| Literature DB >> 35162110 |
Abstract
This study reviewed the mental health problems experienced by office workers exposed to new kinds of work stress, career plateau, and job burnout, due to no-contact teleworking during the COVID-19 pandemic. Human beings tend to evaluate their own qualities to determine their own superiority by comparing themselves with others. Appropriate social comparison helps to promote self-understanding and boost self-esteem. However, in the case of no-contact remote working, where the amount of time spent alone is drastically increased, the information obtained from such social comparisons is naturally insufficient, resulting in the perception of reaching a career plateau. Prolonged anxiety and a sense of helplessness have been shown to cause job burnout; however, so far, few studies have examined career plateau as an antecedent factor for job burnout. This study also considered the moderating effect of regulatory focus in order to closely examine the effect of career plateau on job burnout. According to the regulatory focus theory, differences appear in various psychological processes, such as human choices, judgments, motivations, and attitudes, determined by whether individuals adopt a promotion focus or a prevention focus. This study aimed to verify whether regulatory focus operates in a conditional context, in the process of job burnout following career plateau, to change the magnitude and direction of the influence of career plateau. To this end, a hierarchical regression analysis was performed by collecting data from 202 people working for three Korean companies. As a result of the analysis, it was found that the career plateau had a significant effect on job burnout. This direct effect was still significant even after considering the interaction with regulatory focus. In addition, promotion focus was found to have a negative moderating effect, while prevention focus had no effect on the influence of career plateau on job burnout. This study demonstrated that the negative effects of career plateau, which have been presented in various ways in academia, lead to job burnout under the non-face-to-face teleworking systems implemented due to the COVID-19 pandemic, and suggested that promotion focus can play a positive role in alleviating this dynamic.Entities:
Keywords: COVID-19 pandemic; career plateau; job burnout; regulatory foci
Mesh:
Year: 2022 PMID: 35162110 PMCID: PMC8834611 DOI: 10.3390/ijerph19031087
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Research Model.
Goodness of fit of the measurement model.
| Index | χ2 | df |
| χ2/df | AGFI | TLI | CFI | RMESA |
|---|---|---|---|---|---|---|---|---|
| Cut-off criteria | - | - | - | <2.0 | >0.90 | >0.90 | >0.90 | <0.05 |
| Fit value | 656.3 | 539 | 0.000 | 1.218 | 0.876 | 0.901 | 0.910 | 0.032 |
Reliability and validity.
| Factor | Factor Loading | AVE | CR | Cronbach’s α | |
|---|---|---|---|---|---|
| Career plateau | Hierarchical plateau | 0.523~0.872 | 0.527 | 0.782 | 0.843 |
| Content plateau | 0.588~0.917 | 0.686 | 0.820 | 0.927 | |
| Regulatory foci | Promotion focus | 0.525~0.907 | 0.539 | 0.842 | 0.789 |
| Prevention focus | 0.564~0.783 | 0.506 | 0.671 | 0.710 | |
| Job burnout | Exhaustion | 0.493~0.897 | 0.534 | 0.693 | 0.751 |
| Cynicism | 0.605~0.828 | 0.536 | 0.816 | 0.786 | |
| Job efficacy | 0.523~0.747 | 0.511 | 0.810 | 0.823 | |
Descriptive statistics and correlation matrix.
| Mean | S.D. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1. Age | 38.20 | 2.78 | 1.00 | |||||||
| 2. Gender a | 0.72 | 0.45 | 0.61 * | 1.00 | ||||||
| 3. Company 1 b | 0.37 | 0.48 | 0.18 * | 0.28 ** | 1.00 | |||||
| 4. Company 2 c | 0.31 | 0.46 | 0.11 | 0.08 | 0.02 | 1.00 | ||||
| 5. Career plateau | 3.97 | 0.71 | 0.21 ** | 0.14 * | 0.08 | 0.30 ** | 1.00 | |||
| 6. Promotion focus | 3.73 | 0.81 | 0.01 | −0.03 | 0.06 | 0.19 ** | 0.28 | 1.00 | ||
| 7. Prevention focus | 3.59 | 0.93 | −0.09 | −0.08 | −0.13 | 0.04 | −0.35 ** | −0.26 ** | 1.00 | |
| 8. Job burnout | 3.61 | 1.01 | 0.12 | 0.14 * | −0.06 | 0.22 ** | 0.32 ** | 0.10 | −0.11 | 1.00 |
Note: Sample size = 202; * p < 0.05, ** p < 0.01 (two tailed); a Dummy variables: Female = 0, Male = 1; b Dummy variables: Insurance = 0, Information technology = 1; c Dummy variables: Insurance = 0, Education service = 1.
Regression estimates for job burnout.
| Model 1 | Model 2 | Model 3 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| b | S.E. | t | b | S.E. | t | b | S.E. | t | |
| Age | 0.01 | 0.03 | 0.38 | −0.01 | 0.03 | −0.17 | 0.00 | 0.03 | 0.05 |
| Gender a | 0.29 | 0.19 | 1.52 | 0.29 | 0.19 | 1.51 | 0.26 | 0.18 | 1.43 |
| Company1 b | −0.23 | 0.15 | −1.60 | −0.26 | 0.15 | −1.81 | −0.21 | 0.15 | −1.42 |
| Company2 c | 0.46 | 0.15 | 3.11 ** | 0.30 | 0.16 | 1.88 | 0.26 | 0.17 | 1.54 |
| Career plateau (Cp) | 0.37 | 0.11 | 3.40 ** | 0.31 | 0.12 | 2.55 * | |||
| Promotion focus (Pm) | 0.01 | 0.10 | 0.06 | 0.03 | 0.09 | 0.32 | |||
| Prevention focus (Pv) | −0.03 | 0.08 | −0.40 | −0.01 | 0.07 | −0.16 | |||
| Cp × Pm | −0.38 | 0.18 | −2.13 * | ||||||
| Cp × Pv | 0.08 | 0.21 | 0.37 | ||||||
| R2 (adjusted R2) | 0.08 (0.06) | 0.14 (0.11) | 0.16 (0.12) | ||||||
| F | 3.96 ** | 4.58 ** | 5.36 ** | ||||||
* p < 0.05, ** p < 0.01 (two tailed); a Dummy variables: Female = 0, Male = 1; b Dummy variables: Insurance = 0, Information technology = 1; c Dummy variables: Insurance = 0, Education service = 1; Unstandardized regression coefficients reported for mean centered data.
Figure 2Moderating effect of promotion focus on job burnout.