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Choice model based analysis of consumer preference for drone delivery service

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  • Kim, Sang Hyun

Abstract

It is anticipated that drones will soon be utilized for a range of applications, including delivery service. However, there has been a lack of research on consumer preference between drone delivery service and traditional delivery service. This is the first study to analyze the consumer preference for drone delivery based on a discrete choice model between the drone delivery service and traditional delivery services by truck or motorcycle. The discrete choice model is estimated using a stated preference survey, and potential consumers’ preference is analyzed for representative commodities with different price. The results show that the price and type of commodities influence consumer preference, which also depends on socio-demographic characteristics such as gender, age, and household income. Specifically, it was consistently observed in all cases that the younger the age, the higher the preference for drone delivery service. This study contributes to predicting the consumer preference for drone delivery service before real service offerings and to supporting the establishment of business strategies for companies who prepare for the new market of drone-based delivery.

Suggested Citation

  • Kim, Sang Hyun, 2020. "Choice model based analysis of consumer preference for drone delivery service," Journal of Air Transport Management, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:jaitra:v:84:y:2020:i:c:s0969699719304661
    DOI: 10.1016/j.jairtraman.2020.101785
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    References listed on IDEAS

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    Cited by:

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    3. Amalia Polydoropoulou & Athena Tsirimpa & Ioannis Karakikes & Ioannis Tsouros & Ioanna Pagoni, 2022. "Mode Choice Modeling for Sustainable Last-Mile Delivery: The Greek Perspective," Sustainability, MDPI, vol. 14(15), pages 1-14, July.
    4. Cai, Lanhui & Yuen, Kum Fai & Xie, Diancen & Fang, Mingjie & Wang, Xueqin, 2021. "Consumer's usage of logistics technologies: Integration of habit into the unified theory of acceptance and use of technology," Technology in Society, Elsevier, vol. 67(C).
    5. Burke, Andrew & Zhao, Jingyuan & Miller, Marshall & Fulton, Lewis, 2023. "Vehicle Choice Modeling for Light-, Medium-, and Heavy-Duty Zero-Emission Vehicles in California," Institute of Transportation Studies, Working Paper Series qt7437p058, Institute of Transportation Studies, UC Davis.
    6. Koh, Le Yi & Lee, Jia Yi & Wang, Xueqin & Yuen, Kum Fai, 2023. "Urban drone adoption: Addressing technological, privacy and task–technology fit concerns," Technology in Society, Elsevier, vol. 72(C).
    7. Sham, Rohana & Chong, Han Xi & Cheng-Xi Aw, Eugene & Bibi Tkm Thangal, Thahira & Abdamia, Noranita binti, 2023. "Switching up the delivery game: Understanding switching intention to retail drone delivery services," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    8. Tamakloe, Reuben & Zhang, Kaihan & Atandzi, Jonathan & Park, Dongjoo, 2024. "Examining urban delivery service user profiles and determinants of drone delivery adoption in Ghana considering usage before and after the COVID-19 pandemic," Transport Policy, Elsevier, vol. 146(C), pages 279-294.

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