Those who are more committed to their organizations are assumed to be more productive persons and behave with a higher sense of responsibility and loyalty (Ulrich, 1998). Several different definitions of commitment exist in the literature. Buchanan's (1974) definition is typical: "Commitment is viewed as a partisan, affective attachment to the goals and values of an organization, to one's role in relation to goals and values, and to the organization for its own sake, apart from its pruly instrumental worth" (p. 533). In the definition of Wiener (1982) commitment is conceptualized as a normative motivational process clearly distinctive from instrumental approaches to the explanation of work behavior. Tsui, Egan, and O'Reilly (1992) defined it as an employees' psychological and behavioral attachment to a social group. Scheldon's (1971) definition combines commitment with identification words. He viewed commitment as a psychological attachment

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Current event processing systems simply define an event as a message about a status-change of data or information. As a result, the event models with domain-specific structure and attributes are not neutral to application domains. It is difficult for event models developed for a specific domain to be applied to other domains and event processing system. In this paper, we propose a generic, extensible, and application-domain independent event model based on a new notion of event, and show the proposed event system feasible by applying standard semantic web technologies to the model.

In the current paper, I develop and test a theoretical model aimed at understanding the relationship between friendship and group performance. Specifically, using data from 924 teams in 24 studies, I tested my hypothesized main effect, and key moderator analyses, for how working with friends, versus non-friends, would influence group task performance. Results show that friendship has a positive effect on group task performance (d=0.45). Furthermore, as expected this effect was moderated by task interdependence and task focus/emphasis (i.e. maximizing versus optimizing). In addition to detailing which paths of the model were supported by the data, in my discussion section I detail gaps in the literature and future directions for research in this domain.

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