Determinants of Continuance Intention Toward ChatGPT Premium Among Indonesian University Students: An Information Systems Success Model Perspective for Higher Education Management

Amaninda Jihan Nabila; Wenny Pebrianti; Giriati; Heriyadi; Harry Setiawan

doi:10.61987/jemr.v5i2.1892

Authors

Amaninda Jihan Nabila Universitas Tanjungpura
Wenny Pebrianti Universitas Tanjungpura
Giriati Universitas Tanjungpura
Heriyadi Universitas Tanjungpura
Harry Setiawan Universitas Tanjungpura

DOI:

https://doi.org/10.61987/jemr.v5i2.1892

Keywords:

Generative Artificial Intelligence, Continuance Intention, Digital Learning Management

Abstract

The rapid adoption of generative artificial intelligence (GenAI) tools such as ChatGPT has significantly transformed digital learning practices in higher education; however, empirical evidence on students’ continuance intention toward subscription-based GenAI services remains limited. This study aims to examine the determinants of university students’ continuance intention to use ChatGPT Premium by adopting the Information Systems Success Model (ISSM) within the context of digital learning management. An online survey was conducted with 200 Indonesian university students who had used ChatGPT Premium for at least six months, and the data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that system quality, information quality, and service quality have positive and significant effects on perceived usefulness and price fairness, which subsequently enhance students’ continuance intention. Mediation analysis confirms that perceived usefulness and price fairness significantly mediate the relationships between the three quality dimensions and continuance intention. These findings extend the application of the ISSM by positioning price fairness as a critical evaluative component in subscription-based GenAI services and provide managerial implications for higher education institutions in optimizing digital learning strategies through improved system performance, information reliability, and value-oriented service management to support the sustainable use of GenAI in academic settings.

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