Identifying the Effect of Entrepreneurship Approach on the Banking Industry Development Strategy (Study at Bank Mellat)
Bibliographic record
Abstract
Approach to entrepreneurship and strategy of development is the concepts that have attracted the attention of many researchers and managers from the theoretical and practical dimension, and many companies have used these two concepts, and have won a lot of success. In today's world, the entrepreneurship is known as an engine of local, regional and national economic development. (Ahmadpour, 2007) The process of economic development in developed countries reflects the fact that the economy is under the influence of entrepreneurship. In addition, in the current competitive environment, it is an important factor in the development and survival of the companies. Companies that put the entrepreneurial orientation as their policy approach, through the development of flexible resources, they can increase their long-term potential. (Khanka, 2003) Therefore, in this paper, we examine the relationship between development strategy and entrepreneurship dimensions, including innovation, risk-taking, activism, and competitive aggression in the Mellat bank. The research is applied and is done in correlation way. The questionnaire is used to collect data. The statistical population of the study was limited that 261 statistical samples were used in the analysis. The research findings show that entrepreneurship approaches have a strong and positive impact on its development strategy. The Moderating Competitive dynamic has no impact on the relationship between entrepreneurship approach and development strategy. The findings of this study will help decision-makers to be able to know the importance of entrepreneurial approach so that they can decide appropriate policies to create and launch apps. Based on the results obtained, recommendations are provided for statistical population and bank managers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".