Bridging the Knowledge Gap: The Influence of Strong Ties, Network Cohesion, and Network Range on the Transfer of Knowledge Between Organizational Units
Bibliographic record
Abstract
Prior research has emphasized the importance of boundary spanners in facilitating the transfer of knowledge between organizational units. The successful transfer of knowledge between organizational units is critical for a number of organizational processes and performance outcomes. The empirical evidence on the success of boundary spanners is mixed, however. Research findings indicate boundary spanners can either facilitate or inhibit the flow of knowledge between organizational units. We develop and test a theoretical argument emphasizing the importance of the broader network context in which boundary spanning occurs. In particular, we consider how tie strength, network cohesion, and network range affect the level of knowledge acquired in cross-unit knowledge transfer relationships. An analysis of knowledge transfer relationships among several hundred scientists indicates that each network feature had a positive effect on the level of knowledge acquired in cross-unit knowledge transfer relationships. Our findings illustrate how network features contribute to the flow of knowledge between organizational units and, therefore, how network context contributes to heterogeneity in boundary-spanning outcomes.
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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 | 0.005 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| 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".