The effects of financial literacy and digital literacy on financial resilience: Serial mediation roles of financial inclusion and financial decisions
Bibliographic record
Abstract
The research objective was to analyze the effect of financial literacy and digital literacy on financial inclusion, financial decisions, and financial resilience of MSME's. The design of this research is explanatory quantitative research. The research is a cross-sectional study in which all research variables are measured and observed at one point in time. The sampling technique used is area purposive sampling. The reachable population in this study was 98,567 MSMEs in the Province of Bali, and the research sample was 385. The research instrument used was a questionnaire with a Likert scale. The analysis technique used is a descriptive and inferential analysis using SEM-PLS. The findings of this research reveal 1) a direct positive and significant effect of financial literacy and digital literacy on financial inclusion, financial decisions, and financial resilience of MSMEs; 2) a positive and significant effect of financial literacy and digital literacy on financial resilience of MSMEs through financial inclusion and financial decisions parallelly; and 3) a positive effect of financial literacy and digital literacy on financial resilience of MSMEs through financial inclusion and financial decisions serially, but the effect of digital literacy on financial resilience through financial inclusion and financial decisions serially is insignificant. The findings of this research show the crucial role of financial literacy and digital literacy in increasing financial resilience. Financial inclusion and financial decisions mediate the effect of financial literacy and digital literacy on 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".