IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v81y2017icp51-59.html
   My bibliography  Save this article

Investigating the effect of quality of grammar and mechanics (QGAM) in online reviews: The mediating role of reviewer crediblity

Author

Listed:
  • Ketron, Seth

Abstract

Grammar and mechanics are important components of written communication and provide signals of credibility. Although past research has documented general effects of grammar and mechanics, to date, the influence of quality of grammar and mechanics (QGAM) of online reviews remains largely unexamined. Through the lens of ELM, the present research examines QGAM of a review as a peripheral cue influencing the perceived credibility of a reviewer, finding that reviews with high QGAM have higher perceived credibility and exert a stronger influence on purchase intentions. Meanwhile, reviews with low QGAM are not as credible, influencing purchase intentions less. Product type, review length, and review valence moderate these influences, such that QGAM is more important for reviews of experience goods and reviews of shorter lengths. Further, reviewer credibility fully mediates positive reviews but does not mediate negative reviews. Implications, limitations, and future research directions are discussed.

Suggested Citation

  • Ketron, Seth, 2017. "Investigating the effect of quality of grammar and mechanics (QGAM) in online reviews: The mediating role of reviewer crediblity," Journal of Business Research, Elsevier, vol. 81(C), pages 51-59.
    • Handle: RePEc:eee:jbrese:v:81:y:2017:i:c:p:51-59
    DOI: 10.1016/j.jbusres.2017.08.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S014829631730259X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2017.08.008?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. Sparks, Beverley A. & So, Kevin Kam Fung & Bradley, Graham L., 2016. "Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern," Tourism Management, Elsevier, vol. 53(C), pages 74-85.
    3. 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.
    4. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    5. Klein, Lisa R., 1998. "Evaluating the Potential of Interactive Media through a New Lens: Search versus Experience Goods," Journal of Business Research, Elsevier, vol. 41(3), pages 195-203, March.
    6. 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.
    7. Filieri, Raffaele, 2016. "What makes an online consumer review trustworthy?," Annals of Tourism Research, Elsevier, vol. 58(C), pages 46-64.
    8. Jiménez, Fernando R. & Mendoza, Norma A., 2013. "Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 226-235.
    9. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
    10. Kostyra, Daniel S. & Reiner, Jochen & Natter, Martin & Klapper, Daniel, 2016. "Decomposing the effects of online customer reviews on brand, price, and product attributes," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 11-26.
    11. Ashton, Robert H., 2014. "Wine as an Experience Good: Price Versus Enjoyment in Blind Tastings of Expensive and Inexpensive Wines," Journal of Wine Economics, Cambridge University Press, vol. 9(2), pages 171-182, August.
    12. Casaló, Luis V. & Flavián, Carlos & Guinalíu, Miguel & Ekinci, Yuksel, 2015. "Avoiding the dark side of positive online consumer reviews: Enhancing reviews' usefulness for high risk-averse travelers," Journal of Business Research, Elsevier, vol. 68(9), pages 1829-1835.
    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. Rohit Aggarwal & Michael J Lee & Braxton Osting & Harpreet Singh, 2021. "Improving Funding Operations of Equity‐based Crowdfunding Platforms," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4121-4139, November.
    2. Hui Zhao & Xiaoyuan Wang & Debing Ni & Kevin W. Li, 2023. "The Quality-Signaling Role of Manipulated Consumer Reviews," Group Decision and Negotiation, Springer, vol. 32(3), pages 503-536, June.
    3. Plotkina, Daria & Munzel, Andreas & Pallud, Jessie, 2020. "Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews," Journal of Business Research, Elsevier, vol. 109(C), pages 511-523.
    4. Na Zhang & Ping Yu & Yupeng Li & Wei Gao, 2022. "Research on the Evolution of Consumers’ Purchase Intention Based on Online Reviews and Opinion Dynamics," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    5. Lutz, Bernhard & Pröllochs, Nicolas & Neumann, Dirk, 2022. "Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation," Journal of Business Research, Elsevier, vol. 144(C), pages 888-901.

    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. Zhu, Yongmin & Liu, Miaomiao & Zeng, Xiaohua & Huang, Pei, 2020. "The effects of prior reviews on perceived review helpfulness: A configuration perspective," Journal of Business Research, Elsevier, vol. 110(C), pages 484-494.
    2. Wang, Yihan & Zhong, Ke & Liu, Qihua, 2022. "Let criticism take precedence: Effect of side order on consumer attitudes toward a two-sided online review," Journal of Business Research, Elsevier, vol. 140(C), pages 403-419.
    3. 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.
    4. Román, Sergio & Riquelme, Isabel P. & Iacobucci, Dawn, 2023. "Fake or credible? Antecedents and consequences of perceived credibility in exaggerated online reviews," Journal of Business Research, Elsevier, vol. 156(C).
    5. Tibert Verhagen & Daniel Bloemers, 2018. "Exploring the cognitive and affective bases of online purchase intentions: a hierarchical test across product types," Electronic Commerce Research, Springer, vol. 18(3), pages 537-561, September.
    6. 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).
    7. 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.
    8. Dipankar Das, 2022. "Measurement of Trustworthiness of the Online Reviews," Papers 2210.00815, arXiv.org, revised Nov 2023.
    9. 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).
    10. Martin Gellerstedt & T. Arvemo, 2019. "The impact of word of mouth when booking a hotel: could a good friend’s opinion outweigh the online majority?," Information Technology & Tourism, Springer, vol. 21(3), pages 289-311, September.
    11. 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.
    12. Miyea Kim & Jeongsoo Han & Mina Jun, 2020. "Do same-level review ratings have the same level of review helpfulness? The role of information diagnosticity in online reviews," Information Technology & Tourism, Springer, vol. 22(4), pages 563-591, December.
    13. Ray, Arghya & Bala, Pradip Kumar & Rana, Nripendra P., 2021. "Exploring the drivers of customers’ brand attitudes of online travel agency services: A text-mining based approach," Journal of Business Research, Elsevier, vol. 128(C), pages 391-404.
    14. 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.
    15. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics," Grenoble Ecole de Management (Post-Print) halshs-01923196, HAL.
    16. Liu, Fu & Wei, Haiying & Wang, Xingyuan & Zhu, Zhenzhong & Chen, Haipeng Allan, 2023. "The influence of online review dispersion on consumers’ purchase intention: The moderating role of dialectical thinking," Journal of Business Research, Elsevier, vol. 165(C).
    17. 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.
    18. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Grenoble Ecole de Management (Post-Print) halshs-01923243, HAL.
    19. Bi, Sheng & Liu, Zhiying & Usman, Khalid, 2017. "The influence of online information on investing decisions of reward-based crowdfunding," Journal of Business Research, Elsevier, vol. 71(C), pages 10-18.
    20. 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.

    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:81:y:2017:i:c:p:51-59. 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.