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Introduction
Electronic word-of-mouth/mouse (e-WOM) has recently experienced significant growth due to the proliferation of user-generated content (UGC), content created by ordinary people which can be distributed quickly and easily, primarily through the internet. In 2006, UGC sites attracted 69 million users in the US alone, and in 2007 generated $1 billion in advertising revenue ([22] Internet Advertising Bureau, 2008). By 2011, UGC sites are projected to attract 101 million users in the US. According to the latest [36] OECD report (2007), UGC sites include blogs (e.g. MSN spaces), wikis (e.g. Wikipedia), virtual worlds (e.g. Second life), social networking sites (e.g. Facebook), podcasting (e.g. iTunes), and web sites allowing feedback (e.g. FanFiction.net).
One of the most interesting types of UGC from a marketing perspective is review sites, where consumers share their product experiences in order to help others make more informed purchase decisions. The importance of this type of communication is reflected in the findings from a survey conducted by [34] Nielsen Research (2009) across 50 markets worldwide based on which consumer recommendations is the most trusted form of advertising. Of the respondents 90 per cent rate recommendations from people they know as trustworthy compared to 70 per cent who trust consumer opinions about products and brands posted online. Consumer trust towards online consumer reviews has increased by 9 per cent compared to the respective study from 2007. Paid advertising channels are rated lower on trustworthiness: TV and newspapers (61 per cent); magazines (59 per cent); radio (55 per cent); search engine ads (41 per cent); banner ads (33 per cent); and mobile (24 per cent). The growth in the level of trust toward online recommendations and review sites indicates that an increasing number of users globally share information about products and brands.
Effectively, e-WOM is argued to be more influential than its offline counterpart (WOM), due to its ability to reach a larger number of individuals instantly and on a global scale ([16] Hennig-Thurau et al. , 2004; [39] Phelps et al. , 2004). Based on the [2] Bass (1969) diffusion model, the likelihood to adopt a new product increases as the number of previous buyers rises. Due to the increased visibility of online product reviews from several previous buyers we argue that the...