A Framework for Developing and Aligning a Knowledge Management Strategy
Bibliographic record
Abstract
Businesses today, including non-profits, recognise the need for knowledge management (KM). KM may require new strategies and goals before it can be implemented, or it can be aligned with current business strategies for quicker implementation. The framework presented here is for managers in companies and organisations to use to align their KM strategies with business strategies to improve performance involving financial growth, cost reduction and customer satisfaction. A study of three strategic types of organisations (defender, prospector, analyser) and interviews at a large corporation and a non-profit organisation suggests that the conceptual framework presented in this paper can be verified. More empirical evidence of alignment is planned, as organisations become more sophisticated users of KM. The authors have been working for over three years on the taxonomy and conceptual framework for KM/BS Alignment (also known as KMSABS) and present a procedure for implementation in this paper. The KM/BS Alignment model involves concepts, actors, actions and processes. An important aspect of the methodology is for businesses and organisations to identify their strategic character to support appropriate interactions associated with knowledge. As shown in this paper, product and knowledge managers can affect goal alignment and interaction in an organisation if they implement change based on the suggested framework.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".