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Post diversity: A new lens of social media WOM

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  • Dong, Xiaodan
  • Zhang, Zelin
  • Zhang, YiJing
  • Ao, Xiang
  • Tang, Tanya (Ya)

Abstract

Social media word of mouth (WOM) involves much interaction and engagement between users in their own networks. The feature of reposting on social media allows WOM to spread fast and easily. Original posts or reposts can be easily identified, perceived, and processed by social media users for purchase decisions. Then, we propose to investigate social media WOM post diversity, which reflects the variation of posts. Further, we propose that the relationship between social media WOM post diversity and sales can be conditional on the three previously established dimensions: volume, valence, and variance. The findings, reflecting approximately 1.5 million posts about 51 movies on a social media site, suggest that WOM post diversity, as measured by post entropy, positively associates with movie sales, contingent on low WOM volume, high valence, and high variance.

Suggested Citation

  • Dong, Xiaodan & Zhang, Zelin & Zhang, YiJing & Ao, Xiang & Tang, Tanya (Ya), 2024. "Post diversity: A new lens of social media WOM," Journal of Business Research, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:jbrese:v:170:y:2024:i:c:s0148296323006884
    DOI: 10.1016/j.jbusres.2023.114329
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    References listed on IDEAS

    as
    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Jacquemin, Alexis P & Berry, Charles H, 1979. "Entropy Measure of Diversification and Corporate Growth," Journal of Industrial Economics, Wiley Blackwell, vol. 27(4), pages 359-369, June.
    3. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
    4. Jaebong Son & Jintae Lee & Kai R. Larsen & Jiyoung Woo, 2020. "Understanding the uncertainty of disaster tweets and its effect on retweeting: The perspectives of uncertainty reduction theory and information entropy," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1145-1161, October.
    5. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    6. Sylvaine Castellano & Vincent Dutot, 2017. "Investigating the Influence of E-Word-of-Mouth on E-Reputation," International Studies of Management & Organization, Taylor & Francis Journals, vol. 47(1), pages 42-60, January.
    7. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
    8. Wang, Feng & Liu, Xuefeng & Fang, Eric (Er), 2015. "User Reviews Variance, Critic Reviews Variance, and Product Sales: An Exploration of Customer Breadth and Depth Effects," Journal of Retailing, Elsevier, vol. 91(3), pages 372-389.
    9. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    10. Stephen X. He & Samuel D. Bond, 2015. "Why Is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 41(6), pages 1509-1527.
    11. Zelin Zhang & Kejia Yang & Jonathan Z. Zhang & Robert W. Palmatier, 2023. "Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning Approach," Management Science, INFORMS, vol. 69(4), pages 2339-2360, April.
    12. Brown, Jacqueline Johnson & Reingen, Peter H, 1987. "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 350-362, December.
    13. Langan, Ryan & Besharat, Ali & Varki, Sajeev, 2017. "The effect of review valence and variance on product evaluations: An examination of intrinsic and extrinsic cues," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 414-429.
    14. 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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