You reap what you sow: knowledge hiding, territorial and idea implementation
Bibliographic record
Abstract
Purpose This study aims to build a research model from the perspectives of knowledge hiding and idea implementation to examine what factors influence idea implementation and the cross-level moderating role of team territory climate. Design/methodology/approach Data were collected from universities, 52 (R&D) teams in China via a two-wave survey. The final sample contained 209 team members and their immediate supervisors. Hierarchical linear modeling was used to test hypotheses. Findings The results indicated that individuals’ knowledge-hiding behavior had a significantly negative impact on idea implementation and creative process engagement, which played a mediating role. Team territorial climate played a cross-level moderating role between knowledge hiding and idea implementation. If team territorial climate was at a high level, then the negative connection between knowledge hiding and idea implementation would be weaker. Research limitations/implications Under the perspective of territorial behavior in Chinese cultural, it can help to distinguish territorial behavior and be preventive at individual and team levels. This study not only enables managers to clearly understand the precipitating factors of idea implementation but also provides constructive strategies for alleviating the negative effects of knowledge territoriality on creative process engagement and idea implementation. Originality/value This study constructs a cross-level model to explore the relationship among knowledge hiding, creative process engagement and idea implementation at individual and team levels in the context of Chinese R&D enterprises. Additionally, the study analyzes the influence of territoriality on idea implementation under boundary conditions.
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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 | 0.003 | 0.010 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".