Embracing AI advisors for making (complex) financial decisions: an experimental investigation of the role of a maximizing decision-making style

Purpose This study investigates the impact of experimentally priming a maximizing decision-making style on individuals’ likelihood of using artificial intelligence (AI) advisors for making complex financial decisions, such as building an investment portfolio for their retirement. It examines whether individuals with stronger maximizing tendencies are more likely to perceive algorithms as effective, thereby reducing their algorithm aversion, and ultimately increasing the likelihood of using AI advisors in their financial decision-making. Design/methodology/approach A qualitative pre-study amongst individuals differing in their maximizing tendencies to learn more about the existing usage patterns of AI advisors for financial decisions was combined with a quantitative study to experimentally test our hypotheses. For both studies, US participants were recruited through Prolific. The data were analyzed using thematic analysis in NVivo and regression analysis in the SPSS Process macro. Findings The results show that individuals primed with a maximizing mindset demonstrated a higher likelihood of using AI advisors for their financial decisions. This effect was serially mediated by the perception of enhanced algorithm effectiveness and reduced algorithm aversion. Practical implications This study provides actionable insights for financial service providers such as banks, pension funds and insurance companies into strategies on how to reduce algorithm aversion and encourage greater AI usage in decision-making amongst their (potential) clients. In particular, to increase the likelihood that consumers will rely on AI advisors for financial decisions, financial service providers can induce a maximizing mindset in these individuals by adjusting the wording of their marketing communications material. Originality/value This study extends our understanding of how maximizing tendencies influence the likelihood of using AI advisors. It contributes to the literature by highlighting the role of perceived effectiveness and algorithm aversion and by demonstrating that experimentally inducing a maximizing mindset can increase AI usage for financial decisions; doing so is important as AI can help provide consumers with personalized advice in a cost-effective way.