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Organizational Culture Analysis in the Implementation of Knowledge Management in Government Research Agency

2023· article· en· W4322624289 on OpenAlexaff
Dian Isnaeni Nurul Afra, Insan Ramadhan, Dewi Suci Rafianti, Andri Puji Prasetiyo, Ali Reza Syariati, Lindri Setyaningrum, Agus Sutejo, Triyono Susanto

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsOrganizational cultureKnowledge managementBusinessOrganizational learningAgency (philosophy)Government (linguistics)Transformational leadershipTeamworkKnowledge sharingPublic relationsPolitical scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

In knowledge management implementation, organizational culture is recognized as a driving force and defining factor of the successful knowledge management application. The aim of this study is to identify the condition of organizational culture in a government research agency using Organizational Culture Assessment Instrument (OCAI) and propose strategies that can be taken to attain the expected organizational culture which encourages the implementation of knowledge management. The result shows that the current state of the organization was identified as Market Culture which focuses on targets, deadlines, and goals. On the other hand, the expected culture desired to be Clan Culture in which more cares about teamwork, participation, and empowerment. Several strategies proposed to change the organizational culture are believed to be great for knowledge management processes and activities. To improve knowledge management implementation, the organization must be able to focus on empowering employees, stimulate transformational leadership skills, provide effective rewards, and enhance employees’ participation and engagement.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.009
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.438
Teacher spread0.359 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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