Although artificial intelligence can contribute to decision-making processes, many industry players lag behind pioneering companies in utilizing artificial intelligence-driven technologies, which is a significant problem. Explainable artificial intelligence can be a viable solution to mitigate this problem. This paper proposes a research model to address . Using an experimental design, empirical data is collected to test the research model. This paper is one of the pioneer papers providing empirical evidence about the impact of explainable artificial intelligence on supply chain decision-making processes. We propose a serial mediation path, which includes transparency and agile decision-making. Findings reveal that explainable artificial intelligence enhances transparency, thereby significantly contributing to agile decision-making for improving cyber resilience during supply chain cyberattacks. Moreover, we conduct a post hoc analysis using text analysis to explore the themes present in tweets discussing explainable artificial intelligence in decision support systems. The results indicate a predominantly positive attitude towards explainable artificial intelligence within these systems. Furthermore, the text analysis reveals two main themes that emphasize the importance of transparency, explainability, and interpretability in explainable artificial intelligence.

中文翻译：

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供应链网络弹性中的可解释人工智能和敏捷决策

尽管人工智能可以为决策过程做出贡献，但许多行业参与者在利用人工智能驱动的技术方面落后于先驱公司，这是一个重大问题。可解释的人工智能可以成为缓解这一问题的可行解决方案。本文提出了一个研究模型来解决。使用实验设计，收集经验数据来测试研究模型。本文是提供关于可解释人工智能对供应链决策过程影响的实证证据的先驱论文之一。我们提出了一系列调解路径，其中包括透明度和敏捷决策。调查结果表明，可解释的人工智能可以提高透明度，从而极大地促进敏捷决策，从而提高供应链网络攻击期间的网络弹性。此外，我们使用文本分析进行事后分析，以探索讨论决策支持系统中可解释人工智能的推文中存在的主题。结果表明，人们对这些系统中可解释的人工智能持积极态度。此外，文本分析揭示了两个主题，强调透明度、可解释性和可解释性在可解释人工智能中的重要性。