Exploring Mobile Banking App Security from User’s Perspectives
Bibliographic record
Abstract
Organizationalsupport and improved performance have seen unprecedented enhancement due to the ability of internet technology that is constantly changing the ways and procedures for attaining organizational goals.However, given the volume of digital-related transactions today, especially with mobile internet banking systems, cybersecurity threats, and privacy concerns arising from Internet use by businesses, their employees, and external stakeholders have become prevalent.The detrimental effects resulting from cybersecurity threats have an adverse effect on the confidentiality, integrity, and availability of information for banks and users of their mobile banking app services.The users' knowledge of cybersecurity vulnerability hampers their decision-making about adopting mobile banking.This study examines factors that affect cybersecurity and how mobile banking app users perceive cybersecurity issues that may hinder the banks' ability to expand mobile banking usage amongst their customers.Additionally, this study suggests a conceptual research model that illustrates the relationships between the variables that affect cybersecurity.The study discovered that users view knowledge of potential identity theft, impersonation, and account hijacking as cybersecurity threats that impede their use of mobile banking.The review of literature conducted identified that mobile banking app users who regard these concerns to be real are hesitant to embrace mobile banking.Similarly, the knowledge about cybersecurity threats putting mobile banking app users in danger makes them reluctant to use the app for banking purposes.As a result, mobile banking serves as a reminder to strategically reinforce the security and privacy issues in relation to cybercrime in the banking industry.Practically, the survival of banking in the future will depend on the retention of its mobile banking app users.The study contributes to the theory of cybersecurity, particularly in using the Internet as a platform for mobile banking.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".