Online communities are increasingly important to organizations and the general public, but there is little theoretically based research on what makes some online communities more successful than others. In this article, we apply theory from the field of social psychology to understand how online communities develop member attachment, an important dimension of community success. We implemented and empirically tested two sets of community features for building member attachment by strengthening either group identity or interpersonal bonds. To increase identity-based attachment, we gave members information about group activities and intergroup competition, and tools for group-level communication. To increase bond-based attachment, we gave members information about the activities of individual members and interpersonal similarity, and tools for interpersonal communication. Results from a six-month field experiment show that participants' visit frequency and self-reported attachment increased in both conditions. Community features intended to foster identity-based attachment had stronger effects than features intended to foster bond-based attachment. Participants in the identity condition with access to group profiles and repeated exposure to their group's activities visited their community twice as frequently as participants in other conditions. The new features also had stronger effects on newcomers than on old-timers. This research illustrates how theory from the social science literature can be applied to gain a more systematic understanding of online communities and how theory-inspired features can improve their success.

The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.

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A tagging community's vocabulary of tags forms the basis for social navigation and shared expression. We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system. We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms' effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.

We design a field experiment to explore the use of social comparison to increase contributions to an online community. We find that, after receiving behavioral information about the median user's total number of movie ratings, users below the median demonstrate a 530 percent increase in the number of monthly movie ratings, while those above the median decrease their ratings by 62 percent. When given outcome information about the average user's net benefit score, above-average users mainly engage in activities that help others. Our findings suggest that effective personalized social information can increase the level of public goods provision. (JEL C93, H41, L82)