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Record W2792429697 · doi:10.5539/jms.v8n1p137

The Relationship Between Implementing Knowledge Management Practices in on-the-Job Training and Developing Professional Skills of Oil Industry Employees

2018· article· en· W2792429697 on OpenAlexvenueno aff
Roushanak Rahmani, Esfandiar Doshmanziari, Nasser Asgari

Bibliographic record

VenueJournal of Management and Sustainability · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsLISRELCronbach's alphaKnowledge managementPath analysis (statistics)Knowledge sharingData collectionSample (material)Statistical populationPsychologyVocational educationPopulationPetroleum industryDescriptive statisticsBusinessMedical educationEngineeringComputer scienceStatisticsStructural equation modelingMedicineMathematicsPedagogy

Abstract

fetched live from OpenAlex

In the technology-based organizations that are active in oil industry, the enjoyment of the employees of the specialized knowledge and skills plays a determinative role in their vocational performance. Inter alia the various prerequisites for such specialized skills in the employees, the present study has dealt with the evaluation of the knowledge management effect. The current study paper is an applied research in terms of its objectives and it has been carried out based on descriptive-survey method. The study population included the operational workers of Naft-e-Shomal Excavation Company that reached to a total of 2000 individuals out of whom 322 individuals were selected as the study sample volume based on convenience method. The data collection tool was a standard questionnaire the reliability of which was evaluated based on Cronbach’s alpha method and a value equal to 0.86 was obtained. The data analyses have been undertaken assisted by path analysis and t-test statistical examinations through taking advantage of LISREL software and SPSS software. The results indicated that all four practices of knowledge management (knowledge creation, knowledge storage, knowledge application and knowledge sharing) exert positive and significant effects on the employees’ development of specialized skills. Also, it was figured out that the company is in a good status in terms of both of the abovementioned factors.

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)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
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 science0.0000.000
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 head0.098
GPT teacher head0.411
Teacher spread0.313 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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