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Developing Business Aligned Knowledge Management Strategy

2003· book-chapter· en· W4241592232 on OpenAlex
El‐Sayed Abou‐Zeid

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIGI Global eBooks · 2003
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsPremiseKnowledge managementStrategic managementStrategic alignmentCompetitive advantageBusinessProcess managementComputer scienceStrategic planningStrategic financial managementMarketing

Abstract

fetched live from OpenAlex

With the growing awareness of the crucial role that knowledge can play in gaining competitive advantage, several issues with regard to knowledge management (KM) initiatives have challenged executives. The articulation of the relationship between an organization’s competitive strategy and its knowledge strategy is the most eminent. This chapter addresses the issue of how to align knowledge strategy with enterprise business strategy. Based on the premise that the realization of business value from KM investments requires alignment between business and knowledge strategies, the issue is addressed by developing a strategic alignment model for KM. This model, which is based on the Henderson-Venkatraman strategic alignment model, includes the external domains (opportunities/threat) and internal domains (capabilities/arrangements) of both business (B-) and knowledge (K-) strategies and the relationships between them. Furthermore, it provides alternative strategic choices. The model is used to study a KM initiative at Buckman Laboratories.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.006

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.

Opus teacher head0.030
GPT teacher head0.241
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it