Analysis reveals significant impact of strategy, policies, and skills on Industry 4.0 adoption in India, suggesting focused improvements.
The adoption of Industry 4.0 (I4.0) is redefining the manufacturing standards on a global scale. Furthermore, within developing economies such as India, adoption process is still fragmented due to disparities in variations in internal competencies and external assistance mechanisms. This analysis explores the the impact of three critical and essential enablers Organizational Strategy & Support (OS), Government Policies & Incentives (GP), and Workforce Skill Level (WS) in the implementation of Industry 4.0 in India across all the manufacturing sectors. Partial Least Squares Structural Equation Modelling (PLS-SEM) and is implemented to inspect both contextual variations and structural relationships across various industry segments. Data collection occurred via a systematic survey involving experts across a range of manufacturing industries and analysed by SmartPLS4. The measurement model exhibited excellent reliability and validity, with all constructs shows an internal consistency (Cronbach’s alpha > 0.89) and allowable convergent (AVE > 0.58). The structural model revealed that all three independent variables significantly and positively influence I4.0 implementation, with Organizational Strategy & Support emerging as the most influential predictor (β = 0.471, p < 0.001), succeeded by Government Policies & Incentives (β = 0.213, p < 0.001), and Workforce Skill Level (β = 0.190, p = 0.002). The model shows the result which is 67.5% of the variance in I4.0 outcomes, indicating strong predictive power. This research contributes theoretically by validating a multi-dimensional adoption model and practically by guiding industrial leaders, policymakers, and training institutions to focus efforts where they are most needed—on strategic alignment, policy responsiveness, and workforce development. By addressing these enablers in a coordinated manner, India’s manufacturing sector can achieve a more inclusive and accelerated transformation towards I4.0.
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K. et al. (2026) studied this question.
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