Global ranking of knowledge management and intellectual capital academic journals: a 2021 update
Bibliographic record
Abstract
Purpose The purpose of this study is to update a global ranking list of 28 knowledge management and intellectual capital (KM/IC) academic journals. The list should be periodically updated because the pool of active KM/IC researchers changes, researchers adjust their journal perceptions, citation indices change and new journals appear while others become discontinued. Design/methodology/approach The ranking list was created based on a survey of 463 active KM/IC researchers and journal citation impact metrics (the h-index and the g-index). Findings Journal of Knowledge Management and Journal of Intellectual Capital are ranked A+, followed by The Learning Organization, Knowledge Management Research & Practice, VINE: The Journal of Information and Knowledge Management Systems, Knowledge and Process Management and International Journal of Knowledge Management which are ranked A. VINE, Electronic Journal of Knowledge Management and Online Journal of Applied Knowledge Management have shown the most improvement. The recently established Journal of Innovation & Knowledge has demonstrated a strong performance. Practical implications KM/IC discipline stakeholders may consult and use the ranking list for various purposes, but they should do so with caution. Highly ranked journals are quite likely to have the Clarivate’s Journal Impact Factor or be included in the Clarivate’s Emerging Sources Citation Index. A journal’s longevity is strongly correlated with its citation metrics and is moderately correlated with expert survey scores. Interdisciplinarity is the natural state of the KM and IC research domains, and it should be embraced by the research community. Originality/value This study presents the most up-to-date ranking list of KM/IC academic journals.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.047 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".