IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p12230-d672992.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/12230/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/21/12230/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, July.
    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.
    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. Wang, Fang & Du, Zhao & Wang, Shan, 2023. "Information multidimensionality in online customer reviews," Journal of Business Research, Elsevier, vol. 159(C).

    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. 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).
    2. 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).
    3. Wang, Fang & Du, Zhao & Wang, Shan, 2023. "Information multidimensionality in online customer reviews," Journal of Business Research, Elsevier, vol. 159(C).
    4. Zhanfei Lei & Dezhi Yin & Han Zhang, 2021. "Focus Within or On Others: The Impact of Reviewers’ Attentional Focus on Review Helpfulness," Information Systems Research, INFORMS, vol. 32(3), pages 801-819, September.
    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.
    6. 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.
    7. 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.
    8. Fitria Dwi Puspita & Abdul Wahib Muhaimin & Silvana Maulidah, 2024. "Study of Porang Rice Purchase Intention: Moderating Role of Price Sensitivity," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(1), pages 2630-2642, January.
    9. Irina Kalabikhina & Vadim Moshkin & Anton Kolotusha & Maksim Kashin & German Klimenko & Zarina Kazbekova, 2024. "Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews," Mathematics, MDPI, vol. 12(4), pages 1-17, February.
    10. 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.
    11. Gordon Burtch & Anindya Ghose & Sunil Wattal, 2013. "An Empirical Examination of the Antecedents and Consequences of Contribution Patterns in Crowd-Funded Markets," Information Systems Research, INFORMS, vol. 24(3), pages 499-519, September.
    12. Zhuolan Bao & Wenwen Li & Pengzhen Yin & Michael Chau, 2021. "Examining the impact of review tag function on product evaluation and information perception of popular products," Information Systems and e-Business Management, Springer, vol. 19(2), pages 517-539, June.
    13. Nigam, Nirjhar & Benetti, Cristiane & Johan, Sofia A., 2020. "Digital start-up access to venture capital financing: What signals quality?," Emerging Markets Review, Elsevier, vol. 45(C).
    14. 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.
    15. Krishen, Anjala S. & Dwivedi, Yogesh K. & Bindu, N. & Kumar, K. Satheesh, 2021. "A broad overview of interactive digital marketing: A bibliometric network analysis," Journal of Business Research, Elsevier, vol. 131(C), pages 183-195.
    16. Mohammad Al-Khasawneh & Shafig Al-Haddad & Abdel-Aziz Ahmad Sharabati & Hebatallah Hisham Al Khalili & Lana Laith Azar & Farah Waleed Ghabayen & Leen Mazen Jaber & Mariam Husam Ali & Ra’ed Masa’deh, 2023. "How Online Communities Affect Online Community Engagement and Word-of-Mouth Intention," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    17. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    18. Kuttimani Tamilmani & Nripendra P. Rana & Robin Nunkoo & Vishnupriya Raghavan & Yogesh K. Dwivedi, 2022. "Indian Travellers’ Adoption of Airbnb Platform," Information Systems Frontiers, Springer, vol. 24(1), pages 77-96, February.
    19. Chih-Hung Peng & Dezhi Yin & Han Zhang, 2020. "More than Words in Medical Question-and-Answer Sites: A Content-Context Congruence Perspective," Information Systems Research, INFORMS, vol. 31(3), pages 913-928, September.
    20. Verma, Deepak & Prakash Dewani, Prem & Behl, Abhishek & Pereira, Vijay & Dwivedi, Yogesh & Del Giudice, Manilo, 2023. "A meta-analysis of antecedents and consequences of eWOM credibility: Investigation of moderating role of culture and platform type," Journal of Business Research, Elsevier, vol. 154(C).

    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:gam:jsusta:v:13:y:2021:i:21:p:12230-:d:672992. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.