DEVELOPMENT OF A MODEL TO EVALUATE USERS' TECHNOLOGY READINESS AND ACCEPTANCE IN USING THE SELF-CHECK-IN KIOSK SERVICE AT SOEKARNO-HATTA INTERNATIONAL AIRPORT - Muhammad Faisal Fanani; Umi Laili Yuhana; Ary Mazharuddin Shiddiqi (JUTI: Jurnal Ilmiah Teknologi Informasi, 2024)
The self-check-in kiosk is one of the digital technologies used by the aviation industry to help passengers check in on passenger flights independently and efficiently without the need for a conventional check-in counter at the airport. However, the phenomenon on the ground indicates that many users have not yet used the service. As a result, the check-in area in some of the flight masks often has a long wait. Studies conducted by several airports in campsites such as Malaysia, South Africa, and Switzerland show that self-check-in kiosks do not meet the echoes of users. The same thing happened at Indonesian airports, where the use of self-check-in kiosks was still below 20% of total passenger traffic in 2022–2023. The study introduces the User Experience Technology Readiness and Acceptance Model (UX TRAM), which is used to evaluate user readiness and acceptance of the application of new technologies in the airport environment. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method is used to analyze the research model and the proposed hypothesis. Based on the results of the test of significance and relevance of the relationship in this study, the structural model proposed by the majority is of significant value, except for the variables Innovativeness and Insecurity versus Perceived Ease of Use. Based on the results of the test of the hypothesis carried out, out of 15 hypotheses tested, there are 13 accepted and 2 rejected hypotheses related to the readiness and acceptance of users in the use of new technology on the Self-Check-in Kiosk service at Soekarno-Hatta International Airport. The results of this study show that the proposed research model has varying explanatory strengths (near moderate to substantial/high) as well as predictive strengths that offer better predictable performance.