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Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?

Author

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  • Zhao Du

    (Business School of Sport, Beijing Sport University, Beijing 100084, China
    The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content, Institute of Scientific and Technical Information of China, Beijing 100036, China)

  • Fang Wang

    (Lazaridis School of Business & Economics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada)

  • Shan Wang

    (Department of Finance and Management Science, University of Saskatchewan, Saskatoon, SK S7N 2A5, Canada)

Abstract

With a surging number of online courses on MOOC (Massive Open Online Course) platforms, online learners face increasing difficulties in choosing which courses to take. Online course reviews posted by previous learners provide valuable information for prospective learners to make informed course selections. This research investigates the effects of reviewer experience and expertise on reviewer competence in contributing high-quality and helpful reviews for online courses. The empirical study of 39,114 online reviews from 3276 online courses on a leading MOOC platform in China reveals that both reviewer experience and expertise positively affect reviewer competence in contributing helpful reviews. In particular, the effect of reviewer expertise on reviewer competence in contributing helpful reviews is much more prominent than that of reviewer experience. Reviewer experience and expertise do not interact in enhancing reviewer competence. The analysis also reveals distinct groups of reviewers. Specifically, reviewers with low expertise and low experience contribute the majority of the reviews; reviewers with high expertise and high experience are rare, accounting for a small portion of the reviews; the rest of the reviews are from reviewers with high expertise, but low experience, or those with low expertise, but high experience. Our work offers a new analytical approach to online learning and online review literature by considering reviewer experience and expertise as reviewer competence dimensions. The results suggest the necessity of focusing on reviewer expertise, instead of reviewer experience, in choosing and recommending reviewers for online courses.

Suggested Citation

  • Zhao Du & Fang Wang & Shan Wang, 2021. "Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12230-:d:672992
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    References listed on IDEAS

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    1. 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.
    2. Bonner, Se & Lewis, Bl, 1990. "Determinants Of Auditor Expertise," Journal of Accounting Research, Wiley Blackwell, vol. 28, pages 1-20.
    3. Jennifer Martínez‐Ferrero & Isabel‐María García‐Sánchez & Emiliano Ruiz‐Barbadillo, 2018. "The quality of sustainability assurance reports: The expertise and experience of assurance providers as determinants," Business Strategy and the Environment, Wiley Blackwell, vol. 27(8), pages 1181-1196, December.
    4. Elvira Ismagilova & Yogesh K. Dwivedi & Emma Slade & Michael D. Williams, 2017. "Electronic Word-of-Mouth (eWOM)," SpringerBriefs in Business, in: Electronic Word of Mouth (eWOM) in the Marketing Context, chapter 0, pages 17-30, Springer.
    5. Samer Faraj & Lee Sproull, 2000. "Coordinating Expertise in Software Development Teams," Management Science, INFORMS, vol. 46(12), pages 1554-1568, December.
    6. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    7. Elvira Ismagilova & Yogesh K. Dwivedi & Emma Slade & Michael D. Williams, 2017. "Electronic Word of Mouth (eWOM) in the Marketing Context," SpringerBriefs in Business, Springer, number 978-3-319-52459-7, January.
    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. 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.
    10. Wu, Xiaoyue & Jin, Liyin & Xu, Qian, 2021. "Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews," Journal of Retailing, Elsevier, vol. 97(2), pages 238-250.
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