The Persuasive Power of Algorithmic and Crowdsourced Advice

• 成果类型: Article
• 署名作者: Gunaratne, Junius; Zalmanson, Lior; Nov, Oded
• 署名单位: New York University; New York University Tandon School of Engineering; University of Haifa; New York University; New York University Tandon School of Engineering
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2018.1523534
• 发表日期: 2018
• 页码: 1092-1120
• 关键词: elaboration likelihood model; social-influence; online peer; explanation facilities; recommender systems; source credibility; decision-making; expert-systems; INFORMATION; wisdom

摘要

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.