Two methods (quantitative and qualitative) were conducted to investigate the objectives (research questions) of this study. In addition, the intensity of performance monitoring had a negative impact on the overall well-being of call center employees. Role conflict and positive well-being - (moderating effect of self-efficacy group with high self-efficacy score - N=134).
Role Conflict and Positive Well-Being- (Moderating Effect of Self-Efficacy - Low Self-Efficacy Group - N=117). Role Ambiguity & Role Conflict and Home Adjustment (Moderating Effect of Self-Efficacy - Low Self-Efficacy Group - N=117). Role Ambiguity & Role Conflict and Perceived Stress (Moderating Effect of Peer Support - Low Peer Support Score Group - N=131).
Introduction
The primary purpose of this study is to investigate the effects of work factors (job demands) on the workload of the call center employees. The health and well-being of employees has always been a core factor that must be handled in most of the successful organizations. A review of the literature suggests that four factors have a significant effect on the well-being of call center representatives (Holman, 2004).
Well-being is one of the most studied constructs in the social sciences, especially in psychology. This study will investigate the impact of different job demands on the job satisfaction of call center employees. Some of the call center studies used measures of role conflict and role ambiguity to assess stress in call centers.
This study will investigate the effects of these two dimensions of performance monitoring on the stress and well-being of call center employees (agents). The main objective of this study is to analyze the effects of different job demands - role conflict, emotional labor and performance monitoring on the stress and well-being of call center employees. Furthermore, what are the effects of different work factors (exogenous and endogenous) on stress, adjustment, job satisfaction and well-being of call center employees.
What role does social support (supervisory and co-worker support) play in relationships of job demands with stress and well-being in call center employees. However, most of the studies report a positive relationship of emotional labor with stress and a negative relationship with well-being. Hypothesis 4E: (i) Self-efficacy will reduce the effect of intensity of performance monitoring on positive well-being, and (ii) it will increase the effect of purpose of performance monitoring on positive well-being.
Hypothesis 6E: Self-efficacy will reduce the effect of role ambiguity and role conflict on positive well-being. Hypothesis 9E: Supervisory support will reduce the effects of role ambiguity and role conflict on positive well-being. Hypothesis 10E: (i) Coworker support will reduce the effects of performance monitoring intensity on positive well-being, and (ii) it will increase the effect of performance monitoring goal on positive well-being.
Hypothesis 12E: Coworker support will moderate the effects of role ambiguity and role conflict on positive well-being. It discusses various work factors in the call centers with regard to the health and well-being of the call center.
As mentioned earlier, the main objective of this research work is to investigate the factors of mental health and well-being of call center employees. Strain in this study was operationalized in terms of employees' perceived level of stress and emotional exhaustion. We operationalized the well-being of employees in terms of the positive dimension of well-being, i.e. positive well-being.
The dimensions of the scale are measured by a five-point likert type scale ranging from '1= never' to '5= always'. Factor analysis of the data was performed using the principal component with varimax rotation method. The factor analysis of the performance monitoring items resulted in two factors, that is, Intensity of Performance Monitoring and Purpose of Performance Monitoring.
The reason for dropping this item was its marginal correlation with other items in the scale (Kelloway & Barling, 1990). Factor analysis of the data revealed four factors, namely role ambiguity, inter-resource and intra-role conflicts, conflicting organizational demands and resources, and incompatible policies and role overload. Factor loadings and correlation of factors and psychometric properties of the scale are given in Table 2.7 and Table 2.8, respectively.
Employee well-being was assessed by six dimensions of the positive well-being scale, where each dimension consists of 9 elements. Factorial analysis of the items was performed using the principle component with the varimax rotation method. A 4-point Likert-type scale ranging from 1 (not at all true) to 4 (exactly true) was used to measure individuals' responses.
Greenhaus, Parasuraman and Wormley (1990) developed this scale to assess employees' perceptions of the extent to which they receive supervisory support in their work. This measure has been widely used and has remained one of the most established scales used to measure social support in work settings (Lim, 1996). Following factor analysis and labeling of the factors, correlation analyzes were performed to establish relationships between factors of independent variables and factors of dependent variables.
Results
Sources and intra-role conflict dimension of role ambiguity and role conflict also correlated positively with perceived stress. The dimensions of conflicting organizational demands and resources of role ambiguity and role conflict were positively related to emotional exhaustion. The sources and intrarole conflict dimension of role ambiguity and role conflict also correlated negatively with both dimensions of job satisfaction.
In sum, among the dimensions of three independent variables, intensity of performance monitoring emerged as the best predictor (β = -.38, p < .00) of dissatisfaction on the self-dimension of positive well-being. As is clear from Table 3.7, these are (1) the Intensity of Performance Monitoring dimension of performance monitoring, the Organization's Conflicting Demands and Resources dimension of role ambiguity and role conflict, and area. In summary, among the dimensions of three independent variables, Intensity of Performance Monitoring emerged as the best predictor (β = -.27, p < .00) of the Lack of Mastery and Relationships dimension of positive well-being.
In short, among the dimensions of 3 independent variables, Intensity of Performance Monitoring emerged as the best predictor (β = 0.26, p < 0.00) of home adjustment. In short, among the dimensions of 3 independent variables, Intensity of Performance Monitoring emerged as the best predictor (β = 0.34, p < 0.00) of home adjustment. In short, among the dimensions of 3 independent variables, Intensity of Performance Monitoring emerged as the best predictor (β = -.32, p <.00) of personal stagnation dimension of positive well-being.
In conclusion, among the dimensions of the 3 independent variables, the intensity of performance monitoring emerged as the best predictor (β = -.35, p < .00) of the self-dissatisfaction dimension of positive well-being. In sum, among the dimensions of the 3 independent variables, performance monitoring intensity emerged as the best predictor (β = -.26, p <.00) for the lack of mastery and attitudes dimension of positive well-being. In sum, among the dimensions of the 3 independent variables, performance monitoring intensity emerged as the best predictor (β = -.28, p <.00) of the intention and positive attitude dimensions of positive well-being.
Briefly, among the dimensions of the 3 independent variables, Performance Monitoring Goal emerged as the best predictor (β = .29, p < .00) of the Personal Stagnation dimension of positive well-being. The role ambiguity dimension of role ambiguity and role conflict were positively correlated with self-dissatisfaction. 135 In short, among the dimensions of the 3 independent variables, Performance Monitoring Intensity emerged as the best predictor (β = -.31, p < .00) of the self-dissatisfaction dimension of positive well-being.
Further, the resource and intrarole conflict dimension of role ambiguity and role conflict also correlated positively with emotional exhaustion. In short, among the dimensions of 3 independent variables, Intensity of Performance Monitoring emerged as the best predictor (β = 0.35, p < 0.00).
