Examining the power-law distribution among eWOM communities: a characterisation approach of the Long Tail

A new methodology that mathematically fits the relationship between the power-law distribution and the Long Tail from an eWOM community is developed and a tool for finding niche products inaccessible through conventional channels is defined.

Abstract

Nowadays electronic word-of-mouth (eWOM) communities symbolise a\n\t\t\t\t significant source of information that helps customers to make informed\n\t\t\t\t purchasing decisions. Through eWOM communities, a great audience of\n\t\t\t\t users is able to acquire knowledge from reviews concerning products\n\t\t\t\t and services that are less popular to the majority. The Long Tail effect\n\t\t\t\t is a manifestation of such redistribution of demand from popular\n\t\t\t\t products to niche products. In this paper, a new methodology that\n\t\t\t\t mathematically fits the relationship between the power-law distribution\n\t\t\t\t and the Long Tail from an eWOM community is developed. In addition,\n\t\t\t\t this paper defines a tool for finding niche products inaccessible through\n\t\t\t\t conventional channels. The results are consistent in showing that not all\n\t\t\t\t the categories fitting a power-law distribution are characterised by the\n\t\t\t\t Long Tail phenomenon, and conversely some of those having a Long\n\t\t\t\t Tail do not fit a power-law distribution.