意会
供应链
弹性(材料科学)
适应性
过程管理
知识管理
风险分析(工程)
供应链风险管理
概念框架
外包
适应(眼睛)
业务
因果链
风险管理
弱势群体
心理弹性
供应链管理
实证研究
工程类
计算机科学
情景分析
方案规划
系统思维
相互依存
经验证据
持续性
认知
自动化
背景(考古学)
认知地图
作者
Aamir Rashid,Rizwana Rasheed
标识
DOI:10.1108/scm-07-2025-0656
摘要
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.
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