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  • Research article
  • Open Access

The work–life balance and psychosocial well-being of South Korean workers

Annals of Occupational and Environmental Medicine201830:38

https://doi.org/10.1186/s40557-018-0250-z

  • Received: 27 September 2017
  • Accepted: 28 May 2018
  • Published:

Abstract

Background

It is challenging to balance work and life, and little attention has been paid to the work–life balance and psychosocial well-being of South Koreans. We assessed the association between work–life balance and psychosocial well-being among paid Korean workers.

Methods

This study was based on data from the fourth Korean Working Conditions Survey. We evaluated only paid workers, which constituted 30,649 of the total of 50,007 subjects surveyed. Poor work–life balance was defined based on the goodness of fit between working hours and social commitments. Well-being was measured using the World Health Organization WHO-5 index. Poisson regression with robust variances was used to calculate the estimated prevalence ratios (PRs) with confidence intervals.

Results

Poor work–life balance was associated with poor psychosocial well-being (PR = 1.25; 95% CI 1.21 to 1.28) even after adjusting for work-related and individual characteristics. Poor well-being was associated with low-level job autonomy (PR = 1.06; 95% CI 1.03 to 1.09), working for ≥53 h per week (PR = 1.10; 95% CI 1.06 to 1.14), blue-collar status (PR = 1.16; 95% CI 1.11 to 1.21), low-level support at work (PR = 1.32; 95% CI 1.29 to 1.36), age ≥ 50 years (PR = 1.21; 95% CI 1.15 to 1.26), the female gender (95% CI PR = 1.04; 95% CI 1.01 to 1.07), and cohabitation (living with somebody) (PR = 1.08; 95% CI 1.04 to 1.12). Good well-being was associated with high-intensity work (PR = 0.96; 95% CI 0.94 to 0.99), being the secondary earner in a household (PR = 0.82; 95% CI 0.79 to 0.85), and higher income (PR = 0.75; 95% CI 0.71 to 0.79).

Conclusion

Work–life balance was associated with psychosocial well-being after adjusting for both work-related and individual characteristics.

Keywords

  • Work–life balance
  • Psychosocial well-being
  • KWCS
  • WHO-5 index

Background

Successfully reconciling work and non-work time is challenging regardless of life stage or profession [1]. Both demographic features and the work-related environment have changed in recent times. The traditional sole male breadwinner is less common today. Women work and dual-earning couples are common. The lack of single-parent housing combined with increasing work demands and rapid industrial changes render it harder for workers to balance work with life; stress levels are increasing [2, 3]. It is important to define an appropriate work–life balance and to understand the current situation in South Korea.

No widely accepted definition of work–life balance is yet available; the idea is complex. Suggested definitions include “equilibrium or an overall sense of harmony in work and private life” [4], “an adequate amount of resources to respond effectively to the demands of their and family roles” [5], and “balancing the life demands of various life-roles” [6]. Harmonization of life and work improves mental and physical health. A good balance between work and life improves job satisfaction, psychosocial well-being, and the overall quality-of-life [7]. When work and personal life are poorly balanced the consequences include decreased job satisfaction, poor psychosocial well-being, and a lower quality-of-life [810]. Failure to achieve the required balance impairs mental health, and triggers burnout, depression, and family conflict [11, 12].

The Organization for Economic Cooperation and Development (OECD) ranked South Korea 36th among 38 OECD countries in terms of work–life balance, because of very long working hours, gender inequality, and insufficient time for leisure and personal care. The longer the work hours, the less time is available to spend with others, to engage in leisure activities, and to eat and sleep. Overall well-being is compromised, as are physical and mental health http://www.oecdbetterlifeindex.org/topics/work-life-balance/. Of all Korean workers, 23.1% work ≥50 h/week, compared to the OECD average of 13%. The OECD average for work hours per week is 37.6 h; the South Korean average is 44.3 h [13].

A perceived good balance between work and personal life predicts psychosocial well-being [14]. The World Health Organization questionnaire WHO-5 can be used to evaluate the “state of well-being” and is a useful measure of subjective physical, mental, and social health [15]. The tool also assesses the overall quality-of-life, emotional state, and depression [16]. Impaired psychological well-being may reduce job involvement and increase absenteeism [17]. Individuals with greater psychosocial well-being are more dedicated to work, and more productive and happier, than others [18].

As mentioned above, an imbalance between work and private life is of growing concern in South Korea. However, the issue has been little-studied. Our purpose was to identify associations between work–life balance and psychosocial well-being using data from the fourth Korean Working Conditions Survey.

Methods

Data and study samples

This study was based on the fourth Korean Working Conditions Survey (KWCS 2014) performed by the Korea Occupational Safety and Health Agency. The basic sample design is multistage random sampling. The enumeration districts in the 2010 Population and Housing Census were used for sampling. Data were gathered via face-to-face interviews in homes, using the questionnaire. The survey gathered comprehensive information on working conditions to define workforce changes and the quality of work and life. The survey was performed in 2014 and targeted the economically active population aged ≥15 years who were paid workers or self-employed at the time of interview. The survey data were weighted with reference to the economically active population, in that the sample distributions by region, locality, sex, age, economic activity, and occupation were identical to those of the overall economically active population at the time of the survey. We restricted our analysis to paid workers. Therefore, we included only 30,649 of a total of 50,007 workers. Military personnel and those who did not respond were also excluded [19].

Measures

Psychosocial well-being, our outcome of interest, was measured using five items of the WHO-5 scale: “I feel cheerful and in good spirits/I feel calm and relaxed/I feel active and vigorous/I wake up feeling fresh and rested/My daily life is filled with things that interest me”. Each item is scored from 0 to 5. A raw score (0–25) is calculated by totaling the Figs. A score < 13 indicates poor wellbeing. We dichotomized the scores into good and poor well-being [20].

Work–life balance, our primary exposure of interest, was assessed by a single question: “In general, do your working hours fit in with your family or social commitments outside work?”. The answers were dichotomized as good (“very well” or “well”) and poor (“not very well” or “not at all well”). Other covariates were divided into two categories: Individual and work-related. Of many possible work-related characteristics, we explored job type, weekly work hours, work intensity, job autonomy, job insecurity, and support at work. Job type was categorized as white-collar (managers, professionals, and technicians), service and sales, and blue-collar (agriculture/fishery workers, and skilled workers and machine operators). We divided weekly work hours into 47 h and below, 48–52 h, 53 h and above. The European Working Conditions Survey considered that the standard number of work hours per week was 35–47 h [21]; therefore, we considered that working 48 h and above indicated extended work hours. The Korea Labor Standards Act limits extended working hours to 52 h. We divided extended work hours into 48–52 h and ≥ 53 h. Work intensity, job autonomy, and support at work were divided into high and low, and job insecurity was divided into secure and insecure according to the scoring methods by Lu et al. [22].

We explored individual characteristics including gender, age, education, income, job type, cohabitation status, and contribution to household earnings. All subjects were divided into four age groups: < 30, 30–39, 40–49, and ≥ 50 years; and into four groups by educational level: above middle school; middle school graduate, high school graduate, or community college graduate. Average monthly income was divided into intervals of 1,000,000 won (KRW; the Korean currency). Cohabitation status (yes, no); and the contribution made to household earnings (primary earner, secondary earner, or equal earner), were also evaluated.

Statistical analysis

Work–life balance and work-related and individual characteristics by reference to psychosocial well-being are shown as descriptive statistics. All analyses were carried out using weights. We used the chi-square test to explore the effects of variables on psychosocial well-being. Poisson regression with robust variances was used to determine the estimated prevalence ratios (PRs) for work–life balance and psychosocial well-being (for all samples, and separately for women and men) [23, 24]. Three predictive models were used. Model 1 featured univariate Poisson regression and Models 2 and 3 featured multivariate Poisson regression analysis. Model 2 was adjusted for work-related characteristics (work intensity, weekly working hours, job type, job autonomy, job insecurity, and support at work). Model 3 was adjusted for both work-related and individual characteristics (the covariates of Model 2 plus gender, age, income, cohabitation status, and contribution to household earnings). Variables exhibiting co-linearity were excluded from multivariate analysis. The stratified analysis by women and men did not differ from the results of the overall sample (results not shown). A p-value < 0.05 was considered to reflect statistical significance. All analyses were performed with the aid of SPSS (ver. 23.0).

Results

Subject distribution and sample characteristics

Table 1 shows data on all subjects. Those with a good work–life balance scored significantly higher in terms of psychosocial well-being. Work intensity did not significantly affect psychosocial well-being. Weekly work for < 47 h, high-level job autonomy, a high support level, job security, and a lower contribution to household earnings, were all associated with significantly better psychosocial well-being. Gender did not significantly affect psychosocial well-being. Age ≤ 39 years improved psychosocial well-being, as did higher education, an income ≥3 million KRW, and a white-collar occupation. A poor work–life balance, work for ≥53 h/week, low-level job autonomy, insecure work, low-level support, older age, and a blue-collar position, were all associated with significantly poorer psychosocial well-being.
Table 1

Distribution of sample characteristics with psychosocial well-being

Characteristics

WHO well-being group n (%)

P value

 

Good well-being

Poor well-being

 

Work-life balance

 Good

15,927 (59.1)

11,027 (40.9)

< 0.001

 Poor

4075 (46.3)

4735 (53.7)

 

Work-related characteristics

 Job type

  White collar

3619 (63.0)

2125 (37.0)

< 0.001

  Service

9010 (61.3)

5678 (38.7)

 

  Blue collar

7536 (48.1)

8131 (51.9)

 

Weekly work hours

  ≤ 47

13,322 (58.2)

9576 (41.8)

< 0.001

 48–52

3699 (56.5)

2849 (43.5)

 

  ≥ 53

3065 (47.6)

3370 (52.4)

 

Work intensity

 Low

10,954 (55.7)

8724 (44.3)

0.341

 High

9098 (56.2)

7100 (43.8)

 

Job autonomy

 high

9502 (58.7)

6672 (41.3)

< 0.001

 low

9200 (54.0)

7832 (46.0)

 

Job insecurity

 secure

18,344 (56.5)

14,139 (43.5)

< 0.001

 insecure

717 (51.4)

678 (48.6)

 

Support at work

 high

16,226 (59.4)

11,088 (40.6)

< 0.001

 low

2777 (43.1)

3663 (56.9)

 

Individual characteristics

 Gender

  Male

10,291 (55.7)

8201 (44.3)

0.404

  Female

9887 (56.1)

7741 (43.9)

 

 Age

   < 30

3098 (62.2)

1882 (37.8)

< 0.001

  30–39

5683 (61.7)

3522 (38.3)

 

  40–49

5840 (56.1)

4564 (43.9)

 

   ≥ 50

5559 (48.2)

5975 (51.8)

 

 Education

  Below middle school

1726 (39.9)

2599 (60.1)

< 0.001

  Graduate high school

6859 (52.1)

6306 (47.9)

 

  Above college

11,586 (62.2)

7034 (37.8)

 

 Income (KRW)

   < 1,000,000

2194 (47.6)

2419 (52.4)

< 0.001

  1,000,000–1,999,999

6341 (51.6)

5947 (48.4)

 

  2,000,000–3,000,000

5865 (58.8)

4108 (41.2)

 

   ≥ 3,000,000

5356 (62.9)

3160 (37.1)

 

 Cohabitation status

  No

2792 (53.6)

2420 (46.4)

< 0.001

  Yes

17,386 (56.2)

13,523 (43.8)

 

 Contribution to household earnings

  Primary earner

11,366 (53.6)

9821 (46.4)

< 0.001

  Secondary earner

7281 (60.0)

4863 (40.0)

 

  Equally earner

1397 (56.9)

1060 (43.1)

 

Association between a poor work–life balance and poor psychosocial well-being

Table 2 presents the multivariate Poisson regression data. All models showed that a poor work–life balance and poor psychosocial well-being were associated. On crude analysis, a poor work–life balance was associated with an increased likelihood (crude PR = 1.32, 95% CI 1.29 to 1.35) of poor psychosocial well-being. After adjusting for work-related characteristics (Model 2), the PR changed slightly (PR = 1.24, 95% CI 1.21 to 1.28). When both individual and work-related characteristics were adjusted (Model 3), the association increased slightly (PR = 1.25, 95% CI 1.21 to 1.28). Model 3 showed that the likelihood of poor psychosocial well-being increased in those working ≥53 h/week (PR = 1.10, 95% CI 1.06 to 1.14); blue-collar status (PR = 1.16, 95% CI 1.11 to 1.21); those with low-level job autonomy (PR = 1.06, 95% CI 1.03 to 1.09); those with low-level support at work (PR = 1.32, 95% CI 1.29 to 1.36); female gender (PR = 1.04, 95% CI 1.01 to 1.07); and increasing age (40–49 years [PR = 1.17, 95% CI 1.12 to 1.23], and ≥ 50 years [PR = 1.21, 95% CI 1.15 to 1.95% CI 26]); and cohabitation (PR = 1.08, 95% CI 1.04 to 1.12). The likelihood of poor well-being was lower for those reporting high-level work intensity (PR = 0.96, 95% CI 0.94 to 0.99); incomes ≥1,000,000–1,999,999 KRW (PR = 0.92, 95% CI 0.87 to 0.96); incomes ≥2,000,000–2,999,999 KRW (PR = 0.82, 95% CI 0.79 to 0.87); incomes ≥3,000,000 KRW (PR = 0.5875, 95% CI 0.71 to 0.79); and those contributing as secondary or equal earners (PR = 0.82, 95% CI 0.79 to 0.85; PR = 0.92, 95% CI 0.87 to 0.97, respectively).
Table 2

The association between work-life balance & poor psychosocial well-being in the Korean Working Conditions Survey

 

Poor psychosocial well-being

 

PR (95% CI)

Model 1a

Model 2b

Model 3c

Characteristics

Work-life balance

 Good work-life balance

1

1

1

 Poor work-life balance

1.32 (1.29 to 1.35)

1.24 (1.21 to 1.28)

1.25 (1.21 to 1.28)

Work-related characteristics

Job type

 White collar

 

1

1

 Service

 

1.02 (0.97 to 1.06)

0.99 (0.95 to 1.03)

 Blue collar

 

1.29 (1.23 to 1.34)

1.16 (1.11 to 1.21)

Weekly working hours

  ≤ 47

 

1

1

 48–52

 

0.97 (0.94 to 1.00)

1.00 (0.96 to 1.04)

  ≥ 53

 

1.09 (1.05 to 1.13)

1.10 (1.06 to 1.14)

Work intensity

 Low intensity

 

1

1

 High intensity

 

0.95 (0.93 to 0.98)

0.96 (0.94 to 0.99)

Job autonomy

 High autonomy

 

1

1

 Low autonomy

 

1.05 (1.02 to 1.08)

1.06 (1.03 to 1.09)

Job insecurity

 Secure

 

1

1

 Insecure

 

1.02 (0.96 to 1.08)

0.96 (0.90 to 1.02)

Support at work

 High support

 

1

1

 Low support

 

1.36 (1.33 to 1.40)

1.32 (1.29 to 1.36)

Individual characteristics

 Gender

  Male

  

1

  Female

  

1.04 (1.01 to 1.07)

 Age

   < 30

  

1

  30–39

  

1.04 (0.99 to 1.09)

  40–49

  

1.17 (1.12 to 1.23)

   ≥ 50

  

1.21 (1.15 to 1.26)

 Income (KRW)

   < 1,000,000

  

1

  1,000,000–1,999,999

  

0.92 (0.87 to 0.96)

  2,000,000–2,999,999

  

0.82 (0.79 to 0.87)

   ≥ 3,000,000

  

0.75 (0.71 to 0.79)

 Cohabitation status

  No

  

1

  Yes

  

1.08 (1.04 to 1.12)

 Contribution to household earnings

  Primary earner

  

1

  Secondary earner

  

0.82 (0.79 to 0.85)

  Equally earner

  

0.92 (0.87 to 0.97)

PR Prevalence ratio, CI Confidence interval

a. Crude PR

b. Adjusted for work-related characteristics

c. Adjusted for work-related and individual characteristics

Discussion

This study explored the association between work–life balance and psychosocial well-being. Previous research has found that work–life balance predicts well-being [7, 9, 10, 14]. Of note, two cross-sectional studies obtained some interesting results. Gröpel and Kuhl [14] revealed that psychosocial well-being is positively correlated with work–life balance (β = 0.40, p < 0.001) and negatively correlated with work–family conflict (β = − 0.39, p < 0.001) which is an important cause of poor work-life balance. Grant-Vallone and Donaldson [9] found a significant negative association (β = − 0.29, p < 0.001) between work–family conflict (an important cause of poor work-life balance) and self-reported well-being. In our study, work–life balance was also associated with psychosocial well-being. The crude analysis revealed an association between poor work–life balance and poor psychosocial well-being (PR = 1.32, 95% CI 1.29 to 1.35). Even after adjusting for work-related and individual characteristics, the well-being of the group with a poor work–life balance was significantly lower (PR = 1.25, 95% CI 1.21 to 1.28).

As mentioned in the introduction the definition of work-life balance is still controversial. Due to this controversy it is important to have a look at the various definitions of work-life balance. Greenhaus, Collins, and shaw defined work-life balance as a balance and equity across multiple roles. Greenhaus, Collins, and Shaw also proposed that work-life balance reflects one’s orientation across different life roles, and inter-role phenomenon. Furthermore, they suggest that work-life balance is the extent to which an individual is engaged in – and equally satisfied with – one’s work role and social role with three components including time balance, involvement balance, and satisfaction balance. [7] Grzywacz & Bass [11] and Frone [25] viewed the psychological part of the work-life balance and defined it as an absence of inter-role conflict and higher levels of inter-role facilitation. Se′necal, Vallerand, and Guay proposed that work-life balance depends on time allocation across various life-roles and a subjective sufficiency of the time available for work and social roles. [6] By adapting the questionnaire from the KWCS ‘In general, do your working hours fit in with your family or social commitments outside work?’ we view and evaluate work-life balance as sufficiency of the time available for work and social roles.

In simple terms, work–life balance must consider multiple aspects of work, family needs, and social life [26]. First, it is necessary to understand why work–life balance affects psychosocial well-being. Role theory and the scarcity hypothesis can be used to examine this [9]. Within role theory, the scarcity hypothesis suggests that individuals have fixed amounts of time and energy for multiple roles [27]. Consequently, increased roles lead to higher role conflict, overload, and negative psychological repercussions. This fixed amount of energy and time results in conflict, stress, and anxiety. Previous studies have supported the notion that multiple roles lead to conflict, overload, and stress and have a negative impact on the well-being and performance of employees [26, 28]. As a result, conflict between work and social life could result in objective and psychological conflict [29]. It can also be explained by needs fulfillment [14]. Well-being is enhanced when goals are met. To reach these goals, resources are required, i.e., time, energy, money, and so forth [30]. The resources available for the goals are thought to be the best predictors of well-being [31]. If one sees time as an important resource, the sufficiency of time to reach that goal can affect well-being. However, not all goals affect well-being. Only goals that satisfy important psychological needs increase well-being [32]. As a result, sufficient time available for work and private life will affect well-being if personal needs are met only within that time [33]. Conversely, insufficient time or conflict within the work and non-work domains may decrease the level of well-being due to needs frustration.

Several important outcomes of poor work–life balance have been documented. Besides decreasing well-being, conflict between work and non-work roles leads to psychological symptoms such as stress, increased depression, anxiety [34], increased somatic complaints [26], and poor physical health [28].

We analyzed data on three predictor models and obtained some interesting results. Our finding that a poor work environment was associated with poor psychosocial well-being is in line with previous results. Long work hours and low-level job autonomy were associated with poor well-being. As also found in previous studies, long work hours and low-level job autonomy mean that workers have poor control over both their work and private lives [35]. An earlier study found that long working hours correlates with higher levels of anxiety and depression [36]. If less time is spent at work, and greater control over work is granted to the worker, psychosocial well-being would improve. Job type, the work environment, and socioeconomic status vary among occupations. We assume that blue-collar workers are more susceptible to poor well-being because of lower incomes, longer working hours, and low job flexibility [37]. However, some authors disagree, arguing that white-collar occupations associated with autonomy and flexibility pose greater job demands and responsibilities that spill over from work into the family, negatively affecting well-being [38]. Poor support at work was also associated with poor well-being. Studies have shown that a low level of support at work can cause problems that spill over into family life, degrading the work–life balance further and compromising psychosocial well-being [39, 40]. In studies conducted in Turkey [41] and Thailand [42], greater work intensity significantly predicted lower psychological well-being. In comparison, our study found a positive association between higher work intensity and good psychosocial well-being. We cannot explain this or cite a relevant prior study regarding this result. We speculate that the two relevant terms used to explore this (“working at a very fast pace” and “working to tight deadlines”) may not have adequately explored the work environment.

Female gender and older age increased the likelihood of poor psychosocial well-being, explained by the fact that both age and gender are associated with the emotional state [43]. Cohabitation status (living with someone) was associated with poor well-being, in line with the results of a previous study; family demands can increase stress that spills over into work [37]. This view is supported by role theory, which suggests that conflict between the increased demands of work and social roles may increase stress-related symptoms and lower psychosocial well-being [27]. According to recent studies, however, cohabitation reduces the likelihood of declining physical health and psychological disorders due to a good combination of work-related and partner roles [44]. A lesser contribution to household income was associated with better well-being, consistent with previous findings [38]. Logically, one would think that the higher the proportion of household income earned the greater the burden on that individual.

Our study had some limitations. First, although we identified an association between a poor work–life balance and poor well-being, the cross-sectional nature of the work means that causal and directional inferences cannot be made. To confirm any directional and causal inferences, a cohort study needs to be conducted. Second, our study used the fourth Korean Working Conditions Survey instead of a customized questionnaire. Considering work–life balance characteristics other than work-related variables is also important. In addition to work domains, family and private social domains and personality traits that might affect psychosocial well-being should also be considered. However, the survey does not contain adequate questionnaire items to analyze family or private social factors, such as cultural traditions and social infrastructure. Third, the variables for work–life balance and psychosocial well-being were dichotomized as good and poor, between which there is ambiguity. Despite these limitations, our study is the first to investigate the association between work–life balance and psychosocial well-being using a large nationwide sample of South Koreans. Although directional inferences are difficult to make due to the cross-sectional data, the possibility of reverse causality remains, as poor psychosocial well-being may potentially increase the likelihood of poor work–life balance. As mentioned above, South Korea ranks very low in terms of the OECD Work–Life Balance Index [13]. It is important to have a close look at work–life balance through various data and studies. In our study, it is meaningful that we used the fourth Korean working conditions survey data, which is representative of South Korea.

Conclusions

Poor work–life balance was associated with poor psychosocial well-being even after adjusting for some important confounding factors. We expect that further research will identify causal relationships between work–life balance and psychosocial well-being. Furthermore, our findings, combined with the current situation in South Korea, suggest that it is necessary to implement measures assisting workers to balance their work with their private lives, thus improving well-being.

Abbreviations

CI: 

Confidence Interval

KWCS: 

Korean Working Conditions Survey

PR: 

Prevalence Ratio

WHO: 

World Health Organization

Declarations

Acknowledgements

There is no conflict of interest or financial support to declare.

Availability of data and materials

The raw-data of KWCS (Korean Working Conditions Survey) was offered from Occupational Safety and Health Research Institute and available at.

http://www.kosha.or.kr/www/cmsTiles.do?url=/cms/board/board/Board.jsp?communityKey=B1002&menuId=8303.

Authors’ contributions

JW Yang and CH Suh designed the study and performed the statistical analysis. JW Yang drafted the article, and searched references. CH Suh, CK Lee and BC Son supported to interpret the data and revise the manuscript. All of the authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of Occupational and Environmental Medicine & Institute of Environmental and Occupational Medicine, Pusan Paik Hospital, Inje University, Hospital, 75, Bokji-ro, Busanjin-gu, Busan, 47392, Republic of Korea

References

  1. Emslie C, Hunt K. ‘Live to work’or ‘work to live’? A qualitative study of gender and work–life balance among men and women in mid-life. Gend Work Organ. 2009;16(1):151–72.View ArticleGoogle Scholar
  2. Kossek EE, Pichler S, Bodner T, Hammer LB. Workplace social support and work–family conflict: a meta-analysis clarifying the influence of general and work–family-specific supervisor and organizational support. Pers Psychol. 2011;64(2):289–313.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Cunningham M. Changing attitudes toward the male breadwinner, female homemaker family model: influences of women's employment and education over the lifecourse. Soc Forces. 2008;87(1):299–323.View ArticleGoogle Scholar
  4. Clarke MC, Koch LC, Hill EJ. The work-family Interface: differentiating balance and fit. Fam Consumer Sci Res J. 2004;33(2):121–40.View ArticleGoogle Scholar
  5. Valcour M. Work-based resources as moderators of the relationship between work hours and satisfaction with work-family balance. J Appl Psychol. 2007;92(6):1512.View ArticlePubMedGoogle Scholar
  6. Senécal C, Vallerand RJ, Guay F. Antecedents and outcomes of work-family conflict: toward a motivational model. Personal Soc Psychol Bull. 2001;27(2):176–86.View ArticleGoogle Scholar
  7. Greenhaus JH, Collins KM, Shaw JD. The relation between work–family balance and quality of life. J Vocat Behav. 2003;63(3):510–31.View ArticleGoogle Scholar
  8. Allen TD, Herst DE, Bruck CS, Sutton M. Consequences associated with work-to-family conflict: a review and agenda for future research. J Occup Health Psychol. 2000;5(2):278.View ArticlePubMedGoogle Scholar
  9. Grant-Vallone EJ, Donaldson SI. Consequences of work-family conflict on employee well-being over time. Work Stress. 2001;15(3):214–26.View ArticleGoogle Scholar
  10. Noor NM. Work-family conflict, work-and family-role salience, and women's well-being. J Soc Psychol. 2004;144(4):389–406.View ArticlePubMedGoogle Scholar
  11. Grzywacz JG, Bass BL. Work, family, and mental health: testing different models of work-family fit. J Marriage Fam. 2003;65(1):248–61.View ArticleGoogle Scholar
  12. Peeters MC, Montgomery AJ, Bakker AB, Schaufeli WB. Balancing work and home: how job and home demands are related to burnout. Int J Stress Manage. 2005;12(1):43.View ArticleGoogle Scholar
  13. Organization for Economic Cooperation and Development (OECD) Statistics. Average usual weekly hours worked. http://stats.oecd.org/viewhtml.aspx?datasetcode=AVE_HRS&lang=en.
  14. Gröpel P, Kuhl J. Work–life balance and subjective well-being: the mediating role of need fulfilment. Br J Psychol. 2009;100(2):365–75.View ArticlePubMedGoogle Scholar
  15. Diener E, Wirtz D, Tov W, Kim-Prieto C, D-w C, et al. New well-being measures: short scales to assess flourishing and positive and negative feelings. Soc Indic Res. 2010;97(2):143–56.View ArticleGoogle Scholar
  16. Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 well-being index: a systematic review of the literature. Psychother Psychosom. 2015;84(3):167–76.View ArticlePubMedGoogle Scholar
  17. Harnois G, Gabriel P, Organization WH. Mental health and work: impact, issues and good practices. 2000.Google Scholar
  18. Kelloway EK, Barling J. Job characteristics, role stress and mental health. J Occup Organ Psychol. 1991;64(4):291–304.View ArticleGoogle Scholar
  19. Park J, Kim Y, Han B. Long working hours in Korea: based on the 2014 korean working conditions survey. Saf Health Work. 2017;8(4):343–6.View ArticlePubMedPubMed CentralGoogle Scholar
  20. WHO Collaborating Center for Mental Health. WHO (5) well-being index. Geneva: World Health Organization; 1998.Google Scholar
  21. Parent-Thirion A, Vermeylen G, van Houten G, Lyly-Yrjänäinen M, Biletta I, et al. Eurofound. Fifth European working conditions survey. Luxembourg: Publications. Office of the European Union. 2012;Google Scholar
  22. Lu M-L, Nakata A, Park JB, Swanson NG. Workplace psychosocial factors associated with work-related injury absence: a study from a nationally representative sample of Korean workers. Int J Behav Med. 2014;21(1):42–52.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3(1):21.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–6.View ArticlePubMedGoogle Scholar
  25. Frone MR. Work-family balance. Handbook of occupational health psychology. Washington, DC, US: American Psychological Association; 2003. p. 143–162.Google Scholar
  26. Burke RJ. Some antecedents of work-family conflict. Soc Behav Pers. 1988;3(4):287.Google Scholar
  27. Marks SR. Multiple roles and role strain: some notes on human energy, time and commitment. Am Sociol Rev. 1977:921–36.Google Scholar
  28. Frone MR, Russell M, Cooper ML. Relation of work–family conflict to health outcomes: a four-year longitudinal study of employed parents. J Occup Organ Psychol. 1997;70(4):325–35.View ArticleGoogle Scholar
  29. Greenhaus JH. The intersection of work and family roles: individual, interpersonal. and organizational issues Soc Behav Pers. 1988;3(4):23.Google Scholar
  30. Diener E, Suh E, Oishi S. Recent findings on subjective well-being. Indian J Clin Psychol. 1997;24:25–41.Google Scholar
  31. Diener E, Fujita F. Resources, personal strivings, and subjective well-being: a nomothetic and idiographic approach. J Pers Soc Psychol. 1995;68(5):926.View ArticlePubMedGoogle Scholar
  32. Sheldon KM, Kasser T. Pursuing personal goals: skills enable progress, but not all progress is beneficial. Personal Soc Psychol Bull. 1998;24(12):1319–31.View ArticleGoogle Scholar
  33. Sheldon KM, Elliot AJ. Goal striving, need satisfaction, and longitudinal well-being: the self-concordance model. J Pers Soc Psychol. 1999;76(3):482.View ArticlePubMedGoogle Scholar
  34. Hämmig O, Gutzwiller F, Bauer G. Work-life conflict and associations with work-and nonwork-related factors and with physical and mental health outcomes: a nationally representative cross-sectional study in Switzerland. BMC Public Health. 2009;9(1):435.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Ala-Mursula L, Vahtera J, Kivimäki M, Kevin MV, Pentti J. Employee control over working times: associations with subjective health and sickness absences. J Epidemiol Community Health. 2002;56(4):272–8.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Kleppa E, Sanne B, Tell GS. Working overtime is associated with anxiety and depression: the Hordaland health study. J Occup Environ Med. 2008;50(6):658–66.View ArticlePubMedGoogle Scholar
  37. Voydanoff P, Kelly RF. Determinants of work-related family problems among employed parents. J Marriage Fam. 1984:881–92.Google Scholar
  38. Schieman S, Whitestone YK, Van Gundy K. The nature of work and the stress of higher status. J Health Soc Behav. 2006;47(3):242–57.View ArticlePubMedGoogle Scholar
  39. Grzywacz JG, Marks NF. Reconceptualizing the work–family interface: an ecological perspective on the correlates of positive and negative spillover between work and family. J Occup Health Psychol. 2000;5(1):111.View ArticlePubMedGoogle Scholar
  40. Lourel M, Ford MT, Edey Gamassou C, Guéguen N, Hartmann A. Negative and positive spillover between work and home: relationship to perceived stress and job satisfaction. J Manage Psychol. 2009;24(5):438–49.View ArticleGoogle Scholar
  41. Burke RJ, Koyuncu M, Fiksenbaum L, Acar FT. Work hours, work intensity, satisfactions and psychological well-being among Turkish manufacturing managers. Europe's Journal of Psychology. 2009;5(2):12.Google Scholar
  42. Floro MS, Pichetpongsa A. Gender, work intensity, and well-being of Thai home-based workers. Fem Econ. 2010;16(3):5–44.View ArticleGoogle Scholar
  43. Fabes RA, Martin CL. Gender and age stereotypes of emotionality. Personal Soc Psychol Bull. 1991;17(5):532–40.View ArticleGoogle Scholar
  44. Nordenmark M. Balancing work and family demands: do increasing demands increase strain? A longitudinal study. Scand J Public Health. 2004;32(6):450–5.View ArticlePubMedGoogle Scholar

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