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Record W2310727016 · doi:10.5539/ass.v12n4p138

Entrepreneurship in Social-Media Services in Oman – A Socio-Economic Scanning of the Sultanate

2016· article· en· W2310727016 on OpenAlexvenueno aff
Baby Sam Samuel, Prof. Dr. Joe Sarprasatha

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaEntrepreneurshipDiversification (marketing strategy)Social entrepreneurshipBusinessMarketingScope (computer science)Service (business)Economic growthPolitical scienceEconomicsFinance

Abstract

fetched live from OpenAlex

<p class="a"><span lang="EN-US">The document explores the background of entrepreneurship and social media from the perspective of the Sultanate of Oman, and explores the prevalence and usage of social media and social media business services across the Arab world and in particular, Oman. The review of data and of available literature reveals that entrepreneurship is increasingly being promoted in Oman as part of a strategy to promote the diversification of economy in light of declining oil prices and depleting oil reserves. </span></p><p class="a"><span lang="EN-US">With a view of exploring the scope of social media related services as a means of entrepreneurial ventures, the potential benefits of social media for businesses, the possible opportunities and challenges in entrepreneurship in social media management and marketing services in Oman have been reviewed and highlighted. The review reveals that in Oman and across the Arab region, as social media usage by businesses is gaining prominence, the lack of digital skills could act as a promoter for outsourcing of social media to external social media service agencies. The studies also reveal the gap between client expectations and the currently available services. In this light, the study concludes that this is a viable area for research that merits further exploration.</span></p>

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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