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Integrated Online Consumer Preference Mining for Product Improvement with Online Reviews

Author

Listed:
  • Li Jie

    (School of Economics and Management, Hebei University of Technology, Tianjin300401, China)

  • Lan Qiaoling

    (School of Economics and Management, Hebei University of Technology, Tianjin300401, China)

  • Liu Lu

    (School of Economics and Management, Hebei University of Technology, Tianjin300401, China)

  • Yang Fang

    (School of Economics and Management, Hebei University of Technology, Tianjin300401, China)

Abstract

Exploring consumer preferences for a product is essential for the enterprise in product improvement. Many studies have been conducted in consumer preference. However, few studies have concentrated on evaluating the product and service characteristics of a specific product, to facilitate product and service improvements. This study proposes a systematic research framework for exploring major product and service features that reflect consumer preferences based on the online reviews. By creatively integrating quantitative studies of multiple linear regression and meta-analysis, this study expects to generate a feature-based preference importance ranking. Furthermore, by adopting an importance-satisfaction analysis, we can draw a matrix that is valuable in product improvement. Coupled with the preference rankings, implications for competitive strategies that facilitate product improvement can be drawn. The effectiveness of this methodology is verified by a case study of laptop on the basis of the online reviews from amazon.cn.

Suggested Citation

  • Li Jie & Lan Qiaoling & Liu Lu & Yang Fang, 2018. "Integrated Online Consumer Preference Mining for Product Improvement with Online Reviews," Journal of Systems Science and Information, De Gruyter, vol. 7(1), pages 17-36, March.
    • Handle: RePEc:bpj:jossai:v:7:y:2018:i:1:p:17-36:n:2
    DOI: 10.21078/JSSI-2019-017-20
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    References listed on IDEAS

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    1. Cai, Zhen & Aguilar, Francisco X., 2013. "Consumer stated purchasing preferences and corporate social responsibility in the wood products industry: A conjoint analysis in the U.S. and China," Ecological Economics, Elsevier, vol. 95(C), pages 118-127.
    2. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
    3. Chattaraman, Veena & Rudd, Nancy A. & Lennon, Sharron J., 2009. "Identity salience and shifts in product preferences of Hispanic consumers: Cultural relevance of product attributes as a moderator," Journal of Business Research, Elsevier, vol. 62(8), pages 826-833, August.
    4. Baltas, George & Saridakis, Charalampos, 2013. "An empirical investigation of the impact of behavioural and psychographic consumer characteristics on car preferences: An integrated model of car type choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 92-110.
    5. Li Jie & Xue Wenyi & Yang Fang & Li Yakun, 2017. "An Integrated Research Framework for Effect of EWOM," Journal of Systems Science and Information, De Gruyter, vol. 5(4), pages 343-355, August.
    6. Christy M.K. Cheung & Gloria W.W. Chan & Moez Limayem, 2005. "A Critical Review of Online Consumer Behavior: Empirical Research," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 3(4), pages 1-19, October.
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