Salience Bias in Crowdsourcing Contests

39 Pages Posted: 3 Oct 2017 Last revised: 4 Oct 2017

See all articles by Ho Cheung Brian Lee

Ho Cheung Brian Lee

University of Massachusetts Lowell - The Robert J. Manning School of Business

Sulin Ba

University of Connecticut School of Business

Xinxin Li

University of Connecticut - Department of Operations & Information Management

Jan Stallaert

University of Connecticut - School of Business

Date Written: June 30, 2017

Abstract

Crowdsourcing relies on online platforms to connect a community of users to perform specific tasks. However, without appropriate control, the behavior of the online community might not align with the platform’s designed objective, which can lead to an inferior platform performance. This paper investigates how the feedback information on a crowdsourcing platform and systematic bias of crowdsourcing workers can affect crowdsourcing outcomes. Specifically, using archival data from the online crowdsourcing platform Kaggle, combined with survey data from actual Kaggle contest participants, we examine the role of a systematic bias, namely the salience bias, in influencing the performance of the crowdsourcing workers and how the number of crowdsourcing workers moderates the impact of the salience bias as a result of the parallel path effect and competition effect. Our results suggest that the salience bias influences the performance of contestants, including the winners of the contests. Furthermore, the parallel path effect cannot completely eliminate the impact of the salience bias, but it can attenuate it to a certain extent. By contrast, the competition effect is likely to amplify the impact of the salience bias. Our results have critical implications for crowdsourcing firms and platform designers.

Keywords: behavioral economics, crowdsourcing, salience bias, parallel path effect, competition effect

Suggested Citation

Lee, Ho Cheung Brian and Ba, Sulin and Li, Xinxin and Stallaert, Jan, Salience Bias in Crowdsourcing Contests (June 30, 2017). University of Connecticut School of Business Research Paper No. 17-01. Available at SSRN: https://ssrn.com/abstract=3046806 or http://dx.doi.org/10.2139/ssrn.3046806

Ho Cheung Brian Lee (Contact Author)

University of Massachusetts Lowell - The Robert J. Manning School of Business ( email )

One University Avenue
Lowell, MA 01854
United States

Sulin Ba

University of Connecticut School of Business ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

Xinxin Li

University of Connecticut - Department of Operations & Information Management ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States
(860) 486-3062 (Phone)

Jan Stallaert

University of Connecticut - School of Business ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

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