Emotional labor, occupational identity and work engagement in Portuguese police officers

AbstractAbstractBased on emotional labor theory, we aim to study the relationships between the dimensions of emotional labor (requirements and strategies), work engagement, and occupational identity in Portuguese police officers. Therefore, we intend to explore the possible effects of emotional labor both on work engagement and occupational identity, as well as ways of preventing and/or mitigating the impact of these relationships. We identified a gap in the studies on this subject in Portugal, particularly in the context of police professionals. Thus, a sample of 924 Portuguese police officers of the Public Security Police (PSP) was asked to answer a set of questionnaires: the Emotional Labour Scale, the Emotion Work Requirements Scale, the Utrecht Work Engagement Scale, and the Social Identity Scale. The data obtained was analyzed using correlation and multiple linear regression. Overall, the results revealed relationships between the emotional demands (i.e., suppression of negative emotions and expression of positive emotions) and strategies (i.e., deep and surface acting) of emotional labor and work engagement as well as occupational identity. We discuss these results and propose directions for future research, given the richness of the subject.Keywords: Emotional laborwork engagementoccupational identitypolice officersPSPintervention/prevention Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe data that support the findings of this study are available from the corresponding author, upon reasonable request.Additional informationFundingThis work has been funded by national funds through Fundação para a Ciência e a Tecnologia (FCT), I.P., Project UIDB/05037/2020.Notes1 “Factor analysis partitions the variance of each indicator (derived from the sample correlation/ covariance matrix) into two parts: (1) Common variance or the variance accounted for by the factor, which is estimated based on variance shared with other indicators in the analysis; and (2) unique variance, which is a combination of reliable variance that is specific to the indicator (i.e., systematic factors that influence only one indicator) and random error variance (i.e., measurement error or unreliability in the indicator)” (Brown, 2015 Brown, T. A. (2015). Confirmatory factor analysis for applied research [internet] (2nd ed.). The Guilford Press. www.guilford.com/MSS [Google Scholar], p. 11). It can be said that if the factor loading is .75, the observed variable explains the latent variable variance of (.75^2 = .56) 56%. It is a good measure. If the factor loading is .40, it explains a 16% variance. As a cut point, .33-factor loading can be given, because explains 10% variance.