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Record W4394887008 · doi:10.5267/j.uscm.2024.4.012

The operational impact of e-business on SMEs performance in Saudi Arabia

2024· article· en· W4394887008 on OpenAlexvenueno aff
Faisal Abdulkarim Alkhamis

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFlexibility (engineering)Electronic businessQuality (philosophy)Business caseNew business developmentBusiness processMarketingProcess managementBusiness modelManagementEconomics

Abstract

fetched live from OpenAlex

The study aimed at investigating the impact of e-business on small and medium enterprises (SMEs) operational performance in terms of business flexibility, business quality, and business costs. Research data was harvested via an online questionnaire developed based on previous related works and administered to a sample consisting of 500 owners and managers of industrial and commercial SMEs in Saudi Arabia. Using SmartPLS software, the results pointed out that e-business exerts a significant positive impact on the overall SMEs operational performance. Particularly, the results revealed that e-business results in positive effects on business flexibility, business quality, and business costs. It was observed that the greater impact of e-business was on business flexibility while the impact of e-business on business quality and business costs is roughly similar. These findings suggest that the current SMEs are highly concerned with customer-oriented issues such as marketing channels, customer needs and communications, and on-time delivery. Based on these results, it was concluded that SMEs can use e-business solutions to boost their business operational capabilities with reference to flexibility, quality, and cost.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.389

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 communication0.0000.000
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations5
Published2024
Admission routes1
Has abstractyes

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