Consumer Behavioural Insights for AI-Enabled Personalized Food Waste Reduction in Restaurants

Abstract: Artificial Intelligence (AI) is increasingly enabling restaurants to deliver personalized customer experiences while supporting sustainable business practices. However, the effectiveness of AI-enabled interventions depends on understanding the behavioural factors that influence consumer food waste. This study examines consumer behaviour related to restaurant food waste and explores how behavioural insights can inform AI-driven personalization strategies for food waste reduction. The research is grounded in the Theory of Planned Behaviour (TPB) and is based on primary data collected from 100 restaurant consumers in the Mumbai Suburban region using a structured questionnaire. The study assessed six behavioural constructs: awareness, attitude, behavioural factors, psychological factors, social factors, and willingness to act towards food waste reduction. Descriptive analysis revealed that respondents demonstrated relatively high levels of awareness (Mean = 3.89) and positive attitudes (Mean = 3.92) towards reducing food waste. Psychological factors (Mean = 3.81) and social factors (Mean = 3.64) also indicated favourable behavioural orientations. However, behavioural practices recorded comparatively lower scores (Mean = 3.45), suggesting an attitude–behaviour gap in responsible food consumption. Respondents also expressed a positive willingness to adopt food waste reduction practices (Mean = 3.76), indicating the potential for targeted behavioural interventions. Based on these findings, the study proposes an AI-driven consumer insight framework that can support restaurants in designing personalized food waste reduction strategies, including portion-size recommendations, smart menu suggestions, and digital nudges. Rather than positioning AI as a replacement for managerial decision-making, the framework demonstrates how consumer behavioural data can be leveraged to enhance AI-enabled customer engagement and sustainability initiatives. The study contributes to the growing discourse on AI in Business and Marketing by illustrating the role of behavioural insights in developing customer-centric and sustainable restaurant practices. The findings offer practical implications for restaurant managers seeking to integrate AI-enabled personalization with food waste reduction initiatives in the HORECA sector. Keywords: Artificial Intelligence, Consumer Behaviour, Food Waste, Restaurant Management, Theory of Planned Behaviour, Sustainability, HORECA.