Financial Literacy, Trust, and Socioeconomic Determinants of Borrowers’ Behavior in Credit Card Use: A PLS-SEM Analysis
Bibliographic record
Abstract
Credit cards play a central role in household financial behavior by combining payment and short-term financing functions shaped by socioeconomic, cognitive, and attitudinal factors. This study examines the determinants of credit card use and repayment behavior in Ecuador, focusing on purchasing power, financial literacy, and institutional trust. A quantitative, cross-sectional, and explanatory design was applied to a probabilistic sample of 550 credit card users from Quito and Santo Domingo. Multivariate analyses and Partial Least Squares Structural Equation Modeling (PLS-SEM)—including formative and hierarchical constructs—were used to validate the proposed behavioral framework. The results show that higher income is associated with more responsible repayment, while financial literacy and trust mediate this relationship through cognitive and attitudinal mechanisms. Moderate R2 values and small-to-moderate f2 effect sizes align with patterns observed in other Latin American credit markets. Behavioral differences also emerge across age, gender, and household composition, underscoring the heterogeneity of financial capability in the region. The findings demonstrate that responsible credit card indebtedness depends not only on economic capacity but also on financial knowledge and institutional trust, offering practical implications for financial inclusion policies and targeted education programs in emerging economies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".