1. Introduction
The Job Demands-Resources (JD-R) model [Demerouti et al., 2021; Schaufeli, and Bakker, 2004] attributes the characteristics of work environments and employee well-being. Resulted from JD-R model, the attributions of working setting are separated into two categories: job demands and job resources. Job demands are referred as aspects of work standing in need of the sustained effort and as a result of being related to physical and psychological costs [Xanthopoulou et al., 2007]. Job resources are referred as aspects of work (e.g., physical, social, or organizational) functional in realizing work-related goals, meeting different job demands at psychological costs, and motivating personal growth and development [Llanos-Contreras et al., 2023; Van den Broeck et al., 2008]. Job demands are the most important predictors of burnout [Bakker et al., 2004], whereas job resources are the distinctive predictors of work engagement [Bakker and Demerouti, 2017].
Two independent psychological processes which are health impairment and motivational process [Bakker and Demerouti, 2007] are evoked by these two types of job demands and resources. On the basis of the health impairment process, high levels of job demand requiring sustained physical or mental effort may carry off employees’ resources and bring about energy draining and psychological health symptoms such as burnout [Bakker et al., 2005]. By contrast, on the basis of the motivational processes, the obtainability of job resources results in work engagement [Schaufeli and Bakker, 2004]. The motivational process is incurred by plentiful job resources and may lead to employee engagement.
According to Conservation of Resources (COR) theory [Hobfoll, 2002], there are two main assumptions: (1) individuals invest resources to respond to threatening situations and keep negative outcomes from arising; (2) Individuals not only endeavor to conserve these resources but also store them up. Considering the above assumptions, self-efficacy as personal resource is recognized to the potential moderating role in the relationship between job demands (threats) and burnout (negative outcome). In addition, considering the motivational process of the JD-R model combined with the second assumption of COR theory, self-efficacy as personal resource is recognized to the mediating role to explain job resources enrich efficacy beliefs, which in turn increase employee engagement. This study suggests the combination of COR theory with the buffer hypothesis in the health impairment process of JD-R model and with fostering hypothesis in the motivational process of JD-R model.
Based on the above, this study attempts to take self-efficacy as the core concepts, burnout and engagement in the context of job demands-resources model as its consequence variable. Based on job demands-resources model mainly and resource conservation theory, job demands may affect employee burnout and job resources may affect employee work engagement. Self-efficacy as personal resource plays not only as a mitigator on the impact of job demand on burnout, but also as a mediator on the impact of job resources on work engagement. Through the health impairment process, self-efficacy is expected to moderate the positive job demands—burnout relationship. on the other hand, through the motivational process, self-efficacy is also expected to mediate the positive influence of job resources on work engagement.
2. Literature Review and Hypothesis Building
2.1 The Moderating Role of Self-efficacy
Burnout informs employee about psychosocial risk and health problems in the workplace [Giménez-Espert et al., 2020]. As Maslach, and Leiter [1997] statement: “Energy, involvement, and efficacy—these are the direct opposites of the three dimensions of burnout.” burnout is considered as an erosion whereby “Energy turns into exhaustion, involvement turns into cynicism, and efficacy turns into ineffectiveness”. Burnout is defined as a multidimensional construct that encompasses emotional exhaustion (i.e., feelings overly drained and depleted of emotional and physical resources), depersonalization (i.e., displaying negative, immoderately detached reactions to person being served or cared for.), and lack of personal accomplishment (i.e., feeling incompetent and lacking success at work) [Maslach, 2003; Maslach and Leiter, 2008]. Past studies have examined the impact of job demands on the exhaustion component of burnout [Schaufeli and Bakker, 2004; Bakker et al., 2004; Bakker and Demerouti, 2017; Bakker et al., 2023].
Rather than focal point of task-specific self-efficacy [Yeo and Neal, 2006], this study investigates a general construct, namely, individuals’ perceptions of their capability to master the necessary job skills with motivational and cognitive resources and to satisfy their needs in a broad context [Chen et al., 2001]. The accumulation of successes and persevering positive experiences can enhance general self-efficacy [Chen et al., 2001]. Employees with high level of general self-efficacy have better mastery that assists them to cope with demanding conditions more effectively, and in turn avoid them to experiencing negative outcomes [Bakker et al., 2004]. Self-efficacious employees will focus on motivational and cognitive resources, formulate a comprehensive environment and how they response to it. As a result, they will experience lower levels of burnout. Self-efficacy buffers the relationship between job demands and burnout, that is to say, employees low in self-efficacy show that when job demand is high, their levels of burnout increase. Therefore, the following hypothesis is suggested:
Hypothesis 1: Self-efficacy moderates the relationship between Job demands and burnout.
2.2 The Mediating Role of Self-efficacy
Work engagement is characterized by vigor (i.e., energetic at work with strong mental resilience, willingness to work investment, and perseverance in the face of obstacles), dedication (i.e., work with enthusiasm and passion), and absorption (i.e., being happily immersed in work so as time flies and difficulty in detachment from work), which is a motivational and work-related state of fulfillment [Xanthopoulou et al., 2009]. Work engagement is not a specific and temporary state, but a more general affective-cognitive state.
The attainability of job resources that help achieve work goals [Bakker, 2008] drives the motivational process. Job resources act a motivator because they facilitate employee growth, learning, and development. Work engagement may be promoted by job resources through a motivational process which meet basic needs and which enhances the possibility of achieving work goals. Job resources may be identified as important predictors that highlight engagement on the likelihood of personal development through work. Personal resources are concerned as individuals’ perceptions of their potential to successfully control and influence their environment [Hobfoll et al., 2003]. Self-efficacy as personal resource is included as a principal extension of the JD-R model [Simbula et al., 2011]. Self-efficacy promotes motivation with affecting the challenges individuals run after, the effort they invest, and the persistence with facing difficulties. Self-efficacy indeed acts as a self-motivating mechanism in the relationship between job resource and work engagement. Hence, the following hypothesis is suggested:
Hypothesis 2: Self-efficacy mediates the relationship between Job resources and work engagement.
Grounded on the aforementioned discussion, the research framework is organized as shown in <Figure 1>.
<Figure 1> Hypothesized Model
3. Method
3.1 Samples
The data was collected through online questionnaire. Questionnaire with a clear research purpose introduction were send through the internet such as e-mail and social media. The research population in this study were the employees, who are currently working in Milk JSC established in 1958. This manufacture factory produces and trades 100% milk products.
Data was distributed through questionnaire survey online over 3 months period. A total of 380 survey questionnaires are mailed to Milk JSC’s employee. A sample of 311 questionnaires was collected. Due to incompleteness feedback or loss of data, 74 of these samples were invalid which were removed from total response. As a result, the total 237 response can be used. The effective feedback rate was 76%. Among the employees, 56.5% were female. 33.8% of employees fall in 31-35 years old, 31.6% in 26-30 years old, and 40.5% in 2-5 years. 34.6% of employees’ work experience in industry fall in 6-10 years in their current organization. 50.2% of the employees held a bachelor degree or above.
3.2 Measurement
The survey items used in this study were originally written in English. It is needed to follow back-translation procedure proposed by [Brislin, 1980] to translate them into Mongolian for the accurate understanding. A 5-point Likert-type scale is adopted for all variables measurement with ranging from (1) “a little or no extend” to (5) “a great extension.”
3.2.1 Job Demands
The measurement of job demands was adopted from the 12-item scale developed by [3], is used in this questionnaire. Employees were asked to assess the use of three subdimensions: workload, change in task, and emotional load. Example items are “I have to work extra hard because I have to finish my order”, “I have the proposed changes in my tasks are introduced well”, and “I confronted with things that affect me personally in my work”. Cronbach’s alpha is .895.
3.2.2 Job resources
The measurement of Job resource was adopted from the 6-item scale developed by [3] including two dimensions: autonomy, coaching by supervisor and professional development. Employees were asked to assess the use of two subdimensions: autonomy, and coaching by supervisor and professional development. Example items are “On my job, I have freedom to decide how I do my work”, “My supervisor uses his/her influence to help me solve my problems at work”, and “I have sufficient possibilities to develop myself at work”. Cronbach’s alpha is .736.
3.2.3 Self-efficacy
The measurement of self-efficacy consists of 10 items, which were adopted from [Schwarzer and Jerusalem, 1995], is used in this questionnaire. Example items are “I can always manage to solve difficult problems if I try hard enough”, “I can remain calm when facing difficulties because I can rely on my coping abilities”, and “When I am confronted with a problem, I can usually find several solutions”. Cronbach’s alpha is .770.
3.2.4 Work Engagement
The measurement of work engagement 14-items scale adopted from [Schaufeli et al., 2006] with three dimensions of engagement including vigor, dedication and absorption. Employees were asked to assess the use of three subdimensions: vigor, dedication, and absorption. Example items are “At my work I always persevere, even when things do not go well”, “I find the work that I do full of meaning and purpose”, and “When I am working, I forget everything else around me”. Cronbach’s alpha is .922.
3.2.5 Burnout
The measurement of burnout 15 items scale was adopted from [Maslach and Jackson 1981]. Example items are “I am harder and less sympathetic with people than perhaps they deserve”, “I feel that organizational politics or bureaucracy frustrate my ability to do a good job”, and “I feel that I do not have time to do many of the things that are important to doing a good quality job”. Cronbach’s alpha is .894.
3.2.6 Control Variables
The following variables are controlled for in the hypothesis testing: several demographic variables (i.e. gender, age, and education) and work-related (i.e. work experience in industry) background variables, because they have been found to be related to employee burnout and work engagement. Gender has been found to influence affective commitment [Becker, 2005], because of empathy rooted in women is higher than in men [Lovell et al., 1999]. Age differences of employees are with different positions on themselves, others and work. Young and highly educated employees pay more attention to economic exchange and fair treatment [Wagner and Rush, 2000]. Consequently, gender, age, education, and work experience in industry were chosen within the analyses.
4. Analysis
4.1 Correlation Analysis
Descriptive statistics and correlational analysis of measured variables are reported in <Table 1>. Job demand is significantly and positively related to self-efficacy (γ=.168, p<0.01), and burnout (γ=.701, p<0.01). Job demand is non-significantly related to job resource (γ=.104, p=.109) and work engagement (γ=.098, p=.133), significantly related to self-efficacy (γ=.168, p<0.01). Job resource is significantly and positively related to self-efficacy (γ=.520, p<0.01), and work engagement (γ=.647, p<0.01).
<Table 1> Descriptive Statistics, Correlations and Scale Reliabilities
Note: * p<0.05, **p<0.01; Gender: 1=male; 2=female.
Education: 1=Elementary school; 2=high school; 3=college; 4=Bachelor degree; 5= Master degree; N= 237,
WEiInd: Work experience in industry; JD: Job demand; JR: Job resource; SE: Self-efficacy; WE: Work Engagement
Job resource is non-significantly related to burnout (γ=.069, p=.292). Self-efficacy is significantly and positively related to work engagement (γ=.559, p<0.01). Self-efficacy is non-significantly related to burnout (γ=.095, p=.143). Burnout is non-significantly related to work engagement (γ=.026, p=.691).
4.2 Hierarchical Regression Analysis
As shown M3 in <Table 2>, our analyses showed that self-efficacy moderated the main effect of job demand on burnout. Specifically, the interaction of job demand and self-efficacy on burnout was significant (β=2.086, p <.001).
<Table 2> Regression Analysis
Note: * p<0.05, **p<0.1, ***p<0.01
WEiInd: Work experience in industry; JD: Job demand; JR: Job resource; SE: Self-efficacy; WE: Work Engagement
As shown in <Figure 1>, we plotted the conditional effect of job demand on burnout at low level and high level of self-efficacy (i.e., one standard deviation above and below the mean value of the moderator). The result is supportive of Hypothesis 1.
To test the effect of job resource on self-efficacy, self-efficacy was regressed on LMX with control variables of gender, age, education and work experience in industry shown as <Table 2> (M4: β = 0.512, p < .001).
In accordance with [Baron and Kenny, 1986], conditions are necessary to test H2. The analysis results show a significant mediation effect of self-efficacy. When self-efficacy was entered into the equation as shown in M7, job resource significantly predicted work engagement (β=.472, p<.001), whereas self-efficacy significantly predicted work engagement (β=.287, p<.001). The effect of job resource onwork engagement did drop from .619 (p<.001) to .472 (p<.001). Therefore, H2 was supported.
<Figure 2> The Interaction of Job Demand and Self-efficacy on Burnout
5. Research Discussion and Suggestion
This study aims to test the moderating role of self-efficacy with regard to the JD–R model, and its impact on burnout (hypothesis 1). The other study objective is to test the mediating role of self-efficacy with regard to the JD–R model, and its effect on work engagement (hypothesis 2). Hypothesis 1 and 2, the socalled interaction hypothesis, are confirmed.
5.1 Practical Implication
The first relevant result is that high levels of self-efficacy buffered burnout when job demands are high, as was expected. The empirical evidence of the moderation of self-efficacy in the relationship between job demands and burnout contributes significantly in explaining the health impairment process of the JD-R model. This finding may highlight that this personal resource operates not only at affective-cognition but also at behavioral-practice. Self-efficacy as personal resource of a more practical nature such as an individual ability to allocate time or energy to invest in specific tasks. This attribution may also be vital for human resource manager aware of the specific job demands and burnout prevention.
The mediation of self-efficacy in the relationship between job resources and work engagement contributes significantly. The empirical evidence explains the utility mechanism of the motivational process of the JD-R model. This significant process from job resources to work engagement through self-efficacy mainly emphasizes a more active role in the model’s motivational process. Since through the activation of employees’ beliefs in more capable to perform their tasks the capacity in a resourceful environment, job resources can draw forth more positive appraisals of work engagement situations.
5.2 Limitation and Future Research
As far as the study’s limitations, several limitations in this study are considered to be acknowledged as a starting point for future work. First, due to self-reported data used to identify all variables with the increase of the likelihood of common method, cross-sectional and longitudinal study design are suggested to validate our findings over time for further research. Second, using only a homogeneous sample of Mongolian employees in Milk JSC, it limits the generalizability of the results to the heterogeneity of other occupations Therefore, it is important for future research to study other organizational contexts. Finally, future research can consider different kinds of job demands/resources, as well as personal resources for deepening the complexity of the study model.
References
- Bakker, A. B., "Building engagement in the workplace", In C. L. Cooper & R. J. Burke (Eds.), The Peak Performing Organization (pp. 50-72), London: Routledge, 2008.
- Bakker, A. B. and Demerouti, E., "Job Demands-Resources Theory: Taking Stock and Looking Forward", Journal of Occupational Health Psychology, Vol. 22, No. 3 2017, pp. 273-285. https://doi.org/10.1037/ocp0000056
- Bakker, A. B. and Demerouti, E., "The Job Demands-Resources Model: State of the Art", Journal of Managerial Psychology, Vol. 22, No. 3 2007, pp. 309-328. https://doi.org/10.1108/02683940710733115
- Bakker, A. B., Demerouti, E., and Sanz-Vergel, A., "Job Demands-Resources Theory: Ten Years Later", Annual Review of Organizational Psychology and Organizational Behavior, Vol. 10, No. 12023, pp. 25-53.
- Bakker, A. B., Demerouti, E., and Euwema, M. C., "Job Resources Buffer the Impact of Job Demands on Burnout", Journal of Occupational Health Psychology, Vol. 10, No. 2 2005, pp. 170-180. https://doi.org/10.1037/1076-8998.10.2.170
- Bakker, A. B., Demerouti, E., and Verbeke, W., "Using the Job Demands Resources Model to Predict Burnout and Performance", Human Resource Management, Vol. 43, No. 1 2004, pp. 83-104. https://doi.org/10.1002/hrm.20004
- Baron, R. M. and Kenny, D. A., "The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations", Journal of Personality and Social Psychology, Vol. 51, No. 6 1986, pp.1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173
- Becker, T. E., "Potential Problems in the Statistical Control of Variables in Organizational Research: A Qualitative Analysis with Recommendations", Organizational Research Methods, Vol. 8, No. 3 2005, pp.274-289. https://doi.org/10.1177/1094428105278021
- Brislin, R. W., "Translation and content analysis of oral and written material", In H. C. Triandis and J. W. Berry (Eds.), Handbook of cross-cultural psychology, pp. 349-444, Boston, MA: Allyn and Bacon, 1980.
- Chen, G., Gully, S. M., and Eden, D., "Validation of a New General Self-efficacy Scale", Organizational Research Methods, Vol. 4, No. 1, 2001, pp. 62-83. https://doi.org/10.1177/109442810141004
- Demerouti, E., Bakker, A. B., Nachreiner, F., and Schaufeli, W. B., "The Job Demands-Resources Model of Burnout", Journal of Applied Psychology, Vol. 86, No. 3, 2001, pp. 499-512. https://doi.org/10.1037/0021-9010.86.3.499
- Gimenez-Espert, M. D. C., Prado-Gasco, V. and Soto-Rubio, A., "Psychosocial Risks, Work Engagement, and Job Satisfaction of Nurses during COVID-19 Pandemic", Frontier in Public Health, Vol. 8, 2020, 566896.
- Hobfoll, S. E., "Social and Psychological Resources and Adaptation", Review of General Psychology, Vol. 6, No. 4, 2002, pp. 307-324. https://doi.org/10.1037/1089-2680.6.4.307
- Hobfoll, S. E., Johnson, R. J., Ennis, N. and Jackson, A. P., "Resource Loss, Resource Gain, and Emotional Outcomes among Inner City Women", Journal of Personality and Social Psychology, Vol. 84, No. 3, 2003, pp. 632-643. https://doi.org/10.1037/0022-3514.84.3.632
- Llanos-Contreras, O., Ibanez, M. J. and V. J. Prado-Gasco, "Job-Demand and Family Business Resources in Pandemic Context: How They Influence Burnout and Job Satisfaction", Frontier in Psychology, Vol. 13, 2023, 1061612.
- Lovell, S. E., Kahn, A. S., Anton, J., Davidson, A., Dowling, E., Post, D., and Mason, C., "Does gender Affect the Link Between Organizational Citizenship Behavior and Performance Evaluation?" Sex Roles, Vol. 41, No. 5-6, 1999, pp.469-478. https://doi.org/10.1023/A:1018883018719
- Maslach, C., "Job Burnout: New Directions in Research and Intervention", Current Directions in Psychological Science, Vol. 12, No. 5, 2003, pp. 189-192. https://doi.org/10.1111/1467-8721.01258
- Maslach, C. and Leiter, M. P., "Early Predictors of Job Burnout and Engagement", J. of Applied Psychology, Vol. 93, No. 3, 2008, pp. 498-512. https://doi.org/10.1037/0021-9010.93.3.498
- Maslach, C. and Leiter, M. P., The Truth about Burnout: How Organizations Cause Personal Stress and What to Do about It. San Francisco, CA: Jossey-Bass, 1997.
- Maslach, C. and Jackson, S. E., "The Measurement of Experienced Burnout", Journal of Organizational Behavior, Vol. 2, No. 2, 1981, pp. 99-113. https://doi.org/10.1002/job.4030020205
- Schaufeli, W. B. and Bakker, A. B., "Job Demands, Job Resources, and Their Relationship with Burnout and Engagement: A Multi-Sample Study", Journal of Organizational Behavior, Vol. 25, No. 3, 2004, pp. 293-315. https://doi.org/10.1002/job.248
- Schaufeli, W. B., Bakker, A. B., and Salanova, M., "The Measurement of Work Engagement with a Short Questionnaire: A Cross-National Study", Educational and Psychological Measurement, Vol. 66, No. 4, 2006, pp. 701-716. https://doi.org/10.1177/0013164405282471
- Schwarzer, R. and Jerusalem, M., "Generalized self-efficacy scale", In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in Health Psychology: A User's Portfolio. Causal and Control Beliefs (pp. 35-37), Windsor, United Kingdom: NFER-NELSON, 1995.
- Simbula, S., Guglielmi, D., and Schaufeli, W. B., "A Three-wave Study of Job Resources, Self-efficacy, and Work Engagement among Italian School Teachers", Eur. Journal of Work and Organizational Psychology, Vol. 20, No. 3, 2011, pp. 285-304. https://doi.org/10.1080/13594320903513916
- Van den Broeck, A., Vansteenkiste, M., De Witte, H., and Lens, W., "Explaining the Relationships Between Job Characteristics, Burnout, and Engagement: The Role of Basic Psychological Need Satisfaction", Work & Stress, Vol. 22, No. 3, 2008, pp. 277-294. https://doi.org/10.1080/02678370802393672
- Wagner, S. L. and Rush, M. C., "Altruistic Organizational Citizenship Behavior: Context, Disposition, and Age", The Journal of Social Psychology, Vol. 140, No. 3, 2000, pp. 379-391. https://doi.org/10.1080/00224540009600478
- Xanthopoulou, D., Bakker, A. B., Demerouti, E., and Schaufeli, W. B., "Reciprocal Relationships between Job Resources, Personal Resources, and Work Engagement", Journal of Vocational Behavior, Vol. 74, No. 3, 2009, pp. 235-244. https://doi.org/10.1016/j.jvb.2008.11.003
- Xanthopoulou, D., Bakker, A. B., Demerouti, E., and Schaufeli, W. B., "The Role of Personal Resources in the Job Demands-Resources Model", International Journal of Stress Management, Vol. 14, No. 2, 2007, pp. 121-141.
- Yeo, G. B. and Neal, A., "An Examination of the Dynamic Relationship between Self-efficacy and Performance across Levels of Analysis and Levels of Specificity", Journal of Applied Psychology, Vol. 91, No. 5, 2006, pp. 1088-1101. https://doi.org/10.1037/0021-9010.91.5.1088