The influence of power and information sharing on supply ...

The Influence of Power and Information Sharing on

Supply Chain Relationships and a Firm’s Operational

Performance – Perceptions of Organic Fruit and

Vegetable Growers

Sumangala D. Bandara

BSc (Electrical Engineering), MBA

Submitted in total fulfilment of the requirements of the degree of

Doctor of Philosophy

Faculty of Business and Law

Swinburne University of Technology, Australia

2016

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Abstract Firms in different industry sectors take several initiatives to minimise the time that

goods travel through a supply chain (SC), thus they are able to minimise the total cost.

However, these firms have been unsuccessful in their attempts to find an ideal SC

solution, despite the many improvements that this important field has achieved so far.

Although, the management of activities in a SC, which is popularly known as supply

chain management (SCM), is more than four decades old, it is still evolving as a

discipline. This study explores the influence of power (coercive and non-coercive) and

information sharing on supply chain relationship determinants and subsequently on

performance (SC and firm’s operational performance) of growers in the organic fruit

and vegetable industry. Additionally, it investigates how the important SC relationship

determinants of satisfaction, collaboration, trust and commitment influence SC

relationship success (SC performance) and firm’s operational performance of the

organic fruit and vegetable growers. A comprehensive conceptual model developed

after an extensive literature review, was tested using data obtained using an online

survey administered in Australia. Structural Equation Modeling (SEM) was employed

to examine the influence of the exogenous constructs on each other, and specifically on

the operational performance of firms.

The findings of this study reveal that the sharing of valuable information between SC

partners improves satisfaction, collaboration, trust, commitment and the firm’s

operational performance. These results suggest that sharing valuable information with

SC partners improves the SC relationships and operational performance of the organic

fruit and vegetable growers. Contradictory to previous literature, the results of this

thesis demonstrate that information sharing does not significantly influence the SC

performance (also identified in this study as SC relationships success). The use of

power by major SC partners in dealing with the growers of organic fruit and vegetables

influences SC relationships differently. In this respect the punitive nature of strategies

or actions used by major SC partners negatively impacts collaboration of the growers.

Also, contrary to previous literature, the findings of this thesis did not display

significant negative relationships between coercive power and satisfaction,

commitment, SC relationships success and firm’s operational performance. In fact the

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findings of this study reveal a positive relationship between coercive power and trust.

This can possibly be owing to the fact that the major SC partners take the lead and drive

the growers in achieving their targets. However, growers are happy to continue the

relationship, and trust them due to the business opportunity that they receive through

these strong SC partners.

This study reveals that there are positive relationships between the use of non-coercive

strategies and collaboration, commitment, SC relationship success and the firm’s

operational performance, which are in line with previous findings in different study

contexts. Conversely, the results of this study reveal significant negative relationships

between use of non-coercive strategies and satisfaction and trust. As expected, the

results of this study reveal positive influences between satisfaction and SC relationship

success, collaboration and SC relationships success, trust and commitment, commitment

and SC relationship success, and SC relationship success and firm’s operational

performance. However, contradictory to previous findings, the results of this thesis did

not find a significant positive relationship between trust and SC relationships success.

However, it identified three new positive and significant relationships between

satisfaction and commitment, satisfaction and trust, and also between collaboration and

firm’s operational performance.

The analysis of this study demonstrates that mediation effects emanate as a result of the

relationships formulated in the unique conceptual model of this thesis. It demonstrates

that satisfaction, collaboration, trust and commitment positively mediate the

relationships between information sharing and success of SC relationships. These

results indicate the influence of relationship determinants in achieving improved SC

performances (which is identified in this thesis as SC relationship success). Also, the

relationship determinants (i.e. satisfaction, collaboration, trust and commitment)

negatively mediate between coercive power and SC relationship success, and positively

mediate between non-coercive power and the success of SC relationships. These results

indicate the negative mediation of relationship determinants in the presence of punitive

strategies (coercive), and on the other hand positive mediation when major SC partners

use rewards (non-coercive) to motivate organic fruit and vegetable growers. Further, the

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results of this study reveal that both relationship constructs as well as SC relationship

success invigorate the negative effect of coercive power towards a firm’s operational

performance.

This study has made several important academic contributions. These include the

development of a three-stage unique conceptual framework incorporating information

sharing, power (coercive and non-coercive), relationship constructs and a firm’s

operational performance. This conceptual model can be used to investigate relationships

between growers and other SC partners in the organic fruit and vegetable industry, or on

SC relationships in any other industry sector. Further, it conceptualises power as being

an influential determinant of the relationship constructs, an area which has not

previously been visited in SC studies. The practical implications of this study include

suggestions relating to the use of different types of organisational power in SCs and

their effects on SC performance and firm’s operational performance. The implications

also include the positive influences in sharing strategic information with SC partners.

All stakeholders of the organic fruit and vegetable industry would benefit immensely by

using the findings of this study to strengthen their SC relationships and in turn to

improve SC performance, operational performance of the growers and also by

increasing organic fruit and vegetable exports.

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Acknowledgements I wish to express my sincere gratitude and appreciation to all those fantastic people who

supported in many ways, assisted when needed, encouraged, and inspired me always,

and especially during the four years of this wonderful journey.

My parents supported me in countless ways during the early years of my education,

which provided me the foundation, and also inspired me to embark on this long, but

fascinating journey. Your support came at the right time, and encouraged me in every

aspect of my endeavours. Thank you very much, mom and dad.

My principal supervisor, Associate Professor Antonio Lobo, was crucial in completing

my study. His encouragement, guidance, orders, patience, understanding, trust in me,

active support throughout my candidature, and above all, his desire to see me succeed,

assisted me in reaching the completion of this study. Thank you for your support, to

which I am indebted, and which is very much appreciated.

Dr. Chandana Hewege, my second supervisor was instrumental in starting my PhD.

Your guidance, encouragement, insights and valuable direction have been immensely

helpful throughout my candidature. It is very much appreciated. My third supervisor, Dr

Civilai Leckie, was a wonderful human being, who guided, supported and patiently

helped me in the statistical analysis. Your support enabled me to conclude the data

analysis with much confidence, thank you very much.

Dr. Lakmal Abeysekera and Dr. Chamila Perera were also instrumental in starting my

PhD. They both encouraged me to embark on this journey. Your support has helped me

to achieve this success, and I appreciate it very much.

My good friend Dushan Jayawickrama, whom I met at Swinburne University and who

later became an advisor, mentor, critic, lunch and tea partner, and a close friend. Your

insights have tremendously helped me during the four years of this journey. Thank you

Dushan, you are a true gentleman and a fantastic friend.

Also thank you Anne Cain and faculty of Business and Law, Swinburne University of

Technology for their support, providing me with resources and training during my

candidature. A big thank you to Dr. Jay Daniel Thompson for editing and proofreading

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my thesis within a very limited time. Also, ‘thank you very much’ to all the participants

of this study, whose valuable knowledge and insights yielded the data for this study.

During all these years, this absolutely fantastic lady was patiently waiting to see every

bit of my success. My lovely wife, Manjula was pivotal in this journey. It’s not easy to

look after two kids fulltime, and also to support and inspire me to continue my

education. Most importantly, you never complained or became uneasy with my absence

from important family events. I am extremely lucky to have you as my partner for life.

Thank you for your support and “I LOVE YOU”. Also, my handsome boy Dulvin, and

cute little girl Kenuli, both of you are treasures to me. Your love, understanding,

patience and sweet smiles kept me happy and alive even in most challenging and

demanding situations during the last four years. I know you both enjoyed sitting on my

lap and counting “how many words that I have typed so far”, which I too enjoyed. I love

you both.

Personally, I am very happy to reach the completion of this journey. I was restless,

impatient, agitated and exhausted at times, but happy to see the hard work taking me to

my destination. I pushed myself to my limit, and realised that I was capable of doing

more, which will no doubt benefit me in my future journeys. I am a truly happy father,

and a husband to understand that, I supported my family emotionally and financially

while undergoing full-time studies during these four years.

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Declaration I hereby certify that this study is my own and contains no materials, which are

published previously or has been accepted for the award of any other degrees or

diplomas, except where the due references are made.

Dr. Jay Daniel Thompson, Director of Jay’s Academic Proofreading edited this thesis.

Dr. Thompson only addressed the grammar, and not the thesis’ substantive content. This

study also met the requirements of Swinburne's Human Research Ethics Committee

(SUHREC) in line with the National Statement on Ethical Conduct in Human Research,

under SUHREC 2013/076.

Sumangala Dharmapriya Bandara

Melbourne, Australia – 2016

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Publications associated with this thesis Bandara, SD., Lobo, A. and Hewege, C. (2013) “Evaluating the impact of supply chain

member relationships on organisational performance in the Victorian organic fruit and

vegetable industry – Development of a conceptual framework” ANZAM, Brisbane,

Australia, 20-12 June 2013.

Bandara, SD., Lobo, A. and Hewege, C. (2015) “The influence of supply chain

relationships on firm performance: The case of Australian organic fruit and vegetable

sector” International Conference of Marketing and Supply Chain Management,

Colombo, Sri Lanka, February 2015.

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Abbreviations ACO - Australian Certified Organic

AGFI - Adjusted Goodness of Fit Index

AMOS - Analysis of Moment Structure

AUD - Australian Dollar

AVE - Average Variance Extracted

BDRI - Bio-Dynamic Research Institute

CAGR - Compound Annual Growth Rate

CAPI - Computer-Assisted Personal Interviewing

CATI - Computer-Assisted Telephone Interviewing

CFA - Confirmatory factor analysis

CFI - Comparative Fit Index

CMV - Common Method Variance

CR - Construct Reliability

DAFF - Department of Agriculture Fisheries and Forestry

DC - Distribution Centre

df - Degree of Freedom

EFA - Exploratory Factor Analysis

EM - Expectation Maximization

FIML - Full Information Maximum Likelihood

FL - Standardised Factor Loading

GFI - Goodness of Fit Index

ISM - Institute of Supply Management

JIT - Just In Time

MCAR - Missing Completely At Random

MGSEM - Multiple Group Structural Equation Modeling

MI - Modification Indices

ML - Maximum Likelihood

MTMM – Multi Trait Multi Method

NASAA - National Association for Sustainable Agriculture, Australia

x

NFI - Normed Fit Index

OFC - Organic Food chain

OGA - Organic Growers of Australia

RMSEA - Root Mean Square Error of Approximation

SC - Supply Chain

SCM - Supply Chain Management

SBSR - Success of Buyer-Supplier Relationships

SD - Standard Deviation

SEM - Structural Equation Modeling

SPSS - Statistical Package for Social Sciences/Statistical Product and Service Solutions

SRMSR - Standardised Root Mean Square Residual

Std. FL – Standardised Factor Loadings

SUHREC - Swinburne University's Human Research Ethics Committee

SVME - Swiss Association of Purchasing and Material’s Management

TLI - Tucker-Lewis Index

TOP - Tasmanian Organic Dynamic Producers

UK - United Kingdom

US - Unites States

χ2 - Chi-square

α - Cronbach’s alpha

λ - Regression coefficient ns - Non significant

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Table of Contents

Abstract ............................................................................................................................. ii

Acknowledgements ........................................................................................................... v

Declaration ...................................................................................................................... vii

Publications associated with this thesis.......................................................................... viii

Abbreviations ................................................................................................................... ix

List of tables ................................................................................................................... xvi

List of Figures ................................................................................................................ xix

List of Appendices ......................................................................................................... xxi

Chapter One: Introduction and background of this study ................................................. 1

1 Road map to chapter one ........................................................................................... 2

1.1 Background .................................................................................................... 3

1.1.1 Supply Chain (SC) and Supply Chain Management (SCM) ..................... 3

1.1.2 Influential determinants ............................................................................. 7

1.1.3 Relationship constructs .............................................................................. 8

1.1.4 A firm’s performance................................................................................. 9

1.2 The context of this study .............................................................................. 10

1.3 Significance of this study ............................................................................. 12

1.3.1 Academic implications ............................................................................ 12

1.3.2 Practical implications ............................................................................... 13

1.4 Conceptual framework, research problem/questions and hypotheses .......... 14

1.5 Methodology ................................................................................................ 15

1.6 Outline of Chapters ...................................................................................... 16

Chapter Two: Literature review ...................................................................................... 19

2 Introduction to chapter two ...................................................................................... 20

2.1 A supply chain (SC): Its definition and purpose .......................................... 22

2.1.1 SC relationships ....................................................................................... 26

2.1.2 Cooperation, competition and interdependence of SCs ........................... 29

2.1.3 SC integration .......................................................................................... 31

2.1.4 Context of this study ................................................................................ 32

2.2 SC relationship success and a firm’s operational performance .................... 35

2.2.1 SC relationship success ............................................................................ 35

2.2.2 The performance of firms ........................................................................ 37

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2.3 Relationships among partners of the fruit and vegetable SCs ...................... 40

2.4 Organisational power ................................................................................... 46

2.4.1 Bases of power ......................................................................................... 48

2.4.2 Importance of power and its influence in fruit and vegetable SCs .......... 56

2.5 Theoretical underpinnings of this study ....................................................... 60

2.5.1 The Resource Based View (RBV) and its application within SCs .......... 61

2.5.2 Transaction Cost Economics (TCE) theory and its application in SCs ... 65

2.5.3 Network theory (NT) and its application within SCs .............................. 69

2.6 Information sharing and its influence in SCs ............................................... 74

2.7 SC relationship constructs ............................................................................ 77

2.7.1 Satisfaction............................................................................................... 78

2.7.2 Collaboration ........................................................................................... 79

2.7.3 Trust ......................................................................................................... 80

2.7.4 Commitment ............................................................................................ 82

2.8 Summary and gaps in previous literature ..................................................... 83

2.9 Research problem ......................................................................................... 86

2.10 Chapter summary ......................................................................................... 87

Chapter Three: Conceptual model and development of hypotheses ............................... 88

3 Introduction to chapter three .................................................................................... 89

3.1 Critical review of previous relevant models ................................................. 90

3.2 Development of the proposed conceptual model for the current study ........ 96

3.3 Primary and secondary research questions ................................................... 98

3.4 Explanation of the constructs of the proposed conceptual model ................ 99

3.4.1 Information sharing.................................................................................. 99

3.4.2 Power ..................................................................................................... 101

3.4.3 Satisfaction............................................................................................. 106

3.4.4 Collaboration ......................................................................................... 106

3.4.5 Trust ....................................................................................................... 108

3.4.6 Commitment .......................................................................................... 109

3.4.7 SC relationship success .......................................................................... 110

3.4.8 Firm’s operational performance............................................................. 111

3.5 Summary of hypotheses and proposed final conceptual model ................. 112

3.6 Chapter summary ....................................................................................... 114

Chapter Four: Methodology .......................................................................................... 115

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4 Introduction to chapter four ................................................................................... 116

4.1 Research design .......................................................................................... 117

4.1.1 Deductive and inductive approaches ..................................................... 118

4.1.2 Objectivism ............................................................................................ 120

4.1.3 Positivism............................................................................................... 121

4.1.4 Quantitative research method ................................................................ 122

4.2 Unit of Analysis .......................................................................................... 123

4.3 Development of the survey instrument....................................................... 124

4.3.1 Measurement scales ............................................................................... 126

4.3.2 Pre-testing .............................................................................................. 139

4.3.3 Structure of the final survey instrument ................................................ 140

4.4 Data collection and analysis ....................................................................... 141

4.4.1 Sampling and sample size ...................................................................... 141

4.4.2 Survey participants ................................................................................ 142

4.4.3 Completion of the survey ....................................................................... 143

4.4.4 Data analysis .......................................................................................... 144

4.5 Ethical considerations ................................................................................. 145

4.6 Chapter summary ....................................................................................... 146

Chapter Five: Analysis and findings ............................................................................. 147

5 Introduction to chapter five ................................................................................... 148

5.1 Data preparation procedure ........................................................................ 150

5.1.1 Missing data and imputation .................................................................. 150

5.1.2 Unengaged responses ............................................................................. 150

5.1.3 Reverse coding ....................................................................................... 151

5.1.4 Outliers................................................................................................... 151

5.1.5 Normality ............................................................................................... 151

5.1.6 Non-response bias .................................................................................. 152

5.1.7 Skewness and kurtosis ........................................................................... 152

5.1.8 Sample size and its effects on normality ............................................... 153

5.2 The respondents’ profiles ........................................................................... 153

5.3 Data analysis procedure .............................................................................. 160

5.3.1 Structural Equation Modeling (SEM) .................................................... 160

5.3.2 Two-step approach ................................................................................. 161

5.4 Model evaluation: Fit indices ..................................................................... 162

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5.4.1 Absolute fit Measures ............................................................................ 163

5.4.2 Model comparison and relative fit measures ......................................... 164

5.4.3 Non-centrality-based indices ................................................................. 165

5.5 Assessment of Construct Validity and Reliability...................................... 167

5.5.1 Validity .................................................................................................. 167

5.5.2 Reliability............................................................................................... 169

5.6 CFA for one factor congeneric models ...................................................... 170

5.6.1 Item Codes ............................................................................................. 170

5.6.2 Measurement models for each measured construct or latent variable ... 171

5.6.3 Common method variance ..................................................................... 189

5.6.4 Reliability of constructs ......................................................................... 190

5.6.5 Summary of Constructs ......................................................................... 191

5.7 Overall confirmatory factor analysis .......................................................... 192

5.7.1 Overall CFA for measurement model 1 ................................................. 193

5.7.2 Overall CFA for measurement model 2 ................................................. 197

5.8 Structural model estimation ........................................................................ 201

5.8.1 Path analysis .......................................................................................... 201

5.8.2 Composite variables ............................................................................... 201

5.8.3 Regression coefficient and measurement error for composite variables203

5.8.4 Initial SEM model .................................................................................. 205

5.8.5 Model re-specification ........................................................................... 207

5.8.6 Final SEM model ................................................................................... 208

5.9 Mediation, total, direct and indirect effects between constructs ................ 213

5.9.1 Constructs affecting Satisfaction ........................................................... 215

5.9.2 Constructs affecting collaboration ......................................................... 216

5.9.3 Constructs affecting trust ....................................................................... 216

5.9.4 Constructs affecting commitment .......................................................... 217

5.9.5 Constructs affecting SC relationship success ........................................ 217

5.9.6 Constructs affecting firm’s operational performance ............................ 218

5.10 Chapter summary ....................................................................................... 219

Chapter Six: Discussion and conclusion ....................................................................... 221

6 Introduction to chapter six ..................................................................................... 222

6.1 Rationale of the present study .................................................................... 223

6.2 Discussion of the findings .......................................................................... 226

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6.2.1 Findings associated with influential determinants ................................. 227

6.2.2 Findings associated with SC relationship constructs ............................. 238

6.2.3 Findings associated with performance ................................................... 242

6.3 Discussions relating to total and indirect effects between the constructs .. 244

6.3.1 Indirect effects on SC relationship success............................................ 244

6.3.2 Indirect effects on firm’s operational performance ............................... 247

6.4 Contributions to knowledge ....................................................................... 252

6.4.1 Theoretical contributions ....................................................................... 253

6.4.2 Practical/managerial implications .......................................................... 255

6.5 Limitations of the study .............................................................................. 257

6.6 Directions for future research ..................................................................... 259

6.7 Concluding remarks ................................................................................... 261

References ..................................................................................................................... 263

Appendices .................................................................................................................... 292

xvi

List of tables Table 2.1 Comparison of present versus traditional SCs ................................................ 24

Table 2.2 Key characteristics of partnerships versus traditional relationships ............... 27

Table 2.3 Definitions and dimensions of power in previous literature ........................... 47

Table 2.4 Review of different sources of power ............................................................. 54

Table 2.5 General conceptualisation of organisational theories ..................................... 61

Table 2.6 Comparison between RBV, TCE and NT ....................................................... 72

Table 2.7 The theoretical perspectives of best value and traditional SCs ....................... 73

Table 4.1 Items used to measure information sharing .................................................. 128

Table 4.2 Items used to measure coercive power ......................................................... 129

Table 4.3 Items used to measure non-coercive power .................................................. 130

Table 4.4 Items used to measure satisfaction ................................................................ 132

Table 4.5 Items used to measure collaboration ............................................................. 133

Table 4.6 Items used to measure trust ........................................................................... 134

Table 4.7 Items used to measure commitment .............................................................. 136

Table 4.8 Items used to measure SC relationship success ............................................ 137

Table 4.9 Items used to measure firm’s operational performance ................................ 138

Table 4.10 Description of survey items/questions and their relative sections .............. 140

Table 5.1 Kurtosis values .............................................................................................. 153

Table 5.2 Respondent’s position in the organisation .................................................... 154

Table 5.3 Organic certification ..................................................................................... 155

Table 5.4 Activities of growers ..................................................................................... 156

Table 5.5 Duration of involvement in fruit and vegetable growing .............................. 156

Table 5.6 Experience in organic fruit and vegetable growing ...................................... 157

Table 5.7 Annual turnover of the business.................................................................... 158

Table 5.8 Percentage of produce certified as organic product ...................................... 158

Table 5.9 Fruit and vegetable percentage ..................................................................... 159

Table 5.10 Buyers of organic fruit and vegetables ....................................................... 159

Table 5.11 Duration of the grower’s relationship with present SC partner .................. 160

Table 5.12 Model fit indices and acceptable levels used in this study ......................... 166

Table 5.13 List of codes for constructs and items of survey instrument....................... 170

Table 5.14 Model fit indices for information sharing ................................................... 172

Table 5.15 Regression weights for information sharing ............................................... 173

xvii

Table 5.16 Model fit indices for coercive power .......................................................... 174

Table 5.17 Regression weights for coercive power ...................................................... 175

Table 5.18 Model fit indices for non-coercive power ................................................... 176

Table 5.19 Regression weights for non-coercive power ............................................... 177

Table 5.20 Model fit indices for satisfaction ................................................................ 178

Table 5.21 Regression weights for satisfaction............................................................. 179

Table 5.22 Model fit indices for collaboration.............................................................. 180

Table 5.23 Regression weights for collaboration .......................................................... 181

Table 5.24 Model fit indices for trust............................................................................ 182

Table 5.25 Regression weights for trust ........................................................................ 183

Table 5.26 Model fit indices for commitment .............................................................. 184

Table 5.27 Regression weights for commitment........................................................... 185

Table 5.28 Model fit indices for SC relationship success ............................................. 186

Table 5.29 Regression weights for SC relationship success ......................................... 187

Table 5.30 Model fit indices for firms operational performance .................................. 188

Table 5.31 Regression weights for firm’s operational performance ............................. 189

Table 5.32 Reliability of constructs .............................................................................. 190

Table 5.33 Statistical summary of constructs ............................................................... 191

Table 5.34 Summary of deleted items during one factor CFA analysis ....................... 192

Table 5.35 Model fit indices for Influential Determinants ........................................... 195

Table 5.36 Descriptive Statistics and Correlation Matrix for Study Variables............. 195

Table 5.37 Std. FL, α, CR and AVE for influential determinants ................................ 196

Table 5.38 Model fit indices for SC Relationships, RS and Performance .................... 198

Table 5.39 Descriptive statistics and correlation matrix for study variables ................ 198

Table 5.40 Std. FL, α, CR and AVE for SC relationships, RS and performance ......... 199

Table 5.41 The items deleted during SEM analysis ...................................................... 200

Table 5.42 Descriptive Statistics for Composite variables ........................................... 203

Table 5.43 Regression coefficients and measurement errors for Composite variables 204

Table 5.44 Model fit indices for initial SEM ................................................................ 205

Table 5.45 Model fit indices for final SEM .................................................................. 210

Table 5.46 Results of hypothesis testing ....................................................................... 210

Table 5.47 Proportion of variation explained for each endogenous variable ............... 213

Table 5.48 Constructs affecting satisfaction ................................................................. 216

Table 5.49 Constructs affecting collaboration .............................................................. 216

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Table 5.50 Constructs affecting trust ............................................................................ 217

Table 5.51 Constructs affecting commitment ............................................................... 217

Table 5.52 Constructs affecting SC relationship success.............................................. 218

Table 5.53 Constructs affecting firm’s operational performance ................................. 219

Table 6.1 Constructs and related secondary research questions ................................... 227

xix

List of Figures Figure 1.1 Road map for the introduction chapter ............................................................ 2

Figure 1.2 An integrated supply chain .............................................................................. 4

Figure 1.3 Road map for the study .................................................................................. 16

Figure 2.1 Road map for the literature review chapter ................................................... 21

Figure 2.2 Evolution of SC relationships ........................................................................ 26

Figure 2.3 Power classification ....................................................................................... 53

Figure 3.1 Road map to the conceptual framework chapter ........................................... 90

Figure 3.2 Conceptual model developed by Corsten and Felde (2005) .......................... 91

Figure 3.3 Conceptual model used by Panayides and Lun (2009) .................................. 92

Figure 3.4 Conceptual model developed by Nyaga, Whipple and Lynch (2010) ........... 94

Figure 3.5 Conceptual model developed by Leonidou, Talias and Leonidou (2008) ..... 96

Figure 3.6 Proposed initial conceptual model ................................................................. 97

Figure 3.7 Proposed final conceptual model ................................................................. 113

Figure 4.1 Road map to the methodology chapter ........................................................ 117

Figure 4.2 The process of deduction ............................................................................. 119

Figure 4.3 Deductive and inductive approaches ........................................................... 119

Figure 4.4 Typical research process .............................................................................. 123

Figure 4.5 Main modes of administering a survey ........................................................ 125

Figure 5.1 Road map to the analysis and findings chapter ........................................... 149

Figure 5.2 CFA for information-sharing ....................................................................... 172

Figure 5.3 CFA for coercive power .............................................................................. 174

Figure 5.4 CFA for Non-Coercive Power ..................................................................... 176

Figure 5.5 CFA for satisfaction ..................................................................................... 178

Figure 5.6 CFA for collaboration .................................................................................. 180

Figure 5.7 CFA for trust ................................................................................................ 182

Figure 5.8 CFA for commitment ................................................................................... 184

Figure 5.9 CFA for SC relationship success ................................................................. 186

Figure 5.10 CFA for firm’s operational performance ................................................... 188

Figure 5.11 CFA for information sharing, coercive power and non-coercive power ... 194

Figure 5.12 CFA for relationship constructs, relationship success and performance ... 197

Figure 5.13 Initial SEM model to test hypotheses ........................................................ 206

Figure 5.14 Respecified final SEM model .................................................................... 209

xx

Figure 5.15 Direct effect and multiple step multiple mediator model .......................... 215

Figure 6.1 Road map to the discussion and conclusion chapter ................................... 223

Figure 6.2 Three-stage conceptual framework .............................................................. 226

xxi

List of Appendices Appendix 1: Survey Instrument .................................................................................... 293

Appendix 2: Research Information Statement .............................................................. 301

Appendix 3: Ethics Clearance ....................................................................................... 303

Appendix 4: Summary of Survey Items ........................................................................ 305

Appendix 5: Initial SEM model .................................................................................... 307

Appendix 6: Final SEM model ..................................................................................... 308

1

Chapter One: Introduction and background of this

study

2

1 Road map to chapter one This chapter has been organised into seven sections. Section 1.1 describes the

background of the study, and in doing so, briefly discusses supply chains and their

management. Gaps in the literature are discussed in Section 1.2, while the context of

this study is discussed in Section 1.3. Section 1.4 presents the significance of this study,

including the academic and practical implications. Section 1.5 presents the proposed

conceptual framework, along with the primary and secondary research problems, and a

listing of the hypotheses. The methodology adopted in this study is briefly discussed in

Section 1.6. Section 1.7 outlines the chapters of this entire study. The roadmap of this

chapter is depicted in Figure 1.1.

Figure 1.1 Road map for the introduction chapter

1.1 Background

1.2 The context of this study

1.3 Significance of the study

1.4 Conceptual framework, research problem/research questions and hypotheses

1.5 Methodology

Influential determinants

Supply chains and their management

Relationship constructs

Firm’s overall performance

1.6 Outline of chapters

Academic implications

Practical implications

3

1.1 Background

In the twenty-first century, businesses are forced to embrace increasingly tough and

continuously changing business environments. Coyle et al. (2013) remark that

globalisation, technological advancements, organisational consolidation and power

shifts, empowered consumers, and government policies and regulations are major

factors affecting these changes. The survival of a business in these volatile and

demanding conditions depends on “how much you can change and how fast you can

embrace that change”. Tompkins (2000) suggest that “Change is inevitable, but growth

and improvement are optional”. As the environment around us changes, so do the

businesses and all other connected mechanisms to support these rapid changes.

According to Coyle et al. (2013), supply chain management (SCM) and the associated

logistics activities are key in achieving success in today’s highly competitive and rapid

changing global business environment. Some other researchers (Gunasekaran, Patel &

Tirtiroglu 2001; Lambert, Cooper & Pagh 1998) emphasise that a firm’s operational

performance, as well as the competitive advantage of the twenty-first century

businesses, are strongly linked to supply chain (SC) performance.

In today’s business world, firms compete to capture increased market share. According

to Lambert & Cooper (2000) the competitive battle is now between SCs, and not

between firms as previously identified. This is owing to the importance of sound SCM

strategies in coordinating and moving the product to the end consumer. This aspect is

more important in today’s business environment, than producing a leading-edge

product. Giunipero et al. (2008) emphasise that many firms consider their SCs as being

central to corporate strategy, which therefore acknowledges the vital role that SCs play

in firms. Hence, this study focusses on identifying the influence of information sharing

and organisational power on SC relationships and performance of growers in the

organic fruit and vegetable industry.

1.1.1 Supply Chain (SC) and Supply Chain Management (SCM)

Possession of an efficient SC is key to business success for any organisation. According

to Coyle et al. (2013),

4

SC is an extended enterprise that crosses the boundaries of individual firms to

span the related activities of all the companies involved in the total supply chain.

This extended enterprise should attempt to execute or implement a coordinated,

two-way flow of goods and services, information, cash and demand. (p. 21).

SCs have evolved over the years. The earliest aim of a SC was to provide a mechanism

to minimise transaction costs, hence the selection of SC partners was based purely on

achieving low costs. However, later firms started focusing on establishing a more

relationship-oriented approach to SC, concentrating on creating value delivery to

stakeholders of the SC through strong alliances with selected partners (Giunipero et al.

2008). An individual firm could achieve considerable benefits by engaging in a SC with

selected partners. These benefits include the access to strategic information ( including

supply, demand and market information), cooperation from other partners, rewards

(could be risks at times), and technological advancements through more established

partners (Giunipero et al. 2008).

Source: Coyle et al. (2013)

Figure 1.2 An integrated supply chain

Products, information and cash flow through a SC (reference Figure 1.2), and they are

important to the success of SCM. As depicted in Figure 1.2, integration across many

organisational boundaries is not a simple operation; the SC needs to ensure that it

functions similar to a single organisation in order to satisfy the customer.

The term ‘Supply Chain Management’ is over three decades old, and was initially

viewed as a method to “better manage resources and assets” (p. 8) of organisations

(Ellram & Cooper 2014). There is no commonly agreed-upon definition for SCM

Cash flow

Information flow

Product/service flow

Suppliers Retailers/ Customers Distributors Wholesalers Manufacturers

5

(Chicksand et al. 2012), Coyle et al. (2013) define SCM as “the art and science of

integrating the flows of products, information, and financial through the entire supply

pipeline from the supplier’s supplier to customer’s customer.” (p. 18).

According to Mentzer et al. (2001) SCM can be defined as

the systemic, strategic coordination of the traditional business functions and the

tactics across these business functions within a particular company and across

businesses within the supply chain, for the purposes of improving the long-term

performance of individual companies and the supply chain as a whole. (p. 18).

This definition highlights the important strategic role that SC’s and SCM play by

coordinating various activities within a company, and then replicating that coordination

in the entire SC. Another important purpose of SCs is to improve the individual

performances of involved firms, and also the performance of the entire SC using intense

levels of coordination between its partners.

Also, SCM involves various disciplines, i.e. logistics and transportation, operations,

material and distribution management, marketing, purchasing and information

technology (Giunipero et al. 2008). SCM is an activity that happens across different

firms (Mentzer et al. 2001; Min, Mentzer & Ladd 2007), and it contributes to the

competitive advantage of firms through collaborative activities. According to Chen and

Paulraj (2004), SCM strives to improve the performance of individual firms as well as

the whole SC, “through better use of internal and external capabilities in order to create

a seamlessly coordinated supply chain.” (p. 122).

Collaborative activities between firms promote knowledge acquisition, and as a result

this improves the performance of that relationship (Hernández-Espallardo, Sánchez-

Pérez & Segovia-López 2011). However, there are issues associated with collaborative

relationships in SC’s. Some related concerns include, for example, the question of

which party controls the activities of the SC and which party sets directions for the

entire SC taking a lead role (Fawcett, Magnan & McCarter 2008; Soosay, Hyland &

6

Ferrer 2008). Also, decentralised and limited information flow between firms negatively

impacts inter-firm relationships (Dvoracek 2009).

According to Kähkönen (2014), firms develop relationships with strategically important

partners, and improve their performance through collaborative activities, which benefits

all SC partners. Uninterrupted information flows through the SC assist its partners in

reaching intelligent decisions (Rashed, Azeem & Halim 2010), and also provide insight

into and visibility for SC activities (Coyle et al. 2013).

Power is an important factor that can alter the chemistry of a collaborative arrangement

between SC partners. The most resourceful partner in a SC naturally commands power

over the others due to its resources (Cendon & Jarvenpaa 2001; Cox, Sanderson &

Watson 2001; Gelderman & Van Weele 2004; Svahn & Westerlund 2007). Power is an

important tool in relationship building (Perumal et al. 2012). However, Van Weele and

Rozemeijer (2001) argue that power is an obstacle to collaborative relationships. Also

the capacity of a powerful partner to exert power over the others depends on the type of

SC that they are involved with (Smith 2008). Hence, it can be argued that power

influences SC relationships by influencing various relationship constructs (for example,

satisfaction, collaboration, trust and commitment). Further, SC relationship constructs

are important in establishing close SC relationships (Doney & Cannon 1997; Dorsch,

Swanson & Kelley 1998; Field & Meile 2008; Hewett, Money & Sharma 2002; Morgan

& Hunt 1994). Also, SC relationship constructs are closely associated with and

influence SC relationship success and the performance of firms that are SC partners.

The following sections briefly describe the nine constructs that are included in the

proposed conceptual framework. These constructs are apportioned to three stages:

influential determinants, relationship constructs and performance. According to the

proposed conceptual model, influential determinants influence all the other constructs,

and relationships constructs mediate between influential determinants and performance

(please refer to Figure 3.6).

7

1.1.2 Influential determinants

The degree of collaboration and the strength of relationships between SC partners

depend on several parameters. Information sharing, coercive power and non-coercive

power are identified as being the most important aspects in any SC, hence these are

broadly categorised as influential determinants in this study. These three constructs

have been found to influence all the other SC relationship constructs (i.e. satisfaction,

collaboration, trust, commitment) and also SC relationship success and a firm’s

operational performance (Anderson & Weitz 1992; Handfield et al. 2000; Lee 2000;

Nyaga, Whipple & Lynch 2010).

Information sharing

Transparency is an important aspect in any SC, and particularly in food SC’s, as it

assists in planning future activities. Sharing valuable information between SC partners

greatly assists in achieving transparency in overall SC activities. This allows SC

partners to engage in effective and efficient planning, coordination and execution

throughout the SC. According to Trienekens et al. (2012), transparency can be achieved

through information exchange between SC partners, which then helps in developing

well-coordinated SC activities. As a result, all the stakeholders of the SC are able to

share and understand SC activities without loss, delay or distortion (Deimel, Frentrup &

Theuvsen 2008). Information sharing and coordination between SC partners are

important and strategically necessary in achieving competitive advantage over other

similar SCs (Trienekens et al. 2012). Information sharing in agribusiness type of SCs

assists in improving food quality, product differentiation, logistical and process

optimisation (Van der Vorst 2006). The acceptance of a food product depends on a

combination of factors, namely the price, quality and safety of that product (Trienekens

et al. 2012). These authors further emphasise that the producers of a product, as well as

the partners of the entire food SC, are responsible in influencing these factors. Each SC

partner plays an important and quite different role.

Organisational power

Power influences relationships between SC partners in every SC, but the intensity of the

influence differs owing to various reasons. Also, power is derived through the control of

knowledge (Lado, Dant & Tekleab 2008). Organisational power consists of coercive

8

power and non-coercive power. Non-coercive power is a combination of four types of

power, i.e. reward, legitimate, referent and expert types of power. Dvoracek (2009)

remarks that power relationships are influenced by the level of information sharing

between SC partners. However, Williamson (1981) contends that power arises as a

result of efficient use of resources. Power is derived through the control of resources,

which allows a more resourceful partner to exert power over the others (Pfeffer &

Salancik 1978). These authors further highlight that, power is central to the success of a

firm. Relationships are influenced by power, when SC partners possess unequal

resources (Mikkola 2008), and in many instances the most resourceful SC partner

controls the entire SC. Also, every inter-organisational relationships consists of one

firm, which has greater amount of power than the other partners (Ireland & Webb

2007), and the resources of that firm determine its power position in the SC (Möller &

Svahn 2003; Svahn & Westerlund 2007). Firms create interdependencies with other

firms in an effort to acquire required resources, and as a result these SCs comprise of

partners who are dependent on each other (Pfeffer & Salancik 1978). Smith (2008)

emphasises that the capacity of a SC partner to influence others varies depending on the

SC and its partners. Hence it is necessary to investigate power and the manner in which

it influences relationships in different types of SCs.

1.1.3 Relationship constructs

In this study, satisfaction, collaboration, trust and commitment are constructs that are

used to examine SC relationship. Many previous studies (Doney & Cannon 1997;

Dorsch, Swanson & Kelley 1998; Hewett, Money & Sharma 2002; Morgan & Hunt

1994) identify these constructs as being important in achieving closer SC relationships.

When SC partners are satisfied with each other, they exchange ideas freely between

them (Nyaga, Whipple & Lynch 2010). Satisfaction also assists them to maintain and

continue their relationship (Janvier-James 2012).

Furthermore, close operational activities between SC partners can improve the overall

satisfaction (Schulze, Wocken & Spiller 2008). Collaboration motivates SC partners to

engage in future collaborative activities (Ramanathan & Muyldermans 2011), and it

also creates long-term partnerships in SCs (Ramanathan & Gunasekaran 2014).

9

According to Ciancimino (2010), collaboration (which is achieved through the sharing

of strategic information) assists firms in forming integrated SCs. Panayides and Lun

(2009) identify trust as a pre-requisite for entering into a relationship. Trust is important

in deciding the depth of collaboration, and is a critical element in establishing sound SC

relationships (Matopoulos et al. 2007). A higher degree of trust between firms improves

performance (Lindgreen 2003).

Also, trust positively influences commitment (Hausman & Johnston 2010), satisfaction

(Leonidou, Talias & Leonidou 2008), and performance in inter-firm relationships

(Currall & Inkpen 2002; Ireland & Webb 2007). Palmatier et al. (2006) remark that

commitment positively influences the success of SC relationships. Commitment

towards each other assists SC partners to improve joint actions in relationships

(Hausman & Johnston 2010). Further, commitment between SC partners is essential to

achieve improved relationships in SCs (Van Weele 2009). Hence, it can be stated that

the selected relationship constructs (i.e. satisfaction, collaboration, trust and

commitment) help improve relationships between SC partners, which in turn can

positively influence the performance of these firms.

1.1.4 A firm’s performance

A firm’s performance has been evaluated using various measures, such as relationship

performance, operational performance and financial performance (Corsten & Felde

2005; Nyaga, Whipple & Lynch 2010; Panayides & Lun 2009). However, the

performance in this study (please refer to Figure 3.6) has been evaluated as being a

summation of the SC performance (also identified as SC relationship success) and

firm’s operational performance. Close and well-coordinated SC relationships can

improve the performance of SC partners (Coyle et al. 2008; Karia & Razak 2007; Min

et al. 2005; Rinehart et al. 2004). The success of relationships improves as a result of an

environment which is conducive to SC relationship success (Kannan & Tan 2006).

Satisfaction is important (Field & Meile 2008), and collaboration, trust and commitment

are central in achieving SC relationship success (Kannan & Tan 2006). Information

sharing positively influences SC relationship success (Anderson & Weitz 1992; Kwon

& Suh 2004). Also, coercive power negatively influences SC relationship success

10

(Benton & Maloni 2005; Brown, Johnson & Koenig 1995; Leonidou, Talias &

Leonidou 2008; van Weele & Rozemeijer 2001). Conversely, non-coercive power

positively influences SC relationship success (Gelderman, Semeijn & De Zoete 2008;

Lunenburg 2012). As a result, it can be stated that information sharing, coercive power,

non-coercive power and relationship constructs are closely associated with the success

of SC relationships.

On the other hand, closer relationships between SC partners are associated with the

improved performance of firms that are SC partners (Duffy & Fearne 2004a; Johnston

et al. 2004; Kotabe, Martin & Domoto 2003; Stank, Keller & Daugherty 2001).

Information sharing between SC partners improves a firm’s operational performance (Li

et al. 2006a), coercive power negatively affects a firm’s operational performance

(Brown, Johnson & Koenig 1995; Lai 2007; Ramaseshan, Yip & Pae 2006), and non-

coercive power positively influences (Arend & Wisner 2005; Jonsson & Zineldin 2003;

Lai 2007) a firm’s operational performance. The success in SC relationships improves a

firm’s operational performance (Benton & Maloni 2005; Maloni & Benton 2000;

Narasimhan & Nair 2005), and long-term collaborative relationships improve the

performance of SCs (Field & Meile 2008; Grewal, Levy & Kumar 2009; Singh &

Power 2009). Hence, information sharing between SC partners, coercive power, non-

coercive power and success of SC relationships are important aspects in influencing the

performance of firms that are SC partners.

1.2 The context of this study

The global fruit and vegetable market was estimated at USD 1483bn in 2014 (Fruit and

Vegetables: Global Industry Guide 2015). Further, the industry is growing at a

compounded annual growth rate (CAGR) of 5.9%, and is expected to reach a value of

USD 1971bn in 2019. According to Rowley (2011), this industry is expected to grow

continuously as consumers are searching healthier food options due to health concerns.

As a sub sector of the fruit and vegetable industry, the global organic fruit and vegetable

industry is worth AUD 91bn in 2014 (Australian Organic Market Report 2014). This

industry is continuously growing, and expected to reach AUD 102bn in 2016 at a

compounded annual growth rate (CAGR) of 5.9%. Due to its importance to consumers

11

in their everyday lives, the organic fruit and vegetable industry survived even the global

recession, and reported a double digit growth rate between 2006 and 2008. According to

the Australian Organic Market Report (2014), this industry is expected to grow much

faster than the conventional fruit and vegetable industry. The global organic fruit and

vegetable exports are fifth on the list of top exported products behind meat, processed

foods, dairy, and wine and beverages. North America, Europe, Singapore/Malaysia,

Hong Kong and Japan are the top five exporting countries in the world in 2014

(Australian Organic Market Report 2014).

The logistical and distribution bottle necks negatively affect many industries, and

specially the fresh produce industry, where it is necessary to minimise the time spent on

transportation to improve the quality of produce. As Henderson (1994) remarks,

packaging and intermodal services are two of the most important issues concerning the

development of the fruit and vegetable trade. Grunow and van der Vorst (2010)

emphasise that the production and distribution management in food SCs are intrinsically

dynamic owing to the perishability and quality variations of such products. Hence, there

are important implications for the management with regard to their sorting, processing

and distribution (Trienekens et al. 2012). Moreover, due to the different perspectives of

SC partners and consumers relating to product attributes, there are extra challenges in

aligning SC processes (Linnemann et al. 2006). Also, the operational activities of food

SCs (especially agribusiness SCs) are inherently different to other product SCs. As a

result, SC partners need to take extra care in ensuring that accurate information is freely

available to all the SC partners in order to plan, coordinate and execute an uninterrupted

product flow.

12

1.3 Significance of this study

This study has both academic and practical implications.

1.3.1 Academic implications

As explained previously, there are several gaps in the extant literature. Hence, there are

opportunities to explore various aspects of SCs in order to advance the theoretical

foundation of SCM. Firstly, this study focuses on building a comprehensive model

incorporating influential determinants (information sharing, coercive power and non-

coercive power), relationship constructs (satisfaction collaboration, trust and

commitment) and performance (SC relationship success and a firm’s operational

performance) in a single model. This model can be used to advance the knowledge of

SCs in different industry sectors, in different geographical locations and with different

infrastructure facilities.

The unique proposed model conceptualises the selected constructs to be apportioned to

three stages. Stage one consists of the influential determinants, stage two the

relationship constructs, and stage three the performance, which includes SC relationship

success and an SC firm’s operational performance (please refer to Figure 3.6). As a

result of this unique three-stage conceptual model, and due to various interrelationships

proposed between its individual constructs, several new relationships were revealed.

Many of these relationships were tested and empirically verified. Hence, this study

provides opportunities in conducting new research studies targeting different study

contexts, which would assist in advancing the SCM literature.

The influence of power and information sharing working alongside in a single model

has been under researched in the past. Hence this study addresses that gap. Also, the

unique conceptual model in this study can be incorporated with additional constructs in

order to create a more comprehensive conceptual model for future research studies.

13

1.3.2 Practical implications

This study addresses the very specific issues relating to the SCs of the organic fruit and

vegetable sector. The beneficiaries of this study are various stakeholders including SC

intermediaries, growers, policy makers and consumers. The following are few practical

implications. The results indicate that information sharing enhances the SC relationships

and importantly the firm’s operational performance. This means that sharing of valuable

information between SC partners is important and aids in improving the performance of

SC firms in the organic fruit and vegetable industry. The information shared includes

market information such as preferences and the buying trends of the consumers and the

anticipated demand by analysing previous data. Retailers are also able to share technical

information including ordering, packing and transportation which helps SC partners to

plan and execute the orders more efficiently and cost effectively.

Coercive strategies (example: financial penalties) which are used to handle SC partners

adversely affect the relationships, while non-coercive strategies like offering special

incentives enhance the SC relationships. Hence it is suggested that the firms use more

non-coercive strategies in dealing with their SC partners and to reduce coercive

strategies in dealing with day-to-day activities. Also, the organic fruit and vegetable

growers may perceive the non-coercive strategies such as ‘rewards for compliance’ by

their major SC partners as opportunistic tools to attract them, and as a result it weakens

SC relationships. This may cause the growers to move away from the present

relationship towards competition. Hence, powerful major SC partners of the growers

need to carefully craft their relationship strategies.

Relationship constructs (satisfaction, collaboration, trust and commitment) positively

mediate the relationships between influential determinants (information sharing,

coercive power and non-coercive power) and performance (SC performance and firm’s

operational performance). Hence, SC partners can carefully craft strategies to improve

SC relationships which in turn would enhance the effects of influential determinants on

performance. Also the organisational characteristics revealed a very small percentage of

growers are involved in exporting their products. Due to the enormous market

opportunities for Australian grown fruit and vegetables in overseas markets owing to

14

their superior quality, it is viable to develop this aspect of the trade to substantially

increase the export market share in organic fruits and vegetables.

Following sections briefly introduce the proposed conceptual framework, the primary

research question, secondary research questions and the related hypotheses of this study.

1.4 Conceptual framework, research problem/questions and hypotheses

Several previous studies (Corsten & Felde 2005; Leonidou, Talias & Leonidou 2008;

Nyaga, Whipple & Lynch 2010; Panayides & Lun 2009) have been instrumental in the

development of the proposed conceptual model of this study. Based on these previous

studies and on the literature review, the relationships between the constructs were

formalised, and the conceptual model consists of three stages (please refer to Figure

6.2). The influential determinants influence all the other constructs, SC relationship

constructs mediate the relationships between influential determinants and performance

(which comprises of SC relationships success and a firm’s operational performance).

The primary or overarching research problem identified in this study can be summarised

thus: ‘How do power and information sharing in combination with satisfaction,

collaboration, trust and commitment influence the SC relationship success and

operational performance of firms in the organic fruit and vegetable industry?’

Eight secondary research questions were formulated based on the conceptual model.

These research questions and related hypotheses are as follows:

Research question 1 - Does information sharing positively influence the SC

relationship constructs (collaboration, satisfaction, trust and commitment), SC

relationship success and firm’s operational performance? There are six hypotheses (i.e.

H1a, H1b, H1c, H1d, H1e and H1f) associated with this research question.

15

Research question 2 - Does coercive power negatively influence the SC relationship

constructs (collaboration, satisfaction, trust and commitment), SC relationship success

and a firm’s operational performance? Research question 2 is addressed by six

hypotheses, i.e. H2a, H2b, H2c, H2d, H2e and H2f.

Research question 3 - Does non-coercive power positively influence the SC

relationship constructs (collaboration, satisfaction, trust and commitment), SC

relationship success and a firm’s operational performance? This research question is

addressed by hypotheses H3a, H3b, H3c, H3d, H3e and H3f.

Research question 4 - Does satisfaction of SC partners positively influences the SC

relationship success? Hypothesis H4 is associated with research question 4.

Research question 5 - Does collaboration between SC partners positively influence SC

relationship success? Research question 5 is addressed by hypothesis H5.

Research question 6 - Does trust between SC partners positively influence

commitment and SC relationship success. There are two hypotheses (i.e. H6a, and H6b),

which are associated with research question 6.

Research question 7 - Does commitment of SC partners positively influences SC

relationship success? This research question is addressed by hypothesis H7.

Research question 8 – Is SC relationship success positively related to a firm’s

performance? This research question is addressed by hypothesis H8.

1.5 Methodology

This study utilises the “positivist epistemological assumption” to advance the

knowledge of the performance of firms in SCs of the organic fruit and vegetable

16

industry. A total of nine constructs have been identified as being important for the

study. These nine constructs were used to develop the conceptual framework, which

consists of three broad stages: influential determinants (information sharing, coercive

power and non-coercive power), SC relationship constructs (satisfaction, collaboration,

trust and commitment), and performance (SC performance and firm’s operational

performance). This study adopts a quantitative methodology, and uses an online survey

to collect the required data. As detailed in Chapter 4, the survey is a web-enabled self-

completion questionnaire, which allows potential respondents to complete it in their

own time. The scales used to measure the nine constructs were previously validated and

tested several times by researchers in different study contexts. The sampling frame of

927 organic fruit and vegetable growers yielded 287 usable responses. These responses

were analysed using SEM (specifically path analysis).

1.6 Outline of Chapters

This study consists of six chapters. The following sections briefly describe each of these

chapters. Figure 1.3 presents the roadmap of this study.

Figure 1.3 Road map for the study

Chapter 2 – Literature review

Chapter 1 – Introduction and background of the study

Chapter 3 – Conceptual model and development of hypotheses

Chapter 4 - Methodology

Chapter 5 – Analysis and findings

Chapter 6 – Discussion and conclusion

17

Chapter 1 – Introduction and background to the study

This chapter presents the introduction and background to this study. First it summarises

the context and briefly describes the SCs in the organic fruit and vegetable sector. This

chapter also introduces the main research problem, and the academic and practical

justifications of this study. The chapter moves on to present the conceptual framework

with the related research questions. The chapter concludes by briefly explaining the

study’s methodology, followed by the chapter outline.

Chapter 2 – Literature review

This chapter discusses previous literature pertaining to SCM, the different SC

perspectives and the theories that are used to understand the behaviour of SCs. There is

a discussion about the organic fruit and vegetable industry, and its SCs. The chapter

then focusses on organisational power and information sharing and their role in SCs.

Finally it discusses SC relationship constructs (satisfaction, collaboration, trust and

commitment), SC relationship success and a firm’s operational performance.

Chapter 3 – Conceptual model and development of hypotheses

This chapter endeavours to establish the logical linkages between the key identified

constructs investigated in this study. The conceptual model is developed based on the

literature. The chapter then presents the primary and secondary research questions. The

associated hypotheses are enumerated in relation to the secondary research questions.

Lastly, the chapter presents the proposed conceptual model of this study.

Chapter 4 - Methodology

This chapter introduces the general research design and explains the deductive and

inductive approaches, objectivism, and positivism. The chapter moves on to present

detailed descriptions of the survey instrument, which includes measurement scales,

validity, pre-testing and the structure of the final survey instrument. This chapter also

presents details about data collection and analysis of data, together with ethical

considerations for this study.

18

Chapter 5 – Analysis and findings

This chapter is a particularly important part of this study, in that it presents the analysis

and findings. The first section describes the procedures adopted in data preparation. The

next section presents respondents’ profiles, followed by the procedures of data analysis.

The chapter also includes descriptions of fit indices, and then explains construct validity

and reliability. After performing CFA for one factor congeneric models, the overall

CFA is then conducted in two stages. The chapter discusses structural equation

estimation, which includes path analysis, initial model and then develops the final

model after re-specification of the initial model. The final section of this chapter

presents the mediation which includes direct, indirect effects and total effects based on

the final best-fit model.

Chapter 6 – Discussion and conclusion

This chapter discusses the findings based on the three stages of the conceptual

framework: influential determinants, SC relationship constructs and performance. There

is a discussion of mediation, which (it will be argued) helps explains indirect effects on

SC relationship success and a firm’s operational performance. This chapter presents the

theoretical contributions and practical /managerial implications followed by the

limitations of this study. The final section of this chapter suggests directions for future

research.

19

Chapter Two: Literature review

20

2 Introduction to chapter two This chapter aims to critically review previous literature pertaining to SCs, different

perspectives and theoretical bases encompassing relationships between SC partners, SC

relationship success and firm’s operational performance. Section 2.1 introduces a SC,

describes SC relationships, cooperation, competition and interdependence of SCs, and

introduces the context of this study. Section 2.2 reviews the literature on SC relationship

success and a firm’s operational performance. Relationships between SC partners are

described in Section 2.3. Finally in Section 2.4, the bases of power, as well as the

importance and influence of power in SCs, are discussed.

Section 2.5 discusses underpinning theories, which includes descriptions of resource

based view (RBV), transaction cost economics (TCE) theory, and network theory (NT),

and reviews the application of these approaches within SCs. Information sharing and its

influence in SCs is discussed in Section 2.6. Relationship constructs (satisfaction,

collaboration, trust and commitment) are discussed in Section 2.7, followed by a

summary and gaps of the literature review in Section 2.8. Section 2.9 introduces the

research problem. Finally Section 2.10 provides a summary to the chapter. The road

map for this chapter is presented in Figure 2.1.

21

Figure 2.1 Road map for the literature review chapter

2.1 A supply chain: its definition and purpose

2.0 Introduction to chapter

2.2 SC relationship success and firm’s performance

2.4 Organisational power

2.3 Relationships among partners of the fruit and vegetable SCs

2.5 Theoretical underpinnings

2.7 Relationship constructs

2.6 Information sharing and its influence in SCs

2.8 Summary and gaps in previous literature

2.9 Research problem

2.10 Chapter summary

Cooperation, competition and interdependence of

SCs

SC relationships

Context of this study

Relationship success

Firm’s performance

Bases of power

Importance of power and its influence in fruit and

vegetable SCs

TCE theory and its application within SCs

RBV theory and its application within SCs

NT and its application within SCs

Satisfaction

Trust

Collaboration

Commitment

SC integration

22

2.1 A supply chain (SC): Its definition and purpose

Traditionally, SCs are generally considered to refer to the process of moving materials

and goods, and are viewed as a support function that assists organisations to implement

their strategies (Ketchen & Hult 2007). In the early days, goods were exchanged

between multiple firms and price was the sole determinant of a particular transaction

(Wilson 1996a), which ensured that the buying firm achieved the lowest possible cost.

As such, this view of a SC was based on achieving the lowest possible cost of the final

product (Spekman, Kamauff Jr & Myhr 1998). These authors further suggest that,

multiple partners, arms-length negotiations, formal short-term contracts, evaluation of

partners based on purchase price of goods, and cost-based information bases were some

of the characteristics of traditional SCs. Shin, Collier and Wilson (2000) view this

earlier concept of SCs as a traditional independent procurement and distribution channel

facilitated by independent organisations. Hence, the main function of earlier SCs was to

decrease the product cost, and as a result these SCs did not experience long-term and

closer relationships between their partners. In other words, the earlier SCs apparently

failed to appreciate the strategic importance of long-term relationships as compared to

transactional relationships, which are generally cost-based and short-term. Later, after

identifying the capabilities of a SC as a strategic tool in achieving competitiveness, the

importance of a SC shifted to a new paradigm. Ketchen and Hult (2007) remark that the

SC nowadays is not merely a logistical tool, but has moved from a “function that

supports strategy to a key element of strategy.”(p. 574).

The SC was redefined as a process which designs, develops, optimises and manages the

internal and external components of the supply system (Spekman, Kamauff Jr & Myhr

1998). This process also includes supply of materials, transforming them to finished

products and then distribution of goods and services to customers. With these

developments in mind Christopher (2011) describes the present SCM process as

follows:

The management of upstream and downstream relationships with suppliers and

customers in order to deliver superior customer value at less cost to the supply

chain as a whole (p.3)

This perspective emphasises the value of long-lasting relationships in improving the

overall customer service experience.

23

According to Shin, Collier and Wilson (2000) SCs nowadays consist of interdependent

firms working towards the efficiency of the entire SC. Cooper and Ellram (1993) define

the SC management process as: “An integrative philosophy to manage the total flow of

a distribution channel from the supplier to the ultimate user” (p.13). Today SCs are

considered as one of the most important competitive tools in the global market place.

Many organisations depend on SCs for achieving lower cost, improved quality, less

time to market, and also in gaining valuable market information. Cooper et al. (1997)

emphasise the development of an integrative company management philosophy that is

used to assist in managing the “total flow of channel from the earliest supplier of raw

materials to the ultimate customer, and beyond, including the disposal process” (p.68).

This was reaffirmed by Shaffer, Dalton and Plucinski (2011) who describe the

important corporate role that SCs eventually play in organisations by reshaping the

businesses.

Cooper et al. (1997) assert that each partner in the SC channel directly or indirectly

affects the other partners and as a result performance of the entire SC is dependent on

the level of interrelationships. This leads to the opinion that a supply chain functions as

an integrated whole. As summarised by Cooper and Ellram (1993) the traditional

channel relationships are different to the present day practices in SCs. Anderson and

Narus (1991) suggest that SC relationships nowadays are closer and stronger than in the

past, and they achieve higher levels of inter-firm communications and coordination.

Also, the cost and inventory controls in SCs nowadays are considered to be every

partner’s responsibility. However, in the past these were the responsibilities of

individual partners. Table 2.1 compares some of the elements that distinguish present-

day SCs from traditional ones.

24

Table 2.1 Comparison of present versus traditional SCs

Element Traditional Channel SC

Inventory management

approach Independent efforts

Joint reduction in channel

inventories

Total cost approach Minimize firm costs Channel-wide cost efficiencies

Time horizon Short term Long-term

Amount of information sharing

and monitoring

Limited to needs of current

transaction

As required for planning and

monitoring processes

Joint planning Transaction-based On-going

Compatibility of corporate

philosophies Not relevant

Compatible at least for key

relationships

Breadth of supplier base Large to increase competition

and spread risk

Small to increase the

coordination

Channel leadership Not needed Needed for coordination focus

Amount of sharing risks and

rewards Each on its own

Risks and reward shared over

the long term

Speed of operations,

information and inventory

flows

“Warehouse orientation”

(storage, safety stock)

interrupted by barriers to flows;

localized to channel pairs

“DC” orientation (inventory

velocity) interconnecting flows;

JIT, quick response across the

channel

Information systems Independent Compatible, key to

communications

Notes: DC = Distribution Centre, JIT = Just In Time

Source: Cooper and Ellram (1993)

Cooper and Ellram (1993) demonstrate that joint planning in a SC is a continuous

process, whereas in a traditional channel it was transaction based. In traditional SCs, a

firm chose its partners based on each transaction, and this failed to result in long lasting

mutually beneficial relationships. Further, traditional channels tried to achieve the

lowest possible cost, but nowadays SCs endeavour to achieve channel-wide cost

efficiencies. As depicted in Table 2.1, the inventory management in a traditional

channel depends on each partner’s efforts, and stays within the boundary of that

particular firm. However nowadays in SCs, the firms jointly plan and execute the

inventory management and they are jointly responsible for its efficiency. Similarly, the

information systems were managed by every firm in traditional channels, whereas SCs

nowadays ensure that information systems are compatible and are extremely important

in communications with their partners. Such initiatives assist engaged firms to be

25

transparent, which leads to greater collaboration within the SC. Essentially one of the

firms spearheads the total operation as the active leader which enables the smooth

coordination of SC activities of the channel (Cooper & Ellram 1993).

Every commodity which reaches the retailer’s shelf has arrived there owing to activities

in a SC. The SC connects the total flow of channel from the earliest supplier of raw

materials to the final consumer, and it also stretches and connects the disposal process

(Cooper et al. 1997). This connectivity offers strategic importance to a SC, hence its

earlier view of moving materials and goods from one place to another, has changed to

being a tool which can influence competitive advantage (Ketchen & Hult 2007).

According to Shaffer, Dalton and Plucinski (2011), competitive advantage is

determined by the quality of products, service standards, customisation, and faster

delivery to customers, who are scattered in different geographical locations.

Significantly, many of these requirements are influenced by the SC. Hence, the SC

becomes an important part of any commodity in reaching its customers, and it also

assists in providing the desired service standards to them.

A SC comprises of many partners, and the purpose of this connectivity is to provide

services which include making products available in a desired package, at best possible

quality, at a preferred location, and at the right time. In an effective and efficient SC,

these services are provided at the lowest cost through collective effort of several SC

partners (Shaffer, Dalton & Plucinski 2011). In doing so the role played by SC partners

is of paramount importance as sophisticated consumers continue to demand improved

service standards. In this respect Christopher (2011) remarks that relationships among

SC partners are valuable in meeting improved service standards at the lowest cost.

Hence, it is imperative that a SC fosters strong relationships among its partners.

The relationships between SC partners have evolved over the years. The transaction

relationships which were based on cost-related decisions evolved to strategic alliances,

where SC partners work as a dedicated team to provide sophisticated consumer service

standards. Figure 2.2 depicts the transaction cost based relationships of the early 1990s,

which have evolved and improved as value delivery networks in the present day through

strong alliances among SC partners (Giunipero et al. 2008).

26

Source: compiled from Giunipero et al. (2008)

Figure 2.2 Evolution of SC relationships

The earlier SCs were based on lowest cost transactions, however in recent decades they

have evolved into becoming value delivery networks. These SCs are important in

achieving improved customer service and competitive end user costs for their products.

Nowadays SC partners co-operate and depend on each other in order to achieve

improved service levels. Another important aspect of present day SCs is long-lasting

relationships among their partners. As a result of these strong relationships the SC

partners are all winners.

2.1.1 SC relationships

In the competitive market today, the traditional firm-to-firm competition is not

considered a viable strategy. Instead firms tend to create unique SCs and compete with

other similar SCs. As a result, individual firms (a partner of a SC) achieve cost

advantages and are also able to reduce the time taken for their products to reach the

market. In this respect, Yew Wong and Karia (2010) demonstrate that SC partners plan

and jointly execute logistical activities through better understanding and commitment

among its partners, which they gain through effective relationships. According to these

authors, it is nearly impossible to replace long-term working relationships in SCs.

Early 1990s Minimising transactions costs in the buyer supplier interactions

Early 2000s Firms changed their focus and perspective to a more relationship oriented

approach to SCM

Present Day Firms stressing a value delivery network which is based on strong alliances

alongside significant vertical and horizontal integration

27

Scholars are divided on their views about relationships among SC partners. Since speed,

reliability, cost reduction, improved quality, and flexibility are directly related to closer

relationships among SC partners, several authors (Karia & Razak 2007; Mentzer, Min

& Zacharia 2000) argue that relationships are of paramount importance in order to

achieve better SC performance. Min et al. (2005) remark that mutual trust and

commitment to each other in SCs is crucial in building and maintaining long-term

relationships, which eventually lead to achieving better performance. As described

earlier in the chapter, the SC partners become interdependent on each other and their

relationships are predominantly long-term. These relationships are conceptualised as

being on a continuum which ranges from being primarily transactional at one end to

highly collaborative at the other (Duffy & Fearne 2004a). The main characteristics of

partnerships versus traditional arms-length relationships are enumerated in Table 2.2.

Table 2.2 Key characteristics of partnerships versus traditional relationships

Traditional arms-length relationships Partnerships

Short term focus on individual transactions

Buying decisions made on price

Many suppliers

Low interdependence

Haphazard production and supply schedules

Limited communication restricted between sales

and purchasing

Little co-ordination of work processes

Relationship specific investments avoided

Information is proprietary

Clear delineation of business boundaries

Use of threat to resolve disputes

Unilateral improvement initiatives

Separate activities

Dictation of terms by more powerful firm

Adversarial attitudes/combat

Conflicting goals

Behave opportunistically

Act only in own interest

Win-lose orientation

Commitment to long-term relationships

Buying decisions made on value

Fewer selected suppliers

High interdependence

Order driven production and supply

Open communications facilitated by multi-

level/multifunctional relationships

Integration/ co-ordination of work processes

Increases in relationship specific investments

Information is shared

Creation of inter-company teams

Joint problem solving approach to conflicts

Continuous joint improvement sought

Engage in joint activities

Joint decision making

Co-operative attitudes/teamwork

Compatible goals

Mutual trust exists

Act for mutual benefit

Win-win orientation

Source: Adapted from Duffy and Fearne (2004a)

28

Several advantages accrue to SC partners, when their relationships transition from

traditional arm’s length mode to a closer collaboration. The main objective of

collaborative relationships is to gain competitive advantage over other similar SCs. As

opposed to short-term cost based individual transactions, the collaborative SCs create

long-term relationships which focus on win-win outcomes (Duffy & Fearne 2004a). As

highlighted in Table 2.2, collaborative SC partners make joint decisions, engage in joint

activities, share information freely, coordinate their work processes, have compatible

goals, and most importantly, act for their mutual benefit. Further, according to Duffy

and Fearne (2004a), the purchasing decisions are made based on value and not the price

of products, which therefore elevates them to a competitive position in the market

compared to other similar SCs.

SCs have become the basis for competition between firms, with developments in

globalised trade. For this reason, relationships between SC partners have become

extremely important. This is especially true in the fresh produce trade, where major

buyers have reduced the number of their suppliers in order to achieve strong

competition in the market, and in the process they seek long-term relationships with key

suppliers (Hughes & Merton 1996). Dimitri (1999) suggests that disputes among SC

partners can be solved swiftly and easily with minimum communication, and also with

minimum losses to both parties, when they deal with consistent trade partners. This is

one of the major advantages when there all collaborative partners in the SC of a fresh

produce trade, as the goods are of a perishable nature. In this trade, the relationships

between SC partners facilitate shared resources and important activities between them.

As concluded by Manikas and Terry (2009), partners build short-term relationships in

many perishable SCs, that lead value adding activities like quality control and

packaging to be moved downwards the SC. As a result, the SC is able to achieve

efficient handling of these important activities at warehouses and distribution centres

(Manikas & Terry 2009).

Interestingly, long term relationships have not always helped achieve better results in

SCs. In this respect, Batt (2003) concludes that fresh produce growers’ trust towards

preferred market agents has sometimes decreased with the increased duration of the

relationship between them. This suggests a negative rather than a positive correlation

between trust and the duration of a relationship. The reason for this negative correlation

29

is due to some market agents’ opportunistic desire to take advantage of increased

familiarity with SC partners, which makes the relationship costlier to stay in (Batt

2003).

2.1.2 Cooperation, competition and interdependence of SCs

Cooperation and competition between partners of a single SC are a widely-discussed

phenomena in the SC literature (Christopher & Towill 2000; Ganesan et al. 2009;

Langley et al. 2007; Power 2005; Wilson 1996a; Yew Wong & Karia 2010).

Competition among SC partners can be simply explained as the tendency of an

individual SC partner to act opportunistically to obtain more profits than other partners

in the same SC. In doing so, that particular SC partner neglects the competition from

other similar SCs. As a result, the entire SC may become uncompetitive when compared

to other similar SCs. Opportunism can exist regardless of whether a firm is a SC partner

or not. However, the intensity of the relationship between SC partners can help the

entire SC to achieve strong results and a competitive position in the market as compared

to other similar SCs. Ketchen and Hult (2007) remark that the best value SCs address

opportunism by creating long-term trusting relationships that benefit all SC partners.

Competition between partners of the same SC can negatively influences that SC’s

performance. Ketchen and Hult (2007) suggest that short-term purchasing decisions that

are made reduce transaction costs, are sometimes short-lived. This is owing to lower

quality of outsourced items, which is a result of the opportunistic behaviour of short-

term suppliers. These authors also suggest that these SC partners tend to mistrust each

other and as a result, relationships become short lived. The opportunistic behaviour also

creates long-term effects where short-term suppliers are excluded from SCs unless the

products they offer are unique and not substitutable. Some researchers (Grover &

Malhotra 2003; Halldorsson et al. 2007) reveal that there is a considerable cost

increment to SC partners, when they try to minimise negative effects of opportunism

through controls such as audits and penalty contracts. As a result, the cost increases and

the competitiveness of SC decreases compared to other similar SCs. Therefore, it can be

stated that cooperation among partners of their SC is more beneficial than competition

between them.

30

The efforts of an individual firm to gain competitive advantage in highly aggressive

markets can become ineffective for many reasons. The main reason for this

ineffectiveness is the single firm’s inability to acquire a variety of resources to compete

with resource-rich SCs. In order to avoid this inherent disadvantage, these individual

firms create relationships with resourceful firms to access scarce resources. As a result,

effective supply chains that enhances an individual firm’s competitiveness when

compared to similar SCs are developed. Then, these newly formed resource rich SCs

can compete effectively with other similar SCs within the same market conditions.

Reaffirming this, White (2000) remarks that SC partners need to cooperate among

themselves to be more effective and efficient than the other SCs. As such, Christopher

and Towill (2000) remark that a network of SC partners that can create better structures

and co-ordinate and manage better relationships can improve competitive advantage

over similar SCs which have weak SC relationships. Moreover, stronger relationships

between SC partners tend to create more service oriented SCs, that can conquer and

maintain competitive advantage over other similar SCs (Langley et al. 2007; Power

2005).

Interdependence is common between individual organisations. Irrespective of the type

of commodity that they are involved with, the SC interdependence can be a major

advantage in order to achieve an efficient, effective and competitive SC. Wilson (1996a)

describes a new paradigm of business model, where a SC competes against other similar

SCs and in doing so all partners consolidate their skills and resources and, plan and

coordinate them in order to facilitate a constant and smooth flow of goods to end users.

As Wilson (1996a) further explains, the interdependence of such SC partners is at its

highest level. Further, Kumar (1996) suggests that in retailer-manufacturer

relationships, the company’s level of trust and satisfaction is at its highest, and the level

of perceived conflict is lowest when there is a high level of interdependence with its

relationship partner.

SCs continue to increase their competitive advantage by integrating resources and

capabilities through closer SC relationships among their partners. As Ganesan et al.

(2009) emphasise, SC partners give more importance to the overall SC itself than their

individual firms, in order to achieve competitive advantage over other SCs. Similarly,

closer relationships can influence a SC to achieve improved operational performance,

31

and as a result it can deliver greater service to consumers than its competitors (Kannan

& Tan 2007; Sila, Ebrahimpour & Birkholz 2006; Trent 2004; Zsidisin & Smith 2005).

Reaffirming this, Brown et al. (2005) remark that efficient SCs can satisfy their

customers’ needs, and grow their market share through improved service standards,

such as making available the right merchandise at the right place in right quantities and

at the right time. More recently, Yew Wong and Karia (2010) found that SCs whose

partners have long-term relationships jointly execute their plans in order to achieve

improved service standards. Further, these authors argue that it is nearly impossible to

replace these long-term and well established working relationships of SCs. Hence, it can

be stated that the relationships between SC partners are important, and they can

positively influence the competitiveness of SCs.

2.1.3 SC integration

An integrated SC involves forming essential relationships with skilled and capable

partners in ensuring that they all reach outstanding performance levels, as a result they

can successfully compete with other SCs (Henkoff 1994). According to Fawcett and

Magnan (2002), firms create SC partnerships, align their objectives and integrate their

resources in ensuring that they are able to provide greater value as a SC, which is a

result of SC integration. According to Richey et al. (2010), “integration is the natural

outcome of effective interorganisational management” (p. 238). Fawcett and Magnan

(2002) identify four types of integration. The first is internal, which is cross-functional

process integration. The second is backward integration with first-tier suppliers. The

third is forward integration with first-tier customers. The fourth is complete forward and

backward integration, which is associated with SCM. However, Chen, Daugherty and

Roath (2009) identify two major types of integration. The first is internal integration,

which is within a particular firm’s boundaries but across separate firm functions, while

the second is external integration, which occurs across different firms (for example: SC

partners).

Gimenez and Ventura (2005) argue that SC integration positively and directly influence

performance. Also, external collaboration with SC partners is linked to integration,

which positively impacts logistics performance (Stank, Keller & Daugherty 2001), and

collaborating with SC partners aids integration, which assists in improving the financial

32

performance (Corsten & Felde 2005). Although SC integration is important in achieving

a positive impact on performance, many firms neglect the need to engage in governance

efforts that promote SC integration (Fawcett & Magnan 2002). However, this study

considers information sharing, organisational power (which consists of coercive and

non-coercive power), SC relationships constructs (i.e. satisfaction, collaboration, trust

and commitment) collectively assist in achieving SC integration, which in turn

positively influences a firm’s performance (which consists of SC performance and the

firm’s operational performance).

2.1.4 Context of this study

The global fruit and vegetable industry

The fruit and vegetable industry is a subsector of the horticulture industry, which

includes flowers, nuts, turf and nursery products other than fruit and vegetables

(Department of Agriculture 2015). The global fruit and vegetable market is expected to

reach USD 735 billion in 2015, which represent a 25% growth over five years

(IBISWorld Pty Ltd 2015). According to IBISWorld, in volume this is 690 million tons

in 2015, which is an increase of 5% compared to 2010. Further, MaketLine (2014)

forecast suggest that the vegetables represent 65% of the overall market. According to

Food and Agriculture Organization of the United Nations (2015) the total export trade

of fruit and vegetables generate USD 45 billion.

Rowley (2011) remarks that, although the industry is hardy and viable, there are

challenges in the industry in terms of supply shortages, rising input costs, and

dominance of mega-retailers over the SC. These issues have forced less efficient fruit

and vegetable operators out of the industry, due to their inability to meet the high

quantity and quality specifications of the industry’s largest buyers (Rowley 2011).

Higgins et al. (2011) remark that fruit and vegetable transportation involves complex

spatial and dynamic networks incorporating various factors. These factors include

specialised transportation needs, multiple SC paths, long SCs with multiple stages of

processing/distribution, multiple modes, and climate variability, which make this a

highly complex SC. In this industry, sometimes the locally grown fruit and vegetables

become attractive to consumers compared to imported or interstate transported fruits

33

and vegetables, due to adverse environmental effects of long distance transportation

(Marquez, Higgins & Estrada-Flores 2015). Hence, the fruit and vegetable growers

continuously need to improve the quantity and quality of their produce to compete

locally. The initiatives like developing improved packaging, transportation techniques,

which protect freshness throughout the journey from farm to retailers’ shelf, and

developing state of the art warehouses are able to assist in controlling quality

deterioration.

Rowley (2011) assert that although the vegetable consumption will decrease during the

next five years the overall fresh produce consumption (which is combined fruit and

Vegetable consumption) will increase due to the rising fruit consumption.

Global organic fruit and vegetable industry

The organic movement developed during the 1960s and 1970s as a result of growing

concerns about man-made changes to the natural environment (Pearson, Henryks &

Jones 2010). Only in 1990, though, did organics receive formal recognition as a food

production system by many national governments. According to Halpin (2004),

“organic agriculture is originated with the voluntary efforts of like-minded farmers” (p.

1). The industry mainly consists of not only farmers, but processors, retailers,

wholesalers, input providers, and certifying organisations. The organic fruit and

vegetable growers mainly sell their products to wholesalers, retailers and direct to

consumers, and majority of these SC relationships have been long established (Halpin

2004). Also, this industry is small and fragmented, but there are new SC patterns

emerging, and it is also evidence that the production of organic fruits and vegetables are

demand-led rather than production-driven. Halpin (2004) further explains that

horizontal collaboration is weak in the organic fruit and vegetable sector, which

severely impacts the industry’s ability to produce the required quantity, quality and

consistency to sustain domestic and international markets.

Globally, there are 2 million producers involved in organic growing, and 43.1 million

hectares of agricultural lands (which is 1% of the world’s agricultural lands) are

managed organically (Arbenz et al. 2015). The global organic market value has

increased by AUD 28 billion between 2009 and 2014 to reach AUD 91 billion with a

34

compounded annual growth rate (CAGR) of 7.7% during this period (Australian

Organic Market Report 2014). This report also suggests that the global organic market

value is forecasted to reach 102bn in 2016, with an anticipated CAGR of 5.9% between

2014 and 2016. According to Australian Organic Market Report (2014), the world’s

largest organic food markets are North America, which is valued at AUD 44bn (52.2%

of global market) and Europe, which is valued at AUD 35bn (41.5% of global market).

Further, the organic fruit and vegetable industry grows faster than the conventional fruit

and vegetable sector (Bez et al. 2012).

Pearson, Henryks and Jones (2010) emphasise that main reasons to buy organic foods

are personal health, product quality and concerns about degradation of the natural

environment, although the ranking of these reasons differ depending on specific cultural

and demographic factors. Further, the organic consumers consider that quality of the

organic fruit and vegetables are superior and its SC delivers a fresher product (Pearson

& Henryks 2008). However, these authors also remark that the quality of the organic

fruits and vegetables substantially depend on its particular SC, and especially how the

SC partners coordinate between each other to achieve lowest possible lead-time.

Pearson, Henryks and Jones (2010) remark that apart from established retailers, organic

produce outlets are also emerging as a result of community-level interest in creating

them. According to these authors, cooperative business structures, independent outlets,

farm shops, markets, community-supported agriculture schemes and productive gardens

are popular in selling organic produce.

The supply chain plays an important role in maintaining the quality of fruit and

vegetables. The harvested produce pass through different people and organisations, and

all of them contribute in providing a quality product to the customer, as produce passes

through the SC to reach the final consumer (Hewett 2006). According to Hewett (2003),

quality can be maintained by post-harvest technologies, but cannot be improved.

Furthermore, efficiency of SCs can be improved in order to achieve improved

profitability for all SC partners (Hewett 2003).

35

2.2 SC relationship success and a firm’s operational performance

As explained previously, effective SC relationships can benefit its partners in their short

term goals (improved quality and reduced cost) as well as in their long-term strategic

goals (sustainable improvements in product quality, innovation and enhanced

competitiveness) (Kannan & Tan 2006). Further, the success of SC relationships can

directly and positively influence a firm’s operational performance (Benton & Maloni

2005; Kannan & Tan 2006; Maloni & Benton 2000; Narasimhan & Nair 2005). SC

relationship success can be achieved when the SC partners are effectively working

towards the success of their SC. The following sections describe the SC relationship

success and a firm’s operational performance based on several previous studies, and

their linkage to SC relationships.

2.2.1 SC relationship success

Firms enter into relationships with their partners in order to improve their performance.

The success of the established relationships can help firms to achieve market success.

Kannan and Tan (2006) suggest that efforts made by SC partners to establish an

environment conducive to SC relationship success, have a direct and positive influence

on their SC relationship success. This implies that the SC partners need to be closely

engaged with each other, and work towards their collective goals to achieve success in

their relationships. As a result, a firm carefully selects its SC partners, as success of the

relationship is important to be successful in the business, and also to achieve required

market competitiveness (Kannan & Tan 2006). In this study, SC relationship success is

identified as the “outcome of relationships itself”, which is different to “the broader firm

level performances” (Kannan & Tan 2006, p. 762), which is later identified as the firm’s

operational performance. Further, this study define SC relationship success as the

growers’ perception of how successful the relationships are between them and their

major SC partners in terms of achieving quality of products, lowering monitoring costs,

assistance received during difficult times and increasing communication and

cooperation between SC partners.

The success of a SC relationship is the result of two or more firms engaging in close co-

operation, which is mutually beneficial as compared to when they are in competition

36

(Kannan & Tan 2006). These authors further explain that some firms prefer arms-length

relationships, whereas others prefer more collaborative relationships. According to

Vollmann and Cordon (1998), SC partners achieve relationship success and mutual

growth when they work hard collectively to achieve their goals. On the other hand, the

compatibility with each other is important to be effective SC partners, and in many of

the successful relationships SC partners need to identify each other as important

partners in order to engage in a strong relationship (Vollmann & Cordon 1998). Further

these partnerships need to have like-minded partners, who have the desire to develop

organisational ethos and enthusiasm. As Vollmann and Cordon (1998) emphasise, the

successful relationship “acts as one, with one voice, one set of values and one direction,

none of this is easy, but the payoffs are large, and achieving the results is an

invigorating experience” (p. 693).

There are many benefits to SC partners, when their relationships are successful. For

instance, relationship success yields improvement in SC performance (Benton & Maloni

2005; Maloni & Benton 2000; Narasimhan & Nair 2005). Similarly, Kotabe, Martin and

Domoto (2003) emphasise the importance of successful relationships between SC

partners, which reduces lead-time, improves quality, and achieve product and process

developments. According to Duffy and Fearne (2004a), successful relationships

improve future relationship prospects, which can lead to more collaborative

partnerships. Further, these successful SC relationships achieve improved profitability

and competitiveness as compared to transaction based SC relationships. On the other

hand Field and Meile (2008) empirically demonstrate that, service standards and

performances improve when firms in service industries maintain long-term and stronger

SC relationships. Also, Singh and Power (2009) demonstrate that closer collaborative

relationships are important in improving the performance of SCs. Similarly, close

relationships among SC partners can lower the product costs and improve the quality of

products and their delivery (Kannan & Tan 2006). Conversely, relationships among the

firms are the most important factor in SCs achieving success (Grewal, Levy & Kumar

2009; Simchi-Levi, Kaminsky & Simchi-Levi 2003).

However, the success of every partner in the SC is important achieving SC relationship

success. As Benton and Maloni (2005) argue, the entire SC is responsible for the

competitive delivery of a product or service to the ultimate customer. In a study

37

conducted with senior purchasing and supply managers, Kannan and Tan (2006)

revealed that buyers must go beyond operational criteria ( i.e. price and delivery

performance), and consider strategic orientation and commitment to meet shared goals

and objectives in selecting their suppliers. The management of relationships, which is

over and above a transaction relationship (merely a purchasing relationship), is

important to achieve competitive results (Kannan & Tan 2006). Further, Benton and

Maloni (2005) emphasise the importance of a SC’s relationship success and state:

A product is delivered to the end customer via a supply chain of firms, which

consist of suppliers, manufacturers, and distributors. Individual firms are links in

the supply chain, but a supply chain is only as strong as its weakest link. (p. 2).

As the SC moves from merely a cost saving exercise to a strategically important part in

achieving competitiveness of the involved firms, the relationships among SC partners

and success of them also becomes important (Benton & Maloni 2005). Additionally, the

successes of these relationships are directly linked to the firm’s operational

performance. Many previous studies (Kotabe, Martin & Domoto 2003; Larson &

Kulchitsky 2000; Scannell, Vickery & Droge 2000; Stank, Keller & Daugherty 2001)

have demonstrated that success of these relationships are positively related to the firm’s

operational performance.

2.2.2 The performance of firms

Researchers are faced with many difficulties in measuring SC performance (Cousins &

Menguc 2006), and have used various parameters to do this. However many researchers

(Hult et al. 2006; Krajewski & Ritzman 1996; Neely et al. 1994; Panayides & Lun

2009) agree that speed, cost, quality and flexibility are the most important four

competitive priorities, which are critical in the measurement of SC performance. These

competing priorities can be measured using a variety of measurement items, which are

detailed in Chapter 4 of this thesis. Accordingly, these competing priorities form the

basis for the firm’s operational performance variables in this study, and are detailed

below.

As Handfield and Bechtel (2002) suggest, speed refers to the total time taken from the

initiation of an order to the completion of that particular order through the SC.

38

Achieving speed means “delivering the goods faster” or “on-time delivery”, and the

entire SC focuses on this effort in order to ensure that the particular order is delivered to

the end user within the shortest possible time. On-time delivery ensures that the goods

are delivered according to a promised schedule (Ward et al. 1998), but there can be

trade-offs as a result of this speedy delivery. Ward et al. (1998) suggest that speedy

delivery together with the lowest possible cost, greater flexibility and also the highest

quality of goods, is essential to compete with other similar SCs by delivering according

to a promised schedule.

Customer value can be achieved either by providing increased benefits (i.e. value =

benefits/cost) or through reduced cost (Youndt et al. 1996). Hence, SCs that compete

with each other based on cost can achieve the lowest possible cost, or provide value to

customers which competitors cannot match. Choi and Hartley (1996) identify quality as

one of the most important performance criteria. The quality of the order fulfilment

process is also an important part of an efficient order delivery. According to Youndt et

al. (1996), quality-based SCs increase their product reliability and customer satisfaction

by creating a quality delivery process. These SCs continue to look for ways to increase

the quality of the process to gain competitiveness (Hult et al. 2006). As the number of

firms increases, there is intense competition to provide the customer with the highest

quality products and services at the lowest price. Hence, firms look for different

strategies to gain competitive advantage. Flexibility is one such strategy, and Youndt et

al. (1996) view flexibility as a firm’s ability to stay agile, adaptable and responsive to

the changes demanded by their customers and business partners.

Based on the four competing priorities, many previous studies have conceptualised and

empirically tested key parameters of SC performances. These parameters include

quality improvement (Shin, Collier & Wilson 2000; Tan, Lyman & Wisner 2002),

process improvement (Fullerton, McWatters & Fawson 2003), cost reduction

(Narasimhan & Das 2001), lead times (Shin, Collier & Wilson 2000), delivery

reliability (Narasimhan & Das 2001; Shin, Collier & Wilson 2000; Tan, Lyman &

Wisner 2002), time to market (Tan, Lyman & Wisner 2002) and conformance to

specifications (Shin, Collier & Wilson 2000). This study therefore evaluates the firm’s

operational performance using the above parameters to empirically identify the

performance of firms that are organic fruit and vegetable growers.

39

Researchers have mixed views on how relationships between SC partners influence a

firm’s operational performance in SCs of different industries. Simchi-Levi, Kaminsky

and Simchi-Levi (2003) suggest that SC relationship among its partners is the most

important dimension in determining success in industries. They explain that firms need

to maintain constant communication and contacts with their customers. Similarly,

relationships among SC partners in low cost industries, such as retailing are considered

important in achieving competitive advantage over similar SCs (Grewal, Levy & Kumar

2009). Also, Field and Meile (2008) empirically demonstrate that overall performance,

including customer service, can improve through stronger and long-term relationships

among SC partners in service-oriented industries. The need for a higher degree of

interaction to resolve the increased number of service-related issues is one reason why

stronger relationships among SC partners in such industries are needed. Singh and

Power (2009) suggest that collaborative relationships among SC partners is important to

improve the performance of firms in the manufacturing industry. These authors also

emphasise that resource sharing among partners of a manufacturing firm’s SC is

important in achieving competitiveness over other similar SCs. Relationships among SC

partners influence SC performance differently in different industries.

Conversely, Christopher, Peck and Towill (2006) suggest that SC strategy needs to be

tailored to match the demand characteristics of products, and also to specific market

conditions. They also emphasise that there is no single SC strategy which applies to all

products and markets. Reaffirming this view, Hilletofth (2009) suggests that SC

partners need to create a SC which provides solutions that are appropriate to each

product and market conditions to generate competitiveness over similar SCs. These

views suggest that there is a requirement for differentiated SCs to handle varied demand

characteristics and also to compete with similar SCs.

According to Hobbs and Young (2000), consumers’ preferences in regards to fruit and

vegetable have changed over the years. They emphasise that such products must be

differentiated due to a change in buyers’ preferences towards more heterogeneous

products. In this respect Clements, Lazo and Martin (2008) argue that product

characteristics affect the selection of the kind of relationship ideal between SC partners.

Characteristics such as perishability and fragility in fruits and vegetable industry mean

40

that there are issues related to quality and reliability of supply, which in turn create

more complex transactions between SC partners. Such complexities eventually lead to

tensions in the SC relationships among its partners.

2.3 Relationships among partners of the fruit and vegetable SCs

Fresh fruit and vegetables are regarded as highly perishable and fragile products, which

need special attention and careful coordination in order to attract the best possible price

(White 2000). Further, the short lead time available from the farm to the consumer

necessitates rapid harvesting, packaging, distribution and most importantly proper

coordination of activities in the SC. Wilson (1996a) suggests that due to fundamental

changes in consumer preferences and tastes, the SCs involved in fresh produce need to

minimise distribution and inventory costs and maximise their market opportunities

which are important for them to stay competitive. These changes make management and

coordination of these SC increasingly important. These are issues directly related to

cost, availability, stock outs and quality and a longer shelf life (Wilson 1996a). On the

other hand, SC partners seek product flow strategies, which create greater efficiencies

and economies, as a means of increasing their margins. Thus, closer relationships, open

shared information and communication have been seen as effective ways in achieving

competitive advantage over other similar SCs (Wilson 1996a).

Traditionally, the costs are passed along the SC channel, with each of its partner

endeavouring to minimise its cost at the expense of the other partners (Wilson 1996b).

Such a scenario ultimately increases the cost of the product, which is passed to the

consumer. As a result, the entire SC channel is unable to compete with other channels,

which have fewer overall costs. These traditional channels work with many partners in

order to obtain the lowest possible cost for a particular transaction. Generally speaking,

firms are unlikely to invest in new infrastructure and assets, and they also use inferior

quality products to achieve lower costs (Wilson 1996b). Similarly, Kearney (1994)

argues that there are diminishing returns from conventional relations as it is difficult to

compete solely on cost. However, such strategies have proven to be unsuccessful in the

long run (Kanter 1994; Porter 1998), and organisations strive to foster closer

relationships with important and useful SC partners in order to gain a competitive

position for their products in the market.

41

Many firms invest considerable sums of money in developing their SCs. One of the

main reasons behind these huge investments is the strategic importance of today’s SCs

in shaping their firms’ competitiveness. An efficient SC creates many advantages for its

partners. Wilson (1996a) suggests that suppliers are at an advantage when they have

open, shared information and communication, and also improved market intelligence.

The efficient sharing of information and technology between SC partners tends to

positively influence the level of competition against other similar SCs.

White (2000) asserts that growers and suppliers of fresh fruit and vegetables need the

co-operation and support in both commercial and technical matters, if retailers need

them to be effective and efficient. The spot markets are efficient in distributing

homogeneous products, however Hobbs and Young (2000) argue that agricultural

products are considerably different to each other, and buyers also prefer more

heterogeneous products. As a result, it can be argued that spot markets are unable to

efficiently handle heterogeneous agricultural products. The requirement for improved

information flow between SC partners in a vertically coordinated SC becomes more

important (Hobbs & Young 2000). Since the spot market procurement cannot guarantee

the firms against risk or other exploitation, the retailers move towards contractual or

integrated procurement strategies (Wilson 1996b). Also, the issue of perishability is a

significant factor, especially in the fruit and vegetable industry. Hence the contractual

and integrated procurement arrangements are more common and less riskier in

perishable commodities than in storable commodities (Harrington & Manchester 1986).

While competition in the fresh fruit and vegetable markets becomes intense, SC partners

need to efficiently utilise technology, weather and marketing information in order to

minimise cost and increase competitive advantage (Cook 1999). SC partners share real

time information due to the advantages of the latest technology in the fresh produce

industry, which leads to automatic inventory replenishment and then to adoption of just-

in-time processes in distribution centres (Cook 1999). The increase in the demand for

fresh produce coupled with the need of just-in-time distribution, stimulates the retailers

to work closely with suppliers (Wilson 1996b), hence relationships in a SC has become

important. Palmer (1994) suggests that working together in a SC can improve the

42

stability of prices, provide better financial returns, improve each partner’s ability to

supply what market requires, and provide economies of scale and also marketing

support. Wilson (1996b) argues that the cost associated with the fruit and vegetable

distribution channels can be reduced by having efficient SCs, as the traditional SCs

have numerous bottlenecks. Furthermore, firms in these traditional channels usually

build their own safety margins or buffer stocks, and it is important to smooth these

channels, as leaner and efficient SCs tend to survive in the longer run (Wilson 1996b).

A major banana retailer in the UK has successfully reduced the number of suppliers,

and in turn identified few important suppliers, who are able to handle the SC more

efficiently (Wilson 1996a). As a result, the retailer was able to develop closer

relationships with the selected few suppliers, build supply programmes and also

regularly monitor the quality. As Wilson (1996a) explains, the banana retailer believes

that mutual understanding and trust grows when the coordination and inter-firm

communication increases. Also these SC partners combine their skills and resources,

and engage in collective planning, coordination and investments, which help them to

succeed over and above other similar SCs. Furthermore, Wilson (1996a) explains that

closer relationships have helped traditionally fragmented fruit and vegetable SCs to

achieve rapid structural changes in the last few decades. According to Wilson (1996a),

personal relationships and trust assists in solving operational issues in SCs swiftly, thus

decreasing order cycle time. Also, reduction of order cycle time is of utmost importance

to a SC, which handles perishable goods such as fruits and vegetables (Wilson 1996a).

In a study conducted on SCs of perishable products in northern Europe, Wilson (1996b)

demonstrates that retailers are the major purchasers of growers, and there is increasing

recognition that competitive advantage of fresh fruits and vegetable sector can be

achieved through closely co-ordinated SCs. The author concludes that the UK has the

most sophisticated SCs in the fresh produce retailing, followed by Netherlands, France,

Belgium and Germany (Wilson 1996b). Further, the traditionally fragmented fresh fruit

and vegetable SCs of Northern Europe have seen rapid structural changes in the last few

decades. Also, close relationships, planning, co-ordination and investments facilitate a

smooth and constant flow of goods in these SCs (Wilson 1996b).

43

In a separate study that was conducted in the UK, White (White 2000) reveals that

relationships in the SCs of fruit and vegetable industry have become less exploitative

and more co-operative. These relationships rely on extensive integration and

information sharing. In this industry sector in the UK, SC partners have recognised the

need to build their SC relationships in order to protect their business interests. Hingley,

Lindgreen and Casswell (2006) conclude that SC relationships are improving in the UK

fresh produce industry, however trust, co-operation and collaboration between SC

partners are of paramount importance in order to be more competitive in the market. In

their study of the Russian food industry, Tuominen, Kitaygorodskaya and Helo (2009)

demonstrate that downstream coordination between SC partners hinders the operational

efficiency of the entire SC. Johnson, Weinberger and Wu (2008), suggest that close

involvement and partnerships among SC partners are important in improving the

vegetable industries in India, Indonesia, Thailand, Philippines and Vietnam. These

authors reiterate that growers’ participation and co-operation is the most important

aspect in developing this industry to reach its potential. Furthermore, Hingley (2001)

found that relationships within the fruit and vegetable industry SCs in the UK are

imbalanced and these relationships favour the retailer. However, some of these

relationships have been established and practiced over a long period of time, and some

retailers seek to have locked-in contracts with exclusive fruit and vegetable producers in

order to overcome market competition (Hingley 2001).

Leat and Revoredo-Giha suggest that SC coordination and cooperation amongst its

partners can initially improve the efficiency and effectiveness of the chain, which then

leads to improved competitiveness and long-term sustainability of that particular SC.

Peterson, Pearson and Cornwell (2000) stress that the entire SC needs to plan for their

success, and all partners need to be actively involved in the planning process. Further,

these authors highlight the importance of SC relationships in order to create a common

planning platform. In a study conducted in the UK malting and barley SC, Leat and

Revoredo-Giha (2008) reveal committed and trusting relationships between SC

partners. As a result of these close relationships, all SC partners showed a strong

willingness to resolve their problems. These SC partners enjoyed direct financial

benefits and improved overall business performance.

44

The wastage of produce, which is directly connected to poor SC coordination, is

significant in fruit and vegetable SCs. Fisher (1997) reports that poor coordination in

food SCs in the USA, costs them USD 30 billion annually. As coordination of SCs is

directly related to relationships between SC partners, the above example is a clear

indication of how SC relationships matter (Gunasekaran, Patel & Tirtiroglu 2001).

These authors further highlight that strong partnerships among firms encourage mutual

planning, problem solving, and they also emphasise direct and long-term associations,

which results in higher level of coordination and collaborative activities within the SC.

According to Smith (2008), the level of interdependence between partners of a SC is

one of the most important factors in achieving a commanding position in the

agricultural production process. In this respect, Wilson (1996a) concludes that it is very

difficult to achieve standards of quality if growers are not properly integrated in the SC.

Essentially agri-food products are time-sensitive, and therefore need higher levels of

coordination among SC partners to create and make available a better quality product at

the retailer’s shelf. Sometimes this process starts not only at the harvesting stage, but

even before the growers’ make their decision of what to grow. Strategically, a long-term

forecast needs to be developed jointly by retailers, farmers and processors to link the

farm production plans occurring at the time of planting with anticipated consumer

demand at the time of harvest (Taylor & Fearne 2006). Closer relationships and

coordination between partners of the agri-food SCs are arguably more important than in

other industries due to the unique product characteristics.

Although coordination and closer relationships between SC partners are important in the

fresh fruit and vegetable industry, the relationships are seldom fair in regards to their

division of power, and all SC partners do not practice the same level of commitment to

each other (Gummesson 1996). Morgan and Hunt (1994) believe that conflict and

cooperation are unavoidable characteristics of any inter-firm relationship, and both can

exist simultaneously. Furthermore, Hughes (1994) argues that relationships are

imbalanced by nature, due to the power disparities in SCs. According to Morris,

Brunyee and Page (1998), firms must simultaneously manage adversarial and

cooperative approaches in relationships with their SC partners. According to Kumar

(1996), a large percentage of manufacturers and retailers have imbalanced relationships

in their SC partners.

45

Several previous studies (Hingley 2005; O'Keeffe & Fearne 2002; O’Keeffe 1998),

have highlighted that organisational power and its influence on SC relationships are

extremely relevant in the agri-food industry. There is a diverse range of relationship

types in agri-food SCs, but they all seem to comprise the elements of power and

cooperation (Hogarth-Scott & Parkinson 1993). According to Morris, Brunyee and Page

(1998) and Wilson (1996b), relationships associated with power are often prevalent in

the food industry SCs. Further, Stern and El-Ansary (1996) and Hughes (1994) argue

that agri-food relationships are imbalanced by their nature, and there is an inherent

inequality in these relationships. O’Keeffe (1998) highlights that the size imbalance of

agri-food firms is one of the reasons for their low interdependence, as smaller and less

powerful firms are more dependent on the larger and powerful firms. As a result, the

power asymmetry in this industry enables the larger and powerful firms to exercise their

power and impose their rules on day-to-day SC activities, especially the aspect relating

to the sharing of risks and rewards (Matopoulos et al. 2007).

There are mixed views on how the organisational power pans out in different SC

contexts. In a study conducted in the UK fresh produce industry, White (2000)

demonstrates that SC relationships are not one sided (i.e. always in favour of powerful

retailers) as earlier believed. Sometimes powerful partners become highly dependent on

the less powerful partners and as a result the balance of power evens out (Peppers,

Rogers & Dorf 1999). In a separate study conducted in the fruit and vegetable SCs in

the UK, Hingley (2001) suggests that the ultimate decision making lies with the retailers

due to their absolute buying power, control over branding and disproportionate size.

Hence, suppliers are concerned that the powerful retailers sometimes abuse their

position of power. However some of the suppliers in the UK’s fruit and vegetable

industry believe that the influence of power is counterproductive, and as a result they

strive for effective mutual working relationships (Hingley 2001). Furthermore, the SC

partners can still build trusting relationships in the face of power imbalance, especially

when the powerful SC partner treats the dependent partner with fairness (Kumar 1996).

The following sections endeavour to shed light on organisational power, and how it

influences SC relationships.

46

2.4 Organisational power

More than five decades ago Dahl (1957) broadly defined power as the “ability to

achieve intended effects or goals”. Later, French and Raven (2001) define power as “the

ability to manage the perception of the other party”, and Emerson (1962) defines it as

“the ability of an actor to influence another to act in the manner that they would not

have otherwise”. El-Ansary and Stern (1972) define power as “the ability of the parties

involved in a working relationship to control the decisions concerning the operations of

the venture”, and Hunt and Nevin (1974) describe it as “the ability of one individual or

group to control or influence the behaviour of another” (adapted from Kähkönen 2014,

p. 19-20).

Stern and El-Ansary (1996) view power dependence as an inter-firm organising

mechanism through which, control and coordination are extended beyond the

boundaries of individual firms. They also remark that power dependence maximises the

efficiency and effectiveness of inter-organisational exchange systems. According to

Ireland and Webb (2007) power stems from the ability of one SC partner to influence

other partners to act in a desired manner for economic gains. Further, El-Ansary and

Stern (1972) describe power as a dispositional concept, which denotes the ability of one

party to control the behaviour of the other party. Nelson and Quick (2012) identify

power as the ability to influence someone else. All of the foregoing definitions of power

suggest that one partner in the SC relationship controls the other partner in order to

achieve desired results. As Dwyer, Schurr and Oh (1987) explain, power can give the

relationship a purpose, order and direction, and also it takes the relationship out of the

realm of chance. Based on these definitions and the research approach, this study

defines organisational power as the ability to influence actions and the decision making

of the other partner in a relationship.

Previous literature highlights the different power bases in organisations (the definitions

and different dimensions of power are depicted in Table 2.3). According to French and

Raven (2001), five out of these bases are common and important, and can be identified

as being coercive, reward, legitimate, referent and expert power, based on the resources

available to influence the decisions of the relationship. The coercive source of power is

different from the others, since it involves punishment. However in the other four

47

sources (i.e. reward, legitimate, referent and expert), an individual willingly yields

power to the other (Hunt & Nevin 1974). Later, these sources of power were classified

as coercive (i.e. aggressive) and non-coercive (i.e. non- aggressive) depending on the

intensity of their aggressiveness (Frazier 1983; Hunt & Nevin 1974; Ireland & Webb

2007; Lusch 1977; Lusch & Brown 1982). Molm (1997) argues that coercive power

controls negative outcomes relative to each other through punishments or threatened

sanctions, whereas non-coercive power is the ability of a partner to provide or withhold

rewards in promoting desired behaviours from others. Similarly, Hunt and Nevin (1974)

explain that coercive power involves potential punishment if one party does not adhere

to the demands of other, and non-coercive power involves the willingness of one party

to wield power over the other. Adapting the above classification of power which is

based on intensity of aggressiveness, this study assumes that organisational power is an

amalgamation of coercive and non-coercive power.

Table 2.3 Definitions and dimensions of power in previous literature

Theoretical

approach Prominent authors Definition of power

Dimensions of and

perspectives on power

Distribution/

Marketing

Channel

French and Raven

(1959)

Ability to manage the

perceptions of the other party -

El-Ansary and

Stern (1972)

Ability to control the decision

variables of another member

Power to control

Measurement of power

Wilkinson (1973,

1996)

Ability to affect the decision-

making and/or behaviour -

Hunt and Nevin

(1974)

Ability to control or influence

the behaviour of another

Coercive and non-coercive

influence strategies

Anderson and

Narus (1984)

Ability to affect the other

participant’s outcomes from

the relationship

Fate control, behaviour

control

Etgar (1976) Ability to control decisions

and activities

Economic and non-economic

power

Frazier and

Summons (1984) Ability to influence -

Mohr et al. (1996) Ability to influence

Asymmetry of power

48

Supply

management

Porter (1985) Extent to which to retain most

value created for themselves Determinants of power

Ramsay (1995,

1996)

Interplay between supplier’s

and buyer’s resources and

freedom to obtain those

Purchasing power

Buyer’s power, supplier’s

power

Actual and potential power

Cox (1999, 2001a,

b, 2007) -

Attributes of power

Power structures

Power and RDT and TCE

McDonald (1999) - Power in partnerships

Cox et al. (2000)

Extent to which an actor is

dependent on the other for

particular resources

Structure of power: who gets

what, where, how, when

Kähkönen and

Virolainen (2011)

Ability to influence the

decision-making and actions

Structural perspective

Sources of power

Meehan and Wright

(2011, 2012) Potential to influence

Power priorities

Power as property of

organizations, individuals

or relationships

Source: Adapted from Kähkönen (2014)

As depicted in Table 2.3, the various dimensions of power have been discussed in the

context of two elements of the SC, i.e. distribution/marketing channel and supply

management.

2.4.1 Bases of power

Although there are many power bases, French and Raven (2001) suggest five bases of

power (i.e. coercive, reward, legitimate, referent and expert), which are common and

important. These different power bases are construed according to their strength, range

and also the degree of dependence. There are also different effects on the relationship

between the firm’s partners depending on the manner in which these different power

bases are exercised (French & Raven 2001).

49

Coercive power

Coercive power stems from the expectation held by one relationship partner that the

other, more powerful partner can punish him, if he does not adhere to influences

(French & Raven 2001). Lunenburg (2012) explains coercive power “as the ability to

influence others by punishing them or by creating a perceived threat to do so.” (p. 3).

Coercive power varies from one organisation to another, and tends to result in negative

feelings towards those who use its punitive strategies against others. Hence, coercive

power should be used with caution. The strength of the coercive power depends on the

magnitude of the negative valance multiplied by perceived probability that the less

powerful partner can avoid by conformity. For example, a factory worker receives an

offer of a piece-rate bonus for number of pieces that he produces, and the factory can

fire him if he falls below a given level of production. This is a result of coercive power.

According to French and Raven (2001), coercive power leads to a dependency in

relationships, and the degree of dependence varies with the degree to which the other

partner’s conformity can be observed. However, at times, distinguishing between

coercive power and reward power is difficult. It can be argued that withholding a

reward can be equivalent to a punishment, and withdrawal of punishment can be

equivalent to a reward. French and Raven (2001) argue that, this depends on a particular

situation, and is a psychological decision. However, even though the primary influence

of the coercive power leads to dependence of one on the other, it often produces

secondary changes, which are independent (French & Raven 2001). Since a SC consists

of some more powerful partners as compared to the others in the chain, coercive actions

can commonly feature in day-to-day transactions.

Reward power

Reward power is defined as power which is based on the ability of one partner to reward

the other (French & Raven 2001). Lunenburg (2012) defines it as “a person’s ability to

influence others’ behaviour by providing them with things they want to receive” (p. 3).

This base of power depends on one partner’s ability to administer positive valances and

to decrease or remove negative valances. According to French and Raven (2001), the

strength of reward power depends on the magnitude of the perceived reward of one

50

partner, which can be mediated by the other. The strength of reward power can also be

dependent on the probability that one partner can mediate rewards, as rewards are

perceived by the other partner (French & Raven 2001). For example a factory worker

receives a piece-work rate as an incentive to increase the production level. However, the

probability of one partner receiving the reward is controlled by the more powerful

partner.

Over time, the utilisation of actual reward (instead of promise) tends to increase the

attraction of the reward receiver towards the reward originator (French & Raven 2001).

According to Lunenburg (2012), reward power diminishes as a result of a misalignment

between the offered rewards and what other partner perceives as a reward. Hence, the

reward power originator needs to make a clear connection between the expected

behaviour and the intended reward in order to exercise the reward power effectively

(Nelson & Quick 2012). On the other hand use of rewards to change systems within the

range of reward power has a tendency to increase reward power by increasing the

probability of future promises. Also, the power can be decreased through unsuccessful

attempts to exert reward power outside the range of power (French & Raven 2001).

However, performance can be increased utilising reward power, and by establishing a

clear link between performance and intended rewards (Lunenburg 2012).

Legitimate power

According to French and Raven (2001), legitimate power is the most complex out of all

bases of power that are described here. It is defined as the power,

which stems from internalized values in reward receiver which dictate that

reward originator has a legitimate right to influence reward receiver and that he

has an obligation to accept this influence. (p. 265)

Lunenburg (2012) describes legitimate power or the position power as “a person’s

ability to influence others’ behaviour because of the position that person holds within

the organisation” (p. 2).

Legitimate power emanates from a person’s legitimate authority to make discretionary

decisions, as long as receivers accept this discretion (McShane & Von Glinow 2011).

51

French and Raven (2001) argue that the legitimate power is similar to the concept of

legitimacy of authority. However, the receiver can accept an induction from the

originator merely because the receiver values his word, and does not want to break a

promise that they agreed previously. Hence, it can be argued that legitimate power is

always not role related. In the face of legitimate power, the powerful becomes more

assertive and independent, while the powerless becomes inhibited and dependent on

others (Galinsky, Gruenfeld & Magee 2003; Keltner, Gruenfeld & Anderson 2003).

Further, legitimate power involves standards, which can be accepted by the individual,

and as a result the other partner can assert his power. (French & Raven 2001).

Legitimate power can be established using different cultural/social values and

structures. The cultural values establish a common basis for legitimate power of one

partner over another. French and Raven (2001) explain that culture can specify

characteristics on one partner’s right to prescribe the behaviour of the other, who may

not have those characteristics. Secondly, as a result of the receiver’s acceptance of a

hierarchical social structure, the originator can influence the receiver, and do so using

his superior and legitimate position power. Further, legitimate power largely entails a

relationship between subunits of firms rather than people in these formal organisations,

and rights of these subunits are the basis for the legitimate power (French & Raven

2001).Thirdly, legitimate power is acquired through a designation of a legitimate agent.

As a result, the originator sets behaviours for the receiver using his superior legitimate

power, which is accepted by the receiver. Elections are examples of a process that

legitimises the authority of one individual over others. However, the legitimate power

covers a narrow range of influence, hence it is inappropriate to overstep these

boundaries (Greenberg 2011).

Referent power

Referent power originates through the basis of identification, and Lunenburg (2012)

identifies it as “a person’s ability to influence others’ behaviour because they like,

admire, and respect the individual.” (p. 4). Also, if one partner (admirer) is highly

attracted towards the other (power holder), then the admirer might have a desire to

become closely associated with the power holder (French & Raven 2001). Further, this

association can be established and maintained, if the admirer believes, behaves and

52

perceives as the power holder. As a result of this behaviour, the power holder generates

an ability to influence the behaviour of the admirer although the admirer is unaware of

the referent power of the power holder. For an example, “a charismatic leader can ignite

an entire organisation” (Tosi et al. 2004, p. 4). Lunenburg (2012) remarks that the

referent power develops out of admiration of another, and a desire to be like that person.

French and Raven (2001) explain that the referent power permits power holder to

indulge changes, which are relatively independent. Hence, neither rewards nor promises

arouse resistance in the admirer, as he considers it legitimate for the power holder to

offer rewards.

As explained earlier in this chapter, coercive power is closely connected to the

mediation of punishments, while rewards power is closely connected to rewards. French

and Raven (2001), however argue that referent power is the extent that an admirer gains

satisfaction or avoids discomfort by conformity based on identification. However,

conformity with the majority’s opinion is sometimes based on respect for the

collaborative wisdom of engaged partners, which is then called the expert power (expert

power is explained later). The power holder is valued by the admirer because he likes to

be associated with the power holder, and assumes the attitudes or beliefs of the power

holder. Conversely, when a power holder dislikes a partner and evaluates him

negatively he can exercise negative influence, which emanates as a result of negative

referent power (French & Raven 2001).

Expert power

Lunenburg (2012) defines expert power as “a person’s ability to influence others’

behaviour because of recognised knowledge, skills, or abilities” (p. 4). French and

Raven (2001) suggest that the strength of the expert power of the power holder over the

follower is dependent on the extent of knowledge or perception, which the follower

attributes to the power holder within a given area. The follower can evaluate the power

holder’s expertness based on his own knowledge as well as against an absolute

standard.

Additionally, when organisations become increasingly complex and specialised, the

expert power of its members at all levels of the hierarchy become important (Luthans

53

2011). There are few conditions for expert power to occur between two partners. Firstly,

it is necessary for the follower to think that the power holder has the know-how.

Secondly, the follower needs to trust that the power holder tells the truth, and he is not

trying to deceive him. Also, the follower needs to perceive that the power holder is

credible, trustworthy and relevant in order to grant the expert power (Luthans 2011).

According to French and Raven (2001), the advantage of expert power is that it creates

a new cognitive structure as against the early dependent structure. Also, with the

influence of information expert power can create a more independent structure.

Figure 2.3 Power classification

This study identifies five initial bases of power, which are important in understanding

relationships between two SC partners. As depicted in Figure 2.3, these five initial bases

of power are then categorised into two broad power bases based on the nature of their

influence. Firstly, the coercive power as one broad power base, and secondly, reward,

legitimate, referent and expert power as another broad power base which is identified as

non-coercive power. The organisational power is identified in this study as the

collective influence of coercive and non-coercive power as shown in Figure 2.3.

Sources of power

There are many factors, by which a firm can gain power over other firms. Cendon and

Jarvenpaa (2001) remark that power is derived not through the actions of people, but

from organisational structures, that includes organisational positions, resources and

Reward Organisational Power

Referent

Expert

Coercive sources

Non-coercive sources

Legitimate

54

interconnections among actors, and these are called sources of power. These power

sources are derived through the volume of purchases, number of substitutes and

alternatives available, significance of the market power, possessed resources, type of the

product involved, and capabilities and competencies owned by a particular company

(Cox 2001c; Kähkönen & Virolainen 2011; Meehan & Wright 2012; Ramsay 1994).

These power sources enable one partner in the relationship to acquire and wield power

over the other partner (Cendon & Jarvenpaa 2001), and can be defined as the building

blocks of power positions, that define the actors’ power positions and relations in

networks based on these different sources of power (Kähkönen 2014). The

organisations then exercise the power (gained through previously mentioned power

sources), through some form of action, which can influence, evoke and affect the

behaviour of the other relationship partner. There are different levels of capabilities

within the partners of SCs, where one partner may influence power on the other based

on its capabilities. SC partners, however, select their chain partners based on these

capabilities, and as a result the whole SC can compete against other similar SCs.

Table 2.4 Review of different sources of power

Author Sources of power

Bates and Slack (1998) Size, volume of sales and purchases and key technologies

Up-front investments and lock-ins

Caniels and Gelderman (2005,

2007)

Type of product

Dependence between the actors

Cendon and Jarvenpaa (2001) Resources and interconnections

Organisational positions

Cox (1999) Position and resources

Cox (2001a) Share of the market, number of alternatives, dependencies and lock-ins

Cox (2001b) Economies of scale, information, brands, switching costs, network

effects, share of the market/revenue and number of alternatives

Cox et al. (2001a) Resource dependency, utility resource scarcity, i.e. economies of scale,

information, branding, switching costs and network effects

Cox et al. (2001b) Number of alternatives

Doran et al. (2005) Number of alternatives, type of product and importance of relationship

Doz and Hamel (1998) Unique resources, competences

Negotiation skills

Ford et al. (1998) Financial and positional resources, relationships, technologies, unique

products, brands and market share

55

Gadde et al. (2003) Relationship

Gelderman and van Weele

(2004)

Importance of resources

Substitutability of source, i.e. alternative sources and switching costs

H˚akansson (1986) Control of resources and activities

Johanson and Mattsson (1992) Relationships, network role and resources

McDonald (1999) Control of information and technology, market power, size and

volume of purchases and sales

Medcof (2001) Resource importance, alternatives, discretion and dependencies

Mohr and Nevin (1990) Control of information

Pfeffer (1981) Dependence, importance of resources, alternatives and skills

Control of information and position in network

Pfeffer and Salancik (1978) Resource importance, discretion over the resources, control over the

resources and alternatives

Porter (1985) Buyer/supplier concentration

Switching costs, substitute products, type of product and technology

Ramsay (1995) Interdependence, resources, capabilities, volume of purchases and

sales, type of product, brand and market power

Ramsay (1996) Resources, switching costs, alternatives, type of product, specific

investments and volume of sales and purchases

Sanderson (2004) Resource utility and scarcity, volume of purchases and sales, number

of alternatives and market power

Stannack (1996) Attribution=size of the company (number of employees and turnover)

Delivery times, costs and quality

Svahn and Westerlund (2007) Resources

Thorelli (1986)

Economic base, technology, expertise, trust, legitimacy, market share,

size, share of purchases, importance of the product, alternatives,

switching costs and capabilities

van Weele and Rozemeijer

(1999) Number of alternatives and type of product

Source: Adapted from Kähkönen and Virolainen (2011)

As depicted in Table 2.4, there are varying sources of power. As highlighted, the

“resources” owned and controlled by a particular firm are the most common source of

gaining power (Cendon & Jarvenpaa 2001; Cox, Sanderson & Watson 2001; Gelderman

& Van Weele 2004; Medcof 2001; Svahn & Westerlund 2007). Cox, Sanderson and

Watson (2001) suggest that a firm’s share of a particular market, number of alternatives

to its product, dependencies of other firms and lock-in contacts are other sources of

acquiring power over other firms. Ramsay (1996) reveals that switching costs, product

56

type, specific investments, and the volume of sales and purchases are alternative sources

of gaining power. Furthermore, expertise, trust, legitimacy, size (Thorelli 1986),

interdependence, brand power (Ramsay 1994), control of information (Mohr & Nevin

1990), importance of resources (Medcof 2001) and importance of a particular

relationship (Doran, Thomas & Caldwell 2005) are also used by firms to gain power

over other firms.

2.4.2 Importance of power and its influence in fruit and vegetable SCs

There are differing views regarding the advantages and disadvantages of using power in

SCs. According to Benton and Maloni (2005), it is desirable to consider the role of

power in studies relating to the relationships between partners of any SC. Cox, Lonsdale

and Watson (2003) argue that power needs to be considered in studies relating to buyer–

supplier relationships. Maloni and Benton (2000) argue that SC practices which do not

account for the influences of power cannot be entirely realistic or implementable.

Ireland and Webb (2007) suggest that power is a multi-dimensional construct, which

can be used to evoke desired actions from partners. Similarly, Cox (2007) suggests that

power is one of the main factors which determine the outcomes of many business

transactions. According to Achrol (1997), power is an inexorable tool in inter-

organisational relationships. Hence, it can be argued that power is one of the most

important criteria in maintaining the relationships between SC partners.

Although power is important and central to understand how SCs behave, it is largely

neglected in SC studies (El-Ansary & Stern 1972), and rarely used to explain

relationships between firms (Pilbeam, Alvarez & Wilson 2012). Previous studies have

ignored the significance and the role that power plays in relationships (Hingley 2001).

He, Ghobadian and Gallear (2012) argue that there is a lack of studies examining the

relationship between power and different attributes of SC relationships. Thus, it can be

argued that although power is important and highly relevant to SC relationships,

research in this area is scant. Hence, this study intends to examine the influence of

coercive and non-coercive power on SC relationships, SC relationship success and

operational performance of the firms in organic fruit and vegetable SCs.

57

Although power is a serious obstacle to effective collaboration between SC partners

(McDonald 1999), every inter-organisational relationship comprises one firm which has

greater amount of power relative to other partners (Ireland & Webb 2007). There are

several reasons for this phenomenon. According to Mikkola (2008) relationships

between SC partners are influenced by power, when they possess unequal resources. In

most cases it is the most resourceful SC partner who controls the entire SC. Simply put,

the resources within a firm govern the degree of power one firm has in a SC. The

resources and capabilities of a firm are regarded as the essential sources of power

(Cendon & Jarvenpaa 2001; Cox 1999; Cox, Sanderson & Watson 2001; Doz & Hamel

1998; Medcof 2001; Ramsay 1996). These resources determine the position of power in

the relationship (Möller & Svahn 2003; Svahn & Westerlund 2007). The efforts of

lesser resourced firms’ in acquiring valuable resources initiates interdependencies in the

SCs, and these later creates patterns of dependencies in SCs (Pfeffer & Salancik 1978).

As a result of these dependencies, firms which control valuable resources can wield

power over other firms seeking those resources. Secondly these firms also acquire

power over other firms through transactions. Although goods, money and knowledge

(or information) are the three elements that are exchanged between firms of any SC

(Coyle et al. 2008), the number of transactions and complexity of them differs between

each individual SC partner (Smith 2008). Hence the capacity of any SC partner to exert

influence over other partners varies enormously with the type of SC that they are

involved with (Smith 2008).

Collaborative relationships between SC partners can only develop with a certain balance

of power, and as a result neither partner dominates the other substantially (van Weele &

Rozemeijer 2001). However, all the relationships in a SC network are not entirely

collaborative (Kähkönen 2014). They may instead consist of less collaborative

relationships (Bernardes & Zsidisin 2008; Cousins & Spekman 2003), however these

may not be categorised as “purely transactional” or in other words “arm’s length”

relationships (Parker & Hartley 1997). According to Kähkönen (2014), when one

partner in a SC relationship is more powerful than the other, the power imbalance

naturally prevents stronger collaboration even though the less powerful partner wishes

to be more collaborative.

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Present-day SCs are generally dominated and controlled by powerful retailers. Here the

controlling behaviour emanates through retailers’ knowledge of the SC in terms of

customer preferences. Such domination naturally facilitates retailers to start controlling

the entire SC, and according to Wilson (1996a), large organisations are able to have an

end-to-end visibility of their logistics pipeline commencing from the placement of an

order through to its delivery, as their knowledge of SC grows. Wilson (1996a) further

explains that the main reason for this trend is a retailer’s ability to control its buying

efforts, hence understanding and influencing the source of production due to its power

over the rest of the SC partners.

Furthermore, Cox (2001a) suggests that power sustains the firm’s relationship through

investments. Bachmann (2001) remarks that power is highly conducive to developing

trust between firms. Conversely, Perumal et al. (2012) empirically demonstrate that

power is an antecedent of satisfaction, owing to its important role in the relationship

building process between firms. However, Ireland and Webb (2007) argue that theories

often explain the detrimental effects of organisational power, but ignore its positive

effects on relationships.

Coercive means of power involves punitive, aggressive and forceful behaviour (Frazier

& Rody 1991; Lunenburg 2012). According to Leonidou, Talias and Leonidou (2008)

and Yu and Pysarchik (2002), coercive strategies can negatively influence satisfaction

in SC relationships. Also, the coercive strategies which are associated with punishments

can negatively influence collaboration (Gelderman, Semeijn & De Zoete 2008), trust

and commitment (Gelderman, Semeijn & De Zoete 2008; Hausman & Johnston 2010).

According to van Weele and Rozemeijer (van Weele & Rozemeijer 2001), a power

imbalance between SC partners can prevent them from having a collaborative

relationship. Also, the dominant power holder in a relationship resists collaboration

between SC partners (Batt & Purchase 2004). Furthermore, coercive means of power

can negatively influence the performance of the SC partners (Lai 2007; Ramaseshan,

Yip & Pae 2006).

Contrary to coercive power, non-coercive power can foster a high level of agreement

between SC partners (Leonidou, Talias & Leonidou 2008), and thus positively influence

satisfaction (Ramaseshan, Yip & Pae 2006; Yu & Pysarchik 2002), collaboration

59

(Gelderman, Semeijn & De Zoete 2008), trust (Leonidou, Talias & Leonidou 2008) and

commitment (Hausman & Johnston 2010). Non-coercive strategies can change the

attitude of SC partners and, in doing this, have a positive impact on SC relationships

(Gelderman, Semeijn & De Zoete 2008). These strategies can also drive teamwork and

improve the relationships between SC partners (Arend & Wisner 2005; Jonsson &

Zineldin 2003). The non-threatening behaviour of a SC partner can improve the success

of SC relationships (Lunenburg 2012), and improve the performance of the SC partner

firms (Arend & Wisner 2005; Jonsson & Zineldin 2003).

The present day SCs are different to traditional SCs in terms of their structure, control

and management. One main reason behind this change is the globalised business

environment, in which retailers are forced to achieve lowest possible costs. The second

reason is the presence of heavy competition in the market place. Many large fruit and

vegetable retailers approach growers directly and purchase large quantum of their

produce, thereby bypassing wholesale markets or middle men (Dimitri 1999). This trend

has reduced agents’ share of business, which they controlled in traditional SCs. This has

also forced agents out of their business (Batt 2003). As evident, the power of mega-size

retailers has virtually obliterated small scale farmers, in their quest to achieve lowest

product costs. The entry of major retailers into the organic market has changed this

niche market considerably. As highlighted by Kastel (2006), the entry of Wal-Mart in

the organic sector in the USA has influenced noticeable changes to the organic market

owing to their large scale buying capability. Also many existing organic retailers who

source from North America are unable to compete with China sourced, cheap priced

organic products.

A few decades ago, traditional middlemen assisted in the coordination of SC activities.

However, present day SCs are generally dominated and controlled by powerful retailers.

These mega-retailers influence the production process even before it commences, owing

to the power that they enjoy over other SC partners. In doing so, these mega-retailers go

to the extent of deciding when, where and what to grow, and also go on to check the

quality of fields and seeds. Dimitri (1999) demonstrate that these firms usually monitor

the growth of crops through periodical field inspections, and also conduct laboratory

tests to measure the quality of harvest. These activities are frequently performed to

ensure that the product quality is maintained to the highest standards. The retailers

60

continue to monitor product quality even after harvest until it reaches their stores. They

also influence partners of the entire SC to ensure that the produce has the highest quality

not only on the shelf, but throughout its journey to the shelf. In this respect, Wilson

(1996a) remarks that nowadays these retailers impose standards at every stage of

production and transportation process until it reaches the final destination. Such retailers

even influence the research and development activities, storage and shipping, and

packing processes of the growers (Wilson 1996a). Hence it can be seen that a powerful

partner in the SC influences the less powerful partner in order to create effective,

efficient and a competitive SC compared to other similar SCs.

2.5 Theoretical underpinnings of this study

The main features of a SC is the presence of many individual firms working together to

achieve a common set of goals. According to Halldorsson et al. (2007), SC is a meta-

organisation comprised of many independent SC partners with established inter-

organisational relationships, and importantly it includes integrated business processes

beyond the borders of individual firms. Although many scholars view SC as an

integrative philosophy to manage the flow of goods from supplier to ultimate user,

Christopher (1998) and Harland (1996) suggest that SC involves management of

interconnected firms to collectively provide products and services to the end customer.

Scholars have borrowed theories from other disciplines in order to address the different

activities in SCs. Giunipero et al.(2008) state that activities in SCs involve a

multidisciplinary approach. Similarly, Halldorsson et al. (2007) argue that there are

many theories that can be used to explain SCs and their behaviours, since these are

complex and interconnected socio-economic institutions. As explained earlier a SC

consists of many individual firms, which are working towards the same goal in

achieving success in the market place, and as a result it creates relationships with

selected firms (Christopher 2011; Cooper et al. 1997). These firms establish SC

relationships in order to acquire scarce resources, capabilities and market opportunities.

According to Barney (1991), organisations achieve competitive advantage by

harnessing resources that are valuable and rare. Firms focus on the effort and cost

required to complete a particular activity (Williamson 1981), and in turn they try to

achieve the lowest possible cost to make more profits (Ketchen & Hult 2007). Further,

61

Jones, Hesterly and Borgatti (1997) argue that the outcomes of organisations are a

function of relationships between organisations. As a result, there are various activities

that take place within a SC, when its partners actively collaborate with others in order to

achieve market success. This study intends to leverage on some relevant theories, i.e.

the resource-based view, transaction cost economics and network theory, which are

used to discuss why firms create and maintain relationships between their SC partners.

The general conceptualisations of three main theories discussed in this chapter are

summarised in Table 2.5.

Table 2.5 General conceptualisation of organisational theories

Theory General conceptualisation

Resource-based

view

The resource-based model of competitive advantage suggests that competitive

advantage may be sustained by harnessing resources that are valuable, rare,

imperfectly imitable, and non-substitutable (Barney 1991).

Transaction cost

economics theory

Transaction cost economics focuses on how much effort and cost is required for

two entities, buyer and seller, to complete an activity (economic exchange or

transaction) (Williamson 1981).

Network theory

The Network Theory considers organizational outcomes as a function of the

social relationships between organizations or individuals in an organization

(Jones et al. 1997).

Source: Adapted from Sarkis, Zhu & Lai (2011)

2.5.1 The Resource Based View (RBV) and its application within SCs

Firms do not possess all the resources that are required to effectively and efficiently

perform their tasks, and as explained previously, SC partners need to cooperate with

each other for their existence, survival and success. Hence firms must depend on other

partners of the SC for the resources that are required to succeed (Ulrich & Barney

1984). Firms cannot be self-sufficient in their resources due to their heterogeneous

distribution and imperfect mobility (Barney 1991). Hence SC partners share these

resources between themselves as a vital part of their relationship (Ireland & Crum

2005). According to Sirmon, Hitt and Ireland (2007), it is important to acquire and

develop resources, and also divest resources that are no longer required for the firm to

achieve its strategic objectives. As a result firms need to harness resources that are rare,

62

valuable, imperfectly imitable and also non-substitutable to be competitive (Barney

1991). According to RBV, firms create relations with interested partners to acquire

these important resources. Barney (1991) argues that resources, capabilities and

strategic assets are key elements of RBV. According to Dyer and Singh (1998), some

firms collectively combine their resources in a unique way, and achieve advantages over

other similar competing firms which are unable to do so. Although RBV has been

applied and described in the context of a single firm and its resources, Lavie (2006)

describes its applicability and relevance in networked environments. This author

remarks that partners of a SC can protect, co-develop and share resources from external

imitations by isolation methods, and also a network can protect imperfect

substitutability by working as a chain in competition with similar SCs. Lavie (2006)

also argues that the original conditions (value, rarity, imperfect imitability and imperfect

substitutability) for sustainability of competitive advantage apply in networked

environments.

Resources can be pooled in SCs, and as a result its partners have access to a vast array

of resources which may be impossible to acquire as an individual entity. This creates an

opportunity for a better resourced SC to be more efficient, effective and competitive as

compared to other similar SCs. According to Barney (1991), two fundamental

assumptions are the essence of RBV. These assumptions that resources and capabilities

in firms are heterogeneously distributed among them; and those resources are

imperfectly mobile. In a SC, resources are heterogeneously distributed. Many of these

resources are imperfectly mobile within its network, and are tacit in nature (Yew Wong

& Karia 2010). Hence, there cannot be any competition between SC partners, as

heterogeneity and imperfect mobility described in the RBV, would not hold in

networked environments (Lavie 2006). This creates an opportunity to use the RBV

theory in SCs to explain how networks can achieve competitive advantage using pooled

resources in order to compete with other homogeneous SCs.

Halldorsson et al. (2007), explain that a firm achieves competitive advantage using core

competencies, which is acquired through its resources and capabilities. Similarly, core

competencies of a SC decide its competitiveness between similar SCs. According to

Ramanathan and Gunasekaran (2014), resources are two fold, i.e. tangible resources

such as machines and warehouses or information sharing and use of a SC partner’s

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reputation, which are categorised as intangible resources. Also, there are two major

types of logistic service providers: The first is ‘asset heavy’, which owns sizeable

tangible assets. The second is ‘asset light, but knowledge based, as these service

providers have less tangible assets but they possess a strong base of intangible assets.

As both these types of resources are important, these firms create SCs which achieve

competitiveness by pooling their individual resources towards a common cause. The

RBV advocates that firms acquire access to resources which are valuable, inimitable,

rare and non-substitutable, and gain competitive advantage through these idiosyncratic

resources (Eisenhardt & Martin 2000).

There are different views regarding the possession of resources and their usage and

management in a SC. Rubin (1973) argues that raw resources must be processed in

order to use them. Several other scholars explain that it is not the possession of

resources that makes a firm competitive, but its competence in making use of these

resources effectively (Barney & Arikan 2001; Mahoney & Pandian 1992; Priem &

Butler 2001). Many SCs possess adequate resources to compete with other similar SCs,

however the intensity of competition differs based on how efficiently they exploit these

possessed resources.

Recently there has been a resurgence of interest in the role of a firm’s resources as a

foundation of its strategy. At the corporate strategy level, theoretical interest in the

economies of scope and transaction costs have focussed attention on the role of

corporate resources in determining industrial and geographical boundaries of a firm’s

activities (Zack 1999). As highlighted by Zack (1999), resources are important to any

organisation, although not many individual resources are productive on their own.

Further, Zack (1999) reveals that a team of resources can perform tasks or activities

which create opportunities for a firm to invent value addition to achieve a competitive

edge over other firms in the same market atmosphere. When teams of resources work

together they are called ‘capabilities’. There are several types of resources, and Zack

(1999) categorises these into six types; financial, physical, human, technological,

reputation and organisational resources.

Salaman and Asch (2003) emphasise that coordination and cooperation in relation to the

resources play a major role in achieving productivity in firms. These authors also

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discuss ways in which people behave in an organisation, and the manner in which their

behaviours produce unusual, important and hard to imitate outcomes using readily

available resources. Such outcomes include an organisation’s culture, ways of working,

processes, structures, established routines, and also the organisation’s history. Many of

these capabilities are complex and comprise a large tacit dimension. These are used to

achieve competitiveness when they are developed and accumulated to a level which

competitors cannot match and imitate. Hence these resources are highly valuable in

achieving competitive advantage over similar firms.

Although there are major developments and advances in the field of SC management,

yet there are areas that need attention due to the ever-increasing demands of consumers.

Confirming this, Langley et al. (2007) argue that SC users are dissatisfied with services

in terms of expected cost reductions, increased requirement of logistics services,

trustworthy relationships, advanced information sharing and geographical coverage. In

light of these demands and unsatisfied customers, logistics service providers are

constantly looking for ways and means of improving their service standards. They

achieve growth and competitive advantage through acquisitions, alliances, mergers and

expansions.

However management of resources in a SC is more important than possessing resources

in order to achieve competitive advantage in present day markets (Barney & Arikan

2001; Priem & Butler 2001). These authors further assert that there are many SCs that

possess enough resources to compete with other chains, but the intensity of competition

differs depending on how efficiently they can exploit these resources. In this context the

theory of resource management as highlighted by Sirmon, Hitt and Ireland (2007)

explains how existing resources are structured, bundled to create required capabilities,

and then those bundled resources are leveraged to gain competitive advantage.

Yew Wong and Karia (2010) reveal that, of the many available resources, physical,

knowledge, human and, relational resources are identified in the RBV literature as being

strategic resources for SCs. Furthermore, Reed and DeFillippi (1990) suggest that

intangible resources are difficult to replicate and vague in nature. However, Miller and

Shamsie (1996) remark that intangible resources are used to exploit tangible resources

65

(which are imitable), in gaining competitive advantage over other SCs. Although the

human resources management literature defines ‘human resources’ as being separate to

‘knowledge resources’(Wright, McMahan & McWilliams 1994), Yew Wong and Karia

(2010) argue that these two are very important strategic resources to SCs, and are at

times inseparable.

The SC consist of many individual firms, which are committed to work as a single

entity, and as a result there are varieties of resources available for them, which can be

used in different network coverage and service portfolios (Yew Wong & Karia 2010).

These authors also suggest that the ability of a SC to provide integrated solutions using

imperfectly mobile and heterogeneously distributed resources makes the difference in

achieving competitiveness over other homogeneous SCs. They propose that a skilful

partner is necessary in every SC to acquire and bundle required resources according to

the objectives of that particular SC. This is important in gaining a market share, and this

action of structuring and bundling of available resources is key to achieving competitive

advantage (Yew Wong & Karia 2010).

As explained, the fruit and vegetable growers work closely with their major SC partners

in order to access their market to sell their produce. These growers also maintain these

relationships for long duration; hence they can achieve competitive advantage over their

competitors. Hence RBV can be used to explain the relationships between fruit and

vegetable growers and their major SC partners.

2.5.2 Transaction Cost Economics (TCE) theory and its application in SCs

The neoclassical theory of the firm ignored the friction inherent in transactions, and as a

result the TCE emerged as a theory (Wilson 1996b). Later, firms tried to create

relational exchanges between their partners, which later proved to be more effective

than transaction-based relationships. According to the transaction cost economics

theory, a firm decides whether to form an alliance or not, based on the cost of the initial

and ongoing transactions between the firm and its new alliance partner (Williamson

1991, 2008). The overall goal of TCE is to achieve maximum profitability by

minimising transaction costs within and between organisations (Ketchen & Hult 2007).

66

Hitt (2011) explains that a firm internalises a particular activity, if the alliance incurs

higher costs. Williamson (2008) further explains that firms may prefer internalisation

(hierarchy governance structure), as they gain control over valuable resources and also

prevent the exposure of critical information to current or potential competitors. But

there are disadvantages in internalisation as the firm may not be as efficient as the

external supplier (market-based governance structure) in performing the task due to

external suppliers’ capability and focus on the activity. This helps to perform that task

more effectively than the more diversified firms (Hitt 2011). The choice of the

governance structure depends on their relational efficiency, and the cost of performing a

transaction may be higher in one governance structure than in the other (Grover &

Malhotra 2003). According to Williamson (1985), a hierarchical governance is more

efficient than market governance when the exchange environments possess higher

degree of uncertainty and a small number of potential partners, and also due to the

inability of competitive forces in controlling the supplier opportunism.

According to Sarkis, Zhu and Lai (2011), “transaction costs are the costs of activities

beyond the costs of a product or service that are required to exchange a product or

service between the two entities” (p. 10). Clemons, Reddi and Row (1993) suggest that

the co-ordination cost and transaction risk are the two major components of transaction

cost. Further these authors remark that co-ordination cost consists of the costs

associated with information exchange and also incorporating that information into the

decision-making process. The transaction risk includes the risk that other partners in the

transaction evade, and also includes the asset specific investments made by one partner

in the relationship (Grover & Malhotra 2003).

There are two key assumptions that characterise the TCE theory. These are bounded

rationality and opportunism. According to Simon (1965), bounded rationality means

that although people need to make a rational decision, they are unable to do so due to

physical limitations of accurate evaluation of all possible alternatives. As Grover and

Malhotra (2003), explain there can be issues with information availability, storage,

retrieval and communication, and due to these information deficiencies (which results in

bounded rationality), the partners involved are unable to incorporate all possible

contingencies into their contract, which can result in the rise of renegotiation costs

(which are also called transaction costs). The opportunism emanates from the self-

67

interest and guile of human actors in the relationship, which results in the rise of

transaction costs as there are costs associated in terms of safeguarding assets and

monitoring the behaviour of the partner (Grover & Malhotra 2003). As these authors

explain, transactions related costs are on the rise as a result of bounded rationality and

opportunism, and these two assumptions are distinctly different facets of the TCE

theory. Although there are success stories related to both types of governance structures

(Ketchen & Hult 2007), the priori TCE assumption is that outsourcing is more efficient

than performing the task within the company. Or, worded another way, market

governance is more efficient than hierarchical governance structure owing to the

benefits of competition (Geyskens, Steenkamp & Kumar 2006). Further, these authors

demonstrate that the increase in transactions costs can cause failure to the market

governance, which makes hierarchical governance (vertical integration) more efficient

than market governance.

Opportunism is a major aspect of the TCE theory, and it is the main cause for the

existence of hierarchies and failure of markets. Tsang (2006) explains that the TCE

theory decides the choice of governance structures of managers based on the given

levels of asset specificity, uncertainty and frequency of interactions. The TCE theory

assists managers in identifying relevant activities which are to be outsourced to external

partners in a SC (Halldorsson et al. 2007). According to Halldorsson et al. (2007),

cooperating with external partners reduces total transaction costs, which is one of the

reasons for organisations to form networks. Koh and Venkatraman (1991) suggest that

SC collaboration can be an alternative to market and hierarchical governance structures.

Croom (2001) argues that SC collaboration between the firms reduces the costs that are

associated with monitoring the other business partner. Croom (2001) also argues that

opportunism through mutual trust and process integration increases the probability that

both partners work for the mutual interest of the partnership.

Asset specificity, uncertainty and transaction frequency are the three main dimensions

that increase transaction costs in market governance, which therefore makes it

undesirable (Williamson 1975, 1985). Asset specificity (transaction specific assets)

refers to the transferability of assets that support a given transaction between two firms

(Grover & Malhotra 2003). According to Williamson (1985), asset specificity is crucial

in TCE, which leads to the decision between vertical integration and autonomous

68

contractors. As Grover and Malhotra (2003) argue, highly asset-specific investments are

embedded with costs that have little or no value outside their specific transactions.

Geyskens, Steenkamp and Kumar (2006) suggest that these transaction-specific assets

cannot be redeployed to another transaction as they are tailored to a particular

transaction. Asset specificity can be a result of site specificity, physical asset specificity

and human resource specificity (Zsidisin & Siferd 2001). Redeployability of certain

assets for alternative use may decrease high asset specificity situations, and this

increases bilateral dependency of relationship partners (Tsang 2006). Grover and

Malhotra (2003) suggest that uncertainty refers to unexpected changes in circumstances

surrounding a particular transaction. These uncertainties emanate through the

contingencies surrounding a transaction which cannot be predicted ex-ante in a contract

(due to environmental uncertainty), or due to behavioural uncertainty, which does not

allow ex-post performance verifications (Geyskens, Steenkamp & Kumar 2006). The

environmental uncertainty is related to unpredictability in the environment, technology,

demand volume and variety, and behavioural uncertainty includes performance

evaluation and information asymmetry problems (Grover & Malhotra 2003). These

conditions of uncertainty reiterate the effects of bounded rationality.

Furthermore, Geyskens, Steenkamp and Kumar (2006) argue that uncertainty is only

problematic in the presence of specific assets. These authors also suggest that

uncertainty without transaction specific assets would favour outsourcing, and

uncertainty with transaction specific assets would demand vertical integration (i.e.

hierarchical governance). Transaction frequency is the extent to which transactions

occur between two firms (Geyskens, Steenkamp & Kumar 2006). The cost of the

governance structures must be justified in terms of the amortisation of costs over the

period of time, and recurrent transactions justify asset specific investments. There is

thus a need to deal with uncertainty rather than with non-recurring transactions

(Williamson 1986). Further, Williamson (1985) explains that transaction frequency

prefers hierarchical compared to market governance structure, as the overhead costs of

the former are easier to recover for recurring transactions.

The firms that are engaged in a relationship with one another decide the governance

structure that suits them based on the costs that are associated with the specific

transaction (Williamson 1991). However, these decisions can sometimes be costlier, or

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can turn customers to one’s competitors indefinitely. Ketchen and Hult (2007) suggest

that the most efficient SCs overcome these uncertainties by creating long-term trusting

relationships that benefit all its partners. These authors also suggest that short-term

buying decisions that are made based on transaction costs are sometimes short lived due

to the lower quality of outsourced items, which is a result of opportunistic behaviours of

a few partners in the SC.

On the other hand, TCE assumes that managers are using transaction cost calculations

in determining governance structures for their firms (Williamson 1985). The issue for

firms nowadays, though, is whether managers actually use these calculations when

making contracting decisions (Tsang 2006). As a result there can be cost differences and

the total cost is higher when the quality of goods is taken in to consideration. Since the

TCE is considered to be a tool to ensure minimum cost involved in any particular

transaction, SC partners need to select and work with a suitable set of partners in order

to obtain efficiency, quality and lowest possible cost. Hence, there is a nexus between

the principles of TCE concepts and the need for a SC to exist and operate. As concluded

by many researchers (Grover & Malhotra 2003; Ketchen & Hult 2007), it is relevant to

use TCE in the study of SCs.

Since the transaction costs are dependent on the individual SC partners, growers need to

create alliances with most suitable SC partner, and endeavour to achieve maximum

profitability. They also maintain their relationship with most profitable SC partners and

achieve long term sustainability in the business by minimising the overall operational

costs. It can be argued that these long-term relationships hinder the opportunism in fruit

and vegetable SCs, paving the way for more sustainable and collaborative SCs. Hence,

it is appropriate to use TCE in describing the relationships between the SC partners of

organic fruit and vegetable industry.

2.5.3 Network theory (NT) and its application within SCs

The TCE focuses entirely on economic issues, and as a result it fails to discuss the

personal and social relations (Skjoett-Larsen 1999), which also are important aspects of

a SC. Thus, NT becomes important in the study of SC relationships. Jones, Hesterly and

Borgatti (1997) argue that the organisational outcome is a function of social

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relationships between organisation and individuals in that organisation. Also, the

cooperation between business partners, or how well both partners efficiently cooperate

with each other (reciprocity) is important in achieving improved performance

(Halldorsson et al. 2007). Oliver (1990) suggests that the network theory can be used to

describe the basis for the conceptual analysis of reciprocity between these types of

cooperative relationships. According to Thorelli (1986) the NT can describe, explain

and predict relationships between linked firms.

Furthermore, NT contributes immensely to understanding the dynamics of inter-

organisational relations (Halldorsson et al. 2007). A SC consists of two or more firms,

which work closely to fulfil the required transactions. In essence these firms form a

network, and as a result, the NT has potential to describe the transactions between these

networked firms (Ketchen & Hult 2007). Ketchen and Hult (2007) also argue that

strong and weak ties are the two key elements in the NT. Both of these types of ties are

important and provide certain advantages to the SC. In this respect Ketchen and Hult

(2007) suggest that strong ties provide greater reliability, whereas weak ties provide

improved flexibility. Reliability and flexibility are major advantages for a SC, which

can assist the firms to achieve better results in the competitive market place. The overall

network configuration was not considered when creating weak or strong ties between

firms in traditional SCs (Ketchen & Hult 2007). These authors also suggest that the best

value SCs systematically select a blend of weak and stronger ties in order to match the

specific needs of a particular SC.

Skjoett-Larsen (1999) explains that the fundamental assumption in the network

perspective is that individual firms depend on the resources controlled by other firms,

and these firms get access to the required resources through interactions with other

firms in their network. According to Halldorsson et al. (2007), the level of trust

increases when firms create long-term cooperative relationships and mutual adaptations

to systems and routines through the exchange process, which can be described using

NT. These networked firms invest in relationships with other firms, and as a result gain

knowledge of their network partners. They also have a built-in tendency to make the

relationships stable and stronger over time (Skjoett-Larsen 1999).

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Owing to its descriptive nature, the NT can be applied to the SC context in order to

describe actors, activities and resources in a SC (Halldorsson et al. 2007). These actors

can be defined according to the resources they control and activities they perform

(Skjoett-Larsen 1999). Further Johanson and Mattsson (1987) propose two separate (but

closely linked) types of interactions between the networked firms. The first one is the

exchange processes (which includes information, goods and services, and social

processes). The network partners gradually build up trust through the exchange process

(Skjoett-Larsen 1999). The second one encompasses the adaptation processes (that

includes personal, technical, logistics, legal and administrative elements), which

develop links between firms (Johanson & Mattsson 1987). The adaptation process has

several advantages. Due to the special requirements of transactions the dependability of

partners on each other improves, which helps to strengthen the bonds between the

network partners. Secondly, by adjusting to the mutual needs the partners signal that

they do not attract short-term profit opportunities, but look for mutual, stable and long-

lasting relationships (Skjoett-Larsen 1999). The advantage of being a partner in a

network is the ability to influence not only the direct partners, but also the indirect

partners through the direct partners. Thus, SC partners can influence the partner’s

suppliers and partner’s customers.

The access to complementary resources of other firms is an asset, and NT emphasises

that a firm’s relationship with another firm constitutes its most valuable resource

(Skjoett-Larsen 1999). Since the invisible assets or tacit knowledge are inimitable, they

play a major role in achieving competitive advantage (Nelson & Winter 2009). These

invisible assets are created as a result of external relationships, and they disappear when

that relationship is broken. This is why network relationships should remain stable over

time (Skjoett-Larsen 1999). However, Skjoett-Larsen (1999) further explains that the

networks can be stable and dynamic at the same time. According to Skjoett-Larsen

(1999), new relations are established while the older ones come to an end, and also the

existing relations can change over time. Consequently, the network does not reach an

optimal state of equilibrium, but is in a constant state of movement and change.

The fruit and vegetable growers depend on their major SC partners in order to sustain

their business. Growers create relationships based on mutual trust, and these firms also

create mutual adaptations, routines and systems. Later, these mutual adaptations result

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in the gaining knowledge of their SC partners. Since mutual adaptations are prevalent

between the SC partners of the organic fruit and vegetable industry it is appropriate to

use NT in explaining these SC relationships.

Table 2.6 Comparison between RBV, TCE and NT

Characteristics RBV TCE NT

Behavioural

assumptions

Bounded rationality,

trust


opportunism


trust

Problem orientation Internal competence

development

Efficient governance

structures Dynamic relationships

Time dimension Dynamic Static Dynamic

Unit of analysis Resources and

capabilities Transaction Relations

Nature of relations

Access to

complementary

capabilities

Market failures Access to heterogeneous

resources

Source: Adapted from Skjoett-Larsen (1999)

Table 2.6 compares the three theories that were described in this chapter. Each of these

theories is compared based on five characteristics: behavioural assumptions, problem

orientation, time-dimension, unit of analysis and nature of relationships. The problem

orientation of RBV is internal competence development, of TCE it is efficient

governance structure. NT looks at dynamic relationships. The unit of analysis for RBV

is resources and capabilities. The unit of analysis for TCE is transaction, while for NT it

is relations.

Skjoett-Larsen (1999) suggests that the network theory explains and emphasises the

importance of “personal chemistry” (p. 44) between the relationship partners, builds

trust through long-term cooperative relationships and mutual adjustments to systems

and routines. There is a power structure in any network, and the partners use it to

influence the actions of other partners in their network (Skjoett-Larsen 1999). This

author further reveals that the power decides the role and position of a particular partner

in relation to other partners in the SC. More importantly, the partner’s contradictory and

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common interests, along with its power structure, influence the development of the

network.

Table 2.7 depicts the perspectives of best value and traditional SCs based on the three

main theories (i.e. RBV, TCE and NT) highlighted in this study.

Table 2.7 The theoretical perspectives of best value and traditional SCs

Theoretical

perspective

Best value supply chains Traditional supply chains

Resource based view Assume that unique resources exist at

the supply chain level, and that supply

chains can be inimitable competitive

weapons.

Assume that unique reside within

the firms. Supply chain

management is a tool to

complement these resources

Transaction cost

economics

Focus on total costs, not just

transaction costs, as the basis for

“make or buy” decisions.

Short-term costs play a secondary

role if the potential for long-term,

trusting relationships exists.

Focus on transaction costs as the

basis of “make and buy”

decisions.

Opportunism undermines trust;

short-term costs are a primary

consideration.

Network theory A blend of strong and weak ties that

matches supply chain needs is created

in order to maximize supply chain

performance.

Strong and weak ties formed on a

case-by-case basis rather than

strategically.

Source: Adapted from Ketchen and Hult (2007)

The RBV, TCE and NT highlight (Table 2.7) that the best value SCs with close

connections, coordination and careful planning between SC partners, can effectively

face the competitive markets (Ketchen & Hult 2007). Chan, Au and Chan (2006)

remark that the absence of effective coordination and planning between individual firms

results in inefficiencies in SCs, which then impacts the performance of these individual

firms. Although there are few reasons for these inefficiencies, Sahin and Robinson

(2002) and Narasimhan and Nair (2005) identify information sharing as an important

antecedent in effectively managing these SCs. Inconsistent information flows between

upstream and downstream of SC partners also affects the smooth flow of goods through

the SC (Prajogo & Olhager 2012). Hence, the following section addresses how

information sharing influences the activities of a SC.

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2.6 Information sharing and its influence in SCs

Information sharing within partners of the SC is important in today’s highly competitive

market, which enable these SCs to rapidly respond to the end consumer and also to

achieve seamless coordination between SC partners. Mason-Jones and Towill (1997)

argue that the use of information by each partner of the SC positively improves the

speed of response. Additionally, previous studies (Balsmeier & Voisin 1996;

Childerhouse & Towill 2003; Towill 1997; Turner 1993) have highlighted that up-to-

date, and undistorted flow of information between partners of the SC is important to

create a seamless and competitive SC. Similarly, Handfield et al. (2000) suggest that SC

partners be required to share timely and sensitive information with each other. These

authors suggest that information sharing is one of the most important aspects of SC

management. Jones (1998), and Novack, Langley Jr and Rinehart (1995) argue that

information sharing is a source of competitive advantage for SCs. According to Bechtel

and Jayaram (1997), firms prosper when they take advantage of information sharing.

Information sharing is an important source of power (Table 2.4), which enables

information rich firms in a SC to acquire power over the other firms (Cox 2001c; Cox,

Sanderson & Watson 2001; McDonald 1999; Mohr & Nevin 1990). Power is also

discussed in this study as a major factor which influences SC relationships. Further,

information sharing can lead to satisfaction, collaboration (Lee 2000), trust, and

commitment (Anderson & Weitz 1992; Nyaga, Whipple & Lynch 2010) in SCs.

Information is a strategic asset, and SC partners need to ensure its undisrupted flow

through the SC in order to achieve improved performance (Li et al. 2006b). Mohr and

Spekman (1994) remark that information sharing is the extent to which critical

information is conveyed to one’s relationship partners. According to Cannon and

Perreault Jr (1999), it is the expectation of openly sharing valuable information that is

useful to both the relationship partners. These authors further highlight that the

relationship partners start the information sharing at the initial stage of product design,

which may include sharing information about costs, discussing future product

development initiatives or jointly providing supply and demand forecasts. Open

information sharing in a SC stems from both partners’ willingness to share important

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and sometimes proprietary information (Cannon & Perreault Jr. 1999). As a result of

open information sharing, the relationship partners tend to engage more closely in their

future transactions.

Information creates knowledge in SCs. Hence the flow of information in a SC enables

planning, execution and evaluation of key aspects in that SC (Coyle et al. 2013).

Similarly the SC partners who exchange relevant information with each other on a

regular basis are able to work closely (Stein & Sweat 1998). According to these authors,

shared information assists their firms to understand market needs and quickly respond

to market changes. Since there is a limited, direct line of sight to a SC owing to

geographical locational differences, the information flow can provide an insight into the

SC activities (Coyle et al. 2013). These authors also assert that information needs to

flow between key participants of any SC for long-range or day-to-day decision making.

Similarly, Childerhouse and Towill (2003) argue that highly visible information flow

through the SC is key to achieving an integrated and effective SC.

The linkages between firms in a SC allow information sharing across the SC. As

Cousins and Menguc (2006) explain, improved communication between SC partners is

important in achieving improved SC relationships. Confirming the above, Lalonde

(1998) explains that information sharing is one of the most important ingredients in

creating sound SC relationships. These relationships enable SC partners to work closely

with one another and strongly and overcome competition. Similarly, Larson and

Kulchitsky (2000) empirically establish that accurate and timely information sharing

leads to closer relationships in a SC. The information sharing among SC partners are

important. Johannson (1994) explains that a SC requires its partners to inform each

other thoroughly, and this process of information sharing amongst them is critical to the

performance of the SC. Sharing information between SC partners make each other

knowledgeable about their business, which helps them to act independently (Mohr &

Spekman 1994). Towill, Naim and Wikner (1992) argue that SC is a system in which

the partners are connected through a flow of information.

More recently, Ibrahim and Ogunyemi (2012) have argued that the smooth transfer of

valuable information between SC partners is crucial in achieving sound SC

relationships. Also, a lack of transparency impacts on SC relationships (Narayanan &

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Raman 2004). This emphasises the importance of shared information in relationships.

Overall, information sharing in SCs helps its partners to make better decisions with

regard to SC activities (Cheng 2011), and facilitates the ability to jointly meet customer

requirements through closer SC relationships (Spekman, Kamauff Jr & Myhr 1998).

Furthermore, information sharing enables the smooth flow of goods along SCs (Au &

Ho 2002). A higher level of information sharing results in achieving a shorter order

cycle time and a lower total cost (Lin, Huang & Lin 2002).

The sharing of vital, appropriate and selective information improves collaboration

between SC partners and their firm’s performance (Rashed, Azeem & Halim 2010).

Furthermore, information sharing between SC partners directly influence SC

profitability (Gaonkar & Viswanadham 2001), which can positively influence the

performance of SC partner firms. However, some of the SCs are reluctant to share all

available information, since they perceive information sharing to constitute a loss of

power (Berry, Towill & Wadsley 1994). Feldmann and Müller (2003) describe

information asymmetries across the SCs, which have resulted from conflicting interests

and opportunistic behaviour, and which negatively affect the competitiveness of that

SC. In some instances, firms deliberately distort the information that flows in the SC,

and as result original strategic information cannot reach their competitors, but at the

same time incorrect information can reach their SC partners (Mason-Jones & Towill

1997). Due to these predispositions, sharing information among SC partners becomes a

critical aspect of any SC (Feldmann & Müller 2003).

Information sharing develops an understanding between SC partners, which assists in

building trust in the SC (Hart & Saunders 1997; Kwon & Suh 2004). According to

Baihaqi and Sohal (2012), sharing information between SC partners promote collective

activities in the SC. Lee (2000) argues that information sharing assists in reaching SC

collaboration. Kwon and Suh (2004) discuss the important role that information sharing

plays in improving the level of trust and commitment in SC relationships. As a result, it

can be stated that information sharing is closely associated with relationship building

process in SCs. The following sections define SC relationship constructs (i.e.

satisfaction, collaboration, trust and commitment), and their influence on SC

relationships.

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2.7 SC relationship constructs

SC relationships are multi-sourced, and have connections to operations management,

marketing, purchasing, industrial economics, where each one of them addresses the

relationships using different terminology (Christy & Grout 1994). This study identifies

satisfaction, collaboration, trust and commitment collectively as relationship constructs.

As suggested in many previous studies (Doney & Cannon 1997; Dorsch, Swanson &

Kelley 1998; Field & Meile 2008; Hewett, Money & Sharma 2002; Morgan & Hunt

1994), these relationship constructs are important in building closer SC relationships. In

recent decades, business-to-business relationships have emerged as a new trend. As

such many firms have created relationships with selected and important firms (Ulaga &

Eggert 2006). According to these authors, the current trend is to create closer

relationships with few important business partners. In doing this, these firms create and

deliver value to their customers, which is otherwise impossible as a single entity.

Nowadays the competition is between SCs, and not between firms as traditionally

perceived (Boyer, Frohlich & Hult 2005; Ketchen Jr & Giunipero 2004). Hence SC

relationships are important in mitigating the competition. Through relationships, SC

partners are able to work closely, reduce costs and improve the quality, reliability, speed

and flexibility (Karia & Razak 2007; Mentzer, Foggin & Golicic 2000). These authors

also suggest that SC relationships are strong predictors of SC performance. According

to Christy and Grout (1994), deeply engaged SC partners agree to work on a long-term

basis, engage in substantial information sharing and benchmarking. Such seemingly

divergent relationships provide mechanisms which ensure the achievement of a

competitive, stable and, most importantly, a profitable SC. Choi and Hartley (1996)

remark that well-developed long-term relationships between SC partners can create a

long-lasting effect on the competitiveness of that SC. Pfeffer and Salancik (2003)

suggest that these SC partners try to achieve improved performances by creating long-

term collaborative relationships among them. The following sections describe

satisfaction, collaboration, trust and commitment in relation to SCs.

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2.7.1 Satisfaction

Parasuraman, Zeithaml and Berry (1988) argue that the feeling of satisfaction is a result

of a comparison between perceived performance and one or more comparison standards

such as expectation. Dwyer, Schurr and Oh (1987) suggest that satisfaction results from

the overall positive measure of the aspects of a firms working relationship with another

firm. Further satisfaction in the context of a relationship can be defined in economic

terms (i.e. increased sales and profits) or non-economic terms (i.e. willingness to

exchange ideas and respect) (Nyaga, Whipple & Lynch 2010). As a result of the

satisfaction, the firms which are involved in long-term relationships achieve higher

growth and profitability, than the firms involved in transactional relationships (Kalwani

& Narayandas 1995). These make satisfaction an important aspect in SC relationships in

order to be profitable, and face competition in the market.

When the product’s performance exceeds the expectations, the customer is satisfied and

when it falls below the expected standards, the customer is dissatisfied (Ulaga & Eggert

2006). These authors also assert that satisfaction is a result of a cognitive process, which

compares perceived performance against comparison standards. Also, the feeling of

satisfaction represents an affective state of mind (Geyskens, Steenkamp & Kumar

1999). This study intends to evaluate grower’s satisfaction with the major SC partner,

which is generated as a result of the appraisal of all relevant aspects of their

relationship.

Stronger relationships are associated with greater satisfaction (Field & Meile 2008). As

a result, the firms that can create and maintain long-term relationships, are able to

achieve better performance than firms with short-term, transactional relationships

(Kalwani & Narayandas 1995). The satisfaction of a SC partner is a result of a positive

affective state that has been derived through past experiences in the relationship

(Ganesan 1994). Also, the satisfaction of one SC partner in the relationship results in

increased cooperation between other SC partners. This, in turn, results in reduced

litigation and fewer relationship terminations (Hunt & Nevin 1974; Lusch 1976). Closer

coordination and collaborative activities between SC partners can improve satisfaction

(Schulze, Wocken & Spiller 2008), which assists in maintaining relationships with SC

partners (Janvier-James 2012). According to Nyaga, Whipple and Lynch (2010),

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satisfaction with a SC partners leads the other SC partners to exchange ideas. This

exchange of ideas can help enhance the respect that these partners for each other. Also,

satisfaction leads to trust between SC partners (Leonidou, Talias & Leonidou 2008),

which allows them to solve their issues amicably in their SC (Corsten & Felde 2005).

2.7.2 Collaboration

Collaboration comes into play when organisations realise that working alone cannot

resolve common issues to achieve the desired results (Barratt & Oliveira 2001; Corbett,

Blackburn & Van Wassenhove 1999; Wagner, Macbeth & Boddy 2002). Further,

collaboration implies that two or more SC partners become actively involved and work

together in coordinating activities that span their organisational boundaries, which

satisfy their customer needs (Bowersox 1990; Mentzer, Foggin & Golicic 2000;

Muckstadt et al. 2001). Matopoulos et al. (2007) writes: “Collaboration is about

organisations and enterprises working together and can be viewed as a concept going

beyond normal commercial relationships” (p. 178).

According to Corsten and Felde (2005), collaboration has been researched in many

contexts, and also for an extended period of time. An increasing number of firms have

been moving towards alliances and partnerships. As a result, the traditional arms-length

relationships are disappearing at a rapid pace (Heide & John 1990). According to

Cannon and Perreault Jr (1999), collaboration is a relational exchange, and as Kanter

(1994) suggests this process creates value to both the relationship partners.

Collaboration is a process which requires a high level of purposeful cooperation

between the relationship partners (Spekman 1988), and Heide and John (1990) define it

as “joint action”(p. 25). Using this definition, this study defines collaboration as joint

action between the growers of organic fruit and vegetable and their major SC partner,

and focuses on collaborative product and process development processes.

Collaborative relationships are important, and assists organisations to effectively

respond to unpredictable and dynamic changes in the market place (Hoyt & Huq 2000).

Importantly, Stank, Keller and Daugherty (2001) argue that collaboration with external

SC partners increases internal collaboration of firms, and as a result, the firms improves

their service performances. Also, collaboration is linked to a variety of benefits (Corsten

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& Felde 2005), for example improved logistics service performance (Stank, Keller &

Daugherty 2001), increased quality and lower costs (Larson 1994), improved delivery

(Artz & Norman 1998), and improved performance of firms (Hewett & Bearden 2001).

Collaboration between SC partners is central in building competitive SCs (Langley et al.

2007), and is the main determinant for achieving close relationships within a SC (Min et

al. 2005). Matopoulos et al. (2007), for example, argue that collaboration between SC

partners can change commercial relationships in to more engaged and sustained

relationships. Also, collaboration between SC partners in commercial relationships is

less in comparison to the strategic nature of relationships (Kähkönen 2014). According

to Ramanathan and Muyldermans (2011), collaboration between SC partners motivate

them to engage in future activities. Collaboration creates an atmosphere for individual

firms to form integrated SCs (Cannella & Ciancimino 2010), and as a result they can

achieve higher profits (Simatupang & Sridharan 2005). Seifert (2003) remark that

collaboration transforms individual goals into common strategic goals through shared

efforts.

However, there are also disadvantages associated with close collaborative relationships.

Dyer (1996) suggests that a firm may lose the opportunity of achieving economies of

scale due to close collaboration with another firm, hence it would be unable to achieve

low costs. According to Williamson (1985), a firm may become vulnerable to the

opportunism of the exchange partner, due to asset co-specialisation. Further, in close

collaborations, one firm depends on another for information and knowledge, and as a

result the dependent firm may lose the capability to build and specify the product (Fine

& Whitney 1996). Hence, it can be argued that collaborative relationships are linked to

a firm’s positive as well as negative outcomes.

2.7.3 Trust

The concept of trust is widely discussed in the marketing literature (Anderson & Narus

1990; Doney & Cannon 1997; Moorman, Deshpande & Zaltman 1993; Morgan & Hunt

1994), social exchange literature (Zaheer, McEvily & Perrone 1998), and economics

literature (Sako & Helper 1998). Ulaga and Eggert (2006) suggest that many of the

definitions of trust indicate that one relationship partner acts in the best interest of the

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other party. According to Wilson (1995), “trust is a fundamental relationship model

building block and as such is included in most relationship models.” (p.337). Similarly,

Morgan and Hunt (1994) view trust as a key mediating variable in relational exchanges,

and Moorman, Deshpande and Zaltman (1993) define trust as “a willingness to rely on

an exchange partner in whom it has confidence.” (p. 82). Further, Uzzi (1996) defines

trust as “a unique governance mechanism in that it promotes voluntary, non-obligating

exchanges.” (p. 678).

Trust helps to fulfil promised role obligations, and it also adheres to the norms of

corporate behaviour (Cannon & Perreault Jr. 1999). Anderson and Narus (1990) suggest

that, when trust is present, one relationship partner thinks that the other partner will act

as previously agreed and not unexpectedly, hence there is no negative impact on their

firm. Trust infers that one party in the relationship believes that the other party

possesses the required skills and expertise to perform the expected tasks, and also they

have the intentions and motives which benefit the relationship (Ganesan 1994). In this

study, I focus on the inter-organisational trust between the growers of organic fruit and

vegetable and their major SC partners (Panayides & Lun 2009).

According to Matopoulos et al. (2007), trust is the most important factor in deciding the

depth of collaboration between firms, and it is one of the most critical elements in

establishing and maintaining SC relationships. Trust among SC partners creates a long-

lasting commitment to their relationship (Sako 1992), which helps to maintain better SC

relationships between the firms. Similarly, Wicks, Berman and Jones (1999) suggest

that trust can help eliminate friction in day-to-day operational activities. Trust energises

the relationship between firms to move beyond minimal requirements of a relationship,

and to reach a state of mutual benefits (Panayides & Lun 2009). As Currall and Inkpen

(2002) argue, trust decides whether to depend on another firm, with the expectation that

this particular firm will act according to a common agreement. Also, firms should

possess certain level of trust between them in order to enter into a relationship

(Panayides & Lun 2009). This highlights the importance of trust as pre-requisite in

relationship building. Johnston et al. (2004) suggest that “trust is not simply an input to

a relationship; it is both a pre-condition and an outcome of relationship development.”

(p. 26).

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Trust may arise as result of sharing vital and proprietary information, and also through

exposure to opportunistic behaviour (Bensaou 1999; Bensaou & Venkatraman 1995;

Doney & Cannon 1997). Trust increases the perceived rate of relational returns of

relationship partners through reduced uncertainty of their partner’s actions, which in

turn encourages them to engage in collaborative actions (Madhok 1995). Trust between

SC partners improves satisfaction among them (Leonidou, Talias & Leonidou 2008),

and influences joint action in these relationships (Palmatier et al. 2006). According to

Corsten & Felde (2005) trust between SC partners create clear motives, transparency

and assists in making mutual adaptations. Trust also contributes to achieving SC

relationship success (Krishnan, Martin & Noorderhaven 2006). Trust assists in

achieving successful performances (Lindgreen 2003), and it can diffuse tensions in SC

relationships (Zuurbier 1999). Further, Hausman and Johnston (2010) remark that trust

is positively associated with commitment.

2.7.4 Commitment

There are numerous definitions found for commitment, especially in the marketing and

behavioural literature. Dwyer, Schurr and Oh (1987) define commitment as “an implicit

or explicit pledge of relational continuity between exchange partners” (p. 19). Similarly,

a commitment between SC partners can be suggested by their willingness to exert

considerable effort on behalf of their relationship (Monczka et al. 1998; Spekman,

Kamauff Jr & Myhr 1998). According to Morgan and Hunt (1994) and Nyaga, Whipple

and Lynch (2010), commitment refers to one relationship partner’s belief that the

alliance with the second partner is important, and worth protecting. SC partners achieve

mutual gains through commitment in SC relationships (Anderson & Weitz 1992), hence

this is important to maintain in order to achieve competitive SCs. Adopting the

definition used by Morgan and Hunt (1994) and Nyaga, Whipple and Lynch (2010) this

study identifies commitment as organic fruit and vegetable grower’s belief that the

relationship with major SC partner is important and worth sustaining indefinitely.

Also, SCs need to create strong, long-lasting and successful relationships in order to

compete with other similar SCs. Gundlach, Achrol and Mentzer (1995) suggest that

commitment is essential in developing successful relationships between SC partners.

Dwyer, Schurr and Oh (1987) explain that the quality of SC relationships depends on

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both partners’ commitment to the relationship. As Dorsch, Swanson and Kelley (1998)

explain, one partner in the relationship may exhibit self-interest and demonstrate

something more than a promise, which may disguise its commitment. Further,

commitment creates willingness to perform short-term sacrifices in order to ensure

long-term benefits (Dwyer, Schurr & Oh 1987).

Commitment acts as the objective to maintaining a valued relationship (Moorman,

Zaltman & Deshpande 1992), which can help SC partners in future transactions.

According to Hausman and Johnston (2010) and Van Weele (2009), commitment is

necessary to achieve joint actions between SC partners. Also, commitment positively

influences joint action in SCs (Palmatier et al. 2006). Additionally, SC partners that

possess strong commitment are less likely to leave the relationship with their SC

partners (Morgan & Hunt 1994). Scanzoni (1979) suggests that commitment increases

with elapsed time, and also when the relationship partners commit more resources to the

relationship. Similarly, commitment thrives when SC partners continue to maintain the

relationship for a longer duration (Kumar, Scheer & Steenkamp 1995a). At this stage of

relationship interdependence, partners are satisfied with each other, which may prevent

other firms, who could provide similar benefits entering the relationship (Dwyer, Schurr

& Oh 1987). Hence, relationship constructs (i.e. satisfaction, collaboration, trust and

commitment) are important aspects in relationship building in SCs, assist in maintaining

SC relationships, and then influence the performance of SC partners.

2.8 Summary and gaps in previous literature

As reviewed earlier in this chapter, an efficient SC is based on mutual relationships,

which focuses on cost efficiencies and improved communication processes within the

channel, and one which assists its partners to share risks and rewards over a long period

of time (Cooper & Ellram 1993). The partners of such SCs commit themselves to long-

term relationships, share valuable information, practice joint problem solving, work on

mutual trust, act for mutual benefit and seek continuous improvement (Duffy & Fearne

2004a). Firms as clients build trusting and committed relationships with their SC

partners in an increasingly service oriented industry. Hence efficient SCs are able to

effectively compete with other similar SCs (Christopher & Towill 2000; Langley et al.

2007; Power 2005). Further, close relationships between SC partners are important in

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the fresh produce industry owing to the perishability of those products. These

relationships help SC partners to transfer their important value added activities down

the SC to warehouses and DCs (Manikas & Terry 2009). As a result, firms dealing in

perishable and highly fragile products can reduce the wastage and attract best possible

prices (White 2000).

According to Kannan and Tan (2006), SC partners are able to achieve success in their

relationships by engaging in collaborative efforts between one another. This success in

relationships improves the performance of the SC (Benton & Maloni 2005; Narasimhan

& Nair 2005). The partners of a SC need to plan and execute their strategies taking the

product characteristics and market conditions into consideration in order to improve

performance and to be competitive over similar SCs (Christopher, Peck & Towill 2006;

Hilletofth 2009). Hence, it can be argued that the fruit and vegetable SCs, which handle

perishable and differentiated products (Hobbs & Young 2000) need to carefully plan

and execute their SC activities to achieve improved firm performance.

Although there are five main bases of power (coercive, reward, legitimate, referent and

expert), these can be broadly categorised into two bases, i.e. coercive and non-coercive,

based on their nature of influence (French & Raven 2001). Power imbalances influence

the level of commitment and collaboration between SC partners (Gummesson 1996).

According to Kähkönen (2014), power imbalances hamper strong relationships between

SC partners. Also, O'Keeffe and Fearne (2002) and Hingley (2005) remark that

organisational power is extremely relevant in agri-food SCs in order for them to achieve

success. However Hingley (2001) remarks that organisational power has been

counterproductive in the UK’s fruit and vegetable SCs. He cites examples of SCs where

one or more SC partners are more powerful than the others.

The continuous information sharing between SC partners is vital, as inconsistent

information flows adversely affect the smooth flow of goods through the SC (Prajogo &

Olhager 2012). Also, information sharing between SC partners influences their

satisfaction, collaboration, trust and commitment (Anderson & Weitz 1992; Lee 2000;

Nyaga, Whipple & Lynch 2010), which are important in achieving SC success. Stronger

SC relationships are associated with greater satisfaction (Field & Meile 2008), internal

and external collaboration (Stank, Keller & Daugherty 2001), trust (Panayides & Lun

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2009) and commitment (Anderson & Weitz 1992). Hence, satisfaction, collaboration,

trust and commitment are important aspects of building effective SC relationships.

Although these aspects have been studied and widely discussed in different SC contexts

(Field & Meile 2008; Grewal, Levy & Kumar 2009; Singh & Power 2009), there has

been found to be limited research conducted in the organic fruit and vegetable industry

SCs. Hence, this study attempts to understand specific interactions between partners of

the organic fruit and vegetable SCs, and their influence on a firm’s operational

performance.

Previous studies (Corsten & Felde 2005; Leonidou, Talias & Leonidou 2008; Nyaga,

Whipple & Lynch 2010; Panayides & Lun 2009) have attempted to selectively

investigate constructs that are included in this study. However, no single study has

investigated all the constructs included in this study. As explained previously, all the

nine constructs included in this study are important to SCs owing to their inter-

connectivity and their influence on the performance of SCs/firms. Hence, this study is

unique in its attempt in investigating nine important constructs in a single

comprehensive model. This model can later be used in similar studies (i.e. in different

industry sectors, different geographical contexts and in the same industry targeting

various SC partners) in exploring the influence of information sharing and power on SC

relationships and performance of SC partners.

Although previous studies have attempted in understanding how various concepts

influence business environments in different SC contexts, yet there are knowledge gaps

in the literature (Burgess, Singh & Koroglu 2006). These authors further remark that,

inter-organisational traits including people centred ones have been scantily investigated

in previous SC studies. According to Giunipero et al. (2008) several previous studies

have researched only a narrow or single aspect of the SC. Hence, there are opportunities

to investigate people centred constructs in a SC by providing empirical evidence in a

bid to address the current gaps in the literature.

According to Giunipero et al. (2008), the majority of previous studies have investigated

SCs belonging to the manufacturing and consumer goods industries, and those studies

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have focussed more on the operations management aspects of SCs. Also, several other

studies (Field & Meile 2008; Grewal, Levy & Kumar 2009; Singh & Power 2009) have

investigated contexts associated with the services, retailing and manufacturing

industries. There are other industry sectors whose SCs are completely different to the

that of the manufacturing, retailing and services sectors, mainly owing to their

inherently dynamic SC activities (Grunow & van der Vorst 2010). This provides an

opportunity to explore these under researched-industry sectors in order to make

considerable advancements in the field both from a theoretical and empirical

perspective.

Similarly, power is important and central in understanding the behaviour of SC

relationships. However, as Pilbeam, Alvarez and Wilson (2012) remark, it is rarely

studied in order to explain SC relationships. Hingley (2001) explains that previous

studies have ignored the significant role that power plays in SC relationships. Also,

there are scant studies, which explore relationships between power and different

attributes of SC relationships (He, Ghobadian & Gallear 2012). According to Smith

(2008), the capacity of a powerful partner to influence other SC partners depends on the

type of SC (depends on the type of commodity involved in the SC). Hence, power and

the manner in which it influences relationship constructs and performance of SCs/firms

is significant in addressing the inherent gap in the extant literature.

2.9 Research problem

As explained in the previous sections, and with a view to extending the current theory,

this study investigates SC relationships and how these relationships influence the

performance of firms in the organic fruit and vegetable SCs. Hence, the overarching

research problem of this study is articulated as:

‘How does power and information sharing in combination with satisfaction,

collaboration, trust and commitment influence the success of SC relationships and


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This research problem will be dissected further in the next chapter to formulate relevant

secondary research issues or hypotheses.


The literature review examined and synthesised the previous studies, which are related

to organisational power, information sharing, relationship constructs, SC relationship

success and a firm’s operational performance in various SC contexts. The chapter

introduced the above mentioned constructs, which were later used to develop the

conceptual framework in Chapter 3 of this thesis. Organisational power and information

sharing both were discussed in detail as these were the two independent variables,

which influence all the other constructs of this study as depicted in the proposed

conceptual framework in Chapter 3. The extensive literature review served as the basis

for formulating the hypotheses of this study, which are detailed in Chapter 3.

Study also used three theoretical approaches namely resource based view, transaction

cost economics theory and network theory. These theories were used in this chapter to

describe the reasons for firms to form and maintain SC relationships, and their closer

relationships. All theoretical approaches that were used in this thesis shed light on the

formation of SCs, and their close relationships among its partners. Although the SC

relationship constructs, SC relationship success and performance constructs have been

studied previously, this study becomes unique as it tries to understand how power in

combination with information sharing influences these widely discussed SC

relationships. The critical review of previous literature on SC relationships, power,

information sharing, relationship constructs and performance assist in formulating the

conceptual framework for this study, which is detailed in Chapter 3.

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Chapter Three: Conceptual model and development of

hypotheses

89

3 Introduction to chapter three The previous chapter reviewed relevant literature, which aided in the identification,

explanation and also the formulation of study constructs. This chapter endeavours to

establish logical linkages between key constructs detailed in the previous chapter.

Section 3.1 reviews previous models that are relevant and aided the conceptualisation of

this study, followed by the development of the conceptual model in Section 3.2. Next,

Section 3.3 presents the primary research problem and secondary research questions.

Section 3.4, then explains the constructs that are used in the conceptual model of this

study.

Section 3.5 explains the rationale underpinning the logical relationships between

constructs mentioned in the specific research questions. Then the chapter presents the

formulation of hypotheses which serve as secondary research questions. Finally, a

summary of hypotheses is presented, and Figure 3.7 depicts the proposed final

conceptual model. The roadmap of chapter 3 is depicted in Figure 3.1

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Figure 3.1 Road map to the conceptual framework chapter

3.1 Critical review of previous relevant models

Several previous studies have examined conceptual models incorporating information

sharing, power, satisfaction, collaboration, trust, commitment and performance in

different SC contexts. The hypotheses developed in these conceptual models were

empirically tested using structural equation modeling (SEM). Although there is no

single study incorporating all the constructs elicited in this research study, the

conceptual models which will henceforth be examined are relevant to this study.

3.1 Critical review of previous relevant models

3.0 Introduction to the chapter

3.2 Development of the conceptual model

3.4 Explanation of the constructs of the conceptual model

3.3 Research problem and research questions

3.5 Summary of hypotheses and final proposed conceptual model

3.6 Chapter summary

Power

Information sharing

Satisfaction

Trust

Collaboration

Commitment

SC relationship success

Firm’s performance

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Corsten and Felde (2005) examine buyer-supplier relationship factors and their

influence on firm’s performance. Data was collected from 894 member firms of the

Swiss Association of Purchasing and Material’s Management (SVME), which was

related to the buyers’ perceptions. The mail survey utilised seven-point Likert scale

measures, and yielded 135 completed responses, which were later analysed using SEM.

The results revealed that collaboration influences innovation and financial performance,

trust influences innovation and purchasing cost reduction and dependence influences

innovation. The authors also identified that trust plays an important role in SC

relationships. Figure 3.2 depicts Corsten and Felde’s (2005) final model, in which the

bold lines indicate significant relationships between constructs.

Figure 3.2 Conceptual model developed by Corsten and Felde (2005)

The relationship factors comprised of collaboration and its relational constructs of trust

and dependence. Using Heide and John’s (1990) definition of collaboration as

comprising of joint action, Corsten and Felde (2005) define collaboration as “joint

action in buyer-supplier relationships” (p. 446). Also, these authors argue that

collaboration is influenced by relationship partners’ dependence on each other. Firm

performance was measured using innovation, purchasing cost reduction and financial

indicators. Innovation in this study is defined as the “level of product and process

improvements in conjunction with reduced R&D expenses compared to other supplier

Performance factors

Relationship factors

Innovation

Supplier collaboration

Purchasing cost reduction

Financial performance Dependence

Trust

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relationships” (p. 448) (Corsten & Felde 2005). These authors identify purchasing cost

as the sum of communication, transportation and ordering costs, which directly affect

the changes in final product cost. However, this study argues that the operational

performance of a firm is influenced by four competitive priorities, i.e. speed, cost,

quality and flexibility (Hult et al. 2006; Krajewski & Ritzman 1996; Neely et al. 1994;

Panayides & Lun 2009). Although Corsten and Felde (2005) include trust and

collaborations in their study, the other relationships constructs (i.e. satisfaction and

commitment) were ignored. Their findings reveal that trust and dependence moderate

the impact of collaboration on the dependent performance constructs.

The second model investigated is that of Panayides and Lun (2009), who identify trust

as a significant predictor of positive performance in business relationships. They

collected data using a survey of large scale UK manufacturers of electronic equipment,

and 193 completed responses were analysed using SEM. The unit of analysis of this

study was the dyad between supplier and manufacturer, and cross sectional data was

collected. The items of the survey were measured using a seven-point Likert scales. The

results of this study reveal that trust contributes to the innovativeness, and directly

influences SC performance. In this study, trust is modelled as the single exogenous

variable. Trust has direct and indirect relationships with SC performance, and

innovativeness is identified in the model as the only mediating variable (Figure 3.3).

Figure 3.3 Conceptual model used by Panayides and Lun (2009)

According to Panayides and Lun (2009), “trust between organisations creates an

environment where companies strive to exceed the minimal requirements of a

relationship to increase the likelihood for mutual benefits” (p. 36). Also, a certain level

of trust is a prerequisite to enter into a relationship. Further, these authors adopt the

definition of Sher and Yang (2005), and define innovation as “any incremental or

Innovativeness Trust Supply chain performance

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radical change embodied in product and process and includes change in value activities

such as service and administration” (p. 37).

This study used previously validated scales (Cousins & Menguc 2006; Narasimhan &

Das 2001; Shin, Collier & Wilson 2000; Tan, Lyman & Wisner 2002) to measure the

dependent construct of SC performance. This dependent construct was operationalised

using seven indicators associated with cost reduction, delivery reliability, quality

improvement, conformance to specifications, lead times, time to market and process

improvement. These seven indicators are linked to the four competing priorities, i.e.

speed, cost, quality and flexibility (Hult et al. 2006; Krajewski & Ritzman 1996; Neely

et al. 1994; Panayides & Lun 2009).

Although this study identified that trust positively influences SC performance, it did not

include other important SC relationship constructs (i.e. collaboration, commitment and

satisfaction), and their influence on SC performance. As discussed in Chapter 2, the

organisational power and information sharing are important constructs in shaping the

operational performance of firms in a SC, which were not included in this model

(Panayides & Lun 2009).

The third model investigated is that of Nyaga, Whipple and Lynch (2010), who

empirically investigate how trust and commitment (as key mediating variables) along

with collaborative activities (Information sharing, joint relationship effort and dedicated

investments), influence relationship outcomes. The collaborative activities consisted of

information-sharing, joint relationship effort and dedicated investments, whilst

satisfaction with relationship, satisfaction with results and performance were deemed as

relationship outcomes. Adopting the definition of Dwyer, Schurr and Oh (1987), overall

satisfaction (Nyaga, Whipple & Lynch 2010) is defined as “the overall positive measure

or evaluation of the aspects of a firm’s working relationship with another firm” (p. 105).

These authors suggest that overall satisfaction involves satisfaction with relationships

and satisfaction with results. The information sharing in their study is defined as “the

extent that critical information is conveyed to a party’s relationship partners” (p.103).

The construct of commitment in their study is defined as “the exchange partner’s belief

that the ongoing relationship with another firm is so important as to warrant maximum

efforts at maintaining it” (p.104). According to Morgan and Hunt (1994), the above

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definition explains the committed party’s belief that the relationship is worth keeping,

and to ensure that it is sustained indefinitely. According to Ganesan (1994), trust is

explained as “the extent to which relationship partners perceive each other as credible

and benevolent” (p. 104). As Ganesan (1994) further explains, credibility reflects the

extent of one relationships partner’s belief that the other partner has required the

expertise to perform expected tasks effectively. Also, benevolence results when one

relationship partner believes that other relationship partner has motives and intentions,

which benefits the relationship. Nyaga, Whipple and Lynch (2010) measured the

operational performance in terms of reduced order cycle time, improved on-time

delivery, increased forecast accuracy and improved order processing accuracy.

Figure 3.4 Conceptual model developed by Nyaga, Whipple and Lynch (2010)

The main study consisted of two separate studies, and one examined the buyers’

perceptions, and the other examines suppliers’ perceptions, and was operationalised

using two independent samples. Nyaga, Whipple and Lynch’s (2010) model depicts

commitment and trust as two mediating variables between collaborative activities

(exogenous constructs) and relationship outcomes (endogenous constructs), and trust as

directly influencing commitment. Further, although their model (Figure 3.4) does not

consist of direct relationships between information sharing and relationship outcomes, it

consists of indirect relationships through trust and commitment. Their data was

Satisfaction with relationships

Joint relationship effort

Satisfaction with results

Performance Dedicated

investments

Information sharing

Commitment

Trust

Relationship outcomes Collaborative activities Key mediating variables

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collected using two independent surveys consisting of 370 buyer respondents and 255

supplier respondents, who were previously engaged in collaborative relationships. Their

findings revealed that only two hypothesised relationships (i.e. dedicated investment to

trust, and joint relationship effort to commitment) were not supported with both the

buyer and supplier data, and one hypothesised relationship (i.e. commitment to

performance) was not supported only with the supplier data.

The results of both studies found that the information sharing influences commitment

and trust; trust influences commitment, satisfaction with SC relationship success,

satisfaction with results and performance, and commitment influences satisfaction with

relationships and satisfaction with results (Nyaga, Whipple & Lynch 2010).

Furthermore, the results also show that the commitment influences performance in the

buyers’ study, but does not significantly influence relationships in the suppliers’ study.

Although, Nyaga, Whipple and Lynch (2010) successfully examined information

sharing and its influences on satisfaction and trust, their study failed to include the other

important relationship constructs (i.e. satisfaction and collaboration), and especially the

influence of power in their study.

The fourth model investigated was that of Leonidou, Talias and Leonidou (2008), who

examine power as a driver of commitment and trust in the buyer-seller SC relationships.

Their study consisted of six constructs, and power in this study was identified as being

both coercive and non-coercive (Figure 3.5). The purpose of this study was to examine

the role of these two sources of power as key driving forces of trust and commitment in

SC relationships, through the mediating roles of conflict and satisfaction (Leonidou,

Talias & Leonidou 2008). The study targeted manufacturing firms (registered in the

American Export Register) that were engaged in exporting products overseas and were

based in the USA. A stratified random sample of 1500 potential respondents was used.

A mail survey (all the measures were based on a seven-point Likert scale) was

administered. A total of 201 usable responses were collected, out of which 151 were

from producers of industrial goods.

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Figure 3.5 Conceptual model developed by Leonidou, Talias and Leonidou (2008)

As identified in this study, coercive power is based on one SC partner’s perception that

the other party has the ability to be punitive if his/her requests are not complied with.

Whereas, non-coercive power does not include any aggressive elements that produces

frictions among its partners in the relationship (Leonidou, Talias & Leonidou 2008).

The non-coercive power stemmed from five sources: reward, legitimate, referent, expert

and information (Leonidou, Talias & Leonidou 2008). Due to the strategic importance

of the information sharing in SC relationships (Li et al. 2006b), this study identifies it as

being an influential determinant along with the two broad sources of power (Figure 3.5).

Leonidou, Talias and Leonidou (2008) demonstrated that exercised coercive power

causes disagreements and escalate tensions between SC relationship partners, hence it is

negatively associated with satisfaction. Conversely, the exercised non-coercive power

increases the level of agreement between SC relationship partners. Their study also

identified a positive association between trust and commitment, which invigorates long-

lasting relationships between SC partners (Leonidou, Talias & Leonidou 2008).

3.2 Development of the proposed conceptual model for the current study

Developing long-term relationships with important SC partners is strategically

important (Kähkönen 2014; Ramanathan & Gunasekaran 2014). Power (Kähkönen &

Virolainen 2011), information sharing (Rashed, Azeem & Halim 2010), satisfaction

(Ulaga & Eggert 2006), collaboration (Corsten & Felde 2005), trust (Cannon et al.

2010) and commitment (Chen et al. 2011) are important in fostering these strategically

important SC relationships. Furthermore, the above mentioned determinants, along with

SC relationship success (Kannan & Tan 2006), influence a firm’s performance (Hult et

Exercised coercive power

Trust Commitment

Exercised non-coercive power

Conflict

Satisfaction

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al. 2006) within a particular SC. The conceptual model for this study (which is depicted

in Figure 3.6) has been developed after carefully evaluating all relevant literature in

chapter 2, and also after considering the findings and shortcomings of the four models

described earlier (Section 3.1). This proposed conceptual model categorises information

sharing and power as influential determinants, and collaboration, satisfaction, trust and

commitment as SC relationship constructs (Figure 3.6).

Figure 3.6 Proposed initial conceptual model

As depicted in Figure 3.6, information sharing and the two types of power influence all

the other constructs (Anderson & Weitz 1992; Handfield et al. 2000; Lee 2000; Nyaga,

Whipple & Lynch 2010). The SC relationship constructs (satisfaction, collaboration,

trust and commitment) are associated with the competitive position of the SC, and they

influence SC relationship success and a firm’s operational performance (Corsten &

Felde 2005; Field & Meile 2008; Kumar, Scheer & Steenkamp 1995a; Matopoulos et al.

2007). Finally, the SC relationship success impacts on the operational performance of a

firm (Benton & Maloni 2005; Maloni & Benton 2000; Narasimhan & Nair 2005).

Power

Operational

Performance SC Performance

Influential

Determinants

Firm’s operational

performance SC relationship

success

Satisfaction

Collaboration

Commitment

Trust

Information sharing

Non-coercive

Coercive

SC Relationship

Constructs

Performance

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3.3 Primary and secondary research questions

Figure 3.6 depicts the proposed conceptual framework of this research study and it

consists of nine constructs.

The primary or overarching research problem which has been articulated in the previous

chapter is: ‘How does power and information sharing in combination with satisfaction,

collaboration, trust and commitment influence the success of SC relationships and


In the above research problem, power is defined as being both coercive and non-

coercive. Thus, two separate constructs of power are considered in the proposed

conceptual model, and also in developing the secondary research questions.

Based on the proposed conceptual model the following secondary research questions

have been developed:

RQ1 - Does information sharing positively influence the SC relationship constructs

(collaboration, satisfaction, trust and commitment), SC relationship success and a firm’s

operational performance?

RQ2 - Does coercive power negatively influence the SC relationship constructs



RQ3 - Does non-coercive power positively influence the SC relationship constructs



RQ4 - Does satisfaction of SC partners positively influence the SC relationship

success?

RQ5 - Does collaboration between SC partners positively influence the SC relationship

success?

RQ6 - Does trust between SC partners positively influence the SC relationship success?

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RQ7 - Does commitment of SC partners positively influence the SC relationship

success?

RQ8 - Is SC relationship success positively related to a firm’s operational performance?

3.4 Explanation of the constructs of the proposed conceptual model

Previous studies confirm that power and information sharing can influence SC

relationships. Nyaga et al. (2010) empirically establish that information sharing

influences trust and commitment. Also, power imbalances between SC partners (which

can be positive or negative, depending on the source of power) influence commitment

(Kumar 1996) and trust (Matopoulos et al. 2007).

Additionally, several previous studies suggest that SC relationship determinants such as

trust, commitment, collaboration and satisfaction are considered important in achieving

effective SC relationships, and they also bring about mutually beneficial outcomes to all

partners in a SC (see Ghosh & Fedorowicz 2008; Golicic & Mentzer 2006; Handfield &

Bechtel 2002; Heide & John 1988; Hobbs & Young 2000; Kannan & Tan 2006; Kumar

1996; Kwon & Suh 2004; Nyaga, Whipple & Lynch 2010; Sahay 2003; Ulaga & Eggert

2006; Whipple & Frankel 2000). These researchers stress that the absence of these SC

relationship determinants tend to negatively influence the expected results. These SC

relationship determinants enable SC partners to achieve closer connectivity as compared

to arms-length relationships, thus enhancing a firm’s performance.

3.4.1 Information sharing

RQ1 - Does information sharing positively influence the SC relationship constructs

(collaboration, satisfaction, trust and commitment), SC relationship success and

firm’s operational performance?

According to Mohr and Spekman (1994), the term ‘information sharing’ refers to the

extent to which critical information is shared with one’s SC partners. According to

Monczka et al. (1998), information sharing comprises two components, each based on

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quantity and quality of sharing respectively. These authors write that the quantity aspect

of information sharing is the extent by which proprietary and critical information is

communicated to one’s SC partner.

By sharing valuable information with each other, SCs keep each informed about each

other’s businesses, and over time SC partners are able to act independently in

maintaining their relationship (Mohr & Spekman 1994). Similarly, sharing vital and

proprietary information with the SC relationship partner leads to improved trust

(Bensaou 1999; Bensaou & Venkatraman 1995). As Doney and Cannon (1997) explain,

the sharing of confidential information between SC partners indicates that the partners

trust each other, and also that the partner who shares information has benevolent

intentions.

Few authors have identified the importance of information sharing in SCs. Lalonde

(1998) remarks that information sharing helps enable SC partners to work cohesively

and strongly in order to overcome competition. On the other hand information sharing

in a SC is important in the trust-building process, as sharing information develops an

understanding between SC partners, enabling them to resolve conflicts amicably (Kwon

& Suh 2004). Likewise, Hart and Saunders (1997) emphasise that information sharing is

positively associated with trust between firms.

Lee (2000) argues that information sharing between SC partners is the basic foundation

for collaboration (and coordination) within SCs. Also information sharing between SC

partners provides a catalyst to synchronise the activities across the SC (Baihaqi & Sohal

2012). Childerhouse and Towill (2003) empirically demonstrate that making

information transparent to all SC partners, and streamlining its flow are key factors in

creating an effective and integrated SC. Also, information sharing between SC partners

reduces uncertainty, and this improves the level of trust and commitment in a

relationship (Anderson & Weitz 1992). These authors also found that information

sharing encourages SC partners to commit towards the relationship that they have

formed. According to Kwon and Suh (2004), greater sharing of information between SC

partners reduces uncertainty, which improves the level of trust and commitment towards

the relationship partner.

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Petersen, Ragatz and Monczka (2005) and Monczka et al. (1998) demonstrate that

information sharing has a positive effect on SC performance. Also, information sharing

between SC partners is a requirement to achieving successful SC relationships (Field &

Meile 2008). According to Lee (2004), information sharing is a key determinant in

achieving an agile, adaptable and aligned SC. Devlin and Bleackley (1988) argue that

the systematic availability of information in a SC is a strong predictor of success of

relationships among SC partners. Information sharing along with commitment, trust and

collaboration are associated with the success of relationships between SC partners

(Mohr & Spekman 1994). Thus, it can be argued that information sharing influences

relationships between SC partners and their individual firm’s operational performance.

The following hypotheses are formulated in order to investigate answers to RQ1:

H1a - Information sharing is positively related to satisfaction

H1b - Information sharing is positively related to collaboration

H1c - Information sharing is positively related to trust

H1d - Information sharing is positively related to commitment

H1e - Information sharing is positively related to SC relationship success

H1f - Information sharing is positively related to a firm’s operational performance

3.4.2 Power

Power is a multi-dimensional construct, and it is widespread in many disciplines. As

Ireland and Webb (2007) explain, power can be used to evoke desired actions from the

partners in a relationship. Similarly, El-Ansary and Stern (1972) argue that power is a

dispositional concept which creates an ability of one partner of the relationship to

control the behaviour of the other. Dapiran and Hogarth-Scott (2003) identify power as

“the base atomic particle of relationships” (p.265). According to these researchers,

power between relationship partners helps achieve focussed results. Dwyer, Schurr and

Oh (1987) and Kumar (2005) view power as a vital component of relationships. Also,

these authors remark that power provides the relationship with a purpose, order and

direction; and takes the relationship out of the realm of chance.

According to French and Raven (2001), there are six different sources of power. These

are: coercive, reward, referent, legitimate, expert and information. By using these

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different sources of power, the powerful SC partners influence the decisions of the other

relationship partners. As outlined by Belaya, Gagalyuk and Hanf (2009), these different

power sources have been classified according to a variety of measurement criteria. The

most common classification among them is coercive and non-coercive (Ireland & Webb

2007; Lusch 1976), based on their nature of aggressiveness. Hence, this study classifies

power as coercive and non-coercive.

In the presence of unequal power, SC partners are hesitant to create or maintain

collaborative relationships (Batt & Purchase 2004; van Weele & Rozemeijer 2001).

According to these authors, the most powerful SC partner refrains from getting involved

in an intensive collaborative relationship with other partners in a SC, as it already

controls the SC. These SC partners prefer to keep valuable information to themselves

rather than share it openly with other SC partners (Croom, Romano & Giannakis 2000).

This information-hoarding can affect the relationships within, and performance of the

entire SC. Hence, it can be argued that unequal power distributions of SC partners tend

to affect the relationships of a SC (Kähkönen 2014).

Although the retailers are more powerful in the fruit and vegetable industry in the UK,

they acknowledge the important role played by other partners of their SC in order to be

successful in that industry sector (Hingley, Lindgreen & Casswell 2006). Dapiran and

Hogarth-Scott (2003) demonstrate that, although the retailers acknowledge the

importance of suppliers and their dependence on them, they rely on power (in most

occasions through coercive power) to control these suppliers. This is different to the

behaviour of retailers in the UK. The power base as identified by the suppliers is the

rich information possessed by the retailers (Dapiran & Hogarth-Scott 2003). The

following sections address the issues relating to coercive and non-coercive power

articulated in RQ2 and RQ3.

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Coercive power (Aggressive power)

RQ2 - Does coercive power negatively influence the SC relationship constructs

(collaboration, satisfaction, trust and commitment), SC relationship success and a


Coercive power is based on one party’s perception that the other party may impose

punishment, if requests of latter party are not complied (El-Ansary & Stern 1972; John

1984). According to Pfeffer and Salancik (1978), control of scarce resources by few SC

partners, exerts them with power over other SC partners. These powerful SC partners

also seek these resources to an extent, in which dependency is not mutual but forceful,

due to power imbalance between SC partners (Pfeffer & Salancik 1978). The exercise of

coercive power involves aggressive, forceful and suppressive behaviour, and as a result,

the other parties in the relationship tend to perform tasks that they would not have

undertaken otherwise (Frazier & Rody 1991). This unwilling engagement is most likely

to escalate tension and frustration between the SC partners involved (Frazier & Rody

1991; Rawwas, Vitell & Barnes 1997). The escalation of these tensions results from the

actions of one partner, which the other party either disapproves of, does not possess

resources to carry out, or is offended by.

Power creates opportunities for more powerful partners to act opportunistically through

coercion, which can reduce trust within the relationship (Ireland & Webb 2007). These

situations create disagreements between SC partners, and as a result, can intensify

clashes rather than resolve them (Lusch 1976). Leonidou, Talias and Leonidou (2008)

empirically establish that coercive power negatively influences satisfaction. Also,

several other researchers (Richardson, Swan & Hutton 1995; Yu & Pysarchik 2002)

suggest an inverse association between coercive power and SC satisfaction.

According to Gelderman, Semeijn and De Zoete (2008), coercive strategies which are

associated with punishments, negatively impact on the relationships between SC

partners, and this negatively affects collaboration, satisfaction, trust and commitment

between these SC partners. Gaski (1984) remark that coercive power decrease

satisfaction and commitment in SC relationships. Also, Hausman and Johnston (2010)

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empirically establish that coercive power negatively influences trust and commitment in

SC relationships. This type of power within a relationship constitutes a serious obstacle

to effective collaboration (McDonald 1999). The depth of collaboration depends on the

power position of the relationship’s partners (Cox 2007; Cox, Lonsdale & Watson

2003), and unequal power hinders the attempts of SC partners to build collaborative

relationships between them (van Weele & Rozemeijer 2001). According to these

authors, there is less commitment towards collaboration in the presence of unequal

power between the SC partners. The SC partner that possesses a disproportionate level

of power resists engaging in a collaborative relationship (Batt & Purchase 2004).

Simpson and Mayo (1997) argue that the increased level of coercive strategies between

SC partners negatively impacts the trust and commitment between them. Benton and

Maloni (2005) and (Cox 2001b), suggest that coercive power damages SC relationships,

hence it can be argued that coercive power adversely affects SC relationships.

The following hypotheses can be formulated to investigate answers to RQ2.

H2a - Coercive power is negatively related to satisfaction

H2b - Coercive power is negatively related to collaboration

H2c - Coercive power is negatively related to trust

H2d - Coercive power is negatively related to commitment

H2e - Coercive power is negatively related to SC relationship success

H2f - Coercive power is negatively related to a firm’s operational performance

Non-coercive power (Non-aggressive power)

RQ3 - Does non-coercive power positively influence the SC relationship constructs

(collaboration, satisfaction, trust and commitment), SC relationship success and a


Non-coercive power is derived from four basic sources of power, namely reward,

legitimate, referent, and expert (French & Raven 2001). As Ireland and Webb (2007)

argue, trust co-exists with non-coercive power, and this results in positive relationships

between SC partners. According to these authors, when a SC partner fails to achieve

desired results, the ability of another SC partner to remedy the situation emanates from

the complementary nature of power and trust between them (Ireland & Webb 2007).

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Unlike coercive power, non-coercive power does not include aggressive actions, which

adversely affect the relationships between partners of a SC. As Frazier and Summers

(1984) explain, non-coercive power cultivates relatively high levels of agreement

between SC partners. According to Leonidou, Talias and Leonidou (2008), non-

coercive power fosters a high level of agreement between the SC relationship partners,

and thus positively affects collaboration, satisfaction, trust and commitment.

Ramaseshan, Yip and Pae (2006) and Yu and Pysarchik (2002) demonstrate that non-

coercive power is positively associated with satisfaction. Gelderman, Semeijn and De

Zoete (2008) remark that the purpose of non-coercive strategies is to change the attitude

of the other SC partner, and these strategies are found to have a positive impact on their

relationship. As a result, it can be argued that the non-coercive power positively

influences collaboration, satisfaction, trust and commitment between SC partners.

According to Leonidou, Talias and Leonidou (2008) and Hausman and Johnston (2010),

firms which engage in long-term SC relationships practice non-coercive strategies as

opposed to coercive strategies. Arend and Wisner (2005), and Jonsson and Zineldin

(2003) remark that non-coercive power is a driver for teamwork, improved relationships

in the SC and better individual performance for a firm.


H3a - Non-coercive power is positively related to satisfaction

H3b - Non-coercive power is positively related to collaboration

H3c - Non-coercive power is positively related to trust

H3d - Non-coercive power is positively related to commitment

H3e - Non-coercive power is positively related to SC relationship success

H3f - Non-coercive power is positively related to a firm’s operational performance

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3.4.3 Satisfaction

RQ4 - Does satisfaction of SC partners positively influence the SC relationship

success?

Parasuraman, Zeithaml and Berry (1988) remark that satisfaction is closely associated

with the disconfirmation paradigm. This paradigm postulates that satisfaction emerges

from a comparison between perceived performance and comparison standards (for

example, expectations). If performance exceeds expectation, then the customer is happy

(positive disconfirmation). When the result falls below the expectation, the customer is

dissatisfied or in a state of negative disconfirmation (Ulaga & Eggert 2006).

According to Dwyer (1984), satisfaction is the difference between costs and rewards

that a SC partner receives, and these are measured in terms of financial and social

exchanges between the parties involved. Ruekert and Churchill Jr (1984) and Selnes

(1998) suggest that satisfaction is based on past evaluation outcomes, which can be

rewarding or frustrating. Also, long-term satisfaction leads to trust between the

exchange partners (Leonidou, Talias & Leonidou 2008).

According to Janvier-James (2012), satisfaction is a crucial benchmark for the success

of SCs, and is important in continuing the relationship with SC partners. Also, closer

operational activities between SC partners improve their satisfaction with each other

(Schulze, Wocken & Spiller 2008).

The following hypothesis can be formulated to investigate answers to RQ4

H4 - Satisfaction between SC partners is positively related to SC relationship success

3.4.4 Collaboration

RQ5 - Does collaboration between SC partners positively influence the SC

relationship success?

Collaboration means “joint action” (Heide & John 1990, p. 25), and Spekman, Kamauff

Jr and Myhr (1998) view it as a higher level of purposeful cooperation. According to

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Matopoulos et al. (2007), collaboration is ‘a must’ in present day SCs, and not an option

as it was once perceived. Confirming this, Min et al. (2005) argue that collaboration is

the main determinant in achieving effective SC relationships, and it is considered to be

the ultimate core competency of an organisation. Langley et al. (2007) explain that the

collaborative relationship among SC partners is a differentiating tool, and is considered

a strategic weapon to conquer and maintain the competitive advantage of a SC over

other, similar SCs.

Collaboration is, however, a relational construct, and it creates value for the entire SC in

association with trust, commitment and satisfaction. Collaboration is a result of two or

more parties working together towards a mutual goal. Thus, Batt (2003) argues that SC

partners’ desire to collaborate improves when these SC partners share sensitive market

information with one another. Furthermore, collaboration is achieved as a result of firms

working together for a considerable period of time, and the relationship is more than

commercial in nature (Matopoulos et al. 2007). Collaborative partnerships motivate SC

partners to engage in future activities (Ramanathan & Muyldermans 2011), and as a

result, the SC partners create long-term partnerships (Ramanathan & Gunasekaran

2014).

The collaborative SC relationships reduce inventory levels and associated cost and

improve logistical performances (i.e. reduced cycle time and reduced lead time). Hence

SCs achieve improved operational performances (Daugherty et al. 2006; Whipple &

Frankel 2000). Kähkönen (2014) remarks that collaboration between SC partners in

arm’s length relationships is less than in strategically aligned SCs. Also, collaboration

assists the SC partners to achieve higher incomes as they work as a group with common

goals (Simatupang & Sridharan 2005). According to Cannella and Ciancimino (2010)

collaboration brings together individual organisations in forming integrated SCs through

sharing of valuable information. Collaboration can transform the individual goals of

different firms to common strategic goals, and achieves these goals through shared

efforts and close relationships (Seifert 2003).

The following hypothesis can be formulated to investigate answers to RQ5

H5 - Collaboration between SC partners is positively related to SC relationship success

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3.4.5 Trust

RQ6 - Does trust between SC partners positively influence commitment and SC

relationship success?

Trust is an important tool in any organisation, and in many industries trust acts as a

major ingredient of success. Lindgreen (2003) argues that a higher degree of trust

between organisations aids in achieving successful performance as compared to their

competitors. According to Zuurbier (1999) trust, which emanates as a result of close SC

relationships, assists the SC partners in diffusing the tensions associated with

monitoring and controlling activities in SCs.

Lindgreen (2003) remarks that production know-how, a capable and knowledgeable

workforce, general reliability, timely deliveries on required specifications, sharing

valuable information, and fair prices are important prerequisites in achieving and

maintaining trust between SC partners. Matopoulos et al. (2007) identify trust as the

most important factor in deciding the scope of collaboration between firms. He also

views trust as being a critical element, which affects the formation and maintenance of

SC relationships. According to Zuurbier (1999), trust is important in achieving

coordination between SC partners in the fresh produce industry. Also, trust, which is a

result of personal relationships between SC partners, help fruit and vegetable SCs to

improve their performances (Batt 2003; Wilson 1996b).

Trust between SC partners creates long lasting commitment in their relationship (Sako

1992), and this improved commitment helps in maintaining effective SC relationships

between them. According to Wicks, Berman and Jones (1999), trust eliminates the

friction in day-to-day operational activities, and thus creates higher level of commitment

between SC partners. Trust is important in achieving a positive performance in inter-

firm relationships (Currall & Inkpen 2002; Ireland & Webb 2007). According to

Ireland, Hitt and Webb (2005), trust plays a prominent role in reducing the costs that are

associated with inter-firm relationships. These transaction costs diminish as a result of

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trust based partnerships, which reduce the need for contracts and expensive negotiations

(Wilson 1996b).

Leonidou, Talias and Leonidou (2008) empirically established that trust positively

influences satisfaction in SC relationships. Walter and Ritter (2003) argue that

commitment mediates between trust and joint actions of relationship partners, which

emphasise the important role of commitment in achieving smooth SC relationships.

According to Palmatier et al. (2006), several previous studies have established that trust

positively influences joint action in SC relationships. Further, Hausman and Johnston

(2010) empirically establish that trust positively influences commitment and joint action

between SC partners. Wilson (1996b) remarks that trust-based, long-lasting

relationships enable SC partners to synergise their strengths. Krishnan, Martin and

Noorderhaven (2006) identify trust as being one of the key features that contributes

toward achieving success in SC relationships.


H6a - Trust between SC partners is positively related to commitment

H6b - Trust between SC partners is positively related to SC relationship success

3.4.6 Commitment

RQ7 - Does commitment of SC partners positively influence the SC relationship

success?

Developing an efficient SC and working as a committed team between SC partners to

withstand competition can be difficult, but it is achievable. Commitment towards each

other partner in SCs, is considered a non-separable part of SC relationships, and is

central to a relationship’s success (Morgan and Hunt 1994; Ganesan 1994; Bennett

1996). The duration of the relationship assists in enhancing the commitment (Kumar,

Scheer & Steenkamp 1995a). As White (2000) reveals, large volumes of goods are

traded during the mature stage of a relationship, and commitment at that point is at the

highest level.

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According to Palmatier et al. (2006), several previous studies have empirically

established that commitment consistently and positively influences joint action, which is

linked to the success of SC relationships. Hausman and Johnston (2010) empirically

demonstrate that commitment positively influences joint actions of the SC partners.

Further, Van Weele (2009) remarks that the commitment is necessary to achieve

success in ongoing SC relationships.

The following hypothesis can be formulated to investigate answers to RQ7.

H7 - Commitment between SC partners is positively related to SC relationship success

3.4.7 SC relationship success

RQ8 - Is SC relationship success positively related to a firm’s operational

performance?

Research has demonstrated that close relationships between SC partners can improve

service standards. Rinehart et al (2004) stress that close relationships among SC

partners assist in substantially lowering product costs, reducing lead times, and

improving quality. Some other researchers (e.g. Karia & Razak 2007; Mentzer, Foggin

& Golicic 2000; Min et al. 2005), have argued that speed, reliability, cost reduction,

quality improvements, and flexibility can be achieved through closer SC relationships

between partners. A more recent study has found that strong SC relationships

significantly assist in overcoming service difficulties when dealing with sophisticated

consumers (Coyle et al. 2008).

Kannan and Tan (2006) argue that the outcomes of a SC relationship itself (as an

example, increased communication between SC partners) and the broader firm level

outcomes (as an example, reduced lead-time) as two separate products emanates as a

result of SC relationship success. A firm’s operational performance consists of

improvements in quality and lead time, reduction of costs, and market-related

performances (Kannan & Tan 2006). A SC partner’s efforts in creating a conducive

environment to SC relationship success, positively and directly influences relationship

success (Kannan & Tan 2006).

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According to Anderson, Håkansson and Johanson (1994) and Dwyer, Schurr and Oh

(1987), trust and commitment are critical elements in achieving success in SC

relationships. Field and Meile (2008) suggest that satisfaction is vital in achieving closer

and stronger SC relationships. Kannan and Tan (2006) remark that commitment, trust

and collaboration are central to a meaningful relationship between SC partners. Also,

Duffy and Fearne (2004a) argue that commitment and trust are key mediating variables,

which contribute to relationship success in SCs. Ellram (1995) argues that buyers (i.e.

major SC partner in this study) prefer collaborative relationships over the hands-off

relationships. As a result, buyers can control the dependability of the supply, and

influence the quality and delivery schedules of the supplier (i.e. grower in this study).

Vollmann and Cordon (1998) argue that strong and effective SC relationships are based

on trust, long-run commitment towards each other SC partner, and with the desire to

achieve win/win situations. These authors further reveal that close SC relationships

reduce cost, as well as the cycle time explained previously.

The following hypothesis can be formulated to investigate answers to RQ8.

H8 – SC relationship success is positively related to a firm’s operational performance

3.4.8 Firm’s operational performance

There are three major areas of a firm’s performance namely: marketing, financial and

operational. This study focuses only on the operational performance of the organic fruit

and vegetable growers. Effective SC relationships are closely associated with

performance gains through reduction of costs (e.g. Duffy & Fearne 2004a; Johnston et

al. 2004; Martin & Grbac 2003), improved lead time (e.g. Kotabe, Martin & Domoto

2003; Larson & Kulchitsky 2000), quality improvements (e.g. Johnston et al. 2004;

Kotabe, Martin & Domoto 2003), product availability and market coverage (Groves &

Valsamakis 1998; Stank, Keller & Daugherty 2001).

Additionally, there is a direct relationship between cost reduction and a firm’s

operational performance (Panayides & Lun 2009). Apart from that, activities within SCs

substantially influence performance. Information sharing between SC partners reduces

costs, which is directly associated in improving performance (Lee, So & Tang 2000; Li

et al. 2006a; Mentzer et al. 2001) and profitability (Gaonkar & Viswanadham 2001).

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Coercive strategies used by one SC partner can negatively influence the performance of

other SC partners (Brown, Johnson & Koenig 1995; Lai 2007; Ramaseshan, Yip & Pae

2006). On the other hand, non-coercive strategies drives teamwork and as a result

improves a firm’s performance (Arend & Wisner 2005; Jonsson & Zineldin 2003) and

positively influence SC performance (Brown, Johnson & Koenig 1995; Lai 2007;

Ramaseshan, Yip & Pae 2006). According to several studies (Daugherty et al. 2006;

Hewett & Bearden 2001; Whipple & Frankel 2000), collaboration between SC partners

can improve a firm’s performance. Furthermore, the success of SC relationships

improves quality, lower costs and increases information sharing, all of which can help

increase a firm’s performance (Kannan & Tan 2006; Karia & Razak 2007; Min et al.

2005).

3.5 Summary of hypotheses and proposed final conceptual model

The key research problem of this study was identified in Chapter 2. In Section 3.4, I

identified eight specific research questions. Later, the previously validated four research

models and review of previous literature were used to formulate 24 hypotheses (please

refer Section 3.4) for this study.

H1a - Information sharing is positively related to satisfaction

H1b - Information sharing is positively related to collaboration

H1c - Information sharing is positively related to trust

H1d - Information sharing is positively related to commitment

H1e - Information sharing is positively related to SC relationship success

H1f - Information sharing is positively related to a firm’s operational performance

H2a - Coercive power is negatively related to satisfaction

H2b - Coercive power is negatively related to collaboration

H2c - Coercive power is negatively related to trust

H2d - Coercive power is negatively related to commitment

H2e - Coercive power is negatively related to SC relationship success

H2f - Coercive power is negatively related to a firm’s operational performance

H3a - Non-coercive power is positively related to satisfaction

H3b - Non-coercive power is positively related to collaboration

H3c - Non-coercive power is positively related to trust

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H3d - Non-coercive power is positively related to commitment

H3e - Non-coercive power is positively related to SC relationship success

H3f - Non-coercive power is positively related to a firm’s operational performance

H4 - Satisfaction between SC partners is positively related to SC relationship success

H5 - Collaboration between SC partners is positively related to SC relationship success

H6a - Trust between SC partners is positively related to commitment

H6b - Trust between SC partners is positively related to SC relationship success

H7 - Commitment between SC partners is positively related to SC relationship success

H8 – SC relationship success is positively related to a firm’s operational performance

These 24 hypotheses, along with the final conceptual model (Figure 3.7), are used to

identify the relationships suggested in the primary research problem. The proposed

relationships between the constructs are marked (as depicted in Figure 3.7) using a

combination of numerical and letter codes, which are the same codes that identify the

hypotheses of this study.

Figure 3.7 Proposed final conceptual model

Collaboration

Commitment

Trust

Satisfaction


performance


Non-Coercive Power

Information Sharing

Coercive Power

4 1a

1b 1c

1d

1e

1f

2a

2d

2f 2c

2b

2e

3a

3e

3d

3c

3f

3b

7

8

5

6b

6a

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3.6 Chapter summary

This chapter explained the development of the proposed conceptual model for this

study. First, it reviewed four important studies which explored information sharing,

power (coercive and non-coercive), SC relationships constructs, SC relationship success

and performance in different SC context. These studies failed to incorporate these

important constructs in a single study, though they substantially aided in developing the

conceptual model of this study.

The proposed conceptual model has three major sections: influential determinants,

relationship constructs, and performance (which consist of SC performance and firm’s

operational performance). These sections are identified based on the influence that they

have on the SC. The influential determinants consist of information sharing, coercive

power and non-coercive power, which are assumed to influence all the other study

constructs in the proposed conceptual model. The SC relationships constructs used in

this thesis study are satisfaction, collaboration, trust and commitment. These SC

relationship-constructs influence the success of SC relationships, which is broadly

identified as SC performance. Finally, the model ends with a firm’s operational

performance, which is the operational performance of the organic fruit and vegetable

growers as identified in this study.

The following section of Chapter 3 presented the primary research question, and then

identified eight secondary research questions based on the proposed conceptual model

of this study. The chapter moved on to present twenty four (24) hypotheses, which are

formulated in order to investigate eight secondary research questions. The chapter then

presented a summary of these hypotheses, and the final proposed conceptual model, in

which the construct relationships were named based on the 24 hypotheses.

The following chapter presents the methodology, which evaluate the efficacy of

proposed conceptual model of this study, which is depicted in Figure 3.7.

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Chapter Four: Methodology

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4 Introduction to chapter four This chapter discusses the methodology utilised in this study. Section 4.1 of this chapter

discusses the generic research paradigms and designs, and subsequently provides a

detailed description of inductive and deductive approaches. Additionally, this section

provides a detailed description of the quantitative research methodology adopted in this

study.

Section 4.2 discusses the unit of analysis. The development of the survey instrument is

explained in Section 4.3, which includes description of survey methods, and the

measurement scales used in this study. Each measurement scale is explained with

reference to previous studies from which these scales have been adopted. Also, Section

4.3 presents the pre-testing of the measurement scales, and the final survey instrument.

Section 4.4 discusses the data collection and analysis, which includes description of

sample size, participants of the survey (including their selection process), data gathering

and the procedures adopted in analysing the data. Finally, Section 4.5 presents the

process adopted in obtaining the ethics approval from Swinburne University’s Human

Research Ethics Committee. The road map to this chapter is presented in Figure 4.1.

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Figure 4.1 Road map to the methodology chapter

4.1 Research design

Theories guide and influence researchers, when they collect and analyse data (Bryman

2012). The theoretical considerations present numerous questions, and researchers

answer these questions through engaging in related research. According to Bryman

(2012),

theory is important to the social researcher because it provides backcloth and

rationale for the research that is being conducted. It also provides a framework

4.1 Research design


4.2 Unit of analysis

4.4 Data collection and analysis

4.3 Development of survey instrument

4.5 Ethical considerations

4.6 Chapter summary

Positivism

Deductive and inductive approaches

Quantitative research method

Measurement scales

Sampling and sample size

Completion of the survey

Participants of the survey

Data analysis

Pre-testing

Structure of final survey

Objectivism

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within which social phenomena can be understood and the research findings can

be interpreted (p.20).

Alternatively, Bryman (2012) views theory as a late occurrence, and the result of

collection and analysis of data. These two opposing views about theory and research can

be explained by the deductive and inductive approaches to the relationship between

theory and research.

4.1.1 Deductive and inductive approaches

The deductive theory denotes the most common view of the nature of the relationship

between theory and social research (Bryman 2012). According to Walter (2011),

deductive theory is “a way of developing theory that begins with the idea (theory) and

proceeds to collect data to test the validity of the theory” (p. 50). Similarly, as Bryman

(2012) remarks, the deductive process starts with

the researcher, on the basis of what is known about in a particular domain and of

theoretical considerations in relation to that domain, deduces a hypothesis (or

hypotheses) that must then be subjected to empirical scrutiny (p. 24).

Colton and Covert (2007) argue that the deductive approach works from general to

specific. This approach starts by identifying a construct to be examined and then

identifies ways to operationalise that construct. Also, in a deductive research process,

the researcher deduces hypotheses based on what is already known about a particular

domain along with related theoretical considerations, and then empirically scrutinises

the hypotheses. According to Bryman (2012), these hypotheses need to be skilfully

deduced and then translated into operational terms. Later, data is collected in relation to

the concepts that make up these hypotheses. The deductive research process is depicted

in Figure 4.2.

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Source: Bryman (2012)

Figure 4.2 The process of deduction

On the other hand, an inductive approach commences with observations or findings,

which is the opposite association compared to deduction (Bryman 2012). In the final

step of the induction research process, the researcher deduces the implications of the

findings for the theory, which prompted the whole exercise. These findings of the new

study are then fed back into the stock of theory and the research findings, which are

associated with the specific area of the inquiry (Bryman 2012).


Figure 4.3 Deductive and inductive approaches

1. Theory

3. Data collection

4. Findings

5. Hypotheses confirmed or rejected

6. Revision of theory

2. Hypotheses

Theory

Observations/Findings

Theory

Deductive approach

Inductive approach

Observations/Findings

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According to Bryman (2012), in deductivism, “the purpose of theory is to generate

hypotheses that can be tested and that will thereby allow explanations of laws to be

assessed” (p. 28). In inductivism, “knowledge is arrived at through the gathering of

facts that provide the basis for laws” (p. 28). Figure 4.3 shows the essence of the

difference between deductivism and inductivism. The deductive approach is more often

associated with quantitative research, and appears very linear, with one step following

the next in a clear and logical sequence (Walter 2011). The inductive approach works

from specific generalisations (for example, finite items) to broader generalisations

(Colton & Covert 2007).

There are two main philosophical assumptions in social research. These are ontology

and epistemology (Zou & Sunindijo 2015). Ontological questions deals with the nature

of social entities that are under investigation. There are two contrasting ontological

positions, objectivism and constructivism. According to Zou and Sunindijo (2015),

objectivism asserts that social phenomena are independent of social actors, and are

beyond the influence of these actors. On the contrary, constructivism implies that these

social phenomena are produced through social interactions, and are in constant state of

revision. On the other hand, epistemological questions deal with the process of

understanding the social phenomena, and communicating that knowledge to others, and

consist of two contrasting epistemological positions called positivism and interpretivism

(Zou & Sunindijo 2015). Positivism is an epistemological position that supports the use

of natural science methods to study social phenomena, whereas interpretivism believes

that people and institutions (which are the subjects of social research) are fundamentally

different from those of natural sciences. According to interpretivism, knowledge is

subjective based on the experience and insight of individuals, and it considers that

people and institutions are fundamentally different from the natural sciences. Hence,

knowledge has to be personally experienced; it is not transferrable from one medium to

another.

4.1.2 Objectivism

According to Bryman (2012), objectivism is “an ontological position that implies that

social phenomena confront us as external facts that are beyond our reach or influence”

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(p. 32). This approach considers an organisation to be a tangible object, which has rules,

regulations and adopts standardised procedures in getting things done. Also, Bryman

(2012) remarks that these features vary from organisation to organisation, and as a result

it is assumed that the organisation has a reality, which is external to the individual

within it. Since these organisations represent social order, its inhabitants can feel

pressure to conform to requirements of that organisation. As a result, these inhabitants

learn and apply the values of that organisation, follow its standardised procedures, and

perform their jobs. However, inhabitants face adverse effects when they fail to follow

the instructions (Bryman 2012).

4.1.3 Positivism

Bryman (2012) defines positivism as “an epistemological position that advocates the

application of the methods of the natural sciences to the study of social reality and

beyond” (p.28). Zou and Sunindijo (2015) remark that in positivism, it is believed that

knowledge is gained in an objective way, and is transferred in tangible form. According

to Colton and Covert (2007), positivism is concerned with surface events and

appearances; it establishes meaning operationally. Positivism involves the elements of

both deductive and inductive approaches, and draws a sharp distinction between theory

and research (Bryman 2012). This author considers that in positivism, the role of

research is to test the theories and then provide materials for the development of

theories. As explained previously, this study adopts a positivist approach in exploring

the effects of influential determinants, SC relationship constructs, and SC relationship

success on a firm’s operational performance (also identified as operational performance

in this study).

Zou and Sunindijo (2015) assert that the assumptions of a researcher affects the

selection of methodology (i.e. a general orientation to conduct the social research),

design (i.e. a plan to collect and analyse data), and methods (i.e. instrument to collect

data). This study considers operational performance (also firm’s operational

performance in this study) to be an objective reality. As a result, the ontological position

of this study entails the use of positivist epistemological assumption to advance the

knowledge of operational performance of organic fruit and vegetable growers, in an

objective manner. Based on these assumptions, the most appropriate methodology for

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this study is quantitative methodology, which involves collection of standardised data

using a questionnaire. This data can be analysed statistically and the findings can be

generalised.

4.1.4 Quantitative research method

According to Leedy and Ormrod (2013) and Bryman (2012), quantitative research is the

dominant methodology in social research around the world. Zou and Sunindijo (2015)

remark that quantitative research methodology is a method which

measures variables in a numerical way by using standardised instruments with a

purpose to establish relationships among variables. The process involves the

determination of concepts, variables and hypotheses at the beginning of the

research, which are tested after data have been collected (p.184).

According to Walter (2011), the “quantitative research involves the collection and

analysis of data that can be presented numerically or codified and subjected to statistical

testing” (p. 25). The data for a quantitative research is collected from a population or

from samples, which represent the population. Thus, the findings are generalisable

(Leedy & Ormrod 2013).

Quantitative data analysis is a process of systematic observation, collection and analysis

of quantitative data, which effectively test, verify, reject or proffer revised or different

explanations of social life (Walter 2011). According to Zou and Sunindijo (2015) and

Bryman (2012), quantitative research has an objectivist notion of the social reality,

hence quantitative researchers use established guidelines to conduct their research. They

also remain detached from the phenomena and participants that they investigate in order

to draw unbiased conclusions. These types of studies can be carried out in different

ways, such as through surveys, observation and/or through a population level analysis.

According to Walter (2011), the collected data is analysed using computerised statistical

analysis tools, and most common among them is the SPSS. This study uses SPSS as the

main tool in analysing the data.

A typical research process (as depicted in Figure 4.4) consists of the steps that are

undertaken to carry out the research, commencing by determining the research problem

123

and ending with the report of the results (Zou & Sunindijo 2015). This process is

dynamic, however, and the researcher has flexibility in its implementation. Fellows and

Liu (2015) remark that maintaining coherence and complementarity is essential to

achieve robust research results and conclusions. The literature review results in further

defining the research aim and objectives (As shown in Figure 4.4). The findings are

linked back to the literature as it is important to find existing literature on similarities

and differences, and then to explain the implications of the study findings on practice

and also on knowledge development (Zou & Sunindijo 2015).

Source: Zou and Sunindijo (2015)

Figure 4.4 Typical research process

4.2 Unit of Analysis

According to Walter (2011), a unit of analysis is “a particular instance of what or who

we are researching” (p. 72). The unit of analysis can include individuals, groups, social

Res

earc

h

1. Identify research problem

7. Discuss results and findings Key findings Relationships to current literature Model modification Contributions to knowledge Implications to practice

3. Literature review

4. Develop model and hypothesis

5. Collect data, do experiments or simulations

6. Analyse data, test model or hypothesis

2. Determine research aim and objectives

8. Report results

124

artefacts (i.e. newspaper articles or policy documents), or anything else that is related to

social life (Walter 2011). In a typical research process, the researcher first needs to

come to a conclusion and decide what is to be observed. As a result, the researcher can

select the population (i.e. the collection of all units that the researcher wants to study),

and sample (i.e. set of cases that are selected from the population) that is relevant to the

unit of analysis.

The focus of this study is the organic fruit and vegetable industry. This study intends to

explore the nine constructs (as detailed in Chapter 3) based on the perceptions of the

organic fruit and vegetable growers. Hence, the unit of analysis of this study is the

grower of organic fruit and/or vegetable.

4.3 Development of the survey instrument

The survey method is used when the researcher is interested in investigating more than

one case in their study (Bryman 2012). In a survey, the data is collected at the same

time, which is in contrast to an experimental design, where data is collected over a

period of time. The surveys are versatile and allow the investigation into a range of

topics (Walter 2011). Also their structured format allows collecting information on

respondents’ characteristics, attitudes, values, beliefs, behaviours and opinions. Bryman

(2012) argues that the survey method enables researchers to have a systematic and

standardised method for gauging variation and provides them with a consistent

benchmark. Surveys allow researchers to collect information from a large sample in a

relatively short period of time and provide reliable and valid information on a large

population using a comparatively small sample of respondents (Walter 2011).

According to this author, the survey data are comparative in nature, hence they can be

identified and analysed using statistical analysis techniques, which allows in arriving at

a wide range of robust results.

Although surveys come in many forms (Figure 4.5), the web surveys are appealing to

prospective respondents due to their appearance, specifically their use of colour,

formatting and response styles (Bryman 2012; Walter 2011). Also, web surveys can be

customised, easy to download, designed to filter questions, and able to code a large

number of questions easily. The self-administered survey method, which is used in this

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study, has several advantages. Walter (2011) remarks that the survey method is the

cheapest and most-effective data collection technique, and has a higher level of

respondent anonymity, less interviewer influence/bias, and the respondents are able to

complete the survey in their own time. Also, all question types, including open-ended

questions, can be included in a survey. Surveys are easily and rapidly distributed to

many respondents. The various survey types are shown in Figure 4.5.


Figure 4.5 Main modes of administering a survey

The literature review (Chapter 2) of this study helped to identify the key constructs,

which were later used to develop the final conceptual model shown in Figure 3.7. The

constructs of conceptual model were operationalised by items which used a seven-point

Likert scale (anchored at strongly agree and strongly disagree), and these are multiple

indicators. Walter (2011) defines multiple indicators as “different measures of one

concept; often combined to generate a more valid and reliable measure” (p. 224). There

are several advantages of using multiple indicator measures as compared to single

indictor measures. Multiple indicator measures can offset the effects of

misclassifications of a particular question (Bryman 2012). In contrast, a single indicator

measure captures only a portion of the underlying concept or it is too general.

According to Bryman (2012), a measurement scale with a single item captures only one

Survey

Self-completion questionnaire Structured interview

Face-to-face Supervised Postal Internet Telephone

Paper + pencil CAPI Paper +

pencil CATI Email Web

Embedded Attached

Note: CAPI = Computer-Assisted Personal Interviewing, CATI = Computer-Assisted Telephone Interviewing

126

aspect of the construct that needs to be measured, whereas multiple items can capture a

wide range of aspects of that particular construct.

4.3.1 Measurement scales

There are three main reasons for the use of measurements in quantitative research. First,

the measurement allows the researcher to delineate fine differences between people in

terms of the particular characteristic that is in question (Walter 2011). Second, the

measurement provides a consistent device to gauge these fine differences, which are

difficult to detect. Finally, Walter (2011) remarks that the measurement provides us

with precise estimates about the degree of the relationship between different concepts,

which guides us as to how closely a particular concept of our interest is related to other

concepts.

The following sections attempt to operationalise the constructs which were identified in

the conceptual model depicted in Figure 3.7 in Chapter 3. Each of the nine constructs

(information sharing, coercive power, non-coercive power, collaboration, satisfaction,

trust, commitment, SC relationship success and a firm’s operational performance) are

individually considered, and the selection and the process of formulating the individual

items to measure a particular construct is methodically explained. Since no single

previous study had included all the study constructs that are presented in the conceptual

model of this study, several relevant studies were used in identifying and selecting the

measurement scales.


The following section describes the measurement scales relating to the three constructs

(information sharing, coercive power and non-coercive power), which are broadly

categorised as influential determinants of the conceptual model in this study.

Information sharing

The collaborative activities between SC partners are important in achieving competitive

advantage over similar SCs (Nyaga, Whipple & Lynch 2010). Industries seek long-term

127

relationships with fewer SC partners, with whom they work as a focussed team. Hence,

information sharing between these SC partners become a strategic requirement to

achieve closer collaborative activities in the SC (Min et al. 2005). Mohr and Spekman

(1994) define information sharing in a SC as the “extent that the critical information is

conveyed to a party’s relationship partners” (p. 103). Such information includes that

relating to product design, cost, product development strategy, and supply and demand

forecast (Cannon & Perreault Jr. 1999), all of which assist in building close SC

relationships. The information sharing construct in this study aims to capture the extent

of the information shared between the organic fruit and vegetable growers and their

major SC partners (i.e. retailers, wholesalers, processors, exporters, distributors or

restaurants).

Mohr and Spekman (1994) use several items in measuring information-sharing between

computer dealers and their suppliers. These scaled items were pre-tested using personal

interviews with computer dealers, and were revised iteratively. The reliability analysis

was performed, and the items which possessed low item-to-total correlations were

deleted before the survey commenced (Mohr & Spekman 1994). Similarly, Monczka et

al. (1998) used these items to measure information-sharing in strategic supplier alliance

relationships. The information-sharing measure was methodically examined by subject

area experts and industry professionals to ensure the content validity before its

operationalisation (Monczka et al. 1998). In more recent times, Nyaga, Whipple and

Lynch (2010) used these measurement items to measure the perception of buyers and

suppliers on information sharing based on the relationship between buyers/suppliers and

their SC partners. This study adapts the measurement scale used by Nyaga, Whipple and

Lynch (2010) to gauge the perception of organic fruit and vegetable growers on

information-sharing based on their relationship with the major SCs. Table 4.1 presents a

detailed description of the three items of the information-sharing construct used in this

study.

128

Table 4.1 Items used to measure information sharing

Adapted from Original construct

from which the

item was adapted

Item number used

in the survey

instrument

Item description used in the survey

instrument

Nyaga, Whipple and

Lynch (2010) Information-sharing

1 We inform our major SC partner in

advance of changing needs.

2 In this relationship, it is expected that

any information which might help the

other party will be provided.

3 The parties are expected to keep each

other informed about events or

changes that may affect the other

party.

Coercive power

Coercive power involves the imposition of punishment by one partner of the

relationship to another partner, if the requests of the initial partner are not complied with

(John 1984). Such coercive or aggressive strategies in controlling the SC relationship

partner can impact negatively on the relationship (Gelderman, Semeijn & De Zoete

2008). Brown, Johnson and Koenig (1995) use coercive power strategies in measuring

the sources of marketing channel power in their study in exploring the relationships of

retail store employees with their suppliers in the eastern US metropolitan area. In their

study, the measurement scale items of coercive power is adopted from El-Ansary and

Stern (1972), which were later extended by Hunt and Nevin (1974).

Leonidou, Talias and Leonidou (2008) operationalise five items to measure coercive

power in their study of exercised power as a driver of trust and commitment in cross-

border industrial buyer-seller relationships. These authors adopt the measurement scale

items of exercised coercive power from Brown, Johnson and Koenig (1995), Frazier

and Summers (1986), and Kale (1986). All these scale items intend to record the

aggressive behaviour of one partner over the other SC partner, and they include

financial and other penalties, threatening behaviour, and withholding important support.

Adapting the exercised coercive power scale used by Leonidou, Talias and Leonidou

129

(2008), the coercive power construct of this study is comprised of five items listed in

Table 4.2.

Table 4.2 Items used to measure coercive power


from which the

item was adapted

Item number used

in the survey

instrument


instrument

Leonidou, Talias and

Leonidou (2008)

Exercised non-

coercive power

4 Failing to comply with our major SC

partner’s requests will result in

financial and other penalties for our

farm.

5 Our major SC partner threatens to

withdraw from what they originally

promised, if we do not comply with

their requests.


take legal action, if we do not

comply with their requests.

7 Our major SC partner withholds

important support for our farm, in

requesting compliance with their

demands.


deal with another supplier, in order

to make us submit to their demands.

Non-coercive power

According to French and Raven (2001), non-coercive power is derived from four basic

sources of power: reward, legitimate, referent and expert. Non-coercive power does not

include aggressive elements that create frictions in the relationships among SC partners

(Leonidou, Talias & Leonidou 2008), and cultivates high level of agreement between

relationship partners (Frazier & Summers 1984). Hunt and Nevin (1974) operationalise

the non-coercive power of franchisors by assessing the perceptions of franchisees based

on the quality of assistance offered by franchisors. According to these researchers

franchisors establish expertise, reward the compliance with their requests, create

130

attractive SC relationships and legitimise their attempts in controlling other SC partners

by developing and offering high quality assistance (Hunt & Nevin 1974). Hence, non-

coercive sources of power are categorised as forms of assistance offered to one SC

partner by the powerful partner.

Non-coercive power is operationalised by Brown, Johnson and Koenig (1995) in a

study, which explore the sources of marketing channel power in the relationships

between retail store employees and their suppliers in the eastern US metropolitan area.

Frazier and Summers (1986) operationalise non-coercive power in investigating inter-

firm power within franchise SCs. Kale (1986) uses this model of power to explore the

perceptions of dealers on the power influence strategies of manufacturers. Adapting

these non-coercive power measurement scales, Leonidou, Talias and Leonidou (2008)

operationalise non-coercive power using a scale with five items. These five items intend

to explore the perceptions of organic fruit and vegetable growers about the influence on

them by their major SC partner. These influences relating to incentives, decision

making authority, critical information, demand compliance, as a result of contract and

unique competences of the major SC partner. Table 4.3 presents a detailed description

of the five items used in measuring the non-coercive power of this study.

Table 4.3 Items used to measure non-coercive power


from which the

item was adapted

Item number used

in the survey

instrument


instrument

Leonidou, Talias and

Leonidou (2008)

Exercised non-

coercive power

9 Our major SC partner offers specific

incentives to us when we are

reluctant to cooperate with them.

10

Our major SC partner has the upper

hand in the relationship, due to

power granted to them by the

contract.

11

Our major SC partner demands our

compliance because of knowing that

we appreciate and admire them.

12 Our major SC partner uses their

131

unique competence to make our

company accept their

recommendations.

13

Our major SC partner withholds

critical information concerning the

relationship to better control our

company.


Chapter 3 identifies and broadly categorises four constructs: satisfaction, collaboration,

trust and commitment which are termed relationship constructs, and are depicted in the

conceptual model in Figure 3.7 in that chapter. The following sections describe the

formulation of measurement scales of these four constructs and describe related items.

Satisfaction

According to Parasuraman, Zeithaml and Berry (1988), satisfaction emanates as a result

of the comparison between perceived performance and a comparison standard such as

expectation. Ulaga and Eggert (2006) remark that, when performance exceeds

expectation the customer is satisfied, which is known as positive disconfirmation. In a

study conducted in cooperation with the Institute of Supply Management (ISM), Ulaga

and Eggert (2006) analyse satisfaction of senior purchasing managers of manufacturing

companies with relate to their relationship with the supplier. These authors

operationalised satisfaction using a scale with five items. These items intend to

understand the perception of purchasing managers’ satisfaction in the relationship with

the supplier based on daily activities between the respective firms. The measurement

scale was initially developed using responses of in-depth interviews with senior-level

purchasing managers and after a thorough literature review. The scale items were then

tested using a group of purchasing managers, and subsequently items are selected by

fourteen experts in an item-sorting exercise recommended by Anderson and Gerbing

(1991) in enhancing content validity (Ulaga & Eggert 2006).

132

Adapting the satisfaction measurement scale used by Ulaga and Eggert (2006) with

minor changes to the wordings, this study intends to measure the perception of organic

fruit and vegetable growers on their satisfaction pertaining to relationships with their

major SC partner. The descriptions of the five items of the satisfaction construct are

presented in Table 4.4.

Table 4.4 Items used to measure satisfaction


from which the

item was adapted

Item number used

in the survey

instrument

Item description used in the

survey instrument

Ulaga and Eggert

(2006) Satisfaction

14

Our farm regrets the decision to

do business with our major SC

partner.

15 Our farm is very satisfied with our

major SC partner.

16 Our farm is very pleased with what

our major SC partner does for us.

17 Our farm is not completely happy

with our major SC partner.

18

Our farm would still choose to use

our major SC partner, if we had to

do it all over again.

Collaboration

Spekman, Kamauff Jr and Myhr (1998) argue that collaboration is the highest level of

purposeful cooperation. Collaboration is also the main force in achieving effective SC

relationships (Min et al. 2005). Heide and John (1990) conducted a study to unearth the

determinants of joint action in buyer and supplier relationships in an industrial

purchasing context. In their study the joint action is defined as the “degree of

interpenetration of organisational boundaries” (p. 25). According to Heide and John

(1990) SC relationship partners carryout their activities in a cooperative and coordinated

way, and organisational boundaries are penetrated by the integration of these activities.

The target firms manufactured machinery (except electrical), electrical and electronic

133

machinery, and transportation equipment. The constructs are tested using a seven-point

Likert scale and a subsequent mail survey, which yielded 175 responses. In a separate

study, Zaheer, McEvily and Perrone (1998) explored the effects of inter-organisational

and interpersonal trust and its effects on performance in buyer-supplier relationships in

electrical equipment manufacturing industry. Corsten and Felde (2005) measured

collaboration using a four-item scale adapted from Heide and John (1990) and Zaheer,

McEvily and Perrone (1998). These four items include joint product development,

including technology and process development, target costing and project planning.

Corsten and Felde (2005) used seven-point Likert scale items which were pretested

during a purchasing seminar with thirty participants.

This study adapts the collaboration measurement scale from Corsten and Felde (2005)

with minor changes to wording in the four items used in the scale. This study aims to

measure growers’ perception of collaboration, which is a result of day-to-day activities

between the growers and their major SC partners in the organic fruit and vegetable

industry. The collaboration scale used in this study is presented in Table 4.5.

Table 4.5 Items used to measure collaboration


from which the

item was adapted

Item number used

in the survey

instrument


instrument

Corsten and Felde

(2005)

Supplier

collaboration

19 We are working closely with our

major SC partner in technology

sharing and its development.

20

We are working closely with our

major SC partner in process

development in the supply chain.

21 We are working closely with our

major SC partner in target costing.

22

We are working closely with our

major SC partner in planning of the

supply chain activities.

134

Trust

Trust is important in improving relationships between SC partners, and according to

Lindgreen (2003) it enhances performance in the SC. Kumar, Scheer and Steenkamp

(1995b) define trust as the collection of trust in a partner’s honesty (i.e. partner stands

by its word) and benevolence (i.e. partner is interested in the firm’s welfare). These

authors explore the effects of perceived interdependence on dealer attitudes, and

specifically how SC partners’ interdependence influences trust among them in the

automobile industry. In their study a mail survey is conducted with items using Likert

scales. Trust is operationalised in two parts (i.e. based on honesty and benevolence), and

each of them contain five items (Kumar, Scheer & Steenkamp 1995b). Corsten and

Felde (2005) adapt these trust measures in their study to explore performance effects of

key supplier collaboration. However, these authors use single six-item measures

compared to ten items used by Kumar, Scheer and Steenkamp (1995b). The

measurement scale for trust is developed and then tested with thirty participants of a

purchasing seminar to assess supplier’s honesty and benevolence. This study adapts the

trust scale from Corsten and Felde (2005), with minor word changes. The study intends

to measure growers’ perception of trust, which is a result of day-to-day activities

between the growers and their major SC partners in the organic fruit and vegetable

industry. Table 4.6 present the six-item scale that is used to measure trust in this study.

Table 4.6 Items used to measure trust


from which the

item was adapted

Item number used

in the survey

instrument


instrument

Corsten and Felde

(2005) Trust

23 Whoever is at fault, problems need

to be solved together.

24 Both parties watch the other’s

profitability.

25 Our major SC partner has high

integrity.

26 There are no doubts regarding our

major SC partner’s motives.

27 Both parties are willing to make

135

mutual adaptations.

28

If our major SC partner gives us

some advice, we are certain that it is

an honest opinion.

Commitment

According to Anderson and Weitz (1992), commitment assists in achieving mutual

gains through closer SC partner relationships. Also, the commitment of SC partners

facilitates in exerting considerable effort on behalf of their relationships (Monczka et al.

1998; Spekman, Kamauff Jr & Myhr 1998), and it is the key component to successful

SC relationships (Morgan & Hunt 1994). Employing logistics managers of

manufacturing firms, Moberg and Speh (2003), evaluated how commitment influences

the relationship between questionable business practices and the strength of SC

relationships. These authors used a four-item scale to measure commitment which is

adapted from Wilson and Vlosky (1997), in which respondents were required to rate the

overall quality of the relationship with the chosen trading partner on a scale from one to

ten. Although Moberg and Speh (2003) use a validated measure of commitment, they

also conducted a pre-test of the survey instrument through several logistical managers,

and made few changes to the wordings of the original items to suit the study context.

In a separate study, Nyaga, Whipple and Lynch (2010) adapted the commitment scale of

Moberg and Speh (2003), by examining SC relationships in identifying different

perspectives of buyers and suppliers on collaborative relationships. In their study,

Nyaga, Whipple and Lynch (2010) used a seven-point Likert scale in measuring the

perception of buyers and suppliers on commitment based on the relationship between

buyers/suppliers and their SC partners. This study adapts the measurement scale used by

Nyaga, Whipple and Lynch (2010), with minimal context-specific word changes, in

gauging the perception of organic fruit and vegetable growers on the commitment based

on their relationship with their major SC partners. The measures used to operationalise

commitment in this study are presented in Table 4.7.

136

Table 4.7 Items used to measure commitment


from which the

item was adapted

Item number used

in the survey

instrument


instrument

Nyaga, Whipple and

Lynch (2010) Commitment

29 We expect this relationship to

continue for a long time.

30 We are committed to our major SC

partner.

31 We expect this relationship to

strengthen over time.

32

Considerable effort and investment

has been undertaken in building this

relationship.

Performance

SC relationship success (also known as SC performance) and firm’s operational

performance (also known as operational performance) are broadly identified in this

research as performance (please refer to Figure 3.6).

SC relationship success (SC performance)

The success of relationships among SC partners assists in overcoming service

difficulties (Coyle et al. 2008) and substantially reduces product costs, lead times, and

improves quality of the products (Rinehart et al. 2004). Kannan and Tan (2006) have

studied the success of buyer-supplier relationships (SBSR) in a survey among senior

purchasing and supply managers identified from the Institute of Supply Management

and the Association of Operations Management. The other constructs in the study are

buyer-supplier engagement, supplier selection and firm performance. In their study,

measurement items were identified after an in-depth literature review, and then through

a comparison with company manuals of two manufacturing firms which were closely

connected to the suppliers (Kannan & Tan 2006). The revised items list was then

reviewed by ten industry professionals to verify the accurate representation of industry

practices. Finally, the revised survey instrument was pre-tested by a group of twenty

industry professionals. As a result, Kannan and Tan (2006) have identified a four-item

137

measure for SBSR. This study adopts the SBSR measure with minimum changes to

wordings to measure the perceptions of organic fruit and vegetable growers on SC

relationship success based on quality, SC monitoring cost, assistance received, and

communication with their major SC partners. Table 4.8 presents a description of the

four items used in measuring the SC relationship success in this study.

Table 4.8 Items used to measure SC relationship success


from which the item

was adapted

Item number

used in the

survey

instrument


instrument

Good relationships with our major

supply chain partner has yielded

success in …

Kannan and Tan

(2006)

Success of buyer-

supplier relationship

33 quality of fruits and vegetables to

the end consumer

34 terms of lowering SC monitoring

cost

35 terms of assistance received during

difficult times

36

terms of increasing cooperation and

communication between you and

your major SC partner

Firm’s operational performance (operational performance)

The operational performance, which is identified in this study as a firm’s operational

performance, is achieved through the improvements of delivery reliability,

responsiveness, total cost reduction, lead time, conformance to specifications, process

improvement and time-to-market (Panayides & Lun 2009). Cousins and Menguc (2006)

explored the “implications of socialisation and integration in SC management” (p. 604)

in an empirical study covering buyers and suppliers of pharmaceutical, automotive,

communications, financial services and transportation. The survey in their study was

designed based on a comprehensive literature review, 25 interviews with manufacturing

managers, item selection using 20 purchasing directors/managers, and finally using the

advice of five academics. Also, these authors measured supplier’s operational

performance using a seven-item scale, covering total cost reduction, delivery to

138

schedule, quality improvement, conformance to specifications, lead times, time-to-

market and process improvement (Cousins & Menguc 2006).

Later, Panayides and Venus Lun (2009) adapted the operational performance measure

of Cousins and Menguc (2006), and explored “the impact of trust on innovativeness and

supply chain performance.” (p.35). Their study equated SC performance with the

operational performance of the SC, which is measured with seven-items using seven-

point Likert scales. These authors also conducted a detailed literature review, in-depth

interviews, and a pilot test to investigate the items to ensure the context appropriateness

of selected measures and their individual items. This study adapts the seven-item

operational performance scale used by Panayides and Venus Lun (2009), with context-

specific minor changes to the wording of the items. Further, this measure is intended to

capture the organic fruit and vegetable growers’ perceptions on the improvements to

their firm’s operational performance as a result of the relationships with their major SC

partner. The seven-item firm’s performance scale used in this study is presented in

Table 4.9.

Table 4.9 Items used to measure firm’s operational performance


from which the

item was adapted

Item number used

in the survey

instrument


instrument

As a result of the relationship with

our major SC partner, improvements

have been noticed in the following

areas…

Panayides and Venus

Lun (2009)

Supply chain

performance

37 Reliability of deliveries

38 Responsiveness of our farm to

outside queries

39 Total cost reduction

40

Lead time (time between order

placement and delivery of produce

to your major SC partner)

41 Conformance to specifications

42 Process improvement of our farm

43 Time-to-market

139

4.3.2 Pre-testing

The review of previous literature serves as the main source in identifying the study

constructs and their respective measurement scales of this study. After initial

identification, the constructs and the survey instruments were tested using three

academic professionals and three industry professionals. Based on their views, the study

constructs and their relevant measurement scales were refined to a draft stage after

undertaking necessary changes to represent the organic fruit and vegetable industry.

The next stage was the pre-testing stage of the survey instrument. Eight industry

stakeholders were asked to provide feedback on the content and the clarity of the

individual measurement items, as well as on the layout and structure of the survey

instrument. These stakeholders were as follows,

An organic fruit and vegetable grower with 25 years of experience

An organic fruit and vegetable grower with 15 years of experience

An organic fruit and vegetable wholesaler with 15 years of experience

An organic fruit and vegetable retailer with 10 years of experience

One fruit and vegetable retailer with 25 years of experience

An advocate of sustainable business models in agriculture, food and beverage

industries in Australia

One industry professional and a grower working for an organic fruit and

vegetable certification authority in Australia

An organic fruit and vegetable grower with 10 years of experience, and also an

official of a local organic fruit and vegetable association of Victoria, Australia.

Final changes were made based on the feedback of these stakeholders. The changes

included changes to the format, structure and wording, as all measurements used in this

study are adopted from previous studies conducted in different contexts.

140

4.3.3 Structure of the final survey instrument

The final survey instrument (Appendix 1) used in this study comprises 54

items/questions, with one screening question. These 54 items/questions have been

organised into two sections for ease of understanding and completion. Section one of

the survey consists of 11 questions, and is intended to collect background and/or

demographic information from organic fruit and vegetable growers. The first question

of section one aims to identify whether the respondent is a certified organic fruit and

vegetable grower. If the answer is “yes”, then the respondent is channelled to the second

question, which is related to organic certification bodies in Australia. If the answer is

“no”, the respondent is channelled to the third question, skipping the second question.

The ninth question is related to percentages of fruits and vegetables assuming that

growers only grow these two products, and comprises of two parts. Respondents only

need to fill part one of this question, and part two is automatically filled based on the

answer given in part one (please refer to final survey instrument in Appendix 1). The

tenth question requires the respondents to select their major SC partner based on day-to-

day operations in the organic fruit and vegetable industry. The eleventh question intends

to ascertain the duration of the grower’s relationship with the present SC partner. Some

of the questions are provided an option for respondents to fill their answer, if they are

unable to select most appropriate answer from multiple choices provided. Table 4.10

presents the organisation of the 54 items/questions, their relevant constructs and

respective sections.

Table 4.10 Description of survey items/questions and their relative sections

Construct/description Question/item numbers

Section one

Screening, background and demographic questions 1-11

Section two

Information sharing 12-14

Coercive power 15-19

Non-coercive power 20-24

Satisfaction 25-29

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Collaboration 30-33

Trust 34-39

Commitment 40-43

SC relationship success 44-47

Firm’s operational performance 48-54

Section two of the survey starts with an introduction, which asks respondents to select

the most appropriate answer, and describes the seven-point Likert scale, which is used

in all the questions. The 43 questions (or items) in section two belong to the nine

constructs in this study, and respondents are required to select the most suitable answer

from a scale of 1 (strongly disagree) to 7 (strongly disagree). The appearance of the

survey is developed displaying relevant art works and colours for respondents’ easy of

understanding. Also, there are descriptions for some questions.

4.4 Data collection and analysis

4.4.1 Sampling and sample size

There are two main types of sampling: probability sampling and non-probability

sampling (Walter 2011). Probability sampling is exclusively connected to quantitative

research methodologies, and is mostly used in surveys along with quantitative analysis

techniques. Non-probability sampling is mostly associated with qualitative research

methodologies. Bryman (2012) identifies three sources of sampling bias, use of non-

probability sample (method used to select sample is not random) , inadequate sampling

frame and non-response (sample members refuse to participate). Although it is difficult

to eliminate bias altogether, it needs to be eliminated as much as possible (Bryman

2012).

According to Bryman (2012), the sample size depends on a number of considerations,

and there is no definitive sample size. Time and costs affect the decision about the

sample size and researchers are required to compromise between time and costs, and the

need for precision. Large samples produce smaller standard errors (Walter 2011), and

increase the precision of the sample, and in other words sampling error decrease with

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the increase of sample size (Bryman 2012). However, use of much larger samples to

reduce sampling error provides diminishing returns. Walter (2011) identifies four

factors which define sample size used in any study, i.e. amount of variation in key

variables in the population, confidence level set by the researcher, amount of sampling

error that the researcher is prepared to tolerate and the size of the population. Although

a 95 percent confidence level is generally acceptable, a 99 percent confidence level can

be achieved with an increased sample size, but with additional costs (Walter 2011).

Kline (2011) remarks that the minimum sample size required for a small to medium

sized study model in order to apply SEM, is between 100 to 150, or ten times the

number of parameters to be estimated. On the other hand, the required sample size for

SEM using ML estimation is between 100 to 400 or a ratio of at least five responses per

each construct to be studied (Reisinger & Mavondo 2007). Bollen (1989) highlights that

the ratio of minimum sample size to the number of parameters to be estimated, is three

to five respondents for each parameter.

4.4.2 Survey participants

This study intends to employ growers of the organic fruit and vegetable industry in

Australia as potential survey respondents. The grower must be certified with one of the

organic certification authorities in Australia to be qualified as a prospective respondent.

Since there are several organic certification bodies in Australia, and due to the

unavailability of an organic fruit and vegetable growers list, this study used publicly

available web sites to obtain a detailed list of certified organic fruit and vegetable

growers in Australia. Hence, the following websites and publications were used in

preparing a detailed list of potential respondents. The Australian organic food directory,

the Victorian organic products directory published by the Organic Federation of

Australia (OFA), United States Department of Agriculture (USDA), Australian

Certified Organic (ACO), National Association for Sustainable Agriculture Australia

(NASAA) Certified Organic Pty Ltd, Organic Growers of Australia (OGA), AUS-

QUAL Pty Ltd, Bio-Dynamic Research Institute (BDRI), Safe food production

Queensland, Tasmanian Organic Dynamic Producers (TOP), The Organic Food Chain

Pty Ltd (OFC), TM Organics, Environment society of Australia and Organic farms in

Western Australia. Several web searches yielded a sampling frame with 927 certified

organic fruit and vegetable growers of Australia. This sampling frame contained email

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addresses, telephones numbers and business names (in some cases, only the names of

owners, partners or managers).

All the 927 potential respondents were contacted by email and were sent the “research

information statement” (Appendix 2). This statement introduced the researcher, the

study, purpose, ethical considerations, its benefits to the industry, intended use of

collected data, and contact details of the principal investigator and the student

investigator. The statement advised potential respondents that their participation was

voluntary, and assured them that their responses would be anonymous and confidential.

The “research information statement” also provided full contact details of Swinburne

University's Human Research Ethics Committee (SUHREC). The email contained a

customised link to the survey, which when clicked guided the respondent to the first

question of the survey.

4.4.3 Completion of the survey

The sampling frame was compiled with the email addresses of the 927 potential

respondents (i.e. organic fruit and vegetable growers), and it was assumed that they

would have easy access to internet facilities, which were required to complete the online

survey. The survey was available online via the Opinio survey platform, which

facilitates the production, and publication of web-based surveys and which is the

standard online survey platform used by the Swinburne University of Technology. Also,

the survey was available 24 hours a day, 7 days a week. This enabled potential

respondents to complete the survey in their own time.

The introductory email contained a personalised link, which guided respondents to

‘question one’ of the survey instrument. The time to complete the survey was estimated

to be 15 minutes. Respondents were notified (at the time of their submission) when a

question or a part of a question was inadvertently missed, which assisted them to

complete and re-submit. Once the respondents complete at least one question, the

system provided them with an automatically generated unique identification number.

This identification number remains same, even if the respondent completes the survey

in few steps spanning over few days. The researcher was unable to identify respondents

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using this identification number due to system restrictions. Once the survey was closed

for respondents, the data was downloaded and analysed using SPSS version 22.

4.4.4 Data analysis

The data analysis stage incorporates several elements apart from the application of

statistical techniques to the data that has been collected (Bryman 2012). According to

Bryman (2012), this stage is

fundamentally about data reduction, which is concerned with reducing the large

corpus of information that the researcher has gathered, so that he or she can

make sense of it. Unless the researcher reduces the amount of data, it is more or

less impossible to interpret the material. (p. 13)

After the collection of data has been completed, the raw data was screened to delete the

responses, which were not suitable for analysis. Initially the data was checked for

incomplete responses (missing value analysis), and then data imputation method was

used for the responses, which consisted of 10-15% of missing values. In the next step,

the responses were checked for unengaged responses using standard deviation values.

The reverse coding was performed on one item of the satisfaction construct. The

responses were checked for outliers, normality, skewness and kurtosis, and effects on

normality in sections 5.1.4, 5.1.5, 5.1.6 and 5.1.7 respectively. Respondents’ profiles

were described in section 5.2, and data analysis procedure is discussed in section 5.3.

Model evaluation fit indices are introduced and explained in section 5.4. The validity

and reliability are explained in Section 5.5. Confirmatory factor analyses (CFA) for one

factor congeneric models were performed in Section 5.6, and once the structure of each

construct was confirmed, the reliability were measured for all these constructs in

Section 5.6.3.

The CFA was performed for all the study constructs in two parts (two measurement

models) in Section 5.7. Structural model estimation was presented in section 5.8, which

included path analysis, forming composite variables, regression coefficient and

measurement errors for composite variables, initial SEM model, model re-specification,

and final SEM model. Finally, section 5.9 aimed to explain and identify mediation,

total, direct and indirect effects between each construct.

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4.5 Ethical considerations

The approval for the data collection was sought from the SUHREC in order to comply

with the ethical and legal requirements of this study. The SUHREC has an established

policy, which must be strictly followed prior to any form of data collection, and the

researcher is only authorised to collect data after receiving a written approval from

SUHREC. The process required the completion of a detailed and extensive ethics

application, which included following sections.

Description and justification for this study

The data collection procedures, including risk analysis and benefits of the study

The training requirements of the researchers involved in the study in terms of

professional and ethical conduct

Intended use of the collected data

Detailed description of the participants of the study and the recruitment process

Disclosure information and compensation provided

Data collection and data scrutiny

Data storage and safety

Publication intentions

Data collection commenced immediately after the ethics clearance (Appendix 3) was

granted by the SUHREC. The potential respondents were informed about the study

through an email. Informed consent was implied when the participants voluntarily

commenced the online survey. All the SUHREC ethics guidelines were appropriately

actioned to protect the participants and the researcher, to preserve the integrity of the

study, assure the confidentiality and anonymity of the participants, and also to maintain

the data security. Regular periodic reporting to the SUHREC was undertaken

throughout the duration of the study. The highest ethical behaviour standards have been

exercised in this study as specified by the SUHREC.

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4.6 Chapter summary

This chapter has provided a comprehensive description of the methods employed to

collect data for this study. The initial sections of Chapter 4 described deductive and

inductive approaches and positivism, followed by a description of the unit of analysis.

These sections also provided details of quantitative methodology in collecting and

analysing the data used in this study. The typical research process adopts in social

research was presented in Section 4.2.4. Section 4.4 provided a detailed description of

the development of the survey instrument including a detailed description of the

formulation of measurement scales and their validity. Section 4.4.3 described the pre-

testing stage of the survey instrument, followed by the structure of the final survey

instrument in Section 4.4.4.

A description of sampling frame, methods employed in finding participants and

completion of the survey were described in Section 4.5. Section 4.5.4 provided an

explanation of the data analysis, including the steps adhered in preparing the raw data

for analysis, and final steps in the analysis. Chapter 4 concludes by highlighting the

ethical and legal considerations in Section 4.6, which describes the procedure in

obtaining the ethics clearance from SUHREC for this study. The following chapter

(Chapter 5) provides the analysis and findings of this study.

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Chapter Five: Analysis and findings

148

5 Introduction to chapter five This chapter presents the results of the data collection. Section 5.1 will describe the

preparation process of sample data prior to the analysis, which includes screening for

missing data and imputation, checks for unengaged responses, reverse coding, checks

for outliers, normality, non-response bias, checks for skewness and kurtosis. Section 5.2

will present profiles of survey respondents. Section 5.3 briefly explains the study’s data

analysis procedure, which includes SEM and the two-step approach. Section 5.4

presents model evaluation fit indices, which describe absolute fir measures, model

comparison and relative fit measures, and non-centrality based indices. Validity and

reliability is explained in Section 5.5. Section 5.6 presents the CFA for one factor

congeneric models, which presents items codes, measurement models, common method

variance, reliability of constructs, and a summary of the measured constructs. Section

5.7 describes the overall CFA in two measurement models, while Section 5.8 details the

structural model estimation. The latter section also describes path analysis, initial SEM

model, model re-specification and final SEM model. Mediation, total, direct and

indirect effects between constructs are presented in Section 5.9. A chapter summary is

presented in Section 5.10. The roadmap of this chapter is presented in Figure 5.1.

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Figure 5.1 Road map to the analysis and findings chapter

Unengaged responses

Missing data imputation

Reverse coding

Normality

Outliers

Non-response bias 5.1 Data preparation procedure

5.0 Introduction

5.2 The respondent’s profiles

5.4 Model evaluation: Fit indices

5.3 Data analysis Procedure

5.5 Assessment of Construct Validity and Reliability

5.7 Overall confirmatory factor analysis

5.6 CFA for one factor congeneric models

5.8 Structural model estimation

5.9 Mediation, total, direct and indirect effects between constructs


Skewness and kurtosis

Sample size and its effects on normality

Absolute fit measures

Goodness of fit

Model comparison and relative fit measures

Non-centrality based indices Item codes

Measurement models for each measured construct

or latent variable

Reliability of constructs

Validity

Overall CFA for measurement model 1

Overall CFA for measurement model 2

Constructs affecting collaboration

Constructs affecting satisfaction

Constructs affecting trust

Constructs affecting SC relationship success

Constructs affecting commitment

Constructs affecting performance

Path analysis

Composite variables

Regression coefficient and measurement error for composite variables

Model re-specification

Initial SEM model

Final SEM model

Two-Step Approach

Structural Equation Modeling (SEM)

Summary of constructs

Reliability

Common method variance

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5.1 Data preparation procedure

5.1.1 Missing data and imputation

The total number of responses collected using the online survey were 323, which is

34.84% of the sample of 927. Initial screening of data set revealed that there were 34

incomplete responses containing 95% of missing data. These 34 responses were

removed, which resulted in 289 completed responses.

According to Graham et al. (1997), there are four main imputation methods:

expectation-maximization (EM) algorithm, multiple imputations, multiple group

structural equation modeling (MGSEM), and maximum likelihood (ML). The two

imputation methods considered in this study were full information maximum likelihood

(FIML) and expectation maximisation (EM), both of which are facilitated by the SPSS

software. The EM was deemed more appropriate since it is an iterative imputation

method, and according to Graham et al. (1997), missing values are imputed using many

other variables in a regression model.

Four responses out of the 289 completed surveys had approximately 10-15% of the data

missing. As explained above, these missing data were imputed using the EM facility in

SPSS. The imputation process resulted in Little's MCAR (missing completely at

random) Chi-Square (χ2) = 238.590, df = 210, Sig. = 0.086, which justifies that the EM

imputation method used in this study is valid.

5.1.2 Unengaged responses

The standard deviation was calculated using SPSS version 22 for all completed

responses, and there were two responses with zero (0) standard deviation. This means

that these two respondents were not engaged as required, hence they were deleted. The

balance 287 (30.96% of initial sample of 927) responses had a standard deviation of

above 0.5 (> 0.5), and thus were retained for further analysis.

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5.1.3 Reverse coding

The satisfaction construct was measured using a five-item scale. One item (Sat1) of this

scale was a negatively-worded question. Thus, Sat1 was reverse coded, and renamed as

ReSat1.

5.1.4 Outliers

Outliers are observations of unique combination of characteristics, which are distinctly

different from other observations (Hair et al. 2010). To be distinctly different, a

particular observation should have an unusually high or low value compared to other

observations. There are two types of outliers: univariate outliers and multivariate

outliers (Hair et al. 2010). According to Wong, Wilkinson and Young (2010), the

univariate outliers only exists when the |Z-score| > 3. Analysis results indicate that there

is no |Z-score|, which is greater than 3, hence there are no univariate outliers in this data

set. Subsequently, the data set is analysed to identify possible multivariate outliers.

According to Wong, Wilkinson and Young (2010), multivariate outliers exist, when the

Mahalanobis distance is more than 4 (D2/df > 4). The analysis results indicate that the

Mahalanobis distance for all the cases are below 4, hence there is no multivariate outlier

in the data set.

5.1.5 Normality

Hair et al. (2010) emphasise that the most fundamental assumption of multivariate

analysis is normality, and normal distribution is the benchmark for statistical methods.

These authors also highlight that non-normality is, firstly based on the shape of the data

distribution, and secondly on sample size. The maximum likelihood estimation (MLE or

ML) and generalised least squares (GLS) require the assumption of multivariate

normality of data (Hair et al. 1998; McDonald & Ho 2002; Ory & Mokhtarian 2010;

Reisinger & Mavondo 2007). When data violates the normality, ML and GLS

estimation can produce biased standard errors and incorrect test statistics (Hair et al.

2010; Reisinger & Mavondo 2007). According to Hancock and Mueller (2006), the

greater the non-normality of data, the greater the impact on results. Thus, the data must

be checked rigorously for normality prior to analyses.

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As suggested by Hancock and Mueller (2006), the frequently used indices for non-

normality are skewness and kurtosis, which are used to evaluate the distribution of

observed data. Although there is no clear consensus regarding the acceptable level of

non-normality, previous studies (Chou & Bentler 1995; Curran, West & Finch 1996),

have concluded that skewness and kurtosis values higher than 2 create non-normality.

5.1.6 Non-response bias

The nonresponse bias is a major concern in surveys, and if the persons who responded

differ substantially from those who did not respond, the results would not allow to

predict how the entire sample would have responded (Armstrong & Overton 1977).

According to these authors the most commonly recommended protection against

nonresponse bias is the reduction of the nonresponse bias itself, and estimation of

nonresponse bias is important to understand the effects of nonresponse. Armstrong and

Overton (1977) suggest three methods of estimation, i.e comparisons with known values

for the population, subjective estimates and extrapolation. The extrapolation methods

are based on the assumption that late respondents are more like non-respondents as they

are assumed to have responded due to increased stimulus through follow up methods.

Nonresponse bias is tested in this thesis using mean values of latent constructs, which

are used in the final model. The data set was categorised into two groups, i.e. based on

responses received during first four weeks and responses received after four weeks. The

independent sample t-test is employed using SPSS version 22. The results indicate that

there is no significant (> 0.05) difference between the data collected within four weeks

of launching the survey and the data collected after first four weeks of the survey.

Hence it can be stated that nonresponse bias does not influence the sample data used in

this thesis.

5.1.7 Skewness and kurtosis

The skewness and kurtosis values were calculated for each of the items using SPSS

version 22. All recorded skewness values were within the acceptable value of between

-2 to 2, hence they were deemed acceptable for further analysis. However, four of the

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kurtosis values were greater than 2, which is outside the acceptable value. The highest

kurtosis value recorded was 2.23, for CP3. There were three other kurtosis values which

fell outside the acceptable limit, and these are depicted in Table 5.1. However,

Reisinger and Mavondo (2007) affirm that ML and GLS methods can be used as a slight

or moderate departure from normality. Hence the data was used for further analysis,

even though the kurtosis values were marginally outside the acceptable limit.

Table 5.1 Kurtosis values

Tru1

(Trust 1)

Tru6

(Trust 6)

Sat2

(Satisfaction 2)

CP3

(Coercive Power 3)

No of growers 287 287 287 287

Kurtosis 2.018 2.079 2.220 2.230

Std. Error of kurtosis 0.287 0.287 0.287 0.287

5.1.8 Sample size and its effects on normality

As Hair et al.(2010) argue, it is important to consider the effects of sample size as this

can increase the statistical power by reducing the sample error. If the samples size is

less than 50, there can be significant departures from normality, and these can affect the

results substantially (Hair et al. 2010). The larger sample sizes, however, reduce the

detrimental effect of non-normality, and the effect is negligible for samples of more

than 200. Therefore, non-normality is unlikely to be a problem, since the sample size in

this study is 287.

5.2 The respondents’ profiles

Publicly available websites were used to obtain contact details (names and email

addresses) of organic fruit and vegetables growers. These growers were then invited to

participate in the online survey (see Chapter 4 for more details). Several efforts were

taken to conduct web searches to collect the details of the maximum possible number of

organic fruit and vegetable growers in Australia. As a result, the total number of email

addresses of organic fruit and vegetable growers collected were 927. The online survey

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using the university’s Opinio platform was made live on 5 July 2013. The participants

were informed of the purpose of study, and of the subsequent use of collected data

through an email with a link to the survey. One hundred and sixty two (162) emails did

not go through, suggesting that those email addresses were not valid. Subsequently, two

follow-up emails were sent to improve the response rate. After accounting for missing

data, unengaged responses, and imputation, a final of 287 survey responses were

deemed useful for data analysis.

As depicted in Table 5.2, majority of the survey respondents were owners/partners of

fruit and vegetable farms, and they accounted for 84.7% of the total. Another 14.3% of

the respondents were farm managers. Hence 99% of the total 287 respondents were

major decision makers/or experienced farmers, and they possess high level of

knowledge of SC activities, and also engage daily with their major SC partners. As

explained by Kumar, Stern and Anderson (1993), these respondents (informants) satisfy

the criterion of using key informants due to their knowledge on the SC activities.

Table 5.2 Respondent’s position in the organisation

Position in the farm Frequency Percentage

Owner/Partner 243 84.7

Manager 41 14.3

Other 3 1.0

Total 287 100.0

The names of seven organic certification bodies were included in the survey instrument

for participants to select their respective certification body. As seen in Table 5.3, the

responses indicated that the majority of respondents (46.3%) were certified with

Australian Certified Organic (ACO), while 45.6% of respondents selected National

Association for Sustainable Agriculture, Australia (NASAA) as their certification body.

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Table 5.3 Organic certification

Certification body Frequency Percentage

Australian Certified Organic (ACO) 133 46.3

National Association for Sustainable Agriculture, Australia (NASAA) 131 45.6

Tasmanian Organic Dynamic producers (TOP) 10 3.5

AUS-QUAL Pty Ltd 5 1.7

Organic Food chain (OFC) 2 0.7

Bio-Dynamic Research Institute (BDRI) 1 0.4

Organic Growers of Australia (OGA) 1 0.4

Both NASAA and ACO 3 1.0

Both ACO and BDRI 1 0.4

Total 287 100.0

Another 3.5% of organic fruit and vegetable farms were certified with Tasmanian

Organic Dynamic producers (TOP), 1.7% of farms were certified with AUS-QUAL Pty

Ltd, and 0.7% of farms were certified with Organic Food chain (OFC). 0.4% of farms

were certified with Bio-Dynamic Research Institute (BDRI), and with Organic Growers

of Australia (OGA), which was not in the initial seven certification bodies included in

the survey. Three farms (which represent 1.0% of respondents) were certified with both

NASAA and ACO, and another 0.4% was certified with both ACO and BDRI.

These organic fruit and vegetable growers also engaged in secondary (that is, non-

growing) activities such as retailing, processing and wholesaling. 29.6% of growers (n =

85) are involved in retailing, 26.8% of them (n = 77) undertake the processing of fruits

and vegetables, and 23.7% of growers (n = 68) are involved in wholesaling (Table 5.4).

The data also suggests that there are growers who are performing more than one

secondary activity. Few other growers involved in activities like direct selling (on-farm

sales), exporting of fruits and vegetables and selling through farmers markets. The

growers who exported their produce were less than 1%, which comprised only 2

growers out of 287. 45.6% of the respondents (n = 131), however, indicated that they

were only involved in the growing of organic fruits and vegetables.

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Table 5.4 Activities of growers

Activity Frequency Percentage

Only growing 131 45.6

Retailing 85 29.6

Processing 77 26.8

Wholesaling 68 23.7

Direct selling 3 1.0

Exporting 2 0.7

Farmers markets 1 0.3

As seen in Table 5.5, the majority of the respondents were involved in fruit and

vegetable growing for a long time. As such 79.1% of the growers (n = 227) had more

than 10 years of experience, 39% (n = 112) had more than 15 years of experience, and

24% (n = 69) had more than 20 years of growing experience. Interestingly, only 3.5% of

growers (n = 10) had less than 5 years of experience in fruit and vegetable growing.

Hence, many of the survey respondents are experienced fruit and vegetable growers,

and this also indicates their exposure to SC activities in this industry.

Table 5.5 Duration of involvement in fruit and vegetable growing

Years in growing Frequency Percentage

Less than 5 years 10 3.5

5 to less than 10 years 50 17.4



More than 20 years 69 24.0

Total 287 100.0

Although these respondents were involved in fruit and vegetable growing for a long

time, they were not involved in organic fruit and vegetables growing from the start of

their career. As depicted in Table 5.6, only 64.1% of respondents had more than 10

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years of experience, 17.1% had more than 15 years of experience, and only 5.6% of

them had more than 20 years of experience in organic fruit and vegetable growing.

These data also indicates that many of these growers started with conventional

vegetable growing, and subsequently moved to organic growing, or they grow both at a

later stage.

Table 5.6 Experience in organic fruit and vegetable growing

Years in growing Frequency Percentage






Total 287 100.0

More than a quarter of Australian organic fruit and vegetable growers operate farms that

are small-scale in terms of their annual turnover. As depicted in Table 5.7, 29.3% of

Australian organic fruit and vegetable farms (n = 84) have an annual turnover of less

than AUD 100k. The majority of Australian organic fruit and vegetable growers operate

medium sized farms (farm with turnovers between AUD 100k and 500K), and they

account for 58.9% (n = 169) of the total. 11.9% of organic growers (n = 34) operate

larger farms, which earn in excess of AUD 500k per annum. Out of these farms 9% (n =

26) had an annual turnover of more than AUD one million. Interestingly, 88.2% of

organic fruit and vegetable growers in Australia (n = 253) earn less than AUD 500k per

annum.

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Table 5.7 Annual turnover of the business

Annual turnover (in AUD) Frequency Percentage

Less than 100k 84 29.3

Between 100k to 300k 103 35.9

Between 300k to 500k 66 23.0

Between 500k to 1 million 8 2.8

Above 1 million 26 9.0

Total 287 100.0

Note: Small scale farms = Annual turnover < AUD 100k, Medium scale farms = Annual turnover

between AUD 100 and 500k, Large scale farms = Annual turnover > AUD 500k

As mentioned previously, fruit and vegetable growers of Australia grow a mix of

conventional and organic products. 95.1% of growers grow more organic than

conventional fruits and vegetables in their farms (see Table 5.8 for more details). The

majority of growers (73.5%) grow more than 80% organic produce in their farms. A

small percentage (2.8%) of farms grows less than 20% of organic produce. Hence, these

data indicate that majority of the respondents of the survey are organic fruit and

vegetable growers of Australia.

Table 5.8 Percentage of produce certified as organic product

Percentage of organic produce Frequency Percentage

Less than 20% 8 2.8

Between 20 - 50% 6 2.1

Between 51 - 80% 62 21.6

Between 81 - 100% 211 73.5

Total 287 100.0

Organic growers produce a mix of vegetables and fruits in their farms. However, the

survey results indicate that 20.9% growers (n = 60) produce only organic fruits and they

do not grow organic vegetables in their farms. Another 10.5% of organic growers (n =

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30) do not grow organic fruits, but their 100% organic produce is vegetables. As seen in

Table 5.9, however, the majority (n = 197) of them (68.6%) produce both organic fruits

and vegetables in their farms.

Table 5.9 Fruit and vegetable percentage

Name of organic produce Frequency Percentage

Only fruits 60 20.9

Only vegetables 30 10.5

Both fruits and vegetables 197 68.6

Total 287 100.0

As depicted in Table 5.10, 58.2% and 31.7% of Australian organic fruit and vegetable

growers directly sells their products to wholesalers and retailers, respectively. A smaller

percentage of growers (3.5%) sell their products directly to processors. 6.6% of

Australian organic fruit and vegetable growers use selling methods such as farmers’

markets, local markets, selling direct to consumers, selling produce on farm sales,

supply to distributors, online sales and selling direct to restaurants.

Table 5.10 Buyers of organic fruit and vegetables

Buyer Frequency Percentage

Wholesaler 167 58.2

Retailer 91 31.7

Processor 10 3.5

Other* 19 6.6

Total 287 100.0

Note: Other* includes farmers’ markets, local markets, direct to consumers, on farm sales, supply to

distributors, online sales and direct sale to restaurants

The survey results indicate that many of the growers work with the same SC partner for

many years. As depicted in Table 5.11, 15% of the organic fruit and vegetable growers

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work with the same SC partner more than 15 years, and 4.2% of them work with the

same partner for more than 20 years. Another 36.6% of the organic growers work with

their present SC partner for a period of between 10 and 15 years. 32% of the organic

growers work with their present SC partner for a period between 5 to 10 years, and only

16.4% of the growers work less than 5 years with the present SC partner. The results

also indicate that 83.6% of the organic fruit and vegetable growers work with their

present SC partner for more than 5 years.

Table 5.11 Duration of the grower’s relationship with present SC partner

Years with present SC partner Frequency Percentage






Total 287 100.0

5.3 Data analysis procedure

5.3.1 Structural Equation Modeling (SEM)

This study employs structural equation modeling (SEM) for data analysis. As Hair et al.

(1998) explain, SEM is one of the more advanced analysis tools, and can be used to

analyse multiple variables. SEM is also an extension of several multivariate techniques

such as multiple regression and factor analysis.

Structural equation modeling is a multivariate technique, which can simultaneously test

and estimates a series of interrelated dependency relationships between latent constructs

(Hu & Bentler 1998; Reisinger & Mavondo 2007). These latent (unobservable)

constructs are measured using one or more manifest (observable) variables, and they

can be either independent (exogenous) or dependent (endogenous). Hair et al. (2010)

argue that SEM examines the structure of interrelationships expressed in a series of

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equations, which are similar to multiple regression equations. Reisinger and Mavondo

(2007) explain that the SEM technique combines multiple regression and factor

analyses, which makes it a more robust measurement tool. Although SEM belongs to

the family of multivariate statistical techniques, it can be used to achieve results which

are not possible using first generation statistical methods namely, correlation and

regression analysis (Bagozzi & Yi 2012). These authors also assert that SEM techniques

are able to account for the errors confounding first generation statistical methods.

The use of SEM has spread into research studies in many fields. For example,

education, sociology, psychology, management and marketing fields have used this

technique extensively. Hair et al. (2010) explain that there are two major reasons for this

popularity of SEM. First is SEM’s ability to provide a straightforward method to deal

with multiple variables simultaneously, while providing statistical efficiency. Second,

SEM is able to provide a comprehensive assessment of complex relationships and

provide a transition from exploratory to confirmatory analysis. As explained earlier,

SEM is able to examine a series of dependent relationships simultaneously. Specifically,

this method of analysis can be successfully used when one dependent variable becomes

an independent variable in a subsequent dependence relationship. Hence, it is

appropriate to use SEM in this study to examine complex variable relationships as

depicted in the conceptual model in Chapter 3.

5.3.2 Two-step approach

This study adopts a two-step approach to the data analysis as recommend by Anderson

and Gerbing (1988). In the two-step approach, the measurement model is first analysed

and estimated. Then, the structural model is estimated. In this study, path analysis is

used in the structural model, where reliabilities and measurement errors are fixed when

the structural model is estimated (Hair et al. 2010). Anderson and Gerbing (1988)

suggest that a two-step approach helps prevent model misspecification. Given an

acceptable uni-dimensionality of measurement model, the pattern of coefficients from

measurement model remains stable when alternative structural models are estimated

(Anderson & Gerbing 1988). The two-step approach also helps avoid mis-interpretation

by fixing the measurement paths of the model before estimating the structural paths

(Anderson & Gerbing 1988). Finally, assessing the adequacy of measurement model

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using confirmatory factor analysis (CFA) before estimating the structural model is a

common practice in channels research (Heide & John 1990; Hibbard, Kumar & Stern

2001; Siguaw, Simpson & Baker 1998). In this dissertation, CFA was first used to

validate the measurement model. Path analysis was then used to test hypotheses in the

structural model.

5.4 Model evaluation: Fit indices

Before examining various fit measures, the research ensures that the model is identified

and the model should not show problems such as negative error variances and non-

significant error variance (Bagozzi & Yi 2012). In the next step, the researcher needs to

assess the global measures of it statistics. Several fit statistics (called “goodness of fit

indices”) are utilised to either confirm or reject the priori conceptualised relationships.

Goodness of fit

The degree to which the actual or observed input (covariance or correlations) matrix is

predicted by the proposed or estimated model is known as goodness of fit (Hair et al.

2010). The model fit, or whether the hypothesised measurement model fits the actual

model (that is derived from sample data) can be achieved through the variety of fit

indices (Reisinger & Mavondo 2007). Varieties of goodness of fit indices are used in

various studies, and different statistical programmes supply varying subsets of these.

McDonald and Ho (2002) observe, however, that there is no established mathematical

or empirical basis for their selection and use for a particular study. Also, there is no

agreement on what fit indices are to be used for model evaluation (Reisinger &

Mavondo 2007). These authors also argue that, although various fit indices have been

suggested in studies, it is recommended to use chi-square value, degrees of freedom (df)

and the corresponding p value. Every model can be rejected if only these fit indices are

used (Reisinger & Mavondo 2007). Hence, there are other fit indices such as GFI,

AGFI, SRMR, TLI, NFI, RMSEA and CFI (Bentler & Bonett 1980; Hu & Bentler

1998; Hu & Bentler 1999; Jaccard & Wan 1996; Kline 2011; MacCallum & Austin

2000), which can be used along with chi-square, degrees of freedom and p values in

order to achieve a good model fit with the sample data. As such this study is analysed

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and modelled using SPSS version 22 with AMOS, and the following fit indices are used

to analyse the model fit of this study.

5.4.1 Absolute fit Measures

Chi-square

One of the most popular ways of estimating a model fit is by using chi-square (χ2)

statistics with a degree of freedom (df) (Hu & Bentler 1998; McDonald & Ho 2002).

This is the difference between observed and estimated covariance matrices (Hair et al.

2010).

There are several reasons why chi-square value is not a very good model fit index

(Reisinger & Mavondo 2007). Firstly, since sample size is used to calculate χ2, the large

samples result in large chi-square values. Secondly, chi-square value also depends on

the distribution of variables, and as a result highly kurtosis or skewed variables increase

its value. This means that, CMIN (chi-square value in AMOS is called CMIN) best

performs under the estimation of normality of data. Finally, the models with more

variables produce a larger χ2 value. If chi-square value is small and non-significant

(p>0.05), then the model is said to fit sample data well (Reisinger & Mavondo 2007).

Normed Chi-square (χ2/df)

Normed chi-square (also called ‘relative chi-square’) is the result of chi-square divided

by degrees of freedom, which makes this measure less dependent on sample size. As

Jöreskog (1969) explains this fit index is normally used with large sample sizes, and it

is sensitive to sample size. As such, Reisinger and Mavondo (2007) argue that in large

sample sizes, the smaller differences can become significant, whereas in smaller sample

sizes large differences can become insignificant. Also, a model with a small chi-square

value in relation to degrees of freedom is a better fit for sample data, than a model with

a large chi-square value in relation to its degrees of freedom (Hu & Bentler 1998).

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Goodness of fit index (GFI)

The GFI is known as fit index, and is not sensitive to sample size. However, Hair et al.

(2010) argue that GFI is sensitive to sample size since sampling distribution is

influenced by sample size. The GFI value ranges from 0 to 1, and models with values

close to one are considered to be best fit models. Several scholars (Hu & Bentler 1999;

Reisinger & Mavondo 2007) have agreed that a GFI value above 0.90 (>0.90) is a good

fit of model with sample data.

Adjusted goodness of fit index (AGFI)

AGFI uses the differing degrees of model complexity, and adjusts GFI by a ratio of

degree of freedom used in the model to total degrees of freedom available (Hair et al.

2010). The AGFI values are generally lower than GFI values, and Reisinger and

Mavondo (2007) emphasise that models with AGFI values over 0.90 (>0.90) fit the

sample data well.

Standardised Root Mean Square Residual (SRMR or SRMSR)

According to Cunningham (2010) SRMR is “the average difference between

corresponding elements of the sample and model implied correlation matrices” (p. 5).

Standardised residual can be used to identify potential problems with measurement

models. However, as Hair et al. (2010) argue, standardised residuals are deviations of

individual covariance terms, and do not reflect an overall model fit. Hence, SRMR is

useful to compare the fit across models. Although there is no statistical threshold level

established for SRMR (Hair et al. 2010), Cunningham (2010) suggests that since the

units of measurement are in standardised form, a model which possess a SRMR value of

below 0.05 (< 0.05) is considered to fit the sample data well.

5.4.2 Model comparison and relative fit measures

Tucker-Lewis index (TLI)

TLI takes into account the model complexity, and compares normed chi-square values

for null and specified models. The value of TLI can fall below 0 or above 1, but

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according to Hair et al. (2010), values close to 1 suggest a better fit. Further, Reisinger

and Mavondo (2007) suggest that values below 0.90 (< 0.90) indicate the need to

respecify the model, and values higher than 0.90 ( > 0.90) reflect a good model fit to

sample data.

Normed fit index (NFI)

NFI is the ratio of differences in χ2 value for fitted and null models divided by χ2 value

for the null model (Hair et al. 2010). Although value of NFI ranges between 0 and 1, a

perfect fit model produce a value of 1. The only disadvantage is that the more complex

models end up with higher NFI, which artificially inflates the estimate of model fit

(Hair et al. 2010). According to Reisinger and Mavondo (2007), values below 0.90 (<

0.90) indicate the need to respecify the model, and values higher than 0.90 ( > 0.90)

reflect a good model approximation to sample data.

5.4.3 Non-centrality-based indices

Root Mean Square Error of Approximation (RMSEA)

This is a widely used model fit index, which better represents how well a model fits a

population. According to Hair et al. (2010), RMSEA tries explicitly to correct for both

sample size and model complexity by including each in its computation. Although value

of an acceptable RMSEA is debatable, Reisinger and Mavondo (2007) state that values

between 0.05 and 0.08 reflect a reasonable fit, and values below 0.05 (< 0.05) reflect a

good fit.

Comparative Fit Index (CFI)

CFI is an improved version of normed fit index (NFI), has many desirable properties,

and is insensitive to model complexity. CFI compares the performance of a new model

to performance of a null or independence model (which has zero correlation between all

observed variables). The value of CFI ranges from 0 to 1 (Hair et al. 2010), and the

values close to one suggest a better fit model with the sample data (Hair et al. 2010;

Reisinger & Mavondo 2007). These authors stress that CFI penalises for sample size,

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and provides the best approximation of the population value for a single model. They

also suggest that values > 0.90 reflect a good model fit. Table 5.12 summarises the fit

indices, acceptable level and their interpretation used in this study.

Table 5.12 Model fit indices and acceptable levels used in this study

Goodness of Fit Criterion Acceptable Level Interpretation

Absolute Fit Measures

Chi-square (χ2) Low χ2 value (relative to

degrees of freedom) with

significance level < 0.05

Value > 0.05 reflect acceptable fit

Values between 0.05 and 0.20 good fit

Normed chi-square (χ2/df) Ratio 2:1 or 3:1 Values close to 1 reflect a good fit

Values < 3 reflect acceptable fit

Goodness of Fit Index

(GFI)

0.90 or higher Values > 0.90 reflects a good fit

Adjusted Goodness of Fit

Index (AGFI)

0.90 or higher Values > 0.90 reflects a good fit

Standardised Root Mean

Square Residual (SRMR or

SRMSR) #

0.05 or below Values < 0.05 reflect a good fit

Model Comparison and Relative Fit Measures

Tucker-Lewis Index (TLI) Value close to 1 Values > 0.90 reflect a good fit

Values < 0.90 indicate the need to respecify model

Normed Fit Index (NFI) Value close to 1 Values > 0.90 reflect a good fit

Values < 0.90 indicate the need to respecify model

Non-centrality based indices

Root Mean Square Error of

Approximation (RMSEA)

0.08 or below Values < 0.05 reflect a good fit

Values between 0.05 and 0.08 reflect a reasonable fit

Comparative Fit Index

(CFI)

Value close to 1 Values > 0.90 reflect a good fit

Source: Reisinger and Mavondo (2007), # Cunningham (2010)

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5.5 Assessment of Construct Validity and Reliability

5.5.1 Validity

The extent to which a particular scale or set of measures accurately represent the

concept of interest is known as validity (Hair et al. 2010). There are several forms of

validity. According to Hair et al. (2010) content validity, convergent validity,

discriminant validity and nomological validity have been the most used forms of

validity in previous research.

Content validity

Hair et al. (2010) assert that content validity is the assessment of correspondence of the

variables which are to be included in a summated scale and its conceptual definition

(which is the starting point for creating any summated scale). According to Cavana,

Delahaye and Sekaran (2001),

content validity ensures that the measures include and adequate and

representative set of items that tap the concept. The more the scale items

represent the domain or universe of the concept being measured, the greater the

content validity. In other words, content validity is a function of how well the

dimensions and elements of a concept have been delineated. (p. 213).

According to Hair et al. (2010), the purpose of content validity is to ensure that

selection of items used in the scales also include practical and theoretical consideration

apart from empirical issues. Content or face validity should, however, be established

before theoretical testing when using CFA, and it is impossible to express and correctly

specify a measurement theory if the meaning or the content of every item used is not

clear (Hair et al. 2010). Hence, content or face validity is considered the most important

validity test (Hair et al. 2010).

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Convergent validity

According to Hair et al. (2010), convergent validity explains the extent to which the

scale items of a latent construct, share a high proportion of variance in common. Also,

convergent validity can be established when the scores of the same concept obtained

using two different measuring instruments, are highly correlated (Cavana, Delahaye &

Sekaran 2001). High correlation among the items of used scales indicates that they

measure same intended construct. Also, the convergent validity is achieved when the

standardised factor loadings are higher than the minimum recommended value of 0.50

(Hair et al. 2010). Anderson and Gerbing (1988) explain that the factor loadings of

scale items measuring a construct must be statistically significant in order to achieve

convergent validity.

Discriminant validity

Discriminant validity ensures that two conceptually similar constructs are distinct from

each other (Hair et al. 2010). In this, the correlation is measured between two summated

scales (of two constructs) to ensure that they are different constructs. According to

Hulland (1999), discriminant validity “represents the extent to which measures of a

given construct differ from measures of other constructs in the same model” (p.199).

According to Cavana, Delahaye and Sekaran (2001), “discriminant validity is

established when, based on theory, two variables are predicted to be uncorrelated , and

the scores obtained by measuring them are indeed empirically found to be so” (p. 213).

Hair et al. (2010) argue that the correlation between two scales should be low, in order

to demonstrate that one particular summated scale is sufficiently different from another,

similar construct. Fornell and Larcker (1981) suggest using the Average Variance

Extracted (AVE) to identify the discriminant validity. To satisfy the discriminant

validity requirement, the AVE value need to be greater than the squared correlation

between the two constructs (i.e. variance shared between the particular construct and the

other constructs in the mode) (Hulland 1999). Additionally, the AVE, which used to

measure the amount of variance captured by an underlying factor in relation to the

amount of variance due to measurement error, needs to exceed the value of 0.5 (AVE >

0.5).

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Nomological validity

Nomological validity assesses whether the scales used to measure the constructs

demonstrate relationships (which are shown in conceptual model), are based on theory

or prior studies (Hair et al. 2010). As described in Chapter 4, scales that were used to

operationalise the constructs were adopted from previously published studies; hence

nomological validity is achieved for all constructs used in this study. Also, nomological

validity will be further tested in this dissertation when the structural model is estimated.

5.5.2 Reliability

Assessment of the degree of consistency between multiple measurements of a construct

is known as reliability (Hair et al. 2010). According to Cavana, Delahaye and Sekaran

(2001),

reliability of a measure indicates the extent to which the measure is without bias

(error free) and hence offers consistent measurement across time and across the

various items in the instrument. In other words, the reliability of a measure

indicates the stability and consistency with which the instrument measures the

concept and helps to assess the ‘goodness’ of a measure. (p. 210)

There are many tools that are used to assess reliability. Many research have suggested

various methods to find reliability, which include individual item reliability, Cronbach’s

alpha, the construct reliability, and the average variance extracted (AVE) (Bagozzi & Yi

2012; Fornell & Larcker 1981). These measures increasingly impose more stringent

tests of reliability.

Individual item reliability is equal to its true score variance divided by the total variance

Hair et al. (2010). It can be investigated by calculating the squared of the indicator’s

standardised factor loading. Individual item reliability should exceed 0.50 (Hair et al.

2010). Bagozzi and Yi (2012) argue, however, that there is no universally accepted

standard for minimum acceptable indicators. The reason provided by these authors is

that individual indicator reliabilities may be relatively low at times, but the factor to

which they correspond might perform well in large models. Second, and the most

commonly used measure, is reliability coefficient that assesses the consistency of entire

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scale with Cronbach’s alpha (Bagozzi & Yi 2012; Hair et al. 2010). As outlined by Hair

et al. (2010), the lower limit for Cronbach’s alpha is 0.7, although it can be 0.6 for

exploratory research, and as low as 0.5 for large models (Bagozzi & Yi 2012), with

many latent variables and indicators.

Third, the construct reliability is the measure of internal consistency developed by

Fornell and Larcker (1981). A commonly accepted threshold value is 0.70 (Hair et al.

2010). Finally, the average variance extracted (AVE) is the amount of variance that is

captured by the construct or factor in relation to the amount of variance due to

measurement error (Fornell & Larcker 1981). The AVE should exceed 0.50 (Hair et al.

2010).

5.6 CFA for one factor congeneric models

The following sections depict the CFA results for all the constructs used in this thesis.

This thesis used all previously validated measurement scales, hence EFA was not

performed.

5.6.1 Item Codes

The individual constructs and items of each construct were coded before the CFA is

performed. Table 5.13 provides a list of constructs, construct codes, item codes and

related item numbers.

Table 5.13 List of codes for constructs and items of survey instrument

Construct Construct code Item code Item numbers


Information sharing INFOSHARING Inf 1-3

Coercive power COPOWER CP 4-8

Non-coercive power NONCOPOWER NCP 9-13

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Satisfaction SATSIFACTION Sat 14-18

Collaboration COLLABORATION Col 19-22

Trust TRUST Tru 23-28

Commitment COMMITMENT Com 29-32

Supply chain performance

SC relationship success RELSUCCESS RS 33-36

Operational performance

Firm’s operational performance PERFORMANCE Per 37-43

Source: Developed for the study

5.6.2 Measurement models for each measured construct or latent variable

The variance of each latent variable was set at 1 in order to have a scale for latent

variables. It is deliberately avoided to set first factor coefficient value (or any other

coefficient value) to 1 as this would not generate p values for this particular parameter

(Cunningham 2010). Alternatively, it is required to fix the strongest factor

coefficient/loading to 1 to avoid other standardised loadings exceeding one. Hence, it

was decided to set variances of latent variables at one (1) in performing one factor

congeneric model analysis in this study. The CFA for each construct or dimension of

the proposed conceptual model follows.

CFA for Information-sharing

CFA was run using AMOS for items of information-sharing to obtain the construct

validity. The information sharing construct was measured using a three item scale, and

initial solution of CFA revealed that model was a saturated model. Since saturated

models fail to obtain fit statistics, the variances of two error variables were constrained

to unity. The two error variables constrained were the items with the least difference in

factor loadings (Bollen 1989; Kline 2011).

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The CFA was re-run for the adjusted model for information sharing. The fit indices of

the final model are depicted in Table 5.14, and the final measurement model with factor

loadings is shown in Figure 5.2.

Figure 5.2 CFA for information-sharing

The final CFA solution obtained factor loadings greater than 0.5 (>0.5) for all three

items. This suggests that construct validity was achieved for the information sharing

construct.

Table 5.14 Model fit indices for information sharing

Fit Index Final model fit value

Chi-square (χ2) 0.197 (df = 1, p = 0.657)

CMIN/df 0.197

GFI 1.000

AGFI 0.997

SRMR 0.001

TLI 1.002

NFI 1.000

RMSEA 0.000

CFI 1.000

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The chi-square of the re-specified final CFA model was 0.197, with non-significant p

value (p > 0.05) of 0.657. This suggests that data fits the model well. The other

indicators, GFI, AGFI, TLI, NFI and CFI values were all above 0.90 with SRMR< 0.05

and RMSEA < 0.08. These indices suggest that the model fits well with the data.

Table 5.15 Regression weights for information sharing

Relationship Estimate S.E. C.R. P

Inf1 INFOSHARING 0.919 0.070 20.388 ***

Inf2 INFOSHARING 0.975 0.070 22.725 ***

Inf3 INFOSHARING 0.977 0.073 22.847 ***

*** Significant at p<0.001 level

The regression weights, as shown in Table 5.15, indicate that all three items have

significant relationships with information sharing. The standardised factor scores are

high (close to 1), and are significant at p < 0.001 level.

CFA for Coercive Power

Coercive power was operationalised using five items on a seven-point Likert scale. One

factor CFA congeneric model was tested using AMOS, and final measurement model

with factor loadings is depicted below in Figure 5.3. The CFA analysis suggested that

model fits data well with all five items, and resulting fit indices for coercive power is

shown in Table 5.16.

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Figure 5.3 CFA for coercive power

Figure 5.3 shows that factor loadings for all five items of coercive power are above 0.5

(> 0.5). Although the factor loading for CP1 is 0.52, which is considerably lower than

rest of the factor loadings, it is still above the accepted value of 0.50. Hence, it is

suggested that all five items have significant relationships with coercive power.

Table 5.16 Model fit indices for coercive power


Chi-square (χ2) 9.018 (df = 5, p = 0.108)

CMIN/df 1.804

GFI 0.987

AGFI 0.962

SRMR 0.019

TLI 0.987

NFI 0.985

RMSEA 0.053

CFI 0.993

As depicted in Table 5.16, the chi-square value for coercive power is 9.018, with the

non-significant p value (p>0.05) of 0.108 suggesting that data fits the model well. The

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results (Table 5.16) shows that additional model fit indices GFI, TLI, NFI and CFI

values are all close to 1 which indicate a good model fit. Additionally AGFI value of

0.962 (AGFI>0.95) along with SRMR = 0.019 (SRMR < 0.05) and RMSEA = 0.053

(RMSEA < 0.08) also suggest a good fit of the final model with the data.

Table 5.17 Regression weights for coercive power


CP1 COPOWER 0.518 0.063 8.860 ***

CP2 COPOWER 0.794 0.063 15.297 ***

CP3 COPOWER 0.802 0.057 15.522 ***

CP4 COPOWER 0.754 0.053 14.210 ***

CP5 COPOWER 0.804 0.060 15.581 ***


As shown in Table 5.17, there are 5 items that indicate a strong relationship with

coercive power. The regression weights for all five items are less than one (< 1), but

still significant at p < 0.001 level.

CFA for Non-Coercive Power

One factor congeneric model analysis was run for non-coercive power with all the five

items. The initial fit statistics suggested that model did not fit the sample data. The fit

indices were chi-square= 47.719 with a significant p value (p < 0.05) and GFI=0.931,

AGFI=0.794, TLI=0.926, NFI=0.959, CFI=0.963 values along with higher RMSEA

value (RMSEA = 0.173 > 0.08). These values indicate a poor model fit. Residual

covariance matrix and large modification indices (MI) value suggested the removal of

item number four (NCP4) in this construct (please refer to Appendix 4 for deleted item

descriptions). The revised model of non-coercive power was re-tested with four items

and the results are presented in Figure 5.4 and Table 5.18.

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Figure 5.4 CFA for Non-Coercive Power

The significant factor loadings (> 0.50) of non-coercive power ranged from a low of

0.80 to a high of 0.92 (Figure 5.4). This demonstrates that the new model with four

items fits the sample data well.

Table 5.18 Model fit indices for non-coercive power


Chi-square (χ2) 0.637 (df = 2, p = 0.727)

CMIN/df 0.318

GFI 0.999

AGFI 0.995

SRMR 0.004

TLI 1.005

NFI 0.999

RMSEA 0.000

CFI 1.000

The chi-square value of 0.637 with a non-significant p value (p=0.727) indicates that the

model fits sample data well. Table 5.18 shows that other goodness of fit indices,

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GFI=0.999, AGFI=0.995, TLI=1.005, NFI=0.999 and CFI=1.000 are all above 0.90,

and RMSEA=0.000 and SRMR=0.004. These indices reveal that the model fits the data

for the non-coercive power construct.

Table 5.19 Regression weights for non-coercive power


NCP1 NONCOPOWER 0.849 0.069 17.460 ***

NCP2 NONCOPOWER 0.923 0.089 19.957 ***

NCP3 NONCOPOWER 0.827 0.070 16.770 ***

NCP5 NONCOPOWER 0.798 0.069 15.895 ***


The standardised regression weights (as shown in Table 5.19), indicate significant

relationships between the four items and the construct of non-coercive power.

Additionally, all the factor loadings of the items are significant at p < 0.001 level.

CFA for Satisfaction

One factor congeneric model was performed with all five items to ascertain construct

validity for the construct of satisfaction. The initial fit statistics suggested that the model

did not fit well with sample data. The chi-square= 22.841 with a significant p value

(p<0.05), AGFI=0.911 along with a higher RMSEA value of 0.112 (> 0.08), all indicate

a poor model fit.

Higher standardised residual covariance matrix and higher MI accompanied with

positive parameter change was observed for error variances e1 and e5. Item number one

(Sat1), which was originally negatively worded was removed from the analysis (please

refer to Appendix 4 for deleted item descriptions). The amended model of satisfaction

was re-assessed for construct validity with four items and the results are presented in

Figure 5.5 and Table 5.20.

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Figure 5.5 CFA for satisfaction

Figure 5.5 shows that factor loadings for all four items of satisfaction are above 0.5

(>0.5), ranging from a low of 0.89 (item 2 and 5) to high of 0.96 (item 3). Hence, it is

suggested that all four items have significant relationships with satisfaction.

Table 5.20 Model fit indices for satisfaction


Chi-square (χ2) 2.512 (df = 2, p = 0.258)

CMIN/df 1.256

GFI 0.996

AGFI 0.978

SRMR 0.005

TLI 0.999

NFI 0.998

RMSEA 0.030

CFI 1.000

The goodness of fit measures are: Chi-square=2.512 with a higher p value of 0.285

(p>0.05), GFI=996 and AGFI=0.978, TLI=999, NFI=998, CFI=1.000 (Table 5.20), all

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of which are above 0.90 (> 0.90). These indicate a good model approximation with data.

The other important fit indices; RMSEA=0.030 (< 0.08) and SRMR=0.005 (< 0.05) also

suggest a good model fit for the satisfaction construct.

Table 5.21 Regression weights for satisfaction


Sat2 SATISFACTION 0.890 0.064 19.177 ***





As shown in Table 5.21, the results indicate that all four items have significant

relationships with satisfaction. The standardised factor scores are high (close to 1), and

are significant at p < 0.001 level.

CFA for Collaboration

CFA was run using AMOS to test the one factor congeneric model for collaboration.

Collaboration was operationalised using a four-item scale, and initial model fit indices;

chi-square of 22.951 with a significant p value (p < 0.05), AGFI=0.775, TLI=0.911

along with RMSEA=0.221 which is higher than acceptable level (<0.08). These indices

suggest that model was not a good fit with sample data. Higher standardised residual

covariance and MI prompted the removal of item number one (please refer to Appendix

4 for deleted item descriptions). The model was re-run and the analysis revealed a

saturated model. Since saturated models fail to obtain fit statistics, the variances of two

error variables e3 and e4 (with smallest difference in factor loadings) were constrained

to be equalled. The CFA was re-assessed for the third time to obtain a reasonable model

fit. Factor loadings of items and fit indices of final collaboration construct are shown in

Figure 5.6 and Table 5.22, respectively.

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Figure 5.6 CFA for collaboration

The standardised factor loadings (> 0.50) for all items of collaboration are ranging from

a low of 0.80 to a high of 0.94 (Figure 5.6). Hence, it is suggested that all three items

have significant relationships with collaboration.

Table 5.22 Model fit indices for collaboration


Chi-square (χ2) 0.470 (df = 1, p = 0.493)

CMIN/df 0.470

GFI 0.999

AGFI 0.993

SRMR 0.004

TLI 1.003

NFI 0.999

RMSEA 0.000

CFI 1.000

As depicted above in Table 5.22, the chi-square value for collaboration is 0.470 with

non-significant (p > 0.05) p value of 0.493, which suggests that sample data fits the

model well. The results (Table 5.22) also shows that additional model fit indices GFI,

AGFI, TLI, NFI and CFI values are all above 0.90 (> 0.90), which all indicate a good

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model fit. Additionally SRMR=0.004 (SRMR < 0.05) and RMSEA=0.000

(RMSEA<0.08) also suggest an acceptable model fit with sample data.

Table 5.23 Regression weights for collaboration


Col2 COLLABORATION 0.934 0.078 20.269 ***




As shown in Table 5.23, the regression weights for collaboration suggest that there are

three significant relationships. All three standardised factor scores are high (close to 1),

and are significant at p < 0.001 level.

CFA for trust

The trust construct was operationalised using six items. One factor CFA was performed

using AMOS to ascertain construct validity for trust. Results of the initial analysis

indicated that the model was not a good fit for sample data. Further a large standardised

residual covariance of 2.24 resulted for items Tru1 and Tru2. Also considerable MI

value with a positive parameter change was shown for error variances e1 and e2. Taking

these into consideration, it was decided to remove item number two (please refer to

Appendix 4 for deleted item descriptions). Subsequently, the CFA was re-run with five

items and the results are shown in Figure 5.7 and Table 5.24.

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Figure 5.7 CFA for trust

Figure 5.7 shows that standardised factor loadings of all retained items are above 0.5 (>

0.5), and are ranging from a low of 0.79 to a high of 0.96. This predicts that all five

items have significant relationships with trust.

Table 5.24 Model fit indices for trust


Chi-square (χ2) 8.574 (df = 5, p = 0.127)

CMIN/df 1.715

GFI 0.988

AGFI 0.963

SRMR 0.009

TLI 0.996

NFI 0.995

RMSEA 0.050

CFI 0.998

The absolute fit measures are: chi-square=8.574 with a p value of 0.127 (p > 0.05),

GFI=0.988 and AGFI=0.963 indicates a good model fit with data. The other important

fit indices TLI=0.996, NFI=0.995, CFI=0.998 all of which are above 0.90 (> 0.90)

along with RMSEA=0.050 (< 0.08) and SRMR=0.009 (< 0.05) also suggest a good

approximation of data with the new model for the construct of trust.

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Table 5.25 Regression weights for trust


Tru1 TRUST 0.788 0.070 15.896 ***

Tru3 TRUST 0.958 0.065 21. 907 ***

Tru4 TRUST 0.915 0.069 20.160 ***

Tru5 TRUST 0.906 0.067 19.816 ***

Tru6 TRUST 0.951 0.070 21.598 ***


Results shown in Table 5.25 indicate five significant relationships. All five standardised

factor scores are high (close to 1), and are also significant at p < 0.001 level.

CFA for Commitment

A one factor congeneric model CFA was performed for commitment which had four

items. The resultant indices; Chi-square 15.923 with a significant p value (p<0.05),

AGFI=0.862 which is less than acceptable level (> 0.90) and RMSEA=0.156 which is

way above acceptable level (< 0.08) suggested that model was not a good fit to the

sample data. To obtain a good fit model, item number four (Com4) was removed after

considering standardised residual covariance and modification indices (please refer to

Appendix 4 for deleted item descriptions). Since second model analysis for commitment

with three items obtained a saturated model, the variances of e1 and e3 error variables

were constrained to be equalled, and third model analysis was performed. The final

measurement model with factor loadings is shown in Figure 5.8, and model fit indices

are shown in Table 5.26.

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Figure 5.8 CFA for commitment

The factor loadings for all three items are higher than 0.5 (>0.50), and are ranging from

a low of 0.94 to a high of 0.96. This predicts that all three items have significant

relationships with commitment.

Table 5.26 Model fit indices for commitment


Chi-square (χ2) 1.970 (df = 1, p = 0.160)

CMIN/df 1.970

GFI 0.995

AGFI 0.973

SRMR 0.004

TLI 0.997

NFI 0.998

RMSEA 0.058

CFI 0.999

As shown in Table 5.26, Chi-square=1.970 with non-significant p value of 0.160

(p>0.05) suggests that model fits sample data well. The other goodness of fit indices

(Table 5.26) shows that GFI, AGFI, TLI, NFI and CFI values, all close to 1 indicate a

good approximation of data with final model. Additionally, SRMR=0.004

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(SRMR<0.05) and RMSEA=0.058 (RMSEA < 0.08) also suggest an acceptably fitted

model.

Table 5.27 Regression weights for commitment


Com1 COMMITMENT 0.938 0.068 21.098 ***

Com2 COMMITMENT 0.975 0.072 22.627 ***

Com3 COMMITMENT 0.948 0.074 21.512 ***


As shown in Table 5.27, the regression weights suggest that all three items have

significant relationships with commitment. All three standardised factor scores are high

(close to 1), and are also significant at p < 0.001 level.

CFA for SC relationship success

CFA was initially performed for SC relationship success to assess one factor congeneric

model with all four items, which were used to measure the construct. The initial fit

statistics suggested that model did not fit the sample data well. Chi-square value of

7.126 with a significance p=0.028 value (p<0.05) along with higher RMSEA= 0.095

(>0.08) indicate a poor model fit.

The standardised residual covariance or MI was unable to suggest any single item to be

removed to obtain a model fit. Hence, to obtain a reasonable model fit for SC

relationship success, variances of two error variables e1 and e2 (with smallest difference

in factor loadings) were constrained to unity. The revised model of SC relationship

success was re-assessed and the results are presented in Figure 5.9 and Table 5.28.

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Figure 5.9 CFA for SC relationship success

The final model for SC relationship success obtained factor loadings of above 0.5 (>0.5)

for all four items (Figure 5.9). This predicts that all four items have significant

relationships with SC relationship success.

Table 5.28 Model fit indices for SC relationship success


Chi-square (χ2) 7.293 (df = 3, p = 0.063)

CMIN/df 2.431

GFI 0.988

AGFI 0.960

SRMR 0.022

TLI 0.982

NFI 0.985

RMSEA 0.071

CFI 0.991

As depicted above in Table 5.28, the chi-square value for relationship success is 7.293

with non-significant p value (p>0.05) of 0.063, which suggests that sample data fits the

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model well. Additional model fit indices GFI=0.988, AGFI=0.960, TLI=0.982,

NFI=0.985 and CFI=0.991 values are all above 0.90 (>0.90), and indicate a good model

fit. Additionally, lower SRMR=0.022 (SRMR<0.05) and lower RMSEA=0.071

(RMSEA<0.08) also suggest a good model fit to the sample data.

Table 5.29 Regression weights for SC relationship success


RS1 RELSUCCESS 0.666 0.075 12.093 ***

RS2 RELSUCCESS 0.675 0.076 12.329 ***

RS3 RELSUCCESS 0.801 0.083 15.327 ***

RS4 RELSUCCESS 0.895 0.069 17.889 ***


There are four items that indicates significant relationships with SC relationship success

(Table 5.29). The standardised regression weights for two items are 0.666 and 0.675,

and another two standardised factor scores are high (close to 1). All four standardised

weights are, however, significant at p < 0.001 level.

CFA for firm’s operational performance

There were seven items that were used to operationalise the firm’s operational

performance construct. CFA was performed to establish a one factor congeneric model.

The resultant goodness of fit indices suggests that model was not a good fit with sample

data. Considering standardised residual covariance and modification indices with

positive parameter changes, it was decided to remove item numbers five (Per5) and six

(Per6). CFA for one factor congeneric model was re-run for the amended firm’s

operational performance construct with five items (please refer to Appendix 4 for

deleted item descriptions). The factor loadings of items are depicted in Figure 5.10, and

fit indices are shown in Table 5.30.

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Figure 5.10 CFA for firm’s operational performance

The significant factor coefficients above 0.05 (> 0.50) were noticed for all five items in

the final model (Figure 5.10). The coefficients range from a low of 0.77 (Per3) to a high

of 0.89 (Per4). This predicts that all remaining five items of the revised model have

significant relationships with firm’s operational performance.

Table 5.30 Model fit indices for firms operational performance


Chi-square (χ2) 7.609 (df = 5, p = 0.179)

CMIN/df 1.522

GFI 0.989

AGFI 0.967

SRMR 0.014

TLI 0.995

NFI 0.993

RMSEA 0.043

CFI 0.997

Chi-square value of 7.609 with a non-significant p value of 0.179 (p>0.05), along with

GFI=0.989, AGFI=0.967 indicate a good model fit with data (Table 5.30). The other

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important goodness of fit indices; TLI=0.995, NFI=0.993, CFI=0.997 all of which are

above 0.90 (>0.90), along with a lower RMSEA=0.050 (<0.08) value and a

considerably lower SRMR=0.014 (<0.05), also suggest a good approximation of data

with the revised model of firm’s operational performance.

Table 5.31 Regression weights for firm’s operational performance


Per1 PERFORMANCE 0.811 0.073 16.311 ***

Per2 PERFORMANCE 0.871 0.072 17. 942 ***

Per3 PERFORMANCE 0.768 0.065 14.839 ***

Per4 PERFORMANCE 0.888 0.069 19.019 ***

Per7 PERFORMANCE 0.838 0.069 17.896 ***


Table 5.31 above shows that there are five items which indicate significant relationships

with firm’s operational performance. The standardised regression weight for one item is

0.768, and all the other standardised factor scores are high (close to 1). All five

standardised weights are, however, significant at p < 0.001 level.

5.6.3 Common method variance

Common method variance (CMV) is attributable to the measurement method rather than

to the constructs that the measures represent, and is considered as one of the main

sources of measurement error (Podsakoff et al. 2003). According to Buckley, Cote and

Comstock (1990), CMV refers to the amount of spurious covariance shared among

variables due to the common method used in data collection. Podsakoff et al. (2003)

identify four categories that causes CMV as common rate effects, item characteristic

effects, item context effects and measurement context effects. These effects

differentially influence how the respondents answer questions, thereby resulting in

method biases (Tourangeau, Rips & Rasinski 2000). Although there are four main

methods to asses CMV i.e traditional Multitrait-Multimethod (MTMM) procedure,

CFA-based MTMM techniques, Harman’s single-factor test and Marker-variable test,

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the most widely used approach in assessing CMV is Harman’s single-factor test

(Malhotra, Kim & Patil 2006). CMV was calculated using Harman’s single factor

method in SPSS version 22. The results indicated that the single factor extracted

explained less than 50% of the variance. Hence it can be concluded that CMV does not

affect the sample data used in this thesis.

5.6.4 Reliability of constructs

This study uses internal consistency method, which uses Cronbach’s alpha to measure

reliability of the constructs. As presented in Table 5.32, the Cronbach’s alpha values for

all the constructs have relatively high values, which range from 0.843 to 0.970.

According to Cunningham (2010), the accepted level of Cronbach’s alpha to achieve

reliability is above 0.70 ( > 0.70). Considering this values, all the constructs have

achieved acceptable levels, and as result all these constructs have internal consistency,

hence they are reliable.

Table 5.32 Reliability of constructs

Construct Construct code Cronbach’s Alpha (α)

Information-sharing INFOSHARING 0.970

Coercive power COPOWER 0.853

Non-coercive power NONCOPOWER 0.906

Satisfaction SATISFACTION 0.954

Collaboration COLLABORATION 0.898

Trust TRUST 0.957

Commitment COMMITMENT 0.967

SC relationship success RELSUCCESS 0.843

Firm’s operational performance PERFORMANCE 0.920

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5.6.5 Summary of Constructs

Table 5.33 summarise the statistical details of the final measurement models which

were discussed in the previous sections.

Table 5.33 Statistical summary of constructs

Construct Initial

Items

Deleted

Items

Cronbach

’s Alpha#

P

value**

CMIN/

df GFI AGFI SRMR TLI NFI RMSEA CFI

Bench-Marks*

α>0.7 P>0.05 <3 .90 or

above

.90 or

above

0.05 or

below

Value

close to 1

Value

close to 1

0.08 or

below

Value

close to 1

INFOSHARING 1,2,3 - 0.970 0.657 0.197 1.000 0.997 0.001 1.002 1.000 0.000 1.000

COPOWER 4,5,6,7,

8 - 0.853 0.108 1.804 0.987 0.962 0.019 0.987 0.985 0.053 0.993

NONCOPOWER 9,10,11

,12,13 12 0.906 0.727 0.318 0.999 0.995 0.004 1.005 0.999 0.000 1.000

SATSIFACTION

14,15,

16,17,

18

14 0.954 0.285 1.256 0.996 0.978 0.005 0.999 0.998 0.030 1.000

COLLABORATI

ON

19,20,

21,22 19 0.898 0.493 0.470 0.999 0.993 0.004 1.003 0.999 0.000 1.000

TRUST

23,24,

25,26,

27,28

24 0.957 0.127 1.715 0.988 0.963 0.009 0.996 0.995 0.050 0.998

COMMITMENT 29,30,

31,32 32 0.967 0.160 1.970 0.995 0.973 0.004 0.997 0.998 0.058 0.999

RELSUCCESS 33,34,

35,36 - 0.843 0.063 2.431 0.988 0.960 0.022 0.982 0.985 0.071 0.991

PERFORMANCE

37,38,

39,40,

41,42,

43

41,42 0.920 0.179 1.522 0.989 0.967 0.014 0.995 0.993 0.043 0.997

Sources of Benchmarks: * - Reisinger and Mavondo (2007), ** - Browne and Cudeck (1993), # -

Cunningham (2010)

As detailed previously a total of 7 items were deleted to obtain best fit one factor

congeneric models with sample data. Table 5.34 summarises deleted items and the

corresponding latent constructs.

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Table 5.34 Summary of deleted items during one factor CFA analysis

Construct Deleted Item Code Deleted Item

NONCOPOWER NCP4

4. Our major SC partner uses their unique

competence to make our company accept their

recommendations.

SATISFACTION Sat1 1. Our farm regrets the decision to do business with

our major SC partner.

COLLABORATION Col1 1. We are working closely with our major SC partner

in technology sharing and its development.

TRUST Tru2 2. Both parties watch the other’s profitability.

COMMITMENT Com4 4. Considerable effort and investment has been

undertaken in building this relationship.

PERFORMANCE Per5, per6

As a result of the relationship with our major SC

partner, improvements have been noticed in the

following areas:

5. Conformance to specifications

6. Process improvement of our farm

TOTAL

Original survey

instrument consist of

43 items

Deleted 7 items out of 43

5.7 Overall confirmatory factor analysis

This dissertation follows a measure refining process suggested by Anderson and

Gerbing (1988) and Bagozzi and Yi (2012). Firstly, each construct was subjected to

CFA to verify unidimensionality. The results of congeneric factor CFA were presented

in the previous section. Secondly, sets of measures were organised into two sub-models

depending on the theoretical or conceptual framework (Hibbard, Kumar & Stern 2001).

The first measurement model (measurement model 1) contained influential determinants

(i.e. information sharing, coercive power and non-coercive power) of the model. The

second measurement model (measurement model 2) contains all the other variables

(satisfaction, collaboration, trust, commitment, SC relationship success and firm’s

operational performance).

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5.7.1 Overall CFA for measurement model 1

Information-sharing, coercive power and non-coercive power are categorised as

influential determinants based on the previous literature (see Chapter 3). These three

constructs are influencing the other six constructs as showed in the conceptual model

(Chapter 3) developed for this study. CFA analysis was performed for these three

independent constructs in order to assess convergent and discriminant validity.

As described earlier in this chapter, there were five items that were used to

operationalise the coercive power construct, and all of them were retained after

performing one factor congeneric analysis. However, CFA results for influential

determinants suggest that the model is a poor fit with the sample data. Hence, it was

decided to remove item number one (CP1) considering the standardised residual

covariance and modification indices with positive parameter changes. CFA for

influential determinants was re-run with the amended coercive power construct, which

now consists of only four items. The results are depicted in Figure 5.11, and Table 5.35.

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Figure 5.11 CFA for information sharing, coercive power and non-coercive power

As shown in Table 5.35, CFA revealed that fit indices present a good fit model with

sample data. Chi-square value with a non-significant p value of 0.097, along with GFI,

AGFI, TLI, NFI and CFI values all above 0.90 (> 0.90) shows a good approximation of

model with the data. Lower SRMR and RMSEA values also suggest a good model fit

with sample data.

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Table 5.35 Model fit indices for Influential Determinants


Chi-square (χ2) 90.774 (df = 41, p = 0.097)

CMIN/df 2.214

GFI 0.982

AGFI 0.978

SRMR 0.042

TLI 0.979

NFI 0.972

RMSEA 0.056

CFI 0.979

Table 5.36 presents the mean, standard deviation (SD), correlations between study

variables, and square root of average variance extracted (AVE) values of information

sharing, coercive power and non-coercive power.

Table 5.36 Descriptive Statistics and Correlation Matrix for Study Variables

Construct Mean SD 1 2 3

1. INFOSHARING 5.724 1.586 0.96

2. COPOWER 2.274 0.882 -0.19** 0.79

3. NONCOPOWER 3.951 1.368 0.12* 0.22** 0.85 Notes: Correlations are significant at **p <0.01 (two-tailed), *p <0.05 (two-tailed), ns – not significant; the diagonal elements are square root of AVE values (bold italicised)

The results (Table 5.36) indicate that majority of constructs are significantly correlated

with each other, and correlation coefficients ranging from lower -0.19 to higher 0.22.

Also the correlation coefficients of all constructs are lower than square root of average

variance extracted or AVE values (bold italicised diagonal elements of Table 5.36). As

Hair et al. (2010) suggest, these results indicates discriminant validity amongst

information sharing, coercive power and non-coercive power. The CR and AVE values

are calculated using software that calculates these values using correlation table and

standardised regression weights tables. These two tables are obtained through the CFA

model analysis of particular latent constructs using AMOS.

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Table 5.37 Std. FL, α, CR and AVE for influential determinants

Construct Statement Std.

FL α CR AVE

INFOSHARING

We inform our major SC partner in advance of changing

needs 0.92

0.97 0.97 0.92 In this relationship, it is expected that any information

which might help the other party will be provided 0.98

The parties are expected to keep each other informed

about events or changes that may affect the other party 0.97

COPOWER

Our major SC partner threatens to withdraw from what

they originally promised, if we do not comply with their

requests

0.78

0.85 0.87 062

Our major SC partner threatens to take legal action, if we

do not comply with their requests 0.81

Our major SC partner withholds important support for

our farm, in requesting compliance with their demands 0.76

our major SC partner threatens to deal with another

supplier, in order to make us submit to their demands 0.80

NONCOPOWER

Our major SC partner offers specific incentives to us

when we are reluctant to cooperate with them 0.85

0.91 0.91 0.73

Our major SC partner has the upper hand in the

relationship, due to power granted to them by the

contract

0.91

Our major SC partner demands our compliance because

of knowing that we appreciate and admire them 0.83

Our major SC partner withholds critical information

concerning the relationship to better control our company 0.81

Note: Std. FL = Standardised Factor Loadings, α = Cronbach’s alpha, CR = Construct Reliability, AVE = Average Variance Extracted

The factor loadings (Table 5.37) of all the constructs are above the minimum threshold

value of 0.50 and are significant. These provide evidence of the convergent validity of

the constructs. As suggested by Nunnally and Bernstein (1994), constructs meet an

acceptable level of construct reliability when the value is at 0.70. Based on these

findings, reliability of constructs was measured using Cronbach’s alpha (α) and

construct reliability (CR) values. As such, the results in Table 5.37 reveal that

Cronbach’s alpha and construct reliability values are within the acceptable levels

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(ranging from lower 0.85 to higher 0.97). Hence, construct reliability is achieved for

information sharing, coercive power and non-coercive power.

5.7.2 Overall CFA for measurement model 2

The measurement model 2 contains the measurement of six remaining variables in the

conceptual model which is detailed in Chapter 3.

Figure 5.12 CFA for relationship constructs, relationship success and performance

As depicted in Table 5.38, the chi-square value for SC relationship success is 531.354

with a non-significant p value (p > 0.05) of 0.084, which suggests that model fits the

sample data well. Additional model fit indices GFI=0.974, AGFI=0.967, TLI=0.972,

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NFI=0.970 and CFI=0.971 all above 0.90 (> 0.90), indicate a good model fit.

Additionally lower SRMR=0.044 (SRMR < 0.05) and lower RMSEA=0.067

(RMSEA<0.08) also suggest a good model fit with sample data.

Table 5.38 Model fit indices for SC Relationships, RS and Performance


Chi-square (χ2) 531.354 (df = 237, p = 0.084)

CMIN/df 2.242

GFI 0.974

AGFI 0.967

SRMR 0.044

TLI 0.972

NFI 0.970

RMSEA 0.067

CFI 0.971

Table 5.39 presents the mean, standard deviation (SD), correlations between variables

and square root of average variance extracted (AVE) values of study variables.

Table 5.39 Descriptive statistics and correlation matrix for study variables

Construct Mean SD 1 2 3 4 5 6

1. SATISFACTION 4.749 1.441 0.88

2. COLLABORATION 5.015 1.392 0.34** 0.87

3. TRUST 5.092 1.355 0.71** 0.38** 0.86

4. COMMITMENT 5.085 1.304 0.43** 0.37** 0.67** 0.86

5. REL SUCCESS 4.620 1.267 0.35** 0.48** 0.46** 0.44** 0.90

6. PERFORMANCE 5.082 1.249 0.37** 0.66** 0.62** 0.67** 0.64** 0.84 Notes: Correlations are significant at **p <0.01 (two-tailed), *p <0.05 (two-tailed), ns – not significant; the diagonal elements are square root of AVE values (bold italicised)

As shown in Table 5.39, the majority of the constructs are significantly correlated with

each other, and correlation coefficients ranging from lower 0.34 to higher 0.71.

However, the correlation coefficients of focal constructs are lower than the square root

199

of average variance extracted (AVE) values (bold italicised diagonal elements of Table

5.39). These indicate discriminant validity amongst the focal variables (Hair et al.

2010).

Table 5.40 Std. FL, α, CR and AVE for SC relationships, RS and performance

Construct Statement Std.

FL

α CR AVE

SATISFACTION

Our farm is very satisfied with our major SC partner 0.77

0.92 0.93 0.77

Our farm is very pleased with what our major SC partner

does for us

0.93

Our farm is not completely happy with our major SC partner 0.93

Our farm would still choose to use our major SC partner, if

we had to do it all over again

0.87

COLLABORATION

We are working closely with our major SC partner in

process development in the supply chain

0.93

0.90 0.90 0.75 We are working closely with our major SC partner in target

costing

0.79

We are working closely with our major SC partner in

planning of the supply chain activities

0.88

TRUST

Whoever is at fault, problems need to be solved together 0.78

0.94 0.94 0.75

Our major SC partner has high integrity 0.89

There are no doubts regarding our major SC partner’s

motives

0.90

Both parties are willing to make mutual adaptations 0.86

If our major SC partner gives us some advice, we are certain

that it is an honest opinion

0.88

COMMITMENT

We expect this relationship to continue for a long time 0.91

0.87 0.89 0.74 We are committed to our major SC partner 0.70

We expect this relationship to strengthen over time 0.95

RELSUCCESS

Good relationships with our major supply chain partner has

yielded success in quality of fruits and vegetables to the end

consumer

0.98

0.92 0.94 0.81 Good relationships with our major supply chain partner has

yielded success in terms of lowering SC monitoring cost

0.98


yielded success in terms of assistance received during

0.59

200

difficult times


yielded success in terms of increasing cooperation and

communication between you and your major SC partner

0.99

PERFORMANCE

As a result of the relationship with our major SC partner,

improvements have been noticed in the following areas…

Reliability of deliveries

0.80

0.92 0.92 0.70 Responsiveness of our farm to outside queries 0.87

Total cost reduction 0.77

Lead time (time between order placement and delivery of

produce to your major SC partner)

0.89

Time-to-market 0.84

Note: Std. FL = Standardised Factor Loadings, α = Cronbach’s alpha, CR = Construct Reliability, AVE =

Average Variance Extracted

As shown in Table 5.40, the factor loadings of all study constructs are above the

minimum threshold value of 0.50. According to Hair et al. (2010), these results indicate

the convergent validity of measures.

Additionally, each construct’s reliability was evaluated using Cronbach’s alpha (α) and

construct reliability (CR) values (Nunnally & Bernstein 1994). Table 5.40 further reveal

that Cronbach’s alpha and construct reliability values for all constructs are within the

acceptable levels (ranging from lower 0.87 to higher 0.94). Hence, as suggested by

Nunnally and Bernstein (1994), these constructs demonstrate an acceptable level of

construct reliability.

Table 5.41 The items deleted during SEM analysis

Construct Deleted Item Code Deleted Item

COPOWER CP1

1. Failing to comply with our major SC partner’s

requests will result in financial and other penalties

for our farm

TOTAL

Original survey

instrument consist of

43 items

Deleted 8 items in total (out of 43)

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Table 5.41 show the item deleted during reliability and validity tests of latent constructs.

At the end of these analyses, 8 items in total were deleted from the initial survey

instrument, which consisted 43 items related to 9 latent constructs. The following

sections describe the techniques used in further analysis of the constructs.

5.8 Structural model estimation

5.8.1 Path analysis

Path analysis is used to test the hypotheses in this study. According to Garson (2008),

path analysis is

an extension of the regression model, used to test the fit of the correlation matrix

against two or more causal models which are being compared by the researcher.

A regression is done for each variable in the model as a dependent on others

which the model indicates are causes. The regression weights predicted by the

model are compared with the observed correlation matrix for the variables, and

goodness-of-fit statistic is calculated. (p. 1)

Furthermore, according to Lussier (2011), path analysis is a type of causal modeling,

which is used to examine causal inter-relationships among a set of constructs that are

logically ordered. As Cunningham (2010) explains, path analysis provides a way of

testing more explicit causal models. It uses linear regression to test the causal

relationships among the constructs, which are specified in the conceptual model

(Lussier 2011). Path analysis begins with a model with constructs and arrows

connecting them, which indicate the causal flow, or direction of the effect. These

constructs and directions of the arrows are carefully selected after a thorough review of

previous literature. Also, path analysis allows the researcher to estimate both direct and

indirect effects (Lussier 2011).

5.8.2 Composite variables

The proposed conceptual model was developed using several latent variables which are

not directly measurable or observable, but measured using several other observable

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items. The previous sections of this chapter confirmed the unidimensionality of these

items through one factor congeneric CFA using SPSS version 22 with AMOS.

Composite variables were then created for each latent variable using these selected

items. The single indicator latent variables can be created by aggregating together the

items that form a composite construct.

Sass and Smith (2006) suggest that parcels of items are preferred over individual items

because the small number of indicators are able to reduce estimation error. Also, parcels

of items have an increased chance of achieving multivariate normality than individual

items. These summated scales (parcels) have facilitated ease of use in subsequent

analyses and also provided the researcher with complete control over the calculation

(Hair et al. 2010). Furthermore, Hair et al. (2010) emphasise that summated or parcels

scales can portray complex concepts in a single measure while reducing measurement

errors, which is important in multivariate analysis.

Cunningham (2010) recommends using summated scales to form composite variables,

since taking the average of summed items facilitates the interpretability of scores with

reference to original response scale. On the other hand, average summed composites

generate smaller variances, which protect against unstable data matrices in SEM

(Cunningham 2010). The composites were created by calculating the simple averages of

the items that were confirmed using the overall CFA earlier in this chapter. Table 5.42

shows the descriptive statistics for all newly created composite scales.

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Table 5.42 Descriptive Statistics for Composite variables

Construct Minm Maxm Mean Std.

Deviation

Skewness Kurtosis

Statistic Std.

Error Statistic

Std. Error

Information-sharing 1.00 7.00 5.724 1.586 -1.502 0.144 1.259 0.287

Coercive power 1.00 6.00 2.128 0.948 1.411 0.144 2.193 0.287

Non-coercive power 1.00 6.25 3.951 1.368 -0.742 0.144 -0.587 0.287

Satisfaction 1.00 7.00 5.335 1.388 -1.507 0.144 1.485 0.287

Collaboration 1.00 7.00 5.016 1.391 -1.133 0.144 0.759 0.287

Trust 1.40 7.00 5.383 1.380 -1.630 0.144 1.633 0.287

Commitment 1.33 7.00 5.826 1.578 -1.613 0.144 1.420 0.287

Relationship success 1.00 7.00 5.067 1.179 -1.151 0.144 0.812 0.287

Performance 1.00 7.00 5.082 1.249 -1.015 0.144 0.388 0.287

As shown above in Table 5.42, all the skewness and kurtosis values fall in the range

between -2.00 and 2.00. It can also be noticed that the skewness of the composite

variables are negatively skewed but this is common when using seven-point Likert

scales. Thus, it can be concluded that these composite or summated items have achieved

the normality and can be used for further analyses.

5.8.3 Regression coefficient and measurement error for composite variables

The degree to which the observed values are not the representative of true values is

known as measurement error (Hair et al. 2010). As Hair et al. (2010) further explain,

data entry errors, imprecision of the measurement and the respondent’s inability to

provide accurate information can cause this error. Hence the true effect is partially

masked by the measurement error in computing correlations, which results in less

precise means and weaken correlations (Hair et al. 2010). Cunningham (2010) argues

that measurement error is critical to consider, since the analyses results which ignore

such errors are biased estimators, and hence provide incorrect findings.

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According to Munck’s formula the regression coefficient and measurement errors for

composite latent variables can be calculated using Cronbach’s alpha and standard

deviation. These values resulting from the Munck’s formula are specified as fixed

parameters in SEM.

The two formulae for calculating the above-mentioned fixed values associated with

single indicator latent variables are shown below.

Regression coefficient (λ) = SD(X) √𝛂

Measurement error variance = Var(X) (1 – α)

Since, Var(X) = SD(X)2

Measurement error variance = [SD(X)]2 (1 – α) SD = Standard deviation of the composite variable, α = Cronbach’s alpha (internal consistency reliability

estimate) of the composite variable (Cunningham 2010).

Based on these formulae, regression coefficients and measurement errors were

calculated for each latent variables used in the SEM, and presented in Table 5.43.

Table 5.43 Regression coefficients and measurement errors for Composite variables

Construct Std. Deviation Cronbach’s

Alpha Regression Coefficient

Measurement Error

Information Sharing 1.586 0.970 1.562 0.075

Coercive Power 0.948 0.866 0.882 0.120

Non-coercive power 1.368 0.906 1.302 0.176

Satisfaction 1.388 0.954 1.356 0.089

Collaboration 1.391 0.898 1.318 0.197

Trust 1.380 0.957 1.350 0.082

Commitment 1.578 0.967 1.552 0.082

SC relationship success 1.179 0.843 1.082 0.218

Performance 1.249 0.920 1.198 0.125

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As shown above in Table 5.43, all Cronbach’s alpha and construct reliability values

exceed the threshold of 0.70, which suggests that summated scales have demonstrated a

reasonable level of construct reliability.

5.8.4 Initial SEM model

The data for this study were analysed using SPSS version 22 with AMOS, and used

maximum likelihood (ML) estimation. The initial model was developed based on the

initial conceptual model, which was explained in Chapter 3. There are three independent

variables which directly influence six other variables in the model. These independent

variables also indirectly influence other variables through mediator variables. The

independent variables are correlated with each other, whilst residual errors for

endogenous variables are not correlated. There are no feedback loops in the model and

all relationships are unidirectional. This study model is, thus, recursive in nature

(Holbert & Stephenson 2002).

Figure 5.13 illustrates the initial structural equation model for the fully mediated

conceptual model, and Table 5.44 shows the goodness of fit indices of the initial model.

Table 5.44 Model fit indices for initial SEM


Chi-square (χ2) 137.423 (df = 9, p = 0.000)

CMIN/df 15.269

GFI 0.915

AGFI 0.573

SRMR 0.038

TLI 0.756

NFI 0.936

RMSEA 0.223

CFI 0.939

The results show a considerably higher CMIN/df value of 15.269, a higher RMSEA

value of 0.223 (RMSEA > 0.08 is a poor fit), and lower model fit values for GFI, AGFI,

206

TLI, NFI and CFI. Compared with the accepted model fit values (as depicted in Table

5.44), these fit indices indicate that the initial model (please refer initial structural model

depicted in Appendix 5) does not fit well with sample data, nor all the initial hypotheses

were supported.

Figure 5.13 Initial SEM model to test hypotheses

The initial conceptual model (Chapter 3) was developed based on previous studies

which were conducted in different contexts. Hence, this model may need to be adapted

to suit the present study. On the other hand, there was no single study found which has

incorporated all study constructs that are used in this study, and tested in one model. For

these reasons, the results of poor model fit associated with the initial model can be

expected. As Cunningham (2010) argues, a poor model fit with data can stem from

omission or inclusion of variables or parameters (which arrows to be added and deleted)

between observed variables. As a result, researchers need to respecify the poorly fit

hypothesised model with an aim to identify the true model, thus improving model fit

indices (Cunningham 2010).

Collaboration

Commitment

Trust

Satisfaction


performance

SC relationship

success

Non-Coercive Power

Information Sharing

Coercive Power

Significant Not significant

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5.8.5 Model re-specification

In certain circumstances mentioned earlier, the hypothesised models can be different

from true models (or best fit models). In this respect, Cunningham (2010) stresses that

applied researchers must ascertain the extent that the hypothesised model is consistent

with the true model. Also, a hypothesised model can produce a poor sample covariance

matrix, when it is inconsistent with the true model (Cunningham 2010). Further, re-

specification is required when the researcher tests the initial model, and as a result some

paths among study constructs need to be added or removed to improve model fit (Shook

et al. 2004). Since the initial model does not fit well with the sample data of this study, a

model re-specification was performed on the initial model (Figure 5.12) in few steps, as

explained below.

The most appropriate method to identify the source of model misspecification is

through examining standardised residual values (Cunningham 2010), which represent

the difference between estimated covariance matrix and observed covariance matrix

(Reisinger & Mavondo 2007). According to Cunningham (2010), large standardised

residuals suggest that the hypothesised model has not produced the particular

covariance well. Furthermore, Reisinger and Mavondo (2007) argue that a hypothesised

model need adjustments when a standardised residual value is greater than the absolute

value of 2.58, and are considered statistically significant at 0.05 (5%) level.

Second, modification indices (MIs) are also indicative of a poor model fit to the sample

data (Cunningham 2010). The MI is the minimum drop that occurs to χ2 (chi-square)

value when the indicative parameter is estimated rather than fixed to be zero. Reisinger

and Mavondo (2007) recommend to add paths associated with parameters, which shows

the largest MIs. In doing so, improvement to model fit can be observed through the

reduction in chi-square. Further, Cunningham (2010) outlines that MIs referring to

regression weights are the only MIs that are considered to estimate path models.

Considering the above, the standardised residual covariance matrix (SRCM) of initial

model indicated that there are misspecifications between satisfaction and trust,

satisfaction and commitment, and also between collaboration and performance. These

misspecifications are also consistent with the information obtained through MIs.

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However the MIs are purely statistically driven, and the changes suggested by them

should only be considered after a careful and meaningful consideration of related

theoretical concepts.

Secondly, a respecification can also be achieved by deleting the paths that possess little

explanatory power (Schumacker & Lomax 1996). In other words, the parameters with

insignificant t-values can be deleted in order to obtain a better fit model with sample

data. A parameter that possess a t-value of less than 1.96, indicates that it is not

significantly different from zero at 0.05 (5%), hence that particular path can be deleted

to obtain a better model fit. However, Schumacker and Lomax (1996) argue that these

changes should be theoretically justified. Also, the respecification of the model by

adding and deleting paths needs to be carried out carefully. After each addition or

deletion, the new model should be analysed, and this process should be repeated until

the sample data is a good fit for the model.

After considering several model respecification criteria (explained above), the following

new paths were included in to the initial model to obtain a theoretically sound and a

better fit final model.

A direct relationship between satisfaction and trust

A direct relationship between satisfaction and commitment

A direct relationship between collaboration and performance

As depicted in Figure 5.13, there were several non-significant paths in the initial model.

However these were retained in the final model, since there was no theoretical

justification to delete them.

5.8.6 Final SEM model

The respecified model was then analysed using SPSS version 22 with AMOS, and the

results indicate a better fit model with sample data. The final model is presented in

Figure 5.14. Table 5.44 shows the resultant goodness of fit indices of the final model.

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Figure 5.14 Respecified final SEM model

As shown in Table 5.45, most fit indices of the final model are within the acceptable

levels (Acceptable levels are depicted in Table 5.12). RMSEA value is 0.060 (RMSEA

< 0.080 is a good fit) which indicates a good model fit with sample data. The CMIN/df

value is 2.016 with a non-significant p value along with lower SRMR value of 0.013

(SRMR < 0.05 is a good fit) indicates a good approximation of sample data with the

final model. The other model fit indices of GFI, AGFI, TLI, NFI and CFI also shows

higher values, which suggest a model that fits well with the sample data. The final

model (please refer final structural model depicted in Appendix 6) also supports many

of the hypotheses, which are detailed in Chapter 3.

Collaboration

Commitment

Trust

Satisfaction


performance

SC relationship

success

Non-Coercive Power

Information Sharing

Coercive Power

Significant Not significant New paths

0.829 0.244

0.274

-.144

0.422 -.045

-.142

0.262 0.327

0.514

0.092

0.451 0.190

0.125

0.455

0.091

0.502

0.197

0.102

0.284 -.142

0.276

0.145

-.064

-.039

-.028

-.003

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Table 5.45 Model fit indices for final SEM


Chi-square (χ2) 12.096 (df = 6, p = 0.060)

CMIN/df 2.016

GFI 0.991

AGFI 0.931

SRMR 0.013

TLI 0.983

NFI 0.994

RMSEA 0.060

CFI 0.997

The results from the hypotheses testing for the final model are shown in Table 5.46.

According to the initial model (Figure 5.12), there were 24 hypotheses to be tested, but

6 of them were not supported by the sample data. As mentioned earlier in this chapter,

there are three new relationship paths that have been included in the initial model in

order to obtain a good model approximation with sample data. These three relationship

paths were absent in the initial conceptual model (Chapter 3), which was formulated

using available literature, but identified based on the sample data. With the addition of

three new paths, there are 27 relationship paths in the final model, out of which 21 are

supported and 6 relationships are not supported by the sample data.

Table 5.46 Results of hypothesis testing

Hypotheses Regression paths β value t-value Result

1a INFORMATION SHARING SATISFACTION 0.829*** 24.223 Supported

1b INFORMATION SHARING COMMITMENT 0.274*** 5.802 Supported

1c INFORMATION SHARING RELATIONSHIP SUCCESS 0.145ns 1.674 Not supported

1d INFORMATION SHARING PERFORMANCE 0.262*** 5.398 Supported

1e INFORMATION SHARING TRUST 0.514*** 11.546 Supported

1f INFORMATION SHARING COLLABORATION 0.327*** 6.693 Supported

2a COERCIVE POWER SATISFACTION -0.028ns -0.821 Not supported

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Hypotheses Regression paths β value t-value Result

2b COERCIVE POWER COMMITMENT -0.003ns -0.136 Not supported

2c COERCIVE POWER RELATIONSHIP SUCCESS -0.064ns -1.576 Not supported

2d COERCIVE POWER PERFORMANCE -0.039ns -1.056 Not supported

2e COERCIVE POWER TRUST 0.102*** 3.934 Supported

2f COERCIVE POWER COLLABORATION -0.144** -2.921 Supported

3a NON COERCIVE POWER SATISFACTION -0.142*** -4.117 Supported

3b NON COERCIVE POWER COMMITMENT 0.091*** 3.734 Supported

3c NONCOERCIVE POWER RELATIONSHIP SUCCESS 0.092* 1.979 Supported

3d NON COERCIVE POWER PERFORMANCE 0.125** 3.018 Supported

3e NON COERCIVE POWER TRUST -0.142*** -5.370 Supported

3f NON COERCIVE POWER COLLABORATION 0.451*** 9.163 Supported

4 SATISFACTION RELATIONSHIP SUCCESS 0.244** 3.050 Supported

5 COLLABORATION RELATIONSHIP SUCCESS 0.284*** 6.125 Supported

6a TRUST COMMITMENT 0.502*** 9.672 Supported

6b TRUST RELATIONSHIP SUCCESS -0.045ns -0.440 Not supported

7 COMMITMENT RELATIONSHIP SUCCESS 0.276** 2.735 Supported

8 RELATIONSHIP SUCCESS PERFORMANCE 0.422*** 8.002 Supported

New SATISFACTION COMMITMENT 0.197*** 4.347 Supported

New SATISFACTION TRUST 0.455*** 10.331 Supported

New COLLABORATION PERFORMANCE 0.190*** 4.112 Supported Notes: Significant at: *p < 0.05, **p < 0.01 and ***p < 0.001, ns - not significant

As shown in Figure 5.14 and Table 5.46, the final results consist of 21 significant

relationship paths, whilst 6 are not supported out of the total of 27 relationship paths.

Information-sharing positively and significantly influences satisfaction (β = 0.829,

p<0.001), thus supporting H1a. Information-sharing also positively and significantly

influences commitment (β = 0.274, p<0.001) and trust (β = 0.514, p<0.001), thus

supporting H1b and H1e, respectively. Information-sharing positively and significantly

impacts collaboration (β = 0.327, p<0.001) and firm’s operational performance (β =

0.262, p<0.001), thus supporting H1f and H1d, respectively. The results also indicate that

information sharing does not influence SC relationship success (β = 0.145, p>.10),

212

although it was hypothesised (Chapter 3) based on previous literature and thus H1c is not

supported.

Interestingly coercive power positively influences trust (β = 0.102, p<0.001), thus

supporting H2e, and negatively influences collaboration (β = -0.144, p<0.01), thus

supporting H2f. Coercive power does not influence satisfaction (β = -0.028, p>0.10),

commitment (β = -0.003, p>0.10), SC relationship success (β = -0.064, p>0.10) or

firm’s operational performance (β = -0.039, p>0.10). Thus, H2a, H2b, H2c and H2d are not

supported.

As previously suggested, non-coercive power negatively and significantly predicts

satisfaction (β = -0.142, p<0.001) and trust (β = -0.142, p<0.001), thus supporting H3a

and H3e, respectively. Non-coercive power is positively and significantly influences

commitment (β = 0.091, p<0.001) and collaboration (β = 0.451, p<0.001) thus

supporting H3b and H3f, respectively. Non-coercive power was also found to positively

and significantly impact SC relationship success (β = 0.092, p<0.05) and firm’s

operational performance (β = 0.125, p<0.01), thus supporting H3c and H3d, respectively.

Satisfaction was found to positively and significantly impact SC relationship success (β

= 0.244, p<0.01), thus supporting H4. Also, collaboration positively and significantly

impacts SC relationship success (β = 0.284, p<0.001), thus supporting H5. Trust

significantly and positively influences commitment (β = 0.502, p<0.001), thus

supporting H6a, Trust does not, however, significantly predict SC relationship success (β

= -0.045, p>0.10) as hypothesised, thus meaning that H6b is not supported.

As shown in Table 5.46, commitment is positively and significantly related to SC

relationship success (β = 0.276, p<0.01), thus supporting H7. Also, SC relationship

success positively and significantly influences firm’s operational performance (β =

0.422, p<0.001), thus supporting H8. Satisfaction positively and significantly influences

commitment (β = 0.197, p<0.001) and trust (β = 0.455, p<0.001). Also, collaboration

positively and significantly influences firm’s operational performance (β = 0.190,

p<0.001).

213

The analysis also looked at the proportion of each of endogenous variable which is

explained through the final model. As shown in Table 5.47, the final model explains a

considerable proportion of variation for most of the endogenous variables. Thus the

final model is said to demonstrate a strong explanatory power. Almost 87% of variation

in commitment, 83% of variation in trust, 69% of variation in satisfaction and 61% of

variation in SC relationship success are explained by the final model. Moreover, the

entire model is able to account for 65% of variation in firm’s operational performance.

The lowest variation that is explained by the final model is 36%, which is for the

collaboration construct.

Table 5.47 Proportion of variation explained for each endogenous variable

Construct Proportion of variation explained

Satisfaction 0.685

Collaboration 0.359

Trust 0.825

Commitment 0.866

SC relationship success 0.607

Performance 0.648

5.9 Mediation, total, direct and indirect effects between constructs

According to Baron and Kenny (1986), the mediating effect is a result of a third

construct intervenes between two other related constructs. The mediating variable (also

known as the intervening variable) take inputs from first construct and translate them in

to the output which is the second construct. As such, the mediator variable explains the

relationship between two original constructs (Hair et al. 2010). These authors also argue

that all three constructs need to have a significant correlation among them to satisfy the

mediation requirement. Complete or full mediation is achieved when the mediating

construct explain the complete relationship between two original constructs. Partial

mediation results when the intervening construct does not explain complete relationship

between two original constructs (Hair et al. 2010). When there is only one mediating

variable, then it is said to be a simple mediation (Preacher & Hayes 2008). There can

also be multiple mediators, as shown in Panel B (Figure 5.15). The mediating effects of

214

a model can be defined through direct and indirect effects (Hair et al. 2010). The direct

effect initiates when one construct influences the second construct, but are not

influenced by any other variable in the model (Bollen & Stine 1990), or when the

relationship between two constructs are linked with a single arrow (Hair et al. 2010). As

Bollen and Stine (1990) suggest, indirect effects occur when the effect of first construct

on the second construct is mediated by at least one other variable in the model.

According to Hair et al. (2010), an indirect effect is a result of a sequence of

relationships with at least one mediating construct involved.

There are several methods to determine direct and indirect effects between variables.

The most widely used among them is the method described by Baron and Kenny

(1986). However, many studies (Collins, Graham & Flaherty 1998; MacKinnon, Krull

& Lockwood 2000; Preacher & Hayes 2004, 2008) have highlighted various limitations

associated with this method. The bootstrap methodology introduced by Bollen and Stine

(1990) can overcome these limitations through its superior statistical power. Shrout and

Bolger (2002) argue that bootstrapping approach allows the researcher to examine the

distributions empirically.

As Preacher and Hayes (2008) explain, the total effect on a variable is the sum of direct

and indirect effects.

Direct effect (1) + Indirect effect (2) = Total effect (1) + (2)

As shown in Panel A of Figure 5.15, when one construct (X) influences the other

construct (Y), the total effect (in this case, the same as direct effect) of X on Y is

indicated as c (Hayes 2009). There is no other constructs influence this relationship.

However, in more complex models total effect is the sum of all the direct and indirect

effects. As shown in Panel B of Figure 5.15, a1 = effect of X on M, a2 = effect of X on

W, a3 = effect of M on W, b1 = effect of M on Y, b2 = effect of W on Y, c' = direct effect

of X on Y. As such the total effect of X on Y, vis-à-vis c = c' + a1b1 +a2b2 + a1a3b2

(Hayes 2009).

215

Figure 5.15 Direct effect and multiple step multiple mediator model

There are many indirect influences that can be identified in the final model (Figure

5.13). By using the above method to calculate the total effect, the following sections

describe the antecedents for each of the dependent variables. Corresponding tables

present the regression coefficients (β values) of direct, indirect and total effects

generated using SPSS version 22with AMOS.

5.9.1 Constructs affecting Satisfaction

As presented in Table 5.48, satisfaction has three antecedents, and has only direct

effects. Out of the paths from the three antecedents to satisfaction, information-sharing

has a positive significant effect on satisfaction, while non-coercive power has a negative

significant effect on satisfaction. Coercive power has a non-significant effect on

satisfaction.

Y X

M W a3

a1 b2

a2 b1

c'

Y X c

Panel A

Panel B

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Table 5.48 Constructs affecting satisfaction

Construct Direct Effects (1) Indirect Effects (2) Total Effects (1) + (2)

Information-sharing 0.829*** -- 0.829***

Coercive power -0.028ns -- -0.028 ns

Non-coercive power -0.142*** -- -0.142***

Notes: Significant at: *p < 0.05, **p < 0.01 and ***p < 0.001, ns - not significant

5.9.2 Constructs affecting collaboration

In total, there are three antecedents for collaboration, and all of them have direct effects

on collaboration. As presented in Table 5.49, information sharing and non-coercive

power have positive significant effects on collaboration whilst coercive power has a

negative significant effect on collaboration.

Table 5.49 Constructs affecting collaboration


Information-sharing 0.327*** -- 0.327***

Coercive power -0.144** -- -0.144**

Non-coercive power 0.451*** -- 0.451***


5.9.3 Constructs affecting trust

There are four antecedents of trust. These are: information sharing, coercive power,

non-coercive power and satisfaction (Table 5.51). In information-sharing, coercive

power and satisfaction have positive significant effects on satisfaction, whilst non-

coercive power has a negative significant effect. Information-sharing has a positive

indirect effect through satisfaction. Also, coercive power and non-coercive power have

negative indirect effects though satisfaction but both these effects are non-significant.

217

Table 5.50 Constructs affecting trust


Information-sharing 0.514*** 0.377*** 0.891***

Coercive power 0.102*** -0.013 ns 0.089*

Non-coercive power -0.142*** -0.065 ns -0.207***

Satisfaction 0.455*** -- 0.455***


5.9.4 Constructs affecting commitment

Five antecedents have direct effects on commitment. Of these five antecedents,

information-sharing, non-coercive power, satisfaction and trust have positive significant

effects on commitment (Table 5.51). Only coercive power has a negative, non-

significant effect on commitment and thus, it is not an antecedent to commitment.

Information-sharing has indirect effect on commitment through trust and satisfaction.

Coercive and non-coercive powers have indirect effects on commitment through trust

and satisfaction, but they are non-significant.

Table 5.51 Constructs affecting commitment



Coercive power -0.003 ns 0.039 ns 0.036 ns

Non-coercive power 0.091*** -0.132*** -0.041 ns

Satisfaction 0.197*** 0.228*** 0.425***

Trust 0.502*** -- 0.502***


5.9.5 Constructs affecting SC relationship success

Although SC relationship success has seven direct antecedents, only non-coercive

power, satisfaction, collaboration and commitment have direct and significant effects on

the construct. Information-sharing, coercive power and trust have non-significant direct

218

effects on SC relationships success. Also, five out of seven constructs have indirect

effects on SC relationship success. As shown in Table 5.52, information sharing has

indirect effect on SC relationship success through satisfaction and commitment.

Table 5.52 Constructs affecting SC relationship success


Information-sharing 0.145 ns 0.499*** 0.644***

Coercive power -0.064 ns -0.042 ns -0.106*

Non-coercive power 0.092* 0.091** 0.184***

Satisfaction 0.244** 0.097 ns 0.341***

Trust -0.045 ns 0.139** 0.094 ns

Collaboration 0.284*** -- 0.284***

Commitment 0.276** -- 0.276**


Also, non-coercive power has the indirect effect on SC relationship success through

satisfaction and commitment. Satisfaction also has indirect effect on the SC

relationship success through commitment. Collaboration has a direct effect on SC

relationship success. From these results, it can be concluded that satisfaction and

commitment are the mediators of the relationship between information-sharing and SC

relationship success. Trust and commitment are the mediators of the relationship

between coercive power and SC relationship success. Commitment is the mediator of

the relationship between trust and SC relationship success.

5.9.6 Constructs affecting firm’s operational performance

Firm’s operational performance has eight antecedents which have direct and indirect

effects on the construct. Out of these, five antecedents have direct effects on the

construct, and four of them also have indirect effects on firm’s operational performance.

As presented in Table 5.53, information sharing, non-coercive power, collaboration and

SC relationship success have positive significant total effects on firm’s operational

performance. Satisfaction, trust and commitment only have indirect effects on firm’s

219

operational performance. Coercive power has a non-significant negative effect of firm’s

operational performance. This suggests that trust, commitment and SC relationship

success mediate the relationship between coercive power and a firm’s operational

performance.

Table 5.53 Constructs affecting firm’s operational performance



Coercive power -0.039 ns -0.072*** -0.112**

Non-coercive power 0.125** 0.163*** 0.289***

Satisfaction -- 0.144*** 0.144***

Trust -- 0.040 ns 0.040 ns

Collaboration 0.190*** 0.120*** 0.310***

Commitment -- 0.117** 0.117**



This chapter presented analysis and findings of sample data set. Initially the data set was

prepared for further analysis by performing missing data analysis, imputation, checking

for outliers, skewness and kurtosis. The next section presented profile descriptions of

survey respondents. Later, reliability of the constructs was measured employing internal

consistency method using Cronbach’s alpha.

CFA process was then detailed with descriptions of fit indices, and also included an

acceptable fit values table, which was used in this study. The next section described the

analyses of one factor congeneric CFA models to obtain construct validity for all study

constructs, and 7 items (out of 43 items) were deleted during the process to obtain best

fit models. After descriptions of validity and reliability, the main analysis was

performed in two steps. In step I, reliability and validity were obtained using the CFA

models in two parts. After this, it was observed that a total of 8 items out of 43 original

items were deleted in obtaining a best fit initial model for further analysis.

220

Composite variables were then created by aggregating related items for each construct.

This step was undertaken to reduce estimation error, and also due to the ability of

summated scales to confirm to multivariate normality assumptions than individual

items. After the Cronbach’s alpha values and measurement error for summated scales

were obtained, step two of the analysis (i.e. the confirmatory structural model) was

tested with the sample data. Since the initial model did not fit the sample data well,

model re-specification was performed to obtain the final model, and this fitted the

sample data well.

The final section of this chapter examined the direct, indirect and total standardised

effects of all the drivers of firm’s operational performance. Information-sharing, non-

coercive power, collaboration and SC relationship success were shown to have direct

and significant effects on firm’s operational performance; SC relationship success was

shown to have the largest direct effect on firm’s operational performance. However,

information-sharing observes the largest total effect on firm’s operational performance.

As observed, many other constructs have direct and indirect effects on firm’s


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Chapter Six: Discussion and conclusion

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6 Introduction to chapter six This chapter discusses the findings, implications and conclusions of this study. Section

6.1 discusses the rationale for undertaking the study focussing on the relevant constructs

that were used in developing the conceptual framework. Section 6.2 discusses the

findings based on the three broad areas of the conceptual framework, and related

secondary research questions. The direct, indirect and total effects are discussed in

Section 6.3. This section comprises two sub-sections, which discuss the influences of

indirect effects on SC performance (i.e. SC relationship success) and operational

performance (i.e. a firm’s operational performance) respectively. Section 6.4 presents

the theoretical contribution and practical/managerial implications of this study. Section

6.5 presents the inherent limitations of this study, followed by directions for future

research that is presented in Section 6.6. This chapter concludes with Section 6.7. The

roadmap of this chapter is depicted in Figure 6.1.

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Figure 6.1 Road map to the discussion and conclusion chapter

6.1 Rationale of the present study

Long-term sustained relationships with strategically important partners are important in

achieving improved SC performance (Cousins & Menguc 2006; Kähkönen 2014). The

complex interdependent relationships allow SC partners to improve performance

through engaging in collaborative activities (Kähkönen 2014). Although the traditional

Theoretical contributions

Practical/managerial implications

6.1 Rationale of the present study


6.2 Discussions of the findings

6.3 Original theoretical model revisited

6.4 Contributions to the body of knowledge

6.5 Limitations of the study

6.6 Directions for future research

6.7 Concluding remarks

Discussion related to relationship constructs

Discussion related to influential determinants

Discussion related to overall performance

Indirect effects on SC relationship success

Indirect effects on firm’s performance

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logistical networks focussed entirely on distribution of goods, present-day SCs focus on

aspects like information-sharing, which helps SC partners to make intelligent decisions

(Rashed, Azeem & Halim 2010). These authors demonstrate that SCs involve the flow

of materials, information and capital across the entire SC. Information flow between SC

partners assists them to ensure undisrupted flow of goods through their SC (Li et al.

2006b) and more often they share important proprietary information (Cannon &

Perreault Jr. 1999). Further, information flow between SC partners provides insight and

visibility into SC activities (Coyle et al. 2013). Owing to the important role that

information sharing plays in any SC this aspect is conceptualised here as being an

influential determinant.

According to some researchers, the power of SC partners tends to influence other

partners to act according to a desired manner for economic gains (Ireland & Webb

2007; Nelson & Quick 2012). These powerful SC partners obtain their power through

different power bases, such as coercive, reward, legitimate, referent and expert power

(French & Raven 2001). Many previous researchers (Frazier 1983; Ireland & Webb

2007; Molm 1997) classify these power bases as being coercive and non-coercive power

depending on the intensity of aggressiveness. In SCs, the most resourceful partner is

likely to have controlling power over the other SC partners (Cendon & Jarvenpaa 2001;

Cox, Sanderson & Watson 2001; Gelderman & Van Weele 2004; Svahn & Westerlund

2007). Previous researchers held conflicting views about the influence of power among

SC partners. Van Weele and Rozemeijer (2001) view power as an obstacle to

collaborative relationships in SCs. Conversely, Perumal et al. (2012) empirically

establish that power act as a tool of relationship building in SCs. Further, the capacity

and ability of SC partners to exert power on the other partners varies depending on the

type of SC (i.e. depending on the type of commodity that is distributed in the SC) that

they are involved with (Smith 2008). Due to the important role of power in SC

relationships, coercive power and non-coercive power are included in the final

conceptual model as influential determinants together with information-sharing.

Satisfaction, collaboration, trust and commitment (identified in this study as relationship

constructs) are important in establishing close SC relationships (Doney & Cannon 1997;

Dorsch, Swanson & Kelley 1998; Field & Meile 2008; Hewett, Money & Sharma 2002;

Morgan & Hunt 1994). Through these close relationships, SC partners are able to

225

achieve reduced costs, improved quality, improved reliability of deliveries, speed and

flexibility (Karia & Razak 2007; Mentzer, Foggin & Golicic 2000). Hence, these

relationship constructs help achieve improved SC performances. Satisfaction is crucial

in gaining success in SCs (Janvier-James 2012), collaboration invigorates SC partners

motivation to engage (Ramanathan & Muyldermans 2011), trust improves inter-firm

relationship performance (Currall & Inkpen 2002; Ireland & Webb 2007), and

commitment positively influences joint actions in the SC (Hausman & Johnston 2010).

As a result, these relationship constructs are included in the proposed conceptual model

as they directly influence SC relationship success.

The success of SC relationships directly influences the performance of SC partner firms

(Benton & Maloni 2005; Kannan & Tan 2006; Maloni & Benton 2000; Narasimhan &

Nair 2005). The success of these firms is a result of SC partners’ effective engagement

with each other. Hence, this study suggests that SC relationship success directly affects

a firm’s operational performance.

Information-sharing, power, and the four relationship constructs and their influence on

SC relationship success and firm performance have been studied in various contexts

(Field & Meile 2008; Grewal, Levy & Kumar 2009; Singh & Power 2009). There is,

however, no study that deals with all these constructs simultaneously. Also, it is

important for small firms to depend on their powerful SC partners in order to be

successful in their business. This is due to their partners’ market knowledge (O’Keeffe

1998). There is scant academic research, which investigates information sharing, power,

and relationship constructs and their influence towards SC relationship success and

firm’s operational performance in the specialised organic fruit and vegetable industry.

Hence, this industry, which is economically lucrative and also has bright future

prospects (for example, employment), is used as the context of this study (Bez et al.

2012).

The data was collected from organic fruit and vegetable growers in Australia, using an

online survey. The sampling frame (which consisted of the contact details for the

growers) were obtained using publicly available websites. The perceptions of these

growers with respect to the day-to-day SC activities with their major SC partners were

the focus of this study. The proposed conceptual model was empirically tested using

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Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The

results of these analyses are discussed in the following sections.

6.2 Discussion of the findings

The following sections discuss the findings pertaining to the secondary research

questions which were developed in Chapter 3 in line with the primary or overarching

research problem.

The main objective of this study was to investigate the influence of power, information

sharing and SC relationship constructs on the SC relationship success and firm’s

operational performance in relation to the organic fruit and vegetable industry. Further,

this study intended to identify the relationships between these constructs. Based on the

literature review a conceptual model was proposed (please refer to Figure 3.6 in Chapter

3), and the model was empirically tested using data obtained from an online survey

administered among organic fruit and vegetable growers in Australia. The simplified

three stages associated with the proposed conceptual framework is presented in Figure

6.2.

Figure 6.2 Three-stage conceptual framework

These three stages are the influential determinants, SC relationship constructs and

performance. The influential determinants consist of information sharing and power,

which is operationalised using two individual constructs, coercive and non-coercive

power. The literature review emphasises that these three constructs influence the other

constructs in the proposed conceptual model. SC relationship constructs include

satisfaction, collaboration, trust and commitment. All of these are considered important


SC relationship constructs

Performance (SC +

operational)

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in achieving closer relationships between SC partners, and they also influence the

performance of a SC.

The dependant or outcome construct relating to performance includes SC performance

(which is operationalised as SC relationship success) and firm’s operational

performance (which is operationalised as firm’s performance). The literature review

establishes that SC relationship constructs influence SC relationship success, and then

SC relationship success influences the firm’s operational performance of the SC.

Following from the forgoing discussion, Table 6.1 presents the three stages (reference

Figure 6.2) of the proposed conceptual framework, together with their associated

constructs, research questions and hypotheses. This table aims to discuss the findings in

a logical sequence, addressing each of the research questions.

Table 6.1 Constructs and related secondary research questions

Stage of the conceptual

framework

Constructs Secondary

research questions

Related hypotheses


Information sharing RQ1 1a, 1b, 1c, 1d, 1e, 1f

Coercive power RQ2 2a, 2b, 2c, 2d, 2e, 2f

Non-coercive power RQ3 3a, 3b, 3c, 3d, 3e, 3f

SC relationship constructs

Satisfaction RQ4 4

Collaboration RQ5 5

Trust RQ6 6a, 6b

Commitment RQ7 7

Performance


(SC performance) RQ8 8


performance (operational

performance)

- -

6.2.1 Findings associated with influential determinants

Information-sharing, coercive power and non-coercive power are broadly categorised as

influential determinants in this study, and these three constructs are featured in research

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questions RQ1, RQ2 and RQ3 (the hypotheses related to these research questions are

presented in Table 6.1).

According to Cheng (2011; Kwon & Suh 2004), information-sharing has become an

important issue for SCs. As discussed previously, un-distorted sharing of information

between the partners of a SC improves the capabilities of SCs, and this results in the

seamless flow of materials (Balsmeier & Voisin 1996; Childerhouse & Towill 2003; Li

et al. 2006b; Towill 1997). Further, Lalonde (1998) views information-sharing as one of

the cornerstones of creating solid SC relationships. Moreover, Zhao, Xie and Zhang

(2002) remark that information-sharing significantly affects SC performance and

reduces costs. Power, which is identified in this study as coercive and non-coercive

power, is vital in creating effective relationships (Kumar 2005). Further, Leonidou,

Talias and Leonidou (2008) argue that coercive forms of power increases conflict in SC

relationships, whilst non-coercive forms leads to less conflicts. The following sections

describe the results related to research questions RQ1, RQ2, RQ3, and their associated

hypotheses.

Discussion of information sharing as an influential determinant

The final SEM model analysis results demonstrate that information sharing has positive

and significant influences on five out of the six constructs of the conceptual model. The

greatest influence is on satisfaction (β = 0.829***), which suggests that information

sharing between SC partners increases the level of satisfaction between them.

Consistent with the findings of Lee (2000), this implies that sharing valuable

information between SC partners creates an environment, in which SC partners

willingly exchange ideas, and sometimes proprietary information. This finding is also

consistent with the findings of Nyaga, Whipple and Lynch (2010), which establish that

investments that are targeted to improve information sharing capabilities, result in the

commitment attachment of SC partners towards their relationship. These authors further

establish that this improved level of commitment leads to increase satisfaction between

SC partners. Hence, it follows that both partners in the relationship need an

uninterrupted flow of shared information, which assists them in their day-to-day

operations.

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The results of the hypotheses test using the final SEM model reveals that information

sharing between SC partners positively and significantly influences (β = 0.327***)

collaboration. This confirms the previous findings of Rashed, Azeem and Halim (2010),

who argue that sharing of vital and proprietary decision-making information can

substantially promote collaboration between SC partners. Similarly, Min et al. (2005)

suggest that information sharing between SC partners is important to achieve the

benefits of collaboration. According to Duffy and Fearne (2004b), collaboration

between retailers and their suppliers is hampered if the most powerful SC partner (in

this study, the retailer) does not share important information such as consumer demand.

This is consistent with the finding of this study, which establishes that information

sharing positively and significantly influences collaboration between the organic fruit

and vegetable growers and their major SC partners. Information sharing helps SC

partners to intelligently forecast their supply and demand requirements, and as a result

collaboration between them improves (Byrne & Heavey 2006). Also, these authors

emphasise that information sharing and collaboration between SC partners leads to

overall SC gains.

The results of the final SEM model analysis also suggest that sharing of valuable

information between SC partners positively influences (β = 0.514***) trust between

them. This is in line with the previous findings of Doney and Cannon (1997), which

assert that sharing of confidential information signals that SC partners who share

information can be trusted and their intentions are benevolent. According to Kwon and

Suh (2004), information sharing between SC partners assists in understanding each

other’s routines, and develops mechanisms for conflict resolution, which improves trust

among them. Hence, it can be argued that organic fruit and vegetable growers are able

to understand their SC partners through information sharing, which in turn enhances

their trustworthiness towards each other. This finding ties in with the findings of several

previous studies (Anderson & Weitz 1992; Doney & Cannon 1997; Kwon & Suh 2004;

Monczka et al. 1998), which conclude that information sharing within a SC reduces

uncertainty, and as a result improves commitment in the relationship.

The analysis in the final SEM model revealed that information sharing between SC

partners positively and significantly influences (β = 0.274***) commitment. This

finding concurs with the previous contribution of Anderson and Weitz (1992), where

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information-sharing between SC partners was shown to encourage their commitment

towards each other. Consistent with the findings of Kwon and Suh (2004) and Monczka

et al.(1998), this finding implies that the sharing of information reduces the uncertainty

in the relationship, and this, in turn, improves the level of commitment in the

relationship. In this respect , ashed, Azeem and Halim (2010) suggest that relevant,

accurate and timely information sharing between SC partners reduces temporal and

spatial distance, which enables them to engage in more collaborative decisions.

The results of the final SEM model analysis also demonstrate that sharing of

information between SC partners positively and significantly influences (β = 0.262***)

a firm’s operational performance. This is consistent with the findings of Lee, So and

Tang (2000), who empirically establish that information sharing between SC partners

reduces cost, and cost reduction is directly related to improved operational performance

(also known as firm’s operational performance in this study). According to Panayides

and Lun (2009), there is a direct relationship between cost reduction and improved

firm’s operational performance. Thus, considering these previous findings and the

findings of this study, it can be established that organic fruit and vegetable growers can

improve their firm’s operational performance by sharing information with their SC

partners. In this particular industry, information regarding prospective and seasonal

demand is important for the growers to effectively plan the supply of their produce. This

finding also strengthens the findings of Mentzer et al. (2001) and Li et al. (2006a),

which show that information sharing improves performance of SC firms.

Furthermore, Gaonkar and Viswanadham (2001) empirically demonstrate that

information sharing has a significant impact on supply chain profitability, which is

directly linked to the performance of a SC. According to Rashed, Azeem and Halim

(2010), firms need to carefully select the information to be shared with SC partners, and

use of selective, vital and appropriate information improves firm performance. In the

organic fruit and vegetable sector, ideally retailers and wholesalers need to share with

growers the information relating to type and quantity of produce that would be in

demand, so that growers can plan well in advance. This will substantially reduce the

cost of production, and improves operational performance of the organic fruit and

vegetable growers.

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The results of this study, however, reveal that information sharing does not significantly

influence (β = 0.145ns) SC relationship success. There could be several reasons which

contribute to this result. One plausible reason is that the growers are satisfied with the

amount of information that they already receive from their major SC partners, which

helps them to achieve their present performance. Further, as identified through the

organisational profile data, the turnover of the majority (88.2%) of the organic fruit and

vegetable growers is less than AUD 500k per annum, which indicates that they operate

small scale farms. In comparison to the large scale operations of retailers and

wholesalers, growers seem less powerful. As a result of this power imbalance growers

may not be demanding additional information from their major SC partners, as

additional demands might hamper the present state of their relationship. In both of these

instances, growers are comfortable with the amount of information shared between

them, although it does not contribute to the success of their relationship with major SC

partners. Another reason might be that growers do not consider that information-sharing

will influence their SC relationship success as they already enjoy close relationships

with their major SC partners. Hence, it can be concluded that information sharing

between growers and their major SC partners does not directly influence SC

relationship success in the organic fruit and vegetable industry.

Discussion of coercive power as an influential determinant

According to Gelderman, Semeijn and De Zoete (2008), coercive power motivates

compliance through source-controlled rewards and punishments. Thus, it negatively

impacts on trust. Also, coercive power creates opportunities for the most powerful

partner to behave opportunistically, and as a result this may lower the level of trust in

the relationship. In contrast to the findings of previous studies, however, the findings of

this study reveal that coercive power positively and significantly influences (β =

0.102***) trust. One reason for this anomaly could be the varying influences of

coercive power in different types of SCs. In supporting this argument Lunenburg (2012)

suggests that the effects of coercive power vary depending on the organisation, and they

generate mixed feelings for the users depending on the situation.

Furthermore, according to Kähkönen (2014), neither the source of power nor the

possession of power solely impacts on the nature of SC relationship. The impact,

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however, depends on the willingness to use and exploit one’s power position. Hence, it

can be concluded that trust between growers and their SC partners improves,

irrespective of their pressure on the organic fruit and vegetable growers through

coercive means. Conversely, the organisational profile data reveals that 88.2% of

organic fruit and vegetable growers operate medium or small size (turnover < AUD

500K) farms, and their SC partners are invariably more powerful than them due to their

scale of the operations. As a result, it can be stated that organic fruit and vegetable

growers are dependent on their major SC partners, and receive economic benefits. Also,

according to French and Raven (2001), coercive power leads to dependencies in

relationships. Hence, it can be argued that organic fruit and vegetable growers tend to

work according to guidelines set out by their more powerful partners. Also, the coercive

strategies used by major SC partners positively influence trust, and the organic fruit and

vegetable growers work with them irrespective of these power differences.

The results of the hypotheses tests also demonstrate that coercive power negatively and

significantly influences (β = -0.144**) collaboration. This result is consistent with the

findings of several authors, who have suggested that coercive strategies are associated

with punishments, and thus negatively influence collaboration (Benton & Maloni 2005;

Cox 2001b; Gelderman, Semeijn & De Zoete 2008). The power position of the SC

partner impacts on the depth of collaboration (Cox 2007; Kähkönen 2014), and

collaboration increases when there are no obstacles to decrease or prevent it (Bernardes

& Zsidisin 2008). Hence, it can be stated that adverse effects of coercive power tend to

hinder the collaboration between the organic fruit and vegetable growers and their SC

partners.

The results of the final SEM model analysis reveal that coercive power does not

significantly influence (β = -0.028ns) satisfaction, but it is a non-significant negative

effect. A possible explanation is the growers’ perceptions about the way the coercive

strategies are used in their relationship by the major SC partner. The long-term

relationships that the growers maintain with their major SC partners reveal that they are

comfortable with the relationship, and the power imbalance does not adversely affect

this relationship. Leonidou, Talias and Leonidou (2008), however, have empirically

established that coercive power negatively influences satisfaction, and Yu and

Pysarchik (2002) suggest an inverse association between coercive power and

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satisfaction. The findings of this current study indicate that coercive power does not

inversely influence satisfaction in the organic fruit and vegetable SCs.

Coercive strategies, that are associated with punishments create negative impact on SC

relationships, and negatively influence commitment (Gelderman, Semeijn & De Zoete

2008). Also, Hausman and Johnston (2010) have empirically established that coercive

power creates a negative association with commitment. The findings of this study reveal

that there is a negative relationship between coercive power and commitment, although

the influence is not significant (β = -0.003ns). This might be owing to the willingness of

organic fruit and vegetable growers to accept their SC partner’s coercive strategies.

Since the intervention of powerful major SC partners, the growers have the opportunity

to improve their inter-firm systems, acquire new technologies and also receive valuable

information. Such measures might change the growers’ internal systems, which would

help them improve their efficiency, responsiveness and reduce lead times. As a result,

these coercive strategies do not significantly and negatively influence their commitment

towards the relationship.

According to Lunenburg (2012), coercive power creates negative influences as it uses

punitive and perceived threats, and thus needs to be used with caution. Though several

previous studies (Benton & Maloni 2005; Brown, Johnson & Koenig 1995; Leonidou,

Talias & Leonidou 2008; van Weele & Rozemeijer 2001) establish a negative and

significant relationships between coercive power and SC relationship success, this

present study does not reveal a significant influence. The findings of this study reveal an

insignificant negative influence (β = -0.064ns) of coercive power towards SC

relationship success. This implies that coercive strategies used by major SC partners in

controlling organic fruit and vegetable growers do not affect the success of their SC

relationships. One possible reason might be the desire of growers to adhere to the

demands of major SC partners due to lucrative long term contracts, through which the

growers earn the majority of their revenue. Consequently, organic fruit and vegetable

growers perceive the authoritative behaviour of their major SC partners as an

opportunity to improve their business. Thus, the negative effects of coercive strategies

towards the success of SC relationships appear to be insignificant in the organic fruit

and vegetable SCs.

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Several previous studies (Brown, Johnson & Koenig 1995; Lai 2007; Ramaseshan, Yip

& Pae 2006) have established that coercive strategies negatively influence the

performance of SC partners. However, the results of this study indicate that coercive

power does not significantly influence (β = -0.039ns) a firm’s operational performance,

but rather has a non-significant negative influence on this performance. This might be

owing to the uniqueness of this particular industry. The performance is closely linked to

delivery reliability, cost reduction, lead time, process improvements, time-to-market,

responsiveness to queries and conformance to specifications, all of which are closely

related to inter-firm system improvements and SC coordination (Panayides & Lun

2009). The major SC partners’ pressure on growers to adopt their procedures and

processes might be assisting growers to organise and improve their inter-firm

coordination, which in turn improves their operational performance.

Furthermore, organic growers tend to work with the same SC partners for many years,

although there are power differences between them (the organisational profile data

indicates that 83.6% of the organic fruit and vegetable growers work with same SC

partner for more than 5 years, and 51.6% for more than 10 years). As a result of these

long-term partnerships, growers perceive SC partners’ coercive strategies as means to

develop their business. This helps them to achieve low-cost and efficient measures due

to major SC partner’s market and process expertise. Hence, it can be concluded that

major SC partners’ coercive strategies have an insignificant influence on the operational

performance of growers in the organic fruit and vegetable industry.

Discussion of non-coercive power as an influential determinant

Non-coercive power strategies aim to change the attitude of the SC partner (Gelderman,

Semeijn & De Zoete 2008), and as a result they create a more cordial atmosphere

between SC partners. According to Frazier and Summers (1984), non-coercive power

cultivates high levels of agreement between the relationship partners. Also, non-

coercive power does not include any aggressive actions or behaviours, and thus creates

less frictions in relationships (Leonidou, Talias & Leonidou 2008). According to these

authors, smooth transactions between SC partners create high levels of agreement in

their relationship.

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The results of this study reveal that non-coercive power positively and significantly

influences commitment (β = 0.091***). This result is consistent with the findings of

previous studies (Frazier & Summers 1984; Leonidou, Talias & Leonidou 2008), which

suggest that non-coercive power creates high level of agreement between the SC

partners and positively influences commitment. According to Gelderman, Semeijn and

De Zoete (2008), non-coercive power has positive impact in the relationships, and as

result it positively influences commitment. A previous study by Simpson and Mayo

(1997) demonstrates that increased level of non-coercive strategies in relationships

positively impacts commitment, which is also consistent with the findings of this study.

In a separate study, Hausman and Johnston (2010), empirically establish that non-

coercive power positively influences commitment in SC relationships. Hence, based on

the findings of this study supported by previous studies, it can be stated that

commitment of organic fruit and vegetable growers is positively influenced by major

SC partners’ use of non-coercive power strategies in SCs.

The results of the hypotheses tests reveal that non-coercive power positively and

significantly influences (β = 0.451***) collaboration. The findings of several previous

studies support this result. Leonidou, Talias and Leonidou (2008) empirically

established that non-coercive power initiates high level of agreement between SC

partners, and hence improves collaboration between them. According to Gelderman,

Semeijn and De Zoete (2008), non-coercive strategies change the attitude of the SC

partner. This has a positive impact on the relationships and improves collaboration

between the SC and the partner. The results of this study are consistent with the earlier

findings in that they reveal that the use of non-coercive strategies by grower’s major SC

partner increases the collaboration effort in organic fruit and vegetable SCs.

According to Arend and Wisner (2005), and Jonsson and Zineldin (2003), non-coercive

power drives team-work and improves the relationships between SC partners. Also,

Morgan and Hunt (1994) and Wiertz et al. (2004) emphasise that non-coercive power

influences positive relationships, resulting in successful SC relationships. Consistent

with these findings, the results of the final SEM model analysis reveal that non-coercive

power positively and significantly influences (β = 0.092*) SC relationships success. As

explained by Gelderman, Semeijn and De Zoete (2008), non-coercive strategies can

change the attitude of the other SC partner, and thus contribute to the success of their

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relationship. Non-coercive power influences SC partners by using non-threatening

aspects of power (i.e. rewards power, legitimate power, referent power and expert

power), hence maintaining a positive atmosphere in the SC relationships (Lunenburg

2012). This in turn, results in SC relationship success. Considering these views, the

results of this study reveal that non-coercive power strategies used by growers’ major

SC partners positively influences relationship success.

The results of this study reveal that non-coercive power positively and significantly

influences (β = 0.125**) the firm’s operational performance of the growers firm. Non-

coercive power strategies such as providing critical information, expertise and

incentives, drives teamwork in SCs. This improves relationships between the SC

partners, leading to the improved performance of a firm (Arend & Wisner 2005;

Jonsson & Zineldin 2003). Some researchers (Brown, Johnson & Koenig 1995; Lai

2007; Ramaseshan, Yip & Pae 2006) have established that the non-coercive power

positively influences SC performance. Consistent with these previous findings, the

results of the final SEM model analysis reveal that non-coercive strategies used by

major SC partners in consultation with growers of fruit and vegetable, improves their

firm’s operational performance.

Ramaseshan, Yip and Pae (2006) and Yu and Pysarchik (2002) demonstrate that non-

coercive power is positively associated with satisfaction in SC relationships.

Gelderman, Semeijn and De Zoete (2008) discuss the ability of non-coercive strategies

to influence positive attitudes among SC partners, and as a result they positively

influence satisfaction between SC partners. However, in contrast to these findings, the

results of this study demonstrate that non-coercive power negatively and significantly

influences (β = -0.142***) satisfaction. This anomaly might be due to SC partners’

specific behaviour in the organic fruit and vegetable industry. As explained earlier, non-

coercive power is a combination of reward, legitimate, referent and expert power bases,

and all these power bases endeavour to achieve the desired results of the power holder

using temporal measures. The organic fruit and vegetable growers perceive these

temporal measures (i.e. rewards for compliance, provide critical information, use of

competencies and opportunities through large contracts) with suspicion. Consequently,

although the organic fruit and vegetable growers may comply with major SC partners’

requests, the level of satisfaction with the relationship decreases, since these temporal

237

measures are targeted to achieve short-term compliance. Hence, it can be concluded that

the use of non-coercive strategies by major SC partners significantly reduce the level of

satisfaction between them and the organic fruit and vegetable growers.

According to Ireland and Webb (2007), trust co-exists with non-coercive power, and

results in positive relationships in SCs. Leonidou, Talias and Leonidou (2008) remark

that non-coercive power positively influences trust. Through the change of attitudes of

the SC partner, non-coercive power is able to positively influence trust in SC

relationship partners (Gelderman, Semeijn & De Zoete 2008). Also, Simpson and Mayo

(1997) demonstrate that non-coercive strategies results in positive impact on trust

between SC partners. Further, in a separate study Hausman and Johnston (2010)

empirically establish that non-coercive power positively influences trust in SC

relationships.

The results of this study, however, reveal a negative and significant influence (β = -

0.142***) between non-coercive power and trust. This anomaly might be possible

owing to the following explanation. According to Corsten and Felde (2005) trust

requires that the relationship’s partner has a high level of integrity, good motives, make

mutual adaptations to help other SC partners, endeavour to solve problems together, be

honest and also watch each other’s profitability. Thus, relationship partners work

closely with each other, and are not driven by self-interest. However, with the use of

non-coercive strategies, major SC partners ensure that organic fruit and vegetable

growers work according to their interests, which can be a result of cynicism. Also, these

non-coercive strategies are temporal measures which are tailored to attract the organic

fruit and vegetable growers, and major SC partners can vary them depending on the

situation. This behaviour is consistent with the findings of Lunenburg (2012), who

argues that the power holders uses their bases of non-coercive power (reward,

legitimate, referent and expert) depending on the situation and in varying combinations.

As a result of the self-centred behaviour of major SC partners, organic fruit and

vegetable growers perceive non-coercive strategies as creating negative influence

towards trust building mechanisms, which are also opportunistic to some extent. Hence,

it can be concluded that non-coercive strategies used by major SC partners decrease the

level of trust between them and the organic fruit and vegetable growers.

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6.2.2 Findings associated with SC relationship constructs

Satisfaction, collaboration, trust and commitment are broadly categorised as relationship

constructs in this study, and are associated with research questions RQ4, RQ5, RQ6 and

RQ7 respectively.

Discussion relating to Satisfaction

Field and Meile (2008) suggest that stronger relationships between SC partners are

positively associated with satisfaction. According to Nyaga, Whipple and Lynch (2010),

when SC partners are satisfied with their relationships, they exchange ideas and foster

respect for each other. Also, when performance exceeds their expectations, SC partners

become satisfied with each other (Parasuraman, Zeithaml & Berry 1988; Ulaga &

Eggert 2006), which in turn generates stronger relationships between them. Consistent

with the findings of previous studies, the results of this current study empirically

establish that satisfaction positively and significantly influences (β = 0.244**) the SC

relationship success of the SC. This finding is also in line with the finding of Janvier-

James (2012), which suggests that satisfaction crucially assists in maintaining and

continuing relationships with SC partners. Intense engagement in closer operational

activities improves overall satisfaction in the SC (Schulze, Wocken & Spiller 2008).

Hence, as per the results of the hypotheses tests supported by previous studies, it can be

established that satisfaction between SC partners of the organic fruit and vegetable

industry improves the success of their relationships.

The best fit final model reveals two new links of satisfaction (i.e. one from satisfaction

to commitment and the other from satisfaction to trust), although these two links were

not included in the proposed conceptual model (reference Figure 3.7). The results of the

final SEM model analysis demonstrate that satisfaction positively and significantly

influences (β = 0.197***) commitment. According to Ulaga and Eggert (2006), the

greater level of satisfaction allows SC partners to work with each other, even though

they may have minor service difficulties. Also, committed SC partners expect to work

with the same SC partner, and endeavour to strengthen their relationship over time

(Nyaga, Whipple & Lynch 2010). Such findings of previous studies imply that satisfied

SC partners look forward to working in the SCs. Also, an analysis of organisational

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characteristics of growers reveals that they have long-term working relationships with

their major SC partners (83.6% growers work with same SC partner for more than 5

years). Hence, it can be concluded that organic fruit and vegetable growers’ satisfaction

with major SC partners improves their level of commitment in their SC relationships.

The results of the respecified final model also reveal that satisfaction positively and

significantly influences (β = 0.455***) trust. According to Leonidou, Talias and

Leonidou (2008), long-term satisfaction leads to trust between SC partners, and as a

result they are able to maintain trust for a longer duration owing to their satisfaction

with each other. The findings of the organisational characteristics of this study reveal

that growers are involved in relationships with the same SC partner for a long period of

time (please refer to Table 5.11). This suggests that organic fruit and vegetable growers

are satisfied with their SC partners, and as a result they continue to work with them.

According to Corsten and Felde (2005), trust allows SC partners to solve their

operational issues and continue to work together for a longer duration. Considering

previous literature and the results of this study, it can be concluded that growers’

satisfaction with their major SC partners improves the level of trust with each other in

the organic fruit and vegetable industry.

Discussion relating to Collaboration

The results of the analysis of final SEM model demonstrate that collaboration positively

and significantly influences (β = 0.284***) SC relationship success. When

organisations are unable to work alone to fulfil common issues, they collaborate with

each other to achieve better outcomes (Barratt & Oliveira 2001; Wagner, Macbeth &

Boddy 2002). Through collaboration, SC partners actively involve and work together in

coordinating the activities of their SC (Mentzer, Foggin & Golicic 2000; Muckstadt et

al. 2001), which contribute to the success of their relationships. Collaboration is

considered to be the main determinant in achieving effective SC relationships (Min et

al. 2005), and it is considered a strategic tool in developing a competitive SC (Langley

et al. 2007). According to Matopoulos et al. (2007), collaboration assists in changing SC

relationships from a purely commercial one to a more engaged and sustained

relationship. Also, the organisational characteristics data of this study indicate that

83.6% of the growers possess more than 5 years (> 5 years) of relationship with the

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major SC partner and 51.6% of them have more than 10 years (> 10 years) of

relationship. These results indicate that growers are maintaining successful long-term

relationships with their major SC partners. Considering the findings of previous studies,

and the findings of this study, it can be concluded that collaboration between organic

fruit and vegetable growers and their major SC partners increases the success of their

relationships.

The best fit final model also reveals a link between collaboration and a firm’s

operational performance. The analysis of the respecified final model demonstrates that

collaboration positively and significantly influences (β = 0.502***) operational

performance (also known as firm’s operational performance). According to Stank,

Keller and Daugherty (2001), when SC partners work collaboratively with each other, it

also improves their inter-firm collaboration. Such improvement within individual firms

strengthens their systems, which helps them achieve cost and time efficiencies.

According to Corsten and Felde (2005), collaboration between SC partners are directly

linked to many benefits. Collaboration improves quality of the products and achieve

lower costs (Larson 1994), reduce the length of delivery schedules, (Artz & Norman

1998), improves the logistical performance of the firms (Stank, Keller & Daugherty

2001), and ultimately improves a firm’s performance (Hewett & Bearden 2001). These

firms are able to keep inventory levels to a minimum without impacting their daily

schedules, due to their collaborative efforts together with advanced level of information

sharing between SC partners. This allows these collaborative firms to reduce overall

cycle time as well as lead time, which directly improves the performance of individual

firms (Daugherty et al. 2006; Whipple & Frankel 2000). The perishable nature of fruit

and vegetables need more attention and careful coordination to attract best prices in the

market (White 2000), hence collaboration between SC partners are important for these

firms. Coordination between SC partners is directly associated with cost of the product,

its availability, stock outs and the product’s quality (Wilson 1996a). These in turn

influence a firm’s operational performance. The findings of this study reveal that

collaborative relationships between the organic fruit and vegetable growers and their

major SC partners improve their firm’s operational performance.

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Discussion relating to trust

Trust between SC partners helps build commitment to their relationship and helps the

relationship to last for a long period (Sako 1992). Also, trust in a relationship is a

critical element in establishing and maintaining the relationship (Matopoulos et al.

2007), and it creates mutual benefits for relationship partners (Panayides & Lun 2009).

Furthermore, Hausman and Johnston (2010) suggest that trust positively influences

commitment between SC partners. Consistent with these findings, the results of this

study demonstrate that trust between SC partners positively and significantly influences

(β = 0.502***) commitment to each other. According to Wicks, Berman and Jones

(1999), trust assists in eliminating the frictions in day-to-day operational activities, and

as a result this creates a higher level of commitment of SC partners towards the

relationship. The organisational characteristics data indicate that growers involve in this

industry for long durations (i.e. 95.5% of them > 5 years and 79.1% of them > 10

years), and work with the same SC partner for a considerable period of time (i.e. 83.6%

of them > 5 years and 51.6% of them > 10 years). Considering the results of this current

study and those of previous studies, it can be concluded that trust creates a frictionless

atmosphere between organic fruit and vegetable growers and their SC partners, which

helps them to achieve commitment towards each other.

While trust is one of the most important factors in deciding the success of SC

relationships (Matopoulos et al. 2007), the results of the final SEM model analysis

reveal that trust does not significantly influences (β = -0.045ns) SC relationships

success. This unexpected behaviour of trust could be due to the industry-specific ways

that organic fruit and vegetable growers deal with their long term SC partners.

According to Corsten and Felde (2005), trust is closely connected with integrity,

willingness to make mutual adaptations, clear motives, honesty and transparency of the

SC partners. The organic fruit and vegetable growers are less powerful than their major

SC partners due to the size of operation, resource ownership and large contracts that

major SC partners control. As a result of this superior organisational power, major SC

partners control the relationship, and growers need to work according to the desires of

their major SC partners. Accordingly, it is unlikely that the major SC partners behave

with integrity, mutual adaptions, honesty and transparency as the powerful partner of

the relationship. Hence, this controlling behaviour (of major SC partner) results in the

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deterioration of trust in organic fruit and vegetable SCs, which negatively affects SC

relationship success. Accordingly, it can be concluded that trust between organic fruit

and vegetable growers and their major SC partners does not assist in influencing SC

relationship success.

Discussion relating to commitment

Commitment enhances mutual gains for both partners in SC relationships (Anderson &

Weitz 1992), and it is essential in developing successful SC relationships (Gundlach,

Achrol & Mentzer 1995). The quality of a SC relationship and its success depends on

commitment between the SC partners towards each other (Dwyer, Schurr & Oh 1987).

According to Van Weele (2009), commitment is necessary to achieve success in SC

relationships. Additionally, commitment positively influences joint action between SC

partners (Hausman & Johnston 2010), which assists in achieving SC relationship

success. Consistent with the findings of previous studies, the results of this current study

reveal that commitment positively and significantly influences (β = 0.502***) SC


Also when SC partners possess strong commitment towards each other, their propensity

to leave the relationships is lower (Morgan & Hunt 1994). This supports the findings of

this study, which also reveals that organic fruit and vegetable growers maintain long-

lasting relationships (reference Table 5.11) with their SC partners. In turn, commitment

between them increases with the duration of the relationships, and SC partners commit

more resources to their relationship (Scanzoni 1979). This stabilises the relationship,

resulting in success. Considering the findings of previous studies and those of this

study, it can be concluded that commitment of organic fruit and vegetable growers

towards their major SC partners enhances SC relationship success.

6.2.3 Findings associated with performance

The third stage of the conceptual framework consists of SC performance (also identified

in this study as SC relationships success) and operational performance (also identified

in this study as firm’s operational performance), and are associated with research

question RQ8.

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Discussion relating to SC performance

The performance in this study comprises two components: SC performance (also

identified as SC relationship success) and operational performance (also identified as a

firm’s operational performance). This section describes the influence of SC relationship

success on a firm’s operational performance.

Several previous researchers have discussed the importance of SC relationship success

in achieving firm performance in SCs (Benton & Maloni 2005; Maloni & Benton 2000;

Narasimhan & Nair 2005). According to Kotabe, Martin and Domoto (2003), SC

relationship success reduces lead-time, improves quality, and achieves process

development, all of which are important in increasing a firm’s operational performance.

Also, the performance of SCs improves as a result of long-term, collaborative and

stronger relationships between SC partners (Field & Meile 2008; Singh & Power 2009).

Several other researchers (Grewal, Levy & Kumar 2009; Simchi-Levi, Kaminsky &

Simchi-Levi 2003) have identified SC relationships as the most important dimension in

achieving success in SCs. The analysis of the final SEM model of this study

demonstrates that SC relationship success positively and significantly influences (β =

0.422***) firm’s operational performance (also identified in this study as operational

performance).

SC partners achieve improved quality, lower cost and improved information sharing

through the success of their relationships, and this in turn improves firm performance

(Kannan & Tan 2006; Karia & Razak 2007; Min et al. 2005). Organic fruit and

vegetable growers can improve their market knowledge through their relationships with

their major SC partners, and this therefore enables them to reduce their cost of

production and distribution. The fresher quality of fruits and vegetables commands

higher prices. Thus growers can obtain increased profits as a direct result of their

relationship success. Monitoring each other’s activities is an important step in avoiding

opportunism that is one SC partner trying to earn more profits at the expense of the

other. Also, monitoring incurs substantial cost increments. However, Ketchen and Hult

(2007) remark that SC partners avoid opportunism by engaging in long-term trusting

relationships. Thus, through the success in their relationships, organic fruit and

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vegetable growers are able to achieve the lowest or no monitoring cost, which

substantially reduces their operational costs. Also, according to Coyle et al. (2008),

strong SC relationships assists in improving overall service standards. Considering the

findings of previous studies, and the results of this study, it can be concluded that SC

relationship success between the organic fruit and vegetable growers and their major SC

partners improves their firm’s operational performance.

Previous sections present discussions related to direct effects of influential determinants

and relationship constructs on other variables. However, the final SEM model analysis

also reveals the presence of indirect and total effects on these variables (please refer to

Section 5.11). The following sections present the indirect effects on performance (i.e.

SC relationships success and firm’s operational performance), and are discussed with

reference to direct effects.

6.3 Discussions relating to total and indirect effects between the constructs

An indirect effect is a result of mediation, which occurs when a third construct

intervenes between two other related constructs (Baron & Kenny 1986). According to

Hair et al. (2010), a mediating variable draws inputs from the first variable and transfers

in to the second variable. These three variables need to have significant correlations

between them in order to satisfy mediation. There can be simple or multiple mediations,

and this depends on the number of mediating variables involved (Preacher & Hayes

2008). According to Hair et al. (2010), the mediating effects of a model can be

explained using direct and indirect effects.

6.3.1 Indirect effects on SC relationship success

As depicted in the proposed conceptual model (Figure 3.7), information sharing,

coercive power, non-coercive power and SC relationship constructs (i.e. satisfaction,

collaboration, trust and commitment) were conceptualised as being antecedents of SC

relationship success. However, in the final SEM model it was also found that these

antecedents have both direct and indirect influences on SC relationship success.

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The results of the analysis of the final model reveal that information sharing has a non-

significant direct effect (β = 0.145ns) on SC relationship success. This suggests that the

sharing of valuable market, product and other proprietary information between the SC

partners of organic fruit and vegetable industry does not help these SC partners in

improving the success of their SC relationships. The results of the final SEM model,

however, revealed that information sharing has a significant positive indirect influence

(β = 0.499***) on SC relationship success. As depicted in Figure 5.14, the mediating

variables are satisfaction, collaboration, trust and commitment. This suggests that

information sharing improves SC relationship success in the presence of the identified

relationship constructs. This is consistent with the findings of Lalonde (1998), Baihaqi

and Sohal (2013), and Field and Meile (2008), who emphasise the importance of

information sharing in shaping the SC relationship success. The total effect of

information sharing on SC relationship success is significant and positive (β =

0.644***). This implies that information sharing between SC partners of the organic

fruit and vegetable industry improves the SC relationship success, although the direct

influence between these two constructs is non-significant.

According to final SEM model analysis results, coercive power has a non-significant

negative direct effect (β = -0.064ns) on SC relationship success. This suggests the

negative influence of coercive power towards SC relationship success, which is non-

significant. The results also indicate an indirect relationship between these two

constructs, but the total indirect effect is a non-significant negative influence (β = -

0.042ns). However, the results reveal a negative and significant total effect (β = -0.106*)

of coercive power towards SC relationship success. This result is consistent with

findings of Lunenburg (2012), who remarks that use of coercive power (by the powerful

SC partner) creates negative effects on relationships due to its punitive strategies, and

also by creating a perceived threat to do so. Further, several other studies (Benton &

Maloni 2005; Leonidou, Talias & Leonidou 2008; van Weele & Rozemeijer 2001)

suggest that coercive power can have a negative influence on SC relationship success.

Hence, it can be concluded that coercive power used by major SC partners towards

growers negatively influences SC relationship success in organic fruit and vegetable

SCs.

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The results of this study have revealed that non-coercive power has a positive

significant direct effect (β = 0.092*) on SC relationship success, but with a lower level

of significance (i.e. p < 0.05). This suggests that use of non-coercive power by the

major SC partners enhances SC relationship success. However, it was revealed that non-

coercive power also has a positive indirect effect with a higher level significance (β =

0.091**, p < 0.01) on SC relationship success through mediating variables. The

mediating variables are satisfaction, collaboration, trust and commitment (Figure 5.14).

Consistent with the findings of previous studies (Arend & Wisner 2005; Gelderman,

Semeijn & De Zoete 2008; Lunenburg 2012), this result suggests that non-coercive

power positively influences SC relationship success by mediating through the

relationship constructs with a higher level of significance.

Aside from this, non-coercive power can have a positive and significant total effect (β =

0.184***) on SC relationship success with a much higher level of significance (p <

0.001). These results highlight that the mediating effect of relationship constructs (i.e.

satisfaction, collaboration, trust and commitment) improves the influence of non-

coercive power towards SC relationship success. The reason for this might be the

positive influences of satisfaction (Janvier-James 2012), collaboration (Cannella &

Ciancimino 2010), trust (Hausman & Johnston 2010) and commitment (Van Weele

2009) on achieving success in SC relationships. As a result, it can be concluded that use

of non-coercive strategies, along with SC relationship constructs by major SC partners

in dealing with organic fruit and vegetable growers, substantially improves SC


As explained earlier, satisfaction directly and significantly influences (β = 0.244**) SC

relationships success. The final SEM model analysis also revealed that satisfaction has a

non-significant (β = 0.097ns) indirect effect on SC relationship success. The mediating

variables involved in this indirect effect are trust and commitment. This result reveals

that trust and commitment does not mediate the relationship between satisfaction and

SC relationship success. This can be due to the negative influence of trust (β = -0.045ns)

in the relationship between organic fruit and vegetable growers and their major SC

partners, which does not enhance SC relationship success. A possible reason for this

negative influence can be that although growers closely work with their SC partners due

to long-term contracts, technological advancements and other incentives, they do not

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trust their major SC partners unreservedly. The results also revealed a positive total

effect (β = 0.341***) of satisfaction on SC relationship success, with a higher level of

significance (p < 0.001). A possible reason for the increased level of significance might

be the positive effect of commitment (β = 0.276**) on the SC relationship success.

Also, this is consistent with the findings of Nyaga, Whipple and Lynch (2010), and

Janvier-James (2012), which suggest that satisfaction with relationship partners

improves SC relationship success. Thus, it can be concluded that commitment between

growers and their major SC partners mediates and increases the positive influence of

satisfaction towards SC relationship success, although trust has a negative influence on

SC relationship success in the organic fruit and vegetable SCs.

The results of the final SEM model analysis reveal a non-significant direct influence (β

= -0.045ns) of trust towards SC relationship success. However, the results also reveal a

positive and significant indirect influence (β = 0.139**) of trust towards SC relationship

success. This positive result is due to the mediating effect of commitment, and it is

consistent with the finding of Sako (1992), who emphasises the mediating role of

commitment in improving SC relationship success. According to Sako (1992), trust

between SC partners creates commitment, which then leads to effective SC partner

relationships. Several researchers (Morgan and Hunt 1994; Ganesan 1994; Bennett

1996) emphasise the vital role of commitment in improving SC relationship success.

Furthermore, according to Hausman and Johnston (2010) and Palmatier et al.(2006),

commitment invigorates SC relationship success through joint action. Also, it can be

stated that the organic fruit and vegetable growers stay committed to the relationship

with their major SC partner, due to benefits that they receive from their major SC

partner. Hence, it can be concluded that the mediating role of commitment between

organic fruit and vegetable growers and their major SC partners, assists in positively

influencing the effects of trust on SC relationship success.

6.3.2 Indirect effects on firm’s operational performance

Information sharing, coercive power, non-coercive power, relationship constructs (i.e.

satisfaction, collaboration, trust and commitment) and SC relationship success were

conceptualised as antecedents of a firm’s operational performance. The results of this

study, however, reveal that apart from SC relationship success all the other antecedents

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also have indirect effects on firm’s operational performance (also known as operational

performance).

As discussed earlier, information sharing positively and significantly influences (β =

0.262***) a firm’s operational performance. The results of the final SEM model also

reveal that information sharing has a positive and significant indirect effect (β =

0.334***) on a firm’s operational performance. This effect is a result of mediation, and

the variables involved are satisfaction, collaboration, trust, commitment and SC

relationship success. This finding is consistent with previous literature. Field and Meile

(2008) remark that satisfaction between SC partners assists in achieving better

performance. Further, collaboration (Corsten & Felde 2005; Hewett & Bearden 2001),

and commitment (Hausman & Johnston 2010) between SC partners can help improve

firm performance. Also, success of SC relationships improves firm performance (Karia

& Razak 2007; Min et al. 2005; Rinehart et al. 2004), and trust and information sharing

are insignificant in directly influencing the success of SC relationships. However, the

overall indirect effect on a firm’s operational performance is positive and significant

due to direct significant influences of satisfaction, collaboration and commitment on SC

relationship success. Consistent with the findings of previous studies and the results of

this current study, it can be concluded that satisfaction, collaboration, commitment and

SC relationship success enhance firm’s operational performance through mediation in

organic fruit and vegetable SCs. The results of this study also reveal that information

sharing predicts a positive and significant total effect (β = 0.597***) on a firm’s

operational performance. This is as a result of significant direct and indirect effects of

information sharing on a firm’s operational performance.

Coercive power does not significantly influence firm’s operational performance, but has

a negative direct influence (β = -0.039ns) due to its punitive behaviour. However, the

results also indicate a negative and significant indirect influence (β = -0.072***) of

coercive power towards a firm’s operational performance through the mediating

variables of satisfaction, collaboration, trust, commitment and SC relationship success.

This reveals that mediating variables substantially increase the negative effect of

coercive power in relation to a firm’s operational performance. According to previous

studies, coercive power negatively impacts satisfaction (Leonidou, Talias & Leonidou

2008), collaboration (Gelderman, Semeijn & De Zoete 2008), commitment (Hausman &

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Johnston 2010), and SC relationship success (Benton & Maloni 2005; Gelderman,

Semeijn & De Zoete 2008; Kähkönen 2014) in SC relationships. Consistent with these

findings, a possible reason for the negative indirect effect can be the negative influence

of coercive power towards satisfaction, collaboration, commitment and SC relationship

success. While coercive power positively influences trust of organic fruit and vegetable

growers towards their major SC partner, the results of this study also reveal the negative

influence of trust on SC relationship success. This can be another reason for the

negative indirect effect of coercive power towards a firm’s operational performance.

This suggests that the major SC partner’s punitive strategies (for example, financial

penalties, withdrawal of support for not compliance, not delivering on originally

promises, threatening to take legal action, and threatening to switch to alternative

suppliers) creates dissatisfaction among organic fruit and vegetable growers. They

become unhappy with these coercive controlling strategies, and as result, their

relationships with their major SC partners are negatively affected. This negative effect

then negatively influences the firm’s operational performance. The results of the final

SEM model also predict a negative and significant total effect (β = -0.112**) of

coercive power towards a firm’s operational performance. Hence, it can be concluded

that the use of coercive strategies by major SC partners (i.e. retailers and wholesalers)

negatively influence SC relationships constructs and SC relationships success, which

subsequently mediates in negatively influencing the operational performance of organic

fruit and vegetable growers’ firms.

As discussed earlier, the use of non-coercive power strategies by major SC partners

creates a positive and significant direct influence (β = 0.125**) on a firm’s operational

performance. The results also reveal that non-coercive power has a positive indirect

effect (β = 0.163***) with a higher level of significance on firm’s operational

performance, through mediating variables of satisfaction, collaboration, trust,

commitment and SC relationship success. This suggests that the relationship constructs

invigorate the influence of non-coercive power towards firm’s operational performance

in organic fruit and vegetable SCs. Also, this is supported by findings of previous

studies, which suggest that non-coercive power positively influences collaboration and

commitment, and as a result it positively influences SC relationship success (Arend &

Wisner 2005; Hausman & Johnston 2010; Kähkönen 2014; Leonidou, Talias &

Leonidou 2008). According to Jonsson and Zineldin (2003) SC relationship success

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then positively impacts firm performance. The final SEM model results, however,

suggest that non-coercive power negatively influences satisfaction and trust, while the

positive mediating effects of collaboration, commitment and SC relationship positively

influences a firm’s operational performance. As a result, previous literature, as well as

the findings of this study, suggests that incentives, lucrative contracts, competencies,

critical market/demand information, appreciation and admiration provided by major SC

partners attract organic fruit and vegetable growers towards them. The resultant

relationship improvements then assist growers to improve their firm’s operational

performance. The results of this study also demonstrate a positive and significant total

effect (β = 0.289***) of non-coercive power towards a firm’s operational performance.

Hence, it can be concluded that SC relationship constructs and SC relationship success

mediate between the use of non-coercive strategies by major SC partners (mainly

retailers, wholesalers and processors) and the a firm’s operational performance. As a

result, this positively stimulates the effects of non-coercive power in achieving firm’s

operational performance of growers in the organic fruit and vegetable industry.

Collaboration can have a positive and significant direct influence (β = 0.190***) on a

firm’s operational performance, and as discussed this is due to its ability in improving

coordination between SC partners. The results of the final SEM model analysis also

reveal a positive and significant indirect influence (β = 0.120***) on a firm’s

operational performance, through the mediating variable of SC relationship success. As

suggested by previous literature (Kähkönen 2014; Matopoulos et al. 2007; Min et al.

2005; Ramanathan & Gunasekaran 2014), collaboration in SCs improves coordination

between its partners, which assists in achieving SC relationship success. Furthermore,

Whipple and Frankel (2000) and Daugherty et al. (2006) argue that collaboration

between SC partners improves SC relationship success, which in turn assists in

improving a firm’s operational performance.

Consistent with the above findings, it can be concluded that SC relationship success

positively mediates the relationship between collaboration and a firm’s operational

performance. As a result, SC relationship success assists in improving the effect of

collaboration on the operational performance of firms in organic fruit and vegetable

growers SCs. The results also suggest a positive and significant total effect (β =

0.310***) of collaboration on a firm’s operational performance. This is due to the

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positive direct and indirect influence of collaboration on a firm’s operational

performance.

The results of the final SEM model reveal a positive and significant indirect effect (β =

0.144***) of satisfaction on a firm’s operational performance, through the mediating

variables of trust, commitment and SC relationship success. Commitment facilitates

success in SC relationships (Walter & Ritter 2003), and SC relationships success

directly improves firm performance (Kannan & Tan 2006). Considering these findings

and the results of this study, it can be concluded that commitment and SC relationship

success mediates in elevating the effects of satisfaction in achieving positive operational

performance of firms in organic fruit and vegetable SCs. While trust indicates a

negative influence on SC relationships success, the positive mediating influences of

commitment and SC relationship success on a firm’s operational performance reduces

the negative influences, and posits an overall positive mediation effect on the firm’s

operational performance. Further, it can be argued that the organic fruit and vegetable

growers and their SC partners, who develop commitment towards each other can

success in their SC relationships. This in turn leads to the improved operational

performance of a firm.

While commitment does not influence a firm’s operational performance directly, it can

have a positive and significant indirect effect (β = 0.117**) on a firm’s operational

performance. The mediating variable in this indirect effect is SC relationship success.

This implies that the commitment of organic fruit and vegetable growers assists in

improving SC relationship success, and as a result, it improves the operational

performance of these growers’ firms. Since commitment facilitates SC relationships

success (Walter & Ritter 2003), this result also reveals that growers and their SC

partners focus on commitment building activities like critical information sharing in

order to improve their firm’s operational performance. This result is consistent with the

findings of Karia and Razak (2007), who emphasise the close connectivity between SC

relationships success and a firm’s operational performance. Hence, it can be concluded

that the commitment of organic fruit and vegetable growers improves the success of

their relationships with major SC partners. These relationships are instrumental in

improving their firm’s operational performance.

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According to the respecified final SEM model (reference Figure 5.14), there is no direct

relationship between trust and firm’s performance, but there is an indirect relationship,

which is mediated through the variables of commitment and SC relationship success.

The final model results reveal, however, that the indirect relationship between trust and

firm’s operational performance in non-significant (β = 0.040ns). This non-significant

effect is primarily due to the negative influence of trust on SC relationship success,

which is one of the mediating variables between trust and firm’s operational

performance. A plausible reason for this can be the reduced state of trust between

organic fruit and vegetable growers and their major SC partners. As discussed earlier,

organic fruit and vegetable growers perceive that their major SC partners act

opportunistically in day-to-day transactions. Hence, it can be concluded that trust

between organic fruit and vegetable growers and their major SC partners does not

indirectly influence a firm’s operational performance.

6.4 Contributions to knowledge

This study investigated the influence of organisational power (i.e. coercive and non-

coercive power) and information sharing on SC relationships and the performance of

organic fruit and vegetable growers. In particular, this study investigated the growers’

perceptions based on their day-to-day transactions with their major SC partners. Several

previous studies have investigated the influence of power, information sharing,

satisfaction, collaboration, trust, commitment on SC relationship success and firm

performance (Corsten & Felde 2005; Leonidou, Talias & Leonidou 2008; Nyaga,

Whipple & Lynch 2010; Panayides & Lun 2009). There is, however, no single study

which has comprehensively incorporated all these constructs into a single model. The

organic fruit and vegetable industry is one of the fastest growing food sectors (Henryks

& Pearson 2010), and it is also economically important (Bez et al. 2012). Hence, the

findings of this study have many significant implications for the SC management

literature, as well as for this particular industry sector. The following two sections

explain both the theoretical and practical contributions of this study.

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6.4.1 Theoretical contributions

As explained earlier, this study is unique, in that it investigates several relevant

constructs in a single comprehensive model (Figure 3.6). The nine constructs of this

model include information-sharing, coercive power and non-coercive power as

influential determinants; satisfaction, collaboration, trust and commitment as

relationship constructs; SC relationship success as SC performance and a firm’s

operational performance as operational performance. This study targeted growers of

organic fruit and vegetable industry, and the data was collected in Australia. This

conceptual model can be utilised to investigate the perceptions of other SC players

(retailers, wholesalers, processors, exporters) either within or outside the organic fruit

and vegetable industry.

The conceptual model used in this study broadly consists of three stages: influential

determinants, relationship constructs and performance. The influential determinants

(information sharing, coercive power and non-coercive power) were conceptualised as

influencing the other constructs in the model, owing to the important role that these

three constructs play in any relationship (Dapiran & Hogarth-Scott 2003; Dwyer, Schurr

& Oh 1987; Kumar 2005). This unique model has provided new insights into the inter-

relationships of these constructs and their direct and indirect influence on the

performance of SCs and firms. The results obtained are extremely useful for the organic

fruit and vegetable industry.

Several previous studies were used to underpin the conceptual model of this study.

Corsten and Felde (2005) suggest investigating collaboration, trust and cost reduction in

different industry contexts in order to compare the results. Panayides and Lun (2009)

include trust and SC performance in their conceptual model and collected data from

manufacturers. These authors suggest collecting data from suppliers or adopting dyadic

data collection processes. Leonidou, Talias and Leonidou (2008) suggest that power,

satisfaction, trust and commitment be investigated with regards to different SC partners,

and also in economically different contexts. The context of this study is unique as a

majority of previous studies investigated the perceptions of SC partners in the

manufacturing and service-oriented industries. The organic fruit and vegetable industry

sector is extremely different to those industries. Thus, it was an opportune moment to

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investigate this industry, and in particular the perceptions of the growers who play the

most important role in the entire SC.

Further, this study tried to unearth the influence of information sharing and power,

along with the relationship constructs, on SC performance and operational performance.

SC performance (identified as SC relationships success) is associated with product

quality, lower costs, co-operation and communication between SC partners. Operational

performance (identified as firm’s operational performance), conversely, is associated

with delivery reliability, responsiveness, total cost, lead-time, conformance to

specification, process improvement and time-to-market. Also, the previous sections of

this chapter argued the connectivity of these two performance-constructs to cost

reduction, which enable SC partners to achieve increased profitability. Both SC

performance and operational performance improve the financial performance of SC

partners. Thus, the two performance constructs used in this study are vital for inclusion

in studies that explore financial performance of SCs.

The results of this study have revealed that information sharing does not directly

influence, but has a positive indirect effect on SC performance (i.e. SC relationship

success). This is mainly due to the mediating effects of relationship constructs, which

are satisfaction, collaboration, trust, and commitment. The results also reveal that,

although the coercive strategies do not directly influence a firm’s operational

performance, they can have a negative and significant indirect influence on a firm’s

operational performance. This is due to the mediating variables, which positively

mediate the influence of coercive power on SC relationship success and firm’s

operational performance. These findings elaborate the crucial mediating role (between

information sharing, power and performances) of satisfaction, collaboration, trust and

commitment, and are valuable additions to the existing literature on SC relationships.

Hence, these mediating variables are important in SC relationships and need to be

considered in future studies which explore SC relationship success and operational

performance in the SCs of different industries.

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6.4.2 Practical/managerial implications

The findings of this study provide useful and meaningful insights which can be used by

various stakeholders of the organic fruit and vegetable industry. These implications also

provide valuable insights into other industry sectors in relation to sharing

proprietary/valuable information between SC partners, use of coercive and non-coercive

strategies, and use of relationship constructs (i.e. satisfaction, collaboration, trust and

commitment) in improving relationships in their respective SCs.

The findings of this study suggest that information sharing between SC partners directly

improves satisfaction, collaboration, trust, commitment, and most importantly, the

firm’s operational performance. Information-sharing also indirectly influences SC

relationship success through the mediating variables of satisfaction, collaboration, trust

and commitment. These make information sharing one of the important aspects to

practice in SCs, which is consistent with the findings of Nyaga, Whipple & Lynch

(2010). According to Baihaqi and Sohal (2013), information sharing between SC

partners facilitates the synchronisation of the activities across the SC. Considering

previous studies and also the findings of this study, it can be argued that growers,

retailers, wholesalers, and processors need to understand the influence of uninterrupted

information flow between and within their SC, and they need to build on their

information sharing strategies accordingly. Owing to their direct interaction with

customers, retailers and wholesalers are able to predict the demand both generally and

seasonally. Hence, they can relay this information to the growers, who can plan,

coordinate and execute in advance to meet this intended demand requirements. At the

same time, growers can share their projected supply, and the capacity availability, if

demand is expected to surge. Also the retailers can share the technical information

related to ordering, packing, and transportation, all of which help these SC partners to

plan, execute and coordinate their logistical requirements without unwanted delays in

the SC. Since the information sharing is strategically important to these SCs, they can

acquire user friendly technology including specific software that automatically transfer

data, and enhances the information sharing between SC partners.

The other important aspect is the use of coercive and non-coercive strategies in SCs.

The results revealed that coercive strategies have negative influence towards

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collaboration and does not influence satisfaction, commitment, SC relationship success

or firm’s operational performance. These results caution retailers and wholesalers that

coercive strategies like threatening and financial penalties adversely affect the

relationship as well as the performance of suppliers. On the other hand, use of non-

coercive strategies such as offering specific incentives and use of unique capabilities

improve the level of collaboration, commitment, SC relationship success and firm’s

operational performance. Retailers, wholesalers and the other stakeholders need to be

aware of these adverse effects in using coercive strategies, as well as of the advantages

of using non-coercive strategies. These SC partners can reduce the use of coercive

strategies and cultivate non-coercive strategies in their day-to-day activities, and this

can create improvements in their relationships and performances. These SC partners

must also be aware of the adverse effects of coercive strategies such as withholding

critical information and controlling the growers using their large contractual power.

These coercive strategies can create unpleasant atmosphere in day-to-day transactions

and finally lead to decreased satisfaction and trust.

Conversely, the organic fruit and vegetable growers may perceive major SC partners’

non-coercive strategies such as ‘rewards for compliance’ to be opportunistic tools to

attract them, and as a result this decreases satisfaction and trust in the relationship. This

might allow the organic fruit and vegetable growers to move away from the present

relationship for lucrative offers elsewhere, which adversely affects the supply of

produce to the major SC partners. Hence, powerful retailers and wholesalers need to

carefully craft their relationship strategies and select appropriate techniques in dealing

with their suppliers in order to create collaborative SC relationships. These

collaborative relationships will lead to improved performance.

The results of this study have revealed that relationship constructs such as satisfaction,

collaboration, trust and commitment can positively and significantly mediate the

relationships between influential determinants (information sharing, coercive power and

non-coercive power) and performance (SC performance and firm’s operational

performance). Considering these, the SC partners can develop suitable methods to

improve satisfaction, collaboration, trust and commitment between them. According to

Cannella and Ciancimino (2010) and Batt (2003), collaboration improves as a result of

SC partners sharing sensitive market information. Furthermore, these collaborative SC

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partners motivate each other to engage in future ventures (Ramanathan & Muyldermans

2011), which can expand opportunities for development. Information sharing, fair

pricing and timely deliveries are prerequisites for enhancing trust between SC partners

(Lindgreen 2003). The commitment improves when SC partners are in a relationship for

a long duration of time (Kumar, Scheer & Steenkamp 1995a). Close activities between

SC partners can improve the satisfaction between them (Schulze, Wocken & Spiller

2008). SC partners can concentrate on developing and enhancing satisfaction,

collaboration, trust and commitment between each other using appropriate techniques

like information sharing, long term contracts, fair pricing and close operational planning

and execution. Improvements in these relationship constructs positively mediate the

effects of information sharing and power on SC relationship success and a firm’s


The organisational characteristics of growers in this study reveal that only two of them

are involved in exporting their products. This is less than 0.7% of total 287 respondents.

This is a very low percentage compared to other industries such as dairy, which exports

38% of its production (Dairy Australia Limited 2014) and the beef industry, which

exports 60% of its production (Pricewaterhouse Coopers 2015). Also, according to the

Australian Organic Market Report (2014), there is an increased demand from China for

Australian certified organic products. Hence, the organic fruit and vegetable industry

needs to find ways to increase the production and also the opportunities for exports.

6.5 Limitations of the study

As with any research, this study has its limitations. The following sections acknowledge

and describe these limitations, which can hopefully be avoided in future studies.

First, the sampling frame (consisting of 927 organic fruit and vegetable growers in

Australia) for this study was compiled using publicly available websites, which display

the name lists of organic fruit and vegetable growers in Australia. There can be

instances where some of the organic fruit and vegetable growers are not listed on these

websites, although they are certified organic growers. In some cases, growers

intentionally do not wish to be included on the websites. Also, some published data

does not include the full contact details such as email or telephone contacts. Also, there

258

can be growers who have changed their company names or contact details, but websites

have not been updated with these changes. In all of these instances, the researcher was

unable to invite the growers of organic fruit and vegetables to participate in the online

survey.

The data was collected using an online survey instrument, and as a result only the

growers (who had access to a computer and internet) were able to participate in the

survey. The other organic fruit and vegetable growers were unable to participate due to

limited access to the required technology and hardware. There can be several reasons

for this limited access, including unfamiliarity with computers and the internet,

infrastructure issues in remote areas where growers do not have access to the internet,

and signal interruptions which could hamper them from completing the survey. These

technical difficulties, as well as the growers’ dislike for using computers and the

internet, have deprived these growers of the opportunity to participate in the online

survey. Therefore, the findings of this study reflect the views of a specific population,

who have access to computers, internet, uninterrupted internet connections, and who are

familiar with computers and the internet.

There are several partners in a SC, and all of them are called ‘SC partners’. A complete

SC starts from where the raw materials is located and ends up on retailers’ shelves

(Coyle et al. 2013). Likewise, organic fruit and vegetable SC also consists of many SC

partners. This study, however, only focussed on the growers of the organic fruit and

vegetable industry SC, thus excluding the perceptions of other partners such as retailers,

wholesalers, processors and exporters. Hence, the results represent the views of organic

fruit and vegetable growers, who are also known as the suppliers of this particular

industry. There are opportunities to collect the data focussing on other SC partners of

this industry. The survey could also adopt a dyadic approach where the data is collected

from two SC partners simultaneously. Alternatively, a longitudinal study can be

performed, where data is collected from the same SC partner, but at two different time

horizons.

The performance of a SC also depends on the physical infrastructure of a country or the

region where the logistics operation takes place. This study was conducted targeting the

organic fruit and vegetable growers and the data was collected in Australia. As a

259

developed country, transportation and warehousing facilities (includes temperature

controlled trucks and warehouses) and other SC infrastructure (includes technologically

advance handling equipment at loading bays) in Australia are in a substantially

advanced stage when compared to facilities available in developing or underdeveloped

countries. The perceptions of growers in different countries can be different from the

views of the respondents in this study. Hence, the findings of this study represent the

views of growers, who were supported by improved infrastructure facilities in

performing their day-to-day SC activities.

Finally, this study has categorised performance of a SC firm as ‘performance’, which

has two components. The first is SC performance, which is also identified as SC

relationship success and the second is operational performance, which is associated with

firm’s operational performance of organic fruit and vegetable growers. This study

intentionally disregarded financial performance, as it intended to collect data based on

growers’ perceptions, and not based on the monetary side of their business. As a result,

the findings of this study does not comment or make recommendations based on

financial outcomes of the growers or any other SC partners in this particular industry

sector. The findings of this study are based on the data collected in regard to perceptions

of growers’ as a result of their day-to-day SC activities with their respective major SC

partners.

6.6 Directions for future research

This study focussed on the organic fruit and vegetable industry, and collected data from

its growers in order to investigate the influence of information sharing and power on SC

relationship constructs and performance (i.e. SC performance and operational

performance of firms). This approach was undertaken because of the important role that

growers play in this industry, and also to obtain maximum number of responses.

However, future research could focus on different SC partners in this industry, such as

retailers, wholesalers, processors and exporters. Also, there is a potential to undertake

dyadic, triadic or longitudinal studies using the same constructs that have been explored

in this study.

260

There are inherent limitations owing to the sampling frame being drawn up using

publicly available websites. Future research could focus in obtaining sampling frames

from organic certification bodies of the country, which could increase the potential

number of respondents. Also, future researchers could simultaneously employ a host of

data collection methods such as online, email and/or mail survey. Using various

methods of data collection would enable researchers to increase the final usable

responses.

Potential future research could also focus on other industries, and use the conceptual

model of this study in investigating the influence of information sharing and power on

SC relationship constructs and performance. The handling of perishable products needs

a considerable amount of coordination between SC partners, sound infrastructure and

advanced equipment/technology (Coyle et al. 2013). Some of these needs depend on the

geographical location and economic situation of that country or region. Hence, there is

an opportunity for future researchers to conduct this study in geographically and

economically diverse locations.

This study focussed only on satisfaction, collaboration, trust and commitment

(identified broadly as relationship constructs) as the four most important aspects to

attain cordial SC relationships. There is a potential for future researchers to include

other important relationship aspects in this conceptual model; and to investigate their

influence on SC performance and firm’s operational performance. These relationship

aspects might include coordination, flexibility and dependence, which have been

identified by Kannan and Tan (2006) as important aspects for meaningful relationships.

As explained previously, this study intentionally excluded collecting data relating to

financial performance of the growers. According to Corsten and Felde (2005), financial

performance is an important measurement in accessing overall performance of a SC.

Thus, it is suggested that future studies could focus on financial performance in

understanding the influence of information sharing, and power on SC relationships and

a firm’s operational performance.

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6.7 Concluding remarks

Although several previous studies have investigated the role of information sharing,

power, SC relationship constructs, SC relationship success and firm’s operational

performance, there is scant available research on how all these constructs influence each

other in a single model. With this literature gap in mind, this study has aimed to provide

valuable insights to existing literature and practice by investigating the effects of

information sharing and power on SC relationships, SC performance and an SC firm’s

operational performance. The conceptual model developed and tested in this study is

comprehensive, and can be used in different study contexts.

The findings of this study extend the current literature in understanding how influential

determinants (information sharing, coercive power and non-coercive power) directly

and indirectly influence SC relationships constructs, SC performance and an SC firm’s

operational performance. The findings enrich our understanding of the influence of

coercive and non-coercive strategies on SC relationships, SC performance and an SC

firm’s operational performance; and empirically establish the negative effects of

coercive strategies on relationships and performance.

The mediating role of relationship constructs (satisfaction, collaboration, trust and

commitment) are a highlight of the findings. This study has revealed the mediating

effects of relationship constructs, and their positive effect in stimulating the impact of

influential determinants on SC performance and an SC firm’s operational performance.

The study has also revealed that the negative influence of coercive strategies can be

reduced through relationship building activities like information sharing, fair prices and

assistance when growers facing economic difficulties.

This study provides valuable information to policy makers, industry specialists and to

all SC partners. These include different ways, in which information sharing between SC

partners and organisational power impact SC relationships, SC performance and firm’s

performance, and benefits that the firms can accrue due to these influences. The

findings inform the stakeholders of organic fruit and vegetable industry in several ways,

which can be used to improve their relationships, and an SC firm’s operational

performance. The implications include ways to improve satisfaction, collaboration,

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trust, commitment in order achieve improved SC performance and firm’s operational

performance.

The final sections of this chapter discussed the limitations of this study, and suggested

several ways that these limitations can be minimised in future studies. This chapter then

discussed areas for future research, which can be undertaken in different SC contexts.

The chapter has suggested the inclusion of several other relationship and performance

dimensions (which are not investigated in this study) to extend the conceptual model of

this study.

263

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Appendices

293

Appendix 1: Survey Instrument

A SURVEY OF THE AUSTRALIAN ORGANIC FRUIT AND VEGETABLE GROWERS

If you have inadvertently missed completing some of the data, then upon completion of the survey you would be redirected to the top of the screen. If that happens, please scroll down and you will find the missing data. Once you complete this missing data, you would be able to submit the survey. Sorry for this inconvenience, and please be assured that all your data is extremely important for this study.

1. Are you certified with any organic certification bodies in Australia?

Yes

No

If you have inadvertently missed completing some of the data, then upon completion of the survey you would be redirected to the top of the screen. If that happens, please scroll down and you will find the missing data. Once you complete this missing data, you would be able to submit the survey. Sorry for this inconvenience, and please be assured that all your data is extremely important for this study.

2. Please name the certifying bodies (you may tick more than one box)

AUS-QUAL – AUSQUAL Ltd

ACO – Australian Certified Organic

BDRI Bio – Dynamic Research Institute

NCO – NASAA Certified Organic

OFC – Organic Food Chain

Safe Food Production Queensland

TOP – Tasmanian Organic Dynamic Producers

Other, please name it

3. Apart from growing, are you also involved in any of the following? (you may tick more than one box)

Retailing

Wholesaling

Processing

None of the above

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Other, please specify

4. How long has your business been operating for?

Less than 5 years

5 to Less than 10 years

10 to less than 15 years


More than 20 years

5. How long has your business been producing certified organic products for?

Less than 5 years




More than 20 years

6. What is your position in the organisation?

Owner/Partner

Manager

Supervisor


7. What is the annual turnover of your business (In Australian dollars)?

Less than 100k

Between 100k to 300k

Between 300k to 500k

Between 500k to 1 million

Above 1 million

8. What percentage of your produce is certified as organic products?

Less than 20%

Between 20 - 50%

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Between 51 - 80%

Between 81 - 100%

9. Assuming that you grow only fruit and vegetables, how much percentage of vegetables do you grow? (When you enter this %, the fruit row will automatically change. If you do not grow vegetables enter "0" in that row)

Percentage (%) Vegetables Fruits 10

TOTAL of Fruits & Vegetables 100%

10. To whom do you sell the majority of your produce? (only tick one box)

Retailer

Wholesaler

Processor


11. Duration of your relationship with the present SC partner?

Less than 5 years




More than 20 years

Section Two We are interested in identifying how the relationship between your organisation and your major supply chain (SC) partner (whom you have mentioned in the previous question) has developed over the years. This section focuses on your interactions with the major SC partner, and we intend to investigate some of the relationship traits such as trust, commitment, collaboration, satisfaction, dependence, information sharing and power, and how these influence SC relationship success and finally your organisational performance. These interactions could have occurred in your day-to-day operations in the organic fruit and vegetable industry. Every statement below has a seven point scale ranging from 1 to 7. Please indicate your experience relating to supply chain relationships by selecting one option. 1 means that you Strongly Disagree with the statement, and 7 means that you Strongly Agree with the statement. You may select any of the numbers in between 1 and 7 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 relating to the supply chain (SC) of organic fruit and vegetables.

296

PLEASE ANSWER THE FOLLOWING QUESTIONS KEEPING IN MIND YOUR MAJOR SUPPLY CHAIN (SC) PARTNER WHOM YOU HAVE MENTIONED IN PREVIOUS QUESTION

12. TRUST

Statement Strongly Disagree (1)

Disagree (2)

Slightly Disagree (3)

Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

Whoever is at fault, problems need to be solved together

Both parties watch the other’s profitability

Our major SC partner has high integrity There are no doubts regarding our major SC partner’s motives

Both parties are willing to make mutual adaptations

If our major SC partner gives us some advice, we are certain that it is an honest opinion

13. COMMITMENT


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

We expect this relationship to continue for a long time We are committed to our major SC partner We expect this relationship to strengthen over time

Considerable effort and investment has been undertaken in building this relationship

14. COLLABORATION

We are working closely with our major SC partner in ......

297


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

Technology sharing and its development

Process development in the supply chain

Target costing

Planning of the supply chain activities

15. SATISFACTION


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

Our farm regrets the decision to do business with our major SC partner

Our farm is very satisfied with our major SC partner

Our farm is very pleased with what our major SC partner does for us

Our farm is completely happy with our major SC partner

Our farm would still choose to use our major SC partner, if we had to do it all over again

16. DEPENDENCE


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

It would be difficult to replace our major SC partner

our major SC partner commands resources that we would have difficulties obtaining somewhere else

298

A lot of our success depends on the success of our major SC partner

17. INFORMATION SHARING

Statement Strongly Disagree

(1)

Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

We inform our major SC partner in advance of changing needs In this relationship, it is expected that any information which might help the other party will be provided

The parties are expected to keep each other informed about events or changes that may affect the other party

18. COERCIVE POWER


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

Failing to comply with our major SC partner’s requests will result in financial and other penalties for our farm

Our major SC partner threatens to withdraw from what they originally promised, if we do not comply with their requests

Our major SC partner threatens to take legal action, if we do not comply with their requests

Our major SC partner withholds important support for our farm, in requesting compliance with their demands

Our major SC partner threatens to deal with another supplier, in order to make us submit to their demands

19. NON-COERCIVE POWER


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

Our major SC partner offers specific incentives to us when we are reluctant to cooperate with them

299

Our major SC partner has the upper hand in the relationship, due to power granted to them by the contract

Our major SC partner demands our compliance because of knowing that we appreciate and admire them

Our major SC partner uses their unique competence to make our company accept their recommendations

Our major SC partner withholds critical information concerning the relationship to better control our company

20. SUPPLY CHAIN RELATIONSHIP SUCCESS Good relationships with our major Supply Chain partner has yielded success in ......


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

Quality of fruits and vegetables to the end consumer

Lowering SC monitoring cost

Assistance received during difficult times

Increasing cooperation and communication between us and our major SC partner

21. GROWER'S PERFORMANCE

As a result of the relationship with our major SC partner, improvements have been noticed in the following areas......


Disagree (2)


Neutral (4)

Slightly Agree (5)

Agree (6)

Strongly Agree (7)

Reliability of deliveries Responsiveness of our farm to outside queries Total cost reduction

Lead time (time between order placement and delivery of produce to your major SC partner)

Conformance to specifications

Process improvement of our farm

300

Time-to-market

THANK YOU VERY MUCH FOR YOUR TIME. IT IS VERY MUCH APPRECIATED.

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Appendix 2: Research Information Statement

Dear Sir or Madam,

I am a student at the Swinburne University of Technology, Melbourne, Australia. I am currently

doing a PhD in the Business Faculty in the area of supply chain management and the title of my

PhD is:

“The influence of power and information sharing on supply chain relationships and firm’s

operational performance – perceptions of organic fruit and vegetable growers”.

I would be extremely grateful if you could please participate in the survey which follows,

as this would enable me to obtain data which would address my research questions. I

envisage that it would take you about 15 minutes to complete the survey.

The outcome of the study will be published in my PhD dissertation, in journal articles and also

in articles published by the Australian organic certification bodies. Additionally, the findings of

this study would be disseminated at conferences and seminars organised by the Australian

organic food industry. It is hoped that the results of this study will be used by the organic fruit

and vegetable industry to assist them make better decisions when it comes to creating business

relationships and also in maintaining them. I sincerely appreciate your willingness to complete

this survey.

Completion of this survey is taken as your ‘Informed Consent’ to participate in this research.

‘Informed Consent’ means that:

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 This 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 purely for academic research, and is completely independent of any commercial

interests. If you wish to participate in this survey, you will find the link to it at the end of this

email.

Data security is ensured. All data collected and analysed will be stored in a password protected

computer, and locked in a filing cabinet, in accordance with Swinburne University’s Policy on

the Conduct of Research.

If you have any questions, or would like to have a copy of the findings, please contact:

302

Principal Investigator: Assoc. Prof. Antonio Lobo, Faculty of Business and Enterprise,

Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia, Phone:

(03) 9214 8535 or email [email protected]

Or

Student Investigator: Sumangala Bandara, Faculty of Business and Enterprise, Swinburne

University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia, Phone: 0430794744 or

email [email protected]

This is a gentle reminder that you may participate in this survey only if you grow organic

fruit and vegetables. If not please do not proceed and many thanks for your time

Thanks a million for your willingness to complete the survey, and if you are ready now

please click on this link:

This project has been approved by or on behalf of Swinburne's Human Research Ethics Committee (SUHREC) in

line with the National Statement on Ethical Conduct in Human Research. If you have any concerns or

complaints about the conduct of this project, you can contact:

Research Ethics Officer, Office of Swinburne Research (H68), Swinburne University of Technology, PO Box

218, Hawthorn, VIC 3122, Australia. Tel: (03) 9214 5218 or [email protected]

mailto:[email protected]



303

Appendix 3: Ethics Clearance

Dear Antonio and Sumangala,

SUHREC 2013/076, The influence of power and information sharing on supply chain relationships and firm performance - Perceptions of Organic Fruit and Vegetable Growers A/Prof Antonio Lobo; Mr. Sumangala Bandara FBE Approved duration: 03/05/2013 to 03/05/2015

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 19th April 2013. Your response to the review as e-mailed on 30 April and 3 May 2013 were reviewed.

I am pleased to advise that, as submitted to date, the project may 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 current 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 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 the Research Ethics Office 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 email as part of project record-keeping.

304

Best wishes for project. Yours sincerely, Ann Gaeth _____________________________________

Dr Ann Gaeth Administration Officer (Research Ethics) Swinburne Research (H68) Swinburne University of Technology P O Box 218 HAWTHORN VIC 3122 +61 3 9214 8356 +61 3 9214 5267

305

Appendix 4: Summary of Survey Items

Construct Original Items Deleted Items

NONCOPOWER

1. Our major SC partner offers specific incentives to us when we are

reluctant to cooperate with them 4. Our major SC

partner uses their

unique competence

to make our

company accept

their

recommendations

2. Our major SC partner has the upper hand in the relationship, due to

power granted to them by the contract

3. Our major SC partner demands our compliance because of knowing

that we appreciate and admire them

4. Our major SC partner uses their unique competence to make

our company accept their recommendations

5. Our major SC partner withholds critical information concerning the

relationship to better control our company

SATISFACTION

1. Our farm regrets the decision to do business with our major SC

partner

1. Our farm regrets

the decision to do

business with our

major SC partner

2. Our farm is very satisfied with our major SC partner

3. Our farm is very pleased with what our major SC partner does for us

4. Our farm is not completely happy with our major SC partner

5. Our farm would still choose to use our major SC partner, if we had

to do it all over again

COLLABORATION

1. We are working closely with our major SC partner in technology

sharing and its development 1. We are working

closely with our

major SC partner

in technology

sharing and its

development

2. We are working closely with our major SC partner in process

development in the supply chain

3. We are working closely with our major SC partner in target costing

4. We are working closely with our major SC partner in planning of the

supply chain activities

TRUST

1. Whoever is at fault, problems need to be solved together

2. Both parties

watch the other’s

profitability

2. Both parties watch the other’s profitability

3. Our major SC partner has high integrity

4. There are no doubts regarding our major SC partner’s motives

5. Both parties are willing to make mutual adaptations

6. If our major SC partner gives us some advice, we are certain that it is

an honest opinion

COMMITMENT 1. We expect this relationship to continue for a long time 4. Considerable

306

2. We are committed to our major SC partner effort and

investment has

been undertaken in

building this

relationship

3. We expect this relationship to strengthen over time

4. Considerable effort and investment has been undertaken in

building this relationship

PERFORMANCE

As a result of the relationship with our major SC partner, improvements

have been noticed in the following areas….

1. Reliability of deliveries

As a result of the

relationship with our

major SC partner,

improvements have

been noticed in the

following areas…

5. Conformance to

specifications

6. Process

improvement of our

farm

2. Responsiveness of our farm to outside queries

3. Total cost reduction

4. Lead time (time between order placement and delivery of produce to

your major SC partner)

5. Conformance to specifications

6. Process improvement of our farm

7. Time-to-market

Total Originally there were 43 items in the survey instrument Deleted 7 items

Note: Deleted items are marked in bold

307

Appendix 5: Initial SEM model

308

Appendix 6: Final SEM model

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