Determinants of Financial Behaviour: Does Digital Financial Literacy (DFL) Foster or Deter Sound Financial Behaviour?
Bibliographic record
Abstract
The rise in bankruptcy cases among Malaysia’s younger population shows that youngsters have weak money management skills or financial behaviour (FB). Digital financial goods and services (DFS) have increased in popularity because of social isolation due to COVID-19 disease. Therefore, digital financial literacy (DFL) - financial literacy (FL) from the digital standpoint has spurred. Based on the theory of planned behaviour, DFL is expected to influence oneself in executing good FB. This study examines the role of DFL in influencing students’ FB, incorporating other vital factors, such as FL, financial attitude (FAT), peer influence (PEI), parental influence (PRI), and social media influence (SMI). SmartPLS was used to analyse data from a survey of 183 Malaysian university students using partial least squares (PLS) modelling. The measurement model signified that the instrument utilised was valid and reliable. The result indicated that FL, FAT, PRI, and SMI displayed a significantly positive impact on FB. Meanwhile, DFL negatively affects FB, which surprisingly contradicts the expectation that it could foster sound FB. This study concludes that DFL deters sound FB. In light of DFS's recent ascent in popularity, these results add to the expanding body of knowledge on DFL.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".