Revisiting Knowledge Management Systems: Exploring Factors Influencing the Choices of Knowledge Management Systems in Knowledge-Intensive Organisations
Bibliographic record
Abstract
Limited research attention has been directed toward exploring ways in which organisations' understanding of their activities and the contexts in which their workers work influence the organisations' choice, design, and implementation of knowledge management systems (KMS). In particular, little research and insights exist to guide the successful development and implementation of KMS in knowledge-intensive organisations (KIOs). This oversight is somewhat surprising given that knowledge is a key asset in KIOs and one might therefore expect the design of systems that are used to manage knowledge of paramount interest to KIO researchers and practitioners. Using primarily grounded theory approach this study examines how KIO defining factors, KIO organisational knowledge-intensity attributes and knowledge worker activities relate to the choice of KMS in KIOs. Results of this analysis suggest that both organisational knowledge-intense attributes and knowledge-intense worker activities inform the choice and application of KMS in KIOs. Notably, the results revealed significant differences among participants in their choices of KMS, pointing to the fact that managers and practitioners in KIOs critically consider knowledge-intense factors defining their organisations when choosing and implementing KMS. This study contributes to the knowledge management (KM) literature in general and in particular to the KMS in KIOs theory and practice, where limited attention has been paid to the various ways knowledge-intense organisational and worker-related factors may influence KMS choices, design, and adoption and ultimately organisational KM effectiveness.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".