The individual and social dynamics of knowledge sharing: an exploratory study
Bibliographic record
Abstract
Purpose This paper aims to examine how knowledge sharing behavior is influenced by three sets of dynamics: a rational calculus that weighs the costs and benefits of sharing; a dispositional preference that favors certain patterns of sharing outcomes; and a relational effect based on working relationships. Design/methodology/approach Concepts from social exchange theory, social value orientation, and leader‐member exchange theory are applied to analyze behavioral intentions to share knowledge. The study population consists of employees of a large pension fund in Canada. Participants answered a survey that used allocation games and situational vignettes to measure social value orientation, propensity to share knowledge, and perception of cost and benefit. Findings The results suggest that personal preferences about the distribution of sharing outcomes, individual perceptions about costs and benefits, and structural relationship with knowledge recipients, all affect knowledge sharing behavior significantly. Notably, it was found that propensity to share knowledge is positively related to perceived benefit to the recipient, thus suggesting that evaluation of cost and benefit in social exchange is not limited to self‐interest, but is also influenced by perceived recipient benefit. Moreover, it was found that the relationship with the sharing target (superior or colleague) also influenced sharing. Originality/value Most studies emphasize the organizational benefits of knowledge sharing. This study examines knowledge sharing from the perspective of the individual who approaches knowledge sharing as a social exchange that involves perceptions of costs and benefits, preferences about sharing outcomes, and relationship with the sharing target. The study also introduces innovative methods to measure social value orientation and propensity to share knowledge.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".