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There are a broad set of variables and theoretical models used to understand technology adoption and customers’ usage intention (Hassan & Wood, 2020). These variables include behavioral characteristics of individuals, cultural dimensions, gender differences, environmental facilities, and costs, among others. Considering this scenario, we combined variables from a traditional model (Technology Acceptance Model - TAM) with a comprehensive measure for an individual’s computer self-efficacy (CSE) to improve the understanding of a contemporary technology, which is the case of mobile banking. CSE indicates the capability of an individual to use computers (Compeau & Higgins, 1995) and, in the case of this paper, to provide help to other individuals in solving some problems with computers.
The relevance of the CSE variables in the field of information systems has been recognized by previous research, as pointed out by Luarn and Lin (2005), Lee et al. (2007), and Lee et al. (2011). According to Venkatesh (2000), CSE can be considered as an anchor that influences initial perceptions about the ease of use of a new technology. In line with Venkatesh (2000), recent studies indicate that CSE may influence users’ perceptions about the ease of use of a technology which, in turn, may have an effect on its adoption (Choi et al., 2018; Singh & Srivastava, 2018; Avornyo et al., 2019). Thus, in the present study, we intend to test the effect of CSE on users’ perception of mobile banking’s ease of use.
Since 2000, the number of mobile subscribers has increased significantly in comparison with the number of fixed line subscribers (Picoto, Bélanger & Palma-dos-Reis, 2014). Currently, mobile devices enable individuals to access diverse apps without barriers of location and time. Therefore, understanding the factors related to this technology represents an interest both for practitioners and academics. With the increased use of (and dependence on) the World Wide Web to do banking transactions and other personal activities, privacy and security concerns became an important issue (Junglas, Johnson & Spitzmüller, 2008; Zhang, Weng & Zhu, 2018; Malaquias & Hwang, 2019). Thus, internet and mobile technologies provide innovations for customers, but also face resistance from the market (Laukkanen, 2016). Considering such concerns with privacy and security, we also intend to explore the effect of an individual’s CSE on trust.
There are different ways to estimate the individual’s computer self-efficacy (Compeau & Higgins, 1995; Thatcher & Perrewé, 2002; Yi & Hwang, 2003; Luarn & Lin, 2005; Chakraborty et al., 2008; Al-Somali et al., 2009; Hwang & Grant, 2011; Lee et al., 2011; Zhou, 2012a). These estimations can consider general or specific application levels of CSE, since it is a multi-level construct (Marakas et al., 1998). This means that when analyzing the effect of self-efficacy on adoption of a given technology, it is possible to consider the user self-efficacy, specifically in the use of this technology (specific case), or consider the user self-efficacy in the use of computers in general (general case). In the context of mobile banking, we find a gap regarding studies using this construct (computer self-efficacy) represented by a more comprehensive scale (self-efficacy in the use of computers in general), and its potential effect on intention to use mobile banking.
We expect to expand upon previous literature with this paper, because we consider a scale for CSE that captures abilities to solve problems with computers, as well as with information systems. This scale was grounded on previous research (Durndell, Haag & Laithwaite, 2000). Based on this context, our main purpose is to analyze the role of an individual’s computer self-efficacy in mobile banking context.
This study considers data from respondents of three different countries: Brazil, South Korea (Republic of Korea) and the United States of America (USA). We collected data from three different economies in order to analyze if the potential effect of CSE is equivalent in different environments. These countries have differences in variables related with mobile banking adoption (such as the case of the level of internet users). In addition, they present different localizations (South America, North America and Asia) and different numbers for GDP and population, as shown in Table 1. Therefore, in this study, we developed a quantitative model to analyze the robustness of the results among samples from diverse regions.