network embeddedness, distribution flexibility, and ...

NETWORK EMBEDDEDNESS, DISTRIBUTION FLEXIBILITY, AND PERFORMANCE: FIT MECHANISMS FOR ENVIRONMENTAL CONTINGENCY

A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in Marketing

Kangkang YU

School of Marketing Australian School of Business The University of New South Wales Sydney, Australia

February, 2012

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ABSTRACT A highly volatile and uncertain environment has accelerated the operational speed of distribution channels, with an increasing number of manufacturers implementing quicker and more flexible strategies in their channel management. Although a growing body of literature extends manufacturing flexibility to other processes within the supply chain, few empirical studies investigate the conditions under which distribution flexibility, or more particularly, each type of distribution flexibility, can enhance a firm’s performance. Focusing on distribution flexibility related to a downstream supply chain, this study specifies alternative forms of fit based on contingency theory to analyze how different flexibility strategies adapt to different environmental conditions.

The research uses a mixed method: a survey and multiple case studies. In the survey, the data arise from 212 focal firms in different sectors of Chinese manufacturing industry. The results of partial least squares (PLS) path modelling addresses fit as matching and fit as mediation which, given a stable distribution network as a source of information and opportunism, considers how distribution flexibility, as a response to uncertainty and heterogeneity, mediates the effects of network embeddedness on performance. The findings of an interaction and a subgroup analysis specify fit as moderation which considers how environmental drivers moderate not only the effects of distribution flexibility on performance but also the effects of network embeddedness on distribution flexibility. Specifically, in either an uncertain or a homogeneous environment, logistics flexibility, the resource-based dimension, has relatively stronger effects on short-term performance, while in a certain or heterogeneous environment norm-based relationship flexibility has a stronger effect on short-term performance, while in a homogeneous environment, it has a stronger effect on long-term performance.

The multiple case studies then confirms these patterns of effects and further explores strategic choices of network structures, flexibility strategies and performance orientations as gestalts or covariance under different environmental conditions. The abundant information obtained from the case study not only suggests a three-step decision making process for managers but also implies that future studies could discuss

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different kinds of gestalts and how to minimize the deviation from the baseline (‘best fit’) when the environment changes.

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ACKNOWLEDGEMENTS Composing a Phd dissertation makes me involved in a tough but wonderful journey. If there were not any guidance, encouragement and supports from my teachers, colleagues, friends and families, it would be impossible that I could overcome the difficulties and finally complete the journey. So I would like to use this important part of my dissertation to express my deepest gratitude to everyone who contributed to the achievement of this work.

First and foremost, I would like to express my sincere thanks to my supervisor Prof. Jack Cadeaux who has been supporting me throughout this journey. Whenever I am struggling with any problems and lose my confidence to go on, he would stand aside and show me all the possible gates to Alice’s wonderland. I really appreciate his talent way to teach and guide me, which cultivate my own capability to find the right key to the right gate. Inspired by his rigorous attitude for doing research and enthusiasm in guiding students, I truly believe that the knowledge he teaches me would lighten the future of my academic career as well as my life.

I am especially indebted to Prof. Hua Song in Renmin University of China, who encourages me to go on further study at abroad and constantly provides constructive comments on my work. I also want to extend my appreciation to Mark Uncles, Paul Patterson, Pam Morrison, John Roberts, Ian Wilkinson, Rita di Mascio, Mohammed Razzaque, Jennifer Harris, Liem Viet Ngo, and Nanfeng Luo for their academic guidance and insightful comments on this research. Thanks also go to Nadia Withers, Paula Aldwell and Margot DeCelis for their joyful assistance at various stages, but especially to Nadia who gave me a warm hug when welcoming me in Sydney.

I am grateful for the financial assistance of China Scholarship Council and Australian School of Business. Without the collaborate scholarship, I would not have such a valuable learning experience.

I also want to thank my friends and fellow students in School of Marketing. Companying with them, my journey was full of happiness, venture, and inspirations. vii

Thanks to my seniors, Jie Meng, Dan (Daisy) Liu, Zhirong Duan, Denni Arli, Jiraporn (Nui) Surachartkumtonkun, Warat Winit, Yiming (Stephanie) Huang, Haodong (Harry) Gu, and Ting Yu for their generous assistant and helpful guidance to PhD life. Thanks to my PhD classmates, Bernardo Figueiredo, Fabian Held, Ehsan Ahmed, William Neill, and Ryan Miller, for their critical thoughts and intellectual inputs at the early stage of my journey. Thanks to my close friends, Hong Jiang, Xiao (Hazel) Han, Ning (Chris) Chen, Ce Mo, Nuo (Nora) Xu, Cheng Qian, and Ning (Angela) Ding for cooking and eating together the most professional Chinese dishes in Sydney!

Last, but certainly not least, my sincerest appreciations go to my family and relatives. To my dear dad and mum, thanks for their endless love and beliefs through all my life. To my dear husband and parents in law, thanks for their understanding and sacrifices during these years. To my grandma from my deep heart, thanks for coming to my dreams and let me know- for what you pursue and for whom you love, never give up.

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TABLE OF CONTENTS 1

INTRODUCTION ................................................................................................. 1 1.1

Background to the research............................................................................... 1

1.1.1 Theoretical background ................................................................................ 1 1.1.2 Managerial background................................................................................ 3 1.2

Research questions ............................................................................................ 4

1.3

Scope of the research ........................................................................................ 5

1.4

Overview of method.......................................................................................... 8

1.4.1 Overview part 1 – A survey........................................................................... 9 1.4.2 Overview part 2 – Multiple case studies ..................................................... 10

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1.5

Expected findings and potential contributions................................................ 11

1.6

Structure of the thesis...................................................................................... 12

LITERATURE REVIEW ON FLEXIBILITY................................................. 15 2.1

Introduction ..................................................................................................... 15

2.2

Review scope .................................................................................................. 16

2.2.1 A framework for literature review .............................................................. 17 2.2.2 A focus on distribution flexibility ................................................................ 19 2.3

Definition and dimensions of flexibility ......................................................... 21

2.3.1 Manufacturing flexibility............................................................................. 21 2.3.2 Supply chain flexibility................................................................................ 24 2.3.3 Distribution flexibility ................................................................................. 27 2.4

Drivers of flexibility........................................................................................ 29

2.4.1 Drivers of manufacturing flexibility............................................................ 29 2.4.2 Drivers of supply chain flexibility ............................................................... 32 2.4.3 Drivers of distribution flexibility................................................................. 33 2.5

Sources of flexibility....................................................................................... 35

2.5.1 Sources of manufacturing flexibility ........................................................... 35 2.5.2 Sources of supply chain flexibility............................................................... 36 2.5.3 Sources of distribution flexibility ................................................................ 37 2.6

Performance of flexibility ............................................................................... 43

2.6.1 Performance of manufacturing flexibility ................................................... 43 2.6.2 Performance of supply chain flexibility ...................................................... 44 2.6.3 Performance of distribution flexibility........................................................ 45 ix

2.7 3

Summary ......................................................................................................... 50

LITERATURE REVIEW ON FIT MECHANISMS ....................................... 51 3.1

Introduction ..................................................................................................... 51

3.2

Review scope .................................................................................................. 51

3.2.1 Empirical studies of flexibility .................................................................... 52 3.2.2 Contingency theory ..................................................................................... 54 3.3

Fit in the selection and interaction approaches ............................................... 58

3.3.1 Fit as matching............................................................................................ 58 3.3.2 Fit as mediation .......................................................................................... 59 3.3.3 Fit as moderation ........................................................................................ 61 3.4

Fit in the system approach............................................................................... 62

3.4.1 Fit as gestalts .............................................................................................. 62 3.4.2 Fit as covariation ........................................................................................ 64 3.4.3 Fit as deviation............................................................................................ 65 3.5 4

Summary ......................................................................................................... 66

MODEL DEVELOPMENT................................................................................ 67 4.1

Introduction ..................................................................................................... 67

4.2

Core model: fit in the selection and interaction approaches ........................... 68

4.2.1 Environmental drivers and distribution flexibility: fit as matching............ 70 4.2.2 Network sources, distribution flexibility and performance: fit as mediation71 4.2.3 Environmental drivers, network sources, distribution flexibility and performance: fit as moderation ............................................................................. 79 4.3

Complementary model: fit in the system approach......................................... 86

4.3.1 Focused flexibility strategies in different contexts: fit as gestalts .............. 87 4.3.2 Coalignment flexibility strategies in different contexts: fit as covariation . 89 4.4 5

Summary ......................................................................................................... 90

RESEARCH METHODS.................................................................................... 91 5.1

Introduction ..................................................................................................... 91

5.2

A survey for the core model............................................................................ 93

5.2.1 Measurements ............................................................................................. 93 5.2.2 Data collection .......................................................................................... 108 5.2.3 Sample profile ........................................................................................... 112 x

5.2.4 Analysis procedures .................................................................................. 116 5.3

Multiple case studies for the complementary model..................................... 123

5.3.1 Evidence collection ................................................................................... 124 5.3.2 Sample profile ........................................................................................... 127 5.3.3 Analysis procedures .................................................................................. 130 5.4 6

Summary ....................................................................................................... 132

SURVEY RESULTS AND ANALYSIS........................................................... 133 6.1

Introduction ................................................................................................... 133

6.2

Descriptive analysis ...................................................................................... 133

6.3

Common method bias ................................................................................... 134

6.4

Measurement model assessment ................................................................... 136

6.4.1 Reflective measures................................................................................... 136 6.4.2 Formative measures .................................................................................. 145 6.5

Structural model assessment ......................................................................... 149

6.5.1 Results of the full path model .................................................................... 150 6.5.2 Analysis of fit as matching ........................................................................ 156 6.5.3 Analysis of fit as mediation ....................................................................... 158 6.6

Interactions and subgroup analysis ............................................................... 161

6.6.1 Results of the models with interactions..................................................... 163 6.6.2 Results of subgroup analysis..................................................................... 170 6.6.3 Analysis of fit as moderation..................................................................... 177

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6.7

Post hoc analysis ........................................................................................... 181

6.8

Summary ....................................................................................................... 184

CASE STUDY RESULTS AND ANALYSIS .................................................. 187 7.1

Introduction ................................................................................................... 187

7.2

Within case analysis...................................................................................... 187

7.2.1 Flexibility strategies.................................................................................. 188 7.2.2 Network embeddedness ............................................................................. 194 7.2.3 Performance orientation ........................................................................... 199 7.3

Cross case analysis........................................................................................ 202

7.3.1 Revised propositions based on fit as matching ......................................... 203 7.3.2 Revised propositions based on fit as gestalts or covariation.................... 206 7.4

Summary ....................................................................................................... 210 xi

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DISCUSSION AND CONCLUSIONS............................................................. 212 8.1

Introduction ................................................................................................... 212

8.2

Review of research objectives....................................................................... 212

8.3

Summary and discussion of results ............................................................... 213

8.3.1 Distribution flexibility, its drivers, its sources and its performance......... 213 8.3.2 Interpretation of fit in the selection/interaction approach........................ 216 8.3.3 Interpretation of fit in the system approach.............................................. 221 8.4

Contributions and implications ..................................................................... 223

8.4.1 Theoretical contributions .......................................................................... 224 8.4.2 Managerial implications ........................................................................... 226 8.5

Limitations and future research..................................................................... 229

8.5.1 Limitations and future research for theory ............................................... 229 8.5.2 Limitations and future research for research design................................ 231 REFERENCE .............................................................................................................. 233 APPENDICES ............................................................................................................. 264 Appendix 2-A Literature review of flexibility ......................................................... 265 Appendix 2-B Drivers, sources and performance of supply chain flexibility.......... 271 Appendix 3-A Summary of the empirical studies of flexibility............................... 272 Appendix 5-A Summary table of all constructs ....................................................... 279 Appendix 5-B Questionnaire on flexible distribution channels ............................... 286 Appendix 5-C Interview guide on flexible distribution channels ............................ 298 Appendix 6-A Descriptive statistics for constructs used in the model..................... 305

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LIST OF TABLES Table 2-1 Differences between manufacturing flexibility and supply chain flexibility .18 Table 2-2 Summary of supply chain flexibility dimensions used in the literature .........25 Table 2-3 External sources of volume flexibility (VF) ...................................................36 Table 2-4 Attributes of different network ties.................................................................39 Table 3-1 Alternative forms of fit in contingency theory ...............................................57 Table 4-1 Summary of competing hypotheses for environmental moderation of relationship flexibility effects .........................................................................................86 Table 4-2 Typology of information in the context ..........................................................87 Table 5-1 C-OAR-SE glossary for scale development in survey study ..........................95 Table 5-2 Concepts and measures of distribution network structure ............................100 Table 5-3 Sample composition......................................................................................114 Table 5-4 ANOVA results ............................................................................................115 Table 5-5 A typology for sample selection ...................................................................126 Table 5-6 Business background ....................................................................................129 Table 6-1 Principle component analysis .......................................................................137 Table 6-2 Reliability of all measurement scales (n=212) .............................................140 Table 6-3 AVE and correlation matrix..........................................................................143 Table 6-4 Factor structure matrix of loadings and cross-loadings ................................144 Table 6-5 Post hoc tests of one-way ANOVA ..............................................................149 Table 6-6 Communality, redundancy and GoF (n=212) ...............................................151 Table 6-7 Results of testing the full structural model (Resampling=500) ....................155 Table 6-8 The results of Sobel test................................................................................160 Table 6-9 Testing the moderating effects between network embeddedness and distribution flexibility (Resampling=500) ....................................................................168 Table 6-10 Testing the moderating effects between distribution flexibility and performance (Resampling=500) ...................................................................................169 Table 6-11 Model summary for four subgroups ...........................................................171 Table 6-12 Results of subgroup analysis (Resampling=500) .......................................174 Table 6-13 Summary of results for testing hypotheses about fit as moderation ...........179 Table 6-14 Results of polynomial regressions ..............................................................182 Table 6-15 Summary of hypotheses testing ..................................................................185 Table 7-1 Profiles of each company selected in different contexts ..............................203 xiii

Table 7-2 Cross-case analysis of flexibility strategy ....................................................204 Table 7-3 Cross-case analysis of network and performance.........................................207 Table 8-1 Summary of conceptual arguments and findings of moderating effects related to relationship flexibility ...............................................................................................221 Table 8-2 Flexibility strategies of the four cases ..........................................................222

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LIST OF FIGURES Figure 1-1 The framework of fit mechanisms for environmental contingency ................9 Figure 1-2 Thesis Structure .............................................................................................14 Figure 2-1 Structure of literature review .........................................................................16 Figure 2-2 Distribution flexibility, its drivers, sources and performance .......................21 Figure 2-3 A framework for analyzing manufacturing flexibility ..................................22 Figure 2-4 Hierarchy of flexibility dimensions...............................................................23 Figure 2-5 A framework of analyzing supply chain flexibility.......................................27 Figure 2-6 Illustration of network embeddedness from a focal firm’s perspective ........42 Figure 2-7 Dynamic circles of flexibility and link duration ...........................................49 Figure 3-1 Partism and holism in sample analysis and selection of variables ................52 Figure 3-2 A schematic representation of fit as mediation .............................................60 Figure 3-3 A schematic representation of fit as moderation ...........................................61 Figure 3-4 A schematic representation of fit as gestalts .................................................63 Figure 3-5 A schematic representation of fit as covariance ............................................65 Figure 4-1 The framework of fit mechanisms for environmental contingency ..............67 Figure 4-2 The core model of fit mechanisms in the selection and interaction approaches .........................................................................................................................................69 Figure 4-3 Fit as gestalts of flexibility strategies ............................................................88 Figure 4-4 Fit as covariation of flexibility strategies ......................................................89 Figure 5-1 Framework of methodology design...............................................................93 Figure 5-2 The first level distribution network .............................................................102 Figure 5-3 The instrument development and validation process ..................................118 Figure 6-1 Simplified example of the proposed approach ............................................135 Figure 6-2 Formative construct validation roadmap .....................................................147 Figure 6-3 Plots of means .............................................................................................148 Figure 6-4 The full structural model tested using partial least squares (PLS) ..............154 Figure 6-5 The structural model without environmental drivers ..................................157 Figure 6-6 The structural model without distribution flexibility as mediators .............159 Figure 6-7 Framework for identifying moderator variables .........................................162 Figure 6-8 The product indicator approach for reflective constructs ............................163 Figure 6-9 The two-stage approach for formative constructs .......................................165

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Figure 6-10 Coefficient differences between high uncertainty and low uncertainty subgroups ......................................................................................................................175 Figure 6-11 Coefficient differences between high heterogeneity and low heterogeneity subgroups ......................................................................................................................176 Figure 6-12 Framework of fit as moderation for environmental contingency ..............180 Figure 6-13 Contingency effects of tie strength and environmental uncertainty ..........183 Figure 7-1 Distribution network of Company P ...........................................................195 Figure 7-2 Distribution network of Company Z ............................................................197 Figure 7-3 Distribution network of Company Q ...........................................................198 Figure 7-4 Distribution network of Company J ............................................................199 Figure 8-1 Alternative forms of fit in flexibility strategies ...........................................227

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1 INTRODUCTION

1.1 Background to the research With increasing complexities and uncertainties, it is necessary to implement quicker and more flexible competitive strategies and achieve highly effective operations with agility. In such environments, flexibility, which is the ability to change or react to environmental uncertainty with little penalty in time, effort, cost or performance, then becomes the key to competitiveness (Upton, 1994). This is a very general and abstract definition and the purpose of this dissertation is to clarify and quantify abstract competitive capabilities. The use of the term- “flexibility” throughout this thesis does not mean that rigidity is neglected or unimportant. From another perspective, flexibility can be considered as the opposite side of rigidity, in which sense a lower level of flexibility represents a higher level of rigidity. But the key idea of this thesis is not an either-or choice between flexibility and rigidity but a systematic decision about how to balance or combine different dimensions under different conditions mainly using a contingency approach as the key theoretical grounding.

1.1.1

Theoretical background

A number of streams of literature discuss the concept of flexibility in a manufacturing and supply chain context, but it still lacks a commonly accepted definition. Most of early studies in the operations research area focused on the concept of Manufacturing Flexibility (Slack, 1987; Gupta & Bazacott, 1989; Sethi & Sethi, 1990; Gerwin, 1993; Upton, 1994; Koste & Malhotra, 1999). As several analysts note, the early definitions “refer to the ability to respond to change, and point to the use of flexibility to accommodate uncertainty” (Beach, Muhlemann, Price, Paterson, & Sharp, 2000) (p. 42). However, the literature on manufacturing flexibility mainly takes the viewpoint of a manufacturing system or a production system as a single entity in a supply chain, ignoring the contribution of downstream channel members in achieving flexibility (Kumar, Fantazy, Kumar, & Boyle, 2006).

In the last few years, several streams of literature have increasingly considered all levels and functions in the supply chain from an integrative and customer-oriented perspective: Value Chain Flexibility / Agility (Zhang, Vonderembse, & Lim, 2002; Swafford, Ghosh, & Murthy, 2006), Supply Chain Flexibility or Flexibility in the Supply Chain (Vickery, Calantone, & Dröge, 1999; Prater, Biehl, & Smith, 2001; Duclos, Vokurka, & Lummus, 2003; Lummus, Duclos, & Vokurka, 2003; Pujawan, 2004; 6ȐQFKH]  3érez, 2005 ; Kumar et al., 2006; Stevenson & Spring, 2007; Fantazy, Kumar, & Kumar, 2009), Supply Flexibility (Tachizawa & Thomsen, 2007; Liao, Paul, & Rao, 2010), and Logistics Flexibility (Zhang, Vonderembse, & Lim, 2005). Although these studies have tried to clarify the dimensions or elements of flexibility, and have also tried to understand the different drivers of flexibility and its relationship with firm performance, there still exists confusion about the relations between flexibility and its drivers, sources and performance outcomes, especially long-term orientations.

Within the context of a supply chain, flexibility is driven by market dynamism (Van Hoek, 2001). In the marketing literature, flexibility is defined as a bilateral expectation of willingness to make adaptations as circumstances change (Heide & John, 1992), which is another relational contracting norm (Dahlstrom, McNeilly, & Speh, 1996; Achrol, 1997). Furthermore, many studies in marketing have considered as antecedents of flexibility such dyadic relational factors as dependence, mutuality, trust, specific investments, and continuity expectations (Bello & Gilliland, 1997; Johnson, 1999; Handfield & Bechtel, 2002; Ivens, 2005). However, few studies have developed taxonomies that can distinguish among different categories or types of organizations and contexts and that can systematically consider relationships among the variables within each type.

In other areas such as organization management and strategic management, researchers on Fit (Venkatraman & Camillus, 1984; Drazin & Van de Ven, 1985; Venkatraman, 1989; Miller, 1992), Adaptation (Miller & Friesen, 1980; Chakravarthy, 1982; Hrebiniak & Joyce, 1985) and Organization Change (Greenwood & Hinings, 1996; Armenakis & Bedeian, 1999) are well aware of how very different strategies can be used to adapt organizations to their environments. These theories apply at the higher level of making strategies for the whole organization, while flexibility is at a lower level 2

of implementing strategies through different processes. The studies at the higher level make use of a systematic perspective but do not look into the process itself, while studies at a lower level focus on process but often mix flexibility with its sources and performance outcomes. Thus, based on the definition of flexibility made by Upton (1994), this thesis tries to first clarify the drivers, sources and performance of distribution flexibility from both operations and marketing perspectives and then explore the underlying fit mechanisms based on contingency theory in organization management.

1.1.2 Managerial background

In benevolent environments where a threat does not entail major changes, rigid responses may be the best course of action for some firms. Thus, when perceiving benevolent environments, organizations can simply utilize well-learned or dominant responses to address environmental disturbances (Fredericks, 2005). However, economic globalization, the development of information technology and the diversification of consumer requirements cause many enterprises to face highly volatile and uncertain environments arising from short product life cycles and frequent and unpredictable changes in demand. In such instances, existing routines and procedures may be inappropriate to the extent that a mismatch has occurred between organizational responses and external demands (Fredericks, 2005). Thus, many enterprises now consider flexibility to be one of their core strategic competencies. Furthermore, a stream of supply chain management literature since the early 1990s argues that firms should look beyond manufacturing flexibility to achieve a level of flexibility that adds value to customers (Kumar et al., 2006). In an interview with an associate partner in the Accenture Supply Chain Practice (Wimer, 2001), the partner noted that: “Manufacturers must find a way to align their supply chain partners with a common set of goals and metrics to ensure that all the elements of the supply network are focused on flexibility, speed, and cost”. Therefore, in an increasingly turbulent marketplace, supply chains that are agile, adaptable, and aligned can provide companies with sustainable competitive advantage (Lee, 2004).

The highly volatile and dynamic nature of the contemporary business environment also forces many channel firms to make adaptations in channel relationships and to modify 3

the rules of exchange as circumstances change (Sezen & Yilmaz, 2007). These forwardlooking companies are trying to make their distribution channels more flexible and responsive (Narus & Anderson, 1996). For example, GREE, the world’s largest specialised air conditioner company, carried out a “Vague Refund Profits Policy” to give a certain percentage of whole year profits to distributors whenever competition caused a dramatic decrease in distributors’ profits and also implemented an “Off-season Sales Policy” to give refunds of profits to distributors who pay for goods before the arrival of the peak season (Huang, Dai, & Zhang, 2009). Both reflect their strategic capability to coordinate with distributors flexibly in reaction to changes in the distribution environment. To meet the peak period in 2000 with a higher order fulfillment rate, SmarterKids.com, a leading online educational store and resource, established a new distribution centre designed flexibly with equipment such as interlink racks that would fulfill thousands of orders per day during the regular season and more than 10000 orders per day during the holiday season (Tompkin, 1999). Even since IKEA’s best selling bookcase “Billy” exceeded the German environmental public policy “E-1 standard”, they have carried out several action plans such as highlighting environmentally friendly products in stores, drafting a checklist of how IKEA stores could be environmentally oriented, designing packaging alternatives to maximise the efficient use of transportation space, and stressing the use of the most efficient transportation mode to reduce the number and volume of trips, all of which reflect the strategic capability of IKEA to respond flexibly in physical distribution (Reichert & Larson, 1998). However, the literature on the topic is limited and needs to provide answers to basic questions that managers need to address such as: What is the required distribution flexibility taxonomy? How can channel members implement the required distribution flexibility? What is the impact of the required distribution flexibility on distribution performance?

1.2 Research questions From both a theoretical and managerial perspective, uncertainties in the dynamic market affect and drive distribution flexibility, which is the ability to react to distribution environments by changing the degree to which both resource-based and norm-based distribution activities respond to direct and indirect customers. However, it is not clear 4

how such choices are made and what benefits they generate, which is a challenging topic related to how the fit among environment, competitive strategy, and supply chain strategies influences supply chain and firm performance (Zhao, Flynn, & Roth, 2007). Furthermore, most of the studies on flexibility are limited to operations issues in the supply chain and do not specifically address strategic aspects of distribution channels. It is in this context that we pose our research questions: x

What is distribution flexibility? What are the drivers, sources, and performance outcomes of distribution flexibility?

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How do manufacturers adapt distribution flexibility strategies to changes in the external distribution environment?

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How does the final distribution performance of manufacturers arise through a variety of different distribution flexibility strategies?

To answer the above questions, this research focuses on studies related to manufacturing flexibility and supply chain flexibility from both operations and marketing perspectives in order to analyze distribution flexibility related to a downstream supply chain. On one hand, it is worthwhile to represent the drivers, sources, and performance outcomes of distribution flexibility; while on the other hand, the fit mechanisms for environmental contingency underlying these components are still under consideration. So the main purpose of the present research is to explain the conditions under which different distribution flexibility strategies can enhance a firm’s performance by specifying alternative forms of fit based on contingency theory. To achieve this, different methods are required to justify explanations.

1.3 Scope of the research This thesis focuses on distribution flexibility, the aspect of flexibility related to a downstream supply chain, for several reasons including: (a) considering flexible arrangements in all logistical activities has become more important in a volatile world (Fernie, Sparks and McKinnon, 2010); (b) delivery flexibility as a mass customization strategy leads to mixed service outputs (Yu, 2011); and (c) making distribution channels 5

more flexible and responsive is a mechanism for channel firms to adapt to a highly volatile and dynamic environment. Channel design decisions are difficult to change for reasons of high costs and commitments to idiosyncratic relationships, yet, have a large and lasting impact on the success of a firm’s operations (Anderson & Coughlan, 1987). In fact, a company’s chosen channel intimately affects all of its other marketing decisions (Kotler, 1994). With a new stress on growth and the need to reduce distribution costs, effective and efficient marketing channels have become increasingly important for competing successfully in highly competitive markets all over the world (Rosenbloom, 1999). Yet, no single framework or model relating to behavioral phenomena can apply across all channel systems in the world (Kim & Frazier, 1996). An effective distribution strategy must therefore be able to address the specific issues related to each nation’s environmental idiosyncrasies. Providing an overview of Chinabased research, Zhao, Flynn and Roth (2006) suggest that China is a rich setting for the study of many decision-sciences issues relating to supply chain management and operations strategy. However, logistics strategy research in China has been virtually ignored by scholars compared with the numerous studies about business and operation strategy (Zhao et al., 2007). The present study focuses on managing distribution channels in the People’s Republic of China for at least three reasons.

First, China is an attractive market to many foreign investors. With one-fifth of the world’s population, China has the world’s biggest market. China’s economy has been growing at an average annual growth rate of 10.2% (China, 2009). Moreover, according to UNCTAD FDI Database, China was the second largest recipient of foreign direct investment (FDI) after the United States, with investments flowing into Mainland China amounting to USD 108.3 billion in 2008. Thus, to the extent that many multinational firms are increasingly considering China as one of the top strategic markets, one of the fundamental questions they must address is the type of distribution channel to use. As Stern, EI-Ansary & Coughlan

point out: “There is general agreement that the

distribution channel is the key to any company’s success in China” (p. 502). This is still true nowadays as the added value of China’s manufacturing industry is only 55% of that of the US, indicating the relatively weak earning capacity of both the production and distribution sectors (China, 2009). Investigating flexibility strategies taken by Chinese manufacturers would also provide a guidence for foreign investors. 6

Second, its economic and political environment makes investing in China a big challenge. The list of failed China ventures is long and mainly due to China’s internal business environment which remains largely unknown to western entrepreneurs (Tseng, Kwan, & Cheung, 1995). To the extent that there are ample differences in the channel environment between China and Western countries, channel management techniques that are derived based on the experiences in the West might not be compatible with the requirements in China (Luk, 1998). Although many scholars have confirmed the differences between fully developed economies and transition economies such as China (Child & Lu, 1996; Boisot & Child, 1999; Peng, 2003), the existing channel studies put more emphasis on relationship factors like Guanxi and power (Tseng et al., 1995; Ambler, Styles, & Wang, 1999; Lee, 2001) rather than strategic flexibility in adapting to this specific environment. While in the context of recognizing Guanxi (relationship) as the key for business success in China (Tseng et al., 1995), the final destination of implementing strategic flexibility is to achieve longer link duration with distribution channel members, which is based on the expectations of achieving future goals (Ganesan, 1994).

Last but the most important, structures of marketing channels are diversified in China. Prior to the economic reform movement, China’s centrally planned and three-tier system dominated the distribution sector. After implementing a series of distribution reform programs, the centrally planned three-tier system in China has been changed and a new pattern of multi-channel competition has taken shape (Luk, 1998). The Hong Kong Trade Development Council (HKTDC) (2005) prepared a practical guide to distribution in China, which reports a comprehensive account of different kinds of distribution channels in China at present. In China’s distribution sector, the coverage of its vertical wholesaling system is extensive with wholesalers reaching out to a large number of widely dispersed rural markets at the county level, while the coverage of China’s horizontal retailing system is relatively narrow with retailers tending to concentrate in markets at different levels (HKTDC, 2005). Most manufacturers use a traditional channel composed of both wholesalers and retailers to reach end customers. Goods such as clothing, footwear and small home electrical appliances are primarily distributed through wholesale marts. Another rapidly developing channel employs commercial enterprises or agents to save costs. Other formats such as direct sales, distribution centres, and electronic commerce are also fast growing in China. 7

Specifically, many manufacturers also take a multi-channel approach, adopting systems of agents and distributors on one hand, while setting up their own sales offices on the other hand. Some of these even establish retail stores under their own operation to assume more control over the end market. Overall, the variety of distribution channels in China provides a valuable variance of network structure in the sample. Also, the diversified external environments for different distribution channels could generate a variance of environmental variables in the sample.

As this study is focused on business-to-business channels, all the upstream focal firms are from the manufacturing industry in China. From 1999 to 2007, the average annual growth rate of China’s manufacturing industry is about 21.44% (Li, 2009). According to one prediction, the output of China’s manufacturing industry will be USD 11805.47 billion in 2011 when it will surpass the United States and be the number one manufacturing economy in the world (Li, 2009). Also, the proportion of manufacturing value added to GDP is the largest, which suggests that manufacturing industry makes a great contribution to China’s economy development. The sample in this study almost covers all the categories of manufacturing including such industries as food manufacturing, tobacco processing, textile and garment manufacturing, furniture manufacturing, stationery and sporting goods manufacturing, pharmaceuticals manufacturing, PC and electronic equipment manufacturing, and machine building. Furthermore, the manufacturing output of the top 5 districts studied here: Guangdong Province, Jiangsu Province, Shandong Province, Zhejiang Province and Shanghai together account for 56.64% of total manufacturing output in China. Thus, the data for this research collected from cities in these regions and industries well represents China’s manufacturing sector.

1.4 Overview of method After developing a complete conceptual model of fit mechanisms for environmental contingency as depicted in Figure 1-1, this study empirically partitions it into a core model and a complementary model using mixed methods: a field survey in part one and a multiple case study in part two. Since quantitative methods are able to test causal 8

relationships between constructs in a large sample, this study uses a survey to test key hypotheses about associations among distribution flexibility, environmental drivers, network sources, and performance. However, strategic choices about different flexibility strategies made by firms under different conditions may be unstable and influenced by factors that are hard to detect in a survey, thus, this study uses case methods to illustrate real world manifestations of theoretical constructs and to help assess the complementary model.

Distribution environment Fit as Moderation

Distribution Network

Fit as Matching

Distribution Flexibility

Distribution Performance Fit as Mediation

Fit as Mediation Fit as Covariance

Fit as Gestalt Strong & dense network Neutral network … ...

Fit as Moderation

Fit as Gestalt

Logistics flexibility Relationship flexibility … ...

Short-term performance Long-term performance … ...

Figure 1-1 The framework of fit mechanisms for environmental contingency Note: To guide the readers, the theoretical framework to be empirically examined is presented above, but it is also presented in detail in Chapter 4.

1.4.1 Overview part 1 – A survey x

Objective: This part empirically tests the conceptual framework and accompanying hypotheses in the core model including: (a) the matching between distribution environment and distribution flexibility; (b) the mediating effects of distribution flexibility on the effects of distribution network embeddedness on distribution performance; and (c) the moderating effects of distribution environment on the effects of distribution network embeddedness and 9

distribution flexibility as well as the effects of distribution flexibility on distribution performance. x

Sample: A sample of 212 firms in various manufacturing industries in the top 5 districts in China was surveyed. Qualified key informants ranged from middle managers and senior managers to presidents/vice presidents, who have been involved in the distribution decision-making process or worked with the downstream direct customers.

x

Data collection: A total of 460 firms were approached from March to December 2010 in two ways: local firms that are acquaintances of the author; and managers enrolled in several short-term business training programs held by Renmin University of China. As a result, 262 executives responded to the request, and 212 valid questionnaires were returned, yielding a response rate of 46.09%. Moreover, there was no non-response bias in the final dataset.

x

Data analysis: Structural Equation Modeling (SEM) based on Partial Least Squares (PLS) using Smart PLS 2.0 was applied to test not only reliability and validity of the measurement models but also a series of predicted relationships as stated in the hypotheses. Also, the product indicator approach, moderated regression analysis and sub-group analysis were employed to examine the moderation effects. Finally, polynomial regressions were used in a post hoc analysis for nonlinear effects.

1.4.2

Overview part 2 – Multiple case studies

x

Objective: This part was designed to: (a) further illustrate the processes of implementing flexibility in different activities and whether they are in conflict; (b) if the degree of conflict is high, illustrate how manufacturers make strategic choices among them under different conditions; and (c) if the degree of conflict is low, illustrate how manufacturers balance resources across them.

10

x

Sample: Four manufacturing firms in China were selected which represent different flexibility drivers that reflect a cross-classification among levels of environmental uncertainty and environmental heterogeneity similar to the one made by Nonaka and Nicosia (1979).

x

Data collection: The key informant in each firm participated in a one and a half hour in-depth interview involving semi-structured questions about the structure of their distribution channel, about examples of flexibility in their manufacturing, logistics and marketing activities, as well as about firm performance. The interviews were conducted between December 2009 and January 2010.

x

Data analysis: Multiple case studies using within- and cross-case analysis were employed to explore the three objectives stated above.

1.5 Expected findings and potential contributions Reviewing the literature on flexibility, this study will distinguish distribution flexibility itself from its drivers, sources and performance outcomes, which is ambiguous in the previous literature. The review suggests that network embeddedness is an important external source of distribution flexibility, while distribution flexibility has effects on specific distribution performance outcomes including distribution service quality, relationship satisfaction and link duration. However, these effects are varied under different environmental conditions, which have been neglected in most empirical studies of flexibility. Therefore, the key contribution of this study will be specifying different forms of fit underlying environmental drivers, network sources, and performance outcomes of distribution flexibility based on contingency theory. It explains the conditions under which distribution flexibility, or more specifically, logistics flexibility and relationship flexibility, can enhance a firm’s distribution performance. The expected findings will indicate that the stronger the network ties, the higher will be the level of both logistics flexibility and relationship flexibility, while the denser the network ties, the higher will be the level of logistics flexibility but the lower will be the level of relationship flexibility. A high level of either logistics flexibility or 11

relationship flexibility will have positive effects on distribution service quality and relationship satisfaction, which in turn, will enhance link duration. Furthermore, the strength of all these effects will be determined by environmental uncertainty and heterogeneity which also driver both logistics flexibility and relationship flexibility. Finally, from the perspective of applying contingency theory in operations management, this study will not only develop a mixed approach to identify different forms of fit but will also distinguish how the application of general information theory and transaction cost theory complement contingency theory to explain the key effects.

For managers, the expected findings will suggest that given a choice of a distribution network, a mechanism to adapt to specific environmental conditions involves making strategic choices between logistics flexibility and relationship flexibility, which in turn achieves corresponding short-term or long-term performance. These strategic choices will be further explored in the case study and brought into an extension of implementing a focus strategy or a coalignment strategy. Finally, a significant implication for managers will be a three-step decision making process when they consider what kind of flexibility strategy is appropriate for their company and how to implement the strategy to achieve their goals. The first step would be to scan the task environment of their company by using two dimensions: stability-uncertainty and homogeneity-heterogeneity. The second step would be to choose a flexibility strategy that either focuses on one kind of flexibility or combines several kinds of flexibility as a mechanism to adapt to the environment in which the company is located. The final step would be to be sure that the strategy chosen by the company fits with their network structure as well as their performance orientation. Chapter 8 discusses these implications in detail.

1.6 Structure of the thesis The remainder of this thesis comprises seven chapters: Chapter 2 which reviews the literature on manufacturing flexibility, supply chain flexibility and distribution flexibility, and Chapter 3 which reviews the literature on alternative forms of fit based on contingency theory as the theoretical foundation for model development; based on the literature review, Chapter 4 which develops the conceptual models and discusses 12

corresponding hypotheses and propositions; followed by Chapter 5 which justifies a triangulation method to test hypotheses and revise working propositions; Chapter 6 and Chapter 7 then report results and findings from a survey and multiple case studies respectively; and finally Chapter 8 draws conclusions and discusses contributions, managerial implications, limitations and future research directions. A complete outline of this thesis with key components in each chapter is illustrated in Figure 1-2.

13

Chapter 1 Introduction

Chapter 2 Literature Review on Flexibility x x x x

x x x x x

Chapter 3 Literature Review on Fit mechanisms x

Definition and dimensions of flexibility Drivers of flexibility Sources of flexibility Performance of flexibility

x

Research backgrounds Research questions Scope of the research Overview of method Expected contributions

x

Fit in the selection and interaction approaches Fit in the system approach

Chapter 4 Model Development

Core model -Fit as matching -Fit as mediation -Fit as moderation

x

Complementary model -Fit as gestalts -Fit as covariance

Chapter 5 Research Method x

A survey -Measurements -Data collection -Sample profile -Analysis procedures

x

Multiple case studies -Evidence collection -Sample profile -Analysis procedures

Chapter 6 Survey Results and Analysis x x x x x x

Chapter 7 Case Study Results and Analysis

Descriptive analysis Common method bias Measurement model assessment Structural model assessment Interactions and subgroups Chapter 8 Discussion analysis and Conclusions Post hoc analysis x x x

x x

Within case analysis Cross case analysis

Summary and discussion of results Contributions and implications Limitations and future research

Figure 1-2 Thesis Structure

14

2 LITERATURE REVIEW ON FLEXIBILITY

2.1 Introduction This chapter tries to distinguish four components in the literature on manufacturing flexibility and supply chain flexibility: (a) definition and dimensions of flexibility, (b) drivers of flexibility, (c) sources of flexibility, and (d) performance of flexibility. Flexibility is a capability to change or react to environmental uncertainty that exists in several different forms internal or external to the organization. Most researchers admit that flexibility is complex, multidimensional, and hard-to-capture as a process in itself (Sethi & Sethi, 1990; Koste & Malhotra, 1999). Moreover, the drivers of flexibility (reasons why flexibility exists in different situations) are different from the sources of flexibility (how flexibility is achieved in different contexts) (Jack & Raturi, 2002a). For example, environmental uncertainty, demand variability, variety of products, etc. are drivers; product and process technologies, facility and equipment, worker training/skills, etc. are internal sources, while vendor/supplier network, supplier relationships, strategic alliances in the distribution network, etc. are external sources (Jack & Raturi, 2002a). Finally, different strategies regarding flexibility should achieve different outcomes on different dimensions of performance. Focusing on downstream distribution channels, this thesis then clarifies the drivers, sources and performance of distribution flexibility based on the flexibility literature. In an effort to explain fit mechanisms for environmental contingency underlying these components, the next chapter (Chapter 3) will review contingency theories about alternative forms of fit. Figure 2-1 depicts how each of these elements is integrated into the literature review.

15

Selection/interaction Chapter 3 Fit Mechanisms Fit as matching/mediation/moderation Drivers Environmental contingency

Chapter 2 Flexibility

Sources

Flexibility

Performance

Distribution network

Distribution flexibility

Distribution performance

System Fit as gestalts/covariance

Figure 2-1 Structure of literature review

The current chapter is structured as follows: first, it illustrates the framework we use to summarize related studies and the reasons why we focus on distribution flexibility; second, it reviews the definition and dimensions of manufacturing flexibility and supply chain flexibility, based on which it defines distribution flexibility and its dimensions; third, it explains why different firms need flexibility as a response to drivers which include specific contingency factors; finally, it clarifies sources and performance of distribution flexibility after reviewing the literature on manufacturing flexibility and supply chain flexibility.

2.2 Review scope As a reaction to increasing uncertainty in the business environment, flexibility became a hot topic in a considerable amount of research in operations management most commonly associated with the literature on manufacturing flexibility that emerged in the 1980s and 1990s (Gerwin, 1987; Slack, 1987; Sethi & Sethi, 1990; Gerwin, 1993; Upton, 1994; Koste & Malhotra, 1999). However, a growing body of literature has begun to recognize that it is important to look beyond the flexible factory to the flexible supply chain, reflecting an increasing tendency to extend manufacturing flexibility to other processes within the supply chain (Vickery et al., 1999; Zhang et al., 2002; Duclos et al., 2003; Pujawan, 2004; Sánchez & Pérez, 2005; Zhang et al., 2005; Kumar et al., 16

2006). Using a framework composed of flexibility together with its drivers, sources and performance, the following sections will review literature on manufacturing flexibility and supply chain flexibility. Adopting the similarities and positioning the potential gaps, this study then focuses on clarifying drivers, sources and performance of distribution flexibility as a critical dimension of supply chain flexibility.

2.2.1 A framework for literature review

Using the components of flexibility, its drivers, sources and performance, Appendix 2-A summarizes studies of flexibility in operations management, marketing and supply chain management. The concepts of flexibility are quite different in the literature: (a) the earlier definition of flexibility in manufacturing lies within a strategic choice tradeoff between low cost manufacturing, high quality manufacturing or flexible manufacturing (Gupta & Buzacott, 1989); (b) a widely accepted definition of manufacturing flexibility is as a capability of responding to uncertainty at the same time with lower cost (Upton, 1994); and (c) a definition of flexibility commonly used in marketing is as one of the relational norms (Heide & John, 1992), but it also incorporates supply chain flexibility (Krajewski, Wei, & Tang, 2005; Wang & Wei, 2007). Many researchers in operations management support that a certain dimension of flexibility could arise at any tier of a hierarchical or cross-functional system (Sethi & Sethi, 1990; Sanchez, 1995; Prater et al., 2001). And, finally a new concept of supply chain flexibility is treating it as an output of a mixed system (Zhang et al., 2002; Duclos et al., 2003; Stevenson & Spring, 2007). But this definition often confuses flexibility itself with its sources and performance outcomes.

Since an ambigous definition exists in recent literature on supply chain flexibility, Appendix 2-B tries to separate the drivers, sources and performance outcomes of supply chain flexibility from its definition. In comparison with the literature on manufacturing flexibility, the studies of supply chain flexibility acknowledge the importance of flexibility in meeting customer demands and improving responsiveness rather than confining the study of flexibility to intra-organizational components and to the production environment (Stevenson & Spring, 2007). To the extent that a supply chain should be examined from an integrative, customer-oriented perspective (Vickery et al., 1999), flexibility arises at an intra-firm level as well as at an inter-firm level in that 17

supply chain partners share the responsibility to respond rapidly to customers’ demands at each link of the chain (Kumar et al., 2006). That is the key element difference among the differences between manufacturing flexibility and supply chain flexibility as shown in Table 2-1 below. The following sections will present details of a literature review on manufacturing flexibility and supply chain flexibility as well as highlight differences between them.

Table 2-1 Differences between manufacturing flexibility and supply chain flexibility Manufacturing flexibility

Supply Chain flexibility

Definition

The ability to change or react with Encompass those flexibility little penalty in time, effort, cost dimensions that directly affect a or performance (Upton, 1994). firm’s customers and are the shared responsibility of two or more functions along the supply chain, whether internal or external to the firm (Vickery et al., 1999).

Dimensions

Focus on manufacturing processes such as machine flexibility, operation flexibility, volume flexibility (Gerwin, 1987), product flexibility (Browne, Dubois, Rathmill, Sethi, & Stecke, 1984)

Include not only basic shop floor processes but also company level processes and aggregate chain level logistics (Sánchez & Pérez, 2005), including specific dimensions such as relationship/network flexibility (Stevenson & Spring, 2007).

Drivers

Intrinsic uncertainties and the variability of outputs (Corrêa, 1994)

Supply uncertainty, component commonality, product life cycle, product variety, process variability, customer requirements disparity, and order stability (Pujawan, 2004)

Sources

Intra-organizational factors such as product technology, production management, worker training/skills (Suarez & Cusumano, 1991)

Intra- and Inter-organizational factors such as a robust network and supply chain relationship (Lummus et al., 2003)

Performance

Economic and financial outcomes (ROA, ROI, ROS) (Tombak, 1988; Fiegenbaum & Karnani, 1991)

Add value to customers (Kumar et al., 2006), and link the firm to performance of the wider supply chain (Fantazy et al., 2009) 18

2.2.2

A focus on distribution flexibility

The highly volatile and dynamic nature of the contemporary business environment not only pushes a logistical transformation to quick response and efficient customer response (McKinnon, 1994), but also forces many channel firms to make adaptations in channel relationships and to modify the rules of exchange as circumstances change (Sezen & Yilmaz, 2007). Fernie, Sparks and McKinnon (2010) also indicate that too much focus on efficiency from a cost point of view can tie firms into situations that are undesirable, while considering flexible arrangements in all logistical activities has become more important in a volatile world. Using content analysis based on a total of 55 featured case study reports, Yu (2011) found that delivery flexibility as a mass customization strategy leads to mixed service outputs such as meeting seasonal customer demands, improved customer responsiveness, and improved customer service. However, early research on distribution channels focused on topics such as conflict (Brown & Day, 1981; Gaski, 1984), power (EI-Ansary & Stern, 1972; Frazier, Gill, & Kale, 1989; Frazier & Rody, 1991; Frazier & Antia, 1995), communication (Mohr, Fisher, & Nevin, 1996), relational norms and relationship governance (Heide & John, 1992; Heide, 1994), and long-term orientation (Ganesan, 1994). Although some researchers investigate the effects of channel environmental factors in the demand and competition environment (Etgar, 1977), regulatory environment (Assael, 1968), macro environment (Guiltinan, Rejab, & Rodgers, 1980), and supply environment (Gaski, 1989) on those kinds of behavioural dimensions, few studies recognize those channel environmental factors as drivers of flexibility. While given the choice of a distribution network structure, a mechanism for channel firms to adapt to a highly volatile and dynamic environment is to make their distribution channels more flexible and responsive.

Distribution flexibility, as a resource-based capability, mainly takes the viewpoint of a manufacturing system as a single entity in a supply chain, ignoring the contribution of channel members toward flexibility achievement (Kumar et al., 2006). But the concept of flexibility as another relational contracting norm has been well developed in the marketing literature which uses transaction cost analysis (TCA) to explain the effect of 19

the construct of flexibility in relationships. For example, Fredericks (2005) shows empirically that the formation of strategic alliances and new product development alliances will positively influence firm flexibility. Ivens (2005) also suggests that a service provider’s relationship-specific investments and sense of mutuality positively influences its flexibility. In addition, Sezen and Yilmaz (2007) empirically demonstrate how dependence on and trust in the exchange partner have positive effects on flexibility. However, Handfield and Bechtel (2002) argue that increased perceived buyerdependence, suppliers investing in high levels of human-specific assets, and higher levels of buyer trust are associated with lower levels of supplier responsiveness. Therefore, although they test a connection between flexibility and dyadic relational factors, the direction of the effects differs across studies. It may also due to the neglect of channel environmental factors. Since the distribution function is a vital part of the supply chain, it also has the potential to enhance supply chain flexibility. Adopting from literature on supply chain flexibility, the following sections will present details of distribution flexibility, its drivers, sources and performance (See Figure 2-2): (a) distribution flexibility, which includes both resource-based logistics flexibility and norm-based relationship flexibility, (b) drivers of distribution flexibility including environmental uncertainty and heterogeneity, (c) sources of distribution flexibility including both internal and external sources (although this study focuses on external sources based on network theories by controlling internal sources), and (d) performance of distribution flexibility including both distribution performance and firm performance (although this study only considers distribution performance which ultimately contributes to firm performance).

20

Environmental drivers

x x

Internal Sources

x x

External Sources

Uncertainty Heterogeneity

Distribution Flexibility

x Logistics Flexibility x Relationship Flexibility

Relational Structural

x x

Distribution Performance Short-term Long-term

Firm Performance

Figure 2-2 Distribution flexibility, its drivers, sources and performance

2.3 Definition and dimensions of flexibility Appendix 2-A summarizes a variety of flexibility definitions and dimensions reflecting a confusion about treating flexibility as a strategy, capability, norm, or system. While previous studies on manufacturing flexibility reach an agreement that flexibility is a kind of capability with multiple dimensions responding to environmental changes (Slack, 1987; Swamidass & Newell, 1987; Upton, 1994). Expanding this capability to a supply chain context, many researchers emphasize how the cross-functional dimensions of flexibility arises at the chain level (Vickery et al., 1999; Duclos et al., 2003; Lummus et al., 2003). And distribution flexibility is cited as one of these critical dimensions (Fantazy et al., 2009), which from a hierarchical perspective includes both resourcebased and norm-based dimensions.

2.3.1 Manufacturing flexibility

In the context of manufacturing systems, flexibility is widely accepted as referring to the ability of a system to cope with changes (Gupta & Buzacott, 1989). However, this definition does not explain what it means “to cope” with changes. Kim (1991) distinguishes two meanings of the term manufacturing flexibility: strategic 21

manufacturing flexibility which is not likely to be compatible with a strategy of low cost manufacturing and operational flexibility which is the capacity of a manufacturer to react to the changing environments as well as to bring about changes in manufacturing operations. A definition of combining those two aspects might be “the ability to change or react with little penalty in time, effort, cost or performance” (Upton, 1994) (p. 73). Upton (1994) expanded on the original elements of flexibility, range and response (Slack, 1987; Swamidass & Newell, 1987), and also designed a framework for analyzing manufacturing flexibility as shown in Figure 2-3. He points that the manager should answer the following questions to characterize important types of flexibility: (a) what needs to change or to be adapted to (dimension); (b) what is the general period over which changes will occur (time horizon); (c) Which elements of flexibility are most important (element) (Upton, 1994) (p. 77). This framework has been applied in later empirical studies that measure manufacturing flexibility (D'Souza & Williams, 2000; Narasimhan & Das, 2000).

Range Operational Time period

Uniformity

Tactical Dimension A

Time period

Mobility

Strategic Time period

Flexibility

Dimension B

Figure 2-3 A framework for analyzing manufacturing flexibility Source: Upton D.M. (1994). The management of manufacturing flexibility. California Management Review, Winter, 72-89.

To the extent that manufacturing flexibility has been widely recognized as a multidimensional concept within the manufacturing function, researchers have not reached an agreement on its dimensions. At least 50 different terms for various types of flexibility can be found in the manufacturing literature (Sethi & Sethi, 1990). These dimensions, to name a few, could include machine flexibility, labor flexibility, material handling 22

flexibility, routing flexibility, operation flexibility, expansion flexibility, volume flexibility, mix flexibility (the ability to produce a number of different products at the same point in time (Gerwin, 1987), product flexibility, new product flexibility, and modification flexibility (Browne et al., 1984; Gerwin, 1987; Gupta & Goyal, 1989; Koste & Malhotra, 1999). The various components have been built up over time and it is very reasonable to array these dimensions along several hierarchies. Despite the early typologies such as basic, system, and aggregate (Sethi & Sethi, 1990), or operational, tactical, and strategic (Upton, 1994), a more comprehensive analysis represents five tiers from an operational level to a strategic level as is shown in Figure 2-4 (Koste & Malhotra, 1999). Thus, flexibility could arise at any tier of the hierarchical system. However, Corrêa (1994) represents a view of manufacturing system flexibility as an output of the whole system, an output that also contains system robustness.

Strategic Flexibility

Strategic Business Unit Tier 5

R&D Flexibility System Flexibility Marketing Flexibility

Manufacturing Flexibility

Organizational Flexibility

Modification Flexibility New Product Mix Flexibility Flexibility

Functional Tier 4

Volume Flexibility Expansion Flexibility

Plant Tier 3

Operation Flexibility Routing Flexibility

Machine Flexibility

Labour Flexibility Material Handling Flexibility

Shop Floor Tier 2

Individual Resource Tier 1

Figure 2-4 Hierarchy of flexibility dimensions Source: Koste L.L. and Malhotra, M.K. (1999). A theoretical framework for analyzing the dimensions of manufacturing flexibility. Journal of Operations Management, 18: 75-93. 23

2.3.2 Supply chain flexibility

Given increasing cross-functional and cross-company efforts to increase flexibility, arguably, a focus limited to manufacturing flexibility might be insufficient to deal with a more complex and turbulent environment (Duclos et al., 2003; Lummus et al., 2003; Sánchez & Pérez, 2005; Kumar et al., 2006), which leads to an extension from manufacturing processes to other processes within the supply chain. And it is still applicable in a supply chain context that flexibility is a kind of capability, either proactively or reactively, responding to environmental uncertainty and heterogeneity, manifested in different functions along the supply chain. However, since the supply chain extends beyond the enterprise, supply chain flexibility must also extend beyond the internal flexibility of a single firm (Lummus et al., 2003). Thus, supply chain flexibility should be defined to encompass those flexibility dimensions that directly affect a firm’s customers and that are the shared responsibility of two or more functions along the supply chain, either functions internal to the firm such as marketing or manufacturing or external functions involving participants such as upstream suppliers or downstream channel members (Vickery et al., 1999). Furthermore, a complete definition of supply chain flexibility components should include the flexibility dimensions required by all of the participants in the supply chain in order to successfully meet customer demand (Duclos et al., 2003). Previous studies identify several dimensions and most add several new dimensions to those originally related to manufacturing flexibility (See Table 2-2).

24

Table 2-2 Summary of supply chain flexibility dimensions used in the literature 6ȐQFKH] Lummus and et al. Pérez (2005) (2005)

Types of supply chain flexibility

Vickery et al. (1999)

Zhang et al. (2002)

Duclos et al. (2003)

Pujawan (2004)

Logistics/delivery /distribution flexibility

×

×

×

×

×

Sourcing/supply/ procurement flexibility

×

×

×

×

Product/product development flexibility

×

×

Responsiveness/ market flexibility

×

New product/ launch flexibility

×

×

×

Production/ manufacturing/ routing flexibility

×

Information systems/ spanning flexibility

×

×

Kumar et al. (2006)

Total no. cited

×

×

7

×

×

6

×

×

5

×

×

4

×

×

3

×

3

×

×

3

Operations system flexibility

×

×

2

Organizational flexibility

×

×

2

Trans-shipment flexibility

×

1

Postponement flexibility

×

1

Adapted from: Fantazy, K.A., Kumar, V., & Kumar, U. (2009). An empirical study of the relationships among strategy, flexibility, and performance in the supply chain context. Supply Chain Management: An International Journal, 14(3), 177-188.

25

Based on the earlier framework of Sethi and Sethi (1990), Sánchez and Pérez (2005) proposed the three hierarchies of supply chain flexibility dimensions: basic shop-floor (product, volume), system-company (delivery, transshipment, postponement), and aggregate-chain (launch, sourcing, response, access). The nine dimensions are either internal or external functions along the supply chain. Although previous research on manufacturing flexibility may be more concerned with internal dimensions, supply chain flexibility includes both internal and external aspects but puts more emphasis on the external. Instead of a vertical view, there is a new horizontal (chain) system view that tries to capture each process along the supply chain including sourcing flexibility, manufacturing flexibility and delivery flexibility (Prater et al., 2001; Pujawan, 2004; Kumar et al., 2006; Swafford et al., 2006; Fantazy et al., 2009). Sawhney (2006) also use a simpler typology including input flexibility, process flexibility and output flexibility. However, more researchers support viewing supply chain flexibility as an output of a system including both vertical and horizontal dimensions (Zhang et al., 2002; Duclos et al., 2003; Stevenson & Spring, 2007). Some specific components are added to this system such as spanning flexibility (Zhang et al., 2002), organizational flexibility and information systems flexibility (Golden & Powell, 1999; Duclos et al., 2003; Stevenson & Spring, 2007). Relationship flexibility is well developed in buyer-seller research as one relational norm (Boyle, Dwyer, Robicheaux, & Simpson, 1992; Heide & John, 1992; Ivens, 2005). When it is introduced into operations management literature (Johnston, McCutcheon, Stuart, & Kerwood, 2004; Krajewski et al., 2005; Wang & Wei, 2007), relationship flexibility and network flexibility are also considered as important components in supply chain flexibility (Golden & Powell, 1999; Duclos et al., 2003; Stevenson & Spring, 2007; Tachizawa & Thomsen, 2007; Wang & Wei, 2007; Liao et al., 2010). Adopting Upton’s framework, both a cross-functional dimensions and a hierarchical dimensions are integrated as shown in Figure 2-5.

26

Sourcing/supply flexibility Operational Supply chain flexibility

Manufacturing flexibility

Tactical Distribution/ delivery flexibility

Proactive

Logistics flexibility Reactive

Relationship flexibility Strategic Network flexibility

Figure 2-5 A framework of analyzing supply chain flexibility

2.3.3

Distribution flexibility

Some researchers define distribution flexibility as the ability to provide widespread or intensive distribution coverage (Vickery et al., 1999; Sánchez & Pérez, 2005). However, this definition has several limitations: (a) the notion of flexibility is clearly missing in a definition based on distribution intensity or coverage. Flexibility should be properly defined in terms of the ability to change or react to environments (Upton, 1994). Although widespread or intensive distribution coverage is a potential outcome of distribution flexibility, it does not represent the intrinsic processes or functions of distribution flexibility; (b) the boundary of distribution flexibility in this definition is vague: it is more reasonable to absorb interactive aspects of other flexibility dimensions like relationship flexibility into the scope of distribution flexibility, a construct that is not only resource-based but also norm-based, and (c) although such a definition views distribution flexibility as a kind of ability, such a view is too abstract to explain both adaptive and proactive processes (ClaycomE 'UĘJH  *HUPDLQ ). Thus, this study defines distribution flexibility as the ability to react to distribution environments by changing the degree to which both resource-based and norm-based distribution activities respond to both direct and indirect customers. As one dimension of supply chain flexibility, distribution flexibility also operates either across functions internal to the firm or across external functions involving downstream channel members. Sanchez (1995) suggests that resource flexibility and coordination flexibility are key challenges to strategic managers in dynamic markets. However, he focuses on internal product resources and intra-organizational coordination in dynamic product markets, while the 27

present study emphasizes external network resources and inter-organizational coordination in dynamic distribution channel environments.

The resource-based dimension of distribution flexibility is related to logistics processes since physical distribution management lies on the outbound side of the logistics process, and involves activities such as transportation planning and management, facility structure management (e.g. warehouse location), inventory management, material handling (e.g. packaging and loading), reverse logistics, tracking and delivery (Williamson, Spitzer, & Bloomberg, 1990; Duclos et al., 2003). Logistics flexibility is defined by Swafford, Ghosh and Murthy (2006) as “the availability of a range of options and the ability to effectively exploit them to adapt the process of controlling the flow and storage of materials, finished goods, services, and related information from origin to destination in response to changing marketplace conditions” (p. 124). In the context of downstream distribution channels, the present paper suggests that logistics flexibility involves adjusting storage capacity, delivery capacity or schedules, transportation mode, inventory and other outbound logistics activities in response to varying requirements of both direct and indirect customers. To make such adjustments requires a sufficient quantity and quality of information as a resource.

Arguably, not only a resource-based dimension but also a norm-based dimension should be included in distribution flexibility for two reasons. First, studies of relationship flexibility connect flexibility to relational norms and suggest that relationships will be subject to modification in the light of changed circumstances (Heide & John, 1992; Sezen & Yilmaz, 2007). Furthermore, a complete definition of supply chain flexibility components should include the flexibility dimensions required by all of the participants in the supply chain in order to successfully meet customer demand (Duclos et al., 2003). Thus, in addition to logistics flexibility, which is a kind of resource-based capability, relationship flexibility is another dimension of distribution flexibility. For example, facing fierce competition in the air-conditioner market, GREE, the world’s largest specialised air conditioner company, carried out a “Vague Refund Profits Policy” to give a certain percentage of whole year profits to distributors whenever competition caused a dramatic decrease in distributors’ profits (Huang et al., 2009). They also implemented an “Off-season Sales Policy” to give refunds of profits with an interest rate higher than the bank rate to distributors who pay for goods before the arrival of the 28

peak season (Huang et al., 2009). Such policies help support distributors by sharing the costs of managing stocks in a volatile environment, which in turn can strengthen relationships with distributors. Thus, both policies reflect the strategic capability of GREE to coordinate with their distributors flexibly in reaction to environment changes. Compared with logistics flexibility, a bilateral expectation of willingness to make adjustments of ongoing relationships is required (Heide & John, 1992).

2.4 Drivers of flexibility Before considering how specific actions generate flexibility, an initial step is to analyze why or in what kinds of contexts firms need certain types of flexibility. This review defines a driver of flexibility as a factor that determines the existence and the level of flexibility required as well as its performance. As shown in Appendix 2-A, the drivers of flexibility are consistent in different areas concerning two aspects of environmental information: uncertainty and heterogeneity. Early studies on manufacturing flexibility consider perceived environmental uncertainty as the main driver (Swamidass & Newell, 1987; Corrêa, 1994). While in a broader supply chain context, researchers consider different types of task-related uncertainty and suggest that each dimension has corresponding drivers (Vickery et al., 1999; Dreyer & Grønhaug, 2004). For distribution flexibility, both uncertainty and heterogeneity in the primary task environment of distribution channels are considered as important drivers.

2.4.1

Drivers of manufacturing flexibility

A number of studies suggest that manufacturing flexibility is mainly needed in order to deal with two factors: intrinsic uncertainties and the variability of outputs (Corrêa, 1994). Early empirical studies also confirm the positive relationship between perceived environmental uncertainty and manufacturing flexibility (Swamidass & Newell, 1987). At the same time, with increasingly demanding customers and a tendency toward customization, market-related factors causing flexibility needs are considered in some theoretical papers (Toni & Tonchia, 1998; Kara & Kayis, 2004).

29

x

Uncertainty

Manufacturing flexibility drivers are related to uncertainty as flexibility has been often seen as a reaction to uncertainty (Slack, 1987; Swamidass & Newell, 1987; Gerwin, 1993). Pioneering research based on contingency theory built upon this central concept of uncertainty which sought to capture the environment’s effects on the organization’s functioning (Downey & Slocum, 1975). It has been used both as a descriptor of the state of the organizational environment and as a descriptor of the state of a person who perceives himself/herself to be lacking critical information about the environment (Milliken, 1987). Alternatively, transaction cost theory divides uncertainty into two dimensions: environmental uncertainty and behavioral uncertainty (Rindfleisch & Heide, 1997).

It is argued from contingency research that the same set of stimuli from the environment can foster different levels of perceived uncertainty in different individuals or organizations depending on the previous level of knowledge of the individual and on cognitive processes. For example, Lawrence and Lorsch (1969) suggest that environmental uncertainty is composed of three elements: (a) lack of clarity of information, (b) general uncertainty of causal relationships between decisions and the corresponding results, and (c) time span of feedback about the results of the decision. But Lawrence and Lorsch (1969) do not analyze the relationship between environment and perceived uncertainty. Duncan (1972) assumes that the perception of complexity/dynamism in the environment can be expected to be positively related to the perception of uncertainty. Other authors propose objective measures for uncertainty, which are based on attributes of the environment as indexes of volatility (Snyder, 1987; Cadeaux, 1992; Dreyer & Grønhaug, 2004). However, because of reliability and validity problems pointed by Milliken (1987), many researchers sought to measure both “objective” and Perceived Environmental Uncertainty (PEU). For example, Daft and Weick (1984) suggest that an organization can be viewed as an interpretive system that uses three interlocking stages to interpret or learn of its environment. These stages are scanning or data collection, interpretation or giving the data meaning, and learning or taking action on the basis of the environmental view that has been enacted. Parallel to the three stages of interpretation, three types uncertainty posed by Milliken (1987) are: (a) state uncertainty, unpredictability of the environment or some components of the 30

environment; (b) effect uncertainty, inability to predict the impact of environmental change on the organization; (c) response uncertainty, inability to predict the consequences of a response choice. Gerloff, Muir and Bodensteiner (1991) conduct a factor analysis and found significant loadings on state, effect, and response uncertainty as independent dimensions. Miller and Shamsie (1999) also explore the impact of these three kinds of uncertainty on product line simplicity-specifically on the range of product variations a firm offers. x

Market-related factors

Buyers have become increasingly sophisticated, demanding increasingly customization and, at the same time, product life cycles are shortening during the introductory and growth stages, despite manufacturing process related factors causing flexibility such as uncertainty with respect to machine downtime, material input to the process and delivery times of raw materials, and variations in the workforce. Therefore, a number of authors have also introduced as alternative causes of flexibility such market related factors as the variability of demand, shorter life-cycles of both the products and technologies, wider ranges of products, increased customization and shorter delivery times (Toni & Tonchia, 1998; Kara & Kayis, 2004).

The length of the product life cycle (PLC) is the time between introduction and withdrawal from the marketplace (Bayus, 1994). Many studies hold that life cycles are shrinking. For example, Qualls, Olshavsky and Michaels provide empirical evidence that PLCs are shortening at least during the introductory and growth stages. Yet, there is no strong and consistent empirical evidence that product life cycles are systematically getting shorter over time (Bayus, 1994). The shortening of the PLC presents at least two difficulties: the company must have capabilities to design and produce new products or make changes to existing ones in a short time span; and the rapid evolution may also mean that hardware and software traditionally devoted to manufacturing flexibility will become obsolete (Kara & Kayis, 2004). All of these problems drive firms to seek additional sources of flexibility, such as supply chain flexibility to supplement manufacturing flexibility.

31

When analyzing the variability of outputs of manufacturing systems, Corrêa (1994) suggests two different dimensions: one is the actual variety of outputs, which refers to the range of products the system produces, and the other one is the variation of the system’s outputs during the time period, not only in terms of how the range of products varies but also in terms of how the volume, the mix and the timing of the demanded output vary during the time period. Variety involves offering a wider variety of products and replacing them more frequently than competitors (Womack, Jones, & Roos, 1990). Although the benefits of variety are generally linked to the market, the costs of variety to an organization are in general very sensitive to the amount of variety (Corrêa, 1994). According to Slack, flexibility is one way to achieve variability of outputs costeffectively.

2.4.2

Drivers of supply chain flexibility

As previously noted, earlier researchers have used measures of perceived environmental uncertainty (PEU) when testing the relationship between uncertainty and manufacturing flexibility (Slack, 1987; Swamidass & Newell, 1987; Gerwin, 1993; Corrêa, 1994). However, empirical results from studies by Pagell and Krause (1999; 2004) indicate that the measures of PEU in these studies are not as specific as possible to the particular context under investigation. In a wide supply chain context, uncertainty has been connected with specific tasks. For example, Vickery et al. (1999) try to distinguish different dimensions of supply chain flexibility by testing their connections with different kinds of uncertainty in marketing, product, competitors, demand and production. Also, Dreyer and Grønhaug (2004) try to identify which relative factors of uncertainty (e.g. raw materials, product volume, product mix, gross margins, profitability) drive different types of flexibility. In the framework developed by Sawhney (2006), uncertainty is categorized according to interfaces along the supply chain including input uncertainty, process uncertainty and output uncertainty. Furthermore, recent researchers have acquired task-related measures of environmental changes including manufacturing uncertainty, delivery uncertainty, technological uncertainty, supplier uncertainty, customer uncertainty, and competitor uncertainty (Patel, 2011; Wong, Boon-itt, & Wong, 2011).

32

Furthermore, Pagell and Krause (1999; 2004) also suggest that there may be other factors driving flexibility in addition to environmental uncertainty. Since the construct of flexibility examined in a supply chain is not restricted to operational-based manufacturing flexibility, but broadened to other cross-functional dimensions, the scope of flexibility drivers is also expanded. For example, Pujawan (2004) examines the relationships between drivers and dimensions of supply chain flexibility and summarizes the relationship strength: (a) supply uncertainty and component commonality have strong relationships with sourcing flexibility; (b) product life cycle has a strong relationship with product development flexibility; (c) product variety and process variability have strong relationships with production flexibility; (d) customer requirements disparity and order stability have strong relationships with delivery flexibility. In addition, focusing on supply flexibility, Tachizawa and Thomsen (2007) categorize its drivers into upstream external drivers (e.g. incomplete supply), internal drivers (e.g. low-component commonality along the products of the company) and downstream external drivers (e.g. demand volatility and seasonality). Above all, compared with drivers of manufacturing flexibility, there are several emphases on analyzing drivers of supply chain flexibility: (a) task-related environmental uncertainty; (b) a wide range of drivers beyond uncertainty; and (c) corresponding drivers to each dimension.

2.4.3 Drivers of distribution flexibility

Based on the review, uncertainty is one of the main reasons for a firm to seek flexibility. Some researchers have argued that we should measure uncertainty as a perceptual phenomenon, while others measure uncertainty objectively. But some researchers such as Milliken (1987) have tried to combine both objective and perceived measurement. Furthermore, after comparing differences between the more abstract “general” PEU items and more specific “task-related” PEU items with experiments, Lorenzi, Sims and Slocum (1981) conclude that task-related operationalizations of PEU minimize the impact of individual differences on the perceptual measure of PEU. Achrol and Stern (1988) also show that a sufficient number of task-related items can be found to measure uncertainty in a reliable and consistent manner which cross-validates across independent samples. And the task environment of distribution channels is segmented into three components: (a) primary task environment consisting of immediate suppliers and 33

customers of the dyad as well as regulating agencies or competitors; (b) secondary task environment composed of suppliers of suppliers and customers of customers, actual and potential competitors, regulatory agencies and interest aggregators; and (c) macro environment representing social, economic, political and technological forces (Achrol, Reve, & Stern, 1983). According to previous studies on drivers of supply chain flexibility (Vickery et al., 1999; Tachizawa & Thomsen, 2007), perceived uncertainty in the primary task environment is a critical driver of distribution flexibility.

Besides environmental uncertainty, there may be other potential drivers of distribution flexibility in particular. According to Nonaka and Nicosia (1979), there are two dimensions of the information generated by the environment: stability-uncertainty representing quality of information and homogeneity-heterogeneity representing quantity of information. Leblebici and Salancik (1981) also support this general information argument about uncertainty. Other drivers such as short life cycles of the products also contribute to uncertainty, while variety of products or processes, customer demand variability and increased customization all add to heterogeneity, which is rarely tested in empirical studies of flexibility. However, there are also other theories from organization economics including agency theory and transaction cost theory (Williamson, 1975), which compete with information theory. The basic assumption of Transaction Cost Analysis (TCA) is that if transaction costs including both the direct costs of managing relationships and the possible opportunity costs of making inferior governance decisions are low, economic actors will favor market governance; otherwise, they will favor internal organization (Rindfleisch & Heide, 1997). In addition, TCA assumes that transaction costs are directly related to asset specificity, as well as behavioral and environment uncertainty (Gresov & Drazin, 1997). TCA also proposes that environmental uncertainty arises from information asymmetry rather than information quantity as hypothesized in general information theory. However, TCA has almost exclusively focused on vertical integration (of sales forces, distribution etc.), whether it is applicable for explaining flexibility drivers remains to be examined as a competing set of arguments against information theory.

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2.5 Sources of flexibility Sources of flexibility explain how flexibility is achieved in different contexts. Appendix 2-A categorizes different sources of flexibility in previous studies into intraorganizational and inter-organizational phenomena. It is apparent that early studies of manufacturing flexibility focus on intra-organizational sources (Slack, 1987; Beach et al., 2000), while most studies of supply chain flexibility emphasize many interorganizational sources (Fredericks, 2005; Wang & Wei, 2007; Liao et al., 2010). Both realize the importance of relationships and networks (Kim, 1991; Jack & Raturi, 2002a; Lummus et al., 2003; Tachizawa & Thomsen, 2007), from which resources and information can be seized, delivered and used to form different kinds of capabilities. Thus, there are also external sources of distribution flexibility, underlying which are resource-based and norm-based capabilities. But few empirical studies have tested yet this framework from a network perspective combining both relational and structural embeddedness.

2.5.1

Sources of manufacturing flexibility

Most flexibility sources factors are internal manufacturing and production processes such as factory network, machines and equipment, product technology, production management, worker training/skills, the product development process, rules and regulations, and information systems (Kim, 1991; Suarez & Cusumano, 1991). There is one source, however that authors refer to as “supplier network” or “relationship with suppliers and distributors” (degree of closeness and cooperation, subcontracting technical assistance projects, cross staffing, etc.) which concerns external supply chain and distribution processes. Jack and Raturi (2002) also include external sources of volume flexibility such as supplier relationships and strategic alliances in the distribution network (See Table 2-3). A recent paper by Krause, Handfield and Tyler (2007) also tests the relationship between manufacturing flexibility and some relational factors such as commitment, shared values, length of relationship, and dependence which are all well known as antecedents of flexibility as a relational norm in marketing 35

studies (Johnson & Fornell, 1991; Heide, 1994; Ivens, 2005; Sezen & Yilmaz, 2007), but found that only buyer commitment, shared values and supplier development have significant positive effects. The relationship source brings into consideration sources of manufacturing flexibility from a supply chain respective, because it is connected with inter-organizational relationships rather than just intra-organizational activities.

Table 2-3 External sources of volume flexibility (VF) External Sources

Comment

References

Vender/supplier networks

Impacts the leading-time for orders and the volume range for orders obtainable within a given lead time

Cox (1989)

Supplier relationships (outsourcing) including JIT sourcing

Subcontractors/suppliers can absorb volume fluctuations

Suarez et al.(1995)

Network of plants

Chaining multiple plants increases product mix and VF

Jordan and Graves (1995)

Off-shore plants

Off-shore plants provide surge capacity and support VF

Ferdows (1997)

Strategic alliances in the distribution network

Improve delivery reliability, streamline the supply chain and support VF

Carter and Narasimhan (1990), Cooper et al. (1997)

Source: Jack, E.P., & Raturi, A. (2002). Sources of volume flexibility and their impact on performance. Journal of Operations Management, 20(5), 519-548.

2.5.2

Sources of supply chain flexibility

The sources of supply chain flexibility also have both internal and external facets. Supply chain flexibility analysts borrow important internal sources such as information, operation and control systems, and manufacturing strategy which have been discussed in the manufacturing literature (Kumar et al., 2006; Fantazy et al., 2009). Also, they consider more specific internal sources of supply chain flexibility such as in-bound logistics and organization design (Lummus et al., 2003; Tachizawa & Thomsen, 2007; Braunscheidel & Suresh, 2009). However, flexibility in the supply chain adds the requirement of flexibility within and between all partners in the chain (Duclos et al., 36

2003). This view regards sources of supply chain flexibility as not simply constrained to intra-organizational phenomena. Some factors such as inter-organizational information systems (Golden & Powell, 1999), sourcing or outsourcing (Tachizawa & Thomsen, 2007), supplier network (Lummus et al., 2003), relationships with suppliers and distributors, and information sharing (Wang & Wei, 2007) have already been cited as sources of manufacturing flexibility.

Compared to manufacturing flexibility, there are other external specific sources that are quite important, for example, Tachizawa and Thomsen (2007) suggest that out-bound logistics, alliance formation, new product alliances, external integration are all sources of supply flexibility. Furthermore, the two important components of a flexible supply chain are (a) a robust network (the design of supply chains) and (b) supply chain relationships (various aspects of flexibility in the relationship between buyers and suppliers) (Stevenson & Spring, 2007). Liao, Hong and Rao (2010) divide supply flexibility into two aspects: supplier flexibility and supply network flexibility, which are in turn influenced by supplier selection/development and strategic alliances. Given these perspectives, the present study will argue that a robust network should be considered the source of supply chain flexibility, while relationships entail three possibilities. First, relationship strength in itself is a source: as, for example, Suarez, Cusumano and Fine (1995) examine the effect of relationships with suppliers and subcontractors on focal firm manufacturing flexibility. In addition, Wang and Wei (2007) emphasize the importance of cooperation with channel members to achieve flexibility in the supply chain. However, contracts requiring flexible procurement, in themselves, constitute one aspect of flexibility, and relationship duration, in itself, is arguably a long run output.

2.5.3 Sources of distribution flexibility

As reviewed, the literature on flexibility indicates that network embeddedness is a critical external source. Network embeddedness describes the structure of a firm’s relationship with other firms, specifically, the extent to which a firm is connected to other firms and, in turn, how interconnected those firms are to each other (Granovetter, 1992). The type of network in which an organization is embedded defines the opportunities potentially available, while its position in that structure and the types of interfirm ties it maintains define its access to those opportunities (Uzzi, 1996). There are 37

two types of network embeddedness: relational and structural. Both highlight the informational advantages networks can confer on certain actors (Gulati, 1998). At one extreme, high network embeddedness means that a firm belongs to a dense network, firms tend to know each other well through recurring interactions and interconnected ties that engender familiarity and trust, while at another extreme, low network embeddedness means that a firm belongs to a sparse network in which few of its contacts are connected to each other (Echols & Tsai, 2005). Thus, from a network perspective, external sources such as relationship with suppliers and distributors, information sharing, as well as trust and commitment are indicators of relational embeddedness, while sources like supplier networks, dependence, alliance formation and supplier selection are indicators of structural embeddedness.

A common theme of network research has been to examine the impact of network involvement on organizational performance (Provan, Fish, & Sydow, 2007). Some researchers have investigated the effect of network embeddedness on firm performance (Rowley, Behrens, & Krackhardt, 2000; Zaheer & Bell, 2005). Others focused on the opportunities that an organization could access as a result of its network embeddedness. They treat network embeddedness as a critical independent source of competitive capabilities, knowledge transfer, and network learning, particularly as they affect innovation output and entrepreneurship (McEvily & Zaheer, 1999; Ahuja, 2000; Reagans & McEvily, 2003). Although the reason why network embeddedness is a source of flexibility is not obvious in previous studies, it becomes clearer upon analyzing network theory, that is, through collaborations with other firms in the industry, a firm involves itself in an inter-firm network that contains useful information and resource flows (Echols & Tsai, 2005). According to Gulati (1999), network resources represent the informational advantages associated with a firm’s network of ties. On one hand, rich and fine-grained information is gained through strong and dense ties (Coleman, 1988; Larson, 1992). On the other hand, nonredundant and new information is obtained through “weak ties”- infrequency of interaction (Granovetter, 1973) and “structural holes”- nonredundancy (Burt, 1992). Above all, the present study will argue that high embeddedness facilitates information flows, but generates highly redundant information, while low embeddedness creates novel and new information, but inhibits the free flow of information among members. Therefore, information and resources required to develop different dimensions of distribution flexibility are delivered through 38

distribution networks, because the distribution channel is not just a chain of businesses with one-to-one business-to-business relationships, but a network of multiple businesses and relationships, through which all kinds of resources flow, be they explicit or tacit (Lambert, Cooper, & Pagh, 1998).To get a clear view, the following contents illustrate the attributes of different network ties which are summarized in Table 2-4.

Table 2-4 Attributes of different network ties Information and resource flow Network Ties

Relation

High embeddedness

Low embeddedness

Strong relations

Weak relations

Strong ties are associated with trust and fine-grained information exchanges between partners (Larson, Relational embeddedness 1992; Uzzi, 1997). But the information obtained through such a network tie is more likely to be redundant (Granovetter, 1973; Krackhardt, 1992).

Weak ties more often constitute a “local bridge” to parts of the social system that are otherwise disconnected, and therefore a weak tie is likely to provide new information from disparate parts of the system (Granovetter, 1973; Krackhardt, 1992). Sparse structure

Dense structure A dense network promotes trust and cooperation among Structural its members who are likely embeddedness to possess more common information and knowledge of each other (Coleman, 1988; Gulati, 1998).

x

(Structural holes) Firms embedded in sparsely connected networks will enjoy efficiency and brokerage advantages based on the ability to arbitrage nonredundant information exchanges (Burt, 1992; 2001).

Relational embeddedness

The relational embeddedness or cohesion perspective on networks, including levels of strong and weak ties, stress the role of direct cohesive ties as a mechanism for gaining fine-grained information (Gulati, 1998). Strong ties are shown to provide organizations with two primary advantages. First, strong ties are associated with the exchange of high39

quality information and tacit knowledge. Second, strong ties serve as part of the social control mechanism that governs partnership behaviors. Larson (1992) shows that strong ties incrementally promote and, in turn, enhance trust, mutual gain, reciprocity, and a long-term perspective. Strong ties produce and are governed by relational trust and norms of mutual gain and reciprocity, which grow through a history of interactions (Powell, 1990; Larson, 1992).

Furthermore, much of the early research on the strength of a tie draws on Granovetter’s (1973) conceptualization of ties with a focus on information flows among individuals. Although strong ties are characterized by trust and rich information exchange, much of the information is redundant. Weak ties are conduits across which an actor can access novel information and are more likely than strong ties to be “local bridges” to distant others possessing unique information (Krackhardt, 1992). Granovetter (1982) notes that strong ties have greater motivation to be of assistance and are more easily available, while weak ties provide people with access to information and resources beyond those available in their own social circles. Thus, a weak tie can be beneficial, because it is more likely to embed an actor in (or provide access to) divergent regions of the network rather than to a single densely connected set of actors. x

Structural embeddedness

Information travels not only through proximate ties in networks, but through the structure of the network itself. The structural embeddedness or positional perspective on networks, including dense structure and sparse structure (structural holes), goes beyond the immediate ties of firms and emphasizes the informational value of the structural position each node occupies in the network (Gulati, 1998). A history of dense and extended interfirm linkages provides a firm with expertise in managing such linkages (Gulati, 1993; Anand & Khanna, 2000). Because inter-firm information flows would lead quickly to established norms of cooperation when all firms in an industry have relationships with each other, information on deviant behavior would be readily disseminated and in turn influence the behavior in such a dense network (Walker, Kogut, & Shan, 1997). So Coleman (1988) argues that the optimal social structure is one generated by building dense and interconnected networks. 40

However, ties to multiple actors, who are connected to one another, provide redundant information (Granovetter, 1973; Burt, 1992). Moreover, infrequency of interaction alone may not be a sufficient condition for discovering opportunities if an actor’s weak tie contacts are either connected to each other or connected to the actor’s strong tie contacts (McEvily & Zaheer, 1999). Burt (1992) argues that the network positions associated with the highest economic return lie between, not within, dense regions of relationships. He calls these sparse regions structural holes. A firm occupying many structural holes has few redundant ties and economizes on the number of ties required to access unique information (Burt, 2001). Further, firms situated in structural holes are awarded control benefits because they act as intermediaries between disconnected partners, who rely on the firm to facilitate exchange flows across the network (Burt, 1992; Gulati, 1998). x

Distribution network

With the development of network-based research, marketing researchers in the 1990s began focusing more attention on network dimensions (Ford, 1990; Wilkinson, 1990; Frazier & Lassar, 1996; Achrol, 1997; Achrol & Kotler, 1999; Wilkinson & Young, 2002). However, a distribution channel, which is a kind of vertical market network, is quite different from an intermarket network or opportunity network (Achrol, 1997). At the heart of such a network lies a focal organization, referred to as an ‘integrator’, which organizes the network and coordinates the industry-specific ‘channel’ of suppliers and distributors organized vertically around the focal firm (Achrol & Kotler, 1999). Although either the vertical or horizontal relationships are embedded in the network structure and although interconnectedness may occur either indirectly or directly (Koka & John, 2002), the distribution network members for a common focal firm need not have any direct horizontal ties with one another as they all tie to the hub in similar ways to achieve structural equivalence. For a focal firm having strong direct interconnectedness, members’ dependence may decline while the focal firm itself may increase dependence on the members (Provan, 1993). Furthermore, the information and resources that flow through each node depend on each node’s network position (Zaheer & Bell, 2005; Koka & Prescott, 2008). However, central positions in relatively dense distribution networks are sources of information power simply because of access to downstream customer information (Dahlstrom & Dwyer, 1993; Cadeaux, 1997). For 41

example, a focal firm might exhibit a network structure with high centrality, low density and

high strength allowing for effective performance of a speculative matching

function for product assortments when supply and demand are both heterogeneous and volatile (Cadeaux, 1997). Based on network theory, Figure 2-6 illustrates the situation of a focal firm embedded in a distribution network.

Low embeddedness

High embeddedness

Integrated

Structural holes Focal firm’s 1st-order and 2nd-order network is composed of weak ties. Strong ties

Focal firm’s 1st-order network is composed of mostly strong ties and its 2nd-order network is an integration of weak and strong ties. Weak ties

Focal firm

Focal firm’s 1st-order and 2nd-order network is composed of strong ties. Channel members

Figure 2-6 Illustration of network embeddedness from a focal firm’s perspective Adopted from: Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: a longitudinal study. Administrative Science Quarterly, 45, 425-455; Uzzi, B. (1997). Social structure and competition in interfirm networks: the paradox of embeddedness. Administrative Science Quarterly, 42, 35-67.

A final note is that the production of interorganizational alliance networks is driven by a dynamic process (Gulati, 1999). The dynamic perspective of networks is concerned with the mechanisms through which networks are created and evolve, which can be divided into two sub-perspectives: (a) network evolution and (b) network creation which involves changes in network membership. That is why some studies consider dynamic factors such as alliance formation, new product alliances, and supplier selection as sources of flexibility (Krause et al., 2007; Tachizawa & Thomsen, 2007). However, the scope of network dynamics is different depending on the research purpose. The aim of this study is to find the relationship between flexibility and network linkages 42

rather than investigate the flexibility that arises from a modification of governance structures, from contingency planning, or from contractual adjustments between partners in a dynamic network. Given this scope, the membership in the network is stable but the linkages may be changed in terms of content rather than number.

2.6 Performance of flexibility As shown in Appendix 2-A, early studies of manufacturing flexibility focus on firm performance such as economic, financial and business performance (Suarez, Michaels, & Charles, 1992; Gerwin, 1993). Although these are also examined as performance of supply chain flexibility (Vickery et al., 1999; 6ȐQFKH] 3érez, 2005), recent empirical studies link the firm to a wider supply chain performance such as delivery quality, inventory, customer satisfaction and relationship performance (Zhang et al., 2002; Kumar et al., 2006). Thus, specific indicators oriented to downstream distribution channels are direct outcomes of distribution flexibility. But few studies consider the time sequencing, for example, delivery quality could be short-term orientated, which in turn, affect relationship performance in a long run.

2.6.1 Performance of manufacturing flexibility

While most of the previous studies present analytical models (Kim, 1991; Gerwin, 1993), at least three kinds of empirical studies also exist: taxonomies of flexibility, data based studies of flexibility and performance, and historical and economic analyses of flexibility (Suarez & Cusumano, 1991). In the second group, scholars with backgrounds in both operations management and economics collect and analyze data on flexibility in order to support specific hypotheses about its effects on performance. For example, Swamidass and Newell (1987) find a positive relationship between manufacturing flexibility and economic performance. In addition, Tombak (1988) finds that flexibility positively affects strategic business unit (SBU) performance. In addition, Suarez, Cusumano and Fine (1992) find that achieving high mix or new-product flexibility does not seem to involve a penalty such as lower quality or increased costs. After comparing differences between small and large firms in terms of volume flexibility, Fiegenbaum 43

and Karnani (1991) suggest that small firms are able to trade off cost efficiency for volume flexibility to increase their profits. Considering size of firm as a moderator in the relationship between volume flexibility and firm performance, Jack and Raturi (2002) show that the moderating effect is negative but non-significant.

However, because all of these studies concern flexibility in the manufacturing field, the outcomes that they test are often connected with financial or business performance such as return on investment (ROI), ROI growth, return on assets (ROA), ROA growth, return on sales (ROS), market share, and sales growth. It is interesting that Pagell and Krause (2004) try to expand the measures of manufacturing performance by bringing in such non-financial aspects as product quality, delivery speed, delivery dependability, and new product introduction, but the model does not fit the data well. Furthermore, one item measuring performance in Jack and Raturi’s (2002) study is about customer satisfaction, and which is also the main outcome in Zhang, Vonderembse and Lim’s (2003) study of flexible manufacturing capability. Therefore, the performance of manufacturing flexibility could be a set of variables that influence customer satisfaction such as quality, delivery speed, delivery dependability and innovation.

2.6.2

Performance of supply chain flexibility

Supply chain flexibility may have some very general performance effects to the extent that performance of a supply chain can improve if the entire chain and not just its manufacturing dimension is flexible (Fisher, 1997; Vickery et al., 1999; Lummus et al., 2005). For example, Vickery, Calantone and Droge (1999) find that supply chain flexibility has a general positive effect on firm performance in the furniture industry. In addition, Sánchez and Pérez (2005) explore the relation between dimensions of supply chain flexibility and firm performance among automotive suppliers. These studies measure business performance in terms of growth in return on investment (ROI), ROI growth, market share, return on sales (ROS) and ROS growth. However, it is arguably necessary to link the firm to wider supply chain performance outcomes and value added outputs for channel members and end customers (Kumar et al., 2006; Stevenson & Spring, 2007). For example, in the framework proposed by Lummus, Duclos and Vokurka (2003), a flexible supply chain leads to customer satisfaction and supply chain assets summing the overall supply chain inventory. Also, Zhang, Vonderembse, and 44

Lim (2005) find that logistics flexibility has a strong positive effect on customer satisfaction. In addition, Fantazy, Kumar, and Kumar (2009) suggest that different dimensions of supply chain flexibility have different effects on supply chain performance including both financial performance outcomes and non-financial performance outcomes such as lead time and customer satisfaction.

A problem with some studies is that measures of flexibility are themselves sometimes incorporated in a broader performance measurement system (Neely, Gregory, & Platts, 1995). To help address this problem which, in effect, contaminates the dependent variable with a measure of an independent variable, Stevenson and Spring (2007) split existing studies into those which assess “hard” factors and those which assess “soft” factors. On one hand, hard factors such as measuring the ability to cope with order variations based on costs, inventory levels, lead times, lost sales and profitability are truly the result of supply chain flexibility. Zhang et al. (2002) suggest that price/cost, production innovation, delivery dependability, value to customer/quality, rapidity/timeto-market and service are all competitive advantages created by value chain flexibility. Furthermore, Liao et al. (2010) test the relationship between supply flexibility and supply chain performance including cost, supplier performance, reliability and timebased performance. On the other hand, soft factors which are defined as flexibility in relationships do not mean that a relationship itself is flexible but mean instead that the process of flexibility arises from the relationship between partners. In this sense, soft factors are sources of relationship flexibility (Johnston et al., 2004).

2.6.3

Performance of distribution flexibility

Responding to customer requests for adjustments may create value for the customer while reducing value for the service provider if it has a negative impact on operative efficiency and/or effectiveness in the focal relationship and/or in other customer relationships (Ivens, 2005). This process also applies in business-to-business markets, so the core question here is whether investments into flexibility in distribution channels will pay off. As reviewed, the performance outcomes relevant to supply chain flexibility may not be limited to broader firm performance outcomes but also may include other, more intermediate, external aspects. Thus, from a downstream channel perspective, 45

service quality and relationship performance in a broader sense are arguably included as important performance outcomes for distribution flexibility.

Although it is necessary to measure performance outcomes in several dimensions, the problem with attempting to compare performance across business units on the dimensions proposed by Walker and Ruekert (1987) is that they involve substantial tradeoffs. Donaldson (1984) claims that good performance on one dimension often means sacrificing performance on another, so no single strategy can be expected to perform well on all dimensions no matter how well it is implemented. In particular, the effectiveness with which a particular strategy is implemented might strongly affect performance primarily on dimensions on which the strategy is expected to do well, but may have little effect on other aspects of performance (Walker & Ruekert, 1987). According to this view, different types of distribution flexibility strategies should also perform differently on distinct performance outcomes. x

Short-term performance

Channel systems exist and remain viable through time by performing duties that reduce end users’ search, waiting time, storage, and other costs, which are called the service outputs of the channel by Bucklin (1966). Bucklin (1966) distinguishes three types of service outputs performed by the channel: (a) lot size, the number of units that the customer receives at any given delivery; (b) delivery time, the period that the customer must wait, after ordering, before he receives his goods; (c) market decentralization, the number and dispersion of trading points. Based on this analysis, Coughlan et al. (2001) identify four dimensions of service outputs: (a) bulk-breaking, the end-user’s ability to buy its desired number of units of a product or service; (b) spatial convenience provided by market decentralization of wholesale or retail outlets which increases customer’s satisfaction by reducing transportation requirements and search costs; (c) waiting or delivery time, the time period that the end-user must wait between ordering and receiving goods; and finally (d) the breadth of assortment or product variety available to the end-user. Although Bucklin (1966) and Coughlan et al. (2001) tend to analyze service outputs from the perspective of consumers or end-users, Bienstock, Mentzer and Bird (1997) develop a valid and reliable scale for measuring industrial customers’ (e.g. manufacturers, wholesalers, retailers, government organizations) perceptions of the 46

physical distribution service quality they receive from their supplier. This scale is based on Mentzer, Gomes and Krapfel’s (1989) classification of physical distribution service into three dimensions: (a) timeliness, the order cycle time performance of the entire distribution system linking buyers and sellers; (b) availability, the proportion of units, order lines, or orders completely filled; and (c) quality or condition of the products delivered, depending on the incidence of in-transit damage, shipment of incorrect items, and incorrect shipment quantity. Although Mentzer, Flint and Hult (2001) have developed more comprehensive measures for logistics service quality, the underlying constructs at the higher order are still the three key dimensions above.

Service quality captures the objective performance of distribution systems, while from the perception of customers, cumulative satisfaction is an overall evaluation based on the total purchase and consumption experience with a good or service over time (Fornell, 1992). In distribution channels, the construct of satisfaction is also important in helping to understand behaviors within the channel. Ruekert and Churchill (1984) make clear a definition that “channel member satisfaction comprises the domain of all characteristics of the relationship between a channel member (the focal organization) and another institution in the channel (the target organization) which the focal organization finds rewarding, profitable, instrumental, and satisfying or frustrating, problematic, inhibiting, or unsatisfying” (p. 227). They also test the reliability and validity of alternative measures of this construct of channel member satisfaction which includes four dimensions: a product dimension, a financial dimension, an assistance dimension and a social interaction dimension. However, customer’s affective or emotional state toward a relationship, which reflects satisfaction with the relationship but not overall satisfaction (Palmatier, Dant, Grewal, & Evans, 2006), is not included in the dimensions above. From a cognitive perspective, existing studies in channel management simply measure this construct in terms of whether the relationship is a happy one or whether the partner is satisfied with the relationship (Kumar, Stern, & Achrol, 1992; Webb & Hogan, 2002). Thus, together with distribution service quality, relationship satisfaction helps form a critical index indicating the level of distribution performance. x

Long-term performance

47

Relationship marketing emphasizes that customer satisfaction is a necessary but not sufficient goal of marketing activity; rather, the goal should be to develop a lasting relationship based on a structure of long-term benefits and mutual affinity between buyer and seller (Achrol, 1997). Wilson and Jantrania (1994) define relationship value as those outcomes of a collaborative relationship that enhance partner competitiveness. As one dimension of relationship value, link duration measures the amount of experience that the supplier and the buyer have in dealing with each other, with the resulting routines being described as a form of relation specific assets (Levinthal & Fichman, 1988). Link duration provides trading partners with more behavioral information in varied contexts, which allows for better predictions that should increase each party’s confidence in its partner’s behavior (Anderson & Weitz, 1989; Doney & Cannon, 1997).So the current study defines link duration as a perception of long-term orientation. An actor’s long-term orientation is distinct from the longevity of a relationship though duration of an existing relationship is likely to affect a long-term orientation (Kelly & Thibaut, 1978). A short-term orientation only relates with the options and outcomes of the current period, whereas a long-term orientation focuses on the expectations of achieving future goals (Ganesan, 1994; Min & Mentzer, 2004; Barry & Terry, 2008).

However, the longer the time horizon of a business relationship, the higher the level of uncertainty about the stability of the environment (Ivens, 2005). Such a dynamic process implies that the longer the duration of a relationship, the more that the norm of rigidity will be enhanced and the lower will be the coordination flexibility. As is shown in Figure 2-7, this process constitutes a dynamic circle in which link duration is increased by flexibility in the short run which in turn reduces flexibility in the long run. Although interesting and important, the dynamic reinforcement process is outside the research scope of this study as we use a long-term orientation in an existing relationship rather than the length of relationship as a better indicator of link duration (Ganesan, 1994).

48



Flexibility

Link Duration

 Figure 2-7 Dynamic circles of flexibility and link duration

49

2.7 Summary The purpose of this chapter is to distinguish distribution flexibility from its drivers, sources and performance outcomes by reviewing the literature on manufacturing flexibility and supply chain flexibility. The studies reviewed include: x

An overview of two main streams of studies on flexibility in operations management: manufacturing flexibility and supply chain flexibility, as well as reasons why distribution flexibility is focused;

x

The definition and dimensions of manufacturing flexibility and supply chain flexibility, based on which distribution flexibility is defined together with its two dimensions: logistics flexibility and relationship flexibility;

x

The drivers of manufacturing flexibility and supply chain flexibility, based on which, environmental uncertainty and heterogeneity in distribution channels are reviewed as drivers of distribution flexibility;

x

The sources of manufacturing flexibility and supply chain flexibility, based on which, network embeddedness is reviewed as a critical external source of distribution flexibility;

x

The performance of manufacturing flexibility and supply chain flexibility, based on which, distribution service quality, relationship satisfaction and long-term orientation are reviewed as performance outcomes of distribution flexibility.

The next chapter (Chapter 3) reviews the theories about the fit mechanisms for environmental contingency underlying these components.

50

3 LITERATURE REVIEW ON FIT MECHANISMS

3.1 Introduction The previous chapter (Chapter 2) clarifies environmental drivers, network sources and performance of distribution flexibility by reviewing the literature on manufacturing flexibility and supply chain flexibility. However, the relationships among these components are still under investigation. Although many empirical studies try to examine the associations among flexibility, its sources and performance, few of them address the issue of environmental contingency. After illustrating this gap, primarily by using contingency theory, this chapter tries to specify alternative forms of fit in order to interpret the underlying mechanisms among drivers, sources and performance of distribution flexibility. The current chapter is structured as follows: first, a partism and holism framework is used to review effects examined in 29 recent empirical studies of flexibility, from which the requirement of contingency theory is addressed; and then alternative forms of fit including fit as matching, fit as moderation, fit as mediation, fit as gestalts and fit as covariation are discussed.

3.2 Review scope Using partist and holist approaches for the selection of variables and treatment of samples, the following sections review 29 recent empirical studies of flexibility which are summarized in Appendix 3-A, and then find that while most of the studies locate in the holism quadrants few studies give rich description and complex contingencies for the key relationships in their models. Thus, contingency theory which addresses the association between organizations and environments, is required to illustrate the fit mechanisms for environmental contingency underlying those key effects connected with flexibility.

51

3.2.1 Empirical studies of flexibility

As a general principle in organizational research, extreme partism in the selection of variables can result in specification error, while holism in the treatment of samples can result in unwarranted sample-wide generalizations which might be avoided by segmenting the sample into its homogeneous parts (McKelvey, 1978; Pinder & Moore, 1979). Figure 3-1 illustrates the interaction among the partist and holist tendencies in the treatment of variables and samples. The vertical axis represents the number of variable selected in the study, while the horizontal axis represents the subgroup treatment of samples analyzed in the study. Then, the coordinate axes provide four quadrants which are used to categorize the empirical studies of flexibility as follows.

many

Holism

Broad and rich characterizations, unwarranted generalizations B

Variable Selection

Rich description and complex contingencies D

sample-wide Sample Analysis

segmented

A Specification error and unwarranted generalization

C Piecemeal but more accurate findings

few

Partism

Figure 3-1 Partism and holism in sample analysis and selection of variables Adopted from: Miller, D. (1981). Toward a new contingency approach: the search for organizational gestalts. Journal of Management Studies, 18(1), 1-26.

Quadrant A shows a partist approach with specification error in the selection of variables, coupled with a sample-wide analysis which does not distinguish among different subsamples. In the present context, research questions may be narrowed down 52

and focused on some aspect such as the different dimensions of flexibility and their performance (Young, Sapienza, & Baumer, 2003; Zhang et al., 2003; Dreyer & Grønhaug, 2004; Zhang et al., 2005), (perceived) environmental uncertainty as an antecedent of manufacturing flexibility (Swamidass & Newell, 1987; Vickery et al., 1999), or dependence as an antecedent of norm flexibility (Heide, 1994). However, from Quadrant B Appendix 3-A, we could see that more and more firm relational and behavioral antecedents of flexibility were brought in to enrich the model including human investments (Bello & Gilliland, 1997), trust (Young-Ybarra & Wiersema, 1999), continuity expectation (Johnson, 1999), supplier involvement (Narasimhan & Das, 2000), mutuality (Ivens, 2005), relational governance (Wang & Wei, 2007), as well as shared values and information sharing (Krause et al., 2007). Also, some strategies such as differentiation strategy (Ward & Duray, 2000), integration (Wang & Wei, 2007; Braunscheidel & Suresh, 2009), and innovating strategy, customer oriented strategy and follower strategy (Fantazy et al., 2009) were correlated with flexibility. In addition, other situational factors were considered such as environmental dynamism (Ward & Duray, 2000), product complexity (Bello & Gilliland, 1997), and technological complexity (6ȐQFKH] 3é rez, 2005). But when analysts do consider such factors, they are only treated as antecedents rather than as moderators. Not surprisingly, there are some inconsistent results about the relationship between flexibility and performance.

Quadrant B shows a research approach which selects many variables that richly and broadly describe a research question so that specification error is avoided. As shown in Appendix 3-A, we found most of the studies to be located in this quadrant to the extent that firms were randomly selected as a whole sample rather than split into several subsamples according to situational factors. Such an approach makes a tacit assumption that the same relationship between variables will hold in different contexts. Thus, relationships are usually treated as linear, and no attempt is made to segment the sample to see if the nature of relationships varies from one part of the sample to another. For example, the effects of mutuality, trust, relational governance and balanced specific investments always have positive effects on either norm flexibility (Bello & Gilliland, 1997; Handfield & Bechtel, 2002; Ivens, 2005; 6ȐQFKH] 3érez, 2005 ; Wang & Wei, 2007) or strategic flexibility (Young-Ybarra & Wiersema, 1999). However, there are several conflicting arguments about the relationship between dependence and flexibility. Some claim that buyer or dealer dependence has a positive effect on norm or 53

manufacturing flexibility (Johnston et al., 2004; Krause et al., 2007; Sezen & Yilmaz, 2007), although Handfiled and Bechtel (2002) find a negative effect of buyer dependence on supply chain responsiveness. Others claim that supplier dependence has a negative effect on manufacturing flexibility (Krause et al., 2007), although Sezen and Yilmaz (2007) have not found the effect to be significant. Finally, Heide (1994) suggests that unilateral dependence has a negative effect on norm flexibility, while symmetric dependeQFHKDVDSRVLWLYHHIIHFWRQQRUPIOH[LELOLW\DOWKRXJK6ȐQFKH] 3 érez (2005) find a negative relationship between interdependence and norm flexibility. Furthermore, although many researchers consider flexibility to be always a good strategy for enhancing performance regardless of context and environmental condition (Swamidass & Newell, 1987; Bello & Gilliland, 1997; Vickery et al., 1999; 6ȐQFKH] 3érez, 2005), the overall main effects model replicated by Pagell and Krause (2004) does not fit its data well. Also, Fantazy et al. (2009) found that some dimensions of supply chain flexibility have negative effects on firm performance. Such inconsistent results may arise because these studies never consider how these relationships might vary across different contexts.

Only two studies in Quadrant C (Verdú-Jover, Lloréns-Montes, & García-Morales, 2006; Liao et al., 2010) split samples by firm size, however, they do not consider many other contingency factors which may be influencing or may account for an observed relationship. Johnson (1999) considers uncertainty as a moderator in the relationship between flexibility and strategic integration, although he did not test this effect. Recent studies have begun to test the moderating effect of environmental uncertainty following a contingency theory (Patel, 2011; Wong et al., 2011). In light of contingency generalizations, it is important to determine the conditions under which flexibility, or more particularly, each type of flexibility, can enhance a firm’s performance. Thus, the following sections analyze fit mechanisms based on contingency theory.

3.2.2

Contingency theory

The contingency framework has been widely used not only in strategy and organization studies (Lawrence & Lorsch, 1967; Hofer, 1975; Miles, Snow, Meyer, & Coleman, 1978; Lenz, 1980; Parthasarthy & Sethi, 1993; Jennings & Seaman, 1994) but also in many marketing studies (Ruekert, Walker, & Roering, 1985; Walker & Ruekert, 1987; McKee, 54

Varadarajan, & Pride, 1989; Pelham & Wilson, 1996; Vorhies & Morgan, 2003; Olson, Slater, & Hult, 2005). The essence of the contingency theory paradigm is that there is no universal set of strategies which are optimal for all business or firms, and therefore strategies need to be designed for specific environment contexts. It is a state of adaptation which asserts that managers may adapt the organization’s strategy to cope with changes in the external environment or adapt the organization’s structure and behavior to address the requirements of its strategy (Chakravarthy, 1982). Thus, the contingency approach entails identifying commonly recurring settings and observing how different structures, strategies and behavioral processes fare in each setting (Hambrick, 1983). Traditional contingencies include the environment (Burns & Stalker, 1961), organization size (Child, 1975), and organizational strategy (Chandler, 1962). But the range of contingency factors has been extended to broader environmental variables and organizational characteristics including even competitor, supplier and consumer behavior variables (Hofer, 1975). Therefore, the range of aspects of the organization explained by contingency theory has expanded over time from internal organization structure to inter-organizational relations and strategy (Donaldson, 1995). However, the term “adaptation” is employed in a number of ways, ranging simply from “change” including both proactive and reactive behavior (Miles & Snow, 1978) to a more specific denotation of “reaction” to environmental forces or demands (Astley & Ven, 1983).

Recent contingency analysts strive to identify what constitutes fit and seek to show the effect of fit on performance (Donaldson, 2001). The concept of fit has served as an important building block for theory construction in several areas of research (Aldrich, 1979; Drazin & Van de Ven, 1985) including strategic management and focuses on how elements must fit together to reach a desired configuration (Venkatraman & Camillus, 1984). However, Schoonhoven (1981) criticizes the lack of clarity in such interactive relationships and suggests that the researcher should more clearly specify the “fit” relationship when conducting contingency studies. Addressing this concern, Drazin and Van de Van (1985) interpret fit in selection, interaction and systems approaches (See Table 3-1). Furthermore, Venkatraman (1989) made use of two dimensions to classify different kinds of fit: degree of specificity of the functional form of a fit-based relationship and choice of anchoring the specification of fit-based relationships. He argued that in some settings (e.g. fit as profile deviation, fit as mediation, fit as 55

moderation), researchers specify fit that is intrinsically connected to specific criterion variables, but in other settings (e.g. fit as matching, fit as gestalts, fit as covariance) they adopt a criterion-free specification, which has universal applicability. Reviewing contingency hypotheses in strategic management research that use at least one of the congtingency tools in Venkatraman’s (1989) taxonomy, Boyd et al. (2011) find a trend in the 2000s that both subgroups and mediation continued to be more prevalent and that the gap between interaction and other tools like gestalt, covariation, and profile has become progressively larger. In addition, emphasizing the importance of contingency theory in operations management practices, Sousa and Voss (2008) re-categorize the different forms of fit originally categorized by Venkatraman (1989) by using the three approaches proposed by Drazin and Van de Van (1985) as shown in Table 3-1. They find that the use of the interaction approach is lower and the equifinality argument within the system approach is largely absent in operation management studies. Quite similarly, as shown in the review of empirical studies of flexibility, the selection approach is the main stream approach, while the interaction approach and the system approach are quite rare. The following sections adopt all three approaches and discuss alternative forms of fit.

56

The existence of a significant intervening mechanism between an antecedent variable and the consequent variable.

•

•

This perspective is invoked for strategy concepts in which fit is a theoretical defined match between two related variables.

Response Variable

Response Variable

Fit as mediation

•

•

Fit as matching

Context

The fit between the predictor and the moderator is the primary determinant of the criterion variable.

Fit as moderation

Performance

Fit is the integration of pairs of organizational context - response variables which affect performance.

Context

Fit is seen as a basic assumption underlying congruence propositions between the organizational context and response variables.

Interaction

• •

text

•

The degree of internal coherence among a set of theoretical attributes.

Fit as gestalts

•

•

A pattern of covariation or internal consistency among a set of underlying theoretically related variables.

The degree of adherence to an externally specified profile.

Fit as deviation

Performance

Fit as covariation

•

text

•

Response Variables

•

Context

Fit is the internal consistency of multiple contingencies and multiple response alternatives which affect performance characteristics.

System

57

Adapted from: Drazin, R., & Van de Ven, A.H. (1985). Alternative forms of fit in contingency theory. Administrative Science Quarterly, 30 (4), 514-39; Venkatraman, N. (1989). The concept of fit in strategy research: toward verbal and statistical correspondence. The Academy of Management Review, 14 (3), 423-44; Sousa, R., & Voss, C.A. (2008). Contingency research in operations management practices. Journal of Operations Management, 26, 697-713.

Venkatraman (1989)

Sousa, R. and Voss, C.A. (2008)

Drazin and Van de Van (1985)

Selection

Table 3-1 Alternative forms of fit in contingency theory

3.3 Fit in the selection and interaction approaches Interpretation of fit in the selection and interaction approaches focuses on how single contextual factors affect single structural characteristics. The selection approaches adopted by many early structural contingency theories were in fact congruence theories which simply hypothesized that organizational context was related to structure and process (Drazin & Van de Ven, 1985). Thus, from natural selection and managerial selection perspectives, fit as matching is related to the selection approach for viewing fit as a basic assumption underlying congruence propositions (Sousa & Voss, 2008). However, the selection approach does not examine whether this congruence affects performance, while the interaction approaches address how these pairs of contexts and response variables interact to explain performance (Drazin & Van de Ven, 1985). Thus, fit as mediation and fit as moderation, as categorized by Venkatraman (1989), which explain variations in organizational performance, manifest themselves as corresponding forms of fit in the interaction approaches (Sousa & Voss, 2008).

3.3.1

Fit as matching

Fit as matching simply defines a match between the context and the response variable, which is developed independent of any performance anchor (Venkatraman, 1989). It is on the basis of the natural selection model which, by developing the strongest argument for an environmental perspective, posits that environmental factors select those organizational characteristics that best fit the environment (Aldrich, 1971). By using the term natural ecological selection, Aldrich and Pfeffer (1976) emphasize that the process of organizational change controlled by the environment does not necessarily involve progress to more complex or higher forms of social organization or to better organizations; rather, it simply means that the social organizations are moving toward a better fit with the environment. Many studies using the early structural contingency theory support that organizations adapt their structure in order to maintain fit with changing contingency factors of environment, size and strategy so as to attain high 58

performance (Burns & Stalker, 1961; Chandler, 1962; Lawrence & Lorsch, 1967; Child, 1975). For example, Nonaka and Nicosia (1979) suggest that centralization of the marketing department matches a certain and homogeneous environment while decentralization matches an uncertain and heterogeneous environment.

Based on a natural selection model, the condition of fit as matching between contingency factors such as environmental uncertainty and different kinds of flexibility shows the conditions or assumptions underlying congruence propositions. Therefore, focal firms enjoy virtually no control over exogenous factors but have to match their flexibility decision making processes to the demands of their external environments. Most empirical studies testing drivers of flexibility adopt this selection approach (Swamidass & Newell, 1987; Vickery et al., 1999). They simply test whether correlation or regression coefficients of environmental uncertainty on flexibility is significant. Venkatraman (1989) summarizes three related analytical schemes: (a) the deviation score analysis that the absolute difference between the standardized scores of two variables indicates a lack of fit; (b) the residual analysis (regression) that the residuals from the regression of one variable on the other are used to reflect fit; and (c) the analysis of variance (ANOVA) that is typically used to test for interaction. All three are appropriate for specifying simple bivariate fit rather than a larger system of relationships.

3.3.2

Fit as mediation

Generally speaking, the mediation perspective specifies the existence of a significant intervening mechanism between an antecedent variable and the consequent variable (Venkatraman, 1989). In this case, fit as mediation shares with fit as matching the same theoretical foundation of natural selection. But the mediation perspective decomposes the effects that contingency factors have on firm performance into direct effects versus indirect effects as shown in Figure 3-2. Thus, observed variations in performance can be explained in terms of accompanying variations in structure or conduct. Take the classical industrial organization economics paradigm of “structure ė strategy ė performance” (Bain, 1968) as an example, which is used to test the role that firm-level strategic actions have in influencing the relationship between market-structure characteristics and firm performance. If it is a complete mediation model, firm conduct 59

plays a critical role in translating market-structure opportunities into firm performance. If it is a partial mediation model, it addresses that firm performance is a function of both structural factors and strategic choice.

Strategy

Context

Direct effect

Performance

Figure 3-2 A schematic representation of fit as mediation Adapted from: Venkatraman, N. (1989). The concept of fit in strategy research: toward verbal and statistical correspondence. The Academy of Management Review, 14 (3), 423-44.

Although the empirical studies of flexibility also consider its performance, the effect is examined separately from the drivers or antecedents of flexibility. Thus, the results only illustrate direct effects rather than indirect effects on performance. In fact, the situations of fit as mediation neglected in these studies could include, for example, the mediation of flexibility between contingency factors and performance and between network embeddedness and performance. The former still emphasizes that the match between environment and strategy leads to superior performance, but in the later condition, network embeddedness is treated as a less controllable factor for other channel members. Although a network could be treated as an uncontrollable situation in which the participants are embedded, the distribution network is often recognized as a designed structure from the perspective of the manufacturer as a focal firm. So the former situation is much more important for fit as mediation in distribution channels. A common method to test such fit is within a path-analytic framework, which includes two fundamental issues: the distinction between complete versus partial mediation and the test for the direct and indirect performance effects of fit both using Sobel test (Venkatraman, 1989). More than two regression mediation studies for every structural equation modeling study in a pool of five management journal (Wood, Goodman, Beckmann, & Cook, 2008). 60

3.3.3 Fit as moderation

By assuming that discussions are made correctly, the natural selection model argues that those organizations that fit the environment will survive and that there is no need for them to be concerned with the processes by which such an organization-environment match is achieved (Aldrich & Pfeffer, 1976). Therefore, the natural selection model leaves out questions about how decisions are made in organizations. But the organization could be active, and capable of changing, as well as responding to the environment, in which case the former is considered as important, or even more important, than the latter (Aldrich & Pfeffer, 1976). It rejects the idea central to structural contingency theory that the organization is a rational instrument for goal attainment, but holds that organizations are dependent upon external resources and seek to manage them through a variety of means (Pfeffer & Salancik, 1978). Therefore, the impact of strategy varies across the different levels of environments, termed here as the moderator. In more general terms, a moderator can be viewed categorically or characteristically (Venkatraman, 1989), and it will affect the strength of relationships between strategy variables and performance (Prescott, 1986) (See Figure 3-3).

Context

Strategy

Performance

Figure 3-3 A schematic representation of fit as moderation Adapted from: Venkatraman, N. (1989). The concept of fit in strategy research: toward verbal and statistical correspondence. The Academy of Management Review, 14 (3), 423-44.

The early marketing studies in channel management using contingency theory emphasize environmental determinism in terms of a match between task/policy environments and conduct/structure (Stern & Reve, 1980; Zeithaml & Zeithaml, 1984), but later some researchers have begun to take the moderation approach into consideration. For example, McKee, Varadarajan and Pride (1989) treat market 61

volatility as a moderator between four types of organization strategy and organization performance. Ruekert, Walker and Roering (1985) suggest that macro-environmental conditions and task characteristics moderate the relationship between transactional form/structure of activity and performance of the task. Although rarely examined in previous empirical studies of flexibility, fit as moderation by contingency factors could involve environmental factors moderating the effect of flexibility on performance or the effect of network embeddedness on flexibility. This kind of fit is usually tested by moderated regression analysis (MRA) and subgroup analysis, where Venkatraman (1989) emphasizes four issues for attention: “(a) the distinction between form and strength of moderation, (b) the role and impact of multicollinearity, (c) the comparison of main versus interaction effects, and (d) the requirement of partialling out quadratic effects for testing the moderating effects” (p. 426).

3.4 Fit in the system approach Fit as selection and fit as interaction treat the anatomy of an organization as being decomposable into elements examined independently, but the knowledge can be aggregated to understand the whole organizational system, which is a systems approach reacting against reductionism (Drazin & Van de Ven, 1985). Therefore, many contingencies, structural alternatives, and performance criteria are addressed simultaneously to understand the internal consistency of context-response performance relationships. Sousa and Voss (2008) categorize fit as covariance, fit as gestalts and fit as deviation in a system approach, which depicts fit as a feasible set of equally effective alternative designs, with each design internally consistent with its structural pattern and with each set matching to a configuration of contingencies facing the organization (Drazin and Van de Ven, 1985). However, all three forms of fit in the system approach are absent from empirical studies of flexibility.

3.4.1 Fit as gestalts

When fit is conceptualized and specified using only two variables, it is possible to invoke alternate perspectives that have precise functional forms, but when many 62

variables are used, it is necessary to identify a less precise gestalt mechanism (Venkatraman, 1989). The role of gestalts has been best described by Miller (1981): “Instead of looking at a few variables or at linear associations among such variables we should be trying to find frequently recurring clusters of attributes or gestalts” (p. 5). As shown in Figure 3-4, the position of the fit as gestalts perspective represents how the nature of internal congruence among a set of strategic variables differs across businesses that perform differently. Such a perspective has led strategy to be conceptualized as the combination (profile) of environmental, contextual, and structural elements affecting an organization at any time (Venkatraman & Camillus, 1984).

Under a gestalt mechanism, an organization in a specific context will only perform well if it emphasizes a strategy that fits its designed structure and chosen performance outcomes. Therefore, organizations that attempt to satisfy too many distinct kinds of flexibility strategies will perform poorly; rather, internal coherence among the sets of different types of network structures, different types of flexibility strategies, and different types of performance requires fit as gestalts. Venkatraman (1989) suggests two major analytical issues regarding fit as gestalts: (a) the descriptive validity which requires that the gestalts be interpretable in terms of the theoretical positions implied by fit; and (b) the predictive validity for establishing the performance implications of fit and demonstrating multiple configurations of equally successful strategies.

Context Structure A

Function A

Performance A

Structure B

Function B

Performance B

Structure C

Function C

Performance C

Figure 3-4 A schematic representation of fit as gestalts Adapted from: Venkatraman, N. (1989). The concept of fit in strategy research: toward verbal and statistical correspondence. The Academy of Management Review, 14 (3), 423-44. 63

3.4.2 Fit as covariation

Fit as covariation is a pattern of covariation or internal consistency among a set of underlying theoretically related variables, and it can be best described through an illustration (Venkatraman, 1989). This type of fit assumes that organizations would most likely engage in the development of either one functional demand or functional demands that are not in conflict, with the purpose of cataloging the alternative structures that would satisfy each function. Therefore, if there are several consistent functional demands, a strong covariance among them will lead to superior performance, but when the latitude of structural options is so constrained that there is only one option, called an ideal profile, good performance will not arise.

Only a few studies apply this methdology. For instance, Hult and Ketchen (2001) use resource-based theory to examine how four capabilities combine to create a unique advantage for a firm. Similarly, Tippins and Sohi (2003) identify three attributes that contribute to a firm’s information technology competency. Under the assumption of functional demands without conflicts, covariation among the concrete dimensions of flexibility means that a higher level of all of the dimensions will lead to a higher level of flexibility, which, in turn, leads to a higher level of overall performance. As shown in Figure 3-5, the position of fit as covariation within the classificatory framework differs from the position of the perspective of fit as gestalts in relation to the degree of specification of the functional form. Therefore, if the degree of conflict in different types of flexibility is high, fit will be acted on as gestalts; but if it is low, fit will be acted on as covariation.

64

Context Function A

Function B

Coalignment

Function C

Structure D

Performance D

Figure 3-5 A schematic representation of fit as covariance Adapted from: Venkatraman, N. (1989). The concept of fit in strategy research: toward verbal and statistical correspondence. The Academy of Management Review, 14 (3), 423-44.

3.4.3 Fit as deviation

Fit as profile deviation states that the degree of adherence to an externally specified profile has a significant effect on performance (Venkatraman, 1989). Thus, deviations from ideal-type designs should result in lower performance and the source of the deviation originates in conflicting contingencies. Thus, Venkatraman (1989) explains that: “If an ideal strategy profile is specified for a particular environment, a business unit’s degree of adherence to such a multidimensional profile will be positively related to performance if it has a high level of environment-strategy coalignment. Conversely, deviation from this profile implies a weakness in environment-strategy coalignement, resulting a negative effect on performance” (p. 433). He also emphasizes that: (a) developing the ideal profile, (b) adding differential weights for the multiple dimensions, and (c) using a baseline model to assess the power of the test are all critical issues for analyzing fit as deviation. For example, Zajac, Kraatz and Bresser (2000) model expected levels of change and measured firm departures from the predicted change level. Although it is a potential gap in existing studies of flexibility, to identify an ideal profile is beyond the scope of research questions in the present study.

65

3.5 Summary Using a typology of partism and holism in sample analysis and selection of variables, this chapter first summarizes the recent empirical studies of flexibility and then points out the limitation of neglecting different contexts under which these models operate. Thus, the main objective of the literature review for this chapter is to revisit contingency theory, a theory that considers the fit among organization construct, behaviors and environments and which explains the basic fit mechanisms for environmental contingency underlying drivers, sources, and performance of distribution flexibility. To achieve this, this chapter specifies alternative forms of fit in the selection, interaction and system approaches. Together with the review of relevant literature on flexibility, these mechanism domains provide the foundation for the conceptual models developed in the next chapter (Chapter 4), a core model examining fit in the selection and interaction approaches and a complementary model interpreting fit in the system approach.

66

4

MODEL DEVELOPMENT

4.1 Introduction Based on contingency theory, Chapter 3 discusses alternative forms of fit in the selection, interaction and system approaches. Figure 4-1 depicts fit mechanisms based on the conceptual framework proposed by Venkatraman (1984; 1989) which is explained in the former section. In this framework, fit as matching, fit as mediation and fit as moderation refers to the state in which the focal firm can survive in an uncertain and heterogeneous environment. Fit as gestalts and fit as covariance refer to the internal consistency which holds that the performance of the focal firm can be achieved through a focus or coalignment strategy of flexibility in different activities as well as a multiplicity of different distribution network structures. Accordingly, this dissertation splits the conceptual model into two parts: a core model and a complementary model. All of the hypotheses in the core model illustrated in Section 4.2 are about forms of fit in the selection and interaction approaches, while all of the propositions in the complementary model explained in Section 4.3 are about forms of fit in the system approach.

Distribution environment Fit as Moderation

Distribution Network

Fit as Matching

Distribution Performance

Distribution Flexibility Fit as Mediation

Fit as Mediation

Fit as Gestalt Strong & dense network Neutral network … ...

Fit as Moderation

Fit as Covariance Logistics flexibility Relationship flexibility … ...

Fit as Gestalt Short-term performance Long-term performance … ...

Figure 4-1 The framework of fit mechanisms for environmental contingency 67

4.2 Core model: fit in the selection and interaction approaches Fit in selection and interaction approach, which represents the relationship between the firm and its environment, also functions in terms of adapting distribution flexibility so as to cope with uncertainty and heterogeneity in the external environment or adapting a distribution network to address the requirements of distribution flexibility so as to enhance distribution performance (Chakravarthy, 1982). As shown in Figure 4-2, this study considers three parallel approaches to explain alternative forms of fit in selection and interaction approach: (a) fit as matching (the relationships between distribution environment and distribution flexibility), (b) fit as mediation (distribution flexibility as a mediator between network embeddedness and distribution performance), and (c) fit as moderation (distribution environment moderates not only the effect of distribution flexibility on distribution performance but also on the effect of network embeddedness on distribution flexibility). The following sections will illustrate the hypotheses established according to these fit mechanisms.

68

External Sources

Tie Density

H9

H3a

H7b

Environment Drivers

Environmental Heterogeneity

H10

b H2

Distribution Flexibility

H12

Relationship Flexibility

Logistics Flexibility

H11

a H8

H4 a

H8c

H5

Relationship Satisfaction

H6

Performance of distribution flexibility

Short-term Performance

H8b

Distribution Service Quality

H4b

Long-term Performance

Link Duration

Not hypothesized controlling paths

Hypothesized moderating paths

Hypothesized main paths

Figure 4-2 The core model of fit mechanisms in the selection and interaction approaches

Sources and drivers of distribution flexibility

Internal Sources

Firm Resource

Firm Age

Tie Strength

H1 a

H1b H2a

Environmental Uncertainty

H3 b a H7

69

4.2.1 Environmental drivers and distribution flexibility: fit as matching

The hypotheses of fit as matching concern relationships between different types of drivers of distribution flexibility and different dimensions of distribution flexibility. According to the natural selection model, environmental factors select those organizational characteristics that best fit the environment (Aldrich, 1971), which taken to the extreme suggests that the firm enjoys virtually no control over exogenous factors but must instead adapt its decision making processes to the demands of the external environment. This state of adaptation lies at the core of a traditional contingency theory of how a business organization survives the conditions of its environment (Chakravarthy, 1982). Pioneering research based on contingency theory built upon the central concept of uncertainty which sought to capture the environment’s effects on the organization’s functioning (Downey & Slocum, 1975). Uncertainty has been used both as a descriptor of the state of the organizational environment and as a descriptor of the state of a decision maker who lacks critical information about the environment (Milliken, 1987). The former usage is external and shared across a set of firms, while the latter is largely internal and controllable.

Invoking an adaptive view of organizations, Swamidass and Newell (1987) claim that the operations management literature has paid little attention to the uncertaintymanufacturing strategy connection and then argue that manufacturing flexibility offers the capability to cope with environmental uncertainty. In general, the more that task environments move away from being rich, homogeneous, stable, dispersed, and placid, the more that uncertainty can be expected to increase (Achrol et al., 1983), and the more flexibility that is demanded from the system (Swamidass & Newell, 1987). A number of other researchers support this view and suggest that flexibility is a core manufacturing strategy adaptation to environmental uncertainty or dynamism (Gerwin, 1993; Beach et al., 2000; Ward & Duray, 2000; Dreyer & Grønhaug, 2004). Expanding from intraorganizational to inter-organizational functions, the adaptation to environmental uncertainty also arises in studies of value chain or supply chain flexibility (Vickery et al., 1999; Zhang et al., 2002; Pujawan, 2004; Tachizawa & Thomsen, 2007). Environmental uncertainty manifests itself in several dimensions in these studies (e.g. as supply uncertainty, as demand uncertainty, and as technology uncertainty), for which a given dimension of supply chain flexibility is seen as a reaction to a specific kind of 70

uncertainty. As one dimension of supply chain flexibility, the present study claims that distribution flexibility adapts quite broadly to the environmental uncertainty found in distribution channels. Thus, we hypothesize that:

H1. Higher uncertainty in distribution channels will enhance (a) logistics flexibility and (b) relationship flexibility.

However, as the previous section notes, Pagell and Krause (1999; 2004) argue that there is no significant relationship between environmental uncertainty and operational flexibility. One of the reasons for this inconsistency is that factors causing a need for flexibility are not only manufacturing process-related but also market related as buyers have become more sophisticated and are demanding more customization (Kara & Kayis, 2004). Furthermore, there are also other drivers of flexibility in addition to uncertainty. Many authors have introduced as alternative causes of flexibility such market related factors as the variability of demand, shorter life-cycles of both products and technologies, wider ranges of products, increased customization, and shorter delivery times (Kara & Kayis, 2004). Thompson (1967) proposes two sources of environmental uncertainty: heterogeneity and stability, while Nonaka and Nicosia (1979) divide the information generated by the environment into two dimensions: stability-uncertainty and

homogeneity-heterogeneity.

As

another

dimension

of

the

environment,

heterogeneity, is evidenced by differences in competitive tactics, customer tastes, product lines, and channels of distribution across the firm’s respective markets (Miller, 1987). These differences are only significant to the extent that they require different marketing, production, and administrative practices. In particular, downstream channel members and distributors constitute an important and potentially heterogeneous source of information. Thus, we hypothesize that:

H2. Higher heterogeneity in distribution channels will enhance (a) logistics flexibility and (b) coordination flexibility.

4.2.2 Network sources, distribution flexibility and performance: fit as mediation

As the previous discussion notes, fit as mediation shares with fit as matching the same theoretical foundation as the natural selection model. In the theoretical framework, fit as 71

mediation specifies the existence of a significant intervening mechanism in which distribution flexibility as a process intervenes between network embeddedness and performance. This mechanism is further interpreted into the main routes as is shown in Figure 4.2 which outlines the hypotheses to be presented shortly: (a) the effects of relational embeddedness and structural embeddedness on logistics flexibility which in turn influences short-term performance; and (b) the effect of relational embeddedness and structural embeddedness on relationship flexibility which in turn influences not only short-term but also long-term performance. x

The effect of network embeddedness on relational outcomes

A common theme of strategic network research has been to examine the impact of network involvement on organizational performance, even though the outcomes of focus need not be restricted to organizational outcomes but also can include outcomes of collectivities of organizations (Provan et al., 2007). Thus, researchers at the network level may choose to study the effect of multilevel actions and structures on network level outcomes which, themselves, frequently consist of dyadic relationships between organizations (Burt, 1992; Uzzi, 1997; Ahuja, 2000). While there have been advances in assessing the performance of alliances, few of these efforts have considered the impact of the networks in which firms are embedded on the relative performance of their alliances (Gulati, 1998). Instead, dyad-focused research often has utilized characteristics and attributes of organizations to explain their relationship with other organizations (Provan et al., 2007). Such a research focus also dominates research in relationship marketing. For instance, synthesizing empirical research in relationship marketing in a meta-analytic framework, Palmatier et al. (2006) clarify how the effects of relationship marketing strategies on outcomes such as loyalty and cooperation are fully mediated by one or more of the relational constructs of trust, commitment, relationship satisfaction and/or relationship quality. However, trust, commitment and dependence are considered as components of relationship magnitude (Golicic & Mentzer, 2006), but should not be construed as relational outcomes in a parallel position with relationship satisfaction but should rather be construed as potential antecedents. Zaheer, McEvily and Perrone (1998) test the effects of both interpersonal trust and interorganizational trust on supplier performance via the level of conflict and costs of negotiation in the exchange relationship. However, the potential confusion about the effects of relational constructs 72

may also arise in part from the prevalent tendency to analyze a collection of two-party relationships rather than to analyze network structures. Above all, an extension focusing directly on dyadic relational performance and the effect of embeddedness in a strategic network on such outcomes should help advance the understanding of the nature and importance of embedded ties (Gulati, 1998).

Beyond the scope of business networks, the direct effect of network embeddedness on dyadic relationships is easy to understand in the broader case of social networks. For example, perceived support from the subject’s and the partner’s networks of family and friends will affect romantic involvement in ongoing relationships in terms of emotional attachment, the amount of interaction, and the expectation that the relationship will continue (Parks, Stan, & Eggert, 1983). Ties between boundary spanners (which include the members of the audit committee of a firm’s board of directors, the chief executive officer (CEO), the chief financial officer (CFO) and the chief accounting officer (CAO)) play a major role in the maintenance of auditor-client relationships (Seabright, Levinthal, & Fichman, 1992). However, compared with more general social networks, the situation in business networks is much more complex since the macro-environment is largely uncontrollable and demands adaptation by firms. Thus, the efficiency of a particular network structure depends on the environmental conditions in which firms operate (Burt, 1997; Ahuja, 2000; Reagans & Zuckerman, 2001).

Although some researchers have drawn attention to the contingent benefits of network structure (Burt, 1997; Podolny & Baron, 1997), most studies are at the level of analysis of individual persons and only few consider network structural contingencies at the level of the firm. Those contingency factors that have been examined at the firm level include industry development (Walker et al., 1997), the nature of the industry (Rowley et al., 2000), internal firm capabilities (Zaheer & Bell, 2005), and the type of innovation activity that firms undertake (Vanhaverbeke et al., 2009). Only a study by Koka and Prescott (2008) claims that environmental change is an important contingency factor in the association between interfirm network structure and relative performance. In particular, environmental uncertainty plays a critical role in the distribution channel environment (Achrol & Stern, 1988). Although the structure of social networks or alliance networks could adapt to a macro environment from a dynamic evolving perspective, vertical distribution networks are usually explicitly designed by the focal 73

firm in light of industry norms and standards and are often quite rigid to change in the short term. Given a relatively stable distribution network structure as a source of information, resources, and opportunities, logistics functions and other strategies become the only potentially flexible mechanisms for adaptation to a changing environment. Thus, in the context of distribution channels, relational outcomes are not likely to arise directly from the network in which the firm is embedded, but rather from other strategic responses such as distribution flexibility. x

Logistics flexibility as a mediator between network embeddedness and

relational outcomes

In a network, tie strength reflects the closeness of a relationship between partners, and increases with frequency of interaction and communication (Hansen, 1999). Strong ties are more likely to promote in-depth communication as well as valuable and accurate information exchange (Hoang & Antoncic, 2003). The main reason for these effects is that strong ties incrementally enhance trust and reciprocity (Powell, 1990; Larson, 1992). High levels of trust between partners are positively and significantly related to access to rich information and knowledge transfer (Inkpen, 2000; Szulanski, Cappetta, & Jensen, 2004). Moreover, information transferred is fine-grained and of high-quality (Krackhardt, 1992; Rowley et al., 2000), while the knowledge transferred is uncodified and dependent ‘know-how’ (Hansen, 1999; Kale, Singh, & Perlmutter, 2000). Like strong ties, one of the benefits of dense ties among a group of actors (a closed network) is an environment of strong collective social capital (Coleman, 1988), which promotes shared behavioral norms and cooperation (Rowley, 1997). So for both strong ties and dense ties, the willingness to assist others in facilitating the exchange of fine-grained information and in easing the transfer of tacit knowledge is relevant because it is typically beneficial to both the recipient and the broader community of organizations (Hansen, 1999; Reagans & McEvily, 2003). As has been noted, logistics flexibility is based on efficient transfer from the manufacturer to distribution channel members of a large quantity of firm-specific information and internal knowledge concerning physical inventory levels, locations, and other physical characteristics. Therefore, with stronger ties or denser ties, the manufacturer as the hub firm is likely to acquire larger quantities of information and facilitate the flow of information throughout the network (Provan, 1993). The firm would also spend more time articulating complex knowledge with 74

current partners (Hansen, 1999; Koka & John, 2002; Koka & Prescott, 2008), making it travel faster and spread more widely among network members. During this process, firm-specific capabilities that are difficult to imitate will be promoted and thus the level of logistics flexibility will increase.

In contrast, both weak ties and structural holes bring in new links and novel information (Granovetter, 1973; Burt, 1992). On one hand, however, the necessary interactions for transferring complex knowledge in weak ties are absent in this situation, thus requiring a longer time to transfer knowledge that is highly uncodified and dependent (Hansen, 1999). On the other hand, although a sparse network provides a firm with the ability to efficiently obtain and broker information and resources, such a structure does not generate a governance mechanism to impede opportunism (Rowley et al., 2000). Like a network of weak ties, the ability to develop strong cohesive ties in such a network is attenuated and thus the transfer of tacit knowledge is limited (Koka & Prescott, 2008). Compared with network prominence which results in a dense cohesive network, an entrepreneurial position operationalized using structural holes is also less conducive to the quick mobilization and coordination of resources (Koka & Prescott, 2008). Above all, the marginal benefits of strengthening existing linkages will be low and the marginal costs of strengthening existing links will be relatively high because of the difficulty of managing and facilitating rich and complex information flow and information resources in a sparse network with weak ties. Therefore, in a situation of low relational and structural embeddedness, the willingness to change delivery and logistics processes to match dynamics in the market will be lower than in a situation of high relational and structural embeddedness or, in other words, there will be less logistics flexibility. So, as a positive statement, we hypothesize, ceteris paribus, as follows:

H3. (a) The stronger the distribution network ties and (b) the denser the distribution network ties, the higher will be the level of logistics flexibility.

Logistics flexibility in distribution channels involves material and information flow between the focal firm and channel members and demands agility in activities such as warehousing, inventory and ongoing transportation (Swafford et al., 2006). Responding to those uncertainties that arise during delivery of physical products which directly affect the supply or distribution ability of the market and influence downstream 75

distributors’ operations, logistics flexibility minimizes operational costs, saves delivery time and enhances the consistency of delivery. For example, by adjusting storage capacity or balancing inventory level, the availability of products will be enhanced; and by adjusting delivery capacity, delivery schedule or transportation modes, products could be delivered in a shorter time and in better condition. Therefore, the higher the level of logistics flexibility, the higher will be the level of distribution service quality.

Empirical studies of customer satisfaction in the business-to-consumer market all support the theory that perceived quality has a significant positive effect on overall customer satisfaction (Anderson, Fornell, & Lehmann, 1994; Fornell, Johnson, Anderson, Cha, & Bryant, 1996). Arguably, in the context of distribution channels, the higher the level of distribution service quality which represents the efficiency, accuracy and consistency in the delivery, the more satisfied with the relationship will be the channel members or end customers. Since logistics flexibility may enhance distribution service quality, it may have an indirect effect on relationship satisfaction. Nevertheless, distribution flexibility may have direct effects on relationship satisfaction. First, arguably, the higher the level of logistics flexibility, the higher will be the level of relationship satisfaction. One reason is that by being more responsive to product delivery demands, the focal firm provides evidence that it cares about the relationship and is willing to cooperate and make sacrifices (Ganesan, 1994; Doney & Cannon, 1997), which in turn leads to a higher perception of satisfaction with the relationship (Jonsson & Zineldin, 2003). Furthermore, communication with channel members or end customers about their specific demands will also increase satisfaction with the relationship in that it can lead to a shared understanding of performance outcomes and expectations (Selnes, 1998). Thus, the higher the level of logistics flexibility, the higher will be the level of channel member satisfaction with the relationship with additional influences on purchasing loyalty intentions as indicated in the Satisfaction-Profit Chain (Anderson & Mittal, 2000). Since higher customer loyalty will reduce the need to find new customers and instead allow effort to focus on retaining existing customers, it will result in more experience in dealing with buyer-seller relationships and higher confidence in each party’s behavior (Levinthal & Fichman, 1988; Anderson & Weitz, 1989; Doney & Cannon, 1997). In other words, relationship satisfaction will lead to longer duration of relationships. Based on this analysis, we hypothesize that: 76

H4. Logistics flexibility will have a significant positive effect on (a) distribution service quality and (b) relationship satisfaction.

H5. Distribution service quality will have a significant positive effect on relationship satisfaction.

H6. Relationship satisfaction will have a significant positive effect on link duration. x

Relationship flexibility as a mediator between network embeddedness and

relational outcomes

The marketing literature studies the concept of flexibility as a coordinative mechanism for channel governance from the perspective of relational norms which includes relationship flexibility as a dimension - “a bilateral rather than unilateral expectation of willingness to make adaptations as circumstances change” (Heide & John, 1992) (p. 38). Many empirical studies investigate the relative effects of relational factors on flexibility as one of the three major relational behavior forms (flexibility, information exchange and solidarity). Most of these studies find that the effects on flexibility of mutuality, trust, relational governance and specific investments are significantly positive (Bello & Gilliland, 1997; Handfield & Bechtel, 2002; Ivens, 2005; 6ȐQFKH] 3érez, 2005; Wang & Wei, 2007). The main argument for these effects is based on the transaction cost analysis (TCA) theory that investments in nonredeployable assets can promote sustained cooperation which in turn counterbalances the fear of opportunism by showing good faith embodied in reciprocal agreements. Thus, formal safeguards can be ‘relaxed’ further, which in turn allows initial contractual agreements to be incomplete (Williamson,

1985;

Parkhe,

1993).

Therefore,

strong

ties

which

promote

interorganizational trust and reciprocity by investing in more nonredeployable assets based on the linkages between the focal firm and their channel members will safeguard the focal firm from opportunism and make adjustments of contracts or agreements much easier, which in turn generally enhances the level of relationship flexibility.

However, the association between dependence and flexibility is not consistent. For example, Handfield and Bechtel (2002) find a linear and negative effect of perceived buyer-dependence on supplier responsiveness because of limited alternatives and high 77

complacence. Also, 6ȐQFKH]DQG3érez (2005) hypothesize that supply chain flexibility is negatively related to higher levels of perceived interdependence between firms in the supply chain. However, Sezen and Yilmaz (2007) find that dependence on the supplier exerts a stronger positive effect on dealer flexibility than does trust in the supplier. Those differences could be explained by early research by Heide (1994) which distinguishes between the effects of symmetric dependence and unilateral dependence. In a condition of high symmetric dependence, both parties have constrained the alternatives open to them, which represents a mutual safeguard and a collective incentive to maintain the relationship (Williamson, 1985). Therefore, symmetric dependence encourages flexible adjustment processes. In contrast, in a condition of unilateral dependence, the potential for opportunism will represent a disincentive for the dependent party to show forbearance or flexibility in the first place (Heide, 1994). In a sparse distribution network, where the number of distributors is low and horizontal connections are few, the interdependence between the focal firm and the distributors will be high and investments in relationship-specific assets will be greater, which leads to a high cost of opportunism and a high level of flexibility in adjusting relationships in response to changing environments. Thus, we hypothesize that:

H7. (a) The stronger the distribution network ties and (b) the sparser the distribution network ties, the higher will be the level of relationship flexibility.

Distinct from resource-based logistics flexibility, relationship flexibility is a kind of relational norm (Heide & John, 1992). When firms exhibit flexibility in their behavior toward partners, they implicitly communicate their good faith and good intentions in the relationship (Johnson, 1999). Due to this positive and pro-relationship message, the focal firm is able to apprise partners of potential situations that might influence operations, and in turn partners provide the focal firm with guarantees of future volumes and prices, which may be tied to their cost reduction and quality improvement efforts (Heide & John, 1992). As a result, as Bello and Gilliland (1997) note, “the establishment of bilateral cooperation, in the form of flexible adjustments made by both parties, is likely to increase the effectiveness and efficiency with which channel tasks are performed” (p. 28). Thus, the higher the capability of adjusting ongoing relationships, the better will be the distribution service quality. 78

From a subjective perspective, when channel members or end customers perceive suppliers to be more cooperative and willing to solve problems under changing circumstances, they are more confident of the supplier’s ability and desire to work together and are thus more satisfied with the relationship (Roath & Sinkovics, 2006; Lai, 2007). Moreover, when communicating how to make adjustments to agreements or contracts, the frequency and quality of information exchange determines the degree to which the partners both understand each other’s goals and coordinate their efforts to achieve those goals and attain a relationship that is mutually satisfying (Jonsson & Zineldin, 2003). Therefore, the higher the level of relationship flexibility, the higher will be the level of channel member satisfaction with the relationship.

While these are short-term performance outcomes associated with relationship flexibility, relationship flexibility may also affect long-term performance outcomes. At a higher level of relationship flexibility, uncertainties in the relationship between the focal firm and downstream distribution channel members or end customers will be reduced; and they will increasingly desire to sell or purchase the focal firm’s products or services as well as engage in other businesses, in turn allowing distribution channel members or end customers to become more loyal to the focal firm and thus make both sides willing to maintain a long-term relationship. Above all, we hypothesize as follows:

H8. Relationship flexibility may have a significant positive effect on (a) distribution service quality, (b) relationship satisfaction and (c) link duration.

4.2.3 Environmental

drivers,

network

sources,

distribution

flexibility

and

performance: fit as moderation

Although fit as mediation represents the existence of (a) the effect of network embeddedness on distribution flexibility and (b) the effect of distribution flexibility on distribution performance, the strength of each such relationship may not always be high. Arguably, their strength depends on the moderating effects of both environmental uncertainty and environmental heterogeneity as is illustrated in Figure 4.2. Such fit as moderation arises in a situation when strategic choice and environmental determinism are both high, defining a turbulent context for adaptation (Hrebiniak & Joyce, 1985). Contingency theory is used to explain these moderating effects. Arguably, the 79

explanation of moderating effects for logistics flexibility is mainly based on information theory, while a basis for distinctive moderating effects for relationship flexibility lies in general information theory and transaction cost theory respectively. x

Moderators in the effects of network embeddedness on distribution flexibility

While H3 to H8 make general claims about how logistics flexibility and relationship flexibility mediate the effect of network embeddedness on relational outcomes, they implicitly assume a constant level of environmental conditions for such dynamic market factors. However, the efficiency of a given network structure depends on the environmental conditions in which firms operate (Burt, 1997; Ahuja, 2000; Reagans & Zuckerman, 2001). Each focal firm may in fact face systematically different environmental conditions which not only elicit different strategic responses but also may influence the extent to which such responses are themselves influenced by other antecedents, such as, in this case, network antecedents. Such contingent effects are congruence propositions. Traditional structural contingency theory has dominated the study of organizational design and performance since the 1960s and natural selection perspectives have surfaced and provide some justification for viewing fit as a basic assumption underlying such congruence propositions between organizational context and structure (Drazin & Van de Ven, 1985). Many studies in organization management emphasize the moderating effects of environmental characteristics on the relationship between organization structure and effectiveness (Anderson & Paine, 1975; Koberg & Ungson, 1987). But instead of effectiveness, the present study focuses on distribution flexibility itself as the dependent variable and tries to examine how the distribution environment moderates the effect of network embeddedness on distribution flexibility.

There are some studies in marketing that use structure-environment contingency theories. For example, Nonaka and Nicosia (1979) view the marketing department as a processor of market information and propose that the best organization for processing such information is one that matches the variety of the market environment. Sinkula (1994) argues that the relationship between market information processing and market information supply is contingent on environmental change. Thus, under conditions of low environmental change, increasing the supply of market information will have little effect on information processing; while under conditions of high environmental change, 80

increasing the supply of market information will result in enhanced information processing. In this sense, an uncertain environment contains low quality information that is of low reliability and has only short time span applicability (Nonaka & Nicosia, 1979). Such an environment could not supply sufficient or appropriate information to develop the capability of processing information underlying logistics flexibility or to deliver the positive and pro-relationship message required by relationship flexibility. However, what no one has examined is how different kinds of information supplied by the distribution network itself could compensate for a lack of high quality information in the external environment to the extent that rich and fine-grained information can be gained through strong and dense ties (Coleman, 1988; Larson, 1992). Thus, under greater environmental uncertainty, a relatively higher level of network embeddedness as a source of information is needed to process and deliver information required for a given level of distribution flexibility. In other words, environmental uncertainty should have a positive moderating effect on the association between distribution network embeddedness and distribution flexibility.

As is explained in the context of H7, since relationship flexibility is a kind of relational norm based on investments in specific assets, the explanation for the effect of network embeddedness on relationship flexibility can also rest on an analysis of the transaction costs of asset specificity under small numbers bargaining. Arguably, environmental uncertainty can also moderate this effect. That is, in an uncertain environment, channel members may have asymmetric information which makes those partners who have greater access to information behave opportunistically. As Parkhe (1993) notes, “the more a partner sees the other party as likely to behave opportunistically, the greater will be such perceived vulnerability, and the greater will be the first partner’s aversion toward making nonrecoverable investments” (p. 805). Thus, under an uncertain environment, higher expected opportunism of partners’ behavior will lead to lower perceived value of specific asset investments from the counterpart. Without such investments, expectations of relationship continuity would be lower and formal contracts would be required as mechanisms to constrain opportunism. However, compared to relational contracting, formal contracting tends to be much less flexible as a safeguard (Carson, Madhok, & Wu, 2006). Above all, the interaction between environmental uncertainty and the value of specific assets should have a negative effect on relationship flexibility. As is explained in the context of H7, higher tie strength 81

represents trust and lower tie density represents interdependence which together yield greater investment in assets that are only applicable in a specific relationship and which lose value when the relationship changes. Thus, to the extent that a strong yet sparse network requires such high relational asset specificity (as was discussed in the context of H7), the interaction between environmental uncertainty and such dimensions of network embeddedness will itself have a negative effect on relationship flexibility. In order words, environmental uncertainty negatively moderates the effect.

The above argument yields the following contingency hypotheses as well as competing hypotheses as noted H9c1, H9c2, H9d1 and H9d2:

H9. Higher uncertainty in the distribution environment will increase the strength of the effects of (a) tie strength on logistics flexibility, and (b) tie density on logistics flexibility, and either (c1) increase or (c2) decrease the strength of the effect of tie strength on relationship flexibility, and either (d1) increase or (d2) decrease the strength of the effect of tie density on relationship flexibility.

Different from environmental uncertainty which concerns quality of information, environmental heterogeneity concerns quantity of information (Nonaka & Nicosia, 1979). The higher the heterogeneity is in the distribution environment, the larger will be the number of sources generating information and the more of information will each source generate. As we explained that logistics flexibility as a capability of information processing and relationship flexibility based on goodwill delivery both require compensatory information from distribution networks when the quality of environmental information is low, this is also true when the quantity of environmental information is low. Thus from this perspective, the higher the heterogeneity is in the distribution environment, the weaker will be the effect of network embeddedness on both logistics flexibility and relationship flexibility. However, based on transaction cost theory, information asymmetry is related to high opportunism. While high quantity of information in the heterogeneous environment will release the pressure of information asymmetry which in turn reduce opportunism. Thus, same argument as H9, the higher the heterogeneity is, the stronger will be the effect of network embeddedness on relationship flexibility. Above all, we propose the following hypotheses and competing ones as noted H10c1, H10c2, H10d1 and H10d2: 82

H10. Higher heterogeneity in the distribution environment will decrease the strength of the effects of (a) tie strength on logistics flexibility, and (b) tie density on logistics flexibility, and either (c1) decrease or (c2) increase the strength of the effect of tie strength on relationship flexibility, and either (d1) decrease or (d2) increase the strength of the effect of tie density on relationship flexibility. x

Moderators in the effects of distribution flexibility on distribution performance

Few studies of flexibility have developed taxonomies that can distinguish among different contexts and that can systematically consider the effects of flexibility on performance outcomes within each flexibility type. Instead, the prevailing doctrine is that flexibility is generally a good policy which always has positive effects on firm performance regardless of context or environmental condition (Swamidass & Newell, 1987; Bello & Gilliland, 1997; Vickery et al., 1999; 6ȐQFKH]  érez, 3 2005 ). Nevertheless, researchers in organization management are well aware of how very different strategies can be used to adapt organizations to distinct environments. These researchers use contingency approaches to identify commonly recurring settings and observe how different structures, strategies and behavioral processes fare in each setting (Hambrick, 1983). The essence of the contingency theory paradigm is that there is no universal set of strategies which are optimal for all businesses, and therefore strategies need to be designed for specific environment contexts.

Most commonly, a contingency theory states that the effective level of some planning variable depends on the level of some environmental variables (Cadeaux, 1994). Miller (1979) suggests that “organizations are complex entities and the relationship between two variables may be influenced by many contextual conditions” (p. 296). Or, in other words, a contingency theory usually involves a theory of environmental moderation that is more explicit than a simple matching theory which claims that somehow organization structures and strategies “match” the environments in which they lie. In strategic management, environments have been specifically viewed as key contingency variables understanding the effects of strategy on performance (Prescott, 1986; Kim & Lim, 1988). Thus, it is more important to determine the conditions under which flexibility, or more particularly, each type of flexibility, can enhance a firm’s performance. As an 83

important contingency variable for distribution channels, environmental uncertainty may not only affect the level of distribution flexibility but also moderate the effect of distribution flexibility on performance.

As referred to in the previous section, information theory depicts an uncertain environment as one of low quality information about the market (Nonaka & Nicosia, 1979). Logistics flexibility offers time efficiency and enhances the consistency of delivery by adjusting storage, inventory, and delivery activities in response to customer requirements. Underlying it is a capability of processing relevant market information from channel members or end customers for the focal firm. Thus, to enhance distribution service quality and retain channel members or end customers in an uncertain environment, logistics flexibility is required as a strong capability of processing information. In contrast to logistics flexibility, relationship flexibility increases

distribution

service

quality

and

relationship

satisfaction

via

the

communication of good faith and intentions regarding the relationships. To the extent that a high quantity of less reliable market information in a short time span would disturb the delivery of this positive and pro-relationship message, relationship flexibility is arguably most effective under a certain environment where market information is both long in time span applicability and high in reliability (Nonaka & Nicosia, 1979).

However, from the perspective of transaction cost theory, the greater the uncertainty in the environment, the higher is the level of opportunism (Williamson, 1985). A higher level of opportunism constitutes a threat to the relationship between the focal firm and its channel members or end customers. As noted earlier, relationship flexibility concerns changes in relationship management, where modifications of original contracts or relational norms would be taken in a long-term oriented manner (Heide, 1994). It restrains partners from opportunism through a socialization process that leads to shared values (Bello & Gilliland, 1997). Thus, to retain channel members or end customers, relatively more relationship flexibility is required when greater environmental uncertainty exists. These arguments based on transaction cost theory compete with the arguments based on information theory. In conclusion, we propose the following hypotheses and their competing alternatives (noted as H11c1, H11c2, H11d1, H11d2, H11e1 and H11e2): 84

H11. Higher environmental uncertainty will increase the strengths of the effects of logistics flexibility on (a) distribution service quality and (b) relationship satisfaction, and either (c1) decrease or (c2) increase the strength of the effect of relationship flexibility on distribution service quality, either (d1) decrease or (d2) increase the strength of the effect of relationship flexibility on relationship satisfaction, and either (e1) decrease or (e2) increase the strength of the effect of relationship flexibility on link duration.

As we explained in H10, the higher the heterogeneity in the distribution environment, the larger will be the quantity of information. Therefore, by processing information, logistics flexibility will be required to enhance short-term distribution performance under a heterogeneous environment. Furthermore, a large quantity of information also promotes the delivery of the positive and pro-relationship message required by relationship flexibility to enhance both short-term and long-term distribution performance. Thus, the greater the heterogeneity, the stronger will be the effects of logistics flexibility and relationship flexibility on their associated performance outcomes. In contrast, from the perspective of transaction cost theory, a greater heterogeneity in the environment indicates a lower level of opportunism. Thus, as explained in H11, the threat to relationships would be smaller and the requirements of relationship flexibility to retain partners would be less. Above all, we propose the following hypotheses and competing ones as noted H12c1, H12c2, H12d1, H12d2, H12e1, and H12e2:

H12. Higher environmental heterogeneity will increase the strengths of the effects of logistics flexibility on (a) distribution service quality and (b) relationship satisfaction, and either (c1) increase or (c2) decrease the strength of the effect of relationship flexibility on distribution service quality, either (d1) increase or (d2) decrease the strength of the effect of relationship flexibility on relationship satisfaction, and either (e1) increase or (e2) decrease the strength of the effect of relationship flexibility on link duration.

To make it clear, Table 4-1 summarizes all the competing hypothesis H9 – H12 as follows: 85

Table 4-1 Summary of competing hypotheses for environmental moderation of relationship flexibility effects Moderators

Environmental Uncertainty

Environmental Heterogeneity General

General information theory

Transaction cost theory

Low information quality

High opportunism

High information quantity

Low opportunism

Tie strength ĺ Relationship flexibility

H9c1(+)

H9c2(-)

H10c1(-)

H10c2(+)

Tie density ĺ Relationship flexibility

H9d1(+)

H9d2(-)

H10d1(-)

H10d2(+)

Relationship flexibility ĺ'LVWULEXWLRQVHUYLFH quality

H11c1(-)

H11c2(+)

H12c1(+)

H12c2(-)

Relationship flexibility ĺ5HODWLRQVKLS satisfaction

H11d1(-)

H11d2(+)

H12d1(+)

H12d2(-)

Relationship flexibility ĺ/LQNGXUDWLRQ

H11e1 (-)

H11e3(+)

H12e1(+)

H12e2(-)

Main effects

information theory

Transaction cost theory

4.3 Complementary model: fit in the system approach As the Chapter 3 discussion claims, the system approach treats fit as a feasible set of equally effective alternative designs. In other words, organizations are so different from each other that no two organizations will use the same approach to strategy implementation. Instead of taking contingency factors as antecedents or moderators which affect or interact with other constructs in the whole system, the system approach focuses on internal consistency in a given context rather than on external consistency. In a given context of quality and quantity of information generated (See Table 4-2), fit in the system approach represents various combinations of flexibility in different activities, different network structures, and different performance orientations. It 86

proposes that there are no generic solutions to the “channel performance metric paradox” and many possible different combinations of channel performance characteristics exist, each allowing successful implementation for any of the flexibility strategy types (Valos & Vocino, 2006).

Table 4-2 Typology of information in the context Quality

Low

High

Uncertain and homogeneous

Stable and homogeneous

task environment

task environment

Uncertain and heterogeneous

Stable and heterogeneous

task environment

task environment

Quantity Low

High

4.3.1

Focused flexibility strategies in different contexts: fit as gestalts

The position of the fit as gestalts perspective represents how the nature of internal congruence among a set of strategic variables differs across businesses with different performance. Under this perspective, flexibility in different functions are assumed to be conflicting functional demands, and thus we try to find frequently recurring clusters of attributes or gestalts instead of looking at a few variables or at linear associations among such variables (Miller, 1981). In particular, this perspective predicts that focal firms who attempt to simultaneously satisfy all demands will perform poorly. Therefore, in the present context, once a focused flexibility strategy is chosen, a network structure can be designed to maximize that function, and relatively high performance will result. Consistent with the hypotheses about fit in the selection and interaction approaches, this study will discuss strategic choices as is shown in Figure 4-3: (a) a strategy oriented to short-term performance which focuses on logistics flexibility and matches it with a strong and dense distribution network; and (b) a strategy oriented to long-term performance which focuses on relationship flexibility and matches it with a neutral distribution network.

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Strong and Dense Network

A1. Logistics Flexibility Focused

Short-term Performance

Neutral network

A2. Relationship Flexibility Focused

Long-term performance

……

Ai. X Flexibility Focused

……

Figure 4-3 Fit as gestalts of flexibility strategies

Arguably, the internal consistency of each choice will lead to relatively better corresponding distribution performance. Such a perspective has led strategy to be conceptualized as the combination (profile) of environmental, contextual, and structural elements affecting an organization at any time when the congruence of environmental, contextual and structural complexity increase (Venkatraman & Camillus, 1984). Strategic decisions surrounding these activities involve a trade-off as to how much to invest in the refinement of existing technologies to reap profits today, compared to the invention of new technologies to secure returns in the future when environmental conditions demand new strategies (Levinthal & March, 1981). The proportion of resources allocated to different flexibility strategies differs across environments: that is, the degree to which firms favor one activity over the other depends on environmental conditions. Therefore, how to make strategic choices among distribution channels with different types of flexibility and design a matching system depends on specific conditions of the firm classified by the quality and quantity of information shown in Table 4-2. But internal consistency realized by fitting distribution network and performance orientations with the chosen flexibility strategy determines the success of implementing the strategy (e.g. either increasing revenues, achieving rapid growth, or attaining a large market share). These arguments yield the following working proposition, which is subject to further refinement in light of case-based evidence.

WP1. The focal firm in Context A which fits a strategy focused on Flexibility Ai with Network Ai will have a Performance Orientation Ai.

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4.3.2

Coalignment flexibility strategies in different contexts: fit as covariation

Fit as covariation is a pattern of covariation or internal consistency among a set of underlying theoretically related variables, and it can be best described through an illustration (Venkatraman, 1989). In somewhat strong contrast to the logic of fit as gestalts, fit as covariance means that different kinds of flexibility in cross-functional activities are functional demands that are not in conflict but rather act synergistically. Therefore, the covariation among different types of flexibility will lead to superior performance as is shown in Figure 4-4. So the following working proposition in effect competes with WP1 drawn under the situation of fit as gestalts.

WP2. The focal firm in Context Bi that fits a strategy of Flexibility Coaligment Bi (Ai to Aj) with a mixture of Network Structures Ai to Aj will have a set of Performance Orientation Ai to Aj.

Logistics Flexibility B1. Coalignment

Superior Performance

Relationship Flexibility

X Flexibility

Mixed Network

Figure 4-4 Fit as covariation of flexibility strategies

The degree of specification of the functional form determines the differences between fit as covariation and fit as gestalts. If the resources or capabilities required by different kinds of flexibility are critical and scarce for the focal firm, the degree of conflict in them will be very high and they will fit as gestalts. It means that the focal firm should use a large proportion of its limited resources or capabilities to focus on only one kind of flexibility and put less emphasis on the others. In contrast, if the resources or capabilities required are different and easy to access for the focal firm, the degree of 89

conflict will be very low and they will exhibit fit as covariance. This means that the focal firm can take advantage of plentiful resources or different capabilities to balance all of them. Therefore, different firms are in distinctive situations of achieving equifinality.

4.4 Summary Based on the literature review of flexibility (Chapter 2) and fit mechanisms (Chapter 3), this chapter has developed several theoretical models. On one hand, the core model about adaptation specifies fit as matching, fit as mediation, and fit as moderation. The relevant empirical and theoretical literature such as network theory, contingency theory, general information theory, and transaction cost theory was then discussed and employed to hypothesize the relationships among network embeddedness, distribution flexibility, and performance as well as the moderating effect contributed by the distribution environment. On the other hand, the complementary model about equifinality specifies fit as gestalts and fit as covariation in a given context. Finally, either external consistency between the contingency factors and organizational behaviors or internal consistency of distinctive strategic choices is equally important for understanding and explaining alternative forms of fit. The next chapter will describe the research methods used to test or explain the proposed models.

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5 RESEARCH METHODS

5.1 Introduction The preceding chapters of this dissertation have developed conceptual models according to the literature on flexibility and fit mechanisms. As it is hard to control so many complex factors in a distribution environment to test the causal effects of phenomena, pure experimental research designs and quasi-experimental research designs are not appropriate for this study. Therefore, this study employs a non-experimental design in order to investigate conditions that prevail in a market system without an attempt to change any of them, an approach that can also enhance the level of external validity. Furthermore, as this study does not aim at examining continuity of response or observing changes that occur over time, a cross-sectional research design using both a survey and in-depth interviews will be used and all data will be collected at a single point in time. Since it would be almost impossible to find companies in the same sector who distribute the same products with the same process yet face systematically different environments, we select companies across different sub-sectors of the manufacturing industry in order to obtain variance in distribution environments. The study takes place in China.

The main purpose of this study is to explain and analyze fit mechanisms for environmental contingency, which can be divided into two sub-objectives: one is to analyze external consistency based on natural selection; the other is to explain internal consistency based on strategic choices. Accordingly, Chapter 4 has developed two models: a core model of fit mechanisms in selection and interaction approaches and a complementary model of fit mechanisms in system approach. Therefore, different methods are required for assessing these two different models which are based on different research objectives. This sort of combination of methods in the study of the same phenomenon is referred as triangulation (Denzin, 1978). In this study, triangulation manifests itself in several aspects: data collection triangulation, data sources triangulation and analysis methods triangulation. 91

x

First, both a survey and multiple case studies will be used during the data collection process. As a survey is able to test causal relationships between variables in a large sample, a questionnaire will be developed and then used to collect data in order to test hypotheses in the core model. Yet strategic choices of flexibility in different activities made by firms under different conditions may be unstable and influenced by some factors that are hard to control in a survey. The case study approach is particularly useful when the phenomenon under investigation is difficult to study outside its natural setting and also when the concepts and variables under study are difficult to quantify (Ghauri & Grønhaug, 2005), so in-depth interviews will also be designed to illustrate real world manifestations of theoretical constructs and help assess the complementary model.

x

Second, both quantitative data and qualitative data will be collected in this study. Quantitative data provides information that is easy to analyze statistically and fairly reliable, while qualitative data provides a more in depth and rich description. The two types of data complement each other when combined.

x

Third, a variety of analysis method including partial least squares (PLS) path modeling, the product indicators approach, moderated regression analysis, subgroup analysis, and polynomial regression analysis will be used in this study.

Figure 5-1 describes the overall design which the following sections further justify and analyze.

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Research Objective

Research Designs

x Theory testing x Theory illustration

x Cross-sectional x Triangulation

Data Collection

x Survey x Case study

Data Types

x Quantitative x Qualitative

Analysis Methods

x Structural equation model x Interaction and subgroup analysis

Figure 5-1 Framework of methodology design

5.2 A survey for the core model The core model we have established in Chapter 4 focuses on fit as matching, fit as mediation and fit as moderation. Venkatraman (1989) argues that in these settings, researchers specify fit that is intrinsically connected to specific criterion variables. Therefore, these criterion-based types of fit require a larger scale survey based data set to capture the various criterion variables systematically. The following sections will discuss the design of the survey in respect of questionnaire development process, data collection procedures, sampling frame, and analysis procedures.

5.2.1 Measurements

The development and validation of multi-item scales based on reflective measures has been extensively used in research in marketing, as where issues of single item measures and index construction using formative measures have received little attention (Diamantopoulos & Winklhofer, 2001; Bergkvist & Rossiter, 2007). However, in many 93

cases, there is no difference in the predictive validity of the multiple-item and singleitem measures (Bergkvist & Rossiter, 2007). In addition, in many cases, indicators could be viewed as causing rather than being caused by the latent variable measured by the indicators (MacCallum & Brown, 1993). For the development of scales, this study first clarifies the items for each construct that are based on an extensive literature study and interviews with practitioners. As is described in Table 5-1, the choices of whether to use multiple or single and reflective or formative measures depend on different types of objects and attributes of the construct.

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Table 5-1 Scale development in survey study Construct

Object

Attribute

Distribution performance (Dependent variables) Distribution Delivery time Timeliness service quality Inventory Availability

Rater

Scale formation

Group raters

Reflective multiple

Product

Condition

Relationship satisfaction

Relationship

Satisfied degree

Group raters

Reflective multiple

Link duration

Relationship

Length experienced and expected

Group raters

Reflective multiple

Network embeddedness (Independent variables) Tie strength Linkages Level of embedded in cooperation in network vertical dimension

Group raters

Reflective multiple

Tie density

Level of competition in horizontal dimension

Group raters

Formative multiple

Capability of making changes

Group raters

Reflective multiple

Capability of making changes

Group raters

Reflective multiple

Volatility

Group raters

Reflective multiple

Variety

Group raters

Reflective multiple

Group raters

Single

Group raters

Formative multiple

Linkages embedded in network

Distribution flexibility (Mediators) Logistics Storage flexibility Delivery Inventory Relationship flexibility

Relationship content

Task environment (Moderators) Uncertainty Demand Competition Regulation Heterogeneity

Distributors or Customers’ demand

Firm characteristics (Control variables) Firm age Firm Time of business duration Firm resource mix

Firm

Number of employees Annual sales Total assets

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x

Construct: distribution performance

The dependent variables in this framework are the performance outputs of distribution flexibility. As referred to in Chapter 2, there are several dimensions of distribution performance. In the short run, distribution performance outcomes include distribution service quality and relationship satisfaction. In the long run, channel member satisfaction with the relationship will produce a long-term relationship orientation manifested as link duration.

Service outputs

Channel systems exist and remain viable through time by performing duties that reduce end users’ search, waiting time, storage, and other costs, which are called the service outputs of the channel by Bucklin (1966). Bucklin (1966) distinguishes three types of service outputs performed by the channel: (a) lot size, the number of units that the consumer receives at any given delivery; (b) delivery time, the period that the consumer must wait, after ordering, before he receives his goods; and (c) market decentralization, the number and dispersion of trading points. Based on this analysis, Coughlan et al. (2001) identify four dimensions of service outputs: (a) bulk-breaking or the end-user’s ability to buy its desired number of units of a product or service; (b) spatial convenience provided by market decentralization of wholesale or retail outlets which increases consumer’s satisfaction by reducing transportation requirements and search costs; (c) waiting or delivery time, the time period that the end-user must wait between ordering and receiving goods; and finally (d) the breadth of assortment or product variety available to the end-user. However, the four service outputs discussed here are wide ranging, but may not be exhaustive in all situations. Coughlan et al. (2001) also note several additional potential service outputs: customer education, referring to the formal or informal provision of information before or after the product sale, and after-sales services, including configuration, installation, repairs, maintenance, and warranty service.

Both Bucklin (1966) and Coughlan et al. (2001) analyze service outputs from the perspective of consumers or end-users, but Bienstock, Mentzer and Bird (1997) develop 96

a valid and reliable scale for measuring industrial customers’ (e.g. manufacturers, wholesalers, retailers, government organizations) perceptions of the physical distribution service quality they receive from their supplier. This scale is based on Mentzer, Gomes and Krapfel’s (1989) classification of physical distribution service into three dimensions: (a) timeliness, the order cycle time performance of the entire distribution system linking buyers and sellers; (b) availability: the proportion of units, order lines, or orders completely filled; and (c) quality or condition of the products delivered, which depends on the incidence of in-transit damage, shipment of incorrect items, and incorrect shipment quantity. The five timeliness items had a high Cronbach’s Alpha of 0.93, the five availability times had a high Cronbach’s Alpha of 0.89, and the three condition items had a Cronbach’s Alpha of 0.87 in their original empirical study. Furthermore, physical distribution service quality within a second-order structure also had a high reliability (Alpha=0.95). However, some items had no general meanings for our cross-industry survey (e.g. “The time it takes my supplier to put my order together is consistent” and “This supplier has inventory available near my facility”). Besides, one item of availability, “If this supplier is notified of possible increases in upcoming orders, extra inventory is maintained”, was actually a measure of flexibility as defined in our study. In the end, the present study used three items for each factor of service quality and constructed a second-order model (the original and revised measures are shown in Appendix 5-A).

Satisfaction

Behavioral researchers in marketing have developed a rich body of literature investigating the antecedents and consequences of customer satisfaction viewed as a post-choice evaluative judgment of a specific purchase occasion at the individual level (See Yi 1990 for a review). In contrast, cumulative customer satisfaction as an overall evaluation based on the total purchase and consumption experience with a good or service over time is a more fundamental indicator of the firm’s performance (Johnson & Fornell, 1991; Fornell, 1992; Anderson et al., 1994). As customer satisfaction in industrial marketing is related to different facets of a buyer-seller relationship than is customer satisfaction in consumer marketing, Homburg and Rudolph (2001) also suggest that a cumulative approach is better to measure industrial customers’ satisfaction. In addition, there are two broad types of scales in customer satisfaction 97

surveys. Many studies use a single-item for overall customer satisfaction (Anderson et al., 1994; Bolton & Lemon, 1999), in which case it is hard to capture different nuances related to products and services. However, multi-item measures can better capture customer satisfaction because respondents are also required to evaluate the key components or dimensions of the offer (Oliver, 1980; Fornell, 1992; Phillips & Baumgartner, 2002). Also, the reliability of such multi-item scales is higher than when using single-item scales (Yi, 1990). However, in a business-to-business context, a third type of multiple dimensional scale using multiple-item measures is considered more reasonable (Homburg & Rudolph, 2001).

In distribution channels, the construct of satisfaction is also important in helping to understand behaviors within the channel. Ruekert and Churchill (1984) make a clear definition that “channel member satisfaction comprises the domain of all characteristics of the relationship between a channel member (the focal organization) and another institution in the channel (the target organization) which the focal organization finds rewarding, profitable, instrumental, and satisfying or frustrating, problematic, inhibiting, or unsatisfying” (p. 227). They also test reliability and validity of the direct and indirect measures of channel member satisfaction in terms of four dimensions: social interaction, product, financial, cooperative advertising support and other assistances. Although some researchers make use of single-item scales ranging from “very satisfied” to “very dissatisfied” to measure channel member satisfaction (Rosenberg & Stern, 1971; Wilkinson, 1979), Ruekert and Churchill (1984) use multiple items for each dimension. However, a customer’s affective or emotional state toward a relationship, which captures the satisfaction with the relationship rather than overall satisfaction (Palmatier et al., 2006), is not included in the dimensions above. Existing studies in channel management simply measure this construct from a cognitive perspective in terms of whether the relationship is a happy one or whether the partner is satisfied with the relationship (Kumar et al., 1992; Webb & Hogan, 2002). Webb and Hogan (2002) developed four items for measuring satisfaction among channels which has a high reliability (Alpha=0.82). Thus, we used their measures (See Appendix 5-A).

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Link duration

Link duration measures the amount of experience that the supplier and the buyer have in dealing with each other with the resulting routines sometimes described as a component of relation specific assets (Levinthal & Fichman, 1988). The longer a link lasts, the more behavioral information trading partners gain across varied contexts, which allows for better predictions that should increase each party’s confidence in its partner’s behavior (Anderson & Weitz, 1989; Doney & Cannon, 1997). Thus, items such as believing or expecting the relationship to last a long time, working to improve the quality of partners in the long run, and whether the relationship is expected to be profitable in the long run are often used in measuring long term relationship orientation (Ganesan, 1994; Chen & Paulraj, 2004; Ivens, 2005). In this study, we adopted the seven items designed by Ganesan (1994) representing a retailer’s long-term orientation which captures the focus of a retailer on long-term goals, willingness to offer concessions, long-run profitability of the relationship, and concern for both own and vendor outcomes (the original and revised measures are shown in Appendix 5-A). x

Construct: network embeddedness

Different types of ties in a distribution network are antecedents of distribution flexibility and independent variables in this framework. They are included in two types of network embeddedness: relational and structural (Gulati, 1998). In previous studies, structural variables such as centrality, density, the existence of structural holes, level of governance, and a prevalence of cliques have been commonly examined and used to explain networks and network outcomes at an organizational level (Provan et al., 2007). Although the most popular empirical method used in social network studies involves modeling the structure of the whole network, there is still a tendency to conduct surveys in strategic network studies. As a survey, the present study plans to use the following variables shown in Table 5-2 to measure distribution network structure.

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Table 5-2 Concepts and measures of distribution network structure Concept

Measure

Concept

Measure

Relational Embeddedness

Tie Strength

Structural Embeddedness

Tie Density

Strong Ties

High Strength

Dense Ties

High Density

Weak Ties

Low Strength

Structural Holes

Low Density

Tie strength

The strength of a tie is a combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services that characterize the tie (Granovetter, 1973). In practice, there have been a number of different ways to measure tie strength. Some have measured strong ties as reciprocated nominations and weak ties as unreciprocated nominations (Friendkin, 1980). Some use indications of the “closeness” of a relationship; thus, close friends have been said to be “strong” ties, while acquaintances or friends of friends have been called “weak” ties (Lin, Ensel, & Vaughn, 1981). Numerous other measures of tie strength have also been used or proposed such as frequency of contact, mutual acknowledgement of contact (Friendkin, 1980), the extent of multiplexity within a tie, and even social homogeneity (Marsden and Campell, 1984). Based on a review, Marsden and Campell (1984) distinguish two types of variables used in the measurement models. The first type includes indicators of tie strength based on Granovetter’s definition including “closeness” which measures the intensity of a relationship; “duration” and “frequency” of contact which measure the amount of time spent in a tie, as well as the breadth of “topics” discussed and the extent of “mutual confiding” which represents intimacy. The second type includes predictors of tie strength which are measured as dichotomous variables: kinship, co-worker, and neighbor statuses.

Recent studies in a strategic network context associate strong ties with trust, finegrained information exchange and joint problem-solving arrangements. For example, McEvily and Alfred (2005) develop parallel instruments to measure the extent to which a firm embedded ties with its lead customer and lead supplier in terms of joint problem solving (the degree to which exchange partners share the responsibility for solving problems as they arise), information sharing (the degree to which parties actively 100

exchange information beyond the letter of the contract, that is, information that can facilitate the other party’s activities), and trust (the extent to which members of a focal firm have a collectively held trust orientation toward a customer or supplier firm). In addition, following the conceptualization of tie strength in a project alliance ties portfolio, Tiwana (2008) measures the degree of presence of strong ties using a fiveitem measure designed by Kale et al. (2000) that assesses the extent of close personal interaction, reciprocity, mutual trust, mutual respect, and personal friendship at multiple levels among the members of a project team. In addition, Tiwana (2008) suggests that this approach overcomes the coarseness of using infrequency of interaction as a proxy for tie weakness type concepts as cautioned by McEvily and Zaheer (1999). However, the present study treats a distribution network as a given structure distinguished from relational outcomes, which requires measurement from an objective and behavioral perspective rather than from a subjective perspective. Thus, we developed our measures based on behavioral account items as is shown in Appendix 5-A, which fall within the first type as categorized by Marsden and Campell (1984) and include indicators of “closeness”, “duration and frequency of contact” and “mutual confiding”.

Tie density

From Coleman’s (1988) standpoint, the optimal social structure is one generated by building dense, interconnected networks that promote trust and cooperation among members. But like strong ties with equivalence redundancy (similar information from strong contacts connected to each other), dense ties bring in cohesion redundancy (same source of information from contacts linking to the same third parties) (Burt, 2000). An operationalisation of density for a finite portion of a network or “partial” network is simply the ratio of the total number of actual ties to the total number of possible relationships in that partial network (Boissevain, 1974). Thus, density, D = [2Na/N(N-1)] where Na = actual number of ties, excluding those of the subject and N = the total number of others in the partial network (Cadeaux, 1997).

The approach of calculating this ratio is commonly used in studies of strategic networks. For example, Rowley et al. (2000) measure a firm’s density as the number of existing ties in the ego network (other than those involving the focal organization) divided by the total possible number of ties among its partners that would exist if each partner were 101

tied to every other partner. Schilling and Phelps (2007) use the ratio of existing links in the network to the number of possible pair-wise combinations among firms. These studies have clearly achieved agreement in measuring network density. However, in this study, instead of investigating a whole industry network using secondary data, we focus on the first level of a distribution network (direct customers) from the perspective of the focal firm because the situation in each level is quite different. Such a scenario may present difficulties for the key informant in giving an accurate answer to questions about numbers of actual and potential ties. Therefore, to analyze tie density of these specific types of distribution networks, we separated the construct into two formative components: one concerns the ratio of existing linkages to potential linkages between the focal firm and its direct customers, and the other concerns the ratio of existing linkages to potential linkages between the focal firm’s competitors and its key customer. Since we fixed the number of existing linkages at one (the relationship between the focal firm and the key account shown in Figure 5-2), we only asked about the number of potential linkages and then reverse-scaled the answer (See Appendix 5-A).

The focal firm

The potential competitors

Potential linkages Existing linkages

The key account

The potential direct customers

Figure 5-2 The first level distribution network x

Construct: distribution flexibility

In general, flexibility can be defined as the ability to change or react to environmental uncertainty with little penalty in time, effort, cost, or performance (Upton, 1994). As defined in the previous chapter, distribution flexibility involves changing logistics activities and ongoing relationships adaptively and proactively in an efficient or effective manner to adjust to requirements of both direct and indirect customers. It consists of logistics flexibility and relationship flexibility. In our framework, both are 102

hypothesized as mediators between network embeddedness and distribution performance.

Logistics flexibility

As referred to in Chapter 2, logistics flexibility involves changing delivery, storage and other logistics processes to respond in an efficient manner to varying requirements of both direct and indirect customers regarding order size, inventory, and transportation of tangible physical products. Therefore, conventional measurements for logistics adaptability are useful to measure logistics flexibility. These items have been concerned with flexibility in three aspects of processes: (a) Flexibility in storage. For example, when measuring physical distribution flexibility, Zhang et al. (2005) used items such as “pick and assemble multiple customer orders accurately and quickly at the finished goods warehouse” and “provide multiple kinds of product packaging effectively at the finished goods warehouse”. Other measures are “adjust storage capacity” (Swafford et al., 2006), “vary warehouse space” (Lummus et al., 2003), etc. (b) Flexibility in delivery. For example, Swafford et al. (2006) used items such as “adjust delivery capacity”, “improve delivery reliability”, “change delivery modes when necessary”, etc. Additional measures include “vary transportation carriers” (Lummus et al., 2003), “use multiple transportation modes to meet schedule for deliveries” (Zhang et al., 2005), etc. (c) Flexibility in inventory. For example, when analyzing global supply chain capabilities, Morash and Lynch (2002) designed several items for measuring supply-oriented capabilities

including

“efficient

inventory

deployment”,

“quick

inventory

replenishment”, “postpone inventory movement”, “inventory reduction”, etc. In this study, the measure of logistics adaptability designed by Swafford et al. (2006) was mainly adopted. Internal consistency of the final four-item measurement was not very high (CR=0.62), so we combined it together with the other three deleted items. Details of the items we used are shown in Appendix 5-A.

Relationship flexibility

Relationship flexibility is a type of coordinative modification of interactions between partners based on relationship norms and clearly characterizes, for example, the three items that Heide and John (1992) use to measure flexibility which are borrowed in most 103

of the subsequent studies of this topic: (a) The extent to which flexibility in response to requests for changes is a characteristic of a relationship; (b) The extent to which the parties expect to be able to make adjustments in the ongoing relationship to cope with changing circumstances; and (c) Whether when an unexpected situation arises, the parties would rather work out a new deal than hold each other to the original terms. Some researchers use exactly the same items to measure this kind of flexibility. These include Dahlstrom, McNeilly and Speh (1996) and Bello and Gilliland (1997). Others make some changes for the particular context of their studies. For example, Boyle et al. (1992) use three items from the perspective of dealers in marketing channels: (a) “We are usually willing to make adjustments to contracts with the manufacturer in the face of problems or special circumstances”; (b) “We are willing to put aside contractual terms in order to work through difficult problems raised by this manufacturer”; (c) “We are apt to rework prior agreements with the manufacturer when unforeseen supply or market disturbances arise”. Johnson (1999) adds two reversed items: (a) “The terms of transactions between us and our major supplier are not renegotiable under any circumstances”; and (b) “In our relationship, both firms live with the terms of the transaction until completion, no matter what happens”. Recently, Wang and Wei (2007) extend the basic three-item measure proposed by Heide and John (1992) into six items with high validity and reliability (CR>0.70; AVE>0.70). So we adopted the measure from Wang and Wei (2007) as reliability and validity were improved compared to the early instrument (Heide & John, 1992). But two items, “flexibly deal with complicated problems that neither party could account for” and “solve problems adequately when unexpected situation arises”, which raised confused meanings were eventually deleted according to suggestions from experts and practitioners (the original and revised measures are shown in Appendix 5-A). x

Construct: task environment

Based on contingency theory, environmental conditions are taken into consideration as the moderators between distribution flexibility and distribution performance as well as between network embeddedness and distribution flexibility. Achrol and Stern (1983) suggest that for every dyad there is a primary task environment, a secondary task environment, and a macro task environment. According to our framework, we focus on the primary task environment comprised of immediate suppliers and customers of the 104

dyad. In this situation, regulating agencies and competitors to the channel dyad may be drawn into direct exchange relationships and thus into the primary task environment.

Uncertainty

As referred to in Chapter 2, uncertainty is a core concept in contingency theory and a variety of measures for it have been developed. For example, Duncan (1972) develops an instrument to measure environmental uncertainty that captures the following dimensions: (a) lack of information regarding the environmental factors associated with a given decision making situation; (b) lack of knowledge about the outcome of a specific decision in terms of how much the organization would lose if the decision were incorrect; and (c) the ability or inability to assign probabilities as to the effect of a given factor on the success or failure of a decision unit in performing its function. These items arise from a subjective perspective, but some researchers propose objective measures for uncertainty. For example, Dreyer and Grønhaug (2004) look at the level of uncertainty as fluctuations in the factors studied, using an index - the volatility index developed for describing stock market fluctuations. Thus, if there are big fluctuations from one period to the other, the volatility index(s) will be high, and if there are small fluctuations, it will be low. Cadeaux (1992) defines environmental volatility as a function of changes (or perceived changes) in environmental elements. There are different measures of volatility (Tosi, Aldag, & Storey, 1973; Bourgeois, 1978; Snyder & Glueck, 1982). Snyder (1987) compares the convergent validity of them and developed a measure based on a time series of sales at the four-digit SIC level. However, problems range from findings of poor reliability and lack of validity evidence for measurement instruments to a failure to find clear evidence of a relationship between “objective” characteristics of the organizational environment and perceptions of environmental uncertainty (Milliken, 1987). Downey and Slocum (1975) compare the earlier scales of Duncan (1972) and Lawrence and Lorsch (1969). The results show that the internal reliability of both scales and their respective subscales are supported. But overlap between them is small and comparisons with criterion measures produce disappointing results. Moreover, Achrol (1988) validates the concept-based factor structure of the items against the alternative that item variance is simply decision-related variance. 105

As discussed in the previous chapter, in order to maximize the degree of congruence between objective environmental uncertainty and the perceptual response, it is necessary to develop task-related measures of uncertainty with a consideration of objective factors such as fluctuation and volatility. A firm’s task environment generally includes five external components: customers, competitors, suppliers, regulatory groups, and technological requirements of an industry (Dill, 1958). Duncan (1972) also divided the external environment into several dimensions including a customer component, a supplier component, a competitor component, a socio-political component, and a technological component. Therefore, when measuring changes in a firm’s external environment, researchers often construct measures with these dimensions. For example, Miller (1987) designed items about changes in a firm’s external environment over the past five years including dynamism of technology, heterogeneity of demand, and hostility of competition. In addition, Achrol and Stern (1988) divided environmental dynamism into several dimensions including dynamism in marketing practices, competitor dynamism and customer dynamism. Assessing reseller performance from the perspective of the supplier, Kumar et al. (1992) focused on environment variables such as demand uncertainty and competition uncertainty in a reseller’s territory, an approach which clearly fits the context of distribution channels in this study (See Appendix 5-A). There were five items for measuring the construct of environmental uncertainty in our study, two of them were about competition uncertainty, two of them were demand uncertainty, and one was about regulation and policy uncertainty.

Heterogeneity

Uncertainty concerns the quality of environmental information, while heterogeneity concerns the quantity of information generated by the environment for a seller (Nonaka & Nicosia, 1979). Nonaka and Nicosia (1979) construct two indicators of the heterogeneity-homogeneity dimension: (a) the number of sources (e.g., number of market segments and number of customers in each segment) generating information; and (b) the amount of information that each source may generate and/or require the firm to provide. However, they only use the indicators as a qualitative classification framework; whereas other researchers employ quantitative measures in a survey. For example, Miller (1987) uses a single item to assess the change of needed diversity in production methods and marketing tactics to cater to different customers over the last 106

five years. Achrol and Stern (1988) defined environmental diversity (heterogeneity, complexity) as the degree of similarity or differentiation perceived between the elements of the population dealt with, including organizations, individuals, and any social forces affecting resources. They operationalize the measure as two independent constructs: diversity among individuals and diversity among organizations. In a business-to-business context, this study focuses on the diversity among organizations. Thus, we designed three items about demand variety of channel members or end customers as shown in Appendix 5-A. x

Construct: firm characteristics

Other firm-specific variables could also affect distribution flexibility and distribution performance. These include resource, age, and industrial sector.

Firm resource mix

Firm size has been found to be an important variable in determining flexibility as well as firm performance. For example, using T-tests, Verdú-Jover, Lloréns-Montes and García-Morales (2006) draw the conclusion that the larger the firm size, the higher the operational flexibility, structural flexibility, strategic flexibility, financial flexibility and financial performance. Moreover, Liao, Hong and Rao (2010) compare two structural models for groups of SMEs and large enterprises. The results reveal that firm size has a positive moderating effect on the relationship between supply management and supplier flexibility, but a negative moderating effect on the relationship between supplier flexibility and supply chain performance. Thus, in our framework we would like to control for firm size using a formative construct which is presented by the number of employees, annual sales and total assets. It is renamed as firm resource mix because this construct can measure the effects on distribution flexibility by internal resources of a firm that is capital intensive or human resource intensive. According to the Temporary Provisions for Chinese Small and Median Enterprises (SMEs), SMEs in industry should meet the following principles: the number of employees is below 2000; the value of annual sales is below 300 million RMB; or the value of total assets is below 400 million RMB. Among them, the ones which have more than 300 employees, 30 million RMB annual sales or 40 million assets are medium size, while others are small size. 107

Firm age

Although firm age is not discussed in the previous literature on flexibility, much of the literature on learning curve considers organizational learning as skill building based on repeated execution of similar tasks and the tacitness that arises when learning is experiential (See review by Huber 1991 about experiential learning). For example, Levitt and March (1988) explain how organizations learn from direct experience, how organizations learn from the experience of others, and how organizations develop conceptual frameworks or paradigms for interpreting the experience. Furthermore, Zollo and Winter (2002) suggest that learning processes encompassing experience accumulation, knowledge articulation and knowledge codification are responsible for the evolution of both operating routines and dynamic capabilities. Thus, compared with firms with less experience, firms with more experience would have better learning skills which help in enhancing the capability of being flexible in different processes. So we controlled for firm age by asking a single question requesting the years since the firm was established.

5.2.2 Data collection

To collect data, we first designed a semi-structured questionnaire for the cross-industry survey and then decided unit of analysis, selection of respondents and sampling frame during this procedure. The first step of questionnaire development, verifying what is to be measured, was carried out through the literature review described in the previous section. The remaining steps are as follows: planning a draft of the questionnaire, undertaking a critical review and amending the questionnaire for the main study (Bagozzi, 1994). x

Questionnaire development and pretesting

The questionnaire started with instructions on who should answer, what should be answered, and how to answer. This was then followed by five sections for the key informant to read: (a) an assessment of environmental conditions in the distribution 108

channels where the focal firm is located; (b) an assessment of both relational and structural embeddedness in the first level distribution network; (c) an assessment of three types of flexibility including logistics flexibility, marketing flexibility and relationship flexibility; (d) an assessment of both short-term and long-term distribution performance; and (e) the respondent’s and the focal firm’s background information.

The questionnaire was then translated into Chinese by professional researchers and back-translated into English by someone not involved in the research in order to allow experts to examine each survey item on both versions to establish meaning conformity (Schaffer & Riordan, 2003; Zhao et al., 2006). Both versions were then evaluated by seven academic researchers who served as expert judges to assess face validity. Since all items used to measure each construct were derived from a thorough literature review, only minor modifications such as re-organizing the order of a few questions and also rewording headings and introductions for a better flow were suggested by the panel experts (Farh, Cannella, & Lee, 2006). After that, two stages of pretesting were conducted to determine the effectiveness of the questionnaire concerning question format, wording and order.

Participating pretests

The first stage of the pretest process is that participants are aware that they are taking a pretest. During this process, the translated version of the survey was pre-tested with eleven industry experts (middle or senior managers who are familiar with distribution channel management) from firms in the manufacturing industry in China. Rather than simply fill out the questionnaire, they are involved in an interview setting (face-to-face or over the telephone). In order to gather as much insight from the participants as possible, they were asked to explain reactions to the flow of the questions, whether the instrument was difficult to complete or understand, and even give their interpretation of some specific questions.

Undeclared pretest

According to Converse and Presser (1986), if researchers have the resources to do more than one pretest, it might be best to use a participatory pretest first, then an undeclared 109

test to check the choice of analysis and the standardization of the survey. Therefore, we also conducted two rounds of undeclared pretests. During each pretest, respondents were not informed that it is a pretest and the survey was given just as we intend to conduct it in the field. The aims of the pre-test were:  To ensure that respondents had no trouble following the instructions included and that skip patterns were followed.  To examine the frequencies of the items. If there is little or no variance among responses, the question may not be measuring clearly what we intend.  To check whether there are too many non-responses. Too many non-responses may indicate a poorly constructed scale, or unclear or inappropriate wording.  To see if there are scaled items that received more than one response, no response or write-in answers, and in which case, to refine the response scales. 

To test how long it takes to complete the survey.

Feedback from the pretest along with comments and suggestions from industry experts and academic researchers was incorporated into a revised version of the survey instrument. The majority of the constructs were measured via multi-item measures with five-point scales (See Appendix 5-B). x

Unit of analysis

The unit of analysis is the focal firm’s assessment of the distribution network they had established, the flexibility strategies that they had implemented, and the relationship value they had created together with downstream channel members. The focal firm, located in any sector of the Chinese manufacturing industry, could be a manufacturer or a wholesaler but not a retailer or a pure service provider such as a bank or a restaurant. In the instructions of the questionnaire, the key informant from the focal firm was required first to identify a key product reflecting at least one of the following features: (a) being the largest revenue component of the main business, (b) the largest part of the total unit volume, or (c) involving the greatest proportion of selling effort. Then the key informant identified direct customers considered as first level independent distributors, agents, direct sales offices or branches. From these direct customers, the key informant 110

named a key account that has at least one of the following features: (a) the largest percentage of key product profits, (b) the largest percentage of key product sales revenue, or (c) the largest percentage of key product unit volume. Thus, all of the questions they should answer focus on some of the relevant factors that characterize the first level distribution network and the relationship between the focal firm and the key account. x

Selection of respondents

A key informant approach was used in this study to collect data. Campbell (1955) first suggested that the key informant technique could be used to obtain quantifiable data and that key informants should (a) occupy roles that make them knowledgeable about the issues being researched and (b) be able and willing to communicate with the researcher. Furthermore, Kumar, Stern, and Anderson (1993) conclude that two problems arise in using multiple informants in interorganizational research: the first is the selection problem related to response errors for informants’ roles and competence; and the second is the perceptual agreement problem that arises because of differences in both knowledge and perceptions. According to this view, to ensure reliability with this method, we included a few questions at the end of the questionnaire for selecting qualified respondents. First, the key informant was asked for his/her position in the focal firm. The qualified respondents ranged from middle managers and senior managers to presidents/vice presidents. Second, the key informant was asked about the degree of his/her experience and knowledge and decision involvement connected with distribution channel management. Returned questionnaires were discarded if these items were rated lower than three on a five-point scale. x

Sampling procedure

To obtain the data for testing our framework, we relied on a large-scale survey of local companies in business-to-business environments. As explained in the introduction of the research design, environmental variance was obtained through a cross-industry sampling which covered almost all the sub sectors of manufacturing industry in China from code C1300 to C4300 in the National Economy Industry Category and Code (GBT4754-94) and includes such industries as food manufacturing, tobacco processing, 111

textile and garment manufacturing, furniture manufacturing, stationery and sporting goods manufacturing, pharmaceuticals manufacturing, PC and electronic equipment manufacturing, and machine building. Furthermore, the manufacturing output of the top 5 districts studied here: Guangdong Province, Jiangsu Province, Shandong Province, Zhejiang Province and Shanghai together account for 56.64% of total manufacturing output in China. Thus, the data for this research collected from cities in these regions and industries well represents China’s manufacturing sector.

The data was collected in two ways. On one hand, we approached qualified local firms in manufacturing industry in different districts of China that are acquaintances of the author via email or by phone and briefly described the content of this study. If the respondents agreed to participate in this survey, a questionnaire was then sent to the key informant with a cover letter explaining the objectives of this study. Each key informant was allowed around a half hour to complete the questionnaire. Then a follow up email or phone call was made before picking up the completed questionnaire. Completion of the questionnaire was carefully checked and any problems were followed up promptly at the point of pickup. On the other hand, we tried to contact the managers enrolled in several short-term business training programs held by Renmin University of China. These managers were presidents or owners of their companies and highly involved in strategic decision making processes. After a brief introduction to the research, respondents who agreed to participate were then handed a paper questionnaire. They were allowed around half an hour to complete the questionnaire and then hand it back to the researcher at the class. Any questions were answered during this process. The screening process and the survey administration for those two methods were carried out in exactly the same manner.

5.2.3 Sample profile

From March to November 2010, we approached 460 firms in different sectors of Chinese manufacturing industry. As a result, a total of 262 executives responded to the request for information about their company. This number represents 56.96% of the original firms contacted. The follow up screening process revealed that 212 questionnaires were usable yielding a pure response rate of 46.09% which was considered satisfactory given previous experience with B2B surveys of a similar nature 112

(Zhang et al., 2005; Wang & Wei, 2007; Fantazy et al., 2009; Liao et al., 2010). We also tested non-response bias as shown in the following sections. x

Sample Composition

Table 5-3 shows the total sample was across a wide range of manufacturing industries. In the data set, 54.7% of the firms from which we obtained responses had 300 or fewer employees and 18% had 2000 or more employees. In addition, the companies with annual sales less than 30 million RMB or total assets less than 40 million RMB account for 38.7% and 48.0% respectively, and those with annual sales more than 300 million RMB or total assets more than 400 million RMB occupy 23.1% and 23.6% respectively. Further, 29.2% of the firms in our dataset had been in operation for 5 to 10 years, and 50.9% for more than 10 years.

113

Table 5-3 Sample composition N

%

24 18 8 9 12 10 18 46 21 30 3 199

11.3 8.5 3.8 4.2 5.7 4.7 8.5 21.7 9.9 14.2 1.4 93.9

8 12 22 62 108 212

3.8 5.7 10.4 29.2 50.9 100.0

80 36 13 11 20 14 8 30 212

37.7 17.0 6.1 5.2 9.4 6.6 3.8 14.2 100.0

33 18 20 11 23 30 28 49 211

15.6 8.5 9.4 5.2 10.8 14.2 13.2 23.1 100.0

53 23 10 16 23 17 20 50 212

25.0 10.8 4.7 7.5 10.8 8.0 9.4 23.6 100.0

Manufacturing Category Food and drink Clothing and clothes Wood and furniture Printing and stationery Oil and chemical products Pharmacy Mine and metal Special or general equipment Electrical appliance Telecom and electric products Others like construction Total

Age of the company Less than 1 year 1-2 years 2-5 years 5-10 years More than 10 years Total

Number of Employees 0-100 100-300 300-500 500-600 600-1000 1000-2000 2000-3000 More than 3000 Total

Sales Revenues Less than 5 million RMB 5-10 million RMB 10-20 million RMB 20-30 million RMB 30-50 million RMB 50-150 million RMB 150-300 million RMB More than 300 million RMB Total

Total Assets Less than 10 million RMB 10-20 million RMB 20-30 million RMB 30-40 million RMB 40-100 million RMB 100-200 million RMB 200-400 million RMB More than 400 million RMB Total

114

x

Non-response bias

Non-response bias occurs in statistical surveys if the answers of respondents differ from the potential answers of those who did not answer. There are different ways to test for non-response bias. In this study, we first compared the survey results to some known population parameters. For example, companies that constitute the largest proportion of our

sample

locate

in

the

industry

of

manufacturing

special

or

general

equipment/instrument (27.1%), which is close to the proportion reported by National Bureau of Statistics of China (23.1% in 2009). Besides, the proportion of the number of large enterprises (3254), the number of middle enterprises (38036) and the number of small enterprises (393074) is about 1: 10: 100 (China, 2009). In our sample, the distribution of different sizes of companies is also like such an inverted pyramid. Another commonly used method of testing non-response bias is based on the assumption that late respondents share similar characteristics and response biases with non-respondents (Armstrong & Overton, 1977). Thus, we compared early-returned questionnaires to late-returned questionnaires by post on a number of variables: company age, number of employees, sales revenue and total assets. The results in Table 5-4 show that there are not any significant mean differences between early and late respondents except for annual sales. But as a whole, the other insignificant results still imply that non-response bias is not a big problem in our dataset.

Table 5-4 ANOVA results

Firm Age

Between groups Within groups Total

Sum of Squares 1.55 241.64 243.19

Number of Employees

Between groups Within groups Total

1.29 1420.27 1421.56

1 210 211

1.29 6.76

0.19

0.66

Annual Sales

Between groups Within groups Total

25.55 1338.38 1363.93

1 210 211

25.55 6.37

4.01

0.05

Total Assets

Between groups Within groups Total

1.29 1420.27 1421.56

14.99 1585.94 1600.93

1 210 211

14.99 7.55

1.99

df 1 210 211

Mean Square 1.55 1.15

F

Sig

1.35

0.25

115

5.2.4

Analysis procedures

Based on the theoretical framework and hypotheses proposed in the previous chapter, structural equation modeling was used to analyze the inter-variable relationships. There were two parts: first, a measurement model that assesses reliability and validity of the scales used to measure each latent construct, and second, a structural model that lays out and estimates multiple dependent relationships between the constructs of interest. x

Measurement model analysis

Sample observed variance (O) comes from three aspects: true variance (T), unique variance (U), and error variance (E). This can be expressed by the following function: O=T+U+E. The proportion of E in O is the reliability issue, while the proportion of T in O is a validity issue. Therefore, reliability depends on how much of the variance in scores is attributed to random error (Churchill, 1979), while validity denotes the scientific utility of a measuring instrument (Nunnally & Bernstein, 1994). Above all, the key purpose of measurement model analysis is to test the reliability and validity of the measures. There are two kinds of measures within the total structural model: one kind is a reflective measure such as the measure of distribution flexibility; the other is a formative measure such as that for network density. The methods used to validate these measures are quite different.

Conventional procedures (e.g. confirmatory factor analysis) to be used to assess the validity and reliability of scales composed of reflective indicators are well known (e.g. Gerbing and Anderson, 1988). However, these procedures are not appropriate for composite variables with formative indicators. Diamantopoulos and Winklhofer (2001) conclude that four issues are critical to successful index construction: (a) content specification regarding the scope of the latent variable, that is, the domain of content the index is intended to capture; (b) indicator specification which means that the items used as indicators must cover the entire scope of the latent variable as described under the content specification; (c) indicator collinearity which matters because the formative measurement model is based on a multiple regression, and (d) external validity assessment to examine how well the index relates to measures of other variables. There 116

are two ways to test individual indicator validity (Diamantopoulos, Riefler, & Roth, 2008): one is to use a MIMIC model to simultaneously allow for the estimation of ¤parameters and for the provision of an overall model fit; the other is to assess indicator validity by estimating the indicators’ correlations with an external variable. However, there is a lack of agreement about whether the standard procedures for assessing discriminant validity are equally applicable to formative indexes. Diamantopoulos and Siguaw (2006) propose using the variance of the error term as an indicator of construct validity. In addition, confirmatory tetrad analysis (CTA) also offers a basic test of construct validity (Bollen, 1990). Recently, Henseler (2009) summarizes statistical analyses on both the construct level and the indicator level to assess the validity of formative constructs. According to the analysis above, we settled on the different procedures for validating both reflective and formative measures shown in Figure 5-3.

117

Literature Review

Identification of constructs

Development of the initial instrument

Reflective

Pre-testing of the instrument

Formative

Establishing content of face validity

Content specification

Refinement of items

Indicator specification

Data collection

Data collection

Unidimensionality

Construct level assessment

Reliability

Validity

Indicator level assessment

Correlation

No

Reliable & valid? Yes Instrument

Figure 5-3 The instrument development and validation process Adapted from: Chen, I.J., & Paulraj, A. (2004). Towards a theory of supply chain management: the constructs and measurements. Journal of Operations Management, 22, 119-130; Diamantopoulos, A., & Winklhofer, H.M. (2001). Index construction and formative indicators: an alternative to scale development. Journal of Marketing Research, 38(2), 269-277. 118

x

Structural model analysis

As structural equation model (SEM) tests a hypothesized model statistically to determine the extent to which the proposed model is consistent with the sample data. Chin (1998) concluded that the advantages of SEM are as follows: (a) It models relationships among multiple predictor and criterion variables, (b) It constructs unobservable latent variables, (c) It models errors in measurements for observed variables, and (d) It statistically tests a priori substantive/theoretical and measurement assumptions against empirical data. There are two approaches to structural equation modeling: one is covariance structure analysis with Maximum Likelihood (ML) estimation of parameters using “Hard Modeling” programs such as LISREL, Amos, EQS etc.; the other is deviation analysis based on Partial Least Squares (PLS) using “Soft Modeling” programs such as MATLAB, Smart PLS, PLS Graph etc. Since the inception of contemporary structural equation methodology in the middle 1960s, ML has been the predominant estimation method (Anderson & Gerbing, 1988) and LISREL developed by Jöreskog and Sörbom (1978) has been used for parameter estimation in nearly every application of structural modeling. However, data in the social sciences often do not satisfy the restrictive assumptions underlying ML techniques (Fornell & Bookstein, 1982). PLS, first introduced by Wold (1966) and then receiving a great amount of attention in the field of chemometrics, is becoming a hot topic in the field of management in dealing with the limitations of ML.

The purpose of ML is theory testing and development as concluded by Jöreskog and Wold (1982) “ML is theory-oriented, and emphasizes the transition from exploratory to confirmatory analysis” (p. 270). Therefore, constructing a structural equation model begins from a specifically designated model. The first step of SEM is model specification including relationships between observed variables and latent variables, relationships between latent variables, and fixing some factor loadings in complex models. The second step is model identification to determine whether all the free parameters can be represented by equations made up of at least one factor in the covariance matrix of observed variables. If the model can be identified, the next step is to estimate parameters in the model by minimizing the distance between the underlying 119

covariance matrix ( 6(T ) ) and the sample covariance matrix( S ), which is called the fit function ( F ( S , 6(T )) ). Thus, min F ( S , 6(T )) is the condition of perfect estimation. 6(T ) Where A

' / y A*)/ x º » ' / x */ x  4 G »¼

ª/ y A(*)* '  \ ) A ' / y '  4 H « ' / x )* ' A ' / y «¬

( I  B) 1

The common methods of estimation are ML and GLS. The fit functions are as follows:

FML

^

lg ¦ (T )  tr S ¦ 1 (T )`  lg S  ( p  q )

^>

1 ( )tr I  ¦ (T ) S 1 2

FGLS

@` 2

The final step is to evaluate the fit between the model and the sample by some fit indexes such as the root mean square error of approximation (RMSEA), the normed fit index (NFI), the non-normed fit index (NNFI), or the comparative fit index (CFI).

However, SEM based on PLS is a kind of iteration to approximate the real value of parameters, so whether or not the model can be identified this method ultimately obtains estimated values of the corresponding parameters. In other words, different from SME based on ML, SME based on PLS does not need to identify the model before estimation. Here take SEM with two latent variables as an example. The sample size is N, observed values of xh and y k are x hn and y kn respectively, where n=1…N and all the data are assumed to be standardized. The first step is iteration to get estimated values of latent variables. f1 ¦ (Z h x hn ) (1a)

L Xn

h

f 2 ¦ (Z k y kn ) (1b)

LYn

k

Where f1 and f 2 are standardized operators,

f1

­° 1 r® °¯ N

1

ª

¦ «¬¦ (Z n

h

h

º x hn )» ¼

2

½° 2 ¾ °¿

The same applies to f 2 for Z k and x hn According to the weights, LYn

¦ (Z

h

x hn )  d n (2a)

h

120

y kn

Z k L Xn  d kn (2b)

The first step obtains initial values.

Z k(1)

1 , when k=k0

Z k(1)

0 , when kĮk0

Then, Z k(1) is entered into function (1b) and get L(Yn2 ) ; L(Yn2 ) is entered into function (2a) to get Z h( 2 ) ; Z h( 2 ) is entered into function (1a) to get L(Xn2 ) ; L(Xn2 ) is entered into function (2b) to get Z k( 2 ) . If the result meets the following end condition, the iteration stops; otherwise it reverse to the second step and the iteration continues.

Z ( n )  Z ( n 1)  10 5 or (Z ( n )  Z ( n 1) ) / Z ( n )  10 5 The second step is to regress L Xn , LYn on observed values of the corresponding indicators. x hn

p h L Xn  P hn (3a)

y kn

p k LYn  P kn (3b)

Where P is a residual and p is a regression coefficient. The regression of LYn on L Xn is as follows: LYn

b1 L Xn  e (3c)

Where e is a residual and b1 is regression coefficient. The third step is to obtain initial functions as follows:

L Xn

f1 ¦ (Z h x h ) h

L Yn

f 2 ¦ (Z k y k ) k

Then the intercepts in function (3a), (3b), (3c) can be calculated.

Comparing the two kinds of algorithms ML and PLS, this study opts for partial least squares (PLS) analysis for several reasons. First, PLS does not make the parametric assumption that the data is normally distributed. It can be used when the sample size is quite small because estimation by partial least squares uses limited information rather than the full information estimation of maximum likelihood (Chin, 1998). Based on “rules of thumb”, a model with the number of indicators and casual paths reflected in this study would require close to 500 cases to satisfy ML covariance processing 121

requirements. Second, a covariance-based method usually gets a better model fit when there are less than 10 constructs, while PLS works well when the structural model is large and complex due to the fast convergence rate of partial least squares (Wold, 1985). Third, PLS is particularly appropriate when the model includes formative measurements because partial least squares employs a component-based approach for estimation purposes and can be used to estimate models that use both formative and reflective indicators (Chin, 1998). In our model, we have reflective constructs, formative constructs, as well as single-item constructs. Fourth, PLS allows each indicator to vary in how much it contributes to the composite scores of the latent variables, whereas summed scales mask measurement error through the two-step process in which item aggregation is performed outside the theoretical context (Chin, Marcolin, & Newsted, 2003). Since the framework of a flexible distribution channel in this study is of high complexity but the theory is by no mean mature, PLS is more suitable. It suits exploratory analysis and theory development by maximizing the explained variance of the dependent variables (here these are distribution flexibility and the corresponding performance outcomes) (Barclay, Higgins, & Thompson, 1995; Chin, 1998). Finally, PLS applies a product indicator approach to estimate an underlying interaction construct (Chin et al., 2003). The following section explains why we use PLS to test moderating effects. x

Moderating effects analysis

Structural equation modeling (SEM) to date has predominantly used independent and dependent latent variables to examine linear effects in a theoretical model. A reason for the lack of testing of interaction and nonlinear effects in latent variable models in the research literature may be that the techniques are technically demanding and not well understood (Schumacker & Maroulides, 1998). Kenny and Judd (1984) have developed a solution, but it is quite complicated with many nonlinear constraints and it requires a relatively large sample size to have sufficient power. Li et al. (1998) also compared three approaches for testing moderation by SEM (Jaccard & Wan, 1995; -ĘUHVNRJ  Yang, 1996; Ping, 1996). But there is still an argument about basic issues such as data types, sample size and parsimony, multicollinearity, and statistical distribution problems which are often ignored in different SEM techniques which attempt to test interaction. The techniques for representing and testing interaction effects are familiar in the 122

regression and analysis-of-variance methodologies (Rigdon, Schumacker, & Wothke, 1998). For example, Sharma, Durand and Gur-Arie (1981) made use of the four steps to identify and analyze moderator variables: (a) doing a moderated regression analysis (MRA) procedure; (b) determining whether a variable is a quasi or pure moderator variable; (c) determining whether a variable is moderator or is a criterion or predictor variable; and (d) doing a subgroup test. This is a commonly used method in testing moderating effects, but regression produces biased and inconsistent coefficient estimates along with a loss of statistical power (Chin et al., 2003). The PLS productindicator approach represents a one-step technique by creating all pair-wise product indicators where each indicator from the main construct is multiplied with each indicator from the moderating construct. It requires no additional specification of parameter constrains or assumptions of multivariate normality (Chin et al., 2003), thus in this study it is used together with the four steps to analyze moderating effects embedded in the structure model.

5.3 Multiple case studies for the complementary model Adopting a criterion-free specification, which has universal applicability (Venkatraman, 1989), the complementary model focused on an equifinality mechanism based on theories of fit as gestalts and fit as covariance. In order to illustrate this mechanism from a systematic perspective, we used case analysis for several reasons.

First, the case study method is used to examine the implications of a theory that specifies how a particular set of outcomes are associated with a particular situation, and how applicable the theory is for an organization which finds itself in that particular situation (Yin, 1994). Since these criterion-free kinds of fit cannot be separated from their particular contexts, the case study method is appropriate for this investigation.

Second, according to Eisenhardt (1989) case studies are: “particularly well-suited to new research areas or research areas for which existing theory seems inadequate” (p. 548). Although fit as matching, fit as mediation and fit as moderation are well developed theories, there are few studies of fit as gestalts and fit as covariance and there 123

is no accepted framework for analyzing equifinality. Therefore, a case study is appropriate for the complementary model due to its exploratory nature.

Third, case studies have a distinct advantage when a “how” or “why” question is being asked about “a contemporary set of events over which the investigator has little or no control” (Yin, 1994, p. 4). Thus, the case study method seems appropriate to obtain an in depth knowledge of the processes, sources, and performance outcomes of distribution flexibility and to illustrate how firms make and implement a variety of different distribution flexibility strategies under different distribution environments.

5.3.1 Evidence collection

There were three essential aspects of evidence collection for this case study: first, in addition to the key source of information from interviews as well as direct observations, it contained multiple other sources of evidence including company brochures, newsletters, publications, and archival records which show the background information of each company and its industry in order to categorize the companies according to their external environment; second, the case study data base included case study notes, gathered documents, tabular material and narratives in order to make information traceable and reliable; third, a chain of evidence was developed that links questions asked, data collected and conclusions drawn. The following sections will explain how we collected the evidence through in-depth interviews with key informants from the focal firms selected by purposeful sampling. x

In-depth interviews

To maintain data consistency and improve richness in detail, we used a semi-structured interview approach (Yin, 1994). As shown in Appendix 5-C, the interview guide based on the literature review was comprised of 21 open-ended leading questions together with some probing questions, which are divided into six sections as follows:  Background description of the key informant and his/her company;  An introduction to their industry and description of the changes their distribution environment underwent in the last five years; 124

 An overview of their product characteristics including product life cycle, category structure, and customer demand;  The structure of their distribution network and their policies about distribution channel management;  An explanation of what kinds of operational, marketing, and coordination strategies they would take under changing circumstances;  An assessment of their distribution performance.

The background information of each firm were obtained from its website and the interview guide was sent a few days before the formal interviews were conducted which lasted approximately one and a half to two hours. All the conversations were taperecorded and coded after gaining permission in order to ensure reliability and to transcribe for analysis. If the key informant agreed to share some documentary materials from their company, we would then enter into appropriate confidentiality agreements. x

Unit of analysis

The unit of analysis in this case study is the key informant’s assessment of the structure, strategies, and performance of their downstream distribution channel. Thus, we aimed at collecting direct information from focal firms in different subsectors of manufacturing industry. We contacted the managers from 6-8 focal firms that are acquaintances of the author. Semi-structured in-depth interviews were conducted with qualified key informants who ranged from middle managers and senior managers to presidents/vice presidents all of whom must meet the following requirements: (a) have significant experience in dealing with channel management or working with the downstream distributors/direct customers; (b) be quite knowledgeable about operations and distribution channel management; and (c) be involved in the distribution decisionmaking process and hold opinions that are representative of the organization or business unit as a whole. Moreover, multiple interviewees within one case were encouraged to participate as this study aimed to explore many cross-functional issues. x

Sample selection

125

Since we wanted to compare the differential challenges and responses in multiple firms, we developed a multi-site study (Creswell, 1998). The studies of each site focused on the same issue so we used purposeful sampling (Stake, 1995), because we wanted to explore the differing environmental conditions. We selected manufacturing firms based on different dimensions of flexibility drivers or reasons why flexibility exists in different situations (Jack & Raturi, 2002b). As explained in the previous chapter, Nonaka and Nicosia (1979) make use of two dimensions: certainty-uncertainty and homogeneity-heterogeneity to define the information generated by the markets faced by a seller. The former is about information quality, while the latter is about information quantity. As shown in Table 5-5, we found four types of companies located in different cells of the typology first presented in Chapter 4.

Table 5-5 A typology for sample selection Uncertainty

Low

High

Low

Type I

Type II

High

Type III

Type IV

Heterogeneity

In this qualitative component of the dissertation, we only analysed the evidences from four firms among the interviewed firms that are acquaintances of the author because each representing a distinctive type, which reflects a cross-classification among levels of two dimensions shown in Table 5-5: (a) the level of uncertainty determined by asking such questions as “In the last five years, have there ever been any changes in customer demand?” “In the last five years, have there ever been any changes in competitive activity?” “In the last five years, have there ever been any changes of production technology in your industry?” and “If there have ever been new products introduced in your industry in the last five years, did they become obsolete quickly?” and (b) the level of heterogeneity by asking such questions as “How many different categories of this product exist?” “Do you think the range is wide enough for most of your end customers or do they need more?” and “Is there any segmentation of your end customers? If so, what are the differences in the orders among them?” Based on the responses of these questions, the following section presents how we categorized the firms. 126

5.3.2 Sample profile x

Type I

Company P manufactures, markets, and develops men’s wear and other clothing with world class production lines and auxiliary equipment introduced from abroad. Although they are still at an early stage of development, their growth rate is dramatic, at about 708.9% in recent years. Now they have around 1000 employees and 30 million RMB in annual sales. Their products are mainly sold in Shandong province, where the market is very stable. Suits are their main products and men’s suit styles in China have not changed much for many years. Furthermore, since they focus on county-level cities in the local market, the level of competition they face is relatively low. Thus, compared with the other three companies, Company P is in a relatively stable and homogeneous environment. x

Type II

Company Z is one of the most professional and largest filter press manufacturers in China with leading-edge technology and a wide range of products for the Solid/Liquid Separation industry. They have established 12 branches and more than 150 sales offices in less than 10 years. They now have more than 1300 agents all over the country and their annual sales revenue is above 1.5 billion RMB. A few years ago, the filter press was used exclusively in the coal washing industry, but, as an industry that consumes a high level of energy, its market had been shrinking. Now, many industries connected with solid/liquid separation demand this kind of product including, for example, the chemical engineering industry, the sugar manufacturing industry, and the titanium white industry. This situation also accelerates the upgrading of products. For example, their key machines were of small size two years ago, but now they give priority to energyefficient types of a large size which they claim to be the future trend. Furthermore, prices fluctuate rapidly because of lack of differentiation among competitors. Previously, Company Z’s profit was around 50% but fell to around 30%-40% between 2008 and 2009 because of competition. Thus, although the environment of Company Z is relatively homogeneous, it is also uncertain. 127

x

Type III

Company Q, one of the leading pharmaceutical companies in China, develops, manufactures and markets quality and affordable generic drugs. They now have more than 6000 employees and 6-10 billion RMB annual value production. In 2009, they were chosen as one of the 10 pharmaceutical companies with the largest growth potential in China. Although the pharmaceutical industry is not a big part of Chinese GDP, it is developing very rapidly. They already have three product lines consisting of Oncology, Infectious disease, and Cerebrovascular & Cardiovascular and more than 200 categories of products. The customer demand for most categories has not changed very much in recent years. Although the number of companies producing similar categories of products is increasing, the number of strong competitors is very limited. Most products available are generic drugs and the process of R&D is very arduous, lasting from five to seven years. Thus, although Company Q introduces new products each year, previous products remain on the market for a long time. Therefore, Company Q is in a certain environment although a wide range of product category and a large number of customer segmentation represents that the environment is heterogeneous. x

Type IV

Company J invented the first Automatic Soymilk Maker in the world and engaged mainly in the health-oriented household appliances industry. Their average growth rate has kept above 40% in the most recent five years and the market share of their soymilk maker, the most famous brand in this field, has exceeded 80%. They already have 450 first level distributors and more than 20000 retail outlets all over the country. Their total revenue in 2008 was 4.3 billion RMB. In addition to producing and selling a soymilk maker, Company J has entered many small household appliance categories including those of induction cookers, blenders, juicers, electrical kettles, and electrical pressure cookers, which suggests that their market covers a large number of segmentation and different kinds of customer demands. In 2008, after the so-called “Poison Milk” incident, the market for the soymilk maker became even more intensely competitive as many big competitors quickly entered including brands such as Midea, Supor and Galanz. These entrants promoted similar products at a lower price and even tried to 128

seize market share at or below marginal cost. Therefore, the environment of Company J is not only heterogeneous but is also quite uncertain.

Above all, Table 5-6 profiles these four firms in terms of their distribution environment and Appendix 5-D gives a brief introduction of each company and their key informants.

Table 5-6 Business background Company P

Company Z

Company Q

Company J

Low

High

Low

High

Preferences for product features

Ƹ

Ʒ

Ƹ

Ʒ

Demand volume

Ƹ

Ʒ

---

Ʒ

Number and quality of competitors

Ƹ

Ƹ

Ƹ

Ʒ

Strategies of competitors

Ƹ

Ʒ

Ƹ

Ʒ

Manufacturing technology

Ʒ

Ʒ

Ƹ

Ƹ

Product development

Ƹ

Ʒ

Ƹ

Ʒ

Low

Low

High

High

V

V

T

T

Number of customer segmentation

V

V

T

T

Variability of customer demand

V

T

---

T

Uncertainty x

x

x

Demand

Competition

Technology

Heterogeneity x

Product Category of existing products

x

Customer

Note: “Ʒ” indicates evidence of uncertainty, “Ƹ” indicates evidence of certainty, “T” indicates evidence of heterogeneity, “V” indicates evidence of homogeneity, “---” indicates no evidence

129

5.3.3

Analysis procedures

Based on the theoretical framework and working propositions in the previous chapter, we first enhanced the quality of the research design by integrating the techniques of testing reliability and validity of case studies suggested by Yin (1994) and the techniques of assessing trustworthiness which Lincoln and Guba (1985) consider more appropriate for qualitative studies. Then, within-case analysis and cross-case analysis were used to explain the mechanism. x

The quality of the research design

In a case study, the following techniques are used to deal with validity and reliability. First, since lack of external validity has been the major criticism of case studies, this is an issue that must be addressed during the design of the research. Although Yin (1994) suggests that external validity can best be addressed by using the replication logic of multiple-case studies, that logic is quite hard to apply in this study since there is only a limited number of firms. Instead, Lincoln and Guba (1985) replace external validity with “transferability”, a new term which has a better fit with naturalistic epistemology. They also suggest thick description as a good technique to deal with external validity. Therefore, we adopted thick description in Chapter 7.

Second, there are three elements associated with the establishment of construct validity during the data collection phase (Yin, 1994): (a) using multiple sources of evidence; (b) establishing a chain of evidence; and (c) having key informants review a draft case study report. In this study, we collected multiple sources of evidence such as industry databases, product catalogues, company magazines and websites, and brochures to analyze the background information of each company and its industry. Furthermore, the transcripts of records were first sent back to interviewees in order to ensure accuracy.

Third, internal validity in explanatory case studies relates to making proper inferences from the data, considering alternative explanations, and making use of convergent data, as well as related techniques such as pattern matching, explanation-building and timeseries analysis (Yin, 1994). Thus, we employed searching for pattern-matches and explanation-building during within-case analysis and cross-case analysis. However, 130

Lincoln and Guba (1985) propose that internal validity be replaced with “credibility” and also recommend such techniques as prolonged engagement, persistent observation, triangulation, peer debriefing, and member checks. For example, following this recommendation, the transcripts of the records were coded and checked by all three leading members of the project.

Finally, Yin (1994) suggests two keys to enhanced reliability: the use of a case study protocol and the development of a case study data base. A case protocol includes the interview guide as well as the procedures to be followed in using the test instrument. In this study, to increase the reliability of the case analysis, we used an interview protocol and developed a case study database as recommended by Yin (1994). On one hand, all of the case studies involved on-site visits. Those who agreed to participate in the research were sent a letter of introduction for the research to prepare them for the session. They were also sent a copy of the interview guide so they know what types of questions to expect and the type of documentation that might be requested. On the other hand, the case study data base also included a copy of the completed interview guide or guides for each firm as well as any additional notes taken outside of the interview guide and a detailed summary write-up of each case. In addition, Lincoln and Guba (1985) suggest using a dependability audit to establish “dependability” in place of reliability. Thus, after being sent back to interviewees for accuracy and then coded and checked by members of this project, the transcripts of the records were subsequently audited by other invited researchers in order to establish dependability. x

Within-case and cross-case analysis

A staggering volume of data in a case study drives within-case analysis and the overall idea is to become intimately familiar with each case as a stand-alone entity (Eisenhardt, 1989). Therefore, the first part of analysis involved detailed case study write-ups for each site in order to know the unique pattern of each case before generalizing patterns across cases. In this study, industry type, product characteristics, distribution network structure, type of distribution flexibility and distribution performance were analyzed in each case.

131

Coupled with within-case analysis is cross-case analysis for patterns. Because constructs of the theoretical framework have been suggested by the research problem and tested by a survey, the case study made use of cross-case analysis to select dimensions and then look for within-group similarities coupled with intergroup differences (Eisenhardt, 1989). Based on these similarities and differences, these sample cases were categorized into subgroups and a typology was constructed to explain the mechanism of equifinality based on fit as gestalts and fit as covariance.

5.4 Summary This chapter presented the methodology used in this study to test the hypotheses and revise working propositions developed in the previous chapter. In order to examine the fit mechanisms underlying environmental drivers, network sources and performance of distribution flexibility, different methods are required for two sub-objectives. Specifically, a survey was mainly used to analyze fit in selection and interaction approaches in the core model. A description of the questionnaire development procedures, data collection and sampling frame were covered in this chapter, while results concerning reliability and validity, hypothesis testing, and more detailed analysis are discussed in Chapter 6. In addition, multiple case studies were applied to explain fit in system approaches in the complementary model. The latter part of this chapter illustrated the in-depth interviews, evidence collection and sample selection procedures. Chapter 7 presents the results of with-case analysis and cross-case analysis.

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6 SURVEY RESULTS AND ANALYSIS

6.1 Introduction This chapter presents the results of the survey study and is structured in the following sections: x

Descriptive statistics of the data collected from Chinese manufacturing industry are presented, and common method bias is also discussed;

x

Reliability and validity of the measurement model are assessed in detail using partial least squares analysis;

x

Hypotheses about fit as matching and fit as mediation are tested in the full structural model using partial least squares analysis;

x

Hypotheses about fit as moderation are tested in the structural model with interaction terms and subgroups using partial least squares analysis;

x

Possible non-linear relationships are discussed in post hoc analysis using polynomial regressions;

x

A section summarizing the findings in this survey study is included.

6.2 Descriptive analysis A dataset of 212 cases was used in the final data analysis due to the strict data collection and screening procedure discussed in Chapter 5. To assess the extent and patterns of missing data, we tabulated (a) the percentage of variables with missing data for each case and (b) the number of cases with missing data for each variable (Hair, Black, Babin, Anderson, & Tatham, 2006). Through Missing Value Analysis in SPSS, we found that case 78 has the largest percentage of variables with missing data, which is 17.6%. In addition, 186 cases have no missing data on any variables, so the percentage of cases with missing data is only 12.3%. According to Hair et al. (2006), variables or cases with 50% or more missing data should be deleted; otherwise, lower levels of missing data 133

can often be remedied. As the analysis suggests a pattern of missing completely at random (MCAR), we applied regression imputation which is preferred at a level of missing data from 10% to 20% (Hair et al., 2006).

Q-Q Plot and Box-plot were used to test normality and outliers. Appendix 6-A shows brief descriptive statistics for each set of measures. It also gives a normality test based on skewness and kurtosis values. Kurtosis refers to a measure of the extent to which observations cluster around a central point, while skewness is a measure of the asymmetry of a distribution. If the calculated z value exceeds a critical value (r2.58), the assumption about the normality of the distribution at the 0.01 probability level can be rejected (Hair et al., 2006). In Appendix 6-A, all Zskewness are below the critical value, and five indicators have the Zkurtosis value that exceeds r2.58 slightly, which is only 10.2% of the total indicators. Furthermore, an absolute normal distribution is impossible to find in the real world (Carmines & McIver, 1981). Although covariance-based maximum likelihood (ML) estimation rests on the assumption of a joint multivariate distribution, the partial least squares (PLS) technique of model building uses very general, soft distributional assumptions (Chin, 2010).

6.3 Common method bias Common method bias arises in quantitative research when the covariance caused by the measurement approach rather than the measured trait causes measured relationships between two constructs to either inflate or attenuate compared to the true value (Williams & Brown, 1994). Since we relied on the same key informants to assess all constructs in our framework, common method bias may be a threat to the validity of our findings (Podsakoff, MacKenzie, Podsakoff, & Lee, 2003). To test for common method bias, we first applied the Harman single-factor test. Traditionally, researchers using this technique load all of the variables in their study into an exploratory factor analysis (EFA). If either a single strong factor emerges or the first factor loads significantly on all items, common method variance is most likely present in the data. In this study, we examined the unrotated factor solution and found that there are 13 factors emerging from the factor analysis and that the first factor only accounts for 22.74% of the 134

variance, which indicates that no general factor accounts for the majority of the covariance among the measures.

However, Podsakoff et al. (2003) argue that Harman’s single-factor test does not statistically control for common method variance. Instead, they provide a good overview of other methods and suggest the general idea of adding error correlations or factors to the analysis, allowing the covariance that results from the measurement to escape from the model rather than affect the substantive regression or correlation relationships. However, they do not discuss any methods for controlling for common method variance in a partial least square analysis context. Reviewing recent techniques developed for modeling common method variance, Ylitalo (2009) suggests an approach for PLS path modeling in which a method factor is included as a predictor for all endogenous latent constructs in the model. This means that the calculated values from the outer estimation for the latent constructs are a result of the true relationships between the constructs and error variance caused by measurement (See Figure 6-1).

IV

IV1

IV2

DV

IV3

M1

Method

M2

DV1

DV2

DV3

M3

Figure 6-1 Simplified example of the proposed approach

Since the indicators of the method factor should be theoretically unrelated to any of the constructs of interest and preferably not correlated except for the correlation caused by sharing the same method (Ylitalo, 2009), we chose the three items for measuring respondent background as indicators of the latent common method factor. Then, we examined the significance of the structural parameters both with and without the latent common methods variance factor in the structural model. In the model controlling 135

common method variance, the common method factor was correlated with all endogenous latent constructs including logistics flexibility, relationship flexibility, distribution service quality, relationship satisfaction, and link duration, where common method bias might likely exist. The results show that the significance of the hypothesized path coefficients is not different between the two models, revealing that the effects remain even if a common method factor is included. Thus, these diagnostics imply that the findings are not affected by the use of the same data source.

6.4 Measurement model assessment The traditional two-step approach separating measurement and structural model assessments is based on strict assumptions that the model is correct and the data are multivariate normal, while PLS applies a one step estimation which assumes that constrained measurement error lies within the estimates of the theoretical variables (Chin et al., 2003). Therefore, reliability and validity of a measurement model that represent the adequacy of items to measure constructs are assessed simultaneously with a structural model that illustrates hypothesized relationships among the constructs. As discussed in Chapter 5, all of the measures in this dissertation were adopted from the existing literature. Each construct comprises several reflective indicators, which were adjusted to fit the context in this study except for tie density which is comprised of two formative indicators. As conventional procedures for assessing the validity and reliability of reflective scales are not appropriate for formative scales (Diamantopoulos & Winklhofer, 2001), we validate the two kinds of measurements separately in the following sections.

6.4.1

Reflective measures

In the construct of reflective measures, each manifest variable xij reflects its latent variable ȟj, so each manifest variable is related to its latent variable by the following simple regression: xij

S i 0  S ij [ j  H ij

Where ʌi0 is the intercept, ʌij is the loading of xij , İij is the residual, and ȟj has mean m 136

and standard deviation 1. The only hypothesis made on this model is the predictor specification condition (Wold, 1966):

E ( xij [ j ) S i 0  S ij [ j This hypothesis implies that the residual İij has a zero mean and is uncorrelated with the latent variable ȟj. In this model assessment, a priori knowledge concerns the unidimensionality, reliability, and validity (Tenenhaus, Esposito Vinzin, Chatelin, & Lauro, 2005; Henseler, Ringle, & Sinkovics, 2009). If the data do not fit the model, fit can be improved by removing some manifest variables with weak loadings (Tenenhaus et al., 2005). x

Unidimensionality

Unidimensional measures mean that a set of measured indicators has only one underlying construct (Hair et al., 2006). In PLS path modeling, the block of manifest variables is unidmensional in the meaning of factor analysis. Tenenhaus et al. (2005) recommend three tools to check the unidimensionality of a block: (a) use of principle component analysis of the block, (b) Cronbach’s alpha and (c) Dillon-Goldstein’s ȡ (composite reliability). As shown in Table 6-1, the first eigenvalue of the correlation matrix of the block of manifest variables is larger than 1 and the second one is smaller than one, so the unidimensionality of these blocks is acceptable. The other two methods are also related to reliability assessment which is presented in the next section.

Table 6-1 Principle component analysis Block Distribution Service Quality Timeliness Availability Condition Relationship Satisfaction Link Duration Logistics Flexibility Relationship Flexibility Tie Strength Environmental Uncertainty Environmental heterogeneity

First eigenvalue

Second eigenvalue

1.99 1.54 2.04 2.89 3.13 2.31 2.31 2.58 2.38 1.74

0.63 0.86 0.58 0.65 0.88 0.80 0.71 0.82 0.97 0.68 137

x

Reliability

The first step to determine the reliability of the measurement model is examining the weights and loadings of the individual measures to their respective construct for reflective measures. The individual item weight is the regression beta coefficient of the item on its latent construct, while the individual item loading represents the correlation between each item and its latent construct. According to Chin (1998), the loadings should be greater than 0.70 and items with loadings of less than 0.50 should be dropped. As shown in Table 6-2, Item4 and Item6 of link duration had low loadings around 0.50, indicating they were candidates for deleting. Looking back at the meaning of the two items, which were “we are willing to make sacrifices to help the key account from time to time” and “any concessions we make to help the key account will even out in the long run”, we considered that the description was very absolute and might not be a common situation for most focal firms in distribution channels. Therefore, we dropped the two items. In addition, Item 5 of environmental uncertainty about regulation and policy uncertainty had a very low loading below 0.50. Since this might not be an important factor when considering downstream distribution channels, we decided to delete this item. In the modified model, the loadings of most items left were above 0.70, with a few above 0.60 which are still acceptable when the current context differs from the original context in which the instrument is developed (Barclay et al., 1995).

The measurement model in a reflective scheme assumes that each group of manifest variables is homogenous and unidimensional related to a single variable. Thus, the reliability of the component (internal consistency or homogeneity of a block, of manifest variables) is estimated by either Cronbach’s alpha or Composite Reliability (Chin, 1998). Cronbach’s alpha is the most widely used reliability estimate (Cronbach, 1951), which is calculated using the following equation:

aj

1 ¦ cor ( xij , xi ' j ) p j ( p j  1) i zi ' pj u 1 1 p j 1 cor ( xij , xi ' j )  ¦ p j  1 p j ( p j  1) i zi '

Where xij is the ith manifest variable in block j and pj is the number of manifest variables in block j. 138

Typically, reliability coefficients of 0.70 or higher are considered adequate, but Nunnally and Bernstein (1994) further state that permissible alpha values can be slightly lower (0.60) for new scales. As is shown in Table 6-2, Cronbach’s alpha of each construct exceeds the cut-off value of 0.60 expect for the construct of availability. However, as shown in the calculation, the larger number of indicators composed a latent construct would enhance the value of Cronbach’s alpha. Fornell and Larker (1981) recommend another estimate of Composite Reliability (CR), which is computed as follows: 2

Uj

§ n · ¨ ¦ Oij ¸ u var[ j ©i1 ¹ 2

§ n · § n · ¨ ¦ Oij ¸ u var[ j  ¨ ¦ varH ij ¸ ©i1 ¹ ©i1 ¹

Where Ȝij is the component loading a manifest variable, var(ȟ is factor variance, var(İ) is unique/error variance and where, if var(ȟ) is set at 1, then var(İ)=1- Ȝij2. Usually, CR value of 0.70 or a higher level suggests good reliability (Fornell & Larcker, 1981). Cronbach’s alpha assumes tau-equivalence (identical loadings) and is a lower bound estimate of reliability, while composite reliability should be larger and more accurate if loadings are accurately estimated (Chin, 1998). As is shown in Table 2-3, all CR values are above the cut-off value and much higher than Cronbach’s alpha. Besides, the CR value of reliability and its higher-order factor, service quality, were both above 0.70 although Cronbach’s alpha of reliability is lower. Therefore, the reliability of our reflective scales performed well as a whole.

139

Table 6-2 Reliability of all measurement scales (n=212) Construct & Original Model Items Loading Weight Distribution Service Quality Timeliness SQT1 0.82 0.41 SQT2 0.86 0.43 SQT3 0.75 0.39 Availability SQA1 0.82 0.58 SQA2 0.69 0.38 SQA3 0.62 0.43 Condition SQC1 0.83 0.41 SQC2 0.87 0.42 SQC3 0.78 0.38 Relationship Satisfaction RS1 0.91 0.33 RS2 0.91 0.31 RS3 0.89 0.31 RS4 0.67 0.22 Link Duration LD1 0.78 0.30 LD2 0.84 0.29 LD3 0.77 0.23 LD4 0.56 0.15 LD5 0.75 0.24 LD6 0.57 0.14 Logistics Flexibility LF1 0.72 0.29 LF2 0.79 0.31 LF3 0.84 0.40 LF4 0.67 0.31 Relationship Flexibility RF1 0.78 0.39 RF2 0.77 0.30 RF3 0.80 0.35 RF4 0.68 0.26 Tie Strength TS1 0.62 0.23 TS2 0.73 0.28 TS3 0.74 0.27 TS4 0.73 0.33 TS5 0.76 0.29 Environmental Uncertainty EU1 0.80 0.39 EU2 0.79 0.35 EU3 0.67 0.30 EU4 0.66 0.27 EU5 0.43 0.07 Environmental heterogeneity EH1 0.85 0.57 EH2 0.68 0.31 EH3 0.74 0.42

Modified Model Loading Weight

0.82 0.86 0.75

0.41 0.43 0.39

0.82 0.69 0.62

0.58 0.38 0.43

0.83 0.87 0.78

0.41 0.42 0.38

0.91 0.91 0.89 0.67

0.33 0.31 0.31 0.21

CR 0.87 0.85

Cronbach’s Į 0.83 0.74

0.75

0.52

0.86

0.77

0.91

0.87

0.88

0.81

0.84

0.75

0.84

0.75

0.84

0.76

0.82

0.71

0.80

0.64

0.81 0.35 0.88 0.35 0.77 0.27 dropped 0.74 0.28 dropped 0.73 0.79 0.84 0.67

0.30 0.31 0.39 0.31

0.78 0.77 0.80 0.68

0.39 0.31 0.36 0.26

0.62 0.73 0.75 0.72 0.75

0.23 0.28 0.27 0.33 0.28

0.81 0.40 0.80 0.36 0.67 0.31 0.65 0.28 dropped 0.85 0.68 0.74

0.57 0.31 0.42 140

x

Construct validity

Construct validity is the extent to which the items in a scale measure the underlying abstract or theoretical construct (Churchill, 1979). Construct validity is necessary for meaningful and interpretable research findings and can be assessed in various ways. Hair et al. (2006) summarize a procedure for testing construct validity including face validity, convergent validity and discriminant validity.

Convergent validity

Convergent validity measures the similarity or convergence between the individual items measuring the same construct, which should be achieved ahead of a reliability test (Steenkamp & Trijp, 1991). Several methods are available. First, convergent validity can be assessed by testing whether or not each individual item’s coefficient is greater than twice its standard error (Anderson & Gerbing, 1988). This was confirmed in the PLS outputs. Second, the larger the t-values or the loadings, the stronger the evidence that the individual items represent the underlying factors (Bollen, 1989). Using a boostrap procedure as explained in the following sections, we found that the t-value for each indicator was greater than 1.96, indicating a significant loading (Schumacker & Lomax, 2004). Third, the Average Variance Extracted (AVE) among a set of construct items is a summary indicator of convergence (Fornell & Larcker, 1981). This value can be calculated using the following equation: n

Uj

¦O

2 ij

u var[ j

i 1

¦ Oij2 u var[ j  ¦ varH i p

p

i 1

i 1

Where Ȝij is the component loading of a manifest variable, var(ȟ is factor variance, var(İ) is unique/error variance, and if var(ȟ) is set at 1, then var(İ)=1- Ȝij2. Thus, if AVE value is less than 0.50, the variance due to measurement error is larger than the variance captured by the construct, in which case the construct validity might be problematic (Fornell & Larcker, 1981). Table 6-3 shows that the AVE value of each construct exceeded the cut-off value of 0.50, which indicates that convergent validity of the overall scales is good. 141

Discriminant validity

Discriminant validity measures the extent to which the individual items of a construct are unique and do not measure any other constructs. Confirmatory factor analysis provides a common method of constructing models for all possible pairs of latent constructs to assess discriminant validity. These models are run on each selected pair, allowing for correlation between the constructs and fixing the correlation between the constructs at 1.0. A significant difference in chi-square values for the fixed and free solutions indicates the distinctiveness of the two constructs (Bagozzi, 1994). However, in practice, this test does not always provide strong evidence of discriminant validity because of high correlations. There are two other procedures commonly used to assess the discriminant validity of the measures.

The first is to show that a construct is more strongly related to its own measures than with any other construct. A better test is to compare the square root of the average variance extracted (AVE) with the correlations among constructs. As evidence of discriminant validity, the square root of AVE of each construct should be greater than the correlations between the construct and any other construct (Fornell & Larcker, 1981; Chin, 1998). The matrix shown in Table 6-3 shows that all reflective constructs met this required criterion.

The second and more detailed test is to see how each item relates to each construct. According to Chin (1998), when going down a particular construct column, you should expect to see item loadings to be higher than the cross loadings. Table 6-4 shows the correlations of each item with its intended construct and with all other constructs. Scanning across any particular item row, we find that each item is more strongly correlated with its construct column than with other construct column. Consequently, we concluded that all constructs exhibit satisfactory discriminant validity.

142

0.81 0.51** 0.50** 0.49** 0.39** 0.58** 0.42** -0.32** 0.32** 0.13*

0.71 0.56** 0.53** 0.34** 0.28** 0.46** 0.37** 0.35** -0.36** 0.37** 0.17*

0.51

0.66

0.68

0.72

0.64

0.58

0.57

0.51

---

0.54

0.57

*

Correlation is significant at the 0.01 level (2-tailed).

Correlation is significant at the 0.05 level (2-tailed).

**

SQT

SQA

AVE

Note: The square root of AVE is on the diagonal.

Construct Distribution Service Quality Availability (SQA) Timeliness (SQT) Condition (SQC) Relationship Satisfaction (RS) Link Duration (LD) Logistics Flexibility (LF) Relationship Flexibility (RF) Tie Strength (TS) Tie Density (TD) Environmental Uncertainty (EU) Environmental Heterogeneity (EH) 0.14*

0.18*

-0.23**

0.41**

0.42**

0.37**

0.30**

0.39**

0.82

SQC

0.04

0.26**

-0.17*

0.42**

0.57**

0.40**

0.71**

0.85

RS

0.09

0.25**

-0.14*

0.41**

0.58**

0.32**

0.80

LD

0.76

LF

0.16*

0.21**

-0.29**

0.32**

0.38**

Table 6-3 AVE and correlation matrix

0.20**

0.26**

-0.27**

0.56**

0.75

RF

0.17*

0.17*

-0.16*

0.71

TS

-0.15*

-0.26**

---

TD

0.21**

0.73

EU

143

0.75

EH

Table 6-4 Factor structure matrix of loadings and cross-loadings

SQA1 SQA2 SQA3 SQT1 SQT2 SQT3 SQC1 SQC2 SQC3 RS1 RS2 RS3 RS4 LD1 LD2 LD3 LD5 LF1 LF2 LF3 LF4 RF1 RF2 RF3 RF4 TS1 TS2 TS3 TS4 TS5 EU1 EU2 EU3 EU4 EH1 EH2 EH3

SQA

SQT

SQC

CS

LD

LF

RF

TS

EU

EH

0.82 0.69 0.62 0.47 0.46 0.42 0.42 0.43 0.47 0.34 0.30 0.24 0.29 0.28 0.21 0.23 0.17 0.26 0.38 0.34 0.43 0.38 0.19 0.28 0.23 0.14 0.19 0.18 0.36 0.32 0.32 0.27 0.25 0.22 0.10 0.21 0.12

0.57 0.30 0.26 0.82 0.86 0.75 0.45 0.44 0.36 0.48 0.49 0.39 0.32 0.41 0.43 0.37 0.37 0.28 0.29 0.33 0.26 0.50 0.38 0.45 0.41 0.20 0.27 0.32 0.42 0.27 0.26 0.19 0.31 0.18 0.12 0.10 0.07

0.41 0.26 0.45 0.38 0.43 0.43 0.83 0.87 0.78 0.35 0.36 0.32 0.31 0.30 0.26 0.18 0.22 0.23 0.28 0.34 0.28 0.40 0.22 0.35 0.28 0.30 0.27 0.34 0.31 0.27 0.20 0.13 0.06 0.11 0.08 0.11 0.15

0.34 0.07 0.28 0.48 0.39 0.35 0.36 0.30 0.31 0.91 0.91 0.89 0.67 0.62 0.64 0.49 0.52 0.38 0.25 0.38 0.19 0.49 0.45 0.44 0.32 0.23 0.31 0.31 0.34 0.29 0.26 0.21 0.14 0.14 0.06 -0.05 0.05

0.32 -0.01 0.23 0.50 0.38 0.33 0.20 0.27 0.29 0.67 0.65 0.64 0.43 0.81 0.88 0.77 0.74 0.18 0.18 0.35 0.23 0.44 0.40 0.54 0.35 0.27 0.34 0.32 0.30 0.25 0.32 0.17 0.13 0.07 0.11 0.05 0.04

0.41 0.26 0.30 0.30 0.32 0.33 0.28 0.34 0.30 0.36 0.35 0.34 0.31 0.27 0.29 0.22 0.25 0.73 0.79 0.84 0.67 0.35 0.19 0.27 0.36 0.13 0.22 0.18 0.32 0.27 0.15 0.20 0.11 0.17 0.10 0.14 0.14

0.37 0.07 0.29 0.58 0.44 0.38 0.30 0.37 0.38 0.58 0.52 0.51 0.26 0.54 0.48 0.40 0.41 0.23 0.24 0.38 0.29 0.78 0.77 0.80 0.68 0.35 0.40 0.35 0.46 0.43 0.25 0.17 0.20 0.12 0.23 0.06 0.12

0.36 0.07 0.26 0.42 0.35 0.25 0.31 0.31 0.39 0.41 0.36 0.36 0.27 0.40 0.34 0.28 0.29 0.14 0.18 0.33 0.30 0.59 0.41 0.39 0.25 0.62 0.73 0.75 0.72 0.75 0.24 0.19 -0.02 0.04 0.19 0.02 0.14

0.34 0.22 0.20 0.36 0.23 0.19 0.22 0.13 0.08 0.28 0.27 0.20 0.12 0.31 0.21 0.10 0.16 0.17 0.23 0.09 0.17 0.20 0.21 0.24 0.11 0.14 0.18 0.07 0.06 0.16 0.81 0.80 0.67 0.65 0.17 0.19 0.11

0.16 0.11 0.09 0.18 0.09 0.04 0.06 0.15 0.14 0.05 -0.01 0.02 0.09 0.03 0.08 0.05 0.14 0.11 0.04 0.14 0.18 0.15 0.17 0.20 0.07 0.06 0.13 0.04 0.19 0.16 0.15 0.17 0.15 0.14 0.85 0.68 0.74

144

Nomological validity and face validity

Nomological validity is tested by examining whether the correlations among constructs in a measurement model make sense (Hair et al., 2006). As shown in Table 6-3, the correlation matrix showed that the correlations among the constructs were reasonable as we hypothesized in Chapter 4. In particular, the correlations between moderators and independent variables were lower than those among the others, indicating good nomological validity. Face validity tells us to what extent the measure used seems to be a reasonable measure for what it purports to measure, which must be established prior to any measurement model testing (Hair et al., 2006). A simple test for face validity is to ask for the opinion of others acquainted with the actual topic (Ghauri & Grønhaug, 2005). As we stated in Chapter 5, all the scales used were derived from a thorough literature review and minor modifications were suggested by panel experts. Also, two stages of pretesting were conducted to determine the effectiveness of the questionnaire. The most important guiding principle would be that the theoretical meaning rather than statistical results should always be considered in the first place when dropping any items. Finally, each item’s content or meaning expresses and correctly specifies the measurement theory.

6.4.2 Formative measures In the model for formative measures, it is supposed that the latent variable ȟj is generated by its own manifest variables xij, thus the latent variable is a linear function of its manifest variables plus a residual term as follows:

[j

¦Y

ij

xij  G j

i

where Ȧij is the weight of xij, and įj is the residual. The predictor specification condition supposed to hold is:

E ([ j x1 j ,..., xij )

¦Y

ij

xij

i

This hypothesis implies that the residual įj has a zero mean and is uncorrelated with the manifest variables xij.

145

Because of the different algorithm, the conventional procedures used to assess the validity and reliability of scales composed of reflective indicators are not appropriate for composite variables with formative indicators. In addition to theoretic rationale and expert opinion, Henseler (2009) recommends statistical analyses on both the construct level and the indicator level to assess validity of formative constructs. However, Henseler also also makes a note of caution that formative indicators should never be discarded simply on the basis of statistical outcomes as such actions may substantially change the content of the formative index. x

Construct level

At the construct level, the assessment is to check whether the formative index indeed carries the intended meaning. Chin (2010) develops a checking procedure (Figure 6-2): first, if reflective items exist, the formative index should explain a large part of the variance of an alternative reflective measure of the focal construct; second, if a prior nomological net exists, the relationships between the formative index and other constructs in the path model that are well known through prior research should be strong and significant; third, if the first two conditions could be met, their predictive relevance is valued in the structural model. Since tie density is commonly calculated as an index and seldom treated as a reflective construct in network studies, the predictive relevance was assessed. The results of PLS path modeling show that the formative construct of tie density has significant effects on logistics flexibility and relationship flexibility as we hypothesized, indicating acceptable validity at the construct level, thus we focused more on validity at the individual level.

146

Begin with formative construct

Yes

Redundancy analysis

Monological pattern comparison

Monological analysis

No

Reflective items exist?

Yes

No

Prior nomological net exist?

Monological substitution pattern comparison

Predictive relevance

Figure 6-2 Formative construct validation roadmap Source: Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications. Berlin, Germany: Springer, 655-690. x

Indicator level

At the indicator level, the question arises as to whether each indicator indeed delivers a contribution to the formative index by carrying the intended meaning. First, we used partial least squares (PLS) to estimate weights of the two formative items, both of which were significant. Second, multicollinearity was tested by calculating the variance inflation factor VIF because the formative measurement model is based on a multiple regression

(Diamantopoulos

&

Winklhofer,

2001).

The

results

show

that

multicollinearity was not a problem as each Tolerance value was above 0.10 and each VIF (variance inflation factor) value was less than 10 (Hair et al., 2006). Third, we use alternative questions to confirm the responses to the two reflective items, asking the respondent to choose the type of key account among (a) a first level independent distributor or agent, (b) a first level direct sales office or branch, and (c) a corporate client or customer who has direct connections with the focal firm. Splitting the sample 147

according to response to this question, we implement a one way ANOVA. From a practical view, the focal firm would have the smallest number of potential competitors if its key account is a direct sales office or branch rather than a distributor or a direct customer. Figure 6-3 shows that the quantity of potential competitors of a focal firm whose key account is “a” was larger than a focal firm whose key account is “c” which was larger than a focal firm whose key account is “b”, for which significant differences existed among the means (F(2,196)=3.169, p0.70

RF1: The relationship between me and the key account is able to respond quickly to requests for changes in distribution environment RF2: The key account and I expect to be able to make adjustments in our ongoing relationship to cope with a changing distribution environment RF3: When disagreements arise in transactions, the key account and I would revalue the ongoing situation to achieve a mutually satisfactory solution RF4: When an unexpected situation arises, the key account and I would modify our working agreement rather than hold each other to its original terms (Scale: 1 = Strongly disagree; 5 = Strongly agree)

283

Section 3 Part C Q1-4

Task environment (Moderators) x The level of competitive activity (number and quality) within the dealer’s [neighborhood/territory] is high x There are a number of changes taking place in competitors’ sales and promotional strategies within the dealer’s [neighborhood/territory] x The customers in the dealer’s [neighborhood/territory] vary a lot in their preferences and needs for [renting or buying Uncertainty the supplier’s products] (variability/heterogeneity) x The general customer demand for [the supplier’s products] in the dealer’s [neighborhood/territory] is strong and growing x There are a number of changes taking place in customer’s [supplier’s products] preferences within the dealer’s [neighborhood/territory] Heterogeneity (Miller, 1987) x Needed diversity in production methods and marketing tactics to cater to different customers has dramatically increased Heterogeneity Diversity among organizations (Achrol and Stern, 1988) x Preferred variety of product brands/features x Product preferences in price/quality Miller (1987) Achrol and Stern (1988)

Kumar, Fantazy, and Kumar (1992)

Section 1 Part A Q1-5

Section 1 Part C Q2-4

EU1: Customers’ demand for the key product is strong and growing EU2: There are a number of changes taking place in customers’ preferences for buying the key product EU3: The level of competitive activity is changing (e.g. number or strength of competitors is increasing) EU4: There are a number of changes taking place in competitors’ sales and promotional strategies (Below are additional items suggested by the expert panel and drawn from other studies) EU5: Marketing policies have changed very much (Scale: 1 = Strongly disagree; 5 = Strongly agree) EH1: The key product volume demanded by customers varies a lot between customers EH2: The category mix of the key product demanded by customers varies a lot between customers EH3: Timing of orders of customers varies greatly from customers to customers (Scale: 1 = Strongly disagree; 5 = Strongly agree)

284

Total assets

Annual sales

Employee number

Firm age

SMEs in industry should meet the following principles: the number of employees is below 2000; the value of annual sales is below 300 million RMB; or the value of total assets is below 400 million RMB. Among them, the ones which have more than 300 employees, 30 million RMB annual sales or 40 million assets are medium size, while others are small size.

-

Firm characteristics (Control variables)

Temporary Provisions for Chinese Small and Median Enterprises (SMEs)

-

Less than 1 year 1-2 years 2-5 years 5-10 years More than 10 years 0-100 100-300 300-500 500-600 600-1000 1000-2000 2000-3000 above 3000 Less than 5 5-10 10-20 20-30 30-50 50-150 150-300 More than 300 Less than 10 10-20 20-30 30-40 40-100 100-200 200-400 More than 400

Section 5 Part B Q6

Section 5 Part B Q5

Section 5 Part B Q4

Section 5 Part B Q3

285

Appendix 5-B Questionnaire on flexible distribution channels

Approval No 09670 THE UNIVERSITY OF NEW SOUTH WALES

PARTICIPANT INFORMATION STATEMENT Flexible distribution channels: the construct, antecedents, and performance outcomes You are invited to participate in a study of flexible distribution channels. We hope to learn about the construct and mechanisms of flexible distribution channels. You were selected as a possible participant in this study because your company is a typical downstream channel member. If you decide to participate, you will complete a questionnaire. It will take you about half an hour to complete. The answers will be coded for statistical analysis and the results will be used for academic research. We cannot and do not guarantee or promise that you will receive any benefits from this study. Any information that is obtained in connection with this study and that can be identified with you will remain confidential and will be disclosed only with your permission, except as required by law. Completion of the questionnaire implies that you consent to participate. We plan to publish the results in marketing academic journals. In any publication, information will be provided in such a way that you cannot be identified. Complaints may be directed to the Ethics Secretariat, The University of New South Wales, SYDNEY 2052 AUSTRALIA (phone 9385 4234, fax 9385 6648, email [email protected]). Any complaint you make will be investigated promptly and you will be informed about the outcome. A summary of research findings will be offered at the completion of the study if you are interested. Your decision whether or not to participate will not prejudice your future relations with the University of New South Wales. If you decide to participate, you are free to withdraw your consent and to discontinue participation at any time without prejudice. If you have any questions, please feel free to ask us. If you have any additional questions later, Kangkang Yu (phone 0425863680, email [email protected]) and Jack Cadeaux (phone +61-02-9385-1436, email [email protected]) will be happy to answer them. You will be given a copy of this form to keep.

286

1.

DISTRIBUTION ENVIRONMENT

Among all the products that your company manufactures or sells, identify a key product which may reflect any of the following features: a) the largest revenue component of the main business; b) the largest part of your total sales volume; c) the one on which you spend most of your time and effort to sell. What is the key product? __________________ (Please fill the name of the product) Please respond to all statements and questions in reference to this key product in recent 5 years. Part A 1 Customers’ demand for the key

product is changing

Strongly Strongly Disagree Neutral Agree Disagree Agree 1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 There are a number of changes

taking place in customers’ preferences for buying the key product 3 The level of competitive activity

is changing (e.g. number or strength of competitors is increasing) 4 There are a number of changes

taking place in competitors’ sales and promotional strategies 5 Marketing policies have

changed very much Part B

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 The rate of key product

innovation has substantially increased

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 The rate at which the key

product becomes obsolete is high 3 The production technology of

the key product changes frequently 4 Many new categories of the key

product are being introduced in my industry 5 For the key product, technical

progress makes many new product concepts come true

287

Part C

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 Customers demand a wide

category range for the key product

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 The key product volume

demanded by customers varies a lot between customers 3 The category mix of the key

product demanded by customers varies a lot between customers 4 Timing of orders of customers

varies greatly from customer to customer 5 Customers require a customized

key product frequently

288

2. DISTRIBUTION NETWORK For this key product, direct customers of your company contain the following groups: a) first level independent distributors or agents; b) first level direct sales offices or branches; c) corporate clients or customers who have direct connections with you. Please respond to the following statements and questions in reference to you direct customers. Part A

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 My direct customers vary

widely in the level of information they possess 2 My direct customers have a variety of different backgrounds 3 My direct customers have skills and abilities that complement each other 4 My direct customers differ greatly in their expertise Part B

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 There is close, personal

2

3 4

5

interaction between me and my direct customers There is high reciprocity between me and my direct customers I meet and communicate with my direct customers regularly There are joint problem-solving arrangements between me and my direct customers I share important marketing information with my direct customers

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

Among all direct customers, identify one key account that may have any of the following features: a) the largest percentage of key product revenue; b) the largest percentage of key product sales volume; c) the one on which you spend most of your time and effort to manage. Is the key account (Please tick the corresponding box) ƶA first level independent distributor or agent ƶA first level direct sales office or branch ƶA corporate client or customer who has direct connections with you 289

Please respond to the following statements and questions in reference to your key account. Part C 1. There are many similar direct customers that can replace the position of my key account

Strongly Strongly Disagree Neutral Agree Disagree Agree 1

2

3

4

5

Estimate the number of these direct customers ________________ (Please fill a number) Part C 2. There are many competitors who can threaten the relationship between me and my key account

Strongly Strongly Disagree Neutral Agree Disagree Agree 1

2

3

4

5

Estimate the number of these competitors ________________ (Please fill a number)

290

3. DISTRIBUTION FLEXIBILITY Please respond to all statements and questions in reference to your company and the key account. Part A 1 I introduce new products to the

key account frequently

Strongly Strongly Disagree Neutral Agree Disagree Agree 1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 I adjust product lines to produce

higher volume when the key account’s demand is increasing 3 I can adjust my storage capacity

if the key account’s demand fluctuates 4 I adjust my delivery capacity to

meet volume for delivering products to the key account 5 I make flexible use of multiple

transportation modes to meet the schedule for delivering products to the key account 6 I frequently balance inventory

of the key product between the key account and other direct customers Part B

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 I adjust order fulfillment time

for the key account at the key account’s request 2 I provide a high level of after-

sales services to the key account 3 I customize assortments to

attract new market segments

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4 I mix orders to meet the distinct

delivery requirements of the key account 5 I involve the key account in

improving the key product or service 6 I make significant investments

in the key account’s promotion of the key product

291

Part C

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 The relationship between me

and the key account is able to respond quickly to requests

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 The key account and I expect to

be able to make adjustments in our ongoing relationship 3 When disagreements arise in

transactions, the key account and I would revalue the ongoing situation to achieve a mutually satisfactory solution 4 When an unexpected situation

arises, the key account and I would modify our working agreement rather than hold each other to its original terms 5 I am able to change the level of

the relationship between myself and the key account from a partnership to a strategic alliance or vice versa 6 I am able to initiate offers to

support the key account in response to the key account’s requests

292

4. DISTRIBUTION PERFORMANCE Please respond to all statements and questions in reference to your company and the key account. Part A

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 Orders from the key account are

available in inventory where ordered

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 The time between placing and

receiving an order from the key account is short 3 The time between receiving and

shipping the key account’s order is short 4 The time between placing and

receiving an order from the key account is consistent 5 The products are consistently

available in inventory for the key account Part B

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 A wide assortment of the key

product is available to the key account from us

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 Compared to our competitors,

we offer a wider variety of the key product to the key account 3 We provide competent after-

sales services to key account 4 We respond with accurate

information in response to the key account’s inquiries concerning an order 5 All orders from the key account

are fulfilled accurately (items ordered arrive, no unordered items) 6 All orders from the key account

are delivered undamaged

293

Part C 1 The key account’s customers

ask for the key product

Strongly Strongly Disagree Neutral Agree Disagree Agree 1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 The key product is a good

growth opportunity for the key account 3 The key product is well known

by the key account’s customers 4 The key account’s customers

are willing to pay high prices for the key product 5 The key account would have a

difficult time replacing the key product with similar products 6 The key product performs much

better than its competition in the key account’s market Part D

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 We have a good working

relationship with the key account

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 Our working relationship with

the key account is characterized by open and honest communication 3 When the need arises for us and

the key account to work together, it is always conducted in a positive manner 4 The key account is satisfied

with the arrangement of our overall distribution system 5 The key account is happy to

form a partnership with us

294

Part E

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 The key account wants to sell

our products and show its desire to do so in a number of positive ways

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 It takes the key account a lot of

time, effort, and energy to get attention from us 3 The key account is motivated to

further our business rather than to either sell competing brands or engage in other business activities 4 The key account spends a

higher amount of time and effort on our business relative to others 5 The key account is willing to

promote our sales in many different kinds of ways Part F

Strongly Strongly Disagree Neutral Agree Disagree Agree

1 We believe that over the long

run the relationship between us and the key account will be profitable

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

2 Maintaining a long-term

relationship with the key account is important to us 3 We and the key account focus

on long-term goals in this relationship 4 We are willing to make

sacrifices to help the key account from time to time 5 We expect the key account to be

working with us for a long time 6 Any concessions we make to

help the key account will even out in the long run

295

5. BACKGROUND INFORMATION Part A The following information is only used to valid the information you supplied! 1. Which of the following categories best describes your position in the organization? ƶPresident/Vice-president ƶSenior Manager ƶMiddle Manager ƶEmployee 2. How experienced would you say you are in dealing with distribution channel management? Inexperienced

1

2

3

4

Very Experienced

5

3. How knowledgeable are you of distribution channel management? Not knowledgeable at all

1

2

3

4

Very Knowledgeable

5

4. Including yourself, how many persons were involved in distribution channel management? _________________ (Write number here) 5. How representative are your answer to the questions about distribution channel management among these persons? Not at all representative

1

2

3

4

5

Very representative

296

Part B The following information is only used in categorizing your company! 1. Name of your company: ___________________ 2. Industry of your company: ƶManufacturing ƶManufacturing and distribution ƶWholesale and distribution ƶRetail ƶService (If so please specify: ________________)

3. Age of your company: ƶLess than 1 year ƶ1-2 years ƶ2-5 years ƶ5-10 years ƶMore than 10 years 4. Number of employees in your company: ƶ0-100 ƶ100-300 ƶ300-500 ƶ 500-600 ƶ600-1000 ƶ1000-2000 ƶ2000-3000 ƶabove 3000 5. Annual sales of your company: (Unit: million RMB) ƶLess than 5 ƶ5-10 ƶ10-20 ƶ20-30 ƶ30-50 ƶ50-150 ƶ150-300 ƶMore than 300 6. Total assets of your company: (Unit: million RMB) ƶLess than 10 ƶ10-20 ƶ20-30 ƶ 30-40 ƶ40-100 ƶ100-200 ƶ200-400 ƶMore than 40 297

Appendix 5-C Interview guide on flexible distribution channels

Approval No 09670 THE UNIVERSITY OF NEW SOUTH WALES

PARTICIPANT INFORMATION STATEMENT & CONSENT FORM Flexible distribution channels: the construct, antecedents, and performance outcomes You are invited to participate in a study of flexible distribution channels. We hope to learn about the construct and mechanisms of flexible distribution channels. You were selected as a possible participant in this study because your company is an upstream channel member with a typical distribution network. If you decide to participate, you will take part in an interview. The interview will be recorded with a digital recorder unless you indicate on the consent form that you wish handwritten notes only. It will take you about one hour and a half. The answers will be coded for case study and the results will be used for academic research. We cannot and do not guarantee or promise that you will receive any benefits from this study. Any information that is obtained in connection with this study and that can be identified with you or your organization will remain confidential and will be disclosed only with your permission, except as required by law. If you give us your permission by signing this document, we plan to publish the results in marketing academic journals. In any publication, information will be provided in such a way that you cannot be identified. Complaints may be directed to the Ethics Secretariat, The University of New South Wales, SYDNEY 2052 AUSTRALIA (phone 9385 4234, fax 9385 6648, email [email protected]). Any complaint you make will be investigated promptly and you will be informed out the outcome. A summary of research findings will be offered at the completion of the study if you are interested. Your decision whether or not to participate will not prejudice your future relations with the University of New South Wales. If you decide to participate, you are free to withdraw your consent and to discontinue participation at any time without prejudice. If you have any questions, please feel free to ask us. If you have any additional questions later, Kangkang Yu (phone 0425863680, email [email protected]) and Jack Cadeaux (phone +61-02-9385-1436, email [email protected]) will be happy to answer them. You will be given a copy of this form to keep.

298

THE UNIVERSITY OF NEW SOUTH WALES

PARTICIPANT INFORMATION STATEMENT AND CONSENT FORM (continued)

Flexible distribution channels: the construct, antecedents, and performance outcomes You are making a decision whether or not to participate. Your signature indicates that, having read the information provided above, you have decided to participate. Ƒ7LFNWKLVER[LI\RXGRQRWZLVKWREHWDSH-recorded, then only hand-written notes will be taken

……………………………………………………

Signature of Research Participant

……………………………………………………

(Please PRINT name)

.…………………………………………………….

Signature of Witness

.…………………………………………………….

(Please PRINT name)

……………………………………………………

.…………………………………………………….

Date

Nature of Witness

Kangkang Yu

Jack Cadeaux

School of Marketing

School of Marketing

Phone 0425863680

Phone +61-02-9385-1436

Email [email protected]

Email [email protected]

299

REVOCATION OF CONSENT

Flexible distribution channels: the construct, antecedents, and performance outcomes I hereby wish to WITHDRAW my consent to participate in the research proposal described above and understand that such withdrawal WILL NOT jeopardise any treatment or my relationship with The University of New South Wales, (other participating organisation[s] or other professional[s]). Ƒ7LFNWKLVER[LI\RXGRQRWZLVKWREHWDSH-recorded, then only hand-written notes will be taken

……………………………………………………

.…………………………………………………….

Signature

Date

……………………………………………………

Please PRINT Name

The section for Revocation of Consent should be forwarded to

Kangkang Yu

Jack Cadeaux

School of Marketing

School of Marketing

Phone 0425863680

Phone +61-02-9385-1436

Email [email protected]

Email

[email protected]

300

BACKGROUND Name: Company: Division: Industry: Job: Years in Position: Years with Company: Years in Distribution:

DISTRIBUTION ENVIRONMENT 1. What kind of industry are you in? Could you please give a brief description of this industry? 2. In the last five years, have there ever been any changes in customer demand? If yes, please share an example or two. Probing: Such as preferences for product features, preferences for brands, preferences about product quality/price, preferences about volume and composition, etc.? 3. In the last five years, have there ever been any changes in competitive activity? If yes, please share an example or two. Probing: Such as pricing fluctuation, sales and promotional strategies, valueadded service, etc.? 4. In the last five years, have there ever been any changes made by your suppliers? Probing: Have they increased the price of raw materials or component parts frequently? Did you feel that it is difficult sometimes to procure materials for your major product? If so, please describe the situation. 5. In the last five years, have there ever been any changes of government regulations controlling your industry, especially over distribution? Briefly explain. 6. In the last five years, have there ever been any changes of production technology in your industry? If yes, please share an example or two.

301

PRODUCT CHARACTER 7. What is the major product of your company? Could you please give a brief description of this product? 8. Have there ever been any new products introduced in your industry in the last five years (the time depends on industry)? Did they become obsolete quickly? If so, please describe. 9. How many different categories of this product exist? Probing: Do you think the range is wide enough for most of your end customers or do they need more? Is there any segmentation of your end customers? If so, what are the differences in the orders (e.g. timing, volume, mix, etc.) among them? Explain briefly.

DISTRIBUTION NETWORK 10. How many tiers exist in your distribution channel? Probing: Could you please give a brief description of each tier? Are there any intermediaries such as merchants, agents, retailers, wholesalers, franchisers, etc.? On which tiers are they located and how many of them are in each tier? 11. How do you select distribution channel members? Probing: Are there any standard processes and principles? If so, please describe the main points. If not, please explain the main reasons. 12. How do you control distribution channel members? Probing: Are there any formal contracts about terms and responsibilities of your members? If so, please describe the main points. If not, please explain why. 13. How do you make contact with your distribution channel members? Probing: Directly or indirectly? Do you hold regular meetings with them? If so, how frequently, and what are the concrete processes? Do you have any personal interactions with some channel members? If so, please share an example or two.

DISTRIBUTION FLEXIBILITY 14. When changes have taken place in the environment, what kinds of manufacturing strategies did you undertake? Probing: Did you bring in new technologies, introduce new products, or try to keep original product lines operating normally? Explain briefly. 15. When changes have taken place in the environment, what kinds of logistics 302

strategies did you undertake? Probing: Did you increase storage capacity, open more warehouses, or try to hold the inventory level stationary? Did you increase transportation capacity, use more combinations of transportation modes, or did you still keep initial numbers and kinds of transportation modes? Explain briefly. 16. When changes took place in the environment, what kinds of marketing strategies did you undertake? Probing: Did you change the order fulfillment time, customize assortments and delivery, increase value-added services, offer discounts, or just keep everything the same as usual? Explain briefly. 17. When changes took place in the environment, what kinds of coordination strategies did you undertake? Probing: Did you modify the agreement with your distribution channel members or try to hold to the original terms? Did you initiate offers to support your distribution channel members? If yes, please share an example or two.

DISTRIBUTION PERFORMANCE 18. How do you evaluate the performance of your distribution channel? Probing: What about inventory, is it easy enough to order, and easy to access? What about order time, is it short, and well arranged? What about product delivery, is it accurate, and are assortments sufficiently wide? What about service, can you provide quick feedback, and convenient return procedures? 19. What is your distribution channel members’ assessment? Probing: Do they ever complain about the margins you offer? What kinds of support have you offered to them? Are they pleased to accept or do they complain? 20. How do you evaluate the performance of your distribution channel members? Probing: What are the latest results? Do they pay enough time, effort and energy to your businesses or do they engage in other business activities? 21. On the average, how long standing are the relationships between you and your distribution channel members? Probing: How do you develop, maintain and monitor these relationships? Do you have any guidelines, or you just sign short-term contracts and change them frequently? 303

Appendix 5-D Overview of companies in the interviews Company

Company Q

Company Z

Company J

Company P

Interviewees

Marketing manager, 10 years working experience

R&D manager, 7 years working experience; Marketing manager, 2 years working experience

Public relations manager, 2 years working experience (second-hand materials)

Marketing manager, 7 years working experience (second-hand materials)

One of the most professional and the largest filter press manufacturers with leadingedge technology and wide range product for the Solid/Liquid Separation industry.

Engaged mainly in the industry of healthy household appliances and invented the first Automatic Soymilk Maker in the world.

Manufactures, markets, and develops men's wear and other clothing.

Product/market One of the leading Focus pharmaceutical companies in China, develops, manufactures and markets quality and affordable generic drugs.

Industry structure & performance

Low proportion in GDP; Quick development; High additional profits

Expanding market; Wide area of promotion (coal washing, chemical engineering, sugar manufacturing, titanium white, etc.)

Highly competitive industry in terms of brand, core technology, and distribution channels

Highly competitive industry; Regional clustering

Age

More than 10 years

More than 10 years

5-10 years

5-10 years

Size

Number of employees: 6000; Annual value of production: 6-10 billion RMB (Large size)

Number of employees: 1500; Annual sales: 1.5 billion RMB (Middle size)

Number of employees: 2000; Total revenue in 2008: 4.3 billion RMB (Large size)

Number of employees: 1000; Annual Sales: 30 million RMB (Small size)

304

Appendix 6-A Descriptive statistics for constructs used in the model Constructs and indicators

Mean

SD

Zskewness

Zkurtosis

Q-Q Plot

Out liers

Service Quality (SQ) Timeliness SQT1. Short time between placing and receiving an order

3.90

0.84

-1.17

2.49

OK

No

SQT2. Consistent time between placing and receiving an order

3.92

0.77

-0.74

0.97

OK

No

SQT3. Short time between receiving and shipping the order

3.94

0.78

-0.80

1.34

OK

No

SQA1. Available in inventory where ordered

3.60

1.00

-0.63

0.19

OK

No

SQA2. Consistently available in inventory

3.71

0.92

-0.78

0.76

OK

No

SQA3. A wide assortment is available

3.88

0.87

-0.79

0.76

OK

No

SQC1. Respond with accurate information

4.00

0.72

-0.56

0.52

OK

No

SQC2. Fulfill all orders accurately

4.05

0.73

-0.52

0.64

OK

No

SQC3. Deliver all orders undamaged

3.91

0.86

-0.68

0.45

OK

No

4.13

0.84

-1.54

3.79

OK

No

RS2. Relationship is characterized 4.06 by open and honest communication

0.82

-1.52

3.94

OK

No

RS3. When needs arises to work together, it is always conducted in a positive manner

4.08

0.75

-0.75

1.05

OK

No

RS4. Satisfied with the arrangement of overall distribution system

3.88

0.70

-0.32

0.58

OK

No

LD1. The relationship will be profitable over the long run

4.10

0.79

-1.11

2.14

OK

No

LD2. Maintaining a long-term relationship is important

4.20

0.78

-1.40

3.47

OK

No

Availability

Condition

Relationship satisfaction (RS) RS1. Have a good working relationship

Link Duration (LD)

305

LD3. Focus on long-term goals in this relationship

4.15

0.74

-0.82

1.33

OK

No

LD4. Willing to make sacrifices from time to time

3.86

0.78

-0.74

1.55

OK

No

LD5. Expect to be working with us for a long time

4.22

0.74

-0.95

1.61

OK

No

LD6. Any concessions will even out in the long run

4.02

0.78

-1.03

2.35

OK

No

TS1. Close, personal interaction

3.58

0.94

-0.40

-0.03

OK

No

TS2. High reciprocity

3.92

0.84

-0.60

-0.05

OK

No

TS3. Joint problem-solving arrangements

4.06

0.79

-0.70

0.36

OK

No

TS4. Share important marketing information

3.92

0.84

-0.74

0.69

OK

No

TS5. Meet and communicate regularly

3.78

1.02

-1.02

1.13

OK

No

TD1. Many similar customers that can replace the key

3.27

1.06

-0.26

-0.52

OK

No

TD2. Many competitors who can threaten the relationship

2.76

1.09

-0.30

-0.73

OK

No

LF1. Adjust storage capacity

3.88

0.82

-0.89

1.16

OK

No

LF2. Adjust delivery capacity

3.89

0.84

-0.92

1.32

OK

No

LF3. Flexible use of multiple transportation modes

4.02

0.84

-1.09

1.75

OK

No

LF4. Balance inventory

3.85

0.92

-0.91

0.98

OK

No

RF1. The relationship is able to respond quickly

4.05

0.82

-1.08

1.95

OK

No

RF2. Make adjustments in ongoing relationship

3.84

0.75

-0.55

0.71

OK

No

RF3. Revalue the ongoing situation to achieve a solution

4.09

0.71

-1.02

3.01

OK

No

RF4. Modify the working agreement

3.79

0.82

-0.77

1.03

OK

No

3.58

1.15

-0.58

-0.63

OK

No

Tie Strength (TS)

Tie Density (TD) (Reversed)

Logistics Flexibility (LF)

Relationship Flexibility (RF)

Environmental uncertainty (EU) EU1. Customers’ demand is

306

growing EU2. Customers’ preferences are changing

3.38

1.00

-0.33

-0.60

OK

No

EU3. The level of competitive activity is changing

3.82

1.20

-0.90

-0.13

OK

No

EU4. Competitors’ strategies are changing

3.66

0.93

-0.74

0.20

OK

No

EU5. Marketing policies are changing

3.39

1.05

-0.25

-0.54

OK

No

EH1. Volume demanded varies a lot between customers

3.52

1.03

-0.60

-0.17

OK

No

EH2. Category mix demanded varies a lot between customers

3.90

0.92

-1.03

1.18

OK

No

EH3. Timing of orders varies greatly from customer to customer

3.33

0.92

-0.02

-0.43

OK

No

4.18

1.07

-1.38

1.29

OK

No

FS1. Number of employees

3.37

2.60

0.69

-1.04

OK

No

FS2. Annual sales

4.98

2.54

-0.33

-1.34

OK

No

FS3. Total assets

4.48

2.75

-0.01

-1.58

OK

No

RB1. Experience of distribution channel management

3.50

0.68

0.85

-0.20

OK

No

RB2. Knowledge of distribution channel management

3.60

0.74

0.71

-0.72

OK

No

RB3. Representation of making decisions about distribution channels

3.46

0.66

0.95

0.05

OK

No

Environmental heterogeneity (EH)

Firm Age (FA) FA1. Years of business Firm Size (FS)

Respondent Background (RB)

307