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Record W289194949

The Effect of Knowledge Management Context on Knowledge Management Practices: an Empirical Investigation

2006· article· en· W289194949 on OpenAlexaffabout
Brian Detlor, Umar Ruhi, Ofir Turel, Pierrette Bergeron, Chun Wei Choo, Lorna Heaton, Scott Paquette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversité de MontréalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsKnowledge managementOrganizational learningStructural equation modelingContext (archaeology)BusinessPersonal knowledge managementOrganizational commitmentEmpirical researchEmpirical evidencePsychologyOrganizational effectivenessSocial psychologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Abstract: This paper presents recent research findings on the effects of organizational knowledge management (KM) context on KM practices. Data were collected at a large Canadian law firm via a Web-based survey instrument from over 400 participants comprising professional and support staff working in various office locations. The purpose of the study was to gain insight on the antecedents of knowledge management behaviors in organizations. A theoretical model explicating the impact of an organization’s KM environment on both organizational and individual KM behaviors was developed and tested using structural equation modeling techniques. The moderating effects of age, biological sex, job category, and years spent in the organization were also examined. Results indicate that an organization’s knowledge management environment impacts on both organizational as well as personal knowledge management behaviors. Furthermore, we show that organizational KM behavior also influences personal KM behavior, thus acting as a mediator between the overarching organizational knowledge management policies and practices and the employees ’ individual practices. Based on this empirical evidence, recommendations are suggested for organizations wishing to institutionalize knowledge management initiatives in their firms.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.375
Teacher spread0.327 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2006
Admission routes2
Has abstractyes

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