Entrepreneurial Intensity in the Corporate Sector in Oman: The Elusive Search Creativity and Innovation
Bibliographic record
Abstract
Entrepreneurial firms are attributed with the key characteristic of creativity and innovation which is one of the most intriguing competency that has generated substantial interest of academicians and practitioners and have received considerable attention in the literature. Creativity and innovation have been elevated to this pedestal by the virtue of its ability to enable firms to achieve the necessary competitive advantage in today’s chaotic market place. Ironically creativity and innovation is an elusive commodity and many firms particularly of the larger species struggle to display this competency. This study investigated the dimensions that promote or impede the concentration of creativity and innovation, measured through entrepreneurial intensity in the corporate sector in Oman. Data was collected from four participating organizations from four different sectors through a questionnaire survey. The findings indicate that 52% of the relationships between organizational characteristics and creativity and innovation are explained by entrepreneurial architecture dimensions. At the same time 55% of the relationships between organizational climate and creativity and innovation are explained by entrepreneurial frequency dimensions. Finally 43% of the relationships between macro economic conditions and creativity and innovation are explained by entrepreneurial degree dimensions. Further a general linear model explains 56% of the relationship between entrepreneurial architecture and entrepreneurial intensity dimensions (EF and ED). The findings are quite conclusive indicating that there is a strong relationship between organizational characteristics, organizational climate, macro environmental conditions and creativity and innovation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".