Analyzing the impact of review recency on helpfulness through econometric modeling
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-020-00992-x.pdf
Reference39 articles.
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3. Aakash A, Aggarwal AG, Aggarwal S (2020) Analyzing the impact of e-WOM text on overall hotel performances: a text analytics approach exploring the power of electronic word-of-mouth in the services industry. IGI Global, Hershey, pp 240–264. https://doi.org/10.4018/978-1-5225-8575-6.ch014
4. BrightLocal (2019) Local consumer review survey 2019. https://www.brightlocal.com/research/local-consumer-review-survey/. Accessed 23 Dec 2019
5. Cao Q, Duan W, Gan Q (2011) Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach. Decis Support Syst 50(2):511–521. https://doi.org/10.1016/j.dss.2010.11.009
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