A Long Tail Study of eWOM Communities

Authors: M. Olmedilla, M. R. Martinez-Torres, S. L. Toral

Abstract:

Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels.

Keywords: eWOM, Online user reviews, Long tail theory, Product categorization, Social Network Analysis.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100300

References:

[1] H.-W. Kim, S. Gupta, A comparison of purchase decision calculus between potential and repeat customers of an online store, Decision Support Systems, Vol. 47, Iss. 4, pp. 477–487, 2009.
[2] G. Zacharia, A. Moukas, P. Maes, Collaborative reputation mechanisms for electronic marketplaces, Decision Support Systems, Vol. 29, Iss. 4, pp. 371–388, 2000.
[3] F. J. Arenas-Marquez, M. R. Martinez-Torres, S. L. Toral, Electronic word of mouth communities from the perspective of Social Network Analysis, Technology Analysis & Strategic Management, Vol. 26, Iss. 8, pp. 927-942, 2014.
[4] L. Qiu, J. Pang, K. H. Lim, Effects of conflicting aggregated rating on eWOM review credibility and diagnosticity: The moderating role of review valence, Decision Support Systems, Vol. 54, Iss. 1, pp. 631–643, 2012.
[5] Y. C. Ku, C. P. Wei, H. W. Hsiao, To whom should I listen? Finding reputable reviewers in opinion-sharing communities, Decision Support Systems, Vol. 53, pp. 534–542, 2012.
[6] Y. Chen, J. Xie, Online consumer review: a new element of marketing communications mix, Management Science, Vol. 54, Iss. 3, pp. 477– 491, 2008.
[7] C. Anderson, Long Tail: Why the Future of Business is Selling Less of More, Hyperion Books, New York, NY2008.
[8] A. Odic, M. Tkalčič, J. F.Tasič, and A. Košir, Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System, Interacting with Computers, Vol. 25, no. 1, pp. 74-90, 2013.
[9] F. Zhu, X. Zhang, Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics, The Journal of Marketing, Vol. 74, Iss. 2, pp. 133–148, 2010.
[10] C. Kumar, J. B. Norris, Y. Sun, Location and time do matter: A long tail study of website requests, Decision Support Systems, Vol. 47, pp. 500– 507, 2009.
[11] F. Feather, Future consumer.com. Toronto: Warwick Publishing, 2000.
[12] M. Khammash, G. H. Griffiths, ‘Arrivederci CIAO.com, Buongiorno Bing.com’—Electronic word-of-mouth (eWOM), antecedences and consequences, International Journal of Information Management, Vol. 31, pp. 82–87, 2011.
[13] A. Elberse, Should You Invest in the Long Tail?,Harvard Business Review, Vol. 86, no. 7/8,pp. 88-96, 2008.
[14] S. Standifird, Reputation and ecommerce: eBay auction and the asymmetrical impact of positive and negative ratings, Journal of Management, Vol. 27, Iss. 3, pp. 279–295, 2001.
[15] J. Lee, J. N. Lee, H. Shin, The long tail or the short tail: The categoryspecific impact of eWOM on sales distribution, Decision Support Systems, Vol. 51, pp. 466–479, 2011.
[16] E. Brynjolfsson, M.D. Smith, Y.J. Hu, Goodbye pareto principle, hello long tail: the effect of search costs on the concentration of product sales, in: MIT working paper, 2007.
[17] Elberse, F. Oberholzer-Gee, Superstars and underdogs: an examination of the long tail phenomenon in video sales, in: Harvard Business School Working Paper Series, 07–015, 2007.
[18] M. Sun, How does variance of product ratings matter? Management Science, Vol. 58, Iss.4, pp. 696–707, 2012.
[19] B. Gu, Q. Tang, A. B. Whinston, The influence of online word-of-mouth on long tail formation, Decision Support Systems, Vol. 56, pp. 474–481, 2013.
[20] E. K. Clemons, G. Gao, Consumer informedness and diverse consumer purchasing behaviors: traditional mass-market, trading down, and trading out into the long tail, Electronic Commerce Research and Applications, Vol. 7, no. 1, pp. 3–17, 2008.
[21] B. Pan, X. R. Li, The long tail of destination image and online marketing, Annals of Tourism Research, Vol. 38, Iss. 1, pp. 132-152, 2011.
[22] X. Li, Y. Xu, Y. Zhang, J. Shi, Long Tail Distribution in the Web Usage of a Chinese Learning Website, 2012 International Symposium on Information Science and Engineering (ISISE), pp. 64-67, 2012.
[23] Q. Jiang, C.-H. Tan, C. W. Phang, J. Sutanto, K.-K. Wei, Understanding Chinese online users and their visits to websites: Application of Zipf's law, International Journal of Information Management, Vol. 33, Iss. 5, pp. 752-763, 2013.
[24] A. Mahanti, N. Carlsson, A. Mahanti, M. Arlitt, C. Williamson, A tale of the tails: Power-laws in internet measurements, IEEE Network, Vol. 27, Iss. 1, pp. 59-64, 2013.
[25] A. Clauset, C. R.Shalizi, M. E. J. Newman, Power-law distributions in empirical data, SIAM Review, Vol. 51, pp. 661-703, 2007.