Substantive or Symbolic? The Ethical Influence of Female Directors on Green Innovation Disclosure in Politically Connected Firms
Bibliographic record
Abstract
Motivated by the ethical implications of gender diversity on boards, this study examines the effect of female directors on symbolic green innovation disclosure and substantive green innovation disclosure. The study defines substantive green innovation disclosure as environmentally oriented innovation that produces tangible value creation, evidenced through its positive effect on firm value, distinguishing it from symbolic green innovation that serves rent-seeking or legitimacy purposes. Using a sample of Indonesian manufacturing firms from 2021 to 2023, the research tests the model separately for politically and nonpolitically connected firms to capture the moderating role of political embeddedness. The results reveal that in nonpolitically connected firms, female directors do not significantly affect green innovation disclosure; however, substantive green innovation positively influences firm value, confirming its genuine strategic and ethical impact. In contrast, in politically connected firms, female directors negatively affect green innovation disclosure, and green innovation fails to improve firm value—indicating that political influence turns sustainability efforts into symbolic compliance rather than authentic environmental innovation. These findings extend upper-echelon and legitimacy theories by showing that in patriarchal cultural background, female directors’ ethical orientation negatively affects symbolic green innovation disclosure but do not affect substantive green innovation disclosure.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".