Understanding Restaurant Service Robot Adoption: The Moderating Effect of Age Generations
Keywords:
Customer acceptance and Usage, Trust, Robot Service Technology, Restaurant
Abstract
In the endemic era, the success of robot services in Malaysian restaurants hinges on public acceptance and customer satisfaction. This study explores the critical factors influencing customer attitudes, emphasizing the reliability and efficiency of robots, as well as their capacity to deliver a personalized, human-like dining experience. As the restaurant industry navigates post-pandemic challenges, robot services are poised to revolutionize operations by enhancing safety and addressing public health concerns. Using the Unified Theory of Acceptance and Use of Technology (UTAUT2) as its framework, this study employed a quantitative research methodology, gathering data through survey questionnaires from 225 respondents. Descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM) were utilized to test the hypotheses and evaluate the proposed model. Multivariate analysis revealed no significant generational divide in the adoption of service robots, with both younger and older generations displaying comparable levels of acceptance and usage. The findings underscore customer habit as the most significant predictor of robot service adoption, highlighting the UTAUT2 model's applicability in the Malaysian context. The study also reinforces the importance of considering age-related factors in understanding technology acceptance during the endemic period. Given the positive reception of robot services, the restaurant industry should focus on refining these technologies, ensuring that they meet the diverse needs of different age groups.
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References
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De Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2020). Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots. Tourism Management, 78, 104042. https://doi.org/10.1016/j.tourman.2019.104042
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Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2013). Online drivers of consumer purchase of website airline tickets. Journal of Air Transport Management, 32, 58-64.
Fishbein, M., & Ajzen, I. (1977). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research.
Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: an application of UTAUT model. International Journal of Business and Management, 6(4), 161.
García-Gomez, C.D., Demir, E., Díez-Esteban, J.M., Bilan, Y., (2021). The impact of COVID19 outbreaks on hotels’ value compared to previous diseases: the role of ALFO strategy. Heliyon 7 (8), e07836.https://doi.org/10.1016/j.heliyon.2021.e07836
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM)–cutting edge. Tourism Review. https://doi.org/10.1108/TR022019-0062.
Hair, J.F., Jr Hult, G.T.M., Ringle, C. and Sarstedt, M. (2016), A Primer on Partial Least SquaresStructural Equation Modeling (PLS-SEM), Sage Publications
Hana?ah, M.H. (2020), “Formative vs. re?ective measurement model: guidelines for structural equationmodeling research”,International Journal of Analysis and Applications, Vol. 18 No. 5,pp. 876- 889
Harrington, J., & Schaefer, M. (2014). Extrusion-based microencapsulation for the food industry. In Microencapsulation in the Food Industry (pp. 81-84). Academic Press.
Huber, M. M., Hancer, M., & George, R. T. (2010). A comparative examination of information technology usage in the restaurant industry. Journal of Foodservice Business Research, 13(3), 268-281
Huang, C.-Y., & Kao, Y.-S. (2015). UTAUT2-based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 2015, 603747. https://doi.org/10.1155/2015/603747
Hsu, L., & Wu, P. (2013). Electronic-Tablet-Based Menu in a Full-Service Restaurant and Customer Satisfaction – A Structural Equation Model. International Journal of Business, Humanities and Technology, 3(2), 61 - 71.
Hwang, J., Kim, H., Kim, J. J., & Kim, I. (2021). Investigation of perceived risks and their outcome variables in the context of robotic restaurants. Journal of Travel & Tourism Marketing, 38(3), 263- 281.https://doi.org/10.1080/10548408.2021.1906826
Hwang, J., Kim, H., & Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality and Tourism Management, 38, 102–110. https://doi.org/10.1016/j.jhtm.2019.01.004
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Ivanov SH, Webster C, Berezina K (2017) Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo e Desenvolvimento 27(28): 1501–1517
Jang, H.W., Lee, S.B., (2020). Serving robots: management and applications for restaurant business sustainability. Sustainability 12 (10), 3998. Jang, H.W., Lee, S.B.,
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Published
2025-09-14
How to Cite
Mohd Nazri, N. A. N., Baba, N., & Abdullah, N. (2025). Understanding Restaurant Service Robot Adoption: The Moderating Effect of Age Generations. Information Management and Business Review, 17(3(I), 507-521. https://doi.org/10.22610/imbr.v17i3(I).4489
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