Motivating Experts to Contribute to Digital Public Goods: A Personalized Field Experiment on Wikipedia

Yan Chen (), Rosta Farzan (), Robert Kraut (), Iman YeckehZaare () and Ark Fangzhou Zhang ()
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Yan Chen: School of Information, University of Michigan, Ann Arbor, Michigan 48109; Department of Economics, School of Economics and Management, Tsinghua University, Beijing 100084, China
Rosta Farzan: School of Computing and Information, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
Robert Kraut: School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Iman YeckehZaare: School of Information, University of Michigan, Ann Arbor, Michigan 48109
Ark Fangzhou Zhang: Google LLC, Mountain View, California 94043

Management Science, 2024, vol. 70, issue 5, 3264-3280

Abstract: We conducted a large-scale personalized field experiment to examine how match quality, recognition, and social impact influence domain experts’ contributions to Wikipedia. Forty-five percent of the experts expressed willingness to contribute in the baseline condition, whereas 51% (a 13% increase over the baseline) expressed interest when they received a signal that an article matched their expertise. However, none of the treatments had a significant effect on actual contributions. Instead experts contributed longer and better comments when the actual match between a recommended Wikipedia article and an expert's expertise, measured by cosine similarity, was higher, when they had higher reputation, and when the original article was longer. These findings suggest that match quality between volunteers and tasks is critically important in encouraging contributions to digital public goods and likely to volunteering in general.

Keywords: digital public goods; match quality; machine learning; field experiment (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:5:p:3264-3280

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