Healthcare Technology Adoption at the Group Level

Background

Healthcare technologies are playing a dual role improving productivity of hospitals, clinics, and health administration services and enhancing access to and quality of healthcare (Devaraj, Ow, & Kolhi, 2013; Sun et al., 2013). Examples of such technologies include healthcare information management systems, healthcare document management, healthcare business intelligence software, electronic medical records, mobile health services, and patient monitoring systems, to name a few.

In recent studies, scholars have reported challenges such as underuse, resistance, workarounds and overrides, sabotage, and even abandonment of healthcare technologies (Chang et al., 2007, Holden & Karsh, 2009, Yi et al., 2006). Paradoxically, however, healthcare technologies are critical for enhancing organizational productivity and the quality of healthcare (Devaraj & Kohli, 2010). Thus, it is of utmost importance the understanding of the factors behind their user acceptance.

A great deal of research in this area has focused on theories such as technology acceptance model (TAM), protection motivation theory (PMT), theory of planned behavior (TPB), and the unified theory of use and acceptance of technology (UTAUT) (Sun et al., 2013). Typically these studies tend to adopt an individual level of analysis while little attention has been given to the factors that influence acceptance of healthcare technologies from a group perspective. Examining the acceptance of these technologies at the group level may help explain some of the organizational challenges highlighted above. For instance, understanding the structure that determines how these technologies are embraced by organizational groups is critical because it recognizes that “group members’ individual a priori attitudes about a technology cannot be simply aggregated to predict whether or not the group will decide to adopt a technology, unless every member is in agreement” (Sarker et al., 2005). This is critical since work groups in large organizations are usually given the autonomy to adopt a specific IT based on how it may better support their task needs (Bajwa & Lewis, 2003). In other words, it is both characteristics of the individuals and group interaction processes that unfold over time that determines adoption of healthcare technologies.

We examine the key components of healthcare technology adoption using a group level analysis. The main components of our research model and its relationships are depicted in the figure below. In the following sections we describe the research model, its components, and their combined impact on healthcare IT adoption (Figure 1).

Figure 1.

Research model