The sharing and management of managers' knowledge: A structured literature review
Bibliographic record
Abstract
The management and sharing of managers' knowledge constitute vital components of organizational success and performance. A range of existing studies on managerial knowledge has analyzed various aspects of knowledge sharing and management on a patchwork basis. With future research developments in mind, some stock‐taking of the current body of research studies should now be undertaken for the purpose of obtaining a clearer understanding of how managerial knowledge is and could be shared and managed. Specifically, this study strives to reply to three questions: (1) What is the status of the development of literature concerning managers' knowledge? (2) What are the various conceptualizations of managers' knowledge and of its sharing and management? (3) What are the main determinants of the sharing and management of managers' knowledge that have been identified in the literature? A structured literature review method was used to code and analyze 57 studies published between 1997 and 2016. The results show that 128 authors produced an average of 3.2 publications per year, offering an abundance of conceptualizations of managers' knowledge and of its sharing and management. Several individual and organizational determinants of managerial knowledge sharing and management were identified in our review. The various findings provide a basis for orienting future research in this field.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".