The purpose of this study is to investigate the impact of corporate governance on ESG performance in large publicly listed firms in Indonesia from 2016 to 2023. The study adopts both stakeholder-agency theory and resource dependency theory to explore the relationship between sustainability assurance, board governance characteristics, and the extent of ESG performance. Fixed effects regression controlling both industry and year fixed effects is used to measure the relationship between sustainability assurance, corporate governance characteristics, and ESG performance. We find a positive significant relationship between assurance sustainability reports and ESG performance. Additionally, we also document a positive association between sustainability committees and ESG performance. Adopting the Big Four auditors as a moderating variable, we find a positive relationship between gender-diverse boards and firms audited by the Big Four auditors and sustainability performance. This result suggests that firms with gender-diverse boards audited by the Big Four auditors enhance sustainability performance. Additional robustness tests using GMM estimation, conducted to address endogeneity concerns, corroborated the main test results.

The transformative impact of AI technologies on the financial sector has been a topic of increasing interest. This study investigates ChatGPT’s applications in financial reasoning and analysis and evaluates ChatGPT-4o’s effectiveness and limitations in conducting both basic and complex financial analysis tasks. By designing a series of multi-step, advanced reasoning tasks and establishing task-specific evaluation metrics, we assessed ChatGPT-4o’s performance compared to human analysts. Results indicate that while ChatGPT-4o demonstrates proficiency in basic and some complex financial tasks, it struggles with deep analytical and critical thinking tasks, especially in specialized finance areas. This study underscores the need for meticulous task formulation and robust evaluation in AI financial applications. While ChatGPT enhances efficiency, integrating it with human expertise is crucial for effective decision-making. Our findings highlight both the potential and limitations of ChatGPT-4o in financial analysis, providing valuable insights for future AI integration in the finance sector.

The intention of this study is to investigate carbon reduction initiatives, board characteristics and sustainability assurance in large, listed firms in Indonesia from 2016 to 2023, given the significance of climate change. Based on fixed effects regression controlling industry effects, we find a positive significant relationship between multiple directorships and carbon reduction initiatives. Additionally, we find a statistically significant association between gender-diverse boards and carbon reduction initiatives, as well as a positive association between sustainability assurance and carbon reduction initiatives. When adopting multiple directorships as a moderating variable, our results reveal a positive relationship between long-tenured board members with multiple directorships and carbon reduction initiatives, suggesting that firms with long-tenured directors and multiple directorships reduce carbon footprint. Robustness checks and endogeneity tests corroborate the main results. This study has important implications for regulators, including policymakers, and contributes to theoretical applications.