CONSUMER RESPONSE TO STOCKOUTS IN ONLINE APPAREL SHOPPING
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Mijeong Kim, M.S.
* * * * *
The Ohio State University 2004
Dissertation Committee: Approved by
Professor Sharron J. Lennon, Adviser
Professor Leslie Stoel Adviser
Professor Nancy Stanforth College of Human Ecology Professor Michael Browne
Copyright by Mijeong Kim 2004 ABSTRACT
The primary goal of this research was to investigate how consumers respond to product unavailability from the perspective of discrepancy-evaluation theory of emotion.
This research consists of two studies employing a randomized experiment using a mock website simulating online apparel shopping. In a 2 (timing of notification about stockout: before or after) x 2 (item preference: not preferred or preferred) x 2 (frequency of stockout: once or twice) complete between-subjects factorial design, Study 1 examined:
(1) the effects of timing, preference, and frequency of product unavailability on negative emotion elicited, (2) structural relationships among negative emotion, perception of store image, decision satisfaction, and behavioral intent, and (3) the moderating role of timing, preference, and frequency on the process by which product unavailability influences consumer response. Eight hundred twenty female college students participated in the simulated online apparel shopping Web experiment for Study 1, in which they experienced a different level of product unavailability as a function of timing, preference, and frequency of product unavailability. In a one factor (managerial response) between- subjects design with four levels (standard, substitute, backorder, or financial response),
Study 2 explored the effect of four retail management responses on consumer responses to stockouts. Two hundred thirty-four female college students participated in another
ii simulated online shopping Web experiment developed for Study 2, in which they received one of the four managerial responses at the time they encountered stockouts.
The findings from Study 1 revealed: (1) main effects for timing, preference, and frequency on negative emotion; (2) three two-way interaction effects for timing, preference, and frequency on negative emotion; (3) the effects of negative emotion on perception of store image, decision satisfaction, and behavioral intent; (4) the effect of negative emotion on behavioral intent mediated by perceptions of store image and decision satisfaction; (5) the varied relationship between store image and behavioral intent as a function of timing, preference, and frequency; and (6) the varied relationship between negative emotion and store image, store image and behavioral intent, and decision satisfaction and behavioral intent as a function of three two-way interactions among timing, preference, and frequency. The findings from Study 2 showed the effect of managerial response on negative emotion, perceptions of store image, and behavioral intent.
The findings of these studies add to the extant literature on stockouts by providing
(1) empirical support for the proposed model that illustrates the process by which product unavailability influences consumer response, (2) theoretical insight to understand the stockout phenomena from the perspective of the discrepancy-evaluation theory of emotion, and (3) retail management strategies that retailers can adopt to alleviate the negative impact of stockouts.
iii
Dedicated to my loving husband
Kyuwoon Leon Hwang
iv ACKNOWLEDGMENTS
First and foremost, I owe special thanks to my adviser, Dr. Sharron Lennon for her invaluable advice, support, encouragement, and caring that has motivated me to successfully complete my graduate program. For the last eight years in my life, she has been the best teacher, mentor, and counselor. She has always been my inspiration and role model for striving for excellence in teaching, research, and everything else that I do and have done during time at this university. I am so grateful for having her in my life.
I would also like to extend my sincere gratitude to my committee members for their guidance. Dr. Leslie Stoel has provided intellectual and emotional support throughout my doctoral program and valuable suggestions and comments for my dissertation. Her energy and excitement for my work has always encouraged me to take further steps in conducting research. Dr. Nancy Stanforth has shared her brilliant insight while providing valuable suggestions and constructive feedback during the completion of my dissertation. Dr. Michael Browne has taught me a great deal about structural equation modeling and has provided valuable feedback during the data analyses portion of my dissertation. His encouragement throughout quantitative my psychology minor program was very special and nurturing.
My appreciation is also extended to Dr. Nancy Rudd for her guidance throughout my graduate program. She has always been a wonderful mentor for me. I am also
v grateful to Dr. Gong-Soog Hong and Dr. Loren Geistfeld for their advice and encouragement during the course of my program and my job search.
I would also like to thank my fellow graduate students for their intellectual and emotional support for the last two years: Hyejeong Kim, Wi-suk Kwon, Sejin Ha, Jiwon
Seo, Jung-Hwan Kim, Jiyoung Lim, Young Ha, and Minjung Park. I especially thank my first, last, and the best roommate, Minjung Park, for her friendship and the overwhelming comfort she has given me during my last graduate year.
My special thanks go to my family in Korea and my parents-in-law for their love, support, and prayers. Last, but not least, I thank my wonderful husband for his encouragement, support, and patience. He has provided me the strength I needed to complete this degree. Without his support, my successful completion of this dissertation was not possible.
This dissertation was supported by the Alumni Grant for Graduate Research and
Scholarship (AGGRS) from the Graduate School of The Ohio State University. This award made it possible for me to successfully collect the data for this dissertation.
Finally, I thank God for guiding me all along.
vi VITA
June 7, 1971 ...... Born - Busan, Korea
1995 ...... B.S., Ewha Womans University Major: Clothing and Textiles Seoul, Korea
1997 – 1999 ...... Graduate Teaching and Research Associate Department of Consumer and Textile Sciences The Ohio State University, Columbus, Ohio
1998 ...... Technical Designer Intern, Abercrombie & Fitch Reynoldsburg, OH
1998 – 1999 ...... Freelance Sketcher, Lane Bryant Reynoldsburg, OH
1999 ...... M.S., The Ohio State University Major: Textiles and Clothing
1999 – 2001 ...... Technical Coordinator, Express, Inc. Columbus, OH
2001 – 2003 ...... Internship Coordinator Leadership Development Challenge Grant Graduate Research Associate Department of Consumer and Textile Sciences The Ohio State University
2002 ...... Technical Design Consultant, Express, Inc. Columbus, OH
2003 – 2004 ...... Doris M. and Clifford A. Risley Fellow College of Human Ecology The Ohio State University
vii PUBLICATIONS
Research Publication
1. Kim, M., & Lennon, S. (accepted for publication). The Effects of Customer’s Dress on Salesperson’s Service. Clothing and Textile Research Journal
2. Kim, M., & Lennon, S. (accepted for publication). Content Analysis of Diet Advertisements: A Cross-National Comparison of Korean and U.S. Women's Magazines. Clothing and Textile Research Journal
3. Kim, M., & Lennon, S. (2000). Television Shopping for Apparel in the U.S.: Effects of Perceived Amount of Information on Perceived Risks and Purchase Intentions. Journal of Family and Consumer Sciences Research, 28(3), 301 – 330.
4. Kim, M., & Lennon, S. (2003). The Effects of Visual and Verbal Information on Attitudes and Purchase Intent in Online Shopping: PART II, Abstract published in Proceedings of the International Textiles and Apparel Association (Online). 5. Kim, M., & Lennon, S. (2003). The Effects of Visual and Verbal Information on Attitudes and Purchase Intent in Online Shopping: PART I, Abstract published in Proceedings of the European Institute of Retailing and Services Studies Conference (p.54). 6. Kim, M., & Lennon, S. J. (1999). The Effects of Perceived Amount of Information on Perceived Risks and Purchase Intentions in Television Shopping, Abstract published in Proceedings of the International Textiles and Apparel Association (p.59).
7. Kim, M., & Lennon, S. J. (1998). The Effects of Customer’s Dress on Salesperson’s Service, Abstract published in Proceedings of the International Textiles and Apparel Association (p.95).
8. Kim, M., & Rudd, N. A. (June 1998). Historic Costume. A Multidisciplinary Conference on Holiday, Ritual, Festival, Celebration, & Public Display (pp.15)
FIELD OF STUDY
Major Field: Human Ecology Area of Specialization: Textiles and Clothing Minor Field: Quantitative Psychology viii TABLE OF CONTENTS
Page
Abstract ...... ii
Dedication...... iv
Acknowledgements...... v
Vita...... vii
List of Tables ...... xiii
List of Figures ...... xvi
Chapters:
1. Introduction...... 1
1.1. Overview...... 1 1.2. Problem Statement ...... 6 1.3. Purpose of the Study...... 8 1.4. Definition of Terms...... 11
2. Literature Review...... 13
2.1. Overview...... 13 2.2. Stockout Literature...... 14 2.2.1. Overview of Stockout Problems ...... 14 2.2.2. Causes of Stockouts ...... 17 2.2.3. Stockouts in Online Retailing ...... 20 2.2.4. Apparel Stockouts...... 22 2.2.5. Consequences of Stockouts...... 26 Consumers’ Behavioral Responses to Stockouts...... 27
ix Market Share...... 32 Consumers’ Evaluative Responses to Stockouts ...... 35 Store image ...... 35 Decision satisfaction...... 38 Behavioral Intent...... 40 2.3. Limitations in Prior Research...... 42 2.3.1. Research Methods...... 42 2.3.2. Theoretical Approach...... 44 2.4. Theoretical Framework...... 47 2.4.1. Discrepancy-Evaluation Theory of Emotion...... 47 2.5. Hypothesis Development ...... 51 2.5.1. Part One...... 54 Timing of Notification about Product Unavailability...... 54 Preference for an Unavailable Product ...... 55 Frequency of Product Unavailability...... 58 2.5.2. Part Two ...... 61 2.5.3. Part Three...... 68
3. Methodology...... 71
3.1. Overview...... 71 3.2. Research Design...... 72 3.3. Pretests ...... 72 3.3.1. Pretest 1: Stimulus Development ...... 72 3.3.2. Pretest 2: Instrument Development...... 76 Negative Emotion Items...... 77 Retail Management Strategy...... 77 Testing of Questionnaire...... 81 3.4. Main Study...... 81 3.4.1. Instrument Development...... 81 Negative Emotions ...... 81 Store Image ...... 83 Decision Satisfaction...... 83 Behavioral Intent...... 84 Background and Demographic Information...... 84 3.4.2. Mock Website Development ...... 86 3.5. Procedure ...... 88 3.5.1. Participants’ Recruitment...... 88 3.5.2. Main Study 1...... 89 3.5.3. Main Study 2...... 94
x 4. Analysis and Results ...... 96
4.1. Overview...... 96 4.2. Study 1 ...... 98 4.2.1. Sample Description...... 988 4.2.2. Dependent Variables...... 102 Negative Emotions ...... 102 Store Image ...... 103 Decision Satisfaction...... 103 Behavioral Intent...... 105 4.2.3. Preliminary Analysis and Evaluation of Measures...... 107 Unidimensionality...... 110 Convergent Validity...... 111 Discriminant Validity...... 111 Model Specification...... 115 4.2.4. Hypotheses Testing...... 116 Part One...... 120 Part Two ...... 130 Structural equation modeling...... 130 Fit indices...... 131 Model fit...... 133 Hypothesis Testing...... 133 Part Three...... 138 4.3. Study 2 ...... 150 4.3.1. Sample Description...... 150 4.3.2. Exploratory Study...... 154 Variables...... 154 Negative emotions...... 155 Store image ...... 155 Decision satisfaction...... 156 Behavioral intent...... 156 Multivariate Analysis of Variance ...... 157 Negative emotions...... 158 Store image ...... 159 Decision satisfaction...... 160 Behavioral intent...... 160
xi 5. General Discussion...... 163
5.1. Overview...... 163 5.2. Empirical Findings...... 164 5.2.1. Findings from Study 1 ...... 164 The Effect of Contextual Factors in Stockouts ...... 164 Timing of notification...... 165 Preference for an unavailable item...... 166 Frequency of product unavailability...... 168 Timing by preference interaction...... 168 Timing by frequency interaction...... 169 Preference by frequency interaction...... 169 How Negative Emotion Influences Consumer Response ...... 169 The Moderating Role of Contextual Factors ...... 173 5.2.2. Findings from Study 2 ...... 174 5.3. Implications...... 176 5.3.1. Theoretical Implications and Contributions...... 176 5.3.2. Managerial Implications and Contributions...... 179 5.4. Limitations ...... 183 5.5. Suggestions for Future Research...... 186
Bibliography...... 188
APPENDICES ...... 204
Appendix A: Email Script to Solicit Participants ...... 204 Appendix B: Pretest 1: Website...... 207 Appendix C: Pretest 1: Apparel Stimuli ...... 210 Appendix D: Pretest 2...... 214 Appendix E: Main Study...... 216 Appendix F: Experimental Conditions (Example) ...... 222 Appendix G: Questionnaire ...... 227 Appendix H: Covariance Matrix Analyzed ...... 233 Appendix I: Preliminary Data Screening...... 235 Appendix J: Human Subjects Approval Form...... 237
xii LIST OF TABLES
Table Page
3.1. Rating of Apparel Stimuli...... 75
3.2. Frequency of Mention of Negative Emotion Items from the Pretest...... 78
3.3. Content Analysis of Consumers’ Behavioral Responses to a Hypothetical Stockout Situation...... 80
3.4. Four Managerial Responses to Stockouts ...... 80
3.5. Items for Dependent Variables ...... 85
3.6. Experimental Conditions for Study 1 ...... 89
3.7. Comparison of Process between a Web Experiment and General Online Apparel Shopping ...... 92
3.8. An Example of Experimental Conditions ...... 93
3.9. One Condition from Study 1 to which All Study 2 Participants Were Exposed ...... 95
4.1. Demographic Profile of Participants...... 100
4.2. Participants’ Internet Use...... 101
4.3. Participants’ Prior Experience Related to Stockouts ...... 101
4.4. Descriptive Statistics for Dependent Variables ...... 104
4.5. Reliability for Latent Constructs...... 106
4.6. Final Measurement Items...... 109
4.7. Results from Exploratory Factor Analysis...... 110
xiii 4.8. Measurement Properties from Confirmatory Factor Analysis ...... 112
4.9. Construct Correlation for Discriminant Validity...... 113
4.10. Chi-square Difference Test ...... 114
4.11. Participants’ Rating of Preferred Apparel Items...... 118
4.12. Analysis of Variance for Part One ...... 121
4.13. Number of Participants in Each Experimental Condition for Study 1...... 121
4.14. Timing by Preference Interaction on Negative Emotion...... 127
4.15. Timing by Frequency Interaction on Negative Emotion...... 128
4.16. Preference by Frequency Interaction on Negative Emotion...... 129
4.17. Summary of Model Fit...... 134
4.18. Testing of Hypothesis 12 ...... 144
4.19. Testing of Hypothesis 13 ...... 145
4.20. Testing of Hypothesis 14 ...... 146
4.21. Testing of Hypothesis 15 ...... 147
4.22. Testing of Hypothesis 16 ...... 148
4.23. Testing of Hypothesis 17 ...... 149
4.24. Study 2: Demographic Profile of Participants ...... 152
4.25. Study 2: Participants’ Internet Use ...... 153
4.26. Study 2: Participants’ Prior Experience Related to Stockouts...... 153
4.27. Study 2: Description of Dependent Variables ...... 157
xiv 4.28. Multivariate Analysis of Variance for Study 2 ...... 158
4.29. Descriptive Statistics for Study 2...... 161
4.30. Tukey Post Hoc Comparisons...... 162
5.1. Summary of Hypothesis-test Results for Study 1 ...... 167
xv LIST OF FIGURES
Figure Page
2.1. Proposed Model of Consumer Response to Stockouts...... 53
2.2. Part One of the Proposed Model...... 60
2.3. Part Two of the Proposed Model...... 67
2.4. Part Three of the Proposed Model...... 70
4.1. Model Specification using LISREL Notation...... 117
4.2. Proposed Model of Consumer Response to Stockouts...... 119
4.3. Timing by Preference Interaction on Negative Emotion...... 127
4.4. Timing by Frequency Interaction on Negative Emotion...... 128
4.5. Preference by Frequency Interaction on Negative Emotion...... 129
4.6. Hypothesized Model of Consumer Response to Stockouts (Unstandardized parameter estimates)...... 135
4.7. Hypothesized Model of Consumer Response to Stockouts (Standardized parameter estimates)...... 136
xvi CHAPTER 1
INTRODUCTION
1.1. Overview
“the right product, in the right quantity, at the right place, at the right time”
- a formula for perfect retailing.
Success in retailing lies in a retailer’s ability to satisfy customer needs by offering the right product, in the right quantity, at the right place, at the right time. In order to satisfy customers, retailers need a clear understanding of customer needs, efficient supply chain management, and successful store operations (Fisher, Raman, & McClelland, 2000).
However, the intensified competition in retailing, a growing heterogeneity in consumer preference, and an endless quest for a variety of products make it much more challenging for retailers to satisfy customers’ needs than ever before. Retailers try to provide the products that consumers want, but consumers often encounter a situation in which the product they want to buy is unavailable. It appears that retailers put a lot of effort into wooing consumers to visit their stores via various promotions and advertisements, but put forth less effort to ensure that products are available to consumers. However, if retailers
1 fail to make products available that consumers want to buy, all efforts to entice
consumers to stores may be wasted (ACNielsen, 2002).
Consumers today can choose from a plethora of retail outlets and channels to fulfill their needs. If consumers are not satisfied with their shopping experience with a retailer, they can easily shift their purchasing elsewhere. In general, consumers are not very tolerant about product unavailability and further react to it in ways that are adverse to a retailer’s business (Lugo, 2002). It is important for customers to be able to buy the item they want to buy, when and where they want to buy it. With a changing retailing environment, product availability has become a critical source of competitive advantage for retailers and is now an important aspect of customer service (Fox, 1993; Zinn & Liu,
2001). Retailers may unknowingly let their competitors take away their customers, even the most loyal customers, if they cannot satisfy customers by providing customers with what they want, when and where they want to buy it (Macaluso, 2000). Timing is key to retail success (Fox, 1993).
Temporary unavailability of a product on the sales floor is called a stockout (Pride
& Ferrell, 1997). Despite its potentially detrimental impact on retailers, stockouts have been a rampant problem in retailing (Gruen, Corsten, & Bharadwaj, 2002; Hess &
Gerstner, 1987). Several industry reports have shown the severity and pervasiveness of the stockout problem in retailing. The first study that assessed the retail stockout problem reported that approximately one fifth of consumers experienced stockouts while grocery shopping (Progressive Grocer, 1968a, 1968b). A national survey of stockouts was conducted by the Coca-Cola Retailing Research Council in 1995 (Andersen
Consulting, 1996) and several other national surveys were conducted by practitioners to
2 evaluate the state of the stockout problem in retailing, specifically in grocery stores and in convenience stores (Convenience Store News, 1998; Gruen et al., 2002; McCoy, 2003).
According to these reports, most stores have an 8 to 10 percent out of stock level, and stockout level is higher for promoted items and during busy shopping days. Andersen
Consulting (1996) reported an 8.2 percent stockout level in grocery channels. Grocery
Manufacturers of America (GMA) reported a 7.4 percent out of stock level in the U.S., and an 8.3 percent general out of stock rate worldwide, rising up to 25 percent for some promotional items (Gruen et al., 2002). In convenience stores, a 9.2 percent out of stock level was found on a typical day, rising to 15 percent on advertised items (Convenience
Store News, 1998).
The problem with stockouts is not confined to traditional brick and mortar stores.
One of the most common complaints among catalog shoppers is that the items that they want to buy are out of stock (Consumer Reports, 1987). Reports also show that online retailers have suffered from stockouts due to the poor management of inventory systems
(Forbes, 1999; Los Angeles Times, 1999). While no information is currently available to gauge the extent of a stockout problem in online retailing, several studies support the importance of product availability in online shopping. When online shoppers encounter product unavailability, they easily switch to other websites (Accenture, 2000). It has been found that product availability is a critical factor to online customer satisfaction, and online customers are very intolerant with out of stock situations. Many online customers are convenience-oriented shoppers, but there is nothing convenient about stockouts.
Given the virtually zero switching cost in the context of online shopping, online
3 customers are ready to leave the site if they cannot find or buy what they want immediately because an item is out of stock (Macaluso, 2000).
Despite the drastically changing retail business environment, one basic goal for retailers remains the same - to increase profitability while increasing customer satisfaction and fostering store loyalty (Convenience Store News, 1998). However, stockouts result in substantial revenue losses from both short- and long-term impacts on consumer demand (Fisher et al., 2000; Gruen & Corsten, 2002; Zinn & Liu, 2001). The short-term impact of stockouts may be a loss of sales if consumers delay the purchase or
switch to a competing store. However, the long-term impact of stockouts can be more
severe, resulting in loss of patronage and negative word-of-mouth. Stockouts may initially elicit frustration, but may eventually influence a consumer’s perception of the
product, brand, and store. Gogos (2003) estimated that about 15 percent of promoted
sales are lost due to stockouts and this is equivalent to $19 billion in the total industry.
Data Ventures (2001) projected the loss of $25 billion per year due to stockouts. Some
researchers estimated the cost of stockouts in a catalog company to be a 34 percent loss in
profits (Anderson, Fitzsimons, & Simester, 2001). Financial implications of stockouts
for retailers seem non-negligible. Anderson et al. suggested that the impact of stockouts
is enduring and thus influences future profits.
The biggest problem related to stockouts in the retail industry is that retailers lack
a good understanding of the stockout problem. Although a stockout is a subject as old as
retailing, many retailers are unfamiliar with the causes and consequences of stockouts
and very little is learned as to how to reduce stockouts and mitigate the negative impact
of stockouts on their business (Convenience Store News, 1998). Despite an increasing
4 amount of data that retailers gather about buyers, point of purchase, buying patterns, and more, retailers have been struggling with stockouts for decades without much improvement (Fisher et al., 2000; Gruen & Corsten, 2002). Many retailers still lack a good understanding of both the short- and long-term impacts of stockouts due to a difficulty associated with measuring the costs of stockouts. The cost of overstocks can be readily measured because it can be accurately quantified, but the cost of stockouts cannot be easily measured because lost sales due to stockouts cannot be accurately quantified
(Zinn & Liu, 2001). Yet, retailers are challenged to balance the costs of carrying inventory and the costs of stockouts, because imbalance between supply and demand results in overstocks and/or stockouts and billions are lost in sales and in profit each year
(Gogos, 2003). The high costs of maintaining inventory encourage retailers to reduce their stock keeping units, but at the same time, the costs of stockouts may be substantial, with potential long-term consequences (Schary & Christopher, 1979). An increasing heterogeneity in consumer preference and a never-ending desire for a variety of products exacerbate the intricacy of maintaining an optimal balance between overstocking and stockouts.
Although the stockout problem was noted by marketing scholars and practitioners several decades ago (Peckham, 1963; Progressive Grocer, 1968a, 1968b), most prior research focused on logistic implications of stockouts based on the economic theory of utility maximization (Chiang, 1991; Gupta, 1988). A limited number of researchers investigated consumers’ behavioral responses to stockouts (Emmelhainz et al., 1991;
Peckham, 1963; Walter & Grabner, 1975; Zinszer & Lesser, 1981). However, findings from these studies were mainly descriptive in that they reported a variety of consumers’
5 reactions to stockouts (e.g., substitute for the item, delay the purchase, cancel the
purchase, or go to another store) without theoretical insight. In addition, empirical
findings were greatly inconsistent across studies depending on research methods used
(quasi-experiment vs. interview), research setting (grocery store vs. discount store), or
other artifacts specific to the study. Furthermore, most prior research failed to explain the
underlying process by which stockouts influence consumer response. As a result, extant
literature offers a very fragmented picture of the stockout phenomenon in retailing.
1.2. Problem Statement
Without much improvement in the stockout problem throughout several decades, a changing retail environment challenges retailers to deal with the stockout problem
(Campo, Gijsbrechts, & Nisol, 2003; Corsten & Gruen, 2003). Despite its important implications for retailers and manufacturers, stockout research has been limited to date.
Extant literature illustrates a variety of consumers’ reactions to stockouts, but entails
several critical limitations. First, a predominant portion of prior research employed field studies to investigate consumer response to stockouts or the impact of stockouts on market share. An important limitation of this method is that a causal relationship cannot be drawn due to a lack of control over other extraneous variables and the absence of random assignment (Campbell & Stanley, 1963). Second, most prior research implicitly assumed that the severity of a stockout problem is constant across different individuals, situations, or products. However, there is evidence to suggest that stockout response varies substantially depending on consumers, situations, and products (Campo et al.,
6 2003). Different people will respond to the same stockout differently, or one person will
react differently to similar stockouts depending on the situation. Depending on the circumstance, a consumer may not care whether an item is unavailable to purchase.
Other times, the consumer will get extremely frustrated about a stockout. Likewise, while the nature of stockouts may vary in reality, most prior research largely neglected to take such variables into consideration. Third, although current stockout literature acknowledges heterogeneous consumers’ responses to stockouts, an underlying process by which consumers respond to stockouts is not provided. Thus, little has been learned as to why consumers respond the way they do. Fourth, most prior studies examined consumers’ behavioral reactions to stockouts (e.g., switch stores) and generally overlooked consumers’ evaluative reactions to stockouts. Only a very small number of studies paid attention to some aspects of evaluative reactions such as perception of store image (Schary & Christopher, 1979) and decision satisfaction (Fitzsimons, 2000).
Consumers’ evaluative reactions to stockouts are important because they may influence consumers’ behavioral responses to stockouts and may have enduring effects on consumers. Fifth, prior research failed to provide useful insight that retailers can utilize to reduce the negative impact of stockouts on their business. An improved understanding of consumer response to stockouts will contribute to more accurate and reliable measurements of the cost of stockouts, and subsequently allow retailers to make more informed inventory decisions to balance between overstocks and stockouts. Retailers may further need practical implications that they can incorporate into their strategic decisions or managerial decisions to mitigate the potentially harmful impact of stockouts on retailers, when they occur.
7 1.3. Purpose of the Study
This study attempts to fill a gap in the existing literature by addressing limitations noted in a previous section. First, in order to examine a causal relationship between a stockout and consumers’ reactions, this study will employ a randomized experiment using a mock website simulating online apparel shopping. Experimental design with successful manipulation of causes and random assignment is best-suited to study causal relationships (Shadish, Cook, & Campbell, 2001). The findings from this study will complement previous research findings based on field studies. Second, this study will investigate the relationship between contextual factors in stockouts (e.g., timing of notification about product unavailability, preference for an unavailable item, and frequency of stockouts) and consumers’ emotional and evaluative responses as well as behavioral intent. Unlike previous research that implicitly assumed that the nature of stockouts is constant, this study acknowledges that the nature of stockouts can range from negligible to severe depending on contextual factors. This study will examine whether three contextual factors impact the severity of stockouts by assessing the intensity of negative emotion elicited by product unavailability. Third, this study will provide a theoretical perspective on the process by which product unavailability influences consumer response. Using Mandler’s discrepancy-evaluation theory of emotion (1975,
1984, 1990), this study posits that negative emotion aroused due to product unavailability adversely influences consumers’ evaluative responses, which in turn impact behavioral intent. Fourth, this study will examine perception of store image and decision satisfaction as consumer evaluative responses to stockouts. While a majority of prior
8 studies focused on consumers’ behavioral reactions to stockouts, this study will pay attention to evaluative responses, which are postulated to affect behavioral response to stockouts. Fifth, this research will explore four different management strategies as a retailer’s response to a consumer at the time of stockouts. Four retail management strategies will be tested to assess which strategy is more effective in reducing negative consumer response to stockouts. The findings will provide useful and practical information that can be adopted by online retailers to mitigate the adverse impact of stockouts.
In summary, the purpose of this study is to investigate how consumers respond to stockouts. The following research objectives are developed to accomplish the purpose of the study.
• Research Objective 1: Investigate the effects of three contextual factors in
stockouts (timing, preference, and frequency) on negative emotion elicited by
product unavailability.
• Research Objective 2: Investigate how negative emotion evoked by product
unavailability influences consumers’ evaluative responses (store image and
decision satisfaction) and behavioral intent.
• Research Objective 3: Investigate the moderating role of contextual factors in
stockouts on the process by which negative emotion influences consumer
response.
• Research Objective 4: Explore consumer reaction to four managerial responses
to stockouts and evaluate their effectiveness in mitigating the adverse impact of
stockouts.
9 The research findings from this study will contribute significantly to the stockout literature by providing a comprehensive model that explains how consumers respond to product unavailability and also by complementing previous research findings based on different research methods. Furthermore, this study will provide useful insight to help retailers strategically deal with the stockout problem by mitigating negative consumer response to stockouts.
10 1.4. Definition of Terms
The following terms are used in this study.
1. Affect: An umbrella term for subjective feeling states that include feelings, emotions,
or moods.
2. Consideration sets: A set of products or brands that consumers actually would consider
buying and thus evaluate in making a decision. In this study, consumers are asked
to select four apparel items they would consider buying out of 10 available items
to develop a consideration set.
3. Decision satisfaction: Consumers’ satisfaction with their experience in the decision-
making process. It is both a cognitive and an affective state that consumers may
experience upon making a product selection.
4. Emotion: Intense, short-lived, and high conscious affective states that influence mental
processing and behaviors. People are often aware of the source of their emotion
and its effect. Emotion in this study involves autonomic visceral arousal and
cognitive evaluation based on Mandler’s discrepancy-evaluation theory of
emotion.
5. Inventory control: The procedures used to maintain the desired level of inventory.
They involve perpetual or periodic review of inventory levels and the
management of receipt, storage, and distribution of materials related to inventory
within the firm and between the firm and its vendors.
6. Market share: The proportion of the total sales in terms of dollars or quantities in a
target market that is held by each of the competitors.
11 7. Mood: More diffuse and enduring affective states that bias one’s cognition and
behavioral tendency. People are not often aware of the source of their moods and
the effects of mood.
8. Preference: Consumer preference is reflected in how a consumer acts toward an item.
In a choice context, preference for an item is often expressed in terms of
consideration set membership. Items included in the consideration set are more
preferred items than those not included in the consideration set.
9. Schema: As an abstract representation of one’s experience and knowledge, a schema is
developed through interaction with the environment. As a cognitive system, a
schema helps us organize and make sense of information. In this study, one’s
schema may serve as a source of discrepancy between expectation and actuality in
that one develops expectations of an event based on a schema.
10. Stockouts: A situation in which a product is temporarily unavailable to consumers.
11. Store image: The way consumers perceive the store based on the evaluation of store
attributes (e.g., merchandise, service, physical facilities, promotion, store
atmosphere, and convenience) deemed important by consumers.
12. Online shopping: A type of shopping in which consumers engage in a shopping
process whereby product images and descriptions and services are viewed via the
Internet and computer.
12 CHAPTER 2
LITERATURE REVIEW
2.1. Overview
This chapter comprises five sections that build foundations for the current study.
In the second section, the literature addressing stockouts will be reviewed. An overview
of stockout problems in retailing will be presented followed by causes and consequences of stockouts. Following the literature review, limitations of extant literature will be
discussed in terms of research methods and theoretical foundations in the third section.
In the fourth section, the discrepancy-evaluation theory of emotion (Mandler, 1975) will be presented and discussed as a theoretical framework for the study. In the last section of
this chapter, the proposed model of consumer response to stockouts will be discussed
followed by hypotheses development.
13 2.2. Stockouts Literature
2.2.1. Overview of Stockout Problems
A stockout is defined as an unavailability of a product on the sales floor (Pride &
Ferrell, 1997). Making products available when and where consumers want to buy improves consumer value, builds brand loyalty as well as store loyalty, increases sales, and thus increases profitability of business. Despite the advances in supply chain management and increased investments in inventory-tracking systems, stockouts have been an endemic problem in retailing without much improvement for several decades
(Corsten & Gruen, 2003; Gruen et al., 2002).
Several industry reports have shown the severity and diffusion of stockout problems in the retail industry. A study by the National Association of Food Chains, A.
C. Nielsen Company, and Progressive Grocer (1968a, 1968b) was the first stockout study that looked at consumer behavior, while previous stockout studies focused more on estimations of stockout costs. In two sequence studies, Progressive Grocer investigated over 30 items and shoppers’ reactions to stockouts and found that over 20 percent of shoppers experienced stockouts of the item they wanted to buy. Early studies indicated that 10 to 30 percent of out of stock level was widely accepted as the norm in the retail industry (Mason & Wilkinson, 1976; Progressive Grocer, 1968a, 1968b; Schary &
Christopher, 1979).
More recent trade reports showed that the overall out-of-stock levels vary to a great extent across retailers and stores, but the majority had 8 to 10 percent of stockout
14 level (Convenience Store News, 1998; Corsten & Gruen, 2003; Gruen et al., 2002). The
1995 Coca-Cola Retailing Research Council study documented 8.2 percent stockout level
in supermarket chains. A previous National Housewares study in 1995 found 6 to 12
percent of out of stock level in mass merchandising (Andersen Consulting, 1996). In a
comprehensive study of stockouts combining store audits, scanner data, and personal
interviews with customers and industry representatives, Andersen Consulting revealed
that about 8.2 percent of items were out of stock on a typical afternoon in a national
supermarket chain, rising to about 11 percent on Sundays and 15 percent on advertised
items. The estimated loss due to stockouts was a 6.5 percent loss of category sales.
Convenience Store News (1998) conducted a national stockout study on 15 key
product categories (tobacco, fruit juices/drinks, beer, milk, carbonated beverages, salty
snacks, fountain/frozen drinks, single-serve salty snacks, snack cakes, candy, isotonics,
breads, iced teas, bagged ice, and bottled water) from 25 convenience stores across the
country and with 2,100 consumers. This study observed that 9.2 percent of the items
were out of stock on a typical day and stockout problems lasted all week long resulting in
an estimated 3 percent of total sales lost due to stockout problems.
Despite valiant efforts to eliminate stockout problems in the grocery industry, a
recent study by Grocery Manufacturers of America (GMA) reported 7.4 percent of out of
stock levels in grocery channels (McCoy, 2003). GMA tracked 1600 items from 7
product categories (beer, carbonated soft drinks, cookies and crackers, frozen pizza, milk,
pre-packaged bread, and salty snacks) in 20 stores for 14 consecutive days to conduct a
comprehensive out-of-stock study for food and consumer products and found that an
average of 7.4 percent of the time, consumers encountered stockouts for the item they
15 wanted to buy. This stockout level was translated to 3 percent of supermarket sales at risk, which equates to nearly $200,000 in annual sales per average supermarket and $6 billion of lost sales nationwide. This study further showed that an out of stock level for a
promoted item was higher (an average of 13.1 percent), ranging from 8.3 percent to a
17.1 percent depending of the day of week. Stockout level was the highest when
consumer traffic was high, which lead to a higher likelihood of stockouts. Popular items
were more frequently out of stock and accounted for 45 percent of stockouts observed in
the study. For some promotional items, out of stock level was 25 percent (GMA, 2002).
Another GMA-sponsored study examined stockout problems worldwide and reported an 8.3 percent out of stock rate worldwide (Gruen et al., 2002). Again, out-of- stock level for popular items or promoted items is generally much higher than other items.
This study further revealed that retail store practices are major causes of stockouts through inaccurate forecasting, poor supply management, and poor replenishment practices at stores, accounting for about 75 percent of all stockouts. The estimated sales losses due to stockouts accounted for about 4 percent of total sales.
A field study (Anderson et al., 2001) with a real catalog company found that over
20 percent of the items were out of stock at the time of the order, and nearly one third of customers who ordered from this catalog encountered stockouts. The conversion rate from shoppers to purchasers drastically dropped from 86 percent to 62 percent if the item was out of stock at the time of the order. The estimated loss due to stockouts accounted for a 34 percent reduction in profits of the company studied.
Stockouts are prevalent in retailing. As evidenced in the above industry reports, prevailing retail stockouts are non-negligible because of the potential costs for retailers
16 and manufacturers and critical implications for the business (Campo, Gijsbrechts, &
Nisol, 2000). In a more recent study, Gogos (2003) reported that up to 15 percent of promoted sales are lost due to stockouts and this is translated to $19 billion in the total retail industry. Data Ventures (2001) estimated that $25 billon in sales are lost annually due to retail stockouts. Even with some differences in estimation, the expected financial losses due to stockouts are substantial.
2.2.2. Causes of Stockouts
Why do stockouts occur? Can retailers avoid having stockouts in their stores?
Researchers have identified several factors that contribute to the occurrence and prevalence of stockouts in the retail industry (Fisher, 1997; Fisher et al., 2000; Nexgen,
2003). One factor causing stockouts is inaccurate and inadequate forecasting. With a growing heterogeneity in consumer preference and volatile consumer demand, accurate forecasting has become more difficult to achieve. In addition, for products with a long lead time, accurate forecasting is much harder (Fisher, 1997; Nexgen, 2003). A recent study published in the Harvard Business Review (Fisher et al., 2000) illustrated why many retailers fail to achieve accurate forecasting. According to this report, more than two thirds of retailers test new products before the actual launching. Nonetheless, most of them tend to distrust unfavorable test results and use their own intuitions instead.
Many retailers have a tendency to think that testing of new products in their company is highly unscientific and subsequently do not pay attention to testing results. In addition, even though it is important to update forecasts based on early sales data for accurate
17 forecasting, many retailers fail to analyze early sales data and update forecasting
accordingly, partly because many of them do not have the systems to exploit early sales
data. Average forecast errors can reach up to 55 percent, but with the use of early sales
data, forecasting errors can be reduced up to 18 percent. To make it worse, most of these companies fail to track and predict forecasts accurately. Less than one third of the companies studied in the Harvard Business Review reported that they analyzed the accuracy of their forecast and made improvements accordingly (Fisher et al., 2000).
Most forecasting systems work well at the chain level, but not at the store level (Gogos,
2003).
A second factor that may cause stockouts is inefficient supply chain management.
Efficient supply chain management is a critical component of successful retailing, especially for products with short life cycles, but it has become more challenging. Fisher et al. (2000) criticized that the lack of an “efficiency mentality” among retailers is in part responsible for inefficient supply chain management system. Due to increasingly long lead times, retailers have to make a commitment with suppliers far in advance. If retailers have an efficient supply chain management system, they can use early sales data and respond to demands accordingly. This will reduce overstocking of unpopular items and also minimize stockouts of popular items. If retailers can reduce supply chain speed with a replenishment system, retailers will not have to use markdowns to eliminate overstocks and lose sales and customers due to stockouts again. However, many retailers do not have efficient supply chain management (Fisher et al., 2000; Nexgen, 2003).
A third factor causing stockouts is inaccurate inventory planning. Inventory planning deals with decisions about how much to produce, how much to order, and when
18 to order. Researchers criticized that most retailers do poor jobs in their inventory
planning (DeHoratius, 2002; Fisher et al., 2000; Nexgen, 2003). Many retailers fail to
track stockouts and do not estimate the revenue losses due to stockouts. Fisher et al.
found that only about one third of retailers they studied tracked stockouts and estimated
the sales losses caused by stockouts. Although stockouts and lost sales due to stockouts are endemic, many retailers fail to utilize the tracking of stockouts to achieve an optimal inventory level.
Furthermore, inventory records are often inaccurate at the store-level because of errors in replenishment and sales processes (DeHoratius, 2002). Most retailers have point-of-sale (POS) systems and use this to capture sales data. However, store-level sales are frequently inaccurate because improper handling of returns and/or incorrect scanning at the checkout register. For example, after buying a small shirt, if a customer wants to exchange it for a medium shirt, a salesperson is supposed to scan the small shirt into the
register as a return and then scan a medium shirt as a new purchase. However, a more
common practice is to exchange them without scanning both items into the POS system,
resulting in inaccurate sales data for inventory management (Fisher et al., 2000).
DeHoratius found that 65 percent of the inventory records from one large national retail
chain were inaccurate. Inaccurate inventory records have a substantially negative impact
on inventory control because stores may carry too much inventory without knowing it or
fail to reorder when the item is out of stock. Reduced profits due to such inaccurate
inventory control are substantial.
Inaccurate inventory records may further influence the accuracy of forecasting
because historical sales data are used to estimate future demand within a forecasting
19 system. If no sales transactions occur due to stockouts, and inventory data do not have
such information, a new forecast based on inaccurate data may predict very low or no
demand for the product that may actually be a popular item.
2.2.3. Stockouts in Online Retailing
Online retailers are more susceptible to stockout problems, because they generally carry lower inventories to lower operation costs (Bhargava, Sun, & Xu, 2002). Online retailing is based on the idea of gaining a competitive advantage by lowering operation costs. Lower inventories reduce operation costs and thus provide a competitive advantage for a retailer, but provide a higher likelihood of stockouts for the consumer.
Despite its high vulnerability to stockout problems, no statistical data are available to
gauge the extent of stockout problems in online retailing. Few available reports have
indicated that online retailers have suffered from stockouts due to inaccurate forecasting
problems and poor management of inventory systems (Forbes, 1999; Los Angeles Times,
1999).
While a very limited number of findings is available to show the level of the stock
problem in online retailing, more research findings are available to advocate the
importance of product availability for successful online retailing. According to an
Accenture study (2000), the biggest problem consumers confront during the shopping
process is a stockout. This has a significant implication for online retailers because many
online shoppers then easily switch to another site. Given the high cost of customer
acquisition in online retailing, online retailers cannot afford to lose customers (Accenture,
20 2000). It has been further shown that product availability is a critical factor to satisfy online customers (Macaluso, 2000). Online shoppers are very unforgiving with out of stock situations. If they could not make a purchase and payment in one transaction, they were ready to leave the site (Morphy, 2002). Therefore, what is likely to happen is that stockouts give competitors the opportunity to take the customers away, even the most loyal customers (Macaluso, 2000).
Some research findings suggested the importance of offering in-stock status information in online retailing. Internet Retailer (2004a) found that product availability information was an important factor when choosing online retail sites for shopping. A study by PricewaterhouseCoopers showed that in-stock status information is crucial information that would increase the likelihood of online purchases (Retail Forward, 2001).
In their study of online customer service, Kim and Stoel (2004) found that availability of in-stock status information was a significant aspect of service predicting online purchase intent. Saliba (2001) also found that available in-stock status information was one of several important factors that convert shoppers to purchasers.
Online retailing is in the business of convenience, but there is nothing convenient about stockouts. For successful business, online retailers need to resolve problems associated with their infrastructure to minimize occurrences of stockouts. Otherwise, they may not survive. Online retailers agree with the critical importance of inventory management for business. However, a growing, but still a very small number of online retailers adopt practical technologies (e.g., WebSphere 5.4) to enhance automated order management, which provides customers with in-stock status information and further
21 tracks their orders (Morphy, 2002). This technology allows shoppers to see product availability as they would on a store rack.
In its infancy, online retailing was all about building a web site and user interface mimicking the brick and mortar shopping experience. During the dot.com shake down, many online retailers were forced out of business because of a lack of profitability. Now, moving beyond the shake down stage, online retailing is entering the second phase with an increased focus on customer-centric order fulfillment via real-time inventory management (LeClaire, 2002). Emerging topics in the second phase of online retailing include support for demand forecasting, product optimization, and pricing execution technologies. Real-time inventory management will allow online retailers to avoid overstocks and stockouts. It is evident that product availability is a key factor for a success in future online retail business.
2.2.4. Apparel Stockouts
Apparel products are subject to more frequent stockouts due to characteristics of fashion, namely, short product lifecycles, unpredictable demand, and inaccurate inventory management (Hammond & Kohler, 2000; Rollo, 2002). Apparel has been one of the most difficult product categories for which to predict future demand, because apparel product lifecycles are getting shorter and product proliferation is rapidly increasing (Hammond & Kohler, 2000). Such trends have resulted in increasing demand uncertainty, which adversely affects the accuracy of forecasting (Rollo, 2002). However, accurate forecasting of consumer demand is more critical in the apparel category than for
22 other consumer products (Sproles, 1975) because today’s fashions are tomorrow’s markdowns. Inaccurate forecasting leads to mismatches in demand and supply, which result in markdowns to clear overstocks and lost sales due to stockouts. Retail customers are now so accustomed to markdowns that the portion of goods sold at full price is very low. Retail apparel prices have collapsed in recent years and experienced the steepest price drop in 50 years. Net profits for apparel retailers and manufacturers have shrunk to
5 percent. At the same time, potential full-price sales are lost due to stockouts (Rollo,
2002).
According to Fisher’s (1997) classification of products, there are two types of
products, functional and innovative products. Functional products are commodities with
predictable demand. These products have long-life cycles and are low margin items for
retailers. Despite long lead time, functional products have rare stockout situations because of predictable demand. Functional products require a physically efficient supply chain to reduce costs within the chain. Toothpaste or light bulbs are examples of functional products.
Another type of product based on Fisher’s classification is innovative products with unpredictable demand. Innovative products are new concept goods with a short-life cycle and high margins. Due to unpredictable demand, these items are vulnerable to overstock and/or stockouts. Overstocks lead to serious markdowns to clear inventory, while stockouts lead to lost sales and dissatisfied customers. Innovative products require
a more flexible and responsive supply chain to allow retailers to replenish in response to
POS data. Pokemon toys and Beanie babies are examples of innovative products.
Consumer demands for such products are highly unpredictable because they have no
23 historical data, and the demand for such products is volatile. Especially for innovative products, economic gains from balancing overstock and stockouts are substantial. For example, if a product with a 40 percent contribution margin has a stockout level of 25 percent, lost sales are 10 percent of the total sales, which exceeds most retailers’ profits before taxes (Fisher, 1997; Hammond & Kohler, 2000).
Another example of innovative products are apparel products which have a longer lead time and a more fragmented and globally dispersed manufacturing system, as well as common characteristics of innovative products (short lifecycle, unpredictable demand).
These factors make it much more difficult for apparel retailers to achieve efficient supply chain management (Hammond & Kohler, 2000). Furthermore, apparel items are frequently out of stock due to varying styles, colors, and sizes. If a retailer wants to carry a pair of casual pants (available in 3 colors and 7 sizes), the retailer at a minimum needs to carry 21 items (1 style x 3 colors x 7 sizes) for just one style. Most apparel retailers carry far more styles and also operate multiple locations, and so the problem is magnified.
To make it worse, ineffective inventory management leads to more frequent stockouts of apparel products, especially for a size. Detailed sales data are generally available for retailers to make more accurate inventory management. Nevertheless, many retailers fail to utilize such sales data for more effective and efficient inventory management (Fisher et al., 2000). For example, apparel retailers can track their sales in terms of style, color, and size because each has its own bar code. However, they store the data with style and color information in the central computer and do not keep track of the size of the merchandise sold. This may explain why one out of three shoppers entering an apparel store with an intention to buy something leaves without purchasing because
24 their size is out of stock (Fisher et al., 2000). Retailers need to understand the simple principle that they cannot sell what they do not have in stock. Many retailers suffer from financial losses due to markdowns, while disappointing their customers who cannot find what they want.
A challenge for apparel retailers is that apparel stockouts are more difficult to deal
with compared to other consumer products. In grocery or convenience stores,
substitutions are more viable because same or similar products are often available in a
different size, variety, and brand; however, no such substitutes are readily available in
apparel stores. For example, while it is easy to change the size of a grocery product (half
gallon of milk to one gallon of milk), it is very unlikely that one can substitute a size in
apparel stockouts because an apparel item has to fit one’s body.
The context of this dissertation is online apparel retailing. The characteristics of
online retailing and those of apparel products altogether put online apparel retailers at the
highest risk for stockouts. Furthermore, apparel products are often presented on the website as if they are currently available to ship to a customer if ordered. While currently
no information about the level of online stockout problems or online apparel stockout
problems is available, it will be important for online retailers to understand the impact of
stockouts on consumers and ultimately their businesses.
25 2.2.5. Consequences of Stockouts
When confronted with stockouts, consumers notice, react, and respond to them
(Fitzsimons, 2000). How consumers respond to stockouts determines the magnitude of a stockout’s impact on retailers and manufacturers. The importance of stockout occurrences and potential threats to manufacturers and retailers were noted by marketing scholars and practitioners already in the sixties and seventies (Peckham, 1963; Schary &
Christopher, 1979), but not much attention has been given to consumer response to stockouts since then. More attention has been given to the logistic aspect of stockout problems - estimating the cost of stockouts (Anupindi, Dada, & Gupta, 1998; Bell &
Fitzsimons, 1999; Ferguson, Mason, & Wilkinson, 1979; Schwartz, 1966; Walter &
Grabner, 1975).
The limitation of prior research estimating the cost of stockouts lies in the difficulty of estimating the cost of stockouts, while it is more straightforward to estimate the cost of overstocks. The cost of overstocks can be measured by calculating the cost of carrying additional inventory and losses from markdowns to clear the inventory (Zinn &
Liu, 2001). However, it is not very straightforward to estimate lost sales due to stockouts.
Another limitation lies in that prior research estimating stockout costs has investigated the short-term losses of stockouts such as the cost of lost sales and of backorders, but fails to incorporate the impact of stockouts on both immediate and subsequent demands for products (Schary & Becker, 1978) and other intangible costs such as customer dissatisfaction, negative word-of-mouth, and loss of customer loyalty (Fitzsimons, 2000;
Zinn & Liu, 2001).
26 Furthermore, among a limited number of studies that have examined consumer
response to stockouts, a predominant portion of these studies are primarily descriptive in
nature. They simply illustrated a variety of stockout reactions without explaining the
process by which stockouts influence consumer response. However, because consumers’
responses to stockouts reported in empirical studies vary substantially from case to case
(Emmelhainz et al., 1991; Peckham, 1963; Schary & Christopher, 1979; Straughn, 1991;
Walter & Grabner, 1975; Zinn & Liu, 2001; Zinszer & Lesser, 1981), descriptive research findings do not provide a complete picture of the stockout phenomenon and also do not provide useful insight for retailers. Prior stockout literature leaves a number of issues related to stockouts uncovered and provides little insight into understanding heterogeneous consumer response to stockouts. Although some researchers recognize heterogeneity in consumer response and attempt to explain it, the process by which stockouts influence diverse consumer responses has yet to be uncovered.
Consumers’ Behavioral Responses to Stockouts
Most commonly reported consumer responses when confronted with stockouts
are: (1) substitute for the item, (2) delay the purchase until the next trip to the store, (3)
go to another store to buy the item, or (4) cancel the purchase (Corsten & Gruen, 2003).
Most previous researchers have asked customers to recall how they reacted when they
encountered stockouts or asked them how they would react in a hypothetical stockout
situation. Based on the above 4 possible reactions to stockouts, some researchers
gathered more detailed information about behavioral responses. For example, a
27 consumer can substitute an item by switching a size or number of items of the same brand or by switching brands.
A review of prior literature on stockout behaviors supports that the way consumers respond to stockouts varies to a great extent (Andersen Consulting, 1996;
Emmelhainz et al., 1991; Progressive Grocer, 1968a, 1968b; Schary & Christopher, 1979;
Walter & Grabner, 1975; Zinn & Liu, 2001). Across several empirical studies, a range of
20 to 80 percent of respondents substituted for the out of stock item, a range of 5 to 25 percent of respondents delayed the purchase, and a range of 15 to 50 percent of customers studied left the store without a purchase. Although substitutions seem to be the most dominant behavioral pattern, in other studies, store switching was a more dominant reaction to stockouts (Schary & Christopher, 1979). So, most empirical findings are more exploratory rather than conclusive.
Progressive Grocer (1968a) reported that approximately 48 percent of shoppers they studied substituted for the item when confronted with a stockout and about 25 percent of them delayed the purchase. This industry report further found that over 28 percent of shoppers left the store without making a purchase because the item they wanted was out of stock.
Under a hypothetical stockout situation, Walter and Grabner (1975) asked consumers in liquor stores how they would respond to a stockout. Six different actions were identified: (1) switch brands and buy at a lower price, (2) switch brands and buy at the same price, (3) switch brands and buy at a higher price, (4) buy the same brand, in a different size, (5) make a return trip to the store for the item, and (6) visit another store for the item. Over 80 percent of respondents reported that they would substitute [(1)
28 through (4)] for the out of stock item and nearly 15 percent of them responded that they
would go to another store. Very few were willing to delay the purchase.
In their interviews with 343 customers who experienced at least one stockout in
suburban supermarkets, Schary and Christopher (1979) identified six possible reactions
in the stockout condition: (1) substitute a brand, (2) substitute a different size from the
same brand, (3) buy a different product, (4) delay the purchase until the next trip to the
store, (5) go to another store, and (6) abandon the purchase. Among six possible
reactions, about half of respondents indicated that they would switch to another store and about 20 percent of respondents reported that they would drop the purchase. Only a little over 20 percent of respondents would have substituted for the item.
Using a quasi-experiment, Emmelhainz et al. (1991) examined a variety of consumer responses to stockouts. After intentionally removing five items (the best
selling item of a leading brand) from a discount grocery store, they conducted exit
interviews with consumers who intended to buy from any of the five product categories,
but could not find the exact brand, size, and variety they wanted. This study reported that
39 percent of customers experienced more than one stockout on a given shopping trip.
Findings showed that nearly 73 percent of the interviewees substituted for the out of
stock item, while the other 27 percent did not substitute. Among the 27 percent of
customers who did not substitute, more than half of them indicated that they would visit
another store to purchase the item. Three of the most common responses to stockouts
were: (1) substitute a different brand, (2) substitute a different variety within a brand, and
(3) switch to another store.
29 A study by Convenience Store News (2000) showed that 71 percent of respondents substituted for the product, 17 percent of them went to another store, and 12 percent of them delayed the purchase. This study further found that consumers were so unforgiving with stockouts that most of them stopped going to the store after experiencing 2 or 3 stockouts.
After conducting a quasi-experiment, Zinn and Liu (2001) conducted exit- interviews with 230 customers who experienced stockouts at the checkout counter from four different stores of a chain over a two-week period. They found that more than 60 percent of respondents substituted for the item by switching within the same store and over 20 percent of participants were willing to go to another store, while about 15 percent of them delayed the purchase until a next trip.
Gruen and Corsten (2002) found that when confronted with stockouts, a growing number of consumers were willing to go to another retail outlet to purchase the item they wanted rather than buying a substitute. In this study, 21 to 43 percent of consumers were willing to go to another store to make a purchase, while 7 to 25 percent of consumers were willing to drop the purchase.
Because some other factors (personal-related, product-related, or situational- related) may influence how consumers react to stockouts, several researchers further attempted to identify important factors influencing consumer response to stockouts.
Among several factors examined, demographic variables, brand loyalty, and urgency of a purchase were included in previous studies.
Schary and Christopher (1979) examined how demographic variables affect stockout behaviors and found that age and occupation influenced how consumers respond
30 to stockouts. Brand loyalty also had an impact on the way consumers react to stockouts.
Not surprisingly, they found that brand loyal consumers were more likely to visit another store to find the brand they wanted.
In their study of stockouts, Emmelhainz et al. (1991) investigated how other
factors including product risk, intended product usage, and urgency of a purchase
influence the way consumers react to stockouts. Findings showed that if the perceived
risk of purchasing an alternative brand is high, consumers were less likely to substitute.
As for intended product usage, consumers were more likely to substitute for the item if it
was for regular usage than for a special occasion. In terms of urgency of a purchase, if a
purchase is urgent, consumers were more likely to substitute for the item. While repeated
brand purchase experience affected whether consumers substituted for a brand or
substituted within a brand, it did not influence whether to substitute or not. Strong repeat
brand purchasers were more likely to substitute a variety or a size within the brand. Store
loyalty was a factor determining whether consumers switch stores or delay the purchase.
Results of this research suggest that consumers’ responses to stockouts are more varied
than previously indicated. When confronted with stockouts, at least fifteen different
consumer reactions were identified in this research.
Zinn and Liu (2001) investigated how situational, consumer, and perceived store
characteristics influence the way consumers respond to stockouts. Situational
characteristics refer to the shopping experience on the day the interview was conducted
(e.g., urgency of purchase). Consumer characteristics indicate non-demographic
attributes of consumers (e.g., brand loyalty). Perceived store characteristics are store
attributes perceived by consumers (e.g., perception of store prices). Findings of this
31 study showed that perception of store prices significantly influenced whether consumers
would substitute for the item, delay the purchase, or leave the store. When consumers
perceived that the store offered lower prices, they were more likely to substitute for the
item from the store or to delay the purchase because the price might be higher at other
stores, and they were less likely to leave the store. Urgency of purchase had a significant
effect on the probability of substituting for the item and on delaying the purchase. The
higher the urgency to purchase the item, the more likely that consumers would substitute
for the item and the less likely that they would delay the purchase. Surprise had a
significant effect on the probability of delaying the purchase and on leaving the store.
The less surprised with the stockout, the more likely that consumers would delay the
purchase, and they were less likely to leave the store. Consumers who were surprised
because of a stockout were more likely to leave the store. This study found no
relationship between demographic variables and consumer responses to stockouts.
Market Share
Some previous studies examined the short- and long-term impact of stockouts on
market share, but their findings are inconsistent across studies. While the short-term impact of stockouts on the market share was significant in studies employing an experiment (Charlton & Ehrenberg, 1976) or a quasi-experiment (Motes & Castleberry,
1985; Schary & Becker, 1978), it was negligible in another research study using scanner panel data (Straughn, 1991). The findings of the impact of stockouts on long-term market share are even more diverse across studies. While several researchers have found suggestive evidence to support the enduring impact of stockouts on market share
32 (Anderson et al., 2001; Fitzsimons, 2000; Struaghn, 1991), another study found no significant long-term impact on market share (Charlton & Ehrenberg, 1976).
Charlton and Ehrenberg (1976) conducted an experiment simulating the door-to- door selling of 3 brands of detergent and another 3 brands of tea over 25 weeks and observed changes in consumer purchasing behavior in response to variations in product availability, pricing, and promotion. When stockouts were introduced during the study, the most common response was to switch brands, but return to their usual brand when in stock again. They found no significant long-term impact on market share. Another research study was conducted as a partial replication of Charlton and Ehrenberg’s study using potato chip brands (Motes & Castleberry, 1985). This study found a substantial long-term effect on market share.
In a quasi-experiment using the accidental stockouts (a strike affecting beer distributors in Seattle in 1971), Schary and Becker (1978) examined the impact of stockouts on both short- and long-term market share. They observed that product unavailability had a significant impact on short-term market share. When brands were out of stock for a while, the effect of product unavailability on market share was the greatest both during and immediately after stockouts. Product unavailability may have a long-term effect on market share, but other factors may also have contributed to long- term market share. So, it was difficult to estimate the unique effect of stockouts on long- term market share. Schary and Becker further speculated that stockouts may have an adverse impact on consumer loyalty because once buyer-seller relations are broken, it may be difficult to recover. This study observed that firms did not fully recover their market share after having stockouts due to a strike.
33 Straughn (1991) examined how stockouts are related to market share using weekly store-level scanner panel data and found a negligible short-term impact on the market and a strong negative impact on long-term market share. This is the first study that used scanner data in stockout research. While the short-term effect on market share was negligible, the negative long-term effect on market share was substantial and worse for large market share brands.
Findings from a series of laboratory experiments by Fitzsimons (2000) also supported the concept of stockouts possibly leading to potential long-term effects on market share. Those exposed to a stockout in a simulated online music shopping environment were much more likely to switch to another website, compared to those who did not encounter a stockout.
In their field study with a catalog company, Anderson et al. (2001) found that stockouts negatively impact current orders, and thus have a negative short-term impact on market share. The conversion rate from browsers to purchasers significantly dropped if an item was out of stock at the time of the order. Stockouts were also found to increase cancellation rates. They found that customers tend to cancel an entire purchase or other orders that are available, if one of the items they wanted was out of stock. Even if a backorder does not take long at all, the conversion rate significantly drops and thus cancellation increases. This study found little evidence to support customers substituting for the item when the item they ordered was out of stock. One interesting finding from the study was that if first-time customers encounter stockouts, they are less likely to purchase from that merchant, compared to repeat customers. Anderson et al. suggested that a small reduction in a stockout rate can lead to a substantial short-term impact on
34 profitability. They further found that the impact of stockouts influences the subsequent
purchase behaviors of customers who were recently confronted with stockouts,
suggesting a potential long-term impact on market share.
While most prior research on stockouts focused on whether consumers substitute,
delay, drop, or switch to another store and its potential impact on market share, little
attention has been paid to consumers’ evaluative responses to stockouts. Only a few
researchers examined the impact of stockouts on consumers’ evaluative responses such as
store image (Schary & Christopher, 1979) and decision satisfaction (Bell & Fitzsimons,
1999; Fitzsimons, 2000). Consumers’ evaluative responses may be enduring and thus
affect behavioral response to stockouts. The ramifications of consumer evaluative response to product unavailability may be more substantial than lost sales due to its
enduring long-term impact on consumer behavioral response. Therefore, in order to fill a
gap in existing literature and to improve the understanding of stockout phenomenon, this dissertation focused on two evaluative responses to stockouts (e.g., perception of store image and decision satisfaction) that may subsequently affect behavioral response to stockouts. Behavioral intent was studied as an approximation to behaviors.
Consumers’ Evaluative Responses to Stockouts
Store image. Store image is the way consumers perceive the store, which is
derived from both objective and subjective store attributes (Doyle & Fenwick, 1974-1975;
Martineau, 1958). How consumers perceive the store is based on the evaluation of store
attributes deemed important by consumers (Bloemer & Ruyter, 1997; James, Durand, &
35 Dreves, 1976). Over the course of time, consumers develop images of the stores and
these images affect consumer behaviors (Berry, 1969; Mazursky & Jacoby, 1986).
Researchers speculate that store image has concrete and substantial managerial
relevance, especially in terms of its impact on profitability (Mitchell, 1993). A unique
store image can be an important marketing asset that creates a competitive advantage for
a company (Rosenbloom, 1983). Prior store image research supports that store image is
positively related to store patronage (Bloemer & Ruyter, 1997; Erdem, Oumlil, &
Tuncalp, 1999; Lessig, 1973; Osman 1993; Samli, 1998; Stanley & Sewall, 1976;
Korgaonkar, Lund, & Price, 1985), store loyalty (Lessig, 1973, Sirgy & Samli, 1989),
and shopping expenditure in the store (Hildebrandt, 1988). Therefore, it is critical for
retailers to develop and maintain a favorable store image (Nevin & Houston, 1980; Samli,
1989). The extent to which retailers can attract new customers to a store and maintain
their market position depends on the degree to which retailers can engender positive store
image because customers tend to look for and patronize the stores with favorable store
images (Bearden, 1977; Prasad, 1975).
Substantial research efforts have been made to identify important store attributes
that constitute store image which impacts consumers’ store choice and patronage
behaviors (Dickerson & Albaum, 1977; Hansen & Deutscher, 1977-1978; Lindquist,
1974-1975). Researchers have suggested multiple facets of store image. Lindquist
summarized prior research on store image and identified nine independent aspects of
store image that contribute to the formation of store image. These nine attributes are: (1)
merchandise (quality, assortment, styling or fashion, guarantees, and pricing), (2) service
(general service, salesperson service, ease of merchandising return, delivery service, and
36 credit policies), (3) clientele (social class appeal, self-image congruency, and store personnel), (4) physical facilities (elevators, lighting, air conditioning, store layout, and washrooms), (5) convenience (general convenience, locational convenience, and parking),
(6) promotion (sales promotion, advertising, displays, and symbols and colors), (7) store atmosphere (customer’s feeling of warmth, acceptance, or ease), (8) institutional factors
(reputation and reliability of the store), and (9) post-transaction satisfaction (merchandise in use, returns, adjustment). Using the same data, Mazursky and Jacoby (1986) extracted six of the most frequently examined dimensions of store image: merchandise quality, merchandise pricing, merchandise assortment, locational convenience, salesclerk service, and general service.
Consumers place a different importance on each store attribute depending on their shopping orientation and other situational factors (Gentry & Burns, 1977; Lindquist,
1975). If consumers develop a favorable store image based on their perception of store attributes, they are more likely to patronize the store, while an unfavorable store image due to a negative evaluation of store attributes may justify consumers’ changes of patronage behavior to another store (Spiggle & Sewall, 1987). Therefore, retailers need to identify store attributes that are important to their target customers, and accommodate such attributes to foster favorable store image.
Among store attributes, product-related store attributes (e.g., merchandise assortment, quality, and price) have been found to be most important (Lindquist, 1974-
1975). Kahn and Lehmann (1991) suggested that consumers tend to prefer a store with greater variety because they have more options to choose from.
37 Evidence suggests that store image is negatively influenced by stockouts. In their early study on consumer response to stockouts, Schary and Christopher (1979) found that consumers who encountered product unavailability rated store image significantly lower than those who did not experience a stockout. They used seven store attributes to measure store image. These seven store attributes include: (1) product quality, (2) service to customers, (3) value for money, (4) convenience, (5) variety of products available, (6) product availability, and (7) store location. While Schary and Christopher hypothesized that product availability and convenience are two aspects of store image affected by stockouts, they found that store image in terms of values and product quality were also affected by stockouts. In their interviews with customers who experienced stockouts, Zinn and Liu (2001) found that stockouts adversely impacted consumers’ perceptions of merchandise quality and product availability.
Decision satisfaction. Researchers speculate that a consumer’s overall satisfaction with purchase includes two components. The first component concerns satisfaction derived from the decision process and the second component concerns satisfaction derived from consumption of the good itself (Fitzsimons, Greenleaf, &
Lehmann, 1997). While most consumer satisfaction research has focused on satisfaction related to consumption, decision satisfaction has received much less attention.
Decision satisfaction is defined as consumers’ satisfaction with their experience in the decision process (Fitzsimons et al., 1997). It is both a cognitive and an affective state that consumers may experience upon making a product selection. Decision satisfaction focuses on the consumer decision-making process including need recognition,
38 information search, alternatives evaluation, and purchase (Engel & Blackwell, 1982).
Decision satisfaction is posited to be a distinctive construct from consumption
satisfaction and to contribute to an overall satisfaction judgment (Fitzsimons, 2000;
Westbrook, Newman, & Taylor, 1978). While decision and consumption satisfaction are
expected to be distinct, they are also expected to be positively correlated.
Consumption satisfaction and decision satisfaction may have different impacts on
consumers’ intentions and behaviors (Fitzsimons et al., 1997). Decision satisfaction has
a more direct impact on retailers than the brand, whereas consumption satisfaction has a
more direct impact on the brand than retailers. For example, if a store is out of stock of
an item a consumer wants to buy, this is likely to lower a consumer’s decision
satisfaction which may lead to negative word-of-mouth and lowered purchase intention for the store in which a consumer encounters stockouts. However, this experience is not
likely to impact a consumer’s satisfaction with the branded item he or she wants to buy.
Researchers suggest that decision satisfaction is a more relevant concept in the
study of consumer response to stockouts than consumption satisfaction (Fitzsimons et al.,
1997; Thaler, 1989). Because product unavailability affects the decision process by
eliminating alternatives to choose from, one important evaluative response to stockouts is
consumer satisfaction with the decision process. In their study of consumer satisfaction
with decision making, Westbrook and his colleagues (1978) found that product
availability was one of the most important factors influencing consumer satisfaction with
decision-making. In general, consumers are more satisfied with their decision process
when they have more alternatives to choose from unless the choice decision becomes too
39 complicated with too many choices. Other experimental studies of stockouts further
supported that product unavailability negatively affects consumers’ evaluation of their
decision experience and thus lowers decision satisfaction (Bell & Fitzsimons, 1999;
Fitzsimons, 2000).
Behavioral Intent
The concept of behavioral intent has been one of the most important constructs
because it has a significant impact on customers’ future interaction with a firm.
Behavioral intent indicates “whether customers will remain with or defect from the
company” (Zeithaml, Berry, & Parasuraman, 1996, p.33). Favorable behavioral intent
has been found to have a positive influence on the profitability of a firm (Zeithaml et al.,
1996).
The definition of behavioral intent varies across different research contexts. For example, behavioral intent was defined as the likelihood of returning to the same hospital in a study of patient satisfaction (Woodside, Frey, & Daly, 1989). In another study in a
hotel context, it was defined as a customer’s willingness to provide favorable word-of-
mouth and patronage to the hotel in the future.
In this study, behavioral intent is viewed as a higher order construct that consists of purchase intent and word-of-mouth. Behavioral intent is defined as customers’ purchase intent and intent for word-of-mouth communication. Customers’ purchase intent is viewed as their intent to visit a store for shopping and purchasing without
40 switching to another store, and, word-of-mouth is defined as the likelihood of recommending a store to friends.
Purchase intent includes shopping likelihood and buying likelihood (Juster, 1966;
Morrison, 1979; Whitlark, Geurts, & Swenson, 1993). Previous research has shown that high purchase intent is related to higher actual buying rates rather than low intent of buying (Berkman & Gilson, 1978). While purchase intent does not equate to actual purchase behavior, it has been demonstrated that measures of purchase intent have a good predictability of actual purchase behaviors (Jamieson & Bass, 1989; Stapel, 1971).
Generally, satisfaction is a major determinant of behavioral intent. Previous research supports that customer satisfaction positively influences purchase intent (Oliver,
1981). Prior research has shown that product unavailability negatively affects consumers’ evaluation of their decision experience and thus lowers decision satisfaction.
The lowered decision satisfaction further negatively influences future purchase intent and promotes store switching behaviors (Fitzsimons, 2000; Straughn, 1991). Exposure to stockouts further affects subsequent shopping behavior in such a way that those who experienced stockouts were less likely to return to the same store where they encountered stockouts.
Evidence suggests that word-of-mouth has a significant influence on one’s purchase decision-making. Word-of-mouth communication is perceived as a credible source of information, and thus is very influential on a company’s reputation (Day, 1980).
Prior research findings generally showed that customer satisfaction impacts one’s willingness to engage in positive word-of-mouth (Bearden & Teel, 1983; Oliver & Swan,
41 1989; Yi, 1990). Satisfied customers are likely to provide positive word-of-mouth
communication (Westbrook, 1987). In this study, positive word-of-mouth
communication is defined as a higher likelihood of recommending an online store to
friends.
2.3. Limitations in Prior Research
Prior research on stockouts provides fragmented insight into the process by which consumers respond to stockouts. Although some studies recognize heterogeneity in consumer reactions and attempt to explain it, the process by which stockouts influence diverse consumer response has not yet been found. While most prior studies have investigated consumers’ behavioral responses to stockouts, consumers’ evaluative responses to stockouts have not received much scholarly attention. In addition, not much has been learned about factors determining the severity of stockouts and no causal relationships between such factors and consumer reaction have been established to date
(Campo et al., 2003). Major limitations in prior research seem related to research methods predominantly used in prior research and lack of theoretical insight.
2.3.1. Research Methods
Research methods used to study consumers’ stockout behaviors vary across studies. Some researchers have conducted quasi-experiments; intentionally removing
42 specific items from store shelves during a specific period of time, and then interviewing
customers who wanted to buy any of those removed items (Emmelhainz et al., 1991;
Verbeke, Farris, & Thurik, 1998; Zinn & Liu, 2001). Another type of field study was
also used. Some researchers conducted exit interviews with customers immediately after
they experienced stockouts as they naturally occur (Schary & Christopher, 1979). Walter
and Grabner (1975) interviewed customers at a liquor store in the context of a
hypothetical stockout situation. These studies largely depended on self-reports without
being able to parcel out the impact of other extraneous factors on consumer responses to
stockout. For example, different promotional activities at the time of data collection may
influence how consumers respond to stockouts. If a competing brand is on sale, a
customer may be more willing than usual to switch to a different brand. In addition, personal situations may influence how consumers react to a stockout as well. Time
pressed shoppers may react to stockouts more negatively than others. Due to a lack of
control of other extraneous variables in a field study including a quasi experiment, it is
impossible to assume that stockouts caused certain consumer responses. Although true experiments are desirable to study the causal influence of stockouts on consumer
behaviors, costs are prohibitive in a real-life retail setting.
Another important limitation in prior research is that previous studies implicitly
assumed that the severity of stockout situations is constant across studies and across
individuals. Although empirical investigations were undertaken to study heterogeneous
behavioral reactions to stockouts, they did not take into consideration and examine
factors that may influence the severity of stockouts. This may be largely due to
limitations associated with research methods used in previous research. For example,
43 timing of notification about a stockout is posited to influence consumer response to
stockouts in such a way that late notification may lead to a stronger negative response than early notification (Fitzsimons, 2000; Min, 2003). However, such investigations manipulating timing of notification may not be possible in a field experiment or in field
studies.
Several variations in terms of research settings in previous studies make it
difficult to derive empirical generalizations across studies (Sloot, Verhoef, & Frances,
2002). For example, a small number of product categories (e.g., 2 products in Campo et
al, 2000) and a small number of stores and/or retail formats (e.g., 1 store from one retail
chain in Emmelhainz et al., 1991) were studied in some cases. In addition, product
categories and brand types used in stockout studies were different across studies. Some studies used high-share brands (Verbeke et al., 1998), while others examined consumer reactions to stockouts of both high- and low-share brands (Campo et al., 2000). Another variation across studies is the type of stockout. A single item of a brand may be out of stock or all items in a single brand in a product category might be out of stock. With such differences within a field study, it is difficult to draw a general conclusion.
2.3.2. Theoretical Approach
Most consumer research on stockouts largely lacks a theoretical base in the investigation of stockout problems. Early studies were generally descriptive and illustrated heterogeneous consumer behavioral responses to stockouts without providing
theoretical insight (Emmelhainz et al., 1991; Peckham, 1963; Schary & Christopher,
44 1979; Zinn & Liu, 2001; Zinszer & Lesser, 1981; Walter & Grabner, 1981). Of the more recent studies, few have attempted to investigate the stockout problem with theoretical insight.
First, Corstjens and Corstjens (1995) developed a conceptual framework that explicates consumer response to stockouts as the outcome of tradeoffs between different cost types based on the economic theory of utility maximization. For example, the tradeoff between the cost of switching brands and the cost of switching stores is posited to lead to a different consumer response. Consumer response to stockouts depends on the absolute and relative level of both costs. When the cost of switching brands is lower than switching stores, consumers are more likely to substitute for the item. When both costs are high, consumers are more likely to delay the purchase or to drop the purchase plan.
This approach provides good insight, but is limited in several aspects. First, this approach regards brand switching and store switching as two main consumer reactions, while putting less emphasis on other reactions such as deferring or canceling of the purchase. In addition, this framework has not been empirically tested yet and the proposed two main reactions may not be directly measured, thus results have to rely on self-reports.
Economic theory predicts that rational consumers will maximize their utility by choosing the product alternative with the next highest utility, and thus an unavailable alternative should have no influence on consumer decision-making. However, consumers may not be as rational as the economic theory would predict. Research on context effects suggests that the choice context has an impact on consumer decision-making (Bettman,
Luce, & Payne, 1998). Prior research on consumer response to stockouts provides
45 evidence to suggest that consumers respond in a way that is not consistent with predictions based on the economic theory (Emmelhainz et al., 1991; Fitzsimons, 2000;
Straughn, 1991).
Another theoretical approach used to study consumer behavior related to product unavailability is the psychological reactance theory. Two recent choice studies in the context of stockouts suggested that psychological reactance theory explains why
consumers exhibit some evaluative and behavioral responses when confronted with
stockouts (Fitzsimons, 2000; Min, 2003). As a motivational theory, Brehm’s
psychological reactance theory (1966) explains how an individual responds when his or
her freedom is threatened or eliminated. According to Brehm (1966), when a person’s
freedom to choose is restricted or eliminated, individuals experience psychological
reactance that prompts them to reinstate the threatened freedom.
Focused more on choice behaviors, psychological reactance theory predicts that
an individual who encounters product unavailability is motivated to reassert their
freedom of choice (Brehm, 1966; Brehm & Brehm, 1981; Clee & Wicklund, 1980). The
level of psychological reactance an individual experiences due to product unavailability
may depend on the degree of freedom an individual expects to have prior to choice
(Brehm, 1966) and the importance of threatened freedom to the individual (Clee &
Wicklund, 1980).
Building on reactance theory, Min (2003) posited that individuals will exhibit the
“boomerang effect.” According to Min’s rationale, when individuals encounter product
unavailability, they experience reactance because their freedom of choice is threatened.
In a response to a threatened freedom, individuals are motivated to assert their freedom of
46 choice by selecting a dissimilar alternative rather than a similar alternative. Another
marketing study suggested that consumers seem to experience psychological reactance
when they are aware that someone has intentionally imposed a constraint on a choice, and
subsequently choose an alternative that is opposite of what is recommended as an
alternative (Fitzsimons, 2000). When consumers feel that they are forced to choose an
alternative, they are more likely to resist and go the opposite direction.
However, it is not clear how likely it is that consumers would experience
psychological reactance in general shopping situations. In particular, it is unlikely that
consumers perceive that retailers would intentionally have a product unavailable to
consumers. Also, Wicklund (1974) speculated that if there are many overlapping
qualities among alternatives available in decision-making situations, individuals are less likely to experience psychological reactance. In a typical shopping situation, consumers
have a plethora of alternatives from which to choose. Therefore, consumers may not
experience the level of psychological reactance that would motivate them to attempt to
reestablish their freedom of choice in general shopping contexts.
2.4. Theoretical Framework
2.4.1. Discrepancy-Evaluation Theory of Emotion
This study proposes that negative emotion elicited when confronted with
stockouts is an explanatory variable that drives consumers to negatively react to stockouts.
Mandler’s (1975, 1984, 1990) discrepancy-evaluation theory of emotion offers insight
47 into why consumers negatively react to stockouts. This theory explains that discrepancy between expectation and actuality or some interruption to the usual or habitual way of thinking or acting provide the mechanism by which negative emotions occur. For consumers, product availability is a hidden, but strong assumption when shopping for products. Underlying store choice behavior, there is the presumption that a desired product will be available for the purchase. Therefore, a consumer’s expectation is product availability, while the actuality is a stockout. Discrepancy between a consumer’s expectation of product availability and the actuality of stockouts sets the stage for negative emotions to occur.
As one cognitive theory of emotion, Mandler’s (1975, 1984, 1990) discrepancy- evaluation theory of emotion followed Stanley Schachter’s (Schachter & Singer, 1962) two-factor model of emotion that proposes two basic underlying processes of emotions: autonomic visceral arousal and cognitive evaluations. Both theories suggest that autonomic nervous system (ANS) arousal generates emotions when mediated by cognitive evaluation. However, Mandler’s theory is different from Schachter’s theory in that Mandler identified discrepancy and interruption as origins for visceral arousal, whereas no specific origins were identified in Schachter’s theory. According to Mandler, perceptual or cognitive discrepancies or the interruption of some ongoing action are origins for emotional experience, because discrepancy and interruption innately trigger autonomic visceral arousal. Mandler explained several key reasons why interruption and discrepancy are important conditions for emotions. First, interruption leads to autonomic visceral arousal. Second, the interruption of ongoing cognitive and behavioral activities postulates cognitive and behavioral restructuring. Third, interruption signals important
48 changes occurring in the environment that may result in alternative circumstances of
living and adapting. Autonomic visceral arousal determines the intensity of emotion,
while cognitive evaluation determines the quality of emotion.
Mandler (1975, 1984, 1990) further speculates that a schema is a major source of
discrepancies and interruptions. As a cognitive structure, a schema is an abstract representation of one’s experience and knowledge (Wyer, 1980) and is developed through interaction with the environment (Fiske, 1991). Schemas under the cognitive structure influence an individual’s perception and evaluation of the environment and
provide sources of discrepancies (Mandler, 1985). Expectations are important because
they are related to the occurrence of discrepancy and interruption. People develop expectations of an event based on their schema. If their expectations are violated, a discrepancy occurs due to a lack of congruity in schematic processing. The occurrence of
discrepancies innately causes autonomic visceral arousal which sets the stage for
cognitive evaluation. In the real world, people are unlikely to make an accurate prediction about future events. People’s memory for past experience tends to be imperfect and expectations of future events are rarely the same as the actual events.
Unexpected outcomes are more likely to occur in real life. In the stage of cognitive evaluation, individuals may attempt to integrate input from the environment into the existing knowledge structure, or schemas.
Schemas are also sources of interruption (Mandler, 1984, 1990). The notion of interruption is that an organized response, like a schema, is interrupted whenever the completion of its goal is physically hampered. If incoming information from the environment is congruent with information in the existing schema, new information is
49 readily assimilated into existing knowledge schemas. Individuals under this condition
may not be aware of the cognitive processes. However, incoming information from the
environment may be interrupting if it does not fit with the current schema, and thus, is not
manageable with an existing schema. When a mismatch occurs, information cannot be
readily assimilated into current working schemas. This interruption of an ongoing
process leads to changes in autonomic visceral arousal which triggers cognitive evaluation and in turn emotions.
In this study, product unavailability is expected to cause discrepancy and/or interruption by violating a consumer’s expectation based on his/her schema. As a function of their shopping schema, consumers develop expectations that a product will be available for purchase whenever they want to buy. When confronted with stockouts, a consumer’s expectation is now violated because experiencing a stockout is not congruent with one’s schematic processing about shopping. This discrepancy causes autonomic visceral arousal which sets the stage for cognitive evaluation. When autonomic visceral arousal by discrepancy is paired with conscious cognitive evaluation, negative emotions occur. Cognitive evaluation may depend on consumer characteristics, situational variables, and other factors relevant to individuals. Therefore, two people may experience and exhibit different emotional responses when confronted with the same stockout at the same time, as a function of their cognitive evaluation of a stockout, even when their expectation about product availability is the same.
This study postulates that negative emotion elicited by discrepancy or interruption of one’s schema due to a stockout is an underlying variable that drives consumers to negatively respond to product unavailability. The discrepancy-evaluation theory of
50 emotion can take individual, situational, and other extraneous variables into account to explain consumer response to stockouts. When consumers encounter stockouts, autonomic visceral arousal is automatically triggered and subsequent cognitive evaluation will determine the quality of negative emotion elicited by product unavailability. These two processes for negative emotion elicitation may account for various emotions experienced at the time of stockouts from mere annoyance to extreme frustration.
2.5. Hypothesis Development
Given the substantial impact of stockouts on the profitability of business, it is important for retailers to gain knowledge about factors determining the severity of stockouts and the underlying process by which stockouts influence consumers. Even if stockouts cannot be completely eliminated in a real retail setting, an improved understanding of the stockout phenomenon may help retailers strategically deal with occurrences of stockouts and minimize negative long-term consequences of stockouts.
Based on previous arguments and the discrepancy-evaluation theory of emotion, the following model is proposed to provide a more complete picture of the underlying process by which stockouts influence consumers (See Figure 2.1). The proposed model consists of 3 parts. In the first part, this study acknowledges a different nature of stockouts depending on context and proposes that the following three contextual factors may influence the severity of stockouts by evoking different levels of negative emotions:
(1) the timing of the notification about product unavailability, (2) the consumer’s preference for an unavailable product, and (3) the frequency of product unavailability.
51 The severity of a stockout problem is hypothesized to influence the level of negative
emotions an individual experiences at the time of stockout. The more severe a stockout
problem is to an individual, the more likely that the stronger negative emotion is elicited
by a stockout. Therefore, the degree of negative emotion experienced by consumers may
reflect how severe the stockout problem is to an individual.
The second part of the proposed model suggests that negative emotions aroused
due to stockouts have a negative influence on consumers’ evaluative responses to
stockouts such as perceptions of store image and decision satisfaction and behavioral
intent. The proposed model further suggests the positive relationship between
consumers’ evaluative responses and behavioral intent. The stronger negative emotions
consumers experience due to stockouts, the more likely that they develop unfavorable
store image, are dissatisfied with decision-making process, and further express lower behavioral intent with a firm.
The third part of the model suggests that three contextual factors impacting the
severity of stockouts may moderate how negative emotion influences consumers’
evaluative responses and behavioral intent. Each part of the proposed model will be
discussed in more detail in subsequent sections and hypotheses will be developed to test
the proposed model of consumer response to stockouts.
52
Antecedents Underlying Mechanism Consequences
Part Three
Part One Part Two
Timing Store Image
(+) (-)
(-) Preference Negative Behavioral Emotion Intent
(-) (+)
Frequency Decision Satisfaction
Figure 2.1. Proposed Model of Consumer Response to Stockouts
53 2.5.1. Part One: Relationship between Contextual Factors and Negative Emotions
As shown in Figure 2.2, the first part of a conceptual framework establishes the
relationship between three key contextual factors in stockouts and negative emotions. It
is posited that three contextual factors including timing, preference, and frequency have
effects on the level of negative emotions elicited by stockouts.
Timing of Notification about Product Unavailability
Few empirical research findings support the effect of timing of notification on consumer response. Fitzsimons (2000) posited that the timing of notification about product unavailability may influence consumer response to stockouts. If consumers are notified about product unavailability after they make a choice, they are likely to experience more intense negative emotions as compared to prior notification. His rationale was that if consumers are notified about product unavailability after going
through a deliberative decision process, consumer reactions to stockouts are more negative because consumers are more personally attached to the product and also
committed to the product. Similarly, Zinn and Liu (2001) found that consumers who
were more surprised with a stockout were more likely to leave the store and less likely to
delay the purchase. Late notification about product unavailability is more likely to surprise consumers than early notification.
In the current study, the timing of notification about product unavailability is hypothesized to affect the level of negative emotions in such a way that consumers will
54 experience stronger negative emotions when they are notified about product
unavailability after making a choice compared to early notification. The rationale behind
this hypothesis is that timing of notification influences consumers’ expectations about
product availability. The degree of negative responses elicited from product
unavailability may vary depending on whether it is expected or not (Wicklund, 1974). If
consumers are informed about product unavailability in an early stage of decision-making,
they are more likely to choose other alternatives available and thus avoid unexpected
choice constraint due to stockouts. On the other hand, if consumers are notified about
unavailability after making a choice, this violates consumers’ expectations that a product
is available for purchase and further interrupts their shopping goals (e.g., purchase),
resulting in discrepancies between expectations and actuality. Therefore, it is predicted
that timing of notification about product unavailability will influence the intensity of
negative emotions experienced at the time of stockout.
Hypothesis 1: As compared to those notified about product unavailability prior to
making a choice, consumers notified about product unavailability after making a
choice will experience stronger negative emotions.
Preference for an Unavailable Product
Researchers have stressed the critical impact of an individual preference for consumer choice behaviors (Hutchinson, Kamakura, & Lynch, 2000). Some marketing researchers have supported that preference for an unavailable item influences the magnitude of an actual stockout response (Anupindi et al., 1998; Campo et al., 2003).
55 They warned that even large retailers carrying extended assortments may face substantial
short-term losses of sales if preferred items are out of stock. Ten percent of consumers interviewed reported that they switched to a different store because their favorite item was unavailable (Convenience Store News, 1998). Fitzsimons (2000) also found that consumers reacted more negatively when the item they preferred was out of stock compared to when other items were out of stock.
In a choice context, consumer preference for an item is often expressed in terms of consideration set membership (Fitzsimons, 2000). A consideration set is defined as a set of products or brands that the consumer actually would consider buying (Anderson &
Vincze, 2000). Due to limited cognitive capacity, consumers do not evaluate all available alternatives when making a choice. Rather, they develop a consideration set of alternatives of manageable size to further process their choice decision. In order to develop a consideration set from all available alternatives, a consumer needs to engage in some level of decision processing to make selections from alternatives under the assumption that alternatives are available (Fitzsimons, 2000). In general, consideration set membership is a good indicator of consumer preference of an item. Items included in the consideration set are more preferred items than those not included in the consideration set.
With apparel products, consumers tend to form preferences as instantly as they see them. In online apparel shopping, online shoppers cannot evaluate all possible alternatives at once because a virtually unlimited number of products are available online.
Rather online shoppers are likely to pick several alternatives of a more manageable size
(i.e., develop a consideration set) after browsing pictures of apparel items. If a consumer
56 includes an item in a consideration set, this indicates that a consumer engaged in some
deliberative processing to select the item. If the item a consumer considers buying is
unavailable, this violates the consumer’s expectation about product availability and
further interrupts one’s shopping goals. Because pictures are used to present products on the website whether they are actually available in stock or not, online shoppers presumably expect that products are available if they see them online. Available pictures on the website lead shoppers to believe that products are available, even when they are not.
Preference may impact how consumers respond to product unavailability via salience. Prior research has found that consumers are more likely to notice if their favorite items are unavailable as compared to when their less favorite items are unavailable (Broniarczyk, Wayne, & McAlister, 1998; Fitzsimons, 2000). Product unavailability becomes more salient to consumers if the preferred product is unavailable than if other less preferred products are unavailable. This may further impact the intensity of emotions when confronted with stockouts. It is hypothesized that preference for an unavailable item will influence the intensity of negative emotions experienced at the time of stockout.
Hypothesis 2: As compared to when the not preferred item is unavailable,
consumers will experience stronger negative emotions when their preferred item
is unavailable.
57 Frequency of Product Unavailability
Consumers have been found to be intolerant about stockouts. Convenience Store
News (1998) reported that a majority of consumers stop going to a store after experiencing two or three stockouts. However, no systematic research has been conducted to examine the impact of the frequency of product unavailability on consumer response, although some researchers have warned about the potentially substantial impact of cumulative effects of stockouts (Emmelhainz et al., 1991; Schary & Christopher,
1979). If a consumer faces product unavailability a second or third time, this may have a cumulative effect, which may be a stronger impact on consumer response than the first incidence. Since repetitive unavailability of a product can be a serious interruption to a consumer’s goal of decision-making and thus continuously create discrepancies, frequent stockouts are likely to elicit more negative emotional responses than a single stockout.
Therefore, it is hypothesized that frequency of product unavailability will influence the intensity of negative emotions elicited by a stockout. In this study, consumers were exposed to one or two stockouts only.
Hypothesis 3: As compared to those confronted with product unavailability once,
consumers confronted with product unavailability twice will experience stronger
negative emotions.
58 It is further hypothesized that three contextual factors may interact to evoke negative emotions. The following hypotheses are suggested to capture interactions between contextual factors.
Hypothesis 4: Timing and preference will interact to elicit negative emotional
response to product unavailability.
Hypothesis 5: Timing and frequency will interact to elicit negative emotional
response to product unavailability.
Hypothesis 6: Preference and frequency will interact to elicit negative emotional
response to product unavailability.
59
Antecedents Underlying Mechanism
Timing H1
H2 Preference Negative Emotion
H3
Frequency
Part One
Figure 2.2: Part One of the Proposed Model (Hypotheses 1 to 6) Note. Hypotheses 4 to 6 predicting interactions among independent variables are not shown in the figure
60 2.5.2. Part Two: Relationship between Negative Emotions and Consumer Response
A predominant portion of prior consumer research has focused on the cognitive nature of consumer behaviors and assumed logical consumers (Bettman, 1979; Howard &
Sheth, 1969). Meanwhile an increasing number of researchers proposed that affect plays a critical role in consumer behavior in that affect is not only a source of motivation, but also has a major impact on information processing and choice (Hoffman, 1986; Isen,
1984; Zajonc, 1980). Feelings may be used as a source of information (Frijda, 1986;
Schwarz, 1990). Recent development in social psychology recognizes the central role of affect in the decision-making process (Schwarz & Clore, 1983, 1988). In addition, a growing number of consumer studies have examined feeling-oriented factors on consumer choice and judgments (Barta & Holbrook, 1990; Hirschman & Holbrook,
1982; Rossiter & Percy, 1980) and the role of emotion on purchase and consumption processes (Zeitlin & Westwood, 1986).
Emotion plays a critical role in business success (Babin, Darden, & Babin, 1998;
Cohen, 1990). Business success may depend on how well retailers can maximize emotional states desired by consumers and minimize emotional states not desired by consumers. Prior research in the retail and services domain have shown that emotions aroused during shopping influence approach/avoidance behavior (Hui, Dube, & Chebat,
1997), shopping expenditures (Donavan & Rossiter, 1982), retail preference and store selection (Dawson, Bloch, & Ridgway, 1990), purchase intent (Baker, Grewal, & Levy,
1992), and shopping satisfaction (Machleit & Eroglu, 2000).
61 Previous marketing research suggested that emotion aroused during shopping may influence consumers’ cognitive processing of store atmosphere, service, and/or product quality and such cognitive evaluations influence their shopping behaviors (Chebat,
Chebat, Vaillant, 2001; Chebat & Michon, 2003; Dubé & Morin, 2001; Mattila & Wirtz,
2001). Some found that positive emotions aroused in a shopping environment influenced shoppers’ cognitive evaluations of store atmosphere, sales personnel, and/or product quality, which in turn influenced shoppers’ spending at the store (Chebat et al., 2001).
Prior research generally supports the positive links between positive emotions and an overall satisfaction. In his study with automobile owners and subscribers, Westbrook
(1987) found that negative emotion was negatively related to customer satisfaction and positively related to complaining behavior, while positive emotion was positively related to customer satisfaction and inversely related to complaining behavior. Subsequent research further confirmed the relationship between emotion and satisfaction in the consumption process (Dubé-Rioux, 1990; Mano & Oliver, 1993; Oliver, 1993).
As shown in Figure 2.3, the second part of a conceptual framework establishes the relationship between negative emotions and consumers’ evaluative responses and behavioral intent under the stockout situation. This study proposes that negative emotion is an explanatory variable that drives consumers to negatively react to stockouts. It is postulated that negative emotions elicited by product unavailability influence perceptions of store image, satisfaction with the decision process, and behavioral intent and also evaluative responses such as perceptions of store image and decision satisfaction further impact behavioral intent.
62 Because this study focuses on how negative emotion affects consumers’
evaluative judgments and behavioral intent, it is necessary to define the concept of
emotion for this study. Scholars tend to use terms such as ‘affect’, ‘emotion’, and ‘mood’ vaguely and arbitrarily in different contexts. Affect is an umbrella term for a class of
mental phenomena characterized by a subjectively valenced feeling state, and emotions
or moods are specific examples of affect1 (Cohen & Areni, 1991; Westbrook, 1987).
Both emotions and moods affect consumers’ mental constructs (e.g., perception,
evaluation, judgment, decision making, attitude, intentions), but the nature of influence is
different because their properties are different (Clark & Isen, 1982; Gardner, 1985;
Simon, 1967). The first difference lies in the duration of affective states. Emotion is
short-lived, while mood is enduring. The second difference is the intensity of affective response in that emotion is more intense, while mood is low-intensity. The third difference is the salience of cause. Emotion is a highly conscious affective state with a salient cause, while mood is more diffuse without a salient cause. So, it is easy to pinpoint the source of emotion, but the source of mood cannot be easily identified.
Another difference between emotion and mood is the consequence. Emotion is more likely to react to the source of emotion, while mood is unlikely to directly influence behavior. Instead, mood is likely to bias one’s cognitions and behavioral tendencies.
Emotion may interrupt ongoing behaviors, while mood rarely disrupts ongoing behaviors.
1 Some researchers used mood as a broader term and conceptualized emotion as a motivational view of mood and mood (as defined in Gardner, 1987) as a backdrop view of mood (see Luomala & Laaksonen 2000) for an extensive review). Likewise, affect, emotion, and mood in marketing literature were often interchangeably used without clear conceptual distinctions.
63 In the current study, it is postulated that emotion is aroused by product
unavailability, not mood. First of all, when consumers experience negative feelings at the
time of stockouts, they are likely to be aware of the source or cause of their feelings.
Feelings that consumers may experience at the time of stockouts have specific referents,
which occur as the result of evaluation and interpretation after comparing the actual state
(i.e., product unavailability) with a desired state (i.e., product availability) (Bagozzi,
Gopinath, & Nyer, 1999).
Furthermore, under certain conditions of stockouts, consumers may experience
strong feelings rather than mild feelings. For example, imagine one male online shopper
who spent the last few hours searching and selecting a gift for the upcoming birthday of
his wife. After browsing and debating what to buy, he finally selected one item to buy,
but found that the item he chose was out of stock. Under such a circumstance, he
probably felt extremely mad or frustrated because he spent hours to find out that what he
wanted was not available for purchase. Therefore, the feeling he experienced immediately after the stockout is more likely to be emotion than mood. Furthermore, he may stop shopping for a gift online rather than going to another website immediately after experiencing a stockout because he is frustrated with a stockout. Likewise, stockouts may cause some interruptions of ongoing behavior, while mood is not likely to do that. Prior research findings have supported that product unavailability directly impacts consumers’ behavioral response (Emmelhainz et al., 1991; Fitzsimons, 2000;
Schary & Christopher, 1979; Zinn & Liu, 2001).
While this study posits that negative emotions are elicited by a stockout, types of emotions aroused are different as a function of attribution of the causal agents.
64 Researchers have shown that the mechanism by which consumers attribute causality is
different dependent on context (Bitner, 1990; Folkes, 1990; Westbrook, 1987); internally
attributed (e.g., consumer mistake), externally attributed (e.g., company mistake), or
situationally attributed (e.g., weather). Some negative emotions such as anger, disgust, or
contempt are likely to be aroused if external attributions are made (e.g., a store is
responsible for stockouts), while shame or guilt may be elicited if internally attributed
(e.g., the item is out of stock because I came too late). If situationally attributed (e.g., the
item is too popular), fear or sadness are likely to be aroused. Westbrook (1987)
postulated that negative emotions aroused by external attribution (e.g., anger) are primary
reasons for dissatisfaction. In a retailing context, Machleit and Mantel (2001) found that
the effects of emotions on consumers’ shopping expectation were stronger if externally
attributed (e.g., blame a store), rather than internally attributed (e.g., blame self).
Negative emotions are in general more salient and intense than positive emotions
(Derbaix & Pham, 1991).
This study postulates that consumers are more likely to attribute their negative emotions aroused by product unavailability to the store (e.g., online retailer) than to the self or situation, and thus certain types of negative emotions are likely to be aroused in the stockout situation as a result of an external attribution (e.g., anger, mad). In addition, negative emotions will have a strong influence on the shopping experience because an external attribution leads to more dissatisfaction. Therefore, this study proposes that negative emotions aroused by product unavailability will adversely affect consumers’ evaluative responses such as perceptions of store image and decision satisfaction and
65 consumers’ behavioral intent. Also, perceptions of store image and decision satisfaction are posited to have a positive influence on behavioral intent.
The following hypotheses were developed to investigate the process by which negative emotions influence consumers’ evaluative responses and behavioral intent.
Hypothesis 7: Negative emotion is negatively related to perception of store image.
Hypothesis 8: Negative emotion is negatively related to decision satisfaction.
Hypothesis 9: Negative emotion is negatively related to behavioral intent.
Hypothesis 10: Perception of store image is positively related to behavioral intent.
Hypothesis 11: Decision satisfaction is positively related to behavioral intent.
66
Underlying Mechanism Consequences
Store Image
H10 H7
H9 Negative Behavioral Emotion Intent
H8 H11
Decision
Satisfaction
Part Two
Figure 2.3: Part Two of the Proposed Model (Hypotheses 7 to 11)
67 2.5.3. Part Three: The Moderating Role of Contextual Factors on the Process by Which
Negative Emotion Influences Consumer Response
Part three attempts to examine the moderating role of contextual factors in stockouts (timing, preference, and frequency) on how negative emotion affects consumers’ evaluative responses as well as behavioral intent. It is possible that contextual factors that affect the severity of stockouts may further moderate the relationship between negative emotion and consumer response. For example, the relationship between negative emotion and behavioral intent may be different between early notification and late notification (i.e. timing of notification). The late notification group may show a stronger impact of negative emotion on behavioral intent than early notification group. The following six hypotheses were developed to assess the moderating role of three contextual factors on consumer response to stockouts. Each hypothesis included five sub-hypotheses.
Hypothesis 12: The relationship between negative emotions and consumers’
responses differ as a function of timing of notification.
Hypothesis 13: The relationship between negative emotions and consumers’
responses differ as a function of preference for an unavailable item.
Hypothesis 14: The relationship between negative emotions and consumers’
responses differ as a function of frequency of product unavailability.
68 Hypothesis 15: The relationship between negative emotions and consumers’ responses differ as a function of timing by preference interaction.
Hypothesis 16: The relationship between negative emotions and consumers’ responses differ as a function of timing by frequency interaction.
Hypothesis 17: The relationship between negative emotions and consumers’ responses differ as a function of preference by frequency interaction.
A: negative emotion perception of store image
B: negative emotion decision satisfaction
C: negative emotion behavioral intent
D: perception of store image behavioral intent
E: decision satisfaction behavioral intent
69
Antecedents Underlying Mechanism Consequences
Part Three
Part One Part Two
Timing Store Image H1
H7 H10
H9 Preference H2 Negative Behavioral Emotion Intent
H3 H8 H11
Frequency Decision Satisfaction
Figure 2.4: Part Three of the Proposed Model Note. Hypotheses 12 to 17 are not shown in the model
70 CHAPTER 3
METHODOLOGY
3.1. Overview
This research consists of two studies. In Study 1, a Web-based experiment using
a mock apparel website was conducted to investigate the process by which product
unavailability influences consumers’ evaluative responses and behavioral intent in online
apparel shopping. Based on the proposed model, this study: (1) assessed the effects of
stockouts on negative emotion as a function of three contextual factors (timing,
preference, and frequency), (2) examined the relationship between negative emotions and
consumers’ evaluative responses (e.g., store image and decision satisfaction) as well as
behavioral intent, and (3) examined the moderating role of timing, preference, and frequency on the process by which product unavailability influences consumer response.
In Study 2, a Web-based experiment using a mock apparel website was conducted to explore the effect of four different managerial responses to stockouts on consumers’
reactions.
This chapter presents a description of methods and procedures used to develop
stimuli and instruments and to collect final data. Two pretests were conducted; in the
71 first pretest, mock websites and their contents were developed, and in the second test
instruments for the main study were pretested. The same stimuli and instruments were used in both Study 1 and Study 2. The data collection procedures for Study 1 and Study
2 are each presented in the next section.
3.2. Research Design
The design of Study 1 was a 2 (timing of notification about stockout: before or
after) x 2 (item preference: not preferred or preferred) x 2 (frequency of stockout: once or
twice) complete between-subjects factorial design. The design of Study 2 was a one
factor (managerial response) experimental between-subjects design with four levels
(standard, substitute, backorder, or financial response). Both studies used a randomized
Web experiment simulating online apparel shopping.
3.3. Pretests
3.3.1. Pretest 1: Stimulus Development
The first pretest was conducted to select multiple apparel stimuli for display on
the mock website used in this study. The purpose of this pretest was to select apparel
items that were appealing to the target participants of this study. Because this study
focuses on consumer response to a stockout, it was necessary to provide apparel stimuli
that are more liked and desired by target customers than stimuli that are unappealing to
72 target customers. If apparel stimuli are not attractive to target customers, they would not
be suitable stimuli for simulating a stockout situation in online shopping. For example, if
shoppers do not like any apparel items on a website, they will simply exit the website
without browsing for individual items more carefully. If shoppers find items that they
like from the site, they are more likely to stay at the website and shop. Both clothing
interest (Gurel, 1974) and brand preference (Zaichkowsky, 1985) have been found to be
related to apparel involvement. Therefore, it is reasonable to expect that consumers will
be more likely to be involved with the apparel selection process when they like the
apparel items.
For pretest 1, a mock website was first developed. Pictures of 30 apparel items
were downloaded from commercial websites (See Appendix C). All 30 apparel items
were women’s tops and all were presented on a mannequin (torso only). Thirty apparel
items were pretested in order to select 10 final apparel stimuli for the main study. The
consistency of pictures in terms of background, resolution, angle, brightness, and contrast was achieved and controlled using Adobe Photoshop. The resolution of pictures was maintained to be 360 x 480 pixels across all 30 apparel items.
The pretest used a within-subjects design and was conducted as a Web survey
(See Appendix B). The presentation order of the 30 apparel items was fully randomized.
Forty-eight female college students participated in pretest 1 and received extra course
credit as an incentive. Pretest participants were given a URL for the pretest website and
asked to participate in the Web survey. During the pretest, participants viewed all 30
apparel items and evaluated each apparel item on the following four characteristics using
seven-point rating scales: (1) attractiveness (1: highly unattractive and 7: highly
73 attractive), (2) fashionability (1: highly unfashionable and 7: highly fashionable), (3)
likableness (1: highly unlikable and 7: highly likable), and (4) likelihood to purchase (1:
highly unlikely to purchase and 7: highly likely to purchase). These items were used
because all seemed related to how appealing an item was.
Reliability of the four items was calculated for each apparel item and found to be
reliable (all Cronbach α’s > .90) (See Table 3.1). Based on these reliabilities, scores
from the four items were summed to develop a single indicator of how appealing each item was to participants. Statistics describing the appealingness of each garment were
calculated; the range of these scores varied from 4 to 28. As shown in Table 3.1, the ten
apparel items with the highest median and mean scores were selected for the main study
(See Appendix C). Higher median and mean scores indicate that the item was generally
more appealing to participants compared to items with lower median and mean scores.
Pretest participants also provided feedback on the mock website used for the
pretest in terms of overall appearance and functionality (e.g., how easy to navigate the
site), and this feedback was used in developing a mock website for the main study.
Feedback from participants included the need to add a store name and a banner similar to
real shopping sites. Participants were generally content with the background of the
website and ease of navigation.
74
Apparel Cronbach’s Mean Median S.D. Min. Max. stimuli alpha2 Item 1* .94 20.04 20.50 6.34 5.00 28.00 Item 2 .94 10.73 10.00 5.97 4.00 28.00 Item 3* .96 20.04 21.00 6.21 5.00 28.00 Item 4 .96 15.40 15.50 6.65 4.00 28.00 Item 5 .92 15.02 14.50 5.81 6.00 28.00 Item 6 .94 12.29 11.50 6.52 4.00 28.00 Item 7 .93 15.90 15.50 6.11 4.00 28.00 Item 8 .92 15.85 16.00 5.63 4.00 28.00 Item 9 .90 10.75 9.00 5.61 4.00 24.00 Item 10 .96 16.17 17.00 6.07 4.00 28.00 Item 11* .93 18.69 19.00 5.52 6.00 28.00 Item 12* .93 20.65 21.00 5.29 8.00 28.00 Item 13 .95 14.06 13.00 6.44 4.00 28.00 Item 14 .97 12.48 11.50 7.36 4.00 28.00 Item 15* .91 21.02 22.50 4.57 9.00 28.00 Item 16 .93 15.06 15.00 5.53 4.00 28.00 Item 17 .90 16.60 17.00 5.01 4.00 27.00 Item 18 .96 15.83 16.00 6.70 4.00 28.00 Item 19* .94 19.83 21.00 5.89 4.00 28.00 Item 20* .94 19.06 20.00 5.72 4.00 28.00 Item 21* .95 18.35 20.00 6.60 4.00 28.00 Item 22 .95 11.02 10.00 6.49 4.00 28.00 Item 23 .96 12.52 11.50 6.40 4.00 28.00 Item 24* .93 18.88 19.50 5.92 7.00 28.00 Item 25 .95 15.92 16.00 6.41 4.00 28.00 Item 26 .93 13.06 12.00 5.32 4.00 24.00 Item 27 .93 17.40 19.50 6.53 4.00 28.00 Item 28 .92 15.42 16.00 5.58 4.00 28.00 Item 29 .95 17.42 18.00 6.87 4.00 28.00 Item 30* .93 20.08 20.50 5.06 8.00 28.00 Note. * Items selected for the main study
Table 3.1. Ratings of Apparel Stimuli
2 Reliability of four evaluative dimensions (attractiveness, fashionableness, likableness, and likelihood to purchase) for each apparel item 75 3.3.2. Pretest 2: Instrument Development
Pretest 2 using survey methodology was undertaken to accomplish three objectives (See Appendix D). The first objective of this pretest was to develop negative emotion measures relevant to the context of this study. Because a vast array of emotion measures, albeit irrelevant, is available from literature, it was necessary to select emotion items relevant to the stockout situation. The second objective was to develop potential retail management strategies for Study 2. Little information from prior literature was available to develop potential managerial responses to those who encounter stockouts.
Thus, pretest 2 was used to guide the development of appropriate managerial responses to use at the time a stockout occurs. The third objective was to test a questionnaire for its readability and clarity of content prior to the main study.
Forty-five female undergraduate students participated in pretest 2 and received extra course credit as an incentive. All participants were asked to imagine that they were facing an unexpected stockout. The hypothetical apparel stockout situation was as follows: “Assume that you are shopping for apparel at a clothing store at the mall. You just find one that you really like, but are told that your size is unavailable due to a stockout.” Then using an open-ended question format, participants were asked to describe how they would feel under such a situation and were also asked to illustrate what they would do under such a circumstance. The same participants later tested a final questionnaire and identified unclear question items and confusing wording.
76 Negative Emotion Items
Based on participants’ responses about their possible emotional experience related to stockouts, nineteen different emotion items were initially identified (See Table 3.2).
These emotion items were further reviewed by ten female graduate students majoring in
Textiles and Clothing program in terms of their relevance to the context of this study.
Previous literature on emotions was also reviewed to inform the relevancy of the identified emotion items (Holbrook & Barta, 1987; Pham, Watson, Clark, & Tellegen,
1988; Zeitlin & Westwood, 1986). Based on the pretest and reviews, fourteen emotion items deemed relevant to the current study were selected. These 14 items expressing emotional response to product unavailability were: aggravated, agitated, angry, annoyed, anxious, disappointed, discouraged, frustrated, irritated, mad, sad, unhappy, unpleasant, and upset. These 14 items were used in the main study.
Retail Management Strategy
Due to a lack of prior research to suggest potentially useful managerial responses to stockouts, the pretest was conducted to guide the development of potential managerial responses for Study 2. Participants for pretest 2 were asked to illustrate what they would
do under a hypothetical stockout situation. These responses were content analyzed using
the unit of “mention”. A “mention” is a phrase used to describe a participant’s behavioral
reaction to stockouts. If listed phrases were synonymous or had close meanings,
frequencies were combined. There were a total number of 99 mentions, and as shown in
Table 3.3, they fell into 7 types of behavioral responses to stockouts: (1) ask sales
associates to call another store and see if they can relocate the item (f = 30), (2) go to
77
Negative emotion measures Frequency of mention
Aggravated* 3 Agitated* 3 Angry* 10 Annoyed* 7 Anxious* 4 Concerned 1 Confused 1 Disappointed* 27 Discouraged* 4 Frustrated* 20 Irritated* 14 Mad* 11 Nervous 1 Regretful 1 Sad* 7 Uneasy 1 Unhappy* 3 Upset* 15 Unpleasant* 4
Total 19 items 137 Note. * Items used in the main study
Table 3.2. Frequency of Mention of Negative Emotion Items from the Pretest
78 other stores to look for a similar item (f = 19), (3) check catalog or website to find the item (f = 16), (4) ask if they have a style similar to the one out of stock (f = 14), (5) backorder the item (f = 12), and (6) ask if and when they would restock the item (f = 8).
Among these responses, (1), (3), and (5) were all deemed to indicate one’s willingness to get the item. A backorder response (This item is out of stock. Would you like to backorder this item?) was developed to accommodate online shoppers’ willingness to get the item. The responses (2) and (4) seem to indicate one’s willingness to substitute for the item. A substitute response (This item is out of stock. Would you like to consider other items similar to this item?) was developed to accommodate online shopper’s willingness to substitute for the item.
A financial response (The item is out of stock. But we can offer you a 10% discount on any items you purchase from us.) was developed based on the study by
Anderson et al. (2001). In their study of the stockout problem in catalog shopping,
Anderson et al. (2001) used two different financial responses ($5 off vs. 10% off) along with other responses (i.e., giving reasons for why the item is out of stock). While a financial need was not identified in the pretest, it seemed relevant in the context of online shopping (Bhargava et al., 2002). As a result, the four managerial responses developed for Study 2 included standard, substitute, backorder, and financial response (See Table
3.4).
79
Behavioral responses Frequency (N=99)
(1) ask them to call another store and see if she/he can relocate the item 30 (2) go to other stores to look for a similar item 19 (3) check catalog or website to find the item 16 (4) ask if they have a style similar to the one out of stock 14 (5) backorder the item 12 (6) ask if and when they would restock the item 8
Table 3.3. Content Analysis of Consumers’ Behavioral Responses to a Hypothetical
Stockout Situation
Managerial responses
(1) Standard response This item is out of stock.
(2) Substitute response This item is out of stock. Would you like to consider other items similar to this item?
(3) Backorder response This item is out of stock. Would you like to backorder this item?
(4) Financial response The item is out of stock. But we can offer you a 10% discount on any items you purchase from us.
Table 3.4. Four Managerial Responses to Stockouts
80 Testing of Questionnaires
Participants for pretest 2 read and commented on the questionnaire in terms of readability and clarity of questions at a later date. They also pointed out ambiguous or confusing wording and typos. The questionnaire was further reviewed by graduate students in terms of content and format. Several minor revisions were made based on comments received to correct ambiguous or confusing wording, inaccurate anchoring for scaled items, the order of questions, and typos.
3.4. Main Study
3.4.1. Instrument Development
Study 1 included 3 independent variables (e.g., timing, preference, and frequency of stockouts) used to manipulate experimental conditions, while Study 2 had one independent variable (e.g., retail management response to stockouts) used to manipulate experimental conditions. Both studies used same dependent variables to investigate consumer response to stockouts. The dependent variables included negative emotions, store image, decision satisfaction, and behavioral intent.
Negative Emotions
Based on Mandler’s discrepancy-evaluation theory of emotion, this study posited that when consumers encounter stockouts, negative emotions will be elicited because a
81 stockout creates a discrepancy between a consumer’s expectation about shopping and the
actuality of stockouts or interruption of one’s shopping goal.
The following fourteen negative emotion items were selected based on pretest 1:
aggravated, agitated, angry, annoyed, anxious, disappointed, discouraged, frustrated, irritated, mad, sad, unhappy, unpleasant, and upset (See Table 3.5). These items have been used in prior research on emotions (Mano & Oliver, 1993; Min, 2003; Pham, Cohen,
Pracejus, & Hughes, 2001; Watson et al., 1988; Zeitlin & Westwood, 1986). Among the fourteen items, eight items (e.g., agitated, angry, annoyed, discouraged, frustrated, sad, unpleasant, and upset) were used in prior research on stockouts (Min, 2003) and achieved
adequate reliability (Cronbach’s α = .92)
During the main study, other negative emotional items irrelevant to the current
study were also included as distracters in order to disguise the true purpose of the study
(i.e. response to stockouts) and to minimize the salience of emotional measures of interest.
Revealing the true purpose of the study may distort participant responses to research
questions and exacerbate the problem of research artifacts (Campbell & Stanley, 1963).
Distracter items used in questionnaire included the following 31 emotion items: afraid,
ashamed, astonished, concerned, conflictful, confused, depressed, disgusted, distressed,
distrustful, dominated, embarrassed, enraged, fearful, guilty, helpless, humiliated,
nervous, overstimulated, panicked, powerless, regretful, remorseful, revolted, scornful,
skeptical, sorrowful, surprised, suspicious, tense, and uneasy. In the main study, research
participants were asked to indicate their current feelings based on a five-point scale
ranging from 1 (not at all) to 5 (very much).
82 Store Image
Store image is a multi-dimensional construct (Lindquist, 1974-1975; Mazursky &
Jacoby, 1986). As a possible evaluative response to product unavailability, perceptions of store image were measured using scales developed by Chowdhury, Reardon, and
Srivastava (1998). Chowdhury et al. (1998) suggested six dimensions of store image;
(1) service (5 items), (2) product quality (5 items), (3) product selection (5 items), (4) atmosphere (11 items), (5) convenience (7 items), and (6) prices/values (5 items), and developed multiple items to measure each dimension. The reliabilities of these measures
(Cronbach’s α = .92, .76, .84, .90, .84, and .88, respectively) were established in previous research (Chowdhury et al., 1998) (See Table 3.5).
For this study, the existing store image scales were revised to reflect the context of online apparel shopping. Some items were dropped because of their irrelevancy to online shopping (e.g.,
transactions) to reflect an online shopping environment. A total of 27 items measuring
six dimensions of online store image were used based on a five-point Likert scale ranging
from 1 (strongly disagree) to 5 (strongly agree). See Appendix G for the 27 items used to
measure store image.
Decision Satisfaction
Decision satisfaction was measured using six items used in Fitzsimons’s (2000)
study of stockouts (Cronbach’s α = .83) (See Table 3.5). These items tap consumers’
83 satisfaction with the decision process, rather than overall satisfaction or satisfaction with consumption. All five statements were measured using five-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree).
Behavioral Intent
Behavioral intent in the current study is operationalized as a higher construct comprising purchase intention and word-of-mouth. Behavioral intent was measured using scales developed by Grewal, Baker, Levy, and Voss (2003). Two items were used to measure purchase intent and one item was used to measure word-of-mouth (See Table
3.5). All three items were measured based on a five-point scale ranging from 1 (very unlikely) to 5 (very likely). The reliability of the three items (Cronbach’s α = .88) was established in prior research (Grewal et al., 2003).
Background and Demographic Information
Demographic questions were asked to gather general background information from the research participants in terms of age, academic standing, and ethnicity.
Questionnaires also included 5 questions about general online shopping experience and 5 questions about prior experience related to stockouts (See Appendix G).
84
Dependent variables Items Cronbach’s alpha
Negative emotions 1. Aggravated : Pretest 2 2. Agitated*, ** : Mano & Oliver. 1993; 3. Angry* Min, 2003; Pham, Cohen, 4. Annoyed** Pracejus, & Hughes, 2001; 5. Anxious Watson, Clark, & Tellegen, 6. Disappointed 1988; Zeitlin & Westwood, 7. Discouraged** 1986 8. Frustrated** 9. Irritated 10. Mad 11. Sad* 12. Unhappy 13. Unpleasant* 14. Upset**
Store imagea 1. Service (7 items) .92 : Chowdhury, Reardon, 2. Product quality (3 items) .76 Srivastava, 1988 3. Product selection (4 items) .84 4. Atmosphere (5 items) .90 5. Convenience ( 5 items) .84 6. Prices/values (3 items) .88
Decision satisfaction: 1. I found the process of deciding which apparel items to .83 : Fitzsimons, 2000 buy frustrating (R) 2. Several good options were available for me to choose from 3. I thought the choice selection was good 4. I would be happy to choose from the same set of product options on my next purchase 5. I found the process of deciding which apparel items to buy interesting 6. How satisfied or dissatisfied are you with your experience of deciding which apparel items to buy?
Behavioral intent 1. How likely is that you will shop via this online store? .88 : Grewal, Baker, Levy, & 2. How likely is that you will purchase apparel via this Voss, 2003 online store? 3. How likely is that you will recommend this store to your friends?
Note. * (2, 3, 11, 14) four of six items used in Study 1 of Min’s (2003) research (α=.92) ** (2, 4, 7, 8, 14) five out six items used in Study 2 of Min’s (2003) research (α=.92) a = complete list of all items in found in Appendix G. (R): reverse-scored
Table 3.5. Items for Dependent Variables
85 3.4.2. Mock Website Development
A mock website selling women’s apparel was developed by a professional
website developer to resemble a real shopping website as closely as possible (See
Appendix E). Feedback on the mock website used in pretest 1 was used to guide the
development of a mock website for the main study.
The mock website simulated online stores targeting young women. The site had a
brand name “e Fashion---Fashion Portal” (using flash) and had the appearance of a real
apparel website, although other unnecessary functions (i.e. view checkout) for the study
were disabled. Feedback on the mock website in terms of appearance and functionality
was continuously sought from fellow graduate students. Several minor revisions were made accordingly to enhance the realism of the study and to improve functionality.
The mock website was developed to enable several key functions for this research.
First of all, the website allowed participants to select the items they liked, incorporating individual preference. In this study, preference is hypothesized to moderate how consumers respond to a stockout, in such a way that if their preferred item is unavailable, consumers are likely to react more negatively to stockouts compared to when their not preferred item is unavailable. Unlike some commodities like soft drinks with which consumers tend to have enduring, explicit preferences (Coke vs. Pepsi), consumers do not have such preferences for apparel items because fashion is always changing and so there are always new apparel items. While consumers may have unambiguous preferences for specific apparel brands, they are not likely to have an existing preference for specific apparel items. Instead, consumers may form preferences for an apparel item when they
86 are exposed to it. In consumer choice studies, one important criticism is that most prior
studies assumed homogeneity of preference across individuals (Highhouse, 1996;
Pettibone & Wedell, 2000). This study acknowledges the heterogeneity of individual
preference and incorporates individual true preference for apparel items.
In addition, the website was designed to randomly assign participants to one of eight experimental conditions. This study included three independent variables: (1)
timing of notification about product unavailability (before or after), (2) preference for an
unavailable item (not preferred or preferred), and (3) frequency of product unavailability
(once or twice). For a timing variable, the mock website was developed to notify
participants before or after they selected the final items they wanted to buy. For the
preference variable, the site was designed to allow participants to choose apparel items
they preferred to buy, and then to make either their preferred or not preferred items
unavailable depending on the condition. For a frequency variable, the website was
created to make one of two final items or both items unavailable.
87 3.5. Procedure
This research was reviewed and approved by the Behavioral and Social Science
Human Subjects Institutional Review Board at the Ohio State University (IRB research protocol # 2003B0293, See Appendix J).
3.5.1. Participants’ Recruitment
Female undergraduate students were recruited for this study. Female shoppers are the most dominant visitors to online apparel and beauty sites, making up 63 percent of online apparel and beauty shoppers (Internet Retailer, 2004b) and young women comprise a significant portion of online shoppers (Lee & Johnson, 2001). As young women are viewed as potential online apparel shoppers, the mock website for the study was designed to target young college-aged women. Apparel items used in this study were selected to be appealing to this target group as well.
Research participants for the main study were recruited via email. Five-thousand names and email addresses of female undergraduate students enrolled at a large
Midwestern University were randomly selected by the University Registrar. Invitation emails were sent to each of 5,000 potential respondents to recruit research participants for this study (See Appendix A). Potential respondents could participate by clicking on the research URL available in an invitation email or could reply to an invitation email to participate in the study. A follow-up email was sent to those who replied to an invitation email to give them instructions on how to participate (See Appendix A). Two reminder
88 emails were sent out to those who had not yet participated. The first reminder email was sent out five days after the invitation email and the second was sent out five days after the first reminder email. Because the focus of this study is about stockouts in online apparel shopping, the email recruitment and a Web-based experiment were proposed as the most appropriate methodology for the study. When participants logged onto the website, they were automatically directed to either Study 1 or Study 2 without their knowledge.
3.5.2. Main Study 1
In Study 1, a 2 (timing of notification: before or after) x 2 (item preference: not preferred or preferred) x 2 (frequency of stockout: once or twice) between-subjects factorial design was employed to investigate how consumers respond to product unavailability in online apparel shopping (See Table 3.6).
Timing (T)
Before After
Frequency (F) Frequency (F)
Once Twice Once Twice
Not preferred FPT FPT FPT FPT Preference (P) 1,1,1 2,1,1 1,1,2 2,1,2
Preferred FPT1,2,1 FPT2,2,1 FPT1,2,2 FPT2,2,2
Table 3.6. Experimental Conditions for Study 1
89 When participants logged onto the website using the URL provided, they were
asked to read a cover page describing the research and to type their name to indicate their
agreement on the electronic consent form. When they indicated they would voluntarily
participate in the study, they could proceed to the next page for the experiment.
Prior to the experiment, all participants were asked to read general instructions
developed to encourage high involvement with the experiment (See Appendix E). The
general instructions were as follows: “Imagine that you are given a $150 gift certificate
to purchase apparel via an online store, e fashion. Browse the website and first, select four items that you would consider buying. After evaluating the four items of your initial choice, select two final items you would like to buy. Some of you will be randomly selected to receive an item of your choice or a gift certificate.”
After viewing all items, participants selected the four items they preferred.
Participants were asked to rate the four selected items on four apparel evaluative criteria
(style, color, fit, and fabric) using seven-point scales with endpoints from 1 (very bad) to
7 (very good) and also to choose their size for each item. This process was used to enhance the realism of the online apparel selection process and also foster higher involvement in the experiment. The four apparel evaluative criteria used in this process corresponded to what people generally consider important in apparel purchase decisions
(Eckman, Damhorst, & Kadolph, 1990).
The research website simulated a real online shopping environment, more specifically an apparel selection process. Table 3.7 displays how the pre-determined online apparel selection process in a mock website corresponds to general online apparel
90 selection process. Throughout the website, detailed instructions for each step of the online apparel selection process were available. In order to foster higher involvement for the study, all participants were informed that some randomly selected participants would receive the item they choose during the experiment or a gift certificate. Upon the completion of apparel selection process, participants were asked to complete the questionnaire.
During the apparel selection process, all research participants experienced a different level of product unavailability depending on the experimental condition to which they were randomly assigned. For example, participants directed to cell FPT2,2,2 would initially browse ten apparel items available on the website (for example, items A to J are displayed), and then select the four items they would consider buying (let us assume that A, B, C, and D are selected). They would evaluate the four selected items on four apparel evaluative criteria like they would do to make an apparel purchase decision and then select two final items they would like to buy (let us assume that A and B are chosen). After they select two final items, A and B, they would be notified that both items A and B are out of stock. As an example, Table 3.8 illustrates all experimental conditions when the preferred items were A, B, C, and D (selected from items A to J), and two final items were A and B. See Appendix F.
91
Pre-determined apparel selection General online apparel selection process process in a mock website
Step 1 • Browse ten apparel items available • Quickly explore all apparel items on the site available on the site or webpage
Step 2 • Select four items you would consider • After quick browsing, choose a couple buying of items to focus on
Step 3 • Evaluate each of four selected items • Engage in more careful examinations on four apparel evaluative criteria by seeing a larger view and carefully (style, color, fit, and fabric) using a reading verbal descriptions to learn seven-point scale ranging from 1 more about items of interest. Think (very bad) to 7 (very good) and about what you like about each item indicate your size for each item and how the item satisfies your need
• A larger view for each item is available if needed
Step 4 • Review four selected items one more • Debate which one to choose time
Step 5 • Select two final items that you would • Make a purchase decision like buy
Table 3.7. Comparison of Process between a Web Experiment and General Online
Apparel Shopping
92
Experimental Step 1 Step 2 Step 3 Early Step 4 Late condition notification notification Browse Select Evaluate Select 2 10 tops 4 preferred 4 selected final items items items
1 FPT1,1,1 A - J A, B, C, D J is out of stock A - B
FPT2,1,1 A - J A, B, C, D H is out of stock A - J is out of stock B
2 FPT1,2,1 A - J A, B, C, D A is out of stock A - B
FPT2,2,1 A - J A, B, C, D A is out of stock A - B is out of stock B
FPT1,1,2 A - J A, B, C, D - A J is out of stock B
FPT2,1,2 A - J A, B, C, D - A H is out of stock B J is out of stock
3 FPT1,2,2 A - J A, B, C, D - A A is out of stock B
4 FPT2,2,2 A - J A, B, C, D - A A is out of stock B B is out of stock Note. 1 Because the preferred items are A, B, C, and D, any item(s) from E to J can be out of stock in FPT1,1,1, FPT2,1,1, FPT1,1,2, and FPT2,1,2 conditions. 2 Because the preferred items are A, B, C, and D, any item(s) from A to D can be out of stock in FPT1,2,1 and FPT2,2,1 conditions. 3 Because A and B are selected as two final items, either A or B can be out of stock in FPT1,2,2 condition. 4 Because A and B are selected as two final items, both A and B should be out of stock in FPT2,2,2 condition.
Table 3.8. An Example of Experimental Conditions
93 3.5.3. Main Study 2
The design of Study 2 was a between-subjects experiment with one factor (retail
management strategies) with four manipulated levels (standard, substitute, backorder, or
financial response). Study 2 was designed to explore potential retail management
strategies that can be employed to alleviate negative emotions elicited by product
unavailability and subsequently to mitigate adverse impact on consumers’ evaluative
responses and behavioral intent.
Like Study 1, all research participants for Study 2 browsed ten apparel items
displayed on the mock website and selected four items they would consider buying.
After evaluating each of four selected items on four evaluative criteria (style, color, fit,
and fabric) and selecting their size for each item, they selected two final items they would
like to buy. However, unlike Study 1, all participants for Study 2 experienced stockouts of both items they chose during the experiment. In other words, all of them were exposed to only one of the eight conditions (i.e., FPT2,2,2) used in Study 1 (See Table 3.9). They
were then randomly assigned to receive one of the four different managerial responses to
stockouts (standard, substitute, backorder, or financial response).
As in Study 1 to encourage higher involvement with the experiment process,
participants were also told that some randomly selected participants would receive the
item they choose during the experiment or a gift certificate.
94 Timing
Before After Frequency Frequency
Once Twice Once Twice Preference Not preferred
Preferred Study 2 (FPT2,2,2)
Table 3.9. One Condition from Study 1 to which All Study 2 Participants Were Exposed
95 CHAPTER 4
ANALYSIS AND RESULTS
4.1. Overview
This chapter presents the research findings and results relating to the hypotheses
and the proposed model used in this study. This research is comprised of two studies:
Study 1 investigated how product unavailability influences consumer response, and Study
2 explored different retail management strategies that may be effective in mitigating the adverse impact of product unavailability. In this chapter, results of Study 1 and Study 2 are each presented. For Study 1, research findings are presented in four sub-sections. In section 1, the description of research participants is first presented. In section 2, the description of variables measured is presented. Descriptive statistics using SPSS 12.0 were used to examine general properties of major variables of the study. In section 3, preliminary analyses required for formal hypotheses testing are discussed. Exploratory factor analysis using CEFA and SPSS and confirmatory factor analysis using LISREL
VIII were used to evaluate measurement properties, including the reliability and validity of the measures. In section 4, research hypotheses and the proposed model for Study 1 are tested. Factorial analyses of variance were used to test hypotheses in Part One and
96 structural equation modeling was used to test hypotheses in Part Two and Part Three. For
Study 2, results are presented in two sub-sections. In section 1, the description of the research participants for Study 2 is presented. In section 2, multivariate analysis of variance was performed to explore more effective retail management strategies in reducing the adverse impact of stockouts. Statistical packages used in this research include (1) SPSS version 12.0 for data preparation, descriptive statistics, exploratory factor analysis, factorial analysis of variance, and multivariate analysis of variance, (2)
CEFA for exploratory factor analysis, and (3) LISREL VIII for confirmatory factor analysis and structural equation modeling.
In order to solicit participants for the Web-based experiment, invitation emails were sent to 5,000 randomly selected female students enrolled at a large Midwestern university, and 1,153 responses were collected. Because multiple submissions can be a potential problem with a Web-based study (Schmidt, 1997; Smith & Leigh, 1997), all participants were required to provide their name and a valid university email address to complete the study. This information was used to clean the data with multiple responses.
After cleaning the data for multiple responses and for errors, 1,054 usable responses were obtained. The overall response rate for usable response was 21.1 percent. Among the
1,054 usable responses, 820 respondents participated in Study 1 and 234 respondents participated in Study 2.
97 4.2. Study 1
4.2.1. Sample Description
Of the 5,000 original invitation emails sent, 820 of them completed the online
experiment for Study 1. Because participants were automatically directed to either Study
1 or Study 2, a response rate specific to Study 1 is not available. The mean age of the participants was 21, with a range of 18 to 50 (See Table 4.1 for demographic
information). About 90 percent of the participants were aged between 18 and 22. The
academic standing of the participants was evenly spread out. Freshman and sophomores
combined accounted for nearly 45 percent of the participants. Seniors were the single
largest group accounting for 30.5 percent of all participants. Approximately 80 percent
of the participants were Caucasian. Other ethnic groups combined accounted for about
20 percent; Asian/Pacific Islander (8.5%), African American (7.0%), Multi-cultural
(1.1%), Hispanic (0.9%), Native Americans (0.6%), and other (2.2%).
Information about participants’ general practice with the Internet and online
shopping/buying was also obtained (See Table 4.2). Nearly 80 percent of participants
indicated that they use the Internet very frequently, but their online shopping and buying
practices were not so prevalent. About one third of participants shop online frequently or
very frequently, while another one third shop online infrequently or very infrequently.
Over 95 percent of participants have purchased online. Of these participants, 46 percent
responded that they purchase infrequently or very infrequently, and 19.5 percent purchase
frequently or very frequently. About half the participants indicated that they shop for
98 apparel online sometimes or more frequently. Meanwhile about one third of participants responded that they actually purchase apparel online sometimes or more frequently.
For experience related to stockouts, about half the participants experienced apparel stockouts sometimes, while almost 18 percent experienced apparel stockouts often or very often (See Table 4.3). In store shopping, approximately 28 percent of participants experienced stockouts often or very often. In catalog shopping, about 11 percent of participants seldom experienced stockouts from catalogs. Nearly 15 percent of participants experienced stockouts from catalogs often or very often. In online shopping, about 12 percent of participants had little experience with stockouts online, while about
17 percent often or very often experienced stockouts. A majority of participants further indicated that no actions were taken by retailers at the time of stockouts.
99
Variable Category Mean Frequency Percent (SD) (N=820)
Age Under 20 20.65 318 38.8 20 – 24 (3.41) 452 55.1 25 – 30 30 3.6 Over 30 20 2.5
Academic standing Freshman 196 23.9 Sophomore 172 21.0 Junior 187 22.8 Senior 250 30.5 Graduate 15 1.8
Ethnic background African American 57 7.0 Caucasian American 654 79.8 Hispanic 7 0.9 Native Americans 5 0.6 Asian/pacific islander 70 8.5 Multi-cultural 9 1.1 Other 18 2.2
Table 4.1. Demographic Profile of Participants
100 General Online Online Online Online Internet use shopping purchase apparel apparel shopping purchase
(N=820) f % f % f % f % f % Very infrequently 4 0.5 156 19.0 231 28.2 211 25.7 294 35.9 Infrequently 6 0.7 118 14.4 146 17.8 146 17.8 175 21.3 Sometimes 60 7.3 255 31.1 245 29.9 194 23.7 169 20.6 Frequently 99 12.1 181 22.1 107 13.0 156 19.0 63 7.7 Very frequently 650 79.3 88 10.7 53 6.5 60 7.3 41 5.0 Not applicable 1 0.1 22 2.7 38 4.6 53 6.5 78 9.5
Table 4.2. Participants’ Internet Use
Apparel Stockout in Stockout in Stockout in Received stockout store catalog online compensation shopping shopping shopping
(N=820) f % f % f % f % f % Not often at all (1) 85 10.4 114 13.9 89 10.9 110 12.4 438 53.4
(2) 96 11.7 128 15.6 127 15.5 133 16.2 72 8.8 (3) 405 49.4 314 38.3 244 29.8 271 33.0 95 11.6 (4) 113 13.8 176 21.5 102 12.4 112 13.7 28 3.4 Very often (5) 34 4.1 54 6.6 18 2.2 25 3.0 5 0.6 Not applicable 87 10.6 34 4.1 240 29.3 169 20.3 182 22.2
Table 4.3. Participants’ Prior Experience Related to Stockouts
101 4.2.2. Dependent Variables
This study includes four dependent variables, which are latent constructs in the proposed model. Multiple items were used to measure the four latent constructs.
Descriptive statistics for four latent constructs are presented in this section.
Negative Emotions
Based on a pretest, fourteen negative emotion items were identified as relevant to
the context of this study about online apparel stockouts. The selected fourteen emotions
were: aggravated, agitated, angry, annoyed, anxious, disappointed, discouraged,
frustrated, irritated, mad, sad, unhappy, upset, and unpleasant. Immediately after
participants were exposed to product unavailability, they were asked to rate how well
these emotion terms reflected their feelings using a 5-point scale.
Although there is no prior theory to suggest that the fourteen selected items may
share multiple common factors, an exploratory factor analysis using maximum likelihood
estimation was conducted to assess the unidimensionality of the scale. After examining 1,
2, and 3-factor models for an emotion scale, a one-factor model was deemed most
relevant. There was only one factor with an eigenvalue larger than one (first eigenvalue
was 10.40) (Kaiser, 1960). The scree diagram (Cattell, 1966) also showed that there is
one factor that precedes the last large drop. In addition, Cronbach’s alpha for the
fourteen-item emotion scale was .97. Based on these results, scores from the fourteen
emotion items were summed together to develop a single indicator for negative emotion
construct. Higher scores indicated that participants experienced stronger negative
102 emotions, while lower scores indicated that participants experienced less intensive negative emotions. This single indicator of negative emotion construct was used as a dependent variable in Part one of the proposed model, while it was used as a predictor variable in Part two and Part three of the proposed model. The mean value of negative emotion was 25.2 with a range from 14 to 70 (See Table 4.4). This mean indicates that on average little negative emotion was experienced by participants.
Store Image
Twenty-seven items adopted from Chowdhury et al. (1998) were used to measure
six different facets of store image. Reliability of items for each of the six facets was
calculated and found to be reliable (all Cronbach’s α’s > .89). Based on the established
reliability, items for each facet of store image were summed to generate six indicators for
store image latent construct. Descriptive statistics for the six indicators are displayed in
Table 4.4.
Decision Satisfaction
The decision satisfaction construct was measured using six items from Fitzsimons
(2000). Cronbach’s alpha was calculated to assess the internal consistency of the six
items and was found to be reliable (α = .93). Descriptive statistics of the six indicators
for decision satisfaction are presented in Table 4.4.
103
Range Min. Max. Mean S.D.
Negative emotion NE. Negative emotion 56 14 70 25.21 13.55
Store image S1. Product quality 12 3 15 10.42 3.46 S2. Product assortment 16 4 20 12.55 4.74 S3. Service 28 7 35 21.95 8.18 S4. Convenience 20 5 25 18.25 5.50 S5. Atmosphere 20 5 25 17.64 5.63 S6. Values 20 5 25 15.47 5.64
Decision satisfaction D1*. I found the process of deciding which apparel 4 1 5 3.84 1.24 items to buy frustrating (R) D2. Several good options were available for me to 4 1 5 3.25 1.33 choose from D3. I thought the choice selection was good 4 1 5 3.11 1.31 D4. I would be happy to choose from the same set of 4 1 5 2.90 1.34 product options on my next purchase D5. I found the process of deciding which apparel 4 1 5 3.58 1.24 items to buy interesting D6. How satisfied or dissatisfied are you with your experience of deciding which apparel items to 4 1 5 3.51 1.24 buy?
Behavioral intent B1. How likely is that you will shop via this online 4 1 5 2.96 1.35 store? B2. How likely is that you will purchase apparel via 4 1 5 2.70 1.34 this online store? B3. How likely is that you will recommend this store 4 1 5 2.91 1.36 to your friends?
Note. * Item was reverse-scored
Table 4.4. Descriptive Statistics for Dependent Variables
104 Behavioral Intent
Behavioral intent was measured using three items from Grewal et al. (2003).
Cronbach’s alpha was calculated to assess the internal consistency of the three measures and was found to be reliable (α = .96). Descriptive statistics of the three measures of the behavioral intent construct are presented in Table 4.4.
For measurement assessment, an exploratory factor analysis was conducted to examine factor loadings and item-total correlations of indicators for each of four latent constructs (i.e., negative emotion, store image, decision satisfaction, and behavioral intent) in the model (Churchill, 1979; Steenkamp & Trijp, 1991). Reliability of each construct was assessed based on Cronbach’s alpha and percent of variance explained.
The results are presented in Table 4.5.
As shown in Table 4.5, all measures except for D1 appear to be good indicators for each construct. Although D1 showed a lower factor loading (.64) and item-total correlation (.64), it still exceeded cut-off points suggested (Churchill, 1979; Nunnally,
1978). Thus, it was included in further analysis using confirmatory factor analysis.
105
Factor loading Item-total Percent Cronbach’s correlation variance alpha explained
Negative emotion* NE
Store image 85.2 .96 S1 .90 .88 S2 .91 .90 S3 .93 .91 S4 .93 .91 S5 .94 .92 S6 .94 .92
Decision satisfaction 68.0 .93 D1 .64 .64 D2 .90 .84 D3 .91 .84 D4 .86 .80 D5 .76 .76 D6 .83 .83
Behavioral intent 89.2 .96 B1 .93 .91 B2 .98 .94 B3 .93 .91
Note. * Negative emotion has a single indicator
Table 4.5. Reliability for Latent Constructs
106 4.2.3. Preliminary Analysis and Evaluation of Measures
In this section, measurement quality is assessed in terms of reliability, convergent validity, and discriminant validity using confirmatory factor analysis (CFA). This study followed a two-step approach suggested by Anderson and Gerbing (1988). In a two-step approach, the measurement model is evaluated separately from the full structural model
prior to simultaneous estimation of the measurement and structural models. This enables
comprehensive assessment of construct validity (Bentler, 1978). Campbell and Fiske
(1959) also suggested that the measurement model allows confirmatory assessment of
convergent and discriminant validity.
Following Anderson and Gerbing (1988), the measurement model is first
evaluated and assessed for convergent and discriminant validity. After comprehensive
assessment of the measurement model, it is respecified to purify measures and to reduce
the potential for interpretational confounding. Anderson and Gerbing suggested four
different ways to respecify the measurement: “Relate the indicator to a different factor,
delete the indicator from the model, relate the indicator to multiple factors, or use
correlated measurement errors” (p. 417). They further suggested that the first two
methods for respecification may preserve unidimensional measurement and so are more
preferable. Last two methods can be justified only when they are specified a priori.
Otherwise, they may capitalize on chance and lose interpretability and theoretical
meaningfulness (Bagozzi, 1983; Fornell, 1983; Gerbing & Anderson, 1984).
In this study, the second method for respecification was used. Potentially
problematic indicators were deleted and respecified by the following considerations.
107 Items were considered for respecification if they: (1) displayed a significantly lower item
reliability than that of the other items that are posited to measure the same construct, as
indicated in the squared multiple correlations; (2) showed that path coefficients for the
expected construct are insignificant; (3) showed large residuals with other indicators; (4) shared large variance with other indicators, but due to error and thus unexplainable variance, as indicated in the modification indices for Θδ; or (5) shared common variance
with indicators posited on some other constructs, as indicated by large modification
indices for λ. The respecification decision was made based on both statistically and
content consideration as suggested by Anderson and Gerbing (1988).
For example, D1 was deleted because it had a low squared multiple correlation
(SMC) (.49), while other indicators for the same construct had higher squared multiple
correlations ranging from .69 to .74. This low SMC may indicate that the item is not a
reliable measure of the posited construct. S2 was deleted because it shared common
variance with multiple indicators from other constructs such as decision satisfaction. As
a result of a series of respecification processes, the following six indicators were deleted
from the model: S1, S2, S3, D1, D5, and D6. Table 4.6 shows the final ten indicators
used for the structural model after the respecification of the measurement model. The
respecified measurement model was tested for unidimensionality, reliability, and validity
of constructs.
108
Constructa Indicator Item
Negative emotionb NE Negative emotion
Store imagec S4 Convenience S5 Atmosphere S6 Values
Decision satisfactionc D2 Several good options were available for me to choose from D3 I thought the choice selection was good D4 I would be happy to choose from the same set of product options on my next purchase
Behavioral intentc B1 How likely is that you will shop via this online store? B2 How likely is that you will purchase apparel via this online store? B3 How likely is that you will recommend this store to your friends?
Note. a All measures employ five-point scales b Exogenous construct c Endogenous constructs
Table 4.6. Final Measurement Items
109 Unidimensionality
In order to assess unidimensionality of each latent construct in the model, an exploratory factor analysis was conducted (Kumar & Dillon, 1987). An exploratory
factor analysis for each construct yielded a single underlying factor for each construct.
As shown in Table 4.7, reliabilities, the item-total correlations within each construct, and the percent of variance explained were all high and stable, suggesting unidimensionality
of each construct (Kumar & Dillon, 1987; McDonald, 1981; Steenkamp & Trijp, 1991).
Factor loading Item-total Percent Cronbach’s correlation variance alpha explained
Store image 91.5 .95 S4 .94 .90 S5 .97 .93 S6 .90 .88
Decision satisfaction 87.3 .93 D2 .88 .84 D3 .95 .88 D4 .87 .83
Behavioral intent 92.7 .96 B1 .93 .91 B2 .98 .94 B3 .93 .91
Table 4.7. Results from Exploratory Factor Analysis
110 Convergent Validity
Convergent validity assesses the extent to which different measurement methods concur in their measurement of the same construct, and thus independent measures of the same constructs need to be significantly correlated (Campbell & Fiske, 1959). In
LISREL, convergent validity is assessed by the significant t-values of each item’s estimated path coefficient on its posited latent construct (Anderson & Gerbing, 1988;
Steenkamp & Trijp, 1991). Table 4.8 displays that t-values of all estimated path coefficients were significant at the p < .0001 level. In addition, individual item reliability
(i.e., squared multiple correlations), composite reliability, and average variance extracted all exceeded the minimum standards suggested by Bagozzi and Yi (1988). Significant t- values and high squared multiple correlations support convergent validity (Lusch &
Brown, 1996).
Discriminant Validity
Discriminant validity assesses the extent to which independent measurement methods diverge in their measurement of different constructs. In order to achieve discriminant validity, the correlation between independent measures of different constructs should be negligible. Discriminant validity is assessed by examining whether the confidence interval (plus and minus two standard errors around the correlation coefficients) for any pair of the construct in the model include 1.0 (Anderson & Gerbing,
1988; Bagozzi, 1991; Steenkamp & Trijp, 1991). As shown in Table 4.9, none of them include 1.0 in their confidence interval, indicating that the constructs are distinct from one another.
111
Construct Item Standard t-value Reliability Average factor variance loading extracted
Negative emotion NE 1.00 40.47 1.00
Store image .953 .874 S4 .94 35.36 .805 S5 .97 37.58 .94 S6 90 32.87 .88
Decision satisfaction .93 .81 D2 .88 31.54 .75 D3 .95 35.54 .90 D4 .87 30.66 .78
Behavioral intent .95 .86 B1 .95 36.10 .77 B2 .96 36.53 .91 B3 .88 31.64 .90
Table 4.8. Measurement Properties from Confirmatory Factor Analysis
3 Bagozzi and Yi (1988) proposed 3 types of reliability for assessment of fit of internal structure of a model
2 2 Individual item reliability = λi *var(T)/ λi *var(T) + θii, where T=ηj or ζk, minimum standard (.50 )
4 2 2 Average variance extracted = Σλi *var(T)/ Σλi *var(T) + Σθii, where T=ηj or ζk, minimum standard (.50 )
5 2 2 Composite reliability = (Σλi) *var(T)/ (Σλi) *var(T) + Σθii, where T=ηj or ζk, minimum standard (.60 ) 112
Negative Store image Decision Behavioral emotion satisfaction intent
Negative emotion
-.696 Store image (.02)a (-.73; -.65)b
Decision satisfaction -.64 .92 (.02) (.01) (-.68; -.60) (.90; .93)
Behavioral intent -.49 .79 .77 (.03) (.02) (.02) (-.55; -.43) (.75; .83) (.73; .81)
Note. a(Standard Error), b(Confidence Interval)
Table 4.9. Construct Correlation for Discriminant Validity
In addition, “one-at-a-time chi-square difference tests” between the constrained
(i.e., factor correlation is set at 1.0) and unconstrained models (i.e., factor correlation is freely estimated) are commonly used to assess discriminant validity of each construct. In order to achieve discriminant validity, the fit of unconstrained model should be significantly better than the fit of the constrained model with perfect correlation between two constructs. “A significantly lower χ2 value for the model in which the trait
6 Discriminant validity is determined by whether the confidence interval (± two standard errors around the correlation estimate between the two constructs) includes 1.0 (Anderson & Gerbing, 1988). 113 correlations are not constrained to unity would indicate that the traits are not perfectly correlated and that discriminant validity is achieved” (Bagozzi & Phillips, 1982, p.476).
As shown in Table 4.10, when a constraint of perfect correlation between two
constructs was imposed, the fit of model became substantially worse, thus supporting
discriminant validity of the measures.
Constraint Chi-square df Difference (∆χ2)* Unconstrained 1416.96 99 Negative emotion & Store image 2248.44 100 831.48 Negative emotion & Decision satisfaction 3593.17 100 2176.21 Negative emotion & Behavioral intent 3873.06 100 2456.10 Store image & Decision satisfaction 1742.20 100 325.24 Store image & Behavioral intent 2899.45 100 1482.49 Decision satisfaction & Behavioral intent 2710.97 100 1294.01 Note. *All chi-square differences are statistically significant for 1 df at .0001 level.
Table 4.10. Chi-square Difference Test7
7 When a number of chi-square difference tests are performed for assessments of discriminant validity, the significance level for each test should be adjusted to maintain the “true” overall significance level for the t family of the test (cf. Finn, 1974). This adjustment can be given as αo = 1-(1-αi) , where αo is the overall significance level that should be used for each individual hypothesis test of discriminant validity; and t is the number of tests performed. 114 Model Specification
After assessing reliability and validity of scales, ten variables were included for
the model specification. The input data were prepared using PRELIS program. The maximum likelihood (ML) procedure was used to estimate model parameters with covariance matrices (See Appendix H). Because this study used multi-group analysis to test the proposed model in Part three, a covariance matrix was used (Byrne, Shavelson, &
Muthen, 1989; Jöreskog & Sörbom, 1993). The ML estimation was used because the ML
procedure provides unbiased, more consistent, and more efficient parameter estimates
(Kmenta, 1971; Jaccard & Wan, 1996) and works well with data that are not
multivariately normally distributed (Jaccard & Wan, 1996).
The input data for this study were assessed for the multivariate normality
assumption. Appendix I displays the result of data screening with mean, SD, skewness and kurtosis evaluation. The normality of distribution is evaluated by the values of the
skewness and kurtosis coefficients being close to zero. When skewness coefficients
range from 2 to 3 and kurtosis coefficients range from 7 to 21, the distribution is
considered as moderately nonnormal, while skewness coefficients larger than 3 and
kurtosis coefficients larger than 21 are considered as extremely nonnormal (Curran, West,
& Finch, 1996). Skewness coefficients of the data for this study ranged from -.55 to 2.14
and kurtosis coefficients ranged from -1.24 to 4.70, indicating that the distribution is
moderately nonnormal. The ML procedure is robust to moderate deviations from a multivariate normal distribution (Jaccard & Wan, 1996), and thus the data for this study
were deemed appropriate for structural equation modeling.
115 The model specifications are presented in Figure 4.1 graphically. The proposed
model consisted of four latent constructs with 10 manifest variables (indicators). One
latent construct, negative emotion, is an exogenous latent variable (ξ) and three latent constructs (i.e., store image, decision satisfaction, and behavioral intent) are endogenous latent variables (η). Negative emotion had a single indicator (NE) and other three endogenous constructs had three indicators each.
To standardize the scores for a latent construct, the phi (Φij) matrix was standardized by specifying the variance of an exogenous variable to be 1. Error variances of each of three endogenous latent constructs were set to 1 for identification purpose (i.e., set the diagonal elements of Ψ matrix to be 1).
4.2.4. Hypotheses Testing
The first part of the proposed model investigating the effects of timing, preference,
and frequency on negative emotion (Hypotheses 1 through 6) was tested using factorial
analysis of variance (See Figure 4.2). The second part of proposed model examining the
relationship among negative emotion, store image, decision satisfaction, and behavioral
intent was tested using a single group structural equation modeling (Hypotheses 7
through 11). The third part of the model examining the moderating role of timing,
preference, and frequency on the structural relationship among dependent variables was
tested using multiple group structural equation modeling (Hypotheses 12 through 17).
116 S4 θε11 λy11 Store λy21 θ Image S5 ε22 0 η λy31 1 S6 θε33 NE γ11 β31
λx11 B1 θε77 λy73 Behavioral Negative λy83 γ31 B2 θε88 Emotion Intent ζ η λy93 1 3 B3 θε99
γ21
1 β32
θε44 λy42 D2 Decision λy52 Satisfaction D3 θε55 η 2 λy62 D4 θε66
Figure 4.1. Model Specification using LISREL Notation
117 As a manipulation check, participants’ evaluations of the apparel items they selected during the online experiment were assessed prior to formal hypotheses testing. It was speculated that people are more likely to be involved with the apparel selection process when they like the apparel items rather than when they do not like any items. If participants liked some items presented on the mock website for the study, it is more likely that they were involved with the experiment and thus provide a more realistic picture of how they really react to product unavailability.
During the online experiment, all participants were asked to evaluate the four items they initially selected on the following four dimensions of style, color, fit, and fabric using a seven-point rating scale. Mean values for the preferred items for all four dimensions ranged from 5.35 to 5.67, indicating that participants liked items they selected (See Table 4.11).
Mean SD Range Min. Max.
Style 5.61 .82 6 1 7 Color 5.67 .84 6 1 7 Fit 5.44 .84 6 1 7 Fabric 5.35 1.00 6 1 7
Table 4.11. Participants’ Rating of Preferred Apparel Items
118
Antecedents Underlying Mechanism Consequences
Part Three
Part One Part Two
Timing Store Image
H1 H7 H10
H9 Preference H2 Negative Behavioral Emotion Intent
H11 H3 H8
Frequency Decision Satisfaction
Note. Part One: Hypotheses 1 to 6 tested using factorial analysis of variance (H1-3 for main effects and H 4-6 (not appeared in the diagram) for two-way interaction effects) Part Two: Hypotheses 7 to 11 tested using a single group structural equation modeling Part Three: Hypotheses 12 to 17 tested using multiple group structural equation modeling (not appeared in the diagram)
Figure 4.2. Proposed Model of Consumer Response to Stockouts
119 Part One (Hypotheses 1 to 6)
The first part of the model proposed in this study establishes the relationship
between three key contextual factors in stockouts and negative emotions. It is posited
that three contextual factors including timing, preference, and frequency of product
unavailability have effects on the level of negative emotions experienced when confronted with product unavailability.
Hypotheses 1 to 6 were tested using factorial analyses of variance. The independent variables were timing of notification (before or after), preference for an
unavailable item (not preferred or preferred), and frequency of product unavailability
(once or twice). All three independent variables were used to manipulate experimental
conditions for Study 1. The dependent variable was negative emotion aroused when
confronted with product unavailability. The number of participants in each experimental
condition is presented in Table 4.13.
A 2 (timing) x 2 (preference) x 2 (frequency) between-subjects analysis of variance was calculated on negative emotions participants experienced (See Table 4.12).
There was the significant timing x preference x frequency three-way interaction, F (1,
812) = 15.38, p < .0001. The analysis further revealed significant interactions for timing by preference [F (1, 812) = 273.96, p < .0001], timing by frequency [F (1, 812) = 29.98, p < .0001], and preference by frequency [F (1, 812) = 32.03, p < .0001]. There were significant main effects for timing of notification [F (1, 812) = 343.50, p < .0001], preference [F (1, 812) = 455.34, p < .0001], and frequency of stockouts [F (1, 812) =
49.31, p < .0001].
120 Sum of Mean Source of variations squares df square F* ω2
Timing 24926.86 1 24926.86 343.50 .295 Preference 33043.45 1 33043.45 455.34 .357 Frequency 3578.21 1 3578.21 49.31 .056 Timing x Preference 19880.41 1 19880.41 273.96 .250 Timing x Frequency 2175.45 1 2175.45 29.98 .034 Preference x Frequency 2324.17 1 2324.17 32.03 .037 Timing x Preference x Frequency 1115.77 1 1115.771 15.38 .017 Note. * All are significant at p < .0001.
Table 4.12. Analysis of Variance for Part One
Timing
Before After Frequency Frequency
Once Twice Once Twice Preference FPT111 FPT211 FPT112 FPT212 Not preferred (99) (103) (103) (112)
FPT121 FPT221 FPT122 FPT222 Preferred (99) (98) (97) (109)
Table 4.13. Number of Participants in Each Experimental Condition of Study 1
121 Although a three-way interaction was not predicted in this study, the timing x
preference x frequency interaction was significant from the analysis. However, a close
examination of the effect size using omega squared8 (ω2) revealed that the three-way
2 9 interaction had a small effect (ω F x P x T = .017), according to Cohen’s guideline (1977, pp. 284-288). This suggests that the three-way interaction was statistically significant, but unlikely to have practical significance. Furthermore, there is no theoretical support for a timing x preference x frequency interaction and it is also uninterpretable. Therefore, no further investigation of the three-way interaction was made and the subsequent analyses assumed no three-way interaction.
Three two-way interactions were examined in terms of effect size (See Table
2 4.12). Effect size for a timing by preference interaction was large (ω P x T = .250), while
2 effect sizes for a timing by frequency interaction (ω F x T = .034) and a preference by
2 frequency interaction (ω F x P = .037) were relatively small. Three two-way interactions
were individually examined in hypothesis testing. Effect sizes for a main effect of timing
2 2 (ω T = .295) and preference (ω P = .357) were large, while effect size for a frequency
2 main effect was moderate (ω F = .056).
8 2 Omega squared (ω effect) is the most popular measure of effect size, which represents the proportion of variance accounted for by the treatment manipulation. ω2 is based on two variances, one derived from the 2 2 2 2 treatment populations and the other derived from the total population variance [ω A = σ A / (σ A + σ S/A)]. In this study, partial omega squared was calculated because it is not directly influenced by the presence of 2 2 2 other factorial effects. Keppel (1991) recommended partial omega squared [ω effect = σ effect / (σ effect + 2 2 2 2 2 2 σ error), where σ error = σ S/ABC in a 3-way factorial design] over standard omega squared [ω effect = σ effect /σ T, 2 2 2 2 2 2 2 2 2 where σ T = σ A + σ B + σ C + σ A x B + σ A x C + σ B x C + σ A x B x C + σ S/ABC ] in more than 2-way factorial designs.
9 2 2 2 ω ranges from 0 to 1. According to Cohen, an ω =.15 is considered to be a large effect, ω =.06 is a 2 2 medium effect, and ω =.01 is a small effect. If ω =0, there are no population treatment effects.
122 Hypothesis 1: As compared to those notified about product unavailability prior to
making a choice, consumers notified about product unavailability after making a
choice will experience stronger negative emotions.
Factorial analysis of variance revealed that there was a significant main effect for
timing of notification on the level of negative emotions evoked, F (1, 812) = 343.50,
p < .0001 (ω2 = .295). Inspection of cell means revealed that participants who were notified about product unavailability after they made the final choice (M=30.57,
SD=15.23) exhibited stronger negative emotions than those who were notified before making a choice (M=19.55, SD=8.38). Effect size (ω2 = .295) indicated a large effect of
timing on negative emotions. Approximately 29.5 percent of the total variance in
negative emotion is accounted for by the experimental treatment of timing of notification.
Therefore, hypothesis 1 is supported.
Hypothesis 2: As compared to when the not preferred item is unavailable,
consumers will experience stronger negative emotions when their preferred item
is unavailable.
Factorial analysis of variance revealed that there was a significant main effect for
preference for an unavailable item on the level of negative emotions evoked, F (1, 812) =
455.34, p < .0001 (ω2 = .357). Inspection of cell means revealed that when their preferred item was unavailable (M=31.88, SD=14.71), participants exhibited stronger negative emotions than when their not preferred item was unavailable (M=18.76, SD=8.22).
123 Effect size (ω2 = .357) indicated a large effect of preference on negative emotions.
Approximately 35.7 percent of the total variance in negative emotion is accounted for by the experimental treatment of preference. Therefore, hypothesis 2 is supported.
Hypothesis 3: As compared to those confronted with product unavailability once,
consumers confronted with product unavailability twice will experience stronger
negative emotions.
Factorial analysis of variance revealed that there was a significant main effect for
frequency on the level of negative emotions evoked, F (1, 812) = 49.31, p < .0001
(ω2 = .056). Inspection of cell means revealed that participants exhibited stronger
negative emotions when they encountered product unavailability twice (M=27.40,
SD=15.31) than once (M=22.89, SD=10.94). Effect size (ω2 = .056) indicated a moderate
effect of frequency on negative emotions. Approximately 5.6 percent of the total
variance in negative emotion is accounted for by the experimental treatment of frequency.
Therefore, hypothesis 3 is supported.
Hypothesis 4: Timing and preference will interact to elicit negative emotional
response to product unavailability.
Factorial analysis of variance revealed that there was a significant timing by
preference interaction effect on the level of negative emotions evoked, F (1, 812) =
124 273.96, p < .0001 (ω2 = .250). Simple effects tests indicated that timing of notification was significant only when the preferred item was unavailable, F (1, 812) = 605.11, p < .0001. Inspection of cell means revealed that when the preferred item was unavailable, participants who were notified after they made a choice exhibited stronger negative emotions (M=41.89, SD=11.17) than those who received prior notification
(M=21.00, SD=8.93). This difference was larger when the preferred item was unavailable rather than when the not preferred item was unavailable (See Figure 4.3 and
Table 4.14). Approximately 25 percent of the total variance in negative emotion is accounted for by the timing by preference interaction. Therefore, hypothesis 4 is supported.
Hypothesis 5: Timing and frequency will interact to elicit negative emotional
response to product unavailability.
Factorial analysis of variance revealed that there was a significant timing by
frequency interaction effect on the level of negative emotions evoked, F (1, 812) = 29.98,
p < .0001 (ω2 = .034). Simple effects tests indicated that the effect of frequency was
significant only when participants were notified after making a choice, F (1, 812) = 80.13,
p < .0001. Inspection of cell means revealed that when notified after they made a choice,
participants who faced two stockouts exhibited stronger negative emotions (M=34.33,
SD=16.93) than those who face one stockout (M=26.89, SD=11.97). This difference was larger with late notification than early notification (See Figure 4.4 and Table 4.15).
125 Approximately 3.4 percent of the total variance in negative emotion is accounted for by
the timing by frequency interaction. Therefore, hypothesis 5 is supported.
Hypothesis 6: Preference and frequency will interact to elicit negative emotional
response to product unavailability.
Factorial analysis of variance revealed that there was a significant preference by
frequency interaction effect on the level of negative emotions evoked, F (1, 812) = 32.03,
p < .0001 (ω2 = .037). Simple effects tests indicated that the effect of frequency was
significant only when the preferred item was unavailable, F (1, 812) = 79.05, p < .0001.
Inspection of cell means revealed that when the preferred item was unavailable,
participants who faced two stockouts exhibited stronger negative emotions (M=35.22,
SD=16.16) than those who faced one stockout (M=27.67, SD=11.58). This difference was larger when the preferred item was unavailable rather than when the not preferred item was unavailable (See Figure 4.5 and Table 4.16). Approximately 3.7 percent of the total variance in negative emotion is accounted for by the preference by frequency interaction. Therefore, hypothesis 6 is supported.
Inspections of cell means for negative emotion from above simple effect tests indicate that negative emotion experienced by participants during the experiment was generally moderate, although there were substantial differences between experimental
conditions. While the scale for negative emotion ranged from 14 to 70, the mean values
of negative emotion from experiment groups ranged from 18.14 to 41.89, most of them
being below the midpoint of the scale.
126 Timing before 40.00 after
35.00
30.00
25.00 Negative emotion Negative
20.00
not preferred preferred Preference
Figure 4.3. Timing by Preference Interaction on Negative Emotion
Timing Before After Preference Not preferred 18.14 19.32 Preferred 20.99 41.89
Table 4.14. Timing by Preference Interaction on Negative Emotion
127 35.00 timing before after
30.00
25.00 Negative emotion Negative
20.00
once twice Frequency
Figure 4.4. Timing by Frequency Interaction on Negative Emotion
Timing Before After Frequency Once 19.11 26.89 Twice 20.03 34.33
Table 4.15. Timing by Frequency Interaction on Negative Emotion
128 36.00 Preference not preferred 33.00 preferred
30.00
27.00
24.00 Negative emotion Negative
21.00
18.00
once twice Frequency
Figure 4.5. Preference by Frequency Interaction on Negative Emotion
Preference Not preferred Preferred Frequency Once 18.33 27.67 Twice 19.12 35.22
Table 4.16. Preference by Frequency Interaction on Negative Emotion
129 Part Two (Hypotheses 7 to 11)
The second part of the proposed model establishes the relationship between
negative emotions and consumers’ evaluative responses and behavioral intent as responses to product unavailability. In Part one, it was shown that timing, preference, and frequency of product unavailability affect the level of negative emotions aroused when confronted with product unavailability. In Part two, this study investigated the
relationship among negative emotion, store image, decision satisfaction, and behavioral
intent (See Figure 4.1 for the proposed model).
Structural equation modeling with experimental data. The second part of the
study was tested using structural equation modeling. Although several scholars have
advocated the benefits of using structural equation modeling on experimental data
(Bagozzi, 1991; Bagozzi & Yi, 1988, 1989; Cole, Maxwell, Arvey, & Salas, 1993; Fiske,
Kenny, & Taylor, 1982; Steenkamp & Trijp, 1991), structural equation models have not
been used much with experimental data yet. Fiske et al. (1982, p.108-109) stimulated
more psychologists to use structural equation modeling;
Although structural modeling is useful for inferring causality with correlational data, experimental control adds even more power to the technique. Since experiments manipulate the independent variable directly, one knows that it causes the outcomes (Costner, 1971). There is no possibility of reverse causation, that is, that the effect is actually the cause, and vice versa. Further, “third variables” are convincingly eliminated by experimental random assignment. Without experimental manipulation, estimates of causal effects are subject to alternative explanation. Hence, the conventional wisdom, that structural modeling is appropriate only for survey or observational data and not for randomized experiment is false; causal modeling benefits from and improves inferential inference.
130 Nonetheless, a structural equation modeling approach has been applied to a
limited number of experimental studies since then. Some have criticized that this reluctance for using structural equation models is partially due to “the inertia associated with the traditional methods, ANOVA and MANOVA” (Bagozzi & Yi, 1989) and more complicated data analysis methods (Fiske et al., 1982).
For Part two of this study, structural equation modeling (Jöreskog & Sörbom,
1993) was used to test hypotheses 7 through 11. By pooling across experimental groups, a structural equation modeling was performed with a single group. The focus of Part two was to examine the structural relationship between negative emotion and consumer response in terms of perception of store image, decision satisfaction, and behavioral intent. Since all participants were exposed to stockouts and exhibited different levels of negative emotion as supported in Part one, the relationship between the level of negative emotion and other variables can be examined with pooled data using structural equation modeling.
Fit indices. The following fit indices were used to evaluate model fit. First, the
chi-square fit statistic was considered. The chi-square statistic tests the null hypothesis
that there is no difference between the observed covariance matrix and the expected
covariance matrix from the hypothesized model. When there is a perfect fit between the
observed data and the expected covariance matrix, the chi-square value will be 0. A
significant chi-square value indicates that the hypothesized model is significantly
different from the relations in the observed data, thus rejecting the hypothesized model,
while an insignificant chi-square statistic indicates a good fit of the model to the observed
131 data. However, the chi-square test statistic has long been criticized for its sensitivity to
sample size (Bagozzi & Yi, 1988; Bentler & Bonett, 1980; Hu & Bentler, 1980; Kaplan,
1990; Marsh, Balla, & McDonald, 1988; Wheaton, Muthen, Alwin, & Summers, 1977).
A chi-square statistic is sensitive to both small and large sample sizes. However, it is useful for the chi-square difference test for nested models.
Researchers advised that other fit indices should also be considered to evaluate the fit of the model (Bagozzi & Yi, 1988; Rigdon, 1996). The GFI (Goodness-of-fit index, Hu & Bentler, 1995) indicates an absolute reduction in fit by comparing the hypothesized model to a null model (no relationship among variables). AGFI is GFI adjusted for the degrees of freedom in the model. GFI > .95 and AGFI >.90 suggest a good fit of the model (Jöreskog & Sörbom, 1993).
As a measure of the discrepancy per degree of freedom, RMSEA (Root Mean
Square Error of Approximation) (Steiger, 1990) has gained more attention as a superior fit index over other fit indices (Browne & Cudeck, 1992; Sugawara & MacCallum, 1993).
RMSEA adds a penalty for adding too many parameters in the model and is less sensitive to sample size. When the value of RMSEA is smaller than .05, it would indicate a close fit of the model, while an RMSEA value between .05 and .08 indicates a mediocre fit of the model (Browne & Cudeck, 1992).
TLI (Tucker-Lewis Index) compares the hypothesized model to two reference models, a null model (no relationship among variables) and an ideal model. TLI > .95 indicates a good fit (Hu & Bentler, 1999).
132 Model fit. Based on measurement validations in previous section (4.2.3), the covariance matrix of the measurements was analyzed using the maximum likelihood function of LISREL VIII (Jöreskog & Sörbom, 1993). An overall chi-square (df=29) was
103.99 (p <.0001). The GFI was .98 and AGFI was .96. The RMSEA value was .05, indicating an acceptable fit of the hypothesized model to the data and the TLI was .99.
Although a chi-square test was significant, it was not surprising with the large sample size. All other fit indices suggest that the hypothesized structural equation model fits the
data reasonably (GFI=.98, AGFI=.96, RMSEA=.05, and TLI=.99). All path coefficients
for structural paths and measurement paths were significant. Table 4.17 summarizes the
result of model fit.
Hypothesis Testing
Hypothesis 7: Negative emotion is negatively related to perception of store image.
Hypothesis 7 proposed that negative emotion aroused by product unavailability is
negatively related to perception of store image. The results show a significant negative
relationship between negative emotion and perception of store image (γ11 = -.22, t =
-6.12), implying that the intensity of negative emotion depresses perceptions of store
image. Thus, hypothesis 7 was supported.
133
Parameter ML Standard t-value estimate error
Structural path Negative emotion (ζ1) Store image (η1) γ11 -.22 .04 -6.12*** Negative emotion (ζ1) Decision satisfaction (η2) γ21 -.22 .04 -5.84*** Negative emotion (ζ1) Behavioral intent (η3) γ31 -.09 .04 -2.23* Store image (η1) Behavioral intent (η3) β31 1.02 .08 12.55*** Decision satisfaction (η2) Behavioral intent (η3) β32 .41 .07 5.72***
Measurement model Negative emotion (ζ1) NE λx11 9.01 .22 40.47*** Store image (η1) S4 λy11 4.59 .15 30.09*** Store image (η1) S5 λy21 4.87 .15 31.88*** Store image (η1) S6 λy31 5.40 .14 37.80*** Decision satisfaction (η2) D2 λy42 1.16 .04 32.31*** Decision satisfaction (η2) D3 λy52 1.19 .03 34.82*** Decision satisfaction (η2) D4 λy62 1.15 .04 31.22*** Behavioral intent (η3) B1 λy73 .73 .02 31.73*** Behavioral intent (η3) B2 λy83 .75 .02 33.21*** Behavioral intent (η3) B3 λy93 .72 .02 31.90***
Non-causal relationship Store image (η1) Decision satisfaction (η2) Ψ21 .78 .02 48.98***
Model fit (df=29) Chi-square 103.99 RMSEA .05 90 percent C.I. (.043; .066) GFI .98 AGFI .96 TLI .99
Note. *p<.05, **p<.01, ***p<.001.
Table 4.17. Summary of Model Fit
134 4.59 S4
Store 4.87 Image S5 0 .78 η 5.40 1 S6 NE -.22 (γ11) 1.02 (β31) 9.01 B1 .73 Negative Behavioral -.09 (γ31) .75 B2 Emotion Intent ζ η .72 1 3 B3
-.22 (γ21) .41 (β32) 1
1.16 D2 Decision 1.19 Satisfaction D3 η 1.15 2 D4
Note. All parameter estimates are significant
Figure 4.6. Hypothesized Model of Consumer Response to Stockouts
(Unstandardized parameter estimates)
135 .85 S4
Store .89 Image S5 0 .75 η .98 1 S6 NE -.22 (γ11) .60 (β31) 1 B1 .93 Negative Behavioral .97 -.05 (γ31) B2 Emotion Intent ζ η .94 1 3 B3
-.21 (γ21) .24 (β32) 1
.89 D2 Decision .93 Satisfaction D3 η 2 .87 D4
Figure 4.7. Hypothesized Model of Consumer Response to Stockouts
(Standardized parameter estimates)
136 Hypothesis 8: Negative emotion is negatively related to decision satisfaction.
Hypothesis 8 suggested that negative emotion aroused by product unavailability is negatively related to satisfaction with the decision process. The results show a significant negative relationship between negative emotion and decision satisfaction
(γ21 = -.22, t = -5.84), implying that the stronger negative emotion, the lower the satisfaction with the decision process. Thus, hypothesis 8 was supported.
Hypothesis 9: Negative emotion is negatively related to behavioral intent.
Hypothesis 9 suggested that negative emotion aroused by product unavailability is negatively related to behavioral intent. The results show a significant negative relationship between negative emotion and behavioral intent (γ31 = -.09, t = -2.23), indicating that the intensity of negative emotion reduces behavioral intent. Thus, hypothesis 9 was supported.
Hypothesis 10: Perception of store image is positively related to behavioral intent.
Hypothesis 10 predicted that perception of store image has a positive influence on behavioral intent. The results show a significant positive relationship between store image and behavioral intent (β31 = 1.02, t = 12.55), implying that the more favorable store image, the higher likelihood of behavioral intent. Thus, hypothesis 10 was supported.
137 Hypothesis 11: Decision satisfaction is positively related to behavioral intent.
Hypothesis 11 predicted that decision satisfaction has a positive influence on
behavior intent. The results show a significant positive relationship between decision
satisfaction and behavioral intent (β32 = .41, t = 5.72), implying that the more satisfied
with the decision process, the higher the likelihood of behavioral intent. Thus, hypothesis
11 was supported.
Decomposition of effects further revealed that indirect effects from negative emotion to behavioral intent was significant (t = -6.23). The effect of negative emotion of behavioral intent was mediated by perception of store image and decision satisfaction.
While the influence of negative emotion on store image and decision satisfaction was similar (standardized coefficients -.22 and -.21 respectively), the impact of store image on behavioral intent was much stronger (.60) than that of decision satisfaction on behavioral intent (.24).
Part Three (Hypotheses 12 to 17)
By taking the whole model into account, the objective of Part three was to
investigate whether the structural relationships examined in Part two differ as a function
of timing, preference, and frequency of stockouts. In other words, Part three investigated
the moderating role of timing, preference, and frequency on consumer response to
product unavailability. The result of Part one supported that the intensity of negative
emotion elicited was different as a function of timing, preference, and frequency of
138 stockouts. The results of Part two supported the significant structural relationships
among negative emotion, store image, decision satisfaction, and behavioral intent in
hypothesized direction. Based on the results from Part one and two, Part three examined whether the contextual factors of stockouts can moderate the process by which negative
emotion influences consumer response. Multiple group structural equation modeling
(Bollen, 1989; Jöreskog & Sörbom, 1993) was used to test hypotheses 12 through 17.
Hypotheses 12 to 17 predicted the moderating role of the three independent variables of
the study (timing, preference, and frequency) on the proposed relationship between
negative emotion and consumer response (structural paths; γ11, γ21, γ31, β31, and β32).
One important benefit of using a multiple group analysis with experimental data is that the moderating role of independent variables can be tested using chi-square difference tests by constraining relevant constraints to structural models (Jaccard & Wan,
1996). Nested goodness-of-fit strategy was used to test the moderating relationship of the independent variables. Two steps were required to test each of main and interaction effects. The first step involves a multiple group solution that allows estimating coefficients freely in each group without any constraints across groups. The second step involves imposing constraints making assumptions of no main or interaction effects.
Then, the constrained solution from step 2 is compared to the unconstrained solution from step 1. The difference in model fit in terms of chi-square statistics is calculated. If a chi-square difference statistic is significant, it indicates that the constraint (e.g., no main effect of timing) is wrong because the imposing of such a constraint adversely impacts the model fit. Therefore, in this example, the unconstrained model fits better.
139 For example, when a main effect of timing is under investigation, a first step
involves a multiple group solution without any constraints across groups. The fit of this
model needs to be acceptable to proceed to the next step. In the second step, types of
constraint are determined. For example, if there is no main effect of timing, γ11 (negative
emotion store image) values should be same between group 1 (FPT1,1,1) and 5 (FPT1,1,2), between group 2 (FPT2,1,1) and 6 (FPT2,1,2), between group 3 (FPT1,2,1) and 7 (FPT1,2,2), and
between group 4 (FPT2,2,1) and 8 (FPT2,2,2). Then, these constraints are imposed (thus,
constrained model) and then another multi group analysis is run with such constraints.
Then, the fit of the constrained model is compared to the fit of unconstrained model in
terms of chi-square test statistics. If the difference test10 is significant, the unconstrained
model fits better than the constrained model.
Hypothesis 12: The relationship between negative emotions and consumers’
responses differ as a function of timing of notification.
The chi-square difference tests were used to test for a main effect of timing of
notification about product unavailability on consumers’ responses to stockouts.
Constraints A (γ11), B (γ21), C (γ31), D (β31), and E (β32) were each tested against the
unconstrained model. The results show that timing of notification had a significant
moderating effect on the relationship between store image and behavioral intent (∆χ2
10 ∆χ2 = χ2 (constrained) - χ2 (unconstrained); ∆df = df (constrained) – df (unconstrained)
140 = 13.79, ∆df = 4, p < .05). How store image influences behavioral intent was different depending on when participants were notified about product unavailability. However, timing did not have any substantial moderating effect on other structural paths. Therefore,
only hypothesis 12D was supported.
Hypothesis 13: The relationship between negative emotions and consumers’
responses differ as a function of preference for an unavailable item.
The chi-square difference tests show that preference for an unavailable item had a significant moderating effect on the relationship between store image and behavioral intent (∆χ2 = 9.50, ∆df = 4, p < .05). How store image influences behavioral intent was
different depending on whether the preferred item is unavailable or the not preferred item
is unavailable. However, preference did not have any substantial moderating effect on
other structural paths representing how consumers respond to product unavailability.
Therefore, the data support hypothesis 13D only.
Hypothesis 14: The relationship between negative emotions and consumers’
responses differ as a function of frequency of product unavailability.
The chi-square difference tests show that frequency of product unavailability had
a significant moderating effect on the relationship between store image and behavioral intent (∆χ2 = 9.81, ∆df = 4, p < .05). How store image influences behavioral intent was
different depending on whether participants encounter product unavailability once or 141 twice. However, frequency did not have any substantial moderating effect on other structural paths of the proposed model. Therefore, the data support hypothesis 14D only.
Hypothesis 15: The relationship between negative emotions and consumers’
responses differ as a function of timing by preference interaction.
The chi-square difference tests show that timing and preference moderated the
relationship between negative emotion and store image (∆χ2 = 8.54, ∆df = 2, p < .05),
between store image and behavioral intent (∆χ2 = 19.73, ∆df = 2, p < .001), and between
decision satisfaction and behavioral intent (∆χ2 = 6.76, ∆df = 2, p < .05). Therefore, the
data support hypotheses 15A, 15D, and 15E.
Hypothesis 16: The relationship between negative emotions and consumers’
responses differ as a function of timing by frequency interaction.
The chi-square difference tests show that timing and frequency moderated the
relationship between negative emotion and store image (∆χ2 = 9.20, ∆df = 2, p < .05),
between store image and behavioral intent (∆χ2 = 12.68, ∆df = 2, p < .01), and between
decision satisfaction and behavioral intent (∆χ2 = 11.96, ∆df = 2, p < .01). Therefore, the
data support hypotheses 16A, 16D, and 16E.
142 Hypothesis 17: The relationship between negative emotions and consumers’
responses differ as a function of preference by frequency interaction.
The chi-square difference tests show that preference and frequency moderated the relationship between negative emotion and store image (∆χ2 = 14.71, ∆df = 2, p < .001), between store image and behavioral intent (∆χ2 = 38.57, ∆df = 2, p < .0001), and between decision satisfaction and behavioral intent (∆χ2 = 9.11, ∆df = 2, p < .01). Therefore, the data support hypotheses 17A, 17D, and 17E.
143
Timing Constraints df χ2 Value ∆df ∆χ2
Unconstrained model -- 232 322.88
Constraint A FPT1,1,1 (γ11) = FPT1,1,2 (γ11) 236 325.05 4 2.17 Negative emotion FPT2,1,1 (γ11) = FPT2,1,2 (γ11) Store image (γ11) FPT1,2,1 (γ11) = FPT1,2,2 (γ11)
FPT2,2,1 (γ11) = FPT2,2,2 (γ11)
Constraint B FPT1,1,1 (γ21) = FPT1,1,2 (γ21) 236 324.68 4 1.80 Negative emotion FPT2,1,1 (γ21) = FPT2,1,2 (γ21) Decision satisfaction (γ21) FPT1,2,1 (γ21) = FPT1,2,2 (γ21)
FPT2,2,1 (γ21) = FPT2,2,2 (γ21)
Constraint B FPT1,1,1 (γ31) = FPT1,1,2 (γ31) 236 326.39 4 3.51 Negative emotion FPT2,1,1 (γ31) = FPT2,1,2 (γ31) Behavioral intent (γ31) FPT1,2,1 (γ31) = FPT1,2,2 (γ31)
FPT2,2,1 (γ31) = FPT2,2,2 (γ31)
Constraint D FPT1,1,1 (β31) = FPT1,1,2 (β31) 236 336.67 4 13.79** Store image FPT2,1,1 (β31) = FPT2,1,2 (β31) Behavioral intent (β31) FPT1,2,1 (β31) = FPT1,2,2 (β31) FPT2,2,1 (β31) = FPT2,2,2 (β31)
Constraint E FPT1,1,1 (β32) = FPT1,1,2 (β32) 236 329.60 4 6.72 Decision satisfaction FPT2,1,1 (β32) = FPT2,1,2 (β32) Behavioral intent (β32) FPT1,2,1 (β32) = FPT1,2,2 (β32) FPT2,2,1 (β32) = FPT2,2,2 (β32)
Note. *p <.05, **p<.01, ***p <.001, ****p <.0001 FPT: F [Frequency (1: once vs. 2: twice)] P[Preference (1: not preferred vs. 2: preferred)] T[Timing (1: before vs. 2: after)]
Table 4.18. Testing of Hypothesis 12 (Main Effect of Timing)
144
Preference Constraints df χ2 Value ∆df ∆χ2
Unconstrained model -- 232 322.88
Constraint A FPT1,1,1 (γ11) = FPT1,2,1 (γ11) 236 327.79 4 4.91 Negative emotion FPT2,1,1 (γ11) = FPT2,2,1 (γ11) Store image (γ11) FPT1,1,2 (γ11) = FPT1,2,2 (γ11)
FPT2,1,2 (γ11) = FPT2,2,2 (γ11)
Constraint B FPT1,1,1 (γ21) = FPT1,2,1 (γ21) 236 323.84 4 .96 Negative emotion FPT2,1,1 (γ21) = FPT2,2,1 (γ21) Decision satisfaction (γ21) FPT1,1,2 (γ21) = FPT1,2,2 (γ21)
FPT2,1,2 (γ21) = FPT2,2,2 (γ21)
Constraint C FPT1,1,1 (γ31) = FPT1,2,1 (γ31) 236 329.70 4 6.82 Negative emotion FPT2,1,1 (γ31) = FPT2,2,1 (γ31) Behavioral intent (γ31) FPT1,1,2 (γ31) = FPT1,2,2 (γ31)
FPT2,1,2 (γ31) = FPT2,2,2 (γ31)
Constraint D FPT1,1,1 (β31) = FPT1,2,1 (β31) 236 332.38 4 9.50* Store image FPT2,1,1 (β31) = FPT2,2,1 (β31) Behavioral intent (β31) FPT1,1,2 (β31) = FPT1,2,2 (β31) FPT2,1,2 (β31) = FPT2,2,2 (β31)
Constraint E FPT1,1,1 (β32) = FPT1,2,1 (β32) 236 326.69 4 3.81 Decision satisfaction FPT2,1,1 (β32) = FPT2,2,1 (β32) Behavioral intent (β32) FPT1,1,2 (β32) = FPT1,2,2 (β32) FPT2,1,2 (β32) = FPT2,2,2 (β32)
Note. *p <.05, **p<.01, ***p <.001, ****p <.0001 FPT: F [Frequency (1: once vs. 2: twice)] P[Preference (1: not preferred vs. 2: preferred)] T[Timing (1: before vs. 2: after)]
Table 4.19. Testing of Hypothesis 13 (Main Effect of Preference)
145
Frequency Constraints df χ2 Value ∆df ∆χ2
Unconstrained model -- 232 322.88
Constraint A FPT1,1,1 (γ11) = FPT2,1,1 (γ11) 236 327.70 4 4.82 Negative emotion FPT1,2,1 (γ11) = FPT2,2,1 (γ11) Store image (γ11) FPT1,1,2 (γ11) = FPT2,1,2 (γ11)
FPT1,2,2 (γ11) = FPT2,2,2 (γ11)
Constraint B FPT1,1,1 (γ21) = FPT2,1,1 (γ21) 236 327.60 4 4.72 Negative emotion FPT1,2,1 (γ21) = FPT2,2,1 (γ21) Decision satisfaction (γ21) FPT1,1,2 (γ21) = FPT2,1,2 (γ21)
FPT1,2,2 (γ21) = FPT2,2,2 (γ21)
Constraint C FPT1,1,1 (γ31) = FPT2,1,1 (γ31) 236 327.84 4 4.96 Negative emotion FPT1,2,1 (γ31) = FPT2,2,1 (γ31) Behavioral intent (γ31) FPT1,1,2 (γ31) = FPT2,1,2 (γ31)
FPT1,2,2 (γ31) = FPT2,2,2 (γ31)
Constraint D FPT1,1,1 (β31) = FPT2,1,1 (β31) 236 332.69 4 9.81* Store image FPT1,2,1 (β31) = FPT2,2,1 (β31) Behavioral intent (β31) FPT1,1,2 (β31) = FPT2,1,2 (β31) FPT1,2,2 (β31) = FPT2,2,2 (β31)
Constraint E FPT1,1,1 (β32) = FPT2,1,1 (β32) 236 331.06 4 8.18 Decision satisfaction FPT1,2,1 (β32) = FPT2,2,1 (β32) Behavioral intent (β32) FPT1,1,2 (β32) = FPT2,1,2 (β32) FPT1,2,2 (β32) = FPT2,2,2 (β32)
Note. *p <.05, **p<.01, ***p <.001, ****p <.0001 FPT: F [Frequency (1: once vs. 2: twice)] P[Preference (1: not preferred vs. 2: preferred)] T[Timing (1: before vs. 2: after)]
Table 4.20. Testing of Hypothesis 14 (Main Effect of Frequency)
146
Timing x Preference Constraints df χ2 Value ∆df ∆χ2
Unconstrained model -- 232 322.88
Constraint A FPT1,1,1 (γ11) + FPT1,2,2 (γ11) 234 331.42 2 8.54* Negative emotion = FPT1,2,1 (γ11) + FPT1,1,2 (γ11) Store image (γ11) FPT2,1,1 (γ11) + FPT2,2,2 (γ11) = FPT2,2,1 (γ11) + FPT2,1,2 (γ11)
Constraint B FPT1,1,1 (γ21) + FPT1,2,2 (γ21) 234 328.63 2 5.75 Negative emotion = FPT1,2,1 (γ21) + FPT1,1,2 (γ21) Decision satisfaction (γ21) FPT2,1,1 (γ21) + FPT2,2,2 (γ21) = FPT2,2,1 (γ21) + FPT2,1,2 (γ21)
Constraint C FPT1,1,1 (γ31) + FPT1,2,2 (γ31) 234 323.02 2 .14 Negative emotion = FPT1,2,1 (γ31) + FPT1,1,2 (γ31) Behavioral intent (γ31) FPT2,1,1 (γ31) + FPT2,2,2 (γ31) = FPT2,2,1 (γ31) + FPT2,1,2 (γ31)
Constraint D FPT1,1,1 (β31) + FPT1,2,2 (β31) 234 342.61 2 19.73*** Store image = FPT1,2,1 (β31) + FPT1,1,2 (β31) Behavioral intent (β31) FPT2,1,1 (β31) + FPT2,2,2 (β31) = FPT2,2,1 (β31) + FPT2,1,2 (β31)
Constraint E FPT1,1,1 (β32) + FPT1,2,2 (β32) 234 329.64 2 6.76* Decision satisfaction = FPT1,2,1 (β32) + FPT1,1,2 (β32) Behavioral intent (β32) FPT2,1,1 (β32) + FPT2,2,2 (β32) = FPT2,2,1 (β32) + FPT2,1,2 (β32)
Note. *p <.05, **p<.01, ***p <.001, ****p <.0001 FPT: F [Frequency (1: once vs. 2: twice)] P[Preference (1: not preferred vs. 2: preferred)] T[Timing (1: before vs. 2: after)]
Table 4.21. Testing of Hypothesis 15 (Timing x Preference)
147
Timing x Frequency Constraints df χ2 Value ∆df ∆χ2
Unconstrained model -- 232 322.88
Constraint A FPT1,1,1 (γ11) + FPT2,1,2 (γ11) 234 332.08 2 9.20* Negative emotion = FPT2,1,1 (γ11) + FPT1,1,2 (γ11) Store image (γ11) FPT1,2,1 (γ11) + FPT2,2,2 (γ11) = FPT2,2,1 (γ11) + FPT1,2,2 (γ11)
Constraint B FPT1,1,1 (γ21) + FPT2,1,2 (γ21) 234 324.39 2 1.51 Negative emotion = FPT2,1,1 (γ21) + FPT1,1,2 (γ21) Decision satisfaction (γ21) FPT1,2,1 (γ21) + FPT2,2,2 (γ21) = FPT2,2,1 (γ21) + FPT1,2,2 (γ21)
Constraint C FPT1,1,1 (γ31) + FPT2,1,2 (γ31) 234 324.45 2 1.57 Negative emotion = FPT2,1,1 (γ31) + FPT1,1,2 (γ31) Behavioral intent (γ31) FPT1,2,1 (γ31) + FPT2,2,2 (γ31) = FPT2,2,1 (γ31) + FPT1,2,2 (γ31)
Constraint D FPT1,1,1 (β31) + FPT2,1,2 (β31) 234 335.56 2 12.68** Store image = FPT2,1,1 (β31) + FPT1,1,2 (β31) Behavioral intent (β31) FPT1,2,1 (β31) + FPT2,2,2 (β31) = FPT2,2,1 (β31) + FPT1,2,2 (β31)
Constraint E FPT1,1,1 (β32) + FPT2,1,2 (β32) 234 334.84 2 11.96** Decision satisfaction = FPT2,1,1 (β32) + FPT1,1,2 (β32) Behavioral intent (β32) FPT1,2,1 (β32) + FPT2,2,2 (β32) = FPT2,2,1 (β32) + FPT1,2,2 (β32)
Note. *p <.05, **p<.01, ***p <.001, ****p <.0001 FPT: F [Frequency (1: once vs. 2: twice)] P[Preference (1: not preferred vs. 2: preferred)] T[Timing (1: before vs. 2: after)]
Table 4.22. Testing of Hypothesis 16 (Timing x Frequency)
148
Preference x Frequency Constraints df χ2 Value ∆df ∆χ2
Unconstrained model -- 232 322.88
Constraint A FPT1,1,1 (γ11) + FPT2,2,1 (γ11) 234 337.59 2 14.71*** Negative emotion = FPT2,1,1 (γ11) + FPT1,2,1 (γ11) Store image (γ11) FPT1,1,2 (γ11) + FPT2,2,2 (γ11) = FPT2,1,2 (γ11) + FPT1,2,2 (γ11)
Constraint B FPT1,1,1 (γ21) + FPT2,2,1 (γ21) 234 325.17 2 2.71 Negative emotion = FPT2,1,1 (γ21) + FPT1,2,1 (γ21) Decision satisfaction (γ21) FPT1,1,2 (γ21) + FPT2,2,2 (γ21) = FPT2,1,2 (γ21) + FPT1,2,2 (γ21)
Constraint C FPT1,1,1 (λ31) + FPT2,2,1 (λ31) 234 323.19 2 .31 Negative emotion = FPT2,1,1 (λ31) + FPT1,2,1 (λ31) Behavioral intent (λ31) FPT1,1,2 (λ31) + FPT2,2,2 (λ31) = FPT2,1,2 (λ31) + FPT1,2,2 (λ31)
Constraint D FPT1,1,1 (β31) + FPT2,2,1 (β31) 234 361.45 2 38.57**** Store image = FPT2,1,1 (β31) + FPT1,2,1 (β31) Behavioral intent (β31) FPT1,1,2 (β31) + FPT2,2,2 (β31) = FPT2,1,2 (β31) + FPT1,2,2 (β31)
Constraint E FPT1,1,1 (β32) + FPT2,2,1 (β32) 234 331.99 2 9.11** Decision satisfaction = FPT2,1,1 (β32) + FPT1,2,1 (β32) Behavioral intent (β32) FPT1,1,2 (β32) + FPT2,2,2 (β32) = FPT2,1,2 (β32) + FPT1,2,2 (β32)
Note. *p <.05, **p<.01, ***p <.001, ****p <.0001 FPT: F [Frequency (1: once vs. 2: twice)] P[Preference (1: not preferred vs. 2: preferred)] T[Timing (1: before vs. 2: after)]
Table 4.23. Testing of Hypothesis 17 (Preference x Frequency)
149 4.3. Study 2
4.3.1. Sample Description
Data for Study 2 were collected from 234 female college students. The data collection process for Study 2 was concurrent with the data collection of Study 1. When participants logged onto the research website, they were automatically directed to either
Study 1 or Study 2 website without their knowledge. Therefore, a response rate specific to Study 2 is not available.
The mean age of the participants for Study 2 was 21, with a range of 18 to 51 (See
Table 4.24). Nearly 60 percent of participants were aged between 20 and 24 and over 90 percent of participants were younger than 24. The academic standing of the participants was evenly spread out. In regards to ethnic background, over 80 percent of participants
were Caucasian. Other ethnic groups combined accounted for about 18 percent;
Asian/Pacific Islander (7.7%), African American (6.8%), other (1.7%), multi-cultural
(1.3%), Hispanic (0.4%), and Native Americans (0.4%).
Information about participants’ general practice with the Internet and online
shopping/buying was also obtained (Table 4.25). The distribution of general Internet use is negatively skewed. About 90 percent of participants responded that they use the
Internet frequently or very frequently, but their online shopping and buying practices were not so prevalent. About 29 percent of participants shop online frequently or very frequently, while about 18 percent of participants buy frequently or very frequently.
150 More than 27 percent of participants shop online for apparel frequently or very frequently, while about 14 percent of participants buy apparel online frequently or very frequently.
Information about participants’ prior experience related to stockouts was further obtained (See Table 4.26). For general apparel stockouts, about two thirds of participants experienced stockouts sometimes or more frequently. Participants’ experience with stockouts was similar across various shopping channels. About 66 percent of store shoppers, 52 percent of catalog shoppers, and 55 percent of online shoppers experienced stockouts sometimes or more frequently.
151
Variable Category Mean Frequency Percent (SD) f % Age Under 20 21.36 73 31.2 20 – 24 (4.94) 138 59.0 25 – 30 12 5.1 Over 30 11 4.7
Academic standing Freshman 47 20.1 Sophomore 47 20.1 Junior 60 25.6 Senior 70 29.9 Graduate 10 4.3
Ethnic background African American 16 6.8 Caucasian American 191 81.6 Hispanic 1 .4 Native Americans 1 .4 Asian/pacific islander 18 7.7 Multi-cultural 3 1.3 Other 4 1.7
Table 4.24. Study 2: Demographic Profile of Participants
152 General Online Online Online Online Internet use shopping purchase apparel apparel shopping purchase N=820 f % f % f % f % f % Very infrequently 3 1.3 53 22.6 74 31.6 67 28.6 92 39.3 Infrequently 3 1.3 40 17.1 56 23.9 43 18.4 41 17.5 Sometimes 19 8.1 64 27.4 47 20.1 41 17.5 39 16.7 Frequently 41 17.5 45 19.2 26 11.1 44 18.8 23 9.8 Very frequently 166 70.9 22 9.4 15 6.4 20 8.5 10 4.3 Not applicable 2 .9 10 4.3 16 6.8 19 8.1 29 12.4
Table 4.25. Study 2: Participants’ Internet Use
Apparel Stockout in Stockout in Stockout in Received stockout store catalog online compensation shopping shopping shopping N=820 f % f % f % f % f % Not often at all (1) 37 15.8 31 13.2 36 15.4 31 13.2 99 42.3 (2) 15 6.4 37 15.8 28 12.0 22 9.4 24 10.3 (3) 113 48.3 109 46.6 85 36.6 80 34.2 40 17.1 (4) 31 13.2 37 15.8 23 9.8 38 16.2 16 6.8 Very often (5) 13 5.6 9 3.8 12 5.1 10 4.3 3 1.3 Not applicable 25 10.7 11 4.7 50 21.4 53 22.6 52 22.2
Table 4.26. Study 2: Participants’ Prior Experience Related to Stockouts
153 4.3.2. Exploratory Study
The objective of Study 2 was to explore potential retail management strategies that can mitigate the adverse impact of stockouts. Although the best scenario for retailers is to gain an optimal balance between overstocks and stockouts and thus have no stockouts for consumers, stockouts are likely to occur in real retail situations. Thus, while an ultimate goal of retailers is to minimize stockouts, they also need to develop strategies that may help them deal with stockouts more effectively when they occur.
In order to provide some useful insights for retailers, four different retail management responses to stockouts were explored in Study 2. Because there is no prior literature or theory to suggest which strategy would work better in reducing the adverse impact of stockouts, the approach of Study 2 is exploratory in nature. Multivariate analysis of variance (MANOVA) and subsequent post-hoc comparisons among different strategies were conducted to explore the effect of different managerial responses on consumers’ responses to stockouts.
Variables
Study 2 used a one factor between-subjects design. The independent variable for
Study 2 was type of managerial response to stockouts. There were four levels of managerial responses (standard, substitute, backorder, and financial response). Thus, a single independent variable with four levels was used in Study 2.
Dependent variables used in Study 2 were identical to those used in Study 1.
Participants were exposed to only one experimental condition (FPT2,2,2) and then
154 randomly assigned to receive one of four managerial responses. Then, negative emotion,
store image, decision satisfaction, and behavioral intent were measured to assess the
impact of stockouts on consumer response.
Negative emotions. The same 14 negative emotion items (aggravated, agitated,
angry, annoyed, anxious, disappointed, discouraged, frustrated, irritated, mad, sad,
unhappy, unpleasant, and upset) as Study 1 were used in Study 2. An internal
consistency of fourteen 5-point items was reliable (Cronbach’s alpha = .95). Based on
this reliability, scores of the 14 emotion items were summed and then used as a
dependent variable labeled as “negative emotion.” Higher scores indicate stronger
negative emotion. The mean value of ‘negative emotion’ was 24.1 with a range from 14
to 66 (See Table 4.27). This mean value indicates that on average little negative emotion
was experienced by participants.
Store image. Thirteen items verified as reliable measures of store image in Study
1 were included in Study 2. An internal consistency of thirteen 5-point items was reliable
(Cronbach’s alpha = .94). Based on this reliability, scores from 13 store image items were summed and used as a dependent measure labeled as “store image.” Higher scores of store image indicate more positive evaluation of attributes of store image, while lower scores indicate more negative evaluation of attributes of store image. The mean value of
‘store image’ was 47.9 with a range from 13 to 65 (See Table 4.27). This mean value indicates that on average participants’ perceived store image was favorable.
155 Decision satisfaction. Three items verified as a reliable measure of decision
satisfaction in Study 1 were included in Study 2. Consistency of three 5-point items was
reliable (.88). Based on this reliability, scores from the three items were summed and
used as a dependent variable labeled as “decision satisfaction.” Higher scores indicate that participants are more satisfied with their decision process, while lower scores indicate that they are more dissatisfied with the decision process. The mean value of
‘decision satisfaction’ was 8.6 with a range from 3 to 15 (See Table 4.27). This mean value indicates that on average participants’ satisfaction with the decision process is slightly high.
Behavioral intent. Three items verified as a reliable measure of behavioral intent
in Study 1 were included in Study 2. Consistency of three 5-point items was reliable
(.93). Based on this reliability, scores from the three items were summed and used as a
dependent variable labeled as “behavioral intent.” Higher scores indicate greater intent to
patronize an online store (e-fashion), while lower scores indicate less intent to patronize the
online store. The mean value of ‘behavioral intent’ was 8.5 with a range from 3 to 15
(See Table 4.27). This mean value indicates that on average participants’ behavioral
intent was slightly high.
156
Dependent variables Min. Max. Mean S.D. Cronbach’s alpha
Negative emotion 14 66 24.1 10.8 .95 Store image 13 65 47.9 9.2 .94 Decision satisfaction 3 15 8.6 3.2 .88 Behavioral intent 3 15 8.5 3.4 .93
Table 4.27. Description of Dependent Variables
Multivariate Analysis of Variance
In order to explore more effective managerial response at the time of stockouts,
multivariate analysis of variance was performed. The independent variable was retail
management response (standard, substitute, backorder, and financial) and four dependent
variables were negative emotion, store image, decision satisfaction, and behavioral intent.
For post-hoc comparisons, the Tukey’s test was used. When making all pairwise comparisons as in this study, the Tukey test is recommended over the Scheffé test because it is more powerful for simple comparisons (Keppel, 1991).
There was a significant multivariate main effect for retail management response on the dependent measures of consumer response to stockouts, F (12, 600) = 17.31, p < .0001 (See Table 4.28). Given that the multivariate test was significant, univariate analyses of variance were performed to determine which dependent variables were
157 affected by the independent variables. If the results of the univariate analysis were
significant, Tukey’s test was conducted to determine which managerial responses were
significantly different from each other.
Variable Wilks’ Lambda df F p
Retail management response .456 12, 600 17.31 <.0001
Table 4.28. Multivariate Analysis of Variance for Study 2
Negative emotions. The subsequent univariate analysis of variance revealed that
retail management response had a significant effect on negative emotion, F (3, 230) =
40.84, p < .0001. Tukey’s test was performed for post hoc comparisons to determine
which responses were significantly different (See Table 4.29 and Table 4.30). The results
show a significant difference in negative emotion between three pairs of comparison groups; (1) people who received a standard response (M = 33.94, SD = 8.30) and people who received a substitute response (M = 20.59, SD = 10.04), p < .0001, (2) people who
received a standard response (M = 33.94, SD = 8.30) and people who received a
backorder response (M = 22.05, SD = 9.38), p < .0001, and (3) people who received a
158 standard response (M = 33.94, SD = 8.30) and people who received a financial response
(M = 18.06, SD = 7.35), p < .0001. Post hoc comparisons indicate that a managerial response had a significant effect on negative emotion because people who received any response other than a standard response of “this item is out of stock” experienced less negative emotion. No significant differences were observed from other pairs.
Store image. Univariate analysis of variance revealed that retail management response had a significant effect on store image, F (3, 230) = 30.11, p < .0001. Tukey’s test was performed for post hoc comparisons to determine which responses were significantly different (See Table 4.29 and Table 4.30). The results show a significant difference in store image between three pairs of comparison groups; (1) people who received a standard response (M = 44.68, SD = 7.60) and people who received a financial response (M = 56.24, SD = 6.53), p < .0001, (2) people who received a substitute response (M = 45.61, SD = 8.60) and people who received a financial response (M =
56.24, SD = 6.53), p < .0001, and (3) people who received a backorder response (M =
44.95, SD = 8.59) and people who received a financial response (M = 56.24, SD = 6.53), p < .0001. Post hoc comparisons indicate that a managerial response had a significant effect on store image because people who received a financial response perceived more positive store image than people who received any other of the three responses. No significant differences were observed from other pairs.
159 Decision satisfaction. Univariate analysis of variance revealed that retail
management response did not have a significant effect on decision satisfaction, F (3, 230)
= .815, p = .487.
Behavioral intent. Univariate analysis of variance revealed that retail
management response had a significant effect on behavioral intent, F (3, 230) = 9.77, p
< .0001. Tukey’s test was performed for post hoc comparisons to determine which
responses were significantly different (See Table 4.29 and Table 4.30). The results show a significant difference in behavioral intent between three pairs of comparison groups; (1) people who received a standard response (M = 7.80, SD = 3.09) and people who received a financial response (M = 10.47, SD = 3.13), p < .0001, (2) people who received a substitute response (M = 7.65, SD = 3.22) and people who received a financial response
(M = 10.47, SD = 3.13), p < .001, and (3) people who received a backorder response (M
= 7.65, SD = 3.58) and people who received a financial response (M = 10.47, SD = 3.13),
p < .0001. Post hoc comparisons indicate that a managerial response had a significant
effect on behavioral intent because people who received a financial response showed
greater behavioral intent than people who received any of the other three responses. No
significant differences were observed from other pairs.
160
Dependent variables Managerial response Mean S.D.
Negative emotion Standard 33.94 8.30 Substitute 20.59 10.04 Backorder 22.05 9.38 Financial 18.07 7.35
Store image Standard 44.68 7.60 Substitute 45.61 8.60 Backorder 44.95 8.59 Financial 56.24 6.53
Decision satisfaction Standard 8.70 3.34 Substitute 8.31 2.99 Backorder 8.27 2.96 Financial 9.08 3.36
Behavioral intent Standard 7.80 3.09 Substitute 8.11 3.22 Backorder 7.65 3.58 Financial 10.47 3.13
Table 4.29. Descriptive Statistics for Study 2
161
Standard Substitute Backorder Financial
Negative emotion Standard - 13.35**** 11.88**** 15.87**** Substitute - -1.46 2.52 Backorder - 3.99 Financial - Store image Standard - -.93 -.26 -11.56**** Substitute - .67 -10.63**** Backorder - -11.29**** Financial -
Decision satisfaction Standard - .38 .42 -.39 Substitute - .04 -.77 Backorder - -.81 Financial -
Behavioral intent Standard - -.31 .15 -2.67**** Substitute - .46 -2.36*** Backorder - -2.82**** Financial -
Note. ***p<.001, ****p<.0001
Table 4.30. Tukey Post Hoc Comparisons
162 CHAPTER 5
GENERAL DISCUSSION
5.1. Overview
The purpose of this research was to investigate how consumers respond to stockouts from the perspective of discrepancy-evaluation theory of emotion. In Study 1, this research examined: (1) how three contextual factors (timing, preference, and frequency of product unavailability) influence the severity of stockouts measured as the intensity of negative emotion, (2) how negative emotion elicited by product unavailability influences the way in which consumers respond to product unavailability, and (3) how the process by which consumers respond to stockouts differ as a function of timing, preference, and frequency of product unavailability. In Study 2, this research explored the effect of four retail management responses on consumer response.
This chapter summarizes empirical findings of this dissertation and discusses its implications and contributions. In the second section, empirical findings are first presented. Research implications and contributions are discussed in the third section.
Finally, the limitations of the study are addressed and suggestions for future research are presented.
163 5.2. Empirical Findings
This dissertation research used an experimental design to study consumer
response to product unavailability in online apparel shopping. By using a randomized
experiment, this study attempted to examine the causal relationship between product
unavailability and consumer response in terms of negative emotion, perception of store
image, decision satisfaction, and behavioral intent. In addition, by using a mock website simulating online apparel shopping, this study attempted to improve the realism of the experimental context.
This dissertation included two phases of study. Study 1 investigated the proposed
model of consumer response to product unavailability. Study 2 explored different
managerial responses to stockouts in their effectiveness of mitigating the adverse impact
of stockouts on consumer response. The empirical findings for Study 1 and Study 2 are
each presented.
5.2.1. Findings from Study 1
The Effects of Contextual Factors in Stockouts on Negative Emotion
The proposed model of consumer response to product unavailability in Study 1
was tested with tripartite analyses. In Part one, the effects of three contextual factors in
stockouts, namely, timing, preference, and frequency on negative emotion were tested
using between-subjects factorial analyses of variance (H1 to H6). Table 5.1 provides a
164 summary of all of the hypothesis-test results for Study 1. Results from factorial analysis of variance supported all six hypotheses in Part one.
Timing of notification. The findings of this study revealed that timing of notification had a significant impact on negative emotion in such a way that late notification evokes more negative emotion than early notification. This result is consistent with prior research findings (Bell & Fitzsimons, 1999; Fitzsimons, 2000).
In the late notification condition, people are likely to unconsciously expect that products are available for purchase throughout the decision-making process. Further, they may engage in some level of cognitive processing to make a choice under the assumption of product availability. When exposed to a stockout, such an expectation of product availability is likely to lead to a greater discrepancy with the fact of product unavailability. Shopping goals pursued under such an assumption are likely to be interrupted in a late notification condition. With early notification, consumers are unlikely to expect product availability throughout the decision-making process because they are informed about the status of product availability before engaging in the decision- making process. In addition, their shopping goal is unlikely to be interrupted because they can modify their shopping goals before engaging in the decision-making process in the early notification condition. If consumers know the in-stock status of products, they can shop or plan shopping accordingly.
Furthermore, during the apparel selection process, consumers often think about such things as how an item would look on them, how it would match with their existing wardrobes, and where they can wear it. If notified after making a selection, such
165 considerations developed during the selection process may further aggravate discrepancies between expectation and actuality and interruption of their shopping goal.
If notified early, such cognitive considerations are unlikely to take place.
Preference for an unavailable item. As several marketing researchers have emphasized the importance of individual preference on consumer behaviors (Hutchinson et al., 2000), the findings of this study provided supporting evidence that preference for an unavailable item had a significant impact on negative emotion. People experienced more negative emotion when their preferred item was unavailable than when their not preferred item was unavailable. This finding is consistent with prior research suggesting that consumers are more likely to notice if their preferred item is unavailable
(Broniarczyk et al., 1998) and to negatively respond (Fitzsimons, 2000) than if the not preferred item is unavailable. Prior research further suggested that availability of a preferred item affects store choice behavior (Fitzsimons, 2000).
In this study, consideration set membership was an indication of one’s preference for an item (Broniarczyk et al., 1998; Fitzsimons, 2000). While forming preferences (i.e., developing a consideration set), people unconsciously develop an expectation of product availability. If the item in one’s consideration set (i.e., preferred) is unavailable, this is likely to interrupt one’s shopping goal (e.g., purchase), and this is a source of negative emotion. On the contrary, if an item not in the consideration set is unavailable, a stockout may not have much of an impact on one’s shopping process or shopping goal.
People may not care whether the not preferred item is available or not because it has little relevance to their shopping goal.
166
Hypotheses Results P H1: timing negative emotion Supported A H2: preference negative emotion Supported R H3: frequency negative emotion Supported T H4: timing x preference negative emotion Supported 1 H5: timing x frequency negative emotion Supported H6: preference x frequency negative emotion Supported
P H7: negative emotion store image (-) (A) Supported A H8: negative emotion decision satisfaction (-) (B) Supported R H9: negative emotion behavioral intent (-) (C) Supported T H10: store image behavioral intent (+) (D) Supported 2 H11: decision satisfaction behavioral intent (+) (E) Supported
P H12: (a) timing A Not supported A (b) timing B Not supported R (b) timing C Not supported T (d) timing D Supported 3 (e) timing E Not supported
H13: (a) preference A Not supported (b) preference B Not supported (c) preference C Not supported (d) preference D Supported (e) preference E Not supported
H14: (a) frequency A Not supported (b) frequency B Not supported (c) frequency C Not supported (d) frequency D Supported (e) frequency E Not supported
H15: (a) timing x preference A Supported (b) timing x preference B Not supported (c) timing x preference C Not supported (d) timing x preference D Supported (e) timing x preference E Supported
H16: (a) timing x frequency A Supported (b) timing x frequency B Not supported (c) timing x frequency C Not supported (d) timing x frequency D Supported (e) timing x frequency E Supported
H17: (a) preference x frequency A Supported (b) preference x frequency B Not supported (c) preference x frequency C Not supported (d) preference x frequency D Supported (e) preference x frequency E Supported Note. A (negative emotion store image); B (negative emotion decision satisfaction); C (negative emotion behavioral intent); D (store image behavioral intent); E (decision satisfaction behavioral intent)
Table 5.1. Summary of Hypothesis-test Results for Study 1
167 Frequency of product unavailability. The results of this study revealed that
frequency of product unavailability has a significant impact on negative emotion. People
who encountered two stockouts experienced more negative emotion than people who
encountered one stockout. Prior researchers have warned that the cumulative impact of
stockouts may be greater than the additive impact of a single stockout (Emmelhainz et al.,
1991; Schary & Christopher, 1979). As noted in Convenience Store News (1998), people may be very intolerant of product unavailability. More frequent product unavailability may cause repetitive interruptions to one’s shopping goal, and thus elicit stronger negative emotion.
Timing by preference interaction. The results of this study revealed that the timing by preference interaction was significant. The interaction between timing and
preference was such that only when the preferred item was unavailable, late notification
about product unavailability invoked significantly stronger negative emotion than early
notification. When the preferred item is unavailable, late notification may cause more
serious interruption to one’s shopping goal than early notification. This mostly likely
occurs because such information has more relevance to one’s decision-making. The
mean value of negative emotion in late notification was nearly twice as large as that of
early notification in this study. Meanwhile, when the not preferred item was unavailable,
late notification did not significantly exacerbate the level of negative emotion than early
notification. This may be due to the irrelevance of such information to one’s decision-
making process. The extent to which information about product unavailability interrupts
one’s shopping goal may determine the level of negative emotion.
168 Timing by frequency interaction. The results further revealed that the timing by
frequency interaction was significant. The interaction between timing and frequency was
such that only when people were notified after making a choice, exposure to two
stockouts evoked significantly stronger negative emotion than exposure to one stockout.
Late notification has a stronger impact on negative emotion than early notification and such impact may accumulate with recurrent stockouts. If notified prior to decision- making, likely discrepancy and interruption are relatively low regardless of the frequency of stockouts.
Preference by frequency interaction. The results of this study showed that the
preference by frequency interaction was significant. The interaction between preference
and frequency was such that only when the preferred item was unavailable, exposure to
two stockouts elicited significantly more negative emotion than exposure to a single
stockout. If the preferred item is unavailable, it may cause more substantial interruptions
to one’s shopping goal, and such impact is likely to accumulate with frequent stockouts.
Meanwhile, unavailability of the not preferred item may not cause substantial
interruptions to one’s shopping goal at the first place. Therefore, the frequency of
stockouts may have little impact on negative emotion.
How Negative Emotion Influences Consumer Response
In Part two, this study investigated the process by which negative emotion
influences consumer response to stockouts. This study proposed that negative emotion
elicited by product unavailability is an explanatory variable that drives consumers to
169 negatively react to stockouts. It was proposed that negative emotion has a negative influence on consumers’ evaluative responses and behavioral intent, and evaluative responses have positive influences on behavioral intent. Using single group structural equation modeling, the relationships among negative emotion, store image, decision satisfaction, and behavioral intent were investigated. The results from structural equation modeling supported all five of the hypotheses in Part two (See Table 5.1).
The findings of this study supported hypotheses 7 and 8 predicting the negative influence of negative emotion on two evaluative responses of consumers such as perception of store image and decision satisfaction. As hypothesized, negative emotion had a negative influence on perception of store image and decision satisfaction. The stronger the negative emotion, the less favorable the perception of store image and the
more dissatisfied consumers are with the decision process. When people experience
more negative emotion due to product unavailability, their perception of store image
becomes more unfavorable and they become more dissatisfied with the decision-making
process.
These findings are congruent with prior marketing research supporting the critical
role of emotion on consumer decision-making and judgment (Barta & Holbrook, 1990;
Hirschman & Holbrook, 1982; Rossiter & Percy, 1980; Zeitlin & Westwood, 1986). The relationship between emotion and perception of store image was consistent with previous research findings suggesting that emotion elicited during shopping has a significant impact on perception and evaluation of store environment and service of salespeople
(Chebat et al., 2001; Chebat & Michon, 2003; Dubé & Morin, 2001; Mattila & Wirtz,
2001).
170 Prior research generally supports the significant impact of emotion on overall consumption satisfaction (Dubé –Rioux, 1990; Mano & Oliver, 1993; Oliver, 1993;
Westbrook, 1987). However, the relationship between emotion and decision satisfaction has not been tested much in prior research. Some previous findings from the stockout literature support the substantial impact of product availability on decision satisfaction
(Bell & Fitzsimons, 1999; Fitzsimons, 2000; Westbrook et al., 1978), but emotion was not investigated in these studies. The findings of this study supported that negative emotion has a significant influence on decision satisfaction.
Results of this study further revealed that negative emotion had a negative influence on behavioral intent, comprised of purchase intent and word-of-mouth communication. As stronger negative emotion is elicited, it is likely that one exhibits both lower purchase intent and lower intent to recommend a store to friends. This finding is consistent with prior research supporting the critical impact of emotion on purchase intent (Baker et al., 1992), retail preference and store selection (Dawson et al., 1990), and approach/avoidance behavior (Hui et al., 1997). However, the direct effect of negative emotion on behavioral intent was found to be small in this study.
Results of hypotheses 10 (store image behavioral intent) and 11 (decision satisfaction behavioral intent) further revealed that the effect of negative emotion on behavioral intent was mediated by perception of store image and decision satisfaction.
Examination of the indirect effect of negative emotion on behavioral intent was significant, suggesting that store image and decision satisfaction mediate the relationship between negative emotion and behavioral intent. Negative emotion depresses perception of store image, and unfavorable store image, in turn, decreases behavioral intent. Also,
171 negative emotion adversely influences decision satisfaction, and lowered decision
satisfaction reduces behavioral intent.
The results of the hypotheses tests for Part two provided a more complete picture
of the process by which product unavailability influences consumer response. First,
findings from Study 1 revealed that product unavailability invokes negative emotion, and
the intensity of negative emotion differs as a function of timing, preference, and frequency of stockouts. In Part two, the findings revealed that negative emotion elicited
by product availability is a key variable that further drive consumers’ negative responses
to product unavailability. Negative emotion adversely affects perception of store image
and decision satisfaction in such a way that more negative emotion leads to unfavorable store image and greater dissatisfaction with the decision-making process. Negative emotion further affects behavioral intent both directly and indirectly mediated by store image and decision satisfaction. The mediated effects of negative emotion on behavioral intent were stronger than the direct effect of negative emotion. Also, the mediated effect
of negative emotion via store image was stronger than the mediated effect of negative
emotion via decision satisfaction.
In a stockout situation, consumers are likely to know why they feel negative
emotion. In other words, consumers are aware of the source of their negative emotion.
In such a case, consumers are more likely to blame the store for not having items
available to them (i.e., make an external attribution) than to blame themselves (i.e., make
an internal attribution) or to blame the weather (i.e., make a situational attribution).
When consumers make an external attribution for their negative emotion at the time a
172 stockout occurs, this may significantly deflate perception of store image and decrease
satisfaction with the decision-making process.
The Moderating Role of Contextual Factors on How Consumers Respond to Stockouts
In Part three, multiple group structural equation modeling was performed to
assess the moderating roles of timing, preference, and frequency on the structural
relationships among negative emotion, store image, decision satisfaction, and behavioral
intent. The structural relationships among the four variables were supported in Part two of this study. A series of chi-square difference tests were used to test these moderations
(See Table 5.1).
Results revealed that timing, preference, and frequency had a moderating effect only on the relationship between perception of store image and behavioral intent and had no moderating effect on other structural relationships. The strength of the relationship between perception of store image and behavioral intent may differ as a function of timing, preference, and frequency. Because most people may hold the store responsible for stockouts, such attribution is more likely to affect perceptions of store image and subsequently behavioral intent.
The analyses of the moderating role of the two-way interactions of timing, preference, and frequency revealed that all three two-way interactions (e.g., timing by
preference, timing by frequency, and preference by frequency) had significant
moderating effects on the structural relationship between negative emotion and store image, store image and behavioral intent, and decision satisfaction and behavioral intent.
The strength of the relationship between negative emotion and store image, store image
173 and behavioral intent, and decision satisfaction and behavioral intent may differ as a
function of two-way interactions among timing, preference, and frequency. Because little
negative emotion was elicited in this study, the influence of negative emotion on decision
satisfaction and behavioral intent may have not varied much as a function of interactions
between timing, preference, and frequency of stockouts.
5.2.2. Findings from Study 2
Study 2 explored four retail management responses to stockouts. Four managerial
responses included: standard (This item is out of stock.), substitute (This item is out of
stock. Would you like to consider other items similar to this item?), backorder (This item
is out of stock. Would you like to backorder this item?), and financial response (The item is out of stock. But we can offer you a 10% discount on any items you purchase from us.).
Multivariate analysis of variance and post hoc comparisons using Tukey’s test revealed the significant multivariate main effect of retail management responses on consumer responses including negative emotion, store image, decision satisfaction, and behavioral intent. Subsequent analyses revealed that managerial responses significantly influenced
negative emotions elicited by product unavailability in such a way that people who
received any response other than a standard response showed less negative emotion than
people who received a standard response. Regardless of what retail management offered,
any evidence suggesting that retailers do care about consumers in stockout situations was
effective in alleviating negative emotion aroused by stockouts. Considering the
174 significant impact of negative emotion on consumers’ negative responses as supported in
Study 1, this finding provides useful information for retailers.
Results further revealed that retail management response had a significant effect on store image and behavioral intent in such a way that people who received a financial response showed the most favorable store image and the highest behavioral intent.
People who received a financial response significantly differed from people who received any of the three other responses in their perceptions of store image and behavioral intent.
No differences were observed among people who received the three other responses.
Provision of a financial response had a substantial effect on alleviating the negative impact of stockouts on perceptions of store image and behavioral intent. Meanwhile provision of other managerial responses did not have such effects. Interestingly, substitute and backorder responses had effects on mitigating negative emotion elicited by product unavailability, but had no effects on perception of store image and behavioral intent. These findings are consistent with previous research supporting that financial compensation was more effective in increasing consumer demands than backorder policy without financial compensation (Bhargava et al., 2002). Therefore, this study suggests that a financial response can be an effective strategy to alleviate the negative impact of stockouts when they occur.
175 5.3. Implications
5.3.1. Theoretical Implications and Contributions
The findings of this study advance our knowledge of the stockout phenomenon in both a theoretical and practical sense. While most prior research findings were descriptive to a large extent, this study extends our understanding in stockout literature by proposing and empirically testing the process model of consumer response to stockouts.
First of all, this study revealed the causal relationship between product unavailability and consumers’ negative responses: (1) product unavailability evokes negative emotion because it may create a discrepancy between expectation and actuality or cause interruption of one’s shopping goal; (2) negative emotion elicited by product unavailability has a negative influence on perception of store image, decision satisfaction, and behavioral intent; (3) perception of store image and decision satisfaction mediate the impact of negative emotion on behavioral intent; (4) timing, preference, and frequency of product unavailability influence the strength of the relationship between store image and behavioral intent; and (5) two-way interactions among timing, preference, and frequency influence the strength of the relationship between negative emotion and perception of store image, perception of store image and behavioral intent, and decision satisfaction and behavioral intent.
Second, this study contributes to the theoretical development of consumer response to product unavailability. One of the important limitations in extant stockout literature is the lack of theoretical insight to explain consumer response to product
176 unavailability. Although the economic theory of utility maximization was suggested as a theoretical framework (Corstjens & Corstjens, 1995), it was never empirically tested. In
addition, empirical evidence from consumer choice research (Bettman et al., 1998) and
stockout research (Emmelhainz et al., 1991; Fitzsimons, 2000; Straughn, 1991) suggested
consumer behaviors that were incongruent with what the economic theory would predict.
Some researchers suggested Brehm’s psychological reactance theory as a theoretical framework to explain why consumers negatively respond to stockouts. This theory may provide useful perspective to understand consumers’ choice behaviors. However, it may not adequately explicate consumers’ stockouts response in general shopping situations in
which consumers are unlikely to experience psychological reactance.
Instead, this study may provide useful theoretical insight in understanding why product unavailability negatively influences consumer response. This study suggests that negative emotion is elicited by discrepancies between expectation (i.e., product availability) and actuality (i.e., product unavailability) or by interruptions of one’s shopping goal. Such discrepancies or interruptions set the stage for negative emotion to occur when followed by cognitive evaluation and further negatively impact consumers’ evaluative responses to stockouts and behavioral intent. In a typical shopping situation, people have an expectation that products will be available for purchase. Timing may affect the level of expectation to determine the intensity of negative emotion. When a preferred item is unavailable, the shopping goal is interrupted, leading stronger negative emotion. Results of this study demonstrate that the discrepancy-evaluation theory of emotion can explain and predict how consumers respond to product unavailability.
177 Third, from a methodological point of view, this study successfully examined a causal relationship between a stockout and consumers’ reactions by using a randomized
Web experiment. Most prior research using field studies to collect the data found a variety of consumer reactions to stockouts, but a causal relationship was not conclusive due to the limitation with the research method. This study employed a Web-based experiment with random assignment to test the causal relationship between product unavailability and consumer response. The findings of this study complement previous research findings based on different research methods.
In addition, this research improved the realism of the experimental context by using a mock website closely simulating the actual online apparel shopping process, and incorporating individual preferences during the experimental tasks. Therefore, the findings of this study contribute to the improved understanding of consumer response to stockouts.
Fourth, this study demonstrated that the nature of stockouts is not as constant as implicitly assumed in previous literature. This study revealed that the severity of stockouts measured as the level of negative emotion evoked differ as a function of timing, preference, and frequency. Depending on such contextual factors, the level of negative emotion elicited by product unavailability was different, and this further impacted evaluative responses and behavioral intent. Such information is useful because it can inform retailers how to strategically deal with product unavailability.
178 5.3.2. Managerial Implications and Contributions
The findings of this study provide several useful insights that retailers can utilize
to strategically manage stockout problems. Although the most ideal situation for both
retailers and consumers is not to have stockouts, in reality, occasional stockouts are likely
to occur. Although retailers may not be able to completely avoid having stockouts, the
important issue is how to best manage stockout situations when they occur.
The findings of this study suggest that the severity of stockouts can be controlled by manipulating certain contextual factors in stockouts. This study revealed the important role of timing of notification on consumer response such that late notification causes more severe reactions to the situation than early notification. Retailers may want to notify consumers with in-stock status information at the initial stage of the shopping process. If notified early, consumers are less likely to develop expectations of product availability throughout their decision-making process, thus experiencing less negative emotion with a stockout. When to inform consumers about product unavailability is a more critical managerial decision that may have a substantial impact on retailers than what has been previously thought among retail managers. Also, additional information, such as when an item will be in-stock again, may be useful to help consumers develop expectations that are more congruent with reality.
In online shopping, when products are displayed on the website, shoppers may unconsciously assume that products are available for purchase based on their shopping schema, and continue shopping under such an assumption. Therefore, product unavailability is an unexpected event to consumers who shop for products displayed on
179 the website. If consumers chose an item that is unavailable for purchase, the time and effort they put into choosing that item may have been wasted. In contrast, in store shopping, consumers can more readily figure out whether items are available for purchase or not during the decision-making process. For example, apparel shoppers can go through a rack to see if an item they want is available in their size. When store shoppers select an item, they are generally able to purchase the item. Therefore, product unavailability may not be such an unexpected event to consumers who shop for products displayed at stores.
Some online retailers give notification about product unavailability when the checkout process is nearly complete or sometimes even after a purchase is made (Tamimi et al., 2003). These shoppers probably went through several additional steps after they selected the item for purchase; for example, registered, logged in, provided a shipping address, provided credit card information, chose a shipping and handling option, and chose a gift wrapping option, only to find that the item they chose is not available. A recent content analysis of online apparel websites (Kim, Kim, & Lennon, 2004) found that more than 43 percent of the total websites analyzed (N=110) did not provide in-stock status information. Consistent with Tamimi et al.’s (2003), this study also observed that information about product unavailability was often not provided until the checkout process was nearly complete, leading to dissatisfaction.
Online retailers need to provide in-stock status information at an early stage of the decision-making process to avoid the adverse impact of stockouts. Technologies (e.g.,
WebSphere) are currently available to provide online shoppers with in-stock status information and to allow them to track their orders (Morphy, 2002). As of yet, very few
180 online retailers have adopted such technologies. Real-time inventory management will be a critical factor in determining the success of an online retail business (LeClaire, 2002).
This study also found that availability of the preferred item had a substantial impact on consumer response to stockouts. If the preferred item was not available, it elicited more negative emotion, which in turn led to depressed perceptions of store image and decision satisfaction. Retailers may need to trade off between a wide product assortment and the level of stockouts. The more items a retailer has to carry, the more challenging it is for one to maintain an optimal stock level. A greater assortment is generally desirable, but it also increases the likelihood of having stockouts (Fitzsimons,
2000; Thayer, 1989). Broniarczyk et al. (1998) found that the unavailability of low- preference items did not affect consumers’ perceptions of assortment. This suggests that retailers can reduce their assortments without a negative influence on consumers. The current study also showed that consumer negative response to unavailability of the not preferred item was minimal. Therefore, a more efficient strategy for retailers is to reduce the assortment by eliminating low-preference items, and pay more attention to keeping an optimal level of high-preference items. This will reduce operating costs, at the same time minimize lost sales due to stockouts. More accurate forecasting of consumer demand and analysis of early sales data can help retailers identify high and low-preference items and determine the stock level accordingly.
Frequency of product unavailability had a negative impact on consumer response.
In general, consumers are intolerant of stockouts and they are ready to go somewhere else after experiencing two or three stockouts (Convenience Store News, 1998). Furthermore, the negative impact of stockout frequency is much more severe when the preferred item,
181 rather than the not preferred item, is unavailable. If consumers cannot find their favorite item the second or third time, they are unlikely to patronize a store and more likely to go somewhere else. Given the low switching cost of online shopping, online retailers are very susceptible to losing their customers, even their most loyal customers, if stockouts repeatedly occur, especially for preferred items. This emphasizes the importance of strategic management of the level of the assortment and the effort to maintain an optimal level of stock.
Both the effects of timing of notification and the frequency of product unavailability were more harmful for retailers when the preferred item was unavailable compared to when the not preferred item was unavailable. Although offering a variety of products may improve a retailer’s ability to satisfy heterogeneous consumer preference, at the same time, it increases operating costs and the likelihood of having stockouts.
Therefore, retailers need to find an optimal level of the assortment that would meet diverse consumer preferences and also meet a retailer’s ability to maintain an optimal inventory level.
The proposed model of this study illustrates that the effect of negative emotion on behavioral intent is mediated by the perception of store image and decision satisfaction.
This suggests that online retailers may be able to recover some of the negative effect of stockouts on behavioral intent by enhancing store image. Since store image is multi- faceted (Lindquist, 1974-1975; Mazursky & Jacoby, 1986), retailers may improve other facets of store image to minimize the negative impact of stockouts. In this study, three facets of store image were included in the final model tested: atmosphere, convenience, and value. So, online retailers might offer an upgrade on shipping (e.g., from standard to
182 express delivery) without any charge if an unavailable item can be backordered. This
may to some extent compensate for the inconvenience experienced due to a stockout.
Also, online retailers might offer free shipping or coupons for the consumers’ next
purchase to improve their perception of value associated with shopping with a store.
Study 2 of this research explored different managerial response to consumers who face stockouts. The findings of Study 2 suggest that the financial response (i.e., 10% discount) may be used to alleviate the negative impact of stockouts on consumer response.
An online retailing context makes it easy to implement a price discount as a
compensation for stockouts because it is not costly to make price changes (Bhargava et
al., 2002). Retailers can announce price discounts as soon as they are out of stock of an item and change the item back to full price as soon as it is in stock again. However, such compensation needs an extremely careful planning and implementation. Although financial compensation for stockouts may have a positive impact on retaining orders or shoppers, retailers should also think about the financial burden of offering such a financial incentive. Some researchers have cautioned that financial compensation may reduce profits in the long run, although it may have a positive impact on demand
(Anderson et al., 2001).
5.4. Limitations
There are several limitations that should be recognized for adequate interpretation of the implications drawn from this dissertation. First, this study employed a laboratory
183 experiment using a mock website. Although efforts were made to simulate a real-life
online apparel shopping situation, external validity was sacrificed to achieve internal
validity (Cook & Campbell, 1979). The generalizability of these findings to a real-world
shopping situation should be made with extra caution. In addition, this study included
only female college students due to the limited resources to develop a mock website
designed to target specific population group. Therefore, the findings of this study may
not hold for other populations from different gender, age, and other demographic groups.
Also, the distribution of the ethnicities of the participants was non-normal in that about
80 percent of the participants were Caucasian, and thus, findings may not hold for other
ethnic groups.
Second, scaled question items were used to measure negative emotions in this
study, although emotions may be less accurately measured via self-reports using
questionnaires. Physiological measures of emotion may be more reliable than self- reported emotions, but the cost of the equipment is prohibitive. The problem of measuring negative emotion combined with the artificiality of an experiment may explain
the moderate level of negative emotion found in this study. Although there were
substantial differences among comparison groups, the strongest negative emotion
measured was only at the midpoint of the scale. This may reflect the lack of realism in
the experiment and also the limitation of using self-reports to measure emotion.
Third, slow downloading time may provide another source of negative emotion
irrelevant to the context of this study. All participants were advised to use a cable
modem or DSL connection to participate in the study, in order to control downloading
184 time and time spent to complete experimental task. However, this could not be controlled
because participants could log onto the website posted online and participate when and
where they wanted. However, low overall levels of negative emotion suggest that this
may not have affected them.
Fourth, the effect of frequency on negative emotion measured in this study did not
measure the cumulative impact of stockouts over time. There was no temporal gap
between exposure to the first and second stockouts, because two stockouts occurred
simultaneously in this study. Recurrent stockouts occurring with a temporal gap may
have a different impact on consumer response than multiple stockouts occurring at once.
Fifth, only one product category, women’s apparel, was examined to study consumer response to product unavailability in this study. Consumer responses to the unavailability of apparel items may differ from responses to the unavailability of other product categories. As discussed in earlier chapters, the way consumers form preferences for apparel items are distinct from other consumer products such as soft drinks. In addition, the way consumers engage in the decision-making process for apparel may be distinct from the decision-making process for other products.
Sixth, this study did not measure actual behaviors and used behavioral intent as the best approximation to predict actual behavior. Also, due to the nature of an experiment, this study could not examine whether consumers would substitute, delay, cancel, or switch as behavioral responses to stockouts.
185 5.5. Suggestions for Future Research
Future research needs to be directed to achieve greater generalizability by
including participants from diverse population groups in terms of gender, age, education,
and ethnicity. In addition, how consumers respond to product unavailability may differ
across cultures. Future research may be conducted in other cultures to examine the
similarity and difference of consumer response to product unavailability and the implications for retailers.
Additional research is needed to determine whether product categories play influential roles in consumer response to product unavailability. Apparel products may be distinct from other products in that consumers may not have explicit preferences for a specific apparel item prior to decision-making, while they may have such preferences for other consumer products. Most previous research focused on stockouts of grocery items, but paid less attention to other product categories. Given the possibility that product characteristics may influence how consumers shop and buy, how consumers respond to product unavailability may differ as a function of product categories. Future research needs to include several product categories to examine the effects of product category on consumer response to product unavailability.
Additional research is also needed to determine whether shopping channels have an influence on consumer response to product unavailability. How consumers respond to product unavailability while in-store shopping may be different from online shopping because store shopping involves different switching costs, has social factors (presence of salesperson or shopping with friends), and other external factors that are not pertinent to
186 online shopping. Also, in a store environment, consumers know that they can buy any product they select, unlike in an online environment.
While this research focused on stockouts of products, a similar rationale can be applied to service contexts. For example, when an individual is looking for a hotel room or looking for a specific menu item at a restaurant, she or he may encounter a situation in which no room is available or a menu item is not available at the time of the order.
Unavailability of service occurs in various service settings, and consumers are also likely to respond negatively to unavailable service. Therefore, additional research can be conducted to examine the impact of the temporary unavailability of service on consumer response.
This study examined the short-term impacts of stockouts only. Future research is needed to examine the long-term consequences of stockouts. It is possible that consumers negatively react to stockouts immediately after experiencing stockouts, but such negative responses may subside over time or may persist over time. Future research focusing on the long-term effects of stockouts on consumers and retailers can provide useful insights to contribute to the better understanding of the stockout problem.
187 BIBLIOGRAPHY
Accenture (2000). 9 out of 10 online holiday shoppers experienced problems. Retrieved February 27, 2003, from http://www.accenture.com/xd/xd.asp?it=enweb&d=_dyn%5cdynamicpressrelease.
ACNielsen (2002). Building on relationships. Retrieved March 7, 2003, from http://www.acnielsen.com.ph/news.asp?newsid=70.
Andersen Consulting (1996). Where to look for incremental sales gains: The retail problem of out-of-stock merchandise. The Coca-Cola Retailing Research Council, Atlanta.
Anderson, E., Fitzsimons, G., & Simester, D. (2001). Measuring and mitigating the cost of stockouts in mail order catalogs. Unpublished Manuscript, University of Pennsylvania at Philadelphia.
Anderson, J., & Gerbing, D. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Anderson, C., & Vincze, J. (2000). Strategic marketing management: Meeting the global marketing challenge. Boston, MA: Houghton Mifflin Company.
Anupindi, R., Dada, M., & Gupta, S. (1998). Estimation of consumer demand with stock- out based substitution: An application to vending machine products. Marketing Science, 17(4), 406-423.
Babin, B., Darden, W., & Babin, L. (1998). Negative emotions in marketing research: Affect or artifact? Journal of Business Research, 42, 271-285.
Bagozzi, R. (1983). Issues in the application of covariance structure analysis: A further comment. Journal of Consumer Research, 9, 449-450.
Bagozzi, R. (1991). Structural equation models in marketing research. In W. D. Neal (Ed.), First annual advanced research techniques forum (pp. 335-379). Chicago: American Marketing Association.
188 Bagozzi, R., Gophinath, M., & Nyer, P. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184-206.
Bagozzi, R., & Phillips, L. (1982). Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly, 27, 459-489.
Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Baker, J., Grewal, D., & Levy, M. (1992). An experimental approach to making retail store environmental decisions. Journal of Retailing, 68(4), 445-460.
Barta, R., & Holbrook, M. (1990). Developing a typology of affective responses to advertising. Psychology and Marketing, 7(1), 11-25.
Bearden, W. (1977). Determinant attributes of store patronage: Downtown versus outlying shopping centers. Journal of Retailing, 53(2), 15-22.
Bearden, W., & Teel, J. (1983). Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research, 20(1), 21-28.
Bell, D., & Fitzsimons, G. (1999). An experimental and empirical analysis of consumer response to stockouts. Unpublished manuscript, University of Pennsylvania.
Bentler, P. (1978). The interdependence of theory, methodology, and empirical data: Causal modeling as an approach to construct validation. In D. B. Kandel (Ed.), Longitudinal drug research (pp. 267-302). New York: Wiley.
Bentler, P., & Bonnet, D. (1980). Significance test and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.
Berry, L. (1969). The components of department store image: A theoretical and empirical analysis. Journal of Retailing, 45(1), 3-20.
Bettman, J. (1979). An information processing theory of consumer choice. Reading, MA: Addison-Wesley.
Bettman, J., Luce, M., & Payne, J. (1998). Constructive consumer choice process. Journal of Consumer Research, 25(3), 187-217.
Bhargava, H., Sun, D., & Xu, S. (2002). Stockout compensation policies in electronic retailing. Unpublished manuscript, Pennsylvania State University.
Bitner, M. (1990). Evaluating service encounters: The effects of physical surroundings and employee responses. Journal of Marketing, 54(2), 69-82.
189 Bloemer, J., & Ruyter, K. (1997). On the relationship between store image, store satisfaction, and store loyalty. European Journal of Marketing, 32(5/6), 499-513.
Brehm, J. W. (1966). A theory psychological reactance. New York: Academic Press.
Brehm, S. S., & Brehm, J. W. (1981). Psychological reactance: A theory of freedom and control. New York, NY: Academic Press.
Broniarczyk, S., Hoyer, W., & McAlister, L. (1998). Consumers’ perceptions of the assortment offered in a grocery category: the impact of item reduction. Journal of Marketing Research, 35(2), 166-176.
Browne, M., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21(2), 230-258.
Byrne, B., Shavelson, R., & Muthen, B. (1989). Testing of equivalence of factor covariance and mean structures: The issues of partial measurement invariance. Psychological Bulletin, 105, 456-466.
Campbell, D., & Fiske, D. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105.
Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for research (pp.1-16). Chicago: Rand McNally.
Campo, K., Gijsbrechts, E., & Nisol, P. (2000). Towards understanding consumer response to stockouts. Journal of Retailing, 76(2), 219-242.
Campo, K., Gijsbrechts, E., & Nisol, P. (2003). The impact of retailer stockouts on whether, how much, and what to buy. International Journal of Research in Marketing, 20, 273-286.
Cattell, R. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276.
Charlton, P., & Ehrenberg, A. (1976). An experiment in brand choice. Journal of Marketing Research, 13(2), 152-160.
Chebat, J-C., Chebat, C., & Vaillant, D. (2001). Environmental background music and in- store selling. Journal of Business Research, 54, 115-123.
Chebat, J-C., & Michon, R. (2003). Impact of ambient odors on mall shoppers’ emotions, cognition, and spending: A test of competitive causal theories. Journal of Business Research, 56, 529-539.
190 Chiang, J. (1991). A simultaneous approach to the whether, what, and how much to buy questions. Marketing Science, 10(4), 297-315.
Chowdhury, J., Reardon, J., & Srivastava, R. (1998). Alternative modes of measuring store image: An empirical assessment of structured versus unstructured measures. Journal of Marketing Theory and Practice, 6, 72-86.
Churchill, G. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64-73.
Clark, M., & Isen, A. (1982). Toward understanding the relationship between feeling states and social behavior. In A. Hastorf & A. Isen (Eds.), Cognitive social psychology (pp. 73-108). New York: Elsevir.
Clee, M., & Wicklund, R. (1980). Consumer behavior and psychological reactance. Journal of Consumer Behavior, 6(4), 389-405.
Cohen, J. (1977). Statistical power analysis for the behavioral sciences (Rev. ed.). New York: Academic Press.
Cohen, J. (1990). Attitude affect, and consumer behavior. B. S. Moore & A. M. Isen (Eds.), Affect and social behavior (pp. 152-206). New York: Cambridge University Press.
Cohen, J., & Areni, C. (1991). Affect and consumer behavior. In T. S. Robertson & H. K. Kassarjian (Eds.), Handbook of consumer behavior (pp. 188-240). Englewood Cliffs, NJ: Prentice Hall.
Cole, D., Maxwell, S., Arvey, R., & Salas, E. (1993). Multivariate group comparisons of variable systems: MANOVA and structural equation modeling. Psychological Bulletin, 114(1), 174-184.
Convenience Store News (1998). Special Report: Out of stock study. Retrieved May 19, 2003, from http://www.csnews.com/csnews/reports_analysis/reports/.
Corsten, D., & Gruen, T. (2003). Desperately seeking shelf availability: An examination of the extent, the causes, and the efforts to address retail out-of-stocks. International of Journal of Retail & Distribution, 31(12), 605-617.
Corstjens, J., & Corstjens, M. (1995). Store wars: The battle for mindspace and shelfspace. New York, NY: Wiley.
Costner, H. (1971). Utilizing causal models to discover flaws in experiments. Sociometry, 34, 398-410.
191 Curran, P., West, S., & Finch, J. (1996). The robustness of test statistics to nonnormality and specification errors in confirmatory factor analysis. Psychological Methods, 1, 16-29.
Data Ventures (2001). Data Ventures’ and Procter & Gamble’s out of stock technology ranked #2 on InfoWorld’s top 100 IT innovations for 2001. Retrieved May 19, 2003 from http://www.dataventures.com/press_releases/120301.html.
Dawson, S., Bloch, P., & Ridgway, N. (1990). Shopping motives, emotional states, and retail outcomes. Journal of Retailing, 66(4), 408-427.
Day, R. (1980). Research perspectives on consumer complaining behavior. In C. W. Lamb & P. M. Dunne (Eds.), Theoretical developments in marketing (pp. 211- 215). Chicago, IL: American Marketing Association.
DeHoratius, N. (2002). First measure, then manage. Capital Ideas, 4(1). Retrieved April 16, 2004, from http://gsbwww.uchicago.edu/news/capideas/summer02/measuremanage.html.
Derbaix, C., & Pham, M. (1991). Affective reactions to consumption studies: A pilot investigation. Journal of Economic Psychology, 12(2), 325-355.
Dickerson,J., & Albaum, G. (1977). A method for developing tailormade semantic differentials for specific marketing content areas. Journal of Marketing Research, 14(1), 87-91.
Donovan, R., & Rossiter, J. (1982). Store atmosphere: An environmental psychology approach. Journal of Retailing, 58(1), 34-57.
Doyle, P., & Fenwick, I. (1974-1975). How store image affects shopping habits in grocery chains. Journal of Retailing, 50(4), 39-52.
Dubé, L., & Morin, S. (2001). Background music pleasure and store evaluation intensity effects and psychological mechanism. Journal of Business Research, 54, 107-113.
Dubé-Rioux, L. (1990). The power of affective reports in predicting satisfaction judgments. Advances in Consumer Research, 17, 571-576.
Eckman, M., Damhorst, M., & Kadolph, S. (1990). Toward a model of the in-store purchase decision process: Consumer use of criteria for evaluating women’s apparel. Clothing and Textiles Research Journal, 8(2), 13-22.
Emmelhainz, L., Emmelhainz, M., & Stock, J. (1991). Logistics implications of retails stockouts. Journal of Business Logistics, 12(2), 129-141.
192 Engel, J., & Blackwell, R. (1982). Consumer behavior. New York: Holt, Rinchart, and Winston.
Erdem, O., Ounmil, A, & Tuncalp, S. (1999). Consumer values and the importance of store attributes. International Journal of Retail and Distribution Management, 27(4), 137-144.
Ferguson, C., Mason, B., & Wilkinson, J. (1979). Estimating consumer losses from unavailable advertised grocery specials. Journal of Consumer Affairs, 13(2), 248- 261.
Finn, J. (1974). A general model for multivariate analysis. New York: Holt, Rinehart, & Winston.
Fisher, M. (1997). What is the right supply chain for your product? Harvard Business Review, 78(4), 105-116.
Fisher, M., Rajaram, K., & Raman, A. (2001). Optimizing inventory replenishment of retail fashion products. Manufacturing and Service Operations Management, 3(3), 230-241.
Fisher, M., Raman, A., & McClelland, A. (2000). Rocket science retailing is almost here. Are you ready? Harvard Business Review, 78(4), 115-124.
Fiske, S. T. (1981). Social cognition and affect. In J. Harvey (Ed.), Cognition, social behavior, and the environment (pp.227-264). Hillsdale, NJ: Erlbaum.
Fiske, S., Kenny, D., & Taylor, S. (1982). Structural models for the mediation of salience effects on attribution. Journal of Experimental Social Psychology, 18, 105-127.
Fitzsimons, G. (2000). Consumer response to stockouts. Journal of Consumer Research, 27(2), 249-266.
Fitzsimons, G., Greenleaf, E., & Lehmann, D. (1997). Decision and consumption satisfaction: Implications for channel relations. Unpublished Manuscript, University of Pennsylvania at Philadelphia.
Folkes, V. (1990). Conflict in marketplace: Explaining why products fail. S. Graham & V. S. Folkes (Eds.), Attribution theory: Applications to achievement, mental health, and interpersonal conflict (pp. 143-160). Hillsdale, NJ: Erlbaum.
Forbes (1999, December 27). Santa Flaws.
Fornell, C. (1983). Issues in the application of covariance structure analysis: A comment. Journal of Consumer Research, 9, 443-448.
193 Fox, F. (1993, October). Weather- The next retail frontier. Discount Merchandiser, 33(10), pp. RT6-RT8.
Frijda, N. (1986). The emotions. Cambridge, UK: Cambridge University Press.
Garbing, D., & Anderson, J. (1984). On the meaning of within-factor correlated measurement errors. Journal of Consumer Research, 11, 572-580.
Gardner, M. (1985). Mood states and consumer behavior: A critical review. Journal of Consumer Research, 12, 281-300.
Gentry, J. & Burns, A. (1977). How important are evaluative criteria in shopping center patronage. Journal of Retailing, 53(4), 73-84, 94.
GMA (2002). Full-shelf satisfaction. Reducing out-of-stocks in the grocery channels: An in-depth look at DSD categories. Grocery Manufacturers of America and Roland Berger, Washington, DC.
Gogos, P. (2003). Uncovering the missing component in dynamic forecasting: Store-level consumer demand visibility. Retrieved May 19, 2003 from http:www.executivetechnology.com/ViewSOFull.cfmSOID=53.
Grewal, D., Baker, J., Levy, M., & Voss, G. (2003). The effects of wait expectations and store atmosphere evaluations on patronage intentions in service-intensive retail stores. Journal of Retailing, 79, 259-268.
Gruen, T., & Corsten, D. (2002). Rising to the challenge of out-of-stocks. ECR Journal, The International Commerce Review, 2(2), 45-58.
Gruen, T., Corsten, D., & Braradwaj, S. (2002). Retail out of stocks: A worldwide examination of causes, rates, and consumer responses. Washington, D.C.: Grocery Manufacturers of America.
Gupta, S. (1988). Impact of sales promotions on when, what, and how much to buy. Journal of Marketing Research, 25(4), 342-355.
Gurel, L. (1974). Dimensions of clothing interest based on factor analysis of Creekmore’s 1968 clothing measure (Doctoral dissertation, University of North Carolina at Greensboro, 1974). Dissertation Abstract International, 74-22015.
Hammond, J., & Kohler, K. (2000). E-commerce in the textile and apparel industries. The E-Business Transformation: Sector Developments and Policy Implications. Paper presented at Economic Studies Conference, Washington, DC.
194 Hansen, R., & Deutscher, T. (1977-78). An empirical investigation of attributes importance in retail store selection. Journal of Retailing, 53(4), 58-72.
Hess, J. D., & Gerstner, E. (1987). Loss leader pricing and rain check policy. Marketing Science, 6(4), 358-374.
Highhouse, S. (1996). Context-dependent selection: The effects of decoy and phantom job candidates. Organizational Behavior and Human Decision Processes, 65, 68- 76.
Hildebrandt, L. (1988). Store image and the prediction of performance in retailing. Journal of Business Research, 17, 91-100.
Hirschman, E., & Holbrook, M. (1982). Hedonic consumption: Emerging concepts, methods, and propositions. Journal of Marketing, 46(2), 92-101.
Hoffman, M. (1986) Affect, cognition, and motivation. R. M. Sorrentino & E. T. Higgins (Eds.), Handbook of motivation and cognition: Foundations of social behavior (pp. 244-280). New York: Guilford Press.
Holbrook, M., & Barta, R. (1987). Assessing the role of negative emotions as mediators of consumer response to advertising. Journal of Consumer Research, 14(3), 404- 420.
Howard, J., & Sheth, J. (1969). The theory of buyer behavior. New York, NY: Wiley.
Hu, L., & Bentler, P. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage Publications, Inc.
Hui, M., Dube, L., & Chebat, J. (1997). The impact of music on consumers’ reactions to waiting for services. Journal of Retailing, 73, 87-104.
Hutchinson, J., Kamakura, W., & Lynch, J. (2000). Unobserved heterogeneity as an alternative explanation for reversal effects in behavioral research. Journal of Consumer Research, 27(3), 324-344.
Internet Retailer (2004a, April 6). Conversion is still No. 1- although a fifth of retailers don’t know theirs. Retrieved April 18, 2003, from http://internetretailer.com/printArticle.asp?id=11692.
Internet Retailer (2004b, March 12). Women make up nearly two-thirds of online apparel and beauty shoppers. Retrieved April 17, from http://internetretailer.com/printArticle.asp?id=11521.
195 Isen, A. (1984). Toward understanding of the role of affect in cognition. In R. W. Wyer & T. K. Srull (Eds.), Handbook of social cognition (Vol. 3, pp. 179-236). Hillsdale, NJ: Lawrence Erlbaum.
Jaccard, J., & Wan, C. (1996). Lisrel approach to interaction effects in multiple regression. Thousand Oaks, CA: Sage Publications, Inc.
James, D., Durand, R., & Dreves, R. (1976). The use of multi-attribute attitude model in a store image study. Journal of Retailing, 52(2), 23-32.
Jamieson, L., & Bass, F. (1989). Adjusting stated intention measures to predict trial purchase of products: A comparison of models and methods. Journal of Marketing Research, 26(3), 336-345.
Jöreskog, K., & Sörbom, D. (1993). LISREL 8: User’s reference guide. Chicago, IL: Scientific Software International, Inc.
Juster, T. (1966). Consumer buying intentions and purchase probability: An experiment in survey design. Journal of the American Statistical Association, 61(3), 658-696.
Kahn, B., & Lehmann, D. (1991). Modeling choice among preference and structural heterogeneity in consumer choice. Marketing Science, 15(2), 152-172.
Kaiser, H. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141-151.
Kaplan, D. (1990). Evaluating and modifying covariance structure models: A review and recommendation. Multivariate Behavioral Research, 25(2), 137-155.
Keppel, G. (1991). Design and analysis: A researcher’s handbook (3rd ed.). Upper Saddle River, NJ: Prentice-Hall, Inc.
Kim, M., Kim, J., & Lennon, S. (2004). Evaluation of online apparel service quality: SERVQUAL approach. Manuscript submitted for publication.
Kim, M., & Stoel, L. (2004). Salesperson roles: Are online retailers meeting customer expectations? Manuscript submitted for publication.
Kmenta, J. (1971). Elements of econometrics. New York, NY: McMillan Publishing Company.
Korgaonkar, P., Lund, D., & Price, B. (1985). A structural equations approach toward examination of store attitude and store patronage behavior. Journal of Retailing, 61(2), 39-60.
196 Kumar, A., & Dillon, W. (1987). Some further remarks on measurement-structure interaction and the unidimensionality of constructs. Journal of Marketing Research, 24, 438-444.
LeClaire, J. (2002, August 1). Phase two for e-commerce. E-Commerce News. Retrieved April 18, 2004, from http://www.ecommercetimes.com/perl/story/18840.html.
Lee, M., & Johnson, K. (2000). Exploring differences between Internet apparel purchasers, browsers, and non-purchasers. Journal of Fashion Marketing and Management, 6(2), 146-157.
Lessig, P. (1973). Consumer store images and store loyalties. Journal of Marketing, 37(4), 72-74.
Lindquist, J. (1974-5). Meaning of image. Journal of Retailing, 50(4), 29-38.
Los Angeles Times (1999, December 17). Online shopping still hit or miss with customers. p.C1.
Lugo, L. M. (2002). Building on relationships. Retrieved March 9, 2003, from http://www.acnielsen.com.ph/news.asp?newsID=70.
Luomala, H., & Laaksonen, M. (2000). Contributions from mood research. Psychology and Marketing, 17(3), 195-233.
Lusch, R., & Brown, J. (1996). Interdependency, contracting, and relational behavior in marketing channels. Journal of Marketing, 60(4), 19-38.
Macaluso, N. (2000, May 1). Study: E-shoppers not swayed by loyalty programs. E- Commerce News. Retrieved April 18, 2004, from http://www.ecoomercetimes.com/perl/story/3180.html.
Machleit, K., & Eroglu, S. (2000). Describing and measuring emotional response to shopping experience. Journal of Business Research, 2, 101-111.
Machleit, K., & Mantel, S. (2001). Emotional response and shopping satisfaction: Moderating effects of shopper attributions. Journal of Business Research, 54, 97- 106.
Mail-order companies (1987, October). Consumer Reports, pp.607-614.
Mano, H., & Oliver, R. (1993). Assessing the dimensionality and structure of the consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20(3), 451-466.
197 Marsh, H., Balla, J., & McDonald, R. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effects of sample size. Psychological Bulletin, 103, 391-411.
Martineau, P. (1958). The personality of the retail store. Harvard Business Review, 36, 47-55.
Mandler, G. (1975). Mind and emotion. New York, NY: Wiley.
Mandler, G. (1984). Mind and body: Psychology of emotion and stress. New York, NY: Norton.
Mandler, G. (1985). Cognitive psychology: An essay in cognitive science. Hillsdale, NJ: Erlbaum.
Mandler, G. (1990). A constructivist theory of emotion. In N. S. Stein, B. L. Leventhal, & T. Trabasso (Eds.), Psychological and biological approaches to emotion (pp. 21-43). Hillsdale, NJ: Erlbaum.
Mason, J. B., & Wilkinson, J. (1976). Mispricing and unavailability of advertised food products in retail food outlets. Journal of Business, 49(2), 219-225.
Mattila, A., & Wirtz, J. (2001). Congruency of scent and music as a driver of in-store evaluations and behavior. Journal of Retailing, 77, 273-289.
Mazursky, D., & Jacoby, J. (1986). Exploring the development of store images. Journal of Retailing, 62(2), 145-165.
McCoy, D. (2003). It eliminates out-of-stocks, fill store shelves. Retrieved May 19, 2003, from http://www.groceryheadquarters.com/printarticle.cfm?aid=626.
McDonald, R. (1981). The dimensionality of tests and items. British Journal of Mathematical and Statistical Psychology, 34, 100-117.
Min, K. (2003). Consumer response to product unavailability. Unpublished doctoral dissertation, Ohio State University.
Mitchell, J. (1993). Perception is everything. NPL: National Petroleum News, 85(5), 47.
Motes, W., & Castleberry, S. (1985). A longitudinal field test of stockout effects on multi-brand inventories. Journal of the Academy of Marketing Science, 13(4), 54- 68.
Morphy, E. (2002, July 30). WebSphere commerce 5.4 integrates inventory and order management. Retrieved April 18, 2004, from http://www.crmbuyer.com/perl/story/18221.html.
198 Nexgen (2003). Retail. Retrieved March 9, 2003, from http://www.nexgenix.com/retail.html.
Nevin, J., & Houston, M. (1980). Image as a component of attraction to intraurban shopping areas. Journal of Retailing, 56(1), 77-93.
Nunnally, J. (1978). Psychometric theory. New York, NY: McGraw-Hill Book Company, Inc.
Oliver, R. (1991). Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing, 57(3), 25-48.
Oliver, R. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20, 418-430.
Oliver, R., & Swan, J. (1989). Consumer perceptions of interpersonal equity and satisfaction in transaction: A field survey approach. Journal of Marketing, 53(2), 21-35.
Online shipping still hit or miss with customers (1999, December 17). Los Angeles Times, pp. C1.
Osman, M. (1993). A conceptual model of retail image influences on loyalty patronage behavior. The International Review of Retail, Distribution, and Consumer Research, 31, 149-166.
Peckham, J. (1963). The consumer speaks. Journal of Marketing, 27(4), 21-26.
Pettibone, J. C., & Wedell, D. H. (2000). Examining models of nondominated decoy effects across judgment and choice. Organizational Behavior and Human Decision Processes, 81, 300-328.
Pham, M., Cohen, J., Pracejus, J., & Hughes, D. (2001). Affect monitoring and the primacy of feelings in judgment. Journal of Consumer Research, 28(2), 167-188.
Prasad, V. (1975). Socioeconomic product risk and patronage preferences of retail shoppers. Journal of Marketing, 39(3), 42-47.
Pride, W., & Ferrell, O. (1997). Marketing concepts and strategies. Boston: Houghton Mifflin Company.
Progressive Grocer (1968a, October). The out of stock study: Part I. S1-S16.
Progressive Grocer (1968b, November). The out of stock study: Part II. S17-S32.
199 Retail Forward, Inc. (2001, November). E-retail intelligence updates. Retail Forward, Inc.
Rigdon, E. (1996). CFI vs RMSEA: A comparison of two fit indexes for structural equation modeling. Structural Equation Modeling, 3(4), 369-379.
Rollo, C. (2000, July). The end of the sales rack. Retrieved March 4, 2003, from http://www.clientlogic.com/resources/s/end%20of%20the%20sales%20rack_final.
Rosenbloom, B. (1983). Store image development and the question of congruency. In W. R. Darden & R. F. Lusch (Eds.), Patronage behavior and retail management (pp. 141-150), New York, NY: North-Holland.
Saliba, C. (2001, March 6). Report: E-tail sites need nuts-and-bolts, not splash. E- Commerce News. Retrieved April 18, 2003, from http:.//www.ecommercetimes.com/perl/story/7964.html.
Samli, A. C. (1989). Retail marketing strategy. New York, NY: Quorum.
Samli, A. C. (1998). Strategic marketing for success in retailing. Westport, UT: Quorum Books.
Schary, P., & Becker, B. (1978). The impact of stock-out on market share: Temporal effects. Journal of Business Logistics, 1(1), 31-44.
Schary, P., & Christopher, M. (1979). The anatomy of a stockout. Journal of Retailing, 55(2), 59-70.
Schmidt, W. (1997). World Wide Web survey research: Benefits, potential problems, and solutions. Behavioral Research Methods, Instruments, and Computers, 29, 274- 279.
Schwartz, B. (1966). A new approach to stockout penalties. Management Science, 12(12), B-538-B-544.
Schwarz, N. (1990). Feelings as information: Informational and motivational functions of affective states. In R. M. Sorrentino & Higgins, E. T. (Eds.), Handbook of motivation and cognition: Foundations of social behavior (Vol. 2, pp.527-561). New York: Guilford.
Schwarz, N., & Clore, G. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45(3), 513-523.
200 Shadish, W., Cook, T., & Campbell, D. (2001). Experiments and generalized causal inference (pp. 1-17) in Experimental and quasi experimental design. Boston: Houghton Mifflin Company.
Sheth, J. (1983). Emerging trends for the retailing industry. Journal of Retailing, 59(3), 6-18.
Simon, H. (1967). Motivational and emotional controls of cognition. Psychological Review, 74(1), 29-39.
Sirgy, J., & Samli, A. C. (1989). The store loyalty concept: Dimensions and measurement. In A. C. Samli (Eds.), Retailing marketing strategy (pp.), New York, NY: Quorum.
Sloot, L., Verhoef, P., & Frances, P. (2002). The impact of brand and category characteristics on consumer stockout reactions. Working paper. Rotterdam: Erasmus Research Institute of Management.
Smith, M., & Leigh, B. (1997). Virtual subjects: Using the Internet as an alternative source of subjects and research environment. Behavioral Research Methods, Instruments, and Computers, 29, 496-505.
Spiggle, S., & Sewall, M. (1987). A choice sets model of retail selection. Journal of Marketing, 51(2), 97-111.
Sproles, G. (1975). Fashion preferences and store patronage: A longitudinal study. Advances in Consumer Research, 5(1), 675-681.
Stanley, T., & Sewall, M. (1976). Image inputs to a probabilistic model: Predicting retail potential. Journal of Marketing, 40, 48-53.
Staple, J. (1971). Sales effect of print ads. Journal of Advertising Research, 1(3), 32-36.
Steenkamp, J., & Trijp, H. (1991). The use of LISREL in validating marketing constructs. International Journal of Research in Marketing, 8, 283-299.
Steiger, J. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173-180.
Straughn, K. (1991). The relationship between stock-outs and brand share. Unpublished doctoral dissertation, Florida State University, Florida.
Sugawara, H., & MacCallum, R. (1993). Effect of estimation method on incremental fit indexes for covariance structure models. Applied Psychological Measurement, 17, 365-377.
201 Tamimi, N., Rajan, M., & Sebastianelli, R. (2003). The state of online retailing. Internet Research: Electronic Networking Applications and Policy, 13(3), 146-155. Thaler, R. (1989). Mental accounting and consumer choice. Marketing Science, 4(3), 199-214.
Thayer, W. (1989, July). The return of stockouts. Progressive Grocer, 68, 28-32.
Verbeke, W., Farris, P., & Thurik, R. (1998). Consumer response to the preferred brand out-of-stock situation. European Journal of Marketing, 32(11/12), 1008-1028.
Walter, C. & Grabner, J. (1975). Stockout cost models: Empirical test in a retail situation. Journal of Marketing, 39(3), 56-68.
Watson, D., Clark, L., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063-1070.
Westbrook, R. (1987). Product/consumption-based affective responses and postpurchase process. Journal of Marketing Research, 24(3), 258-270.
Westbrook, R., Newman, J., & Taylor, J. (1978). Satisfaction/dissatisfaction in the purchase decision process. Journal of Marketing, 42(4), 54-60.
Wheaton, B., Muthen, B., Alwin, D., & Summers, G. (1977). Assessing reliability and stability in panel models. In D. R. Heise (Ed.), Sociological methodology, San Francisco: Jossey-Bass.
Whitlark, D., Geurts, M., & Swenson, M. (1993). New product forecasting with a purchase intention survey. Journal of Business Forecasting, 12(3), 18-21.
Wicklund, R. (1974). Freedom and reactance. Hillsdale, N.J.: Erlbaum.
Woodside, A., Frey, L., & Daly, R. (1989). Linking service quality, customer satisfaction, and behavioral intention. Journal of Health Care Marketing, 9(4), 5-17.
Wyer, R. S. (1980). The acquisition and use of social knowledge: Basic postulates and representative research. Personality and Social Psychological Bulletin, 6(4), 558- 573.
Yi, Y. (1990). A critical review of customer satisfaction. In V. A. Zeithamal (Ed.), Review of marketing (pp.68-123). Chicago, IL: American Marketing Association.
Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341-352.
202 Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, 151-175.
Zeithaml, V., Berry, L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.
Zeitlin, D., & Westwood, R. (1986). Measuring emotional response. Journal of Advertising Research, 26(5), 34-44.
Zinn, W., & Liu, P. C. (2001). Consumer response to retail stockouts. Journal of Business Logistics, 22(1), 49-71.
Zinszer, P., & Lesser, J. (1981). An empirical evaluation of the role of stock-out on shopping patronage processes. Advances in Consumer Research, 8, 221-224.
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APPENDIX A
EMAIL SCRIPT TO SOLICIT PARTICIPANTS
204 (1) Invitation Email
TITLE: FEMALE PARTICIPANTS NEEDED!!
Greetings! Hello, my name is Minjeong (Mijeong) Kim, a doctoral candidate in the Department of Consumer and Textile Sciences at the Ohio State University. I am writing this email to ask for your help in participating in my dissertation research. I am conducting research to investigate diverse consumer behaviors in the context of online apparel shopping and need female volunteers to participate in the study. The study is done as a Web survey using a mock apparel website. Participants will engage in an apparel selection process similar to what we usually do when selecting apparel items to buy.
If you are willing to participate in the study, please reply to this email. I will send you the follow-up email containing an URL (Web address) and more detailed information about the study. Because this is a Web-based study, you can participate in the study when and where convenient for you. The survey will take about 15 minutes to complete. Upon completion of the survey, randomly selected participants will receive the apparel item they choose during the study or a $20 gift certificate. Your participation is strictly voluntary, but I would really appreciate it if you can help me out to complete my dissertation.
I apologize for sending you the email without your permission, but appreciate your time and consideration. I am anxiously looking forward to receiving your reply to this email!! Please feel free to email me ([email protected]) if having any questions. Thank you very much!
Best regards,
Minjeong Kim, Doctoral candidate Dr. Sharron Lennon, Professor Dept. of Consumer and Textile Sciences Dept. of Consumer and Textile Sciences 265 Campbell Hall 230 Campbell Hall 1787 Neil Avenue 1787 Neil Avenue Ohio State University Ohio State University Columbus, OH 43210-1295 Columbus, OH 43210-1295 Tel: 614-688-4234 Tel: 614-292-4384 Email: [email protected] Email: [email protected]
205 (2) FOLLOW-UP EMAIL
TITLE: THANK YOU FOR YOUR INTEREST!
Dear:
Thank you very much for your response to my email. I am very excited with your interest in participating in my dissertation research. Your participation will be a GREAT help for my dissertation!
Well, let me first describe my research a little bit more in detail. Title of my dissertation is “A Theoretical Investigation of Online Shopping Behaviors.” I am trying to examine diverse consumer behaviors when selecting apparel items in online shopping. The study will be conducted as a Web-based survey using a mock apparel website. You can participate in the study by logging onto the following URL (www.thinkanswer.com). When you log onto the website, in the first page, you will be asked to indicate whether your participation is voluntary and you agree to participate in the study. If you confirm that you voluntarily agree to participate in the study, you can proceed to the simulated online apparel shopping sites. Please carefully read instructions given in each page and follow the instructions. Your task is to browse ten apparel items and eventually select two items that you would like to buy. After choosing two apparel items, you will be asked to complete the questionnaire measuring your perceptions and behavioral intention regarding online apparel shopping. Your task will be simple and straight-forward, similar to what we all do in apparel shopping situation and it will take approximately 15 minutes to complete. Upon completion, randomly selected participants will receive the apparel item they choose during the study or a $20 gift certificate.
Please note that your participation is voluntary and your response will be kept confidential. When I receive your response from the Web server, your response is already aggregated with all other responses without any identifying information. In addition, what we need is the aggregate data, not individual responses. So, please be assured that your response will be kept confidential.
Please complete your research participation by due date (will be given) and feel free to email me if having any questions/concerns/comments. I very much look forward to receiving your completed survey! I deeply appreciate your help with my dissertation research.
Thank you!
Minjeong Kim, Doctoral candidate Dr. Sharron Lennon, Professor
Dept. of Consumer and Textile Sciences Dept. of Consumer and Textile Sciences 265 Campbell Hall 230 Campbell Hall 1787 Neil Avenue 1787 Neil Avenue Ohio State University Ohio State University Columbus, OH 43210-1295 Columbus, OH 43210-1295 Tel: 614-688-4234 Tel: 614-688-4234 Email: [email protected] Email: [email protected]
206
APPENDIX B
PRETEST 1: WEBSITE
207
208
209
APPENDIX C
PRETEST 1: APPAREL STIMULI
210 (1) THIRTY ITEMS TESTED
211
212 (2) TEN FINAL ITEMS SELECTED
213
APPENDIX D
PRETEST 2
214 Extra Credit Opportunity!!
Direction: please print out this form and answer the following questions. This is due this Friday, 3-7-03. You can give it to me or drop off my mail in room 265.
• Assume that you are shopping for apparel at a clothing store at the mall. You just find one that you really like, but are told that your size is unavailable due to a stockout.
1. How would you feel to learn that the item you want to buy is out of stock? Please list at least three feelings you might have under the circumstances.
2. What would you do after you find out the item is unavailable? Please list at least two things you would do in the situation and explain why.
215
APPENDIX E
MAIN STUDY
216 217
218
219
220
221
APPENDIX F
EXPERIMENTAL CONDITIONS (EXAMPLE)
222 FPT1,1,1
FPT2,1,1
223 FPT1,2,1
FPT2,2,1
224 FPT1,1,2
FPT2,1,2
225 FPT1,2,2
FPT2,2,2
226
APPENDIX G
QUESTIONNAIRE
227 PLEASE TELL US ABOUT ONLINE APPAREL SELECTION PROCESS YOU JUST EXPERIENCED BY RESPONDING TO THE FOLLOWING QUESTIONS.
Section A. We would like to know how you feel now. Please indicate the number that best reflects your current feelings.
Not Very at all Neutral much 1 Afraid 1 2 3 4 5 2 Aggravated 1 2 3 4 5 3 Agitated 1 2 3 4 5 4 Angry 1 2 3 4 5 5 Annoyed 1 2 3 4 5 6 Anxious 1 2 3 4 5 7 Ashamed 1 2 3 4 5 8 Astonished 1 2 3 4 5 9 Concerned 1 2 3 4 5 10 Conflictful 1 2 3 4 5 11 Confused 1 2 3 4 5 12 Depressed 1 2 3 4 5 13 Disappointed 1 2 3 4 5 14 Discouraged 1 2 3 4 5 15 Disgusted 1 2 3 4 5 16 Distressed 1 2 3 4 5 17 Distrustful 1 2 3 4 5 18 Dominated 1 2 3 4 5 19 Embarrassed 1 2 3 4 5 20 Enraged 1 2 3 4 5 21 Fearful 1 2 3 4 5 22 Frustrated 1 2 3 4 5 23 Guilty 1 2 3 4 5 24 Helpless 1 2 3 4 5 25 Humiliated 1 2 3 4 5 26 Irritated 1 2 3 4 5 27 Mad 1 2 3 4 5 28 Nervous 1 2 3 4 5 29 Overstimulated 1 2 3 4 5 30 Panicked 1 2 3 4 5 31 Powerless 1 2 3 4 5 32 Regretful 1 2 3 4 5 33 Remorseful 1 2 3 4 5 34 Revolted 1 2 3 4 5
228 Not Very at all Neutral much 35 Sad 1 2 3 4 5 36 Scornful 1 2 3 4 5 37 Skeptical 1 2 3 4 5 38 Sorrowful 1 2 3 4 5 39 Surprised 1 2 3 4 5 40 Suspicious 1 2 3 4 5 41 Tense 1 2 3 4 5 42 Uneasy 1 2 3 4 5 43 Unhappy 1 2 3 4 5 44 Unpleasant 1 2 3 4 5 45 Upset 1 2 3 4 5
Section B. Please indicate the number that best indicates the degree to which you agree or disagree with each of the following statements. (SD= Strongly Disagree, SA= Strongly Agree)
SD Neutral SA
1 I found the processing of deciding which apparel 1 2 3 4 5 items to buy frustrating 2 Several good options were available for me to 1 2 3 4 5 choose from 3 I thought the choice selection was good 1 2 3 4 5
4 I would be happy to choose from the same set of 1 2 3 4 5 product options on my next purchase occasion 5 I found the process of deciding which apparel 1 2 3 4 5 items to buy interesting
Extremely Extremely Dissatisfied Satisfied How satisfied or dissatisfied are you with your 6 experience of deciding which apparel items to 1 2 3 4 5 buy?
229
Section C. Based on your experience with e Fashion today, please indicate the number that best indicates your expectation of e Fashion as an online apparel store.
Store Image Measures SD Neutral SA
1 e Fashion offers good quality clothing items 1 2 3 4 5
2 e Fashion offers a variety of clothing items 1 2 3 4 5
3 e Fashion has what I want 1 2 3 4 5
4 e Fashion makes clothing available to customers 1 2 3 4 5
5 e Fashion has what I want in stock 1 2 3 4 5
6 e Fashion carries many fashionable clothing 1 2 3 4 5
7 I like clothing sold at the e Fashion online store 1 2 3 4 5
8 e Fashion provides good customer service 1 2 3 4 5
9 e Fashion offers convenient payment options 1 2 3 4 5
10 e Fashion offers good return policy 1 2 3 4 5
11 e Fashion offers reliable delivery service 1 2 3 4 5
12 e Fashion offers secure online transactions 1 2 3 4 5
13 I feel safe in purchasing clothing from 1 2 3 4 5 e Fashion 14 I am pleased with the service I received at 1 2 3 4 5 e Fashion 15 e Fashion is convenient to shop 1 2 3 4 5
16 e Fashion is easily accessible 1 2 3 4 5
17 It is easy to browse an e Fashion website 1 2 3 4 5
18 I do not have a problem in shopping via 1 2 3 4 5 e Fashion 19 e Fashion allows to me find information that I 1 2 3 4 5 need 20 e Fashion offers a reliable shopping environment 1 2 3 4 5
230 21 e Fashion offers a pleasant shopping site 1 2 3 4 5
22 e Fashion website is visually pleasing 1 2 3 4 5
23 e Fashion website is visually appealing 1 2 3 4 5
24 It is fun to browse e Fashion website 1 2 3 4 5
25 e Fashion offers value for customers 1 2 3 4 5
26 The prices at e Fashion are fair 1 2 3 4 5
27 e Fashion is likely to offer promotions 1 2 3 4 5
Section D. Consider that e Fashion is an active online store. Please indicate the number that best represents your thoughts based on your shopping experience with e Fashion today.
Very Very Unlikely Neutral Likely 3 How likely is it that you will shop for apparel via this online store? 1 2 3 4 5 4 How likely is it that you will purchase apparel via this online store? 1 2 3 4 5 5 How likely is it that you will recommend this store to your friends? 1 2 3 4 5
Section E. We would like to know a little bit about you and your experience. Please answer the following questions.
1. What is your age? ______years
2. What is your academic standing? Freshman Sophomore Junior Senior Graduate Students
3. How would you classify yourself? Caucasian American African American Native American Asian/ Pacific Islander Hispanic Multi-cultural Other
231 4. Please answer the following questions based on your own experience (NA: None Applicable, VI: Very Infrequently, VF: Very Frequently).
NA VI Average VF
1 How often do you use the Internet? 1 2 3 4 5
2 How often do you shop online? 1 2 3 4 5
3 How often do you purchase online? 1 2 3 4 5
4 How often do you shop for apparel online? 1 2 3 4 5
5 How often do you purchase apparel online? 1 2 3 4 5
7. We would like to know your prior experience related to apparel shopping. Please answer the following series of questions based on your own experience during last two years (NA: None Applicable, NO: Not Often, VO: Very Often)
NA NO Sometimes VO
1 How often have you experienced product 1 2 3 4 5 unavailability when shopping for apparel? 2 How often have you experienced product 1 2 3 4 5 unavailability from in-store shopping? 3 How often have you experienced product 1 2 3 4 5 unavailability from catalog shopping? 4 How often have you experienced product 1 2 3 4 5 unavailability from online shopping? 5 How often have you been provided with any 1 2 3 4 5 compensations for unavailable products?
232
APPENDIX H
COVARIANCE MATRIX ANALYZED
233
D2 D3 D4 B1 B2 B3 NE S4 S5 S6
D2 1.77
D3 1.46 1.71
D4 1.36 1.44 1.80
B1 1.14 1.14 1.16 1.88
B2 1.12 1.10 1.13 1.67 1.83
B3 1.13 1.12 1.13 1.59 1.65 1.80
NE -2.31 -2.29 -2.31 -2.93 -2.68 -2.50 82.17
S4 4.62 4.32 4.34 4.91 4.78 4.67 -9.88 30.28
S5 5.06 4.83 4.79 5.15 5.10 4.97 -11.35 28.12 31.72
S6 5.32 5.23 5.22 5.77 5.68 5.77 -10.70 26.05 27.59 31.78
234
APPENDIX I
PRELIMINARY DATA SCREENING
235
Skewness Kurtosis Skewness Kurtosis Skewness & Variable Mean SD T coefficient coefficient kurtosis Z p Z p χ2 p
NE 20.06 9.07 63.37 2.14 4.70 16.38 .000 9.62 .000 360.95 .000
S4 18.25 5.50 94.95 -.55 -.59 -6.03 .000 -.4.92 .000 60.55 .000
S5 17.64 5.63 89.69 -.40 -.78 -457 .000 -772 .000 80.44 .000
S6 15.47 5.64 78.58 .05 .92 .54 .000 -10.88 .000 118.61 .000
D2 3.26 1.33 70.10 -.23 -1.12 -2.69 .007 -18.60 .000 352.98 .000
D3 3.11 1.31 68.08 -.07 -1.12 -.77 .443 -18.25 .000 333.52 .000
D4 2.90 1.34 61.95 .10 -1.17 1.18 .23 -21.35 .000 457.12 .000
B1 3.04 1.37 63.59 -.06 -1.24 -.71 .48 28.66 .000 822.13 .000
B2 2.96 1.35 62.77 .01 -1.21 .11 .91 -24.70 .000 609.96 .000
B3 2.70 1.34 57.52 .24 -1.14 2.78 .006 -19.66 .000 394.23 .000
236
APPENDIX J
HUMAN SUBJECT APPROVAL FORM
237
238