Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
With the rapid rise in labour costs in China’s hotel industry, service robots have emerged as a potential solution to enhance service efficiency and reduce operational expenses. However, their adoption rate in Chinese hotels remains low. While prior studies have primarily explored technical performance and costs from a managerial perspective, there is a lack of systematic methodologies examining adoption barriers from the lens of guests’ negative emotions. This study employs web-crawling technology to collect 20,900 low-rated reviews from six major Chinese online travel platforms. Using Latent Dirichlet Allocation (LDA) topic modelling combined with computational grounded theory, the authors identified ten key barriers to the adoption of service robots in hotels. Notably, this study introduces “Cultural Misfit”, “Frequent Malfunctions”, and “Inconvenient Operation” as distinct barriers. It also reveals a cascading effect involving service quality, functional utility, and expectation alignment, highlighting that multidimensional interactions drive technology acceptance. These findings provide theoretical and practical insights for optimising service robot deployment, offering new perspectives to improve service efficiency and user acceptance in China’s hotel industry.
Rocznik
Tom
Strony
1--26
Opis fizyczny
Bibliogr. 91 poz., tab., wykr.
Twórcy
autor
- School of Business, Institute of Vocational Technology, SIP No. 1 Ruoshui Road, Suzhou Industrial Park, Suzhou city, Jiangsu province, China, 215123
autor
- Hezhou University, No. 3261, Xiaohe Avenue, Babu District, Hezhou city, Guangxi province, China, 542899
Bibliografia
- Bandura, A. (1986). Social Foundations of Thought and Ac tion: A Social-Cognitive View. The Academy of Man agement Review, 12(1).
- Barnes, S. B. (2006). A privacy paradox: Social network ing in the United States. First Monday, 11(9). doi: 10.5210/fm.v11i9.1394
- Belanche, D., Casaló, L. V., & Flavián, C. (2020). Frontline robots in tourism and hospitality: Service enhance ment or cost reduction? Electronic Markets, 31(3), 477-492. doi: 10.1007/s12525-020-00432-5
- BenMessaoud, C., Kharrazi, H., & MacDorman, K. F. (2011). Facilitators and barriers to adopting robotic-assisted surgery: Contextualizing the Uni f ied Theory of Acceptance and Use of Technol ogy. PLoS ONE, 6(1), e16395. doi: 10.1371/journal. pone.0016395
- Blei, D. M. (2012). Probabilistic topic models. Com munications of the ACM, 55(4), 77-84. doi: 10.1145/2133806.2133826
- Bowen, J., & Morosan, C. (2018). Beware hospitality indus try: The robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726-733. doi: 10.1108/ WHATT-07-2018-0045
- Brandstötter, M., Komenda, T., Breitenhuber, G., Rathmair, M., Steiner, M., Laflamme, C., Müller, A., & Hofbaur, M. (2022). A method to enhance the flexibility of col laborative human-robot workspaces through an ex tended safety perspective. Procedia CIRP, 112, 197 202. doi: 10.1016/j.procir.2022.09.072
- Buerkle, A., Eaton, W., Al-Yacoub, A., Zimmer, M., Kinnell, P., Henshaw, M., Coombes, M., Chen, W. H., & Lohse, N. (2023). Towards industrial robots as a service (IRaaS): Flexibility, usability, safety and busi ness models. Robotics and Computer-Integrated Manu facturing, 81, 102484. doi: 10.1016/j.rcim.2022.102484
- Çalişkan, G., & Sevim, B. (2023). Use of service robots in hospitality: An observational study in terms of technology acceptance model. Tour ism and Hospitality Research, 25(2), 167-179. doi: 10.1177/14673584231198438
- Cao, K., Yin, H., & Wang, J. (2025). Introducing robot or not? Decisions of competing hotels. International Journal of Hospitality Management, 126, 104034. doi: 10.1016/j.ijhm.2024.104034
- Cao, X., Xu, Y., Yao, Y., & Zhi, C. (2023). An improved hy brid A* algorithm of path planning for hotel service robot. International Journal of Advanced Computer Science and Applications, 14(10). doi: 10.14569/IJAC SA.2023.0141091
- Ceccarelli, M. (2011). Problems and issues for service ro bots in new applications. International Journal of Social Robotics, 3(3), 299-312. doi: 10.1007/s12369 011-0097-8
- Ceglowski, J., & Golub, S. S. (2012). Does China still have a labor cost advantage? Global Economy Journal, 12(3), 1850270. doi: 10.1515/1524-5861.1874
- Chang, C., Shao, B., Li, Y., & Zhang, Y. (2022). Factors influencing consumers’ willingness to accept ser vice robots: Based on online reviews of Chinese hotels. Frontiers in Psychology, 13. doi: 10.3389/ fpsyg.2022.1016579
- Chen, J., Zhang, Y., & Wang, L. (2025). The impact of service robot communication style on consumers’ continued willingness to use. Collabra: Psychology, 11(1). doi: 10.1525/collabra.128020
- Chiang, A.-H., & Trimi, S. (2020). Impacts of service ro bots on service quality. Service Business, 14(4). doi: 10.1007/s11628-020-00423-8
- Choi, Y., Choi, M., Oh, M., & Kim, S. (2019). Service robots in hotels: Understanding the service quality percep tions of human-robot interaction. Journal of Hospi tality Marketing & Management, 29(6), 1-23. doi: 10.1080/19368623.2020.1703871
- Choi, Y., Oh, M., Choi, M., & Kim, S. (2020). Explor ing the influence of culture on tourist experi ences with robots in service delivery environ ment. Current Issues in Tourism, 24(5), 1-17. doi: 10.1080/13683500.2020.1735318
- Chung, M. J., & Cakmak, M. (2018,). “How was your stay?”: Exploring the use of robots for gathering customer feedback in the hospitality industry. In 2018 27th IEEE International Symposium on Robot and Hu man Interactive Communication (RO-MAN) (pp. 947-954). Nanjing, China: IEE. doi: 10.1109/RO MAN.2018.8525604
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. doi: 10.1177/001316446002000104
- Collins, G. R. (2020). Improving human-robot interactions in hospitality settings. International Hospitality Re view, 34(1), 61-79. doi: 10.1108/IHR-09-2019-0019
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information tech nology. Journal of Risk and Uncertainty, 18(3), 321 325. doi: 10.1023/A:1011156710779
- Ding, P., Chong, K. M., Tan, T. H., Zhang, W., Gao, J., & Zhang, Q. (2023). Investigating behavioural in tention toward adopting artificial intelligence service robots technology in hospitality in China. Environ ment-Behaviour Proceedings Journal, 8(26), 389 397. doi: 10.21834/e-bpj.v8i26.4990
- Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1). doi: 10.1186/s40537-015-0015-2
- Ganesh, S. R. (1980). Institution building for so cial and organizational change: An apprecia tion. Organization Studies, 1(3), 209-227. doi: 10.1177/017084068000100301
- Garcia, S., Strüber, D., Brugali, D., Di Fava, A., Pelliccione, P., & Berger, T. (2022). Software variability in service robotics. Empirical Software Engineering, 28(2). doi: 10.1007/s10664-022-10231-5
- García, S., Strüber, D., Brugali, D., Di Fava, A., Pellic cione, P., & Berger, T. (2023). Software variabil ity in service robots (Summary). Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume A, 268-268. doi: 10.1145/3579027.3608999
- Goel, P., Kaushik, N., Sivathanu, B., Pillai, R., & Vikas, J. (2022). Consumers’ adoption of artificial intelli gence and robotics in hospitality and tourism sector: Literature review and future research agenda. Tour ism Review, 77(4), 1081-1096. doi: 10.1108/TR-03 2021-0138
- Guo, L., Gong, L., Xu, Z., Wang, W., & Chen, M.-H. (2024). T he role of service robots in enhancing customer satisfaction in embarrassing contexts. Journal of Hos pitality and Tourism Management, 59, 116-126. doi: 10.1016/j.jhtm.2024.04.008
- Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfac tion analysis using latent Dirichlet allocation. Tour ism Management, 59, 467-483. doi: 10.1016/j.tour man.2016.09.009
- Gupta, S., Mishra, R. S., Singal, G., Badal, T., & Garg, D. (2020). Corridor segmentation for automatic ro bot navigation in indoor environment using edge devices. Computer Networks, 178, 107374. doi: 10.1016/j.comnet.2020.107374
- Gutsche, K., Genovese, J., Serstjuk, P., & Altindis, S. (2025). User-centered design of professional social service robots. AHFE International, 160. doi: 10.54941/ ahfe1005806
- Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292-1307. doi: 10.1016/j.ipm.2018.05.006
- Han, H., Kim, S. I., Lee, J.-S., & Jung, I. (2024). Understand ing the drivers of consumers’ acceptance and use of service robots in the hotel industry. International Journal of Contemporary Hospitality Management, 37(2), 541-559. doi: 10.1108/IJCHM-02-2024-0163
- Heuer, T., Schiering, I., & Gerndt, R. (2019). Privacy-cen tered design for social robots. Interaction Studies, 20(3), 509-529. doi: 10.1075/is.18063.heu
- Hong, H., Jung, H., Park, K., & Ha, S. (2018). SeMo: Ser vice-oriented and model-based software frame work for cooperating robots. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11), 2952-2963. doi: 10.1109/ TCAD.2018.2857339
- Huang, D., Chen, Q., Huang, J., Kong, S., & Li, Z. (2021). Customer-robot interactions: Understanding cus tomer experience with service robot. International Journal of Hospitality Management, 99, 103078. doi: 10.1016/j.ijhm.2021.103078
- Ivanov, S., Seyitoğlu, F., & Markova, M. (2020). Hotel managers’ perceptions towards the use of robots: A mixed-methods approach. Information Technology & Tourism, 22(4), 505-535. doi: 10.1007/s40558-020 00187-x
- Jeong, H., Lee, H., Kim, C., & Shin, S. (2024). A survey of ro bot intelligence with large language models. Applied Sciences, 14(19), 8868. doi: 10.3390/app14198868
- Jia, S. J., Chi, O. H., & Lu, L. (2024). Social robot privacy concern (SRPC): Rethinking privacy concerns with in the hospitality domain. International Journal of Hospitality Management, 122, 103853. doi: 10.1016/ j.ijhm.2024.103853
- Kapur, P., & Williams, J. D. (2025). Balancing efficiency and human touch: The role of AI and robotics in hospitality. Artificial Intelligence, Machine Learning, & Robotics in Business, 1(1), 49-51. doi: 10.32473/ aimlrb.1.1.138286
- Kim, S., Kim, J., Badu-Baiden, F., Giroux, M., & Choi, Y. (2021). Preference for robot service or human ser vice in hotels? Impacts of the COVID-19 pandem ic. International Journal of Hospitality Management, 93, 102795. doi: 10.1016/j.ijhm.2020.102795
- Lajante, M., & Dohm, N. C. (2024). Customer’s social cognition in service recovery satisfaction with hu man vs robot agent. International Journal of Quality and Service Sciences, 16(4), 498-518. doi: 10.1108/ IJQSS-07-2024-0098
- Lee, H., Ma, H., & Xiao, G. (2023). Ready for robot as sistance? Exploring gender influences on service robot adoption in luxury vs. economy hotels. Jour nal of Marketing Development and Competitiveness, 17(4). doi: 10.33423/jmdc.v17i4.6663
- Lei, C., Hossain, M. S., & Wong, E. (2023). Determinants of repurchase intentions of hospitality services deliv ered by artificially intelligent (AI) service robots. Sus tainability, 15(6), 4914. doi: 10.3390/su15064914
- Lestari, N. S., Rosman, D., Chan, S., Nawangsari, L. C., Na talina, H. D., & Triono, F. (2022). Impact of robots, artificial intelligence, service automation (RAISA) acceptance, self-efficacy, and relationship qual ity on job performance. In 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1-6). Medan, Indonesia: IEEE. doi: 10.1109/ICORIS56080.2022.10031336
- Leung, X. Y., Zhang, H., Lyu, J., & Bai, B. (2023). Why do hotel frontline employees use service robots in the workplace? A technology affordance theory perspec tive. International Journal of Hospitality Manage ment, 108, 103380. doi: 10.1016/j.ijhm.2022.103380
- Liu, W., Ni, S., & Tuo, Y. (2019). Usability testing and re quirements analysis of service robot. In 2019 11th International Conference on Intelligent Human Machine Systems and Cybernetics (IHMSC) (pp. 225-228). Hangzhou, China. doi: 10.1109/IHM SC.2019.00059
- Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734. doi: 10.5465/ amr.1995.9508080335
- McCartney, G., & McCartney, A. (2020). Rise of the ma chines: Towards a conceptual service-robot research framework for the hospitality and tourism indus try. International Journal of Contemporary Hospital ity Management, 32(12), 3835-3851. doi: 10.1108/ IJCHM-05-2020-0451
- Milohnić, I., & Kapeš, J. (2024). Exploring the barriers and prospects for service robot adoption in the hotel in dustry: A management perspective. European Jour nal of Tourism Research, 38, 3805. doi: 10.54055/ejtr. v38i.3387
- Nikolenko, S. I., Koltcov, S., & Koltsova, O. (2016). Topic modelling for qualitative studies. Jour nal of Information Science, 43(1), 88-102. doi: 10.1177/0165551515617393
- Qi, H., Han, Z., & Feng, X. (2024). Research on hotel ser vice robot management system based on artificial intelligence. In 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS) (pp. 265-269). Bristol, United Kingdom: IEEE. doi: 10.1109/AIARS63200.2024.00055
- Ranieri, C. M., & Romero, R. A. F. (2016). An emotion based interaction strategy to improve human-robot interaction. In 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR) (pp. 31-36). Recife, Brazil: IEEE. doi: 10.1109/LARS-SBR.2016.13
- Rasheed, H. M. W., He, Y., Khizar, H. M. U., & Abbas, H. S. M. (2023). Exploring consumer-robot inter action in the hospitality sector: Unpacking the rea sons for adoption (or resistance) to artificial intelli gence. Technological Forecasting and Social Change, 192, 122555. doi: 10.1016/j.techfore.2023.122555
- Rasmussen, M. K., Schneider-Kamp, A., Hyrup, T., & Go dono, A. (2024). New colleague or gimmick hurdle? A user-centric scoping review of the barriers and fa cilitators of robots in hospitals. PLOS Digital Health, 3(11), e0000660. doi: 10.1371/journal.pdig.0000660
- Ren, Q., Hou, Y., Botteldooren, D., & Belpaeme, T. (2024). No more mumbles: Enhancing robot intelligibil ity through speech adaptation. IEEE Robotics and Automation Letters, 9(7), 6162-6169. doi: 10.1109/ LRA.2024.3401117
- Richard, L. J., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. doi: 10.2307/2529310
- Romero-Gonzalez, C., Martinez-Gomez, J., & Garcia-Va rea, I. (2020). Spoken language understanding for social robotics. In 2020 IEEE International Confer ence on Autonomous Robot Systems and Competitions (ICARSC) (pp. 152-157). Ponta Delgada, Portugal: IEEE. doi: 10.1109/ICARSC49921.2020.9096175
- Rosete, A., Soares, B., Salvadorinho, J., Reis, J., & Amorim, M. (2020). Service robots in the hospitality indus try: An exploratory literature review. Exploring Ser vice Science, 377, 174-186. doi: 10.1007/978-3-030 38724-2_13
- Sadangharn, P. (2022). Acceptance of robots as co-workers: Hotel employees’ perspective. International Jour nal of Engineering Business Management, 14. doi: 10.1177/18479790221113621
- Saeki, W., & Ueda, Y. (2024). Sequential model based on human cognitive processing to robot accep tance. Frontiers in Robotics and AI, 11. doi: 10.3389/ frobt.2024.1362044
- Said, N., Ben Mansour, K., Bahri-Ammari, N., Yousaf, A., & Mishra, A. (2024). Customer acceptance of hu manoid service robots in hotels: moderating effects of service voluntariness and culture. International Jour nal of Contemporary Hospitality Management, 36(6), 1844-1867. doi: 10.1108/IJCHM-12-2022-1523
- Schmiedel, T., Müller, O., & vom Brocke, J. (2018). Topic modeling as a strategy of inquiry in organizational re search. Organizational Research Methods, 22(4). doi: 10.1177/1094428118773858
- Senthamarai, M., Kishore, R. K., Vikram, U. & Kathir, R. (2024). The role and progression of serving robots in hospitality and service industries. International Journal for Multidisciplinary Research, 6(5). doi: 10.36948/ijfmr.2024.v06i05.27961
- Stavropoulou, P., Spiliotopoulos, D., & Κουρουπέτρογλου, Γ. (2020). Voice user interfaces for service robots: Design principles and methodology. Lecture Notes in Computer Science, 489-505. doi: 10.1007/978-3-030 49282-3_35
- Stirpe, M., Brugnoli, B., Donelli, G., Francolini, I., & Vuot to, C. (2020). Poloxamer 338 affects cell adhesion and biofilm formation in Escherichia coli: Potential ap plications in the management of catheter-associated urinary tract infections. Pathogens, 9(11), 885. doi: 10.3390/pathogens9110885
- Taecharungroj, V., & Mathayomchan, B. (2019). Analysing TripAdvisor reviews of tourist attractions in Phuket, T hailand. Tourism Management, 75, 550-568. doi: 10.1016/j.tourman.2019.06.020
- Tractinsky, N., Katz, A. S., & Ikar, D. (2000). What is beauti ful is usable. Interacting with Computers, 13(2), 127 145. doi: 10.1016/S0953-5438(00)00031-X
- Tsushima, Y., Yamamoto, S., Ravankar, A. A., Luces, J. V. S., & Hirata, Y. (2025). Task planning for a fac tory robot using large language model. IEEE Robot ics and Automation Letters, 10(3), 2383-2390. doi: 10.1109/LRA.2025.3531153
- Tung, V. W. S., & Au, N. (2018). Exploring customer experi ences with robotics in hospitality. International Jour nal of Contemporary Hospitality Management, 30(7), 2680-2697. doi: 10.1108/IJCHM-06-2017-0322
- Tuomi, A., Tussyadiah, I. P., & Stienmetz, J. (2020). Ap plications and implications of service robots in hospitality. Cornell Hospitality Quarterly, 62(2). doi: 10.1177/1938965520923961
- Tussyadiah, I. P., & Park, S. (2018). Consumer evaluation of hotel service robots. Information and Communi cation Technologies in Tourism 2018, 308-320. doi: 10.1007/978-3-319-72923-7_24
- Vallverdú, J., & Trovato, G. (2016). Emotional affordances for human-robot interaction. Adaptive Behavior, 24(5), 320-334. doi: 10.1177/1059712316668238
- Wang, R., Hao, J.-X., Law, R., & Wang, J. (2019). Examin ing destination images from travel blogs: A big data analytical approach using latent Dirichlet alloca tion. Asia Pacific Journal of Tourism Research, 24(11), 1092-1107. doi: 10.1080/10941665.2019.1665558
- Wang, X., Zhang, Z., Huang, D., & Li, Z. (2023). Consumer resistance to service robots at the hotel front desk: A mixed-methods research. Tourism Manage ment Perspectives, 46, 101074. doi: 10.1016/j. tmp.2023.101074
- Wang, Z., Huang, J., Xiong, N., Zhou, X., Lin, X., & Ward, T. L. (2020). A robust vehicle detection scheme for intelligent traffic surveillance systems in smart cit ies. IEEE Access, 8, 139299-139312. doi: 10.1109/AC CESS.2020.3012995
- Wu, X., & Huo, Y. (2023). Impact of the introduction of service robots on consumer satisfaction: Empiri cal evidence from hotels. Technological Forecasting and Social Change, 194, 122718. doi: 10.1016/j.tech fore.2023.122718
- Xie, M., & Kim, H. (2022). User acceptance of hotel service robots using the quantitative Kano model. Sustain ability, 14(7), 3988. doi: 10.3390/su14073988
- Xu, J., Hsiao, A., Reid, S., & Ma, E. (2023). Working with service robots? A systematic literature review of hospitality employees’ perspectives. International Journal of Hospitality Management, 113, 103523. doi: 10.1016/j.ijhm.2023.103523
- Yang, J., & Chew, E. (2020). A systematic review for service humanoid robotics model in hospitality. Interna tional Journal of Social Robotics. International Journal of Social Robotics, 13(6), 1397-1410. doi: 10.1007/ s12369-020-00724-y
- Ye, H., Sun, S., & Law, R. (2022). A review of robotic appli cations in hospitality and tourism research. Sustain ability, 14(17), 10827. doi: 10.3390/su141710827
- Ying, S., Chan, J. H., & Qi, X. (2020). Why are Chinese and North American guests satisfied or dissatis f ied with hotels? An application of big data analy sis. International Journal of Contemporary Hospital ity Management, 32(10), 3249-3269. doi: 10.1108/ IJCHM-02-2020-0129
- Yörük, T., Akar, N., & Özmen, N. V. (2023). Research trends on guest experience with service robots in the hos pitality industry: A bibliometric analysis. European Journal of Innovation Management, 27(6), 2015 2041. doi: 10.1108/EJIM-09-2022-0530
- Zahedifar, R., Baghshah, M. S., & Taheri, A. (2025). LLM controller: Dynamic robot control adaptation using large language models. Robotics and Autonomous Sys tems, 186, 104913. doi: 10.1016/j.robot.2024.104913
- Zhang, Y., Ran, X., Luo, C., Gao, Y., Zhao, Y., & Shuai, Q. (2022). “Only visible for three days”: Mining microblogs to understand reasons for using the Time Limit setting on WeChat Moments. Comput ers in Human Behavior, 134, 107316. doi: 10.1016/j. chb.2022.107316
- Zhang, Y., Wang, X., Wu, X., Zhang, W., Jiang, M., & Al Khassaweneh, M. (2019). Intelligent hotel ROS based service robot. In 2019 IEEE International Conference on Electro Information Technology (EIT) (pp. 399-403). Brookings, USA: IEEE. doi: 10.1109/ EIT.2019.8834040
- Zheng, T., Wu, F., Law, R., Qiu, Q., & Wu, R. (2021). Identify ing unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting ap proach. International Journal of Hospitality Manage ment, 92, 102658. doi: 10.1016/j.ijhm.2020.102658
- Zhong, L., Verma, R., Wei, W., Morrison, A. M., & Yang, L. (2022). Multi-stakeholder perspectives on the im pacts of service robots in urban hotel rooms. Tech nology in Society, 68, 101846. doi: 10.1016/j.tech soc.2021.101846
- Zia, A., & Alotaibi, A. (2024). Navigating the Customer Experience Landscape: Unraveling the Dynamics of AI-driven Chatbot Services for FMCG Retailers. Journal of Comprehensive Business Administra tion Research, 1(3), 113-123. doi: 10.47852/bon viewJCBAR42023726
- Zuo, S. (2023). How can hospitality industry improve customer satisfaction by determining the relevant degree of robot staff implementation? Journal of Research in Social Science and Humanities, 2(4), 49 68. doi: 10.56397/JRSSH.2023.04.06
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d5e4a8dc-e3d0-43f4-838c-fba9b06f6289
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.