Hoang Pham, Rutgers, The State University of New Jersey

The era of AI, through its focus on the reliability and statistical machine computing of intelligent systems in everyday applications and the service industry, has experienced a dramatic shift in recent years. Such systems require reliable and timely responses.

Articles concerning new theoretical research and methods in advanced reliability and statistical computing for intelligent systems are solicited. Preference will be given to papers with real-world applications over purely theoretical papers.

Potential topics

• Mathematical reliability and statistical methods
• Big data modeling and prediction
• Statistical learning algorithms, models, and theories
• Machine learning models for intelligent systems
• Text mining and deep machine learning
• Intelligent system dependability and performability
• Reliability modeling and optimization
• High-dimensional data analysis
• Statistical inference for intelligent systems
• Industrial case studies in intelligent systems, including field and service robotics, medical care, education, visual surveillance, intelligent transportation, etc.

Timeline

• September 30, 2026: Submission deadline

Christian Bartelheimer, University of Göttingen

Daniel Beverungen, Paderborn University

Sara Hofmann, University of Agder

Lysanne Lessard, University of Ottawa

Digital transformation continues to reshape how organizations create, deliver, and capture value. At the same time, service has become a crucial lens for understanding contemporary information systems (IS) phenomena. Value is co-created across organizational boundaries, enacted through digital technologies, and embedded in networks, platforms, and ecosystems. The Topical Collection on Digital Transformation & Service at Electronic Markets seeks to advance theoretically grounded and relevant IS research at the intersection of digital transformation and service.

We invite submissions that foreground service and digital transformation as they are established, debated, and further developed within the IS discipline, and in particular in Electronic Markets. Submitted articles should demonstrate a clear engagement with core IS literature to identify a well-defined research gap that matters for practice and theory, and articulate a convincing contribution to recent IS conversations. We are particularly interested in papers that move beyond broad claims about the consequences of digital transformation and instead examine how information technology (IT) artifacts ought to be designed and how they can be applied to foster value co-creation.

A central expectation of this Topical Collection is that submissions 'white-box' the role of IT artifacts. We seek papers that explicate how IT artifacts shape and are shaped by the digital transformation of organizations, for instance by enabling or transforming customer relations, value propositions, and service exchange. Studies may focus on (smart) service systems or ecosystems at varied levels, including communities, organizations, inter-organizational arrangements, platforms, data spaces, and society. In contrast, we do not seek papers that treat information systems as 'black boxes' or limit their analysis to summative business implications without theorizing the underlying structures, mechanisms, and affordances of IT artifacts through which these implications emerge. Manuscripts presenting IT artifacts that use or expand large language models or other large-scale artificial intelligence technologies must also be unpacked and contextualized, and presented in a way that is theoretically anchored and generates contributions at the intersection of digital transformation, service, and information systems.

We invite contributions from various epistemological positions and methodological traditions common in the IS discipline. Suitable submissions may include (action) design science, conceptual papers, qualitative studies, including case studies and Delphi studies, quantitative research, including experiments, surveys, and data-driven studies, and mixed-methods work. Literature reviews are also welcome, provided that they deliver strong theoretical implications and develop a forward-looking research agenda that reaches considerably beyond reporting the current state of research.

We particularly encourage submissions that investigate the role of digital technologies in real-world service scenarios. Studies that focus on inter-organizational contexts such as service networks, digital platforms, data spaces, or business ecosystems are especially relevant, as they touch on core topics of Electronic Markets. Submissions may examine established organizations, start-ups, public-sector organizations, communities, or other actor constellations in which digital technologies enable, constrain, or transform service exchange and value co-creation.

Potential topics

• Digital transformation and service in information systems
• Design and application of IT artifacts to foster value co-creation
• Smart service systems and ecosystems at multiple levels (communities, organizations, inter-organizational arrangements, platforms, data spaces, and society)
• Customer relations, value propositions, and service exchange enabled by digital technologies
• Role of digital technologies in real-world service scenarios
• Inter-organizational contexts such as service networks, digital platforms, data spaces, and business ecosystems
• Digital transformation in established organizations, start-ups, public-sector organizations, and communities
• IT artifacts using or expanding large language models and other large-scale artificial intelligence technologies
• Generative AI and digital responsibility in service contexts

Timeline

• Invalid DateTime: Submission deadline

Timeline

• September 1, 2026: Full paper submission deadline

Timeline

• September 30, 2027: Submission deadline

Timeline

• December 1, 2026: Submission deadline

Timeline

• December 31, 2026: Full paper submission deadline

Timeline

• September 15, 2026: Full paper submission deadline

Timeline

• November 30, 2026: Submission deadline

The interdepartmental virtual special issue on AI for finance and business decisions aims to explore the transformative impact of AI and attract top-quality research on the economics, methodology, and applications of AI and novel data analytics in practice and research in finance and business decisions.

Both theoretical and empirical work is welcome, and studies developing new methods for decision making, optimization, inference and prediction, and interdisciplinary research are encouraged. The special issue especially welcomes submissions that transcend the departmental boundaries and explore unconventional frameworks.

Potential topics

• Economics of AI and machine learning
• Methodology and algorithms for decision making, optimization, inference and prediction
• Applications of AI and novel data analytics in finance and business
• Theoretical and empirical work on AI in practice
• Interdisciplinary research combining AI with business and finance
• Novel frameworks transcending traditional departmental boundaries

Timeline

• December 31, 2025: Submission deadline

Hyun-Jung Kim, KAIST

Shu-Kai Fan, National Taipei University of Technology

Fugee Tsung, Hong Kong University of Science and Technology

Thomas Volling, Technical University Berlin

Jang Ho Kim, Korea University

Dong-Young Lim, Ulsan National Institute of Science and Technology

Recent advances in artificial intelligence (AI), optimization, and data-driven analytics are fundamentally transforming decision-making processes in complex systems operating under uncertainty. Modern industrial and service systems, including manufacturing, logistics, supply chains, transportation, energy, and healthcare, increasingly face dynamic and stochastic environments characterized by demand fluctuations, disruptions, incomplete information, evolving system states, and operational risks. To effectively manage such complexity, there is growing interest in intelligent decision-making methodologies that integrate learning, prediction, optimization, simulation, and adaptation. In particular, emerging technologies such as reinforcement learning, foundation models, generative AI, agentic AI, digital twins, and hybrid AI-optimization frameworks are enabling the development of more autonomous, adaptive, and data-driven operational systems. Despite these advances, significant challenges remain regarding scalability, robustness, interpretability, real-time implementation, and the successful deployment of AI-driven decision-making approaches in real-world industrial environments.

This Special Issue aims to provide a platform for cutting-edge research on AI-driven decision making under uncertain environments, with particular emphasis on methodologies and real-world applications that combine AI techniques with operations research, optimization, simulation, control, and industrial engineering approaches. The issue welcomes both theoretical and applied contributions addressing uncertainty-aware intelligent decision-making across a broad range of industrial and service systems.

The Special Issue is organized in conjunction with APIEMS 2026. Selected high-quality papers presented at the conference will be invited to submit extended versions for possible publication in the Special Issue. The Special Issue will also be open to general submissions from researchers worldwide. All submitted manuscripts will undergo the standard rigorous peer-review process of the International Journal of Production Research.

The Special Issue particularly encourages interdisciplinary studies demonstrating practical relevance, industrial applicability, and managerial insights for complex decision-making problems under uncertainty. To foster transparency, reproducibility, and cumulative scientific progress, open science practices such as the sharing of (synthetic) data, code, models, prompts, and comprehensive replication materials are strongly encouraged and highly valued.

Potential topics

• AI-driven decision-making under uncertainty
• Production planning and scheduling in stochastic and dynamic environments
• Reinforcement learning for uncertain industrial systems
• Stochastic optimization and robust operational strategies
• AI-enabled statistical quality control and process improvement
• Hybrid AI and optimization approaches for uncertain environments
• Data-driven optimization and prescriptive analytics
• AI-enhanced supply chain and logistics management under disruptions
• Real-time and adaptive decision-making systems
• Simulation-based optimization and digital twins under uncertainty
• Agentic AI and autonomous industrial systems
• Explainable and trustworthy AI for operational decision-making
• AI for resilient and sustainable operations
• Industrial applications and case studies of AI-driven decision systems

Timeline

• October 1, 2026: Submissions open
• January 31, 2027: Submission deadline