Financial resilience of small and medium enterprises in Bali
Bibliographic record
Abstract
The economy of Bali heavily relies on the tourism sector, leading to economic vulnerability. There are numerous challenges in Bali's economy, with COVID-19 being one of the most prominent examples. During the COVID-19 pandemic, Bali's economy experienced the deepest contraction and slowest recovery compared to other provinces in Indonesia. Amid this economic vulnerability, it is important to conduct research on the financial resilience of SMEs in Bali. This research aims to analyze the influence of financial literacy on financial resilience, as well as the mediating role of financial performance and fintech adoption on the impact of financial literacy on financial resilience among SMEs in Bali. The research method used is a quantitative approach with a sample of 177 SMEs in Bali, selected through non-probability sampling techniques. Data were analyzed using descriptive and inferential statistics, with SEM-PLS in SmartPLS 3.0 program. The test results indicate that financial literacy has a positive and significant influence on financial resilience, financial performance, and fintech adoption. Financial performance and fintech adoption also have a positive and significant impact on financial resilience. Furthermore, financial performance and fintech adoption can partially mediate the influence of financial literacy on financial resilience in SMEs in Bali. The findings of this research are beneficial for the development of Knowledge-Based View theory and Technology Acceptance Model, as well as for SMEs in Bali and the government as policy makers in efforts to enhance 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.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".