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.
Similar content being viewed by others
References
Aakash A, Aggarwal AG (2019) Role of EWOM, product satisfaction, and website quality on customer repurchase intention intention. In: Carvalho J, Sabino E (eds) Strategy and superior performance of micro and small businesses in volatile economies. IGI Global, Hershey, pp 144–168. https://doi.org/10.4018/978-1-5225-7888-8.ch010
Aakash A, Aggarwal AG (2020) Assessment of hotel performance and guest satisfaction through eWOM: big data for better insights. Int J Hosp Tour Administr. https://doi.org/10.1080/15256480.2020.1746218
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
BrightLocal (2019) Local consumer review survey 2019. https://www.brightlocal.com/research/local-consumer-review-survey/. Accessed 23 Dec 2019
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
Chen M-Y, Teng C-I, Chiou K-W (2019) The helpfulness of online reviews. Online Inf Rev. https://doi.org/10.1108/OIR-08-2018-0251
Chua AY, Banerjee S (2016) Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Comput Hum Behav 54:547–554. https://doi.org/10.1016/j.chb.2015.08.057
Fan Z-P, Che Y-J, Chen Z-Y (2017) Product sales forecasting using online reviews and historical sales data: a method combining the Bass model and sentiment analysis. J Bus Res 74:90–100. https://doi.org/10.1016/j.jbusres.2017.01.010
Filieri R, Raguseo E, Vitari C (2019) What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics. Int J Hosp Manag 77:333–341. https://doi.org/10.1016/j.ijhm.2018.07.013
Fresneda JE, Gefen D (2019) A semantic measure of online review helpfulness and the importance of message entropy. Decis Support Syst 125:113117. https://doi.org/10.1016/j.dss.2019.113117
Geetha M, Singha P, Sinha S (2017) Relationship between customer sentiment and online customer ratings for hotels—an empirical analysis. Tour Manag 61:43–54
Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512. https://doi.org/10.1109/TKDE.2010.188
González-Rodríguez MR, Martínez-Torres R, Toral S (2016) Post-visit and pre-visit tourist destination image through eWOM sentiment analysis and perceived helpfulness. Int J Contemp Hosp Manag 28(11):2609–2627. https://doi.org/10.1108/IJCHM-02-2015-0057
Gursoy D (2019) A critical review of determinants of information search behavior and utilization of online reviews in decision making process (invited paper for ‘luminaries’ special issue of international journal of hospitality management). Int J Contemp Hosp Manag 76:53–60. https://doi.org/10.1016/j.ijhm.2018.06.003
Hotelogix (2015) Does your hotel have enough reviews to get you views? https://www.hotelogix.com/blog/2015/08/14/does-your-hotel-have-enough-reviews-to-get-you-views/. Accessed 24 Dec 2019
Hu N, Koh NS, Reddy SK (2014) Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decis Support Syst 57:42–53. https://doi.org/10.1016/j.dss.2013.07.009
Kincaid JP, Fishburne Jr RP, Rogers RL, Chissom BS (1975) Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. Retrieved IST technical report numbers are not necessarily unique document numbers
Lee S, Choeh JY (2018) The interactive impact of online word-of-mouth and review helpfulness on box office revenue. Manag Decis 56(4):849–866. https://doi.org/10.1108/MD-06-2017-0561
Lee P-J, Hu Y-H, Lu K-T (2018) Assessing the helpfulness of online hotel reviews: a classification-based approach. Telemat Inform 35(2):436–445. https://doi.org/10.1016/j.tele.2018.01.001
Leonardo (2017) How tripadvisor’s algorithm works and how to rank higher. https://blog.leonardo.com/tripadvisor-algorithm/. Accessed 24 Dec 2019
Liu Z, Park S (2015) What makes a useful online review? Implication for travel product websites. Tour Manag 47:140–151. https://doi.org/10.1016/j.tourman.2014.09.020
Lu S, Wu J, Tseng S-LA (2018) How online reviews become helpful: a dynamic perspective. J Interact Market 44:17–28. https://doi.org/10.1016/j.intmar.2018.05.005
Mariani MM, Borghi M, Gretzel U (2019) Online reviews: differences by submission device. Tour Manag 70:295–298. https://doi.org/10.1016/j.tourman.2018.08.022
Miao Q, Li Q, Dai R (2009) AMAZING: a sentiment mining and retrieval system. Expert Syst Appl 36(3):7192–7198. https://doi.org/10.1016/j.eswa.2008.09.035
Otterbacher J (2009) ‘Helpfulness’ in online communities: a measure of message quality. Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems, Boston, MA, USA, 955–964. 10.1145/1518701.1518848
Ren G, Hong T (2019) Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews. Inf Process Manage 56(4):1425–1438. https://doi.org/10.1016/j.ipm.2018.04.003
Roshchina A, Cardiff J, Rosso P (2015) Twin: personality-based intelligent recommender system. J Intell Fuzzy Syst 28(5):2059–2071. https://doi.org/10.3233/IFS-141484
Salehan M, Kim DJ (2016) Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis Support Syst 81:30–40. https://doi.org/10.1016/j.dss.2015.10.006
Sharma H, Tandon A, Kapur P, Aggarwal AG (2019) Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS. Int J Syst Assur Eng Manag 10(5):973–983. https://doi.org/10.1007/s13198-019-00827-4
Shin S, Chung N, Xiang Z, Koo C (2019) Assessing the impact of textual content concreteness on helpfulness in online travel reviews. J Trav Res 58(4):579–593. https://doi.org/10.1177/0047287518768456
Srivastava V, Kalro AD (2019) Enhancing the helpfulness of online consumer reviews: the role of latent (content) factors. J Interact Market 48:33–50. https://doi.org/10.1016/j.intmar.2018.12.003
Sun X, Han M, Feng J (2019) Helpfulness of online reviews: examining review informativeness and classification thresholds by search products and experience products. Decis Support Syst 124:113099. https://doi.org/10.1016/j.dss.2019.113099
Tandon A, Aakash A, Aggarwal AG (2020) Impact of EWOM, website quality, and product satisfaction on customer satisfaction and repurchase intention: moderating role of shipping and handling. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-020-00954-3
Thompson CG, Kim RS, Aloe AM, Becker BJ (2017) Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic Appl Soc Psychol 39(2):81–90. https://doi.org/10.1080/01973533.2016.1277529
Wan Y (2015) The Matthew effect in social commerce: the case of online review helpfulness. Electron Markets 25(4):313–324. https://doi.org/10.1007/s12525-015-0186-x
Wu PF (2013) In search of negativity bias: an empirical study of perceived helpfulness of online reviews. Psychol Market 30(11):971–984. https://doi.org/10.1002/mar.20660
Wulff DU, Hills TT, Hertwig R (2015) Online product reviews and the description–experience gap. J Behav Decis Making 28(3):214–223. https://doi.org/10.1002/bdm.1841
Xie KL, Chen C, Wu S (2016) Online consumer review factors affecting offline hotel popularity: evidence from tripadvisor. J Travel Tour Market 33(2):211–223. https://doi.org/10.1080/10548408.2015.1050538
Zhao P, Wu J, Hua Z, Fang S (2019) Finding eWOM customers from customer reviews. Ind Manag Data Syst 119(1):129–147. https://doi.org/10.1108/IMDS-09-2017-0418
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tandon, A., Aakash, A., Aggarwal, A.G. et al. Analyzing the impact of review recency on helpfulness through econometric modeling. Int J Syst Assur Eng Manag 12, 104–111 (2021). https://doi.org/10.1007/s13198-020-00992-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-020-00992-x