Timeline

• December 31, 2026: Submission deadline

Timeline

• December 31, 2026: Submission deadline

Timeline

• September 30, 2026: Submission deadline

Timeline

• October 31, 2026: Submission deadline

Timeline

• July 31, 2026: Submission deadline

Timeline

• October 30, 2026: Full paper submission deadline

Yung Kyun Choi, Dongguk University

Tae Hyun Baek, Sungkyunkwan University

For most of advertising's history, creative work has been relatively slow, scarce, and expensive. A campaign moved from brief to concept to finished asset through weeks of human labor, and the cost of producing each execution limited how many ideas a brand could test and how finely it could tailor them. Generative AI is potentially collapsing those constraints. Tools that draft copy, generate images and video, and produce thousands of message variants in minutes are now becoming embedded in the daily workflow of agencies, brands, and platforms. The result is not a marginal efficiency gain but a potential reordering of how advertising creative is imagined, made, evaluated, and valued. For an industry whose competitive advantage has long rested on creativity, arguably few developments matter more.

Creativity has always been central to how advertising works. Decades of research establish that creative advertising, generally defined as work that is both divergent and relevant, drives attention, processing, memory, and ultimately sales. Yet what counts as creative has never been settled. It is judged differently by creatives, account managers, and clients, and it turns on originality, artistry, and strategic fit in ways that resist easy measurement. Generative AI forces these questions open again. When a model can produce a polished, on-brief execution in seconds, the premium may shift from craft and execution toward ideas, taste, judgment, and the ability to direct the machine. Early evidence is mixed: AI appears able to lift the measured creativity and effectiveness of individual work, in some field settings to apparently superhuman levels, while tending to push outputs across many users toward sameness. Whether AI expands the creative frontier or flattens it is now an empirical and managerial question of importance.

These dynamics are touching every stage of the creative process. In ideation and concepting, AI can act as a brainstorming partner that generates and recombines directions faster than most teams, shifting the human role toward editing, curating, and directing. In asset production, text-to-image and text-to-video systems are compressing the cost and time of finished creative, making volume and localization feasible where they were once uneconomic. In personalization, the same tools make it possible to generate near-infinite variants tuned to context, audience, and moment, reviving long-standing ambitions for dynamic, one-to-one creative while raising fresh questions about distinctiveness and brand coherence. Each shift promises scale, but scale at the expense of differentiation may be a poor trade for brands that compete on standing out.

The people and organizations that make advertising are being remade alongside the work. Generative AI is reshaping which skills are scarce, which tasks are automated, and how value is captured across the agency and client relationship. It raises hard questions about the future of creative talent: which roles disappear, which are augmented, and which new ones, such as prompt strategist, AI creative director, or model curator, emerge. It may also unsettle agency business models built on billable hours and production fees as the marginal cost of production approaches zero. How agencies, in-house teams, and platforms reorganize creative labor, and how they preserve the human judgment clients still pay for, is likely to shape the structure of the industry in the coming years.

Building on a fast-growing body of scholarship, this special issue invites theoretically rigorous and managerially useful research on how AI is changing advertising creativity. Consistent with the mission of the Journal of Advertising Research and its strong practitioner readership, submissions should make a clear contribution to advertising theory while offering actionable guidance for the creatives, agencies, brands, and platforms living through this transition. The special issue is especially interested in work that moves past the observation that AI is disruptive to specify how, where, why, and for whom it improves or degrades creative outcomes.

Potential topics

• How does AI change the way creative ideas are generated, selected, and refined, and where in that process is human judgment most valuable?
• When does AI assistance widen the range of creative directions a team explores, and when does it narrow it?
• How should briefs, brainstorming, and creative workflows be redesigned around generative tools?
• What is the most effective division of labor between human creatives and AI across ideation, drafting, and refinement?
• How does near-zero marginal cost production change what advertising creative gets made, and how much of it?
• What is gained and lost when finished assets are generated rather than crafted?
• How does AI-produced creative compare with human-produced work on effectiveness, quality, and cost?
• How does production at scale change media planning, creative testing, and iteration?
• How does generative AI change dynamic creative optimization and one-to-one message tailoring?
• How can brands produce thousands of variants without eroding distinctiveness and brand consistency?
• When does personalized AI creative outperform a single strong idea, and when does it not?
• How do consumers respond to creative that is visibly machine-tailored to them?
• How are agencies, in-house teams, and platforms reorganizing creative labor around AI?
• Which creative roles and skills are being automated, augmented, or newly created?
• How does AI reshape the agency and client relationship, the pitch process, and value capture?
• What happens to agency business models when the cost of production approaches zero?
• Does AI change how creativity is defined, judged, and rewarded in advertising?
• How should originality, distinctiveness, and craft be valued when execution becomes commoditized?
• Does widespread AI use homogenize advertising creative, and how can brands resist sameness?
• How should creative awards, evaluation standards, and quality benchmarks adapt?
• What methods best capture the effect of AI on creative outcomes?
• How can creativity itself be measured at scale across large volumes of AI-generated work?
• How can researchers study homogenization, distinctiveness, and the diversity of creative output?
• When does AI-generated creative help or hurt brand building and long-term equity, and what guardrails keep it on-brand and effective?

Timeline

• January 15, 2027: GMC Tokyo 2027 submission deadline
• July 22, 2027: GMC Tokyo 2027 conference
• September 1, 2027: Special issue submission window opens
• October 15, 2027: Special issue manuscript deadline

Giovanni Schiuma, Università LUM

Péter Baranyi, Corvinus University

Francesco Santarsiero, Università della Basilicata

Dagmara Lewicka, AGH University of Science and Technology

The intersection of Knowledge Management (KM) and Artificial Intelligence (AI) is rapidly transforming how organizations create, share, and apply knowledge to pursue sustainability. In this context, 'intelligent knowledge' refers to the management of knowledge systems that dynamically combine human cognitive capacities with advanced AI-driven tools and models to facilitate ethical, inclusive, and purpose-driven innovation. This Special Issue examines how intelligent knowledge management systems can enhance organizations' innovation capacity and align their efforts with environmental, social, and governance (ESG) commitments and the Sustainable Development Goals (SDGs).

The convergence of Knowledge Management (KM) and Artificial Intelligence (AI) is increasingly recognized as a pivotal driver for advancing organizational sustainability in the digital era. In today's complex business landscape, organizations face challenges in developing intelligent knowledge systems—AI-augmented frameworks designed to support sustainable decision-making, foster innovation, and ensure strategic alignment with long-term value creation goals. The emergence of generative AI and large language models (LLMs) is transforming knowledge processes, enabling organizations to dynamically retrieve, synthesize, and apply knowledge to address complex sustainability challenges.

While traditional Knowledge Management (KM) has primarily focused on the systematic processes of creating, storing, sharing, and applying knowledge, the integration of Artificial Intelligence (AI) introduces new dimensions of computational intelligence that enhance and, in some cases, automate these foundational functions. AI-driven technologies are increasingly supporting knowledge-based decision-making, thereby fostering the emergence of intelligent knowledge systems that can reason, learn, and act based on context-aware insights to promote sustainable and purpose-driven outcomes. Within KM theory, the enduring distinction between tacit and explicit knowledge remains highly relevant: while AI excels in processing and managing explicit knowledge, the challenge of capturing and leveraging tacit knowledge persists, particularly in enabling meaningful and ethical human-AI collaboration. Recent studies emphasize that AI should be viewed as augmenting, rather than replacing, human expertise in sustainability-oriented knowledge work, thereby reaffirming the centrality of co-creation within contemporary knowledge management frameworks.

The evolution of KM in the AI era thus calls for new conceptual models and dynamic strategies that can support knowledge environments aligned with long-term sustainability objectives. As organizations strive to integrate human and AI cognition into their knowledge systems, the ability to flexibly interact with and adapt through AI-generated insights becomes a defining capability for intelligent knowledge management. The impact of AI on organizational learning and strategic knowledge flows must be critically assessed from a sustainability perspective, recognizing that AI technologies can either enhance or threaten knowledge-based resilience and responsible innovation. Generative AI and machine learning models are reshaping the landscape of KM, revolutionizing how organizations capture, synthesize, and apply knowledge to create sustainable value.

While large language models (LLMs) and AI-driven knowledge repositories significantly increase the speed and scope of knowledge retrieval and decision support, they also introduce critical risks related to epistemic validity, algorithmic bias, and the sustainability of automated knowledge production. These challenges are especially acute when AI-generated knowledge informs decisions with significant social or environmental implications, where the accuracy, contextual sensitivity, and ethical integrity of outputs are vital for maintaining organizational trust, legitimacy, and long-term innovation capacity.

This Special Issue aims to foster a deeper understanding of how intelligent knowledge systems, powered by the integration of AI technologies, human cognition, and contemporary KM practices, are transforming the pursuit of sustainability, innovation, and responsible governance within organizations. We invite contributions that critically explore how human-AI collaboration can be harnessed to develop resilient, ethical, and sustainability-oriented knowledge ecosystems. Beyond conceptual and theoretical contributions, we particularly encourage empirical studies that provide evidence-based insights into how intelligent knowledge processes enhance sustainability performance, strengthen ESG strategies, and promote adaptive, long-term value creation. Given the transformative impact of AI on knowledge flows and strategic decision-making, special attention will be devoted to research that examines the capabilities, limitations, and practical applications of AI-enabled knowledge systems, including generative AI tools and large language models (LLMs), in advancing sustainable organizational practices.

Potential topics

• The design of intelligent knowledge systems integrating AI technologies and human cognitive capacities
• Reimagining knowledge management (KM) practices to align with ESG commitments and sustainable development goals (SDGs)
• Human-AI collaboration models to augment human expertise in knowledge work
• The transformative impact of generative AI and large language models (LLMs) on knowledge processes
• Integrating Human cognitive systems with AI-enabled technologies
• Human-based emotional knowledge and AI-based rational knowledge
• Challenges in Managing Tacit and Explicit Knowledge in AI-Enhanced Knowledge Management Environments
• Development of transformative leadership competencies for navigating digital complexity and fostering sustainable innovation
• Strategic knowledge intelligence approaches for building resilience, responsible innovation, and adaptive capabilities
• Democratization of knowledge access and support for inclusive value co-creation through intelligent knowledge systems
• Risks and challenges of automated knowledge production and ensuring the sustainability of AI-generated knowledge
• Governance models and policy frameworks to guide the ethical evolution of intelligent knowledge systems

Timeline

• January 15, 2026: Submissions open
• June 1, 2026: Review process on a rolling basis
• August 16, 2026: Manuscript submission deadline

Steve Yang, Stevens Institute of Technology

Jing Chen, Cardiff University

Aparna Gupta, Rensselaer Polytechnic Institute

Zachary Feinstein, Stevens Institute of Technology

William Knottenbelt, Imperial College

This special issue aims to explore the specific financial modelling issues related to Artificial Intelligence (AI), in particular Generative AI and Large Language models (LLMs) modelling and applications in finance. It seeks to offer new insights in consideration of Generative AI/LLM innovation that brings changes to the traditional finance topics such as asset pricing, financial intermediation, financial markets & investment, behavioural finance, banking, accounting, insurance, etc. The special issue particularly encourages work that advances the understanding of how Gen AI and LLM modelling brings forward ethical implications, benefits financial inclusion and/or enhances regulatory and policy changes.

The launch of ChatGPT in November of 2022 and its rapid adoption have demonstrated the promise of Generative AI/LLMs and brought a renewed interest in finance research on artificial intelligence applications. Generative AI has the potential to revolutionize various aspects of finance by enabling better data generation, analysis, decision-making, and risk management. One example of the potential impact is a recent paper on how FinBERT (a finance focused large language model) can improve several existing areas of information processing in finance. However, this is only the beginning, and it is anticipated that more research on modelling and applications using Generative AI is needed.

In the era of increased AI influence, ethical concerns often revolve around the use of big data, decentralized finance, artificial intelligence (AI), and how they affect customer privacy. At the same time, regulatory challenges are equally significant, with AI introducing new methods and risks that may not fit well within existing frameworks. For example, Generative AI can reduce costs associated with predictions and decisions but raises concerns about data privacy and the stifling of human-centred innovation. These challenges beg further research to bring better understanding.

Potential topics

• Asset pricing with Generative AI and LLMs
• Financial intermediation applications of Generative AI and LLMs
• Financial markets and investment strategies using Generative AI and LLMs
• Behavioural finance and AI applications
• Banking and AI applications
• Accounting and AI applications
• Insurance and AI applications
• Ethical implications of Generative AI and LLMs in finance
• Financial inclusion through Generative AI and LLMs
• Regulatory and policy changes driven by AI in finance
• Data generation and analysis using Generative AI
• Risk management with Generative AI and LLMs
• FinBERT and finance-focused language models
• Data privacy concerns in AI-driven finance
• Cost reduction through AI predictions while managing innovation

Timeline

• September 15, 2026: Manuscript deadline

Konstantin Hondros, Helmut Schmidt University

Leonhard Dobusch, University of Innsbruck

Astrid Mager, Austrian Academy of Sciences Vienna

Patricia Aufderheide, American University Washington

Patrick Cohendet, HEC Montréal

Creativity refers to the generation of novel and valuable ideas and artifacts and is central to the management and organization of innovation. Mimicking creativity, generative artificial intelligence (GenAI) technology rapidly generates potentially novel and valuable content such as text, images, music, or video. The multiple implications of GenAI for the management and organization of creativity and innovation are exemplified in the burgeoning literature on creative problem solving, the organization of creative and knowledge-based work, employee creativity, and human-nonhuman interaction in creative processes.

Running through organizational discussions of GenAI and creativity, but often not made explicit, are copyright issues, such as rights management of training data or ownership of generated content. Copyright refers to the legal framework that protects but also delineates ownership of creative content. GenAI, and digitization more broadly, is moving copyright from the legal niches of the creative industries to the center of practices and routines in organizations and everyday digital life. Any activity related to GenAI requires the management of copyright within and across organizations. Yet an in-depth discussion of the interrelation between creativity and copyright in the context of GenAI is missing from management and organization studies, as well as social science at large.

This Call for Papers suggests that the accelerating developments of digitization and GenAI call for more attention to the interrelation between creativity and copyright in organizations. At the same time, this provides a unique opportunity to study how organizations deal with regulatory uncertainty due to rapid technological developments. Such a mismatch between technology and regulation opens up all kinds of opportunities and challenges for creative organizing and organizing creativity alike.

We particularly encourage studies that use empirical methods to explore everyday practices of dealing with GenAI and copyright within and beyond organizational contexts, but conceptual submissions are also very welcome. Empirically, we invite contributions from all kinds of fields potentially affected by GenAI and its relationship to copyright – from the creative industries and journalism to online platforms and services, to research and science itself. We welcome contributions from different disciplines interested in the empirical analysis of technology and innovation – ranging from organization and management studies, media and communication studies, cultural studies, information studies, sociology, political science, history, science and technology studies, musicology, to activist research and practice-based approaches.

The aim of this Special Issue is to provide fundamental insights in the management and organization of the interrelation between copyright and creativity in the context of GenAI. We will achieve this by showcasing how this interrelation unfolds in various creative and innovative arenas, such as creative industries, journalism, digital platforms, and open science. We expect contributions to make copyright a more accessible research topic across disciplines and contexts by fostering an interdisciplinary and transdisciplinary community around the topic of copyright and creativity in organization and management. Through this special issue, we aim to occupy a central position in the discourse about organizing and managing creativity in the context of GenAI and emerging digitalization.

Potential topics

• Creativity in the shadow of GenAI: GenAI crucially transforms creative practices and knowledge production that we only start to understand, particularly regarding the interrelation between creativity and copyright. How does GenAI affect the creation and management of creative content? How do creative, artistic practices change through the use of GenAI? What role does copyright play in creative transformations and organizational changes co-produced with GenAI?
• Doing copyright: A practice lens of 'living copyright' is missing in much of the organization and management literature in the context of creativity, but also in other contexts and fields. How does GenAI foster practices related to copyright within and across organizations? Who are the actors and actants doing copyright in the context of GenAI? How are these new practices shaping the organization of creativity? How is emerging regulation of GenAI affecting organizations and organizational creativity?
• Conflicts over copyright: Organizations are involved in many conflicts over copyright that touch upon creativity in multiple ways, but we know little about them. How do conflicts over copyright foster or impede with creativity and the generation of creative content? What is the role of digitization in these conflicts, and how are these conflicts evolving with GenAI? How do these conflicts affect creativity in organizations? How do conflicts over copyright and AI relate to managing technological innovation?
• Inequality and copyright: Copyright plays an important role in distributing access to creative content and to knowledge in global economies more broadly. How does GenAI affect access to knowledge within and across organizations? Who actually owns copyright and how does GenAI influence ownership structures? What are the conventions and managerial practices for allocating copyright among actors and agents in organizations? What are the consequences of copyright inequality for creativity?
• Alternatives to copyright: Commons-based creation and knowledge-sharing approaches have proven invaluable for creativity in many organizations, especially in the context of digitization. What are the alternatives to exclusive copyright regimes in the context of GenAI? How do alternatives to copyright provide counter-imaginaries in digital environments? How do such approaches challenge or facilitate the management and organization of creativity in the context of GenAI?
• Researching copyright: Copyright is ubiquitous, yet highly abstract and complex, both in its legal structure and in its real-world impact on creativity. Making copyright-sensitive creativity and innovation research more accessible to the discourses of management and organization is also a methodological issue. What are methodologically promising ways to investigate the interrelation between copyright and creativity in organization and management? What are the specific obstacles in the context of GenAI?

Timeline

• January 25, 2026: Deadline for abstract submission for paper development workshop
• February 27, 2026: Online paper development workshop
• September 30, 2026: Manuscript submission deadline

William Y. Degbey, University of Vaasa

Maria Pajuoja, University of Vaasa

Matti Pihlajamaa, VTT Technical Research Centre of Finland

Baniyelme D. Zoogah, McMaster University

Waymond Rodgers, University of Texas

Artificial Intelligence (AI) is defined as the frontier of computational advancements that references human intelligence in addressing ever more complex decision-making problems. A key development is Generative Artificial Intelligence (Gen AI), particularly Large Language Models (LLMs) like ChatGPT, which can process and generate human-like language, extract insights, and produce creative outputs. Gen AI has enormous potential to significantly boost national economies. Its widespread adoption could increase GDP in the EU region by +8% (EUR 1.2–1.4 trillion) over the next ten years, provided innovators are equipped with the necessary skills and capabilities.

Yet, adoption remains uneven. For instance, European countries average a 54 percent adoption rate, below the global average of 61 percent. This uneven adoption is compounded by the global competition for skilled talent and persistent workforce shortages, particularly in digital and AI-related fields. McKinsey estimates that up to 12 million job transitions may be required due to AI, underscoring the urgent need for reskilling and entrepreneurial innovation.

This Special Issue aims to advance empirical understanding of how, when, and why humans engage with Gen AI in innovation. The focus is on Gen AI as an innovation enabler, inviting submissions to explore innovation processes, human capital resources, and organizational capabilities. The call welcomes single- and multi-level studies that examine Gen AI's role in shaping innovation within and across organizations and encourages contributions that take an interdisciplinary perspective. Submissions should address managers' pressing challenges in finding new ways to support and improve innovation by investigating how managers can effectively enhance how employees and executives use Gen AI.

Technological advancements have made Gen AI more accessible and affordable across industries. Gen AI's impacts are not limited to what organizations innovate but also change how they innovate. Gen AI can be used to replace existing innovation processes, reinforce them, or reveal unforeseen ways of developing new products and services. It may assist in the identification of opportunities, idea generation, and evaluation of innovation potential, reshaping the front-end of innovation processes, and enable more rapid experimentation and improved customer understanding. Yet, experiences and use cases remain evolving and fragmented, creating uncertainty for managers.

Furthermore, Gen AI transforms innovation processes at multiple interrelated levels, whereas existing research has mostly examined them in isolation. Consequently, this Special Issue encourages a multi-level perspective, including individual, team, and organizational level processes.

Microfoundations research shows that innovation is fundamentally driven by people, not organizations, since it is individuals who identify opportunities, generate solutions, and implement them. To understand how Gen AI enhances innovation outcomes, such as faster development of better-quality products, services, and processes, it is necessary to examine how individual innovators use Gen AI, what influences its use, and what results emerge from these AI-augmented efforts. Team-level dynamics are equally important, as teams have become the core unit of modern organizations. Gen AI can support team innovation by sparking dynamic discussions and promoting diverse thinking in design processes, supporting ethical decision-making, and by helping overcome challenges in group decision-making. Finally, scholars have called for more research on how leadership can support AI adoption and digital transformation. Leaders must build new capabilities as AI implementation is often challenging, human-AI interaction may involve complications, and a new range of ethical concerns must be considered.

The rapid evolution of Gen AI demands not only technical adaptation but also new approaches to manage the associated processes, tensions, and frustrations. As Gen AI profoundly alters work design, skill requirements, and professional identities, employees face both new opportunities and psychological challenges. To ensure they can develop the AI-related skills needed for the evolving roles, organizational and managerial support mechanisms have to be developed. Yet, there is limited understanding of the organizational capabilities required to implement this change successfully. Effective support may involve building Gen AI literacy, fostering experimentation, and promoting continuous learning.

At its best, AI is strategically deployed to generate business value and competitive advantage, but without targeted support, organizations risk de-skilling, employee resistance, and ineffective adoption that undermines innovation goals. Organizational practices for managing innovation are also being challenged by Gen AI, especially in terms of ethical governance. While the benefits of Gen AI in innovation are promising, ethical concerns remain underexplored. Key principles include respect for intellectual property, truthfulness, robustness, recognition of malicious uses, sociocultural responsibility, and human-centric design.

Overall, the Special Issue seeks papers that provide empirical contributions on the evolving role of Gen AI in innovation to deepen psychological and social insights into management practices to support, improve, and offer practical guidance to help managers navigate Gen AI adoption and integration. The listed themes are neither exhaustive nor exclusive; authors are encouraged to explore other themes aligned with the aims of the Special Issue. The journal welcomes contributions from scholars of innovation, organizational behavior, management, HR, strategy, technology, and practitioners.

Potential topics

• How is Gen AI being used in innovation processes at different levels?
• How do organizations apply Gen AI to replace, reinforce, or reveal innovation activities, and what are the resulting effects on, for example, process efficiency, novelty, and responsiveness to market needs?
• How do varied patterns of Gen AI use across process phases and organizational levels influence the dynamics and management of innovation processes?
• How do individual innovators use Gen AI in their work, what influences this usage, and what are the measurable outcomes of AI-augmented innovation efforts in terms of product, service, or process quality and speed?
• In what ways does Gen AI influence innovation team dynamics, including collaboration, resilience, and decision-making, particularly in virtual or hybrid team environments?
• What psychological and behavioral effects emerge from using Gen AI in innovation settings, such as changes in employee engagement, collaboration dynamics, or perceptions of autonomy?
• How do managerial competencies influence the successful implementation of Gen AI in organizational innovation efforts?
• What organizational capabilities need to be developed to successfully implement, integrate, and support the use of Gen AI in innovation efforts?
• How are organizations supporting continuous learning and skill development for employees working with Gen AI, and what mechanisms are most effective in enabling the strategic use of Gen AI?
• What ethical challenges do organizations face when deploying Gen AI in innovation activities, and how are they addressing issues such as intellectual property, transparency, and sociocultural responsibility?

Timeline

• January 15, 2026: Opening date for manuscripts submissions
• August 31, 2026: Closing date for manuscripts submission

This Journal of Enterprise Information Management Special Issue seeks to understand Agentic Artificial Intelligence and Generative AI (GenAI) in healthcare, and how these technologies impact the governance, strategy, and value creation of healthcare organizations. In technological innovation, digital technologies are reconfiguring value creation processes and prompting organizations to develop new adaptive strategies. In healthcare, this transformation is driving the adoption of innovative solutions to enhance value for stakeholders while supporting more personalised, predictive, and preventive models of care. AI and GenAI are emerging as strategic levers for optimising resource allocation, supporting new care delivery paradigms, and accelerating research and development. The rapid emergence of Agentic AI systems introduces a further step in this transformation, with AI technologies moving from reactive tools towards semi-autonomous systems able to plan, coordinate and monitor actions across complex organizations.

Healthcare is a relevant setting for examining how innovation management shapes competitiveness, sustainability, and value-creation capabilities. Recent debate has shifted from a focus on the technical performance of AI systems to broader concerns related to implementation, accountability, trustworthiness, evaluation, and organisational sustainability. This shift is crucial in healthcare, where Agentic AI and GenAI are not merely digital transformation tools, but sociotechnical systems that potentially affect clinical practices, decision-making processes, care coordination, patient-doctor relationships, resource allocation and costs optimization.

AI and GenAI are sociotechnical systems with growing autonomy and interactive capabilities, thereby raising new questions around trust, responsibility, human oversight, and governance. As such, they pose a significant challenge to enterprise information management, affecting processes, data, professional roles, compliance, procurement, and monitoring systems.

The deployment of Agentic AI and GenAI occurs in high-risk, highly regulated, data and human-intensive settings. Healthcare organizations must balance innovation with patient safety, care quality, ethical and regulatory issues, data protection and human oversight preservation. Errors, biases and unclear accountability may affect patients, professionals and healthcare ecosystems.

This Special Issue seeks theoretical and empirical contributions examining how health systems, healthcare organizations, and providers develop capabilities, governance structures, and evaluation practices to move from experimentation to technology adoption and integration. Particular attention will be given to agentic workflow integration, responsible value creation, data governance, clinical and managerial accountability, human oversight, professional role reconfiguration, patient-doctor relationship, organisational capabilities and compliance with existing regulatory frameworks.

By focusing on healthcare as the empirical and theoretical context, this Special Issue aims to generate new insights into how Agentic and GenAI systems can be responsibly embedded in healthcare organizations while balancing innovation, safety, equity, trust, regulatory compliance and measurable clinical, organisational, and societal value.

Potential topics

• How are GenAI and Agentic AI reshaping clinical, administrative, and managerial workflows in healthcare organizations?
• How do healthcare organizations govern Agentic AI systems across care pathways?
• What organizational capabilities are needed to move from experimental GenAI applications to integrated and scalable Agentic healthcare systems?
• How can healthcare organizations ensure meaningful human oversight when AI systems become more autonomous, proactive, and embedded in clinical or administrative processes?
• How do GenAI and agentic AI create, capture, or potentially destroy value for different healthcare stakeholders?
• How do Agentic and GenAI systems transform healthcare knowledge management?
• How can healthcare organizations evaluate and measure the clinical, organizational, economic, ethical, and societal value generated by GenAI and Agentic AI adoption?
• What governance mechanisms are needed to ensure accountability, transparency and regulatory compliance in AI-enabled healthcare organizations?
• How do GenAI and Agentic AI affect decision-making processes within healthcare organizations?
• How can healthcare organizations manage risks related to automation bias, inequitable outcomes and over-reliance on AI?
• How do agentic AI and GenAI support healthcare system sustainability?

Timeline

• January 1, 2027: Opening date for manuscript submissions
• June 30, 2027: Closing date for manuscript submissions

Mark Camilleri, University of Malta

Generative Artificial Intelligence (GenAI) and Agentic Artificial Intelligence (Agentic AI) are transforming how services are designed, delivered, experienced and led. While GenAI refers to systems, such as large language models (LLMs), that produce content in response to human prompts; Agentic AI technologies may be considered as active agents that can implement tasks (rather than merely functioning as passive generators). The latter can monitor situations, allocate resources, initiate and manage processes as well as co-ordinate multiple activities. Hence, Agentic AI algorithms and their governance affect service outcomes.

Generative AI capabilities often constitute the communicative and cognitive foundations of Agentic AI. In other words, many Agentic AI systems rely on GenAI models to reason, communicate and interact. Together, these AI technologies challenge conventional assumptions about agency, control, responsibility and value creation in service environments. Unlike earlier forms of automation and analytics, these AI systems can engage in social interactions, reason in a contextual manner and may dynamically adapt to changing situations. As such, they raise profound theoretical questions about anthropomorphism, social presence, trust, autonomy, creativity, emotion, accountability, responsibility and moral agency.

These capabilities indicate that Generative and Agentic AI represent more than incremental advances in automated technologies. They introduce different forms of interaction and agency that cannot be fully explained by utility-driven adoption frameworks. Consequently, there is a growing need for theory-driven and conceptually rigorous research that explains how, why and under what conditions Generative and Agentic AI are deployed, adapted, governed, or even resisted in service environments.

This special issue seeks to advance services marketing research by encouraging scholars to utilize, extend, integrate or critically evaluate existing theories to investigate user engagement with Generative and Agentic AI across diverse service settings. The guest editorial team particularly welcomes submissions that move beyond descriptive accounts. Prospective contributions are expected to offer strong theoretical explanations of AI acceptance and usage in services.

Submissions that integrate multiple perspectives, compare existing conceptual frameworks and develop new theoretical models specific to GenAI and Agentic AI in services are especially encouraged for this special issue. The special issue welcomes conceptual, qualitative, quantitative, experimental or mixed-methods approaches, provided that the contributing authors demonstrate strong theoretical grounding and relevance to the underlying objectives of this journal.

Potential topics

• Theoretical perspectives on Generative and Agentic AI adoption in service environments
• Comparative or multi-theoretical frameworks for studying human-AI interaction in services
• Anthropomorphism, social presence and human-AI relationships
• Perceived affordances, interface design and service experiences
• Emotions, expectations and psychological responses to AI
• Adoption, acceptance and continued use of AI in services
• Trust, ethics, accountability and relational governance
• AI as a service actor within socio-technical systems
• Contextual and contingency-based perspectives
• Value co-creation, value co-destruction and service outcomes
• Organizational, strategic and policy implications of Generative and Agentic AI in services

Timeline

• June 23, 2026: Opening date for manuscripts submissions
• February 26, 2027: Closing date for manuscripts submission

Thomas Hess, University of Munich (LMU)

Ioanna Constantiou, Copenhagen Business School

Niki Panteli, Lancaster University

Generative AI (GenAI) has rapidly become a general-purpose technology that reshapes how information is created, curated, and consumed. GenAI broadly refers to a class of AI models that generate seemingly new content in the form of text, images, audio, or video. In the media context, where value is built around the provision and use of content, GenAI has attracted particular attention. For instance, it enables the near-instant creation of journalistic articles, marketing texts, or audiovisual material, and it supports personalization by dynamically adapting media offerings to individual user preferences. The advent of the Internet had already marked a profound transformation in the delivery and consumption of content. It made user-generated content possible, catalyzed multi-sided platforms, and enabled unprecedented personalization. This transformation brought new players into the media sector, as technology companies entered the market and traditional media firms were forced to develop significant digital competencies for the first time.

GenAI is expected to have an equally profound impact on the media industry by expanding complementary innovation, lowering barriers for content creation, and altering the economics of matching and recommendation at scale. Despite recent advances, our knowledge is still limited. Existing research has begun to shed light on the impact of GenAI on textual news app users’ willingness to pay, yet it is unknown whether similar effects extend to audiovisual content. Moreover, there are initial indications of how journalists’ productivity may change with the use of GenAI. At the same time, the potential for entirely new GenAI-based products remains largely unexplored. In particular, little is known about the extent to which audience discussions can be moderated and managed, or about the new forms of public media provision that GenAI might enable. These questions are especially pressing given the central role of media in shaping public opinion and broader societal developments, including political attitudes.

The aim of this special issue is to advance IS research on this emerging field. We invite contributions that examine the role of GenAI in the provision and use of media offerings. Analyses may focus on individuals, organizations, or industries. Studies may address GenAI on the level of systems, their effects, or their management. Submissions should be firmly grounded in the technology itself and its implications for media ecosystems. This special issue aims to stimulate innovative investigations of the transformative role of GenAI in the provisioning and use of public media. In contrast to closed settings, such as private messaging services, the recipients of public communication cannot be predetermined or exhaustively specified in advance. Accordingly, the domain of interest spans both online media, including digital platforms and social media, and traditional media such as print and television. We welcome qualitative and quantitative empirical studies as well as design-oriented research. Submissions should provide a clear academic contribution by advancing theory and knowledge in the Information Systems discipline. While practical relevance and managerial implications are highly valued, they are not sufficient on their own; academic advancement is essential.

Potential topics

• Provision of Content for the Media
• Use of Content provided by the Media
• Embedding of the Media in Society

Timeline

• December 1, 2025: Full paper submission opens
• April 30, 2026: Full paper submission closes
• June 30, 2026: Desk check
• September 30, 2026: Feedback on the first version
• January 31, 2027: Submission revision 1
• April 30, 2027: Feedback on revision 1
• June 30, 2027: Submission revision 2
• July 31, 2027: Final decision