Empirical Modeling of Industry 4.0 Enablers: Insights from Indian Manufacturing Through PLS-SEM and CB-SEM

The article’s main focus is on identifying the key enablers that are making Industry 4.0 adoption easier, utilizing structural equation modeling via SPSS version 26. A comprehensive examination of previous studies led to the identification of 10 main enablers and 35 associated sub-enablers. Data collected from 182 manufacturing companies in India, selected by simple random sampling, was used for quantitative research. The analysis basically depends on PLS-SEM and CB-SEM (Partial Least Squares and Covariance-Based Structural Equation Modeling) path modeling. The findings indicate that technological enablers such as data analytics and artificial intelligence, computational power and connectivity, technologies that integrate physical and digital systems, and other enabling technologies are crucial to Industry 4.0 adoption. Additionally, organizational enablers (including a supportive organization, government efforts and promotions, and human resources) are also found to be significant contributors to Industry 4.0 implementation. Additionally, the study identified a significant mediating effect between technological and organizational enablers, emphasizing the importance of collaborative visualization mechanisms, established through bootstrapping with bias-corrected confidence intervals. Strengthening technological, organizational, and collaborative capabilities through Industry 4.0 adoption allows firms to attain improved operational performance while advancing sustainability objectives. These results contribute to the present understanding of Industry 4.0 adoption by offering useful implications for policymakers and industry practitioners. These insights guide managers and policymakers in structuring digital transformation initiatives.