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There is a rich body of information systems (IS) literature about the factors that influence individuals when choosing a technology. These factors are based on the end users’ perceptions of the technology (i.e. perceived usefulness, effort expectancy, and facilitating conditions (e.g. Venkatesh et al. 2003), the opinions of significant others (i.e., subjective norms (e.g. Titah & Barki, 2009), the context of usage (i.e., hedonic and utilitarian (e.g. Lowry et al. 2015), and so forth. A common assumption that is made regarding the decision-making process of users is that an individual is able to sufficiently and personally evaluate the usefulness of a technology and is willing to invest the necessary cognitive effort required to make such decisions. In reality, however, many users are constrained by bounded rationality, that is, their ability to act is limited by factors such as information, cognitive restrictions, and available time (Simon, 1976). For instance, the lack of personal information about alternatives motivates individuals to find mechanisms to cope with the resulting uncertainty (Banerjee, 1992). In such circumstances, observing other users’ technology choice decisions and receiving information about the popularity of alternatives can substantially influence individuals’ decisions (Bikhchandani, 1992).
Because of the widespread use of the Internet and various online platforms, an individual can browse and observe other users’ choices a technology and how popular a certain technology is. Perceived uncertainty, combined with the observation of other users’ decisions, can lead to herd behavior phenomenon, defined as “everyone doing what everyone else is doing, even when their private information suggests doing something quite different” (Banerjee, 1992). For instance, when an individual lacks sufficient personal information and is uncertain about a software, obtaining information about the number of times it has been downloaded may motivate them to imitate the majority and adopt the software.
A number of studies in the IS literature have investigated the roles of herd behavior and popularity information in users’ decision making in different areas, such as the online purchasing of music (Dewan et al., 2017), online reviews (Li and Hitt, 2008; Gao et al. 2017; Zhou and Guo, 2017; Wang et al., 2018), Internet marketing (Tucker and Zhang, 2011; Kang et al. 2016; Liu & Tang, 2018; McCole et al. 2019), software use (Duan et al., 2009), crowdsourcing (Bretschneider and Leimeister, 2017; Banken et al. 2019), and IS security behaviors (Vedadi and Warkentin, 2020). Sun (2013) examined the influence of herd behavior using two complementary theoretical mechanisms: discounting one’s own information, which is the degree to which one disregards personal beliefs about a technology in making an adoption decision; and imitating others, which is the degree to which one follows previous adopters in choosing a certain form of technology and found that when users receive information about the popularity of a technology, they tend to discount their own limited information and imitate others’ actions.
Although the extant literature identifies important factors that influence herd behavior in the IS context, this research domain is still in its infancy, and there are numerous research questions in this area remain unanswered. Regarding the fact that trust has been found to have a substantial influence on decision making under conditions of uncertainty (e.g. Mayer et al. 1995), we believe that it is a key factor that should be investigated in the IS herd behavior context. Lewis and Weigert (1985) argued that if transactions took place under certain circumstances, there would be no need for trust. As evidenced in the e-commerce literature, when individuals struggle to make purchasing decisions due to insufficient information, they need other mechanisms to compensate their information deficiency (e.g. Cheung et al. 2014). Hence, trust can be a means of reducing perceived uncertainty because it allows individuals to accept the vulnerability associated with accepting the information conveyed by the signals sent by the vendors and existing customers of a product (i.e. Pavlou et al., 2007; Stoecklin-Serino & Paradice, 2009; Zhang et al. 2009).