Assessing the Impact of ESG Performance on Firm Competitiveness: A Meta-Frontier DEA and OLS Regression Analysis of Global Optical Component Firms
Bibliographic record
Abstract
The optical component industry is an industry that generates a high profit, yet it has rarely been explored on its management in the previous research. This study aims to investigate the impact of environmental (E), social (S), and governance (G) performance (ESGP) on firm competitiveness. Specifically, by using ordinal least square (OLS) regression, we analyze how overall ESGP and its components (E, S, G) impact the competitiveness including component of marketability, profitability, and innovation ability of global optical component firms, including those from Taiwan, Japan, China, and South Korea. We developed a meta-frontier data envelopment analysis (DEA) model including meta frontier, group frontier, and operational technology gap ratio to estimate firm competitiveness and analyzed 95 firm-year observations from 2016 to 2020. The meta-frontier DEA results indicate that optical component firms from Taiwan and Japan exhibit relatively higher competitiveness. Additionally, the ordinary least squares and truncated regression results reveal a significant positive association between ESGP and firm competitiveness, aligning with the resource-based view theory, where firms that performs in ESG practices obtaining unique resources and capabilities would develop a long-term competitive advantages, and also aligning with stakeholder theory, where firms that engage in ESG attract support from stakeholders, leading to a higher firm performance and competitiveness. The findings of the provides insights for the optical policymakers of the proper resource allocation to maximize firm competitiveness under the influence of ESGP.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.017 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".