ROBOTS AT THE TABLE: INTERACTION QUALITY AND TECHNOLOGY ACCEPTANCE SHAPE CUSTOMER REVISIT IN RESTAURANT
DOI:
https://doi.org/10.55197/qjssh.v7si5.1742Keywords:
interaction quality, perceived ease of use, perceived usefulness, revisit intention, restaurant service robotsAbstract
Restaurant service robots are increasingly deployed in food service establishments to perform table service, deliver food, and handle routine customer interactions, yet their ability to generate repeat patronage remains uncertain. This study examines how interaction quality and core technology acceptance factors, namely, perceived ease of use and perceived usefulness, directly influence customer intentions to revisit robot-assisted restaurants. Data were collected between October 2024 and January 2025 across 18 participating full-service and casual dining restaurants in Malaysia. A purposive online survey produced 251 usable responses from verified adult patrons who had experienced restaurant service robots during the preceding six months. Data were analysed using partial least squares structural equation modeling (PLS-SEM via SmartPLS 4). The structural results reveal that perceived usefulness (β = 0.248, p < 0.001) and perceived ease of use (β = 0.150, p < 0.05) directly drive customer revisit intention, explaining 11.5% of the total variance (R2 = 0.115). Conversely, interaction quality exerts no significant direct effect on revisit intention (β = 0.003, p > 0.05). These findings demonstrate that a pleasant robot encounter does not, by itself, secure customer return unless translated into tangible utility and effortlessness. Restaurant operators should prioritize seamless functional execution, clear interface usability, and visible operational benefits such as reduced waiting times.
References
[1] Ashfaq, M., Haq, I.U., Pham, H.C., Jan, I.U., Shahzad, K. (2025): Critical determinants of intentions to revisit smart hotels: An examination of smart service interactional experience, emotions, and technophilia. – Journal of Hospitality Marketing and Management 34(5): 627-650.
[2] Belanche, D., Casaló, L.V., Flavián, C. (2021): Frontline robots in tourism and hospitality: Service enhancement or cost reduction? – Electronic Markets 31(3): 477-492.
[3] Chen, C.-F., Girish, V.G. (2023): Investigating the use experience of restaurant service robots: The cognitive-affective-behavioral framework. – International Journal of Hospitality Management 111: 12p.
[4] Choi, B.J., Kim, H.S. (2013): The impact of outcome quality, interaction quality, and peer-to-peer quality on customer satisfaction with a hospital service. – Managing Service Quality 23(3): 188-204.
[5] Davis, F.D. (1989): Perceived usefulness, perceived ease of use, and user acceptance of information technology. – MIS Quarterly 13(3): 319-340.
[6] Figueiredo, I.G., Moutinho, P.F., Duarte, P.O., Macedo, A.C. (2026): Customers’ intentions to use restaurants with robot waiters: An extension of the Technology Acceptance Model. – Journal of the Knowledge Economy 17(2): 3687-3713.
[7] Fornell, C., Larcker, D.F. (1981): Evaluating structural equation models with unobservable variables and measurement error. – Journal of Marketing Research 18(1): 39-50.
[8] Guan, X., Gong, J., Li, M., Huan, T.-C. (2022): Exploring key factors influencing customer behavioral intention in robot restaurants. – International Journal of Contemporary Hospitality Management 34(9): 3482-3501.
[9] 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.
[10] Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M. (2019): When to use and how to report the results of PLS-SEM. – European Business Review 31(1): 2-24.
[11] Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. (2021): A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). – SAGE Publications, Thousand Oaks, California 384p.
[12] Henseler, J., Ringle, C.M., Sarstedt, M. (2015): A new criterion for assessing discriminant validity in variance-based structural equation modeling. – Journal of the Academy of Marketing Science 43(1): 115-135.
[13] Huang, D., Chen, Q., Huang, J., Kong, S., Li, Z. (2021): Customer-robot interactions: Understanding customer experience with service robots. – International Journal of Hospitality Management 99: 14p.
[14] Huang, D., Chen, Q., Huang, S., Liu, X. (2024): Consumer intention to use service robots: A cognitive-affective-conative framework. – International Journal of Contemporary Hospitality Management 36(6): 1893-1913.
[15] Kao, W.K., Huang, Y.S. (2023): Service robots in full- and limited-service restaurants: Extending the Technology Acceptance Model. – Journal of Hospitality and Tourism Management 54: 10-21.
[16] Kim, H., So, K.K.F., Wirtz, J. (2022): Service robots: Applying social exchange theory to better understand human-robot interactions. – Tourism Management 92: 12p.
[17] Kock, N. (2015): Common method bias in PLS-SEM: A full collinearity assessment approach. – International Journal of e-Collaboration 11(4): 1-10.
[18] Kock, N., Lynn, G.S. (2012): Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. – Journal of the Association for Information Systems 13(7): 546-580.
[19] Molinillo, S., Rejón-Guardia, F., Anaya-Sánchez, R. (2023): Exploring the antecedents of customers’ willingness to use service robots in restaurants. – Service Business 17(1): 167-193.
[20] Rana, N.P., Begum, N., Faisal, M.N., Mishra, A. (2025): Customer experiences with service robots in hotels: A review and research agenda. – Journal of Hospitality Marketing and Management 34(2): 145-174.
[21] Ringle, C.M., Wende, S., Becker, J.-M. (2024): SmartPLS 4 (Version 4.1.1.1) [Computer software]. – SmartPLS GmbH, Oststeinbek, Germany 100p.
[22] Santiago, J., Borges-Tiago, M.T., Tiago, F. (2024): Embracing RAISA in restaurants: Exploring customer attitudes toward robot adoption. – Technological Forecasting and Social Change 199: 12p.
[23] Seo, K.H., Lee, J.H. (2021): The emergence of service robots at restaurants: Integrating trust, perceived risk, and satisfaction. – Sustainability 13(8): 15p.
[24] Song, B., Zhang, M., Wu, P. (2022): Driven by technology or sociality? Use intention of service robots in hospitality from the human-robot interaction perspective. – International Journal of Hospitality Management 106: 12p.
[25] Um, S., Chon, K., Ro, Y. (2006): Antecedents of revisit intention. – Annals of Tourism Research 33(4): 1141-1158.
[26] Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., Martins, A. (2018): Brave new world: Service robots in the frontline. – Journal of Service Management 29(5): 907-931.
[27] Yang, H., Song, H., Xia, L., Yang, A. (2024): Understanding the consequences of robotic interaction quality and outcome quality: A three-phased affordance theory-based approach. – Journal of Hospitality Marketing and Management 33(6): 763-783.
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Copyright (c) 2026 MUHAMMAD AMIRUDDIN ALIAS, DAHLAN ABDULLAH, NI MADE EKA MAHADEWI, ER UMMI KALSUM, HANAFI HAMZAH, I PUTU HARDANI HESTI DUARI
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