Modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration

    Meng Zhao   Affiliation
    ; Chenxi Zhang   Affiliation
    ; Yiqi Hu Affiliation
    ; Zeshui Xu Affiliation
    ; Hao Liu Affiliation


With the development of e-commerce, an increasing number of online reviews can serve as a promising data source for enterprises to improve online products. This paper proposes a method for modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Firstly, the attributes concerned by consumers are extracted from online reviews, and sentiment analysis of the extracted attributes is carried out using Standford CoreNLP. Secondly, to identify the types of product attributes, an improved Kano model is proposed based on the effects of product attributes on consumer total utility. On this basis, different attribute types are illustrated from the perspective of risk attitude. Then, the consumer aspirations are mined based on the risk attitudes of different attributes and the attribute impact on consumer satisfaction. According to the risk attitudes and aspirations of different attributes, the quantified satisfaction functions are constructed to provide more objective and accurate improvement suggestions. Finally, the proposed method is applied to the hotel service improvement to illustrate the effectiveness.

First published online 13 April 2021

Keyword : Kano, online reviews, satisfaction function, aspiration, risk attitude

How to Cite
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.
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May 25, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.


Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52–77.

Berger, C., Blauth, R., & Boger, D. (1993). Kano’s methods for understanding customer-defined quality. Center for Quality Management Journal, 2(4), 3–36.

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.

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.

Bi, J. W., Liu, Y., Fan, Z. P., & Zhang, J. (2020a). Exploring asymmetric effects of attribute performance on customer satisfaction in the hotel industry. Tourism Management, 77, 104006.

Bi, J. W., Liu, Y., & Fan, Z. P. (2020b). Crowd intelligence: Conducting asymmetric impact-performance analysis based on online reviews. IEEE Intelligent Systems, 35(2), 92–98.

Boo, S., & Busser, J. A. (2018). Meeting planners’ online reviews of destination hotels: A twofold content analysis approach. Tourism Management, 66, 287–301.

Brandt, D. R. (1988). How service marketers can identify value-enhancing service elements. Journal of Services Marketing, 2(3), 35–41.

Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521.

Chang, Y. C., Ku, C. H., & Chen, C. H. (2019). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management, 49, 263–279.

Culotta, A., & Cutler, J. (2016). Mining brand perceptions from Twitter social networks. Marketing Science, 35(3), 343–362.

Fan, Z. P., Che, Y. J., & Chen, Z. Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, 90–100.

Fan, Z. P., Li, G. M., & Liu, Y. (2020). Processes and methods of information fusion for ranking products based on online reviews: An overview. Information Fusion, 60, 87–97.

Gao, S., Tang, O., Wang, H., & Yin, P. (2018). Identifying competitors through comparative relation mining of online reviews in the restaurant industry. International Journal of Hospitality Management, 71, 19–32.

Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and Outlook. Journal of Consumer Research, 5(2), 103–123.

Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646–675.

Jiang, C., Liu, S., Lin, Z., Zhao, G., Duan, R., & Liang, K. (2016). Domain-aware trust network extraction for trust propagation in large-scale heterogeneous trust networks. Knowledge-Based Systems, 111, 237–247.

Jin, J., Liu, Y., Ji, P., & Kwong, C. K. (2019). Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering, 19(1).

Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2), 263–292.

Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control, 14(2), 39–48.

Khan, F. H., Qamar, U., & Bashir, S. (2016). SWIMS: Semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis. Knowledge-Based Systems, 100, 97–111.

Lee, H. C., Rim, H. C., & Lee, D. G. (2019). Learning to rank products based on online product reviews using a hierarchical deep neural network. Electronic Commerce Research and Applications, 36, 100874.

Lee, Y. C., & Huang, S. Y. (2009). A new fuzzy concept approach for Kano’s model. Expert Systems with Applications, 36(3), 4479–4484.

Li, Y., Qin, Z., Xu, W., & Guo, J. (2015). A holistic model of mining product aspects and associated sentiments from online reviews. Multimedia Tools and Applications, 74(23), 10177–10194.

Li, H., Bhowmick, S. S., & Sun, A. (2010). Affinity-driven prediction and ranking of products in online product review sites. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (pp. 1745–1748).

Liang, X., Liu, P., & Wang, Z. (2019). Hotel selection utilizing online reviews: A novel decision support model based on sentiment analysis and DL-VIKOR method. Technological and Economic Development of Economy, 25(6), 1139–1161.

Liao, H., Wu, X., Mi, X., & Herrera, F. (2020). An integrated method for cognitive complex multiple experts multiple criteria decision making based on ELECTRE III with weighted Borda rule. Omega, 93, 102052.

Liu, S., Jiang, C., Lin, Z., Ding, Y., Duan, R., & Xu, Z. (2015). Identifying effective influencers based on trust for electronic word-of-mouth marketing: A domain-aware approach. Information Sciences, 306, 34–52.

Liu, Yang, Bi, J. W., & Fan, Z. P. (2017a). A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm. Information Sciences, 394–395, 38–52.

Liu, Yang, Bi, J. W., & Fan, Z. P. (2017b). A method for ranking products through online reviews based on sentiment classification and interval-valued intuitionistic fuzzy topsis. International Journal of Information Technology and Decision Making, 16(06), 1497–1522.

Liu, Y., Jiang, C., & Zhao, H. (2018). Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums. Decision Support Systems, 105, 1–12.

Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 55–60). Association for Computational Linguistics.

Marković, S., Šegarić, K., & Raspor, S. (2010). Does restaurant performance meet customers’ expectations? An assessment of restaurant service quality using a modified DINESERV approach. Tourism and Hospitality Management, 16(2), 181–195.

Martí Bigorra, A., Isaksson, O., & Karlberg, M. (2019). Aspect-based Kano categorization. International Journal of Information Management, 46, 163–172.

Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.

Neirotti, P., Raguseo, E., & Paolucci, E. (2016). Are customers’ reviews creating value in the hospitality industry? Exploring the moderating effects of market positioning. International Journal of Information Management, 36(6), 1133–1143.

Ou, W., Huynh, V. N., & Sriboonchitta, S. (2018). Training attractive attribute classifiers based on opinion features extracted from review data. Electronic Commerce Research and Applications, 32, 13–22.

Pang, Q., Wang, H., & Xu, Z. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143.

Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In Natural language processing and text mining (pp. 9–28). Springer.

Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer requirements from online reviews: A product improvement perspective. Information & Management, 53(8), 951–963.

Siering, M., Deokar, A. V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52–63.

Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.

Socher, R., Perelygin, A., Wu, J. Y., 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). Association for Computational Linguistics.

Song, M., & Chambers, T. (2014). Text mining with the Stanford CoreNLP. In Measuring scholarly impact (pp. 215–234). Springer.

Tan, H., Lv, X., Liu, X., & Gursoy, D. (2018). Evaluation nudge: Effect of evaluation mode of online customer reviews on consumers’ preferences. Tourism Management, 65, 29–40.

Ting, S.-C., & Chen, C. N. (2002). The asymmetrical and non-linear effects of store quality attributes on customer satisfaction. Total Quality Management, 13(4), 547–569.

Tseng, C., Wu, B., Morrison, A. M., Zhang, J., & Chen, Y. (2015). Travel blogs on China as a destination image formation agent: A qualitative analysis using Leximancer. Tourism Management, 46, 347–358.

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.

Violante, M. G., & Vezzetti, E. (2017). Kano qualitative vs quantitative approaches: An assessment framework for products attributes analysis. Computers in Industry, 86, 15–25.

Wang, Y., Lu, X., & Tan, Y. (2018). Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines. Electronic Commerce Research and Applications, 29, 1–11.

Xiao, S., Wei, C. P., & Dong, M. (2016). Crowd intelligence: Analyzing online product reviews for preference measurement. Information & Management, 53(2), 169–182.

Xu, Q., Jiao, R. J., Yang, X., Helander, M., Khalid, H. M., & Opperud, A. (2009). An analytical Kano model for customer need analysis. Design Studies, 30(1), 87–110.

Yang, B., Liu, Y., Liang, Y., & Tang, M. (2019). Exploiting user experience from online customer reviews for product design. International Journal of Information Management, 46, 173–186.

Yen, C. L., & Tang, C. H. (2015). Hotel attribute performance, eWOM motivations, and media choice. International Journal of Hospitality Management, 46, 79–88.