Enterprise Information Systems

Technological and Social Shaping of Emerging Technologies in Healthcare

Overview

Within mere hours of returning to the White House, President Donald Trump took a dramatic step in reshaping the future of artificial intelligence (AI) by repealing the guardrails put in place by former President Joe Biden. This abrupt policy reversal has sent shockwaves through the tech community, sparking both optimism about accelerated AI innovation and apprehension about a new “wild west” of limited oversight. While high-profile supporters such as venture capitalist welcome fewer restrictions, others caution that the Biden administration’s initiatives on AI safety and ethical standards, many of which already yielded research findings and policy recommendations, risk being mitigated. As new US government remains tight-lipped about how it will regulate advanced AI, the international community is left to speculate: Will this herald an era of fast-paced development that bypasses cautionary measures, or will additional safeguards eventually surface to address concerns around privacy, discrimination, and global competition? This unfolding scenario sets the stage for a reexamination of AI governance, investment, and cross-border collaboration, an opportune moment for researchers to analyze the interplay between regulatory shifts and technological progress.

Scholars have noted that changes in political and policy interventions can profoundly affect how nations or organizations invest in and govern emerging technologies, particularly in AI (Goos & Savona, 2024; Meyer & Brouthers, 2023). For example, some countries may double down on government-led AI initiatives to retain technological sovereignty, while others may opt for more laissez-faire approaches that prioritize market-driven innovation (West & Allen, 2018). As these dynamics unfold, they reshape competitive advantages, strategic alliances, and pathways of innovation. At the organizational level, a potential relaxation in AI governance, such as easing compliance requirements or loosening data protection measures, could bolster corporate and industrial innovations in the short run. Firms might benefit from lower costs associated with regulatory compliance and gain rapid market entry for AI-based products and services (Gama & Magistretti, 2025). However, insufficient oversight can exacerbate risks related to data privacy, algorithmic bias, and ethical concerns (Siala & Wang, 2022). Kronblad et al. (2024) propose the concept of “institutional blackboxing,” describing how the technical complexities and operational procedures of AI decisions are obscured or hidden within institutional frameworks. This blackboxing prevents scrutiny and accountability, allowing injustices to persist unaddressed within the systems’ operations.

This lack of accountability and transparency can further lead to fragmented standards across regions, as weakened governance makes it increasingly difficult for multinational companies to navigate varying legal requirements and compete effectively on a global scale (Coche et al., 2024). The risks associated with fragmented standards underscore the critical role of key enablers identified by Pramanik et al. (2024), which influence AI readiness across both developed and developing economies. These enablers, scientific research output, internet infrastructure, and public consumption expenses, highlight the universal challenges and opportunities faced by nations in harnessing AI. As these enablers play a pivotal role in shaping AI governance models, they also reflect each economy’s ability to strategically leverage digital transformation. Therefore, in managing their AI ecosystems, industries and governments must carefully assess these factors to effectively navigate the complexities introduced by varied political and policy landscapes.

In light of these developments, there is a pressing need for scholarly investigations into how international and political uncertainties shape AI innovation, especially in terms of governance models, investment flows, cross-border collaborations, and competitive dynamics. We invite submissions that explore, but are not limited to, the following themes: (1) the influence of shifting geopolitical contexts on AI research and development priorities; (2) comparative studies of AI policy frameworks across different countries; (3) the implications of relaxed governance for ethical AI, data protection, and social welfare; and (4) strategies for multinational enterprises to navigate AI innovation in volatile regulatory environments. We particularly welcome interdisciplinary perspectives that draw on economics, political science, information systems, organizational studies, and operations management. By publishing in this special issue, authors will contribute to a deeper understanding of how AI innovation can be managed, sustained, and directed for societal benefit amidst evolving global uncertainties.

Potential Topics

• How do shifting political contexts and leadership changes (e.g., the Trump administration’s approach to AI) shape countries’ AI R&D investments and strategic alliances?
• What are the short-term and long-term implications of relaxed AI governance on economic performance, data privacy, and algorithmic bias?
• Under what conditions can reduced AI regulations foster or hinder innovation ecosystems in sectors such as healthcare, finance, manufacturing, and transportation?
• How can policymakers and organizations balance the need for rapid AI innovation with the ethical and social risks arising from limited oversight or fragmented governance?
• How might relaxed AI governance in certain countries influence global competitive dynamics, international collaborations, and the uneven distribution of AI capabilities?
• What strategies can multinational enterprises adopt to navigate complex regulatory landscapes, protect intellectual property, and maintain data security while pursuing AI innovation?
• Which governance models or policy frameworks from different regions (e.g., EU vs. US vs. Asia) most effectively balance innovation, accountability, and social welfare in AI?
• How can scenario planning and forecasting methods be applied to model the impact of political volatility on AI investments, talent flows, and market structures?

References

Coche, E., Kolk, A., & Ocelík, V. (2024). Unravelling cross-country regulatory intricacies of data governance: the relevance of legal insights for digitalization and international business. Journal of International Business Policy, 7(1), 112-127.

Gama, F., & Magistretti, S. (2025). Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of AI applications. Journal of Product Innovation Management, 42(1), 76-111.

Goos, M., & Savona, M. (2024). The governance of artificial intelligence: Harnessing opportunities and mitigating challenges. Research Policy, 53(3), 104928.

Kronblad, C., Essén, A., & Mähring, M. (2024). When justice is blind to algorithms: Multilayered blackboxing of algorithmic decision making in the public sector, MIS Quarterly, 48(4), 1637-1662.

Meyer, K. E., Li, J., & Brouthers, K. D. (2023). International business in the digital age: Global strategies in a world of national institutions. Journal of International Business Studies, 54(4), 577-598.

Pramanik, P., Jana, R. K., & Ghosh, I. (2024). AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework. Technological Forecasting and Social Change, 205, 123482.

Siala, H., & Wang, Y. (2022). SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Social Science & Medicine, 296, 114782.

West, D. M., & Allen, J. R. (2018). How artificial intelligence is transforming the world. Brookings Institution. Available at: https://platform.debateproject.eu/wp-content/uploads/youzify/groups/7/2024/05/How-artificial-intelligence-is-transforming-the-world-Brookings.pdf.