The power of sustainability, corporate governance, and millennial leadership: Exploring the impact on company reputation
Bibliographic record
Abstract
In an era of challenging business and increasingly fierce competition, the company (business) reputation has become an increasingly valuable and vital asset. To maintain a good reputation, this study aims to explain what internal factors affect the business reputation and test the consistency of agency theory as a solution in explaining the influence of internal factors such as sustainability, millennial director, financial distress, board of commissioners, and company size on business reputation. The research used the power of panel data analysis, complemented by advanced statistical techniques such as Robust, Fixed Effects, Ordinary Least Square Regression, and Random Effects. This method is executed using Stata software, which offers incredible flexibility to seamlessly connect theoretical concepts and empirical data related to research variables. Results of this research show that sustainability and a board of commissioners are not able to have a significant influence on the business reputation; millennial directors and financial distress have a negative influence on the business reputation, while the company size has a significant positive effect on the business reputation. This research makes a valuable contribution to the company's management in considering important factors that can affect the business reputation, as well as taking appropriate steps to maintain and improve its reputation amid increasingly fierce business competition.
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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 | 0.004 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".