.958
3
.942
13.456
68.508
4
.790
11.286
79.794
5
.620
8.852
88.647
6
.480
6.862
95.509
7
.314
4.491
100.000
Extraction Method: Principal Component Analysis.
a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.
305
Pattern matrixa
Component
1
2
3.2 Organic food looks nice
.882
-.115
3.1 Organic food has a pleasant texture
.865
.043
3.3 Organic food smells nice
.694
.119
3.9 Organic food is good value for money
-.119
.779
3.5 Consuming organic food is trendy
.149
.648
3.6 Organic food has high nutritional value
.179
.643
3.7 Generally speaking, the higher the price of a product, the higher the quality
-.051
.485
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalisation.
a. Rotation converged in 5 iterations.
Structure matrix
Component
1
2
3.1 Organic food has a pleasant texture
.878
.302
3.2 Organic food looks nice
.848
.148
3.3 Organic food smells nice
.730
.326
3.9 Organic food is good value for money
.113
.743
3.6 Organic food has high nutritional value
.371
.697
3.5 Consuming organic food is trendy
.342
.693
3.7 Generally speaking, the higher the price of a product, the higher the quality
.094
.470
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalisation.
Component correlation matrix
Component
1
2
1
1.000
.298
2
.298
1.000
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalisation.
306
Table C: EFA results for the regulatory construct
KMO and Bartlett’s test
Kaiser-Meyer-Olkin measure of sampling adequacy.
Bartlett’s Test of Sphericity
.771
Approx. chi-square
225.354
df
15
Sig.
.000
Total variance explained
Initial Eigenvalues
Extraction sums of squared loadings
Component
Total
% of Variance
Cumulative
%
Total
% of
Variance
Cumulative
%
1
2.558
42.627
42.627
2.558
42.627
42.627
2
.963
16.056
58.683
3
.798
13.308
71.990
4
.726
12.094
84.084
5
.505
8.416
92.499
6
.450
7.501
100.000
Extraction Method: Principal Component Analysis.
307
Component matrixa
Component
1
3.10 When I buy a food product, I always read the label
.567
3.11 I don’t trust organic food certification bodies
.401
3.14 Lack of adequate government control of media allows advertisers to take advantage of
consumers
.637
3.15 Logos depicting types of organic foods should be controlled
.783
3.16 More land should be allocated for organic farming
.702
3.17 The market for organic and green is chaotic
.750
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
308
Table D: EFA results for the lifestyle construct
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett’s Test of Sphericity
.748
Approx. Chi-Square
660.854
df
55
Sig.
.000
Total Variance Explained
Extraction Sums of Squared
Loadings
Initial Eigenvalues
Rotation Sums of
Squared
Loadingsa
Component
Total
% of
Variance
Cumulative
%
Total
% of
Variance
Cumulative
%
Total
1
3.624
32.944
32.944
3.624
32.944
32.944
3.090
2
1.553
14.118
47.062
1.553
14.118
47.062
2.562
3
1.330
12.087
59.149
1.330
12.087
59.149
1.929
4
.924
8.399
67.548
5
.869
7.900
75.448
6
.599
5.441
80.890
7
.580
5.276
86.165
8
.520
4.730
90.895
9
.458
4.168
95.063
10
.314
2.851
97.914
11
.229
2.086
100.000
Extraction Method: Principal Component Analysis.
a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.
309
Pattern Matrixa
Component
1
2
3
3.34 I love fashionable and trendy products
.812
.071
.078
3.32 I often make impulse purchases
.736
.106
.102
Lifestyle_9 3.36 I am often influenced by advertisements of new products
.682
.013
.006
3.33 I always try something new and unique
.679
.101
.149
3.35 It does not hurt to be trendy if I feel like it
.673
.108
.063
3.28 When I like something, I will buy it without too much deliberation
.032
.916
.004
3.29 I always do whatever I feel like and whenever I feel like it
.068
.898
.099
3.30 The sole purpose of making money is to spend it
.187
.672
.025
3.38 My friends often consult me when they cannot make up their own mind
.010
.066
.829
3.37 I can easily influence people around me during conversation
.077
-.027
.734
3.39 I have a strong desire to be successful
.036
.088
.590
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalisation.
a. Rotation converged in 5 iterations.
Structure Matrix
Component
1
2
3
3.34 I love fashionable and trendy products
.809
.217
.285
3.32 I often make impulse purchases
.745
.340
.112
3.35 It does not hurt to be trendy if I feel like it
.693
.328
.134
3.36 I am often influenced by advertisements of new products
.685
.245
.179
3.33 I always try something new and unique
.684
.153
.315
3.28 When I like something, I will buy it without too much deliberation
.281
.906
.137
3.29 I always do whatever I feel like and whenever I feel like it
.264
.890
.219
3.30 The sole purpose of making money is to spend it
.409
.732
.129
3.38 My friends often consult me when they cannot make up their own mind
.210
.065
.821
3.37 I can easily influence people around me during conversation
.282
.166
.759
3.39 I have a strong desire to be successful
.152
.167
.594
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalisation.
310
Component Correlation Matrix
Component
1
2
3
1
1.000
.340
.268
2
.340
1.000
.155
3
.268
.155
1.000
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalisation.
Table E: EFA results for the Ethnocentrism construct
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.689
Bartlett’s Test of Sphericity Approx. Chi-Square
167.247
df
3
Sig.
.000
Total Variance Explained
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative
%
1
2.073
69.115
69.115
2.073
69.115
69.115
2
.531
17.708
86.822
3
.395
13.178
100.000
Component
Extraction Method: Principal Component Analysis.
311
Component Matrixa
Component
1
3.40 Chinese people should buy domestic products rather than imported products
.864
3.41 China should levy heavy tariff on foreign products to reduce their quantity into
China
.821
3.44 Chinese people should not buy foreign products, because it would hurt domestic
business, and causes more unemployment
.808
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
Table F: EFA results for the beliefs and attitudes construct
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett’s Test of Sphericity
Approx. Chi-Square
.900
830.123
df
45
Sig.
.000
Total Variance Explained
Initial Eigenvalues
Component
Total
Extraction Sums of Squared Loadings
% of Variance Cumulative %
1
4.898
48.985
48.985
2
.947
9.473
58.458
3
.856
8.560
67.018
4
.668
6.677
73.695
5
.622
6.225
79.920
6
.521
5.213
85.133
7
.469
4.694
89.828
8
.390
3.900
93.728
9
.342
3.416
97.143
10
.286
2.857
100.000
Total
4.898
% of Variance Cumulative %
48.985
48.985
Extraction Method: Principal Component Analysis.
312
Component Matrixa
Component
1
3.24 I believe organic food has superior quality
.801
3.23 Organic food is good for the environment
.778
3.18 Organic food is good for myself and my family’s health
.736
3.27 Organic food labels mean high quality food products
.729
3.25 I personally think I should always buy organic food
.711
3.20 I like the brands associated with organic food
.703
3.19 Organic food has no harmful effects
.699
3.22 Organic food does not contain pesticides
.686
3.21 I worry about harmful chemicals in my food
.627
3.26 To me, it is important that the food I usually eat can be easily found in the food outlets
near my house or workplace
.476
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
Table G: EFA Results for the Pre-purchase evaluation construct
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett’s Test of Sphericity
Approx. Chi-Square
df
Sig.
.711
287.968
10
.000
313
Total Variance Explained
Compo
nent
Initial Eigenvalues
Total
% of Variance
Extraction Sums of Squared Loadings
Cumulative %
1
2.512
50.235
50.235
2
.964
19.283
69.518
3
.792
15.844
85.362
4
.466
9.315
94.677
5
.266
5.323
100.000
Total
% of Variance
2.512
Cumulative %
50.235
50.235
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1
4.3 I am interested in experiencing the benefits of using organic foods
.851
4.2 I intend to try out organic food products
.839
4.4 It is likely that I will buy organic food products when they become available
.803
4.5 I can recall the brand names and labelling of some of the organic food products
.485
4.1 I am eager to check out organic food products because of advertisements and
promotion
.452
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
314
Table H: EFA results for the Behavioural/purchase intention construct
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett’s Test of Sphericity
.704
Approx. Chi-Square
234.229
df
3
Sig.
.000
Total Variance Explained
Initial Eigenvalues
Extraction Sums of Squared Loadings
Component
Total
% of Variance Cumulative %
1
2.233
74.431
74.431
2
.469
15.635
90.066
3
.298
9.934
100.000
Total
% of
Variance
Cumulative %
2.233
74.431
74.431
Extraction Method: Principal Component Analysis.
315
Component Matrixa
Component
1
4.7 I will recommend usage of organic foods to my friends and relatives
.890
4.8 If I had to purchase organic foods again, I would make the same choice
.876
4.6 I will probably use organic food products in the future
.821
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
316
Appendix 5: Detailed analysis of the main study (study 2)
Table A: CFA for the product-related construct
Discriminant validity for product _related construct
F2
F1
Product_5
Product_6
Product_7
Product_9
Product_1
Product_2
F2
1.000
F1
.833
1.000
Product_5
.579
.482
1.000
Product_6
.528
.439
.305
1.000
Product_7
.235
.196
.136
.124
1.000
Product_9
.457
.380
.264
.241
.108
1.000
Product_1
.524
.629
.303
.276
.123
.239
1.000
Product_2
.535
.643
.310
.282
.126
.244
.405
1.000
Product_3
.555
.666
.321
.293
.131
.253
.419
.428
Product_3
1.000
It suggested that there is lack of discriminant validity. Although product _7 shows significant difference, one
item can’t form a construct.
CFA for product-related measure (after deleted statement 3.7/3.9)-one factor
Sample Covariances (Group number 1)
Product_6
Product_5
Product_3
Product_2
Product_1
Product_6
.688
.179
.189
.159
.172
Product_5
Product_3
Product_2
Product_1
.656
.230
.193
.193
.538
.228
.215
.549
.255
.581
Condition number = 4.730
Eigenvalues
1.409 .521 .454 .331 .298
317
Standardised regression weights (Group number 1-Default model)
Product_1
Product_2
Product_3
Product_5
Product_6
<--<--<--<--<---
PRODUCT_-RELATED
PRODUCT_-RELATED
PRODUCT_-RELATED
PRODUCT_-RELATED
PRODUCT_-RELATED
Estimate
.625
.650
.658
.537
.445
Implied correlations (Group number 1-Default model)
Product_6
Product_5
Product_3
Product_2
Product_1
Product_6
1.000
.239
.293
.290
.278
Product_5
Product_3
Product_2
Product_1
1.000
.353
.349
.335
1.000
.428
.411
1.000
.406
1.000
Standardised residual covariances (Group number 1-Default model)
Product_6
Product_5
Product_3
Product_2
Product_1
Product_6
.000
.801
.538
-.928
-.173
Product_5
Product_3
Product_2
Product_1
.000
.994
-.810
-.661
.000
-.208
-.768
.000
1.306
.000
CMIN
Model
Default model
Saturated model
Independence model
NPAR
10
15
5
CMIN
16.690
.000
803.505
DF
5
0
10
P
.005
CMIN/DF
3.338
.000
80.350
RMR, GFI
Model
Default model
Saturated model
Independence model
RMR
.013
.000
.166
GFI
.993
1.000
.678
AGFI
.978
PGFI
.331
.517
.452
318
Baseline Comparisons
Model
Default model
Saturated model
Independence model
NFI
Delta1
.979
1.000
.000
RFI
rho1
.958
.000
IFI
Delta2
.985
1.000
.000
TLI
rho2
.971
.000
CFI
.985
1.000
.000
RMSEA
Model
Default model
Independence model
RMSEA
.050
.291
LO 90
.025
.274
HI 90
.077
.308
PCLOSE
.450
.000
Standardised RMR = .0224
Table B: CFA for the regulatory construct
Sample Correlations (Group number 1)
Regulatory_1
Regulatory_5
Regulatory_6
Regulatory_7
Regulatory_1
1.000
Regulatory_5
.116
1.000
Regulatory_6
.244
.338
1.000
Regulatory_7
.106
.213
.265
1.000
Regulatory_8
.171
.264
.353
.264
Regulatory_8
1.000
Condition number = 3.225
Eigenvalues
1.964 .921 .796 .711 .609
319
Standardised Regression Weights: (Group number 1-Default model)
Regulatory_8
Regulatory_7
Regulatory_6
Regulatory_5
Regulatory_1
<--<--<--<--<---
REGULATION
REGULATION
REGULATION
REGULATION
REGULATION
Estimate
.545
.421
.669
.490
.316
CMIN
Model
Default model
Saturated model
Independence model
NPAR
10
15
5
CMIN
8.065
.000
442.258
DF
5
0
10
P
.153
CMIN/DF
1.613
.000
44.226
RMR, GFI
Model
Default model
Saturated model
Independence model
RMR
.013
.000
.144
GFI
.997
1.000
.805
AGFI
.990
PGFI
.332
.707
.536
NFI
Delta1
.982
1.000
.000
RFI
rho1
.964
IFI
Delta2
.993
1.000
.000
TLI
rho2
.986
Baseline Comparisons
Model
Default model
Saturated model
Independence model
.000
.000
CFI
.993
1.000
.000
RMSEA
Model
Default model
Independence model
RMSEA
.026
.215
LO 90
.000
.198
HI 90
.057
.232
PCLOSE
.891
.000
Standardised RMR = .0183
320
Table C: CFA for the Lifestyle measures
Standardised Regression Weights: (Group number 1-Default model)
Estimate
Lifestyle_9 <--- VARIETY-_SEEKING
.537
Lifestyle_7 <--- VARIETY-_SEEKING
.792
Lifestyle_6 <--- VARIETY-_SEEKING
.791
Lifestyle_5 <--- VARIETY-_SEEKING
.669
Lifestyle_3 <--- SELF-_INDULGENCE
.607
Lifestyle_2 <--- SELF-_INDULGENCE
.825
Lifestyle_1 <--- SELF-_INDULGENCE
.786
Lifestyle_11 <--- OPINION-_LEADERSHIP
.665
Lifestyle_10 <--- OPINION-_LEADERSHIP
.728
Lifestyle_12 <--- OPINION-_LEADERSHIP
.454
Standardised Residual Covariances (Group number 1-Default model)
Lifestyle
_12
Lifestyle
_12
Lifestyle
_10
Lifestyle
_11
Lifestyle
_1
Lifestyle
_2
Lifestyle
_3
Lifestyle
_5
Lifestyle
_6
Lifestyle
_7
Lifestyle
_9
Lifestyle
_10
Lifestyle
_11
Lifestyl
e_1
Lifestyl
e_2
Lifestyl
e_3
Lifestyl
e_5
Lifestyl
e_6
Lifestyl
e_7
Lifestyl
e_9
.000
-.503
.000
.366
.102
.000
-2.006
.409
-.254
.000
-1.263
.021
-.247
.255
.000
.100
2.836
-.063
-.281
-.360
.000
.221
-.186
-1.617
.061
1.341
4.382
.000
.974
-1.299
-1.012
-1.555
-1.142
.505
.058
.000
1.071
.516
-.376
-.731
-.771
2.563
-.764
.722
.000
1.343
2.978
2.430
.750
.956
2.959
1.567
-.894
-.996
.000
CMIN
Model
Default model
Saturated model
Independence model
NPAR
23
55
10
CMIN
125.621
.000
2768.959
DF
32
0
45
P
.000
CMIN/DF
3.926
.000
61.532
321
RMR, GFI
Model
Default model
Saturated model
Independence model
RMR
.037
.000
.245
GFI
.973
1.000
.519
AGFI
.954
PGFI
.566
.412
.424
Default model
Saturated model
Independence model
NFI
Delta1
.955
1.000
.000
RFI
rho1
.936
IFI
Delta2
.966
1.000
.000
TLI
rho2
.952
RMSEA
Model
Default model
Independence model
RMSEA
.056
.255
Baseline Comparisons
Model
.000
LO 90
.046
.247
HI 90
.067
.263
.000
CFI
.966
1.000
.000
PCLOSE
.157
.000
Standardised RMR = .0424
Table D: CFA for Ethnocentrism measures
Sample Correlations (Group number 1)
Ethnocentric_1 Ethnocentric_2 Ethnocentric_5
Ethnocentric_1
1.000
Ethnocentric_2
.618
1.000
Ethnocentric_5
.577
.562
1.000
Condition number = 5.702
Eigenvalues
2.172 .447 .381
Sample moments: Eigenvalues 2.172, .447, .381, it suggests one factor solution
Standardised Regression Weights: (Group number 1-Default model)
Ethnocentric_5 <--- ETHNOCENTRISM
Ethnocentric_2 <--- ETHNOCENTRISM
Ethnocentric_1 <--- ETHNOCENTRISM
Estimate
.724
.771
.801
322
CMIN
Model
Default model
Saturated model
Independence model
NPAR
5
6
3
CMIN
.132
.000
925.508
DF
1
0
3
P
.717
CMIN/DF
.132
.000
308.503
RMR, GFI
Model
Default model
Saturated model
Independence model
RMR
.004
.000
.389
GFI
1.000
1.000
.593
AGFI
.999
PGFI
.167
.185
.296
Baseline Comparisons
Model
Default model
Saturated model
Independence model
NFI
Delta1
1.000
1.000
.000
RFI
rho1
1.000
.000
IFI
Delta2
1.001
1.000
.000
TLI
rho2
1.000
.000
CFI
1.000
1.000
.000
RMSEA
Model
Default model
Independence model
RMSEA
.000
.575
LO 90
.000
.544
HI 90
.062
.606
PCLOSE
.907
.000
Standardised RMR = .0020
Table E: CFA for the beliefs and attitudes measures
Sample Correlations (Group number 1)
Personal_10 Personal_8 Personal_7
Personal_10
1.000
Personal_8
.390
1.000
Personal_7
.422
.482
1.000
Personal_6
.286
.366
.474
Personal_3
.275
.394
.361
Condition number = 5.268
Personal_6
Personal_3
1.000
.243
1.000
323
Eigenvalues
2.493 .777 .707 .550 .473
Sample moments: Eigenvalues, it suggests one factor solution
Standardised Regression Weights: (Group number 1-Default model)
Estimate
Personal_3 <--- PERSONAL
.502
Personal_6 <--- PERSONAL
.572
Personal_7 <--- PERSONAL
.753
Personal_8 <--- PERSONAL
.670
Personal_10 <--- PERSONAL
.555
CMIN
Model
Default model
Saturated model
Independence model
NPAR
10
15
5
RMR, GFI
Model
Default model
Saturated model
Independence model
RMR
.016
.000
.201
GFI
.990
1.000
.637
AGFI
.970
PGFI
.330
.456
.425
Default model
Saturated model
Independence model
NFI
Delta1
.976
1.000
.000
RFI
rho1
.953
IFI
Delta2
.981
1.000
.000
TLI
rho2
.963
RMSEA
Model
Default model
Independence model
RMSEA
.062
.320
CMIN
22.781
.000
960.071
DF
5
0
10
P
.000
CMIN/DF
4.556
.000
96.007
Baseline Comparisons
Model
.000
LO 90
.038
.303
HI 90
.089
.337
.000
CFI
.981
1.000
.000
PCLOSE
.193
.000
Standardised RMR = .0253
324
Table F: CFA for Pre-purchase evaluation measures
Sample Correlations (Group number 1)
Prepurchase_4 Prepurchase_3 Prepurchase_2 Prepurchase_1
Prepurchase_4
1.000
Prepurchase_3
.523
1.000
Prepurchase_2
.482
.496
1.000
Prepurchase_1
.202
.231
.306
1.000
Condition number = 4.540
Eigenvalues
2.158 .860 .506 .475
Sample moments: Eigenvalues 2.158, 0.860, 0.506, 0.475, it suggests one factor solution
Standardised Regression Weights: (Group number 1-Default model)
Prepurchase_1
Prepurchase_2
Prepurchase_3
Prepurchase_4
<--<--<--<---
PRE-PURCHASE_EVALUATION
PRE-PURCHASE_EVALUATION
PRE-PURCHASE_EVALUATION
PRE-PURCHASE_EVALUATION
Model
Default model
Saturated model
Independence model
Estimate
.346
.699
.725
.699
CMIN
NPAR
CMIN
8
13.953
10
.000
4 749.317
DF
2
0
6
RMR, GFI
Model
RMR
GFI
Default model
.014
.992
Saturated model
.000 1.000
Independence model
.145
.680
P
.001
CMIN/DF
6.977
.000
124.886
AGFI
.962
PGFI
.198
.467
.408
Baseline Comparisons
NFI RFI
IFI
Model
Delta1 rho1 Delta2
Default model
.981 .944
.984
Saturated model
1.000
1.000
Independence model
.000 .000
.000
TLI
rho2
.952
.000
CFI
.984
1.000
.000
325
Model
Default model
Independence model
RMSEA
RMSEA LO 90
.044
.080
.365
.343
HI 90
.122
.387
PCLOSE
.081
.000
Standardised RMR = .0255
Table G: CFA for Behavioural/purchase intention measures
Sample Correlations (Group number 1)
Be_intention_1
1.000
.437
.427
Be_intention_2
Be_intention_3
Be_intention_1
Be_intention_2
1.000
Be_intention_3
.416
1.000
Condition number = 3.304
Eigenvalues
1.854 .585 .561
Sample moments: Eigenvalues 1.854, 0.585, 0.561, it suggests one factor solution
Standardised Regression Weights: (Group number 1-Default model)
Estimate
Be_intention_3 <--- PURCHASE_INTENTION
.650
Be_intention_2 <--- PURCHASE_INTENTION
.640
Be_intention_1 <--- PURCHASE_INTENTION
.670
Model
Default model
Saturated model
Independence model
NPAR
5
6
3
CMIN
CMIN
.343
.000
461.587
DF
1
0
3
RMR, GFI
Model
RMR
GFI
Default model
.004 1.000
Saturated model
.000 1.000
Independence model
.161
.733
P
.558
CMIN/DF
.343
.000
153.862
AGFI
.999
PGFI
.167
.466
.366
326
RMSEA
Model
Default model
Independence model
RMSEA
LO 90
HI 90
PCLOSE
.000
.405
.000
.375
.072
.437
.844
.000
Baseline Comparisons
NFI RFI
IFI
Model
Delta1 rho1 Delta2
Default model
.999 .998
1.001
Saturated model
1.000
1.000
Independence model
.000 .000
.000
TLI
rho2
1.004
.000
CFI
1.000
1.000
.000
Standardised RMR = .0049
327
Appendix 6: Discriminant validity
Table A: Discriminant validity for section one
ETHNOCENTRISM
SELF_INDULGENCE
VARIETY_SEEKING
ETHNOCENTRISM
1.000
SELF_INDULGENCE
.248
1.000
.177
.463
1.000
VARIETY_SEEKING
REGULATORY
REGULATORY
PRODUCT_RELATED
.046
.044
.127
1.000
PRODUCT_RELATED
.126
.176
.231
.272
1.000
Ethnocentric_1
Ethnocentric_2
Ethnocentric_5
Lifestyle_1
Lifestyle_2
Lifestyle_3
Lifestyle_6
Lifestyle_7
Lifestyle_9
Regulatory_5
Regulatory_6
Regulatory_7
Regulatory_8
Product_1
Product_2
Product_3
Product_5
Product_6
.792
.782
.725
.195
.205
.149
.140
.146
.088
.024
.029
.020
.024
.078
.083
.081
.067
.056
.196
.194
.180
.788
.826
.602
.365
.381
.230
.023
.028
.020
.023
.108
.116
.113
.094
.078
.140
.139
.128
.365
.383
.279
.788
.823
.497
.066
.080
.057
.068
.142
.152
.148
.123
.103
.036
.036
.033
.034
.036
.026
.100
.104
.063
.522
.635
.449
.534
.168
.179
.174
.145
.121
.100
.099
.092
.139
.145
.106
.182
.190
.115
.142
.173
.122
.146
.616
.659
.641
.532
.444
Table B: Discriminant validity for section two
PRE-PURCHASE_EVALUATION
PURCHASE_INTENTION
PERSONAL_ATTITUDES
Prepurchase_2
Prepurchase_3
Prepurchase_4
Be_intention_1
Be_intention_2
Be_intention_3
Personal_6
Personal_7
Personal_8
Personal_10
PRE-PURCHASE
_EVALUATION
1.000
.771
.484
.652
.696
.767
.515
.503
.493
.293
.368
.307
.268
PURCHASE
_INTENTION
PERSONAL
_ATTITUDES
1.000
.573
.503
.537
.591
.668
.652
.640
.347
.435
.363
.317
1.000
.316
.337
.371
.383
.374
.366
.606
.760
.634
.554
328
.000
.288
.000
.239
.062
.127
.321
.000
.000
.038
.037
.016
LIFESTYLE
-.069
.197
.383
.000
.000
.282
PRODUCT
.067
.379
.274
.000
.000
.000
REGULATORY
LIFESTYLE
PRODUCT
REGULATORY
BELIEFS/ATTITUDES
PRE_-PURCHASE
BEHAVIOUR_INTENTION
.000
.000
.081
.133
.161
.210
.145
LIFESTYLE
.000
.093
.026
.119
.079
ETHNOCENTRISM
.371
.000
.000
.000
.077
.197
PRODUCT
.332
.000
.000
.000
.000
.054
REGULATORY
.122
BELIEFS/
ATTITUDES
.000
.000
.000
.000
.000
.000
PRE_PURCHASE
.000
.000
.000
.000
.000
.000
BEHAVIOUR_INTENTION
.000
.000
.000
.000
.000
.000
.000
.232
.621
.000
.000
.000
.196
.000
SELF_INDULGENCE
.000
.000
.000
.000
.000
.000
.000
.000
SELF_INDULGENCE
.000
.000
.000
.000
BEHAVIOUR_INTENTION
.000
.000
.000
PRE_PURCHASE
.000
.000
.000
BELIEFS/
ATTITUDES
.000
.000
.000
Table B: Standardised Indirect Effects (Group number 1-Default model)
LIFESTYLE
PRODUCT
REGULATORY
BELIEFS/
ATTITUDES
PRE_PURCHASE
BEHAVIOUR_INTENTION
ETHNOCENTRISM
Table A: Standardised Direct Effects (Group number 1-Default model)
Appendix 7: The output of the final model
.000
329
VARIETY_SEEKING
.000
.000
.000
.000
.000
.000
.000
.000
VARIETY_SEEKING
.000
.000
.000
LIFESTYLE
PRODUCT
REGULATORY
ATT/BEL
PRE_-PURCHASE
BEHAVIOUR_INTENTION
SELF_INDULGENCE
VARIETY_SEEKING
.000
.288
.081
.372
.222
.337
.766
.601
.161
.246
.193
LIFESTYLE
.321
.093
.026
.158
.116
ETHNOCENTRISM
.000
.000
.301
.000
.000
.282
.461
.395
PRODUCT
.000
.000
.399
.000
.000
.000
.274
.432
REGULATORY
.000
.000
.354
.000
.000
.000
.000
.196
ATT/BEL
Table C: Standardised Total Effects (Group number 1-Default model)
.000
.000
.000
.000
.621
.000
BEHAVIOUR_INTENTION
.000
.000
.000
.000
.000
PRE_PURCHASE
.000
.000
.000
.000
.000
.000
.000
.000
SELF_INDULGENCE
.000
.000
.000
.000
.000
.000
.000
.000
330
VARIETY_SEEKING
.000
.000
.000
.000
.000
Table D: Regression Weights: (Group number 1-Default model)
Estimate S.E. C.R. P
LIFESTYLE
<--- ETHNOCENTRISM
.121 .022 5.386 ***
PRODUCT
<--- LIFESTYLE
.415 .087 4.788 ***
REGULATORY
<--- PRODUCT
.308 .058 5.354 ***
ATTITUDES
<--- PRODUCT
.450 .066 6.820 ***
ATTITUDES
<--- REGULATORY
.294 .053 5.567 ***
ATTITUDES
<--- LIFESTYLE
.404 .098 4.111 ***
ATTITUDES
<--- ETHNOCENTRISM .024 .026 .926 .355
PRE_-PURCHASE
<--- ATTITUDES
.223 .068 3.257 .001
PRE_-PURCHASE
<--- REGULATORY
.464 .066 7.066 ***
PRE_-PURCHASE
<--- LIFESTYLE
.119 .105 1.131 .258
PRE_-PURCHASE
<--- PRODUCT
.264 .071 3.695 ***
PRE_-PURCHASE
<--- ETHNOCENTRISM .027 .029 .922 .356
VARIETY_-SEEKING
<--- LIFESTYLE
1.000
SELF_-INDULGENCE
<--- LIFESTYLE
1.692 .262 6.448 ***
BEHAVIOUR_-INTENTION <--- PRE_-PURCHASE
.566 .058 9.729 ***
BEHAVIOUR_-INTENTION <--- ATTITUDES
.241 .061 3.947 ***
BEHAVIOUR_-INTENTION <--- LIFESTYLE
.222 .095 2.347 .019
BEHAVIOUR_-INTENTION <--- PRODUCT
-.085 .062 -1.363 .173
BEHAVIOUR_-INTENTION <--- REGULATORY
.075 .061 1.234 .217
BEHAVIOUR_-INTENTION <--- ETHNOCENTRISM
.011 .026 .420 .674
331
Table E: Factor loadings in the final best-fit model
Standardised Regression Weights: (Group number 1-Default model)
Estimate
Product_6
<--- PRODUCT
.481
Product_5
<--- PRODUCT
.542
Product_3
<--- PRODUCT
.645
Product_2
<--- PRODUCT
.622
Product_1
<--- PRODUCT
.613
Regulatory_8 <--- REGULATORY
.529
Regulatory_7 <--- REGULATORY
.494
Regulatory_6 <--- REGULATORY
.632
Regulatory_5 <--- REGULATORY
.485
Lifestyle_9
<--- VARIETY_-SEEKING
.497
Lifestyle_7
<--- VARIETY_-SEEKING
.823
Lifestyle_6
<--- VARIETY_-SEEKING
.789
Lifestyle_3
<--- SELF_-INDULGENCE
.601
Lifestyle_2
<--- SELF_-INDULGENCE
.825
Lifestyle_1
<--- SELF_-INDULGENCE
.790
Ethnocentric_5 <--- ETHNOCENTRISM
.724
Ethnocentric_2 <--- ETHNOCENTRISM
.784
Ethnocentric_1 <--- ETHNOCENTRISM
.790
Personal_6
<--- ATTITUDES
.594
Personal_7
<--- ATTITUDES
.742
Personal_8
<--- ATTITUDES
.651
Personal_10 <--- ATTITUDES
.571
Prepurchase_2 <--- PRE_-PURCHASE
.652
Prepurchase_3 <--- PRE_-PURCHASE
.700
Prepurchase_4 <--- PRE_-PURCHASE
.763
Be_intention_3 <--- BEHAVIOUR_-INTENTION
.643
Be_intention_2 <--- BEHAVIOUR_-INTENTION
.650
Be_intention_1 <--- BEHAVIOUR_-INTENTION
.666
332
Table F: Squared Multiple Correlations:
(Group number 1-Default model)
ETHNOCENTRISM
LIFESTYLE
PRODUCT
REGULATORY
BELIEFS/ATTITUDES
PRE_-PURCHASE
BEHAVIOUR_-INTENTION
SELF_-INDULGENCE
VARIETY_-SEEKING
Prepurchase_4
Be_intention_1
Be_intention_2
Be_intention_3
Prepurchase_3
Prepurchase_2
Personal_10
Personal_8
Personal_7
Personal_6
Ethnocentric_1
Ethnocentric_2
Ethnocentric_5
Lifestyle_1
Lifestyle_2
Lifestyle_3
Lifestyle_6
Lifestyle_7
Lifestyle_9
Regulatory_5
Regulatory_6
Regulatory_7
Regulatory_8
Product_1
Product_2
Product_3
Product_5
Product_6
Estimate
.000
.103
.083
.080
.413
.395
.664
.587
.361
.583
.443
.423
.413
.490
.425
.326
.423
.550
.352
.625
.614
.525
.624
.680
.361
.622
.677
.247
.235
.400
.244
.279
.376
.387
.416
.293
.231
333
dimension3
dimension2
dimension3
dimension3
dimension3
dimension3
Total_purchase_intention
Tukey HSD
(I) 5.11 Please specify the (J) 5.11 Please specify the highest
highest level of education you level of education you have
have completed: (tick one)
completed: (tick one)
1
2
3
4
5
2
1
3
4
5
3
1
2
4
5
4
1
2
3
5
5
1
2
3
4
*. The mean difference is significant at the 0.05 level.
Hypothesis H7C
Table A: Multiple Comparisons
Appendix 8: Hypotheses seven
Mean Difference
(I-J)
-.208*
-.371*
-.258*
-.292*
.208*
-.164
-.050
-.084
.371*
.164
.113
.079
.258*
.050
-.113
-.034
.292*
.084
-.079
.034
Std. Error
.074
.071
.066
.083
.074
.060
.054
.074
.071
.060
.050
.071
.066
.054
.050
.066
.083
.074
.071
.066
Sig.
.040
.000
.001
.004
.040
.051
.881
.785
.000
.051
.153
.799
.001
.881
.153
.986
.004
.785
.799
.986
Lower Bound
-.41
-.56
-.44
-.52
.01
-.33
-.20
-.29
.18
.00
-.02
-.12
.08
-.10
-.25
-.21
.06
-.12
-.27
-.15
Upper Bound
-.01
-.18
-.08
-.06
.41
.00
.10
.12
.56
.33
.25
.27
.44
.20
.02
.15
.52
.29
.12
.21
95% Confidence Interval
334
N
94
161
202
368
93
918
Mean
3.47
3.68
3.84
3.73
3.76
3.72
dimension2
dimension3
dimension3
dimension3
Total_purchase_intention
Tukey HSD
(I) 5.7 Please indicate your (J) 5.7 Please indicate your
monthly family income? monthly family income?
(tick one)
(tick one)
1
2
3
4
2
1
3
4
3
1
2
4
4
1
2
3
*. The mean difference is significant at the 0.05 level.
dimension3
Std. Deviation
.575
.533
.591
.568
.564
.575
Table C: Multiple Comparisons
Hypothesis H7d
1
2
3
4
5
Total
Total_purchase_intention
Table B: Descriptives
Mean Difference
(I-J)
-.182*
-.104
-.206*
.182*
.078
-.025
.104
-.078
-.103
.206*
.025
.103
Std. Error
.059
.042
.042
.030
.058
.019
Std. Error
.043
.055
.076
.043
.057
.077
.055
.057
.085
.076
.077
.085
Sig.
.000
.238
.034
.000
.521
.989
.238
.521
.618
.034
.989
.618
2
2
2
1
2
1
Maximum
5
5
5
5
5
5
Upper Bound
-.07
.04
-.01
.29
.23
.17
.25
.07
.11
.40
.22
.32
95% Confidence Interval
Minimum
Lower Bound
-.29
-.25
-.40
.07
-.07
-.22
-.04
-.23
-.32
.01
-.17
-.11
95% Confidence Interval for Mean
Lower Bound
Upper Bound
3.35
3.59
3.60
3.76
3.76
3.92
3.67
3.79
3.65
3.88
3.69
3.76
335
1
2
3
4
Total
N
394
310
147
66
917
Total_purchase_intention
Mean
3.63
3.81
3.74
3.84
3.73
Table D: Descriptives
Std. Deviation
.545
.572
.643
.540
.576
Std. Error
.027
.033
.053
.066
.019
95% Confidence Interval for Mean
Lower Bound
Upper Bound
3.58
3.69
3.75
3.88
3.63
3.84
3.71
3.97
3.69
3.76
Minimum
2
2
1
2
1
Maximum
5
5
5
5
5
336
Table E: Descriptive Statistics of important attributes (23 statements)
N
Minimum Maximum
Std.
Mean Deviation
2.19 Certification relating to the quality of organic food
948
1
5
4.24
.842
2.4 The overall quality of organic food
950
1
5
4.24
.829
2.20 Enforcement relating to the quality of organic food
951
1
5
4.22
.868
2.23 Information about the nutritional value of organic
958
food
1
5
4.15
.851
2.18 Food safety in relation to organic food
949
1
5
4.14
.890
2.22 Correct labelling of organic food
950
1
5
4.07
.832
2.9 The environmental benefits of organic food
950
1
5
3.93
.929
2.5 The price of organic food
947
1
5
3.90
.875
2.8 The value of organic foods relative to its price
946
1
5
3.88
.923
2.17 Government regulations and policies relating to the
947
sale of organic food
1
5
3.87
.961
2.1 The taste of organic food
960
1
5
3.79
.845
2.6 The availability of organic food in convenience stores
946
and supermarkets
1
5
3.76
.867
2.2 The smell of organic food
951
1
5
3.73
.847
2.12 The knowledge of organic food products
947
1
5
3.69
.924
2.13 The awareness of organic food products
951
1
5
3.57
.913
2.16 The brand name of the organic food
956
1
5
3.52
.930
2.21 Packaging of organic food
952
1
5
3.41
.973
2.15 Organic food that is produced in China
950
1
5
3.39
.978
2.3 The appearance of organic food
947
1
5
3.34
.947
2.14 The country of origin of the organic food
949
1
5
3.30
1.043
2.7 The promotion and advertising of organic food
940
1
5
3.27
.977
2.11 The social status of people purchasing organic food
950
1
5
2.35
1.103
2.10 The idea of face saving (mianzi) when purchasing
948
organic food
1
5
2.30
1.084
Valid N (listwise)
859
337
Own Consumption
For Children
For Elderly
Gift
For Family
Total
254
90
27
11
328
710
35.8
12.7
3.8
1.5
46.2
100.0
Percent
35.8
12.7
3.8
1.5
46.2
100.0
Valid Percent
35.8
48.5
52.3
53.8
100.0
Cumulative Percent
2.1 The taste of organic Own Consumption
food
For Children
For Elderly
Gift
For Family
Total
2.2 The smell of organic Own Consumption
food
For Children
For Elderly
Gift
For Family
Total
2.3 The appearance of Own Consumption
organic food
For Children
For Elderly
N
254
89
27
11
327
708
253
90
27
11
322
703
254
90
27
Mean
3.83
3.82
3.63
3.45
3.83
3.82
3.86
3.58
3.67
3.64
3.71
3.75
3.25
3.40
3.48
Std.
Deviation
.888
.716
1.115
.820
.847
.858
.846
.793
.961
.674
.836
.840
.974
.909
1.051
Std. Error
.056
.076
.214
.247
.047
.032
.053
.084
.185
.203
.047
.032
.061
.096
.202
95% Confidence Interval
for Mean
Lower
Bound
Upper Bound
3.72
3.94
3.67
3.97
3.19
4.07
2.90
4.01
3.74
3.92
3.75
3.88
3.76
3.97
3.41
3.74
3.29
4.05
3.18
4.09
3.62
3.80
3.68
3.81
3.13
3.37
3.21
3.59
3.07
3.90
Minimum
1
2
1
2
1
1
1
1
2
3
1
1
1
1
2
Table G: Descriptives results of single intended use and important attributes
Valid
Frequency
Table F: Single usage group
Maximum
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
338
2.8 The value of organic
foods relative to its price
2.7 The promotion and
advertising of organic food
2.6 The availability of
organic food in convenience
stores and supermarkets
2.5 The price of organic
food
2.4 The overall quality of
organic food
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
11
323
705
250
89
27
11
326
703
250
90
27
11
324
702
250
89
27
10
324
700
252
89
27
11
320
699
249
90
27
11
325
702
3.36
3.41
3.36
4.32
4.09
4.37
4.27
4.24
4.25
3.88
4.04
3.81
3.82
3.93
3.92
3.73
3.81
4.00
3.80
3.79
3.78
3.20
3.27
3.37
3.64
3.27
3.25
3.94
3.84
3.59
3.73
3.88
3.88
.809
.966
.964
.773
.821
.629
.786
.821
.798
.872
.806
.962
.603
.877
.866
.904
.864
.784
.632
.870
.875
1.022
.939
.792
.809
.965
.974
.944
.898
.971
.905
.879
.909
.244
.054
.036
.049
.087
.121
.237
.045
.030
.055
.085
.185
.182
.049
.033
.057
.092
.151
.200
.048
.033
.064
.099
.152
.244
.054
.037
.060
.095
.187
.273
.049
.034
2.82
3.31
3.28
4.23
3.92
4.12
3.74
4.15
4.20
3.77
3.88
3.43
3.41
3.83
3.85
3.62
3.63
3.69
3.35
3.69
3.71
3.07
3.07
3.06
3.09
3.16
3.18
3.83
3.66
3.21
3.12
3.78
3.82
3.91
3.52
3.43
4.42
4.26
4.62
4.80
4.33
4.31
3.98
4.21
4.20
4.22
4.02
3.98
3.84
3.99
4.31
4.25
3.88
3.84
3.33
3.47
3.68
4.18
3.37
3.33
4.06
4.03
3.98
4.33
3.98
3.95
2
1
1
1
1
3
3
1
1
2
2
2
3
1
1
1
1
3
3
1
1
1
1
2
3
1
1
1
1
2
2
1
1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
339
2.9 The environmental Own Consumption
benefits of organic food
For Children
For Elderly
Gift
For Family
Total
2.10 The idea of face saving Own Consumption
(mianzi) when purchasing For Children
organic food
For Elderly
Gift
For Family
Total
2.11 The social status of Own Consumption
people purchasing organic For Children
food
For Elderly
Gift
For Family
Total
2.12 The knowledge of Own Consumption
organic food products
For Children
For Elderly
Gift
For Family
Total
2.13 The awareness of Own Consumption
organic food products
For Children
For Elderly
Gift
For Family
Total
2.14 The country of origin Own Consumption
of the organic food
For Children
For Elderly
254
88
27
11
326
706
253
90
27
11
324
705
253
89
27
11
327
707
252
89
27
11
326
705
253
90
27
11
326
707
253
90
27
4.01
3.69
3.96
3.91
3.94
3.93
2.16
2.44
2.19
2.64
2.30
2.27
2.14
2.58
2.48
3.18
2.30
2.30
3.66
3.71
3.70
3.73
3.74
3.71
3.38
3.81
3.85
4.09
3.63
3.58
3.17
3.36
3.30
.937
.951
.898
.944
.901
.923
1.094
1.040
1.111
1.120
1.082
1.085
1.078
1.085
1.189
1.079
1.080
1.096
.920
.829
1.171
1.009
.916
.917
.975
.748
.770
.701
.867
.902
1.042
.952
1.171
.059
.101
.173
.285
.050
.035
.069
.110
.214
.338
.060
.041
.068
.115
.229
.325
.060
.041
.058
.088
.225
.304
.051
.035
.061
.079
.148
.211
.048
.034
.066
.100
.225
3.89
3.49
3.61
3.27
3.84
3.86
2.02
2.23
1.75
1.88
2.18
2.19
2.01
2.36
2.01
2.46
2.18
2.22
3.55
3.53
3.24
3.05
3.64
3.64
3.26
3.65
3.55
3.62
3.53
3.51
3.04
3.16
2.83
4.12
3.89
4.32
4.54
4.03
4.00
2.29
2.66
2.62
3.39
2.42
2.35
2.28
2.81
2.95
3.91
2.41
2.38
3.78
3.88
4.17
4.41
3.84
3.77
3.50
3.97
4.16
4.56
3.72
3.64
3.30
3.55
3.76
1
1
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
1
1
2
2
3
1
1
1
1
1
5
5
5
5
5
5
5
5
5
4
5
5
5
5
5
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
340
2.19 Certification relating to
the quality of organic food
2.18 Food safety in
relation to organic food
2.17
Government
regulations and policies
relating to the sale of
organic food
2.16 The brand name of the
organic food
2.15 Organic food that is
produced in China
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
Own Consumption
For Children
For Elderly
Gift
For Family
Total
11
325
706
254
90
27
11
325
707
254
90
27
11
327
709
253
90
27
11
326
707
252
89
27
11
326
705
253
90
27
11
323
704
3.36
3.37
3.29
3.33
3.38
3.26
3.36
3.40
3.37
3.35
3.51
3.52
3.55
3.56
3.48
3.87
3.72
3.93
4.18
3.88
3.86
4.27
3.87
3.93
4.27
4.10
4.12
4.30
4.08
4.22
4.27
4.27
4.25
.924
1.048
1.038
.995
1.023
1.095
.924
.946
.977
.940
.838
.975
1.036
.944
.935
1.012
.936
.958
.982
.933
.964
.859
.919
.997
.786
.903
.899
.906
.738
.801
.905
.799
.834
.279
.058
.039
.062
.108
.211
.279
.052
.037
.059
.088
.188
.312
.052
.035
.064
.099
.184
.296
.052
.036
.054
.097
.192
.237
.050
.034
.057
.078
.154
.273
.044
.031
2.74
3.25
3.22
3.21
3.16
2.83
2.74
3.30
3.30
3.23
3.34
3.13
2.85
3.46
3.41
3.75
3.53
3.55
3.52
3.78
3.79
4.16
3.67
3.53
3.74
4.00
4.06
4.18
3.92
3.91
3.67
4.18
4.19
3.98
3.48
3.37
3.46
3.59
3.69
3.98
3.50
3.44
3.46
3.69
3.90
4.24
3.67
3.55
4.00
3.92
4.30
4.84
3.98
3.93
4.37
4.06
4.32
4.80
4.20
4.19
4.41
4.23
4.54
4.88
4.36
4.31
2
1
1
1
1
1
2
1
1
1
1
1
2
1
1
1
1
2
2
1
1
1
1
2
3
1
1
1
2
2
3
1
1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
341
2.20 Enforcement relating Own Consumption
to the quality of organic For Children
food
For Elderly
Gift
For Family
Total
2.21 Packaging of organic Own Consumption
food
For Children
For Elderly
Gift
For Family
Total
2.22 Correct labelling of Own Consumption
organic food
For Children
For Elderly
Gift
For Family
Total
2.23 Information about the Own Consumption
nutritional value of organic For Children
food
For Elderly
Gift
For Family
Total
252
89
27
11
326
705
253
90
27
11
326
707
252
90
26
11
326
705
253
90
27
11
327
708
4.28
4.16
4.04
4.36
4.22
4.23
3.36
3.50
3.56
3.36
3.52
3.46
3.99
4.14
3.96
4.18
4.10
4.06
4.20
4.08
3.81
4.18
4.15
4.15
.877
.838
.940
.809
.861
.865
.977
1.030
1.121
1.027
.914
.962
.904
.801
.871
.751
.822
.851
.852
.851
1.388
.874
.816
.863
.055
.089
.181
.244
.048
.033
.061
.109
.216
.310
.051
.036
.057
.084
.171
.226
.046
.032
.054
.090
.267
.263
.045
.032
4.17
3.98
3.67
3.82
4.13
4.17
3.24
3.28
3.11
2.67
3.42
3.39
3.88
3.98
3.61
3.68
4.01
4.00
4.10
3.90
3.27
3.59
4.06
4.08
4.39
4.33
4.41
4.91
4.32
4.30
3.48
3.72
4.00
4.05
3.62
3.53
4.10
4.31
4.31
4.69
4.19
4.13
4.31
4.26
4.36
4.77
4.24
4.21
1
2
2
3
1
1
1
1
1
2
1
1
1
2
2
3
1
1
1
2
1
3
1
1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
342
2.1 The taste of organic Between Groups
food
Within Groups
Total
2.2 The smell of organic Between Groups
food
Within Groups
Total
2.3 The appearance of Between Groups
organic food
Within Groups
Total
2.4 The overall quality of Between Groups
organic food
Within Groups
Total
2.5 The price of organic Between Groups
food
Within Groups
Total
2.6 The availability of Between Groups
organic food in convenience Within Groups
stores and supermarkets
Total
2.7 The promotion and Between Groups
advertising of organic food Within Groups
Total
2.8 The value of organic Between Groups
foods relative to its price
Within Groups
Total
2.9 The environmental Between Groups
benefits of organic food
Within Groups
Sum of Squares
2.513
517.617
520.130
6.624
488.799
495.422
4.468
649.169
653.637
4.095
443.328
447.422
2.297
522.911
525.208
2.072
533.162
535.234
2.844
659.337
662.180
3.598
576.055
579.654
6.517
594.219
Table H: ANOVA
df
4
703
707
4
698
702
4
700
704
4
698
702
4
697
701
4
695
699
4
694
698
4
697
701
4
701
F
.853
2.365
1.204
1.612
.765
.675
.748
1.088
1.922
Mean Square
.628
.736
1.656
.700
1.117
.927
1.024
.635
.574
.750
.518
.767
.711
.950
.900
.826
1.629
.848
.105
.361
.559
.609
.548
.169
.308
.052
Sig.
.492
343
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
2.17
Government
regulations and policies
relating to the sale of
organic food
2.18 Food safety in Between Groups
relation to organic food
Within Groups
Total
2.19 Certification relating to Between Groups
the quality of organic food Within Groups
Total
2.20 Enforcement relating Between Groups
2.16 The brand name of the
organic food
2.15 Organic food that is
produced in China
2.14 The country of origin
of the organic food
2.13 The awareness of
organic food products
2.10 The idea of face saving
(mianzi) when purchasing
organic food
2.11 The social status of
people purchasing organic
food
2.12 The knowledge of
organic food products
705
4
700
704
4
702
706
4
700
704
4
702
706
4
701
705
4
702
706
4
704
708
4
702
706
4
700
704
4
699
703
4
600.737
7.854
820.478
828.332
22.929
825.099
848.028
.839
591.383
592.221
20.527
554.022
574.549
6.126
754.181
760.307
.943
673.441
674.385
6.932
611.934
618.866
3.103
652.588
655.692
12.555
556.461
569.016
3.357
485.637
488.994
2.358
.248
6.502
.210
.845
5.132
.789
3.948
1.208
.786
.839
.695
.589
.835
1.994
.246
3.139
.795
.776
.930
1.733
.869
.236
.959
1.424
4.877
5.732
1.175
1.532
1.076
1.675
1.964
1.172
.534
.306
.004
.503
.094
.912
.224
.000
.911
.001
.154
344
to the quality of organic Within Groups
food
Total
2.21 Packaging of organic Between Groups
food
Within Groups
Total
2.22 Correct labelling of Between Groups
organic food
Within Groups
Total
2.23 Information about the Between Groups
nutritional value of organic Within Groups
food
Total
524.956
527.313
3.954
649.647
653.601
2.910
506.344
509.254
4.176
521.839
526.016
700
704
4
702
706
4
700
704
4
703
707
1.068
1.006
1.406
.988
.925
.727
.723
1.044
.742
.750
.230
.404
.371
345
For Family
For Gift
For Elderly
For Children
For Own
Consumption
(I) 5.2.Single
usage group
For Children
2.13 The awareness of organic For
Own
food products
Consumption
2.11 The social status
of people purchasing
organic food
Tukey HSD
Dependent Variable
dimension3
dimension3
dimension3
dimension3
dimension3
dimension3
Own Consumption
For Elderly
Gift
For Family
Own Consumption
For Children
Gift
For Family
Own Consumption
For Children
For Elderly
For Family
Own Consumption
For Children
For Elderly
Gift
Gift
For Children
For Elderly
Gift
For Family
Own Consumption
For Elderly
Gift
For Children
For Elderly
Gift
For Children
For Elderly
Gift
For Family
(J) 5.2.Single usage group
Std. Error
.129
.217
.332
.134
.219
.334
.091
.134
.238
.346
.130
.219
.238
.388
.217
.334
.346
.388
.332
.091
.130
.217
.332
.282
.109
.180
.274
.074
.109
.195
.284
Mean Difference (I-J)
-.145
.114
-.337
-.442*
-.339
-1.040*
-.154
.442*
.103
-.598
.288
.339
-.103
-.700
.185
1.040*
.598
.700
.885
.154
-.288
-.185
-.885
.012
-.432*
-.472
-.711
-.246*
.432*
-.041
-.280
Table I: Five intended usages: Multiple Comparisons
.009
.993
.419
.174
.533
.993
.371
.914
.016
.419
.371
.061
.434
.174
.914
.061
1.000
.001
.067
.071
.009
.001
1.000
.862
Sig.
.794
.985
.848
.009
.533
.016
.434
.08
-.55
-1.55
-.07
-.26
-.75
-1.76
-.41
.13
-.35
-.36
-.02
-.09
-.64
-.78
-1.79
-.76
-.73
-.96
-1.46
-.45
.13
-.57
-1.06
.81
.75
.35
.64
.94
.55
.36
.78
1.95
1.55
1.76
1.79
.40
.07
.41
.02
.78
-.13
.02
.04
-.04
.73
.49
.50
346
95% Confidence Interval
Lower Bound
Upper Bound
-.50
.21
-.48
.71
-1.24
.57
-.81
-.08
-.94
.26
-1.95
-.13
-.40
.09
For Children
2.18 Food safety in relation to For
organic food
Own
Consumption
For Family
For Gift
For Elderly
For Children
For Family
FOR Gift
For Elderly
dimension3
dimension3
dimension3
dimension3
dimension3
dimension3
dimension3
dimension3
dimension3
For Family
Own Consumption
For Children
Gift
For Family
Own Consumption
For Children
For Elderly
For Family
Own Consumption
For Children
For Elderly
Gift
Own Consumption
For Elderly
Gift
For Family
Own Consumption
For Children
Gift
For Family
Own Consumption
For Children
For Elderly
For Family
Own Consumption
For Children
For Elderly
Gift
For Children
For Elderly
Gift
For Family
Own Consumption
For Elderly
Gift
.185
.472
.041
-.239
.226
.711
.280
.239
.465
.246*
-.185
-.226
-.465
.165
-.007
-.034
-.052
.172
.007
-.027
-.044
.199
.034
.027
-.017
.216*
.052
.044
.017
.401*
.340
-.007
.168
-.401*
-.061
-.408
.106
.180
.195
.318
.178
.274
.284
.318
.272
.074
.106
.178
.272
.114
.205
.298
.111
.189
.205
.333
.187
.287
.298
.333
.286
.078
.111
.187
.286
.110
.181
.275
.075
.110
.196
.285
.403
.067
1.000
.944
.709
.071
.862
.944
.430
.009
.403
.709
.430
.602
1.000
1.000
.990
.892
1.000
1.000
.999
.958
1.000
1.000
1.000
.045
.990
.999
1.000
.003
.327
1.000
.165
.003
.998
.608
-.10
-.02
-.49
-1.11
-.26
-.04
-.50
-.63
-.28
.04
-.47
-.71
-1.21
-.15
-.57
-.85
-.36
-.34
-.55
-.94
-.55
-.59
-.78
-.89
-.80
.00
-.25
-.47
-.76
.10
-.15
-.76
-.04
-.70
-.60
-1.19
.47
.96
.57
.63
.71
1.46
1.06
1.11
1.21
.45
.10
.26
.28
.48
.55
.78
.25
.69
.57
.89
.47
.98
.85
.94
.76
.43
.36
.55
.80
.70
.83
.74
.37
-.10
.47
.37
347
*. The mean difference is significant at the 0.05 level.
For Family
For Gift
For Elderly
dimension3
dimension3
dimension3
For Family
Own Consumption
For Children
Gift
For Family
Own Consumption
For Children
For Elderly
For Family
Own Consumption
For Children
For Elderly
Gift
-.233
-.340
.061
-.347
-.172
.007
.408
.347
.175
-.168
.233
.172
-.175
.107
.181
.196
.319
.179
.275
.285
.319
.273
.075
.107
.179
.273
.187
.327
.998
.813
.871
1.000
.608
.813
.969
.165
.187
.871
.969
-.52
-.83
-.47
-1.22
-.66
-.74
-.37
-.53
-.57
-.37
-.06
-.32
-.92
.06
.15
.60
.53
.32
.76
1.19
1.22
.92
.04
.52
.66
.57
348
Appendix 9: Referred Journal, Book and Conference Publications
associated with this thesis
Chen, J 2009, ‘A study investigating the determinants of consumer buyer behaviour relating
to purchase of organic food products in China: a project proposal’, ANZMAC doctoral
colloquium, Melbourne, 27-29 Nov.
Chen, J, Lobo, A & Mascitelli, B 2009, ‘Consumer buyer behaviour associated with the
purchase of organic food products in China: development of a conceptual model’, Global
Business and Technology Association 11th annual international conference, Prague, 8-11
July, pp. 206-212.
Chen, J, Lobo, A & Mascitelli, B 2010, ‘Buyer behaviour of organic food in China:
consumers have their say’, Global Business and Technology Association 12th annual
international conference, Mpumalanga, South Africa, 5-9 July, pp. 87-94.
Chen, J 2010, ‘Factors determining organic food consumption: a first national study in
China’, BioFach China conference on international organic food markets and development,
Shanghai, 27-28 May.
Chen, J, 2010, ‘China agri-business feature: Australia’s share of China’s organic pie’,
Australia China Connections, Nov./Dec, pp.32-33
Chen, J & Lobo, A 2010, ‘An exploratory study investigating the dimensions influencing
consumers’ purchase intention relating to organic food in Urban China’, Australia & New
Zealand Marketing Academy, University of Canterbury, Christchurch, 29 Nov.-1 Dec.
Chen, J, Lobo, A & Mascitelli, B 2011, ‘Investigating the buyer behaviour of organic food
in urban China’, Global Business and Technology Association 13th annual international
conference, Istanbul, 12-16 July.
349
Chen, J 2011 ‘Important attributes to pre-purchase evaluation relating to organic food in
urban China’, 3rd Scientific Conference of the International Society of Organic Agriculture
Research (ISOFAR)-the 17th Organic World Congress, Namyangju, South Korea, 28 Sept 1 Oct, pp.84-87.
Wijesinghe, JMRC & Chen, J 2011, ‘Environmental marketing: a source of competitive
advantage’, 2nd International Conference on Business & Information, Kelaniya, Sri Lanka,
20 Oct.
Chen, J & Lobo, A 2012, ‘Organic food products in China: determinants of consumers’
purchase intentions’, International Review of Retail, Distribution and Consumers Research,
vol. 22, no. 3, pp. 293-314.
Chen, J & Lobo, A 2012, ’Consumption of organic food in urban China: investigating
determinants important to buyers and a segmentation analysis of their usage pattern’,
Business growth in emerging markets: a debate on critical perspectives, Nova, Hauppauge,
NY.
Lobo, A & Chen, J 2012, ‘The influence of consumers’ lifestyle segments on the purchase
intention of organic food in urban China’, Academy of World Business, Marketing &
Management Development conference, Budapest, 16-19 July.
350