Predictors of Digital Fraud: Evidence from Thailand
Bibliographic record
Abstract
This study examined the complex interplay of demographic characteristics, behavioral patterns, and technological factors that contribute to digital fraud victimization within the context of a developing economy, focusing specifically on Thailand. Utilizing data collected from 1200 respondents and applying binary logistic regression analysis, the research identified key predictors of fraud exposure, including age, income, student status, use of portable devices, and social media engagement. A paradoxical finding emerged: stronger perceived digital security was associated with higher fraud risk, indicating that overconfidence in platform safeguards may unintentionally increase vulnerability. Interestingly, users’ perceptions of digital security—such as confidence in identity verification and password protocols—were positively associated with fraud victimization, indicating potential cognitive biases and overconfidence in digital environments. The findings revealed a high prevalence of fraud experiences among participants, highlighting the gap between perceived and actual digital safety. These results emphasized the urgent need for user-centered fraud prevention measures, enhanced digital literacy, and targeted public awareness campaigns. The study contributes to the broader understanding of cybersecurity challenges in emerging markets and offers policy-relevant insights for strengthening digital financial resilience.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".