Understanding job satisfaction in the causal attitude network (CAN) model.

Job satisfaction researchers typically assume a tripartite model, suggesting evaluations of the job are explained by latent cognitive and affective factors. However, in the attitudes literature, connectionist theorists view attitudes as emergent structures resulting from the mutually reinforcing causal force of interacting cognitive evaluations. Recently, the causal attitudes network (CAN; Dalege et al., 2016) model was proposed as an integration of both these perspectives with network theory. Here, we describe the CAN model and its implications for understanding job satisfaction. We extend the existing literature by drawing from both attitude and network theory. Using multiple data sets and measures of job satisfaction, we test these ideas empirically. First, drawing on the functional approach to attitudes, we show the instrumental-symbolic distinction in attitude objects is evident in job satisfaction networks. Specifically, networks for more instrumental features (e.g., pay) show stable, high connectivity and form a single cluster, whereas networks regarding symbolic features (e.g., supervisor) increase in connectivity with exposure (i.e., job tenure) and form clusters based on valence and cognitive-affective distinction. We show these distinctions result in "small-world" networks for symbolic features wherein affective reactions are more central than cognitive reactions, consistent with the affective primacy hypothesis. We show the practical advantage of CAN by demonstrating in longitudinal data that items with high centrality are more likely to affect change throughout the attitude network, and that network models are better able to predict future voluntary turnover compared with structural equation models. Implications of this exciting new model for research and practice are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).