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An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content

Author

Listed:
  • Hanyang Luo

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

  • Wugang Song

    (College of Management, Shenzhen University, Shenzhen 518060, China)

  • Wanhua Zhou

    (College of Management, Shenzhen University, Shenzhen 518060, China)

  • Xudong Lin

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

  • Sumin Yu

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

Abstract

This work attempts to develop a novel framework to reveal the preferences of Chinese car users from online user-generated content (UGC) and guides automotive companies to allocate resources reasonably for sustainable design and improve existing product or service attributes. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words, and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users’ satisfaction with different attributes based on the rules and weigh the importance of attributes using the TF-IDF method, thus constructing an importance-satisfaction gap analysis (ISGA) model. Finally, a case study is used to realize the framework evaluation and analysis of the twenty top-mentioned attributes of a small-sized sedan, and the dynamic ISGA-time model is constructed to analyze the changing trend of the importance of user demand and satisfaction. The results show the priority of resource allocation/adjustment. Fuel consumption and driving experience urgently need resource input and management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13336-:d:1233782
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    References listed on IDEAS

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    Cited by:

    1. Saurav Kumar, 2023. "Red Ocean Strategy: A Literature Review," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 91-100.

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