Analyzing Customer Engagement with Gamification Approach in the Banking Sector Using the Rasch Model
Bibliographic record
Abstract
This study investigates the instruments used to measure consumer engagement in mobile banking applications that employ gamification strategies.Customer engagement plays a crucial role in the effectiveness of e-marketing strategies, particularly for relationships, products, services, and brands.Gamification has emerged as a promising approach for promoting content marketing.Primary data from active users of mobile banking applications were collected from various banks and analyzed using the Rasch model to assess the instrument's efficiency.The study involved 451 participants and considered demographics, customer involvement, psychological aspects, game elements, and 26 dimensions.The results indicate that the average Outfit Mean Square (MNSQ) scores for individuals and items obtained from analyzing 49 questions with the Rasch model fall within the desirable range of 0.5-1.5.The mean values of INFIT and OUTFIT MNSQ, as well as INFIT and OUTFIT ZSTD, closely approximate the desired level.Additionally, the instrument demonstrates strong reliability with a coefficient alpha of 0.94.In conclusion, the findings suggest that the respondents understood the instruments employed in this study well and effectively facilitated data collection.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".