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Examining the impact of review tag function on product evaluation and information perception of popular products

Author

Listed:
  • Zhuolan Bao

    (The Chinese University of Hong Kong, Shenzhen)

  • Wenwen Li

    (Fudan University)

  • Pengzhen Yin

    (Hefei University of Technology)

  • Michael Chau

    (The University of Hong Kong)

Abstract

Since online reviews have become an increasingly important information source for consumers to evaluate products during online shopping, many platforms started to adopt review mechanisms to maximize the value of such massive reviews. In recent years, the review tag function has been adopted in practices and leading the research of sentiment and opinion extraction techniques. However, the examination of its impact has been largely overlooked. In this paper, by proposing a framework through the lens of attribution theory, we look into the effect of the review tag function on two focal outcomes. One is the evaluation of highly-rated popular products, the other is the helpfulness perception of product reviews. Experimental methods and qualitative analysis were utilized to test our hypotheses. Our findings demonstrate the importance of tag function application as it further increases consumers’ product evaluation for popular products. We also found that different tag function appearances influence consumers’ cognitive biases in review helpfulness perception.

Suggested Citation

  • Zhuolan Bao & Wenwen Li & Pengzhen Yin & Michael Chau, 2021. "Examining the impact of review tag function on product evaluation and information perception of popular products," Information Systems and e-Business Management, Springer, vol. 19(2), pages 517-539, June.
  • Handle: RePEc:spr:infsem:v:19:y:2021:i:2:d:10.1007_s10257-021-00532-5
    DOI: 10.1007/s10257-021-00532-5
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    References listed on IDEAS

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    1. 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.
    2. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    3. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.
    4. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    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. 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.
    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. Dezhi Yin & Sabyasachi Mitra & Han Zhang, 2016. "Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth," Information Systems Research, INFORMS, vol. 27(1), pages 131-144, March.
    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.
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