用机器学习分析美国消费者对运输认证食品的偏好，发现安全与能源证书最受青睐。
Investigating U.S. Consumer Demand for Food Products with Innovative Transportation Certificates Based on Stated Preferences and Machine Learning Approaches
- 基于陈述偏好实验和机器学习，评估五类运输认证的吸引力。
- 安全与能源类证书显著提升购买意愿，价格与产品类型也有影响。
- 研究结果可指导食品供应链优化，适合政策制定者与企业参考。
本文利用机器学习模型，研究美国消费者对带有创新运输认证食品的购买行为。在已有供应链可追溯性研究基础上，识别出运输因素在消费者决策中具有重要影响。为此，进一步设计实验以明确消费者重视的具体运输属性，并提出五类创新认证：运输方式、物联网（IoT）、安全措施、能源来源及必须到达日期（MABDs）。实验还控制了产品特性和决策者因素。结果显示，消费者对运输环节中的安全与能源类证书表现出明显偏好。研究同时分析了价格、产品类型、认证类别及决策者因素对购买选择的影响。最终，研究为优化食品供应链系统提供了数据驱动的建议。
原文摘要 · Abstract (English)
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.
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