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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yan Chen & Rosta Farzan & Robert Kraut & Iman YeckehZaare & Ark Fangzhou Zhang, 2024. "Motivating Experts to Contribute to Digital Public Goods: A Personalized Field Experiment on Wikipedia," Management Science, INFORMS, vol. 70(5), pages 3264-3280, May.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:5:p:3264-3280
    DOI: 10.1287/mnsc.2023.4852
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