The Manufacture and Service Companies Differ Leverage Impact to Financial Performance
Bibliographic record
Abstract
The purpose of this research is to examine the leverage from firm. The firms use leverage to expand their source of fund by using external fund such as debt. By usingdebt, financial performance of the firm will develop. Beside the leverage, the use of size and inflation are also considered to be the factors that influence the financial performance while the firms are using leverage. As an independent variable, size is reflected by the assets and the leverage or debt by using the debt ratio to the total of assets. Then,the financial performance is reflected by using the return on the measured assets. Inflation as a control variable is included in this research to know the effect towards the financial performance. In this research, firms are divided into two sectors, there are manufacture and service sector. By using the manufacture and service sectors in order to know each effect of leverage toward the financial performance, this research focuses to the unique characteristic of these two sectors. Knowing which sector is influenced more by the leverage than the others, will guide the urgency of this research. This research used the pooled data regression method, 468 data entries of 156 listed firms in Indonesian Stock Exchange. This research was conducted from 2015 until 2017. The result shows that leverage significantly has a negative effect towardthe financial performance and the size positively influences financial performance. Manufacture sector is influenced more in leverage towardsthe financial performance, and the service sector is influenced more on size towardsthe financial performance.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".