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This study aims to investigate the impact of firms’ digital orientation (FDO) on corporate green innovation (CGI) among Chinese firms, examining the effects of financial constraint as the mediator and exploring heterogeneous effects across different firm contexts.

Using a sample of 28,697 firm-year observations from Chinese A-share listed companies (2008–2021), we employ a novel multidimensional measure of FDO derived from textual analysis of corporate annual reports. CGI is quantified using patent-based metrics. We utilize fixed-effects panel data models as benchmark regression to quantify FDO’s impact on CGI. Later, we utilize two-stage least squares, alternate measure for core explanatory variable, alternate as well as lead measures for explained variable and propensity score matching to tackle concerns for potential endogeneity.

Our results unveil a substantial positive connection between FDO and CGI. This connection is facilitated through the alleviation of financial constraints. Furthermore, heterogeneity analysis shows that the impact of FDO on CGI is more pronounced for state-owned enterprises, firms in areas with lower financial technology development and politically connected firms.

Our findings suggest that managers should view FDO as a strategic posture that can drive sustainable innovation, not just as a technological imperative. Policymakers should consider the role of FDO when designing policies to promote CGI, particularly in less-developed regions.

This study extends current understanding by: (1) Employing a comprehensive multidimensional measure of FDO that goes beyond the existing technologically focused digital transformation matrices. (2) Identifying financial constraints as a key mediating mechanism in the FDO–CGI relationship. (3) Revealing heterogeneous effects across different firm contexts, providing nuanced insights into how institutional and environmental factors moderate this relationship.

The increasing awareness and adoption of technology, particularly artificial intelligence (AI), reshapes industries and daily life, fostering a proactive approach to risk management and leveraging advanced analytics, which may affect the stock price crash risk (SPCR). The main objective of the current study is to explore how AI adoption influences SPCR.

This study employs an Ordinary Least Squares (OLS) fixed-effect regression model to explore the impact of AI on SPCR in Chinese A-share listed companies from 2010 to 2020. Further, number of robustness analysis (2SLS, PSM and Sys-GMM) and channel analysis are used to validate the findings.

The primary findings emphasize that AI adoption significantly reduces SPCR likelihood. Further, channel analysis indicates that AI adoption enhances internal control quality, contributing to a reduction in firm SPCR. Additionally, the observed relationship is notably more pronounced in non-state-owned enterprises (non-SOEs) compared to state-owned enterprises (SOEs). Similarly, this distinction is heightened in nonforeign enterprises (non-FEs) as opposed to foreign enterprises (FEs). The study finding also supports the notion that financial analysts enhance transparency, reducing the SPCR. Moreover, the study results consistently align across different statistical methodologies, including 2SLS, PSM and Sys-GMM, employed to effectively address endogeneity concerns.

Our study stands out for its distinctive focus on the financial implications of AI adoption, particularly how it influences firm-level SPCR, an area that has been overlooked in previous research. Through the lens of information asymmetry theory, agency theory, and the economic implications of integrating AI into financial markets, our study makes a substantial contribution in mitigating SPCR.

This study underscores the pivotal role of AI adoption in influencing stock markets for enterprises in China. Embracing digital strategies, fostering transparency and prioritizing talent development are key for reaping substantial benefits. The study recommends regulatory bodies and service providers to promote AI adoption in strengthening financial supervision and ensure market stability, emphasizing the importance of investing in technologies and advancing talent development.

In the era of knowledge economy, the significance of intellectual capital has been increasing globally. Similarly, recent studies have focused on the importance of green intellectual capital in mitigating environmental degradation. However, only a few studies have analysed green intellectual capital and its impacts in the specific case of Pakistan. Hence, this study aims to investigate the effects of green intellectual capital on green innovation adoption in Pakistan’s manufacturing small and medium-size enterprises (SMEs).

We used a data sample of 235 SMEs, gathered from the four manufacturing sectors of Pakistan including: textile, chemical, pharmaceutical and steel and analysed using a multiple regression analysis approach.

The empirical results of this research indicate that green human capital and green structural capital significantly increase green innovation adoption. However, it must be noted that green relational capital has a positive but insignificant impact on green innovation adoption in manufacturing SMEs in Pakistan.

The findings and recommended policy measures of this study are important for the managers of manufacturing SMEs and policymakers to mitigate environmental destruction and achieve sustainable development through green intellectual capital.