Online Shopping Intentions: Antecedents and Moderators of Shopping Intention Formation in New Fields of E-Commerce (Handel und Internationales Marketing Retailing and International Marketing) 3658376619, 9783658376611

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Handel und Internationales Marketing Retailing and International Marketing Bernhard Swoboda · Thomas Foscht Hanna Schramm-Klein Hrsg.

Anne Fota

Online Shopping Intentions Antecedents and Moderators of Shopping Intention Formation in New Fields of E-Commerce

Handel und Internationales Marketing Retailing and International Marketing Series Editors Bernhard Swoboda, Universität Trier, Trier, Germany Thomas Foscht, Karl-Franzens-Universität Graz, Graz, Austria Hanna Schramm-Klein, Lehrstuhl für Marketing, Universität Siegen, Siegen, Germany

Die Schriftenreihe fördert die Themengebiete Handel und Internationales Marketing. Diese charakterisieren – jedes für sich, aber auch in inhaltlicher Kombination – die Forschungsschwerpunkte der Herausgeber. Beide Themengebiete werden grundsätzlich breit aufgefasst; die Reihe bietet sowohl Dissertationen und Habilitationen als auch Tagungs- und Sammelbänden mit unterschiedlicher inhaltlicher und methodischer Ausrichtung ein Forum. Die inhaltliche Breite ist sowohl im Sinne eines konsumentenorientierten Marketings wie auch einer marktorientierten Unternehmensführung zu verstehen. Neben den Arbeiten, die von den Herausgebern für die Schriftenreihe vorgeschlagen werden, steht die Reihe auch externen wissenschaftlichen Arbeiten offen. Diese können bei den Herausgebern eingereicht und nach einer positiven Begutachtung publiziert werden. The book series focuses on the fields of Retailing and International Marketing. These two areas represent the research fields of the editors—each of them as a single research area, but also in combination. Both of these research areas are widely understood. Consequently, the series provides a platform for the publication of doctoral theses and habilitations, conference proceedings and edited books, as well as related methodological issues that encompass the focus of the series. The series is broad in the sense that it covers academic works in the area of consumer-oriented marketing as well as the area of marketoriented management. In addition to academic works recommended by the editors, the book series also welcomes other academic contributions. These may be submitted to the editors and will be published in the book series after a positive assessment.

More information about this series at https://link.springer.com/bookseries/12697

Anne Fota

Online Shopping Intentions Antecedents and Moderators of Shopping Intention Formation in New Fields of E-Commerce

Anne Fota Wilnsdorf, Germany Anne Fota, Dissertation, Universität Siegen, 2022

ISSN 2626-3327 ISSN 2626-3335 (electronic) Handel und Internationales Marketing Retailing and International Marketing ISBN 978-3-658-37661-1 ISBN 978-3-658-37662-8 (eBook) https://doi.org/10.1007/978-3-658-37662-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Responsible Editor: Marija Kojic This Springer Gabler imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Acknowledgments

It’s done! And although in the last few years it seemed like the process was dragging on and on and I could hardly wait to finally be done, looking back the time has flown by. And there are two special reasons for that: On the one hand, the doctoral time was one of the best times in my life, because I learned a lot about myself: I started to push my limits, to deal with my own doubts and quirks, but above all, the PhD showed me that I shouldn’t do anything in life because others expect it of me or because I feel forced, but because I do it for myself. On the other hand, because of the great people I met and also call my friends. When I started at the Chair of Marketing and Retailing, I didn’t expect to experience so much support, love and most of all fun. Work colleagues quickly became friends, many of whom will hopefully accompany me throughout my life. Therefore, I would like to thank especially the following colleagues: Tobias Röding, Theresia Mennekes, Jan-Lukas Selter, Julian Schmitz, Maria Bergmann, Eric Schell, Dr. Florentine Frentz, Dr. Florian Neus, Dr. Frederic Nimmermann, Prof. Dr. Sascha Steinmann, Robér Rollin, PD Dr. Michael Schuhen, Minou Seitz and Manuel Froitzheim. Special thanks to Dr. Katja Wagner: I could not have asked for a better office colleague! As well as to Dr. Gerhard Wagner: With your help, I not only managed to get started at the chair, but also conducted my first scientific studies, so I am very grateful to you for that. My thanks also go to our colleagues from the neighboring chairs, especially to Julia Müller, Jonas Brühl, Timo Jenne, Dr. Samaneh Azarpour and Meike Stephan, who also contribute a lot to the fact that I go to work every day with joy.

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Acknowledgments

A special thank you goes to my PhD supervisor Prof. Dr. Hanna SchrammKlein: Without you, my PhD would not be what it is now. I thank you for the constructive criticism, support, encouragement and demand and your motivating words. Thank you for giving me the opportunity to grow beyond myself and expand my horizons! In addition, I would also like to thank the advisor committee members of this work. I would like to thank Prof. Dr. Tilo Halaszovich for his helpful comments and the time invested, as well as Prof. Dr. Arndt Werner for taking over the chairmanship of the associated committee. Another thank you goes to Mrs. Carmen Richter, to whom I have come with whatever concern and experienced support. Many thanks for the many great conversations. Furthermore, I would like to thank all my friends, many of whom have accompanied me on my way since childhood and high school and have always believed in me and motivated me. As well as my family and in-law family, especially my grandparents and my great aunt, who have always shown me that they are proud of me. And most of all I would like to thank my own little family: Kai, it was a great blessing that we did our doctorates at the same time, which resulted in a lot of understanding for each other and motivating each other. But also beyond that, there is no other person who supports me so much and by whom I feel so loved, even if things sometimes don’t go as wished. Thank you so much for always being there to catch me and for giving me strength. For this I will be eternally grateful! I am also so grateful for Charlie, who always gives me so much love and joy, and who has helped me so much to also get time away from work and recharge my batteries. Dad, I will never forget how happy you were when I told you I was starting my PhD. Thank you for always giving me the best education, always supporting me, and always telling me I could do it, even when I doubted it. I don’t think there is a person who believes in me so much as you do and to whom I am so grateful for that! Mom, I thank you for always giving me so many opportunities, sparing no effort and expense for my education and training, pushing me, and teaching me to question things and think for myself. I know that there is no one who would have been more proud of me and who would have read this thesis with more joy. To you and Dad I would like to dedicate this work. And as I write this thank you note, I realize once again how blessed I am to have such wonderful people in my life. For that I am very grateful and I promise to pass on the support and patience that was given to me during this time.

Acknowledgments

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During the PhD time, my life was marked by many ups and downs, which made it clear to me once again that it is the people who accompany you in life that matter. Therefore, as proud as I am of my dissertation, the deeper goes the feeling of gratitude to have such wonderful people in my life. Wilnsdorf

Anne Fota

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Evolution of Traditional E-Commerce into Emerging Future Consumption Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Theoretical Foundation and Central Domains in the Various Subareas of E-Commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Research Gaps in the Various Subareas of E-Commerce . . . . . . . 1.4 Science-Theoretical Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Structure and Content of the Essays . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Focus of the Essays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Essay 1—A Cross-National Comparison of Consumers’ Cross-Border Online Shopping Intentions in Germany and Romania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Essay 2—Development of a Motivation–Trust–Vulnerability (MTV) Framework for Cross-Border Online Shopping: A Cross-National Application to Chinese and German Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Essay 3—A Qualitative Study of Consumer Perceptions and Experiences Related to Voice-Commerce . . . . . . . . . . . . . . . . . 2.5 Essay 4—An Experimental Investigation of the Use of Artificial Intelligence in the Context of Complaint Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Essay 5—From Owning to Renting through Rental-Commerce Websites—A Qualitative Analysis of the Importance of Ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 7 16 24 27 27

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2.7 Essay 6—Will Renting Substitute Buying? Drivers of User Intention to Participate in Rental-Commerce . . . . . . . . . . . . . . . . . . 2.8 Overview of Essays and Related Research Characteristics . . . . . . 3 Essays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 A Cross-National Comparison of Consumers’ Cross-Border Online Shopping Intentions in Germany and Romania . . . . . . . . . 3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Theoretical Foundations and Hypotheses . . . . . . . . . . . . . . 3.1.4 Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.7 Conclusion and Implications . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Development of a Motivation–trust–vulnerability (MTV) Framework for Cross-Border Online Shopping: A Cross-national Application to Chinese and German Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Theoretical Foundation and Hypotheses . . . . . . . . . . . . . . . 3.2.3 Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.6 Conclusion and Implications . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 A Qualitative Study of Consumer Perceptions and Experiences related to Voice-Commerce . . . . . . . . . . . . . . . . . 3.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Theoretical Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.4 Empirical Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.5 Conclusion and Implications of Study 1 and 2 . . . . . . . . . 3.4 An Experimental Investigation of the Use of Artificial Intelligence in the Context of Complaint Management . . . . . . . . . 3.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Literature Review and Hypotheses . . . . . . . . . . . . . . . . . . . . 3.4.3 Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.6 Conclusion and Implications . . . . . . . . . . . . . . . . . . . . . . . . .

39 41 43 43 43 45 51 59 69 71 74

76 76 80 87 94 98 100 103 103 109 113 120 150 155 155 159 164 169 174 177

Contents

3.5 From Owning to Renting through Rental-commerce Websites—A Qualitative Analysis of the Importance of Ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Theoretical Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4 Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.7 Conclusion and Implications . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Will Renting Substitute Buying? Drivers of Consumer Intention to Participate in Rental-Commerce . . . . . . . . . . . . . . . . . . 3.6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 Theoretical Foundations and Hypotheses . . . . . . . . . . . . . . 3.6.4 Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.7 Conclusion and Implications . . . . . . . . . . . . . . . . . . . . . . . . .

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178 178 180 183 187 188 193 199 201 201 203 206 213 218 219 221

4 General Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Core Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Research and Theoretical Implications and Directs for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

225 225 233

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviations

AI AMOS ANCOVA ANOVA AVE B2B B2C BI C2C CAT CB CCT CFA CFI CR d_G d_ULS df e-commerce e.g. EUR EUT FG Fig. GNI H

Artificial intelligence Analysis of a moment structures Analysis of covariance Analysis of variance Average variance extracted Business to business Business to consumer Behavior intention Consumer to Consumer Consumer acculturation theory Covariance-based Consumer culture theory Confirmatory factor analysis Comparative fit index Composite reliability Geodesic distance Squared Euclidean distance Degrees of freedom Electronic commerce for example Euro Expected utility theory Focus group Figure Gross national income Hypothesis

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i.e. IP IPA IoT M MA MICOM ML MS MTV N NFI n.s. p p. P2P PLS R R2 RMS_theta RMSEA RQ SC SD SEM Sig. SPSS SRMR t Tab. TAM TLI TTF UC USD VIF χ2 α β

Abbreviations

that is to say Interview partner Intelligent personal assistant Internet of things Mean Motivation ability framework Measurement invariance of composite models Maximum likelihood Mean squares Motivation–trust–vulnerability framework Number of sample size Normed fit index Not significant p-value Page Peer to peer Partial least squares Reversed item R-squared (coefficient of determination) root mean squared residual covariance matrix Root mean square error of approximation Research question Subcategory of a category system Standard deviation Structural equation model Significance level Statistical package for the social sciences Standardized root mean square residual t-statistic Table Technology acceptance model Tucker-Lewis-Index Task technology fit model Upper category of a category system US-Dollar Variance inflation factor Chi-Square Cronbach’s alpha Beta (standardized coefficient)

List of Figures

Figure 1.1 Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4

Figure 3.5

Figure 3.6

Figure 3.7 Figure 3.8 Figure 3.9 Figure 3.10 Figure 3.11

General Conceptual Framework of Antecedents and Inhibitors to Participate in E-Commerce . . . . . . . . . . . . Conceptual model for investigating cross-border online shopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of PLS SEM for German and Romanian sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual model of the motivation-trust-vulnerability framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditional effect of trust on interaction between benefits and vulnerability: Chinese sample (N = 356) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditional effect of trust on interaction between benefits and vulnerability: German sample (N = 452) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison between the traditional e-commerce buying process and the voice-commerce buying process based on Kotler and Keller’s (2016) five-step model of the buying process . . . . . . . . . . . . . . . . . . . . . . . . . Integrated TAM/TTF model (Davis 1989; Goodhue 1992; Dishaw and Strong 1999) . . . . . . . . . . . . . . . . . . . . . . Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scenarios of the Experiment . . . . . . . . . . . . . . . . . . . . . . . . . Summarization of the results to a preliminary explanatory model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theoretical framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8 53 70 86

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114 118 159 165 198 213

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List of Tables

Table Table Table Table Table Table Table

2.1 2.2 3.1 3.2 3.3 3.4 3.5

Table 3.6 Table Table Table Table

3.7 3.8 3.9 3.10

Table 3.11 Table 3.12 Table 3.13 Table 3.14 Table 3.15 Table 3.16

Essays Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of Essays and Research Characteristics . . . . . . . . . Cross-Border E-Commerce Literature Overview . . . . . . . . . . Comparison between Germany and Romania . . . . . . . . . . . . Formative measurement instruments . . . . . . . . . . . . . . . . . . . . Reflective measurement instruments . . . . . . . . . . . . . . . . . . . . Discriminant validity assessment and inter-construct correlations: German sample (N = 175) . . . . . . . . . . . . . . . . Discriminant validity assessment and inter-construct correlations: Romanian sample (N = 141) . . . . . . . . . . . . . . . Corrlelation table for the German sample (N = 175) . . . . . . Corrlelation table for the Romanian sample (N = 141) . . . . Correlation values for compositional invariance . . . . . . . . . . Economical and cultural comparison between China and Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement instruments: Factor Loadings . . . . . . . . . . . . . . Discriminant validity assessment and inter-construct correlations: Chinese sample (N = 356) . . . . . . . . . . . . . . . . Discriminant validity assessment and inter-construct correlations: German sample (N = 452) . . . . . . . . . . . . . . . . Correlation table for the Chinese sample (N = 356) . . . . . . Correlation table for the German sample (N = 452) . . . . . . Effects of motivation, trust and vulnerability on cross-border online purchase intention . . . . . . . . . . . . . . .

28 41 49 60 62 64 66 66 66 67 68 88 90 92 92 92 93 95

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List of Tables

Table 3.17 Table 3.18 Table Table Table Table Table Table

3.19 3.20 3.21 3.22 3.23 3.24

Table Table Table Table Table Table Table

3.25 3.26 3.27 3.28 3.29 3.30 3.31

Effects of motivation, trust and vulnerability on past cross-border online purchase . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of motivation, trust and vulnerability on satisfaction with cross-border online purchases . . . . . . . . Demographics of the focus group discussions . . . . . . . . . . . . Category system of the focus groups . . . . . . . . . . . . . . . . . . . Demographics of the experimental observations . . . . . . . . . . Category system of the experimental study . . . . . . . . . . . . . . Scales used in the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ANOVA for the influence of avatar, reaction, and compensation on the behavioral intention and ANCOVA with anthropomorphism and the evaluation of redress as mediators . . . . . . . . . . . . . . . Mean values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correlation table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of the participants . . . . . . . . . . . . . . . . . . . . . . . . . . Category system rental-commerce . . . . . . . . . . . . . . . . . . . . . . Items and outer loadings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correlation table for the scales of the framework . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

96 97 122 124 137 141 167

171 174 174 188 188 215 217 219

1

Introduction

1.1

The Evolution of Traditional E-Commerce into Emerging Future Consumption Opportunities

In recent decades, the rapid development of networks and technologies in information communications has created a borderless digital world. Information and knowledge gaps between countries are reduced or closed by information networks (e.g., Legner et al., 2017). Falling information costs simplify communication processes and lead to enormous transformations in the production of goods and services. Thus, the world has become closely connected, and products and services can be delivered immediately and anywhere in the world through networks. The Internet offers exporters new opportunities since access to new markets is increasing, efficiency in accepting global customer orders is increasing, and the processing of inquiries is improving. Therefore, consumption is possible wherever there is Internet access (Ternès et al., 2015). As a result of digitized access to goods and services, more consumers are opting for electronic commerce (ecommerce) over traditional shopping channels, such as brick-and-mortar stores and direct sales. E-commerce represents all forms of digital transactions of business processes between companies and their customers via public and private networks (Schinzer and Thome, 2000). Accordingly, e-commerce includes all business processes in the fields of business-to-consumer (B2C), businessto-business (B2B), or consumer-to-consumer (C2C). According to Fritz (2000), electronic retailing or e-commerce is geared towards the “initiation, negotiation and/or handling of goods processes.” According to Chaffey (2015), the term “ecommerce” extends beyond the mere buying and selling of goods. He defines e-commerce as “all electronically mediated information exchanges between an organization and its external stakeholders.” Thus, in its definition, e-commerce

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Fota, Online Shopping Intentions, Handel und Internationales Marketing Retailing and International Marketing, https://doi.org/10.1007/978-3-658-37662-8_1

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Introduction

includes not only the purchase process but also preceding and further aspects related to the purchase. Looking primarily at the statistics, one can see the relevance of this branch of business. According to Retail Research, retail e-commerce sales in Europe are estimated to account for over 15% of retail trade in 2021 (Retail Research, 2020). Furthermore, retail e-commerce sales worldwide reached $3.354 trillion in 2019, while sales are expected to increase worldwide to $6.388 trillion by 2024 (eMarketer, 2020). Online shopping requires Internet access. In Europe, threequarters of the population have access to the Internet, and 45% shop online, from which 54% already shopped cross-border (ECommerce Europe, 2017). The increasing globalization of world trade and the digitalization of society have also led to consumers increasingly looking beyond their own borders when shopping online (China Cross-Border E-Commerce Guidebook, 2019). Moreover, despite the general assumption that online shopping is mainly a Western phenomenon, e-commerce is booming in developing countries (Saeed et al., 2017). Other scientific studies also show the benefits for consumers and their improved standard of living made possible by e-commerce trade—for example, people living in rural and remote areas in the BRIC countries fighting poverty (Karine, 2021). Moreover, online platforms are a popular way for consumers to participate in e-commerce. As one of the leading online providers, Amazon Inc. has increased its annual income by nearly $300 trillion in the last 10 years, $159 trillion of which was generated by third-party businesses alone. This implies a 52% increase in third-party business and a 25% increase through Amazon directly (Amazon Annual Report, 2018). eBay, on the other hand, had around 97 million users in 2011, generating an income of $3.2 trillion for the entire year. In the third quarter of 2019 alone, the number of active buyers increased to 183 million, and the income reached $2.6 trillion, 59% of which were from international businesses (eBay, 2020). Due to the continuous removal of barriers, online retailing has grown, as shown in the examples above, and is now one of the most dynamic and important economic sectors in many countries. National and, especially, international trade is often seen as a key factor for economic growth (Kalini´c et al., 2019) and increasing employment levels across the European Union. However, according to the European Commission, even after the adoption of the EU directive, online retailing still accounted for only 7.2% of total European trade in 2015 (European Commission, 2015). This means that it is far from having exhausted its economic potential. However, depending on the region, e-commerce is currently expected to account for 10–15% of all online business traffic and increase online income from the current $80 trillion to between $250 and $350 trillion by 2025

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(The Boston Consulting Group, 2014). Asia will hereby account for 40% of total income, followed by Europe with 25% and North America with 20%. In this context, China is losing its image as a pure production country and is adapting to the consumption habits of the developed world with its new online technologies. However, above all, the global COVID-19 pandemic has shown that online retailing can represent a stable and growing consumption opportunity, especially for consumers and particularly in times of crisis. For example, e-commerce has grown from 16–27% in the USA and from 18–30% in the United Kingdom within eight weeks (The Bosten Consulting Group, 2020). Therefore, the restrictions on public and private life that accompanied the COVID-19 pandemic served as a “booster” for e-commerce. While retailers and most brick-and-mortar stores (e.g., fashion and shoe stores, electronics stores, bookstores, and some hardware stores) were forced to close for weeks during the countries’ lockdowns due to the risk of infection from the virus, many consumers shifted their shopping activities to the Internet. Whether to compensate for the lack of brick-and-mortar shopping opportunities or because of the extra free time consumers received due to the lockdown, online shopping websites were able to record 22 billion visits in June 2020, compared to only 16.07 billion in January 2020 (SEMrush, 2020). Many marketplaces, most notably Amazon, emerged as winners during the COVID-19 pandemic, reporting high sales gains. Amazon, for example, was able to increase its stock by 63.6%, and that alone until July 2020. This development additionally favored cross-border e-commerce, as a 21% year-over-year increase in global cross-border sales was noted from January–June 2020, with a 53% year-overyear sales growth in the second quarter alone (Global-e, 2020). Furthermore, despite the relaxation of the pandemic’s public live restrictions in some countries in spring and summer 2021, which almost without exception allow brick-andmortar retail to resume without restriction, German online retail is forecast to grow by over 17% year-over-year in 2021 (Handelsverband Deutschland, 2021). Notably, e-commerce is not only a welcome alternative to traditional offline consumption but also offers consumers the opportunity to shop in a variety of ways by offering other sub-forms of e-commerce, which indeed all originate from the basic principle of e-commerce but have different characteristics and, thus, serve different consumer wishes and requirements. Consequently, e-commerce is not only an established shopping channel but is also constantly evolving and gaining more relevance in consumers’ everyday shopping behavior, offering them ever new consumption opportunities (Karine, 2021). Important developments in e-commerce, which are worth examining in more detail, as these offer the greatest new consumption opportunities for consumers, are cross-border e-commerce,

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artificial intelligence in e-commerce, and rental-commerce. The common factor among these subareas of e-commerce is that through these new different consumption opportunities, new consumer behaviors emerge, such as a stronger global orientation, automation in purchase behavior, or easier and more convenient purchase decisions. In this context, e-commerce is characterized above all as a channel that is becoming ever more differentiated and, thus, serves the increasingly diverse consumption needs of consumers. One of these future consumption opportunities, as mentioned before, is the growing global orientation, which is also reflected in consumers’ consumption behavior. Therefore, cross-border online shopping emerged as a consumption opportunity to consume globally by ordering products from another country (Wagner et al., 2016). Thus, cross-border e-commerce enables consumers to shop online in other countries, which they may never have physically visited before, and benefit from the product offerings in those countries. However, in addition to the benefits of gaining access to products from another market, consumers also face new obstacles and risks that may arise from trading with retailers in other countries (Jian Wang u.a., 2010). For example, cultural and language barriers, as well as uncertainties about product quality or legal rights, can dampen the cross-border e-commerce shopping experience and, in the worst case, harm the consumer monetarily or health-wise (Lin u.a., 2018; Safari und Thilenius, 2013). These hidden risks reduce the cross-border e-commerce purchase intention if consumers are aware of them and prevent them from making otherwise beneficial purchases. However, it becomes particularly problematic when consumers are not aware of these potential risks, and problems arise after they have made a purchase at a foreign online retailer, or when they do not even realize that they have made a cross-border purchase and are thus unaware of the potential risks in the first place. Although risks and hurdles should also be reduced by politics and consumer protection through official, internationally applicable legal regulations, as well as by the foreign online retailer itself, to enable a risk-free cross-border e-commerce shopping experience, the importance of competence education is particularly evident here to create mature consumers who recognize risks themselves at an early stage and know how to help themselves in the event of problems. Here, especially in the context of cross-border e-commece consumer informedness can be useful (Han and Kim, 2019). Nevertheless, one possibility to reduce the need for consumers’ self-help and effort while still reducing potential risks is offered by automated and partly controlled processes, which additionally simplify decision-making activities (e.g., by automating product searches or pre-selections) and significantly facilitate purchasing procedures for consumers (e.g., automated payment processes). Hence,

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the integration of artificial intelligence in e-commerce poses new challenges and opportunities not only for suppliers and retailers but also for consumers who can benefit from intelligent helpers in the purchasing process (Heinemann, 2017). Thus, the use of artificial intelligence represents a new usage option in terms of input devices and interface, offering both novel consumption opportunities and a simplification of consumption for consumers through automatization (Ahmed et al., 2016). Although this integration of artificial intelligence into everyday life and e-commerce, such as in the form of voice-commerce (using a digital voice assistant or a chatbot as a communication interface between consumers and companies), is intended to make the shopping process easier and more convenient, problems can also be identified here that need to be taken into account. On the one hand, there is a great deal of uncertainty regarding the use and data security of artificial intelligence (Klein et al. 2020), and, on the other hand, especially among older and less technology-savvy consumers, there is a lack of competence and knowledge to benefit from these new usage options. Furthermore, undeveloped intelligent systems can become an obstacle for consumers who rely on them and depend on their adequate functioning in the form of task accomplishment (Moore et al. 2017). Therefore, these hurdles still limit the potential benefits of using smart systems in the everyday lives of many consumers and demonstrate the enormous potential for improvement, especially with regard to building trust between humans and machines. Another way to minimize potential risks, increase consumer trust, and ensure more safety for consumers in e-commerce is through rental-commerce. Here, consumers now increasingly have the option of temporarily renting products from an online retailer instead of buying them as usual (PwC, 2019). Therefore, different intrinsic and extrinsic motivations of consumers are addressed, resulting in new consumption opportunities, which, above all, support consumers’ consumption flexibility. This model represents a new business concept with new service options, as it addresses new possibilities of payment, as well as the change in the consumption society and its new understanding of property and ownership. Here too, however, not only advantages can be identified, such as almost unrestricted access to all products, but also disadvantages, such as, above all, psychological hurdles, which make the temporary use of a product, without being able to realize ownership or claim to this product, still an unfamiliar business model for many consumers, while the contractually regulated return of the products can also feel like a loss to consumers, especially if they have become emotionally attached to it (Peck and Shu, 2009; Pierce et al., 2001). Additionally, although subscription models, in which consumers pay an agreed monthly price for access to products or services, offer a strong incentive for many consumers (Tussyadiah, 2015),

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they do not always offer consumers a financial advantage in the long term and can quickly become an unforeseen cost trap. These different areas of e-commerce show that consumers can search for products overseas, rent products if they are too expensive or if they are unsure whether they need them permanently, shop online comfortably only with their own voice, or enjoy a 24-hour service using chatbots. However, while these subareas can all be assigned to e-commerce, they are all based on different technologies and concepts and, therefore, differ in their implementation and in the motivation and intention of consumers to use them. In addition, some of these e-commerce areas have been used by consumers for several years, such as cross-border e-commerce or conversational-commerce. However, particularly in recent years, there has been a growing increase in the use of all these four subareas of e-commerce due to, among other things, the constant improvement of technology and artificial intelligence, the creation of alternative consumption options to meet the increasingly individual and diverse lifestyles of consumers, the reduction of trade restrictions, and the increased blurring of market boundaries. Thus, this thesis deals with the different areas of e-commerce that consumers encounter more in their everyday life, which offer them new possibilities to participate in online commerce by using them. Thus, these subareas are shaping the buying behavior of consumers significantly and are becoming more relevant for their future consumption behaviors. Exploring these growing consumption opportunities may eventually also help to answer the overall research question and objective of this thesis: Which antecedents and moderators influence consumers’ shopping intention formation in the new fields of e-commerce? Consequently, this thesis seeks to contribute to the following general research objectives in key e-commerce domains: • Cross-Border E-Commerce: investigating shoppers’ behavior intention in the case of shopping online from a foreign country market. • Voice-Commerce: investigating shoppers’ behavior intention in the case of shopping online with the help of digital voice assistants. • Conversational-Commerce: investigating shoppers’ behavior intention in the case of interaction with chatbots in complaint management. • Rental-Commerce: investigating shoppers’ behavior intention in the case of renting products online. While numerous advantages for consumers result from these new developments of e-commerce, additional new risks and hurdles arise, which consumers are either not aware of or do not know how to deal with, or which completely prevent them

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from participating in these new subareas of e-commerce and taking advantage of the benefits they offer. Indeed, all these risks can lead to consumer disadvantage and limitations of various kinds, like e.g., monetary, emotional/psychological, or even health (Baker et al., 2005). Thus, these disadvantages of these new ecommerce developments can restrict consumers from obtaining the best possible benefits for them in the market and leave them worse off than other market participants (Berg, 2015). Consequently, consumer vulnerability is emerging as an outcome of these e-commerce developments, as well as differentiation into new forms of consumption and new systems of ownership. This rising consumer vulnerability is a phenomenon that is increasing and becoming more present with the additional opportunities of e-commerce. Consequently, for consumer protection, it is essential to validate which different problems and potential hurdles and risks consumers face depending on the form of e-commerce, especially since it appears that consumer vulnerability is not only a problem for the consumer himself but also has negative social and economic consequences. This leads to the need to understand the different processes in e-commerce and, therefore, reduce consumer vulnerability in every area of e-commerce. However, this reduction proves to be complex, as special challenges must be considered to satisfy the needs of the potentially vulnerable. At the same time, the increasing complexity of market activities, such as is the case with the different forms of e-commerce, increases the vulnerability of the consumers involved (Brennan et al., 2017). Therefore, given the importance of e-commerce for consumers and businesses, the aim of this thesis is to deepen knowledge in this area. Consumer vulnerability should, therefore, receive greater attention in consumer research to gain the best understanding of how to limit it and, thus, allow consumers to participate in the new subareas of e-commerce unrestrictedly.

1.2

Theoretical Foundation and Central Domains in the Various Subareas of E-Commerce

This dissertation investigates how consumer intentions to use different subareas of e-commerce evolve. A research model was designed, shown in Figure 1.1, to generalize these basic relationships and postulated effects. This general conceptual framework forms the basic concept of this thesis, which is reflected in the research questions of all six essays in this thesis. Nevertheless, each essay presents its own elaborated research framework, which is fundamentally related

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to this theoretical framework, but, depending on the essay, draws on further theories that support the investigation of the research questions posed and, thus, focuses on the respective context of the essay.

Figure 1.1 General Conceptual Framework of Antecedents and Inhibitors to Participate in E-Commerce

The above conceptual research model is primarily regarded as a basic framework. Thus, the model is intended to provide general explanations of how consumer intentions to shop online in different contexts are formed. For this purpose, different influencing factors are considered as main effects, which act either as antecedents or inhibitors of e-commerce participation. These influencing factors act as possible drivers or barriers to form a positive purchase or consumption intention, which, in turn, allows conclusions to be drawn about the actual behavior formation of consumers. While barriers have been shown to weaken shopping intention, drivers generally strengthen consumers’ intention to shop online. This is motivated by the assumption that these drivers usually result in a benefit for the consumer, representing a concrete value or utility to them. This utility can then be used to derive the probability of purchase behavior based on the strength of the intention, depending on how strong this utility is for the consumer. This consequently results in a reasoned action by the consumer based on the perceived advantages or potential disadvantages that they will receive from this action, here, in the case of online purchasing.

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This assumption, which is the basic pillar in the conceptual research model, is mainly supported by the Expected Utility Theory (Fishburn 1968) and Theory of Reasoned Action (Fishbein and Ajzen, 1977). Fishburn (1968) defines the Utility Theory as follows: “Utility theory is concerned with people’s choices and decisions. It is concerned also with people’s preferences and with judgements of preferability, worth, value, goodness or any number of similar concepts.” According to Mongin (1997), the Expected Utility Theory implies that decision-makers decide between risky and unsafe alternatives by calculating their expected utility values, resulting in consumers always choosing the alternatives that have the greatest expected benefit. From the perspective of the present research studies of this thesis, the differentiation of e-commerce into the new subareas was designed to offer consumers additional benefits compared to traditional e-commerce. These additional benefits and advantages are cited as the antecedents to participating in e-commerce in the conceptual framework presented here. For example, the international component of cross-border e-commerce offers consumers the benefit of accessing a wider range of products, be offered a wider price range, or participating in other cultures by purchasing foreign products. From the perspective of artificial intelligence, these systems offer consumers utility in the form of facilitation in everyday life, in which commands and tasks can be carried out by intelligent systems without the users having to move around and carry out the tasks themselves. Consumers, in particular, benefit when intelligent chatbots can ensure faster and more efficient problem handling when shopping online. Finally, rental-commerce serves as an alternative form of e-commerce that generates additional utility in contrast to classic e-commerce by making products available to consumers that they would otherwise not be able to afford. This new business model also offers consumers the advantage that they can order the latest model or fashion at any time and are not permanently bound to old models that are no longer used or worn over time and thus, no longer provide any utility to the consumer. In traditional e-commerce, these additional, expected benefits are not realizable for the consumers. Therefore, the expected benefit is an incentive for consumers to make purchases or participate in a certain consumption model. As consumers have different perceptions of benefits, the actual benefit for an individual is based on the perception of what they get and what they have to give in return (Zeithaml, 1988). The expected benefit is, thus, composed of the perceived advantages and risks associated with the purchase (Chen and Dubinsky, 2003). In this context, the benefit of a purchase is always composed of a trade-off of advantages and risks, where the advantages are motivating elements of the purchase decision, while the risks may lead to the purchase not being carried out (Forsythe et al., 2006). Consequently, the risks arising from the new subareas

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of e-commerce, which are referred to as inhibitors to participate in e-commerce by the conceptual framework, may lead to increased consumer vulnerability that may also inhibit consumer intention to shop online. While the (Expected) Utility Theory primarily describes the psychological processes and how consumers deal with the advantages and potential risks of online shopping, referred to as antecedents and inhibitors in the conceptual framework of this thesis, the formation of the behavioral intention (to shop online) may be explained by the Theory of Reasoned Action (Fishbein and Ajzen, 1977). The basic assumption of the Theory of Reasoned Action is that the immediate variable for behavior is the behavioral intention. Behavioral intention is understood in the Theory of Reasoned Action as the degree to which an individual plans to perform a particular action. More specifically, it is the individual’s assessment of whether or not they expect or plan to perform an action in a given future timeframe (Wagner, 2016). With regard to the present studies, this means that it is to be investigated to what extent the consumers surveyed can imagine participating in the different subareas of e-commerce. Behavioral intention is influenced by two factors: the attitude towards the action or behavior, which is primarily considered in this work, and the social norm (Fishbein and Ajzen, 1977). The attitude towards the action or behavior reflects how a person feels about a certain behavior (i.e., whether they perceive the behavior as positive or negative). It depends on whether the person expects the action to lead to the expected consequence and the associated value or utility (Montano and Kasprzyk, 2015), as mentioned above. This action would be, for example, online shopping via the “new” subareas of e-commerce and deciding which advantages or disadvantages arise for the consumer because of the use. The second factor, the subjective norm, reflects the person’s expectation of the extent to which the behavior is of importance to other relevant persons. It should therefore be noted that the Theory of Reasoned Action was designed to predict reasoned action. These two theoretical approaches explain how consumer expectations and evaluations are formed, in which potential benefits and risks are counterweighted to determine the trade-off for the consumer. These expectations and evaluations are attributed a mediating character in the conceptual framework, as they result from the evaluation of antecedents and inhibitors to participate in e-commerce and exert a direct influence on the consumer intention to shop online. This weighingup of the antecedents and inhibitors of e-commerce also forms an expectation and evaluation of the respective subareas in e-commerce and, thus, help consumers to assess whether or not they offer an additional, expected benefit, which eventually influences the shopping intention. In summary, the assumed relationships in the

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conceptual framework between the respective antecedents, the inhibitors to participate in e-commerce, and the intention to shop online, as well as the resulting relationships between consumer expectations and evaluations and the consumer intention to shop online, can be supported by both the Expected Utility Theory and the Theory of Reasoned Action. Since some of the new subareas of e-commerce depend on the use of new technologies, it is worth also examining the Technology Acceptance Model (TAM), which is a central model of acceptance developed by Davis (1989) and explains the adaptation of technological innovations (Gunnesch-Luca, 2014). Furthermore, the Internet experience, which is also gained in e-commerce, is part of the model and is based on various theories of social psychology, especially the Theory of Reasoned Action by Ajzen and Fishbein (1980) and the Theory of Planned Behavior by Ajzen (1991). The model is used to predict the acceptance of new technologies and states that the attitude, intention, and persuasion of consumers have a causal connection (Chen et al., 2002). The central elements of the model include the aspect of perceived benefit and perceived usability (Davis, 1989). Perceived benefit is understood to mean that a person believes that, to some extent, the benefit of a new technology will increase their performance or productivity (Panda and Swar, 2013). Davis understands perceived usability as the estimated effort of a person when using a new technology. Thus, the perceived benefit and the perceived simplicity of usage lead to a higher willingness to make use of new technological possibilities (Gunnesh-Luca, 2014). Pavlou (2003) also makes the statement that the TAM is a theory to not only explain technical adaptation but also predict general online consumer behavior. It also considers the factors of trust and perceived risks (Pavlou, 2003), of which their influence on online shopping intentions has already been predicted in several studies (Van der Heijden et al., 2003; Verhoef and Langerak, 2001). Although the TAM generally supports the theoretical assumptions already presented before, it additionally places them in the context of technology usage. Because of this, the discussed relationships should primarily serve as a basic principle for the use of technology, such as the use of artificial intelligence, for example, in the form of digital voice assistants in voice-commerce or chatbots in conversational-commerce, which are dealt with in two of the six essays. This additional theoretical explanation is therefore important since with cross-border e-commerce and rental-commerce, the well-known basic features of online shopping remain so that the purchasing process is mainly identical to that of traditional e-commerce. Meanwhile, with voice-commerce and conversational-commerce, a new input device and interface are created that were previously unknown to consumers. Therefore, a more detailed understanding of the usage acceptance of these two subareas of e-commerce is needed here.

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In addition to the antecedents and inhibitors, which exert an influence on the consumer intention to shop online, as well as the consumer expectations and evaluations, other factors that may act as moderators are also considered in the conceptual framework. These moderators can be of different nature, such as general consumer attitudes or characteristics, which arise individually or situationally but are also culturally shaped. Hofstede (2009), for example, assumes that consumers from different countries have different behavioral and attitudinal expressions due to their different cultural influences. Hence, the behavior of consumers can also be influenced by cultural factors, which will be scientifically investigated in two of the subsequent essays. One theoretical approach that takes up this concept is the Consumer Culture Theory (CCT). The CCT is a research discipline that investigates the influence of social and cultural components on consumer behavior. According to Arnould and Thompson (2005), the CCT is classified as follows: “[…] it refers to a family of theoretical perspectives that address the dynamic relationships between consumer actions, the marketplace, and cultural meanings” (Arnould and Thompson, 2005). Therefore, the focus of the CCT—which is a collection of different theoretical approaches—is on the complexity of cultural constructs in consumer research. Cultural awareness can have both positive and negative effects on purchase intentions. For example, the first essay shows how a culturally open and cosmopolitan attitude strengthens the cross-border e-commerce intention, while an ethnocentric attitude can weaken it. This awareness is described with the term consumer culture, “[…] a system composed of individuals who share specific values, skills, and knowledge relevant to engaging in consumer behavior” (Peñaloza, 1989). On the one hand, consumer culture stands for the importance of one’s own culture and how this is reflected in the consumer’s buying behavior. On the other hand, however, the term also includes integrated global connections, through which local cultures are connected with each other (e.g., through migration or globalization) (Arnould and Thompson, 2005). From the conceptual framework, all these presented and postulated relationships are influenced by the context. This context is expressed in the different subareas of e-commerce (cross-border e-commerce, voice-commerce, conversational-commerce, and rental-commerce), to which this model is adapted, specified, and examined. Thus, depending on the respective context or subarea of e-commerce, different determinants in the form of antecedents, inhibitors, consumer expectations, and evaluations, as well as moderators, are applied in the conceptual model. This also shows why examining the different subareas of e-commerce is relevant: all these subareas change the classic framework of

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e-commerce or its traditional functioning. Depending on their intentions and motivations, consumers can choose from this pool of subareas, which have emerged from the traditional e-commerce, and satisfy different needs. Therefore, with the new subareas, new rules and conditions appear respectively, which change the ecommerce buying behavior of consumers. For example, cross-border e-commerce includes an international component in contrast to classic e-commerce, which primarily takes place in the consumer’s home market (Wagner et al., 2016). In contrast, voice-commerce and conversational-commerce introduce new input and conversation mechanisms, as well as an emotional component, through the personification and humanization of the artificial intelligence used (Hecker et al., 2017; Callejas et al., 2011). Finally, in rental-commerce, consumers are confronted with a new classification of the conventional understanding of ownership, in which they primarily rent products temporarily instead of buying them (PwC, 2019). Overall, different new conditions and parameters are introduced and can be observed due to the presented different subareas. Therefore, a generalization of the individual subareas is not possible and would not contribute to a better understanding of the consumers’ intention to shop online. To prevent this, the different subareas of e-commerce must be examined, taking into account their particularities, to determine practicable implications for research and practice. Beginning with cross-border e-commerce, while traditional or domestic online shopping still respects the boundaries of the national market, consumers are increasingly benefiting from a greater variety of prices and products on international online markets (Anastasiadou, 2019). In contrast to conventional e-commerce, the international component plays a decisive role here, changing the traditional conditions of online shopping. Thus, this new access to offers from foreign online retailers is changing consumers’ expectations and evaluations of this shopping process and, therefore, the general online shopping behavior of consumers, as they are being offered an additional opportunity to consume beyond their national borders (Wagner et al., 2016). However, consumers not only benefit from the advantages offered by foreign retailers but must also learn to validate foreign online offers by considering the potential risks they would not be exposed to with a domestic online retailer. Moreover, this international component means that consumers face cultural and legal changes compared to domestic online shopping (Safari and Thilenius, 2013). This also results in a further differentiation from traditional e-commerce, as consumers must now have additional knowledge and skills to protect themselves from these internationally different underlying conditions, which can have negative consequences for them. The danger of increased consumer vulnerability due to this international component becomes particularly clear here, such that cross-border e-commerce

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represents a subarea that should receive additional special attention from politics and consumer protection. While traditional online shopping via non-mobile devices, such as a computer, or mobile devices, such as a laptop, tablet, or smartphone, primarily uses a screen and a physical input form to search for products, which can then be viewed online and finally ordered, voice-commerce represents a new type of technologysupported possibility for online shopping (Hoy, 2018). Instead of entering the search term by typing it, the input is made by a digital voice assistant using one’s own voice. Thus, no input via a manual input device, such as a keyboard, is necessary (Gaikwad et al., 2010), enabling consumers to order products and services in a new way. Compared to traditional e-commerce, voice-commerce allows consumers to save time and shop online more conveniently, as the search and order process is triggered by voice alone, and an order can be placed flexibly from anywhere without the consumer having to hold a device in their hands. Thus, this subarea is distinguished by its increased comfort and usability due to the reduced input form (Heinemann, 2017). This enables consumers, for whom access would otherwise be reduced or even denied entirely, to participate in consumption. Physically restricted consumers, in particular, can thus comfortably shop online and satisfy their consumer needs without having to rely on third parties. However, compared with the other subareas of e-commerce, data protection concerns play a particularly important role here, as the eavesdropping of consumers by a digital voice assistant may result in the unintentional sharing of information (Klein et al. 2020; Verbraucherzentrale NRW, 2018). Thus, the likelihood of disclosing personal data in voice-commerce is higher. While cross-border e-commerce and rental-commerce do not differ in their basic use and mode of operation from traditional online shopping, voice-commerce is a new technology whose usage and restrictions are not yet comprehensively regulated by law. This is also where the concept of conversational-commerce, as another area of e-commerce, takes off. Conversational-commerce refers to online retailing that occurs via different conversation channels, like e.g. chat interfaces in social networks, on websites, or in instant messengers (Messina, 2016). The intention of conversational-commerce is to make the customer’s buying process as comfortable, personalized, and consultative as possible. For this purpose, conversational-commerce differs from traditional online shopping, in particular, in terms of efficiency. Due to the immediate feedback of the chat offer or voicebot, there is no waiting time and the chatbot’s full attention to the consumer is continuously given (Messina, 2015). For this reason, chatbots are often used in the service area to answer (standardized) questions and provide initial assistance. However, in traditional e-commerce, consumers sometimes

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have to wait a long time for customer service responses, or transactions take longer due to a lack of automatization. This increased efficiency and convenience can be found in both voice-commerce and conversational-commerce, as both subareas make use of artificial intelligence that automates (online shopping) processes (Cui et al., 2017). These two subareas of e-commerce do not offer an expansion of the product range, as in cross-border e-commerce, or a new type of and access to consumption, as in rental-commerce, but they enable online shopping via new technologies, so-called intelligent systems. However, while voice-commerce is used via speech, conversational-commerce focuses on the interaction between online retailers and consumers via chat systems, with chatbots serving as consumers’ counterparts, for example (Berkowitz, 2016). Thus, with conversational-commerce, the consumer also does not have to purchase or use any additional technology or device, as is the case with voice-commerce. Instead, the required systems are provided by the online retailer as an addition to traditional channels and are immediately accessible to all consumers. However, especially for consumers who want to always be up to date with the latest technology or wear the latest fashion, another way of participating in e-commerce can be a solution. Rental-commerce, representing the last of the four subareas of e-commerce, makes it possible to rent a variety of products from an online shop for a limited period and have them sent to a consumer’s home. Here, the consumer does not own the products; the company does. However, the consumer is allowed to use the products to their fullest extent for an agreed period and then sends them back (Zervas et al. 2017). Strictly speaking, it is not about online shopping but about online renting, which is carried out via online shops or platforms, as in traditional online shopping. Due to this new consumption model, other evaluation processes occur in consumers when they participate in rental-commerce compared to traditional e-commerce. Since in rental-commerce consumers must return the ordered products after a few weeks or months and have no claim to ownership, a different attachment to these products is formed, such that a general change in consumers’ sense of ownership may arises (e.g., Furubotn and Pejovich, 1972). Therefore, this unconventional way of consuming is changing consumers’ general view of property and its value in society, as well as consumers’ shopping behavior compared to classical online shopping. By paying a lower monthly rent, which many consumers can afford, compared to the full price one would otherwise have to pay for the product, consumers are now consuming products that they would not otherwise be able to purchase (frequently). Here, especially, branded products get an additional distribution channel by rental-commerce, which can lead to a changed self-image

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Introduction

of consumers (C˘at˘alin and Andreea, 2014) and may, therefore, influence their overall consumption behavior and lifestyle. These different subareas of e-commerce presented above influence the purchasing and consumption behavior of consumers in different ways. Therefore, the use of e-commerce by consumers should not be considered uniformly since different determinants play different roles depending on the e-commerce subarea, and thus, different implications for research and (marketing) practice can be derived from it. Hence, although the procedures of the e-commerce variations are, in principle, almost identical, other motivations, as well as concerns and risks, play an essential role in forming consumers’ behavioral intention, which differ from traditional e-commerce. In summary, although research in recent years has dealt with general ecommerce issues (e.g., Nisar and Prabhakar, 2017; Laudon and Traver, 2016; Delone and McLean, 2004; Rayport and Jaworski, 2003) and partially examined related phenomena in the context of the subareas of e-commerce listed above (e.g., Zhu et al., 2019; Tuzovic and Paluch, 2018; Xu et al., 2017; Rifkin 2001), the studies to date show that there is a need for further research, especially for the subareas of e-commerce that have been addressed. For this purpose, the presented theoretical framework in the form of the “General Conceptual Framework of Antecedents and Inhibitors to Participate in E-Commerce” serves as the underlying theory for all six essays in this thesis. To provide a more comprehensive explanation of the phenomena described in this thesis, the theoretical approaches presented above (the Expected Utility Theory/Theory of Reasoned Action, the TAM, and the CCT) were also used. These established theories offer an additional explanation for the postulated relationships between the determinants under consideration.

1.3

Research Gaps in the Various Subareas of E-Commerce

The relationships proposed in the general conceptual framework (see Figure 1.1) underpin the research objectives of this thesis. However, while the conceptional framework presents fundamental relationships that are addressed in the six essays, it also illustrates that these postulated relationships can differ and be influenced depending on the specific context; in this case; the respective subarea of e-commerce. Thus, depending on each subarea under consideration, different research questions and research foci arise, which are explained by the respective

1.3 Research Gaps in the Various Subareas of E-Commerce

17

characteristics of the subareas. Therefore, the main questions of this dissertation focus on how the intentional behavior of consumers is influenced depending on the different subareas of e-commerce and how this intention can be increased, which are answered both qualitatively and quantitatively. However, the focus here is on the individual research gaps in the individual subareas. These subareas of e-commerce each have specific research questions, which consider the different phenomena of the subareas, such that an overview can be successively presented in the subsequent chapter. Unlimited product availability, low prices, and broad selections are the main drivers that convince German consumers to order products from abroad (McKinsey, 2017). When consumers purchase products from abroad online, this is called cross-border e-commerce (Wagner et al., 2016). This increasingly important form of shopping is currently growing at an annual rate of 25%, while sales of 245 billion Euros are forecast in Europe alone for 2022 (CBCommerce.EU, 2019). With the increasing importance of cross-border online retailing, the significance of consumer behavior in relation to it is also increasing. Therefore, the investigation of consumer behavior in international online retailing is an important aspect of consumer research (Holtforth, 2017). Both consumers and retailers are confronted online with various factors that influence their purchasing decisions. In addition to the advantages of cross-border online retailing, which can arise due to low prices, product availability, or convenience, it also involves various risks related to financial, qualitative, or service-related aspects (International Post Corporation, 2010). How individual consumers’ reaction to risks and benefits is based on their perception and the benefits they attribute to foreign products (Forsythe et al., 2006). In addition to the benefits, cultural, sociological, or personal factors can also influence consumers’ purchasing decisions (Kotler et al. 2010). An important aspect in this context is the personal attitude towards foreign products. Consumers can have both cosmopolitan (Zeugner-Roth et al., 2015) and domestic preferences (Shimp and Sharma, 1987), which can have different effects on the attitude towards cross-border e-commerce. Consumer behavior in cross-border online retailing is an area that has been little empirically researched so far (Wagner et al., 2016). Because there are relevant differences in market development, economic conditions, and culture that could lead to discriminative market factors across nations, it can be assumed that emerging and advanced country markets are likely to differ with regard to cross-border online shopping behavior. The investigation of cross-national differences and similarities in consumer behavior in cross-border e-commerce is particularly useful for companies that serve several country markets and consumers of different

18

1

Introduction

nations. The perceived advantages and risks of cross-border e-commerce for consumers in different countries are still unexplored. Especially due to the different cultural, economic, and political influences, different importance of cosmopolitanism and ethnocentrism on the purchase intention in cross-border e-commerce can be assumed, so that it can also be analyzed whether these effects vary between the different population groups. While international online retailing has become particularly prevalent in advanced markets, the potential benefits that consumers may receive from cross-border e-commerce due to limited domestic product choices or unattractive prices in the home market seem particularly appealing for emerging markets. Therefore, cross-border e-commerce is a global phenomenon, but it can nevertheless be observed that the proportion of cross-border online shopping activities and the attitude towards cross-border online shopping varies greatly from country to country (PayPal, 2018). Therefore, it is important to consider the country-specific differences when investigating cross-border online shopping behavior to derive country-specific strategies for the internationalization of cross-border e-commerce. Thus, the first research question arises: • What factors motivate or constrain consumers in advanced and emerging country markets to make cross-border online purchases? While better prices or access to products that are unavailable in one’s country are an incentive for cross-border online retailing (Kalini´c et al., 2019), cross-border e-commerce is nevertheless associated with numerous uncertainties and risks that deter consumers from shopping with foreign online retailers (Safari and Thilenius, 2013). Consumers feel uncertainties, for example, regarding the final price, which is made up of the product price, taxes, and other fees, or regarding the return of goods and the resolution of problems that could arise, such as in the event of a defective product or late delivery. In this regard, it would be particularly beneficial to speak the language of the online retailers to be able to communicate with them or to read the guidelines for returning goods, if they have been published online. In addition, the payment method and currency of the foreign online merchant may also be a criterion for excluding the consumer from shopping with them. On the one hand, the consumer may not be able to follow the transaction process when the money is refunded, and on the other hand, the currency conversion may result in a different amount being refunded. Furthermore, the likelihood of having to bear the cost of international delivery if a faulty product is returned creates a feeling of insecurity and can increase consumer vulnerability. Overall, this insecurity and vulnerability in (cross-border) e-commerce

1.3 Research Gaps in the Various Subareas of E-Commerce

19

include: 1) lack of information and experience, 2) missing knowledge of customer rights, 3) language barriers, and 4) difficulties in returning the product (Kawa and Zdrenka, 2016; Safari and Thilenius, 2013). Moreover, especially for consumer research, as a part of marketing, the concept of “consumer vulnerability” plays a special role. Consumer vulnerability is seen here as an indicator that can shed light on the extent to which individual consumer needs and the concept of marketing fit together (Brennan et al., 2017). Consumer vulnerability exerts a relevant influence on the strategic orientation of marketing measures. For example, “vulnerable consumers” have less confidence in the market and its products and services. In this context, individual consumer vulnerability is also understood as the increased probability of making unfortunate purchasing decisions, which are associated with potential losses, whereby the unfortunate purchase decision, from the consumer’s perspective, is a wrong decision that does not correspond to their economic interest (Berg, 2015). The consequences of this are a lower purchase intention and, thus, lower sales by companies (Roy and Sanyal, 2017). Thus, an increased perceived consumer vulnerability may weaken cross-border ecommerce intention as a direct driver but may also have a moderating influence on the effect of consumer motivation on the intention to shop cross-border online. In addition, effective consumer protection is even an essential part of European Union policy, as set out in the Treaty on the Functioning of the European Union and in the Charter of Fundamental Rights of the EU (European Parliament, 2019). For both theory and practice, it is therefore important to examine how consumers deal with this potential vulnerability and whether it affects their intention to make cross-border online purchases. Hence, the second research question arises: • Which role does the perceived vulnerability play in consumers’ cross-border online shopping intention? Following the worldwide establishment of e-commerce and, more specifically, mobile commerce as popular sales channels through aspects such as convenience and availability, recent alternatives to online shopping are emerging with the aim of exceeding the previous advantages of traditional e-commerce (Deges, 2020). Thus, voice-commerce, as one of these new alternatives, represents the process of ordering products on the Internet using a digital voice assistant (Mari, 2019). In this context, digital assistants, such as the voice-based Amazon Echo or text-based chatbots, have gained in relevance in recent years and will become increasingly important for everyday life in the future (Maedche et al., 2019). For example, in 2019, $786 million was invested in start-ups working with speech recognition

20

1

Introduction

technology. This growth confirms the great interest of companies in this innovative technology, which is becoming increasingly attractive to consumers. In 2020, more than 20 million U.S. consumers made a purchase using a voice-controlled device, primarily purchasing groceries or other retail products (PYMNTS, 2020). Additionally, forecasts predict that by 2024, 8.4 billion digital voice assistants will be used worldwide, more than the entire world’s population (Juniper Research, 2020). Often, potential risks are addressed, such as voice assistants collecting or sharing data and large security gaps (Hoy, 2018). However, proponents of voice assistants argue that there are some opportunities, especially for disabled and elderly people, such as communication for people with dementia (Wolters et al., 2016). The relevant aspect of convenience in online shopping is bringing the ordering of products via a voice assistant increasingly to the fore (Swaminathan et al., 1999). By developing voice assistants with screens, such as the Amazon Echo Show, digital voice assistants are increasingly optimized for shopping. Combined with the increasing sales of these, voice-commerce represents an interesting new sales channel. Due to the new initial establishment of digital voice assistants in the everyday life of consumers and the so far very low use of these for online purchases, there is hardly any research on the role of digital voice assistants in the consumption of consumers, how they are used for voice-commerce by consumers, and what obstacles consumers must struggle with. Since voicecommerce shows special characteristics compared to other forms of e-commerce, where manual entering for a product search and a purchase is still necessary, the question arises, “Which factors can strengthen or weaken the intention to use voice-commerce?” Hence, the third research question: • What factors influence consumers’ participation intention of voice-commerce, and what opportunities and threats are perceived by them? The business-to-consumer contact is shifting more to the Internet, which is available to customers around the clock. Since companies today differentiate themselves less through their products, they must place more value on the type and quality of their services (Schmitt and Schneider, 2001). This is where customer service comes into play. The use of digital service contacts in customer service should make it more accessible to customers and faster overall (Franke and Schulz, 2016). The success factor service, especially in a complaint process, thus plays an increasingly important role in the differentiation of the company. For this reason, the use of so-called chatbots has become increasingly popular for companies in recent years. They can offer a 24/7 service and have become more economical by working more efficiently and avoiding rising personnel costs (Cui

1.3 Research Gaps in the Various Subareas of E-Commerce

21

et al., 2017). This is naturally also used in complaint management. Especially during a complaint, it is decided how the relationship between the customer and the company will look in the future and whether the company, despite the complaint, can continue to bind and satisfy the consumer. Therefore, the complaint process plays an essential role for the success of a company and receives special attention in its process optimization. With the help of automation processes, enabled by artificial intelligence, customers can, for example, resolve their complaints directly with a chatbot instead of having to put up with long waiting times. The faster a company can react to the individual complaint of a customer, the faster their anger and frustration towards the company will disappear (Harrison-Walker, 2001). Due to the growing progress in research, the use of digital consultants in the corporate sector is likely to increase in the future. Although this interaction falls into the field of conversational-commerce, which has been researched several times, the usage of chatbots by companies has only increased in recent years due to the improvement of the artificial intelligence behind it. Thus, the problems that the use of such artificial intelligence brings with it are increasingly coming to the fore. For example, the self-learning robot “Tay” from Microsoft used racist and national socialist expressions after a while. The program had learned these slogans from users and distributed them (Graff, 2016). Such undesirable side effects can limit the trust of a potential customer in a chatbot and, thus, affect customer satisfaction. Given that there is a significant correlation between service quality and customer satisfaction (Sureshchandar et al., 2002), companies should ensure that their service system works well and that no problems occur. This should, above all, bind customers to the company, which is considerably cheaper than acquiring new customers (Reichheld and Schefter, 2000). Therefore, it is necessary to investigate the acceptance of chat offers, especially in the B2C area, to discover and understand weak points during the complaint management process. While online ordering processes mainly take place without intensive communication between the customer and the company and are already highly automated, customers in the complaint process enter close contact with the company and validate this personal interaction. Here, in particular, the use of artificial intelligence in the form of a chatbot generates added value to control the interaction and the handling of the chatbot, which represents the company, better than with a human service employee. The aim of the fourth essay is, therefore, to investigate how the behavior and characteristics of a chatbot affect the customer’s service experience in a complaint process. Thus, the fourth research question arises:

22

1

Introduction

• How does the behavior and appearance of a chatbot avatar affect consumers’ repurchase intention in a complaint process? That the role of ownership will change in society and that in the future consumers will increasingly pay for the temporal use of products instead of owning them has already been predicted by some economists (e.g. Rifkin, 2000). Thus, it was assumed that in a fast-moving society with ever new products and innovations, it would become increasingly unimportant to own things. This trend is referred to as the “Sharing Economy” or often “Collaborative Consumption.” It describes the sharing of resources, products, and services, such as car sharing, room sharing, or media streaming (Belk, 2010). Moreover, due to digitalization and the trends associated with it, the consumer behavior of many people has changed considerably in recent years. Several studies have already shown that the Sharing Economy and, especially, the business branch of the rental industry, called rental-commerce, are growing business models. PwC (2019) stated that already in 2017, 39% of all people in Germany used services from the Sharing Economy, and according to KPMG (2017), 41% of those surveyed would like to see more subscription and rental models in the future. In addition, revenues for the entire sector are predicted to be $335 billion by 2025 (Marchi and Parekh, 2015). The primary goal of companies in rental-commerce is to make a profit by renting out consumer durables. While rental-commerce is part of the Sharing Economy, it differs from most other sharing models in its focus on the commercial component (Fota et al., 2019) and can, therefore, be classified in the subfield of sharing-commerce. Therefore, rental-commerce platforms are very similar to conventional e-commerce platforms. The difference is that the focus is on renting rather than buying goods, and thus, there is no transfer of ownership; instead, the tenant is merely granted the rights to use the goods (Benoit et al., 2017). This change in consumer behavior raises the question of why more consumers are choosing to rent products instead of buying and owning them for the long term. In investigating users’ perception and evaluation of the rental-commerce concept, one might suppose that feelings of ownership might influence their intention to participate, based on the Property Rights Theory, the Theory of Perceived Ownership, and the Endowment Effect. These theories all give a different perspective on users’ attitude towards ownership and what value property has for society. The Theory of Perceived Ownership, for example, states that people who come into contact with a good for a certain time develop a feeling of ownership (Peck and Shu, 2009). In some cases, the very idea of owning that good is enough to reinforce this effect. This leads, among other things, to the assessment of a good as more valuable than it actually is, which suggests a connection to the

1.3 Research Gaps in the Various Subareas of E-Commerce

23

Endowment Effect (Kahneman et al., 1991). Therefore, there are already some theoretical approaches that could explain the meaning of ownership. However, so far, there has been no investigation of whether these phenomena can also be applied in the context of the Sharing Economy, specifically in rental-commerce, and explain consumers’ participation intentions in this subarea of e-commerce. Hence, the fifth research question arises: • What role does ownership play in today’s consumer society, and what motivates consumers to give up property and instead prefer to have only temporary access to products? The economy of sharing has been the subject of great interest in recent years (Zervas et al., 2017). Factors influencing the development of the Sharing Economy from a social perspective include the striving for sustainability and the end of over-consumption (Sikorska and Grizelj, 2015), whereby consumers break away from the striving for possession (Peitz and Schwalbe, 2016). In addition, especially due to the global financial crisis in 2008, consumers seem to have become more detached from possessions, whereby the focus is on access to goods but not on ownership of these goods (Sikorska and Grizelj, 2015). Such consumer wishes and expectations are met by the Sharing Economy (Haucap, 2015), which can take various forms and orientations. Researchers in the field have established various categorizations to distinguish sharing models from each other (e.g., Botsman and Rogers, 2010; Schor, 2014.). In addition to consumer-to-consumer models, business-to-consumer models are also developing, which expand the business activities of companies (Eichhorst and Spermann, 2015). As a sharing model from the business-to-consumer sector, rental-commerce can also be seen, which is currently gaining attention in the retail sector (Lamprecht, 2018). This involves the rental of products, which creates the possibility of using goods only for a certain period without having to own all the products themselves (e.g., Grover Group GmbH, 2018). However, whether and to what extent the Sharing Economy and rental-commerce have an impact on sustainability, such that, for example, resources can be saved, remains unclear (Peitz and Schwalbe, 2016). Thus, the sustainability of sharing models is not entirely clear and is subject to criticism. In addition, rental-commerce has a different characteristic compared to other business models in the sharing economy, as the focus seems to be less on ecological than on economic goals, both for the consumer and the company. Therefore, the assumption arises that there are other factors that could influence the behavioral intentions of consumers in rental-commerce than the sustainability aspect. The examination of the subject of rental-commerce makes

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1

Introduction

it clear that this new type of e-commerce concept has hardly been researched from a scientific perspective so far. However, as the rental business is increasingly becoming a trend (Lamprecht, 2018), the sixth essay focuses on the question of drivers and barriers of rental-commerce from a consumer perspective. This basic research in this subfield of e-commerce can serve as a groundwork to support future research that addresses more specific questions in this context. Thus, the sixth and last research question arises: • Which factors drive or prevent consumers from participating in rentalcommerce, and how do they differ from the already known driving forces of established sharing models? This dissertation attempts to answer relevant research questions in the various subareas of e-commerce based on previous considerations and research studies. The findings resulting from the examination of the individual research questions should create added value for both research and practice. The creation of this new knowledge serves as a further basis for future consumer research in the field of e-commerce and enables the elaboration of practice-relevant implications from which retailing, politics, and consumer protection will benefit.

1.4

Science-Theoretical Classification

More business models and trends are evolving in online retailing, which increasingly blur and merge into each other. Consequently, the boundaries between the different forms of online shopping opportunities can no longer always be clearly drawn, although all e-commerce forms have a common goal: the value creation for the consumer (Chen et al., 2019). Since the entirety of consumer behavior in online retailing cannot be examined scientifically in this dissertation, this work provides an overview of the most essential trends in e-commerce, which are expected to have the greatest impact on consumer behavior in the future. An observation of the different e-commerce subareas should also receive scientific consideration, since the actions of individuals occur in dependence on the respective context (cf. Section 1.2) and, hence, cannot be regarded as generally applicable (e.g., Branch and Pennypacker, 2013). Thus, the necessity of a comprehensive consideration of the corresponding different e-commerce contexts can be justified. These selected e-commerce phenomena should, therefore, help to expand the empirical basic research in these areas and explain and extend concrete theoretical relationships presented in the conceptual model, among others.

1.4 Science-Theoretical Classification

25

Therefore, the present research not only relies on already established theoretical approaches but also aims to generate new theoretical explanatory patterns that extend the previous ones and provide additional understanding about the behavior of individuals in social and economic science research. Thus, the research interest of this dissertation consists of a theoretical scientific goal, which can also be applied practically. Therefore, in the foreground is the increase in knowledge, which should not only find theoretical application but also provide practical implications (Marsh, 2002; Shin et al., 2001). In this way, insights should be created on a meta-level through the different objectives of the research focuses, which are all located in the field of e-commerce. In this context, the goal from a scientific-theoretical point of view is to distance oneself from one’s own thoughts and emotions and try to adopt a holistic and unbiased perspective towards the subjects under investigation (Harding, 2015; Ratner, 2002) to enable a practical-normative approach (Heinen, 1992). Among other things, methodologies, such as theory pluralism, are used to promote epistemology, which tries to capture reality and truth in the best possible way by viewing them from multiple perspectives (Midgely, 2011; Bohman, 1999). Another methodological approach to get closer to the social reality, especially in social research, is quality criteria (Schirmer, 2009). One of these quality criteria is objectivity, which should be given both in the implementation, evaluation, and interpretation of research results. Accordingly, a research result is only objective if it is not influenced by subjective assessments and perceptions of the observer (Schirmer 2009). However, for the formation of perceptions, one needs preconditions and limitations of cognition, which are shaped from individual to individual by personal characteristics, culture, or experiences gained, and, thus, do not allow for a universally valid reality or truth (Bowden and Green, 2010; Flick 2011). Nevertheless, with an empirical consideration and the inclusion of the perception of multiple individuals, it has tried to come as close as possible to reality and truth (House, 1991). Regarding this, theoretical basic assumptions are used, such as motivation theories, (cf. Expected Utility Theory, Section 1.2), which simplify the theoretical behavior of individuals by their generality, but also explain it as close to reality as possible. For this purpose, different inductive and deductive methods were used, which should give, on the one hand, exemplary (in the form of qualitative interviews), explorative, and heuristic findings about phenomena that have hardly been researched in the investigated constellation so far and which should help to build theory, as well as quantitative phenomena, on the other hand, which describe correlations, which are based on theoretical concepts and test them empirically (Sobh and Perry, 2006). Theoretical assumptions and

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Introduction

hypotheses are used, which are based on already existing descriptive or confirmatory findings and perceptions. However, the “appropriateness of methods and theories” to the object matter (e.g., Flick 2011), which states that the methodology and theory to be applied are determined and defined by the respective object of investigation, was also considered in this dissertation. Additionally, next to the objectivity, Schirmer cites reliability as another quality criterion that helps to describe social reality. Specifically, it addresses the dependability and accuracy of the research results. In case of a repetition under the same research conditions, a reliable measurement leads to the same results, which strengthens the explanatory value of the observed correlations and phenomena. The last quality criterion is validity. If the research questions can be answered with the help of the applied method, or if what should be measured is actually measured, one speaks of a valid observation. The results of the research thus gain credibility (Schirmer 2009) and help to ensure the truth validity of the research, which gives information about how close a statement or observation is to the actual truth and reality (Maxwell, 1992). This dissertation attempts to fulfill all three of these quality criteria to provide a form of knowledge creation. In doing so, the goal is to evaluate existing theoretical knowledge and further develop it with the help of own studies.

2

Structure and Content of the Essays

2.1

Focus of the Essays

While in the previous chapters, the conceptual framework and the basic research questions of the respective essays were presented, as well as a holistic scientifictheoretical classification of the presented phenomena, the present chapter follows up on these findings to obtain a more detailed overview of the essays’ content, which fit with the above theoretical foundation. The following subchapters thus provide a quick summary of the scientific relevance, the research questions, the methodology, and the central results and implications of the individual essays. All six essays (see Table 2.1) deal with newly observed developments and trends in the various subareas of e-commerce and are embedded in different theoretical phenomena. However, among other things, the most relevant constructs of the six essays and their interrelationships are also placed in relation to the conceptual framework and the theories presented in Chapter 1. By examining the various subareas, consumer behavior in e-commerce is illuminated from several perspectives and thus enables a holistic overview of the new shopping and consumption behavior in online environments. In this manner, new insights can be derived, which can provide added value for marketing science and workable implications for practice.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Fota, Online Shopping Intentions, Handel und Internationales Marketing Retailing and International Marketing, https://doi.org/10.1007/978-3-658-37662-8_2

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2

Structure and Content of the Essays

Table 2.1 Essays Overview Essay Title

Subarea of E-Commerce

A Cross-National Comparison of Consumers’ Cross-Border Online Shopping Intentions in Germany and Romania

Cross-Border E-Commerce

Development of a Motivation–Trust–Vulnerability (MTV) Framework for Cross-Border Online Shopping: A Cross-National Application to Chinese and German Consumers

Cross-Border E-Commerce

A Qualitative Study of Consumer Perceptions and Experiences Related to Voice-Commerce

Voice-Commerce

An Experimental Investigation of the Use of Artificial Intelligence in the Context of Complaint Management

Conversational-Commerce

From Owning to Renting Through Rental-Commerce Websites—A Qualitative Analysis of the Importance of Ownership

Rental-Commerce

Will Renting Substitute Buying? Drivers of User Intention to Participate in Rental-Commerce

Rental-Commerce

2.2

Essay 1—A Cross-National Comparison of Consumers’ Cross-Border Online Shopping Intentions in Germany and Romania

The purpose of the first essay is to investigate factors that motivate or constrain consumers in advanced and emerging country markets to make cross-border online purchases. Cross-border online shopping describes the phenomenon of consumers conducting online purchases in foreign countries instead of buying online in their home country market (Wagner et al., 2016). While market data indicates globally increasing numbers of cross-border online purchases (PayPal, 2018), little is known about the benefits that drive or the risks that impede consumers’ intention for cross-border online shopping. To advance the understanding of the “crossborder online shopper,” customers from an advanced country (i.e., Germany) are compared with customers from an emerging market (i.e., Romania). Furthermore, this research contributes to the knowledge that further factors affect (directly or by moderation) cross-border online shopping intentions and whether there are differences of cross-border online shopping behaviors across country markets.

2.2 Essay 1—A Cross-National Comparison of Consumers’ …

29

To test the hypotheses and account for cross-national differences, an empirical study in two European countries was conducted: Germany and Romania. Data was generated for the main study using two, with regard to contents, identical online questionnaires. For testing the hypotheses, partial least squares (PLS) structural equation modeling was used. As only partial measurement invariance could be obtained, separate models for the German and the Romanian samples and performed multigroup analysis using SmartPLS 3 was calculated. In these models, only experienced cross-border online shoppers were included (N = 136 for the German sample, N = 80 for the Romanian sample). While the findings show that consumers’ perceived benefits have a significant positive influence on the cross-border online purchasing intention in advanced and emerging markets, with regard to perceived risks, a negative significant effect on cross-border online purchasing intention for both samples could be found, too. However, it could be observed that in Romania, as an emerging market, potential risks are perceived more strongly than in the advanced German market. These more strongly perceived risks could also promote a stronger perceived consumer vulnerability, which could be reinforced by the comparatively still weak experience of Romanian consumers with international online purchases and markets. This is also where the conceptual framework and the theoretical framework can be linked with the present results (cf. Section 1.2): The perceived benefits and risks in this essay represent the antecedents and inhibitors of the research model to participate in e-commerce, here, in cross-border e-commerce, which affect the overall shopping intention. The (Expected) Utility Theory (Fishburn, 1970) also describes the perceived trade-off between the benefits and risks in which the consumer validates what utility they will gain by participating in cross-border ecommerce and how large this utility will be. Additionally, the CCT (Arnould and Thompson, 2005) also finds application in the first essay. On the one hand, significant positive influences of cosmopolitan attitudes on cross-border e-commerce intention were found for both the German and the Romanian sample. Hence, cosmopolitanism, which represents a high level of consumer interest and curiosity in other cultures and international exchange, appears to drive cross-border online purchase intentions for both advanced and emerging markets. Especially for emerging markets, such as Romania, a low availability of own domestic online stores could explain the openness to foreign online stores, which greatly expand consumers’ consumption choices. On the other hand, effects of ethnocentric influences could only be found empirically for the German consumers. The results for the German sample, therefore, indicate that tendencies towards ethnocentrism that favor home country purchasing seem to also challenge consumer openness and

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2

Structure and Content of the Essays

willingness to globalize their purchases. These two sociological and cultural attitudes can be seen as (quasi-) moderators in the conceptual framework and help to better understand consumer intention formation. This study and its findings therefore provide valuable insights for online retailers about the trade-off consumers face in cross-border e-commerce and the influence these perceived benefits and risks have on their purchase intentions. In addition, by looking at two culturally and economically different country markets, it is shown how sociological and cultural attitudes influence consumers’ cross-border online commerce in the same or different ways. These differences in cross-border e-commerce intentions should be further explored through future research with additional countries that exhibit different characteristics.

2.3

Essay 2—Development of a Motivation–Trust–Vulnerability (MTV) Framework for Cross-Border Online Shopping: A Cross-National Application to Chinese and German Consumers

The purpose of the second essay is to investigate and explain the causal mechanism of how benefits motivate cross-border online purchasing intentions despite the perceived vulnerability in uncertain foreign online shopping environments. As already pointed out in the first essay, the growing consumer confidence in cross-border e-commerce is also becoming apparent (European Commission, 2017), such that advantages and possibilities of use are increasingly perceived by consumers. However, obstacles are still emerging that prevent consumers from using cross-border e-commerce to a wider extent. These obstacles and concerns when shopping at a foreign online retailer relate, for example, to payments that could be rejected in cross-border online shopping or delivery problems that may exist between individual countries (PayPal, 2018). Consumers differ regarding their ability of dealing with such risks. One reason might be consumer vulnerability that may occur due to individual characteristics, individual and situational states, or external conditions (Baker et al., 2005; Browne et al., 2015). Therefore, despite the increasing cross-border e-commerce numbers worldwide, little is known about the determinants and obstacles that hinder consumers in crossborder e-commerce. There is also little research on how and why consumers choose to engage in cross-border e-commerce activity. In addition, insights of a survey conducted by PayPal (2018) indicate that the share of cross-border

2.3 Essay 2—Development of a Motivation–Trust–Vulnerability (MTV) …

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online shopping activities differs across country markets, and attitudes to crossborder online shopping vary considerably across regions. Hence, it is important to account for country-specific differences when investigating cross-border online shopping behavior. To increase the theoretical contribution, the second essay focuses on explaining the mechanism that motivates online shoppers to become cross-border online shoppers. Building on the motivation–ability (MA) theoretical framework (Merton, 1957), in the second essay, a motivation–trust–vulnerability (MTV) framework is developed to explain behavioral decision-making in situations of uncertainty and vulnerability. This new framework is applied to cross-border online shopping, which is characterized by uncertain benefits, potential losses, and increased vulnerability of making cross-border online purchases at foreign online vendors. By merging the ability dimension from the MA framework with a concept from consumer psychology, it is shown that vulnerability can be conceptualized as “reverse ability.” Therefore, lower levels of perceived ability (i.e., lack of knowledge and lack of skills) are considered equal to higher levels of vulnerability (Shultz and Holbrook, 2009), which is a relevant factor in situations that involve uncertain outcomes, as is the case with cross-border online purchases. Therefore, the MTV framework (1) explains the mechanism of crossborder online shopping by considering the effects and interactions of motivation, trust, and vulnerability, (2) offers a new conceptualization of perceived vulnerability, and (3) is applicable to culturally and economically distinct country markets. To test the generalizability of the framework, two culturally and economically distinct country markets were selected: China and Germany. One reason for this selection was that they show certain differences with regard to the two cultural dimensions: uncertainty avoidance and individualism–collectivism. Data was generated for further analyses and hypothesis testing using two online questionnaires that are identical regarding their content but translated both in national languages, German and Mandarin Chinese. While the Chinese questionnaires were distributed via e-mail and social networks, the German data was collected using a local research agency panel of adult online shoppers. Hence, a dataset was obtained consisting of N = 808 consumers, of which 452 are from Germany (51.8% female, M age = 44.02 years), and 356 are from China (54.5% female, M age = 28.22 years). The maximum likelihood estimation procedure with AMOS 25 was used to conduct CFA of the measurement model on the Chinese and German datasets individually. Next to the cross-border online purchase intention, two further outcome variables were added: actual cross-border online purchases and satisfaction with past cross-border online purchases.

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The research model used in the second essay also aligns with the conceptual framework of this dissertation: similar to the first essay on cross-border e-commerce, the perceived benefits stand for the antecedents to participate in e-commerce, which are assumed to influence the intention to shop (crossborder) online. Again, the overarching (Expected) Utility Theory (Fishburn 1970) describes the phenomenon that consumers evaluate the utility (here, the benefits) upon which subsequently they form their cross-border e-commerce intention. Additionally, the (quasi-) moderators from the conceptional framework (cf. Section 1.2) are reflected in the second essay by the trust towards online vendors and the perceived vulnerability of the consumers. These two (quasi-) moderators are assumed, following the conceptual framework, to exert both a moderating influence on the relationships between the other determinants of the model and a direct effect on the cross-border e-commerce behavioral intention. The results of the analyses in the context of cross-border online shopping show that while perceived benefits build the main motivation, and trust towards foreign online vendors has a positive effect, perceived vulnerability has a negative direct effect on the intention to purchase cross-border online. At the same time, the interaction of trust in foreign online vendors and perceived vulnerability increases the motivation to conduct cross-border online purchases. However, while these direct effects are largely consistent across the Chinese and German samples, support is found only for the Chinese sample for a significant moderating effect of vulnerability, while there is a small but non-significant positive effect in the German sample. These differences in the significance and strength of the postulated influences, especially concerning the trust and vulnerability aspects between the German and the Chinese samples, illustrate the influence of culture and its impact on the different consumer perception and attitude formation, as described by the CCT (Arnould and Thompson, 2005) (cf. Section 1.2).

2.4

Essay 3—A Qualitative Study of Consumer Perceptions and Experiences Related to Voice-Commerce

The third essay aims to investigate the factors influencing the intention of consumers to participate in voice-commerce, as well as the opportunities and risks they perceive when participating in voice-commerce. The use of digital voice assistants is still largely unexplored, especially in the consumer sector, and poses challenges for both consumers and other stakeholders. Voice assistants could fundamentally change shopping behavior (Mari, 2019;

2.4 Essay 3—A Qualitative Study of Consumer Perceptions …

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Sims, 2019; Sands, 2017); therefore, voice-commerce is also gaining in importance. While voice assistants can provide support at the individual touch points within the customer journey, they also raise questions about data security and privacy. Especially today, when consumers are generally longing for more privacy and data protection, these aspects are also becoming more relevant regarding digital voice assistants (Mihale-Wilson et al., 2017; Easwara Moorthy and Vu, 2015). To optimize the shopping experience for consumers via voice-controlled technologies, it is advisable to identify possible measures to improve the use of digital voice assistants and gain insight into consumer behavior and wishes. Especially regarding voice-commerce, which consumers have so far been rather skeptical about, solutions should be found. However, so far, no study has investigated the actual use of digital voice assistants for online shopping in the context of exploratory studies, resulting in a research gap, which will be explored with this qualitative and experimental study. To answer the research questions of the third essay, two different studies were conducted. For the first study, a qualitative survey in the form of focus group discussions was chosen. The recruited participants were between 18 and 73 years old, and 62% were male. This enables the generation and explorative collection of opinions, behaviors, and experiences on a specific topic on which findings are available so far. The qualitative content analysis with inductive category formation, according to Mayring (2016), was chosen as the evaluation technique for the qualitative survey. The sample of focus group discussions consists of 71 equally distributed participants. The chosen number of participants should ensure the possibility of sufficiently active participation and expression of opinions of the participants (Blank, 2011). The focus of the second study is the observation and analysis of the behavior of the participants during an ordering process with a digital voice assistant. The participants were to conduct three different order processes with an Amazon Echo Dot, known as Alexa. During the ordering process, which was divided into different phases, the participants and their interaction with Alexa were filmed. To additionally consider possible differences between different product categories and product complexity, three products of different price ranges and complexity, which were also classified as low- and high-involved products, were ordered one after the other. In the observational study, a total of 25 consumers participated, with 52% of the participants being male. Combining the findings of the qualitative survey and the observational study, a clear view of consumers regarding the suitability of digital voice assistants for purchasing products on the Internet results. The assessment of digital voice assistants depends strongly on their benefit and added value for the customer (Hörner, 2019). This is again linked to Fishburn’s Expected Utility Theory (1970), as well

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as the core of the conceptual framework of this thesis (cf. Section 1.2), in which numerous antecedents and inhibitors could be identified, which influence the intention to use digital voice assistants for online shopping. Above all, numerous inhibitors could be observed, which are particularly restraining for the intention to use. The voice assistant used in this study, the Amazon Echo or Alexa, has some missing functions that are necessary for a smooth ordering process. Thus, the advantages and opportunities found in the results, representing the antecedents in the conceptual framework (convenience and speed, user-friendliness, ease of use, and assistance in everyday life), are currently weighted less strongly by the participants than the disadvantages and risks (lack of visualization, susceptibility to errors, restriction of a free product choice, data protection problems, and security aspects). In addition, the observational studies were often characterized by communication problems between the participants and Alexa. As the study by Tuzovic and Paluch (2018) shows, shopping in voice-commerce is more suitable for everyday consumer goods and low-involvement products. Both studies carried out in this paper can confirm these findings, as most participants mentioned that they would buy food, products for everyday use, and generally products in a lower price range via this shopping channel. The reason for this is the acceptable risk of losing only low amounts of money. Furthermore, in the focus group discussions, but especially in the subsequent observations and interviews, it became clear how strongly the already existing consumer expectations and the consumer evaluations based on the experiences with intelligent systems influence the consumers’ opinions towards voice-commerce and their intention to participate in it, referring to the conceptual framework of this dissertation. In addition, the results show that the interaction between young and older consumer groups with the digital voice assistant can differ significantly. Therefore, an adaptation of the digital voice assistant, not only in terms of its TAM elements (Davis, 1986) but also in terms of its communication and emotional interaction, to the respective consumer group can significantly influence their expectations and evaluations to use digital voice assistants for online-shopping.

2.5

Essay 4—An Experimental Investigation of the Use of Artificial Intelligence in the Context of Complaint Management

The fourth essay focusses on the effects of a complaint answer with a humanvs. robot-like chatbot avatar. As in the third essay, the focus here is on the use of new technology in the form of intelligent systems and, therefore, differs from the

2.5 Essay 4—An Experimental Investigation of the Use of Artificial …

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other four essays. In the context of complaint management, it will be examined how artificial intelligence can best be presented both visually and in terms of content so that the consumer’s intention to buy from this supplier again increases despite complaints. The results of the third study, which focuses on the use of digital voice assistants, have already shown that how consumers will communicate and work in the future will be decisively influenced by the networking of human and machine (Hecker et al., 2017). In the digital age, marketing automation systems that support the independent execution of repetitive marketing tasks are also becoming more common. Chatbots are a part of the automation of marketing, which are technologically evolving and learning more about consumers through interaction, thus becoming virtual service butlers that make life easier for consumers (Schüller and Schuster, 2017). According to a report by Grand View Research (2017), the global chatbot market will grow to $1.25 billion by 2025, growing at 24.3% annually, representing significant growth. In addition, innovations in the areas of artificial intelligence and machine learning will improve the capabilities of chatbots and significantly drive the market (MarketInsider, 2017). This represents a great opportunity for companies to distinguish from other competitors, since they must establish good quality and price as well as an excellent service policy. Specifically, the after-sales service should guarantee that customers having problems are supported and satisfied. Therefore, good complaint management is inevitable. With the increase of economically profitable chatbots, it is possible to provide a 24/7 service to the customers (Cui et al., 2017). To ensure that this service is provided in the best possible way, the presentation and behavior of a chatbot to the consumer in complaint management needs to be examined more closely. The third essay investigates the extent to which the choice of an avatar, robot- or human-like, affects the consumer behavior intention in the context of an online complaint. Hence, on the one hand, the influence of a human vs. robot avatar on consumer behavior and the role of the avatar’s empathy in an online complaint will be investigated. On the other hand, the influence of compensation on the behavioral intention will be examined, as well as the additional effect of anthropomorphic characteristics by the chatbot. An experimental study with a 2 (human vs. robot) x2 (empathetic vs. nonempathetic) x2 (voucher vs. no voucher) between-subject design was conducted using an online survey. A total of 389 participants participated in the study, recruited randomly via social media channels and online forums (N > 30 in each condition, M age = 29.00 years, SD = 12.49, 57.1% women). The experiment began with the presentation of a scenario. This initial scenario was the same for all participants in the study, in which online-ordered headphones arrived broken

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at the consumer’s home. Then, each participant was shown a chat history. This chat history varied in its avatar presentation and content. Each participant was, therefore, shown only one of eight different manipulation versions that were created. A total of three manipulations were made: either a human-like or robot-like avatar was shown as the contact person (experimental factor avatar), the chatbot showed empathy for the consumer or not (experimental factor reaction), and the consumer was offered compensation by the chatbot in the form of a voucher or not (experimental factor compensation). To test the hypotheses, several ANOVAs were conducted. To identify whether there are mediating variables influencing the effects of the experimental factors on the behavioral intention, multiple analysis of covariance (ANCOVA) were also run. A significant positive influence of the avatar on the behavioral intention was found, as well as for reaction and compensation, whereby the effect strength (partial eta squared) for compensation was observed to be the strongest for all three experimental factors. In addition, the strongest mean value for the behavioral intention was observed for the scenario of an empathic human-like avatar, which gives compensation. However, no significant interaction effects between each of the three experimental factors could be reported. The results also show that, the primary driver for the human-like avatar is empathy. Moreover, the behavioral intention can be explained by the mediating influences of anthropomorphism and the evaluation of redress. Again, the determinants studied are related to the conceptual framework: the various specifications of the three experimental factors represent either antecedents or inhibitors to participating in e-commerce (cf. Section 1.2). At the same time, anthropomorphism and the evaluation of the redress function as consumer expectations and evaluations from the conceptual framework, which, like the antecedents and inhibitors, exert an influence on the consumer intention to shop online, respectively on the intention to return to this specific online retailer. These expectations and evaluations, in turn, are influenced by the three experimental factors. Depending on the specifications of the experimental factors (e.g., a human-like or robot-like avatar, an empathetic or non-empathetic avatar, or a compensation in the form of a voucher which is offered or not offered by the avatar), the expectations and evaluations of the consumers regarding the anthropomorphic characteristics of this avatar and its redress can be evaluated and influenced differently. The results show that the choice of the avatar, the reaction, and the compensation play a decisive role in influencing consumer behavior and, thus, increase the probability that the customer, despite a complaint, returns and buys again from the retailer. Therefore, through the use of chatbots, companies can not only reduce employees’ costs but also increase service satisfaction.

2.6 Essay 5—From Owning to Renting through Rental-Commerce …

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Essay 5—From Owning to Renting through Rental-Commerce Websites—A Qualitative Analysis of the Importance of Ownership

Using the example of rental-commerce, the fifth essay focuses on providing information on when consumers can renounce own possession and which determinants play a role in the decision-making process to use products temporarily rather than to own them. Thus, the fifth essay, as well as the last one, deals with a new kind of business model that rethinks the traditional system of buying and owning. For instance, to solve the problem that resources are not fully utilized (Peitz and Schwalbe, 2016), numerous sharing platforms have been established (Theurl, 2016), the best known of which are Uber and Airbnb (Eichhorst and Spermann, 2015). The status and figures of such companies show that the development of the Sharing Economy is progressing rapidly. For example, Airbnb, a platform on which private suppliers and tenants of housing meet, was founded in 2008, and four years later, it can already record around four million guests and over ten million booked nights (Zervas et al., 2017). The desire for more sustainability and conservation of resources is associated with the Sharing Economy, while some consumers are moving away from the pursuit of possession (Peitz and Schwalbe, 2016). However, ownership is also often associated with prestige and the freedom to use products whenever needed or wanted. Therefore, ownership and non-ownership can be perceived as both an advantage or disadvantage by consumers (Moeller and Wittkowski, 2010), or, following the conceptual framework from Section 1.2, as antecedents or inhibitors that influence the consumers’ purchase or consumption decisions negatively or positively. Looking at the literature, it can be stated that there are opposing theoretical approaches, such as the Property Rights Theory (Furubotn and Pejovich, 1972), the Theory of Perceived Ownership (Pierce et al., 2001), and the Endowment Effect (Thaler, 1980), that show the varying relevance of ownership for consumers. All these above theories describe in basic terms the expectations consumers have about ownership and property and how they evaluate them, depending on what antecedents or inhibitors are associated with ownership and property. Thus, they describe an essential part of the overarching conceptual framework, using the example of rental-commerce, a sub-model of the Sharing Economy and a subarea of e-commerce. This leads to the research question of what value ownership still holds for consumers today and what determinants induce consumers to renounce ownership and prefer to rent products online? To address these research questions, a qualitative study was conducted to investigate the phenomenon of feelings of ownership using the example of

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rental-commerce in a holistic approach, as well as to identify determinants that strengthen the intention to participate in rental-commerce. For this, a problem-oriented semi-structured interview guideline was developed. Altogether, 28 German participants agreed to take voluntarily part in this study, of which four persons were formed to a focus group, and 24 participants were questioned in individual interviews. The conducted interviews were then recorded, transcribed, and coded for thematic analysis with the software MAXQDA. Lastly, the transcripts were evaluated in a structured qualitative content analysis following Mayring (2010). In rental-commerce, the consumer is still given the opportunity to take full advantage of the product and benefit from its utility. How this perceived utility is estimated by the consumer can be depicted by the (Expected) Utility Theory (Fishburn 1970), which is considered in the overall theoretical framework of this dissertation (cf. Section 1.2): Rental-commerce offers a certain utility to the consumer, which results from the antecedents and inhibitors of possession and ownership and their evaluation and expectations of them. Depending on what expectations one has about possessions (e.g., product-depended benefits that possessions can provide to consumers) and how one evaluates them (e.g., in terms of their emotional value to consumers), the behavioral intention to order or not to order products from rental-commerce retailers is formed. As such, the results of the qualitative study also demonstrate that the importance of ownership often seems to be product- and emotion-dependent. Thus, the results show that the decision to own a product permanently or to use it only temporarily depends mainly on several factors, in the form of antecedents or inhibitors, which influence this consumer decision. However, due to the comparatively short period of use in rental-commerce, it is questioned how strongly the emotional bond between the consumer and the rented object is evaluated and whether the same expectations are placed on the rented product (e.g., with regard to product life or quality) as with a purchased product. On the one hand, the results give further insights about why experiencing a product is more important to the consumer than owning it (Furubotn and Pejovich, 1972). On the other hand, the study’s results show that for some consumers, it is more important to possess products, especially depending on their age. The circumstances that drive consumers to make this choice or form this behavioral intention are presented in more detail in the results of the study. For example, consumers expect an increased access to products when participating in rental-commerce, which acts as a particularly relevant influencing factor, respectively antecedent for the intention to use rental-commerce. However, it was also observed that older participants in the study, in particular, evaluate the emotional

2.7 Essay 6—Will Renting Substitute Buying? …

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value of a product as more important and would, therefore, prefer to own a product rather than rent it. Thus, the results of the fifth essay also clarify the postulated relationships between the antecedents and inhibitors, consumer expectations and evaluations, and consumer intention of the conceptual framework and contribute to a better understanding of consumers’ online shopping and participation in e-commerce.

2.7

Essay 6—Will Renting Substitute Buying? Drivers of User Intention to Participate in Rental-Commerce

The objective of the sixth essay is to investigate consumers’ motivation to participate in rental-commerce using a quantitative study. The main goal is to analyze what determinants are relevant in driving consumers’ behavioral intentions to participate in rental-commerce and which determinants influence behavioral intention the most. In rental-commerce, consumers receive access to products for a monthly fee without owning them (KPMG, 2017). Although they can make full use of them for their intended usage, they must return them after an agreed period, as they have no claim to ownership of the products. Therefore, rental-commerce is not only a subarea of e-commerce but also a model of the growing Sharing Economy and can be classified as sharing-commerce. A reason for the Sharing Economy’s increasing growth is the changing consumption behavior of the society. Moreover, technological developments in information and communication technologies and networking are of particular importance (Peitz and Schwalbe, 2016). Looking at the consumer expectations and evaluations proposed in the conceptual framework in Section 1.2, it appears that consumers have different expectations when they participate in rental-commerce compared to traditional e-commerce: By participating in this new consumption model, consumers expect more choice and variety-seeking, the opportunity to test more products, access to products they could not otherwise afford, and general cost savings through a comparatively lower monthly rental price (Lawson et al., 2016). In addition to the expectations of these benefits, many of these aspects also influence consumer evaluations of rental-commerce, some even on an emotional level. For example, the feeling of group belonging can be strengthened by gaining access to certain products and lifestyle through the rental-commerce model. Or an environmentally conscious and resource-saving motive could lead to an increased desire to rent products that can still be used by another consumer after their temporary usage, making the product itself more sustainable (Böcker and Meelen, 2017).

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However, the increased frequency of orders and returns when constantly renting new products compared to traditional e-commerce results in more logistics, which negatively influences the environment. In this way, different potential drivers and barriers can also be named for rental-commerce. However, since rental-commerce stands out as a special part of the Sharing Economy, while at the same time manages the balancing act to the area of e-commerce, the antecedents and inhibitors that drive or inhibit consumers’ participation in this new business model should be examined more closely. The aim of this essay is, therefore, to compare the different theoretical approaches to the shared economy and, especially, to rentalcommerce with the results of a quantitative study. Through the empirical study, a better understanding of rental-commerce should be gained, and important results for further research and practical implementation of rental-commerce should be provided. To test the hypotheses, a quantitative study in Germany was conducted by generating data for the main study via an online questionnaire. Altogether, a dataset of N = 689 participants was obtained, of which 65.6% were female (M age = 29.20 years, SD = 11.97). To test the hypotheses, PLS structural equation modelling was used, and bootstrapping procedures (5,000 samples) were applied to assess the significance of the parameter estimates. The results of the fourth essay show that economic benefit, complexity, safety, and knowledge of terms and use are predictors for the behavioral intention. Based on the conceptual framework, these influencing factors form either antecedents or inhibitors, which are weighted against each other according to the (Expected) Utility Theory (Fishburn 1970) to determine the general utility for the consumers, which, in turn, influences behavioral intention (cf. Section 1.2). Surprisingly, sustainability has no significant influence on the intention to rent a product via a rental-commerce website. Instead, when participating in rentalcommerce, lower prices must be paid compared to a purchase, resulting in a financial benefit and motivational factor for many consumers. Moreover, it is important for consumers to feel safe with regard to products and data. Therefore, rental-commerce retailers are recommended to clearly and comprehensibly mark information on data protection regulations and consumer information for their consumers on their websites so that it is easily accessible (Bhatnagar and Ghose, 2004; Caudill and Murphy, 2000), as safety and knowledge of terms and use represent essential antecedents of the rental-commerce intention. However, perceived complexity (of the renting procedure) could be seen as a decisive inhibitor of the rental-commerce intention, weakening the expected utility of renting products.

2.8 Overview of Essays and Related Research Characteristics

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Overview of Essays and Related Research Characteristics

Table 2.2 provides an overview of the six essays and the corresponding research characteristics. For each essay, the research objective is summarized and information about the research design is provided. The sample size for each study and the various methodologies used are illustrated. In summary, 2,039 participants were acquired to participate in online surveys, experimental studies, interviews, and field studies to expand the knowledge for research and propose meaningful implications for practice in the different areas of e-commerce. Table 2.2 Summary of Essays and Research Characteristics Objective

Design

Sample Size

Methodology

Cross-national Online Survey

Germany: N = 136 Romania: N = 80

PLS-SEM, multi-group analysis

Essay 2 Investigating Cross-national causal mechanism Online Survey of how benefits motivate cross-border online purchasing intentions despite the perceived vulnerability in uncertain foreign online shopping environments

China: N = 256 Germany: N = 365

Confirmatory factor analysis, CB-SEM

Essay 1 Investigating factors that motivate or constrain consumers in advanced and emerging country markets to make cross-border online purchases

(continued)

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Table 2.2 (continued) Objective Essay 3 A qualitative study of consumer perceptions and experiences related to voice-commerce

Design

Sample Size

Study 1: Focus Study 1: N = 71 Group Interviews Study 2: N = 25 Study 2: Field Study

Methodology Structured content analysis

Essay 4 Investigating the Online use of artificial Experiment intelligence in the context of complaint management

N = 389

AN(C)OVA

Essay 5 Investigating Interviews when consumers renounce own possession and which determinants play a role in the decision-making process to use products temporarily rather than to own them

N = 28

Structured content analysis

Essay 6 Investigating Online Survey which determinants are relevant in driving consumers’ behavioral intentions to participate in rental-commerce

N = 689

PLS-SEM

3

Essays

3.1

A Cross-National Comparison of Consumers’ Cross-Border Online Shopping Intentions in Germany and Romania

3.1.1

Introduction

The Internet as a global medium raises consumers’ awareness of foreign online shopping destinations. Brands and products not available in the domestic online market become increasingly visible, preferable, and deliverable to consumers (Cleveland et al., 2014). The result is that if consumers are not satisfied with retailers, brands, products, or prices in the domestic online market, they shop online across country borders. According to recent market research by McKinsey (2017), cross-border e-commerce transactions are growing at a rate of 25% per year and were expected to account for one-fifth of worldwide e-commerce transactions by 2020. The numbers from a global PayPal (2018) survey with approximately 34,000 consumers conducted in 31 country markets indicate that, in some cases, the share of cross-border online shopping transactions as well as consumers’ attitudes toward cross-border online purchases differ vastly across country markets. Hence, it is important to account for country-specific differences when investigating cross-border online shopping behavior. For this research, our understanding of cross-border online shopping relates to consumers shopping in a foreign online store that ships from outside their domestic country. We thus differentiate cross-border online shopping from consumers shopping at a foreign online retailer that targets shoppers through country-specific websites and a physical presence in the country of the shopper’s residence, for example Amazon.co.uk/.de/.fr (Sinkovics et al., 2013; Yamin and Sinkovics,

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Fota, Online Shopping Intentions, Handel und Internationales Marketing Retailing and International Marketing, https://doi.org/10.1007/978-3-658-37662-8_3

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2006). This distinction is important because when consumers are (digitally) crossing a country border for a transaction, several conditions change, resulting in diverse additional benefits (drivers) and risks (barriers) of cross-border online shopping, compared to domestic online shopping. Especially with regard to the risks of cross-border purchases, consumers express a variety of concerns, such as the costs of shipping, duties, fees, and taxes, long delivery times, or not receiving the (correct) item, all of which oppose a range of benefits, including cheaper prices or the availability of unique brands and products (PayPal, 2018). For retail managers, customers’ awareness of cross-border online shopping is of relevance for two key reasons: first, the potential growth opportunities in foreign markets and, second, increasing competition in the home market, as foreign online retailers might sell to domestic customers. Market size, rule of law, common language, and the logistics performance of a target country, among other things, influence online retailers’ foreign market choice (Schu and Morschett, 2017). Although cross-border e-commerce is accompanied by a number of risks (e.g., costly returns) and barriers (e.g., legal restrictions), it represents a significant growth opportunity, and more online retailers are extending their e-commerce business to foreign countries; that is, they are providing cross-border deliveries to customers abroad (Ecommerce Europe, 2016). Even if retailers are not actively trying to sell their merchandise internationally through the adaption of offerings to foreign customers or international marketing efforts, unsolicited online orders may come from outside the home country. If online retailers understand the intentions and characteristics of the growing segment of consumers who shop online across borders, it might be possible to prevent domestic customers from shopping abroad and to lead foreign shoppers to the domestic online store (Boeuf and Senecal, 2014). Therefore, it is of strategic relevance to investigate how the perceived benefits and risks of cross-border online shopping affect individuals’ intention to engage in cross-border online shopping. Our study is among the first to conceptualize and empirically investigate the relationship between perceived benefits and risks and consumers’ crossborder online shopping intention that considers different county markets. In addition, while previous literature delivers valuable insights into the risks that inhibit consumers from shopping online across borders (e.g., Cheng et al., 2008; Safari and Thilenius, 2013), the benefits that might drive consumers to purchase from foreign retailers have not been explored in these works. In particular, the present research contributes to existing knowledge by responding to the following research questions:

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1. What benefits drive and what barriers impede consumers’ intention to conduct cross-border online shopping? 2. Which further factors affect (directly or by moderation) cross-border online shopping intentions? 3. Are there differences in cross-border online shopping behaviors across country markets? By dealing with the above-mentioned questions, this research contributes to developing marketing theory by (1) offering a conceptual model for cross-border online shopping behavior and (2) theorizing and empirically testing the effects of specific behavioral traits (foreign traveling) and attitudinal beliefs (cosmopolitanism, ethnocentrism) on the intention to shop at foreign online retailers. In addition, we (3) account for cross-national differences by testing our model in two different country markets (one advanced economy and one emerging country market). Moreover, the practical contribution of this study for marketing management and retailers is mainly twofold: (1) We illustrate the specific benefits and risks that consumers perceive with regard to cross-border online shopping, and (2) we discuss how retailers can align their strategies with cross-border online shopper behavior to successfully establish international operations in online retailing.

3.1.2

Literature Review

A broad range of literature relates to consumers physically shopping in foreign countries, whereas relatively sparse research investigates cross-border online shopping. The term “cross-border” is similar to classic “outshopping,” and the two terms are sometimes used synonymously in the literature (Schleiden and Neiberger, 2020). In fact, cross-border shopping can be categorized as a subcategory of outshopping—that is, international outshopping (Sullivan and Kang, 1997; Lau et al., 2005). Outshopping, in general, is defined as the purchase of goods by customers outside their local shopping area (Herrmann and Beik, 1968) and has been investigated with regard to the “outshopper” (Hawes and Lumpkin, 1984), outshopper segment descriptors (Jarratt, 2000), and identifying and attracting outshoppers (LaForge et al., 1984), among others. In the offline context, outshopping means that consumers shop in a neighboring region or country instead of in their otherwise usual or immediate region. In the case of online outshopping, the shopping area is again significantly enlarged compared with offline outshopping (Lee et al., 2009). In literature, the characteristics of consumers (e.g.,

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demographic variables and shoppers’ attitudes) and the benefit offerings of retailers (e.g., quality, selection, and the prices of goods offered) have been identified as relevant determinants of outshopping (Kumar Velayudhan, 2014). In addition, a distinction must be made between cross-border offline shopping and cross-border online shopping. Cross-border offline outshoppers travel to other countries to satisfy their economic (e.g., better quality, lower price, or more variety) or socio-psychological (e.g., entertainment, shopping enjoyment, or status seeking) needs (Sharma et al., 2018). Moreover, researchers have investigated the determinants of cross-border shopping (Guo and Wang, 2009), the influence of demographic and retail characteristics on international outshopping (Piron, 2002), and consumers’ foreign outshopping motives (Jian Wang et al., 2010). Cross-border shopping literature also emphasizes the difference between consumers who travel to another country for the explicit purpose of shopping (e.g., Lau et al., 2005; Sullivan et al., 2012) and the purchasing activities of consumers who shop away from home when visiting a country for business or a vacation (Sharma et al., 2018). Compared to domestic forms of outshopping, the international experience may result in differences in the consumer’s perception: The foreign retailer’s language might differ from that of the consumer’s home country; taxes on goods might be different, and exchange rates might need to be taken into account; the consumer may experience disorientation from a different cultural environment; and the attitude toward foreign products, brands, and retailers may play an important role in shopping intentions (Boeuf and Senecal, 2014). Cross-border online shopping differs from domestic online shopping (because it involves an international component) or physically international cross-border shopping (because it involves no traveling). For example, Wagner et al. (2016) define the difference between domestic and cross-border online shopping as the addition of an international component, and they emphasize that the seller must be located in a country different from the buyer’s home country. The decisive characteristics are therefore that the online purchase takes place between a retailer and a consumer from two different countries and that the purchased goods cross at least one country border when they are delivered. However, the retailer does not have to be physically located in the destination country (Qi et al. 2020). Thus, cross-border online shopping involves a greater distance dimension that must be overcome (Kim et al., 2017). Through the Internet, barriers of time and space that have arisen in the course of globalization and internationalization can be overcome (Yamin and Sinkovics, 2006). Consumers can thus shop worldwide

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without having to move, regardless of their location, and they are therefore more geographically and temporally flexible than when shopping offline. Consumer behavior is hence likely to be driven by some additional or distinct determinants that must be analyzed and understood (Boeuf and Senecal, 2014). For example, Boeuf and Senecal (2014) were able to identify seven factors on the basis of a literature analysis: the quality of the foreign offer, familiarity with and trust in foreign retailers, language differences between the buyer and seller, any costs incurred by the buyer, a fundamentally ethnocentric attitude, and susceptibility to interpersonal influences. However, to date, other authors have confirmed the actual influence of these factors only for individual aspects (Schleiden and Neiberger, 2020). In addition, research on cross-border online shopping (i.e., international outshopping performed online) is sparse, with a few noteworthy exceptions. For example, previous research has often dealt with the identification of risks that may arise in relation to cross-border consumer shopping and trust in online stores (Guo et al., 2018; McKnight et al., 2002; Lee and Turban 2001). These factors play an important role in business-to-consumer (B2C) commerce (Kim et al., 2017) and therefore receive special attention. In addition, similar phenomena are already known from national e-commerce (Chang et al., 2005) and thus represent a useful point of contact. The findings of Blum and Goldfarb (2006) revealed that consumers would rather visit shopping websites from countries that are physically close than from countries that are far away. However, their study is limited to the consumption of digital goods (e.g., music or games), whose value to consumers may differ from that of physical goods (Vendrell-Herrero et al., 2018). Cheng et al. (2008) found that three extrinsic cues (e-tailer brand equity, guarantee quality, and country of e-tailer) positively affect the e-tailer service quality perceived by foreign online shoppers, which in turn reduces their perceived cross-border online purchasing risk. A qualitative study by Safari and Thilenius (2013) analyzed consumers’ perceived uncertainty and trust when purchasing from foreign online retailers. According to their findings, consumers perceive specific uncertainties when purchasing from foreign online retailers; these uncertainties arise due to a lack of information about the legal system, specific national customer rights, language difficulties, and the complexity of product returns. Moreover, Cheng et al. (2019) and Sinkovics et al. (2007) confirm that disadvantages such as language barriers arise, for example, due to cultural inequalities between countries. Furthermore, according to a study by Lin et al. (2018), cross-border online shopping mainly creates uncertainties in the areas of finance (exchange rate losses, credit card theft, loss of personal data), logistics (long delivery times, freight and customs fees, complicated returns process), and information (unfamiliar retailer, unfamiliar product packaging, product

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conditions, and promotional materials) compared to domestic online retailing. In addition, further research suggests that the purchase intention of consumers is also positively affected by their degree of informedness (Han and Kim, 2019), assuming that information and knowledge reduce perceived uncertainties. The study by Wagner et al. (2016) is one of the first to empirically investigate cross-border online shopping behavior; however, their research is limited to only one country market. In addition to the risks, their study also investigated the benefits perceived by consumers when buying online. The authors were able to confirm that consumers value online purchasing because of the greater product selection. Cardona et al. (2015) also reported that, in addition to price advantages, the greater product variety offered by online purchasing compared with offline purchasing is the main driver for consumers increasingly switching to online retailing. In addition, Kim et al. (2017) addressed the effects and potentials of long distances in cross-border e-commerce and proposed solutions regarding how suppliers can reduce the distance perceived by consumers. In addition to psychological convergence through websites that meet consumers’ needs and the reduction of cost barriers through pricing strategies that redistribute transportation costs, they focus primarily on minimizing time barriers with the help of express delivery services. Further studies also indicate that distance generally plays a much smaller role in cross-border online shopping than it does in offline retailing (Gomez-Herrera et al., 2014). Table 3.1 lists the most important studies in the field of cross-border ecommerce, as well as research that significantly shapes the understanding thereof: In summary, a variety of research approaches exist in the field of cross-border e-commerce. However, as digitization and globalization continue to advance, new factors are constantly playing an important role that must also be investigated. To date, few studies have been conducted in the comparatively young field of cross-border e-commerce and cross-border online shopping; therefore, there is still a great need for research. As explained above, some factors influencing cross-border online shopping have already been identified in research, but empirical verification is often lacking. Another research gap results from the fact that aspects of cultural influences on cross-border online shopping behavior have hardly been investigated, if at all.

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Table 3.1 Cross-Border E-Commerce Literature Overview Author

Topic

Blum and Goldfarb (2006)

Exploration of the relevance of the law of gravity in the case of digital goods consumed over the Internet

Boeuf and Senecal (2014)

Development of a conceptual framework that identifies online outshopping antecedents

Cardona et al. (2015)

Investigation of consumer perceptions of cross-border e-commerce in the EU Digital Single Market

Chang et al. (2005)

Identification of areas that aid in the development of a better understanding of the dynamics of a customer’s decision to shop online

Cheng et al. (2008)

Examination of whether extrinsic cues affect purchase risk at international e-tailers and the mediating effect of perceived e-tailer service quality

Cheng et al. (2019)

Development of a talent training model for cross-border e-commerce from the perspective of enterprises

Gomez-Herrera et al. (2014)

Investigation of the drivers of and impediments to cross-border e-commerce in the EU and whether distance still matters for online trade in physical goods

Guo and Wang (2009)

Exploration of cross-border outshopping determinants and the mediating effect of outshopping enjoyment

Guo et al. (2018) Exploration of sellers’ trust and risk of chargeback fraud in cross-border e-commerce Han and Kim (2019)

Investigation of the role of information technology use for increasing consumer informedness in cross-border e-commerce

Hawes and Lumpkin (1984)

Examination of the characteristics and mobility of “outshoppers”

Herrmann and Beik (1968)

Exploration of shoppers’ movements outside their local retail area

Jian Wang et al. (2010)

Investigation of Chinese consumers’ international outshopping motives from a cultural perspective based on Hofstede’s four cultural dimensions

Kim et al. (2017) Examination of the distance effects on cross-border e- commerce and the importance of express delivery in reducing the time dimension of distance Kumar Velayudhan (2014)

Understanding of influences on the prevalence of rural retailing institutions of periodic markets and outshopping

LaForge et al. (1984)

Identification of factors attracting consumer outshoppers (continued)

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Table 3.1 (continued) Author

Topic

Lau et al. (2005)

Investigation of Chinese cross-border shopping behavior

Lee and Turban (2001)

Testing of a trust model for consumer Internet shopping

Lee et al. (2009)

Investigation of the relationship between consumer outshopping-related characteristics and the preference for Internet shopping

Lin et al. (2018)

Examination of service justice as the antecedent factor of dysfunctional customer behavior in cross-border e-commerce and how it interacts with negative emotions and service dissatisfaction

McKnight et al. (2002)

Validation of measures for a multidisciplinary, multidimensional model of trust in e-commerce

Piron (2002)

Examination of the international shopping behavior and attitudes of Singaporeans in neighboring Malaysia and the influence of demographic and retail characteristics on outshopping

Qi et al. (2020)

Investigation of companies’ motivations for selecting cross-border e-commerce as a foreign market entry mode

Safari and Thilenius (2013)

Understanding of consumers’ foreign online purchasing process through a study of their perceived uncertainty and trust when purchasing from foreign online vendors

Schleiden and Examination of the extent to which the increase in ecologically Neiberger (2020) oriented sensitivity has an impact on a consumer’s decision to make a cross-border online purchase Sharma et al. (2018)

Extension of the concept of customer perceived value to the tourist outshopping context and exploration of the differences in the antecedents and outcomes of customer perceived value between cross-border and international outshoppers

Sinkovics et al. (2007)

Investigation of cultural adaptation in cross-border e-commerce by building on Hofstede’s and Hall’s cultural framework

Sullivan and Kang (1997)

Exploration of the information sources and motivational attributes of Canadian cross-border shoppers

Sullivan et al. (2012)

Exploration of Mexican national cross-border shopping and retail tourism

Vendrell-Herrero Investigation of the role of cross-border strategic alliances and the et al. (2018) centralization of expertise decisions in enhancing product-service innovation in MMNEs Wagner et al. (2016)

Investigation of the determinants and moderators of consumer’s cross-border online shopping intentions

Yamin and Sinkovics (2006)

Exploration of the effects of online internationalization on the psychic distance perceptions of internationalizing firms

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Theoretical Foundations and Hypotheses

3.1.3.1 Conceptual Model To understand the drivers and barriers of consumers’ cross-border online shopping decisions and thus to address our first research question, we draw on Expected Utility Theory, which states that decision makers choose between risky or uncertain prospects by comparing the values of their expected utility (Fishburn, 1970). Customers’ anticipation of gains is primarily a result of expected benefits related to cross-border online shopping. Losses, or the sacrifices associated with obtaining the expected benefits, are mainly determined by the risks of cross-border online shopping. Since online shoppers are renowned for being motivated to maximize their benefits and minimize risk, both the perceived benefits and the risks of cross-border online shopping are expected to play important roles in explaining cross-border online shopping behavior (Forsythe et al., 2006). Consumer Culture Theory (CCT) serves as the basis for introducing further determinants and moderators of the aforementioned relationships and to answer our second research question. CCT refers to a conglomeration of theoretical perspectives addressing the dynamic relationships among consumer actions, marketplaces, and cultural meanings (Arnould and Thompson, 2005). In the CCT context, cosmopolitanism has been investigated with regard to its role in shaping consumer goals (Thompson and Tambyah, 1999). In line with this perspective, we explain the relationship between cosmopolitanism and cross-border online shopping behavior following Consumer Acculturation Theory, which explains how consumers acquire skills and knowledge that affect their behavior in a foreign cultural context (Luedicke, 2011). Due to the growing global penetration of mass media, consumers are exposed to various cultures and consumption styles. In this way, they can develop an affinity with certain foreign countries, when the culture, landscape, entertainment, people, and politics there align with their home country (Nes et al., 2014). However, in the context of this study, a cosmopolitan consumer may even be described as “an open-minded individual whose consumption orientation transcends any particular culture, locality or community and who appreciates diversity including trying products and services from a variety of countries” (Riefler and Diamantopoulos, 2009). We hence expect that cosmopolitanism affects one’s intention to shop online across borders. Additionally, according to CCT, foreign traveling represents a relevant factor because experiences with foreign countries may broaden the mind of the consumer with regard to international marketplaces (Cao et al., 2014). For consumers, foreign traveling is often linked to shopping purposes, thereby enhancing

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experiences of country-specific products, foreign brands, or local retailers (Kinley et al., 2012). Therefore, the more a consumer travels to foreign countries, the more likely it is that their perceptions of the risks of shopping abroad decrease, while perceived benefits are likely to become more obvious (e.g., price differences, availability of exclusive products). We consequently assume that foreign traveling might be another relevant determinant of cross-border online purchases. While some authors consider foreign traveling as a prerequisite for cosmopolitanism (e.g., Riefler et al., 2012), others distinguish cosmopolitans from travelers (e.g., Cleveland et al., 2014). In this context, Hannerz (1990) notes that foreign travelers may be regarded as mere spectators of a host country, while cosmopolitans represent active participants in a foreign culture. Moreover, consumers may be cosmopolitans, even without traveling to foreign countries, for example through the influence of global media such as music, books, television, or the Internet (Craig and Douglas, 2006). Following this reasoning, we consider foreign traveling and cosmopolitanism as two independent constructs. With regard to international relations, consumer ethnocentrism is often regarded as an important factor, influencing consumer purchasing decisions. The construct is rooted in Attitudinal Theory (Fishbein and Ajzen, 1977) and describes the beliefs held by consumers that the purchase of foreign products is unpatriotic and harmful to the domestic market (Shimp and Sharma, 1987). In general, consumer ethnocentrism tendencies are seen as contradictory to cosmopolitanism tendencies (Zeugner-Roth et al., 2015). For example, the findings of Cleveland et al. (2009) show that cosmopolitan consumers score low on consumer ethnocentrism. Riefler and Diamantopoulos (2009) encourage a comparison of consumer cosmopolitanism and consumer ethnocentrism in terms of their predictive power in foreign product purchases. Drawing on these thoughts, we assume that ethnocentric consumers perceive purchases at foreign online stores as morally inappropriate because such purchases could hurt the domestic economy and put fellow citizens out of their jobs. Our conceptual model, which summarizes our theoretical reasoning, is illustrated in Figure 3.1. We propose that the relationships under review represent general causal connections across the constructs. Therefore, we suggest that the general framework is valid for investigating cross-border online shopping intentions in different country markets.

3.1 A Cross-National Comparison of Consumers’ …

Foreign Traveling

H3 (+) H4a (+)

H3a (+) Perceived Benefits

H1 (+) H3b (-)

Perceived Risks

H4 (+)

Intention to Cross-Border Online Purchasing

H4b (-)

H5a (-)

H2 (-) H5b (+) Ethnocentrism

Controls: Age, Gender, Income, Online Affinity

Cosmopolitanism

53

H5 (-)

Figure 3.1 Conceptual model for investigating cross-border online shopping

3.1.3.2 Hypotheses Development In contrast to perceived benefits, Expected Utility Theory assumes that consumers will generally refrain from making risky decisions if the expected benefits are not large enough (Fishburn, 1968). Therefore, expected risks are assumed to negatively influence purchase decisions (Forsythe et al., 2006). Furthermore, in line with the online shopping literature on domestic online shopping perceptions, benefits and risks are seen as important antecedents of consumers’ online shopping behavior (Bhatnagar and Ghose, 2004; Forsythe et al., 2006). Nevertheless, in the field of cross-border online commerce, only a small number of empirical studies have been conducted regarding perceived benefits and risks. However, empirical findings confirm that online shoppers who perceive more benefits from online shopping will purchase more online than those who perceive online shopping as less beneficial (Rohm and Swaminathan, 2004). Conversely, consumers who perceive more risks associated with online purchases will be deterred from online shopping (Forsythe et al., 2006). A cross-national study by Zeng and Hao (2016) also demonstrates that consumers’ purchase intention is

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influenced by the formation of perceived value, which is defined by consumers’ perceived risk and perceived quality. On the one hand, Cross-Border online purchases are somewhat different from domestic online purchases and appear to be partly motivated by the perception that a foreign retailer’s offering is superior in comparison to that of local (online) retailers (Boeuf and Senecal, 2014). Moreover, economic factors, such as saving money, are relevant determinants for making purchases abroad (Piron, 2002). On the other hand, consumers have less knowledge and less control over the purchase situation when shopping online in foreign countries. In general, their perceived level of risk increases when making purchases abroad (Jian Wang et al., 2010). With regard to cross-border online shopping, additional risks become relevant (e.g., risk and uncertainty with regard to taxes in the foreign country, import customs, exchange rates, or communication). Furthermore, a study by Wagner et al. (2016) empirically investigated the influence of perceived benefits on the purchase decision of consumers in Germany. The study examined the online search and purchase behavior of consumers who already had experience with online shopping and identified perceived advantages, such as product variety and exclusivity, as stimulating factors of cross-border online retailing. In addition to perceived benefits, the study investigated the influence of perceived risks on both the purchase decision and the intention to conduct online searches in crossborder online commerce. Similar findings are presented in a study analyzing the factors influencing Taiwanese consumers’ cross-border online retail purchasing behavior. The results of the study confirm that a greater number of perceived benefits increases one’s intention to engage in cross-border online shopping. The study also examined three aspects of perceived risk: communication costs, waiting times, and return shipping costs. The expected negative effect of these aspects on the intention to conduct cross-border online shopping could be confirmed for waiting times and return costs (Huang and Chang, 2017). Moreover, cross-border online shopping is perceived as riskier compared to traditional shopping channels, with information risk being a typical risk that consumers face in this context. For example, online stores are unreliable, meaning that the information provided by online stores is incomplete, or online sellers intentionally misrepresent information, taking advantage of customers who do not understand foreign languages to realize their own interests (Liao 2002). In summary, perceived benefits are expected to be positive predictors of future intentions to visit and purchase from an online retailer abroad, while perceived risks are expected to be negatively related to future intentions to purchase online across borders (Forsythe et al. 2006):

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H1. H2.

55

The perceived benefits of cross-border online shopping have a positive effect on one’s intention to make cross-border online purchases. The perceived risks of cross-border online shopping have a negative effect on one’s intention to make cross-border online purchases.

Following CCT, we expect several direct and moderating effects. First, foreign traveling, also referred to as international traveling, for work or pleasure—hence indicating consumers’ international experience (Samiee et al., 2005)—is relevant as a determinant of consumers’ elaboration processes when deciding to shop online across borders. In particular, foreign traveling positively enhances consumers’ perceptions of foreign brands, products, and retailers, thus also making them more attractive to consumers (Samiee et al., 2005). Furthermore, Cao et al. (2014) theorize and empirically validate that foreign traveling increases one’s sense of generalized trust, leading consumers who travel to also trust foreign retailers more than those who do not travel, which again is likely to positively affect consumers’ intention to buy from a foreign online retailer. In addition, a strong relationship between the frequency of foreign traveling and overall trust has been confirmed, strengthening consumers intention to participate in crossborder activities the more often they travel. However, greater knowledge and experience of foreign brands, products, and retailers due to traveling is not only likely to (directly) foster one’s intention to make cross-border online purchases, but also likely to (indirectly) influence the relevance of perceived benefits and risks on one’s cross-border online shopping intention. In other words, consumers’ travel experience not only directly and positively influences their overall intention to participate in cross-border e-commerce activities, but also plays a role in how they evaluate and assess the influences of perceived benefits and risks of crossborder e-commerce on their intention to participate in cross-border activities. This evaluation of the effects of perceived benefits and risks on cross-border intention is shaped by consumers’ travel experiences. Although it is impossible for every travel experience to be positive, people with affirmative travel experience manage to significantly reduce uncertainty, since they learn how to deal with new and challenging situations with every travel experience (Cao et al., 2014). Thus, consumers who travel regularly gain additional knowledge and skills that they can use and which support them in new, unfamiliar situations abroad. This means that unforeseen risks that influence their cross-border e-commerce intention can be minimized from the beginning and handled more competently, or at least, the deterrent effect of those risks on consumers who frequently travel can generally be reduced (Williams and Baláž, 2013). Furthermore, consumers’ personal experience abroad can create familiarity with foreign products and manufacturers,

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reducing their evaluation of risks on future cross-border e-commerce activities. This confidence among consumers allows them to focus more on the benefits of their travel experience, such that the higher the foreign traveling experience is, the stronger the effect of perceived benefits is on the intention to conduct crossborder activities. Conversely, the higher the foreign traveling experience is, the lower the effect of perceived risk is on this intention. Therefore, it is believed that foreign traveling can not only directly influence consumers’ cross-border ecommerce intention, but also mitigate the effect between risks in cross-border e-commerce and one’s intention to participate in such commerce. Moreover, it can foster an effect between the benefits of and the intention to participate in cross-border e-commerce. In summary, we thus suppose the following: H3.

Foreign traveling has a direct, positive effect on one’s intention to make cross-border online purchases, and it (a) increases the effect of perceived benefits and (b) decreases the effect of perceived risks on that intention.

Cosmopolitan consumers attribute a higher value to novel consumption experiences and choose from the range of products that the world has to offer (Askegaard et al., 2005). Cosmopolitan consumers are understood as consumers who are open to cultural exchange and actively seek contact with other cultures, as well as appreciate the diversity of cultures (Hannerz, 1990). The term cosmopolitanism refers to the attitudes, beliefs, and characteristics of specific individuals (Cleveland et al., 2009). Evidence from consumer culture acculturation research indicates that cosmopolitanism is an important factor in the adoption of new consumer cultures (e.g., Hofstede, 1984; Arnould und Thompson, 2005), which can result from transnational structures and globalization, among other things. This adaptation is reflected in consumers’ behavior through their preference for foreign products. Thus, cosmopolitanism is expected to have a direct, positive influence on purchase intention in cross-border online shopping (Thompson and Tambya, 1999). Moreover, Riefler and Diamantopoulos (2009) confirm that cosmopolitan consumers have a higher preference and exhibit higher purchase tendencies for foreign products than non-cosmopolitan consumers. Since cosmopolitan consumers are more curious about and experienced with foreign brands, products, and retailers than their non-cosmopolitan counterparts, they are more informed about the “true” relevance of making cross-border online purchases. Zeugner-Roth et al. (2015) investigated the influence of consumer cosmopolitanism on product evaluation and purchase intention by surveying Austrian and Slovenian consumers. Participants were asked about domestic as well as Italian products. The results revealed that cosmopolitanism has a positive

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influence on the product evaluation of foreign products in Austria and on the purchase intention of foreign products in Slovenia. The effect of cosmopolitanism on cross-border shopping intention was further investigated in a study by Parts and Vida (2013), in which consumer groups in Estonia and Slovenia were empirically examined. Significant findings pointed to a positive influence of cosmopolitanism on purchase intention in cross-border retailing. Therefore, it is implied that cosmopolitan consumers tend to consume foreign products more than non-cosmopolitan consumers. In addition, other studies suggest that a cosmopolitan consumer attitude positively influences consumption intention (Wagner et al., 2016; Cleveland et al., 2009; Dubois and Duquesne, 1993). Therefore, a positive relationship is also assumed between cosmopolitanism and cross-border online shopping intention. Next to this hypothesized direct effect between cosmopolitanism and consumers’ cross-border online shopping intention, a moderating effect of cosmopolitanism is also assumed, following a study by Tran (2020) that investigated the moderating effect of cosmopolitanism between online trust and consumers’ purchase intention. Since the level of cosmopolitanism affects consumers’ attitude toward life, it also influences their perception and evaluation of the antecedents and relationships with which they are confronted. Therefore, cosmopolitanism may be associated with a shift in one’s evaluation of the benefits and risks associated with cross-border online shopping, meaning that the influence of the perceived benefits and risks on cross-border purchase intentions is assessed and influenced depending on consumers’ cosmopolitanism. By preferring foreign goods, cosmopolitan consumers may also be inclined to perceive the benefits of cross-border e-commerce even more strongly and the risks less so, due to their high affinity for foreign products (Riefler and Diamantopolous, 2009). This could mean that the more cosmopolitan consumers are, the more positively they will evaluate the influence of perceived benefits in cross-border e-commerce on their intention to buy from a foreign online retailer. Moreover, the influence of perceived risks on their intention to buy from a foreign online retailer is reduced due to their cosmopolitanism and their accompanying focus on cultural benefits. Thus, the general assessment of the benefits and risks of cross-border e-commerce on cross-border purchase intention may be shaped by consumers’ cosmopolitan attitudes. Therefore, we propose the following: H4.

Cosmopolitanism has a direct, positive effect on one’s intention to make cross-border online purchases, and it (a) increases the effect of perceived benefits and (b) decreases the effect of perceived risks on that intention.

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Ethnocentrism, in contrast, seems to trigger the opposite effect. Consumer ethnocentrism functions as a normative construct that implies a domestic country bias (Fischer and Zeugner-Roth, 2017). Ethnocentric consumers prefer domestic over foreign products, as they deem the consumption of foreign products to be immoral (Zeugner-Roth et al., 2015). An ethnocentric attitude is therefore characterized by the rejection of foreign cultures and the strong expression of one’s own culture (Cleveland et al., 2009; Shimp and Sharma, 1987). Specifically, consumer ethnocentrism means that consumers evaluate the purchase of imported products as problematic because it could harm their own economy, could promote unemployment, and is generally unpatriotic (Shimp and Sharma, 1987). Consumers with strong ethnocentric attitudes thus prefer local products over imported items even if they themselves experience an economic disadvantage as a result (Cleveland et al., 2009). Several studies demonstrate that ethnocentrism has a negative effect on consumers’ attitude toward and intentions to purchase foreign products (Shankarmahesh, 2006). In addition, it can be observed that consumer ethnocentrism, as well as cosmopolitanism, exert different influences on consumers depending on whether they come from developed or developing countries (Jin et al., 2015). Piron (2002) investigated the relationship between international outshopping and ethnocentrism and subsequently introduced the term “shopping ethnocentrism,” which refers to one’s ethnocentric tendencies regarding outshopping. He concluded that consumers with high ethnocentrism may prefer to shop locally. Thus, although ethnocentric consumers acknowledge the benefits of foreign products, it is presumed that they would rather choose a domestic option. In contrast, risks are perceived more strongly when buying foreign products (Herche, 1992). Zeugner-Roth et al. (2015) tested the influence of consumer ethnocentrism on both product evaluation and the intention to purchase foreign products. The authors found a negative influence of ethnocentrism on that intention among Austrian consumers. Furthermore, another study examined the behavior of Malaysian consumers in cross-border shopping. It explored, among other things, the effect of ethnocentrism on the evaluation of foreign products and found a significant negative effect (Kuncharin and Mohamed, 2013). Thus, it can be concluded that an increase in ethnocentric tendencies leads to a poorer evaluation of foreign products, resulting in a lower intention to purchase them. However, similar to cosmopolitanism, ethnocentrism is a general consumer worldview that influences consumers’ perception and evaluation of relationships. Therefore, ethnocentrism is expected to have not only a direct, negative effect on one’s purchase intention in cross-border online shopping, but also an indirect effect on the relationship between perceived benefits and risks on the one hand and one’s intention to shop from a foreign online retailer on the other. For example, Yen (2018) shows that

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ethnocentrism positively moderates the relationship between perceived quality and one’s intention to buy domestic products. Following these results and considering that perceived quality can be deemed a perceived benefit, an opposite effect can be expected for considerations in cross-border e-commerce, such that ethnocentrism a) negatively moderates the relationship between perceived benefits and one’s cross-border online shopping intention and b) positively moderates the relationship between perceived risks and this intention. Thus, on the one hand, an aversion to foreign products reduces the influence of the existing benefits of cross-border e-commerce on one’s intention to buy products from foreign retailers, as the positive influence of these benefits is weakened by ethnocentrism. On the other hand, the effect of the perceived risks of foreign products, which can be observed on the intention, is additionally reinforced by ethnocentric attitudes. In summary, ethnocentric attitudes can be assumed to have a direct, negative effect on one’s cross-border online shopping intention because ethnocentric attitudes promote the purchase of traditional, local products (Cleveland et al., 2009), while ethnocentric attitudes also influence how consumers evaluate and perceive relationships. This therefore indicates that consumers who display an ethnocentric attitude would refrain from cross-border online shopping, overrating the risks and underrating the benefits thereof. We thus postulate the following: H5.

3.1.4

Ethnocentrism has a direct, negative effect on one’s intention to make crossborder online purchases, and it (a) decreases the effect of perceived benefits and (b) increases the effect of perceived risks on that intention.

Empirical Study

3.1.4.1 Procedure and Sample To test the hypotheses and to account for cross-national differences, we conducted an empirical study in two European countries: Germany and Romania. The comparison of two countries is of particular relevance in cross-national research to allow for a systematic validation of concepts and measurements in different environments and thus to make a possibly better statement about the general validity of the results (Chabowski et al., 2016). We chose Germany and Romania for our study for several reasons. First, both countries are members of the European Union. They are hence part of the European Single Market, which creates similar terms and conditions for consumers’ cross-border online shopping within and outside of the European Union. Second, there are relevant differences in market

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size, economic conditions, and market development that could affect cross-border online shopping behavior. Third, according to the International Monetary Fund (2017), Germany is an advanced economy, while Romania is still an emerging market, characterized by a significantly lower level of economic development; thus, differences in consumer perception may occur between those two countries (Morgeson et al., 2015). A further comparison of both country markets is presented in Table 3.2. Table 3.2 Comparison between Germany and Romania Germany

Romania

Source

Population (2020)

83.1 million

19.3 million

Eurostat (2021)

Median age (2020)

45.9

42.8

GNI (2019, Q4)

891,942 US$

56,882 US$

Internet users (2020)

95%

85%

Online shoppers (2020)

83%

38%

Online sales (2020)

83.3 billion e

3.6 billion e

Ecommerce News Europe (2020)

Import of goods and services (2019)

41.1% of GDP

44.2% of GDP

The World Bank (2021)

Exports of goods and services (2019)

46.9% of GDP

40.4% of GDP

We generated data for our main study using two content-identical online questionnaires (one in the German language to address German consumers and one in the English language to address Romanian consumers). We applied the translation-back-translation procedure to guarantee translation adequacy (Chidlow et al., 2014). The questionnaires were distributed via e-mail and social networks to potential respondents. In total, we obtained data from N = 409 consumers (228 from Germany and 181 from Romania). Before analyzing the data, a data cleaning procedure was employed. First, the data sets of all participants with a short processing time were eliminated because this indicated little engaged behavior with regard to reading and answering all questions. Furthermore, plausibility checks were conducted; for example, the variances of the answers of constructs that contained reverse-coded items were investigated, and the data of participants whose answers contained variances of zero among all items were eliminated. Lastly, we

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omitted the data of consumers whose residence and nationality differed, as they would have included consumers with another temporary place of residence (e.g., expatriates) and therefore potentially diverse domestic country markets. After this data cleaning procedure, a data set of N = 316 consumers (56% female, Mage = 31.79 years, 175 German and 141 Romanian) could be included in the further analysis. While 68.4% of the respondents had already made a cross-border online purchase, 28.5% had not yet done so, and 3.2% indicated that they did not know if they had ever purchased online across borders. In our further analyses, we focused only on respondents who already have experience with cross-border online shopping, since only these respondents are able to adequately assess the associated benefits and risks (Wagner et al., 2016).

3.1.4.2 Adaption and Development of Measures A measure of validation and model testing were conducted using SmartPLS 3 (Ringle et al., 2015). We mainly relied on established multi-item scales from previous studies that we identified and modified to fit the context of our study. Each construct was measured using a seven-point Likert scale. However, especially with regard to the perceived benefits and perceived risks of cross-border online shopping, we could not rely solely on established scales. Therefore, we generated our survey data based on an adaptation of the former measurement instruments following a multistep procedure. We first investigated relevant online shopping, outshopping, and cross-border literature to understand the potential drivers, barriers, and moderators of cross-border online shopping intention. While relying on former research, we had to adapt existing measurement instruments to measure the perceived benefits and risks of cross-border online shopping. Therefore, to validate, select, and extend these scales to our analyses, we additionally held semi-structured interviews with 12 experts (managers, politicians, and jurists) with knowledge on cross-border online shopping and 20 online shoppers (with and without cross-border shopping experience). We then conducted an online pretest with 125 undergraduate exchange students from different countries to assess the reliability and comprehensibility of our adapted scales, thereby confirming our measurement instruments. The items to measure perceived benefits and risks were adapted and extended from the measures of Bhatnagar and Ghose (2004), Forsythe et al. (2006), and Chen et al. (2002). In total, 16 items were derived and pretested to cover financial benefits; benefits related to product selection, exclusiveness, service, and the comfort of cross-border shopping; enjoyment; and convenience aspects. The formative scale for measuring the perceived risks of cross-border online shopping, after pretesting the instrument, resulted in 18 items, comprising financial or

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payment-related risk, shopping risk, transaction risk, product risk, legal risk, and communications-related risk aspects. We validated the formative scales according to the recommendations by Diamantopoulos and Winklhofer (2001) and checked for collinearity of the formative indicators by analyzing variance inflation factors (VIFs). None of the indicators revealed severe multicollinearity problems (see Table 3.3). Table 3.3 Formative measurement instruments Formative instruments (Seven-point Likert scale: 1 = strongly agree, 7 = strongly disagree)

VIF

Mean Values (Standard Deviation)

Germany Romania Germany

Romania

Perceived benefits of cross-border online shopping Online shopping abroad is low-priced.

2.190

2.465

3.68 (1.40) 3.60 (1.80)

Products in foreign online stores are offered at lower prices than they are in domestic/local stores.

1.984

3.243

3.30 (1.43) 3.04 (1.62)

Because of the exchange rates, it is more valuable to shop in foreign online stores.

1.328

1.555

4.03 (1.44) 4.06 (1.69)

I can get all products that I need in foreign online stores.

2.005

1.875

4.51 (1.56) 3.70 (1.87)

Foreign online stores offer me everything 2.254 that I need.

2.764

4.07 (1.66) 3.55 (1.64)

Foreign online stores offer a very wide selection of products.

1.756

1.851

2.74 (1.31) 2.31 (1.30)

At foreign online stores, I can get all the products that are relevant to me.

1.802

2.976

3.52 (1.38) 3.58 (1.72)

Foreign online stores offer exclusive brands/products.

1.656

1.774

3.22 (1.34) 3.24 (1.66)

Foreign online stores offer very good services.

1.472

1.849

4.05 (1.20) 2.98 (1.42)

Shopping abroad online helps me to differentiate myself from others.

1.673

1.579

5.01 (1.79) 3.98 (1.80)

When I shop in foreign online stores, I 1.767 can purchase products that nobody else in my country has.

2.416

3.70 (1.79) 3.20 (1.74)

Shopping abroad online is convenient.

2.183

3.71 (1.49) 3.13 (1.60)

2.314

(continued)

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63

Table 3.3 (continued) Formative instruments (Seven-point Likert scale: 1 = strongly agree, 7 = strongly disagree)

VIF

Mean Values (Standard Deviation)

When I shop in foreign online stores, I can often try something new.

1.664

2.272

3.88 (1.52) 2.94 (1.63)

It is fun to shop in foreign online stores.

2.000

1.794

4.21 (1.37) 3.38 (1.54)

Online shopping abroad is useful.

2.026

2.262

3.88 (1.50) 2.71 (1.31)

When I shop in foreign online stores, I can save time.

1.558

1.560

4.46 (1.60) 3.45 (1.92)

Germany Romania Germany

Romania

Perceived risks of cross-border online shopping When online shopping abroad, I would be afraid that… … payment-related problems might occur.

2.378

3.206

3.72 (1.67) 3.48 (1.71)

… my preferred payment method might not be an option.

1.693

1.930

4.22 (1.66) 3.79 (1.89)

… my credit data might not be safe.

2.521

3.401

3.51 (1.78) 3.63 (1.89)

… I might not receive the products that I ordered.

2.362

3.561

3.34 (1.66) 3.50 (1.84)

… my personal data might not be secure.

2.218

3.979

3.65 (1.68) 3.73 (1.84)

… legal problems might occur.

1.966

2.098

3.21 (1.63) 4.22 (1.69)

… I might not be able to make warranty claims.

2.850

2.928

2.76 (1.51) 3.11 (1.80)

… I might be charged additional fees (e.g., for payment transactions).

1.824

2.177

2.99 (1.69) 3.21 (1.70)

… delivery might take too long.

1.649

1.625

2.63 (1.51) 2.48 (1.41)

… delivery to my address might not be possible.

1.613

1.711

4.29 (1.84) 3.65 (2.00)

… customer service might not understand 3.148 me.

2.035

4.07 (1.77) 4.19 (1.86)

… I might not be able to contact customer service at the necessary time.

2.537

2.828

3.35 (1.75) 3.43 (1.76)

… communication problems might occur. 3.040

2.384

4.22 (1.88) 3.53 (1.70) (continued)

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Table 3.3 (continued) Formative instruments (Seven-point Likert scale: 1 = strongly agree, 7 = strongly disagree)

VIF

Mean Values (Standard Deviation)

Germany Romania Germany

Romania

… placing an order might be complicated. 2.460

1.772

4.49 (1.82) 4.81 (1.71)

… that products are counterfeits.

2.594

2.195

3.54 (1.80) 3.41 (1.69)

… that fraud will happen.

3.260

2.534

3.41 (1.68) 3.80 (1.76)

… that I’m not allowed to order specific products from abroad.

1.566

1.756

4.15 (1.85) 4.05 (1.84)

… that return of goods is not possible / too expensive.

2.851

2.697

2.37 (1.53) 2.78 (1.68)

Cross-border online purchase intention, cosmopolitanism, ethnocentrism, and foreign traveling were measured using established reflective scales. To capture cross-border online purchase intention, we adapted Pavlou’s (2003) scale, which consists of three items, to the context of our study. Cosmopolitanism was measured using the five-item scale developed by Cleveland et al. (2014). To capture foreign traveling, we applied Cleveland and Laroche’s (2007) five-item scale (but dropped one item when refining the scale). For measuring ethnocentrism, we used Cleveland et al.’s (2009) four-item scale. With regard to these scales’ reflective indicators, we assessed their unidimensionality using exploratory factor analyses, and we evaluated the measurement model’s internal consistency. An average variance extracted (AVE) of not less than .664 for all reflective scales, Cronbach’s alpha of .834 and above, and a composite reliability (CR) of .889 and above are all satisfactory and reflect high levels of scale consistency (see Table 3.4). Table 3.4 Reflective measurement instruments Reflective instruments (Germany/Romania) (Seven-point Likert scale: 1 = strongly agree, 7 = strongly disagree)

Outer Loadings Germany

Romania

Foreign Purchase Intention (Cronbach’s Alpha = .953/.845, CR = .969/.905, AVE = .913/.762) Given the chance, I intend to visit foreign online stores to make purchases.

.959

.826 (continued)

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Table 3.4 (continued) Reflective instruments (Germany/Romania) (Seven-point Likert scale: 1 = strongly agree, 7 = strongly disagree)

Outer Loadings Germany

Romania

Given the chance, I expect to purchase items from foreign online stores in the future.

.959

.871

I will likely purchase items in foreign online stores.

.948

.919

Ethnocentrism (Cronbach’s Alpha = .916/.865, CR = .941/.884, AVE = .799/.664) One should not buy from foreign online stores, because this hurts domestic businesses and causes unemployment.

.927

.919

It is not right to purchase from foreign online stores, because .898 it puts countrymen out of jobs.

.969

One should always buy products from domestic online stores. .833

.674

We should purchase from online stores in our home country instead of letting other countries get rich off of us.

.646

.916

Cosmopolitanism (Cronbach’s Alpha = .925/.928, CR = .942/.946, AVE = .765/.778) I like to observe people from other cultures to see what I can .864 learn from them.

.725

I am interested in learning more about people who live in other countries.

.830

.904

I enjoy exchanging ideas with people from other cultures and .911 countries.

.921

I like to learn about other ways of life.

.889

.899

I enjoy being with people from other countries to learn about .877 their unique views and approaches to life.

.943

Foreign Traveling (Cronbach’s Alpha = .840/.834, CR = .891/.889, AVE = .671/.670) I prefer spending my vacations outside the country that I live .866 in.

.741

While vacationing, I prefer staying in my home country rather than visiting another country (reversed).

.815

.709

I often think about going to different countries and doing some travelling.

.794

.914

Visiting foreign countries is one of my favourite things.

.800

.891

Note: Outer Loadings derived from SmartPLS 3.

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We assessed all reflective scales for discriminant validity by applying Fornell and Larcker’s (1981) criterion, indicating that discriminant validity should not be a problem because no construct shares more variance with any other construct than with its own indicators (see Table 3.5 and Table 3.6) (Tables 3.7 and 3.8). Table 3.5 Discriminant validity assessment and inter-construct correlations: German sample (N = 175) FPI

Foreign Traveling

Cosmopolitanism

FPI

.913

Foreign Traveling

.020

.671

Cosmopolitanism

.058**

.185**

.765

Ethnocentrism

.165**

.272

.007

Ethnocentrism

.799

Note: Squared correlations are shown below the diagonal, AVEs on the main diagonal (bold); FPI = Foreign purchase intention; * if p < 0.05, ** if p < 0.01, *** if p < 0.001.

Table 3.6 Discriminant validity assessment and inter-construct correlations: Romanian sample (N = 141) FPI

Foreign Traveling

Cosmopolitanism

FPI

.762

Foreign Traveling

.047

.670

Cosmopolitanism

.050*

.072**

.778

Ethnocentrism

.005

.071*

.052*

Ethnocentrism

.664

Note: Squared correlations are shown below the diagonal, AVEs on the main diagonal (bold); FPI = Foreign purchase intention; * if p < 0.05, ** if p < 0.01, *** if p < 0.001.

Table 3.7 Corrlelation table for the German sample (N = 175) FPI FPI Foreign Traveling Cosmopolitanism Ethnocentrism

Foreign Traveling 1

.104

.104

1

.240** −.406***

.409*** .067

Cosmopolitanism .240**

Ethnocentrism −.406***

.409***

.067

1

−.083

−.083

1

*significant at p < .05; ** significant at p < 0.01; *** significant at p < .001

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Table 3.8 Corrlelation table for the Romanian sample (N = 141) FPI FPI

Foreign Traveling 1

Cosmopolitanism

Ethnocentrism

.227*

.223*

−.072

Foreign Traveling

.227*

1

.234*

−.077

Cosmopolitanism

.223*

.234*

1

−.228*

Ethnocentrism

−.072

−.077

−.228*

1

*significant at p < .05; ** significant at p < 0.01; *** significant at p < .001

3.1.4.3 Method To test our hypotheses, we employed partial least squares (PLS) structural equation modeling because of its ability to present both formative and reflective latent constructs (Jarvis et al., 2003) and because it is more suitable in the case of small sample sizes (below 250) (Reinartz et al., 2009). Assessing the applicability of frameworks developed in one country to other countries (i.e., accounting for measurement invariance) is an important step in establishing the generalizability of consumer behavior research (Steenkamp and Baumgartner, 1998). To test for measurement invariance, we followed Henseler et al.’s (2016) three-step procedure, which accounts for configural invariance (i.e., equal parameterization and way of estimation), compositional invariance (i.e., equal indicator weights), and the equality of composite mean values and variances. Configural invariance requires that the same basic factor structure exists in all groups (in terms of number of constructs and items associated with each construct), which is this case in our data. To account for compositional invariance, we applied the MICOM (measurement invariance of composite models) procedure (5,000 permutation runs), which was implemented in the SmartPLS 3 software (Ringle et al., 2015). With a value of .547, perceived benefits showed the lowest c value (original correlation value) of all composites in the model. The permutation test substantiated that none of the c values are significantly different from one (p > .05), as can be seen in Table 3.9. We could therefore conclude that compositional invariance was established for all composites in our model. Having accounted for configural and compositional invariance in Steps 1 and 2, we could compare the path coefficients of German and Romanian consumers using a multigroup analysis. Finally, in Step 3, we assessed the composites’ equality of mean values and variances across country groups. We found that no full measurement invariance was established. The permutation test results revealed that the mean value (M-Diff.Benefits = .308, p < .05; M-Diff.Cosmopolitanism = .375, p < .05; M-Diff.Ethnocentrism = .269, p < .05) of some composites in the

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German sample did significantly differ from the results in the Romanian sample. We consequently could not analyze a total model using the pooled data of both country groups. Table 3.9 Correlation values for compositional invariance

Original Correlation (c value) Perceived benefits of cross-border online shopping

0.547ns

Perceived risks of cross-border online shopping

0.726ns

Foreign purchase intention

0.999ns

Foreign traveling

0.972ns

Cosmopolitanism

0.998ns

Ethnocentrism

0.997ns

ns = not significant

The evaluation of both the antecedents and the outcome measures in the model stem from the same person, which might produce a common method bias (Podsakoff et al., 2003). In our questionnaire, we included a marker variable that is conceptually independent from the latent variables in our study. We chose the item, “Winter is getting colder and colder every year,” as it is theoretically unrelated to the constructs of our model. The item served as a proxy for common method variance. We included the marker variable in a structural model containing all substantive variables as a common method factor, following the suggestion of Podsakoff et al. (2003). The marker variable was included as a latent variable that directly affects every other variable in the model. The modified model shows a consistent pattern of results, with only marginal changes in path coefficients and no changes in significance levels as compared to the model without the marker variable. In our model, we controlled for the effect of several variables related to respondents’ demographics (i.e., age and gender) as well as shopping-relevant aspects such as monthly income (measured on a six-point scale, 1 = less than 500 EUR, 6 = 4,000 EUR and more) and online shopping affinity (share of online shopping compared to shopping in physical stores). As we could only obtain partial measurement invariance, we calculated separate models for the German and the Romanian sample and performed multigroup analysis (Awanis et al., 2017). Only experienced cross-border online shoppers were included in these models (N =

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69

136 for the German sample, N = 80 for the Romanian sample). We applied bootstrapping procedures (5,000 samples) to assess the significance of the parameter estimates because PLS makes no distributional assumption. In PLS, the objective is prediction versus fit (Fornell and Cha, 1994); therefore, offering a general conclusion on overall goodness of fit for the PLS model is controversially discussed (Dijkstra and Henseler, 2015; Henseler et al., 2014). However, we applied SmartPLS 3, which provides a number of model fit criteria. All model fit criteria (see Figure 3.2) indicate adequate model specification for both models (Dijkstra and Henseler, 2015). The R2 values of the intention to purchase across borders and the Stone-Geisser criterion (Q2 values > .233), which assesses the predictive relevance of the models, indicate an adequate model specification for all calculated models (Hair et al., 2012).

3.1.5

Results

The results of a multigroup analysis to compare the path coefficients of the German and the Romanian samples revealed no significant differences regarding any of the paths (p > .1). With respect to H1, our data supports a positive, significant impact of perceived benefits on consumers’ intentions to make purchases online across borders for both the German and Romanian samples (see Figure 3.2). The effect of perceived benefits was higher in the Romanian sample than in the German sample, even though the impact was not significantly different. In addition, the impact of perceived risk on participants’ intention to make cross-border purchases was high and negative in both samples, thus supporting H2. The negative effect of perceived risk on the intention to engage in cross-border online purchasing was higher in the Romanian sample than in the German sample, albeit again not significantly different between samples. Our results thus show that for consumers in an emerging country market, in our case Romania, the impact of both the benefit and risk perceptions on one’s intention to engage in cross-border online purchasing is higher than in more advanced economies, as in our German sample. Comparing the impact of risk and benefit perceptions, however, suggests another difference: In the German sample, benefits were of significantly higher relevance in forming intentions to make cross-border online purchases, and Germans thus seem to be rather driven by temptation and opportunity seeking, whereas in the Romanian sample, both perceived risks and benefits were almost of equal importance. Therefore, even though both countries are part of the European Union and hence subject to similar import conditions, country-specific and cultural differences seem to drive risk in emerging countries such as Romania.

3

German Sample: Only Experienced Cross-Border Online Shoppers (N=136)

Cosmopolitanism

Foreign Traveling

.032ns

-.041ns Perceived Benefits

-.103ns

Intention to Cross-Border Online Purchasing

-.146ns

R²=.461

.378*** -.009ns

Perceived Risks

.214***

-.030ns

-.299*** -.041ns Ethnocentrism

-.152***

Essays

Age=-.050ns, Gender=.040ns, Income=..069ns, Online Affinity=-.007ns

70

Romanian Sample: Only Experienced Cross-Border Online Shoppers (N=80)

Cosmopolitanism

Foreign Traveling

.050ns

-.028ns Perceived Benefits

.072ns

Intention to Cross-Border Online Purchasing

-.109ns

R²=.555

.413*** .036ns

Perceived Risks

.237***

.112ns

-.419*** -.052ns Ethnocentrism

-.083ns

Age=.034ns, Gender=.013ns, Income=-.032ns, Online Affinity=-.053ns

Model Fit: SRMR=.081; d_ULS=9.682; d_G=4.204; NFI=.689; rms Theta=.134

Model Fit: SRMR=.092; d_ULS=12.517; d_G=8.621; NFI=.555; rms Theta=.179 Note: *** path coefficients significant at p ≤ .01 if t-value ≥ 1.96 (two-tailed), ns = not significant; Bootstrapping procedure: 5,000 samples.

Figure 3.2 Results of PLS SEM for German and Romanian sample

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71

With regard to the moderating effects, since interestingly not supporting our hypotheses H3a/b, H4a/b, and H5a/b, the effects of perceived benefits and perceived risks were not moderated by foreign traveling, cosmopolitanism, or ethnocentrism. The evaluation of benefit or risk perception was thus not subject to influences of those variables that draw from CCT, neither for the Romanian nor the German sample. However, two of these variables exerted a direct influence on consumers’ cross-border online purchase intentions. For both samples, foreign traveling, which offers individuals opportunities to outshop in other countries while being away from home, did not have any effect on consumers’ cross-border online purchasing intention. Hence, we must reject H3. Cosmopolitanism exerted high and positive impacts in both the samples, thereby supporting H4. Cosmopolitanism in both emerging and advanced economies thus seems to drive consumers’ cross-border online purchasing intention directly instead of performing moderating effects on risk and benefit evaluation. Interestingly, even though ethnocentrism showed no moderating influence in both samples, in the German sample, it had a significant and negative effect on intentions to make purchases online across borders. Even though the Romanian and German samples did not differ significantly with regard to the level of ethnocentrism (ethnocentrism: MGermany = 5.23, MRomania = 4.96, p = .160), the impact of ethnocentrism differed: The effect was negative and significant in the German sample, but not significant in the Romanian sample. Nevertheless, as our multigroup analysis indicates, we cannot confirm significant differences between these paths across both samples. With regard to the effect of our control variables, none of the variables yielded a significant influence, neither in the German nor in the Romanian sample. Overall, the comparison of the two samples highlights the stability of our model with regard to different consumer nationalities in cross-border online shopping.

3.1.6

Discussion

By exploring the determinants of individuals’ decision to shop online across borders, a comprehensive understanding of their corresponding buying behavior is gained. However, while most previous research has focused on the perceived risks of cross-border online shopping, our findings demonstrate that the perceived benefits of cross-border online shopping have a positive influence on one’s crossborder online purchase intention. This seems to hold true both for emerging and for advanced countries (i.e., Romania and Germany in the case of our study). Consumers seem to value the specific benefits that online stores abroad offer (e.g., a wide product selection or exclusive brands). Therefore, foreign online retailers

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should primarily increase the value of their products and services such that consumers can perceive more benefits when they shop at these stores. The more benefits consumers perceive for a particular shopping channel, the greater their willingness to buy will be. These results are consistent with previous research findings, such as those by Shergill and Chen (2005), who found a strong effect of advantage regarding online purchasing on one’s intention to shop online, as well as with the findings of Topalo˘glu (2012), showing that utilitarian and hedonistic benefits are the main drivers to participate in e-commerce. With regard to perceived risks, we found a negative, significant effect on crossborder online purchasing intention for both samples. This finding indicates that the perceived risks of cross-border online shopping create barriers to cross-border online purchasing. The risk perception seems to be of slightly higher importance in emerging markets (i.e., in Romania), with a stronger significant effect than the perceived benefits, thus indicating that in emerging markets, consumers seem to feel more vulnerable when it comes to making cross-border online purchases. One reason for this could be that Romanian consumers primarily shop on domestic websites and rarely order from foreign online retailers (PayU, 2020); therefore, they have less experience with and trust in foreign online shops. Unfortunately, our study could not cover country-specific risk and thus risk that does not relate to the cross-border aspects of online shopping but that is linked to country-internal aspects, such as social or political risk in emerging countries (Giambona et al., 2017). These, however, might explain the higher relevance of risk in such countries. Future studies could address this issue by examining the impact of individual risk and benefit categories per country when they are not considered as an index or bundled construct, as in this study in the form of perceived advantages and disadvantages. This deeper investigation could provide further clarification, especially for emerging markets, regarding the behavioral intention of consumers who, according to the present results, perceive a particularly stronger influence of risks in cross-border e-commerce. In addition, consumers from emerging countries still have low experience with cross-border shopping activities, and consumers in the specific country that we included in our study (i.e., Romania), despite being part of the European Union, have much less experience with open markets compared to more advanced countries. Nonetheless, our results indicate that for Romanian consumers, the utility of cross-border online shopping makes dealing with the corresponding risks worthwhile. With regard to the influence of the examined perceived benefits and risks on one’s cross-border e-commerce shopping intention, it can thus be stated that for consumers, the search for, among other things, the lowest price, the largest selection, and the highest exclusivity seems to be a reason to buy from foreign

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rather than domestic retailers. For the benefits to be visible to consumers, online retailers must hence first and foremost reduce the risks to consumers because these risks can diminish the positive effects of the benefits and reduce the benefits of foreign products (Chen and Dubinsky, 2003). According to our analyses of the moderating effects of foreign traveling, cosmopolitanism, and ethnocentrism on one’s intention to make cross-border online purchases, none of the relationships was moderated by these variables in either of the samples. However, further studies should be conducted to explore the precise impact of cosmopolitanism, as well as that of ethnocentrism and foreign traveling. The results also show that, contrary to the hypotheses formulated, consumers’ foreign travel experience has no direct influence on their intention to buy from a foreign online retailer. Thus, no influence of foreign traveling on cross-border e-commerce could be observed for the German or the Romanian sample. Nevertheless, findings suggest direct influences of cosmopolitanism for both samples and, only for the German sample, a direct effect of ethnocentrism on cross-border online purchasing intentions. Accordingly, a cosmopolitan attitude among consumers leads to a higher cross-border online shopping intention. Comparing this finding with the study by Wagner et al. (2016), which states that the cosmopolitanism of experienced online shoppers increases their online search for products, it can additionally be concluded that a cosmopolitan attitude, related to a high interest in and curiosity about other cultures and international interchange seems to foster cross-border online purchasing intentions. This effect is opening up important avenues for further retailer strategies in internationalizing their online stores: It highlights that cosmopolitan consumers can easily be attracted to foreign online stores, and their openness to other cultures expands perspectives for intensifying the conversion of store visitors (e.g., for obtaining information) to store customers. Furthermore, the right consumer segmentation can be helpful for online retailers (Jayawardhena, 2007), for example to target consumers with cosmopolitan tendencies. The influence and significance of ethnocentrism have hardly been considered empirically in the context of cross-border e-commerce research to date, but they are mainly included in theoretical considerations (Boeuf and Sénécal 2014). Therefore, the results of this study offer added value for research and indicate the high relevance of ethnocentrism, at least for German consumers, who are representative of an advanced nation in this study. With regard to cross-border online shopping intention, a negative correlation with consumer ethnocentrism could be observed. This means that the more pronounced a person’s ethnocentrism is, the lower their intention is to make a cross-border online purchase. Managers and

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companies should be aware of this and develop appropriate measures to give ethnocentric consumers convincing arguments to participate in cross-border online shopping. Possible incentives for ethnocentric consumers could be the benefits to domestic and local retailers when ordering across borders, for example by establishing cooperation between foreign and domestic online shops. Here, too, further research must be conducted on the part of academia to evaluate the influence of specific factors, such as language and cultural barriers, especially for ethnocentric consumers. These findings could then help reduce both internal and external barriers and expand consumers’ consumption options to their advantage.

3.1.7

Conclusion and Implications

Through technological advancements and ongoing globalization, cross-border online shopping is gaining relevance for consumers (in the form of a global market place) and for retailers (in the form of a worldwide customer base), not only in more advanced but also in emerging countries. Our study contributes to this relevant topic by investigating the drivers of and barriers to consumers’ intention to make cross-border online purchases, as well as by examining the moderators affecting these relationships. In particular, we combine knowledge from international marketing, outshopping, and e-commerce research to derive and empirically validate the benefits and risks specific to cross-border online shopping, as perceived by the consumer. To advance the understanding of the “cross-border online shopper,” we compared customers from an advanced country (i.e., Germany) with customers from an emerging market (i.e., Romania). The insights from this study entail implications that are relevant for marketing theory and retail management. In particular, the findings help one to understand the perceived benefits and risks of cross-border online shopping. Moreover, the findings constitute a helpful instrument for future research in the area of international online shopping, and they form a foundation for retail managers to understand the trade-offs that consumers face when considering making cross-border online purchases. With regard to our sample, both in the German and Romanian samples, the majority of respondents (68.4% overall) has already shopped online across borders, indicating that this type of shopping is the norm rather than the exception. It is likely that global competition will reinforce the pressure on local (online) retailers and offer opportunities for a worldwide business model for retailers that will develop their online stores to address the needs of cross-border online shoppers (Boeuf and Senecal, 2014).

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Further implications can be derived from the impact of additional influential factors (i.e., cosmopolitanism and consumer ethnocentrism). Cosmopolitanism increases the probability of consumers visiting online stores abroad to search for product-related information and to make purchases. However, consumer ethnocentrism opposes this relationship. Our results for the German sample indicate that the tendencies toward nationalism, populism, and fostering home-country purchases, which are unfortunately observable across nations, also seem to challenge consumers’ openness and willingness to globalize their purchases. Consumers appear to be illogical in their buying behavior—cosmopolitan on the one hand, yet ethnocentric on the other. This might be one of the most important challenges for the future. Retailers will need to consider this when devising their international strategies. For example, in markets where consumer ethnocentrism plays an important role, a retail brand being locally responsive with regard to international markets or striving to achieve the appearance of a local company might be a more suitable strategy than emphasizing its global aspects. Our study, however, has a number of limitations that should be addressed in future research. With regard to our scales, we did not differentiate between the diverse types of perceived benefits and risks that comprise our formative scale. In our sample, respondents who indicated that they had never shopped online abroad might have done so without recognizing it, and similarly, selfproclaimed cross-border shoppers might have shopped from a domestic online retailer. However, we controlled for this fact by asking respondents to name the online store and domain of their last and most frequent purchases abroad. Future research might examine transaction data that entails information about the residence of shoppers, thus aiding in identifying actual cross-border online shoppers. In this study, we did not differentiate between online marketplaces and independent online stores. International market places, such as Amazon, eBay, and Rakuten, allow foreign retailers to sell to a domestic market, which makes it more difficult for customers to recognize whether they are purchasing from an online retailer in their home market or abroad. Even though our study of cross-border online shopping is, by definition, related to international marketing research, our data was collected in only two country markets, both part of the same area of economic integration (i.e., the European Union). Both country markets differ significantly in terms of the level of economic development, especially with regard to consumer income levels. However, we did not include cultural variables in our study to analyze further differences. We also did not consider the respective domestic online shop structures and options, which could also influence the demand for offers from foreign online retailers. For example, traffic on Romanian independent online websites is comparatively low, while Romanian consumers

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mainly shop on domestic marketplaces (PayU, 2020). Thus, a preference for foreign online marketplaces compared to foreign independent online stores could be suspected, based on domestic online store structures and shopping behavior. Moreover, a lack of availability of local options could strengthen consumers’ interest in foreign online retail choices, thereby increasing their cross-border ecommerce purchase intention. In addition, further studies have found an impact of country image on one’s intention to purchase (e.g., Wang et al., 2012). Therefore, the image of the country in which a foreign online retailer is located might also play an important role in consumers’ cross-border purchase behavior and should thus be investigated. In our study, we could hint at some differences between the observed countries; however, the main effects remained stable across both countries, only indicating some small differences. Therefore, while we suppose that our results hold stable across countries, further research should explore and compare the drivers of and barriers to cross-border online shopping across other and a higher number of countries and cultures to ensure stability or to elaborate on differences.

3.2

Development of a Motivation–trust–vulnerability (MTV) Framework for Cross-Border Online Shopping: A Cross-national Application to Chinese and German Consumers

3.2.1

Introduction

Fostered by globalization and the increasing digitalization of markets, crossborder e-commerce has become a recent development in international business. When looking at the turnover of cross-border e-commerce, which amounted to 137 billion euros in Europe in 2018 and is estimated to reach 245 billion euros in 2022, the steadily growing success as well as the growth potential of crossborder online commerce becomes clear (Cross-Border Commerce Europe, 2019). This is also confirmed by comparing the behavior of German Internet users in the period from 2017 to 2019. While 56% of those surveyed in 2017 already stated that they used cross-border e-commerce, this figure had already risen to 71% two years later (PwC, 2019). Moreover, the global Covid-19 pandemic and the resulting temporary closures of many brick-and-mortar stores, as well as travel restrictions, have contributed not only to the overall increase in the importance of e-commerce, but also to the rise in cross-border e-commerce sales in particular (Global-e, 2020).

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In this research, we aim to investigate and explain the causal mechanism of how benefits motivate cross-border online purchasing (intention, behavior and satisfaction) despite the perceived vulnerability in uncertain foreign online shopping environments. From a theoretical perspective, we develop a framework that provides an explanation of the mechanism that links the main independent variable to the dependent variable and then explain which interaction variables modify this mechanism (Andersson et al., 2014). In particular, this research contributes to international business knowledge by answering the following research question: What is the underlying mechanism that steers cross-border online purchasing behavior, and can this mechanism be observed across countries? By addressing this research question, our study contributes to developing international business theory in three ways. First, by drawing on the motivation–ability (MA) framework (Merton, 1957), we develop and test a motivation–trust–vulnerability (MTV) conceptual framework, providing details about the mechanism that motivates cross-border online purchasing. By investigating this mechanism, our research focuses on the arrows (effects) and the determinants (constructs), i.e., we clarify how and under what conditions perceived benefits motivate cross-border online purchasing (Thomas et al., 2011). Second, we introduce a new conceptualization of the consumer vulnerability, adding a new component to international business knowledge. As Shultz and Holbrook (2009) suggest, this conceptualization uses lack of knowledge and lack of skills as two underlying dimensions of vulnerability. Therefore, we propose and explain why, in the context of cross-border e-commerce, vulnerability can be promoted by the lack of ability by avoiding common evaluation biases when consumers should judge their own vulnerability (Jones and Middleton, 2007). Third, we account for cross-national applicability by testing our model in two large e-commerce country markets (Germany and China). This cross-national investigation provides a first indication of the cross-national applicability of the MTV framework. Moreover, as the practical contribution of this study with regard to international business management, we illustrate the interrelationships of cross-border e-commerce motivation, based on perceived benefits, and trust towards foreign online vendors (two dimensions that can be directly influenced by firms’ marketing activities) with the perceived vulnerability of cross-border online shopping (an external dimension that arises from the uncertain environment of cross-border e-commerce). We also discuss how retail managers and policy makers can cope with the vulnerability of cross-border online shoppers and perform trust-building activities.

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In answering the research question, particular importance is attached to consumer vulnerability. While global market surveys suggest that cross-border online shoppers are motivated to digitally cross country borders for specific benefits, such as better prices and more choice when shopping with foreign online vendors (PayPal, 2018), in the context of cross-border e-commerce, the international component also creates additional risks, compared to domestic online shopping, which can promote consumer vulnerability. While vulnerability, originating from the disciplines of philosophy and social policy, is decribed as a consequence of harm caused by external actions (Goodin, 1986), an alternative definition of the term can be found in sociology. Here, vulnerability is defined as the inability to protect oneself against potential threats that could affect one’s life, development, or well-being, indicating that vulnerability is influenced not only by external factors, but also by internal ones. Despite the differing definitions of the term, there is consensus in the literature regarding the implications of vulnerability. According to this, it generally describes a condition in which a person can be harmed or is inhibited from achieving his or her goal due to physical, mental, or monetary limitations (Browne et al., 2015). Consumer vulnerability exerts a relevant influence on the strategic direction of marketing efforts. For example, vulnerable consumers exhibit lower trust towards the market and its products and services. Since trust in foreign online vendors seems to be already lower compared to domestic online vendors, e.g. due to less consumer experience and security, consequences like lower purchase intentions may arise (Roy and Sanyal, 2017), weaking cross-border e-commerce. This consumer vulnerability does not only influence consumption behavior, but also has financial, physical and psychological consequences. The consumers are neither aware of which goods and services have a positive influence on them (Cui and Choudhury, 2003; Burden, 1998; Brenkert, 1998) nor which products are harmful to them (Smith and Cooper-Martin, 1997; Ringold, 1995). Not consuming certain goods can also lead to decreased well-being on the part of the consumer (Cartwright, 2015; Burden, 1998). Overall, it can be seen that consumer vulnerability is not only a problem for consumers themselves, but also comes with negative societal and economic consequences. In addition, consumers are not always aware of their vulnerability, even if they rate their own skills and knowledge low. Thus, it is also important to distinguish between actual and perceived consumer vulnerability. In summary, this results in the need to better understand consumer vulnerability as well as to reduce it. Although even in domestic e-commerce, when consumers are not familiar with the retailer and a higher level of perceived risk und uncertainity is natural and to be expected, however, this reduction of vulnerability proves to be complex, especially in international markets which are

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influenced by various different legal, economic, administrative, or cultural obstables compared to the consumer’s home market. Compared to domestic online shopping, to date, cross-border online shopping is still highly unregulated, and a formal legal system to protect potential cross-border online shoppers is lacking (BEUC, 2017). This higher lack of regulation and consumer protection creates additionally uncertainties and increases vulnerability, creating extra barriers to cross-border e-commerce and inhibiting cross-border online transactions. Additionally, the general increasing complexity of market activities, as it is the case with cross-border e-commerce, seems therefore to increases the vulnerability of the consumers involved (Brennan et al., 2017). Therefore, knowledge about the interaction between the causes and inhibitors of cross-border e-commerce transactions is relevant for the activities, strategies, and decision-making processes of multinational vendors and policy makers. Taking this into account, we argue that cross-border e-commerce differs from domestic e-commerce with regard to how perceived benefits motivate and additional uncertainties inhibit purchasing from foreign online vendors. Therefore, the causal mechanism that explains online purchasing behavior must be reevaluated for cross-border e-commerce. While marketing research provides knowledge about the motivation to shop online (albeit limited to domestic markets) and international business research provides an understanding of the motives of firms to cross borders to conduct business (e.g., Brouthers et al., 2016), the motivation of consumers to cross borders for online purchasing is underresearched. Since the strength of international business research depends on the ability to identify the main theoretical mechanisms through which the dependent variable arises, this lack of knowledge on the mechanism of cross-border e-commerce indicates an important research gap (Reeb et al., 2012). In particular, there is a lack of knowledge about how firms have to configure their internationalization strategy and marketing activities for cross-border e-commerce based on what motivates and inhibits online shoppers to make cross-border online purchases. Therefore, more precise theorizing on the mechanisms through which perceived benefits motivate cross-border online purchases is required. However, in this research, our understanding of cross-border e-commerce involves sales from a foreign online vendor who ships from outside the buyer’s home country. Thus, we differentiate cross-border e-commerce from online sales of an online retail company that targets foreign buyers via country-specific websites and has a physical and legal presence in the country of the buyer’s residence, e.g., Amazon.co.uk/.de/.fr. This distinction is important because when online buyers are actively crossing the national country border to purchase online, a number of conditions change; as a result, cross-border e-commerce involves additional

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benefits (motivation) and uncertainties (vulnerability) (Guo et al., 2018). Another reason why foreign online vendors with a stand-alone website are considered is that consumers often do not perceive and judge individual online retailers who offer their products on marketplaces, but the marketplace itself, like Amazon. A marketplace with a certain reputation also creates trust and (legal) security, from which online sellers benefit because these characterisitcs are also transferred from the intermediary, like a marketplace, to the retailer (Hong and Cho, 2011). This makes it difficult to distinguish whether a purchase was made due to the advantages of the marketplace or the foreign online vendor itself.

3.2.2

Theoretical Foundation and Hypotheses

3.2.2.1 Literature Overview and Conceptual Model Our work is rooted in and extends the previous literature on physical crossborder commerce and previous studies on national online shopping behavior of consumers. Scholars have conducted extensive research individually on each of these two domains, but in combination, they build a new research field. For a comprehensive understanding of what motivates and inhibits cross-border ecommerce, it is necessary to differentiate cross-border online purchases from physical cross-border purchases and from domestic online purchases. Scholars often study so-called “outshopping”, i.e., consumers’ purchases of goods from outside their local shopping areas (Herrmann and Beik, 1968). National business studies identify cross-border buyers (e.g., Hawes and Lumpkin, 1984) and the determinants that motivate cross-border purchasing behavior, e.g., the quality, selection, and prices of the offered goods (Kumar Velayudhan, 2014). Additionally, international business studies focus on the cross-border purchasing that occurs when consumers travel abroad to make purchases, i.e., the cross-border shopping phenomenon (Clark, 1994). For example, Sharma et al., (2018) study cross-border purchasing motivation (economic and sociopsychological), while other scholars examine determinants, demographics and/or retail characteristics in the cross-border context (Piron, 2002). Furthermore, scholars compare consumers who travel abroad for the explicit purpose of purchasing with those who buy abroad when visiting a country while on vacation (e.g., Sullivan et al., 2012). These studies show that diverse factors affect foreign (vs. domestic) shopping behaviors. Compared with domestic forms of purchasing and international physical cross-border purchasing, the perceived benefits that motivate cross-border online purchasing and the uncertainties that increase perceived vulnerability significantly differ. When online shoppers digitally cross borders,

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they can experience products and brands from other countries and cultures without physically leaving their domestic market. In addition, cross-border online shoppers can search for the cheapest global prices or the best available quality; alternatively, they can be motivated to support a foreign company by directly purchasing products at the origin (Guo et al., 2018). However, cross-border online shoppers may also experience uncertainties with regard to their knowledge (e.g., the legal terms for the import of goods) and skills (e.g., language proficiency or the handling of foreign payment services). In summary, these differences indicate that it is necessary to consider the factors that affect cross-border online purchasing and their interrelationships, i.e., the mechanism of cross-border online shopping. With regard to domestic online buy, there are a number of studies that provide knowledge about what motivates consumers to buy online in their national markets (e.g., Childers et al., 2001; Forsythe et al., 2006). These studies identify factors such as convenience and the variety and quality of products as perceived benefits that motivate online purchasing. Furthermore, these studies reveal the uncertainties of online shopping, such as uncertainties with regard to financial risks, product risks and time risks, which increase perceived vulnerability and can create barriers to online shopping intentions (Childers et al., 2001). Cross-border e-commerce differs from domestic e-commerce because it involves an international component and offers a distinct range of additional benefits and uncertainties. Clearly, more choice and more options can enhance consumers’ cross-border e-commerce motivation, but perceived uncertainties can increase when transaction partners and goods are in different countries with varying norms, rules and legal systems. Hence, compared to domestic e-commerce, the complexity of cross-border e-commerce increases, and this increasing complexity in combination with the lack of regulation and consumer protection in cross-border e-commerce suggests that consumers’ perceived vulnerability affects the mechanism of cross-border online shopping. We use Merton’s (1957) MA theoretical framework as a conceptual starting point to explain the mechanism that motivates cross-border online purchasing intentions. The original MA framework postulates that a combination of motivation and ability shapes the nature and intensity of actions (Merton, 1957). Based on the general influences of motivation and ability, scholars can apply the MA framework to determine the tendency to perform any specific behavior (Burnkrant, 1976). Therefore, scholars in various fields, including marketing (e.g., Grewal et al., 2001; Sprott et al., 2001) and international business (e.g., Bahadir et al., 2015; Minbaeva et al., 2003), adopt and apply the MA framework in their studies. In line with these studies, we adopt and extend the MA

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framework to the field of cross-border e-commerce to explain what mechanism motivates cross-border online purchasing intentions. While the mechanisms, i.e., realtionships, of the framework might be also applicable to domestic settings, the levels of the determinants used might be different in an international context. Such said, it is assumed that in cross-border e-commerce lower levels of trust, but higher levels of vulernability apply, making it especially meaningful to test for the MA framework in an international environment. According to the MA framework, motivation and ability are conceptualized as two interrelated but distinct antecedents of human behavior. Research findings suggest that the interaction of the ability to perform a specific behavior and the motivation to perform this behavior affect actual consumer behavior (Sprott et al., 2001). Research regards variables that reflect consumers’ motivation and ability as important preconditions for behavioral actions (Moorman and Matulich, 1993). In the following, we theorize and explain the selection and interrelationship of motivation, trust and vulnerability (as a proxy for reverse ability) in our framework, identify variables that reflect each of these causes and derive hypotheses.

3.2.2.2 Hypotheses Development According to the MA framework, the psychological driver that influences the degree to which an individual is inclined to perform a behavior is motivation (Rauch et al., 2015). Vroom’s (1964) expectancy theory can explain the development of the motivation to make cross-border online purchases. Expectancy theory argues that behavior results from conscious choices among alternatives whose purpose is to maximize gains. In the context of cross-border online shopping, consumers’ anticipation of gains is primarily a result of the expected benefits related to cross-border online shopping. These benefits, like e.g., a broad selection and exclusivity of foreign brands and products, form consumers’ motivation to participate in cross-border e-commerce (Wagner et al., 2016). Previous studies widely analyze the direct effect of perceived benefits on purchasing intentions, and from the theoretical perspective, there is a consensus on the relationship (e.g., Forsythe et al., 2006). Therefore, we state that this is a well-known argument, and we assume that the relationship between consumers’ motivation, following the MA framework and based on consumers’ perception of cross-border online-shopping benefits and intended behavior, will hold for cross-border online shopping. Thus, this direct effect serves merely as our baseline hypothesis, as we focus on the less understood interaction effects (Andersson et al., 2014). Our baseline hypothesis postulates the following:

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The motivation to participate in cross-border online shopping, based on perceived benefits, increases the cross-border online purchasing (intention, behavior, satisfaction).

Drawing on the MA framework, we first elaborate the connecting relationship of ability and vulnerability and then argue why we include the perceived vulnerability of cross-border online shopping in our framework. Ability refers to the physiological and cognitive capabilities that enable an individual to perform a behavior effectively (Rauch et al., 2015). To conceptualize ability, we follow previous research that regards ability as an individual’s knowledge and capability to acquire the relevant skills to carry out a particular task (e.g., Siemsen et al., 2008). Theoretically, this conceptualization of ability and its effects is rooted in Bandura’s (1977) self-efficacy theory, which states that individuals assess whether they have the required skills or knowledge desired to achieve their goals through their beliefs about their ability to successfully perform a particular behavior. Stewart and Pavlou (2002) suggest that a consumer’s ability to complete an online transaction represents an attempt by the consumer to acquire information in a structure in which the desired information is uncertain. For cross-border online transactions, consumers need to acquire even more skills (e.g., language proficiency) and information (e.g., rights and obligations) to compensate for the additional uncertainties of international online transactions. Moreover, cross-border online shoppers need specific knowledge and skills to be able to calculate the total cost of a cross-border online transaction (which possibly includes the exchange rate, delivery costs, customs duty, or tariffs). If consumers do not possess this financial knowledge and cannot acquire these skills, they are financially vulnerable with regard to cross-border online shopping. Although a high level of knowledge and skills does not necessarily mean that consumer vulnerability is low, it can be assumed that a lack of skills and knowledge fosters consumer vulnerability. Further areas of cross-border online shopping vulnerability involve legal (e.g., asserted rights), cultural (e.g., unknown language of customer service), physical (e.g., harmful products) and privacy (e.g., weak data protection regulation) factors (Guo et al., 2018). To integrate the concepts of ability and vulnerability, we adopt the perspective of Shultz and Holbrook (2009), who state that the lack of knowledge and the lack of capabilities to acquire the relevant skills and information to perform a specific task (i.e., lack of ability), create consumer vulnerability. In particular, Shultz and Holbrook (2009) suggest a two-dimensional conceptualization of vulnerability in which consumers are doubly vulnerable if they (1) do not know what is beneficial for them and (2) do not have the skills or other resources needed

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to acquire what would benefit them. Regarding this conceptualization, vulnerability is a phenomenon whose expression is influenced by consumers’ existing or lacking abilities. But while a higher level of capability does not necessarily mean that it leads to a lower level of vulnerability, as other external influences may in turn promote it, a lower level of capability certainly leads to a higher level of vulnerability. This new conceptualization of perceived vulnerability (as reverse ability), which we introduce in this research, refers to the uncertainties of cross-border online shopping and the related vulnerability of international online shoppers. It also has another advantage: Because it is often difficult for individuals to assess their own vulnerability, the evaluation of one’s own ability instead of vulnerability should avoid social desirability bias with regard to the interpretation of vulnerability as weakness or naivety (Jones and Middleton, 2007). Moreover, we propose that perceived vulnerability will have two contradictory effects on consumer behavior: a negative direct effect on cross-border online purchasing intentions and a positive moderating effect on the relationship between consumers’ cross-border e-commerce motivation and cross-border online purchasing intentions. We assume the negative direct effect because previous research confirms that perceived vulnerability is one of the determinants that negatively influences the intention to purchase products online (Faqih, 2013). Consumers who perceive more uncertainties associated with online purchases will be deterred from online shopping (Forsythe et al., 2006). Therefore, we theorize that the more insecure consumers feel with regard to their abilities, respectively skills and knowledge with regard to cross-border online shopping, the more vulnerable they will feel and the greater the likelihood that they will refrain from cross-border online purchasing. However, the existence of vulnerability might also cause consumers to focus on their motivations to participate in cross-border e-commerce, like the perceived benefits, and repress potential negative outcomes, thus leading to an overestimation of the gains that they will obtain from cross-border online shopping (Jones and Middleton, 2007). As our theoretical argument for the positive moderating effect of perceived vulnerability on the relationship between consumers’ crossborder e-commerce motivation and cross-border online purchasing intentions, we propose that because vulnerability involves lack of knowledge and lack of skills, the perceived motivation to participate in cross-border e-commerce that is easily accessible (e.g., cheaper prices) overlay the inherent vulnerability, increasing the effect of consumers’ cross-border e-commerce motivation on their intentions. This effect is due to an imbalance between gains and losses in the cognitive weighting mechanism (Kahneman and Lovallo, 1993). In particular, potential losses are not perceived because they are not known (because of no previous experiences or

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information), or they are cognitively repressed as a strategy of dissonance reduction (Harmeling et al., 2015). Therefore, we conclude that vulnerable consumers, i.e., individuals who have less knowledge and skills with regard to cross-border online shopping, might be more motivated by perceived benefits because they directly experience certain advantages of cross-border online shopping without having the knowledge or the skills to assess the uncertainties. We can model the proposed relationship as a moderating (two-way interaction) effect in which the impact of consumers’ cross-border e-commerce motivation on cross-border online purchase intentions is strengthened when the level of vulnerability is higher. In summary, keeping in mind that the lack of consumer ablitity and knowledge promotes consumer vulnerability, we propose the following: H2.

Perceived vulnerability has (a) a negative direct effect on cross-border online purchasing (intention, behavior, satisfaction) but (b) positively moderates the relationship between consumers’ cross-border e-commerce motivation and cross-border online purchasing (intention, behavior, satisfaction).

As research on Internet shopping argues that shopping online inherently involves higher levels of uncertainty than does shopping at a physical store, we argue that cross-border online shopping involves higher levels of uncertainty than does domestic online shopping (Lim et al., 2004). In online environments in which vendors’ true intentions are especially difficult to assess, trust is a crucial antecedent of behavioral activities (Bleier and Eisenbeiss, 2015). Potential crossborder online shoppers often lack information on the foreign online vendor (e.g., whether the store is legitimate, what payment service is available or whether the products offered are genuine or counterfeit). When information about foreign online vendors is lacking, trust serves as a key foundation on which online shoppers base their purchase decisions (Urban et al., 2009). Therefore, in the uncertain environment of cross-border e-commerce, trust functions as a catalyst for transactions, as it reduces perceived risks (Pavlou, 2003). Consequently, we theorize that consumers who generally trust foreign online vendors tend to be more tolerant of a higher level of uncertainty when transacting with foreign vendors, which should positively affect their intention to make cross-border online purchases. While we control for the above postulated direct effect of trust towards foreign online vendors, our focus is on the interaction effect of trust and perceived vulnerability on the relationship between consumers’ cross-border e-commerce motivation and cross-border purchase intentions, i.e., we postulate a three-way interaction in which trust moderates the moderating effect of perceived vulnerability. Our argumentation for this three-way interaction effect derives from the theoretical

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rationale of the relationship between trust and vulnerability. Trust is a way of dealing with vulnerability such that individuals who trust still feel vulnerable but the more they trust, the less they expect to actually be harmed (Tsui-Auch and Möllering, 2010). Previous research discusses the relationship between perceived vulnerability and trust with regard to whether trust comes before or after vulnerability (Bigley and Pearce, 1998). We follow the perspective of Mayer et al. (1995), who assume that trusting individuals start from a neutral position from which they decide to increase or decrease vulnerability. In this sense, trust is not risk taking per se; rather, it is the willingness to take risk (Mayer et al., 1995). Because trust increases one’s willingness to accept uncertainties and risks based on positive expectations of the intentions or behaviors of another party, we assume that trust towards foreign online vendors increases the effect of perceived vulnerability on cross-border online purchase intentions (Rousseau et al., 1998). Accordingly, we postulate the following: H3.

Trust towards foreign online vendors has (a) a positive direct effect on crossborder online purchasing (intention, behavior, satisfaction) and (b) positively moderates the moderating effect of vulnerability on the relationship between consumers’ cross-border e-commerce motivation and cross-border online purchasing (intention, behavior, satisfaction).

Our conceptual model of the MTV framework summarizes our theoretical reasoning, as illustrated in Figure 3.3.

Figure 3.3 Conceptual model of the motivation-trust-vulnerability framework

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Empirical Study

3.2.3.1 Procedure and Sample A large number of previous studies have already shown that national culture has a decisive influence on the way consumers perceive, think and behave (e.g. Kemper et al. 2011; Mooij, 2003). Different cultural influences, especially between mature and emerging markets, also reveal institutional differences as well as differences in human skills and competencies (Anokhin et al., 2008). To test the generalizability of our framework, we therefore select two culturally and economically distinct country markets: China and Germany. One reason why China and Germany were selected is that both countries show certain differences with regard to the cultural dimensions uncertainty avoidance and individualism–collectivism (see Table 3.10), which research identifies as being the most relevant to online shopping because of their link to the willingness to accept the potential risks of online shopping and to trust unknown online vendors (Lim et al., 2004). The different degrees to which the two cultural dimensions uncertainty avoidance and individualism-collectivism are expressed according to Hofstede (2019), indicate that the determinants of the MTV framework, the motivation to participate in cross-border e-commerce, as well as trust and perceived vulnerability in cross-border e-commerce, may be assessed and perceived differently by the participants in these two countries. Thus, it can be assumed that the postulated relationships between the individual determinants are also influenced to different degrees due the respective characteristics of the two cultural dimensions. In this way, the theoretical relationships of the MTV framework can be conceptually tested with the help of two countries whose cultural dimensions differ in strength, enabling a better understanding of the framework’s general applicability. Also, in addition to political, ideological, and cultural differences, differences can also be seen between advanced and emerging country markets in terms of consumers’ expectations and preferences of an online store (Feng et al., 2004). Additionally, another reason why we selected China and Germany is that both countries are among the five largest e-commerce markets worldwide, indicating a large tendency to engage in and the relevance of online shopping (Eshopworld, 2018). In particular, there are relevant differences in market size, economic conditions, market development and culture that can lead to discriminative market factors between Germany and China and that can affect the mechanism of crossborder online shopping. A comparison between these two country markets based on various economic and cultural criteria is presented in Table 3.10.

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Table 3.10 Economical and cultural comparison between China and Germany China

Germany

Source

Population (2017)

1.4 billion

82.7 million

GNI per capita (2017)

8,690 US$

43,490 US$

The World Bank (2019)

Imports goods and services (2017)

2.2 trillion US$

1.5 trillion US$

Exports of goods and services (2017)

2.4 trillion US$

1.7 trillion US$

Median age (2018)

37.4

47.1

World Population Review (2019)

Online shoppers (2018) 72%

77%

Eshopworld (2018)

Online sales (2018)

636.1 billion US$

70.4 billion US$

Uncertainty avoidance score

30

65

Individualism score

20

67

Hofstede (2019)

We generated data for our further analyses and hypothesis testing using two online questionnaires that are identical with regard to their content (both in national languages: German and Mandarin Chinese). For this purpose, we performed translation-back-translation (from German to Mandarin Chinese), conducted by four independent coders (Chinese native speakers living in Germany with fluent German language proficiency), to guarantee translation adequacy while considering the cultural context (Chidlow et al., 2014). We distributed the Chinese questionnaires via e-mail and social networks, generating a convenience sample of Chinese online shoppers. To collect the German data, we used a local research agency online panel of adult online shoppers. Before analyzing the data, we eliminated the data sets of all participants with a processing time that is less than half the median (less than 10 minutes) because this low processing time indicates a low level of engagement with regard to reading and answering all the questions. Furthermore, we conducted plausibility checks, e.g., eliminating the data of the participants whose answers contain variances of zero among all items. We obtain a data set consisting of N = 808 consumers, of whom 452 are from Germany (51.8% female, Mage = 44.02 years, SD = 15.15) and 356 are from China (54.5% female, Mage = 28.22 years, SD = 7.72). A total of 64.2% of the German and 53.4% of the Chinese respondents have already made a cross-border online purchase. To account for differences in demographics between the Chinese and German samples, we included gender, age, income and online shopping

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affinity (number of online purchases within the last twelve months) as control variables in our analysis.

3.2.3.2 Adoption and Development of Measures We relied on established multi-item scales from previous studies that we identified and modified to fit the context of our study (see Table 3.11). To capture cross-border online purchasing intentions, we adapted Pavlou’s (2003) online purchasing intention scale, which consists of three items. To account for cross-border online purchasing behavior, we used the answer to the question (single-itemscale) of whether the respondents have made cross-border online purchases in the past. This variable is a dichotomous variable reflecting self-reported past cross-border online purchase behavior (0 = not made cross-border online purchases; 1 = made cross-border online purchases). We evaluated satisfaction with cross-border online shopping, which is a variable that indicates an outcome of the expectations and actual performance of cross-border online purchases, only for the respondents who have already made cross-border online purchases in the past. To measure satisfaction, we adapted three items (e.g., “I think that I did the right thing when I purchased at foreign online vendors.”; α = 0.77) from Cronin et al. (2000). To measure the consumers’ cross-border e-commerce motivation of cross-border online shopping, we adapted three items from Meuter et al., (2005), focusing on perceived benefits, to the context of our study. Trust is measured using a three-item scale (Yoon, 2009). Perceived vulnerability is measured as a reflective construct with a two-factor structure, with each factor consisting of three items, that encompasses perceived lack of knowledge and perceived lack of skills, as proposed by Shultz and Holbrook (2009). We adapted a measurement scale for perceived lack of knowledge and perceived lack of skills from Grewal et al. (2001) to address the lack of a compatible vulnerability scale in the literature that captures consumers’ perceived lack of knowledge and skills to successfully conduct cross-border online shopping. By measuring perceived vulnerability with items of ability dimensions, we decided to use a reverse-coded scale approach; studies usually apply such an approach to measure constructs that have negative connotations or that are difficult for respondents to assess, such as emotional stability (Cucina et al., 2019), job complexity (Morgeson and Humphrey, 2006) or role ambiguity (Rizzo et al., 1970). We measured all constructs via seven-point Likert scales. Additionally, we pretested the measurement scales via an online survey with students from both countries to assess the reliability and comprehensibility of our adapted scales; the pretesting confirmed the reliability and comprehensibility of our measurement instruments.

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Table 3.11 Measurement instruments: Factor Loadings Measurement instrument (China/Germany) (7-point Likert scale: 1 = Factor Loading strongly agree, 7 = strongly disagree) China Germany Intention to cross-border online purchase (α = 0.90/0.98, CR = 0.89/0.98, AVE = 0.74/0.95) Given the chance, I intend to visit foreign online vendors to shop online.

0.87

0.98

Given the chance, I expect to order items from foreign online vendors 0.94 in the future.

0.97

I will likely purchase items from foreign online vendors.

0.97

0.76

Satisfaction with cross-border online purchases (α = 0.83/0.92, CR = 0.90/0.95, AVE = 0.75/0.85) I think that I did the right thing when I purchased at foreign online vendors.

0.84

0.95

I am satisfied with my previous purchases from foreign online vendors.

0.89

0.88

In my opinion, foreign online vendors offer satisfactory products and services.

0.87

0.94

Consumers’ cross-border e-commerce motivation (α = 0.83/0.88, CR = 0.84/0.87, AVE = 0.63/0.70) I think purchasing from an online vendor outside my home country would be beneficial.

0.78

0.84

I think purchasing from an online vendor outside my home country can lead to good results.

0.86

0.81

I think purchasing from an online vendor outside my home country can have certain advantages.

0.73

0.85

Trust towards foreign online vendors (α = 0.92/0.94, CR = 0.92/0.95, AVE = 0.79/0.85) Foreign online vendors are reliable.

0.88

0.90

Foreign online vendors are trustworthy.

0.92

0.97

I trust foreign online vendors.

0.87

0.90

I know everything I need to know to make wise foreign online purchases. (R)

0.88

0.94

I know everything I need to know to competently purchase from foreign online vendors. (R)

0.88

0.97

Perceived vulnerability (Reverse ability) Lack of perceived knowledge (α = 0.91/0.96)

(continued)

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Table 3.11 (continued) Measurement instrument (China/Germany) (7-point Likert scale: 1 = Factor Loading strongly agree, 7 = strongly disagree) China Germany I know everything I need to know to successfully purchase from foreign online vendors. (R)

0.87

0.93

I can easily acquire all skills and information to make wise foreign online purchases. (R)

0.79

0.97

I can easily acquire all skills and information to competently purchase from foreign online vendors. (R)

0.85

0.98

I can easily acquire all skills and information to successfully purchase 0.88 from foreign online vendors. (R)

0.96

Lack of perceived skills (α = 0.88/0.98)

Note: α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted; (R) = reverse-coded; Factor loadings derived from CFA in AMOS25.

3.2.3.3 Method Reliability and Validity We investigated the dimensionality, reliability, and validity of our construct measures via exploratory and confirmatory factor analysis (CFA). The average variance extracted (AVE) values not less than 0.63 for all scales, the Cronbach’s alpha values of 0.83 and above and the composite reliability (CR) values of 0.84 and above are all satisfactory and reflect high levels of scale consistency (see Table 3.11). In addition, we assessed all reflective scales for discriminant validity by applying Fornell and Larcker’s (1981) criterion, indicating that discriminant validity should not be a problem because no construct shares more variance with any other construct than with its own indicators (see Table 3.12 and Table 3.13) (Tables 3.14 and 3.15). To conduct CFA of the measurement model on the Chinese and German datasets individually, we used the maximum likelihood (ML) estimation procedure with AMOS 25. The model yields an acceptable model fit with the sample data from China (χ2 /df = 3.71; RMSEA = 0.09; CFI = 0.95; TLI = 0.93; SRMR = 0.05) and Germany (χ2 / df = 2.58; RMSEA = 0.06; CFI = 0.99; TLI = 0.98; SRMR = 0.03). Each factor loading is statistically significant, and the standardized values are above the recommended threshold of 0.70 (Germany: 0.81 and above; China: 0.73 and above) for all items (Bagozzi and Yi, 1988).

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Table 3.12 Discriminant validity assessment and inter-construct correlations: Chinese sample (N = 356) CBOPI

MOT

TRU

CBOPI

0.86

MOT

0.52***

0.79

TRU

0.48***

0.50***

0.89

VUL

0.12***

0.19***

0.15***

VUL

0.98

Note: Squared correlations are shown below the diagonal, AVEs on the main diagonal (bold); CBOPI = cross-border online purchase intention; MOT = motivation (cross-border online shopping motivation based on perceived benefits); TRU = trust towards foreign online vendor; VUL = perceived vulnerability of cross-border online shopping; *** if p < 0.01. Table 3.13 Discriminant validity assessment and inter-construct correlations: German sample (N = 452) CBOPI

MOT

TRU

CBOPI

0.97

MOT

0.50***

0.83

TRU

0.46***

0.40***

0.92

VUL

0.46***

0.35***

0.42***

VUL

0.86

Note: Squared correlations are shown below the diagonal, AVEs on the main diagonal (bold); CBOPI = cross-border online purchase intention; MOT = motivation (perceived benefit of cross-border online shopping); TRU = trust towards foreign online vendor; VUL = perceived vulnerability of cross-border online shopping; *** if p < 0.01. Table 3.14 Correlation table for the Chinese sample (N = 356) CBOPI CBOPI MOT

1 .344***

MOT .344*** 1

TRU

.552***

.356***

VUL

.422***

.234***

TRU

VUL

.552***

.422***

.356***

.234***

1 .355***

*significant at p < .05; **significant at p < 0.01; ***significant at p < .001

.355*** 1

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Table 3.15 Correlation table for the German sample (N = 452) CBOPI CBOPI

1

MOT .774***

MOT

.774***

TRU

.691***

.720***

1

VUL

.576***

.549***

TRU

VUL

.691***

.576***

.720***

.549***

1 .575***

.575*** 1

*significant at p < .05; **significant at p < 0.01; ***significant at p < .001

Measurement Invariance We performed tests of multigroup invariance to examine the equivalence of the proposed measurement model across the two country samples. Following the procedures suggested by Steenkamp and Baumgartner (1998), we first estimated a multigroup CFA model without any restrictions on the parameters across country groups. The overall model fit is sufficient, thus supporting configural invariance (global χ2 /df = 2.52; RMSEA = 0.04; CFI = 0.97; TLI = 0.97; SRMR = 0.03). Next, we assessed metric invariance by constraining the factor loadings in the two groups to be equal and comparing this model with another model in which the factor loadings are free to be estimated across groups. The results indicate that the two samples are not fully invariant because the constrained model has a significantly higher chi-square (χ2 (20) = 197.5, p < 0.05). Therefore, we find that there is no full metric invariance (i.e., we cannot analyze a total model using the pooled data of both country groups). However, the test results indicate partial metric invariance; thus, we can estimate two independent models and compare the results (Awanis et al., 2017). Common Method Bias In our analyses, the evaluations of both the antecedents and the outcome measures in the model stem from the same person, which might produce common method bias (Chang et al., 2010). Following the suggestion of Podsakoff et al. (2003) to account for common method variance, we took several approaches and perform several tests. Ex ante, we assured the respondents of the anonymity and confidentiality of the study and indicate that there are no right or wrong answers and that they should answer as honestly as possible. Moreover, we counterbalanced the order of questions relating to different scales and constructs and randomized the order of the items in our online survey. We also included a marker variable in our questionnaire that is conceptually independent of the latent variables in our study. Specifically, we chose a variable to measure the consumer’s charity

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participation likelihood because it is theoretically unrelated to the constructs of our model. The marker variable is not significantly related to any of the variables in the model; therefore, the results of the marker variable testing provide further evidence that common method variance is not a serious problem in our study.

3.2.4

Results

Because we obtained partial metric invariance, we calculated separate models for the Chinese and German samples. To test our hypotheses, we first conducted covariance-based structural equation modeling (CB-SEM) in AMOS 25. CB-SEM provides a means of accounting for measurement error, allows comparison of nested models for hypothesis testing, and accommodates moderated moderation models. This estimation method makes it possible to test of each of the proposed moderators and to derive further insights into the moderation mechanism. Table 3.16 shows all the results of our hypothesis tests. All model fit criteria indicate an adequate model specification for both models: China (χ2 /df = 2.94; RMSEA = 0.07; CFI = 0.94; TLI = 0.92; SRMR = 0.04) and Germany (χ2 / df = 2.27; RMSEA = 0.05; CFI = 0.98; TLI = 0.98; SRMR = 0.03). In the following, we report the results for the outcome variable cross-border online purchasing intention and refer to Table 3.17 and Table 3.18 for the cross-border online purchasing behavior and satisfaction results. The R2 values of cross-border online purchase intentions for Germany (0.73) and China (0.64) indicate an adequate model specification for all calculated models. Regarding H1, our data support our baseline hypothesis that consumers’ cross-border e-commerce motivation motivate cross-border online purchase intentions for both the Chinese (β = 0.48, p < 0.01) and German (β = 0.49 p < 0.01) samples. Regarding H2a, vulnerability has a negative effect, thus inhibiting cross-border online purchase intentions for both Chinese (β = −0.25, p < 0.01) and German (β = −0.20, p < 0.01) online shoppers. Regarding the hypothesized two-way interaction effect (H2b) in which vulnerability positively affects the relationship between consumers’ cross-border e-commerce motivation and cross-border online purchase intentions, we find that the moderating effect is significant only for the Chinese sample (β = 0.11, p < 0.05); in contrast, the moderating effect is not significant for the German sample (β = 0.07, p = 0.20). Our data for both the Chinese (β = 0.33, p < 0.01) and German (β = 0.28, p < 0.01) samples demonstrate the positive direct effect of trust on cross-border purchase intentions, supporting H3a.

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Regarding the proposed three-way interaction in which trust enhances the moderating effect of vulnerability on the relationship between consumers’ crossborder e-commerce motivation and cross-border purchase intentions, we find a significant effect for the Chinese sample (β = 0.12, p < 0.01) and an even stronger effect for the German sample (β = 0.27, p < 0.01), supporting H3b. Regarding our control variables, we find only two significant effects. For the Chinese sample, income has a small effect (β = −0.07**, p < 0.05) on cross-border online purchase intentions, while for the German sample, age affects cross-border online purchase intentions (β = −0.07**, p < 0.05). Table 3.16 Effects of motivation, trust and vulnerability on cross-border online purchase intention Chinese Sample (C)

German Sample (G)

Beta Coefficient Sig.

Beta Coefficient Sig.

Hypothesis C/G

MOT → CBOPI

β = 0.48***

0.00

β = 0.49***

0.00

H1

✔/✔

VUL → CBOPI

β = −0.20***

0.00

β = −0.25***

0.00

H2a

✔/✔

MOT x VUL → CBOPI

β = 0.11*

0.05

β = 0.07

0.20

H2b

✔/✖

TRU → CBOPI

β = 0.33***

0.00

β = 0.28***

0.00

H3a

✔/✔

MOT x TRU x VUL → CBOPI

β = 0.12**

0.01

β = 0.27***

0.00

H3b

✔/✔ ✖/✔

Age → CBOPI

β = 0.04

0.25

β = −0.07**

0.01

Control

Gender → CBOPI

β = 0.04

0.19

β = −0.03

0.26

Control

✖/✖

Income → CBOPI

β = −0.07**

0.03

β = −0.03

0.28

Control

✔/✖

0.62

β = 0.00

0.93

Control

✖/✖

Online Shopping β = 0.02 Affinity → CBOPI Sample Size (N)

356

452

Note: standardized beta coefficients are shown. MOT = motivation (perceived benefit of cross-border online shopping); VUL = perceived vulnerability of cross-border online shopping; TRU = trust towards foreign online vendors; CBOPI = cross-border online purchase intention; C/G = China/Germany; *** if p < 0.01, ** if p < 0.05, * if p < 0.10.

While the proposed relationships between motivation, trust and vulnerability lead to comparable effects, fewer of these effects are significant, especially with regard to the Chinese sample (see Table 3.17 and Table 3.18). However, this finding might be a methodological issue and the result of the smaller sample sizes when we consider actual purchases (Reinartz et al. 2009).

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Table 3.17 Effects of motivation, trust and vulnerability on past cross-border online purchase Chinese Sample (C)

German Sample (G)

Hypothesis C/G

Beta Coefficient Sig.

Beta Coefficient Sig.

MOT → CBOP

β = 0.21**

0.02

β = 0.20**

0.01

H1

✔/✔

VUL → CBOP

β = −0.26***

0.00

β = −0.27***

0.00

H2a

✔/✔

MOT x VUL → CBOP

β = −0.07

0.26

β = 0.12

0.17

H2b

✖/✖

TRU → CBOP

β = 0.17**

0.04

β = 0.24***

0.00

H3a

✔/✔

MOT x TRU x VUL → CBOP

β = −0.02

0.75

β = 0.35***

0.00

H3b

✖/✔

Age → CBOP

β = −0.03

0.58

β = −0.23***

0.00

Control

✖/✔

Gender → CBOP

β = 0.18***

0.00

β = 0.00

0.95

Control

✔/✖

Income → CBOP

β = −0.07

0.16

β = 0.08*

0.05

Control

✖/✔

0.79

β = 0.03

0.45

Control

✖/✖

Online Shopping β = 0.01 Affinity → CBOP Sample Size (N)

356

452

Note: standardized beta coefficients are shown. MOT = motivation (perceived benefit of cross-border online shopping); VUL = perceived vulnerability of cross-border online shopping; TRU = trust towards foreign online vendors; CBOP = cross-border online purchase (yes/no); C/G = China/Germany; *** if p < 0.01, ** if p < 0.05, * if p < 0.10.

Moreover, we corroborate the CB-SEM analyses by conducting moderated moderation analyses using Model 3 of Hayes’ SPSS macro PROCESS (Hayes, 2018). The output of the PROCESS analyses allows us to draw more accurate conclusions about the three-way interaction effect of trust, vulnerability and motivation. As input for the PROCESS calculation, we create composite scales of each latent factor that are weighted based on each item’s factor loading in AMOS. Then, using the PROCESS Model 3 macro, we calculate bias-corrected bootstrap confidence intervals using 5,000 resamples. The results of the moderated moderation analyses using PROCESS are similar to those of the CB-SEM analysis; hence, we focus on the additional analysis methods that PROCESS offers. In particular, we want to identify regions in the range of trust in which the effect of vulnerability on the relationship between consumers’ cross-border e-commerce motivation and cross-border online purchases intention is significant. To do so, we use floodlight analysis based on the Johnson–Neyman technique (Hayes, 2018). Floodlight analysis is appropriate when the continuous moderating variable lacks

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Table 3.18 Effects of motivation, trust and vulnerability on satisfaction with cross-border online purchases Chinese Sample (C)

German Sample (G)

Hypothesis C/G

Beta Coefficient Sig.

Beta Coefficient Sig.

MOT → SAT

β = 0.17

0.21

β = 0.28***

0.00

H1

✖/✔

VUL → SAT

β = −0.26***

0.00

β = −0.26***

0.00

H2a

✔/✔

MOT x VUL → SAT

β = 0.15

0.21

β = 0.15**

0.04

H2b

✖/✔

TRU → SAT

β = 0.24**

0.04

β = 0.39***

0.00

H3a

✔/✔

MOT x TRU x VUL → SAT

β = 0.11

0.36

β = 0.12*

0.05

H3b

✖/✔

Age → SAT

β = 0.02

0.70

β = 0.10**

0.02

Control

✖/✔

Gender → SAT

β = −0.02

0.77

β = −0.01

0.70

Control

✖/✖

Income → SAT

β = 0.11*

0.07

β = −0.01

0.79

Control

✔/✖

0.46

β = −0.03

0.45

Control

✖/✖

Online Shopping β = 0.04 Affinity → SAT Sample Size (N)

190

290

Note: standardized beta coefficients are shown. MOT = motivation (perceived benefit of cross-border online shopping); VUL = perceived vulnerability of cross-border online shopping; TRU = trust towards foreign online vendors; SAT = Satisfaction with cross-border online purchases; C/G = China/Germany; *** if p < 0.01, ** if p < 0.05, * if p < 0.10.

natural values for high vs. low levels (as in the case of trust) and the intention is to overcome the arbitrariness of using standard deviation of the moderator variable, as done in the spotlight analysis (Grinstein and Riefler, 2015). Therefore, our floodlight analysis serves to identify the range(s) of trust for which the moderating effect of perceived vulnerability becomes significant. The results of the floodlight analyses are illustrated in Figure 3.4 for the Chinese sample and Figure 3.5 for the German sample. Figure 3.4 shows that for the Chinese sample, the interaction between consumers’ cross-border e-commerce motivation and vulnerability transitions (orange colored area) between statistically nonsignificant and significant when trust = −1.17 (mean trust). Above this value, there is a significantly positive two-way interaction between benefits and vulnerability. Below this value, vulnerability does not moderate the effect of benefits on cross-border online purchase intentions. Figure 3.5 shows that for the German sample, the interaction between benefits and vulnerability transitions (orange colored area) between statistically nonsignificant and significant when trust = −2.67 (mean

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trust) and trust = 0 (mean trust). Below the value of −2.67 and above the value of 0 (mean trust), there is a significantly positive two-way interaction between benefits and vulnerability. Comparing the two country samples, we find that the floodlight analyses suggest that for German online shoppers, a higher level of trust is necessary so that perceived vulnerability is compensated and increases the effect of consumers’ cross-border e-commerce motivation on cross-border online purchasing intentions. For the Chinese sample, the three-way interaction effect becomes significant even at a below average level of trust, but the effect is less pronounced compared to the German sample.

Conditional Two-Way Interaction between Vulnerability and Benfits

95% CI Upper Limit

Point Estimate

95% CI Lower Limit

Trust Note: Orange colored area indicates range when the three-way interaction of benefits, trust and vulnerability is significant.

Figure 3.4 Conditional effect of trust on interaction between benefits and vulnerability: Chinese sample (N = 356)

3.2.5

Discussion

The results of our analyses in the context of cross-border e-commerce provide empirical support for the appropriateness and cross-national applicability of the MTV framework. In particular, our findings show that—in line with expectancy theory—consumers’ cross-border e-commerce motivation build the main driver and trust towards foreign online vendors also has a positive effect. Perceived

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Conditional Two-Way Interaction between Vulnerability and Benefits

95% CI Upper Limit

Point Estimate 95% CI Lower Limit

Trust Note: Orange colored area indicates range when the three-way interaction of benefits, trust and vulnerability is significant.

Figure 3.5 Conditional effect of trust on interaction between benefits and vulnerability: German sample (N = 452)

vulnerability decreases cross-border online purchasing (intention, behavior, satisfaction). These direct effects are largely consistent across the two country samples from China and Germany and in line with previous findings on the inhibiting effects of vulnerability (e.g., Tsui-Auch and Möllering, 2010). Regarding the proposed moderation effect, the results are less consistent and challenge previous understanding of the role of perceived vulnerability. We find support for a significant moderating effect of vulnerability only for the Chinese example, while there is a small but nonsignificant positive effect in the German sample. However, the moderated moderation effect, i.e., the three-way interaction of trust towards foreign online vendors and perceived vulnerability, significantly increases the motivation to make cross-border online purchases for both country samples. In light of the findings of our floodlight analysis, it seems that vulnerability entails a reinforcing effect of cross-border online shopping behavior when a certain level of trust is present. Therefore, in line with Tsui-Auch and Möllering (2010), we find that negative effects of vulnerability can be absorbed by building trust. However, while Tsui-Auch and Möllering (2010), propose only a direct relationship between trust and perceived vulnerability, we theorize and empirically validate an interaction effect. In this regard, our findings suggest that the vulnerability construct may challenge the established relationships between key variables of

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consumer behavior and that the inclusion of perceived vulnerability as a moderator variable can result in unexpected changes in well-researched relationships. By shedding light on these relationships, the MTV framework provides more fine-grained theoretical clarity regarding the mechanism that motivates crossborder online shopping, and it offers implications for retail managers and policy makers who have to deal with uncertain situations that increase vulnerability of consumers.

3.2.6

Conclusion and Implications

Our study contributes to international business and marketing theory by extending and respecifiying the MA framework (Merton, 1957) to advance our understanding of the mechanism through which perceived benefits motivate cross-border online shopping behavior. To understand this mechanism, we go beyond the simplistic argument “it depends” and consider the underlying interactions of the relationship between cross-border online shopping motivation and intention to understand the conditions under which this relationship applies (Andersson et al., 2014). We show that for cross-border online shopping, the ability factor is reciprocally identical to the concept of vulnerability, as Shultz and Holbrook (2009) suggest, meaning that lower levels of ability equal higher levels of vulnerability. Therefore, this research also offers implications for the conceptualization of vulnerability. Here, we theorize and demonstrate the higher-order reflective structure of perceived vulnerability as a combination of perceived lack of knowledge and perceived lack of skills. Our findings make an important contribution to online shopping research by identifying that vulnerability affects cross-border online shopping in two different ways: via a direct negative effect and via an indirect positive moderating effect. With the MTV framework, we therefore extend research on domestic online shopping that focuses solely on benefits and losses (e.g., Forsythe et al., 2006), but not on the lack of knowledge and lack of skills which creates perceived vulnerability and inhibits cross-border online purchasing intentions. The introduction of a new conceptualization of perceived vulnerability into the international marketing literature and its integration in a holistic MTV framework offer a certain potential for helping to explain effectiveness in intercultural interactions. In our two initial analyses, the MTV framework demonstrates its cross-cultural applicability and the potential to explain the mechanism of cross-border online shopping. Therefore, the MTV framework appears robust across countries with different cultural and economic backgrounds. Naturally, the usefulness of this

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new framework must establish its validity in a series of further studies. Because relationships depend on particular environmental conditions, the MTV framework might be appropriate to understand further motivation mechanisms in situations that involve uncertain outcomes and high levels of vulnerability. Therefore, the MTV framework might also be applicable to further studies in international business where situations with increased uncertainty occur, for example, how the vulnerability (e.g., lacking language skills or limited knowledge about Chinese culture) of international managers operating in transition economies such as China and their trust towards the multinational corporation (MNC) affect their work motivations (Tsui-Auch and Möllering, 2010). The creation of the MTV framework is a step that is consistent with the development of a mid-range theory that links vulnerability perceptions to consumer behavior and international activities. Previous research on the topic of perceived vulnerability in international business studies is scarce and considers vulnerability mostly as an antecedent to managerial practices (Tsui-Auch and Möllering, 2010). Derived from the MA framework, the combination of motivation and ability is primarily applied in the context of the main effect and as static factors influencing desired outcomes (e.g., Bahadir et al., 2015; Grewal et al., 2001). Although scholars agree that behavior across various contexts is a function of ability (or vulnerability) and motivation, there is no general agreement on the mechanism through which these factors operate (Siemsen et al., 2008). Indeed, it is possible that these factors combine in different ways across differing contexts. In line with Minbaeva et al. (2003), we show that effects not only result from the impact of individual variables, but also from the interaction of these variables. Moreover, mixed research results suggest that high levels of ability and motivation are not always valid precursors of behaviors (Moorman and Matulich, 1993). For example, research finds that consumers who are moderately motivated and moderately able to perform some activities, perform most effectively (Bettman and Park, 1980). These mixed findings indicate that with regard to the interaction of motivation and ability, a relationship of “the more, the better” might not exist in all cases. Our conceptualization of vulnerability as reverse ability might contribute to explaining these contradictory findings to some extent and help in examining the boundaries of the MA theoretical framework (Andersson et al., 2014). A major implication of our findings for international business practice is that vulnerability may serve as an internationalization barrier to the cross-border activities of consumers that may counteract the cross-border e-commerce efforts of companies to sell to international shoppers online. In particular, our findings suggest that managers must cope with vulnerability and take measures to decrease

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vulnerability. Marketing strategies should aim to neutralize feelings of vulnerability and to take advantage of consumer trust (Bart et al., 2005). The negative impact of vulnerability on cross-border online purchasing intentions indicates a chance for online vendors to increase consumers’ willingness to make crossborder online purchases by increasing knowledge and skills. Online vendors can increase knowledge by providing relevant information, for example, about the expected costs, delivery time, and return policy. Regarding skills, cross-border ecommerce should be manageable by customers with the same set of skills that are necessary for domestic online shopping to reduce the uncertainties and barriers of cross-border online shopping. For example, online vendors should provide the same language, currency and payment services as in the domestic market of the online shopper. Doing so is important because only customers who are satisfied with their cross-border online shopping experiences will continue to buy from foreign online vendors and thus represent loyal and long-term profitable customers. Whenever, consumers are potentially vulnerable, the question arises, if marketing activities that target the vulnerable consumer group are unethical (Jones and Middleton, 2007). Firms are urged to avoid intended or unintended unethical marketing strategies for cross-border e-commerce, for example by concealing the actual place of location or displaying nontransparent delivery or return shipment costs. Moreover, retail managers are encouraged to develop a normative prescriptive framework for ethical conduct on the part of the cross-border e-commerce business that considers vulnerability of foreign online shoppers. These measures would also help to increase trust in foreign online vendors, which as our findings suggest, is necessary to strengthen the positive relationship between consumers’ cross-border e-commerce motivation and the intentions to conduct cross-border online purchases (Bart et al., 2005). That trust is important for online transactions is well-known, but its interaction with perceived vulnerability underlines the relevance of this construct for online relationship building even more. Additionally, cross-border online shopping vulnerability arises because there are almost no existing international rules or norms in place to protect crossborder online shoppers. Most existing trade agreements between countries were signed in the predigital era to cover traditional flows of goods. Because crossborder e-commerce is related to direct shipments from foreign online vendors to customers in countries abroad, there is a lack of control and influence. This situation makes it even more important for governmental institutions and policy makers to help consumers develop the necessary knowledge and skills with regard to cross-border online shopping. Such knowledge and skills can be maintained by providing relevant information and educating consumers or by developing and offering tools that support online-shoppers, such as cross-border tax and tariffs

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calculators. Moreover, policy makers can take measures to help online shoppers to access the trustworthiness of foreign online vendors, to avoid a general frivolously trusting that can result in vulnerability. Our research has some limitations, which can serve as starting points for future research. Because our study is based on just two country samples, future research must assess the generalizability of the theoretical model by applying it to further country markets. Replications of our study that use different country combinations and that capture additional motivational determinants or further moderators are necessary to establish the generalizability and robustness of our findings. For example, consumer characteristics such as consumer ethnocentrism or cosmopolitanism may also influence or moderate cross-border online purchasing intentions (Riefler et al., 2012). Additionally, our measurement of the constructs that reflect motivation, trust and vulnerability relies on self-reporting by participants and is thus subject to the criticisms leveled at all self-report measures (see Donaldson and Grant-Vallone, 2002). Further research might use other measures and techniques to operationalize the MTV dimensions. For example, research might examine transaction data that include information on the residence of shoppers, thus helping to obtain a more concrete picture of cross-border online transactions and objective vulnerability that arises in cross-border e-commerce transactions between two specific country markets without trade and legal agreements. Moreover, we focus specifically on trust towards foreign online vendors because in online transactions, the online vendor is the key actor with regard to the exchange of money for goods. However, we cover only general trust and do not focus on the brands, specific vendors or the countries of origin of online vendors. In addition, future research should consider further dimensions of trust, such as trust in technologies, institutions or service providers for payment and delivery, that are also related to cross-border online shopping.

3.3

A Qualitative Study of Consumer Perceptions and Experiences related to Voice-Commerce

3.3.1

Introduction

During the course of progressive digitization, consumers are constantly confronted with new technologies. Rapid advances in the field of communication technology and the explosive growth of the Internet of Things (IoT) make comprehensive networking possible, for example through computer-based devices. This intelligent environment offers the potential to support consumers in their

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living and consumption environment and to make associated tasks more convenient and/or efficient (Ahmed et al., 2016). As part of this development, the use of digital voice assistants is currently in particular focus. Large international companies, such as Amazon, Google, Apple, Microsoft, and Samsung, play a special role as key players and are involved in the development and expansion of digital voice assistants, such as smart speakers (e.g., Amazon’s Alexa or Google Home) or integrated voice applications in smartphones (e.g., Apple’s Siri). The main features of this new technology are elements of the human–computer interaction and the task management and service delivery that this technology can provide to consumers. They are based on software that uses a combination of artificial intelligence techniques, such as automatic speech recognition, text-to-speech synthesis, and natural language comprehension (Gaikwad et al., 2010). The aim is to conduct natural conversational interactions with humans, exclusively by speech (Hauswald et al., 2015). Digital voice assistants are often also referred to as intelligent personal assistants (IPAs) or, if they are portable, mobile assistants. IPAs are thus applications that use information inputs such as the user’s voice, images, or contextual information to help answer questions, make recommendations, and perform actions or “tasks” (Hauswald et al., 2015). The range of functions extends from query services (e.g., weather and news services) or information from search engines to instructions for action (e.g., playing music, managing one’s appointment calendar, or making calls) through to the holistic task fulfillment of complex processes, such as smart home control or the handling of product selection and ordering processes in purchasing. Digital voice assistants can be found in various forms; for example, they are integrated into smartphones and computers, or they can be found in specially designed devices (Hoy, 2018). So far, however, few studies on the application and use of digital voice assistants in online shopping and a consumption context can be found in the literature. The growing popularity of digital voice assistants is also reflected in the sales performance of smart speakers: In the first three quarters of 2017, more than 17 million smart speakers were delivered worldwide. In addition, another 16 million were sold during the Christmas season (The Guardian, 2018). This development signals a massive change in the usage and reception behavior of web content, such as online shopping websites. Experts estimate sales of 200 million smart speaker units by 2023 (IDC, 2019). Furthermore, a study by the Bundesverband Digitale Wirtschaft (2017) revealed that 56% of German consumers were already using digital voice assistants in 2017. In the shopping and service context, one third of another study’s respondents stated that they would rather use a voice

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assistant than visit a shop or bank themselves, based on a three-year perspective (Capgemini, 2018). Voice assistants have hence already become part of the everyday life of many consumers. However, with the increased usage of digital voice assistants, consumers are confronted with a new technology with which they have little experience to date. The challenges for manufacturers and retailers are to convince consumers to use the technology and to encourage them to continue using it. The number of current users and those who want to use digital voice assistants in the future is growing (Capgemini, 2018). Yet, from a consumer protection perspective, it is problematic that knowledge and research is lacking regarding the drivers and barriers that either lead consumers to use digital voice assistants or prevent them from doing so, the potential risks for consumers from possibly ambiguous communicative usage in the context of different fields of application, and the relevance and viability of such assistants. From the perspective of consumer research, it is thus particularly important to determine how consumers can realize the greatest possible benefits at the lowest possible risk, while simultaneously being supported in their sustainable consumption behavior. Using approaches from technology acceptance and technology usage research, a primary objective is to understand how consumers accept and use digital voice assistants and what influences their behavioral intentions. Especially the interactions between consumers and digital voice assistants in the context of consumption are still largely unexplored. Here, due to consumers’ special needs and their possibly lower level of competence with regard to this novel use of information and communication technologies, an increasing consumer vulnerability can be assumed compared to other e-commerce alternatives. This consumer vulnerability arises as soon as consumers cannot achieve the most optimal outcome for themselves in a situation, are disadvantaged in market events, or even suffer harm in, for example, financial or health terms (Baker et al., 2005). Such disadvantages can arise due to, among other things, external situational influences, but especially due to a lack of consumers’ knowledge and abilities (Shultz and Holbrook, 2009). In the case of using digital voice assistants for consumption and online shopping, most consumers have not yet gained any experience and thus have no knowledge about processes, potential risks, and opportunities. Voice-commerce (i.e., shopping via digital voice assistants) therefore represents a special extension of traditional online shopping and presents consumers with new challenges that they have not encountered in the past. These hurdles are favored above all by the characteristics of digital voice assistants: While digital voice assistants are integrated as an IoT element, the input and output of information is based only on sound and speech. Here, not-yet-perfect

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speech usage or only sequential processing of speech on the part of the digital voice assistant can lead to comprehension problems in the purchasing process. Thus, in addition to the personal characteristics and situational circumstances of consumers (Baker, et al., 2005), the quality of usability when utilizing digital voice assistants can also be decisive in terms of how easy or difficult it is for consumers to participate in voice-commerce. Here, too, the aspect of consumer vulnerability is relevant. If usability is poor in voice-commerce, then consumers may feel overwhelmed, and the purchasing process will not be smooth. As a result, consumers may have to focus more on completing the purchase process than on the actual task at hand, which is selecting the best possible product. How consumers deal with these new challenges and how much they influence consumer behavior allow us to make statements about consumer vulnerability and the usability of digital voice assistants in the consumption context. This is especially because perceived hurdles and risks could reduce the expected utility of voice-commerce for consumers and thus also permanently reduce their intention to use it (Fishburn, 1968). For instance, consumers would primarily order groceries, toilet paper, or pizza online via digital voice assistants, suspecting a consumer preference for less risky or habitualized products (PwC, 2019). In summary, no knowledge is available on how consumers, particularly those with different levels of experience, use digital voice assistants for online shopping and at what point they might be at risk, what data is (consciously and unconsciously) released, and whether there is actually a control of consumption, as is often discussed in the media. For example, a control of product selection by a third-party may impede consumers from making independent decisions, and it can regulate and manipulate consumer consumption. The aim here is often to achieve a greater profit for companies, although this does not always result in the optimal consumption solution for the consumer and can therefore disadvantage them, even increasing consumer vulnerability (Baker et al., 2015). Such control regulation may arise especially when using digital voice assistants. One reason for this is that only a reduced preselection of products might be presented by the digital voice assistant, and the free choice of products or the availability of certain product variants might be limited. An overview of alternative products and prices is therefore only possible to a limited extent or, in extreme cases, does not take place at all, due to the low transparency and loss of control compared to online shopping on a computer or mobile phone. However, the presentation of several products would be too complex without a screen; therefore, without preselection, it would hardly be possible for consumers to handle the mass of information that is only shared via voice.

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However, the use of digital voice assistants not only comes with disadvantages and risks, but also offers considerable advantages to consumers, especially older ones or those who are no longer able to be as physically mobile as before. For example, purchasing and decision-making processes can be simplified and made much more convenient. Depending on the preselection and configuration of the systems with which the voice assistants communicate, particularly advantageous product and choice options for consumers can also be promoted, such as sustainable or energy-efficient products. In particular, for those consumers with nutritional restrictions, digital voice assistants would enable a preselection of products meeting a defined nutritional guideline to support a healthier lifestyle. Especially if the purchasing competence is limited due to certain competence deficits or if specific vulnerabilities exist, these can be reduced by introducing the interface of digital voice assistants. With the conscious commitment to reduce one’s own private autonomy and privacy at this point, digital voice assistants can also offer individual advantages, for example with regard to health. Aspects of the hedonistic element of shopping processes should also not be underestimated. They can be promoted by voice assistants in a specific form during the shopping experience, such as through gamification elements, which are perceived as positive in this context and can be used, for example, in the context of nudging approaches, not only to promote one’s shopping experience, but also to positively influence the sustainability and advantageousness of one’s purchase and consumption decisions. This study investigates how consumers use digital voice assistants in voicecommerce and what problems arise that hinder this usage. In the context of voice-commerce, the focus is on potential implementation options as well as the interaction between digital voice assistants and consumers, since the human–computer interaction is of central importance to these intelligent systems (Hauswald et al., 2015). Here, the focus is on the use of standalone digital voice assistants, which are, for example, not integrated into a smartphone, but are primarily used as an individual device, such as Amazon’s Alexa or Google Home. This paper addresses the manufacturers and distributors (retailers) of digital voice assistants as well as policymakers and consumer protection organizations, all of whom should use the results to generate improvements in usage and preserve the consumer as an object worth protecting. The ease and speed of ordering by voice in particular suggests the potential for increased sales, as it will be even easier for consumers to order products online at any time and with minimal effort. Optimized usage and risk reduction of voice-commerce is therefore an important incentive for companies, as they would benefit from the resulting increase in

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usage and a growing new sales channel. However, the results also present interesting implications for consumers themselves, illuminating their use of digital voice assistants, especially voice-commerce, from different perspectives and revealing possible dangers and risks. Thus, the implications for all stakeholders primarily relate to reducing consumer vulnerability, which benefits not only consumers themselves, but also manufacturers and retailers in the long run. The aims of this research are to gain insight into consumers’ use of such digital voice assistants in the shopping process and to develop measures for optimization, support consumer literacy, and improve their usage. To this end, we formulated the following three research questions: RQ 1: RQ 2:

RQ 3:

What role do digital voice assistants play in consumers’ shopping and consumption behavior? How do consumers use and interact with digital voice assistants when participating in voice-commerce, and where do opportunities and threats for consumers arise? What measures can be taken to support consumer literacy and protection as well as to improve consumer usage when participating in voice-commerce?

To answer these three research questions, a review of the current literature is first provided, after which two qualitative studies are presented. Since none of the participants had yet engaged in voice-commerce at the time of study, in the first study, consumers were asked about their perceptions and expectations of the use of this type of commerce. The results of this qualitative study not only provide insight into the current general usage behavior of consumers with digital voice assistants, but above all also highlight the perceived opportunities and benefits of voice-commerce. In addition, the expected risks and disadvantages of voicecommerce in particular are identified, and optimization potentials are shown that could increase usability and reduce consumer vulnerability when shopping via a digital voice assistant. In the second study, a real purchase process with a digital voice assistant was carried out in a scenario-based setting. Here, the concrete behavioral patterns of consumers toward digital voice assistants could be determined on the basis of the individual phases of a purchase process, and the assessment of consumers’ experiences after actual participation could be divided into positive and negative. These two studies differ in their methodology and focus of investigation. On the one hand, the first study surveyed the perceptions of consumers who have not yet participated in voice-commerce and thus primarily communicate their expectations. On the other hand, the second study aimed to present the perceptions and assessments of consumers after they had engaged

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in a real purchase process with a digital voice assistant and had thus gained real experience with the technology. After the results of both studies are discussed individually, we offer a final critical discussion of the results across both studies.

3.3.2

Literature Review

Voice assistants are speech-based user interfaces that interpret human language and offer a range of applications, for example playing music and writing lists or messages (Hoy 2018). Digital voice assistants have the ability to communicate with users, process requests contextually, expand knowledge, and learn from mistakes (Smith, 2018). Hence, they serve a wide range of applications in both business and consumer contexts. Not only healthcare, the automotive industry, and customer service, but also functions such as authentication and identification as well as those in the area of smart homes are typical fields of application in which functions of digital voice assistants can be integrated (Stucke and Ezrachi, 2017). Especially in the field of ambulatory (elderly) care, intelligent systems such as digital voice assistants could offer added value and help consumers who suffer from limited mobility or have little social contact (Hellwig et al., 2018). In addition, retailers are increasingly experimenting with bringing voice assistants into physical stores to help consumers with customer service, thereby bridging a gap to commercial adoption, which is becoming the focus of potential applications for digital voice assistants. Moreover, recent studies have shown that many (online) retailers are already increasingly using digital voice assistants as an additional sales channel, for example in the cooperation between Walmart and Google Home. Furthermore, 22% of consumers who order food online already use digital voice assistants for their orders, and a growing number of household appliances, especially kitchen appliances (e.g., from the South Korean brand Samsung), are being linked to digital assistants online (GlobalWebIndex, 2018). This makes the online food industry a particularly attractive market for online shopping using digital voice assistants. This process of placing online orders via digital voice assistants is known as voicecommerce, voice shopping, or v-commerce. Voice-commerce is generally defined in the literature as a consumer’s interaction with platforms or apps that use speech recognition to enable transactions over the phone and on other devices (Harris and Dennis, 2002). Galanxhi-Janaqi and Nah (2004) define voice-commerce in more detail: Computer-based speech technologies such as speech recognition, voice identification, and text-to-speech technology help consumers to conduct

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business transactions. They see voice-commerce as a subset of e-commerce. Software reacts to a keyword and forwards the consumer’s voice to a server. This server then processes the voice as a command, interprets it, and provides the assistant with the appropriate information—uncomplicated, in real time, and anytime (Kumar et al., 2015). Voice-commerce is therefore an emerging e-commerce channel that is not limited to the transaction phase of the purchase process, but includes all commerce functions that (potentially) allow users to search for a product, take note of reviews, add items to a shopping list, track their orders, and access customer service. Voice-commerce has the potential to transform all phases of the consumer journey, from search through to automated repurchase (Munz and Morwitz 2019; Sun et al. 2019). In addition to the potential applications of digital voice assistants, some studies have also explored consumer attitudes and perceptions toward them, focusing primarily on the personality and humanity of digital voice assistants. The humanization of intelligent systems and artificial intelligence, also known as anthropomorphism, involves imitating human characteristics, such as appearance; movements; and, in the case of digital voice assistants, speech or, in other words, communication (DiSalvo et al., 2002). This humanization of voice assistants facilitates their adoption in a variety of application areas by using human-like characteristics to increase consumers’ acceptance of these systems (Fong et al., 2003). Some early studies have already shown that over time, consumers perceive intelligent systems as increasingly human, sometimes making it more difficult for them to distinguish between humans and machines (Cassell et al., 1994). Further research has investigated the effect of the personification or integration of emotions in the design of digital voice assistants (e.g., Callejas et al., 2011), creating a kind of own personality that strengthens the human–voice-assistant relationship. In this way, consumers build up more trust in digital voice assistants if they are characterized by sociability and humanity. This in turn has a positive influence on consumers’ intention to use such assistants, their credibility, and their entertainment value (e.g., Bartneck et al., 2009). Above all, the use of voice assistants leads to a significant increase in consumers’ cognitive and emotional trust when shopping online, since immediate and direct forms of communication are evaluated as more trustworthy (Qiu and Benbasat, 2005). Regarding the consumption context, the use of voice assistants is more authentic compared to traditional online shopping, as voice-guided assistants make communication in the shopping process seem more human and thus more similar to that in stationary retail (Tuzovic and Paluch, 2018). However, to date, interactions with digital voice assistants have often led to disappointment on the part of the user. This is

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because the lack of conversational competence still makes such interactions too different from a natural conversation between humans (Moore et al. 2017). Despite the adaptation of human-like communication, certain prerequisites must be met by the consumer to ensure an optimal and convenient interaction with a digital voice assistant. For example, to use such an assistant, consumers can formulate a search query freely, but they must follow certain rules when activating special so-called “skills,” such as voice apps, by executing a command according to a predefined scheme. This mixed-initiative dialogue has the advantage that, depending on the scenario, the right approach can be used to support consumers in their intentions (Hackenberg, 2013). This should afford them a certain degree of flexibility, while reducing the risk of misidentifications and uncertainties. These types of interactions can be carried out in any situation, such as cooking, shopping, and lying on the couch, or when the consumer is on the go, thus increasing the convenience compared to other devices that cannot be utilized for online shopping by using speech alone. Akhter (2015), for instance, argues that the feeling of comfort in carrying out transactions in an online environment, such as online shopping, is as important for consumers as in a stationary shop. According to Kraus et al. (2019), in the context of digital voice assistants, consumers have even higher demands and expectations in terms of convenience than with traditional e-commerce. Thus, existing literature mentions the aspects of convenience, simplicity, and efficiency in the context of voice-commerce as positive and determining factors for the usage of a digital voice assistant for shopping (Kraus et al., 2019; Purc˘area 2018). The use of speech as the most natural and comfortable way of communicating consequently favors online shopping. Especially in the case of repeat or re-purchases, voice assistants can be helpful due to faster performance (Hörner, 2019). To many consumers, the possibility of the automation of online shopping for daily and regular goods using voice assistants appears to be a welcome simplification of their everyday lives (Heinemann, 2017). In addition to increasing (shopping) convenience through easy interaction between digital voice assistants and consumers, these assistants can support or even control consumers in certain consumption decisions through voicecommerce. For instance, with the help of digital voice assistants, certain diets and nutritional guidelines can be followed more easily, for example by such assistants recommending healthier recipes based on the user’s settings or by not suggesting foods that are harmful to the consumer when shopping online. This “nudging” can be enabled because digital voice assistants collect relevant information about consumers, learn from this data, and identify consumption patterns to predict future individual behaviors and to know users’ needs, preferably before

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consumers themselves are aware of them (Shankar 2018, Kaplan and Haenlein, 2019). Through the process of continuous data collection and data analysis of individual consumers, smart devices can improve and tailor the services offered to the user (Giebelhausen, 2014; Holzwarth, 2006), thereby promoting a healthier lifestyle, for example. Further research suggests that 65–70-year-olds who make greater use of information and communication technologies in their daily lives combine this use with physical and emotional independence and positive characteristics (Vroman et al., 2015). In addition, the continuous development of customer relationships is enabled (Purc˘area, 2018), as voice-commerce is an approach in which communication between consumers and brands is at the forefront of the customer journey (Piyush et al., 2016). However, not only benefits but also risks and dangers for consumers can be identified from this data collection. For example, 70% of consumers who use digital voice assistants are concerned that these devices will affect their privacy, while 65% fear that their personal data will be used by companies (GlobalWebIndex, 2018). In addition, there is a risk that digital voice assistants may become active without users’ permission because of unclear words and sounds (Verbraucherzentrale NRW, 2018), which also increases the risk of unwanted conversations being recorded or unintentional or incorrect orders being placed. For consumers, this could result in psychological or financial vulnerability, among other things. From a commercial perspective, voice applications also represent a new source of revenue for companies, where, for example, through so-called in-app purchases, users can seamlessly purchase products using the payment options associated with their account. The basis for a successful purchase via digital voice assistants is consequently the data input provided by consumers in advance. This requires private data, such as their delivery address and bank details, otherwise no order can be executed. However, there are considerable concerns about the monitoring and misuse of data when using digital voice assistants (Klein et al. 2020). For instance, mainly “vulnerable” consumer groups, such as the elderly or very young and unexperienced consumers (Bala and Müller, 2014), face the potential risks associated with the use of digital voice assistants for online shopping, as they may not be able to recognize or deal with these risks and avoid them. Yet, it is precisely these potentially vulnerable consumer groups (e.g., those who rely on healthcare) who benefit from voice-commerce. Reducing the potential risks and barriers associated with this type of commerce, such as low transparency and loss of control, can contribute to a reduction of consumer vulnerability (Dinter et al., 2014).

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Theoretical Foundation

Using Kotler and Keller’s five-step model of the buying process, the differences between a non-speech-based purchase and a voice-commerce purchase can be illustrated. The phases are divided into problem identification, information search, comparison, purchase decision, and post-purchase (Kotler and Keller, 2016), Figure 3.6 depicts these phases and illustrates the differences between traditional e-commerce and voice-commerce in the buying process. As an example, some differences between the two channels might already be observed in the problem identification phase. Although a need for a product arises from internal stimuli, such as hunger, external stimuli (i.e., external influences) can also evoke consumers’ consumption and buying intentions (Kotler and Keller, 2016). This “nudging” from the outside can be facilitated by the use of digital voice assistants, which can proactively and easily draw a consumer’s attention to potential products in the first phase, namely, problem identification. Although consumers can also be actively made aware of products through advertising, among other things, when surfing the Internet, this nudging by a digital voice assistant can principally take place automatically all day and night, regardless of whether the user is active on the Internet or is cooking or lying on the couch without actively using the voice assistant. However, this could be accompanied by a greater lack of transparency for consumers, as the nudging by Alexa, Siri, and others does not make it clear whether the platform, a brand itself, or another third party wants to trigger the purchase request. In addition, the possible advantages of marketing in voice-commerce are the digital voice assistant’s successful recognition of the problem based on additional indirect information (e.g., when the assistant is connected to the refrigerator and recognizes that it is empty) combined with a suitable range of products offered to solve the problem. Here, intelligent systems can link up and immediately identify a consumer’s future need or desire on the spot, whereas in traditional e-commerce, a retailer does not know exactly when consumers have consumed a product and need a new one, potentially irritating them with unnecessary product offers. In the information search phase, where the aim is to find a suitable provider, there are major differences between traditional e-commerce and voice-commerce with regard to product selection (Hörner, 2019). On the one hand, when making a normal online purchase using a computer, laptop, tablet, or smartphone, consumers can independently search numerous marketplaces and suppliers, with visual presentation options and almost unlimited information (Deges, 2020). On the other hand, the search for information using digital voice assistants is more limited. In voicecommerce, either the voice assistant can actively make an offer to the consumer

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Figure 3.6 Comparison between the traditional e-commerce buying process and the voice-commerce buying process based on Kotler and Keller’s (2016) five-step model of the buying process

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by identifying the problem, or the consumer can make a direct search query, although this is likely to be limited to the offer of a manufacturer or provider, as is the case with Amazon Echo (Hörner, 2019). Thus, the information search in voice-commerce is characterized by reduced transparency and information, compared to a non-speech-based e-commerce information search phase, whereby without the preselection of products in voice-commerce, it would be hardly cognitively possible for consumers to process all information about several proposed products, which would make this purchase phase too complex. Differences in the comparison phase, where offers are compared, are similar to those in the information phase. In traditional e-commerce, consumers can compare the alternatives found on the basis of individual criteria and through aspects such as product information, pictures, or reviews, and they can also use search engines to find the best offer (Deges, 2020). In contrast, product images cannot be viewed with digital voice assistants without a screen. For example, due to the less mediarich nature of voice-commerce, but a higher cognitive effort, consumers tend to look for fewer alternatives (Maity and Dass, 2014). This can, similarly to the previously mentioned limitation to one provider, limit consumers’ choices in voice-commerce. Moreover, in e-commerce, the consumer can assess the online shop before making a purchase and decide whether the provider is trustworthy (Deges, 2020). In addition to the time, financial, functional, and physical risks that must be borne by the consumer in traditional e-commerce (Hörner, 2019), the same risks also exist in voice-commerce, along with additional risks, such as possible misuse of the purchase function by unwanted persons or voices or through communicative misunderstandings between human and machine (Hoy, 2018). An important influencing factor in this phase is the opinion of other people, for example in the form of customer reviews or recommendations (Kotler and Keller, 2016), which is difficult to include in voice-commerce due to the lack of review presentations. Moreover, providing personal and financial data in voice-commerce is different from traditional e-commerce (Hörner, 2019). In most cases, consumers no longer need to type in personal information, as their payment data and method are usually already saved in the digital voice assistant and are automatically recalled with every purchase, making the transaction phase easier and faster. The model of the buying process according to Kotler and Keller (2016) not only describes the process of a purchase, but also can be used, as explained above, to draw a comparison between different purchase forms on the basis of the sequential buying phases. This makes it possible for one to identify the advantages and disadvantages of the purchase forms under consideration, which provides information on whether a purchase form or a new technology used for

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shopping is also suitable for fulfilling this task. As a consequence, for new technologies, such as digital voice assistants, to be successful and make everyday life easier, they must be generally accepted and used by consumers (Davis, 1989). For example, the Technology Acceptance Model (TAM) by Davis (1986) and the Task Technology Fit Model (TTF) by Goodhue (1992) can be used to explain the antecedents of user behavior of regarding a new technology (i.e., digital voice assistants for voice-commerce). The TAM is used to explain user acceptance in terms of information technologies (Davis et al. 1989) by examining the influence of external factors on internal beliefs and drawing conclusions about the circumstances under which consumers use technologies or view their use as unacceptable. Both behavioral and attitudinal acceptance play an essential role here. Technology acceptance is achieved when consumers accept an innovation and repeatedly use it (Kollmann 1998). While the TAM thus assesses consumers’ technology acceptance and therefore their intention and behavior formation, the TTF focuses primarily on the technology itself and its suitability to perform a desired task (Goodhue, 1992). Therefore, the external requirements of the task to be performed and the internal functionality of the technology are used to measure the level of performance of the technology to successfully accomplish the task. To describe the potential of a technology to perform a desired task, the drivers for using a novel technology are combined in the TAM/TTF model using the following variables: 1) perceived ease of use, perceived usefulness, and intention of use in the TAM and 2) the constructs task–technology fit and tool functionality in the TTF (Davis, 1989; Goodhue, 1992; Dishaw and Strong, 1999). This combined model demonstrates the complexity of consumers’ intention formation and decision to actually use a technology. Looking at the TAM/TTF model in more detail, it can be seen that perceived usefulness and ease of use are associated as two essential constructs of technology acceptance (Pavlou 2003). According to Davis (1989), perceived usefulness represents the degree to which consumers expect that using a particular system or technology would improve their performance or bring them closer to a desired goal. A technology to which consumers attribute a high degree of usefulness is consequently one for which they believe that the relationship between performance and usefulness is positive. Thus, perceived usefulness influences consumers’ attitudes and usage intentions. Perceived ease of use, on the other hand, refers to the extent to which consumers believe that using a system or technology is easy. Thus, the easier and more efficiently consumers can achieve a desired result or perform an action with the help of a technology, the higher their intention to use this technology. The degree of intention to use a technology,

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which depends on the described usefulness and ease of use thereof, will in turn determine whether a technology is actually used eventually (Davis 1989). Regarding the TTF, instead of focusing on consumer behavior formation, it focuses on assessing the fit of a technology to complete a particular task. On the one hand, the external task requirements and characteristics play a role in the assessment of the technology fit. These requirements must be met by the technology, meaning that the technology must be adapted to the task characteristics to be able to complete the task. The more specific these requirements are, the higher the technology fit must be. On the other hand, the internal technology characteristics, which make up the functionality of the technology for the task, also play a role in the assessment of the task–technology fit. The better the technology functions are adapted to the task, the more suitable it is for fulfilling the task (Goodhue 1992). The variables’ tool functionality, namely, the functionalities of the technology and task requirements (i.e., the requirements of the task to be completed) therefore form the task–technology fit—that is, the potential or degree of fit of the technology to fulfill the task (Goodhue and Thompson, 1995). Here, an intersection between the two theoretical models arises: The “task–technology fit” variable, which describes the suitability of a technology for a specific task, exerts an influence on the perceived ease of use and the perceived usefulness of the technology from the TAM. Thus, the fit of a technology can indicate how useful it will be, as well as how easy it will be for the consumer to use it for the intended task. Both usefulness and ease of use then influence one’s intention to use this technology—or, in the case of this study, the digital voice assistant— to purchase products, which ultimately has a direct influence on actual usage. In addition, the task–technology fit construct also directly influences consumers’ actual use of the technology (Dishaw and Strong, 1999). In the present study, the acceptance of a digital voice assistant for the online purchase of products is examined with the help of the integrated TAM/TTF model. The condition for further use of a voice assistant for shopping is thus the potential of this assistant to fulfill the task desired by the consumer. Figure 3.7 graphically depicts the variables of the model. Therefore, the consumer perceives shopping via the voice assistant as userfriendly and useful if the functionalities of the assistant allow the purchase to be carried out as desired. In this way, the requirements in the shopping phases of the buying process described by Kotler and Keller (2016) reflect the tasks that digital voice assistants must accomplish as a technology to ensure successful online shopping: Depending on the shopping phase, the voice assistant should be able to identify the user’s problem, start a search query for a product, provide information, offer and compare alternatives, complete the purchase, and/or query and

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Perceived Ease of Use

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Actual Tool Use

Perceived Usefulness TAM

Tool Functionality

TaskTechnology Fit

TTF Task Requirements

Figure 3.7 Integrated TAM/TTF model (Davis 1989; Goodhue 1992; Dishaw and Strong 1999)

cancel the order. If the functionalities of the voice assistant meet the requirements of the task, then the device is considered useful by consumers, and their intention to use the assistant for online shopping would be positively influenced. However, when evaluating the suitability of digital voice assistants for online shopping, it is important to consider what tasks they actually need to perform. Although the suitability of such assistants for a complete shopping process is observed and evaluated in the context of this study, the question arises as to whether they should be used for all or only for select phases of the buying process. In omnichannel systems in particular, digital voice assistants could support only a part of the entire shopping process. The degree of the technology’s fit, as well as the associated intention to use and actual use, should always be precisely matched to the task under consideration and be delimited from other tasks. In addition to these technical and utilitarian requirements, which influence consumers’ intention to use digital voice assistants for online shopping, the hedonic and affective aspects are also important for the users of such assistants. Since in voice-commerce, compared to other subareas of e-commerce, a stronger impression of interacting with a “real” seller or shopping assistant is created due to the use of speech by the voice assistant, the humanization of digital voice assistants plays a significant role. The humanization or anthropomorphizing of things, also called animism, is a phenomenon in which non-living objects are humanly designed and brought to life by giving them human characteristics, traits, or appearances (Puzakova et al., 2009; Kühn, 2018), as is the case with digital voice assistants. Animism consists of three stages, which represent the degree of humanization in ascending order. In the first stage, the personality

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traits of an individual who is known from advertising are transferred to the corresponding brand. Associating brands or objects with people from one’s personal environment reflects the second stage. Finally, in the third stage, the complete humanization or anthropomorphizing of the object or brand takes place. In this process, human characteristics, such as thinking or emotionality, are transferred to objects (Fournier, 1998). This humanization can be seen, among other things, in the naming process. For example, the Amazon Echo voice assistant is usually addressed as the female name Alexa, and its voice is also reminiscent of a human female voice. Moreover, people engage with voice assistants by means of verbal and natural communication, as if talking to a real person. Since artificial intelligence and algorithms ensure a constant improvement of voice assistants’ software, communication with them is continuously improving. For users, the boundaries between the world of inanimate objects and humans are hence gradually dissolving (Dörrenbächer and Plüm, 2016). This becomes clear in a user’s emotional reactions to statements or activities made by a voice assistant. It can thus be assumed that the interaction and perception of consumers using a digital voice assistant is different from that of consumers using traditional e-commerce devices, such as computers, laptops, tablets, or smartphones, in which this humanization does not play a role and no human-like communication takes place (except when using a voice assistant on a smartphone). As a result, the purchasing process may be perceived differently and offers a new way of consuming and ordering online. Therefore, a digital voice assistant can be perceived by way of this animism as a kind of real shopping assistant, which lends a personal touch to the online shopping process. Due to the constant communication between a consumer and a voice assistant, it can be assumed that the principle of animism is present in the different purchase phases. The qualitative study hence also analyses how participants feel during an ordering process via speech and where the animism in voice-commerce still has its limits. The present study is an exploratory investigation, as it takes a closer look at a research area that has not yet been intensively explored, and there are still few research approaches in the field to draw on. Therefore, instead of focusing on individual aspects, a holistic approach is taken to gain a general understanding of consumers’ use of digital voice assistants, specifically in a consumption context. The models and theories cited in this chapter are intended to help simplify the complexity of this approach. On the one hand, Kotler and Keller’s (2016) phases of the buying process create a framework for the individual areas of voice-commerce to be examined. These areas differ in terms of their tasks, and a differentiated handling of these phases, both by the digital voice assistant and by consumers themselves, can thus be possible. In addition to the classification of

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the individual tasks, this study also focuses on a) the suitability of digital voice assistants for the individual tasks in the buying process and b) their resulting usefulness and user-friendliness (TAM/TTF model by Davis, 1989; Goodhue, 1992; Dishaw and Strong, 1999). After considering how suitable the technology is in general, one should also assess how consumers feel (emotionally) about interacting with digital voice assistants and how they perceive this interaction (e.g., Fournier, 1998). For example, while a technology may be appropriate for completing a task, if consumers do not feel comfortable using it, this may still reduce their intention to use it. Thus, the models presented above demonstrate the theoretical requirements for successful voice-commerce. However, the literature does not clarify whether a digital voice assistant should serve every buying phase of the shopping process, whether the assistant’s perfect functionality is crucial for all tasks of the shopping process, or whether interacting with a real human being is most important to consumers. Thus, the study offers different approaches that may influence consumers’ behavioral intention and therefore aims to determine which of these approaches is ultimately decisive for participation in voice-commerce. In conclusion, the qualitative approach is suitable for this research area, which has not yet been extensively explored, as quantitative approaches are better suited to measuring already known objects, while qualitative data serves as a sound and meaningful source to understand and classify events in a specific context. The qualitative approach also allows us to identify possible connections between the different presented theoretical concepts or to create and refine new conceptual frameworks (Miles and Huberman, 1994). In addition, compared to quantitative research, the qualitative research approach is characterized by greater flexibility, which allows the research process to be open (Lamnek, 2010).

3.3.4

Empirical Studies

3.3.4.1 Procedure, Sample, Method Study 1 This section explains the methodological approach, which was adapted to the research objective and is considered suitable for answering this study’s research questions. Two consecutive studies were conducted with different foci. The first study deals with the general understanding, attitudes, and expectations of (inexperienced) participants regarding digital voice assistants, especially voicecommerce, while the second study focuses on participants’ actual use and implementation of a voice-commerce process, which they subsequently evaluate themselves on the basis of their collected experiences. The procedures, sample, and approach of Study 1 are presented below.

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As part of the first study, a qualitative survey was conducted to investigate the use of voice-commerce, with the example of digital voice assistants in marketing, based on structured focus group discussions. A focus group consists of a group of people who are systematically selected and assembled to discuss the selected topic from personal experience and to comment on the current state of the art (Acocella, 2012). In this way, for example, the risks and uncertainties of participation in voice-commerce can be identified and then clarified to improve consumer protection. Group discussions also allow researchers to gain insight into consumers’ motives and have the advantage that during the discussion, participants exchange information with one another (Blank, 2011). However, the results cannot be generalized, because the participants were selected arbitrarily within the population, making this an explorative rather than a representative study (Esser et al., 2011). Nevertheless, this research approach is particularly suitable for the evaluation of focus group discussions, as it takes into account the context of textual components. It has been shown that identical text or discussion elements can have different intentions in different situations, especially in group discussions (Mayring, 2010). Moreover, according to Mayring, latent structures of meaning are considered, since meanings are not objectively or lexically defined. Furthermore, the frequency as well as the presence and absence of statements are questioned (Mayring, 2002). Thus, to provide structure and clarity to the content collected, the participants’ statements were, among other things, systematically categorized, abstracted, and compared with one another (Spiggle, 1994). This approach ensured that the three research questions of this study were answered, with the core content of the theories (see Section 3.3.3) serving as the underlying basis for the investigation. Hence, looking at the three research questions, this qualitative analysis makes it possible to generate novel insights for the specific, new field of application of e-commerce regarding the antecedents of voice-commerce usage (opportunities/advantages vs. risks/disadvantages), the usage itself (as a consequence of the extended advantages or disadvantages), and last but not least, measures that could help either to reduce the risks or disadvantages that have an inhibiting effect on the use of voice-commerce or to build up consumers’ usage competencies. Following Mayring (2015), a structured interview guide was developed, according to which the focus group discussions were organized. These discussions took place in a familiar atmosphere to enable a pleasant dialog character and the uninhibited expression of opinions. At the beginning, the term “digital voice assistant” was defined for all participants in the group discussions. This was followed by the presentation of a video on YouTube, in which the functions of

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digital voice assistants were explained based on Amazon’s Alexa. This explanation and description of digital voice assistants and voice-commerce ensured that all participants in the data collection process had the same level of knowledge and understanding of the context to be studied. The video presentation of the functionalities helped (inexperienced) participants to vividly imagine the capabilities and limitations of a digital voice assistant and its interaction with consumers in everyday life. In this study, Amazon’s Alexa was selected as a representative of digital voice assistants for voice-commerce use, since the purchasing process with other manufacturers and brands (in Germany) was not yet as advanced as that with Amazon’s Alexa. Thus, Alexa has the highest probability of successfully carrying out a purchase process, such that a more intensive interaction between participant and digital voice assistant, which could be evaluated, was assumed. The guidelines of the questionnaire were structured in four segments. To provide easy access to the topic, introductory questions were asked regarding participants’ attitudes, usage behavior, and knowledge of online shopping and digital voice assistants. This was followed by general questions about online shopping via digital voice assistants as well as perceived external- and self-control via these devices. Finally, questions on optimization measures and future perspectives concerning voice-commerce were discussed in the groups. Before the actual group discussions, a pretest with 12 participants was used to check the understanding of the questions and to design the general group discussion situation. Within approximately two months, 71 German participants were acquired for the actual group discussions, and they were interviewed in 12 focus groups. Each group discussion lasted between 60 and 90 minutes. The gender distribution was 38.03% women and 61.97% men, with the youngest participant being 18 and the oldest 73 years old (see Table 3.19). Table 3.19 Demographics of the focus group discussions N

Age

Age Structure

Gender

71

M = 45.97 (SD = 17.45)

18–29 years = 23 participants; 30–59 years = 28 participants; 60–73 years = 20 participants

61.97 % men

All 12 focus group discussions were recorded, transcribed using f4, and then coded using MAXQDA. A category system was designed for coding using the structured qualitative content analysis, based on the work of Mayring (2010). First, inductive upper categories were formed, which in turn were subdivided

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into subcategories. All relevant parts of the text were assigned to the respective subcategories, and a tabular overview of the categories was created. The aim of the analysis was to answer the three research questions on the basis of the developed categories. An inductive approach was followed throughout the entire study, which means that individual empirical observations were used to draw conclusions about the generality (Fereday and Muir-Cochrane, 2006). To be able to generalize statements best, in qualitative social research, documenting the implementation well and thus making the research process comprehensible are particularly important (Mayring, 2010).

3.3.4.2 Results Study 1 Following Mayring’s procedure of qualitative content analysis, inductive categories were formed from the focus group discussions, and four upper categories were created, which are listed in Table 3.20. Categories form a group of statements that fit together thematically and can be summarized. Among other things, they can pertain to the attitudes, perceptions, influencing factors, or intentions of the respondents. An upper category thematically covers several contents together and is divided into subcategories that consider different aspects or characteristics of the respective upper category. Each of the upper categories for Study 1 consists of two to three subcategories. The first upper category relates to the usage behavior and attitudes of consumers with regard to digital voice assistants. Statements about chances and advantages are made in the second upper category. The third upper category examines the risks and limitations of digital voice assistants from a consumer perspective. Last but not least, the fourth upper category deals with measures to optimize shopping via digital voice assistants. The term “IP” (interviewed person) represents the person from whom the cited quote originates, while the abbreviation “FG” (focus group) stands for one of the 12 focus groups. UC1: Usage behavior and attitude The first upper category relates to participants’ general usage behavior regarding voice-commerce to date. Since their previous usage experience was limited or even non-existent, participants’ expectations and usage intentions toward digital voice assistants and voice-commerce were captured within this category, offering a first impression of their attitude toward this new consumption option. SC 1.1: Product characteristics and price category In the first subcategory, the focus is on the products that participants would potentially order via voice-commerce or the characteristics that products must have

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Table 3.20 Category system of the focus groups Upper Categories (UC)

Subcategories (SC)

UC1

Usage behavior and attitude

SC 1.1 SC 1.2 SC 1.3

Product characteristics and price category Fields of application Future perspectives

UC2

Opportunities and advantages

SC 2.1 SC 2.2 SC 2.3

Convenience and speed Support for everyday tasks User interface

UC3

Risks and limitations

SC 3.1 SC 3.2 SC 3.3

Data protection and security Product presentation and visualization Error susceptibility

UC4

Optimization measures

SC 4.1 SC 4.2

Requirements for retailers and manufacturers Recommendations for consumer protection and politics

to be considered suitable for voice-commerce, such as corresponding to a certain price category. For instance, regarding price, the participants disagreed on whether to spend more or less when buying with digital voice assistants, with the majority leaning toward spending more when using such assistants regularly to make purchases. When asked about the suggested price for a purchase with a voice assistant, sums that participants considered to be low (15–200 euros) were usually mentioned: “One would probably start with smaller amounts. See how the whole thing works and see if I can increase the value and buy more products” (IP6, FG1). With low-risk products—“daily needs” (IP5, FG2)—buying via voice assistants was more conceivable than with risky products. Participants stated that they were more likely to imagine purchasing via voice assistants if they were buying brand products (IP6, FG4) or products where visualization was not absolutely necessary, because they were already known (IP5, FG3). Voice-commerce was also more conceivable for participants when it came to re-purchases and followup purchases: “I would buy things of daily life, i.e., simply recurring products, where you know that this is a good and quick solution” (IP6, FG2). SC 1.2: Fields of application The next subcategory focuses on when (i.e., in which situations) the participants would use digital voice assistants or participate in voice-commerce and

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which application areas and tasks they consider suitable for digital voice assistants in the context of voice-commerce. In the various focus group discussions, it became clear that no participant had yet made purchases via a digital voice assistant (FG1,2,3,4). Isolated experience with the use of digital voice assistants was available, but only for tasks outside of voice-commerce. However, participants imagined the most likely use of voice assistants at home rather than in areas that interfered with privacy (IP6, FG1). In society as well as in public, this was excluded for most: “Alone, if at all” (IP3, FG3). Furthermore, participants considered potential support for consumption via digital voice assistants in the following purchase phases: re-purchases, order status query, order placement, product search. The respondents rejected support from such assistants in payment processing: “I just wouldn’t enter my credit card information or my payment functions via Alexa” (IP2, FG1). For the participants, it made a difference whether the payment data was entered in writing or transmitted to digital voice assistants (FG1). SC 1.3: Future perspectives The participants also discussed general future perspectives on voice-commerce as well as their own intentions to use or not use it. Many of them stated that they would not be willing to shop via a digital voice assistant in the future (FG1,2,3). However, some of them noted that they would like to try out the functionalities (FG2). For IP6 from FG2, the use is too “risky” for security reasons, and IP6 and IP3 from FG3 claimed that the use of the technology reduces personal contact and could lead to “loneliness of people.” Only in FG4 were all participants willing to order “little things” (FG4) via voice. Nevertheless, almost all of the participants agreed that the frequency of shopping in this “new way” will increase in the future and that the topic will become more socially accepted (e.g., FG2). Furthermore, the participants expressed that in the near future, consumers will have no choice but to use this technology. They believe that interaction via voice will be firmly integrated into most devices and that old models that are not state of the art will be abolished: “They’re forcing it on you” (IP3, FG3). Participants from FG4 predicted that, especially in subsequent generations, shopping by voice will become part of everyday life, and more money will be spent on voice orders (IP3, FG4). UC2: Opportunities and advantages The second upper category comprises participants’ evaluations of the advantages and potential of digital voice assistants, especially in the context of voice-commerce. The main focus here is on highlighting positive features

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and characteristics that can make life easier for consumers and simplify the accomplishment of tasks. SC 2.1: Convenience and speed In the first subcategory of opportunities and advantages, the primary theme is the increasing convenience of using voice-commerce, which is ensured by the unique use of voice alone, as well as the time savings through routinized and short voice commands. For example, voice assistants can bring about time savings and more comfort from the participants’ point of view, such as through faster checkout for repeat orders: “It would make it easier for these recurring things if you could simply say, ‘oh man, I need something again […]’ then that’s something that is immediately done by voice-commerce” (IP2, FG2). Moreover, participants mentioned, for example, that voice commands are faster than operating via keyboard (IP2FG2). The convenience and speed by omission of a manual device was also discussed as an advantage: “For this purpose, the mobile phone might be omitted—or I wouldn’t have to start the computer, which is a huge procedure” (IP2, FG3). SC 2.2: Support for everyday tasks Furthermore, in this subcategory, digital voice assistants are discussed as potential support systems that simplify the accomplishment of tasks and, as their name suggests, as real everyday assistants. The participants found the function of voice shopping particularly useful for disabled and physically limited people: “If you are blind, it makes a lot of sense” (IP4, FG1). In addition, the use of voice assistants in the health sector was assumed to make everyday life easier: “In many areas, I believe that the voice assistant can be helpful, for example, when someone is ill and has limited mobility […]” (IP5, FG2). In this context, such assistants can be generally supportive and should be explicitly developed for use by vulnerable consumer groups who are, for example, physically limited. Participants were of the opinion that using a voice assistant can be an instrument for coping with everyday tasks (IP5, FG1). Shopping by voice is not bound to place and time, which is another advantage. The participants also rated the functionality as useful in situations where both hands are used for other purposes, such as cooking and cleaning (IP6, FG4). In addition, some participants mentioned the facilitation of decision-making as an advantage. If a digital voice assistant suggests only a few products, then one can more easily and quickly choose from them (IP1, FG4). If products are offered that have been preselected based on certain criteria, then the consumer is also relieved of the effort of research. This aspect was also considered in a positive way, especially in the areas of sustainability and energy

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efficiency (IP1, FG4). Furthermore, with regard to the facilitation of decisionmaking, the existence of a digital wish list was mentioned. This enables reference persons to access another person’s list of desired products for gifting purposes (IP6, FG4). SC 2.3: User interface The last subcategory of the second upper category addresses the simplified handling and high usability of digital voice assistants and the fact that this easy-to-use interface could, in principle, simplify the purchasing process to a maximum. The participants perceived it as advantageous that they do not have to pay attention to their spelling (IP1, FG1) and do not have to type (IP2, FG1). The fact that voice alone controls the input means that hardly any physical effort is required, making the usage requirements low (IP6, FG4). The conversation in the focus groups revealed that there are no concerns about user-friendliness: “Yes, simply so, so that everyone can do it” (IP3, FG3) and “It is made user-friendly” (IP5, FG3). The skills and knowledge required to operate voice assistants are therefore estimated to be lower than those required to use non-voice-controlled devices. Moreover, since user accounts can also be linked to one another, manual data entry is no longer necessary (IP6, FG4). In addition, “the assistant is trained on me” (IP6, FG4); it recognizes the voice of the owner better and can make suggestions based on individual preferences. Likewise, the independence of spatial orientation (i.e., the fact that one can communicate with digital voice assistants over a greater distance) is seen as an advantage (IP2, FG1). UC3: Risks and limitations The third upper category describes the potential and perceived barriers and risks related to the use of digital voice assistants and, in particular, voice-commerce. These barriers and risks arise primarily from the limitations arising from digital voice assistants through the sole use of speech, and they could complicate or even inhibit consumers’ usage of these systems. SC 3.1: Data protection and security This subcategory focuses on the most frequently discussed disadvantages and risks of voice-commerce: the risk of misuse of personal data and one’s own privacy. The participants feared permanent monitoring (IP4, FG1) and data manipulation (IP4, FG1), with the discussions focusing on the misuse of personal data and voice (IP6, FG1). The participants also displayed great skepticism toward data storage and its processing (IP1, FG1). Another disadvantage mentioned was the loss of an overview of the total price of an order (IP2, FG4).

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Compared to one’s conscious data entry at a computer, voice purchasing is characterized by missing control options. The participants said they do not know when the device is listening (IP4, FG3), and particular concerns also arose with regard to payments made by voice: “I would prefer to buy everything on account” (IP4, FG3). A further risk in the eyes of the participants is the problem of how to prove that they did not place orders themselves via their digital voice assistant if unauthorized recordings of their voices are used by third parties for purchase transactions (IP5, FG2). In addition, participants criticized not being able to precisely control which product they receive (e.g., they are “afraid that one might get a fake” product [IP6, FG3]) and to whom their data is forwarded. SC 3.2: Product presentation and visualization Furthermore, the relevance of visual presentation in the voice-commerce buying process is discussed in the following subcategory. Visualization usually helps consumers to better assess product characteristics, but is not given in most digital voice assistants due to their lack of screens, representing a common limitation for most digital voice assistants on the market. This aspect of product presentation is especially often emphasized in connection with shopping for clothes. The picture and the description have a decisive influence on consumers’ purchase decision (IP4, FG1). In addition, visual representations support orientation. Product presentation thus plays a major role in the purchase process for most participants, which is why they classified the lack of visualization (in the majority of digital voice assistants) as highly disturbing in voice-commerce, which has an effect on their willingness to buy. Although one advantage of digital voice assistants is that consumers are no longer bound to a screen, but can move around freely without holding a device, IP3 in FG4 also criticized the lack of inspiration without pictures presented on a screen. Moreover, the assistant’s lack of visual support to guide a consumer through the ordering process with instruction steps was also criticized (IP2, FG2). When buying via digital voice assistants, the missing picture is therefore a disadvantage not only for product selection but also for order processing. A participant stated that without visualization, one must memorize a large amount of information dictated by the digital voice assistant, which was perceived as a challenge (IP6, FG2). Many participants also criticized the limited product selection, as only one product is usually suggested due to the limitation to voice and the lack of a screen (IP4, FG1). Furthermore, the order of the search results is determined by an algorithm, which was also a negative criterion for the participants (IP6, FG4). Thus, there is a risk that product offers from new companies will be lost and that unconscious manipulation might occur (IP6, FG4). Lastly, “And there are also brands that pay for standing at the top” (IP5, FG4).

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SC 3.3: Error susceptibility The third subcategory of risks and disadvantages deals with the immature technical implementation of current interaction with digital voice assistants, which still entails a fairly high error rate in usage. For example, participants criticized the error susceptibility of digital voice assistants in the form of voice recognition inaccuracies, which can ultimately lead to a poorer user experience. The participants saw a danger in communication problems, for example through unclear pronunciation or misunderstanding of voice commands, which ultimately may lead to the faulty execution of voice commands and the delivery of the wrong products (IP2, FG2) Another disadvantage is the incorrect differentiation of voices: “For example, even if I sit together with my brother and I set a timer, his mobile phone also turns on” (IP6, FG4). The restriction on verbal communication, which is tied to certain formulations or a fixed vocabulary so that digital voice assistants can understand the commands, was also criticized (IP3, FG2), with IP5 from FG3 stating, “Linguistic understanding is not always given.” UC4: Optimization measures Based on the risks and limitations of using digital voice assistants, especially for voice-commerce, the participants’ proposed optimization measures themselves reflect requirements and recommendations for action that could, among other things, minimize or even completely eliminate the disadvantages presented by voice assistants. The measures proposed are divided into requirements for action by retailers and manufacturers, on the one hand, and recommendations for action by consumer protection and policymakers on the other. SC 4.1: Requirements for retailers and manufacturers In the first subcategory of optimization measures, requirements for action are formulated for managers, which focus on both optimizing the security of the use of digital voice assistants and improving the shopping experience and userfriendliness for consumers when participating in voice-commerce. Beginning with security measures, participants indicated that they would like to see an attempt to build up more trust between digital voice assistants and consumers in the future (FG1). Furthermore, they would like more transparency regarding the further processing of their data and prefer personal data to be deleted after each purchase (IP3, FG2). Concerning the location of the data storage device, the participants would feel most secure with a location in Germany or in Europe (IP2, FG3). They also suggested that above a certain purchase amount, in addition to voice confirmation of the purchase, digital assistants should request a second authorization from consumers, for example by means of a fingerprint (IP6, FG4).

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Overall, the participants found biometric verifications to be highly reliable. Since they have the feeling that someone might be eavesdropping on them on familiar devices despite the fact that the voice controls are switched off, consumers should be able to completely switch off the technology (IP7, FG2). Overall, the digital voice assistant technology is to be further improved to reduce error rates, such as voice recognition. Since payment processing is the main source of obstacles, it should be made less risky by newly developed encryption methods (IP6, FG4). In addition to the security measures that retailers and manufacturers should take, participants also want to see an increase in the shopping experience and usability with voice-commerce. Concerning the shopping experience, they most frequently suggested an integrated display in smart speakers (IP1, FG1). In addition, a projection or hologram on the wall (FG2) was also mentioned as a possible optimization: “Speech is appealing, but not like pictures” (IP6, FG3). In FG4, participants also expressed the wish to be able to enlarge and reduce images by motion and gesture control (IP6, FG3), stimulating the presentation and visualization of the product: “It’s as if I had touched the product” (IP3, FG3). Another wish was to have an overview of the products added to the shopping cart, the purchase sum, and the order history (IP5, FG1). In the discussions, it turned out that the feeling of control and the possibility of intervention and revocation are of great importance for participants (IP8, FG2). In addition, participants expressed the need to receive e-mails about the order stages (IP1, FG2). A further improvement of the shopping experience is the possibility to have technical details and customer reviews read out loud (IP2, FG3). In addition, consumers should be able to set filter options via voice for which they can define their own specifications (IP5, FG4). Some participants also considered the active presentation of individually selected products by voice assistants, where they proactively make suggestions (IP2, FG3). SC 4.2: Recommendations for consumer protection and politics In the second subcategory of optimization measures, participants’ wishes and suggestions are recorded; they are addressed directly to consumer protection organizations and policymakers. For instance, the need to improve data and consumer protection was mentioned frequently, which is why data protection issues should be assigned a high priority. For one thing, the participants would like the data protection laws to be tightened (IP3, FG1), enabling an external protection function to be performed by third parties. However, next to external and legal regulations that provide protection for consumers, they also want to be able to protect themselves through education and training. This education and training in the use of digital voice assistants can be ensured in the form of information events

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and workshops on data protection laws, such that consumers are informed of their rights (IP3, FG2). Furthermore, the participants would like an “introduction” (IP4, FG2) not only to the general usage of digital voice assistants, but also specifically for the consumption environment. For example, a purchase transaction via digital voice assistants could be demonstrated to provide consumers with a sense of security and help them build skills and confidence in using digital voice assistants. They thus recognize the use of the new technology as a “learning process” (IP2, FG3): “I think a certain amount of training would be quite good” (IP3, FG2). After an intensive familiarization and several times of use, their willingness to use the voice assistant will likely increase in the same way as shopping with a smartphone has become common practice today: “Paying by mobile phone, for example. You just have to try it out and then you become more secure, and I think voice-commerce makes me feel the same way. When you see that it works, you will trust it afterwards” (IP2, FG3). The participants were skeptical about whether politicians have the necessary knowledge and motivation to actively improve consumer protection in the field of voice-commerce. However, they trust politicians to have more interest in this topic compared to retailers who, in the eyes of the interviewees, are pursuing their own interests and are therefore not motivated to change consumer protection, as it may make the offering of products via digital voice assistants more complicated and difficult for them (FG1).

3.3.4.3 Discussion Study 1 In this section, the results of Study 1 are critically discussed. In all of the focus groups, participation in the discussion was high, which confirms people’s general interest in and the relevance of this new technology. Considering the underlying theories of this research, a theoretical gain in knowledge can be obtained from the focus group discussions, which additionally helps to answer the research questions of the study. With regard to the first research question, the results of the study indicate that digital voice assistants still play a minor role in consumers’ shopping and consumption behavior, primarily due to the still-lacking functionality of digital voice assistants. This is also one of the reasons why the participants have hardly any experience with digital voice assistants to date and why none of them has ever purchased products using the technology. However, the animism theory has raised the assumption that, in addition to the technical and utilitarian requirements that voice-commerce must bring with it, hedonic and emotional aspects also play an essential role both in consumers’ interaction with digital voice assistants and in their participation in voice-commerce. However, the focus group discussions suggest that animism— the humanization of digital voice assistants—plays a subordinate role, in contrast

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to the TAM/TTF model’s basic principles (Davis 1989; Goodhue 1992; Dishaw and Strong 1999), which participants repeatedly mentioned as prerequisites for the use of voice-commerce. While the humanization of digital voice assistants could thus be a requirement or motivation for (further) using technologies in the long term, the components of the TAM/TTF model represent the requirements and motivation for starting to use a technology in the first place. Thus, although both theories can be seen as antecedents of technology use, there are differences in the applicability and relevance of these theoretical approaches in the technology use process. While technical and practical prerequisites must be fulfilled at the start of use and should be continued throughout the entire usage process, the emotional component plays a secondary role, although it can lead to long-term use of a technology and, in contrast to the functionality of a technology, influences the strength of consumers’ emotional and affective attachment to it. Therefore, although the components of the TAM/TTF model serve as basic requirements, the animism approach could theoretically be extended to describe the intensity of the usage of a technology. Considering not only the theoretical findings but also the concrete statements of the participants in the study, it becomes clear that the handling of digital voice assistants, especially voice-commerce, is still characterized by uncertainties, high transaction costs, performance expectations, and a lack of experience. With regard to the TAM, it can also be stated that the interviewees perceive the current usefulness of voice assistants as low, since for them the disadvantages, such as data protection and comprehension problems or a high probability of misdelivery, outweigh the benefits (Davis et al., 1989). For example, all focus groups rated data protection and security issues as the most relevant, confirming earlier research studies (e.g., Hoy, 2018). This topic was addressed most frequently and, in the eyes of the participants, has the most urgent need for improvement. The participants would like to see the option of deactivating voice assistants to avoid constant monitoring, their data deleted after a certain period of time, and detailed information notices on consumer rights regarding voice assistants. In addition, to reduce transaction costs, user-friendliness and service quality should be continuously improved (e.g., Kassim and Abdullah, 2010). Some of the participants also deem the perceived usefulness of digital voice assistants to be low, and some participants are of the opinion that they do not have the necessary skills and abilities to use voice assistants. This is also reflected in their desire for training and one-time demonstrations on the use of voice assistants. Since both influencing factors of the TAM seem to have low positive magnitudes, this illustrates the currently low intention to use digital voice assistants for shopping. As a further

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disadvantage, the participants expressed the error-proneness of digital voice assistants, which can also increase the transaction costs for potential users and may decrease the expected performance of such assistants. In contrast, the most important advantages and opportunities from the participants’ point of view are aspects of increased speed and convenience when participating in voice-commerce, since they were mentioned most often. The participants appreciate fast and efficient solutions and seek to complete tasks comfortably. Thus, the facilitation of everyday life for different consumer groups could also be highlighted, and, among other things, participants praised the userfriendly operation. Hence, despite their lack of experience, many participants see medium-aged consumers (between 30 and 59 years of age) as an important target group for voice-commerce: They have the necessary financial means and a high purchasing power compared to other generations, but they unfortunately often experience time problems due to the balance between family and work (e.g., Martin and Turley 2004; Lissitsa and Kol, 2016), which means that a more convenient and faster shopping process would make everyday life easier for this target group. Furthermore, the participants see the most potential and opportunities in the use of voice-commerce especially among older and sick people, to make their everyday life easier, which is also supported by current research (Hoy, 2018). However, setting aside specific target groups or areas of application for voicecommerce and focusing solely on the purchasing process itself, according to a study by Capgemini (2018), consumers can imagine the support provided by digital voice assistants in order-status inquiries, shopping list creation, product research, and the possibility of transferring a product from one’s wish list directly to their shopping cart. Furthermore, the participants in this study even discussed the possible support of digital voice assistants within the customer journey for product suggestions. However, the results of the focus group discussions call for a rethinking of the typical buying process proposed by Kotler and Keller (2016). While the purchase phase model depicts information search (Phase 2) as one of the most decisive phases, which essentially shapes the further course of a consumer’s purchase process and purchase decision, the results indicate that the characteristics and limitations accompanying digital voice assistants (e.g., the interaction primarily limited by speech or the lack of display options/screens in most cases) reduces product choice. Phase 3, the comparison phase, can also be reduced in the case of digital voice assistants without a screen, such that the consumer does not necessarily have the opportunity to compare several products with one another, although this would be technically possible and can also depend on the digital voice assistant used. However, since consumers are accustomed to

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being guided through the online shopping process by visual aids, the absence of these aids could lead to confusion as well as increased ordering efforts. One consequence of this is the increase in transaction costs for consumers (Williamson, 1998). This result raises the question of how the characteristics of digital voice assistants influence consumers’ shopping behavior in the long term and how the purchase phases, as they have existed in the classic sense (see Kotler and Keller 2016), must be adapted in voice-commerce. This adaption may also influence the general decision-making process and purchase decision (Phase 4) of consumers. The adaption of the purchase phases can bring advantages, including time saving, since an ever-strengthening flood of product information exists on the market, making it sometimes time consuming to find the right product. Otherwise, this can also be disadvantageous for the consumer, who is no longer in control but is primary controlled by the digital voice assistant. The purchase process thus loses transparency for the consumer, and there is an increased risk of manipulation by third parties. While voice-commerce can also reduce forms of consumer vulnerability, there is a risk that this vulnerability is enhanced at other points when consumers participate in voice-commerce, since they might no longer decide for themselves what the best possible product or decision is for them. In some cases, however, it can be beneficial to “nudge” consumers with low levels of competence or knowledge toward a product that is beneficial to them, especially if they are unable to evaluate the suitability of products on their own. However, precautions must be taken a) to ensure that the product choice is individually advantageous to the consumer and b) to prevent manipulation by third parties, such as companies that want to enrich themselves by selling their product regardless of whether it is the best choice for the consumer. Moreover, during the discussions, these two-sided opinions on preselected product recommendations and minimal product presentation via digital voice assistants were discussed. On the one hand, some participants are of the opinion that the reduced product selection enables them to make a product choice easily and thus makes shopping more efficient and comfortable. On the other hand, the reduced product selection and visualization was perceived as an encroachment on users’ own freedom to decide and as a form of lack of control in the purchasing process. One consequence of the restricted purchasing process is that the participants tend to be more risk-averse; hence, when it comes to voice-commerce and shopping via digital voice assistants, they would prefer to order low-involvement products or already well-known products, such as consumer goods or everyday products, which can also be confirmed by previous research results (Tuzovic and Paluch, 2018). Those products are associated with a lower personal risk, which is important to the consumers, especially due to their increased need for

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security. Given the continuing lack of confidence, the participants would also pay more attention to brand reputation and would prefer their trusted suppliers when participating in voice-commerce. Therefore, the survey demonstrates that uncertainty prevails among the majority of participants regarding shopping via a digital voice assistant, since there are still some obstacles to overcome. By creating positive experience values and creating a high level of service quality (Kassim and Abdullah, 2010), however, trust can be built up. In any case, many participants already recognize the potential of voice-commerce and are therefore of the opinion that the frequency of shopping via voice will increase in the future and become generally socially accepted.

3.3.4.4 Procedure, Sample, Method Study 2 To answer the research questions in greater depth, a second study was conducted in addition to the focus group discussions of Study 1. Study 2 involved empirical observations with subsequent qualitative interviews. The second study allowed us to assess the extent to which self-disclosure and actual behavior correspond, since they often differ. Observations aim to reveal the behavior of persons in certain situations rather than to document verbal expressions (Brosius et al., 2016). Accordingly, to gain a realistic insight, a structured observation was carried out under conditions that were as natural as possible (Mayring, 2015). The type of observation was chosen, in which the researcher controls the procedure on the basis of a semi-standardized observation guide and can perceive the best possible internal perspective through active participation and closeness to the observation participant (Mayring, 2016). Before the observational study, we explained the research project to the participants. This was followed by an explanation of what is meant by a digital voice assistant. Afterwards, the participants were shown a YouTube video about the general functioning of digital voice assistants, using Amazon’s Alexa as an example. This ensured that all participants in the observational study had a similar level of knowledge and understanding of digital voice assistants in order to guarantee the comparability of the study. As in Study 1, Alexa was selected as a representative of digital voice assistants for voice-commerce in Study 2. Since a purchase process was to be carried out with a digital voice assistant, Amazon’s Alexa was chosen here as well, given that Alexa has the highest probability of successfully carrying out a purchase process among the digital voice assistants available at this time. Participants’ demographic data was subsequently recorded. Observations were made, if possible, at each participant’s home or in a familiar environment to create an atmosphere that was as natural as possible. The actual observational study

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then began with Step 1, a warm-up phase in which participants were required to interact with a digital voice assistant (in this case, Amazon’s Echo Dot), asking, for example, about the weather forecast for the coming days or a joke. The purpose of this warm-up phase was to enable the participants, regardless of their previous experience with digital voice assistants, to experience and learn the functioning and interaction and feel more comfortable using such assistants before the actual observation began. The participants then had to carry out several ordering processes using the digital voice assistant. During these order processes, both low-involvement products, such as milk and a water boiler, and a smartphone as a high-involvement product had to be purchased via the digital voice assistant. Here, milk was the cheapest product, while the water boiler was comparatively more expensive, and the mobile phone was chosen as the most expensive product in the observational study. However, these three products differ not only in their price category, but also in their complexity and their need for product explanation. Step 2 dealt with the problem formulation, where participants in the observation were to inform the Echo Dot that they no longer had the product “milk” in stock. The main objective was to determine how the digital voice assistant reacts to this problem and whether it initiates a corresponding purchase search request. In Step 3 of the observation, the product “milk” was then to be ordered by sending a concrete purchase order to the digital voice assistant. Participants had to subsequently ask the Echo Dot to make a binding purchase of the desired product and then, in Step 5, ask for the delivery status of the order. Finally, the consumer was asked to cancel their previous purchase. If, during one of these steps, participants had problems formulating a suitable command for the digital voice assistant, after several attempts, an index card with the corresponding sample set was handed to them so that the respective step could be performed. Since the participants were supposed to order a water boiler and a mobile phone in addition to the milk, the purchase procedure was carried out in the same way for the other two products, resulting in a set of three purchase cycles. To ensure that each observation was similar in its basic structure, an observation guide was developed. To be able to record any statements and reactions, all participants were filmed. Throughout the experiment, the observer took notes to record the behavior of the participants based on certain pre-categorizations. These included reaction and body interaction with the digital voice assistant, facial expressions and gestures, tonality and communication with the digital voice assistant, and the participants’ speaking pace. Following the observation, qualitative, open-ended interviews ensued using a semi-structured interview guide and lasting on average eight minutes. The purpose of the interviews was to develop an initial, detailed mood picture on

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the previously conducted observation study and research topic by allowing the participants to share their impressions, points of criticism, and suggestions for improvement of a real shopping experience with a digital voice assistant. Thus, in addition to the pure observations and the insights that can be drawn from them, further information could be gained about the participants’ perception and assessment of such technology after an actual use of voice-commerce. A total of 25 participants between 23 and 80 years took part in the observation study, with a balanced gender distribution (52% male), as can be seen in Table 3.21. The aim was to reach as broad an age spectrum as possible to gain a better overview of the different consumer groups and their behavior toward voice assistants. Table 3.21 Demographics of the experimental observations N

Age

Age Structure

Gender

25

M = 45.20 (SD = 19.60)

23–29 years: eight participants; 30–59 years: nine participants; 60–80 years: eight participants

52.00% men

The first evaluation of the observational study was based on a systematic screening of the recorded video footage according to the previously mentioned fixed pre-categories. For a more detailed evaluation of the qualitative survey, the same procedure as in Study 1 was applied. First, after the data collection, all interviews were anonymized and transcribed with the program f4. These transcripts were coded using a predominantly deductive-inductive category system and evaluated with the help of the MAXQDA program. This procedure corresponds to a “structuring” (Mayring, 2015), in which certain criteria are defined in advance to be able to answer the research questions posed in a targeted manner. Here, large amounts of text are reduced to essential components to devise core statements and make the text verifiable through the rules of a qualitative content analysis. Additional categories were also established during the coding process. Mayring (2015) refers to this procedure as a “summary,” as individual, reduced contents of the interviews are combined into new, meaningful categories. Thus, deductive-inductive upper categories were supplemented and extended by inductive subcategories.

3.3.4.5 Results Study 2 The main results of the observations are presented below. Usage behavior is summarized on the basis of the phases of the buying process according to Kotler and

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Keller (2016), and the usage attitude is described on the basis of the interviews conducted after the observations and the resulting category system. Phase 1: Problem identification To better understand and analyze user behavior during the buying process, the participants’ behavior was analyzed through the video recordings with the phases of a buying process according to Kotler and Keller (2016). The first phase consists of the problem identification. The participants told the voice assistant their “problem” by stating their need for a certain product. Participants’ initial reactions to the voice assistant’s answers were amusing: They smiled and responded with statements such as, “Alexa, today I have a hard time with you” (B7), when Alexa was not reacting as desired. Before and during the first ordering process, all participants still had a relaxed posture and were partially directed toward the voice assistant. Some of the participants had their first difficulties when formulating the problem, because the voice assistant did not understand the voice command and therefore did not execute it. Sometimes, the participants tried different ways to communicate with the voice assistant, for example slowing down their speaking speed and breaking some words into syllables. In addition, some participants initially forgot to address the voice assistant with the name “Alexa.” As a result, the voice assistant did not react, and the participants were surprised that they did not receive an answer until they remembered that the assistant only reacts when the name “Alexa” is mentioned. Thereupon, some participants exhibited annoyance at having to say the name before each command. At the beginning, one participant started to thank the voice assistant after each sentence and smiled while doing so. Moreover, the participants were initially amused when the voice assistant gave incorrect answers—they smiled and joked about it. We also quickly noticed that some participants humanized the voice assistant and communicated with it like a real person. They answered the voice assistant, looked at it with different glances while speaking, and changed their tonality in between. Moreover, it was noticeable that older participants made eye contact with Alexa more often than younger participants who only made eye contact when formulating a command. The classification of emotions in Phase 1 initially showed slightly positive emotions due to calm or neutral behavior as well as slightly negative emotions due to observed boredom in some participants. Strong negative emotions quickly developed due to nervousness, annoyance, or perplexity after misunderstood communication and wrong product suggestions. Phase 2: Information search When searching for information, the participants made a direct search query. They told the voice assistant what they wanted to buy. All participants pronounced

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these sentences confidently during all three ordering processes. When using the voice assistant, they felt more confident in this phase than in the previous phase. Some participants folded their hands while starting the information search. Few problems were encountered in the search for information. When one of the participants placed an order, the voice assistant repeatedly responded by saying, “I am not sure.” The participant repeated his command several times; he then closed his eyes and took a deep breath in and out, interlocking his arms until he rephrased his command. For most participants, the voice assistant responded with a correct answer and suggested a suitable product to the participant. For one participant, instead of a mobile phone, a mobile phone holder was suggested during the ordering process, which the participant was amused about. The other product suggestions all matched the search queries. The voice assistant suggested a product and listed all product data and a product description immediately afterwards. However, this list was so long that the participants became impatient—they gave the impression that the answer was too long for them and that the voice assistant should finish speaking. Sometimes, the participants also rolled their eyes because they did not want to hear the whole answer, at times even losing their patience to continue the buying process because it took too much time. Some participants also made this clear by gesturing with their hands: either rubbing them together or tapping them on their legs. After the participants received the product information, they were sometimes uninterested in continuing the ordering process. Some participants turned their gaze away from the voice assistant and spoke faster than before. In general, however, the older participants were more patient and optimistic in their search for possible products. For example, one older participant remained persistent in her mobile phone search and tried out different words and brands, such as “iPhone” and “Samsung.” Other participants also made search queries with specific brand names. In addition, a younger participant wanted to be given a delivery date and more product information, as well as reviews about the water boiler. The emotions in the second phase can be characterized by strong negative emotions, such as annoyance or irritation. These were caused by wrong product suggestions, a lack of choice, and a lack of information. In addition, slightly positive emotions due to calmness could be observed among the older participants. However, strong positive emotions such as surprise and enthusiasm rarely appear. Phase 3: Purchase decision As no alternative products were suggested by the voice assistant, the sole product named during the search query was immediately added to the shopping cart. Hence, the phase of evaluating alternatives (Phase 3: comparison phase) could

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not be carried out. This was followed by the actual purchase. The participants told the voice assistant to go to the checkout with the product in the shopping cart to complete the purchase. However, this phase was characterized by ambiguities and communication problems. For example, Alexa’s previously placed product order could not be finalized because the product in the shopping cart had to be purchased via an app or website. After the actual purchase of the product did not work, confused, irritated, surprised, questioning, angry, rejecting, and perplexed facial expressions could be observed. Despite this, some participants repeated their commands often and reworded them, but showed themselves to be very uncertain in this situation. Some participants shrugged their shoulders, indicating that they were stuck and helpless. During the rephrasing, the participants also tried to change their tonality. Here too, they dissected some words into their constituent syllables. Some of them tried to speak louder, and their eyes turned directly to the voice assistant. As the participants spoke more slowly, it was noticed that they turned their gaze away from the voice assistant more often, rolling their eyes in the process. Noticeable among the older participants were longer commands or sentences and human-like communication, such as, “Alexa, thank you,” “Alexa, that doesn’t seem to be milk,” or “Alexa, am I mumbling?” One participant even apologized to the digital voice assistant for faulty communication. In contrast, the youngest participant was not patient and interrupted the buying process after several attempts with strongly negatively loaded and insulting statements. Despite more patient and persistent behavior among the older participants through repeated attempts to check the shopping cart, confusion and annoyance quickly emerged on the user side. In the course of the three ordering rounds, the participants’ motivation and willingness to carry out the ordering processes thus declined. Toward the end, all participants spoke faster than at the beginning of the purchasing process, increasingly gesticulated with their hands, and increasingly turned away from the voice assistant. After the participants had tried all possibilities to finish the purchase, the purchase process was canceled. Some participants shrugged their shoulders and looked questioningly in the direction of the camera. In this third phase, slightly pronounced negative emotions, such as tiredness or depression, could be observed with regard to the unsuccessful purchase. The minority showed cheerful facial expressions or strongly pronounced positive emotions. Mostly, this phase was characterized by strong negative emotions, such as annoyance or anger, due to the unsuccessful purchase. Finally, it should be noted that the fourth (check and trace order) and fifth (cancel order) phases could not be carried out due to the infeasibility of the purchase. Therefore, they could not be observed in the study.

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After observing three purchase processes with the digital voice assistant for every participant in Study 2, a short interview was conducted with each participant to learn more about their impressions and experiences. The analysis of these 25 interviews from the experimental study resulted in the following three upper categories and 11 subcategories, which are listed in Table 3.22: Table 3.22 Category system of the experimental study Upper Categories (UC)

Subcategories (SC)

UC1

Opportunities and advantages

SC 1.1 SC 1.2 SC 1.3

Technical possibilities Product information and suggestions Suitable product categories

UC2

Risks and disadvantages

SC 2.1 SC 2.2 SC 2.3 SC 2.4 SC 2.5

Technical barriers Erroneous communication Restricted visual representations Reduced product choice Misuse of data and restriction of privacy

UC3

Optimization measures

SC 3.1 SC 3.2 SC 3.3

Structure and guidance Product selection and filter adjustment Personal and individual customizations

UC1: Opportunities and advantages The first upper category deals with the benefits and potential of digital voice assistants, or voice-commerce, which the participants identified during their use. Thus, in contrast to the focus group discussions, it is not expectations that are discussed here, but the participants’ real experience values that could support and promote the use of voice-commerce. SC 1.1: Technical possibilities The first subcategory of opportunities and advantages pertains to the technical capabilities and competencies of digital voice assistants and how mature they are for consumers to easily interact with them or make purchases online. The technical functions that a voice assistant can currently already perform are seen as a great opportunity for voice-commerce. These functions are mainly limited to the use and processing of language. Possibilities mentioned are the “very pleasant (…) and clear” voice (IP2), the “quite good word recognition” (IP11), or generally

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the understanding of the voice command. It was also noted that “it (…) always understood its name quite well” (IP4). This category shows that the basic functions required for shopping via a voice assistant are present. SC 1.2: Product information and suggestions The next subcategory focuses primarily on Phase 2 of the buying process, namely, information search. Here, we evaluated how suitable digital voice assistants are for product suggestions. The presentation of suitable suggestions and important product information was mentioned as another opportunity of voice-commerce. IP9 evaluated Alexa’s suggestions as “okay,” and IP21 thought that the voice assistant found him “a beautiful water boiler.” IP14 found it positive that the voice assistant “explained exactly what [the water boiler] can do and what is included.” It was also considered advantageous that the digital voice assistant “directly selected a product” (IP7) in response to the search query and thus “always sought out the best option” (IP24). SC 1.3: Suitable product categories This subcategory shows which products are considered suitable and of high potential for voice-commerce in the eyes of the participants. Most participants prefer to buy cheap products, such as “food” (IP4) and “everyday items (…), shampoo, toilet paper” (IP2), or products up to “maximum 20e” (IP8). IP1 stated, “you must first have a clear idea of what you want” (IP16), which means that prior information about a product should be provided through other channels. Moreover, IP8 said that he would order products from well-known brands and would also buy medium-priced products, since there is generally a two-week return policy when ordering products online, which mitigates the additional perceived risks when shopping via voice-commerce. Nevertheless, few participants would order expensive products, such as a television or a mobile phone, using voice assistants. UC2: Risks and disadvantages The second upper category addresses the risks and disadvantages as well as the limitations that the participants experienced during the purchasing process via the digital voice assistant. These are primarily technical and functional limitations, as well as restrictions on consumer choice and privacy. SC 2.1: Technical barriers The risks and disadvantages in this subcategory arise due to the partially faulty and incorrect implementation of the purchasing process. Here, the participants were primarily confronted with fundamental technical problems. As the most

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frequently mentioned category in the interviews, missing technical requirements hinder the smooth flow of a complete shopping order process. The fact that products could be placed in the shopping cart by the voice assistant, but not bindingly ordered, was seen by the participants as an obstacle to voice-commerce shopping. Since it should actually be technically possible for a voice assistant to complete the purchase, many participants reacted with uncertainty, impatience, and annoyance. In addition, it is a disadvantage that during the buying process, one cannot ask the digital voice assistant to perform an additional task flexibly (IP11), such as asking for product reviews to be read aloud. SC 2.2: Erroneous communication In this subcategory, participants’ main criticism was of the display feature of digital voice assistants (i.e., speech and communication)—above all, pertaining to the immature (linguistic) communication and interaction between humans and machines. Faulty communication between the consumer and the voice assistant was also a frequently mentioned obstacle in voice-commerce shopping. The opinion of most participants was that even after they repeated commands several times, the digital voice assistant did not understand exactly what the user wants due to the lack of flexibility in the free formulation of sentences or commands. Moreover, they remarked on the faulty recognition of one’s own voice and of important words such as “checkout” or on the voice assistant’s lack of reaction, which has the consequence that led to the shopping process being canceled. The consequence of this is the missing realization of commands. Furthermore, participants criticized the unnecessary and superfluous communication of the voice assistant because “she talks too much (…)” (IP9). The outcomes that participants mentioned included unwanted product suggestions, possibly excessive demand and stress on the voice assistant or on the consumer, and the interruption of communication. The latter was made clear by the statement, “at some point, you no longer know (…) what to say” (IP5). SC 2.3: Restricted visual representations The third subcategory of risks and disadvantages concerns the missing screen, which is the case for most digital voice assistants that are used and sold. This feature seems to be a strong obstacle to voice-commerce in particular, especially when products are to be purchased that require visualization in advance. Nearly all participants see missing images as a limitation in product selection. The following statements are examples of the necessity of visual representations in the buying process: “In this case, one wants to have an idea what a product looks like” (IP17) and “you would like to see it somehow before you buy something” (IP9). In

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addition, some participants did not ascribe this necessity to all products, as evidenced by the statement, “With milk, it didn’t bother me much, but with the water boiler, I would say it did” (IP4). SC 2.4: Reduced product choice This subcategory addresses the limited and controlled selection of products in the voice-commerce process (primarily due to the often missing screen and the product presentation via voice), which does not allow for an own or extended selection of products. This lack of product choice was also criticized by almost all participants. Statements such as, “that’s a bit restrictive” (IP11) or “you always get only one device suggested, which always has only one price, and I don’t know right away, is there something cheaper or something comparable?” (IP5), illustrate that the lack of product alternatives makes the ordering process more difficult. Furthermore, the random selection of a certain product represents a restriction with regard to one’s own wishes because the digital voice assistant “puts it immediately into the shopping cart without asking you beforehand if you are satisfied with it or if you want another one” (IP16). SC 2.5: Misuse of data and restriction of privacy The last subcategory addresses the participants’ fear of what happens to their data as well as how much their privacy is abused through the use of digital voice assistants, especially in voice-commerce, where sensitive financial data is transferred. The lack of perceived security is an often-cited barrier to voice-commerce. However, the participants’ views are divided regarding the personal and financial data that the voice assistant has access to when shopping. IP4 said that he would order products via voice-commerce from online shops that he had already shopped at in the past and that hence already have his data, thus making no difference. In contrast, IP13 took a more critical view of the potential dangers arising from the use of voice-commerce: “I am actually against it.” Participants feel that there is a risk of having to pass on personal data to the voice assistant, and most participants feel their privacy will be violated. UC3: Optimization measures In the third and last upper category, participants’ proposed measures for action are discussed on the basis of their experiences. These measures were developed based on the potential and advantages that the participants experienced during the purchasing process with digital voice assistants, as well as primarily on their negative experiences.

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SC 3.1: Structure and guidance This subcategory refers to the lack of assistance from digital voice assistants in voice-commerce and ways in which the purchase process could be made more structured. A noteworthy measure that would make shopping in voice-commerce easier is a clearly defined structure and presentation of instructions on the course of the ordering process. Some participants would like “a more structured ordering process” (IP3), “clearly structured commands” (IP2), or “simple commands (…), which can then be implemented” (IP22). One participant described feeling “left relatively alone” (IP12), and another stated that it would be beneficial if the digital voice assistant would help the consumer to some degree in the process (IP3). Missing support and explanations of how to proceed with the ordering process are thus perceived as obstacles in voice-commerce. SC 3.2: Product selection and filter adjustment In the second subcategory, the participants drew on their experiences with the digital voice assistant without a screen and presentation option. Thus, a need for optimization is seen primarily in the limited and pre-controlled product selection. For instance, some respondents explicitly wish to see greater product choice as an optimization measure, as described exemplarily in the statement, “other products should be displayed” (IP7). An integrated filter setting for products would also be desirable, but was mentioned by only one participant. As an example, IP3 mentioned filtering by price range or manufacturer when searching for mobile phones. SC 3.3: Personal and individual customizations In the last subcategory, as an optimization measure, the participants proposed that the product suggestions and the purchasing process itself should be adapted more individually to the respective consumer. This alludes in particular to the fact that a digital voice assistant is perceived as a kind of personal assistant, which should therefore also make individual adjustments and get to know “its” consumer, including their preferences and allergies, among other things. Hence, some participants would like to see personal preferences considered in the search for the right product. “Questions (…) what exactly do I want” (IP1), “what fat content should the milk contain” (IP16), and “organic or non-organic” (IP4) are statements about the inclusion of personal wishes regarding the products.

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3.3.4.6 Discussion Study 2 By combining the findings of the observational study and the conducted interviews, consumers’ views on the suitability of digital voice assistants for purchasing products on the Internet can be derived based on their experiences made in a real purchase process. Especially the results of the observational studies provide new insights into the interaction between humans and digital voice assistants. What is striking is the body language of the participants in all phases of the purchase process with the digital voice assistant. While consumers in traditional e-commerce or other subareas of e-commerce primarily complete the buying process in front of a screen, consumers in voice-commerce are situated between the classic, digital buying process and a perceived “face-to-face” purchase where they sit across from or interact with a real (buying) assistant or salesperson (here, the digital voice assistant). This feeling is reinforced by the humanization of that assistant. While the purchase process via computer, tablet, or cell phone is usually quite unemotional, the participants in the study displayed many facial expressions and gestures when issuing commands to the digital voice assistant, answering it, or receiving a response, similar to a salesperson or shopping consultant sitting across from them. For example, at the beginning of the purchase process with the digital voice assistant, the participants’ mood was still positive and relaxed; they made jokes at the expense of the digital voice assistant, or they mocked it. In addition, they smiled at the assistant, thanked it, and talked to it as if it was a real person. In the event of repeated misunderstanding on the part of the digital voice assistant, the participants’ reactions noticeably changed in terms of their gestures and facial expressions. With increasing impatience, they folded their hands together, for example, or tapped their fingers impatiently on their leg or the table, shrugged their shoulders, rolled their eyes, or closed them in annoyance. Changes in voice could also be detected during the purchase process. In the beginning, the participants spoke slowly and clearly, but as the process progressed, many of them began to speak faster and changed their pitch. These observations offer new insight into the interaction between consumers and digital voice assistants because, in contrast to other technologies or traditional e-commerce, the participants had strongly outward, emotionally bound reactions. This could be seen above all in negative emotions, such as impatience, nervousness, anger, or helplessness. Furthermore, we observed that in the case of problems or an increase in complexity (of the tasks), participants made direct “eye contact” with the digital voice assistants. In addition to the strong involvement of participants’ emotions in the interaction with the digital voice assistant, we also determined that certain forms of

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behavior can be assigned to different age groups. For example, it is striking that older participants on average made more “eye contact” with the digital voice assistant, especially when they received a response from it. In contrast, many younger participants only looked at the assistant when they addressed it directly. In addition, we noticed that the older participants were much more patient than their younger counterparts, even if the voice assistant did not execute a command as desired, or they showed greater persistence when the assistant misunderstood them or selected the wrong product. Thus, on average, older participants made several attempts to correct the voice assistant or to eventually perform the task successfully. In addition to the older participants’ different behavior toward the process, their social interactions with the digital voice assistant also differed noticeably from the younger participants in the study. For example, older participants used social interaction rules more often and treated the voice assistant with more patience, politeness, and respect by saying “please” and “thank you” or even apologizing when the digital voice assistant did not understand them. Here, the communication was strongly reminiscent of a real conversation between two people at “eye level.” In comparison, younger participants frequently interrupted the digital voice assistant when the answer was too long for them or an unwanted action was performed. The younger participants were clear that a technology was being used here (and not a living shopping assistant), and it was not treated with much patience and respect. Our impression was that while the older participants treated the voice assistant as an equal, living being, the younger participants treated it as a machine that was there to perform tasks for them. In addition to notable differences in handling and behavior between younger and older consumers, other personality traits were identified when using the digital voice assistant that differentiate the participants in the observational study. It is noticeable that some participants focused primarily on the security aspects of voice-commerce and, depending on these, decided whether or not to use this technology and this type of consumption and shopping. While these participants tended to focus on the potential dangers and disadvantages for them when using voice-commerce, other participants were driven by the innovation of voice-commerce and were rather euphoric about the use of digital voice assistants. These participants are primarily fascinated by (new) technical possibilities, curious about the integration of these into their everyday lives, and tend to be consumers who generally like to try out new innovations. Participants who saw advantages in the use of digital voice assistants, for the general public as well as for themselves, were also potentially positive about voice-commerce. These participants see the use of digital voice assistants as a way to enrich themselves

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and benefit from voice-commerce by providing them with an additional consumption option that makes the purchasing process even faster and more convenient. Finally, consumers can also be identified who, as with other technologies, tend to use voice-commerce only if it is also used by others. With these consumers, the social component plays an important role, as does the fact that they follow trends. They may hence only use a technology to belong to a group, even if their own motivation or enthusiasm for it is not primarily in the foreground, as in the case of the technology-euphoric user or the benefits-seeking or opportunistic user. The results of the observational study thus lead to the consideration of how the interaction with a digital voice assistant is shaped in relation to different consumer groups and whether the “character” of such an assistant should be adapted to the respective consumer group. For older consumers in the study, not only the functionality of the digital voice assistant, but also the social added value that the interaction with it contained, seemed to play a role. Here, the new findings can be linked to the antecedents of the TAM/TTF model (Davis, 1989; Goodhue, 1992; Dishaw and Strong, 1999), especially for younger consumer groups, whereas the technology usage intention of older consumers should be reconsidered and even theoretically extended. Especially for the older group, the approach of animism (e.g., Fournier 1998) seems to play a more important role than for younger consumers because here the integration of digital voice assistants into their everyday life as a social component, based on the observations, seems more likely. Therefore, the higher average patience span and the human-like communication by the older participants make it clear that a digital voice assistant could serve as a form of social contact and make everyday life easier, especially for older consumers, confirming the possibility of successfully anthropomorphizing digital voice assistants (e.g., Bartneck et al., 2008; Złotowski et al., 2015). Focusing next on the interviews conducted after the observation, the main disadvantage that was perceived by the participants during the ordering process is that no alternative products were suggested to them in the information search phase. They were thus impatient at first because the voice assistant provided too much information for the only suggested product, while in the next step they were surprised that no alternatives were suggested to them. Most of the participants wanted several alternatives to compare, since they stated that they would not buy a product without having heard information about alternative products. While some participants even found the product selection to be an aid to decision-making, the lack of choice of alternatives is a major disadvantage in voice-commerce for many participants. In most cases, the product selection provided by Amazon Echo is based on the current first placement of a product in the

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search for a keyword. Most of the time, these products have been awarded “Amazon’s Choice” (Cheris et al., 2017), giving broad scope for an arbitrary selection of products and brands in order to be able to sell them better (Mari, 2019). However, consumers want a selection of different products with different prices and features in order to be able to compare alternatives. In this context, we observed that more concrete words had to be mentioned to narrow down the search. While none of the participants became more concrete when searching for milk, some of them searched for concrete mobile phone brands, such as “Apple” and “Samsung,” or for concrete mobile phones, such as the “iPhone 11,” becoming more self-confident and independent during the last purchasing round. The aspect of missing visual representations in voice-commerce was also attributed a high value. For most participants, the absence of a product image was a missing decision criterion for the purchase. Product images help to build one’s attention to a product and trust in the supplier, and they play a central role in products with the decision criterion of appearance (Di et al., 2014). The results of this work demonstrate that in the case of low-involvement products such as milk, consumers do not necessarily need a product image, whereas in the water boiler purchase process, the desire for a product image was often expressed. A further disadvantage is the issue of data protection. According to the results, the participants would not disclose their complete private data. They are not sure where their personal data, such as voice recordings, are stored, and they are skeptical about whether that information will be passed on to third parties or whether they will be bugged. While stored voice recordings have significant benefits for the manufacturers of digital voice assistants (e.g., to improve features) or for police criminal investigations (Orr and Sanchez 2018), the participants in this study perceived this system to be restrictive and not secure enough. As illustrated in a study by Tuzovic and Paluch (2018), voice-commerce is more suitable for everyday consumer goods and low-involvement products. This can be confirmed by the findings of the present study, as most participants mentioned that they would use a digital voice assistant to buy low-involvement products, such as food, everyday consumer goods, and generally products in the low-price range. The reason for this is the acceptable risk of comparatively low monetary losses. In addition to functioning technical requirements, such as enabling a purchase, some measures can be developed to improve the buying experience when using a digital voice assistant. Insights from the qualitative surveys suggest that most participants would like to have guidance for the ordering process. Moreover, insights from the observations and the interviews revealed that some participants did not know which commands they had to formulate in the further procedure

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or that they needed more assistance from the voice assistant. As a measure to improve usage of such devices, the provision of further process steps and a more structured purchasing process can therefore be suggested. In addition, the voice assistant’s queries combined with the filtering of consumer-preferred product features would make the purchasing process easier. Participants would also like to have a screen where they can see the product to be sure they are ordering the right one. One measure of improvement would therefore be to attach a small screen to the voice assistant. Furthermore, due to high security concerns on the part of consumers, it is important to ensure that personal and financial data is secured (e.g., Hoy, 2018). Since buyers of digital voice assistants use the cloudbased system behind the physical device, in addition to the physical device itself, clear statements regarding the storage and transfer of data should be described in the purchase and usage contract. Nonetheless, despite the implementation problems with ordering in the observational study, the participants see the potential and benefits of digital voice assistants and regard them as helpful by eliminating the need for lengthy information searches and enabling consumers to perform tasks more efficiently. This enables a simple and time-saving usability and hence higher consumer satisfaction compared to traditional e-commerce (Kraus et al., 2019).

3.3.5

Conclusion and Implications of Study 1 and 2

The goal of the two aforementioned studies was to gain insight into consumer behavior and consumer attitudes toward shopping with voice assistants. The focus groups, interviews, and observational study were taken as a research basis, and our aim was to answer the three research questions. We investigated the relevance of voice assistants for consumers, as well as the handling of these assistants and the associated opportunities and risks. In addition, it became clear which measures consumers would like to see implemented to support consumer education and improve usage. With regard to the first research question, digital voice assistants and the associated shopping still play a minor role for the users of these technologies. Some consumers still regard online shopping with skepticism, as there are many risks involved. The use of voice assistants is not widespread, and the majority of consumers either prefer to use their smartphone to order products or choose the traditional option of visiting retail stores. This is partly due to them not possessing a voice assistant, not having any technical interest, and fearing data misuse (Hoy, 2018). In the case of owning a voice assistant, there was a lack

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of knowledge about the possibility and realization of voice-commerce. During the study, the voice assistant primarily took on the role of disclosing information about products. With regard to the question of how consumers handle digital voice assistants, it can be stated that most participants were, at least in the beginning, polite with the assistant. They talked to it as they would with a real person, which was facilitated by the humanization of the digital voice assistant, for example by the language it used (e.g., Bartneck et al., 2008; Castelo and Boegershausen, 2016; Araujo, 2018). This often resulted in a communicative conversation between the participant and the voice assistant. Participants’ initial shyness thus quickly changed to energetic and open communication with the voice assistant. Nevertheless, it could be shown that the used voice assistant still had some deficits in the understanding of the participants. Regarding the perceived limitations of voice-commerce, the limited selfdetermination of product selection in this type of commerce poses the risk of increased consumer vulnerability and makes measures to strengthen consumer education and skills particularly important (Baker et al., 2005). If safety concerns are addressed and the function of purchase is given, then digital voice assistants would be well suited for the purchase of low-involvement products in particular (Tuzovic and Paluch, 2018). However, the lack of a product selection that includes consideration of desirable product features is an obstacle that disregards the individuality of consumers. In contrast, several opportunities for consumers could also be derived from the results: All participants see substantial advantages for older or movement-restricted people. With the help of voice-commerce, a voice assistant could serve as a shopping aid or help in one’s household to operate smart home devices. Moreover, it offers a reminder function that seniors could use, for example for important appointments or medication, and is also available as a communication device and contact for single persons. The voice assistant could even contact a trusted person in an emergency situation. The results of the observations have already shown that older consumers in particular entered into a stronger social interaction with the digital voice assistant, and many potentials could result from this. For instance, older consumers who can no longer participate in traditional consumption (due to immobility or illness) and find traditional e-commerce too impersonal or long for interaction with a real person, such as in bricks-and-mortar stores, can use voice-commerce to satisfy their consumption needs on the one hand and to meet their social needs on the other. However, this applies to older consumers more strongly, since younger consumers in the purchasing process were less interested in social interaction than in completing the purchase process quickly and successfully. In this way, trust and an emotional bond can build up between older consumers and digital voice assistants

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in particular. In addition to the social components, digital voice assistants could also simplify the decision-making process for older consumers, who often seem overwhelmed by the flood of products and information on the Internet. Therefore, such assistants could contribute to increasing the functionality and effectiveness of older individuals’ consumption. For example, a digital voice assistant could be informed of requirements regarding a certain diet (e.g., in the case of diabetes); the assistant could then select the appropriate products directly for the consumer, or the individual could be informed when certain advantageous offers are available. In this way, the independence of many older consumers, who would otherwise be dependent on the help of third parties or excluded from consumption options, can be secured and prolonged. New insights for theory can also be derived from the results. At the beginning of the study, different approaches were presented that describe technology acceptance and use on the one hand (TAM/TTF model by Davis, 1989; Goodhue, 1992; Dishaw and Strong, 1999) and the meaning of the humanization of objects on the other (e.g., Fournier 1998). The two studies conducted have led to a better understanding of when these theoretical models apply to the use of digital voice assistants, which are representative of technologies in general. They have also highlighted differences in the relevance of these theories depending on the consumer group under consideration. For example, we found that the components of the TAM/TTF model (e.g., usability, usefulness, or task fit) are decisive for trying out a technology in the first place and using it permanently. The (emotional) intensity of use, as well as the degree to which a consumer likes to use artificial intelligence, however, can be influenced by the humanization of these technologies. It has also been shown that different generations value or interact with humanized technologies differently. For example, older consumers display significantly higher social interaction with digital voice assistants (e.g., through their gestures, mimics, and the way they communicate with the assistant), compared to younger consumers. An interplay of these two approaches, which address the functional and utilitarian component of technology use on the one hand and the emotional, affective motivation to use a technology on the other hand, thus seems to significantly influence consumers’ usage intention. Future research should therefore empirically examine the interaction effects of the TAM/TTF model and the animism approach. Yet, implications for politics, consumer protection, and manufacturers can be derived from the focus group discussions, observations, and interviews. Politicians should introduce laws that create more transparency on data processing and consumer rights. In addition, they should introduce guidelines for the producers of smart speaker technology, which include specifications for production

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and development, such that, for example, the elimination of speech recognition is guaranteed. In addition, manufacturers should offer various encryption methods so that data cannot be accessed by third parties and is better secured. To optimize consumer education, we also propose training and education on topics such as digital voice assistants, voice-commerce, and data protection. For example, upon first-time use of voice assistants, training should be offered to consumers, including a demonstration of an ordering process and the development of their competence and skill in the use of such assistants. This is recommended because some participants fear that vulnerable consumer groups, especially the elderly (e.g., Bala and Müller, 2014), who are generally more likely to have problems participating in certain market activities, may lack the skills and competence to use voice assistants. Nevertheless, the high willingness and interest to learn among the different age groups of the participants illustrates the generally existing motivation to learn how to use digital voice assistants. By implementing improvement measures, consumers’ performance expectations of digital voice assistants could increase, which may lead to a higher acceptance and positive attitude of usage. Finally, the benefits might overweigh the risks, resulting in a higher expected utility of voice-commerce and therefore in a positive usage intention (e.g., Fishburn, 1968). In summary, policymakers, consumer protection bodies, retailers, and manufacturers should investigate ways to improve the user experience of digital voice assistants and find solutions to raise consumer awareness of voice-controlled technologies. For example, the participants in this study see product visualization through digital voice assistants as a potential optimization measure, as they emphasized that the possibility of product presentation can influence their purchase decision. From the participants’ point of view, visualization for certain product groups, such as for some clothing items, is necessary for them to consider voice-commerce, as visual presentation supports the building of trust and enables better control and information possibilities, which participants are already accustomed to from traditional online shopping (e.g., Di et al., 2014). A visualization option for digital voice assistants, for example by using a screen or a hologram, as is already the case for a few models, is therefore desired by many of the participants surveyed. However, visualization is less important not only for some product groups, but also when already familiar products are to be repurchased. For instance, products such as coffee or other groceries can be (automatically) reordered as part of the Internet of Things, for which visualization would also not be necessary. Here, one should also be able to adapt the digital voice assistant itself (e.g., in its communication and language) as well as its performance of tasks and usability for the respective consumer groups, since the observations revealed significant differences in the interaction with digital voice

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assistants, especially between younger and older consumer groups. It is important that developers and retailers take consumer wishes into account, enter into an exchange with them, and offer more security, especially with regard to data protection, in order to build consumer confidence in digital voice assistants. Only when trust and security are in place, is there a chance that consumers will take a more positive view of voice-commerce and ultimately use it. Due to the consideration of only one specific voice assistant in the study (Amazon Echo), the binding of functions to specific devices is an important limitation of this work. The functions of other digital voice assistants, which could significantly change the order process, were not analyzed here. A comparison of the purchase processes of different voice assistants can hence be seen as a further research need. Additionally, the small number of observed participants and a restriction to German consumers represents a further limitation. Therefore, a comparison between consumers in different country markets could be considered in further research (e.g., Choi and Lee, 2003; Gefen et al., 2005; Hallikainen and Laukkanen, 2018), as the use of digital voice assistants as well as voice-commerce itself can vary greatly between countries. Furthermore, the study examined a large number of participants from different generations, but no subdivision into specific consumer groups took place. However, the results already suggest that specific consumer types are emerging in the use of intelligent systems such as digital voice assistants. These consumer types differ, for example, in terms of their attitudes, preferences, or behavior. For example, the following consumer types can already be derived and identified from the studies: the securityor privacy-conscious consumer, the technology-euphoric user, the benefit-seeking or opportunistic user, and the hanger-on user. Future research should build on these findings and investigate which consumer types use digital voice assistants and voice-commerce, how they differ from one another, and which consumer characteristics are typical for each group (e.g., age, gender, experience, lifestyle, etc.). Moreover, social influence is a component that shapes consumers’ acceptance of and attitude toward digital voice assistants. This influence was high in our focus group discussions, as group dynamics were established. Many of the group participants stated that they pay attention to the opinions of other people. This explains, among other things, why they would not use digital voice assistants in public or when in company, as they fear being judged negatively by others due to the currently general limited use of such devices. However, we also observed that less controversial opinions arose within a single focus group than between different focus groups, which leads to the assumption that the participants in one focus group tailored their opinions to the rest of the group. Thus, there is a greater

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risk that not all consumers in the focus group discussions of Study 1 expressed their exact and true opinions (unconsciously or because they were ashamed of having a different opinion), thus leading to an expression bias of the results, compared to the individual interviews in Study 2. In Study 2, however, there was a risk that, although the observation was carried out in participants’ homes to ensure that it was as natural as possible and that it took place in participants’ own environments, a bias due to situational influences arose. This bias could possibly result from the fact that the study did not take place in an identical and neutral environment, and situational influences from the outside could thus have existed that influenced the participants differently during the study and which the interviewer could not control for.

3.4

An Experimental Investigation of the Use of Artificial Intelligence in the Context of Complaint Management

3.4.1

Introduction

In recent years, the retail industry has been enjoying increasing profits through new sales and service opportunities enabled by constant digitization. Not only does the industry uses artificial intelligence (AI) through Industry 4.0 and the Internet of Things, but online retailing also gains new opportunities through intelligent helpers and automatism, which prove to be advantageous both economically and in handling. Often this AI is personified in either a physical (e.g., smart speakers, such as Amazon’s Alexa or Google Home) or sometimes only in a virtual (such as information or communication technology) form to make it more tangible and real for the consumer. This AI is often presented in socalled chatbots, which are virtual communication partners that enable written communication via chat. Usually, these chatbots are personalized by a name and a virtual, visual appearance. They allow the impression of communicating with a real person as an equivalent to a human service employee in a company. The market size of chatbots grew rapidly to $250 million US dollars in 2017, and the market size is expected to exceed $1.34 billion US dollars by 2024 (Pise, 2018). From the business perspective, 15% of 80 German companies already use chatbots for customer communication (Akhtar et al., 2019). Furthermore, chatbots are used in 28% of the real estate business, followed by the travel industry (16%), education (14%), health care (10%) and finance (5%) (Waghmare, 2019). Additionally, more than 21% of adults in the US and more than 80% of Generation

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Z use both language- as well as textbots for information searches and shopping (Del Valle, 2018), which reveals that innovation in AI and machine learning may enhance the ability of chatbots to drive the market (Grand View Research, 2017). The problem with the rapid increase is that many companies do not have significant experience with chatbots and AI. They lack the expertise to define the core aspects of the chatbot and fail to realize that it is possible to make chatbots as human as possible or to emphasize the artificial aspect (Hill et al., 2015; Jenkins et al., 2007). Because of their skills and ever-increasing number, chatbots are of significant relevance for companies, and thus, the optimization of chatbots has gained relevance. Here, digitization impacts complaint management, especially for companies that sell services or products to end consumers. For instance, classical human-human interaction is continuously being replaced by human-machine interactions (Zumstein and Hundertmark, 2017). Instead of a human, a chatbot manages and processes the complaints or at least manages the initial contact with the customer. The advantages for the company are automated processes and reduced personnel costs, while the consumer enjoys the advantage of being able to contact customer service 24 hours a day because the chatbot accesses all relevant information through predefined algorithms and programming and, in the best case, remains patient and friendly at all times. Based on prior research regarding general complaint management strategies, one could assume that a successful complaint handling process using a chatbot has a positive effect on the relationship between customer and company, whereas a negative experience leads to a negative impact, which afterward could lead to negative word of mouth (Davidow, 2003). Thus, the success factor of service is increasingly important in differentiating companies. Chatbots can provide infinite service and are economically more profitable for a company (Cui et al., 2017) because complaint behavior influences loyalty and profitability (Holloway et al., 2005). The faster a company can respond to a customer’s complaint, the faster that anger and frustration toward the company may vanish (Harrison-Walker, 2001), meaning that effective complaint handling can lead to a decrease in consumers’ anger (Homburg and Fürst, 2007). With the diffusion of the Internet and the increase of digitization, many complaints are now made online, and former communications channels have been replaced by online channels and online communication, such as via chatbots (Cho et al., 2002). Specifically, Generation Y and following generations tend to make online complaints since they are knowledgeable about technology and are influenced by social groups. However, they are also more willing to remain with a service provider or a retailer in case of a problem (Soares et al., 2017). When an online complaint is made, nearly everyone has

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the opportunity to read the anger of other consumers because it often occurs in a public digital place. This in turn can negatively influence other consumers who want to inform themselves about products or services. They may conclude that the quality is not sufficient or the customer service is poor, which may affect their behavioral intentions, especially purchase decisions (Hennig-Thurau and Walsh, 2004). Furthermore, the repurchase decisions for existing customers can be affected by the complaint handling strategy. Customers who complain because of a service failure tend to display higher repurchase intentions than customers who had no negative experiences in an online environment (Bijmolt et al., 2014). Paired with the last aspect, open communication regarding a failure can generate a positive image of the company and thus a positive service recovery (van Vaerenbergh et al., 2019). Applied to the context of chatbots in complaint management, conversational style and behavior are crucial in designing chatbots (OthlinghausWulhorst et al., 2019). A chatbot that is polite and answers in a friendly manner is evaluated more positively, which is also expected to have an influence during a complaint. Therefore, proper complaint handling can be a useful tool for companies to strengthen customer loyalty and future purchase intentions by increasing the expected benefit of the purchase (Othlinghaus-Wulhorst et al., 2019). When a company’s response consists of, for example, apologies and sincerity rather than primarily promotional information, the relationship quality between company and customer increases, and the behavioral intention of the customer is positively affected (Li et al., 2020). This explains the importance for companies to consider their complaint handling process. Previous studies concentrate on the optimization of complaint management to increase customer experience and satisfaction, thus preventing customers from leaving after a complaint (Fornell and Wernerfelt, 1987; Orsingher et al., 2010). However, studies as well as businesses have also investigated the application of AI regarding their influence on consumers’ attitudes toward the AI and related usage intentions (Gursoy et al., 2019; Ustundag and Cevikcan, 2018). Furthermore, former research has revealed that humanlike characteristics can have a positive influence on the behavioral intentions concerning technology acceptance and satisfaction with the service (Purington et al., 2017; Wagner et al., 2019). Specifically, the visual appearance can be a decisive influencing factor in the evaluation of the chatbot, since this appearance, in addition to the written communication, represents the initial perception of the chatbot. Therefore, the use of chatbots as a form of AI is becoming more attractive for companies, as their state of development allows competent and efficient usage. Moreover, costs can be reduced because there is no longer any need for human employees to manage customer complaints.

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AI can simulate empathy or act objectively and in a fact-oriented manner (Bittner and Shoury, 2019), and although both automation and AI are primarily about efficiency, it can be observed repeatedly that consumers want warm, friendly interactions, especially in the service area, and that this is a decisive factor in the evaluation of corporate service (Boninsegni et al., 2020; Goodwin and Smith, 1990; Shaw Brown and Sulzer-Azaroff, 1994). Numerous studies indicate that empathy is a decisive element in interpersonal interactions and in the context of relational perspectives (Betzler, 2019; Main et al., 2017). For instance, several studies have demonstrated that empathy is a crucial factor for service marketing, not only offline, but also in a virtual space (Bove, 2019; Feng et al., 2004; Varca, 2009; Wieseke et al., 2012). Applied to chatbots, these results suggest that a humanlike and more empathic representation of a chatbot may achieve positive consumer perceptions. We thus focus on this aspect with our research and investigate the importance of a human representation of a chatbot on the one hand and the effect of an affective and empathic reaction of AI on the behavioral intention of the consumer on the other hand. Additionally, the literature indicates that economic incentives are a predominant reason for many consumers to purchase from a retailer and that compensation is a key factor in the event of a complaint (Davidow and Dacin, 1997; Osarenkhoe and Komunda, 2013). To summarize, to our knowledge, there are scant studies that combine the aspect of complaint management strategies and the effects of different avatar representations by comparing humanlike characteristics. However, the increasing opportunities for consumers to receive information at any time of day or night and to consume such information flexibly and individually mean that the appeal and benefits of AI are becoming increasingly apparent. For the future, it is to be expected that AI will act more efficiently in handling tasks and solving problems both for the company and the customer. Thus, it may be positive for retailers not only to improve the handling and consumer-friendliness of AI, such as chatbots, but also to expand the human and emotional components that many consumers desire. In virtual contexts, everything seems distant, anonymous or machine-like; therefore, empathy and humanity could minimize this perceived distance and build a positive bond between retailer and customer. Thus, the findings of this study should allow online merchants and companies that use or are considering using chatbots to decide which visual features and characteristics the AI should present to enable the customer to have a positive experience, especially in complaint handling, where it is often decided whether the customer will remain with the company, as well as to increase profits in the long run. Hence, the purpose of this study is to examine how the chatbot’s reaction to the customer as well as the graphic representation of a robot-like chatbot avatar or a humanlike avatar affect

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the customer’s behavioral intention in a complaint process. Thus, the research questions are as follows: (1) To what extent does the choice of an avatar (robot or human) affect the customer’s behavioral intention? (2) To what extent does the less empathic or more empathic behavior of an avatar influence the behavioral intention? (3) What effect does the additional provision of monetary compensation have on the behavioral intention? (4) To what extent do human characteristics generally play a relevant role?

3.4.2

Literature Review and Hypotheses

We developed our conceptual framework based on anthropomorphism and complaint management research (see Figure 3.8). In summary, we propose that the representation of the chatbot avatar itself, its reaction to customers and the offer of a monetary compensation influence the customers’ behavioral intention when these factors are mediated by anthropomorphism and evaluation of redress.

Figure 3.8 Conceptual Model

Generally, the term complaint management is understood as a defensive reaction by the company to gain customer loyalty. Complaint management has the task of transforming the dissatisfaction of a customer into satisfaction. This can be accomplished by monetary recompense or other forms of compensation (Fornell and Wernerfelt, 1987). Therefore, complaint management covers the receipt, investigation, management and prevention of customer complaints (Johnston,

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2001). It not only involves repairing, rectifying or replacing a defective product or service, but also satisfying the customer subsequent to a defect (Fornell and Wernerfelt, 1988). Customers form opinions about the service quality of a company on the basis of communication with the contact person (Burgers et al., 2000). The literature reveals that service quality has a significant effect on future buying intentions, which means that customers are more willing to repurchase from a company if they know that the service is responsive (Taylor and Baker, 1994). In this study, this service quality is reflected in the appropriateness and handling of the service employee—here, the chatbot—with a complaint. This is reflected by the empathic reaction of the service employee toward the customer and by the offer of compensation for circumstances that have arisen, which in this case is a monetary redress. Chatbots are computer programs that process linguistic input from a consumer and then generate an intelligent answer (Khan and Das, 2018). During an interaction with the customer, the chatbot simulates the human language through integrated algorithms to make the communication between a human and the computer more natural (Abu and Atwell, 2007). The majority of the time, chatbots recognize certain phrases and provide ready-made answers (Zumstein and Hundertmark, 2017). If the program that customers communicate with is visualized by, for example, a human being, an animal, or a robot, then this visualization is called an avatar. This represents a virtual character that companies or people can use to represent themselves (Holzwarth et al., 2006). In this context, anthropomorphism is described as attributing human characteristics to non-human things (Bartneck et al., 2009). Since this study investigates the effect of two representative chatbot avatars, in addition to the monetary compensation and the reaction, we must examine the literature to justify the choice of the investigation of the visual representation. Previous research has indicated that the pictorial presentation influences consumers’ behavior in online communities (Steinmann et al., 2015) and that the appearance of the avatar influences the psychophysiological reactions of consumers (Ciechanowski et al., 2019). When chatbots are representatives and human replacements for the company, it seems to be important to make them similar to human beings because people evaluate those who are similar to themselves more positively. Therefore, a humanlike morphology affects the behavioral intention (Wexelblat, 1998) and evokes the reaction of the consumer in that the consumer treats chatbots as humans, evaluating them according to their appearance and social behavior (Kim and Sundar, 2012). Go and Sundar (2019) mention that the visual presentation of the chatbot can be more human or artificial. Thus, by manipulation of visual cues, consumers perceive the chatbot differently. For

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example, images of humans on a website convey human contact (Cyr et al., 2009); similarly, the presentation of the human avatar as a chatbot can be identified as this “human” contact. Visual cues are one part of social cues (Feine et al., 2019) that affect consumers’ behavior by using, for example, chatbots. In this context, humanlike visual cues symbolize the chatbot’s social presence (Lee and Oh, 2015). Speaking theoretically, social presence was originally defined as the “degree of salience of the other person in the interaction” (Short et al., 1976). Another definition was provided by Biocca et al. (2003), who define social presence as the feeling of “being with another in a mediated environment.” Therefore, the sense of human contact appears to be essential. Applied to this study, an anthropomorphic presentation such as a human avatar can be perceived as that other person during a complaint process. However, the representation of the chatbot and thus the visual attributes are not the only factors that foster the feeling of being with another person; the chatbot’s reaction in a critical situation, such as during a complaint, is also essential. Moreover, the presence of an avatar generally may provide an impression of face-to-face communication, which can positively affect the evaluation of the communication and thus the retailer (Holzwarth et al., 2006). According to Biocca et al. (2003) and Ogara et al. (2014), the feeling of being with others can be induced by, for example, the representation of the avatar or social cues including empathy and problem solving. Additionally, the interaction with the retailer, which in this study occurs through the chatbot, is a dimension of social presence (Lu et al., 2016). In general, the Social Presence Theory explains how consumers chose the communication channel and that different channels have various potential to awaken consumers’ awareness about the presence of another social actor (Mennecke et al. 2011). Therefore, consumers can be motivated to repurchase when anthropomorphic elements can be identified. It has been found in former studies that social presence has an influence on the shopping intention, which is why we argue that in a critical situation, such as a complaint, the humanlike representation and behavior of the avatar can affect the repurchase intention (Jiang et al., 2019). Originally, humans were accustomed to the presence of another human who shared the same space with them. With the increase of digital counterparts, humans may become accustomed to communicating with a robot in situations such as complaints. Nevertheless, they expect similar patterns from human-human interaction to be present in the human-computer interaction and thus draw human traits on the chatbot. This is related to their desire to be socially connected. Studies have demonstrated that the reason people using shadow IT in their workplaces

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is because they are seeking the social presence of others (Mallmann and Maçada, 2019). Comparatively, Reeves and Nass (1996) reveal that people treat technology, such as avatars, socially as soon as the computer or the technical device indicates social signals; in our case, for example, this is empathy in the form of the computer’s “reaction.” This leads to the assumption that in this study, the AIs, as humanlike and empathic avatars, have a positive influence on consumers’ behavioral intentions. Thus, we want to investigate whether there is a difference in the perception of two variations of the avatar (humanlike vs. robot-like) and which type of avatar reaction most affects the behavioral intention. Moreover, literature confirms that the familiarity with an avatar or an object increases when people add humanlike characteristics to it (Seeger et al., 2017). However, this effect is reversed when the anthropomorphism is excessive and makes it overly difficult for consumers to determine whether they are interacting with a real human or a machine, leading to confusion and a negative effect (Mori, 1970). Combined with this, humanlike cues, such as the appearance and the language style, influence perceived anthropomorphism of an avatar and the behavior toward a company (Araujo, 2018). Specifically, social presence is an important factor in avatars and service interactions (Xu et al., 2017). Furthermore, referring to the service environment, previous research has presented that avatars’ humanlike characteristics are seen as decisive factors for consumer reactions and satisfaction. Moreover, social presence and cues are associated with an increased behavioral intention (Fan et al., 2016; Johnson and Acquavella, 2012). According to Gursoy et al. (2019), anthropomorphic cues influence the acceptance of artificial devices in service encounters, such as the use of chatbots for receiving complaints. As mentioned in the Computers are Social Actors paradigm (CASA) (Nass et al., 1994), technology itself, including chatbots, is seen as a social character with anthropomorphic cues that influence the behavioral intention. The CASA paradigm means that humans treat computers socially as soon as they indicate any kind of human behavior. One reason for anthropomorphizing is the desire for a social interaction (Epley et al. 2007), which can be also seen in a conversation with a chatbot that demonstrates human characteristics, such as appearance and empathy. Moreover, social presence of any kind that is symbolized through anthropomorphism and the interactivity itself is reported as an influencing factor for the perception of anthropomorphism and on the behavioral intentions (Bente et al., 2008; Sundar et al., 2016), resulting in additional positive intentions. Based on the literature, both demonstrating empathy and attentively listening to a complaint have a crucial influence on the experience of customer service

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(Parasuraman et al., 1988). Especially in complaint management, empathy can be a decisive factor in generating customer complaint satisfaction (Stauss, 2002). Empathic contact persons generate positive emotional reactions from the customer, whereby they build a positive relationship with the other person more quickly. This is primarily intended to generate customer loyalty and influence the customer’s behavioral intention, since the customer is more likely to terminate a negative relationship (Liljander and Strandvik, 1995). Moreover, the contact persons are expected to place themselves in the shoes of the customers and understand their feelings (Burgers et al., 2000). Applied to our research, the contact person is a chatbot as a representative for the company. Studies in the health care context reveal that empathic and emotional chatbots are preferred over non-empathic ones (Liu and Sundar, 2018), which means that consumers transfer their expectations for human contact persons to the virtual contact person—the chatbot. This is further confirmed by van der Zwaan’s (2014) study, in which a chatbot was inserted for victims of cyberbullying. The chatbot that expressed sympathy was evaluated more positively, and participants were more willing to continue working with it compared to those that demonstrated no empathy (Bickmore and Picard, 2004). Moreover, research generally reveals that the behavioral intention can be influenced by the empathy of a chatbot (Malik et al., 2020). Therefore, the empathy of a chatbot should allow the customers to feel that their personal needs have priority. In particular, consumers prefer to receive an answer from a chatbot instead of waiting for a response from a human contact person (Ambawat and Wadera, 2019); therefore, it is important to investigate empathy in a critical context, such as during a complaint process. In addition, our study addresses company complaint management as a service process, in which chatbot avatars are involved. In complaint contexts, however, not only the avatar plays a role, but also the type of complaint management strategy is important. Companies offer either tangible or intangible benefits when processing complaints. Tangible benefits, for example, include monetary compensations (e.g., vouchers or discounts), while intangible benefits imply nonmonetary factors, such as apologies. Both tangible and intangible elements can be utilized as redress in complaint management situations (Estelami, 2000; Gelbrich and Roschk, 2011). Some studies have found that compensation has an influence on the behavioral intention generally (Sparks and McColl Kennedy, 2001). Gelbrich et al. (2015) demonstrated that for future behavioral intention, the degree of the compensation is dependent on the relationship between customer and company, but generally, they find that it is necessary to offer at least some compensation. Moreover, especially in the online environment, immediate

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compensation is expected by consumers to overcome their dissatisfaction and to allow them to feel that they have been treated fairly (Ong and The, 2016). In summary, we hypothesize: H1:

H2:

H3:

3.4.3

The behavioral intention is influenced positively by (a) the human likeness of the avatar, (b) the empathic reaction, (c) the compensation offered by the avatar and (d) the interaction between these. The effect of (a) the visual representation of the avatar, (b) the avatar’s reaction, (c) the compensation and (d) the interaction between these on the behavioral intention is mediated by anthropomorphism. The effect of (a) the visual representation of the avatar, (b) the avatar’s reaction, (c) the compensation and (d) the interaction between these on the behavioral intention is mediated by evaluation of redress.

Empirical Study

To verify the hypotheses, we conducted an experimental study with a 2 (human vs. robot) ×2 (less empathic vs. more empathic) ×2 (no voucher vs. voucher) between-subject design using an online survey. The subjects were recruited randomly via social media channels and online forums. Once the participants began the questionnaire, they were asked to read the scenario and imagine themselves in it. Concurrently, they were provided with information about the complaint process and about the talk with a chatbot. The experiment began with the presentation of a scenario, after which each participant was presented with a chat history that varied in its avatar presentation and content. The service failure—that a pair of ordered headphones were delivered broken and that an exchange of the product was suggested—was constant in all scenarios. They were asked to imagine that the ordered headphones were broken and that they complain about this to the retailer with the chat function. Based on the introduction, they know that the contact person they are communicating with is a chatbot. However, there was a difference in the representation of the selected avatar, which visualized the contact person. Either a human or a robot was visualized as the contact person for the complaint. Moreover, the chatbot apologized for the problem, or the answer offered no form of personal apology. Furthermore, there was a manipulation by offering or not offering a voucher as compensation for the service failure. These variants resulted in eight versions of the complaint handling for the same initial problem (see Figure 3.9).

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Human - more empathic - voucher

Robot - more empathic - voucher

Human - less empathic - voucher

Robot - less empathic – voucher

Figure 3.9 Scenarios of the Experiment

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Human - more empathic – no voucher

Robot - more empathic – no voucher

Human - less empathic – no voucher

Robot - less empathic – no voucher

Figure 3.9 (continued)

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The questions were based on well-established multi-item scales from the literature in the context of complaint management and anthropomorphism (see Table 3.23). All scales were measured with 7-point Likert scales (1 = “I totally disagree”—7 = “I totally agree”). To measure the behavioral intention, we used four iems from Bhattacherjee (2001) (e.g., “In the future I will use offers from this retailer again.”, α = .815). Based on Davidow’s (2003) study, we adopted the three item scales of evaluation of redress (e.g., “The answer from my contact person left me in an improved position than I was before the problem.”, α = .765). Additionally, we relied on Bartneck et al. (2009) measuring anthropomorphism with three items (e.g., “artificial—alive”, α = .821) via a semantical differential from 1 to 7. A manipulation check was conducted to ensure that the participants in the survey perceived the manipulations as intended. For the representation of the avatar (human vs. robot), we confirm a successful manipulation. The participants were asked to choose whether they saw a human or a robot chatbot. The analysis reveals that the majority of the participants who were presented with the human chatbot identified the avatar presentation correctly (83%), while the majority of those who were assigned to the robot chatbot thought that they had contact with a robot-like chatbot (92%). The participants who answered incorrectly were not

Table 3.23 Scales used in the study Constructs (Cronbach’s Alpha)

Items

Sources

Redress After receiving the reply Adapted from Davidow (α = .77; Mean = 5.05; SD from my contact person, I (2003) = 1.24) am in better position than before the complaint. The answer from my contact person left me in an improved position than I was before the problem. The result that I received from my contact person put me in a situation that was better than before the complaint. Anthropomorphism Fake—Natural (α = .82; Mean = 3.72; SD Artificial—Alive = 1.40) Unconscious—Conscious

Adapted from Bartneck et al. (2009) (continued)

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Table 3.23 (continued) Constructs (Cronbach’s Alpha)

Items

Sources

Behavioral Intention All in all, I will buy offers Adapted from Bhattacherjee (α = .82; Mean = 4.59; SD from this retailer again in the (2001) = 1.13) future. In the future I will use offers from this retailer again. I will expand my activities with this retailer if possible. It is likely that I will buy from this retailer again in the future. Manipulation Checks: Reaction The contact person seemed Adapted from Homburg and (α = .78; Mean = 4.96; SD to have great interest in the Fürst (2005) = 1.11) problem of the customer. The contact person understood the problem exactly. The contact person treated the customer roughly. The contact person tried very hard to solve the problem. All in all, the way in which the contact person responded to its customer’s report was fair. Compensation

No voucher—voucher

Own development

Avatar

Human Robot

Own development

considered in this study. Consequently, 389 participants contributed to the study (n > 30 in each condition, Mage = 29.00, SD = 12.49, 57.1% women). Additionally, a t-test indicated that the characteristics between each experimental factor, reaction (less empathic vs. more empathic; Mmore empathic = 4.75, SD = 1.12; Mless empathic = 4.41, SD = 1.12) and compensation (no voucher vs. voucher; Mvoucher = 4.82, SD = 1.12; Mno voucher = 4.37, SD = 1.10), differed significantly from each other (reaction: t = −2.947, p < .01; compensation: t = −4.022, p < .001). The compensation participants were asked with a semantic differential (“no voucher” to “voucher”) whether they were offered a voucher. To check for

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the reaction, five items were adapted from Homburg and Fürst (2005) (e.g., “The contact person understood the problem exactly,” α = .780). Both scales were based on a 7-point Likert scale (1 = “I totally disagree”—7 = “I totally agree”).

3.4.4

Results

Since a significant direct effect between the experimental factors and the behavioral intention is necessary to determine mediations, we conducted several ANOVAs to test our hypotheses. A significant positive influence of the avatar on the behavioral intention is found (p < .01), as well as for reaction (p < .01) and compensation (p < .001); the effect strength (partial eta squared) for compensation is observed to be the strongest for all three experimental factors, while R2 is reported to be .091. Thus, the hypotheses (H1a–c) can be confirmed, indicating a positive influence of all three experimental factors on the behavioral intention (see Table 3.24). To identify whether mediating variables influence the effects of the experimental factors on the behavioral intention, we ran multiple analyses of covariance (ANCOVA) based on previous literature (Hayes and Preacher, 2013; Song and Zinkhan, 2008). To verify whether the postulated variables anthropomorphism and evaluation of redress mediate the form of service recovery, we first investigated following the approach of Baron and Kenny (1986) the direct effect of the experimental factors on the mediators. Therefore, anthropomorphism and evaluation of redress are included as covariates. For all three experimental factors, a significant positive effect on anthropomorphism can be determined (avatar: p < .001; reaction: p < .05; compensation: p < .05), as well as a significant positive effect on evaluation of redress (avatar: p < .01; reaction: p < .05; compensation: p < .05). Additionally, anthropomorphism and evaluation of redress both have a positive significant influence on the behavioral intention (anthropomorphism: p < .001; redress: p < .001). By decreasing the mean square (MS) of the main effect, we can observe the mediation effect: for anthropomorphism as a mediator, we can observe a direct only mediation for avatar (p > .05) and a complementary mediation for both reaction (p < .05) and compensation (p < .001) as experimental factors. Consequently, H2a is confirmed since anthropomorphism entirely mediates the effect between avatar and behavioral intention. Moreover, we can confirm H2b and H2c since anthropomorphism mediates the effect between reaction and behavioral intention as well as between compensation and behavioral intention complementarily. Concerning the evaluation of redress, the main effects of all three experimental factors on the behavioral intention are worsened by the presence of evaluation of redress as a mediator, which, according to Zhao

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et al. (2010), results in a complementary mediation (avatar: p > .05; reaction: p > .05; compensation p > .001). These results present that the behavioral intention can be explained by the complementary mediating influence of evaluation of redress; hence, we also confirm H3a-c. Examining H2d and H3d, we see that the interaction between the three experimental factors on the behavioral intention is mediated neither by anthropomorphism nor evaluation of redress; thus, we reject both hypotheses. The strongest mean value for the behavioral intention is observed for the scenario of a more empathic human avatar that offers compensation (M = 5.03, SD = 1.21), while the mean value for the scenario of a more empathic robot avatar that offers compensation is slightly lower for the behavioral intention (M = 4.77, SD = 1.06; see Table 3.25). However, when the mean values reveal differences, there is no significant effect between these two groups. The lowest mean values for the behavioral intention are observed for a human avatar (M = 4.41, SD = 1.02) and a robot avatar (M = 3.97, SD = 1.17, t = 2.08 p < .05) that are each less empathic and provide no compensation. No significant interaction effects between each of the three experimental factors can be reported. Therefore, we cannot confirm H1d. The results further present that in the main effects, the primary driver for the human avatar is empathy, as it has a higher mean value for a more empathic reaction that offers no compensation (M = 4.86, SD = 1.02) compared to offering compensation with a less empathic reaction (M = 4.68, SD = .99). Furthermore, the mean values here portray a difference in the perception, whereas statistically, no significance can be seen between these two groups. Nevertheless, the absolute mean values together with previous studies confirm that empathy is more important than compensation when the avatar is human. Additionally, when the avatar is robot-like, empathy is also more important than compensation. To identify whether mediating variables influence the effects of the experimental factors on the behavioral intention, we ran multiple analyses of covariance (ANCOVA) based on previous literature (Hayes and Preacher, 2013; Song and Zinkhan, 2008). To verify whether the postulated variables anthropomorphism and evaluation of redress mediate the form of service recovery, we first investigated following the approach of Baron and Kenny (1986) the direct effect of the experimental factors on the mediators. Therefore, anthropomorphism and evaluation of redress are included as covariates. For all three experimental factors, a significant positive effect on anthropomorphism can be determined (avatar: p < .001; reaction: p < .05; compensation: p < .05), as well as a significant positive effect on evaluation of redress (avatar: p < .01; reaction: p < .05; compensation: p < .05). Additionally, anthropomorphism and evaluation of redress both have a

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positive significant influence on the behavioral intention (anthropomorphism: p < .001; redress: p < .001). By decreasing the mean square (MS) of the main effect, we can observe the mediation effect: for anthropomorphism as a mediator, we can observe a direct only mediation for avatar (p > .05) and a complementary mediation for both reaction (p < .05) and compensation (p < .001) as experimental factors. Consequently, H2a is confirmed since anthropomorphism entirely mediates the effect between avatar and behavioral intention. Moreover, we can confirm H2b and H2c since anthropomorphism mediates the effect between reaction and behavioral intention as well as between compensation and behavioral intention complementarily. Concerning the evaluation of redress, the main effects of all three experimental factors on the behavioral intention are worsened by the presence of evaluation of redress as a mediator, which, according to Zhao et al. (2010), results in a complementary mediation (avatar: p > .05; reaction: p > .05; compensation p > .001). These results present that the behavioral intention can be explained by the complementary mediating influence of evaluation of redress; hence, we also confirm H3a-c. Examining H2d and H3d, we see that the interaction between the three experimental factors on the behavioral intention is mediated neither by anthropomorphism nor evaluation of redress; thus, we reject both hypotheses (Table 3.24). Finally, correlations are presented in Table 3.26. Table 3.24 ANOVA for the influence of avatar, reaction, and compensation on the behavioral intention and ANCOVA with anthropomorphism and the evaluation of redress as mediators F

Sig.

η2

Independent Variable(s)

Dependent Variable

Avatar (human vs. robot)

Behavioral Intention

9.098

.003

.023

Reaction (less empathic vs. more empathic)

Behavioral Intention

8.684

.003

.022

Compensation (voucher vs. no voucher)

Behavioral Intention 16.185

.001

.040

Avatar * Reaction

Behavioral Intention

1.227

.269

.003

Avatar * Compensation

Behavioral Intention

2.896

.069

.010 (continued)

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Table 3.24 (continued) F

Sig.

η2

Independent Variable(s)

Dependent Variable

Reaction * Compensation

Behavioral Intention

.365

.546

.001

Avatar * Reaction* Compensation

Behavioral Intention

.017

.896

.000

Avatar (human vs. robot)

Anthropomorphism

12.312

.001

.031

Reaction (less empathic vs. more empathic)

Anthropomorphism

4.687

.031

.012

Compensation (voucher vs. no voucher)

Anthropomorphism

4.298

.039

.011

Avatar (human vs. robot)

Evaluation of Redress

6.711

.010

.017

Reaction (less empathic vs. more empathic)

Evaluation of Redress

4.488

.035

.011

Compensation (voucher vs. no voucher)

Evaluation of Redress

4.247

.040

.011

Mediation Effects: Experimental Factor; Mediator

Decrease of Mean Square

Avatar (human vs. robot); Anthropomorphism

Behavioral Intention

3.001

.084

.008

67.40%

Reaction (less empathic vs. more empathic); Anthropomorphism

Behavioral Intention

5.047

.025

.013

48.67%

Compensation (voucher vs. no voucher); Anthropomorphism

Behavioral Intention 11.961

.001

Avatar (human vs. robot); Evaluation of Redress

Behavioral Intention

.029

4.824

0.30

.012

34.85%

47.71%

(continued)

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Table 3.24 (continued) F

Sig.

η2

Independent Variable(s)

Dependent Variable

Reaction (less empathic vs. more empathic); Evaluation of Redress

Behavioral Intention

5.339

.021

.014

46.69%

Compensation (voucher vs. no voucher); Evaluation of Redress

Behavioral Intention 12.156

.001

.031

32.34%

Avatar * Reaction; Anthropomorphism

Behavioral Intention

.345

.557

.001

71.88%

Avatar * Compensation; Anthropomorphism

Behavioral Intention

2.362

.125

.006

18.44%

Reaction * Compensation; Anthropomorphism

Behavioral Intention

.297

.586

.001

18.63%

Avatar * Reaction* Compensation; Anthropomorphism

Behavioral Intention

.001

.979

.000

94.12%

Avatar * Reaction; Evaluation of Redress

Behavioral Intention

1.192

.276

.003

2.85%

Avatar * Compensation; Evaluation of Redress

Behavioral Intention

1.210

.272

.003

58.22%

Reaction * Compensation; Evaluation of Redress

Behavioral Intention

.359

.549

.001

1.64%

Avatar * Reaction* Compensation; Evaluation of Redress

Behavioral Intention

.101

.751

.000

–

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Table 3.25 Mean values Avatar

Reaction

Compensation

Mean Value

SD

Humanlike

Less empathic

No voucher Voucher

4.41 4.68

1.02 0.99

More empathic

No voucher Voucher

4.86 5.03

1.02 1.21

Less empathic

No voucher Voucher

3.97 4.71

1.16 1.17

More empathic

No voucher Voucher

4.19 4.77

1.02 1.06

Robot-like

Effects of the experimental factors on the BI were measured on a 7-point-Likert scale according to Bhattacherjee (2001) (1 = “I totally disagree”—7 = “I totally agree”).

Table 3.26 Correlation table Behavioral Intention Behavioral Intention

1

Anthropomorphism

.415***

Redress

.381***

Anthropomorphism .415*** 1 .277***

Redress .381*** .277*** 1

* significant at p < .05; ** significant at p < 0.01; *** significant at p < .001

3.4.5

Discussion

This study investigated the difference between a human and non-human avatar as they provided different complaint answers. The results demonstrate that the choice of the avatar, its reaction and the compensation each play a decisive role in influencing consumer behavior during a complaint. Based on the results, despite a complaint, consumers are likely to purchase from retailers again when the avatar employs more empathic reactions, is portrayed as humanlike or when it offers some kind of compensation. Furthermore, the compensation has the predominant effect on the behavioral intention, indicating that offering an amount of money is essential in successful management of the complaint process. This aligns with previous research that illustrates that compensation leads to an increased behavioral intention (Gelbrich et al., 2015). The fact that compensation exerts the strongest influence on the behavioral intention compared to other experimental factors is additionally illustrated by the effect strength, known as the partial eta squared (η2 = .040). Additionally, the mean values reveal that consumers are

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more likely to shop at a retailer again if the avatar presentation is that of a human rather than a robot, regardless of whether the robot is as empathic as and offers the same compensation as the human avatar. This demonstrates that the anthropomorphization and social presence of the avatar presentation has a decisive role on consumer behavior, which is confirmed by other studies (Jiang et al., 2019) but is also further discussed in the mediation analysis of this study. Additionally, considering the interaction between the experimental factors, empathy has a greater influence on the behavioral intention than compensation when a human avatar is presented. Therefore, the relevance of the avatar’s humanity is decisive for consumers, as they are seeking social presence and behavior in the avatar due to its visual human presentation (Mallmann and Maçada, 2019). Moreover, the results demonstrate that the representation of the avatar has a stronger effect on anthropomorphism than the reaction or compensation. This indicates that, although empathizing and offering a voucher is important, the human likeness is evaluated depending on the appearance of the chatbot. Again, the effect of the avatar is stronger than the effect of its reaction and the effect of compensation on evaluation of redress, meaning that the representation of the chatbot is more important for consumers’ feelings about how they were treated than empathic words or even the offering of a voucher. However, previous research portrays that consumers expect an immediately accessible and responsive redress procedure from retailers (Ong and The, 2016)16) and that overcompensation and an apology can repair consumer trust in the retailer in the event of an abuse of trust (Cui et al., 2018). Thus, although the voucher does not seem to be the most decisive factor in evaluation of redress, offering redress, especially if presenting the avatar as a robot cannot be avoided (e.g., if the company mascot is to be used as an avatar), may increase the consumer intention to return to this retailer, as seen in the results above; the robot presentation generally exerts a worse influence on the behavioral intention compared to the humanlike avatar. Additionally, the mean values of a human avatar on evaluation of redress are higher (M = 5.20, SD = 1.27) than a robot avatar on evaluation of redress (M = 4.88, SD = 1.20), which again presents the higher influence of a humanlike avatar (t = 2.59, p < .010). Therefore, consumers place more emphasis on who offers the redress than what kind of redress is offered. These human visual cues may be preferred by consumers, as they suggest a social presence of the chatbot (Lee and Oh, 2015), and make them more similar to consumers as social beings (Mallmann and Maçada, 2019). Furthermore, for anthropomorphism and evaluation of redress, the representation of the avatar has the strongest effect size compared to the other experimental factors.

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Additionally, the direct effects are mediated by the perception of anthropomorphism and evaluation of redress. For example, the perception of the avatar is mediated entirely by anthropomorphism, while it only causes complementary mediations for reaction and compensation as experimental factors. This indicates that the behavioral intention depends on how human and alive the avatar seems, regardless of whether it is a human or a robot. Therefore, anthropomorphism mediates the compensation’s influence on the behavioral intention more significantly than the avatar’s presentation influences the behavioral intention, indicating that the behavioral intention depends on how humanlike the consumers perceive the offered compensation. Similar results can be observed for redress as a mediator: the influence of the choice of the avatar as well as that of the reaction and the compensation on the behavioral intention is mediated by the influence of evaluation of redress in a complementary mediation, but their direct impact on the behavioral intention does not disappear completely; rather, it continues to indicate their significance. Therefore, we observe that the behavioral intention is increased by consumers’ evaluation of redress when an empathic reaction or compensation is offered; however, choosing an avatar that is perceived as alive and vivid, regardless of whether it appears as a human or a robot, is decisive for forming the behavioral intention. For companies, this is a first hint regarding creating a chatbot to offer their customers after-sales service because, in the worst case, a bad service experience is connected with the most radical form of reaction: to stop future purchases and move to another company (Bolfing, 1989). Therefore, the behavior of the company is decisive for the consumer’s perception. In this case, the behavior of the chatbot is a representation of a human employee. The difficulty is that the chat-text is typically scripted, but companies are working on the recognition of free texts and their understanding in order create a natural conversation (Zumstein and Hundertmark, 2017). Since the results present that anthropomorphism strengthens consumers’ behavioral intentions, embedding humanlike behavior is essential. One explanation is that people are searching for social contact and are accustomed to feeling a social presence when they are communicating why they prefer anthropomorphic elements in chatbots. Consequently, sharing the same space to feel connected also occurs through digital counterparts, and companies have the opportunity to increase the feeling of a socially present character by adding humanlike traits.

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177

Conclusion and Implications

Our results indicate that it is an important premise to offer consumers a adequate after-sales service to bind them to the company, which is considerably cheaper than acquiring new customers (Reichheld and Schefter, 2000). As chatbot technologies have become an integral part of everyday consumer life, evaluation of the chatbot performance is critical to achieving and maintaining this competitive advantage (Lin and Hsieh, 2011). Aligning with Davidow (2003), we can confirm that successful complaint handling with a chatbot leads to a more positive behavioral intention and thus a positive effect on the relationship between customer and company. Past research has illustrated that empathy is a decisive factor in service marketing, especially in the online environment (Bove, 2019), and our findings demonstrate that this holds true during a complaint process as well. Hence, this indicates both that consumers want a friendly interaction with the chatbot in the service area (Boninsegni et al., 2020) and the importance of the creation of a humanlike chatbot. Conceptually, friendly complaints reflect customers’ opinions for positive relationships with the company, while neglect and redress-seeking complaints reflect the lack of connection (Ro, 2013). This partially contradicts our results, in that evaluation of redress is essential for the behavioral intention. It is important to create an empathic avatar but also to offer compensation. As literature illustrates that an economic incentive is one reason for consumers to repurchase from a retailer after a complaint (Osarenkhoe and Komunda, 2013), we can confirm these results. However, it would be interesting to investigate if the degree of the compensation depends on the appearance of the avatar. Perhaps a robot-like avatar is more forgivable than a human because consumers know of their lack of intelligence. For instance, some studies reveal that it is more important to present the combination of an admission of guilt and additionally to offer money during an online complaint without an avatar (Marx and Nimmermann, 2017). Consequently, it would be interesting to transfer this to a complaint process with chatbot avatars and investigate whether an admission of guilt also works with a robot-like chatbot appearance since feeling guilty is attributed to humans rather than to robots. The perception of a chatbot and the expectation also influence the evaluation of the service quality. Maintaining a significant level of service quality is a key factor in achieving a competitive advantage for service providers. Investigating the combination of the perceived service quality and chatbots would be an interesting future research field.

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Although this research has produced interesting results, there are also limitation since the study is based on a limited German sample and is scenario based. Therefore, we recommend investigating whether the cultural background has an impact on the perception of the avatar and its behavior; additionally, we recommend replicating the study in an actual field experiment. Moreover, the Rsquared value is sufficient to determine a first clue, but there are probably other influencing factors between the two groups, such as attentiveness or whether the response time is evaluated differently between a human and a robot, which can be disclosed through further research.

3.5

From Owning to Renting through Rental-commerce Websites—A Qualitative Analysis of the Importance of Ownership

3.5.1

Introduction

Digitization, online retailing and mobile apps have already had an impact on ownership purchasing and user behavior in the last two decades. Particularly the music and media industry has suffered strongly and faced the constant challenges and consequences of digitization. The entire media usage behavior has changed. Streaming dominates these two industries today. The user consumes, but longer focuses primarily on the purchase of property, but on the purchase of rights of use—and this trend is increasingly spreading to other areas as well. This trend is even reinforced by the fact that users are increasingly tending to share products they consume with other users. “What’s mine is yours” is what Botsman and Rogers (2011) call their work on collaborative consumption, and it sums up what the sharing economy is all about. In recent years, great interest has been devoted to the economy of sharing. Companies as well as private individuals can offer goods and services as providers that would otherwise remain unused, while users can use these goods and services for a certain period of time at low cost (Zervas et al., 2014). In the sharing economy, collaborative consumption is therefore in the focus in which resources are not only consumed jointly but also sustainably (Belk, 2014; Hamari et al., 2016; Hawlitschek et al., 2016; Böcker and Meelen, 2017). But the sharing economy can occur in various forms and orientations. Researchers in this field establish different categorizations in order to separate sharing models from each other (e.g. Botsman and Rogers, 2011; Schor and Fitzmaurice 2014). With a view to current studies by KPMG (2017) and

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PwC (2017), especially the sharing model of rental-commerce, i.e. renting products with the help of information and communication technologies, is becoming more and more popular among both companies and users. According to KPMG’s Consumer Barometer (KPMG, 2017), 43% of the respondents are interested in renting products in the future. While already over 75% of the respondents believe that borrowing or renting products is sometimes more advantageous than buying them. Low costs are therefore the most important advantage for users (PwC, 2019). In addition, previous studies have already focused on rental models of the sharing economy, but are mainly concerned with conventional (not online mediated) rental models. Examples include models such as renting an apartment, which usually involves high rates and additional running expenses (e.g. monthly costs), high-priced luxury products, which are usually limited to special occasions and short periods, like renting a sports car, as well as non-commercial forms of sharing, as often used by family and friends and which have a specific social character such as sharing food or clothing to prevent waste (Miron, 1995; Bock et al., 2005; Zervas et al., 2017). What is new, is that the increasing relevance of rental models and the associated use of unowned products is leading to changes in consumer behavior, especially concerning everyday items. In rental-commerce, user behavior is moving away from “classic” forms of consumption under property law towards the alternative of “non-ownership” consumption via Internet platforms that offer rental options. “Non-ownership” means that users order products from an online retailer which they are allowed to use to the extent of their attributed product use. However, the user does not own these products: He or she pays a monthly fee to be allowed to use them and must return the products to the retailer, the actual owner, after an agreed time. In the case of non-proprietary consumption, only the right of use is transferred to the user, while the full rights (use but also ownership) are reserved exclusively for the owner (Moeller and Wittkowski, 2010). Since the rental-commerce model is still relatively new and not many companies are yet active in this area, so far little is known about the drivers and barriers that lead user to rent products online rather than buying them. This could be due to the fact that they have not yet had any experience with this model and are therefore not familiar with it. One of biggest challenges in the sharing economy is the lack of customer confidence in online activities and transactions (Cheung and Lee, 2000; Dervojeda et al., 2013), which therefore might also be the case for rental-commerce. Hence, the examination of the subject of rentalcommerce makes it clear that this concept has so far hardly been researched from a scientific perspective, which is probably due to the novelty of this business model. Since rental-commerce, in contrast to other models of the sharing

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economy, has a somewhat different character, a research gap can be assumed in this context: The circumstances in which the simple use of a product is preferred by the consumer to actual possession and vice versa are not yet apparent from the literature. In addition, this consumer study shows the advantages and disadvantages, motivations, expectations, influencing factors and perceived risks from the consumers’ point of view. Thus, this study focuses on answering the following research questions: RQ1: RQ2:

What relevance do ownership and possession have for consumers nowadays, using rental-commerce as an example? Which determinants tempt consumers to renounce ownership and prefer to rent products online instead?

The aim of the discussion is to compare previous theoretical approaches on the sharing economy and in particular on rental-commerce with the results of a qualitative study. The fact that users rent products instead of buying them can be explained by the property right theory, which states that experiencing a product is more important for the user than its actual possession (Furubotn and Pejovich, 1972; Baumeister et al., 2015). However, both the theory of perceived ownership and the endowment effect imply the opposite: according to these two theories, users develop feelings and emotions toward an object that may influence their behavioral intentions, binding them to the object (Thaler, 1980; Pierce et al., 2001). Since rental-commerce, in contrast to other models of the sharing economy, has a somewhat different character, a research gap can be assumed in this context: while these contrasting theoretical concepts seem to play a role, no study has, to our knowledge, discussed these central points concerning rental-commerce. In order to get a comprehensive overview of rentalcommerce, a qualitative study will provide insights into the topic. This consumer study shows the advantages and disadvantages, motivations, expectations, influencing factors and perceived risks from the users’ point of view. The empirical study should contribute to the understanding of rental-commerce and generate managerial implications for companies both in rental-commerce as well as in traditional retail businesses but also provide important results for further research.

3.5.2

Literature Review

Some studies present alternative forms of consumption in which users gain access to products or services and use them conjointly without a transfer of ownership,

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offering an access to the topic of the sharing economy (e.g. Lawson et al., 2016; Benoit et al., 2017). For example, Bardhi and Eckhardt (2012) report that collaborative consumption as a form of the sharing economy, takes place when users have “temporary access” to products instead of taking possession of them, focusing on the joy of sharing, sustainability aspects and social responsibility (Hamari et al., 2016). Generally, users see in sharing an opportunity to use products more cheaply and, among other things, to take account of environmental protection (Matzler et al., 2014). For environmentally conscious users, the hope of being able to create more sustainability through the sharing economy will be a decisive motivation to make use of these kind of business models. But while a number of studies have dealt with different concepts of the sharing economy, to the best of our knowledge, rental-commerce—as a specific type of business model in the sharing economy—has been neither generally defined nor classified into the different characteristics of the existing sharing concepts. So, while it is clear that rental-commerce can be understood as part of the sharing economy, it is helpful to reconsider categorizations of this concept and assign rental-commerce to the corresponding categories in order to further understand how rental-commerce can be categorized in the sharing economy. Rental-commerce, as the name suggests, can therefore be assigned to the area of “commerce”, since companies conduct business through the rental of products and, for example, conclude rental agreements with users, resulting in a B2C business concept. Therefore, an important difference to other sharing models is that instead of private individuals, companies act as providers. Thus, when distinguishing between commercial and non-commercial sharing models (Henten and Windekilde, 2016), rental-commerce can clearly be assigned to the commercial models since it focuses on making profit. While many other sharing economy models communicate that they focus on sustainability aspects or a community idea, rental-commerce websites are built like traditional e-commerce websites which attract potential users with low rents and economic advantages. The aim of the website providers, which are often subsidiaries of already well-known large online providers, is to conduct lucrative business by offering attractive deals to users. Also, in the classification of Botsman and Rogers (2011), rental-commerce can most likely be classified in the area of “product service systems”, since rentalcommerce providers enable access to products for a certain period of time. So, the providers charge users a fee for the temporary use of products, who then enter into a tenancy agreement for the period of use. According to Belk (2010), these forms of consumption have a second common feature, as well: the dependence on information and communication technologies to provide access to products or services. Therefore, the contact between user and provider takes place via

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information and communication technology websites, either directly between the provider and user or through a third-party-run online website. Although providers aim to achieve further primary profits with this new business concept as well as to bind users who would otherwise not be able to afford to buy certain products, looking at the user side, this does not mean that there are only economic incentives encouraging users to rent products like financial flexibility and access to consumption (Owyang, 2013). Although these aspects continue to play an overriding role, environmental protection has become an issue of great importance in today’s society. Rental-commerce therefore has the potential to conserve resources, as the actual use of the products can be made more efficient in the rental process, leading to optimal use of the products. But, rental-commerce, compared to traditional sharing models, does not describe classic sharing in the original sense, where the focus is on joint benefit. Rather, it is on a profitable business relationship. Users do not share a product at the same time but use it one after the other, concentrating on their own needs’ satisfaction and not on collaborative usage. Some common features of many sharing models also apply to rental-commerce, such as using products for a certain period of time without acquiring ownership of these products, but that are not fully used otherwise, using information and communication technology (Belk, 2014). When engaging in rental-commerce, users pay a contractually-agreed price for the duration of use monthly and can then use the product to its full extent. The user can determine the rental period themselves, usually under the condition of a minimum rental period specified by the provider. Financial flexibility through consumption and access to consumption, which not every individual could previously afford, represent economic advantages (Owyang, 2013). This gives users the opportunity to purchase products for a certain period of time at a reasonable and lower price (Zervas et al., 2014). But, due to the novelty of rental-commerce, there is also a lot of uncertainty among users regarding their rights and obligations, increasing the possibility of privacy risks, but also risk of quality flaws like poor hygiene, traces of usage, and health issues (Bardhi and Eckhardt, 2012; Lee et al., 2016) since products offered by rental-commerce providers are often of an intimate nature, like clothing, electronics, or durable household goods. Additionally, for many users, this new business model might seem to be very complex and not sufficiently transparent, rising questions about the process itself, return policies, and security aspects. Hence, rental-commerce might also has a different value to users compared to other sharing concepts, addressing different user needs, such as a need for change or variety-seeking (e.g. trying new things for a special period of time), testing and

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trying (e.g. for users who are unsure if the product is really worth it), strengthening environmental awareness and sustainability (e.g. reducing the likelihood of products, such as children’s clothing, being thrown away or destroyed after a certain period of time), social needs (e.g. for those who cannot afford certain products but also want to have the feeling of group belonging), but also financial needs (e.g. spending less money) (e.g. Lawson et al., 2016; Böcker and Meelen, 2017). The concept of rental-commerce can be summarized by certain characteristics, whereby it does not differ in its entirety from other business models of the sharing economy but represents a branch of them. So while some features can be observed individually or partially aggregated in other business models of the sharing economy, the bundling of the following characteristics are considered specific to rental-commerce, distinguishing it from these other sharing concepts: (1) rental offers are offered by companies to users (B2C); (2) rental-commerce takes place via digital websites, using information and communication technology; (3) rental-commerce involves renting different types of products for everyday use that are used several times over a longer period of time, such as durable household goods, electronics, and clothing; (4) users choose a rental period and pay installments instead of the purchase price, whereby each provider has its own terms and conditions; (5) there is no transfer of ownership, but providers may offer a purchase option at the end of the rental period; (6) and, last but not least, there is no “community spirit” necessarily involved.

3.5.3

Theoretical Foundations

Investigating users’ perception and evaluation of the rental-commerce concept, we suppose that feelings of ownership might influence their intention to participate, based on the property rights theory, theory of perceived ownership as well as the endowment effect. These theories all give a different perspective on users’ attitude towards ownership and what value property has for society. Thus, there are some theoretical approaches which explain the participation of users in rentalcommerce and which advantages arise for them although they only receive usage rights but no rights of ownership. By the acquisition of property, owners bind themselves to this property, which can also lead to disadvantages for them (Moeller and Wittkowski, 2010). Such disadvantages may arise, for example, where there is a lack of space or confusion for the consumer about his own possessions due to a large number of belongings. This can result in physical as well as in psychological ties and restrictions for the consumer. The possession of (especially expensive) products may also create

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a certain psychological responsibility towards them, so that, for example, one wants to protect the own property from being stolen or destroyed. In this way, consumers have a stronger bond with objects that belong to themselves or for which they have invested a great deal of effort, like money or time for example, compared to objects or products that do not belong to them and which they only use for a very limited time period. Because in rental-commerce consumer contracts insurances or at least have the possibility to enter an insurance contract that in case of damages they are not liable themselves. Concluding that in contrast to pure possession, they have much less responsibility and commitments. These disadvantages referred to consumers belongings can therefore be perceived as burdens, which can have a financial or social character, but can also result in less flexibility (Schaefers, Lawson, and Kukar Kinney 2015). With rental-commerce, on the one hand, this burden is reduced by offering a financial advantage in which the user is only tied to a product for a limited period of time. On the other hand, users can also adapt to trends more quickly, being more independent in their choice of products they want to use. In addition, property rights theory states that the value of a product is determined not only by whether the user owns it or not, but also by what it is used for (Furubotn and Pejovich, 1972). Although products represent property in the traditional sense through their acquisition, property rights theory describes the meaning of property-free and proprietary consumption as well as which rights arise from it for both the actual owner and the user (Alchian and Demsetz, 1973). Here, the actual value of the product is measured on the basis of the user’s experience with it. This emphasizes the relevance of actual product usage as opposed to mere ownership, whereby alternative forms of consumption, such as those found mainly in the sharing economy, gain in importance. This would also be the case with rental-commerce, where the user is allowed to order and use a product for a limited period of time, but does not own the proprietary right to this product and therefore must return it after the contractually agreed time period. The proprietary rights remain exclusive with the provider of the rental-commerce website (Moeller and Wittkowski, 2010). Furthermore, according to Maslow’s hierarchy of needs, the hierarchies’ fourth stage defines esteem needs for oneself and the desire for respect from others (Maslow, 1943). Through rental-commerce, users can also have access to expensive products they would otherwise not be able to afford as well as get a feeling of group belonging for a limited period of time, making the rental business model attractive to them. However, there are additional influences that distinguish consumers’ perception between the different forms of sharing and renting in the sharing economy: According to Peck and Shu (2009), a frequent use of an object increases the value

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of the perceived property, assuming that forms of the sharing economy such as collaborative consumption or access-based consumption do not trigger any strong feelings of ownership or fears of loss, since the usage of goods and services is very limited. Sometimes, users have access to products even simultaneously with other users, which also makes it clear to them that the product is not their own. But in rental-commerce, the user receives exclusive rights and control over the rented property for a certain period. So, although the rental period of products is individual in rental-commerce, there often are minimum rental periods, implying that a longer period of use can be assumed in comparison to other sharing business models like car sharing, where users can rent a car only for a few hours or days. Since users know that their access to this product is still only limited, they rent a product to test it extensively to consider whether to buy it or not, or they are thinking of making use of the purchase option at the end of the rental period, there might be a strong usage of the product, so that the involvement of the user to that product could increase higher in comparison to other sharing concepts. This can cause the development of feelings of ownership already during the ordering process although the user only sees a description of the product, without receiving it yet. Due to this kind of usage in the context of rental-commerce, feelings of ownership might arise from the mere idea that the product belongs or can be belong to the user, since experience of psychological property has a positive effect on the attitude towards the property object (Van Dyne and Pierce, 2004). This effect might even strengthen after the agreed rental period, as users develop emotions and feelings towards the product, perceiving the return as a loss (Peck and Shu, 2009). Because rental-commerce websites are built similar to traditional e-commerce websites, users get the impression to buy a product online as usual, thus acquire property, which could trigger the same emotions and feelings of ownership, although this is not the case with rental-commerce. So, another theory that could serve as an explanation for or against participation in rental-commerce is the theory of perceived ownership. As with the previous theories, the theory of perceived ownership describes that, dependent on the duration of usage, users build emotions towards products that support their psychological attachment to them and strengthen users’ urge to own property (Pierce et al., 2001). This psychological connection towards objects is not unusual for users (Dittmar, 1992), strengthening the assumption that this emotional development can also take place through participation in rental-commerce. In addition, users purchase products not only for utilitarian reasons, but also for hedonistic reasons (e.g. Chiu et al., 2014). Thus, users frequently choose products that not only provide them with a functional benefit, but with which they can identify and position themselves in society (Baumeister et al., 2015),

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forming a kind of extended ego through it (Belk, 1988). This can lead to a personal connection to a product in which it is also intended to improve the user’s quality of life (Chen, 2009). As a result, the loss or return of such a product, which contributes to the identification and personal value of the user, represents a considerable disadvantage for the user, which he would try to prevent (Ferraro et al., 2011). This might be a problem in the concept of rental-commerce, since not only products are rented for which the user only has a limited functional use (e.g. a drilling machine), but also products which the user would otherwise not be able to afford, thus fulfilling a personal and societal function (e.g. high branded products). The user is given the opportunity of self-fulfillment, but only for a short period of time which ends with the loss of the product and the associated advantages. Thaler (1980) already suggests that according to the endowment effect, users perceive the purchase of products and property as a gain, while the sale of them represents a loss. Therefore, users find it easier to acquire property than to sell it. The value of a product that is sold is perceived to be higher than it actually is, since not only the monetary value but also the personal value plays a role for the user (Thaler, 1980). In rental-commerce, the (emotional) value of a product may be perceived higher at the time it has to be returned then at the time it was rented. Thus, the literature provides three different theoretical explanations which could provide a basis for understanding the relevance of ownership and possession for today’s consumers using the example of rental-commerce. Thus, property rights theory supports the idea of rental-commerce, in which the value of a product is not determined by whether the user of the product also owns it, but is defined by its actual use. This would lead to a greater perceived benefit from rental-commerce, as consumers would be offered an additional opportunity to use products for an intended purpose without being financially or permanently bound to this product, gaining more flexibility. In contrast, the theory of perceived ownership and the endowment effect suggest that, in contrast to other sharing models, in which only a very short period of product use by consumers takes place, due to the comparatively long rental and thus usage period, consumers build up an emotional bond with the products, so that they consider the return of these products as a loss. This could lead to a situation where the intention to participate in rental-commerce is weakened by the possibility to experience a loss. In addition, these three theories can be used to investigate further determinants which motivate consumers to prefer temporary rights of use for a product by renting it over ownership. To gain more insight into which effect predominates and what the main determinants are that impact consumer participation in rental-commerce, we therefore followed an exploratory research approach.

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Empirical Study

3.5.4.1 Procedure, sample and method A qualitative study was conducted to investigate the phenomenon of feelings of ownership and time-limited usage rights, as well as what relevance possessions still have for consumers nowadays using the example of rental-commerce in a holistic approach. Also, it shall be identified which determinants strengthen the intention to participate in rental-commerce. Compared to quantitative research, qualitative research approaches are distinguished by a much greater openness and flexibility (Lamnek, 2010). Taking into account the previous literature, a problem-orientated semi-structured interview guideline was first developed. At first, each participant was ensured confidentiality and anonymity during the whole process. They should answer questions based on whether they have tried rental-commerce so far, if they would try it (again) in the future, as well as on their previous experiences. At the beginning, demographical questions and questions about the attitude towards property in general as well as their experience with rental-commerce were queried to get an easy access to the topic. Furthermore, we gave a brief introduction into the research topic and especially in rental-commerce to ensure that everyone has the same level of knowledge. To ensure objectivity no priori assumptions were made. Afterwards we covered different aspects like benefits and risks or motivations and barriers of rental-commerce. During a period of approximately 14 weeks (between June 2018 and September 2018) we recruited German participants and conducted semi-structured interviews either in person or via phone. In the end, 28 persons agreed to take voluntarily part in this study whereby four persons were formed to a focus group (FG) and 24 participants were asked individually (IP). To achieve heterogeneity data, we included people with different ages, education levels, and experiences in rental-commerce. The experience levels included participants with no experiences up to those who have done rental-commerce several times. The age of the interviewees was between 23 and 67 years old (Mage = 39.32 years, SD = 13.69) whereby the gender distribution was balanced (50.0% men) and the average time of the interviews were about 20 minutes (see Table 3.27). The focus group was chosen to get more insights through a discussion and unfamiliar ideas. All interviews were recorded, transcribed with the software f4 and coded for thematic analysis with the software MAXQDA based on the grounded theory approach. The codes were compared and categorized into benefits and risks of rental-commerce for participants. The categories were discussed between the coauthors and distinguished after repeated readings.

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Table 3.27 Overview of the participants N

Age

Age Structure

Gender

28

M = 39.32 (SD = 13.69)

18–29 years = 9 participants; 30–49 years = 11 participants; 50–67 years = 8 participants

50.0% men

The first step is the inspection of all data sentence by sentence by two coders to ensure different perspectives, increased creativity, and decreased failures (Corbin and Strauss, 2008), wherein the transcripts are evaluated in a structured qualitative content analysis according to Mayring (2010). In addition, a separate coding of the transcripts by the two coders ensures an intercoder reliability for the qualitative content analysis performed. Since the text passages were assigned to the same categories by the two coders, the reliability of the coding can be confirmed. The saturation was reached with 20 interviews, so the last four individual interviews were coded directly and the focus group gained no further insights but confirmed the encoding which was done before. The aim of the content analysis was to identify categories based on the comparison of the individual encodings by the two coders with regard to the research questions. In this process, so-called upper categories (UC) are inductively formed, which in turn are subdivided into subcategories (SC). Herby, all relevant text passages are assigned to corresponding upper and subcategories and a category-based overview was created.

3.5.5

Results

The analysis revealed the following seven categories (see Table 3.28), which are presented in detail in the following: Table 3.28 Category system rental-commerce Upper Categories (UC)

Subcategories (SC)

UC1

Attitude towards Property and Ownership

UC2

Consumer Motivation

SC 2.1 SC 2.2 SC 2.3 SC 2.4

Economic Value Sustainability Access to Products Knowledge

UC3

Requirements for the Retailer

SC 3.1 SC 3.2

Safety and Trust Reduction of Complexity

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UC1: Attitude towards Property In case of rental-commerce, there is no transfer of ownership, the consumer receives “only” rights of use. It is noticeable that especially the elderly of the interviewees rated property as very important and of great importance (e.g., IP20). Property “is very important to me. The significance is high. With my property I can determine when I used it and how I used it “(IP2). IP6 adds: “It’s important to me that I own certain things, so I can call them my property”. Also, IP8 confirms that control and ownership are special. FG2 sums up that ownership “also involves a bit of freedom”. For IP12, ownership is also more important: “It is important to be able to call certain things someone’s property”. The question of why it is important reciprocated IP12, “I cannot tell you exactly. I think it is the feeling”. The personal relationship to an object is essential for the choice of a tenancy. IP5 comments, “Let’s say I rent something personal, it’s totally stupid that I have to give it back after a few months. Then all my memories are gone”. Personal value can therefore be understood as a component of memory. In addition, it became apparent that fears of loss can arise from the return of the rented property: “Money must be spent without me holding a permanent object in my hands” (IP7). In contrast, FG3 states that “property binds”, while IP1 implements the value of property as “moderately important; only when things have a personal value.” Here, the emotional component is decisive for the value of property. Some participants also differentiate depending on the product (FG1). “It depends on the object, but usually property is relatively important to me,” according to IP9. Even though rental-commerce is generally about “gathering experience” (IP7), it depends on what experience is gathered and how crucial the product is. UC2: Consumer Motivation SC 2.1: Economic Value The results show that the perceived economic value is the most relevant motivator for rental-commerce. Rental-commerce is chosen by consumers due to its low cost and the connected cost advantages (e.g., IP19,21, FG1,3). “The price is very important” (IP8). “Renting is still cheaper and more flexible than buying” (IP1). The economic value for rental-commerce is “less than the actual purchase price” (IP6). “I rent thing because it’s cheap” (IP13). Moreover, the cost factor of rentalcommerce greatly reduces the cost of purchase. “Someone who cannot afford the ownership of a product may benefit from rental-commerce” (IP11). IP3 has a similar view when stating that “especially technical devices that have a high price

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you don’t just buy yourself” are worth renting. The cost factor therefore strongly includes the acquisition costs that the consumer saves in rental-commerce. “Instead of buying objects, you can borrow ten objects for the same amount” (IP5). But there are also critical views: If the rent exceeds the actual purchase price, there is no profitability for the consumers, leading to a disadvantage (IP2). One reason for the non-profitability of rental-commerce is mainly caused by the rental period (IP17, FG4). “Especially with prolonged use a purchase possibly is more economical” (IP7). SC 2.2: Sustainability More than half of the participants stated that sustainability and the positive impact on environment is a major factor concerning rental-commerce, mentioning that renting products helps making them more valuable because they can be re-rented after one person’s use to the next consumer instead of remaining unused (IP22, FG3,4). Another person agrees that this will result in less waste and resources, which generally means that the environment will be relieved (IP16). IP1 estimates that “fewer resources are consumed (…), less waste is produced and objects handled more consciously”. IP14 appreciates that, “you do not have to have everything, but you have the possibility to borrow something. We live in a throwaway society anyway”. Rental-commerce is “more bearable for the environment” (IP6). It contributes to the “conservation of natural and financial resources” (IP7). IP9 refers to “cost and resource efficiency” and “sustainability” and expresses that this is very important. Overall, a positive impact on consumption and the environment is expected (e.g., IP17,13, FG2), as e.g. the usage of the products increases (e.g., IP1, 19,20), less wastage takes place (e.g., IP6,16,20), and less resources are consumed (IP16,22). IP5 argues against the factor of sustainability by mentioning that “the resources are also not really spared by the constant dispatch of the goods”. SC 2.3: Access to Products A big advantage of rental-commerce is the flexibility of trying different products. Afterwards, the consumers often can decide, whether they really want to buy the product for the full price or not (e.g., IP7,18,19,21, FG2,3,4). “I can easily return the product I rented after the rental period” (IP2). According to the results of this study, the flexibility of testing products appeals to consumers with a particular interest in technology and early adopters: “Especially in the technological area, where you are constantly confronted with new and further developments, it is always about getting something newer, better and faster” (IP11). Rental-commerce therefore offers consumers an opportunity to try out

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new innovations. Especially with new technologies, the first models can still be prone to problems resulting in a replacement by an improved version on the market after a few months. Thus, consumers can gain access to such innovative products by renting them for a limited period of time without having to make a decision to buy the new model repeatedly for a lot of money after a few months. Consumers, who need products exclusively for a special occasion or pursue a special hobby, also profit from the test possibilities in rental-commerce (e.g., IP7). In addition, products such as children’s clothing and toys (e.g., IP1,16,20,23), or especially technical products, are worth renting in comparison to buying, given the temporary product use or rapid obsolescence of technical objects (e.g., IP5,11,18,24). Also, there are some products that do not offer a big potential for rental-commerce websites like hygiene or long-term products. IP1 states, “Things of daily use, which either wear out quickly or are permanent” are not worthwhile in rental-commerce. Another important point is the functionality of the product, so the products should not lose their function and quality through the rent cycle (e.g., IP10,14, FG3,4). IP2 also requires “that the function of the products is not restricted, that I can use the product fully”. IP6 emphasizes that the condition of the product is important. He sees the supplier’s task in “cleaning the products and checking them for defects” (IP6). IP8 does not contradict this, who would also like to have the “assurance of a good condition and functionality of the products”. “The products must be carefully examined and, if necessary, maintained and repaired so that the next consumer receives a functional product without restrictions” (IP11). SC 2.4: Knowledge So far, the experience with rental-commerce websites is still relatively low, therefore the knowledge about rental-commerce is also not very widespread. Some respondents are uncertain about the consequences in the case of damage (e.g., IP18,21). This uncertainty can arise since most consumers are not familiar with rental-commerce websites and, as IP20 explains, have not engaged in rentalcommerce contracts yet (IP20). IP5 sees rental-commerce “still in the initial phase and [it] is still little known”. “The information is rather scarce; I came across the rental of clothes only by accident” (IP1). Consumers want to rent from a website that they perceive as well-known, well-established (IP18,21,24), and with which they already have had good experience (IP2,3,9,23). Especially because many have little experience with rental-commerce, others’ reviews are perceived as important and considered as external knowledge to make a rational decision. IP8 states that consumers can “get plenty of information” and thereby

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easily acquire knowledge about rental-commerce (FG3). IP4 sees no risk in finding information, “I think there is no risk. One can inquire about the retailer or look at other reviews” (IP4). The information about the process should be reliable (IP22). Ratings or reviews are read by almost all interview partners (e.g., IP8), affecting their purchase or rental behavior through the unrestricted availability of information and external experience (IP20,21,23, FG1), while other consumers prefer to get to know the products themselves (IP14,15,21, FG2). According to IP5, especially negative reviews are interesting “ I look at the negative reviews in particular to get an idea about possible problems”. UC3 “Requirements for the Retailer” SC 3.1: Safety and Trust While consumers’ knowledge, which they have gained both through their own experience and through experience shared by others, provides a rational and cognitive basis for forming their behavioral intention, trust embodies an emotional feeling, which influences consumer decisions affectively. Trust is mentioned by almost all participants as an essential factor (e.g., IP1, 18,24). Here, it is not only about trust in the company, but also about process implementation to ensure product quality (e.g., IP22,17,18). “I want to be sure that the products are flawless. If this is not the case, I change the retailer” (IP1). While IP15 believes that you have as much confidence in renting as you would in a purchase, IP22 says trust in renting could be even more important. The majority of respondents rely on the contract between consumer and retailer as a basis of trust. This gives the consumer a kind of safety (IP7,19,20, FG1,2). Some importance in terms of trust and safety was awarded to the hygiene aspect (IP15,21, FG1,3). IP1 emphasizes “the reliability of the company with regard to cleanliness” as a relevant factor (IP1). “The cleaning of the products is important”. The products offered for rental must be “absolutely clean and functional” (IP5). Hygiene also influences product selection (IP4, FG4). The reliance on the retailer to check products properly, to clean and to put them in the factory condition, before re-renting, is great. “Of course, you never know what others have done with the products, so uncertainty is probably always present” (IP11). SC 3.2: Reduction of Complexity “Complexity” does not only include the usability and the simplicity of the process, but also the delivery service and the terms of payment. For example, the friendliness of the user interface as well as the visual impression are also of importance for the majority, as well as a simple and trouble-free process (e.g.,

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IP7,14,21). For most consumers it is important that the products can be delivered home, and many also would like the products to be picked up after the rental period. (e.g., IP23,24). In individual discussions, the wish was also expressed that the possibility of agreeing on a delivery time in advance should be given. (IP21). IP19 states that, depending on the product, to book an assembly and disassembly if needed would also reduce complexity (IP19). In addition, an optimal and uncomplicated online ordering process is desired (IP1,3). IP9 also calls for different delivery models in terms of subscriptions and for “simple payment methods” in the interest of simplicity. Moreover, IP10 wishes the implementation of payment methods “via app without reentering bank data”.

3.5.6

Discussion

One of the purposes of the study was to capture the new phenomenon of rentalcommerce, to get a deep understanding about participant’s ideas of the critical determinants of rental-commerce as well as differentiate it from other sharing models and to work out drivers and barriers to participate in rental-commerce. Based on some research studies from the field of the sharing economy, we refer to the developed categories of our qualitative investigation: Attitude towards property and ownership, consumer motivation, and requirement for the retailer. Similar to previous studies, the results of the interviews and focus group stress out that economic benefits have a positive influence on the intention to use rental-commerce websites (Hamari et al., 2016). Price is a relevant factor when it comes to rental-commerce, as cost savings often are a priority for many users. High acquisition costs are avoided if products are only needed temporarily. If, however, the user intends to use the product in the long term, no given profitability can be seen in rental-commerce. So, the rental period must be taken into account, because depending on the product and duration of the rental, the sum of the rental costs might quickly exceed the purchase price of the product. Thus, the providers of the products are also concerned with the development of profit-maximizing price models, which also accommodate users in the sharing economy, such as rental-commerce (Provin et al., 2016). The perception of a product is largely controlled by price (Jacoby and Olson, 1997), but the rental period also plays an important role in the user’s decision-making process (Tan, 2018). The price is usually perceived by the users as what they have to sacrifice in order to receive the product (Zeithaml, 1988). According to Bolton and Lemon (1999), a price is perceived as fair or unfair depending on a user’s own inner preferences. This perception has an effect on the satisfaction of the user with the

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product and thus on his or her behavioral intention to use this product (Bolton and Lemon, 1999). Therefore, the economic benefits should be highlighted by rental-commerce providers to attract more users to this new business model. A further category, which apart from the monetary advantages plays an important role for the users, are the products, which can be rented online, and their special significance in rental-commerce. Rental-commerce offers users the opportunity to test products for their own needs temporary without incurring high costs. So, users are not committed to the rented products, but can easily test them for a certain time period, while they should fulfill a benefit in this short term. This results in an increased users’ independence and flexibility (Singh, 2016). Rentalcommerce can thus contribute to a greater willingness to buy a product due to given flexibility and testing options, since a purchase option after the rental period was requested by the interviewees several times. In line with these results, Shu and Hsieh (2016) have found that the different factors involved in behavioral intentions are influenced by the time factor, since users want to lead an increasingly flexible life, which is why a long rental period is regarded as burdensome and inflexible by users (Singh, 2016). Moreover, rental-commerce websites offer convenience, which is reflected in the permanent full functionality of the products and their interchangeability. There is no product guarantee that expires after six months. IP3 sees a market potential for rental-commerce and other alternative forms of consumption for “expensive, scarce goods that are as standardized as possible”. Furthermore, the basic idea of the property right theory is that the value of a product is determined not only by its properties, but above all by the usage possibilities with the product. As usage relevance increases, the value of products also increases. Also, Lawson et al. (2016) observe that the multiple use of a product makes sharing concepts an environmentally friendly form of consumption by maximizing the use of this product. This is contradicted in particular by IP5 by predicting that the possibility of renting products online, which one would otherwise not be able to afford, increases consumption, which in turn leads to a higher frequency in logistics and environmental pollution. Nevertheless, for the interviewees, environmental factors are also drivers, but weigh less than the price. But, looking at platforms and services that offer this alternative form of consumption, as is the case in rental-commerce, the respondents often speak of a sustainable marketplace that optimizes the “ecological, social and economic consequences of consumption” and yet meets the interests of current and future generations (Luchs et al., 2011). Along with previous assumptions (Luchs et al., 2011; Lawson et al., 2016), sustainability is a relevant driver. Through sharing in general there can be a sustainable form of consumption and an improvement of

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the ecology (Cherry and Pidgeon, 2018). Even rarely used products can be used several times which can lead to a reduced usage of resources (Martin, 2016). In line with previous studies, this examination points out that sustainability influences the attitude towards rental-commerce (Hamari et al., 2016). But like IP5 there are also opponents that rental-commerce is not really sustainable. Contrary, this business model leads to an even higher consumption of products. This could be due to the product category and the concept of what the participants have in mind. Further studies could examine whether the sustainability aspect is more likely to be noticed for certain products or depending on the product category offered by the online provider. Concerning safety issues, providers must strictly check their rental properties and ensure the functionality, cleanliness, hygiene and safety of the product. Thus, the general risk in the rental business is considered to be comparatively high. According to PricewaterhouseCoopers, the biggest disadvantages that stand out in sharing models such as rental-commerce relate to product quality, security and data protection (PwC, 2019). For example, IP10 appealed: “Politicians should create more framework conditions for rental-commerce”, which underlines the desire for more regulation and security of users. Also trust in rental-commerce and in its providers is, according to the respondents, of great importance. There, trust can also be created by the seriousness of the provider and its image. Thus, both relationship commitment and trust determine the effectiveness of user behavior (Morgan and Hunt, 1884). Besides, Hawlitschek et al. (2016) also show that in the sharing economy it is not only about trusting the provider, but also about trusting other users who use the product as well as the product itself. According to Cui et al. (2018), trust transfer theory allows users to build trust between objects with similar characteristics but also business ties, like products that can be rented on a rental-commerce website. Rental-Commerce providers should guarantee more transparency and safety throughout the rental process concerning data privacy, payment, return, and product quality as well as using quality seals to increase users’ trust. In addition, the importance of rating portals and review pages is extraordinarily high, not only building trust towards a rental-commerce provider, but also establishing a solid knowledge base for users with the help of previous user’ experience and information. Online reviews can have a positive effect on users as well as inform them about the process or risks (Mudambi and Schuff, 2010), while they support providers with improving their external image and help them identifying improvement potential (Scott and Orlikowski, 2014). On the opposite side, anonymous postings and fake profiles open opportunities for bullying and exploitation (Mayzlin et al., 2013), as well as the manipulation of reviews (Hu

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et al. 2012). Kroschke and Steiner (2017) show that while positive reviews seem to have less impact than expected, negative reviews increase users’ perceived risk as well as decrease their expected benefits. This shows, that it is easy for users to collect information and experience from others that can significantly form their knowledge about rental-commerce, influencing their user behavior. Bhatnagar and Ghose (2004), for example, already confirmed that the higher the users’ experience with a website is and the greater their knowledge about their possibilities is, the more likely they are to order from that website. Users feel more secure the more experience they have with a website and the more familiar it seems to them (Laroche et al., 1996). Consequently, rental-commerce providers should provide potential users with all relevant information and guarantee a clear process structure as the need for security is here very high for users (Bart et al., 2005). Access to online reviews, but also a familiar structure of the website and the rental process as in traditional e-commerce, is recommended. Another factor that determines whether users participate in rental-commerce is the perceived complexity. Following Thompson et al. (1991), users’ intention to participate in rental-commerce will be influenced by the perceived level of complexity, whereby users want to reduce the complexity and the resulting effort to rent products from a rental-commerce website successfully and easily. Rental-commerce providers, therefore, have to make the usability of the website or app as well as the customer journey as easy as possible to enable users to conclude a lease within a few clicks. Standardized payment methods like PayPal are also extremely welcome, making all transactions convenient for the users. From a logistical point of view, the availability of products must be ensured without long waiting and delivery times. As a rule, a delivery should only take a few days, as many users are used to fast delivery times of giant online market places. Decisive for users are individual leases with flexible rental periods including insurance for claims. In order to create a sense of community spirit, rental-commerce providers could set up a forum, supporting users in exchanging experience and giving advice to each other, but also to get in direct contact with the users themselves and to help out directly in case of problems or uncertainties. As far as the first research question is concerned, it can be seen that the decision to own a product permanently or to use it temporarily depends on several factors: Therefore, perceived ownership is questionable: the user experiences the product and all its features as well as the utility of the object. However, due to the usually short rental period, it can be assumed that the user does not establish a deeper connection to the product. On the one hand, the user only has limited access to the product and knows that he or she must return it after the lease has

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ended, and on the other hand, the use itself is in the foreground. In fact, rentalcommerce does not transfer ownership, as it is the case with a purchase, and the legal treatment of those models that allow access but not ownership is not yet sufficiently clear compared to traditional purchase (Bardhi and Eckhardt, 2012). According to the respondents, the development of self-identity is rather unlikely in rental-commerce due to the lack of emotional connection between the user and the object. However, the endowment effect might occur and therefore confirm psychological ownership since respondents would like to purchase the product after the rental period. Hence, they consider the product to be more valuable than they would have before participating in rental-commerce. Thus, the interviewees’ purchase request underlines once again the importance of ownership, which is also discussed in many other researches when it comes to user voluntary contributing in a user-artifact relationship (e.g. Fang et al., 2017). Often, the literature also refers to non-ownership consumption due to burdens by property. However, these described burdens do not seem to confuse users much in their desire to consume. In addition, the study did not show very different results in terms of property value. Furthermore, property is considered very important in the over-50s group, while the under-40s group owns property that can be perceived as either unimportant or important. Moreover, the importance of ownership seems often to be product-dependent. Concerning the second research questions, if all factors are considered, which were mentioned and discussed in the qualitative study as relevant factors in the context of rental-commerce, these determinants can be combined to a model approach, which can offer an explanation for consumers’ increasing participation in rental-commerce even though they renounce potential property rights and thereby only have rights of use for an ordered product (see Figure 3.10). Although the influences of the determinants “economic value”, “sustainability”, “access to products”, “knowledge”, “safety and trust”, as well as “reduction of complexity” based on the results of the interviews conducted can be illustrated well in this model approach, additional studies are needed to examine the classification of the “attitude towards property and ownership” on today’s consumer behavior more closely. Here, the results of the study as well as the theoretical approaches from the literature show that feelings of ownership represent complex emotions which are very situational, dependent on one’s own characteristics as well as product-dependent. Especially these factors, which shape the attitude towards property and ownership, are worth investigating more closely in future research in order to understand the background to the formation of consumers’ property-relevance and perception. While there is a clear trend for less interest in possession but increasing consumer consumption and sharing models (PwC,

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Figure 3.10 Summarization of the results to a preliminary explanatory model

2019; Schor, 2016), the factors influencing the consumer’s decision to renounce property should be explicitly defined in order to gain a better understanding of consumer decisions in the context of the Sharing Economy and e-commerce. Nevertheless, referred to theoretical aspects, this study demonstrates that dependent on the product category and the age as well as the duration of the rental period either the property rights theory or perceived ownership and the endowment effect arise. Based on the results, especially when only testing products for a short period of time and items with no personal meaning, the use and following the property rights theory overweighs which means that the participation in rental-commerce is more likely. Contrary, the intention to participate is weaken by choosing a very long renting agreement due to the fact that an emotional bond to the product can appear and the return is seen as a loss. Consumers can evaluate the rented product as their own and thus as their possession since they use it e.g., every day and get used to it. In other words, they call it “mine” meaning that they will not hand it back symbolizing an affective reaction. In this case perceived ownership and the endowment effect occur. But there is still the opportunity to buy the product instead of returning it in order to circumvent loss aversion as a driver for the endowment effect (Shu and Peck 2011). Moreover, the older people in this study evaluate property as much more important which may result into a distance against rental-commerce based on perceived ownership and the endowment effect. But for the future, there is an increasing probability

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that the property rights theory and thus the usage of a product instead of the possession will become more important. One reason might be that the younger generation and the next generations are growing up with the trend of consuming and experiencing instead of purchasing and owning.

3.5.7

Conclusion and Implications

The aim of this study was to obtain a better understanding of the perceived determinants and factors of rental-commerce through a qualitative study. Thereby, the results of earlier studies have been largely proven and additional new findings and implications for both theory and rental-commerce providers can be derived. Based on already established sharing concepts, the new business model of rentalcommerce also strengthens the trend of using products instead of owning them. However, the focus here is not on the joint exchange of products, but on the short-term exclusive usage right. Thus, on the one hand, our results primarily confirm the property rights theory and give further insights about why experiencing a product is more important to the user than owning it (Furubotn and Pejovich, 1972; Baumeister et al., 2015). For many users, the flexibility and the possibility to try out different products, without committing to them in the long term, are attractive advantages of the online rental model. On the other hand, with regard to the endowment effect and the theory of perceived ownership, the study’s results show that for some users it is more important to possess products depending on their age. The results also make it clear that increased access to products appears to be a particularly relevant factor influencing the intention to use rental-commerce and that, for this reason, consumers are even willing to have less property in order to have access to more products. This can be seen in particular for products from the technology sector, children’s clothing and generally products with a short period of use. For certain advantages (e.g. using the latest products), the benefits of one’s own possession can thus be exchanged for a temporary right of use, showing that the theory about ownership needs to be reconsidered. This shows that it is important to conducted continuous research in the field of ownership in order to determine when the use of products is more important than the ownership of products. It also becomes clear in the interviews and focus group that the product category and rental period are decisive factors for the success of rental-commerce. So far, this business model has been based on long-term rental agreements for consumer durables. There is a need for action to be identified, for example by

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creating short-term leases. In summary, it can be said that rental-commerce websites can offer, among other things, many financial and social advantages for the users and that they will therefore continue in growth in the future, which can also be confirmed by further consumer surveys (KPMG, 2017; PwC, 2017). In particular, this might be the case for fast-moving product categories, where new product models are constantly appearing on the market at short intervals, like in the technology sector. However, this study also has some limitations, which we would like to address in the following. Firstly, there is only little knowledge about rental-commerce due to the novelty of this business model, so that this research should offer a holistic overview of the most important determinants. In this respect, the initial compiled description and classification of rental-commerce in the world of the Sharing Economy can be confirmed by the fact that the participants in this new business model are primarily interested in economic benefits and flexible access to (innovative) products, rather than joint or ecological benefits in a community. Thus, the question arises whether the basic idea of sustainability and shared consumption still have the same relevance for novel business models that originate from the Sharing Economy, or whether the Sharing Economy is increasingly being used as a profitable business model that sells itself as a sustainable concept through, for example, “sharewashing”, the misleading of consumers by businesses by purposely portraying an image of social and ecological principles while actually not involving them, therefore relegating the original basic ideas to the background and placing economic profit in the foreground (Hawlitschek et al., 2018; Schormair, 2019). Further research could hence deal with the role of “sharewashing” in rental-commerce and examine its relevance and consequences for consumers. Secondly, in order to verify the general validity of our results, future studies should also acquire participants from other countries, since the sample was only collected in Germany. Particularly in the area of e-commerce but also in the field of collaborative consumption, previous studies have addressed the importance of cultural influences (e.g. Gefen et al., 2005; Piscicelli et al., 2015; Hallikainen and Laukkanen, 2018). Furthermore, no distinction was made between participants who have already participated in rental-commerce and participants who have not yet rented products on any rental-commerce website. Since, in this context, the experience and knowledge of the technical and organizational processes of the rental-commerce business model could provide an influence, a distinction between these two user groups would be recommended. Further studies could, for example, also consider personality traits and general user attitudes like sustainable lifestyle, change seeking or technology affinity, which may moderate users’

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influence on behavior intention. Moreover, his study pointed out different categories which are relevant for users when participating in rental-commerce. So, it would be interesting if the results of this qualitative study can also be confirmed in a quantitative study, using a representative sample.

3.6

Will Renting Substitute Buying? Drivers of Consumer Intention to Participate in Rental-Commerce

3.6.1

Introduction

Nearly 20 years ago, Rifkin assumed that society was on its way to changing the relevance of possession. In his opinion, the importance of ownership for Western society was beginning to decline markedly, and he supposed that access to and use of objects would become more relevant than owning them (Rifkin, 2001). Indeed, nowadays, users increasingly seem to seek access to products and prefer to pay for the experience of temporarily accessing them rather than buying and owning them (Bardhi and Eckhardt, 2012). This phenomenon is closely related to the “sharing economy”. The business model of the “Sharing Economy” is characterized by an ever faster spread and explosive growth of companies. Airbnb and Uber in particular provide an example of the growing trend as well as the profound changes in the business environment and have become global players within a few years (PwC, 2019; Schor, 2016). One further example of this growing trend are rental models, which are one way to participate in the sharing economy that will be considered more closely in this study. According to recent studies by KPMG (2017) and PwC (2017), the relevance of rental models and barter transactions is constantly increasing. Also, a 2018 study conducted by PricewaterhouseCoopers in six European countries showed that 44% of the approximately 4,500 consumers surveyed had already taken advantage of shared economy offers. Younger adults under 40 years of age are particularly attracted to this business model, with men showing higher usage (PwC, 2019). What is new, is that the increasing relevance of rental models and the associated use of unowned products is leading to changes in consumer behavior, especially concerning everyday items. Unlike some other sharing models, the focus in rental-commerce is only on the business-to-consumer (B2C) market. In contrast to the peer-to-peer area, this model represents an extended approach of the sharing economy business model (Eichhorst and Spermann, 2015), within which the consumers are enabled to obtain resources for their needs from companies that are usually provided via their own platforms (PwC, 2019). The focus is on the growing interest

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of consumers to rent products instead of buying and owning them. The trend is thus moving towards the sharing or temporary access of products. So, in rentalcommerce, user behavior is moving away from “classic” forms of consumption under property law towards the alternative of “non-ownership” consumption via internet platforms that offer rental options. “Non-ownership” means that users use a product but do not have property rights. In the case of non-proprietary consumption, only the right of use is transferred to the consumer, while the full rights (use and ownership) are reserved exclusively for the owner, here the company (Moeller and Wittkowski, 2010). As a result, companies can benefit from the trend towards “collaborative consumption” in the context of the Sharing Economy and expand their offerings through new creative approaches (Matzler et al., 2014). Surprisingly, as far as we know, no study has yet brought up empirical insights on consumer motivation to participate in rental-commerce or adequately addressed management implications to enhance the acceptance of rental-commerce websites. The results of previous research have referred to a number of rental models and primarily addressed high-priced luxury products, conventional (i.e. not online mediated) rental models, or non-commercial forms of sharing (Zervas et al., 2017; Hudders and Pandelaere, 2014; Bock et al., 2005; Miron, 1995). Rental-commerce, which is focused on in this research, has a different character, and we have supposed that it might address different user needs, such as need for change or variety-seeking (e.g. trying new things for a special period of time), financial needs (e.g. spending less money), testing and trying (e.g. for users who are unsure if the product is really worth it), and social needs (e.g. for those who cannot afford certain products but also want to have the feeling of group belonging). Therefore, the fact that the principle of the Sharing Economy and thus also rental-commerce is meeting with increasing consumer approval is primarily due to the savings in costs and resources (PwC, 2019). But, also the creation of new jobs, ensuring access to resources and above all that consumption can be made possible for people in lower income groups, ultimately strengthening the sharing community (PwC, 2018). It also seems that above all the joy of the activity of shared consumption and the resulting benefits of the joint economy and collaborative consumption is a decisive point for the use of Sharing Economy offers. The issue of sustainability is also in focus here, as the goal of this business model not only brings monetary benefits, but is also capable of mitigating social problems such as environmental pollution, hyper-consumption and poverty (Hamari et al., 2016). In addition, the global economic crisis of 2008 caused financial problems, which in turn made the desire for access to products more important than the ownership of products (Sikorska and Grizelj, 2015). Here, we

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see a gap in the literature: while there are already studies as well as knowledge about general sharing concepts and drivers that enhance consumers’ intention to participate in other sharing models, no study has, to our knowledge, discussed these central points and drivers concerning the model of rental-commerce, which differs in several features from the other models of the sharing economy and thus represents a special new form and possibility of e-commerce participation. Therefore, we have addressed as research questions: (1) What determinants are relevant in driving inexperienced users’ behavioral intentions to participate in rental-commerce? (2) Which drivers are most important in influencing behavioral intentions? To answer these questions, we based our study on well-established determinants from literature in the context of sharing models. Hereby, we can contribute to a more profound understanding of the motivations by respecting the relevant drivers of a novel branch within the sharing economy. Thus, based on this more holistic approach to participating in rental-commerce, we can derive implications for further research and managerial implications.

3.6.2

Literature Review

To the best of our knowledge, rental-commerce—as a specific type of business model in the sharing economy—has not yet been generally defined. Nevertheless, a number of studies have dealt with non-ownership consumption, access-based services, collaborative consumption, and sharing or co-owning which offer an access to the topic, presenting alternative forms of consumption in which users gain access to products or services and use them conjointly without a transfer of ownership (e.g. Benoit et al., 2017; Lawson et al., 2016). While commercial retailers charge a fee for the temporary use of products, in rental-commerce, users enter into a tenancy agreement for the period of use. According to Belk (2010), these forms of consumption have a second common feature, as well: a dependence on information and communication technologies to enable access to products or services. As such, in this study, we focused on information and communication mediated rental-commerce models. To understand the differences between the alternative consumption forms, the following provides a very short overview of the most important types of related consumption forms before concentrating on rental-commerce: Access-based business models such as Car2Go or DriveNOW differ from traditional renting, as they are market-mediated and digitally processed (Botsman and Rogers, 2010) and provide users with limited access to products in return for an access fee, while the legal ownership remains with the service retailer

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(Schaefers et al., 2015). They are often web-based and/or app-based, such as car and bike sharing services, short-term rental models for high-priced products like designer fashion, and peer-to-peer websites. Sharing or co-owning is often between two or more individuals and is mediated through social mechanisms. These forms do not imply ownership transfer or shared ownership, which leads to simultaneous or sequential use (Benoit et al., 2017). In general, collaborative consumption focuses on special websites, primarily in peer-to-peer sharing, with shorter periods of agreed consumption in which resources are consumed jointly and sustainably (Böcker and Meelen, 2017; Hamari et al., 2016; Belk, 2014; Hawlitschek et al., 2016). In contrast to this, the rental-commerce business model concentrates on economic motives by operating “pseudo-sharing” (Hawlitschek et al., 2016). Compared to traditional sharing models, pseudo-sharing does not describe classic sharing in the original sense, where the focus is on joint benefit. Rather, it is on a profitable business relationship. Users do not share a product at the same time but use it one after the other, concentrating on their own needs’ satisfaction and not on collaborative usage. Economic aspects, such as money saving, have primary relevance, while the feeling of being part of a community and of consuming resources sustainably is of less importance (Belk, 2014). Concerning rental-commerce, there are two actors: the retailer and the user. For the latter, there is no transfer of ownership but, rather, a fixed period of agreed consumption time, wherein the phenomenon is predominantly mediated through market mechanisms instead of social mechanisms as in the sharing or co-owning environment (Benoit et al., 2017). When engaging in rental-commerce, users pay a contractually-agreed price for the duration of use and can then use the product to its full extent. The user can determine the rental period themselves, usually under the condition of a minimum rental period specified by the provider. The contact between user and provider takes place via information and communication technology websites, either directly between the provider and user or a through third-party-run online website. However, as the concept of rental-commerce is relatively new, there is a lot of uncertainty among users regarding their rights and obligations, which might also lead to an increasing possibility of risks. Aside from certain benefits (e.g. economic reward) that users may experience in the context of the sharing economy as well as in rental-commerce, they must also face perceived costs—including privacy and security risks and the risk of quality flaws like poor hygiene, traces of usage, and health issues—that influence their intentions to participate in forms of collaborative consumption (Lee et al., 2016; Bardhi and Eckhardt, 2012). One reason for this is that products offered by rental-commerce providers are often of an intimate nature, e.g. clothing or durable household

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goods, and the user thus has to show some higher level of trust. Hence, forms of consumption like rental-commerce require a higher tolerance of risk compared to traditional buying processes (Santana and Parigi, 2015). Considering the differences between the rental-commerce model and other alternative forms of consumption in the sharing economy, the concept of rentalcommerce differs in the characteristics of ownership, platform type, financial transaction, type of goods, potential risks, and supplier intention, as described in the following: In rental-commerce, for example, consumers receive a temporary right of use for a product. However, due to the rental period of several weeks or even months, this renal period is considerably higher than in other sharing models where the right of use is often only transferred for a few hours or days (Hamari et al., 2015). The next notable characteristic is the type of platform on which the goods and services are offered. While there are sharing business models which are either P2P or B2C platforms, or sometimes both (Laukkanen and Tura, 2020), renal-commerce focuses only on B2C business. Thus, financial transactions with a clear commercial revenue and monetary profit interest by the rental-commerce company are also in the foreground, while many other sharing models also operate on a free basis. Moreover, there are two different categories of goods in the Sharing Economy, material and immaterial. Material goods, on the one hand, are understood to be durable goods, consumable goods and luxury goods. Durable goods also include objects that can be used several times over a longer period of time (furniture, clothing, cars, etc.). The second type is represented by consumable goods which are (fully) consumed during their use. These include food, cosmetics or electricity. Luxury goods include items that represent a luxury, such as expensive cars or designer fashion. Immaterial goods, on the other hand, include services and digital goods. With regard to these classifications, rental-commerce focuses on the rental of durable goods, i.e. products for everyday use, as well as, in part, luxury goods in the form of designer fashion. A further aspect is the risks: a distinction is made between sensitive data, which can be misused, and material damage, where a good can be damaged and thus constitute a disadvantage for one of the consumers. In addition, consumers perceive as yet unclarified legal situations as a risk, as many sharing models are still very new and therefore lack experience in this area. Rental-commerce, in particular, offers a target for these three listed potential risks. Although there is also a risk of data misuse in many other sharing models (Lutz et al., 2018), there is a particularly high risk of rented products showing heavy traces of usage due to poor hygiene or excessive use, especially in rental-commerce. In addition, there is a risk that more intensive use of products in rental-commerce could make consumers unclear about

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their rights of use and the associated limits of usage and ownership, thus confusing consumers. There is also the risk of not wanting to return a rented product because consumers have become emotionally attached to it due to the comparatively long rental periods. Last but not least, the intentions of the providers of Sharing Economy platforms may also differ. Thus, providers could be guided by social, profit-oriented or sustainable intentions (Laukkanen and Tura, 2020). While several reasons may apply to a business model, the primary intention of rental-commerce providers is profit-oriented behavior, while participating consumers may also be guided by sustainable and social motives in addition to cheap offers. As Lawson et al. (2016) stated, social status, flexibility, trying different things, variety-seeking, and loyalty are factors that explain why users select rental models, while greed and materialism were identified as factors that lead to an aversion to rental models. This study will not only expand these factors but will also take determinants concerning the rental websites into consideration. Other motives to take part in this business model can be economic, ecological, and social (Böcker and Meelen, 2017).

3.6.3

Theoretical Foundations and Hypotheses

3.6.3.1 Theoretical Basis Based on the Expected Utility Theory, the Theory of Loss Aversion, and the SelfDetermination Theory, we investigated inexperienced users’ perceptions in the environment of rental-commerce websites and postulated that feelings of ownership influence the perception of different determinants and thus build the basis for user behavior. The use of a system is not only influenced by behavior intentions but also by facilitating conditions and behavioral expectations (Venkatesh et al., 2008). In particular, the consideration of consumer behavior in this study revealed whether an inexperienced consumer intends to participate in rental-commerce in the future. This aided in gaining a better understanding of consumers’ motivations and formulating adequate management implications. We firstly draw upon Expected Utility Theory (EUT) in order to understand the motives and inhibitory factors of consumers’ rental-commerce participation intention. According to the study of Fishburn (1968), EUT stressed that decision makers choose between risk and uncertain prospects by comparing their expected utility values. In this developed theorem of expected utility, the individual finding and making decisions is in the foreground (Dubra et al., 2004). According to the EUT, after assessing the risks and uncertainties and comparing the respective expected benefits, individuals and decision-makers decide on an available option

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(Mongin, 1997). However, it is important to say that in reality it is difficult to make an exact or even perfect statement about a possible benefit. In particular, the degree of uncertainty mentioned above can vary greatly with regard to different retail formats. Thus, new business models of e-commerce may have a higher risk factor than traditional retail stores. Because decisions are made under uncertainty and risk, based on different levels of valuation, consumers tend to maximize the expected rather than the actual benefits. Thus, the higher the perceived level of uncertainty, the greater the variance in the perceived value of the total bundle of benefits. The higher this variance is, the lower is the expected benefit for the consumer. This shows that consumers only make purchases via the Internet when the expected benefit has a value greater than zero (Bhatnagar and Ghose, 2004). The application of the theory of expected utility is intended to make clear that consumers must perceive and recognize a clear benefit before deciding to accept or consider a Sharing Economy offer, especially a rental-commerce offer. Which perceived benefits drive consumers to turn away from possessions and prefer to use products for a limited period of time, which represents a kind of loss of possessions and is associated with risks such as lack of hygiene or higher long-term costs, still needs to be investigated. However, these expected benefits of participating in rental-commerce appear, judging by the increasing number of uses, to be greater than the possible disadvantages. Such a benefit could be that the latest trends can always be followed, so that costs can be saved and clothing does not have to be accumulated, which is associated with perceived economic savings and environmental sustainability. Based on the principle of loss aversion, which is also known as loss avoidance and describes the effect that people tend to feel a loss more painfully than a gain, the question arises as to whether renting products makes sense at all and whether it can lead to lasting consumer satisfaction (Bucheli, 2020). According to the Theory of Lost Aversion, at the end of the rental period, consumers lose not only the product but also the financially necessary means for renting the products, so that they face a double loss compared to buying a product and thereby owning it. This also illustrates the relevance of research and the desire to find out why more and more consumers are nevertheless participating in the rental trend and accepting this supposed loss. Here, the Self-Determination Theory could possibly offer an explanatory approach (Deci and Ryan, 2012). This theory describes the further development of the personality based on the motivation of an individual. The fulfilment of the basic needs of autonomy, competence and belonging increases motivation. Autonomy includes the need for self-determination and selfresponsibility and expresses the decision for a free and self-directed way of life. Properties, in contrast, bind consumers, often go hand in hand with responsibility

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and can also be perceived as a burden, which means that desired actions cannot always be carried out. Hiring products can therefore provide a remedy and promote flexibility and self-determination for consumers, as they do not have to choose and commit to one product permanently, but can use a new product every few weeks. In terms of competence, the focus is on developing one’s own skills. Particularly in sharing concepts, the focus is on personal and social development through sharing and sustainable consumption (Laukkanen and Tura, 2020), which is intended to promote not only personal development, but also social development. Finally, the aspiration of an individual to belong to a group and also to be accepted is part of the basic need for belonging. By renting products, acceptance within a group can be increased with little effort, for example by renting expensive or current products, through whose use the consumer can define himself and compare himself with others. The question of the familiarity and social competence of the consumers of sharing models is therefore also at the center of attention here (Bruhn et al., 2015). Thus, social needs, individual needs, and the need for self-fulfillment can also be found in Maslow’s hierarchy of needs (Maslow 1943), which describes the importance of this socio-psychological motivation for the lives of consumers, and thus in everyday consumption.

3.6.3.2 Hypotheses Development and Conceptual Model In order to answer this study’s research questions, a number of hypotheses were proposed, based on a literature review. According to Leismann et al., (2013), economically effective use of natural resources is important in order to generate wealth in the future. Thus, through the repeated use of products, sharing concepts can generally prove to be an environmentally friendly and sustainable form of consumption, as the use of a product is maximized (Lawson et al., 2016). This leads to one of the key intentions of users to share products: sustainability (Hamari et al., 2016). In addition, unlike private property, these solutions almost always have a positive impact on the environment (Möhlmann, 2015). Following Luchs et al. (2011), different online websites attempt to promote the concept of sustainable consumption in regard to the “environmental, social and economic consequences of consumption”. As such, apart from the economic benefit, it is a kind of ideology. Since the products offered on rental-commerce websites are not used only once but several times by different users, it could be argued that the usage of these products will be maximized, leading to a higher sustainability and reducing the likelihood of products (such as children’s clothing) being thrown away or destroyed after a certain period of time. Thus, it is not only an economic benefit but also an ideological one. Since Hamari et al. (2016) found that sustainability is an important factor in collaborative consumption, we therefore

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hypothesized that perceived sustainability influences users’ behavioral intentions towards rental-commerce. H1.

Perceived sustainability has a positive effect on the intention to rent products.

Rental-commerce is not only assumed to have an ecological and thus environmentally friendly and sustainable aspect but also an economic one. According to Hamari et al. (2016), the sharing economy can offer economic benefits for users. Past studies on collaborative consumption have confirmed this assumption, as they have discovered that financial benefits are the key to motivating peer-to-peer sharing (Tussyadiah, 2015). Renting products might be cheaper than buying them, so people can afford more expensive products when using rental-commerce. Therefore, participation in sharing can also be a rational, use-maximizing behavior in which the user replaces the exclusive ownership of products with the cheaper options of a rental-commerce retailer (Hamari et al., 2016). In rental-commerce, renting is a more cost-effective option than purchasing a new product. Kim et al. (2008) defined the perceived benefits in terms of online shopping, but the definition can also be specifically applied to the economic benefits of rental-commerce by the extent to which the user believes that they will benefit financially from an online transaction with a particular rental-commerce company. This allows for cost and time savings as well as access to resources (Hamari et al., 2015). By eliminating fixed costs like the purchase price, costs can be reduced in rentalcommerce for the users’ benefit (Demary, 2015). They also gain more time and energy because they no longer need to spend much of it on considerations (e.g. in the form of product and dealer comparisons). The more economic benefits (and thus advantages) users perceive for themselves with a rental-commerce website, the more willing they are to enter into online transactions. Therefore, we assumed: H2.

Perceived economic benefits have a positive effect on the intention to rent products online.

Research on online shopping has shown that trust is an important influence factor on user behavior. For example, if the user decides to buy from an online retailer, they have confidence in the retailer and its website as well as in the internet transactions. The user relies on the company’s actions and technology. Consequently, they trust that the company is honest with them and that it deals with high quality products (Flavián and Guinalíu, 2006; Wiencierz and Röttger, 2017). In addition, the user also trusts that the company’s products and services will offer exactly what is available and promised on the website. At the same time, trust in the

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website often represents a proxy for trust in the online retailer (Mukherjee and Nath, 2007). Trust is a multidimensional model in the field of information and communication technology. Consequently, there are different types and forms of trust, e.g. disposition to trust, institution-based trust, trusting beliefs, and trusting intentions, which influence and shape users’ trust-related behavior in e-commerce (McKnight et al., 2002). According to Suh and Han (2003), a website that creates trust can improve the behavioral intentions towards shopping on it. Leonard (2012) also assumed that trust in the seller influences an individual’s attitude towards internet commerce. This was confirmed by other studies (Pennington et al., 2004; Verhagen et al., 2006), as well, which also showed that repurchase intentions are significantly increased by trust in the vendor (Fang et al., 2014). Adapting these e-commerce results to rental-commerce leads to the assumption that behavioral intentions to participate in rental-commerce depend on trust in the retailer. The greater a user’s trust in the rental-commerce retailer, the more positive their behavioral intentions will be. In addition, according to Cheung et al.’s research model on e-commerce, trust in internet transactions is influenced by the perceived security of users, among other things (Cheung et al., 2000). Other studies have assumed that the intention to buy (e.g. from an online retailer) is influenced by trust (e.g. Pavlou, 2003; Liu et al., 2005). Therefore, we hypothesized: H3.

Perceived trust has a positive effect on the intention to rent products online.

As a new economic model form, rental-commerce still offers users many uncertainties and risks compared to physical shopping and renting in a brick-andmortar store, as rental-commerce companies are not yet widespread. In contrast to physical shopping or renting, users cannot check the quality of a product, the handling of their data, or the safety precautions before placing an order. Especially when doing rental-commerce, there may be huge insecurity concerning traces of usage when receiving a used product. Users are not able to estimate the functionality and look of the product, and moreover, they cannot inspect the chosen product beforehand. Thus, the users do not know what to expect from the retailer (Lee and Turban, 2001). In addition, there is no physical interaction with the seller, since rental-commerce takes place exclusively via the internet. Therefore, the user is not in a position to effectively evaluate the offered products or to verify the identity of the rental-commerce retailer. As a result, and also due to possible hacker attacks and viruses, there is the possibility of fraud and misuse of users’ data (Flavián and Guinalíu, 2006). As internet purchases are perceived as riskier than physical purchases, users need even more certainty from companies

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that they are protected from such threats (Kassim and Abdullah, 2010). It must be ensured that these promises and intentions are translated into reality (Flavián and Guinalíu, 2006). In rental-commerce, it is not only about the private data and the payment methods but also about the quality of the product and the conditions to return it. This is especially important because the products will be used and then returned, so there may be high uncertainty concerning the return conditions. This shows that safety is an important factor in the relationship between companies and users, which is why we hypothesized the following: H4.

Perceived safety has a positive effect on the intention to rent products online.

Complexity shows the degree to which an innovation, like a new technology or business model, is perceived to be difficult to use and requires a high level of effort to be performed (Rogers and Showmaker, 1971). For many users, rental-commerce still seems very complex and not sufficiently transparent, raising questions about return policies, security aspects, and the process itself. However, previous research has shown that, in general, the more complexity there is, the more potential disadvantages there might be for users (e.g. Triandis, 1980; Davis, 1989; Cheung et al., 2000). According to Nieschlag et al. (1997), the higher the perceived risk via the internet, the more users tend to shop in stationary retail outlets. This could ultimately be due to the negatively-perceived complexity. Conversely, as the purchasing risk decreases, the willingness to shop by distance selling increases (Nieschlag et al., 1997). It is questionable whether the rented services in rental-commerce are apparent to users. Finally, several studies have already investigated the negative influence of increased complexity on the use of information technology, the intention to make purchases, and the adoption of a “green consumerism” (e.g. Thompson et al., 1991; Teo et al., 1999). H5.

Perceived complexity has a negative effect on the intention to rent products online.

As experience bundles the already-accumulated knowledge of something, the experience value is used as a popular parameter to map users’ knowledge. Thus, this experience value can be used for the assessment of knowledge. Alba and Hutchinson (1987) stated that experience with a brand results from the knowledge and familiarity of the user with this brand category. Users who have had positive experiences with shopping on the internet and who have tried out new technologies and ways of shopping tend to buy online more often, as they estimate the perceived risks to be lower compared to those who have not yet gained experience

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with internet shopping (Miyazaki and Fernandez, 2001). Many years of experience with online shops increase users’ self-confidence and leads them to use their previous knowledge to reduce any uncertainties (Bhatnagar and Ghose, 2004). In this way, users influence their self-assessment with regard to their knowledge and know-how. Further research also demonstrated that knowledge about a particular brand or product has a direct positive effect on the intention to buy from an online retailer (Chen and He, 2003; Wang and Yang, 2008). Here, knowledge about a brand that also offers its products in the form of an online shop reflects familiarity and experience with the brand, which are relevant implications for self-assessment (Laroche et al., 1996). Thus, it can be assumed that, similar to knowledge about brands and products, higher knowledge about a new technology or business model (like rental-commerce) may increase the intention to use and buy from the online retailers. In addition, users’ knowledge of terms of use is particularly influenced by the information content of the rental-commerce website. Users evaluate their purchasing decisions on the basis of how strong the information quality of the online shop is and how well they know that their privacy and data are protected by the online shop (Kim et al., 2008; Forsythe and Shi, 2003). It is of great importance for users to know exactly what happens to their personal data and payment information. The online shop must be able to assure users that their information will not be passed on to third parties and that there is a high quality of information service (Park and Kim, 2003). This so-called assurance to users by the online shop acts as the users’ knowledge of what is happening with their information and influences their loyalty and buying behavior (Sarkar, 2011; Hoffman et al., 1999). Wang et al. (1998) also found out that buying behavior on the internet is influenced by the extent to which the information transparency of online shops is used to create a user-oriented knowledge system. Further research has also shown that users wish to avoid uncertainty and that they strive for secure information when buying online (Wang et al., 1998). Risk-averse users are increasingly shopping in online shops if the shops provide the users with better information about their security precautions and data protection regulations. As such, the exchange of communication and subsequent transfer of knowledge play an important role in the purchasing decision of unsettled users in particular (Forsythe et al., 2006; Bhatnagar and Ghose, 2004). Therefore, it can be assumed that users’ knowledge of terms of use in rental-commerce could have a positive influence on their intentions to rent online. We thus supposed: H6.

Knowledge of rental-commerce has a positive influence on the intention to rent products online.

3.6 Will Renting Substitute Buying? Drivers …

H7.

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Knowledge of terms of use has a positive influence on the intention to rent products online.

On the basis of these hypotheses, the following theoretical framework can be developed (see Figure 3.11):

Figure 3.11 Theoretical framework

3.6.4

Empirical Study

3.6.4.1 Procedure and sample To test the hypotheses, we conducted a quantitative study. Before we went into the field with our study, we did a pretest. This was to be used to check the online questionnaire for incomprehensibilities and errors. After this pre-test, some improvements were made in terms of comprehensibility and wording before the main study was sent to participants from Germany via e-mail and social networks. Before our main study, we conducted a pretest (N = 30) to ensure that the questionnaire was understandable and to determine whether there were interesting results. Afterwards, we generated data for our main study with an online questionnaire, which was distributed to potential respondents via e-mail and social networks. Before analyzing the data, data sets of all participants who did not answer the survey completely were eliminated. In addition, we decided that this study concerns the intentions to use rental-commerce of actual non-users, hence, we only included inexperienced users in the study. After this procedure, we obtained a data set of N = 652 random German participants in total, of which 63.3% were female (Mage = 29.51 years, SD = 12.13).

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3.6.4.2 Adoption and Development of Measures While we primarily relied on well-established, reflective multi-item scales from previous studies that we modified to fit the context of our study, all constructs were measured via seven-point Likert scales. We adapted Bhattacherjee’s (2001) scale of collaborative consumption behavioral intention, which consists of four items, to the context of our study to capture the online renting intention. Following Bock et al. (2005), perceived economic benefits were measured. The approaches of Chai et al. (2015) and Bhattacherjee (2002) were used to measure trust. Knowledge of rental-commerce was adapted from Flynn et al. (1996) to our context, as well as perceived sustainability and perceived complexity, both of which were measured using a four-item scale (Cheung et al., 2000; Hamari et al., 2016). Furthermore, two three-item scales were used to measure safety and knowledge of terms and use (Wolfinbarger and Gilly, 2003; Bart et al., 2005). We decided to use these scales because most of them were previously applied in studies investigating drivers for collaborative consumption and sharing or in the field of e-commerce. Therefore, they fit the context of our research, so it was assumed that they could also be used adequately in this study (see Table 3.29). We assessed the reflective indicators’ unidimensionality using exploratory factor analysis and also measured the models’ internal consistencies. In addition, we supposed that multicollinearity was not a problem, since variance inflation factors (VIF) were tested and all showed a measurement below the recommended threshold of 10 (Hair et al., 2011). Moreover, high levels of scale consistency could be observed, as the average variance extracted (AVE not less than .67 for all reflective scales), Cronbach’s alpha of .86 and above, and composite reliability (CR) of .91 and above were all satisfactory (see Table 3.29). No items were removed during the measurement data model analysis, except for two items of knowledge of rental-commerce. Here, the items “I rarely come across offers from rental-commerce websites that I haven’t heard of” and “Compared to most other people, I know less about rental-commerce. (R)” had to be removed to provide a high scale consistency of the construct. Additionally, by applying Fornell and Larcker’s (1981) criterion, we assessed all reflective scales for discriminant validity, indicating that discriminant validity should not be a problem because no construct shared more variance with any other construct than with its own indicators (Table 3.30).

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Table 3.29 Items and outer loadings Reflective instruments (7-point Likert scale: 1 = strongly agree, 7 = strongly disagree)

Outer Loadings

Behavioral Intention (Bhattacherjee, 2001) (α = .955, CR = .967, AVE = .881) All things considered, I expect to continue with rental-commerce often in the future.

.957

I can see myself engaging in rental-commerce more frequently in the future.

.948

I can see myself increasing my rental-commerce activities if possible.

.901

It is likely that I will frequently participate in rental-commerce in the future.

.946

Sustainability (Hamari et al., 2015) (α = .937, CR = .955, AVE = .841) Rental-commerce helps save natural resources.

.914

Rental-commerce is a sustainable mode of consumption.

.910

Rental-commerce is ecological.

.931

Rental-commerce is environmentally friendly.

.913

Economic Benefits (Bock et al., 2005) (α = .888, CR = .930, AVE = .817) I can save money if I participate in rental-commerce.

.902

My participation in rental-commerce benefits me financially.

.938

My participation in rental-commerce can improve my economic situation.

.870

Trust (Chai et al., 2015; Bhattacherjee, 2002) (α = .875, CR = .909, AVE = .668) I trust that the offered rental-commerce products will be displayed as expected.

.790

The providers of rental-commerce websites are truthful.

.792

I trust that the rental-commerce provider provides enough safeguards to .820 protect me from liability for damage I am not responsible for. Rental-commerce websites provide a robust and safe environment in which I can use the service.

.840

Overall, rental-commerce is trustworthy.

.842

Safety (Wolfinbarger and Gilly, 2003) (α = .863, CR = .916, AVE = .784) (continued)

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Table 3.29 (continued) Reflective instruments (7-point Likert scale: 1 = strongly agree, 7 = strongly disagree)

Outer Loadings

I feel like my privacy is protected at rental-commerce websites.

.894

I feel safe in my transactions with rental-commerce websites.

.900

Rental-commerce websites have adequate security features.

.862

Complexity (Cheung et al., 2000) (α = .878, CR = .916, AVE = .732) The participation in rental-commerce websites is complicated; it is difficult to understand what is going on.

.895

The participation in rental-commerce websites involves too much time.

.812

It takes too long to learn how to participate in rental-commerce websites to make it worth the effort.

.864

In general, rental-commerce websites are very complex to use.

.849

Knowledge of Rental-Commerce (Flynn et al., 1996) (α = .905, CR = .927, AVE = .681) I feel quite knowledgeable about rental-commerce.

.861

Among my circle of friends, I’m one of the experts for renting products .818 on a rental-commerce website. I know pretty much about rental-commerce.

.879

I do feel very knowledgeable about rental-commerce.

.846

When it comes to rental-commerce, I really don’t know a lot. (R)

.711

I have heard of most of the new offers from rental-commerce websites that are around.

.820

Knowledge of Terms of Use (Bart et al., 2005) (α = .920, CR = .949, AVE = .861) I know my rights and obligations when participating in rental-commerce websites.

.943

My knowledge of my rights and obligations when participating in rental-commerce websites is very great.

.930

My knowledge of my legal claims and obligations when participating in .912 rental-commerce websites is above average.

3.6.4.3 Method To test our hypotheses, we used partial least squares (PLS) structural equation modelling, as the reason for a PLS method is based on the consideration that the research objective is prediction. We applied bootstrapping procedures (5,000 samples) to assess the significance of the parameter estimates. In PLS, the objective is prediction versus fit (Fornell and Cha, 1994), therefore giving a general

Safety

.204***

Knowledge of Terms of Use

−.034 .056 .013

.012

−.011

.230***

.296***

1

.534***

.429***

−.035

.268***

.278***

.534***

1

.314***

.242***

.246***

−.048

.622***

1

.296***

.278***

.392***

.246***

.622*** −.048

.243***

.194***

−.200***

1

1

.035

.172***

.483***

1

.172***

.194***

.056

−.200***

−.035

.012

.268*** .230*** −.034

.212***

1

.483***

.035

.243***

.242***

.013

−.011

.204***

Complex-ity Knowledge of Knowledge Rental-Commerce of Terms of Use

.377*** −.177***

Safety

* significant at p < .05; ** significant at p < 0.01; *** significant at p < .001

.212***

Knowledge of Rental-Commerce

−.177***

.392***

.377***

Trust

Complexity

.314***

.429***

Economic Benefits

1

Sustain-ability

Behavioral Intention

Behavioral Sustain-ability Economic Trust Intention Benefits

Table 3.30 Correlation table for the scales of the framework

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conclusion on overall productness of fit for the PLS model is controversially discussed (Dijkstra and Henseler, 2015; Henseler et al. 2014). However, we applied SmartPLS 3, which provides a number of model fit criteria. Model fit criteria with SRMR = .050 and NFI = .877 indicate adequate model specification (Dijkstra and Henseler, 2015). Additionally, we controlled for the effect of variables that relate to respondents’ demographics (i.e. age and gender) as well as shopping relevant aspects, such as their monthly income (measured on a six-point scale: 1 = less than 500 EUR, 6 = 4,000 EUR and more) and their affinity to use offerings of the sharing economy. To prevent common method bias, which might occur since evaluation and outcome measures were answered by the same person, a marker variable was included as a latent variable that directly affected the other variables in the model but was conceptually independent from the other variables in the questionnaire (Podsakoff et al., 2003). It can be confirmed that the model with the marker variable shows consistent results compared to the model without the marker variable. There were only marginal changes in the path coefficient but no changes in significance levels.

3.6.5

Results

The R2 (adjusted R2 ) of the dependent variables reports a value of .348 (.341), and the Q2 reports a value of 0.280, indicating an adequate model specification, as results of .20 and above for R2 and results larger than 0 for Q2 are already considered high in the field of consumer behavior research (Hair et al., 2011). Therefore, 34.8% of the variance of the behavior intention can be explained with the chosen constructs in our model. Regarding the effects of the independent determinants, we see that many of the predicted influences can be confirmed: Beginning with perceived sustainability, H1 can be confirmed, showing a significant influence on the BI (p < .01). Additionally, H2—the hypothesized effect—can be confirmed, assuming that perceived economic benefits have a significant impact on the BI (p < .001). As in previous studies (e.g. Pavlou, 2003; Liu et al., 2005), the intention to participate in rental-commerce is influenced by the perceived trust, thus confirming H3 (p < .01). With regard to the hypothesized effect of H4, a significant influence can be confirmed, indicating a positive impact of perceived safety on the BI (p < .05). In terms of perceived complexity, the assumed negative significant impact on the BI can be confirmed for H5 (p < .001). Lastly, on the one hand, H6 (the knowledge of rental-commerce on the BI) shows a positive significant influence (p < .001),

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while, on the other hand, there is no significant influence of knowledge of terms of use on the BI, therefore rejecting H7 (see Table 3.31). Table 3.31 Results T-Statistic

VIF

Sustainability → BI

Stand. Coef. .100**

2.424

1.520

Economic Benefits → BI

.290***

6.925

1.460

Trust → BI

.165**

3.499

1.801

Safety → BI

.110*

2.085

1.819

Complexity → BI

−.150***

4.383

1.091

Knowledge of Rental-Commerce → BI

.146***

3.581

1.434

Knowledge of Terms of Use → BI

.074ns

1.786

1.428

Note: BI = behavioral intention; N = 652; PLS algorithm: maximum iterations = 300; bootstrapping procedure: cases 652; Samples = 5,000; *significant at p < .05; **significant at p < 0.01; ***significant at p < .001; ns = not significant.

3.6.6

Discussion

One of the purposes of this study was to capture the new phenomenon of rentalcommerce in its entirety and differentiate it from other sharing models as well as to work out the relevant determinants that affect behavioral intentions. The results confirmed most of the predicted hypotheses. In regard to the first research question, perceived sustainability, perceived economic benefits, trust, perceived safety, and knowledge of rental-commerce positively influenced behavioral intentions, while perceived complexity negatively influenced behavioral intentions. Surprisingly, knowledge of terms of use had no impact on the intention to use rental-commerce. To answer the second research question, it can be observed that perceived economic benefits especially influenced the rental-commerce intentions the most, followed by trust, complexity, and knowledge of rental-commerce. In line with previous assumptions (Lawson et al., 2016; Luchs et al., 2011), H1 can be confirmed. For inexperienced users, sustainability is a motive to participate in rental-commerce (e.g. by hoping to reduce economical waste). Further studies could examine whether the sustainability aspect varies for certain products or depends on the product category offered by the online retailer. Additionally, we observed a positive effect of perceived economic benefits on the BI in our data. Following previous studies (Hamari et al., 2016; Demary, 2015), H2 can

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be confirmed. When participating in rental-commerce, lower amounts have to be paid compared to a purchase, which users perceive as a financial benefit and motivational factor (Tussyadiah, 2015). In the moment, this is more important to them than owning the product. Therefore, the monthly installments are less significant than the total purchase price. If products are only wanted for a trial or only needed for a short period of time, the financial benefits cannot be denied. Often, gradations are made between new and used products, whereby the financial advantage is greater for the used products. Moreover, H3 can be confirmed, verifying earlier studies (Cheung et al., 2000; Pavlou, 2003; Liu et al., 2005). Thus, trust has a significant impact on BI. The results indicated that trust has a significant influence on the BI, since trust (e.g. about the quality of the products or the reliability of the retailer) plays a decisive role for inexperienced users. One reason for this might be that users know that the products could have been rented by other users before and maybe face the risks of quality flaws or poor hygiene, so they evaluate trust building as more relevant and must rely on a certain quality standard being maintained. As Lee and Turban (2001) demonstrated, it is important for inexperienced users to feel safe regarding products and data. As with traditional internet purchases, where users need security in the settlement process (Kassim and Abdullah, 2010), safety is an important driver of rental-commerce. Thus, H4 can be confirmed. If the users feel safe, they are more likely to take part in rental-commerce, and this is an especially critical factor for companies, which should be even more careful in demonstrating a high level of transparency in terms of parameters such as data, payment, settlement, return, and quality. For this purpose, quality seals and the representation of real and satisfied user experiences should be placed on their website. Regarding H5, the negative effect of complexity on the behavior intentions can be confirmed, too. Thus, it can be assumed from the literature that the higher the perceived complexity of rentalcommerce is, the lower the users’ intentions to use the rental-commerce website and rent products from the retailer (Thompson et al., 1991). This effect can be explained by the fact that the users would have to make a greater effort in order to achieve the desired goal, i.e. the rental of a product (Rogers and Showmaker, 1971). In addition, hypothesis H6 can be confirmed, suggesting that a high level of knowledge about rental-commerce reinforces the intention to rent products on a rental-commerce website. As such, the more someone seems to know about their own possibilities to participate in rental-commerce and therefore builds a form of experience, the more likely they will buy on such a website. This assumption can be confirmed by previous studies (e.g. Bhatnagar and Ghose, 2004). The knowledge forms a security for an inexperienced user, which in turn reduces the risks of renting from an online retailer. This confidence in one’s own knowledge

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can be built up by visiting several rental-commerce websites with the same structure to establish some kind of familiarity (Laroche et al., 1996). This positive correlation also demonstrates that the knowledge gained must be positive. Otherwise, there would be no positive intention to shop there. Therefore, looking at both the negative influence of perceived complexity and the positive influence of knowledge of rental-commerce, it is recommended that rental-commerce website retailers should establish clear and transparent information structures. A rental-commerce website should offer users the opportunity to find all relevant information quickly in order to inform them about all important steps of their online rental, reducing complexity and increasing information knowledge. This could also include a simple and clear design of the rental-commerce website. The user must experience the feeling of being well informed so that their intention to rent something from the retailer increases. A visual listing of all payment, shipping, and service options could consolidate this effect. Thus, internal security would be established and ultimately lead to a rent. Surprisingly, users’ knowledge of terms of use seemed to have no significant effect on their intentions to use rental-commerce websites, rejecting H7. Some rated knowledge of terms of use in rental-commerce as more important while others rated it as less important, leading to the unexpected result and no clear significant direction.

3.6.7

Conclusion and Implications

Rental-commerce is characterized by the fact that the basic scheme of the modern market system has changed. The focus is no longer on exchange but on shortterm access to property. In other words, the focus is on the use of products, not ownership. In light of the results, to what extent such rental models strengthen environmental awareness and sustainability should also be considered. In this case, the rental model increases the users’ comfort by offering a certain flexibility in contrast to ownership. User durables can be tested without any problems (taking into account the minimum rental period) and without the risk of a bad purchase. On the one hand, this further stimulates consumption, as the user can now afford products that they would not be able to pay for otherwise. On the other hand, there is an increase in logistics due to the permanent back-and-forth shipment of rented products. The benefits are different: users do not have to wait long for the ordered products and may even be able to order products that are not yet available in the in-store business. With the possibility of being able to afford products that would not be affordable otherwise, rental-commerce also reaches users who cannot or

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do not want to spend a certain amount of money for products but who want to enjoy “new” devices for a certain period of time. These users can try to determine whether an investment really makes sense or whether it is not worth saving money for. In addition, users who want to try something new (e.g. who are looking for variety-seeking) are addressed. Instead of always buying the latest model, they can rent the product for a certain period of time and then rent the successor, as well. Therefore, they are always up-to-date. Often, especially in the field of information and communication technology, there are new innovations every few months—such as with improved devices like smartphones—which are often associated with financial losses when reselling due to the fast value loss of these technologies. With the rental-commerce model, the product is simply sent back, and a newer model is ordered if necessary, which is much more flexible than buying the product. In contrast, it must not be assumed that there is always no or reduced risk in rental-commerce. For example, one can fall into financial difficulties during the rental period by not being able to pay the rental fee, or a case of damage or loss can occur, the costs of which must be borne. It is also possible that users may rent a product that does not bring the desired performance. In the worst case, the users are bound by a contractually-agreed minimum rental period of several months, during which they still have to pay the rental fee, even if the device is not used. A classic faulty purchase can be resold to reduce the financial damage, whereas this is impossible in the case of a “faulty rental”. Although the financial risk is lower compared to the purchase, the user does not acquire any property and thus no equivalent value. Not only for users but also for some companies, rental-commerce can provide extra revenues and thus represent a lucrative, expanded business model. For example, the global online clothing rental market alone is estimated to have a value of 1.9 billion USD by the end of 2023, with an annual growth rate of over 10% (Research Nester, 2019). In addition to the traditional sale of products, companies have the option to build a website extension for a side-activity to reach a new target group. For instance, users who feel insecure about buying may try the product first through the possibility of renting. Without this option, it is likely that such users would not buy any products at all, leaving the company without any revenue. Therefore, with the option of renting, companies can still generate additional sales and profit. However, in the long run, companies need to decide whether rental-commerce should be an additional side-activity or the main branch of income. Compared to traditional retail websites, rental-commerce websites can offer users more flexibility and the option to experience the product first, improving overall user experience in comparison to competitors. There are also advantages for companies that focus on rental-commerce activities as their pure

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business model because they can make multiple profits on a single product by renting it several times. Thus, it is important that rental-commerce websites are aware of increasing logistic operations and effort regarding the quality control of returned products. The results demonstrated that rental-commerce providers should highlight the economic benefits to users and emphasize that they can save money while simultaneously mitigating their quest for the latest and newest products. However, it should be noted that access to information on the process should be kept transparent, simple, and concise in order to reduce complexity and inform users sufficiently. Here, a quality seal would be advantageous, as it would provide additional security for users (Miyazaki and Krishnamurthy, 2005). In order to create more authenticity and build trust, it is possible to incorporate customer reports on the website that rate the process and quality of the service and products. In addition, rental-commerce retailers have the opportunity to set up a kind of forum in which users can exchange views. This would do justice to the original philosophy of the sharing economy, i.e. to create a sense of community that binds users in a way that simultaneously accommodates information and communication technology. To answer the initial question (“Will renting substitute buying?”), it could be argued that rental-commerce needs to be further developed and that inexperienced users need to be more informed that they can rent products online, as many users are unaware of this option or have not done it yet. Based on the study’s findings that general knowledge of rental-commerce promotes the intention to use rental-commerce websites, it could be assumed that the more informed users are, the more likely they will participate in rental-commerce. In the context of the Expected Utility Theory, other determinants, such as sustainability, economic benefits, trust, safety, and complexity, seem to contribute to the assumption that rental-commerce might be a substitute to buying. Particularly concerning variety-seeking and flexibility, renting seems to be a promising concept for users. Overall, based on the results, we supposed that rental-commerce websites will undergo further growth but will still constitute an add-on option to traditional purchases. Nevertheless, for certain product categories, renting might be more likely to become a substitute for buying in the future. For example, in terms of digital products—e.g. CDs and video discs that developed from a physical carrier to an online-based medium—this might be an option. Among fast-developing product categories like technology and electronics, it may be possible that users will tend to use rental options in the future rather than buy these products. However, this study also had some limitations, which will be addressed in the following: firstly, the sample was collected in Germany and largely consisted of a younger age group, somewhat limiting the generalizability of the results.

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Particularly in the area of e-commerce, previous studies have shown that there are cultural diversity and cultural differences concerning the adoption of new IT models, and influencing factors may vary between countries (Choi and Lee, 2003; Gefen et al., 2005; Hallikainen and Laukkanen, 2018). However, studies in the field of collaborative consumption have addressed the importance of cultural influences (e.g. Piscicelli et al., 2015). Aside from cultural effects, geographical factors can also have an influential role in the use of rental-commerce, which has been proven by some studies that investigated consumer behavior in e-commerce (Ren and Kwan, 2009). Therefore, future studies should acquire participants from other countries in order to verify the general validity of the results. In addition, the age distribution of the data sample could have influenced the results. As this study’s sample primarily consisted of younger age groups (with an average age of 29.51 years), it reached the main target groups of today’s rental-commerce providers, so the results are important for rental-commerce practices. However, the younger average age of the participants may have affected the users’ perceptions differently than older age groups. The younger participants generally had more experience with e-commerce and information and communication technologies. Therefore, the factors influencing the formation of intentions and the intentions’ strength may differ compared to less experienced or older participants. Furthermore, the study only included participants who had not yet rented products on rental-commerce websites. As such, it would be interesting to examine whether the investigated drivers of users who have already participated in rental-commerce are equal to those of inexperienced users, as well as the motivation for repeated use of rental-commerce websites. In this context, a distinction between these two user groups would be recommended, as experience with and knowledge of the technical and organizational processes of the rental-commerce business model could provide an influence again. Moreover, the product category might play a significant role, e.g. trust may be more important when renting a car than when renting a board game. Therefore, further research could highlight whether there are different influencing factors regarding the product category.

4

General Discussion and Conclusion

4.1

Core Results

Both the respective literature reviews and the results of the six essays listed illustrate the relevance and ubiquity of the four subareas of e-commerce in consumers’ everyday online shopping. On the one hand, the results represent an important basis for marketing and consumer research to support a better understanding of the behavioral psychological motives of consumers and better evaluate correlations in shopping behavior. On the other hand, the practice benefits from the newly gained insights, as online retailers, in particular, can use them to better adapt their offers to consumer needs and optimize consumers’ online shopping experience, leading in return to increasing sales. The findings of the different essays are supported by a common conceptual framework, which focuses primarily on the consumer’s consideration and weighing up of utility and which could be confirmed by the qualitative and quantitative studies. Thus, this chapter summarizes the most relevant results of all six essays to answer the research questions posed at the beginning, discuss the most important implications and limitations of the studies, and provide suggestions for future research directions. Thus, the implications for research, management, and further research recommendations of the studies are discussed in the following and summarized in the context of e-commerce. Here, the core results from the research fields of cross-board ecommerce, voice-commerce, conversational-commerce, and rental-commerce are summarized and correlations of the research findings are evaluated on a metalevel. In doing so, the findings are illustrated along the conceptual framework from Section 1.2 so that a general consideration of the overarching questions of this dissertation can occur.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Fota, Online Shopping Intentions, Handel und Internationales Marketing Retailing and International Marketing, https://doi.org/10.1007/978-3-658-37662-8_4

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Looking at the established conceptual framework from Section 1.2, the focus is particularly on the main effect of this theoretical model. Thus, it is assumed that antecedents and inhibitors to participate in e-commerce influence consumer intention to shop online. This postulated effect represents the basic structure of the investigation in all six essays, which can also be uniformly confirmed empirically. Antecedents of Online Shopping Intentions in the New Subareas of E-Commerce From the results of the first and second essay, the perceived benefits of crossborder online shopping, which represent the antecedents in the conceptual framework, have a positive influence on the intention to buy cross-border online and are, thus, a crucial indicator of the purchase intention. This seems to be true for both emerging and advanced countries (i.e., in the case of the first essays for Romania and Germany, as well as for Germany and China in the second essay). This overarching term of “benefits” was used because they are used here as an overarching term for concrete factors through which consumers perceive an advantage in e-commerce. Online shops abroad offer consumers, for example, specific advantages, such as a wide range of products, exclusive brands, or cheaper alternatives, which are decisive reasons to order these products from a foreign retailer. In addition to the benefits of the cross-border e-commerce, further antecedents were identified in the other subareas of e-commerce, which positively affect the consumer intention to shop online. For instance, trust could be identified, which exerts a positive direct influence in cross-border e-commerce research as well as in voice-commerce and rental-commerce. Looking again at cross-border e-commerce, trust is primarily about trusting the foreign online retailer and trusting that the cross-border online purchase will be trouble-free for the consumer, despite the potential additional risks. If trust in the retailer and the cross-border process can be strengthened, this trust acts as a driver and increases consumer intention to use cross-border e-commerce and its benefits. Regarding the use of artificial intelligence in e-commerce, as examined in the third essay, trust is even shown to be one of the most important determinants of usage intention. One reason is that many consumers are unsure or inexperienced in the use of smart systems and are especially afraid of data misuse. Therefore, the trust towards the digital voice assistant itself is important (e.g., does it record hiddenly?), as well as towards the companies behind the artificial intelligence (e.g., what do the companies do with the gained and partly private information, and will they pass it on to third parties?). Due to the comparatively closer and permanent integration of intelligent systems in everyday life (e.g., in the form of induced voice assistants

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in smartphones or voice devices in consumers’ homes), trust plays a more prominent role for this subarea of e-commerce in particular. Next to voice-commerce, trust also has a positive impact on consumers’ behavioral intention when it comes to renting products online, as trust (e.g., in terms of product quality or retailer reliability) plays a critical role for inexperienced users in rental-commerce. One reason could be that users are aware of the re-renting of the products by multiple consumers, and therefore, the risk of poor quality or hygiene might seem very present to them. Hence, they rate trust building as more relevant and must rely on companies’ compliance with a certain quality standard. Along with trust, perceived security is one of the essential antecedents in the subareas of e-commerce. It is also important for inexperienced users to feel safe about products and personal data. As with conventional Internet purchases, where consumers need the certainty of security in the transaction process (Kassim and Abdullah, 2010), security is an important driver of rental-commerce. This is a particularly critical factor for companies that should be even more careful to demonstrate a high degree of transparency regarding parameters, such as data and payment, billing, return, and quality, since consumers are more likely to participate in rental-commerce the more secure they feel with the rental-commerce retailer or website. Another type of security can also be the knowledge through which consumers can protect themselves from unfavorable incidents when shopping online, and this, therefore, positively influences the online purchase intention. Therefore, the results indicate that a high level of knowledge about rental-commerce reinforces the intention to rent products on a rental-commerce website. The more someone knows about their options for participating in the rental business and, therefore, builds up a form of experience and security, the more likely it is that they will buy on such a website. The knowledge provides a kind of security for an inexperienced user, which, in turn, reduces the risk of renting from an online retailer. In addition, the studies presented found further antecedents that act as drivers to explain why consumers shop online. Regarding consumers’ crossborder e-commerce activities, the results show direct positive influences of cosmopolitanism for the Romanian and German sample. Thus, cross-border online purchasing intentions seem to be positively influenced by cosmopolitanism (i.e., the interest and curiosity about other cultures and international exchange), going along with the CCT (cf. Section 1.2). Additionally, according to the consumers surveyed, the most decisive driver in the field of voice-commerce is convenience. This is clearly where the added value for the consumer lies. Therefore, language, as the most natural form of communication, enables an even more seamless and effortless shopping experience for the consumer, which is one of

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the main advantages of using voice-commerce. Among other things, it was also found that for older consumers, in particular, it is not only functionality that is decisive for the use of voice-commerce but also the social interaction with a digital voice assistant. Thus, the humanization of digital voice assistants can be not only a functional but also an emotional help and enrichment, especially for older consumers who are limited in their mobility and social interaction. Regarding the results of the conversational-commerce study in the context of complaint management, it can be observed that the probability that the customer will return to the retailer and buy from them again, despite a complaint, increases depending on the choice of avatar, the empathic reaction of the avatar, and the offered compensation, which all represent antecedents with different specifications. Thus, it could be confirmed that the more human-like and emphatic the avatar is and the more the avatar offers a compensation, which all three serve as specifications of the antecedents, the more positive the consumer’s intention to buy again from this online retailer. Thus, consumer intention and subsequent behavior can be influenced by these three influencing variables. The behavior of the service contact person (here, the chatbot) in the event of a complaint is crucial for the perception of the consumer since, in the worst case, a bad service experience could lead to the consumer stopping future purchases and switching to another provider or retailer. Further antecedents could be found in the two rental-commerce studies, which have a special characteristic compared to the other essays. While cross-border e-commerce, voice-commerce, and conversational-commerce retain the original character of online shopping, rental-commerce represents a new business model that also challenges the traditional idea of ownership. Consumers are thus enabled to engage in a new, additional type of consumption that ties in with the principles of the Sharing Economy and, therefore, favors additional drivers than in the other subareas of e-commerce. In rental-commerce, the consumer experiences the product with all its properties and benefits. However, it is rather unlikely that the consumer will develop an emotional connection to the rented product due to a usually short rental period of only a few weeks. Due to this, the development of a self-identity in rental-commerce between the user and the object is rather unlikely in the opinion of the interviewed participants. However, the results provide further insights into why the experience of a product may be more important for the consumer than the possession of that product (Furubotn and Pejovich, 1972; Baumeister et al., 2015), resulting in the intention to rather rent than buy. Particular drivers and antecedents here are flexibility and the possibility of testing different products without committing to them in the long term. The literature also often refers to a non-ownership consumption interest by the consumers due to

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perceived burdens that can arise from the ownership of goods, therefore strengthening the intention to participate in rental-commerce. It can also be noted that the importance of ownership often seems to also be product-dependent. This could be particularly true for fast-moving product categories, where new product models are constantly appearing on the market at short intervals, such as in the technology sector or the fashion industry. The importance of the product category, as well as the rental period, is, therefore, clearly discussed in the interviews and the focus group as decisive factors for the success of rental-commerce. For example, there is a desire for short-term rental contracts to ensure the flexibility and freedom of the consumers. In the sixth essay, for inexperienced users of rental-commerce, sustainability issues are an additional motive for participating in rental-commerce, as consumers hope to reduce economic waste by renting products. Moreover, a positive effect of the perceived economic benefit on the behavioral intention of the consumer can be observed. When participating in rental-commerce, depending on the overall rental period, lower amounts are paid compared to a purchase. This is perceived as a financial advantage and motivation factor by many consumers (Tussyadiah, 2015) and is considered more important than owning the product at this moment. Therefore, the monthly payment rates are perceived as a smaller financial burden compared to the total purchase price—especially if the products are only needed for testing, or for a short period, such that the financial benefits cannot be denied. Inhibitors of Online Shopping Intentions in the New Subareas of E-Commerce In addition to the numerous antecedents listed above, which could be empirically identified in the six studies, inhibitors, as proposed in the conceptual framework, have also emerged that impede consumers’ intention to shop online. In the first essay, both the German and the Romanian sample show a negative significant influence of perceived risks of cross-border e-commerce on the crossborder online purchasing intentions. This result suggests that the perceived risks of cross-border online shopping constitute obstacles to cross-border online shopping and, thus, inhibit consumers’ purchase intentions. In addition, risk perception seemed to be slightly more important in emerging markets, such as Romania, than in advanced countries, suggesting that consumers in emerging markets seem to feel more vulnerable when buying from a foreign online retailer. However, country-specific risks that do not relate to cross-border aspects of online shopping but to domestic aspects, such as social or political risks in emerging markets, could provide an explanation for the higher relevance of risk in such countries. Moreover, consumers from emerging markets still have less experience with cross-border shopping activities. In addition, Romanian consumers, although part

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of the European Union, have, so far, had less experience with open markets than, for example, German consumers. Moreover, a direct negative effect of ethnocentrism on the intention of cross-border online purchases could be found for the German sample, showing that ethnocentric attitudes, serving here as inhibitors, may lead to a reduced intention to shop from a foreign online retailer. Furthermore, perceived vulnerability reduces consumers’ cross-border online shopping intentions. Yet, this direct effect is broadly consistent across the two countries’ samples studied, China and Germany, confirming earlier findings on the inhibitory effects of vulnerability (e.g., Tsui-Auch and Möllering, 2010). This vulnerability is also reflected in the other essays, for example, in the form of privacy concerns. The results of the voice-commerce study, for example, show that data protection is a central barrier for the consumers investigated. This is closely linked when there is a lack of trust in digital voice assistants. In particular, fears about permanent monitoring by voice assistants seem to be omnipresent. In addition, there are also security concerns during the ordering and payment process, confirming earlier research by Hoy (2018). The results also show that the lack of distribution is a barrier that currently has a major impact on consumers’ use of voice-commerce, as this lack of distribution is associated with little experience and confidence in using voice-commerce. Moreover, the social environment, in particular, could have an influence on usage. The more voice-commerce users there are in the consumer’s environment, the more likely it is that perceived security and trust can spread through collective use. The acceptance of voicecommerce is, thus, directly linked to the acceptance of digital voice assistants. If consumers use digital voice assistants even more actively in their everyday lives, the path to ordering via them is not far off. Although some consumers would currently use digital voice assistants to order mainly everyday items, the lack of trust and visualization is one of the main obstacles. Regarding the lack of visualization, all consumers surveyed confirm a strong barrier creating a feeling of lack of control and self-determination, which can result in consumer vulnerability. From the rental-commerce essays, the results show that it is more important for older consumers to own products than use them only for a short period, in contrast to younger participants. For example, property is considered important in the group of consumers over 50 years old, while the group of consumers under 40 years old owns property that is perceived as either unimportant or important, depending on the product. Thus, the evaluation of a potential antecedent and a potential inhibitor in this context is dependent on consumer and product characteristics. While the temporary rental and use of products can be seen as an advantage for certain consumer groups or product groups, it appears that the purchase and ownership of products in this study was consistently rated as positive

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by older consumers or for certain products. Consequently, for a certain consumer group and selected products, the basic principles of rental-commerce, renting products instead of owning them, serve as an inhibitor for participation in this ecommerce model. The sixth essay also confirms the negative effect of complexity on behavioral intentions, so that it can be assumed that the higher the perceived complexity of rental-commerce, the lower the intentions of consumers to use the website of the rental retailer and rent products from the latter. This effect can be explained by the fact that consumers must make greater efforts, which might outweigh the expected utility and benefits (Fishburn, 1970). In summary, the studied relationships between antecedents and inhibitors of online shopping intention, which are proposed in the conceptual framework of this dissertation, represent the different drivers and barriers of the various subareas of e-commerce. On the one hand, perceived benefits (cross-border ecommerce); determinants such as trust and security (cross-border e-commerce, voice-commerce, rental-commerce), cosmopolitanism (cross-border e-commerce), convenience (voice-commerce), humanness (voice-commerce, conversationalcommerce), and empathy of an avatar; the offered compensation in case of a complaint (conversational-commerce); and knowledge, economic benefit, and sustainability (rental-commerce) seem to be current antecedents in e-commerce. On the other hand, perceived risks (cross-border e-commerce), as well as further determinants such as ethnocentrism (cross-border e-commerce), consumer vulnerability, the lack of control and self-determination (cross-border e-commerce, voice-commerce), privacy concerns (voice-commerce), and complexity (rentalcommerce) could be identified as current inhibitors to consumers’ e-commerce participation. In addition, the intention to use rental-commerce was either positively or negatively influenced depending on the age of the consumer, as well as the product group. Therefore, these results illustrate the factors consumers include in their considerations when deciding whether or not to shop online. This decision thus depends on the utility which the consumers expect due to weighing up the perceived benefits and risks (cf. Section 1.2). Moderators of the Relationships Under Review In addition to the main effects that were proposed in the conceptual framework, different moderating influences are also considered in the essays. These moderations could give an additional explanation about the relationship between the antecedents and inhibitors to participate in e-commerce and the consumer intention to shop online, as seen in Section 1.2. Hence, some moderation variables were tested to evaluate whether an increase or decrease of the postulated main effects of the conceptual framework could be observed. However, the analyses

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of the moderating effects of the first cross-border e-commerce essay show that in both the German and Romanian samples, foreign travel, cosmopolitanism, or ethnocentrism have not shown a moderating influence on the relationships to the intention of cross-border online purchases. Moreover, with regard to the proposed moderating effect of the second cross-border e-commerce study, the results are less consistent and call into question the previous understanding of the role of perceived vulnerability. For example, a significant moderating effect of vulnerability could only be confirmed for the Chinese sample, whereas in the German sample, there was only a small positive effect, which is, however, not significant. However, the moderated moderation effect (i.e., the threefold interaction of trust towards foreign online retailers and perceived vulnerability) significantly increases the motivation to make cross-border online purchases for both country samples. Due to the findings of a floodlight analysis, it seems that vulnerability entails a reinforcing effect of cross-border online shopping behavior when a certain level of trust is present. In line with the results of Tsui-Auch and Möllering (2010), it can be concluded that negative effects of vulnerability can be mitigated by trust building. However, while they propose only a direct relationship between trust and perceived vulnerability (Tsui-Auch and Möllering, 2010), an interaction effect is theorized and can be empirically validated. In this respect, these results suggest that the vulnerability construct can challenge the established relationships between key variables of consumer behavior and that the inclusion of perceived vulnerability as a moderator variable can lead to unexpected changes in well-researched relationships. Consumers’ Expectations and Evaluations in the Context of Online Shopping Intentions As the final part of the conceptual framework, mediating effects are considered, in the form of consumer expectations and evaluations. While the fourth essay about the use of artificial intelligence in complaint management depicts the single study considering mediators, the effects of the three experimental factors are positively mediated by the perception of anthropomorphism and by the evaluation of redress. Therefore, the perception of the avatar is completely mediated by anthropomorphism, while for the other two experimental factors, reaction and compensation, there is only a complementary mediation. The results show that the behavioral intention depends on how human-like and alive the avatar is represented—regardless of whether it is depicted as a human or a robot avatar. Similar results can be observed for redress as a mediator: The influence of the choice of the avatar as well as the reaction and compensation on behavioral intention is

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mediated by the influence of the evaluation of the redress in the form of a complementary mediation. Nevertheless, the direct effect of the three experimental factors on the behavioral intention does not disappear completely, showing their direct influence and importance despite the redress. Thus, while it is observed that behavioral intention can be increased by the perceived evaluation of redress by consumers when an empathic reaction or a compensation is offered, the choice of an avatar that is perceived as human-like or alive, whether shown as a human or a robot, is crucial for behavioral intention formation. The present results show that the relationships and correlations of the conceptual framework apply in all six essays. Thus, with the help of the different main topics, but in their entirety, the factors that play a role in forming and influencing today’s consumer intentions in e-commerce can be explained. However, it is also crucial that these relationships are dependent on the respective context and the subareas of e-commerce and that they influence these individually (cf. Section 1.2).

4.2

Practical Implications

Based on the core results of the individual essays, relevant implications for (retail) management, politics, and consumer protection, as well as for consumers can be drawn from the six presented essays. In the following segments, the findings are presented in a structured overview for the three main areas of implications and discussed in an overarching manner. Implications for Management From the results of the studies examined, numerous implications can be derived for online retailers, manufacturers (e.g., of digital voice assistants), and management, which should help to provide consumers with an improved online shopping experience. To encourage consumers to participate more in e-commerce, they need to be made more aware of the benefits they receive from online shopping. The relevance of this general illustration of benefits is particularly addressed in the first two essays in the context of cross-border e-commerce. For instance, the results of the first essay will specifically help retail managers to understand what kind of trade-off consumers have to make when considering cross-border online purchases. From the German and Romanian samples, the majority of participants (68.4% in total) have already made cross-border online purchases. To make consumers be able to perceive more benefits when they shop in foreign online stores, what foreign online retailers should consider first is to increase the value

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of their products and services. The more benefits consumers will perceive in a certain shopping opportunity, the more positive the consumer’s attitude towards this purchase option, and the greater their willingness to shop from this retailer. In addition to driving awareness to general benefits, online retailers should also communicate concrete advantages and drivers of e-commerce directly to consumers. For example, the search for the lowest price, the greatest choice, and the highest exclusivity seems to be a reason for consumers to buy from foreign rather than domestic retailers. Moreover, foreign online sellers should strictly control the product quality. The product quality is one of the driving forces for the development of a retail industry and trade flows (e.g., Hallak, 2003; 2006). The reason why consumers give up their own domestic products and resolutely trust overseas products is the authentic guarantee brought by cross-border e-commerce. If consumers have doubts about the product quality of a foreign retailer or even the foreign market itself, it could be deadly for the foreign firms’ and markets’ sustainable development (e.g., Hudson and Jones, 2003). Hence, cross-border online retailers should cautiously choose reliable suppliers and establish intelligent supplier systems to control the product quality by ensuring the integrity of the source channels. Online sellers should also engage in comprehensive control of products testing, inspection, and origin verification. In addition, some latest technologies, such as cloud computing, Internet of Things, and big data analysis, could be adopted in cross-border online transactions to provide consumers with timely anti-counterfeiting, identification, and products tracing services by using, for example, QR codes (Chen, 2018). Foreign online stores should also simplify transaction processes. Normally consumers are more inclined to accept simple, convenient, and efficient online shopping systems because they do not have much time or patience to learn how to register for numerous accounts, search and collect information, compare product characteristics, complete orders with unfamiliar payments methods, or understand complex foreign trading terms. As the first point of contact in cross-border ecommerce, the interface of online websites should be concise, clearly arranged, and uncomplicated. Product characteristics and corporate information should be listed out clearly at first glance for consumers, as well as made recognizable to the consumers that they are ordering from a foreign online retailer. Extensive shopping information, such as logistics, payment methods, risk warnings, and privacy statements, should be reasonably sorted and briefly (but clearly) shown to consumers so that they can grasp all the cross-border online shopping-related information in the shortest time. Therefore, for the benefits to be apparent to consumers, online retailers must reduce the risks for consumers above all because risks can reduce positive effects of benefits and reduce the perceived benefits of

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foreign products (Wang et al., 2010; Chen and Dubinsky, 2003). These potentially perceived risks can make the e-commerce environment more uncertain for consumers and put them in vulnerable positions. This can be underlined by the findings of the second essay since a potential consumer vulnerability can, among others, serve as a barrier to the internationalization of consumers’ cross-border activities. This vulnerability may counteract and mitigate the cross-border e-commerce efforts of companies to sell online to international buyers. The findings suggest, in specific, that managers need to deal with vulnerability and take action to reduce it. Marketing strategies should aim to neutralize both feelings of vulnerability and actual risks. This negative impact of vulnerability on cross-border online purchasing intentions suggests an opportunity for online retailers to increase consumers’ willingness to make crossborder online purchases by enhancing their knowledge and skills, thus arming them against this vulnerability and its possible negative consequences. By providing relevant information (e.g., expected costs, delivery time, and return policy), online retailers can increase consumers’ knowledge. Cross-border e-commerce should also be mastered by consumers with the same skills required for domestic online shopping to reduce the uncertainties and barriers to cross-border online shopping compared to domestic online shopping. Among other things, the local language, currency, and the same payment services should be offered as in the online shopper’s home market since only consumers who are satisfied with their experience of cross-border online shopping will continue to buy from foreign online retailers, thus representing loyal and long-term profitable customers (e.g., Flavián et al., 2006). Retailers should avoid intentional or unintentional unethical marketing strategies for cross-border e-commerce (e.g., by deliberately misleading consumers into believing that their shop is a domestic online shop or by not disclosing hidden costs, such as non-transparent delivery or return costs). In addition, retailers should create a business environment for ethical behavior on the part of cross-border e-commerce that considers and minimizes the risks faced by foreign online shoppers. These efforts would also help to increase confidence in foreign online retailers, which, as the results of the second study suggest, is necessary to strengthen the positive relationship between perceived benefits and the intentions to participate in cross-border e-commerce. While the importance of trust for online transactions is well-known from numerous previous studies (e.g., Guo et al., 2018; Fang et al., 2014; Bart et al., 2005), its interaction with perceived vulnerability further underlines the relevance of this determinant for building strong online relationships. However, not only in cross-border e-commerce, efforts and assistance should be made to reduce consumer vulnerability and insecurities. The research findings

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show here as well that some measures to improve the purchase experience when using a digital voice assistant should be developed. Insights from the qualitative surveys of the third essay show that most participants would like to have guidance during the ordering process. Due to faulty communication, the presentation of clear words and commands is needed, which the digital voice assistant can implement. Insights from the observations and the interviews show that some participants did not know which commands they had to formulate in the further process or that they needed more assistance from the voice assistant. Therefore, as a measure to improve usage, the provision of further process steps and a more structured purchasing process can be suggested. Considering the lack of product selection in voice-commerce, statements of the participants from the studies show that more product suggestions should be named and explained in more detail. In addition, placing questions by the voice assistant combined with the filtering of product features would make the personal purchasing experience easier. Moreover, the practical experiment in the form of an observation of actual usage has shown that the loss of control goes hand in hand with the fact that no manual control mechanisms are offered to confirm purchases and orders. Developers of smart speakers must address this obstacle, for example, by attaching a button to the smart speaker to verify orders or purchases. Amazon has taken a first step in this direction by introducing a language code as a control mechanism or parental control. Using a four-digit language code, consumers can confirm their order via Alexa (Amazon, 2020). However, this extends the ordering process by a further step. Therefore, the recommendations mentioned here should be treated with caution, as an extension of the ordering process in favor of the user’s control can have a negative effect on the simple and time-saving usability of voice-commerce (Kraus et al., 2019). Thus, the voice-commerce studies above show that one problem that online retailers need to address is the lack of knowledge and uncertainty of consumers with new systems or the unknown generally, as is the case with the use of artificial intelligence for online shopping. This can be observed for rental-commerce as well since consumers also experience a new business model with which they have gathered comparatively less experience. Identical to shopping from a foreign online retailer or shopping via digital voice assistants, for companies setting up a rental business, it also seems particularly important to provide consumers with carefreeness and flexibility and, above all, reduce uncertainty and mistrust. As an example, it should be noted that access to information about the process should be kept transparent, simple, and concise to reduce complexity and keep users adequately informed. A quality seal would be advantageous here, as it would provide additional security for users (Miyazaki and Krishnamurthy, 2005). To

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create more authenticity and to build trust, it is possible to include customer reports on the website that evaluate the process and the quality of the service and products. However, consumers’ expectations and evaluations of the retailer’s actions also play an essential role, especially in situations where the consumer is already in a vulnerable and disadvantaged position and, therefore, needs help. Although many customers in online retailing sometimes want some advice during the buying process, they often lack confidence in taking advantage of consulting and service offers by digital avatars. A key factor in achieving competitive advantages for service providers when using chatbots is maintaining a high level of service quality. As chatbot technologies have become an integral part of everyday consumer life, assessing chatbot performance is crucial to achieving and maintaining this competitive advantage (Lin and Hsieh, 2011). Hence, online retailers need to invest time in contact and interaction with customers to learn more about their purchasing behavior and intentions. Service personnel, such as chatbots, must, therefore, be able to empathize with the consumers so that they understand their needs and can react appropriately. In addition, the information and knowledge base of this service personnel should be constantly expanded to be able to answer customer questions professionally and satisfactorily (De Ruyter and Wetzels, 2000). Therefore, companies should focus primarily on the quality of their services in the complaint process. For customers, it can be of importance whether the avatar is more human- or robot-like, or how empathetic the service contact is. However, more importantly, they expect a positive service experience, where additional compensation is particularly helpful. They must also be able to trust the service contact. An artificially intelligent chatbot can probably generate this trust better than a mere rule-based chatbot, as the latter can reach its limits fast, especially in more complicated situations and conversations. A technically mature, artificially intelligent chatbot can, therefore, be of great benefit to companies in the area of complaint management. Friendly consumer complaints reflect their concerns about the positive relationship with the company, while consumers who neglect and seek redress for complaints reflect the lack of such a relationship (Ro, 2013). This partly contradicts the findings of the fourth essay, where the assessment of the redress plays a key role in behavioral intention. Therefore, it can be assumed that it is important to create an empathetic avatar and offer some kind of compensation. Therefore, the results of the quantitative analysis show that avatars embedded in websites can be helpful in enabling consumers to interact in a seemingly social setting; additionally, a stronger bond and satisfaction of the consumer with the shop can be created. If the avatars are displayed in the

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right way, they can improve the overall image that the consumer has of the company. By referring, for example, to the match-up hypothesis (e.g., Kamins, 1990; Till and Busler, 2000), it can also be assumed that the consumer’s perception of an avatar depends on its appearance and personality traits. By assigning human characteristics to a non-human figure, which is known as anthropomorphism (e.g., Bartneck et al., 2008; Złotowski et al., 2015), the consumer’s perception of the brand or retailer may improve, too (Castelo and Boegershausen, 2016). Besides the reduction of vulnerability and barriers, there are further implications for retailers, which should be applied especially in the context of rental-commerce. It becomes clear in the interviews and focus group that the rental period is a decisive factor for the success of rental-commerce. So far, this business model has been based on long-term rental agreements for consumer durables. Therefore, there is a need for action to be identified, for example, by creating short-term leases. This also means that companies should be aware of their role as rental companies and not as sellers, which entails some different requirements and procedures than those of traditional e-commerce. The choice of products is also important, as not all products are suitable for rental, and, as the products are to be rented several times, they should also be of an appropriate quality and durability while simultaneously covering the company’s costs. Rental-commerce websites can offer many financial and social advantages for the consumers and will continue in growth in the future, which might be the case, in particular, for fast-moving product categories, where new product models are constantly appearing on the market at short intervals, such as in the technology or fashion sector. In general, it is advisable to deal with and react to consumer wishes as a service provider to create customer benefit and establish the business (e.g., Kassim and Abdullah, 2010). In addition, retailers of rental-commerce could set up a kind of forum where the consumers can exchange information. This would do justice to the original philosophy of the Sharing Economy, that is, to create a sense of community among consumers (e.g., Hamari et al., 2015) and between consumers and providers. Since it has been made clear that trust plays a critical role in rental-commerce, and consumers tend to trust well-known and established companies rather than new ones, it is particularly advisable to expand existing companies with rental models or to establish a rental business in cooperation with well-known retailing partners. In addition to traditional product sales, companies doing traditional e-commerce business, therefore, have the opportunity to build a website extension for a secondary activity to reach a new target group. For example, consumers who feel unsure about making a purchase can first try the product by renting it. Without this option, it is likely that such consumers would not buy any products at all, leaving the company with no revenue.

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By doing so, companies can still generate additional sales and profits with the option of renting. However, there are also advantages for companies that focus on rental-commerce activities as their pure business model, as they can make multiple profits from a single product by renting it several times. Therefore, it is important that rental-commerce websites are aware of the increasing logistical processes and the increasing effort for quality control of returned products. Since a certain interest of consumers in rental-commerce is evident, it is advisable for rental-commerce providers to draw attention to their business through marketing and advertising and, thus, provide consumers with more information and greater presence. By doing that, rental-commerce can provide extra revenues and represent a lucrative, expanded business model. The worldwide online market for clothing rental alone, for instance, is estimated to have an annual growth rate of over 10%, while being worth $1.9 billion by the end of 2023 according to Research Nester (2019). Furthermore, the right consumer segmentation can be helpful for online retailers (O’Connor and O’Keefe, 2000), especially in international markets. Since the analysis in cross-border e-commerce has shown that cosmopolitanism can also be an explanatory factor of the buying intention, retailers should try to address consumer groups that show cosmopolitan tendencies. However, consumers in certain country markets, such as Germany, also show ethnocentric tendencies. This should be considered by retailers in the development of their international strategies. In markets where consumer ethnocentrism plays a significant role, foreign online retailers could focus on local responsibility with regard to international markets, cooperate with domestic retailers, or try to achieve an outer appearance and rely on the same values similar to local companies. Implications for Consumer Policy and Consumer Protection Following the results of the essays, implications can also be drawn for policymakers and consumer protections that make consumption via the new subareas of e-commerce safer and more attractive to consumers. For example, the vulnerability aspects, arising from cross-border online purchases as discussed above, result from absent international rules or standards to protect cross-border online shoppers. The problem here is that most existing trade agreements between countries were concluded in the pre-digital era. However, as cross-border e-commerce focuses on direct deliveries from foreign online retailers to customers abroad, these mostly obsolete trade agreements lack both governmental and institutional control and influence. This makes it even more important for government institutions and policymakers to help consumers develop the necessary knowledge and skills to make the right decisions concerning cross-border online purchases

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and protect themselves from potential risks. These knowledge and skills can be ensured by providing relevant information and educating consumers, preferably at a very young age, as first consumption experiences are made in early childhood (e.g., Frentz, 2020). In addition, tools to assist online shoppers, such as calculators for cross-border taxes and tariffs, should be provided. Further measures to reduce consumer vulnerability in cross-border online shopping include quality securing certifications, such as online quality seals, and flexible exchange rights, which provide more security to the consumers. Online retailers could also take measures by better mapping experience goods, for example, by expanding virtual product testing. The results of the voice-commerce studies also make it clear that the qualification of consumers is of particular importance, especially in the context of voice-commerce since, here, new technologies and intelligent systems are used. Consumer protection can support consumers to help themselves by providing comprehensible information about the benefits and risks of voice-commerce. With this information, consumers can weigh up the advantages and disadvantages, thus encouraging a critical approach to the topic that is geared to their own interests and needs (e.g., Fishburn, 1968). However, to achieve this, new ways of educating and providing information must first be found to reach consumers of all age groups. For example, perceived control is a key factor influencing the vulnerability of consumers. According to Baker et al. (2005), consumers appear vulnerable when they lose their autonomy of action and freedom of choice. The analysis showed that perceived low control over the buying process seems to influence the consumers’ behavioral intention to order products via a digital voice assistant negatively. Here, consumer protection can build on these findings to address the lack of resources and means to increase consumers’ perceived control over the purchasing process and, thus, enable the successful use not only of voice-commerce but also of other subareas of e-commerce. Implications for Consumers Although external measures that ensure a smooth and successful participation in e-commerce are crucial, there are also internal precautions and measures that consumers must take to support the most optimal consumption for them. Beginning with rental-commerce, for consumers, it is first advisable to get a comprehensive impression of the offers in rental-commerce, as the concept is little known so far. Compared to traditional retail websites, rental-commerce websites can offer consumers more flexibility and the ability to experience the product first, improving the overall user experience compared to competitors. Which products consumers

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rent for which purpose is something they should try out and find out for themselves. Nevertheless, it is especially important for customers to pay attention to the different processes and offers of the rental companies. Consumers should be aware that a long rental period can mean that the monthly rental price can eventually exceed the actual purchase cost of the product. Therefore, the minimum term for renting products should be taken into account, as well as, for example, insurances, which are offered by some companies. Some companies combine rental with subscription models, while others offer different prices with regard to new and used goods. If, for example, consumers want to test products before buying them, or keep the option of buying open, such rental models are recommended that offer to purchase the products less the rental fees already paid. The range of products on offer and the procedures vary between rental providers, and consumers should inform themselves in advance about the conditions and pay attention to the contractual arrangements. Consumers should also be aware of the environmental consequences of increased logistics and more parcels being shipped through rental-commerce. Consumers should also always read the terms and conditions of the rental-commerce website to be aware of the consequences in case of loss or damage of the rented product. This increased caution also applies to other e-commerce subareas, such as cross-border e-commerce, so consumers should always look at the retailer websites’ terms and conditions to determine whether they are ordering from a domestic or foreign online shop and to better assess whether that retailer is trustworthy or not. In conclusion, the results show that many implications for management and from politics and consumer protection, as well as for consumers themselves, relate primarily to the reduction and avoidance of consumer vulnerability, which has a decisive negative impact on consumer intention. Thus, many of the proposed actions are based on minimizing external and internal risks and strengthening consumers’ competence and knowledge access.

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Research and Theoretical Implications and Directs for Future Research

The results of the individual essays illustrate the added value of the conducted studies for research and theory and allow implications to be derived that both provide additional explanatory value for existing theories and enrich existing research knowledge. In the following, the research and theoretical implications for the results of the different studies are depicted. In addition to numerous implications

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for both research and practice, this dissertation also brings with it a few limitations that highlight potential research interests and remaining research gaps, which have been addressed by the essays. Consequently, the core results and conclusions of this work lead to new questions and provide an additional basis for subsequent research studies. Thus, future research recommendations in ecommerce are presented. However, instead of addressing all the findings of the six essays, only those that provide the most crucial added value for further research studies will be addressed. In doing so, the presentation of the research implications will be structurally oriented on the conceptual framework from Section 1.2 so that the overarching and superordinate relationships, which are based on this model and have been addressed in the different chapters of this dissertation, are finally illustrated. Research Implications Based on Antecedents and Inhibitors of Online Shopping Intentions in the New Subareas of E-Commerce While the conceptual framework focuses primarily on the main effect of the relationship of antecedents and inhibitors with the intention to shop online (cf. Section 1.2), several different determinants can also be identified in the six research studies examined, which act as antecedents and inhibitors for the different subareas of e-commerce, thus supporting the theoretical framework. Here, for example, in the context of international marketing and cross-border e-commerce, it is the specific benefits and risks of cross-border online shopping, as perceived by the consumers. To promote the understanding of the “cross-border online shopper,” consumers from an advanced country, Germany in both essays, with consumers from emerging markets, Romania in the first essay and China in the second, have been compared. The marketing theory can thus be expanded and better explained with the findings of these two studies. In summary, the results of both essays help to examine the perceived benefits and risks of cross-border online shopping and provide a useful tool for future research on international online shopping to understand what kinds of trade-offs consumers are willing to make when considering participation in cross-border online shopping. However, other determinants can also be identified in the studies, which can be regarded as antecedents or inhibitors for the intention to participate in ecommerce, depending on the circumstances. For instance, especially for older consumers, the emotional value of products seems to play a greater role, such that they are less willing to participate in rental-commerce compared to younger consumers. Nevertheless, for certain product categories, renting could be a lucrative alternative to buying in the future. In the case of rapidly developing product categories, such as technology, electronics, or fashion, consumers may be more

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inclined to use rental options in the future rather than regularly buying new products at full price. Therefore, results of the two essays on rental-commerce show that both previous research and theories can be confirmed and, thus, do not allow any generalizable and universally applicable statement about the consumers’ perception of property and ownership. Thus, whether ownership is perceived as a driver or barrier to the rental-commerce participation depends on different conditions. Therefore, the present essays generate some statements about the advantages and disadvantages of rental-commerce, which could be investigated in greater depth by further qualitative and quantitative studies. However, the desire for ownership rather than temporary use of products depends on the product group and age of the consumer. These findings, therefore, show that the theoretical implications should be considered in a more differentiated way in future research since attitudes towards ownership may depend on external (e.g., product-related) and internal (e.g., consumer characteristics) conditions that affect the applicability of the respective theories. For example, research could be conducted on whether the intention to rent products instead of buying them is related to the product category or type (e.g., hedonic vs. utilitarian products). Furthermore, it could be investigated which consumer groups primarily participate in rental-commerce shops by also evaluating different sinus milieus. The results obtained can be evaluated in the form of a comprehensive online survey. The theoretical foundations of the Sharing Economy, from which some statements about rental-commerce are derived, also reveal several topics that can be addressed by scientific research. For example, the focus should continue to be on education and understanding of the perception of ownership and its relevance for consumers’ consumption behavior in the future. Since only unexperienced consumers were included in the study, consumers could be interviewed after the use of rental-commerce offers and asked about their attitude towards lost and returned possessions to learn more about the background of ownership perception and the overall resulting consumer behavior. Similar to rental-commerce, conversational-commerce is also an e-commerce subarea, which is gaining more popularity and gradually entering consumers’ everyday life. However, it still has comparatively low actual user numbers, which is why the studies focus especially on the behavioral intention of consumers. Like with rental-commerce, here, it would also be interesting to investigate whether participants who have already used conversational-commerce in the past and have, therefore, gained experience with it will name the same barriers and drivers as the unexperienced consumers or whether other influencing factors may play a more important role. This distinction between consumer experience levels could be relevant not only in voice-commerce or rental-commerce but also in dealing with complaints, as such an investigation could help retailers to offer a more

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differentiated and adapted response to their customers in complaint management (e.g., Hsiao et al., 2016). Although the present studies have been able to highlight some motivations at the individual level, this is a topic that could well be explored in more depth in future research. In addition, the many perceived benefits of the different subareas of ecommerce also favor increased logistics due to a higher online-shopping frequency, resulting in the problem that increased (especially international) returns have a negative impact on the environment. Furthermore, international online retailers destroy masses of products that consumers return, including products that are new and undamaged, such as furniture, clothes or electronics. This raises the question of whether consumers, especially those who participate in crossborder e-commerce, are even aware of these negative environmental impacts when buying products online. Especially with a view to rental-commerce, this new business model has led to a higher number of parcels shipped, which might have an increasingly negative impact on the environment. Thus, further research studies could investigate the environmental awareness of consumers in cross-border e-commerce and rental-commerce more closely. Interviews and online surveys could be used to determine whether environmental awareness is present in ecommerce and what effects it has on consumers’ future shopping intentions and behavior. In addition, future research should investigate how the usage of sharing models and product orders made cross-border could actually be made more sustainable and environmentally friendly. Thus, the research field of sustainability and environmental awareness represents a relevant potential research area, not only for cross-border e-commerce or rental-commerce, as already described, but also as a possible determinant for other areas of e-commerce, which try to describe the changed and adapted consumer behavior in today’s world. Research Implications Based on the (Quasi-) Moderators of the Relationships Under Review The greatest gains in theoretical knowledge and implications for research were made by the (quasi)-moderators of the studies. One of the most outstanding findings of this dissertation is the importance of consumer vulnerability, which was given a special role in several of the essays examined. What is special about this determinant is that it exerts a direct influence on consumer intention to shop online and moderates the relationship between antecedents and inhibitors on consumer intention. The concept of consumer vulnerability is particularly wellillustrated theoretically in the second essay, and interesting research implications can be derived from the insights gained.

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The second essay on cross-border online shopping contributes to international business and marketing theories by extending and re-specifying the MA framework from Merton (1957) to promote the understanding of the mechanism by which perceived benefits drive consumers to participate in cross-border online e-commerce. Thus, the second essay on cross-border e-commerce illustrates that in cross-border online shopping, the ability factor is reciprocally identical with the concept of vulnerability, as also suggested by Shultz and Holbrook (2009). Thus, lower ability levels correspond to higher vulnerability levels, creating new implications for the conceptualization of consumer vulnerability. The reflective structure of perceived vulnerability is understood here as a combination of the perceived lack of knowledge and the perceived lack of skills. The findings that vulnerability influences cross-border online shopping in two different ways, via a direct negative effect and via an indirect positive moderation effect, represent an important contribution to the study of online shopping research. Thus, the MTV framework can be used to extend research on domestic online shopping, which has so far focused primarily on the benefits and risks (e.g., Forsythe et al., 2006), but not on the lack of knowledge and skills that create perceived vulnerability and thus inhibit consumers’ cross-border online shopping intentions. So far, there are few studies that examine the perceived vulnerability in international business and marketing research. Although science agrees that behavior in different contexts is a function of the ability (or, conversely, vulnerability) and motivation of consumers, there is, as yet, no general scientific agreement on the mechanism by which these factors apply (Siemsen et al., 2008). In addition, the second essay demonstrates the intercultural applicability of the MTV framework, as well as its potential to explain the mechanism of cross-border online shopping, making this theoretical framework robust for countries with different cultural and economic backgrounds. Thus, it can be stated that the usefulness and applicability of this new research framework should be proven in further studies. The MTV framework could be suitable for understanding further motivational mechanisms in situations that involve uncertain outcomes and a high degree of vulnerability, as the relationships depend on certain environmental conditions. Therefore, the MTV framework should also be explored in further studies in the context of marketing and consumer research, where consumers find themselves in situations of increased uncertainty (e.g., due to the lack of language skills or limited knowledge of and experience with another culture). In addition, consumer vulnerability poses particular challenges for marketing and its subareas of consumer research since it is associated with, among other things, lower confidence but higher perceived risk of consumers. For example, the first essay showed that risks that can

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lead to consumer vulnerability are perceived differently across different cultures (in this case, Germany and Romania). Thus, consumer vulnerability may also be perceived differently or play different roles depending on the culture, influencing the purchasing behavior of a country’s consumers. A key way to overcome these challenges is to understand the subjective views of consumers on consumer vulnerability. Despite the existence of some different research approaches, each of which sheds light on different aspects of consumer vulnerability, this aspect surrounding the actual perception and assessment of a consumer with regard to their (potential) vulnerability in the context of consumption has little application so far and should be investigated in more detail. Consumer vulnerability is often regarded as a permanent condition due to the presence of special characteristics (Mansfield and Pinto, 2008). However, authors of central research approaches assume consumer vulnerability as a temporary state (Baker et al., 2005), such that every consumer can experience vulnerability depending on the situation (Brennan et al., 2017; Mansfield and Pinto, 2008), such as in cross-border online shopping due to additional risks. Despite the large number of research approaches in this area (Linz, 2017; Visconti, 2016; Baker et al., 2015), there is still no uniform definition of terms in the consumer context (Brennan et al., 2017; Mansfield and Pinto, 2008). Most of these studies are devoted to the conceptualization of the construct, while few studies focus on the empirical investigation of consumer vulnerability (Roy and Sanyal, 2017; Shi et al., 2017; Kenning and Wobker, 2013; Burden, 1998). Hence, of central importance is that perception in this context (i.e., information processing) is understood as an active process that is shaped by the subjectivity and selectivity of the individual consumer. The investigation of the subjective view of the consumer can provide information about both the causes and the subjective perception of vulnerability in the context of consumption. In addition, it allows knowledge about whether and what measures consumers take to reduce their perceived vulnerability. However, the phenomenon of consumer vulnerability is not only an issue in the cross-border e-commerce context but can also be identified in the other subareas of e-commerce. Above all, the voice-commerce study clearly showed the vulnerability of consumers due to a lack of knowledge and skills, but more importantly due to a lack of consumer control and determination in the shopping process via a digital voice assistant. In accordance with the purchase phase model, the voice assistant shortens the pre-purchase phase considerably by taking over the information search and the evaluation of product alternatives and presenting the products that it has evaluated as most suitable like a personal advisor, such as in stationary retail. Moreover, the overall low price range, which the

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participants were willing to pay when shopping via a digital voice assistant, indicates that the relationship established in the purchase phase model between the pre-purchase phase and cognitive effort with regard to product selection is interdependent in both directions: The classical assumption of the model is that the pre-purchase phase is shortened if the consumer intends to buy low-involvement products (Kotler and Keller, 2016). The results of the study now show the opposite case: A pre-purchase phase shortened by external circumstances (in this case, by the pre-selection of the digital voice assistant) results in the fact that lowinvolvement products are purchased primarily. The traditional purchase phase model must, therefore, be adapted when artificial intelligence is integrated and applied in the purchase process since certain prerequisites, such as the independent and self-determined search for products by the consumers, as is the case in classic e-commerce, can no longer be guaranteed due to the external control of the digital voice assistant in voice-commerce. Strengthening transparent communication and knowledge transfer and enhancing consumer literacy and education are approaches that should be theoretically explored in future research to reduce vulnerability. However, the importance of decreasing consumer control and selfdetermination (e.g., due to the rising usage of intelligent systems and artificial intelligent) in the context of increased consumer vulnerability should also be considered in future e-commerce research. These findings, however, can already help to better understand the theoretical construct of consumer vulnerability by identifying possible factors that may influence and facilitate it. In addition to consumer vulnerability, other (quasi-) moderators were also considered, as well as determinants identified that should receive attention as (quasi-) moderators in future studies. The two cross-border e-commerce studies have particularly shown that cultural influences should be given greater consideration in the use of e-commerce, especially in the use of new technology. Indeed, the results show that depending on the country observed, the results vary in strength or even in the existence or non-existence of certain relationships, which are founded on the conceptual framework. For instance, from the first essay on cross-border e-commerce, further research implications can be derived from the effects of additional (quasi-) moderators observed: While cosmopolitanism increases the likelihood that consumers will visit online shops abroad to seek product-related information and make purchases, consumer ethnocentrism contradicts this relationship and reduces their intention to shop at a cross-border online shop. The results for the German sample, thus, suggest that the ethnocentric tendencies also seem to minimize the openness and willingness of consumers to globalize their purchases. The purchasing behavior of consumers, therefore, appears to be contradictory—cosmopolitan on the one hand but ethnocentric on

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the other. The investigation of these contrasting behavioral and attitudinal characteristics, among others in the context of the CCT, as discussed in Section 1.2, could be one of the greatest challenges for future research. Therefore, while in the first essay about cross-border e-commerce, the influence of cosmopolitanism seems to be constant in both the German and Romanian samples, differences can be observed with regard to the effect of ethnocentrism. To understand whether these effects have a general validity, as well as what other implications these sociological characteristics have on consumer purchasing behavior, future studies on e-commerce, especially focusing on cross-border shopping, should be conducted, for example, with samples from other countries. In particular, it would be desirable to study and compare several countries or regions with significantly different country characteristics and cultural backgrounds. The cultural dimensions of Hofstede (e.g., Hofstede, 2009; Hofstede and Bond, 1984) could then also be applied here. Possible further approaches that could be additionally investigated are the purchase frequency of online shoppers, social media influences (for example, by influencers), and socio-demographic factors, as well as the influence of credit card ownership. Further studies can also expand the scope of research to more diverse influencing factors. Several research variables, such as product characteristic, exchange rate, and trade policy, could be added to the research interest to explore what other factors will influence the foreign online purchase intention. Additionally, a comparison between different cross-border online shopping models would be valuable. Further studies could, thus, investigate how cross-border e-commerce intention and the general cross-border online shopping behavior of consumers change depending on whether they receive offers from a foreign online marketplace (e.g., eBay or Small Global) or an independent online shop (e.g., adidas.com). Such research could provide a detailed insight into consumers’ motivation, knowledge, and perceived risk towards different types of businesses. Research Implications based on Consumers’ Expectations and Evaluations with Regard to Online Shopping Intentions in the New Subareas of E-Commerce In addition to the main effect of the antecedents and inhibitors, as well as the influence of the (quasi-) moderators proposed in the conceptual framework, research implications based on mediators in the form of consumer expectations and evaluations are also considered. For this consideration, the third and fourth essays particularly provide new insights for academia and research in the context of voice-commerce and conversational-commerce. The results show that the essay’s findings of the animism approach should be used to remove possible barriers by establishing a personal relationship between digital voice assistants and users. In this context, the theoretical significance of

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personal and individual communication within the anthropomorphism approach (e.g., Bartneck et al., 2008; Złotowski et al., 2015) could be investigated more intensively to better understand the intended use of digital voice assistants by consumers. The aim should be to achieve the greatest possible degree of reciprocal interaction so that the digital voice assistant is ideally completely humanized in the sense of the animism approach (e.g., Dörrenbächer and Plüm, 2016). This also corresponds to the wishes of the consumers, who, for example, prefer individual customer profiles. The fact that the study’s participants maintained “eye contact” with the voice assistant throughout the entire process could indicate that they have already oriented themselves to the usual human interaction rules and evaluate the voice assistant to be human-like. Additionally, the Theory of Animism can be put into context with the purchase phase model by Kotler and Keller (2016) in the context of voice-commerce. Due to the constant communication of the user with the voice assistant to be guided through the purchase process, it can be concluded that the principle of animism is, therefore, present in every purchase phase. The qualitative study, therefore, also analyzed how the participants feel during an ordering process via speech and where animism still has its limits in voice-commerce. The results of the conversational-commerce study in the context of complaint management also support these conclusions: It is essential for (online) companies to meet, if not exceed, consumer expectations with the help of good customer service. Thus, the consumer is more likely to be loyal to the company and more likely to recommend it to other potential consumers (Reichheld and Schefter, 2000). According to Ro (2013), friendly complaints from customers reflect the positive relationship with the company or the need to maintain it, while neglect and redress-seeking consumer behavior reflects the lack of a positive relationship between companies and consumers. However, the results of the fourth essay show that redress by the company and its evaluation by the consumers play a significant role in the behavioral intention and decide whether or not the consumer would purchase again from this retailer. Here, research still needs to clarify the role of redress and consumers’ attitudes and behavior resulting from use in different contexts. Thus, the research results show that it is important to create an empathic avatar, as well as offer some kind of compensation. Furthermore, the animism and anthropomorphistic theoretical approaches (e.g., Bartneck et al., 2008; Złotowski et al., 2015; Dörrenbächer and Plüm, 2016) can also be confirmed in this study, as the human-like avatar resulted in a stronger positive behavioral intention than the robot-like avatar. In contrast to existing theories, such as the Uncanny Valley Theory (Mori, 1970; 2012), which states that a too human-like design of nonliving or artificially created objects can have a frightening and alien effect on

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General Discussion and Conclusion

consumers, it can be assumed that the more human-like an avatar is displayed, the greater the consumer’s willingness to buy from this retailer again. Concerning directions for future research, both the results of the voicecommerce and conversational-commerce studies should be confirmed quantitatively, with a larger sample, as well as with participants from other country markets to, for example, investigate whether anthropomorphistic characteristics are valuated differently between cultures. Since the research results of the crossborder e-commerce studies have shown that the postulated relationships in the conceptual framework are dependent on the influence of consumers from different countries, it can also be suggested that examining consumers from different countries or cultures could also provide scientific value in these e-commerce subareas. Moreover, especially with regard to anthropomorphism, it would be advisable to conduct additional experimental studies with real-life settings to validate whether consumers react different to direct use in comparison to a hypothetical scenario, with which they should only imagine the usage. When looking at further research possibilities from a company’s point of view, additional forecasts about the future increase in purchasing via voice assistants raise not only infrastructural questions, such as whether, for example, a proprietary system of a voice assistant should be implemented in the customer journey, but also the question of what the future implementation must look like in everyday handling with regard to buying advice and purchasing. With special regard to the implementation of artificial intelligence in complaint management, it would be interesting to investigate whether the amount of compensation depends on the appearance of the avatar used. Perhaps a robot-like avatar is more forgiving than a human because consumers are aware of their comparatively lower intelligence. The influence of the perception of the chatbot and the expectations can also have an impact on the quality of service. Maintaining a high level of service quality is a key factor in achieving competitive advantages for service providers (e.g., Kassim and Abdullah, 2010). The combination of perceived service quality in the dependence of the usage of chatbots would be an interesting future field of research. In today’s constantly changing environment, it is, therefore, helpful to know the characteristics and motivations of consumers when shopping online (Akar and Nasir, 2015). It is advisable to take a detailed look at the antecedents and inhibitors to participate in e-commerce to consider the socio-demographic information and sociological characteristics of potential customers and consider their expectations and evaluations into account when designing marketing strategies. Thus, the results of this dissertation should help to uncover, among other things, drivers and barriers of potential consumers for the different subareas of e-commerce, which may be of interest to retailers and academia.

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