More Knowledge, More Experience, Less Debt? The Mediating Role of Money Management on the Effects of Financial Knowledge and Experience on Consumer Debt
Bibliographic record
Abstract
The present study aims to serve two major purposes. The first purpose is to examine whether individuals with better financial knowledge and experience are less likely to be indebted. The second purpose is to examine whether money management acts as a mediator between the influences of financial knowledge and financial experience on consumer debt. Data were collected from questionnaire survey of 440 individuals at working age in Bangkok. Results from regression analysis indicated that individuals who have better financial knowledge and experience do not have less debt. Financial knowledge has insignificant direct impact on consumer debt, while financial experience is associated with higher debt. Financial experience reduces fear and caution when using credit, hence lead to more debt. In addition, when testing the mediating effects of money management, findings revealed that money management does not mediate the influence of financial knowledge on debt, but it mediates the effect of financial experience on debt. Individuals, who can manage their money well, have lower debt, regardless of financial knowledge. Individual who can use their financial experience to better manage their money have lower debt. This study shed lights on policy implementation in reducing debt problem. Financial education that emphasizes only on numeric calculation and mathematic formula may not sufficient to reduce debt burden problem. Policies should aim at shaping consumers’ money management behavior, particularly the behaviors in managing their cash, expenditure, budget, saving, credit, and insurance to reduce their debt burden problems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".