The Persuasive Power of Algorithmic and Crowdsourced Advice

Abstract

Prior research has shown that both advice generated through algorithms and advice resulting from averaging peers’ input can impact users’ decision-making. However, it is not clear which advice type is more closely followed and if changes in decision-making should be attributed to the source or the content of the advice. We examine the effects of algorithmic and social advice on decision-making in the context of an online retirement saving system. By varying both the advice’s message and the attributed messenger, we assess what it is about the advice that people follow. We find that both types of advice have a positive effect on users’ saving performance, and that users follow advice presented as coming from an algorithmic source more closely than advice presented as crowdsourced. Our results shed light on how people view and follow online advice, and on information systems’ persuasive effects under conditions of uncertainty.

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