Why Do People Resist Healthcare IT? Literature Analysis, Model Testing, and Refinement.
Samhan, Bahae Khalaf
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Health Information Technology (HIT) has the potential of improving the overall performance of healthcare organizations. However, there are worldwide evidence of HIT resistance and avoidance behaviors. IS researchers have provided valuable insights about these behaviors towards IT in general. Very limited work has aimed to explain these behaviors in healthcare settings and especially towards Electronic Health Record (EHR) systems. In this dissertation, we investigate the resistance and avoidance behaviors in the context of a hospital, specifically having the EHR system as the focal technology for my study. Overall, the dissertation is comprised of five chapters and subscribes to a mixed-methods approach. The first chapter will provide an extensive review the literature to highlight the current status of research on technology resistance and the main research gaps. In the second chapter, we empirically validate the User Resistance Model (Kim and Kankanhalli, 2009). We aim to explain the healthcare providers’ resistance to change from the paper-based recording system to the new EHR system. Based on an analysis of survey data from healthcare providers, we investigate the effects of the key determinants from URM on EHR resistance. The third chapter is a qualitative research in which we collect responses from healthcare providers using open-ended questions. We use the Reasoned Causal Mapping (RCM) methodology to uncover the main predictors of EHR resistance and the key concepts shaping those predictors. In the fourth chapter, we aim to explain the EHR avoidance behaviors after the implementation of the system is complete and has been enforced to all users. We adopt the Technology Threat Avoidance Theory (TTAT) (Liang and Xue, 2009) and empirically test the complete proposed conceptual model. Based on an analysis of survey data from healthcare providers, we investigate the effects of perceived threats and perceived avoidability on avoidance motivations, and the direct effects of avoidance motivation on avoidance behavior. In the fifth chapter, we use RCM to reveal the main constructs impacting EHR avoidance as well as the key concepts forming these constructs. We analyze qualitative data collected from healthcare providers using open-ended questions. Contributions to research and practice are discussed within each chapter.