Research on Continuance Intention for Business Undergraduates to Use Simulation Practice Teaching System

Background and Aim: The rapid integration of simulation technologies in business education has highlighted the need to understand psychological factors driving students' sustained use of such systems. Existing research lacks focus on continuance intention mechanisms in Chinese business education, particularly in simulation-based learning contexts. This study addresses this gap by integrating the Technology Acceptance Model (TAM), Expectation-Confirmation Model (ECM), and Information Systems Success Model (ISSM) to examine how perceived usefulness (PU), perceived ease of use (PEOU), confirmation (CON), interactivity (INT), and system quality (SYQ) influence satisfaction (SAT) and continuance intention (CNI). Materials and Methods: A quantitative survey was conducted with 756 business undergraduates from Zhanjiang University of Science and Technology who completed simulation-based courses. Data were collected using a validated 5-point Likert scale (28 items) and analyzed via structural equation modeling (SEM) and confirmatory factor analysis (CFA) in AMOS 24 and SPSS 26. Judgmental sampling ensured representativeness, with reliability (Cronbach’s α> 0.8) and validity (Fornell-Larcker criteria) rigorously tested. Results: All six hypotheses were supported: PU (β=0.184, p<0.001), PEOU (β=0.224, p<0.001), CON (β=0.196, p<0.001), INT (β=0.205, p<0.001), and SYQ (β=0.215, p<0.001) significantly influenced SAT, explaining 75.3% of its variance. SAT strongly predicted CNI (β=0.775, p<0.001), accounting for 60% of its variance. The impact hierarchy ranked PEOU > SYQ > INT > CON > PU, with system ease of use being the strongest driver. Conclusion: This study validates the pivotal role of satisfaction in mediating the relationship between cognitive evaluations (e.g., ease of use) and continuance intention. Practical implications emphasize optimizing interface simplicity and system stability to enhance user experience. Limitations include sampling bias (76.6% accounting majors) and cross-sectional data. Future research should explore longitudinal effects and cross-cultural comparisons to generalize findings.