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

• 成果类型: Article
• 署名作者: Chen, Yan; Farzan, Rosta; Kraut, Robert; Yeckehzaare, Iman; Zhang, Ark Fangzhou
• 署名单位: University of Michigan System; University of Michigan; Tsinghua University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University; Alphabet Inc.; Google Incorporated
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4852
• 发表日期: 2024
• 关键词: digital public goods; match quality; Machine Learning; field experiment

摘要

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.