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Consumer Acceptance and Adoption of AI Robo-Advisors in Fintech Industry

Author

Listed:
  • Asrar Ahmed Sabir

    (Division of Management & Administrative Science, UE Business School (UEBS), University of Education, Lahore 54770, Pakistan)

  • Iftikhar Ahmad

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Hassan Ahmad

    (Business School, Liaoning University, Shenyang 110000, China)

  • Muhammad Rafiq

    (Graduate Business School, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Muhammad Asghar Khan

    (Department of Electrical Engineering, Hamdard Institute of Engineering and Technology, Hamdard University, Islamabad 44000, Pakistan)

  • Neelum Noreen

    (Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, Petaling Jaya 47810, Malaysia
    Department of Computer and Information Sciences, Gulf Colleges, Hafr Al Batin 39952, Saudi Arabia)

Abstract

Artificial intelligence (AI) has provided significant help in many fields of life. This study proposed a framework that helped in understanding customers’ attitudes about the adoption of Robo-advisors. The role of the Technology Readiness Index moderated as one of the primary relationships. A total of 208 potential users of Robo-advisor services provided the data that confirmed the validity of the model. This model provided the input for structural equation modeling and analysis of the study hypotheses. The results indicated that consumers showed positive attitudes about Robo-advisor services, with the moderating effect of Technology Readiness Index dimensions, namely, contributors and inhibitors. Perceived ease of use, perceived usefulness, and perceived convenience influenced consumers in developing positive attitudes about this service. Financial businesses can design better AI Robo-advisor services to fulfill the requirements of a wide range of consumers. This proposed framework contributes to the consumers’ understanding of behavioral intentions for the use of Robo-advisors in FinTech.

Suggested Citation

  • Asrar Ahmed Sabir & Iftikhar Ahmad & Hassan Ahmad & Muhammad Rafiq & Muhammad Asghar Khan & Neelum Noreen, 2023. "Consumer Acceptance and Adoption of AI Robo-Advisors in Fintech Industry," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1311-:d:1091640
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    References listed on IDEAS

    as
    1. Richard H. Thaler & Shlomo Benartzi, 2004. "Save More Tomorrow (TM): Using Behavioral Economics to Increase Employee Saving," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages 164-187, February.
    2. Simona Naspetti & Serena Mandolesi & Jeroen Buysse & Terhi Latvala & Philippa Nicholas & Susanne Padel & Ellen J. Van Loo & Raffaele Zanoli, 2017. "Determinants of the Acceptance of Sustainable Production Strategies among Dairy Farmers: Development and Testing of a Modified Technology Acceptance Model," Sustainability, MDPI, vol. 9(10), pages 1-16, October.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Florian Glaser & Zwetelina Iliewa & Dominik Jung & Martin Weber, 2019. "Towards Designing Robo-advisors for Unexperienced Investors with Experience Sampling of Time-Series Data," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph (ed.), Information Systems and Neuroscience, pages 133-138, Springer.
    5. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    6. Hausman, Angela V. & Siekpe, Jeffrey Sam, 2009. "The effect of web interface features on consumer online purchase intentions," Journal of Business Research, Elsevier, vol. 62(1), pages 5-13, January.
    7. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    8. Lee, In & Shin, Yong Jae, 2018. "Fintech: Ecosystem, business models, investment decisions, and challenges," Business Horizons, Elsevier, vol. 61(1), pages 35-46.
    9. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
    10. Meuter, Matthew L. & Ostrom, Amy L. & Bitner, Mary Jo & Roundtree, Robert, 2003. "The influence of technology anxiety on consumer use and experiences with self-service technologies," Journal of Business Research, Elsevier, vol. 56(11), pages 899-906, November.
    11. Lopez, Juan Carlos & Babcic, Sinisa & De La Ossa, Andres, 2015. "Advice goes virtual:how new digital investment services are changing the wealth management landscape," Journal of Financial Perspectives, EY Global FS Institute, vol. 3(3), pages 156-164.
    12. Hohenberger, Christoph & Spörrle, Matthias & Welpe, Isabell M., 2017. "Not fearless, but self-enhanced: The effects of anxiety on the willingness to use autonomous cars depend on individual levels of self-enhancement," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 40-52.
    13. Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
    14. John Hulland, 1999. "Use of partial least squares (PLS) in strategic management research: a review of four recent studies," Strategic Management Journal, Wiley Blackwell, vol. 20(2), pages 195-204, February.
    15. Hirschman, Elizabeth C, 1980. "Innovativeness, Novelty Seeking, and Consumer Creativity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 7(3), pages 283-295, December.
    16. Ozturk, Ahmet Bulent & Bilgihan, Anil & Nusair, Khaldoon & Okumus, Fevzi, 2016. "What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience," International Journal of Information Management, Elsevier, vol. 36(6), pages 1350-1359.
    17. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    18. Man Lai Cheung & Ka Yin Chau & Michael Huen Sum Lam & Gary Tse & Ka Yan Ho & Stuart W. Flint & David R Broom & Ejoe Kar Ho Tso & Ka Yiu Lee, 2019. "Examining Consumers’ Adoption of Wearable Healthcare Technology: The Role of Health Attributes," IJERPH, MDPI, vol. 16(13), pages 1-16, June.
    19. Illum, Steven F. & Ivanov, Stanislav H. & Liang, Yating, 2010. "Using virtual communities in tourism research," Tourism Management, Elsevier, vol. 31(3), pages 335-340.
    20. Noelia Romero Castro & Juan Piñeiro Chousa, 2006. "An integrated framework for the financial analysis of sustainability," Business Strategy and the Environment, Wiley Blackwell, vol. 15(5), pages 322-333, September.
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