Abstract
The fierce competition in automobile industry makes buying a car to be a daunting task for customers, since it is a high-risk decision due to the intrinsic imperceptibility of car products. Nowadays, customers are more willing to take some advice from online reviews before making a purchase decision in the era of big data. Therefore, we try to develop a novel data-driven method to make reasonable decisions for customers when they buy cars. To achieve the goal, our study first collects data from online reviews rather than questionnaires. To better depict the ambiguity and complexity of online reviews, we then utilize the hesitant probabilistic fuzzy set (HPFS) and sentiment analysis to quantify evaluations. Taking customers’ psychological cognition into account, a novel method combining the prospect theory and term frequency is used to measure the weights of different attributes. After that, we further propose an improved multiplicative multi-objective optimization by ratio analysis (MULTIMOORA) approach to rank the products. Specifically, the dominance degrees from TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method are extended in three sub-ranking methods in MULTIMOORA, namely ratio system method, reference point method, and full multiplicative form method to prioritize all car products for customers. Finally, we provide some product improvement suggestions for car manufacturers.
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References
Aytaç Adalı, E., & Tuş Işık, A. (2017). The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem. Journal of Industrial Engineering International, 13(2), 229–237.
Baltas, G., & Saridakis, C. (2013). An empirical investigation of the impact of behavioural and psychographic consumer characteristics on car preferences: an integrated model of car type choice. Transportation Research Part A-Policy and Practice, 54, 92–110.
Bi, Y. Y., Qiu, Y. J., Sha, Z. H., Wang, M. X., Fu, Y., Contractor, N., & Chen, W. (2021). Modeling multi-year customers’ considerations and choices in China’s auto market using two-stage bipartite network analysis. Networks and Spatial Economics, 21(2), 365–385.
Biswas, T. K., & Das, M. C. (2018). Selection of hybrid vehicle for green environment using multi-attributive border approximation area comparison method. Management Science Letters, 8(2), 121–130.
Biswas, T. K., & Saha, P. (2019). Selection of commercially available scooters by new MCDM method. International Journal of Data and Network Science, 3(2), 137–144.
Brauers, W. K. M., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 445–469.
Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24.
Brauers, W. K. M., & Zavadskas, E. K. (2012). Robustness of MULTIMOORA: A method for multi-objective optimization. Informatica, 23(1), 1–25.
Büyüközkan, G., & Güler, M. (2021). A combined hesitant fuzzy MCDM approach for supply chain analytics tool evaluation. Applied Soft Computing, 112, 107812.
Chen, X., Zhao, L., & Liang, H. M. (2018). A novel multi-attribute group decision-making method based on the MULTIMOORA with linguistic evaluations. Soft Computing, 22(16), 5347–5361.
Chen, Y., Lawell, C. Y. C. L., & Wang, Y. S. (2020). The Chinese automobile industry and government policy. Research in Transportation Economics, 84, 100849.
Chen, Z. S., Liu, X. L., Chin, K. S., Pedrycz, W., Tsui, K. L., & Skibniewski, M. J. (2021). Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: case study of high-speed rail system in china. Information Fusion, 69, 22–39.
Darko, A. P., Liang, D. C., Xu, Z. S., 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, 119262.
Darko, A. P., & Liang, D. C. (2022). Modeling customer satisfaction through online reviews: A flowsort group decision model under probabilistic linguistic settings. Expert Systems with Applications, 195, 116649.
Ding, Q. Y., Wang, Y. M., & Goh, M. (2021). TODIM dynamic emergency decision-making method based on hybrid weighted distance under probabilistic hesitant fuzzy information. International Journal of Fuzzy Systems, 23(2), 474–491.
Divsalar, M., Ahmadi, M., Ebrahimi, E., & Ishizaka, A. (2022). A probabilistic hesitant fuzzy Choquet integral-based TODIM method for multi-attribute group decision-making. Expert Systems with Applications, 191, 116266.
Du, Y. F., Chen, Z. S., Yang, J., Morente-Molinera, J. A., Zhang, L., & Herrera-Viedma, E. (2023). A textual data-oriented method for doctor selection in online health communities. Sustainability, 15(2), 1241.
Du, Y. F., Liu, D., & Duan, H. X. (2022). A textual data-driven method to identify and prioritise user preferences based on regret/rejoicing perception for smart and connected products. International Journal of Production Research, 60(13), 4176–4196.
Du, Y. F., Liu, D., Morente-Molinera, J. A., & Herrera-Viedma, E. (2022). A data-driven method for user satisfaction evaluation of smart and connected products. Expert Systems with Applications, 210, 118392.
Gao, J., Xu, Z. S., & Liao, H. C. (2017). A dynamic reference point method for emergency response under hesitant probabilistic fuzzy environment. International Journal of Fuzzy Systems, 19(5), 1261–1278.
Girubha, R. J., & Vinodh, S. (2012). Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component. Materials and Design, 37, 478–486.
Gomes, L. F. A. M., & Lima, M. M. P. P. (1992). TODIM: Basics and application to multicriteria ranking of projects with environment impacts. Foundations of Computing and Decision Sciences, 16(4), 113–127.
He, S. F., & Wang, Y. M. (2022). Evaluating new energy vehicles by picture fuzzy sets based on sentiment analysis from online reviews. Artificial Intelligence Review, 56(3), 2171–2192.
Hu, J. H., Yang, Y., & Chen, X. H. (2018). A novel TODIM method-based three-way decision model for medical treatment selection. International Journal of Fuzzy Systems, 20(4), 1240–1255.
Ji, F. X., Cao, Q. W., Li, H., Fujita, H., Liang, C. Y., & Wu, J. (2023). An online reviews-driven large-scale group decision making approach for evaluating user satisfaction of sharing accommodation. Expert Systems with Applications, 213, 118875.
Jiang, Y. P., Liang, X., & Liang, H. M. (2017). An I-TODIM method for multi-attribute decision making with interval numbers. Soft Computing, 21(18), 5489–5506.
Jin, J., Jia, D. P., & Chen, K. J. (2021). Mining online reviews with a Kansei-integrated Kano model for innovative product design. International Journal of Production Research, 60(22), 6708–6727.
Joshi, D. K., Awasthi, N., & Chaube, S. (2022). Probabilistic hesitant fuzzy set based MCDM method with applications in portfolio selection process. Materials Today-Proceedings, 57, 2270–2275.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
Khan, F., Ali, Y., & Khan, A. U. (2020). Sustainable hybrid electric vehicle selection in the context of a developing country. Air Quality Atmosphere and Health, 13(4), 489–499.
Kim, E. G., & Chun, S. H. (2019). Analyzing online car reviews using text mining. Sustainability, 11(6), 1611.
Li, M. Y., & Cao, P. P. (2019). Extended TODIM method for multi-attribute risk decision making problems in emergency response. Computers and Industrial Engineering, 135, 1286–1293.
Li, Q., Hu, H., Ma, L. Y., Wang, Z. G., Arıcı, M., Li, D., Luo, D., Jia, J. J., Jiang, W., & Qi, H. B. (2022). Evaluation of energy-saving retrofits for sunspace of rural residential buildings based on orthogonal experiment and entropy weight method. Energy for Sustainable Development, 70, 569–580.
Liang, D. C., Dai, Z. Y., Wang, M. W., & Li, J. J. (2020). Web celebrity shop assessment and improvement based on online review with probabilistic linguistic term sets by using sentiment analysis and fuzzy cognitive map. Fuzzy Optimization and Decision Making, 19(4), 561–586.
Liang, W., Goh, M., & Wang, Y. M. (2020). Multi-attribute group decision making method based on prospect theory under hesitant probabilistic fuzzy environment. Computers and Industrial Engineering, 149, 106804.
Liao, N. N., Wei, G. W., & Chen, X. D. (2021). TODIM method based on cumulative prospect theory for multiple attributes group decision making under probabilistic hesitant fuzzy setting. International Journal of Fuzzy Systems, 24(1), 322–339.
Lin, Z. M., Huang, C., & Lin, M. W. (2021). Probabilistic hesitant fuzzy methods for prioritizing distributed stream processing frameworks for IoT applications. Mathematical Problems in Engineering, 2021, 6655477.
Liu, H. C., Zhao, H., You, X. Y., & Zhou, W. Y. (2019). Robot evaluation and selection using the hesitant fuzzy linguistic MULTIMOORA method. Journal of Testing and Evaluation, 47(2), 1405–1426.
Liu, P. D., & Li, Y. (2019). An extended MULTIMOORA method for probabilistic linguistic multi-criteria group decision-making based on prospect theory. Computers and Industrial Engineering, 136, 528–545.
Liu, P. D., & Teng, F. (2019). Probabilistic linguistic TODIM method for selecting products through online product reviews. Information Sciences, 485, 441–455.
Liu, Z. M., Song, W., Liu, D. Q., & Lu, J. (2018). Exploring brand preference and its spatial patterns in the Chinese automobile market. Journal of Spatial Science, 63(2), 399–417.
Nayak, B. B., Abhishek, K., & Mahapatra, S. S. (2018). Parametric appraisal of WEDM taper cutting process using maximum deviation method. Materials Today-Proceedings, 5(5), 11601–11607.
Nepal, B., Yadav, O. P., & Murat, A. (2010). A fuzzy-AHP approach to prioritization of CS attributes in target planning for automotive product development. Expert Systems with Applications, 37(10), 6775–6786.
Qin, Q. D., Liang, F. Q., Li, L., Chen, Y. W., & Yu, G. F. (2017). A TODIM-based multi-criteria group decision making with triangular intuitionistic fuzzy numbers. Applied Soft Computing, 55, 93–107.
Sakthivel, G., Ilangkumaran, M., Nagarajan, G., Raja, A., Ragunadhan, P. M., & Prakash, J. (2013). A hybrid MCDM approach for evaluating an automobile purchase model. International Journal of Information and Decision Sciences, 5(1), 50–85.
Shen, R. P., Liu, D., & Shen, H. S. (2023). Detecting review manipulation from behavior deviation: A deep learning approach. Electronic Commerce Research and Applications, 60, 101283.
Song, Y. M., Li, Y. H., Zhu, H. L., & Li, G. X. (2023). A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior. Journal of Retailing and Consumer Services, 73, 103303.
Sun, B. Z., Zhang, M., Wang, T., & Zhang, X. R. (2020). Diversified multiple attribute group decision-making based on multigranulation soft fuzzy rough set and TODIM method. Computational and Applied Mathematics, 39(3), 186.
Teng, F., & Liu, P. D. (2021). A large group decision-making method based on a generalized Shapley probabilistic linguistic Choquet average operator and the TODIM method. Computers and Industrial Engineering, 151, 106971.
Tian, Z. P., Liang, H. M., Nie, R. X., Wang, X. K., & Wang, J. Q. (2023). Data-driven multi-criteria decision support method for electric vehicle selection. Computers and Industrial Engineering, 177, 109061.
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.
Wan, Q. Z., & Yu, Y. (2020). Power load pattern recognition algorithm based on characteristic index dimension reduction and improved entropy weight method. Energy Reports, 6, 797–806.
Wang, L., Wang, Y. M., & Martínez, L. (2019). Fuzzy TODIM method based on alpha-level sets. Expert Systems with Applications, 140, 112899.
Wang, Y., Sun, B. Z., Zhang, X. R., & Wang, Q. (2020). BWM and MULTIMOORA-based multigranulation sequential three-way decision model for multi-attribute group decision-making problem. International Journal of Approximate Reasoning, 125, 169–186.
Wang, Y. R., Lu, X., & Tan, Y. J. (2018). Impact of product attributes on customer satisfaction: an analysis of online reviews for washing machines. Electronic Commerce Research and Applications, 29, 1–11.
Wu, J., Zhang, G. Y., Xing, Y. M., Liu, Y. J., Zhang, Z., Dong, Y. C., & Herrera-Viedma, E. (2023). A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors. Applied Intelligence, 53(16), 19093–19114.
Wu, X. L., Liao, H. C., Xu, Z. S., 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.
Xu, Z. S., & Xia, M. M. (2011). Distance and similarity measures for hesitant fuzzy sets. Information Sciences, 181(11), 2128–2138.
Xu, Z. S., & Zhou, W. (2017). Consensus building with a group of decision makers under the hesitant probabilistic fuzzy environment. Fuzzy Optimization and Decision Making, 16(4), 481–503.
Yildiz, A., & Ergul, E. U. (2014). Usage of fuzzy multi-criteria decision making method to solve the automobile selection problem. The Journal of Engineering and Fundamentals, 1(1), 1–10.
Zhang, J., & You, T. H. (2020). Method for selecting desirable product(s) through multiple attribute online reviews considering customer’s aspirations. Journal of Industrial Engineering and Engineering Management, 34(5), 24–31.
Zhang, Z., & Li, Z. L. (2021). Personalized individual semantics-based consistency control and consensus reaching in linguistic group decision making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(9), 5623–5635.
Zhang, Z., & Li, Z. L. (2022). Consensus-based TOPSIS-sort-B for multi-criteria sorting in the context of group decision-making. Annals of Operations Research, 325(2), 911–938.
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China [Nos. 62276217, 61876157, 71571148], the Sichuan Science and Technology Program [No. 2022JDJQ0034], the Chongqing Key Laboratory Project of Computational Intelligence [No. 2020FF03], the Chengdu Philosophy and Social Science Program [No. 2023CS124], the project of Philosophy and Social Science Research Innovation Team in Kunming University of Science and Technology [No. CXTD2023004], the Fundamental Research Funds for the Central Universities [No.2682024ZTPY021].
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Liu, D., Xu, J. & Du, Y. An integrated HPF-TODIM-MULTIMOORA approach for car selection through online reviews. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-05972-z
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DOI: https://doi.org/10.1007/s10479-024-05972-z