The Effect of Information Technology Acceptance Model, Diffusion of Innovation, Social Cognitive Learning, and Channel Expansion Theory on Behavioral Intentions of People in Generation Z in Bangkok to Use the DoctorMe Applications

Abstract

Health problems in Thailand are escalating because the nation has entered into an aging society era. There are more patients, which make it hard for everyone to access hospital care. DoctorMe is the only application in Thailand that provides self-care advice at no charge. DoctorMe could enhance a capacity of public health services. It promotes self-care, which results in diminishing reliance on healthcare professional. When the number of application download is taken into account, the application shows a low success rate in creating a behavioral intention to use. There are only 0.285 percent of Thai smartphone users who have downloaded DoctorMe. This research aims to 1) study factors that comprise the behavioral intention to use, 2) determine an impact that such factors have on the behavioral intention to use, and 3) develop a model to represent the behavioral intention to use among the target population. This research employs a mixed-method research approach, between a qualitative and a quantitative research. For the qualitative research, 17 semi-structured interviews are carried out with individuals, who never use DoctorMe and belong to the target group of Generation Z in Bangkok. A taxonomy analysis is employed for data analysis.  For the quantitative research, samples are 300 individuals, who belong to the Generation Z in Bangkok. The sample size complies to the sample size requirements for structural equation model. A multi-stage sampling technique is chosen, and data is collected through questionnaires. For data analysis, the second order confirmatory factor analysis in structural equation modelling is selected. The research finds that 1) technology acceptance model (TAM), diffusion of innovation theory (DOI), social cognitive theory (SCT), and channel expansion theory (CET), which comprise the behavioral intention to use, are high, 2) each has a factor loading score (l) of: TAM (l = 0.88), DOI (l = 0.91), CET (l = 0.91) and CET (l = 0.67). Each factor has an impact on the behavioral intention to use at a statistic significance level of .05, and 3) the author proposes a model called the TDSC (TAM-DOI-SCT-CET) model. It presents concepts and methods to improve Generation Z’s behavioral intention to use DoctorMe. This research presents information on mHealth usage among citizens, in which there is limited information, and determines factors that make up a behavioral intention to use. It also provides know-hows to mHealth entrepreneurs to improve a behavioral intention to use, which could promote usage among current users, and attract new groups of users.  In addition, it promotes the Thailand 4.0 policy, especially on the part responsible by the Ministry of Public Health, through networks of private bodies and citizens so that they are educated in managing their own behaviors.