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This research aims to determine the factors that influence the adoption of safe entry station (SES) as a health detection technology. There are six main constructs that will be studied, namely Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Technology Readiness, towards Behavioural Intention. Data collection was carried out using a survey distributed to 824 participants by analysis was carried out using the Structural Equation Model. The research findings show a significant relationship between technology readiness and behavioral intention regarding the use of safe entry station. The results of this research specifically show that the application of artificial intelligence in safe entry station health detection technology has a significant positive impact on increasing accuracy in the health examination process. Furthermore, this research provides insight into substantial practical implications in various business sectors, highlighting the importance of integrating safe entry station with organizational systems. The academic implications contained in this research will make a positive contribution to the development of knowledge and theory in the field of safe entry station technology adoption and can provide a strong basis for further research, while the managerial implications of this research lie in the ability to further design effective implementation strategies in various sectors.
Rahardja et al. (Tue,) studied this question.