Artificial intelligence and informatics in sustainable marketing: Enhancing green consumer engagement
Abstract
Marketing has shifted from transactional promotion to value-oriented engagement, where sustainability and environmental responsibility increasingly influence consumer decision-making; Artificial Intelligence (AI)-driven sustainable marketing leverages informatics, data intelligence, and ethical analytics to coordinate marketing approaches with green consumer expectations and long-term engagement. However, the effectiveness of AI-driven sustainable marketing is constrained by context-specific consumer perceptions and variations in ethical information governance practices. To observe how AI-driven sustainable marketing mechanisms influence green consumer engagement through informatics-based insights, eco-oriented personalization, green information transparency, and responsible data utilization. The data were collected using a structured questionnaire from environmentally conscious consumers; out of 587 responses, 465 valid samples were gathered. The conceptual framework includes AI-enabled sustainability cues, green information transparency, ethical data governance, and perceived environmental value as predictors of green consumer engagement, analyzed using reliability analysis, exploratory factor analysis (EFA), multiple regression, and structural relationship modelling via IBM SPSS and AMOS. The outcomes show that all AI-driven sustainable marketing factors, perceived environmental value, and green trust significantly influence green consumer engagement, with effects ranging from β = 0.203–0.319, t = 2.54–3.13, p ≤ 0.011.AI-driven sustainable marketing, supported by informatics and ethical analytics, significantly enhances green consumer engagement and provides organizations with a strategic approach to integrate environmental responsibility with competitive marketing performance.
Fields of Study & Subjects
Environmental Sustainability in Business
AI in Service Interactions
Digital Marketing and Social Media
Publication details
- Journal / source
- International Journal of Data and Network Science
- Publisher
- Growing Science
- Volume
- 10
- Issue
- 3
- Pages
- 1289–1302
- Publication date
- 1 January 2026
- Language
- EN