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To Study the Impact of Adoption and Trust in AI-Based Robo-Advisory Platforms Among Investors in Raipur
Subject area: Management and Commerce · Area of research: Finance
DOI: https://doi.org/10.64388/IREV9I11-1717442
Abstract
This research investigates the factors influencing the adoption of AI-based robo-advisory platforms among retail investors in Raipur, Chhattisgarh. As financial services transition toward automation, understanding how individual investors in emerging markets perceive non-human financial advice is critical. While most studies focus on Tier-1 metropolitan areas, this research addresses a significant geographical gap by providing localized insights into a Tier-2 commercial hub. Using a quantitative descriptive design, primary data was collected from 75 active investors through purposive sampling. The study utilizes the Technology Acceptance Model (TAM) to analyse how variables such as trust, perceived risk, and perceived ease of use affect an investor’s willingness to use AI platforms. Furthermore, it bridges a behavioural gap by measuring actual capital allocation—the percentage of wealth investors are willing to entrust to algorithms. Data was analysed using Microsoft Excel, employing manual calculations for Pearson Correlation and Independent t-tests to verify hypotheses. The findings aim to assist fintech developers and financial institutions in building trust and increasing digital adoption within localized Indian markets. The study concludes that addressing the "trust deficit" is essential for the long-term success of AI-driven wealth management in regional economies.
Keywords
Robo-Advisory, Artificial Intelligence, Wealth Management, Behavioural Finance, Trust Deficit, Algorithmic Aversion, Retail Investors, Fintech Adoption, Tier-2 Cities, Capital Allocation
References
[1] Bhatia, A. (2020). Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context.
[2] Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology.
[3] Elomari, R. (2025). Consumer Perception of Robo-Advisors: Trust, Adoption Barriers, and Behavioral Insights. In Multidimensional Research Insights.
[4] Fan, L. &. (2020). The Utilization of Robo-Advisors by Individual Investors: An Analysis Using Diffusion of Innovation and Information Search Frameworks. In Journal of Financial Counseling and Planning.
[5] Kulkarni, M. S. (14 Mar 2025). The role of robo-advisors in behavioural finance, shaping investment decisions.
[6] Lingam Naveen, S. S. (2026). From Algorithms to Action : The Role of Trust and Engagement in Adopting Robo-Advisors.
[7] Mittal, R. (2025). Adoption of Robo-Advisory in Investment Management: Insights from Individual Investors.
[8] Nain, I. R. (2024). An empirical analysis of the antecedents and barriers to adopting robo-advisors for investment management among Indian investors.
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[10] Panwar, D. M.-D.-M. (2025). Analyzing the Impact of AI-Driven Financial Advisory Services on Investment Decision-Making. In International Journal of Environmental Sciences.
[11] Yi, T. Z. (2023). The Adoption of Robo-Advisory among Millennials in the 21st Century: Trust, Usability and Knowledge Perception. In Sustainability.
How to cite this paper
@article{1717442,
author = {Akhil Jaiswal, Prof. (Dr.) Monica Sainy},
title = {To Study the Impact of Adoption and Trust in AI-Based Robo-Advisory Platforms Among Investors in Raipur},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {614-620},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717442.pdf},
abstract = {This research investigates the factors influencing the adoption of AI-based robo-advisory platforms among retail investors in Raipur, Chhattisgarh. As financial services transition toward automation, understanding how individual investors in emerging markets perceive non-human financial advice is critical. While most studies focus on Tier-1 metropolitan areas, this research addresses a significant geographical gap by providing localized insights into a Tier-2 commercial hub. Using a quantitative descriptive design, primary data was collected from 75 active investors through purposive sampling. The study utilizes the Technology Acceptance Model (TAM) to analyse how variables such as trust, perceived risk, and perceived ease of use affect an investor’s willingness to use AI platforms. Furthermore, it bridges a behavioural gap by measuring actual capital allocation—the percentage of wealth investors are willing to entrust to algorithms. Data was analysed using Microsoft Excel, employing manual calculations for Pearson Correlation and Independent t-tests to verify hypotheses. The findings aim to assist fintech developers and financial institutions in building trust and increasing digital adoption within localized Indian markets. The study concludes that addressing the "trust deficit" is essential for the long-term success of AI-driven wealth management in regional economies.},
keywords = {Robo-Advisory, Artificial Intelligence, Wealth Management, Behavioural Finance, Trust Deficit, Algorithmic Aversion, Retail Investors, Fintech Adoption, Tier-2 Cities, Capital Allocation},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717442}
}