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This paper aims to examine the relationship between the community and environment disclosures and the long-run share price performance of Malaysian initial public offering (IPO) companies.

This study used secondary data through the content analysis of the annual reports and DataStream of 115 sampled IPOs listed on Bursa Malaysia from 2007 to 2015. The present study incorporated weighted least squares and quantile least squares to evaluate the relationship between the community and environment disclosures and IPO performance.

The results show a positive and significant relationship between the extent and quality of community disclosures and IPO performance; while the extent and quality of environment disclosures have a negative and positive relationship, respectively, with IPO performance. These results suggest that community and environmental activities can be considered an effort to enhance Malaysian IPOs.

These results suggest that Malaysian IPO companies should be involved consistently in corporate social responsibility disclosure, i.e. community and environmental activities, as they have a significant impact on the performance of Malaysian IPOs. The findings can facilitate financial institutions and regulatory agencies in driving companies to be more responsible regarding community and environmental disclosures.

To the best of the authors’ knowledge, this study provides new insights into the relationship between the community and environment disclosures and the performance of Malaysian IPO companies.

In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.