“Artificial Intelligence-Enabled Restaurant Services and Customer Experience: A Conceptual Framework for Customer Satisfaction and Loyalty”

AI is of increasing significance in the food service industry, reshaping the way restaurant operators serve customers, enhancing customer experience, and managing operations. A lot of restaurants are increasing their use of AI-enabled technologies, such as intelligent ordering systems, chatbots, personalized food recommendations, self-service technologies, service robots, and AI-powered mobile applications. While these technologies can increase operational convenience and efficiency, customer experience is more complicated. Consumers may enjoy speed, convenience, and individuality; privacy, trust, loss of human interaction, and even AI service glitches are also concerns. This concept paper attempts to fill this gap by suggesting a conceptual framework from the Stimulus-Organism-Response (S-O-R) approach for examining the nexus between AI-powered restaurant services and customer experience, which will eventually lead into customer satisfaction and loyalty.

Artificial intelligence, customer experience, restaurants, food service industry

1. Brakus, J. J., Schmitt, B. H., & Zarantonello, L. (2009). Brand experience: What is it? How is it measured? Does it affect loyalty? Journal of Marketing, 73(3), 52–68. https://doi.org/10.1509/jmkg.73.3.052 [Google Scholar] [Crossref]

2. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

3. Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience: An overview of experience components that co-create value with the customer. European Management Journal, 25(5), 395–410. https://doi.org/10.1016/j.emj.2007.08.005 [Google Scholar] [Crossref]

4. Gursoy, D. (2025). Artificial intelligence (AI) technology, its applications and the use of AI powered devices in hospitality service experience creation and delivery. International Journal of Hospitality Management, 129, 104212. https://doi.org/10.1016/j.ijhm.2025.104212 [Google Scholar] [Crossref]

5. Huang, D., Chen, Q., Huang, J., Li, Z., & Kong, S. (2021). Customer-robot interactions: Understanding customer experience with service robots. International Journal of Hospitality Management, 99, 103078. https://doi.org/10.1016/j.ijhm.2021.103078 (ScienceDirect) [Google Scholar] [Crossref]

6. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459 [Google Scholar] [Crossref]

7. Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9 [Google Scholar] [Crossref]

8. Kim, H., So, K. K. F., & Wirtz, J. (2022). Two decades of customer experience research in hospitality and tourism: A bibliometric analysis and thematic content analysis. International Journal of Hospitality Management, 100, 103082. https://doi.org/10.1016/j.ijhm.2021.103082 (ScienceDirect) [Google Scholar] [Crossref]

9. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420 [Google Scholar] [Crossref]

10. Li, M., Sun, X., Hua, M., & Qiu, H. (2025). Artificial intelligence features and their service outcomes: A meta-analysis. Journal of Hospitality Marketing & Management, 34(1), 46–71. https://doi.org/10.1080/19368623.2024.2391856 (Taylor & Francis Online) [Google Scholar] [Crossref]

11. Li, M., Sun, X., Hua, M., & Qiu, H. (2022). Proactivity or passivity? An investigation of the effect of service robots' proactive behaviour on customer co-creation intention. International Journal of Hospitality Management, 106, 103271. https://doi.org/10.1016/j.ijhm.2022.103271 [Google Scholar] [Crossref]

12. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press. [Google Scholar] [Crossref]

13. Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), 1210. https://doi.org/10.3390/su11041210 [Google Scholar] [Crossref]

14. Seo, K. H., & Lee, J. H. (2021). The emergence of service robots at restaurants: Integrating trust, perceived risk, and satisfaction. Sustainability, 13(8), 4431. https://doi.org/10.3390/su13084431 (IDEAS/RePEc) [Google Scholar] [Crossref]

15. Shorbaji, M. F., Alalwan, A. A., & Algharabat, R. (2025). AI-enabled mobile food-ordering apps and customer experience: A systematic review and future research agenda. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 156. https://doi.org/10.3390/jtaer20030156 (MDPI) [Google Scholar] [Crossref]

16. Song, H., Wang, Y.-C., Yang, H., & Ma, E. (2022). Robotic employees vs. human employees: Customers' perceived authenticity at casual dining restaurants. International Journal of Hospitality Management, 106, 103301. https://doi.org/10.1016/j.ijhm.2022.103301 [Google Scholar] [Crossref]

17. 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. https://doi.org/10.1108/JOSM-04-2018-0119 [Google Scholar] [Crossref]

18. Zaitouni, M., & Murphy, K. S. (2025). Self-service technologies (SST) in the U.S. restaurant industry: An evaluation of consumer perceived value, satisfaction, and continuance intentions. Journal of Foodservice Business Research, 28(2), 245–276. https://doi.org/10.1080/15378020.2023.2229582 (Taylor & Francis Online) [Google Scholar] [Crossref]

19. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50–64. https://doi.org/10.1509/jmkg.64.3.50.18024 [Google Scholar] [Crossref]

20. Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156 [Google Scholar] [Crossref]

21. Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001 [Google Scholar] [Crossref]

22. Klaus, P., & Maklan, S. (2013). Towards a better measure of customer experience. International Journal of Market Research, 55(2), 227–246. https://doi.org/10.2501/IJMR-2013-021 [Google Scholar] [Crossref]

23. Kandampully, J., Zhang, T., & Jaakkola, E. (2018). Customer experience management in hospitality: A literature synthesis, new understanding and research agenda. International Journal of Contemporary Hospitality Management, 30(1), 21–56. https://doi.org/10.1108/IJCHM-10-2015-0549 [Google Scholar] [Crossref]

24. Chathoth, P. K., Ungson, G. R., Harrington, R. J., & Chan, E. S. W. (2016). Co-creation and higher order customer engagement in hospitality and tourism: A critical review. International Journal of Contemporary Hospitality Management, 28(2), 253–273. https://doi.org/10.1108/IJCHM-03-2014-0152 [Google Scholar] [Crossref]

25. Bolton, R. N., Gustafsson, A., McColl-Kennedy, J. R., Sirianni, N. J., & Tse, D. K. (2014). Small details that make big differences: A radical approach to consumption experience as a firm's differentiating strategy. Journal of Service Management, 25(2), 253–274. https://doi.org/10.1108/JOSM-01-2014-0037 [Google Scholar] [Crossref]

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