A study of inclusive supervisory behaviors, workplace social inclusion and turnover intention in the context of employee age
Bibliographic record
Abstract
Purpose The Australian retail industry is facing skills shortages while mature and old-age workers are experiencing high unemployment rates. This study focuses on understanding organizational inclusion and turnover intentions in the context of employee age. Design/methodology/approach Survey data were collected from 502 retail supervisors and employees. Findings Drawing on socioemotional selectivity theory and social exchange theory, the findings indicate: no difference in inclusive supervisory behaviors perceptions for different age groups; a significantly higher workplace social inclusion perceptions among employees aged 55 plus than among employees aged 35–44; a significantly lower turnover intention among employees aged 55 plus and 45–54 years than other age groups; a positive relationship between inclusive supervisory behaviors and workplace social inclusion and a negative relationship between workplace social inclusion and turnover intention which was stronger for older employees than for younger employees. Practical implications The findings present a business case for hiring older employees and indicate that managers need to prioritize inclusion. Originality/value This study addresses the underexplored area of employee age differences in inclusion and turnover perceptions among retail employees. It links inclusive supervisory behaviors, social inclusion and turnover intention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".