The effects of financial literacy and digital literacy on financial resilience: Serial mediation roles of financial inclusion and financial decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it