Valérie Fernandez, Telecom Paris

Bénédicte Geffroy, IMT Atlantique

Liette Lapointe, McGill University

Jean-François Berthevas, Université de La Rochelle

As Orlikowski and Iacono (2001) emphasize, information systems (IS) researchers not only have the opportunity to influence the future, but they also have the responsibility to do so by documenting what digital technologies change, capturing what disappears, and what is being created.

In the healthcare sector, recent years have been marked by massive growth in innovations and the implementation of digital technologies: the development of mobile health, the deployment of digital patient monitoring devices, the operationalization of integrated electronic medical records systems, the introduction of artificial intelligence applied to clinical diagnostics, the development of immersive therapies via virtual reality, and the use of blockchain technology. However, beyond the proliferation of innovative digital devices, what now stands out seems to be of a different order: their interdependence, which nurtures new forms of complexity, the quantification at work (data-intensive medicine), and the unprecedented modes of instantaneous calculation (algorithmic medicine) that support them, as well as the connectivity challenges (telemedicine).

Data analysis and Artificial Intelligence (AI), driven by the acceleration of data collection in the sector, are ushering in disruptions and radical innovations in healthcare. Rapid advancements in AI are creating new opportunities in healthcare. These technologies alone have a transformative power on the industry and society. But through their integration, their disruptive effects increase exponentially, raising new issues.

Thus, the more organizations collect data due to increased integration of digital devices, the more the issue of privacy protection becomes critical. With the dissemination of self-learning algorithms, cybersecurity concerns become even more critical. This new generation of digital innovations also raises new governance questions, particularly regarding the involvement of new stakeholders in healthcare system analysis, represented by the intervention of non-human agents in automated and algorithmic decision systems.

Similarly, explainability, accountability, relationships between healthcare professionals, and the organization of work represent major challenges associated with the use of AI in healthcare organizations. The complexity of algorithms and the colossal volume of data used by AI make it extremely difficult to understand and thus explain the functioning and results produced by AI. The question of responsibility for healthcare professionals and the organizations to which they belong becomes even more acute when they use this technology. From a managerial perspective, the use of AI is a major disruptive element in healthcare management systems, contributing to the transformation of relationships among stakeholders and the emergence of new work organizations that require new skills. Providing patients with broader access to healthcare via telemedicine or connected devices for remote monitoring profoundly changes the organization of care and the role of the patient. It also raises numerous challenges in terms of health equity and trust.

For some, this represents a significant break. For others, technological advancements in healthcare oscillate between continuity and a shift. Many studies have shown that the impact of early generations of information technology on healthcare has been far below expectations. And even though the COVID crisis accelerated the digital transformation of the healthcare sector, the sector, and even more so the medico-social sector, remain marked by slow adoption of innovations in digital health technologies.

The transformative potential of digital technologies in the healthcare sector remains complex to grasp, as it is embedded in structural specificities. The complexity of the domain is related to the multiplicity of competing and/or cooperating actors (industrials, consumers, patients, healthcare professionals, insurance companies), and an institutional environment characterized by multiple levels of regulation and the power of professional bodies. The ongoing challenge of integrating legacy systems, the diversity of technological platforms, and strict regulations pose major interoperability issues. Governments, providers, and users must agree on interoperability and standardization frameworks in a constantly evolving regulatory context. Each new technology integrated into the healthcare pathway introduces a multiplicity of changes in the existing context, creating obstacles and resistance that may affect the social and spatial organization of care, the division of medical and paramedical labor, and interactions among various stakeholders. Power dynamics at play in the healthcare industry are highlighted in studies on IS governance and the direction of AI development, and internal power dynamics within a healthcare organization, as well as external dynamics that often play out between patients and caregivers.

Is the transformative power of digital technologies in healthcare likely to improve the effectiveness and efficiency of healthcare systems? As the World Health Organization (2021) reminds us, digital innovations aim to transform the delivery of healthcare services, making them more accessible, personalized, and potentially more efficient.

Emerging technologies in healthcare present new challenges and renew issues due to the unpredictable discontinuities brought about by technologies like AI and their integration, or even their expansion across the entire sector. They offer significant opportunities for future research and the potential to renew theoretical and methodological approaches in IS management. In order to overcome past difficulties and address the complexity of the field, scholars advocate for interdisciplinary approaches in healthcare and information technology. The healthcare field represents a rich environment from which new theories can be developed and existing theories extended in the field of IS.

This special issue is open to contributions from interdisciplinary and sociotechnical perspectives, often considered one of the foundational viewpoints in IS discipline. Submissions should focus on the ongoing transformations driven by innovative digital health technologies and grasp the magnitude of these changes. Contributions on the medico-social sector, which is currently under-researched, will be appreciated. Contributions may include empirical, theoretical, or meta-analysis papers.

Potential topics

• The challenges of AI in healthcare (ethics, responsibility, etc.)
• Patient empowerment and healthcare democracy
• The 'techno-push' dynamics of digital health technologies and industrial influence on the pace and direction of innovations in this field (particularly AI)
• The challenge of new forms of multi-level governance in healthcare
• The new generation of digital health technologies and their vulnerabilities
• The care pathway and technologies
• Hospital-city cooperation/coordination

Timeline

• September 30, 2025: Submission Deadline
• December 20, 2025: First-round Notification
• March 20, 2026: Revision Deadline
• June 1, 2026: Second/Final Decision by Invited Editors
• July 1, 2026: Publication Decision by Editorial Board
• September 1, 2026: Publication Date

Guoqing Zhang, University of Windsor

Jiaguo Liu, Dalian Maritime University

Hakan Yildiz, Wayne State University

The reconfiguration of global supply chains has become increasingly urgent amid escalating tariff uncertainty and shifting international trade policies. Tariff-induced disruptions are reshaping sourcing strategies, manufacturing footprints, logistics networks, and market access worldwide.

While tariff-related supply chain research is not new, today's environment is marked by unprecedented levels of uncertainty and complexity. High tariff rates, retaliatory measures, and escalating trade tensions have created significant challenges for supply chain reconfiguration and management in practice. The unprecedented challenges of tariff uncertainty highlight the need for new decision-making models, offering significant opportunities to advance the literature and address pressing real-world challenges.

This special issue invites high-quality contributions that develop and apply Operations Research (OR) and Artificial Intelligence (AI) methods to address the challenges and opportunities in global supply chain reconfiguration under tariff uncertainty. We welcome theoretical developments, methodological innovations, applied modelling studies, and quantitatively supported managerial insights. Interdisciplinary research that integrates OR, AI, supply chain management, and international economics, particularly with real-world case applications, is especially encouraged.

Manuscripts should be original, unpublished, and prepared according to submission guidelines. Submissions are expected to have strong methodological contributions in OR and/or AI, with clear relevance to global supply chain reconfiguration under trade policy uncertainty.

Potential topics

• Supply network redesign and optimization under tariff uncertainty
• Robust and stochastic optimization models for tariff-driven supply chain planning
• Global supply chain reconfiguration under trade policy uncertainty
• AI-powered dynamic supply chain adaptation and tariff response strategies
• Dynamic production, sourcing, and logistics strategies facing tariff risks
• Supply chain resilience and risk management for tariff disruptions
• Logistics and warehousing for global e-commerce and omnichannel supply chains
• Maritime network and logistics optimization with tariff impacts
• Hybrid OR–machine learning for adaptive decision-making in global supply chains
• Multi-echelon inventory management under fluctuating tariff policies
• AI and data-driven methods for trade policy analysis and supply chain impacts
• Optimization models and algorithms for large-scale global supply chain problems

Timeline

• October 31, 2026: Manuscript submission deadline

Matteo Rocca, University of Insubria

Davide La Torre, Skema Business School

Constantin Zopounidis, Technical University of Crete

This special issue aims to publish selected papers presented during the 16th International Conference on Multiple Objective Programming and Goal Programming (MOPGP'25) that will be held on 1–3 July 2025, in Varese, Italy. Contributions arising from papers presented at the conference should be substantially extended and cite the conference paper where appropriate. The special issue will also consider papers not presented during the conference. Original and unpublished work not currently under consideration in any other journal is welcome.

The intersection of Multiple Objective Optimization (MOP), Goal Programming (GP), and Artificial Intelligence (AI) creates a robust framework for addressing complex decision-making challenges in Economic and Social Sciences. MOP provides structured methods to evaluate and prioritize conflicting objectives, while GP helps setting specific goals to be achieved by the decision maker. AI leverages data analytics and machine learning to process large datasets, revealing insights that improve the accuracy and the robustness of decision models and predictions.

The scientific quality of the contributions is the main criterion in the selection process.

Potential topics

• Advancements in Multiple Objective Programming Techniques
• Goal Programming Techniques and Formulations
• Goal Programming Approaches in Public Policy
• AI-Enhanced Decision Support Systems for Resource Allocation
• Data-Driven Methods in Economic and Social Decision-Making
• Integrating Machine Learning with MOP
• Multicriteria Deep Learning
• Applications of MOP and GP in Sustainable Development
• Real-Time Decision-Making Frameworks Using AI
• Comparative Studies of MOP and GP in Various Contexts
• Multi-Criteria Decision Analysis in Sustainable Economics
• Multiple Criteria Decision Making in Environmental Economics
• Optimization Models for Social Welfare
• Behavioural Insights in Multi-Objective Decision-Making
• Metaheuristics and Computational Methods in MOP
• MOP and MCDM in AI applications
• Innovative Applications to Economic and Social Sciences

Timeline

• June 30, 2027: Submission deadline

V. Kumar, Brock University

The use of new-age tools for transformative marketing strategies is an emerging area with several unexplored dimensions. While prior research has established a conceptual foundation, significant gaps remain unaddressed. Existing studies primarily focus on theoretical frameworks or conceptual models, lacking sufficient empirical validation of how these technologies influence consumer attitudes, decision-making, and purchasing behavior.

Furthermore, there is a lack of empirical research quantifying the return on investment (ROI) of integrating these advanced technologies into marketing campaigns. Previous research has also not thoroughly examined the tension between hyper-personalized marketing enabled by GAI and consumer privacy concerns, particularly in the context of Blockchain's ability to ensure data traceability.

This special issue aims to encourage researchers to explore these critical issues and bridge the existing research gaps.

Potential topics

• Use of new-age tools for transformative marketing strategies
• Empirical validation of how technologies influence consumer attitudes, decision-making, and purchasing behavior
• Return on investment (ROI) of integrating advanced technologies into marketing campaigns
• Tension between hyper-personalized marketing and consumer privacy concerns
• Blockchain's ability to ensure data traceability in marketing contexts
• Generative AI (GAI) applications in marketing

Timeline

• August 31, 2026: Submission deadline

Stefan Gold, Technical University of Munich

Antoine Harfouche, Paris Nanterre University

Paul Jones, Swansea University

Maryam Lotfi, Cardiff University

Guoqing Zhao, Swansea University

Unethical labour practices represent a pressing global issue, with an estimated 50 million people currently living in such conditions worldwide. The prevalence of unethical labour practices is expected to increase due to ongoing regional conflicts, economic downturns, and restrictive migration policies in major Western countries. Digital technologies, such as blockchain, artificial intelligence (AI), and cloud computing, promise significant benefits in addressing unethical labour practices and have consequently attracted increasing scholarly attention.

Modern slavery is one of the most serious forms of unethical labour practices, commonly conceptualized as the recruitment, movement, harbouring or receiving of children, women, or men through means such as coercion, deception, abuse of vulnerability, or the use of force, for the primary purpose of exploitation. It encompasses multiple practices, with prevalent forms including child slavery, human trafficking, forced labour, descent-based slavery, bonded labour, forced and early marriage, and domestic servitude. According to the latest Global Estimates of Modern Slavery, an estimated 49.6 million people are living in conditions of modern slavery worldwide, including 22 million in forced marriage and 27.6 million in forced labour. Ongoing regional conflicts and expulsionist migration policies are expected to further exacerbate modern slavery by intensifying violence, displacing populations, and disrupting social and economic structures.

Digital technologies encompass a diverse range of tools, systems, and devices that leverage digital information and processes to enhance activities. These technologies include but are not limited to AI, blockchains, internet of things (IoT), big data analytics, cloud computing, and virtual and augmented reality. Big data and cloud computing are increasingly integrated into enterprise resource planning systems in manufacturing to support informed decision-making. Generative AI applications are being adopted in higher education to enable more efficient information access and content creation.

Digital technologies promise significant benefits for addressing unethical labour practices. Monitoring technologies such as remote sensors and worker voice technologies can enhance the identification of unethical labour practices within supply chains. Machine learning and AI can detect patterns of trafficking and slavery in large datasets, facilitating identification of at-risk areas or vulnerable individuals. Blockchain technology can be employed to track supply chain elements, helping to mitigate labour exploitation. Additionally, digital technologies have been proposed as effective tools to support the social inclusion of survivors of modern slavery.

The potential of digital technologies to address unethical labour practices has been consistently highlighted by various international organizations. The United Nations Human Rights Office has proposed that digital technologies are an effective means to prevent and address contemporary forms of slavery. Research conducted in partnership with the International Organization for Migration and other organizations has indicated that although digital technologies can support adults with lived experience of unethical labour practices in reintegrating into society, limited research constrains deeper understanding of these mechanisms.

Although existing studies have examined the role of digital technologies in addressing unethical labour practices, significant research gaps remain. Current literature has primarily examined technologies from the perspective of focal firms or single technology approaches, while other digital technologies such as biometrics recognition and satellite imagery employed by governments and non-governmental organizations also hold significant potential. Literature reviews indicate that while unethical labour practices remain pressing, there is limited research examining how digital technologies can be employed to monitor, detect, report, and address unethical labour practices, and there is a need for further empirical research to advance understanding in this area.

This special issue invites scholars and practitioners to examine the interplay between digital technologies and unethical labour practices, particularly the ways in which digital technologies can be employed to monitor, detect, report, and remediate unethical labour practices in pursuit of an ethical, responsible, and sustainable society. We welcome original, theory-driven research that makes significant contributions to advancing knowledge on this topic. Submissions may use various research methodologies, including theoretical and conceptual approaches, analytical and modelling, case studies and interviews, surveys, and literature reviews. All submissions must clearly identify the specific types of digital technologies being examined.

We particularly welcome studies that move beyond surface-level narratives and dominant discourse to provide nuanced and sophisticated insights into the nexus between digital technologies and unethical labour practices. Submissions that challenge existing paradigms, explore underexamined dimensions, or offer innovative perspectives and frameworks are especially encouraged. We seek contributions that extend theoretical boundaries and yield practical implications, thereby informing policymakers, business leaders, and wider society on how to leverage digital technologies to drive meaningful and lasting change in the fight against unethical labour practices.

Potential topics

• Role of blockchain, artificial intelligence, and cloud computing in addressing unethical labour practices
• Monitoring and detection of modern slavery and human trafficking using digital technologies
• Machine learning and AI applications for identifying trafficking patterns and at-risk populations
• Blockchain technology for supply chain transparency and labour exploitation mitigation
• Remote sensors and worker voice technologies for identifying unethical practices in supply chains
• Digital technologies for supporting social inclusion and reintegration of modern slavery survivors
• Biometric recognition and satellite imagery applications by governments and NGOs
• Digital technology adoption mechanisms for mitigating unethical labour practices
• Theoretical and empirical frameworks for digital technology deployment in combating labour exploitation
• Policy implications and business leadership strategies for leveraging digital technologies against unethical labour practices

Timeline

• February 15, 2027: Submission deadline

Jyoti Choudrie, University of Hertfordshire

Sherah Kurnia, The University of Melbourne

Gabrielle Peko, University of Auckland

David Sundaram, University of Auckland

Information and Communication Technologies (ICTs) and Artificial Intelligence (AI) are driving profound transformations across organisational, economic, and societal contexts, reshaping value creation, structures, and processes. As technologies such as generative and agentic AI, blockchain, and the Internet of Things (IoT) continue to evolve, scholarly attention must increasingly move beyond initial adoption toward understanding how these technologies are integrated, adapted, and leveraged over time. While prior research has extensively examined technology adoption decisions, comparatively less attention has been paid to post-adoption dynamics, including continuance, adaptation, and routinization. Moreover, the realization of value from ICT and AI investments remains uneven and insufficiently understood, as adoption does not necessarily translate into organizational or societal benefits. These gaps highlight the need for deeper theoretical and empirical insights into how technologies generate sustained value and transformation.

At the same time, the widespread deployment of ICTs and AI introduces complex risks and tensions, including ethical concerns, digital inequality, cybersecurity threats, and unintended consequences. In parallel, emerging forms of human–AI interaction and digital platforms are reshaping work practices, decision-making, and socio-technical relationships in ways that require renewed scholarly attention.

This special issue seeks to advance cutting-edge research that moves beyond initial adoption to examine post-adoption phenomena, value realization, and transformation across individual, organizational, and societal levels. While this special issue emphasizes post-adoption and impact, we also welcome novel perspectives on early-stage adoption where they offer new theoretical insights or address emerging technologies and contexts.

We invite original contributions employing diverse methodological approaches, including empirical, theoretical, experimental, design science, and review-based research.

Potential topics

• Technology assimilation, routinization, and deep integration
• Post-adoption behaviors: continued use, discontinuance, and switching
• Value creation, capture, and measurement in ICT and AI use
• Cross-cultural and comparative post-adoption studies
• Changes to work practices, organizational structures, and decision-making
• ICT-enabled institutional and governance transformation
• Digital platforms, ecosystems, and network effects
• Public sector and societal impacts of sustained technology use
• Success and failure of digital transformation initiatives
• Algorithmic bias, transparency, and accountability
• Privacy, cybersecurity, and data governance challenges
• Overuse, misuse, and unintended consequences of ICTs and AI
• Environmental and societal externalities of digital technologies
• Regulation and governance of AI and digital technologies
• Organizational strategies for effective post-adoption management
• Public policy for sustainable and ethical technology use

Timeline

• July 15, 2026: Submission of abstract (up to 300 words)
• August 15, 2026: Notification of abstract acceptance
• December 4, 2026: Submission of full paper
• April 5, 2027: Notification of first-round reviews
• July 1, 2027: Revised manuscripts due
• October 29, 2027: Notification of second-round reviews
• November 30, 2027: Final versions due
• January 31, 2028: Expected final decision

Andrea Generosi, Pegaso University

Luigi Gallo, Pegaso University

Valerio De Luca, Pegaso University

Maura Mengoni, Marche Polytechnic University

Josef Spjut, NVIDIA

Lucio De Paolis, University of Salento

The past decade has seen explosive growth in video data (live and on-demand), driven by ubiquitous cameras, mobile devices, and online platforms. From city CCTV and body-cams to retail, sports, tele-health, and social live commerce, organizations now operate end-to-end video streaming pipelines at massive scale. In parallel, advances in deep learning, spanning video transformers, self-supervised pretraining, and multimodal large models, have transformed what intelligent systems can infer from continuous visual data, moving beyond static clips to long-horizon, low-latency understanding of events, activities, and interactions.

Affective computing adds a new dimension by interpreting emotional and social signals in video and multimedia. For example, research shows that portraying positive emotions and trust cues in video significantly increases viewer engagement. By combining facial expression, gesture, and acoustic analysis, advanced AI can infer user sentiment and intent. These capabilities enrich social-media intelligence, recommender systems, and customer journey analysis.

This special issue seeks cutting-edge work at the intersection of video analytics and affective computing, harnessing emotion and intent cues in visual data to enhance information systems, and about AI for video streaming: methods, systems, and governance for real-time ingestion, analysis, and decisioning over continuous video flows.

The goal is to bridge computer-vision analytics and affective models (extracting sentiment, engagement, trustworthiness, etc.) to design intelligent, human-centered systems that inform decision-making and adaptive services. The special issue is interested in methods that fuse visual, auditory, and textual data (multimodal analysis) to detect emotion, sentiment, engagement, gaze, gesture, posture, prosody and language in real time. Such techniques enable insights from live-streamed user interactions or "experience mining" in marketing and e-commerce. Deep-learning advances enable machines to recognize facial expressions, gestures, tone of voice and other cues to infer sentiment, engagement and intent. Recent work demonstrates that Convolutional Neural Networks combining video and audio input can predict viewers' emotions with high accuracy, achieving around a 75% AUC on real-world video-ads data. Other research has integrated language and vision: one study built an "Affective Mimicry Index" from CEO interview videos, using computer vision together with large language models, and found that higher video-derived empathy scores correlate with better firm performance. These examples show how multimodal video analytics can extract rich affective signals, from basic expressions to higher-level trust and empathy cues. The aim is to bring these advances into intelligent information systems that are explainable, fair, and aligned with human values.

Potential topics

• Scalable video understanding: algorithms and architectures for analyzing large-scale video streams (e.g. in surveillance, social media or enterprise) to support decision-making and business analytics.
• Emotion and sentiment analysis: detection of affective and engagement cues in video (e.g. customer-journey recordings, advertisement viewing, user-generated content).
• Multimodal fusion: methods that combine vision with audio, text or other modalities to enrich social-media intelligence and contextual analysis.
• Streaming-native AI and MLOps: online inference under tight latency budgets, stream processing/windowing, drift detection, A/B testing and safe rollouts, cost–latency–accuracy trade-offs, and end-to-end observability for production pipelines.
• Video(-language) models for streaming: multimodal LLMs/VLMs, memory and token compression for long-horizon video, retrieval-augmented streaming
• Real-time and distributed architectures: edge/cloud or federated systems for real-time video analytics at scale, including considerations of latency, bandwidth and privacy.
• Explainability, fairness and compliance: approaches to make affective video AI transparent and trustworthy, addressing ethical, legal and regulatory challenges.
• Adaptive learning: techniques for domain adaptation, continual learning or few-shot learning to handle evolving video streams and reduce the need for large labeled datasets.
• Case studies and applications: empirical studies in domains such as healthcare (e.g. remote patient monitoring), finance (e.g. behavioral analytics), education (e.g. XR/serious games environments), and smart environments.

Timeline

• October 30, 2026: Submission deadline

Brian King, Texas A&M University

Babak Taheri, Texas A&M University

Danae Manika, Brunel University of London

Kyoung Jun Lee, Kyung Hee University

The aim of this special issue is to advance scholarly understanding of Responsible and Trustworthy Artificial Intelligence in tourism and hospitality, by assembling cutting-edge research, innovative methodologies, and critical reflections. As AI technologies become embedded in service delivery, marketing, operations, and consumer experience, transformative opportunities are accompanied by potential ethical challenges. Issues of fairness, accountability, transparency, and trust are particularly pressing as the sector adopts AI in pursuit of personalization, efficiency, and engagement. Responsible AI is linked with the concept of trustworthy AI, which reflects a stronger technological view and focuses on systems that are lawful, ethical, and robust throughout their lifecycle. Earlier research in tourism and hospitality has shown the need for interdisciplinary inquiry regarding the technical, managerial, legal, and societal dimensions of responsible AI adoption.

This special issue aims to provide a dedicated forum for interdisciplinary inquiry into the tensions and synergies that are evident between innovation, ethical responsibility, and human experience. It particularly encourages submissions that highlight the design, adoption, and governance of AI-enabled digital platforms, data-sharing ecosystems, and information infrastructures shaping tourism and hospitality. Papers may investigate the broader societal impacts of AI and topics such as bias and fairness in algorithmic decision-making, consumer trust in AI systems, sustainable and frugal AI applications, digital twins for experience innovation, and governance and regulatory frameworks and human-centered design. This builds on prior Electronic Markets contributions on responsible and trustworthy AI and extends them into the domain of tourism and hospitality. This special issue will foster dialogue across disciplines to highlight best practices as well as challenges in the design, deployment, and governance of responsible AI systems. Ultimately, it seeks to open up pathways that will ensure enhanced customer experiences through AI-driven innovation in tourism and hospitality, while upholding ethical, social, and environmental responsibility.

Electronic Markets is a Social Science Citation Index (SSCI)-listed journal in the area of information management and information systems. All papers should fit the journal scope. This special issue invites submissions that investigate the critical dynamics, challenges, and transformative potential of responsible and trustworthy AI in tourism and hospitality. We welcome studies that deploy quantitative, qualitative, or mixed methods approaches, providing that they demonstrate methodological rigor and scholarly relevance. Suitable contributions may include conceptual and theoretical papers, empirical investigations, case-based analyses, position papers, and integrative reviews.

Potential topics

• Ethical AI frameworks in tourism and hospitality: fairness, accountability, transparency, and inclusivity.
• AI-driven consumer experiences: balancing personalization, convenience, and privacy.
• AI robustness and risk: testing booking, pricing, and chatbot vulnerabilities, modeling threats, and strengthening resilience.
• Trust, reliability and safety: building consumer trust, ensuring reliable performance, and managing risks.
• Auditing AI systems: ongoing evaluations in pursuit of fairness, accuracy, and compliance.
• Generative AI in marketing: pursuing responsibility in branding, engagement, and consumer journeys.
• Sociotechnical and cultural dimensions: cross-cultural and historical perspectives on AI adoption.
• AI, sustainability, and CSR: supporting or hindering responsible and sustainable practices in tourism and hospitality.
• Future directions: conceptual frameworks and policy challenges for responsible and trustworthy AI in service innovation and resilience.
• Social and labor impacts: the effects of adopting AI on employment, equity, and workforce well-being.
• Frugal AI and digital twins: cost-effective and resource-conscious applications for operations and guest experiences.
• Algorithmic transparency and rights: making AI decisions understandable and offering recourse for affected consumers.
• Dark side of AI: risks of manipulation, surveillance, over-automation, and addictive design.
• Corporate digital responsibility (CDR) in the age of AI within tourism and hospitality.
• AI on digital platforms: exploring recommender systems, marketplaces, and platform governance.
• Data ecosystems: sharing and leveraging user/usage data across digital travel and hospitality platforms.
• Trustworthy AI principles: ensuring AI systems in tourism and hospitality are lawful, ethical, and technically robust.
• Verification, validation, and explainability: designing AI that is interpretable and auditable for multiple stakeholders.
• Trustworthy AI and consumer journeys: balancing automation with transparency and user empowerment.

Timeline

• September 15, 2026: Submission deadline

Rainer Schmidt, Munich University of Applied Sciences

Rainer Alt, Leipzig University

Alfred Zimmermann, Reutlingen University

The Topical Collection on Artificial Intelligence-based Assistants and Platforms focuses on assistant systems (e.g. chatbots, recommenders) that are based on artificial intelligence and the interaction with users via declarative natural-language interfaces. They are present in various forms as well as industries and often assume a platform logic when services and devices from different providers are included.

The Topical Collection relates to a minitrack at the Hawaii International Conference on System Sciences (HICSS) and comprises research on novel methods, models, processes, and approaches related to the design, implementation, deployment, operation, and optimization of AI-based assistants and platforms for the digital economy.

Potential topics

• Design, implementation, and deployment of AI-based assistants and platforms
• Novel methods and models for AI-based assistants (e.g., chatbots, recommenders)
• Natural language interfaces and user interaction with AI systems
• Platform logic and integration of services from different providers
• Operation and optimization of AI-based assistants and platforms for the digital economy
• AI-based assistants in various industries and applications

Timeline

• Invalid DateTime: Submission deadline

Timeline

• July 17, 2027: Full paper submission deadline

Timeline

• October 1, 2026: Submission deadline

Timeline

• November 30, 2026: Full paper submission deadline

Potential topics

• Artificial intelligence and machine learning applications in financial markets
• Algorithmic trading and high-frequency trading
• AI-driven market microstructure and price discovery
• Systemic risk and financial stability implications of AI
• Regulatory frameworks and governance of AI in finance
• Market efficiency and anomalies in AI-dominated markets
• Risk management and portfolio optimization with AI
• Ethical considerations and fairness in AI-driven finance
• Impact on market participants and trading strategies
• Data quality, bias, and robustness of AI models in finance

Timeline

• April 30, 2027: Full paper submission deadline

Timeline

• February 12, 2027: Submission deadline

Timeline

• February 28, 2027: Submission deadline

Timeline

• February 28, 2027: Full paper submission deadline

Timeline

• July 31, 2026: Submission deadline

Timeline

• November 1, 2026: Full paper submission deadline

Timeline

• August 1, 2026: Submission deadline

Timeline

• November 30, 2026: Full paper submission deadline

This special issue invites submissions exploring the applications, implications, and innovations of Large Language Models (LLMs) in the tourism industry and for enhancing tourist experiences. The rapidly evolving landscape of artificial intelligence presents unprecedented opportunities and challenges for tourism stakeholders, from destination management organizations to hospitality providers to individual travelers.

Potential topics

• Applications of LLMs in tourism management and operations
• LLMs for personalized tourist experiences and recommendations
• Natural language processing applications in tourism marketing
• LLMs for tourism chatbots and customer service
• Language translation and communication in tourism contexts
• LLMs for travel planning and itinerary generation
• Sentiment analysis and tourist feedback analysis using LLMs
• LLMs for tourism research and data analysis

Timeline

• August 31, 2026: Full paper submission deadline

Timeline

• August 30, 2026: Full paper submission deadline

Timeline

• November 30, 2026: Full paper submission deadline

This special issue focuses on the intersection of sustainable maritime transportation and artificial intelligence technologies. Maritime transportation plays a crucial role in global commerce and logistics, accounting for a significant portion of international trade. However, the shipping industry faces considerable environmental challenges, including greenhouse gas emissions, fuel consumption, and pollution. Artificial intelligence and machine learning offer promising opportunities to address these sustainability challenges while improving operational efficiency.

The special issue welcomes contributions that explore innovative AI-based solutions for enhancing sustainability in maritime transportation. This includes research on optimizing vessel performance, reducing carbon footprints, improving safety protocols, and developing intelligent systems for port and logistics management. We are particularly interested in studies that demonstrate practical applications of AI technologies in real-world maritime contexts.

Potential topics

• AI and machine learning applications in maritime transportation
• Optimization of shipping routes and fuel efficiency
• Autonomous vessels and maritime automation
• Environmental impact assessment and emissions reduction
• Port operations and logistics optimization
• Maritime safety and risk management
• Data analytics for sustainable shipping
• Integration of renewable energy in maritime transport
• Smart navigation systems
• Digitalization of maritime supply chains

Timeline

• October 1, 2026: Full paper submission deadline

Timeline

• February 15, 2025: Full paper submission deadline

This special issue focuses on the applications and implications of Large Language Models (LLMs) in the tourism sector. We invite research exploring how LLMs can enhance tourist experiences, improve tourism service delivery, and support both tourism businesses and individual travelers. The issue welcomes empirical studies, theoretical frameworks, case studies, and critical analyses of LLM technologies in tourism contexts.

Potential topics

• Applications of LLMs in tourism industry
• LLMs for personalized tourist recommendations
• Natural language processing for tourism content
• Chatbots and virtual assistants in tourism
• Tourist information retrieval using LLMs
• Language translation for tourism
• Sentiment analysis of tourist reviews
• LLM-based travel planning and itinerary generation
• Multilingual tourism communication
• AI ethics in tourism applications

Timeline

• August 31, 2026: Submission deadline

Claudine Gartenberg, .pop

Sharique Hasan, .pop

Lamar Pierce, .pop

Christopher Bail, .pop

Hengchen Dai, .pop

Oliver Hauser, .pop

Hatim Rahman, .pop

Dennis Zhang, .pop

This special issue asks a fundamental question about artificial intelligence and social science: do we want it to produce faster, cheaper versions of what we already do, or do we want fundamentally new science? Returning to Organization Science's founding mission—Daft and Lewin's 1990 call to break out of the "normal science straitjacket" and March's "exploration of new possibilities"—we want to shift our focus to how AI is changing the production of science and how it can expand our knowledge, rather than merely increasing the number of papers through efficiency and reduced labor.

In this call for science, we seek contributions that reimagine what a social science research contribution is in an AI-enabled world, encouraging wild ideas and radical innovation over obvious incremental improvement. We are not looking for conventional full-length papers with AI-related content, nor "AI slop"—we want the innovative applications themselves.

The issue follows a three-stage process—a research proposal and prototype, a collaborative development phase with an in-person workshop, and finalization—culminating in short Science/Nature-style articles and shorter "letters," all treated as true peer-reviewed contributions. We welcome submissions from scholars across the social sciences and adjacent fields, so long as they address organizational or managerial implications, broadly interpreted.

Potential topics

• AI-enabled research loops under human direction
• Reusable research infrastructure
• New forms of measurement
• AI-enabled qualitative and theory-building work
• Synthetic social systems
• New approaches to established research designs
• Critical or boundary-setting work on the limits of AI-enabled science
• Reimagining social science research contributions in an AI-enabled world

Timeline

• October 1, 2026: Submissions Open
• November 1, 2026: Submissions Close

Paul Leonardi, .pop

Alex Murray, .pop

Frank Nagle, .pop

Nelson Phillips, .pop

Juliana Schroeder, .pop

Paula Ungureanu, .pop

Elisa Villani, .pop

Agency is central to organizational sciences and heavily debated. Assumptions about who can act, what acting requires, and who is answerable for results underlie theories of motivation, leadership, decision making, control, value creation, careers, institutions, and technological change. These views largely developed before intelligent technologies began participating in organizational action. As analytic, generative, and agentic AI increasingly participate in organizational action, intention, execution, and consequence—traditionally assumed to reside in a single human actor—are coming apart, and with them our settled sense of who exercises agency.

This special issue seeks theoretically ambitious work, whether conceptual or empirically grounded, that takes the changing production of action as its starting point. The issue is organized around two complementary aims: to reconsider agency itself—what it is, where it resides, and how it operates when action is co-produced by people and machines—and to understand how the changing nature of agency reshapes the phenomena in organizational fields, from organizational behavior and careers to management and control, strategy, entrepreneurship, institutions, the sociology of work, information systems, and the governance of autonomous action.

Submissions are welcomed from across the organizational sciences and from neighboring disciplines in sociology, economics, psychology, and the philosophy of action. Given the rapid pace of change in agentic technologies, the issue will follow an accelerated review process.

Potential topics

• Agency in organizations and how it changes with AI participation
• Co-production of action by people and machines
• Reconceptualizing agency: what it is, where it resides, and how it operates
• Impact on organizational behavior and careers
• Management and control in the age of AI
• Strategy and entrepreneurship with agentic technologies
• Institutional implications of changing agency
• Sociology of work and AI
• Information systems and autonomous action
• Governance of autonomous action
• Motivation, leadership, and decision-making with AI
• Value creation with intelligent technologies

Timeline

• February 1, 2027: Submissions due

René Riedl, University of Applied Sciences Upper Austria & Johannes Kepler University Linz

Jan vom Brocke, University of Münster

Jella Pfeiffer, Karlsruhe Institute of Technology

Robert Gleasure, Copenhagen Business School

The advent of increasingly powerful artificial intelligence systems (e.g., large language models such as ChatGPT, diffusion models, graph neural networks, or vision transformers) used in domains such as healthcare, manufacturing, software development and finance, together with other contemporary digital technologies (e.g., autonomous cars, immersive worlds, and neuroadaptive interfaces), has fundamentally reshaped human interaction with information systems. As these technologies become embedded in work, consumption, private life, and society in general, the ways in which they affect human cognition, emotion, and social interaction remain poorly understood.

A growing body of research highlights that neurophysiological methods can provide unique insights into these phenomena across a broad range of IS contexts. NeuroIS—the interdisciplinary field that integrates neuroscience and psychophysiological approaches with information systems—has been at the forefront of this movement. Originating at the International Conference on Information Systems in Montréal in 2007, NeuroIS celebrates its 20-year anniversary in 2027. This milestone provides an opportune moment to reflect on the progress achieved, highlight challenges, and chart new directions for NeuroIS research in an era of rapid technological change.

Over the past two decades, NeuroIS research has established the potential of neurophysiological methods such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), eye tracking, and various other neurophysiological measures to enrich IS theory. These approaches have been shown to improve prediction accuracy of different IS phenomena, uncover hidden cognitive and affective mechanisms, and open new methodological horizons. A particularly important trajectory in this research stream has been the integration of neurophysiological and behavioral levels of analysis by linking neural responses to observable IS-related behaviors and self-report measures.

In an era of pervasive digitalization, this integration becomes even more essential. Understanding not only what individuals do when interacting with advanced digital systems, but also how their brains and bodies adapt, allows researchers to explain emergent behaviors such as overreliance, automation bias, algorithm aversion, new forms of collaborative cognition, or cognitive debt. Neurophysiological measures enable the study of phenomena that may remain inaccessible to self-reports or behavioral observation alone, such as unconscious biases, implicit trust, or neural markers of attention and memory.

Beyond neurophysiology, this special issue also welcomes work that integrates genetic perspectives into IS research. Prior publications have demonstrated the promise of genetics for explaining individual differences in IS-related behaviors. Combining genetic and neurophysiological approaches may provide a powerful multi-level framework to understand how contemporary digital technologies affect humans at both biological and behavioral levels.

This special issue seeks high-quality, theory-driven, and methodologically rigorous research that examines the neurophysiological and related biological foundations, mechanisms, and effects of contemporary IS technologies and sociotechnical systems—incorporating AI as an important case, but not limiting the scope to AI. We invite submissions from information systems scholars, neuroscientists, psychologists, computer scientists, geneticists, and other disciplines to advance understanding of how contemporary IS reshapes human cognition, affect, decision-making, and social interaction.

This special issue encourages contributions across the full range of IS research traditions. Neurophysiological and genetic insights may advance design-oriented research by informing new principles for human-centered and neuroadaptive system design. They may also enrich IS economics research by offering a biological basis for understanding productivity, well-being, or value creation in digitally mediated contexts. Studies grounded in organizational behavior, strategy, or societal perspectives are highly relevant if they integrate or reflect on neurophysiological evidence.

In line with EJIS' tradition of intellectual openness, this special issue explicitly welcomes visionary, provocative, and contrarian contributions that challenge taken-for-granted assumptions, problematize dominant narratives, or open up unconventional research directions.

Potential topics

• How does sustained interaction with AI-enabled systems (e.g., LLMs) and other advanced digital tools reshape neural networks associated with language, reasoning, and memory?
• Does reliance on AI systems and other forms of automation/digital decision support alter attentional control, working memory, metacognition, or problem-solving processes at the neurophysiological and behavioral levels?
• What are the implications of digitally augmented cognition for neuroplasticity, skill acquisition, and expertise development over time?
• How can integrated neurophysiological and behavioral measures provide a fuller picture of technology-mediated cognitive processes?
• How do individuals' brains respond to empathetic or anthropomorphic cues exhibited by conversational AI and other interactive systems?
• What neurophysiological correlates underlie trust, reliance, distrust, or skepticism toward AI systems and other algorithmic or platform-based systems?
• How does emotional regulation change when decision-making is mediated by AI support, algorithmic decision aids, or digital nudges?
• What are the neurophysiological mechanisms of stress, fatigue, or overload in contexts of AI-assisted work and other digitally intensified work settings?
• How does technological mediation in team collaboration influence the neurophysiological foundations of social coordination, empathy, and shared attention?
• What neural mechanisms underlie shifts in authority, leadership, and influence when AI becomes a co-decision-maker?
• How do cultural differences modulate neurophysiological responses to AI and other digitally mediated collaboration settings?
• How do gender, age, or personality differences modulate neurophysiological responses to IS artifacts?
• How might AI-driven systems and other algorithmic designs reinforce or mitigate cognitive biases?
• What neural signatures accompany ethical dilemmas and moral decision-making in AI-mediated contexts?
• How does long-term use of AI and other digitally intensive work systems shape neurophysiological well-being, stress, or mental health?
• How can neurophysiological insights inform the design of AI systems and other digital systems?
• What are the neurophysiological underpinnings of productivity, efficiency, or value creation in digitally mediated economic interactions?
• How can genetic and neurophysiological approaches together explain individual differences in the adoption and use of digital systems?
• Which neurophysiological tools are best suited for investigating contemporary IS phenomena?
• How can hybrid approaches combining neurophysiology with computational methods enrich IS theory development?
• How can multi-level research designs integrate genetic, neurophysiological, behavioral, self-report, and organizational data?

Timeline

• March 31, 2026: Two-page abstracts due
• April 30, 2026: Feedback on abstracts
• May 31, 2026: Paper development workshop (in person event in Vienna, Austria, and virtual)
• August 31, 2026: First-round paper submission
• November 30, 2026: First-round decisions
• February 28, 2027: First-round revisions due
• May 31, 2027: Second-round decisions
• August 31, 2027: Second-round revisions due
• October 31, 2027: Final decisions

Associate editors

• Bonnie B. Anderson, Brigham Young University
• Dinko Bačić, Loyola University Chicago
• Colin Conrad, Dalhousie University
• Verena Dorner, Vienna University of Economics and Business
• Nadine R. Gier-Reinartz, Heinrich-Heine-University Düsseldorf
• Milena Head, McMaster University
• Alan R. Hevner, University of South Florida
• Qiqi Jiang, Copenhagen Business School
• Marion Korosec-Serfaty, University of Québec in Montréal
• Alexander Maedche, Karlsruhe Institute of Technology
• Gernot R. Mueller-Putz, Graz University of Technology
• Pierre-Majorique Léger, HEC Montréal
• Mario Nadj, University of Duisburg-Essen
• Fiona Nah, Singapore Management University
• Adriane Randolph, Kennesaw State University
• Ofir Turel, University of Melbourne
• Eric A. Walden, Texas Tech University
• Peter Walla, Sigmund Freud Private University Vienna
• Dezhi Wu, University of South Carolina

Mariachiara Barzotto, University of Bath

Jennifer Clark, The Ohio State University

AI is increasingly understood as an emerging enabling technology with potentially far-reaching implications for regional development. As both an industry in its own right and a technology increasingly integrated across incumbent sectors, AI has the potential to reshape production systems, innovation dynamics, regional governance, and the organisation of economic activity across space. Its growth raises important questions about technological change, the spatial organisation of new industries, the evolving practices of regional development, and the transformation of existing sectors.

AI may also be understood as a technology that influences how knowledge is produced, legitimised, and acted upon. In this sense, its significance extends beyond industrial transformation alone: AI may also affect how regions are governed and planned. This perspective resonates with long-standing debates in Regional Studies on technological change, digitalisation, and place-based adaptation, where innovation has been shown to reshape both regional economic trajectories and the institutional capacities through which development is pursued.

There is growing interest in Regional Studies in identifying the regional implications of new technologies, such as AI as a platform technology integrated across incumbent industries. Recent scholarship has begun to engage some of these issues, including its role in green technology specialisation, global innovation systems and industrial rise, new path creation, and the role of system-building agency in new path creations. Yet these discussions remain fragmented across literatures, empirical domains, and scales of analysis. This Special Issue seeks to advance a more coherent research agenda that examines AI simultaneously as an emerging industry, as a tool and infrastructure of regional development governance, and as an enabling technology whose diffusion may alter the spatial organisation of existing sectors.

The Call for Papers invites conceptually robust, empirically grounded, and methodologically rigorous contributions that examine the regional development implications of AI as an emerging enabling technology. Contributions may address the economic geography of the AI industry, the effects of AI on the governance, administration, and planning of regional development, and the ways in which the diffusion and adoption of AI across incumbent sectors may reshape patterns of agglomeration, dispersion, and regional economic change.

Potential topics

• The economic geography of the AI industry: spatial organisation of AI production, agglomeration and dispersion patterns, factors determining spatial organisation (skills, capital, infrastructure, energy, regulatory regimes)
• AI and the governance of regional development: impact on administration and management, effects on decision-making processes and outcomes, changes to regional development and planning practices (citizen engagement, public administration, systems optimisation, transportation)
• AI diffusion and the restructuring of incumbent industries: how adoption alters regional geography of other sectors, substitution effects with other technologies, new patterns of concentration or dispersion, analogues from previous enabling technologies

Timeline

• October 16, 2026: Abstract deadline
• December 1, 2026: Full manuscript deadline

Cristina Alaimo, ESSEC Business School

Lauren Waardenburg, ESSEC Business School

Jonny Holmström, Umeå University

Lior Zalmanson, Tel Aviv University

Reza M. Baygi, VU Amsterdam

We are living through a profound epistemic shift in the history of computation. Contemporary AI systems learn from, operate through, and increasingly generate data at unprecedented scale. Unlike symbolic approaches associated with GOFAI (Good Old-Fashioned AI), advances in machine learning and deep learning have repositioned data—not rules—as the primary substrate of computational intelligence. This transformation compels us to rethink the foundations of AI scholarship within the IS discipline and to develop new theoretical vocabularies to understand the data-AI nexus as a complex socio-technical phenomenon.

Data are sociotechnical artefacts shaped by institutional practices, historical trajectories, cognitive frames, and semiotic conventions. As AI systems are built on data, learn from data, and reshape data through processes of classification, prediction, and generation, data are simultaneously increasingly crafted to meet the requirements of AI systems. Training datasets are curated, cleaned, labelled, augmented, and optimized with particular model architectures in mind. In this recursive dynamic, the boundary between data and AI becomes blurred, demanding renewed conceptual attention to how organisations' social fabric alters in the wake of AI.

Experts and organisations primarily encounter and shape AI through data practices: generating inputs, curating datasets, fine-tuning models, evaluating outputs, and conducting quality control. To understand AI in organisational contexts, we must therefore unpack the socio-cognitive and technical processes of data production, cleaning, transformation, and governance; that is, the data work embedded in everyday organisational routines. These processes extend beyond individual tasks to encompass institutional histories of data: organisational memory, sedimented classification systems, collective sensemaking, and enduring cognitive frames. Such historically layered infrastructures shape both how AI systems are designed and how their outputs are interpreted.

AI is no longer trained only on data understood as structured database entries. Today, AI systems rely on an expanding range of materials, digital and analogue alike, that are transformed into training input. For the first time, AI also generates data. Synthetic data—text, images, code, and other artefacts generated by machines—are becoming a key input for model training, fine-tuning, and evaluation. The growing use of synthetic data for training purposes may be motivated by performance considerations, but also by issues of privacy, scarcity, access, and governance. When synthetic data, disconnected from social practice, recursively feed new models, the challenge becomes institutional, as human actors defer to AI even where scepticism and independent judgment are needed for legitimizing AI-generated material.

We invite submissions that advance our understanding of the data-AI nexus by foregrounding a dialogue between existing research traditions on data and AI, which have often remained disconnected. We particularly welcome theoretically ambitious and empirically rich studies that move beyond purely technical accounts of AI to explore its socio-technical, organisational, and epistemic dimensions. We encourage submissions that interrogate how data shape AI systems, how AI systems reshape the data they consume, and what happens when data becomes increasingly detached from practices of representation and meaning making.

We aim to catalyse how IS scholars conceptualise and study the interdependencies between data, AI, and social practice. We seek contributions that treat the data-AI nexus not as a technical backdrop but as a core analytical problem that reshapes how knowledge is produced, validated, and institutionalized in digital societies. We welcome empirical and conceptual contributions embracing a broad range of paradigms, methodological approaches, and levels of analysis, valuing diversity in theories, methods, and genres.

Potential topics

• Data and AI co-constitution: exploring where and when intelligence in AI emerges in computational architectures, datasets, or their entanglement, and how choices about data selection, curation, labelling, and historical accumulation shape what AI systems can know and do
• AI as a data-maker: conceptualizing AI as an active producer of data that structures, generates, and transforms the data it relies on, including the rise of AI agents and recursive dynamics where synthetic data becomes input for other AI systems
• Data decoupled from practice: examining consequences of data production detached from institutionalized knowledge practices or situated practices of meaning-making, and implications for governance and decision-making
• The social life of the data-AI nexus: tracing institutional, historical, and cognitive trajectories of data as it is produced, curated, transformed, and mobilized, including how organizational memory and AI agents reshape institutional dynamics
• Theoretical and methodological advances: reconceptualizing constructs such as intelligence, data quality, ground truth, training, and synthetic data as relational, temporal, and emergent phenomena
• Cross-level insights: studying how assumptions about data and intelligence are embedded in AI systems and reshape organizational practices and societal outcomes
• Data work in organizations: examining curation, labelling, cleaning, evaluation, model development practices, and integration of generative AI into workflows
• Data, AI, and grand challenges: addressing climate change, public health, inequality, digital governance through responsible AI in contexts with incomplete, biased, or synthetic data

Timeline

• May 28, 2026: Call launch at the Theorizing Data & AI Conference
• January 15, 2027: Initial paper submission deadline
• April 15, 2027: First round authors notification
• May 1, 2027: Workshop for first round authors during Theorizing Data & AI Conference
• July 31, 2027: Invited revisions deadline
• October 31, 2027: Second round authors notification
• January 15, 2028: Final revision deadline

Associate editors

• Aleksi Aaltonen, Ida Asadi Someh
• Ioanna Constantiou, Domenico di Prisco
• Mayur Joshi, Ekaterina Jussupow
• Jannis Kallinikos, Tomislav Karačić
• Stan Karanasios, Alexander Kempton
• Angelos Kostis, Harris Kyriakou
• Christine Legner, Kalle Lyytinen
• Eric Monteiro, Jeff Parsons
• Mike Power, Jan Recker
• Paavo Ritala, Marta Stelmaszak Rosa
• Lauri Wesssel,

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

Jingsha He, Beijing University of Technology

Sohail S. Chaudhry, Villanova University

Galina Ilieva, University of Plovdiv

Against the backdrop of the rapid development of e-commerce enterprises and the increasing maturity of data intelligence technologies, the organic integration of the two has created innovative digital business models that transcend traditional e-commerce operations, such as enhanced commodity transactions through predictive analytics, deepened user interactions via personalized recommendations, and streamlined supply chain collaboration with real-time optimization. This has brought tremendous changes to e-commerce enterprises and has become a core engine driving enterprise upgrading. The e-commerce enterprises use big data analysis to optimize business strategies, improve user experience, and make informed decisions. For example, Amazon leverages data intelligence to conduct real-time analysis of massive transaction data. During promotional periods, it adjusts prices on a minute-by-minute basis based on factors such as competitors' prices and users' purchasing intentions, giving full play to the role of big data value mining in optimizing business strategies. The "Thousands of People, Thousands of Faces" system improves user experience and stickiness through intelligent data analysis. E-commerce platforms have established intelligent supply chain systems, which realize efficient collaboration of warehousing networks by integrating sales data, inventory data, and logistics data.

However, as e-commerce enterprises driven by data intelligence rapidly develop, many practical problems have emerged in areas such as operational efficiency, data handling, and competitive differentiation. The surge in orders brought by massive transactions makes it urgent to improve service efficiency and sales levels; the complexity of data processing and the dynamics of e-commerce (including difficulties in processing structured and unstructured data) lead to system processing delays, which further exacerbate this problem; over-reliance on historical data reduces recommendation accuracy, and similar algorithms across multiple platforms result in a lack of differentiation in enterprise competition, restricting the innovative development of e-commerce.

This special issue will explore the innovative development of e-commerce enterprises under data intelligence to address these current challenges. This special issue aims to explore feasible paths to overcome technical bottlenecks and enhance models by bringing together innovative academic theories and practical cases from e-commerce enterprises, offering forward-looking and practical ideas for enterprise development, and promoting exchanges and cooperation in the field of data intelligence among global e-commerce enterprises.

Potential topics

• Paths to enhance e-commerce efficiency driven by big data
• Data intelligence empowers innovation in e-commerce operations and service upgrading
• Optimization of complex data processing in e-commerce operations through data intelligence
• AI enables solving the problems of structured and unstructured data processing in e-commerce
• E-commerce marketing strategies and accurate capture of user behavior based on in-depth AI analysis
• AI enables optimization of the guidance effectiveness and accuracy of recommendation systems for e-commerce users
• Generative AI breaks through algorithm homogenization to realize personalized services in e-commerce
• Data intelligence promotes the format innovation of integration between e-commerce and physical retail

Timeline

• September 30, 2026: Submission of manuscript
• November 15, 2026: First notification
• December 31, 2026: Submission of revised manuscript
• February 15, 2027: Final paper due

Alexandra Brintrup, University of Cambridge

Thomas Choi, Arizona State University

George Huang, Hong Kong Polytechnic University

Dmitry Ivanov, Berlin School of Economics and Law

Recent advances in Agentic Large Language Models (LLMs) are reshaping the discourse on how autonomous decision systems might operate in complex environments such as supply chains. The motivation for the special issue is to facilitate rigorous research in the transformative potential of agentic technology for Supply Chain Management (SCM). A key objective is to foster collaboration and unlock synergies by merging diverse perspectives from operations and supply chain management, AI, complexity science, and industrial engineering.

An AI agent is characterised as one that has a predefined or emergent goal and uses cognitive tools to achieve its objectives, such as situational awareness, data-driven learning and prediction, decision optimisation, negotiation, and problem-solving with human or other AI agents. Supply chain management scholars have long studied agent-based systems, especially multi-agent systems (MAS) since the 2000s. However, research has stalled as MAS were slow to develop, difficult to code, debug and scale, and were prone to severe coordination problems. Most supply chain agent research was not adopted in the industry.

Recent advances in LLMs have now brought in a new paradigm that might reverse this trend: LLM agents are widely expected to usher in a new era where specialist programming knowledge is no longer required. LLM agents utilize a large language model as their information processing unit for core reasoning, planning, and decision-making. They can also utilize bespoke tools and recall past actions and conversations to generate contextual awareness. Whereas in the past, specialist MAS agents required rigorously defined rules and frameworks to operate, LLM agents begin their operation having already learned from vast amounts of open data. A generic knowledge base enables LLM agents to adapt to complex environments in real-time. This new paradigm makes agents much more flexible and scalable. LLM agents can interact with humans in natural language, coordinate or compete with other LLM agents, use custom tools to perform tasks such as search and optimisation, query documents and databases, and search the web.

Large multi-national corporations like Walmart and Siemens are already experimenting with agentic LLMs to automate their supply chain tasks. Supply chain information system providers, such as SAP, Microsoft, and Google, are actively developing supply chain agent platforms and interoperability initiatives to enable cross-organizational supply chain automation.

Academic agentic supply chain research is in its infancy, with research fragmented across computer science, operations research, and manufacturing engineering. Early research points to its potential for overcoming supply chain inefficiencies through rapid access to data, improved planning, and cross-organisational negotiation, but also warns against agentic AI mirroring human bias, challenges in the verification of output and lack of precise language leading to wrong decisions. As this field continues to evolve, it is crucial for researchers to stay informed about the latest developments, raise awareness of potential challenges, and contribute to the growing body of knowledge on the application of agentic AI in supply chain management.

This Special Issue seeks to integrate multi-disciplinary research from various perspectives on shaping agentic automation in the supply chain. A variety of submissions and perspectives are welcome. Technical solutions, advanced modeling, mixed-methods, rigorous quantitative and qualitative empirical research, experimental and analytical methodologies with practical industry, managerial, and policy implications are welcome.

Potential topics

• Discourse on utilising agentic systems for complex scenarios in supply chain management: Risk and disruption management, logistics and supply chain optimisation, transportation routes, inventory management, quality control, demand forecasting, and warehouse planning and location, and cash flow predictions
• Supply network design with agentic technology: Supplier relationship configurations, agentic digital twins to simulate inventory flows, sustainability implications across the supply chain, circular supply chains, supply chain visibility, and supply chain financing
• Interorganisational agentic systems: Effective multi-agent negotiation and coordination, the design of mediative and persuasive agentic systems, preservation of organisational privacy during multi-agent communication
• Hybrid systems: Integration of agentic systems with blockchain, IoT, Omniverse, and traditional multi-agent systems
• Emergence and Complexity: Unintended consequences of agentic deployment at the system scale, governance, trustworthiness and safety, centralised versus decentralised control, human-in-the-loop agentic systems
• Technical challenges: Performance evaluation, efficient task division, ablation analysis and back testing, sensitivity analysis, agentic architectures operating in high uncertainty environments, long-term horizon reasoning, overcoming hallucinations

Timeline

• September 30, 2026: Manuscript deadline

Sandra Fisher, FH Münster

Janet Marler, University at Albany - SUNY

The 10th e-HRM Conference in Muenster, Germany (June 2026) provided a platform for scholars from around the world to share research and leading-edge insights into electronic/digital human resource management in the midst of another major technological revolution. This special issue highlights key research papers presented at the conference and also invites authors who were unable to attend the conference to submit their related research to this special issue.

Artificial intelligence and robotics may prove as transformative for economic growth and human potential as electrification, mass production, and electronic telecommunications in their eras. The release of ChatGPT in November 2022 highlights the rapid advance of AI technologies, illustrating that such advancement is not necessarily continuous but can occur in sudden bursts. Previously heralded as a tool for automation and increased efficiency, narratives about AI now make a broader claim of a new era of human-AI collaboration in which AI performs routine tasks freeing professionals to take on more value-added work. But this claim is not new. Such promises were made 20 years ago with the introduction of the internet and the expansion of electronic telecommunications, and use of ERP and HRIS technologies.

One goal of the conference is to examine the ways in which AI can advance the science and practice of human resource management, but we also want to remain mindful of the potential limitations and boundaries of AI, such as concerns with inequality and bias, hallucinations, lack of transparency, issues with trust or distrust in the technology and skill erosion. Given these limitations, which HRM processes and workflows can or should be automated, and which augmented? What other technologies might be used along with AI or instead of AI? What can we imagine is next after the big wave of AI?

To advance scholarship and to provide evidence-based, independent guidance, academic researchers have a crucial role to play in influencing how AI technologies are used in organizations to improve outcomes for all stakeholders. It is important therefore to build on the well-developed management literature that already exists. Over the last two decades research on e-HRM, also referred to as digital HRM or d-HRM, shows how a wide range of configurations involving computer hardware, software and electronic networking resources which both enable intended or actual HRM activities not only automates but also transform HRM processes in intended and unintended ways. Digital transformations have also been extensively researched in the traditional HR functions of recruitment, selection, leadership, learning and development, and compensation.

Research in e-HRM has been enriched with such concepts as cloud computing, big data and people analytics, social media, chatbots, gamification, the internet of things, robots, artificial intelligence, machine learning, and other emerging technologies. E-HRM has also evolved to include new forms of organizing, such as industry 4.0 and digital-platform business models. Industry 4.0 redefines the relationship between human workers and machines, and requires a new approach to HRM, or HRM 4.0. Moreover, the emergence of advanced systems, software agents and/or robots which work alongside human workers has resulted in human-robot 'white-collar teams'. The platform or gig economy has created a new type of contingent worker, gig workers who have little or no connection to job providers, who are managed algorithmically through platforms. The digital platform is an important vehicle of their employment relationship, since it creates a new ecosystem for employment/work relations and institutes the use of algorithms/software robots for executing HRM activities. These developments pose new challenges to and opportunities for the e-HRM field, as well as expand and enrich its scope.

With the release of large language models (LLMs) like ChatGPT, Claude, and Gemini, generative AI and AI agents now make communicating with and accessing information from information technology much easier, democratizing insights from HR data and HR analytics. There has been an explosion of AI for HRM start-ups. Despite these impressive advances, the question remains, what is different about AI in the e-HRM context? Do many of the traditional challenges and agendas of e-HRM remain or are there other characteristics that need further study? How is AI different from the preceding electronic technologies and how does this make a difference to the practice of HRM?

AI has three specific material characteristics—constant change, invisibility, and inscrutability—that may challenge traditional approaches to the study of technology and work. Individually these characteristics are not problematic but in combination they challenge applying earlier research frameworks to the study of AI in organizations. The production and use of AI involves a broad swathe of stakeholders in and across organizations whose assumptions and interests influence design, development, implementation, and use. This calls for a broader view, tracing interactions across contexts and expanding analysis upstream from use.

To deliver value for all stakeholders, HR professionals need to be aware of the technology-driven context in which they work and the assumptions about HRM processes and practices that are embedded in the technology. Rather than accepting the dictates of software developers external to the organization, HR leaders need to develop HRM practices that deliver value to their organization and employees. Thus, the aim of much current e-HRM research is to uncover e-HRM outcomes or consequences that lead to an enhanced value of HRM for both organizational members and other stakeholders, eschewing determinative technological pressures. Given that e-HRM aims to create value within and across organizations for targeted employees and management and covers all possible integration mechanisms and content between HRM and Information Technology (IT), this call invites papers dealing with a broad scope of technology application to managing people inside and across organizations.

Potential topics

• New theories and methodologies to study AI/HRM
• New HRM strategies
• AI-Human collaboration at work
• e-HRM/AI value propositions
• AI/Technology-enabled HR roles
• AI Agents' roles and intended and unintended outcomes in HRM
• AI/Technology-enabled HR functions: recruitment, selection, performance management, leadership, training and development, coaching, compensation and employee relations
• Algorithmic management of employees or gig workers
• Gig-worker management and employment/work relations
• e-HRM and employee experience
• e-HRM and employee wellbeing
• AI and Bias in decision-making
• AI and job design/crafting
• HR data management and confidentiality
• People Analytics and AI
• Digital talent management
• Employee and digital onboarding systems
• Gamification in HRM
• Robots and artificial intelligence (AI) in HRM
• Employer branding and digital communication
• AI/e-HRM and trust/ethics

Timeline

• Invalid DateTime: Special issue completed
• July 1, 2026: Submission portal opens
• October 31, 2026: Special Issue Submissions due

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

Saverio Barabuffi, Scuola Superiore Sant'Anna

Giulio Ferrigno, Scuola Superiore Sant'Anna

Letizia Mortara, University of Cambridge

Yogesh K. Dwivedi, King Fahd University of Petroleum and Minerals

Innovation increasingly emerges from collaboration among a diverse set of actors (firms, startups, universities, public institutions, and non-profits) who expand and recombine their knowledge bases. These collaborative relationships are of utmost importance to open innovation processes, enabling the co-creation of product and process innovations and enhancing the impact and diffusion of novel solutions.

Prior research has robustly shown that organizations select partners based on the complementarities or distances between their knowledge structures, often leveraging cognitive proximity to drive technological diversification or exploring distant domains to avoid lock-in and enable radical breakthroughs.

However, open innovation has acquired new and largely unexplored facets in recent years. The rapid growth of the availability of a large volume of structured and unstructured data poses unprecedented opportunities to understand and guide open innovation dynamics. This massive amount of data, often referred to as Big Data, is fundamentally reshaping how firms search for external knowledge, identify complementarities, and govern open innovation processes. In this context, Artificial Intelligence technologies provide a method of invention that shifts the boundary between human-led and machine-led knowledge production. AI is being adopted rapidly and pervasively across industries, generating profound effects on organizations.

Recent advancements in AI, such as Large Language Models, represent a leap forward in this transformation. When embedded in data-driven approaches, these powerful tools are expected to allow organizations to systematically map technological trajectories, detect emerging knowledge fields, and support external search strategies through the automation of the analysis of millions of documents and informational signals. As a result, AI will not simply enhance analytical efficiency, but actively contribute to redefining the scope, boundaries, and modalities of knowledge search within open innovation processes.

Despite some recent investigations in the innovation field, our understanding of how recent advancements and adoption of AI technologies can promote and shape open innovation processes remains fragmented and incomplete. Existing studies have largely focused on the role of AI and Big Data in relation to innovation outcomes or on how they help mapping technological landscapes, while much less evidence and theory is available about how AI-driven tools intervene upstream in the formation, governance, and evolution of collaborative innovation. We still lack insights into how AI influences partner selection, reconfigures knowledge search strategies, alters power and coordination mechanisms within innovation ecosystems, and reshapes the roles of firms, universities, and public actors in open innovation settings.

While AI-driven tools promise to expand collaboration opportunities and improve coordination across heterogeneous actors, they also raise new organizational, strategic, and governance challenges, including issues of transparency, algorithmic bias, control over decision-making, and unequal access to data and computational capabilities. Addressing these open questions is crucial to understanding when, how, and under what conditions advances in AI can effectively promote open innovation, rather than merely optimize existing practices.

This Special Issue seeks to advance theory based on empirical research on the role of AI in enabling, shaping, and governing open innovation processes across firms, industries, and innovation systems. It calls for works which support an understanding of the role of AI technologies in creating new opportunities for firms to innovate, to redesign industry boundaries, and generate new value systems and partnership networks.

We invite scholars to move beyond the what digital tools question to engage with the how and why they alter open innovation dynamics. We welcome conceptual, methodological, and empirical contributions, using qualitative, quantitative, mixed or computational approaches, to explore how advances in AI, including Large Language Models, generative AI, and other advanced machine learning techniques, actively enable, reshape, and govern collaborative innovation and open innovation processes. We particularly encourage submissions that move beyond descriptive applications of AI to investigate its role as a driver of partner selection, coordination, and knowledge integration.

The Special Issue aims to engage a multidisciplinary audience and stimulate scholarly debate at the intersection of AI, collaboration, and open innovation, across multiple levels of analysis, ranging from individuals and teams to organisations, inter-organizational networks, ecosystems, and innovation ecosystems.

Potential topics

• AI and Inbound Open Innovation: Partner Search and Knowledge Scouting
• How do AI-based tools reshape the classic trade-off between search breadth and depth in open innovation? Can algorithmic scouting explore distant knowledge domains more efficiently than traditional methods?
• To what extent can AI overcome local search biases, revealing latent complementarities across industries, regions, or technologies that human managers may overlook?
• Can AI-powered analysis of diverse data sources democratize access to innovation ecosystems, or does it favor incumbents with larger digital footprints?
• Orchestration & Governance of Innovation Networks
• How do AI tools enable algorithmic governance of knowledge flows in multi-partner networks?
• How can AI help coordinate heterogeneous actors, including firms, universities, NGOs, and governments, within mission-oriented innovation systems?
• How are platforms leveraging AI-tools to shape technological trajectories and orchestrate complementors in ecosystems?
• What are the implications of AI-mediated orchestration for value capture, appropriation, and transparency in collaborative innovation?
• Knowledge Flows, Spillovers and Innovation Mapping
• How do generative AI and Natural Language Processing techniques uncover tacit knowledge flows and early-stage spillovers invisible to traditional patent- or publication-based metrics?
• How do AI tools improve the mapping of technological landscapes, identify white spaces, and detect emerging trajectories to inform strategic decisions such as make, buy, or ally?
• What methods best integrate multiple data streams to track cross-sectoral and cross-regional knowledge diffusion enabled by AI?
• AI-Enabled Absorptive Capacity and Human AI interaction
• How should absorptive capacity be reconceptualized when AI tools, such as LLMs, assist in the recognition of external knowledge?
• What is the optimal division of labor between AI systems and human R&D managers in scanning, interpreting, and assimilating external knowledge?
• How can AI support organizational learning while mitigating barriers such as the Not Invented Here syndrome, especially when AI identifies previously unknown sources of innovation?

Timeline

• Invalid DateTime: Expected publication of the Special Issue
• September 1, 2026: Submission window opens
• September 30, 2026: Full paper submission deadline

Kirsten Cowan, University of Edinburgh

Yang Feng, University of Florida

Seth Ketron, University of St. Thomas

Aidin Namin, Loyola Marymount University

The rapid evolution of generative artificial intelligence (AI) is revolutionizing the way consumers search, retrieve, and interact with product information, offerings, and brands. The traditional search landscape, once dominated by Google's algorithmic filtering and search engine optimization (SEO) strategies, is being transformed by AI-generated summaries and zero-click searches. This paradigm shift is disrupting conventional SEO approaches and revenue models, reshaping how consumers encounter and process information and make choices while forcing the industry to adapt and reinvent.

As AI continues to influence search results and decision-making, several key trends are emerging. First, the nature of search is changing, with AI increasingly mediating our interactions and altering the way we engage with search results. Is search still a pull activity, where consumers initiate queries and receive advertising responses aligned with their intent, or are we witnessing a shift towards AI-led search experiences that unfold without explicit human initiation? Second, we are delegating search-related tasks to AI, potentially leading to a future where AI autonomously performs search functions, bypassing traditional some stages of the consumer journey, including information processing. Third, our current interactions with AI are largely functional, focusing on utilitarian offerings and cognitive evaluations. However, as AI evolves, we may see a shift towards more experiential, hedonic, and social evaluations, where AI assists us in discovering new goods and services that cater to our emotional, social, and moral needs.

The implications for advertising are profound. As AI-generated content and zero-click searches become more prevalent, advertisers must adapt their strategies to reach consumers in a landscape where traditional search results are no longer the primary touchpoint. This may involve shifting focus from keyword-based advertising to more contextual, conversational, and experiential approaches, such as sponsored content, influencer partnerships, and immersive brand experiences. Further, the rise of AI-driven search raises important questions about the role of advertising in shaping consumer decision-making, the potential for bias in AI-generated recommendations, and the need for greater transparency and accountability in advertising practices.

Building upon recent contributions that focus on GenAI search and advertising, this special section aims to inspire the future of advertising applications within the consumer search phase. We welcome submissions that make substantive theoretical and practical contributions in the broader domain of advertising research, irrespective of methods. This includes either conceptual or empirical work. Empirical work (experiments, surveys, modeling, qualitative, etc.) should be appropriately executed, given best practices in the discipline.

Potential topics

• How advertisers can effectively leverage AI-generated content and zero-click searches to reach their target audiences
• The impact of AI-generated content on search results and consumer decision-making
• The evolution of SEO strategies in response to AI-driven search algorithms
• The role of AI in shaping consumer journeys and experiential marketing
• How AI has shifted and/or will shift psychological mechanisms in search, including but not limited to empowerment, attention, memory, processing styles, and cognitive bias
• How social, cultural, and other contextual differences might lead different consumer segments or markets to react to and utilize AI differently in search
• The role of AI in relation to marketer-controlled vs. nonmarketer-controlled sources of information (e.g., ads or website content vs. customer reviews or social media) and consumers' relative reliance on each
• The effects of AI on speed, locus of control, and effort in consumer search
• The potential for AI-driven advertising to enhance or undermine consumer trust and brand loyalty
• The impact of AI on ad pricing and revenue models, including the potential for more nuanced and contextual pricing strategies
• The role of AI in facilitating more personalized and relevant advertising search experiences, and the potential trade-offs with consumer privacy
• The need for new metrics and benchmarks to measure the effectiveness of AI-driven advertising campaigns, and the potential for AI to provide more granular and actionable insights into consumer behavior and preferences
• The potential for AI to create more intimate and human-like interactions with consumers
• The ethical implications of AI-driven search and advertising practices
• The future of search interfaces, including voice-activated, image-based, and multimodal search
• The interplay between AI-generated search experiences and consumer autonomy
• The implications of AI-driven search ecosystems for market competition and brand visibility
• The role of AI transparency and explainability in consumer trust and regulatory compliance
• The sustainability and environmental impact of AI-driven search and advertising ecosystems
• Cross-platform integration and continuity in AI-mediated consumer journeys
• How the reduced visibility of paid versus organic influence in conversational AI affects consumer understanding, disclosure, and trust in recommendations
• How the growing tendency to treat AI systems as social or companion-like partners might reshape the value of impressions, expectations of authenticity, and marketplace pricing for AI-mediated advertising interactions

Timeline

• June 1, 2026: Submission window opens
• September 7, 2026: Manuscript deadline

Eunju Ko, Yonsei University

Teresa Sádaba, ISEM Fashion Business School, Universidad de Navarra

Carmen Valor, Universidad Pontificia Comillas

The luxury sector is undergoing a profound transformation, marked by complexity, volatility, and new societal and technological challenges. Traditional growth models show signs of fatigue, while institutional pressures push brands toward more authentic commitments to sustainability, ethics, and inclusion. Simultaneously, luxury brands face a rapidly shifting competitive landscape, with changes in global markets, consumer expectations, and technological innovation.

In this context, advertising is central to redefining the identity, desirability, and legitimacy of luxury brands. As Ko (2020) noted in a previous International Journal of Advertising special issue, the study of luxury advertising requires continuous theoretical and methodological renewal. Building on that foundation, this special issue seeks to advance conceptual, empirical, and interdisciplinary insights into how luxury advertising is being reimagined through artificial intelligence (AI), emerging technologies, influencer ecosystems, and sustainability. We especially invite contributions that explore the tensions between tradition and innovation, authenticity and automation, exclusivity and accessibility, and symbolism and data-driven personalization.

Artificial intelligence (AI) and related technologies are reshaping luxury advertising by enabling hyper-personalization, predictive analytics, and novel creative outputs. From generative content to immersive AR and metaverse campaigns, these tools extend luxury's narrative potential and promise new consumer experiences. Yet research highlights ambivalence in consumer responses: AI-generated advertising can appear inauthentic and misaligned with luxury's aura of craftsmanship and artistry. Success often depends on trust and perceived humanness, with consumers more receptive when AI is positioned as augmenting rather than replacing human creativity. This suggests that the integration of AI into luxury advertising not only offers opportunities but also introduces tensions that demand deeper theoretical exploration.

We invite research that advances theoretical and empirical understanding of how luxury brands can adopt AI without eroding symbolic value or authenticity. Relevant questions include how AI-generated avatars and chatbots alter perceptions of brand aesthetics, how predictive modeling balances personalization with transparency and privacy concerns, and under what conditions immersive technologies (NFTs, AR filters, metaverse campaigns) enhance rather than commodify the luxury experience. Current evidence remains fragmented, highlighting opportunities for deeper investigation of cultural, ethical, and aesthetic implications.

Influencers—whether human, virtual, or AI-generated—have become central to luxury advertising, shaping consumer aspirations and brand legitimacy across digital platforms. Their effectiveness, however, is contingent on credibility, authenticity, exclusivity, and cultural context, raising persistent tensions in luxury communication. While collaborations with influencers can amplify reach and relatability, over-commercialization risks diminishing attention, trust, and distinctiveness. At the same time, AI-powered tools increasingly influence this ecosystem—identifying influencers, generating personalized recommendations, and even producing content. These innovations complicate the balance between technological precision and the symbolic codes of luxury, highlighting an urgent need for systematic inquiry.

We welcome studies that clarify how different types of influencers—celebrity, micro, or virtual—shape luxury consumers' perceptions, and how effects vary across platforms, cultures, and generations. Key questions include how storytelling fosters parasocial ties, how authenticity can be preserved in AI-mediated collaborations, and how influencers interact with institutional pressures such as regulation and sustainability expectations. Advancing this line of inquiry requires not only testing effectiveness but also theorizing influence in a landscape where human creativity, algorithmic personalization, and aspirational symbolism intersect.

Research on sustainable advertising has yielded insights into framing, cues, and consumer responses, as well as unintended outcomes such as skepticism. Yet a decade after Taylor's calls for more research on green and luxury advertising, significant theoretical gaps remain. We lack guidance on how luxury brands can credibly design sustainability communications that align aspirational and ethical values. Progress requires moving beyond 'what works' to explaining why and under what conditions messages resonate, and examining both short- and long-term outcomes.

We invite research that reconceptualizes sustainable advertising as a mechanism not only for achieving marketing goals but also for advancing consumer well-being, social good, and environmental progress. Key questions include how to sustain consumer motivation post-purchase, what consequences follow eco-friendly luxury consumption, and how advertising can strengthen volition to bridge the intention–behavior gap. Emerging practices such as minimalist appeals and 'quiet luxury' illustrate pathways toward less materialistic lifestyles, but mainstream adoption remains elusive. Understanding which brand characteristics enable these shifts is an urgent research need.

Luxury's associations with craftsmanship, authenticity, and durability provide a platform for credible sustainability positioning. Yet tensions persist when practices such as recycled materials or fair labor standards conflict with luxury's symbolic codes of exclusivity. Future research should examine message strategies that integrate sustainability without diluting aspirational value and assess whether long-term equity gains offset short-term trade-offs. While much attention has focused on framing effects, higher-order execution formats—storytelling, branded content, interactive experiences, or influencer collaborations—remain underexplored.

AI, influencers, and sustainability are not independent forces but interdependent dynamics reshaping luxury advertising. AI-driven personalization transforms influencer practices; influencers amplify or undermine sustainability claims; and immersive technologies alter how consumers interpret exclusivity, ethics, and authenticity. Understanding luxury advertising thus requires integrative perspectives that connect these domains. We especially welcome interdisciplinary and cross-methodological submissions that bridge these themes to advance both theoretical and practical knowledge.

Potential topics

• How can influencers disclose data-driven targeting practices while preserving transparency and trust?
• How do AI-generated avatars and human influencers co-create content, and what balance sustains authenticity and aspiration?
• In what ways does AI-enabled personalization enhance luxury advertising without undermining exclusivity and symbolic value?
• How do AI-powered influencers and proliferating collaborations shape consumer trust, differentiation, and purchase decisions?
• How do platform-specific affordances (e.g., Instagram, TikTok, WeChat) shape storytelling, authenticity cues, and parasocial ties?
• How do different types of influencers (celebrity, micro, virtual, AI-generated) shape perceptions of authenticity, aspiration, and brand legitimacy?
• In what ways does the proliferation of influencer collaborations affect consumer trust, attention, and differentiation in luxury markets?
• How do AI-powered tools for influencer identification, personalization, and content creation alter the dynamics of luxury influence?
• How do influencers interact with broader institutional forces, such as regulation, ethics, and sustainability expectations?
• Under what conditions do sustainable luxury communications operate effectively, and why?
• How do sustainable advertising strategies influence consumers beyond purchase—shaping post-purchase satisfaction, long-term loyalty, and lifestyle adoption?
• How can luxury advertising integrate sustainability (e.g., recycled materials, fair labor) without diluting aspirational value?
• Which execution formats (storytelling, branded content, live events, influencer collaborations) most effectively balance marketing and societal objectives?
• How can luxury brands use communication to encourage minimalist consumption ('buy less but better') while maintaining aspiration and desirability?

Timeline

• August 31, 2026: Manuscript submission deadline for direct submissions to the International Journal of Advertising

Linda Tuncay Zayer, Loyola University Chicago

Jing Yang, Boston University

Shu-Chuan (Kelly) Chu, DePaul University

With the rapid evolution of artificial intelligence, social media algorithms, robotic interfaces, and immersive technologies such as virtual and augmented reality (VR/AR), the boundaries between technology, persuasive influence, and human activities have blurred. In this evolving landscape, the individual and collective well-being of consumers, as well as society at large, is increasingly at risk and warrants critical scholarly attention.

Consumer well-being is a multidimensional concept that encompasses individual and shared experiences, which can be manifested through cognitive, affective, and psychophysical states. Moreover, well-being is contextualized, with advertising holding the potential to enhance or diminish well-being at various micro, meso, and macro levels. At a broader level, societal well-being extends these concerns to collective equity, inclusion, and information integrity, highlighting how advertising technologies shape civic life, cultural representation, and public trust.

Emerging technologies such as generative AI have revolutionized how advertising messages are created, optimized, and delivered. While AI promises efficiency and personalization, research has also highlighted the risk of deception, diminishment of authenticity, and misrepresentation. At the same time, when human-AI collaboration is transparent and meaningful, it may enhance empowerment, creativity, and perceived control.

Robotic and embodied agents, such as in-store robots or voice-enabled AI assistants, are emerging as persuasive venues. These technologies can increase comfort and engagement, such as through emotional support, but may also evoke discomfort or distrust if perceived as overly human-like or manipulative. Their deployment raises new ethical questions about safety, accessibility, labor replacement, and the equitable distribution of technological benefits.

Examining the ethics and well-being impacts of social media platforms, drawing on advanced algorithmic targeting, is crucial due to the prevalence of deepfakes, misinformation, and extreme and misogynistic content. Although algorithms can enhance the relevance of information and increase efficiency and effectiveness, there could be negative results for consumer and societal well-being. Building digital literacy among vulnerable populations and diminishing the harms of social media must be a priority for future scholarship.

Immersive and metaverse advertising further redefines persuasion through embodiment and sensory engagement and holds sociological and psychological implications as well as new challenges to consumer well-being and consciousness. The heightened presence might enhance the learning and persuasion outcomes but could also result in consumer vulnerability if the ad boundaries or data protection are unclear. Ethical design, consent, inclusion, and transparency are crucial to protecting autonomy in such sensor-rich environments.

These transformations signal both opportunity and responsibility. This special issue seeks contributions that advance conceptual, empirical, and policy insights to ensure that innovation in advertising remains human-centered, ethical, and responsible and enhances well-being. All methodological approaches are welcome.

Potential topics

• Ethical implications of AI, social media, AR, VR, metaverse, and other emerging technologies
• Ethics and well-being related to AI companions, bots, and virtual influencers
• Personalized content generation and psychological manipulation concerns
• Algorithmic amplification of beneficial versus harmful advertising content
• Anthropomorphic design ethics and emotional manipulation through physical presence of AI/Robot
• Consent and autonomy in human-robot advertising/marketing communication interactions
• Parasocial relationships with advertising/marketing communication robots and their exploitation
• Consumer understanding and control of integrated AI advertising experiences
• Data sharing and privacy concerns across social media platforms and emerging technologies
• Building AI and digital media literacy
• Children's well-being related to AI, social media and new technologies
• Elderly consumers and potential exploitation through advertising systems embodied by emerging technologies
• Consumer involvement in advanced advertising system design and governance
• Professional ethics for AI-automated decision-making
• Deepfakes and synthetic media ethics in commercial contexts
• Synthetic data and digital twins in the advertising context
• Automation and replacement of advertising industry employees by AI
• Bias detection, measurement, and mitigation strategies across digital applications
• Bias and representational harms of AI-generated advertising and other emerging technologies
• Extremism, misogyny, and gender-based violence enabled by AI, social media, and emerging technologies
• Immersive storytelling, embodiment, and consumer well-being in hybrid digital–physical advertising experiences
• Sustainability issues related to the use of AI in advertising industry
• AI, energy consumption, and the carbon footprint of the advertising industry

Timeline

• August 31, 2026: 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

This Special Issue aims to advance understanding of how AI is reshaping work and employment for underrepresented groups, and to stimulate research on how HRM can foster more inclusive, equitable, and human-centered AI-enabled workplaces. It seeks to bring together multidisciplinary scholarship examining both the opportunities and risks of AI for women, migrants, refugees, older workers, people with disabilities, and Indigenous employees. These aims will be met by inviting conceptual, empirical, and review-based contributions that examine bias, accessibility, trust, human–AI collaboration, inclusive design, and responsible governance in AI-HRM systems. In doing so, the Special Issue will generate new theoretical insights, practical implications, and future research directions that position HRM scholarship to respond more effectively to the challenges and possibilities of AI-driven organizational change, while helping organizations foster more equitable, inclusive, and human-centered human–AI interactions.

This Special Issue offers an original contribution by bringing AI, HRM, and diversity management into one integrated conversation, rather than treating them as separate streams. Its novelty lies in advancing understanding of how AI reshapes workplace inclusion, exclusion, and inequality for underrepresented groups, while also showing how HRM can design more fair, ethical, and human-centered AI systems. The issue moves beyond predominantly technical and productivity-focused debates by encouraging new theory on algorithmic bias, intersectionality, trust, and human–AI collaboration, alongside practical insights on inclusive AI design, reskilling, AI literacy, equitable adoption, and governance. It also highlights the importance of human oversight, minority voice, and cross-sector collaboration in reducing bias and improving responsible AI implementation. By bridging the fields of HRM, organizational behavior, information systems, and diversity and inclusion, this Special Issue contributes to positioning HRM scholarship at the forefront of debates on responsible AI and inclusive organizational transformation.

Potential topics

• AI in Talent Acquisition and Selection: How do AI-based recruitment and selection systems create, reproduce, or mitigate biases against candidates from underrepresented groups (e.g., people with disabilities, older workers, migrants), and what mechanisms can ensure equitable outcomes in AI-assisted hiring?
• Accessibility and Inclusion in Employment: In what ways can AI technologies be designed to enhance workplace accessibility, enable equitable participation, and expand employment opportunities for employees with disabilities or other marginalized groups?
• Human–AI Collaboration and Bias Mitigation: How can human oversight, input from minority employees, and inclusive design processes help AI systems learn more equitably—reducing bias and performing more human-like or human-better tasks in HR contexts?
• Talent Development, Reskilling, and Learning in the Age of AI: How can HRM leverage AI-enabled learning, development, and reskilling systems to support diverse employees and prepare both workers and organizations for ethical AI use? How can organizations use talent development strategies to train both humans and AI systems toward ethical and inclusive performance?
• Ethical, Legal, and Governance Challenges in AI-HRM: What are the ethical and legal implications of using AI in managing diversity and disability inclusion, and how can organizations establish responsible AI governance frameworks aligned with human rights and DEI principles?

Timeline

• January 9, 2026: Opening date for manuscripts submissions
• January 3, 2027: Closing date for manuscripts submission

Digitalisation has multiplied the media, channels, and touchpoints through which consumers and firms interact, reshaping the customer journey around digital experiences. In parallel, the rapid maturation of Machine Learning, Deep Learning, and especially Artificial Intelligence (AI), is pushing both scholars and managers to rethink how human actors engage with, rely on, and derive value from technological systems, and to revisit the design of these interactions as well as their governance.

A growing body of research shows that AI is increasingly becoming a central shopping companion across the entire customer journey, supporting consumers from product discovery and evaluation in the pre-purchase stage, to basket building and checkout at purchase, and into post-purchase activities such as customer service and returns.

Compared with traditional recommender systems, AI companions are powered by language models and conversational interfaces that do more than filter information. They can operate as interactive decision partners, engaging consumers in dialogue, adaptively tailoring support, and providing cognitive scaffolding. Through these mechanisms, AI companions can directly shape how preferences are constructed, how uncertainty is interpreted, and how choices are ultimately made.

The conversational nature of AI-mediated assistance introduces dynamics that are not yet fully understood. These include real-time persuasion and framing, the delegation of decisions to 'the system', the emergence and calibration of trust, perceived agency and controllability, the role of explanation and disclosure, and the effects of anthropomorphism. Taken together, these elements can make the interaction feel closer to an ongoing relationship than to a single, isolated touchpoint.

The literature has devoted little attention to how AI companions affect downstream outcomes that matter for both retail performance and consumer welfare. In particular, we still know too little about whether, and under what conditions, an AI companion can increase or decrease basket conversion among consumers who are already expected to purchase, or how conversational assistance may contribute to monitoring and shaping assortment status. Outcomes such as basket conversion, assortment shaping, and returns are strategically consequential for firms, platforms, and shoppers, yet they remain less systematically examined than upstream constructs such as adoption, user experience, trust, and intention-based measures.

Our understanding remains incomplete regarding the conditions under which assistant guidance improves preference-product fit and long-term value, versus when it accelerates decisions that amplify mismatch, encourage opportunistic trial behaviours, or increase operational costs through returns. This Special Issue aims to address this gap by inviting new conceptual, methodological, qualitative, and quantitative contributions that advance insight into this domain.

Potential topics

• What psychological and informational mechanisms explain the effect of AI companions on basket conversion? How do these effects vary with trust, perceived agency, and disclosure in conversational assistance?
• To what extent do AI companions alter assortment exploration and basket structure in terms of variety seeking, complementarity, substitution patterns, and price sensitivity?
• Which conversational features (e.g., framing, explanations, tone, memory) act as key drivers?
• When does AI-induced conversion translate into higher decision quality and ex post satisfaction, and when does it instead generate regret, decision deferral, or dependence on assistance?
• What is the impact of AI companions on returns, distinguishing between informational mismatch, fit errors, overbuying, and opportunistic behaviours, and how do these dynamics vary across product categories and channels (online vs. omnichannel)?
• How can companion design (e.g., explainability, user controls, limits on persuasion, 'pro-social' nudges) reduce returns and increase decision quality without depressing conversion and revenues?
• What are the distributional effects of AI companions on consumers with different levels of digital literacy and vulnerability, and which policies or governance standards are needed to ensure transparency, accountability, and fairness?

Timeline

• January 5, 2026: Opening date for manuscripts submissions
• January 11, 2026: Closing date for manuscripts submission
• April 15, 2027: Final acceptance date

Jens Hogreve, Catholic University of Eichstätt-Ingolstadt

Ilias Danatzis, King's Business School

Joy Field, Boston College

Kristina Lindsey Hall, Louisiana State University

Marah Blaurock, Catholic University of Eichstatt-Ingolstadt

The service-profit chain (SPC) is one of the most influential concepts in service research. Introduced by Heskett et al. (1994), the SPC emphasizes the significance of both internal and external service quality for a firm's long-term financial performance. It bridges service companies' internal and external environments by positing employee satisfaction, loyalty, and productivity as mediators between internal and external service quality, and customer satisfaction and loyalty as mediators between external service quality and financial outcomes. In doing so, the SPC links three domains: (a) internal marketing, encompassing internal service quality and employee attitudes and behaviors; (b) external marketing, reflecting external service quality and customer attitudes and behaviors; and (c) firm performance, including revenue growth and profitability. Over the past three decades, the SPC has gained considerable scholarly attention and continues to inspire organizations that place employees and customers at the center of their strategies.

Now is a particularly timely moment for the Journal of Service Management to devote a special issue to the SPC. Despite its widespread influence, recent developments have challenged its original assumptions, calling for both revision and reimagination. Three developments in particular highlight the need for renewed scholarly attention.

First, the digital transformation of service work, including the rise of service robots, automation, and artificial intelligence (AI), is profoundly reshaping the roles of service employees. These changes alter the meaning of internal service quality and affect how customers evaluate service encounters. SPC research must therefore account for the opportunities and unintended consequences of AI-driven transformations.

Second, employee and customer well-being have become central concerns in both academic scholarship and practical applications. Well-being increasingly influences transformative service research and may complement—or even substitute—the SPC's traditional satisfaction measures. Understanding whether well-being is a stronger predictor of loyalty and performance outcomes is a critical next step.

Third, external shocks and turbulent environments, from pandemics to economic crises, challenge the stability of SPC relationships. These shocks not only critically disrupt service provision but also have profound effects on customer-employee interactions. Service employees must quickly adapt to shifting processes, environments, and often enforce service rules that are not well received by customers, adding another layer of stress to their already challenging jobs. Disruptive events, therefore, raise questions about how resilient the SPC framework is and how firms can adapt internal and external marketing practices to maintain performance in uncertain times.

Taken together, these challenges provide an impetus to further develop and extend the SPC. This special issue aims to build on its rich legacy while critically assessing how it must evolve to remain relevant. By explicitly including practitioners' perspectives, the issue will not only illustrate how the SPC is lived in practice but also identify where its core links need to be rethought. We call for an interdisciplinary perspective, engaging scholars across marketing, management, operations, finance, and human resources to reflect, revise, and reimagine the SPC for the next generation of service research.

Potential topics

• How do service employees adapt to and engage with AI and automation, and impact SPC relationships? What roles do technostress, co-design opportunities, or AI functions play in shaping acceptance?
• How does AI reshape customer experiences and loyalty? Does it complement or substitute for human frontline interactions, and what unintended effects may emerge?
• What are the optimal levels of AI implementation within the SPC across contexts, and how do they vary by industry, leadership, or employee/customer loyalty levels?
• Does employee or customer well-being serve as a stronger predictor of loyalty and performance than traditional satisfaction measures?
• How do well-being and satisfaction interact, and what are their respective short-term versus long-term effects on firm performance?
• How do external shocks and turbulent environments moderate SPC relationships?
• How do service employees adapt to shifting processes, environments, and interaction demands during disruptive events, and how does this adaption affect key SPC links?
• How does enforcing service rules - especially when facing customer resistance or misbehavior - affect employee well-being, service quality, and the strength of SPC linkages?
• To what extent are SPC links universal versus context-sensitive (e.g., across industries, cultures, or service types)?
• What new methodological approaches (e.g., longitudinal, experimental, multi-source, big data) can uncover causal, non-linear, or feedback effects within the SPC?
• How do under-researched internal service marketing practices (e.g., workplace design, digital tools for customer service) influence employee satisfaction, loyalty, and productivity?
• Which HRM systems and practices are most effective in enhancing internal service quality, and how do they jointly shape employee and organizational outcomes?
• How do alternative work arrangements (e.g., gig, remote, hybrid) influence employee satisfaction and SPC outcomes?

Timeline

• January 7, 2026: Opening date for manuscripts submissions
• January 11, 2026: Closing date for manuscripts submission

Zuzana Virglerova, Tomas Bata University in Zlín

Nicola Capolupo, San Raffaele Roma University

The aim of this special issue is to explore how algorithmic and behavioral biases influence the adoption, use, and outcomes of Artificial Intelligence (AI) in organizational settings. As AI systems increasingly shape decision-making, human resource management, operational processes, and strategic planning, understanding how biases emerge and interact within socio-technical systems has become a critical scholarly and societal challenge.

The special issue seeks to integrate interdisciplinary perspectives to explain how biases arise both from AI systems and from the human and organizational decisions that design, implement, interpret, and govern them. It offers an original contribution by bridging two research streams that are often treated separately: algorithmic biases embedded in data, models, and computational architectures, and behavioral biases rooted in human cognition, organizational routines, and institutional structures. Building on socio-technical systems theory, AI is conceived as co-constructed by technologies, individuals, and the environments.

The originality of this issue lies in examining the dynamic interplay between technical biases (e.g., biased training data, model opacity, feedback loops) and cognitive and organizational biases (e.g., overconfidence, automation bias, anchoring, resistance to change), and how these interactions shape organizational outcomes. Recent research has emphasized the role of behavioral biases in organizational and entrepreneurial decision-making, highlighting the contextual and institutional moderators of such biases. Emerging work further demonstrates how digital technologies and AI systems interact with managerial cognition and organizational structures, creating hybrid forms of bias that are neither purely human nor purely algorithmic. These insights underscore the need for integrated frameworks and methodologies that capture the socio-technical nature of AI adoption in organizations.

The topicality of this special issue is reinforced by the rapid diffusion of AI across organizational domains, including human resource management and recruitment, decision support systems, circular economy and sustainability models, consumer behavior analytics, and work process automation. Public debates on algorithmic discrimination, opacity, accountability, and the future of work have intensified, highlighting the urgent need for rigorous research on fairness, transparency, and governance in AI-enabled organizations. Furthermore, global regulatory initiatives such as the European AI Act, along with ethical frameworks proposed by international organizations, emphasize the societal relevance of understanding and mitigating bias in AI-driven processes.

Empirical qualitative and quantitative studies are welcome; however, submissions must be based on international data or multi-country contexts and should not be limited to a single national perspective. Literature reviews are welcome if they are systematic or bibliometric in nature; exploratory or narrative metasyntheses will not be accepted. Conceptual papers are also encouraged, only if offering exceptionally strong and innovative theoretical insights.

Potential topics

• Algorithmic biases in AI systems applied to HR and decision-making processes
• Cognitive and behavioral biases of users interacting with AI systems
• Governance, ethics, and regulation of AI in organizations
• Methodologies for identifying and mitigating socio-technical biases in work processes
• Organizational and social impacts of AI on equity, environment, inclusion, and job quality

Timeline

• June 1, 2026: Opening date for manuscripts submissions
• July 31, 2027: Closing date for manuscripts submission

Teemu Laine, Tampere University

Tuomas Korhonen, Tampere University

Carsten Rohde, Copenhagen Business School

Vesa Tiitola, Tampere University

The special issue will reflect and revisit the role and value of management accounting (MA) in the current evolving and complex business context. The value of management accounting lies in its ability to serve managers in different roles in their decision making and broader managerial work, all situated in their operational environment. The nature of managerial work and the potential support from MA therein are being challenged by several intertwined contemporary developments: a) technology advancements, such as algorithms and the artificial intelligence (AI) penetration to many professions, MA included, b) human judgement, ethical considerations and responsible behaviour, and c) global and local issues that influence the business context, such as geopolitics and climate change.

The originality of the special issue stems from its integrative view on the timely challenges influencing MA and the necessity to revisit the foundations of management accounting and control. Now, we need to take a closer look at the implications of the timely challenges with a realistic, but rather long-term view – 2020s and beyond. The MA profession has witnessed some remarkable changes, but some issues have remained: decisions are still made under uncertainty, with limited relevant data available, and human judgement is still fundamental to set the directions for organizations and take responsibility of decisions. Meanwhile, new requirements for information provision emerge as well as new challenges of understanding the local and global business contexts.

MA is a wide and connected field that builds new knowledge for a wider array of business needs, processes and problems. MA is situated in time and place, i.e., in a context. Indeed, the desired perspectives of the special issue are connected to the organizational practices: operations, offerings, business models and value-generation processes in different types of organizations. All these practices could/would benefit from MA: but how is MA (information) different in the 2020s compared to earlier years? What is expected from MA in the future when we look at where the operational environment is and its multiplicity of trajectories could be? Detailed examinations of these perspectives in the contemporary MA contexts as well as research methods to engage with those practices remain too scarce. Especially, there is a need for contributions on the new and desired roles of MA based on in-depth empirical understanding and evidence, especially qualitative in nature.

The special issue stems from the Manufacturing and Service Accounting Research (MSAR) 2026 conference, in Tampere, 6/2026, with the theme "Understanding, extending and redefining the value of management accounting in organizations". MSAR 2026 hosts scholars interested in both the traditional "core" MA topics and those that are more emergent in the 2020s and beyond. A special feature of the conference is the tradition of in-depth access to real-life cases and practices, which greatly benefits the aims of this special issue.

Potential topics

• Revisiting the Foundations of Management Accounting (MA) and Management Control (MC)
• Digitalization, Artificial Intelligence, and the "Post-Analytics" Era
• Human Judgment and Accountability in the Age of AI
• Sustainability, Ethics, and the Evolving Role of Management Accounting
• Transformation and Innovation in MA&MC Systems and Practices
• Management Accounting that Expands over Organizational Boundaries
• Emerging Business Models and Performance Paradigms
• Inter-, Multi-, and Transdisciplinary MA Studies that Increase Practical Relevance of Research

Timeline

• June 8, 2026: Opening date for manuscripts submissions
• September 30, 2026: Closing date for manuscripts submission

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

Victoria-Sophie Osburg, Catholic University Eichstätt-Ingolstadt

Dina Khalifa, Regent's University London

Jonas Holmqvist, KEDGE Business School Bordeaux

Vignesh Yoganathan, Aston University

The concept of luxury is undergoing significant changes in the digital age, merging traditional ideas of exclusivity and craftsmanship with innovative digital experiences. Technology increasingly influences our daily lives as luxury brands increasingly apply digital tools to enhance the customer experience, reshaping the luxury markets and fundamentally altering how luxury is perceived, experienced, and consumed. Additionally, changing societal norms and consumer expectations require luxury brands to engage in new ways, such as through brand activism. This transformation impacts how luxury brands connect with consumers and deliver value.

Virtual contact points are increasingly replacing or supplementing human interactions, providing personalized and seamless experiences. Consumers can now display their luxury consumption on social media and explore and interact with luxury products through immersive virtual experiences, such as digital flagship stores, which blur the lines between physical and digital realms. Furthermore, digital technologies enable unprecedented levels of personalization.

As luxury services represent a rapidly growing sector, integrating service robots in luxury retail presents a complex landscape of opportunities and challenges. Robots can enhance efficiency and create novel customer experiences, but their integration into luxury settings may also threaten the human-centric approach despite that some luxury consumers prefer a strong human interaction and/or taking on an active role themselves. This situation presents a dilemma for luxury brands, particularly regarding how much they should substitute or augment human staff to provide the best customer experience.

Moreover, entirely new avenues for luxury consumption are emerging. Virtual real estate, for instance, has become a new opportunity for luxury investment and expression, with the market for virtual luxury goods projected to reach $50 billion by 2030. Digitalization can also empower consumers, for instance, the use of Blockchain can increase traceability, allowing consumers to follow the entire production process and supply chains of their products.

These examples illustrate how luxury consumption is undergoing fundamental changes in the digital era. At the same time, there are also risks involved, and some luxury brands deliberately limit their digital interactions to retain exclusivity. As luxury brands navigate these drastic changes, it is essential to understand consumer reactions toward these newly emerging forms of luxury. As luxury consumption becomes more widespread, understanding the impact this has on consumers becomes crucial, and the role technology plays in this context. A theory-driven approach is needed to thoroughly examine whether luxury consumption should be redefined in the digital age. Moreover, the discourse on the (in)compatibility of sustainability and luxury consumption must be adapted to the digital context, as new ethical and responsibility-related questions may arise.

Potential topics

• Drivers of consumer adoption of technological solutions in luxury markets
• The evolving understanding of luxury among consumers in the digital age
• The impact of digital technologies on luxury value perceptions and consumer experiences
• Consumers' preferences and needs regarding the joint implementation of technology and a human touch in luxury consumption
• Consumer reactions to virtual luxury goods and experiences
• Consumer agency and engagement in luxury through digital tools
• Tensions that consumers face when brands integrate luxury with technology
• Luxury brands' opportunities to navigate technological innovation while maintaining brand heritage
• Managing the paradox of the ephemerality of technology with the permanence of luxury brand identity
• The role of social media and influencers in shaping digital luxury consumption, including virtual influencers
• The integration of physical and digital luxury experiences in omnichannel strategies
• Consumers' ethical considerations in virtual luxury markets, including sustainability, traceability, and inclusivity
• Generational differences in the adoption of digital luxury experiences and how luxury brands can cater to cross-generational consumer needs

Timeline

• March 6, 2026: Opening date for manuscripts submissions
• November 30, 2026: Closing date for manuscripts submission