مشخصات مقاله | |
ترجمه عنوان مقاله | هوش مصنوعی قابل توضیح و تصمیم گیری چابک در تاب آوری سایبری زنجیره تامین |
عنوان انگلیسی مقاله | Explainable artificial intelligence and agile decision-making in supply chain cyber resilience |
نشریه | الزویر |
انتشار | مقاله سال ۲۰۲۴ |
تعداد صفحات مقاله انگلیسی | ۱۰ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۸٫۲۵۲ در سال ۲۰۲۲ |
شاخص H_index | ۱۸۰ در سال ۲۰۲۴ |
شاخص SJR | ۲٫۲۱۱ در سال ۲۰۲۲ |
شناسه ISSN | ۰۱۶۷-۹۲۳۶ |
شاخص Quartile (چارک) | Q1 در سال ۲۰۲۲ |
فرضیه | دارد |
مدل مفهومی | دارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی صنایع – مهندسی کامپیوتر – مهندسی فناوری اطلاعات |
گرایش های مرتبط | لجستیک و زنجیره تامین – هوش مصنوعی – اینترنت و شبکه های گسترده – داده کاوی |
نوع ارائه مقاله |
ژورنال |
مجله | سیستم های پشتیبانی تصمیم – Decision Support Systems |
دانشگاه | a Department of Marketing and Supply Chain Management, Willie A. Deese College of Business and Economics, North Carolina Agricultural and Technical State University, Greensboro, USA |
کلمات کلیدی | هوش مصنوعی قابل توضیح – تصمیم گیری چابک – تاب آوری سایبری – آزمایش – داده کاوی |
کلمات کلیدی انگلیسی | Explainable artificial intelligence – Agile decision making – Cyber resilience – Experiments – Data mining |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.dss.2024.114194 |
لینک سایت مرجع | https://www.sciencedirect.com/science/article/pii/S0167923624000277 |
کد محصول | e17725 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract ۱ Introduction ۲ Theoretical support and research model ۳ Methodology ۴ Results ۵ Discussion and conclusions CRediT authorship contribution statement Declaration of competing interest Data availability References |
بخشی از متن مقاله: |
Abstract 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 how explainable artificial intelligence can impact decision-making processes. 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.
Introduction Automotive industry leaders, such as Tesla, have made substantial investments in artificial intelligence (AI) to expedite the introduction of self-driving vehicles to the market, enhancing their competitive capabilities. The integration of AI in supply chain operations has played a crucial role in enabling Tesla to optimize its operational costs [64] while simultaneously facilitating the establishment of a Gigafactory in China [78]. We are witnessing a rapid digital transformation driven by the integration of AI in supply chain management. The COVID-19 pandemic forced companies and organizations to expedite the digitalization of their operations [6]. To enhance their competitive edge, prominent companies, including Amazon, Walmart, Alibaba, Siemens, and Toyota have embraced AI-based technologies to automate and digitalize their operations and supply chain activities [1,32]. Digital transformation also introduces new possibilities for potential cyberattacks. However, using AI-based technologies for decision-making during cyberattacks (e.g., American Express monitoring; [75]) offers significant advantages that outweigh the potential losses incurred.
Although AI-based technologies can contribute to decision-making processes in operations and supply chain management, many industry players are lagging behind pioneering companies in utilizing AI-driven technologies, which is a main problem. Therefore, the adoption of AI-based technologies in decision-making processes, particularly during sensitive situations such as cyberattacks, may encounter potential barriers that delay their usage. Due to its vital advantages, AI has received much attention from decision-makers to address resilient case studies [45] and other sensitive problems such as healthcare [69]. The lack of explanations of the underlying AI processes leads to the rejection of AI in decision support systems [60]. Leveraging AI-powered decision-making platforms can significantly facilitate and expedite the decision-making process, which results in improved overall performance. For instance, the Colonial Pipeline, a U.S. oil supplier, faced a cyberattack and, after a week of deliberation, opted to pay around $4.4 million to solve the issue [21]. A quick decision on the first day through agile decision-making could have saved them money and enabled the uninterrupted continuation of their operations with stockholders.
Results We employed regression analysis to test the hypothesized relationship between research model variables. We conducted required tests relating to the experimental design along with method assumptions and bias checks. ۴٫۱٫ Design check |