Smart pricing for sustainability: AI's role in reducing food waste in retail

Purpose This study investigates how Artificial Intelligence (AI)-driven dynamic pricing can help supermarkets optimize profits from perishable foods by adjusting prices based on real-time product expiry data, thereby reducing food waste. Design/methodology/approach The study formulated a conceptual framework and evaluated it through a quantitative research design. Primary data was gathered using structured surveys administered to 176 supermarket retailers located in Karnataka, India. The dataset was examined through descriptive statistics, reliability testing, and Structural Equation Modeling (SEM) employing SmartPLS-4 to confirm the hypothesized linkages among price determinants, dynamic pricing, and waste minimization Findings The analysis confirms that AI-based dynamic pricing strategies significantly positively impact waste reduction and profitability. Key determinants include product characteristics, demand, and inventory levels. The model demonstrated good reliability and validity, though some consumer demographic factors showed weaker than expected loadings. Research limitations/implications The sample utilized in this study is restricted to a single region within India, which could constrain the wider applicability of the results. Subsequent investigations should consider examining the suggested AI-based algorithms in real-time retail settings across varied geographic regions and cultural contexts. Practical implications: This research provides supermarket managers with a validated framework for implementing AI-driven pricing to reduce waste and increase profits. It highlights the operational factors that most significantly influence the success of such strategies. Originality/value: This is one of the first studies to empirically validate a model linking AI-driven dynamic pricing directly to waste reduction in the context of Indian retail, addressing a significant gap in both literature and practice. 177