Motivating supervisors during disability accommodation: a comprehensive examination on job demand and resources theories
Bibliographic record
Abstract
Purpose This study develops and tests a comprehensive model that examines whether dimensions of supervisors’ job demands and resources influence their work motivation through their job strain levels while managing disability accommodation (DA). Design/methodology/approach The proposed model leverages the assumptions of established job demand and resources theories, including demand-ability fit, job demand-control, job demand-control-support, and effort-reward balance models. Then, we tested with the quantitative data from 335 British, Canadian, American, Australian, Dutch, and German supervisors with recent DA experience. Findings This study found support for the proposed model. Job control and social support directly affected work motivation, while job strain did not mediate the relationship between job control and social support and work motivation. The results suggest that employers looking to improve the likelihood of DA success should focus on providing adequate job control, social support, and rewards to supervisors responsible for accommodating employees with disabilities. Practical implications This research enhances our understanding of how additional DA responsibilities impact supervisors and aids in the development of effective DA management policies and interventions, providing robust support for practitioners. Originality/value This study contributes to extending the DA literature by testing the applicability of different theoretical models to explain the effect of the additional DA responsibility on supervisors’ job demand, strain, and motivation levels and identify the resources to mitigate them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".