AI-Based Decision Support System for Learning Growth Performance Workforce Use Certainty Factor and Constraint Evaluation

Abstract

This study presents the design and computational evaluation of an AI-based decision support system for workforce performance in an AI-driven environment under constrained conditions. Previous research has largely emphasized technological determinants and adequate conditions, while neglecting the constraint mechanisms that limit achievable performance. To address this gap, this study integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) into a unified computational framework. Empirical data from 200 manufacturing workers was used to derive model parameters. The results identified Engagement Level as a key performance driver (β = 0.519), while Self-Regulation Ability emerged as a critical constraint (d = 0.189). These findings were operationalized into a Certainty Factor-based expert system that models performance as a function of positive contribution, negative influence, and constraint thresholds. The proposed model was evaluated using classification metrics, achieving 87% accuracy, 0.85 precision, 0.86 recall, and an F1-score of 0.85. The results demonstrate strong predictive capabilities and confirm that performance is jointly determined by enabling and constraining factors. This study contributes by bridging statistical analysis and AI system design, providing a constraint-aware decision support model for performance evaluation in complex operational environments.