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Identification of the to-be-improved product features based on online reviews for product redesign

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  • Lei Zhang
  • Xuening Chu
  • Deyi Xue

Abstract

Acquisition of customer needs usually serves as the basis for the identification of to-be-improved features for the product redesign process. However, the customer's true needs tend to be non-obvious and are difficult to extract from the data source like interviews or market survey. In the era of Big Data, with the advances in e-commerce, the customer's online review has become one of the most important data source to reveal the insight of customer's preference. In this paper, an online-review-based approach is introduced to identify the to-be-improved product features. The product features and corresponding opinions are extracted and reduced based on the semantic similarity. A structured preference model based on the semantic orientation analysis is constructed. A redesign index is subsequently introduced to measure the priority of redesign for each feature, and a target feature selection model is created to identify the to-be-improved features from candidate features considering engineering cost, redesign lead time and technical risk. A case study for smartphones is developed to demonstrate the effectiveness of the developed approach. In the future study, the online reviews may be combined with the traditional survey data to provide a more effective and reliable identification on the to-be-improved product features.

Suggested Citation

  • Lei Zhang & Xuening Chu & Deyi Xue, 2019. "Identification of the to-be-improved product features based on online reviews for product redesign," International Journal of Production Research, Taylor & Francis Journals, vol. 57(8), pages 2464-2479, April.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:8:p:2464-2479
    DOI: 10.1080/00207543.2018.1521019
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    Cited by:

    1. Chao He & Zhongkai Li & Dengzhuo Liu & Guangyu Zou & Shuai Wang, 2023. "Improving the functional performances for product family by mining online reviews," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2809-2824, August.
    2. Xiao, Yan & Li, Congdong & Thürer, Matthias & Liu, Yide & Qu, Ting, 2022. "User preference mining based on fine-grained sentiment analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    3. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
    4. Huang, Shupeng & Potter, Andrew & Eyers, Daniel & Li, Qinyun, 2021. "The influence of online review adoption on the profitability of capacitated supply chains," Omega, Elsevier, vol. 105(C).
    5. Hanyang Luo & Wugang Song & Wanhua Zhou & Xudong Lin & Sumin Yu, 2023. "An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content," Sustainability, MDPI, vol. 15(18), pages 1-29, September.
    6. Yakubu, Hanan & Kwong, C.K., 2021. "Forecasting the importance of product attributes using online customer reviews and Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 171(C).

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