Adoption and Resistance to Robo-Advisory Services: A Narrative Review

Abstract

Purpose: This paper synthesizes and critically evaluates academic literature on the adoption and resistance toward robo-advisory services, with particular focus on trust, technology acceptance, and behavioural perspectives. By concentrating on emerging markets, especially India, the study aims to identify dominant theoretical patterns, contextual dynamics, and unresolved research gaps influencing investor engagement with automated financial advice.

Design/methodology/approach: The study adopts a narrative literature review approach based on a transparent and structured identification of prior research. Peer-reviewed journal articles published between 2015 and 2025 were sourced mainly from Scopus and Web of Science, with selective support from Google Scholar. An interpretive thematic synthesis was employed to integrate insights from technology acceptance models, behavioural finance, and human computer interaction.

Findings: The review indicates that robo-advisor adoption is not determined by technological efficiency alone, but emerges from an interaction of functional, psychological, and contextual factors. While performance expectancy and ease of use provide a basic foundation for adoption, trust plays a central mediating role. Behavioural tendencies such as algorithm aversion, overconfidence, and preference for human expertise continue to restrict diffusion, particularly in high-stakes financial decisions. In the Indian context, social influence, security concerns, and status quo bias further intensify resistance.

Originality/Value: By integrating adoption-focused and resistance-focused perspectives and distinguishing between cognitive and affective trust, this review offers a theory-driven synthesis that strengthens understanding of robo-advisory adoption in emerging market settings.

Paper Type: View Point