Emotion Regulation in the Context of Customer Mistreatment and Felt Affect: An Event-Based Profile Approach

• 成果类型: Article
• 署名作者: Diefendorff, James M.; Gabriel, Allison S.; Nolan, Megan T.; Yang, Jixia
• 署名单位: University System of Ohio; University of Akron; University of Arizona; City University of Hong Kong
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0000389
• 发表日期: 2019
• 页码: 965-983
• 关键词: affect; EMOTIONAL LABOR; emotion regulation; EXPERIENCE SAMPLING METHODOLOGY; multilevel profile analysis

摘要

Variable-centered views of emotional labor suggest that high customer incivility and employee-felt negative affect should co-occur with high employee emotion regulation. Similarly, low customer incivility and employee positive affect should be accompanied by low emotion regulation. We theorize that these theory-based configurations of emotional labor variables represent only a subset of the possible ways that emotional labor events unfold. We propose that there are distinct subpopulations of emotional labor events, some of which conform to this standard view of emotional labor and some of which deviate from this model and that these distinct configurations suggest different underlying theoretical processes with implications for employee well-being. To investigate these ideas, we adopt an event-centered view (i.e., event-level profiles) that seeks to identify distinct configurations of emotional labor events. In a sample of 246 call center employees who provided ratings of 7,331 customer service interactions, results from multilevel latent profile analysis (MLPA) revealed 8 distinct event-level profiles, some of which align with variable-centered approaches and some of which suggest new ways to think about such events. We then linked these profiles to the event-level well-being outcomes of emotional exhaustion and psychological vitality, showing both longitudinal and concurrent effects. Finally, supplemental analyses detailed how this event-level profile approach differed from standard variable-centered analyses.