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Bihari, Anshita, Dash, Manoranjan, Kumar, Anil, Muduli, Kamalakanta, Luthra, Sunil and Samadhiya, Ashutosh (2026) The impact of AI-powered robo-advisors on investor decision-making: the moderating role of financial knowledge. VINE Journal of Information and Knowledge Management Systems. ISSN 2059-5891 (In Press)
Purpose: In today’s dynamic world, innovative technological advancements are reshaping and revolutionizing traditional practices. One such transformation is the advent of AI-driven robo-advisors (RAs). This study examines the effectiveness of AI-powered RAs in decision-making by integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to elucidate the rationales behind the practical use of RAs.
Design/methodology/approach: Data were collected from 365 respondents using simple random sampling. An integrated approach of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) techniques was utilised to test the proposed model and examine the robustness of the hypotheses. Emotional and cognitive biases that influence investors’ perceptions of RAs were analysed, with age, income, and financial literacy used as moderators to measure individuals’ behaviours and traits.
Findings: The findings reveal that—perceived usefulness, perceived ease of use, and performance expectancy—along with the contextual predictors such as, emotional bias and cognitive bias, act as positive mediators for the adoption of RAs among consumers. The study also finds that age, income, and financial literacy significantly moderate the relationships between UTAUT components and behavioural intention to use RAs.
Practical implications: The study provides insights for financial service providers and policymakers to design strategies that promote the adoption of AI-based RAs by addressing emotional and cognitive biases and improving users’ perceived usefulness and ease of use of these technologies.
Originality/value: This study is among the first to explore how behavioural biases can explain the adoption of RAs among investors. It contributes to the existing literature by integrating TAM and UTAUT models with psychological constructs to better understand investors’ decision-making in the context of AI-powered financial advisory services.
![[thumbnail of Revised Manuscript - Accepted Version.pdf]](https://repository.londonmet.ac.uk/style/images/fileicons/text.png "Revised Manuscript - Accepted Version.pdf")
Restricted to Repository staff only until 29 September 2026.
Available under License Creative Commons Attribution Non-commercial 4.0.
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