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A novel approach for product competitive analysis based on online reviews

Author

Listed:
  • Zhen He

    (Tianjin University)

  • Lu Zheng

    (Tianjin University)

  • Shuguang He

    (Tianjin University)

Abstract

Recently, online reviews have become a prevalent information source for competitive analysis because they provide rich information on the voices of customers. Based on online reviews, we propose a novel method named Integrated-Degree based K-shell decomposition (ID-KS) to conduct competitive analysis via product comparison networks. Under the consideration of feature differences among products, we apply text-mining approaches and ID-KS to convert online reviews into competitive insights including competitor identification, product comparison, product ranking, brand comparison and market-structure analysis. To validate the feasibility and the effectiveness of ID-KS, we demonstrate our approach in two cases, SUV cars and laptops, and compare it with state-of-the-art methods. The results show that ID-KS analyzes product comparison networks more effectively and properly, and it derives comprehensive comparative insights that are not fully captured by existing studies.

Suggested Citation

  • Zhen He & Lu Zheng & Shuguang He, 2023. "A novel approach for product competitive analysis based on online reviews," Electronic Commerce Research, Springer, vol. 23(4), pages 2259-2290, December.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:4:d:10.1007_s10660-022-09534-y
    DOI: 10.1007/s10660-022-09534-y
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    References listed on IDEAS

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    1. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    2. Jian-Wu Bi & Yang Liu & Zhi-Ping Fan & Erik Cambria, 2019. "Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7068-7088, November.
    3. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    4. Candi, Marina & Jae, Haeran & Makarem, Suzanne & Mohan, Mayoor, 2017. "Consumer responses to functional, aesthetic and symbolic product design in online reviews," Journal of Business Research, Elsevier, vol. 81(C), pages 31-39.
    5. Lee, Kiljae & Yu, Chunyan, 2018. "Assessment of airport service quality: A complementary approach to measure perceived service quality based on Google reviews," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 28-44.
    6. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    7. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    8. Kim Hua Tan & Guojun Ji & Chee Peng Lim & Ming-Lang Tseng, 2017. "Using big data to make better decisions in the digital economy," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 4998-5000, September.
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