Green minds, clean life: How does a sustainability-oriented university convert norms into pro-environmental behaviors with the moderating effect of ai adoption

Abstract

Purpose – This research objective is to investigate how a sustainability-oriented university influences students' personal (PN), subjective (SN), and descriptive norms (DN), along with the connection between these norms and student’s pro-environmental behavior (PEB). Moreover, this study also focuses on analyzing the moderating effect of AI adoption (AIA) on the correlation between norms and PEB. Eventually, practical and theoretical implications will be proposed formed on the findings of the quantitative testing. Design/methodology/approach – This research utilizes an empirical survey-based quantitative approach. The data were provided by 352 students studying at universities in the top 1000 sustainable universities around the world according to the QS World University Rankings: Sustainability. The data was later evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings – The results revealed that Sustainable University (SU) makes a strong impact on students’ norms (including PN, SN, and DN). While the result indicates that DN and PN directly influence PEB, SN is shown to be not. However, the moderating effect of AI adoption only supports the relationship between this norm (SN) and PEB. Theoretical and practical contributions – This is one of the research initiatives in this field that aims to study the influence of sustainability-oriented universities on student norms. Its goal is to understand the interaction between those orientations and students' norms-driven PEB and the moderating effect of AI applied in sustainable practices. This study contributes to strengthening the VBN theory and expands this theoretical model with theories that predict behavior through other standards such as TPB and the Focused Theory of Normative Behavior. On that shore, this research discovered a new moderating factor which is the application of AI. This research also proposes some practical implications for universities and stakeholders based on the findings to develop students' pro-environmental behavior through Norms (PN and DN) and suggests several future pathways for AI development based on regulatory relationships.