The technological enhancement and its impact on corporate financial performance in the context of the industrial revolution 4.0: The case of Vietnam
Bibliographic record
Abstract
Businesses play an important role in the economy in most countries. Businesses contribute to increased productivity, output and jobs for the economy. Therefore, governments of countries always create favorable business environments to help businesses operate more efficiently, and thereby contribute to the economy. Transforming the industrial revolution 4.0 has brought businesses certain benefits to operations, improving productivity and efficiency. Using data in real estate businesses, through regression analysis, the research results confirm the technology factor has not yet affected the financial performance of enterprises, which can show that businesses need enough time to absorb technology in production activities to have a change in its output. In addition, there exists the negative relationship of leverage in the business and financial performance. Or it can also be confirmed that enterprises that choose their own capital are often more effective than enterprises that choose capital from loans and external financing. The study also confirms that enterprises with the ability to manage total asset turnover have higher financial efficiency. However, the research shows that interest rates have a negative effect on business operations, businesses with high interest rates have a negative effect on business operations, and vice versa.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".