More work,better health? The moderation effect of employee-organizational psychological distance

Abstract

Based on the data obtained from 1014 Chinese employees, this study clarified the relationship between work hours, psychological distance, and the occupational mental health of employees. This study revealed a curvilinear and almost inverted U-shaped relationship between work hours and occupational mental health. Furthermore, the results showed that the optimum interval of work hours to maintain high-quality occupational mental health was indicated as “typical overtime work,” and the employee-organizational psychological distance may positively moderate this inverted U-shaped relationship, and a “close” employee-organizational psychological distance may alleviate the pressure of work hours and help to maintain high-quality occupational mental health.

Keywords

employee-organizational psychological distance moderation effect occupational mental health U-shaped relationship work hours

Introduction

At the end of the 20th century, the rise of positive psychology led to a shift from traditional psychological focus on negative factors to positive and optimistic factors (Seligman and Csikszentmihalyi, 2000). Against this backdrop, the keywords related to individual mental health and well-being appeared frequently in psychological literature, particularly in the area of organizational psychology, and these have become the core concepts of positive psychology research. The rapid development of positive psychology has further promoted the birth and development of positive organizational behavior. Scholars, such as Luthans (2002), began to introduce positive psychology into organizational behavior research. As a result, the occupational mental health of employees, which refers to their psychological status, has gradually attracted the attention of those from all walks of life (Żołnierczyk-Zreda et al., 2017). Early studies of positive organizational behavior failed to strictly differentiate between the mental health of employees and that of the general population, and the emergence of concepts such as “job-related mental health” and “non-job-related mental health” marked a gradual independence and deepening of the study of employee’s occupational mental health (Kelloway and Barling, 1991). Warr was one of the earliest scholars to propose the concept of occupational mental health, and he suggested that individual occupational mental health should include factors such as “job-related affective well-being,” “competence,” “aspiration,” and “negative job carry-over.” Warr (1992) furthermore proposed that job-related affective well-being should be regarded as an integral part of occupational mental health. In comparison to other organizational variables that represent a single perspective, occupational mental health may reflect complex psychological characteristics more synthetically and also promote the quality of occupational life among employees and more effective organizational performance (Huetter-mann and Bruch, 2019).

Sparks et al. (2001) highlighted that one of the most important changes in the workplace relates to the general extension of work hours, and they suggested that extended work hours is an important factor that affects occupational well-being. After that, the relationship between work hours and employee mental health has gradually attracted the attention of scholars (Wagner-Hartl and Kallus, 2017). Most research on work hours focused on developed countries such as the United States and Japan, while researches in developing countries were slightly limited. Moreover, numerous studies have shown that the number of work hours in developing countries is higher than that in developed countries (Liu et al., 2018), and the psychological harm caused by long work hours in developing countries was more serious than that in developed countries (Fitzgerald et al., 2015). Especially for the current China, studies have shown that overtime work by Chinese employees ranked among the highest in East Asia (Yamashita et al., 2016), and the issue of job-related time pressure among Chinese workers needs urgent attention. Taking 2017 as an example, according to the Organisation for Economic Co-operation and Development (OECD) database and the China Labor Statistics Yearbook, the average number of weekly work hours in OECD countries was 38.35 hours, whereas the number was 46.2 hours in China. Based on this calculation, in 2017, Chinese employees worked an average of 2402 hours per year for 53 weeks, far more than the 1710 hours per year in Japan, once a big overtime country. Therefore, we believed that studying the mental health of employees in this typical overtime work country has considerable practical significance.

Based on the view of the resources theory, it was believed that individual self-regulation was a limited resource (Stucke and Baumeister, 2010), and its information processing was a cognitive task that need to consume limited psychological resources (Hallez and Droit-Volet, 2017). Long work hours accelerated the loss of individual psychological resources and increased job burnout. Therefore, some scholars suggested a negative relationship between overtime work and occupational mental health (Afonso et al., 2017; De et al., 2017). However, some scholars found that time-related stress may also promote certain aspects of employees’ mental health. For example, Widmer et al. (2012) found that time-related pressure may enhance well-being by increasing organization-based self-esteem. Therefore, we assumed that there was a turning point in the relationship between work hours and occupational mental health, and this study believes that there is an optimal interval between work hours and occupational mental health of Chinese employees, at which interval the occupational mental health of employees may maintain the highest. Therefore, this study proposed Hypothesis 1:

Hypothesis 1. There is an inverted U-shaped relationship between work hours and Chinese employee occupational mental health, that is, there is an optimal work hour interval at which employee occupational mental health is the highest, and if the number of work hours is lower or higher than the optimal interval, employee occupational mental health would be lower than the optimal level.

The “employee-organizational psychological distance” (EOPD) refers to an employee’s subjective judgment and evaluation of the relationship between themselves and the organization and is used to describe the degree of agreement or fit between employees and the organization (Chen and Li, 2017). The concept of psychological distance provides a new theoretical framework for understanding employees’ occupational behaviors. In organizational practice, employees’ subjective judgment of organizational psychological distance leads to emotional experience, which is often manifested as psychological attraction or rejection (Agnew et al., 2004). Individuals engage with a high psychological involvement with organizations that are perceived as “closer,” and some positive psychological variables are activated, which can result in high-quality occupational mental health (Li et al., 2018). In contrast, when the psychological distance between employees and organizations is perceived as “distant,” their psychological involve-ment is relatively low, and employees’ perceptions of long work hours become objective and essential. Therefore, they would be easily concerned about the negative characteristics of long work hours, and accordingly, their mental health would be adversely affected. Therefore, in order to better analyze the relationship between employees’ work hours and mental health, this study tried to include psychological distance as a moderated variable into the model of employees’ work hours and mental health, so as to better characterize the internal influencing mechanism between them, and Hypothesis 2 is proposed as follows:

Hypothesis 2. EOPD may significantly moderate the relationship between work hours and occupational mental health.

Methods

Data sources

In order to ensure the representativeness of the samples, this study covered participants from both the eastern and western regions of China. Studies have shown that the eastern was the most densely populated regions of China (Li et al., 2018), and the number of employees of it was higher than that of the western region (Ye et al., 2015). Therefore, we divided the investigation into two stages by combining directional typical and random sampling (Lusinchi, 2017). In the first stage, the research team focused on obtaining the data of employees in eastern China, and the second stage of investigation concentrated on supplementing the data of eastern China, as well as obtaining the relevant data of western China. This study collected data by providing paper questionnaires on-site, sending network questionnaires links, and telephone interviews.

A total of 2000 questionnaires were distributed and 1508 questionnaires were retrieved. After the elimination of low-quality questionnaires (e.g. there are a large number of unselected items and more than ten questions with the same answer continuously), 1014 valid questionnaires were obtained, with an effective rate of 67.2 percent. Among these samples, 601 were collected by Internet and telephone interviews, and 413 were collected through on-site distribution of paper questionnaires. The detailed information of samples is shown in Table 1.

Table 1.

Basic information of samples.

Gender	N	Marital status	N	Industry	N
Male	553	Unmarried	348	Agriculture, animal husbandry, fishery	30
Female	461	Married	587	Extractive	58
Age (years)	N	Others	79	Manufacturing	111
⩽20	81	Income (monthly/RMB)	N	Electricity, gas	54
21–30	226	⩽3000	96	Construction	30
31–40	227	3001–5000	389	Community service	108
41–50	221	5001–10000	374	Hydrogeological	80
51–60	201	10,001–20,000	96	Sports, health	53
⩾61	58	20,001–50,000	44	Transportation	58
Education	N	⩾50,001	15	Education, culture	67
⩽Junior	25	Job position	N	Wholesale, retail	37
Specialist, high	212	Junior staff	428	Scientific	61
Bachelor	492	Junior managers	188	Finance, insurance	67
⩾Master	285	Middle managers	176	Party, government	72
		Top managers	55	Others	128
		Others	167

As shown in Table 1, the age, gender, and marital distribution were normal distribution among those who participated in this study, and the occupations that were investigated in this study included various sectors, such as education, health, culture, finance, and transportation.

Measurements

The control variable referred to the additional variables that may impact the results except those particular research variables of one research. Generally, it would be more appropriate to take those additional variables into analysis to exclude the influence of them (Chen and Lei, 2018), and such method has also been widely used in many areas (Liao, 2017). Therefore, we introduced some demographic information as control variables into this study, such as gender and education level. Meanwhile, organizational variables, such as industry (Peters et al., 2018) and job position (Herr et al., 2018), have been proved to have great impact on employees’ mental health. Thus, this study also adopted some occupational variables as control variables.

The occupational mental health scale used in this study comprises the affective well-being scale and additional mental health scale which were developed by Warr (1990). The affective well-being scale consists in 12 items and was divided into two subscales: “anxiety-content” (employees’ emotional tendencies in respect to anxiety-content) and “depression-enthusiasm” (emotional tendencies in respect to depression-enthusiasm). The reliability coefficients of those scales were 0.76 and 0.80, respectively. The “additional mental health measurements” scale consisted in three primary types of occupational behaviors with 16 items that included “reports of job competence (individual’s ability to cope successfully with certain work events),” “aspirations (individual tendency to set goals and participate in goal-oriented activities),” and “negative job carry-over (the spill-over effect of work on leisure and family life, and the consequences of dysfunction)” (Versey and Tan, 2018). This scale was validated and revised by Sevastos et al. (1992), which has good reliability and validity. All the scales were scored using a 5-point Likert-type scale, that is, “1” indicated “complete inconsistency” and “5” indicated “complete compliance.” The scale contains items such as “I am not very interested in my job” and “I find my job quite difficult.”

Work hours represented the average number of work hours per week over the period of 1 month, and such information was provided by the respondents. According to the regulations of the State Council of the People’s Republic of China regarding work hours, an individual should work no more than 8 hours per day or 40 hours per week. This study classified “work” into five categories, and the limit was set at 40 hours per week. Thus, “1” indicates <40 hours, “2” indicates 41–50 hours, “3” indicates 51–60 hours, “4” indicates 61–70 hours, and “5” indicates >71 hours. The latter four categories were defined as overtime work. The measurement of this project was to require the participants to choose their actual work hours of last week.

Chen and Li (2017) expanded the EOPD to include six dimensions, namely, experience distance, behavioral distance, emotional distance, cognitive distance, space-time distance, and objective social distance, and furthermore developed the EOPD scale which consists in a total of 44 items that cover a wider range than the conventional model. Hence, we adopted the EOPD scale developed by H. Chen and S. Li, which used a 5-point Likert-type scale, such that “1” indicates “complete inconsistency” and “5” indicates “complete compliance.” For the overall reliability of the scale, Cronbach’s alpha was 0.971, and Cronbach’s alpha of each latent variable was 0.956, 0.953, 0.940, 0.876, 0.833, and 0.737. The scale contains items such as “I worked in the organization for a period of time and generated feelings” and “Over the course of the work, I gained a good understanding of the organization in which I work.”

Data analysis

This study utilized SPSS 22.0 to carry out the descriptive statistical analysis, correlation analysis, and regression analysis, and a structural equation was developed using AMOS 23.0 to test the validity of each scale. In addition, the regression equation developed by Lin et al. (2016) was introduced to test the inverted U-shaped relationship between work hours and occupational mental health as well as the moderation effect of EOPD. Specifically, equation (1) was used to test the inverted U-shaped relationship, and equation (2) was used to assess the moderation effect of psychological distance

M H_{0} = β_{c 0} + β 1 W H + β 2 W H^{2} + ε_{0}

(1)

\begin{array}{l} M H_{i} = β_{c 1} + β_{3} W H + β_{4} W H^{2} + β_{5} P D \\ + β_{6} P D \times W H + β_{7} P D \times W H^{2} + ε_{1} \end{array}

(2)

Results

In this study, Cronbach’s alpha was used to measure the reliability of each scale. At the same time, the validity of the scale was verified using confirmatory factor analysis. The results of the test are shown in Table 2.

Table 2.

The test of measurements.

Variables	Cronbach’s alpha	Chi-square value	CFI	TLI	NFI	GFI	RMSEA
Occupational mental health	0.872	2380.79	0.905	0.910	0.866	0.903	0.027
EOPD	0.970	3880.06	0.924	0.915	0.905	0.849	0.060

CFI: comparative fit index; TLI: Tucker–Lewis index; NFI: normed fit index; GFI: goodness-of-fit index; RMSEA: root mean square error of approximation; EOPD: employee-organizational psychological distance.

Cronbach’s alpha value of overall occupational mental health was 0.872, and the reliability coefficients of each subscale ranged from 0.800 to 0.875; Cronbach’s alpha of EOPD was 0.970, and the reliability coefficients of each subscale ranged from 0.885 to 0.939. The results of confirmatory factor analysis showed that the scale fitted well, and the factor loads of each sub-dimension and item were significantly higher than 0.5, which indicated that the convergence validity of the scale was ideal. In addition, the survey results were more direct and accurate as the work hours were measured by requiring the subjects to directly choose the average work hours per week within 1 month before the self-filled questionnaire, so the reliability and validity analysis were not conducted.

Descriptive statistics and relevant results

The means, standard deviations, and correlation coefficients of variables are shown in Table 3.

Table 3.

The descriptive statistics and correlation analysis of variables.

	WH	PH	JC	JA	NJ	AM	EM	PD	ED	BD	ED	CD	CD	CD
PH	0.440**
JC	0.231**	0.727**
JA	0.425**	0.843**	0.503**
NJ	0.425**	0.808**	0.309**	0.606**
AM	0.332**	0.883**	0.793**	0.775**	0.476**
EM	0.386**	0.820**	0.323**	0.569**	0.925**	0.552**
PD	0.248**	0.626**	0.517**	0.492**	0.430**	0.635**	0.476**
ED	0.510**	0.382**	0.182**	0.366**	0.321**	0.340**	0.360**	0.369**
BD	0.418**	0.355**	0.245**	0.358**	0.246**	0.339**	0.264**	0.423**	0.620**
ED	0.442**	0.378**	0.297**	0.358**	0.271**	0.343**	0.273**	0.392**	0.550**	0.707**
CD	0.488**	0.377**	0.287**	0.374**	0.262**	0.354**	0.262**	0.400**	0.502**	0.602**	0.639**
CD	0.606**	0.502**	0.367**	0.477**	0.388**	0.451**	0.367**	0.405**	0.503**	0.475**	0.487**	0.718**
CD	0.602**	0.464**	0.318**	0.429**	0.389**	0.401**	0.356**	0.357**	0.399**	0.385**	0.424**	0.651**	0.779**
M	3.66	2.86	2.71	2.98	2.87	2.84	2.90	2.97	2.93	2.64	2.98	2.92	2.39	2.94
SD	1.23	0.88	1.16	1.07	1.07	1.03	1.06	0.90	0.97	0.78	0.98	0.98	0.86	1.03

JC: job competence; JA: job aspiration; NJ: negative job carry-over; PD: psychological distance; ED: experiential distance; BD: behavioral distance; ED: emotional distance; CD: cognitive distance; SD: spatial-temporal distance; OD: objective social distance; AM: anxiety-contentment; EM: depression-enthusiasm; WH: work hour.

p < .05; **p < .01.

Table 3 shows that the mean for work hours was 3.66, which belongs to the interval of 51–60 hours and also indicates that overtime work was a serious issue for the participants. The average score of occupational mental health was 2.86, which is slightly lower than the median, and the mean of EOPD was 2.97. The correlation analysis showed that the variables were significantly correlated and could be used for further data processing.

Hypothesis testing

Hierarchical multiple regression analysis was used to examine the inverted U-shaped relationship between work hours and occupational mental health. Taking occupational mental health as the dependent variable, the process was as follows: (1) introduce control variables into the equation, (2) introduce work hours, and (3) introduce the square term of work hours.

Table 4 shows that the regression coefficient of the work hours’ square term was significant (–2.055, p < 0.001), and the ΔR² of Model 3 was larger than that of Model 2, indicating an inverted U-shaped relationship between work hours and occupational mental health. At the same time, we also used the quadratic curve to describe their relationship, which is shown in Figure 1.

Table 4.

The test of work hours and occupational mental health.

Variable	Model 1	Model 2	Model 3
Gender	−0.112**	−0.036	−0.033
Age	0.003	0.015	0.022
Marital status	−0.015	−0.004	0.010
Monthly income	0.059	0.044	0.026
Educational level	−0.067*	−0.051	0.025
Tissue location	−0.033	−0.042	−0.039
Working life	−0.035	−0.051	−0.031
Work hours		0.433***	2.445***
Square of work hours			−2.055***
R ²	0.020	0.201	0.384
ΔR²	0.013	0.194	0.378
F	2.969***	31.534***	69.484***

p < .05; **p < .01; ***p < .001.

Figure 1.

The inverted U-shaped relationship between work hours and occupational mental health.

Figure 1 indicates that with the increase in work hours, the occupational mental health shows an inverted U-shaped trend. The symmetrical axis was 3.6, which belongs to “overtime work,” according to the categories contained in our questionnaire, that is, employees who work a moderate amount of overtime have the highest level of occupational mental health.

Based on the formula illustrated in section “Data analysis,” this study examined the moderation effect of EOPD. In Models 1–4, “anxiety-contentment” was taken as a dependent variable in the regression equation. Models 5–8 introduced “depression-enthusiasm” as a dependent variable, and the results are shown in Table 5.

Table 5.

The moderation role of employee-organizational psychological distance.

Variable	Anxiety-contentment						Depression-enthusiasm
Variable	M1		M2	M3		M4	M5		M6	M7		M8
Gender	–0.057		−0.048*	–0.048		−0.046	–0.006		−0.001	–0.003		−0.003
Age	–0.012		−0.022	–0.020		−0.023	0.042		0.037	0.045		0.044
Marital status	0.057**		0.031	0.032		0.034	–0.035		−0.048	–0.044		−0.044
Monthly income	0.087**		0.037	0.037		0.039	–0.032		−0.058	–0.053		−0.053
Educational level	–0.015		−0.031	–0.032		−0.035	0.025		0.017	0.014		0.013
Tissue location	–0.062		−0.048*	–0.049		−0.047	–0.025		−0.018	–0.023		−0.023
Working life	–0.001		0.018	0.019		0.019	–0.082		−0.072	–0.067		−0.067
WH	2.145***		0.234***	0.281***		−0.694***	2.044***		1.057***	1.291***		1.213***
WH²	–1.865***		−0.056	–0.028		0.964***	–1.688		−0.754***	–0.616***		−0.536**
PD			0.576***	0.644***		0.263			0.297***	0.635***		0.604***
PD × WH				–0.113		1.331***				–0.559***		−0.443
PD × WH²						−1.163***						−0.093
R ²	0.279		0.444	0.444		0.449	0.287		0.331	0.342		0.343
ΔR²	0.272		0.438	0.438		0.442	0.281		0.324	0.335		0.335
F	43.11***		79.95***	72.75***		67.96***	44.882***		49.589***	47.447***		43.456***
Variable	Job competence				Job aspiration				Negative job carry-over
Variable	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12
Gender	−0.047	−0.040	−0.039	−0.035	−0.052	−0.043	−0.045	−0.042	0.006	0.010	0.006	0.007
Age	−0.049	0.034	−0.034	−0.027	−0.012	0.006	0.006	0.012	0.060	0.068	0.069	0.070
Marital status	0.027	0.015	0.014	0.019	0.046	0.032	0.036	0.040	−0.039	−0.045	−0.040	−0.039
Monthly income	0.112	0.078	0.077	0.078	0.038	−0.001	0.002	0.003	−0.063	−0.081	−0.075	−0.075
Educational level	−0.019	−0.030	−0.030	−0.036	0.004	−0.010	−0.013	−0.018	0.062	0.056	0.052	0.051
Tissue location	−0.019	−0.012	−0.011	−0.010	−0.054	−0.045	−0.049	−0.049	−0.018	−0.014	−0.020	−0.020
Working life	0.016	0.023	0.022	0.022	0.015	0.023	0.028	0.027	0.079	−0.075	−0.069	−0.069
WH	2.101***	0.863***	0.828***	0.756***	1.615***	0.166	0.360	−0.949	2.099***	1.440***	1.737***	1.585***
WH²	−1.928***	−0.754***	−0.775***	−0.835***	−1.224***	0.149	0.266	1.596***	−1.699***	−1.075***	−0.897***	−0.742***
PD		0.369***	0.318***	0.278***		0.432	0.714	0.222		0.197	0.628***	0.751***
PD × WH			0.204***	2.376***			−0.466	1.426***			−0.714***	0.944***
PD × WH²				1.848***				1.527***				0.717***
R ²	0.393	0.460	0.460	0.461	0.255	0.346	0.354	0.362	0.321	0.340	0.359	0.360
ΔR²	0.387	0.454	0.454	0.456	0.248	0.339	0.347	0.354	0.315	0.333	0.352	0.353
F	72.18	85.29	44.46	71.23	38.15	53.03	49.92	47.25	52.69	51.59	50.98	46.70

PD: psychological distance; WH: work hours; WH²: square of work hours.

p < .05; **p < .01; ***p < .001.

With respect to Model 4, Table 5 shows that both the interaction coefficients of psychological distance and work hours, and the square interaction coefficients of work hours were all significant, which indicated that psychological distance may effectively moderate the relationship between work hours and “anxiety-contentment.” While in Model 8, the interaction coefficients of psychological distance were not significant.

From Table 5, the results showed that despite experiencing the same time-related pressure, a high-level psychological distance could contribute to maintaining a high-quality occupational mental health. This study examined the dynamic relationship between work hours, EOPD, and occupational mental health, as shown in Figure 2.

Figure 2.

The moderation role of employee-organizational psychological distance.

As shown in Figure 2(a), we set the X-axis and Y-axis as equal units, namely the work hours of employees, and set the Z-axis as the employee’s occupational mental health. It can be seen in Figure 2(a) that with the extension of employee’s work hours, the occupational mental health of employees experienced a change from “low–high–low,,” which showed a clear inverted U-shaped relationship between the work hours and occupational mental health. In Figure 2(b), we reset the Y-axis as the EOPD, and the relationship between work hours and occupational mental health has changed significantly. Especially within the long work hours (i.e. the end of X-axis), employees’ occupational mental health showed an obvious reverse improvement trend.

Discussion

The average number of weekly work hours was 3.66, which constituted “typical overtime work” according to our questionnaire. The results indicate a common norm regarding overtime work among Chinese employees. We consider that the reason is closely related to China’s collectivist culture. Compared with individualism, the self-perceptions and thinking of collectivists often revolve around the team (Dierdorff et al., 2011), and an emphasis is placed on collective goals and trust (Binder, 2017). As a typical collectivist culture (Woodhams et al., 2015), the Chinese advocate hard-work and dedication (Zhao and Heyman, 2017), and overtime work has gradually internalized into their inherent awareness. Some cultures even regarded overtime work as one of the characteristics of an “ideal employee” (Ganster et al., 2018). In such a climate, employees’ psychological anxiety could be alleviated by extending their work hours in order to satisfy social expectations. Moreover, China has always been a country that has a high power distance (Edmondson, 1999), and employees are sensitive to group deviation and tend to obey organizational rules without criticism. Therefore, when faced with the overtime needs of organizations, especially in situations of perceived pressure from their own groups, more employees would choose to work overtime. In addition, the results showed that the effect of control variables on occupational mental health became less significant after the introduction of work hours, which showed that the influence of control variables was gradually weakening.

This study demonstrates that the relationship between work hours and occupational mental health is not a simple linear, but an inverted U-shaped relationship, and the EOPD can significantly moderate this relationship. This conclusion not only offers novel insight into understanding the relationship between work hours and occupational mental health but also provides important practical guidance for intervening employees’ occupational mental health.

On one hand, this study presents the changing process of the relationship between work hours and occupational mental health, which breaks the previous single explanation mechanism on them (Iglesias-Rios et al., 2019; Widmer et al., 2012). We speculate that this inverted U-shaped relationship between work hours and occupational mental health may be related to activation theory. According to the activation theory, individual has an optimal activation interval, in which individual psychological factors such as motivation, work performance, and well-being would be maintained at high level (Pan et al., 2018). Therefore, this study suggests that there also exists an optimal interval for individual work hours to make employees work at the optimal level. Moreover, this study establishes a transmission mechanism and influence path among work hours, psychological distance, and occupational mental health, which is an extension of previous researches.

On the other hand, the inverted U-shaped relationship model demonstrates the existence of a matching mechanism of work hours in maintaining employees’ occupational mental health, and it provides new practical possibilities for managers to improve employees’ occupational mental health. The analysis of the data showed that the appropriate work hours interval of the sample group was 51–60 hours per week, which indicated that extending work hours may not damage employees’ mental health, but overwork seriously would certainly reduce the quality of employees’ mental health. The discussion of this inverted U-shaped relationship claims that managers should pay attention to the management of work hours to keep high psychological production (the apex of the curve in Figure 1), while not paying attention to or extending the work hours of employees excessively may lead to the poor state of employees (the rising part and the falling part of the curve in Figure 1). The work hours proved to be a double-edged sword in this study. Therefore, this study encourages managers to improve employees’ occupational mental health by controlling their work hours effectively. For example, the managers could guide to establish an atmosphere of decent work (Cooke et al., 2019) and adopt scientific selection and deployment to ensure the optimal match between work hours and occupational mental health (Kuroda and Yamamoto, 2019).

Another important contribution of this study was to verify the positive moderation role of EOPD between work hours and occupational mental health, that is, a “close” EOPD may effectively help to maintain or improve occupational mental health. This conclusion provided important practical guidance for managers to relieve employee’s stress by reducing the psychological distance between employees and organizations. According to the construal level theory, individuals tend to interpret objects in an abstract and essential way when they perceive the objects as “distant” (Stillman et al., 2017) and would more likely to notice the negative effects of overtime work on their psychology and physiology. Thus, their occupational mental health would be adversely affected. While in “close” psychological distance situations, where individuals perceive their distance toward organization as “close,” and the outcomes of decision-making are incoherent and task-independent (Trope and Liberman, 2010).

There were still some limitations in this study: (1) the data of this study were obtained from participants’ retrospective answers, and there may be some memory bias; (2) this study explored the general relationship between work hours and mental health of employees and failed to analyze the demographic variables or other control variables that may influence employees’ occupational mental health; (3) this study only found the correlation between work hours and occupational mental health, but failed to prove the causal link between them; and (4) although the Chinese employees selected in this study are typical representatives of the current overtime working group, the selection of samples still has some limitations, so it is more suitable for the universal conclusion of other countries to be further explored.

Footnotes

Acknowledgements

We would like to thank all of our participants involved in this study.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

This study was carried out in accordance with the recommendations outlined by the Ethical Codes of Consulting and Clinical Psychology of the Chinese Psychological Society. The protocol was approved by the Ethics Committee of the China Occupational Safety and Health Association-Occupational Mental Health Professional Committee, and all subjects in our study gave written informed consent in accordance with the Declaration of Helsinki.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Major project of National Social Science Funding of China (Grant No. 16ZDA056), the National Natural Science Funding of China (Grant Nos. 71473248, 71673271), the Think Tank of Green Safety Management and Policy Science (2018 “Double First-Class” Initiative Project for Cultural Evolution and Creation of CUMT 2018WHCC03), the Jiangsu Philosophy and Social Sciences Excellent Innovation Cultivation Team (2017), the 333 High-level Talents Project of Jiangsu Province (2016), the Innovation Team Program of the China University of Mining and Technology (Grant No. 2015ZY003), “13th Five Year” Brand Discipline Construction Funding Project of China University of Mining and Technology (2017), and the Fundamental Research Funds for the Central Universities, CUMT(2019CXNL07). The study design and subjects, data collection, and analysis were the responsibility of the authors. The funding body did not play a role in the above activities.

ORCID iD

Hong Chen

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