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Record W3112602785 · doi:10.5430/ijba.v12n1p1

Knowledge Management as Support for Innovation of Public Projects

2020· article· en· W3112602785 on OpenAlexvenueno aff
Camila Marques de Lima, Flávio de São Pedro Filho, Elca Pereira da Silva, Tiago Garcia Araujo, Francisco Alexandre Bellinassi Paim

Bibliographic record

VenueInternational Journal of Business Administration · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge managementKnowledge sharingBusinessAsset (computer security)Knowledge value chainInnovation managementPersonal knowledge managementService (business)Process (computing)Knowledge economyProduct (mathematics)Organizational learningComputer scienceMarketing

Abstract

fetched live from OpenAlex

Knowledge is an important organizational asset and it is essential to ensure efficient performance and competitiveness. Knowledge Management (KM) appears, therefore, as an important tool to guarantee the identification, absorption, creation, sharing and application of organizational knowledge. Innovation is seen as the creation or improvement of methods, practices, technology, product or service. In this scenario, this research had as a general objective to carry out a study of the conceptual foundations of Knowledge Management which are valid for innovation in public projects and as specific objectives to carry out a theoretical-conceptual survey on Knowledge Management, characterize the relationship between Knowledge Management and innovation and point out the valid indications in this study that support the innovation of public projects. The question to be answered was: How can Knowledge Management be used to support innovation in public projects? This research was elaborated through the Content Analysis Method and presented, as a result, the conclusion that the KM process as a whole has practices that aim to create in an organizational environment conducive to the emergence of innovative thinking encouraging the sharing of knowledge and experiences, the search for solutions and making individuals better qualified.

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)0.002
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: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.247
GPT teacher head0.431
Teacher spread0.185 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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