 Big data analytics capability and supply chain: a perspective of socio-technical theoryJing Tang,
Zuge Yu,
Yeming Gong (),
Ajay Kumar () and
Xianhao Xu
Post-Print from HAL Abstract: Purpose: This study aims to investigate how big data analytics (BDA) capability contributes to competitive advantage in supply chain management by examining the distinct and integrated roles of technical and social subsystems from a socio-technical theory (STT) perspective. Design/methodology/approach: A sequential mixed-methods approach was adopted, integrating quantitative and qualitative data. A three-wave survey was conducted with 316 global firms, followed by semi-structured interviews with representatives from 67 firms. To ensure continuing relevance, a robust qualitative validation phase was conducted in 2025 with 33 firms to reexamine these mechanisms amid emerging artificial intelligence (AI) technologies. Quantitative analysis tested a structural equation model, while qualitative insights provided validation and a deeper understanding of the mechanisms. Findings: The results identify four mechanisms through which BDA capabilities affect competitive advantage: two within the technical and social subsystems independently, and two through integrated pathways. Technical mechanisms, particularly those enhancing information system quality and supply chain performance, exert the strongest influence. Social alignment and integrated configurations also play crucial roles. The findings demonstrate that aligning technological infrastructure with human expertise is key to realizing the competitive potential of BDA in supply chains. The 2025 validation confirms that AI applications now operate as advanced analytical tools within existing BDA infrastructures, thereby reinforcing the original socio-technical model. Research limitations/implications: The findings are based on large firms in logistics-intensive industries, which may limit generalizability to smaller organizations or other sectors. Future research should examine industry-specific and cross-cultural variations in BDA implementation and outcomes. Practical implications: Managers should develop BDA personnel expertise and invest in system quality simultaneously. Prioritizing technical synergy or social alignment depending on organizational context can enhance supply chain performance and competitive advantage. Integrated strategies are especially effective in dynamic environments. Social implications: In the social subsystem, organizations should prioritize the continuous development of their BDA personnel expertise capability through comprehensive training programs to enhance technical, business and relational expertise. Leveraging human expertise strategically for relationship management can significantly enhance coordination and performance. Originality/value: This study extends current literature by applying STT to BDA in supply chain contexts, revealing both independent and combined roles of social and technical subsystems. It contributes a novel framework that explains the nuanced pathways of value creation through BDA capability, supported by mixed-method empirical evidence. Keywords: Supply chain relationship quality; Supply chain performance; Supply chain relationship performance; Big data analytics capability; Competitive advantage; Information system quality (search for similar items in EconPapers) Published in Supply Chain Management: An International Journal, inPress, pp.1 - 26. ⟨10.1108/SCM-04-2025-0366⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05603062 Access Statistics for this paper More papers in Post-Print from HAL |
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