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Product innovation based on online review data mining: a case study of Huawei phones

Author

Listed:
  • Hui Zhang

    (Hangzhou Dianzi University)

  • Huguang Rao

    (Hangzhou Dianzi University)

  • Junzheng Feng

    (Hangzhou Dianzi University)

Abstract

Online reviews contain a plethora of useful information that plays a vital role in consumer choices and provides a dominant reference for enterprises to adopt strategies for product development and improvement. The paper examines the www.zol.com.cn website as a source of information and Huawei Mate phones as a research target, processes the data of consumers’ online reviews on three types of Huawei Mate phones, discusses the correlations between online reviews and phone improvement, and proposes some suggestions for future product improvement. This empirical study shows that the correlation between the change in the degree of feature satisfaction and phone improvement is strong; the correlation between the change in the degree of feature satisfaction and product improvement is stronger than that between the change in the degree of feature attention and product improvement. Enterprises can determine the direction and contents of phone improvement based on information from reviews on its preceding phone model. This study can help enterprises master market requirements, understand consumers’ behaviour and improve the quality and efficiency of product innovation.

Suggested Citation

  • Hui Zhang & Huguang Rao & Junzheng Feng, 2018. "Product innovation based on online review data mining: a case study of Huawei phones," Electronic Commerce Research, Springer, vol. 18(1), pages 3-22, March.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:1:d:10.1007_s10660-017-9279-2
    DOI: 10.1007/s10660-017-9279-2
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    2. Kyungbo Park & Jeonghwa Cha & Jongyi Hong, 2023. "Developing a Framework for Evaluating and Predicting Management Innovation in Public Research Institutions," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
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    4. Shugang Li & Fang Liu & Yuqi Zhang & Boyi Zhu & He Zhu & Zhaoxu Yu, 2022. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review," Mathematics, MDPI, vol. 10(19), pages 1-26, September.
    5. Xiaoguang Wang & Tao Lv & Lei Fan, 2022. "New Energy Vehicle Consumer Demand Mining Research Based on Fusion Topic Model: A Case in China," Sustainability, MDPI, vol. 14(6), pages 1-13, March.
    6. Yuanyuan Guo & Yanqing Wang & Chaoyou Wang, 2019. "Exploring the Salient Attributes of Short-Term Rental Experience: An Analysis of Online Reviews from Chinese Guests," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
    7. Satish Kumar & Weng Marc Lim & Nitesh Pandey & J. Christopher Westland, 2021. "20 years of Electronic Commerce Research," Electronic Commerce Research, Springer, vol. 21(1), pages 1-40, March.
    8. 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).
    9. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    10. Jianping Li & Yinhong Yao & Yuanjie Xu & Jingyu Li & Lu Wei & Xiaoqian Zhu, 2019. "Consumer’s risk perception on the Belt and Road countries: evidence from the cross-border e-commerce," Electronic Commerce Research, Springer, vol. 19(4), pages 823-840, December.

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