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Over the past three decades, end users have been working with increasingly sophisticated and complex technologies (Kanter, 2000; Sykes, Venkatesh, & Gosain, 2009). These tools provide end-users with a range of features, flexibility, and functionality that can help them perform tasks more efficiently, and help them accomplish new, more sophisticated tasks. For example, current spreadsheet software (e.g. Microsoft Excel) allows data to be imported quite easily, which improves efficiency. End-users can also use the software to explore data for trends and patterns via the Data Analysis tool, which is a more sophisticated use of the software (Evans, 2013). It can take the end-user a substantial amount of time to learn the technology, fit the technology to his/her current tasks (and vice versa), and then decide if the tool can be applied to new, more sophisticated tasks. There is an understandable concern among managers that tools with such lengthy learning curves need to be used as effectively as possible. Researchers have therefore been focusing on better predicting and explaining the use of technology, not just the initial decision to adopt (Ortiz de Guinea & Markus, 2009).
Several research models explaining the initial adoption of technology have been adapted for examinations of technology use (Barki, Titah, & Boffo, 2007; Kim & Malhotra, 2005; Po-An Hsieh & Wang, 2007; Venkatesh, Thong, & Xu, 2012). In addition, individual differences among end-users may have an important role in explaining the variance of long-term technology use. Differences among Individual end-users affect perceptions and behaviors, and many adoption models incorporate such differences. For example, differences in gender, age, and experience moderate the behavioral intention to use a technology (Venkatesh, Morris, Davis, & Davis, 2003; Venkatesh et al., 2012), and differences in self-efficacy (Compeau & Higgins, 1995) have direct effects on computer use. One individual difference that has received substantial research attention is experience (Lee, Kozar, & Larsen, 2003; Legris, Ingham, & Collerette, 2003), with the assertion being that more experience with a technology leads to more positive attributions regarding that technology, and therefore greater use (Gefen, Karahanna, & Straub, 2003). Although this assertion seems intuitive, research results have been inconsistent. Some studies have found that experience has a positive effect on perceptions and behavior, others have found a negative experience effect, and still others have found no effect (Gefen et al., 2003).
To help clarify the effects of experience on technology perceptions and use, we assimilated research from organizational behavior and personnel psychology that has proven effective at predicting job performance. In addition, our post hoc analyses further demonstrate the strength of our findings, and lend support to current sentiments regarding the importance of prior use in explaining the continued use of technology.