Contributions to Management Science

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Contributions to Management Science

For further volumes:

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Jella Pfeiffer

Interactive Decision Aids

in E-Commerce

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Jella Pfeiffer

Johannes Gutenberg-Universit¨at Mainz Lehrstuhl f¨ur Wirtschaftsinformatik und BWL Jakob-Welder Weg 9 55128 Mainz Germany [email protected] ISSN 1431-1941 ISBN 978-3-7908-2768-2 e-ISBN 978-3-7908-2769-9 DOI 10.1007/978-3-7908-2769-9

Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011942413 © Springer-Verlag Berlin Heidelberg 2012

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Acknowledgements

At the beginning of my doctorate, I had not anticipated that it would connect me to many people, bring me to many countries, and would follow a very evolutionary process inspiring me not to leave the scientific path. I am grateful for this interesting part of my life and I would like to express my thanks to some special people who have supported me throughout these past years.

First and foremost, I am very grateful to my supervisor Dr. Franz Rothlauf. Franz, you have always supported me no matter how complicated my ideas were and how far they carried me. Furthermore, you have taught me a lot about scientific writing and how to structure my thoughts, and I have the feeling that I will never stop learning from you. Whenever I needed your advice, you were there for me. You have quite simply played the role of “PhD Supervisor” – particularly in the German sense of the word – perfectly! But besides all that, I appreciate your friendship very much.

My second acknowledgment goes to Dr. Ulrich Hoffrage, who was very kind to serve as the second reviewer in the committee. His profound and extraordinary knowledge in the field of decision-making is remarkable. I am very happy to be able to share and discuss ideas with Ulrich and I am very much looking forward to do further research with him in the future.

Most importantly, I would like to say many thanks to my family for all their help and support. They have showed me the importance of being optimistic and earnest, and they have taught me to be curious about the world.

Next, I want to thank all my colleagues from my department for wonderful discussions and their immense support during these last few years. In particular, I would like to thank Heike Kirsch for all her help and support. I would also like to thank Dr. Daniel Schunk, who was the first to inspire me to undertake a research project during my studies in Mannheim, and who has given me valuable feedback on my present work. Daniel, I still remember endless hours of discussing and generating ideas well into the evening in the SFB 504 building. Similarly, I am also grateful to Dr. Martin Meißner, from whom I learned about preference measurement and with whom I had great discussions. Furthermore, I would like to thank Dr. Ren´e Riedl for his good ideas and cooperation in several projects, as

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viii Acknowledgements

well as Dr. Eduard Brandst¨atter for very inspiring discussions on decision-making behavior. Moreover, I would like to thank Felix Vogel who helped me a lot with implementing INTACMATO, and Melanie Bloos as well as my brother Thies Pfeiffer who both proofread parts of my dissertation.

I would like to express my thanks to those who participated in the studies and to several students who helped me to conduct the studies. The successful completion of my research is directly related to your support.

One very big thank you goes to Eric Bonabeau, Ph.D., from Icosystem Corpo-ration. He is such an inspiring man, and has made many things possible for me, including allowing me to be part of a company, where both the science-world and the real-world go closely hand-in-hand. I also thank Dejan Duzevik from Icosystem, who is as excited as I am about decision-making behavior and with whom I had great discussions.

I would like to thank all my friends who were always there for me. My close friends, Silke, Susanne and my old friends from Mannheim, the friends I have found among my colleagues, and the friends I have found abroad during my time at Icosystem and Harvard in Cambridge (USA). Last but not least, I would like to thank Mine, who has not been discouraged despite coming into my life during the final and most strenuous stage of my doctorate.

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List of Figures

Fig. 1.1 Rigor-, relevance-, and build-cycle.. . . 7

Fig. 2.1 Choice process. . . 16

Fig. 2.2 Value functions.. . . 18

Fig. 3.1 Example of a choice task. . . 48

Fig. 3.2 Experimental procedure. . . 56

Fig. 3.3 Effect of SM on information acquisition (PCPM). . . 60

Fig. 3.4 Effect of SM on information acquisition (CBC). . . 60

Fig. 4.1 Two mappings of alternatives to decision strategies.. . . 70

Fig. 4.2 Construction of a choice task from a solution string. . . 73

Fig. 4.3 Controlling for the attribute range.. . . 77

Fig. 4.4 Controlling for the attractiveness difference.. . . 77

Fig. 4.5 Controlling for the correlation of attribute vectors. . . 78

Fig. 4.6 Influence of the independent variables on the relative frequency of the observed strategies.. . . 81

Fig. 4.7 Interaction effects for MAJ. . . 85

Fig. 4.8 Interaction effects for EBA. . . 85

Fig. 5.1 Welcome screen on myproductadvisor.com. . . 95

Fig. 5.2 Types of interactive decision aids. . . 95

Fig. 5.3 Product-comparison matrix on cdw.com.. . . 96

Fig. 5.4 Screening phase with IIMT on cdw.com.. . . 97

Fig. 5.5 Screening phase without IIMT on panasonic.com . . . 97

Fig. 5.6 Product-comparison matrix on myproductadvisor.com. . . 99

Fig. 5.7 Industries among the 100 analyzed websites. . . 101

Fig. 5.8 Websites per industry offering a product-comparison matrix.. . . 102

Fig. 5.9 Attribute importance dialog on myproductadvisor.com.. . . 106

Fig. 5.10 Filter on myproductadvisor.com. . . 106

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xiv List of Figures

Fig. 5.11 User navigation on myproductadvisor.com.. . . 107

Fig. 5.12 Results of Study: IIMT vs. RA. . . 109

Fig. 6.1 IIMT: REMOVE. . . 118

Fig. 6.2 IIMT: PAIRWISE COMPARISON and MARKd iff er e nces. . . 119

Fig. 6.3 IIMT: MARK and REMOVEmar ked. . . 119

Fig. 6.4 Choice process. . . 120

Fig. 6.5 IIMT: SORT and FILTER. . . 121

Fig. 6.6 First IIMT-prototype. . . 122

Fig. 6.7 Results from the thinking aloud usability study. . . 124

Fig. 6.8 Final design of INTACMATO. The tooltip explains the IIMT functionality. . . 125

Fig. 7.1 Positions of decision strategies in the effort-accuracy space. . . 130

Fig. 7.2 The technical environment as additional factor influencing decision making. . . 131

Fig. 7.3 Effort for WADD without support of IIMT. . . 136

Fig. 7.4 Effort for WADD with support of IIMT. . . 137

Fig. 7.5 Effort for EBA without support of IIMT. . . 138

Fig. 7.6 Effort for EBA with support of IIMT. . . 139

Fig. 7.7 Effort for LEX without support of IIMT. . . 140

Fig. 7.8 Effort for LEX with support of IIMT. . . 141

Fig. 7.9 The extended effort-accuracy framework.. . . 145

Fig. 8.1 Experimental Design. . . 157

Fig. 8.2 Screenshot of webstores for group few IIMT. . . 158

Fig. 8.3 Screenshot of webstore for group no IIMT . . . 158

Fig. 8.4 Evaluation of design criteria. . . 161

Fig. 8.5 Results of hypothesis testing . . . 163

Fig. 8.6 Mean of clicks for group all IIMT . . . 167

Fig. 8.7 IIMT used at least once for group all IIMT. . . 168

Fig. 8.8 Mean of clicks for group few IIMT . . . 169

Fig. 8.9 IIMT used at least once for group few IIMT. . . 170

Fig. 8.10 Applied strategies (analysis of final choices). . . 172

Fig. 9.1 Number of decision strategies which are supported by each decision aid. . . 187

Fig. 9.2 Cumulative number of decision strategies which are supported by each decision aid. . . 187

Fig. A.1 Characteristics of decision strategies. . . 192

Fig. A.2 Effort for ADD without support of IIMT. . . 198

Fig. A.3 Effort for ADD with support of IIMT. . . 199

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List of Figures xv

Fig. A.5 Effort for COM with support of IIMT. . . 201

Fig. A.6 Effort for CONJ without support of IIMT. . . 201

Fig. A.7 Effort for CONJ with support of IIMT. . . 202

Fig. A.8 Effort for DIS without support of IIMT. . . 202

Fig. A.9 Effort for DIS with support of IIMT. . . 203

Fig. A.10 Effort for DOM without support of IIMT. . . 204

Fig. A.11 Effort for DOM with support of IIMT. . . 205

Fig. A.12 Effort for EQW without support of IIMT. . . 206

Fig. A.13 Effort for EQW with support of IIMT . . . 206

Fig. A.14 Effort for FRQ without support of IIMT. . . 207

Fig. A.15 Effort for MAJ without support of IIMT. . . 208

Fig. A.16 Effort for FRQ with support of IIMT. . . 209

Fig. A.17 Effort for MAJ with support of IIMT. . . 209

Fig. A.18 Effort for MCD without support of IIMT. . . 210

Fig. A.19 Effort for MCD with support of IIMT . . . 211

Fig. A.20 Effort for LED without support of IIMT. . . 212

Fig. A.21 Effort for LED with support of IIMT. . . 213

Fig. A.22 Effort for SAT without support of IIMT. . . 213

Fig. A.23 Effort for SAT with support of IIMT. . . 214

Fig. A.24 Effort for SAT+ without support of IIMT. . . 214

Fig. A.25 Effort for SAT+ with support of IIMT. . . 215

Fig. B.1 Final design of IIMT: FILTER. . . 218

Fig. B.2 Final design of IIMT: MARKdiff. . . 219

Fig. B.3 Final design of IIMT: MARKmanually(positive). . . 220

Fig. B.4 Final design of IIMT: MARKmanually(negative).. . . 221

Fig. B.5 Final design of IIMT: SORThierarchically. . . 222

Fig. B.6 Final design of IIMT: SORTmanually. . . 223

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List of Tables

Table 1.1 Guidelines for the design-science approach.. . . 8

Table 2.1 Product-comparison matrix. . . 17

Table 2.2 Examples of correlation of attribute vectors. . . 33

Table 2.3 Attribute ranges and attractiveness differences. . . 35

Table 3.1 Design of preference measurement used in CBC and PCPM. . . 57

Table 3.2 SM for the three stages. . . 58

Table 3.3 Alternatives with at least one fixation. . . 58

Table 3.4 Correlations between measures of complexity and breadth and depth of search. . . 59

Table 4.1 Attributes and attribute levels used in the experiment. . . 69

Table 4.2 Quality of solutions found by GA versus randomly generated choice tasks (mean and standard deviation). . . 76

Table 4.3 Correlations of complexity measures. . . 78

Table 4.4 Observed versus expected usage of strategies. . . 80

Table 4.5 Wilcoxon tests on the relative frequencies for task-based measurements. . . 83

Table 4.6 Wilcoxon tests on the relative frequency for the context-based measurements.. . . 83

Table 4.7 Binary logit models testing interaction effects between AR and AC, AD and AC, respectively.. . . 84

Table 4.8 Relative frequencies of the observed strategies for different combinations of AR and AC . . . 85

Table 4.9 Results of cluster analysis. . . 86

Table 5.1 Number of products in the product-comparison matrices. . . 100

Table 6.1 List of IIMT implemented in INTACMATO. . . 117

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xviii List of Tables

Table 7.1 Elementary Information Processes.. . . 130

Table 7.2 Elementary Communication Processes. . . 132

Table 7.3 Effort-reduction when using IIMT . . . 142

Table 8.1 Example of a pairwise Hamming distance of two. . . 155

Table 8.2 Mean values for evaluation criteria. . . 163

Table 8.3 Results of hypotheses tests with effect sizes. . . 164

Table 8.4 Frequencies of strategies which explain choices.. . . 172

Table 8.5 Occurrences of mixed strategies. . . 176

Table A.1 Decision strategies and alternative name conventions.. . . 191

Table A.2 Summary of studies on choice task complexity.. . . 193

Table C.1 Pages with highest Google PageRank.. . . 225

Table C.2 Measures (A): RA vs. IIMT. . . 228

Table C.3 Measures (B): RA vs. IIMT. . . 229

Table C.4 Measures (A): evaluation of INTACMATO.. . . 230

Table C.5 Measures (B): evaluation of INTACMATO. . . 231

Table C.6 Measures (C): evaluation of INTACMATO. . . 232

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Acronyms

ADD Additive difference rule ANOVA Analysis of variance COM Compatibility test CONJ Conjunctive strategy DIS Disjunctive strategy DOM Dominance strategy DSS Decision support system(s) EBA Elimination by aspect strategy

ECP Elementary communication process(es) EIP Elementary information process(es) EQW Equal weight heuristics

EV Expected value

FRQ Frequency of good and/or bad features heuristic GA Genetic algorithm

IDA Interactive decision aid(s)

IIMT Interactive information management tool(s)

INTACMATO Prototype for interactive information management tools LED Minimum difference lexicographic rule

LEX Lexicographic heuristic LTM Long-term memory

MAJ Simple majority decision rule

MD Median

M Mean

RA Recommendation agent(s) SAT Satisficing heuristic SATC Satisficing-plus heuristic SD Standard deviation SE Standard error

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xx Acronyms SI Search index SM Strategy measure STM Short-term memory TTF Task-technology fit vs. Versus

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Symbols

˛ Cronbach’s alpha

aij Attribute level of attribute i and alternative j

Ai Vector of possible attribute levels of attri

Attrw Vector of attributes ordered decreasingly according to attribute weight

altk D .a1k; : : : ; amk/ Alternative vector

attrl D .al1; al2; : : : ; aln/ Attribute vector

asp./ Aspiration level function (equals 0 in case aspiration level is met)

ˇi k Part-worth utility of occurrence k of attribute i

c Fitness for correlation of attribute vectors

ct Choice task

d.altj/ Deterministic component of u.altj/

df Degrees of freedom

ds Decision strategy

DSu The set of strategies without multiple mappings

"j Error term of u.altj/

F Fitness

O

f Effect size for ANOVA

Fr obust _{Robust fitness}

H.ct/ Entropy of a choice task

l The length of the genotype

n Number of alternatives

m Number of attributes

mp Fitness for mapping

p Mutation probability

.altj/ The probability that alternative j is chosen

r Pearson’s correlation coefficient

rattr Number of attribute-wise transitions

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xxii Symbols s Fitness for attribute range/attractiveness difference

t .x/ t-value of the T-test, x: degrees of freedom

u.altj/ Overall utility value of alternative j

v.aij/ Attribute value of attribute i and alternative j

wi Attribute importance, attribute weight

Xj i k Binary variable is 1 if altj contains occurrence k of

Contributions to Management Science