Information Seeding and Knowledge Production in Online Communities: Evidence from OpenStreetMap
研究利用OpenStreetMap的自然实验，发现信息播种虽提供基础知识，但过高水平反而降低后续贡献和长期质量，不过对人口密集城市和忠实用户有益。
The wild success of a few online communities (such as Wikipedia) has obscured the fact that most attempts at forming such communities fail. This study evaluates information seeding, an early-stage intervention to bootstrap online communities that enables contributors to build on externally sourced information rather than have them start from scratch. I analyze the effects of information seeding on follow-on contributions using data on more than 350 million contributions made by more than 577,000 contributors to OpenStreetMap, a crowd-sourced map-making community seeded with data from the U.S. Census. I estimate the effect of seeding using a natural experiment in which an oversight caused about 60% of U.S. counties to be seeded with a complete census map, while the rest were seeded with less complete versions. Although access to basic knowledge generally encourages downstream knowledge production, I find that a higher level of information seeding significantly lowered follow-on contributions and contributor activity on OpenStreetMap, and was associated with lower levels of long-term quality. However, seeding did benefit densely populated urban areas and did not discourage more committed users. To explain these patterns, I argue that information seeding can crowd out contributors’ ability to develop ownership over baseline knowledge and thereby disincentivize follow-on contributions. This paper was accepted by Chris Forman, information systems.