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Record W3134971930 · doi:10.48110/joi.v2i1.25

Stakeholders' Engagement and Performance Efficiency at Oil and Gas Industry in Yemen

2021· article· en· W3134971930 on OpenAlexaff
Mohammed Ahmed Al-Haddad, Mohammed Saleh Al-Abed

Bibliographic record

VenueJournal of Impact · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsImpact
Fundersnot available
KeywordsUpstream (networking)MidstreamBusinessPetroleum industryDownstream (manufacturing)Sample (material)Process (computing)Customer engagementMarketingProcess managementPerformance indicatorEngineeringComputer science

Abstract

fetched live from OpenAlex

Oil and Gas industry in Yemen is considered as the biggest and variable sector. Therefore, the performance efficiency is very important to be attained during all the activities and phases that include searching, drilling, upstream, midstream, and downstream. As a crucial factor, stakeholders’ engagement theories have been emphasized the central role of stakeholders’ engagement to strategic planning efforts in contemporary organizations. This study therefore examined the impact of stakeholders’ engagement on the performance efficiency (time, cost, and quality) at the Yemeni oil and gas industry. The quantitative method was employed and online questionnaire was used as a primary source for collecting data. The sample size was 312, selected from three oil and gas companies; namely; Yemen Liquid Natural Gas Company, Safer Exploration and Production Oil Company and OMV Company. This study targeted managers and non-managers. The results show that stakeholders’ engagement has a significant relationship with the performance efficiency. In addition, stakeholders’ engagement has a significant impact on the performance efficiency. The results suggest the consideration of early stakeholders’ engagement in the planning, development, implementation, controlling and evaluation the performance. Managers should enhance every step regarding the participation of the stakeholders in the decision-making process. In addition, more effective practices will support achieving performance efficiency.

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 score0.113
Threshold uncertainty score0.379

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 science0.0000.000
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 head0.040
GPT teacher head0.264
Teacher spread0.224 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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