Whose Algorithm Says So: The Relationships between Type of Firm, Perceptions of Trust and Expertise, and the Acceptance of Financial Robo-Advice

Financial advisors seek to accurately measure individuals’ risk preferences and provide sound personalized investment advice. Both advice tasks are increasingly offered through automated online technologies. Little is known, however, about what drives individuals’ acceptance of such automated financial advice and, from a consumer point of view, which firms may be best positioned to provide such advice. We generate novel insights on these questions by conducting a real-world empirical study using an interactive automated online tool that employs an innovative computer algorithm to build pension investment profiles, the “Pension Builder,” and a large, representative sample. We focus on the role that two key firm characteristics have on consumer acceptance of pension investment advice generated by computer algorithms running on automated interactive online tools: profit orientation and role in the sales channel. We find that consumers’ perceptions of trust and expertise of the firm providing the automated advice are important drivers of advice acceptance (besides a strong impact of the satisfaction with the consumer–online tool interaction), and that these constructs themselves are clearly influenced by the for-profit vs. not-for-profit orientation and the product provider vs. advisor only role in the sales channel of the firm providing the advice. We discuss the implications of our findings for marketers and policy makers and provide suggestions for future research.