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

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.

Source: Technological Forecasting and Social Change - November 3, 2019 Category: Science Source Type: research