Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice

Abstract

Adaptive online platforms, powered by artificial intelligence, commonly referred to as robo-advice, steadily increase their market share. Yet these comparably new financial services are critically understudied. Little is known about why some investors adopt robo-advice for something as essential as asset allocation. The current paper tries to close this gap by shedding light on the causal effect of investor overconfidence on the propensity of using robo-advice. The study proposes a theoretical framework that combines the divergence of opinion hypothesis with consumer behavior insights and information technology diffusion research. The framework is empirically tested on the Investor Sample of the 2015 National Financial Capability Study, a subsample of 2000 US investors. The results from a series of generalized linear, structural, and semiparametric models show that in a pre-chasm market, overconfident investors have a significantly higher propensity of adopting robo-advice. While higher financial literacy seems to decrease robo-advice uptake, unjustified confidence in one’s knowledge causally increases it. Willingness to take financial risk cannot account for the significantly increased adoption of robo-advice among overconfident investors. The findings help managers to better position robo-advice by offering behavioral insights into their user base. In addition, the results outline a managerial tool to take demand-side actions to increase the likelihood of an end-user innovation crossing the chasm.