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Record W2513236016 · doi:10.5539/jms.v6n3p127

The Effect of Management Policy & Process on Adopting Entrepreneurship Aspects by Jordanian Universities

2016· article· en· W2513236016 on OpenAlexvenueno aff
Firas Rifai, A.S.H. Yousif

Bibliographic record

VenueJournal of Management and Sustainability · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsConsolidation (business)EntrepreneurshipAuditBusinessSample (material)Process (computing)Data collectionStatistical analysisAccountingMarketingPublic relationsSociologyPolitical scienceFinanceStatisticsComputer scienceMathematicsSocial science

Abstract

fetched live from OpenAlex

This paper aims at exploring the role of the university managerial systems and policies in the process of implementing the concept of entrepreneurship and innovation by Jordanian universities. A questionnaire was used as a means of data collection. Seven Jordanian universities were chosen from both governmental and private sectors and 320 questionnaires were distributed to a random sample of managerial staff of these seven universities. The resulting data was carefully viewed, audited and statistically analyzed using the most appropriate statistical tests. The outcomes and results of the statistical analysis clearly indicated that the three independent variables (i.e. The university managerial process related to conducting the entrepreneurial concept implementation problems and obstacles, the university general managerial and financial policies and managerial staff awareness of entrepreneurial aspects) had a positive impact on both dependent variables individually and collectively (i.e. university expansion and the consolidation of university competitive advantage).

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)0.007
metaresearch head score (Gemma)0.025
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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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