Predictors of Knowledge Transfer:A Biographical Analysis of Authors in Leading Management Journals
Bibliographic record
Abstract
The creation and transfer of new knowledge is considered a central role of business schools in most developed countries. Yet, an important question in knowledge creation and transfer remains – knowledge for whom? This debate has taken place and has led to the emergence of distinct knowledge transfer channels (KTC’s) oriented towards two audiences: academics and practitioners. In this exploratory study, we examine KTC decision making at an individual-level by empirically examining the factors that predict a scholar to choose one KTC over another. This research builds on institutional theory to suggest that the knowledge transfer output of individual faculty is rooted in a legitimacy judgment that is influenced by personal- and contextual-level factors. The personal factors analyzed include academic socialization, practitioner socialization, alumni prestige, and career stage, while the contextual-level factors include both business school rank and orientation. A biographical analysis, over 36 months, of the first (lead) authors of 429 articles from four highly ranked academic- and practitioner-oriented peer- reviewed journals was undertaken. Results show that a scholar’s socialization and career stage are important predictors of KTC output.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".