This study aims to examine the determinants of smartwatch user satisfaction health monitoring among patients with cardiovascular diseases by using the stimulus–organism–response and the unified theory of acceptance and use of technology.
The research used a self-administered online questionnaire collecting 444 valid responses. Structural equation modeling and artificial neural networks were used to analyze the data using AMOS and SPSS software. The model is collated from the literature.
The result indicated that performance expectancy, effort expectancy and hedonic motivations directly impact smartwatch satisfaction. In addition to the noted constructs, price value and facilitating conditions were found to indirectly impact satisfaction through trust in artificial intelligence (AI)-enabled devices. Moreover, the findings revealed that social influence and habit directly and indirectly impact smartwatch satisfaction, among patients with cardiovascular diseases, through cyberchondria.
This study contributes to the literature and practitioners in the healthcare sector by focusing the scope of the research on patients with cardiovascular diseases and AI-enabled smartwatches. The findings provide a pathway for marketers to facilitate health monitoring for heart disease patients. Applying the model can enhance the quality of health monitoring while lowering the costs of the process.
