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Implementing Business-To-Business Online Reverse Auctions IMPLEMENTING BUSINESS-TO-BUSINESS ONLINE REVERSE AUCTIONS BY LOAY SEHWAIL Bachelor of Science University of Jordan Amman, Jordan 1999 Master of Science Oklahoma State University Stillwater, Oklahoma 2001 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY July, 2006 COPYRIGHT BY LOAY SEHWAIL JULY, 2006 ii IMPLEMENTING BUSINESS-TO-BUSINESS ONLINE REVERSE AUCTIONS Dissertation Approved: Dr. Ricki G. Ingalls Dissertation Advisor Dr. David B. Pratt Dr. Camille DeYong Dr. William D. Warde Dr. Dan Tilley Dr. A. Gordon Emslie Dean of the Graduate College iii ACKNOWLEDGEMENTS With deep gratitude, I extend my sincere appreciation to the members of my dissertation committee: Drs. Ricki G. Ingalls, Camille DeYong, David Pratt, William D. Warde, and Dan Tilley, who provided me with knowledge and encouragement to help make my dream become a reality. I really thank my committee for all the meetings, endless intelligent conversations, and debates. You have taught me the meaning of research. I am particularly grateful to my chair, Dr. Ricki G. Ingalls, who has affected my life in many positive ways, for his friendship, guidance, and support from the first day we met. I would not have been writing this acknowledgement without his mentoring, support, and friendship. Thank you Dr. Ingalls. Special thanks must go to the Institute of Supply Management (ISM) for its support in providing the contact information for the survey sample. Additionally, I would like to recognize Dr. Rick Boyle for his help, cooperation, and fast response to any inquiry or question. During this journey of research, there are many people who, like lamp posts, stood beside the road at different angles, appeared at different moments, and guided me all the way to where I am now. Without their guidance and encouragement, I would not have been able to make this journey. Thanks to all of you. iv Without the support of my family, none of this would have been possible. I dedicate this dissertation to my role model in life, my father, Munir, who taught me integrity, honesty, and the importance of higher education, and to my mother, Sabiha, whose spiritual support has been encouraging me since the day I was born. I also dedicate this dissertation to my sister, Lama, and my brothers, Feras and Tareq, for their unconditional love and support. My family contribution to my success cannot be quantified or described. Finally, I gratefully acknowledge my wife, Yen-Ping Leow-Sehwail, who provided ongoing support and encouragement. To my wife Yen-Ping: without your support, in every possible form, I would not have been able to do this. Please know that I am truly grateful to you, and that this achievement would have been a lot more difficult without you. I love you. v TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1 1.1 Overview 1 1.2 Problem Statement 3 1.3 Research Purpose 7 1.4 Contribution of the Research 8 1.5 Outline of Dissertation 10 CHAPTER 2: LITERATURE REVIEW 12 2.1 Introduction 12 2.2 The Impact of Electronic Commerce on Supply Chain Management 14 2.3 Electronic Procurement 16 2.4 Online Marketplace 20 2.5 Online Marketplace Classification Dimensions 22 2.5.1 Ownership Dimension 22 2.5.2 Stake-Holders Focus Dimension 23 vi 2.5.3 Commerce Model Dimension 24 2.5.4 Revenue Model Dimension 26 2.6 Online Reverse Auctions 27 2.6.1 Reasons for Using Online Reverse Auctions 29 2.6.2 Online Reverse Auctions Risks and Conditions 31 2.6.3 Types of Auctions 33 2.6.4 Differences between Online and Manual/Physical Auctions 35 2.7 Buyer-Supplier Relationships 37 2.8 Summary of the Literature Review 39 CHAPTER 3: HYPOTHESES OF THE RESEARCH 40 3.1 Introduction 40 3.2 Academic Research on Reverse Auctions Implementations 40 3.3 The Fit between Auction Design and Reduction in Purchase Price 48 3.4 The Fit between Product Type and Reduction in Purchase Price 50 3.5 The Fit between the Product Type and the Auction Application 54 3.6 The Fit between Auction Application and Strategic Supplier Alliance 57 3.7 Successful Implementation 65 vii CHAPTER 4: RESEARCH METHODOLOGY 68 4.1 Introduction 68 4.2 Data Gathering Tool 68 4.3 The Sample 76 4.4 Data Collection 77 CHAPTER 5: ANALYSIS AND FINDINGS 80 5.1 Introduction 80 5.2 Survey Response 81 5.3 Early versus Late Response 84 5.4 Sample Descriptive Statistics 85 5.5 Reliability and Validity Analysis 89 5.5.1 Reliability Analyses 89 5.5.2 Validity Analyses 103 5.6 Hypotheses Testing 109 5.6.1 Scale Descriptive Analyses 109 5.6.2 Hypotheses Tests 111 viii CHAPTER 6: SUMMARY, CONCLUSIONS & FUTURE RESEARCH 155 6.1 Introduction 155 6.2 Research Conclusions 156 6.3 Summary of the Research Study 159 6.4 Limitations of the Study and Future Research Guidelines 180 BIBLIOGRAPHY 185 APPENDIX A : SURVEY INSTRUMENT 204 APPENDIX B : IRB APPROVAL 210 APPENDIX C : DESCRIPTIVE STATISTICS 212 APPENDIX D : RELIABILITY ANALYSIS 220 APPENDIX E : VALIDITY ANALYSIS 258 APPENDIX F : SCALE DESCRIPTIVE STATISITCAL ANALYSIS 270 APPENDIX G : HYPOTHESIS ANALYSIS 275 ix LIST OF TABLES Table 3-1: Online Reverse Auction Implementation Literature Summary 41 Table 4-1: Construct Measures before Scale Purification 75 Table 4-2: Online Reverse Auction Surveys Response Rates 76 Table 4-3: Hypotheses Statistical Analyses Procedures 79 Table 5-1: Summary of Industries Surveyed by SIC Code 83 Table 5-2: Summary of Reliability Criteria 90 Table 5-3: Reliability Analysis: Auction Design (Format) Scale 91 Table 5-4: Reliability Analysis: Auction Design (Event Organization) Scale 92 Table 5-5: Reliability Analysis: Reduction in Purchase Price Scale 93 Table 5-6: Reliability Analysis: Auction Application (Power Based Bargaining) Scale 94 Table 5-7: Reliability Analysis: Collaborative Problem Solving Scale 94 Table 5-8: Reliability Analysis: Successful Event Implementation Scale 95 Table 5-9: Reliability Analysis: Attribute of the Alliance (Trust) 96 Table 5-10: Reliability Analysis: Attribute of the Alliance (Commitment) 97 Table 5-11: Reliability Analysis: Attribute of the Alliance (Interdependence) 97 Table 5-12: Reliability Analysis: Attribute of the Alliance (Coordination) 98 x Table 5-13: Reliability Analysis: Information Quality Scale 99 Table 5-14: Reliability Analysis: Information Participation Scale 100 Table 5-15: Reliability Scale: Information Sharing 101 Table 5-16: Reliability Analysis: Strategic Supplier Alliance Scale 102 Table 5-17: Construct Measures after Scale Purification 106 Table 5-18: Summary of Hypotheses Tests Supported by the Data 112 Table 5-19: Summary of Hypotheses Tests not Supported by the Data 113 Table 5-20: Test of H1 114 Table 5-21: Filtering Data Criteria for Further Analysis 115 Table 5-22: Companies’ Experience in Using Reverse Auctions 116 Table 5-23: Test of H2 117 Table 5-24: Test of H3 118 Table 5-25: Test of H4a 119 Table 5-26: Test of H4b 121 Table 5-27: Test of H4c 122 Table 5-28: Test of H5a 123 Table 5-29: Test of H5b 124 Table 5-30: Test of H5c 125 Table 5-31: Tests to Validate Regression Assumptions 130 xi Table 5-32: H7a Outliers 131 Table 5-33: Cook's Distance and Leverage Stastic Value for H7a Outliers 132 Table 5-34: K-S Normality Test for H7a Regression Residuals 133 Table 5-35: Correlation between Independent Variables and Residuals for H7a 134 Table 5-36: Multicollinearity test values for H7a 135 Table 5-37: H7a Regression Coefficients by Auction Experience 140 Table 5-38: H7b Regression Coefficients by Auction Experience 143 Table 5-39: H7c Regression Coefficients by Auction Experience 146 Table 5-40: Test of H8 152 Table 5-41: Test of H8 Based on Auction Experience 153 Table 5-42: Test of H9 153 Table 5-43: Test of H9 Based on Auction Experience 154 Table 6-1: Hypothesis Testing Results 157 xii LIST OF FIGURES Figure 1-1: Research Structure and Content 11 Figure 2-1: The Wyld (2000) e-Procurement Model 17 Figure 2-2: Main Motivations for using Online Marketplaces 20 Figure 3-1: The Kraljic Purchasing Portfolio Model (Modified from Kraljic 1983) 52 Figure 3-2: Moher and Spekman (1994) Model 60 Figure 3-3: Research Hypothesized Model 67 Figure 5-1: Survey Percentages by SIC Division Code 82 Figure 5-2: Respondents' Job Titles 85 Figure 5-3: Frequency Data on Year 2004 Annual Sales 87 Figure 5-4: Frequency Data on Year 2004 Annual Purchasing Volume 87 Figure 5-5: Frequency Data on Total Number of Employees 88 Figure 5-6: Frequency Data on Number of Purchasing Employees 88 Figure 5-7: Plot of Residuals against Predicted Values for H7a 131 Figure 5-8: Histogram of H7a Residuals 132 Figure 5-9: Normal P-P Plot of H7a Regression Standardized Residuals 133 Figure 5-10: H7a Test for Independence of the Residuals 134 xiii Figure 5-11: Plot of Residuals vs. IVs to Assess Linearity for H7a 136 Figure 5-12: Homoscedasticity Visual Tests for H7a 137 Figure 5-13: H7a WLS Regression Output 138 Figure 5-14: H7a OLS Regression Output 139 Figure 5-15: H7b Multiple Regression Output 142 Figure 5-16: H7c Multiple Regression Output 145 Figure 5-17: H6a Regression Output 147 Figure 5-18: H6a Regression Output - Power based Bargaining Assumption 149 Figure 5-19: H6b Regression Output 150 Figure 5-20: H6c Regression Output 151 xiv CHAPTER 1: INTRODUCTION 1.1 Overview This research is motivated by the recent phenomenal growth in the use of business-to-business (B2B) online reverse auctions, by organizations on a global basis.
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