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Unveiling the Nexus Between Use of AI-Enabled Robo-Advisors, Behavioural Intention and Sustainable Investment Decisions Using PLS-SEM

Author

Listed:
  • Nargis Mohapatra

    (School of Economics and Commerce (KSEC), Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar 751024, India)

  • Sameer Shekhar

    (School of Economics and Commerce (KSEC), Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar 751024, India)

  • Rubee Singh

    (Institute of Business Management, GLA University, Mathura 281406, India)

  • Shahbaz Khan

    (Department of Industrial Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Gilberto Santos

    (Design School, Polytechnic Institute Cavado Ave, Campus do IPCA, 4750-810 Barcelos, Portugal)

  • Sandro Carvalho

    (Technological School, 2Ai—Applied Artificial Intelligence Laboratory Polytechnic Institute of Cavado and Ave, 4750-810 Barcelos, Portugal)

Abstract

The study examines the nexus between AI-driven technology, i.e., robo-advisors, and the behavioural intention of investors towards sustainable investment decisions considering government regulations and sustainable investment awareness as the moderating variables. A total of 372 responses were collected from across India through a structured questionnaire along identified variables from the TAM and UTAUT theories under the select constructs, i.e., trust, perceived risk, user-friendliness, perceived usefulness, and emotional arousal. This is with reference to the use of robo-advisors to unearth the extent to which they influence the behavioural intention and finally the sustainable investment decisions taking into account government regulations and sustainable investment awareness as the moderating variables. The results derived by using PLS-SEM reveal that all the five factors are having a significant impact on the behavioural intention for sustainable investment decisions of the investors. Further, both sustainable investment awareness and government regulations have been found to have a moderating impact on shaping the behavioural intention of the investors with respect to most of the variables. The results of the study come up with significant suggestions for the government, financial institutions, and the investors as well as the academicians, and therefore, have policy implications, managerial implications, and theoretical implications. The constructs and moderating variables considered here can further be used for studying the behavioural intentions. The robo-advisory service providers may emphasize developing the algo ensuring trust, usability, and friendly interface in a manner that tends to minimize the perceived risk and emotional arousal leading to the use of robo-advisors pushing the intention of the investors towards sustainable investment.

Suggested Citation

  • Nargis Mohapatra & Sameer Shekhar & Rubee Singh & Shahbaz Khan & Gilberto Santos & Sandro Carvalho, 2025. "Unveiling the Nexus Between Use of AI-Enabled Robo-Advisors, Behavioural Intention and Sustainable Investment Decisions Using PLS-SEM," Sustainability, MDPI, vol. 17(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3897-:d:1642786
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    References listed on IDEAS

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    1. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    2. Jochen Wirtz & Johannes Hofmeister & Patricia Y. P. Chew & Xin (David) Ding, 2023. "Digital service technologies, service robots, AI, and the strategic pathways to cost-effective service excellence," The Service Industries Journal, Taylor & Francis Journals, vol. 43(15-16), pages 1173-1196, December.
    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. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    5. Gaurav Talan & Gagan Deep Sharma, 2019. "Doing Well by Doing Good: A Systematic Review and Research Agenda for Sustainable Investment," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    6. Tan Zi Yi & Noor Ashikin Mohd Rom & Nurbani Md. Hassan & Mohamad Shaharudin Samsurijan & Andrew Ebekozien, 2023. "The Adoption of Robo-Advisory among Millennials in the 21st Century: Trust, Usability and Knowledge Perception," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    7. Aleksandrina Aleksandrova & Valentina Ninova & Zhelyo Zhelev, 2023. "A Survey on AI Implementation in Finance, (Cyber) Insurance and Financial Controlling," Risks, MDPI, vol. 11(5), pages 1-16, May.
    8. Utz, Sebastian & Wimmer, Maximilian & Steuer, Ralph E., 2015. "Tri-criterion modeling for constructing more-sustainable mutual funds," European Journal of Operational Research, Elsevier, vol. 246(1), pages 331-338.
    9. Ming-Lang Tseng & Phan Anh Tan & Shiou-Yun Jeng & Chun-Wei Remen Lin & Yeneneh Tamirat Negash & Susilo Nur Aji Cokro Darsono, 2019. "Sustainable Investment: Interrelated among Corporate Governance, Economic Performance and Market Risks Using Investor Preference Approach," Sustainability, MDPI, vol. 11(7), pages 1-15, April.
    10. Mikhail Beketov & Kevin Lehmann & Manuel Wittke, 2018. "Robo Advisors: quantitative methods inside the robots," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 363-370, October.
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