Measuring knowledge spillovers transfer from scholars in business schools: validation of a multiple-item scale
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop and validate a 12-item scale of knowledge spillovers transfer (KST) from scholars in business schools to practitioners outside academia. Design/methodology/approach A sample of 807 faculty members from 35 Canadian business schools was used for the psychometric evaluation of the questionnaire. The reliability of the scale was assessed by Cronbach’s alpha. The construct validity was examined through exploratory and confirmatory factor analyses. The nomological validity was assessed by analyzing the prediction of two output indicators by means of KST using structural equation modeling and by testing differences in KST according to other related variables. Findings The psychometric properties obtained indicate that the instrument is reliable and valid, which invites to its use as a diagnostic tool of KST from scholars in business schools to users outside academia. Research limitations/implications The KST questionnaire developed and validated in this study can be considered as a useful practical tool enabling the assessment of business scholars’ KST activities. Practical implications The KST questionnaire developed may enlighten business schools’ administrators and policy-makers to identify and implement actions to improve the transfer of knowledge between research and practice. Originality/value To the best of the authors’ knowledge, despite the wide range of quantitative measures proposed in the literature, this is the first study that aims to present a comprehensive, accurate and validated scale to measure KST from scholars in business schools to practitioners outside academia.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".