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INFLUENCES OF POWER UPON SUPPLY CHAIN RELATIONSHIPS: AN ANALYSIS OF THE AUTOMOTIVE INDUSTRY
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University
By
Michael Maloni, M.A.
*****
The Ohio State University
1997
Dissertation Committee: A p ^ v e d by
Professor W.C. Benton, Adviser
Professor Amelia Carr
Professor Glenn W. Milligan Business Adminiâtcatîdn CMduate Program UMI Number: 9 8 0 1 7 4 1
Copyright 1998 by Maloni, Michael J.
All rights reserved.
UMI Microform 9801741 Copyright 1997, by UMI Company. All rights reserved.
This microform edition is protected against unauthorized copying under Title 17, United States Code.
UMI 300 North Zeeh Road Ann Arbor, MI 48103 ABSTRACT
Supply chain management involves the extensive coordination of planning and processes among firms within the supply chain. Such integration should conceptually generate decreased uncertainty, enhanced responsiveness, and reduced costs, thus translating to higher customer satisfaction and subsequent supply chain profitability. Supply chain management necessitates an environment of mutuality and cooperation, but inter-firm power influences may upset this relational nature. Thus, power presents a potential barrier to effective supply chain integration. This dissertation sought to investigate the presence of power within the supply chain in attempt to understand power as a critical component of the supply chain management environment.
The research methodology involved a survey of suppliers in the automobile industry, a pioneer of supply chain management. The survey instrument served to gather perceptions of power bases, critical inter-firm relationship elements, performance, and satisfaction.
Structural equation modeling was then utilized to analyze the causal effects of power bases within the supply chain.
The research produced several important findings. First, expert, referent, and reward power sources were found to have significant positive effects upon the relationship between buyer and supplier, while coercive and legal legitimate power sources were
u found to have significant negative impacts. As a second major finding, the research verified that a strong supply chain relationship was perceived to have a beneficial influence upon supplier satisfaction as well as supplier, buyer, and supply chain performance.
Based on the results, the research indicates that supply chain strategy must incorporate power effects or risk compromising its functional effectiveness. The research also reveals evidence for the use of power management as a tool for enhancing supply chain integration. Such findings justify the need for further supply chain power research to provide additional insight into power management strategies and effects.
Ill Dedicated to my parents who have never wavered
IV ACKNOWLEDGMENTS
I am forever grateful to my advisor. Dr. W.C. Benton, who provided solid guidance, direction, and enthusiasm throughout the research. Dr. Benton taught me the value of carefully planned, well executed scholarly research. Without his support, this dissertation as weU as my subsequent graduation would not have been possible.
I would like to thank the committee members. Dr. Amelia Carr and Dr. Glenn Milligan, for their critical insight with the research analysis and dissertation composition. I would also like to thank Dr. David A. Schilling and Dr. Bernard J. La Londe for their critical guidance during my coursework.
Further thanks are extended to Jeff Trimmer of the Chrysler Corporation, David Curry of Honda of America, and Gunter Schmirler of General Motors for their assistance with research direction and contact lists. I am also grateful to all the survey respondents. Without their thoughtful participation, the research would not have been a success.
I would like to thank the National Association of Purchasing Management which provided a critical financial support through their doctoral dissertation grant program. I am in further gratitude to numerous colleagues who provided instrumental guidance throughout the research project including fellow Ph.D. candidates Rebecca Duray, Ling Li, Seungwook Park, and Darryl Wilson. Furthermore, Frederik Schlingemann, Sara Moeller, John Griffin, and Jim Morrison made the long days and nights in Page Hall fun and bearable.
Brad Damewood of Andersen Consulting deserves much recognition for his consistent guidance in serving as my primary mentor in both work and life.
I would like to recognize the Glassware Club, a steadfast symbol of Nittany Lion masculinity.
For their undying support, I would like to thank my parents who have inspired me to pursue advanced degrees and have never wavered in their support. My new in-laws are also pretty sharp.
Finally, I wish to thank my wife and best friend, Rachel, who helped me in more ways than she will ever know.
Here we go Steelers here we go!
Life is a journey, not a guided tour.
VI VTTA
1988-1991 ...... B.S. Quantitative Business Analysis, Penn State University University Park, PA
1991-1992 ...... Analyst Programmer, Armstrong World Industries Lancaster, PA
1992-Present ...... Graduate Teaching and Research Associate, Max M. Fisher College of Business The Ohio State University Columbus, OH
1992-1995 ...... Master of Arts (Business Administration) Max M. Fisher College of Business The Ohio State University Columbus, OH
PUBLICATIONS
Maloni, Michael J. and W.C. Benton, "Supply Chain Parmerships: Opportunities for Operations Research," European Journal of Operational Research, Vol 101, No 3, 1997, pp 419-429.
FIELDS OF STUDY
Major Field: Business Administration
vu TABLES OF CONTENTS
Abstract...... ii
Dedication ...... iv
Acknowledgments...... v
Vita...... vii
List of Tables...... xiv
List of Figures...... xvi
CHAPTERS
CHAPTER I - INTRODUCTION...... 1 1.1 Operations as a Competitive Weapon ...... I 1.1.1 Operations Leadership ...... 1 1.1.2 Critical Elements for Best Practice Manufacturing...... 3 1.1.3 Supply Chain Management and Competitive Operations ...... 5 1.1.4 Barriers to Supply Chain Management ...... 7 1.2 Supply Chain Partnerships ...... 9 1.2.1 Benchmark Supplier Partnershipping: Chrysler Corporation ...... 10 1.2.2 Importance of Supply Chain Partnership Awareness ...... 11 1.3 Motivation and Scope of Research ...... 11 1.4 Research Problem ...... 13 1.5 Research Overview ...... 14 1.6 Contributions...... 17
vm 1.6.1 Findings ...... 18 1.7 Summary...... 18
CHAPTER 2 - REVIEW OF LITERATURE...... 20 2.1 Introduction...... 20 2.2 Partnerships - Definition and Overview ...... 21 2.3 Traditional Sourcing ...... 24 2.3.1 Reduced Supplier Bases ...... 26 2.4 Supply Chain Partnerships ...... 27 2.4.1 Benefits of Supplier Partnerships ...... 30 2.4.2 Risks of Supplier Partnerships ...... 32 2.4.3 Partnership Implementation and Critical Success Factors ...... 33 2.5 Research Gap: Power in Supply Chain Partnerships ...... 37 2.6 Influences of Power...... 39 2.6.1 Definition of Power...... 39 2.6.2 Bases of Power...... 40 2.6.3 Influence Strategies...... 42 2.6.4 Problems with Analyses of Power Bases ...... 43 2.6.5 Dichotomization of Power Sources...... 45 2.6.6 Exercised and Unexercised Power ...... 47 2.6.7 Reciprocity and Countervailing Power ...... 48 2.7 Power Influences Upon Channel Relationships ...... 49 2.7.1 Power and Dependence ...... 50 2.7.2 Power, Commitment, and Trust...... 52 2.7.3 Power, Cooperation, and Compliance ...... 53 2.7.4 Power and Conflict ...... 54 2.7.5 Power and Satisfaction ...... 56 2.7.6 Power, Performance, and Profitability ...... 57 2.8 Research Hypotheses ...... 58 2.8.1 Power and Relationship Hypotheses ...... 60 2.8.2 Relationship and Performance Hypotheses ...... 61 2.8.3 Relationship/Performance and Satisfaction Hypotheses ...... 62 2.8.4 Contributions...... 62
IX 2.9 Summary...... 63
CHAPTER 3 - RESEARCH DESIGN AND METHODOLOGY...... 64 3.1 Introduction...... 64 3.2 Experimental Design ...... 64 3.2.1 Identification of Research Focus ...... 65 3.2.1.1 Current State of the Automobile Industry...... 66 3.2.1.2 Drivers in the U.S. Automobile Industry...... 66 3.2.1.3 The U.S. Automobile Industry as Study Focus...... 69 3.2.2 Foundations for Instrument Design and Data Collection ...... 71 3.2.3 Supplier Lists ...... 72 3.3 Instrument Design...... 73 3.3.1 Item Design ...... 74 3.3.2 Instrument Development - Literature Review...... 74 3.3.2.1 Influences of Power...... 74 3.3.2.2 Relationship Elements ...... 75 3.3.2.3 Performance and Satisfaction ...... 76 3.3.3 Instrument Development - Pilot Testing ...... 76 3.3.4 Content Validity ...... 77 3.4 Data Collection...... 77 3.4.1 Response ...... 80 3.4.2 Non-response Bias ...... 81 3.5 Summary and Overview of Data Analysis...... 84
CHAPTER 4 - DESCRIPTION OF DATA...... 85 4.1 Introduction...... 85 4.2 Demographics of Respondents ...... 85 4.3 Manufacturer Benchmarking Assessment...... 87 4.3.1 Important Factors in Customer Assessment...... 91 4.4 Summary Statistics...... 93 4.4.1 Summary Statistics - Power...... 93 4.4.2 Summary Statistics - Relationship Elements ...... 94 4.4.3 Summary Statistics - Performance and Satisfaction...... 95 4.5 Summary Statistics - Chrysler versus Honda...... 99 4.5.1 Chrysler versus Honda Results...... 99 4.6 Unidimensionality ...... 104 4.6.1 Power Influence Items...... 104 4.6.1.1 Power - Correlations among the Bases ...... 107 4.6.2 Relationship Items ...... 108 4.7 Reliability...... 110 4.7.1 Cronbach's Alpha Values ...... I l l 4.8 Summary and Overview of Future Chapters ...... 112
CHAPTER 5 - MODEL FITTING...... 114 5.1 Introduction...... 114 5.2 Research Variables ...... 114 5.2.1 Power Variables...... 115 5.2.2 Relationship Variables ...... 115 5.2.3 Performance/Satisfaction Variables...... 117 5.3 An Overview of Structural Equation Modeling...... 118 5.3.1 Structural Equation Modeling...... 118 5.3.2 Fitting the Structural Model - A Two Step Approach ...... 119 5.4 Overview of Research Models ...... 120 5.4.1 Model Justification...... 121 5.4.2 Description of Research Models ...... 122 5.5 Assessment of Measurement Models...... 127 5.5.1 Measurement Model Fit Procedure...... 127 5.5.2 Modification of the Measurement Model ...... 130 5.5.3 Reliability and Validity of Constructs and Indicators ...... 130 5.5.3.1 Composite Reliability ...... 131 5.5.3.2 Variance Extracted Estimates ...... 132 5.5.3.3 Construct Validity...... 132 5.5.4 Operationalized Measurement Models ...... 133 5.5.4.1 Non-mediated Power, Supplier Performance Measurement Model 133 5.5.4.2 Remaining Non-mediated (Mn-m and Mn-sc) Measurement Models ..137 5.5.4.3 Coercive-mediated (Mc-s, Mc-m, and Mc-sc) Measurement Models .. 140
XI 5.5.4.4 Reward-mediated (Mr-s, Mr-m, and Mr-sc) Models ...... 144 5.6 Assessment of Structural Models...... 148 5.6.1 Structural Model Fit Procedure...... 148 5.6.1.1 Sample Size ...... 152 5.6.2 Modification of the Structural Model...... 153 5.6.2.1 Problems of Model Fitting...... 153 5.6.3 Operationalized Structural Models...... 154 5.6.3.1 Non-mediated, Supplier Performance (Mn-s) Structural Model ...... 155 5.6.3.2 Remaining Non-mediated (Mn-m, Mn-sc) Structural Models ...... 158 5.6.3.3 Coercive-mediated (Mc-s, Mc-m, Mc-sc) Structural Models...... 160 5.6.3.4 Reward-mediated (Mr-s, Mr-m, Mr-sc) Structural Models ...... 160 5.6 Summary...... 164
CHAPTER 6 - RESEARCH FINDINGS AND INSIGHT...... 165 6.1 Introduction...... 165 6.2 Critical Research Questions and Model ...... 165 6.3 Power Effects On Research Hypotheses and Results ...... 167 6.3.1 Power Effects on the Buyer-Supplier Relationship Hypotheses ...... 168 6.3.2 Power Effects on Relationship — Results ...... 169 6.3.3 Power Effects On Relationship — Insight ...... 170 6.3.3.1 Non-Mediated Power Influences ...... 171 6.3.3.2 Reward-Mediated Power Influences ...... 173 6.3.3.3 Coercive-Mediated Power Influences ...... 174 6.4 Relationship Effects on Performance ...... 176 6.4.1 Relationship Effects On Performance — Hypotheses ...... 176 6.4.2 Relationship Effects on Performance — Results ...... 177 6.4.3 Relationship Effects on Performance — Insight ...... 177 6.5 Relationship/Performance Effects on Satisfaction ...... 179 6.5.1 Effects on Satisfaction — Hypotheses ...... 180 6.5.2 Effects On Satisfaction — Results...... 181 6.4.3 Effects on Satisfaction — Insight ...... 182 6.4.3.1 Need for Further Satisfaction Research ...... 183 6.6 Conclusion...... 184
XU CHAPTER 7 - CONCLUSIONS...... 185 7.1 Introduction...... 185 7.2 Power and the Supply Chain ...... 185 7.3 Research Overview ...... 186 7.3.1 Research Design ...... 187 7.3.2 Analysis and Findings...... 188 7.3.3 Research Contributions...... 189 7.4 Limitations and Subsequent Directions for Future Research...... 191 7.5 Concluding Thoughts ...... 193
APPENDICES
A: Supply Chain Literature ...... 197 B: Summary of Power Literature...... 200 C: Notes from Chrysler Meeting ...... 204 D: Notes from Honda Meeting ...... 206 E: Notes from General Motors...... 208 F: Survey Instrument...... 210 G: Introductory Postcard for Survey Mailings ...... 216 H: First Round Cover Letter...... 217 I: First Reminder Postcard ...... 219 J: Second Round Cover Letter ...... 220 K: Second Reminder Postcard ...... 221
LIST OF REFERENCES...... 234
xui LIST OF TABLES
Table 1.1: Barriers to Supply Chain Management ...... 8 Table 2.1: Discrete versus Relational Business Strategies...... 29 Table 2.2: Traditional Versus Partnership Supply Strategies ...... 30 Table 2.3: Potential Benefits of Supplier Partnerships ...... 32 Table 2.4: Supplier Partnership Implementation Steps ...... 34 Table 2.5: Supplier Partnership Critical Success Factors ...... 35 Table 2.7: Attitudes-Behavior Influences and Communication Strategies ...... 42 Table 2.8: Dichotomizations of Power Sources and Strategies ...... 46 Table 3.1: U.S. New Car Sales - 1996 ...... 67 Table 3.2: Summary of Survey Constructs...... 78 Table 3.3: Summary of Survey Responses...... 81 Table 3.4: Chi-square Test of Non-Response Bias ...... 83 Table 4.1 : Categories of Products/Services of Respondents ...... 86 Table 4.2: Demographics of Respondents ...... 86 Table 4.3: Benchmarking Scores for Usable (n=130) Responses ...... 89 Table 4.4: Benchmarking Scores for Suppliers of all Five Manufacturers...... 90 Table 4.5: Basis for Allocation of Points in Benchmarking Assessment ...... 91 Table 4.6: Summary Statistics for Power Bases...... 96 Table 4.7: Summary Statistics for Relationship Elements ...... 97 Table 4.8: Summary Statistics for Performance/Satisfaction...... 98 Table 4.9: Two Population T-Tests for Power Bases ...... 101 Table 4.10: Two Population T-Tests for Relationship Elements ...... 102 Table 4.11: Two Population T-Tests for Performance and Satisfaction Variables 103 Table 4.12: Factor Analysis for the Bases of Power Items ...... 105 Table 4.13: Inter-factor Correlations for Bases of Power...... 106 Table 4.14: Initial Factor Analysis for Relationship Elements Items ...... 109
XIV Table 4.15: Secondary Factor Analysis for Relationship Elements Items ...... 109 Table 4.16: Values of Cronbach Alpha for Reliability Assessment ...... I ll Table 5.1 : Description of the Power Bases Variables ...... 116 Table 5.2: Description of the Relationship Variable ...... 116 Table 5.3: Description of Performance and Satisfaction Variables ...... 117 Table 5.4: Abbreviations for for the Nine Research Models ...... 123 Table 5.5: Fit Indices for Initial Measurement Models ...... 134 Table 5.6: Fit Indices for Final Measurement Models ...... 135 Table 5.7: Measurement Model Properties - Mn-s...... 136 Table 5.8: Inter-Factor Correlations - Mn-s ...... 136 Table 5.9: Measurement Model Properties - Mn-m ...... 138 Table 5.10: Inter-Factor Correlations - Mn-m...... 138 Table 5.11: Measurement Model Properties - Mn-sc ...... 139 Table 5.12: Inter-Factor Correlations - Mn-sc...... 139 Table 5.13: Measurement Model Properties - Mc-s...... 141 Table 5.14 : Inter-Factor Correlations - Mc-s...... 141 Table 5.15: Measurement Model Properties - Mc-m ...... 142 Table 5.16: Inter-Factor Correlations - Mc-m...... 142 Table 5.17: Measurement Model Properties - Mc-sc ...... 143 Table 5.18: Inter-Factor Correlations - Mc-sc ...... 143 Table 5.19: Measurement Model Properties - Mr-s ...... 145 Table 5.20: Inter-Factor Correlations - Mr-s...... 145 Table 5.21: Measurement Model Properties - Mr-m ...... 146 Table 5.22: Inter-Factor Correlations - Mr-m...... 146 Table 5.23: Measurement Model Properties - Mr-sc ...... 147 Table 5.24: Inter-Factor Correlations - Mr-sc...... 147 Table 5.25: Fit Indices for Final Structural Models...... 156 Table 6.1: Results of the Power bases-Relationship Hypotheses Tests ...... 170 Table 6.2: Results of the Relationship-Performance Hypotheses Tests ...... 178 Table 6.3: Results of the Performance/Supplier Satisfaction Hypotheses Tests ...... 182
XV LIST OF FIGURES
Figure 1.1: Cyclical Benefits of Supply Chain Management ...... 6 Figure 1.2: Overview of Research ...... 15 Figure 1.3: A Conceptual Model of Power in the Supply Chain ...... 16 Figure 2.1: Research Taxonomy ...... 21 Figure 2.2: Continuum of Inter-firm Relationships...... 22 Figure 2.3: Stages in Intra and Inter-Firm Integration ...... 25 Figure 2.4: Overview of Research ...... 60 Figure 2.5: Causal Model of Power Influences...... 60 Figure 3.1: Cumulative New Car Sales in the United States - 1996 ...... 67 Figure 3.2: Overview of Survey Instrument Development...... 73 Figure 4.1: 95% Confidence Intervals for Benchmarking Scores (n=130)...... 89 Figure 4.2: 95% Confidence Intervals for Benchmarking Scores ...... 90 Figure 5.1: Generalized Research Model ...... 121 Figure 5.3: Mn-s Model ...... 123 Figure 5.4: Mn-m Model ...... 124 Figure 5.5: Mn-sc Model ...... 124 Figure 5.6: Mc-s Model ...... 125 Figure 5.7: Mc-m Model ...... 125 Figure 5.8: Mc-sc Model ...... 125 Figure 5.9: Mr-s Model ...... 126 Figure 5.10: Mr-m Model ...... 126 Figure 5.11: Mr-sc Model ...... 126 Figure 5.12: Final Measurement Model - Non-mediated Power ...... 137 Figure 5.14: Final Measurement Model - Reward-mediated Power ...... 144 Figure 5.15: Generalized Structural Model - Non-mediated Power ...... 154 Figure 5.16: Final Structural Model - Mn-s...... 157
XVI Figure 5.17: Final Structural Modal - Mn-m...... 159 Figure 5.18: Final Structural Model - Mn-sc...... 159 Figure 5.19: Final Structural Model - Mc-s...... 161 Figure 5.20: Final Structural Model - Mc-m...... 161 Figure 5.21: Final Structural Model - Mc-sc...... 162 Figure 5.22: Final Structural Model - Mr-s...... 162 Figure 5.23: Final Structural Model - Mr-m...... 163 Figure 6.1: General Research Model ...... 167 Figure 6.2: Research Model with Hypotheses ...... 167
xvu CHAPTER 1
INTRODUCTION
1.1 O perations as a C om petitive W eapon Historically, operations has been targeted as a cost center within the firm. Corporate strategy was set without involving operations strategy, leaving operational functions such
as procurement, manufacturing, and distribution to simply minimize costs. Over the last few decades, however, firms have begun to refocus operations as a profit center through integration of operations into corporate strategy. This repositioning of operations functions has bestowed a new and effective source of competitive advantage in an increasingly demanding market place.
1.1.1 Operations Leadership
The many facets of operational functions allows operations to be asserted as a competitive weapon through numerous strategies. For one, firms may become a cost leader. Development of improved production and design processes can lead to elimination of wastes and subsequent decreased costs. This cost advantage translates directly to decreased prices for customers and creates the possibilities for enhanced market share. For instance, the U.S. steel industry has seen the emergence of mini-mills over the last few decades. Many of these mini-mills such as Nucor have found a competitive price advantage in the use of scrap steel, allowing them to compete against larger, integrated competitors.
Firms may also gain a competitive advantage through quality leadership. Creation of better, longer-lasting products satisfies customers, allowing firms to command higher prices and retain market share. For instance, over the last two decades enhanced product quality has allowed Japanese auto manufacturers such as Toyota and Honda to gain a strong foothold in U.S. market that has historically been monopolized by the U.S. manufacturers. To remain in business, the U.S. manufacturers have endeavored to improve their own quality levels, and product quality is now positioned as a critical marketing advantage in the industry. Likewise, customers have benefited from improved products and extensive warranty and roadside assistance programs.
Many firms have also targeted operations as a source of design leadership. Faster and more effective design allows firms to be the first to enter new markets and subsequently establish themselves as a market leader. Hewlett-Packard (HP) provides one such example as their own product research and development allowed them to be the first to penetrate the laser printing market. With little competition, HP captured extensive market share at premium prices. As competitors entered the market, HP's experience had already push them far ahead technologically, allowing ±em to maintain their dominance.
Yet another powerful operation strategy involves flexibility leadership. The increasingly competitive marketplace has yielded a more demanding consumer. As customer interests and demands change, those firms which are able to respond quickly can gain an advantage over competitors. Dell computers offers itself as one such example. The personal computer (PC) market is driven by such a rapid rate technological growth that products can become obsolete within just a year. Dell's approach of mass customization allows them to respond quickly to customer demands, delivering a customized product within days. Dell has subsequently become a market leader without owning retail stores.
1.1.2 Critical Elements for Best Practice Manufacturing The modem business world has seen increased international competition, making corporate survival a more arduous task. The above strategies of cost, quality, design, and flexibility leadership allow flnns the ability to compete by positioning operations as a competitive weapon. In today's business world, this competitive edge is not only a must for a market leader but a virtual necessity for all firms just to maintain corporate survival. Creation and maintenance of operational leadership, however, remains a difficult and complex process, and firms must foster a set of new, critical elements in order to develop best practice operations.
First, firms must develop a sincere corporate attitude toward a dedication to operations. The belief and commitment toward developing operations as a profit driver requires high level organizational support that is communicated throughout the firm. It also includes the place of operations as a prominent role in corporate strategy with a vision of industry and worldwide best practice. This leads to a second necessary corporate element of continuous improvement. An established competitive advantage means little if a firm is not able to sustain it, and thus, leaders must retain an fundamental spirit of anticipation
(instead of reaction) to changes and problems. Realizing that most major advances result from small changes over time, the firm must create a corporate environment of continuos product and process improvement. This requires employee training and empowerment as these small changes will more effectively come from operations personnel rather than high level, strategic heads.
Yet another component required to position operations as a source of advantage is the elimination of waste. To be able to reduce costs and react to change, a firm must be lean and flexible. This necessitates a general corporate attitude toward elimination of excess in inventories, personnel, and processes. Such an orientation of lean manufacturing has helped propel many responsive Japanese manufacturers to success. A fourth element that fosters the environment for best practice operations is technology. Knowledge of self, competitors, and the general business world is critical to corporate success, and modem technology can be utilized to enhance information flows both inside and outside the firm.
Technology may also be exploited to facilitate improved product design, production processes, materials flows, and order processing. This enhances the responsiveness of the firm, allowing them to remain on the cutting edge of competition.
The above critical elements of attitude, process improvement, waste elimination, and technology are necessary in allowing operations to serve as a competitive advantage within the firm, yet such an operations-oriented strategy, however, must extend beyond the firm. A product is delivered to the end customer via a supply chain of firms consisting of suppliers, manufacturers, distributors, and other vendors. Most firms are simply a link in the supply chain, and a chain can only be as strong as its weakest link. Thus, a manufacturer can not be responsive without responsive suppliers, and the benefits of such reaction can not be transferred to the end customer unless the distributors align with this strategy as well. As another example, a manufacturer can not produce quality products without quality parts, thus pushing quality responsibility down to its suppliers.
1.13 Supply Chain Management and Competitive Operations This discussion leads to the conclusion that a single firm can not necessarily position itself as an operations leader without the help of the other firms in the chain. Thus, a fifth element necessary for competitive operations is supply chain control. Specifically, the supply chain consist of all firms which serve to deliver a product or service to the end customer. Japanese manufacturers achieved tight control over their supply chains with the keiretsu which Ellram and Cooper [1993, p. 2] describe as "business consortia which rely on cooperation, coordination, and joint ownership and control to competitively position businesses and industry." While keiretsu activities are driven by the unique legal and cultural environment in Japan, transferring the keiretsu-like concepts to the United States has yielded the concept of supply chain management.
Supply chain management involves the strategic and process coordination of firms within the supply chain to deliver satisfaction the ultimate customer. While each firm in the supply chain has been traditionally driven by self profitability, the notion of supply chain management involves optimization of synergistic relationships between supply chain members to ultimately satisfy the end customer. The concept evolves from controlling the supply chain as a single process rather than a sum of independent transactional relationships. The expected end result is a mutually beneficial, win-win partnership that creates a synergistic supply chain in which the entire chain is more effective than the some of its individual parts. Ideally, supply chain management represents a win-win, utopian goal of circular benefits (Figure 1.1). A firm obtains its own profitability and success by creating customer value
in terms of a functional, quality product at an acceptable price. Firms within the supply
chain can reduce their own costs and increase performance through supply chain management, thus, enabling the chain to deliver a higher quality value package to the
customer. The satisfied customer in turn rewards the supply chain with their loyal buying power, allowing profitability to be transferred back down throughout the supply chain. This in turn instigates further supply chain integration and responsiveness, causing the cycle to repeat.
SUPPLY CHAIN MANAGEMENT Increased coordination leading to decreased costs, faster cycle times, higher quality benefiting the customer
SATISFIED CUSTOMERS Increased loyalty, volume and repeat business leading to greater profitability of supply chain
Figure 1.1: Cyclical Benefits of Supply Chain Management
As mentioned above, a manufacturer may be able to position their firm as a market leader through exploitation of best practice operations, but the nature of the linked firms in the supply chain necessitates that the suppliers and distributors must be part of this strategy.
A manufacturer can not become a industry leader without supply chain support, and thus, supply chain management is a prerequisite for developing a competitive advantage for operations. As an example, the Japanese auto manufacturers brought their cutting edge production and supply chain processes to the United States. The subsequent intense competition push many U.S. firms such as Chrysler and American Motors Corporation to the brink of bankruptcy. In response, the U.S. manufacturers had to re-engineer their own
production processes to stay afloat and eventually found that much of this change actually
originated with the supplier base. The manufacturers have therefore developed a new
orientation to the supply chain, transferring design, cost reduction, and quality issues to their suppliers. By taking a lead in supply chain management among the U.S.
manufacturers, Chrysler was able to rebound as an industry leader.
1.1.4 Barriers to Supply Chain Management
Supply chain management offers promise for U.S. firms. Intense implementation challenges, however, often prevent effective exploitation of supply chain management benefits, thus proving detrimental to any planned operations advantage (Table 1.1). For one barrier, the nature of individual, autonomistic firms makes it difficult to create
involved cooperative relationships with other firms. Specifically, supply chain
management necessitates sharing of traditionally proprietary information, strategy, planning, and goals, and most firms do not feel comfortable exposing such elements to other firms, fearing loss of control. Furthermore, interfirm collaboration requires each participating firm to create a high level of awareness of both themselves and their partners, and such a cognition is often difficult to effectively accomplish. Yet another problem involves the inability of chain members to focus on one mutual goal of supply chain rather than individual performance. Other significant barriers to execution of supply chain management include a lack of understanding of the customer (who is the true customer and what do they really want), communication gaps (difficult for separated members to communicate), a lack of understanding of the true supply chain (what firms are in the chain), the enormous size of many supply chains (difficulty in coordination of hundreds or even thousands of firms), a lack of effective leadership (who is the best leader), corporate egocentrism (self pride may create myopic strategy), and finally, a deficiency of mutuality (profits and rewards are not shared equally).
Failure to share information Fear of loss of control Lack of self awareness Lack of parmer awareness Enormity of supply chain Inability to recognize goals Lack of customer understanding Lack of understanding of supply chain Myopic strategies Deficiency of mutuality
Table 1.1: Barriers to Supply Chain Management
On the basis of the above barriers to supply chain management, development of an integrated supply chain remains an extremely difficult task. It represents a new way of doing business, and most firms are not prepared or even necessarily willing to effectively integrate the chain. Though a distant goal, however, the concept of supply chain management remains a critical factor for the long term success of the manufacturing firms, and while complete supply chain management may be out of reach for most firms, application of its concepts will improve their competitive advantage. Furthermore, as the supply chain best practice firms are able to improve quality, lead times, and availability while decreasing costs, the demanding customer will continue to demand more. As a result, the efficient supply chains will prosper, and the traditional, competitive supply chains will not survive. For this reason, supply chain management remains a critical element for positioning operations as a source of competitive advantage, and thus serves as a desirable goal for industry practitioners and a promising source of supply chain research.
1.2 SUPPLY CHAIN PARTNERSHIPS
Critical to the implementation of supply chain management techniques is the supply chain partnership. Also termed a strategic alliance, a supply chain partnership is a relationship formed between two independent entities in supply channels to achieve specific objectives and benefits, and it is these partnerships that form the essential building blocks of supply chain management. The high levels of information flow and subsequent coordination of error-firee deliveries required by supply chain management require manufacturers to build tighter bonds with a few suppliers. Once traditionally driven by competition, the supplier relationships for many manufacturing firms have thus matured from an adversarial relationship to one of supply chain partnership.
Within the win-win partnership dyad, buyer and supplier share goals as well as inherent risks through joint planning and control, seeking to create a supply chain with increased information flow and enhanced loyalty. Like the overall goal of supply chain
management, such coordination allows for improved service, technological innovation, and product design with decreased cost. Ideally, the end result for both firms should be a decreased uncertainty, yielding greater control of costs, cycle times, inventory, quality,
and ultimately, customer satisfaction.
1.2.1 Benchmark Supplier Partnershipping: Chrysler Corporation As an example of supply chain partnerships, the Chrysler Corporation is a leader in developing intimate relationships with its suppliers. When Chrysler*s team designed its new LH line (Dodge Intrepid, Eagle Vision, Chrysler Concorde) and new compact sedans (Neon), Chrysler outsourced more than 70% of its parts to a limited amount of suppliers.
In order to achieve this supply chain partnership arrangement, Chrysler invited several key suppliers into the early stages of the development process and actually presourced
95% of the component parts for its new sedan by choosing vendors prior to the design stage [Kamath and Liker, 1994]. In doing so, they eliminated the competitive bidding process. Several of Chrysler's supply chain parmers, like their Pacific Rim competitors, have full responsibility in developing the components themselves and coordinating with other sub-contractors to carry our the component development process. In the end, the
LH line was developed from scratch in 39 months versus the usual five to six years, and the new Neon line was developed in only 31 months. [Raia, 1993; Carbone, 1993]
Furthermore, Chrysler’s Supplier Cost Reduction Effort (SCORE) has lead to 10,000 new ideas since 1993 and has resulted in $2.3 billion in supply chain savings, one-third of such the suppliers keep [Smith, 1994]. Chrysler has also utilized supplier involvement to become a virtual vehicle manufacturer as its suppliers accept more responsibility and do
1 0 more assembly. While Chrysler takes a role in part design, it leaves a significant portion of the assembly to suppliers, cutting its own costs and increasing overall production efficiency in the process. Each Chrysler plant once carried between $25 million to $27 million of inventory, but the number has dropped to below $8 million as supplier boundary personnel work directly with plant purchasing personnel [Bradley, 1995].
1.2.2 Importance of Supply Chain Partnership Awareness Like supply chain management, the frequency of parmershipping is increasing in industry [La Londe and Masters, 1990], but implementation still remains a difficult process. And like supply chain management, buyer-supplier partnering extends beyond a simple inter firm relationship to involve integration of confidential and vital processes like strategy formation, planning, information flow, and operations. Thus, both researchers and industry practitioners must clearly comprehend when, why, and how effective partnerships are formed as well as when, why, and how these parmerships are maintained. The research described here seeks to contribute to such an understanding through an analysis of buyer-supplier relationships.
1.3 M o tivatio n AND Scope OF Research
To remain competitive, U.S. manufacturing firms must build stronger and more effective relationships with their suppliers. As they attempt to implement such practice, however, they will need to endure a paradigm shift firom the traditional transactional supplier orientation to a relational one in which long-term, trusting alliances among a small number of suppliers prevail [Matthyssens and Van de Bulte, 1994; Treleven, 1987]. Ideally, firms must break down the traditionally competitive barriers within the supply chain to increase information flow and resource coordination necessary for an effective
11 relationship environment. Likewise, the supply chain literature base research should be
able to support this transition with practical partnershipping research.
Both conceptual [Ellram, 1991; Landeros, et al, 1995] and empirical [EUram and Hendrick, 1995; Stuart, 1993; Graham et al, 1994], in nature, logistics research has been
extremely optimistic about the promise and effectiveness of supply chain partnerships Much of this literature focuses upon the theoretical shifts to relational supplier alliances [Matthyssens and Van de Bulte, 1994; Manoochehri, 1984] as well as critical implementation factors for successful partnerships [Ellram, 1991; Dwyer et al., 1987; Landeros et al., 1995]. Further examination of the supply chain literature base, however, reveals critical yet unaddressed issues. One such topic is inter-firm power.
Defined, power involves the ability of one firm (the source) to influence the intentions
and actions of another (the target). Much of the partnership literature infers a critical
environment of equality and cooperation, but such a state often proves difficult to attain
due to the realistic increased chance for opportunism by power holders [Pilling and Zhang, 1992]. For instance, in Pacific Rim supply chain management, a manufacturer usually controls numerous small, dependent suppliers and subcontractors, creating an opportunity for manipulation and exploitation by the powerful manufacturer Although the Asian business culture works to neutralize the influential use of power, Ramsay [1990, p. 4] cautions that "it can hardly be described as egalitarian" and further forecasts that "in the West, this kind of situation typically leads to the exploitation of the weaker party by the strong." Furthermore, other skeptics have challenged that American implementation of Pacific Rim developed production processes have simply resulted in pushing inventory and uncertainty to supply chain members that wield less authority
1 2 [Oliver, 1990] as suppliers hold superfluous inventory to meet delivery schedules [Chapman, 1989; Freeland, 1991; Hill and VoUmann, 1986].
Thus, the notion of strategic alliances throughout the supply chain as developed by
current research idealistically offers promise for a win-win relationship but is often
remiss in ignoring the balance of power within the partnership. The current literature makes the ambitious assumption that both constituents of the supplier-buyer dyad are both willing and able to cultivate a mutually beneficial relationship, but it may be argued
that a firm with significant power may not find it necessary to establish the win-win
alliance as they can achieve their own profitability and effectiveness through control of their dependents. In other words, the firms with the bargaining power have little if no reason to yield control or withhold exercise of such power. In seeking their own
profitability and success, the dominant firms may be better off pursuing their own individual supply chain agendas, submitting to a joint planning partnership only as much as the balance of power dictates. This dramatically challenges possibilities for
implementation of supply chain partnerships and contests the effectiveness of supply
chain research. Consequently, some researchers have pushed for partnership research
directed toward a more realistic outlook for implementation [Pilling and Zhang, 1992].
1.4 Research Problem
This research described in this dissertation contends that the current supply chain research has yet to thoroughly address the effects of power influences upon supplier partnerships, and without such an examination, the literature base is neither complete nor practical. The research described herein seeks to examine the presence of power in the supply chain and the effects of such power on buyer-supplier partnerships. The research investigates
13 the extent to which influences of power are utilized for control within the relationships between buying and selling firms as well as how such power affects satisfaction and performance of the entire supply chain. In doing so, the research judges the validity of current supply chain partnership literature, thus offering insight into the development processes of realistic supplier partnering.
Critical questions addressed by the research include;
How does the influence of a power imbalance (asymmetry) affect cooperative relationships between buyers and suppliers?
How does the influence of power affect elements critical to supply chain relationships?
How does the influence of power affect supplier, buyer, and supply chain performance?
How does the influence of power affect satisfaction within the supply chain?
Does a the influence o f a power imbalance prohibit the establishment of long term, mutually beneficial partnerships?
1.5 Research Overview
On the basis of an extensive review of both supply chain and power literature, this study combined two streams of research in providing one of the first works to address power issues in the supply chain. The research sought to establish empirical evidence into the bases of power between suppliers and manufacturers, yielding insight as to why firms are able to hold power over others. The research also quested to examine how such power affects critical elements of buyer supplier relationships including cooperation, commitment, trust, compliance, conflict, and conflict resolution. Finally, the research
14 sought to link the power-affected relationship environment with perceptions of
performance and satisfaction within the supply chain. Figure 1.2 summarizes this flow.
i.
! ii! 4 ■
..
. ...
.'Vi*r»lv » I' IlM l»_'...yr''5!3U:'.l|(iO'
Figure 1.2: Overview of Research
The U.S. automobile industry provided an excellent source of focus in which to address
the research objectives. Specifically, the industry has relied heavily upon buyer-supplier relationships for competitive advantage and has thus served as a pioneer of supply chain
management within the United States. Furthermore, ± e nature of a few powerful
manufacturers and a relatively large supplier base has yielded a natural environment of
power asymmetry. To verify the research goals and their application to the auto industry,
meetings were arranged with several of the manufacturers. These manufacturers
recognized their power advantage but also identified improved supplier relations as a critical driver of their corporate strategy. Thus, the research was able to rely upon support and assistance directly from industry practitioners to enable the development of a significant and practical study.
The research methodology revolved around a general model (Figure 1.3) of power
influence of the buyer-supplier relationship and subsequent effects upon performance and
15 satisfaction. It was hypothesized that power directly influenced the relationship
environment between the buyer and supplier. The relationship then, in turn, was conceptualized to affect performance of the supply chain members as well as the satisfaction of the suppliers. Due to the intricate nature of the dependence relationships
within this model, structural equation modeling was targeted as the best analysis tool.
Performance \ mm.-
Figure 1.3: A Conceptual Model of Power in the Supply Chain
A validated survey instrument was developed and administered mailed to critical suppliers for two manufacturers identified as industry leaders in supply chain management. In support of the research model, this survey data sought opinions of supply chain power bases, relationship elements, and satisfaction/performance measures.
The survey data was collected and validated, and the fit between the data and model was assessed. Verification of the model allowed for statistical testing of hypothesized relationships between research constructs which subsequently leads to critical insight for both supply chain researchers and industry practitioners.
16 1.6 Contributions While the ultimate benefits of the research are left to the judgment of the reader, the research sought to provide a meaningful contribution to the direction of supply literature and enhance the implementation of buyer-supplier partnerships in practice. With regard
to research, the study hopefully adds a more realistic dimension to the current "conceptual" based supply chain management studies by measuring the power factor for
the first time. If power structures are, as contended, interfering with effective supplier alliances, the research will provide insight into the effects of the power, thus examining
the reality of such partnerships. The intentions are not to arrest the current literature direction but rather to steer it toward a more realistic and implementable base. Research contributions include the following questions and research findings:
• What role does the influence of power play in buyer-supplier relationships?
• How should power management be incorporated into supply chain research?
• Does the current literature base need to be modified for power variables?
• How can the understanding o f power facilitate future supply chain research?
On the practice dimension, managers must understand how the influence of power affects
their buyer-supplier relationships. The research seeks to provide a tool to help managers both recognize the presence of different forms of supply chain power and understand the role of power within their supply chain relationships. In doing so, the research seeks to help managers incorporate the power into supply chain strategies, helping firms to establish more effective partnerships to benefit both their own firm and the entire supply chain. Such contributions to industry can be summarized with the following questions:
17 • What types o f power does a firm hold over others in the supply chain?
• How do supply chain partners perceive their power?
• What types o f power do other partners hold?
• What role does power play in the development of supply chain relationships?
• How will power affect the maintenance o f supply chain relationships?
• How does power influence performance and satisfaction within the supply chain?
• How do firms best incorporate power into supply chain strategies?
1.6.1 Findings
The research results offered significant insight for the above contributions. First, it was
found that non-mediated and reward-mediated power bases will enhance the nature of the
relationship between buying and selling firms. It was found that coercive-mediated
power strategies will have a detrimental effect on such relationships, however. With regard to performance, improved supply chain relationship were found to have a significant positive effect on performance within the chain. Finally, it was found that
supplier satisfaction was driven by the nature of the supply chain relationships with their customers rather than supply chain performance. These findings highlight the need power awareness in supply chain strategy as well as further power research in the supply chain literature base.
1.7 S ummary
This chapter has presented a general overview of the research scope, problem, and
methodology as well as possible contributions and limitations. The following chapters seek to expose the research in finer detail. Chapter 2 concentrates on related literature
18 bases, justifying the motivation and direction for the research as well as providing a foundation for the research hypotheses. Chapter 3 discusses the general research design and data collection efforts, while Chapter 4 reveals initial descriptive analysis of the survey data. Chapter 5 reviews the process of fitting the data to the conceptualized measurement and structural models, leading to Chapter 6 which details the testing of research hypotheses and subsequent insight. Finally, Chapter 7 serves as a review of the entire research project and discusses possible directions for future research.
19 CHAPTER 2
REVIEW OF LITERATURE
2.1 Introduction
Chapter 2 presents a review of relevant literature with respect to the research. (Figure 2.1 portrays an overview of the literature review taxonomy.) The review draws upon extensive research in both supplier partnership and distribution channel power to examine the shortcomings in the supply chain literature in addressing the influences of power between buying and selling firms. The review begins with an examination of generalized inter-firm parmerships then extend into the logistics and operations literature with two related concepts: Traditional Sourcing and Supplier Parmerships. In doing so, it offers proposed benefits of each as well as implementation challenges for American firms, contending that power will influence effective execution of both traditional sourcing and supplier parmerships. Next, a discussion of power as explored primarily by researchers of distribution channels will then be explored and subsequently applied to the supply channel, yielding insight into power implications for supply chain partnerships. In the end, the review will utilize knowledge gained from the distribution channel literature to formulate critical research hypotheses for examination of power effects on supply chain parmerships.
2 0 Power EHects upon Bases o( Power commitment Power In cooperation Power DtaoUxiOon compliance Channels conflkt satisfaction K Muence Strategies performance
CRITICAL RESEARCH GAP: POWER IN BUYER-SUPPLIER RELATIONSHIPS
BeneRts/RisKs
Supply Ctiain Partnerships Partnerships Implementation
Critical Success Factors
Figure 2.1: Research Taxonomy
2.2 Partnerships - Definition AND Overview
Though partnershipping has received abundant recognition over the last few decades from both researchers and practitioners alike, the concept of a partnership is perhaps as old as or even older than business itself. Although many firms engage in partnershipping activities, the specific interpretation of a strategic alliance or partnership (the terms partnership and alliance will be used interchangeably in this dissertation) is at best vague. To understanding the context of a partnership, it is helpful to consider a continuum of inter-firm relationships (Figure 2.2) as extended from both Lambert et al. (1996) and
Cooper and Gardner (1993). A basic interaction between two firms involves a discrete arm's length relationship that has a relationship equivalent to the length of time it takes to
2 1 complete the single transaction. Other interactions may involve significantly more attention by one or both firms and involve what this research deems as a special influence transaction. An example of such may be found in the national account groups of firms in which specific resources, both human and non-human, are devoted to handling larger, more important buyers.
Discrete, Special Verticai Arm’s Length Influence r h h Joint Venture integration Transaction Transaction ■ H
RELATIONSHIP INTENSITY
Figure 2.2: Continuum of Inter-firm Relationships
Parmerships move beyond special influence transactions by involving efforts by both firms to coordinate functional activities. Lambert et al. (1996) breaks down parmerships into three tiers based on the intensity and duration of the leadership. Tier I partnerships entail short-term, single function/division coordination. Tier H parmerships extend coordination to integration and encompass multiple activities over a longer time span.
Finally, Tier m parmerships dilate into what Lambert et al. (p. 3) deem "significant levels of operational integration." Though independent. Tier IH firms view their parmers as difficult-to-replace extensions of themselves. Beyond a partnership, firms may want to evenmally involve themselves financially in significantly large, capital intensive projects with their partners (joint ventures) or even go as far to completely outright purchase or be purchased by the parmer (vertical integration). Although the concepts of joint ventures and vertical integration expand beyond the scope of the dissertation research, a primary
2 2 argument for implementation of strategic partnerships in fact involves receiving benefits
of joint ventures and vertical integration without the ownership commitment.
To narrow the above discussion, Lambert, et al. [p. 2] formally defines a partnership as:
a tailored business relationship based on mutual trust, openness, shared risk, and shared reward that yields a competitive advantage, resulting in business performance greater than would be achieved by the firms individually.
Several key concepts are critical to the essence of the above definition with the first of
which being interdependence. Partnershipping involves an interdependent relationship of coordination planning and strategy. Ultimately, these partners work toward a mutual goal
that benefits all parties. The second significant component of the above definition is the
notion of synergy in that within the partnership, the two firms create a whole that functions better than the sum of its parts. The two concepts of interdependence and synergy are two guiding attitudes with the concept of a partnership, but significantly more elements exist as necessary pieces of the alliance. Such factors will be discussed later and represent a critical rudiment of the dissertation research.
Despite a formal definition of partnershipping, the orientation of partnership activities in practice are still often vague. When do firms officially move into the realm of partnerships? When can two firms officially call themselves partners? What if one firm considers the other its partner but the title is not reciprocated? This obscurity complicates the orientation of the dissertation research as two different sets of partnerships may be significantly different in terms of coordination. One projected output
23 of the research, however, will involve the understanding of the factors necessary for a true win-win partnership.
This chapter will next continue by applying and subsequently extending partnershipping concepts specifically to the supply chain. To do so, it is first necessary to present the evolutionary background of supply chain strategy as this is a primary driver for the intensified focus of supply chain partnerships.
2.3 T raditional S ourcing Firms have taken bold steps to break down both intra and inter firm barriers to smooth uncertainty and enhance control of supply and distribution channels (Figure 2.3). The evolution of intra-firm functional integration has occurred for most firms over the last few decades, and the current push is toward external integration with both suppliers and customers. Supply chain partnerships bridge the barrier between buyer and vendor, leading manufacturers to ally with a reduced supplier base.
Historically, American manufacturers have formulated supply strategy around the transparent benefits of a large competitive supplier base. An abundant collection of suppliers encourages competition which the manufacturer can exploit to negotiate lower costs, higher quality, reasonable delivery times, and special exigencies. Such a strategy enhances ultimate manufacturer bargaining power as well as shelters against interruptions in supply due to strikes and other unforeseen problems. To the contrary, many Asian and American firms have recognized the benefits of a contrarian concept of single sourcing (use of only one supplier for a part) which leads to an abatement of adversarial attitudes, lower switching costs, and decreased shipping errors. The association with the single
24 source can also lead to quantity and relationship based discounts as well as a decreased cost of quality. [Treleven, 1987; Bartholomew, 1984]
Functional Independence
MaterialsPurchasing Production Distribution
Functional Integration
Materials Manufacturing Distribution Management Management Management
Internal Integration
Materials Manufacturing Distribution Management Management Management
External Integration
Internal Suppliers Supply Chain Customers
Supply Chain Partnerships
Source: Stevens [1989]
Figure 2.3: Stages in Intra and Inter-Finn Integration
25 2.3.1 Reduced Supplier Bases Some researchers argue that implementation of many new manufacturing techniques
necessitates a reduction in the number of suppliers. Brown and Inman [1993] found that
the two primary factors in emulation of Asian production techniques are reduced vendor lot sizes and single sourcing. Other research does proclaim that single sourcing is not as widespread in Japan as believed and that many Pacific Rim manufacturers actually exercise a single/dual hybrid approach [Hines, 1995]. In a practical example, Chrysler Corporation single sources an individual product (such as tires for a particular car line) but will have two or three suppliers for the commodity (tires in general) in case of problems ["Stallkamp", 1994]. Likewise, Newman [1989] also promotes dual over single sourcing to reduce the potential for power influence.
Despite the variance in opinions about the size of the supplier base, the major issue
remains that a closer relationship with suppliers sanctions a reduced number of suppliers
[Graham et al., 1994]. As one example, Emshwiller [1991] found an increased tendency
toward a smaller supplier base in the United States. With regard to the automobile industry, component part sales have increased while the number of suppliers has
drastically decreased. A study by International Business Development Corporation (IBD) reports that in 1983, the automobile industry relied upon approximately 10,000 suppliers to purchase $103 billion in components parts from Tier 1 suppliers. Ten years later, such purchases rose to $122 billion while the number of Tier 1 suppliers fell to just 450. IBD predicts that this supply base will fall to 150 by the year 2003. [Fitzgerald, 1996]. As an example of a reduced supplier base, Chrysler has been able to reduce its total (all Tiers)
26 supplier base from 3200 vendors in 1985 to 1200 now, and 90% of their buys actually come from just 150 "Tier I" suppliers [Smith, 1994]. Furthermore, Chrysler was able to develop their LH line with approximately 200 suppliers versus the typical 600 to 700 [Raia, 1993].
The objective of a drastically reduced supplier base precludes an acceptance of supplier partnerships for a firm must accept dependence upon fewer suppliers before they can internalize legitimate forms of supply chain management [Cooper and Ellram, 1993] and supplier partnerships. As much literature that exists to examine a reduced supplier base, one primary, often overlooked problem, power asymmetry, may potentially inhibit the implementation of such a strategy in Western cultures. Ramsay [1990] argues that the
Asian supplier is not a "supplier" in the traditional sense of the word, and single sourcing will not mechanically work in the United States as the power imbalance will lead to opportunism and exploitation. Thus, relying on a reduced supplier base requires a transformation of engraved Western supply chain practices, and such a change may not be completely possible due to behavioral and cultural considerations. Efforts to investigate the implementation of reduced supplier bases have neglected the role of power asymmetry and thus have not realistically challenged the reality of single sourcing.
2.4 Supply C h ain P artnerships
A Harvard Business School study concluded that a key driver in the decline of U.S. competitiveness in the international marketplace has originated from investing less in intangible benefits such as supplier relations [MacBeth and Ferguson, 1994]. According to Mac Neil [1980. p. 60], it is impossible to operate as a discrete entity, but while virtually no firm engages in completely discrete engagements, conventional Western and
27 American business practices have been more oriented toward discrete than relational. Traditionally, U.S. firms have based their drive for success on autonomy and have viewed competition as a Darwinistic keeper of American superiority. Long-run U.S. firm planning has been independent, and considerable efforts are taken to ensure privacy of
corporate information.
Over recent decades however, firms within the supply chain have begun to realize the advantages enjoyed from sharing of technology, information, and planning with other firms, even competitors, and many modernistic business thinkers will claim that not only is a more open and relational attitude advantageous but actually essential and inevitable in maintaining a competitive advantage. As shown in Table 2.1, the idea of relationalism between firms seeks to move away from the concept of discrete transactions, breaking down traditional inter-firm barriers. Firms unite to share information and planning efforts, thus, reducing uncertainty as well as increasing control. In the end, the partners
reap the benefits of the joint effort. Niederkofler [1991], Ring and Van de Yen [1992,
1994], and Bergquist et al. [1995] provide basic overviews for strategic partnering, and
Appendix A offers a summarized review of partnershipping literature, primarily oriented to the supply chain. Maloni and Benton [1997] also offer a review of supply chain partnership literature.
Recognizing partnershipping between buyer and supplier as a fundamental driver for the success of the Pacific Rim supply chain processes, the American firms have begun to emulate these supplier alliances. While Asian firms are not completely responsible for the move to supplier partnerships (Farmer [1976] proposes that many supplier alliances were imminent due to raw materials shortages, oil crises, government price control, and
28 general changes in attitudes), the primary root of the success of the concept lies with the
Pacific Rim. Modem manufacturing improvements such as Just-in-Time require the tighter control generated by the supply chain partnership, and there is growing evidence that Western firms have begun to implement such relational strategies [O'Neal, 1989; Spekman, 1988]. La Londe and Masters [1990] report an increase in partnering over the past decade and indicate this trend will continue into the future.
Contractural Element
Duration
Transferability (switching parties)
Attitude
Communication
Information
Planning and Goals
Benefits and Risks
Problem Solving
Table 2.1: Discrete versus Relational Business Strategies
Initial efforts to involve suppliers began with the inclusion of suppliers in cross functional sourcing teams. Trent and Monczka [1994] suggest that the establishment of such teams will improve supply chain effectiveness. Supply alliances, however, extend well beyond this notion to an even more relational level of exchange in which parmers create an intensive, interdependent relationship from which both can mutually benefit.
29 Supply partnerships emphasize a direct, long term association, encouraging mutual planning and problem solving efforts. Table 2.2 displays the critical elements of a supply partnership in comparison to traditional thinking.
• Price emphasis for supplier selection • Multiple criteria for supplier selection • Short-term contracts for suppliers • Long-term alliances with suppliers • Bid evaluation • Intensive evaluation of supplier value-added • l_arge supplier base • Few suppliers • Proprietary information • Shared information • Power driven problem solving • Mutualproblem solving improvement improvement success sharing success sharing
Adapted from Stuart [1993]
Table 2.2: Traditional Versus Partnership Supply Strategies
2.4.1 Benefits of Supplier Partnerships While many firms have sought vertical integration through acquisition to harness supplier expertise, [Dwyer, 1993], MacBeth and Ferguson [1994], and Ellram [1991d] argue that parmerships can provide similar benefits without necessity of ownership and arduous exit barriers. Suppliers can gain from higher quality, and transaction costs may be reduced through economies of scale, decreased administrative and switching efforts, process integration, coordination of processes, and quantity discounts [Treleven, 1987; Newman, 1988; Wilson, Dant, and Han, 1990]. Furthermore, the relationship will be enhanced by stability of market on the vendor side and of supply on the buyer side. Scott and
Westbrook [1991] propose several further benefits specific to manufacturing process
30 including set-up time reduction, improved process-oriented layout, better product design, and enhanced data capture. In less tangible benefits, both firms can benefit from increased communication and goal congruence, leading to enhanced conflict resolution, less probability of opportunism, and decreased risk fi-om externalities.
Both Cooper and EUram [1992] and Ellram [1991a] present a valuable, detailed list of possible advantages of supply chain relationships composed from several sources. Moreover, Landeros and Monczka [1989] propose benefits of supplier-buyer partnerships under different manufacturing strategies including cost leadership, product differentiation, and market-segment focusing. Finally, Stuart and Mueller [1994] offer a longitudinal look at continuos quality improvement recognized through supplier partnerships. Table 2.3 best summarizes the conceptual benefits of supplier partnerships.
A limited amount of the supply chain literature has been directed at testing the recognized benefits of the supply paitnership. Graham et al. [1994] found that the supplier partnerships lead to improved quality of supplier operations, improved quality of parts, decreased supplier costs, and improved reaction to buyer changes to delivery date. Such improvements tend not to be recognizable until after three years of alliance. Kalwani and Narayandas [1995] examine the empirical benefits of partnering with suppliers, finding that such suppliers are able to achieve higher profitability than their non-partnering competitors.
To date, however, research on partnerships has been primarily either analytical or conceptual. Thus, one purpose of the research described herein is to empirically test the
31 ability to recognize the benefits of supplier partnerships as conjectured by the analytical
models and conceptual ideas.
Reduced Uncertainty for Buyers In Cost Savings • material costs • economies of scaie in • quality ordering • timing production • reduced supplier base easier to manage transportation • decreased administrative costs • fewer switching costs Reduced Uncertainty for Suppliers In • enhanced process integration • market technical or physical integration • understanding of customer needs improved asse t utilization • product specifications Time Management Reduced Uncertainty for Both In • faster product development • convergent expectation and goals • faster new products to market • reduced effects from externalities • improved cycle time • reduced opportunism • increased communication and feedback Shared Risks and Rewards • joint investements • joint research and development Joint Product and Process Development • market shifts • faster product development • increased profitabilty • increased shared technology • greater joint involvement of product design Stability • lead times Greater Flexibility • priorities and attention
Adapted from Dwyer [1993]; Ellram [1991a,c,d; 1993]; Kalwani and Narayandas [199S]; Landeros and Monczka [1989]; MacBeth and Ferguson [1994]; Newman [1988]; Scott and Westbrook [1991]; Treleven [1987];
Table 2.3: Potential Benefits of Supplier Partnerships
2.4.2 Risks of Supplier Partnerships With its many benefits, supply chain partnerships retain several inherent risks that can be potentially damaging to participants. First and foremost, heavy reliance on one partner
32 can be disastrous if the partner does not meet expectations [MacBeth and Ferguson, 1994]. Also, firms risk decreased competitiveness due to loss of partnership control, complacency [Kalwani and Narayandas, 1995], and over specialization with an affirmed partner. Furthermore, Leavy [1994] cautions that firms may overestimate partnership benefits while ignoring potential shortcomings and declares a need for more research examining direct comparisons of the conventional and partnership strategies.
Subsequently, Leavy offers a bridge between the two schools of thinking, declaring there are beneficial insights from both conventional and partnership perspectives. Finally,
Newman [1989] notes that partnerships may actually open the weaker party up to influence potential and suggests that competition may abate power.
2.4.3 Partnership Implementation and Critical Success Factors Before a firm can enjoy the benefits of a buyer-supplier partnership, they must first endure the complicated and intricate partnership implementation process. A supplier partnership involves a significant attitudinal as weU as structural change from traditional supply arrangements, so the allying firms must be meticulous to ensure a true win-win partnership is developed. Ellram [1991a]; Dwyer et al. [1987]; Frazier, Spekman, and O'Neal [1988], Lyons, Krachenberg and Henke [1990]; and Landeros et al. [1995] offer guides to the implementation process, which is summarized in Table 2.4. Heide and John [1990]; Helper [1991]; and Salmond and Spekman [1992] empirically examine relationship determinants.
The first step in the supplier partnership implementation process includes the strategic verification of the need for a supplier partnership. Here, the firm must evaluate the potential risks and benefits of a partnership in comparison to traditional processes. Next,
33 criteria for potential partners are developed, and candidates are assessed. Once a partner is selected, the establishment of the actual relationship provides the next critical step in
which the partners must create a complete sense of awareness about the needs and
participation of all involved parties. The final step in the partnershipping process
includes the maintenance of the relationship to either enhance its development or bring about its dissolvement.
1. Establish strategic need for partnership
2. Develop partner criteria, evaluate suppliers, and select partner
3. Formally establish partnership
4. Maintain and refine partnership (possible reduction or dissolvement)
Table 2.4: Supplier Partnership Implementation Steps
The entire partnership implementation process holds many elements critical to the success of the relationship (see Table 2.5), and Ellram [1991a, 1991c, 1995] provides general
resources for success factors. One rudiment that must be established immediately is top management advocacy [Ellram, 1991a; Stuart, 1993]. This requires overcoming social and attitudinal barriers as well as managerial, procedural, and structural obstacles associated with corporate change [MacBeth and Ferguson, 1994]. In practice, overcoming the social and attitudinal barriers and managerial practices may prove to be extremely difficult if not impossible.
34 In the supplier selection/evaluation step, Leavy [1994] warns that the chance of choosing the wrong supplier presents a severe problem in partnershipping, and there is a stream of supplier partnership literature that attempts to explore supplier evaluation and selection [Ellram, 1990 and 1991a; Newman, 1988; Spekman, 1988]. Ellram [1991a] offers a list
of additional selection criteria for supply chain partners including such elements as cultural compatibility, long term strategic plans, financial stability, technology/design
capability, management compatibility, and location.
Throughout • top management support • communication • central coordination
In Initai Strategic Analysis Phase • social and attitudinal barriers • procedural dn structural barriers
in Supplier Evaluation and Selection Phase • total cost and profit benefit • partner capabilities • cultural compabaility • management compatability • financial stability • location
In Partnership Establishment Phase • perception and needs analysis • intense interaction • documentation
In M aintenance P hase •trust • social exchange • goodwill • boundary personnel • flexability • performance measurment • conflict management skills
Table 2.5: Supplier Partnership Critical Success Factors
35 In the final steps, establishing and maintaining the relationship necessitates several factors, and Landeros et al. [1995], Ellram and Hendrick [1995] provide rich sources for such elements. (See Table 4) Overall, Niederkopler [1991] suggests that the most important attitudinal factors involve cooperation, trust, and goodwill as well as the ability to both be flexible and handle conflicts. Furthermore, attitude and shared goals are described as success factors [Ellram, 1990; Ellram, 1991a], and Ellram [1995] shows empirically that two importance antecedents of partnership failure are lack of trust and deficiency of shared goals. Much of the literature also emphasizes communication as well [Landeros et al, 1995; Ellram and Hendrick, 1995]. In a related sense, Landeros et al. [1995, p. 4] found that "product information and social exchange among partners lead to both cooperation and adaptation." Katner [1989] examines partnership imbalances with resources, information, and benefits. Other critical success factors will include effective performance measurement as well as proper establishment of boundary personnel and procedures.
Ultimate dissolution of the partnership may be necessary if the firms are unable to successfully work through the critical steps of paitnership formation or synergies can not be recognized. Graham et al. [1994] found, however, that partnership benefits tend not to be realized until three years after formation, so firms must be patient in their approach. Also, Ellram [1991a] warns that abandonment of partners may lead to suspicion, making future partners difficult to attract. Ultimately, Dwyer et al. [1987, p. 20] report that "little is known about disengagement", so dissolution may offer a pessimistic yet rich source of research. The dissertation research will attempt to develop the "rule of thumb" that can be used to decide whether or not a partnership has sufficient potential.
36 2 .5 Research Ga p : P ow er in S upply C h ain Partnerships
To summarize the aforementioned discussions, supplier parmering is being promoted by the literature as a source of increased competitive advantage through a reduced supplier base and the establishment of win-win, mutually beneficial supplier alliances. However, there is a significant void in this literature regarding potential effects of power asymmetry within supply chain relationships. Cooper and Gardner [1993, p 24] propose that strategic alliances will negate power influences through a balanced relationship but encourage critical testing for '"real world' evidence." Williamson [1985] proposes that opportunism plays a major role in the buyer-vendor dyad, implying that a firm with power will use it. Provan and Skinner [1989] provide support for this. Overall, however, there is little examination of the role of power in the development of supply chain alliances. Thus, it remains vital to evaluate each of the steps in Table 2.4 and the success factors given in Table 2.5 in a more realistic environment that includes the power variable.
A few works do acknowledge the implications of power within the supply chain, but this research does not sufficiently analyze power issues. Pilling and Zhang [1992] found that some firms engage in a "constrained win-win" partnership that retains checks and balances to prevent opportunism by power holders. Leavy [1994] proposes that power and dependence management is vital to the supplier relationship, and Ramsay [1990] warns that single sourcing will not work in the U.S. due to the tendency to exploit power.
Monczka et al. [1993] propose use of rewards and activities fitting the notion of referent power for developing supplier capabilities. Works such as Christy and Grout [1994] and McMillan [1990] discuss defenses against opportunism in the supply chain, and Grout and Christy [1993] examine incentives for on time delivery for suppliers. Stuart and McCutcheon [1995] attempt to investigate power in supplier alliances but fail to arrive at
37 significant findings about the impact of perceived power. Naumann and Reck [1982] explore a buyer’s bases of power and found that such bases were relatively invariable to variables such as the phase and situation of the supply chain process as well as buyer background. Despite varying stream of literature that hints about power implications, researchers have yet to directly examine the effects of power in supply chain relationships.
With a lack of significant research directed to power in supply chain partnerships, the role of power in supply chain relationships has not been established, and the supplier partnering literature is based on an unrealistic assumption of power symmetry. Thus, the critical question remains;
How does power variable affect supply chain relationships?
Until this question can be effectively addressed, the supplier partnership literature does not an acceptable level of validity and can not be recommended for implementation.
In contrast to the void of literature on supply chain power, distribution channel researchers have produced a wealth of exploration of power effects within marketing channels. Appendix B reveals the wealth of distribution channel power literature as well as the scarcity of supply side power research. The next section will review the established power literature, offering implications of the effect of the power variable on the previous supply research.
38 2.6 Influences OF Pow er
The notion of inter-firm power holds its roots in social sciences (psychology, social psychology, and political science) literature and has been extensively developed by marketing channel researchers. The following review of the power literature seeks first to briefly review the distribution channel power literature then transfer the findings to the supply chain. Thus, the review will attempt to assess the impact of the literature findings on strategic supply partnerships. The power component will begin with the definition of power then as well as the bases of channel power then examine dichotomization as well as exercise of power. Finally, the effects of power and influence strategies upon critical channel elements will be evaluated.
2.6.1 Definition of Power A comprehension of power starts with its basic definition. Gaski [1984] provides a collection of several similar definitions of power taken fi-om the social sciences literature:
A has the power over B to the extent that A can get B to do something that B would not otherwise do [Dahl, 1957]
When an agent, O, performs an act resulting in some change in another agent, P, we say that 0 influences P. If O has the capability of influencing P, we say that O has power over P. [Cartwright, 1965]
El-Ansary and Stem [1972] apply the notion of power to marketing channels:
... the power o f a channel member [is] his ability to control the decision variables in the marketing strategy o f another member in a given channel at a different level of distribution. It should be different from the influenced member's original level of control over his own marketing strategy.
With a supply-side orientation, the dissertation research will establish its definition of power in the supply chain as the ability of one channel member (the source) to influence
39 the actions and intentions of another channel member (the target). As admonished by
Gaski and Nevin [1985, p. 130], it suffices to set the definition as "an ability, a potential, rather than the actual alteration of behavior," as the potential to influence often has significant effects.
2.6.2 Bases of Power Power can originate from many different sources and exercised via a multitude of strategies. A vast amount of power literature is oriented toward "bases" or "sources" of power which primarily addresses the orientation of why a firm holds the ability to influence. The first offering of bases of power comes from French and Raven [1959]. (Table 2.6) A multitude of further research has offered extensions and new concepts to power sources but most analyses remain closely related to the French and Raven classification.
The source has the ability to mediate rewards to the target.
The source has the ability to mediate punishment to the target Also termed as Coercive.
. I The source has the iegitimate right to influence behavior over the target. This is based primariiy on acceptance of roles (Traditional Legitimate) as well as judiciary restraints (Legal Legitimate).
The target will allow influence by the source to maintain identification with the source. Also termed as Referent.
The source holds distinctive knowledge, information, and skills that are valuable to the target. Also termed as Information.
Table 2.6: Bases of Power
40 Reward power refers to the ability of one partner in the relationship to control valued resources whether such resources be tangible or intangible. With a supply chain notion, a customer might have the ability to offer more business or long-term contracts while a supplier might be able to offer lower prices, better service, or improved technology. Punishment power, which is also termed as coercive power, involves the ability to take disciplinary action over partners. In the supply chain, customers may attempt to withdraw business or cancel contracts, and suppliers may attempt to raise prices or offer poorer service. Reward and punishment power are closely related since withholding an expected reward may be considered a form of punishment.
Legitimate power refers to recognition of the right to hold authority over others. Legitimate power originates from perceived standing or status and thus is only present if the power target believes the power source retains the natural privilege to such power. Suppliers may believe that their customers have the right to authority within the supply chain or customers may hold such beliefs about their suppliers. Identifîcation or referent power regards a partner's desire to be associated with another out of admiration from them. Such power originates from affection or respect. Within the supply chain, partners may respect the business practices or position of a chain member and feel they are obligated to respond to the firm out of allegiance. Finally, expert (or information) power refers to the ability of a member of a relationship to control knowledge. With expert power, power targets believe the source has knowledge and competencies that will lead them to take proper action and thus, it is in the target's best interest to respond to the source. In the supply chain, supply chain members may hold market, technology, or
41 process knowledge, and subsequently, the other channel members may yield control,
believing such knowledge will lead to the best decisions.
2.6.3 Influence Strategies Directly related to the bases of power is the concept of influence strategy which describes the method by which power may be mediated. Firms may choose to exert power in many different ways, and a summary of such methods is revealed in Table 2.7 (adapted from Frazier and Sheth’s [1985] classification of attitude-behavior influence and
communication strategies). Influence strategies range from simple control and exchange of information through issuing of rewards and punishments. Often, multiple influence strategies may be used in a single attempt.
supplied information and knowiedge withheld or manipulated information
examples of desired attitidue or behavior statement of that a "good" channel member would do statement of what a "poor" channel member would do
suggestion or endorsement cautionary statement solicitation for proposed behavior mandate
pledged retumed response returned sanctionary response reminder or threat of legal bindings
monetary based compensation non-monetaiy based compensation monetary based discipline non-monetary based discipline
Adapted from Frazier and SheUi [1985]
Table 2.7: Attitudes-Behavior Influences and Communication Strategies
42 It is of interest to note that other potential power influences exist that are not listed above due to the fact those strategies are not specifically mediated to the target. Examples of such include traditional legitimate and identification power as well as expert power. One must be cautious to recognize the difference between expert power and information control. Expert power is held naturally by a source, but the intended mediation or control of the information involved more of a reward/punishment power source.
2.6.4 Problems with Analyses of Power Bases The notion of each power base as well as strategy is relatively elementary, but several issues create potential problems for researchers. For one, there exists no defined boundaries for the different bases and strategies. Because of the ambiguity of the notion of power, perception plays a critical role in the power struggle, affecting both the intention and communication of the power type utilized. What might be perceived as one strategy by a source may be preconceived otherwise by the target (for instance, a recommendation being interpreted as a request or threat), creating the potential for misinterpretation and subsequent discord. Researchers have also disagreed about the significance of certain power strategies. For example, Frazier and Summers [1984] found, a promise which is typically thought of as a positive strategy may actually viewed negatively. Overall, the haze surrounding the power bases and strategies compounds research difficulty and creates the potential to lead to vague and deceptive results. Gaski
[1988] highlights the ambiguity with the challenge of whether or not power remains too abstract of a concept to be measurable.
A second obstacle with power source and strategy categorization lies with its inherent a priori orientation [Michie and Sibley, 1985] which has the potential to not only bias the
43 research but cause it to miss the true power relationship as well. Gaski [1988, p. 32] emphasizes the use of factor analysis "for detecting the underlying construct," and thus, determining validity of measurement numerics remains a vital step in the power evaluation.
Another problem with power classification exists with longitudinal factors in that the nature and orientation of relationships change over time, affecting types and amounts of power employed. Researchers have not thoroughly addressed these issues in the literature. Introducing time (in the forms of business cycles [Michie and Sibley, 1985] as well as history and length of relationship) into the power modeling may help validate
power study results. Lusch and Brown [1982] examine the effects of power historically in channel relationships. They found that targets will attribute less (not more) power to
the source after the source exercises non-economic sources of power because the target seems to unknowingly adopt the source's values and believes they are acting independently of the source.
In a final problem with power bases, much of the research that attempts to model power fails to incorporate the effects of power sources on one another, potentially biasing research results. For instance, Gaski [1986] found that use of reward power sources has a positive impact upon expert, referent, and legitimate power sources. On the other hand, punishment yields a negative impact. Thus, researchers may be simplifying complex, interacting power relationships.
44 2.6.5 Dichotomization of Power Sources
Many researchers have further attempted to simplify power sources and strategies through dichotomization in order to direct research goals as well as explain the logic of power effects [Johnson et al, 1993]. Prominent dichotomizations include coercive/non- coercive, economic/non-economic, direct/indirect, contingent/non-contingent, altered/unaltered perceptions, and mediated/non-mediated. Table 2.8 offers a taxonomy of power source dichotomization as well as explains how the power bases are classified under each of the dichotomizations.
One popular dichomotization is mediated/non-mediated. Mediated power sources which include reward, coercive, and legal legitimate, involve influence strategies that the source specifically administers to the target with the direct intention of provoking bringing about some action. Such mediated bases represent the competitive, negative uses of power traditionally associated with organizational theory. Mediated power sources which are more relational and positive in orientation include expert, referent, and traditional legitimate. These power bases occur as a natural part of business transactions and do not necessitate intention from the source. In fact, the source may not even be aware that some mediated power bases may exist.
Numerous researchers [Lusch and Brown, 1982; Frazier and Rody, 1991; Frazier and
Summers, 1984] have found that the power sources tend to use non-coercive influence strategies which is logically supported by the idea that use of coercion may risk the power advantage [Boyle and Dwyer, 1995; Frazier and Rody, 1991]. Furthermore, other sources [Stem and Heskett, 1969; Bacharach and Lawler, 1980] specifically show that non- coercive techniques can enhance the power advantage. In contrast, other researchers
45 [Frazier, Gill, and Kale, 1989; Dwyer and Walker, 1981] argue that the power source will
tend to utilize mediated power strategies since such forms require less time to implement.
Coercive vs (Punishment) Non-coercive (Reward, Expertise, Legitimate, Referent)
Economic vs (Reward, Punishment) Non-Economic (Expert, Legitimate, Referent)
Direct vs (Reward, PunishmenL Legal Legitimate) indirect (information. Traditional Legitimate, Referent)
Contingent vs (Reward, Punishment) Non-Contingent (Expert, Legitimate, Referent)
Perceptions Altered vs (Information, Recommendation) Perceptions Unaitered (Reward, Threats, Legalistic, and Requests)
Mediated vs (Reward, Coercion, Legal Legitimate) Non-mediated (Referent, Expert, Traditional Legitimate, Information)
Table 2.8: Dichotomizations of Power Sources and Strategies
46 Despite these research findings for distribution channels, ambiguity, as in the case of power bases, creates a problem with power dichotomization. The boundaries between dichotomies are often hazy, and influence types may span both categories. For instance, theoretically a reward may not be directly or perhaps not at all linked to economic resources, and information from expert and referent sources can lead to economic gain
[Gaski, 1984]. Perception also plays a role in complicating the boundaries between power source dichotomy. For example, a reward that is viewed as non-coercive by the source may be perceived as a coercive punishment. In general, researchers must be cautious not to blindly follow previous dichotomizations of power bases and should carefully validate proposed classifications for each individual study.
2.6.6 Exercised and Unexercised Power Beyond the aforementioned dichotomies of power sources, one more arrangement, exercised versus unexercised power, deserves analysis. Early distribution channel research did not acknowledge exercised versus unexercised power [Gaski, 1988], and other research such as Cronin et al. [1994] attempts to focus efforts toward exercised power alone. Indicating a complex relationship between exercised and unexercised power, Gaski [1988] legitimately warns that power holders need not necessarily exercise their power to obtain the desired response from the target. In deed, the presence and potential of power may be enough to create a situation of perceived dependence and or bring about some desired behavior. Gaski and Nevin [1985] provide another source in the analysis of exercised versus unexercised power, finding that in many cases, exercise of power has stronger effects than power that is not exercised.
47 Because of the potential interaction as well as ambiguity involved with exercised and unexercised power in a relationship, it is critical to consider both simultaneously in research. Concentrating research on exercised power could cause the underestimation of the strength of the power relationship. In fact, Gaski and Nevin [1985] warn that the stronger the amount of held power, the less likely that power needs to be exercised. Furthermore, previous exercise of power may alleviate the need to exercise it in the future, so the power history between a source and target firm can affect the need for the actual exercise of power. Given the nature of the dependence of the supply chain members, these results from exercise and unexercised power is expected to be a major contribution of the dissertation research.
2.6.7 Reciprocity and Countervailing Power To understand the complete potential of power influences, it remains critical to understand how a target firm will react in the form of both reciprocity (returning power influences) and countervailing power (attempting to stop use of the other's power). In first considering reciprocity, the nature of a power relationship dictates that a source holds authority over a target, hinting that only the source will be able to exercise power bases. One stream of literature demonstrates the fact that the target will often attempt or at least desire to strike back with similar forms of power [Frazier and Summers, 1986; Stem and Gorman, 1969, Frazier and Rody, 1991]. This holds critical implications for the source's choice of appropriate power strategies as the source must be careful not to bring back upon itself what it has exercised.
Directly related to idea of reciprocity of power is the notion of countervailing power in which target firms may utilize to avoid the effects of power exercised by the source.
48 Gaski [1984 p. 25] defines countervailing power as "channel member B's ability to inhibit channel member A’s power over B's decision variables." In other words, countervailing power is the ability of the target to stop the source from exercising power. Etgar [1976] provides another source for analysis of countervailing power. Countervailing power implies the presence of non-pervasive power. If a firm had complete power control over another, it is unlikely that the target firm would be able countervail any power efforts. However, if a firm's power was non-pervasive, the target firm may be able to threaten reciprocity in another area in which it might hold a power advantage. Lusch and Ross
[1985] found that power among food brokers and wholesalers was issue specific as opposed to pervasive, but overall, the pervasiveness of power no doubt varies greatly due to industry and firm specific factors. Countervailing power clouds the power relationship since the existence of power in such a situation does not imply that it may be utilized.
The result would seemingly be a stalemate. Gaski [1984] proposes, the existence of countervailing power may increase channel conflict and decrease satisfaction to the holder, but others may argue that countervailing power would increase satisfaction to the holder in that they would avoid power exercised upon them. Reciprocity by definition is usually formally or informally used in most buyer-seller transactions, the findings of the dissertation supply chain research will make significant contribution to the literature.
2.7 P ow er Influences Upon C h annel Relationships
Extremely complex in nature, power serves as a composite relationship-oriented variable, affecting both the target and source in many transparent as well as concealed ways. The following section will examine the literature concerning effects of influence strategies on critical relationship factors including dependence, commitment, trust, compliance.
49 cooperation, conflict, satisfaction, performance, and profitability. Much of the distribution channel research has examined the effects of influence strategies on these different elements, and researchers have generally used the approach of examining the tendencies to employ mediated/non-mediated or coercive/non-coercive sources. This literature will tend to refer to mediated and coercive power sources as competitive strategies and non-mediated and non-coercive as relational strategies.
Exploration of the effects of power on factors of the supplier-buyer alliance will provide the key to understanding the concept of ±e power-partnership link that is under investigation. In each area, this paper will show how power asymmetry will affect the implementability of a true partnership and will conclude each area with suggested implications for supply chain partnerships. In doing so, it will pull together the previous partnership and power literature, demonstrating that the power variable must be included in any examination of supply chain partnershipping.
2.7.1 Power and Dependence
The notion of power in an inter-firm relationship implies target dependence upon the source, else the target would not need to subject itself to the unbalanced relationship. Emerson [1962] suggests that power is a direct result of dependence, and research by Brown et al. [1983] found that the extent of dependence was directly induced by perceptions of power. However, Provan and Gassenheimer [1994] found problems with the exercised power-dependence relationship in the presence of high commitment, and both Etgar [1976] and El-Ansary and Stem [1972], however, found little or no relationship between power and dependence. Gaski [1984] offers an explanation that
50 power sources and dependence are inseparable, and thus, dependence measurement will
not add insight to the presence of power since they essentially measure the same concept.
With the subordinate relationship caused by dependence, one might logically conclude that the target-source relationship would be fairly strong (due to necessity) since the target would be forced to abide by the will of the source. Keith et al. [1990] and Skinner et al. [1992] found that greater dependence is associated with greater cooperation in the relationship. Gassenheimer and Calantone [1994] found that economic dependence increases compliance, but this same work as well as El Ansary [1975] found no relationship between dependence and conflict.
The distribution channel research points to the idea that the dependence created by power could possibly yield a closer relationship, but the presence of dependence does not invite a relationship like that of the simple conceptual partnership. As found from the partnership literature [Niederkopler, 1991; Ellram, 1990; 1991a], critical elements of supplier partnerships include trust, goodwill, shared goals, and social exchange. A state of dependence would indicate compliance rather than cooperation on the part of the target due to necessity rather than out of trust and goodwill. Thus, critical dependence questions that prove significant to supply chain partnerships will be addressed by the research
Does a state o f dependence negate the possibility for critical supply chain partnership success factors such as goodwill, trust, and shared goals?
Does a state o f dependence create a relationship that differs from a true, mutually beneficial supply chain partnership?
51 2.7.2 Power, Commitment, and Trust Two factors driven by power and critical to the partnership dyad are commitment and trust. Commitment may be defined as the feeling of being emotionally impelled. The distribution channel literature has found that the relationship between power and commitment is dependent upon the origins of the commitment. Brown et al. [1995b] discusses commitment in the form of compliance (instrumental) as well as identification and involvement (normative). Brown et al. found that use of mediated power is consistent with higher instrumental commitment but lower normative conunitment while non-mediated power was associated with higher normative commitment. In other words, mediated power forces the target to be committed to maintain the relationship for their own competitiveness and sustenance, but the genuine psychological commitment would be lower due to the resentment over the subordinate situation. On the other hand, non- mediated power sources can increase true commitment. Ultimately, normative commitment in the form of attitude and goodwill is implied by the supply chain literature as a critical success factor for supply chain partnerships. The distribution literature indicates that non-mediated power will enhance normative commitment while mediated strategies will destroy it. Thus, power used positively by the source may enhance a partnershipping relationship.
Bradlad and Eccles [1989, p. 104] defines trust as "a type of expectation that alleviates the fear that one's exchange partner will act opportunistically," directly implying the need to curb power influences. Gulati [1995] states that trust implies faithfulness and is built overtime as firms interact while Dwyer, et al. [1987] report that trust is critical in extended inter-firm relationships. Kumar, et al. [1995] found that both trust and commitment increased with expanded interdependence, but like Anderson and Weitz
52 [1992] found that inter-firm asymmetry will defeat both trust and commitment. The
previous commitment findings imply however that it may be possible to increase trust
through relational (non-mediated, non-coercive) power efforts. The subsequent key commitment and trust questions significant to supply chain partnership research that will
be tested by the dissertation research include:
• Are true trust and normative commitment possible in an inter-firm environment of power asymmetry?
• Can a target in an unbalance power relationship experience normative commitment or is such commitment merely instrumental?
• How do relational and competitive power strategies affect normative trust and commitment?
• Can a power source use relational power strategies to enhance normative commitment and trust?
2.7.3 Power, Cooperation, and Compliance Power essentially attempts to force a target to comply with the source's desires, and like commitment, the level of compliance and more importantly cooperation are critical to the relationship tenure as well as profitability [Stem and Reve, 1980]. Defined, compliance is action without inhere desire, and thus, compliance remains a relatively easy factor to measure since it implies action not feeling. The literature has found a positive relationship between non-coercive power and compliance [Skinner et al, 1992], but Gassenheimer and Calantone [1994] also found a positive relationship between coercive power and compliance. These findings point to the idea that power yields compliance regardless of the power strategy.
53 Cooperation, however, endures as a more difficult idea to mark due to the fact that it
implies internal agreement with the actions. A target acting as the source wishes reveals compliance but true cooperation may not be determined without an assessment of the targets internal reasons for compliance. Thus, there can be compliance without cooperation. Hunt et al. [1987] found a positive relationship between non-coercive power
and cooperation, and likewise, Skinner et al. [1992] found that coercive power held a negative association with cooperation. Such findings are related to the concept of normative commitment as non-coercive strategies will improve internalization of actions.
In a supply chain partnership, cooperation needs to be the key driver of strategy, and the
presence of compliance would essentially void a true partnership. The distribution channel literature as well as logic would, thus, imply that use of coercive power will cause compliance and eradicate a paitnership. Thus, the major compliance and cooperation questions to be tested by the dissertation research include:
• Can cooperation exist in power imbalance supply chain partnership relationship?
• Does relational power strategies actually enhance cooperation and strengthen a supply chain partnership?
2.7.4 Power and Conflict
According to Raven and Kruglanski [1970, p 70.], conflict may be defined as "tension between two or more social entities ... which arises from incompatibility of actual or desired responses." Etgar [1979] states that conflict is present in the channel when one member hinders goal attainment and performance of another. Researchers [Reve and
Stem, 1979; Stem and El-Ansary, 1977; Robicheaux and El-Ansary, 1977] tend to agree
54 that conflict is an omnipresent factor in any channel relationship. Gaski [1984] provides an excellent review of conflict literature.
The literature [Skinner et al, 1992, Lusch, 1976b, Wilkinson, 1981, Frazier and Rody,
1991] has suggested that the level of conflict is associated positively with coercive power is negatively associated with non-coercive power. Lusch [1976b] found that the use of threats increased the conflict intensity. Schul and Babakus [1988] found that the direct effects of reward (negative) and threats (positive) on conflict were insignificant but the indirect effects were significant. Results from Brown et al. [1983] include that supplier use of economic (reward and coercion) power sources is associated with retailer’s perceptions of conflict. Wilkinson [1981] found that both coercive and non-coercive power contribute equally to conflict experienced in a relationship. With regard to conflict effects on performance, Lusch [1987] suggests that conflict curbs performance, and Skinner et al. [1992] found conflict decreases satisfaction.
Conflict will obviously be harmful if not extinguishing to supply chain partnerships, and the partnershipping literature has promoted conflict management and resolution skills as an important factor to the success of the paitnership. The distribution literature implies that Competitive power sources will increase conflict, but positive use of power can reduce conflict. Subsequently, the critical issues for supply chain partnerships include:
• Does a power imbalance in a supply relationship create a level o f conflict that will harm and subsequently destroy a supply chain partnership?
• Does use of relational power strategies decrease conflict and promote a supply chain partnership?
• How does power affect conflict resolution?
55 2.7.5 Power and Satisfaction Satisfaction in the supply chain may be defined as the feeling of contentment with the relationship. Surprisingly, little research [Michie and Sibley, 1985] has been undertaken to examine power effects on channel member satisfaction, perhaps because the issue is straight-forward and highly intuitive. In turn, the literature [Hunt and Nevin, 1974; Lusch, 1976a; Michie and Sibley, 1985] has found relatively positive effects of non- coercive power on satisfaction and negative effects from coercion. Wilkinson [1981] found that non-coercive power enhanced satisfaction with other channel members but found no relationship between satisfaction and non-coercive sources. Wilkinson [1979] also found that fact that increased control may lead to satisfaction for the controlling firm but also found this relationship may reverse itself under situations of high control. In more recent work, Skinner et al. [1992] found that satisfaction had a positive relationship with cooperation and a negative one with conflict, and Ganesan [1994] found that satisfaction is a significant factor in achieving long-term relations.
Ultimate supply chain partner satisfaction remains the overriding factor in determining the future of a supply chain partnership. Williams Walton [1996] found that drivers of satisfaction within supply chain partnerships include relationship oriented factor such as planning, mutuality, interdependence, operational information exchange, and extendedness. Without satisfaction, supply chain members will be unable to generate the psychological factors such as trust, commitment, and goodwill that are necessary for the partnership to be sustained. The supply chain partnership literature must address the effects of power on satisfaction to see if satisfaction can exist for both parties in the situation of power asymmetry. The dissertation research will test the following supply chain partnership questions:
56 How does power influence satisfaction with in the supply chain?
Within a power imbalance, can the target firm experience sufficient levels of satisfaction to retain commitment to the supply chain relationship?
Within a power imbalance, can the source firm retain sufficient levels of satisfaction to retain commitment to the supply chain relationship?
2.7.6 Power, Performance, and Profitability A final of the effects of power concerns the ultimate performance and subsequent profitability of the channel members as well as the supply chain itself. Performance may be defined as the ability to execute intentions and goals. Etgar [1976] confirmed that channel member performance can be affected by power as well as countervailing power. Brown et al. [1995b] found that use of mediated power erodes performance of the target, while use of non-mediated power will improve the target's opinion of the source's performance. Furthermore, the study found the target's opinion of source performance was positively related to the amount of target normative commitment and negatively related to instrumental commitment. Stem and Reve [1980] report that the power holders will enjoy higher profitability and found that cooperation in the channel can increase overall profitability. Noordewier, et al. [1990] report that increased relational elements will lead to increased performance.
Two implications for supply chain partnerships, one negative and one positive, evolve from the literature. First, it appears that a power source can benefit from increased profitability and performance through utilization of such power, suggesting that a partnership may not be necessary for their own success. On the other hand, the literature also suggests that non-mediated forms of power can actually help performance.
57 supporting the notion of a true win-win partnership. Thus, important issues for supply chain partnerships to be tested are:
How does power influence source performance and profitability in a power asymmetric supply chain relationship?
How does power influence target performance and profitability in a power asymmetric supply chain relationship?
How does power influence supply chain performance and profitability in the supply chain?
2.8 R esearch Hypotheses The previous literature review examined two streams of research in exposing a research gap. The supply chain literature discussed necessary factors for the development and maintenance of successful buyer-supplier relationships, but inter-firm power was not considered in that analysis. On the other hand, power has been examined extensively in distribution channels, and the second part of the literature review sought to provide a background of power issues. Although the distribution channel power research raises many important issues, three somewhat conflicting impressions that recur:
• Coercive and mediated (competitive) power strategies seem to destroy the critical factors in a relationship.
• Non-coercive and non-mediated (relational) power may possibly enhance critical relationship factors, hinting that not only can a partnership exist under a power relationship, it may be made stronger through positive power use.
The presence o f power o f any nature may negate the crucial relationship factors, making a supply chain partnership either non desirable or impossible.
58 While the above statements may seem obvious to many, it remains critical that both
researchers and firms understand the true influences of power when considering firm interaction. As supply chain research has yet to address power influences, it becomes necessary to study power in the supply chain to maintain the realism and integrity of the
research. This dissertation sought to fulfill such a role by being one of the first works to
address power influences with specific application to the supply chain.
The research design was adapted directly from the distribution channel literature. Figure 2.4 presents an overview of how the research attempted to investigate power effects in the supply chain. Figure 2.5 represents this same idea in a path model. First, it was proposed that power will have significant influence upon factors that are critical to the relationship between buyer and supplier such as cooperation, commitment, trust, compliance, conflict,
and conflict resolution. Following the distribution channel literature this relationship was expected to be direct for non-mediated power sources and inverse for mediated sources.
Next, it was conceptualized that the power affected relation would then affect the
performance and satisfaction of the chain. Based on the supply chain literature, a stronger
relationship was sought to have a positive impact upon both satisfaction (supplier) and perceived performance (supplier, buyer, and supply chain). Furthermore, it was proposed that satisfaction will also have a direct effect upon performance.
This conceptualized model led to the development of specific research hypotheses regarding power influences within the supply chain which are presented below. Each hypothesis essentially tested the significance of a causal path in the model. The effects of independent variables on dependent variables are denoted with the y parameters, and the effects of dependent upon dependent variable are indicated by the P parameters.
59 c//nKK/.(VnraX . j
■ ' i
(rVijÀ' ;i:« j v '-mV.
1,'C . ,, j
■ ?TT: '
Figure 2.4: Overview of Research
---
Figure 2.5: Causal Model of Power Influences
2.8.1 Power and Relationship Hypotheses
Based on the literature review and discussions with industry experts, the power bases were dichotomized into mediated (legal legitimate, reward, coercive) and non-mediated
(expert, referent, legitimate).These hypotheses test the significance of the ability of mediated power sources to harm the degree of relationship between buyer and supplier as well as non-mediated sources to enhance this degree of relationship. In the corresponding model in Figure 2.5, these hypotheses test the significance of yiA and 73 5 respectively.
60 Hou: Non-mediated (expert, referent, legitimate) power sources will have no effect upon the degree of relationship between buyer and supplier.
Hau: Non-mediated power sources will a significant positive effect upon the degree of relationship between buyer and supplier.
Hoib: Mediated power (reward, coercive, and legal legitimate) sources will have no effect upon the degree of relationship between buyer and supplier.
Haib: Mediated power sources will a significant effect negative upon the degree of relationship between buyer and supplier.
2.8 .2 Relationship and Performance Hypotheses This next set of research hypotheses will test the relationship between the degree of relationship and the performance of the supplier, buyer, and supply chain. These hypotheses refer to the P 23 path.
Ho2a: The degree of relationship has no effect upon supplier performance.
Haaa: The degree of relationship has a significant positive effect upon supplier performance.
Ho2b: The degree of relationship has no effect upon buyer performance.
Hazb: The degree of relationship has a significant positive effect upon buyer performance.
H o 2c: The degree of relationship has no effect upon supply chain performance.
Hazc: The degree of relationship has a significant positive effect upon supply chain.
61 2.8.3 Relationship/Performance and Satisfaction Hypotheses This next set of research hypotheses will test the relationship between the degree of relationship and the supplier satisfaction as well as the relationship between performance (supplier, buyer, and supply chain) upon supplier satisfaction. Such hypotheses correspond to the Pi3 and P 12 paths.
H0 3 ; The degree of relationship has no effect upon supplier satisfaction.
Has: The degree of relationship has a significant positive effect upon supplier performance.
H o4a: Supplier performance has no effect upon buyer satisfaction.
Ha4a: Supply performance has a significant positive effect upon buyer satisfaction.
Ho4b: Buyer performance has no effect upon buyer satisfaction.
Ha4b: Buyer performance has a significant positive effect upon buyer satisfaction.
H o4c: Supply chain performance has no effect upon buyer satisfaction.
Ha4c: Supply chain performance has a significant positive effect upon buyer satisfaction.
2.8.4 Contributions
Testing of the above hypotheses was projected to lead to significant insight about influences of power within the supply chain. For researchers, it sought to substantiate power as a critical variable in supply chain relationships, thus seeking to steer supply
62 chain research in a slightly more realistic direction. For practitioners, it expected to show how the effects of power oriented strategies can affect their supply chain relationships and in turn affect performance and satisfaction. This would presumably improve supply chain relationships and subsequently the effectiveness of power wary manufacturers and suppliers.
2.9 Sum m ary This chapter has classified some of the most significant research on supply chain partnerships and for the first time integrates it with the wealth of "power in the channel" literature. A major portion of the current supply chain literature makes the ambitious assumption that both constituents in the supplier-buyer dyad are both willing and able to cultivate a mutually beneficial relationship without regard to the power construct. Supply chain partnership literature that does not address power constructs does not realistically examine possibilities for a capable partnership and remains naive in its critique of partnershipping effectiveness. By combining the established power and supply chain literature bases, a set of research hypotheses was developed that would provide crucial insight for supply chain power issues. Research design and data collection will be presented in the next chapter.
63 CHAPTERS
RESEARCH DESIGN AND METHODOLOGY
3.1 Introduction
The objective of the proposed research was to understand sources of power and the effects of power influences upon buyer-supplier relationships. Subsequently, the literature review from the previous chapter has served to develop a set of critical research questions regarding analysis of power within buyer-supplier relationships. To address such questions, the research gathered survey data to measure perceived power and relationship constructs. The siuvey targeted suppliers in the automotive industry which has served as a pioneer of supply chain integration in the United States. This chapter will overview the basic research design including a description of the research population and sample as well as survey instrument design and testing. Furthermore, this chapter will discuss data collection efforts and the subsequent testing of non-response bias.
3.2 E xperimental Design
The extensive literature review described in Chapter 2 allowed the research to identify a significant research gap as well as set the foundations for research objectives and testable hypotheses. The next stage of the research consisted of the formulation of a design to
64 address these goals and hypotheses. Because little supply chain power research existed, a large scale, empirical study was selected as the best research design. Case studies would offer insight into power effects but would be unable to authenticate general power effects.
Furthermore, power as a topic is sensitive to personal consciousness and perception and is not representable through published, objective data. An applied survey instrument, however, would be able to extract perceptions of power and its effects upon the supply chain. Thus, the use of empirical research was selected as the best research tool given the goals and objectives. Consistent with other power research (see Chapter 2), such an analysis would allow for an investigation of the trends of power effects within the supply chain and thus offer a meaningful direction for future supply chain power research.
3.2.1 IdentlHcation of Research Focus Because power environments are industry specific, the research sought to target one industry as a focus for the study. Candidate industries required two primary characteristics. For one, supply chain management must serve as a critical industry driver so that the research would offer useful and significant results. Second, the industry must retain an identifiable power structure as without such, results would be vague and uninterpretable.
While different industries are in varying stages of implementing supply chain management efforts, one of the more developed is the automobile industry. To explain, the import of high quality, fuel efficient, and competitively priced cars from Japan in the 1970's and 1980's forced American automobile manufacturers to become competitive or go out of business. Subsequently, one critical success factor in the industry has proven to be effective supplier partnering. Furthermore, the industry retains an significant climate
65 of power asymmetry. Given these two elements, the automobile industry was able to serve as an excellent source of study given the research objectives. The next sections will highlight the current industry state as weU as key industry drivers in support of the industry as a appropriate focus for the research.
3.2.1.1 Current State of the Automobile Industry Table 3.1 reveals the current state of competition in the U.S. automobile industry. One impression is that relatively few manufacturers account for most of the auto production for the U.S. market. Although the U.S. Big Three (General Motors, Ford, and Chrysler) has been hit hard by foreign competition, they still retain significant market share, and these numbers have strengthened since the early 1990's. Profitability has also been relatively strong for the Big Three over the last few years as well. The Big Three along with the two primary Japanese transplant manufacturers (Honda and Toyota) sell over 85% of new automobiles in the U.S. market (Figiure 3.1). Given the high cost of automobiles and the fact that over 1.35 million vehicles were sold in the United States in
1996, a tremendous amount of revenue is associated with just five manufacturers. This indicates a significant supply chain power advantage in favor of the manufacturers as they hold an oligopolistic control over market sales.
3.2.1.2 Drivers in the U.S. Automobile Industry
Given the market share of the larger auto manufacturers, there are many critical industry wide issues that significantly affect supply chain processes in the United States. Each of these drivers holds implications for manufacturer-supplier relationships. First, both the U.S. and Japanese transplant firms are attempting to utilize supply chain management to position operations as a source of competitive environment within the industry. This
66 pressure extends back to the coordination of suppliers and manufacturers to decrease costs, increase quality, and accept more product design responsibilities.
Manufacturer Vehicles Sold Percentage Cumulative U.S. Market of Total Percentage GM 435,897 31.74% 31.74% Ford 341,963 24.90% 56.63% C h ry s le r 228,828 16.66% 73.29% Toyota 98,315 7.16% 80.45% Honda 72,891 5.31% 85.76% Nissan 65,122 4.74% 90.50% Mazda 27,062 1.97% 92.47% VW/Audi 18,492 1.35% 93.82% Hyundai 14,311 1.04% 94.86% Mitsubishi 13,751 1.00% 95.86% Other 56,866 4.14% 100.00% TOTAL 1,373^79 100.00% 100.00%
Source: Automotive News, July 7,1997
Table 3.1: U.S. New Car Sales - 1996
OnmialvaktannlSIKn-U.S.Ai*amiUaMUiky, 1996
Fora Chrysler Toyota honoa Source: Automotive News, July 7,1997
Figure 3.1: Cumulative New Car Sales in the United States -1996
57 Next, the focus on product quality in the industry continues to intensify as customer expectations rise and products warrantees elongate. Subsequently, manufacturers and suppliers are under pressure to implement strict quality benchmarking programs such as QS9000, IS09000, and error free delivery. Much of this quality control has been transferred back to the suppliers which are now responsible for more design and assembly
efforts. Cycle time to market is also another big issue. Traditionally, new market line development has been a long and costly process, but the manufacturers are working more
closely with suppliers to design higher quality lines almost half the time and cost. Thus,
the manufacturer-supplier relationship serves a stronger and more critical role in product development.
As another industry driver, safety has risen to become a major industry crux as much of
new design efforts are oriented toward improving safety for the passengers.
Manufacturers are under pressure to meet and surpassed government imposed safety restrictions, and some manufacturers have targeted safety as a source of advantage by
maintaining safety design best practice. This integration of heighten safety efforts into production processes translates back directly to buyer-supplier coordinated product
development and improvement, thus necessitating strong working supply chain relationships.
Finally, one further significant issue that has been affecting the U.S. auto industry is the changing consumer which has grown to be more demanding than ever. The arrival of the
Japanese competition in the 1970's with higher quality cars has transformed consumer apathy into to assertiveness as customers now seek more variety, higher quality, better customer service, and extended reliability in the car purchases. This forces manufacturers
68 and suppliers to build better vehicles without significant price increases, necessitating stronger partnership relationships to improve responsiveness, quality, and overall value while reducing costs.
Overall, many significant issues are driving the focus and direction of the automobile industry in the United States, critically affecting both U.S. and Japanese transplant firms alike. With product advertising, operations has become the industry driver for success. In order to confront significant cost, quality, safety, and responsiveness initiatives, manufacturers have placed greater reliance upon their supplier relationships to deliver best practice. Thus, the manufacturer that is able to develop the most effective supplier relationships will be able to spearhead the critical industry issues and subsequently sustain a competitive advantage within a vast and profitable market.
3.2.1.3 The U.S. Automobile Industry as Study Focus
The history and subsequent current state of the United States auto industry described above reveal several key reasons why the industry remains a strong choice for the research focus. Such reasons, which are described below, include the importance of effective supply chain management within the industry, the definable industry power structure, prior research, and the visibility of the industry.
First, the development of effective buyer-supplier relationships remains a critical source of competitive advantage. Intense international competition and the key drivers of the industry necessitate a close working relationship between the manufacturers and suppliers. Such supply chain management is critical to product development, cost reduction, quality improvement, and market responsiveness. Although effective buyer-
69 supplier relationships remain a critical factor within the industry, the implementation and subsequent benefits of the supplier alliances are still undergoing a maturation process. This warrants the generation of research directed toward promoting a better awareness of supply chain relationships, and the research described here fulfills such a role.
A second strength of the industry lies with its definable power structure. Gaski [1984] warns that research in industries without a clear power structure has often lead to inconclusive results, but the auto industry has a relatively transparent structure. With the
Big Three U.S. manufacturers in General Motors, Ford, and Chrysler as well as Japanese transplant companies Toyota and Honda, the automotive industry retains a relatively oligopolistic power structure, forcing suppliers to bow to the power of manufacturers. The power variable, thus, will play a determining role with supplier partnerships in the industry, and offers a suitable environment for a study of power effects.
Furthermore, many power studies (such as Boyle et al., 1992; Brown and Day, 1981;
Frazier, 1983; Frazier and Summers, 1984, 1986; Lusch, 1976a, 1976b, 1976c; Lusch and Brown, 1982) have been undertaken on the distribution side of the automotive industry. The research described here offers the first power research within the supply side of the industry and thus provides a complementary balance to the prior distribution research.
As a final strength, the automobile industry is both an important and visible element of the U.S. as well as world economies. Vehicle production is the largest manufacturing activity in the world with over 50 million cars produced and 470 million in use yearly worldwide [Maxton and Wormald, 1995]. Each minute, 95 new cars are built and sold throughout the world, and just a day's worth of production would create a line of vehicles
70 almost 400 miles long [Maxton and Wormald, 1995]. Thus, most people are able to identify with the industry because of its stature, aiding in the overall comprehension and acceptance of the results of this study.
Overall, the U.S. automobile industry offered a strong choice as a focus for supply chain power research. Both manufacturers and suppliers in the industry must understand how the power variable influences partnerships so the benefits of their alliances may be maximized. Such a concept is critical to the long-term success of the industry participants, both the power holders and targets. Studying supplier partnerships within U.S. and Japanese transplant manufacturers, the research is in the position to provide significant benefit to the automotive industry through increased awareness of the partnershipping process. Likewise, the research is able to provide useful direction for supply chain research.
3.2.2 Foundations for Instrument Design and Data Collection Given the automobile industry as a research focus, the next step involved the establishment of the foundations for data collection. This stage was critical in that the effectiveness of the data set would make or break the ultimate success of the study. To deliver this significance, the research team sought to meet with industry practitioners to verify direction for the research design and analysis. Six primary automobile manufacturers (Chrysler, Ford, General Motors, Honda, Nissan, Toyota) were initially invited to participate in the project, and all six responded with interest. Phone interviews were conducted with Ford and Toyota. Field visits were conducted with three of these manufacturers including General Motors, Chrysler, and Honda. These meetings were set
71 up with each of these manufacturers at their own facilities. Details are provided in
Appendix C (Chrysler), D (Honda), and E (GM).
The manufacturer meetings as well as other industry research revealed that the
manufacturers had achieved different levels of success in implementing supply chain
management. Some manufacturers, like Chrysler and Honda, were already capitalizing upon integrated supply relationships to gain competitive advantage in the industry.
Others, like General Motors, however, still struggled to set effective supply chain
integration strategies. Given such disparity, it was decided to set the research focus as a
benchmark study so that it would generate an industry-wide awareness of best practice.
Based upon industry research, Chrysler and Honda were targeted for comprehensive data collection. Such manufacturers represented industry leaders in integration of the supply chain through buyer-supplier partnerships. Specifically, Honda, as a Japanese transplant
firm, has proven to be a pioneer of supply chain management within the U.S. market, and Chrysler has led the U.S. Big Three in the same category. As part of the analysis
described in Chapter 4, the research was able to verify Chrysler and Honda as industry
leaders, thus positioning the research as a benchmark study of supply chain relations.
3.2.3 Supplier Lists Both Chrysler and Honda were asked to provide address lists of their most critical
suppliers. Chrysler supplied 178 contacts, and Honda supplied 392 contacts. These lists consisted of individuals with high level, strategically-oriented positions, having titles such as President, CEO, and Chairman. The data was entered into spreadsheet format and verified twice for entry accuracy. The data was then filtered for problems. Some
72 companies were also removed from the Honda list because they were Honda subsidiaries.
Given a 570 total contact names supplied by the two participating firms, a total of 525 were considered usable after data cleansing. One hundred seventy-seven (33.7%) of these contacts were for Chrysler, and the other 348 (66.3%) were for Honda. This sample size would allow for suitable testing of the research hypothesis.
3.3 I n s t r u m e n t DESIGN
As the supplier contact lists were being coordinated, the survey instrument was also being developed. The survey sought to collect an extensive amount of information regarding supplier opinions of their relationships with their customers to allow for testing of the research hypotheses. A two stage process was utilized to design the questionnaire. First, an extensive literature was used to generate statement items related to the research constructs of interest. In the second stage, the advice of industry experts was sought to help refine the survey to an effective form. This two-step process helped to generate an instrument that would produce reliable and unbiased scales. Figure 3.2 outlines the survey development process, and starting with the literature review.
Extensive Face-to-face meetings Survey pilot Round 1 review of with automobiie testing with • Introductory postcard literature for manufacturers to industry experts, • Survey #1 • Reminder postcard #1 examples of establish mutual leading to questions research goals as well questionaire Round 2 and formats as generate lists of refinement • Survey round #2 supplier contact • Reminder postcard #2 Round #3 (if necessary)
Figure 3.2: Overview of Survey Instrument Development
73 3.3.1 Item Design The survey instrument utilized several statement items to measure each research construct. A seven point Likert scale for each statement, ranging from Strongly Disagree through Neither Agree or Disagree to Strongly Agree. Many of the items for each construct were coded inversely to prevent consistent marking of the same answer [Alreck and Settle, 1985], and items from different scales were intermixed to avoid detection of the constructs by the respondents [Flynn, et. al., 1990; Carmines and Zeller, 1979]. The next section describes the foundations for the specific survey items.
3.3.2 Instrument Development • Literature Review The constructs required by the research may be grouped into three power, relationship, and performance/satisfaction dimensions. An extensive review of the literature was undertaken to provided a foundation for the survey statements, and the wealth of power research in distribution channels yielded a generous amount of candidate items. This review of the literature allowed for the development of an initial survey with strong content validity. A more detailed description of the survey items based on the major research dimensions is presented below. Such descriptions offer referrals to the actual final survey version as well as literature references.
33.2.1 Influences o f Power
Power remains an accepted factor in most supply chain relationships, yet as discussed in Chapter 2, little research has been undertaken to address supply chain power influences. Power theory holds much of its roots in the social and political sciences French and Raven [1959], and the distribution channel literature has since applied such theory to logistics oriented processes. Based on the foundation of French and Raven [1959] as well
74 as subsequent distribution power research (Appendix B), the subdimensions of power as detailed in Chapter 2 were selected to include expert (information), referent (identification), traditional legitimate, legal legitimate, reward, and coercive (punishment). Given the final copy of the operationalized survey instrument in Appendix F , expert power is represented in survey statements IVa-d, referent in IVe-h, legitimate, IVi-1, legal legitimate in IVm-p, reward in IVq-t, and coercive in IVu-x. Sources that provided survey statements for the power constructs included Gaski [1986; 1988], Brown, et al. [1995b], Boyle, et al., [1995], and Boyle, et al., [1992].
S.3.2.2 Relationship Elements
It was conceptualized that the power variables influence a set of factors that remain critical to the supply chain relationship. The supply chain literature discussed in Chapter 2 emphasized several of such factors including commitment, cooperation, trust, conflict, conflict resolution, and compliance. Although these elements were defined in Chapter 2, a review of the concepts is presented here. First, commitment refers to the idea of being emotionally impelled, and Gaski [1998] provided statements for such a factor. Survey items (Appendix F) Hi-k represent operationalized measures of commitment. Cooperation constitutes association for mutual benefit. Skinner, et al. [1992] offers cooperation statements which were adapted as survey items IIb,d and VIu-w.
Trust refers to the reliance that a partner will not act opportunistically, and Gulati [1995] serves as a good reference for statement items for this topic. Survey statements VIe-h represent items for trust. Next, conflict may be described by competitive or opposing action while conflict resolution includes attempts to alleviate such friction. Gaski [1988] as well as Frazier and Rody [1991] provide sources for conflict oriented statements. In
75 the survey, conflict is represented by Vli-m whüe conflict resolution is represented by
VIn-p. Finally, Brown, et al., [1995b] provides sources for compliance which entails action without inherent desire. Survey items Vlq-t represent the compliance measures.
3.3.2.S Performance and Satisfaction
Finally, the research sought to examine the effects of the power-affected relationship
variables upon satisfaction and performance. With regard to this research, performance refers to the ability to execute the goals of the supply chain. Although many aspects of
supply chain member performance are available, the research sought only an understanding of general, overall measure of perceived performance. Discussions with industry experts however allow for an interpretation of the drivers of performance measures including improved product development, decreased costs, improved quality,
and increased productivity. Items Va,d,g represent supplier performance, items Vb,e,h represent manufacturer performance, and items Vc,f,i represent supply chain performance.
Next, satisfaction may be defined as contentment within the relationship, and Gaski
[1998] and Skinner, et al. [1992] serve as sources for example statement items for this
factor. The satisfaction analyzed in the research specifically targeted satisfaction of the suppliers with their relationship with the particular manufacturers. Satisfaction was operationalized in the actual survey via items Vla-d.
3.3.3 Instrument Development - Pilot Testing Given a first draft of the survey generated from the literature search, the second design step consisted of feedback from industry experts. These experts provided valuable
76 assistance with validation of language, issues, and context. The feedback was collected
both during the aforementioned meetings with the manufacturers as well as through interviews with a sample of suppliers in the industry. The ideas and judgments were taken into consideration for the final draft of the survey which is presented in Appendix F. Table 3.2 will help the reader associate survey items with research constructs.
3.3.4 Content Validity Survey research necessitates that the survey instrument demonstrates validity. The validity of an item or scale refers to its ability to effectively measure the factor or concept
it is supposed to measuring. Several forms of validity, each with a related but different orientation, exist. "Content validity refers to the extent to which measurement reflects
the domain of the concept," [Drdge, 1997, p. 12]. Unlike construct validity, no statistical
methods exist to establish content validity, so content validity must be assessed through
subjective measures. Content validity was established through the extensive review of supply chain and distribution channel power literature as well as the feedback from
experts during instrument design. Construct validity which assesses the effectiveness of scale indicator power was also determined. Unlike content validity, construct validity may be assessed statistically with confirmatory factor analysis and will thus, be discussed with the analysis of the survey data in Chapter 5.
3.4 D a t a C o l l e c t io n After the survey instmment was designed and validated, the survey was sent to the supplier contacts. Data collection took place in several steps design to yield an effective response rate and included several rounds of survey mailings and reminder postcards.
Efforts to collect data started with an initial introductory post card (Appendix G) which
77 served to introduce the research project to the supplier contacts and announce the coming
surveys. It was felt that this would generate interest among the suppliers, and thus, encourage response when the larger mailing of the surveys and cover letter arrived. mm Power Expert (Knowledge) IV a-d 17-20 Referent (Identifîcation) IVe-h 21-24 Legitimate IVi-i 25-28 Legai Legitimate rV m-p 29-32 Reward IV q-t 33-36 Coercive (Punishment) IV u-x 3 7 ^
Relationship Commitment ni-k 9-11 Compliance VI q-t 65-68 Conflict VI ij,l,m 58-61 Conflict Resolution VI n-p4 62-64 Cooperation n b.d; VI u-w 2,4; V I69-71 Trust VIe-h 54-57
Consequence Supplier Satisfaction VI a-d 50-53 Supplier Performance Va,d,g 41,44,47 Manufacturer Performance V b,e,h 42,45,48 Supply Chain Performance Vc,f,i 43,46.49
Table 3.2: Summary of Survey Constructs
The first survey round followed three days after the mailing of the introductory postcard. This mailing included a personalized cover letter (Appendix H), a self-addressed.
78 postage-paid return envelope, and an individual survey for both Chrysler and Honda. Suppliers that did not serve both manufacturers were asked to indicate such and return both surveys anyway. The surveys were printed on two different paper colors to enhance the understanding that the two surveys represented two different manufacturers.
A follow-up postcard (Appendix I) was mailed a week after the first survey round to remind the supplier contacts of the surveys and encourage response. Next, a second survey round was mailed approximately four weeks after the first survey round to contacts who had yet to respond. Once again the survey round included a personalized cover letter (Appendix J), a self-addressed, postage-paid return envelope, and an individual survey for both Chrysler and Honda. A second follow-up postcard (Appendix K) was sent a few days later to remind the non-respondents about the second survey mailing. The strong results of the data collection (35.2% response rate) reveal that the survey mailing process utilized proved to be effective.
Several techniques as indicated by Hyim et al. [1990]as well as Alreck and Settle [1985] were specifically utilized to ensure a strong response rate including postage-paid return envelopes and follow up contacts. Furthermore, respondents were promised a copy of survey results, but no compensation was extended. No other strategies (such as phone calls) were utilized in attempt to enhance the response rate. Though the survey participants were not promised anonymity, they were assured confidentiality in that their responses would not be identified with their firm or themselves personally. It was felt that this was important to the response rate since some suppliers may not feel comfortable with honestly completing the survey due to fear of manufacturer retaliation.
79 3.4.1 Response Twelve of the original 525 supplier contacts involved problems or errors that did not allow the contact to either receive or complete the survey. Such problems included unfixable address errors and corporate policies of non-participation in external surveys.
Furthermore, one supplier was actually a wholly own subsidiary of one of the manufacturers, and another did not deal directly with either manufacturer. A total of fourteen such non-surveyable contacts were dropped from consideration. Without any further information about other possible problems from non-respondents, a total of 511 possible valid participants were contacted through survey mailings.
A detailed summary of response rates may be found in Table 3.3. The two rounds of surveys yielded a total of 195 respondents (38.16%) with 137 from the first round and 58 from the second round. (Round 1 response was defined as participants who responded to the first survey mailing and likewise for Round 2.) Some of the responses were deemed unusable due to incompleteness, missing data, or excessive response of "no opinion".
Such responses included fourteen from the first round and three from the second round.
The cleansing yielded a final tally of 180 responses for a final response rate of 35.23% which compares favorably to other retiun rates in the fields of operations management and logistics. This included 125 (24.46% of all suppliers contacted and 69.44% of all respondents) from Round 1 and 55 (10.76% of all suppliers contacted and 30.56% of all respondents) from Round 2. 71 (39.44% of all respondents) of the responses were from Chrysler supplied contact names, and the remaining 109 (60.56% of all respondents) were from Honda supplied names. Of the 180 respondents, 49 (27.22% of all respondents) provided usable responses for both Chrysler and Honda, leaving a total of 229 (180 + 49) data points for analysis.
80 1 Chrysler" Honda - Total % of Total Participants V ^-xv^ 'Tbtal^ . 173 338 511 100%
CJsableReturns /f:T:r-?%}Rouhd#; ^ 47 78 125 24.46% 24 31 55 10.76% 71 109 180 35.23%
Respbhdehts SuppIjdng &Rb 18 28 46 9.00%
' .. Rbun Total Data Points Rountf l 75 96 171 Round 2 26 32 58 Total : 101 128 229 Table 3.3: Summary of Survey Responses 3.4.2 Non-response Bras Given the responses, the research sought first to analyze non-response bias. "If those who chose not to respond are systematically different from those who did respond, the generalizahility of the results may he compromised," [Flynn et al., 1990, p. 263]. Essentially, if the set of non-respondents significantly differs from the respondent set, there may he some factor biasing the return [Alreck and Settle, 1983]. Because some firms (especially those with negative views of the manufacturers) may not respond out of fear of retaliation from the more powerful manufacturers, non-response bias could play a significant role in this research. Thus, the research tested for non-response bias in two ways: non-respondent interviews and Chi-square tests. First, a sample of the respondents were contacted via telephone and asked for reasons for their lack of response. Most survey non-respondents replied that they were too busy to complete the questionnaire. A few others stated that their company did not respond to 8 1 external surveys as a practice. One non-respondent declared that they received too many surveys to allow for participation in all surveys. In no cases did any of the non respondents indicate any reason for not responding that was specific to the origin and/or content of the research. Next, Chi-square goodness of fit tests were run to examine respondent frequencies [Flynn et al., 1990; Alreck and Settle, 1985]. Such tests examine the characteristics of respondents versus the original sample of supplier contacts to assure that the frequencies are not statistically different from the original mailing list. If non-response bias does not exist, the proportions of the members of each of these groups should not significantly difference. Chi-square goodness of fit tests were run to compare suppher contact origins (Chrysler or Honda) for the respondent set versus the original sample. The hypotheses and subsequent test statistic are given as follow: Ho: The observed distribution does not differ from the expected distribution. Ha: The observed distribution differs from the expected distribution The Chi-square statistic is calculated as: where: Oi is the number of observed occurrences in a category Ej is the number of expected occurrences in a category k is the number of given categories 82 A large Chi-square statistic will indicate grounds for rejection of the null hypothesis (Ho). Several tests were run for the respondent set, and Table 3.4 summarizes the results for these tests. First, the total respondent set was tested against the original sample. Of the 180 respondents, 71 were Chrysler supplied contacts and 109 were Honda supplied contacts. The Chi-square statistic equaled 1.77 which retains a p value of greater than .10, indicating that the null hypothesis should not be rejected. CB) (Q (E>: 1 , - _ Respondents prigihal Responses Æ m s LOii-sqii^, Oiothroundsy Sample Recelv«I - - Responsigs TestStatlstia 173 71 60.94 1.66 338 109 119.06 0.85 511 180 180 2.51 p>.10 . Res^ndeatsi%M#%^: (roundly^ Chrysler 173 47 42.32 0.52 Honda 338 78 82.68 0.27 -Total 511 125 125 0.78 p>.10 Respondents (round 2) Chrysler 173 24 18.62 1.55 Honda 338 31 36.38 0.80 Total 511 55 55 2.35 p> .10 Respondents (returning two surveys) Chrysler 173 19 16.59 0.35 Honda 338 30 32.41 0.18 Total 511 49 49 0.53 S > J O ------ Notes: (A) Respondent set of interest (B) Number of suppliers in each category (C) Number of surveys returned (D) Number of expected responses, (B)*(180)/511 (E) Chi-square test statistic = (C - D)^/D (F) P-value of test statistic. A large p-value indicates failure to reject Ho indicating that no significant differences between the observed and expected frequencies Table 3.4: Chi-square Test of Non-Response Bias 83 Next, a similar Chi-square goodness of fit test was run for both individual rounds ( I and 2) versus the original sample. The Chi-square statistic for Round 1 equaled .78 while the statistic for Round 2 was 2.35. Each of these statistics retains a p-value that exceeds . 10, once again indicating failure to reject the null hypothesis. Finally, a Chi-square goodness of fit test was run for the respondents returning two samples (doubles) versus the original population. Of the 180 double respondents, 19 were Chrysler supplied contacts and 30 were Honda supplied contacts. The Chi-square test yielded a statistic of .53 and a p-value greater than .10, indication of failure to reject the null hypothesis. In summary, each of the Chi-square goodness of fit tests indicated no signs of non-response bias and aligns with the aforementioned sample of non-respondents. 3.5 S um m ary and O verview of Data Analysis This chapter has presented the basic research design which entailed a survey of suppliers within the automobile industry. The chapter also includes the orientation of the population and sample, the design and validation of the survey instrument, collection of the survey data, and assessment of non-response bias. In all, the discussion in this chapter sets the stage for the data analysis to be described in future chapters. Data description including summary statistics, unidimensionality, and reliability will be covered in Chapter 4. The measurement and structural modeling, will then be covered in Chapter 5, leading to analysis of the hypothesis tests in Chapter 6. 84 CHAPTER 4 DESCRIPTION OF DATA 4.1 Introduction The previous chapter discussed the research methodology and design. This chapter provides a comprehensive description of the collected data, starting with an overview of respondent demographics. To understand the importance of the research, a benchmarking assessment of supplier relations within the automotive industry will then be presented. The chapter will also review basic summary statistics for the research constructs and extends this analysis to allow for a direct comparison of the responses for Chrysler and Honda. Finally, the unidimensionality and reliability of the data scales will be assessed in preparation for the measurement and structural modeling in Chapter 5. 4.2 DEMOGRAPHICS OF RESPONDENTS To obtain a general understanding of the respondent attributes, several standard demographics measures including products/services supplied, percentage and value of sales to the manufacturer, quality certification, and number of employees were collected. The ranked frequencies of the products and/or services provided by the suppliers is displayed in Table 4.1. Bearing in mind that a respondent may select more than one 85 category, chassis and power train components were found to be the most frequently marked categories. Most of the remaining categories were relatively clustered together in frequency, indicating that each of the categories was well represented in the data. Category - : Connt. .;.;.Per;-:. Chassis components 54 23.6% Power train components 54 23.6% Interior components 33 14.4% Exterior components 32 14.0% Stamping components 28 12.2% Electrical components 27 11.8% Other 24 10.5% T ransportation/logistics 24 10.5% Tooling/equipment/construction 12 5.2% Non-production services 6 2.6% Table 4.1: Categories of Products/Services of Respondents Next, the suppliers were asked to estimate the average percentage of their total sales as well as the total dollar amount of sales purchased by the manufacturer of interest (Table 4.2). The average percentage was found to be 23.52%, indicating that the manufacturers accounted for a relatively large proportion of the supplier's sales. The average dollar amount of sales was found to lie between $5 to $50 million. The number of employees per firm averaged approximately 7,000. Gatëgoiy f'HValoëc*-: QS9000 : IS09000 # Salw' of Aies Certified Certified Employees 23.52% 3.39 125 yes 112 yes 6949.11 26.28% 1.50 21055.72 Table 4.2: Demographics of Respondents 86 Finally, information about quality certification with specific regard to IS09000 and QS9000 was collected. IS09000 (International Organization for Standardization) seeks to offer standardization of quality management issues. Firms attempting to register for certification must meticulously map and refine the control of processes such as inspection, purchasing, distribution, and training. One hundred twelve of the respondents, which constitutes approximately half of the total respondent group, report that they currently have or will soon qualify for IS09000 certification. One must bear in mind that the steep cost of certification may prevent small suppliers from achieving such certification. Related to IS09000, QS9000 was developed by the Big Three U.S. manufacturers (GM, Ford, and Chrysler) and retains a closer orientation to the automotive industry. Just over half (125 of 229 respondents) indicate that they currently or will soon fulfill QS9000 certification standards. 4.3 M anufacturer B enchmarking ASSESSMENT Before the summary statistics associated with the research constructs are reviewed, this section will serve to establish a benchmarking assessment of supplier relations in the U.S. automotive industry. This understanding of industry best practice will help the reader to focus upon the importance and relevance of the summary statistics to be presented later. Specifically, Section Vm of the survey (Appendix B) sought to establish a comparison of supplier opinions about the different major manufacturers in the automobile industry. The statement read "In considering your relationships with the following firms, please allocate a total of 1(X) points among them based on their quality as a customer" and included Chrysler, Ford, General Motors, Honda, and Toyota. Chapter 3 revealed that these five manufacturers accounted for over 85% of U.S new vehicle sales in 1996. 87 With the point allocation, an assessment of the relative quality of the manufacturers through the eyes of the suppliers was measured. If all the manufacturers supplied by the particular respondent have the perceived quality as a customer, the score for each should be equal at 100 divided by the number of firms supplied. Scores differing from this average score would indicate above or below average opinions. This allowed suppliers to unbiasedly rate their customers, thus, offering an industry benchmark of supplier relationship efforts. The scores for each response were examined. Any score sets that failed to total to exactly 100 were removed from consideration as were responses that indicated the respondent supplied only one of the five listed manufacturers. This left 130 usable supplier responses, and 41 of these actually supplied all five manufacturers. The score sets for response were taken as a percentage of the expected response given the supplier considered all their manufacturer customers as equals. For instance, if a respondent supplied 4 manufacturers, the expected score for each would be 25. If a manufacturer achieved its expected score of 25, its resulting indice would be 25 divided by 25 equaling 1. Thus, the benchmark indice would assume a value of one if the supplier considered the manufacturer to retain average quality as a customer. Subsequently, an indice greater than one would indicate an above average rating for customer quality while a below average score would be below one. Table 4.3 reveals summary statistics for these customer quality indices. With an average overall rating of 1.42, Chrysler retained the strongest reputation among the suppliers while Honda ranked second with a mean score of 1.10. The ranks of the remaining three manufacturers were found to be Toyota (mean of .96), Ford (.91), and GM (.72). 95% 88 confidence intervals were constructed for each score and are displayed in Figure 4.1 to offer a visual representation of the scores. The scores were also tested for significance in difference from the average value of one. Both Chrysler and Honda showed evidence of significant above average ratings while Ford and GM demonstrated significance in below average ratings. Toyota demonstrated no significant difference from one. Bendmuirk&i^Scbres CiuTsIer Honda Toyota All usàbl&allocations - mean • 1.42 0.91 0.72 1.10 0.96 n=130 stdév 0.467 0.428 0.405 0.545 0.398 8.84 -2.14 -7.25 1.76 -0.86 iWPryalue; <.01 0.03 <.01 0.08 >.10 ; ig ,CpUntd^.7 97 108 113 98 69 Table 4.3; Benchmarking Scores for Usable (n=130) Responses Figure 4.1: 95% Confidence Intervals for Benchmarking Scores (n=130) To gain further insight regarding supplier opinions of their customers, this same analysis was conducted for the 41 respondents of the 130 of study which indicated that they supplied all five manufacturers. These results (Table 4.4) were similar to the previous, finding Chrysler with the highest average rating at 1.40. Honda followed with 1.06, then 89 Toyota with .95, Ford with .87, and GM with .72. Figure 4.2 displays 95% confidence intervals for the mean score for each firm. Also, t-tests were also run for significance in difference from the average value of one revealed that Chrysler retained a significant above average rating while Ford and GM demonstrate significant below average ratings. Both Honda and Toyota demonstrated no significant difference from one. Benchmarking Scores : Ctayder r-TFbrd:-',: f . Honda Toyota SuppIy^allS^ - mean. 1.40 0.87 0.72 1.06 0.95 n = 41 - stdev^ 0.561 0.427 0.445 0.565 0.392 t-stat: 4.51 -2.01 -3.98 0.72 -0.78 p-vàlùe <.01 0.04 <.01 >.10 >.10 count : 41 41 41 41 41 Table 4.4: Benchmarking Scores for Suppliers of all Five Manufacturers Figure 4.2: 95% Confidence Intervals for Benchmarking Scores (supply all 5) Chrysler and Honda were originally selected for the focus of the study based on an extensive literature review of the industry as well as discussions with industry experts. The above results for the benchmarking assessment verifies this best practice, indicating that these two firms set the industry best practice for fostering relationships with their 90 suppliers. Therefore, the ultimate results found by this research are generalizeable to the entire automobile industry as a benchmarking of supplier relationships within the industry. This enhances the significance of the research and the benefit of the results to the automotive industry. 4.3.1 Important Factors in Customer Assessment Section VUI also provided insight into the orientation of the above supplier relations benchmarking responses. Respondents were asked to mark important factors for the basis of their selection of customer quality, allowing them to choose one or more among commitment, cooperation, trust, satisfaction, performance, and other. These results were tallied for the 130 suppliers providing response to the benchmarking assessment (Table 4.5). Of these factors, commitment (98 out of 130 responses, 75.4%), cooperation (107, 82.3%), and trust (93,71.5%) were checked most frequently. Both satisfaction (33, 25.4%) and performance (56,43.1%) were chosen less by less than half of the respondents, and no consensus replies were provided for the "other" category. These proportions were examined for significance in difference from .50 (50% of respondents), and commitment, trust, and cooperation retained significance greater than .50. Furthermore, satisfaction was found to retain significance less than .50, while performance demonstrated no significant difference. ### À ilu^Ieà %%6unt I 98 107 93 33 56 0.754 0.823 0.715 0.254 0.431 5.79 7.37 4.91 -5.61 -1.58 ^prvaluet <.01 <.01 <.01 <.01 >.10 Table 4.5: Basis for Allocation of Points in Benchmarking Assessment 91 These results show that in judging the quality of the manufacturers as customers, the suppliers are more focused on relational elements such as commitment, cooperation, and trust. Satisfaction and performance seem to carry less weight in such an assessment. This is not to say that the suppliers are not concerned about performance and satisfaction, however. It merely indicates that the suppliers seem to be more relationally oriented and value those customers that seek to foster sincere and mutual business partnerships. One explanation for the lack of significance of performance and satisfaction as indicators of customer assessment may be derived from supplier expectations. Because the primary performance measures in the industry are associated with the manufacturer, the suppliers may accept their own performance measures surrogately through the manufacturer. Thus, these suppliers seek to maintain their relationships with the best practice manufacturers as they figure their own success will be inevitable due to their aligmnent with these manufacturer. This would be especially true over the last few years as the manufacturers have enjoyed great profitability. The structural modeling in Chapters 5 and 6 provide more insight in to this notion. Overall, the benchmarking assessment reveals the importance of manufacturer strategy toward supplier management. The suppliers value those manufacturers that foster relational exchange with their suppliers. This indicates that those manufacturers that are focused upon building strong supplier partnerships should place emphasis on enhancing the relationship itself. This yields direct implications for supply chain strategy in practice and further inflates the value of the research insights. 92 4.4 S um m a ry S tatistics The above benchmarking assessment of the primary automobile manufacmrers revealed the potential significance of the research results. This section will commence the primary research analysis with an overview of summary statistics for the collected data. Specifically, descriptive statistics were generated for each survey statement, and the overview of such findings will offer initial insight for the structural modeling to be discussed in the next chapter. Tables 4.6,4.7, and 4.8 display such descriptive statistics. Each statement was sought responses based on a Likert scale from 1 (strongly disagree) to 7 (strongly agree) with 4 indicating a neutral score. Included in the summary statistics are the mean and standard deviation of the score for each statement as well as a t-test for significance from a neutral score of 4. As will be shown, the summary statistics, which are discussed in further detail below, reveal a relatively favorable industry environment. 4.4.1 Summary Statistics - Power The summary statistics for the power constructs (Table 4.6) reveal much about the perceived state of power management among the manufacturers in the industry. Almost all the items demonstrate significance firom a neutral score with the strongest of which being responses that supported the positive presence of both expert and referent power in the buyer-supplier relationships. This suggests that the suppliers consider the manufacturers to retain significant industry knowledge, and the suppliers also seek to align themselves with these manufacturers. The t-tests also indicated significance for the presence of legitimate power, implying that the suppliers feel they should be expected to follow the manufacturer’s lead. Moreover, the t-tests support that legal legitimate power 93 is not present in the buyer-supplier relationships, supporting the industry trend of moving away from contracts and formal agreements. Finally, the t-tests remained inconclusive about the presence of rew>ard and coercive power. For reward, two items showed significant support for its presence while the other two significantly argued a lack of reward power behavior. Likewise, only two of four items showed significant support for coercive power. Overall, the summary statistics for the power statements painted a fairly positive picture about the influence structure in the industry. Relationally oriented power bases such as expert, referent, and legitimate attained the strongest responses while there was little support for the presence of competitively oriented power bases such as reward and coercive. 4.4.2 Summary Statistics - Relationship Elements Like the power items, the summary statistics for the critical relationship elements between the buyers and suppliers were primarily positively oriented (Table 4.7). The t- tests reveal a significance presence of commitment, trust, and cooperation as well as a lack ofcompliance in the buyer-supplier relationships. Furthermore, the t-tests also implied a lack of conflict in the buyer-supplier relationships and an effective process of conflict resolution. Overall, the results of the relationship elements t-tests reveal an industry environment that would tend to foster partnership-oriented relationships. It is important to remind the reader that previous benchmarking results have portrayed the targeted manufacturers, Chrysler and Honda, to be industry leaders in supplier relations. Thus, the results from the relationship elements summary statistics divulge a best practice relationship environment rather than that of the industry as a whole. 94 4.4.3 Summary Statistics • Performance and Satisfaction Next, the summary statistics for performance and satisfaction (Table 4.8) were also strongly favorable. First, the t-statistics for supplier performance, manufacturer performance, and supply chain performance retained highly significant values. These results indicate that the suppliers seemed to view their relationships with the manufacturers as enhancing the performance of themselves, manufacturers, and the supply chain. The positive performance measures are indicative about the linked nature of the supply chain. It appears that suppliers perceive that an improved relationship allows them access to critical information flows and planning activities, allowing them to do their job better. Their performance in the form of better designed, higher quality products at reduced costs then translates to the manufacturer in the form better cars at competitive prices. This enhances sales, market share, and customer loyalty, benefiting the entire supply chain. This offers support for the ultimate goal of supply chain management in improving the performance of all supply chain members. Furthermore, the t-tests for satisfaction reveal that the suppliers were primarily satisfied with their relationships with the manufacturers. All means for the four satisfaction measures were highly significant. This makes intuitive sense given the results of the power, relationship, and performance measures. The suppliers view their buyer relationships positively and perceive their own performance as having improved as a result, high satisfaction would flow naturally from such an environment. Once again, it is important to remember that such results represent a benchmarking environment rather than the composite industry. 95 (A) (B) : A-,:/-:; : r. I (D) / i .;0E) . - 0 (G) Power ?Meair; Sti)èT -T-stat Sign Var&ble 17 Ejqpeit ^ - is an expert in automotive industry 6.08 0.97 31.51 *** 18 We respect judgment of - 5.50 1.12 19.84 *** 19 People from - do not know what they are doing 2.45 1.47 -15.52 *** 20 - retains business expertise 5.14 1.15 14.62 *** 21 Referent We admire way - runs their business 4.98 1.33 10.86 *** 22 We do what - asks because we are proud to be 5.02 1.29 11.71 *** affiliated 23 We talk up - to our colleagues 5.58 1.23 18.92 *** 24 - views us as an important "team member" 5.27 1.50 12.50 *** 25 Lej^timate - should stay out of our business 2.97 1.66 -9.17 *** 26 - has no right to tell us what to do 3.06 1.50 -9.31 *** 27 We should accept requests and recommendations 4.94 1.28 10.83 *** 28 Customers have right to expect suppliers to follow 5.18 1.32 13.19 *** 29 Legal - refers to agreement to gain our compliance 3.84 1.61 -1.43 Legitimate 30 - refers to legal agreement when attempting influence 2.94 1.59 -9.90 *** 3L - uses sales agreement as "tool" to get us to agree 3.21 1.65 -7.04 *** 32 - makes biased interpretations of agreements to gain 2.86 1.56 -10.83 *** cooperation 33 Reward . - emphasizes they will do in return for cooperation 4.22 1.53 2.13 ** 34 - offers incentives when we had been reluctant 3.32 1.43 -7.01 *** 35 By going along with -, we will be favored 4.93 1.34 10.26 *** 36 - offers rewards so we will go with their wishes 3.32 1.40 -7.23 *** 37 Coercive If we do not do as asked, we will not receive good 4.46 1.55 4.36 *** treatment from - 38 If we do not agree, - could make things difficult 4.76 1.60 6.99 *** 39 - makes clear that failing to comply results in 4.05 1.76 0.39 penalties 40 - threats to reduce business if demands not met 4.13 1.84 1.07 A. Question Number B. Variable of Measurement C. Abbreviated Statement from Survey D. Arithmetic Mean E. Standard Deviation F. T-statistics calculated as (D - 4)/(E*sqrt(229)) G. Significance - *** indicate significance at .01, ** at .05, and * at .10 Table 4.6; Summary Statistics for Power Bases 96 CA) . -(B) OE) (E) (G) PoWérVàr^Ië rnmmms&mrn Mean StDer T-stat Sign 2 Coopérai s ■ Relationship better described as a "cooperative" 5.45 1.39 15.78 *** 4 Perform well together 5.85 1.10 25.46 *** 69 Our future goals best reached by working with - 6.18 1.03 32.05 *** 70 We can't count on - to give us support others 2.66 1.67 -12.18 *** receive 71# - helps us in getting job done 5.36 1.18 17.47 Do not want to replace - as a partner 6.50 0.95 39.68 *** 10 Committed to preservation of good 6.72 0.67 61.04 *** relationships with - 11 Our firm believes in - as a partner 6.14 1.15 28.11 *** 54 Trust - is concerned about our welfare 4.83 1.52 8.24 *** 55 - considers how its decisions/actions affect us 4.61 1.49 6.20 56 We can not trust - 2.43 1.61 -14.76 57 - looks out for our best interests 4.22 1.47 2.31 ** 58 C onflict- We don't like things - does 2.83 1.56 -11.34 *** 59 - prevents us from doing what we want to do 3.85 1.59 -1.44 60 - does not have our best interests at heart 3.39 1.54 -6.05 61 We disagree with - on critical issues 3.28 1.51 -7.24 ««« 62 Conflict Discussions with - in areas of disagreement are 4.89 1.15 11.74 *** Resolution productive 63 Discussions in tend to create more problems 3.01 1.43 -10.47 *** 64 Discussions increase effectiveness/strength of 4.83 1.31 9.53 *** relationship 65 Compliance Our private views difrerent from those we 2.49 1.70 -13.44 express publicly 66 Unless rewarded, we see no reason to expend 2.59 1.61 -13.30 *** extra effort 67 How hard we work for - is linked to reward 3.50 1.74 -4.34 *** 68 Bargaining is necessary to obtain favorable 4.76 1.61 7.15 *** terms of trade A. Question Number B. Variable of Measurement C. Abbreviated Statement from Survey D. Arithmetic Mean E. Standard Deviation F. T-statistics calculated as (D - 4)/(E*sqrt(229)) G. Significance - *** indicate significance at .01, ** at .05. and • at .10 Table 4.7: Summary Statistics for Relationship Elements 97 (A) (D) (E) (R (G) Power Variable atemr StDeV T-stat Sign 50 Satisfaetioa Dealing with - benefits our company 6.06 1.00 31.23 *** 51: We are satisfied with dealings with - 5.36 1.51 13.64 *** 52 Would discontinue selling to - if could 1.76 1.41 -24.00 *** 54 - is good company to do business with 5.98 1.17 j 25.68 *** 41 PerfonnShi»-^ Performance of our firm has improved a result 5.40 1.20 17.76 *** Su^piiir of our association with - 44 Efficiency of relationship has improved our 5.23 1.22 15.21 *** performance 47 Without -, our performance not as good 4.75 1.66 6.88 *** 42 Performance - - s performance has improved 5.18 1.31 13.63 *** Manufàctnrer 45 Efficiency of relationship has improved - 4.97 1.24 11.79 *** performance 48 Without us, -'s performance not as good 4J7 1.46 5.90 *** 43 Performance- Performance of supply chain has improved 4.96 1.26 11.55 *** Supply Chain 46 Efficiency of relationship has improved supply 4.90 1.18 11.50 *** chain performance 49 Without relationship, supply chain performance 4.46 1.33 5.20 *** would not be as good A. Question Number B. Variable o f Measurement C. Abbreviated Statement from Survey D. Arithmetic Mean E. Standard Deviation F. T-statistics calculated as (D - 4)/(E*sqrt(229)) G. Significance - *** indicate significance at .01, ** at .05, and * a t . 10 Table 4.8: Summary Statistics for Performance/Satisfaction 98 4.5 S u m m a r y S t a t is t ic s - C h r y s l e r v e r s u s H o n d a The previous review of the summary statistics unveiled a relatively positive supply chain environment between both Chrysler and Honda and their supplier bases. These findings offer insight for the current state and future of supply chain relationships within the industry. This section seeks to extend the analysis of the summary statistics to a direct comparison of the Chrysler versus Honda responses. Although Chrysler and Honda retain the best reputation with suppliers, the benchmarking results indicated that Chrysler was the stronger of the two. This section seeks to offer insight to this difference as well as provide an example of the differences between a U.S. and Japanese transplant automobile manufacturer. 4.5.1 Chrysler versus Honda Results The responses for both Honda and Chrysler were separated and tallied as separate samples. The research then sought to test for significance in difference of means using independent population t-tests. The results of the comparison t-tests (taken as the Chrysler mean minus the Honda mean) which are presented in Tables 4.9,4.10, and 4.11 offer intriguing results about the similarities and differences between the responses of the two supplier bases. Of the power bases items, very few demonstrate a significant difference of means (Table 4.9), implying a similar power structure among the two manufacturers. Among the critical relationship element items, however, many items showed significantly different means, generally favoring Chrysler (Table 4.10). Of the elements, Chrysler consistently ranked higher than Honda with regard to cooperation, commitment, compliance, and conflict resolution. Chrysler also bettered Honda with regard to conflict though one of 99 the four items did not demonstrate significance. The results for trust were inconclusive as only two of four items retained significance favoring Chrysler. Thus, despite an apparent lack of difference in the power environment between Honda and Chrysler, suppliers seemed to favor the general state of the buyer-supplier relationship with Chrysler over that of Honda. The comparison of the satirfaction items indicate that Chrysler generates slightly more satisfaction among its suppliers (Table 4.11). These results also indicate that Chrysler's supplier relationships are more effective at enhancing both manufacturer and overall supply chain performance. There was little evidence, however, concerning a difference in supplier performance between the two. Thus, it is apparent that Chrysler suppliers see better performance results due to their buyer-supplier relationship than do the Honda suppliers, but these suppliers do not necessarily feel that their own performance is improved. The general results from the comparison of supplier perceptions of Chrysler and Honda as supply chain partners yield opportunities for future research. Although these two manufacturers are considered benchmarks among their competitors in fostering supplier relations, some differences between the two exist. Further research would allow for investigation of the reasons for the perceived differences and reveal contrasts in general supply chain strategy. Such differences could simply originate from general corporate strategy or hold deeper foundations in that basis of one being a U.S. manufacturer and the other being a Japanese transplant manufacturer. 100 (Ay P- : W . 0 ) 1 iOveralT 1 0 m ■ V a ria b le : 1 '■ 'S e m 1 {Aat'r: valuer sig 17 6.06 St 6.08 0.95 6.04 1.01 0.31 0.76 18 5.49 5.54 1.00 5.45 1.22 0.62 0.53 19 2.45 2.31 1.41 2.56 1.50 -1.32 0.19 20 5.14 5.27 1.04 -, 5.04 1.21 131 0.13 21 Refei^^Power i 4.97 5.10 1.16 4.88 1.42 1.31 0.19 22 5.03 r. 5.20 1.22 4.90 1.33 1.76 0.08 * 23 5.59 5.89 1.02 5.35 1.30 3.52 0.00 24 5.27 5.63 1.23 4.98 1.64 3.35 0.00 25 L*^dmate Power 2.97 2.94 1.57 3.00 1.70 -0.27 0.79 26 3.06 2.94 1.42 3.15 1.55 -1.05 0.30 27 4.91 4.87 1.22 4.95 1.30 -0.44 0.66 28 5.17 5.13 1.27 5.20 1.34 -0.43 0.67 29 Legal Legitiinate Power 3.86 3.91 1.57 3.83 1.65 0.39 0.70 30 2.94 2.91 1.57 2.97 1.60 -0.27 0.78 31 3.22 3.41 1.65 3.07 1.64 1.54 0.13 32 2.88 2.87 1.52 2.89 1.57 -0.09 0.93 33 Reward Power 4.20 4.39 1.44 4.05 1.59 1.63 0.11 34 3.31 3.42 1.39 3.23 1.46 0.95 0.34 35 4.91 4.98 1.26 4.86 1.41 0.68 0.50 36 3.33 3.40 1.48 3.27 1.34 0.66 0.51 37 Coetdve Power 4.44 4.34 1.49 4.52 1.61 -0.86 0.39 38 4.73 4.66 1.52 4.79 1.68 -0.59 0.55 39 4.06 3.95 1.70 4.15 1.81 -0.85 0.40 40 4.12 3.84 1.74 - 4.34 1.90 -2.06 0.04 A. Question number B. Variable of measurement C. Aggregate mean D. Mean for Chrysler E. Standard deviation for Chrysler F. Mean for Honda G. Standard deviation for Honda H. T-statistic for difference of means I. P-value fort-statistic for difference of means J. Significance of the T-Statistic - *♦* indicates significance at .01, ** at .05, * at .10 Table 4.9: Two Population T-Tests for Power Bases 101 'i % 1 m (J) q & l ? 'MàmÂ-. i 1 A11f t 2 Cooperaflott-ii 5.45 5.67 1.19 5.27 1.50 2.24 0.03 4 : 5.84 5.96 0.99 5.75 1.14 1.49 0.14 69 6.17 6.41 0.67 5.98 1.21 3.35 0.00 « 70 2.63 2.43 1.56 2.80 1.69 -1.72 0.09 « 71 5.34 5.50 1.15 5.21 1.18 1.83 0.07 9 Coinndtment f . 6.50 6.74 0.50 6.30 1.14 3.89 0.00 10 r .■■■:" ;■ J- 6.71 6.87 0.34 6.59 0.82 3.58 0.00 ««« I t 6.14 6.41 0.84 5.93 1.31 3.35 0.00 54 Trust 4.83 5.10 1.23 4.63 1.66 2.48 0.01 ** 55 4.62 4.77 1.36 4.51 1.56 1.37 0.17 56 2.44 2.27 1.53 2.57 1.68 -1.42 0.16 57 4.23 4.45 1.40 4.06 1.50 1.97 0.05 * 58 Conflict - 2.83 2.53 1.35 3.07 1.68 -2.68 0.01 59 3.85 3.74 1.42 3.93 1.72 -0.90 0.37 60 3.37 3.17 1.46 3.53 1.59 -1.78 0.08 $ 61 3.28 3.01 1.39 3.49 1.54 -2.48 0.01 ** 62 Conflict 4.87 5.12 1.06 4.67 1.19 3.00 0.00 *** Resolution 63 3.01 2.80 1.31 3.17 1.46 -1.99 0.05 64 4.83 5.88 1.11 4.63 1.39 2.70 O.Ol 65 Compliance . 2.48 2.15 1.31 2.75 1.89 -2.84 0.01 *** 66 2.59 2.34 1.46 2.78 1.73 -2.11 0.04 67 3.52 3.75 1.76 3.33 1.67 1.86 0.06 * 68 4.78 4.55 1.65 4.95 1.56 -1.87 0.06 $ A. Question number B. Variable of measurement C. Aggregate mean D. Mean for Chrysler E. Standard deviation for Chrysler F. Mean for Honda G. Standard deviation for Honda H. T-statistic for difference of means I. P-value fort-statistic for difference of means J. Significance of the T-Statistic - indicates significance at .01, ♦* at .05, • at .10 Table 4.10; Two Population T-Tests for Relationship Elements 102 (A) 0») - (O - .4 ® . (G) ® 0 ) O veran m i # V ariab le ..M ean : -M ean: betr ' ■ ';ber.'^ fcstàt SiK *«« 5 0 Satisfaction" : 6.06 6.26 0.74 5.91 1.13 2.69 0.01 ** 51 5.34 5.61 1.25 5.13 1.63 2.57 0.01 5 2 1.76 1.53 1.16 1.95 1.56 -2.28 0.02 5.97 6.25 0.90 ,1' 5.76 1.31 3.34 0.00 * 41 Péiiôniiance* 5.39 5.54 1.11 5.27 1.28 1.68 0.09 S nppU er 4 4 5.22 5.38 1.13 5.09 1.31 1.72 0.09 * 4 7 4.76 4.94 1.62 4.62 1.67 1.48 0.14 4 2 P à f o rm a n c e >' 5.19 5.50 1.22 4.94 1.29 3.38 0.00 *** ManuAîAm^ÿz «*♦ 4 5 4.96 -i. 5.24 1.23 - 4.74 1.17 - 3.10 0.00 4 8 4.59 4.92 1.40 4.32 1.41 3.21 0.00 *** ** 43 P èrfo m u in cè - ' 4.95 5.17 1.30 4.77 1.19 2.39 0.02 Supply Chain 4 6 4.90 5.13 1.23 4.71 1.10 2.68 0 .01 49 4.46 4.71 1.37 4.26 1.24 2.64 0.01 **♦ A. Question number B. Variable of measurement C. Aggregate mean D. Mean for Chrysler E. Standard deviation for Chrysler F. Mean for Honda G. Standard deviation for Honda H. T-statistic for difference of means I. P'value fort-statistic for difference of means J. Signiricance of the T-Statistic - indicates significance at .01, ** at .05, * at .10 Table 4.11: Two Population T-Tests for Performance and Satisfaction Variables 103 4.6 Uniddvbensionality The previous sections of this chapter analyzed summary statistics for the survey responses. In doing so, the general environment of the supply chains of the two manufacturers of study was revealed. The remaining portions of this chapter seek to review analyses of the research constructs in preparation for the measurement and structural modeling to be explored in Chapter 5. This section analyzes the unidimensionality of the data scales as a requirement for data reliability and validity. A measurement scale should be unidimensional in that it only measures one construct, and unidimensionality is a prerequisite for the establishment of both reliability and validity [Anderson and Gerbing, 1988; Droge, 1997]. To establish unidimensional constructs, the research employed exploratory factor analysis [Hair, et al., 1992], using oblique rotations due to the expected interdependency of factors. Unidimensional scales should retain significant factor loadings for the factor they are hypothesized to measure and hold insignificant loadings on all other factors. (Note: The research used a cut off of .40 and above for significance of factor loadings [Hair, et al., 1992].) Scales that do not demonstrate sufficient unidimensionality can not be considered either reliable or valid and may thus be removed from subsequent modeling. The results of the factor analyses, which are presented below, not only offer verification of unidimensionality but also yield insight in the relationships between the factors. The unidimensionality analyses are separated for the power, relationship, and performance/satisfaction constructs. 4.6.1 Power Influence Items Questions 17-40 were intended to represent the different sources of power including expert (questions 17-20), referent (21-24), legitimate (25-28), legal legitimate (29-32), 104 reward (33-36), and coercive (37-40). An initial factor analysis of these items produced 7 factors with eigenvalues greater than one as summarized in Table 4.12. The items for legal legitimate (statements 29-32), Reward (33-36), and Coercive (37-40) loaded together as expected, and each had negligible loadings on other factors. Furthermore, items 17-19 and 20-24 also yielded two unidimensional factors in expert and referent power bases respectively, but item 20 loaded on referent instead of expected expert power. Item 20 was thus removed from further analysis. Factor Loadings question expert refôent Ieg-2 leg-1 ' legal leg reward coercive 17 58* 32 -2 -8 4 -5 13 69* 23 -2 4 0 14 -5 19 . 61* -15 9 6 -8 -4 -10 20 17 51* 12 17 -1 5 -6 21 10 79* 4 -7 11 -6 1 22 -15 81* 4 11 2 -3 -2 23 -2 72* 0 1 -4 0 -4 24 4 66* -10 -9 -8 6 -12 25 15 12 64* -8 4 0 -6 26 -3 2 95* 2 -1 -2 3 27 -6 19 1 65* -1 5 -2 28 8 -9 -4 93* 3 -6 2 29 11 10 -2 -1 86* -12 2 : - 3 0 -8 0 5 -2 89* 1 -2 ■ ."3t> -1 -7 6 1 89* 12 2 32 -9 -4 -10 6 73* 4 7 ■ 33 1 10 -12 -1 6 66* -2 34 3 -12 -3 -2 9 77* -12 35 -8 22 10 9 -18 47* 15 36 1 -4 8 -4 -4 70* 10 - - . 3 7 ; -10 -2 5 0 0 4 64* 0 -9 4 2 0 7 75* 39 2 2 -3 2 2 -4 91* ,--■->-■40 =■- 6 -3 -7 -4 5 -4 80* 1 eigenvalues - 4.2359 25.5726 1.5541 2.4804 10.9484 5.4304 7.2344 1 Table 4.12: Factor Analysis for the Bases of Power Items 105 INTER-FAa rOR CORRELATIONS ( factors-i S expert -referent- Ieg-2 legal leg reward co erciv e e x n e r t- r 100* 56* 13 38 -28 13 -37 r e R r e h t # C 56* 100* 23 50* -34 29 -37 13 23 100* 16 3 7 18 le e r l 38 50* 16 100* -38 12 -27 lœàl lëa*' • -28 -34 3 -38 100* 13 44* ' r e w a A -, 13 29 7 12 13 100* 9 -37 -37 _____ LS______i22___ 44* _____ 2 , .„ , ___ lqq : ___ Table 4.13: Inter-factor Correlations for Bases of Power Finally, the items for legitimate power (25-28) split into two unidimensional and uncorrelated factors (25-26 and 27-28), and further examination of the questions revealed ambiguity between the orientation of the questions. Thus, the legitimate power items seemed to be measuring more than one factor. Due to this lack of unidimensionality, all legitimate power base items were removed from further analysis. In all, unidimensionality was established for five power base factors including expert (without item 20), referent, legal legitimate, reward, and coercive power while legitimate power was removed from the analysis due to a lack of unidimensionality. The eigenvalues represent the total amount of variance represented by a factor. Larger eigenvalues indicate that a particular factor retain greater importance in forming the distinct factors. Of the unidimensional power bases, referent power by far retained the largest eigenvalue at 25.57. Legal legitimate retained the second largest eigenvalue at 10.95, then coercive at 7.23, reward at 5.43, and finally, expert at 4.24. These eigenvalues offer an indication 106 4.6.1.1 Power - Correlations among the Bases The inter-factor correlation matrix for the power bases generated by the factor analysis (Table 4.13) was used to gain further insight into the relationships between the different power bases. To review, it was originally conceptualized in Chapter 2 that the power bases would dichotomize into non-mediated (expert, referent, legitimate) and mediated (reward, coercive, and legal legitimate) sources. Subsequently, the inter-factor correlation matrix should demonstrate strong correlations among the grouped bases, but the results did not completely align with the conceptualized grouping. To explain, a strong correlation was found between referent and expert as well as legal legitimate and coercive bases. Reward power, however, did not retain strong correlations with any of the bases. Bearing in mind that the legitimate base were removed from lack of dimensionality, the correlations of the power bases lead to a trichotomization of the bases into three power strategies hence described as non-mediated (expert, referent), coercive- medlated (legal legitimate, coercive), and reward-mediated (reward). Such a trichotomization indicates the perceived nature of power bases in the industry and will drive the modeling in Chapter 5. The link between expert and referent power bases as non-mediated bases remains fairly transparent as suppliers seek to relate themselves with the manufacturer that retain significant industry expertise. The relationship between legal legitimate and coercive power is also clear in that the suppliers see both as harmful, mediated bases. Reward power in itself makes up the last category, failing to align with the other mediated sources as originally conceptualized. This implies that although reward power is mediated to the target suppliers, these suppliers do not necessarily view reward bases in a same orientation as coercive or legal legitimate power. Thus, reward power seems to represent a unique power strategy in the automotive industry. 107 4.6.2 Relationship Items Factor analysis was also applied to establish the unidimensionality of the relationship scales. Specifically, questions 2,4,9-11,54-71 17-40 were intended to represent the six different aspects of the buyer-supplier relationship which include cooperation (items 2,4, 69-71), commitment (9-11), trust (54-57), conflict (58-61), conflict resolution (62-64), and compliance (65-68). An initial factor analysis with an oblique rotation found 5 factors (see Table 4.14 ) and was immediately able to establish the unidimensionality of four constructs in commitment (9-11), trust (54, 55,57), conflict (58-61), conflict resolution (62, 63, 64). Item 56 was removed as an indicator of trust because it failed to retain a significant loading. Item 63 also proved to be troublesome also as it loaded significantly on conflict resolution as intended but also retained a significant negative loading on conflict. Despite this apparent lack of unidimensionality, item 63 was retained as an indicator of conflict resolution because of its importance to the research. The unidimensionality of the remaining items (65-71, 2,4) remained ambiguous after the initial factor analysis. These items were then isolated for a second level factor analysis (see Table 4.15 ). Items 2,4, 69,70, 71 proceeded to load onto a single factor though item 4 was removed due to its significant negative loading on conflict in the first level factor analysis. The intended compliance items (65-68), however, still failed to load onto a single unidimensional factor, and thus, all compliance items were removed from consideration. In the end, the unidimensionality of the five constructs were established as commitment (9-11), cooperation (69,70, 71,2), trust (54, 55,57), conflict (58-61), and conflict resolution (62, 63, 64) while compliance (65-68) was removed due to a lack of unidimensionality. 108 Factor Loadings question ; conunit trust .conflict : cont res, r - - T 78* -2 -10 -5 -11 85* -4 9 2 3 72* 18 0 -2 -3 9 80* 2 2 -5 m m -5 82* -12 6 8 11 11 -47* 11 4 6 72* -13 1 12 -7 -6 70* 4 0 . m m : -2 -9 65* 10 -7 60 -I -28 53* -1 9 61 5 12 79* -7 -3 62 2 3 3 86* -3 63 I -9 -40* 55* -5 64 0 4 8 91* 12 65 -4 -6 47* -15 11 66 1 -10 13 -5 78* 67 -7 12 -10 7 44* 68 10 -20 38 7 6 69 35 -6 0 17 -18 70 8 26 •8 29 -15 71 0 20 -24 31 6 2 30 15 -35 16 8 4 35 4 -43* 7 16 eigenvalues 3.7321 2.5578 28.2155 3.2575 1.6706 Table 4.14: Initial Factor Analysis for Relationship Elements Items Factor I Lbadinj^ question cooperation Compliance:? 65 T -62* 15 66 -26 63* _ 67 13 47* 68 -32 13 - 69 42* -11 - 7 0 ■ 57* -17 7 i;. 60* 5 ' - . 4 - 86* 14 .. 77* 20 eigenvalues? 6.6965 1.4331 Table 4.15: Secondary Factor Analysis for Relationship Elements Items 109 4.7 Reliability The previous section described isolation of unidimensional factors as the first step in preparation for the data analysis. This section assesses the reliability of these unidimensional scales. Specifically, "reliability measures the extent to which a questiormaire, summated scale, or item which is repeatedly administered to the same people will yield the same results," [Flyim et al., 1990, p 265], and reliability is a necessary but not sufficient condition for validity [Droge, 1997; Carmines and Zeller, 1979]. The research utilized internal consistency through calculation of Cronbach's alpha as the assessment of reliability [Cronbach, 1951]. This coefficient alpha provides a lowest possible reliability estimate for a scale and is calculated as: rxx U -1, where: N = # items constituting in the scale = variance of the summated scale score Z = sum of variances for all items in the scale Alpha will assume a value between zero and one. High values of alpha indicate reliability since the greater item correlation will cause combined variance (Z Sj2) to retain a value close to zero. Nunnally [1978] suggests that a level of .70 is considered acceptable for established scales while a minimum level of .60 is acceptable for newly developed scales. The next section will review the results of the alpha values for the research scales. 110 4.7.1 Cronbach's Alpha Values Cronbach's coefficient alpha was calculated for the items for each survey construct, and these are summarized in Table 4.16. For the five power constructs that remained after unidimensionality verification, each retained an acceptable level of alpha, including .72 for expert, .83 for referent, .92 for legal legitimate, .75 for reward, and .87 for coercive. Furthermore, the alphas for the five relationship elements constructs also retained acceptable alpha levels with commitment at .83, cooperation at .71, trust at .89, conflict at .81, and conflict resolution at .86. Finally, the three different performance measures (supplier, manufacturer, and supply chain) also retained significant alpha values at .83, .84., and .85 respectively while the satisfaction held an alpha value of .80. Given the results of the internal consistency assessments, the reliability of the research constructs were thus established. Cronbach's I FACTOR Variable Question# Alpha Power Expert 17-19 0.72 Referent 21-24 0.83 Legal Legitimate 29-32 0.92 Reward 33-36 0.75 ... * . -/ . . • Coercive 37-40 0.87 Relationship - Commitment 9-11 0.83 Conflict 58-61 0.81 Conflict Resolution 62-64 0.86 Cooperation 2,4,69-71 0.71 Trust 54-57 0.89 Performance Supplier Performance 41,44,47 0.83 Manufacturer Performance 42,45,48 0.84 Supply Chain Performance 43,46,49 0.85 Satisfaction Satisfaction 50-53 0.80 Table 4.16: Values of Cronbach Alpha for Reliability Assessment 111 4.8 S ummary AND Overview OF Future C hapters This chapter has presented an overview of the description survey data including an assessment of the best practice supplier relations environment, summary statistics, unidimensionality, and reliability. Critical highlights of the chapter include; • A benchmarking assessment portrayed Chrysler and Honda as the best practice among the manufacturers for supplier relations, indicating the research modeling will also produce benchmark results. • The survey responses presented a relatively positive picture of supplier relations among the two participating manufacturers. Relational power bases (such as expert and referent) were most prevalent while competitive power bases (such a legal legitimate and coercive) were notably absent. The scores for the relationship as well as performance and satisfaction items were also strongly positive. • A direct comparison of Chrysler versus Honda through independent and dependent t- tests revealed that similar power environments for the two but also showed some indication for a stronger supplier relationship environment for Chrysler. • Most of the scales for the research constructs seemed to be unidimensional in nature, but items for compliance as well as legitimate power had to be removed from analysis due to a lack of unidimensionality. Internal consistency checks were used to establish the reliability of the remaining factors. 112 • Although it was conceptualized that the power bases would dichotomize into mediated (reward, coercive, legal legitimate) and non-mediated (expert, referent, legitimate), the inter-factor correlation pointed to a trichotomization of non-mediated (expert, referent), coercive mediated (coercive, legal legitimate), and reward mediated (reward). This affected the modeling to be describes in Chapter 5. The next chapter. Chapter 5, will describe the primary research modeling, reviewing the fit of the conceptualized measurement and causal models. Chapter 6 will then discuss the testing of the research hypotheses based on the modeling in Chapter 5. 113 CHAPTERS MODEL FITTING 5.1 Introduction The previous chapter served to review summary statistics for the data as well as establish unidimensional and reliable measurement scales. This chapter will review the fitting of the conceptualized structural models using a two-step approach. The first step involves assessment of a measurement model through confirmatory factor analysis, and the second step analyzes the corresponding structural model as it estimates the causal relationships among the factors. Based on different sources of power and distinct indicators of performance, the research assessed nine different models using this two step process. The chapter will start with a review of research variables and models. Next, the model fit process for both the measurement and structural steps will be detailed, and the chapter will then review the actual fit results for the research models. These results ultimately serve to evaluate the research hypotheses which will be discussed in Chapter 6. 5.2 Research V ariables The tests for unidimensionality and reliability in Chapter 4 served to prepare the data scales for the measurement and structural analyses to be discussed in this chapter. To 114 help the reader follow the extensive modeling to come, this section will serve to describe and summarize the scales. Such descriptions are broken down below into power, relationship, performance, and satirfaction variables. 5.2.1 Power Variables After tests for unidimensionality and reliability, five constructs remained for the bases of power (Table 5.1]. Each of these variables served as latent, exogenous variables within the structural models and include expert (Y4, EXP), referent (Ys, REF), legal legitimate (Ye, LLEG), coercive (Y?, COER), and reward (Ys, REW). These variables were measured directly by their respective items from the survey. Based on the initial factor analyses during the unidimensionality tests discussed in Chapter 4, it was found that such power bases were most appropriately trichotomized into three power strategies including non-mediated (expert, referent), coercive-mediated (coercive, legal legitimate), and reward-mediated (reward). The effects of each of these distinct power strategies will be analyzed with the structural equation modeling. 5.2.2 Relationship Variables The degree of relationship between the supplier and manufacturer was represented by a single latent, endogenous factor, relationship (Ys, RELA), which was indicated by composite scales of commitment, cooperation, trust, conflict, and conflict resolution (Table 5.2). (Note: Compliance was removed due to a lack of unidimensionality.) Scales were constmcted for each of these indicator variables as a factor weighted average of their respective statement items that remained after the unidimensionality and reliability tests. Hair, et al. [1992] contends that the use of factor-weighted scales is effective in representing a latent factor when the scales are shown to be valid and reliable. 115 Power S tra ^ Factor : Indicators Survey Item 1 NON-MEDIATED ^ : Expert Y 4 EXP Xl3 17 X u 18 Xis 19 Referent Ys REF Xl7 21 Xl8 22 X l9 23 X 20 24 COERÇiyEiME»IATED- Coercive Y6 COER X 21 37 X 22 38 X23 39 X 24 40 Legal Y7 LLEG X 25 29 Legitimate X26 30 X 27 31 X28 32 REWARD-MEDIATED Reward Ys REW X29 33 X30 34 X3I 35 X32 36 Table 5.1: Description of the Power Bases Variables Indicators; Survey Factor Ifactqr-W^ted scale) V" Items RELATIONS^» Y3 RELA Commitment (X8) 9-11 Cooperation (X 9 ) 2, 69-71 Trust (Xio) 29-32 Conflict (X ll) (reverse coded) 37-40 Conflict Resolution (X 12) 33-36 Table 5.2: Description of the Relationship Variable 116 These resulting five factor weighted scales were denoted as Xs (commitment), X 9 (cooperation), Xio (trust), Xu (conflict), and X12 (conflict resolution). Because conflict, Xu. is a negative indicator of relationship, it was recoded inversely to represent a positive indicator of relationship (i.e., a lack of conflict) like the other four indicator scales. 5.2.3 Performance/Satisfaction Variables Three different measures of performance were collected by the survey instrument including supplier performance, manufacturer performance, and supply chain performance. Each is measured directly by its respective items from the survey instrument (Table 5.3). These performance constructs serve as latent, endogenous factors in the model hence known as Yza (S-PRF) fox supplier performance, Yzb (M-PRF) for manufacturer performance, and Yzc (SC-PRF) for supply chain performance. Finally, the satisfaction factor (Y1, SAT) was also measured directly by its items from the survey. Factor .7 % -y -k; Indicators Survey Items SUPPLIER PERFORMANCE Y2a S-PR F X5a 17 X6a 18 X7a 19 MANUFACTURER PERFORMANCE Yzb M -PR F XSb 21 Xôb 22 X7b 23 SUPPLY(%MNPE#OmW^#%^Y2c SC-PRF X5c 29 Xbc 31 ___ X7c 32 Yi SA T X i 50 X2 51 X3 52 X4 Table 5.3: Description of Performance and Satisfaction Variables 117 5.3 An Over view of Structural E quation M odeling The models under study by the research involved a series of linked, causal relationships among the latent factors, and many statistical tools are available for the analysis of these dependence relationships. Regression remains the technique perhaps most frequently considered first when analyzing dependence relationships, but regression will only allow one dependent variable. Other statistical models such as multivariate analysis of variance and canonical correlation permit the use of multiple dependent variables, but such tools are limited in that they may only represent a single relationship between independent and dependent variables. On the other hand, structural equation modeling measures multiple relationships among independent and dependent variables, thus accommodating aggregated dependence relationships simultaneously in one comprehensive model [Hair, et al., 1992; James, et. al, 1982; Bollen, 1989]. Given the multiple dependence relationships in the research models, structural equation modeling was the only suitable statistical tool available to assess the models. Also known as covariance structure modeling, latent variable analysis, and LISREL, structural equation modeling (SEM) is described in further detail below. 5.3.1 Structural Equation Modeling The methodology of this research utilized structural equation modeling [Bollen, 1989; Bentler and Chou, 1986; Shanna, 1996; Hatcher, 1994] to assess of fit of the conceptualized models to the collected survey data. An explanation of SEM, which is described here, begins with the statistical notion of path. A path identifies a causal link between two factor, indicating that one factor (independent variable) affects the other (dependent variable). Given more than two factors, a path (or structural) model specifies 118 a set of inter-related paths in which a dependent variable for one path may become an independent variable in another. A path model may be conceptualized for research purposes so that a path analysis may be conducted to assess the ability of the model to account for the dependence relationships found in a collected data set. Thus, a model is said to fit if sufficiently explain the variance in the data. Structural equation modeling may be expanded to include latent variables. A latent variable represents hypothetical constructs that can not be directly observed and thus must measured by its effect on a set of manifest (measurable) variables. The research in this dissertation sought to assess causal paths between several latent variables such as power, relationship, performance, and satisfaction. The survey instrument was created to collect responses to manifest variables that will then serve as indicator variables for the latent factors. Structural equation modeling may be used to assess both the ability of the manifest variables to measure the latent factors of interest and the significance of the causal paths conceptualized by the research. 5.3.2 Fitting the Structural Model - A Two Step Approach Anderson and Gerbing [1988] (see also Hair, et al., 1992; Kenny, 1979; Medsker, et al., 1994) propose a two-step approach to development of structural equation modeling with latent variables, and a similar methodology was employed by this research. A structural equation model with latent variables consists of two elements, the measurement model and the structural model. The measurement model examines the relationship between the indicator variables and the latent constructs they attempt to measure. Thus, developing an acceptable measurement model ensures that the indicator scales are accurately measuring the latent constructs. With respect to the research project, each of the factors (power. 119 relationship, performance, and satisfaction) in the models represent latent constructs, and the collected survey data was utilized to provide measurement of these latent constructs. Confirmatory factor analysis via SAS was employed to test the fit of the data to the measurement model, and this analysis also allowed for testing of construct (convergent and discriminant) validity. Once the latent factors are found to be measurable, Anderson and Gerbing's second step involves assessment of the structural model which explores the conceptualized dependence relationships between the factors. Thus, the structural model is simply an extension of the measurement model in which causal paths are specified between some of the latent factors. The structural portion of the research models is represented by the conceptualized paths between the latent factors (such as the effect of power upon relationship). S AS was utilized to evaluate the structural model fit which allows for the testing of the research hypotheses as proposed in Chapter 2. The following sections will overview the research models, present model fitting procedures for Anderson and Gerbing's two step approach, and exhibit the results of the actual model fitting. 5.4 Overview OF Re se arc h M odels In its mission of investigating the influence of power upon supply chain relationships, the research employed several related models, and this section seeks to overview this modeling. The general research model was introduced in Chapter 1 and is reviewed as Figure 5.1. This model conceptualizes that manufacturer power affects the nature of the relationship between the manufacturer and supplier by influencing critical elements such as cooperation, commitment, trust, and conflict. The power-affected relationship then influences perceived performance and satisfaction. 120 :»;13 f"r» Figure 5.1: Generalized Research Model 5.4.1 Model Justification Motivation for this model was driven by the synthesis of the supply chain and distribution channel literature discussed in Chapter 2. An overview of the model foundations are explained here, starting with an explanation of the power-relationship component. The orientation of the power variables as dichotomized into mediated and non-mediated sources is applied from Brown, et al. [1995b] and Kasulis, et al. [1979]. The nature of the buyer-supplier relationship factor was compelled by the critical partnership elements provided by the supply chain literature [Niederkopler, 1991; Ellram, 1991c, 1995; Stuart, 1992]. Given the critical relationship elements, the conceptualized dependence of this relationship factor upon the power sources was adapted from several sources including Brown et al. [1995b], Skinner, et. al [1992], Stem and Reve [1980], and Gaski [1984]. Many distribution channel works have analyzed the relationship of power upon performance [Etgar, 1976; Brown, et al. 1995b] and satisfaction [Hunt and Nevin , 1974; Lusch, 1976a; Michie and Sibley, 1985]. The research in this dissertation followed the orientation of such works as Noordewier et al. [1990] and Skinner, et al. [1992] in that power will indirectly affect performance and satisfaction through the nature of the inter firm relationship. Thus, the relationship-performance and relationship-satisfaction 121 models were included in the model. Finally, following implications from Brown, et al. [1995b], a path was added from performance to satisfaction to represent a conceptual dual effect of the relationship and performance upon supplier contentment. 5.4.2 Description of Research Models This general model provides a framework for the specific research models assessed in this chapter. Specifically, based on the factor analysis of Chapter Four, three unique power strategies were derived from the data including non-mediated (expert, referent), coercive- mediated (coercive, legal legitimate), and reward-mediated (reward). Each of these power bases was tested in their own models. Furthermore, three measures of performance were assessed including that of the supplier, manufacturer, and supply chain. Each of these performance measures also received individual consideration. Based on the three power strategies and three performance measures, a total of nine models (Figure 5.2) were evaluated by the research. These models are presented in Figures 5.3-5.11. The models were given abbreviated names for ease of tracing, and these names are presented in Table 5.4. The assessment of each of these structural models by Anderson and Gerbing's two step approach follows in the next two sections, starting first with the measurement models. NON-MEDIATED SUPPLIER (expen. referent) ^ PERFORMANCEPERFORMANCE N . RELATIONSHIP COERCIVE.MEDIATED ' (commim^t. lîî,X ^ _ MANUFACTURER MANUFACTURER ____ ^_ SATISFACTION (coercive. legal legiumau) ► coopcrauon, cooperation, conflict.coniuci. \n . ^ PERFORMANCE PERFORMANCE ^ SATISFACTION conflict resolution) REWARD-MEDIATED^y ^ SUPPLY CHAIN (reward) ' PERFORMANCE 3xlx3xl=9 models Figure 5.2: Flowchart for Research Models 122 M n-s model with non-mediated power and supplier performance Mh m model with non-mediated power and manufacturer performance ■ Ma-iscvL model with non-mediated power and supply chain performance model with coercive-mediated power and supplier performance M em model with coercive-mediated power and manufacturer performance ■'wSKft model with coercive-mediated power and supply chain performance model with reward-mediated power and supplier performance Mr-m ; model with reward-mediated power and manufacturer performance M r-sc model with reward-mediated power and supply chain performance Table 5.4: Abbreviations for the Nine Research Models XU XM xw: X h Xto X7t X t X lt Xl$ XI X% X3 X4 X17 Xw Xà'XâB Figure 5.3: Mn-s Model 123 xtr'M:x>4W PSAT^^:- Figure 5.4: Mn-m Model m •xi ; :» X3 » Xit : X lt Xl9! X » a y SAT REF Figure 5.5: Mn-sc Model 124 A t. X2£ Xs- X» \ I / 7 " % » a » ». » x»:xi^ Figure 5.6; Mc-s Model ai’ xSiai!»? COERt- X» ; X$d XÜÙ JÙ ^/X a — ■ g v . Xtf ; x a r 70f^ x a a # Figure 5.7: Mc-m Model XT; X m :XlC Xm i i s r Figure 5.8: Mc-sc Model 125 709= Tüoi »t ' xn: \IZ \ 3 i Z i % j z ' #SAW: Figure 5.9: Mr-s Model ^ :X& x% A29 X30 X31 A32 \\.LZ/ M?l?ÈRi^ w / / Figure 5.10: Mr-m Model xa»; XM » f ; :xàî Z \ 1 7 Figure 5.11: Mr-sc Model 126 5 ^ A sse ssm e n t OF M e a su r em en t M o d els The first step of Anderson and Gerbing's two step approach to structural equation modeling involves the use of confirmatory factor analysis to fit the measurement model. The measurement model essentially evaluates the ability of the indicator (measurable) variables to model the latent (unobserved, hypothetical) factors of interest, and the measurement model provides a good fit to the data if the indicator variables effectively measure these latent factors. This section overviews the measurement model fit assessment procedure, including evaluation of construct validity. Each of the nine research models was assessed in SAS following this procedure, and the latter part of the section reviews the actual fit results. 5.5.1 Measurement Model Fit Procedure With the assessment of a measurement model by confirmatory factor model, all latent factors are allowed to covary with one another. Given this characteristic, the nine measurement models for the proposed research are displayed in Figures 5.3 through 5.11. The research utilized SAS to test the fit between the measurement model and the data, and several different measures were employed to assess fit including a chi-square test, goodness of fit indices, significance tests for factor loadings, and analysis of residuals. The nature of such measures will be discussed in more detail next before the actual evaluations of the models are reviewed. Chi-square Test Both confirmatory factor analysis and structural equation modeling analyze the covariance matrix of the data to produce the subsequent fitted model, and a predicted covariance matrix may be reproduced from the model fit output. This predicted 127 covariance matrix should be very similar to the actual covariance matrix if the model does a sufficient job of explaining the actual relationships between the research variables. A chi-square test allows a test of model fit via comparison of the two covariance matrixes. If the model does a sufficient job of explaining the relationships between the research factors, then little difference should exist between the actual and predicted covariance matrixes [Long, 1983b]. Thus, the null hypothesis for the test is given as: Ho: the model fits the data Ha: the model does not fit the data Given the null hypothesis, small values of the chi-square statistic indicate sufficient fit [Long, 1983b]. James et al. [1982] and Marsh, et al. [1988] indicate that with large samples, the chi-square statistic often will be significant even if the model does fit the data well, and thus, most researchers look for a "small" chi-square statistic [Hatcher, 1994]. Likewise, Hatcher [1994] and Medsker, et al. [1994] promote a rule of thumb that in practice, the chi-square statistic should be at most twice the degrees of freedom. Non-normed and Comparative Fit Indices The non-normed fit index (NNFI) [Bentler and Bonett, 1980] and the comparative fit index (CFI) [Bentler, 1989] also provide measures the quality of model fit [Medsker, et al., 1994]. Many researchers also use the normed fit index (NFI), but NNFI and CFI offer better fit indices "as they are less likely to produce biased estimates in small samples," (Hatcher, 1994, p. 291). Both indices assume a value between zero and one with higher values indicating better fit. As a rule of thumb, acceptable values for both the NNFI and CFI are at least .90. 128 T-Tests fo r Factor Loadings The factor loadings for each indicator variable measure the ability of that indicator to effectively measure its underlying latent construct [Hair, et al. 1992]. Similar to the nature of a regression coefficient, a factor loading for each indicator variable upon its factor should thus differ significantly from a value of zero, yielding the hypotheses: Ho: the factor loading equals zero Ha: the factor loading is significantly different from zero This hypothesis is tested with a t-statistic, and thus, large values indicate a significant factor loading. If an indicator variable does not retain a significant loading, that variable should be either dropped or reassigned to another latent variable. Hatcher [1994] warns that the standard errors for the factor loadings should be checked before the loading significance tests as near-zero error values may be indicative of an estimation problem. Furthermore, the significance of factor loadings is an indicator of convergent validity which will be discussed later. Residual Matrix As previously mentioned, if the predicted model accurately describes the actual relationships found within the data, then the predicted model covariance matrix should be relatively similar to the covariance matrix of the actual data. Residuals serve to measure the difference between the two matrixes. The residuals should thus be either zero or near zero if the model provides a good fit to the data. "Normalized residuals over 2 are generally considered large and therefore problematic," [Hatcher, 1994, p. 301], and the distribution of normalized residuals should be symmetrical and centered on zero. 129 Problematic residuals are indicative of variables that do not fit well in the model [Anderson and Gerbing, 1982; Medsker, et al. 1994; Sharma, 1996]. 5.5.2 Modification of the Measurement Model If any of the previously discussed measures of fit prove unacceptable, it may be necessary to modify the measurement model. Two general actions may be taken for modification including adding a path from a latent factor to an indicator variable and dropping an indicator variable all together [Bentler and Chou, 1987; Chou and Bentler, 1990; Medsker, et al. 1994]. "The Lagrange multiplier test estimates the reduction in the model chi-square that would result from freeing a fixed parameter and allowing it to be estimated," [Hatcher, 1988, p. 308]. Essentially the Lagrange multiplier test thus offers insight into the reassignment of an indicator variable to other latent factors [Bollen, 1989; Sharma, 1996]. To be valid, however, such a reassignment should be theoretically justifiable [MacCuUum,, 1986]. On the other hand, the Wald test estimates the change in the model chi-square statistic given an indicator variable is expelled. It is worthy to note that dropping an indictor variable will actually increase the Chi-square statistic but it will also free one degree of freedom thus, possibly improving the overall fit of the model. 5.5.3 Reliability and Validity of Constructs and Indicators As mentioned in previous chapters, the measurement model should exhibit both reliability and validity. The research already established content validity (Chapter 3) through the literature review and pilot testing as well as tested reliability through internal consistency (Chapter 4). As an output, however, the factor analytic model allows for further evaluation of both reliability and validity. This section will detail reliability with 130 respect to the composite factors as well as variance extracted estimates. Furthermore, construct validity in the form of convergent and discriminant validity will be discussed. 5.5.3.1 Composite Reliability Assessment of Cronbach’s alpha [Cronbach, 1951] in the earlier stages of analysis in Chapter 4 provided estimates of internal consistency form of rehability for the research variables. Confirmatory factor analysis also provides evaluation of reliability for the factors as a composite of its indicators [Hair, et al., 1992]. The index for composite reliability may be calculated as [Fomell and Larcker, 1981]: (Z 4 )= Œ 4)"+ S ''‘"-(£,) I where: Lj is the standardized factor loading for indicator i Var (ej) is the error variance for indicator i and equals 1 - (Lj)2 This indice is analogous to Cronbach’s alpha as it measures the internal consistency of the indicator variables for the factors. If the measures are consistent, then the error variances for the indicator should be relatively small in comparison to the standardized loadings. Like alpha, this reliability indicator will thus range from zero to one with larger value indicating stronger reliability. Likewise, minimally acceptable levels of the composite reliability are set at .60 to .70 [Hatcher, 1994]. 131 5.5.3.2 Variance Extracted Estimates Variance extracted estimates represent variance due to an underlying factor versus pure measurement error [Fomell and Larcker, 1981; Hatcher, 1994; Hair, et al., 1992]. The index for variance extracted estimates may be calculated as: 2V+lK"-(E,) I where: Lj is the standardized factor loading for indicator i Var (ej) is the error variance for indicator i and equals 1 - (L^)^ The variance extracted estimates will retain a value between zero and one with larger values being better in that they indicate more of the variance is accounted for by the underlying factor. Fomell and Larcker [1981] suggest a minimally acceptable level of the variance extracted estimate to be .50, but Hatcher [1994] advises that lower levels may be considered acceptable. 5.5.3.3 Construct Validity Content validity was established for the survey via the extensive literature review and subsequent pilot testing of the survey instrument. Confirmatory analysis allows for the assessment of construct validity which involves the appraisal of both convergent and discriminant validity [Bollen, 1989; Droge, 1997]. First, convergent validity [Campbell and Fiske, 1959] measures the extent to which different items measure the same constmct and may be assessed through examination of the t-test for the factor loadings in the 132 measurement model. The factor loadings for all item indicators should load on the same factor, and the t-scores for the loadings should significantly differ from zero. Given these characteristics, convergent validity may be verified. Discriminant validity measures the extent to which items intended for different scales do not measure other constructs [Campbell and Fiske, 1959]. Anderson and Gerbing [1988] (see also Bollen, 1989; Hatcher, 1994] suggest one method to assess discriminant validity in the confidence interval test of the inter-factor correlations. Specifically, if the confidence interval for the correlation between two factors does not include the value of 1.0, discriminant validity is sufficiently demonstrated. 5.5.4 Operationalized Measurement Models The previous section reviewed the extensive procedures necessary to assess the fit of the measurement model. This section operationalizes this procedure in fitting of the measurement component of the nine different research models. Each of the following sub-section discusses the aforementioned fit measures of both the initial and final measurement models as well as addresses indicators of composite reliability, variance extracted estimates, convergent validity, and discriminant validity. The fitting of the first model will be discussed in detail, while the rest of the fittings are merely summarized to avoid redundancy. Table 5.5 exhibits the fit indices of the initial measurement models, and Table 5.6 reveals these fit indices for the final measurement models. 5.5.4.1 Non-mediated Power, Supplier Performance (Mn-s) Measurement Model In a confirmatory factor analytic model, each latent variable is allowed to covary with all other factors. Thus, the basic measurement model conceptualizes five factors including 133 the non-mediated power sources in expert (y4) and referent (y5), relationship (y3), supplier performance (y2a), and supplier satisfaction (y 1). This factor model was run in SAS, yielding a marginally acceptable fitting model (see Mn-s column of Table 5.5). To improve the fit, two indicator variables were dropped including X20, in indicator for referent power (y5), and XI, an indicator of satisfaction. The decision to remove these variables was based on the large associated residuals as well as large values for Wald indices [Hair, et al, 1992; Costner and Schoenberg, 1979; Glymour, 1988]. A minimum of three indicator variables still remained for each latent factor. Suggest^Yatue .Mn-s Mn-m Mn-sc Mc-s Mc-m Mc-sc Mr-s Mr-m Mr-sc Chi-square < 2 * d .f. 331.31 291.95 287.23 350.59 323.96 338.55 212.30 190.97 197.45 (d.f.) 142 142 142 160 160 160 98 98 98 o n > .9 0 0.93 0.94 0.94 0.94 0.94 0.94 0.94 0.95 0.94 NNFI > .9 0 0.91 0.93 0.93 0.93 0.93 0.93 0.93 0.94 0.93 GFI >.90 0.87 0.84 0.88 0.87 0.88 0.87 0.90 0.90 0.90 RMR 0.08 0.08 0.08 0.11 0.11 0.11 0.11 0.11 0.10 T-Tests > 1.96 all sig all sig all sig all sig all sig all sig all sig all sig all sig (loadings) Table 5.5: Fit Indices for Initial Measurement Models The new model without the two dropped indicator variables was run, producing a model with an acceptable fit as shown in the Mn-s column of Table 5.6. The value of the chi- square statistics was less than two times the degrees of freedom, and both NFI and NNFI yielded values greater than the minimally acceptable level of .90. Also, the t-tests for the 134 factor loadings (see Table 5.7) for each of the variables were significant at a .01 level with no near-zero standard errors. The plot of residuals produced a relatively symmetric pattern centered around zero with few large residuals. Each of these fit measures thus indicated an acceptable fit. Figure 5.12 presents the final non-mediated power measurement model. SuggatedyaUte Mh-s Malm M h^ Mc-sc Mr-s Mr-m Mr-sc C h i-sq u are < 2*d.f. 190.29 185.50 179.38 173.70 180.38 194.46 111.48 116.47 116.49 (d .f.) 109 109 109 109 109 109 71 71 71 CFI > .9 0 0.96 0.96 0.97 0.97 0.97 0.96 0.98 0.97 0.97 NNFI > .9 0 0.95 0.95 0.96 0.97 0.96 0.96 0.97 0.96 0.96 GFI > .9 0 0.91 0.92 0.92 0.92 0.92 0.92 0.94 0.93 0.93 RMR 0.07 0.08 0.08 0.10 0.10 0.10 0.08 0.09 0.09 T -T ests > 1.96 all sig all sig all sig all sig all sig all sig all sig all sig all sig (loadings) Table 5.6: Fit Indices for Final Measurement Models Tables 5.7 and 5.8 display information regarding composite reliability, variance extracted estimates, and construct validity. The indicators for composite reliability for each factor surpassed the minimum .70 level, and each of the variance extracted estimates were greater than the suggested .50 benchmark. Furthermore, convergent validity was verified by the significance of the factor loadings for each of the indicator variables. Discriminant validity was also established via examination of the confidence intervals for the inter- factor correlations as none of these intervals included the value of one. 135 C o n ^ c te andIhBrcafi>rs> . Variance StahdAaAd: iRèHa^lÿr; :: E x a cted -Ejrffinate i ^ î Sàtisfàctibh; (Yl)' • : — 0.772 * 0.536 0.857 15.403 0.734 ■ >• j - 1. • JD- 0.613 9.842 0.376 n ' " " ' 0.705 11.779 0.497 SuppU^ferkrnum^^ 0.847 * 0.652 XSà:'. 0.833 14.640 0.694 0.911 16.653 0.830 - X7a; 0.657 10.664 0.432 Rfilationship (Y3) ^ ■' V 0.889 * 0.617 X8 (Conumtment) ■ 0.714 12.153 0.510 X9 (Cooperation) 0.870 16.293 0.757 0.791 14.070 0.626 XII (Conflict) 0.802 14.356 0.644 X12 (Conflict Res.) ' 0.740 12.768 0.547 1 Expert Power (Y4) - 0.762 * 0.527 XI3 0.665 10.696 0.443 XI4 0.912 16.056 0.832 X15 0J53 8.539 0.305 Referent Power (YS) 0.798 ♦ 0.570 XI? ; 0.810 13.865 0.656 XI8 0.699 11.352 0.488 X19 0.752 12J38 0.566 Table 5.7: Measurement Model Properties - Mn-s Factors Estimate Error C l (Low) Cl (High) Contains 1? Y4.Y5 0.7186 0.0487 0.6212 0.816 No Y3, Y4 0.7587 0.0405 0.6777 0.8397 No Y3.Y5 0.8077 0.0367 0.7343 0.8811 No Y2a,Y4 0.6258 0.0518 0.5222 0.7294 No Y2a,Y5 0.6861 0.0486 0.5889 0.7833 No Y2a,Y3 0.5819 0.0522 0.4775 0.6863 No Yl. Y4 0.7728 0.0443 0.6842 0.8614 No Yl, Y5 0.7555 0.0468 0.6619 0.8491 No Yl. Y3 0.861 0.0227 0.8156 0.9064 No Yl. Y2a 0.5586 0.0582 0.4422 0.675 No Table 5.8: Inter-Factor Correlations - Mn-s 136 RER Figure 5.12: Final Measurement Model - Non-mediated Power 5.5.4.2 Remaining Non-mediated (Mn-m and Mn-sc) Measurement Models The fit process for the remaining eight research models is the same as that described above and each of these iterations produced similar fit results. To avoid redundancy, the fit of the remaining eight models are only summarized here in this and the next two sections. To start with the remaining non-mediated power models, the Mn-m and Mn-sc models were fit with a similar process to that of the aforementioned Mn-s model with the single exception of the different performance measures. The fit process for these initial measurement models indicated marginal fit, and to be consistent with the Mn-s model, the X20 (Referent) and XI (Satisfaction) indicator variables were dropped in both Mn-m and Mn-sc models. As shown in the Mn-m and Mn-sc columns of Table 5.6, this resulted in acceptably fitting measurement models based on the fit indices. Tables 5.9-5.12 establish the composite reliability, convergent validity, and discriminant validity for the Mn-m and Mn-sc models. 137 Constru(^and DidicaWrs^^^ ^ ^ j V Variance NonrM ediàii^fower ' - ^ ::~^k SW dardlM d -ReliahOitÿ / Em ^ctW M anufaduretf^Péifbrnüaü:e:'i % : ; f.-con^oatft Estlniate 1 Satisfactfon (Y l)f 0.772 * 0.536 1 X2 ' y.: . - . % ' . 0.857 15.414 0.735 0.613 9.849 0.376 1 0.704 11.764 0.496 M a n iifa tâ ^ Be o r ^ 0.845 * 0.647 - x% : ..z.re? 0.792 13.187 0.626 X6b 0.900 15.521 0.809 X7b : - - - -■ 0.712 11.591 0.507 Relationship (Y3) 0.889 * 0.617 X8 (Commitment) 0.716 12.210 0.513 X9 (Cooperation). 0.868 16.244 0.754 XIO (Tnist) ' ''i- ■ »■ ' *' 0.792 14.086 0.627 ixil (C6nffict)v > ; ’ 0.802 14.341 0.643 X12ÏConfIictRès.I 0.740 12.763 0.547 Expert Power (Y4) 0.762 * 0.528 0.659 10.529 0.434 X14 0.919 16.072 0.845 X15 0.552 8.518 0.305 Referent Power (YiS) 0.801 * 0.574 X17 0.788 13.252 0.621 X18 0.721 11.748 0.519 X19 0.762 12.665 0.581 Table 5.9: Measurement Model Properties - Mn-m r Factors Estimate • ■ Error CKCbw) ICKHigh): Contains!? Y4.Y5 0.7107 0.0493 0.6122 0.8092 No Y3.Y4 0.7550 0.0409 0.6733 0.8367 No Y3.Y5 0.8064 0.0369 0.7326 0.8801 No Y2b, Y4 0.2823 0.0710 0.1403 0.4244 No Y2b, Y5 0.3405 0.0716 0.1973 0.4837 No Y2b, Y3 0.3062 0.0687 0.1688 0.4435 No Y1.Y4 0.7686 0.0446 0.6795 0.8578 No Y1.Y5 0.7563 0.0468 0.6627 0.8499 No Y1.Y3 0.8612 0.0227 0.8157 0.9066 No Yl. Y2b 0.2708 0.0739 0.1230 0.4185 No Table 5.10: Inter-Factor Correlations - Mn-m 138 % r i^ 1 îstahdÉÈdl^ g m s Î R ^ û i ^ V Extnmtcd • «T-composii&j ,;Est&nafe 1 0.772 * 0.536 1 X2 0.859 15.452 0.737 1 ~ ' ■ . . V' ; ..." 0.615 9.885 0.378 I: 0.701 11.707 0.492 SnppIfiraainPerft^^ 0.854 * 0.662 X5C-, \ 0.821 13.995 0.674 • X 6 c : ■ 0.861 14.886 0.740 • •* - - - ' •. 1 < - - •'. 0.756 12.590 0.571 Reladon^p (Y3) ; . - ; 0.889 * 0.617 X8(Commitment) x 0.717 12.218 0J14 X9 (Coopeiatiori) v^ : : L v.. 0.868 16.234 0.753 Xiorn#^ : y ; 0.793 14.106 0.628 XlL(ConfIict)- 0.801 14.311 0.641 X12 (Conflict Res.) 0.741 12.792 0.549 Expert Power (Y4) 0.762 * 0.527 ■ X13 0.662 10.605 0.439 X14 0.915 16.000 0.837 X15 0.553 8.528 0.306 Referent Power (Y5) 0.801 * 0.574 X17 0.790 13.295 0.624 X18 0.723 11.797 0.523 X19 0.758 12J68 0.574 Table 5.11: Measurement Model Properties - Mn-sc Factors Estimate Error - C l (Low); d (H ig h ) Contains 1? Y4.Y5 0.7132 0.0492 0.6148 0.8116 No Y3, Y4 0.7574 0.0407 0.6760 0.8389 No Y3. Y5 0.8063 0.0369 0.7325 0.8801 No Y2c, Y4 0.3410 0.0695 0.2020 0.4799 No Y2c, Y5 0.3584 0.0713 0.2158 0.5009 No Y2c, Y3 0.3417 0.0676 0.2064 0.4770 No Yl. Y4 0.7709 0.0445 0.6820 0.8598 No Y1.Y5 0.7556 0.0468 0.6619 0.8493 No Y1.Y3 0.8609 0.0227 0.8155 0.9064 No Yl. Y2c 0.2620 0.0747 0.1126 0.4113 No Table 5.12: Inter-Factor Correlations - Mn-sc 139 5.5.4.3 Coercive-mediated (Mc-s, Mc-m, and Mc-sc) Measurement Models The coercive-mediated power models (Mc-s, Mc-m, and Mc-sc) conceptualize the coercive (y6) and legal legitimate (y7) power bases with the relationship (y3), performance (y2a, y2b, or y2c), and satisfaction (yl) variables. These three models differ only with respect to the performance measure of either supplier, manufacturer, and supply chain performance. Given the fit indices from initial analysis of these measurement models (Table 5.5), the indicators X24 (Coercive), X25 (Legal Legitimate), and XI (satisfaction) were dropped based on the modification indices and residuals. The resulting models produced acceptable fit with respect to values of the chi-square, NFI, and NNFI statistics (Table 5.6). The factor loadings for the indicator variables as well as the plot of residuals also indicated an acceptable fit. As shown in Tables 5.13 though 5.18, composite reliabilities, variance extracted estimates, convergent validities, and discriminant validities were shown to be satisfactory for all three models. Thus, the final measurement models for coercive-mediated power (Figure 5.13) were deemed acceptable. XZt XZZ XD x& XTi: - y X26 xa* SAT LLEG Figure 5.13: Final Measurement Model - Coercive-mediated Power 140 CoDsifucte and.indiiàtprs /-Variance I CoérdvéiM ei^aUii'Fover ' -M f . Eirtiacted ;-f'Estima Satfafiwtibn (XI) V ; 0.772 * 0.536 .V: -r 0.859 15.433 0.739 0.611 9.780 0.373 0.704 11.740 0.496 1 0.848* 0.654 0.842 14.675 0.709 1 X6a - ' ■ : - 0.897 16.028 0.805 'X7a-- / J.T-- 0.669 10.865 0.448 Rdationsliip (X3) > 0.888 * 0.615 X8 (Commitment) 0.695 11.728 0.483 X9 (Cooperation) 0.881 16.654 0.777 XIO (Trust) ; 0.773 13.606 0.598 xir^iiflicO-:rr ^ 0.805 14.451 0.649 XI2i(C5nfllctRes.) - ^ W 0.755 13.153 0.570 Coerdve Power (Y6) 0.842 * 0.646 -X 22 :- " \.f ' . 0.615 9.808 0.378 X23 : -:>v ^.r: 0.891 16.062 0.794 X24 ' 0.875 15.638 0.765 Legal Legitimate Power (XT) r 0.898 * 0.747 3C25 ' / : ^ - ; 0.781 13.685 0.610 X26 0.922 17.475 0.849 X27 0.885 16.396 0.783 Table 5.13: Measurement Model Properties - Mc-s Factors ' ' - Estimate : Eri»r ' C l (Low) Cl (High) Contains 1? Y7. Y6 0.4670 0.0593 0.3484 0.5856 No Y3.Y7 -0.5052 0.0562 -0.6176 -0.3928 No Y3. Y6 -0.6303 0.0483 -0.7269 -0.5337 No Y2a,Y7 -0.2538 0.0696 -0.3929 -0.1147 No Y2a,Y6 -0.2161 0.0720 -0.3600 -0.0722 No Y2a, Y3 0.5844 0.0522 0.4800 0.6888 No Yl. Y7 -0.3828 0.0673 -0.5173 -0.2483 No Y1.Y6 -0.5891 0.0560 -0.7010 -0.4772 No Y1.Y3 0.8580 0.0228 0.8123 0.9037 No Yl. Y2a 0.5589 0.0584 0.4421 0.6757 No Table 5.14 : Inter-Factor Correlations - Mc-s 141 Constructs and fiidicatqrS' Variance Stan&nilzed Rettabfllty Extracted M à n ü fiu :^ : Loadfcgv E - i f c E&timate 0.772 * 0.536 0.859 15.403 0.737 0.614 9.833 0.377 m m 0.702 11.695 0.493 MaiaofâttDKr3Péifornûmce:(Y2b) 0.845* 0.648 0.795 13.258 0.631 ' : 0.896 15.453 0.802 X7b : ■;■■■: 0.714 11.634 0.510 0.889 * 0.616 I X8 (Gbmnutment) ? 0.703 11.895 0.494 X9(Cooperatibn). . V ^ 0.875 16.448 0.766 XlOCTnist): : : : ? 0.776 13.671 0.602 XllXConflict) ' 0.807 14.477 0.651 XI2fConblctRe&W r 0.753 13.084 0.567 Coercive Power ^ ^ u ^ 0.876 * 0.705 0.736 12.514 0.542 X23 -• i: 0.939 17.777 0.882 X24 - r : 0.831 14.789 0.690 Legal In tim a te Power (Y7) 0.898 * 0.747 1 X25 0.781 13.670 0.609 X26 0.922 17.500 0.851 1 X27 0.884 16.385 0.782 s Table 5.15: Measurement Model Properties - Mc-m Factors • Estimate Error Cl (Low)' GICHlgh) Contains 1? Y7.Y6 0.4670 0.0593 0.3484 0.5856 No Y3. Y7 -0.5052 0.0562 -0.6176 -0.3928 No Y3. Y6 -0.6303 0.0483 -0.7269 -0.5337 No Y2b, Y7 -0.2538 0.0696 -0.3929 -0.1147 No Y2b, Y6 -0.2161 0.0720 -0.3600 -0.0722 No Y2b. Y3 0.5844 0.0522 0.4800 0.6888 No Yl. Y7 -0.3828 0.0673 -0.5173 -0.2483 No Yl. Y6 -0.5891 0.0560 -0.7010 -0.4772 No Yl. Y3 0.8580 0.0228 0.8123 0.9037 No Y l, Y2b 0.5589 0.0584 0.4421 0.6757 No Table 5.16: Inter-Factor Correlations - Mc-m 142 Constructs and Indicators : VaHahbe CoerciverM ei^d . . . ' . • Stàndàrdized! ;Reilabm^ Bctracted 'Estimate Satfefactfon Cri) - - : 0.772 * 0.536 0.860 15.432 0.739 :-.-r 0.614 9.845 0.377 0.701 11.656 0.491 SuppIj^'Chaih Peiibnnan^^ 0.855 * 0.663 0.820 13.967 0.672 0.858 14.816 0.735 0.762 12.716 0.580 RelattonsBp 0.889 * 0.616 0.703 11.895 0.494 X9 CCooperation) - ! ’iX^ c 0.875 16.448 0.766 XIO (Rust): # V r'AxxxV: 0.776 13.671 0.602 0.807 14.477 0.651 XïlïCohflictRes:) # : 0.753 13.084 0.567 Coercive Power (Y6): 0.876 * 0.705 ' -.V- ' •; ;v:--- - % : : ■ 0.735 12.499 0.541 X23 0.941 17.830 0.885 X24 X '"^x: 0.830 14.772 0.689 LegalT^gitimate PowW (Y7) : 0.887 * 0.724 X25_: r - : .. 0.781 13.675 0.610 X26 ., . ' 0.922 17.493 0.850 X27 0.844 16.386 0.713 1 Table 5.17: Measurement Model Properties - Mc-sc 1 . Factors . ‘ Estimate : Error Cl (Low) C l (High) Contains 1? Y4. Y5 0.4544 0.0589 0.3367 03721 No Y3, Y4 -0.5054 0.0563 -0.6179 -0.3929 No Y3.Y5 -0J993 0.0497 -0.6986 -0.5000 No Y2c, Y4 -0.1038 0.0739 -0.2515 0.0440 No Y2c, Y5 -0.0250 0.0745 -0.1739 0.1240 No Y2c, Y3 0.3389 0.0678 0.2034 0.4744 No Yl. Y4 -0.3834 0.0672 -03179 -0.2489 No Y1.Y5 -0.5658 0.0564 -0.6786 -0.4531 No Y1.Y3 0.8597 0.0228 0.8140 0.9053 No Yl. Y2c 0.2601 0.0747 0.1107 0.4096 No Table 5.18: Inter-Factor Correlations - Mc-sc 143 5.5.4A Reward-mediated (Mr-s, Mr-m, and Mr-sc) Models Earlier exploratory factor analysis revealed that reward power source did not align well with either mediated or coercive-mediated power bases. Thus, reward-mediated power is represented in its own set of models (Mr-s, Mr-m, and Mr-sc) which differ in respect to performance measures. The fit of the initial models (Table 5.5) suggested removal of the indicator variables X31 and XI, yielding final measurement models of acceptable fit as revealed in Table 5.6. Once again, composite reliabilities, variance extracted, convergent validities, and discriminant validities yielded no significant issues (Tables 5.19-5.25). Thus, the final measurement models for reward-mediated power (Table 5.14) were deemed acceptable. xiiXfic: XÆ -J X » ; X »J X3K X32i X2 ; X* ^ S A U Figure 5.14: Final Measurement Model - Reward-mediated Power 144 Cônstmc(sànd6dlcat6ty ; Variance I S g m d b n l& VÉxiiaiitéd : ■Satisfei^biroa)r::^^^ 0.772 * 0.536 0.859 15.445 0.739 0.606 9.701 0.368 0.708 11.832 0.501 0.848 * 0.653 0.838 14.546 0.702 0.903 16.128 0.815 0.666 10.795 0.444 0.888 * 0.616 X8 (Cbmmitmfint) ' 0.705 11.918 0.497 X9 (Coojp^on) : 0.883 16.654 0.780 ijaOCTtkt)^:^ 0.781 13.760 0.610 XII (Conflict) ; ,' 0.799 14.237 0.639 X12fOcinfirctRes.^ 0.743 12.820 0.552 Reward Power (Y4) 0.756 * 0.509 X29 0.644 9.342 0.414 X30 0.777 11.178 0.604 X32 0.713 10.312 0.509 1 Table 5.19: Measurement Model Properties - Mr-s Factors Estimate Error CKLow) C l (High) Contains 1? Y3.Y4 0.1692 0.0779 0.0134 0.3250 No Y2a,Y4 0.0858 0.0801 -0.0743 0.2460 No Y2a,Y3 0.5849 0.0521 0.4807 0.6890 No Y l. Y4 0.2729 0.0792 0.1145 0.4313 No Yl. Y3 0.8575 0.0228 0.8118 0.9032 No Yl. Y2a 0.5592 0.0583 0.4426 0.6757 No Table 5.20: Inter-Factor Correlations - Mr-s 145 Constructs ànÆIhdIcators v Variance 1 S t É ( Ê r ^ ReUabfli^ ; Attracted * -«'■. 1. : Estimate 0.772 * 0.536 0.860 15.443 0.739 0.611 9.784 0.373 0.704 11.738 0.495 M m iuB ëtuw 0.854 * 0.662 0.818 13.902 0.669 0.861 14.870 0.742 ■ :X7b z ;/ , " 0.759 12.637 0.576 Relationship (Y3) z ^ 0.889 * 0.616 X8 (Cboumtmeht) 0.711 12.040 0.505 X9 iCCooperation) ;- 0.876 16.427 0.768 XIO (Trust) 0.784 13.841 0.615 X I1 (Conflict) ' 0.801 14.204 0.641 X12(GonfllctRes.V- 0.744 12.818 0.553 Reward Power CY4) 0.756 * 0.509 X29 0.645 9.364 0.416 X30 , : 0.778 11.190 0.605 X32 - : ■ 1 0.711 10.279 0.505 Table 5.21; Measurement Model Properties - Mr-m Factors Estimate Error C l (Low). CL(Hlgh) Cohtainsl? Y3. Y4 0.1713 0.0779 0.0155 0.3272 No Y2b, Y4 0.0742 0.0812 -0.0881 0.2365 No Y2b, Y3 0.3409 0.0676 0.2057 0.4762 No Yl. Y4 0.2725 0.0792 0.1141 0.4309 No Y l. Y3 0.8592 0.0228 0.8136 0.9048 No Yl. Y2b 0.2623 0.0746 0.1131 0.4115 No Table 5.22: Inter-Factor Correlations - Mr-m 146 Constract? ànÆIndI&tdrs ■. Variance I Reflabiflty .Extracted Supply~Chtmt.P€a^ormdhce , m â a ^ g . *'- comj^tc - EstiËiate .-T ■■■ Satisfactfoo'CYl) • 0.772 * 0.536 0.860 15.443 0.739 X3',, . : v?.vr.:',I' 0.611 9.784 0.373 0.704 11.738 0.495 Supply Chai&P^roriiiM^^ 0.854 * 0.662 0.818 13.902 0.669 0.861 14.870 0.742 X7c- ' , <- : :■' 0.759 12.637 0.576 Reladonship (Y3) 0.889 * 0.616 X8 ((^inmitmBnt) ' - - i: 0.711 12.040 0.505 X 9 (ebop«iatibn) 0.876 16.427 0.768 XlOCTrustX , , 0.784 13.841 0.615 X II (Conflict), 0.801 14.204 0.641 XlZrConflictRes.) 0.744 12.818 0.553 Reward Power (Y4) . 0.756 * 0.509 - X29 0.645 9.364 0.416 X30 ; 0.778 11.190 0.605 X32 0.711 10.279 0.505 Table 5.23: Measurement Model Properties - Mr-sc - Factors Estimate Error ’ a (Low) : C l (High) Contains 1? Y3. Y4 0.1713 0.0779 0.0155 0.3272 No Y2b, Y4 0.0742 0.0812 -0.0881 0.2365 No Y2b, Y3 0.3409 0.0676 0.2057 0.4762 No Yl. Y4 0.2725 0.0792 0.1141 0.4309 No Yl. Y3 0.8592 0.0228 0.8136 0.9048 No Y l. Y2b 0.2623 0.0746 0.1131 0.4115 No Table 5.24: Inter-Factor Correlations - Mr-sc 147 5.6 ASSESSMENT OF STRUCTURAL MODELS As the first step in Anderson and Gerbing's two step approach to structural equation modeling, the previous section offered insight for testing the measurement model which examines the ability of the indicator variables to predict the latent factors. Once the latent factors are found to be measurable, Anderson and Gerbing's second step anedyzes the structural model which is simply an adaptation of measurement model in which causal relationships are specified between some of the latent factors [Sharma, 1996; Hatcher, 1992; Hair, et al., 1992]. Assessment of structural model fit analyzes the significance of these conceptualized causal paths, and allows for the testing of research hypotheses. The general procedure for assessing structural model fit is discussed below then the fit results for the nine stmctural models are reviewed. 5.6.1 Structural Model Fit Procedure Similar to the measurement model, several measures were utilized to assess structural model fit including analysis of the chi-square test, fit indices, significance tests for factor loadings and paths, residuals, parsimony indices, relative fit indices, and relative parsimony indices. These fit measures be reviewed in further detail below. Many of such measures have been discussed in detail with the measurement modeling, and the reader may refer to Section 5.5.1 for references and insight. Chi-square Test The orientation of the chi-square test with the structural model is the same as that of the measurement model. Smaller values of the text statistics indicate a better fit, but significance of the chi-square is not sufficient grounds for fit rejection, however. As the 148 previous rule-of-thumb was suggested, the chi-square test should be no more than twice the degrees of freedom given an appropriate fit of the model. Non-normed and Comparative Fit Indices Both the comparative fit index (CFI) and the non-normed fit index (NNFI) offer further measures of model fit. The CFI and the NNFI may range between zero and one, and as before, it is suggested that both should be at least .90 in value. Significance Tests for Factor Loadings and Path C o^cients Like the measurement model, the factor loadings of the indicator variables should be significantly different from zero. In the structural model, the specified causal paths between the factors should also retain significant values. Residuals As in the measurement model, the plot of the normalized residuals should retain a symmetric distribution centered around zero, and there should be relatively few outliers. Any outliers may be interpreted with help from modification indices. Parsimony Ratio and Parsimonious Normed Fit Index "With other factors held constant, the most desirable theoretical model is the most parsimonious," [Hatcher, 1994, p. 382] in that a simplistic model is desired over a complicated model. Adding more variables may increase the fit of the data to the model, but such action harms the parsimony of the model. Thus, tradeoffs between fit and parsimony must be examined carefully [Medsker, et al., 1994]. Two measures exist to examine parsimony level. First, James et al. [1982] proposes the parsimony ratio (PR) as 149 PR = ^ dfo where: is the degrees of freedom for the model of interest dfo is the degrees of fteedom for the null model The null model does not predict any relationship between the research variables and is thus the most parsimonious model possible. Thus, PR wül take on a value between zero and one with higher values indicative of a more parsimonious model. Although PR is not necessarily meaningful on its own, it may be used to choose between competing versions of the same model. James et al. [1982] also offer the parsimonious fit index or parsimonious normed-fit index (PNFI) which is simply the PR multiplied by the normed fit index (NFI). Netemeyer et al. [1990] and Williams and Hazur [1986] suggest a minimally acceptable level of .60 for PNFI while Mulaik et al. [1989] suggest a minimal level of .50. PNFI = {PRXNFI) Relative Normed-Fit Index One primary concern with the previously discussed indices is that they simultaneously examine both the measurement and structural portions of the proposed model. Thus, strong values for the NNFI and CFI may result more from the measurement model rather than the structural model. To obtain a better idea about the fit of the isolated structural 150 model, Mulaik et al. [1989] offers the relative normed-fit index (RNFI) [Medkser, et al., 1994] which is calculated as: F - F RNFI= “— " j~ 4 fj where; Fu is the chi-square value for the uncorrelated variables model Fj is the chi-square value for the model of interest Fm is the chi-square value for the measure model dfj is the degrees of freedom for the model of interest dfjn is the degrees of freedom for the measurement model While the model of interest and the measurement model have already been discussed, the uncorrelated variables model has yet to be addressed. This uncorrelated model eliminates any relationship between latent factors, predicting that all factors are uncorrelated. The RNFI will assume a value between zero and one, and it may be interpreted similarly to the NFI with the value of .90 being minimally acceptable. Relative Parsimony and Relative Parsimonious Fit Index The relative parsimony ratio (RPR) [Mulaik et al., 1989] examines the parsimony of the structural portion of the model and is calculated as follows: RPR = ^ ~ ~” dfo-df„ 151 where; is the degrees of freedom for the model of interest dfm is the degrees of freedom for the measurement model dfy is the degrees of freedom for the uncorrelated variables model RPR eliminates the influence of the degrees of freedom from the measurement model [Medsker, et al., 1994], and the ratio will vary from 0 to 1 with higher values being more desirable. While the RPR is not necessarily useful by itself, it does allow for comparison of two models in similar fashion as the PR. The relative parsimonious-fit index (RPFI) [Mulaik et al., 1989] combines the information found in the RNFI and the RPR by multiplying the two together [Medsker, et al., 1994]. Like the RPR, the RPFI will aid the researcher in choosing between two models as higher values of the RPFI are more desirable. RPFI = {RPRXRNFI) 5.6.1.1 Sample Size Given the above review of fit indices for structural equation modeling, it is important to discuss sample size. Most researchers agree that at least five observations are needed for each parameter of the model [Hair, et al., 1992, Hatcher, 1994]. MacCullum, et al. [1992] suggests that large sample sizes (greater than 800) are necessary if many modifications will be made to the research model. Other sources report, however, that large sample sizes (greater than 400) increase the sensitivity of fit indices and thus cause all indices to 152 insinuate poor fit. Hair, et al. [1992] report a suggest that the sample size range between 100 and 200 observations. In the research in this dissertation, data collection efforts led to 229 observations. This number aligned well with minimum sample sizes suggested by the literature base and allowed for at least five observations for each parameter in the research models. Few modifications were also made to the research models to further maintain the respectability of this sample size. 5.6.2 Modification of the Structural Model The above section discusses the fit assessment procedure for the structural model. If the indices suggest a poor fit, theoretically justified modifications may then be made to the model to reveal the true relationship between the variables. Such modification is similar to that of the measurement model and involves the Wald test (to examine path elimination) as well as the Lagrange multiplier test (to examine path additions). The proposed research will take every step to reveal the true underlying theoretical model, but as will be discussed below, great care must be taken not to "over-fit" the model to the data. 5.6.2.I Problems of Model Fitting One major concern with model modification involves "data snooping" wherein modification leads to a model that capitalizes on the incidental fit of the data to the model. Thus, the particular data may happen to fit the model, but the model may not be generalized to other data sets. Hatcher [1994] offers several recommendations for avoiding this so-called "data snooping" including limiting the number of modifications. 153 requiring theoretical justification of changes, considering of alternate a priori models, and use of large samples. 5.6,3 Operationalized Structural Models Given the above discussions of structural equation modeling procedures, this section will review the actual structural models operationalized by the research. The generic structural model for the research conceptualizes the relationship (y3) between the buyer and supplier the power being influenced by power (whether it be non-mediated, coercive- mediated, or reward-mediated). This power-affected relationship then affects performance (y2) and satisfaction (yl). It is also hypothesized that this satisfaction is also influence by performance. As an example. Figure 5.15 represents one such model for non-mediated power (expert, y4 and referent, y5) and supplier performance (y2a). Xl3 Xl4, Xl3 M r 'xi:- xsh Xio! XIÏ: ;Xtt > XT/-t.». * Figure 5.15: Generalized Structural Model - Non-mediated Power An SEM model consists of both independent and dependent variables, but a dependent variable within one equation may become an independent variable within another 154 equation. Structural equations look similar to that of regression with the addition of inter related dependent variables. Thus, the current example model may be represented by the equations and the fitting of the structural model provides estimation of the y and P paths. Y3 = Y34 Y4 + 735 Y5 + ^3 Y2 = 323 Y3 + C2 Yl = 3 1 2 Y2 + 3i3 Y3 + ; i where: Yi represents the latent factor i yji is the causal effect of exogenous variable i upon endogenous variable j Pji is the causal effect of endogenous variable i upon endogenous variable j Çj represents the error term for the equation for endogenous variable j 5.6.3.1 Non-mediated, Supplier Performance (Mn-s) Structural Model This section initiates the actual fitting of the structural models and continues in the next two sections. In general, the stmctural model will closely resemble the measurement model except for two changes. First, the model now specifies the conceptualized causal paths between some of the factors, and second, only the independent variables are permitted to covary. Given the establish measurement model for Mn-s in section 5.5.4.1, the corresponding stmctural model was mn in SAS. Table 5.25 displays the fit indices associated with the final stmctural model. The value of the chi-square statistic was less than twice the degrees of freedom, and both the NFI and NNFI far surpassed the minimum suggested .90 level. Also, the t-tests for both the factor 155 loadings and path coefficients yielded significance with no near-zero standard errors. Furthermore, the plot of residuals was symmetric, centered about zero, and retained relatively few extreme values. Finally, the PNFI eclipsed .60 while the RNFI surpassed the suggested .90 value, indicating a parsimonious model. In full, the measures of fit suggested a relatively strong fit of the model to the data for the non-mediated power, supplier performance model. Suggested Yalm Mh-s Mn-m. Mn-sc- Mc^ Mc-m Mc-sc Mr-s Mr-m Mr-sc Chi-square < 2*d.f. 221.64 190.90 185.45 187.40 195.23 209.98 116.98 121.75 121.61 (d.f.) 113 113 113 113 113 113 73 73 73 CFI > .9 0 0.95 0.96 0.97 0.97 0.97 0.96 0.97 0.97 0.97 NNFI > .9 0 0.94 0.96 0.96 0.96 0.96 0.95 0.97 0.96 0.96 GFI > .9 0 0.90 0.91 0.92 0.92 0.91 0.91 0.93 0.93 0.93 RMR 0.08 0.08 0.08 0.11 0.12 0.12 0.08 0.09 0.09 R-sq all sig all sig all sig all sig ail sig all sig all sig all sig all sig (endog) Parsimony PR 0.83 0.83 0.83 0.83 0.83 0.83 0.80 0.80 0.80 PNFI > .60 0.79 0.76 0.76 0.77 0.77 0.76 0.75 0.74 0.74 RNFI > .9 0 0.96 1.00 1.00 0.98 0.97 0.97 0.99 0.99 0.99 RPR 0.40 0.40 0.40 0.40 0.40 0.40 0.33 0.33 0.33 RPFI 0.38 0.40 0.40 0.39 0.39 0.39 0.33 0.33 0.33 Table 5.25; Fit Indices for Final Structural Models Figure 5.16 summarizes the results for the model path coefficients as well as the fit indices. Given this Mn-s model, the path equations were found to be: 156 XlS Xl« XlS Xb XU: Xli 3;9gg:%j;gi ^.0904 JÜ iü Xf X17Î X » , X % 0.3338 Y, 0.4311 Yj r-squared = .75 (4.67) (5.83) (t-value) 0.8348 Yj r-squared = .40 (9.45) (t-value) 1.5267 Yj 0.0904 Yg, r-squared = .75 (13.51) (-1.30) (t-value) Figure 5.16: Final Structural Model - Mn-s The significance of the t-statistics in the first equation indicates that both expert (y4) and referent (y5) significantly affect the relationship factor (y2), showing initial support for the basic premise of the research that power will affect buyer-supplier relationships. In turn, this power-affected relationship (y2) was found to significantly influence supplier performance (y2a) as shown in the second equation. Finally, with regard to satisfaction, although the relationship variable (y3) was found to have a significant effect upon satisfaction (yl), no relationship was found between supplier performance (y2a) and satisfaction (yl) as shown in the third equation. This implies that the quality of the supplier's relationship with the manufacturer is more critical to their satisfaction than their actual performance. The above inferences relate directly to the hypothesis testing which will be discussed in further detail in Chapter Six. 157 5.6.3.2 Remaining Non-mediated (Mn-m, Mn-sc) Structural Models The previous section discussed the fit assessment of the non-mediated, supplier performance (Mn-s) structural model, and this section will analyze the fit of the remaining non-mediated power models (Mn-m and Mn-sc). Because the fit procedure for these models is identical to the one (Mn-s) above, the results are merely summarized. The results for the fit indices, as displayed in Table 5.25, revealed that the two remaining non-mediated power models provided a strong fit to the data. The final Mn-m structural model is displayed in Figure 5.17 while the final Mn-sc model is exhibited in Figure 5.18. Both models align with the results of the Mn-s model in that the paths between the power bases and the relationship factor prove significant in a positive direction and the two power bases tend to significantly covary with one another. The paths between relationship and performance (manufacturer and supply chain) were also significant as were the paths between relationship and satisfaction. Like the Mn-s model, the paths between performance and satisfaction were found not to be significant. The results firom the three non-mediated power models discussed in this and the previous section reveal evidence for the positive effective of non-mediated power sources upon the buyer-supplier relationship. The subsequent power-affected relationship was found to significantly influence performance of the supplier, manufacturer, and supply chain as well, offering confirmation of the value of fostering effective relationships. These findings and subsequent insight will be discussed in more depth in Chapter 6. 158 Xœ Xu- Xu w X9k. X ^ X ik \JT ^ •“ ■•» , ^ w ' <.- -ti-i . : ■ ^ te . .-f. -.0523 teteSte M768 3267 ••• 0.3256 Y4 0.4267 Y5 r-squared = .72 (4.50) (5.66) (t-value) ‘2b 0.4171 Y3 r-squared = .10 (4.45) (t-value) 1.4768 Y 3 0.0523 Yjb r-squared = .93 (15.5) (-0.94) (t-value) Figure 5.17: Final Structural Model - Mn-m X u Xl« x h \.L/ X*^ xte Xw; . Y4 V :-r" a s sssipr? . .76oq«» XII XU XI» 15126 • « 3 2 3 0 ••• 0.3300 Y4 0.4230 Y5 r-squared = .72 (4.56) (5.63) (t-value) ‘ 2c 0.4318 Y 3 r-squared = .10 (4.94) (t-value) 1.5126 Y3 0.1237 Y%. r-squared = .93 (15J2) (-2.00) (t-value) Figure 5.18: Final Structural Model - Mn-sc 159 5.6.33 Coercive-mediated (Mes, Mc-m, Mc-sc) Structural Models The three coercive-mediated power models (Mc-s, Mc-s, Ms-sc) were fit using an identical procedure as above. The fit indices (Table 5.25) verify an acceptable fit for all three models, and the results for each specific model are displayed in Figures 5.19, 5.20, and 5.21. Both coercive power and legal legitimate power were found to retain significant negative effects upon the relationship with legal legitimate seeming to have a consistently stronger effect. Furthermore, the covariances between coercive and legal legitimate power were found to be positive significant. The remaining paths of the model align with previous results in that the paths between relationship and performance as well as relationship and satisfaction were found to be positively significant while the performance-satisfaction models were found to be insignificant. In all, the fit of the coercive-power models reveal that the coercive and legal legitimate power bases tend to have a detrimental effect upon the buyer-supplier relationship environment. This point will be further deliberated in Chapter 6. 5.Ô.3.4 Reward-mediated (Mr-s, Mr-m, Mr-sc) Structural Models The results for the fitting of the reward-mediated power models (Mr-s, Mr-m, Mr-sc) are displayed in Figure 5.22, 5.23, and 5.24. Once again, support was found to indicate acceptable model fit. The paths between reward power and relationship maintained a positive significance, but the p-value for the paths retained a value slightly greater than .01. This indicates that though significant at some levels, the reward-relationship paths were not as strong as the power-relationship paths found in the non-mediated and coercive-mediated models. In the previous models, the relationship-performance and relationship satisfaction paths were found to retain positive significance, and the performance-satisfaction paths were found to be insignificant. 160 XM X tt X23 XS» xb XI» SfERR XI X r %» 1.095m ••• I/WW8 J417 ••• -0.1585 Yg 0.2417 Yy r-squared = .40 (-3.92) (-6.62) (t-value) Y2. 0.7446 Y3 r-squared = .33 (8.42) (t-value) 1.5008 Y 3 0.0212 Yy. r-squared = .91 (13.53) (0.32) (t-value) Figure 5.19: Final Structural Model - Mc-s XÙ xn! xa \ r z f CpER0f^54g ^ à m 1.0934 1.4282 J4 3 6 '* * -0.1548 Yg 0.2436 Yy r-squared = .40 (-3.86) (-6.69) (t-value) ‘2b 0.3688 Y 3 r-squared = .08 (3.95) (t-value) 1.4282 Y 3 0.0076 Y%, r-squared = .91 (15.37) (-0.13) (t-value) Figure 5.20: Final Structural Model - Mc-m 161 Xu X» iXfj I.09i JS25 — \ 1 7 1/K39 .2417 ••• -0.1556 Yg 0.2417 Yy r-squared = .40 (-3.87) (-6.64) (t-value) ^2c 0.3825 Y 3 r-squared = .11 (4.40) (t-value) 1.4539 Y 3 0.0681 Y%. r-squared = .92 (15.38) (-1.09) (t-value) Figure 5.21: Final Structural Model - Mc-sc X5k XÀ XT. XX X9 XM X li X rt XX » x« ,1544 77613 PEW 1.4359 CSAT- 0.1544 Yg r-squared = .04 (2.49) (t-value) ^2a 0.7613 Y3 r-squared = .34 (8.55) (t-value) 1.4539 Y 3 0.0036 Y2, r-squared = .92 (13.49) (-0.05) (t-value) Figure 5.22: Final Structural Model - Mr-s 162 Vt r- j»g X»: X3ft XK; m m m 3921 ••• m m 1.4562 « • 0.1555 Yg r-squared = .04 (232) (t-value) '2b 0.3921 Y 3 r-squared = .09 (4.17) (t-value) 1.4562 Y3 0.0307 Y25 r-squared = .93 (15.35) (-034) (t-value) Figure 5.23: Final Structural Model - Mr-m X» X3D- X S ' Y f .1569 •• REW% * ^ ü f e s 0.1569 Yg r-squared = .04 (235) (t-value) '2 c 0.4054 Y3 r-squared = .12 (4.63) (t-value) 1.4864 Y 3 0.0952 Ygg r-squared = .93 (15.36) (-1^2) (t-value) Figure 5.24: Final Structural Model - Mr-sc 163 5.6 S ummary Chapter 5 has served to reveal evidence supporting the research suppositions of power's effect upon the nature of the relationship between buyer and supplier. In all, nine structural models were assessed using a two-step approach. Significant preliminary findings may be summarized as follows: • Non-mediated power sources (expert and referent) were found to retain significant effects upon the buyer-supplier relationship. • Coercive-mediated power sources (coercive and legal legitimate) maintained significant negative effects upon the relationship. • Reward-mediated power was found to have a positive effect upon the buyer-supplier relationship, but this significance was not found to be as strong as that of the other power effects. • In all models, the power-affected buyer-supplier relationship was found to have a significant positive effect upon both performance and satisfaction. The paths between performance and satisfaction, however, were consistently found to be insignificant. The modeling and subsequent findings presented in this chapter directly translate to the testing of the research hypotheses. Chapter 6 will review the hypothesis testing and offer critical insight for practitioners and researchers alike. 164 CHAPTER 6 RESEARCH FINDINGS AND INSIGHT 6.1 Introduction The measurement and structural models in Chapter 5 revealed that the research models are valid for this study. Given the results of such modeling, the research hypotheses (Chapter 2) will be evaluated through path significance in this chapter. The results of the hypothesis tests are reviewed and interpreted, yielding insight into influences of power on supply chain relationships. These results wül hopefully reveal the importance of effective power management as an approach to enhance the integration of the supply chain, leading to critical awareness of supply chain power influences for both practitioners and researchers. 6.2 C ritical Researc h Questions and M odel Before the hypothesis tests and subsequent results are discussed, the critical research questions and model will be reviewed to re-establish the foundations and potential contributions of the research. To begin, a recent stream of logistics literature exists to analyze integration of the supply chain, contending that supply chain members can coordinate planning and processes to improve the performance of the entire supply chain. 165 Such supply chain management should enhance customer satisfaction and generate greater profitability throughout the chain, thus offering significant benefits for buyers and suppliers. Inter-firm power influences, however, may serve to upset the relational environment within the chain, posing a significant barrier to effective supply chain management. Little research exists, though, to analyze influences of power influences within the supply chain. This dissertation sought to fulfill this research gap through an empirical study of power influences within the supply chain. Given its aggressive implementation of supply chain management as well as a definable power structure, the automobile industry was selected as a research focus. The research examined power influences within the industry, analyzing a set of critical research questions: • How do the different bases of power affect the relationship environment between buyer and supplier? • How are the performance and satisfaction within the supply chain influenced by a power asymmetric relationship? • How must power influences be incorporated into supply chain strategy and research? Subsequently, the basic research model given in Figure 6.1 was developed to provide a framework for the analysis of such questions. This path model analyzes the causal relationships between the research constructs, first conceptualizing that supply chain power will influence the nature of the buyer-supplier relationship. In turn, this power- influenced relationship affects performance and satisfaction within the chain. Each of these conceptualized paths was translated directly to a set of research hypotheses (Figure 6.2), and the fitting of the structural equation models in Chapter 5 allowed for these 166 hypotheses to be tested for significance. The results of these tests for significance are presented in the next section. r.Soureesofîââ BwymSSupp RélîftiohshI Figure 6.1: General Research Model HO'AW , Ho4 (a,b,c) Powerr Figure 6.2: Research Model with Associated Hypotheses 6.3 P o w e r E f f e c t s O n Re se a r c h H y po t h e se s AND Results The results of the hypothesis tests for the influences of power on the buyer-supplier relationship are presented herein. The hypotheses are partitioned into three categories: power effects on the relationship, relationship effects on performance, and relationship/performance effects on satisfaction. The hypotheses are first reviewed, then the test results are discussed. Insight based on the hypothesis tests are also offered, and specific interpretations are presented as they relate to the automobile industry. 167 6.3.1 Power Effects on the Buyer-Supplier Relationship Hypotheses One of the primary goals of the research was to test the effects of power sources on buyer-supplier relationships. Initially, power bases had been dichotomized into mediated (reward, coercive, and legal legitimate) and non-mediated (expert, referent, legitimate) sources, hypothesizing that the mediated bases would have a negative effective on the relationship while non-mediated would have a positive effect. This yielded the two sets of hypotheses as described in Chapter 2 and presented again here: Ho I a: Non-mediated (expert - y4, referent - y5) power sources will have no effect on the degree of relationship (y3) between buyer and supplier. H a ia : Non-mediated (expert - y4, referent - y5) power sources will have a significant positive effect on the degree of relationship (yS) between buyer and supplier. Hoib: Mediated (reward - y7, coercive - y5, legal legitimate - y6) power sources will have no effect on the degree of relationship (yS) between buyer and supplier. Haib: Mediated (reward - yl, coercive - y5, legal legitimate - y6) power sources will a significant negative effect on the degree of relationship (y3) between buyer and supplier. These research hypotheses are positioned to offer some of the first empirical evidence of power influences within the supply chain. Significance of the y34 (expert) and y35 (referent) paths in the non-mediated power research models (Mn-s, Mn-m, Mn-sc) will lead to rejection of Ho la, implying that non-mediated power does affect the nature of the buyer-supplier relationship. Failure to reject indicates no association between non- mediated power and the relationship. Likewise, path significance for t35 (coercive) and Y36 (legal legitimate) in the coercive-mediated power models (Mc-s, Mc-m, Mc-sc) as 168 well as path significance for t37 (reward) in the reward-mediated models (Mr-s, Mr-m, Mr-sc) offers grounds for rejection of Ho lb, indicating evidence for significant mediated- power effects. (Note; The reader is reminded that legitimate power was dropped from the research models in the initial exploratory stages of the research due to a lack of unidimensionality among the legitimate power research items.) 6.3.2 Power Effects on Relationship — Results The results of the path significances for the mediated and non-mediated power strategies are summarized in Table 6.1. First, it was found in the three non-mediated power models (Mn-s, Mn-m, and Mn-sc) that the influence of both expert and referent power demonstrated a significant effect on buyer-supplier relationships. Thus, Hoia is rejected, supporting the research claim the expert and referent power will have a significant positive impact on the relationship environment between the buying and selling firms. Furthermore, given the three coercive-power models (Mc-s, Mc-m, and Mc-sc), both coercive and legal legitimate power sources retain significant negative influence on the relationship at with p-values less than .01. This caused Hoib to be rejected for coercive and legal legitimate bases, thus supporting the research claim of the detrimental effect of competitively oriented, coercive power strategies on the relationship between the buying and selling firms. Finally, in consideration of the reward-power models (Mr-s, Mr-m, and Mr-sc), the research had initially hypothesized that reward power as mediated bases would have a negative influence on the relationship. The results from the structural modeling revealed that reward power actually retained a positive influence on the relationship, but the 169 significance of this effect causes Hoib for reward power to be rejected. Thus, although such results are not entirely conclusive, some indication of support exists for the positive influence of reward power on the nature of the buyer-supplier relationship. This positive effect was not as strong as that found of non-mediated sources, however. Hypotbè^ : Power Base Model rPath : P-Valne^ Ihterprefation Ho la Expert Mn-s y34 <.0I Reject Ho Expert power has a Mn-m <.0I Reject Ho significant positive effect Mn-sc <■01 Reject Ho on the buyer-supplier relationship Hola Referent Mn-s 735 <.0I Reject Ho Referent power has a Mn-m <.0I Reject Ho significant positive effect Mn-sc <.01 Reject Ho on the buyer-supplier relationship Hoib Coercive Mc-s 736 <.01 Reject Ho Coercive power has a Mc-m <01 Reject Ho significant negative effect Mc-sc <•01 Reject Ho on the buyer-supplier relationship Hoib Legal Mc-s Y37 <.0I Reject Ho Legal legitimate power has Legitimate Mc-m <01 Reject Ho a significant negative Mc-sc <.0I Reject Ho effect on the buyer- supplier relationship Hoib Reward Mr-s 738 >01. <.05 Incon Reward power shows some Mr-m > .01. < .05 clusive evidence for a significant Mr-sc >.0I,<.05 positive efiect on the buyer-supplier relationship. Table 6.1: Results of the Power Bases-Relationship Hypothesis Tests 6.3.3 Power Effects On Relationship - Insight The results from the power effects on the relationship represent some of the first empirical evidence to demonstrate power as a significant influence of the relationship between buying and selling firms. Specifically, the power effects hypothesis test results 170 establish that influences of power do affect the nature of the buyer-seller relationship as measured by the critical relational elements of cooperation, commitment, trust, conflict, and conflict resolution. Thus, the power variable serves as a significant factor in supply chain relationships and must be considered an important part of supply chain strategy. Beyond the basic importance of power a significant factor in the supply chain, the research tested how the specific bases of power affect the supply chain relationships. Specifically, the results from the power effects hypotheses provided evidence that both non-mediated (expert and referent) and reward-mediated power bases improve the nature of the supply chain relationships. On the other hand, it was found that coercive-mediated power bases (coercive and legal legitimate) prove detrimental to the orientation of the buyer-supplier relationships. These findings highlight the need for an awareness of power bases within the supply chain as well as an understanding of how the bases will affect critical chain decision-making and perceptions. The discussions below highlight such insight specific to the three power strategies (non-mediated, coercive-mediated, and reward-mediated) found by the research. 5.3.3.1 Non-Mediated Power Influences Non-mediated power was shown to positively influence the buyer-supplier relationship. Though power often carries a negative connotation, the positive results from the non- mediated power bases indicate beneficial uses of power. Thus, power asymmetry does not negate the potential for effective supply chain relationships and can actually be used to enhance the relationships. Subsequently, expert and referent power offer two influential approaches for improving supply chain control and effectiveness. Firms must 171 recognize their own sources of expert and referent power, and subsequently use these power bases to foster the relationships with their supply chain partners. These findings offer optimistic opportunities for auto manufacturers to further improve integration of their supply chain. With regard to expert power, the summary statistics reviewed in Chapter 4 showed that the suppliers considered Chrysler and Honda as retaining significant expert and referent power in comparison to GM, Ford, and Toyota. It has been shown that such power bases subsequently improve the nature of the buyer- supplier relationships, thus implying sources for effective supply chain strategy. The manufacturers need to understand what expertise the suppliers value and leverage this expertise as supply chain incentives. Furthermore, recognizing that their suppliers also retain significant expertise, the manufacturers can enhance their own expert power position by serving as knowledge/expertise brokers for their supply chain members through coordination of supplier design and production efforts. In all, positioning expert power as a focus of the relationships within the automobile industry will facilitate supply chain integration goals through enhancement of a tighter, cooperative chain. With regard to referent power, the non-mediated power-relationship path significances indicate that referent power, like expert power, may be administered as a method by which to strengthen management of the supply chain within the automobile industry. This indicates that the manufacturers should promote their supplier network as a team- environment that revolves around the manufacturer. Such a notion is already operationalized by Chrysler in their "Extended Family" program. Furthermore, as non- mediated power strategies are utilized to improve the effectiveness of the supply chain, the manufacturer's referent power will be further enhanced. 172 6.3.3.2 Reward-Mediated Power Influences The results from the power-relationship hypotheses revealed some evidence for significant positive effects of reward-mediated strategy. Origination from the obscurity between reward and coercive power bases (i.e. a withdrawn or ungranted reward may be seen as a coercive act), the distribution channel literature has emphasized reward power as a harmful influence on inter-firm relationships. To the contrary, this research has found reward power to retain a positive orientation. The suppliers in the automobile industry seem to approve of reward-oriented programs, and thus, such programs may be utilized to offer some improvement in the relational nature of the supply chain relationship environment. This supports the manufacturers use of reward programs to drive quality, product development and cost reduction efforts. Two concerns complicate the use of reward programs in the automobile industry. For one, the reward-relationship paths from the structural modeling only retained significance with p-value between .01 and .05 and therefore, can not be considered as strong as the positive effects from the non-mediated bases. Hence, a firm may be better off pursuing referent and expert power bases as their effects may offer more promise for building stronger relationship. The second concern with the use of a reward-mediated power strategy revolves around the aforementioned potential to mistakenly interpret a reward as coercion. The research has shown that such coercion will be detrimental to the supply chain relationship. Thus, power holders seeking to implement reward programs must realize the potential harmful effects, implying that a power holder must utilize reward power carefully. Considering the beneficial effects of the previously discussed non 173 mediated power, a power-bolder would most likely be better off pursuing non-mediated power sources instead of reward-mediated strategies if possible. Such insight holds prominence for the automotive industry as supplier reward programs are utilized extensively throughout the industry. Examples of such reward systems include repeat business, shared savings (such as Chrysler's SCORE program), and supplier performance awards. While the manufacturer may offer such rewards with intentions of strengthening their supplier relationships, the reward-mediated hypotheses' results reveal that the positive relational benefits of reward power strategies appear to be somewhat limited and implementation remains a precarious process. Thus, to facilitate more effective supply chain integration, the manufacturers may be better off emphasizing the expert and referent power bases and de-emphasizing the reward programs. 6.3.3.3 Coercive-Mediated Power Influences The results from the coercive-mediated power strategies hypothesis tests revealed that coercive and legal legitimate power bases will be detrimental to supply chain relationships. Two important insights arise from such results. First, although coercive- oriented strategies may be necessary at times, extreme use of coercion will damage the relational orientation of the supply chain environment. Thus, a power holder should not blindly enforce its power through coercive means as they will harm their supply chain relationships. Given the linked nature of the supply chain, this detriment would most likely come back to wound the power holder. As a second insight, the harmful effects of coercive power influences could actually serve to negate attempts to integrate the supply chain, serving a counter-productive role to supply chain strategies. Thus, the research 174 demonstrates strong evidence for the fact that coercive power strategies do not have a place in supply chain integration-oriented strategies. Coercive power has been a historically visible part of the U.S. automotive industry. The oligopoly of the few large manufacturers allows them to make significant demands of their suppliers. Although implementation of supply chain management techniques has served to reduce the implementation of coercion, such power still abundantly exists. As one example. General Motors, the largest customer in the industry, still retains a reputation as a punishing customer through use coercive strategies. With over $70 billion in annual purchases, GM may see their buying power as leverage, but this study demonstrates evidence that this strategy will harm their supply chain relations. Not surprisingly, the results from the benchmarking assessment in Chapter 4 revealed that GM is considered the worst in terms of customer quality. Such coercive strategies impede the ability of GM to integrate the supply chain and position it as a source of competitive advantage. This will most likely significantly harm GM in the future. The negative significance of the legal legitimate power paths offer further insight for the automotive industry. Specifically, the industry has recently moved away from traditional, formal contracts and emphasized more informal, cooperative agreements. Such a movement serves will negate the potential for use of legal legitimate power and thus promote integration of the supply chain. Thus, this research offers support for this tendency to reduce the importance of formal contracts 175 6.4 Relationship E ffects ON Perform ance The previous section reviewed the effects of power on the buyer-supplier relationships, revealing that power plays a prominent role in supply chain relations. To investigate the importance of power influences within supply chain management, the study sought to evaluate the effects of the power-affected relationship factor on performance of the supply chain. If this relationship affects performance, then the research would further expose the need for power control within the chain. Performance as perceived by the supplier was analyzed with three measures: supplier performance, manufacturer performance, and supply chain performance. 6.4.1 Relationship Effects On Performance - Hypotheses The research conceptualized that the strength of the buyer-supplier relationship should have a significant impact on supplier, manufacturer, and supply chain performance. This yielded three sets of hypotheses: H o 2a: The degree of relationship (y3) has no effect on supplier performance (y2a). Ha2a: The degree of relationship (y3) has a significant positive effect on supplier performance (y2a). Hozb: The degree of relationship (y3) has no effect on the buyers’ performance (y2b). Hazb: The degree of relationship (y3) has a significant positive effect on buyer performance (y2b). H o 2c: The degree of relationship (y3) has no effect on supply chain performance (y2c). Hazc: The degree of relationship (y3) has a significant positive effect on supply chain performance (y2c). 176 These three sets of hypotheses examine the significance of the P2a3, 32b3, and P2c3 paths in the research models. If the paths retain values significantly greater than zero, then the respective hypotheses are rejected, and a significant effect exists. Failure to reject indicates no significant effect between relationship and performance. The results from the relationship-performance hypotheses are discussed next. 6.4.2 Relationship Effects on Performance — Results Summarized results for the relationship-performance hypothesis tests may be found in Table 6.2. Analysis of the supplier performance models (Mn-s, Mc-s, Mr-s) reveals that Ho2a is rejected, indicating that the relationship had a significant effect on supplier performance. The manufacturer performance models (Mn-m, Mc-m, Mr-m) also lead to rejection of Hozb, insinuating that the relationship retained a significant influence on manufacturer performance. Finally, the results for supply chain performance were similar to that of the manufacturer performance as the results of Mn-sc, Mc-sc, Mr-sc indicate that Ho 2c should be rejected. Thus, the relationship between buying and supplying firms appears to impact the performance all members of the supply chain. 6.4.3 Relationship Effects on Performance — Insight The results of the relationship-performance hypothesis tests offer several significant insights for the supply chain. First, the research reveals empirical evidence that supply chain integration generates enhanced performance within the supply chain. The tighter relationship appears to allow the manufacturers and suppliers to accomplish more effective execution of modem production strategies. Thus, a supply chain orientation within corporate strategy is a valuable source of competitive advantage as all members of 177 the supply chain can benefit from enhanced performance. This finding suggests that success-oriented firms should implement coordinated supply chain management business strategies. Hypothesis rPèrfornumçë; |;P atI^ i^ResuEK r ^Interpretation Ho2a Supplier Mn-s P2a3 <.01 Reject Ho The buyer-supplier Mc-s <.01 Reject Ho relationship has a Mr-s <.01 Reject Ho significant positive effect supplier performance Ho2a Manu Mn-m P2b3 <.01 Reject Ho The buyer-supplier facturer Mc-m <.01 Reject Ho relationship has a Mr-m <.01 Reject Ho significant positive effect supplier performance Ho2b Supply Mn-sc P2c3 <.01 Reject Ho The buyer-supplier Chain Mc-sc <.01 Reject Ho relationship has a Mr-sc <.01 Reject Ho significant positive effect supplier performance Table 6.2: Results of the Relationship-Performance Hypothesis Tests Secondly, the results from the relationship-performance hypothesis tests indicate that the power holder (the manufacturer in this case) may benefit from effective power management in that their own performance is improved. Power has been shown to influence supply chain relationships, and subsequently, this relationship has been shown to influence performance. Thus, the influence of power will have a transitive effect on performance. This dissertation has earlier challenged that a power holder may not have a meaningful reason for yielding their power advantage. The research findings, however, offer rationalization for the source to control their authority as they can actually enhance their own performance through effective use of power. This indicates that a power holder should be intensely concerned with control of their authority for their own gain and should focus on the power management strategies as discussed in the previous section. 178 The relationship-performance findings align with the linked notion of the supply chain. One member can not seek competitive advantage without the participation of the other members. Furthermore, both improvements and impairments at distinct points within the chain tend to affect the other chain members. Due to the nature of power asymmetry, modifications to power strategy must originate with the power holder, and the relationship-performance results offer instigation for such changes. With regard to the automotive industry. Most of the industry power lies with the manufacturers that have been reluctant to yield their power advantage over the suppliers. With performance being such a critical industry driver, the relationship-performance hypothesis test results indicate that the manufacturers should carefully manage their power influence for their own self benefit. Once again, this highlights the need for awareness of power influences. Promotion of expert and referent power bases can come back to help the manufacturer just as coercive and legal legitimate bases may come back to be detrimental to the manufacturers. This indicates that supply chain integration is a truly a competitive weapon, and relationally-oriented auto manufacturers such as Chrysler and Honda will continue to generate competitive advantage. 6.5 Relationship /P erform ance Effects on Satisfaction The previous section discussed the results of the relationship-performance links, revealing that the power-affected relationship influences the performance of the supplier, buyer, and aggregate supply chain. The third and final section of the research model conceptualized supplier satisfaction as being influence by two causal paths, one from the relationship and the other from performance. The main purpose of these paths were to investigate the drivers of supplier satisfaction. 179 6.5.1 Effects on S atisfaction - H ypotheses Satisfaction with regard to this research may be defined as a feeling of contentment with the supply chain relationship. Four sets of supplier satisfaction hypotheses were developed. The first set examines the effect of the buyer-supplier relationship on supplier satisfaction, conceptualizing that a stronger relationship should increase such satisfaction. Similarly, the remaining three hypotheses examine the significance of the (supplier, manufacturer, and supply chain) performance versus supplier satisfaction links. These four sets of hypotheses are given as follows: Ho3: The degree of relationship (y3) has no effect on supplier satisfaction (yl). Has: The degree of relationship (y3) has a significant positive effect on supplier satisfaction (yl). Ho4a: Supplier performance (y2a) has no effect on buyer satisfaction (yl). Ha4a: Supply performance (y2a) has a significant positive effect on buyer satisfaction (yl). Ho4b: Buyer performance (y2b) has no effect on buyer satisfaction (yl). Ha4b: Buyer performance (y2b) has a significant positive effect on buyer satisfaction (yl). Ho4c: Supply chain performance (y2c) has no effect on buyer satisfaction (yl). Ha4c: Supply chain performance (y2c) has a significant positive effect on buyer satisfaction (yl). Hypothesis Ho3 examines the significance of the p 13 (relationship — satisfaction) path. If the path coefficient is significantly greater than zero then a significant relationship 180 would exist and Ho3 would be rejected. Likewise, the significances of the P 12a (supplier performance - satisfaction), pl2b (manufacturer performance - satisfaction), and P 12c (manufacturer performance - satisfaction) paths offer grounds for acceptance or rejection for the hypotheses Ho4a, Ho4b, and Ho4c respectively. 6.5.2 Effects O n S atisfaction - Results Table 6.3 displays the outcome of the satisfaction drivers hypotheses. First, the relationship-satisfaction paths retained highly significant positive relationships, indicating the quality of the buyer-supplier relationship has potent effects on supplier satisfaction. Thus, Ho3a is consistently rejected, substantiation that the buyer-supplier relationship serves as a fundamental driver of supplier satisfaction. Despite the significant results for the relationship-satisfaction links, the results of the tests for the performance-satisfaction path significances (Table 6.3) proved to be consistently insignificant. This presents a counter-intuitive result, not supporting distribution channel research such as Skinner et al. [1992]. In most cases, no significance relationship was found between any of the performance measures (supplier, manufacturer, or supply chain) and satisfaction. Ho3b is not rejected for all three supplier performance models (Mn-s, Mc-s, and Mr-s) as is Ho3c for all manufacturer performance models (Mn-m, Mc-m, and Mr-m). One of the supply chain performance models (Mn-sc), however, did find negative relationship between supply chain performance and satisfaction, though the p-value was only slightly less than . 10. The performance-satisfaction paths in the other two supply chain performance models (Mc-sc and Mr-sc) were insignificant, however. Given the aggregate results of the nine models with respect to performance-satisfaction paths, virtually no support existed to establish performance as a driver of supplier satisfaction. 181 . SatxsfbcUbnL; i HypothMk M odd; Ho3 Relationship All 313 <.01 Reject The buyer-supplier models for all models Ho relationship has a significant positive effect supplier satisfaction Ho4a Supplier Mn-s 3l2a >.10 Accept Supplier performance has Performance Mc-s Ho no signihcant impact on Mr-s supplier satisfaction Ho4b Manufacturer Mn-m 3 1 2 >.10 Accept Manufacturer Performance Mc-m b Ho performance has no Mr-m significant impact on supplier satisfaction Ho4c Supply Chain Mn-sc 3l2c .05 for Mn-sc, Accept Supply chain performance Performance Mc-sc > .10 for Ho has no significant impact Mr-sc Mc-sc and on supplier satisfaction Mr-sc Table 6.3: Results of the Performance/Satisfaction Hypothesis Tests 6.4.3 E ffec ts o n S atisfaction - Insight The results for the satisfaction hypothesis tests provide for some of the most intriguing results of the research in that the relationship-satisfaction path retained consistent significance but the performance-satisfaction paths did not. Thus, supplier satisfaction seems to be primarily driven by the nature of the buyer-supplier relationship as opposed to performance. Conceptually, the suppliers should be most concerned with their own ultimate performance, even in a supply chain integrated environment, yet the suppliers appear to be more concerned with the nature of the relationship. Thus, if the power holder is attempting to promote satisfaction, they should emphasize a relationship driven supply chain strategy rather than a performance based strategy. 182 The results for satisfaction influence hypotheses are indicative about the orientation of supplier strategy in the automobile industry. Specifically, it appears that the suppliers hold most concern with the alignment of their firm with the manufacturers. In other words, they place their own strategic emphasis on the maintenance of their relationship with their suppliers rather than their own performance or that of the supply chain. Such a conclusion may result from one or both of two rationale. First, since the suppliers are far removed from the end customers in the industry and subsequent ultimate performance of the supply chain, they are myopically unable to see their own true performance. Second and more likely, the suppliers may subscribe to the concept of success by association in that alignment with a proven manufacturer partner will automatically translate to performance of the supplier. Thus, performance would be viewed by the supplier as a fixed result of their association with the manufacturer. This finding may result from the benchmarking orientation of the research project. The finding that supplier satisfaction is driven by the supply chain relationships does not necessarily depreciate the importance of performance in the relationship. In fact, it actually seems to establish the role of performance as an accepted, omni-present industry factor. The suppliers realize that they must generate performance within the chain. The suppliers appear to believe, however, that as long as they can maintain their relationship with the manufacturer, this performance will be created as a natural output. 6.4.3.1 Need for Further Satisfaction Research The research findings with respect to satisfaction drivers indicate a need for further satisfaction research. The performance-satisfaction path may have been found insignificant for one of several reasons with the first of which being that given the 183 conceptualized model, performance does not have a large effect on satisfaction. This is not to say that performance has no effect on satisfaction. Rather it indicates that the effect is overpowered by the significance of the relationship-satisfaction path. Another reason might be that the sample size was not sufficient to allow for detection of path significance. Given the Chapter 5 discussion of appropriate sample for SEM, however, this would not seem to be the case. A final reason for the insignificance of the performance-satisfaction paths might have originated from model misspecification. The literature base, however, did not offer suggestion for such, and furthermore, the modification indices did not suggest changes to the model. Regardless of the reason for the performance-satisfaction path insignificance, more research is needed to verify the true effect of performance on satisfaction. Such research could expand the satisfaction variable (similar to Williams Walton, 1996) to further investigate satisfaction drivers in supply chain relationships. 6.6 C o n c lu sio n On the basis of the findings from the structural modeling and subsequent hypothesis testing, insight into the effects of power on the supply chain have been offered in this chapter. The research has revealed that power does affect the nature of supply chain relationships and that the relationship subsequently affects the performance and satisfaction within the supply chain. This provides valuable implications for the direction of applied supply chain management, allowing the research to significantly justify its original objectives in creating an awareness of the role of power within the supply chain. The entire research project will be reviewed in Chapter 7, concluding this dissertation. 184 CHAPTER? CONCLUSIONS 7.1 Introduction This dissertation has established some of the first empirical evidence for effects of power within the supply chain, offering contributions to the supply chain literature base. This chapter will bring the research project to a close, summarizing and highlighting previous chapters. Limitations of the research will also be discussed in highlighting directions for future supply chain power research. The chapter wUl then close with final thoughts about the need for disciplined use of power influences as a critical element of supply chain strategy, 7.2 Pow er AND THE Supply C hain Inter-firm power may be defined as the ability of one firm (the source) to influence the actions and intentions of another firm (the target). Several sources of power, both positive and negative, exist to affect the operational strategies and processes of both the power target and the source. Distribution channel literature has shown that the influences of power affect critical inter-firm relationship elements as well as firm performance and 185 satisfaction. Despite such effects, most firms may not be completely aware of the broad scope of power dimensions and thus may not actively manage their power. One paradigm in the corporate world that has emerged over the last few decades involves a movement to a more relational way of doing business. Firms are breaking down their own corporate barriers to recognize the synergy generated from shared strategy and processes. The move to relational business transactions has found significant potential in the supply chain. A recent flow of operations and logistics literature has promoted coordination of supply chain activities as a source of competitive advantage through reduced uncertainty, shared risk, enhanced responsiveness, faster cycle times, more effective product development, reduced costs, and higher quality. A relational orientation, though, complicates the role of power within inter-firm interactions. For instance, power may interfere with the mutuality and sincerity of inter firm alliances, inducing the power source to control use of its power. On the other hand, allying firms may expose themselves to further opportunistic behavior by conniving partners, thus, increasing the prominence of power within the relationship. Little research exists, however, to challenge the role of power with such supply chain integration. This directly challenges the effectiveness and utility of current supply chain management research. This dissertation has served to fill the gap within the supply chain literature base by offering an initial empirical analysis of power within the supply chain. 7.3 RESEARCH OVERVIEW Despite the lack of power influence research in the supply chain literature, an abundance of such research exists in retail-oriented, distribution channel literature. Based on a 186 synthesis of distribution channel power and supply chain literature, three primary objectives were developed to focus the research: • to understand how the different bases of power affect the relationship between buying and selling firms • to investigate how the power driven relationship affects the performance of the supplier, manufacturer, and entire supply chain • to examine how supplier satiffaction is affected by both supply chain performance and the relationship between buying and selling firms • to measure the effect o f power influences within the automobile industry By investigating such objectives, the research sought to enhance the effectiveness of supply chain strategy for practitioners and subsequently improve the realistic direction of supply chain research. Such goals are critical to the positioning of supply chain management as a source of competitive advantage. 7.3.1 Research Design The U.S. automobile industry was targeted as a source of study for the research. The industry has been a pacesetter in supply chain integration in the United States, but an oligopolistic manufacturing base has created a significant power imbalance within the industry. Although integration of the supply chain has served to generate a more trustful and sincere industry environment, power still plays a prominent role in manufacturer- supplier relations. Thus, the industry provides an effective source of study given the research objectives, allowing this dissertation to offer significant contributions to the direction of supply chain research. Meetings with industry practitioners verified the 187 benefits of the research objectives, allowing the research to offer significant contributions to not only the literature base but to industry practitioners as well. As power remains a subjective, perceive factor within the supply chain, empirical research was selected as the best method by which to measure inter-firm power sources and the subsequent effects of power on the supply chain relationships. The research instrument was initially developed based on an extensive literature review and was later refined and legitimated with help from industry experts. Seeking to offer a benchmark study, the research focused on industry best practices. The survey was sent to industry suppliers, identified by Chrysler and Honda, both industry leaders in supply chain integration efforts. Five hundred and eleven surveys were mailed, and 180 were returned for a response rate of 35.2%. 7.3.2 Analysis and Findings Given the collected data, initial stages of the data analysis both verified Chrysler and Honda as industry benchmarks and divulged a favorable power environment among these manufacturers. To ultimately establish the role of power influences within the chain, path analytic modeling was used to test conceptualized causal relationships between the research constructs of power, relationship, performance, and satisfaction. The research models were found to provide a strong fit to the data, allowing for significance testing of the research hypotheses. The results of the hypothesis tests offered critical insight into the effects of power on the nature of the relationships between buying and selling firms in the industry. The following highlight the major research findings: 188 • Non-mediated (expert, referent) power sources as well as reward-mediated power sources have significant positive effects on the relationship between buyers and suppliers though the non-mediated effects were consistently the stronger of the two. Such findings imply that the proper use of non-mediated and reward power can enhance partnershipping and supply chain management efforts. • Coercive-mediated power bases were found to have a significant negative effect on the relationships between buying and selling firms. This indicated that misuse of coercive power influences can destroy the relationship between buyers and suppliers and serve to neutralize programs and policies toward integration of the supply chain. • The power-affected relationship was found to have significant impact on the performance of the supplier, manufacturer, and the supply chain. The most significant of these effects were oriented to supplier performance. Such findings provide persuasive justification for integration of the supply chain as well as effective power management within the chain. • Supplier satisfaction was found to be primarily driven by the relationships between buying and selling firms as opposed to actual performance. This indicates that the suppliers are primarily concerned with aligning themselves with the powerful manufacturers and expect performance to result from this association. 7.3.3 Research Contributions These research findings yield critical insight for the orientation of both practitioner supply chain strategy and supply chain research. Such contributions verify the effectiveness of 189 the nature in achieving its goals and objectives. Above all, the research reveals the significance of the role that power influences play within the supply chain. It substantiated that power retains critical effects on the nature of supply chain relationships as well as the performance and satisfaction within the chain. Thus, practitioners must be aware of the power influences within their supply chain and understand how such power can affect their supply chain efforts as well as their own performance. Hence, supply chain strategy that does not acknowledge or account for power variables can not be entirely effective. The research also yielded significant insight to the positioning of power within supply chain strategy. Within the automobile industry, the research findings indicate that the manufacturers as power sources can emphasize non-mediated strategies in expert and referent power as well as reward-mediated strategies to enhance integration of the supply chain. Such improved supply chain relations were shown to benefit the power holder as well as the entire supply chain through greater overall performance. In a similar argument, the research established evidence for the detriment of coercive-mediated power strategies in that such power strategies deter the beneficial integration of the supply chain. The recognition of power management as a valuable and potentially profitable approach will help practitioners to establish more effective supply chain strategy and promote the supply chain as a source of competitive advantage. The research findings also contribute to the direction of future supply chain research, allowing such research to retain a more practical and realistic role. The evidence of power effects establishes the necessity of supply chain research to incorporate power into the research design and analysis. A lack of consideration of power influences could lead 190 to ineffectual and possibly erroneous research results and insight. Hence, researchers must understand power's presence and effects within their analytical, empirical, and conceptual research, and this dissertation has set a foundation for such an understanding. Likewise, researchers must accept a more aggressive role in addressing supply chain power. The research presented in this dissertation, however, hopefully generates insight for such future research. 7.4 L im itations and S ubsequent Directions fo r Future Research Given the objectives, this research has served well in addressing the influences of power within the supply chain. The significance of this research issues a need for further study of power influences within the supply chain. The findings as well as limitations of the research yield numerous directions for future supply chain power oriented research. Ideas for such are described below: • Other industries — The research offered a benchmark of supply chain relations in the automobile industry which has its own unique supply chain and power environment. Thus, the research findings are not necessarily directly generalizable to other industries. Similar research could be undertaken in other industries to investigate the consistency of the research findings, and this research in this dissertation serves to establish foundations for such analyses. It is important, however, that targeted industries must retain definable power structures or subsequent results could be inconclusive. • Field studies — This empirical research has yielded the influence trends of power effects, but application of field studies can reveal much of the specifics about the 191 effects of power strategies in the supply chain. Such cases could be targeted to specific situations of positive power use as well as environments of negative power influences. Objective Measures o f Performance — The research sought subjective measures of performance in the form of supplier perceptions. Performance measures taken from financial and other objective sources could be utilized as well to offer a comparison to the subjective measures. Such objective measures could include quality, productivity, and profitability oriented numerics. Expanded measures o f performance — The performance measures used by the research were simple, generic questions subject to supplier perception and interpretation. These items could be vastly expanded to include different aspects of performance, offering more specified effects of power. Expanded measures of satisfaction — Like performance, expanded measures of satisfaction [see Williams Walton, 1996] could be examined to gain a better understanding of power effects on partner contentment. Longitudinal analyses — The research only offers a snapshot of power effects in the automobile industry at one moment in time. Application of similar research over years or even decades could reveal changes in the power environment as well as the long term effects of power. This could provide critical evidence and support as supply chain management becomes a more consequential factor in industry. 192 • Dyadic perspectives ~ The research only offered opinions from the power target side of the power relationship. An extensive, dyadic survey that would include the manufacturers as power sources would lead to an interesting comparative analysis of power influences. This would facilitate awareness and understanding of supply chain partner perceptions. Such an analysis could be further expanded to three or more echelons within the supply chain. This would offer an integrated supply chain management orientation. 7.5 C oncluding Thoughts The automobile industry in the United States represents a breeding ground for power research. The industry consists of five manufacturers that account for 85% market share, and these manufacturers source from a supplier base of thousands. Such an oligopolistic buying structure has created a power asymmetric environment. With a few manufacturers comprising a large percentage of the suppliers' sales, the supplier must bow to the authority of the buyers or risk financial collapse. This power imbalance is delineated in the demographics of the survey respondents from this research as the manufacturers accounted for an average of 23.52% of respondent business. To compound the problems created by the power imbalance, firms in the automobile industry face intense competition. Each year the manufacturers are pressed to build higher quality, technologically advanced cars while maintaining competitive prices. The supplier base is directly affected by such pressure. The industry power imbalance has allowed the manufacturers to relinquish many of the responsibilities for product and process improvement to the suppliers. These suppliers are the key to maintaining the 193 competitive capacities of the manufacturers, and those suppliers that can not perform are systematically exiled to financial ruin. To recognize the synergy from coordination between manufacturer and suppliers as well as among the suppliers themselves, there has been an industry-wide inclination toward integration of the supply chain. The members of the supply chain synthesize processes and strategies, allowing the entire chain to work together to attack pressures from cost reduction, faster cycle times, and increased quality benchmarks. Supply chain management grants the supply chain a potential source of competitive advantage and will become an increasingly important part of the industry strategy. The intense coordination necessary for effective supply chain integration necessitates a reduced supplier base, however. Where manufacturers were once producing vehicles with thousands of suppliers, successful firms are now manufacturing better cars with just hundreds of suppliers. The large pie of purchased parts and materials, thus gets divided among fewer players, and more is at stake for the suppliers. The suppliers must strive to become best practice in order to gain the critical preferred status with the manufacturers, and these preferred suppliers must maintain best practice or face effortless replacement from the large base of competitors. This intensifies the power imbalance within the industry. The manufacturers can maintain the attitude that the suppliers must maintain pace with the industry or lose a potentially ruining amount of their business. Manufacturers in the automobile industry have been aware of their power advantage, and suppliers have long suffered from competitive, coercive power influences from these manufacturers. The use of such coercive strategy is best exemplified by General Motors. 194 As the largest manufacturer in the industry, GM purchases over $70 billion dollars of components and materials from suppliers annually. GM has capitalized on its buying power, maintaining a demanding and arrogant attitude in its supplier relations. This dissertation has revealed the results of such a strategy as the respondents ranked GM last of the five primary auto manufacturers in terms of customer quality. Representatives from GM may not care about their suppliers' opinions. Whether they are ranked first or last, GM still retains the power to control their supplier base as they please. They understand their use of coercive power, and as long as they feel it is the best supplier management strategy to take, their power strategy will not change. This signifies the main dilemma in power asymmetry: the power target can not alter the situation. It is the power source that must enact the change in the relationship, finding some motivation to change its opportunistic tendencies. Perhaps the most valuable contribution of this research is that it offers evidence for incentives to avoid the use of competitive power. The research has shown that relational uses of power through expert and referent as well as reward sources can be used to strengthen the nature of the relationships between buying and selling firms. Thus, power may be utilized as an approach to promote effective integration of the supply chain. The research has also indicated that these enhanced supply chain relationships can yield performance benefits to all members of the supply chain, including the power source. Hence, the power source should manage its own power influences for its own good. This discussion highlights the importance of power awareness as well as recognition of power as a valuable approach for increasing the competitive positioning of the entire 195 supply chain. Thus, practitioners need to take a long, hard look at their own awareness of power within the supply chain. They must understand power influences as well as the prevalent existing power bases. The power source must become conscious of its available power bases and subsequently, promote the positive bases while carefully controlling the harmful, coercive bases. Furthermore, it may not simply be enough to effectively manage power as the mere ability to exercise power may be enough to bring about desired action. The power holder must create an environment of trust to assure the target that competitive power sources will not be exercise in any fashion. The role of supply chain management in industry will only intensify, and the findings of this research suggest that power management must become a prominent part of supply chain strategy. Firms like GM that choose not to manage up their power advantage effectively risk not only harm to the rest of the supply chain but to themselves as well. Thus, it is the contention of this research that these firms will be unable to sustain supply chain management as a competitive advantage and will be surpassed by those that are able to develop a more relationally oriented chain. Beyond its valuable contributions to the inspiration of supply chain management, this research only provides an iititial glance at power influences within the supply chain. More research is needed to examine the effects of power management on supply chain strategy, and this dissertation hopefully provides substantial encouragement for further power analysis. Such a research direction will enrich the evolution of supply chain management and subsequently allow manufacturing to be better positioned as an effective source of competitive advantage. 196 APPENDIX A SUPPLY CHAIN LITERATURE B * B Akacumand HCase Studies MHB « Case Study Dale (1995) Examples Cavinato Conceptual Total Cost (1991) Analysis Das and Conceptual * Partnering for JIT Goyal (1989) Ellratn Conceptual « m « Partnership (1991a) Development Ellram Case Studies Product Life Cycle (1991b) Effects Ellram Conceptual * $ Supply Chain (1991d) with Case Management Studies Ellram (1993) Case Studies m $ * «« Implementation Ellram (1995) Survey $ Success Factors Ellram and Case Study ** « Implementation Edis (1996) Ellram and Survey 1 ♦ 1 Partner Hendrick Perceptions (1995) Farmer Conceptual $ * Early Study (1976) _ 197 Graham, et al. Regression ** Long Term (1994) Impacts 1 Green (1974) Conceptual * * Anti-Partnership 1 Hahn, et al. Conceptual « Partnering for JTT (1983) Johnson, et Factor Analysis *« Japanese/U.S. al. (1993) Partnerships Lambert, et Case Studies «« Partnership al. (1996) Development and Maintenance Landeros, et Conceptual *« Partnership al. (1995) (with Maintenance Interviews) Landeros, et Interviews * * Effects on al. (1989) Corporate Strategies Maloni and Literature *«« $ Review of Supply Benton Review Chain Partnership (1997) Literature Matthyssens Conceptual * General and Van De Introduction Bulte (1994) Mohr and Multiple « $ Critical Success Spekman Regression Factor Verification (1994) Monczka, et Regression * * Relationship a. (1995) Predictors Newman Conceptual * * Anti-Partnership (1989) Nicderkofler Case Studies « Influencing I (1991) Factors 1 Ramsay Conceptual * Anti-Partnership (1996) Richeson, et Correlation * Communication al. (1995) Ring and Van Conceptual $ General De Ven Partnershipping (1992) Ring and Van Conceptual « Developmental de Ven Processes (1994) 198 Rognes Conceptual « m Negotiation (1995) Rosenberg Conceptual « Partnering for JTT and Campbell (19851 Sandelands Conceptual « Reduced Supplier (1994) Bases Stuart (1993) Correlation « * « Influencing Factors Stuart and Basic * *• Partnership McCutcheon Implementation (1987) Problems Thomas and Mathematical * Review of | Griffin (1996) Modeling Operations Research Models Williams Regression * * Determinant of Walton Satisfaction (1996) 199 APPENDIX B POWER LITERATURE '.iLP-irlte- } ' W f . ' - ■ rSrr^rri r&lYh'Ts - Anderson and Weitz Boyle and Causal Dwyer (1995) Boyle, et al. Causal Relationship (1992) (LISREL) Structures Brill (1994) Causal Brown and Factor Day (1981) Analysis Brown, et al. Causal Economic/ (1983) Noneconomic Brown, et al. Causal Direct/ (1995a) Indirect Brown, et al. Causal Mediated/ (1995b) (USREL) Nonmediated Cronin, et al. Factor Exercised/ (1994) Analysis, Unexercised Regression 200 EI-Ansaiy Factor Q975) Analysis CorrelationEl-Ansary CorrelationEl-Ansary Perceptions and Stem Etgar(1976) Canonical Counter- Correlation vailing Etgar (1978a) Factor, Contractual/ Discriminant Conventional Analysis Channels Etgar (1978b) Regression Economic/ Noneconomic Frazier Factor (1983) Analysis Frazier and Correlation Coercive/ Reciprocity Rody(1991) Noncocrcive Frazier and Correlation Summers (1984) Frazier and Correlation Coercive Summers (1986) Gaski (1984) Conceptual Coercive/ Exercised/ Noncocrcive Unexercised. Gaski (1986) Causal (LISREL) Gaski (1988) Causal Exercised/ (LISREL) W Unexercised Gaski and Regression Coercive/ Exercised/ Nevin (1985) Noncoercive Unexercised Gassenheimer Causal Coercive and Calantone (1994) 201 Gassenheimer Correlation/ Market Share and Scandura Regression Gassenheimer Factor, Path , et al. (1989) Analysis, Regression Gassenheimer Causal Coercive , et al. (1994) Gundlach and Human Coercive/ Cadotte Simulation Noncoercive (1994) Gundlach, et Human ai. (1995) Simulation Hunt and Regression Coercive/ Nevin (1974) Noncoercive Hunt, et al. Causal Coercive/ (1987) (USREL) Noncoercive John (1984) Structural Contingent/ Opportunism Equations Non- Contingent Johnson, et Factor International al. (1993) Analysis Keith, et al. MANOVA (1990) Kumar Causal [1995] Lusch Regression (1976b) Lusch Regression Coercive/ (1976c) Noncoercive Lusch and Non- Pervasiveness Ross (1985) parametric Lusch and Correlation/ Economic/ Brown (1982) Regression Noneconomic 202 McAlister, et Human al. (1986) Simulation Michie and Principal Coercive/ Sibley (1985) Components, Noncocrcive Regression Miles, et al. Conceptual (1994) Naumann and MANOVA Reck (1982) » Pro van and Correlation/ Gassenheimer Regression (1994) Reve and Conceptual Stem (1979) (Lit Review) Richardson Correlation Coercive/ and Noncoercive Robicheaux (1992) Robicheaux Conceptual and El- Ansary (1977) Schul and Causal Coercive/ Babakus (LISREL) Rewards (1988) Skinner, et al Causal Coercive/ (1992) (USREL) Noncoercive Stem and Conceptual Reve (1980) Stem, et al. Human (1973) Simulation Wilkinson Path Analysis (1981) 203 APPENDIX C NOTES FROM CHRYSLER MEETING Chrysler Corporation - November 22, 1997 Attendance: P Jeffrey Trimmer, Chrysler Director, Operations & Strategy, Procurement & Supply Thomas H. Moening, Chrysler Manager Value Analysis, Procurement & Supply W.C. Benton, Ohio State (presenter) Michael Maloni, Ohio State Background Chrysler has an interesting background in the automobile industry as of the last decade. After facing near bankruptcy in 1989, Chrysler has rebounded under Thomas Stallkamp, V.P. of Procurement, to become a leader among the U.S. Big Three in terms of product development. Although they can not be considered as advanced as the Japanese transplants, they comparable to world-class. Their products lines include Chrysler, Dodge, Plymouth, and Jeep with their tmcks and mini-vans seemingly the strength of their offerings. The smallest among of the Big Three, Chrysler purchases approximately $35 billion in armual purchases which puts them about half the size of GM. Without as much volume power as Ford and GM, they have relied more heavily on a progressive supplier management program known as their "Extended Enterprise". This extended enterprise seems to serve as the attitude of communication and coordination that drives supplier interaction. Specific programs and practices under this posture include long-term (business for life) supplier commitments, presourcing and target costing, supplier advisory roundtables, resident engineers, cross-functional development teams, and in- 204 house suppliers. The most notable program is their Supplier Cost Reduction Evaluation Effort (SCORE) which drives suppliers to reduce costs through improvement and streamline of component design. SCORE saved approximately $1 billion in overall costs in 1996, and a total of $2.3 billion since it was first implemented in 1993. Chrysler has used its proactive supplier management programs to become a leader in product development in the industry. New line development has been reduced to about 3 years in comparison the usual 4 to 5 years, and new line development costs are less than half of comparable competitor lines. Both per vehicle profit and market share are up significantly, and their Dodge Caravan was named 1996 Motor Trend Car of the Year. Although Chrysler still lags behind competitors in product quality their comeback from near bankruptcy has been remarkable. Meeting Overview The meeting actually started with a slide presentation by Mr. Trimmer about the procurement programs. He recognized the intimidation, antagonistic methods of supplier management in the past. Due to increased customer expectations, brand loyalty, competition, and profit pressure, they realized the need to focus supply/procurement as a source of competitive advantage rather than a cost center. They then developed their "Extended Enterprise" concept to drive their purchasing processes. Keep ideas now include continues improvement, joint effort, training/awareness, coordination, and communication. Commodity and supplier strategies are centralized while providing focused support to platform teams. Our presentation seemed to fit well with the ideals behind their presentation. Much discussion was generated about the goals of the research, and they seemed positive about all aspects. Mr. Trimmer and Mr. Moening actually offered several interesting ideas about the research design including the administration of the survey to both supplier high level management and lower level operating personnel as well as using the research to provide a longitudinal analysis of supplier perceptions. In the end, they agreed to provide supplier contact lists, and in suit with the entire meeting, we parted on a positive note. Personal Opinions Participation of Chrysler is a significant benefit to the research. It is clear that Chrysler is the leader among the U.S. auto manufacturers as revealed by their overall attitude for improvement. They seemed hungry for the future which is a polar opposite to GM, and thus, they recognized the value of the research to their firm. Chrysler's participation will also provide a comparison to Honda. It will be interesting to see how Chrysler compares to Honda across all aspects of the questionnaire, and the results may offer a spin-off case study paper of America versus Japanese transplant supplier management programs. ______ 205 APPENDIX D NOTES FROM HONDA MEETING Honda of American Manufacturing, Inc. - December 12, 1997 Attendance; David A. Curry, Honda, Purchasing Staff Administrator W.C. Benton, Ohio State (presenter) Michael Maloni, Ohio State (presenter) Background As an auto manufacturer whose manufacturing and purchasing processes were developed in Japan, Honda of America is regarded as having world class purchasing. Recently, Purchasing awarded Honda a medal of professional excellence "for its true understanding of the value of the supply chain and for its long-term approach to developing world-class suppliers" [p. 32, Fitzgerald, 1995]. In the article, Dave Nelson, Vice President of Purchasing, points to several issues that drive their purchasing practices including cost reduction, quality improvement, product research and development, and "teaching self- reliance" to suppliers. Honda was the first of the Japanese auto manufacturers to build plants in America (1982) and actually began motorcycle operations in 1979. They have car (Marysville for Accords and East Liberty for Civics), motorcycle (Marysville), and engine (Anna) plants in Ohio and purchase approximately $4.6 billion annually. Currently, they are attempting to source completely from local (versus Japanese) suppliers though some of these local suppliers are Japanese transplants as well. 206 Meeting Overview The meeting consisted of our presentation to Mr. Curry who seemed to receive it warmly. He appeared to completely understand and promote the focus of effective supplier management. He offered Honda's participation in the form of Honda supplier contacts and has since maintained a relatively active role in the development of the research. Our meeting concluded with lunch with other Honda staff including Rick Mayo, Purchasing Manager, and Jonathan R. Stegner, Purchasing Staff Administrator. Honda has since foUowed-up with a list of approximately 400 suppliers located throughout the United State and Canada. The contact names are at the strategic level with many CEO's and Presidents. Though based in North American, about 25% of the companies seem to have Japanese heads. Personal Opinions It is easy to understand why Honda purchasing practices are among the best in class. As one obvious reason, they have been successful at transplanting Japanese developed processes here in the United States. More importantly, though, is their attitude toward supplier management. They seem to understand that they are ahead of their U.S. competition but do not seem complacent to maintain status quo. They realize that they still have a lot to learn and are excited about change and development. Thus, they seem to view our study as extremely beneficial to them and realize the mutual goals. Honda's participation is a major gain for the research project. Results from Honda suppliers will provide a critical comparison to U.S. manufacturers, and it will be interesting to see the difference between Japanese transplant Honda and U.S. based Chrysler. Although their size is much smaller than Chrysler ($5 billion versus $35 billion in annual purchases), they would probably have significant power advantages over their suppliers just for bearing the Honda name. It is expected that they will score high on the referent power section of the questionnaire. 207 APPENDIX E NOTES FROM GENERAL MOTORS MEETING General Motors - November 12,1997 Attendance; Gunter Schmlrler, General Motors, Advanced Purchasing and Global Sourcing W.C. Benton, Ohio State (presenter) Michael Maloni, Ohio State Background GM does not have a favorable industry reputation with suppliers due to coercive purchasing tactics. GM purchases approximately $70 billion in outside products and services as well as $18 billion from GM subsidiary Delco. Apparently, GM tends to use its purchasing power to control its supply base, and subsequently, suppliers tend to rank them as a very poor customer in comparison to other manufacturers. Such problems become excessively evident in the early 1990's with GM's head of purchasing, Jose Ignacio Lopez de Arriotua. Lopez utilized strong-arm tactics to force supplier cost reductions and gave little validity to supplier contracts. Harold Kumer replaced Lopez in July, 1994 after Lopez left for VW. GM has recently settled suits with VW concerning GM proprietary information transferred to VW by Lopez. Kutner has received some favorable reviews in the trade journals [Minahan, 1996; Smith, 1995a, b] for streamlining purchasing functions and attempting to improve relations with suppliers. Specific programs include increased communication (Ambassador program), productivity improvement teams (PICO), lifetime contracts, supplier recognition, and minority supplier promotions. For example, in 1995, GM recognized 154 best suppliers with an extravagant function in Vierma, Austria. 208 With GM's past problems with suppliers, we felt that they could greatly benefit from out study, perhaps more than any other manufacturer in the industry. Our work would help them understand the results of their supplier management strategies and provide an unbiased source supplier opinions. Overall, we felt that our study would facilitate work already set in motion by Harold Kutner. Meeting Overview The meeting consisted of our presentation to Mr. Schmirler who did not take a relatively active role. Most of his remarks consisted of clarification of GM policies and programs. He seemed favorable toward most of the presentation which concluded with a discussion of critical supplier management issues. One interesting issue that became apparent in the presentation is GM unawareness of their supplier relations. Although it appears that they are familiar with their negative reputation, Mr. Schmirler seemed to believe that their supplier management programs were among the best in class. He did not see a problem with their purchasing practices, even in comparison to Japanese transplants such as Toyota and Honda. Overall, he seemed quite satisfied with GM's place among suppliers. It appeared that we had Mr. Schmirler sold on the benefits of the study, but the meeting came to abrupt end after he told us (after contacting GM legal services) that they could not release supplier contact information to us. It was then apparent that GM did not want to put much of an effort into participation in our study and would be quite satisfied with their current perceived status. Personal Opinions It is in the opinion of the author that GM tends to have an inflated attitude about the quality of their purchasing program. Personal observations of their process models lead one to believe that their programs are so severely antiquated that they could be considered among the worst in class. It seems that they do not have an idea of just how bad they are as well as the severity of the effects of their own supplier management programs. The sheer size of their purchasing volume allows them to be disoriented firom best practice. Although currently GM is reaping the rewards of a favorable automobile industry in the form of high profits, it is in the opinion of the author that improving their purchasing programs to emulate leaders such as Chrysler and Honda would benefit them tremendously. 209 Please Note Page(s) not included with original material and unavailable from author or university. Filmed as received. 210-218 APPENDIX F - H UMI APPENDIX I FIRST REMINDER POSTCARD Please Help Us In Our Research Recently you were sent two Supply Chain Management Surveys from researchers at The Ohio State University designed to collect opinions of buyer-supplier relations within the automobile industry. Your participation is critical to the outcome of our analysis. If you have already returned the survey, thank you for your prompt response and participation. If you have not, please take the time to complete the questionnaire and mail your responses to: Michael Maloni Fisher College of Business 302 Hagerty Hall Columbus, Ohio 43210-1399 or fax your response to: (614) 292-1272 Your participation in this research is greatly appreciated. If you have not received ± e survey forms or have any questions, please call Michael Maloni at (614) 292-2978. 219 APPENDIX J SECOND ROUND COVER LETTER «HEADER» Dear «MR» «LNAME», You recently received a set of surveys seeking opinions of buyer-supplier relationships within the automobile industry. As an important industry supplier your participation is critical to the success of our study and will help improve industry supply chain relations. If you have already returned the surveys, please accept our sincere appreciation. If not, another copy of the surveys has been enclosed. Please take the time to complete the survey. If you feel that there are others in your organization who would be better able to complete the survey, feel free to forward it to them. This is an independent study that is NOT sponsored by any auto manufacturer. Your responses will be strictly confidential and will not be identified with you or your firm. In return for your participation, you will receive a summary of the research results. A postage-paid, self addressed return envelope has been provided for your convenience. If you have any questions or concerns about the study or survey, please contact Michael Maloni at (614) 292-2978 or maloni. 1 @ osu.edu. We appreciate your time and value your thoughtful participation. Sincerely, W.C. Benton, Ph.D. Michael Maloni 220 APPENDIX K SECOND REMINDER POSTCARD Please Help Us In Our Research Recently you were sent a second copy of two Supply Chain Management Surveys from researchers at The Ohio State University designed to collect opinions of buyer- supplier relations within the automobile industry. We need your help. Your responses will offer valuable insight to our analysis. If you have already returned the survey, thank you for your prompt response and participation. If you have not, please take the time to complete the questionnaire and mail your responses to: Michael Maloni Fisher College of Business 302 Hagerty Hall Columbus, Ohio 43210-1399 or fax your response to: (614) 292-1272 Your participation in this research is greatly appreciated. If you have not received the survey forms or have any questions, please call Michael Maloni at (614) 292-2978. 221 LIST OF REFERENCES Ahire, Sanjay L., Damodar Y. Golhar, and Matthew A. 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