Older workers' knowledge seeking from younger coworkers: Disentangling countervailing pathways to successful aging at work
Bibliographic record
Abstract
Summary Increasing age diversity in the workplace has led to growing research attention to the knowledge transfer between older and younger employees. The existing literature on age‐diverse knowledge exchange has mostly focused on knowledge transfer from older to younger employees as a means of knowledge retention. In this study, we change perspectives by aiming to understand how and when older employees' knowledge seeking from younger coworkers is related to their successful aging at work (i.e., the motivation and ability to continue working). Grounded in the self‐regulatory process model of successful aging at work, we predict two countervailing pathways: a positive self‐enhancing path via perceived learning and a negative self‐protective path via embarrassment. In a time‐lagged study with 764 older employees, we found that their knowledge seeking from younger coworkers was positively related to motivation to continue working and workability via perceived learning and negatively related to workability via embarrassment. We further examined older employees' positive intergenerational affect as a boundary condition and found a buffering effect on the negative path to workability. This research shows that knowledge transfer from younger to older employees is a net contributor to successful aging at work and embarrassment can be mitigated by positive intergenerational affect.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".