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A computer-based approach for analyzing consumer demands in electronic word-of-mouth

Author

Listed:
  • Chung-Yi Lin

    (National Cheng Kung University)

  • Shu-Yi Liaw

    (National Pingtung University of Science and Technology)

  • Chao-Chun Chen

    (National Cheng Kung University)

  • Mao-Yuan Pai

    (National Cheng Kung University)

  • Yuh-Min Chen

    (National Cheng Kung University)

Abstract

Consumer opinions are one of the most valuable assets that enterprises have, and thus questionnaires are often employed to investigate the views of consumers. However, this approach requires a large amount of human labor and time, and, most importantly, it cannot automatically find out consumers’ needs. However, many consumers now share their appraisals of products or services through electronic word-of-mouth (eWOM). Since these usually reflect consumer needs, and thus their demands, collecting and analyzing eWOM data has become a key task for many businesses. Nonetheless, current eWOM-related research focuses on its transmission, influence, issues, and marketing, and there seem to be very few studies that apply eWOM to develop consumer needs analysis systems. In order to effectively collect and analyze eWOM data, this study proposes a computer-based approach for analyzing consumer demands. The approach utilizes sentiment analysis to develop extraction methods for use with eWOM appraisals. It thus uses eWOM appraisals to find out consumer demands. This work integrates eWOM with information technology to develop an approach to computerize consumer needs analysis. It is expected that the results will help enterprises to improve the quality of their products and market competitiveness.

Suggested Citation

  • Chung-Yi Lin & Shu-Yi Liaw & Chao-Chun Chen & Mao-Yuan Pai & Yuh-Min Chen, 2017. "A computer-based approach for analyzing consumer demands in electronic word-of-mouth," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 225-242, August.
    • Handle: RePEc:spr:elmark:v:27:y:2017:i:3:d:10.1007_s12525-017-0262-5
    DOI: 10.1007/s12525-017-0262-5
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    References listed on IDEAS

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    1. Grigoroudis, E. & Siskos, Y., 2004. "A survey of customer satisfaction barometers: Some results from the transportation-communications sector," European Journal of Operational Research, Elsevier, vol. 152(2), pages 334-353, January.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    3. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    4. Janet Currie & Stefano DellaVigna & Enrico Moretti & Vikram Pathania, 2010. "The Effect of Fast Food Restaurants on Obesity and Weight Gain," American Economic Journal: Economic Policy, American Economic Association, vol. 2(3), pages 32-63, August.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Nieto, Jannine & Hernández-Maestro, Rosa M. & Muñoz-Gallego, Pablo A., 2014. "Marketing decisions, customer reviews, and business performance: The use of the Toprural website by Spanish rural lodging establishments," Tourism Management, Elsevier, vol. 45(C), pages 115-123.
    7. Kim, Moon-Koo & Park, Myeong-Cheol & Jeong, Dong-Heon, 2004. "The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services," Telecommunications Policy, Elsevier, vol. 28(2), pages 145-159, March.
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    Cited by:

    1. Hoon S. Choi & Michele Maasberg, 2022. "An empirical analysis of experienced reviewers in online communities: what, how, and why to review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1293-1310, September.
    2. Erik Ernesto Vazquez, 2021. "Effect of an e-retailer’s product category and social media platform selection on perceived quality of e-retail products," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 139-157, March.
    3. Supriyo Mandal & Abyayananda Maiti, 2022. "Network promoter score (NePS): An indicator of product sales in E-commerce retailing sector," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1327-1349, September.

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    More about this item

    Keywords

    Electronic Word-of-Mouth (eWOM); Knowledge management; Consumer demands; Sentiment analysis;
    All these keywords.

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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