Understanding the nexus among big data analytics capabilities, green dynamic capabilities, supply chain agility and green competitive advantage: the moderating effect of supply chain innovativeness

Abstract

Purpose

The current study examines the impact of big data analytics capabilities (BDAC) on supply chain performances of the manufacturing firms. Based on the underpinning of resource-based view (RBV) theory, the current study will highlight the significance of BDAC on green dynamic capabilities (GDC), supply chain agility (SCA) and green competitive advantage (GCA). Furthermore, the study examines the moderating effect of supply chain innovativeness (SCI) on the relationship between GCA and firm performance (FP).

Design/methodology/approach

Online survey method was employed for the data collection from the 331 managers employed in Pakistan Stock Exchange (PSX)-listed manufacturing firms. The hypothesized model was tested using partial least squares structural equation modeling (PLS-SEM) technique.

Findings

The study results indicate that BDAC has a positive influence on both GDC and SCA, leading to enhanced GCA. Furthermore, the results demonstrate that GCA significantly and positively impacts FP, and the relationship between them is positively moderated by SCI.

Originality/value

This study developed a novel theoretical perspective based on RBV theory and provided empirical evidence that manufacturing firms' performances are significantly influenced by BDAC, GDC and SCA. The study results provide valuable practical implications top management regarding the effectiveness of BDAC and SCA in the supply chain. The findings further highlight the significance of SCI strengthening relationship between GCA and FP.

Citation

Li, W., Waris, I. and Bhutto, M.Y. (2024), "Understanding the nexus among big data analytics capabilities, green dynamic capabilities, supply chain agility and green competitive advantage: the moderating effect of supply chain innovativeness", Journal of Manufacturing Technology Management, Vol. 35 No. 1, pp. 119-140. https://doi.org/10.1108/JMTM-07-2023-0263