Green knowledge to sustainability: the role of inter-organizational learning and technological innovation

Abstract

In this paper, sustainable manufacturing is discussed in the manufacturing sector of Pakistan to enhance the interplay of green intellectual capital (GIC), inter-organizational learning (IOL) and technological innovation (TI) as drivers of sustainable performance. Drawing on the Resource-Based View (RBV), Natural Resource-Based View (NRBV) and Dynamic Capabilities View (DCV), the study examines how GIC contributes to sustainable performance through IOL and under the boundary condition of TI. The International Organization for Standardization (ISO) 14001-certified companies' top-level and middle-level managers performed the data collection method by distributing 300 online questionnaires; data was analyzed using partial least squares structural equation modelling in SMART-PLS 4. The findings specify that GIC directly influences sustainable performance with IOL allocation as a mediator and TI as a moderator, approving the applicability of the RBV. The results highlight the importance of strategic resource management and technological incorporation in progressing sustainable manufacturing practices. Structural equation modeling demonstrates that hypotheses are significantly positive for this study. The limitations of this study include its cross-sectional design and the fact that has few generalizability factors, suggesting future research to explore cultural differences, alternative methods, and additional variables. The findings suggest that manufacturing managers should strengthen green knowledge and skills, promote IOL with external partners, and align TI with sustainability-related operational improvements. The study advances a resource-conversion explanation of sustainable performance by distinguishing GIC as a strategic green knowledge resource, IOL as the mechanism through which this resource is mobilized across organizational boundaries, and TI as the technological boundary condition that strengthens the translation of learning into sustainable performance. By integrating the complementary roles of RBV, NRBV, and DCV, the study explains not only whether GIC contributes to sustainable performance, but also how and under what technological conditions this contribution is realized in an emerging-economy manufacturing context.