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THE INFLUENCE OF ONLINE VISUAL MERCHANDISING ON CONSUMER EMOTIONS: MODERATING ROLE OF CONSUMER INVOLVEMENT
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the
Graduate School of The Ohio State University
By
Young Ha, M.S.
* * * * *
The Ohio State University 2006
Dissertation Committee:
Approved by
Professor Sharron J. Lennon, Advisor Professor Leslie Stoel Professor Susan Zavotka Professor Michael Browne
Advisor
College of Human Ecology
Copyright by Young Ha
2006
ii
ABSTRACT
The current research consists of two studies. The purpose of Study 1 was to
investigate 1) the effects of peripheral cues presented in the apparel websites on
consumers’ emotions (pleasure and arousal) under low situational involvement, 2) the
influence of product involvement (personal relevance of clothing products) as a
moderator of the relationship between peripheral cues and emotions under the low
involvement situation, 3) the influence of emotions on consumer response behaviors, and
4) the mediating effects of emotions on the relationship between peripheral cues and
response behaviors (purchase intention and approach behaviors). A convenience sample
of 157 female college students participated in an online experiment using a mock website
for Study 1. In a between-subjects experiment with one factor (peripheral cues) having
two levels (presence vs. absence), Study 1 found: 1) main effects for peripheral cues on
consumer pleasure and arousal, 2) a stronger effect for peripheral cues on pleasure and
arousal for consumers with a low level of clothing product involvement rather than with a
high level of clothing product involvement, 3) direct effects of consumer emotions on
purchase intention and approach behaviors, and 4) indirect effects of peripheral cues on
purchase intention and approach behaviors via consumer pleasure and arousal.
The purpose of Study 2 was to examine 1) the effects of web cues – central cues
(product-related stimuli) and peripheral cues (stimuli not directly related to the product) –
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on emotions, 2) the influence of emotions on consumer response behaviors (satisfaction,
purchase intention, and approach behaviors), 3) the effects of situational involvement
(e.g., purchase situation vs. browsing situation) as a moderator of the relationship
between web cues and emotions (pleasure and arousal), and 4) the mediating effects of
emotional states on the relationship between web cues and response behaviors. A
random sample of 1634 female undergraduate students participated in an online
experiment using a mock website for Study 2. Employing a 2 (situational involvement:
high vs. low) x 2 (central cues: medium amount vs. high amount) x 2 (peripheral cues:
presence vs. absence) between subjects’ factorial design, Study 2 revealed: 1) direct
effects for central cues on pleasure and for peripheral cues on arousal, 2) the influence of
pleasure and arousal on satisfaction, purchase intention, and approach behaviors, 3) the
effects for central cues on consumer pleasure and arousal under high situational
involvement (purchasing situation) and effects for peripheral cues on consumer emotions
under low situational involvement (browsing situation), and 4) the mediating effects of
consumer emotions on the relationship between web cues and consumer response
behaviors.
The findings of Study 1 and Study 2 1) provide valuable information for apparel
online retailers developing successful apparel online stores using various web cues that
may attract both online browsers and purchasers, 2) extend online visual merchandising
research by empirically investigating how various web cues presented in apparel websites
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influence consumer emotions that in turn affect consumer response behaviors under
different involvement conditions, and 3) combine the ELM and the S-O-R paradigms to
explain and predict consumer responses to online visual merchandising under different
involvement conditions.
v
Dedicated to my parents and my loving husband, Jin Nam
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ACKNOWLEDGMENTS
I first and foremost thank my advisor Dr. Sharron J. Lennon from the bottom of
my heart for her constant support, encouragement, assistance, and advice, which made
this dissertation possible. Her enthusiasm and inspiration have always stimulated me to
take steps forward in research and teaching. Throughout my whole life, she has been the
best mentor. Without her endless support during my graduate program at this university,
the completion of my degree was not possible.
I would also like to express my special thanks to my committee members for their
thoughtful and valuable suggestions for my dissertation. My heartfelt thanks go to Dr.
Leslie Stoel for her sincere support during the past five years. She has supported me to
make steady progress in my research as well as teaching. Her incessant guidance and
encouragement allowed me to gain confidence in my ability to complete my dissertation.
I also wish to express my appreciation to Dr. Susan Zavotka for her valuable suggestions
and feedback on my dissertation research, particularly on the website design. My
gratitude is extended to Dr. Michael Browne for his invaluable lectures on factor analysis
and structural equation modeling and for his helpful comments on my dissertation. His
brilliant insight and lessons were exceptional.
I am greatly indebted to Dr. Nancy A. Rudd for her thoughtful concern and help
during my graduate program. Her continuous guidance and comments on my teaching
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were extremely precious for my career development. My appreciation is also extended to
Dr. Gong-Soog Hong who has provided helpful advice and support for my teaching. My
thanks also go to Dr. Loren Geistfeld for his constant encouragement for my teaching and
work.
I wish to express my thanks to my best friends Jihye Park at Iowa State University,
Jungmin Ha, and Juhee Kim in Korea for their genuine support and love. I am also
thankful to my colleagues who have contributed to my dissertation in numerous ways:
Sejin Ha, Hunju Im, Hyejeong Kim, Junghwan Kim, Minjeong Kim, Wisuk Kwon,
Jiyoung Lim, Jeesun Park, and Minjung Park. My special gratitude goes to Hyunju Im
and Jeesun Park for their special companionship and assistance during the last quarter of
my graduate study. I would also like to thank Ann Glenn for sharing joys and difficulties
with me while teaching.
My sincere thanks go to my parents and sister in Korean and my parents-in-law
for their endless love and encouragement through my life. I owe perhaps the greatest
thanks to my husband, Jin Nam, who has given me his unwavering support for my work.
The completion of my dissertation would have been unfeasible without his continuing
support and patience.
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VITA
February 14, 1974..………………………………… Born in Seoul, Korea
February 1998..…………………………………….. B.A. Hanyang University Major: Textiles and Clothing Seoul, Korea September 1999 – August 2002…………………… M.S. The Ohio State University Major: Textiles and Clothing Columbus, OH March 2001 – August 2005 ……………………….. Graduate Teaching Associate
Department of Consumer Sciences The Ohio State University Columbus, OH
July 2004 – September 2004……………………….. Graduate Research Associate Department of Consumer Sciences The Ohio State University Columbus, OH September 2005 – December 2005 …………………Lecturer
Department of Consumer Sciences The Ohio State University Columbus, OH
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PUBLICATIONS
Research Publication 1. Ha, Y., & Stoel, L. (2004). Internet apparel shopping behaviors: The influence of general innovativeness. International Journal of Retail and Distribution Management, 32 (8), 377-385. 2. Ha, Y., & Lennon, S. (2005). Effects of satisfaction with local shopping condition in rural areas on Internet apparel shopping behavior. Abstract published in Proceeding of the International Textiles and Apparel Association, available at: www.itaaonline.org
3. Lennon, S., Ha, Y., Johnson, K., Damhorst, M. L., Jasper, C., Lyons, N. (2005). Online shopping for apparel, food, and home furnishings products as a form of outshopping. Abstract published in Proceeding of the International Textiles and Apparel Association, available at: www.itaaonline.org.
4. Ha, Y., & Lennon, S. (2005). Effects of apparel website atmospherics on consumer emotions and purchase intention. Abstract published in Proceeding of 2005 Korean Society of Clothing and Textiles and the Japan Society of Home Economics [CD-ROM].
5. Ha, Y., & Lennon, S (2005). Rural consumers’ Internet apparel shopping: Innovativeness and beliefs. Abstract published in Proceedings of American Collegiate Retailing Association [CD-ROM].
6. Ha, Y., & Lennon, S. (2005). Effects of attractive model and image movement in apparel websites on judgments of clothing fashionability and purchase intention. Abstract published in the first Global Symposium for Consumer Sciences (GSCS), available at: www.consumersciences.org
7. Ha, Y., Kwon, W., & Lennon, S. (2004). Online visual merchandising: A cross national approach. Abstract published in Proceeding of the International Textiles and Apparel Association, available at: www.itaaonline.org.
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8. Ha, Y. & Lennon, S. (2003). Rural Ohio consumers’ Internet apparel shopping: Innovativeness and perceptions of the Internet and Internet shopping. Abstract published in Proceeding of the International Textiles and Apparel Association, available at: www.itaaonline.org.
9. Lee, K. H., Ha, Y., & Read, E. (2002). Improving reference citing skills and pre-class learning activities via image assignments in a beginning aesthetics course. Abstract published in Proceeding of the International Textiles and Apparel Association, available at: www.itaaonline.org.
FIELD OF STUDY
Major Field: Human Ecology Area of Specialization: Textiles and Clothing Minor Field: Quantitative Psychology
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TABLE OF CONTENTS
Page
Abstract ............................................................................................................................... ii
Dedication ........................................................................................................................... v
Acknowledgments.............................................................................................................. vi
Vita................................................................................................................................... viii
List of Table................................................................................................................... xviii
List of Figure................................................................................................................... xxii
Chapters
1. Introduction............................................................................................................. 1
1.1. Introduction...................................................................................................... 1 1.2. Problem Statement ........................................................................................... 4 1.3. Purpose of the Study ........................................................................................ 6 1.4. Significance of the Study ................................................................................. 7 1.5. Definition of Terms.......................................................................................... 9
2. Literature Review.................................................................................................. 11
2.1. Background Literature ................................................................................... 12 2.1.1. Visual Merchandising in Store........................................................ 12 2.1.2. Visual Merchandising in Apparel Store.......................................... 13 2.1.3. Visual Merchandising in General Websites.................................... 16
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2.1.4. Visual Merchandising in Apparel Websites ................................... 17 2.2. Theoretical Framework.................................................................................. 21 2.2.1. S-O-R Paradigm.............................................................................. 21 The Effects of Emotions on Consumer Behaviors:
The Effects of Store Environment ................................................ 23 The Effects of Emotions on Consumer Behaviors: The Effects of Site Environment................................................... 25
2.2.2. Elaboration Likelihood Model: Involvement and Persuasion ........ 27 Websites as Persuasion ................................................................. 27 Central and Peripheral Routes ...................................................... 28 Involvement .................................................................................. 28 Product Involvement..................................................................... 29 Situational Involvement ................................................................ 31
2.2.3. Applying the S-O-R Paradigm and Involvement into Internet Shopping Context....................................................... 34
2.3. Proposed Models and Hypotheses ................................................................. 39 2.3.1. Main Study 1................................................................................... 41
The Effects of Peripheral Cues on Emotional States .................... 41 The Moderating Effect of Product Involvement between S-O ..... 42 The Effects of Emotional States on Response Behaviors............. 44 The Mediating Effects of Emotional States between S-R ............ 45
2.3.2. Main Study 2................................................................................... 48 Part I: The Effects of Web Cues on Emotional States .................. 48 Part II: The Effects of Emotional States on Response Behaviors ...................................................................... 52 Part III: The Moderating Effects of Situational Involvement between S-O.................................................................................. 55 Part IV: The Mediating Effects of Emotional States between S-R.................................................................................. 59
3. Pilot Studies .......................................................................................................... 64 3.1. Pilot Study 1................................................................................................... 66 3.2. Pilot Study 2................................................................................................... 71 3.3. Pilot Study 3................................................................................................... 76 3.4. Content Analysis............................................................................................ 79
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4. Main Study 1......................................................................................................... 84 4.1. Method ........................................................................................................... 85 4.1.1. Research Design and Experimental Manipulations ........................ 85 4.1.2. Instrument Development................................................................. 87 4.1.3. Procedure ........................................................................................ 88 4.2. Analyses and Results ..................................................................................... 90 4.2.1. Description of Participants.............................................................. 90 4.2.2. Manipulation Check........................................................................ 92 4.2.3. Dependent Variables....................................................................... 93 Emotional States: Pleasure and Arousal ....................................... 93 Purchase Intention......................................................................... 94 Approach Behaviors...................................................................... 95 4.2.4. Assessment of Measurement Properties ......................................... 97 Convergent Validity.................................................................... 102 Unidimensionality....................................................................... 104 Discriminant Validity.................................................................. 105 Assessment of Reliability ........................................................... 108 Model Specification .................................................................... 110 Data Screening ............................................................................ 110 4.2.5. Hypothesis Testing........................................................................ 114 Hypothesis 1................................................................................ 114 Hypothesis 2................................................................................ 117 Hypotheses 3 and 4 ..................................................................... 126 Model fit.......................................................................... 126 Hypothesis 5................................................................................ 134 The direct effects of peripheral cues on
response behaviors .......................................................... 135 The mediating effects of emotional states ...................... 136
5. Main Study 2....................................................................................................... 138 5.1. Method ......................................................................................................... 139 5.1.1. Research Design and Experimental Manipulations ...................... 139 5.1.2. Instrument Development............................................................... 143 Emotional States ......................................................................... 143 Satisfaction.................................................................................. 144 Purchase Intention....................................................................... 144
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Approach Behavior ..................................................................... 144 Perceived Amount of Information .............................................. 145 Perceived Quality of Web Appearance....................................... 145 Situational Involvement .............................................................. 146 Demographic Information and Prior Experiences ...................... 147 5.1.3. Website Development................................................................... 147 Apparel Stimuli Preparation for the Websites ............................ 148 Mock Website Development for the Main Study 2 .................... 148 5.1.4. Recruitment of Participants........................................................... 150 5.1.5. Experiment Procedure for Study 2................................................ 151 5.2. Analysis and Results .................................................................................... 153 5.2.1. Description of Participation .......................................................... 153 5.2.2. Manipulation Check...................................................................... 158 Situational Involvement .............................................................. 158 Central Cues................................................................................ 159 Peripheral Cues ........................................................................... 160 5.2.3. Dependent Variables..................................................................... 163 Emotional States: Pleasure and Arousal ..................................... 163 Satisfaction.................................................................................. 164 Purchase Intention....................................................................... 164 Approach Behaviors.................................................................... 164 5.2.4. Assessment of Measurement Properties ....................................... 166 Convergent Validity.................................................................... 169 Unidimensionality....................................................................... 171 Discriminant Validity.................................................................. 172 Assessment of Reliability ........................................................... 175 Testing Invariance of Measurement Model over Groups ........... 177 Model Specification .................................................................... 180 Data Screening ............................................................................ 184 5.2.5. Hypotheses Testing....................................................................... 185 Part One ...................................................................................... 187 Hypothesis 1.................................................................... 187 Hypothesis 2.................................................................... 194 Part Two...................................................................................... 200 Hypothesis 3.................................................................... 200 Part Three.................................................................................... 208
Hypothesis 4.................................................................... 208 Part Four...................................................................................... 224 Hypothesis 5.................................................................... 224
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6. Discussion and Conclusions ............................................................................... 244 6.1. Discussion .................................................................................................... 245 6.1.1. Findings from Study 1 .................................................................. 245 The Effects of Peripheral Cues on Emotional States .................. 245 The Moderating Effect of Product Involvement between S-O ... 248 The Effects of Emotional States on Response Behaviors........... 251 The Mediating Effects of Emotional States Between S-R.......... 254 6.1.2. Findings from Study 2 .................................................................. 258 The Effects of Web Cues on Emotional States........................... 260 The Effects of Emotional States on Response Behaviors........... 263 The Moderating Effect of Situational Involvement
between S-O................................................................................ 266 The Mediating Effects of Emotional States between S-R .......... 269
6.2. Implications.................................................................................................. 274 6.2.1. Managerial Implications for Study 1 ............................................ 274 6.2.2. Managerial Implications for Study 2 ............................................ 277 6.2.3. Theoretical Implications ............................................................... 285 6.3. Limitations ................................................................................................... 288 6.3.1. Homogeneity of the Sample Population ....................................... 288 6.3.2. Simulated Situational Involvement............................................... 289 6.3.3. Limited Product Category............................................................. 289 6.4. Recommendations for Future Studies.......................................................... 290 List of References ........................................................................................................... 292 APPENDICES ................................................................................................................ 303 Appendix A: Pilot Study 1.................................................................................. 303 Recruitment Letter .................................................................................. 304 Questionnaire for Low Involvement....................................................... 305 Questionnaire for High Involvement ...................................................... 307 Appendix B: Pilot Study 2 – Websites ............................................................... 310
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Appendix C: Pilot Study 2 – Apparel Stimuli .................................................... 313 Thirty Four Pairs of Pants Rated in the Pilot Study 2............................. 314 Two Items Selected for Pilot Study 4 ..................................................... 317 Five Items Selected for the Main Experiment ........................................ 318 Appendix D: Pilot Study 3 – Website and Questionnaire................................... 319 Appendix E: Main Study 1 – Website: Manipulations for Peripheral Cues ....... 323 Main Page: Peripheral Cues – Absence .................................................. 325 Main Page: Peripheral Cues – Presence.................................................. 326 Product Page: Peripheral Cues – Absence .............................................. 327 Product Page: Peripheral Cues – Presence.............................................. 328 Peripheral Cues: Presence – Flashing Image and Colorful Icons ........... 329 Appendix F: Main Study 1 – The Questionnaire ................................................ 330 Appendix G: Main Study 2 – Invitation Email................................................... 333 Invitation Email for High Involvement................................................... 334 Invitation Email for Low Involvement ................................................... 336 Appendix H: Main Study 2 – Instruction Page................................................... 338 Instruction Page: Peripheral Cues – Absence ......................................... 339 Instruction Page: Peripheral Cues – Presence......................................... 340 Appendix I: Main Study 2 – Scenario Page........................................................ 341 Scenario Page: Peripheral Cues – Absence, Involvement – Low........... 342 Scenario Page: Peripheral Cues – Presence, Involvement – Low .......... 343 Scenario Page: Peripheral Cues – Absence, Involvement – High .......... 344
Scenario Page: Peripheral Cues – Presence, Involvement – High.......... 345 Appendix J: Main Study 2 – Main Page ............................................................. 346 Main Page: Peripheral Cues – Absence .................................................. 347 Main Page: Peripheral Cues – Presence.................................................. 348 Appendix K: Main Study 2 – Product Page........................................................ 349 Product Page: Peripheral Cues – Absence,
Central Cues – Medium Amount ............................................................ 350 Product Page: Peripheral Cues – Absence, Central Cues – High Amount.................................................................. 351 Product Page: Peripheral Cues – Presence, Central Cues – Medium Amount ............................................................ 352 Product Page: Peripheral Cues – Presence, Central Cues – High Amount.................................................................. 353 Peripheral Cues: Presence – Flashing Image and Colorful Icons ........... 354 Product Page: Size Chart – Peripheral Cues – Absence ......................... 355 Product Page: Size Chart – Peripheral Cues – Presence......................... 356
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Appendix L: Main Study 2 – Purchase Page for High Involvement .................. 357 Purchase Page: Peripheral Cues – Absence ............................................ 358 Purchase Page: Peripheral Cues – Presence............................................ 359 Appendix M: Main Study 2 – The Questionnaire............................................... 360 Appendix N: Standardized Residual Evaluated in Main Study 1 ....................... 364 Appendix O: Data Screening for Normality Test ............................................... 366 Appendix P: Standardized Residual Evaluated in Main Study 2........................ 368 Appendix Q: Data Screening for Normality Test ............................................... 370 Appendix R: Human Subjects Approval Form for Study 1................................ 372 Appendix S: Human Subjects Approval Form for Study 2 ................................ 374
xviii
LIST OF TABLE Table Page 2.1. Summary of hypotheses in Study 1 and Study 2 .................................................. 63 3.1. Frequencies of web cues listed in Pilot Study 1 ................................................... 70 3.2. Descriptive statistics and reliabilities of apparel items selected in
Pilot Study 2 for Study 1 and Study 2 .................................................................. 74
3.3. Web cues listed in Pilot Study 2 that participants reported paying attention to when they purchased clothing online................................................. 75
3.4. The popular apparel websites participants listed in Pilot Study 2 ........................ 75 3.5. The extent to which web cues are product related rated in Pilot Study 3............. 78 3.6. Coding categories used in Content Analysis and frequencies of the
product related web cues presented in apparel websites....................................... 82 3.7. Number of product related web cues available in the 15 apparel
websites analyzed in the Content Analysis........................................................... 83 4.1. Demographic profile of participants ..................................................................... 91 4.2. Descriptive statistics of dependent variables ........................................................ 96 4.3. Final measurement items for each of four latent constructs ............................... 101 4.4. Factor loadings, t-values, and item reliability for convergent validity ............... 103 4.5. Chi-square difference tests for discriminant validity.......................................... 106 4.6. Correlations and confidence intervals for discriminant validity......................... 107 4.7. Composite reliability and AVE of latent constructs ........................................... 109
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4.8. Descriptive statistics for clothing involvement items......................................... 118 4.9. Mean differences for pleasure influenced by peripheral cues and product
involvement interaction ...................................................................................... 124 4.10. Mean differences for arousal influenced by peripheral cues and product
involvement interaction ...................................................................................... 124 4.11. Summary of measurement and structural models and model fit for
Hypotheses 3 and 4 ............................................................................................. 129 4.12. Multiple regression analysis for purchase intention in Hypothesis 5 ................. 137 4.13. Multiple regression analysis for approach behaviors in Hypothesis 5 ............... 137 5.1. The eight treatments in Study 2 .......................................................................... 142 5.2. Demographic descriptions of participants .......................................................... 156
5.3. Participants’ prior Internet usage and online browsing/purchasing
Experiences ......................................................................................................... 157 5.4. Descriptive statistics for manipulation check items in Study 2 .......................... 162 5.5. Descriptive statistics of dependent variables ...................................................... 165 5.6. Final measurement items for each of five latent constructs................................ 168 5.7. Factor loading, t-values, and item reliability for convergent validity................. 170 5.8. Chi-square difference tests for discriminant validity.......................................... 173 5.9. Correlations and confidence intervals for discriminant validity......................... 174 5.10. Composite reliability and AVE of latent constructs ........................................... 176 5.11. The results of testing the invariance of the measurement model........................ 179 5.12. Summary of the model fit for the proposed model in Hypothesis 1................... 189
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5.13. Estimated means of pleasure and arousal in Hypothesis 1 ................................. 193 5.14. Summary of the model fit for the proposed model in Hypothesis 2................... 195 5.15. Estimated means of pleasure and arousal in Hypothesis 2 ................................. 199 5.16. Summary of measurement and structural models and model fit in Part 2 .......... 202 5.17. Summary of the model fit for Model 1 (high involvement) in
Hypothesis 4a...................................................................................................... 211 5.18. Summary of the model fit for Model 2 (low involvement) in
Hypothesis 4a...................................................................................................... 212 5.19. Estimated means of pleasure and arousal in Hypothesis 4a ............................... 216 5.20. Summary of the model fit for Model 1 (high involvement) in
Hypothesis 4b...................................................................................................... 218 5.21. Summary of the model fit for Model 2 (low involvement) in
Hypothesis 4b...................................................................................................... 219 5.22. Estimated means of pleasure and arousal in Hypothesis 4b ............................... 223 5.23. Summary of the model fit for the model tested the direct effects of
central cues on response behaviors ..................................................................... 228 5.24. Estimated means of satisfaction, purchase intention, and
approach behaviors ............................................................................................. 229 5.25. Summary of measurement and structural models and model fit in
Hypothesis 5a...................................................................................................... 231 5.26. Estimated intercepts of satisfaction, purchase intention, and approach
behaviors in Hypothesis 5a ................................................................................. 234
5.27. Summary of the model fit for the model tested the direct effects of peripheral cues on response behaviors................................................................ 237
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5.28. Estimated means of satisfaction, purchase intention, and approach behaviors ............................................................................................. 238
5.29. Summary of measurement and structural models and model fit in Hypothesis 5b...................................................................................................... 240
5.30. Estimated intercepts of satisfaction, purchase intention, and approach behaviors in Hypothesis 5b................................................................................. 243
6.1. Summary of hypotheses testing results in Study 1 ............................................. 257 6.2. Summary of manipulations used in Study 2 ....................................................... 259 6.3. Summary of hypotheses testing results in Study 2 ............................................. 273
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LIST OF FIGURES
Figure Page 2.1. The S-O-R paradigm............................................................................................. 23 2.2. The S-O-R paradigm in the online shopping context ........................................... 38 2.3. The proposed model in the main study 1 .............................................................. 47 2.4. Part one of the model in Study 2 (Hypotheses 1 and 2)........................................ 51 2.5. Part two of the model in Study 2 (Hypotheses 3a to 3f)....................................... 54 2.6. Part three of the model in Study 2 (Hypotheses 4a to 4b) .................................... 58 2.7. Part four of the hypothesized model for Study 2 (Hypotheses 5a and 5b) ........... 61 2.8. The proposed model for Study 2........................................................................... 62 3.1. A summary of pilot tests ....................................................................................... 65 4.1. Model specification for Study 1.......................................................................... 112 4.2. The proposed model in Study 1 .......................................................................... 113 4.3. Effects of peripheral cues and product involvement on pleasure ....................... 123 4.4. Effects of peripheral cues and product involvement on arousal ......................... 125 4.5. Unstandardized parameter estimates in the proposed model for
Hypotheses 3 and 4 ............................................................................................. 130
4.6. Completely standardized parameter estimates in the proposed model for Hypotheses 3 and 4 ....................................................................................... 131
5.1. Model specification for Parts 1 and 3 in Study 2................................................ 182
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5.2. Model specification for Part 2 and Part 4 in Study 2.......................................... 183 5.3. The proposed model in Study 2 .......................................................................... 186 5.4. Unstandardized parameter estimates in the proposed model for
Hypothesis 1........................................................................................................ 190
5.5. Completely standardized parameter estimates for Hypothesis 1 ........................ 191 5.6. Unstandardized parameter estimates in the proposed model for
Hypothesis 2........................................................................................................ 196
5.7. Completely standardized parameter estimates for Hypothesis 2 ........................ 197 5.8. Unstandardized parameter estimates in the proposed model for Part 2.............. 203 5.9. Completely standardized parameter estimates in the proposed model
for Part 2 ............................................................................................................. 204
5.10. Unstandardized parameter estimates in Models 1 and 2 for Hypothesis 4a ....... 213 5.11. Completely standardized parameter estimates in Models 1 and 2 for
Hypothesis 4a...................................................................................................... 214
5.12. Unstandardized parameter estimates in Models 1 and 2 for Hypothesis 4b ....... 220 5.13. Completely standardized parameter estimates in Models 1 and 2 for
Hypothesis 4b...................................................................................................... 221
5.14. Unstandardized parameter estimates in the model for Hypothesis 5a ................ 232 5.15. Completely standardized parameter estimates in the model for
Hypothesis 5a...................................................................................................... 233
5.16. Unstandardized parameter estimates in the model for Hypothesis 5b................ 241 5.17. Completely standardized parameter estimates in the model for
Hypothesis 5b...................................................................................................... 242 6.1. The results of Hypotheses 1a and 1b .................................................................. 247
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6.2. The results of Hypotheses 2a and 2b .................................................................. 250 6.3. The results of Hypotheses 3 and 4 ...................................................................... 253 6.4. The results of Hypothesis 5................................................................................. 256 6.5. The results of Hypotheses 1 and 2 in Part 1........................................................ 262 6.6. The results of Hypothesis 3 in Part 2 .................................................................. 265 6.7. The results of Hypothesis 4 in Part 3 .................................................................. 268 6.8. The results of Hypothesis 5 in Part 4 .................................................................. 272
1
CHAPTER 1
INTRODUCTION
1.1. Introduction
Internet users are increasing in the world as well as in the U.S. According to
Computer Industry Almanac Inc, about 934 million were global Internet users in 2004
and global users are projected to reach 1.35 billion in 2007 (“Population Explosion,”
2005). The rate of American households with computers increased from 51% in 2000 to
61.8% in 2003 and 54.6% of American households had Internet access in 2003 (“A
Nation Online: Entering the Broadband Age,” 2004). U.S. Internet population reached
185.6 million in 2005 (“Population Explosion,” 2005) and 140.6 million are considered
active users who go online at least once a month (Burns, 2005a). In addition, remarkable
changes were made in the relative distribution of the various types of Internet access in
the U.S. American households with high-speed Internet or broadband Internet access
increased from 9.1% in 2001 to 19.9% in 2003 while dial-up Internet connections
decreased by 12.7% during the same period (“A Nation Online: Entering the Broadband
Age,” 2004). These statistics show a change in Internet connections from dial-up service
2
to high-speed Internet connections in American households. Internet users with high-
speed Internet connections also engaged in more types of online activities in the areas of
entertainment, banking, purchasing products, or gathering information than those with
dial-up connections (“A Nation Online: Entering the Broadband Age,” 2004).
As the Internet population grows, the Internet influences the shopping patterns of
in-home shoppers. Internet shopping has grown rapidly, and Internet shoppers can find
product information easily using the Internet (Ward and Lee, 2000). Compared to 15%
of the global average, the U.S. led the world with the highest proportion of online
shoppers at 32% of all Internet users (Greenspan, 2002). U.S. online sales reached
$141.4 billion in 2004 and are predicted to approach $172.4 billion in 2005, representing
a growth of 22% from 2004 (Burns, 2005b). Eighty-one percent of adults with Internet
access have purchased online since they started using the web (“Statistics: US Online
Shoppers,” 2002). Thirty-two percent of Internet users have used the Internet for
shopping (Greenspan, 2002) and apparel is one of the most frequently purchased online
merchandise categories (Greenspan, 2004). U.S. online apparel sales during the 2003
holiday season reached more than $3.7 billion—representing a 40% increase over the
same period in 2002—followed by toys and video games with $2.2 billion (Rush, 2004).
Visual merchandising is an important strategic tool in fashion marketing (Lea-
Greenwood, 1998). Visual merchandising as the total store environment including
merchandise presentation, store design and image, mannequins, props and materials,
lighting, graphics, and signage influences product sales and store image in the retail
setting (Cahan & Robinson, 1984; Diamond & Diamond, 2003). Previous research found
that store environments (e.g., lighting, color, and music) influenced consumers’
3
emotional states such as pleasure and arousal that in turn influenced consumer response
behaviors (Baker, Levy, & Grewel, 1992; Bitner, 1992; Buckley, 1991; Donovan &
Rossiter, 1982; Donovan, Rossiter, Marcoolyn, & Nesdale, 1994; Spies, Hesse, & Loesch,
1997). Higher levels of pleasure and arousal induced by store environments increased
purchase intention (Babin, Hardesty, & Suter, 2003; Fiore & Kimle, 1997) and approach
behaviors (Donovan & Rossiter, 1982). This indicates the importance of visual
merchandising in retail stores for increasing purchase intention and approach behaviors.
As the population of Internet shoppers grows, the effects of visual merchandising
in websites have gained attention from researchers. Previous studies showed that site
designs and merchandising attract customers and influence their satisfaction with Internet
shopping (Harris, 1998; Szymanski & Hise, 2000). More attractive and pleasurable site
stimuli may influence consumers’ purchase decisions (Menon & Kahn, 2002). When a
website creates pleasure for consumers, there is a positive effect on approach behaviors
(Menon & Kahn, 2002). Website designs make consumers return to the websites (Rice,
1997). In addition, ease of site navigation (Rice, 1997; Szymanski & Hise, 2000) and
entertaining experiences (Rice, 1997) make people enjoy and come back to the websites
later. Extensive and higher quality product information also affects consumers’
satisfaction in Internet shopping (Szymanski & Hise, 2000).
The inability to try on apparel products before purchase is a major concern for
consumers when purchasing apparel using an in-home method (Kim & Lennon, 2000;
Kwon, Paek, & Arzeni, 1991; Park, Lennon, & Stoel, 2005). Kim and Lennon (2000)
found that the amount of product and service information in a television shopping
program segment reduced perceived risk and increased purchase intention. Fabric and
4
color information and product details can also reduce perceived risk in the catalog
shopping context (Kwon et al., 1991). In an Internet shopping context, Then and Delong
(1999) suggested that more information regarding visual aspects of apparel products such
as a variety of images and of different product views can generate higher purchase
intention for consumers and in turn, increase sales for e-business. Viewing apparel
products in a variety of combinations can help consumers assess how they might look
wearing those items (Allen, 1999). Mix and match suggestions in apparel sites may also
increase consumers’ purchase intentions that in turn increase apparel sales on the Internet
(Allen, 2000; Then & Delong, 1999).
1.2. Problem Statement
Although the effects of website visual merchandising have gained attention from
previous researchers (Allen, 1999; Menon & Kahn, 2002; Szymanski & Hise, 2000; Then
& Delong, 1999), it is surprising that so little empirical research related to visual
merchandising in apparel websites has been conducted. Previous Internet apparel
shopping studies have focused on demographic issues (Goldsmith & Goldsmith, 2002;
Kim, Damhorst, & Lee, 2002), purchasers vs. browsers (Goldsmith & Goldsmith, 2002;
Lee & Johnson, 2002), and the effect of prior experiences with the Internet or Internet
shopping on Internet apparel shopping (Goldsmith & Goldsmith, 2002; Yoh & Damhorst,
2003).
The Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1996), developed
in the context of persuasion, may be a useful guide for research into online visual
5
merchandising. According to the ELM, central cues are defined as factors that influence
consumer attitudes under a high involvement condition and that require cognitive effort
to process (Petty et al., 1983; Petty & Cacioppo, 1996). Consumers under the high
involvement condition focus on analysis of product relevant information such as the
message argument. Alternatively, peripheral cues are defined as factors that are
sufficient to produce an initial attitude change with minimal cognitive processing. In the
low involvement condition consumer attitudes are influenced by the persuasive peripheral
cues such as a pleasant environment or attractive sources (Petty et al., 1983; Petty &
Cacioppo, 1996). Based on the assumption that online purchasing is analogous to a
persuasion situation, conceptualizing web cues as central or peripheral may offer some
useful insights. Applying the ELM in Internet shopping context, Eroglu, Machleit, and
Davis, (2003) found that peripheral cues presented in apparel websites influence pleasure
and arousal that in turn affect consumer response behaviors in low situational
involvement (browsing situation). However, the effects of central cues available on
apparel websites on consumers’ emotional reactions and behavioral responses have not
been investigated.
In the retail setting, consumers examine apparel products using visual and tactile
senses. However, Internet shoppers can rely only on visual information available on the
screen (e.g., verbal descriptions and product images). Because apparel cannot be
physically experienced online, Internet shopping is a riskier way to purchase apparel
products than in-store shopping. Therefore, it is necessary to understand whether or not
certain types of web cues (both central cues and peripheral cues) on apparel websites
have an impact on consumers’ emotions (pleasure and arousal) that in turn influence
6
consumer response behaviors (e.g., satisfaction, purchase intention, and approach
behaviors). In addition, situational involvement or product involvement may have a
significant effect on the relationship between different types of web cues and consumers’
emotional states. Consumers browsing the websites with a purchasing goal may be more
likely to attend to central cues (product related verbal and pictorial information) rather
than peripheral cues (background color, colorful icons, and flashing images), while those
without a purchasing goal may be more affected by peripheral cues. In other words,
highly involved consumers may be influenced by central cues whereas low involved
consumers may be affected by peripheral cues. Thus, it is also essential to investigate
how the effects of different web cues on emotional states are changed by the levels of
situational or product involvement.
1.3. Purpose of the Study
The purpose of Study 1 is to 1) investigate the effects of peripheral cues presented
in the apparel websites on consumers’ emotional states (pleasure and arousal) under low
situational involvement, 2) examine the influence of product involvement (personal
relevance of clothing products) as a moderator of the relationship between peripheral
cues and emotional states (pleasure and arousal) under the low involvement situation, 3)
assess the influence of emotional states on consumer response behaviors (purchase
intention, and approach behaviors), and 4) examine the mediating effects of emotional
states on the relationships between peripheral cues and response behaviors (purchase
intention and approach behaviors).
7
The purpose of Study 2 is to 1) examine the effects of web cues—central cues
(product-related stimuli) and peripheral cues (stimuli not directly related to the
product)— on emotional reactions, 2) assess the influence of emotional states on
consumer response behaviors (satisfaction, purchase intention, and approach behaviors),
3) investigate the effects of situational involvement (e.g., purchase situation vs. browsing
situation) as a moderator of the relationship between web cues and emotional reactions
(pleasure and arousal); e.g., how central cues and peripheral cues influence consumers’
emotions under different levels of involvement, and 4) assess the mediating effects of
emotional states on the relationships between web cues and response behaviors
(satisfaction, purchase intention, and approach behaviors).
1.4. Significance of the Study
The findings from this study will provide valuable information for apparel online
retailers. Since it is impossible to closely examine apparel products using the tactile
sense in an online context, consumers planning to buy apparel items from online retailers
may rely on verbal or pictorial cues (central cues) that describe an apparel item. Due to
the inability to inspect items on the Internet, more descriptive central cues (high amount
of verbal information, larger views with various angles, and complete mix and match
suggestions) may help consumers evaluate apparel products before making an online
purchase decision. More detailed verbal information, larger views from various angles
(front, back, side, and detail views), and complete mix and match suggestions may induce
more pleasure and arousal that in turn influence consumers’ satisfaction, purchase
8
intention, and approach behaviors. This effect is expected to be stronger for consumers
in the high situational involvement (e.g., purchasing situation) than in the low situational
involvement (e.g., browsing situation). For consumers browsing the websites without a
purchasing goal, peripheral cues such as the presence of background color, colorful icons,
and flashing images may have significant effects on consumers’ pleasure and arousal that
consequently influence consumers’ satisfaction, purchase intention, and approach
behaviors.
Based on the results, this study will provide a comprehensive model that describes
the relationship among various web cues presented on apparel websites, emotional states
(pleasure and arousal) experienced while browsing the websites, and consumers’
response behaviors. The model will also explain how consumers in different situations
react to various web cues and how product involvement influences the relationship
between peripheral cues and consumer emotions. Therefore, the results of the study may
help online apparel retailers develop new strategies for visual merchandising of their
websites to attract various consumer groups in different situations or with different levels
of product involvement.
9
1.5. Definition of Terms
The following terms are used in this study and defined as follows:
1. Approach behaviors: In this study, approach behaviors are defined as desire to
shop or explore in the website or the likeability of the website.
2. Central cues: Central cues include both verbal and pictorial cues that could be
directly related to a purchasing goal (e.g., product related web cues and service
related web cues). In this study, central cues are manipulated by different
amounts of product related web cues.
3. Emotion: Emotion is defined as affective states that focus on pleasure and arousal
as expected reactions to various web stimuli that in turn influence consumer
response behaviors.
4. Internet browsing: Internet browsing is defined as information search activities on
the Internet without a particular purpose of product purchasing.
5. Internet purchasing or shopping: Internet purchasing or shopping is defined as an
online buying activity as a consequence of browsing the websites.
6. Peripheral cues: Peripheral cues include both verbal and pictorial cues that are not
directly related to the purchasing goal (e.g., brand logo, background color, icons,
and layout). In this study, peripheral cues are manipulated by the presence or
absence of background color, various colorful icons, and a flashing brand logo.
7. Product involvement: Product involvement is defined as the personal relevance of
clothing products in this study. It is more a permanent personal involvement than
situational involvement.
10
8. Product related web cues: Product related web cues are defined as the information
that describes an individual apparel item (e.g., product care information, fabric
information, color information, country of origin information, style information,
size information, fit information, larger views, mix and match, and price
information).
9. Purchase intention: In this study, purchase intention is defined as consumers’ plan
to buy apparel products from the websites. The decision may be affected by their
browsing or shopping experience in the apparel websites.
10. Satisfaction: In this study, satisfaction is defined as consumers’ behavioral
responses as a result of the fulfillment of their shopping or browsing experience at
the apparel website.
11. Service related web cues: Service related web cues are defined as the information
related to services provided from online stores such as delivery information,
contact information, customer services information, security information, return
policy information, and account information.
12. Situational involvement: Situational involvement is defined as personal relevance
for a specific situation. In this study, a purchasing situation is considered to
induce high situational involvement and browsing a website is considered to
induce low situational involvement (Eroglu et al., 2003).
11
CHAPTER 2
LITERATURE REVIEW
This chapter describes background literature, theoretical framework, and
hypotheses development for Study 1 and Study 2. In the first section, previous studies
addressing the effects of emotions induced by store or site environments on consumer
behaviors and reporting the effects of visual merchandising in retail stores and in the
websites are reviewed. In the second section of the chapter, the Stimuli-Organism-
Response paradigm (Mehrabian & Russell, 1974) and the Elaboration Likelihood Model
(Petty & Cacioppo, 1996) are discussed as theoretical frameworks for this study. In the
third section, hypotheses development is presented along with the proposed models for
Study 1 and Study 2.
12
2.1. Background Literature
2.1.1. Visual Merchandising in Store
According to textbook authors, visual merchandising is defined as “the
presentation of a store and its merchandise in ways that will attract the attention of
potential customers and motivate them to make purchases” (Diamond & Diamond, 2003,
p. 5). Before using the term, visual merchandising, display was used to describe the job
of a “window trimmer” who produced visual or artistic aspects of the merchandising
presentation (Cahan & Robinson, 1984; Diamond & Diamond, 2003). Today’s visual
merchandising emphasizes the total store environment including merchandise
presentation, store design and image, mannequins, props and materials, lighting, graphics,
and signage (Cahan & Robinson, 1984; Diamond & Diamond, 2003). Visual
merchandising is therefore concerned with both the visual and the marketing functions of
the store environment to enhance the store image and to increase sales (Cahan &
Robinson, 1984; Diamond & Diamond, 2003). Walters and White (1987) emphasized
that visual merchandising should arouse consumers’ interest about products and stimulate
them to buy more products. Using theme-oriented props in the retail store, visual
merchandising may increase mood which can influence purchasing (Tyreman & Walton,
1998). For example, Sears stores displayed men’s dress shirts with typewriters, globes,
and old law books to create the feeling of the professional office (Tyreman & Walton,
1998). Theme-oriented props help consumers understand how and/or where they will use
13
the products; the way the products are displayed influences consumers’ feeling about the
product (Tyreman & Walton, 1998).
In addition, visual merchandising must coordinate the entire merchandising, in-
store design, and space allocation (Walters & White, 1987). Sometimes, improper
elements incompatible with consumers’ expectations can hurt store image (Cahan &
Robinson, 1984; Walters & White, 1987). Thus, the quality of visual merchandising is
thought to be very important to increase sales as well as to enhance the image of the store
(Cahan & Robinson, 1984; Walters & White, 1987).
2.1.2. Visual Merchandising in Apparel Stores
Some researchers have also studied visual merchandising. Lea-Greenwood (1998)
emphasized the importance of visual merchandising as a strategic tool in fashion
marketing. Visual merchandising can attract consumers to enter stores and can
communicate brand image (Lea-Greenwood, 1998). Kerfoot, Davis, and Ward (2003)
investigated the effect of visual merchandising stimuli on consumers’ brand recognition,
liking for displays, browsing, and purchase intention in women’s retail apparel stores.
Through interviews they found a number of elements which influenced consumers’
perceptions of the apparel retail stores. The elements included merchandise color,
manner of presentation, awareness of fixtures, path finding, sensory qualities of materials
and the effects of lighting. The results showed that the coordination of merchandise color
influenced purchase intention. The use of a variety of colors was perceived as attractive
and appealing and it influenced respondents’ browsing tendency. In addition, four
14
different methods of presentation influenced respondents’ perceptions. Hanging
merchandise presentation was perceived as the most attractive presentation method
because it was visible, suggested mix and match items, and helped people visualize
outfits. Mannequin presentation also generated a positive perception in that it showed
entire designs and suggested how a garment might fit. However, a folding presentation
and the use of rails led to some negative perceptions in terms of difficulty of assessing.
Neatness of folded clothing made respondents hesitate to disturb the display. Also, the
tendency to browse and the perceptions of the quality of the store were related to path
finding. Path finding is “the provision of a clear route” and “noticeably affected
propensity to browse” (Kerfoot et al., 2003, p. 149). Path finding shows where to start
and provides a natural way to navigate in the store. Path finding is influenced by
merchandise density and display density. Respondents tended to perceive more browsing
and display space as indicative of better quality and more expensive brands. Sensory
quality of materials such as flooring, fixtures, and hangers and lighting also affected
respondents’ perceptions. The results showed that there was a significant relationship
between consumers’ perceptions of visual merchandising and the development of
approach and avoidance behavior (Kerfoot et al., 2003).
A recent study found significant effects for merchandise coordination on product
evaluation and purchase intention in the apparel retail store setting (Lam & Mukherjee,
2005). Well coordinated apparel items induced higher aesthetic response toward two
complementary products as a whole (enjoyable, nice-looking, pleasing, attractive, good
appearance, and beautiful) than poorly coordinated items. The study also revealed that
the social impressions (socially acceptable, fashionable, popular, high in status, desired
15
impression, and approved by others) of apparel items were influenced by the coordination
of apparel items. Well coordinated items conveyed a more positive impression than
poorly coordinated items. Aesthetic response and social impression had positive effects
on product evaluation (good, favorable, desirable, like, and very useful) that in turn
influenced consumers’ purchase intention for the product (Lam & Mukherjee, 2005).
Supporting the previous research (Kerfoot et al., 2003), the study suggests that well
coordinated apparel items may enhance consumers’ aesthetic response and product
evaluation and consequently, improve store/brand image and sales.
Sen, Block, and Chandran (2002) investigated the effect of window displays in
clothing stores on consumers’ shopping decisions such as store entry and product
purchase. The results showed that consumers looking for store image and product fit
information from window displays were more likely to enter a store than those looking
for merchandise, promotional, and fashion information from window displays. The study
suggests that window displays in clothing stores should present better product fit
information and convey store image to draw more customers into the store. However, in
terms of purchase behavior, consumers looking for fashion information and fit
information from displays tended to purchase products in the store, but consumers
looking for store image displays were less likely to buy products in the store. Results
indicate that consumers looking for product fit information (i.e., information that allows
consumers to assume the fit of the product with consumers’ physical or symbolic selves)
from window displays may enter the store to examine and purchase the products on
display while consumers looking for store image from displays may enter the store to
browse and gain additional information related to store image but may not purchase the
16
products (Sen et al., 2002). This result suggests that product fit information in displays is
the most important cue to draw more customers into a store as well as to make them
purchase in the store. Based on the results, Sen et al. (2002) recommend that clothing
retailers should use mannequins with idealized body types representing their target
consumers’ body types.
2.1.3 Visual Merchandising in General Websites
In the retail store, visual merchandising means floor layout, interior design,
signage, in-store promotion, and product mix that facilitate purchasing (Harris, 1998).
Applying this proven notion to the Internet environment can offer a completely new
standpoint for designing websites to be more profitable. Harris (1998) suggested some
ideas that give direction for applying those proven concepts in the retail store to website
designs. Instead of a floor plan and signage, online graphics, photos, and other design
elements can be used to attract customers to websites and to get them to the products.
Merchandise categories available on the homepage (e.g., the order of the list) may lead
customers in the right direction on the retail websites. In terms of display and music, a
large colorful photo image of the product and price presented right next to the image of
the product will attract customers’ attention and also music on the opening page can
create an exciting mood for the customers (Harris, 1998).
Rice (1997) researched website design factors that make customers keep returning
to a website. Based on a pretest, the survey finally had 12 questions that fell into two
areas: the evaluation of design and technology of the websites and the emotional
17
experience of the user during the visit. The survey also included a question about site
patronage intention and an open-ended question tapping the most important factors that
affect decisions to return or not return to the site. The results showed that making
websites with design a top priority was the most important factor to make people return
to the websites. The second was making people enjoy the websites: making it easy to
find what they were looking for and offering them a novel and entertaining experience.
The next factors in order were the quality of the organization/layout of the site, the degree
of uniqueness, ease of finding information, excitement, visual attractiveness, ease of
navigation, and the speed of the websites (Rice, 1997). The study offers some important
guidelines to make websites attract more attention from customers.
Szymanski and Hise (2000) studied determinants of customer satisfaction in
Internet shopping. The results showed that satisfaction with Internet shopping was
influenced by perceptions of site design and merchandising (including product assortment
and product information). More extensive and higher quality product information
affected consumers’ satisfaction in Internet shopping. Sites designed to be fast,
uncluttered, and easy to navigate played an important role in consumers’ satisfaction.
2.1.4. Visual Merchandising in Apparel Websites
For apparel related websites, the significance of the layout and design of the
websites has been emphasized by Then and Delong (1999) and Allen (2000). According
to Then and Delong, (1999), visual design on Internet apparel sites can be considered
analogous to retail store layout. Thus, the main goal for website design is similar to that
18
for store design in that both websites and stores want consumers to come in, to enjoy the
environment, and to purchase products.
Perceived risk related to the inability to try on apparel products before purchase is
a major concern for consumers when purchasing apparel in-home (Kim & Lennon, 2000;
Kwon et al., 1991; Park et al., 2005). Park et al. (2005) found that there was a negative
relationship between perceived risk and apparel purchase intention in Internet apparel
shopping. This means that if perceived risks are reduced in Internet apparel shopping,
then consumers’ purchase intentions will be increased. Kim and Lennon (2000) found
that the amount of product and service information was negatively related to perceived
risk and positively related to purchase intentions in a television shopping context.
Therefore, to reduce perceived risk and enhance purchase intentions in Internet shopping,
apparel websites should offer richer and more intensive product information using a
variety of sources of product presentation.
Then and Delong (1999) indicated that if Internet retailers offer more information
through the visual display of apparel products using a variety of images, then consumers
will purchase more apparel products in the Internet. Visual aspects of product
presentation such as images of the online product in its closest representation of end use,
displayed in conjunction with similar items, and from various angles such as front, back,
and side view can generate higher purchase intentions for consumers and in turn, increase
higher selling for e-business (Allen, 2000; Then & Delong, 1999).
Images including both static and kinetic graphics can make a website page look
more interesting (Rowley, 2002). Then and Delong (1999) also suggested that a three-
dimensional apparel display can be helpful in minimizing the uncertainties of apparel
19
shopping on the Internet. Apparel product presentation can be enhanced with 3D views
(Allen, 1999). Viewing apparel products in a variety of combinations can help
consumers examine how they might look wearing those items (Allen, 1999). Park et al.
(2005) tested the effects of product movement (kinetic image) in apparel websites on
mood, perceived risk, and purchase intention. The study found that people exposed to a
product in motion (kinetic image) tended to have more positive mood and greater
purchase intention than those exposed to the product not in motion (static image). The
results also showed that kinetic image reduced consumers’ perceived risk (Park et al.,
2005).
Apparel sites often have some mix and match suggestions to create a complete
look by combining pants, shirts, and accessories (Allen, 2000). J. Crew is the best
example of excellent merchandising in that they combine individual items to suggest a
complete look. Customers are not likely to purchase all those combined outfits but
seeing the suggested coordination may increase sales (Allen, 2000). Mix and match
suggestions for each item may increase consumers’ purchase intention in Internet
shopping (Then & Delong, 1999).
For apparel products, fabric and color information and product details are
considered important determinants of consumers’ response behaviors such as satisfaction,
site patronage, and purchase intentions. Kwon et al. (1991) suggested that to reduce
perceived risk for apparel products in catalog shopping, retailers should offer accurate
and complete pictures of products and describe products in detail especially for color,
texture, and fabric description. Fiore and Yu (2001) found that catalog pages with fabric
samples enhanced attitude toward the apparel product in comparison to those without
20
fabric samples. Inaccurate color information causes the loss of sales and increases
product returns and complaints in online apparel stores (Nitse, Parker, Krumwiede,
Ottaway, 2004). Consumers dissatisfied with apparel products delivered in a color that
was different from what was expected would not purchase products from online apparel
stores in the future (Nitse et al., 2004).
According to Park and Stoel (2002), more than two-thirds or more of the apparel
websites they analyzed provided some type of color information (21 out of 31 merchants
provided both visual and verbal color descriptions). However, sensory types of product
information were not really available in apparel websites. For example, fabric
construction (e.g., woven and knit), texture/fabric hand (comfortable, soft, and heavy),
and mix and match suggestions were offered by only about half or fewer of apparel
websites analyzed. In addition, only 55 % (17 out of 31) of the sites provided larger
views to see more detail and only one apparel site offered a 3D product rotation
presentation. Finally, this study suggested that sensory and experiential product
information should be added to apparel websites to increase sales (Park & Stoel, 2002).
Then and Delong (1999) indicated the importance of using human models for
apparel product presentation to show the natural drape of the garment on the human body.
Given choices, respondents chose mannequin display as the best, flat display as the
second, and sketches as the last for apparel product presentation on websites. This result
shows that if a human body is not available, then consumers will prefer mannequins, as
the best form for apparel product presentation. Thus, on apparel websites, a human
model, at least a mannequin, should be used for apparel product presentation to offer
better information to consumers and as a result, to improve their sales.
21
2.2. Theoretical Framework
2.2.1. S-O-R Paradigm
According to Mehrabian and Russell (1974), much of the research in
environmental psychology has focused on the effects of physical stimuli (e.g., things of
the everyday physical environment) on human emotions (e.g., pleasure, arousal, and
dominance, or ‘PAD’) and the effects of physical stimuli on a variety of behaviors (e.g.,
satisfaction, purchase intention, and approach-avoidance behavior). In terms of physical
stimuli, most research has relied on the sensory variables such as color, sound,
temperature, and texture. Consumer emotions are conceptualized as three dimensions:
pleasure, arousal, and dominance. Pleasure and arousal can be easily measured by self-
report or by observation of positive facial expressions. Emotional pleasure refers to the
degree to which a consumer feels happy, pleased, satisfied, contented, or hopeful while
arousal is consumer emotion that refers to the extent to which a consumer feels
stimulated, excited, or aroused. Dominance can be measured through verbal reports or by
observations of body posture or facial expression. Although the S-O-R paradigm
suggests three dimensions to measure consumer emotions, previous research has found
that dominance had only little or no effect on consumer behaviors (Donovan & Rossiter,
1982; Donovan et al., 1994) and therefore was not used in recent environmental research
(Eroglu et al., 2003; Menon & Kahn; 2002; Sherman, Mathur, & Smith, 1997). In
addition, because participants will be controlled by instructions in an experimental study,
it is expected that dominance experienced by participants will not differ by various web
22
cues. Thus, in the present study pleasure and arousal will be used to measure consumer
emotions felt by people while browsing the websites.
As mediators, these emotional states cause various consumer response behaviors
(Mehrabian & Russell, 1974). Physical stimuli (e.g., store design) in the environment
influence consumer emotions (e.g., pleasure and arousal) that serve as mediating
variables in determining a variety of consumer response behaviors such as satisfaction,
purchase intention, and approach behaviors in a retail setting. A high level of pleasure
and arousal elicited by environmental stimuli in retail stores and in websites enhance
satisfaction (Eroglu et al., 2003; Machleit & Mantel, 2001; Spies, Hesse, & Loesch,
1997), purchase intention (Babin & Babin, 2001; Fiore et al., 2005; Spies et al, 1997),
and approach behaviors such as desire to explore and desire to shop (Eroglu et al., 2003;
Menon & Kahn, 2002).
Mehrabian and Russell (1974) proposed the theoretical framework with the
outline of the important variables that take place in most situations; it is called the Stimuli
(S)—Organism (O)—Response (R) paradigm (See Figure 2.1). This S-O-R paradigm has
been examined and developed by researchers applying it to the retail context (Baker et al.,
1992; Baker, Parasuraman, & Grewal, 2002; Bitner, 1992; Buckley, 1991; Donovan &
Rossiter, 1982; Donovan et al., 1994; Spies et al., 1997), who found that the paradigm
worked well in retail situations. Applying the S-O-R model to an Internet shopping
context, a recent study (Eroglu et al., 2003) found that the effects of consumer emotions
as intervening variables between various web cues and consumer response behaviors in
an Internet retail setting are analogous to those in an in-store retail shopping context.
Emotions (pleasure and arousal) induced by atmospheric stimuli presented in the website
23
had a significant effect on consumer response behaviors such as satisfaction and approach
behaviors (Eroglu et al., 2003).
Stimulus Organism Response
Figure 2.1. S-O-R paradigm (Mehrabian & Russell, 1974).
The Effects of Emotions on Consumer Behaviors: The Effects of Store Environment
Several researchers have examined the S-O-R model in retail store environments
and found that consumers’ emotions induced by environmental stimuli influenced
consumers’ response behaviors such as satisfaction, purchase intention, and approach
behaviors. Higher levels of pleasure and arousal from the store environment enhanced
shoppers’ purchase intentions (Fiore & Kimle, 1997). Donovan and Rossiter (1982)
Environmental stimuli -- Things of the everyday physical environment --sensory cue (color, music, temperature)
Emotional Responses Pleasure Arousal Dominance
Behavioral Responses Approach-avoidance behavior
24
tested the S-O-R model in retail settings, especially focusing on three emotional
experiences—pleasure, arousal, and dominance—and suggested that this framework is a
good starting point for studying consumer response behaviors in the retail context. They
found that retail environmental stimuli influenced consumers’ emotions such as pleasure,
arousal, and dominance and these emotional experiences were powerful determinants of
consumer behaviors in the retail store. Pleasure induced by store stimuli such as lighting
and music had an effect on consumer behaviors including positive attitudes toward store
environment, enjoyment of shopping, intention to revisit the store, intention to spend, and
intention to browse more. In a later study, Donovan et al. (1994) extended Donovan and
Rossiter’s (1982) study in that the later one used a broader sample, measured emotions
during the shopping rather than after the shopping, and recorded the effects of the store
stimuli on actual shopping behaviors rather than behavioral intention. They found that
pleasure elicited by the store environment was a significant determinant of unplanned
time spent in the store and unplanned purchasing in the store (Donovan, et al., 1994).
Baker et al. (1992) examined various aspects of store atmospherics—ambient
cues and social cues—and their effects on the retail patronage decision based on the S-O-
R model. They found that ambient factors such as music and lighting significantly
influenced consumers’ pleasure for the low social factor (e.g., only one employee) but not
for the high social factor (e.g., three employees and greeting of one employee). Social
cues alone influenced consumers’ arousal in the store. These affective states—arousal
and pleasure—had a great impact on consumers’ purchase intentions (Baker et al., 1992).
Sherman et al. (1997) studied the effects of various stimuli (social factor, overall
image, design factor, and ambient factor) in apparel retail stores on consumer emotions
25
(pleasure and arousal) which in turn influence consumers’ purchasing behavior. The
results showed positive effects of social factors and design factors on pleasure and a
positive effect of ambient factors on arousal. Consumer emotions induced by a variety of
factors in the store environment enhanced the amount of money and time spent in the
store and the number of items purchased in the store (Sherman et al., 1997).
Color is one of the most important design elements to communicate style and
mood in retail store environments since it is the first thing a customer notices in the store
(Colborne, 1996). Exciting colors attract our eye and make a store look alive (Colborne,
1996). Color in retail stores appears to influence consumer emotions such as pleasure
and arousal (Bellizi & Hite, 1992; Crowley, 1993) that in turn influence consumer
behavioral intentions. Several studies have been conducted to examine the influence of
store colors on consumers’ shopping behaviors such as approach behavior and purchase
intention (Babin et al., 2003; Bellizi, Chrowley, & Hasty, 1983). Consumers’ evaluations
and excitement induced by the color of store environments were positively related to
store patronage intentions and purchase intentions (Babin et al., 2003).
The Effects of Emotions on Consumer Behaviors: The Effects of Site Environment
Based on the S-O-R model suggested by Mehrabian and Russell (1974), Menon
and Kahn (2002) examined the effects of consumers’ emotions, induced by atmospheric
stimuli in websites, on consumers’ later shopping behaviors. The results showed that if
the websites created pleasure for consumers, then there was a positive effect on approach
behaviors (e.g., browsing more, engaging in unplanned purchasing, and seeking out more
stimulating products). The study suggested some important insights for website
26
designers and Internet retailers. Accordingly, Internet retailers should be concerned
about consumers’ emotions stimulated by the initial website/page since the initially
encountered webpage and products may shape consumers subsequent shopping behaviors
(Menon & Kahn, 2002).
Eroglu et al. (2003) examined the S-O-R model suggested by Eroglu, Machleit,
and Davis (2001) in online store environments. They created two new apparel websites
(including t-shirts and sweatshirts with custom imprinting options), one only with high
task relevant cues and another with both low task relevant and high task relevant cues.
The two websites basically contained the same high task relevant cues but one website
added low task relevant cues such as a dark green (instead of black color) for the text, a
pale gray background for a sweatshirt with the logo on it (instead of no background),
photographs of the designers with their profiles, an affiliation graphic, and animated
Visa/Mastercard logo on the ordering page. Results showed that site atmospheric cues
(background color, text color, and other colorful graphics) influenced the level of
pleasure which in turn influenced consumer attitude. In addition, consumer emotions
(pleasure and arousal) as mediators had significant effects on response behaviors such as
satisfaction (including likelihood of revisit) and approach behaviors (intention to stay and
explore).
In the online apparel shopping context, consumers’ emotions elicited by product
presentation (Park et al., 2005) and image interactivity (Fiore, Jin, & Kim, 2005) also
influence consumer response behaviors. Park et al. (2005) found that a positive mood
induced by product movement (product views in motion) shown in apparel websites
influenced purchase intention. Fiore et al.’s (2005) study revealed that image
27
interactivity was a stimulating experience while browsing apparel websites and positively
influenced consumer emotions (pleasure and arousal) that consequently affected
consumers’ approach behaviors.
2.2.2. Elaboration Likelihood Model: Involvement and Persuasion
Websites as Persuasion
Advertisements primarily serve two basic roles: to inform and to persuade (Singh
& Dalal, 1999). In other words, advertisements provide information related to a
particular product or service that in turn persuades consumers to buy the product or
service. Websites as a mass medium resemble traditional advertisements in that they
perform the same fundamental roles (to inform and to persuade) (Joines, Scherer, &
Scheufele, 2003; Singh & Dalal, 1999). Websites are designed to provide information
and consequently to persuade consumers to purchase products from the websites. The
ability to purchase products is a unique advantage of websites (Joines et al., 2003). The
function of some websites is informational, while others have a commercial purpose and
sell products in addition to providing information. In both cases websites serve as a form
of advertising (Berthon, Pitt, & Watson, 1996; Joines et al., 2003; Singh & Dalal, 1999).
Using the Internet consumers can access and browse websites from any place at any time
and purchase products directly from commercial websites. Based on the assumption that
online purchasing is analogous to persuasion situations, the Elaboration Likelihood
Model (ELM) (Petty & Cacioppo, 1996) developed in the context of persuasion, may
provide some useful insights for research in online shopping contexts.
28
Central and Peripheral Routes
According to the ELM (Petty & Cacioppo, 1996), there are two different routes to
persuasion or attitude change—a central and a peripheral route. Under the central route
people’s attitude change will depend on cognitive processing while under the peripheral
route people’s attitude change will depend on minimal cognitive processing. Therefore,
central route processing tends to create more permanent attitude change than peripheral
route processing. The peripheral route is not very permanent nor very successful for
changing attitudes especially under high involvement conditions (Petty & Cacioppo,
1996).
Under the high involvement condition central cues such as the number of issue-
relevant arguments in the message influences attitude change but source credibility as a
peripheral cue has no significant effect. However, under the low involvement condition
attitude change is determined basically by peripheral cues such as attractiveness of the
source, but not by the number of issue-relevant arguments in the message. Persuasion
arises from careful thinking about the issue or message via the central route and
persuasion results from non-issue-relevant cues such as source attractiveness via the
peripheral route (Petty & Cacioppo, 1996). The effects of different types of involvement
on persuasion have also been studied in the consumer behavior context (Zaichkowsky,
1986).
Involvement
Zaichkowsky (1986) categorized the antecedents of involvement into three
factors: person factors, stimulus factors, and situation factors. Personal factors include
29
one’s inherent needs, importance, interest, and values related to a particular object.
Different people may have inherently different levels of involvement for a particular
product (i.e., product involvement). Stimulus factors are related to the physical
characteristics of the stimulus such as differentiation of alternatives, source of
communication, and content of communication. Situational factors also influence the
levels of involvement. Different situational factors such as a purchasing vs. no
purchasing situation may have an impact on consumer’s level of involvement related to a
particular product. When a purchase is perceived as important, consumers may be
motivated to make a careful decision based on the quality of information (Zaichkowsky,
1986). For example, consumers may pay greater attention to car information on
advertisements if they are thinking of buying a car. However, if consumers are not
looking for a new car, then they may not pay attention to car advertisements.
Zaichkowsky (1985) developed the personal involvement inventory (PII) with 20 items to
measure different levels of involvement. Although these items were originally developed
to measure personal involvement with products, the study suggested that it can be used to
measure situational involvement. Previous research (Garlin & McGuiggan, 2002; Shao,
Baker, & Wagner, 2004; Stafford & Stern, 2002; Zaichkowsky, 1986; Zaichkowsky,
1994) demonstrated the sensitivity of the measure toward different types of situational
involvement (e.g., purchase vs. no purchase) for the same product.
Product involvement
Product involvement is a more enduring involvement (Wells & Prensky, 1996)
and levels of involvement with a same product vary greatly across people (Zaichkowsky,
30
1985). Therefore, consumers with high product involvement experience constant high
involvement with a particular product category (e.g., clothing product) as a result of
personal factors that generate greater needs, importance, interest, and value related to the
product (Zaichkowsky, 1986). For example, consumers with greater interest, needs,
value, and importance for clothing products have an enduring involvement with clothing
products. By contrast, the average or low involved consumers become highly involved
with clothing products only when they are in a specific purchasing situation, but are less
involved in clothing products when they are not in a purchasing situation. Kinley,
Conrad, and Brown (1999) found that consumers who had a high level of clothing
involvement were more likely to use a magazine, a television, a store window, and in-
store displays to obtain information related to clothing products than those who had a low
level of clothing involvement.
In comparison to low involved consumers, high involved consumers were found
to search for more information about the product before they purchased to process
relevant information in detail (Petty, Cacioppo, & Schumann, 1983; Petty & Cacioppo,
1996; Zaichkowsky, 1985). Macias (2003) found that consumers with high product
involvement had higher comprehension of a website than those with low product
involvement. Because interactivity is often thought to be more effortful, consumers with
high product involvement are more likely to interact with features of a website (e.g.,
clicking icons in order to request more information) than those with low product
involvement (Cho, 1999, 2003; Macias, 2003). Cho’s (1999) study further revealed that
consumers with low product involvement tended to click a banner ad in a larger size or
with dynamic animation more than one of average size or with no dynamic animation.
31
However, the size of ads or dynamic animation had no effect for consumers with high
product involvement (Cho, 1999). The results support the ELM developed by Petty and
Cacioppo (1996). Consumers in the low involvement condition were more likely to
attend to peripheral cues such as icons with dynamic animation, image sizes, and
background color, while those in the high involvement condition tended to have high
motivation to request more information and contents related to the product (central cues)
in order to examine the product in detail (Petty, et al., 1983; Petty & Cacioppo, 1996).
Consumers with low product involvement are less likely to seek and utilize information
and to compare different brands than those with high product involvement (Mittal, 1989)
Situational involvement
Situational involvement such as purchase or usage occasions tends to enhance the
level of involvement with a particular product (Wells & Prensky, 1996). Consumers who
are highly involved with a particular situation (e.g., browsing with a purchasing goal)
may engage in different behaviors in comparison to those who are low involved with the
situation (e.g., browsing without a purchasing goal). People are more motivated to
allocate the cognitive effort required to evaluate the true merits of an issue or a product
under a high involvement situation rather than under a low involvement situation (Petty,
et al., 1983; Petty & Cacioppo, 1996). Attention and elaboration are expected to be
enhanced by increasing the personal relevance of the products at a particular time and
situation (Petty et al., 1983; Zaichkowsky, 1986).
Previous research examined the moderating effect of situational involvement in
brand evaluation or attitude (Karson & Korgaonkar, 2001; Kokkinaki & Lunt, 1999;
32
Maoz & Tybout, 2002; Park & Hastak, 1995), in advertising effectiveness (Dotson &
Hyatt, 2000; Petty et al., 1983), in expectations of service quality (Shao et al., 2004), and
in consumer emotions (Eroglu et al., 2003; Mano, 1997). Park and Hastak (1995) studied
the effects of involvement as a moderator on persuasion during message exposure. The
results showed that involved subjects tended to spend a longer amount of time for brand
evaluation than uninvolved subjects. Involved subjects remembered more information in
the advertisement than uninvolved subjects. In addition, message quality had a greater
impact on brand attitude under the high involvement situation rather than under the low
involvement situation, while source credibility had a greater impact on brand attitude
only under the low involvement situation (Park & Hastak, 1995).
Dotson and Hyatt (2000) found that high involved people had a more favorable
attitude toward the brand advertised with strong arguments and had greater purchase
intentions for the ad with strong arguments. The results are consistent with the prediction
of the ELM that under high involvement situations people process product information
more carefully than under low involvement situations (Dotson & Hyatt, 2000).
Argument quality as a central cue has more effect on persuasion under high situational
involvement, whereas expertise or an attractive model as a peripheral cue has more effect
on persuasion under low situational involvement (Petty et al., 1983; Petty & Cacioppo,
1984). Petty et al. (1983) found that argument quality tended to be a more important
determinant of purchase intentions under high situational involvement than under low
situational involvement. Involvement also influenced consumers’ recall of brand name.
Strong arguments increased brand name recall and brand name recognition under high
involvement. However, under low situational involvement consumers were more likely
33
to recall the brand name of the product when they were exposed to famous endorsers
(Petty et al., 1983).
Shao et al., (2004) investigated the effects of appropriateness of service contact
personnel dress on expectations of service quality. Consistent with the ELM, the study
found that the appropriateness of personnel dress as a peripheral cue had a greater impact
on consumer expectations of service quality and purchase intention under the low
involvement condition rather than under the high involvement condition (Shao et al.,
2004).
In Internet apparel shopping contexts, Eroglu et al.’s (2003) study supports the
tenets of the ELM in that only under the low involvement situation low task relevant cues
(peripheral cues such as background color, text color, animated icons) influenced
consumer emotions. However, under the high involvement situation low task relevant
cues (peripheral cues) had no effects on consumer emotions (Eroglu et al., 2003). The
results demonstrated the effects of situational involvement as a moderator on the
relationship between peripheral cues and consumer emotions.
34
2.2.3. Applying the S-O-R Paradigm and Involvement into Internet Shopping Context
Online store environments are different from traditional store environments.
Applying the S-O-R paradigm into an online shopping context, Eroglu et al. (2001)
suggested a conceptual model for atmospheric qualities of online retailing. Eroglu et al.
(2001) developed different terms to explain online store environments using the S-O-R
paradigm. The stimuli on the Internet consist of visible and audible cues to Internet
shoppers. Those cues may be divided into those that are high task relevant and those that
are low task relevant. High task relevant cues include site descriptors such as verbal and
pictorial information directly relevant to consumers’ shopping goal. Information on the
products, price, sale, delivery, and return policies are examples of verbal content related
to shopping goals. Pictures of the products and navigational support (e.g., site map,
search tool, menu bar of page) are other examples of high task relevant cues in an
Internet shopping context. Whereas low task relevant cues include site information not
directly relevant to shopping goals such as colors, borders and background patterns, fonts,
animation, music, icons, pictures for decorative purposes, and even amount of white
space. Low task relevant cues can create a mood or image of the site and can make the
verbal content easy or difficult to read. Verbal content directly related to the shopping
goal is typically the most task relevant cue in the Internet. However, in the case of
clothing, a picture of the product is also a very important high task relevant cue because
consumers buy clothing based on style or design (Eroglu et al., 2001).
As a whole, these task relevant cues will influence shoppers’ affect and cognition
that finally influence their shopping outcomes (Eroglu et al., 2001). Affective states have
35
three different dimensions—Pleasure, Arousal, and Dominance (PAD)—as expected
reactions to the stimuli (Mehrabian & Russell, 1974). Eroglu et al. (2001) add the
cognitive state to the model that describes consumer’s internal mental processes and
includes attitudes, beliefs, attention, comprehension, memory, and knowledge. In the
Internet shopping context, the cognitive state concerns “how online shoppers interpret
information provided on the screen, choose from alternative sites and products as well as
their attitudes toward the virtual stores, and so forth (Eroglu et al., p. 181, 2001).”
During the shaping of these attitudes, Internet shoppers deal with questions about
whether they like Internet shopping as an alternative shopping method and whether they
like the processes of Internet shopping. Therefore, affective and cognitive states serve as
mediators between atmospheric stimuli of the online store and consumers’ behavioral
responses (Eroglu et al., 2001).
In addition, Eroglu et al. (2001) add two moderators between the S-O
relationships in the online shopping context. Those are 1) involvement—the degree of
personal relevance—and 2) atmospheric responsiveness—the tendency to be influenced
by the qualities of immediate physical environment. The idea of involvement is also
found in the ELM proposed by Petty et al. (1983). High task relevant cues influence
highly involved shoppers to pursue a central cognitive process that creates a positive
affective and attitudinal state. Low involved shoppers may be more interested in and
influenced by the low task relevant cues (Eroglu et al., 2001). This means that high task
relevant cues may require more thinking to process information about the products than
low task relevant cues. Therefore, under the high involved Internet apparel shopping
condition (e.g., browsing with a purchasing goal), persuasion may arise from thinking
36
about the high task relevant cues that influence consumers’ emotional states that in turn,
influence consumers’ shopping behaviors such as satisfaction, purchase intention,
approach behaviors. Under the high involved shopping condition, people may have more
favorable attitudes toward the apparel website with high task relevant cues in comparison
to the website with low task relevant cues, while under the low involvement condition
people may have more favorable attitudes toward low task relevant cues presented in the
apparel website.
As responses to the S-O-R paradigm, Eroglu et al. (2001) suggest different
avoidance-approach behaviors in the context of Internet shopping. Approach behaviors
consist of positive outcomes such as intention to stay, explore, spend more money, and
revisit the sites. On the other hand, avoidance behaviors are negative outcomes such as a
desire to leave and not return (Eroglu et al., 2001). Figure 2.2 shows the S-O-R paradigm
suggested by Eroglu et al. (2001) to explain the role of atmospheric characteristics in the
online store setting. In a later study, Eroglu et al. (2003) empirically tested this model
and the results supported this S-O-R model in online store environments.
Eroglu et al. (2003) investigated the effects of atmospheric web cues (low task
relevant cues) on affective states (pleasure and arousal) that affect cognitive states
(attitude) and examined both affective and cognitive states as mediators between
atmospheric web cues and consumer behavioral responses (satisfaction and approach
behavior). They also assessed the moderating effects of involvement and atmospheric
responsiveness between S-O. The results showed a positive effect of atmospheric web
cues on pleasure that in turn positively influenced consumer attitude toward the website.
Both affective (pleasure and arousal) and cognitive states (attitude) had positive impacts
37
on consumer response behaviors such as satisfaction and approach behaviors. The results
also showed that the effect of the atmospheric web cues (low task relevant cues) on
consumer response behaviors were mediated by the emotions felt by consumers. High
levels of pleasure and arousal elicited by site atmospheric cues increased consumers’
satisfaction and approach behaviors. In addition, the relationship between web cues and
consumer emotions were moderated by involvement. The effect of atmospheric web cues
(low task relevant cues) on pleasure was significant only under the low involvement
condition (e.g., browsing without a purchasing goal) (Eroglu et al., 2003).
Applying the S-O-R paradigm and the ELM to the Internet shopping context the
model proposed and tested by Eroglu et al. (2001, 2003) provides valuable insight into
the effects of peripheral cues (low task relevant cues) on consumer emotions, thereby
influencing consumer response behaviors. Although the importance of central cues (high
task relevant cues: web cues directly related to consumer’s purchasing goal) in increasing
purchase intention for and satisfaction with apparel in the online context has been
emphasized in previous research (Allen, 2000; Eroglu et al., 2001; Park & Stoel, 2002;
Then & Delong, 1999), the effects of central cues on the emotions and behavioral
responses were not empirically tested in Eroglu et al.’s (2003) study.
38
Stimulus Organism Response
Figure 2.2. S-O-R paradigm in the online shopping context (Eroglu et al., 2001, p. 179).
Online Environment
Cues • High Task Relevant • Low Task Relevant
Internal States
• Affect • Cognition
Shopping Outcomes
• Approach • Avoidance
Involvement
Atmospheric Responsiveness
39
2.3. Proposed Models and Hypotheses
According to the S-O-R paradigm (Mehrabian & Russell, 1974), stimuli in
websites influence consumers’ emotions in terms of pleasure and arousal that in turn
influence consumers’ response behaviors such as satisfaction, purchase intentions, and
approach behaviors (e.g., desire to explore or shop). The ELM explains the effects of
involvement on persuasion and attitude change (Petty & Cacioppo, 1996). According to
the ELM, under a high involvement condition, central cues (e.g., product related web
cues) provided in the websites will affect persuasion, whereas under a low involvement
condition, peripheral cues will affect persuasion. Thus, the present study hypothesizes
that consumers under the high involvement condition will likely be affected by central
cues (e.g., different product views such as front, side, back, and details, the amount of
verbal information, and mix and match suggestions) shown in apparel websites, while
those in the low involvement condition will likely be affected by peripheral cues (e.g.,
colorful icons and background color) in the websites.
Blending the S-O-R paradigm and the ELM, the present study hypothesizes 1)
different effects for central and peripheral web cues on consumer emotions that in turn,
influence consumer response behaviors (satisfaction, purchase intention, and approach
behaviors) and 2) the effect of involvement (product involvement and situational
involvement) as a moderator of the S-O relationship.
The current research consists of two studies. Study 1 proposes 1) the effects of
peripheral cues presented in the apparel websites on consumers’ emotions (pleasure and
arousal) under low situational involvement, 2) the influence of product involvement
40
(personal relevance of clothing products) as a moderator of the relationship between
peripheral cues and emotions (pleasure and arousal), 3) the impact of emotions on
consumer response behaviors (purchase intention, and approach behaviors), and 4) the
mediating effects of consumer emotions between peripheral cues and response behaviors
(purchase intention and approach behaviors). Figure 2.3 describes the hypotheses and the
proposed model for Study 1.
The second study suggests 1) the effects of web cues—central cues (product-
related web cues) and peripheral cues (web cues not directly related to product)— on
consumer emotions, 2) the influence of emotions on consumer response behaviors
(satisfaction, purchase intention, and approach behaviors), 3) the effects of situational
involvement (purchase situation vs. browsing situation) as a moderator of the relationship
between web cues (central cues and peripheral cues) and consumer emotions (pleasure
and arousal), and 4) the mediating effects of emotions between web cues and response
behaviors (satisfaction, purchase intention, and approach behaviors). Hypotheses (See
Table 2.1) and proposed models for Study 1 (Figure 2.3) and Study 2 (Figure 2.8) will be
developed and discussed in detail in the following sections.
41
2.3.1. Main Study 1
The Effects of Peripheral Cues on Emotional States
According to the S-O-R paradigm (Mehrabian & Russell, 1974), stimuli in
websites influence consumers’ emotions in terms of pleasure and arousal. Previous
research supports the effects of stimuli in retail stores (Babin et al., 2003; Baker et al.,
1992; Bellizi & Hite, 1992; Crowley, 1993; Donovan & Rossiter, 1982; Donovan et al.,
1994) and websites (Eroglu et al., 2003; Fiore et al., 2005; Menon & Kahn, 2002) on
consumers’ emotions. Store environmental cues such as music (Baker et al., 1992;
Donovan & Rossiter, 1982; Donovan et al., 1994), lighting (Baker et al., 1992; Donovan
& Rossiter, 1982; Donovan et al., 1994), and color (Babin et al., 2003; Bellizi & Hite,
1992; Crowley, 1993) influence consumers’ pleasure and arousal.
In an online shopping context, web environmental cues such as background color,
text color, animated logo, moving image, and image interactivity influence consumers’
pleasure and arousal while they are browsing the websites (Eroglu et al., 2003; Fiore et
al., 2005; Menon & Kahn, 2002). According to the ELM, these web cues are categorized
as peripheral cues that influence consumer emotions under low involvement situations
(Petty & Cacioppo, 1996). Therefore, based on previous research, the following
hypothesis was developed.
Hypothesis 1. Under the low involvement situation site atmospheric cues (peripheral
cues) will influence emotional reactions.
42
Hypothesis 1a. As compared to those exposed to the website without peripheral
cues, those exposed to the website with peripheral cues will experience more
pleasure.
Hypothesis 1b. As compared to those exposed to the website without peripheral
cues, those exposed to the website with peripheral cues will experience more
arousal.
The Moderating Effect of Product Involvement between S-O
Consumers high in product involvement have greater needs and interest in a
particular product, rate it as more important, and assign greater value to it than those low
in product involvement (Zaichkowsky, 1986). For example, consumers high in clothing
involvement may have consistent needs and interest in clothing, rate it as more important,
and assign greater value to it across situations. As a type of enduring involvement (Wells
& Prensky, 1996), product involvement may influence consumers’ attention to web cues
while browsing apparel websites. For example, highly involved consumers search for
more information about the product (Petty et al., 1983; Petty & Cacioppo, 1996;
Zaichkowsky, 1985).
Consumers high in product involvement are more likely to interact with website
features (e.g., by clicking icons in order to obtain more information) than those low in
product involvement (Cho, 1999, 2003; Macias, 2003). Supporting the ELM, Cho (1999)
found that consumers with low product involvement tended to click a large banner ad or
one with dynamic animation more than an average size banner ad or one with no dynamic
animation. However, the size of ads or dynamic animation had no effect for consumers
43
high in product involvement (Cho, 1999). Consumers in the low involvement condition
tended to attend to peripheral cues such as icons with dynamic animation, larger image
sizes, and background color, while those in the high involvement condition tended to be
affected by central cues such as product or service related information (Petty, et al., 1983;
Petty & Cacioppo, 1996). Consumers with a low level of product involvement are less
likely to seek and utilize information and to compare different brands than those with a
high level of product involvement (Mittal, 1989). Hence, based on previous research, it
is hypothesized that peripheral cues will have a greater effect on consumers with a low
level of product involvement rather than those with a high level of product involvement.
This rationale led to the second hypothesis.
Hypothesis 2. Product involvement (i.e., personal relevance of clothing products) will
moderate the relationship between peripheral cues and emotional reactions.
Hypothesis 2a. Peripheral cues will have a stronger effect on pleasure for people
with low product involvement than those with high product involvement.
Hypothesis 2b. Peripheral cues will have a stronger effect on arousal for people
with low product involvement than those with high product involvement.
44
The Effects of Emotional States on Response Behaviors
According to Mehrabian and Russell (1974), consumer emotions as mediators
affect various consumer response behaviors (Mehrabian & Russell, 1974). High levels of
pleasure and arousal induced by the environmental stimuli enhance purchase intention
(Babin & Babin, 2001; Baker et al., 1992; Spies et al, 1997) and approach behaviors such
as desire to explore and desire to shop (Eroglu et al., 2003; Menon & Kahn, 2002).
Previous research examined the influence of store colors on consumers’ shopping
behaviors such as approach behavior and purchase intention (Babin et al., 2003; Bellizi,
Chrowley, & Hasty, 1983). Consumers’ evaluations and excitement induced by the color
of store environments were positively related to store patronage intentions and purchase
intentions (Babin et al., 2003). Applying the S-O-R paradigm to the Internet apparel
shopping context, Eroglu et al. (2003) found significant effects of emotions (pleasure and
arousal) on consumer approach behaviors. Based on this literature the third and fourth
hypotheses were developed.
Hypothesis 3. Emotional states such as pleasure and arousal experienced from the
apparel website will influence purchase intention.
Hypothesis 3a. Pleasure will be positively related to purchase intention.
Hypothesis 3b. Arousal will be positively related to purchase intention.
Hypothesis 4. Emotional states such as pleasure and arousal experienced from the
apparel website will influence approach behaviors.
45
Hypothesis 4a. Pleasure will be positively related to approach behaviors (desire
to explore or shop and likability of the websites).
Hypothesis 4b. Arousal will be positively related to approach behaviors (desire to
explore or shop and likability of the websites).
The Mediating Effects of Emotional States between S-R
The S-O-R paradigm illustrates the mediating effects of consumer emotions
between environmental stimuli and consumer response behaviors (Mehrabian & Russell,
1974). Based on the S-O-R paradigm, previous research investigated the effects of
consumers’ emotions as mediators between atmospheric stimuli and consumers’
shopping behaviors (Eroglu et al., 2003; Menon & Kahn, 2003; Sherman et al, 1997;
Spies et al., 1997). Pleasure and arousal tend to mediate the relationship between
atmospheric stimuli and consumer purchasing behaviors in retail stores (Sherman et al,
1997) and in online stores (Eroglu et al., 2003; Menon & Kahn, 2003). Consumer
emotions elicited by atmospheric stimuli positively influenced approach behaviors (e.g.,
browsing more, engaging in unplanned purchasing, and seeking out more stimulating
products) in the Internet shopping context (Eroglu et al., 2003; Fiore et al., 2005; Menon
& Kahn, 2003). This suggests that Internet retailers should think about consumers’
emotions experienced while browsing the websites that may shape consumers’
subsequent shopping behaviors. Therefore, it is predicted that consumer emotions
(pleasure and arousal) will mediate the relationship between peripheral cues presented in
apparel websites and consumer response behaviors such as purchase intention and
approach behaviors. This argument led to the development of the following hypothesis.
46
Hypothesis 5. Emotional states such as pleasure and arousal will mediate the
relationship between peripheral cues and consumers’ response behaviors (purchase
intention and approach behaviors).
47
Figure 2.3. The proposed model in the main study 1.
Peripheral Cues
Arousal
Pleasure
Product Involvement
Approach Behavior
Patronage Intention H1a
H1b
H2a and H2b
H3a
H4a
H4b
H3b
H5
Stimulus Organism Response
48
2.3.2. Main Study 2
Part I: The Effects of Web Cues on Emotional States
The S-O-R paradigm developed by Mehrabian and Russell (1974) describes the
effects of various stimuli available in retail stores and in websites on consumers’
emotions. Supporting the S-O-R paradigm, Donovan and Rossiter (1982) found that
retail environmental stimuli such as lighting and music influenced consumers’ emotions
such as pleasure and arousal. In a later study, using a broader sample Donovan et al.
(1994) measured emotions during shopping rather than after shopping as Donovan and
Rossiter had done and found a significant effect for store environment on consumer
emotions.
Various stimuli (social factors, overall image, design factors, and ambient factors)
available in apparel retail stores tend to increase consumers’ pleasure and arousal
(Sherman et al., 1997). As one of the most important design elements that communicates
style and mood in retail store environments, color influenced consumer emotions such as
pleasure and arousal in retail stores (Bellizi & Hite, 1992; Crowley, 1993).
Given that the online store environment is different from a traditional store
environment, Eroglu et al. (2001) developed different terms to explain online store
stimuli—high task relevant cues and low task relevant cues. High task relevant cues
include site descriptors such as verbal and pictorial information directly relevant to
consumers’ purchasing goal. Information related to the products, price, sale, delivery,
and return policies, pictures of the products, and navigational support (e.g., site map,
49
search tool, menu bar of page) are examples of high task relevant cues in the Internet
shopping context. Low task relevant cues include web cues not directly relevant to
shopping goals, for example colors, borders and background patterns, fonts, animation,
music, icons, pictures for decorative purpose, and even amount of white space. Verbal
content directly related to the shopping goal are typically the most task relevant online
cues. However, in the case of clothing, a picture of the product is also a very important
high task relevant cue because consumers buy clothing based on style or design (Eroglu
et al., 2001). In relation to the ELM (Petty & Cacioppo, 1996), high and low task
relevant cues can be categorized as central cues and peripheral cues, respectively. As a
whole, these web cues (both central cues and peripheral cues) may influence shoppers’
pleasure and arousal (Eroglu et al., 2001). In a later study, Eroglu et al. (2003) found a
positive effect for atmospheric web cues on consumer emotions (pleasure and arousal) in
the apparel online shopping context. Based on the S-O-R paradigm and previous
research, it is hypothesized that the amount of central cues1and the presence or absence of
peripheral cues presented in apparel websites will influence consumer pleasure and
arousal experienced while browsing the websites (See Figure 2.4 for the proposed model
in Part 1). This rationale led to the development of the following hypotheses.
1 All websites must have a minimal amount in order for consumers to buy
50
Hypothesis 1. Central cues (e.g., number of different product views-front, back, sides,
details, amount of verbal information, and amount of mix and match suggestions) will
influence consumers’ emotional reactions experienced from an apparel website.
Hypothesis 1a. As compared to those exposed to the website with a medium
amount of central cues, consumers exposed to the website with a high amount of
central cues will experience more pleasure.
Hypothesis 1b. As compared to those exposed to the website with a medium
amount of central cues, consumers exposed to the website with a high amount of
central cues will experience more arousal.
Hypothesis 2. Peripheral cues (e.g., pictorial icons and background colors) will
influence consumers’ emotional reactions experienced from the apparel website.
Hypothesis 2a. As compared to those exposed to the website without peripheral
cues, consumers exposed to the website with peripheral cues will experience more
pleasure.
Hypothesis 2b. As compared to those exposed to the website without peripheral
cues, consumers exposed to the website with peripheral cues will experience more
arousal.
51
Stimulus Organism
Figure 2.4. Part one of the model in Study 2 (Hypotheses 1 and 2).
Central Cues
Peripheral Cues Arousal
Pleasure H1a
H1b
H2a
H2b
52
Part II: The Effects of Emotional States on Response Behaviors
According to the S-O-R paradigm, consumer emotions cause various consumer
response behaviors (Mehrabian & Russell, 1974). Pleasure and arousal induced by the
store or site environmental cues enhance satisfaction (Eroglu et al., 2003; Machleit &
Mantel, 2001; Spies et al, 1997), purchase intention (Babin & Babin, 2001; Baker et al.,
1992; Spies et al, 1997), and approach behaviors such as desire to explore and desire to
shop (Eroglu et al., 2003; Menon & Kahn, 2002). For example, consumers’ evaluations
and excitement elicited by the colors of store environments were positively related to
store patronage intentions and purchase intentions (Babin et al., 2003). Positive emotions
induced by the servicescape influenced behavioral intentions (Hightower, Brady, &
Baker, 2002). Consumers with a positive mood elicited by the pleasant store
environment were likely to spend more money in the store and were highly satisfied with
the store (Spies et al., 1997). Applying the S-O-R paradigm to the Internet apparel
shopping context, Eroglu et al. (2003) investigated the effects of consumer emotions on
consumer satisfaction and approach behaviors (desire to explore or shop and likability of
the websites). High levels of pleasure and arousal induced by various web cues tend to
increase consumers’ satisfaction and approach behaviors (Eroglu et al., 2003).
Accordingly, it is predicted that consumer emotions such as pleasure and arousal will be
positively related to various consumer response behaviors (satisfaction, purchase
intention, and approach behaviors) (see Figure 2.5 of the proposed model for Part 2).
Based on this rationale, the following hypothesis was developed.
53
Hypothesis 3. Emotional states will be positively related to consumers’ response
behaviors.
Hypothesis 3a. Pleasure will be positively related to consumers’ satisfaction.
Hypothesis 3b. Pleasure will be positively related to consumers’ purchase
intention.
Hypothesis 3c. Pleasure will be positively related to consumers’ approach
behaviors (desire to explore or shop and likability of the websites).
Hypothesis 3d. Arousal will be positively related to consumers’ satisfaction.
Hypothesis 3e. Arousal will be positively related to consumers’ purchase
intention.
Hypothesis 3f. Arousal will be positively related to consumers’ approach
behaviors (desire to explore or shop and likability of the websites).
54
Organism Response
Figure 2.5. Part two of the model in Study 2 (Hypotheses 3a to 3f).
Purchase Intention
Arousal
Pleasure
Approach behavior
Satisfaction H3a
H3b
H3d
H3c
H3e
H3f
55
Part III: The Moderating Effect of Situational Involvement between S-O
According to the ELM (Petty & Cacioppo, 1996), there are two different routes to
persuasion—a central and a peripheral route. Persuasion arises from careful thinking
about an issue or message via the central route or from non-issue-relevant cues such as
source attractiveness via the peripheral route (Petty & Cacioppo, 1996). Under the high
involvement condition central cues such as the number or strength of issue-relevant
arguments in the message influence persuasion but source credibility as a peripheral cue
has no significant effect. Under the low involvement condition persuasion is determined
basically by peripheral cues such as attractiveness of the source, but not by the number of
issue-relevant arguments in the message (Petty & Cacioppo, 1996).
The level of involvement is influenced by different situations (Wells & Prensky,
1996). For example, consumers who browse apparel websites with a purchasing goal
may have higher situational involvement than consumers who browse websites without a
purchasing goal. People are more motivated to allocate the cognitive effort required to
evaluate the true merits of a product under the high involvement situation rather than
under the low involvement situation (Petty et al., 1983; Petty & Cacioppo, 1996).
Attention is expected to be enhanced by increasing the level of involvement at a
particular time and situation (Petty et al., 1983; Zaichkowsky, 1986).
Supporting the ELM, previous research has found that situational involvement
moderated the effect of central cues and peripheral cues on brand evaluation or attitude
(Karson & Korgaonkar, 2001; Kokkinaki & Lunt, 1999; Maoz & Tybout, 2002; Park &
Hastak, 1995), on advertising effectiveness (Dotson & Hyatt, 2000; Petty et al., 1983), on
56
expectations of service quality (Shao et al., 2004), and on consumer emotions (Eroglu et
al., 2003; Mano, 1997). Park and Hastak (1995) studied the effects of involvement as a
moderator on persuasion during message exposure and found that message quality as a
central cue had a greater impact on brand attitude under the high situational involvement
condition rather than under the low situational involvement condition, while source
credibility as a peripheral cue had a greater impact on brand attitude only under the low
situational involvement condition (Park & Hastak, 1995). Under high involvement
situations, people process product information more carefully than under the low
involvement situation (Dotson & Hyatt, 2000). Increasing consumer’s involvement
causes them to attend to central cues and affects attitude formation (Petty & Cacioppo,
1996).
Thus, central cues have more effect on persuasion in the high situational
involvement condition, whereas peripheral cues have more effect on persuasion in the
low situational involvement condition (Petty et al., 1983; Petty & Cacioppo, 1984;).
Consistent with the ELM, Shao et al. (2004) found that the appropriateness of personnel
dress as a peripheral cue had a greater impact on consumer expectations of service quality
and purchase intention under low involvement rather than under high involvement (Shao
et al., 2004). Eroglu et al.’s (2003) study also supports the prediction of the ELM in that
only under low situational involvement, low task relevant cues (peripheral cues such as
background color, text color, animated icons) influenced consumer emotions. However,
under high situational involvement, low task relevant cues (peripheral cues) had no effect
on consumer emotions (Eroglu et al., 2003). Based on the ELM and previous research, it
is hypothesized that central cues will have a greater impact on consumer pleasure and
57
arousal under high situational involvement rather than under the low situational
involvement. In addition, in the low situational involvement condition, peripheral cues
will have a greater influence on consumer emotions (pleasure and arousal) (Figure 2.6
describes the proposed model in Part 3). Based on this rationale the following hypothesis
was developed.
Hypothesis 4. Situational involvement will moderate the relationship between web cues
and emotional reactions.
Hypothesis 4a. Central cues will have a stronger effect on emotional reactions
under a high involvement situation than under a low involvement situation.
Hypothesis 4b. Peripheral cues will have a stronger effect on emotional reactions
under a low involvement situation than under a high involvement situation.
58
Stimulus Organism
Figure 2.6. Part three of the model in Study 2 (Hypotheses 4a to 4b).
Central Cues
Peripheral Cues
Arousal
Pleasure
H4a and H4b
Situational Involvement
59
Part IV: The Mediating Effects of Emotional States between S-R
The S-O-R paradigm predicts the mediating effects of consumer emotions
between environmental stimuli and consumer response behaviors (Mehrabian & Russell,
1974). According to the S-O-R paradigm, environmental stimuli (e.g., store design)
influence consumer emotions (e.g., pleasure and arousal) that serve as mediating
variables in determining various consumer response behaviors such as satisfaction,
purchase intention, and approach behaviors in retail stores (Mehrabian & Russell, 1974).
Supporting the S-O-R paradigm, previous research revealed significant mediating effects
of consumers’ emotions between environmental cues (in stores and in websites) and
consumers’ response behaviors (Eroglu et al., 2003; Menon & Kahn, 2003; Sherman et al,
1997; Spies et al., 1997).
In the Internet shopping context, Eroglu et al. (2001, 2003) predicted that various
web cues (central and peripheral cues) would affect consumer emotions that consequently
influence consumer response behaviors, such as satisfaction and approach behaviors.
Eroglu et al. (2003) found that the effect of web cues on consumer response behaviors
were mediated by consumer emotions experienced while browsing the websites. Pleasure
and arousal elicited by various web cues presented in websites positively influenced
satisfaction, purchase intention, and approach behaviors (e.g., browsing more, engaging
in unplanned purchasing, and seeking out more stimulating products) (Eroglu et al., 2003;
Fiore et al., 2005; Menon & Kahn, 2003). This suggests that Internet apparel retailers
should consider emotions felt by consumers while browsing the websites that may
influence consumers’ consequent shopping behaviors.
60
Based on the S-O-R paradigm and previous research, it is proposed that consumer
emotions such as pleasure and arousal will mediate the relationship between type of cue
(central and peripheral cues) and consumers’ response behaviors (satisfaction, purchase
intention, and approach behaviors) (see Figure 2.7 for the proposed model for Part 4 in
Study 2). Based on the previous rationale, the following hypothesis was developed.
Hypothesis 5. Emotional states will mediate the relationship between type of cue and
consumers’ response behaviors.
Hypothesis 5a. The relationships between central cues and response behaviors
(satisfaction, purchase intention, and approach behaviors) will be mediated by
emotional states.
Hypothesis 5b. The relationships between peripheral cues and response behaviors
(satisfaction, purchase intention, and approach behaviors) will be mediated by
emotional states.
61
Figure 2.7. Part four of the hypothesized model for Study 2 (Hypotheses 5a and 5b).
Central Cues
Peripheral Cues
Purchase Intention
Arousal
Pleasure
Situational Involvement
Approach behavior
Satisfaction
H5a and H5b
Stimulus Organism Response
62
Figure 2.8. The proposed model for Study 2.
Central Cues
Peripheral Cues
Purchase Intention
Arousal
Pleasure
Situational Involvement
Approach behavior
Satisfaction H1a
H1b
H2a
H2b
H4a and H4b
H3a
H3b
H3d
H3c
H3e
H3f
Part Four
Part One Part Two
Part Three
H5a and H5b
Stimulus Organism Response
63
Hypotheses
Study 1 Study 2
As compared to those exposed to the website without peripheral cues, those exposed to the website with peripheral cues will experience more pleasure.
H1a H2a
As compared to those exposed to the website without peripheral cues, those exposed to the website with peripheral cues will experience more arousal.
H1b H2b
As compared to those exposed to the website with a medium amount of central cues, consumers exposed to the website with a high amount of central cues will experience more pleasure.
N/A H1a
As compared to those exposed to the website with a medium amount of central cues, consumers exposed to the website with a high amount of central cues will experience more arousal.
N/A H1b
Peripheral cues will have a stronger effect on pleasure for people with low product involvement than those with high product involvement.
H2a N/A
Peripheral cues will have a stronger effect on arousal for people with low product involvement than those with high product involvement.
H2b N/A
Pleasure will be positively related to consumers’ satisfaction.
N/A H3a
Pleasure will be positively related to purchase intention.
H3a H3b
Pleasure will be positively related to approach behaviors.
H4a H3c
Arousal will be positively related to consumers’ satisfaction.
N/A H3d
Arousal will be positively related to purchase intention.
H3b H3e
Arousal will be positively related to approach behaviors.
H4b H3f
Central cues will have a stronger effect on emotional reactions under a high involvement situation than under a low involvement situation.
N/A H4a
Peripheral cues will have a stronger effect on emotional reactions under a low involvement situation than under a high involvement situation.
N/A H4b
Emotional states such as pleasure and arousal will mediate the relationship between central cues and consumers’ response behaviors.
N/A H5a
Emotional states such as pleasure and arousal will mediate the relationship between peripheral cues and consumers’ response behaviors.
H5 H5b
Table 2.1. Summary of Hypotheses in Study 1 and Study 2.
64
CHAPTER 3
PILOT STUDIES
This chapter describes pilot studies conducted to develop manipulations for the
two main studies. Three pilot studies and one content analysis were conducted to select
appropriate stimuli (e.g., apparel items, situational involvement, central cues, and
peripheral cues) for the two main experiments. The purpose of the first pilot study was to
develop and examine the situational involvement manipulations, the purpose of the
second pilot study was to select apparel stimuli for Study 1 and Study 2, and the purpose
of the third pilot study and content analysis were to develop manipulations for central
cues. Figure 3.1 presents the summary of each pilot test in detail.
65
Pilot Study 1: Develop Manipulations for Situational Involvement
1. Develop situational involvement manipulations based on previous research (Eroglu et al., 2003; Zaichkowsky, 1986) and check the effectiveness.
2. Check web cues that gain more attention under different situational involvements.
Pilot Study 2: Select Apparel Stimuli
1. Select apparel stimuli for Study 1 (2 items) and Study 2 (5 items). 2. Check web cues that gain more attention when purchasing clothing online
(high involvement situation) and compare the results with Pilot Study 1. 3. Check the popular apparel websites among participants for Content
Analysis.
Pilot Study 3: Prepare for Content Analysis
1. Based on Pilot Study 1, Pilot Study 2, and previous research (Eroglu et al., 2001), the list of web cues representing both product related central cues and peripheral cues was developed.
2. Check the extent to which the web cues were product related. 3. Results of Pilot Study 3 were used to develop a coding frame for Content
Analysis.
Content Analysis: Develop Manipulations for Central Cues
1. Develop a coding frame representing product related central cues based on Pilot Study 3.
2. Fifteen apparel websites listed in Pilot Study 2 were content analyzed. 3. Based on the results, central cues (medium amount vs. high amount) were
selected for Study 1 and Study 2.
Figure 3.1. A summary of pilot tests.
66
3.1. Pilot Study 1
Pilot Study 1 was conducted to develop and test situational involvement
manipulations and to determine which web cues are attended to in different involvement
situations. According to Zaichkowsky (1986), consumers use different cues to evaluate
products depending on their level of involvement. Eroglu et al. (2001) proposed that
under a high involvement situation (e.g., online apparel purchasing) consumers use high
task relevant cues such as verbal content or pictures of the merchandise related to
shopping goals (i.e., central cues). The authors further suggest that under the low
involvement situation (e.g., online browsing without a purchasing goal) consumers may
be more influenced by low task relevant cues such as colors, icons, and decorative
pictures (i.e., peripheral cues) not directly related to the purchasing goal. To check which
web cues gain more attention under different levels of situational involvement,
participants were asked to list what they recalled from the website after browsing.
The design of Pilot Study 1 was a between-subjects experiment with one factor
(situational involvement) having two levels (low vs. high). One British apparel website
(www.asos.com) was selected to use as a template for Pilot Study 1 to eliminate the
effects of participants’ previous experiences with U.S. apparel websites on the results.
The pilot study was conducted in a laboratory setting with computers. Seventy four
students from a consumer science class participated in Pilot Study 1 for extra credit.
Participants were randomly assigned to one of two treatment groups (high vs. low
involvement situation).
67
Participants were asked to read a scenario (high or low involvement situation)
before they started browsing. According to Zaichkowsky (1986), different situational
factors such as purchasing (high involvement) versus no purchasing (low involvement)
may have an impact on consumer’s level of involvement. Eroglu et al. (2003)
manipulated situational involvement with two different scenarios: a purchasing situation
and a browsing situation. Thus, based on previous research two different scenarios
manipulating situational involvement were created in Pilot Study 1. For the low
involvement situation, participants were asked to read the following scenario: “Imagine
that you find a clothing website today. Now, you are going to visit one clothing website.
Browse and look around the website for a while,” and to fill out a survey. Participants in
the high involvement situation read the following scenario: “Now, you are going to visit
one clothing website. Imagine that you have been given a $100 gift certificate to
purchase apparel products from the website. Remember! After browsing the website, you
should be able to identify and describe two items that you would like to purchase from
the website.” See Appendix A for more information.
Since situational involvement was manipulated in the pilot study, participants
were asked to rate their level of involvement while browsing the website using 7-point
scales (1-not at all and 7-very much so). The mean scores for low involvement (N = 31)
and high involvement (N = 43) were 5.31 and 5.72 respectively. Univariate analysis of
variance (F (1, 72) = 4.183, p < .05) revealed that participants in the high involvement
situation felt more involved than those in the low involvement situation. Therefore, the
results indicated that the manipulation for situational involvement was successful.
68
To determine web cues which participants paid attention to under different levels
of situational involvement (high or low) participants were asked to list what they
remembered after browsing the website. Types of recalled web cues were classified as
central or peripheral cues based on previous research (Eroglu et al., 2001). Eroglu et al.
(2001) indicated that a picture of products, a verbal description of products, size
information, the price, and terms of ordering and shipping could be central cues directly
relevant to consumers’ shopping goal (e.g., buying a pair of pants) that in turn influence
consumers’ emotions and behaviors. Whereas more decorative web cues such as
beautiful background colors and graphics, graphical images, fonts, animation, music, and
icons, and pictures other than products could be peripheral cues not directly related to
consumers’ shopping goals. Central cues were further classified as service related or
product related. Table 3.1 shows the list of web cues mentioned by participants in low or
high involvement situations. Number of web cues recalled were summed and entered
into chi-square difference tests to check for significant differences between the two
involvement groups (See Table 3.1). Under the high involvement situation, participants
tended to recall more web cues relevant to a purchasing goal (i.e., central cues such as
service and product related information), whereas participants under the low involvement
situation were more likely to remember web cues related to peripheral cues such as
different kinds of icons and the layout of the website. According to Zaichkowsky, 1986),
under the high involvement situation (e.g., purchasing) consumers are more likely to pay
attention to central cues (e.g., the quality of information) rather than peripheral cues (e.g.,
attractive model). Results of Pilot Study 1 also showed that participants recalled more
cues related to products rather than cues related to services in both low and high
69
involvement situations. Chi-square difference tests showed significant differences
between the number of recalled product related central cues and service-related central
cues in both low (χ2 = 11.842, p < .005) and high involvement (χ2 = 23.701, p < .001).
This could indicate that consumers are more likely to pay attention to product related
cues rather than service related cues.
70
Web Cues Frequency
High involvement
(N = 39)a
Low involvement
(N = 31)
χ2(1)
Central Cues
Service related account info 9 1 4.764* company info 4 2 n/a
contact info 5 1 n/a customer service 5 3 n/a delivery info 17 11 .284 Faq (Frequently asked questions) 2 0 n/a return policy 3 4 n/a security info 3 1 n/a
Total 48 23 4.069*
Product related colors for products 21 10 1.818 descriptions of products 16 5 3.569 different views of products (larger views, back views)
7 7 .185
fabric 6 2 n/a pictures of products 11 2 4.401* prices 17 8 1.529 size (size, size chart) 10 9 .073 mix and match suggestions 17 7 2.223 wash, care instruction 4 3 n/a
Total 109 53 8.789** Peripheral Cues
Web cues not related to a purchasing goal
brand logo 2 4 n/a layout 2 7 n/a icons (France icon, in stock icon, check out icon, payment icon)
2 15 13.308***
news (news letter subscription) 3 3 n/a Total 9 29 15.800***
Note. a Different sample size for the high involvement treatment group is due to missing data, * p < .05, ** p < .01, *** p < .001 Table 3.1. Frequencies of web cues listed in Pilot Study 1.
71
3.2. Pilot Study 2
The second pilot study was conducted to select appropriate apparel items for the
mock apparel websites used in Study 1 and Study 2. The purpose of the second pilot
study was twofold. The first was to choose apparel stimuli that minimized variance as a
result of style. Since the study is interested in consumers’ responses to the various web
cues (central cues and peripheral cues) during shopping or browsing, apparel items
should be in fashion and attract the participants of the study (i.e., young female college
students) to keep them browsing and shopping. In the high involvement situation (e.g.,
purchasing situation) participants were asked to select one item that they would like to try
or buy from the website. The use of unattractive products could have influenced the
results of the study. Therefore, choosing apparel items that were appealing to
participants was important. The second purpose of this pilot study was to check web cues
that attract attention when purchasing clothing products online (i.e., high involvement
situation) and compare with the results of Pilot Study 1.
Images of 34 pairs of pants were downloaded from several commercial apparel
websites (See Appendix C). All items were presented on a mannequin form (lower torso
only) and images were prepared and edited using Adobe Photoshop 7 to retain the
consistency of image quality in terms of resolution, background color, brightness, and
contrast. Any noticeable brand logos were removed from the images to avoid any
possible effects of brand on the results of the study. All items were shown in front and
back views on white background and the resolution of images was retained to be 300 x
360 pixels. Thirty four pairs of pants were evaluated to select seven final apparel items
72
for the two main studies (two items for Study 1 and five items for Study 2) (See
Appendix C).
Pilot Study 2 was conducted as an online survey (See Appendix B). In addition to
evaluating the pants, respondents indicated whether or not they had purchased clothing
online, websites they had purchased clothing from (open-ended format), and what they
paid attention to on clothing websites (open-ended format). One hundred twenty seven
female undergraduate students from two consumer science classes participated in the
pilot study for extra credit. Thirty four pairs of pants were randomly ordered in the
survey. Participants viewed all 34 pairs and evaluated each pair in terms of attractiveness,
fashionability, and likability using 7-point unipolar scales (e.g., Unattractive – Attractive).
Reliabilities of the three items for all 34 apparel items were found to be adequate
(Cronbach’s αs > .86) (See Table 3.2). Scores for the three items were summed to select
the final apparel items for Study 1 and Study 2. Five apparel items with the highest mean
and median scores were selected for Study 2 and two items with the next two highest
scores were selected for Study 1 (See Table 3.2 and Appendix C).
Eighty-one participants had purchased clothing from the Internet and 114
participants had browsed for clothing on the Internet. Participants who had purchased
clothing from online stores were asked to write what they paid attention to on the apparel
websites when they purchased clothing on the Internet. Table 3.3 describes web cues
mentioned by participants. Thirteen out of 16 items mentioned were types of product
related web cues such as color of products, style, fit information, and fabric. The other
three items, not product related, were brand name, security of websites, and shipping
information which could also influence consumers’ behavior when purchasing clothing
73
products online. A chi-square difference test was performed to check if the difference
between the number of product related web cues and other web cues listed in Pilot Study
2 was significant and it was found to be significant, χ2 = 8.895, p < .01. Results of Pilot
Study 1 (See Section 3.1 and Table 3.1) and Pilot Study 2 (Table 3.3) show that
participants are more likely to attend to product related central cues rather than service-
related central cues in purchasing situations (i.e., high involvement situation).
Participants were also asked to list the apparel websites from which they had
purchased clothing products. Table 3.4 shows the list of the popular apparel websites
provided by participants. Content analysis of these apparel websites was conducted to
identify central cues used on existing apparel websites (See Section 3.1.4).
74
Apparel
Items
Cronbach’s α
Mean
Median
S.D.
Min.
Max.
Item 1 ** .86 14.20 14.00 3.45 3 21 Item 2 * .95 15.58 16.00 3.66 3 21 Item 3 .96 10.14 10.00 4.61 3 21 Item 4 .96 12.41 12.00 4.24 3 21 Item 5 .97 11.43 11.00 5.14 3 21 Item 6 * .95 14.96 15.00 3.95 3 21 Item 7 * .96 14.81 15.00 4.17 3 21 Item 8 .96 12.48 12.50 4.43 3 21 Item 9 * .97 14.52 16.00 5.01 3 21 Item 10 .96 11.39 12.00 4.62 3 21 Item 11 .98 11.50 11.00 5.78 3 21 Item 12 * .97 14.33 15.00 4.63 3 21 Item 13 .95 12.53 13.00 6.01 3 21 Item 14 .97 12.21 12.00 5.20 3 21 Item 15 .94 7.47 6.00 4.79 3 21 Item 16 .95 8.17 6.00 4.83 3 21 Item 17 .97 13.25 14.00 5.70 3 21 Item 18 ** .96 13.53 14.00 4.70 3 21 Item 19 .96 13.10 14.00 5.14 3 21 Item 20 .95 11.49 12.00 5.06 3 21 Item 21 .95 11.36 11.00 5.98 3 21 Item 22 .97 8.93 8.00 4.70 3 21 Item 23 .92 7.18 5.00 4.87 3 21 Item 24 .96 12.29 12.00 4.90 3 21 Item 25 .96 11.11 10.00 6.27 3 21 Item 26 .94 5.74 3.00 4.27 3 21 Item 27 .96 11.67 12.00 5.10 3 21 Item 28 .97 9.01 9.00 4.72 3 21 Item 29 .97 11.11 12.00 5.90 3 21 Item 30 .95 11.16 12.00 4.43 3 21 Item 31 .97 12.22 12.00 5.75 3 21 Item 32 .97 9.67 10.00 5.27 3 21 Item 33 .97 11.75 12.50 5.39 3 21 Item 34 .95 9.80 10.00 5.63 3 21
Note. * Items selected for the main study 2, ** Items selected for the main study 1. Table 3.2. Descriptive statistics and reliabilities of apparel items selected in Pilot Study 2 for Study 1 and Study 2.
75
Web Cues Frequencies
Brand name 3 Color of products 15 Descriptions (details, cut) 10 Detail pictures (larger views in many angles and pictures of products) 10 Fit information (fit on the model) 12 Materials (fabric) 10 Price 25 Product variety 1 Product availability 1 Quality of products 5 Sales 9 Security of websites 2 Shipping information 3 Size (size chart, measurement) 15 Style (style, look, design, fashion) 18 Theme (mix and match) 2
Table 3.3. Web cues listed in Pilot Study 2 that participants reported paying attention to when they purchased clothing online.
Websites
Frequencies
www.victoriasecret.com 36 www.gap.com 17 www.jcrew.com 14 www.abercrombie.com 13 www.delias.com 13 www.bananarepublic.com 12 www.nordstrom.com 11 www.ae.com 9 www.urbanoutfitter.com 9 www.forever21.com 8 www.bebe.com 8 www.alloy.com 7 www.guess.com 6 www.oldnavy.com 6 www.ardenb.com 5
Table 3.4. The popular apparel websites participants listed in Pilot Study 2.
76
3.3. Pilot Study 3
The purpose of Pilot Study 3 was to check if the web cues classified as product
related by participants in Pilot Studies 1 and 2 are actually perceived to be product related
by a different set of participants. The result of Pilot Study 3 was used to develop a coding
frame representing product related central cues for content analysis of popular websites
(See Section 3.1.4). First, based on Pilot Study 1 (Table 3.1), Pilot Study 2 (Table 3.3),
and previous research (Eroglu et al., 2001), 22 web cues representing both central cues
(16) and peripheral cues (6) were generated (See Table 3.5). The web cue mentioned as
‘descriptions of products’ in Pilot Studies 1 and 2 was subdivided as pockets, waist,
inseam, and enclosure details to be more specific. Then, the 22 web cues were evaluated
in terms of the extent to which they were product related. According to Pilot Studies 1
and 2, people tend to pay more attention to product related central cues (e.g., fabric, color
of products, design details etc.) rather than service related central cues (e.g., return policy,
shipping and delivery information). In addition, since the focus of the main study was on
products, central cues related to products rather than services were evaluated in Pilot
Study 3 and the results were used to develop a coding frame for content analysis.
Pilot Study 3 was conducted as an online survey (Appendix D). Fifty nine female
undergraduate students in a consumer science class participated in the survey for extra
credit. Using 5-point Likert-type scales with endpoints ranging from 1 (very unlikely) to
5 (very likely) participants were asked to indicate the extent to which a list of web cues
were product related. Results are shown in Table 3.5.
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As expected, product related central cues classified in Pilot Studies 1 and 2 were
evaluated as more product related (see Table 3.5) than any of the web cues. Mean and
median scores for all product related web cues were close to 4 or above, with detail views
as the highest, whereas mean and median scores for peripheral web cues were close to 3
or lower. Results of this pilot study suggest that 16 web cues (e.g., detail views, price
information, design details, waist, fit information, inseam, style information, color
information, size information, product care information, fabric information, enclosure
details, larger views, side views, mix and match suggestions, and originality of the
product) are types of product related central cues and that 6 web cues (e.g., placement of
images, images other than products on the background, colors used on the website, font
size, background color, font color) are types of peripheral web cues (Table 3.5). The 16
product related central cues were used to develop a coding frame for content analysis
(See Section 3.1.4).
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Web Cues Mean
Median S.D. Min. Max.
detail views 4.61 5.00 .68 2 5 price information 4.60 5.00 .78 2 5 design details (e.g., pockets) 4.60 5.00 .78 2 5 waist (e.g., low or high) 4.58 5.00 .78 2 5 fit information 4.56 5.00 .76 2 5 inseam (e.g., length) 4.54 5.00 .76 2 5 style information 4.52 5.00 .74 3 5 color information 4.51 5.00 .89 2 5 size information 4.49 5.00 .78 3 5 product care information (e.g., machine wash, hand wash etc.) 4.49 5.00 .93 1 5
fabric information 4.47 5.00 .93 2 5 enclosure details (e.g., zipper, buttons) 4.47 5.00 .89 2 5
larger views 4.35 5.00 .88 2 5 side views 4.33 5.00 .87 2 5 mix and match suggestions (e.g., suggested coordinated items such as sweaters, handbag, shoes etc.)
3.93 4.00 1.04 1 5
originality of the product (e.g., country of origin) 3.67 4.00 1.17 2 5
placement of images 3.07 3.00 1.25 1 5 images other than products on the background 2.86 3.00 1.20 1 5
colors used on the website 2.63 2.00 1.21 1 5 font size 2.40 2.00 1.33 1 5 background color on the website 2.40 2.00 1.18 1 5 font color 2.39 2.00 1.32 1 5
Table 3.5. The extent to which web cues are product related rated in Pilot Study 3.
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3.4. Content Analysis
To finalize selection of appropriate central cues for the main experiments and to
increase reality of the mock websites, existing apparel websites were analyzed to check
which types of product related central cues are available in existing apparel online stores.
This information was used to develop mock websites for the main studies. The 15
popular apparel websites identified in Pilot Study 2 were analyzed in terms of the
existence of various product related central cues.
A coding frame with 15 categories for content analysis was developed based on
web cues reflecting product related central cues rated in Pilot Study 3. Design details
(pockets and other details) and larger views (front, back, and side view) were divided into
subcategories (See Table 3.6). Thus, the final coding frame had 18 categories (See Table
3.6).
Two graduate students were trained by the researcher and coded 15 U.S. apparel
websites according to the coding frame working independently for the entire coding
procedure. First, coders logged onto each apparel website and then went to the ‘women’s
pants’ section. Next, they clicked on each pair of pants to go to the individual product
page. On each product page, coders used the categories established in the coding frame.
They were asked to browse at least ten pairs of pants, if available, on each apparel
website to check the consistency of the content across product pages. Each category
could be coded as ‘present’ or ‘absent’. If more than five product pages contained the
same content, then those were coded as ‘present’. Reliability of coding was calculated
using Perreault and Leigh’s (1989) reliability index which takes chance agreement into
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account (Kolbe & Burnett, 1991). The inter-coder reliability between the two coders
was .97 (97 %). Disagreements between the two coders were due to one coder’s failure
to find an occurrence of a particular content. After the coding was complete, results from
the two coders were compared and disagreements were discussed. After the discussion,
agreements between the two coders reached 100%.
Ten categories of web cues were present on 10 or more of the content analyzed
websites (see Table 3.6). Those 10 (out of the 18) categories of web cues, most frequently
presented in existing apparel websites, were used to create a baseline amount of central
cues for the mock websites used in Study 2. These cues were: color information,
enclosure details, fabric information, inseam (length), front larger view, one mix and
match item, country of origin, price information, size information, and style information.
All 18 categories of web cues were used in the high amount of central cues condition.
Thus, the mock websites with the medium amount of central cues contained 10 types of
product related web cues while the mock websites with the high amount of central cues
contained all 18 types of product related web cues. Although mix and match suggestions
were presented in more than 14 of the analyzed apparel websites, only seven websites
provided mix and match suggestions for all products. The number of suggested items for
each product varied from one item to three items and those items could be tops, shoes,
handbags, and other accessories. The other seven websites presented mix and match
suggestions for selected items only. Due to the inconsistencies in products for which mix
and match suggestions were presented and also in numbers of mix and match suggestions
within and across the websites, ‘mix and match suggestions’ in the mock websites used in
Study 2 were manipulated by the number of suggested items (one for the medium amount
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of central cues and three for the high amount of central cues). So both conditions had
some level of mix and match items suggested.
As shown in Table 3.7, the number of available product related web cues in the
product page for each apparel website was also counted. The Jcrew website contained
the largest amount of product related web cues (N = 18) in the product page followed by
Bananarepublic website (N = 17). These two websites are examples of websites with a
high amount of central cues. Abercrombie, alloy, ardenb, delias, and bebe are examples
of the websites with a medium amount of central cues in the product page. When
consumers are shopping for apparel products, they like to physically inspect the products
in terms of fit, color, size, design, and fabric. Due to the nature of the online apparel
purchase process, the inability to examine apparel products and the uncertainty about
color, fabrics, and fit can cause high risk perceptions related to in-home shopping
(Bhatnagar, Misra, & Rao, 2000). Previous research suggested that in nonstore shopping
ample product related information should be provided by using a variety of sources (Kim
& Lennon, 2000; Then & Delong, 1999). Thus, commercial apparel websites should
provide at least a medium amount of product related central cues to reduce uncertainty
about the products and consequently to sell products. As shown in the results of the
content analysis, the popular commercial apparel websites listed in Pilot Study 2 offer at
least a medium amount of central cues (e.g., delias, bebe, Abercrombie, alloy, and
ardenb). To increase the reality of the two main studies, the mock websites used in Study
1 and Study 2 contained at least a medium amount of product related central cues.
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Coding categories Frequencies (N = 15)
Color information 12 * Design details (e.g., pockets) Pockets 7 Other details
(belt loops, stitching)
6
Detail views (e.g., close-up views for front, back, and side, zoom options) 4
Enclosure details (e.g., zipper, buttons) 11 * Fabric information 15 * Fit information 7 Inseam (e.g., length) 11 * Larger views Front 15 * Back 7 Side 1 Mix and match suggestions (e.g., suggested coordinated items such as sweaters, handbag, shoes etc.)
14* Consistent a = 7
Inconsistent b = 7 Originality of the product (e.g., country of origin) 14 * Price information 15 * Product care information (e.g., machine wash, hand wash etc.) 9
Size information 15 * Style information 12 * Waist (e.g., low or high) 6
Note. a all product pages have at least one mix and match suggestion, b some product pages have mix and match suggestions and others do not, * items included in the mock websites for Study 2 with the medium amount of central cues. Table 3.6. Coding categories used in Content Analysis and frequencies of the product related web cues presented in apparel websites.
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Websites
Number of product related information cues
www.jcrew.com 18 www.bananarepublic.com 17 www.gap.com 13 www.nordstrom.com 13 www.ae.com 13 www.urbanoutfitter.com 13 www.forever21.com 13 www.oldnavy.com 13 www.victoriasecret.com 12 www.guess.com 11 www.delias.com 10 www.bebe.com 10 www.abercrombie.com 9 www.alloy.com 9 www.ardenb.com 9
Table 3.7. Number of product related web cues available in the 15 apparel websites analyzed in the Content Analysis.
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CHAPTER 4
MAIN STUDY 1
This research contains two main studies. This chapter presents the method,
analyses, and results of Study 1. The purpose of Study 1 was to examine 1) the effects of
peripheral cues on emotional reactions (pleasure and arousal) under a low involvement
situation, 2) the effects of emotions on consumers’ response behaviors (purchase
intention and approach behaviors), 3) the effects of product involvement as a moderator
between S-O, and 4) the mediating effect of emotions between peripheral cues and
response behaviors. The method part illustrates research design and experimental
manipulations, data collection procedure, and instrument development for main study 1.
Analyses and results describe demographics of participants, dependent variables,
preliminary analyses, and hypotheses testing for Study 1. This research was exempted
from IRB review (Protocol number # 2004E0259, see Appendix R).
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4.1. Method
4.1.1. Research Design and Experimental Manipulations
The design of Study 1 was a between-subjects experiment with one factor
(peripheral cues) with two levels (presence vs. absence). Two mock apparel websites
each with a different amount of peripheral cues were created. Each website consisted of
an instruction page, a scenario page to control the situational involvement, a main page
showing two apparel items together selected in Pilot Study 2, a product page for each of
two apparel items, and a survey page. Each product page had a link to the larger view
(See Appendix E).
Two apparel stimuli selected in Pilot Study 2 were edited for the mock websites
used in Study 1. Front views of two items were prepared in a small size (150 x 190
pixels) and a medium size (225 x 300 pixels). Items in the small size were displayed in
the main page and ones in the medium size were displayed in each product page
(Appendix E). The resolution of front and back larger views was retained to be 450 x
450 pixels for the two apparel items.
Eroglu et al. (2001) indicated that web cues such as beautiful background colors
and patterns, fonts, animation, and graphical icons to click rather than simple underlined
textual hyperlinks could be examples of peripheral cues that might influence consumers’
emotion or the image of an online store. In a later study, Eroglu et al. (2003) used text
with a dark green color rather than black, a background image of a product with the brand
logo, and a green graphic for the links for the website with peripheral cues. In the present
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study, to manipulate peripheral cues in the mock website, the website with the peripheral
cues had text in a blue color (rather than black), a flashing pink brand logo (i.e., heart dot
is blinking), pink and yellow-green icons with roll-over images, and a pink background
with an E-Style logo pattern (See Appendix E). Since the mock online stores used in the
study were targeting young female consumers that are characteristic of the potential
participants for the study, a pink color thought to be more appealing to the target market
was used as a main color in the mock websites with the presence of peripheral cues. In
western culture, pink is traditionally perceived as a feminine color (Clark, 2003). In
addition, pink has been one of the most popular colors in women’s clothing since 2003
(Pink Chic, 2005). The most popular apparel online store listed in Pilot Study 2 (See
Table 3.4), ‘Victoria’s Secret’, introduced a lingerie line ‘Pink’ in 2003 targeting 18 to 22
year old female consumers and now the ‘Pink’ line is found in more than 1000 Victoria’s
Secret stores (Prior, 2005). Victoria’s Secret brand targeting female consumers also uses
pink background for its website. In addition to the brand logo and the background pattern,
previous research also accentuated the importance of the amount of “white space” that
surrounds pictures of the products (Chatterjee, 2001; Eroglu et al., 2001; Eroglu et al.,
2003). Thus, the mock website with the peripheral cues had plenty of white background
surrounding pictures of the products to make the products stand out on the webpage.
White background (about 5 inches x 5.5 inches in 1024 by 786 resolution) surrounded the
descriptions as well as the pictures of the products. On the other hand, the website
without peripheral cues had a static brand logo with a black color, no color icons (i.e.,
text icons with an underline), and white background without any pattern (See Appendix
E).
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To control the effects of central cues in Study 1, the two websites contained the
same amount of product related central cues. All product related cues listed in Table 3.6
excluding detail and side views and mix and match suggestions were included in the
mock website. Detail and side views were excluded because those were hardly found in
existing apparel online stores (See Table 3.6). Due to the inconsistency across existing
apparel websites, mix and match suggestions were also excluded in Study 1 (See Section
3.1.4).
According to previous research (Eroglu et al., 2003; Zaichkowsky, 1986),
browsing without a purchasing goal generates a low level of situational involvement.
Thus, to induce a low involvement situation, all participants were asked to read the same
scenario: “Now, you are going to visit one clothing website. Browse and look around the
site for a while.” This scenario was developed based on Eroglu et al.’s (2003) study and
Pilot Study 1. After browsing the website, participants were asked to finish the survey.
4.1.2. Instrument Development
The dependent measures in Study 1 (See Appendix F) contained five parts. Part 1
assessed respondents’ emotional states (pleasure and arousal) using 12 7-point semantic
differential scales (Mehrabian & Russell, 1974). In Part 2, intention behaviors were
measured by four items using 5-point Likert type scales ranging from 1 (unlikely) to 5
(likely) (Park, Lennon, & Stoel, 2005) and approach behaviors were assessed by four
items using 5-point Likert type scales ranging from 1 (not at all) to 5 (very much so)
(Huang, 2003). Personal involvement with clothing products using 10 7-point semantic
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differential scales (Zaichkowsky, 1994) were assessed in Part 3. Part 4 checked the
perceived amount of information using 5 items with 5-point Likert scales ranging from 1
(strongly disagree) to 5 (strongly agree) (Kim & Lennon, 2000) and user-perceived
quality of web appearance using 5 items with 5-point Likert scales ranging from 1
(strongly disagree) to 5 (strongly agree) (Aladwani & Palvia’s, 2002). Part 5 asked for
respondents’ demographic information such as age (open-ended format) and ethnicity
(selecting a category). Reliability of each dependent measure was established in previous
research. See Section 5.1.2 for detailed information about each instrument.
4.1.3. Procedure
Study 1 was conducted as an online experiment. A convenience sample of 157
female undergraduate students from three different consumer science classes at the Ohio
State University participated in this study for extra credit. Female undergraduate students
were recruited for the study because women are significant Internet apparel purchasers
and browsers (Lee & Johnson, 2002). Apparel stimuli and the mock websites were
developed to target young female consumers.
An instruction and the URL for the survey were given to students during the class.
When participants logged on to the website, they were randomly assigned to one of two
treatment groups (presence or absence of peripheral cues). All participants were asked to
read the instructions on the first page of the website and then read the scenario (low level
of situational involvement) on the next page. After reading the scenario, participants
were asked to browse the two pairs of pants selected in Pilot Study 2. Upon the
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completion of browsing, participants went to the survey page to finish the dependent
measures. See Appendix E for more information.
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4.2. Analyses and Results
This part explains the research analyses and results of Study 1. Study 1 examined
how the peripheral cues and the product involvement influence consumer emotions that
in turn, influence consumer behaviors. Analyses and results of Study 1 are explained in
five sections: the description of participants, manipulation check, the description of
variables measured in Study 1, preliminary analyses to test the validity and reliability of
the measures, and hypotheses testing. Descriptive statistics were used to describe the
research participants and each variable including consumer emotions (pleasure and
arousal), purchase intention, and approach behaviors. A confirmatory factor analysis was
used to evaluate measurement properties. Multivariate analyses of variance were used to
test Hypotheses 1 and 2. Structural equation modeling was used to test Hypotheses 3 and
4. Multiple regression analysis was used to assess Hypothesis 5. Descriptive statistics,
multivariate analyses of variance, and univariate analyses of variance were analyzed by
using SPSS 13.0. Confirmatory factor analysis and structural equation modeling were
analyzed by using Lisrel 8.7 (Jöreskog & Sörbom, 2004).
4.2.1. Description of Participants
All participants were female college students. The mean age of participants
(N=136) was 21, with a range of 18 to 30. More than 80% of participants were aged
between 18 and 22. About 75% of participants were Caucasian American.
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Other participants were African American (9.8%), Asian/Asian American (9.1%),
Hispanic American (3.8%), and other (3%). See Table 4.1 for demographic information.
Demographics Frequencies
Percent
Age Under 20 18 13.2% 20 – 22 96 70.6% 23 – 25 17 12.5% 26 – 30 5 3.7% Total 136* 100% Ethnic Background African American 13 9.8% Caucasian American 98 74.2% Hispanic American 5 3.8% Asian/Asian American 12 9.1% Native American 0 0% Other 4 3.0% Total 132* 100%
Note. * Different Ns are due to missing information. Table 4.1. Demographic profile of participants.
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4.2.2. Manipulation Check
In Study 1, peripheral cues were manipulated with two levels (presence vs.
absence). A manipulation check was performed to determine if participants would
perceive different levels of peripheral cues manipulated in the mock websites.
Participants were randomly assigned to browse only one of two treatment conditions
(presence or absence of peripheral cues) and then asked to assess their perception of the
quality of web appearance for the manipulation check using 5 items with 5-point scales
ranging from 1 (strongly disagree) to 5 (strongly agree). Five items include 1) The
website looks attractive, 2) The website looks organized, 3) The website uses fonts
properly, 4) The website uses colors properly, and 5) The website uses multimedia
features properly.
The reliability of five items were found to be adequate (Cronbach’s α = .91). The
five items were summed to check differences in perceptions across two groups. In order
to test for significant differences in quality perceptions between presence and absence of
peripheral cues, univariate analysis of variance was performed with the levels of
peripheral cues as the independent variable and the perceived quality of web appearance
as the dependent variable. The results revealed a main effect for peripheral cues
(presence or absence) on the perceived quality of web appearance, F (1,154) = 10.028, p
< .01. This indicates that there was a difference between presence and absence of the
mock websites on the perceived quality of web appearance. Mean scores for absence and
presence of peripheral cues were 3.48 (SD = .80) and 3.89 (SD = .79), respectively. A
higher score indicates a higher perceived quality of web appearance. Based on the means,
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participants who browsed the website with peripheral cues perceived the web appearance
of the mock website to be higher quality, while ones who browsed the website without
peripheral cues perceived the web appearance of the mock website to be lower quality.
This suggests that peripheral cues were successfully manipulated in Study 1 since the
website with them was rated higher than the one without them.
4.2.3. Dependent Variables
Study 1 includes four dependent variables, pleasure, arousal, purchase intention,
and approach behaviors (desire to explore or shop and likability of the websites).
Pleasure and arousal were dependent variables for Hypotheses 1 and 2 and purchase
intention and approach behaviors (desire to explore or shop and likability of the websites)
were dependent variables for Hypotheses 3, 4, and 5. All four variables were latent
constructs in the proposed model for Study 1. Multiple items were used to measure the
four latent constructs. Descriptive statistics for four latent constructs are presented in this
section.
Emotional States: Pleasure and Arousal
After browsing the mock website, participants were asked to assess their feelings
using 12 7-point semantic scales (Mehrabian & Russell, 1974). Each pleasure and
arousal scale included six items. Pleasure was measured by happy—unhappy, pleased—
annoyed, satisfied—unsatisfied, contented—melancholic, hopeful—despairing, and
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relaxed—bored and arousal was assessed by stimulated—relaxed, excited—calm,
frenzied—sluggish, jittery—dull, wide-awake—sleepy, and aroused—unaroused.
The reliability of items for pleasure and arousal was established (Cronbach’s α
= .91 and .91, respectively). Based on these results, scores from the six items tapping
pleasure (arousal) were summed to develop a single score for pleasure (arousal) and used
to test Hypotheses 1 and 2. Higher scores indicated that participants experienced more
pleasure or arousal, while lower scores indicated that participants experienced less
intensive emotions. Summed scores for pleasure and arousal were used as dependent
variables in multivariate analysis of variance for Hypotheses 1 and 2 and used as
independent variables in multiple regression analysis for Hypothesis 5. To test
Hypotheses 3 and 4 the six items for pleasure (arousal) were used as multiple indicators
for the pleasure (arousal) latent construct. Descriptive statistics for the six indicators for
each of the pleasure and arousal latent constructs are shown in Table 4.2.
Purchase Intention
Purchase intention was measured by four items used in previous research (Park et
al., 2005). The reliability of the items was calculated to check the internal consistency of
the items and was found to be reliable (Cronbach’s α = .88). The four items were used as
multiple indicators for the purchase intention latent construct in the proposed model for
Hypothesis 3. Purchase intention was used as the dependent variable in Hypothesis 3.
Summed scores for purchase intention were used as the dependent variable in Hypothesis
5. Descriptive statistics of the four indicators for purchase intention are described in
Table 4.2.
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Approach Behaviors
By using four items used in previous research (Huang, 2003) approach behaviors
were measured. Cronbach’s α was calculated to assess the internal consistency of the
four items and was found to be adequate (α = .94). Approach behaviors were used as the
dependent variable in Hypothesis 4 and four items were used as multiple indicators for
approach behavior latent construct in the proposed model for Hypothesis 4. Item scores
were summed and used as a dependent variable (approach behaviors) in Hypothesis 5.
Descriptive statistics of the four items for approach behaviors are presented in Table 4.2.
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Range Min. Max. Mean SD Emotional Reactions Pleasure P1 Happy – Unhappy 5 2 7 5.04 1.05 P2 Pleased – Annoyed 6 1 7 5.11 1.16 P3 Satisfied – Unsatisfied 6 1 7 5.03 1.24 P4 Contented – Melancholic 5 2 7 5.12 1.12 P5 Hopeful – Despairing 6 1 7 4.91 1.37 P6 Relaxed – Bored 6 1 7 4.87 1.31 Arousal A1 Stimulated – Relaxed 6 1 7 4.06 1.51 A2 Excited – Calm 6 1 7 3.91 1.38 A3 Frenzied – Sluggish 6 1 7 4.01 1.24 A4 Jittery – Dull 6 1 7 3.72 1.14 A5 Wide-awake – Sleepy 6 1 7 4.03 1.41 A6 Aroused – Unaroused 6 1 7 3.83 1.43 Purchase Intention PI1 How likely is it that you would buy
clothing items if you happened to see them from E-style.com?
4 1 5 2.82 1.28
PI2 How likely is it that you will buy the apparel item from E-style.com in the next 12 months?
4 1 5 2.61 1.28
PI3 How likely is it that you will shop for apparel from E-style.com when you buy apparel in the upcoming year?
4 1 5 2.64 1.29
PI4 How likely is that you will buy apparel from E-style.com when you find something you like?
4 1 5 3.06 1.23
Approach Behaviors AB1 How much would you enjoy
exploring this site? 4 1 5 3.04 1.21
AB2 Do you like this site? 4 1 5 3.29 1.07 AB3 To what extent is this site a good
opportunity to shop? 4 1 5 3.31 1.05
AB4 Would you enjoy shopping in this site? 4 1 5 3.15 1.15
Table 4.2. Descriptive statistics of dependent variables.
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4.2.4. Assessment of Measurement Properties
In theory testing and theory development research achieving unidimensionality
and construct validity2 of measurements is essential (Anderson & Gerbing, 1982;
Anderson & Gerbing, 1988; Koufteros, 1999). In contemporary structural equation
model testing or developing a theory, a confirmatory factor analysis (CFA) is a better
technique than an exploratory factor analysis (EFA) when assessing unidimensionality
(internal consistency and external consistency) and construct validity (convergent validity
and discriminant validity) of the measurements (Anderson & Gerbing, 1982; Anderson &
Gerbing, 1988; Anderson, Gerbing, & Hunter, 1987; Baggozi, 1981; Baggozi, Yi, &
Phillips, 1991; Baggozi, Yi, & Nassen, 1999; Gerbing & Anderson, 1984; Gerbing &
Anderson, 1988; Koufteros, 1999). Since a CFA is conducted on the entire set of
measures posited to measure each of latent constructs simultaneously, it directly tests the
unidimensionality of measures internally as well as externally and also evaluates the
construct validity of the measurement items that share each of latent variables (Anderson
& Gerbing, 1982; Anderson & Gerbing, 1988; Anderson et al., 1987; Baggozi, 1981;
Baggozi et al., 1991; Baggozi et al., 1999; Gerbing & Anderson, 1984; Gerbing &
Anderson, 1988; Koufteros, 1999). On the other hand, previous research indicates that an
EFA is mainly useful in early stages of scale development particularly when a detailed
theory is missing (Anderson & Gerbing, 1988; Churchill, 1979; Gerbing & Anderson,
1988; Koufteros, 1999). Some critical pitfalls of the EFA technique in testing the quality
2 Construct validity measures “the psychometric accuracy of the latent variable by examining its association with other latent variables. Two of the most widely examined aspects of construct validity are convergent validity and discriminant validity” (Grefen, 2003, p.30).
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of measurements are also presented in prior research. First, EFA does not provide any
explicit evaluation of unidimensionality and construct validity (Anderson & Gerbing,
1988; Churchill, 1979; Koufteros, 1999) and second, it does not account for external
consistency of unidimensionality (Anderson & Gerbing, 1988; Gerbing & Anderson,
1988; Koufteros, 1999).
The general structural equation model consists of two conceptually distinct
models: a measurement model and a latent variable model (or a structural model)
(Anderson & Gerbing, 1982; Bollen, 1989). The measurement model specifies the
relations between indicators (observed variables) and the latent variables (theoretical
constructs). On the other hand, the latent variable model shows the causal relations
among the latent variables (Anderson & Gerbing, 1982; Bollen, 1989; Segars & Grover,
1993). Anderson and Gerbing (1988) suggested a two-step modeling approach: the
measurement model should be assessed and respecified prior to the simultaneous
estimation of the measurement and latent variable model. The measurement model
should exhibit a satisfactory level of validity and reliability before performing the
analysis of the full model and if necessary, the measurement model should be respecified
to avoid interpretational confounding in the full model (Anderson & Gerbing, 1982;
Bagozzi, 1981; Fronell & Larker, 1981). Following the two-step approach the quality of
measurements for each latent construct (i.e., the measurement model) was evaluated and
assessed in terms of reliability, convergent validity, unidimensionality, and discriminant
validity by conducting a CFA before testing the proposed model in Study 1.
After comprehensive evaluation of the measurement model, measurement items
posited to measure each latent variable were respecified to purify measures and to reduce
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the potential for interpretational confounding. Four basic methods to respecify the
measurements were suggested by Anderson and Gerbing (1988): 1) relate the
measurement to a different latent construct, 2) remove the measurement from the model,
3) relate the measurement to multiple latent constructs, or 4) correlate measurement
errors in the model. The first two methods are preferred because they preserve the
potential to have unidimensionality of the measurements. The last two methods are
acceptable only when they are based on a priori theory (Anderson & Gerbing, 1988).
Particularly, the uncritical use of correlated measurement errors for the respecification
may take advantage of chance, cause interpretational confounding, and lose theoretical
meaningfulness (Bagozzi, 1983; Fornell, 1983; Gerbing & Anderson, 1984). Based on
the assessment of the measurement model, respecifications of the measures were
performed by removing the problematic measurements from the model (the second
method suggested by Anderson and Gerbing). The following criteria were used to select
the measurement items for respecification: 1) if measurements’ path coefficients on the
posited latent construct were insignificant, 2) if item reliability (e.g., squared multiple
correlation) of the measurement was lower than the .5 standard (Bagozzi & Yi, 1991;
Bollen, 1989), 3) if the measurement had large residuals with other indicators, 4) if the
measure had highly correlated unexplainable error variances with other indicators (large
modification indices for Θδ), and 5) if the measurement shared common variance with
other indicators posited to measure other latent constructs (large modification indices for
λ). As recommended by Anderson and Gerbing (1988), the model modification was
made based on statistical and theoretical consideration.
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Table 4.3 shows the final 12 items posited to measure four latent constructs in the
model tested in Study 1. Three indicators remained for each of four latent constructs in
the final model. P5, P6, and A1 were eliminated because their squared multiple
correlations did not meet the .5 criterion (.47, .49, and .49 respectively) whereas the other
indicators exhibited squared multiple correlations (item reliability) ranging from .55
to .87, exceeding the .5 standard. Since P3, A1, A2, A4, and PI3 had highly correlated
error variances with other indicators, they were removed from the model. AB2 was
deleted because it shared common variance with other indicators from other latent
constructs such as pleasure and purchase intention. After the completion of measurement
assessments and respecifications, the adjusted measurement model was assessed in terms
of construct validity, unidimensionality, and construct reliability by conducting a CFA.
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Latent Constructs Indicators Measurement Items
P1 Happy – Unhappy P2 Pleased – Annoyed P4 Contented – Melancholic
A3 Frenzied – Sluggish A5 Wide-awake – Sleepy
Pleasure Arousal
A6 Aroused – Unaroused
PI1 How likely is it that you would buy clothing items if you happened to see them from E-style.com?
PI2 How likely is it that you will buy the apparel item from E-style.com in the next 12 months?
Purchase Intention
PI4 How likely is that you will buy apparel from E-style.com when you find something you like?
AB1 How much would you enjoy exploring this site?
AB3 To what extent is this site a good opportunity to shop?
Approach Behaviors
AB4 Would you enjoy shopping in this site?
Table 4.3. Final measurement items for each of four latent constructs.
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Convergent Validity
Convergent validity is the extent of convergence seen when different attempts are
made to measure the same construct through maximally different methods. Convergent
validity examines the extent of correlation between item measures of a construct across
multiple methods of measurement (Grefen, 2003). Correlations between the different
measures of the same construct should be statistically significant and sufficiently large
(Campbell & Fiske, 1959). In a CFA, convergent validity is assessed by each
measurement’s (observed variable) path coefficient (i.e., factor loading) on its posited
latent variable (Anderson & Gerbing, 1988; Bagozzi et al., 1991; Koufteros, 1999). The
corresponding t-values (the ratio of path coefficient to its respective standard error)
indicate whether factor loadings are significant or not (Bagozzi et al., 1991; Bollen, 1989;
Koufteros, 1999). In addition to significant path coefficients t-values, the CFA exhibits
item reliability (i.e., squared multiple correlations). Squared multiple correlations that
exceed the .5 standard support convergent validity (Bagozzi & Yi, 1991; Bollen, 1989).
As indicated in Table 4.4, all path coefficients were significant at the p < .0001
level indicating that all measurement items are significantly related to their specified
latent constructs. In addition, squared multiple correlations of all indicators exceeded
the .5 standard (Bagozzi & Yi, 1991; Bollen, 1989). Significant t-values as well as high
squared multiple correlations indicated that convergent validity was achieved.
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Latent Constructs
Indicators Unstandardized factor loading
Completely standardized
factor loading
t-values Item reliability
Pleasure P1 .92 .87 13.35*** .77 P2 .96 .83 12.31*** .69 P4 1.00 .90 13.84*** .80 Arousal A3 .94 .76 10.72*** .58 A5 1.24 .89 13.37*** .79 A6 1.20 .84 12.33*** .71 Purchase Intention
PI1 1.13 .88 13.55*** .78
PI2 .97 .76 10.90*** .58 PI4 .88 .72 10.04*** .51 Approach Behaviors
AB1 1.11 .91 14.66*** .83
AB3 .88 .84 12.93*** .71 AB4 1.07 .94 15.58*** .89
Note. *** p < .0001
Table 4.4. Factor loadings, t-values, and item reliability for convergent validity.
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Unidimensionality
If each construct is measured by multiple items and each measurement item
measures only one latent construct, the set of measurement items defining each construct
is unidimensional (Anderson & Gerbing, 1982). Two criteria—internal consistency and
external consistency—are used to assess unidimensionality (Anderson & Gerbing, 1988;
Koufteros, 1999). Internal consistency is met if multiple indicators of each latent
construct converge to measure a single construct and external consistency is met if
multiple indicators of each latent construct are widely different from the indicators of
other latent constructs in the model (Anderson & Gerbing, 1988; Koufteros, 1999). In
CFA, both internal and external consistencies of the unidimensionality are assessed by
the evaluation of the model fit along with standardized residuals, modification indices,
and expected change.
An overall chi-square was not significant (χ2 = 53.84, df = 48, p = .2607)
indicating a good fit of the model to the data. The RMSEA was .028 and the NNFI index
was 1.00. The GFI was .95 and the AGFI was .91. All fit indices are good within
acceptable ranges indicating strong evidence of the internal and external consistency of
the unidimensionality. In addition, standardized residuals and modification indices with
expected change, which can also provide useful information in the assessment of the
measurement model and particularly unidimensionality, were inspected. No standardized
residuals were greater than 2.58 (or less than -2.58) (Grefen, 2003; Joreskog & Sorbom,
1989), no modification indices for λx and Θδ were significantly large (all less than 5)
(Grefen, 2003), and completely standardized expected change for λx and Θδ were not
significantly large (all less than .3), providing significant evidence of unidimensionality
105
(Koufteros, 1999). Appendix N shows standardized residuals for the 12 measurement
items.
Discriminant Validity
Discriminant validity is the extent to which a latent construct and its measures
differ from other constructs and their indicators (Churchill, 1979; Grefen, 2003).
Discriminant validity can be assessed by three methods: 1) perform chi-square difference
test for the constrained (correlation between two estimated latent constructs is set to 1)
and unconstrained model (correlation between two constructs is freely measured); if the
chi-square value for the unconstrained model is significantly lower than the value for the
constrained model, it indicates that two latent constructs are not perfectly correlated and
that discriminant validity is achieved (Anderson & Gerbing, 1988; Bagozzi & Phillips,
1982; Bagozzi et al., 1991; Koufteros, 1999), 2) determine whether a confidence interval
constructed by the correlation between two latent constructs plus or minus two standard
errors includes 1; if the confidence interval does not include 1, it is the evidence of
discriminant validity (Anderson & Gerbing, 1988; Koufteros, 1999), and 3) compare the
average variance extracted (AVE) with the squared correlation between constructs; if
AVE for a construct is higher than the squared correlation between the construct and
other constructs, discriminant validity is established (Fornell & Larker, 1981; Koufteros,
1999; Segars, 1997). For the first method, the chi-square test should be performed for
one pair of latent constructs at a time rather than as a simultaneous test of all pairs of
latent constructs (Anderson & Gerbing, 1988).
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Based on the first method of evaluating discriminant validity, all the chi-square
differences between the unconstrained and constrained models were significant (See
Table 4.5), thus indicating discriminant validity of the measures. Evidence of
discriminant validity is also supported by the second method. As shown in Table 4.6,
none of the confidence intervals include the value of 1 supporting discriminant validity.
Additional evidence of discriminant validity is also provided by the third method. The
AVE for each latent variable was higher than the squared correlation between the
construct and all other constructs (See Table 4.6). This indicated that the measures for
each latent variable shared more common variance with their posited construct than any
variance shared with other constructs, thus discriminant validity was achieved.
Constraint
Chi-square
df
Chi-square difference (Δχ2)
df = 1 Unconstrained model 53.84 48 Pleasure and Arousal 220.62 49 166.78*** Pleasure and Purchase Intention 353.70 49 299.86*** Pleasure and Approach Behaviors 227.65 49 173.81*** Arousal and Purchase Intention 183.72 49 129.88*** Arousal and Approach Behaviors 204.81 49 150.97*** Purchase Intention and Approach Behaviors 69.44 49 15.60**
Note. *** p < .0001, ** p < .005
Table 4.5. Chi-square difference tests for discriminant validity3
3 “When a number of chi-square difference tests are performed for assessements of discriminant validity, the significance level for each test should be adjusted to maintain the “true” overall significance level for the family of the test (cf. Finn, 1974). This adjustment can be given as α0 = 1-(1-αi)t, where α0 is the overall significance level that should be used for each individual hypothesis test of discrminant validity; and t is the number of tests performed” (Anderson & Gerbing, 1988, p. 416).
107
Latent Constructs
Pleasure
Arousal
Purchase Intention
Approach Behaviors
Pleasure .75e Arousal .55a
(.07)b
(.41, .69)c .30d
.70
Purchase Intention .50
(.07) (.36, .64)
.25
.57 (.07)
(.43, .71) .33
.67
Approach Behaviors .54
(.06) (.42, .66)
.29
.62 (.06)
(.50, .74) .38
.82 (.02)
(.78, .86) .64
.81
Note. a Correlation, b Standard Error, c Confidence Interval, d Squared Correlation, e Average variance extracted.
Table 4.6. Correlations and confidence intervals for discriminant validity.
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Assessment of Reliability
Reliability is the consistency of the measurement which is different from the
validity of measures (Anderson et al., 1987; Bollen, 1989). After the unidimensionality
of a set of measures is established, its reliability should be assessed because even
unidimensional measures will not be useful if they have unacceptably low reliability
(Gerbing & Anderson, 1988). A typical reliability assessment, Cronbach α coefficient, is
a commonly used index for evaluating reliability. However, since it is based on restricted
assumptions of equal importance of all indicators, reliability in structural equation
modeling is assessed by composite reliability and average variance extracted (AVE)
which are defined in terms of the factor loading of the measurement (Fornell & Larcker,
1981: Gerbing & Anderson, 1988; Koufteros, 1999: Zhang, Lim, & Cao, 2004).
Composite reliability means that a set of measures posited to measure a latent construct is
consistent in their measurement. In other words, highly reliable latent constructs are
those in which the measures are highly intercorrelated meaning that they are all
measuring the same latent construct (Fornell & Larcker, 1981). No generally acceptable
value has been established for composite reliability. A value of .80 or higher is
considered as a strong composite reliability (Grefen, 2003). Some researchers suggests
different standard value such as .60 (Bagozzi & Yi, 1988) or .70 (Lusch & Brown, 1996;
Sun & Zhang, 2004; Segars, 1997). A value of .70 is often cited as the normal threshold
(Segars, 1997). The AVE measures the variance captured by the measures relative to
measurement error, and it should be greater than .50 to justify the reliability (Bagozzi &
Yi, 1988; Fornell & Larcker, 1981; Segars, 1997). Higher variance extracted values
happen when the measures are truly representative of the latent construct. As shown in
109
Table 4.7, the composite reliability of all latent constructs exceeded .80, indicating the
strong composite reliability. In addition, the AVE estimates for four latent constructs
exceeded the .50 critical value indicating the further evidence of the reliability.
Latent Constructs
Composite reliability4
Average variance extracted (AVE)5
Status
Pleasure .90 .75 Accepted Arousal .87 .70 Accepted Purchase Intention .86 .67 Accepted Approach Behaviors .93 .81 Accepted
Note. Minimum standards for composite reliability and AVE are .80 and .50, respectively.
Table 4.7. Composite reliability and AVE of latent constructs.
4 Composite reliability = (Σλi)2/{(Σλi)2+Σθi} (Fornell & Larcker, 1981; Grefen, 2003; Segars, 1997) 5 Average variance extracted (AVE) = (Σλi
2)/{(Σλi2)+Σθi} (Fornell & Lalrcker, 1981; Grefen, 2003; Segars,
1997)
110
Model Specification
Once an acceptable measurement model is available, an evaluation of the
structural model can begin. Structural equation modeling was used to test Hypotheses 3
and 4 in Study 1. After the assessment of the measures, 12 indicators were selected for
the model specification. The structural model specified in Study 1 is presented in Figure
4.1. The proposed model consisted of four latent variables with 12 indicators (manifest
variables). Two latent constructs (pleasure and arousal) were exogenous latent variables
(ξ) and the other two latent constructs (purchase intention and approach behaviors) were
endogenous latent variables (η). Each of four latent constructs had three indicators. For
identification purposes, the variances of the two exogenous latent constructs were set to
one (set the diagonal elements of Φ matrix to one) and error variances of two endogenous
latent constructs were set to one (set the diagonal elements of Ψ matrix to one) (Boker &
McArdle, 2005).
Data Screening
The data were prepared using PRELIS program and assessed for the multivariate
normality assumption. Appendix O shows the data screening result for the observed
variables presenting mean, SD, skewness, and kurtosis. The distribution of the observed
variable was evaluated based on skewness and kurtosis. The distribution with skewness
and kurtosis equal to zero are considered as multivariate normal. Skewness equal to 2
and kurtosis equal to 7 are considered as moderately nonnormal and skewness equal to 3
and kurtosis equal to 21 are considered as severely nonnormal (Curran, West, & Finch,
1996). As shown in Appendix O, all skewness coefficients of the variables were close to
111
zero (ranged from .001 to .733) and all kurtosis coefficients were close to zero (ranged
from -1.184 to .778), indicating that the distribution of the observed variables were
multivariate normal. Therefore, the maximum likelihood (ML) function was used to
estimate model parameters with a covariance matrix in Study 1. Under the multivariate
normality assumptions and the proper model specification, the ML procedure provides
asymptotically unbiased, consistent, and efficient parameter estimates and standard errors
(Bollen, 1989).
112
Figure 4.1. Model specification for Study 1.
Arousal ξ2
Pleasure ξ1
Approach Behavior η2
Purchase Intention η1
P1 P2 P4 PI1 PI2 PI4
A3 A5 A6 AB1 AB3 AB4
θδ11 θδ22 θδ33
θδ44 θδ55 θδ66
θε11 θε22 θε33
θε44 θε55 θε66
λx11 λx21 λx31
λx42 λx52 λx62
λy11 λy21 λy31
λy42 λy52 λy62
γ11
γ21
γ12
γ22
1
1
ς1
ς2
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Note. Hypotheses 1 and 2 were analyzed using multivariate analyses of variance, Hypotheses 3 and 4 were tested using a structural equation modeling, Hypothesis 5 was assessed using a multiple regression analysis.
Figure 4.2. The proposed model in Study 1.
Peripheral Cues
Arousal
Pleasure
Product Involvement
Approach Behavior
Purchase Intention H1a
H1b
H2a and H2b
H3a
H4a
H4b
H3b
H5
Stimulus Organism Response
114
4.2.5. Hypothesis Testing
The design of Study 1 was a between-subjects experiment with one factor
(peripheral cues) with two levels (presence vs. absence). Hypothesis 1 examined in the
low situational involvement how the peripheral cues influence emotional states.
Hypothesis 2 investigated under the low involvement situation how the product
involvement influences the relations between peripheral cues and emotional states.
Hypotheses 3 and 4 examined the relationships between emotional states such as pleasure
and arousal and consumer behaviors such as purchase intention and approach behaviors.
Hypothesis 5 assessed the mediating effects of emotional states between peripheral cues
and response behaviors. See Figure 4.2 for the detailed information.
Hypothesis 1
Hypothesis 1. Under the low involvement situation, site atmospheric cues
(peripheral cues) will influence emotional reactions.
Hypothesis 1 was tested using a multivariate analysis of variance. The
independent variable was absence or presence of peripheral cues and the dependent
variables were pleasure and arousal. Between subjects multivariate analysis of variance
revealed a significant multivariate main effect for peripheral cues on emotional reactions
experienced during browsing the websites, F (2, 151) = 4.95, p < .01. Univariate
between subjects analysis of variance revealed significant main effects for peripheral
cues on pleasure, F (1, 152) = 7.15, p < .01 and on arousal, F (1, 152) = 8.36, p < .005.
115
In addition, omega squared (ω2) was calculated to measure treatment magnitude. The
index omega squared6 provides a relative measure of the treatment effect, reflecting the
proportion of variation accounted for by the treatment manipulation in an experiment
(Keppel, 1991). Omega squared ranges from 0 to 1, zero as no treatment effects.
According to Cohen’s suggested convention, an ω2 equal to .01 is considered to be a
small effect, an ω2 equal to .06 is considered to be a medium effect, and an ω2 equal
to .15 is considered to be a large effect. Regarding treatment effects, a small effect size
(ω2 = .01) represents the lower limit of a meaningful effect and a medium effect size is
considered to be meaningful and definitely worthy of the experiment. Due to effects of
error variance, the potential of a large value of ω2 is highly unlikely. A significant F
statistic implies that ω2 is also significantly greater than zero (Keppel, 1991).
Hypothesis 1a. As compared to those exposed to the website without peripheral
cues, those exposed to the website with peripheral cues will experience more
pleasure.
Univariate analysis of variance revealed a significant main effect for peripheral
cues on pleasure experienced while browsing the websites, F (1, 152) = 7.15, p < .01, ω2
= .045. Mean scores for pleasure [M = 5.32, SD = .99] in the presence of peripheral cues
were higher than those for pleasure [M = 4.88, SD = 1.01] in the condition without
peripheral cues. The results indicated that participants experienced more pleasure while
browsing the website with the presence of peripheral cues as compared to participants in
6 ω2
A = σ2A / (σ2
A + σ2S/A)
116
the condition without peripheral cues. Effect size (ω2 = .045) was close to the medium
effect according to Cohen’s guides. Nearly 4.5% of variance in pleasure is accounted for
by the experiment treatment of the peripheral cues. Thus, Hypothesis 1a was supported.
Hypothesis 1b. As compared to those exposed to the website without peripheral
cues, those exposed to the website with peripheral cues will experience more
arousal.
Univariate between subjects analysis of variance revealed a significant main
effect for peripheral cues on arousal experienced while browsing the websites, F (1, 152)
= 8.36, p<.005, ω2 = .052. Mean scores for arousal [M = 4.26, SD = 1.18] in the
presence of peripheral cues were higher than those for arousal [M = 3.66, SD = 1.12] in
the condition without peripheral cues. The results indicated that participants experienced
more arousal while browsing the website with the presence of peripheral cues as
compared to participants in the condition without peripheral cues. According to Cohen’s
convention, effect size (ω2 = .052) was close to the medium effect. Approximately 5.2%
of variance in arousal is accounted for by the experiment treatment of peripheral cues.
Therefore, Hypothesis 1b was supported.
117
Hypothesis 2
To test for a moderating effect of product involvement (with clothing) on the
relationship between peripheral cues and emotional states, participants were categorized
into low or high product involvement groups. To identify groups according to
participants’ product involvement with clothing, scores of the 10 items measuring
product involvement were summed (See Table 4.8). Then, the median value for the
summed product involvement items was calculated (median = 50). Six participants
received the median score. Participants with scores greater than or equal to the median
value were categorized into high product involvement group (N = 79), and subjects with
lower scores than the median value were categorized into low product involvement group
(N = 75).
118
Items Min. Max. Mean Median SD Unimportant-Important 1 7 5.59 6 1.42 Irrelevent-Relevent 1 7 5.60 6 1.41 Means a lot to me-Means nothing to mea
1 7 5.03 5 1.73
Valuable-Worthlessa 1 7 5.25 5 1.48 Boring-Interesting 1 7 5.21 6 1.67 Unexciting-Exciting 1 7 5.14 5 1.67 Appealing-Unappealinga
1 7 5.28 6 1.63
Mundane-Fascinating 1 7 4.97 5 1.63 Not needed-Needed 1 7 5.35 5.5 1.64 Involving-Uninvolvinga 1 7 4.88 5 1.57 Cronbach’s α = .957 Summed Product Involvement with Clothing
10 70 52.19 50.00 13.42
Note. aReverse coded
Table 4.8. Descriptive statistics for clothing involvement items.
119
Hypothesis 2. Under the low involvement situation, product involvement (i.e.,
personal relevance of clothing products) will moderate the relationship between
peripheral cues and emotional reactions.
Hypothesis 2 was tested using multivariate analyses of variance. A 2 (product
involvement) x 2 (peripheral cues) between subjects multivariate analysis of variance was
conducted to evaluate the product involvement by peripheral cues two-way interaction.
Product involvement and peripheral cues were the independent variables and emotional
states were the dependent variables. There was a significant multivariate two-way
interaction, F (2, 150) = 3.13, p < .05. The analysis further revealed significant main
effects of peripheral cues (F (2, 150) = 4.49, p < .05) and product involvement (F (2,
150) = 7.17, p < .001) on emotional states. Univariate analyses of variance revealed the
significant main effects for peripheral cues on pleasure (F (1, 151) = 6.66, p < .05) and on
arousal (F (1, 151) = 7.08, p < .01). Univariate analysis of variance revealed a significant
main effect for product involvement on pleasure (F (1, 151) = 13.99, p < .001). The main
effect for product involvement on arousal was not significant (F (1, 151) = 1.57, p
= .212). Further analysis showed a significant interaction effect for product involvement
by peripheral cues on pleasure (F (1, 151) = 3.98, p < .05) and arousal (F (1, 151) = 5.33,
p < .05).
Keppel (1991) suggested testing simple effects when the interaction is significant.
The interaction effect in the study was found to be significant, indicating the simple
effects of peripheral cues were not the same at each level of product involvement. To
120
evaluate the simple effects of peripheral cues the sample was divided into two product
involvement groups.
Multivariate analysis of variance was conducted for each of two product
involvement groups (low vs. high). The independent variable was absence or presence of
peripheral cues and the dependent variables were pleasure and arousal. Between subjects
multivariate analysis of variance for low product involvement group revealed a
significant multivariate main effect for peripheral cues on emotional reactions
experienced during browsing the websites, F (2, 73) = 6.18, p < .005. On the other hand,
the result of multivariate analysis of variance for the high product involvement group was
not significant, F (2, 75) = .093, p = .912. These results indicate that the effects of
peripheral cues on emotional states are significant only for participants with low personal
relevance of clothing products. For the low product involvement group, to ascertain
which of the dependent variables contributed to the overall significant multivariate main
effects, univariate between subjects analyses of variance were computed for pleasure and
arousal. The results revealed a significant main effect for peripheral cues on pleasure (F
(1, 74) = 8.25, p < .005) and arousal (F (1, 74) = 10.22, p < .005). Omega squared (ω2)
was also calculated to measure the magnitude of the treatment effects in the study.
121
Hypothesis 2a. Peripheral cues will have a stronger effect on pleasure for people
with low product involvement than those with high product involvement.
Results of multivariate analysis of variance for low product involvement group
showed significant effects for peripheral cues on emotional states experienced while
browsing the websites and univariate between subjects analysis of variance revealed a
significant effect for peripheral cues on pleasure, F (1, 74) = 8.25, p < .005, ω2 = .087.
According to Cohen’s suggested convention, the effect size (ω2) indicated a moderate to
large effect of peripheral cues on pleasure. Nearly 8.7 % of the total variance in pleasure
is accounted for by the treatment of peripheral cues. Inspection of cell means of pleasure
revealed that when product involvement was low, participants who browsed the website
with the presence of peripheral cues experienced greater pleasure [M = 5.18, SD = 1.18]
than those who browsed the website without peripheral cues [M = 4.43, SD = 1.03]. This
difference was larger when the product involvement was low rather than when the
product involvement was high (See Table 4.9 and Figure 4.3). For the high product
involvement group multivariate analysis of variance was non-significant so further
univariate analysis of variance was not conducted. Since the effect of peripheral cues on
pleasure was significant only for the group with low product involvement and not for the
group with high product involvement, peripheral cues had a stronger effect on pleasure
for participants with low product involvement than for those with high product
involvement. Thus, Hypothesis 2a was supported.
122
Hypothesis 2b. Peripheral cues will have a stronger effect on arousal for people
with low product involvement than those with high product involvement.
Between subjects multivariate analysis of variance for low product involvement
group revealed a significant multivariate main effect for peripheral cues on emotional
reactions and univariate analysis of variance revealed a significant main effect for
peripheral cues on arousal, F (1, 74) = 10.22, p < .005, ω2 = .108. According to Cohen’s
rule, effect size (ω2) indicated a moderate to large effect of peripheral cues on arousal.
Nearly 10.8 % of the total variance in arousal is accounted for by the treatment of
peripheral cues. Inspection of cell means of arousal revealed that when product
involvement was low, participants exposed to the website with the presence of peripheral
cues exhibited greater arousal [M = 4.30, SD = 1.22] than those exposed to the website
without peripheral cues [M = 3.37, SD = 1.17]. This difference was larger when the
product involvement was low rather than when product involvement was high (See Table
4.10 and Figure 4.4). For the high product involvement group there were no significant
multivariate effects for peripheral cues on emotional states, thus no further analysis was
performed. The results suggested that the effect of peripheral cues on arousal was
significant only for the low product involvement group; thus, peripheral cues had a
stronger effect on arousal for participants with low product involvement than those with
high product involvement. Therefore, Hypothesis 2b was supported
123
presenceabsence
Peripheral Cues
5.60
5.40
5.20
5.00
4.80
4.60
4.40
Plea
sure low
highproduct involvement
4.43
5.18
5.38
5.47
Figure 4.3. Effects of peripheral cues and product involvement on pleasure.
124
Product Involvement
Low (N = 75) High (N = 79) Mean SD Mean SD
Absence 4.43 1.18 5.38 .88 Peripheral Cues Presence 5.18 1.03 5.47 .94
Difference .75 .09
Table 4.9. Mean differences for pleasure influenced by peripheral cues and product involvement interaction.
Product Involvement
Low (N = 75) High (N = 79) Mean SD Mean SD
Absence 3.37 1.22 4.03 1.02 Peripheral Cues Presence 4.30 1.27 4.10 1.06
Difference .93 .07
Table 4.10. Mean differences for arousal influenced by peripheral cues and product involvement interaction.
125
presenceabsence
Peripheral Cues
4.40
4.20
4.00
3.80
3.60
3.40
3.20
Aro
usal low
highproduct involvement
3.37
4.30
4.03
4.10
Figure 4.4. Effects of peripheral cues and product involvement on arousal.
126
Hypotheses 3 and 4
To test Hypotheses 3 and 4 structural equation modeling using Lisrel 8.7
(Jöreskog & Sörbom, 2004) was used. The focus of Hypotheses 3 and 4 was to
investigate the relationships between emotional states (pleasure and arousal) induced by
peripheral cues in the websites and consumer response behaviors (purchase intention and
approach behaviors). The proposed model (Figure 4.1) consisted of four latent variables
with 12 indicators (manifest variables). Two latent constructs (pleasure and arousal)
were exogenous latent variables (ξ) and the other two latent constructs (purchase
intention and approach behaviors) were endogenous latent variables (η). The model was
analyzed using the maximum likelihood (ML) procedure with a covariance matrix.
Model fit. To assess the fit of the model to the data, chi-square, RMSEA, GFI,
AGFI, and NNFI were computed. The chi-square statistic measures the difference
between the observed covariance matrix and the expected covariance matrix obtained
when the model is fit to the sample (Bollen, 1989). The chi-square statistic tests the null
hypothesis that there is no difference between the observed covariance matrix and the
expected matrix from the hypothesized model. A significant chi-square statistic indicates
that the null hypothesis of perfect fit is rejected while an insignificant chi-square statistic
indicates that the null hypothesis of perfect fit is not rejected and the hypothesized model
is plausible. However, due to the sensitivity of the chi-square statistic to sample sizes
and to models with large numbers of indicators, the statistically significant chi-square
value, indicating the significant departure of the observed covariance matrix from the
127
expected covariance matrix, is often found with large sample sizes even though the
discrepancy is small. In contrast, the insignificant chi-square statistic is often found with
very small sample sizes although the discrepancy is not small (Bagozzi & Phillips, 1982;
Bagozzi & Phillips, 1991; Bentler & Bonett, 1980; Bollen, 1989; Hu & Bentler, 1995;
Segars & Grover, 1993). Due to the potential problems with the chi-square statistic in
evaluating the fit of the model, other model fit indices were also used to assess the model
fit such as RMSEA, NNFI, AGFI, and GFI.
The RMSEA (root mean square error of approximation), the GFI (goodness of fit
index), and the AGFI (adjusted goodness of fit index) are examples of the Absolute Fit
Indices assessing how well an a priori model reproduces the sample data (Hu & Bentler,
1999). The RMSEA is a measure of the discrepancy per degree of freedom for the model
and thus, imposes a penalty for adding more parameters to the model without
substantially improving the discrepancy between the population covariance matrix and
the fitted matrix (MacCallum, Browne, & Sugawara, 1996). Guidelines for interpretation
of the RMSEA are suggested by previous research: RMSEA less than .05 indicates a
close fit of the model, RMSEA in the range of .05 to .08 indicates a fair fit, and RMSEA
greater than .10 indicates a poor fit (Brown & Cudeck, 1992; MacCallum et al., 1996).
The GFI measures the relative amount of the variances and covariances in the
sample covariance matrix that are predicted by the model estimate of the population
covariance matrix (Bollen, 1989; MacCallum & Hong, 1997). Although the GFI has
been commonly used to test a model fit, it has a significant limitation. The GFI increases
as more parameters are introduced into a model. It favors more complex models over
simpler models. Therefore, the AGFI was suggested by previous research (Joreskog &
128
Sorbom, 1984). The AGFI is an adjusted GFI for the degrees of freedom of a model
relative to the number of variables (Bollen, 1989; MacCallum & Hong, 1997). The GFI
greater than or equal to .95 and the AGFI greater than .90 are widely acceptable criteria
suggesting a good fit of the model (Kim, 2005; MacCallum & Hong, 1997).
The NNFI (Non-Normed Fit Index) is one of the Incremental Fit Measures
comparing a given model under the study to two reference models: a null model
(uncorrelated indicators in the population) and an ideal model (a true model). NNFI
greater than or equal to .95 is a desirable threshold value, indicating a good fit (Bentler &
Bonett, 1980; Bollen, 1989; Hu & Bentler, 1999).
The results of fitting the structural model to the data indicate that the model had a
good fit (Table 4.11). All path coefficients for the measurement model were significant,
indicating the validity of the observed variables posited to measure latent constructs. The
specified relationships between emotional states and consumer response behaviors were
supported by significant t-values. An insignificant chi-square 53.84 (df = 48, p = .26)
indicated that the null hypothesis of perfect fit was not rejected. A small RMSEA (.028)
indicated a close fit of the model according to Brown and Cudeck (1992). GFI was .95,
AGFI was .91, and NNFI was 1.00. All fit indices within acceptable ranges (See Table
4.11) suggested that the proposed model in Study 1 fits the data very well. Table 4.11
presents the results of the model fit including all path coefficients for the measurement
and structural models. Figure 4.5 and 4.6 showed all parameter estimates
(unstandardized and standardized) calculated in the proposed model.
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Parameters ML estimates
Standard errors
t-values
Structural Model Pleasure (ξ1) Purchase Intention (η1) γ11 .34 .13 2.68** Pleasure (ξ1) Approach Behaviors (η2) γ21 .39 .12 3.25** Arousal (ξ2) Purchase Intention (η1) γ12 .54 .13 3.99*** Arousal (ξ2) Approach Behaviors (η2) γ22 .62 .13 4.73*** Measurement Model Pleasure (ξ1) P1 λx11 .92 .07 13.35*** Pleasure (ξ1) P2 λx21 .96 .08 12.31*** Pleasure (ξ1) P4 λx31 1.00 .07 13.84*** Arousal (ξ2) A3 λx41 .94 .09 10.72*** Arousal (ξ2) A5 λx51 1.24 .09 13.37*** Arousal (ξ2) A6 λx61 1.20 .10 12.33*** Purchase Intention (η1) PI1 λy11 .89 .07 12.06*** Purchase Intention (η1) PI2 λy21 .77 .08 10.15*** Purchase Intention (η1) PI4 λy31 .69 .07 9.45*** Approach Behaviors (η2) AB1 λy41 .83 .06 13.45*** Approach Behaviors (η2) AB3 λy51 .66 .05 12.08*** Approach Behaviors (η2) AB4 λy61 .80 .06 14.10*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .55 .07 8.42*** Purchase Intention (η1) Approach Behaviors (η2)
ψ21 .70 .06 11.78***
Model fit Chi-square (χ2) 53.84
df = 48 p = .26
Acceptable Criteria RMSEA .028 Less than .05 : Close fit
The range of .05 to .08: Fair fit Greater than .10 : Poor fit
(Brown & Cudeck, 1992; MacCallum et al., 1996)
GFI .95 Greater than or equal to .95: Good fit
(Kim, 2005; MacCallum & Hong, 1997)
AGFI .91 Greater than .90: Good fit (Kim, 2005; MacCallum & Hong, 1997)
NNFI 1.00 Greater than or equal to .95: Good fit
(Bentler & Bonett, 1980; Bollen, 1989; Hu & Bentler, 1999)
Note. **p < .01, ***p< .001
Table 4.11. Summary of measurement and structural models and model fit for Hypotheses 3 and 4.
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Note. All path coefficients were significant.
Figure 4.5. Unstandardized parameter estimates in the proposed model for Hypotheses 3 and 4.
Arousal ξ2
Pleasure ξ1
Approach Behavior η2
Purchase Intention η1
P1 P2 P4 PI1 PI2 PI4
A3 A5 A6 AB1 AB3 AB4
.92 .96 1.00
.94 1.24 1.20
.89 .77 .69
.83 .66 .80
.34
.39
.54
.62
1
1
.55 .70
ς1
ς2
1
1
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Figure 4.6. Completely standardized parameter estimates in the proposed model for Hypotheses 3 and 4.
Arousal ξ2
Pleasure ξ1
Approach Behavior η2
Purchase Intention η1
P1 P2 P4 PI1 PI2 PI4
A3 A5 A6 AB1 AB3 AB4
.87 .83 .90
.76 .89 .84
.88 .76 .72
.91 .84 .94
.26
.29
.43
.46
ς1
ς2
.55 .79
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Hypothesis 3. Emotional states such as pleasure and arousal experienced from
the apparel website will influence purchase intention.
Hypothesis 3 proposed that emotional states induced by the peripheral cues in the
websites will influence purchase intention. The results of the structural model showed
significant effects of emotional states on purchase intention (pleasure: γ11 = .34, t = 2.68,
p < .01, arousal: γ12 = .54, t = 3.99, p < .001), indicating that emotional states experienced
while browsing the apparel websites influenced purchase intention behaviors.
Hypothesis 3a. Pleasure will be positively related to purchase intention.
Hypothesis 3a proposed that pleasure induced by the peripheral cues presented in
the apparel website will be positively related to purchase intention. The significant path
coefficient showed a positive effect of pleasure on purchase intention (γ11 = .34, t = 2.68,
p < .01), indicating that participants who experienced more pleasure during browsing the
websites tended to have higher purchase intention toward the website they browsed.
Therefore, Hypothesis 3a was supported.
Hypothesis 3b. Arousal will be positively related to purchase intention.
Hypothesis 3b proposed that arousal induced by the peripheral cues while
browsing the websites will be positively related to purchase intention. The significant
path coefficient showed a positive effect of arousal on purchase intention (γ12 = .54, t =
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3.99, p < .001), implying that participants who experienced more arousal while browsing
the websites tended to have higher purchase intention. Therefore, Hypothesis 3b was
supported.
Hypothesis 4. Emotional states such as pleasure and arousal experienced from
the apparel website will influence approach behaviors (desire to explore or shop
and likability of the websites).
Hypothesis 4 predicted that emotional states induced by the peripheral cues in the
websites will influence approach behaviors. The results of the structural model showed
significant effects of emotional states on approach behaviors (pleasure: γ21 = .39, t = 3.25,
p < .001, arousal: γ22 = .62, t = 4.73, p < .001), indicating that emotional states
experienced while browsing the apparel websites had an effect on approach behaviors.
H4a. Pleasure will be positively related to approach behaviors (desire to explore
or shop and likability of the websites).
Hypothesis 4a proposed that pleasure induced by the peripheral cues presented in
the apparel websites will be positively related to approach behaviors. The significant
path coefficient showed a positive effect of pleasure on approach behaviors (γ21 = .39, t =
3.25, p < .001), implying that participants who experienced more pleasure while
browsing the apparel websites tended to have more positive approach behaviors.
Therefore, Hypothesis 4a was supported.
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H4b. Arousal will be positively related to approach behaviors (desire to explore
or shop and likability of the websites).
Hypothesis 4b predicted that arousal induced by the peripheral cues in the apparel
websites will be positively related to approach behaviors. The significant path coefficient
showed a positive effect of arousal on approach behaviors (γ22 = .62, t = 4.73, p < .001),
indicating that participants who experienced more arousal during browsing the apparel
websites tended to have more positive approach behaviors. Therefore, Hypothesis 4b
was supported.
Hypothesis 5
Hypothesis 5. Emotional states such as pleasure and arousal will mediate the
relationship between peripheral cues and consumers’ response behaviors
(purchase intention and approach behaviors).
According to Baron and Kenny (1986), the basic causal relationships involved in
mediation are the direct effect of the independent variable (peripheral cues) and the effect
of the mediators (emotional states) on the dependent variables (response behaviors) and
the direct impact of the independent variable (peripheral cues) on the mediators
(emotional states). If the effect of the independent variable on the dependent variables
decreases or disappears when the proposed mediators (emotional states) are entered into
the model, it will be a strong demonstration of mediating effects of the mediators (Baron
& Kenny, 1986). Baron and Kenny suggested three steps to test the mediating effects: 1)
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regress the mediator on the independent variable, 2) regress the dependent variables on
the independent variable, and 3) regress the dependent variables on both the independent
variable and on the mediators. Hypotheses 1 and 2 supported the direct effects of
peripheral cues (independent variable) on emotional states (mediators), and Hypotheses 3
and 4 suggested the direct effects of emotional states (mediators) on response behaviors
(dependent variables). To test the mediating effects of pleasure and arousal on the
relationship between peripheral cues and response behaviors Hypothesis 5 1) tested the
direct effect of independent variable (peripheral cues) on the dependent variables
(response behaviors) using multivariate analysis of variance and 2) assessed the change in
the magnitude of the influence of the independent variable (peripheral cues) on the
dependent variable (response behaviors) when the mediators were added to the analysis.
The direct effects of peripheral cues on response behaviors. The independent
variable was absence or presence of peripheral cues and the dependent variables were
purchase intention and approach behaviors. Between subjects multivariate analysis of
variance revealed a significant multivariate main effect for peripheral cues on response
behaviors, F (2, 147) = 6.01, p < .005. Univariate between subjects analysis of variance
further revealed a significant main effect for peripheral cues on approach behaviors, F (1,
148) = 4.75, p < .05, ω2 = .029 but not on purchase intention, F (1, 148) = .077, p = .78,
ω2 = .001. Mean scores for approach behaviors [M = 3.47, SD = .94] in the presence of
peripheral cues were higher than those for approach behaviors [M = 3.09, SD = 1.06] in
the condition without peripheral cues. The results indicated that participants had higher
approach behaviors toward the website with the presence of peripheral cues as compared
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to participants in the condition without peripheral cues. Effect size (ω2 = .029) was a
small to the medium effect according to Cohen’s guides. Nearly 2.9% of the variance in
approach behaviors is accounted for by the experimental treatment of the peripheral cues.
The mediating effects of emotional states. Multivariate analysis of variance had
revealed a significant multivariate main effect for peripheral cues on response behaviors;
in the next step, the change in the magnitude of the influence of the independent variable
(peripheral cues) on the dependent variable (response behaviors) when the mediators
were added to the model was assessed using multiple regression analyses. To test the
mediating effects of pleasure and arousal on the relationship between peripheral cues and
response behaviors, the mediators (pleasure and arousal) and the independent variable
together were entered into a regression equation predicting response behaviors. The
overall multiple regression analyses showed that approximately 28% and 41% of total
variance in purchase intention and approach behaviors respectively were accounted for
by a linear combination of the two mediators and the independent variable (See Tables
4.12 and 4.13). Results of the multiple regression analyses revealed that when the two
emotion variables were added to the model, the effects of peripheral cues on the
dependent variables were found to be not significant (purchase intention: t = -1.721, p
= .09, approach behaviors: t = -.052, p = .96). On the other hand, two emotional
variables (pleasure and arousal) were significantly related to purchase intention (pleasure:
t = 2.876, p < .005, arousal: t = 4.935, p < .001) and approach behaviors (pleasure: t =
4.693, p < .001, arousal: t = 5.485, p < .001). In sum, results indicated that there was no
significant effect of peripheral cues on consumers’ response behaviors when emotion
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variables were added. In other word, the relationship between peripheral cues and
response behaviors (purchase intention, and approach behaviors) were mediated by
emotional states (pleasure and arousal). Therefore, Hypothesis 5 was supported.
Variable SS df MS F Sig. Regression 52.038 3 17.346 20.762 .000*** Residual 122.810 147 .835 Total 174.848 150
Note. R2 = .298, Adjusted R2 = .283, ***p < .001
Table 4.12. Multiple regression analysis for purchase intention in Hypothesis 5.
Variable SS df MS F Sig. Regression 65.864 3 21.955 35.382 .000*** Residual 89.974 145 .621 Total 155.839 148
Note. R2 = .423, Adjusted R2 = .411, ***p < .001
Table 4.13. Multiple regression analysis for approach behaviors in Hypothesis 5.
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CHAPTER 5
MAIN STUDY 2
This research consists of two main studies. This chapter describes the method,
analyses, and results of Study 2. Based on the proposed model, Study 2 includes four
main parts: 1) Part 1 examined the effects of type of cue (central cues and peripheral
cues) on emotional reactions (pleasure and arousal), 2) Part 2 assessed the effects of
emotions on consumers’ response behaviors (satisfaction, purchase intention, and
approach behaviors), 3) Part 3 examined the effects of situational involvement as a
moderator between S-O, and 4) Part 4 investigated the mediating effects of emotions on
the relationship between type of cue and consumers’ response behaviors. The method,
research design, experimental manipulations, data collection procedure, and instrument
development for main Study 2 are presented. Analyses and results describe
demographics of participants, dependent variables, manipulation checks, preliminary
analyses (assessment of measurement properties and testing invariance of measurement
model over groups), and hypotheses testing for Study 2. This research was exempted
from IRB review (Protocol number # 2005E0018, see Appendix S).
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5.1. Method
5.1.1. Research Design and Experimental Manipulations
A 2 x 2 x 2 between subjects’ factorial design was employed in Study 2:
situational involvement (high vs. low) x central cues (medium amount vs. high amount) x
peripheral cues (presence vs. absence). One of two different scenarios was randomly
given to participants to manipulate situational involvement in main study 2 (See
Appendix I). Participants in the high involvement situation were given the following
scenario: “Imagine that you have been given a $100 gift certificate to purchase clothing
from an online apparel store, E-style.com. Please browse for five pairs of pants on the
website for a while and select one item that you would like to purchase. Then, finish the
survey after shopping the site”. Participants in the low involvement situation were given
the following scenario: “Imagine that today you find the online apparel store, E-style.com.
Browse the website for a while and finish the survey after browsing the site”. These
scenarios were developed based on Eroglu et al.’s (2003) study and Pilot Study 1 and
then were edited for Study 2.
Petty et al. (1983) used free gifts to foster situational involvement in their
experiment. Their participants under the high involvement situation were allowed to
choose a free gift (Edge) from the products examined in the study, while those under the
low involvement situation were allowed to select a free gift from other products
(toothpastes) not related to the study (Petty et al., 1983). Analogously to Petty et al. and
to boost the reality of the purchasing situation (high involvement situation) in Study 2,
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participants in the high involvement situation were informed that randomly selected
participants would receive the apparel item selected during the experiment or a cash
award, while participants in the low involvement situation were informed that randomly
selected participants would receive a cash award only.
Four different apparel websites were created to manipulate central cues and
peripheral cues (See Appendices H through L): 1) medium amount of central cues and
absence of peripheral cues, 2) medium amount of central cues and presence of peripheral
cues, 3) high amount of central cues and absence of peripheral cues, and 4) high amount
of central cues and presence of peripheral cues. Based on results of the content analysis
(See Section 3.4), the websites with medium amount of central cues contained 10 product
related web cues (e.g., front larger view, standard verbal information related to product,
and one item for mix and match suggestion) and the websites with high amount central
cues contained 18 product related web cues (e.g., larger views for front, back, side, and
details, more specific verbal information, and three items for mix and match suggestion).
See Table 5.1 and Appendix K for more information.
Peripheral cues were manipulated by presence or absence of colorful icons and
background colors with a brand logo pattern. In the same manner as Study 1, pink was
used as a main color in the mock websites with the presence of the peripheral cues (See
Section 4.1.1 for more information). The website with peripheral cues consisted of
colorful icons, a flashing brand logo image with color, and pink background color with a
brand logo pattern. The website without peripheral cues contained text icons without
colorful images, a static brand logo image (non-flashing) in black, and white background
without any pattern. See Appendices H through L for manipulations of peripheral cues
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used in main study 2. Participants were randomly assigned to one of two levels of
situational involvement in one of four different treatment combinations of central cues
and peripheral cues for a total of eight treatments. Table 5.1 shows the manipulations of
each of the eight treatments.
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High Involvement Situation
Low Involvement Situation
Central Cues (Medium Amount)
Central Cues (High Amount)
Central Cues (Medium Amount)
Central Cues (High Amount)
Peripheral Cues (Presence: colorful icons with a roll-over image, a flashing brand logo image with color, colorful menu bars, background colors with a brand logo pattern, and colorful texts and images)
(1) Medium amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, and inseam measurement) One mix & match suggestion Only front larger view
(2) High amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, inseam measurement, fit information, waist information, design details (pockets, belt, and/or stitching), and item care) Three mix & match suggestions Front, back, side, and detail views
(3) Medium amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, and inseam measurement) One mix & match suggestion Only front larger view
(4) High amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, inseam measurement, fit information, waist information, design details (pockets, belt, and/or stitching), and item care) Three mix & match suggestions Front, back, side, and detail views
Peripheral Cues (Absence: text icons without colorful images, a static brand logo image with black color, grey menu bars, white background without any pattern, achromatic text colors: black and grey except ‘sale’ menu)
(5) Medium amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, and inseam measurement) One mix & match suggestion Only front larger view
(6) High amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, inseam measurement, fit information, waist information, design details (pockets, belt, and/or stitching), and item care) Three mix & match suggestions Front, back, side, and detail views
(7) Medium amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, and inseam measurement) One mix & match suggestion Only front larger view
(8) High amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, inseam measurement, fit information, waist information, design details (pockets, belt, and/or stitching), and item care) Three mix & match suggestions Front, back, side, and detail views
Table 5.1. The eight treatments in Study 2.
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5.1.2. Instrument Development
The dependent measures in Study 2 contained 5 major parts. Part 1 measured
participants’ emotional states (pleasure and arousal) after shopping or browsing the
website. Part 2 assessed participants’ response behaviors such as satisfaction, purchase
intentions, and approach behaviors. In Part 3 perceived amount of information and user-
perceived quality of web appearance were assessed to check the manipulations. The
effectiveness of situational involvement was assessed in Part 4. In Part 5 participants
provided demographic information and the extent of their prior experiences with the
Internet and Internet shopping. See Appendix M for all items used in Study 2.
Emotional States
Pleasure and arousal were measured by using 12 7 -point semantic differential
scales (Mehrabian & Russell, 1974). The measures included six pleasure items (happy—
unhappy, pleased—annoyed, satisfied—unsatisfied, contented—melancholic, hopeful—
despairing, and relaxed—bored) and six arousal items (stimulated—relaxed, excited—
calm, frenzied—sluggish, jittery—dull, wide-awake—sleepy, and aroused—unaroused).
These measures have been used extensively to measure emotional responses toward
physical retail environments (Baker et al., 1992; Donovan & Rossiter, 1982; Dovovan et
al., 1994; Fiore & Kimle, 1997) and website environments (Eroglu et al., 2003; Fiore, Jin,
& Kim, 2005; Menon & Kahn, 2002). The reliabilities of pleasure and arousal
(Cronbach’s α = .93 and .90, respectively) were found to be adequate in previous research
(Fiore et al., 2005).
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Satisfaction
Satisfaction with browsing or shopping experiences at E-style.com was measured
by 4 items used in Eroglu et al. (2003): 1) I enjoyed visiting E-style.com, 2) I was
satisfied with my shopping experience at E-style.com, 3) Given a choice, I would
probably not go back to E-style.com (reverse coded), and 4) I would recommend E-
style.com to other people. These items were measured using 5-point Likert scales
ranging from 1 (strongly disagree) to 5 (strongly agree). The reliability of the four items
was found to be adequate (Cronbach’s α = .88) in prior research (Eroglu et al., 2003).
Purchase Intention
Purchase intention in Internet apparel shopping was measured by adapting
questions used in Park et al. (2005) using 5-point Likert-type scales ranging from 1
(unlikely) to 5 (likely). The measures included 1) How likely is it that you would buy
clothing items if you happened to see them from E-style.com?, 2) How likely is it that
you will buy the apparel item from E-style.com in the next 12 months?, 3) How likely is
it that you will shop for apparel from E-style.com when you buy apparel in the upcoming
year?, and 4) How likely is that you will buy apparel from E-style.com when you find
something you like?. The reliability of these measures (Cronbach’s α = .89) was
established in previous research (Park et al., 2005).
Approach Behavior
Approach behaviors were measured by four questions used in Huang’s (2003)
study (Cronbach’s α = .82). All four items were measured using 5-point Likert-type
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scales ranging from 1 (not at all) to 5 (very much so). Items included 1) How much
would you enjoy exploring this site?, 2) Do you like this site?, 3) To what extent is this
site a good opportunity to shop?, and 4) Would you enjoy shopping in this site?.
Perceived Amount of Information.
By using 5-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly
agree), perceived amount of information — central cues — on the site was measured.
The items included 1) The website you browsed today contained very much information,
2) From browsing the website, I learned a great deal about the product, 3) The website
was very informative, 4) After browsing the website, I know enough to make an informed
purchase decision, and 5) I can fully trust information given by the website. These
questions are from Kim and Lennon (2000) and revised for Internet shopping. The
reliability of the five items were found to be reliable (Cronbach’s α = .94) in Kim and
Lennon’s (2000) study.
Perceived Quality of Web Appearance
Perceived quality of web appearance — peripheral cues — was measured using
five items used in Aladwani and Palvia’s (2002) study. The intent was to use perceived
quality of web appearance as a manipulation check of the peripheral cues because the
mock websites with two different levels of peripheral cues were manipulated by presence
or absence of the peripheral web cues (e.g., colorful background, flashing icon). If
differences were found across the two levels of peripheral cues such that the website
having the peripheral cues received higher ratings of web appearance, this would be
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evidence that participants were affected by those cues. Since the website with the
peripheral cues used a more appealing color to the target participants along with various
multimedia features (hyperlinks with roll over image and a flashing image), the website
with the the peripheral cues could be perceived as higher quality than the website without
peripheral cues. All five items were assessed using 5-point Likert scales ranging from 1
(strongly disagree) to 5 (strongly agree). The measures included 1) The website looks
attractive, 2) The website looks organized, 3) The website uses fonts properly, 4) The
website uses colors properly, and 5) the website uses multimedia features properly. The
reliability of these five items (Cronbach’s α = .87) were established in previous research
(Aladwani & Palvia, 2002).
Situational Involvement
Participants’ level of involvement was also measured to assess the effectiveness
of the situational involvement manipulations using ten 7-point semantic differential
scales developed by Zaichkowsky (1994): important—unimportant, irrelevant—relevant,
means a lot to me—means nothing to me, valuable—worthless, boring—interesting,
unexciting—exciting, appealing—unappealing, mundane—fascinating, not needed—
needed, and involving—uninvolving. Cronbach’s α of the 10 items ranged from .91
to .95 in prior research (Zaichkowsky, 1994). These items were originally developed to
measure personal involvement with a certain product (Zaichkowsky, 1985). However,
research (Garlin & McGuiggan, 2002; Shao et al., 2004; Stafford & Stern, 2002;
Zaichkowsky, 1986; Zaichkowsky, 1994) also demonstrated the sensitivity of the
measure toward different situational involvement (e.g., purchase or no purchase).
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Therefore, same scale items were used to measure personal involvement with clothing
(Study 1) and involvement with clothing shopping or clothing browsing situations (Study
2).
Demographic Information and Prior Experiences
Participants’ demographic information such as age, ethnicity, and academic
standing were gathered for background information. Participants were asked to fill in the
blank for age (open-ended) and check the response (closed-ended) corresponding to their
ethnicity and academic standing. Prior experiences with the Internet and Internet
shopping were also assessed using five questions 1) How often do you use the Internet?,
2) How often do you browse online for information search?, 3) How often do you
purchase online?, 4) How often do you browse online for apparel information search?,
and 5) How often do you purchase apparel online? These items were adopted from Ha
and Stoel’s (2004) research and edited for the study. All five items were measured based
on a 6-point scale ranging from 0 (Never) to 5 (Very often).
5.1.3. Website Development
For the low involvement situation, the mock apparel website consisted of an
instruction page, a scenario page for the situational involvement manipulations, the main
page showing all five items together, a product page for each of five apparel items, and
the survey page. For the high involvement situation, the mock website contained all
pages included in the low involvement situation plus a purchasing page to select one item
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that participants would like to buy from the website. In the high involvement conditions,
each product page had links to the front and back larger views, alternative views (side
view, three close-up views), and the size chart (See Appendices H through L).
Apparel Stimuli Preparations for the Websites
Five final apparel stimuli selected in Pilot Study 2 were edited for the mock
websites used in Study 2. Front views of all items were prepared in a small size (150 x
190 pixels) and a medium size (225 x 300 pixels). Items in the small size were displayed
in the main page (Appendix J) and those in the medium size were displayed on each
product page (Appendix K). The resolution of front and back larger views was retained
to be 450 x 450 pixels for all five apparel items. Four different alternative larger views
including a side view and close-up views from three different angles (side, front, and
back) retained the resolution of 450 x 450 pixels (Appendix K).
Mock Website Development for the Main Study 2
Mock apparel websites with different manipulations were developed using a web-
design program, Macromedia Dreamweaver MX 2004, to simulate online apparel stores
as closely as possible. A brand name ‘E-Style.com’ was created and used for the mock
websites to avoid the influence of existing brand names on the results of the study. The
brand logo was edited using Adobe Photoshop 7 and Imageready 7 and used to
manipulate different types of peripheral cues in the mock websites (e.g., a flashing brand
logo, a background with a brand logo). The mock websites had the appearance and the
functions of a real apparel online store, even though several functions (e.g., shopping bag,
149
order status, customer service), which were unrelated to the study, were disabled.
Comments on the websites were continuously sought from graduate students in Textiles
and Clothing major to increase the reality of the websites.
The websites were designed to allow participants to browse among five pairs of
pants selected in Pilot Study 2. After reading instructions and a given scenario (high or
low involvement manipulations), participants started browsing the main page displaying
the five pairs of pants. Participants could click images or titles of five pairs of pants to
search for information about each pair of pants. Each product page consisted of different
levels of central cues and peripheral cues depending on the treatment combination to
which they were assigned. In each product page, participants were able to find
information for each item and could click for different larger views and a size chart.
After browsing each product page, participants were asked to go back to the main
page to browse for other items. After they finished browsing for all five items,
participants under the high involvement situation were asked to fill out an order form for
one item that they would like to buy from the website and then moved to the next page to
finish the survey. Participants in the low involvement situation went directly to the
survey page upon the completion of browsing. All pages in the mock website excluding
the five product pages were manipulated only by peripheral cues (presence vs. absence).
See Appendices H to L for more information.
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5.1.4. Recruitment of Participants
A random sample was drawn from students at the Ohio State University. Eight-
thousand email addresses of female undergraduate students at the Ohio State University
were randomly selected by the University Registrar. Female undergraduate students were
recruited for the study because women are more likely than men to be Internet apparel
shoppers. Women make up more than 52% of the U.S. Internet users (Rush, 2004) and
clothing and shoes are the most popular products for female Internet shoppers (Greenspan,
2003). According to previous research, young women are significant Internet apparel
purchasers and browsers (Lee & Johnson, 2002). Thus, apparel stimuli and the mock
websites were developed to target young female undergraduate students.
Before sending the first invitation emails to potential participants, 8,000 email
addresses were randomly categorized into eight groups using an Excel program. Each
group with one thousand email addresses was randomly assigned to one of the eight
treatment conditions. Therefore, potential participants were randomly assigned to one of
eight treatment groups. Participants were recruited via email for Study 2. Because of
dissimilar incentives for the two levels of situational involvement (See Section 5.1.1),
two types of invitation letters were created (See Appendix G). The first invitation emails
were sent to recruit research participants for the study. Potential respondents were able to
participate by clicking the URL provided in the invitation email. Two reminder emails
were sent. The first reminder was emailed to non-responders six days after the invitation
email; people who did not respond to the first or second invitation were sent another
reminder email six days after the first reminder email.
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5.1.5. Experiment Procedure for Study 2
Study 2 employed a 2 (situational involvement: high or low) x 2 (central cues:
medium or high amount) x 2 (peripheral cues: absence or presence) between-subjects
factorial design to examine how consumers respond to different amounts of central cues
and peripheral cues under different situational involvement.
The study was conducted as an online experiment. When participants went to the
website by clicking the URL provided in the invitation email, they were asked to read the
instruction page describing the purpose of the research and providing brief instructions
for the experiment. After reading the instruction page, participants were asked to go to
the next page to start the experiment (See Appendix H).
In the next page, all participants were asked to read a given scenario developed to
manipulate situational involvement (high or low) (See Appendix I). After reading the
given scenario, participants moved to the main page to start browsing the website.
Participants were able to click the title or the image of each product to go to the
individual product page to gather more information about each item (See Appendices J
and K).
After browsing for all five items, participants under the high involvement
situation went to the purchase page to complete an order form for one product that they
would like to buy from the website. In the purchase page (See Appendix L), participants
were asked to choose one item among five pairs of pants and select their size and inseam
for the product. For the shipping information, participants were asked to provide their
full name and email address. This process was used to foster high involvement and to
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enhance the reality of the online apparel purchasing process. Upon the completion of the
purchasing process, participants went to the survey page to complete the dependent
measures. In the case of the low involvement situation, participants moved to the survey
page right after browsing the website without the purchasing step.
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5.2. Analysis and Results
Study 2 investigated how the central and peripheral cues affect consumer
emotions in different situational involvements that in turn influence consumer behaviors.
Analyses and results of Study 2 are presented in four sections: the description of
participants, manipulation checks, the description of variables measured in Study 2,
preliminary analyses to check the reliability and validity of the measures and to test
invariance of measurement model across groups, and hypotheses testing for the four parts
of the proposed model in Study 2. Descriptive statistics were used to describe the
research participants and each variable including consumer emotions (pleasure and
arousal), satisfaction, purchase intention, and approach behaviors. Multivariate analyses
of variance were used for manipulation checks. Confirmatory factor analyses were used
to evaluate measurement properties, and structural equation modeling was used to test
hypotheses. Single group structural equation modeling and multi-group structural
equation modeling were used for hypotheses testing of the proposed model in Study 2.
Descriptive statistics, multivariate analyses of variance, and univariate analyses of
variance were analyzed using SPSS 13.0. Confirmatory factor analyses and structural
equation modeling were analyzed using Lisrel 8.7 (Jöreskog & Sörbom, 2004).
5.2.1. Description of Participants
Invitation emails were sent to 8,000 randomly selected female undergraduate
students at the Ohio State University. All participants were asked to provide their name
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and a valid university email address to complete the survey. This information was used
to purify the data with multiple responses. After cleaning the data for multiple
submissions and other errors, 1694 responses remained. In addition to cleaning the data
with multiple responses, the responses having more than 30% missing values were
removed. Upon the completion of the data cleaning regarding missing information, 1634
usable responses were obtained. The overall response rate for usable data was 20.4%.
All participants were female college students. The mean age of participants was
21, with a range of 18 to 62. About 90% of participants were aged between 18 and 23.
About 80% of participants were Caucasian American. Other participants were African
American (7.4%), Asian/Asian American (6.4%), Hispanic American (2.6%), Native
American (.2%), and other (3.9%). The academic standing of the participants excluding
seniors was evenly spread out. Seniors were the single largest group accounting for
about 36 % of all participants. See Table 5.2 for demographic information.
Information about participants’ prior Internet usage and previous online browsing
and purchasing experiences was also assessed (See Table 5.3). A majority of participants
answered that they use the Internet and browse online for information search very often.
Approximately, 81% of participants use the Internet very often. More than 90% of
participants browse online for information search often or very often, 22% and 69%,
respectively. About one fourth of participants answered that they purchase products
online often or very often. Although online apparel browsing and purchasing are not yet
prevalent compared to general online browsing and purchasing activities, participants
tend to browse and purchase apparel products online quite often. Over 95% of
participants had browsed for apparel products online and over 85% of participants had
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purchased apparel online. Among these participants, nearly 45% responded that they
browse online for apparel products often or very often and about 23% answered that they
purchase apparel products online often or very often. Only about 14% of participants had
not purchased apparel products online. See Table 5.3 for more detailed information.
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Demographics Frequencies
Percent
Age Under 20 442 28.8% 20 – 22 857 55.9% 23 – 25 152 9.9% 26 – 30 54 3.5% 31and Over 29 1.9% Total 1534* 100% Ethnic Background African American 113 7.4% Caucasian American 1209 79.5% Hispanic American 39 2.6% Asian/Asian American 97 6.4% Native American 3 .2% Other 60 3.9% Total 1521* 100%
Academic Standing
Freshmen 252 16.6% Sophomore 323 21.3% Junior 330 21.7% Senior 539 35.5% Other 74 4.9% Total 1518* 100%
Note. *Different Ns are due to missing information.
Table 5.2. Demographic descriptions of participants.
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General Internet Usage
Online Browsing
Online Purchasing
Online Apparel
Browsing
Online Apparel
Purchasing f % f % f % f % f % Never 1 .1 0 0 54 3.6 63 4.2 211 13.9 Very not often
13 .9 10 .7 317 20.9 221 14.6 431 28.4
Not often 10 .7 15 1.0 247 16.3 197 13.0 242 15.9 Sometimes 100 6.6 109 87.2 515 33.9 363 23.9 341 22.4 Often 172 11.3 333 22.0 221 14.6 365 24.1 168 11.1 Very often 1226 80.6 1045 69.1 164 10.8 308 20.3 127 8.4
Table 5.3. Participants’ prior Internet usage and online browsing/purchasing experiences.
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5.2.2. Manipulation Check
Situational Involvement
In Study 2, situational involvement was manipulated with two levels (low vs.
high) so a manipulation check was performed to determine if participants’ involvement
differed as a function of the two levels of situational involvement manipulated in the
mock websites. Participants were randomly assigned to only one of two involvement
treatment conditions (low or high situational scenario). Upon the completion of browsing
(low involvement) or purchasing (high involvement), participants were asked to rate 10
items using 7-point semantic differential scales measuring their levels of involvement in
situations (See Table 5.4). Of the 10 items, four items were reverse coded.
The reliability of 10 items was established (Cronbach’s α = .92). The 10 items
were summed to test participants’ level of involvement in different situations. In order
to test for significant differences between two treatment conditions, univariate analysis of
variance was performed: situational involvement was the independent variable and
participants’ level of involvement was the dependent variable. The results showed a
significant main effect for involvement manipulation on participants’ level of
involvement, F (1, 1564) = 9.783, p < .01. Mean scores for low and high involvement
situations were 39.20 (SD = 11.90) and 41.00 (SD = 10.87), respectively, indicating a
higher score as higher involvement. Inspection of mean scores revealed that participants
in the high involvement situation were more likely to be involved than those in the low
involvement situation. This indicates that participants’ level of involvement differed in
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two situational involvement conditions. Therefore, situational involvement was
successfully manipulated in Study 2.
Central Cues
Central cues were manipulated with two levels (medium amount vs. high amount)
in Study 2 and participants were randomly assigned to browse one of two treatment
conditions (medium or high amount of central cues). To verify if participants would
perceive a different amount of central cues manipulated in the mock websites, a
manipulation check was performed. Since central cues were manipulated by different
amounts of product related information in Study 2, participants were asked to assess their
perception of the amount of information presented in the websites for the manipulation
check using 5 items with 5-point scales. Five items included 1) the website you browsed
today contained very much information, 2) from browsing the website, I learned a great
deal about the product, 3) the website was very informative, 4) after browsing the website,
I know enough to make an informed purchase decision, and 5) I can fully trust
information given by the website. See Table 5.4 for descriptive statistics.
The reliability of the five items was found to be satisfactory (Cronbach’s α = .92).
The five items were summed to test for differences between the two treatment groups
(medium vs. high amount central cues) and entered into a univariate analysis of variance.
This procedure was conducted to verify a significant difference between two treatment
conditions of central cues: the levels of central cues as the independent variable and the
perceived amount of information as the dependent variable. The results revealed a
significant main effect for central cues on perceived amount of information, F (1, 1601) =
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47.217, p < .001, implying that perceived amount of information was significantly higher
for participants who browsed the websites with the high amount of central cues than for
those who browsed the sites with the medium amount. Mean scores for medium and high
amount of central cues were 14.97 (SD = 4.51) and 16.51 (SD = 4.47), respectively. A
higher score indicates that participants perceived a higher amount of information.
Inspection of cell means showed that participants who browsed the website with the high
amount of central cues perceived a greater amount of information in the mock website
than those who browsed the website with the medium amount of central cues. Thus, the
websites with different amounts of central cues were perceived to display different
amounts of information and the manipulation was successfully operationalized in Study 2.
Peripheral Cues
In Study 2, peripheral cues were manipulated with two conditions (presence vs.
absence). Participants were randomly assigned to browse one of two treatment
conditions. In order to determine whether participants would perceive different levels of
peripheral cues manipulated in the websites, a manipulation check was performed.
Because websites with two different levels of peripheral cues may have presented
different quality of web appearance aspects in terms of color, fonts, and multimedia
features, participants were asked to evaluate the perceived quality of web appearance for
the manipulation check using 5 items with 5-point scales. The five items include 1) The
website looks attractive, 2) The website looks organized, 3) The website uses fonts
properly, 4) The website uses colors properly, and 5) The website uses multimedia
features properly. See Table 5.4 for detailed information.
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Cronbach’s α was .92, indicating the adequate reliability of five items. The five
items were summed and entered into a univariate analysis of variance for a manipulation
check. This procedure was performed to verify significant differences between two
treatment groups in terms of the perceived quality of web appearance. Presence or
absence of peripheral cues was the independent variable and the perceived quality of web
appearance was the dependent variable. The results revealed a significant main effect for
presence or absence of peripheral cues on participants’ perceptions of web appearance, F
(1, 1583) = 15.322, p < .001. Mean scores for absence and presence of peripheral cues
were 17.91 (SD = 4.08) and 18.73 (SD = 4.12), respectively. A higher score indicates a
higher perceived quality of web appearance. Inspection of cell means showed that
participants who browsed the website with the presence of peripheral cues tended to
perceive the web appearance to be higher quality than those who browsed the website
without peripheral cues. This implies that the perceived quality of web appearance was
significantly higher for participants who browsed the websites with the presence of
peripheral cues. Thus, the peripheral cue manipulation affected perceptions of the mock
websites in Study 2.
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Min. Max. Mean SD Situation Involvement Unimportant-Important 1 7 3.61 1.51 Irrelevent-Relevent 1 7 3.71 1.59 Means a lot to me-Means nothing to
mea 1 7 3.60 1.56
Valuable-Worthlessa 1 7 4.21 1.41 Boring-Interesting 1 7 4.35 1.54 Unexciting-Exciting 1 7 4.06 1.45 Appealing-Unappealinga 1 7 4.67 1.48 Mundane-Fascinating 1 7 3.90 1.36 Not needed-Needed 1 7 3.70 1.46 Involving-Uninvolvinga 1 7 4.35 1.47 Sum of situation involvement 10 70 40.16 11.39 Reliability .92 Central Cues The website you browsed today
contained very much information 1 5 3.21 1.05
From browsing the website, I learned a great deal about the product
1 5 3.21 1.07
The website was very informative 1 5 3.29 1.05 After browsing the website, I know
enough to make an informed purchase decision
1 5 3.17 1.10
I can fully trust information given by the website
1 5 2.88 1.01
Sum of central cues 5 25 15.75 4.55 Reliability .92 Peripheral Cues The website looks attractive 1 5 3.46 1.04 The website looks organized 1 5 3.88 .84 The website uses fonts properly 1 5 3.79 .87 The website uses colors properly 1 5 3.61 1.01 The website uses multimedia features
properly 1 5 3.58 .98
Sum of peripheral cues 5 25 18.32 4.12 Reliability .92
Note. a Items reverse coded.
Table 5.4. Descriptive statistics for manipulation check items in Study 2.
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5.2.3. Dependent Variables
Study 2 includes five dependent variables, pleasure, arousal, satisfaction,
purchase intention, and approach behaviors. Pleasure and arousal were dependent
variables for Part 1 and Part 3. Satisfaction, purchase intention, and approach behaviors
were dependent variables in Part 2 and Part 4. All five variables were latent constructs in
the proposed model in Part 2 and Part 4. In order to measure each of five latent
constructs multiple items were used. Descriptive statistics for five latent variables are
presented in this section.
Emotional States: Pleasure and Arousal
After the completion of browsing for five pairs of pants in the mock websites,
participants were asked to rate their current feelings using 12 7-point semantic scales
(Mehrabian & Russell, 1974). Each of pleasure and arousal was measured by 6 items
(See Table 5.5). The reliabilities for pleasure and arousal were found to be adequate
(Cronbach’s α = .91 and .83, respectively). Higher scores indicate that participants
experienced more pleasure or arousal, while lower scores indicate that participants
experienced less intensive emotions. Six items for each of pleasure and arousal were
used as multiple indicators for pleasure and arousal latent constructs in the proposed
model for Study 2. Descriptive statistics for the six indicators for each of pleasure and
arousal latent constructs are shown in Table 5.5.
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Satisfaction
Four items used in Eroglu et al. (2003) were used to measure satisfaction with
browsing or shopping experiences at E-style.com. The reliability of the four items was
found to be adequate (Cronbach’s α = .82). Satisfaction was used as the dependent
variable in Part 2 and Part 4 of Study 2. Four items were used as multiple indicators for
satisfaction latent construct in the proposed model. Table 5.5 illustrates descriptive
statistics of the four items measuring satisfaction.
Purchase Intention
Purchase intention was measured by the four items used in Park et al.’s (2005)
study. The reliability of the four items was estimated and was found to be adequate
(Cronbach’s α = .92). The four items were used as multiple indicators for the purchase
intention latent construct in the proposed model for Study 2. Purchase intention was used
as the dependent variable in Part 2 and Part 4. Descriptive statistics of the four indicators
for purchase intention are described in Table 5.5.
Approach Behaviors
By using four items from previous research (Huang, 2003) approach behaviors
were measured. Cronbach’s α was calculated and was found to be reliable (α = .91).
Approach behaviors were used as the dependent variable in Part 2 and Part 4. The four
items were used as multiple indicators for approach behavior latent construct in the
proposed model for Study 2. Descriptive statistics of the four items for approach
behaviors are presented in Table 5.5.
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Min. Max. Mean SD Emotional Reactions Pleasure P1 Happy – Unhappy 1 7 5.10 1.15 P2 Pleased – Annoyed 1 7 4.97 1.39 P3 Satisfied – Unsatisfied 1 7 4.93 1.42 P4 Contented – Melancholic 1 7 5.02 1.28 P5 Hopeful – Despairing 1 7 4.87 1.31 P6 Relaxed – Bored 1 7 4.78 1.57 Arousal A1 Stimulated – Relaxed 1 7 4.06 1.50 A2 Excited – Calm 1 7 3.90 1.54 A3 Frenzied – Sluggish 1 7 3.61 1.08 A4 Jittery – Dull 1 7 3.57 1.07 A5 Wide-awake – Sleepy 1 7 3.91 1.47 A6 Aroused – Unaroused 1 7 3.74 1.38 Satisfaction SA1 I enjoyed visiting E-style.com 1 5 3.55 1.02 SA2 I was satisfied with my shopping experience
at E-style.com 1 5 3.44 1.05
aSA3 Given a choice, I would probably not go back to E-style.com
1 5 3.28 1.23
SA4 I would recommend E-style.com to other people
1 5 3.20 1.11
Purchase Intention PI1 How likely is it that you would buy clothing
items if you happened to see them from E-style.com?
1 5 2.90 1.20
PI2 How likely is it that you will buy the apparel item from E-style.com in the next 12 months? 1 5 2.55 1.21
PI3 How likely is it that you will shop for apparel from E-style.com when you buy apparel in the upcoming year?
1 5 2.62 1.21
PI4 How likely is that you will buy apparel from E-style.com when you find something you like?
1 5 3.28 1.20
Approach Behaviors AB1 How much would you enjoy exploring this
site? 1 5 3.31 1.10
AB2 Do you like this site? 1 5 3.42 .95 AB3 To what extent is this site a good opportunity
to shop? 1 5 3.36 .96
AB4 Would you enjoy shopping in this site? 1 5 3.31 1.04
Note. a Item reverse coded.
Table 5.5. Descriptive statistics of dependent variables.
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5.2.4. Assessment of Measurement Properties
As indicated in Study 1 (See Section 4.1.4 for more information), the
measurement properties were assessed in terms of unidimensionality (internal and
external consistency) and construct validity (convergent validity and discriminant
validity) using a confirmatory factor analysis (CFA). The general structural equation
model consists of two conceptually distinct models: a measurement model and a latent
construct model (Anderson & Gerbing, 1982; Bollen, 1989). Following the two-step
approach according to Anderson and Gerbing (1988) the measurement model specifying
the relations between indicators and the latent constructs was evaluated and respecified
before conducting the analysis of the full model.
After comprehensive assessment of the measurements, the measurement model
was respecified to purify measures and consequently, to reduce the possibility of
interpretational confounding in the full model. As suggested by Anderson and Gerbing
(1988), the measurement model was respecified by deleting the problematical
measurements from the model. The following principles were used to respecify the
measures: 1) if a measurement’s path coefficients on its posited latent construct were
insignificant, 2) if item reliability (e.g., squared multiple correlation) of the measurement
was lower than the .5 standard (Bagozzi & Yi, 1991; Bollen, 1989), 3) if the
measurement had large residuals with other indicators, 4) if the measure had highly
correlated unexplainable error variances with other indicators (large modification indices
for Θδ), 5) if the measurement shared common variance with other indicators posited to
measure other latent constructs (large modification indices for λ). The model
167
modification was made based on statistical and theoretical consideration (Anderson &
Gerbing, 1988; Bollen, 1989).
After the respecification of the measurement model, 15 final items posited to
measure the five latent constructs remained. Each of the five latent variables had three
indicators in the final model. A1, A2, and SA3 were removed due to their small squared
multiple correlations (.41, .45, and .20, respectively) that did not meet the .5 standard
(Bagozzi & Yi, 1991; Bollen, 1989). P6 and AB2 were deleted because these shared
common variance with other indicators posited to measure other latent variables. Since
highly correlated error variance with other indicators was present, P3, P5, A4, and PI3
were also eliminated. Table 5.6 shows the final 15 items for the model. After the
assessment and respecification of the measurement model, the respecified model was
evaluated in terms of construct validity, unidimensionality, and construct reliability by
performing a CFA.
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Latent Constructs Indicators Measurement Items Pleasure P1 Happy – Unhappy P2 Pleased – Annoyed P4 Contented – Melancholic Arousal A3 Frenzied – Sluggish A5 Wide-awake – Sleepy A6 Aroused – Unaroused Satisfaction SA1 I enjoyed visiting E-style.com SA2 I was satisfied with my shopping experience at E-
style.com SA4 I would recommend E-style.com to other people Purchase Intention PI1 How likely is it that you would buy clothing items if you
happened to see them from E-style.com? PI2 How likely is it that you will buy the apparel item from E-
style.com in the next 12 months? PI4 How likely is that you will buy apparel from E-style.com
when you find something you like? Approach Behaviors AB1 How much would you enjoy exploring this site? AB3 To what extent is this site a good opportunity to shop? AB4 Would you enjoy shopping in this site?
Table 5.6. Final measurement items for each of five latent constructs.
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Convergent Validity
Convergent validity was assessed by examining each measurement’s path
coefficient (factor loading) with a significant t-value on its posited latent construct and
each observed variable’s squared multiple correlation. As shown in Table 5.7, all path
coefficients were significant at the p < .0001 level indicating that all measurement items
are significantly related to their specified latent constructs. In addition, squared multiple
correlations of all indicators exceeded the .5 standard (Bagozzi & Yi, 1991; Bollen, 1989)
supporting the achievement of convergent validity of the model. Significant factor
loadings and high squared multiple correlations indicated that convergent validity of the
measurement model was achieved.
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Latent Constructs
Indicators Unstandardized factor loading
Completely standardized
factor loading
t-values Item reliability
Pleasure P1 .93 .81 37.33*** .65 P2 1.17 .84 39.49*** .71 P4 1.02 .80 36.73*** .64 Arousal A3 .70 .65 26.03*** .50 A5 1.12 .76 30.80*** .58 A6 .98 .71 28.61*** .51 Satisfaction SA1 .92 .91 46.07*** .82 SA2 .93 .89 44.48*** .78 SA4 .84 .76 35.30*** .58 Purchase Intention
PI1 1.06 .89 43.99*** .79
PI2 1.04 .86 41.70*** .73 PI4 .90 .76 34.68*** .57 Approach Behaviors
AB1 .86 .79 37.28*** .62
AB3 .80 .83 40.23*** .69 AB4 .95 .91 47.00*** .84
Note. ***p < .0001
Table 5.7. Factor loading, t-values, and item reliability for convergent validity.
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Unidimensionality
Unidimensionality of the measurement model was assessed by examining internal
consistency and external consistency. Both internal and external consistencies were
evaluated by assessing the model fit along with standardized residuals, modification
indices, and expected change.
An overall chi-square was significant (χ2 = 354.15, p < .0001). Since the chi-
square statistic is sensitive to large sample sizes and models with large numbers of
indicators, the significant chi-square is not surprising (Bagozzi & Phillips, 1982; Bagozzi
& Phillips, 1991; Bentler & Bonett, 1980; Bollen, 1989; Hu & Bentler, 1995; Segars &
Grover, 1993). Due to the potential problems with the chi-square statistic in evaluating
the fit of the model, other model fit indices were also used to assess the model fit such as
RMSEA, NNFI, AGFI, and GFI. The RMSEA was .046 and NNFI index was .99. The
AGFI was .96 and the GFI was .97. All other model fit indices are good within
acceptable ranges (See Table 4.11) supporting the strong internal and external
consistencies of the unidimensionality. In addition to the overall model fit, standardized
residuals and modification indices with expected change provided useful information
with respect to the unidimensionality of the model. No standardized residuals were
greater than 2.58 (or less than -2.58) (Grefen, 2003; Joreskog & Sorbom, 1989).
Modification indices for λx and Θδ were all less than 5 (Grefen, 2003) and completely
standardized expected change in chi-square statistics was all less than .3 (Koufteros,
1999), supporting significant evidence of the unidimensionality of the measurement
model. Appendix P shows standardized residuals for the 15 measurement items used in
Study 2.
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Discriminant Validity
Discriminant validity was assessed by three methods : 1) perform chi-square
difference test for the constrained (correlation between two estimated latent constructs is
set to 1) and unconstrained model (correlation between two constructs is freely
measured); if the chi-square value for the unconstrained model is significantly lower than
the value for the constrained model, it indicates that two latent constructs are not
perfectly correlated and that discriminant validity is achieved (Anderson & Gerbing,
1988; Bagozzi & Phillips, 1982; Bagozzi et al., 1991; Koufteros, 1999), 2) determine
whether a confidence interval constructed by the correlation between two latent
constructs plus or minus two standard errors includes 1; if the confidence interval does
not include 1, it is the evidence of discriminant validity (Anderson & Gerbing, 1988;
Koufteros, 1999), and 3) compare the average variance extracted (AVE) with the squared
correlation between constructs; if AVE for a construct is higher than the squared
correlation between the construct and other constructs, discriminant validity is
established (Fornell & Larker, 1981; Koufteros, 1999).
Based on the results of the first method, all the chi-square differences between the
unconstrained and constrained models were significant (See Table 5.8), indicating
discriminant validity of the measures. The second method also supported discriminant
validity of the model. As indicated in Table 5.9, all confidence intervals do not include
the value of 1 providing the evidence of discriminant validity. The results of the third
one provided an additional evidence of discriminant validity of the measures. The AVE
for each latent construct was higher than the squared correlation between the construct
and all other constructs (See Table 5.9), indicating that the measures for each latent
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construct shared more common variance with their underlying construct than any
variance shared with other constructs, thus discriminant validity was achieved.
Constraint
Chi-square
df
Chi-square difference (Δχ2)
df = 1 Unconstrained model 351.61 80 Pleasure and Arousal 1238.43 81 886.82*** Pleasure and Satisfaction 1642.02 81 1290.41*** Pleasure and Purchase Intention 2299.60 81 1947.99*** Pleasure and Approach Behaviors 2035.02 81 1683.41*** Arousal and Satisfaction 1308.66 81 957.05*** Arousal and Purchase Intention 1370.89 81 1019.28*** Arousal and Approach Behaviors 1327.99 81 976.38*** Satisfaction and Purchase Intention 2122.21 81 1770.60*** Satisfaction and Approach Behaviors 1112.15 81 760.54*** Purchase Intention and Approach Behaviors 1037.12 81 685.51***
Note. *** p < .0001
Table 5.8. Chi-square difference tests for discriminant validity7.
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 family of the test (cf. Finn, 1974). This adjustment can be given as α0 = 1-(1-αi)t, where α0 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” (Anderson & Gerbing, 1988, p. 416).
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Latent Constructs
Pleasure
Arousal
Satisfaction
Purchase Intention
Approach Behaviors
Pleasure .67e Arousal .52a
(.02)b
(.48, .56)c .27d
.51
Satisfaction .68
(.02) (.64, .72)
.46
.47 (.02)
(.43, .51) .22
.73
Purchase Intention
.43 (.02)
(.39, .47) .18
.41 (.03)
(.35, .47) .17
.65 (.02)
(.61, .69) .42
.70
Approach Behaviors
.57 (.02)
(.53, .61) .33
.45 (.03)
(.39, .51) .20
.80 (.01)
(.78, .82) .64
.82 (.01)
(.80, .84) .67
.72
Note. a Correlation, b Standard Error, c Confidence Interval, d Squared Correlation, e Average variance extracted.
Table 5.9. Correlations and confidence intervals for discriminant validity.
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Assessment of Reliability
The reliability in structural equation modeling is evaluated by composite
reliability and average variance extracted (AVE) which are defined in terms of the factor
loading of measurement (Fornell & Larcker, 1981: Gerbing & Anderson, 1988; Grefen,
2003; Koufteros, 1999: Zhang, Lim, & Cao, 2004). As indicated in Table 5.10, the
composite reliability of all latent constructs except ‘arousal’ exceeded .80, supporting
strong composite reliability. Although it was not as strong as the .80 standard, the
reliability of the ‘arousal’ latent construct was also higher than the normal acceptable
level (.70) suggested by previous research (Lusch & Brown, 1996; Sun & Zhang, 2004).
The AVE estimates for five latent constructs were also good within the acceptable range
(over .50) (Bagozzi & Yi, 1988; Fornell & Larcker, 1981), providing further evidence of
strong reliability.
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Latent Constructs
Composite reliability8
Average variance extracted (AVE)9
Status
Pleasure .86 .67 Accepted Arousal .75 .51 Accepted Satisfaction .89 .73 Accepted Purchase Intention .87 .70 Accepted Approach Behaviors .88 .72 Accepted
Note. Minimum standards for composite reliability and AVE are .70 and .50, respectively.
Table 5.10. Composite reliability and AVE of latent constructs.
8 Composite reliability = (Σλi)2/{(Σλi)2+Σθi} (Fornell & Larcker, 1981; Grefen, 2003; Segars, 1997) 9 Average variance extracted (AVE) = (Σλi
2)/{(Σλi2)+Σθi} (Fornell & Lalrcker, 1981; Grefen, 2003; Segars,
1997)
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Testing Invariance of Measurement Model over Groups
In Part 1, Part 3, and Part 4 of Study 2, causal relations between latent constructs
and means of latent constructs in the proposed model were compared by different
treatment groups (e.g., medium amount of central cues vs. high amount of central cues).
It is assumed that at least the invariance of the model form (models in different treatment
groups should have the same form) and the invariance of factor loadings should hold
before testing the equality of regression parameters and estimating mean differences of
latent variables across groups (Bollen, 1989; Jöreskog & Sörbom, 1996). The invariance
of the model form across groups is achieved if the model for each group has the same
indicators (i.e., the same measurements) that load on the same latent constructs and that
have the same patterns of fixed, free, and constrained parameters (Bollen, 1989). Bollen
suggested a testing hierarchy of the invariance across groups applicable to measurement
models: 1) first, test if the form of the model is the same in different groups (Hform) and if
this holds, then 2) assess the equality of factor loadings (i.e., the coefficients linking the
latent to the observed variables) across groups (HΛx) and if this holds, then 3) add the
equality of measurement error variances to the test (HΛxΘδ) and if this holds, then 4) add
the equality of the covariance matrices in different groups to the assessment (HΛxΘδΦ).
The third and forth ones are interchangeable in the hierarchy according to the research
interests. To go further to assess the invariance of the general structural equation model
with latent variables (i.e., testing the equality of regression coefficients between latent
variables or estimating mean differences of latent constructs across groups) at least the
first two assumptions should hold (Bollen, 1989; Jöreskog & Sörbom, 1996). Following
178
the steps suggested by Bollen, the invariance of the measurement model for different
groups was assessed before testing the proposed hypotheses in Study 2.
Table 5.11 shows the results of testing the invariance hypotheses. The Hform
hypothesis showed a good match to the data with the NNFI equal to .99 and the RMSEA
equal to .051. In group comparisons, the GFI fit index is useful because it is calculable
for each group (Bollen, 1989). However, the AGFI is not measurable in group
comparisons. The GFIs for different groups ranged from .91 to .94. All the fit indices
indicated that all groups had the same model form. The next hypothesis HΛx also showed
a good fit to the data with the NNFI equal to .99 and the RMSEA equal to .048. The
RMSEA decreased for HΛx. The GFIs were greater than .90 for all treatment groups.
Since the hierarchies of invariances contain nested models (e.g., HΛxΘδ is nested in HΛx),
chi-square difference tests were performed to assess relative fit (See Table 5.11). The
chi-square difference between Hform and HΛx was 73.34 (Δdf = 70) which is not
statistically significant, indicating that all factor loadings appeared to be equal for all
treatment groups (i.e., more constrained model, HΛx is better than Hform). Thus, the next
hypothesis HΛxΘδ added the constraint that the measurement error variances are equal for
all groups. The chi-square difference of HΛx and HΛxΘδ was 257.53 with Δdf = 105,
which is statistically significant (p < .001), suggesting that the equality of the
measurement error variances across groups is not tenable. Thus, it was unnecessary to
assess the next hypothesis HΛxΘδΦ . Overall, the results of testing the invariance of the
measurement model support the hypotheses that all treatment groups have the same
model form and that all factor loadings are the same across groups. Accordingly, the
179
model form and factor loadings for different groups were set equal in the proposed model
tested in Part 1, Part 3, and Part 4 for Study 2.
Hypotheses
RMSEA
Chi-
square
df
Chi-square difference (Δχ2)
Status
Hform (Same model form across groups)
.051
973.12
640
Hold
HΛx (Same Λx across groups)
.048
1046.46
710
HΛx - Hform= 73.34
(Δdf = 710 - 640 =70)
Hold
HΛxΘδ (Same Λx and Θδ across groups)
.054
1303.99
815
HΛxΘδ - HΛx= 257.53** (Δdf = 815 – 710 =105)
Not Hold
Note. **p < .001
Table 5.11. The results of testing the invariance of the measurement model.
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Model Specification
Once the measurement model is assessed and found to be acceptable, an
evaluation of the structural model can begin. Structural equation modeling was used to
test hypotheses in Study 2. After the assessment of the measures, 15 indicators were
selected for the model specification. The structural model specified in Study 2 is
presented in Figure 5.1 for Part 1 and Part 3 and in Figure 5.2 for Part 2 and Part 4. The
proposed model in Parts 1 and 3 included two exogenous (ξ) latent constructs (pleasure
and arousal) with six manifest variables (Figure 5.1). The proposed model in Part 2 and
Part 4 consisted of five latent variables with 15 indicators (manifest variables) (Figure
5.2). Two latent constructs (pleasure and arousal) were exogenous latent variables (ξ)
and the other three latent constructs (satisfaction, purchase intention, approach behaviors)
were endogenous latent variables (η). Each of the five latent constructs had three
indicators.
For identification purposes, the variances of the two exogenous latent constructs
were set to one (set the diagonal elements of Φ matrix to one) and the error variances of
the three endogenous latent constructs were set to one (set the diagonal elements of Ψ
matrix to one) in Part 2 and Part 4 (See Figure 5.2) (Boker & McArdle, 2005). In a single
population study an origin of latent construct is fixed by assuming that all observed
indicators are measured in deviations from their means and that the means of all latent
constructs are zero so that generally the unit of measurement of each latent construct is
fixed either by setting a variance of each latent construct to 1 or by fixing one factor
loading for each latent construct to a reference variable (Bollen, 1989; Jöreskog &
Sörbom, 1996). In multi-group mean comparison analyses, however, these restrictions
181
can be somewhat relaxed by assuming that the latent variables are on the same scale in all
groups. Since means of latent constructs in the proposed model were compared by
groups in Part 1 and Part3 the new restrictions were required to identify the model
(Bollen, 1989; Jöreskog & Sörbom, 1996). The way to do this is to set the means of the
latent variables equal to zero in one group and to scale each latent variable to one of its
observed variables (e.g., λ11 = 1) (Bollen, 1989; Jöreskog & Sörbom, 1996). Under these
assumptions, it is possible to estimate and compare the means of the latent variables
across groups. Consequently, λs for P1 and A3 were scaled to 1 to its posited latent
construct (pleasure and arousal) in Parts 1 and 3 (See Figure 5.1).
182
Figure 5.1. Model specification for Parts 1 and 3 in Study 2.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
θδ11 θδ22 θδ33
θδ44 θδ55 θδ66
1 λx21 λx31
1 λx52 λx62
φ22
φ11
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Figure 5.2. Model specification for Part 2 and Part 4 in Study 2.
Arousal ξ2
Pleasure ξ1
Approach Behavior η3
Satisfaction η1
P1 P2 P4
SA1 SA2 SA4
A3 A5 A6
AB1 AB3 AB4
θδ11 θδ22 θδ33
θδ44 θδ55 θδ66
θε11 θε22 θε33
θε77 θε88 θε99
λx11 λx21 λx31
λx42 λx52 λx62
λy11 λy21 λy31
λy73 λy83 λy93
γ11 γ21
γ12
γ22
1
1
Purchase Intention η2
γ32
γ31 PI1
PI2
PI4
θε44
θε55
θε66
λy42
λy52
λy62
ς1
ς2
ς3
1
1
1
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Data Screening
The data used in Study 2 were prepared using PRELIS program and assessed for
the multivariate normality assumption before testing hypotheses. Appendix Q shows the
data screening result for the 15 observed variables analyzed in the proposed model for
Study 2, presenting mean, SD, skewness, and kurtosis. The distribution of the observed
variables was evaluated based on skewness and kurtosis. The distribution with skewness
and kurtosis equal to zero are considered as multivariate normal (Curran et al., 1996).
Skewness and kurtosis equal to 2 and 7 respectively are considered as moderately
nonnormal. Skewness equal to 3 and kurtosis equal to 21 are considered as severely
nonnormal (Curran et al., 1996). As shown in Appendix Q, all skewness coefficients of
the variables were close to zero (ranged from -.739 to .219) and all kurtosis coefficients
were close to zero (ranged from -1.006 to .690), indicating that the distribution of the
observed variables were approximately multivariate normal. Under the multivariate
normality assumptions and the proper model specification, the maximum likelihood (ML)
procedure provides asymptotically unbiased, consistent, and efficient parameter estimates
and standard errors (Bollen, 1989). Therefore, the ML function was used to estimate
model parameters with a covariance matrix in Study 2.
185
5.2.5. Hypotheses Testing
The design of Study 2 was a 2 x 2 x 2 between-subjects experiment with three
factors: situational involvement (high vs. low) x central cues (medium amount vs. high
amount) x peripheral cues (presence vs. absence). Study 2 consisted of four main parts.
Part 1 (Hypotheses 1 and 2) of the proposed model examined the influence of type of cue
(central cues and peripheral cues) on emotional states (pleasure and arousal) and mean
differences in emotional states over different treatment groups were estimated and
compared using multi-group structural equation modeling. Part 2 (Hypotheses 3)
investigating the effects of emotions on consumers’ response behaviors (satisfaction,
purchase intention, and approach behaviors) was analyzed using a single group structural
equation model. Part 3 (Hypothesis 4) of the proposed model assessing the effects of
situational involvement as a moderator between S-O was tested using multi-group
structural equation modeling. Part 4 (Hypothesis 5) investigated the mediating effects of
emotions on the relationship between type of cue and consumers’ response behaviors and
the model was assessed using multi-group structural equation modeling. See Figure 5.3
for the proposed model in Study 2.
186
Figure 5.3. The proposed model in Study 2.
Central Cues
Peripheral Cues
Purchase Intention
Arousal
Pleasure
Situational Involvement
Approach Behavior
Satisfaction H1a
H1b
H2a
H2b
H4a and H4b
H3a
H3b
H3d
H3c
H3e
H3f
Part Four
Part One Part Two
Part Three
H5a and H5b
Stimulus Organism Response
187
Part One (Hypotheses 1 and 2)
The first part of the proposed model investigated the effects of central cues and
peripheral cues on consumers’ emotional states such as pleasure and arousal. Mean
differences in emotional states over different treatment groups were estimated and
compared using multi-group structural equation modeling. Means of two exogenous
latent variables in the model (Figure 5.1) were compared over groups with different
levels of central cues (Hypothesis 1) or peripheral cues (Hypothesis 2).
Hypothesis 1. Central cues (e.g., number of different product views-front, back,
sides, details, amount of verbal information, and amount of mix and match
suggestions) will influence consumers’ emotional reactions experienced from an
apparel website.
Hypothesis 1 was tested using multi-group structural equation modeling. The
purpose of Hypothesis 1 was to examine the influence of different amounts of central
cues on emotional states (pleasure and arousal). The model included two latent variables
(pleasure and arousal) with 6 manifest variables (Figure 5.1). Means of pleasure and
arousal were compared by two groups with the high amount and the medium amount of
central cues. Means of the latent variables were set to zero in the group with the high
amount of central cues (group 1) but estimated in the group with the medium amount of
central cues (group 2). Factor loadings for P1 and A3 posited to measure the latent
188
variables were set to 1 for the identification purpose (Figure 5.1). The model was
analyzed using the maximum likelihood (ML) function with a covariance matrix.
Model fit. Since the AGFI is not measurable in a multi-group comparison, the
model fit was assessed by chi-square, RMSEA, GFIs, and NNFI. An overall chi-square
(df=24) was not significant (χ2 = 30.28, p = .17), indicating that the null hypothesis of
perfect fit was not rejected. The GFIs for each of the two groups (high amount vs.
medium amount of central cues) were calculated (.99 and 1.00, respectively). The NNFI
was 1.00 and the RMSEA value was .018. All fit indices were within acceptable ranges
(See Table 4.11) and indicated a good fit of the proposed model in Hypothesis 1 to the
data. All path coefficients for the model were significant, supported by significant t-
values. See Table 5.12 for the summary of the model fit. Figure 5.4 (unstandardized
solution) and Figure 5.5 (completely standardized solution) show all parameter estimates
calculated in the proposed model.
189
Parameters ML estimates
Standard errors
t-values
Model Paths Pleasure (ξ1) P1 λx11 1.00 n/aa n/aa
Pleasure (ξ1) P2 λx21 1.30 .04 33.81*** Pleasure (ξ1) P4 λx31 1.13 .03 32.71*** Arousal (ξ2) A3 λx41 1.00 n/aa n/aa
Arousal (ξ2) A5 λx51 1.58 .07 21.72*** Arousal (ξ2) A6 λx61 1.35 .06 21.25*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .34 .03 10.60*** Model fit Chi-square (χ2) 30.38
df = 24 p = .17
RMSEA .018 GFI Group 1=.99
Group 2=1.00
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.12. Summary of the model fit for the proposed model in Hypothesis 1.
190
Note. All path coefficients were significant.
Figure 5.4. Unstandardized parameter estimates in the proposed model for Hypothesis 1.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
1 1.30 1.13
1 1.58 1.35
.34
191
Figure 5.5. Completely standardized parameter estimates for Hypothesis 1.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
.79 .85 .80
.67 .77 .70
.53
192
Hypothesis 1a. As compared to those exposed to the website with a medium
amount of central cues, consumers exposed to the website with a high amount of
central cues will experience more pleasure.
Hypothesis 1a proposed that participants exposed to the website with the high
amount of central cues (group 1) will experience more pleasure than those exposed to the
medium amount of central cues (group 2). The estimated group means of the latent
variables are shown in Table 5.13. The mean of group 2 is interpreted as the mean
difference in pleasure between two groups with different amount of central cues. The
mean of pleasure for group 2 was -.14 with a significant t-value, indicating that
participants in group 1 (high amount of central cues) experienced significantly more
pleasure than those in group 2 (medium amount of central cues). Thus, Hypothesis 1a
was supported.
Hypothesis 1b. As compared to those exposed to the website with a medium
amount of central cues, consumers exposed to the website with a high amount of
central cues will experience more arousal.
Hypothesis 1a proposed that participants exposed to the website with the high
amount of central cues (group 1) will experience more arousal than those exposed to the
medium amount of central cues (group 2). The estimated group means of the latent
variables are presented in Table 5.13. The mean of group 2 is interpreted as the mean
difference in arousal between two groups with different amount of central cues. The
193
mean of pleasure for group 2 was -.05, indicating that participants in group 1 experienced
more arousal than those in group 2. However, the mean difference (t = -1.27) was not
statistically significant. Thus, Hypothesis 1b was not supported.
Pleasure Arousal High amount of central cues (Group 1, N=830)
0 0
Medium amount of central cues (Group 2, N = 804)
-.14 (.05)a
-2.96b**
-.05 (.04)a
-1.27b
Note. a Standard error, b t-values; ** p < .005
Table 5.13. Estimated means of pleasure and arousal in Hypothesis 1.
194
Hypothesis 2. Peripheral cues (e.g., pictorial icons and background colors) will
influence consumers’ emotional reactions experienced from the apparel website.
Hypothesis 2 was tested using multi-group structural equation modeling. The
focus of Hypothesis 2 was to assess the effects of presence or absence of peripheral cues
on emotional states (pleasure and arousal). Two exogenous latent variables (pleasure and
arousal) with 6 manifest variables were included in the proposed model (Figure 5.1).
Means of pleasure and arousal were estimated and compared across two treatment groups
manipulated by the presence or absence of peripheral cues. Means of the latent variables
were set to zero in the group with the presence of peripheral cues (group 1) but estimated
in the group with the absence of peripheral cues (group 2). Factor loadings for P1 and A3
posited to measure the latent variables were set to 1 for identification purposes (Figure
5.1). The model was tested using the maximum likelihood (ML) procedure with a
covariance matrix.
Model fit. The model fit was assessed by chi-square, RMSEA, GFIs, and NNFI.
An overall chi-square (df=24) was not significant (χ2 = 33.05, p = .10), indicating that the
null hypothesis of perfect fit was not rejected. The GFI for both groups (presence vs.
absence of peripheral cues) was .99. The NNFI was 1.00 and the RMSEA value was .021.
All fit indices were within acceptable ranges (See Table 4.11), supporting a good fit of
the proposed model in Hypothesis 2 to the data. All path coefficients for the model were
significant, supported by significant t-values. Table 5.14 presents the summary of the
195
model fit. Figure 5.6 (unstandardized solution) and Figure 5.7 (completely standardized
solution) show all parameter estimates estimated in the proposed model.
Parameters ML estimates
Standard errors
t-values
Model Paths Pleasure (ξ1) P1 λx11 1.00 n/aa n/aa
Pleasure (ξ1) P2 λx21 1.31 .04 33.79*** Pleasure (ξ1) P4 λx31 1.14 .03 32.64*** Arousal (ξ2) A3 λx41 1.00 n/aa n/aa
Arousal (ξ2) A5 λx51 1.60 .07 21.58*** Arousal (ξ2) A6 λx61 1.37 .06 21.14*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .31 .03 10.12*** Model fit Chi-square (χ2) 33.05
df = 24 p = .10
RMSEA .021 GFI Group 1=.99
Group 2=.99
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.14. Summary of the model fit for the proposed model in Hypothesis 2.
196
Note. All path coefficients were significant.
Figure 5.6. Unstandardized parameter estimates in the proposed model for Hypothesis 2.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
1 1.31 1.14
1 1.60 1.37
.31
197
Figure 5.7. Completely standardized parameter estimates for Hypothesis 2.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
.79 .85 .80
.66 .77 .70
.51
198
Hypothesis 2a. As compared to those exposed to the website without peripheral
cues, consumers exposed to the website with peripheral cues will experience more
pleasure.
Hypothesis 2a suggested that participants who browsed the website with the
peripheral cues present (group 1) will experience more pleasure than those who browsed
the website without peripheral cues (group 2). The group means of the latent variables
are shown in Table 5.15. The mean of group 2 is interpreted as the mean difference in
pleasure between two groups manipulated by the presence or absence of peripheral cues.
The mean of pleasure for group 2 was -.09, suggesting that participants in group 2
(absence of peripheral cues) experienced less pleasure than those in group 1 (presence of
peripheral cues). However, the mean difference (t = -1.91) was not statistically
significant. Therefore, Hypothesis 2a was not supported.
Hypothesis 2b. As compared to those exposed to the website without peripheral
cues, consumers exposed to the website with peripheral cues will experience more
arousal.
Hypothesis 2b suggested that participants exposed to the website with the
peripheral cues present (group 1) will experience more arousal than those exposed to the
website without peripheral cues (group 2). The mean of group 2 is interpreted as the
mean difference in arousal between two groups manipulated by the presence or absence
of peripheral cues. The group means of the latent variables are presented in Table 5.15.
199
The mean of arousal for group 2 was -.13 with a significant t-value, supporting that
participants in group 2 (absence of peripheral cues) experienced significantly less arousal
than those in group 1 (presence of peripheral cues). Thus, Hypothesis 2b was supported.
Pleasure Arousal Presence of peripheral cues (Group 1, N=807)
0 0
Absence of peripheral cues (Group 2, N = 827)
-.09 (.05)a -1.91b
-.13 (.04)a
-3.14b**
Note. a Standard error, b t-values; ** p < .005
Table 5.15. Estimated means of pleasure and arousal in Hypothesis 2.
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Part Two (Hypothesis 3)
The second part of the proposed model investigated the effects of emotional states
(pleasure and arousal) induced by central cues and peripheral cues presented in the
websites on consumer response behaviors (satisfaction, purchase intention, and approach
behaviors). The proposed model in Part 2 (Figure 5.8) consisted of five latent variables
with 15 indicators (manifest variables). Two latent constructs (pleasure and arousal)
were exogenous latent variables (ξ) and the other three latent constructs (satisfaction,
purchase intention, and approach behaviors) were endogenous latent variables (η). The
model was analyzed using the maximum likelihood (ML) function with a covariance
matrix.
Hypothesis 3. Emotional states will be positively related to consumers’ response
behaviors.
Model fit. Hypothesis 3 was tested using single group structural equation
modeling. To assess the fit of the model to the data, chi-square, RMSEA, GFI, AGFI,
and NNFI were computed. The results of fitting the structural model to the data indicated
that the model had a good fit (Table 5.16). All path coefficients for the measurement
model were significant, indicating the validity of the observed variables posited to
measure latent constructs. The specified relationships between emotional states and
consumer response behaviors were statistically significant, as supported by significant t-
values. An overall chi-square was 351.61 (df = 80, p < .0001). Since the chi-square
201
statistic is sensitive to large sample sizes, the significant chi-square is not surprising. All
other fit indices were within acceptable ranges (See Table 4.11), suggesting that the
proposed model in Part 2 fits the data very well. The RMSEA (.046) indicated a close fit
of the model according to Brown and Cudeck (1992). The GFI was .97, the AGFI
was .96, and the NNFI was 1.00. Table 5.16 presents the results of the model fit
including all path coefficients for the measurement and structural models. Figure 5.8 and
5.9 show all parameter estimates (unstandardized and standardized) calculated in the
proposed model.
202
Parameters ML estimates
Standard errors
t-values
Structural Model Pleasure (ξ1) Satisfaction (η1) γ11 .83 .05 17.63*** Pleasure (ξ1) Purchase Intention (η2) γ21 .33 .04 8.79*** Pleasure (ξ1) Approach Behaviors (η3) γ31 .57 .04 13.80*** Arousal (ξ2) Satisfaction (η1) γ12 .23 .04 5.46*** Arousal (ξ2) Purchase Intention (η2) γ22 .30 .04 7.40*** Arousal (ξ2) Approach Behaviors (η3) γ31 .27 .04 6.70*** Measurement Model Pleasure (ξ1) P1 λx11 .93 .02 37.28*** Pleasure (ξ1) P2 λx21 1.17 .03 39.61*** Pleasure (ξ1) P4 λx31 1.02 .03 36.85*** Arousal (ξ2) A3 λx41 .70 .03 26.02*** Arousal (ξ2) A5 λx51 1.12 .04 30.79*** Arousal (ξ2) A6 λx61 .98 .03 28.60*** Satisfaction (η1) SA1 λy11 .66 .02 39.11*** Satisfaction (η1) SA 2 λy21 .67 .02 38.29*** Satisfaction (η1) SA 4 λy31 .60 .02 31.98*** Purchase Intention (η2) PI1 λy41 .93 .02 41.41*** Purchase Intention (η2) PI2 λy51 .91 .02 39.63*** Purchase Intention (η2) PI4 λy61 .79 .02 33.43*** Approach Behaviors (η3) AB1 λy71 .69 .02 34.89*** Approach Behaviors (η3) AB3 λy81 .64 .02 37.26*** Approach Behaviors (η3) AB4 λy91 .76 .02 42.09*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .52 .02 21.45*** Satisfaction (η1) Purchase Intention (η2) ψ21 .51 .02 21.08*** Satisfaction (η1) Approach Behaviors(η3) ψ31 .68 .02 35.18*** Purchase Intention (η2) Approach Behaviors (η3)
ψ32 .76 .02 48.72***
Model fit Chi-square (χ2) 351.61
df = 80 p < .0001
RMSEA .046 C.I. (.041; .051) GFI .97 AGFI .96 NNFI 1.00
Note. ***p< .001
Table 5.16. Summary of measurement and structural models and model fit in Part 2.
203
Note. All path coefficients were significant.
Figure 5.8. Unstandardized parameter estimates in the proposed model for Part 2.
Arousal ξ2
Pleasure ξ1
Approach Behavior
η3
Satisfaction η1
P1 P2 P4
SA1 SA2 SA4
A3 A5 A6
AB1 AB3 AB4
.93 1.17 1.02
.70 1.12 .98
.66 .67 .60
.69 .64 .76
.83
.33
.23
.30
1
1
Purchase Intention
η2
.27
.57 PI1
PI2
PI4
.93
.91
.79
.52
ς1
ς2
ς3
.51
.76
.68
1
1
1
204
Figure 5.9. Completely standardized parameter estimates in the proposed model for Part 2.
Arousal ξ2
Pleasure ξ1
Approach Behavior
η3
Satisfaction η1
P1 P2 P4
SA1 SA2 SA4
A3 A5 A6
AB1 AB3 AB4
.81 .84 .80
.65 .76 .71
.91 .89 .76
.79 .83 .91
.60
.29
.16
.26
Purchase Intention
η2
.22
.45 PI1
PI2
PI4
.89
.86
.75
ς1
ς2
ς3
.52
.80
.65
.82
205
Hypothesis 3a. Pleasure will be positively related to consumers’ satisfaction.
Hypothesis 3a proposed that pleasure induced by the web cues presented in the
apparel website will be positively related to satisfaction. The significant path coefficient
showed a positive effect for pleasure on satisfaction (γ11 = .83, t = 17.63, p < .001),
indicating that participants who experienced more pleasure while browsing the websites
tended to have higher satisfaction with the website they browsed. Therefore, Hypothesis
3a was supported.
Hypothesis 3b. Pleasure will be positively related to consumers’ purchase
intention.
Hypothesis 3b predicted that pleasure induced by the web cues shown in the
apparel website will be positively related to purchase intention. The significant path
coefficient indicated a positive effect for pleasure on purchase intention (γ12 = .33, t =
8.79, p < .001), suggesting that participants who experienced more pleasure while
browsing the websites were likely to have higher purchase intention toward the website
they browsed. Thus, Hypothesis 3b was supported.
206
Hypothesis 3c. Pleasure will be positively related to consumers’ approach
behaviors (desire to explore or shop and likability of the websites).
Hypothesis 3c suggested that pleasure induced by the web cues offered on the
apparel website will be positively related to approach behaviors. The significant path
coefficient showed a positive effect of pleasure on approach behaviors (γ13 = .57, t =
13.80, p < .001), supporting that participants who experienced more pleasure when
browsing the apparel websites were likely to have more positive approach behaviors.
Consequently, Hypothesis 3c was supported.
Hypothesis 3d. Arousal will be positively related to consumers’ satisfaction.
Hypothesis 3d proposed that arousal induced by the web cues presented in the
apparel websites will be positively related to consumer satisfaction. The significant path
coefficient indicated a positive effect of arousal on satisfaction (γ12 = .23, t = 5.46, p
< .001), implying that participants who experienced more arousal while browsing the
apparel websites were more likely to be satisfied with their shopping experience at the
mock websites. Therefore, Hypothesis 3d was supported.
207
Hypothesis 3e. Arousal will be positively related to consumers’ purchase
intention.
Hypothesis 3e predicted that arousal induced by the web cues shown in the
apparel websites will be positively related to consumer purchase intention. The
significant path coefficient showed a positive effect for arousal on purchase intention (γ22
= .30, t = 7.40, p < .001), indicating that participants who experienced more arousal while
browsing the websites tended to have higher purchase intention toward the website they
browsed. Thus, Hypothesis 3e was supported.
Hypothesis 3f. Arousal will be positively related to consumers’ approach
behaviors (desire to explore or shop and likability of the websites).
Hypothesis 3f suggested that arousal induced by the web cues offered on the
apparel website will be positively related to approach behaviors. The significant path
coefficient indicated a positive effect for arousal on approach behaviors (γ33 = .27, t =
6.70, p < .001), indicating that participants who experienced more arousal when browsing
the apparel websites were likely to exhibit more approach behaviors. Therefore,
Hypothesis 3f was supported.
208
Part Three (Hypotheses 4)
The third part of the proposed model investigated if situational involvement
moderates the relationship between web cues (central cues and peripheral cues) and
consumers’ emotional states (pleasure and arousal). To test Hypothesis 4 mean
differences in emotional states over different treatment groups were estimated and
compared according to two different situational involvements using multi-group
structural equation modeling.
Hypothesis 4. Situational involvement will moderate the relationship between
web cues and emotional reactions.
Hypothesis 4 was assessed using multi-group structural equation modeling. The
focus of Hypothesis 4 was to assess the moderating effects of the situational involvement
on the relationship between web cues and emotional states (pleasure and arousal). Two
exogenous latent variables (pleasure and arousal) with 6 manifest variables (indicators)
were included in the proposed model (See Figure 5.1). Means of pleasure and arousal
induced by different web cues were estimated and compared across the two levels of
situational involvement. Means of pleasure and arousal were scaled to zero in the group
with the high amount of central cues in Hypothesis 4a and in the group with the presence
of peripheral cues in Hypothesis 4b. Means of the latent variables were estimated in the
group with the medium amount of central cues in Hypothesis 4a and with absence of
peripheral cues in Hypothesis 4b. Factor loadings for P1 and A3 posited to measure the
209
latent variables were set to 1 for identification purposes (Figure 5.10). The model was
tested using the maximum likelihood (ML) procedure with a covariance matrix.
Hypothesis 4a. Central cues will have a stronger effect on emotional reactions
under a high involvement situation than under a low involvement situation.
Hypothesis 4a proposed that the relationship between central cues and emotional
states (pleasure and arousal) will be moderated by situational involvement. The sample
was split into high and low involvement. The multi-group structural equation modeling
was conducted for each group (high vs. low involvement). Mean differences of pleasure
and arousal induced by different amount of central cues were estimated and compared
across the two different levels of involvement to test Hypothesis 4a.
Fit of the model in the high involvement condition (Model 1). To assess the fit of
the model to the data, chi-square, RMSEA, GFI, and NNFI were computed. An overall
chi-square was 29.96 (df = 24, p = .186), indicating the null hypothesis of perfect fit was
not rejected. All other fit indices were within acceptable ranges (See Table 4.11),
suggesting that the model fits the data very well. The RMSEA (.024) indicated a close fit
of the model according to Brown and Cudeck (1992). The GFI for each of the two
treatment groups (high amount (group 1) vs. medium amount of central cues (group 2))
was .99. The NNFI was 1.00. Table 5.17 presents the summary of the model fit
including all path coefficients for the measurement model. Figure 5.10 and 5.11 show all
parameter estimates (unstandardized and standardized) calculated in the model.
210
Fit of the model in the low involvement condition (Model 2). The model fit was
assessed by chi-square, RMSEA, GFI, and NNFI. The non-significant chi-square
indicated that the null hypothesis of perfect fit was not rejected (χ2 = 22.56, df = 24, p
= .546). The GFI for each of the two treatment groups [high amount (group 1) vs.
medium amount of central cues (group 2)] was .99. The NNFI was 1 and the RMSEA
value was .010. All fit indices within acceptable ranges (See Table 4.11) and provided
strong evidence of a good model fit. All path coefficients for the model were significant,
supported by significant t-values. See Table 5.18 for the results of the model fit. Figure
5.10 and 5.11 show all parameter estimates (unstandardized and standardized) calculated
in the model.
211
Parameters ML estimates
Standard errors
t-values
Model Paths Pleasure (ξ1) P1 λx11 1.00 n/aa n/aa
Pleasure (ξ1) P2 λx21 1.23 .05 24.17*** Pleasure (ξ1) P4 λx31 1.12 .05 24.01*** Arousal (ξ2) A3 λx41 1.00 n/aa n/aa
Arousal (ξ2) A5 λx51 1.70 .11 14.88*** Arousal (ξ2) A6 λx61 1.38 .09 14.58*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .30 .04 7.38*** Model fit Chi-square (χ2) 29.96
df = 24 p = .186
RMSEA .024 GFI Group 1=.99
Group 2=.99
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.17. Summary of the model fit for Model 1 (high involvement) in Hypothesis 4a.
212
Parameters ML estimates
Standard errors
t-values
Model Paths Pleasure (ξ1) P1 λx11 1.00 n/aa n/aa
Pleasure (ξ1) P2 λx21 1.39 .06 23.08*** Pleasure (ξ1) P4 λx31 1.15 .05 21.78*** Arousal (ξ2) A3 λx41 1.00 n/aa n/aa
Arousal (ξ2) A5 λx51 1.48 .09 15.77*** Arousal (ξ2) A6 λx61 1.32 .09 15.39*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .35 .05 7.25*** Model fit Chi-square (χ2) 22.56
df = 24 p = .546
RMSEA .010 GFI Group 1=.99
Group 2=.99
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.18. Summary of the model fit for Model 2 (low involvement) in Hypothesis 4a.
213
Note. All path coefficients (high involvement/low involvement) were significant.
Figure 5.10. Unstandardized parameter estimates in Models 1 and 2 for Hypothesis 4a.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
1 1.23/ 1.39 1.12/
1.15
1 1.70/1.48
1.38/1.32
.30/.35
214
Note. Path coefficients: high involvement/low involvement
Figure 5.11. Completely standardized parameter estimates in Models 1 and 2 for Hypothesis 4a.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
.80/.78 .82/.88 .82/.79
.63/.69 .77/.77 .68/.71
.47/.52
215
Mean comparisons. Hypothesis 4a proposed that central cues will have a stronger
effect on emotional states (pleasure and arousal) under the high involvement situation
(Model 1) than under the low involvement situation (Model 2). The estimated group
means of the latent variables (pleasure and arousal) in Model 1 and Model 2 are shown in
Table 5.19. The means of group 2 (medium amount of central cues) are interpreted as the
mean differences in pleasure and arousal between the two groups with different amounts
of central cues. As shown in Table 5.19, the effects of different amounts of central cues
on emotional states were significant only in the high involvement condition, supporting a
moderating effect of situational involvement between central cues and emotional states.
In Model 1, the estimated means of pleasure and arousal for group 2 were -.24 and -.21,
respectively with significant t-values, indicating that participants in group 2 (medium
amount of central cues) experienced significantly less pleasure and less arousal than those
in group 1 (high amount of central cues) under the high involvement situation. Whereas
in Model 2, the estimated means of two emotional states were not significantly different,
suggesting that emotional states were not significantly influenced by different amounts of
central cues in the low involvement situation. Thus, Hypothesis 4a was supported.
216
Model 1 (High involvement) Model 2 (Low involvement) Pleasure Arousal Pleasure Arousal High amount of central cues (Group 1, N= 440)
0 0 0 0
Medium amount of central cues (Group 2, N = 427)
-.24 (.07)a
-3.29b***
-.21 (.07)
-3.00**
-.07 (.08) -.87
.10 (.08) 1.16
Note. a Standard error, b t-values; ** p < .005, *** p < .001
Table 5.19. Estimated means of pleasure and arousal in Hypothesis 4a.
Hypothesis 4b. Peripheral cues will have a stronger effect on emotional reactions
under a low involvement situation than under a high involvement situation.
Hypothesis 4b predicted that the relationship between peripheral cues and
emotional states (pleasure and arousal) will be moderated by different levels of
situational involvement. The sample was divided into high and low involvement as a
function of the verbal instructions. The multi-group structural equation modeling was
conducted for each group (high vs. low involvement). Mean differences of pleasure and
arousal induced by the presence or absence of peripheral cues were estimated and
compared by two different involvement groups to test Hypothesis 4b.
217
Fit of the model in the high involvement condition (Model 1). To test the fit of the
model to the data, chi-square, RMSEA, GFI, and NNFI were calculated. An overall chi-
square was 28.23 (df = 24, p = .251), indicating that the null hypothesis of perfect fit was
not rejected. All other fit indices within acceptable ranges (See Table 4.11) also
supported model fit. The small RMSEA (.020) indicated a close fit of the model. The
GFI for each of the two treatment groups [presence (group 1) vs. absence of peripheral
cues (group 2)] was .99. The NNFI was 1.00. Table 5.20 shows the summary of the
model fit including all path coefficients for the measurement model. Figure 5.12 and
5.13 illustrate all parameter estimates (unstandardized and standardized) measured in the
model.
Fit of the model in the low involvement condition (Model 2). The model fit was
assessed by chi-square, RMSEA, GFI, and NNFI. The non-significant chi-square
indicated that the null hypothesis of perfect fit was not rejected (χ2 = 29.04, df = 24, p
= .219). The GFI for each of the two treatment groups [presence (group 1) vs. absence of
peripheral cues (group 2)] was .99. The NNFI was 1. The RMSEA value was .023,
indicating a close fit of the model. All fit indices within acceptable ranges (See Table
4.11) provided strong evidence of a good fit. All path coefficients for the model were
significant, supported by significant t-values. See Table 5.21 for the results of the model
fit. Figure 5.12 and 5.13 show all parameter estimates (unstandardized and standardized)
computed in the model.
218
Parameters ML estimates
Standard errors
t-values
Model Paths Pleasure (ξ1) P1 λx11 1.00 n/aa n/aa
Pleasure (ξ1) P2 λx21 1.25 .05 24.21*** Pleasure (ξ1) P4 λx31 1.13 .05 24.02*** Arousal (ξ2) A3 λx41 1.00 n/aa n/aa
Arousal (ξ2) A5 λx51 1.73 .12 14.59*** Arousal (ξ2) A6 λx61 1.42 .10 14.39*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .29 .04 7.32*** Model fit Chi-square (χ2) 28.23
df = 24 p = .251
RMSEA .020 GFI Group 1=.99
Group 2=.99
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.20. Summary of the model fit for Model 1 (high involvement) in Hypothesis 4b.
219
Parameters ML estimates
Standard errors
t-values
Model Paths Pleasure (ξ1) P1 λx11 1.00 n/aa n/aa
Pleasure (ξ1) P2 λx21 1.40 .06 23.11*** Pleasure (ξ1) P4 λx31 1.15 .05 21.77*** Arousal (ξ2) A3 λx41 1.00 n/aa n/aa
Arousal (ξ2) A5 λx51 1.49 .10 15.63*** Arousal (ξ2) A6 λx61 1.33 .09 15.25*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .33 .05 6.89*** Model fit Chi-square (χ2) 29.04
df = 24 p = .219
RMSEA .023 GFI Group 1=.99
Group 2=.99
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.21. Summary of the model fit for Model 2 (low involvement) in Hypothesis 4b.
220
Note. All path coefficients (high involvement/low involvement) were significant.
Figure 5.12. Unstandardized parameter estimates in Models 1 and 2 for Hypothesis 4b.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
1 1.25/ 1.40 1.13/
1.15
1 1.73/1.49
1.42/1.33
.29/.33
221
Note. Path coefficients: high involvement/low involvement
Figure 5.13. Completely standardized parameter estimates in Models 1 and 2 for Hypothesis 4b.
Arousal ξ2
Pleasure ξ1
P1 P2 P4
A3 A5 A6
.80/.77 .83/.88 .82/.78
.62/.69 .77/.79 .69/.70
.51/.51
222
Mean comparisons. Hypothesis 4b predicted that peripheral cues will have a
stronger effect on emotional states (pleasure and arousal) under the high involvement
situation (Model 1) than under the low involvement situation (Model 2). The estimated
group means of the latent variables (pleasure and arousal) in Model 1 and Model 2 are
shown in Table 5.22. The means of group 2 (absence of peripheral cues) are interpreted
as the mean differences in emotional states between groups with different levels of
peripheral cues. As presented in Table 5.22, the effect of peripheral cues on pleasure and
arousal was significant only in the low involvement situation, supporting a moderating
effect for situational involvement between peripheral cues (stimuli) and emotional states
(organism). Under the low involvement situation the estimated means of pleasure and
arousal for group 2 were -.17 and -.25, respectively with significant t-values, indicating
that in the low involvement condition participants exposed to the websites without
peripheral cues (group 2) experienced significantly less pleasure and arousal than those
exposed to the websites with the presence of peripheral cues (group 1). Whereas under
the high involvement situation the estimated means of two emotional states were not
significant, suggesting that emotional states were not significantly influenced by the
presence or absence of peripheral cues in the high involvement condition. Therefore,
Hypothesis 4b was supported.
223
Model 1 (High involvement) Model 2 (Low involvement) Pleasure Arousal Pleasure Arousal Presence of peripheral cues (Group 1, N=405)
0 0 0 0
Absence of peripheral cues (Group 2, N = 425)
-.03 (.07)a -.41b
-.10 (.07)
-1.32
-.17 (.08)
-2.14*
-.25 (.08)
-2.95**
Note. a Standard error, b t-values; * p < .05, ** p < .005
Table 5.22. Estimated means of pleasure and arousal in Hypothesis 4b.
224
Part Four (Hypothesis 5)
The results of Part 1 and Part 3 revealed the significant effects of different web
cues on emotional states when browsing the apparel websites. In addition, the result of
Part 2 supported the significant effects of emotional states on consumers’ response
behaviors. By taking the whole model into account, the fourth part of the proposed
model in Study 2 investigated the mediating effects of emotional states between web cues
and consumers’ response behaviors. In Part 4, to assess the mediating effects of
emotional states Hypothesis 5 tested to determine 1) the direct effect of web cues on
consumers’ response behaviors (satisfaction, purchase intention, and approach behaviors)
and 2) the change in the magnitude of the effect of web cues on consumers’ response
behaviors when the mediators (pleasure and arousal) were added to the model. The
models were tested using the maximum likelihood (ML) procedure with a covariance
matrix.
Hypothesis 5. Emotional states will mediate the relationship between type of cue
and consumers’ response behaviors.
The significant effects of different web cues on emotional states were found in
Part 1 and Part 3 and the significant effects of emotional states on consumers’ response
behaviors were supported in Part 2. Hypothesis 5 assessed the mediating effects of
emotional states on the relationship between web cues and consumers’ response
behaviors by testing the change in the magnitude of the effects of web cues on
225
consumers’ response behaviors when mediating variables (pleasure and arousal) were
added to the model that was used to test the direct effects of web cues on response
behaviors. If the effect of web cues on consumers’ response behaviors decreases when
emotional states are added to the model, it indicates the significant mediating effect of
emotional states between web cues and response behaviors. The proposed model in
Hypothesis 5 (Figure 5.16) consisted of five latent variables with 15 indicators (manifest
variables). Two latent constructs (pleasure and arousal) were exogenous latent variables
(ξ) and the other three latent constructs (satisfaction, purchase intention, and approach
behaviors) were endogenous latent variables (η). Multi-group structural equation
modeling was used to test Hypotheses 5a and 5b. The sample was split by two levels of
central cues for Hypothesis 5a and two levels of peripheral cues for Hypothesis 5b. The
models were tested using the maximum likelihood (ML) procedure with a covariance
matrix.
Before assessing the mediating effects of emotional states on the relationship
between web cues and response behaviors, the direct effects of web cues on response
behaviors should be tested (Baron & Kenny, 1986). Thus, multi-group structural
equation modeling was conducted to test the direct effects of web cues on consumers’
response behaviors. Mean differences of three latent variables were estimated and
compared by different levels of web cues presented on the websites. The proposed model
(Figure 5.14) consisted of three exogenous latent variables (ξ) with 9 indicators (manifest
variables). Factor loadings for SA1, PI1 and AB1 posited to measure the latent variables
were set to 1 for the identification purposes.
226
Hypothesis 5a. The relationships between central cues and response behaviors
(satisfaction, purchase intention, and approach behaviors) will be mediated by
emotional states.
Hypothesis 5a predicted the mediating effects of emotional states between central
cues and response behaviors in the proposed model. The sample was split by two
different levels of central cues (high amount vs. medium amount). To test for the
mediating effects, first the direct effect of central cues on consumers’ response behaviors
was assessed. Second, the change in the extent of the effects of central cues on consumer
response behaviors when emotional states were added to the model was tested.
The direct effects of central cues on response behaviors. The sample was split by
the two levels of central cues to compare the means of response behaviors across groups
exposed to different amounts of central cues. Means of three latent constructs were
scaled to zero in the group with the high amount of central cues. Means of the latent
variables were estimated in the group with the medium amount of central cues.
It was predicted that participants exposed to the website with the high amount of
central cues (group 1) would have greater satisfaction, purchase intention, and approach
behaviors than those exposed to the medium amount of central cues (group 2). The
model fit was assessed by chi-square, RMSEA, GFIs, and NNFI. An overall chi-square
(df=69) was significant (χ2 = 290.57, p <.0001) which is not surprising with the large
sample size. The GFIs for both groups (high amount vs. medium amount of central cues)
were .95 and .97, respectively. The NNFI was .99. The RMSEA value was .063,
227
indicating a fair fit (Brown & Cudeck, 1992; MacCallum et al., 1996). All fit indices
were within acceptable ranges (See Table 4.11), supporting a moderate fit of the model to
the data. All path coefficients for the model were significant, supported by significant t-
values. Table 5.23 presents the summary of the model fit and parameter estimates
measured in the model.
The estimated group means of the latent variables are shown in Table 5.24. The
mean of group 2 is interpreted as the mean difference in consumers’ response behaviors
between two groups exposed to different amount of central cues. The means of
satisfaction, purchase intention, and approach behaviors were -.12, -.05, and -.09,
respectively, supporting that participants in group 1 (high amount of central cues) had
higher satisfaction, purchase intention, and approach behaviors than those in group 2
(medium amount of central cues). The mean difference for satisfaction and approach
behaviors were supported by significant t-values (See Table 5.24). However, the effect
on purchase intention was not statistically significant. Thus, the prediction of direct
effects for central cues on response behaviors was partially supported.
228
Parameters ML estimates
Standard errors
t-values
Model Paths Satisfaction (η1) SA1 λy11 1 n/aa n/aa Satisfaction (η1) SA 2 λy21 1.01 .02 49.27*** Satisfaction (η1) SA 4 λy31 .91 .02 38.17*** Purchase Intention (η2) PI1 λy41 1 n/aa n/aa Purchase Intention (η2) PI2 λy51 .98 .02 43.80*** Purchase Intention (η2) PI4 λy61 .85 .02 36.37*** Approach Behaviors (η3) AB1 λy71 1 n/aa n/aa Approach Behaviors (η3) AB3 λy81 .93 .03 36.88*** Approach Behaviors (η3) AB4 λy91 1.11 .03 41.55*** Model fit Chi-square (χ2) 290.57
df = 69 p < .0001
RMSEA .063 C.I. (.055; .070) GFI Group 1 = .95
Group 2 = .97
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.23. Summary of the model fit for the model tested the direct effects of central cues on response behaviors.
229
Satisfaction Purchase Intention
Approach Behaviors
High amount of central cues (Group 1, N=830)
0 0 0
Medium amount of central cues (Group 2, N = 804)
-.12 (.05)a
-2.48b*
-.05 (.06) -.84
-.09 (.04)
-1.97*
Note. a Standard error, b t-values; * p < .05
Table 5.24. Estimated means of satisfaction, purchase intention, and approach behaviors.
Mediating effects of emotional states. In the next step the change in the
magnitude of the influence of central cues on response behaviors when the mediators
(pleasure and arousal) were added to the model was assessed using multi-group structural
equation modeling. The intercepts of endogenous latent variables (satisfaction, purchase
intention, and approach behavior) were computed to test the group difference. The
intercepts were set to zero in the group with the high amount of central cues (group 1) but
estimated in the group with the medium amount of central cues (group 2). The intercepts
of group 2 are interpreted as a measure of the effect of central cues on consumers’
response behaviors in the model.
The model fit was assessed by chi-square, RMSEA, GFI, and NNFI. An overall
chi-square was 460.12 (df = 191, p < .0001). Although the chi-square statistic was
230
significant, it was not surprising with the large sample size and the large number of
indicators. All other fit indices indicated a good fit of the model to the data (See Table
4.11 for acceptable ranges). The GFI for each of two treatment groups [high amount
(group 1) vs. medium amount of central cues (group 2)] was .96 and .97, respectively.
The NNFI was .99. The RMSEA value was .042, indicating a close fit of the model. All
path coefficients for the measurement and structural model were significant, supported by
significant t-values. See Table 5.25 for the results of the model fit. Figure 5.14
(unstandardized solution) and Figure 5.15 (completely standardized solution) show all
parameter estimates estimated in the proposed model.
The intercepts of satisfaction, purchase intention, and approach behaviors were
presented in Table 5.26. No intercepts in group 2 were significantly different from those
in group 1, indicating that there was no significant effect for central cues on consumers’
response behaviors when emotional state variables were added. However, significant
structural coefficients revealed the significant effects of pleasure and arousal induced by
different amounts of central cues on consumer response behaviors (See Table 5.25). In
sum, the results suggested that the relationship between central cues and response
behaviors (satisfaction, purchase intention, and approach behaviors) were not direct and
tended to be mediated by emotional states (pleasure and arousal). Therefore, Hypothesis
5a was supported.
231
Parameters ML estimates
Standard errors
t-values
Structural Model Pleasure (ξ1) Satisfaction (η1) γ11 .82 .05 17.57*** Pleasure (ξ1) Purchase Intention (η2) γ21 .34 .04 8.81*** Pleasure (ξ1) Approach Behaviors (η3) γ31 .57 .04 13.78*** Arousal (ξ2) Satisfaction (η1) γ12 .22 .04 5.44*** Arousal (ξ2) Purchase Intention (η2) γ22 .29 .04 7.34*** Arousal (ξ2) Approach Behaviors (η3) γ31 .26 .04 6.57*** Measurement Model Pleasure (ξ1) P1 λx11 .93 .02 37.45*** Pleasure (ξ1) P2 λx21 1.16 .03 39.44*** Pleasure (ξ1) P4 λx31 1.02 .03 36.83*** Arousal (ξ2) A3 λx41 .71 .03 26.10*** Arousal (ξ2) A5 λx51 1.12 .04 30.96*** Arousal (ξ2) A6 λx61 .98 .03 28.60*** Satisfaction (η1) SA1 λy11 .66 .02 39.20*** Satisfaction (η1) SA 2 λy21 .67 .02 38.36*** Satisfaction (η1) SA 4 λy31 .61 .02 32.24*** Purchase Intention (η2) PI1 λy41 .93 .02 41.44*** Purchase Intention (η2) PI2 λy51 .91 .02 39.80*** Purchase Intention (η2) PI4 λy61 .79 .02 33.54*** Approach Behaviors (η3) AB1 λy71 .69 .02 34.94*** Approach Behaviors (η3) AB3 λy81 .64 .02 37.34*** Approach Behaviors (η3) AB4 λy91 .76 .02 42.18*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .50 .03 14.30*** Satisfaction (η1) Purchase Intention (η2) ψ21 .54 .03 17.08*** Satisfaction (η1) Approach Behaviors(η3) ψ31 .70 .03 27.85*** Purchase Intention (η2) Approach Behaviors (η3)
ψ32 .77 .02 36.25***
Model fit Chi-square (χ2) 460.12
df = 191 p < .0001
RMSEA .042 C.I. (.037; .046) GFI Group 1a = .96
Group 2b = .97
NNFI .99
Note. a Group 1 (high amount of central cues), b Group 2 (medium amount of central cues); ***p< .001
Table 5.25. Summary of measurement and structural models and model fit in Hypothesis 5a.
232
Note. All path coefficients were significant.
Figure 5.14. Unstandardized parameter estimates in the model for Hypothesis 5a.
Arousal ξ2
Pleasure ξ1
Approach Behavior
η3
Satisfaction η1
P1 P2 P4
SA1 SA2 SA4
A3 A5 A6
AB1 AB3 AB4
.93 1.16 1.02
.71 1.12 .98
.66 .67 .61
.69 .64 .76
.82
.34
.22
.29
1
1
Purchase Intention
η2
.26
.57 PI1
PI2
PI4
.93
.91
.79
ς1
ς2
ς3
1
1
1
.50
.70
.54
.77
233
Figure 5.15. Completely standardized parameter estimates in the model for Hypothesis 5a.
Arousal ξ2
Pleasure ξ1
Approach Behavior
η3
Satisfaction η1
P1 P2 P4
SA1 SA2 SA4
A3 A5 A6
AB1 AB3 AB4
.81 .84 .80
.66 .76 .71
.91 .89 .76
.79 .83 .91
.59
.29
.16
.26
Purchase Intention
η2
.21
.46 PI1
PI2
PI4
.89
.86
.76
ς1
ς2
ς3
.79
.50
.82
.63
234
Satisfaction Purchase Intention
Approach Behaviors
High amount of central cues (Group 1, N=830)
0 0 0
Medium amount of central cues (Group 2, N = 804)
-.03 (.06)a -.52b
.02 (.05) .45
-.02 (.06)
-.35
Note. a Standard error, b t-values
Table 5.26. Estimated intercepts of satisfaction, purchase intention, and approach behaviors in Hypothesis 5a.
Hypothesis 5b. The relationships between peripheral cues and response behaviors
(satisfaction, purchase intention, and approach behaviors) will be mediated by
emotional states.
Hypothesis 5b proposed the mediating effects of emotional states between
peripheral cues and response behaviors in the proposed model. The sample was divided
into the presence or absence of peripheral cues. To test for the mediating effects, first the
direct effects of peripheral cues on consumers’ response behaviors were assessed.
Second, the change in the extent of the effect for peripheral cues on consumers’ response
behaviors when emotional states were added to the model was tested.
235
The direct effects of peripheral cues on response behaviors. The sample was split
by two levels of peripheral cues to compare the means of response behaviors across
groups exposed to the presence or absence of peripheral cues. Means of three latent
constructs were scaled to zero in the group with the presence of peripheral cues. Means
of the latent variables were estimated in the group with no peripheral cues.
It was proposed that participants exposed to the website with the presence of
peripheral cues (group 1) would have higher satisfaction, purchase intention, and
approach behaviors than those exposed to the absence of peripheral cues (group 2). A
chi-square, RMSEA, GFIs, and NNFI were computed to assess the model fit. An overall
chi-square was 310.78 (df = 69, p <.0001). Because the chi-square statistic is sensitive to
the large sample size, the significant chi-square is not surprising. The GFIs for both
groups (presence vs. absence of peripheral cues) were .95 and .97, respectively. The
NNFI was .99. The RMSEA value was .066, indicating a fair fit (Brown & Cudeck,
1992; MacCallum et al., 1996). All fit indices were within acceptable ranges (See Table
4.11), suggesting a moderate fit of the model to the data. All path coefficients for the
model were significant. Table 5.27 presents the summary of the model fit and parameter
estimates measured in the model.
The mean of group 2 is interpreted as the mean difference in consumers’ response
behaviors between two groups manipulated by the presence or absence of peripheral cues.
The estimated group means of the latent variables are presented in Table 5.28. The group
means of satisfaction, purchase intention, and approach behaviors were -.08, -.08, and -
.12, respectively, suggesting that participants in group 1 (presence of peripheral cues) had
higher satisfaction, purchase intention, and approach behaviors than those in group 2
236
(absence of peripheral cues). However, the mean difference was statistically significant
for approach behaviors only. The mean differences in satisfaction and purchase intention
were not statistically significant. Therefore, the direct effect of peripheral cues on
response behaviors was partially supported.
237
Parameters ML estimates
Standard errors
t-values
Model Paths Satisfaction (η1) SA1 λy11 1 n/aa n/aa Satisfaction (η1) SA 2 λy21 1.01 .02 49.23*** Satisfaction (η1) SA 4 λy31 .91 .02 38.21*** Purchase Intention (η2) PI1 λy41 1 n/aa n/aa Purchase Intention (η2) PI2 λy51 .98 .02 43.78*** Purchase Intention (η2) PI4 λy61 .85 .02 36.39*** Approach Behaviors (η3) AB1 λy71 1 n/aa n/aa Approach Behaviors (η3) AB3 λy81 .92 .03 36.92*** Approach Behaviors (η3) AB4 λy91 1.11 .03 41.60*** Model fit Chi-square (χ2) 310.78
df = 69 p < .0001
RMSEA .066 C.I. (.058; .073) GFI Group 1 = .95
Group 2 = .97
NNFI 1.00
Note. a The values are not available because the path coefficients were set to 1 for the identification purpose; ***p< .001
Table 5.27. Summary of the model fit for the model tested the direct effects of peripheral cues on response behaviors.
238
Satisfaction Purchase Intention
Approach Behaviors
Presence of peripheral cues (Group 1, N=807)
0 0 0
Absence of peripheral cues (Group 2, N = 827)
-.08 (.05)a -1.58b
-.08 (.06) -1.49
-.12 (.04)
-2.71*
Note. a Standard error, b t-values; * p < .05
Table 5.28. Estimated means of satisfaction, purchase intention, and approach behaviors.
Mediating effects of emotional states. The change in the magnitude of the
influence of peripheral cues on response behaviors when the mediators (pleasure and
arousal) were entered to the model was assessed using multi-group structural equation
modeling in the next step. The intercepts of endogenous latent variables (satisfaction,
purchase intention, and approach behavior) were calculated to test the group difference.
The intercepts were set to zero in the group with the presence of peripheral cues (group 1)
but estimated in the group with the absence of peripheral cues (group 2). The intercepts
of group 2 are interpreted as a measure of the effect of peripheral cues on consumers’
response behaviors in the model.
To assess the fit of the model to the data, chi-square, RMSEA, GFI, and NNFI
were computed. An overall chi-square was 509.25 (df = 191, p < .0001). A significant
chi-square was not surprising with the large sample size and the large number of
239
indicators. All other fit indices indicated a good fit of the model to the data (See Table
4.11 for acceptable ranges). The GFI for each of two treatment groups (presence (group
1) vs. absence of peripheral cues (group 2)) was .95 and .97, respectively. The NNFI
was .99. The RMSEA value was .045, indicating a close fit of the model. All path
coefficients for the measurement and structural model were significant, supported by
significant t-values. See Table 5.29 for the results of the model fit. Figure 5.16
(unstandardized solution) and Figure 5.17 (completely standardized solution) show all
parameter estimates estimated in the proposed model.
The intercepts of satisfaction, purchase intention, and approach behaviors were
presented in Table 5.30. No intercepts in group 2 were significantly different from those
in group 1, indicating that there was no significant effect for peripheral cues on
consumers’ response behaviors when emotional variables were added. On the other hand,
significant structural coefficients indicated the significant effects of pleasure and arousal
induced by peripheral cues on consumer response behaviors (See Table 5.29). In other
words, the results demonstrated that the relationship between peripheral cues and
response behaviors (satisfaction, purchase intention, and approach behaviors) were not
direct and were mediated by emotional states (pleasure and arousal). Therefore,
Hypothesis 5b was supported.
240
Parameters ML estimates
Standard errors
t-values
Structural Model Pleasure (ξ1) Satisfaction (η1) γ11 .82 .05 17.59*** Pleasure (ξ1) Purchase Intention (η2) γ21 .33 .04 8.74*** Pleasure (ξ1) Approach Behaviors (η3) γ31 .56 .04 13.75*** Arousal (ξ2) Satisfaction (η1) γ12 .23 .04 5.57*** Arousal (ξ2) Purchase Intention (η2) γ22 .30 .04 7.48*** Arousal (ξ2) Approach Behaviors (η3) γ31 .27 .04 6.62*** Measurement Model Pleasure (ξ1) P1 λx11 .92 .02 37.20*** Pleasure (ξ1) P2 λx21 1.16 .03 39.75*** Pleasure (ξ1) P4 λx31 1.02 .03 36.90*** Arousal (ξ2) A3 λx41 .69 .03 25.83*** Arousal (ξ2) A5 λx51 1.12 .04 30.90*** Arousal (ξ2) A6 λx61 .98 .03 28.61*** Satisfaction (η1) SA1 λy11 .66 .02 39.15*** Satisfaction (η1) SA 2 λy21 .67 .02 38.30*** Satisfaction (η1) SA 4 λy31 .60 .02 31.98*** Purchase Intention (η2) PI1 λy41 .93 .02 41.38*** Purchase Intention (η2) PI2 λy51 .91 .02 39.58*** Purchase Intention (η2) PI4 λy61 .79 .02 33.47*** Approach Behaviors (η3) AB1 λy71 .69 .02 34.96*** Approach Behaviors (η3) AB3 λy81 .64 .02 37.35*** Approach Behaviors (η3) AB4 λy91 .76 .02 42.13*** Non-causal Relationship Pleasure (ξ1) Arousal (ξ2) φ21 .52 .03 15.04*** Satisfaction (η1) Purchase Intention (η2) ψ21 .53 .03 16.59*** Satisfaction (η1) Approach Behaviors(η3) ψ31 .71 .03 28.27*** Purchase Intention (η2) Approach Behaviors (η3)
ψ32 .75 .02 33.86***
Model fit Chi-square (χ2) 509.25
df = 191 p < .0001
RMSEA .045 C.I. (.040; .050) GFI Group 1a = .95
Group 2b = .97
NNFI .99
Note. a Group 1 (presence of peripheral cues), b Group 2 (absence of peripheral cues); ***p< .001
Table 5.29. Summary of measurement and structural models and model fit in Hypothesis 5b.
241
Note. All path coefficients were significant.
Figure 5.16. Unstandardized parameter estimates in the model for Hypothesis 5b.
Arousal ξ2
Pleasure ξ1
Approach Behavior
η3
Satisfaction η1
P1 P2 P4
SA1 SA2 SA4
A3 A5 A6
AB1 AB3 AB4
.92 1.17 1.02
.69 1.12 .98
.66 .67 .60
.69 .64 .76
.82
.33
.23
.30
1
1
Purchase Intention
η2
.27
.56 PI1
PI2
PI4
.93
.91
.79
ς1
ς2
ς3
.52
.71
.75
.53
1
1
1
242
Figure 5.17. Completely standardized parameter estimates in the model for Hypothesis 5b.
Arousal ξ2
Pleasure ξ1
Approach Behavior
η3
Satisfaction η1
P1 P2 P4
SA1 SA2 SA4
A3 A5 A6
AB1 AB3 AB4
.80 .84 .80
.65 .76 .71
.91 .88 .75
.79 .83 .91
.59
.29
.17
.26
Purchase Intention
η2
.21
.45 PI1
PI2
PI4
.89
.86
.75
ς1
ς2
ς3
.52
.78
.63
.81
243
Satisfaction Purchase Intention
Approach Behaviors
Presence of peripheral cues (Group 1, N=807)
0 0 0
Absence of peripheral cues (Group 2, N = 827)
.01 (.06)a .17b
.00 (.05) .06
-.07 (.06)
-1.26
Note. a Standard error, b t-values
Table 5.30. Estimated intercepts of satisfaction, purchase intention, and approach behaviors in Hypothesis 5b.
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CHAPTER 6
DISCUSSION AND CONCLUSIONS
The purpose of the research was to examine the impact of web cues, product
involvement, and situational involvement on consumers’ emotions that in turn, influence
their response behaviors based on the Elaboration Likelihood Model and the Stimuli-
Organism-Response paradigm. This research consists of two main studies. Study 1
investigated: 1) the effects of peripheral cues on emotions (pleasure and arousal) under a
low involvement situation (Hypothesis 1), 2) the effect of product involvement as a
moderator between peripheral cues and consumer emotions (Hypothesis 2), 3) the effects
of emotions on consumer response behaviors (purchase intention and approach
behaviors) (Hypotheses 3 and 4), and 4) the mediating effect of emotions between
peripheral cues and response behaviors (Hypothesis 5). Table 6.1 presents the results of
Hypotheses testing in Study 1. Study 2 explored: 1) the effects of type of cue (central
cues and peripheral cues) on emotions (pleasure and arousal) (Hypotheses 1 and 2), 2) the
effects of emotions on consumer response behaviors (satisfaction, purchase intention, and
approach behaviors) (Hypothesis 3), 3) the effects of situational involvement as a
moderator between web cues and emotions (Hypotheses 4), and 4) the mediating effects
245
of emotions on the relationship between type of cue and consumer response behaviors
(Hypotheses 5). Table 6.3 shows the results of Hypotheses testing in Study 2. This
chapter 1) summarizes the findings of Study 1 and Study 2, 2) discusses research
implications for Study 1 and Study 2, 3) addresses limitations, and 4) suggests future
research.
6.1. Discussion
6.1.1. Findings from Study 1
The Effects of Peripheral Cues on Emotional States
In Study 1 all participants were exposed to low situational involvement
manipulated by giving them instructions to browse the website. To induce a low
involvement situation, all participants were asked to read the same scenario: “Now, you
are going to visit one clothing website. Browse and look around the site for a while.”
The effects of peripheral cues (atmospheric web cues: fonts, icons, and background color)
on emotions were tested using between-subjects multivariate analyses of variance.
Hypotheses (1a and 1b) were supported by the results of the analysis (Table 6.1 and
Figure 6.1). The results revealed that the peripheral cues (presence or absence of)
significantly influenced consumers’ emotions (pleasure and arousal). People exposed to
the websites with the presence of peripheral cues (a pink background with a logo pattern,
a flashing pink brand logo, and icons with roll-over images) exhibited more pleasure and
arousal than people exposed to the websites without those peripheral cues (white
246
background, a static brand logo with black color, and text icons). This suggests that
when peripheral cues are presented on apparel websites, people are more likely to feel
pleased and aroused than when peripheral cues are not presented on the apparel websites.
As expected, in the low involvement situation the presence of peripheral cues had a great
effect on consumer emotions. This result is consistent with the S-O-R paradigm
(Mehrabian & Russell, 1974) and the findings of prior research (Babin et al., 2003; Baker
et al., 1992; Donovan & Rossiter, 1982; Donovan et al., 1994; Eroglu et al., 2003; Menon
& Kahn, 2002) that examined the effects of atmospheric cues on consumers’ pleasure and
arousal in either in-store or online settings.
247
Note. * p < .01, ** p < .005
Figure 6.1. The results of Hypotheses 1a and 1b.
Peripheral Cues
Arousal
Pleasure
F (1, 152) = 7.15*
F (1, 152) = 8.36**
Stimulus Organism
MANOVA: F (2, 151) = 4.95*
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The Moderating Effect of Product Involvement between S-O
The effect of product involvement (with clothing products) on the relationship
between peripheral cues and emotions was tested using a 2 (high vs. low product
involvement) x 2 (presence vs. absence of peripheral cues) between subjects multivariate
analysis of variance. Product involvement with clothing products was measured using 10
items (Zaichkowsky, 1994). To test for a moderating effect of product involvement (with
clothing) on the relationship between peripheral cues and emotions, participants were
split into low or high product involvement groups based on the median value. The two
Hypotheses (2a and 2b) were supported by the results (Table 6.1). The results showed
that clothing product involvement had a significant impact on the relationship between
peripheral cues and consumers’ emotions (pleasure and arousal). The effects of
peripheral cues on emotions were only significant for people for whom clothing products
had low personal relevance (i.e., low level of clothing involvement) (See Figure 6.2). No
effects of peripheral cues on emotions were found for people for whom clothing products
had high personal relevance (i.e., high level of clothing involvement). Emotions
experienced by people with high product involvement were not significantly influenced
by peripheral cues presented on the websites. This indicates that peripheral cues may
have a stronger effect on pleasure or arousal for consumers with a low level of product
involvement than for those with a high level of product involvement. In other words,
when situational involvement is controlled and low, different levels of product
involvement (personal relevance of the clothing products) moderate the relationship
between peripheral cues and the emotions experienced by consumers.
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These results support the ELM (Petty & Cacioppo, 1996) that explains the
influence of peripheral cues on various consumer behaviors under a low involvement
condition. According to the ELM, in comparison with the highly involved consumers,
consumers in the low involvement condition tend to pay more attention to peripheral cues
(atmospheric cues). Therefore, peripheral cues affect pleasure and arousal for consumers
with low levels of product involvement as compared to those with high levels of product
involvement.
250
Note. a The effects of peripheral cues on emotional states are significant only for low product involvement group; ** p < .005
Figure 6.2. The results of Hypotheses 2a and 2b.
Peripheral Cues
Arousal
Pleasure
F (1, 74) = 8.25a**
F (1, 74) = 10.22a**
Stimulus Organism
MANOVA for low product involvement: F (2, 73) = 6.18** MANOVA for high product involvement: F (2, 75) = .093
251
The Effects of Emotional States on Response Behaviors
The focus of Hypotheses 3 and 4 in Study 1 was to assess the effects of emotions
(pleasure and arousal) induced by peripheral cues on consumer response behaviors
(purchase intention and approach behaviors) using single group structural equation
modeling (See Figure 6.3 for the results).
The results showed that pleasure had significant effects on purchase intention and
approach behaviors. People who experienced more pleasure while browsing the apparel
website were likely to have higher purchase intention for the website they browsed than
those who experienced less pleasure. In addition, pleasure had a great impact on
consumer approach behaviors. Pleasure was positively related to likelihood of exploring
in or shopping from the website. Pleasure experienced by consumers while browsing the
website may significantly influence their purchase intention and approach behaviors
(likely to explore or shop).
Arousal also had significant effects on consumer response behaviors such as
purchase intention and approach behaviors. People who experienced more arousal while
browsing the website tended to have greater purchase intention than those who
experienced less arousal. Moreover, approach behaviors (likelihood of exploring or
shopping) were influenced by arousal experienced by people while browsing the apparel
website. Arousal was positively related to likelihood of exploring in or shopping from
the website. These results indicate that arousal experienced by consumers while browsing
may lead to more positive response behaviors toward the apparel online retailers.
These results are consistent with the S-O-R paradigm suggesting the significant
effects of the emotions on consumer response behaviors (Mehrabian & Russell, 1974). In
252
addition, the findings support previous studies that investigated the effects of emotions on
purchase intention (Baker et al., 1992; Fiore & Kimle, 1997; Fiore et al., 2005; Park et al.,
2005) and approach behaviors (Donovan & Rossiter, 1982; Eroglu et al., 2003; Fiore et
al., 2005; Huang, 2003; Hui, Dube, & Chebat, 1997; Menon & Kahn, 2002; Sherman et
al., 1997).
253
Note. ** p < .01, *** p < .001
Figure 6.3. The results of Hypotheses 3 and 4.
Arousal
Pleasure
Approach Behavior
Patronage Intention
t = 2.68**
t = 3.25**
t = 4.73***
t = 3.99***
Organism Response
Chi-square (χ2) RMSEA GFI AGFI NNFI 53.84, p=.26 .028 .95 .91 1.00
254
The Mediating Effects of Emotional States between S-R
Hypothesis 5 in Study 1 predicted consumer emotions as mediators between
peripheral cues and response behaviors. To test if emotions were mediating the
relationship between peripheral cues and response behaviors, first the direct effects of
peripheral cues on response behaviors were assessed (Baron & Kenny, 1986). The
effects of peripheral cues presented in the apparel websites on response behaviors such as
purchase intention and approach behaviors were assessed using multivariate analysis of
variance. The results revealed the critical impact of peripheral cues on approach
behaviors but not on purchase intention. People exposed to the website with the presence
of peripheral cues (a pink background with a logo pattern, a flashing pink brand logo, and
icons with roll-over images) were more likely to explore or shop the website and tended
to like the website more than people exposed to the website without the peripheral cues.
However, there was no direct effect for peripheral cues on purchase intention. Purchase
intention did not significantly differ as a function of exposure to the website. .
In the next step, emotions (pleasure and arousal) were added to the analysis.
Multiple regression analysis was conducted to test the mediating effects of emotions
(Baron & Kenny, 1986). The results showed that there were no significant effects for
peripheral cues on consumer response behaviors when emotions were added to the
analysis (See Figure 6.4). However, pleasure and arousal as mediators had significant
impacts on approach behaviors and purchase intention (See Figure 6.4). The findings
suggest that pleasure and arousal induced by peripheral cues while browsing the apparel
websites may affect consumers’ purchase intentions and approach behaviors. In other
words, the effects of peripheral cues (background color and colorful icons) on purchase
255
intentions and approach behaviors are not direct and appear to be mediated by people’s
emotions experienced while browsing the apparel website. The results are consistent
with the S-O-R paradigm (Mehrabian & Russell, 1974) that proposes that emotions are
mediating variables in determining a variety of consumer response behaviors such as
purchase intentions and approach behaviors (e.g., likely to explore and shop). Results are
also congruent with findings from previous research (Eroglu et al., 2003) that suggest that
emotions experienced by shoppers mediate the effects of online atmospherics on
shopping outcomes (satisfaction and approach behaviors).
256
Note. **p < .01, ***p< .001
Figure 6.4. The results of Hypothesis 5.
Peripheral Cues
Arousal
Pleasure
Approach Behavior
Patronage Intention H1a and H2a
H1b and H2b
t = 2.876**
t = 4.935***
t = 5.485***
t = 4.693***
Stimulus Organism Response
t = -1.721
t = -.052
257
Hypotheses
Status
H1a As compared to those exposed to the website without peripheral cues, those exposed to the website with peripheral cues will experience more pleasure.
Supported
H1b As compared to those exposed to the website without peripheral cues, those exposed to the website with peripheral cues will experience more arousal.
Supported
H2a Peripheral cues will have a stronger effect on pleasure for people with low product involvement than those with high product involvement.
Supported
H2b Peripheral cues will have a stronger effect on arousal for people with low product involvement than those with high product involvement.
Supported
H3a Pleasure will be positively related to purchase intention.
Supported
H3b Arousal will be positively related to purchase intention.
Supported
H4a Pleasure will be positively related to approach behaviors.
Supported
H4b Arousal will be positively related to approach behaviors.
Supported
H5 Emotional states such as pleasure and arousal will mediate the relationship between peripheral cues and consumers’ response behaviors.
Supported
Note. All hypotheses were tested under low situational involvement.
Table 6.1. Summary of hypotheses testing results in Study 1.
258
6.1.2. Findings from Study 2
Based on the proposed model, Study 2 includes four main parts: 1) Part 1
examined the effects of type of cue (central cues and peripheral cues) on emotions
(pleasure and arousal), 2) Part 2 assessed the effects of emotions on consumer response
behaviors (satisfaction, purchase intention, and approach behaviors), 3) Part 3 examined
the effects of situational involvement as a moderator between S-O, and 4) Part 4
investigated the mediating effects of emotions on the relationship between type of cue
and consumer response behaviors. A 2 x 2 x 2 between subjects’ factorial design was
employed in Study 2: situational involvement (high vs. low) x central cues (medium
amount vs. high amount) x peripheral cues (presence vs. absence). Table 6.2 shows
experimental manipulations used in Study 2. In this section, the findings from the four
parts of Study 2 are presented.
259
Manipulations Manipulation Check
Situational Involvement
F (1, 1564) = 9.78**
High involvement (Purchasing situation)
1. Rewards: Apparel items selected during browsing or cash award 2. Given scenario: “Imagine that you have been given a $100 gift certificate to purchase clothing from an online apparel store, E-style.com. Please browse for five pairs of pants on the website for a while and select one item that you would like to purchase. Then, finish the survey after shopping the site” 3. Order form: Complete the order form for an item
M = 41.00 SD = 10.87
Low involvement (Browsing situation without purchase)
1. Rewards: Cash award only 2. Given scenario: “Imagine that today you find an online apparel store, E-style.com. Browse the website for a while and finish the survey after browsing the site” 3. No order form
M = 39.20 SD = 11.90
Central Cues F (1, 1601) = 47.22*** High amount 1. High amount of verbal information
(Color, price, size, fabric, enclosure, and style information, country of origin, inseam measurement, fit information, waist information, design details (pockets, belt, and/or stitching), and item care) 2. Three mix & match suggestions 3. Front, back, side, and detail views
M = 16.51 SD = 4.47
Medium amount 1. Medium amount of verbal information (Color, price, size, fabric, enclosure, and style information, country of origin, and inseam measurement) 2. One mix & match suggestion 3. Only front larger view
M = 14.97 SD = 4.51
Peripheral Cues F (1, 1583) = 15.32*** Presence colorful icons with a roll-over image, a flashing brand
logo image with pink color, colorful menu bars, pink background with a brand logo pattern, and colorful texts (blue) and images
M = 18.73 SD = 4.12
Absence text icons without colorful images, a static brand logo image with black color, grey menu bars, white background without any pattern, achromatic text colors: black and grey except ‘sale’ menu
M =17.91 SD = 4.08
Note. ** p < .01, *** p < .001 Table 6.2. Summary of manipulations used in Study 2.
260
The Effects of Web Cues on Emotional States
The proposed model for Part 1 in Study 2 investigated the effects of web cues
(central cues and peripheral cues) on consumers’ emotions (pleasure and arousal) while
browsing apparel websites. Mean differences in emotions across different treatment
groups were calculated and compared using multi-group structural equation modeling.
The results of Hypotheses 1 and 2 are presented in Figure 6.5.
The results of Hypothesis 1 showed that the means of pleasure and arousal were
higher for people exposed to the website with the high amount of central cues (product
related web cues) than for those exposed to the website with the medium amount of
central cues. People who were exposed to the websites with the high amount of product
related web cues (high amount of verbal information, three mix and match suggestions,
and front, back, side, and detail larger views) tended to experience significantly more
pleasure than those who exposed to the websites with the medium amount of product
related web cues (medium amount of verbal information, one mix and match suggestion,
and front larger view only). Although the means of arousal experienced by shoppers
were higher in the group exposed to the high amount of central cues, the difference in
arousal across two treatment groups was not statistically significant. In this research,
arousal experienced by shoppers while browsing the apparel websites did not
significantly differ as a function of the amount of central cues presented in the website.
In the case of peripheral cues, the results were reversed. The results of
Hypothesis 2 showed that the means of pleasure and arousal were higher for people
exposed to the website with the presence of peripheral cues (pink background with the
brand logo pattern and colorful icons with roll over images) than for those exposed to the
261
website without peripheral cues (white background color and links with texts). People
who were exposed to the websites with the presence of peripheral cues tended to
experience significantly more arousal than those who exposed to the websites with the
absence of peripheral cues. Although the pleasure experienced by shoppers was higher in
the group exposed to the presence of peripheral cues, the difference in pleasure across
two treatment groups was not statistically significant. Pleasure experienced by shoppers
during browsing the website did not significantly differ by the presence or absence of
peripheral cues shown in the apparel website.
In sum, before taking the effects of the situational involvement into account,
central cues tended to affect pleasure (and not arousal) while peripheral cues tended to
affect arousal (and not pleasure).
262
Note. a high amount of central cues, b medium amount of central cues, c presence of peripheral cues, d absence of peripheral cues; Negative t-values indicate that groups with the medium amount of central cues and with the absence of peripheral cues have lower pleasure and arousal; ** p < .005
Figure 6.5. The results of Hypotheses 1 and 2 in Part 1.
Central Cues
Peripheral Cues Arousal
Pleasure t = -2.96**
t = -1.27
t = -1.91
t = -3.14**
Stimulus Organism
Chi-square (χ2) RMSEA GFI NNFI Central cues: 30.38, p=.17 .018 .99a/1.00b 1.00 Peripheral cues: 33.05, p=.10 .021 .99c/.99d 1.00
263
The Effects of Emotional States on Response Behaviors
Part 2 in Study 2 assessed the effects of consumers’ emotions (pleasure and
arousal) induced by web cues while browsing the websites on consumer response
behaviors (satisfaction, purchase intention, and approach behaviors) using single group
structural equation modeling. As shown in Table 6.3, all six Hypotheses (3a, 3b, 3c, 3d,
3e, and 3f) predicting the effects of the emotions on consumer response behaviors were
supported. Also see Figure 6.6 for the results of Hypothesis 3.
Pleasure had significant effects on satisfaction, purchase intention, and approach
behaviors. People who experienced more pleasure while browsing the apparel website
were more likely to be satisfied with the website they browsed than those who
experienced less pleasure. Pleasure also influenced consumers’ purchase intentions.
People who experienced more pleasure tended to have higher purchase intentions than
those who experienced less pleasure. In addition, pleasure had a great impact on
consumers’ approach behaviors. Those who experienced more pleasure were more likely
to explore or shop in the website than those who experienced less pleasure. Pleasure
experienced by consumers while browsing the website may significantly influence their
satisfaction with the website, purchase intentions, and approach behaviors (likely to
explore or shop).
Arousal also had significant effects on consumer response behaviors such as
satisfaction, purchase intention, and approach behaviors. People who experienced more
arousal while browsing the website tended to have higher satisfaction than those who
experienced less arousal. The positive effect of arousal on purchase intention was also
found. More aroused people were likely to have greater purchase intentions than less
264
aroused people. Approach behaviors (likely to explore or shop) were also influenced by
the arousal experienced while browsing the apparel website. More aroused people were
more likely to explore or shop more in the apparel website than less aroused people.
These results indicate that high arousal experienced by consumers may result in more
positive response behaviors toward the apparel online retailers.
The results are congruent with the findings from Study 1. Both Study 1 and Study
2 found significant effects for emotions on consumer response behaviors. These findings
support the concept of the S-O-R paradigm predicting significant effects for emotions on
consumer response behaviors (Mehrabian & Russell, 1974). The results also support
previous research that found the effects for emotions on various consumer response
behaviors such as satisfaction (Eroglu et al., 2003), purchase intentions (Baker et al.,
1992; Fiore & Kimle, 1997; Fiore et al., 2005; Park et al., 2005), and approach behaviors
(Donovan & Rossiter, 1982; Eroglu et al., 2003; Fiore et al., 2005; Huang, 2003; Hui et
al., 1997; Menon & Kahn, 2002; Sherman et al., 1997).
265
Note. ***p< .001
Figure 6.6. The results of Hypothesis 3 in Part 2.
Purchase Intention
Arousal
Pleasure
Approach behavior
Satisfaction t = 17.63***
t = 8.79***
t = 5.46***
t = 13.80***
t = 7.40***
t = 6.70***
Organism Response
Chi-square (χ2) RMSEA GFI AGFI NNFI 351.61, p < .0001 .046 .97 .96 1.00
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The Moderating Effect of Situational Involvement between S-O
The results of Part 3 revealed significant effects for situational involvement on the
relationship between web cues and consumers’ emotions. As presented in Table 6.3,
Hypotheses 4a and 4b were supported. Hypotheses 4a and 4b were tested using multi-
group mean comparison structural equation modeling. The results are shown in Figure
6.7.
The result of Hypothesis 4a showed that central cues affected emotions (pleasure
and arousal) under the high involvement situation, but had no effect on emotions under
the low involvement situation (See Figure 6.7). Under high situational involvement (e.g.,
purchasing situation) people exposed to the apparel website with the high amount of
central cues were likely to experience significantly more pleasure and arousal than those
exposed to the medium amount of central cues. Whereas under the low involvement
situation the emotions (pleasure and arousal) experienced by people did not significantly
differ by the different amount of central cues provided in the website.
The result of Hypothesis 4b revealed that peripheral cues affected emotions
(pleasure and arousal) under the low involvement situation (e.g., browsing situation
without a purchasing goal) (See Figure 6.7). However, under the high involvement
situation (e.g., purchasing situation) peripheral cues had no impact on emotions. People
in the low involvement condition experienced more pleasure and arousal when they were
exposed to the website in which peripheral cues were present, as compared to when the
peripheral cues were absent. However, in the high involvement condition people’s
pleasure and arousal were not significantly influenced by the presence or absence of
peripheral cues. This result is consistent with the ELM and prior research that shows the
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significant effect of online atmospheric cues (peripheral cues) on pleasure under the low
involvement situation but not under the high involvement situation (Eroglu et al., 2003).
Overall, the results indicate that different types of web cues may influence the
level of pleasure and arousal felt by consumers while browsing the apparel websites.
Consistent with the ELM (Petty & Cacioppo, 1986), the effects of different types of web
cues on consumers’ emotions are moderated by situational involvement (high vs. low).
Under the high involvement situation central cues (product related web cues) rather than
peripheral cues (atmospheric web cues: background color and colorful icons) may have
greater effect on the level of pleasure and arousal experienced by consumers, while under
the low involvement situation peripheral cues rather than central cues may have more
impact on the emotion felt by consumers.
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Note. a high amount of central cues, b medium amount of central cues, c presence of peripheral cues, d absence of peripheral cues; Negative t-values indicate that groups with the medium amount of central cues and with the absence of peripheral cues have lower pleasure and arousal; * p < .05, ** p < .005, *** p < .001
Figure 6.7. The results of Hypothesis 4 in Part 3.
Central Cues
Peripheral Cues
Arousal
Pleasure t = -3.29***/-.87
t = -3.00***/1.16
t = -.41/-2.14*
t = -1.32/-2.95**
Stimulus Organism
Chi-square (χ2) RMSEA GFI NNFI Central cues (high inv/low inv): 29.96/22.56, p=.186/.546 .024/.010 .99a,.99b/.99,.99 1.00 Peripheral cues (high inv/low inv): 28.23/29.04, p=.251/.219 .020/.023 .99c,.99d/.99,.99 1.00
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The Mediating Effects of Emotional States between S-R
In Part 4, the study investigated the mediating effects of emotions between web
cues and consumer response behaviors. To test if emotions are mediating the relationship
between web cues and response behaviors, first the direct effects of web cues on response
behaviors were tested (Baron & Kenny, 1986) using multi-group mean comparison
structural equation modeling and then the change in the extent of the effects of web cues
on response behaviors was assessed using multi-group structural equation modeling. As
presented in Table 6.3 and Figure 6.8, Hypotheses 5a and 5b were supported.
Hypothesis 5a revealed significant mediating effects of emotions between amount
of central cues presented on the websites and response behaviors (satisfaction, purchase
intention, and approach behaviors). The direct effect of amount of central cues on
response behaviors was significant for satisfaction and approach behaviors. Although
purchase intentions were not statistically affected, people who were exposed to the high
amount of central cues had higher satisfaction and approach behaviors than those exposed
to the medium amount of central cues. When emotions was added to the model, the
effects of amount of central cues on consumer response behaviors disappeared (See
Figure 6.8). All significant regression coefficients (the effects of emotions on response
behaviors) indicated the strong effects of pleasure and arousal on consumer response
behaviors (satisfaction, purchase intention, and approach behaviors). The results
revealed that the effects of different amounts of central cues presented in the apparel
websites on consumer response behaviors were mediated by pleasure and arousal
experienced by people while browsing the website.
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The significant mediating effects of pleasure and arousal between presence or
absence of peripheral cues and consumer response behaviors (satisfaction, purchase
intention, and approach behaviors) were also found from Hypothesis 5b. The direct
effects of presence or absence of peripheral cues on response behaviors were significant
for approach behaviors. Although satisfaction and purchase intention were not
statistically affected, people exposed to the website with peripheral cues had higher
approach behaviors than those exposed to the website with no peripheral cues. When
emotions were added to the model, the effects of peripheral cues on consumer response
behaviors disappeared (See Figure 6.8). All significant regression coefficients (the
effects of emotions on response behaviors) indicated the strong effects of pleasure and
arousal on consumer response behaviors (satisfaction, purchase intention, and approach
behaviors). The results revealed that pleasure and arousal induced by the presence or
absence of peripheral cues shown in the apparel websites mediated the relationships
between peripheral cues and various response behaviors. This result is congruent with
the finding from Study 1, supporting the mediating effects of emotions between
peripheral cues and consumer response behaviors.
As a whole, the results of Hypotheses 5a and 5b suggest that consumers’ emotions
(pleasure and arousal) induced by different levels of web cues while browsing or
shopping the apparel websites may have significant effects on consumer response
behaviors. In other words, the effects of web cues on satisfaction, purchase intention, and
approach behaviors are not direct and tended to be mediated by the emotion felt by
consumers while browsing or shopping the apparel websites with various types of web
cues. The findings strongly support the S-O-R paradigm that predicts the emotions as
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mediators in the relationship between various stimuli and consumer response behaviors
(Mehrabian & Russell, 1974). The results are consistent with earlier studies that found
the mediating effects of pleasure and arousal between the site or store atmospheric cues
and consumer response behaviors (satisfaction, purchase intention, and approach
behaviors) (Baker et al., 1992; Donovan & Rossiter, 1982; Donovan et al., 1994; Fiore &
Kimle, 1997; Eroglu et al., 2003; Menon & Kahn, 2002).
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Note. The direct effects of central cues a and peripheral cues b on response behaviors after adding the emotions to the model; c high amount of central cues, d medium amount of central cues, e presence of peripheral cues, f absence of peripheral cues; *** p < .001
Figure 6.8. The results of Hypothesis 5 in Part 4.
Central Cues
Peripheral Cues
Purchase Intention
Arousal
Pleasure
Approach behavior
Satisfaction
H1a and H4a
H1b and H4a
H2a and H4b
H2b and H4b
Stimulus Organism Response
t = -.52a/.17b
t = .45/.06
t = -.35/-1.26
Chi-square (χ2) RMSEA GFI NNFI Central cues: 460.12, p < .0001 .046 .96c/.97d .99 Peripheral cues: 509.25, p < .0001 .045 .95e/.97f .99
t = 17.57***
t = 8.81***
t = 13.78***
t = 5.44***
t = 7.34***
t = 6.57***
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Hypotheses
Status
Part 1
H1a Central cues Pleasure
Supported
H1b Central cues Arousal
Not Supported
H2a Peripheral cues Pleasure
Not Supported
H2b Peripheral cues Arousal
Supported
Part 2
H3a Pleasure Satisfaction
Supported
H3b Pleasure Purchase intention
Supported
H3c Pleasure Approach behaviors
Supported
H3d Arousal Satisfaction
Supported
H3e Arousal Purchase intention
Supported
H3f Arousal Approach behaviors
Supported
Part 3
H4a Situational involvement The relation between central cues and emotional states (pleasure and arousal)
Supported
H4b Situational involvement The relation between peripheral cues and emotional states (pleasure and arousal)
Supported
Part 4
H5a Mediating effect of emotional states Between central cues and response behaviors (satisfaction, purchase intention, and approach behaviors)
Supported
H5b Mediating effect of emotional states Between peripheral cues and response behaviors (satisfaction, purchase intention, and approach behaviors)
Supported
Table 6.3. Summary of hypotheses testing results in Study 2.
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6.2. Implications
6.2.1. Managerial Implications for Study 1
The findings from Study 1 provide helpful information for apparel online retailers
developing effective online store designs that may attract online browsers. While
browsing apparel websites without a particular goal in mind (i.e., a purchasing goal),
people have a low level of situational involvement so that peripheral cues presented in the
websites influence browsers’ feelings of pleasure and arousal (Eroglu et al., 2003).
Consistent with Eroglu et al.’s research (2003), Study 1 found that while browsing
apparel websites without a purchasing goal (low situational involvement), people who
were exposed to the websites with peripheral cues (background color with a logo pattern,
blue text color, pink brand logo with flashing image, and colorful icons with roll over
images) exhibited greater pleasure and arousal than those exposed to the websites without
peripheral cues (white background without a log pattern, grey text color, static brand logo
with black color, and text icons). This indicates that peripheral cues such as background
color, text color, flashing image, and colorful icons in apparel websites may play a
significant role in enhancing the feelings of pleasure and arousal experienced by online
browsers. Accordingly, online apparel retailers may wish to use various peripheral cues
such as colors for a background or for icons and flashing images rather than static images
to build more pleasant and arousing apparel websites that attract browsers’ attention and
consequently influence their emotions while browsing the websites. Although online
browsers may not make purchases on their first visit, browsers who are more attracted to
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apparel websites and who experience more pleasure and arousal while browsing may like
the websites better (Sherman et al., 1997) and have higher tendency to revisit the
websites later to buy products (Eroglu et al., 2003; Fiore et al., 2005).
Study 1 revealed a significant effect for peripheral cues on pleasure and arousal
felt by online browsers who do not have a purchasing goal (low situational involvement).
However, even under low situational involvement, people vary in their level of enduring
product involvement with clothing (Zaichkowsky, 1986) that possibly moderates the
relationship between peripheral cues and emotions. The findings of Study 1 revealed a
significant moderating effect for clothing product involvement on the relationship
between peripheral cues and emotions (pleasure and arousal). The effects of peripheral
cues on emotions were only significant for people with a low level of clothing product
involvement. Pleasure and arousal experienced by people with high product involvement
were not significantly affected by the peripheral cues presented in apparel websites. This
indicates that peripheral cues induce more pleasure and arousal for online browsers with
the low level of clothing product involvement than for those with a high level of clothing
product involvement. This further emphasizes the important role of peripheral cues
(background color, text color, flashing image, and colorful icons) in apparel websites for
attracting online browsers who have a low level of clothing product involvement. People
with low clothing involvement may have less interest in, place less importance on, and
value clothing products less than those with high clothing involvement (Zaichkowsky,
1986). As such, those with low clothing involvement are likely not the hard-core clothing
shoppers. The low involved consumers are more likely to attend to and to be persuaded
by peripheral cues such as icons with dynamic animation, large images, and background
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color (Cho, 1999; Petty, et al., 1983; Petty & Cacioppo, 1996). To attract and persuade
online browsers with low clothing involvement, the presence of peripheral cues in
apparel websites may be essential. Therefore, apparel online retailers are recommended
to use peripheral cues such as colorful images and icons to enhance pleasure and arousal
experienced by low involvement online browsers and consequently to persuade them to
stay and browse longer in the websites and to return to the websites to purchase products.
The findings in Study 1 revealed significant effects for emotions on response
behaviors such as purchase intention and approach behaviors. People who experienced
more pleasure and arousal while browsing apparel websites were likely to have greater
purchase intention and approach behaviors (e.g., desire to explore or shop). This
indicates that online browsers who feel pleased, happy, and contented are more likely to
have greater purchase intention and to enjoy exploring and shopping in the websites. In
addition, online browsers more stimulated and aroused by apparel websites tend to have
greater intention to purchase products from the websites in the future and tend to enjoy
shopping in and exploring the websites. In the online apparel shopping context,
browsers’ emotions tend to affect their response behaviors. Hence, it is important for
online apparel retailers to build a pleasant and arousing online browsing environment to
enhance consumers’ purchase intentions and approach behaviors. As suggested before,
using peripheral cues such as background color, colorful icons, and flashing images may
help online retailers develop more pleasant and arousing apparel websites that
accordingly generate more positive consumer response behaviors under the low
involvement condition.
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In addition, Study 1 revealed that emotions mediated the relationship between
peripheral cues and various consumer response behaviors. The effects of environmental
cues on consumers’ response behaviors tended to be indirect via emotions rather than
direct (Eroglu et al., 2003; Mehrabian & Russell, 1974). Pleasure and arousal felt by
online browsers may function as mediators between peripheral cues presented in apparel
websites and browsers’ subsequent behaviors. Since emotions induced by various
peripheral cues presented in apparel websites have strong effects on consumer response
behaviors (Eroglu et al., 2003), online apparel retailers should consider consumer
emotions that could be stimulated by apparel websites when they select a background
color, icons, and colorful images for their commercial websites.
6.2.2. Managerial Implications for Study 2
Considering both central and peripheral cues the findings from Study 2 provide
valuable information that apparel online retailers can utilize to develop a successful
apparel website using various web cues. Without considering situational involvement,
Study 2 revealed significant effects for peripheral cues on arousal and central cues on
pleasure. Peripheral cues tended to increase consumers’ arousal rather than pleasure,
while central cues tended to enhance consumers’ pleasure rather than arousal. People
exposed to the websites with peripheral cues (background color with a logo pattern, blue
text color, pink brand logo with flashing image, and colorful icons with roll over images)
experienced greater arousal than those exposed to the websites without peripheral cues
(white background without a log pattern, grey text color, static brand logo with black
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color, and text icons). In addition, people who were exposed to the websites with the
high amount of central cues (high amount of verbal information, three mix and match
suggestions, and front, back, side, and detail larger views) exhibited greater pleasure than
those who were exposed to the websites with the medium amount of central cues
(medium amount of verbal information, one mix and match suggestion, and front larger
view only). According to the results, a function of peripheral cues presented in apparel
websites may be to enhance consumers’ arousal while browsing the websites. On the
other hand, central cues may play a significant role in escalating pleasure experienced by
consumers during browsing. The high amount of central cues and the presence of
peripheral cues may enhance the feeling of pleasure and arousal felt by consumers while
browsing apparel websites. People exposed to the website with the high amount of
central cues experienced more pleasure than those exposed to the website with the
medium amount of central cues, while those exposed to peripheral cues tended to
experience more arousal than those exposed to the website without the peripheral cues.
Given these mixed results, online apparel retailers should consider both types of cues to
be important to provide.
Taking involvement into account, the findings of Study 2 revealed a significant
moderating effect for situational involvement on the relationship between web cues and
consumer emotions (pleasure and arousal). According to the ELM (Petty & Cacioppo,
1996), under low situational involvement (i.e., browsing websites without a purchasing
goal) peripheral cues may affect persuasion, while under high situational involvement
(i.e., browsing websites with a purchasing goal) central cues may influence persuasion.
As expected, while browsing apparel websites without a purchasing goal (low situational
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involvement), people exposed to the websites with peripheral cues (background color
with a logo pattern, blue text color, pink brand logo with flashing image, and colorful
icons with roll over images) exhibited greater pleasure and arousal than those exposed to
the websites without peripheral cues (white background without a logo pattern, grey text
color, static brand logo with black color, and text icons) and the results are consistent
with the findings in Study 1. The effects of peripheral cues on emotions were not
significant under the high situational involvement (i.e., browsing websites with a
purchasing goal). These results both support the ELM and indicate the important role of
peripheral cues in persuasion under the low situational involvement. Peripheral cues
presented in apparel websites such as background color, text color, flashing images, and
colorful icons increased pleasure and arousal for online browsers. Online apparel
retailers may desire to use colors for a background or icons and to use flashing images
rather than static images to create more pleasant and arousing apparel websites that in
turn arouse and provide pleasure to online browsers while browsing apparel websites.
Although a purchase may not be made on their first visit, online browsers who experience
greater pleasure or arousal while browsing the apparel website may be more satisfied
with the website (Eroglu et al., 2003) and return to the website later to purchase products
(Fiore et al., 2005). To appeal to frequent online browsers and consequently, to persuade
them to buy products in the future, online retailers may also need to regularly update
websites with various peripheral cues (e.g., background color, images other than
products) that in turn build a positive image for the websites. Innovative site design that
differentiates the apparel retail website from other online stores may help online retailers
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attract consumers and survive in the competitive online market place (“Design Web Sites
to Merchandise and Communicate with Consumers, Experts Say,” 2005).
Supporting the ELM, the findings of Study 2 also revealed that central cues (the
amount of verbal information, larger views from various angles, and mix and match
suggestions) influenced pleasure and arousal for consumers with a particular purchasing
goal (high situational involvement) rather than those without a purchasing goal (low
situational involvement). Since it is impossible to closely inspect apparel products using
the tactile sense in online contexts, consumers planning to buy apparel products from
online retailers may rely on verbal or pictorial cues (central cues) that illustrate an apparel
product (Then & Delong, 1999). Showing clear pictures of the products may be critical
because people are not able to touch the product online (Emerson, 2000). More
descriptive central cues (high amount of verbal information, larger views with various
angles, and complete mix and match suggestions) may help consumers more fully
evaluate apparel products before making an online purchase decision (Then & Delong,
1999). This effect may be stronger for consumers in high situational involvement (e.g.,
purchasing situation) than in low situational involvement (e.g., browsing situation) (Petty
& Cacioppo, 1996). As predicted, the effects of central cues on emotions were
significant under high situational involvement (i.e., browsing websites with a purchasing
goal) but not significant under low situational involvement (i.e., browsing websites
without a purchasing goal). While browsing apparel websites with a purchasing goal
(high situational involvement), people exposed to the websites with the high amount of
central cues (high amount of verbal information, larger views with various angles, and
complete mix and match suggestions) experienced greater pleasure and arousal than those
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exposed to the websites with the medium amount of central cues (medium amount of
verbal information, only front larger view, and one mix and match suggestion). This
underscores the significant role of central cues in persuasion under the high situational
involvement. The high amount of central cues provided in apparel websites may
augment pleasure and arousal felt by online shoppers with a purchasing goal because the
cues describing apparel products may substitute for product examination since the tactile
sense cannot be used. Hence, online apparel retailers may wish to provide a high amount
of central cues rather than the medium amount of central cues to evoke greater pleasure
and arousal experienced by online shoppers while browsing the website with a
purchasing goal. Because online apparel shoppers browsing the websites with a
purchasing goal may make an immediate purchase when they find a product they like,
online retailers should provide a high amount of central cues to aid in product evaluation
and accelerate purchase decisions. To attract potential online purchasers and to facilitate
product purchasing from the websites, online apparel retailers should provide more than a
minimum amount of central cues describing each product in detail. These cues are
unlikely to appeal to low involved consumers, who will be affected by peripheral cues.
Therefore, both central and peripheral cues in apparel websites may play an important
role in persuading consumers under different levels of situational involvement.
As shown in Study 1, the findings in Study 2 also revealed significant effects for
emotions (pleasure and arousal) on consumer response behaviors (satisfaction, purchase
intention, and approach behaviors). People who experienced greater pleasure and arousal
while browsing apparel websites tended to be satisfied with the website they browsed and
to have greater purchase intention and approach behaviors (e.g., desire to explore or
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shop) than those who experienced less pleasure and arousal. Higher levels of pleasure
and arousal felt by online browsers and shoppers may enhance satisfaction, purchase
intention, and approach behaviors (Eroglu et al., 2003; Fiore et al., 2005; Menon & Kahn,
2002). Consumers who feel pleased, happy, and contented tended to have greater
satisfaction with the website (e.g., enjoy visiting the website, satisfied with shopping
experience at the website, recommend the website to other people). In addition, those
who experienced greater pleasure and arousal while browsing the website were more
likely to have higher purchase intention and to enjoy exploring and shopping in the
websites. Consumers more stimulated and aroused by apparel websites tended to have
greater satisfaction with the websites, to have greater intention to purchase products from
the websites in the future, and to enjoy shopping in and exploring the websites. In online
apparel shopping contexts, consumers’ emotions are likely to perform important roles
that affect consumers’ response behaviors. Hence, it is important for online apparel
retailers to build pleasant and arousing apparel websites to increase consumers’
satisfaction, purchase intentions, and approach behaviors. Various web cues may help
online apparel retailers develop more pleasant and arousing apparel websites that in turn
generate more positive consumer response behaviors. As previously suggested, both
central and peripheral cues in apparel websites may play significant roles in increasing
pleasure and arousal felt by consumers as a function of level of involvement that
accordingly affects consumer response behaviors such as satisfaction, purchase intentions,
and approach behaviors.
Consistent with Study 1, the findings of Study 2 strongly supported the S-O-R
paradigm predicting the emotions as mediators in the relationships between various types
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of web cues and consumer response behaviors (Mehrabian & Russell, 1974). Pleasure
and arousal induced by central and peripheral cues presented in apparel websites tended
to influence consumers’ subsequent behaviors such as satisfaction, purchase intention,
and approach behaviors. In other words, the effects of central and peripheral cues on
consumers’ response behaviors were more likely to be indirect through their emotions
rather than direct. As supported before, central and peripheral cues have strong effects on
consumer emotions that in turn influence consumer response behaviors. The greater
pleasure and arousal experienced by consumers while browsing the websites, the higher
the satisfaction, purchase intention, and approach behaviors that were exhibited. Pleasure
and arousal induced by apparel websites may enhance consumer satisfaction with the
websites, intention to purchase products from the websites, and the tendency to explore
or shop in the websites.
Overall, Study 2 provides valuable information to online apparel retailers in
developing new strategies for visual merchandising of their websites to attract both online
browsers (without a purchasing goal) and shoppers (with a purchasing goal). Peripheral
cues may be useful for online apparel retailers to create the websites that catch the
attention of new customers or online browsers. To attract online browsers without
intention to purchase, online apparel retailers may need to differentiate their websites
from other online retailers in terms of appearance. More colorful icons and background
and flashing images may influence online browsers’ pleasure and arousal that in turn
affect their subsequent behaviors such as satisfaction, purchase intention, and approach
behaviors. On the other hand, central cues may be helpful to build the websites that
influence consumers with a purchasing goal rather than those without a purchasing goal.
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Since they cannot try on apparel items online, consumers planning to buy an apparel
product via the Internet may be attracted to more detailed verbal information and product
pictures (i.e., cues directly related to their goal) rather than colorful images or icons not
directly related to their goal (i.e., peripheral cues). Providing informative merchandise
displays may help consumers decide what to purchase and build great trust toward the
websites (“Design Web Sites to Merchandise and Communicate with Consumers, Experts
Say,” 2005). Product pages with more central cues (product related web cues) such as
product related verbal information, mix and match suggestions, color swatches, product
views in different angles, and a virtual model may increase apparel online sales. Fit
information and mix and match suggestions could make shoppers more satisfied and
consequently reduce returns (“Retail Web Sites Need to Improve Usability,” 2004).
Therefore, highly involved potential online purchasers may experience great pleasure and
arousal when they are exposed to the website with extensive product related web cues
and accordingly more pleased or aroused people may make an instant purchase when
they found a product they like.
Although this study found a significant role for peripheral cues in persuading
online apparel browsers, in a real situation as more colorful and/or moving images are
added to the website, downloading time gets longer. Particularly, a background image or
color needs a longer time to download than a white background and consequently it may
have negative effects on consumer emotions and response behaviors. Thus, considering
downloading time it is very important for online retailers to balance the amount of
peripheral cues or the file size of every image in each page when designing their websites.
As found in this study, online browsers without a purchasing goal tend to be affected by
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peripheral cues while online shoppers with a purchasing goal are more likely to be
influenced by central cues (product related information). Online retailers may need to
use more peripheral cues in the homepage (i.e., the opening page of a website) because it
is the first page of the website that gains more attention from online browsers. Whereas
online retailers may wish to use fewer peripheral cues in the product page (i.e., the page
in which each product is presented) because central cues, that attract online shoppers
rather than online browsers, are more important for the product page. For example, the
opening page of apparel websites may contain a colorful background with a brand logo, a
flashing brand logo, and colorful icons with a roll-over image while to reduce
downloading time the product page may include a flashing brand logo and colorful icons
with a white background rather than a colorful background image.
6.2.3. Theoretical Implications
One of the significant limitations in online visual merchandising research is the
lack of theoretical approach to explain diverse consumer reactions to various web cues
presented in apparel websites. Besides, although the effects of website visual
merchandising have gained attention from previous researchers (Allen, 1999; Fiore et al.,
2005; Menon & Kahn, 2002; Szymanski & Hise, 2000; Then & Delong, 1999), it is
surprising that so little empirical research has been conducted to examine consumer
responses to visual merchandising in apparel websites. Applying the ELM (Petty &
Cacioppo, 1996) and the S-O-R paradigm (Mehrabian & Russell, 1974) in Internet
shopping context, Eroglu et al., (2003) empirically tested the effects for peripheral cues
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on consumer emotions that in turn influence consumer response behaviors and found that
peripheral cues (cues not directly related to a purchasing goal) presented in apparel
websites influence pleasure and arousal that in turn affect consumer satisfaction and
approach behaviors under low situational involvement (browsing situation). However,
the effects of central cues (cues directly related to a purchasing goal) available on apparel
websites on consumers’ emotions and behavioral responses have not been investigated.
Therefore, to empirically support the applicability of two theoretical approaches in online
visual merchandising research, the current study investigated the effects for both central
cues and peripheral cues on consumer emotions and also examined how the effects are
changed by the levels of situational or product involvement.
Blending the S-O-R paradigm and the ELM, Study 1 and Study 2 empirically
tested the effects for diverse web cues on consumer emotions and behaviors and
examined the moderating effects of different involvement conditions (product
involvement in Study 1 and situational involvement in Study 2). The findings of both
studies contribute to the theoretical development in studying the role of visual
merchandising in apparel websites. Two studies strongly support the applicability of the
S-O-R paradigm and the ELM together in understanding consumer responses to online
visual merchandising under different involvement conditions.
Supporting the S-O-R paradigm, Study 1 and Study 2 provide a comprehensive
model that describes the relationship among various web cues (peripheral and central
cues) presented on apparel websites, emotional states (pleasure and arousal) experienced
while browsing the websites, and consumers’ response behaviors (satisfaction, purchase
intention, and approach behaviors). The model also sustains the ELM describing how
287
consumers in different situations react to various web cues and how product involvement
influences the relationship between peripheral cues and consumer emotions.
The findings of Study 1 support the S-O-R paradigm (Mehrabian & Russell,
1974) describing the effects of atmospheric cues (colorful icons, background image, and
flashing brand logo) presented in apparel websites on consumers’ emotions that in turn
affect their subsequent behaviors such as purchase intention and approach behaviors
while browsing the websites without a purchasing goal (low situational involvement). In
addition, significant moderating effects for clothing product involvement on the
relationship between peripheral cues and consumer pleasure and arousal strongly support
the ELM (Petty & Cacioppo, 1996). In sum, blending the S-O-R paradigm and the ELM,
Study 1 provides valuable theoretical insight in understanding how peripheral cues (site
atmospheric cues) influence online apparel browsers’ emotions and subsequent behaviors
and how the effects for peripheral cues on emotions differ by the levels of clothing
product involvement.
Taking both central and peripheral cues into consideration the findings in Study 2
suggest a comprehensive model supporting the S-O-R paradigm and the ELM. However,
Part 1 in Study 2 revealed that without considering situational involvement the effects for
central and peripheral cues on pleasure and arousal were only partially supported. This
demonstrates that the S-O-R paradigm alone may not provide useful theoretical insight in
understanding consumer responses to various web cues regarding online visual
merchandising. Considering situational involvement Study 2 revealed the significant
effects for peripheral cues on consumer emotions only under low situational involvement
(browsing without a purchasing goal) and the significant effects for central cues on
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consumer emotions only under high situational involvement (browsing with a purchasing
goal). According to the results, the ELM rather than the S-O-R paradigm more clearly
explains the relationship between various web cues (visual merchandising in apparel
websites) and consumer emotions. Supporting the S-O-R paradigm Study 2 also found
that the effects for various web cues on consumer response behaviors were indirect via
consumer emotions rather than direct. Overall, consistent with Study 1, Study 2 confirms
the applicability of blending the two theoretical approaches, the S-O-R paradigm and the
ELM, in studying the relationship between online visual merchandising and consumer
responses with respect to the effects of different situational involvement.
6.3. Limitations
Although this study provides valuable information regarding visual
merchandising in online apparel stores, there are several limitations recognized in the
current study: 1) the study used a homogenous sample, 2) situational involvement was
manipulated, and 3) product category was limited to one product category, apparel.
6.3.1. Homogeneity of the Sample
The sample used in the study was relatively homogeneous in terms of age and
gender. Since the mock websites used in Study 1 and Study 2 were designed to target
young female consumers, all participants in the study were female and the majority of
participants were aged between 18 and 23. Therefore, the implications of Study 1 and
289
Study 2 may be only applicable to online apparel retailers who target young female
consumers. In addition, the ethnicity of the sample population in the study was not
evenly distributed. Since participants were mostly Caucasian American, the results may
not hold for different ethnic groups. In sum, the findings of the study may not be
generalizable to different age, gender, or other demographic consumer groups.
6.3.2. Manipulated Situational Involvement
Although a large amount of effort was made to simulate a real time online apparel
browsing or purchasing situation in the experiment, several factors related to the
experimental procedure may reduce the reality of the study. First, participants may not
have perceived the experimental task as outlined in the scenario as real browsing or
purchasing situations. Particularly under high situational involvement because
participants are not really making a purchase online, they may feel less involved in
comparison to a real purchasing situation. In addition, several disabled functions (e.g.,
customer service, shipping information) and limited product assortments in the mock
website may reduce the realism of online shopping.
6.3.3. Limited Product Category
This study was necessarily limited in the use of only one product category,
women’s apparel, to examine the effects of various web cues presented in apparel online
stores on consumer emotions and response behaviors while browsing or shopping in the
290
apparel websites. Web cues appropriate for one product category may not be appropriate
for other product categories. For example, a color swatch provides key information
helping consumers make a purchase decision particularly for pricier apparel items while
it is a low priority for electronics or sporting goods (“With Site Design, Product Category
Should Guide Display Tool Selection,” 2002). Therefore, the findings of the study
should be interpreted with caution when it is applied to different product categories
available online retail stores.
6.4. Recommendations for Future Studies
Since the sample used in the study was relatively homogeneous in terms of age
and gender (i.e., young female students), examining whether the findings of the current
study are generalizable to other relevant populations could be an important next step.
Thus, future studies need to investigate how diverse consumer groups react to various
web cues available in apparel websites. Consumers with different demographic
characteristics in terms of age, gender, ethnicity, income, and education may exhibit
different responses to online visual merchandising in apparel websites.
Additional research needs to further establish the generalizability of the findings
of the study across products. Effective online visual merchandising techniques may vary
across different product categories. A virtual model can be an important tool in apparel
online stores while video can be an important cue for electronics (“With Site Design,
Product Category Should Guide Display Tool Selection,” 2002). Therefore, future
research should be conducted to find web cues which play an important role in online
291
stores for different product categories. Further studies also need to investigate how those
web cues influence consumer emotions and response behaviors.
To increase the reality of the study future researchers need to more realistically
manipulate situational involvement. In the current study, a scenario was provided to
manipulate high situational involvement and participants did not make actual purchases.
Thus, future studies could use a valid gift certificate to allow participants to make real
purchases rather than pretend to do it.
Finally, future studies could extend the current study by reexamining the effects
for visual merchandising cues on consumer emotions and response behaviors across
different types of shopping contexts (e.g., in-store shopping, catalog shopping, television
shopping, and online shopping contexts). Available visual merchandising cues may vary
across diverse shopping contexts. While this study focused on visual merchandising in
apparel websites, future research may apply similar logic to diverse shopping contexts.
Future studies need to investigate how visual merchandising in various shopping contexts
can be used effectively in enhancing consumer pleasure and arousal that in turn influence
consumer response behaviors.
292
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APPENDIX A
PILOT STUDY 1
304
(1) Pilot Study 1: Recruitment Letter Dear Participants,
I’m a Ph. D Candidate of Textiles and Clothing in the department of Consumer and Textile Sciences at the Ohio State University. You are being asked to participate in a study of Internet apparel shopping. This study is concerned with group data and not with your individual responses. Therefore, your responses will remain confidential. Your name will not be associated with the data we collect. Please, understand that your participation in this study is entirely voluntary. You may discontinue participation at any time. I realize your time is at a premium. I greatly appreciated your help and you may expect to take 10 to 15 minutes to participate and respond to the questionnaire. Do not write your name anywhere. After you complete the questionnaire, please return it to me. If you have any question, pleases feel free to ask any questions you may have. Thank you very much. Sincerely, Young Ha Ph. D Candidate Dep. Of Consumer and Textile Sciences Ohio State University (614) 688-4234 [email protected]
Dr. Sharron J. Lennon Professor Dep. Of Consumer and Textiles Sciences Ohio State University (614) 292-4384 [email protected]
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(2) Pilot Study 1: Questionnaire for Low Involvement
PART 1. Please, read the following carefully.
Imagine yourself in the following situation. Imagine that you find a clothing website today. Now, you are going to visit one clothing website. Browse and look around the website for a while. ************************************************************************ Here is the website for you to visit: www.asos.com ************************************************************************ Please, do NOT move on to the next page until you finish browsing the website. If you finish browsing the website, then now CLOSE the website that you browsed and move on to the next page.
306
Now, make sure that you closed the website that you browsed and please, answer the
following questions. Now, you cannot go back to the website!!!
PART 3. Please fill in the blank.
1. List and describe what you saw on the website.
i. _________________________________
ii. _________________________________
iii. _________________________________
iv. _________________________________
v. _________________________________
vi. _________________________________
vii. _________________________________
viii. _________________________________
ix. _________________________________
x. _________________________________
PART 4. Please fill in the blank or check the response which best answers the questions
that follow.
2. Age __________
3. Ethnic Background
_______ African American
_______ Caucasian American
_______ Hispanic/Hispanic American
_______ Native American
_______ Asian/Asian American
_______ Other
4. How likely were you involved in browsing the websites today?
Not at all Very much so
1 2 3 4 5 6 7
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(3) Pilot Study 1: Questionnaire for High Involvement
PART 1. Please, read the scenario carefully.
Imagine yourself in the following situation.
Now, you are going to visit one apparel website. Imagine that you have been given a $100 gift certificate to purchase apparel products from the website. Remember! After browsing the website, you should be able to identify and describe two items that you would like to purchase from the website. ************************************************************************ Here is the website for you to visit: www.asos.com ************************************************************************ Please, do NOT move on to the next page until you finish browsing the website. If you finish browsing the website, then now CLOSE the website that you browsed and move on to the next page.
308
Now, make sure that you closed the website that you browsed and please, answer the
following questions. Now, you cannot go back to the website!!!
PART 2. List and describe two apparel items.
1. __________________________________________________________________
__________________________________________________________________
2. __________________________________________________________________
__________________________________________________________________ PART 3. Please fill in the blank.
3. List and describe what you saw on the websites.
i. _________________________________
ii. _________________________________
iii. _________________________________
iv. _________________________________
v. _________________________________
vi. _________________________________
vii. _________________________________
viii. _________________________________
ix. _________________________________
x. _________________________________
PART 4. Please fill in the blank or check the response which best answers the questions
that follow.
4. Age __________
5. Ethnic Background
_______ African American
_______ Caucasian American
_______ Hispanic/Hispanic American
309
_______ Native American
_______ Asian/Asian American
_______ Other
6. How likely were you involved in browsing the websites today?
Not at all Very much so
1 2 3 4 5 6 7
310
APPENDIX B
PILOT STUDY 2 – WEBSITE
311
312
313
APPENDIX C
PILOT STUDY 2 – APPAREL STIMULI
314
Thirty Four Pairs of Pants Rated in the Pilot Study 2
315
316
317
Two Items Selected for Main Study 1
318
Five Items Selected for the Main Study 2
319
APPENDIX D
PILOT STUDY 3 – WEBSITE AND QUESTIONNAIRE
320
321
322
323
APPENDIX E
MAIN STUDY 1 – WEBSITE: MANIPULATIONS FOR PERIPHERAL CUES
324
325
Main page: Peripheral cue – Absence
326
Main page: Peripheral cue – Presence
327
Product page: Peripheral cue – Absence
328
Product page: Peripheral cue – Presence
329
Peripheral Cues – Presence: Flashing Image and Colorful Icons
1. Brand Logo Flashing Image: Heart dot is blinking (blank heart to filled heart)
2. Colorful Icons with Rollover Image: When a mouse is over the icon, the color of the edge is changing.
330
APPENDIX F
MAIN STUDY 1 – THE QUESTIONNAIRE
331
*WITHOUT GOING BACK TO THE WEBSITE, PLEASE TELL US ABOUT YOUR THOUGHTS BY RESPONDING TO THE FOLLOWING QUESTIONS. Part A. We would like to know how you feel after browsing E-Style.com website. Please indicate the number that best describes your current feelings.
Unhappy 1 2 3 4 5 6 7 Happy Annoyed 1 2 3 4 5 6 7 Pleased
Unsatisfied 1 2 3 4 5 6 7 Satisfied Melancholic 1 2 3 4 5 6 7 Contented
Despairing 1 2 3 4 5 6 7 Hopeful Bored 1 2 3 4 5 6 7 Relaxed
Relaxed 1 2 3 4 5 6 7 Stimulated Calm 1 2 3 4 5 6 7 Excited
Sluggish 1 2 3 4 5 6 7 Frenzied Dull 1 2 3 4 5 6 7 Jittery
Sleepy 1 2 3 4 5 6 7 Wide-awake Unaroused 1 2 3 4 5 6 7 Aroused
Part B. Assume that E-Style.com is an active apparel online store. Please indicate the number that best represents your thoughts based on your browsing experience with E-Style.com today.
Unlikely LikelyHow likely is it that you would buy clothing items if you happened to see them from E-Style.com?
1 2 3 4 5
How likely is it that you will buy the apparel item from E-Style.com in the next 12 months?
1 2 3 4 5
How likely is it that you will shop for apparel from E-Style.com when you buy apparel in the upcoming year?
1 2 3 4 5
How likely is it that you will buy apparel from E-Style.com when you find something you like?
1 2 3 4 5
Part C. Please check the response which best represent your thoughts based on your browsing experience with E-Style.com.
Unlikely LikelyHow much would you enjoy exploring this site? 1 2 3 4 5 Not
at all Very
much Do you like this site? 1 2 3 4 5 To what extent is this site a good opportunity to shop? 1 2 3 4 5 Would you enjoy shopping in this site? 1 2 3 4 5
332
Part D. Please rate the following items based on your browsing experience with E-Style.com today. Clothing is ___________________.
Unimportant 1 2 3 4 5 6 7 Important
Irrelevant 1 2 3 4 5 6 7 Relevant Means a lot to me 1 2 3 4 5 6 7 Means nothing to me
Valuable 1 2 3 4 5 6 7 Worthless Boring 1 2 3 4 5 6 7 Interesting
Unexciting 1 2 3 4 5 6 7 Exciting Appealing 1 2 3 4 5 6 7 Unappealing Mundane 1 2 3 4 5 6 7 Fascinating
Not needed 1 2 3 4 5 6 7 Needed Involving 1 2 3 4 5 6 7 Uninvolving
Part E. Please check the response which best answers the questions that follow.
Strongly
disagree Strongly
agreeThe website you browsed today contained very much information.
1 2 3 4 5
From browsing the website, I learned a great deal about the product.
1 2 3 4 5
The website was very informative. 1 2 3 4 5 After browsing the website, I know enough to make an informed purchase decision.
1 2 3 4 5
I can fully trust information given by the website. 1 2 3 4 5 The website looks attractive. 1 2 3 4 5 The website looks organized. 1 2 3 4 5 The website uses fonts properly. 1 2 3 4 5 The website uses colors properly. 1 2 3 4 5 The website uses multimedia features properly. 1 2 3 4 5
Part G. Please fill the blank or check the response which best answers the question that follow.
1. Age ____________
2. Ethnic background
____ African American ____ Caucasian American ____ Native American ____ Hispanic/Hispanic American ____ Asian/Asian American ____ Other
333
APPENDIX G
MAIN STUDY 2 – INVITATION EMAIL
334
(1) Invitation Email for High Involvement
TITLE: PARTICIPANTS NEEDED FOR OSU RESEARCH
Greetings!
Hello, my name is Young Ha, a doctoral candidate in the Department of Consumer Sciences at the Ohio State University. I am writing this email to ask for your help in participating in an important OSU research.
Among 15000 OSU female students, you have been selected to participate in a research study of online shopping behaviors. The purpose of this research is to investigate various consumer behaviors in online shopping stores. The result of this research will provide better guidelines for online retailers to better serve online shoppers. Your participation in this study is invaluable to this research and a growing scholarship in online shopping.
The study will be done as a Web survey using a mock apparel website. You can participate in the study by logging onto the following URL (http://hec.osu.edu/forms/young/estudy). IF THIS LINK DOES NOT WORK BY JUST CLICKING IT, THEN PLEASE COPY AND PASTE THIS LINK TO YOUR WEB BROWSER. Please carefully read instructions given in each page and follow the instructions. Your task is to browse for five pairs of pants on the website and then, choose one item that you would like to purchase. After selecting one item, you will be asked to complete the questionnaire measuring your perceptions and behaviors regarding online apparel shopping. Your task will be simple and straight-forward, similar to what we all do in apparel shopping situation.
Because this is a Web-based study, you can participate in the study when and where convenient for you. The survey will take approximately 15 to 20 minutes to complete. Upon completion, randomly selected participants will receive the apparel item they choose during the study or a $20 or $40 cash award. Your participation is strictly voluntary, but I would really appreciate it if you can help me out to complete this research.
Please note that 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.
I apologize for sending you the email without your permission, but appreciate your time and consideration. Please feel free to email me ([email protected]) if having any questions/concerns/comments. I very much look forward to receiving your completed survey! I deeply appreciate your help with this research.
335
Thank you very much!
Best regards,
Young Ha, Doctoral candidate Dr. Sharron Lennon, Professor Dept. of Consumer Sciences Dept. of Consumer 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]
336
(2) Invitation Email for Low Involvement
TITLE: PARTICIPANTS NEEDED FOR OSU RESEARCH
Greetings!
Hello, my name is Young Ha, a doctoral candidate in the Department of Consumer Sciences at the Ohio State University. I am writing this email to ask for your help in participating in an important OSU research study.
Among 15000 OSU female students, you have been selected to participate in a research study of online shopping behaviors. The purpose of this research is to investigate various consumer behaviors in online shopping stores. The result of this research will provide better guidelines for online retailers to better serve online shoppers. Your participation in this study is invaluable to this research and a growing scholarship in online shopping.
The study will be done as a Web survey using a mock apparel website. You can participate in the study by logging onto the following URL (http://hec.osu.edu/forms/young/onlinestudy ). IF THIS LINK DOES NOT WORK BY JUST CLICKING IT, THEN PLEASE COPY AND PASTE THIS LINK TO YOUR WEB BROWSER. Please carefully read instructions given in each page and follow the instructions. Your task is to browse for five pairs of pants on the website. After browsing five apparel items, you will be asked to complete the questionnaire measuring your perceptions and behaviors regarding online apparel shopping. Your task will be simple and straight-forward, similar to what we all do in apparel shopping situation.
Because this is a Web-based study, you can participate in the study when and where convenient for you. The survey will take approximately 15 to 20 minutes to complete. Upon completion, randomly selected participants will receive a $20 or $40 cash award. Your participation is strictly voluntary, but I would really appreciate it if you can help me out to complete this research.
Please note that 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.
I apologize for sending you the email without your permission, but appreciate your time and consideration. Please feel free to email me ([email protected]) if having any questions/concerns/comments. I very much look forward to receiving your completed survey! I deeply appreciate your help with this research.
Thank you very much!
337
Best regards,
Young Ha, Doctoral candidate Dr. Sharron Lennon, Professor Dept. of Consumer Sciences Dept. of Consumer 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]
338
APPENDIX H
MAIN STUDY 2 – INSTRUCTION PAGE
339
Instruction Page: Peripheral Cues – Absence
340
Instruction Page: Peripheral Cues – Presence
341
APPENDIX I
MAIN STUDY 2 – SCENARIO PAGE
(MANIPULATIONS FOR SITUATION INVOLVEMENT)
342
Scenario Page: Peripheral Cue – Absence, Involvement – Low
343
Scenario Page: Peripheral Cue – Presence, Involvement – Low
344
Scenario Page: Peripheral Cue – Absence, Involvement – High
345
Scenario Page: Peripheral Cue – Presence, Involvement – High
346
APPENDIX J
MAIN STUDY 2 – MAIN PAGE
347
Main Page: Peripheral Cues – Absence
348
Main Page: Peripheral Cues – Presence
349
APPENDIX K
MAIN STUDY 2 – PRODUCT PAGE
350
Product Page: Peripheral Cues – Absence, Central Cues – Medium Amount
351
Product Page: Peripheral Cues – Absence, Central Cues – High Amount
352
Product Page: Peripheral Cue – Presence, Central Cue – Medium Amount
353
Product Page: Peripheral Cue – Presence, Central Cue – High Amount
354
Peripheral Cues – Presence: Flashing Image and Colorful Icons
1. Brand Logo Flashing Image: Heart dot is blinking (blank heart to filled heart)
2. Colorful Icons with Rollover Image: When a mouse is over the icon, the color of the edge is changing.
355
Product Page: Size Chart – Peripheral Cues – Absence
356
Product Page: Size Chart – Peripheral Cues – Presence
357
APPENDIX L
MAIN STUDY 2 – PURCHASE PAGE FOR HIGH INVOLVEMENT
358
Purchase Page: Peripheral Cues – Absence
359
Purchase Page: Peripheral Cues – Presence
360
APPENDIX M
MAIN STUDY 2 – THE QUESTIONNAIRE
361
*WITHOUT GOING BACK TO THE WEBSITE, PLEASE TELL US ABOUT YOUR THOUGHTS BY RESPONDING TO THE FOLLOWING QUESTIONS. Part A. We would like to know how you feel after browsing E-Style.com website. Please indicate the number that best describes your current feelings.
Unhappy 1 2 3 4 5 6 7 Happy Annoyed 1 2 3 4 5 6 7 Pleased
Unsatisfied 1 2 3 4 5 6 7 Satisfied Melancholic 1 2 3 4 5 6 7 Contented
Despairing 1 2 3 4 5 6 7 Hopeful Bored 1 2 3 4 5 6 7 Relaxed
Relaxed 1 2 3 4 5 6 7 Stimulated Calm 1 2 3 4 5 6 7 Excited
Sluggish 1 2 3 4 5 6 7 Frenzied Dull 1 2 3 4 5 6 7 Jittery
Sleepy 1 2 3 4 5 6 7 Wide-awake Unaroused 1 2 3 4 5 6 7 Aroused
Part B. Please check the response which best answers the questions that follow based on your browsing experience with E-Style.com today. (SD: Strongly Disagree, SA: Strongly Agree)
SD SA
I enjoyed visiting E-Style.com. 1 2 3 4 5 I was satisfied with my shopping experience at E-Style.com. 1 2 3 4 5
Given a choice, I would probably not go back to E-Style.com. 1 2 3 4 5
I would recommend E-Style.com to other people. 1 2 3 4 5 Part C. Assume that E-Style.com is an active apparel online store. Please indicate the number that best represents your thoughts based on your browsing experience with E-Style.com today.
Unlikely LikelyHow likely is it that you would buy clothing items if you happened to see them from E-Style.com? 1 2 3 4 5
How likely is it that you will buy the apparel item from E-Style.com in the next 12 months? 1 2 3 4 5
How likely is it that you will shop for apparel from E-Style.com when you buy apparel in the upcoming year?
1 2 3 4 5
How likely is it that you will buy apparel from E-Style.com when you find something you like? 1 2 3 4 5
362
Part D. Please check the response which best represent your thoughts based on your browsing experience with E-Style.com.
Unlikely LikelyHow much would you enjoy exploring this site? 1 2 3 4 5
Not at all
Very much
Do you like this site? 1 2 3 4 5 To what extent is this site a good opportunity to shop? 1 2 3 4 5 Would you enjoy shopping in this site? 1 2 3 4 5
Part E. Please check the response which best answers the questions that follow.
Strongly
disagree Strongly
agreeThe website you browsed today contained very much information. 1 2 3 4 5
From browsing the website, I learned a great deal about the product. 1 2 3 4 5
The website was very informative. 1 2 3 4 5 After browsing the website, I know enough to make an informed purchase decision. 1 2 3 4 5
I can fully trust information given by the website. 1 2 3 4 5 The website looks attractive. 1 2 3 4 5 The website looks organized. 1 2 3 4 5 The website uses fonts properly. 1 2 3 4 5 The website uses colors properly. 1 2 3 4 5 The website uses multimedia features properly. 1 2 3 4 5
363
Part F. Please rate the following items based on your browsing experience with E-Style.com today. Browsing E-Style.com is ___________________.
Unimportant 1 2 3 4 5 6 7 Important
Irrelevant 1 2 3 4 5 6 7 Relevant Means a lot to me 1 2 3 4 5 6 7 Means nothing to me
Valuable 1 2 3 4 5 6 7 Worthless Boring 1 2 3 4 5 6 7 Interesting
Unexciting 1 2 3 4 5 6 7 Exciting Appealing 1 2 3 4 5 6 7 Unappealing Mundane 1 2 3 4 5 6 7 Fascinating
Not needed 1 2 3 4 5 6 7 Needed Involving 1 2 3 4 5 6 7 Uninvolving
Part G. Please fill the blank or check the response which best answers the question that follow.
3. Age ____________
4. Ethnic background
____ African American ____ Caucasian American ____ Native American ____ Hispanic/Hispanic American ____ Asian/Asian American ____ Other
5. What is your academic standing?
____ Freshmen ____ Sophomore ____ Junior ____ Senior ____ Other
6. Please answer the following questions based on your own experience.
Never
Not often
Neutral
Very Often
How often do you use the Internet? 0 1 2 3 4 5 How often do you browse online for information search? 0 1 2 3 4 5
How often do you purchase online? 0 1 2 3 4 5 How often do you browse online for apparel information search? 0 1 2 3 4 5
How often do you purchase apparel online? 0 1 2 3 4 5
364
APPENDIX N
STANDARDIZED RESIDUAL EVALUATED IN MAIN STUDY 1
365
366
APPENDIX O
DATA SCREENING FOR NORMALITY TEST
367
368
APPENDIX P
STANDARDIZED RESIDUAL EVALUATED IN MAIN STUDY 2
369
370
APPENDIX Q
DATA SCREENING FOR NORMALITY TEST
371
372
APPENDIX R
HUMAN SUBJECTS APPROVAL FORM FOR STUDY 1
373
374
APPENDIX S
HUMAN SUBJECTS APPROVAL FORM FOR STUDY 2
375
