Wanhao Zhang, Norwegian University of Science and Technology (NTNU)

Younghoon Chang, University of Nottingham Ningbo China

Sameer Kumar, University of Malaya

Smart systems are becoming increasingly central to contemporary information technology. Across homes, workplaces, healthcare settings, educational environments, mobility systems, public services and platform-mediated environments, they are no longer merely digital tools that support pre-existing human activities. They increasingly function as sociotechnical arrangements that sense, classify, predict, coordinate, recommend and intervene in everyday practices, reshaping relations between humans, machines, organisations and institutions. For the purposes of this call, smart systems are understood as digitally enabled sociotechnical systems that integrate sensing, data processing, algorithmic decision-making and automated or semi-automated actions to coordinate, mediate and shape human activities across social and organisational contexts. Such systems include, but are not limited to, AI-enabled platforms, intelligent service systems, smart health technologies, smart homes, digital workplaces, automated mobility systems, learning technologies, public-sector digital systems and hybrid human-AI systems.

Contemporary digital transformation is therefore not only a technical process of innovation, automation or optimisation. It also involves the reorganisation of relations between people, institutions, infrastructures, data, expertise and everyday routines. Work on everyday practices and social change is relevant here because it shows how technologies, meanings, materials, competences and future expectations become entangled in the transformation of social life. A key challenge is to understand how smart systems become socially consequential through design, use, institutional embedding, organisational routines, user negotiation, datafication and everyday practice. Interfaces, platforms, data architectures, algorithmic classifications, dashboards, monitoring systems and automated feedback mechanisms may all participate in shaping conduct, allocating responsibility, structuring choices and stabilising norms. The central concern is not only how smart systems are governed, but also how smart systems themselves become part of the governance of sociotechnical transformation. This includes questions of behaviour, interaction, coordination, authority, accountability, trust, resistance, inclusion, exclusion and power. Hybrid intelligence, human-centred AI and human-AI collaboration are especially relevant where they help explain how human and machine intelligence are combined, coordinated, contested or governed in organisational, institutional or everyday contexts.

This special issue invites contributions that examine smart systems not only as technical innovations, but also as systems of behaviour, interaction, coordination, authority and governance. Its broader aim is to advance human-centred, behavioural, organisational and sociotechnical understandings of smart systems and their role in contemporary digital transformation.

Potential topics

• Smart systems in homes, workplaces, healthcare, education, mobility, public services and platform-mediated environments
• Human-centred AI, hybrid intelligence and human-AI collaboration in organisational and everyday settings
• Conceptual, methodological and design-oriented approaches to studying governance in smart systems
• Algorithmic decision-making, coordination, compliance and accountability
• User adaptation, negotiation, resistance, appropriation and trust in relation to smart technologies
• AI ethics, information privacy, surveillance, transparency and asymmetrical power in digital systems
• Smart and digital health, care technologies and digitally mediated wellbeing
• Digital work, algorithmic management, robotic management and the reconfiguration of organisational routines
• Datafication, monitoring, prediction and automated feedback as modes of governance
• Inclusion, exclusion, vulnerability and inequality in smart sociotechnical environments
• Smart cities, digital government and platform-based public services
• Comparative, cross-cultural or interdisciplinary studies of smart systems and sociotechnical transformation

Timeline

• October 20, 2026: Manuscript deadline

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