Employee engagement (Job Demands-Resources)
HR / Organizational Behavior
The Job Demands-Resources (JD-R) model is the theoretical gold standard for explaining how work demands and resources affect satisfaction and performance via engagement. A classic mediator chain across five constructs — ideal for studying indirect effects and f² effect sizes.
- N
- 350
- LVs
- 5
- R²(JP)
- 0.40
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Theoretical background
Demerouti, Bakker, Nachreiner, and Schaufeli (2001) formulated the JD-R model as a universal framework for work-stress research: every job decomposes into demands and resources. Bakker and Demerouti (2017) updated the state of the art and integrated engagement explicitly as the bridge to positive outcomes.
Empirically the model has received broad support, especially in meta-analyses (Christian, Garza & Slaughter, 2011). Engagement turns out to be the central mediator: resources act mainly indirectly via engagement on performance.
The bundled dataset follows the Bakker & Demerouti mediator logic exactly: job demands and resources influence engagement, which in turn drives satisfaction and performance. The theoretical prediction is directly replicable.
Structural model
Two exogenous antecedents (Demands, Resources), Engagement as mediator, Satisfaction and Performance as outcomes. Classic reflective 5-LV model.
Job Demands
Workload, time pressure, role conflict.
Job Resources
Autonomy, feedback, support, development.
Work Engagement
Vigor, dedication, absorption at work.
Job Satisfaction
Overall satisfaction with the job.
Job Performance
Self-rated and observed performance.
Hypotheses
| H1 | JD → WE | - | High demands reduce engagement (exhaustion path). |
| H2 | JR → WE | + | Resources raise engagement (motivation path). |
| H3 | JR → JS | + | Resources additionally affect satisfaction directly. |
| H4 | WE → JS | + | Engagement raises satisfaction. |
| H5 | WE → JP | + | Engagement is the central driver of performance. |
| H6 | JS → JP | + | Satisfaction translates into higher performance. |
Methodology & data
N = 350 synthetic employee responses on 18 reflective indicators (seven-point Likert). Real JD-R studies typically run with N = 200 to 600. The dataset reproduces the average path coefficients reported in the meta-analysis by Christian et al. (2011).
Expected results
- R²(JP) ≈ 0.40
- Engagement and satisfaction together explain about 40 % of variance in self-rated performance — substantial for self-report studies.
- WE → JP ≈ 0.42
- The strongest path to performance. Confirms the core JD-R claim that engagement is the direct lever.
- JD → WE negative
- Demands act negatively on engagement (exhaustion path). A textbook example of an expected negative relationship.
Reproduce in 60 seconds
- 1
Clone the project
A single click in your OpenPLS workspace creates a fully editable copy: model, indicators, and dataset, all linked and ready to go.
- 2
Run the computation
OpenPLS solves outer weights, path coefficients, R², HTMT, SRMR, and bootstrap confidence intervals in a few seconds.
- 3
Compare against expected metrics
The key metrics documented below come from the published original. Your computed values should fall inside the bootstrap 95 % CIs.
References
- Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. doi.org/10.1037/ocp0000056
- Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. doi.org/10.1037/0021-9010.86.3.499
- Christian, M. S., Garza, A. S., & Slaughter, J. E. (2011). Work engagement: A quantitative review and test of its relations with task and contextual performance. Personnel Psychology, 64(1), 89–136. doi.org/10.1111/j.1744-6570.2010.01203.x