Workers in Taiwan are commonly required to work long hours by their employers. The results of a survey by Taiwan’s Ministry of Labor, for example, indicated that, in 2014, employees in Taiwan worked an approximate average of 2134.8 hours, a yearly total similar to, but somewhat higher than, those of workers in South Korea and Japan (2124 and 1729 hours, respectively) (“The OECD Teaching,” n.d.) [
Past investigations have found that both work-life balance and job satisfaction are impacted by overtime work [
The role of work in employees’ lives has also been significantly affected in both positive and negative ways by technological advances and globalization. For example, competitive employment pressures have increased even as various social reforms have been manifested. As a result of such pressures, job burnout has become a growing problem, particularly in high-pressure fields such as the banking and technology industries [
The effort-recovery model provides a useful framework for explaining how the effort expended by an individual on work or nonwork activities may eventually damage the individual’s health through a series of psychological, physiological, and behavioral processes. Meijman and Mulder [
The main objective of this study was to develop the effort-recovery model and the control of occupational stress into a theoretical framework, which is shown in Figure
Tested conceptual model.
According to our conceptual model, the causal effects of long work hours can be apportioned into its indirect effects on the dependent variables through mediators (a × c1) (a × c2) and into its direct effects on the dependent variables (paths b1 and b2). Path a represents the effect of work hours on the proposed mediator, and paths c1 and c2 represent the effects of the mediator on the dependent variables, through which the effects of long work hours are effectively portioned out. (Note: path c’ connects to the solid boxes indicating the main concepts of the model, while the dotted box around “perceived control over time” indicates that it acts as a moderator [M2] of the effects contributing to and resulting from working hours and occupational stress.)
To investigate the health of overtime workers in the high-tech and banking industries in Taiwan (both of which have high proportions of workers who work long hours), this study utilized a cross-sectional design. A total of 369 exempt employees ranging in age from 20 to 65 years old were recruited. This recruitment was conducted over two distinct periods, with 193 participants being recruited at high-tech industries during the first recruitment period and 176 participants being recruited at banking industries during the second recruitment period. The institutional review board of National Chengchi University in Taiwan approved the study, and all of the participants provided informed consent.
A total of four questionnaires regarding occupational stress levels, work-life balance, job satisfaction, and perceived control over time were administered (though again, only some of the participants received the questionnaire regarding their perceived control over time).
The job stress questionnaire developed by Cooper and Marshall [
The work-life balance questionnaire used in this study to gather information on the participants’ schedules and the balance or lack thereof between their work and free time has also been used in previous studies [
The job satisfaction questionnaire used in this study, which has previously been reported to have an overall Cronbach’s alpha coefficient ranging from 0.73 to 0.78 [
The perceived control over time scale used in this study, the items of which were also rated using a 5-point Likert scale, was based on the Time Management Behavior Scale developed by Macan et al. [
The demographic statistics (gender) of the study participants are presented in percentages. A descriptive analysis was conducted to determine the distribution of the data from the four questionnaires. An examination of the raw data in four scales carried out prior to data analysis revealed that less than 1% of the data were missing. Normality test in four scales was examined. Natural logarithm transformation was performed if the normality assumption did not fit. Bivariate Pearson’s correlations were used to explore the relationships between scales. Finally, path analyses were conducted to determine any cause-and-effect relationships among the concepts measured by scales from the questionnaires. A linear regression analysis was performed to evaluate the relations between a dependent variable and one (simple linear regression) or more (multiple linear regression) explanatory variables. More specifically, the structural model was calculated in order to determine the statistical significance, if any, of the path coefficients between the various observed variables. In the mediation process, the relationship between the independent variable (X) and the dependent variable (Y) is hypothesized to be an indirect effect (path c’) that exists due to the influence of a third variable. The minimum sample size for principal components analysis was estimated by 30-50 observations of 4 variables, for a total of 120-200 observations. SAS 9.3 was used in all the analyses, and the alpha value was set at 0.05.
Ethical approval for this study was obtained from the Research Ethics Committee, National Chengchi University, Taipei, Taiwan Joint Institutional Review Board (approval no. NCCU-REC-201508-I042).
The demographic information of the study participants is shown in Table
Demographics of the participants(n = 369).
Variables | N | % |
---|---|---|
Gender | ||
Male | 185 | 50.1 |
Female | 184 | 49.9 |
Marital | ||
Single | 186 | 50.4 |
Married | 171 | 46.3 |
Divorced | 10 | 2.7 |
Widowed | 1 | 0.3 |
Cohabiting | 1 | 0.3 |
Education level | ||
Junior high school | 4 | 1.1 |
Senior high school | 14 | 3.8 |
College | 187 | 51.1 |
Masters/Doctorate | 161 | 44 |
Seniority in the workplace | ||
<1 year | 54 | 14.6 |
1-4 years | 136 | 36.9 |
5-9 years | 87 | 23.6 |
10-14 year | 61 | 16.5 |
15+ years | 31 | 8.4 |
Shift work | ||
No | 364 | 98.6 |
Yes | 5 | 1.4 |
|
||
Variables | Mean | SD |
|
||
Age (years) | 36.11 | 7.34 |
Hours of work per week | 46.21 | 8.21 |
Comparison of work-related factors between participants who reported working overtime and those who did not.
Variables | Score Range | ≥48hrs | ≤48hrs |
|
||
---|---|---|---|---|---|---|
(n=241) | (n=128) | |||||
Mean | SD | Mean | SD | |||
Occupational stress (OS) | 15~75 | 45.12 | 7.36 | 41.30 | 7.94 | <.001 |
Perceived control over time (PCT) | 5~ 25 | 15.36 | 2.77 | 16.52 | 2.79 | 0.01 |
Work and life balance (WLB) | 15~105 | 57.65 | 8.75 | 51.59 | 8.95 | <.001 |
Job satisfaction (WSA) | 6~30 | 19.43 | 3.92 | 20.12 | 4.05 | 0.11 |
Independent Sample t-test was used.
The correlation matrix for this study is displayed in Table
Pearson correlation coefficients between working hours, perceived control over time, occupational stress, work-life balance, and job satisfaction (N =369).
Age | HOUR | PCT | OS | WLB | WSA | |
---|---|---|---|---|---|---|
Age | 1 | |||||
HOUR | -.129 |
1 | ||||
PCT | -.064 | -.189 |
1 | |||
OS | -.144 |
.220 |
-.683 |
1 | ||
WLB | -.089 | .270 |
-.513 |
.460 |
1 | |
WSA | .070 | -.051 | .395 |
-.553 |
-.205 |
1 |
Hour = working hours; PCT = perceived control over time; OS = occupational stress; WLB = work-life balance; WSA = job satisfaction.
The path analysis results are presented in Table
Regression analyses results indicating the effects of occupational stress as a mediator of the associations between work-life balance, working hours, and job satisfaction (N=369).
Independent Variables | Dependent Variables |
|
t | p |
|
F | ||
---|---|---|---|---|---|---|---|---|
Model 1 | Path a | HOUR(X) | OS(M1) | 0.22 | 4.317 |
<.001 | 0.048 | 18.64 |
Model 2 | Path b1 | HOUR(X) | WLB(Y1) | 0.177 | 3.798 |
<.001 | 0.241 | 58.02 |
Path c1 | OS(M1) | WLB(Y1) | 0.421 | 9.004 |
<.001 | |||
Model 3 | Path b2 | HOUR(X) | WSA(Y2) | 0.074 | 1.67 | 0.096 | 0.311 | 82.46 |
Path c2 | OS(M) | WSA(Y2) | -0.569 | 12.789 |
<.001 |
Hour = working hours; PCT = perceived control over time; OS = occupational stress; WLB = work-life balance; WSA = job satisfaction.
Path analysis and path coefficients for the mediating and moderating impacts of results (
The term “moderator” is used to refer to any quantitative or qualitative variable that has an effect or effects on the direction and/or strength of the association between a dependent or criterion variable and a corresponding independent or predictor variable. In the specific context of a correlational analysis framework, a moderator consists of a third variable that exerts an effect on the zero-order relationship between two other variables [
In testing the moderator effects, the current study used the data from the second recruitment period alone (N = 176), as only the participants recruited in that period answered the questionnaire regarding perceived control over time. As indicated by the results listed in Table
Regression analyses results indicating the effects of perceived control over time as a moderator of the association between occupational stress and working hours (N=176).
Independent Variables | Dependent Variables |
|
t | p |
|
F |
---|---|---|---|---|---|---|
HOUR (X) | OS (Y) | 0.132 | 2.3779 |
0.019 | 0.510 | 59.304 |
PCT (M2) | -0.655 | -11.976 |
< .001 | |||
HOUR |
0.163 | 2.994 |
0.003 |
To the best of our knowledge, this study constitutes the first investigation of occupational stress that has made use of both perceived control over time as a moderator and cross-sectional mediation in order to investigate the experiences of high-tech and banking industry employees. The study results indicated that occupational stress acts as mechanism in the links between working hours and work-life balance and job satisfaction. According to our results, problems in occupational stress and alertness resulting from being burdened with higher working hours seem to have many harmful ramifications for work-life wellbeing, such as work-life imbalance and job dissatisfaction. Furthermore, those participants who reported having high perceived control over time were less prone to also report having highly stressful workloads or long working hours.
In previous studies, it was found that long working hours were associated with job-related role stressors (including workload, role ambiguity, and role conflict). As such, workers usually be divorced of work pressures during off-job time and recovery-related self-efficacy [
Our results imply that occupational stress acts as a partial mediator between work-life balance and working hours, while also acting as a full mediator between reported job satisfaction and working hours. These findings seem to indicate that both work-life balance and job satisfaction are decreased by longer working hours, while also suggesting that occupational stress plays a key role in workers’ performance. These findings are consistent with those of past reports regarding people working in a variety of other industries [
A previous study found that occupational stress is affected by a worker’s level of perceived control over his or her time [
There were several limitations to this study. First, the sample of participants came exclusively; the high-tech and banking industries and the workloads of employees in those industries typically vary on a seasonal basis. As such, it may not be appropriate to generalize the study findings to other industries. With that in mind, future research focused on other industries and occupations that also require long working hours (e.g., certain roles in healthcare or law enforcement) would be worthwhile, as would investigations aimed specifically at measuring the job stress and psychological conditions of workers who work over 60 hours each week. A second limitation of the current study is that it was a cross-sectional study. Because of that, it is not possible to make any causal interpretations regarding the associations among the number of hours worked, work-life balance, occupational stress, and job satisfaction. Accordingly, future studies that utilize either an experimental or longitudinal study design would be worthwhile.
In conclusion, this study found evidence that occupational stress acts as a powerful mediator of the relationships among long working hours, work-life imbalance, and job dissatisfaction in employees in high-stress industries such as the high-tech and banking industries. Furthermore, it is possible that perceived control over time plays a protective role that affects recovery-related self-efficacy in the face of long working hours and occupational stress. From a welfare of workers perspective, a focus on developing more optimistic attitudes in organizational contexts can promote physical and mental health through time management, stress management, leisure arrangements, etc., thereby enhancing workers’ sense of control over their working hours and work-life, increasing their healthy behaviors, and enhancing their quality of life and competitiveness.
The data used to support the findings of this study are included within the article.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
The authors declare that there are no conflicts of interest.