The influence of neurodiversity management on affective commitment and turnover intention: the role of neurodiversity awareness
Bibliographic record
Abstract
Purpose In the wake of labor shortages in the retail industry, there is value in highlighting a business case for employing neurodivergent individuals. Drawing on signaling theory, this study explores whether perceived neurodiversity management (neurodiversity policies and adjustments) helps enhance neurodiversity awareness and affective commitment and whether affective commitment leads to lower turnover intention. Design/methodology/approach A cursory content analysis of publicly available documents of randomly selected four retail organizations was undertaken, which was followed by an online survey of the Australian retail workforce, leading to 502 responses from supervisors and employees. Findings The content analysis shows that retail organizations barely acknowledge neurodiversity. The findings of the main study indicate that neurodiversity policies are positively associated with both neurodiversity awareness and affective commitment, while adjustments were positively linked to affective commitment. Moreover, affective commitment was negatively associated with turnover intention. Affective commitment also mediated the negative effects of neurodiversity policies and adjustments on turnover intention. Originality/value This study supports, extends and refines signaling theory and social exchange theory. It addresses knowledge gaps about the perceptions of co-workers and supervisors in regard to neurodiversity management. It provides unprecedented evidence for a business case for the positive attitudinal outcomes of neurodiversity policies and adjustments. The findings can help managers manage neurodiversity for positive attitudinal outcomes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".