Digital Technology Investment Breadth and Energy-Cost Intensity in Manufacturing: Evidence on Automated Quality Control and Managerial Human Capital

Digital technology investment expands manufacturers’ technical resources, but its sustainability implications depend on whether those resources become embedded in operating routines. This conversion remains underexamined. Drawing on socio-technical systems theory and the natural-resource-based view (NRBV), this study examines automated quality control (AQC) as an operational pathway linking digital technology investment breadth (DTIB) to lower energy-cost intensity, with managerial human capital—represented empirically by top-manager educational attainment—as a proposed first-stage boundary condition. The empirical analysis uses 3534 manufacturing establishments from nine World Bank Firm-level Adoption of Technology (FAT) survey contexts and estimates sampling-weighted models with design-based inference and stratified bootstrap tests. DTIB is positively associated with AQC adoption, and AQC adoption is associated with lower energy-cost intensity. The pooled DTIB association with lower energy-cost intensity is positive in the primary weighted model but is sensitive to survey weighting, the exclusion of India, and cross-country heterogeneity, indicating that the association is context dependent. The interaction with top-manager educational attainment is positive in the primary linear probability model, but its strength varies across nonlinear and extended-control specifications. The overall statistical indirect association is positive. The between-group difference in conditional indirect associations is positive in the primary bootstrap analysis but is sensitive to the confidence-interval method. These cross-sectional findings emphasize the operational conversion of digital resources while treating the proposed managerial boundary condition as suggestive rather than conclusive.