Validation of the Chinese Version of the Multidimensional Workaholism Scale

Abstract

The aim of this study was to translate the Multidimensional Workaholism Scale (MWS) into Chinese and then test its reliability and validity among full-time Chinese employees in two stages. In Study 1 (N = 220), the MWS was translated and exploratory factor analysis was conducted resulting in a four-factor solution consistent with the original MWS: motivational, cognitive, emotional, and behavioral. In Study 2 (N = 425), confirmatory factor analysis showed that a four-factor, bifactor model was the best fit for the data. Configural, metric, and scalar invariance models were tested which demonstrated that the Chinese version of the MWS did not differ across gender, age, and job position groups. Finally, workaholism and engagement were related and distinct from one another, and they correlated with emotional exhaustion, work-family conflict and life well-being uniquely. This study indicated that the Chinese version of the MWS is a valid and reliable tool for Chinese employees, and this has important practical implications for the individual health and career development of Chinese working adults.

Keywords

workaholism Chinese adults scale validation measurement invariance

As labor market conditions rapidly change to match the environment of modern society, both competition and instability are intensifying in the workplace. Given this context, employees must continually put more effort into their work to remain competitive (Shimazu et al., 2015; van Beek et al., 2012). This is also true of China; factors such as rapid urban growth, financial pressure, and high job instability have escalated the overall level of competition in the workplace. (Westwood & Lok, 2003; Yu et al., 2020). Particularly in recent years, working adults in China compete incessantly in daily work, and have to invest much more time and energy to gain a competitive advantage.

Indeed, research on “workaholism”, which describes individuals who feel an excessive desire to work (Oates, 1971), has grown considerably in the last few years (Balducci et al., 2020; Scafuri Kovalchuk et al., 2019). A crucial concern is the negative outcomes of workaholism. A meta-analysis has found that workaholism is associated with ill-health (physical and mental health) and burnout, increased work-family conflict (WFC), and lower job and life satisfaction (Clark et al., 2016). However, there is no consensus in the organizational literature on how workaholism should be conceptualized, nor is there full agreement on how to measure it (Andreassen, 2014; Clark et al., 2020; Ng et al., 2007). This also constitutes the main hindrance to theoretical and empirical research progress on the matter (Clark et al., 2016).

To address this problem, Clark and colleagues (2020) identified key attributes from the current conceptualizations and measures used for workaholism based on a comprehensive overview, they proposed workaholism as a multidimensional construct and developed the Multidimensional Workaholism Scale (MWS). The goal of this study was to translate the MWS into Chinese and test its reliability and validity among full-time Chinese employees, with the aim of facilitating research progress on workaholism in China.

Workaholism

Originally, Oates (1971) coined the term “workaholism” to describe individuals who felt an excessive desire to work. Since then, workaholism has been conceptualized in a variety of ways. It was first defined based on the amount of time an individual spent at work, for example, 50 hr per week was considered high (Mosier, 1983). However, this may not be accurate for everyone. For example, not all employees who work long hours do so voluntarily, meaning that workaholism may be better defined based on how much discretionary time is spent working (e.g., Scott et al., 1997; Mudrack & Naughton, 2001; Ng et al., 2007), the internally uncontrollable motivation to engage in work (Clark et al., 2016; e.g., Spence & Robbins, 1992; Snir & Harpaz, 2012), and/or the tendency to work beyond requirements (Mudrack & Naughton, 2001; Schaufeli et al., 2008a).

Therefore, Clark et al. (2020) proposed the concept of multidimensional workaholism consisting of motivational, cognitive, emotional, and behavioral dimensions. Among these, the motivational dimension is based on self-determination theory, and refers to the inner pressure or compulsion to work (e.g., an uncontrollable need or inner “push” to work; Snir & Harpaz, 2012; Taris et al., 2010). The cognitive dimension is prominent in most conceptualizations of workaholism, and refers to persistent or uncontrollable thoughts about work, even when one is not working or on vacation (Clark et al., 2016). The emotional dimension refers to the presence of negative emotions when not working or being prevented from working, and may manifest in the form of guilt, anger, and/or other anxiety symptoms (Clark et al., 2014). Finally, the behavioral dimension refers to excessive work; that is, working beyond required work, including periods of engagement lasting many additional hours (Snir & Harpaz, 2012) and/or spending personal or leisure time on work activities (Ng et al., 2007). Each of these four dimensions is an insufficient indicator of workaholism when taken alone, but combine to create a fuller picture of the condition.

In China, Confucianism has persisted as a major cultural force for thousands of years, particularly regarding the core values of occupational devotion, hard work, and diligence (Tian, 2004), all of which may also motivate Chinese employees to work excessively. Furthermore, over the past 40 years of reform and opening-up, tremendous changes have taken place in China, bringing with a flood of Western influence, through which competition and instability has intensified in the workplace. Excessive work is now perceived as a way to gain competitive advantage in the workplace, and consequently people feel they have to invest more time on their work. Indeed, several studies have examined workaholism in a variety of cultural contexts and found that employees in China tend to score higher than employees in Western European countries (e.g., Q. Hu et al., 2014).

However, there is little research on workaholism in China to provide a comprehensive account of how workaholism is understood by Chinese working adults today. Zheng et al. (2010) identified four aspects of workaholism through interviews and empirical research, and validated its structure from enterprises in Beijing. They found that workaholism consists of work fulfillment (experience of energy and motivation in work), work priority (put work first and prioritize it above everything), excessive work (high investment of time and energy in work), and high work demands (high requirements in work). This study proposed that the components of workaholism were conceptually similar to the MWS, indicating the relevance of cross-cultural concept and the potential to validate the MWS in China.

The Present Study

The goal of the present study was to translate the MWS into Chinese and then test its reliability and validity among full-time Chinese employees. To accomplish this goal, we gathered data from full-time employees in China through online surveys. In Study 1, we translated the MWS and invited a diverse sample of working adults to participate in the online survey. The result of exploratory factor analysis (EFA) supported a four-factor solution; all of the items were clustered in their respective factors which demonstrated good reliability. In Study 2, we collected data from a new sample of full-time employees to test the validity of the MWS. First, we conducted a series of confirmatory factor analyses (CFAs) to examine the factor structure; then, we further tested its validity by assessing the relationship between the MWS and its dimensions and related constructs.

Study 1: Validating the Chinese Version of the Multidimensional Workaholism Scale

Method

Scale Translation

The multidimensional measure of workaholism is a 16-item questionnaire comprising four distinct dimensions (motivational, cognitive, emotional, and behavioral; four items each). In this study, respondents were asked to indicate their level of workaholism on a 5-point Likert-type scale, in which 1 = never true, 2 = seldom true, 3 = sometimes true, 4 = often true, and 5 = always true.

We used the classic back-translation method (Brislin, 1980) for the scale validation process. First, all items were independently translated into Chinese by two bilingual (Chinese and English) experts in career psychology. Then, we compared and evaluated the translations jointly and reached a consensus on the translation of the scale. Second, the translated Chinese version was back translated by another academic bilingual researcher who was not otherwise related to this study. After the back-translation, we invited two researchers to compare the back-translated versions with the original English items to check the meaning of the items. Finally, we adjusted the wording of items and reached the conclusion that they were consistent with the original MWS.

Participants

The sample in this study comprised 220 full-time employees in China. More specifically, it included 116 (52.7%) females and 104 males (47.3%), with a mean age of 31.4 years (SD = 6.87; range = 20–55 years). Education levels consisted of high school and below (n = 16, 7.3%), undergraduate degree (n = 108, 49.1%), master’s degree or above (n = 95, 43.2%), and those who chose not to answer (n = 1, 0.5%). The average number of working years was 8.21, with an average of 47.4 hr per week. The majority were employees of various companies (n = 93, 42.3%), followed by civil servants (n = 56, 25.5%), primary and secondary school teachers (n = 28, 12.7%), researchers (n = 11, 5.0%), and those in other occupations (n = 32, 14.5%). Most of them were front-line employees (n = 154, 70%), and others were middle and senior managers (n = 66, 30%).

Procedure

Participants were recruited from a survey website (http://www.obhrm.com/), which is an online data collection service where individuals can complete research tasks, and in return gain professional feedback about their personality traits and work characteristics. First, an online informed consent form was presented as the first page of the study, and a “next step” button then allowed access to the study. Next, a link to the survey that was posted on the survey website was provided, containing the 16-item Chinese version of the MWS, and demographic information asking for age, gender, tenure, occupation, average working hours per week, and job position. Finally, each participant who completed the questionnaire was provided with a report about their work characteristics. All participants were assured of confidentiality and anonymity.

Participants originally included 278 full-time employees (defined as working ≥ 35 hr per week). However, 58 were ultimately excluded from the analysis, including 18 who reported working less than 35 hr per week, 33 who failed two attention check items that were set up in advance, and seven outliers who responded carelessly, according to the Mahalanobis Distance (D) using the R package “careless” (Yentes & Wilhelm, 2018).

Results

Exploratory Factor Analysis

Consistent with Clark and colleagues’ (2020) original study, we expected the factors would be correlated to form an overall workaholism scale. Considering that multivariate normality of the variables may not have been a tenable assumption, our first step was to conduct an EFA using the principal axis factoring method with a promax rotation in IBM SPSS Version 26 (Costello & Osborne, 2005; Kanh, 2006). The Kaiser-Meyer-Olkin measure of sample adequacy was 0.91, while the Bartlett’s test of sphericity was significant (p < .001), thus indicating that the sample was suitable for factor analysis.

Additionally, we tested the factor structure using parallel analysis (Horn, 1965). Parallel analysis is one of the most accurate methods for determining the number of factors that should be extracted (Hayton et al., 2004). It involves randomly generating a group of simulation data matrix (the number of variables and observations is the same as the actual data), and then comparing the number of eigenvalues of the simulation data with the real data. Eigenvalues in the real data larger than the simulation data should be retained (Duffy et al., 2017; B. P. O’ Connor, 2000).

The break in our parallel analysis result, scree plot, and percentage of variance explained by the factors, all supported a four-factor solution. Table 1 shows the factor loadings for each item, all of which were clustered on their respective primary factors, with factor loadings above .35 (in a range of .38 to .91). This indicated that the four factors conformed to the MWS in general, including the four items forming “motivational” (explaining 14.2% of the variance in workaholism), four items forming “cognitive” (explaining 16.8% of the variance), four items forming “emotional” (explaining 13.2% of the variance), and four items forming “behavioral” (explaining 12.3% of the variance). The MWS total and all four subscales had adequate to good internal consistency coefficients as follows: .92 (total), .81 (motivational), .86 (cognitive), .81 (emotional), and .80 (behavioral). Finally, when we tested the correlations among the subscales, the four factors of the MWS were significantly correlated with each other (range of .46 to .62).

Table 1.
Results of the Exploratory Factor Analysis in Study 1.

Items Factor Loading

F1 F2 F3 F4

Motivational (动机)

Q1. I always have an inner pressure inside of me that drives me to work.
在我内心总有一股压力，驱使我去工作。 .90 .01 −.02 .00

Q2. I work because there is a part inside of me that feels compelled to work.
我工作是因为我的内心在迫使着我去工作。 .38 .29 −.14 −.02

Q3. I have a strong inner desire to work all of the time.
我内心有想要一直工作的强烈愿望。 .50 .21 .09 .03

Q4. There is a pressure inside of me that drives me to work.
有一种内在压力，驱使我去工作。 .82 −.18 .07 .10

Cognitive (认知)

Q5. I feel like I cannot stop myself from thinking about working.我无法让自己不去想工作上的事情。 −.06 .77 .10 −.02

Q6. In general, I spend my free time thinking about work.总体来说，空闲时间我也都在想工作上的事情。 .03 .52 .20 .12

Q7. At any given time, the majority of my thoughts are work related.任何时候，我大多都在想工作上的事情。 .12 .66 .06 .00

Q8. It is difficult for me to stop thinking about work when I stop working.下班后，我也很难不去想工作上的事情。 −.04 .87 −.06 .00

Emotional (情感)

Q9. I feel upset if I have to miss a day of work for any reason.如果我某天没能去工作，不管是出于什么原因我都会心烦意乱。 .01 .12 .57 .09

Q10. I am almost always frustrated when I am not able to work.当我不能去工作的时候，我总是觉得很烦恼。 .24 .09 .49 −.07

Q11. I feel upset if I cannot continue to work.如果无法继续工作，我会感到很沮丧。 .19 −.02 .65 −.07

Q12. When something prevents me from working, I usually get agitated.当有什么事情让我不能工作时，我通常会烦躁不安。 −.19 −.03 .91 .01

Behavior (行为)

Q13. When most of my coworkers will take breaks, I keep working.当大多数同事都去休息的时候，我仍继续工作。 −.08 .29 −.02 .64

Q14. I work more than what is expected of me.我完成工作任务/内容比别人期望我完成的还要多。 .22 −.18 .00 .65

Q15. I tend to work longer hours than most of my coworkers.与大多数同事相比，我花在工作上的时间更多。 −.13 .24 −.06 .66

Q16. I tend to work beyond my job’s requirements.我完成的工作任务/内容往往会超出职位/职业本身的要求。 .07 −.09 .04 .67

Note. N = 220. Bolded corresponding to the factor they load on.

Study 2: Confirmatory Factor Analysis and Validation of the Chinese Version of the Multidimensional Workaholism Scale

Study 1 showed that the Chinese version of the MWS had good internal consistency; each of the four subscales were theoretically sound and highly intercorrelated. We then progressed to Study 2, in which we confirmed the scale’s validity based on related constructs. To further test the reliability and validity of the MWS among Chinese employees, we ran a series of CFAs, including a correlated four-factor model, a correlated three-factor model, a single factor model, a second order four-factor model, and a bifactor model. Additionally, we tested the measurement invariance of the Chinese version of the MWS using multigroup confirmatory factor analysis, across gender, age, and job position groups. Then, consistent with the original scale development study (Clark er al., 2020) and previous studies (Di Stefano & Gaudiino, 2019; Schaufeli et al., 2008b; Shimazu et al., 2015), we hypothesized that workaholism and engagement are distinct from one another, and that they uniquely relate to various outcomes. Workaholics would experience more emotional exhaustion and WFC, and lower life well-being. By contrast, engaged employees would experience less emotional exhaustion and WFC, and higher life well-being.

Method

Participants and Procedure

The sample for this study comprised 425 individuals, including 252 females (59.3%) and 173 males (40.7%), with a mean age of 29.9 years (SD = 7.08; range = 19–60 years). Education levels were high school and below (n = 79, 18.6%), undergraduate degree (n = 216, 50.8%), master’s degree or above (n = 128, 30.1%), and those who chose not to answer (n = 2, 0.5%). The average number of working years was 8.21, with an average of 47.4 hrs per week. The majority were employees of various companies (n = 180, 42.4%), followed by civil servants (n = 44, 10.4%), primary and secondary school teachers (n = 54, 12.7%), researchers (n = 17, 4.0%), and those from other occupations (n = 130, 30.5%). Most of them were front-line employees (n = 254, 59.8%), and others were middle and senior managers (n = 171, 40.2%).

We asked 30 alumni of a large public university in China, who now work in a variety of different organizations, to invite no more than 20 of their colleagues or employed friends to participate in the survey. They sent links of the online survey to the participants’ personal e-mail addresses and confidentiality and anonymity were assured. Alumni were paid 20 Chinese yuan (approximately $3.02) for every completed questionnaire returned. An online informed consent form was presented as the first page of the study, and a “next step” button then allowed access to the study. The survey link also contained the 16-item Chinese version of the MWS from Study 1, the scales of other constructs used in Study 2, demographic information asking for age, gender, tenure, occupation, average working hours per week, and job position.

Participants originally included 498 full-time employees. Following the same procedure used in Study 1, 73 were ultimately excluded from the analysis, including 27 who reported working less than 35 hr per week, 30 who failed two attention check items, and 16 outliers identified via the R package “careless” (Yentes & Wilhelm, 2018).

Measures

Workaholism

The Chinese version of the MWS validated in Study 1 was used to assess workaholism, and the internal consistency reliability of the MWS in this study was .93. The four subscales of the MWS had good internal consistency coefficients (Cronbach’s α) as follows: .82 (motivational), .89 (cognitive), .89 (emotional), and .87 (behavioral).

Engagement

The Chinese version of the Utrecht Work Engagement Scale, which was originally developed by Schaufeli et al. (2002), was used to assess engagement (Zhang & Gan, 2005). Each item was rated on a 7-point Likert-type scale, ranging from 1 (never) to 7 (always). These were spread across three dimensions, including vigor (e.g., “At my job, I feel strong and vigorous”), dedication (e.g., “I am enthusiastic about my job”), and absorption (e.g., “I feel happy when I am working intensely”). Scafuri Kovalchuk et al. (2019) have provided it has good reliability estimates higher than .80 and significantly associated with workaholism, WFC, and emotional exhaustion. Good reliability and validity were also demonstrated for the Chinese version of this scale (Y. Li et al., 2021; Zeng et al., 2019). In this study, the total score reliability was .91, and the reliability for each of the subscales was as follows: .77 (vigor), .81 (dedication), and .83 (absorption).

Emotional exhaustion

The Chinese version of the Maslach Burnout Inventory-General Survey, which was originally developed by Maslach and Jackson (1981), was used to assess emotional exhaustion (C. Li & Shi, 2003). The emotional exhaustion subscale consisted of five items that were each rated on a 7-point Likert-type scale, ranging from 0 (never) to 6 (always). Example items include the following: “I feel fatigued when I get up in the morning and have to face another day on the job” and “I feel burned out from my work.” F. Cheung et al. (2018) found the emotional exhaustion subscale has a good reliability higher than .90 and significantly associated with workaholism. Wang et al. (2017) and Chen et al. (2018) all reported good reliability (higher than .90) for the Chinese version of this scale. In the present study, the internal consistency reliability was .91.

Work-family conflict

The Chinese version of the WFC scale which was originally developed by Gutek et al. (1991), was used to assess WFC (Gao & Zhao, 2014). The WFC scale consists of two subscales, including work interference with family (WIF; five items) and family interference with work (FIW; four items). Each item was rated on a 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Example items include the following: “After work, I come home too tired to do some of the things I’d like to do” (WIF) and “I’m often too tired at work because of the things I have to do at home” (FIW). Calvo-Salguero et al. (2012) have provided reliability estimates higher than .70 for the Spanish version of the scale. Gao and Zhao (2014) reported good reliability (higher than .70) for the Chinese version of this scale, which is significantly associated with role overload and job satisfaction. In this study, the WFC scale produced a Cronbach’s α of .84, and the reliability for each of the subscales is as follows: .81 (WIF) and .82 (FIW).

Life well-being

This factor was measured using the life well-being subscale of the employee well-being scale developed by Zheng et al. (2015). The subscale consists of six items each rated on a 7-point Likert-type scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Examples include the following: “Most of the time, I do feel real happiness” and “I am close to my dream in most aspects of my life.” Xu et al. (2019) provided it has good reliability estimates higher than .90 and significantly associated with burnout and engagement. In this study, the reliability of life well-being subscale was .91.

Results

Confirmatory Factor Analyses

To test the factor structure of the MWS-Chinese version, we used the lavaan package in R 4.0.2 with maximum likelihood estimation (Rosseel, 2012). Models were assessed using the same fit indices of the original study (Clark et al., 2020): χ² (χ² with a significant p value), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Criteria for the CFI, TLI, and RMSEA have ranged from CFI ≥ .90, TLI ≥ .90, RMSEA and SRMR ≤ .10 to CFI ≥ .95, TLI ≥.95, RMSEA ≤ .06, and SRMR ≤ .08 (L. T. Hu & Bentler, 1999; Weston & Gore, 2006). To assess the factor structure of the MWS-Chinese version, we tested a series of models: a four-factor model, a three-factor model, an unidimensional one-factor model, a higher order model, and a bifactor model. Table 2 shows the goodness-of-fit indices related to these models.

The four-factor model comprised the four MWS factors which were allowed to be correlated with each other. This model had an acceptable fit to the data, χ² (98) = 354.61, p < .001, CFI = .94, TLI = .93, RMSEA = .08, 90% CI [.07, .09], and SRMR = .07. All items significantly loaded on primary factors.

The three-factor model (motivational, cognitive, and emotional combined) was compared with the four-factor model, χ² (101) = 643.15, p < .001, CFI = .88, TLI = .85, RMSEA = .11, 90% CI [.10, .12], and SRMR = .07. Although the χ² for the three-factor model was significant, the fit criteria suggested that this model was not a good fit for the data. The CFI change was greater than 0.01 (△CFI = .06), and the RMSEA change was greater than 0.01 (△RMSEA = .03; G. W. Cheung & Rensvold, 2002), indicating that the models were practically different.

The four-factor model was also compared with an unidimensional one-factor model which required all items to be loaded onto a single factor. This model had poor fit to the data, χ² (104) = 1244.15, p < .001, CFI = .74, TLI = .70, RMSEA = .16, 90% CI [.15, .17], and SRMR = .09. The change in CFI and RMSEA values was greater than 0.01 (△CFI = .20, △RMSEA = .08), indicating that the models were practically different.

The higher order model regressed the four factors onto a higher factor. It included not only a correlated four-factor model, but also a higher-order workaholism factor labeled as workaholism, this model had an acceptable fit to the data, χ² (100) = 354.86, p < .001, CFI = .94, TLI = .93, RMSEA = .08, 90% CI [.07, .09], and SRMR = .07. The change was very small, indicating that the models were not different.

The bifactor model had a general workaholism factor which allowed 16 items freely loaded on, along with the uncorrelated four factors. This model had better fit than the correlated four-factor model, χ² (88) = 233.81, p < .001, CFI = .97, TLI = .95, RMSEA = .06, 90% CI [.05, .07], and SRMR = .04, and this change was significant, △CFI = .03, △RMSEA = .02). Therefore, we selected the bifactor model as the final model. Figure 1 depicts this model.

In the bifactor model, omega was an estimate of the internal reliability of the MWS total score. Omega was 0.95 for the general factor, 0.67 for motivational, 0.63 for cognitive, 0.71 for emotional, and 0.73 for behavioral. Coefficient omega hierarchical (ωH) was the proportion of the variance that the general factor contributed to the MWS total score. Omega H for the general factor was .85, for the subscale factors, ωH was .31 (motivational), .17 (cognitive), .27 (emotional), .35 (behavioral), indicating that the reliability of the subscale factors decreased owing to the general factor. Finally, explained common variance (ECV) was the proportion of the variance that the general factor accounted for in the common variance with all factors. For the MWS factor, the ECV was .48, indicating that the general factor contributed to 48% of the common variance.

Table 2.
Confirmatory Factor Analyses in Study 2.

Model χ² df p CFI TLI RMSEA SRMR

Correlated Four-Factor Model 354.61 98 <.001 .94 .93 .08 .07

Correlated Three-Factor Model 643.15 101 <.001 .88 .85 .11 .07

Single Factor Model 1,244.15 104 <.001 .74 .70 .16 .09

Second Order Four-Factor Model 354.86 100 <.001 .94 .93 .08 .07

Bifactor Model 233.81 88 <.001 .97 .95 .06 .04

Note. χ² = chi-square statistic; CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Figure 1.
Final confirmatory bifactor model in Study 2.

Factorial Invariance

Since the bifactor model had the best fit to the data, we conducted invariance tests for gender, age, and job position (Table 3). Similar to previous studies (Autin et al., 2019; Duffy et al., 2017), we first created two categories per group. For gender, we compared men and women, and for job position, we compared front-line employees and managers. For age, we split the group at the mean (31.4) to create two categories: those who responsed with ≤31 and those who responsed with ≥32. After creating comparison groups, we tested configural (M0), metric (M1), and scalar (M2) models across groups.

First, the fit indices showed that the configural model (M0) had a modestly good fit to the data across gender, age, and job position groups (Table 3). Indices for gender groups were: χ²(176) = 343.04, p < .001, CFI = .96, RMSEA = .07, 90% CI [.056, .077]. Indices for age groups were: χ²(176) = 343.27, p < .001, CFI = .96, RMSEA = .07, 90% CI [.056, .077]. Indices for job position groups were: χ²(176) = 349.67, p < .001, CFI = .96, RMSEA = .07, 90% CI [.058, .079]. These models suggested that the factor structure were maintained across groups, and provided a baseline model to compare subsequent models.

Next, we conducted a metric invariance test (M1) by constraining all factor loadings to be the same across groups, and compared fit indices of the metric model with those of the configural model. Invariance is supported if the changes in CFI (△CFI) are less than or equal to .010 and the changes in RMSEA (△RMSEA) are up to .010 or .015. (G. W. Cheung & Rensvold, 2002). Fit indices showed that the metric model did not result in a marked decline compared with the configural model (Table 3). Changes in fit were as follows: gender (△CFI = .003, (△RMSEA = .002), age (△CFI = .002, (△RMSEA = .003), and job position (△CFI = .002, (△RMSEA = .003), indicating that the metric and configural models did not significantly differ.

Finally, we conducted a stronger test of invariance (scalar invariance, M2) by constraining the item intercepts to be the same across groups. We again compared the fit indices of the metric and scalar models, and changes in fit were as follows: gender (△CFI = .002, (△RMSEA = .000), age (△CFI = .000, (△RMSEA = .001), and job position (△CFI = .000, (△RMSEA = .002), indicating that the scalar model did not significantly differ from the metric model. Therefore, the factor structure of the Chinese version of the MWS, factor loadings, and indicator intercepts were maintained across gender, age, and job position groups.

Table 3.
Test of Measurement Invariance of the Bifactor Model Across Gender, Age, and Job position in Study 2.

Model χ² df CFI RMSEA [90%CI] △CFI △RMSEA

Gender

M0 (configural) 343.04 176 .962 .067[.056, .077]

M1 (metric) 382.74 203 .959 .065[.055, .074] .003 .002

M2 (scalar) 401.47 214 .957 .064[.054, .074] .002 .000

Age

M0 (configural) 343.27 176 .962 .067[.056, .077]

M1 (metric) 378.88 203 .960 .064[.054, .074] .002 .003

M2 (scalar) 388.94 214 .960 .062[.052, .072] .000 .002

Job position

M0 (configural) 349.67 176 .960 .068[.058, .079]

M1 (metric) 387.30 203 .958 .065[.055, .075] .002 .003

M2 (scalar) 399.94 214 .957 .064[.054, .074] .000 .001

Note. χ² = chi-square statistic; CFI = comparative fit index; RMSEA = root mean square error of approximation; △△CFI = the change of CFI; △△RMSEA = the change of RMSEA.

Validity Estimates

We investigated the intercorrelations between the MWS subscale and total scale scores with engagement, emotional exhaustion, WFC, and life well-being (Table 4). There was a significant correlation between the MWS and engagement. Further, the MWS was positively correlated with emotional exhaustion and WFC, and not significantly correlated with life well-being. On the contrary, engagement was negatively associated with emotional exhaustion and positively correlated with life well-being, and no significant association between engagement and WFC was found. Overall, workaholism and engagement were distinct from one another, and they uniquely related to various outcomes.

Based on the multidimensional conception and development of workaholism, we expected the MWS total score and four factors to be positively correlated with emotional exhaustion and WFC and negatively associated with life well-being, and most of the tested variables showed significant associations with the MWS in the predicted directions. Specifically, all subscales significantly and positively correlated with emotional exhaustion (rs = .18 to .28) and WFC (rs = .26 to .39). However, the correlations of the subscales with life well-being were nonsignificant (rs = .03 to .07).

Table 4.
Descriptive Statistics and Correlations in Study 2.

Variable 1 2 3 4 5 6 7 8 9

1. MWS —

2. Motivational .82* —

3. Cognitive .88* .63* —

4. Emotional .84* .58* .65* —

5. Behavioral .80* .51* .63* .54* —

6. Engagement .52* .40* .42* .46* .45* —

7. Emotional exhaustion .27* .19* .28* .18* .26* −.17* —

8. Work-family conflict .40* .26* .39* .35* .33* .08 .60* —

9. Life well-being .07 .06 .07 .07 .03 .49* −.20*** −.03 —

Mean 2.53 2.69 2.54 2.20 2.71 3.96 3.08 2.85 4.23

Standard deviation .76 .90 .94 .93 .88 1.02 .97 .68 1.29

Note. N = 425. MWS = multidimensional workaholism scale.

p < .01. p < .05. p < .001.

Discussion

The aim of the present study was to translate the MWS into Chinese and then demonstrate its reliability and validity among full-time employees. We translated the MWS and conducted an EFA in Study 1, that supported a four-factor solution. Then, we collected data from a new sample to confirm the factor structure of the MWS using CFA; Additionally, we tested invariance models according to gender, age, and job position and tested the validity through assessing the relationship between MWS dimensions and related constructs. Overall, this study revealed the MWS is a valid and reliable measure of workaholism in China.

Consistent with the scale development study (Clark et al., 2020), we conducted an EFA to test the factor structure of workaholism, and found all items evidenced adequate factor loadings on their respective primary factors, low cross-loading on other factors, and good internal consistency coefficients. Additionally, the four factors of the MWS were conceptually distinct and correlated with each other. In general, the four factors extracted and then confirmed fitted the theoretical assumption of the workaholism construct well, and were consistent with the Chinese conceptualization of workaholism to some extent (Zheng et al., 2010). The MWS provides a valid and reliable tool to extend the research of workaholism in a Chinese context.

To confirm the factor structure obtained from Study 1, we conducted a series of CFAs. We tested five models in Study 2, including two correlational models, a single factor model, a second order four-factor model, and a bifactor model. However, some differences were observed between the Chinese version of the MWS and the original scale (Clark et al., 2020). The bifactor model was best fit to the data, indicating that the MWS also shares a general factor loading on all items.

The findings suggested good reliability of the general MWS factor, although the reliability of the four subscales was lower when the general factor variance contributed to the total score, indicating that a full-scale MWS score is a reliable measure of workaholism, and researchers must be cautious in using scores of the four particular factors in future research. Studies should better represent multidimensional workaholism as a bifactor model. As shown in Figure 1, the four items of each factor should be set to load on their respective primary factor, and all 16 items should be set to freely load on a general workaholism factor. It is also worth noting that the four factors are uncorrelated in the bifactor model. Alternatively, if only focusing on the workaholism general factor, researchers could load the four factors onto a general workaholism factor, as this would represent the shared variance among subscales, and the unique variances these factors accounted for would be treated as error (Duffy et al., 2017).

Tests for invariance across gender, age, and job position were also conducted. Configural, metric, and scalar models were run successively and the change of fit indices did not substantially differ across gender, age, and job position groups, indicating that the factor structure, factor loadings, and indicator intercepts were maintained across gender, age, and job position groups. This result suggested that the general structure of the MWS and the responses to the scale itself may be equally valid across different groups, although people may experience different levels of workaholism due to their demographic characteristics.

To provide further validity evidence, we investigated the relationship between MWS dimensions and total scores, with engagement, emotional exhaustion, WFC, and life well-being. First, there was a significant positive correlation between the MWS and engagement, and they correlated with emotional exhaustion, WFC, and life well-being uniquely. Overall, consistent with the scale development study (Clark et al., 2020), workaholism and engagement were related and distinct from one another; the MWS evidenced its discriminant validity with engagement.

Next, the subscales of the MWS demonstrated construct validity by the assessment of their relationships with other variables. There were significantly positive correlations between the MWS subscales with emotional exhaustion and WFC, and insignificant correlation between the subscales with life well-being. Workaholics always put substantial pressure on themselves, work longer than their colleagues even sacrificing their personal or leisure time, which limits the time needed to rest and recover from excessive effort (Bakker et al., 2014), which in turn causes them to experience higher emotional exhaustion. Moreover, workaholics have less time and cognitive energy to maintain relationships or achieve a proper work-family balance, which often leads to conflict (Hauk & Chodkiewicz, 2013). These results are all consistent with a recent meta-analysis on the antecedents and consequences of workaholism (Clark et al., 2016). Contrary to our hypothesis, we found no significant relationship between the MWS subscales and life well-being. This result is likely due to the fact that workaholics are more likely to obtain fulfillment from their work, this satisfaction of needs probably buffering the negative effects of excessive work on their life well-being to some extent. Future research should further assess the long-term associations between workaholism and life well-being, perhaps by searching for moderating or mediating variables that may account for these inconsistent findings.

Practical Implications

Given the phenomenon of the labor market in China, this study’s findings have important practical implications for both physical and mental health, as well as the career success of Chinese working adults. In this regard, the Chinese version of the MWS may help individuals identify whether they are prone to excessive and/or compulsive work habits. From the perspective of sustainability, employees may thus have better chance of developing more harmonious passions for work while avoiding obsessive tendencies (Tóth-Király et al., 2020). This is especially pertinent for newcomers and young adults in China, who are in the early stages of their careers, which are characterized by fierce competition and high job instability. Although workaholism may improve short-term performance, long spates of excessive and/or constant work do not necessarily help, instead resulting in negative long-term consequences (Balducci, 2020; Ng et al., 2007). This may present in the form of sleep disorders, relational conflicts, and decreased job satisfaction, all of which are known to hinder career success. As such, it is highly important for individuals to identify whether they experience any degree of workaholism using the Chinese version of the MWS.

Chinese organizations should still implement measures to buffer its possible detrimental effects (Balducci et al., 2020). There are also some important distinctions to make regarding the outcomes. Different from workaholism, work engagement is known as a healthy and productive form of heavy work investment, which is associated with many positive outcomes and may moderate the relationship between workaholism and negative outcomes (Scafuri Kovalchuk et al., 2019; Spagnoli et al., 2020). From this perspective, organizations should strive to promote work engagement in a way that limits the possibility of workaholism. This can be facilitated by providing employees with the resources they need to achieve their work goals (Balducci et al., 2020). In the meantime, practitioners may provide internal and/or external training, such as mindfulness training (Zeijen et al., 2018) and counseling targeted at workaholism by using the MWS. In this way, employees can improve their understanding of workaholism and its possible influences, which will aid them in buffering any negative effects.

Finally, organizational culture and leadership are known as stable antecedents in this context, meaning that organizations should foster environments in which excessive work is not encouraged (M. A. O’Connor, 2005). For example, administrations should avoid the practice of incentivizing and praising workaholic behavior, while also encouraging employees to participate in activities such as outdoor sports, thus emphasizing good health and positive long-term outcomes with a focus on mutual development. To help realize this ideal, supervisors should be trained to manage risks that promote workaholism, reduce working time, and implement work–life interventions designed to disconnect employees from work when needed. Meanwhile, supervisors should also behave as role models by exhibiting a healthy work–life balance while avoiding excessive investments into work-related activities (Tóth-Király et al., 2020; Van Wijhe et al., 2010).

Limitations and Future Research Direction

Despite the important theoretical and practical implications discussed above, this study also had some limitations that, nevertheless, highlight areas for future research. First, we relied on cross-sectional data, but to clarify the long-term consequences of workaholism, longitudinal research is needed. Second, we only focused on typical outcomes of workaholism, more individual differences need to be considered, such as perfectionism. Third, our participants were mostly well-educated; future studies could validate the MWS in diverse subpopulations in China such as those with relatively lower education levels. Finally, additional native research is needed in China. While there is no explicit evidence to suggest workaholism is structured differently in China than in Western culture, current research in China is always based on the conception and measures originally developed in the West (e.g., Q. Hu et al., 2014), and it is possible that scale development from the ground up in China may yield different results.

Items	Factor Loading
Motivational (动机)
Q1. I always have an inner pressure inside of me that drives me to work. 在我内心总有一股压力，驱使我去工作。	.90	.01	−.02	.00
Q2. I work because there is a part inside of me that feels compelled to work. 我工作是因为我的内心在迫使着我去工作。	.38	.29	−.14	−.02
Q3. I have a strong inner desire to work all of the time. 我内心有想要一直工作的强烈愿望。	.50	.21	.09	.03
Q4. There is a pressure inside of me that drives me to work. 有一种内在压力，驱使我去工作。	.82	−.18	.07	.10
Cognitive (认知)
Q5. I feel like I cannot stop myself from thinking about working.我无法让自己不去想工作上的事情。	−.06	.77	.10	−.02
Q6. In general, I spend my free time thinking about work.总体来说，空闲时间我也都在想工作上的事情。	.03	.52	.20	.12
Q7. At any given time, the majority of my thoughts are work related.任何时候，我大多都在想工作上的事情。	.12	.66	.06	.00
Q8. It is difficult for me to stop thinking about work when I stop working.下班后，我也很难不去想工作上的事情。	−.04	.87	−.06	.00
Emotional (情感)
Q9. I feel upset if I have to miss a day of work for any reason.如果我某天没能去工作，不管是出于什么原因我都会心烦意乱。	.01	.12	.57	.09
Q10. I am almost always frustrated when I am not able to work.当我不能去工作的时候，我总是觉得很烦恼。	.24	.09	.49	−.07
Q11. I feel upset if I cannot continue to work.如果无法继续工作，我会感到很沮丧。	.19	−.02	.65	−.07
Q12. When something prevents me from working, I usually get agitated.当有什么事情让我不能工作时，我通常会烦躁不安。	−.19	−.03	.91	.01
Behavior (行为)
Q13. When most of my coworkers will take breaks, I keep working.当大多数同事都去休息的时候，我仍继续工作。	−.08	.29	−.02	.64
Q14. I work more than what is expected of me.我完成工作任务/内容比别人期望我完成的还要多。	.22	−.18	.00	.65
Q15. I tend to work longer hours than most of my coworkers.与大多数同事相比，我花在工作上的时间更多。	−.13	.24	−.06	.66
Q16. I tend to work beyond my job’s requirements.我完成的工作任务/内容往往会超出职位/职业本身的要求。	.07	−.09	.04	.67

Model	χ²	df	p	CFI	TLI	RMSEA	SRMR
Correlated Four-Factor Model	354.61	98	<.001	.94	.93	.08	.07
Correlated Three-Factor Model	643.15	101	<.001	.88	.85	.11	.07
Single Factor Model	1,244.15	104	<.001	.74	.70	.16	.09
Second Order Four-Factor Model	354.86	100	<.001	.94	.93	.08	.07
Bifactor Model	233.81	88	<.001	.97	.95	.06	.04

Model	χ²	df	CFI	RMSEA [90%CI]	△CFI	△RMSEA
Gender
M0 (configural)	343.04	176	.962	.067[.056, .077]
M1 (metric)	382.74	203	.959	.065[.055, .074]	.003	.002
M2 (scalar)	401.47	214	.957	.064[.054, .074]	.002	.000
Age
M0 (configural)	343.27	176	.962	.067[.056, .077]
M1 (metric)	378.88	203	.960	.064[.054, .074]	.002	.003
M2 (scalar)	388.94	214	.960	.062[.052, .072]	.000	.002
Job position
M0 (configural)	349.67	176	.960	.068[.058, .079]
M1 (metric)	387.30	203	.958	.065[.055, .075]	.002	.003
M2 (scalar)	399.94	214	.957	.064[.054, .074]	.000	.001

Variable	1	2	3	4	5	6	7	8	9
1. MWS	—
2. Motivational	.82***	—
3. Cognitive	.88***	.63***	—
4. Emotional	.84***	.58***	.65***	—
5. Behavioral	.80***	.51***	.63***	.54***	—
6. Engagement	.52***	.40***	.42***	.46***	.45***	—
7. Emotional exhaustion	.27***	.19***	.28***	.18***	.26***	−.17***	—
8. Work-family conflict	.40***	.26***	.39***	.35***	.33***	.08	.60***	—
9. Life well-being	.07	.06	.07	.07	.03	.49***	−.20***	−.03	—
Mean	2.53	2.69	2.54	2.20	2.71	3.96	3.08	2.85	4.23
Standard deviation	.76	.90	.94	.93	.88	1.02	.97	.68	1.29

Footnotes

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.

Funding

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This research was supported by the Outstanding Innovative Talents Cultivation Funded Programs 2019 of Renmin University of China and The National Natural Science Foundation of China (grant id: 71772171).

ORCID iD

Yan Xu

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