IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v30y2019i3p711-725.html
   My bibliography  Save this article

Leveraging User-Generated Content for Product Promotion: The Effects of Firm-Highlighted Reviews

Author

Listed:
  • Cheng Yi

    (Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing 100084, China, Institute of Internet Industry, Tsinghua University, Beijing 100084, China)

  • Zhenhui (Jack) Jiang

    (Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore 117418, Faculty of Business and Economics, The University of Hong Kong, Hong Kong)

  • Xiuping Li

    (NUS Business School, National University of Singapore, Singapore 119245)

  • Xianghua Lu

    (School of Management, Fudan University, Shanghai 200433, China)

Abstract

User-generated content (UGC) is increasingly used in the marketing communication mix for promoting products. This research investigates how firms can actively manage consumer-generated reviews in the form of highlighting authentic reviews at firms’ discretion. Whereas highlighting a positive review is expected to lead to positive product evaluations, this practice may elicit consumers’ skepticism if consumers are explicitly informed of the promotional intent of the firm. In three studies, we examine the effect of presenting a firm-highlighted review on consumers’ consumption intention and behavior. Our findings confirm that highlighting a positive consumer review can effectively attract consumers’ attention to this review. However, the heightened attention does not always lead to higher consumption likelihood. In particular, the extremity of a highlighted review will interact with the variance of the review context as well as the reputation of the firm being reviewed to determine the effect of the firm-highlighting practice on consumers’ consumption behavior. When other reviews convey mixed opinions or when the firm has not established a strong reputation, highlighting a positive but less extreme review may effectively improve the likelihood of consumption, but highlighting a review that is extremely positive will not.

Suggested Citation

  • Cheng Yi & Zhenhui (Jack) Jiang & Xiuping Li & Xianghua Lu, 2019. "Leveraging User-Generated Content for Product Promotion: The Effects of Firm-Highlighted Reviews," Information Systems Research, INFORMS, vol. 30(3), pages 711-725, September.
    • Handle: RePEc:inm:orisre:v:30:y:2019:i:3:p:711-725
    DOI: 10.1287/isre.2018.0807
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2018.0807
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2018.0807?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Boush, David M & Friestad, Marian & Rose, Gregory M, 1994. "Adolescent Skepticism toward TV Advertising and Knowledge of Advertiser Tactics," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 165-175, June.
    2. Zhenhui Jiang & Izak Benbasat, 2007. "Research Note---Investigating the Influence of the Functional Mechanisms of Online Product Presentations," Information Systems Research, INFORMS, vol. 18(4), pages 454-470, December.
    3. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    4. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    5. Alison Jing Xu & Robert S. Wyer, 2010. "Puffery in Advertisements: The Effects of Media Context, Communication Norms, and Consumer Knowledge," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 329-343, August.
    6. David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
    7. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    8. 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.
    9. Goldberg, Marvin E & Hartwick, Jon, 1990. "The Effects of Advertiser Reputation and Extremity of Advertising Claim on Advertising Effectiveness," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(2), pages 172-179, September.
    10. Alba, Joseph W, et al, 1994. "The Influence of Prior Beliefs, Frequency Cues, and Magnitude Cues on Consumers' Perceptions of Comparative Price Data," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(2), pages 219-235, September.
    11. 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.
    12. Lafferty, Barbara A. & Goldsmith, Ronald E., 1999. "Corporate Credibility's Role in Consumers' Attitudes and Purchase Intentions When a High versus a Low Credibility Endorser Is Used in the Ad," Journal of Business Research, Elsevier, vol. 44(2), pages 109-116, February.
    13. Cheng Yi & Zhenhui (Jack) Jiang & Izak Benbasat, 2017. "Designing for Diagnosticity and Serendipity: An Investigation of Social Product-Search Mechanisms," Information Systems Research, INFORMS, vol. 28(2), pages 413-429, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Minh-Tri Ha & Giang-Do Nguyen & Thi Huong-Thanh Nguyen & Bich-Duyen Nguyen, 2023. "The use of dietary supplements and vitamin consumption during and after the Covid pandemic in Vietnam: a perspective of user-generated content," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    2. Hong, Ming & Wang, Heyong, 2021. "Research on customer opinion summarization using topic mining and deep neural network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 88-114.
    3. Wu, Xiaoyue & Jin, Liyin & Xu, Qian, 2021. "Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews," Journal of Retailing, Elsevier, vol. 97(2), pages 238-250.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    2. 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.
    3. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    4. Kick, Markus, 2015. "Social Media Research: A Narrative Review," EconStor Preprints 182506, ZBW - Leibniz Information Centre for Economics.
    5. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    6. Dominik Gutt, 2018. "In the Eye of the Beholder? Empirically Decomposing Different Economic Implications of the Online Rating Variance," Working Papers Dissertations 40, Paderborn University, Faculty of Business Administration and Economics.
    7. Dipankar Das, 2022. "Measurement of Trustworthiness of the Online Reviews," Papers 2210.00815, arXiv.org, revised Nov 2023.
    8. Yang, Wenjuan & Zhang, Jiantong & Yan, Hong, 2022. "Promotions of online reviews from a channel perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    9. Liu, Yong & Gan, Wen-xue & Zhang, Qi, 2021. "Decision-making mechanism of online retailer based on additional online comments of consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    10. Hyejin Mun & Chul Ho Lee & Hyunju Jung & Ceran Yasin, 2023. "Clash of reputation and status in online reviews," Information Technology and Management, Springer, vol. 24(1), pages 55-77, March.
    11. Yabing Jiang & Hong Guo, 2015. "Design of Consumer Review Systems and Product Pricing," Information Systems Research, INFORMS, vol. 26(4), pages 714-730, December.
    12. Colmekcioglu, Nazan & Marvi, Reza & Foroudi, Pantea & Okumus, Fevzi, 2022. "Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews," Journal of Business Research, Elsevier, vol. 153(C), pages 235-250.
    13. Dongpu Fu & Yili Hong & Kanliang Wang & Weiguo Fan, 2018. "Effects of membership tier on user content generation behaviors: evidence from online reviews," Electronic Commerce Research, Springer, vol. 18(3), pages 457-483, September.
    14. Guo, Qiaozhen & Chen, Ying-Ju & Huang, Wei, 2022. "Dynamic pricing of new experience products with dual-channel social learning and online review manipulations," Omega, Elsevier, vol. 109(C).
    15. David Godes, 2012. "The Strategic Impact of References in Business Markets," Marketing Science, INFORMS, vol. 31(2), pages 257-276, March.
    16. Lirong Chen & Wenli Li & Hao Chen & Shidao Geng, 2019. "Detection of Fake Reviews: Analysis of Sellers’ Manipulation Behavior," Sustainability, MDPI, vol. 11(17), pages 1-13, September.
    17. Linyi Li & Shyam Gopinath & Stephen J. Carson, 2022. "History Matters: The Impact of Online Customer Reviews Across Product Generations," Management Science, INFORMS, vol. 68(5), pages 3878-3903, May.
    18. Li, Yiming & Li, Gang & Tayi, Giri Kumar & Cheng, T.C.E., 2019. "Omni-channel retailing: Do offline retailers benefit from online reviews?," International Journal of Production Economics, Elsevier, vol. 218(C), pages 43-61.
    19. Tao Lu & May Yuan & Chong (Alex) Wang & Xiaoquan (Michael) Zhang, 2022. "Histogram Distortion Bias in Consumer Choices," Management Science, INFORMS, vol. 68(12), pages 8963-8978, December.
    20. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:30:y:2019:i:3:p:711-725. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.