Information Seeding and Knowledge Production in Online Communities: Evidence from OpenStreetMap
49 Pages Posted: 30 Sep 2017
Date Written: September 28, 2017
The wild success of a few online community-produced knowledge goods, notably Wikipedia, has obscured the fact that most attempts at forming online communities fail. A large body of work analyses motivations behind user contributions to successful, online communities but less is known, however, about early-stage interventions that might make online communities more or less successful. This study evaluates information seeding, a popular practice to bootstrap online communities by enabling contributors to build on externally-sourced information rather that starting from scratch. I analyze the effects of information seeding on follow-on contributions using data from more than 350 million contributions made by over 577,000 contributors to OpenStreetMap, a Wikipedia-style digital map-making community that was seeded with data from the US Census. To estimate the effects of information seeding, I rely on a natural experiment in which an oversight caused about 60% of quasi-randomly chosen US counties to be seeded with a complete Census map, while the rest were seeded with less complete versions. While access to knowledge generally encourages follow-on knowledge production, I find that a higher level of information seeding significantly lowered follow-on knowledge production and contributor activity on OpenStreetMap and was also associated with lower levels of long-term quality. I argue that information seeding can crowd out contributors’ ability to develop ownership over baseline knowledge and disincentivize follow-on contributions in some circumstances. Empirical evidence supports this explanation as the mechanism through which a higher level of information seeding can stifle rather than spur knowledge production in online communities.
Keywords: OpenStreetMap, Crowdsourcing, Open Innovation, Online Communities
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