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How Online Reviews Become Helpful: A Dynamic Perspective

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  • Lu, Shuya
  • Wu, Jianan
  • Tseng, Shih-Lun (Allen)

Abstract

Online product reviews aid consumer decision making. Although many studies show that review characteristics have salient effects on review helpfulness, little research has investigated whether such effects change temporally. To bridge this research gap, we study the dynamic formation of review helpfulness by considering the behaviors of three major players in a typical review system: consumers, the review hosting firm, and reviewers. This study uses both dynamic and static drivers of review helpfulness to examine temporal changes in their effects on review helpfulness, along two time characteristics of a post: its lifespan and its timing. Daily data collected from Amazon show that for long post lifespans or late post timing, the effects of static drivers and the spillover effect of dynamic drivers weaken, but the carryover effect of dynamic drivers strengthens. For vendors to leverage user reviews of a product, high-quality reviews posted early are extremely important and should be cultivated diligently. Sorting by review quality attributes, such as review length, also can effectively prolong the time window for reviewers to write high-quality detailed reviews.

Suggested Citation

  • Lu, Shuya & Wu, Jianan & Tseng, Shih-Lun (Allen), 2018. "How Online Reviews Become Helpful: A Dynamic Perspective," Journal of Interactive Marketing, Elsevier, vol. 44(C), pages 17-28.
    • Handle: RePEc:eee:joinma:v:44:y:2018:i:c:p:17-28
    DOI: 10.1016/j.intmar.2018.05.005
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    3. Azer, Jaylan & Anker, Thomas & Taheri, Babak & Tinsley, Ross, 2023. "Consumer-Driven racial stigmatization: The moderating role of race in online consumer-to-consumer reviews," Journal of Business Research, Elsevier, vol. 157(C).
    4. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    5. Gang Chen & Lihua Huang & Shuaiyong Xiao & Chenghong Zhang & Huimin Zhao, 2024. "Attending to Customer Attention: A Novel Deep Learning Method for Leveraging Multimodal Online Reviews to Enhance Sales Prediction," Information Systems Research, INFORMS, vol. 35(2), pages 829-849, June.
    6. Yanni Ping & Chelsey Hill & Yun Zhu & Jorge Fresneda, 2023. "Antecedents and consequences of the key opinion leader status: an econometric and machine learning approach," Electronic Commerce Research, Springer, vol. 23(3), pages 1459-1484, September.
    7. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    8. Abhishek Tandon & Aakash Aakash & Anu G. Aggarwal & P. K. Kapur, 2021. "Analyzing the impact of review recency on helpfulness through econometric modeling," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 104-111, February.
    9. Fernandes, Semila & Panda, Rajesh & Venkatesh, V.G. & Swar, Biranchi Narayan & Shi, Yangyan, 2022. "Measuring the impact of online reviews on consumer purchase decisions – A scale development study," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    10. Ina Garnefeld & Sabrina Helm & Ann-Kathrin Grötschel, 2020. "May we buy your love? psychological effects of incentives on writing likelihood and valence of online product reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 805-820, December.
    11. Ina Garnefeld & Tabea Krah & Eva Böhm & Dwayne D. Gremler, 2021. "Online reviews generated through product testing: can more favorable reviews be enticed with free products?," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 703-722, July.
    12. Abhishek Tandon & Aakash Aakash & Anu G. Aggarwal & P. K. Kapur, 0. "Analyzing the impact of review recency on helpfulness through econometric modeling," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-8.

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