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Analysing Consumer Adoption Intentions of Drone-enabled Food Delivery Services: The Moderating Role of Uncertainty Avoidance

Author

Listed:
  • Shakeel Ahmad

    (Zhengzhou University, Henan, China)

  • Xue Chaogai

    (Zhengzhou University, Henan, China)

  • Sayed Kifayat Shah

    (Shenzhen University, Shenzhen, China)

  • Jozsef Popp

    (John von Neumann University, Hungary and WSB University, Poland)

  • Angel Acevedo-Duque

    (Universidad Autonoma de Chile, Public Policy Observatory, Santiago, Chile)

Abstract

While drone-enabled food delivery continues to gain prominence, a notable research gap exists in comprehending the impact of real-life psychological challenges — specifically autonomy, competence, and relatedness — on consumer adoption intention in various cultural contexts. This study explores the identified research gap by analysing these challenges through the self-determination theory lens, employing partial least squares structural equation modelling to evaluate survey responses from 423 participants in South Korea and China. The results of our study revealed that the autonomy problem has no significant effect, while the competency problem has a significant positive impact on adoption intentions for drone-enabled food delivery services in both South Korea and China. The effect of relatedness was positive and significant in China, but negative and significant in the South Korean context. These data also demonstrated a moderating impact of cultural dimension (uncertainty avoidance) on a relationship between these factors and adoption intention, demonstrating how culture impacts consumer adoption behaviour. Our results in this research are one of the first to contextualise basic issues by filling gaps in the current literature and offering valuable insights for food service businesses. Our study’s results will also guide the formation of effective drone-enabled food delivery systems in diverse societies by supporting businesses in tailoring their methods to match consumer behaviours.

Suggested Citation

  • Shakeel Ahmad & Xue Chaogai & Sayed Kifayat Shah & Jozsef Popp & Angel Acevedo-Duque, 2025. "Analysing Consumer Adoption Intentions of Drone-enabled Food Delivery Services: The Moderating Role of Uncertainty Avoidance," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 27(69), pages 606-606, April.
    • Handle: RePEc:aes:amfeco:v:27:y:2025:i:69:p:606
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    References listed on IDEAS

    as
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    3. Jingbo Yuan & Sayed Kifayat Shah & Jozsef Popp & Angel Acevedo-Duque, 2024. "Understanding the Forced Adoption of an AI-Based Health Code System in China," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(66), pages 550-550, Aprilie.
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    5. Lin, Jiabao & Guo, Jinyuan & Turel, Ofir & Liu, Shan, 2020. "Purchasing organic food with social commerce: An integrated food-technology consumption values perspective," International Journal of Information Management, Elsevier, vol. 51(C).
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    More about this item

    Keywords

    drone-enabled food delivery; uncertainty avoidance; autonomy; competence; relatedness;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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