Investigating the effect of financial literacy and financial inclusion on operational and sustainable supply chain performance of SMEs
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 purpose of this study was to analyze the effect of financial literacy on operational performance, financial inclusion on operational performance and the effect of financial literacy on sustainable supply chains and the impact of financial inclusion on sustainable supply chains in SMEs in Indonesia. The research method is quantitative through online surveys with the Google form, data collection by distributing online questionnaires to 590 SMEs owners in Indonesia who were selected by simple random sampling. The online questionnaire was designed using a Likert scale of 5 and distributed via social media. Data analysis used structural equation modeling of partial least squares (SEM-PLS) with data processing tools using SmartPLS 3.0 software. The stages of data analysis are validity-reliability test and hypothesis, or significance test used in this study using a Google form which will be distributed to respondents. This questionnaire measurement method uses a Likert Scale of 5, namely Strongly Disagree (STS), (2) Answers Disagree (TS), (3) Neutral Answers (N), (4) Answers Agree (S), Strongly Agree (SS). The results of this study indicate that financial literacy had a positive and significant effect on operational performance, financial inclusion had a positive and significant effect on operational performance, financial literacy had a positive and significant effect on sustainable supply chains and financial inclusion had a positive and significant effect on sustainable supply chains in SMEs in Indonesia. The novelty of this research is the model relationship between Financial Literacy and Financial Inclusion, Operational Performance, SME Sustainable Supply Chain which has never been explained in previous studies.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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