The role of good corporate governance and transformative big data analysis in improving company financial performance
Bibliographic record
Abstract
The financial performance of a company reflects its ability to run and manage its operations while strictly adhering to prudent financial administration principles. Good financial performance often mirrors the implementation of Good Corporate Governance (GCG) principles in a company. The application of GCG provides a solid foundation for a company to conduct its operations transparently, ethically, and accountability. The objective of this research is to analyze the implementation of GCG and the capabilities of big data analysis on financial performance, as well as to examine the mediating role of big data analysis in the relationship between GCG and financial performance. The research method employed is quantitative, and data were obtained through a survey questionnaire distributed using a Likert Scale of 1-5. Random sampling was employed to select 258 samples from manufacturing companies that are State-Owned Enterprises (SOE/BUMN) listed on the Indonesia Stock Exchange (ISE/BEI). Data collection took place from March 2023 to May 2023. Respondents included staff and managers from these BUMN companies. The collected data were analyzed using Structural Equation Modeling (SEM) with SmartPLS software. The research findings indicate that GCG has a positive and significant influence on big data analysis, providing a foundation for digital transformation. Furthermore, GCG also contributes positively and significantly to the financial performance of the company. Big data analysis has proven to have a positive impact on financial performance, indicating the role of technology in optimizing financial results. Another interesting finding is that big data analysis mediates the relationship between GCG and financial performance, highlighting the crucial role of technology in connecting good corporate governance practices with optimal financial outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".