Crowd-Judging on Two-Sided Platforms: An Analysis of In-Group Bias

Kwan, A P, Yang, S and Zhang, A H (2024) Crowd-Judging on Two-Sided Platforms: An Analysis of In-Group Bias. Management Science, 70 (4). pp. 2459-2476. ISSN 0025-1909

Abstract

Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms’ customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowdsourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich data set from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin and when jurors perceive that their in-group’s interests are threatened. However, the bias diminishes as jurors gain experience: a user’s bias reduces by nearly 95% as experience grows from zero to the sample median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size through either a larger case panel or aggressively recruiting new jurors may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool.

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Item Type:	Article
Subject Areas:	Management Science and Operations
Date Deposited:	20 Jun 2023 09:36
Date of first compliant deposit:	20 Jun 2023
Last Modified:	21 Nov 2024 02:34
URI:	https://lbsresearch.london.edu/id/eprint/2707

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