What strategy of human resource management to retain older workers?
Bibliographic record
Abstract
Purpose Based on the theory of conservation of resources (Hobfoll, 1989), the purpose of this paper is to propose job satisfaction as a mediator between the use of generativity and affective occupational commitment. The authors tested the mediating role of affective occupational commitment on the relationship between job satisfaction and retirement preparation. Design/methodology/approach A sequential mediation model was tested by the method of indirect effects based on a bootstrap analysis (Preacher and Hayes, 2004) based on 3,000 replications with a 95% confidence interval. The statistical treatments were carried out with the AMOS software V.22. Data were collected for a sample of 340 older workers (bridge and retirees) in Québec, Canada. Findings Results indicate that generativity was related positively to affective occupational commitment via job satisfaction. Moreover, job satisfaction was also related positively to retirement preparation through affective occupational commitment. Practical implications The results can be helpful to guide organizational efforts at retaining older workers, and also recruiting and selecting those who want to return to work after retiring. They provide an insight on the effect of one of the main human resources practices or strategies, that is, programs aiming to attract and retain older workers to stay in the workplace and to encourage retirees to return to work in the form of bridge employment for example. Originality/value The study adds to the existing literature by examining a sequential mediation model to understand the relationship between organizational resources, job attitudes and retirement planning. It thus answers the call for more research and a theoretical framework on these critical variables for the retirement decision-making process. The findings can also contribute to the field of knowledge retention and fulfill some gaps in the literature on this topic. Indeed, examining the use of generativity in the study can help researchers and practitioners to better understand the reasons that encourage older workers to continue working and retirees to return to work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".