How Perceived Risk Shapes User Satisfaction and Continuance Intention Toward AI-Based Applications in Higher Education ?

Sumaryanto Sumaryanto; Nadia Adriane  Ricadonna; Nani Irma  Susanti

doi:10.56442/ijble.v7i1.1333

Authors

Sumaryanto Sumaryanto Universitas Slamet Riyadi
Nadia Adriane Ricadonna Universitas Slamet Riyadi
Nani Irma Susanti Universitas Dharma AUB

DOI:

https://doi.org/10.56442/ijble.v7i1.1333

Keywords:

AI-based applications, Technology Acceptance Model, Expectation Confirmation Theory, perceived risk, student satisfaction

Abstract

This research explores factors influencing university students' satisfaction and intention to continue using AI-based applications. By analyzing the roles of perceived ease of use, usefulness, and risk, the study assesses how these elements drive technology engagement and the extent to which perceived risk moderates user experience. A survey was administered to 210 university students within a quantitative research framework. Findings reveal that perceived usefulness and ease of use drive continuance intention through the mediation of satisfaction. However, perceived risk showed no significant effect, challenging previous empirical evidence that emphasizes its role as a key moderator in technology adoption. The study concludes that improving the functionality and usability of AI tools is key to driving student persistence. Given that perceived risk plays a less significant role than previously theorized, institutions should focus on promoting user-friendly and impactful AI solutions to maximize technology integration and sustained usage.

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