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Record W1992573885 · doi:10.1108/13673270710832181

A systems‐based dynamic knowledge transfer capacity model

2007· article· en· W1992573885 on OpenAlexaff
Robert Parent, Mario Roy, Denis St‐Jacques

Bibliographic record

VenueJournal of Knowledge Management · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsKnowledge managementKnowledge transferAbsorptive capacityComputer scienceContext (archaeology)Process (computing)Knowledge baseKnowledge value chainOriginalitySystems thinkingComplex adaptive systemDisseminationManagement scienceOrganizational learningEngineeringArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is twofold: to understand how recent developments in systems thinking and social construction can influence understanding of knowledge transfer (KT); and to propose a new systems‐based knowledge transfer model. Design/methodology/approach The paper is a review of the literature on knowledge transfer, systems thinking and social construction leads to the proposal of a new KT paradigm. Findings The Dynamic Knowledge Transfer Capacity model (DKTC) found in this paper identifies the components required for social systems to generate, disseminate and use new knowledge to meet their needs. The model includes pre‐existing conditions, (need and prior knowledge) and four categories of capacities (generative, disseminative, absorptive and adaptive/responsive) that social systems must possess for KT to take place. Research limitations/implications The paper shows that the DKTC model is particularly well suited to analyzing complex systems with multiple stakeholders as opposed to small‐scale knowledge transfer systems. Empirical analysis in complex systems environments will help verify, enrich and generalize the model. Practical implications The paper sees that in an increasingly knowledge‐based economy, the ability to base decisions on the latest knowledge is vital for the success of organizations. The capacity for effective and sustained exchange between a system's stakeholders (researchers, government, practitioners, etc.); exchanges characterized by significant interactions reflected within the DKTC model, results in the appropriate use of the most recent discoveries in the decision making process. Originality/value The paper proposes a new knowledge transfer paradigm that views knowledge as a systemic, socially constructed, context‐specific representation of reality. The proposed knowledge transfer model is in sharp contrast to past attempts, focusing attention on the capacities that must be present in organizations and social systems as a precondition for knowledge transfer to occur.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.005
Scholarly communication0.0050.009
Open science0.0030.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0170.002

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 head0.170
GPT teacher head0.404
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations170
Published2007
Admission routes1
Has abstractyes

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