Supply chain learning and performance: a meta-analysis

Chen, L., Jiang, M., Li, T., Jia, F. and Lim, M. K. (2023) Supply chain learning and performance: a meta-analysis. International Journal of Operations and Production Management, (doi: 10.1108/IJOPM-05-2022-0289) (Early Online Publication)

Abstract

Purpose: This paper aims to provide a comprehensive understanding of the supply chain learning (SCL)–performance relationship based on the existing empirical evidence. Design/methodology/approach: We sampled 54 empirical studies on the SCL–performance relationship. We proposed a conceptual research framework and adopted a meta-analytical approach to analyse the SCL–performance relationship. Findings: The results of the meta-analysis confirm the positive effects of SCL on the performance of both firms and supply chains. In addition, building on the knowledge-based view, we found that learning from customers has a stronger positive effect on performance than does learning from suppliers, while joint learning has a stronger positive effect on performance than does absorptive learning. Business knowledge had a greater effect on performance than did general knowledge, process knowledge or technical knowledge, while explicit knowledge had a stronger effect than tacit knowledge. Moreover, the SCL–performance relationship is moderated by performance measure and industry type but not by regional economic development, highlighting the broad applicability of SCL. Originality/value: This study is the first meta-analysis on the SCL–performance relationship. It differentiates between learning from customers and learning from suppliers, examines a more comprehensive list of performance measures and tests five moderators to the main effect, significantly contributing to the SCL literature.

• Item Type: Articles
• Additional Information: The authors gratefully acknowledge the financial support by Natural Science Foundation of China Young Scientist Fund (no. 71902159) and the Research Development Funding (RDF-19–01–17, RDF-16– 02–36) of Xi’an Jiaotong-Liverpool University.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Chen, L., Jiang, M., Li, T., Jia, F., and Lim, M. K.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: International Journal of Operations and Production Management
• Publisher: Emerald Group Publishing Ltd.
• ISSN: 0144-3577
• ISSN (Online): 1758-6593
• Published Online: 11 January 2023
• Copyright Holders: Copyright © 2022 Emerald Publishing Limited
• First Published: First published in International Journal of Operations and Production Management 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy