Towards Understanding the Consumer Behavior of Mobile Banking Applications for Management Decision Makers
Bibliographic record
Abstract
The rapid growth of mobile applications in the banking sector has been increasing due to the ease of use and saving the time of users. However, the fierce competition between banks draws attention to the factors that increase the use of banking mobile applications to maintain the level of satisfaction of the customers of each bank. One of the crucial aspects that decision-makers need to understand is the consumer behavior of mobile banking applications. Therefore, this research paper aims to present a comprehensive exploration of mobile banking adoption by examining the interplay of cultural dynamics, usability, cross-cultural comparisons, economic factors, and the significance of longitudinal insights. It elucidates how cultural norms and societal expectations impact adoption, emphasizing regional variations. The study also dissects mobile banking app interfaces to enhance user-friendliness, tailoring them to diverse user preferences. Cross-cultural comparisons shed light on the interplay between culture, economics, and regulations. It scrutinizes economic factors, considering income levels and financial literacy while emphasizing the importance of longitudinal studies for tracking evolving adoption patterns. This research not only advances our knowledge of mobile banking adoption but also offers practical insights for banks, policymakers, and service providers as they navigate the rapidly evolving mobile banking landscape in an increasingly digital world.
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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 | 0.008 |
| 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 | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".