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The development of information technology and wide adoption of social media by consumers have accelerated the distribution of online reviews. Such reviews, also known as electronic word-of-mouth (eWOM), mainly reflect consumers’ opinions, evaluations, and feelings about products and services. Online reviews are easily accessible through various channels, including e-commerce websites, online review sites, and discussion forums (Cheung, Lee, & Rabjohn, 2008; Lee & Youn, 2009). It has been shown that online reviews are more effective at influencing consumers than are traditional media, because users are more likely to refer to online reviews posted by unknown consumers (ChannelAdvisor, 2011). For instance, one report showed that 78 percent of online consumers are influenced by online reviews when they make purchase decisions (eMarketer, 2013). Thus, user-generated online reviews are increasingly regarded as a powerful source of information that facilitates consumer decision making (Khatwani & Srivastava, 2017; Li, Li, Yen, & Zhang, 2016).
Given the importance of online reviews in influencing consumer decision-making behavior, researchers have been attracted to explore how online review characteristics can influence consumer perceptions, attitudes, and behaviors (Erkan & Evans, 2016; Li et al., 2016; Shen, Zhang, & Zhao, 2016). However, two research gaps are evident in the literature. First, most studies focus on the benefits that online reviews can bring, such as perceived information usefulness (Xia & Bechwati, 2008). The potential risks posed by online reviews are largely ignored. It has been shown that many organizations have utilized the anonymity feature of online reviews to advocate for their own products or services by spreading biased opinions (Magnini, 2011; Zhang, Carpenter, & Ko, 2013). Thus, the potential risks brought by online reviews, especially positive reviews, should also be considered in research to provide a comprehensive understanding of the characteristics of online reviews in influencing consumer decision-making behavior.
Second, based on the Elaboration Likelihood Model (ELM), researchers have demonstrated that individuals vary in their ability and motivation when evaluating online reviews and making purchasing decisions. However, when examining the effects of online reviews on consumer behavior, researchers have mainly focused on the moderating effects of involvement (Lee, Park, & Han, 2008), prior knowledge (Park & Kim, 2008), gender (Zhang, Cheung, & Lee, 2014), and product characteristics (Zhu & Zhang, 2010). Studies exploring the moderating role of consumer skepticism are limited (Reimer & Benkenstein, 2016; Sher & Lee, 2009). In a recent survey, 80 percent of consumers reported that they were concerned about the authenticity of online reviews and suspicious of positive reviews (Williams, 2012). Thus, consumers may develop suspicious attitudes toward positive online reviews (Larson & Denton, 2014; Tarafdar, Pullins, & Ragu-Nathan, 2014; Willemsen, Neijens, & Bronner, 2012), which may further influence their decision-making behavior (Darke & Ritchie, 2007). Zhang et al. (2016) argued that some unexpected results in the online review literature (Dou, Walden, Lee, & Lee, 2012; Qiu, Pang, & Lim, 2012) may have resulted from the exclusion of the effects of Internet users’ skepticism. Consequently, individuals’ skepticism levels should be considered when examining the effects of positive online reviews on consumer perceptions and behaviors (Zhang et al., 2016).