Exploring service improvement through importance-performance analysis considering the reliability of multiple online platforms
DOI: https://doi.org/10.3846/tede.2025.23838Abstract
Service improvement has emerged as a pivotal task for hoteliers to ensure competitive advantage. This study proposes a service improvement method based on online reviews from multiple platforms considering the reliability of online platforms and different evaluation modes, where the reliability of an online platform is defined based on the number of online reviews on that platform and the degree of review helpfulness. In our method, Latent Dirichlet Allocation model is utilized to extract keywords, and lexicon-based sentiment analysis methods are employed to analyze the sentiment of online reviews on each platform considering different evaluation modes. The importance of attributes on each platform is measured by the TextRank method. A multi-platform-oriented importance-performance analysis model is constructed based on the integrated performance and the importance of attributes, so as to classify attributes and formulate service improvement strategies. A case study about hotel service improvement is implemented to illustrate the effectiveness of the method. Results show that the attributes classification results considering the reliability of multiple platforms is more reasonable compared to the results based on a single platform, providing more effective service improvement strategy and clearer view of attribute status on various platforms for hoteliers.
Keywords:
service improvement strategies, multiple online platforms, importance-performance analysis, sentiment analysis, probabilistic linguistic term setHow 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
Albayrak, T., Cengizci, A. D., Caber, M., & Fong, L. H. N. (2021). Big data use in determining competitive position: The case of theme parks in Hong Kong. Journal of Destination Marketing & Management, 22, Article 100668. https://doi.org/10.1016/j.jdmm.2021.100668
Ban, O. I., Droj, L., Tușe, D. A., & Botezat, E. (2022). Operationalization of importance-performance analysis with nine categories and tested for green practices and financial evaluation. Technological and Economic Development of Economy, 28(6), 1711–1738. https://doi.org/10.3846/tede.2022.17653
Bi, J.-W., Liu, Y., Fan, Z.-P., & Zhang, J. (2019). 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
Boley, B. B., & Jordan, E. (2023). Leveraging IPA gap scores to predict intent to travel. Journal of Hospitality and Tourism Management, 57, 97–101. https://doi.org/10.1016/j.jhtm.2023.09.006
Chen, K., Tsai, C.-F., Hu, Y.-H., & Hu, C.-W. (2024). The effect of review visibility and diagnosticity on review helpfulness – An accessibility-diagnosticity theory perspective. Decision Support Systems, 178, Article 114145. https://doi.org/10.1016/j.dss.2023.114145
Darko, A. P., Liang, D., Xu, Z., Agbodah, K., & Obiora, S. (2023). A novel multi-attribute decision-making for ranking mobile payment services using online consumer reviews. Expert Systems with Applications, 213, Article 119262. https://doi.org/10.1016/j.eswa.2022.119262
Feng, Y., Yin, Y., Wang, D., Dhamotharan, L., Ignatius, J., & Kumar, A. (2023). Diabetic patient review helpfulness: Unpacking online drug treatment reviews by text analytics and design science approach. Annals of Operations Research, 328, 387–418. https://doi.org/10.1007/s10479-022-05121-4
Gai, T., Wu, J., Liang, C., Cao, M., & Zhang, Z. (2024). A quality function deployment model by social network and group decision making: Application to product design of e-commerce platforms. Engineering Applications of Artificial Intelligence, 133, Article 108509. https://doi.org/10.1016/j.engappai.2024.108509
Ganguly, B., Sengupta, P., & Biswas, B. (2024). What are the significant determinants of helpfulness of online review? An exploration across product-types. Journal of Retailing and Consumer Services, 78, Article 103748. https://doi.org/10.1016/j.jretconser.2024.103748
Glaveli, N., Manolitzas, P., Palamas, S., Grigoroudis, E., & Zopounidis, C. (2023). Developing effective strategic decision-making in the areas of hotel quality management and customer satisfaction from online ratings. Current Issues in Tourism, 26(6), 1003–1021. https://doi.org/10.1080/13683500.2022.2048805
Jia, H., Shin, S., & Jiao, J. (2022). Does the length of a review matter in perceived helpfulness? The moderating role of product experience. Journal of Research in Interactive Marketing, 16(2), 221–236. https://doi.org/10.1108/JRIM-04-2020-0086
Jin, W., Gai, T., Cao, M., Zhou, M., & Wu, J. (2024). A personalized bidirectional feedback mechanism by combining cooperation and trust to improve group consensus in social network. Computers & Industrial Engineering, 188, Article 109888. https://doi.org/10.1016/j.cie.2024.109888
Kou, G., Yang, P., Peng, Y., Xiao, H., Xiao, F., Chen, Y., & Alsaadi, F. E. (2021). A cross-platform market structure analysis method using online product reviews. Technological and Economic Development of Economy, 27(5), 992–1018. https://doi.org/10.3846/tede.2021.12005
Li, M., & Huang, P. (2020). Assessing the product review helpfulness: Affective-cognitive evaluation and the moderating effect of feedback mechanism. Information & Management, 57(7), Article 103359. https://doi.org/10.1016/j.im.2020.103359
Liang, Y. (2024). Crowdsourcing incentive mechanisms for cross-platform tasks: A weighted average maximization approach. Engineering Applications of Artificial Intelligence, 133, Article 108008. https://doi.org/10.1016/j.engappai.2024.108008
Liu, H., Wu, S., Zhong, C., & Liu, Y. (2023a). The effects of customer online reviews on sales performance: The role of mobile phone’s quality characteristics. Electronic Commerce Research and Applications, 57, Article 101229. https://doi.org/10.1016/j.elerap.2022.101229
Liu, Z., Liao, H., Li, M., Yang, Q., & Meng, F. (2023b). A deep learning-based sentiment analysis approach for online product ranking with probabilistic linguistic term sets. IEEE Transactions on Engineering Management, 71, 6677–6694. https://doi.org/10.1109/TEM.2023.3271597
Lu, L., Xu, P., Wang, Y. Y., & Wang, Y. (2023). Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms. Journal of Business Research, 169, Article 114298. https://doi.org/10.1016/j.jbusres.2023.114298
Luo, Y., He, J., Mou, Y., Wang, J., & Liu, T. (2021). Exploring China’s 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. Tourism Management Perspectives, 37, Article 100769. https://doi.org/10.1016/j.tmp.2020.100769
Lutz, B., Pröllochs, N., & Neumann, D. (2022). Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation. Journal of Business Research, 144, 888–901. https://doi.org/10.1016/j.jbusres.2022.02.010
Ma, B., Wong, Y. D., Teo, C.-C., & Wang, Z. (2024). Enhance understandings of Online Food Delivery’s service quality with online reviews. Journal of Retailing and Consumer Services, 76, Article 103588. https://doi.org/10.1016/j.jretconser.2023.103588
Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77–79. https://doi.org/10.1177/002224297704100112
Mejia, C., Bąk, M., Zientara, P., & Orlowski, M. (2022). Importance-performance analysis of socially sustainable practices in US restaurants: A consumer perspective in the quasi-post-pandemic context. International Journal of Hospitality Management, 103, Article 103209. https://doi.org/10.1016/j.ijhm.2022.103209
Mirtalaie, M. A., Hussain, O. K., Chang, E., & Hussain, F. K. (2018). Extracting sentiment knowledge from pros/cons product reviews: Discovering features along with the polarity strength of their associated opinions. Expert Systems with Applications, 114, 267–288. https://doi.org/10.1016/j.eswa.2018.07.046
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
Pimpalkar, A., & Jeberson Retnaraj, R. (2022). MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis. Expert Systems with Applications, 203, Article 117581. https://doi.org/10.1016/j.eswa.2022.117581
Quan, L. J., Kim, J. J., & Han, H. (2022). Customer views on comprehensive green hotel selection attributes and analysis of importance-performance. Journal of Travel & Tourism Marketing, 39(6), 535–554. https://doi.org/10.1080/10548408.2022.2162657
Verma, D., Dewani, P. P., Behl, A., Pereira, V., Dwivedi, Y., & Del Giudice, M. (2023). A meta-analysis of antecedents and consequences of eWOM credibility: Investigation of moderating role of culture and platform type. Journal of Business Research, 154, Article 113292. https://doi.org/10.1016/j.jbusres.2022.08.056
Wu, X., & Liao, H. (2019). A consensus-based probabilistic linguistic gained and lost dominance score method. European Journal of Operational Research, 272(3), 1017–1027. https://doi.org/10.1016/j.ejor.2018.07.044
Wu, J., & Yang, T. (2023). Service attributes for sustainable rural tourism from online comments: Tourist satisfaction perspective. Journal of Destination Marketing & Management, 30, Article 100822. https://doi.org/10.1016/j.jdmm.2023.100822
Wu, X., Liao, H., & Tang, M. (2023a). Decision making towards large-scale alternatives from multiple online platforms by a multivariate time-series-based method. Expert Systems with Applications, 212, Article 118838. https://doi.org/10.1016/j.eswa.2022.118838
Wu, X., Liao, H., & Zhang, C. (2023b). Importance-performance analysis to develop product/service improvement strategies through online reviews with reliability. Annals of Operations Research, 342, 1905–1924. https://doi.org/10.1007/s10479-023-05594-x
Wu, D. C., Cao, C., Wu, J., & Hu, M. (2024a). Wine tourism experiences of Chinese tourists: A tourist-centric perspective. International Journal of Contemporary Hospitality Management, 36(8), 2601–2631. https://doi.org/10.1108/IJCHM-07-2023-1003
Wu, X., Liao, H., & Tang, M. (2024b). Product ranking through fusing the wisdom of consumers extracted from online reviews on multiple platforms. Knowledge-Based Systems, 284, Article 111275. https://doi.org/10.1016/j.knosys.2023.111275
Salimi, N. (2021). Opportunity recognition for entrepreneurs based on a business model for sustainability: A systematic approach and its application in the Dutch dairy farming sector. IEEE Transactions on Engineering Management, 70(11), 3728–3744. https://doi.org/10.1109/TEM.2021.3082872
Shin, J., Joung, J., & Lim, C. (2024). Determining directions of service quality management using online review mining with interpretable machine learning. International Journal of Hospitality Management, 118, Article 103684. https://doi.org/10.1016/j.ijhm.2023.103684
Yang, Z., Ouyang, T., Fu, X., & Peng, X. (2020). A decision-making algorithm for online shopping using deep-learning-based opinion pairs mining and q-rung orthopair fuzzy interaction Heronian mean operators. International Journal of Intelligent Systems, 35(5), 783–825. https://doi.org/10.1002/int.22225
Yang, Y., Xia, D.-X., Pedrycz, W., Deveci, M., & Chen, Z.-S. (2024). Cross-platform distributed product online ratings aggregation approach for decision making with basic uncertain linguistic information. International Journal of Fuzzy Systems, 26, 1936–1957. https://doi.org/10.1007/s40815-023-01646-3
Zhang, C., Xu, Z., Gou, X., & Chen, S. (2021). 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, M., Sun, L., Wang, G. A., Li, Y., & He, S. (2022a). Using neutral sentiment reviews to improve customer requirement identification and product design strategies. International Journal of Production Economics, 254, Article 108641. https://doi.org/10.1016/j.ijpe.2022.108641
Zhang, Y., Liang, D., & Xu, Z. (2022b). Cross-platform hotel evaluation by aggregating multi-website consumer reviews with probabilistic linguistic term set and Choquet integral. Annals of Operations Research, 1–35. https://doi.org/10.1007/s10479-022-05075-7
Zhang, D. F., Shen, Z. F., & Li, Y. (2023). Requirement analysis and service optimization of multiple category fresh products in online retailing using importance-Kano analysis. Journal of Retailing and Consumer Services, 72, Article 103253. https://doi.org/10.1016/j.jretconser.2022.103253
Zhang, C. X, & Xu, Z. S. (2024). Gaining insights for service improvement through unstructured text from online reviews. Journal of Retailing and Consumer Services, 80, Article 103898. https://doi.org/10.1016/j.jretconser.2024.103898
Zhao, M., Li, L., & Xu, Z. (2021). Study on hotel selection method based on integrating online ratings and reviews from multi-websites. Information Sciences, 572, 460–481. https://doi.org/10.1016/j.ins.2021.05.042
Zhao, M., Liu, M., Xu, C., & Zhang, C. (2024). Classifying travellers’ requirements from online reviews: An improved Kano model. International Journal of Contemporary Hospitality Management, 36(1), 91–112. https://doi.org/10.1108/IJCHM-06-2022-0726
View article in other formats
CrossMark check
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.