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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
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    References listed on IDEAS

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    1. 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.
    2. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
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