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It's Not Just What You Say, But How You Say It: The Effect of Language Style Matching on Perceived Quality of Consumer Reviews


  • Liu, Angela Xia
  • Xie, Ying
  • Zhang, Jurui


This study explores the role of language style in the perceived quality of online reviews. Drawing from research in psychology and sociology, we posit that language style matching (LSM) of a review—the degree to which the language style of a review matches the language style of intended customers—directly influences the perceived quality of the review. We also propose that LSM should matter more when processing fluency is greatly needed such as when customers learn about new products and process complicated product information. Using restaurant reviews from Yelp, we calculate the LSM score for every review to measure the distance between the language style of the focal review and the typical language style of the restaurant's intended customers. We find that LSM has a significant and positive effect on the number of useful votes received by a review. In addition, the effect of LSM is more pronounced for less familiar restaurants and for more complicated reviews. We discuss the implications of these findings for online review platforms, restaurant managers, and online review writers and close by identifying several opportunities for further research.

Suggested Citation

  • Liu, Angela Xia & Xie, Ying & Zhang, Jurui, 2019. "It's Not Just What You Say, But How You Say It: The Effect of Language Style Matching on Perceived Quality of Consumer Reviews," Journal of Interactive Marketing, Elsevier, vol. 46(C), pages 70-86.
  • Handle: RePEc:eee:joinma:v:46:y:2019:i:c:p:70-86
    DOI: 10.1016/j.intmar.2018.11.001

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    References listed on IDEAS

    1. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
    2. Cheng, Yi-Hsiu & Ho, Hui-Yi, 2015. "Social influence's impact on reader perceptions of online reviews," Journal of Business Research, Elsevier, vol. 68(4), pages 883-887.
    3. 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.
    4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    5. Jan-Benedict E. M. Steenkamp & Inge Geyskens, 2014. "Manufacturer and Retailer Strategies to Impact Store Brand Share: Global Integration, Local Adaptation, and Worldwide Learning," Marketing Science, INFORMS, vol. 33(1), pages 6-26, January.
    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. Eliana V. Jimenez-Soto & Richard P. C. Brown, 2012. "Assessing the Poverty Impacts of Migrants’ Remittances Using Propensity Score Matching: The Case of Tonga," The Economic Record, The Economic Society of Australia, vol. 88(282), pages 425-439, September.
    8. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    9. Alberto Abadie & Guido W. Imbens, 2012. "A Martingale Representation for Matching Estimators," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 833-843, June.
    10. Abadie, Alberto & Imbens, Guido W., 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 1-11.
    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. 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.
    13. Olivier Toubia & Andrew T. Stephen, 2013. "Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?," Marketing Science, INFORMS, vol. 32(3), pages 368-392, May.
    14. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    15. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    16. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
    17. Janiszewski, Chris & Meyvis, Tom, 2001. " Effects of Brand Logo Complexity, Repetition, and Spacing on Processing Fluency and Judgment," Journal of Consumer Research, Oxford University Press, vol. 28(1), pages 18-32, June.
    18. Shyam Gopinath & Jacquelyn S. Thomas & Lakshman Krishnamurthi, 2014. "Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance," Marketing Science, INFORMS, vol. 33(2), pages 241-258, March.
    19. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
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