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Analyzing the impact of review recency on helpfulness through econometric modeling

Author

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  • Abhishek Tandon

    (University of Delhi)

  • Aakash Aakash

    (University of Delhi)

  • Anu G. Aggarwal

    (University of Delhi)

  • P. K. Kapur

    (Center for Interdisciplinary Research, Amity University)

Abstract

The eWOM helpfulness and its effect on customer buying behavior are well recognized. All previous helpfulness related studies mainly focus on the determinants of review helpfulness. However, the helpfulness of newly posted eWOM over earlier online reviews (eWOM) has not yet been studied within the context of hospitality and tourism sector. The aim of this paper is to analyze the impact of review recency on the helpfulness of that review. This study also examines the interaction of eWOM recency with eWOM text characteristics such as length, sentiment, and readability on their helpfulness. Our findings show that recently posted eWOM receives more helpful votes than those were posted earlier. Our results also support that lengthy reviews collect more helpful ratings even after becoming old. Our research adds to the social science studies related to eWOM helpfulness. Limitations and future research directions have been also discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:1:d:10.1007_s13198-020-00992-x
    DOI: 10.1007/s13198-020-00992-x
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    References listed on IDEAS

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    1. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    2. 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.
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