User Acceptance of Healthcare Robots Through Extended UTAUT2: A Mixed Method Approach

Abstract

Abstract Background Technological change is reshaping the economic, social and cultural scenarios in which we live. In the health care sector, the response to this change is observed with the advent of eHealth, that employ technologies to support both the healthcare professionals and managers and the patient in his or her care journey. The utilization in practice of these technologies moves the attention to the users. Focusing on the patient side, this study proposes a model aimed at understanding the intention to use the eHealth technology. Methods A model based on UTAUT2 has been tested using a mixed approach combining Partial Leas Squares (PLS) and crisp-set Qualitative Comparative Analysis (csQCA) with the aim of identifying recipes fostering eHealth adoption. Data was collected through an online survey on a sample of 208 respondents. PLS and csQCA helped identify causal combinations of variables that lead the PLS and csQCA are complementary analytical approaches providing novel and more reliable information. Results The PLS analysis show that the model strongly predicted the intention to use healthcare robot (R-square=0.759). According to the model's path coefficients, Trust in Technology and Performance Expectation are the most powerful significant predictors of intentions to use the robot (TRU: 0.259, ρ<0.001; PE: 0.236, ρ<0.01). Hedonic Motivation (0.180, ρ <0.01) and Social Influence (0.126, ρ <0.05) predicted intention to use positively, while Technology Anxiety (-0.177, ρ <0.001) predicted intention to use negatively. The csQCA reveals four configurations, including the PLS identified factors. The results indicate that several conditions that were not significant in PLS are sufficient when combined with other conditions. Conclusions This study emphasizes the importance of adopting an integrated approach centered on performance expectancy, trust in technology, social influence, and hedonic motivation while paying attention to anxiety when using technologies. Our findings could support the practitioners in activating actions oriented to enhance the intention to use of a specific eHealth technology, keeping in account not only the technological aspects but all the variables that affect its utilization.