Motivating Contributions to Public Information Goods

public information goods;Wikipedia;user-generated content;Management;Business and Economics;Information

Zhang, FangzhouRosenblat, Tanya ;

University of Michigan

关键词: public information goods;    Wikipedia;    user-generated content;    Management;    Business and Economics;    Information;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/147670/arkzhang_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This dissertation investigates how to motivate contributions to public information goods, characterized by non-rivalry by nature and non-excludability by choice. With the running example of Wikipedia - the online encyclopedia that grants free access to everyone and relies entirely on the inputs from volunteers, I present three designed-based empirical studies that explore how individual contributions are affected by 1) reduction in the number of collaborators, 2) private benefit, number of readers, and 3) group membership.Chapter II studies how the reduction in group size affects the collaborative behavior in contributing to Wikipedia articles. Exploiting a natural experiment at the Chinese Wikipedia, we find that the level of contribution and conflict within the group drop on articles that face a shock, whereas centralization increases. Interestingly, the impact of a shock on activity increases with shock level, whereas the impact on centralization and conflict is higher for moderate shock levels than for very small or very high shock levels.Chapter III examines how the private benefit of citation and acknowledgement and the social impact from the public goods can motivate contributions from domain experts. Using a randomized field experiment, we invite 3,974 academic economists to contribute to Wikipedia articles relevant to their researches. The results show that experts are significantly more interested in contributing when citation benefit is mentioned. Furthermore, cosine similarity between a Wikipedia article and the expert;;s paper abstract is the most significant factor leading to more and higher-quality contributions, indicating that better matching is a crucial factor in motivating contributions to public information goods.Chapter IV examines the potential of team-based approach on motivating individual contributions to Wikipedia. Employing a panel data set across over 9,000 Wikipedia editors and exploiting the variations in the exposure to WikiProject due to quasi-randomness in the association of Wikipedia articles to WikiProject, we find that joining a WikiProject has a sizable impact on the level of contribution, measured by both the number and size of revisions. Further analysis on the patterns in the behavior of WikiProject members indicates that the recommendation of articles can be the factor driving the impact of WikiProject.