Motivating Experts to Contribute to Digital Public Goods: A Personalized Field Experiment on Wikipedia - Chen, Yan; Farzan, Rosta; Kraut, Robert; YeckehZaare, Iman; Zhang, Ark Fangzhou (Management Science, 2024)
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. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Funding: This work was supported by the National Science Foundation through [Grant SES-1620319] awarded to Carnegie Mellon University and the University of Michigan. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4852 .