Online citizen science offers a low-cost way to strengthen the infrastructure for scientific research and engage members of the public in science. As the sustainability of online citizen science projects depends on volunteers who contribute their skills, time, and energy, the objective of this study is to investigate effects of motivational factors on the quantity and quality of citizen scientists' contribution. Building on the social movement participation model, findings from a longitudinal empirical study in three different citizen science projects reveal that quantity of contribution is determined by collective motives, norm-oriented motives, reputation, and intrinsic motives. Contribution quality, on the other hand, is positively affected only by collective motives and reputation. We discuss implications for research on the motivation for participation in technology-mediated social participation and for the practice of citizen science.

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The notion of information quality (IQ) has been investigated extensively in recent years. Much of this research has been aimed at conceptualizing IQ and its underlying dimensions (e.g., accuracy, completeness) and at developing instruments for measuring these quality dimensions. However, less attention has been given to the measurability of IQ. The objective of this study is to explore the extent to which a set of IQ dimensions-accuracy, completeness, objectivity, and representation-lend themselves to reliable measurement. By reliable measurement, we refer to the degree to which independent assessors are able to agree when rating objects on these various dimensions. Our study reveals that multiple assessors tend to agree more on certain dimensions (e.g., accuracy) while finding it more difficult to agree on others (e.g., completeness). We argue that differences in measurability stem from properties inherent to the quality dimension (i.e., the availability of heuristics that make the assessment more tangible) as well as on assessors'reliance on these cues. Implications for theory and practice are discussed.

Social recommender systems utilize data regarding users' social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data-beyond consumption profile similarity-is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.'s (1992) IS Design Theory by introducing the notion of "applied behavioral theory," as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient's advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems.