Paper Number
ECIS2026-2461
Abstract
Traditional centralized online dispute resolution (ODR) in bilateral platforms faces challenges of high costs and perceived fairness issues. Crowd judging, as an emerging crowdsourced online dispute resolution mechanism, facilitates a paradigm shift in platform governance by decentralizing adjudicative power to users; however, its decision-making mechanisms remain unclear. This study investigates how the characteristics of evidence presented by disputing parties influence the decisions of third-party jurors in crowd judging contexts. Integrating the Elaboration Likelihood Model and Confirmation Bias Theory, the research constructs a framework incorporating central and peripheral cues. This model is empirically examined using real dispute case data collected from a second-hand e-commerce platform, aiming to reveal how evidence features shape users’ voting behavior. This research not only enriches the theoretical understanding of crowdsourced dispute resolution within the Information Systems field but also provides and practical insights for designing fairer and more efficient crowd judging systems.
Recommended Citation
Li, Zengxi; WANG, Guangxu; and Lu, Angela, "How Does Evidence Persuade Crowd Jurors? A Study On Crowdsourced Online Dispute Resolution Based On The Elm Model" (2026). ECIS 2026 Proceedings. 10.
https://aisel.aisnet.org/ecis2026/platforms/platforms/10
How Does Evidence Persuade Crowd Jurors? A Study On Crowdsourced Online Dispute Resolution Based On The Elm Model
Traditional centralized online dispute resolution (ODR) in bilateral platforms faces challenges of high costs and perceived fairness issues. Crowd judging, as an emerging crowdsourced online dispute resolution mechanism, facilitates a paradigm shift in platform governance by decentralizing adjudicative power to users; however, its decision-making mechanisms remain unclear. This study investigates how the characteristics of evidence presented by disputing parties influence the decisions of third-party jurors in crowd judging contexts. Integrating the Elaboration Likelihood Model and Confirmation Bias Theory, the research constructs a framework incorporating central and peripheral cues. This model is empirically examined using real dispute case data collected from a second-hand e-commerce platform, aiming to reveal how evidence features shape users’ voting behavior. This research not only enriches the theoretical understanding of crowdsourced dispute resolution within the Information Systems field but also provides and practical insights for designing fairer and more efficient crowd judging systems.
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