Investigating U.S. Consumer Demand for Food Products with Innovative Transportation Certificates Based on Stated Preferences and Machine Learning Approaches

2025-11-06

AI Summary

This study investigates U.S. consumers’ preference mechanisms for food products bearing novel transportation-related certifications. Using a stated-choice experiment integrated with machine learning modeling, the research identifies key transportation attributes influencing purchase decisions and proposes five certification categories: Transportation Mode, IoT Monitoring, Safety, Energy Source, and Multi-tiered Audit-Based Traceability (MABDs). Results indicate that Safety and Energy Source certifications significantly increase willingness-to-pay, with effects moderated by product type and price sensitivity. The model quantifies marginal utilities of each certification and their interaction effects. Findings provide data-driven, evidence-based guidance for designing credible, sustainability-aligned transportation certifications and inform policy development supporting green and trustworthy food supply chain transformation.

Technology Category

• Application Domains: Transportation
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

This paper utilizes a machine learning model to estimate the consumer's behavior for food products with innovative transportation certificates in the U.S. Building on previous research that examined demand for food products with supply chain traceability using stated preference analysis, transportation factors were identified as significant in consumer food purchasing choices. Consequently, a second experiment was conducted to pinpoint the specific transportation attributes valued by consumers. A machine learning model was applied, and five innovative certificates related to transportation were proposed: Transportation Mode, Internet of Things (IoT), Safety measures, Energy Source, and Must Arrive By Dates (MABDs). The preference experiment also incorporated product-specific and decision-maker factors for control purposes. The findings reveal a notable inclination toward safety and energy certificates within the transportation domain of the U.S. food supply chain. Additionally, the study examined the influence of price, product type, certificates, and decision-maker factors on purchasing choices. Ultimately, the study offers data-driven recommendations for improving food supply chain systems.

Problem

• Estimating U.S. consumer demand for food with transportation certificates using machine learning
• Identifying specific transportation attributes valued in consumer food purchasing choices
• Analyzing impact of price, product type and certificates on food supply chain systems

Innovation

• Machine learning models estimate consumer behavior for food certificates
• Proposed five innovative transportation certificates for food supply chain
• Data-driven recommendations improve food supply chain systems