IDEAS home Printed from https://ideas.repec.org/a/eee/joinma/v46y2019icp70-86.html
   My bibliography  Save this article

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

Author

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

Abstract

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1094996818300689
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intmar.2018.11.001?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. 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.
    2. 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.
    3. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    4. 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.
    5. 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.
    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. Stephan Seiler & Song Yao & Wenbo Wang, 2017. "Does Online Word of Mouth Increase Demand? (And How?) Evidence from a Natural Experiment," Marketing Science, INFORMS, vol. 36(6), pages 838-861, November.
    13. 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.
    14. 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.
    15. 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.
    16. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    17. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
    18. Janiszewski, Chris & Meyvis, Tom, 2001. "Effects of Brand Logo Complexity, Repetition, and Spacing on Processing Fluency and Judgment," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(1), pages 18-32, June.
    19. 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.
    20. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
    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. Wen Zhang & Qiang Wang & Jian Li & Zhenzhong Ma & Gokul Bhandari & Rui Peng, 2023. "What makes deceptive online reviews? A linguistic analysis perspective," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    2. Azer, Jaylan & Anker, Thomas & Taheri, Babak & Tinsley, Ross, 2023. "Consumer-Driven racial stigmatization: The moderating role of race in online consumer-to-consumer reviews," Journal of Business Research, Elsevier, vol. 157(C).
    3. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    4. 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.
    5. Park, Junsung & Yoo, Joon Woo & Cho, Youngju & Park, Heejun, 2023. "Examining the impact of service robot communication styles on customer intimacy following service failure," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    6. 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.
    7. Pezzuti, Todd & Leonhardt, James M. & Warren, Caleb, 2021. "Certainty in Language Increases Consumer Engagement on Social Media," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 32-46.
    8. Esmark Jones, Carol L. & Hancock, Tyler & Kazandjian, Brett & Voorhees, Clay M., 2022. "Engaging the Avatar: The effects of authenticity signals during chat-based service recoveries," Journal of Business Research, Elsevier, vol. 144(C), pages 703-716.
    9. Tim Kollmer & Andreas Eckhardt & Victoria Reibenspiess, 2022. "Explaining consumer suspicion: insights of a vignette study on online product reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1221-1238, September.
    10. Caitlin de la Roche & Tiago de Barros & Tinashe Chuchu & Brighton Nyagadza & Marike Venter de Villiers, 2022. "An Investigation on Consumer Perceptions of Email and Social Media Marketing: An Advertising Case in South Africa," International Review of Management and Marketing, Econjournals, vol. 12(4), pages 29-37, July.

    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. 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.
    3. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    4. Liye Ma & Baohong Sun & Sunder Kekre, 2015. "The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, INFORMS, vol. 34(5), pages 627-645, September.
    5. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    6. 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.
    7. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    8. 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.
    9. Gaurav Sabnis & Rajdeep Grewal, 2015. "Cable News Wars on the Internet: Competition and User-Generated Content," Information Systems Research, INFORMS, vol. 26(2), pages 301-319, June.
    10. 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.
    11. Wang, Fang & Du, Zhao & Wang, Shan, 2023. "Information multidimensionality in online customer reviews," Journal of Business Research, Elsevier, vol. 159(C).
    12. Young-Jin Lee & Kellie B. Keeling & Andrew Urbaczewski, 2019. "The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales," Information Systems Frontiers, Springer, vol. 21(4), pages 829-844, August.
    13. Jake Hoskins & Shyam Gopinath & J. Cameron Verhaal & Elham Yazdani, 2021. "The influence of the online community, professional critics, and location similarity on review ratings for niche and mainstream brands," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1065-1087, November.
    14. Beth L. Fossen & David A. Schweidel, 2017. "Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity," Marketing Science, INFORMS, vol. 36(1), pages 105-123, January.
    15. 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.
    16. Xingyu Chen & Xing Li & Dai Yao & Zhimin Zhou, 2019. "Seeking the support of the silent majority: are lurking users valuable to UGC platforms?," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 986-1004, November.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:joinma:v:46:y:2019:i:c:p:70-86. 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-interactive-marketing/ .

    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.