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Exploiting user experience from online customer reviews for product design

Author

Listed:
  • Yang, Bai
  • Liu, Ying
  • Liang, Yan
  • Tang, Min

Abstract

Understanding user experience (UX) becomes more important in a market-driven design paradigm because it helps designers uncover significant factors, such as user’s preference, usage context, product features, as well as their interrelations. Conventional means, such as questionnaire, survey and self-report with predefined questions and prompts, are used to collect information about users’ experience during various UX studies. However, such data is often limited and restricted by initial setups, and they won’t easily allow designers to identify all critical elements such as user profile, context, related product features, etc. Meanwhile, with widely accessible social media, the volume and velocity of customer-generated data are fast-increasing. While it is generally acknowledged that such data contains important elements in understanding and analyzing UX, extracting them to assist product design remains a challenging issue. In this study, how UX data underlying product design can be isolated and restored from customer online reviews is examined. A faceted conceptual model is proposed to elucidate the crucial factors of UX, which serves as an operational mechanism connecting to product design. A methodology of establishing a UX knowledge base from customer online reviews is then proposed to support UX-centered design activities, which consists of three stages, i.e., UX discovery to extract UX data from a single review, UX data integration to group similar data and UX network formalization to build up the causal dependencies among UX groups. Using a case study on smart mobile phone reviews, examples of UX data discovered are demonstrated and both customers and designers concerned key product features and usage situations are exemplified. This study explores the feasibility to discover valuable UX data as well as their relations automatically for product design and business strategic plan by analyzing a large volume of customer online data.

Suggested Citation

  • Yang, Bai & Liu, Ying & Liang, Yan & Tang, Min, 2019. "Exploiting user experience from online customer reviews for product design," International Journal of Information Management, Elsevier, vol. 46(C), pages 173-186.
  • Handle: RePEc:eee:ininma:v:46:y:2019:i:c:p:173-186
    DOI: 10.1016/j.ijinfomgt.2018.12.006
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    References listed on IDEAS

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    1. Sheng, Margaret L. & Teo, Thompson S.H., 2012. "Product attributes and brand equity in the mobile domain: The mediating role of customer experience," International Journal of Information Management, Elsevier, vol. 32(2), pages 139-146.
    2. Zhan, Jiaming & Loh, Han Tong & Liu, Ying, 2009. "On macro- and micro-level information in multiple documents and its influence on summarization," International Journal of Information Management, Elsevier, vol. 29(1), pages 57-66.
    3. Xu, Xun & Wang, Xuequn & Li, Yibai & Haghighi, Mohammad, 2017. "Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors," International Journal of Information Management, Elsevier, vol. 37(6), pages 673-683.
    4. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    5. Kim, Dong-hyu & Lee, Heejin, 2016. "Effects of user experience on user resistance to change to the voice user interface of an in‑vehicle infotainment system: Implications for platform and standards competition," International Journal of Information Management, Elsevier, vol. 36(4), pages 653-667.
    6. Ahmad, Shimi Naurin & Laroche, Michel, 2017. "Analyzing electronic word of mouth: A social commerce construct," International Journal of Information Management, Elsevier, vol. 37(3), pages 202-213.
    7. Palese, B. & Usai, A., 2018. "The relative importance of service quality dimensions in E-commerce experiences," International Journal of Information Management, Elsevier, vol. 40(C), pages 132-140.
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    Cited by:

    1. Son, Youngdoo & Kim, Wonjoon, 2023. "Development of methodology for classification of user experience (UX) in online customer review," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    2. Jiao, Hao & Wang, Lindong & Yang, Jifeng, 2023. "Standing head and shoulders above others? Complementor experience-based design and crowdfunding success on digital platforms," Technovation, Elsevier, vol. 128(C).

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