Muhammad Imran Rasheed, University of Surrey

Barbara Rita Barricelli, Università degli Studi di Brescia

Waseem Ul Hameed, Universiti Utara Malaysia

Artificial intelligence is increasingly becoming an interactive workplace technology through which employees communicate, collaborate, make decisions, and perform everyday work tasks. From generative AI assistants and algorithmic decision-support systems to intelligent monitoring tools and conversational agents, workers are engaging with AI systems on a continuous basis. Consequently, understanding how employees interact with AI technologies, experience AI-mediated work processes, develop trust in intelligent systems, and adapt their behaviours in response to AI-generated outputs has emerged as a central challenge for Behaviour & Information Technology research.

While existing research has extensively examined technological adoption and performance outcomes, comparatively less attention has been devoted to the behavioral, cognitive, relational, and psychological implications of intelligent systems in workplace settings. Emerging technologies are redefining human-computer interaction in organizational contexts and raising important questions regarding trust in AI, algorithmic transparency, digital autonomy, technostress, cognitive overload, human-AI collaboration, and behavioral adaptation.

While different special issues have recently focused on artificial intelligence in organisations, information systems and the future of work, little has been done to understand workplace AI from the perspective of human–computer interaction and human-centered design. Existing special issues have primarily addressed topics such as technological adoption, organizational outcomes, digital transformation, and AI implementation. In contrast, this Special Issue focuses on the human-intelligent system interaction, covering employee experience, trust calibration, behavioral adaptation, user engagement, explainability, cognitive processes, and the design of psychologically sustainable AI-enabled workplaces.

The Special Issue proposed is well aligned with the mission of Behaviour & Information Technology and brings together research on human behaviour, user experience, interaction design, intelligent systems and digital work. The special issue aims to contribute to a better understanding of not only what AI does in organizations, but also how employees experience, interpret, interact with, and adapt to intelligent technologies in everyday work settings.

In addition to empirical and theoretical contributions, the Special Issue strongly encourages design-oriented and design science research that develops, evaluates, and validates human-centred AI systems, intelligent interfaces, explainability mechanisms, trust-enhancing design features, and interaction designs that promote positive employee experiences and responsible AI use.

This special issue focuses on the intersection of intelligent technologies, workplace behavior, and information systems. Contributions examine how AI and digital systems influence individual, team, and organizational behavior in technology-mediated work environments. The issue is especially interested in studies investigating behavioral and cognitive responses to intelligent systems, human-AI interaction dynamics, digital work experiences, and the broader organizational implications of AI-enabled technologies. Contributions employing quantitative, qualitative, mixed-method, longitudinal, experimental, computational, ethnographic, and design-science approaches are highly encouraged.

Potential topics

• Employee interaction with AI-enabled systems
• Human reliance, overreliance, and resistance toward AI
• Behavioral adaptation to intelligent technologies
• Human-AI collaboration and hybrid decision-making
• Trust formation in intelligent systems
• Algorithmic transparency and explainable AI
• Employee perceptions of AI fairness and accountability
• Behavioral responses to opaque decision systems
• Technostress, digital exhaustion, and AI fatigue
• Cognitive overload and information processing under AI systems
• AI dependency and employee wellbeing
• Digital distraction and attention fragmentation
• AI-supported autonomy and employee empowerment
• Algorithmic management and behavioral control
• Digital surveillance and monitoring technologies
• Behavioral consequences of automated performance systems
• Ethical design of workplace AI systems
• Human-centered intelligent systems
• Responsible AI implementation in organizations
• Designing psychologically sustainable digital work environments
• AI-enabled learning systems and digital upskilling
• Employee agility and adaptability in AI-enabled workplaces
• Behavioral implications of reskilling and task transformation
• Human learning behavior in technology-mediated contexts
• AI-supported teamwork and collaboration
• Virtual collaboration and intelligent communication systems
• Social interaction patterns in AI-mediated environments
• Team trust and coordination under intelligent systems
• AI in healthcare, hospitality, education, finance, and public services
• Gig work, platform labor, and digital employment systems
• Cross-cultural and cross-national comparisons
• Remote and hybrid work environments
• User experience (UX) of workplace AI systems
• Human-centered design of intelligent workplace technologies
• Conversational AI and intelligent assistants at work
• Adaptive and personalized workplace interfaces
• AI usability and user acceptance in organizational settings
• Interaction design for human-AI collaboration
• Human oversight and human-in-the-loop systems
• Trust calibration in intelligent systems
• Employee engagement with AI-enabled technologies
• Interface design for responsible and ethical AI use
• User perceptions of agency, control, and autonomy in AI-mediated work environments

Timeline

• July 31, 2027: Manuscript submission deadline

Asha Thomas, Wrocław University of Science and Technology

Moreno Frau, Corvinus University of Budapest

Dominyka Venciūtė, ISM University of Management and Economics

Across contemporary organizations, advances in artificial intelligence (AI) are transforming AI from a discrete technological resource into a systemic organizational capability that actively shapes decision-making, business model innovation, and competitive advantage. Traditionally, AI interfaces have largely been reactive, responding to human prompts and predefined inputs. The emergence of Agentic Artificial Intelligence represents a fundamental shift, as agentic systems are designed to operate with increasing autonomy, enabling goal-driven planning, workflow orchestration, coordination across systems, and machine-initiated action with limited human intervention.

For organizations, this growing autonomy presents both significant opportunities and substantial risks. Agentic AI promises new forms of value creation by enhancing efficiency, scalability, personalization, and decision quality across organizational functions such as human resource management, marketing, customer engagement, knowledge management, and operations. At the same time, the delegation of agency to autonomous systems heightens concerns related to governance, transparency, accountability, and oversight, particularly when organizations have limited visibility into how agentic systems reason, learn, and act. Moreover, misaligned interactions and problematic resource integration may produce unintended negative outcomes, underscoring the coexistence of value creation and value co-destruction in AI-enabled organizational processes.

Despite these unresolved challenges, agentic AI is no longer a speculative phenomenon. Organizations have already begun embedding agentic systems into core practices, including recruitment, onboarding, performance management, customer service, marketing operations, and knowledge-intensive work. This diffusion reflects a broader shift in which AI is increasingly understood as a normalized and enduring component of contemporary organizational and marketing systems rather than a temporary technological trend. In parallel, early implementations in knowledge management demonstrate how agentic systems can unify fragmented knowledge bases, dynamically adapt insights, and support continuous organizational learning.

From an academic standpoint, these developments challenge existing organizational and information management theories. While socio-technical systems theory, agency theory, and organizational learning have traditionally conceptualized AI as a support tool within human-centric systems, they offer limited explanatory power for autonomous, multi-agent systems capable of independent coordination and action. In marketing and customer engagement contexts, interactive value formation is increasingly shaped by emotional and relational dynamics emerging from human–AI interactions, further complicating assumptions about control and responsibility. As such, new theoretical perspectives are needed to capture agency, accountability, and knowledge dynamics in AI-enabled organizations.

Potential topics

• How does Agentic AI enable new forms of value creation, capture, and measurement in organizations? Under what conditions can agentic systems also lead to value co-destruction or unintended negative outcomes due to misaligned autonomy, resource integration, or decision logic?
• How do organizations design governance, accountability, trust, and regulatory compliance mechanisms for Agentic AI systems operating with increasing autonomy? What challenges arise when human oversight is limited or distributed across functions?
• How do ethical considerations, responsibility, and moral agency evolve when AI systems act as semi-autonomous organizational actors rather than decision-support tools?
• How does Agentic AI reshape business process redesign, orchestration, and automation across organizational functions such as marketing, human resource management, operations, finance, and customer engagement?
• In what ways is artificial intelligence becoming normalized within organizational and marketing practice, shifting from experimental adoption to routinized, AI-embedded decision-making and workflows?
• How does Agentic AI influence workforce transformation, the future of work, and human resource management practices, including recruitment, performance evaluation, learning, and employee autonomy?
• How does the adoption of Agentic AI differ across organizational contexts, such as small and medium-sized enterprises versus large corporations, and what factors shape successful implementation and impact?
• How do emotional, relational, and interactional dynamics shape human–AI engagement in Agentic AI–driven sales, marketing, and customer experience contexts?
• How can human–AI collaboration and human-in-the-loop design be sustained when AI systems increasingly initiate actions, coordinate tasks, and learn autonomously?
• How do multi-agent systems, coordination mechanisms, and organizational architectures evolve as multiple human and artificial agents interact within complex socio-technical environments?
• How can existing theories of agency, organizational learning, and socio-technical systems be extended or reconfigured to explain machine agency and autonomous action in Agentic AI–enabled organizations?
• How does Agentic AI transform knowledge management, organizational learning, and decision support by enabling systems that not only retrieve and integrate knowledge but also reason, adapt, and act upon it?

Timeline

• March 1, 2026: Opening date for manuscript submissions
• September 30, 2026: Closing date for manuscript submissions

Mark Camilleri, University of Malta

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

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

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

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

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

Potential topics

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

Timeline

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