Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach

Abstract

Abstract Wearable healthcare technology (WHT) has the potential to improve access to healthcare information especially to the older population and empower them to play an active role in self-management of their health. Despite their potential benefits, the acceptance and usage of WHT among the elderly are considerably low. However, little research has been conducted to describe any systematic study of the elderly's intention to adopt WHT. The objective of this study was to develop a theoretical model on the basis of extended Unified Theory of Acceptance and Use of Technology (UTAUT2) with additional constructs- resistance to change, technology anxiety, and self-actualization, to investigate the key predictors of WHT adoption by elderly. The model used in the current study was analyzed in two steps. In the first step, a Structural Equation Modeling (SEM) was used to determine significant determinants that affect the adoption of WHT. In the second step, a neural network model was applied to validate the findings in step 1 and establish the relative importance of each determinant to the adoption of WHT. The findings revealed that social influence, performance expectancy, functional congruence, self-actualization, and hedonic motivation had a positive relationship with the adoption of WHT. In addition, technology anxiety and resistance to change posed important but negative influences on WHT acceptance. Surprisingly, the study did not find any significant relationship between effort expectancy and facilitating conditions with behavioral intention to use WHT by the elderly. The results of this research have strong theoretical contributions to the existing literature of WHT. It also provides valuable information for WHT developers and social planners in the design and execution of WHT for the elderly.