Yichuan Wang, University of Sheffield

Minhao Zhang, University of Bristol

Francesco Schiavone, University of Naples Parthenope

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. 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. 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. However, insufficient oversight can exacerbate risks related to data privacy, algorithmic bias, and ethical concerns. 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. 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?

Timeline

• November 15, 2026: Manuscript deadline

Lauri Wessel, European University Viadrina Frankfurt (Oder)

Melanie Reuter-Oppermann, Maastricht University

Roxana Ologeanu-Taddei, University of Montpellier

Hannes Rothe, University of Duisburg-Essen

Sirkka L. Jarvenpaa, The University of Texas at Austin

Digital health is associated with the use and development of digital technologies to improve health. Use, development, and design of the according technologies have in recent years led to a considerable shift. Specifically, practices affecting an individual's health have been shifted from inside hospitals into wider digital health ecosystems where providers, patients, their loved ones, and laypersons interact to jointly shape how care is provided. IS research has greatly advanced in terms of better understanding the technological foundations of this shift such as artificial intelligence (AI) applications, sensor-based technologies, and smartphone apps. However, at least two major trajectories of research arise from this shift and its underlying technologies.

First, creating value in digital health ecosystems demands to take into account diverse kinds of value to be potentially created through these technologies. Financial value is an important kind of value to be created in digital health ecosystems; however, it is by far not the sole kind of value that matters in these settings. It is important to understand which kinds of value technology helps to create as well as how, why, and when it does so. Second, creating value is not per se synonymous with distributing it so that an important line of inquiry is about how to distribute value among ecosystem participants and over time.

We are asking for papers speaking to these topics and see two broad ways in which submissions to our special issue could do so. The first is cumulative and much in line with how research in IS and adjacent fields such as computer science, management, and medicine is conventionally done. It resides in furthering our understandings and toolkits for creating value in digital health ecosystems. However, the more data are available the higher is arguably the potential for misuse, especially when genomic and bio data are in question. This is why research about creating value from data logically calls for research about how to distribute value among ecosystems participants.

The second way to address the abovementioned research trajectories is consistent with promoting contrarian studies. We see much promise of contrarian studies investigating the creation and distribution of value in digital health ecosystems. Research about digital health is replete with assumptions touching on the outcomes that large volumes of data are likely to generate. This stands in stark contrast to the fact that on the ground floor where most clinicians work the data are hardly ever available in the format, quality or volume needed to even remotely live up to these expectations. It is, therefore, paramount to offer fundamentally new ways of thinking about creating and distributing value in digital health ecosystems.

Potential topics

• The role of data management and data sharing in digital health ecosystems
• Designing for measuring kinds of value arising from new digital technologies like XR, 5G, web 3.0, and machine or hybrid learning in digital health ecosystems
• The role of digital health ecosystems during pandemics or natural disasters; specifically with an eye toward how data help to distribute value among ecosystem participants
• (Data-driven) change of professional roles, identities, and institutions in digital health ecosystems
• The difference between creating value for intervention vs. for prevention
• Design of inclusive and responsible digital technologies for healthcare and well-being
• Digital tools and use of digital health data to connect different participants of health service networks, to support decision making and to improve organizational processes
• Negative consequences of digital technologies implementation in healthcare, such as health givers burnout and patients' anxiety
• The role of digital tools like virtual coaching for autonomy of health care providers and patients

Timeline

• March 1, 2026: Submission of extended abstracts/declaration of interest to submit papers to the special issue
• November 30, 2026: Full paper submission deadline
• March 31, 2027: First round decisions due
• September 30, 2027: Revisions due
• December 15, 2027: Second round decisions due
• February 28, 2028: Third round revisions due (if necessary)
• May 31, 2028: Final decisions due

Associate editors

• Aleksi Aaltonen, Stevens Institute of Technology
• Saeed Akhlaghpour, University of Queensland
• Aycan Aslan, IT University Copenhagen
• Anna Essén, Stockholm School of Economics
• Heiko Gewald, HS Neu-Ulm
• Martin Gersch, Freie Universität Berlin
• Camille Grangé, HEC Montréal
• Maike Greve, Copenhagen Business School
• Farkhondeh Hassan Doust, University of Auckland
• Alexander Kempton, University of Oslo
• Charlotte Koehler, European University Viadrina Frankfurt (Oder)
• Johann Kranz, LMU
• Jan-Marco Leimeister, University of St. Gallen
• Wolfgang Maaß, Saarland University
• Bogdan Negoita, HEC Montréal
• Lemai Nguyen, Deakin University
• Guy Paré, HEC Montréal
• Scott Thiebes, Karlsruhe Institute of Technology
• Manuel Trenz, University of Göttingen
• Cristina Trocin, Católica Porto Business School
• Marjolein van Offenbeek, University of Groningen
• Polyxeni Vassilakopoulou, University of Agder
• Till Winkler, Fernuniversität Hagen