arbiter
JournalsAccount

Unveiling the Impact of Big Data Analytics on Supply Chain Resilience: Theorisation and Empirical Evidence Using a Multi‐Method Approach

Jiabao Lin et al.

Information Systems Journal2026https://doi.org/10.1111/isj.70041article
AJG 4ABDC A*
Weight
0.50

Abstract

Organisations should harness big data analytics to transform their supply chains and archive resilience. Despite the critical role of big data analytics in shaping supply chain resilience, existing literature on their relationship remains starkly fragmented and inconclusive. To address this critical paucity and reconcile the prevailing inconsistencies, we employed a multi‐method research design, combining both qualitative and quantitative approaches to confirm our theoretical model. We first utilise deductive qualitative analysis across multiple cases to validate the underlying mechanisms and boundary conditions through which big data analytics capability influences supply chain resilience. Subsequently, our quantitative analysis unveils that the nexus between big data analytics capability and supply chain resilience operates through two mediated pathways (traditional and digital SC capabilities) under institutional environments (i.e., government intervention and guanxi). Specifically, government intervention amplifies both mediating effects, whereas guanxi weakens the mediation effect of digital supply chain capability. Furthermore, our results reveal contingency‐dependent mediation pathways linking big data analytics capability and supply chain resilience. We find that weak institutional forces position digital supply chain capability as the primary pathway, whereas strong institutional forces shift the emphasis toward traditional supply chain capability as the central mechanism linking BDA capability to supply chain resilience. This study contributes to the emerging literature on Information Systems (IS) by theoretically exploring and empirically validating the mechanisms and boundary conditions through which big data analytics capability affects supply chain resilience.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1111/isj.70041

Or copy a formatted citation

@article{jiabao2026,
  title        = {{Unveiling the Impact of Big Data Analytics on Supply Chain Resilience: Theorisation and Empirical Evidence Using a Multi‐Method Approach}},
  author       = {Jiabao Lin et al.},
  journal      = {Information Systems Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/isj.70041},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Unveiling the Impact of Big Data Analytics on Supply Chain Resilience: Theorisation and Empirical Evidence Using a Multi‐Method Approach

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.