The Wrap Effect In Online Review Sets Revisited: How Perceived Usefulness Mediates The Effect On Intention Formation

1.IntroductionOnline reviews are information posted by existing consumers about their consumed products or services on e-commerce or third party websites (e.g., Amazon.com, Yelp, TripAdvisor) [Zhu et al. 2014]. Studies indicate that 78% of consumers consider online reviews before making a purchase, while 44% post reviews themselves [eMarketer 2013]. Compared to advertising, reviews are more likely seen by prospective customers as trustworthy and credible [Chih et al. 2013], as they do not trigger persuasion knowledge (i.e., the attitude bias that results from the awareness of a persuasion attempt) [Friestad & Wright 1994; Purnawirawan et al. 2012]. Electronic word-of-mouth (eWOM) is any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet [Hennig-Thurau et al. 2004]. eWOM can be spread, for example, through brand communities (e.g., Apple, Lego), professional (LinkedIn) and non-professional networking sites (Facebook), consumers' review websites (e.g., TripAdvisor, Cnet) and (micro) blog sites (Twitter). Although the importance of eWOM in consumer behavior has been studied extensively [Cheung & Thadani 2012; Doh & Hwang 2009; Park & Lee 2009], there is little understanding of how the mix of positive and negative reviews in an online review set affects attitude formation and behavioral intentions.In the academic literature on electronic word of mouth, there are only a few studies that have investigated the impact of a set of reviews instead of a single review. Additionally, the few studies on the impact of the balance (the degree of positivity or negativity) of a set of reviews have often assumed that people form impressions and attitudes on the basis of an accurate assessment of a positively, a neutrally and a negatively balanced set, but have ignored the effect of review sequence [Doh & Hwang 2009; Lee et al. 2008]. The current study builds on the research of Purnawirawan et al. [2012] who introduced the concept of the "wrap effect" (or the primacy-recency reinforcement effect) in online review sets. Wrapping occurs when the first and the last review in a set are of the same valence. Specifically, they showed that, for a neutrally balanced review set (containing an equal number of positive and negative reviews), when the first and last review in the set are both positive (positive wrap), readers' recall and impression about the reviews is positively biased, compared to when they were exposed to a non-wrapped review set. Conversely, when the first and last review are both negative (negative wrap), impressions are biased negatively. In a second study [Purnawirawan et al. 2012b], they also found that both positive and negative wrapping can increase the perceived usefulness of a review set. Purnawirawan et al. [2012, 2012b] thus demonstrated that a mix of positive and negative online reviews triggers cognitive biases, depending upon the order in which these reviews are read. More specifically, "wrapping" is important: wrapped review sets have a different effect on readers' responses than nonwrapped review sets.The focus of the Purnawirawan et al. [2012] study was to use the wrap effect to predict review impression and the attitude towards a service (a hotel). The first contribution of the present study is that it extends the findings of Purnawirawan et al. [2012] to behavioral intentions, i.e. the intention to perform a certain behavior, more specifically purchase intention and intention to recommend the reviewed service, and provides insight into the mediating role of perceived usefulness in this process. Second, in both studies by Purnawirawan et al. [2012, b], the number of reviews read was kept constant for reasons of internal validity. In the present study, we enhance the external validity of the research by leaving respondents free to read as many reviews as they want. …