The research problem that this study addressed is healthcare professionals’ resistance to using Electronic Medical Record systems (EMR), which appears to hinder productivity in the healthcare industry (Cherry, Ford, & Peterson, 2011; Ferris, 2010). Healthcare professionals perceive that EMR systems are difficult to use; therefore, a preference still exists for paper charting patients’ medical records (Price, 2010). According to Compeau and Higgins (1995), Computer Self-Efficacy (CSE) is “an individual’s perception of his or her ability to use a computer in the accomplishment of a job task” (p. 193). The role played by healthcare professionals’ CSE in mastering computer systems may be critical to using or resisting EMR systems (Ilie, Seha, & Sun, 2009). Physician adoption of EMR systems has long been studied by Information Systems (IS) studies; however, CSE and its part in the resistance to using EMR systems as documented in prior research to require further investigation (Morton, 2008; Nixon, 2009; Price, 2010). Healthcare professionals’ resistance to using EMR systems is a significant problem because these systems have been shown to induce work efficiency, reduce costs, and provide accurate patient tracking capabilities (Block, 2008). Despite these benefits, one study found that about 52% of healthcare professionals still resist implementations of EMR systems (Nov & Schecter, 2012). Resistance to using EMR systems puts both hospitals and patients at greater risk of possible medical mishaps (Block, 2008; Ilie, Courtney, & Van Slyke, 2007).
Computer Self-Efficacy is considered one of the most important constructs in social cognitive theory in that it provides direct insight into human intentions and behavior (Torkzadeh, Pflughoeft, & Hall, 1999). CSE, in the context of EMR, refers to a healthcare professional’s abilities to perform patient care tasks when he or she uses computers (Compeau et al., 1999). It is defined as a judgment of one’s capability to apply computer knowledge to a specific task (Stephens & Shotick, 2002). It can represent an individual assessment of self-skill sets necessary to use technology effectively for performing tasks related to patient care.
Perceived Complexity refers to the degree to which information systems are viewed as being difficult to use (Tornatzky & Klein, 1982). PC has been found to be closely related to perceived ease of use in the literature (Moore & Benbasat, 1991; Thong, 1999; Van Slyke, Belanger, & Comulane, 2004). In fact, many scholars view PC as being the conceptual opposite of perceived ease of use, which explains the degree to which a prospective user would expect EMR systems to be free of effort (Davis et al., 1989). On that basis, perceived ease of use was omitted and PC adopted for this study.
Attitude Toward EMR is defined as a disposition to respond either favorably or unfavorably to a technology, institution, or event (Whitten, Buis, & Mackert, 2007; Zheng, Padman, Johnson, & Diamond, 2005). ATE was found to be strongly affected by previous personal experiences with computer systems in either professional or personal settings (Boonstra & Broekhuis, 2010; Schoen et al., 2009). EMR implementation has been studied as an individual decision made by physicians based on their attitudes toward technology, standards of care, and administration (Zandieh et al., 2008). However, resistance as it relates to ATE is scarce in the literature.
Anxiety in the context of EMR systems use is defined as a fear of electronic health records when using them or fearing the possibility of having to use a computer (Chua, Chen, & Wong, 1999; Cork et al., 1999; Embi, 2007). Anxiety is considered an emotional fear of a potential negative outcome, such as rendering a system inoperable or appearing computer-illiterate in the eyes of others (Brosnan & Lee, 1998; Kaushal et al., 2009). From an information-processing perspective, the negative frame of mind associated with high anxiety diminishes cognitive resources for task performance (Brown & Coney, 1994; Dixon & Stewart, 2000).
Peer Pressure – Homophily is a social process where opinions are generated by social leaders who are similar in various ways to people choosing whether to resist a particular situation or innovation (McPherson et al., 2001). Actually, Tarde (1903) first presented this idea of homophily, describing a social practice that he deemed critical to the adoption of technology; however, his theory did not extend to the resistance of technology. Rogers (2010) transferred the class nature of homophily to the employment status of individuals in the 21st century, noting that most diffusion networks in business are more interpersonal in nature, “occurring between persons with similar jobs and education levels” (p. 287).
Resistance – IS research has provided rich insights into why technologies are needed and the reason people use them, but has not given enough attention to the question why IT workers resist technologies, and what factors are impacting implementation/usage of systems (Cenfetelli 2004; Lapointe & Rivard 2005; Kim & Kankanhalli 2009). Laumer and Eckhardt (2012) noted that user resistance to technology has been growing, in particular when implementation projects are initiated by the IT department rather than business operations. End-users often become resentful for having to cope with yet another system. They become disruptive to the implementation process (Cenfetelli, 2004).