Journal of Strategic Marketing

AI-driven marketing and Sustainability

Artificial Intelligence (AI) is rapidly transforming marketing practices, reshaping how organisations analyse consumer behaviour, design marketing strategies, and engage with customers. AI technologies, including machine learning, predictive analytics, generative AI, and intelligent recommender systems, are increasingly embedded in marketing decision-making processes, enabling firms to personalise communications, optimise supply chains, and automate customer interactions (Guha et al., 2021; Puntoni et al., 2021; Grewal et al., 2025; Van Hau et al., 2025).

At the same time, sustainability challenges such as climate change, biodiversity loss, and plastic pollution demand new approaches capable of shifting both consumer behaviour and market systems toward more responsible and regenerative futures. Marketing scholars have emphasised the important role marketing can play in addressing global challenges by influencing consumption patterns, supporting sustainable innovations, and enabling behavioural change (Chandy et al., 2021; White et al., 2019).

The intersection of AI and sustainable marketing therefore represents an emerging and important research frontier. AI-driven tools have the potential to promote sustainable consumption by enabling personalised sustainability nudges, improving resource efficiency, and supporting circular economy initiatives (Satornino et al., 2024). For example, AI can analyse large-scale behavioural data to identify opportunities for waste reduction, optimise logistics and production processes, and recommend environmentally responsible consumption choices.

However, the growing integration of AI in marketing also raises important concerns. AI systems may introduce algorithmic bias, lack of transparency, privacy concerns, and the risk of reinforcing unsustainable consumption patterns through hyper-personalised marketing strategies (Mariani et al., 2022; Pentina et al., 2023). Furthermore, the environmental footprint of AI infrastructure and data-intensive technologies raises additional sustainability challenges that require careful consideration.

Despite increasing research on AI in marketing and sustainable consumption, relatively little work has examined how AI-driven marketing practices influence sustainability outcomes across individual, organisational, and system-level contexts (Van Hau et al., 2025). Understanding these dynamics is critical for ensuring that AI technologies contribute to sustainable futures rather than exacerbating environmental and social challenges.

This Special Issue invites empirical (qualitative and quantitative) and conceptual papers that advance understanding of the relationship between AI-driven marketing and sustainable futures. We welcome interdisciplinary perspectives from marketing, consumer behaviour, sustainability studies, information systems, and public policy that examine the opportunities, risks, and governance of AI-driven marketing in sustainability contexts.

Submissions may address questions including, but not limited to:

AI and sustainable consumer behaviour
• AI nudging and recommender systems that encourage sustainable choices
• Consumer perceptions, trust, and acceptance of AI-enabled sustainability initiatives
• AI-driven segmentation for targeting sustainable consumption behaviours

AI-enabled sustainable marketing strategies
• AI insights supporting sustainable product innovation and development
• AI applications in sustainability branding and communication
• Personalised marketing and its influence on responsible consumption

AI and sustainable marketing systems
• Predictive analytics for reducing waste and overproduction
• AI-enabled platforms supporting reuse, repair, sharing, and circular consumption

Ethical and responsible AI in marketing
• Transparency, fairness, and accountability in AI-driven marketing systems
• Consumer privacy and data ethics in sustainability-focused marketing
• Governance frameworks for responsible AI use in marketing

Technology and sustainability transitions
• Generative AI and sustainability communication
• Digital platforms enabling collaborative or sustainable consumption
• AI shaping sustainability narratives and consumer engagement

Cross-level perspectives
• Interactions between individual behaviour change and system-level transformation
• Policy and regulatory responses to AI-driven sustainability initiatives
• Cross-cultural perspectives on AI-enabled sustainability practices

Generative AI and sustainable marketing
• Role of generative AI in sustainability communication and storytelling
• Risks of misinformation or greenwashing through generative AI
• Consumer responses to generative AI–generated sustainability content
• Use of generative AI in sustainable product design and innovation

References

Chandy, R. K., Johar, G. V., Moorman, C., & Roberts, J. H. (2021). Better marketing for a better world. Journal of Marketing, 85(3), 1–9.

Grewal, D., Satornino, C. B., Davenport, T., & Guha, A. (2025). How generative AI is shaping the future of marketing. Journal of the Academy of Marketing Science.

Guha, A., Grewal, D., Kopalle, P., Haenlein, M., Schneider, M., Jung, H., & Hawkins, G. (2021). How artificial intelligence will affect the future of retailing. Journal of Retailing, 97(1), 28–41.

Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776.

Pentina, I., Xie, T., Hancock, T., & Bailey, A. (2023). Consumer–machine relationships in the age of artificial intelligence: A systematic literature review and research directions. Psychology & Marketing, 40, 1593–1614.

Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151.

Satornino, C. B., Du, S., & Grewal, D. (2024). Using artificial intelligence to advance sustainable development in industrial markets: A complex adaptive systems perspective. Industrial Marketing Management, 116, 145–157.

White, K., Habib, R., & Hardisty, D. (2019). How to shift consumer behaviors to be more sustainable: A literature review and guiding framework. Journal of Marketing, 83(3), 22–49.

Van Hau, P., McIver, T., Robertson, K., & Thaichon, P. (2025). Effective customer segmentation using the recency, frequency, and monetary framework, combined with density-based clustering. Journal of strategic marketing, 1-23.

Van Hau, P., Robertson, K., Thyne, M., Hamlin, R., & Rundle-Thiele, S. (2025). Reducing single-use plastics (SUPs) consumption: A synthesis of I and S level approaches. Sustainable futures, 10, 101087.