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How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model

Author

Listed:
  • Navid Aghakhani

    (University of Tennessee Chattanooga)

  • Onook Oh

    (University of Colorado Denver)

  • Dawn Gregg

    (University of Colorado Denver)

  • Hemant Jain

    (University of Tennessee Chattanooga)

Abstract

This study extends our understanding of what makes an online review useful by examining the effects of review quality (i.e., as a composite variable of review comprehensiveness and review topic consistency) on review usefulness, and the moderating effects of source credibility on the relationship between review quality and review usefulness. The Elaboration Likelihood Model, convergence theory, and cueing effect literature are used to define the variables of review comprehensiveness and review topic consistency. Analyses of 27,517 restaurant reviews from Yelp show that review topic consistency has a positive effect on review usefulness, but, contrary to our hypothesis, review comprehensiveness has a negative effect on review usefulness. We also found source credibility positively moderates the effect of review comprehensiveness on review usefulness, but negatively moderates the effect of review topic consistency on review usefulness. Theoretical and practical implications are discussed.

Suggested Citation

  • Navid Aghakhani & Onook Oh & Dawn Gregg & Hemant Jain, 2023. "How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model," Information Systems Frontiers, Springer, vol. 25(4), pages 1513-1531, August.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:4:d:10.1007_s10796-022-10299-w
    DOI: 10.1007/s10796-022-10299-w
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    References listed on IDEAS

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    1. Ivar Krumpal, 2013. "Determinants of social desirability bias in sensitive surveys: a literature review," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2025-2047, June.
    2. Stephanie Watts Sussman & Wendy Schneier Siegal, 2003. "Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption," Information Systems Research, INFORMS, vol. 14(1), pages 47-65, March.
    3. 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.
    4. 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.
    5. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    6. Alton Y.K. Chua & Snehasish Banerjee, 2015. "Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 354-362, February.
    7. Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.
    8. Albert E. Mannes, 2009. "Are We Wise About the Wisdom of Crowds? The Use of Group Judgments in Belief Revision," Management Science, INFORMS, vol. 55(8), pages 1267-1279, August.
    9. Ismagilova, Elvira & Slade, Emma & Rana, Nripendra P. & Dwivedi, Yogesh K., 2020. "The effect of characteristics of source credibility on consumer behaviour: A meta-analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    10. Jie Ren & Jeffrey V. Nickerson, 2019. "Arousal, valence, and volume: how the influence of online review characteristics differs with respect to utilitarian and hedonic products," European Journal of Information Systems, Taylor & Francis Journals, vol. 28(3), pages 272-290, May.
    11. Navid Aghakhani & Onook Oh & Dawn G. Gregg & Jahangir Karimi, 2021. "Online Review Consistency Matters: An Elaboration Likelihood Model Perspective," Information Systems Frontiers, Springer, vol. 23(5), pages 1287-1301, September.
    12. William H. Greene, 1994. "Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models," Working Papers 94-10, New York University, Leonard N. Stern School of Business, Department of Economics.
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