Emotion work and well-being of human resource personnel in a mining industry
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Human Resource personnel as part of their daily jobs provide a service to other employees within a mining industry. These service workers may experience dissonance between their actual feelings and the feelings they are expected to display. For these service workers to be more engaged at work, emotional intelligence and social support is vital. If these factors are not in place, their well-being may be in jeopardy. The objective of this research was to determine the relationship between Emotion Work, Emotional Intelligence, Well-being and Social Support of service workers in a human resource field within a mining industry. A cross-sectional survey design was used. The study population (n = 229) consisted of human resource personnel in the Limpopo and North West Province. The Greek Emotional Intelligence Scale (GEIS), Frankfurt Emotion Work Scales, Utrecht Work Engagement Scale, Oldenburg Burnout Inventory and Social Support Scale, as well as a biographical questionnaire, were used as measuring instruments. Cronbach alpha coefficients, factor analysis, inter-item correlation coefficients, Pearson product moment correlation coefficient and stepwise multiple regression analysis were used to analyse the data. An analysis of the data indicated that correlations between the following constructs are statistically and practically significant. The results show that Positive Display is statistically and positively practically significantly related (medium effect) to Interaction Control. aring/Empathy is positively practically significantly related to Positive Display (medium effect). Furthermore the Control of Emotions (medium effect) and Emotion Management (large effect) are both positively practically significantly related to Caring/Empathy. Emotional Resilience however is negatively practically significantly related to Caring and Empathy (medium effect). Emotion Expression Recognition is positively practically significantly related to Control of Emotion (medium effect). However, both Exhaustion (medium effect) and Emotional Resilience (medium effect) are negatively practically significantly related to Control of Emotions. Engagement is positively practically significant (medium effect) to Emotion Management. Emotion Resilience (medium effect) positively correlates with Exhaustion while Engagement (medium effect) negatively correlates with Exhaustion. Engagement positively practically correlates with Resilience (medium effect). Social Support of both supervisor and co-workers positively relates to engagement to a medium effect. Principal component analysis performed on the GEIS resulted in a four-factor solution. The first factor was Caring and Empathy, which includes the willingness of an individual to help other people and understand others' feelings. The second factor was Control of Emotion, which is the ability of the individual to control and regulate emotions within themselves and others. Emotion Expression/Recognition, which is the ability of the individual to express and recognise his or her own emotional reactions, was the third factor, and the fourth was Emotion Management, which is the ability of an individual to process emotional information with regard to perception, assimilation, understanding and management of emotions. All four factors correlate with that of the GEIS originally developed by Tsaousis (2007) and accounted for 31% of the total variance in emotional intelligence. A Multiple Regression Analysis with Exhaustion as dependent variable was carried out. The results show that Emotion Work factors accounted for 2% of the total variance and Emotional Intelligence factors for 12% of the total variance. More specifically it seems that the lack of Caring and Empathy and Emotion Management predicted Exhaustion in this regard. However, when Emotional Intelligence factors were entered into the model, an increase of 10% variance was shown of the variance explained in Exhaustion. Emotion Work, Emotional Intelligence and Social Support predicted 14% of the variance explained in the level of Exhaustion by participants. A Multiple Regression analysis with Emotional Resilience as dependent variable was carried out. The results show that Emotion Work factors accounted for 6% of the total variance. More specifically; it seems that Dissonance predicted the level of Emotional Resilience. When Emotional Intelligence factors were entered into the model, an increase of 15% was shown. Caring and Empathy and Control of Emotions predicted Emotional Intelligence the best. Lastly, when Social Support factors were entered into the regression analysis, the variance explained showed an increase of 5%. Support of Family and Others predicted Emotional Resilience the best. In total, Emotion Work, Emotional Intelligence and Social Support factors explained 20% of the variance in Emotional Resilience. A Multiple Regression Analysis with Engagement as dependent variable with Emotion Work factors, Emotional Intelligence factors and Social Support as predictors of Engagement was done. Entry of Emotion Work factors at the first step of the regression analysis did not produce a statistically significant model and only accounted for 1% of the variance. However, when Emotional Intelligence factors were entered in the second step of the analysis, it accounted for approximately 7% of the variance. More specifically, it seems that Caring and Empathy predicted Engagement. When Social Support factors were entered into the third step of the analysis, an increase of 27% was found. All the Social Support factors (Social Support of Family and Others, Supervisors and Co-workers) accounted for 27% of the variance explained in Engagement. Emotion Work, Emotional Intelligence and Social Support predicted 33% of the total variance explained in the level of Engagement. Limitations within the study were identified, and recommendations were made for human resource personnel in a mining industry, as well as for future research.