Linking the digital finance, e-competence and e-finance quality on Indonesian MSMEs performance in the digital 5.0 era
Bibliographic record
Abstract
In this digital era, digital technology plays an important role in finance for Micro-, Small and Medium-sized Enterprises (MSMEs). This research aims to analyze the relationship between digital finance technology and performance, the e-quality of financial reports and performance, and finally the relationship between e-competence and performance. The research method used in this research is quantitative survey research. This research uses an online questionnaire as a tool to collect data from respondents. Research data was obtained by distributing online questionnaires to 682 MSMEs owners who were determined using a simple random sampling method. The questionnaire was designed to contain statement items and the Likert scale used in this research was a Likert scale. The data analysis method used in this research is structural equation modelling partial least squares (PLS-SEM) with data processing tools, namely SmartPLS 4.0 software. The results of the research analysis show that e-finance has a positive and significant relationship with performance. The e-quality of financial reports has a positive and significant relationship with performance. Finally, e-competence has a positive and significant relationship to performance. E-The quality of the financial reports produced will indicate whether the performance accountability of a government agency is good or not. Accountability for the performance of government agencies that present financial reports by government accounting standards will produce quality financial reports.
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".