<![CDATA[Digital transformation in higher education demands the sustainable use of Learning Management Systems (LMS). However, most previous studies have predominantly focused on cognitive aspects such as perceived usefulness and user satisfaction, overlooking the motivational dimensions that drive long-term engagement. This study aims to explore how System-Provided Autonomy Support (S-PAS)—the degree of autonomy support embedded within the system—shapes users’ continuance intention to use LMS through psychological need satisfaction and autonomous motivation. Employing an exploratory qualitative approach, data were collected through in-depth interviews, focus group discussions (FGDs), task-based observations, and critical discourse analysis of institutional policies and system micro-copies across several universities under the coordination of LLDIKTI Region IX Sultanbatara. Thematic analysis revealed that users’ experiences with LMS are influenced by five key dimensions of S-PAS: meaningful choice, rationale provision, non-controlling language, emotional validation, and informational feedback. When the system and policies offer flexibility, empathetic communication, and clear rationales behind instructions, users exhibit higher levels of autonomous motivation and stronger commitment to LMS continuance. Conversely, administrative rigidity, technostress, and opaque surveillance practices undermine psychological need satisfaction and weaken users’ continuance intention. The study extends the application of Self-Determination Theory (SDT) to educational technology by introducing S-PAS as a conceptual framework that bridges system design and user motivation. Practically, the findings offer strategic guidance for policymakers and developers to design LMS environments that are more autonomy-supportive, human-centered, and sustainable. Future research is encouraged to quantitatively validate the S-PAS model across diverse institutional and cultural contexts to enhance the generalizability and theoretical robustness of these findings.]]>
