FROM ONLINE REVIEWS TO SERVICE GOVERNANCE: A REVIEW OF SERVICE QUALITY RESEARCH IN HOSPITALITY AND TOURISM
DOI:
https://doi.org/10.35631/JTHEM.1144017Keywords:
Hospitality And Tourism, Online Reviews, Review Analytics, Service Governance, Service Quality, Service RecoveryAbstract
Standard bibliometric summaries no longer capture what online review research in hospitality and tourism is actually doing — the corpus has grown too large and internally varied for that. Applied to a Scopus corpus of 243 articles, bibliometric mapping and latent Dirichlet allocation (K = 8) recover four functional domains: managerial response, experience evaluation, computational analytics, and contextual extension. These domains do not carry equal influence. T6 (Service failure and recovery) leads the taxonomy with 13.61% prevalence and a citation impact of 40.93; T4 (NLP/LLM-enabled review understanding) has expanded in recent visibility but carries the lowest impact score in the set (28.25). The field is not simply becoming more technological—it is becoming more stratified, with a durable managerial core sitting alongside an experimental analytics layer. A literature-derived governance framework links these thematic streams to monitoring signals, response priorities, and evaluation metrics. The framework is not a field-tested operational model, but it makes explicit a logic for service quality governance that the accumulated evidence already supports.
Downloads
References
Ali, U., Arasli, H., Arasli, F., Saydam, M. B., Capkiner, E., Aksoy, E., & Atai, G. (2023). Determinants and impacts of quality attributes on guest perceptions in Norwegian green hotels. Sustainability, 15(6), 5512. https://doi.org/10.3390/su15065512
Alharbi, A., Pandit, A. P., Wilk, V., Rosenberger, P. J., III, & Miah, S. J. (2025). Artificial intelligence-enabled conversational agents in tourism and hospitality: A systematic literature review and future research directions. Asia Pacific Journal of Tourism Research, 31(1). https://doi.org/10.1080/10941665.2025.2555204
Alsayat, A. (2023). Customer decision-making analysis based on big social data using machine learning: A case study of hotels in Mecca. Neural Computing and Applications, 35(6). https://doi.org/10.1007/s00521-022-07992-x
Arici, H. E., Çakmakoğlu Arici, N., & Altinay, L. (2023). The use of big data analytics to discover customers’ perceptions of and satisfaction with green hotel service quality. Current Issues in Tourism, 26(2), 213–228. https://doi.org/10.1080/13683500.2022.2029832
Azer, J., & Alexander, M. J. (2018). Conceptualizing negatively valenced influencing behavior: Forms and triggers. Journal of Service Management, 29(3), 433–458. https://doi.org/10.1108/JOSM-12-2016-0326
Bi, D., Kong, J., & Gao, Y. (2024b). Modeling the effect of service failures and hotel response on customer satisfaction: Evidence from online reviews. Journal of Hospitality and Tourism Management, 58, 269–285. https://doi.org/10.1016/j.jhtm.2024.01.006
Bi, D., Xie, K. L., & Li, X. (2024a). Prevention of negative online customer reviews: A dynamic and compensation perspective. Journal of Hospitality and Tourism Management, 58, 119–131. https://doi.org/10.1016/j.jhtm.2024.01.006
Bi, J.-W., Zhu, X.-E., & Han, T.-Y. (2024). Text analysis in tourism and hospitality: A comprehensive review. Journal of Travel Research, 63(8), 1847–1869. https://doi.org/10.1177/00472875241247318
Botunac, I., Brkić Bakaric, M., & Matetić, M. (2024). Comparing fine-tuning and prompt engineering for multi-class classification in hospitality review analysis. Applied Sciences, 14(14), 6254. https://doi.org/10.3390/app14146254
Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research. Tourism Management, 29(4), 609–623. https://doi.org/10.1016/j.tourman.2008.01.005
Cantallops, A. S., & Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International Journal of Hospitality Management, 36, 41–51. https://doi.org/10.1016/j.ijhm.2013.08.007
Chen, W., Gu, B., Ye, Q., & Zhu, K. X. (2019). Measuring and managing the externality of managerial responses to online customer reviews. Information Systems Research, 30(1), 81–96. https://doi.org/10.1287/isre.2018.0781
Chen, Y.-F., & Law, R. (2016). A review of research on electronic word-of-mouth in hospitality and tourism management. International Journal of Hospitality & Tourism Administration, 17(4), 347–372. https://doi.org/10.1080/15256480.2016.1226150
Grechyn, V., & McShane, I. (2021). “Seriously, Australia, why are you so stingy with Wi-Fi?”: Customer satisfaction with Wi-Fi speed in Australian hotels and lessons for public Wi-Fi provision. Journal of Hospitality and Tourism Technology, 12(4), 747–764. https://doi.org/10.1108/JHTT-01-2020-0025
Grljević, O. (2025). Topic modeling in hospitality and tourism research: Application areas, business insights, and managerial implications. Hotel and Tourism Management, 13(2), 137–153. https://doi.org/10.5937/menhottur2500010G
Ho, B., Mayberry, T. R., Nguyen, K. L., Dhulipala, M., & Pallipuram, V. K. (2024). ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews. Machine Learning with Applications, 15, 100522. https://doi.org/10.1016/j.mlwa.2023.100522
Hsueh, J.-T., & Hsu, S.-H. (2026). A generative pretrained transformer framework for museum visitor experience analysis through aspect-based sentiment analysis. Engineering Applications of Artificial Intelligence, 167, 113817. https://doi.org/10.1016/j.engappai.2026.113817
Hussain, A., Li, M., Kanwel, S., Asif, M., Jameel, A., & Hwang, J. (2023). Impact of tourism satisfaction and service quality on destination loyalty: A structural equation modeling approach concerning China resort hotels. Sustainability, 15(9), 7713. https://doi.org/10.3390/su15097713
Kim, J. M., Liu, J., & Park, K. K.-C. (2023). The dynamics in asymmetric effects of multi-attributes on customer satisfaction: Evidence from COVID-19. International Journal of Contemporary Hospitality Management, 35(10), 3626–3648. https://doi.org/10.1108/IJCHM-02-2022-0170
Ku, C.-H., Chang, Y.-C., & Wang, Y. (2024). How to strategically respond to online hotel reviews: A strategy-aware deep learning approach. Information & Management, 61(5), 103970. https://doi.org/10.1016/j.im.2024.103970
Lata, S., & Rana, K. (2021). What are the determinants of consumers’ online reviews adoption for hotel bookings: A structural equation modelling approach. Enlightening Tourism, 11(1), 50–76. https://doi.org/10.33776/et.v11i1.5354
Le, H. T. M., Phan-Thi, T.-A., Nguyen, B. T., & Nguyen, T. Q. (2025). Mining online hotel reviews using big data and machine learning: An empirical study from an emerging country. Annals of Tourism Research Empirical Insights, 6(1), 100170. https://doi.org/10.1016/j.annale.2025.100170
Li, J. J. (2025). Generative artificial intelligence in tourism management: A review. Tourism Management Perspectives, 54, 101320. https://doi.org/10.1016/j.tmp.2024.101320
Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468. https://doi.org/10.1016/j.tourman.2007.05.011
Liu, P., Xu, Y., & Li, Y. (2025a). An FMEA decision support model for hotel risk assessment based on the risk tolerance of multi-type travelers and online reviews. Information Sciences, 690, 121525. https://doi.org/10.1016/j.ins.2024.121525
Liu, W., Yuan, Y., Jiang, Y., & Mou, J. (2025b). Reforming the SERVQUAL model for accommodation sharing services: A mixed-method approach. Data and Information Management, 9(4), 100102. https://doi.org/10.1016/j.dim.2025.100102
Liu, X., Ji, R., & Yang, J. (2022). Do hotel responses matter? A comprehensive perspective on investigating online reviews. Journal of Retailing and Consumer Services, 67, 102973. https://doi.org/10.1016/j.jretconser.2022.102973
Lunkes, R. J., Codesso, M., Deggau, L. P., & Camargo, M. E. (2026). How managers’ perceptions of online reviews enhance digital innovation in hotels: The role of technological opportunism. International Journal of Hospitality Management, 133, 104416. https://doi.org/10.1016/j.ijhm.2025.104416
Nusair, K., Butt, I., & Nikhashemi, S. R. (2019). A bibliometric analysis of social media in hospitality and tourism research. International Journal of Contemporary Hospitality Management, 31(7), 2691–2719. https://doi.org/10.1108/IJCHM-06-2018-0489
Olawuyi, O. S., & Kleynhans, C. (2025). A bibliometric analysis of service quality in the hospitality industry (2014–2024). Administrative Sciences, 15(6), 215. https://doi.org/10.3390/admsci15060215
Olson, E. D., & Ro, H. (2020). Company response to negative online reviews: The effects of procedural justice, interactional justice, and social presence. Cornell Hospitality Quarterly, 61(3), 258–270. https://doi.org/10.1177/1938965519892902
Palese, B., Piccoli, G., & Lui, T.-W. (2021). Effective use of online review systems: Congruent managerial responses and firm competitive performance. International Journal of Hospitality Management, 96, 102976. https://doi.org/10.1016/j.ijhm.2021.102976
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
Park, S. B., Jang, J., & Ok, C. M. (2016). Analyzing Twitter to explore perceptions of Asian restaurants. Journal of Hospitality and Tourism Technology, 7(4), 405–422. https://doi.org/10.1108/JHTT-08-2016-0042
Permatasari, W., Maghrifani, D., & Wibowo, A. (2025). A qualitative exploration of guest satisfaction with budget hotels from online review: The case of Indonesia. Cogent Business & Management, 12(1), 2475987. https://doi.org/10.1080/23311975.2025.2475987
Qiu, S., Li, M., Mattila, A. S., & Yang, W. (2018). Managing the face in service failure: The moderation effect of social presence. International Journal of Contemporary Hospitality Management, 30(3), 1315–1333. https://doi.org/10.1108/IJCHM-06-2016-0315
S, S., & Anusree, A. (2016). The impacts of customers’ observed severity and agreement on hotel booking intentions: Moderating role of webcare and mediating role of trust in negative online reviews. Tourism Review, 71(2), 77–89. https://doi.org/10.1108/TR-08-2015-0037
Sann, R., Lai, P.-C., & Liaw, S.-Y. (2020). Online complaining behavior: Does cultural background and hotel class matter? Journal of Hospitality and Tourism Management, 43, 113–125. https://doi.org/10.1016/j.jhtm.2020.02.004
Shen, Z., Yang, X., Liu, C., & Li, J. (2021). Assessment of indoor environmental quality in budget hotels using text-mining method: Case study of top five brands in China. Sustainability, 13(8), 4490. https://doi.org/10.3390/su13084490
Shin, J. S., Kim, J., Choe, J. Y. J., & Hwang, J. (2024). Determining directions of service quality management using online review mining with interpretable machine learning. Tourism Management, 101, 104858.
Skovoroda, R., Yang, W., Chen, B., & Buck, T. (2025). Exploring serial patterns in negative hotel reviews. Annals of Tourism Research Empirical Insights, 6(2), 100199. https://doi.org/10.1016/j.annale.2025.100199
Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310–1323. https://doi.org/10.1016/j.tourman.2010.12.011
Tarkang, M. E., Öztüren, A., & Alola, U. V. (2022). Can website quality moderate the relationship between information-task-fit and electronic word of mouth? Journal of Public Affairs, 22(3), e2476. https://doi.org/10.1002/pa.2476
Tran, D. T., Nguyen, T. S., & Huynh, T. N. (2025). Satisfaction with response: The impact on potential customers’ intent to stay through perceived service quality. Annals of Tourism Research Empirical Insights, 6(1), 100143. https://doi.org/10.1016/j.annale.2025.100143
Wang, J., & Yu, X. (2021). The driving path of customer sustainable consumption behaviors in the context of the sharing economy based on the interaction effect of customer signal, service provider signal, and platform signal. Sustainability, 13(7), 3826. https://doi.org/10.3390/su13073826
Wang, N., Liu, S., Si, G., & Zhang, M. (2026). To respond or to wait? The effect of customer review differentiation on hotel response priority. International Journal of Hospitality Management, 132, 104381. https://doi.org/10.1016/j.ijhm.2025.104381
Wang, S., Wang, Q., Cui, Q., & Lan, T. (2025). Artificial intelligence in tourism: A systematic literature review and future research agenda. Sustainability, 17(20), 9080. https://doi.org/10.3390/su17209080
Wąsowicz-Zaborek, E. (2025). National culture as a factor in visitors’ evaluations of hotel services. International Journal of Hospitality Management, 125, 104009. https://doi.org/10.1016/j.ijhm.2024.104009
Xiang, Z., Schwartz, Z., Gerdes, J. H., Jr., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120–130. https://doi.org/10.1016/j.ijhm.2014.10.013
Xie, K. L., Zhang, Z., & Zhang, Z. (2014). The business value of online consumer reviews and management response to hotel performance. International Journal of Hospitality Management, 43, 1–12. https://doi.org/10.1016/j.ijhm.2014.07.007
Xu, C., Wang, G., Nicolau, J. L., & Liu, X. (2025). Enhancing the interaction between guests and hotel managers: The value of guest-generated titles. Tourism Management, 110, 105201. https://doi.org/10.1016/j.tourman.2025.105201
Yadegaridehkordi, E., Nilashi, M., Md. Nasir, M. H., Momtazi, S., Samad, S., Supriyanto, E., & Ghabban, F. (2021). Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. Technology in Society, 65, 101528. https://doi.org/10.1016/j.techsoc.2021.101528
Yan, X., Yao, L., & Zhou, D. (2024). Optimizing tourism service quality in 5G multimedia environments using deep learning: A model-based empirical study. Informatica, 48(22). https://doi.org/10.31449/inf.v48i22.6806
Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28(1), 180–182. https://doi.org/10.1016/j.ijhm.2008.06.011
Yu, X., & Cheng, M. (2025). Multimodality in tourism and hospitality: A critical and narrative review. Tourism Management, 111, 105245. https://doi.org/10.1016/j.tourman.2025.105245
Zhang, H., Xiang, Z., & Zach, F. J. (2025). Generative AI vs. humans in online hotel review management: A task-technology fit perspective. Tourism Management, 110, 105187. https://doi.org/10.1016/j.tourman.2025.105187
Zhang, M., Yi, F., & Gursoy, D. (2026). The effects of generative artificial intelligence on consumers in hospitality and tourism: A systematic review and future research directions. International Journal of Hospitality Management, 133, 104452. https://doi.org/10.1016/j.ijhm.2025.104452
Zheng, X., Huang, J., Wu, J., Sun, S., & Wang, S. (2023). Emerging trends in online reviews research in hospitality and tourism: A scientometric update (2000–2020). Tourism Management Perspectives, 47, 101105. https://doi.org/10.1016/j.tmp.2023.101105
