Bibliographic record
Abstract
Purpose This paper to examine full knowledge sharing (KS) and partial KS in order to test the proposition that they are separate behaviors with different characteristics, risks, and motivations for the informer and subsequently different predictors. Design/methodology/approach Employed knowledge workers completed two questionnaires over a two‐week period regarding their attitudes, situational factors, individual differences, and KS behaviors with their close colleagues in their workplace. Findings Results support the proposition that they are different albeit related behaviors. Full KS is enabled by intentions for full KS. Partial KS is enabled by the uniqueness of the knowledge, interpersonal distrust of close colleagues, and inhibited by perceived value of knowledge. Management support, interpersonal trust and distrust enable intentions for both full and partial KS, then propensity to share further enables full KS, and psychological ownership further enables intentions for partial KS. Research limitations/implications The findings from the study suggest that researchers should specify which sharing behavior they are examining (full or partial). Future research should also examine the outcomes of these two behaviors to see whether the assumed benefits of sharing knowledge apply to both of them. Practical implications The findings of the study provide some insight for practitioners on what motivates full versus partial KS. Originality/value The study challenges the assumption that KS is a single behavior, and starts to parse out the complexities within the KS literature with respect to predictors of actual KS behaviors.
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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 | 0.005 | 0.044 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".