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Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India

Author

Listed:
  • Rishi Manrai

    (Amity University)

  • Kriti Priya Gupta

    (Symbiosis International (Deemed University) (SIU))

Abstract

The purpose of this paper is to investigate the investor perception toward artificial intelligence (AI)/Robo advisory services and factors influencing behavioral intention to adopt the same. The study assimilated factors from TAM theory and extended them by adding two vital factors, subjective norms, trust in service as well as service provider. The respondents of the study were investors in the stock market having a basic understanding of investment. Convenience sampling was used to collect data of 252 responses from Delhi NCR during January and February 2020. The results of the study emphasized the role of trust on service as well as subjective norms to be significant variables affecting AI-based investment. Other variables such as perceived usefulness, perceived ease of use, and attitudes were also found statistically significant. This study is an important contribution to the existing body of knowledge in the area of technology adoption because the study significantly explains (R2 value = 82.9%) the factors affecting robo advisory adoption in investment service. The study also suggests important clues for service-providing companies to frame their business strategy in such a way that they can attract maximum clients and achieve a competitive advantage.

Suggested Citation

  • Rishi Manrai & Kriti Priya Gupta, 2023. "Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 1-14, March.
    • Handle: RePEc:pal:jofsma:v:28:y:2023:i:1:d:10.1057_s41264-021-00134-9
    DOI: 10.1057/s41264-021-00134-9
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    1. Richard P. Bagozzi & Utpal M. Dholakia, 2006. "Open Source Software User Communities: A Study of Participation in Linux User Groups," Management Science, INFORMS, vol. 52(7), pages 1099-1115, July.
    2. Alsajjan, Bander & Dennis, Charles, 2010. "Internet banking acceptance model: Cross-market examination," Journal of Business Research, Elsevier, vol. 63(9-10), pages 957-963, September.
    3. Shanmuganathan, Manchuna, 2020. "Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    4. Bong Keun Jeong & Tom E Yoon, 2013. "An Empirical Investigation on Consumer Acceptance of Mobile Banking Services," Business and Management Research, Business and Management Research, Sciedu Press, vol. 2(1), pages 31-40, March.
    5. Ali Tarhini & Nalin Asanka Gamagedara Arachchilage & Ra'ed Masa'deh & Muhammad Sharif Abbasi, 2015. "A Critical Review of Theories and Models of Technology Adoption and Acceptance in Information System Research," International Journal of Technology Diffusion (IJTD), IGI Global, vol. 6(4), pages 58-77, October.
    6. Kieran Mathieson, 1991. "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior," Information Systems Research, INFORMS, vol. 2(3), pages 173-191, September.
    7. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    8. 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.
    9. Shirley Taylor & Peter A. Todd, 1995. "Understanding Information Technology Usage: A Test of Competing Models," Information Systems Research, INFORMS, vol. 6(2), pages 144-176, June.
    10. Liébana-Cabanillas, Francisco & Marinkovic, Veljko & Ramos de Luna, Iviane & Kalinic, Zoran, 2018. "Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 117-130.
    11. Martina Raue & Lisa A. D'Ambrosio & Joseph F. Coughlin, 2020. "The Power of Peers: Prompting Savings Behavior Through Social Comparison," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(1), pages 1-13, January.
    12. 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.
    13. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
    14. Hyunju Shin & Riza Casidy & Alyssa Yoon & So-Hyang Yoon, 2016. "Brand trust and avoidance following brand crisis: A quasi-experiment on the effect of franchisor statements," Journal of Brand Management, Palgrave Macmillan, vol. 23(5), pages 1-23, September.
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