Nisreen Ameen, University of Reading

Federico Iannacci, University of Sussex

Daniel Gozman, University of Sydney Business School

James S. Denford, Royal Military College of Canada

This special issue examines how artificial intelligence embedded in public services shapes policy-making and how policies configure AI's development and use. The dynamic interplay between technology and policy intensifies with AI's scale and opacity. The issue seeks to understand how this reciprocal relationship creates, distributes or undermines public value—encompassing efficiency, equity, transparency, participation and welfare.

The special issue invites Information Systems research that theorises AI as central to policy formation and enactment. Manuscripts should examine how AI systems shape policy problems and evidence, how organisations translate AI policies into practice, and how communities participate in defining public value. Qualitative, quantitative, design science and practitioner-oriented contributions are welcome.

All submissions must include dedicated policy implications sections. Research may be set in public, private or nonprofit sectors and should address whose interests are encoded in AI systems, who benefits, who bears risks and whose experiences remain invisible.

Appel publié par Information Systems Journal. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Creation and Distribution of Public Value: How AI creates, distributes or undermines public value, including efficiency, innovation, equity, participation, dignity, sustainability, security and welfare, and how related trade-offs are managed.
• AI Use and the Production of Policy: How AI models, data, classifications, system design and organisational routines shape policy problems, evidence, interventions and evaluation, and how policies evolve through AI-enabled systems.
• AI Governance Beyond Regulatory Compliance: How accountability, transparency, explainability, human oversight and decision authority are enacted in practice, and how AI governance can address societal purposes and outcomes beyond regulatory compliance.
• Policy Translation and Organisational Capability: How organisations translate AI-policy ambitions into procurement, system design, workforce skills, professional roles, implementation and service delivery, and build capabilities to sustain public value.
• Participation, Inequality and Contestation: How AI-enabled policies affect citizens and marginalised groups, reshape voice, access and opportunity, and enable affected communities to participate in defining public value.
• IS Knowledge and Policy Formation: How IS theory and evidence inform AI policy, and how research, practitioner insights and research–practice collaborations can connect evidence, policy, organisational practice and societal outcomes.

Timeline

• June 15, 2027: Submission deadline

Shahriar Akter, University of Wollongong

Carolyn Strong, Cardiff University

Marketing is experiencing rapid transformation through AI adoption, yet over 80% of AI initiatives fail to deliver expected financial returns. Despite most CMOs experimenting with AI, fewer than 10% have successfully scaled it across marketing workflows. The gap between AI potential and actual value creation highlights the need for marketing processes centered on humans orchestrating AI, channels, and personalization as a continuous growth engine.

This special issue seeks high-quality research examining how human-centered AI can enhance strategic decision-making in marketing. It welcomes papers addressing responsible AI principles such as beneficence, autonomy, security, justice, and explicability, alongside practical challenges in marketing-specific AI governance, security risks in autonomous agents, algorithmic bias mitigation, and the reskilling of marketing teams for effective human-AI collaboration.

Appel publié par Journal of Strategic Marketing. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Continuous insights: Using multiple data sources for segmentation, campaign execution, and analytics with AI collaboration to predict consumer behaviours
• Security in Agentic creativity: Addressing data breaches, cyberattacks, and security risks in agentic AI systems, including automatic campaign scaling and optimization
• Trust and algorithmic biases: Implementing auditing protocols to identify and correct distortions in data and models
• AI governance and ethical principles: Establishing AI governance frameworks and marketing-specific ethical guidelines for socially beneficial AI adoption
• Human-centred marketing skills: Reskilling and upskilling marketing talent in data analytics, hyper-personalisation, chatbots, and virtual assistants for strategic decision-making
• AI failures: Understanding why AI projects fail due to data quality, customer needs identification, expectations management, and cultural readiness in human-AI collaboration

Timeline

• October 30, 2026: Abstract deadline
• March 31, 2027: Manuscript deadline

Sara Wilkinson, University of Technology Sydney

Johnny Wong, University of Technology Sydney

Biyanka Ekanayake, University of Technology Sydney

This special issue examines how artificial intelligence is transforming the built environment across construction, real estate, facilities management, and urban planning. While AI technologies offer significant opportunities for innovation and efficiency through applications like energy optimization, smart building control, and predictive maintenance, the sector faces critical challenges regarding data integrity, algorithmic bias, privacy, and transparent decision-making.

The special issue seeks high-quality original research exploring AI's use, impact, risks, and future potential in the built environment. It welcomes contributions spanning construction automation, property valuation, smart buildings, urban planning, generative design, AI governance and ethics, sustainability applications, security systems, human-AI collaboration, and professional education in AI.

Appel publié par Building Research & Information. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• AI in construction: automation, robotics, quality control, progress monitoring, risk and safety analytics
• AI in property and real estate: valuation models, market intelligence, customer engagement, asset management, predictive analytics
• Smart buildings and facilities management: IoT-enabled optimisation, energy management, predictive maintenance, AI-driven knowledge systems
• AI in urban planning and infrastructure: spatial modelling, forecasting, environmental assessment, transport and mobility analytics
• Design and engineering applications: generative design, simulation, optimisation, digital twins, parametric modelling
• Explainable AI (XAI): AI governance, ethics, and regulation: transparency, bias, data integrity, privacy, and responsible GenAI in the built environment
• AI for sustainability and ESG: carbon modelling, resource optimisation, lifecycle assessment
• Security and risk management: computer vision for surveillance, access control, anomaly detection
• Multimodal and agentic AI: advanced AI systems applied to complex built environment challenges
• Human-AI collaboration: professional adoption, skills, workflows, and organisational change
• The challenge of educating and training built environment professionals in AI

Timeline

• September 30, 2026: Deadline for submission
• December 31, 2026: Deadline for review
• February 28, 2027: Deadline for revised submission
• April 30, 2027: Deadline for approval final manuscript

Yanto Chandra, City University of Hong Kong

Greta Nasi, Bocconi University

Jiasheng Zhang, Chinese University of Hong Kong

This special issue examines the negative consequences of AI adoption in public organizations, particularly how algorithmic systems can amplify existing inequalities and undermine democratic governance. The call recognizes that while AI offers potential improvements to service delivery, the benefits are unevenly distributed globally, with marginalized populations facing heightened risks related to biased datasets, algorithmic discrimination, and erosion of accountability.

The special issue seeks theoretical and empirical scholarship reconsidering how public administration frameworks must evolve to address AI-related challenges. Scholars are invited to investigate systemic downsides including workforce displacement, economic inequality, and uneven global access to AI innovations, as well as how governance mechanisms can embed fairness, transparency, and inclusiveness to protect democratic values and serve the public interest.

Appel publié par Public Administration and Development. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• North vs South disparities in AI access and innovation
• Exploitation of labor in AI data collection and platform economies
• Human rights implications of AI-driven decision-making
• Accessibility to public services
• The role of AI systems in perpetuating or challenging gender, racial, and cultural discrimination in public administration and welfare policies
• Experiments on how citizens respond to diversity, inclusion and equity issues in the increasing use of AI by governments
• Influence of dominant languages in AI training and its impact on linguistic diversity
• How minority languages of the world can maintain their survival in the age of AI
• Embedding fairness, transparency, and accountability into AI systems
• Legal and regulatory frameworks for responsible AI deployment

Timeline

• April 1, 2026: Call for Papers published
• September 1, 2026: Hybrid (online and offline) workshop in Hong Kong
• November 1, 2026: Online workshop or in-person workshop in Italy
• January 1, 2027: Full papers submitted
• April 1, 2027: Peer reviews completed
• October 1, 2027: All resubmissions completed
• December 1, 2027: Final reviews completed
• March 1, 2028: Final publication of all articles

Joon Mo Ahn, Korea University

Alberto Di Minin, Scuola Superiore Sant'Anna

Sungjoo Lee, Seoul National University

Ahreum Hong, Kyung Hee University

This special issue addresses how artificial intelligence systems function as autonomous innovators rather than tools within R&D management. As AI systems including large language models and physical AI become active participants in innovation processes, existing frameworks assuming human actors must be reconceptualized. The core challenge extends beyond how AI assists humans to how much cognitive work should be delegated to AI and what consequences this has for organizations, ecosystems, and intellectual property.

The special issue examines four interconnected themes: determining optimal levels of AI delegation; understanding how agentic and physical AI reshape innovation ecosystem architectures as autonomous actors; reconceptualizing absorptive capacity for evaluating AI-generated knowledge; and addressing IP ownership and inventorship when non-human systems generate patentable outputs. Collectively, these themes interrogate how the distinction between AI-as-tool and AI-as-innovator requires new governance mechanisms and strategic frameworks for innovation management.

Appel publié par R and D Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Is there an inverted-U relationship between the degree of AI delegation in R&D and innovation performance, mirroring the over-search paradox?
• How do firms determine the optimal breadth and depth of AI involvement across different stages of the R&D process? Specifically, under what conditions does algorithmic management enhance or hinder innovation performance?
• What is the AI-era analogue of the NIH syndrome: how does uncritical acceptance of AI-generated knowledge erode internal expertise?
• What governance architectures enable effective human–AI teaming while preserving accountability, creativity, and strategic judgment?
• How do agentic AI systems alter the architecture of knowledge flows in open innovation ecosystems, and what new governance mechanisms are required?
• How does the participation of autonomous AI agents change the logic of platform-based open innovation, including roles, incentives, and boundary conditions?
• How do digital twins and AI-enabled simulation reshape the scope and speed of distributed experimentation across organisational boundaries?
• What new forms of inter-organisational trust, contracting, and coordination are needed when AI agents act as innovation partners?
• How must absorptive capacity (or dynamic capabilities) be reconceptualised when the primary external knowledge source is an AI system rather than a human partner?
• What organisational routines and managerial processes enable firms to transform AI-generated knowledge into innovation value?
• What individual competencies — technical, cognitive, and relational — distinguish high-performing innovators in human–AI contexts?
• What learning mechanisms allow firms to continuously upgrade AI-related innovation capabilities over time, particularly in relation to exploration and exploitation?
• How should inventorship and IP ownership be attributed when agentic AI systems autonomously generate patentable outputs, and what theoretical frameworks from open innovation research best capture this challenge?
• What strategic logic governs firms' decisions to release model weights, training data, or fine-tuned AI capabilities into open-source commons?
• How do trained model weights, fine-tuning data, and emergent AI capabilities constitute a new category of strategic asset, and how do firms govern access to and monetisation of these assets?
• Under what regulatory and institutional conditions does the open-source AI movement accelerate versus impede innovation diffusion?

Timeline

• May 30, 2026: PDW (paper development workshop) at R&D Management Workshop, Seoul, Korea
• July 4, 2026: PDW at KOSIME summer conference, Jeju, Korea
• December 1, 2026: Special Issue Submission Open
• June 30, 2027: Deadline for SI Submission
• July 1, 2028: Publication of SI Articles

Aljona Zorina, NEOMA Business School

Ella Hafermalz, Vrije Universiteit Amsterdam

Thomas Grisold, WU Vienna

Aron Lindberg, Stevens Institute of Technology

Mahya Ostovar, University of Galway

Digital visibility—making oneself or others visible through digital technologies—has become central to modern organizing, yet remains theoretically fragmented. Algorithmic systems and digital infrastructures increasingly determine how individuals and collectives become visible, valued, or excluded, fundamentally reshaping coordination, communication, and collaboration. The special issue addresses how visibility has shifted from a secondary organizational feature to a primary mechanism through which strategy, power, identity, and modes of organizing are constructed.

Appel publié par European Journal of Information Systems. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Technologies and infrastructures of visibility, including blockchains, algorithmic social media feeds, AI-generated content, and data infrastructures
• Non-human agents and automated systems, including bots, digital twins, and avatars as participants in constructing visibility
• Data practices and visibility construction, including data capture, inclusion, omission, and the politics of data
• Decision support and critical systems in healthcare, security, engineering, and hiring, and how they influence visibility
• Experiences and social dynamics of digital invisibility, including visibility work, filter bubbles, and hyper-personalization
• Strategy, power, and digital identity management, including algorithmic performance and visibility games
• Non-human governance and political economies, including algorithmic management and blockchain-based systems
• Trust, truth, and security in digitally visible environments, including verification and watermarks
• Individual and societal impacts of digital visibility on well-being, identity, autonomy, and civil liberties
• Organizational and workplace consequences of digital visibility on trust, autonomy, innovation, and knowledge sharing
• Ethical and governance consequences of digital visibility systems, including accountability mechanisms and stakeholder roles

Timeline

• September 7, 2026: Optional abstract submission for Paper Development Workshop
• October 5, 2026: Feedback on abstracts via email
• December 1, 2026: Invited five-page abstract due for Paper Development Workshop
• December 9, 2026: Online Paper Development Workshop
• March 31, 2027: First-round submissions to the journal due
• June 30, 2027: First-round decisions expected
• July 30, 2027: Revision Plan Meeting with SI Editors (optional)
• September 30, 2027: Second-round revisions due
• November 30, 2027: Second-round decisions expected
• February 28, 2028: Third and final revisions submissions due
• April 30, 2028: Final decisions expected

Associate editors

• Andreas Eckhardt, University of Innsbruck
• Robert Fichman, Boston College
• Andreas Hein, University of St. Gallen
• Mayur Joshi, Telfer School of Management
• Lise Justesen, Copenhagen Business School
• Waldemar Kremser, Johannes Kepler University
• Virginia Leavell, Cambridge Judge Business School
• Julian Lehmann, Arizona State University
• Silvia Masiero, University of Oslo
• Afshin Mehrpouya, University of Edinburgh Business School
• Josh Morton, Leeds University Business School
• Stella Pachidi, King's Business School
• Mariia Petryk, George Mason University
• Ursula Plesner, Copenhagen Business School
• Carolina Alves de Lima Salge, University of Georgia
• Karla Sayegh, Cambridge Judge Business School
• Lisen Selander, University of Gothenburg
• Maha Shaikh, ESADE
• Maura Soekijad, Vrije Universiteit Amsterdam
• Marta Stelmaszak-Rosa, UMass Amherst
• Elmira Van den Broek, Stockholm School of Economics
• Joao Veira Da Cunha, IESEG
• Bei Yan, Stevens Institute of Technology

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

Younghoon Chang, University of Nottingham Ningbo China

Sameer Kumar, University of Malaya

Smart systems are now deeply embedded in various social contexts, functioning as sociotechnical arrangements that sense, classify, predict and intervene in everyday practices. This special issue seeks to understand how these digitally enabled systems reshape relations between humans, machines, organisations and institutions, and how they become consequential through design, institutional embedding and everyday use.

The issue addresses both how smart systems are governed and how they themselves participate in governing sociotechnical transformation. Key concerns include questions of behaviour, interaction, coordination, authority, accountability and power, particularly examining hybrid intelligence and human-AI collaboration. The special issue invites submissions examining smart systems not merely as technical innovations but as systems shaping governance, interaction and behaviour in organisational, institutional and everyday contexts.

Appel publié par Behaviour & Information Technology. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

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

This special issue examines how shifting political contexts and policy decisions influence artificial intelligence innovation, governance, and investment globally. The call addresses the interplay between regulatory changes, technological development, and organizational strategy in response to evolving geopolitical uncertainties and governance frameworks around AI.

The issue seeks interdisciplinary research exploring how relaxed or tightened AI regulations affect innovation ecosystems, competitive dynamics, and cross-border collaboration. It welcomes submissions analyzing governance models, investment strategies, and the balance between rapid AI development and ethical considerations such as data protection and algorithmic fairness across different economic and institutional contexts.

Appel publié par Enterprise Information Systems. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• How do shifting political contexts and leadership changes 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 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

Stefan Seidel, University of Cologne

Nicholas Berente, University of Notre Dame

Hong Guo, Arizona State University

Wonseok Oh, Korea Advanced Institute of Science and Technology

Digital technologies and artificial intelligence are disrupting established institutional practices and creating significant ethical, regulatory, and policy challenges. Organizations and governments must navigate tensions between addressing risks associated with data privacy, algorithmic bias, and labor displacement while fostering innovation and competitiveness.

The regulatory landscape is rapidly evolving through frameworks like the EU AI Act and GDPR to embed ethical protections. However, digital technologies operate at a pace that traditional regulatory approaches struggle to match, leading to novel mechanisms where technology itself serves as a tool for implementing and monitoring compliance.

This special issue invites sociotechnical and information systems research examining how ethical concerns, regulatory requirements, and policy initiatives interact within organizational and institutional contexts. Studies should employ diverse methodologies to investigate governance practices, regulatory effectiveness, compliance mechanisms, and how emerging technologies address institutional challenges.

Appel publié par MIS Quarterly. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Technology and AI ethics
• Data ethics
• Technology regulation
• AI regulation
• Data regulation
• Institutional governance
• Risk management
• Digital governance
• Sociotechnical perspectives
• Regulatory design
• Compliance and enforcement
• RegTech and SupTech
• Algorithmic regulation
• Organizational adaptation to regulation
• Data privacy and GDPR
• Responsible AI
• AI governance
• Sustainability and ESG
• Market regulation
• Self-regulation
• Legal technology
• Responsible data governance
• Bias and fairness in AI
• Algorithm accountability
• Labor and deskilling
• Human dignity in AI systems
• Regulatory sandboxes
• Innovation and regulation balance

Timeline

• November 1, 2026: Submissions Open
• November 13, 2026: Submissions Closed

Associate editors

• Alessandro Acquisti, Massachusetts Institute of Technology
• Alexander Benlian, Technical University of Darmstadt
• Min Chen, George Mason University
• Aaron Cheng, The London School of Economics and Political Science
• Ioanna Constantiou, Copenhagen Business School
• John Dong, Nanyang Technological University
• Anna Essén, Stockholm School of Economics
• Haiyang Feng, Tianjin University
• Stefan Feuerriegel, Ludwig Maximilian University of Munich
• Uri Gal, The University of Sydney
• James Gaskin, Brigham Young University
• Goh Khim Yong, National University of Singapore
• Anand Gopal, Nanyang Technological University
• Brad Greenwood, George Mason University
• Dominik Gutt, RWTH Aachen University
• Ola Henfridsson, University of Miami
• Nina Huang, University of Miami
• Jannis Kallinikos, The London School of Economics and Political Science
• Gerald C Kane, University of Georgia
• Wolf Ketter, University of Cologne
• Cameron Kormylo, University of Notre Dame
• Natalia Levina, New York University
• Zhepeng Li, The University of Hong Kong
• Kalle Lyytinen, Case Western Reserve University
• Min-Seok Pang, University of Wisconsin-Madison
• Hani Safadi, University of Georgia
• Nachiketa Sahoo, Boston University
• Juliana Sutanto, Monash University
• Mari-Klara Stein, TalTech
• Lai Wei, Shanghai Jiao Tong University
• Yi Yang, The Hong Kong University of Science and Technology
• Lior Zalmanson, Tel Aviv University
• Nan Zhang, University of Notre Dame
• Xia Zhao, University of Georgia
• Youngjin Yoo, London School of Economics and Political Science