Bibliographic record
Abstract
In the present study, the relationship between advertising and attracting financial resources of the bank has been investigated with respect to the role of mediating emotional factors in Tose'e Ta'avon bank of Northern Khorasan province.In this research, the independent variable is the "advertisement "and dependent variable is "financial recourses" and moderator variable is "emotional factors ".The research method used in this research is descriptive-survey based on correlation and from the point of view of purpose is a functional and from the perspective of the time is cross-sectional.The present study has two statistical societies; the first statistical population is consisted of the all employees and managers of the branches of Tose'e Ta'avon bank of Northern Khorasan province (55 people) and sample size is 55 people based on the census and the second community consists of the customers of the Tose'e Ta'avon bank of Northern Khorasan, with a sample size of 385 people.Sampling is from type of the simple random and in order to gathering information in the literature section has been carried out a study of library studies and in the research indices section has been used from questionnaire.To analyze the collected data, were used the analytical statistics in descriptive and inferential statistics.Initially, by using descriptive statistics were obtained a cognitive status and demographic characteristics of the respondents and then, the research model has been fitted with help of the structural equation modeling technique.The obtained results of the research showed that advertising is effective in attracting financial resources.Also, emotional factors will have no moderating effect on this relationship.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.927 | 0.906 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".