Books as a knowledge translation mechanism: citation analysis and author survey
Bibliographic record
Abstract
Purpose As a response to the claims that much of management academic research is irrelevant from the practitioner perspective, this study aims to empirically investigate whether books serve as effective knowledge distribution agents and whether peer‐reviewed publications are used in the development of book content. Design/methodology/approach A citation analysis of 40 authored and nine edited books was done, followed by a survey of 35 book authors. Findings This study refutes the previous claims that management academic research has made little impact on the state of practice. Peer‐reviewed sources, such as refereed journals, book chapters, and conference proceedings, are used to develop the content of knowledge management and intellectual capital (KM/IC) books. Even though most business professionals do not directly read academic articles, the knowledge existing in these articles is delivered to them by means of books and textbooks. Practical implications Scholarly research has played a significant role in developing the KM/IC field. This study confirms the existence of the indirect knowledge dissemination channels where books serve as knowledge transmission agents. Therefore, academics should not change their research behavior. Instead, infrastructure should be developed to facilitate the transition of scholarly knowledge to practitioners. The question is not whether academic research is relevant, instead it is whether it reaches practitioners in the most efficient way. Originality/value This is the most comprehensive empirical investigation of the role of books in academic knowledge transition ever conducted.
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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 | 0.017 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.036 | 0.053 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".