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Record W2758703914 · doi:10.5539/ibr.v10n10p113

Impact of Knowledge Sharing on Competitive Priorities: The Moderating Role of Social Media (An Applied Study in Jordanian Telecommunication Companies)

2017· article· en· W2758703914 on OpenAlexvenueno aff
Khaled Mahmoud Al-Shawabkeh

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge sharingBusinessSocial mediaCompetitive advantageKnowledge managementMarketingSample (material)Stratified samplingComputer science

Abstract

fetched live from OpenAlex

This study aims to identify knowledge sharing and its dimensions (Donating knowledge & Collecting knowledge) and its impact on competitive priorities: (Cost, Flexibility, and Quality) and the role of social media as a moderating variable in Jordanian telecommunication companies. The study population is consisted of (3) Jordanian Telecommunication Companies: (Zain, Orange, and Umniah). The study used equal stratified random sample. To collect the primary data a questionnaire survey was distributed to (134) managers. The questionnaire consisted of (30) items of close ended response type.The study reached set of findings; there is a significant statistical impact of knowledge sharing (donating knowledge & collecting knowledge) on competitive priorities in Jordanian telecommunication companies at level (a£ 0.05(; and there is a significant statistical impact at level (a£ 0.05(of social media on improving the impact of knowledge sharing on competitive priorities in Jordanian telecommunication companies. The study recommended increased knowledge sharing among employees and between departments through encouragement and practicing of knowledge sharing activities among companies' staff. And the need to motivate employees who are use social media for knowledge sharing in a work environment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.552

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.085
GPT teacher head0.402
Teacher spread0.317 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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