THE ROLE OF PERCEIVED ENVIRONMENTAL RESPONSIBILITY IN ARTIFICIAL INTELLIGENCE-ENABLED RISK MANAGEMENT AND SUSTAINABLE DECISION-MAKING

Abstract

As artificial intelligence (AI) becomes increasingly embedded in sustainability governance, understanding the social and ethical contexts that influence its deployment is critical. This study investigates the role of Perceived Environmental Responsibility (PER) as a central construct in moderating stakeholder trust, enhancing AI adoption, and improving the performance of AI-enabled risk management systems in environmentally sensitive decision-making. While AI offers capabilities such as predictive analytics, real-time monitoring, and environmental impact simulations, its effectiveness is often shaped by how stakeholders interpret the intentions and values of the organizations that deploy these technologies. This research explores the hypothesis that PER not only influences stakeholder acceptance of AI but also determines organizational readiness, transparency outcomes, and alignment with sustainability objectives. Using a systematic meta-analysis approach guided by PRISMA methodology, the study synthesizes findings from 122 peer-reviewed articles published between 2010 and 2024. The findings reveal that PER functions as both a moderating and mediating variable: it amplifies trust in AI-based decisions, enhances stakeholder interpretability of complex models, and improves strategic integration of AI into environmental, social, and governance (ESG) frameworks. Organizations with high PER demonstrated stronger performance in AI-based compliance reporting, emissions management, and environmental decision-making, while those with low PER experienced reputational risk, stakeholder skepticism, and underutilization of AI tools. The study contributes to the literature on stakeholder theory, responsible innovation, and sustainable technology adoption by positioning PER as a foundational variable in ethical AI integration. It also aligns PER with the principles of the Triple Bottom Line (TBL) and ESG reporting, emphasizing its operational and reputational importance in digital sustainability governance. By synthesizing evidence across sectors and regions, the research provides actionable insights for organizations seeking to implement AI responsibly. It concludes that PER is not simply a passive perception but an active enabler of technological legitimacy, ethical alignment, and long-term sustainability success in AI-supported decision environments.