Autonomy in design: reconciling sport employee experiences with forced job design alterations
Bibliographic record
Abstract
Rationale/Purpose This study adopted the frameworks of human resource management and development to explore how forced adjustments to job designs influenced sport employees’ psychological well-being. Job design reflects the work environment and employee tasks as designed by managers. The pandemic context emphasized the prominence of job design within human resource management models and the need to proactively manage factors related to job design to enable employee well-being.Design/methodology/approach Semi-structured interviews were conducted with 16 sport employees regarding their direct experience with job design, the resulting influences on their psychological well-being, and future job designs in the sport workplace.Findings The findings of the study included a discussion of how participants altered their job designs to realize improved psychological well-being. A significant factor in connecting job design with psychological well-being was increased autonomy.Practical Implications The agency to engage in positive alterations to job designs provided enhanced psychological well-being by helping participants grow personally and professionally. Improved psychological well-being could benefit sport employees and organizations.Research Contribution This study adds to the growing literature concerning strategic human resource management that can be used to provide improved experiences for sport employees and improve the functionality of sport organizations through enhanced job design.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".