Evaluating consumer shopping, delivery demands, and last-mile preferences: An integrated MDCEV-HCM approach

Ali Riahi Samani , Ahmadreza Talebian , Sabyasachee Mishra , Mihalis Golias
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Abstract

The effective implementation of innovative last-mile delivery approaches depends on understanding two key elements: (i) consumer demand (who places an online order, where, and how) and (ii) consumers’ delivery needs and preferences. This study, first, proposes a disaggregated online demand modeling framework utilizing Multiple Discrete-Continuous Extreme Values (MDCEV) to estimate consumer shopping behavior and households’ home delivery and pick-up demands across different commodity types. Second, a Hybrid Choice Model (HCM) is introduced to assess the competitiveness of three innovative last-mile delivery modes, (i) Autonomous Delivery Robots, (ii) Crowdsourced Delivery, and (iii)Automated Parcel Lockers, considering consumer attitudes toward these technologies. Subsequently, we conduct elasticity analysis for cost and commodity type, revealing consumers’ Willingness-to-Pay for various last-mile delivery methods. The proposed framework is applied to a dataset acquired through an online survey distributed among residents of the State of Tennessee, USA. Analyzing results show that consumers can be categorized into five latent segments according to their shopping preferences: traditional shoppers, benefit seekers, e-shopping enthusiasts, omnichannel consumers, and Indifferent customers. Results indicate that businesses should focus on delivery time for e-shopping enthusiasts and omnichannel consumers, while accessibility to APLs may encourage traditional shoppers and benefits seekers to transition to online shopping. Also, latent variable analysis shows that while perceived risk hinders adoption, perceived benefits and ease of use drive acceptance. The findings of this study highlight the importance of a tailored approach to adopting innovative delivery solutions, ensuring a balance between cost, accessibility, and consumer priorities to meet evolving demands.
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有效实施创新的 "最后一英里 "配送方法取决于对两个关键要素的了解:(i) 消费者需求(谁在网上下单、在哪里下单、如何下单)和 (ii) 消费者的配送需求和偏好。本研究首先利用多重离散-连续极值(MDCEV)提出了一个分类在线需求建模框架,以估算不同商品类型的消费者购物行为以及家庭的送货上门和取货需求。其次,我们引入了混合选择模型(HCM)来评估三种创新的最后一英里配送模式的竞争力,即(i)自主配送机器人、(ii)众包配送和(iii)自动包裹柜,同时考虑消费者对这些技术的态度。随后,我们对成本和商品类型进行了弹性分析,揭示了消费者对各种最后一英里配送方式的支付意愿。我们将所提出的框架应用于在美国田纳西州居民中开展的在线调查所获得的数据集。分析结果表明,消费者可根据其购物偏好分为五个潜在细分市场:传统购物者、利益追求者、电子购物爱好者、全渠道消费者和无动于衷的顾客。结果表明,对于电子购物爱好者和全渠道消费者,企业应关注送货时间,而APL的可及性可能会鼓励传统购物者和利益追求者过渡到网上购物。此外,潜在变量分析表明,感知风险阻碍了消费者的采用,而感知利益和易用性则推动了消费者的接受。这项研究的结果突出表明,在采用创新型交付解决方案时,必须采取量身定制的方法,确保在成本、可及性和消费者优先级之间取得平衡,以满足不断变化的需求。
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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