Knowledge-centered culture and knowledge sharing: the moderator role of trust propensity
Bibliographic record
Abstract
Purpose – This research aims to evaluate if knowledge-centered culture (KCC) fosters knowledge sharing equally across employees with different levels of trust propensity, an enduring individual characteristic. Design/methodology/approach – A cross-sectional questionnaire study was conducted with 128 US-based employees. Findings – The authors found that KCC only promoted knowledge sharing in individuals with high levels of trust propensity. For individuals with low levels of trust propensity, KCC had no effect on knowledge sharing. Research limitations/implications – The authors focused exclusively on trust propensity as a moderator. Future research could analyze the role of other enduring individual differences in the relationship between KCC and knowledge sharing. Practical implications – A KCC may be inefficient in promoting knowledge sharing in employees with low propensity to trust. Recruitment and selection of individuals with a high propensity to trust is a possible solution to enhance the association between KCC and knowledge sharing in organizations. Originality/value – By identifying an enduring individual characteristic that shapes the relationship between KCC and knowledge sharing, the authors move toward the development of a contingent view of KCC and show that KCC fosters knowledge sharing differently across employees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".