| Literature DB >> 35457670 |
Giulia Sciotto1, Francesco Pace2.
Abstract
The aim of the study was to verify whether the frequency of face-to-face interactions with the public at work can reveal differences in how people react to emotional regulation demands. In particular, we investigated the mediating role of surface acting (a strategy of dealing with emotional dissonance) in the relationship between two typical job stressors (workload and mental load) and two outcomes closely related to work-related well-being: employees' general health and the need for recovery. Prior studies investigating the detrimental effects of emotional dissonance mostly focused on service workers. However, in light of a survey conducted by the European Agency for Safety and Health at Work (2016) highlighting the growing psycho-social risk constituted by intense human interactions in the workplaces, even in unexpected categories of workers, we hypothesize that emotional demands may also be a concern for those who do not specifically interface with clients as part of their job duties. The results of the multi-group analysis of front-office (N = 734) and back-office (N = 436) Italian workers showed that surface acting fully mediates the relationship between workload and general health among back-office workers, while it only partially mediates this relationship among front-office workers. Furthermore, surface acting is positively associated with the need for recovery and negatively with general health, with higher values for back-office workers. The findings support the hypothesis that the emotional demands are not only a service worker issue and highlight the need to address emotional regulation strategies to enhance the quality of life in and outside the workplace for all employees.Entities:
Keywords: emotional labor; health; need for recovery; stress; surface acting; wellbeing
Mesh:
Year: 2022 PMID: 35457670 PMCID: PMC9024759 DOI: 10.3390/ijerph19084800
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Means, standard deviations, and correlations among study variables (back-office sample N = 436; front-office sample N = 734).
| Variable | Mean | SD | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|---|---|
|
Workload | 2.89 (2.77) | 0.87 (0.92) | 1 | 0.328 ** | 0.242 ** | 0.640 ** | −0.269 ** |
|
Mental Load | 3.41 (3.42) | 0.69 (0.74) | 0.305 ** | 1 | 0.210 ** | 0.225 ** | −0.069 |
|
Surface Acting | 2.34 (2.41) | 0.97 (0.97) | 0.276 ** | 0.210 ** | 1 | 0.287 ** | −0.354 ** |
|
Need for Recovery | 2.28 (2.20) | 0.94 (0.93) | 0.668 ** | 0.220 ** | 0.476 ** | 1 | −0.355 ** |
|
General Health | 2.36 (2.39) | 0.77 (0.76) | −0.293 ** | −0.033 | −0.442 ** | −0.415 ** | 1 |
Correlations below the diagonal are for the back-office sample and correlations above the diagonal are for the front office sample. Mean and SD for the front office sample are in parentheses. ** p < 0.01.
Goodness-of-fit values of CFA models, multi-group test for measurement invariance, and the structural models testing the study hypotheses (ML estimation; back-office sample N = 436; front-office sample N = 734).
| Models | Model Fit | ||||||
|---|---|---|---|---|---|---|---|
| χ2 | df | CFI | TLI | RMSEA (90% CI) | ΔM | ΔCFI | |
| Model for back-office | 242.664 | 113 | 0.944 | 0.933 | 0.051 (0.042–0.060) | ||
| Model for front-office | 300.177 | 113 | 0.945 | 0.932 | 0.049 (0.042–0.055) | ||
| M1: Configural | 420.529 | 225 | 0.932 | 0.917 | 0.055 (0.046–0.063) | ||
| M2: Metric | 441.323 | 238 | 0.929 | 0.919 | 0.054 (0.046–0.062) | M1-M2 | 0.003 |
| M3: Scalar | 468.849 | 250 | 0.924 | 0.917 | 0.055 (0.047–0.062) | M2-M3 | 0.005 |
| M4: Strict | 502.014 | 266 | 0.918 | 0.916 | 0.055 (0.048–0.042) | M3-M4 | 0.006 |
| Structural model for back-office | 163.852 | 68 | 0.944 | 0.926 | 0.057 (0.046–0.068) | ||
| Structural model for front-office | 187.711 | 68 | 0.952 | 0.934 | 0.050 (0.041–0.058) | ||
| Structural model across groups | 393.162 | 152 | 0.944 | 0.933 | 0.052 (0.046–0.058) | ||
Figure 1The structural model for the back-office sample (N = 436). * p < 0.05 **, p < 0.01.
Figure 2The structural model for the front-office sample (N = 734). * p < 0.05 **, p < 0.01.
Indirect effects using bootstrapping with 5000 replications (ML estimation; back-office sample N = 436; front-office sample N = 734).
| Indirect Effects | Est. | S.E. | 95% CI |
|---|---|---|---|
| Back-office workers sample | |||
| Workload → Surface Acting → Need for Recovery | 0.248 ** | 0.044 | 0.179, 0.322 |
| Workload → Surface Acting → General Health | −0.257 ** | 0.054 | −0.353, −0.177 |
| Front-office workers sample | |||
| Workload → Surface Acting → Need for Recovery | 0.216 ** | 0.038 | 0.155, 0.282 |
| Workload → Surface Acting → General Health | −0.166 ** | 0.035 | −0.226, −0.133 |
**, p < 0.01.