The Persuasive Power of Algorithmic and Crowdsourced Advice

Junius Gunaratne, Lior Zalmanson, Oded Nov

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

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.

Original languageEnglish
Pages (from-to)1092-1120
Number of pages29
JournalJournal of Management Information Systems
Volume35
Issue number4
DOIs
StatePublished - 2 Oct 2018
Externally publishedYes

Funding

FundersFunder number
Financial Industry Regulatory Authority Foundation
Fulbright Foundation
National Science Foundation
John D. and Catherine T. MacArthur Foundation
Google
Marketing Science Institute

    Keywords

    • algorithmic advice
    • and phrases: online advice
    • crowdsourced advice
    • crowdsourcing
    • decision-making
    • investment advice
    • online persuasion
    • personal finance
    • retirement portfolios
    • uncertainty

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