AI-Driven Big Data Analytics and Supply Chain Resilience in B2B Markets: Insights from the Chinese Manufacturing Sector

Abstract

Purpose: In business-to-business (B2B) markets, supply chain resilience (SCR) not only signals a firm’s risk-management strength but also anchors brand value and long-term buyer–supplier partnerships. As digital technologies penetrate operations, AI-driven big data analytics (AI-BDA) has become pivotal for bolstering SCR, yet its specific pathways remain under-examined. Guided by information-processing theory and the dynamic capability view, this study models how AI-BDA capability (AI-BDAC) fosters SCR in manufacturing firms. Design/methodology/approach: Based on results gathered from an investigation of 245 Chinese manufacturing enterprises, using regression analysis and bootstrap analysis, the proposed model examines how AI-BDAC enhances SCR through anticipation capability (AC) and improvisation capability (IC) and discusses the boundary conditions affecting this relationship. Survey data from 245 Chinese manufacturers were analyzed with hierarchical regression and bootstrapping. The model tests the direct effect of AI-BDAC on SCR, the mediating roles of anticipation capability (AC) and improvisation capability (IC), and the moderating influence of social capital (SC). Findings: AI-BDAC significantly enhances SCR both directly and indirectly through AC and IC. Moreover, SC amplifies the positive impacts of AC and IC on SCR. Originality/value: For B2B marketing and supply chain managers, the results highlight AI-BDA as a strategic asset that strengthens SCR while enabling firms to respond swiftly to market fluctuations, deepen B2B customer relationships, and advance collaborative marketing efforts. The study extends information-processing and dynamic capability perspectives to an AI-enabled supply chain context, offering actionable guidance for market adaptability and business sustainability.