The re‐structuring of the information technology infrastructure library (ITIL) implementation using knowledge management framework
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide reinforcement for ITIL V 2.0 implementation process through knowledge management principles embedded in enterprise management‐engineering framework (EMEF). Design/methodology/approach EMEF has been amended to include knowledge management (KM) activities that are imperative for a melioration of ITIL implementation. The framework of four domains has been documented in detail. Additionally, the three major amendments of structure, architecture, and context have been suggested for a configuration management database (CMDB) to comply with KM principles. Findings There are strong indications that implementing ITIL by following the system‐thinking approach may add and sustain competitive advantage. This may be achieved through the leveraging of knowledge, improvement of core competencies, and fostering a customer‐consciousness approach. The apprehension of knowledge continuum components, and the differentiation between knowledge types, are critical for fortifying the ITIL process path and supporting the decision‐making process throughout ITIL implementation. The four layers of the integrative management domain will significantly contribute to the tuning of operational misalignment between IT and business, and the betterment of the employee and processes effectiveness. The similarities found between ontology objects and CMDB configuration items will raise CMDB information to a higher level of conceptualization. Originality/value This paper will be valuable for ITIL customers, decision makers, and implementers by providing a more complete framework allowing organizations to attain effectiveness, efficiency and innovation throughout ITIL implementation.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".