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Antecedents and consequences of the key opinion leader status: an econometric and machine learning approach

Author

Listed:
  • Yanni Ping

    (The Peter J. Tobin College of Business, St. John’s University)

  • Chelsey Hill

    (Feliciano School of Business, Montclair State University)

  • Yun Zhu

    (The Peter J. Tobin College of Business, St. John’s University)

  • Jorge Fresneda

    (New Jersey Institute of Technology)

Abstract

Key Opinion Leaders (KOLs) have an undeniable influence on businesses. Many online review communities, such as Yelp, give KOL users prominent status in their communities as cues of source trustworthiness. Using both econometric analysis and machine learning methods, we adopt an antecedents and consequences framework to investigate the drivers of KOL status and their economic impact on businesses. We find that a user’s social activity is more important in determining KOL status than the reviews themselves. On the consequences side, the paper shows that the first KOL review significantly boosts sales, regardless of the actual rating assigned by the KOL. After confirming this sales boost, we use random forest regression to predict sales using KOL review characteristics, including text. It is found that the number of KOL reviews as the most influential feature in predicting sales. This research contributes to the existing literature by adding a more granular, holistic investigation into KOLs in online consumer review communities.

Suggested Citation

  • Yanni Ping & Chelsey Hill & Yun Zhu & Jorge Fresneda, 2023. "Antecedents and consequences of the key opinion leader status: an econometric and machine learning approach," Electronic Commerce Research, Springer, vol. 23(3), pages 1459-1484, September.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:3:d:10.1007_s10660-022-09650-9
    DOI: 10.1007/s10660-022-09650-9
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    References listed on IDEAS

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    1. Lu, Shuya & Wu, Jianan & Tseng, Shih-Lun (Allen), 2018. "How Online Reviews Become Helpful: A Dynamic Perspective," Journal of Interactive Marketing, Elsevier, vol. 44(C), pages 17-28.
    2. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    3. Fang, Bin & Ye, Qiang & Kucukusta, Deniz & Law, Rob, 2016. "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Elsevier, vol. 52(C), pages 498-506.
    4. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    5. Bart de Langhe & Philip M. Fernbach & Donald R. Lichtenstein, 2016. "Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings," Journal of Consumer Research, Oxford University Press, vol. 42(6), pages 817-833.
    6. Xianghua Lu & Sulin Ba & Lihua Huang & Yue Feng, 2013. "Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews," Information Systems Research, INFORMS, vol. 24(3), pages 596-612, September.
    7. Kaiquan Xu & Jason Chan & Anindya Ghose & Sang Pil Han, 2017. "Battle of the Channels: The Impact of Tablets on Digital Commerce," Management Science, INFORMS, vol. 63(5), pages 1469-1492, May.
    8. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    9. 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.
    10. 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.
    11. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    12. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    13. Alton Y.K. Chua & Snehasish Banerjee, 2015. "Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 354-362, February.
    14. Casaló, Luis V. & Flavián, Carlos & Ibáñez-Sánchez, Sergio, 2020. "Influencers on Instagram: Antecedents and consequences of opinion leadership," Journal of Business Research, Elsevier, vol. 117(C), pages 510-519.
    15. Paul A. Pavlou & Angelika Dimoka, 2006. "The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation," Information Systems Research, INFORMS, vol. 17(4), pages 392-414, December.
    16. Jiménez, Fernando R. & Mendoza, Norma A., 2013. "Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 226-235.
    17. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
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