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The analytics of product-design requirements using dynamic internet data: application to Chinese smartphone market

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Listed:
  • Xinjun Lai
  • Qixiang Zhang
  • Qingxin Chen
  • Yunbao Huang
  • Ning Mao
  • Jianjun Liu

Abstract

To accommodate the diverse users demands for consumer products, enterprises need to design and develop different lines of products according to different groups of users. Dynamic internet data, including product reviews, user attributes, and product configurations, are utilised to model users' stochastic product choice behaviours and mine the product design requirements of features, performance levels, and quantity. First, the web crawler is applied to collect internet data, and then the data are structured and the demand information is retrieved. Second, a product choice model is employed to capture the heterogeneity and correlation of user demands on product features. In particular, users' implicit requirements in terms of product function and performance are elicited from the text mining of product reviews. Third, incorporating various user requirements mined from dynamic internet data, graph theory analysis is introduced into design generation, product improvement, and market analysis. A case study on Chinese smartphones is presented, where the results show that the proposed method is practical and suitable for product-design analysis using the large volume of dynamic internet data.

Suggested Citation

  • Xinjun Lai & Qixiang Zhang & Qingxin Chen & Yunbao Huang & Ning Mao & Jianjun Liu, 2019. "The analytics of product-design requirements using dynamic internet data: application to Chinese smartphone market," International Journal of Production Research, Taylor & Francis Journals, vol. 57(18), pages 5660-5684, September.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:18:p:5660-5684
    DOI: 10.1080/00207543.2018.1541200
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    Cited by:

    1. Pan, Yu & He, Sylvia Y., 2023. "An investigation into the impact of the built environment on the travel mobility gap using mobile phone data," Journal of Transport Geography, Elsevier, vol. 108(C).
    2. 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.
    3. Pan, Yu & He, Sylvia Y., 2022. "Analyzing COVID-19’s impact on the travel mobility of various social groups in China’s Greater Bay Area via mobile phone big data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 263-281.

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