Information Seeding and Knowledge Production in Online Communities: Evidence from OpenStreetMap

• 成果类型: Article
• 署名作者: Nagaraja, Abhishek
• 署名单位: University of California System; University of California Berkeley
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3764
• 发表日期: 2021
• 页码: 4908-4934
• 关键词: online communities; Knowledge production; crowdsourcing; INNOVATION; DIGITIZATION

摘要

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.