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Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption
Abstract
Abstract Customers increasingly orchestrate their everyday activities with the support of technology, with services increasingly adopting AI-based applications. Yet, research is still in its infancy and has been largely conceptual. Therefore, based on data collected from 238 young consumers, analyzed using PLS-SEM, this study focuses on users’ motivations to adopt intelligent digital voice assistants in service encounters. Findings show that functional, social and relational elements drive adoption, untangle crossover effects between them and reveal the moderating role of experience and need for human interaction. While empirically validating and extending the Service Robot Acceptance Model by Wirtz and colleagues, this study provides evidence that anthropomorphism is not universally positive and adds a new perspective regarding the underexplored role of customer-robot rapport building. The study contributes to a more holistic understanding of digital voice assistants’ adoption and provides managerial guidance on how to successfully implement such technologies.
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Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
SPSS and SAS procedures for estimating indirect effects in simple mediation models
Preacher, K. J., & Hayes, A. F. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03206553 · Cited by 17,383
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Research works printed but not linked: 46
- Bilgihan, 2016, Gen Y customer loyalty in online shopping: An integrated model of trust, user experience and branding, Computers in Human Behavior, 61, 103, 10.1016/j.chb.2016.03.014 · DOI · Paper page
- Bitner, 2000, Technology infusion in service encounters, Journal of the Academy of Marketing Science, 28, 138, 10.1177/0092070300281013 · DOI · Paper page
- Belanche, 2020, Service robot implementation: A theoretical framework and research agenda, The Service Industries Journal, 40, 203, 10.1080/02642069.2019.1672666 · DOI · Paper page
- Belanche, 2019, Artificial Intelligence in FinTech: Understanding robo-advisors adoption among customers, Industrial Management & Data Systems, 119, 1411, 10.1108/IMDS-08-2018-0368 · DOI · Paper page
- Blut, 2016, Factors influencing the acceptance of self-service technologies: A meta-analysis, Journal of Service Research, 19, 394, 10.1177/1094670516662352 · DOI · Paper page
- Dabholkar, 2003, Understanding consumer motivation and behavior related to self-scanning in retailing, International Journal of Service Industry Management, 14, 59, 10.1108/09564230310465994 · DOI · Paper page
- Diamantopoulos, 2001, Index construction with formative indicators: An alternative to scale development, Journal of Marketing Research, 38, 269, 10.1509/jmkr.38.2.269.18845 · DOI · Paper page
- De Keyser, 2019, Frontline Service Technology infusion: Conceptual archetypes and future research directions, Journal of Service Management, 30, 156, 10.1108/JOSM-03-2018-0082 · DOI · Paper page
- Delcourt, 2013, Effects of perceived employee emotional competence on customer satisfaction and loyalty: The mediating role of rapport, Journal of Service Management, 24, 5, 10.1108/09564231311304161 · DOI · Paper page
- Demoulin, 2016, An integrated model of self-service technology usage in a retail context, International Journal of Retail & Distribution Management, 44, 540, 10.1108/IJRDM-08-2015-0122 · DOI · Paper page
- Duffy, 2003, Anthropomorphism and the social robot, Robotics and Autonomous Systems, 42, 177, 10.1016/S0921-8890(02)00374-3 · DOI · Paper page
- Fernandes, 2017, The effect of self-checkout quality on customer satisfaction and repatronage in a retail context, Service Business, 11, 69, 10.1007/s11628-016-0302-9 · DOI
- Gelderman, 2011, Choosing self-service technologies or interpersonal services – the impact of situational factors and technology-related attitudes, Journal of Retailing and Consumer Services, 18, 414, 10.1016/j.jretconser.2011.06.003 · DOI · Paper page
- Gremler, 2000, Customer-Employee Rapport in Service Relationships, Journal of Service Research, 3, 82, 10.1177/109467050031006 · DOI · Paper page
- Gummerus, 2019, Technology in use - characterizing customer self-service devices, Journal of Services Marketing, 33, 44, 10.1108/JSM-10-2018-0292 · DOI
- Hair, 2011, PLS-SEM: Indeed a silver bullet, Journal of Marketing Theory and Practice, 19, 139, 10.2753/MTP1069-6679190202 · DOI · Paper page
- Henseler, 2009, The use of partial least squares path modeling in international marketing, Advances in International Marketing, 20, 277, 10.1108/S1474-7979(2009)0000020014 · doi:10.1108/s1474-7979(2009)0000020014 · Paper page
- Kowalczuk (2018). Consumer acceptance of smart speakers: A mixed methods approach. Journal of Research in Interactive Marketing, 12(4), 418-431. · DOI · Paper page
- Kumar, 2016, Research Framework, Strategies, and Applications of Intelligent Agent Technologies (IATs) in Marketing, Journal of the Academy of Marketing Science, 44, 24, 10.1007/s11747-015-0426-9 · DOI · Paper page
- Kunz, 2019, Future service technologies: Is service research on track with business reality?, Journal of Services Marketing, 33, 479, 10.1108/JSM-01-2019-0039 · DOI
- Kim, 2019, Eliza in the uncanny valley: Anthropomorphizing consumer robots increases their perceived warmth but decreases liking, Marketing Letters, 30, 1, 10.1007/s11002-019-09485-9 · DOI · Paper page
- Larivière, 2017, “Service Encounter 2.0”: An investigation into the roles of technology, employees and customers, Journal of Business Research, 79, 238, 10.1016/j.jbusres.2017.03.008 · DOI · Paper page
- Liébana-Cabanillas, 2014, Antecedents of the adoption of the new mobile payment systems: The moderating effect of age, Computers in Human Behavior, 35, 464, 10.1016/j.chb.2014.03.022 · DOI · Paper page
- Lin, 2019, Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services, Journal of Hospitality Marketing & Management., 29, 530, 10.1080/19368623.2020.1685053 · DOI · Paper page
- Lowe, 2019, Consumers and technology in a changing world, European Journal of Marketing, 53, 1038, 10.1108/EJM-06-2019-966 · DOI
- Lu, V., Wirtz, J., Kunz, W., Paluch, S., T Martins, A. & Patterson, P. (2020). Service robots, customers, and service employees: what can we learn from the academic literature and where are the gaps? Journal of Service Theory & Practice, 30(3), 361-391. · DOI · Paper page
- Marinova, 2017, Getting smart: Learning from technology-empowered frontline interactions, Journal of Service Research, 20, 29, 10.1177/1094670516679273 · DOI · Paper page
- Meuter, 2000, Self-service technologies: Understanding consumer satisfaction with technology-based service encounters, Journal of Marketing, 64, 50, 10.1509/jmkg.64.3.50.18024 · DOI · Paper page
- McLean, 2019, Hey Alexa...examine the variables influencing the use of artificial intelligent in-home voice assistants, Computers in Human Behavior, 99, 28, 10.1016/j.chb.2019.05.009 · DOI · Paper page
- Moorman, 1993, Factors affecting trust in market research relationships, Journal of Marketing, 57, 81, 10.1177/002224299305700106 · DOI · Paper page
- Mori, 1970, Bukimi no tani (the uncanny valley), Energy, 7, 33
- Moussawi, 2020, How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents, Electronic Markets
- Qiu, 2020, Enhancing hospitality experience with service robots: The mediating role of rapport building, Journal of Hospitality Marketing & Management, 29, 247, 10.1080/19368623.2019.1645073 · DOI · Paper page
- Podsakoff, 2012, Sources of method bias in social science research and recommendations on how to control it, Annual Review of Psychology, 65, 539, 10.1146/annurev-psych-120710-100452 · DOI · Paper page
- Rafaeli, 2017, The Future of Frontline Research: Invited Commentaries, Journal of Service Research, 20, 91, 10.1177/1094670516679275 · DOI · Paper page
- Robinson, 2019, Frontline encounters of the AI kind: An evolved service encounter framework, Journal of Business Research., 116, 366, 10.1016/j.jbusres.2019.08.038 · DOI · Paper page
- Sarstedt, 2011, Multi-Group Analysis in Partial Least Squares (PLS) Path Modeling, Advances in International Marketing, 22, 19
- Schepers, 2007, A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects, Information & Management, 44, 90, 10.1016/j.im.2006.10.007 · DOI · Paper page
- Solomon, 1985, A role theory perspective on dyadic interactions: The service encounter, Journal of Marketing, 49, 99, 10.1177/002224298504900110 · DOI · Paper page
- Surprenant, 1987, Predictability and personalization in the service encounter, Journal of Marketing, 51, 86, 10.1177/002224298705100207 · DOI · Paper page
- Tinwell, 2011, Facial expression of emotion and perception of the Uncanny Valley in virtual characters, Computers in Human Behavior, 27, 741, 10.1016/j.chb.2010.10.018 · DOI · Paper page
- van Doorn, 2017, Domo Arigato Mr. Roboto: Emergence of Automated Social Presence in Organizational Frontlines and Customers’ Service Experiences, Journal of Service Research, 20, 43, 10.1177/1094670516679272 · DOI · Paper page
- van Pinxteren, 2019, Trust in humanoid robots: Implications for services marketing, Journal of Services Marketing, 33, 507, 10.1108/JSM-01-2018-0045 · DOI · Paper page
- 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
- Wirtz, 2018, Brave new world: Service robots in the frontline, Journal of Service Management, 29, 907, 10.1108/JOSM-04-2018-0119 · DOI · Paper page
- Yang, 2019, Understanding user behavior of virtual personal assistant devices, Information Systems and e-Business Management, 17, 65, 10.1007/s10257-018-0375-1 · DOI · Paper page
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Unreal influence: leveraging AI in influencer marketing
Sands, S., Campbell, C. L., Plangger, K., & Ferraro, C. (2022) · European Journal of Marketing · doi:10.1108/ejm-12-2019-0949 · Cited by 452
Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106763 · Cited by 402
Understanding the user satisfaction and loyalty of customer service chatbots
Hsu, C.-L., & Lin, J. C.-C. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103211 · Cited by 361
Service robots: Applying social exchange theory to better understand human–robot interactions
Kim, H., So, K. K. F., & Wirtz, J. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104537 · Cited by 322
Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-04-2023-0061 · Cited by 315
Metadata
- Authors
- Fernandes, T., & Oliveira, E.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 122
- Pages
- 180-191
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W3086863954
- Cited by
- 598
- References
- 26
- FWCI
- 24.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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