In the previous chapter methodology and measures of the present study were reported. As described, a questionnaire was developed which included all the measures for data collection. Data were collected from various call centre organizations. In this chapter results of the analyses of data are reported. The factor analyses results and psychometric properties of various measures have been reported in the chapter 2.
The main domain of this study is to examine the relationship between various work factors and their impact on the stress, job satisfaction, and well- being of the employees. The other variables (namely, self-efficacy, supervisory support, and coworker support) are taken into account as they also influence the relationships of work factors and its consequences either directly or indirectly.
A model of the possible dynamics of variables based on the literature review has also been shown in chapter 1. Thus in this chapter the relationship as per the model would be tested and established. This analysis will also help to answer the research questions.
For the convenience of the readers, research questions are being reproduced here:
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1. How monitoring of calls induces or increases stress and emotional exhaustion in call center employees and how it affects their job satisfaction, home adjustment and positive well-being?
2. How emotional labor affects (increases) employee stress and emotional exhaustion and (decreases) job satisfaction, home adjustment and positive-well-being?
3. How role ambiguity and role conflict affects (increases) stress and emotional exhaustion in employees and how it affects their job satisfaction, home adjustment and positive well-being?
4. Whether self-efficacy belief plays a moderating role between relationships of work demands and stress and well-being of call centre employees?
5. What role social support (supervisory and coworker support) play among relationships of the work demands with stress and well-being in call centre employees?
6. How unusual working condition in the call center affects employees’
adjustment (home) and job satisfaction and how it increases stress and emotional exhaustion?
The above research questions were based on two models to study work stress namely, Demand-control-support model (Karasek, 1979; Johnson and Hall, 1988) and Transaction model of stress (Lazarus and Folkman, 1984). The
93 results will help to test these theoretical approaches of work stress, to find whether they are successful in explaining work stress in a call centre setting.
In order to answer the research questions raised in chapter 1, data were analyzed using the following scheme:
Two types of analyses- correlation analysis and stepwise multiple regression analyses were carried out to see the relationships between work demands and mental health and well-being of the call centre employees.
Correlation analysis were carried out considering the factors of emotional labor (4; Intensity with Variety, Frequency with Deep Acting, Variety with Deep Acting, and Surface Acting), performance monitoring (2; Intensity of Performance Monitoring, Purpose of Performance Monitoring), and role ambiguity and role conflict (4; Role Ambiguity, Resource and Intra-role Conflict, Conflicting Organizational Demands and Resources, Incompatible Policies and Role Overload), and Perceived stress. Correlation analysis were also carried out to examine the moderating effects of three variables namely, self-efficacy, supervisory support, and coworker support on the relationship between independent and dependent variables. At the next level stepwise regression analysis was also done to investigate possible predictors of criterion variables.
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Relationships of Emotional Labor, Performance Monitoring, and Role Ambiguity and Role Conflict with Perceived Stress
Correlation Results
Correlation between Emotional Labor and Perceived Stress
It can be observed from the Table 3.1, that only one dimension of emotional labor, i.e., Surface Acting had significant positive correlation with perceived stress.
Correlation between Performance Monitoring and Perceived Stress
It is evident from Table 3.1, that one dimension of performance monitoring, i.e., Intensity of Performance Monitoring had significant positive correlation with perceived stress. Purpose of Performance Monitoring did not correlate with perceived stress.
Correlation between Role Ambiguity and Role Conflict and Perceived Stress
It can be observed from Table 3.1, that Role Ambiguity dimension of role ambiguity and role conflict had significant positive correlation with perceived stress. Resource and Intra-role Conflict dimension of role ambiguity and role conflict also correlated positively with perceived stress. Conflicting Organizational Demands and Resources, and Incompatible Polices and Role Overload dimensions of role ambiguity and role conflict did not correlate with perceived stress.
95 Stepwise Multiple Regression Analysis Results
For analyzing the relative effect of independent variables on perceived stress, stepwise multiple regression analysis was carried out considering factors of emotional labor (4 factors: Intensity with Variety, Frequency with Deep Acting, Variety with Deep Acting and Surface Acting), Performance monitoring (2 factors: Intensity and purpose), and Role ambiguity and role conflict (4 factors: Role Ambiguity, Resource and Intra-role Conflict, Conflicting Organizational Demands and Resources, and Incompatible Policies and Role Overload) as independent variables and perceived stress as dependent variable.
As evident from Table 3.3, that (1) Resource and Intra-role Conflict, and Incompatible Policies and Role Overload dimensions of role ambiguity and role conflict and Intensity of Performance Monitoring dimension of performance monitoring were significant predictors of perceived stress; (2) the maximum variance of 7% was explained by Resource and Intra-role Conflict, while Intensity of Performance Monitoring explained 4% of the variance; and Incompatible Policies and Role Overload explained 2% of the variance; (3) Moreover Resource and Intra-role Conflict was found to have positive relationship (β = .24, p < .00) with perceived stress. Intensity of Performance Monitoring also showed positive relationship with perceived stress (β).
Incompatible Policies and Role Overload dimension of role ambiguity and role conflict have shown a negative relationship with perceived stress (β).
In short, among the dimensions of independent variables, Resource and Intra-role Conflict emerged as the best predictor (β = .24, p < .00) of perceived stress.
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Relationships of Emotional Labor, Performance Monitoring, and Role Ambiguity and Role Conflict with Emotional Exhaustion
Correlation Results
Correlation between Emotional Labor and Emotional Exhaustion
It can be observed from Table 3.1, that two dimensions of emotional labor, i.e., Intensity with Variety, and Variety with Deep Acting exhibited significant positive relationship with emotional exhaustion.
Correlation between Performance Monitoring and Emotional Exhaustion
As evident from Table 3.1, that the relationship of Intensity of Performance Monitoring dimension of performance monitoring with emotional exhaustion was found highly significant and positive. Purpose of Performance Monitoring dimension of performance monitoring did not correlate with emotional exhaustion, but the insignificant relationship was negative.
Correlation between Role Ambiguity and Role Conflict and Emotional Exhaustion
It is observed from Table 3.1, that Resource and Intra-role Conflict dimension of role ambiguity and role conflict had significant positive correlation with emotional exhaustion. Conflicting Organizational Demands and Resources dimension of role ambiguity and role conflict correlated positively with emotional exhaustion. Role Ambiguity, and Incompatible
97 Polices and Role Overload dimensions of role ambiguity and role conflict did not correlate with emotional exhaustion.
Stepwise Multiple Regression Analysis Results
In order to observe the relative effect of independent variables on emotional exhaustion, stepwise multiple regression analysis was carried out considering factors of emotional labor (4 factors: Intensity with Variety, Frequency with Deep Acting, Variety with Deep Acting and Surface Acting), Performance monitoring (2 factors: Intensity and purpose) and Role ambiguity and role conflict (4 factors: Role Ambiguity, Resource and Intra-role Conflict, Conflicting Organizational Demands and Resources, and Incompatible Policies and Role Overload) as independent variables and emotional exhaustion as dependent variable.
It can be observed from Table 3.4, that (1) Intensity of Performance Monitoring dimension of performance monitoring, Resource and Intra-role Conflict dimension of role ambiguity and role conflict, Variety with Deep Acting and Frequency with Deep Acting dimensions of emotional labor were significant predictors of emotional exhaustion; (2) the maximum variance, that is, 9% was explained by Intensity of Performance Monitoring, while Resource and Intra-role Conflict explained 4% variance; (3) Variety with Deep Acting, and Frequency with Deep Acting explained 2% and 1% variance respectively;
(4) Further, Intensity of Performance Monitoring, Resource and Intra-role Conflict, and Variety with Deep Acting showed a positive relationship with emotional exhaustion, while Frequency with Deep Acting of has recorded negative relationship with emotional exhaustion.
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In short, among the dimensions of 3 independent variables, Intensity of Performance Monitoring turnout as the best predictor (β = .22, p < .00) of emotional exhaustion.
Relationships of Emotional Labor, Performance Monitoring, and Role Ambiguity and Role Conflict with Job Satisfaction
Correlation Results
Correlation between Emotional Labor and Job Satisfaction
It is evident from the Table 3.1, that one dimension of emotional labor- Frequency with Deep Acting had significant positive correlation with only one dimension job satisfaction, i.e., Social-Intrinsic Satisfaction. Surface Acting as a dimension of emotional labor correlated negatively with only one dimension of job satisfaction, i.e., Extrinsic-Intrinsic Satisfaction. Other two dimensions of emotional labor- Intensity with Variety, and Variety with Deep Acting did not correlate with any dimension of job satisfaction.
Correlation between Performance Monitoring and Job Satisfaction
It can be observed from Table 3.1, that Intensity of Performance Monitoring dimension of performance monitoring had significant negative correlations with both the dimensions of job satisfaction, i.e., Social-Intrinsic Satisfaction and Extrinsic-Intrinsic Satisfaction. Further, Purpose of Performance Monitoring correlated positively only with Social-Intrinsic Satisfaction.
99 Correlation between Role Ambiguity and Role Conflict and Job Satisfaction
It is evident from Table 3.1, that Role Ambiguity dimension of the role ambiguity and role conflict had significant negative correlations with both the dimensions of job satisfaction- Social-Intrinsic satisfaction and Extrinsic- Intrinsic Satisfaction. Resource and Intra-role Conflict dimension of role ambiguity and role conflict also correlated negatively with both the dimensions of job satisfaction. Further, Conflicting Organizational Demands and Resources dimension of role ambiguity and role conflict had significant negative correlations only with Social-Intrinsic Satisfaction. Incompatible Policies and Role Overload dimension of role ambiguity and role conflict did not correlate with any dimension of job satisfaction.
Stepwise Regression Analysis Results
In order to analyze the relative effect of independent variables on job satisfaction stepwise multiple regression analysis was carried out considering factors of emotional labor (4 factors: Intensity with Variety, Frequency with Deep Acting, Variety with Deep Acting and Surface Acting), Performance monitoring (2 factors: Intensity and purpose) and Role ambiguity and role conflict (4 factors: Role Ambiguity, Resource and Intra-role Conflict, Conflicting Organizational Demands and Resources, and Incompatible Policies and Role Overload) as independent variables, whereas dependent variable was factors of job satisfaction (Social-Intrinsic Satisfaction, and Extrinsic-Intrinsic Satisfaction).
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Predictors of Social-Intrinsic Satisfaction
It can be observed from Table 3.5, that (1) Resource and Intra-role Conflict, and Role Ambiguity dimensions of role ambiguity and role conflict, and Intensity of Performance Monitoring were the significant predictors of Social-Intrinsic Job Satisfaction; (2) the maximum variance, that is, 9% was explained by Resource and Intra-role Conflict dimension of role ambiguity and role conflict followed by Role Ambiguity which explained 6% variance with respect to Social-Intrinsic dimension of job satisfaction; (3) Intensity of Performance Monitoring explained 5% variance in Social-Intrinsic Job Satisfaction; (4) Further, all the predictors were found to have negative association with Social-Intrinsic Job Satisfaction.
In short, among the dimensions of 3 independent variables, Role Ambiguity proved to be the best predictor (β = -.27, p < .00) of Social-Intrinsic Satisfaction.
Predictors of Extrinsic-Intrinsic Satisfaction
It is evident from Table 3.5, that (1) Intensity of Performance Monitoring dimension of performance monitoring, Resource and Intra-role Conflict and Role Ambiguity dimensions of role ambiguity and role conflict were the significant predictors of Extrinsic-Intrinsic Job Satisfaction; (2) The maximum variance, that is, 6% was explained by Intensity of Performance Monitoring with respect to Extrinsic-Intrinsic dimension of job satisfaction, while Resource and Intra-role Conflict and Role Ambiguity explained 2%
variance each, for the Extrinsic-Intrinsic Job Satisfaction; (3) Further, all the predictors have shown a negative impact on Extrinsic-Intrinsic Job Satisfaction.
101 In short, among the dimensions of 3 independent variables, Intensity of Performance Monitoring emerged as the best predictor (β = -.20, p < .00) of Extrinsic-Intrinsic Job Satisfaction.
Overall, Intensity of Performance Monitoring, Role Ambiguity and Resource and Intra-role Conflict were turned out to be the significant predictors of overall job satisfaction. Among the dimensions of dependent variable (job satisfaction), Social-Intrinsic Satisfaction was the best predicted factor.
Relationships of Emotional Labor, Performance Monitoring, and Role Ambiguity and Role Conflict with Home Adjustment
Correlation Results
Correlation between Emotional Labor and Home Adjustment
It can be observed from Table 3.1, that Intensity with Variety dimension of emotional labor had significant positive correlation with home adjustment.
Two other dimensions of emotional labor Variety with Deep Acting, and Surface Acting also correlated positively with home adjustment. Only one dimension of emotional labor, i.e., Frequency with Deep Acting did not correlate with home adjustment.
Correlation between Performance Monitoring and Home Adjustment
It is evident from Table 3.1, that, Intensity of Performance Monitoring dimension of performance monitoring had significant positive correlation with TH-988_04614102
home adjustment, whereas Purpose of Performance Monitoring dimension of performance monitoring correlated negatively with home adjustment.
Correlation between Role Ambiguity and Role Conflict and Home Adjustment
It can be observed from Table 3.1, that Role Ambiguity dimension of role ambiguity and role conflict had significant positive correlation with home adjustment. All other three dimensions of role ambiguity and role conflict, i.e., Resource and Intra-role Conflict, Conflicting Organizational Demands and Resources, and Incompatible Policies and Role Overload also correlated positively with home adjustment.
Stepwise Multiple Regression Analysis Results
For analyzing the relative effect of independent variables on home adjustment, stepwise multiple regression analysis was carried out considering factors of emotional labor (4 factors: Intensity with Variety, Frequency with Deep Acting, Variety with Deep Acting and Surface Acting), Performance monitoring (2 factors: Intensity and purpose) and Role ambiguity and role conflict (4 factors: Role Ambiguity, Resource and Intra-role Conflict, Conflicting Organizational Demands and Resources, and Incompatible Policies and Role Overload) as independent variables, while home adjustment was dependent variable.
The results given in Table 3.6, showed that (1) Intensity of Performance Monitoring dimension of performance monitoring, and Role Ambiguity, Incompatible Policies and Role Overload, Resource and Intra-role Conflict dimensions of role ambiguity and role conflict, and Intensity with Variety
103 dimension of emotional labor were significant predictors of home adjustment;
(2) the maximum variance, that is, 9% was explained by Intensity of Performance Monitoring; (3) Role Ambiguity, Incompatible Policies and Role Overload, and Resource and Intra-role Conflict explained 7%, 4% and 3%
variance respectively; (4) Moreover, all the predictors have shown a positive association with home adjustment; (5) Intensity of Performance Monitoring explained 1% variance with respect to home adjustment.
In short, among the dimensions of 3 independent variables, Role Ambiguity emerged as the best predictor (β = .32, p < .00) of home adjustment.
Relationships of Emotional Labor, Performance Monitoring, and Role Ambiguity and Role Conflict with Positive Well-being
Correlation Results
Correlation between Emotional Labor and Positive Well-being
It can be observed from Table 3.2, that Frequency with Deep Acting dimension of emotional labor had significant positive correlations with two dimensions of positive well-being, i.e., Overall-Well-being, and Purpose and Positive Relationship.
Surface Acting dimension of emotional labor had significant negative correlations with Personal Stagnation, Dissatisfaction with self, Social Isolation and Lack of Purpose, and Lack of Mastery and Relations dimensions of positive well-being. Further, Intensity with Variety dimension of emotional labor negatively correlated with Personal Stagnation, and with Lack of Mastery TH-988_04614102
and Relations. Variety with Deep Acting of emotional labor did not correlate with any dimension of positive well-being.
Correlation between Performance Monitoring and Positive Well-being
As evident from Table 3.2, that Intensity of Performance Monitoring had significant negative correlation with Personal Stagnation, Dissatisfaction with Self, Social Isolation and Lack of Purpose, Lack of Mastery and Relations, and Purpose and Positive Relationship.
The other dimension of performance monitoring, i.e., Purpose of Performance Monitoring correlated positively with Overall Well-being, and Purpose and Positive relationship, dimensions of positive well-being.
Correlation between Role Ambiguity and Role Conflict and Positive Well-being
It can be observed from Table 3.2, that the Role Ambiguity dimension of the role ambiguity and role conflict had significant negative correlations with Overall Well-being, and Purpose and Positive Relations dimensions of positive well-being. Role Ambiguity correlated positively with only one dimension of Positive well-being, i.e., Dissatisfaction with self.
Resource and Intra-role Conflict had significant negative correlations with all the dimensions of Positive well-being- Personal Stagnation, Dissatisfaction with self, Social Isolation and Lack of Purpose, Lack of Mastery and Relations, and Purpose and Positive Relationship, except Overall Well- being dimension.
105 Further, Conflicting Organizational Demands and Resources dimension of role ambiguity and role conflict also correlated negatively with Personal Stagnation, Dissatisfaction with self, Social Isolation and Lack of Purpose, Lack of Mastery and Relations dimensions of positive well-being. Conflicting Organizational Demands and Resources did not correlate with Overall Well- being and Purpose and Positive Relationships dimensions of positive well- being.
Incompatible Policies and Role Overload dimension of role ambiguity and role conflict had significant negative correlations with Personal Stagnation, and Lack of Mastery and Relations. Incompatible Policies and Role Overload positively correlated only with Purpose and Positive Relationships.
Stepwise Multiple Regression Analysis Results
For analyzing the relative effect of independent variables on positive well-being, stepwise multiple regression analysis was carried out considering factors of emotional labor (4 factors: Intensity with Variety, Frequency with Deep Acting, Variety with Deep Acting and Surface Acting), Performance monitoring (2 factors: Intensity and purpose) and Role ambiguity and role conflict (4 factors: Role Ambiguity, Resource and Intra-role Conflict, Conflicting Organizational Demands and Resources, and Incompatible Policies and Role Overload) as independent variables, while each factor of positive well- being (Overall Well-being, Personal Stagnation, Dissatisfaction with Self, Social Isolation and Lack of Purpose, Lack of Mastery and Relations, Purpose and Positive Relationship) was considered as dependent variable.
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Predictors of Positive Well-being Dimensions Predictors of Overall Well-being
The results reported in the Table 3.7, showed that (1) Role Ambiguity and Incompatible Policies and Role Overload of dimensions of role ambiguity and role conflict, Purpose of Performance Monitoring dimension of performance monitoring, and Frequency with Deep Acting and Intensity with Variety of emotional labor were significant predictors of Overall Well-being dimension of positive well-being; (2) The maximum variance, that is, 31% was explained by Role Ambiguity with respect to Overall Well-being dimension of positive well-being; (3) Purpose of Performance Monitoring, and Incompatible Policies and Role Overload explained 7% and 2% of variance respectively; (4) Frequency with Deep Acting and Intensity with Variety explained 1% each of variance with respect to Overall Well-being; (5) The relationships of Role Ambiguity, Incompatible Policies and Role Overload, and Intensity with Variety with Overall Well-being were found to be negative.
In short, among the dimensions of 3 independent variables, Role Ambiguity emerged as the best predictor (β = -.37, p < .00) of Overall Well- being.
Predictors of Personal Stagnation
It can be observed from Table 3.7, that (1) Intensity of Performance Monitoring and Purpose of Performance Monitoring dimensions of performance monitoring, and Resource and Intra-role Conflict, Incompatible Policies and Role Overload, and Conflicting Organizational Demands and