Research paradigms of contemporary knowledge management studies: 1998‐2007
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the research paradigms of contemporary knowledge management studies in the past decade using citation and co‐citation analysis. Design/methodology/approach Research in any academic area often clusters into informal networks that focus on common questions in common ways, and the accumulated knowledge often flows between members of these networks, revealed in patterns of citations. The research paradigms of a given field can be identified by analyzing corresponding knowledge flows and citation and co‐citation process. The methods used in the study include citation analysis, co‐citation analysis, and social network analysis. Findings The paper draws an intellectual map of knowledge flows between knowledge management scholars. Key research themes and concepts as well as their relationships in the field of knowledge management are identified. Research limitations/implications An in‐depth analysis of the relationships between knowledge management research and industrial practices should be conducted in future in order to examine the impact of academic research on knowledge management and the management of knowledge accumulated in the practice. Originality/value The paper profiles knowledge management studies in the past decade and presents a solid foundation for a better understanding of different research paradigms in the area of knowledge management. It helps identify the invisible network of knowledge management studies that traces the evolution of knowledge management research, which thus provides a new perspective on knowledge management research.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.033 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".