Recommendation agents (RAs) are software agents that elicit the interests or preferences of individual consumers for products, either explicitly or implicitly, and make recommendations accordingly. RAs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products online. They can reduce the information overload facing consumers, as well as the complexity of online searches. Prior research on RAs has focused mostly on developing and evaluating different underlying algorithms that generate recommendations. This paper instead identifies other important aspects of RAs, namely RA use, RA characteristics, provider credibility, and factors re-1 Dov Te'eni was the accepting senior editor for this paper. Joey George was the associate editor. Moez Limayem, Patrick Chau, and Mark Silver served as reviewers. lated to product, user, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RAs. It goes beyond generalized models, such as TAM, and identifies the RA-specific features, such as RA input, process, and output design characteristics, that affect users' evaluations, including their assessments of the usefulness and ease-of-use of RA applications. Based on a review of existing literature on e-commerce RAs, this paper develops a conceptual model with 28 propositions derived from five theoretical perspectives. The propositions help answer the two research questions: (1) How do RA use, RA characteristics, and other factors influence consumer decision making processes and outcomes? (2) How do RA use, RA characteristics, and other factors influence users' evaluations of RAs? By identifying the critical gaps between what we know and what we need to know, this paper identifies potential areas of future research for scholars. It also provides advice to information systems practitioners concerning the effective design and development of RAs.

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O nline social networks greatly facilitate social exchange among friends. At times, for amusement, individuals may be targeted by friends' playful teases, which often involve exposing individuals' private embarrassing information, such as information that reveals their past indecent behavior, mischief, or clumsiness. Although individuals sometimes do enjoy the humor, they might also be offended by the involuntary exposure. Drawing on social exchange theory, this paper elucidates the consequences of an embarrassing exposure in online social networks. Specifically, this study examines the effects of information dissemination and network commonality on individuals' exchange assessment as well as how this assessment shapes their behavioral responses. The results of our experiment provide strong evidence that information dissemination and network commonality jointly influence individuals' perceived privacy invasion and perceived relationship bonding. In addition, whereas perceived privacy invasion increases transactional avoidance, it reduces approach behavior. Furthermore, whereas perceived relationship bonding impedes both transactional avoidance and interpersonal avoidance, it leads to approach behavior. The theoretical and practical implications of the findings are discussed.