Taking it with you when you leave?: a proposed model and empirical examination of attitudes and intentions to share knowledge before retiring
Bibliographic record
Abstract
Record numbers of employees are retiring in Canada (Conference Board of Canada, 2009), and with their exit, copious amounts of organizational knowledge could be exiting too (Collins, 2007). In this thesis, I propose and test a model of attitudes and intentions towards knowledge sharing with 252 retiring and recently retired employees. The results suggested that the partially mediated alternative model fit the data the best, where affective commitment, job satisfaction, and perceived organizational support predicted attitudes towards knowledge sharing, which in turn positively predicted tacit and explicit knowledge sharing intentions, as well as negatively predicted intentions to hoard knowledge. There were also significant positive direct paths between job satisfaction and intentions to share tacit and explicit knowledge, as well as a significant negative direct path between job satisfaction and intentions to hoard knowledge. Lastly, organizational policies and practices (tacit and explicit), personal perceived knowledge value (tacit and explicit), and financial stake (explicit) were significant moderators. Study findings and limitations, as well as future research directions are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".