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Online communities provide millions of people every day with information, companionship, support, and fun. These communities need regular maintenance to function. Tasks such as welcoming new members, reviewing contributions, and building community-specific databases typically fall to a few dedicated members. Concentrating responsibility in the hands of a few valuable leaders makes communities vulnerable to leaders' leaving and limits communities' ability to grow and provide value. We study the design of member-maintained online communities, systems where many members help perform upkeep. A key design challenge is motivating members to contribute toward maintenance. Social science theories help to explain why people contribute to groups. We use these theories to design two general mechanisms for increasing people's motivation to contribute. The collective effort model from social psychology suggests people are more likely to contribute to a group if they believe their contributions matter. Editorial review can foster this belief by promoting good content and suppressing bad content. We build review systems that involve the whole community, where review is performed by peers, experts, or no one. Peer review performs about as well as expert review in both motivating contributions and providing effective review, but no review does very poorly. We also explore whether contributions must be reviewed before being made available to the community. Mathematical models suggest that making contributions available right away increases value more quickly, and does just as well in the long run, as requiring prior review. These models can inform the design of review systems. Public goods theory from economics suggests people will contribute more to group resources if the cost of contributing drops. We use intelligent task routing---matching people with tasks they are likely to do---to reduce contribution costs. We develop a number of generally useful task routing algorithms. Experiments in a movie database and in Wikipedia show these algorithms are very effective at increasing people's motivation to contribute. By using theory to support our designs, testing them in multiple domains, and distilling our results into usable artifacts such as guidelines, models, and algorithms, we hope to help designers build better systems and better communities.
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