Evolution of Knowledge Management in Business
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While investigating the growth of knowledge management in academic literature and in consultancy firms Wilson (2002) in his article “The nonsense of knowledge management”, argues that the fields of information science and information systems, should clearly distinguish between the term “information” and “knowledge” in order to assure their respective importance within organizations.The purpose of this article is to analyze the evolution of the knowledge management as a field of study that clearly differentiates itself from the information system. It investigates the integration of technology in knowledge creation and identifies progress made in KM on the subject of business using information system with the successful utilization of tacit knowledge concepts.The study consists of a systemic review of articles on knowledge management from Web of Science and Esearch databases since 2003. The study used three search strings “knowledge management”, “knowledge management” and “tacit”, and “knowledge management” and “explicit”. This study may not have covered all articles and reports in KM. Yet, based on the chosen research methodology, it seems reasonable to assume that the review process covered a large share of the studies available.The literature concerning the evolution of the Knowledge Management (KM) has highlighted that KM as a strategy and tool is now more in line with the basic definition of knowledge and wisdom. The advancement in Information Technology (IT), has supported knowledge capture process by utilizing the human dimension of KM that emphasize on knowledge context. The main contribution of this study is to confirm the close relationship of dependency of IT and KM.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it