Supply chain learning and environmental performance: leveraging relational capital and information technology

Abstract

Purpose

While the importance of supply chain learning (SCL) is widely recognized by both academia and industry, the mechanisms through which SCL is developed and its effects on environmental performance remain insufficiently understood. This study conceptualizes and empirically investigates the role of relational capital and information technology (IT) in enhancing SCL and improving environmental performance.

Design/methodology/approach

Drawing on socio-technical system theory (STS) and a knowledge-based view, this research employs structural equation modeling to test the proposed model, utilizing survey data collected from Chinese manufacturing companies.

Findings

The results demonstrate that relational capital with supplier and customer significantly enhances both supplier and customer learning. While IT does not directly impact supplier and customer learning, it exerts an indirect influence through its positive effect on relational capital. In other words, relational capital mediates the relationship between IT and supplier and customer learning. Additionally, this study finds that both supplier and customer learning enhance environmental performance.

Practical implications

This study provides actionable insights for managers, emphasizing the importance of relational capital in fostering SCL and improving environmental performance. By understanding these relationships, managers can develop more effective strategies for leveraging SCL as a tool for sustainability.

Originality/value

This study adds to the existing body of knowledge in supply chain management by offering a more nuanced and holistic model to explain how relational capital, IT and SCL interact to influence environmental performance, particularly within the context of Chinese manufacturing companies.

Citation

Haq, M.Z.U. and Cao, G. (2025), "Supply chain learning and environmental performance: leveraging relational capital and information technology", Industrial Management & Data Systems, Vol. 125 No. 2, pp. 483-503. https://doi.org/10.1108/IMDS-01-2024-0067