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Organizational identity provides an increasingly large number of researchers with a theoretical lens for examining current transformations of the university. The primary objective of this chapter is to report an extensive, systematic overview of the literature published on the subject between 1972 and 2014. The analysis of 120 empirical studies reveals a literature which is rich but dispersed, in theoretical, epistemological, and methodological terms. Thriving since the 2000s, it is mainly American but increasingly globalized. After identifying six main research categories according to the distinctions found in the organizational identity literature, we propose a series of avenues for discussion bearing on the status of identity as an indicator of changes at work in the university, their level and depth.

The study of humor has a long tradition in philosophy, sociology, psychology, anthropology, and communications. Evidence from these fields suggests that humor can have effects on creativity, cohesiveness, and performance, but organizational scholars have paid it relatively little attention. We hope to “jump-start” such a research program. To do this, we first outline the theoretical rationale underlying the production and appreciation of humor, namely, its motivational, cognitive, and emotional mechanisms. Next, we review the literature linking humor to creativity, cohesiveness, and other performance-relevant outcomes. In particular, we note how this literature is theoretically well-grounded, but that the empirical findings are largely correlational and/or based on qualitative research designs. Finally, we go beyond the current humor literature by developing specific predictions about how culture might interact with humor in organizational contexts. Throughout the paper, we discuss possible research directions and methodological issues relevant to the study of humor in organizations.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.