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Organizational identification links together organizational and member identity, yet we currently lack theory explicating the role of organizational and member identity variations in members' evaluations of organizations as identification targets. In this theoretical paper, I outline a model of organizational identification that aims to do three things—account for the role of identity in the identification process, integrate and extend disparate approaches to organizational identification, and illuminate social comparison processes underlying members' organizational evaluations. The model proposes that members undertake two identity comparisons to assess the value of organizational membership for identification purposes. In one, they compare the organization's current identity with their own identity, allowing them to assess the organization's ability to meet their motivation for self-continuity. In the other, they compare the organization's current identity with its expected identity, allowing them to assess the organization's ability to meet their motivation for self-esteem. After introducing the identity congruence framework, I apply to it the identity orientation lens to make specific predictions about how organizational and member identity shape the nature and outcomes of the specific social comparison content drawn upon in each of the two identity comparisons. This analysis reveals how the metrics used to evaluate organizations fundamentally vary by organizational and member identity. Implications for organizational studies are addressed, including those related to organizing and stakeholder theory.

This book explores the policy and practice possibilities offered by a social model of child protection. Drawing on developments in mental health and disability studies, it examines the conceptual, political and practice implications of this new framework.

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For single networks, the older literature concentrated on conditionally uniform models. Various types of latent space models have been developed: for discrete, general metric, ultrametric, Euclidean, and partially ordered spaces. Exponential random graph models were proposed long ago but now are applied more and more thanks to the non-Markovian social circuit specifications that were recently proposed. Modeling network dynamics is less complicated than modeling single network observations because dependencies are spread out in time. For modeling network dynamics, continuous-time models are more fruitful. Actor-oriented models here provide a model that can represent many dependencies in a flexible way. Strong model development is now going on to combine the features of these models and to extend them to more complicated outcome spaces.