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Record W4391060605 · doi:10.5267/j.uscm.2023.11.012

The impact of procurement agility and procurement sustainability on organizational performance in UAE’s entities: The mediating role of corporate governance

2024· article· en· W4391060605 on OpenAlexvenueno aff
Nawaf Alawadhi, Muhammad Turki Alshurideh

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementBusinessSustainabilityContext (archaeology)Process managementCorporate governanceChief procurement officerAgile software developmentMarketingKnowledge managementFinanceManagementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Today, there is a need to develop businesses procurement activities through adopting agile and sustainable procurement practices. The key aim of this research is to identify the influence of both procurement agility and sustainability on organizational performance. The study creates a relationship between the procurement agility and procurement sustainability in the organizational performance with the mediating role of corporate governance. The research framework involves two critical predictors including procurement agility and procurement sustainability. The data was collected with a quantitative cross-sectional methodology within UAE’s business entities context through adaptation of a well-designed questionnaire that was distributed to different businesses like Aviation, Hospitality and Telecommunication (320 responses). The study findings supported a hypothesized model with a significant influence of procurement agility and procurement sustainability on organizational performance. In addition, corporate governance mediated the relationship between procurement agility and organizational performance. The study concluded with the growing non-traditional procurement activities for better organizational outcomes.

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 score0.411
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.227
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

Citations2
Published2024
Admission routes1
Has abstractyes

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