A Study of the Effect of AI Robo-Advisors on Investors’ Intention, Participation, and Performance in West Bengal by Prritish Lalwani

A Study of the Effect of AI Robo-Advisors on Investors’ Intention, Participation, and Performance in West Bengal by Prritish Lalwani

This study empirically investigates the structural dynamics driving retail investment behaviour in Kolkata, West Bengal, focusing on the intersection of automated wealth management (AI Robo-Advisors) and cognitive financial capability. While traditional financial theory and FinTech adoption models often isolate technological trust from economic literacy, this paper synthesises the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) to evaluate their joint impact on investment outcomes. Using cross-sectional survey data from retail investors, we deploy a sequential regression framework to test the mediating pathways from technological acceptance and financial literacy to active stock market participation and subjective portfolio performance. The findings reveal that perceived trust in algorithmic tools (Belanche et al., 2019) and baseline financial literacy (Lusardi & Mitchell, 2014) serve as critical, independent catalysts for cultivating investment intention. This intention subsequently acts as a strong predictor of active market participation (γ = 0.528), which directly translates into enhanced goal-based portfolio performance (δ = 0.389). The study concludes that decisive technological engagement, when paired with foundational economic knowledge, successfully overcomes traditional algorithmic aversion and the risk-averse saving habits endemic to regional metropolitan centres.

1. INTRODUCTION

Over the past decade, the convergence of financial technology (FinTech) and artificial intelligence (AI) has fundamentally restructured the global financial landscape. Historically, strategic asset allocation and dynamic portfolio management were exclusive services reserved for high-net-worth individuals, while average retail investors faced severe information asymmetry and high transaction friction. Today, AI-driven robo-advisors utilise algorithmic optimisation, Modern Portfolio Theory (MPT), and machine learning to deliver accessible, low-cost portfolio management, democratising the mechanics of personal wealth generation (D’Acunto et al., 2019).

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