Antecedents of the Intention to Use Implantable Technologies for Nonmedical Purposes: A Mixed-Method Evaluation

Identificador:  imarina:9414874

Handle: https://hdl.handle.net/20.500.11797/imarina9414874

Autores:  de Andres-Sanchez, Jorge; Arias-Oliva, Mario; Souto-Romero, Mar

Resumen:
This study investigated the rise of implantable or cyborg technologies, also known as insideables, which offer the potential to improve health-related conditions and enhance the capabilities of healthy individuals. This research focused on the acceptance of insideables among university students in Spain, utilizing the unified theory of acceptance and use of technology (UTAUT) framework along with analytical tools such as partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The PLS-SEM analysis revealed that factors such as performance expectancy, effort expectancy, and social influence positively influenced the intention to use insideables. However, the fsQCA revealed that no single variable is a necessary condition for explaining technology acceptance or rejection. Instead, a combination of constructs is needed to understand both intention to use and rejection. Configurational analysis emphasized the importance of factors such as performance expectancy, social influence, and hedonic motivation in explaining technology acceptance, whereas effort expectancy and perceived risk were less conclusive in their impact on behavioral intention. Moreover, the research revealed that the configurations related to the acceptance and rejection of insideables are asymmetrical. This study sheds light on the complex dynamics of implantable technology acceptance and provides valuable insights into the factors influencing its adoption. From a theoretical perspective, the sequential use of both correlational and configurational methods within the UTAUT framework allows us to gain a deeper understanding of the reasons behind the adoption of emerging technology rather than using only one data analysis methodology.