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Record W4410619791 · doi:10.46793/ebm24.003k

REGIONAL DIFFERENCES IN ASSESSING THE KEY CORPORATE ENTREPRENEURSHIP FACTORS: STUDY FROM SERBIA, CROATIA AND B&H

2025· article· en· W4410619791 on OpenAlexaboutno aff
Ljiljana Kontić, Nebojša Janićijević, Mirta Benšić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipKey (lock)BusinessRegional scienceComputer scienceGeographyFinanceComputer security

Abstract

fetched live from OpenAlex

The main aim of this study was to investigate similarities and differences in the key corporate entrepreneurship factors in selected regions from Serbia, Croatia and Bosnia and Herzegovina. The key corporate entrepreneurship factors were Management Support, Work discretion, Rewards, Time availability, and Organizational Boundaries. Methodological tool has been Corporate Entrepreneurship Assessment Instrument (CEAI). The written permission to use the CEAI questionnaire was provided by the authors. Mainly, CEAI model has been used in developed economies i.e. United States of America and Canada. This was the first used of CEAI in the regional context in emerging economies. Previous researches has been conducted in Serbia. This study filled the gap between developed and emerging economies in the context of the corporate entrepreneurship. The respondents were 240 managers from the region. In the selection process of companies main criteria have been economic and financial standing, and regional importance. The correspondent analysis has been used. The findings revealed similar assessment of Management support and Work discretion by managers in Croatia and Bosnia and Herzegovina. The Time availability factor has been different assessed by managers from region. Managers from Bosnia and Herzegovina were differed from the rest of the region in assessment Organizational Boundaries. The higher rating of Organizational boundaries had observed managers from Croatia.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.090
GPT teacher head0.268
Teacher spread0.178 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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