Big data analytics capabilities and supply chain performance: testing a moderated mediation model using partial least squares approach

Abstract

Purpose

In this study, the authors investigate the effect of big data analytics capability (BDAC) on supply chain performance (SCP) to assess the mediating effect of supply chain innovation (SCI) and the moderating effect of a data-driven culture (DDC).

Design/methodology/approach

The authors collected the primary data through an online questionnaire survey from the manufacturing sector operating in Jordan. The authors used 420 samples for the final data analysis, which the authors performed via partial least squares structural equation modelling using SmartPLS 3.3.9 software.

Findings

The results indicate that BDAC has a strong relationship with SCI and SCP. SCI shows a positive relationship with SCP as well as a mediating effect on SCI. The authors confirmed that DDC moderated the relationship between SCI and SCP.

Originality/value

The authors developed a conceptual and empirical model to investigate the relationship between BDAC, SCI, DDC and SCP. The authors contributed new theoretical and managerial insights that add value to the supply chain management literature through testing the moderated-mediated model of these constructs in Jordan’s manufacturing sector.

Citation

AL-Khatib, A.W. and Ramayah, T. (2023), "Big data analytics capabilities and supply chain performance: testing a moderated mediation model using partial least squares approach", Business Process Management Journal, Vol. 29 No. 2, pp. 393-412. https://doi.org/10.1108/BPMJ-04-2022-0179