Knowledge transfer in project-based organisations: A dynamic granular cognitive maps approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Motivation is crucial for enhancing the effectiveness of knowledge transfer. This study aims to investigate how motivation for knowledge transfer and organisational context influence the effectiveness of knowledge transfer in project-based organisations. We further identify the key factors of motivation in an organisation context. We applied Dynamic Granular Cognitive Maps (DGCMs) to reveal the influencing mechanism of motivation and organisation context. Results show that three factors – knowledge transfer involvement, knowledge transfer satisfaction, and knowledge psychological ownership – are global controlled variables that reflect knowledge transfer performance. The motivation factors balanced reciprocity, avoiding punishment, organisational affective commitment, and achievement motivation are more important than others for knowledge transfer. Moreover, organisation context has a serious impact on knowledge transfer performance. Based on these results, a series of strategies are recommended to improve knowledge transfer in project-based organisations. This study offers a new approach to establishing crucial organisational relationships based on empirical evidence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it