Corporate entrepreneurship and debt financing: evidence from the GCC countries
Bibliographic record
Abstract
Purpose – The Gulf Cooperation Council (GCC) member countries have recently given tremendous emphasis to corporate entrepreneurship. The purpose of this paper is to investigate whether the lack of entrepreneurship in publicly listed GCC firms affects their ability to acquire debt financing. Design/methodology/approach – Using stochastic frontier approach, the paper estimates an optimal revenue function given labor costs, operating expenses, and existing physical infrastructure of an organization. The paper estimates the difference between the optimal and actual level of firm revenues from a revenue frontier function, which can be partially resulted from managerial inefficiency due to the lack of corporate entrepreneurship. The paper uses fixed-effect panel regression and simultaneous equations system to determine the effect of such inefficiency on firms’ debt financing. Findings – The main finding is that as entrepreneurial activities increase, firms’ ability to borrow from banks also increases. Results also indicate that increased borrowing improves internal governance practices and indirectly compel the management to become more efficient. Research limitations/implications – Results exhibit how improving entrepreneurship affects firms’ access to external financing when the financial markets are underdeveloped and are plagued with information asymmetry and agency problems. Practical implications – The paper provides insights for policy makers in the GCC and other emerging countries where entrepreneurial activities are becoming a priority. Originality/value – The paper develops a new proxy measure of entrepreneurship in public firms and advances our knowledge about the importance of entrepreneurship in finance.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".