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

62 Pages Posted: 27 May 2020

See all articles by Yan Chen

Yan Chen

University of Michigan at Ann Arbor - Department of Economics

Iman Yeckehzaare

affiliation not provided to SSRN

Date Written: April 25, 2020

Abstract

We use a large-scale personalized field experiment on Wikipedia to examine the effect of motivation on domain experts' contributions to digital public goods. In our baseline condition, 45% of the experts express willingness to contribute. Furthermore, experts are 13% more interested in contributing when we mention the private benefit of contribution, such as the likely citation of their work. In the contribution stage, using a machine learning model, we find that greater matching accuracy between a recommended Wikipedia article and an expert's expertise, together with an expert's reputation and the mentioning of public acknowledgement, are the most important predictors of both contribution length and quality. Our results show the potential of scalable personalized interventions using recommender systems to study drivers of prosocial behavior.

Keywords: digital public goods, matching accuracy, machine learning, field experiment

JEL Classification: C93, H41

Suggested Citation

Chen, Yan and Yeckehzaare, Iman, Motivating Experts to Contribute to Digital Public Goods: A Personalized Field Experiment on Wikipedia (April 25, 2020). Available at SSRN: https://ssrn.com/abstract=3588132 or http://dx.doi.org/10.2139/ssrn.3588132

Yan Chen (Contact Author)

University of Michigan at Ann Arbor - Department of Economics ( email )

105 South State Street
Ann Arbor, MI 48109
United States

Iman Yeckehzaare

affiliation not provided to SSRN

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