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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".