The impact of AI-powered robo-advisors on investor decision-making: the moderating role of financial knowledge

Purpose In today’s dynamic world, innovative technological advancements are reshaping and revolutionizing traditional practices. One such transformation is the advent of artificial intelligence (AI)-driven robo-advisors (RAs). This study aims to examine 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 modelling and artificial neural network techniques was used 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. Research limitations/implications This study is limited to self-reported data from individual investors, which may introduce common method bias. Future studies could include longitudinal data and cross-cultural samples to enhance generalisability. 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 To the best of the authors’ knowledge, 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.