Article Preview
Top1. Introduction
One of the most notable and well-established streams of research in Information Systems (IS) over the past four decades has been focused on how and why people adopt information technology. The need to investigate the factors influencing successful adoption arises, in part, due to the complex interplay between people and technology. Technology adoption research, therefore, seeks to clarify the factors that contribute to the success and failure of information systems and technologies (Wills, El-Gayar & Bennett, 2008).
Recently, healthcare has been transferred from hospitals and nursing facilities to the patient home, which leads to what is commonly known as home healthcare (The Personalized Medicine Coalition, 2013; AHIP Center for Policy and Research, 2010). This initiative has been undertaken broadly by healthcare industry in the U.S. to reduce readmission costs and transportation costs, and to improve pos-hospitalization healthcare quality and finally increase patient independency (The Joint Commission, 2011). Moreover, the rapid increase of the older adult population, which is expected to reach 21 percent in the U.S. by 2030 will create new challenges for our society. The growing population of those with disabilities will also create the need for more nursing and home-care services from the healthcare industry (U. S. Census Bureau, 2005).
Today, it is almost unimaginable to consider this home healthcare initiative without information technology. Clinical decision support systems, mobile health systems (mHealth), sensor based monitoring systems (SMS) and longitudinal electronic medical records (EMR’s) promise to make clinical information available at the right place and right time, thereby reducing error and increasing safety and quality (Black et al., 2011). One of these promising technologies is home healthcare robots (HHRs) (Advait & Kemp, 2010, Choi et al, 2008), which is the focus of this study.
It is critical that we understand the factors that influence the HHRs adoption. HHRs that are perceived negatively by stakeholders are no longer applicable like any product or service, customers must be satisfied or they will look elsewhere to fulfill the jobs they are interested in completing. Most previous HHRs research has focused on technology, implementation and algorithmatic design issues (Advait & Kemp, 2010; Choi, Anderson, Glass, & Kemp, 2008; King, Tiffany, Jain & Charles, 2012; Fan, Chen, Fan, Glass & Kemp, 2010). However, little research has focused on user perceptions, needs and preferences of such technology. This research, therefore, aims to fill the knowledge gap by leveraging the UTAUT model.
The current research makes several contributions to the literature. First, it enables robot designers and service providers to understand what influence stakeholders’ decision to adopt HHRs; second, it enriches the literature on technology adoption by extending the related theories to the HHRs domain; third, it enhances the theoretical foundation of HHRs research by innovatively applying technology acceptance models to explain the adoption of HHRs; fourth, it extends the technology acceptance models by introducing perceived security.
The remainder of this paper is organized as follows: the next section reviews the related literature. The third section introduces the research model and hypotheses. Section four describes the research method. The fifth reports the analysis and results and followed by discussion and conclusion in section six, and section seven, respectively.