Decoding tourist satisfaction for sustainable economic development: a multi-method configuration framework using online reviews
DOI: https://doi.org/10.3846/tede.2025.23251Abstract
Online reviews are crucial to understanding tourist satisfaction (TSA) in the digital tourism era. This study deconstructs the factors leading to high TSA performance in reviews, offering guidance for long-term economic benefits for destinations and businesses. Building on the three-factor theory, we create a framework utilizing text mining, affective distribution computing, and fuzzy-set qualitative comparative analysis (fsQCA) to identify patterns driving high TSA. We employ topic modeling to extract destination attributes from reviews, quantifying their performance through affective distribution computing. An enhanced Kano model classifies tourist needs based on emotional expressions in reviews. We investigate how basic, performance and excitement attributes interact and influence TSA. Additionally, we apply the coupling coordination degree model (CCDM) to analyze attribute interconnections within configurations. Our results show that no single attribute leads to specific outcomes; relatively, high TSA results from a combination of attributes. This study identifies three normative causal recipes and is the first to clarify the complex interactions in satisfaction management within the three-factor theory framework, addressing a significant knowledge gap. Ultimately, our operational guidelines aim to sustain the economic vitality of the tourism industry.
First published online 14 July 2025
Keywords:
tourism economy, tourist satisfaction, three-factor theory, FsQCA, online reviewsHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aliedan, M. M., Sobaih, A. E. E., & Elshaer, I. A. (2021). Influence of cities-based entertainment on tourist satisfaction: Mediating roles of destination image and experience quality. Sustainability, 13(19), Article 11086. https://doi.org/10.3390/su131911086
Anderson, E. W., & Mittal, V. (2000). Strengthening the satisfaction-profit chain. Journal of Service Research, 3(2), 107–120. https://doi.org/10.1177/109467050032001
Angelov, D. (2020). Top2vec: Distributed representations of topics. Arxiv. https://doi.org/10.48550/arXiv.2008.09470
Balazs, J. A., & Velásquez, J. D. (2016). Opinion mining and information fusion: A survey. Information Fusion, 27, 95–110. https://doi.org/10.1016/j.inffus.2015.06.002
Bi, J.-W., Liu, Y., Fan, Z.-P., & Cambria, E. (2019a). Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. International Journal of Production Research, 57(22), 7068–7088. https://doi.org/10.1080/00207543.2019.1574989
Bi, J.-W., Liu, Y., Fan, Z.-P., & Zhang, J. (2019b). Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews. Tourism Management, 70, 460–478. https://doi.org/10.1016/j.tourman.2018.09.010
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation Journal of Machine Learning Research, 3(2003), 993–1022.
Brunner-Sperdin, A., Peters, M., & Strobl, A. (2012). It is all about the emotional state: Managing tourists’ experiences. International Journal of Hospitality Management, 31(1), 23–30. https://doi.org/10.1016/j.ijhm.2011.03.004
Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67–80. https://doi.org/10.1016/j.dss.2013.08.008
Chen, J., Becken, S., & Stantic, B. (2022). Assessing destination satisfaction by social media: An innovative approach using importance-performance analysis. Annals of Tourism Research, 93, Article 103371. https://doi.org/10.1016/j.annals.2022.103371
Chen, C., Zhang, C., & Xu, Z. (2024). Online reviews-driven Kano-QFD method for service design. IEEE Transactions on Engineering Management, 71, 8153–8165. https://doi.org/10.1109/TEM.2024.3387579
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(2011), 2493–2537.
Cui, C., Wei, M., Che, L., Wu, S., & Wang, E. (2022). Hotel recommendation algorithms based on online reviews and probabilistic linguistic term sets. Expert Systems with Applications, 210, Article 118503. https://doi.org/10.1016/j.eswa.2022.118503
Das, R., Ahmed, W., Sharma, K., Hardey, M., Dwivedi, Y. K., Zhang, Z., Apostolidis, C., & Filieri, R. (2024). Towards the development of an explainable e-commerce fake review index: An attribute analytics approach. European Journal of Operational Research, 317(2), 382–400. https://doi.org/10.1016/j.ejor.2024.03.008
Dong, Q., Zhong, K., Liao, Y., Xiong, R., Wang, F., & Pang, M. (2023). Coupling coordination degree of environment, energy, and economic growth in resource-based provinces of China. Resources Policy, 81, Article 103308. https://doi.org/10.1016/j.resourpol.2023.103308
Dul, J. (2016a). Identifying single necessary conditions with NCA and fsQCA. Journal of Business Research, 69(4), 1516–1523. https://doi.org/10.1016/j.jbusres.2015.10.134
Dul, J. (2016b). Necessary condition analysis (NCA) logic and methodology of “necessary but not sufficient” causality. Organizational Research Methods, 19(1), 10–52. https://doi.org/10.1177/1094428115584005
Dul, J., van der Laan, E., & Kuik, R. (2020). A statistical significance test for necessary condition analysis. Organizational Research Methods, 23(2), 385–395. https://doi.org/10.1177/1094428118795272
Feng, Y., Gao, Y., Xia, X., Shi, K., Zhang, C., Yang, L., Yang, L., & Cifuentes-Faura, J. (2024). Identifying the path choice of digital economy to crack the “resource curse” in China from the perspective of configuration. Resources Policy, 91, Article 104912. https://doi.org/10.1016/j.resourpol.2024.104912
Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420. https://doi.org/10.5465/amj.2011.60263120
Fu, H., Xiao, Y., Mensah, I. K., & Wang, R. (2023). Exploring the configurations of learner satisfaction with moocs designed for computer science courses based on integrated LDA-QCA method. Education and Information Technologies, 29, 9883–9905. https://doi.org/10.1007/s10639-023-12185-7
Geremew, Y. M., Huang, W.-J., & Hung, K. (2024). Fuzzy-set qualitative comparative analysis as a mixed-method and analysis technique: A comprehensive systematic review. Journal of Travel Research, 63(1), 3–26. https://doi.org/10.1177/00472875231168619
Greckhamer, T. (2016). CEO compensation in relation to worker compensation across countries: The configurational impact of country-level institutions. Strategic Management Journal, 37(4), 793–815. https://doi.org/10.1002/smj.2370
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv. https://doi.org/10.48550/arXiv.2203.05794
Gupta, S., Deodhar, S. J., Tiwari, A. A., Gupta, M., & Mariani, M. (2024). How consumers evaluate movies on online platforms? Investigating the role of consumer engagement and external engagement. Journal of Business Research, 176, Article 114613. https://doi.org/10.1016/j.jbusres.2024.114613
Han, X., Wang, Y., Yu, W., & Xia, X. (2023). Coupling and coordination between green finance and agricultural green development: Evidence from China. Finance Research Letters, 58, Article 104221. https://doi.org/10.1016/j.frl.2023.104221
Harkison, T. (2018). The use of co-creation within the luxury accommodation experience–myth or reality? International Journal of Hospitality Management, 71, 11–18. https://doi.org/10.1016/j.ijhm.2017.11.006
He, S.-F., Pan, X.-H., Wang, Y.-M., Zamora, D. G., & Martínez, L. (2024). A novel multi-criteria decision making framework based on evidential reasoning dealing with missing information from online reviews. Information Fusion, 106, Article 102264. https://doi.org/10.1016/j.inffus.2024.102264
Hernández-Rojas, R. D., & Huete Alcocer, N. (2021). The role of traditional restaurants in tourist destination loyalty. PLoS ONE, 16(6), Article e0253088. https://doi.org/10.1371/journal.pone.0253088
Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132–140. https://doi.org/10.1086/208906
Kano, N. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control, 31(4), 147–156.
Kirilenko, A. P., & Stepchenkova, S. (2025). Facilitating topic modeling in tourism research: Comprehensive comparison of new AI technologies. Tourism Management, 106, Article 105007. https://doi.org/10.1016/j.tourman.2024.105007
Klaus, P. G. (1985). Quality epiphenomenon: The conceptual understanding of quality in face-to-face service encounters. In J. A. Czepiel, M. R. Solomon, & C. F. Surprenant (Eds.), The service encounter: Managing employee/customer interaction in service business (pp. 17–33). Lexington Books.
Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018). Deep learning for affective computing: Text-based emotion recognition in decision support. Decision Support Systems, 115, 24–35. https://doi.org/10.1016/j.dss.2018.09.002
Kumar, S., Sahoo, S., Ali, F., & Cobanoglu, C. (2023). Rise of fsQCA in tourism and hospitality research: A systematic literature review. International Journal of Contemporary Hospitality Management, 36(7), 2165–2193. https://doi.org/10.1108/IJCHM-03-2023-0288
Lee, C. K. H. (2022). How guest-host interactions affect consumer experiences in the sharing economy: New evidence from a configurational analysis based on consumer reviews. Decision Support Systems, 152, Article 113634. https://doi.org/10.1016/j.dss.2021.113634
Lee, C. K. H., Tse, Y. K., Leung, E. K. H., & Wang, Y. (2024). Causal recipes of customer loyalty in a sharing economy: Integrating social media analytics and fsQCA. Journal of Business Research, 181, Article 114747. https://doi.org/10.1016/j.jbusres.2024.114747
Liu, P., Zhu, B., Yang, M., & De Baets, B. (2024). High-quality marine economic development in China from the perspective of green total factor productivity growth: Dynamic changes and improvement strategies. Technological and Economic Development of Economy, 30(6), 1572–1597. https://doi.org/10.3846/tede.2024.22018
López-Guzmán, T., Uribe Lotero, C. P., Pérez Gálvez, J. C., & Ríos Rivera, I. (2017). Gastronomic festivals: Attitude, motivation and satisfaction of the tourist. British Food Journal, 119(2), 267–283. https://doi.org/10.1108/BFJ-06-2016-0246
Luo, H., Wang, H., & Wu, Y. (2024). Digital financial inclusion and tourism development. International Review of Economics & Finance, 90, 207–219. https://doi.org/10.1016/j.iref.2023.12.001
Lv, S., Xiao, A., Qin, Y., Xu, Z., & Wang, X. (2024). A decision framework for improving the service quality of charging stations based on online reviews and evolutionary game theory. Transportation Research Part A: Policy and Practice, 187, Article 104168. https://doi.org/10.1016/j.tra.2024.104168
Mak, A. H. N. (2017). Online destination image: Comparing national tourism organisation’s and tourists’ perspectives. Tourism Management, 60, 280–297. https://doi.org/10.1016/j.tourman.2016.12.012
Mittal, V., Ross Jr, W. T., & Baldasare, P. M. (1998). The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions. Journal of Marketing, 62(1), 33–47. https://doi.org/10.1177/002224299806200104
Murphy, L., Moscardo, G., Benckendorff, P., & Pearce, P. (2011). Evaluating tourist satisfaction with the retail experience in a typical tourist shopping village. Journal of retailing and Consumer Services, 18(4), 302–310. https://doi.org/10.1016/j.jretconser.2011.02.004
Oliver, R. L., Rust, R. T., & Varki, S. (1997). Customer delight: Foundations, findings, and managerial insight. Journal of Retailing, 73(3), 311–336. https://doi.org/10.1016/S0022-4359(97)90021-X
Pan, M., Li, N., & Huang, X. (2022). Asymmetrical impact of service attribute performance on consumer satisfaction: An asymmetric impact-attention-performance analysis. Information Technology & Tourism, 24, 221–243. https://doi.org/10.1007/s40558-022-00226-9
Pang, Q., Wang, H., & Xu, Z. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143. https://doi.org/10.1016/j.ins.2016.06.021
Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set qualitative comparative analysis (fsQCA): Guidelines for research practice in information systems and marketing. International Journal of Information Management, 58, Article 102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310
Pappas, N. (2021). COVID19: Holiday intentions during a pandemic. Tourism Management, 84, Article 104287. https://doi.org/10.1016/j.tourman.2021.104287
Park, J., & Lee, B. K. (2021). An opinion-driven decision-support framework for benchmarking hotel service. Omega, 103, Article 102415. https://doi.org/10.1016/j.omega.2021.102415
Park, Y., Fiss, P. C., & El Sawy, O. A. (2020). Theorizing the multiplicity of digital phenomena: The ecology of configurations, causal recipes, and guidelines for applying QCA. Management of Information Systems Quarterly, 44, 1493–1520. https://doi.org/10.25300/MISQ/2020/13879
Peng, L., Cui, G., Chung, Y., & Li, C. (2019). A multi-facet item response theory approach to improve customer satisfaction using online product ratings. Journal of the Academy of Marketing Science, 47, 960–976. https://doi.org/10.1007/s11747-019-00662-w
Perdomo-Verdecia, V., Garrido-Vega, P., & Sacristán-Díaz, M. (2024). An fsQCA analysis of service quality for hotel customer satisfaction. International Journal of Hospitality Management, 122, Article 103793. https://doi.org/10.1016/j.ijhm.2024.103793
Pereira-Moliner, J., Villar-García, M., Molina-Azorín, J. F., Tarí, J. J., López-Gamero, M. D., & Pertusa-Ortega, E. M. (2024). Using tourism intelligence and big data to explain flight searches for tourist destinations: The case of the costa blanca (Spain). Tourism Management Perspectives, 51, Article 101243. https://doi.org/10.1016/j.tmp.2024.101243
Pocchiari, M., Proserpio, D., & Dover, Y. (2024). Online reviews: A literature review and roadmap for future research. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2024.08.009
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98–125. https://doi.org/10.1016/j.inffus.2017.02.003
Pu, Z., Zhang, C., Xu, Z., & Wang, X. (2023). A fuzzy decision support model for online review-driven hotel selection by considering risk attitudes of customers. Journal of the Operational Research Society, 75(7), 1407–1420. https://doi.org/10.1080/01605682.2023.2249938
Qin, X., Chen, Y., Rao, Y., Xie, H., Wong, M. L., & Wang, F. L. (2021). A constrained optimization approach for cross-domain emotion distribution learning. Knowledge-Based Systems, 227, Article 107160. https://doi.org/10.1016/j.knosys.2021.107160
Qin, Y., Wang, X., & Xu, Z. (2022). Ranking tourist attractions through online reviews: A novel method with intuitionistic and hesitant fuzzy information based on sentiment analysis. International Journal of Fuzzy Systems, 24, 755–777. https://doi.org/10.1007/s40815-021-01131-9
Ragin, C. C. (2014). The comparative method: Moving beyond qualitative and quantitative strategies. University of California Press. https://doi.org/10.1525/9780520957350
Rassal, C., Correia, A., & Serra, F. (2023). Understanding online reviews in all-inclusive hotels servicescape: A fuzzy set approach. Journal of Quality Assurance in Hospitality & Tourism, 25(6), 1607–1634. https://doi.org/10.1080/1528008X.2023.2167761
Rihoux, B., & Ragin, C. C. (2009). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Sage Publications. https://doi.org/10.4135/9781452226569
Roelen-Blasberg, T., Habel, J., & Klarmann, M. (2023). Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research? International Journal of Research in Marketing, 40(1), 164–188. https://doi.org/10.1016/j.ijresmar.2022.04.004
Sánchez-Franco, M. J., & Aramendia-Muneta, M. E. (2023). Why do guests stay at Airbnb versus hotels? An empirical analysis of necessary and sufficient conditions. Journal of Innovation & Knowledge, 8(3), Article 100380. https://doi.org/10.1016/j.jik.2023.100380
Scarpi, D., Confente, I., & Russo, I. (2022). The impact of tourism on residents’ intention to stay. A qualitative comparative analysis. Annals of Tourism Research, 97, Article 103472. https://doi.org/10.1016/j.annals.2022.103472
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press. https://doi.org/10.1017/CBO9781139004244
Shin, S., & Nicolau, J. L. (2022). Identifying attributes of wineries that increase visitor satisfaction and dissatisfaction: Applying an aspect extraction approach to online reviews. Tourism Management, 91, Article 104528. https://doi.org/10.1016/j.tourman.2022.104528
Singh, J. P., Irani, S., Rana, N. P., Dwivedi, Y. K., Saumya, S., & Roy, P. K. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355. https://doi.org/10.1016/j.jbusres.2016.08.008
Slevitch, L., & Oh, H. (2010). Asymmetric relationship between attribute performance and customer satisfaction: A new perspective. International Journal of Hospitality Management, 29(4), 559–569. https://doi.org/10.1016/j.ijhm.2009.09.004
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1631–1642). Seattle, Washington, USA. Association for Computational Linguistics. https://doi.org/10.18653/v1/D13-1170
Steriopoulos, E., Khoo, C., Wong, H. Y., Hall, J., & Steel, M. (2024). Heritage tourism brand experiences: The influence of emotions and emotional engagement. Journal of Vacation Marketing, 30(3), 489–504. https://doi.org/10.1177/13567667231152930
Subramanian, A. M., Nishant, R., Van De Vrande, V., & Hang, C. C. (2022). Technology transfer from public research institutes to SME: A configurational approach to studying reverse knowledge flow benefits. Research Policy, 51(10), Article 104602. https://doi.org/10.1016/j.respol.2022.104602
Sun, M., Ryan, C., & Pan, S. (2015). Using Chinese travel blogs to examine perceived destination image: The case of New Zealand. Journal of Travel Research, 54(4), 543–555. https://doi.org/10.1177/0047287514522882
Sun, X., Lin, B., Chen, Y., Tseng, S., & Gao, J. (2019). Can commercialization reduce tourists’ experience quality? Evidence from Xijiang Miao village in Guizhou, China. Journal of Hospitality & Tourism Research, 43(1), 120–140. https://doi.org/10.1177/1096348017736569
Tuo, G., Feng, Y., & Sarpong, S. (2019). A configurational model of reward-based crowdfunding project characteristics and operational approaches to delivery performance. Decision Support Systems, 120, 60–71. https://doi.org/10.1016/j.dss.2019.03.013
Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94, Article 101582. https://doi.org/10.1016/j.is.2020.101582
Velikova, N., Slevitch, L., & Mathe-Soulek, K. (2017). Application of Kano model to identification of wine festival satisfaction drivers. International Journal of Contemporary Hospitality Management, 29(10), 2708–2726. https://doi.org/10.1108/IJCHM-03-2016-0177
Vu, H. Q., Li, G., & Law, R. (2019). Discovering implicit activity preferences in travel itineraries by topic modeling. Tourism Management, 75, 435–446. https://doi.org/10.1016/j.tourman.2019.06.011
Wang, F., Liu, Z., Shang, S., Qin, Y., & Wu, B. (2019). Vitality continuation or over-commercialization? Spatial structure characteristics of commercial services and population agglomeration in historic and cultural areas. Tourism Economics, 25(8), 1302–1326. https://doi.org/10.1177/1354816619837129
Wang, X., Dong, Q., & Zhang, B. (2022). Analytical framework and empirical study of user needs for online reviews based on Kano model. Information Studies:Theory & Application, 45(02), 160–167.
Wang, R., Wu, C., Wang, X., Xu, F., & Yuan, Q. (2023). E-tourism information literacy and its role in driving tourist satisfaction with online travel information: A qualitative comparative analysis. Journal of Travel Research, 63(4), 904–922. https://doi.org/10.1177/00472875231177229
Wang, M. M., & Jia, Z. Y. (2024). Investigating the correlation between building fasade design elements and tourist satisfaction – Cases study of Italy and the Netherlands. Habitat International, 144, Article 103001. https://doi.org/10.1016/j.habitatint.2024.103001
Wattanacharoensil, W., Fakfare, P., Manosuthi, N., Lee, J.-S., Chi, X., & Heesup, H. (2024). Determinants of traveler intention toward animal ethics in tourism: Developing a causal recipe combining cognition, affect, and norm factors. Tourism Management, 100, Article 104823. https://doi.org/10.1016/j.tourman.2023.104823
Wu, X., Liao, H., Xu, Z., Hafezalkotob, A., & Herrera, F. (2018). Probabilistic linguistic multimoora: A multicriteria decision making method based on the probabilistic linguistic expectation function and the improved borda rule. IEEE Transactions on Fuzzy Systems, 26(6), 3688–3702. https://doi.org/10.1109/TFUZZ.2018.2843330
Wu, X., & Liao, H. (2021). Modeling personalized cognition of customers in online shopping. Omega, 104, Article 102471. https://doi.org/10.1016/j.omega.2021.102471
Wu, J., Chen, J., Yang, T., & Zhao, N. (2024a). How to stay competitive: An innovative concept to assess the business competitiveness using online restaurant reviews. International Journal of Hospitality Management, 122, Article 103836. https://doi.org/10.1016/j.ijhm.2024.103836
Wu, J., Zhao, N., & Yang, T. (2024b). Wisdom of crowds: Swot analysis based on hybrid text mining methods using online reviews. Journal of Business Research, 171, Article 114378. https://doi.org/10.1016/j.jbusres.2023.114378
Xiao, R., Yu, X., Xiang, T., Zhang, Z., Wang, X., & Wu, J. (2021). Exploring the coordination between physical space expansion and social space growth of China’s urban agglomerations based on hierarchical analysis. Land Use Policy, 109, Article 105700. https://doi.org/10.1016/j.landusepol.2021.105700
Xu, X. (2022). A growing or depreciating love? Linking time with customer satisfaction through online reviews. Information & Management, 59(2), Article 103605. https://doi.org/10.1016/j.im.2022.103605
Xu, X., & Li, Y. (2016). The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach. International Journal of Hospitality Management, 55, 57–69. https://doi.org/10.1016/j.ijhm.2016.03.003
Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013). A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web, 1445–1456. ACM Digital Library. https://doi.org/10.1145/2488388.2488514
Yang, G., Bai, X., & Yang, S. (2023a). Analysis strategy configurations in risk taking using fuzzy set qualitative comparative analysis model. Technological and Economic Development of Economy, 29(3), 981–1004. https://doi.org/10.3846/tede.2023.18779
Yang, T., Wu, J., & Zhang, J. (2023b). Knowing how satisfied/dissatisfied is far from enough: A comprehensive customer satisfaction analysis framework based on hybrid text mining techniques. International Journal of Contemporary Hospitality Management, 36(3), 873–892. https://doi.org/10.1108/IJCHM-10-2022-1319
Zhang, C., Xu, Z., Gou, X., & Chen, S. (2021a). An online reviews-driven method for the prioritization of improvements in hotel services. Tourism Management, 87, Article 104382. https://doi.org/10.1016/j.tourman.2021.104382
Zhang, T., Yin, P., & Peng, Y. (2021b). Effect of commercialization on tourists’ perceived authenticity and satisfaction in the cultural heritage tourism context: Case study of Langzhong ancient city. Sustainability, 13(12), Article 6847. https://doi.org/10.3390/su13126847
Zhang, M., Zhao, L., Zhang, Y., Liu, Y., & Luo, N. (2021c). Effects of destination resource combination on tourist perceived value: In the context of Chinese ancient towns. Tourism Management Perspectives, 40, Article 100899. https://doi.org/10.1016/j.tmp.2021.100899
Zhang, F., Wang, F., Yao, S., & Fu, F. (2023). High-speed rail and tourism expansion in China: A spatial spillover effect perspective. Technological and Economic Development of Economy, 29(6), 1753–1775. https://doi.org/10.3846/tede.2023.19813
Zhang, C., Cheng, X., Li, K., & Li, B. (2024). Hotel recommendation mechanism based on online reviews considering multi-attribute cooperative and interactive characteristics. Omega, 130, Article 103173. https://doi.org/10.1016/j.omega.2024.103173
Zhao, M., Zhang, C., Hu, Y., Xu, Z., & Liu, H. (2021). Modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Technological and Economic Development of Economy, 27(3), 550–582. https://doi.org/10.3846/tede.2021.14223
Zhao, M., Liu, M., Xu, C., & Zhang, C. (2023). Classifying travellers’ requirements from online reviews: An improved Kano model. International Journal of Contemporary Hospitality Management, 36, 91–112. https://doi.org/10.1108/IJCHM-06-2022-0726
Zhou, Y., Xue, H., & Geng, X. (2015). Emotion distribution recognition from facial expressions. Proceedings of the 23rd ACM International Conference on Multimedia, 1247–1250. ACM Digital Library. https://doi.org/10.1145/2733373.2806328
View article in other formats
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.