We live in the era of big data that dramatically transforms the way we make decisions (Janssen, van der Voort, & Wahyudi, 2017). Big data is the “data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” (Manyika, Chui, Brown et al., 2011). New information and communication technologies (ICTs) have enabled the big data trend by providing the capability to capture and store huge amounts of consumer data which serves as the core of the big data trend (Chen, Chiang, & Storey, 2012). When properly collected, stored, and processed, consumer data may allow organizations to understand customer behaviors and preferences. Such knowledge is valuable in customizing and personalizing products and services to meet customer needs, thereby equipping companies with a competitive advantage (Erevelles, Fukawa, & Swayne, 2016). While businesses are eager to access customer data, privacy factor remains the most salient issue that must be solved before organizations could capitalize on the value of a data-centric service economy (Janssen & van den Hoven, 2015; TRUSTe, 2011). Given that each piece of data leaves behind electronic trails of customer activities, individuals are concerned about how companies collect and use their private information (Janssen & Kuk, 2016; Morey, Forbath, & Schoop, 2015) This situation, together with the increasing number of online information leaks, heightens customers’ privacy concerns toward information risk (Drinkwater, 2016). Therefore, it is important that companies are aware and capable of handling the risks because they could pose longterm damaging effects on companies as well as cause economic losses (Culnan, 1993). The risks have led governments to enact privacy regulations and policies (e.g., European Directive EC 95/461995 and United States Federal Trade Commission (FTC)’s Fair Information Practice Principles (FIPPs)) to protect people from potential harmful acts. Companies must comply with these regulations and devise effective privacy management strategies to address privacy issues. This would require knowledge of how people make decisions about revealing and concealing private information. Petronio (2012)’s communication privacy management (CPM) theory used a boundary metaphor to explain how people make decisions about revealing and concealing information, which is known as ‘privacy boundary formation.’ In impersonal contexts such as those between customers and companies, the form by which companies use customer data (i.e., organizational information practices) is salient to the formation of an individual’s privacy boundary (Dinev, Xu, Smith, & Hart, 2013; Metzger, 2007). In the process of forming privacy boundary, consumers also reference their governments’ privacy regulations (Xu, Dinev, Smith, & Hart, 2011). Weighing the interplay among consumers’ privacy boundary formation, organizations’ information practices, and government’s regulations as well as the current findings in the literature, we realize that there are gaps that have to be addressed so that a better understanding of consumers’ privacy boundary formation can be achieved. First, previous research has not fully examined the effect of government’s privacy policy.