Linking the digital finance, e-competence and e-finance quality on Indonesian MSMEs performance in the digital 5.0 era
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
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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| 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