INTEGRATION OF TAM AND UTAUT MODELS IN ANALYZING USER SATISFACTION OF ONLINE TRANSPORTATION MODES
DOI:
https://doi.org/10.33579/krvtk.v11i1.6430Keywords:
Integration, TAM, UTAUT, online transportationAbstract
The development of information technology and digitalization has significantly transformed the transportation sector. The presence of online transportation services such as Gojek, Grab, Maxim, and Shopee in Yogyakarta has increased accessibility, convenience, and efficiency for urban communities. However, user satisfaction remains a challenge due to unmet expectations, long waiting times, and various technical issues within the applications [1]. Therefore, an in-depth examination of the factors influencing user satisfaction is necessary. This study aims to identify and analyze the determinants of user satisfaction with online transportation services in Yogyakarta by integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). This integrative approach is used because it provides a comprehensive explanation of user perceptions and behavioral responses to technology. This study adopted a quantitative method with an exploratory sequential mixed methods design. The population consisted of active users of online transportation applications in Yogyakarta, selected through stratified proportional random sampling. Data were collected using a Likert-scale questionnaire and semi-structured interviews. Data analysis was conducted using Structural Equation Modeling (SEM) with AMOS to examine the relationship between the TAM and UTAUT variables. The results of the hypothesis testing, which included 13 hypotheses, showed that 9 hypotheses were accepted (significant) and 4 were rejected (insignificant). Overall, the integrated TAM–UTAUT model successfully explained most of the factors influencing user satisfaction and behavioral intention, particularly through the significant roles of Attitude Toward Using (ATU), Social Influence (SI), Perceived Ease of Use (PEOU), Effort Expectancy (EE), Behavioral Intention (BI), and Performance Expectancy (PE). The findings indicate that emotional factors, perceived ease of use, user experience, and social pressure exert a stronger influence on user satisfaction and intention than rational considerations such as functional benefits or infrastructure readiness. Therefore, service providers are encouraged to prioritize interface simplification, expectation management, and social influence-based marketing strategies rather than simply adding technical features or emphasizing operational efficiency.
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