DETERMINANTS OF CUSTOMER SATISFACTION TOWARDS ROBOTIC WAITERS AMONG GEN Z CUSTOMERS IN MALAYSIA: A CONCEPTUAL PAPER
DOI:
https://doi.org/10.24191/VoA.v22i2.13438Keywords:
Customer Satisfaction, Service Efficiency, Speed of Service, Perceived Ease of Use Perceived UsefulnessAbstract
This purpose of the study is to investigate the customer satisfaction among Generation Z towards robotic waiters in restaurants. There are four predictors including Speed of Service, Perceived Usefulness (PU), Service Efficiency and Perceived Ease of Use (PEOU). The Gen Z customer is preferred in the study as they are the group that can use and accept high technological products and always looks for uniqueness especially related to robotic waiters. The underpinning theory of this research is Technology Acceptance Model (TAM) which explains how users accept and adopt new technology. The quantitative research is employed and a questionnaire which covers five constructs, namely Customer Satisfaction, Speed of Service, Perceived Usefulness (PU), Service Efficiency and Perceived Ease of Use (PEOU). A minimum total of 200 respondents from Gen Z customers in a survey for their acceptance of robotic waiters in restaurants. The correlation analysis is applied to examine the relationship between all variables, and the multiple regression analysis to analyse the impact of all independent variables on Customer Satisfaction towards robotic waiters. The results will provide an insight towards the application of robotic waiters and the ways to improve the customer satisfactions whenever being served by the robotic waiters. It might create a trend to encourage more restaurants to invest in a technological product which might be able to solve the manpower shortage and employee turnover problem in the industry.
References
Abhari, S., Jalali, A., Jaafar, M., &Tajaddini, R. (2022). The impact of Covid-19 pandemic on small businesses in tourism and hospitality industry in Malaysia. Journal of Research in Marketing and Entrepreneurship, 24(1), 75-91.
Adanan, A., Azalea, N., Izzat, A., Darson, M. D., Khairuman, A., Hasim, M., Binti, N. H., & Wasilan, M. (2024). Serving The Future: Factors Influencing Consumer Acceptance of Robotic Waiters in Restaurants. Information Management and Business Review, 16(3), 210-216.
Anna. (2025). Robot Waiters in Malaysian Restaurants: Trends & Costs. Retrieved July 29, 2025, from https://www.eats365pos.com/my/node/647
Aydin, İ. (2021). Investigation of the effect of robot waiter usage desire on word-of-mouth communication and robot waiter usage attitude in restaurants. Turkish Business Journal, 2(4), 93-105.
Azeem, M., Ahmed, M., Haider, S., & Sajjad, M. (2021). Expanding competitive advantage through organizational culture, knowledge sharing, and organizational innovation. Technology in Society, 66, 101635.
Bassiouni, D.H., & Hackley, C. (2014) Generation Z children's adaptation to digital consumer culture: A critical literature review. Journal of Customer Behaviour, 13(2),113-133. Available from https://doi:10.1362/147539214X14024779483591.
Becker, M., Mahr, D., & Odekerken-Schröder, G. (2023). Customer comfort during service robot interactions. Service Business, 17(1), 137–165. Available from https://doi.org/10.1317/s11628-022-00499-4
Benítez-Márquez, M. D., Sánchez-Teba, E. M., Bermúdez-González, G., & Núñez-Rydman, E. S. (2022). Generation Z Within the Workforce and in the Workplace: A Bibliometric Analysis. Front. Psychol. 12:736820. Available from https://doi: 10.3389/fpsyg.2021.736820.
Berezina, K., Ciftci, O., & Cobanoglu, C. (2019). Robots, artificial intelligence, and service automation in restaurants. In Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality (pp. 185–219). Emerald. Available from https://doi.org/10.1108/978-1-78756-687-320191010
Borghi, M., Mariani, M. M., Wirtz, J., & Vega, R. P. (2023). The impact of service robots on customer satisfaction & online ratings: The moderating effects of rapport and contextual review factors. Marketing Science, Advance online publication. Available form https://doi.org/10.1312/mar.21903
Corbisiero, F., & Ruspini, E. (2018). Millennials, generation Z and the future of tourism. Journal of Tourism Futures, 4(1), 3-6.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30. Available from https://doi.org/10.1080/07421222.2003.11045748
Dixon, J., Hong, B., & Wu, L. (2021). The robot revolution: managerial and employment consequences for firms. Management Science, 67 (9), 5586–5605. Available from https://doi.org/10.1287/ mnsc.2020.3812.
Dorsey, J., Villa, D. (2020) Zconomy; How will Generation Z change the future of business -and what to do about It, New York: Harper
Durai, A. (2022). The Rise of Robot Waiters in Malaysia, Driven by Labour Shortage in F&B Sector.
The Star Online. Retrieved July 30, 2025, from
https://www.thestar.com.my/lifestyle/living/2022/08/24/the-rise-of-robot-waiters-in-malaysia
Enholm, I. M., Papagiannidis, E., Mikalef, P., &Krogstie, J. (2021). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 1-26.
European Travel Commission. (2020). Study on generation Z travellers. https://etc- corporate.org/reports/studyon-generation-z-travellers/.
Francis, T. & Hoefel, F. (2018) True Gen': Generation Z and its implications for companies. McKinsey
& Company, 12. Retrieved July 31, 2025 from
http://www.drthomaswu.com/uicmpaccsmac/Gen%20Z.pdf
Gu, H., Li, B., Ryan, C., Tang, Y., & Yang, X. (2023). From darkest to finest hour: Recovery strategies and organizational resilience in China’s hotel industry during the COVID-19 pandemic. Journal of China Tourism Research, 1-24.
Guszkowski, J. (2022). Consumer Trends. Retrieved December 21, 2025, from https://www.restaurantbusinessonline.com/consumer-trends/grubhub-2025-was-year-convenience-stores-became-meal-destinations
Haque, A., Ahmad Suki, A., Maruf, T. I. & Uzir, M. U. H. (2025). Shaping Customer Attitudes: The Role of AI-Driven Robotic Waiters in Restaurant Business. Journal of Scope, 15 (2), 45 – 74.
Haryanto, A. T., Haryono, T., Sri, H., & Sawitri, R. (2017). International Review of Management and Marketing Market Orientation, Learning Orientation and Small Medium Enterprises Performance: The Mediating Role of Innovation. International Review of Management and Marketing, 7(1), 484–491.
Ivanov, S., & Webster, C. (2019). Economic fundamentals of the use of robots, artificial intelligence, and service automation in travel, tourism, and hospitality. In Robots, artificial intelligence, and service automation in travel, tourism and hospitality (pp. 39-55). Emerald Publishing Limited.
Iyanda, A. R., & Azeez, T. A. (2024). A Multilingual Restaurant Serving Robot Request Management System. Ife Journal of Technology, 29(1), 24–33. Retrieved from https://ijt.oauife.edu.ng/index.php/ijt/article/view/228
Kotler, P., & Armstrong, G. (2012). Principles of Marketing (14th ed.). Essex, Pearson Education Limited.
Kuder, G. F., & Richardson, M. W. (1937). The Theory and Estimation of Test Reliability. Psychometrika, 2, 151- 160.
Lanier, K. (2017). 5 things HR professionals need to know. Strategic Human Resource Review. 16, 288–290. Available from https:// doi: 10.1108/SHR-08-2017-0051
Lim, J. (2018). Ipoh's Nam Heong Restaurant Has Hired and Trained 6 Robot Waitresses to Serve
Customers. Retrieved July 29, 2025, from https://says.com/my/lifestyle/get-your-food-and-drinks-served-by-robots-at-this-kopitiam-in-ipoh
Liu, Y., Li, H., & Carlsson, C. (2021). Factors driving the continued use of artificial intelligence-based conversational agents: An empirical study. Computers in Human Behavior, 120, 106728. Available from https://doi.org/10.1016/j.chb.2021.106728
Magano, J., Silva, C., Figueiredo, C., Vitória, A., Nogueira, T., and Pimenta Dinis, M. A. (2020). Generation Z: fitting project management soft skills competencies—A mixed-method approach. Journal of Education Science, 10, 187. Available from https://doi: 10.3390/educsci10070187
Marin-Pantelescu Andreea, & Ștefan-Hint Mihaela. (2024, March). The Preferences of Generation Z for the Digitalisation of the Hospitality Industry. Paper presented at the 18th International Conference on Business Excellence, Bucharest, Romania.
Marsden, P. (2017). Artificial Intelligence Timeline Infographic – From Eliza to Tay and beyond. Retrieved July 29, 2025, from https://digitalwellbeing.org/artificial-intelligence-timeline-infographic-from-eliza-to-tay-and-beyond/
Maruf, T. I., Kowsar, A.M., Haque A.K.M. Haque, A. A., Siddique, M. M., Sohail, N., Mannan, M., (2024). The Future of Dining: Robotics Hand-In Restaurant Service Revolutionizes Usages Experience in Kuala Lumpur. Journal of Education and Social Sciences, 27(1), 83-93.
McKeever, M., Diffley, S., & O'Rourkel, V. (2021). Generation Z: An Exploration of Their Unique Values Driving Brand Affinity. Conference Paper. Irish Academy of Management Conference, August 25, 2021. Ireland: Waterford IT.
Molinillo, S., Rejon‑Guardia, F., Anaya‑Sanchez, R. (2013). Exploring the antecedents of customers’ willingness to use service robots in restaurants. Service Business, 17, 167–193. Available from https://doi.org/10.1007/s11628-022-00509-5
Novak, M. (2012). The Disco-Blasting Robot Waiters of 1980s Pasadena. Retrieved July 30, 2025, from https://www.smithsonianmag.com/history/the-disco-blasting-robot-waiters-of-1980s-pasadena-70137340/
Oliver, R. L. (1997). Satisfaction: A Behavioral Perspective on the Consumer, Singapore. McGraw-Hill.
Roscoe, J. T. (1975). Fundamental research statistics for the behavioral sciences (2nd ed.). New York: Holt Rinehart and Winston.
Shah, T. R., Kautish, P., & Mehmood, K. (2023). Influence of robots service quality on customers’ acceptance in restaurants. Asia Pacific Journal of Marketing and Logistics, 35 (12), 3117 – 3137. Available from https://doi.org/10.1108/APJML-09-2022-0780
Shin, H.H., & Jeong, M. (2020). Guests’ perceptions of robot concierge and their adoption intentions. International Journal of Contemporary Hospitality Management, 32(8), 2613–2633. Available from https://doi.org/10.1108/IJCHM-09-2019-0798
Suomäki, A., Kianto, A., & Vanhala, M. (2019). Work engagement across different generations in Finland. Knowledge and Process Management, 26(2), 140–151. Available from https://doi.org/10.1002kpm.1604
Tahir, H., Waggett, C. & Hoffman, A. (2013). Antecedents of Customer Satisfaction: An E-CRM Framework. Journal of Business and Behavior Sciences, 25(2), 112-120.
Tavitiyaman, P., Zhang, X. Y., & Tsang, W. Y. (2020). How Tourist Perceive the Usefulness of Technology Adoption in Hotels: Interaction Effect of Past Experience and Education Level. Journal of China Tourism Research, 18(1), 1 – 12. Available https://doi.org/10.1080/19388160.2020.1801546
TMEF (The Malaysian Entrepreneurs Festival). (2022). The Rise of Robot Waiters in Malaysia, Driven by Labour Shortage In F&B Sector. Retrieved July 29, 2025, from https://tmef.com.my/sme-news-details.php?id=10454&page=485
Tuomi, A., Tussyadiah, I. P., & Stienmetz, J. (2021). Applications and implications of service robots in hospitality. Cornell Hospitality Quarterly, 62(2), 232-247.
Wan, L.C., Chan, E.K., & Luo, X. (2020). Robot Come to Rescue: how to reduce perceived risk of infectious disease in Covid19-stricken consumers? Annal of Tourism Research, Elsevier, 88(C). Available from https://DOI: 10.1016/j.annals.2020.103069
Wirtz, J., Kunz, W., & Paluch, S. (2021). The service revolution, intelligent automation and service robots. The European Business Review, January – February, 38-44.
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