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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:69563", author = "M. Olmedilla and M. R. Martinez-Torres and S. L. Toral", title = "A Long Tail Study of eWOM Communities", 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.", volume = "9", number = "6", pages = "1374-5", }
