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Record W114240433

ORGANIZATIONAL PERFORMANCE WITH ENVIRONMENTAL KNOWLEDGE INTENSITY: RESOURCE- VS. KNOWLEDGE-BASED PERFORMANCE

2012· article· en· W114240433 on OpenAlexaff
Tae Hun Kim, Ronald T. Cenfetelli, Izak Benbasat

Bibliographic record

VenueInternational Conference on Information Systems · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOrganizational performanceKnowledge managementOrganizational learningContingency theoryContingencyOrganizational behavior and human resourcesBusinessOrganizational commitmentKnowledge value chainComputer sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

In the current knowledge-oriented society, knowledge is a core strategic component for organizations. Successful knowledge management is therefore a key factor in organizational performance. At the same time, some studies have assumed that knowledge management performance is distinguished from the general features of organizational performance. Thus, this study conceptualizes organizational performance. With data collected from 150 Korean firms, we examine a two-factor model in which organizational performance consists of two different dimensions: knowledge management performance and firm performance. In addition, the twofactor model is analyzed across two sub samples (i.e., high and low knowledge-intensive firms) divided by environmental knowledge intensity, a key contextual factor on which organizational performance depends. Our findings raise issues on the causal relationship between knowledge-based success and firm performance, also suggesting the contingency understanding of organizational performance across organizations and industries. On the basis of the results, we discuss implications and future directions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.034
GPT teacher head0.262
Teacher spread0.228 · 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.

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

Citations3
Published2012
Admission routes1
Has abstractyes

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