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Cited by 75AI in Service InteractionsTechnology Adoption and User BehaviourCustomer Service Quality and Loyalty

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Facilitating retail customers’ use of AI-based virtual assistants: A meta-analysis

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Blut, Markus, Wünderlich, Nancy V., Brock, Christian·2024·Journal of Retailing
DOI 10.1016/j.jretai.2024.04.001
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Abstract

Retailers rely on virtual assistants (VAs), such as Amazon's Alexa and chatbots, to deliver 24/7 customer service at low costs, as well as novel shopping opportunities. Despite improved VA capabilities due to artificial intelligence (AI), many retailers still struggle to convince customers to become repeat users of VAs. Therefore, to establish recommendations for how to facilitate VA use, this meta-analysis extracts 2,766 correlations from 244 independent samples of customers interacting with VAs. The results suggest that customer-, VA-, and shopping occasion–related factors all influence technology use. Price value is the strongest driver, followed by support, social influence, and anthropomorphism. Performance risk, competence, and trust matter to lesser extents. These factors exert strong indirect effects by triggering two customer responses: cognitive and emotional. Negative emotions emerge as a particularly important mediator. Finally, several VA types enhance or weaken the noted effects, including whether they are intelligent/less intelligent, commercial/noncommercial, voice-/text-based, and avatar-/non-avatar-based. The results suggest no one-size-fits-all approach applies for VAs, because their performance varies across customer responses. The current meta-analysis provides in-depth guidance for retailers seeking to select appealing VAs.

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  • Recent literature in marketing research has largely focused on how consumers interact with AI systems and how these AI systems change customer journeys and referrals.

    “Recent literature in marketing research has largely focused on how consumers interact with AI systems and how these AI systems change customer journeys and referrals (e.g., Aggarwal et al. 2024;Blut et al. 2024;Gelbrich et al. 2025;Liu et al. 2024).”

    From Machine marketing: rethinking the customer in the age of generative AI (2026) · cites this paper as Blut et al. 2024

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Research works printed but not linked: 72

  1. Ayyagari, 2011, Technostress: Technological Antecedents and Implications, MIS Quarterly, 35, 831, 10.2307/41409963 · DOI
  2. Bagozzi, 2022, AI Service and Emotion, Journal of Service Research, 25, 499, 10.1177/10946705221118579 · DOI
  3. Bagozzi, 1999, The Role of Emotions in Marketing, Journal of the Academy of Marketing Science, 27, 184, 10.1177/0092070399272005 · DOI · Paper page
  4. Bartneck, 2009, Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots, International Journal of Social Robotics, 1, 71, 10.1007/s12369-008-0001-3 · DOI · Paper page
  5. Blut, 2022, Meta-Analysis of the Unified Theory of Acceptance and Use of Technology, Journal of the Association for Information Systems, 23, 13, 10.17705/1jais.00719 · DOI · Paper page
  6. Blut, 2016, Factors Influencing the Acceptance of Self-Service Technologies: A Meta-Analysis, Journal of Service Research, 19, 396, 10.1177/1094670516662352 · DOI
  7. Blut, 2021, Understanding Anthropomorphism in Service Provision, Journal of the Academy of Marketing Science, 49, 632, 10.1007/s11747-020-00762-y · DOI · Paper page
  8. Castelo, 2019, Task-Dependent Algorithm Aversion, Journal of Marketing Research, 56, 809, 10.1177/0022243719851788 · DOI · Paper page
  9. Chang, 2010, Effects of Message Framing, Vividness Congruency and Statistical Framing on Responses to Charity Advertising, International Journal of Advertising, 29, 195, 10.2501/S0265048710201129 · DOI
  10. Chen, 2021, Understanding Automated Conversational Agent as a Decision Aid, Internet Research, 10.1108/INTR-11-2019-0447 · DOI
  11. Chen, 2013, Temporal Contiguity and Negativity Bias in the Impact of Online Word of Mouth, Journal of Marketing Research, 50, 463, 10.1509/jmr.12.0063 · DOI
  12. Clarkson, 2013, The Desire for Consumption Knowledge, Journal of Consumer Research, 39, 1313, 10.1086/668535 · DOI
  13. Cohen, 1992, A Power Primer, Psychological Bulletin, 112, 155, 10.1037/0033-2909.112.1.155 · DOI · Paper page
  14. Davis, 1989, User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, 35, 982, 10.1287/mnsc.35.8.982 · DOI · Paper page
  15. Davis, 2012, An Investigation of Shopping Trip Value, In-Store Shopping Value and Retail Format, Journal of Retailing and Consumer Services, 19, 229, 10.1016/j.jretconser.2012.01.004 · DOI
  16. Dellaert, 2020, Consumer decisions with artificially intelligent voice assistants, Marketing Letters, 31, 335, 10.1007/s11002-020-09537-5 · DOI
  17. Dodds, 1991, Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations, Journal of Marketing Research, 28, 307
  18. Dogra, 2021, An Investigation of Indian Generation Z Adoption of the Voice-Based Assistants (VBA), Journal of Promotion Management, 27, 673, 10.1080/10496491.2021.1880519 · DOI
  19. Ellway, 2016, What's Wrong with IVR System Service?, Journal of Service Theory and Practice, 26, 387, 10.1108/JSTP-02-2015-0040 · DOI
  20. Featherman, 2003, Predicting e-Services Adoption: A Perceived Risk Facets Perspective, International Journal of Human-Computer Studies, 59, 451, 10.1016/S1071-5819(03)00111-3 · DOI · Paper page
  21. Fishbach, 2007, Be Better or Be Merry: How Mood Affects Self-Control, Journal of Personality and Social Psychology, 93, 158, 10.1037/0022-3514.93.2.158 · DOI · Paper page
  22. Fix, 2006, Emotion-Based Norm Enforcement and Maintenance in Multi-Agent Systems, Autonomous Agents and Multiagent Systems, 105
  23. Frank, 2023, The Paradox of Technology, Psychology & Marketing, 40, 554, 10.1002/mar.21740 · DOI
  24. Gao, 2018, Alexa, My Love: Analyzing Reviews of Amazon Echo, IEEE SmartWorld, 372
  25. Gashami, 2014, Understanding the Trade-Off Between Privacy Concerns and Perceived Benefits in SaaS Individual Adoption, PACIS, 354
  26. Gelbrich, 2021, Emotional support from a digital assistant in technology-mediated services: Effects on customer satisfaction and behavioral persistence, International Journal of Research in Marketing, 38, 176, 10.1016/j.ijresmar.2020.06.004 · DOI
  27. Golder, 2023, Learning from data: An empirics-first approach to relevant knowledge generation, Journal of Marketing, 87, 319, 10.1177/00222429221129200 · DOI
  28. Grewal, 2018, Meta-Analysis: Integrating Accumulated Knowledge, Journal of the Academy of Marketing Science, 46, 9, 10.1007/s11747-017-0570-5 · DOI
  29. Guha, 2021, How artificial intelligence will affect the future of retailing, Journal of Retailing, 97, 28, 10.1016/j.jretai.2021.01.005 · DOI
  30. Hasan, 2020, Consumer Trust and Perceived Risk for Voice-Controlled Artificial Intelligence, Journal of Business Research, 131, 591, 10.1016/j.jbusres.2020.12.012 · DOI
  31. Heerink, 2008, The Influence of Social Presence on Acceptance of a Companion Robot by Older People, Journal of Physical Agents, 2, 33
  32. Holzwarth, 2006, The Influence of Avatars on Online Consumer Shopping Behavior, Journal of Marketing, 70, 19, 10.1509/jmkg.70.4.019 · DOI
  33. Huang, 2022, A framework for collaborative artificial intelligence in marketing, Journal of Retailing, 98, 209, 10.1016/j.jretai.2021.03.001 · DOI
  34. Hong, 2014, A framework and guidelines for context-specific theorizing in information systems research, Information Systems Research, 25, 111, 10.1287/isre.2013.0501 · DOI · Paper page
  35. Huang, 2022, Predicting Continuance Intention to Fintech Chatbot, Computers in Human Behavior, 129, 10.1016/j.chb.2021.107027 · DOI
  36. Hubert, 2019, The influence of acceptance and adoption drivers on smart home usage, European Journal of Marketing, 53, 1073, 10.1108/EJM-12-2016-0794 · DOI
  37. Ischen, 2022, Is Voice Really Persuasive? The Influence of Modality in Virtual Assistant Interactions and Two Alternative Explanations, Internet Research, 32, 402, 10.1108/INTR-03-2022-0160 · DOI
  38. Johnson, 2005, Cognitive and Affective Trust in Service Relationships, Journal of Business Research, 58, 500, 10.1016/S0148-2963(03)00140-1 · DOI
  39. Karahanna, 1999, The Psychological Origins of Perceived Usefulness and Ease-of-Use, Information and Management, 35, 237, 10.1016/S0378-7206(98)00096-2 · DOI
  40. Kock, 2004, The Psychobiological Model, Organization Science, 15, 327, 10.1287/orsc.1040.0071 · DOI
  41. Kreijns, 2022, Social Presence: Conceptualization and Measurement, Educational Psychology Review, 34, 139, 10.1007/s10648-021-09623-8 · DOI
  42. Kuberkar, 2020, Factors Influencing Adoption Intention of AI Powered Chatbot for Public Transport Services within a Smart City, International Journal of Emerging Technologies in Learning, 11, 948
  43. Latif, 2020, Speech Technology for Healthcare, IEEE Reviews in Biomedical Engineering, 14, 342, 10.1109/RBME.2020.3006860 · DOI
  44. Laumer, 2019, Chatbot Acceptance in Healthcare: Explaining user Adoption of Conversational Agents for Disease Diagnosis, ECIS
  45. Lazarus, 1991, Cognition and Motivation in Emotion, American Psychologist, 46, 352, 10.1037/0003-066X.46.4.352 · DOI · Paper page
  46. Le Bigot, 2004, Searching Information with a Natural Language Dialogue System, Applied Ergonomics, 35, 557, 10.1016/j.apergo.2004.06.001 · DOI
  47. Le Bigot, 2007, Effects of Speech-and Text-Based Interaction Modes in Natural Language Human-Computer Dialogue, Human Factors, 49, 1045, 10.1518/001872007X249901 · DOI
  48. Lee, 2003, The Technology Acceptance Model: Past, Present, and Future, Communications of the Association for Information Systems, 12, 752
  49. Li, 2020, Dimensions of Artificial Intelligence Anxiety Based on the Integrated Fear Acquisition Theory, Technology in Society, 63, 10.1016/j.techsoc.2020.101410 · DOI
  50. Loewenstein, 2001, Risk as feelings, Psychological Bulletin, 127, 267, 10.1037/0033-2909.127.2.267 · DOI · Paper page
  51. Mori, 2012, The Uncanny Valley, IEEE Robotics & Automation Magazine, 19, 98, 10.1109/MRA.2012.2192811 · DOI · Paper page
  52. Mou, 2017, Understanding Trust and Perceived Usefulness in the Consumer Acceptance of an E-Service, Behaviour & Information Technology, 36, 125, 10.1080/0144929X.2016.1203024 · DOI
  53. Noble, 2013, The Effects of Brand Metaphors as Design Innovation, Journal of Product Innovation Management, 30, 126, 10.1111/jpim.12067 · DOI
  54. Nowak, 2018, Avatars and Computer-Mediated Communication: A Review of the Definitions, Uses, and Effects of Digital Representations, Review of Communication Research, 6, 30, 10.12840/issn.2255-4165.2018.06.01.015 · DOI
  55. Phipps, 2017, Routines Disrupted: Reestablishing Security Through Practice Alignment, Journal of Consumer Research, 44, 361, 10.1093/jcr/ucx040 · DOI · Paper page
  56. Pitardi, 2021, Unveiling the Drivers of Consumers’ Trust in Voice-Based Artificial Intelligence, Psychology and Marketing, 38, 626, 10.1002/mar.21457 · DOI
  57. Rosenthal, 1979, The File Drawer Problem and Tolerance for Null Results, Psychological Bulletin, 86, 638, 10.1037/0033-2909.86.3.638 · DOI · Paper page
  58. Rzepka, 2022, Voice Assistant vs. Chatbot, Information Systems Frontiers, 24, 839, 10.1007/s10796-021-10226-5 · DOI
  59. Sailunaz, 2018, Emotion Detection from Text and Speech: A Survey, Social Network Analysis and Mining, 8, 1, 10.1007/s13278-018-0505-2 · DOI
  60. Shankar, 2018, How Artificial Intelligence (AI) is Reshaping Retailing, Journal of Retailing, 94, vi, 10.1016/S0022-4359(18)30076-9 · DOI
  61. Shen, 2006, Social Influence for Perceived Usefulness and Ease-of-Use of Course Delivery Systems, Journal of Interactive Online Learning, 5, 270
  62. Shimp, 1982, Warranty and Other Extrinsic Cue Effects on Consumers' Risk Perceptions, Journal of Consumer Research, 9, 38, 10.1086/208894 · DOI
  63. Tam, 2012, The moderating role of perceived risk in loyalty intentions: An investigation in a service context, Marketing Intelligence & Planning, 30, 33, 10.1108/02634501211193903 · DOI
  64. Van der Heijden, 2004, User acceptance of hedonic information systems, MIS Quarterly, 28, 695, 10.2307/25148660 · DOI · Paper page
  65. Venkatesh, 2000, Determinants of Perceived Ease of Use, Information Systems Research, 11, 342, 10.1287/isre.11.4.342.11872 · DOI · Paper page
  66. Venkatesh, 2000, A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science, 46, 186, 10.1287/mnsc.46.2.186.11926 · DOI · Paper page
  67. Venkatesh, 2003, User Acceptance of Information Technology: Toward a Unified View, MIS Quarterly, 27, 425, 10.2307/30036540 · DOI · Paper page
  68. Venkatesh, 2012, Consumer Acceptance and Use of Information Technology, MIS Quarterly, 36, 157, 10.2307/41410412 · DOI · Paper page
  69. Venkatesh, 2016, Unified Theory of Acceptance and Use of Technology, Journal of the Association for Information Systems, 17, 328, 10.17705/1jais.00428 · DOI · Paper page
  70. Wei, 2021, The Impacts of Perceived Risk and Negative Emotions on the Service Recovery Effect for Online Travel Agencies, Frontiers in Psychology, 12
  71. Wünderlich, N. V., & Paluch, S. (2017). A Nice and Friendly Chat with a Bot, International Conference on Information Systems.
  72. Xia, 2004, The Price is Unfair! A Conceptual Framework of Price Fairness Perceptions, Journal of Marketing, 68, 1, 10.1509/jmkg.68.4.1.42733 · DOI · Paper page

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Metadata

Authors
Blut, M., Wünderlich, N. V., & Brock, C.
Affiliations
  • Durham University (GB)
  • Technische Universität Berlin (DE)
  • University of Rostock (DE)
Corresponding author
Brock, Christian
ORCID
Venue
Journal of Retailing
Publisher
Elsevier BV
Published
2024-06
Volume
100
Issue
2
Pages
293-315
ISSN
0022-4359 (ISSN-L)
DOI
10.1016/j.jretai.2024.04.001
OpenAlex
W4396859888
Cited by
75
References
11
Citations by year
2026: 35 · 2025: 36 · 2024: 4
FWCI
2.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Retailing (publishedVersion, cc-by)
  • Durham Research Online (Durham University) (publishedVersion, other-oa)
  • Durham Research Online (Durham University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
Keywords
virtual assistants, chatbots, artificial intelligence adoption, retail customer service, price value, social influence, anthropomorphism, trust, perceived performance risk, consumer emotions, negative emotions, cognitive responses, avatars, technology acceptance

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