2021journal article|125 citations

Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services

Lixuan Zhang, Iryna Pentina, Yuhong Fan

Journal of Services Marketing

Plain Language Summary

This experimental study compares consumer perceptions of human financial advisors versus automated robo-advisors. While highly expert human advisors remain preferred, robo-advisors outperform low-expertise human advisors on trust and competence measures. The findings suggest that robo-advisors represent a viable alternative in financial services, particularly when high-quality human expertise is unavailable. Consumer technology readiness influences these preferences.

Abstract

Purpose This study aims to investigate the differences in consumers’ perceptions of trust, performance expectancy and intention to hire between human financial advisors with high/low expertise and robo-advisors. Design/methodology/approach Three experiments were conducted. The respondents were randomly assigned to human advisors with high/low expertise or a robo-advisor. Data were analyzed using MANCOVA. Findings The results suggest that consumers prefer human financial advisors with high expertise to robo-advisors. There are no significant differences between robo-advisors and novice financial advisors regarding performance expectancy and intention to hire. Originality/value This pioneering study extends the self-service technology adoption theory to examine adoption of robo-advisors vs human financial advisors with different expertise levels. To the best of the authors’ knowledge, it is among the first studies to address multi-dimensionality of trust in the context of artificial intelligence-based self-service technologies.

Key Findings

Consumers generally prefer human financial advisors with high expertise over robo-advisors
Robo-advisors are perceived as more trustworthy and competent than low-expertise human advisors
Trust and performance expectancy are key factors differentiating preferences between human and robo-advisors
Consumer characteristics such as technology readiness moderate preferences for advisory service type