Investigating the effect of financial literacy and financial inclusion on operational and sustainable supply chain performance of SMEs
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.
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 | 0.002 | 0.012 |
| 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 | 0.002 | 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".