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Using the Social Influence of Electronic Word-of-Mouth for Predicting Product Sales: The Moderating Effect of Review or Reviewer Helpfulness and Product Type

Author

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  • Sangjae Lee

    (College of Business Administration, Sejong University, Seoul 05006, Korea)

  • Joon Yeon Choeh

    (Department of Software, Sejong University, Seoul 05006, Korea)

Abstract

The social engagement of eWOM (electronic word-of-mouth) can reduce the threat of adverse selection in e-commerce. As studies that examine the social influence of eWOM are rare, the present work suggests the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales. The volume of eWOM, which is defined as the multiplication of the average length by the number of reviews, is shown to be moderated by review and reviewer helpfulness and search goods to affect product sales. Review ratings are moderated by reviewer helpfulness, and review extremity is positively (negatively) moderated by search (experience) goods and review helpfulness to affect product sales. As previous studies of differentiated sampling strategies that consider review helpfulness for predicting product sales using eWOM are lacking, this study compares the prediction power of business intelligence methods for different subsamples of products created according to high or low review and reviewer helpfulness levels. The subsample with high review or reviewer helpfulness demonstrates greater prediction performance than the subsample with low review or reviewer helpfulness when eWOM variables are used as predictors of product sales. Hence, preliminary filtering data preprocessing should consider review or reviewer helpfulness as a crucial criterion of the data quality. This will contribute to the sampling or preprocessing strategy used to predict product sales using eWOM.

Suggested Citation

  • Sangjae Lee & Joon Yeon Choeh, 2020. "Using the Social Influence of Electronic Word-of-Mouth for Predicting Product Sales: The Moderating Effect of Review or Reviewer Helpfulness and Product Type," Sustainability, MDPI, vol. 12(19), pages 1-18, September.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7952-:d:419659
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    References listed on IDEAS

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    1. Papathanassis, Alexis & Knolle, Friederike, 2011. "Exploring the adoption and processing of online holiday reviews: A grounded theory approach," Tourism Management, Elsevier, vol. 32(2), pages 215-224.
    2. David A. Reinstein & Christopher M. Snyder, 2005. "The Influence Of Expert Reviews On Consumer Demand For Experience Goods: A Case Study Of Movie Critics," Journal of Industrial Economics, Wiley Blackwell, vol. 53(1), pages 27-51, March.
    3. Filieri, Raffaele & Alguezaui, Salma & McLeay, Fraser, 2015. "Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth," Tourism Management, Elsevier, vol. 51(C), pages 174-185.
    4. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    5. Sambashiva Rao Kunja & Acharyulu GVRK, 2018. "Examining the effect of eWOM on the customer purchase intention through value co-creation (VCC) in social networking sites (SNSs)," Management Research Review, Emerald Group Publishing Limited, vol. 43(3), pages 245-269, March.
    6. 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.
    7. Chetna Kudeshia & Amresh Kumar, 2017. "Social eWOM: does it affect the brand attitude and purchase intention of brands?," Management Research Review, Emerald Group Publishing Limited, vol. 40(3), pages 310-330, March.
    8. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    9. Chong (Alex) Wang & Xiaoquan (Michael) Zhang & Il-Horn Hann, 2018. "Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings," Information Systems Research, INFORMS, vol. 29(3), pages 641-655, September.
    10. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    11. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
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    4. Dawei Liu & Jinyang Yu, 2024. "Impact of perceived diagnosticity on live streams and consumer purchase intention: streamer type, product type, and brand awareness as moderators," Information Technology and Management, Springer, vol. 25(3), pages 219-232, September.

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