The determinants of home healthcare robots adoption: An empirical investigation
Introduction
Healthcare is one of the largest growing burdens on a nation's economy [1]. Health expenditures in the U.S. neared $2.6 trillion in 2010 [2]. Health spending accounted for 17.9% of the nation's GDP in total [3], and is expected to increase faster than national income over the next ten years. Therefore, controlling the rising healthcare cost continues to be a major policy priority. Among the existing proposals for reducing the long-term cost include patient-centered medical delivery systems, funding for comparative effectiveness research, and wider use of health IT in the delivery system [4]. One of the most effective initiatives being implemented today is home healthcare [5].
In recent years, healthcare has been transferred from hospitals and nursing facilities to the patient home [4], [5]. This home healthcare initiative has been undertaken broadly by healthcare industries in the U.S. to reduce readmission and transportation costs; improve pos-hospitalization healthcare quality; and increase patient independency [5]. Furthermore, the rapid increase of the older adult population (expected to reach 21 percent in the U.S. by 2030), and the growing population of people with disabilities will create the need for more nursing and home-care services [6].
Home healthcare robots (HHRs) are one of the emerging technologies that promise to make clinical information available at the right place and time, thereby reducing human-error and increasing safety and quality. In the last few years, HHRs have started helping professionals, including nurses, doctors, therapists and physicians, provide home health cares and services to their patients in a variety of forms such as monitoring personal health and safety, providing medication management and scheduling, detecting people lying on the floor, assisting in physical, cognitive and occupational therapy and nursing tasks (e.g., monitoring the blood pressure and bed bath).
Despite the promise of HHRs to reduce healthcare cost, research has repetitively shown that more than 40% of previous IT developments in various sectors including the health sector have failed or been abandoned [7], [8], [9], [10]. One of the major factors leading to the failure is an inadequate understanding of the sociotechnical aspects of IT, particularly how people and organizations adopt IT [11], [12]. Accordingly, the ultimate success of HHRs hinges on whether we can address their associated technological, social, and managerial challenges. The current research aims to understand the adoption of HHRs from these above aspects.
There has been a stream of technology acceptance research in the information systems field [13], [14], [15], [16]. Since HHRs are at an early stage of diffusion, it is critical that we understand these sociotechnical factors that influence their adoption. However, HHRs have not been studied from the technology acceptance perspective to date. Previous robotics research has focused on technical implementation as well as technology development and clinical application [17], [18], [19], [20], but there has been limited discussion of social and managerial issues that might be equally important for robot success. To fill the knowledge gap, this research integrates technology acceptance research and HHR research to provide insights into home healthcare adoption.
This research makes several contributions to the literature. First, it enriches the technology adoption literature by extending existing theories to the domain of HHRs; second, it enhances the theoretical foundation of home healthcare research by applying technology acceptance models to explain robot adoption; third, it extends the technology acceptance models by introducing several new constructs such as trust, privacy, ethical and legal concerns; fourth, it enables robot designers and service providers to improve their products or services by suggesting a list of preferable tasks and services.
Section snippets
Home healthcare initiative
In the first decade of the 21st century, great attention was devoted to the U.S. society's need for access to health care and health care delivery. To date, there has been an increasing focus on the transition of care into the home. Health care is increasingly occurring in residential settings rather than in professional medical settings [21]. The Centers for Medicare and Medicaid Services (CMS) estimates that 8090 home health care agencies in the United States provide care for more than 2.4
Research model and hypotheses development
To explain the usage intention of HHRs, we contextualized the original UTAUT model by making modifications to its constructs. In addition, we identified four new constructs, including trust, privacy concerns, ethical concerns and legal concerns. These constructs were developed based on two primary sources of information: literature review and interviews with health information technology academics and professionals. Our research model is shown in Fig. 1.
Method design
We used surveys for data collection in testing the research model. In addition, we also adopted qualitative methods.
Sample profile
Among a total of 166 responses that were received, 108 were complete and valid, which was greater than the number suggested by power analysis, as discussed earlier. Table 2 reports demographic statistics of the participants.
As shown from Table 2, males accounted for 63% of the participants, the majority of the participants belonged to the 18–33 years old age group (77.7%), about two-fifth of the participants had a graduate degree (39.8%), and more than one third (36.2%) are professionals. The
Discussion
This study primarily sought to identify the determinants of the usage intention of HHRs. The findings suggest that the intention is a function of performance expectancy, social influence, trust, privacy concerns, ethical concerns and facilitating conditions. These factors have substantial predictive power accounting for more than half of the variance in explaining the adoption of HHRs.
Among the influential factors, social influence was found to be the strongest determinant, which is consistent
Implications
This research has a number of theoretical and practical implications. Theoretically, the research provides a model for explaining the usage intention of HHRs, which not only enhances the theoretical foundation of HHRs research, but also expands the application of technology adoption theories to the domain of home healthcare. To the best of our knowledge, this is the first quantitative research study in the domain of HHRs that takes into account of the perceptions of various stakeholders. We
Conclusions
This research is attempted to explain the usage intention of HHRs. The research model extends UTAUT with new constructs and domain. The results show that performance expectancy, social influence, facilitating conditions, trust, privacy concerns, ethical concerns are directly associated with stakeholders’ intention to use HHRs, while effort expectancy is indirectly associated with it. The study also identifies preferred tasks for HHRs. These findings have significant implications for home
Author contributions
Contributors AA and LZ contributed to the conception, design, acquisition, analysis, and interpretation of data, drafted, revised and finally approved the manuscript.
Competing interests
The authors have no competing interests to declare.
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