Bibliographic record
Abstract
One of the most important advantages of organizations is their effect on the customers' behavior.Therefore, organizations are always looking for ways to maintain their customers using these methods.Using gamification is a method in which the application of the techniques and game elements for non-game purposes increases customer motivation for voluntary participation in desirable organization activities as well as improving their behavior.The dimensions of the gamification have the essential role in achieving this goal; therefore, the purpose of the present study is to investigate the effects of two Gamification aspects of performance and attitude on customer behavior of Mellat Bank in Shahrood city.This research is practical in terms of purpose and is descriptive-survey in terms of nature; In this way, the library method was used to collect information about the subject's background and researcher-made questionnaire with acceptable and appropriate reliability and validity that it needed to collect the necessary data with the aim of testing research hypotheses.Regarding the research purpose, the statistical population of this study is customers of selected branches of Mellat bank in Shahroud city and the method was random cluster sampling.Regression coefficient and meaningful level were used to test the hypotheses.The results of the research show that the dimensions of performance and attitude of the Gamification strategy have a significant and positive effect on the behavior of customers of selected branches of the Mellat Bank.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.926 | 0.922 |
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".