Examining The Mediating Effect of Technology Attitude on‎Retail Investors’ Adoption of Artificial Intelligence inInvestment Decisions

  • Authors

    • Sarjas M K Research scholar, School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu, India
    • Dr G. Velmurugan Professor, School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu, India
    https://doi.org/10.14419/17m55835

    Received date: December 17, 2025

    Accepted date: January 15, 2026

    Published date: January 23, 2026

  • Artificial Intelligence; Attitude; Behavioural intention; PLS- SEM; TAM; Technology Adoption
  • Abstract

    The purpose of this study is to investigate the variables influencing the adoption of artificial intelligence among retail investors in ‎South India, with a specific focus on the mediating role of belief in AI in the usage experience. The ‘Technology Acceptance ‎Model’ (TAM) is the background of this research. Four hundred samples were gathered using a structured questionnaire in a ‎selected state in South India. Of these 97, unsatisfactory samples were eliminated, leaving 303 genuine samples for analysis. ‎‎‘Partial Least Squares Structural Equation Modelling’ (PLSEM) was used to examine the planned idea. The findings reveal that ‎Perceived Functional Usefulness, Personalized Usefulness, and ease of use significantly affect the behavioural intention to adopt ‎AI. Moreover, attitude towards AI acted as a mediating variable between these factors and intention to use AI. The intention to use ‎something was primarily determined by people’s perceived usefulness and level of ease of use. Personalized usefulness had a ‎positive influence, although not as intense. Data from people’s own reports is a limitation of the study. Self-report is often ‎influenced by social desirability bias, which means people may exaggerate positive feelings or intentionally think to match socially ‎acceptable responses. Factors like geographical context and AI security issues are the future scope of this study. The results are ‎very useful for banks, investment firms, and policymakers planning to use AI in India and other places. In contributing to the ‎extant literature, the study developed a country-specific framework and underlined the mediating influence of attitude on AI ‎adoption‎.

  • References

    1. Ahmad, N. A., Drus, S. M., Kasim, H., & Othman, M. M. (2019). Assessing the content validity of the enterprise architecture adoption questionnaire (EAAQ) among content experts. 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 160–165. https://doi.org/10.1109/ISCAIE.2019.8743918.
    2. Ajzen, I. (1991). The theory of planned behaviour. Organisational Behaviour and Human Decision Processes, 50(2), 179. https://doi.org/10.1016/0749-5978(91)90020-T.
    3. Ajzen, I., & Fishbein, M. (1977). Attitude-behaviour relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888–918. https://doi.org/10.1037/0033-2909.84.5.888
    4. Antonides, G. and N. L. Van der Sar. “Individual Expectations, Risk Perception and Preferences in Relation to Investment Decision Making. “Journal of Economic Psychology, 11, (1990), pp. 227–245.) https://doi.org/10.1016/0167-4870(90)90005-T.
    5. Aprilia, C., & Amalia, R. (2023). Perceived security and technology continuance theory: An analysis of mobile wallet users’ continuance inten-tion. Global Business Review, 09721509221145831. https://doi.org/10.1177/09721509221145831.
    6. Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modelling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54(101473), 101473. https://doi.org/10.1016/j.tele.2020.101473
    7. Asnakew, Z. S. (2020). Customers’ continuance intention to use mobile banking: Development and testing of an integrated model. The Review of So-cionetwork Strategies, 14(1), 123–146. https://doi.org/10.1007/s12626-020-00060-7
    8. Atwal, G., & Bryson, D. (2021). Antecedents of intention to adopt artificial intelligence services by consumers in personal financial invest-ing. Strategic Change, 30(3), 293–298. https://doi.org/10.1002/jsc.2412.
    9. Au, C.-D., Klingenberger, L., Svoboda, M., & Frère, E. (2021). Business model of sustainable robo-advisors: Empirical insights for practical imple-mentation. Sustainability, 13(23), 13009. https://doi.org/10.3390/su132313009
    10. Babel, B., Buehler, K., Pivonka, A., Richardson, B., & Waldron, D. (2019). Derisking machine learning and artificial intelligence. McKinsey Global Institute.
    11. Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: Application of Technol-ogy–Organisation–Environment framework. Sustainability, 16(5), 1864. https://doi.org/10.3390/su16051864.
    12. Bagozzi, R. P., Baumgartner, H., & Yi, Y. (1992). State versus action orientation and the theory of reasoned action: An application to coupon us-age. The Journal of Consumer Research, 18(4), 505. https://doi.org/10.1086/209277.
    13. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among Custom-ers. Industrial Management + Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/IMDS-08-2018-0368
    14. Bergmann, M., Maçada, A. C. G., de Oliveira Santini, F., & Rasul, T. (2023). Continuance intention in financial technology: a framework and meta-analysis. International Journal of Bank Marketing, 41(4), 749–786. https://doi.org/10.1108/IJBM-04-2022-0168.
    15. Bessadok, A., & Hersi, M. (2025). A structural equation model analysis of English for specific purposes students’ attitudes regarding computer-assisted language learning: UTAUT2 model. Library Hi Tech, 43(1), 36–55. https://doi.org/10.1108/LHT-03-2023-0124
    16. Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214. https://doi.org/10.1016/S0167-9236(01)00111-7.
    17. Bhattacherjee, A., & Lin, C.-P. (2015). A unified model of IT continuance: three complementary perspectives and crossover effects. European Journal of Information Systems: An Official Journal of the Operational Research Society, 24(4), 364–373. https://doi.org/10.1057/ejis.2013.36.
    18. Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organisations. The Bottom Line Managing Library Finances, 33(4), 359–375. https://doi.org/10.1108/BL-08-2020-0057.
    19. Choung, H., David, P., & Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human-Computer Interaction, 1–13. https://doi.org/10.1080/10447318.2022.2050543.
    20. Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in account-ing. Accounting Education, 30(2), 107–130. https://doi.org/10.1080/09639284.2021.1872035
    21. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Infor-mation Systems, 13(3), 319. https://doi.org/10.2307/249008.
    22. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges, and re-search agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021.
    23. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Pa-per: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71(102642), 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642.
    24. Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174–187. https://doi.org/10.1016/j.giq.2015.06.003.
    25. Fishbein, M., & Ajzen. (1980). Understanding attitude and predicting social behavior. Gefen, D., Karahanna, E., & Straub, D. W. (2003a). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519.
    26. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. JMR, Journal of Marketing Research, 18(3), 382. https://doi.org/10.2307/3150980.
    27. Grealish, A., & Kolm, P. N. (2021). Robo-advisory: From investing principles and algorithms to future developments. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3776826
    28. Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpin-nings and research agenda. International Journal of Information Management Data Insights, 4(1), 100232. https://doi.org/10.1016/j.jjimei.2024.100232
    29. Hair, J. F., Jr, Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069.
    30. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2018). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203.
    31. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modelling in market-ing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
    32. Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management + Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130.
    33. Hasan Emon, M. M., Hassan, F., Hoque Nahid, M., & Rattanawiboonsom, V. (2023). Predicting the adoption intention of the artificial intelligence ChatGPT. AIUB Journal of Science and Engineering (AJSE), 22(2), 189–199. https://doi.org/10.53799/ajse.v22i2.797.
    34. Heinze, K. L., & Heinze, J. E. (2018). Individual innovation adoption and the role of organizational culture. Review of Managerial Science. https://doi.org/10.1007/s11846-018-0300-5.
    35. Hill, R. J., Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention and behaviour: An introduction to theory and research. Contemporary Sociolo-gy, 6(2), 244. https://doi.org/10.2307/2065853.
    36. Inan, D. I., Hidayanto, A. N., Juita, R., Soemawilaga, F. F., Melinda, F., Puspacinantya, P., & Amalia, Y. (2023). Service quality and self-determination theory towards continuance usage intention of mobile banking. Journal of Science and Technology Policy Management, 14(2), 303–328. https://doi.org/10.1108/JSTPM-01-2021-0005
    37. Jnr, B. A., & Petersen, S. A. (2023). Using an extended technology acceptance model to predict enterprise architecture adoption in making cities smarter. Environment Systems & Decisions, 43(1), 36–53. https://doi.org/10.1007/s10669-022-09867-x.
    38. Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. International Journal of Information and Learning Technology, 38(1), 1–19. https://doi.org/10.1108/IJILT-05-2020-0090
    39. Kelly, S., Kaye, S.-A. y Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telemat. Inform. 77:101925. https://doi.org/10.1016/j.tele.2022.101925
    40. Khlaif, Z. N., Sanmugam, M., & Ayyoub, A. (2022). Impact of technostress on continuance intentions to use mobile technology. The Asia-Pacific Ed-ucation Researcher. https://doi.org/10.1007/s40299-021-00638-x.
    41. Kwon, J., & Vogt, C. A. (2010a). Identifying the role of cognitive, affective, and behavioral components in understanding residents’ attitudes toward place marketing. Journal of Travel Research, 49(4), 423–435. https://doi.org/10.1177/0047287509346857.
    42. Kwon, J., & Vogt, C. A. (2010b). Identifying the role of cognitive, affective, and behavioral components in understanding residents’ attitudes toward place marketing. Journal of Travel Research, 49(4), 423–435. https://doi.org/10.1177/0047287509346857
    43. Lada, S., Chekima, B., Karim, M. R. A., Fabeil, N. F., Ayub, M. S., Amirul, S. M., Ansar, R., Bouteraa, M., Fook, L. M., & Zaki, H. O. (2023). De-termining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation Technology Market and Complexity, 9(4), 100144. https://doi.org/10.1016/j.joitmc.2023.100144.
    44. Li, W. (2025). A study on factors influencing designers’ behavioral intention in using AI-generated content for assisted design: Perceived anxiety, per-ceived risk, and UTAUT. International Journal of Human-Computer Interaction, 41(2), 1064–1077. https://doi.org/10.1080/10447318.2024.2310354
    45. Liao, C., Palvia, P., & Chen, J.-L. (2009). Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management, 29(4), 309–320. https://doi.org/10.1016/j.ijinfomgt.2009.03.004.
    46. Mahalakshmi, V., Kulkarni, N., Pradeep Kumar, K. V., Suresh Kumar, K., Nidhi Sree, D., & Durga, S. (2022). The Role of implementing Artificial Intelligence and Machine Learning Technologies in the financial services Industry for creating Competitive Intelligence. Materials Today: Proceed-ings, 56, 2252–2255. https://doi.org/10.1016/j.matpr.2021.11.577.
    47. Malaquias, R. F., & Hwang, Y. (2019). Mobile banking use: A comparative study with Brazilian and U.S. participants. International Journal of Infor-mation Management, 44, 132–140. https://doi.org/10.1016/j.ijinfomgt.2018.10.004
    48. Manrai, R., & Gupta, K. P. (2023). Investors’ perceptions on artificial intelligence (AI) technology adoption in investment services in India. Journal of Financial Services Marketing, 28(1), 1–14. https://doi.org/10.1057/s41264-021-00134-9.
    49. Masoud, E., & AbuTaqa, H. (2017). Factors affecting customers’ adoption of E-banking services in Jordan. Information resources management jour-nal, 30(2), 44–60. https://doi.org/10.4018/IRMJ.2017040103
    50. McInish, T. H., & Srivastava, R. K. (1984). The nature of individual investors’ heterogeneous expectations. Journal Of Economic Psychology, 5(3), 251–263. https://doi.org/10.1016/0167-4870(84)90025-4
    51. Morosan, C. (2014). Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel. International Journal of Contemporary Hospitality Management, 26(2), 246–271. https://doi.org/10.1108/IJCHM-11-2012-0221.
    52. Nagadeepa, C., Pushpa, A., Mukthar, K. P. J., Rurush-Asencio, R., Sifuentes-Stratti, J., & Rodriguez-Kong, J. (2024). User’s continuance intention towards Banker’s Chatbot service – A technology acceptance using SUS and TTF model. https://doi.org/10.1108/S1479-351220240000036006
    53. New frontiers of robo-advising: Consumption, saving, debt management, and taxes. (2023). In Machine Learning and Data Sciences for Financial Markets (pp. 9–32). Cambridge University Press. https://doi.org/10.1017/9781009028943.003
    54. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. JMR, Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405.
    55. Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: Exploration of key determinants and extension of the technolo-gy acceptance model. Telematics and Informatics, 31(3), 376–385. https://doi.org/10.1016/j.tele.2013.11.008
    56. Park, J. Y., Ryu, J. P., & Shin, H. J. (2016). Robo advisors for portfolio management. Advanced Science and Technology Letters, 141, 104–108. https://doi.org/10.14257/astl.2016.141.21.
    57. Pavlou, P. A. (2002a). A theory of planned behavior perspective on the consumer adoption of electronic commerce. MIS Quarterly, 30, 115–143. https://doi.org/10.2307/25148720
    58. Phongsatha, T. (2024). Every coin has two sides: Navigating factors of generative PreTrained transformer adoption intentions among educators in Thailand. Pakistan Journal of Life and Social Sciences, 22(1). https://doi.org/10.57239/PJLSS-2024-22.1.00424.
    59. Phoon, K., & Koh, F. (2017). Robo-advisors and wealth management. The Journal of Alternative Investments, 20(3), 79–94. https://doi.org/10.3905/jai.2018.20.3.079.
    60. Premkumar, G., Ramamurthy, K., & Liu, H.-N. (2008). Internet messaging: An examination of the impact of attitudinal, normative, and control belief systems. Information & Management, 45(7), 451–457. https://doi.org/10.1016/j.im.2008.06.008.
    61. Queensland University of Technology, AU, & Bidar, R. (2018). Customer value perception toward the use of mobile banking applications. In Australasian Conference on Information Systems 2018. University of Technology, Sydney.
    62. Rahi, S., Khan, M. M., & Alghizzawi, M. (2020). Extension of technology continuance theory (TCT) with task technology fit (TTF) in the context of Internet banking user continuance intention. International Journal of Quality & Reliability Management, 38(4), 986–1004. https://doi.org/10.1108/IJQRM-03-2020-0074
    63. Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2023). Adoption of artificial intelligence in banking services: an empirical analy-sis. International Journal of Emerging Markets, 18(10), 4270–4300. https://doi.org/10.1108/IJOEM-06-2020-0724.
    64. Robo-advisory: From investing principles and algorithms to future developments”, Chapter of book “Machine learning in financial markets: A guide to contemporary practice.” (n.d.). Cambridge University Press.
    65. Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access: Practical Innovations, Open Solutions, 10, 125666–125678. https://doi.org/10.1109/ACCESS.2022.3225555
    66. Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191–205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022.
    67. Singh, S., Sahni, M. M., & Kovid, R. K. (2020). What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model. Management Decision, 58(8), 1675–1697. https://doi.org/10.1108/MD-09-2019-1318
    68. Strzelecki, A. (2024). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 32(9), 5142–5155. https://doi.org/10.1080/10494820.2023.2209881.
    69. Østerlund, C., Jarrahi, M. H., Willis, M., Boyd, K., and Wolf, T. (2021). Artificial intelligence and the world of work, a co-constitutive relationship. J. Assoc. Inf. Sci. Technol. 72, 128–135. https://doi.org/10.1002/asi.24388
    70. Suh, B., & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3–4), 247–263. https://doi.org/10.1016/S1567-4223(02)00017-0.
    71. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research : ISR, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144.
    72. Tertilt, M., & Scholz, P. (2017). To advise, or not to advise how robo-advisors evaluate the risk preferences of private investors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2913178
    73. Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1–2), 641–652. https://doi.org/10.1007/s10479-020-03918-9.
    74. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Sci-ence, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
    75. Venkatesh, V., Davis, F. D., & Zhu, Y. (2022). A cultural contingency model of knowledge sharing and job performance. Journal of Business Re-search, 140, 202–219. https://doi.org/10.1016/j.jbusres.2021.07.042
    76. Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: U.s. vs. China. Journal of Global Information Technology Management, 13(1), 5–27. https://doi.org/10.1080/1097198X.2010.10856507.
    77. Wang, Y., Liu, C., & Tu, Y.-F. (2021). Factors affecting the adoption of AI-based applications in higher education: An analysis of teachers’ perspec-tives using structural equation modeling. Journal of Educational Technology & Society, 24(3), 116–129. https://doaj.org/article/e2fe5622b1fa4f88a3f1f90285a4126a.
    78. Watson, D. (2019). The rhetoric and reality of anthropomorphism in artificial intelligence. Minds and Machines, 29(3), 417–440. https://doi.org/10.1007/s11023-019-09506-6
    79. Yu, L., & Li, Y. (2022). Artificial Intelligence decision-making transparency and employees’ trust: The parallel multiple mediating effect of effective-ness and discomfort. Behavioral Sciences, 12(5), 127. https://doi.org/10.3390/bs12050127
    80. Zhu, J., & Huang, F. (2023). Transformational leadership, organizational innovation, and ESG performance: Evidence from SMEs in Chi-na. Sustainability, 15(7), 5756. https://doi.org/10.3390/su15075756.
  • Downloads

  • How to Cite

    M K, S. ., & Velmurugan , D. G. . (2026). Examining The Mediating Effect of Technology Attitude on‎Retail Investors’ Adoption of Artificial Intelligence inInvestment Decisions. International Journal of Accounting and Economics Studies, 13(1), 401-412. https://doi.org/10.14419/17m55835