Supply chain cognitive resilience through AI-powered risk mitigation and autonomous resilience
本研究构建了一个整合人工智能、协作协调、自主韧性成熟度和组织感知敏捷性的供应链认知韧性框架，通过对美国数字受限地区中小制造企业的679份有效问卷进行结构方程模型分析，发现人工智能与人类解释的结合能实现主动适应中断，感知敏捷性在其中起中介作用。
Purpose This study aims to develop a comprehensive framework for supply chain cognitive resilience by integrating artificial intelligence, collaboration and coordination, autonomous resilience maturity and organizational sensemaking agility. It addresses critical gaps in existing literature, including the limited empirical focus on the interaction between AI-driven automation and cognitive resilience, especially within economically vulnerable regions. Design/methodology/approach This study uses a quantitative research design, using covariance-based structural equation modeling (CB-SEM) in Analysis of Moment Structures (AMOS) to empirically validate the proposed conceptual framework. Data was collected through a structured survey using cluster and systematic random sampling techniques, resulting in 679 valid responses being obtained from supply chain professionals across SME manufacturers operating in digitally constrained regions of the United States (Mississippi, West Virginia and Arkansas). Findings This study reveals that supply chain resilience in small and medium-sized enterprises is achieved by integrating artificial intelligence and human interpretation, enabling proactive adaptation to disruptions such as port strikes. Artificial intelligence enhances risk detection through real-time analytics, while collaboration and autonomous systems drive rapid responses, validated through collective interpretation. Sensemaking agility mediates these processes, ensuring context-sensitive actions and fostering resilience as anticipatory adaptability rather than mere recovery, particularly in resource-constrained regions. Originality/value This research bridges two major theoretical domains, DCV and sensemaking, to present a unified, human–AI collaborative approach to resilience. It introduces a maturity-based resilience model and addresses the underexplored role of CC in AI-enabled adaptation. Moreover, it contributes new insights relevant to digitally disadvantaged supply chains, offering a pathway for inclusive and scalable resilience strategies in the face of systemic disruptions.