Defining the common feature: task-related differences as the basis for dyadic identity

In this paper, we present an experimental study that examines the influence of work-goal differences and informational differences on the formation of a common identity in dyads. We show that when both kinds of differences are present within a dyad, these differences-- just like similarities--come to be seen as defining dyadic features. Furthermore, mediational analysis shows that as the accumulation of differences results in a clearer conception of the dyad, it fosters dyadic identification. This is not the case when dyad members only differ from each other in one respect (i.e. either in work goals or information), while remaining similar in the other. The results are explained in the light of recent insights and developments in the social identity tradition

Volunteering for charity: pride, respect, and the commitment of volunteers

This study builds upon and extends the social-identity-based model of cooperation with the organization (T. R. Tyler, 1999; T. R. Tyler & S. L. Blader, 2000) to examine commitment and cooperative intent among fundraising volunteers. In Study 1, structural equation modeling indicated that pride and respect related to the intent to remain a volunteer with an organization, and that this relation was mediated primarily by normative organizational commitment. In Study 2, structural equation modeling indicated that the perceived importance of volunteer work was related to pride, that perceived organizational support related to the experience of respect, and that pride and respect mediated the relation between perceived importance and support on the one hand and organizational commitment on the other. Overall, the results suggest that volunteer organizations may do well to implement pride and respect in their volunteer policy, for instance to address the reliability problem (J. L. Pearce, 1993)

Morality and Social Identity

Moral norms and values are key features of human essence, that provide the standards against which behavior is evaluated. Some moral norms and values are universally endorsed (e.g., “do no harm”), others can be more specific (e.g., “eat no meat”). Professional, cultural or religious groups and communities often define their own unique system of moral norms that true group members are expected to adhere to. These are used to identify ‘proper’ group members, regulate the behavior of individuals, and sanction those who transgress them. This is functional to the extent that such guidelines help provide groups and their members with a unique and distinct social identity. Yet they can also constitute a source of social tension and intergroup conflict. This hallmark feature of human morality represents an important challenge to contemporary societies

Temporal networks in collaborative learning: A case study

Social Network Analysis (SNA) has enabled researchers to understand and optimize the key dimensions of collaborative learning. A majority of SNA research has so far used static networks, ie, aggregated networks that compile interactions without considering when certain activities or relationships occurred. Compressing a temporal process by discarding time, however, may result in reductionist oversimplifications. In this study, we demonstrate the potentials of temporal networks in the analysis of online peer collaboration. In particular, we study: (1) social interactions by analysing learners' collaborative behaviour, part of a case study in which they worked on academic writing tasks, and (2) cognitive interactions through the analysis of students' self-regulated learning tactics. The study included 123 students and 2550 interactions. By using temporal networks, we show how to analyse the longitudinal evolution of a collaborative network visually and quantitatively. Correlation coefficients with grades, when calculated with time-respecting temporal measures of centrality, were more correlated with learning outcomes than traditional centrality measures. Using temporal networks to analyse the co-temporal and longitudinal development, reach, and diffusion patterns of students' learning tactics has provided novel insights into the complex dynamics of learning, not commonly offered through static networks