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Article
Publication date: 14 January 2025

Abdullah Al Mamun, Mohammad Nurul Hassan Reza, Qing Yang and Norzalita Abd Aziz

Implementing big data analytics (BDA) for supply chain ambidexterity (agility and adaptability) and green supply chain (GRSC) presents various organizational challenges. These…

Abstract

Purpose

Implementing big data analytics (BDA) for supply chain ambidexterity (agility and adaptability) and green supply chain (GRSC) presents various organizational challenges. These include leveraging BDA capabilities to balance agility and adaptability, integrating this combined approach with GRSC and aligning these efforts to enhance firm performance. This study explores the associations between BDA, supply chain agility and adaptability, GRSC and their impact on firm performance.

Design/methodology/approach

Incorporating a resource-based view and contingency theory, we developed a research framework and validated it with data from 355 Chinese firms. Partial least squares structural equation modeling was used to analyze the data.

Findings

The findings demonstrate that BDA capabilities had direct impact on supply chain agility and adaptability, GRSC and firm performance. Moreover, the combination of supply chain agility and adaptability affected GRSC; which in turn significantly influenced firm performance. Supply chain agility and adaptability mediated the relationship between BDA capabilities and GRSC. Additionally, GRSC mediated the relationship between BDA capabilities, supply chain agility and adaptability and firm performance.

Originality/value

This study offers both a theoretical and empirical examination of the relationships between BDA capabilities, supply chain agility and adaptability, GRSC and firm performance. By assessing the direct and mediating effects of these factors on China’s industrial sector, it presents new theoretical and practical insights into BDA and GRSC, thereby enhancing the value of the existing literature.

Details

Journal of Enterprise Information Management, vol. 38 no. 2
Type: Research Article
ISSN: 1741-0398

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