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User Acceptance of Hotel Service Robots Using the Quantitative Kano Model

Author

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  • Muzi Xie

    (Graduate School of Hospitality & Tourism Management, Sejong University, Seoul 05006, Korea)

  • Hong-bumm Kim

    (College of Hospitality & Tourism Management, Sejong University, Seoul 05006, Korea)

Abstract

With today’s rapid technological developments, many have applied artificial intelligence and robot technology to the tourism and hotel industries, with hotel service robots (HSRs) being gradually developed. At present, more technology development companies have focused their attention on improving HSRs’ different attributes to improve their acceptance by users, thereby enhancing market competitiveness and improving customer loyalty. Understanding consumer acceptance of HSRs is important. Based on a literature review of the user’s acceptance of HSR attributes and HSRs’ current development status, some factors and attributes were extracted. For the questionnaire’s design and data extraction, the quantitative Kano model was used. The data obtained were compiled and analyzed using Microsoft Excel and SPSS. This study aims to (1) qualitatively apply the perceived value theory to develop specific HSR attributes and (2) quantitatively examine these attributes concerning public acceptance. By integrating the Kano model with the perceived value theory, this study provides empirical evidence of a nonlinear relationship between HSRs’ perceived value and user acceptance by exploring various attributes affecting the user’s acceptance of HSRs and the degree of change brought by the different attributes. The research result reveals the multidimensional impacts of perceived value, prompting users to embrace newer HSR technologies.

Suggested Citation

  • Muzi Xie & Hong-bumm Kim, 2022. "User Acceptance of Hotel Service Robots Using the Quantitative Kano Model," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3988-:d:781436
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    References listed on IDEAS

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    1. Daniel Belanche & Luis V. Casaló & Carlos Flavián & Jeroen Schepers, 2020. "Service robot implementation: a theoretical framework and research agenda," The Service Industries Journal, Taylor & Francis Journals, vol. 40(3-4), pages 203-225, March.
    2. Ha-Won Jang & Soo-Bum Lee, 2020. "Serving Robots: Management and Applications for Restaurant Business Sustainability," Sustainability, MDPI, vol. 12(10), pages 1-15, May.
    3. El-Adly, Mohammed Ismail, 2019. "Modelling the relationship between hotel perceived value, customer satisfaction, and customer loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 322-332.
    4. Sharareh Kermanshachi & Thahomina Jahan Nipa & Halil Nadiri, 2022. "Service quality assessment and enhancement using Kano model," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-17, February.
    5. Gursoy, Dogan & Chi, Oscar Hengxuan & Lu, Lu & Nunkoo, Robin, 2019. "Consumers acceptance of artificially intelligent (AI) device use in service delivery," International Journal of Information Management, Elsevier, vol. 49(C), pages 157-169.
    6. Stock, Ruth & Merkle, Moritz, 2017. "A Service Robot Acceptance Model: User acceptance of humanoid robots during service encounters," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 123630, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. Huiyue Ye & Sunny Sun & Rob Law, 2022. "A Review of Robotic Applications in Hospitality and Tourism Research," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    2. Xin Chen & Chengxie Ma & Chengyu Tao, 2022. "Factors Influencing the Sustainability of Stroke Rehabilitation Services in Community: An Analysis Based on Kano Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.

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