Towards an Effective M-Health Application: Exploring Factors Affecting the Adoption and Acceptance of M-Health Applications in Saudi Arabia

Abstract

Background and Objective:The development of mobile health apps seeks to improve the efficiency of healthcare services by providing patients with additional features and options. All across the globe, individuals have come to appreciate these substantial advantages. However, in other developing nations, such as Saudi Arabia, the adoption of such technology has not been as broad as could be anticipated. Discovering what motivates patients (users) to utilise m-health apps is a significant step in establishing the most effective ways to increase their use in the future. Therefore, this research was conducted in line with the demands for further scientific investigations focusing on the factors affecting the adoption and acceptance of mobile health applications in Saudi Arabia. The UTAUT model has formed the basis for the development and expansion of the research model. This study intends to make a significant contribution by empirically investigating users' behavioural intentions about m-health app adoption and acceptance and the variables influencing them from various perspectives: technological, health, personal, and external perspectives.

Methods:In this research, a sequential mixed method, including quantitative and qualitative data, was employed to recruit users of Saudi Arabian public healthcare providers' mobile health applications. Phase one of the study involved distributing a survey questionnaire to participants in order to validate the proposed research model statistically. The survey received responses from 409 valid users. Following the exclusion of incomplete replies, a sum of 343 responses were included in the data analysis. The quantitative analysis was divided into three stages: descriptive data analysis, measurement model assessment, and structural model assessment. The study model was validated using various quantitative analysis methods, such as Confirmatory Factor Analysis (CFA) and Structural Equation Modelling with Partial Least Squares (SEM-PLS). The Partial Least Squares (PLS) technique was used to assess and confirm the hypotheses' relationships to the conceptual model. Statistical analysis using the PLS approach is based on Structural Equation Modelling (SEM). SmartPLS is a well-known PLS-SEM tool that has been employed in data analysis. The second phase, the qualitative technique, was used to explain quantitative findings further and elicit more participants' information. The qualitative study approach used was in-depth interviews, which comprised fifteen meetings with actual mobile health users. The semi-structured interview approach guided the interviews in this study. The research has applied four processes for analysing the interviews, including guided questions relating to the model constructs: (1) Preparing a written transcript of the interview's contents. (2) Interview transcripts are being read, coded, and analysed. (3) Evaluating the basic constructs questions relevant to the proposed model is the objective of this exercise. (4) Incorporating interview results into quantitative data in order to confirm and amplify them.

Results:Twelve hypotheses were formulated in order to address the issues posed in the study. In order to assess the importance of the relationships among the variables examined in this study, these hypotheses were analysed quantitatively and qualitatively in two stages. From the quantitative analysis, facilitating condition is positively and significantly associated with use behaviour (β=0.172, t=3.345, p<0.01). Moreover, seeking health awareness and advice (β=0.226, t=2.578, p=0.010), trust (β=-0.132, t=1.987, p=0.047), personal innovativeness in IT (β=0.227, t=4.444, p=0.000), prior mobile apps usage experience (β=0.123, t=2.514, p=0.012), social influence (β=0.129, t=2.462, p=0.014), and healthcare authorities influence (β=0.225, t=3.757, p<0.001) have significant influence with behaviour intention.