On Consumer Decision Strategies: New Approaches for Studying And
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Universit´ede Lausanne Facult´edes sciences sociales et politiques On Consumer Decision Strategies: New Approaches for Studying and Aiding Preferential Choices Th`esepr´esent´ee`ala Facult´edes sciences sociales et politiques de l'Universit´ede Lausanne pour obtenir le grade de docteur en psychologie par Nils Reisen Lausanne 2009 iii In psychology, invoking \strategies" to explain funny data is the last refuge of the clueless. Steven Pinker iv Acknowledgements First of all, I want to thank my two main supervisors Ulrich Hoffrage and Fred Mast for their support and guidance. Special thanks go to Ulrich Hoffrage for his constant and always immediate assistance. His door is always open, both literally and figuratively. I extend my appreciation to my three colleagues Chris White, Jan K. Woike, and Sebastian Hafenbr¨adl. They were always open for questions and we had many enriching discussions. I also want to thank all the other colleagues who provided help with the planning, execution, and analysis of my experiments. Moreover, I would like to thank the following people for their help with the experiments. For Experiment 1, my thanks go to Giovanni Rivera Diaz, Ada Lezama Lugo, Huseyin Cumhur Tekin, and Eren Vardarli for their help with preparing the material, writing the program, and collecting the data, and Chris M. White for help with the simulations. For Experiment 2, I thank Lucas Sinclair for programming MouseWorkshop, Dario Bombari for his help with the eye tracking equipment, Gregory Affolter, Richard Ciapala, Julien Finci, Gabriella Sinicco, and Vasko Vitanov for their help with the data collection and Felix Reisen for his help with the programming of the data analyses. For Experiment 3, I thank Joanes Grandjean for his good work as an experimenter and his helpful comments, and, last but not least, all the participants of my experiments for their cooperation. Furthermore, I am indebted to Chris M. White, Jan K. Woike, John Antonakis, Christian Zehnder, Marika Angerfelt, Jon Baron and four anonymous reviewers for helpful comments on several earlier versions of this dissertation as well as on a manuscript, which is the basis of Part I. Likewise, my thanks go to the members of the dissertation committee for their helpful input. I also want to thank my partner, M´onicaE. Casta~nedaTinoco, my family and all my friends for their constant support during these almost four years. Finally, I am grateful for financial support provided by the Schweizerischer Nationalfonds (Grant numbers 105511{111621/1 and 100011{116111/1). v vi ACKNOWLEDGEMENTS Abstract This dissertation focuses on the strategies consumers use when making purchase decisions. It is organized in two main parts, one centering on descriptive and the other on applied decision making research. In the first part, a new process tracing tool called InterActive Process Tracing (IAPT) is pre- sented, which I developed to investigate the nature of consumers' decision strategies. This tool is a combination of several process tracing techniques, namely Active Information Search, Mouselab, and retrospective verbal protocol. To validate IAPT, two experiments on mobile phone purchase de- cisions were conducted where participants first repeatedly chose a mobile phone and then were asked to formalize their decision strategy so that it could be used to make choices for them. The choices made by the identified strategies correctly predicted the observed choices in 73% (Experiment 1) and 67% (Experiment 2) of the cases. Moreover, in Experiment 2, Mouselab and eye tracking were directly compared with respect to their impact on information search and strategy description. Only minor differences were found between these two methods. I conclude that IAPT is a useful research tool to identify choice strategies, and that using eye tracking technology did not increase its validity beyond that gained with Mouselab. In the second part, a prototype of a decision aid is introduced that was developed building in particular on the knowledge about consumers' decision strategies gained in Part I. This decision aid, which is called the InterActive Choice Aid (IACA), systematically assists consumers in their purchase decisions. To evaluate the prototype regarding its perceived utility, an experiment was conducted where IACA was compared to two other prototypes that were based on real-world consumer decision aids. All three prototypes differed in the number and type of tools they provided to facilitate the process of choosing, ranging from low (Amazon) to medium (Sunrise/dpreview) to high functionality (IACA). Overall, participants slightly preferred the prototype of medium functionality and this prototype was also rated best on the dimensions of understandability and ease of use. IACA was rated best regarding the two dimensions of ease of elimination and ease of comparison of alternatives. Moreover, participants choices were more in line with the normatively oriented weighted additive strategy when they used IACA than when they used the medium functionality prototype. The low functionality prototype was the least preferred overall. It is concluded that consumers can and will benefit from highly functional decision aids like IACA, but only when these systems are easy to understand and to use. Keywords: Decision strategies, process tracing, Mouselab, eye tracking, preferential choice, con- sumer decision making, decision aids, online marketing vii viii ABSTRACT Contents 1 General introduction 1 1.1 Theories of choice . .3 1.1.1 The normative theory of choice . .5 1.1.2 Descriptive theories of choice . .6 1.2 Overview and contributions of the dissertation . 10 1.3 Contributions . 11 I Identifying Decision Strategies 13 2 Introduction to Part I 15 2.1 Process tracing techniques . 16 2.1.1 Information search: Mouselab, eye tracking, and the method of Active Information Search . 16 2.1.2 Information integration: Retrospective verbal protocol . 18 2.2 The method of InterActive Process Tracing (IAPT) . 19 2.2.1 Approaches similar to IAPT . 20 3 Experiment 1: Validation of IAPT 23 3.1 Hypotheses . 23 3.1.1 Prediction accuracy . 23 3.1.2 Information search . 24 3.2 Method . 26 3.2.1 Participants . 26 3.2.2 Task and stimuli . 26 3.2.3 Design . 27 3.2.4 Procedure . 27 3.2.5 Payment . 29 3.3 Results . 29 3.3.1 Described strategies . 31 3.3.2 Prediction accuracy . 32 ix x CONTENTS 3.3.3 Information search . 35 3.4 Discussion . 38 3.4.1 Information search vs. described strategies . 39 4 Experiment 2: IAPT and eye tracking 43 4.1 Hypotheses . 46 4.1.1 Prediction accuracy . 46 4.1.2 Information search . 47 4.2 Method . 48 4.2.1 Participants . 48 4.2.2 Task and stimuli . 48 4.2.3 Apparatus . 49 4.2.4 Design and procedure . 50 4.2.5 Payment . 51 4.3 Results . 51 4.3.1 Deferrals . 51 4.3.2 Described strategies . 51 4.3.3 Prediction accuracy . 52 4.3.4 Information search . 54 4.4 Discussion . 61 II Aiding Preferential Choices 65 5 Decision support systems and decision aids 67 5.1 Decision aiding on the internet . 69 5.1.1 Web-based decision aids: An overview . 71 5.1.2 Related literature . 74 5.1.3 Caveats of web decision aids: The trust issue . 75 5.2 The InterActive Choice Aid (IACA) . 76 5.2.1 Phase 1: Pre-selection of alternatives . 77 5.2.2 Phase 2: Comparison of alternatives . 79 5.2.3 Strengths and weaknesses of the pre-selection phase . 82 5.2.4 Strengths and weaknesses of the comparison phase . 83 6 Experiment 3: Evaluating IACA 87 6.1 Hypotheses . 88 6.2 Method . 90 6.2.1 Material . 90 6.2.2 Participants and payment . 95 CONTENTS xi 6.2.3 Design . 95 6.2.4 Procedure . 95 6.3 Results . 97 6.3.1 Decision time . 97 6.3.2 Usability questionnaire . 98 6.3.3 Comparison questionnaire . 100 6.3.4 Semi-structured interview . 102 6.4 Discussion . 105 6.4.1 Suggested improvements of IACA . 107 7 General conclusions 111 References 117 A Appendix: Questionnaire items 127 A.1 Usability questionnaire . 127 A.2 Comparison questionnaire . 129 A.3 Semi-structured interview . 130 B Stimuli 133 B.1 Experiments 1 & 2 . 133 B.2 Experiment 3 . 142 xii CONTENTS List of Tables 1.1 Utility theories . .5 2.1 Strengths and weaknesses of four process tracing techniques . 17 3.1 Number of participants who chose each attribute (Exp. 1) . 30 3.2 Three examples of participants' strategies (Exp. 1) . 31 4.1 Number of participants who chose each attribute (Exp. 2) . 52 4.2 Three examples of participants' strategies (Exp. 2) . 53 6.1 Attributes available in the three prototypes . 91 B.1 Stimuli used in Experiments 1 & 2 . 133 B.2 Stimuli used in Experiment 3: a) Mobile phones . 142 B.3 Stimuli used in Experiment 3: b) Digital cameras . 149 xiii xiv LIST OF TABLES List of Figures 3.1 Screen-shot of the process-tracing measure (Exp. 1) . 28 3.2 Prediction accuracy (Exp. 1) . 32 3.3 The six variants of WADD . 33 3.4 Mean proportion of accesses per attribute rank (Exp. 1) . 37 4.1 Screen-shot of the process-tracing measure (Exp. 2) . 49 4.2 Prediction accuracy.