Configurational Analysis of the Driving Paths of University EFL Teachers’ Digital Competence

This study examines the configurational factors influencing digital competence among university foreign language teachers using fuzzy-set qualitative comparative analysis (fsQCA). Specifically, it investigates how technological factors (perceptions of AI), organizational factors (institutional training and administrative support), environmental factors (peer influence and the policy environment for educational digitalization), and individual factors (AI anxiety, professional title, and teaching experience) combine to produce distinct digital competence outcomes. Survey data were collected from 93 EFL teachers at Chinese higher education institutions and analyzed using fsQCA to identify sufficient condition combinations associated with high and low digital competence. The results revealed three sufficient pathways to high digital competence and two to low digital competence, demonstrating both equifinality and causal asymmetry. In particular, perceptions of AI and external support emerged as core conditions across multiple high-competence configurations, while high AI anxiety did not preclude high competence when accompanied by strong perception and institutional support. Low-competence pathways were characterized by the concurrent absence of multiple enabling conditions rather than the inverse of high-competence pathways. These findings highlight that digital competence development is inherently configurational, with no single factor being independently sufficient. Implications are discussed for multi-dimensional policy design, differentiated teacher training programs, and sustained institutional investment in supporting EFL teachers’ digital development.