This study explores technologies for measuring plate waste at hotel breakfasts and implementing tailored behavioral interventions. Using semi-structured interviews with Israeli hotel managers and chefs, we examined their attitudes toward strategies such as smaller portions, social messaging, and photographing meals during consumption to assess food waste. Most participants expressed a willingness to participate in such experiments as long as they were conducted in a non-intrusive manner. Concerns about guest perceptions, privacy, and potential biases were also noted. The findings highlight the potential of combining behavioral nudges, gamification, and advanced technologies, such as artificial intelligence (AI) and machine learning (ML), to effectively measure and reduce food waste. Our research emphasizes the importance of culturally sensitive, data-driven approaches in the hospitality sector to measure plate waste. The study contributes by suggesting integrative methods linking demographic data to food waste patterns, offering practical insights for policy and practice aimed at promoting sustainability in hotels.
Appel et al. (Wed,) studied this question.
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