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Understanding the Order Effect of Online Reviews: A Text Mining Perspective

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Listed:
  • Sambit Tripathi

    (University of Massachusetts Lowell)

  • Amit V. Deokar

    (University of Massachusetts Lowell)

  • Haya Ajjan

    (Elon University)

Abstract

Online reviews are aimed to help prospective buyers in their decision-making. While prior research has focused on the economic impact of ratings, review volume, helpfulness and sentiments, open research questions remain regarding the evolution of text attributes associated with online reviews. Using a large dataset, we extract sentiment intensity, along with novel attributes – product usage contexts and product features – present in each online review and analyze their pattern over the temporal order of the reviews. Results indicate that sentiment intensity as well as the number of product features and usage contexts diminish with respect to the increase in review order, suggesting that earlier reviews tend to have more information for prospective customers. However, the declining trend of sentiment intensity is less when reviews mention a higher number of product features and usage contexts. These findings contribute to the literature while having key practical implications for e-commerce websites, retailers, and customers.

Suggested Citation

  • Sambit Tripathi & Amit V. Deokar & Haya Ajjan, 2022. "Understanding the Order Effect of Online Reviews: A Text Mining Perspective," Information Systems Frontiers, Springer, vol. 24(6), pages 1971-1988, December.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:6:d:10.1007_s10796-021-10217-6
    DOI: 10.1007/s10796-021-10217-6
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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. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    3. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    4. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    5. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    6. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    7. Al-Natour, Sameh & Turetken, Ozgur, 2020. "A comparative assessment of sentiment analysis and star ratings for consumer reviews," International Journal of Information Management, Elsevier, vol. 54(C).
    8. Paul A. Pavlou & Angelika Dimoka, 2006. "The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation," Information Systems Research, INFORMS, vol. 17(4), pages 392-414, December.
    9. Nachiketa Sahoo & Chrysanthos Dellarocas & Shuba Srinivasan, 2018. "The Impact of Online Product Reviews on Product Returns," Information Systems Research, INFORMS, vol. 29(3), pages 723-738, September.
    10. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    11. Bin Gu & Jaehong Park & Prabhudev Konana, 2012. "Research Note ---The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products," Information Systems Research, INFORMS, vol. 23(1), pages 182-196, March.
    12. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
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