Unhealthy dietary habits are a major preventable risk factor for widespread non-communicable diseases (NCD). Diet counseling is effective in managing diet-related NCDs, but constrained by its manual nature and limited (clinical) resources. To address these challenges, we propose a fully automated diet counseling system FoodCoach. It monitors people's food purchases using digital receipts from loyalty cards and provides structured dietary recommendations. We introduce the FoodCoach system's recommender algorithm and architecture, along with evaluation results from a two-arm randomized controlled trial involving 61 participants. The trial results demonstrate the technical feasibility and potential for scalable, fully automated diet counseling, despite not showing a significant change in participants' food purchase healthiness. We further show how others can deploy and extend the FoodCoach system in their own context and provide all relevant component implementations. Our core research contributions are: 1) a novel dietary recommendation algorithm designed and implemented with clinical experts, and 2) a scalable system architecture that employs a knowledge graph for enhanced interoperability and applicability to diverse domains and data sources. From a practical perspective, FoodCoach can augment traditional diet counseling through automatic diet monitoring and evaluation modules. Additionally, it streamlines the counseling process, conserving clinical resources, and ultimately contributing to the reduction of NCD prevalence.

Approximately one-third of all food produced for human consumption is either lost or wasted. Given the central position of retailers in the supply chain, they have the potential to effectively reduce consumer food waste by implementing targeted interventions. To do so, however, they should target distinct consumer groups. In this research, we use a unique data set comprising the grocery shopping data of customers who use loyalty cards, complemented with food waste reports, to derive three distinct target groups: traditionals, time-constrained, and convenience lovers. Based on the general behavioral change literature, we discuss diverse target group-specific interventions that retailers can implement to reduce consumer food waste. Overall, we pave a research path to examine how retailers and marketing can effectively shift consumer behavior toward more sustainable food and shopping practices and assume responsibility within the food supply chain.

Price elasticities are central empirical measures in marketing, informing pricing, promotion planning, or evaluations of consumer responsiveness. Yet estimates may depend not only on brands, categories, and markets but also on the analytical decisions researchers make in transforming raw purchase data into elasticities. We use a many-analyst design in which 156 independent research teams (RTs) estimate brand price elasticities for 68 meat-substitute country-brand combinations from the same dataset. Each team retained discretion over its analytical approach, including choices concerning modeling, aggregation, fixed effects, control variables, and endogeneity correction. Results show substantial heterogeneity in both choices and estimates. Across 10,415 brand price elasticity estimates, the average is -0.61. However, the variation across the 156 RTs is considerable: the average brand price elasticity per RT ranges from -7.3 to +3.1, with a standard deviation of 0.84. Choices related to time aggregation, fixed effects, time controls, and marketing-mix covariates explain the largest share of variation in reported estimates. Because analytical choices occur in configurations rather than in isolation, we cluster RTs and reveal distinct analytical pathways associated with different patterns of estimates. Our findings contribute to elasticity research by identifying modeling decisions that warrant particular scrutiny and to the open science debate by supporting the call for greater transparency about analytical decisions.