The purpose of this study is to examine how artificial-intelligence capabilities in business education catalyse green innovation (GI) and how those innovations translate into stronger sustainable performance (SP) and circular-economy (CE) outcomes. It also evaluates whether big-data analytics and knowledge-management systems strengthen the pathway from artificial-intelligence capability to GI.
Data were collected through structured questionnaires from 712 participants occupying high-level academic and administrative positions across 25 universities in North America, Europe, Asia and the Middle East. A two-wave, time-lagged design with approximately four weeks’ separation (Wave 1: AIC/big data analytics/KMS; Wave 2: GI/SP/CE) provided temporal and source separation. Analysis combined partial least squares (PLS) structural equation modelling (including PLSpredict for out-of-sample performance) and a cross-validated artificial neural network to capture potential non-linear effects.
Artificial-intelligence capability is positively associated with GI, and GI is linked to stronger SP and more circular economy practices. Big-data analytics and knowledge-management systems each amplify the influence of artificial-intelligence capability on GI, with moderation magnitudes best characterised as modest yet statistically significant (small f²) and practically meaningful in aggregate. Prediction tests indicate meaningful out-of-sample relevance, and the neural-network analysis corroborates the salience of data and knowledge infrastructures alongside GI in explaining sustainability outcomes.
The design is cross-sectional at analysis despite time-lagged measurement and relies on self-reported assessments, which constrains causal inference. These concerns are mitigated through rigorous validity checks and prediction assessment. Future research should incorporate longitudinal designs, objective operational metrics and broader organisational contexts to verify and extend these relationships.
Business schools and university leaders seeking tangible sustainability gains should co-invest in artificial intelligence capabilities, enterprise-level analytics and knowledge management infrastructures, and explicitly channel these assets towards green process and product initiatives. Establishing cross-functional governance and dashboards that track CE indicators can accelerate impact and guide continuous improvement. Ethical guardrails (privacy, bias auditing and green-IT practices) are recommended to avoid unintended consequences.
The paper advances scholarship by linking artificial-intelligence capability to GI and downstream sustainability within business education, and by demonstrating that data and knowledge systems are critical first-stage contingencies. Methodologically, it integrates PLSpredict with neural networks to deliver prediction-validated evidence that is theoretically grounded and directly actionable for institutions seeking to pursue sustainability leadership. The study also maps HEI-specific sustainability dimensions to sustainable development goals to support audited, management-relevant reporting.
