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Untact: Customer’s Acceptance Intention toward Robot Barista in Coffee Shop

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  • Hye Jin Sung

    (Department of Foodservice Management, Pai Chai University, Daejeon 35345, Korea)

  • Hyeon Mo Jeon

    (Department of Hotel, Tourism, and Foodservice Management, Dongguk University-Gyeonju, Gyeongju 38066, Korea)

Abstract

Restaurants have been using robots to meet the increasing consumer demand for food customization and safety and contactless service operations. This study identified the antecedent factors influencing a customer’s attitude and acceptance intention toward a robot barista. To this end, we conducted a questionnaire survey from 10 to 24 January 2020, on a sample of 317 Korean consumers who purchased coffee prepared by a robot barista. We based the analysis on the following determinant factors of the extension of the technology acceptance model (ETAM): perceived enjoyment, perceived usefulness, need for interaction, perceived risk, and perceived innovativeness. The results showed that perceived usefulness had the highest impact on acceptance intention, followed by perceived enjoyment and the need for interaction. Perceived usefulness and innovativeness positively influenced acceptance intention. These results confirmed the significance of the determinant factors in inducing customers’ attitude and acceptance intention toward a robot barista. This study extends the research on the application of artificial intelligence and the fourth industrial revolution technologies in the food service industry, and hence contributes toward the preparations for the post-Covid-19 period. It also offers practical implications for sustainable coffee shop management.

Suggested Citation

  • Hye Jin Sung & Hyeon Mo Jeon, 2020. "Untact: Customer’s Acceptance Intention toward Robot Barista in Coffee Shop," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8598-:d:430255
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    Cited by:

    1. Jun-Ho Bae & Hyeon-Mo Jeon, 2022. "Exploring the Relationships among Brand Experience, Perceived Product Quality, Hedonic Value, Utilitarian Value, and Brand Loyalty in Unmanned Coffee Shops during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    2. Se-Ran Yoo & Seon-Hee Kim & Hyeon-Mo Jeon, 2022. "How Does Experiential Value toward Robot Barista Service Affect Emotions, Storytelling, and Behavioral Intention in the Context of COVID-19?," Sustainability, MDPI, vol. 14(1), pages 1-14, January.
    3. Wang, Xueqin & Wong, Yiik Diew & Sun, Shanshan & Yuen, Kum Fai, 2022. "An investigation of self-service technology usage during the COVID-19 pandemic: The changing perceptions of ‘self’ and technologies," Technology in Society, Elsevier, vol. 70(C).
    4. Soyoung An & Thomas Eck & Huirang Yim, 2023. "Understanding Consumers’ Acceptance Intention to Use Mobile Food Delivery Applications through an Extended Technology Acceptance Model," Sustainability, MDPI, vol. 15(1), pages 1-14, January.
    5. Tianyang Huang, 2022. "What Affects the Acceptance and Use of Hotel Service Robots by Elderly Customers?," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    6. Seon Hee Kim & Se Ran Yoo & Hyeon Mo Jeon, 2022. "The role of experiential value, novelty, and satisfaction in robot barista coffee shop in South Korea: COVID-19 crisis and beyond," Service Business, Springer;Pan-Pacific Business Association, vol. 16(3), pages 771-790, September.
    7. Young Joong Kim & Jung Sook Park & Hyeon Mo Jeon, 2021. "Experiential Value, Satisfaction, Brand Love, and Brand Loyalty toward Robot Barista Coffee Shop: The Moderating Effect of Generation," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    8. Jaepil Yoo, 2021. "The Structural Relationship between Service Quality and Sustainable Use Intention of Voice Search Technology in Korea," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    9. Chong Li & Yingqi Li, 2023. "Factors Influencing Public Risk Perception of Emerging Technologies: A Meta-Analysis," Sustainability, MDPI, vol. 15(5), pages 1-37, February.

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