Examining Resistance to Financial Robo-Advisors in India: An In-Depth Look

Published Date : 20/08/2025 

This study explores the resistance towards Financial Robo-Advisors (FRAs) among retail investors in India, focusing on functional and psychological barriers, and the moderating role of attitudes towards Artificial Intelligence (AI). 

The financial landscape is rapidly evolving, and one of the most promising innovations in this domain is the rise of Financial Robo-Advisors (FRAs). These digital platforms use advanced algorithms and artificial intelligence (AI) to provide personalized financial advice and investment management services. However, despite their potential benefits, many retail investors in India remain hesitant to adopt FRAs. This study delves into the reasons behind this resistance, grounded in innovation resistance theory (IRT).

The study aims to understand the impact of various barriers on resistance to FRAs, including functional barriers such as perceived complexity and usability, and psychological barriers such as inertia, overconfidence bias, and data privacy concerns. Additionally, it examines the role of users' attitudes towards AI as a moderating factor in this resistance.

To gather data, the researchers employed purposive sampling, collecting responses from 409 retail investors in India. The data was then analyzed using structural equation modeling (SEM) to identify the key determinants of resistance and their impact on the intention to use and recommend FRAs.

The findings of the study reveal that, with the exception of the value barrier, all other barriers significantly contribute to resistance towards FRAs. Among these, inertia emerges as the strongest determinant. This suggests that many investors are resistant to change and prefer to stick with traditional methods of financial advice and management. Overconfidence bias and data privacy concerns also play significant roles in this resistance.

Resistance to FRAs not only hinders the adoption of these innovative tools but also affects the likelihood of users recommending them to others. This has implications for the growth and acceptance of FRAs in the Indian market.

The study further explores the moderating effect of users' attitudes towards AI. The results indicate that a positive attitude towards AI can significantly weaken the influence of inertia, overconfidence bias, and data privacy concerns on resistance. This suggests that fostering a positive perception of AI among users could help overcome some of the barriers to FRA adoption.

The research provides valuable insights for both theoretical and practical applications. From a theoretical standpoint, it enriches the understanding of innovation resistance theory (IRT) in the context of financial technology (Fintech). Practically, it offers recommendations for FRA providers and financial institutions to enhance the adoption of these tools in emerging markets like India. These recommendations include educating users about the benefits of AI, addressing data privacy concerns, and providing user-friendly interfaces to reduce perceived complexity.

In conclusion, the study highlights the importance of addressing both functional and psychological barriers to FRA adoption and the role of positive AI attitudes in mitigating resistance. By understanding these factors, stakeholders can develop strategies to promote the widespread use of FRAs, ultimately benefiting both investors and the financial industry as a whole.

Chitkara Business School, part of Chitkara University in Punjab, India, is a leading institution in the field of business education and research. The school is committed to fostering innovation and providing actionable insights to address real-world challenges. This study is a testament to the institution's dedication to advancing knowledge in the domain of financial technology and investor behavior.

CBS International Business School, part of CBS University of Applied Sciences in Mainz, Germany, and Christ University in Delhi, India, also contributed to this research, bringing a global perspective to the study. Their expertise in international business and finance adds depth to the findings and recommendations, making the study particularly relevant for a global audience. 

Frequently Asked Questions (FAQS): 

Q: What are Financial Robo-Advisors (FRAs)?

A: Financial Robo-Advisors (FRAs) are digital platforms that use advanced algorithms and artificial intelligence (AI) to provide personalized financial advice and investment management services to retail investors.

Q: What are the main barriers to FRA adoption identified in the study?

A: The study identified several barriers, including inertia, overconfidence bias, data privacy concerns, and perceived complexity, with inertia being the strongest determinant.

Q: How does a positive attitude towards AI influence resistance to FRAs?

A: A positive attitude towards AI can significantly weaken the influence of inertia, overconfidence bias, and data privacy concerns on resistance to FRAs.

Q: What is the impact of resistance on the intention to use and recommend FRAs?

A: Resistance to FRAs impedes both the intention to use these tools and the likelihood of recommending them to others, affecting their adoption in the market.

Q: What are the practical recommendations for FRA providers to enhance adoption?

A: FRA providers should focus on educating users about the benefits of AI, addressing data privacy concerns, and providing user-friendly interfaces to reduce perceived complexity. 

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