Utilizing PLS-SEM and NCA for Understanding Workforce Performance - Agus Mansur; Imam Djati Widodo; Dicky Suryapranatha (Angkasa: Jurnal Ilmiah Bidang Teknologi, 2026)
This study explores workforce performance in AI-driven environments where individuals operate under dual-role conditions. Existing studies emphasize technological factors while neglecting human behavioral constraints and the distinction between sufficient and necessary conditions. This study addresses this gap by integrating Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis to identify both enabling and limiting factors. Data were collected from 200 manufacturing workers in Indonesia. The results show that engagement level is the main driver of performance (β = 0.519), while self-regulation capability has a strong effect (β = 0.345). NCA revealed that self-regulation capability is the key necessary condition and main bottleneck (d = 0.189), indicating that high performance cannot be achieved without a minimum threshold. AI system support acts as an enabling factor (d = 0.166), while digital capability shows a weak effect (d = 0.065). These findings highlight that performance is not only driven by engagement but is constrained by self-regulatory capabilities. Future research should extend this model using longitudinal data, multi-sector analysis, and additional variables such as organizational support and system complexity to improve model generalizability and robustness