Risk management driven by AI has the potential to play an important role in sustainable decision-making by offering predictive insights and scenario modeling capabilities. This proactive approach empowers top management to align informed decisions in accordance with sustainability goals while optimizing resource allocation and mitigating risks. While existing research examined the benefits of AI risk management, this study addresses the underexplored question of how AI risk management impacts sustainable decision-making, particularly when considering the mediating role of perceived environmental responsibility. A structural equation modeling (SEM) technique was employed on a dataset comprising 428 senior managers from the Pakistani construction sector. The results revealed that AI-driven risk management is highly relevant to sustainable decision-making. Further, perceived environmental responsibility was found to have a partial mediating effect. These results hold a significant promise for organizations seeking to integrate AI for environmental sustainability goals. The findings of the study enhance the understanding of how AI-driven risk management is a driving mechanism empowering decision-makers to make more sustained decisions in the construction sector.

Construction project management usually has a high risk of safety-related accidents. An opportunity to proactively improve safety performance is with near-miss information, which is regarded as free lessons for safety management. The research status and practice; however, presents a lack of comprehensive understanding on what near-miss information means within the context of construction safety management. The objective of this study is to fill in this gap. The main findings enrich the comprehensive understanding of the near-miss definition, the near-miss causation model, and the process of near-miss management. Considering that near-misses are more tacit and obscure than accidents, the process for near-miss management involves eight stages: discovery, reporting, identification, prioritization, causal analysis, solution, dissemination, and evaluation. The first three stages aim to make near-misses explicit. The other five are adopted to better manage near-miss information, compiled in a well-designed near-miss database (NMDB). Finally, a case study was conducted to show how near-miss information can be utilized to assist in construction safety management. The main potential contributions here are twofold. Firstly, corresponding findings provide a knowledge framework of near-miss information for construction safety researchers who can go on to further study near-miss management. Secondly, the proposed framework contributes to the guidance and encouragement of near-miss practices on construction sites.

During the past several years, many governments and non-government organizations in the world made efforts to promote policies and activities regarding corporate social responsibility and proposal of relevant regulations. However, scandals of international financial organizations and transnational corporations are enduring issues, which threaten to reduce social responsibility to a mere corporate slogan. This is not only the basis for sustainable operations but, also it is a vital academic issue. Understanding the factors behind the intention of a corporation’s social responsibility practice is a problem that governments and other governance organizations urgently need to solve and is also an issue that scholars and other relevant workers need to pay attention to and investigate. This study tries to discuss the behavioral intention behind social responsibility practices, and to point out exogenous factors, corresponding with theory of reasoned action, among the factors proposed by literature regarding corporate social responsibility. Then we apply structural equation modelling to analyze each hypothesis of the study. Finally, the results show several determinants which empirically affect behavioral intentions towards social responsibility practices. This study serves as a supplement for present literature, which did not clearly explain the reason why corporations hesitate to put social responsibility into action. In addition, although the theory of reasoned action was widely used to discuss the motive of various reasoned actions, the current study might be a pioneer in using theory of reasoned action to discuss the behavioral models for corporate social responsibility practices and discussing the applicability of the theory of reasoned action based on empirical data.