A STIN Model Adoption for Chatbot in Higher Education Online Learning

Abstract

This study delves into the adoption of chatbot technology in higher education, with a focus on Indonesian online learning environments. Recognizing the potential of AI-driven tools to address academic support gaps, particularly in developing regions, the research explores how performance expectancy, effort expectancy, and facilitating conditions influence students' behavioral intentions and subsequent adoption of chatbots for academic use. The study employs Structural Equation Modeling (SEM) to analyze survey data from a diverse sample of university students, enabling a nuanced understanding of the complex relationships among these factors. The findings reveal that performance expectancy—the belief that chatbots will enhance academic performance and facilitating conditions, such as internet access and institutional support, play significant roles in motivating students to adopt chatbots. However, effort expectancy, or the perceived ease of use, does not directly drive adoption intentions. This suggests that students prioritize practical benefits over user-friendliness, an insight valuable for universities aiming to implement effective chatbot systems. Moreover, the results align with the Socio-Technical Interaction Network (STIN) model, which emphasizes the need for a cohesive social and technical framework to foster technological acceptance. The STIN model’s perspective underscores that students' engagement with chatbots is not just a matter of usability but also of how well the technology is supported by the broader educational infrastructure. This study offers actionable insights for Indonesian universities and other institutions in similar contexts, proposing that enhancing campus resources, like reliable internet access and technical support, can drive chatbot adoption. By focusing on performance-based benefits and strengthening the socio-technical environment, universities can effectively integrate AI-based learning tools, addressing both technical and socio-cultural barriers. Such initiatives support students’ learning experiences and foster an adaptive academic ecosystem where AI tools serve as essential assets in overcoming resource limitations. Thus, the study contributes a practical roadmap for advancing e-learning in resource-constrained settings through strategic support of AI technology adoption.