Engaging Mature-Age Workers Through Mature-Age Practices: Examining the Roles of Focus on Opportunities and Work Centrality
Bibliographic record
Abstract
Abstract Given the global trend of labor force aging and the ongoing challenge of engaging mature-age workers, researchers have begun to explore human resource practices that are tailored to the needs of mature-age workers. However, knowledge about how such practices influence older individuals’ motivation at work is limited. Drawing upon signaling theory, we developed and examined a model that specifies why and when mature-age practices are helpful in engaging mature-age workers. Using time-lagged data from 135 Chinese workers aged 40 years or above, we found that mature-age practices are associated with mature-age workers’ focus on opportunities. Moreover, mature-age practices had a positive indirect effect on mature-age workers’ work engagement through their focus on opportunities. This positive indirect effect of mature-age practices on work engagement via focusing on opportunities was stronger for mature-age workers with lower rather than higher work centrality. The findings are discussed in terms of their theoretical implications for the aging workforce management literature and practical implications are provided for managers seeking to engage mature-age workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".