Tourists' Expectations of Experience and Visit Intention in Smart Hospitality: A Moderated Mediation Model of Service Robots
DOI:
https://doi.org/10.62270/jirms.v6i2.110Keywords:
Service robot enjoyment, cross-cultural hospitality, scenario-based experience, tourist experience expectations, technology-mediated experience, Emerging market TourismAbstract
Purpose —The proposed study examines the effect of technologically empowered service characteristics, i.e., service robot enjoyment, infrastructure quality, and activity diversity in smart hospitality, on Tourist Experience Expectations (TEEE) and Visit Intention (VINT). It fills a gap that is very crucial since it assesses the design triggers in a culturally contextualized manner and also examines how tourists respond to anthropomorphic robots’ characteristics.
Design/Methodology/Approach —The study used a cross-sectional experimental design based on a scenario study of 256 Pakistani tourists who were conversant with technology-mediated services. This moderated mediation model was based on the Stimulus Organism Response (S-O-R) model, and it measured the direct effects of the stimulus as well as the conditional interactions. The measures of constructs were based on culturally pre-validated scales, and analysis was performed using Partial Least Squares Structural Equation Modelling (PLS-SEM), which is suitable to analyse the data in small samples, non-normal data, and complex interaction models.
Findings —The enjoyment of service robot, variety of activities, and infrastructure had significant influence on expectations and behavioural intention through TEEE. There was no direct effect of environmental ambiance, which indicated its reduced salience in the context of robotic hospitality. Perceived anthropomorphism failed to moderate the relationship between enjoyment and expectation, suggesting that culture does not embrace a human-like appearance of robots and that it is not universally desirable.
Originality/Value —The work provides one of the earliest culturally adaptable conceptions of stimulus asymmetry in robot tourism and an argument against anthropomorphic suppositions in the design of hospitality. It builds on the S-O-R paradigm by putting TEEE as a central cognitive-affective processor and exposes the contextual limits to emotional technology acceptance.
Practical Implications —he operators of the Pakistani hospitality sector ought to focus on functionality-first robot design and diversify infrastructure and activity offerings to trigger the development of meaningful expectations. The experiential cues can be combined with behavioural outcomes through scenario-based marketing and segmentation strategies, particularly to younger demographics that are more tech-receptive.
References
Ahmed, S. (2017). Robot serves food at Multan Pizzeria
Ali, F., Kim, W. G., & Ryu, K. (2016). The effect of physical environment on passenger delight and satisfaction: Moderating effect of national identity. Tourism Management, 57, 213-224. DOI: https://doi.org/10.1016/j.tourman.2016.06.004
Alma Çallı, B., Çallı, L., Sarı Çallı, D., & Çallı, F. (2023). The impact of different types of service robots usage in hotels on guests’ intention to stay. Journal of Hospitality and Tourism Technology, 14(1), 53-68. DOI: https://doi.org/10.1108/JHTT-09-2021-0266
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of marketing research, 14(3), 396-402. DOI: https://doi.org/10.1177/002224377701400320
Asif, M., & Fazel, H. (2024). Digital technology in tourism: a bibliometric analysis of transformative trends and emerging research patterns. Journal of Hospitality and Tourism Insights, 7(3), 1615-1635. DOI: https://doi.org/10.1108/JHTI-11-2023-0847
Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the academy of marketing science, 40(1), 8-34. DOI: https://doi.org/10.1007/s11747-011-0278-x
Belanche, D., Casaló, L. V., Schepers, J., & Flavián, C. (2021). Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The Humanness‐Value‐Loyalty model. Psychology & Marketing, 38(12), 2357-2376. DOI: https://doi.org/10.1002/mar.21532
Bendoly, E., Donohue, K., & Schultz, K. L. (2006). Behavior in operations management: Assessing recent findings and revisiting old assumptions. Journal of operations management, 24(6), 737-752. DOI: https://doi.org/10.1016/j.jom.2005.10.001
Bernaki, W., & Marso, S. (2023). Tourist experience in destinations: Rethinking a conceptual framework of destination experience. Journal of Marketing Research and Case Studies, 20(23), 1-19. DOI: https://doi.org/10.5171/2023.340232
Cabrilo, S., Leung, R., Tsai, F.-S., & Dahms, S. (2024). “I am served by a Robot!”: internal antecedents of customer acceptance of robotic hotel-service agents. Journal of Organizational Change Management, 37(7), 1427-1445. DOI: https://doi.org/10.1108/JOCM-08-2023-0315
Chen, S. X., Wu, H.-C., & Cheng, D. (2025). The influences of immersive sensory cues on immersive experience recommendation intentions in a digital exhibition: A cognitive-affective-conative model. Event Management.
Cheng, W., & Hwang, J. (2025). Robots' anthropomorphic designs: Psychological mechanism of consumer acceptance in restaurant contexts of service success, failure, and recovery. International Journal of Hospitality Management, 131, 104322. DOI: https://doi.org/10.1016/j.ijhm.2025.104322
Choi, Y., Oh, M., Choi, M., & Kim, S. (2021). Exploring the influence of culture on tourist experiences with robots in service delivery environment. Current Issues in Tourism, 24(5), 717-733. DOI: https://doi.org/10.1080/13683500.2020.1735318
Cummins, R. A., & Gullone, E. (2000). Why we should not use 5-point Likert scales: The case for subjective quality of life measurement. Proceedings, second international conference on quality of life in cities,
Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational statistics & data analysis, 81, 10-23. DOI: https://doi.org/10.1016/j.csda.2014.07.008
Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS quarterly, 39(2), 297-316. DOI: https://doi.org/10.25300/MISQ/2015/39.2.02
Dusek, G. A., Yurova, Y. V., & Ruppel, C. P. (2015). Using social media and targeted snowball sampling to survey a hard-to-reach population: A case study. International Journal of Doctoral Studies, 10, 279-299. DOI: https://doi.org/10.28945/2296
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: a three-factor theory of anthropomorphism. Psychological review, 114(4), 864. DOI: https://doi.org/10.1037/0033-295X.114.4.864
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. DOI: https://doi.org/10.1108/EBR-11-2018-0203
Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123. DOI: https://doi.org/10.1504/IJMDA.2017.087624
Huang, A., Ozturk, A. B., Zhang, T., de la Mora Velasco, E., & Haney, A. (2024). Unpacking AI for hospitality and tourism services: Exploring the role of perceived enjoyment on future use intentions. International Journal of Hospitality Management, 119, 103693. DOI: https://doi.org/10.1016/j.ijhm.2024.103693
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of management information systems, 11(4), 87-114. DOI: https://doi.org/10.1080/07421222.1995.11518061
Ivanov, S. (2025). Robots. In Encyclopedia of Tourism (pp. 883-884). Springer. DOI: https://doi.org/10.1007/978-3-030-74923-1_712
Khan, K., Gogia, E. H., Shao, Z., Rehman, M. Z., & Ullah, A. (2025). The impact of green HRM practices on green innovative work behaviour: empirical evidence from the hospitality sector of China and Pakistan. BMC psychology, 13(1), 1-17. DOI: https://doi.org/10.1186/s40359-025-02417-5
Khan, S., & Mehmood, S. (2024). Factors affecting innovation resistance of fast-food employees’ usage intention of robots: an integrative perspective. Journal of Hospitality and Tourism Insights, 7(3), 1456-1474. DOI: https://doi.org/10.1108/JHTI-08-2023-0515
Li, Y., Zhou, X., Jiang, X., Fan, F., & Song, B. (2024). How service robots’ human-like appearance impacts consumer trust: a study across diverse cultures and service settings. International Journal of Contemporary Hospitality Management, 36(9), 3151-3167. DOI: https://doi.org/10.1108/IJCHM-06-2023-0845
Liu, B., Moyle, B., Kralj, A., & Thomas, N. (2025). Psychology of the tourist experience: a synthesis of behavioural and cognitive theoretical approaches. Current Issues in Tourism, 1-19. DOI: https://doi.org/10.1080/13683500.2025.2533884
Liu, B. Q., & Goodhue, D. L. (2012). Two Worlds of Trust for Potential E-Commerce Users: Humans as Cognitive Misers. Information Systems Research, 23(4), 1246-1262. DOI: https://doi.org/10.1287/isre.1120.0424
Liu, G. G., Benckendorff, P., & Walters, G. (2025). Human–robot interaction research in hospitality and tourism: trends and future directions. Tourism Review, 80(4), 847-870. DOI: https://doi.org/10.1108/TR-04-2024-0266
MacDorman, K. F. (2024). Does mind perception explain the uncanny valley? A meta-regression analysis and (de) humanization experiment. Computers in Human Behavior: Artificial Humans, 2(1), 100065. DOI: https://doi.org/10.1016/j.chbah.2024.100065
Model, E. T. (2022). 5 The Impacts of AI, Robots, and. Robots and AI: A New Economic Era, 123.
Phillips, W., & Jang, S. (2007). Destination image and visit intention: Examining the moderating role of motivation. Tourism Analysis, 12(4), 319-326. DOI: https://doi.org/10.3727/108354207782212387
Pine, B. J., & Gilmore, J. H. (1998). Welcome to the experience economy.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-891. DOI: https://doi.org/10.3758/BRM.40.3.879
Qian, Y., & Wan, X. (2024). Influence of robot anthropomorphism on consumer attitudes toward restaurants and service providers. International Journal of Hospitality Management, 123, 103939. DOI: https://doi.org/10.1016/j.ijhm.2024.103939
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 & Management, 34(2), 145-174. DOI: https://doi.org/10.1080/19368623.2024.2403640
Roselli, C., Lapomarda, L., & Datteri, E. (2025). How culture modulates anthropomorphism in Human-Robot Interaction: A review. Acta Psychologica, 255, 104871. DOI: https://doi.org/10.1016/j.actpsy.2025.104871
Russell, J. A., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of research in Personality, 11(3), 273-294. DOI: https://doi.org/10.1016/0092-6566(77)90037-X
Said, N., Ben Mansour, K., Bahri-Ammari, N., Yousaf, A., & Mishra, A. (2024). Customer acceptance of humanoid service robots in hotels: moderating effects of service voluntariness and culture. International Journal of Contemporary Hospitality Management, 36(6), 1844-1867. DOI: https://doi.org/10.1108/IJCHM-12-2022-1523
Saputra, F. E., Buhalis, D., Augustyn, M. M., & Marangos, S. (2024). Anthropomorphism-based artificial intelligence (AI) robots typology in hospitality and tourism. Journal of Hospitality and Tourism Technology, 15(5), 790-807. DOI: https://doi.org/10.1108/JHTT-03-2024-0171
Sarstedt, M., Hair Jr, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian marketing journal, 27(3), 197-211. DOI: https://doi.org/10.1016/j.ausmj.2019.05.003
Sarstedt, M., Hair Jr, J. F., & Ringle, C. M. (2023). “PLS-SEM: indeed a silver bullet”–retrospective observations and recent advances. Journal of Marketing theory and Practice, 31(3), 261-275. DOI: https://doi.org/10.1080/10696679.2022.2056488
Scholze, A., Potkonjak, S., Marcel, F., Folinazzo, G., & Townend, N. (2022). Scenarios for learning–scenarios as learning: A design-based research process. In Activating linguistic and cultural diversity in the language classroom (pp. 113-139). Springer. DOI: https://doi.org/10.1007/978-3-030-87124-6_6
Schweitzer, M. E., & Gibson, D. E. (2008). Fairness, feelings, and ethical decision-making: Consequences of violating community standards of fairness. Journal of Business Ethics, 77, 287-301. DOI: https://doi.org/10.1007/s10551-007-9350-3
Sheng, C. W., & Chen, M. C. (2013). Tourist experience expectations: questionnaire development and text narrative analysis. International Journal of Culture, Tourism and Hospitality Research, 7(1), 93-104. DOI: https://doi.org/10.1108/17506181311301390
Shum, C., Kim, H. J., Calhoun, J. R., & Putra, E. D. (2024). “I was so scared I quit”: Uncanny valley effects of robots’ human-likeness on employee fear and industry turnover intentions. International Journal of Hospitality Management, 120, 103762. DOI: https://doi.org/10.1016/j.ijhm.2024.103762
Song, X., Li, Y., Leung, X. Y., & Mei, D. (2024). Service robots and hotel guests’ perceptions: anthropomorphism and stereotypes. Tourism Review, 79(2), 505-522. DOI: https://doi.org/10.1108/TR-04-2023-0265
Sun, S., Ye, H., & Law, R. (2025a). Cognitive–analytical and emotional–social tasks achievement of service robots through human–robot interaction. International Journal of Contemporary Hospitality Management, 37(1), 180-196. DOI: https://doi.org/10.1108/IJCHM-12-2023-1880
Sun, S., Ye, H., & Law, R. (2025b). A scoping review of robotic technology in hospitality and tourism. Journal of Hospitality and Tourism Technology, 16(2), 286-304. DOI: https://doi.org/10.1108/JHTT-08-2023-0247
Taecharungroj, V. (2022). Experiential brand positioning: Developing positioning strategies for beach destinations using online reviews. Journal of Vacation Marketing, 13567667221095588. DOI: https://doi.org/10.1177/13567667221095588
Tussyadiah, I. P., Zach, F. J., & Wang, J. (2020a). Do travelers trust intelligent service robots? Annals of Tourism Research, 81(C).
Tussyadiah, I. P., Zach, F. J., & Wang, J. (2020b). Do travelers trust intelligent service robots? Annals of Tourism Research, 81, 102886. DOI: https://doi.org/10.1016/j.annals.2020.102886
Wang, C., Qu, H., & Hsu, M. K. (2016). Toward an integrated model of tourist expectation formation and gender difference. Tourism Management, 54, 58-71. DOI: https://doi.org/10.1016/j.tourman.2015.10.009
Wu, M., Tan, G. W.-H., Aw, E. C.-X., & Ooi, K.-B. (2023). Unlocking my heart: Fostering hotel brand love with service robots. Journal of Hospitality and Tourism Management, 57, 339-348. DOI: https://doi.org/10.1016/j.jhtm.2023.10.014
Yang, S., Isa, S. M., Yao, Y., Xia, J., & Liu, D. (2022). Cognitive image, affective image, cultural dimensions, and conative image: A new conceptual framework. Frontiers in Psychology, 13, 935814. DOI: https://doi.org/10.3389/fpsyg.2022.935814
Yörük, T., Akar, N., & Özmen, N. V. (2024). Research trends on guest experience with service robots in the hospitality industry: a bibliometric analysis. European Journal of Innovation Management, 27(6), 2015-2041. DOI: https://doi.org/10.1108/EJIM-09-2022-0530
Zha, D., Marvi, R., & Foroudi, P. (2024). Embracing the paradox of customer experiences in the hospitality and tourism industry. International Journal of Management Reviews, 26(2), 163-186. DOI: https://doi.org/10.1111/ijmr.12343
Zhang, J., Li, S., Zhang, J.-Y., Du, F., Qi, Y., & Liu, X. (2020). A literature review of the research on the uncanny valley. International conference on human-computer interaction, DOI: https://doi.org/10.1007/978-3-030-49788-0_19
Zhang, L., Wei, W., Yu, H., Sharma, A., & Olson, E. D. (2023). Transformation of consumer expectations for well-being in hospitality: A systems framework. Journal of Hospitality & Tourism Research, 47(4), NP4-NP17. DOI: https://doi.org/10.1177/10963480221141601
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