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The authors examine the effect of greenhouse gas (ghg) assurance on cost of debt, and the effect of board gender diversity on cost of debt, for an international sample of listed companies.

Utilizing firm-level data and a quantile regression approach, this study examines the effects of greenhouse gas assurance and board diversity on cost of debt by employing an international sample of firms during 2015–2021.

The authors find that in firms with a relatively low cost of debt the external assurance of greenhouse gas emissions and gender diversity could significantly contribute to a reduction of cost of debt. Furthermore, other measures of board diversity that are linked with independent directors and skilled directors seem to contribute to an increase of firms' cost of debt in the lower end of distribution. Drawing from the agency theory, the authors showcase the fact that ghg assurance reduces information asymmetry and therefore agency costs such as borrowing costs and signals to the stakeholders a long-term commitment to excellence.

This study is the first that provides insights on the relationship between ghg assurance, board diversity and cost of debt.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.