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

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  • 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
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    References listed on IDEAS

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    1. Filieri, Raffaele, 2016. "What makes an online consumer review trustworthy?," Annals of Tourism Research, Elsevier, vol. 58(C), pages 46-64.
    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. 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.
    4. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    10. 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.
    11. 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.
    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.
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    Cited by:

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    2. 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.
    3. 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.
    4. 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.
    5. 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.

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