CONTINUANCE INTENTION OF AI-BASED CHATBOT SERVICES IN HIGHER EDUCATION: EVIDENCE FROM INDONESIA
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Chatbot berbasis Kecerdasan Buatan (Artificial Intelligence/AI) semakin banyak digunakan di pendidikan tinggi untuk meningkatkan layanan mahasiswa. Namun, masih sedikit kajian yang meneliti faktor-faktor yang mendorong keberlanjutan penggunaan teknologi ini. Lambatnya response oleh layanan kemahasiswaan mendorong pemahaman lebih dalam mengenai determinan niak belernajutan dalam mempergunakan chatbot berbasi AI dengan menerapkan
kerangka Technology Continuance Theory (TCT). Penelitian ini mempergunakan pendekatan kuantitatif dengan metode sampling purposive dimana data survei dikumpulkan dari 294 mahasiswa dan dianalisis menggunakan Partial Least Squares–Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa kualitas informasi berpengaruh signifikan terhadap persepsi kemudahan penggunaan, sementara kualitas sistem meningkatkan konfirmasi dan persepsi kegunaan. Namun, kualitas layanan tidak berpengaruh signifikan terhadap persepsi kegunaan. Baik persepsi kegunaan maupun kemudahan penggunaan terbukti berperan penting dalam mendorong niat keberlanjutan penggunaan VARA. Secara teoretis, penelitian ini memperluas penerapan TCT dalam konteks pendidikan tinggi dengan menunjukkan bagaimana kualitas informasi dan kualitas sistem secara bersama-sama membentuk niat keberlanjutan penggunaan. Secara praktis, temuan ini memberikan arahan bagi universitas untuk meningkatkan desain chatbot melalui keakuratan, keandalan, dan kemudahan penggunaan informasi. Penguatan faktor-faktor tersebut sangat penting untuk mendukung transformasi digital yang berkelanjutan dalam layanan mahasiswa.
Artificial Intelligence (AI)-based chatbots are increasingly being adopted in higher education to enhance the quality of academic services. However, research on the factors influencing the sustained use of these technologies remains limited. This study aims to identify the determinants of students’ continuance intention to use AI-based chatbots by applying the Technology Continuance Theory (TCT) framework. This quantitative research used a purposive sampling method, where data were collected from 294 students and analyzed using Partial Least Squares structural equation modeling (PLS-SEM). The results indicate that information quality significantly influences perceived ease of use, whereas system quality enhances both confirmation and perceived usefulness. In contrast, service quality had no significant effect on perceived usefulness. Perceived usefulness and ease of use are key factors in shaping students’ intention to continue using chatbots. This study extends the application of TCT in higher education by demonstrating how information and system quality jointly influence continuance intention. In practice, the results can guide universities in improving chatbot design by ensuring accuracy, reliability, and usability. Strengthening these elements is vital for sustaining the digital transformation of student services.
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