Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online

• Published in: Management science 2024-03, Vol.70 (3), p.1613-1634
• Main Authors: Manzoor, Emaad, Chen, George H., Lee, Dokyun, Smith, Michael D.
• Format: Article
• Language: English
• Subjects: causal inference with text; Citizen participation; Decision making; Donations; double machine-learning; Heuristic; Information processing; Management science; Mathematical models; Persuasion; Power; reputation systems; Reputations
• ISSN: 0025-1909; 1526-5501
• DOI: 10.1287/mnsc.2023.4762

Deliberation among individuals online plays a key role in shaping the opinions that drive votes, purchases, donations, and other critical offline behavior. Yet, the determinants of opinion change via persuasion in deliberation online remain largely unexplored. Our research examines the persuasive power of ethos —an individual’s “reputation”—using a seven-year panel of over a million debates from an argumentation platform containing explicit indicators of successful persuasion. We identify the causal effect of reputation on persuasion by constructing an instrument for reputation from a measure of past debate competition and by controlling for unstructured argument text using neural models of language in the double machine-learning framework. We find that an individual’s reputation significantly impacts their persuasion rate above and beyond the validity, strength, and presentation of their arguments. In our setting, we find that having 10 additional reputation points causes a 31% increase in the probability of successful persuasion over the platform average. We also find that the impact of reputation is moderated by characteristics of the argument content, in a manner consistent with heuristic information processing under cognitive overload. We discuss managerial implications for platforms that facilitate deliberative decision making for public and private organizations online. This paper was accepted by Anandhi Bharadwaj, information systems. Funding: This research was supported in part by the University of Wisconsin Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation, and by the Carnegie Mellon University Graduate Student Assembly and Provost’s Office. Computing support was provided in part by the Social Science Computing Cooperative at the University of Wisconsin Madison. Supplemental Material: Data and the online appendix is available at https://doi.org/10.1287/mnsc.2023.4762 .