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

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