THE EFFECT OF LIQUIDITY, GOOD CORPORATE GOVERNANCE, AND COMPANY SIZE ON COMPANY’S FINANCIAL PERFORMANCE (STUDY IN THE PANDEMIC TIME OF COVID-19)
Bibliographic record
Abstract
The purpose of this study is to determine and analyzed the impact of pandemic time of covid 19 to financial performance of company that can be analysed from 3 variable independent, such as: liquidity, good corporate governance, and company size. The research is in companies that listed in Indonesian Stock Exchange (IDX) during pandemic time of covid 19. The sampling technique used purposive sampling and the type of data uses is quantitative with regression analysis method. Based on the test results, it is concluded that institutional ownership has a significant effect on the company's financial performance. Besides having its own meaning, this research also has its own limitations. First, analysis only for Indonesia area (because researcher only took the sample only from companies that listed in IDX). If the sample of companies increases and different countries will give different result and analysis. Second, this research only analyzed the impact during second quarter during pandemic time, if time is extended for one year, will be give different result and supposed to be if the time extended, the result will give more implications. The research implication are as follows: For all high level management in companies, to strengthen decision making during pandemic time of covid 19 should be check and analyzed the financial statement of companies and can give more valuable interpretations analysis for all the user of financial statement.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".