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Record W2945871105 · doi:10.5539/ibr.v12n6p58

The Role of Knowledge Management on Competitive Advantage in Jordan Manufacturing Companies from Employees Perspectives

2019· article· en· W2945871105 on OpenAlexvenueno aff
Salameh Al- Nawafah, Mohammad Nigresh, Ali. K. Tawalbeh

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageBusinessSample (material)Knowledge managementKnowledge sharingMarketingComputer science

Abstract

fetched live from OpenAlex

The study aimed to investigate the role of knowledge management on competitive advantage in Jordan manufacturing companies from employee’s perspective. The research used the descriptive analytical methodology. In addition a self administrated questionnaire was developed according to research hypothesis and objectives for the purpose of achieve the study objectives. The research sample consisted of. 255 subjects. The self administrated questionnaires were distributed over 0 research sample, 240 questionnaire were collected, therefore the research sample is 240.. All gathered data were checked and coded then analyzed by using the social Packaging statistical System (SPSS). The study concluded that there is a relationship between knowledge management and competitive advantage in Jordanian industrial companies from the point of view of administrative employee perspectives. In addition the data also concluded that here is a relationship between knowledge generation and competitive advantage. Also there is a relationship between knowledge storage and competitive advantage and there is a relationship between knowledge sharing and competitive advantage in Jordanian industrial companies. The study revealed hat there is a relationship between knowledge application and competitive advantage.... The study recommended that Jordan manufacturing companies have to encourage knowledge management use and to notify their employees with the motives behind such use for obtaining their support.

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)0.000
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: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.018
GPT teacher head0.308
Teacher spread0.290 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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