Artificial Intelligence in Sustainable Marketing: A Systematic Review of Consumer Engagement and Food Waste Reduction Strategies

Abstract

The food waste problem globally contributes to 8-10% of anthropogenic greenhouse gas emissions annually and leads to economic losses amounting to $1 trillion per year, making it a severe threat to the sustainability of the environment and social justice (United Nations Environment Program, 2024). Although food science and logistics of food supply chains have been discussed widely in the literature regarding possible technological solutions, the marketing issues related to the application of artificial intelligence for the promotion of sustainable behavior and food reuse have not been analyzed sufficiently. This systematic review aims at addressing the above gap in the literature by analyzing the influence of the new technology on consumer decision-making and helping marketers use AI to decrease the level of food waste by enhancing consumer engagement. Based on theoretical backgrounds like the Stimulus-Organism-Response Model, Service-Dominant Logic, and the Theory of Planned Behavior, the paper summarizes 30 articles written between 1991 and 2026 in accordance with the PRISMA criteria (Kumar & Singh, 2025). Three main mechanisms used by AI technologies to promote sustainable marketing results were found, namely, personalized engagement and behavioral nudging through AI-enabled apps, predictive analytics for identifying consumer preferences and targeting consumers accurately, and digital platforms for facilitating value co-creation in reducing food waste. It is concluded that AI stimuli (personalization, interactivity, usability) play an important role in consumer engagement; however, self-efficacy turns out to be a key mediator between engagement and behavioral intention. This study contributes to the marketing body of knowledge as it uses existing behavioral theories and gives managerial recommendations for applying AI in marketing practice.