AI and IB: Theoretical Challenges and Strategic Implications
DETAILS
Call for Papers – AI and IB: Theoretical Challenges and Strategic Implications
Journal: Journal of World Business (JWB)
Publisher: Elsevier
Journal metrics: Impact Factor 8.8, CiteScore 19.2
Submission deadline: 15 September 2026
This special issue examines how artificial intelligence reshapes international business (IB), global strategy, and multinational‑enterprise (MNE) behaviour. It specifically focuses on the international dimension of AI, including cross‑border knowledge flows, location and entry‑mode choices, global value chains, institutional diversity, and the reconfiguration of firm‑specific and location‑specific advantages in a world where intelligence is increasingly distributed across human–machine systems.
Why this issue matters
AI stands apart from earlier digital technologies because it intervenes directly in learning, decision‑making, and value creation, rather than only improving coordination and transactions.
Generative AI and agentic AI systems challenge long‑standing IB theories (e.g., OLI paradigm, internalization theory, Uppsala model, institutional theory, global factory framework) that were developed for an era of human‑centred cognition and bounded rationality.
The SI aims to re‑theorise and empirically document how AI transforms MNEs’ learning, governance, innovation, and strategic decision‑making across borders, and to build a dedicated “AI and IB” research agenda.
Core themes and research directions
Contributions are invited on theoretical and empirical work that advances understanding of AI’s impact on international business. The special issue follows the five‑theme structure proposed by Buckley et al. (2026):
AI and International Business Theory
How AI redefines ownership, location, and internalization advantages and the boundaries of the firm.
Whether the locus of advantage shifts from ownership of assets to orchestration of hybrid human–AI systems across borders.
How to adapt OLI, internalization theory, the Uppsala model, and the global‑factory framework, and whether new cross‑disciplinary perspectives (e.g., cognitive science, information systems) are needed.
AI and Cross‑Border Learning, Knowledge, Innovation, and Skills
How AI changes the role of subsidiaries as sources and integrators of local knowledge.
The balance between AI‑accelerated learning and experiential learning, and the geography of AI‑driven innovation.
New governance and contractual arrangements for AI‑enabled knowledge collaboration and protection of proprietary data and IP.
Human–AI skills and capabilities required for managing international learning and innovation.
AI and Decision‑Making, Governance, Resources, Capabilities, and Performance
How AI affects strategic decision‑making, centralisation vs decentralisation of decision rights, and managerial cognition.
When AI reduces cognitive biases and information asymmetries and when it introduces new forms of algorithmic bias, opacity, and path dependence.
How MNEs embed AI into unique, hard‑to‑copy resource and capability configurations and how performance should be evaluated in adaptive, partially autonomous AI‑enabled systems.
AI and Institutions, Grand Challenges, and Global Strategy
How heterogeneous AI regulations, data‑governance regimes, techno‑nationalism, and geopolitical tensions affect location choices, entry modes, and global‑strategy formulation.
The role of AI in global‑value‑chain resilience, sustainability, and environmental impact.
Institutional architectures and regulatory frameworks needed to govern AI across borders and balance innovation, inclusion, and responsibility.
The creation or displacement of jobs across sectors and regions under AI‑driven globalisation.
Methodological Challenges in Studying AI and IB
How to operationally define and measure “AI adoption” and human–AI interaction across firms, countries, and industries.
Appropriate combinations of qualitative, quantitative, computational, longitudinal, simulation‑based, and experimental methods for studying AI in IB.
Ensuring rigour, transparency, and ethical accountability when AI is both object and instrument of research.
The need for interdisciplinary collaboration (IB, computer science, data engineering, behavioural research) to advance AI‑IB scholarship.
Guest editors
Peter J. Buckley, University of Manchester, United Kingdom
Stefano Elia, Politecnico di Milano, Italy
Olli Kuivalainen, LUT Business School, Finland
Mattia Pedota, Politecnico di Milano, Italy
Sebastian Krakowski, Stockholm School of Economics, Sweden
Supervising Editor / JWB Editor‑in‑Chief:
Ajai Gaur, Rutgers Business School, USA
Submission details
Submission portal: Editorial Manager for Journal of World Business:
https://www.editorialmanager.com/jwbSubmission deadline: 15 September 2026
All manuscripts must follow the journal’s Guide for Authors:
https://www.sciencedirect.com/journal/journal‑of‑world‑business/publish/guide‑for‑authors
Contributions should explicitly address AI‑driven cross‑border processes and global‑strategy implications, and may be conceptual, empirical, or mixed‑methods, provided they advance both theory and practise in international business.
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