Emotional Labour and Perceived Stress at Workplace—HR Analytics

Abstract

Human resource (HR) analytics is a technique for collecting and analysing human resource data to improve organisation performance. Emotional labour is the psychological work individuals perform in conforming to emotional display rules in a given environment. The perceived stress at the workplace and emotional labour at the workplace are known to have negative health consequences for employees and compromise the performance of organisations. The study with these metrics significantly influences decision-making for individuals and businesses. Data from serving officers with twenty years of experience from the Armed Forces and Civil Services revealed emotional labour and demand-resource work stress by both the services at varying levels. Multi-supervised machine learning techniques like logistic regression (LR), Naive Bayes (NB), support vector machine (SVM) and decision tree have are considered to build a model. The data collected from Armed Forces and Civil Service officers were used for features extraction. The dataset was loaded into the model to identify, classify and analyse the emotional labour and perceived workplace stress. Findings based on the display of emotional behaviour at the workplace and the perceived workplace stress by the employees were plotted. The model’s performance was evaluated using a variety of machine learning techniques. This study is probably one of the few projects to report the correlation between emotional labour and stress at the workplace. The data revealed emotional labour, primarily as deep acting, in 76.1 and 72.2% of officers of the two services, respectively. The concept can be developed further to investigate the effects of occupational stress on health metrics and the changing priorities of the people in the wake of prevailing pandemic and rise in attritions rate.