This study examines how lecturers’ digital adaptation influences teaching performance through the mediating role of digital commitment within the ongoing digital transformation of higher education. Despite the rapid expansion of digital transformation research, lecturer-centered evidence remains limited, particularly regarding how psychological commitment translates digital capability into sustained teaching performance in emerging-economy higher education contexts such as Indonesia. Using a quantitative explanatory (predictive) design, data were collected from lecturers at private universities in Java, Indonesia, between January and July 2025 and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that digital adaptation enhances lecturers’ digital commitment and teaching performance, while digital commitment plays a partial mediating role, functioning as a motivational bridge that strengthens the impact of digital adaptation on pedagogical outcomes, with all hypothesized effects statistically significant. These results demonstrate that technological competence alone is insufficient; sustained teaching innovation depends on lecturers’ psychological engagement and the presence of supportive institutional environments. Theoretically, this study integrates the Technology Acceptance Model (TAM) and the Ability–Motivation–Opportunity (AMO) framework to conceptualize a Digital Human Capital Model in Education, explaining how technological adaptation (capability) and digital commitment (motivation) jointly shape teaching performance. Practically, the study suggests that universities should invest in faculty development initiatives, digital pedagogy training, and recognition systems that reinforce lecturers’ long-term engagement with technology-enhanced teaching. Overall, the study contributes to the human-centered digital transformation literature by showing that sustainable educational innovation emerges from the synergistic alignment between faculty capability, motivation, and institutional learning ecosystems.
Kurniawan et al. (2026) studied this question.