Skip to main content
SpringerLink
Log in

How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating

  • Published:
Group Decision and Negotiation Aims and scope Submit manuscript

Abstract

The decision on which hotel to book is a high-cost process for individuals and even more for groups due to the information explosion on online travel agent platforms. To assist group selecting the optimal hotel that meets the majority affective expectations, this study proposes a group hotel selection frame for booking with Kansei text mining and consensus coordinating. Firstly, to quantify the tourists’ affective needs for hotels, a Kansei-related dictionary towards hotel domain is constructed by Kansei text mining. Secondly, the bilattice-based Kansei score of hotels is defined and measured to represent hotel affective information scientifically. Then, a group consensus model with minimum adjustment cost is introduced to coordinate consistency of individual preferences in a tourist group. Moreover, a group hotel ranking based on group affective preference is interactively generated to meet the group personalized demands. Finally, a case study on Trip.com is conducted to demonstrate the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Ben-Arieh D, Easton T (2007) Multi-criteria group consensus under linear cost opinion elasticity. Decis Support Syst 43(3):713–721

    Article  Google Scholar 

  • Bi J, Liu Y, Fan Z, Cambria E (2019) Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. Int J Prod Res 57:7068–7088

    Article  Google Scholar 

  • Cao M, Wu J, Chiclana F, Ureña R, Herrera-Viedma E (2020) A personalized consensus feedback mechanism based on maximum harmony degree. IEEE Trans Syst Man Cybern A 51(10):6134–6146

    Article  Google Scholar 

  • Casaló L, Flavián C, Guinalíu M, Ekinci Y (2015) Do online hotel rating schemes influence booking behaviors? Int J Hosp Manage 49:28–36

    Article  Google Scholar 

  • Chang J, Li H, Bi J (2021) Personalized travel recommendation: a hybrid method with collaborative filtering and social network analysis. Curr Issues Tour 25(14):2338–2356

    Article  Google Scholar 

  • Cheng C, Chen C (2018) Creating excellent and competitive motels services. Int J Contemp Hosp M 30(2):836–854

    Article  Google Scholar 

  • Chen M, Hsiao Y, Chang K, Lin M (2019) Applying big data analytics to support Kansei engineering for hotel service development. Data Technol Appl 53:33–57

    Google Scholar 

  • Dong Y, Xu Y, Li H, Feng B (2010) The OWA-based consensus operator under linguistic representation models using position indexes. Eur J Oper Res 203(2):455–463

    Article  Google Scholar 

  • Guo F, Hu M, Duffy VG, Shao H, Ren Z (2021) Kansei evaluation for group of users: A data-driven approach using dominance-based rough sets. Adv Eng Inform 47:101241

    Article  Google Scholar 

  • Herrera-Viedma E, Herrera F, Chiclana F (2002) A consensus model for multiperson decision making with different preference structures. IEEE Trans Syst Man Cybern A 32(3):394–402

    Article  Google Scholar 

  • Jeong H, Kim M (2019) HGGC: A hybrid group recommendation model considering group cohesion. Expert Syst Appl 136:73–82

    Article  Google Scholar 

  • Jin J, Jia D, Chen K (2022) Mining online reviews with a Kansei-integrated Kano model for innovative product design. Int J Prod Res 60(22):6708–6727

  • Kano N, Seraku K, Takahaski F, Tsuji S (1984) Attractive quality and must-be quality. J Soc Qual Control 14(2):38–48

    Google Scholar 

  • Kim D, Perdue RR (2013) The effects of cognitive, affective, and sensory attributes on hotel choice. Int J Hosp Manag 35:246–257

    Article  Google Scholar 

  • Kwok P, Lau H (2019) Hotel selection using a modified TOPSIS-based decision support algorithm. Decis Support Syst 120:95–105

    Article  Google Scholar 

  • Liu S, Ríos-Insua D (2020) Group decision making with affective features. Group Decis Negot 29(5):843–869

    Article  Google Scholar 

  • Llinares C, Page AF (2011) Kano’s model in Kansei Engineering to evaluate subjective real estate consumer preferences. Int J Ind Ergonom 41(3):233–246

    Article  Google Scholar 

  • Martínez P, Del Bosque IR (2013) CSR and customer loyalty: the roles of trust, customer identification with the company and satisfaction. Int J Contemp Hosp M 35:89–99

    Article  Google Scholar 

  • Ming L, Yan H (2016) Applying Kansei engineering to service design: a case study of budget hotel service. In: International conference on service systems and service management. IEEE, pp 1–6

  • Nie R, Tian Z, Wang J, Chin K (2020) Hotel selection driven by online textual reviews: applying a semantic partitioned sentiment dictionary and evidence theory. Int J Hosp Manag 88:102495

    Article  Google Scholar 

  • Peng H, Zhang H, Wang J (2018) Cloud decision support model for selecting hotels on TripAdvisor.com with probabilistic linguistic information. Int J Hosp Manag 68:124–138

    Article  Google Scholar 

  • Scerri MA, Jenkins JM, Lovell G (2017) A grounded theory model of service language in Australia’s luxury hotels. J Hosp Tour Manag 33:82–92

    Article  Google Scholar 

  • Shi Y, Peng Q (2021) Enhanced customer requirement classification for product design using big data and improved Kano model. Adv Eng Inform 49:101340

    Article  Google Scholar 

  • Sun Q, Wu J, Chiclana F, Fujita H, Herrera-Viedma E (2021) A dynamic feedback mechanism with attitudinal consensus threshold for minimum adjustment cost in group decision making. IEEE Trans Fuzzy Syst 30(5):1287–1301

    Article  Google Scholar 

  • Tavana M, Mousavi S, Mina H, Salehian F (2020) A dynamic decision support system for evaluating peer-to-peer rental accommodations in the sharing economy. Int J Hosp Manag 91:102653

    Article  Google Scholar 

  • Victor P, Cornelis C, De Cock M, Da Silva P (2009) Gradual trust and distrust in recommender systems. Fuzzy Set Syst 160(10):1367–1382

    Article  Google Scholar 

  • Violante MG, Vezzetti E (2017) Kano qualitative vs quantitative approaches: an assessment framework for products attributes analysis. Comput Ind 86:15–25

    Article  Google Scholar 

  • Wang L, Wang X, Peng J, Wang J (2020) The differences in hotel selection among various types of travelers: a comparative analysis with a useful bounded rationality behavioural decision support model. Tour Manag 76:1–16

    Article  Google Scholar 

  • Wang S, Wu J, Chiclana F, Sun Q, Herrera-Viedma E (2022) Two stage feedback mechanism with different power structures for consensus in large-scale group decision-making. IEEE T Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2022.3144536

    Article  Google Scholar 

  • Wang T, Zhou M (2020) A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction. Int J Ind Ergonom 76:102901

    Article  Google Scholar 

  • Wang W, Li Z, Tian Z, Wang J, Cheng M (2018) Extracting and summarizing affective features and responses from online product descriptions and reviews: a Kansei text mining approach. Eng Appl Artif Intel 73:149–162

    Article  Google Scholar 

  • Wang W, Wang J, Li Z, Tian Z, Tsui E (2019) Multiple affective attribute classification of online customer product reviews: a heuristic deep learning method for supporting Kansei engineering. Eng Appl Artif Intel 85:33–45

    Article  Google Scholar 

  • Wang X, Wang S, Zhang H, Wang J, Li L (2021) The recommendation method for hotel selection under traveler preference characteristics: a cloud-based multi-criteria group decision support model. Group Decis Negot 30(6):1433–1469

    Article  Google Scholar 

  • Wu J, Chang J, Cao Q, Liang C (2019a) A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust. Comput Ind Eng 127:853–864

    Article  Google Scholar 

  • Wu J, Dai L, Chiclana F, Fujita H, Herrera-Viedma E (2018) A minimum adjustment cost feedback mechanism based consensus model for group decision-making under social network with distributed linguistic trust. Inform Fus 41:232–242

    Article  Google Scholar 

  • Wu J, Hong Q, Cao M, Liu Y, Fujita H (2021a) A group consensus-based travel destination evaluation method with online reviews. Appl Intell 52(2):1306–1324

    Article  Google Scholar 

  • Wu J, Ma X, Chiclana F, Liu Y, Wu Y (2022) A consensus group decision making method for hotel selection with online reviews by sentiment analysis. Appl Intell 52:10716–10740

  • Wu J, Sun Q, Fujita H, Chiclana F (2019b) An attitudinal consensus degree to control the feedback mechanism in group decision-making with different adjustment cost. Knowl-Based Syst 164:265–273

    Article  Google Scholar 

  • Wu J, Zhao Z, Sun Q, Fujita H (2021b) A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network. Inform Fus 67:80–93

    Article  Google Scholar 

  • Wu Y, Zhang Z, Kou G, Zhan H, Chao X, Li C, Dong Y, Herrera F (2021c) Distributed linguistic representations in decision making: taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence. Inform Fus 65:165–178

    Article  Google Scholar 

  • Yager R (1996) Quantifier guided aggregation using OWA operators. Int J Intell Syst 11(1):49–73

    Article  Google Scholar 

  • Yan H, Li M (2021) An uncertain Kansei engineering methodology for behavioral service design. IISE Trans 53:497–522

    Article  Google Scholar 

  • Yu S, Du Z, Xu X (2020) Hierarchical punishment-driven consensus model for probabilistic linguistic large-group decision making with application to global supplier selection. Group Decis Negot 30(6):1343–1372

    Article  Google Scholar 

  • Yu S, Wang J, Wang J, Li L (2018) A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Appl Soft Comput 67:741–755

    Article  Google Scholar 

  • Zhang C, Zhao M, Cai M, Xiao Q (2020a) Multi-stage multi-attribute decision making method based on online reviews for hotel selection considering the aspirations with different development speeds. Comput Ind Eng 143:106421

    Article  Google Scholar 

  • Zhang H, Zhao S, Kou G, Li C, Dong Y, Herrera F (2020b) An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: research paradigms and challenges. Inform Fus 60:65–79

    Article  Google Scholar 

  • Zhang G, Dong Y, Xu Y, Li H (2011) Minimum-cost consensus models under aggregation operators. IEEE Trans Syst Man Cybern A 41(6):1253–1261

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (71971124, 71932005); the Liberal Arts Development Fund of Nankai University (ZB21BZ0106); and the One Hundred Talents Program of Nankai University (63223067).

Author information

Authors and Affiliations

  1. College of Tourism and Service Management, Nankai University, Tianjin, 300350, China

    Jia-Li Chang & Hui Li

  2. School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China

    Jian Wu

Authors
  1. Jia-Li Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Li.

Ethics declarations

Conflict of interest

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, JL., Li, H. & Wu, J. How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating. Group Decis Negot 32, 327–358 (2023). https://doi.org/10.1007/s10726-022-09810-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10726-022-09810-0

Keywords

Search

Navigation