IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3132-d1372717.html
   My bibliography  Save this article

Construction of Product Appearance Kansei Evaluation Model Based on Online Reviews and FAHP: A Case Study of Household Portable Air Conditioners

Author

Listed:
  • Yuanjian Du

    (Department of Industrial Design, Hanyang University, ERICA Campus, Ansan 15588, Republic of Korea)

  • Meng Zhang

    (Department of Industrial Design, Hanyang University, ERICA Campus, Ansan 15588, Republic of Korea)

  • Mobing Cai

    (Department of Engineering Science, University of Oxford, Oxford OX1 3BH, UK)

  • Kyungjin Park

    (Department of Industrial Design, Hanyang University, ERICA Campus, Ansan 15588, Republic of Korea)

Abstract

Meeting the personalized needs of users is the key to achieving the sustainable success of a product. It depends not only on the product’s functionality but also on satisfying users’ emotional needs for the product’s appearance. Therefore, researchers have been conducting research focusing on Kansei engineering theory to determine users’ emotional needs effectively. The initial process involves accurately extracting and filtering emotional data and Kansei words from consumers. Thus, we propose an evaluation model to efficiently obtain, screen, and sort these Kansei words based on Kansei engineering, using household portable air conditioners as research subjects. By integrating techniques for online user comment mining methods, users’ Kansei terms related to the product’s appearance can be gathered efficiently. These terms are then combined with image samples and filtered to determine a final set of 16 Kansei word pairs. Subsequently, the fuzzy analytic hierarchy process (FAHP) is utilized to prioritize these terms, and the fuzzy comprehensive evaluation (FCE) method is used to validate the results and determine the applicability of the evaluation model. The results showed that Kansei words could be quickly and objectively acquired using existing text mining techniques on online reviews. Moreover, the weights of different Kansei terms of the product’s appearance in the consumer’s perception are accurately produced through the FAHP. This evaluation model marks a significant advancement in accurately obtaining users’ emotional data in Kansei engineering. It offers valuable guidance for designing products that meet users’ personalized needs, enhancing design efficiency and reducing resource wastage at the early stages of designing, and improving the sustainability development of Kansei engineering.

Suggested Citation

  • Yuanjian Du & Meng Zhang & Mobing Cai & Kyungjin Park, 2024. "Construction of Product Appearance Kansei Evaluation Model Based on Online Reviews and FAHP: A Case Study of Household Portable Air Conditioners," Sustainability, MDPI, vol. 16(8), pages 1-21, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3132-:d:1372717
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3132/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/8/3132/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    2. Sunny Han Han & Huimin Zhang, 2022. "Progress and Prospects in Industrial Heritage Reconstruction and Reuse Research during the Past Five Years: Review and Outlook," Land, MDPI, vol. 11(12), pages 1-19, November.
    3. Lei Li & Lin Lu & Yuchen Xu & Xiaolong Sun, 2020. "The spatiotemporal evolution and influencing factors of hotel industry in the metropolitan area: An empirical study based on China," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-23, May.
    4. Yinghui Huang & Hui Liu & Lin Zhang & Shen Li & Weijun Wang & Zhihong Ren & Zongkui Zhou & Xueyao Ma, 2021. "The Psychological and Behavioral Patterns of Online Psychological Help-Seekers before and during COVID-19 Pandemic: A Text Mining-Based Longitudinal Ecological Study," IJERPH, MDPI, vol. 18(21), pages 1-19, November.
    5. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    6. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    7. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    2. Ian Sutherland & Youngseok Sim & Seul Ki Lee & Jaemun Byun & Kiattipoom Kiatkawsin, 2020. "Topic Modeling of Online Accommodation Reviews via Latent Dirichlet Allocation," Sustainability, MDPI, vol. 12(5), pages 1-15, February.
    3. Sunyoung Hlee & Hanna Lee & Chulmo Koo, 2018. "Hospitality and Tourism Online Review Research: A Systematic Analysis and Heuristic-Systematic Model," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    4. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    5. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    6. Young Joon Park & Jaewoo Joo & Charin Polpanumas & Yeujun Yoon, 2021. "“Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    7. Zajadacz Alina & Minkwitz Aleksandra, 2020. "Using Social Media Data to Plan for Tourism," Quaestiones Geographicae, Sciendo, vol. 39(3), pages 125-138, September.
    8. Zhu, Yongmin & Liu, Miaomiao & Zeng, Xiaohua & Huang, Pei, 2020. "The effects of prior reviews on perceived review helpfulness: A configuration perspective," Journal of Business Research, Elsevier, vol. 110(C), pages 484-494.
    9. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print halshs-01923243, HAL.
    10. Miyea Kim & Jeongsoo Han & Mina Jun, 2020. "Do same-level review ratings have the same level of review helpfulness? The role of information diagnosticity in online reviews," Information Technology & Tourism, Springer, vol. 22(4), pages 563-591, December.
    11. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    12. Filieri, Raffaele & Lin, Zhibin & Pino, Giovanni & Alguezaui, Salma & Inversini, Alessandro, 2021. "The role of visual cues in eWOM on consumers’ behavioral intention and decisions," Journal of Business Research, Elsevier, vol. 135(C), pages 663-675.
    13. Wu, Ruijuan & Chen, Jiuqi & Lu Wang, Cheng & Zhou, Liying, 2022. "The influence of emoji meaning multipleness on perceived online review helpfulness: The mediating role of processing fluency," Journal of Business Research, Elsevier, vol. 141(C), pages 299-307.
    14. Manes, Eran & Tchetchik, Anat, 2018. "The role of electronic word of mouth in reducing information asymmetry: An empirical investigation of online hotel booking," Journal of Business Research, Elsevier, vol. 85(C), pages 185-196.
    15. Raksmey Sann & Pei-Chun Lai & Hui-Chen Chang, 2020. "Does Culture of Origin Have an Impact on Online Complaining Behaviors? The Perceptions of Asians and Non-Asians," Sustainability, MDPI, vol. 12(5), pages 1-37, February.
    16. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Grenoble Ecole de Management (Post-Print) halshs-01923243, HAL.
    17. Filieri, Raffaele & Acikgoz, Fulya & Du, Hao, 2023. "Electronic word-of-mouth from video bloggers: The role of content quality and source homophily across hedonic and utilitarian products," Journal of Business Research, Elsevier, vol. 160(C).
    18. Flavio Martins & Maria Fatima Almeida & Rodrigo Calili & Agatha Oliveira, 2020. "Design Thinking Applied to Smart Home Projects: A User-Centric and Sustainable Perspective," Sustainability, MDPI, vol. 12(23), pages 1-27, December.
    19. Jochen Wulf, 2020. "Development of an AHP hierarchy for managing omnichannel capabilities: a design science research approach," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 39-68, April.
    20. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3132-:d:1372717. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.