IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v98y2022i4p724-740.html
   My bibliography  Save this article

Relative persuasiveness of repurchase intentions versus recommendations in online reviews

Author

Listed:
  • Ravula, Prashanth
  • Jha, Subhash
  • Biswas, Abhijit

Abstract

This paper examines the effects of loyalty expressions (i.e., repurchase intentions vs. recommendations) on review persuasiveness. Specifically, we propose that repurchase intentions have a stronger positive effect on review persuasiveness compared to recommendations because of reviewer credibility. We test the above proposition using both an empirical dataset and multiple experimental studies. In addition, we examine frequency of purchase as a boundary condition for our proposition. Accordingly, we find that for frequent purchases, repurchase intentions (vs. recommendations) increases credibility, which, in turn, augments review persuasiveness. For infrequent purchases, however, we observe that recommendations (vs. repurchase intentions) enhance review persuasiveness, which occurs because of increased credibility. This research offers contributions to theory in the areas of online reviews, loyalty, source credibility, and cue-diagnosticity, as well as to practice regarding how firms should seek to elicit loyalty expressions (i.e., repurchase intentions vs. recommendations) when soliciting reviews.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jouret:v:98:y:2022:i:4:p:724-740
    DOI: 10.1016/j.jretai.2022.06.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435922000410
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jretai.2022.06.001?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. Sarah G. Moore, 2012. "Some Things Are Better Left Unsaid: How Word of Mouth Influences the Storyteller," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 38(6), pages 1140-1154.
    2. Jin, Ying & Su, Meng, 2009. "Recommendation and repurchase intention thresholds: A joint heterogeneity response estimation," International Journal of Research in Marketing, Elsevier, vol. 26(3), pages 245-255.
    3. M. Joseph Sirgy, 2018. "Self-congruity theory in consumer behavior: A little history," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 28(2), pages 197-207, April.
    4. Kumar, V. & Pozza, Ilaria Dalla & Ganesh, Jaishankar, 2013. "Revisiting the Satisfaction–Loyalty Relationship: Empirical Generalizations and Directions for Future Research," Journal of Retailing, Elsevier, vol. 89(3), pages 246-262.
    5. Rajkumar Venkatesan & Alexander Bleier & Werner Reinartz & Nalini Ravishanker, 2019. "Improving customer profit predictions with customer mindset metrics through multiple overimputation," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 771-794, September.
    6. Sirgy, M Joseph, 1982. "Self-Concept in Consumer Behavior: A Critical Review," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(3), pages 287-300, December.
    7. Campbell, Margaret C & Kirmani, Amna, 2000. "Consumers' Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(1), pages 69-83, June.
    8. Grant Packard & Andrew D. Gershoff & David B. Wooten, 2016. "When Boastful Word of Mouth Helps versus Hurts Social Perceptions and Persuasion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 43(1), pages 26-43.
    9. Ryan Hamilton & Kathleen D. Vohs & Ann L. McGill, 2014. "We'll Be Honest, This Won't Be the Best Article You'll Ever Read: The Use of Dispreferred Markers in Word-of-Mouth Communication," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 41(1), pages 197-212.
    10. Peck, Joann & Childers, Terry L, 2003. "Individual Differences in Haptic Information Processing: The "Need for Touch" Scale," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(3), pages 430-442, December.
    11. 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.
    12. Khare, Adwait & Labrecque, Lauren I. & Asare, Anthony K., 2011. "The Assimilative and Contrastive Effects of Word-of-Mouth Volume: An Experimental Examination of Online Consumer Ratings," Journal of Retailing, Elsevier, vol. 87(1), pages 111-126.
    13. 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.
    14. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
    15. 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.
    16. Michael Hair & Timucin Ozcan, 2018. "How reviewers’ use of profanity affects perceived usefulness of online reviews," Marketing Letters, Springer, vol. 29(2), pages 151-163, June.
    17. 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.
    18. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, 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. 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. 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).
    3. Guyt, Jonne Y. & Datta, Hannes & Boegershausen, Johannes, 2024. "Unlocking the Potential of Web Data for Retailing Research," Journal of Retailing, Elsevier, vol. 100(1), pages 130-147.
    4. 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).
    5. Kübler, Raoul V. & Lobschat, Lara & Welke, Lina & van der Meij, Hugo, 2024. "The effect of review images on review helpfulness: A contingency approach," Journal of Retailing, Elsevier, vol. 100(1), pages 5-23.

    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. 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).
    2. Ifie, Kemefasu, 2020. "Excellent Product … But Too Early to Say: Consumer Reactions to Tentative Product Reviews," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 35-51.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Koukova, Nevena T. & Wang, Rebecca Jen-Hui & Isaac, Mathew S., 2023. "“If you loved our product”: Do conditional review requests harm retailer loyalty?," Journal of Retailing, Elsevier, vol. 99(1), pages 85-101.
    8. Gottschalk, Sabrina A. & Mafael, Alexander, 2017. "Cutting Through the Online Review Jungle — Investigating Selective eWOM Processing," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 89-104.
    9. Yao, Qi & Kuai, Ling & Wang, Cheng Lu, 2022. "How frontline employees' communication styles affect consumers' willingness to interact: The boundary condition of emotional ability similarity," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    10. 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.
    11. 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.
    12. 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.
    13. Munzel, Andreas, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 96-108.
    14. Meiseberg, Brinja, 2016. "The Effectiveness of E-tailers’ Communication Practices in Stimulating Sales of Niche versus Popular Products," Journal of Retailing, Elsevier, vol. 92(3), pages 319-332.
    15. 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.
    16. Kübler, Raoul V. & Lobschat, Lara & Welke, Lina & van der Meij, Hugo, 2024. "The effect of review images on review helpfulness: A contingency approach," Journal of Retailing, Elsevier, vol. 100(1), pages 5-23.
    17. Jifeng Luo & Ying Rong & Huan Zheng, 2020. "Impacts of logistics information on sales: Evidence from Alibaba," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 646-669, December.
    18. 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).
    19. Bin Guo & Shasha Zhou, 2017. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 17(4), pages 585-608, December.
    20. Xiaomo Liu & G. Alan Wang & Weiguo Fan & Zhongju Zhang, 2020. "Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis," Information Systems Research, INFORMS, vol. 31(3), pages 731-752, September.

    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:jouret:v:98:y:2022:i:4:p:724-740. 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: https://www.journals.elsevier.com/journal-of-retailing .

    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.