Collaborative Culture, Learning Community Engagement, and Digital Transformation: Drivers of Learning Innovation in Indonesian Vocational Schools
Abstract
This study investigates how collaborative culture, learning community engagement, and digital transformation of teachers influence learning innovation in vocational schools in Bone Bolango Regency, Gorontalo Province, Indonesia. Using a quantitative correlational design with path analysis, data were collected from 130 teachers across six public vocational schools through stratified random sampling method. A validated Likert-scale questionnaire was administered, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test direct and mediating effects. The results showed that collaborative culture (β = 0.156; p > 0.05), learning community engagement (β = 0.146; p > 0.05), and teacher digital transformation (β = 0.215; p > 0.05) had positive but non-significant direct effects on learning innovation. The only statistically significant relationship was between learning community engagement and teacher digital transformation (β = 0.233; p = 0.013). The model explained 13.6% of the variance in learning innovation (R² = 0.136) and had an acceptable fit (SRMR = 0.107). Mediation analysis revealed that teacher digital transformation partially mediated the relationship between collaborative culture and learning innovation. The findings suggest that learning innovation in vocational schools requires comprehensive strategies, including strengthened school-industry partnerships, robust professional learning communities, sustained digital transformation training, and improved technology infrastructure. This study highlights the critical role of digital transformation as both a direct predictor and mediator in fostering sustainable learning innovation.
Fields of Study & Subjects
Education and Communication Studies
Vocational and Entrepreneurial Education
Vocational Education and Training
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 11 February 2026
- Language
- ANG