A structured literature review of scientometric research of the knowledge management discipline: a 2021 update
Bibliographic record
Abstract
Purpose The purpose of this study is to conduct a structured literature review of scientometric research of the knowledge management (KM) discipline for the 2012–2019 time period. Design/methodology/approach A total of 175 scientometric studies of the KM discipline were identified and analyzed. Findings Scientometric KM research has entered the maturity stage: its volume has been growing, reaching six publications per month in 2019. Scientometric KM research has become highly specialized, which explains many inconsistent findings, and the interests of scientometric KM researchers and their preferred inquiry methods have changed over time. There is a dangerous trend toward a monopoly of the scholarly publishing market which affects researchers’ behavior. To create a list of keywords for database searches, scientometric KM scholars should rely on the formal KM keyword classification schemes, and KM-centric peer-reviewed journals should continue welcoming manuscripts on scientometric topics. Practical implications Stakeholders should realize that the KM discipline may successfully exist as a cluster of divergent schools of thought under an overarching KM umbrella and that the notion of intradisciplinary cohesion and consistency should be abandoned.Journal of Knowledge Managementis unanimously recognized as a leading KM journal, but KM researchers should not limit their focus to the body of knowledge documented in the KM-centric publication forums. The top six most productive countries are the USA, the UK, Taiwan, Canada, Australia and China. There is a need for knowledge brokers that may deliver the KM academic body of knowledge to practitioners. Originality/value This is the most comprehensive, up-to-date analysis of the KM discipline.
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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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.077 | 0.051 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".