The Impact of Negative and Positive Affectivity on the Relationship Between Work-Related Psychological Factors and Work Engagement in Japanese Workers: A Comparison of Psychological Distress

DOI: https://doi.org/10.21203/rs.3.rs-2391559/v1

Abstract

Background: A previous study has shown that Japanese individuals generally exhibit behavior that suppresses the expression of positive emotions, which are strongly affected by personality traits. The current study aimed: (a) to determine the extent to which negative or positive affectivity is related to work engagement (WE) and psychological distress among Japanese workers. (b) To determine the extent to which negative and positive affectivity influence the relationship between work-related psychosocial factors and WE and psychological distress.

Methods: A total of 1,000 full-time Japanese regular workers responded to an online survey that measured demographic variables, negative and positive affectivity, job demands and resources, WE, and psychological distress. A hierarchical multiple regression analysis was conducted separately, which used WE and psychological distress as dependent variables.

Results: The proportion of variance explained by negative and positive affectivity was lower for WE than for psychological distress. However, the proportion of variance defined by job demands and resources was higher for WE than for psychological distress. The interaction was only significant for the relationship between negative affectivity and job resources on psychological distress.

Conclusion: The results of this study emphasize the need to focus preferentially work environmental factors to improve WE among Japanese workers. However, workers’ WE would enhance by focusing on individual factors such as affective traits.

Background

Work engagement (WE) refers to “a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption” [1] and is beneficial for both individual workers and organizations. Studies have indicated that WE is associated with improved physical and mental health [2, 3]. Furthermore, WE is associated with job performance [2, 4] and business growth [5]. Therefore, several researchers have developed intervention programs and verification methods in the workplace environment to enhance WE [6, 7].

To improve the mental health of employees, industrial and organizational psychologists and practitioners who evaluate either the psychosocial environments in the workplace or the effect of workplace intervention programs ought to ensure the accurate and sensitive documentation of job stressors as well as the mind and body state of employees [8]. Furthermore, the influence of individual factors, such as sociodemographic variables and worker personalities, should be considered to a significant extent. For example, negative affectivity (NA) [9, 10] is generally known that a personality trait strongly associated with work environment factors and stress responses [11, 12]. Therefore, statistically controlling negative affectivity had been recommended to distinctly determine the relationship between job stressors and stress responses [13, 14]. On the other hand, scholars have cautioned that statistical control removes true variance and distorts the effects of causal variables (e.g., job stressors and stress responses) and is thus not desirable [15]. Thus, the influence of a worker’s personality is exceedingly and intricately associated with workplace environmental factors or the worker’s stress response.

Similarly, this association might also be the case for the WE. The job demands-resources model includes WE as part of its motivational process [16, 17]. Furthermore, previous studies indicate that WE is enhanced by the abundance of job resources, such as job control and workplace support [18, 19]. On the other hand, in the job demand-resource model, although personality traits are included in personal resources, their position remains indefinite [17]. Bakker et al. [20] reported that extraversion in the five-factor model of personality theory is positively associated with job resources or WE. Furthermore, workers’ personality accounts for approximately 30–50% of the variance in WE [21, 22]. Thus, in addition to the association of negative affectivity between work environment factors, and stress response [11, 12], personality factors might be strongly associated with the relationship between job resources and WE.

A study comparing WE measurements between Japanese and Dutch people ought to consider cultural differences when interpreting WE values as Japanese people tend to suppress positive emotions while self-enhancement for Dutch people can represent lower measurement accuracy [23]. Furthermore, studies have shown that the Japanese are more likely to suppress expressions of positive emotions in contrast to Europeans and Americans due to cultural customs [24–26]. Iwata et al. [27] compared the factor structure of the State-Trait Anxiety Inventory (STAI) between Japanese and Western individuals and found that the personality traits of Japanese people largely determined positive emotions. Therefore, WE measurements among Japanese individuals exhibit a stronger reflection of their personality traits in contrast to Europeans and Americans.

Several studies have been conducted in various countries to examine the relationship between work environment factors and WE [18, 19]. However, no study has been conducted to quantitatively examine the relationship between personality and work environment factors or WE among Japanese workers. Young et al. [22] have recommended the use of personality assessments to improve WE which might be influenced by the worker’s personality. If personality traits are more strongly associated with WE and job resources among Japanese workers [27], it follows that personality assessment is exceedingly effective in improving their WE. Therefore, the present study aimed to clarify the relationship between personality and WE in relation to work environment factors.

Watson and Tellegen [28] found that the various moods nursed by humans can be categorized into two domains, namely, negative and positive affect. Subsequently, Watson et al. [10] discovered that negative and positive affect are pertinent in both state and trait ratings and thus referred to these trait domains as NA and positive affectivity (PA). High NA describes the ease of evoking negative emotions such as “scary” or “sluggish,” while high PA refers to the ease of evoking positive emotions such as “energetic” or “lively” [10]. This two-dimensional factor structure is common in Japan, the United States, and Europe [29], and the NA and PA roughly correspond to the dominant personality traits of neuroticism and extraversion in five-factor model, respectively [30]. Therefore, we used NA and PA as the variables of affective traits for this study.

In this study, the relationships between psychological distress and affective traits or work environmental factors were examined concurrently in relation to WE. Psychological distress [31] is used frequently in occupational stress research and previous studies show that there is no cultural difference between Japanese and Europeans or Americans in terms of stress responses comprising negative aspects such as depression and anxiety [24, 26, 32]. Thus, by comparing the results of psychological distress without cultural differences and WE with cultural differences, the extent to which affective traits influence the association between WE and work environmental factors can be clarified. This study investigates whether individuals should focus on external factors such as the workplace or individual factors such as affective traits to improve WE or reduce psychological distress among Japanese workers.

The objectives of this study were twofold: (a) to determine the extent to which workers’ affective traits explain WE and psychological distress, and (b) to determine the extent to which affective traits can impact the relationship between work environment factors, WE, and psychological distress.

Methods

Study design and data collection

A cross-sectional survey was conducted among registered marketing research monitors and an internet survey firm (Rakuten Insight, Inc). Thus, the participants provided their data using the internet. The inclusion criteria of the participants were as follows: (a) Japanese and (b) full-time employees of the organization. Self-employed, part-time, and unemployed workers were excluded from this study. The Internet survey company recruited monitor workers until the target number was reached based on the inclusion criteria. The recruited workers were able to access a self-report questionnaire of the present study.

Participants

To increase the likelihood of obtaining a representative sample of Japanese workers, the population was assigned proportions according to gender, age, and place of residence based on population estimates published by the Statistics Bureau of the Ministry of Internal Affairs and Communications [33]. Furthermore, to minimize gender differences in the analysis, the gender proportions were set to be equal. Data were obtained from 1,000 Japanese workers (i.e., 504 men and 496 women). The mean age of the participants was 45.6 years (standard deviation, 13.0).

Measures

Affectivity traits

The negative and positive affectivity of the participants was measured using the Japanese version of the Positive and Negative Affect Schedule (PANAS) [34]. This version of the PANAS consists of 16 items, namely, eight items for negative affect and eight items for positive affect. Typically, the PANAS requires respondents to rate the frequency of their feelings during the past four weeks on a six-point scale ranging from 1 (never) to 6 (always). However, this study focused on measuring stable traits of both negative and positive affect. Thus, the PANAS instructions were revised from “How often have you felt these moods in the past month?” to “To what extent do you usually feel these moods?” The items were scored on a six-point scale ranging from 1 (totally disagree) to 6 (totally agree).

Work-related psychosocial factors

Job demands and resources were measured using the Brief Job Stress Questionnaire (BJSQ) [35]. “Job demands” comprised three items for both the quantitative and the qualitative workload. The items were scored on a four-point scale ranging from 1 (very much) to 4 (not at all). “Job resources” consisted of nine items, namely, three for job control and six for support from supervisors and co-workers. All job resources items were scored on a four-point scale ranging from 1 (very much) to 4 (not at all). A high score for job demands indicated a high workload while a high score for job resources indicated extensive workplace resources.

Work engagement

The WE among the participants was assessed using the Japanese version of the Utrecht Work Engagement Scale (UWES) [36]. The UWES consists of three subscales (i.e., vigor, dedication, and absorption), which each comprise three items scored on a seven-point scale ranging from 0 (never) to 6 (always). The overall score for the UWES is the sum of the three subscales.

Psychological distress

The Kessler 6 (K6) scale [37] was used to measure psychological distress. K6 requires respondents to describe how frequently they have experienced each statement during the past 30 days. The items were scored on a five-point scale ranging from 0 (none of the time) to 4 (all of the time).

Demographic variables

Several variables were analyzed in the questionnaire, namely, age, gender, educational background, marital status, number of children, occupation, duration in the current job, and night shift.

Statistical Analysis

First, the correlation coefficients between each variable and Cronbach’s alpha coefficients were estimated. Thereafter, a hierarchical multiple regression analysis of WE and psychological distress was performed before entering the independent variables in Model 1 in the following order: age, gender, and career in the current job. In Model 2, job demands and resources were used as occupational factors while affective factors were used in Model 3. In Model 4, the two two-way interactions (job demands × PA or NA, and job resources × PA or NA) were inserted to analyze the interactive effects between occupational and affective factors [38]. Finally, the interactions were calculated after centering each variable using its mean to account for multicollinearity issues. The statistical analyses were performed using R, version 4.1.0.

Results

Preliminary analyses

Table 1 shows the demographic characteristics of the participants. Approximately 60% of the participants held university or graduate school degrees. Furthermore, it was estimated that 70% of the participants were non-manual workers.

Table 2 shows the correlation coefficients between the variables with Cronbach’s alpha coefficients. This study found moderate associations between age and career in terms of the current job, NA and psychological distress, PA and WE, as well as job resources and WE. Cronbach’s alpha coefficients were greater than 0.80 for all variables.

Hierarchical multiple regression analysis

Tables 3 and 4 show the hierarchical multiple regression analysis results, with WE and psychological distress as the respective dependent variables.

In Model 1, only age was positively associated with WE. Furthermore, including occupational factors in Model 2 resulted in a significant increase in the coefficient of determination (ΔR2 = 0.24). Moreover, age, job demands, and resources were all positively associated with WE. Model 3 includes affective factors and a significant increase was observed in the determination coefficient (ΔR2 = 0.17). Thus, the WE variances associated with occupational and affective factors were higher for occupation factors than for affective factors. Furthermore, age, gender, job demands, resources, and PA were positively associated while NA was negatively associated with WE. Regarding the change in the standardized regression coefficient of occupational factors in Models 2 to 3, job resources decreased (β = 0.19) despite the absence of change in job demands. In Model 4, the coefficient of determination did not increase significantly and no interactions were associated with WE.

In the context of psychological distress, age was negatively associated with psychological distress in Model 1. The inclusion of occupational factors in Model 2 resulted in a significant increase in the coefficient of determination (ΔR2 = 0.16). Furthermore, job demands in Model 2 were positively associated while age or job resources were negatively associated. In Model 3, the affective factors were introduced and the coefficient of determination increased significantly (ΔR2 = 0.26). Thus, the variances in psychological distress explained by occupational and affective factors were more significant for the affective factors in contrast to the occupational factors. Moreover, age, job resources, and PA were negatively associated, while job demands and negative affectivity were positively associated. For the change in the standardized regression coefficient of occupational factors in Models 2 to 3, a decrease was found in job demands and resources (job demands: β = 0.11, resources: β = 0.21). In Model 4, the coefficient of determination increased significantly (ΔR2 = 0.01). A simple slope analysis showed that psychological distress did not change with job resources when NA was low (mean = 1 SD). However, when NA was high (mean + 1 SD), job resources significantly reduced psychological distress (see Fig. 1).

Discussion

This study aimed to determine the extent to which WE and psychological distress are associated with affective components in a sample of Japanese workers. Furthermore, we examined the extent to which the affective components are associated with relationships between psychosocial work environment, WE, and psychological distress. The results indicated that the proportion of variance associated with the affective factors was higher for psychological distress than for WE. Conversely, the proportion of variance explained by occupational factors was higher for WE than for psychological distress. Moreover, a significant interaction was found only for NA while job resources related to psychological distress were not significant in WE.

Work engagement and psychological distress variances associated with occupational and affective factors

In the present study, affective factors accounted for nearly 20% of the variance in the WE. Furthermore, the percentage of variances in WE associated with affective traits was approximately 30% (∆R2 = 0.324) despite changing the input order of the occupational and affective factors in Models 2 and 3 (Table 3). These results were comparable to those of Fukuzaki and Iwata [21], who validated their findings using the five-factor model. Contrastingly, even when comparing the effects of psychological distress in the present study, the influence of affective factors on WE was small. Thus, the result differed from Iwata et al [24].

The current results suggest that focusing on workplace environmental factors is more effective than focusing on individual factors to improve WE among Japanese workers. However, affective factors are associated with WE and account for approximately 20–30% of the variance in WE. Thus, WE among workers can be enhanced by focusing on individual factors such as their affective traits.

Langelaan et al. [39] reported that the discriminant indicators of WE exhibited a tendency of high extraversion and low neuroticism among personality traits. Generally, extraversion corresponds to PA and neuroticism corresponds to NA [10, 40]. Young et al. [22] found that PA accounted for 90.6% of the total proportion of both NA and PA, explaining the variance in WE (i.e., 9.4% for NA and 90.6% for PA). We entered the affective factors one by one and re-calculated the NA and PA ratio for ∆R2 in Model 3 of Table 3. The results showed that the PA and the NA were 80.8% and 19.2%, respectively. In other words, the proportion NA was higher than the findings reported by Young et al. [22]. Several international studies comparing personality traits of the five-factor model have reported high neuroticism as one of the typical personality traits among Japanese individuals [41, 42]. Particularly, Japanese people have stronger NA than individuals from other countries. Therefore, the affective factor that characterizes the high WE of Japanese workers is a high level of PA and expressed as low NA as an essential characteristic.

Importance of environmental work factors in enhancing WE and preventing deterioration from psychological distress

Job demands and resources explained the higher percentage of the variance observed in WE as opposed to psychological distress. This result suggests that WE among Japanese workers can be enhanced effectively by increasing emphasis on extrinsic factors, such as work environment in contrast to individual factors (i.e., reducing psychological distress). In the context of occupational factors, Models 2 and 3 (Tables 3 and 4, respectively) exhibit a lower change for WE in the standardized regression coefficients for job demands and resources in contrast to psychological distress. Moreover, the R2 of the demographic variables was equal (WE: adjusted R2 = 0.03; psychological distress: adjusted R2 = 0.05) for both WE and psychological distress. Therefore, when researchers want to evaluate accurately and sensitively the change of psychosocial factors in the workplace, such as improving the workplace environment among Japanese workers, positive indicators (e. g., WE) should be used in addition to negative indicators (e.g., psychological stress responses). Because, positive indicators, such as WE, are less influenced by individual factors such as affective traits, and those indicators strongly reflect the influence of work environment factors.

Studies have consistently implied that measures to enrich job resources are essential for improving WE [18, 19]. Model 3 (Tables 3 and 4) shows that job resources significantly impacted all dependent variables and were more influential on WE than job demands when the demographic variables and affective factors were controlled. Similarly, job demands and resources have an equal influence on psychological distress. These findings indicate that enriching job resources can reduce psychological distress and improve WE regardless of the affective traits of the worker. Furthermore, the results of the interaction between NA and job resources on psychological distress indicate that the psychological distress of workers with higher NA is mitigated by many job resources in the workplace although they are likely to experience psychological distress [43, 44]. Therefore, enriching job resources is vital to improving WE and alleviating psychological distress.

Clinical practice using personality assessment in industrial and organizational psychology

Approximately 30% of the variance in psychological distress was attributed to NA and PA. Furthermore, this proportion was higher than the proportion observed for WE. Sakai et al. [45] examined the association between temperament and job stress factors in a cross-sectional survey of Japanese workers. They suggested that mental health professionals in industrial and organizational areas should assess workers’ temperament tendencies and provide relevant guidance based on the differences in workers’ vulnerability and severity of job stress. Considering the strong relationship between NA and psychological distress (Model 4 in Table 4; β = 0.56), the present study argues that NA should be regarded as a critical assessment factor for mitigating mental health problems among workers. Therefore, personality assessment has been proposed as a mental health measure for staff evaluation and as a clinical tool for wokers with mental health problems [22, 45, 46]. As indicated in the current study, psychological distress in Japanese workers had a stronger association with affective traits as opposed to WE. Thus, personality assessment may be an effective means for preventing mental health problems such as anxiety or depression in contrast to improving WE.

Limitations

This study has several limitations. First, the causal relationships between variables could not be addressed as this was a cross-sectional study. Second, the participants in this study were all registered monitors chosen by the same Internet survey company; thus, selection bias might have affected the results. Third, additional personality factors not used in the present study would be associated with WE (e.g., conscientiousness in the five-factor model or proactive personality) [22, 30, 47]. Therefore, future studies should include these personality factors to verify these results. Finally, this survey was conducted via the Internet in November 2020, during the coronavirus 2019 (COVID-19) pandemic. Furthermore, some of the companies where the study participants worked might have been operating remotely to prevent the spread of infection. Therefore, the changes in work patterns and daily lifestyles might have influenced the findings of this study.

Conclusion

The results of this study emphasize the need to prioritize work environmental factors to improve WE among Japanese workers due to the limited influence of affective traits on WE in contrast to psychological distress. However, both NA and PA have significant effects on WE. Thus, this study underscores that focusing on individual factors such as affective traits can significantly enhance WE. In contrast to WE, psychological distress is more strongly influenced by affective traits. Therefore, there might be a significant benefit associated with prioritizing the individual factors of Japanese workers to reduce psychological distress.

Abbreviations

BJSQ

Brief Job Stress Questionnaire

COVID-19

Coronavirus 2019

K6

Kessler 6

NA

Negative Affectivity

PA

Positive Affectivity

PANAS

Positive and Negative Affect Schedule

STAI

State-Trait Anxiety Inventory

UWES

Utrecht Work Engagement Scale

WE

Work Engagement

Declarations

Competing interests

The authors declare no conflicts of interest associated with this manuscript.

Funding

This study was supported by a 2019 Productivity Research Grant, administered by the Japan Productivity Center.

Authors’ contributions

This work was carried out in collaboration with all authors. Author TF designed the study, managed the analyses of the study, and prepared the draft of the manuscript. Author NI participated in the statistical analysis and assisted in the draft of the manuscript. All the authors read and approved the final manuscript.

Acknowledgments

None.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

This study was approved by the Research Ethics Committee of the Faculty of Medicine, Tottori University, Japan (no. 20A100). All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. Informed consent was obtained from all participants using a web-based form before the research commenced. The instructions guaranteed: (a) the protection of personal information, (b) the removal of personally identifiable information from the data, and (c) that there would be no disadvantage to individuals who did not participate in the survey.

Consent for publication

Not applicable.

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Tables

Tables 1 to 4 are available in the Supplementary Files section.