IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v154y2023ics0148296322008517.html
   My bibliography  Save this article

Role of gender in the creation and persuasiveness of online reviews

Author

Listed:
  • Ravula, Prashanth
  • Bhatnagar, Amit
  • Gauri, Dinesh K

Abstract

Online reviews play an important role in consumers’ purchase journeys and therefore have received considerable research attention. Yet research is limited as to whether and how a reviewer’s gender affects persuasiveness. In response, we analyze more than one million reviews posted on the website Yelp and find that an author’s gender indeed affects review text and, consequently, persuasiveness. Specifically, we find that (1) reviews posted by women (vs. men) are more authentic, less analytical, more positive-affective, and less negative-affective and (2) authentic, analytical, negative-affective text increases the persuasiveness of reviews while positive-affective text lowers it. We also find that the persuasiveness of reviews from women (vs. men) depends on product category, suggesting that retailers should consider the product category and authors’ gender when ranking reviews.

Suggested Citation

  • Ravula, Prashanth & Bhatnagar, Amit & Gauri, Dinesh K, 2023. "Role of gender in the creation and persuasiveness of online reviews," Journal of Business Research, Elsevier, vol. 154(C).
  • Handle: RePEc:eee:jbrese:v:154:y:2023:i:c:s0148296322008517
    DOI: 10.1016/j.jbusres.2022.113386
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296322008517
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2022.113386?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Richard, Marie-Odile & Chebat, Jean-Charles & Yang, Zhiyong & Putrevu, Sanjay, 2010. "A proposed model of online consumer behavior: Assessing the role of gender," Journal of Business Research, Elsevier, vol. 63(9-10), pages 926-934, September.
    2. 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.
    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.
    4. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, June.
    5. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    6. Gruen, Thomas W. & Osmonbekov, Talai & Czaplewski, Andrew J., 2006. "eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty," Journal of Business Research, Elsevier, vol. 59(4), pages 449-456, April.
    7. Sam Ransbotham & Nicholas H. Lurie & Hongju Liu, 2019. "Creation and Consumption of Mobile Word of Mouth: How Are Mobile Reviews Different?," Marketing Science, INFORMS, vol. 38(5), pages 773-792, September.
    8. Aggarwal, Praveen & Vaidyanathan, Rajiv & Venkatesh, Alladi, 2009. "Using Lexical Semantic Analysis to Derive Online Brand Positions: An Application to Retail Marketing Research," Journal of Retailing, Elsevier, vol. 85(2), pages 145-158.
    9. 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.
    10. Yinlong Zhang & Lawrence Feick & Vikas Mittal, 2014. "How Males and Females Differ in Their Likelihood of Transmitting Negative Word of Mouth," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(6), pages 1097-1108.
    11. 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.
    12. Purnawirawan, Nathalia & Eisend, Martin & De Pelsmacker, Patrick & Dens, Nathalie, 2015. "A Meta-analytic Investigation of the Role of Valence in Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 31(C), pages 17-27.
    13. Karen Page Winterich & Vikas Mittal & William T. Ross Jr., 2009. "Donation Behavior toward In-Groups and Out-Groups: The Role of Gender and Moral Identity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 36(2), pages 199-214.
    14. Ravula, Prashanth & Jha, Subhash & Biswas, Abhijit, 2022. "Relative persuasiveness of repurchase intentions versus recommendations in online reviews," Journal of Retailing, Elsevier, vol. 98(4), pages 724-740.
    15. 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.
    16. S. Christian Wheeler & Jonah Berger, 2007. "When the Same Prime Leads to Different Effects," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(3), pages 357-368, July.
    17. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    18. 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.
    19. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    20. 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.
    21. Sarah G. Moore, 2015. "Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 42(1), pages 30-44.
    22. Kim, Junyong & Gupta, Pranjal, 2012. "Emotional expressions in online user reviews: How they influence consumers' product evaluations," Journal of Business Research, Elsevier, vol. 65(7), pages 985-992.
    Full references (including those not matched with items on IDEAS)

    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. Moon, Sangkil & Kim, Seung-Wook & Iacobucci, Dawn, 2024. "Dynamic relationship changes between reviewers and consumers in online product reviews," Journal of Retailing, Elsevier, vol. 100(1), pages 70-84.
    2. Meek, Stephanie & Wilk, Violetta & Lambert, Claire, 2021. "A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews," Journal of Business Research, Elsevier, vol. 125(C), pages 354-367.
    3. Agnieszka Zablocki & Bodo Schlegelmilch & Michael J. Houston, 2019. "How valence, volume and variance of online reviews influence brand attitudes," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 61-77, June.
    4. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    5. Ismagilova, Elvira & Dwivedi, Yogesh K. & Slade, Emma, 2020. "Perceived helpfulness of eWOM: Emotions, fairness and rationality," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    6. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    7. Wang, Fang & Karimi, Sahar, 2019. "This product works well (for me): The impact of first-person singular pronouns on online review helpfulness," Journal of Business Research, Elsevier, vol. 104(C), pages 283-294.
    8. Ravula, Prashanth & Jha, Subhash & Biswas, Abhijit, 2022. "Relative persuasiveness of repurchase intentions versus recommendations in online reviews," Journal of Retailing, Elsevier, vol. 98(4), pages 724-740.
    9. 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.
    10. Guha Majumder, Madhumita & Dutta Gupta, Sangita & Paul, Justin, 2022. "Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis," Journal of Business Research, Elsevier, vol. 150(C), pages 147-164.
    11. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    12. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
    13. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    14. Cai, Xiaowei & Cebollada, Javier & Cortiñas, Mónica, 2023. "Impact of seller- and buyer-created content on product sales in the electronic commerce platform: The role of informativeness, readability, multimedia richness, and extreme valence," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    15. 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).
    16. 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.
    17. Wang, Fang & Menon, Kalyani & Ranaweera, Chatura, 2018. "Dynamic trends in online product ratings: A diagnostic utility explanation," Journal of Business Research, Elsevier, vol. 87(C), pages 80-89.
    18. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    19. Hernández-Ortega, Blanca, 2020. "When the performance comes into play: The influence of positive online consumer reviews on individuals' post-consumption responses," Journal of Business Research, Elsevier, vol. 113(C), pages 422-435.
    20. Bigne, Enrique & Ruiz, Carla & Curras-Perez, Rafael, 2024. "How consumers process online review types in familiar versus unfamiliar destinations. A self-reported and neuroscientific study," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

    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:eee:jbrese:v:154:y:2023:i:c:s0148296322008517. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.