Translating management research into practice: a six-step path to engage stakeholders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scholars have observed that management research can miss opportunities to translate its findings into practice. Some emphasize the importance of academic-practice collaboration in designing, implementing, and disseminating management research to ensure academic rigor and practitioner relevance. These views align well with evidence-based management perspectives. This paper’s objective aims to describe what a research-to-practice translation path might resemble. This effort describes a six-step model to bridge research and practice identification, engagement, dissemination, exploitation, evaluation, and refresh. It draws on diverse sources of information obtained via purposeful sampling to provide illustrative examples to reflect how researchers or practitioners who translate their work into practice engage with these steps. This work’s contributions involve a roadmap for translation and extension of prior works in the extant literature calling for academic-practice collaboration in designing, implementing, and disseminating management research and multiple research-to-practice experience examples to illustrate how scholars and practitioners embrace such efforts for each phase. It also extends the ongoing academic conversation on this topic. This work proposes avenues for future research to address the limitations of this descriptive narrative. It seeks to refine the proposed model that can aid management researchers in their efforts to translate their works for managers and other practitioners.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it