Telecare Adoption Model Based on Artificial Neural Networks

Telecare will become a trend in the twenty-first century (Haux, 2006; Miller, 2007). With respect to telecare, evaluation studies are few in number. Furthermore, few studies were carried out using nonlinear structural models (such as the nonlinear neural network model) to explore the users’ adoption. Therefore, the purpose of this study is to utilize the healthcare information adoption model (HIAM) that it is created first time by Huang (2010), and use it to establish telecare adoption by artificial neural networks (ANNs). According to the healthcare information adoption model (HIAM) Huang, 2010), the research structure underlying this study is shown in Fig. 1. H1.An individual’s attitude toward using (ATT) and behavioral intention to use (BI) with respect to telecare are found to be positively associated. H2a.Perceived ease of use (PEOU) has a direct effect on the ATT of telecare. H2b.PEOU has a direct effect on PUB (perceived usefulness and benefits). H3. The stronger the perceived usefulness and benefits (PUB) of telecare, the stronger is the ATT of telecare. H4. The stronger the perceived disease threatens (PDT) (which includes perceived susceptibility and perceived severity), the stronger is the ATT of telecare. H5. The stronger is the perceived barriers of taking action (PBTA) of telecare, the weaker is the ATT of telecare. H6. The stronger the cues to action (CUES), the stronger is the ATT of telecare. H6a.The stronger the external cues to action (ECUE), the stronger is the ATT of telecare. H6b.The stronger the internal cues to action (ICUE), the stronger is the ATT of telecare.