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June 4, 2026

Innovation performance in manufacturing: the interplay among lean manufacturing, Industry 4.0 technologies, knowledge management and digital infrastructure

Key Points

This study aims to investigate the effects of lean manufacturing, Industry 4.0 technologies, knowledge management, and digital infrastructure on innovation performance in the manufacturing sector.
Mixed-methods quantitative approach with survey data from 263 respondents in Ethiopia's manufacturing sector.
Analyzed data using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA).
Examined mediating roles of I4.0 technologies and knowledge management, and moderating effects of digital infrastructure.
Lean manufacturing significantly influences Industry 4.0 technologies and knowledge management, enhancing innovation performance.
While the direct link from lean manufacturing to innovation performance is statistically insignificant, knowledge management and Industry 4.0 technologies mediate this relationship.
Digital infrastructure strengthens lean manufacturing's influence on Industry 4.0 technologies but does not significantly affect the relationship between lean manufacturing and innovation performance.

Abstract

Purpose In the contemporary digital era, innovation represents a fundamental source of competitive advantage and sustainability within the manufacturing industry. Drawing on this premise, this study empirically examines how lean manufacturing (LM), Industry 4.0 Technologies (I4.0T), knowledge management (KM) and digital infrastructure (DI) influence manufacturing industry innovation performance (MIIP). It investigates the mediating roles of I4.0T and KM, and the moderating effect of DI, in elucidating the relationships among these constructs. Design/methodology/approach This study employs a mixed-methods, quantitative approach based on the resource-based view (RBV) and dynamic capabilities theory (DCT). Survey data from 263 respondents in Ethiopia's manufacturing sector were analyzed using partial least squares structural equation modeling (PLS-SEM) to test relationships, while fuzzy-set qualitative comparative analysis (fsQCA) identified configurations leading to high MIIP. Findings PLS-SEM reveals that LM has a significant influence on I4.0T and KM, which in turn enhance MIIP. While LM's direct link to MIIP is positive, it remains statistically insignificant. KM and I4.0T mediate between LM and MIIP. DI strengthens the LM to I4.0T relationship but does not significantly moderate LM to MIIP. FsQCA identifies KM as necessary for high MIIP and reveals several equifinal combinations of LM, I4.0T, KM and DI driving innovation performance. Originality/value This study advances RBV and DC Theory by developing and testing an integrated framework explaining how LM, I4.0T, KM and DI shape MIIP in emerging economy contexts. By combining PLS-SEM and fsQCA, the study captures net effects and configurational pathways through which operational, technological and knowledge-based capabilities drive innovation outcomes. The findings show that DI acts as a boundary condition shaping how lean and technological capabilities translate into innovation performance, revealing multiple equifinal pathways for achieving innovation success.

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