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) 2019 APA, all rights reserved).

中文翻译：

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了解因果态度网络 (CAN) 模型中的工作满意度。

工作满意度研究人员通常假设一个三方模型，表明对工作的评估可以通过潜在的认知和情感因素来解释。然而，在态度文献中，联结主义理论家将态度视为由相互作用的认知评估的相互增强的因果力产生的新兴结构。最近，因果态度网络（CAN；Dalege 等人，2016 年）模型被提出，作为这两种观点与网络理论的整合。在这里，我们描述了 CAN 模型及其对理解工作满意度的影响。我们通过借鉴态度和网络理论来扩展现有文献。使用多个数据集和工作满意度的衡量标准，我们凭经验测试这些想法。首先，借鉴态度的功能方法，我们展示了态度对象的工具符号区别在工作满意度网络中很明显。具体来说，更多工具性特征（例如薪酬）的网络表现出稳定、高连接性并形成单个集群，而有关象征性特征（例如主管）的网络与暴露（即工作任期）的连接性增加并基于效价形成集群和认知-情感的区别。我们展示了这些区别导致符号特征的“小世界”网络，其中情感反应比认知反应更重要，与情感首要假设一致。我们通过在纵向数据中证明具有高中心性的项目更有可能影响整个姿态网络的变化来展示 CAN 的实际优势，并且与结构方程模型相比，网络模型能够更好地预测未来的自愿离职。讨论了这种令人兴奋的研究和实践新模型的影响。（PsycINFO 数据库记录 (c) 2019 APA，保留所有权利）。