Academic articles
Practitioner articles
Working papers
Books
Book chapters
Case studies
Other publications
Subject(s)
Economics, politics and business environment; Ethics and social responsibility
Keyword(s)
exploitation, vignettes, fairness, power, distribution
ISSN (Online)
1540-5907
ISSN (Print)
0092-5853
Subject(s)
Human resources management/organizational behavior; Strategy and general management
Keyword(s)
Social networks, network ties, organizational research
ISSN (Online)
2631-7877
ISSN (Print)
2631-7877
Keyword(s)
financial statement analysis, machine learning, earnings forecasting
JEL Code(s)
C53, G10, M41
Volume
80
Subject(s)
Finance, accounting and corporate governance
Keyword(s)
IPOs, going public, external financing, organizational economics, human resource management
JEL Code(s)
G32, G34, M50, D20
ISSN (Online)
1540-6261
Subject(s)
Strategy and general management; Technology, R&D management
Keyword(s)
alliance termination; disintegration, innovation strategy, open innovation closure, relationship dissolution, tie dissolution
ISSN (Online)
2688-2639
ISSN (Print)
2688-2612
Subject(s)
Strategy and general management
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
machine-learning, rational inattention, human-machine collaboration, cognitive effort
JEL Code(s)
L23, M11, O33, O31, D24
Problem definition: Artificial intelligence (AI) is rapidly transforming the research and practice of supply chain management. Yet its impact depends on how effectively it is integrated with the theories, methods, and fundamental principles of operations management (OM), which must also evolve to account for the informational, incentive, and institutional changes brought by AI. The OM community has an important role and responsibility to lead in shaping not only how AI transforms supply chains, but also how the supply chains that enable AI are designed to be sustainable, resilient, and equitable. Methodology/results: This vision statement organizes the discussion around five layers of the interaction between AI and supply chain managementâintelligence, execution, strategy, human, and infrastructure. It synthesizes recent research and industry practice to show how AI enhances forecasting, planning, decisionmaking, risk management, and humanâmachine collaboration, and also examines the supply chains that support AI. Finally, it highlights persistent challenges in data quality, model integration, governance, and workforce adaptation. Managerial implications: Realizing AIâs promise in supply chain management requires reliable data and infrastructure, integration of learning and optimization, transparent and explainable decision systems, and a long-term commitment to humanâAI collaboration. Together, these elements form the foundation for resilient, adaptive, and trustworthy supply chains in the AI era.
© 2026, INFORMS
Volume
28
Journal Pages
ivâxix, 687â1009, iii
ISSN (Online)
1526â5498
ISSN (Print)
1523-4614
Subject(s)
Entrepreneurship; Marketing; Strategy and general management; Technology, R&D management
Keyword(s)
radical innovation, sales performance, fear of failure, loss of face, sales support systems, change readiness
Subject(s)
Economics, politics and business environment
Keyword(s)
Formalization, Tax Avoidance, VAT, Personal Income Tax
JEL Code(s)
O17, H26, H24, H25
Volume
18
Journal Pages
141â80
ISSN (Online)
ISSN 1945-774X
ISSN (Print)
ISSN 1945-7731
Subject(s)
Economics, politics and business environment
Keyword(s)
Order statistics, sampling without replacement, decreasing returns, consumer search
JEL Code(s)
D43, L11
We study sampling from a finite population without replacement when seeking an extreme (lowest or highest) value. An example is a buyer searching for the lowest price. Itis well known that there are decreasing returns to sampling from continuous populations: the expected minimum is a decreasing and discretely convex function of the sample size. We show that is true for sampling without replacement from a finite population. We also give a simple sufficient condition on population values for the properties to hold for other order statistics.
With permission of Elsevier
Volume
264