Entrepreneurial orientation and digital transformation as drivers of high organizational performance: Evidence from Iraqi private banks
Bibliographic record
Abstract
This research investigated how digital transformation affects the relationship between entrepreneurial orientation and organizational performance among private banks in Baghdad, Iraq. The study population consisted of 5,000 individuals holding various positions, including general manager, deputy general manager, department head, section head, and employee. To obtain a representative sample, the researchers employed the stratified equal random sampling method and randomly selected participants. As a result, a total of 406 questionnaires were distributed. However, only 197 questionnaires were valid and suitable for analysis, representing a percentage of 49% of the distributed questionnaires. The primary means of gathering information was through a questionnaire, and the data obtained were analyzed using inferential statistical techniques. The research findings indicated that the different aspects of entrepreneurial orientation, such as being proactive, taking risks, and flexibility, had a significant and beneficial influence on the organization's overall performance and its ability to operate effectively and engage socially. Nonetheless, the innovation aspect did not have a notable impact on the performance of the organization and its related dimensions. In addition, the research indicated that digitalization has a beneficial impact on the relationship between entrepreneurial orientation and the performance of an organization, making it more powerful. The research suggested that private banks operating in Baghdad province ought to promote innovation, foster productive social interactions, enhance their operational efficiency, facilitate digital transformation, and leverage their moderating influence to improve the performance of the organization by boosting entrepreneurial orientation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".