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The rapid development of information communication technology (ICT) has spawned a variety of new products and services for business and communication among which users may select according to their preferences. An ICT user is thus frequently in a position to decide whether to keep using a given current service or to switch to another (Parthasarathy & Bhattacherjee, 1998). His or her decision to continue or discontinue use of a service is referred to as post-adoption behavior (Parthasarathy & Bhattacherjee, 1998). This paper specifically focuses on post-adoption continuance behavior, that is, the choice whether to keep using products and services presently in use. Continuance behavior is an important factor for service providers to understand, simply because the cost of acquiring new customers—searching for them, setting up new accounts, and initiating them to the information systems—is five times that of retaining existing customers (Bhattacherjee, 2001; Parthasarathy & Bhattacherjee, 1998).
Studies of post-adoption behavior have found that cultural characteristics play an important role (De Mooij, 2004; Straub, 1994; Van Slyke, Lou, Belanger, & Sridhar, 2004; McCoy, Everard, & Jones, 2005), because culture has a strong effect on how a user interprets a system’s content and functions (Hiller, 2003). A system feature appropriate for users in one culture may not be appropriate for users in others without significant adaptation.
Uncertainty avoidance (UA) is one cultural factor known to exert a strong influence on post-adoption behavior (Frank, Sundqvist, Puumalainen, & Taalikka, 2001). UA is a measure of how well individuals tolerate unpredictable and unstructured situations or contexts (Hofstede, 1997; Veiga, Floyd, & Dechant, 2001). In the context of current ICT, two factors make it especially important to examine the impact of UA on post-adoption behavior. First, UA has been found to influence substantially users’ initial adoption behaviors with new services or products (Veiga et al., 2001). People with high UA tend to stick with traditional technologies and to be slow in accepting new ones. Conversely, people with low UA tend to adopt new technologies quickly and easily (Hofstede, 1997). This distinctive effect of UA on initial adoption behavior strongly suggests that UA may also influence post-adoption behavior significantly (Lippert & Volkmar, 2007). Second, newer ICT services, including mobile data services (MDS), are part of a ubiquitous computing environment, and are thus used in a far broader range of contexts than traditional ICT services, which are restricted to relatively familiar environments like offices and houses (Evers & Day, 1997). This diversity of use and circumstance entails greater uncertainty in the use of information technologies; new and complicated connection methods, abstract or unfamiliar icons, and high usage fees further increase the uncertainty (Albers & Kim, 2000; Chae, Kim, Kim, & Ryu, 2002). The trend of growing uncertainty in the ICT environment suggests UA may have substantial effects on post-adoption behavior with current ICT services.
The goals of this study are, first, to construct a theoretical model to analyze the impact of UA on continuance behavior at the level of the individual user in the MDS domain, and, second, to identify specific effects of UA by conducting empirical tests through the proposed model. While many studies have examined UA at the national level, the present study focuses on UA at the level of the individual user. UA heterogeneity within countries differs significantly from that between countries, and understanding the former will be more helpful to service providers who want to identify homogeneous market segments in a given country.