SSCI征稿：人工智能与国际商务领域新探索

本期征稿期刊：Journal of World Business

【主题】人工智能与国际商务：理论挑战与战略启示

【关键词】人工智能，国际商务，跨国企业，知识创新，战略决策

征稿期刊

Journal of World Business

期刊指标

IF 6.6 (JCR 2025)

SSCI

Q1 (BUSINESS 56/323)

ABS 4 / ABDC A* / FMS A

新锐期刊大类1区TOP

年发文量40

征稿主题

AI and IB: Theoretical Challenges and Strategic Implications

细分领域

AI and International Business Theory

AI challenges core assumptions underlying dominant IB theories, such as the traditional notions of ownership advantage, internalization, experiential learning, and firm boundaries, which may need to be revisited. Illustrative research questions include: How does AI redefine existing ownership and location advantages, and contribute to the emergence of new ones? Will the locus of advantage move from the ownership to the orchestration of hybrid human–AI systems across borders? Does AI reduce or amplify transaction costs? How does this affect internalization, quasi-internalization, and ecosystem-based governance? How should the OLI paradigm, internalization theory, the Uppsala model, or the global factory framework be adapted to account for predictive, generative, and agentic AI? How can we redefine a global factory when AI agents autonomously coordinate and optimize global value chains, reducing managerial oversight to episodic orchestration? Could we see the rise of “virtual multinationals”, i.e., firms that operate globally through algorithmic coordination without significant physical presence? Do we require new theoretical perspectives and contamination with other disciplines (e.g., cognitive science and information systems) to explain multinational behavior beyond existing IB frameworks?

AI and Cross-Border Learning, Knowledge, Innovation, and Skills

AI transforms how MNEs acquire, combine, and apply knowledge across borders by detecting patterns beyond human cognition, recombining knowledge into novel outputs, and autonomously identifying learning opportunities and adapting routines in response to global feedback. Relevant questions include: How does AI alter the role of subsidiaries as sources and integrators of local knowledge? Does AI accelerate, substitute for, or complement experiential learning in internationalization processes? How does AI affect the geography of innovation and the balance between local adaptation and global standardization? Will knowledge spillovers become faster and more pervasive or, conversely, will proprietary data infrastructures deepen knowledge and innovation asymmetries between data-rich and data-poor firms? How will innovation itself be organized in hybrid humane-machine systems? How can MNEs design governance and contractual mechanisms that allow AI-enabled collaboration while safeguarding proprietary knowledge and data assets? What new human–AI skills and capabilities are required to manage AI-enabled learning and innovation across borders?

AI and Decision-Making, Governance, Resources, Capabilities, and Performance

AI reshapes how MNEs make strategic decisions, allocate decision rights, and build competitive advantage: while AI can reduce information asymmetries and cognitive biases, it may also introduce new forms of algorithmic bias and opacity. Indicative questions include: How does AI affect strategic decision-making in international contexts? Can existing notions of managerial cognition and bounded rationality still explain the strategic behavior of MNEs? When does AI reduce biases, and when does it introduce new forms of algorithmic myopia, overconfidence, or data-driven path dependency? When does AI enable centralization versus decentralization of decision rights within MNEs? How do MNEs embed commoditized AI technologies into unique resource and capability configurations that sustain competitive advantage? How should performance be evaluated when decision systems are adaptive, opaque, and partially autonomous?

AI and Institutions, Grand Challenges, and Global Strategy

AI is deeply embedded in institutional environments characterized by regulatory fragmentation, techno-nationalism, sustainability concerns, and geopolitical tension; these conditions shape both the opportunities and risks associated with AI-driven internationalization. Potential questions include: How do heterogeneous AI regulations, data governance regimes, and institutional trust affect location choices and global strategy? How can MNEs balance global integration with technological autonomy amid competing data and governance regimes? Will AI contribute to the bifurcation of the global economy into rival technological blocs? How can MNEs use AI to enhance global value chain resilience without increasing systemic fragility or environmental costs? What types of institutional architectures and regulatory frameworks do we need to govern and harmonize AI across borders, and to balance innovation, inclusion, and responsibility in the global digital economy? What role do MNEs play in shaping transnational norms and governance frameworks for responsible AI use? Will AI ultimately substitute or create jobs across different sectors and regions?

Methodological Challenges in Studying AI and International Business

Studying AI in IB raises significant methodological challenges due to the adaptive, opaque, and rapidly evolving nature of AI systems: capturing human–AI interaction across borders requires methodological innovation and pluralism (Bosma & van Witteloostuijn, 2024; Delios et al., 2024). Relevant questions include: How can researchers empirically study learning and decision-making in hybrid human–AI systems? Ranging from analytical tools to autonomous agents, how can “AI adoption” be meaningfully operationalized and compared across firms, countries, and industries? What combinations of qualitative, quantitative, computational, comparative, longitudinal, simulation-based, and experimental methods are best suited to study AI in IB? How can scholars ensure rigor, transparency, and ethical accountability when AI is both the object and the instrument of research? Do we need interdisciplinary collaboration among IB scholars, computer scientists, data engineers, and/or behavioral researchers to advance theory, methods, and ethical accountability in the study of AI in international business?

重要时间

Submission Deadline:15 September 2026

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