Affective events theory as a theoretical lens for improving the working environment of academics in developing economies

Abstract

PurposeThe rapid pace of progress in academic institutions in developing economies has created stressful and relatively toxic workplaces, resulting in different negative organizational outcomes indicating the need to transform universities into healthier academic workplaces. However, a review of the higher education literature in both developed and developing countries shows that the antecedents and consequences of academics' affective states has been a relatively unexplored area. Hence, our study aims at testing basic tenets of Affective Events Theory (AET) in a higher education context to address this issue.Design/methodology/approachThis is a quantitative study which applies CB-SEM methodology in analyzing the collected data from 2,324 academics in Malaysian higher education sector. We analyzed the data using EQS software package.FindingsOur results provided substantial support for the applicability and relevancy of AET in higher education domain. Specifically, welfare and supervisory support were identified as the two work environment features which significantly and equally contribute to academics' job satisfaction. In addition, the results showed that positive affect, in comparison with negative affect, was three times stronger in influencing academics' job satisfaction.Practical implicationsGiven the considerable role of positive affect in our study, higher education policy makers are urged to make relevant policies to transform universities into more emotionally safe workplaces. In addition, policies should be formulated in a way that encourages supervisory support and decreases workloads to ensure that the conflicts in general are reduced among academics.Originality/valueThis work is the first large-scale study testing the main tenets of AET in the higher education context. In addition, it addresses the problem of multivariate normality and solves this problem based on the robust methodology which corrects standard errors and fit indices, thereby providing more precise and unbiased results.