Determinant Factors of Retail Trading Company Funding Decision to Reduce Company Financial Distress
Bibliographic record
Abstract
Retail trading companies are one of the pioneers in supporting Indonesia's GDP growth with a contribution of 12.56%, and still making a high and positive contribution in the first quarter of 2020. A high contribution does not guarantee that the company's financial condition is adequate. BPS data (2020) shows that there was a decline in revenue for trading companies during the pandemic. Companies must establish appropriate funding policies to improve efficient business operations so as not to experience financial distress that could potentially bankrupt. This study aims to analyze the determinants in determining funding that can reduce the financial distress of retail trading companies. The data used are the financial statements of retail companies from 2018 and 2019 with the method used is descriptive quantitative. Path analysis method to analyze hypotheses using the SME model in SPSS AMOS 26. The results of this study indicate; (i) company size 0.335, (ii) tax 0.196, and (iii) revenue growth 0.195 are determinants of funding, but they cannot reduce financial distress. If the manager decides to finance using these three factors, it will not affect the company's financial distress
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".