Knowledge sharing as a give-and-take practice: the role of the knowledge receiver in the knowledge-sharing process
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate whether openness to receive and openness to share knowledge drive employees to share knowledge with colleagues in the workplace. The authors also investigate what, if any, influence knowledge sharing has on performance at both individual and work unit levels. Design/methodology/approach Data were collected from 237 employees from eight banks in Kuwait. Structural equation modeling techniques were used to test the hypotheses. Findings The knowledge receiver’s openness to receive and openness to share knowledge influence the provider’s knowledge-sharing behavior. The latter positively affects the provider’s job performance and the work unit’s innovation performance. Furthermore, knowledge utilization strengthens knowledge sharing’s positive effect on work unit innovation. Research limitations/implications The findings of this study are industry and country specific and, therefore, would likely not be applicable to other settings. Thus, similar future research targeting different industries and/or countries is warranted. As a cross-sectional study, this research can also benefit from subsequent longitudinal studies. Practical implications Organizations should create a culture conducive to sharing knowledge. For example, managers should assure employees that knowledge shared with coworkers will be well received and utilized, remove barriers to new knowledge utilization and create awareness among employees that sharing knowledge benefits knowledge providers as well as knowledge providers. Originality/value The authors provide evidence of how the knowledge receiver’s openness to receive and to share knowledge affect the provider’s knowledge sharing. The authors also provide insights into how knowledge sharing drives job performance and innovation.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".