Negotiate, reciprocate, or cooperate? The impact of exchange modes on inter-employee knowledge sharing
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the impact of exchange modes – negotiated, reciprocal, generalized, and productive – on inter-employee knowledge sharing. Design/methodology/approach Based on the affect theory of social exchange, a theoretical model was developed and empirically tested using a survey of 691 employees from 15 North American credit unions. Findings The negotiated mode of knowledge exchange, i.e. when a knowledge contributor explicitly establishes reciprocation conditions with a recipient, develops negative knowledge sharing attitude. The reciprocal mode, i.e. when a knowledge donor assumes that a receiver will reciprocate, has no effect on knowledge sharing attitude. The generalized exchange form, i.e. when a knowledge contributor believes that other organizational members may reciprocate, is weakly related to knowledge sharing attitude. The productive exchange mode, i.e. when a knowledge provider assumes he or she is a responsible citizen within a cooperative enterprise, strongly facilitates the development of knowledge sharing attitude, which, in turn, leads to knowledge sharing intentions. Practical implications To facilitate inter-employee knowledge sharing, managers should focus on the development of positive knowledge sharing culture when all employees believe they contribute to a common good instead of expecting reciprocal benefits. Originality/value This is one of the first studies to apply the affect theory of social exchange to study knowledge sharing.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".