{"title":"Impact of strategic performance measures on performance: The role of artificial intelligence and machine learning","authors":"Vipul Garg , Janeth Gabaldon , Suman Niranjan , Timothy G. Hawkins","doi":"10.1016/j.tre.2025.104073","DOIUrl":null,"url":null,"abstract":"<div><div>This study highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on business logistics and operations, driving efficiency and strategic decision-making. With the logistics AI market anticipated to reach USD 31.58 billion by 2028, AI and ML’s role in enhancing operational performance and reducing costs is evident. Despite this, the application of AI and ML in improving strategic performance measures—namely Information Sharing, Decision Synchronization, and Logistics Efficiency—and their influence on firm and operational performance remains underexplored.</div><div>This research bridges this gap by leveraging the Dynamic Capabilities View to explore how AI and ML technologies influence the relationship between strategic performance indicators and both firm and operational performance. Utilizing a multi-method analysis, including PLS-SEM and fuzzy-set Qualitative Comparative Analysis, we explore the complex dynamics between strategic performance outcomes and the integration of AI and ML technologies. Our findings from PLS-SEM indicate that AI and ML significantly influence Firm Performance but not Operational Performance. Further analysis highlights that logistics efficiency, integrated with AI and ML, can enhance firm performance, showcasing AI and ML as critical components of firm success.</div><div>This study contributes to the fields of information systems and supply chain management by offering an innovative perspective on how AI and ML can empower firms, particularly within the United States and Canadian Business to Business and Business to Government sectors, to improve their firm and operational performance. It provides a strategic framework for managers to leverage these technologies effectively, enriching both theory and practice.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"198 ","pages":"Article 104073"},"PeriodicalIF":8.8000,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554525001140","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
This study highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on business logistics and operations, driving efficiency and strategic decision-making. With the logistics AI market anticipated to reach USD 31.58 billion by 2028, AI and ML’s role in enhancing operational performance and reducing costs is evident. Despite this, the application of AI and ML in improving strategic performance measures—namely Information Sharing, Decision Synchronization, and Logistics Efficiency—and their influence on firm and operational performance remains underexplored.
This research bridges this gap by leveraging the Dynamic Capabilities View to explore how AI and ML technologies influence the relationship between strategic performance indicators and both firm and operational performance. Utilizing a multi-method analysis, including PLS-SEM and fuzzy-set Qualitative Comparative Analysis, we explore the complex dynamics between strategic performance outcomes and the integration of AI and ML technologies. Our findings from PLS-SEM indicate that AI and ML significantly influence Firm Performance but not Operational Performance. Further analysis highlights that logistics efficiency, integrated with AI and ML, can enhance firm performance, showcasing AI and ML as critical components of firm success.
This study contributes to the fields of information systems and supply chain management by offering an innovative perspective on how AI and ML can empower firms, particularly within the United States and Canadian Business to Business and Business to Government sectors, to improve their firm and operational performance. It provides a strategic framework for managers to leverage these technologies effectively, enriching both theory and practice.
期刊介绍：
Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management.
Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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