Translating management research into practice: a six-step path to engage stakeholders
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.281 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.018 | 0.042 |
| Scholarly communication | 0.039 | 0.048 |
| Open science | 0.007 | 0.043 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".