Transactions at Your Fingertips: Influential Factors in Information Security Behavior for Mobile Banking Users
Bibliographic record
Abstract
In today's digital era, the concept of transactions at your fingertips has revolutionized how we conduct financial transactions, allowing us to conduct them anywhere and anytime.Unfortunately, this is followed by inappropriate information security-related behaviors, such as using the same password for multiple accounts and assuming transactions with public WiFi are fully secure, etc. Inappropriate behaviors related to information security increase the risk of cybercrime.Therefore, this study aims to explore the factors that are relevant to fostering positive information security behaviors among mobile banking users in Indonesia.The constructs in this study consist of password management, infrastructure management, email management, security perception, and privacy concerns.Data collected from 197 respondents was derived from distributing online questionnaires and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) techniques and descriptive analysis.This study reveals that security perceptions contribute the most to fostering positive information security behavior, followed by infrastructure management, privacy concerns, email management, and password management.Based on the descriptive analysis from the security perception section, mobile banking users should be more aware that using public WiFi for financial transactions is risky.On the other hand, in Indonesia, mobile banking users have shown a good indication of concern for the security of their devices, which needs to be maintained.This research can be a reference for service providers to educate their users and create regulations such as mandatory password changes.These can minimize the risk of cybercrime among mobile banking users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".