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Record W4312185897 · doi:10.5267/j.uscm.2022.9.008

Entrepreneurial competence and information technology capability as indicators of business success

2022· article· en· W4312185897 on OpenAlexvenueno aff
Ahmad Marei, Azzam A. Abou-Moghli, Maha Shehadeh, Hanadi A. Salhab, Mohammed d Othman

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCompetence (human resources)Information technologyMarketingTest (biology)EntrepreneurshipKnowledge managementManagementEconomics

Abstract

fetched live from OpenAlex

The importance of entrepreneurial competencies and information technology capabilities has increased in the last few decades because of the strategic role played by the entrepreneurs. Therefore, this study aims to investigate the impact of entrepreneur competency and information technology capability on business success. A survey questionnaire was conducted to test the influence of entrepreneur competency and information technology capability. This questionnaire was filled by 403 participants who were recruited through Jordan Chamber of Commerce so as to identify the business owners who had started their companies in the recent years. The findings of the study showed that entrepreneurial competencies have a positive effect on business success and information technology which was used as a mediating role has a positive impact on both entrepreneurial competencies and business success. The findings of this research will be helpful to entrepreneurs and policy makers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2022
Admission routes1
Has abstractyes

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