IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/vyid10.1007_s13198-020-00992-x.html
   My bibliography  Save this article

Analyzing the impact of review recency on helpfulness through econometric modeling

Author

Listed:
  • 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, 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.
    • Handle: RePEc:spr:ijsaem:v::y::i::d:10.1007_s13198-020-00992-x
    DOI: 10.1007/s13198-020-00992-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-020-00992-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-020-00992-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    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. Abhishek Tandon & Aakash Aakash & Anu G. Aggarwal, 2020. "Impact of EWOM, website quality, and product satisfaction on customer satisfaction and repurchase intention: moderating role of shipping and handling," 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. 11(2), pages 349-356, July.
    4. Geetha, M. & Singha, Pratap & Sinha, Sumedha, 2017. "Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis," Tourism Management, Elsevier, vol. 61(C), pages 43-54.
    5. 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.
    6. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2019. "What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics," Post-Print hal-03511270, HAL.
    7. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    8. Himanshu Sharma & Abhishek Tandon & P. K. Kapur & Anu G. Aggarwal, 2019. "Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS," 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. 10(5), pages 973-983, October.
    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. Gobinda Roy & Rajarshi Debnath & Partha Sarathi Mitra & Avinash K. Shrivastava, 2021. "Analytical study of low-income consumers’ purchase behaviour for developing marketing strategy," 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(5), pages 895-909, October.

    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. 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.
    2. 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).
    3. 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.
    4. Yi, Jisu & Oh, Yun Kyung, 2022. "The informational value of multi-attribute online consumer reviews: A text mining approach," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    5. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    6. 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).
    7. Kong, Juan & Lou, Chen, 2023. "Do cultural orientations moderate the effect of online review features on review helpfulness? A case study of online movie reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    8. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    9. Shan, Wei & Wang, Jiaxuan & Shi, Xiaoxiao & David Evans, Richard, 2024. "The impact of electronic word-of-mouth on patients’ choices in online health communities: A cross-media perspective," Journal of Business Research, Elsevier, vol. 173(C).
    10. Enrique Bigne & Carla Ruiz & Carmen Perez-Cabañero & Antonio Cuenca, 2023. "Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 281-314, March.
    11. Lien Thi Kim Nguyen & Hao-Hsuan Chung & Kristine Velasquez Tuliao & Tom M. Y. Lin, 2020. "Using XGBoost and Skip-Gram Model to Predict Online Review Popularity," SAGE Open, , vol. 10(4), pages 21582440209, December.
    12. Fang Lyu & Jaewon Choi, 2020. "The Forecasting Sales Volume and Satisfaction of Organic Products through Text Mining on Web Customer Reviews," Sustainability, MDPI, vol. 12(11), pages 1-23, May.
    13. Colmekcioglu, Nazan & Marvi, Reza & Foroudi, Pantea & Okumus, Fevzi, 2022. "Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews," Journal of Business Research, Elsevier, vol. 153(C), pages 235-250.
    14. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    15. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
    16. Zhang, Ziqiong & Qiao, Shuchen & Chen, Ying & Zhang, Zili, 2022. "Effects of spatial distance on consumers' review effort," Annals of Tourism Research, Elsevier, vol. 94(C).
    17. Zajadacz Alina & Minkwitz Aleksandra, 2020. "Using Social Media Data to Plan for Tourism," Quaestiones Geographicae, Sciendo, vol. 39(3), pages 125-138, September.
    18. 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.
    19. Li, Yuanshuo & Zhang, Zili & Pedersen, Susanne & Liu, Xudong & Zhang, Ziqiong, 2023. "The influence of relative popularity on negative fake reviews: A case study on restaurant reviews," Journal of Business Research, Elsevier, vol. 162(C).
    20. Srikanth Parameswaran & Pubali Mukherjee & Rohit Valecha, 2023. "I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews," Information Systems Frontiers, Springer, vol. 25(2), pages 853-870, April.

    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:spr:ijsaem:v::y::i::d:10.1007_s13198-020-00992-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.