Artificial intelligence (AI) has emerged as a potentially transformative technology for promoting environmental sustainability. AI’s capacity for resource optimization, cost reduction and waste management offers compelling promise. However, research into the factors driving AI adoption for environmental purposes, particularly within developing nations, remains limited. This study aims to address this knowledge gap by proposing a theoretical framework that merges the “Unified Theory of Acceptance and Use of Technology” with contextual variables to illuminate the motivations behind individuals’ usage of AI-driven environmental tools.
This research uses cross-sectional data collected via an online survey questionnaire. Analysis of data collected from 364 undergraduate students, who enrolled in the Jordanian “Higher Educational Institutions”, was conducted based on “structural equation modelling” using AMOS 25.0 software. Accordingly, this analysis evaluated the validity and reliability of the measurement scales and the empirical support for the theoretical model and hypothesized relationships.
This study finds significant relationships between “green AI usage” and “performance expectancy” (PE), “effort expectancy” (EE), “social influence” (SI), “green thinking” (GTH) and “anthropomorphism” (ANTH). Additionally, “green behaviour” (GB) was shown to positively influence “environmental sustainability” (ES). Interestingly, “facilitating conditions” and optimism did not significantly impact green behaviour.
This research enriches the existing body of knowledge on AI and environmental sustainability while providing actionable recommendations for practitioners, policymakers and those engaged in AI product development.
