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An Integrated Framework of Product Kansei Decision-Making Based on Hesitant Linguistic Fuzzy Term Sets

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HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies (HCII 2020)

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Abstract

Kansei adjectives have the advantage of close to consumers’ perception of a product. But consumers may show hesitation and opinion discrepancy while expressing their preferences through comparative Kansei adjectives. To address this, this article investigates hesitant linguistic expression and its application in product Kansei decision-making. An integrated framework is firstly presented based on hesitant fuzzy linguistic term sets (HFLTSs), which involves a consensus model for assessing consistency of consumers’ preferences, particle swarm optimization (PSO) method for adjusting Kansei opinions when agreement fails, and the technique for order preference by similarity to an ideal solution (TOPSIS) for yielding ranked product solutions. An example of charging piles design was used to illustrate the necessity of considering consumers’ hesitation in Kansei decision-making. With the proposed method, the consensus level of consumers’ preferences is enhanced from 0.8339 to 0.9052, and the overall satisfaction degree is also improved. Furthermore, the results of Kansei decision-making through optimizing Kansei preferences are significantly different from that without optimization. This improvement demonstrates that hesitance and consensus change will influence design decision-making and they should be considered in product Kansei decision-making. The given example shows the validity and suitability of the proposed approach.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (Grant No. 51805043), the Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102259202), the China Postdoctoral Science Foundation (Grant No. 2019M663604), and Shaanxi innovation capability support project of China (Grant No. 2020PT-014). We are grateful of their support. We would also like to thank the anonymous reviewers for their invaluable comments and suggestions.

Author information

Authors and Affiliations

  1. School of Construction Machinery, Chang’an University, Xi’an, 710064, China

    Yan-pu Yang & Gang-feng Wang

  2. Xi’an Zhongqing Packaging Machinery Co. Ltd., Xi’an, 710086, China

    Jun-wen Shi

  3. Light Industrial Xi’an Mechanic Design Research Institute Co. Ltd., Xi’an, 710086, China

    Jun-wen Shi

Authors
  1. Yan-pu Yang
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  2. Jun-wen Shi
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  3. Gang-feng Wang
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Corresponding author

Correspondence to Yan-pu Yang .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  3. World Usability Day and Bentley User Experience Center, Newton Center, MA, USA

    Elizabeth Rosenzweig

  4. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  5. San Jose State University, San Jose, CA, USA

    Abbas Moallem

  6. Eindhoven Univesity of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Matthias Rauterberg

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Yang, Yp., Shi, Jw., Wang, Gf. (2020). An Integrated Framework of Product Kansei Decision-Making Based on Hesitant Linguistic Fuzzy Term Sets. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, PL.P., Moallem, A., Rauterberg, M. (eds) HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies. HCII 2020. Lecture Notes in Computer Science(), vol 12423. Springer, Cham. https://doi.org/10.1007/978-3-030-60114-0_24

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  • DOI: https://doi.org/10.1007/978-3-030-60114-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60113-3

  • Online ISBN: 978-3-030-60114-0

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