Decision Analytics and Service Science Track

Digital technologies are becoming central to advancing sustainability goals across organizations, communities, and societies, while recent advances in artificial intelligence (AI), machine learning, and decision intelligence are reshaping how complex socio-technical systems are designed, governed, and optimized. As environmental challenges intensify, there is growing recognition that AI-enabled decisionsupport systems must move beyond static optimization toward adaptive, context-aware approaches capable of navigating the complexity and uncertainty inherent in sustainability transitions.

In alignment with the latest international sustainability initiatives, including the United Nations’ Sustainable Development Goals (SDGs) and the Paris Agreement, this minitrack invites research and practitioner contributions that explore the role of AI-driven analytics, decision support, and intelligent systems in advancing sustainability efforts. This minitrack focuses on the intersection of adaptive AI, decision intelligence, and sustainable systems within the context of green information systems (Green IS), examining how intelligent, data-driven approaches can be designed, deployed, and governed to enable environmentally responsible, socially equitable, and economically viable outcomes.

We welcome theory-driven and applied research on (generative) AI, foundation models, responsible AI, decision intelligence, environmental analytics, and next-generation digital technologies, such as edge AI, IoT, and hybrid cloud architectures, for sustainability applications. Submissions may also address conceptual advances in sustainable information system design and AI-driven decision technologies that contribute to SDGs. Possible topics Include (but are not limited to):

• AI-powered analytics and decision technologies for climate resilience and green transitions
• Generative AI and large language models for supporting the Sustainable Development Goals (SDGs)
• Decision intelligence frameworks integrating environmental, social, and governance (ESG) metrics
• Machine learning for environmental sensing, prediction, and early warning systems
• Smart agriculture, precision farming, and circular economy innovations
• Energy informatics and AI-driven energy efficiency solutions
• Environmental intelligence and decision support for ecological sustainability
• Environmental knowledge acquisition, reasoning, and computational modeling
• Environmental impacts of AI systems and strategies for sustainable AI development
• Sustainable Environmental Management Information Systems (EMIS)
• AI-enhanced Environmental Decision Support Systems (EDSS)
• Geographic Information Systems (GIS) for climate adaptation and sustainability
• Green IS design principles and sustainable IT infrastructure
• AI-driven environmental cyberinfrastructure and digital twins for sustainability
• AI in environmental risk assessment and resilience planning
• Evaluation of the sustainability impacts of AI-driven interventions

We invite submissions that aim to advance theory, methods, and practice at the frontier of adaptive AI and decision intelligence, while highlighting their role in shaping more sustainable and responsible digital futures.

Minitrack Co-Chairs:

Omar El-Gayar (Primary Contact)
Dakota State University
Omar.El-Gayar@dsu.edu

Abdullah Wahbeh
Slippery Rock University
abdullah.wahbeh@sru.edu

Ahmed Elnoshokaty
California State University San Bernardino
ahmed.elnoshokaty@csusb.edu

Industrial value creation is undergoing a fundamental structural transformation. Resource constraints, market saturation, geopolitical volatility, and societal expectations challenge firms to move beyond linear, product-centric logics. Industrial competitiveness increasingly depends on the deliberate shaping of service-based ecosystems that enable growth, resilience, and circular value creation.

This transformation is not primarily technological but institutional and strategic. It requires reconfiguring governance mechanisms, incentive structures, standards, contracts, and inter-organizational collaboration. In this new business logic, ecosystems become the central unit of analysis: firms compete and innovate as part of interconnected networks rather than in isolation.

This minitrack examines how industrial ecosystems are intentionally shaped along three interrelated strategic pathways:

• Circularity: This pathway examines the institutional work required to close resource loops and maintain product value over time through restorative practices like remanufacturing and refurbishment.
• Growth: This pathway explores the transformation to create additional customer value by taking a product- to a more service-centric approach (servitization).
• Resilience: This pathway focuses on the capacity of industrial ecosystems to adapt, evolve, or reshape under exogenous shocks and crisis.

We welcome theoretically plural contributions grounded in diverse perspectives, such as Service-Dominant (S-D) logic, Institutional Theory, Systems Thinking, Information Systems (IS), or Strategic Management. Also, we encourage researchers to analyze these transformations across multiple (nested) levels, and longitudinal. Hereby we welcome conceptual, empirical, and design science contributions. Topics might include (but are not limited to):

• Resilient Service Ecosystems and Circular Services
  1. Design and management of services and business models (e.g., product-as-a-service, equipment-asa- service, pay-per-use, sharing economy, outcome-based-contracting)
  2. Organizational transformation towards resiliency: strategies, competencies, and change management and the role of technology in shaping this transformation
• Value Co-Creation
  1. Analyzing the collaborative processes and mechanisms through which multiple actors (e.g., suppliers,
  2. customers, competitors, and regulators) integrate resources to co-create value within industrial ecosystems
  3. Understanding exploring how the perception of value (“value in context”) changes across different institutional settings and how ecosystems adapt to these diverse requirements
• Institutional Dynamics
  1. Institutions, governance mechanisms and collaboration strategies in industrial ecosystems (e.g., organizational change, market shaping)
  2. Investigating how actors intentionally create, maintain, or disrupt institutions (norms, rules, and beliefs) to establish new service-led or circular market logics
• Digital Technologies as Enablers
  1. Strategic management of digital infrastructures to coordinate ecosystem actors and enable servicebased growth
  2. Organizational practices for embedding data-driven decision-making into industrial transformation processes
  3. Capability development and managerial roles for orchestrating data-enabled service ecosystems
• Tensions, Paradoxes, Trade-offs, and Structural Constraints
  1. Examining how path dependence and structural inertia constrain strategic options and influence ecosystem reconfiguration
  2. Analyzing trade-offs and paradoxes between efficiency, scalability, resilience, and circular value creation, including unintended consequences such as rebound and backfire effects
  3. Investigating how asymmetric resource dependencies and control over critical assets, interfaces, or data shape coordination mechanisms, or ecosystem stability

This minitrack welcomes contributions from academics, practitioners, and policymakers to advance both theoretical understanding and practical applications toward sustainable and resilient industrial ecosystems.

Minitrack Co-Chairs:

Christian Koldewey (Primary Contact)
Paderborn University and Fraunhofer Institute for Mechatronics Systems Design IEM
christian.koldewey@hni.upb.de

Martin Ebel
Ruhr-Universität Bochum
Martin.Ebel@isse.ruhr-uni-bochum.de

Johannes Winter
L3S Research Center at Leibniz University Hannover and Technical University Braunschweig
winter@L3S.de

Understanding differences between users and user groups is central to advancing knowledge about technology use. Yet, in prior information systems (IS) research, such differences have often been treated superficially or reduced to overly simplistic categorisations. Some user groups are routinely overlooked or framed as deviations from presumed “normal” users, contextual influences are insufficiently considered, and user characteristics are reduced to simple control variables. How IS research conceptualizes, categorizes, and compares users shapes what we come to know about technology use, and whether we generate deeper insight, or reinforce stereotypes and obscure heterogeneity. This minitrack provides a space for researchers to explore technology use as it unfolds for diverse users in everyday life, over time, and across personal, social, and organizational contexts.

The rapid development and widespread diffusion of AI—especially large language models (LLMs) and generative AI—further heightens the importance of studying user differences. AI has the potential to act as an equalizer by bridging IT skill gaps through adaptive nterfaces. However, unequal access to resources and varying levels of AI literacy may instead contribute to widening existing digital divides. The rise of AI also compels us to reconsider what kinds of digital literacy individuals need and how to support these skills across different user groups. Furthermore, AI applications trained on biased data have already been shown to amplify biases in areas such as job recruitment and healthcare, underscoring the ethical and societal relevance of examining user differences in technology use.

A recurring issue in prior research is that user differences are reduced to simple control variables, with study samples limited to narrow segments of the population (e.g., working-age employees in organizational settings). Even when differences are identified, studies frequently provide limited explanation of why these differences emerge, how they vary across contexts of use, and what their broader implications are for research and practice. As a result, our understanding of user heterogeneity in technology use remains fragmented, contributing to oversimplified generalizations, and designs that fail to account for variation across users and situations.

These limitations become critical when access to essential services, such as healthcare, and participation in social life increasingly rely on digital technologies. Technology use is no longer purely voluntary but a necessity. For example, the diffusion of self-service technologies in public services may overlook differences in users’ capabilities, if target users are treated as a homogeneous group. Also, discussions about the risks or benefits of social media or AI use require more nuanced insight into how technologies are experienced in different social and institutional contexts. These challenges call for research that examines user differences in technology use across age, life situations, and other contextual dimensions in a systematic and analytically grounded manner.

In this minitrack, we invite multidisciplinary and methodologically diverse studies that place user differences at the center of investigations into technology use both in personal and in organizational contexts. We welcome research that compares user groups, focuses on the distinctive characteristics of specific groups, or treats user differences as a central construct in research design and analysis. Contributions that critically examine prevailing assumptions and stereotypes related to technology use are encouraged.

We invite empirically grounded studies that advance more nuanced understandings of user heterogeneity. We welcome innovative theoretical and methodological approaches, rigorous, creative, and responsible research that advances a richer and more inclusive understanding of technology use across diverse users and contexts. Relevant topics for this minitrack include, but are not limited to:

• Differences and similarities in technology acceptance and use between various user groups – both in personal and in organizational contexts
• Stereotypes related to technology acceptance and use
• Discrimination (e.g., ageism) related to technology acceptance and use
• Age, generational and other user differences in the context of digital divide and digital exclusion
• User differences in various dark side of IT use phenomena (e.g., technostress)
• How major life events (e.g., entering the workforce, retirement) shape technology use across the life span
• Multi-dimensional approaches to exploring variations in technology use, considering factors such as culture, access, daily activities, personality, values and personal interests
• Digital habit formation, habit-driven technology use; technology habits across different user groups
• AI as an equalizer or divider
• How LLMs are utilized and experienced in different contexts and user groups

Minitrack Co-Chairs:

Anna Sell (Primary Contact)
Linnaeus University
anna.sell@lnu.se

Riitta Hekkala
Aalto University
riitta.hekkala@aalto.fi

Markus Makkonen
Tampere University
markus.makkonen@tuni.fi

Pirkko Walden
Åbo Akademi University
pirkko.walden@abo.fi

We would like to invite papers, which address a broad and methodologically varied range of topics related to artificial intelligence systems evaluation. A wide range of methodological approaches and results are welcomed, but can include:

• Evaluation of AI systems, that is, AI applications or agents built on top of AI foundation models
• Empirical evaluation of prototype or deployed AI applications or agents
• Empirical studies evaluating a proprietary or open source model’s performance holistically or on a specific task or benchmark
• Case studies evaluating AI systems deployed in particular industry organizations or sectors
• Comparative evaluation of two or more systems or models for particular tasks
• Comparative evaluations of AI systems against human performance on particular tasks
• Proposal of new evaluation approaches and/or task-specific benchmarks
• Evaluation of prompting and prompt engineering approaches
• Evaluation of AI systems in education
• Proposal and evaluation of novel prompting approaches and techniques
• Proposals for evaluating new aspects of AI models or systems not yet commonly measured
• Case studies of the performance of AI models and systems when deployed in real-world settings or use cases
• Evaluation of the computational performance of AI systems or models
• Evaluation of the social impact of AI systems
• Novel frameworks for AI system evaluation

In the post-ChatGPT world with increasingly powerful AI foundation models, and AI-based applications and agents rapidly becoming available and already being used and deployed by consumers and businesses in real-world settings, the evaluation of AI systems is one of the most significant areas requiring urgent and significant research inquiry. Even model providers such as OpenAI, Anthropic and Google have regularly expressed that they do not fully understand the capabilities, limitations and characteristics of their leading-edge AI models or even how to fully evaluate them. The current state of rapid AI systems and model deployment provides a rich and timely foundation for studies in the areas of: empirical AI systems evaluation, comparative systems evaluation, empirical model evaluation, evaluation of prompting strategies, investigation of specific cognitive capabilities of AI systems, evaluation of reasoning capabilities, theoretical results on model performance or limitations, comparison with human capabilities, consideration of how and in which ways models exceed human capabilities and in which ways they lag behind human capabilities and how this is measured. Indicative topics could include:

In the post-ChatGPT world with increasingly powerful AI foundation models, specialized fine-tuned models and AI-based applications and agents rapidly becoming available and already being used and deployed by consumers and businesses in real-world settings, the evaluation of AI systems is one of the most significant areas requiring rapid and significantly increased research inquiry.

Even model providers such as OpenAI, Anthropic and Google have regularly expressed that they do not fully understand the capabilities, limitations and characteristics of their leading-edge AI models or even how to fully evaluate them. The current state of rapid AI model development and AI system deployment provides a rich and timely foundation for studies in the areas of: empirical system evaluation, comparative system evaluation, empirical model evaluation, evaluation of prompting strategies, investigation of specific cognitive capabilities of AI systems, evaluation of reasoning capabilities, theoretical results on model performance or limitations, comparison with human capabilities, consideration of how and in which ways models exceed human capabilities and in which ways they lag behind human capabilities and how this is measured. Indicative topics could include:

• Evaluation of AI Systems
• Empirical evaluations of AI systems (AI applications or agents)
• Evaluation of publicly available foundation models or model fine-tunes
• Comparative evaluations of models for specific cognitive capabilities e.g. reasoning, code generation
• Case studies of AI systems evaluation for given real-world use cases
• Novel or improved approaches to AI systems evaluation
• Evaluation of prompting techniques and prompt engineering strategies
• Evaluation of the limits of prompt engineering
• AI systems evaluation metrics
• Defining and tracking performance metrics of AI systems and models
• Evaluation of AI systems in education
• Human in the loop systems and model evaluation
• Evaluation of AI agents
• Benchmarks for evaluating AI agents in given domains
• The use of benchmarks in AI evaluation
• The proposal of new benchmarks
• Evaluation and quantification of AI systems risk
• Evaluation of efficiency and scalability of models
• Evaluation of the performance benefits of inference-time scaling of compute or thinking time
• Evaluation of how AI systems can augment human performance in given tasks
• Theoretical results in relation to AI system evaluation
• Theoretical results in relation to predicting model capabilities
• Computational paradigms and frameworks for evaluating AI systems
• Ethics, legal and socio-technical issues in AI-based systems
• Frameworks for evaluating AI system alignment
• Evaluation of systems and model code generation and programming assistance capabilities
• Evaluation of negative AI model characteristics: dishonesty, sycophancy, deceptiveness and inaccuracy
• Multimodal model evaluation
• Evaluating societal impact of AI systems

Minitrack Co-Chairs:

Robert Steele (Primary Contact)
Quantic School of Business and Technology
rsteele@quantic.edu

Julia Rayz
Purdue University
jtaylor1@purdue.edu

Radmila Juric 
ALMAIS Consultancy
radjur3@gmail.com

This minitrack explores the impact and role of digitalization in processes, tools, infrastructures, and ecosystems at the group, firm, institutional, and organizational levels. Understanding digitalization and the characteristics of digital artifacts is essential, particularly for digital service firms, as the shift toward digital operations enables more efficient and effective knowledge exchange—critical for business practice and sustained performance in service contexts. These issues are especially relevant for firms developing and scaling their activities through digital infrastructures and ecosystems.

Successful business operations, marketing, and sales increasingly depend on the effective use of decision‑making analytics and service management. This requires continuous adaptation of digital business models to environmental changes, geopolitical turbulence, and disruptions (e.g., crisis situations, geographic or cross‑cultural challenges, and issues related to information misuse and mistrust). Furthermore, integrating sustainability considerations into existing business models through digitalized services merits further academic attention. Solutions such as AI, digital platforms, and ecosystems can support organizations in addressing these challenges and serve as pivotal tools for digital businesses. Moreover, the growing autonomy and opacity of algorithmic systems raise new challenges related to governance, trust, accountability, and ethical decision making in digital services.

This minitrack offers a multidisciplinary view, drawing on decision analytics, service science, and their applications in marketing and management. We invite papers with theoretical and practical relevance that appeal to a broad audience of academics and practitioners. We welcome qualitative, quantitative, and mixed‑method studies, as well as advanced conceptual work, particularly those that foster cross‑pollination across research fields. Submissions from both researchers and practitioners are encouraged. Potential topics in our mini-track may include, but are not limited to:

• AI, Digital platforms and ecosystems in the context of digital service business
• Digitalization in the context of service firms and organizations
• The role of digital artifacts and AI in decision analytics
• The transformative potential of AI and digital platforms in reshaping business models, decision analytics, value co‑creation, and innovation
• AI‑enabled strategies for scaling and expanding digital services locally and internationally
• AI, digital infrastructures, and decision‑analytics in contexts of geopolitical turbulence, crises, and environmental uncertainty
• Predictive Analytics investigating how AI algorithms can analyze historical data to predict future market trends, decision analytics, and foster digital services
• Decision analytics and the role of information misuse and mistrust
• Governance, ethical considerations, and responsible AI use in digital service ecosystems
• Human–AI collaboration, user trust, and adoption in decision‑analytics and digital service ecosystems
• The role of AI and digital tools to solve or generate sustainability challenges and needs in existing business model
• Practitioner papers and field studies in topics above

Minitrack Co-Chairs:

Mika Gabrielsson (Primary Contact)
Hanken School of Economics
mika.gabrielsson@hanken.fi

Arto Ojala 
University of Vaasa
arto.ojala@uwasa.fi

Sara Fraccastoro
University of Eastern Finland and University G. d’ Annunzio
sara.fraccastoro@uef.fi

Building a system that remains fully protected and secure in every situation against every attacker is an extremely ambitious and likely unattainable objective. Researchers should continue to push systems closer to stronger security guarantees, but it is also essential to develop techniques for adaptive defenses when an attacker bypasses existing controls. Deception-based cyber defense and cyberpsychology research contribute directly to this aim by rebalancing the inherent asymmetry of computer defense by, for example, increasing an attacker’s workload and risk while reducing that of the defender.

Cyber deception is a defensive technique that explicitly accounts for the human element in a cyber-attack. It is a promising tactic for disrupting an attack because it does more than merely deny access: it can also lead the attacker to squander time and effort. In addition, deception enables a defender to exploit false beliefs held by the attacker, thereby reshaping the attacker’s decision-making process in ways that can extend beyond what any static defense can achieve.

A critical component of cybersecurity is to understand the cognition and behavior of both cyber defenders and cyber attackers. Cyberpsychology research advances the science of human behavior and decision-making in cyberspace to understand, anticipate, and influence cyber operators’ behavior, with the aim of improving defender success while minimizing attacker success. It also works to ensure scientific rigor and to quantify the effectiveness of defensive methods.

In cyberspace, an attacker knows only what can be inferred through observation of the target network. The intruder is often physically located far from the network they are trying to penetrate. Unfortunately, modern networks and systems frequently and unintentionally expose more information to an attacker than defenders would prefer. At the same time, to counter this accidental leakage, the network owner can deliberately disclose only what they want the attacker to learn, including purposefully deceptive information. Because network information is typically complex and incomplete, it creates a natural setting for embedding deception: chaos can create opportunity. Deception, along with other cyberpsychology techniques, can shift an attacker’s mindset, confidence, and decision-making process in ways that may produce effects more significant than those of traditional defenses. Moreover, employing deception for defensive purposes can give the defender, at a minimum, partial control over what an attacker knows, and thus create opportunities for strategic interaction with the attacker.

These research efforts demand an interdisciplinary approach; therefore, this minitrack is seeking papers spanning multiple disciplines. To effectively and strategically induce cognitive biases, increase cognitive load, and exploit heuristic thinking in ways that make systems harder to attack, it is essential to understand attacker cognition and behavior. Likewise, deeper insight into cyber defenders will help strengthen the cognitive gates of cyber defense. Studying both attackers and defenders also enables better decision analytics for cybersecurity. Topics of interest include, but are not limited to:

• Science of Deception (e.g., evaluation techniques, deception frameworks applied to cyber)
• Practice of Cyber Deception (e.g., case studies, deception technology, deception detection)
• Understanding/influencing the cyber adversary (e.g., adversary emulation, measures of effectiveness, decision analytics for cyber operators)
• Leveraging influence principles and cognitive heuristics in cyber defense systems
• AI as applied to cyber deception and psychological-based mitigations for defense
• Psychological and social-cultural adversarial mental models that can be used to estimate and anticipate adversarial mental states and decision processes
• Cognitive Modeling of cyber tasks
• Adversary observation/learning schemas through both active multi-level “honey bait” systems and passive watching, in conjunction with active learning and reasoning to deal with partial information and uncertainties
• Oppositional Human Factors to induce cognitive biases and increase cognitive load for cyber attackers
• Metrics for quantifying deception and other cognitive altering techniques’ effectiveness in driving adversary mental state and in determining optimized deception information composition and projection
• Experimental Design, approaches, and results
• Theoretical formulation for a one-shot or multiple rounds of attacker/defender interaction models
• Identification of social/cultural factors in mental state estimation and decision manipulation process
• Cyber maneuver and adaptive defenses
• Cyber defense teaming
• Protecting our autonomous systems from being deceived
• Policy hurdles, solutions, and case studies in adoption of cyber deception and similar technologies
• Predicting, understanding, and protecting against insider threats
• Analyzing the effects of insider attacks
• Human factors and the insider threat problem
• Examining the causes of an insider threat from a behavioral science perspective
• Measuring the effectiveness of mitigation technologies and methodologies
• Models and algorithms for decision analytics for autonomous or adaptive cyber defense
• Decision analytics to analyze various deceptions and psychological approaches and how to improve them
• Decision analytics related to improved cyber defense operations

Selected papers from this minitrack will be recommended to the editors of Organizational Cybersecurity: Practice, Process, and People for fast-track review and publication of an extended version of the HICSS paper. The extended version must include at least 30% new material, cite the HICSS paper, and include an explicit statement about the increment (e.g., new results and findings, better description of materials, etc.) in the cover letter.

Minitrack Co-Chairs:

Kimberly Ferguson-Walter (Primary Contact)
Leidos
Kimberly.J.FergusonWalter@leidos.com

Ryan Gabrys
Naval Information Warfare Center
ryan.c.gabrys.civ@us.navy.mil

Paul Yu
U.S. Army Combat Capabilities Development Command Army Research Laboratory
paul.l.yu.civ@army.mil

Chelsea Johnson
Leidos
Chelsea.k.johnson@leidos.com

Data science refers to the processing and analysis of data – in all its structured, unstructured, or multimodal varieties – to extract meaningful insights for business. Such insights are obtained through statistical procedures, scientific methods, computational techniques, experiments, and advanced machine learning and generative algorithms. Machine learning and Generative AI have become so widespread that many business decisions are now improved not only via predictive models that learn from historical data but also through autonomous agents and reasoning engines capable of complex problem-solving. While many methods and algorithms have been developed for the scientific study of data for better business decisions, there is a constant need for improving the quality and/or accuracy of decisions, adapting these methods to agentic and generative paradigms, enabling the development of new products or services, and modifying methods to improve their transparency, explainability, and human-collaborative potential.

This minitrack focuses on decision-support aspects of data science, machine learning, and Generative AI, with specific emphasis on developing novel methods or models, adapting existing methods to emerging fields such as autonomous workflows, and discovering knowledge and actionable insights. A representative list of general topic areas covered in this minitrack (not meant to be complete or comprehensive) is provided below.

• New or improved methods and algorithms in data science and/or machine learning.
• New or improved standardized processes and methodologies in data science.
• Novel, interesting, and impactful applications of data science and machine learning for supporting better managerial decision-making processes.
• Security, ethical, and privacy issues in data science and machine learning.
• Explainability, interpretability, and transparency of machine learning models.
• Natural language processing methodologies and innovative applications in business decision-making.
• Agentic AI, autonomous agents, and multi-agent systems for automating complex business workflows.
• Human-AI collaboration frameworks for teaming, delegation, and oversight in critical decision loops.
• Generative AI methodologies for decision support, including RAG (Retrieval-Augmented Generation) and fine-tuned LLMs for specialized industry decisions

Extended versions of the papers accepted for presentation in this minitrack will be invited for a fast-track review and publication consideration in the Journal of Business Analytics and AI in Business [Frontiers in AI].

Minitrack Co-chairs:

Dursun Delen (Primary Contact)
Oklahoma State University
dursun.delen@okstate.edu

Behrooz Davazdahemami
University of Wisconsin – Whitewater
davazdab@uww.edu

Hamed Majidi Zolbanin
University of Dayton
hmzolbanin@udayton.edu

The minitrack provides a discussion forum for researchers interested in theoretical and practical problems related to digital service design, innovation, and management. The key drivers in this area of study are the multiplying technological opportunities for digital services stemming from new technologies like artificial intelligence (AI) powered services, cyber-physical systems, generative AI, humanoid robots, virtual/augmented reality, wearable devices and related services and so on.

There are substantial opportunities for digital technology and digitalization-driven digital service research in industrial and business-to-business settings and the consumer space. These opportunities exist particularly where innovation activities increase the digitization of services and service processes. In a broad sense, digital services can be defined as systems that enable value co-creation and limit value co-destruction through developing and managing information technology (IT)-enabled processes that integrate system value propositions with customer value drivers. They draw on different technologies such as sensors, real-time analytics of data, augmented and virtual realities, computer hardware, software, and human and system actors. Such technologies form a platform where different actors assemble the service in situ. As a result, the embedded systems of today and the Internet-of-things of tomorrow are the precursors for the upcoming era of cybernized service innovations.

This fast-moving research area raises interesting questions. For example, traditional development approaches focus on improving the efficiency and effectiveness of design, innovation, and management processes and methods. However, the design of such services may require an emphasis on the socio-psychological aspects, such as the value-in-use and user/consumer/co-creator experiences. Generative AI solutions as part of service systems create new creative opportunities for digital service research. Or they may even enable new self-service design approaches. Digital services create novel ways of engaging customers and other actors in service ecosystems, raising the question of effective patterns of such digital actor engagement. Moreover, digital services facilitate data-driven and analytics-based service design and management, particularly if the service is linked to the physical world through sensors and/or people’s interactions or they open entirely new ways of interacting with service systems, be it voice-based or in multi-modal ways.

This minitrack invites submissions on, but are not limited to, digital service design, innovation, and management of:

• AI-powered services
• Analytics-supported services
• Cyber-physical and IoT-enabled services
• Generative AI and Agentic AI-based services
• Design and management of human-centered, technology-enabled services
• IT-enabled service innovations
• Metaverse based services
• Service ecosystems, platforms, and novel architectures
• Service Robots and Service Robot enabled services
• Services enabled by natural language assistants
• Services using smart television, smart watches, wearables, mobile devices, phones, or other technologies

Minitrack Co-Chairs:

Tuure Tuunanen (Primary Contact)
University of Jyväskylä
tuure@tuunanen.fi

Jan Marco Leimeister
University of St.Gallen
janmarco.leimeister@unisg.ch

Suvi Nenonen
Stockholm School of Economics
suvi.nenonen@hhs.se

Christoph Peters
University of the Bundeswehr Munich
christoph.peters@unibw.de

The use of Artificial Intelligence (AI) in the context of decision analytics and service science has received significant attention in academia and practice alike. The rapid dissemination of generative AI, particularly large language models, has contributed to the urgency of addressing the question of how AI will and should influence the future of work and daily life. One central obstacle to the responsible use of AI systems is their opacity. Many AI systems are “black boxes” – not only for developers but particularly for users and decision makers. In addition, the development and use of AI is associated with many risks and pitfalls like biases in data or predictions based on spurious correlations (“Clever Hans” phenomena), which eventually may lead to malfunctioning or biased AI and hence technologically driven discrimination.

This is where research on Explainable Artificial Intelligence (XAI) comes in. Also referred to as “transparent,” “interpretable,” or “understandable AI”, XAI aims at producing explainable AI systems, while maintaining a high level of learning performance (prediction accuracy); thereby empowering human stakeholders to understand, appropriately trust, and effectively manage the emerging generation of intelligent systems. It comprises both post-hoc explainability methods and intrinsically interpretable machine learning. One key challenge of XAI is to provide meaningful explanations for humans that effectively shape human-AI interaction, such as impacting the task performance of users.

With a focus on decision support, this minitrack aims to explore and extend research on how to establish the explainability of intelligent black box systems,including both predictive AI and Generative AI (GenAI) . We especially seek contributions that investigate XAI and emerging GenXAI approaches from users’, developers’, or governments’ perspectives. We invite submissions from all application domains, such as healthcare, finance, e-commerce, retail, public administration, or others. Technically and method-oriented studies, case studies, as well as design science or behavioral science approaches are welcome. Topics of interest include, but are not limited to:

• Users’ perspective on XAI
  1. Theorizing XAI-human interactions
  2. Presentation and personalization of AI explanations for different target groups
  3. XAI to increase situational awareness, compliance behavior, and task performance
  4. XAI for transparency and unbiased decision making
  5. XAI to foster reflections and learning
  6. Explainability of AI in crisis situations
  7. Explainability of generative AI and foundation models (GenXAI)
  8. GenXAI approaches for LLM and multimodal systems
  9. Potential harm of explainability in AI
  10. Mental models and cognitive biases associated with the explainability of AI
• • Developers’ perspective on XAI
  1. XAI to open, control, and evaluate black box algorithms
  2. Using XAI to identify bias in data and algorithms
  3. Explainability and Human-in-the-Loop development of AI
  4. XAI to support interactive machine learning
  5. Prevention and detection of deceptive AI explanations
  6. XAI to discover deep knowledge and learn from AI
  7. Uncertainty in explanations
  8. Post-hoc explainability and intrinsically interpretable machine learning
• Organizations’ and governments’ perspectives on XAI
  1. XAI and compliance
  2. Explainability and AI policy guidelines, such as the AI Acts
  3. Evidence-based benefits and challenges of XAI expectations and implementations
  4. Ethical AI and GenAI frameworks and regulatory expectations
  5. Organizational implications of XAI
  6. Integration of XAI into organizational processes

Minitrack Co-Chairs:

Maximilian Förster (Primary Contact)
University of Ulm
maximilian.foerster@uni-ulm.de

Masoud Afshari-Mofrad
Macquarie University
MasoudAfshari.Mofrad@mq.edu.au

Florian Brachten
Ruhr University Bochum
florian.brachten@rub.de

Elisa Gagnon
Bishop’s University
egagnon@ubishops.ca

Financial services are being rapidly transformed by advances in data, algorithms, interactive AI, and decentralized infrastructures. From automated lending and robo-advisors to generative-AI-driven customer agents and real-time fraud detection across fiat and digital asset classes, fintech brings substantial opportunities for improved decision quality, personalization, and scale—and concomitant risks to fairness, transparency, and systemic stability.

Under the Decision Analytics and Service Science umbrella, this minitrack explores the intersection of fintech and analytics: how new models, agents, and interfaces change decision processes for firms, regulators, and consumers; how services are designed and delivered in digitally mediated and blockchainenabled financial ecosystems; and what managerial, technical, and policy solutions can ensure trustworthy, robust, and inclusive outcomes.

We invite work that develops or evaluates methods for financial prediction, anomaly and fraud detection, credit scoring, algorithmic trading, risk assessment, and customer-facing financial agents, including applications of machine learning, generative AI/LLMs, multi-agent systems, and hybrid human-AI workflows. We also welcome research into explainability, auditability, and reproducibility of fintech models, evaluations of public and proprietary datasets, experiments on human-AI decision collaboration, and interdisciplinary studies of regulation, privacy, operational resilience, and social impact in both traditional banking and decentralized finance (DeFi). Contributions of all methods (analytical models, large-scale empirical studies, experiments, systems/demos, design papers, and policy analysis) are suitable.

This minitrack aims to foster dialogue between analytics researchers, service scientists, practitioners, and policy experts on how to deploy fintech innovations responsibly and effectively. We welcome papers covering a broad range of topics related to Fintech, AI, and financial analytics. Potential topics include, but are not limited to:

• Generative AI in Finance: Applications of LLMs for financial forecasting, sentiment analysis, parsing financial statements, and automated reporting
• Agentic AI and Automation: The use of autonomous agents in trading, wealth management, and customer service operations
• Fraud Detection & Security: Advanced machine learning techniques for anomaly detection, anti-money laundering (AML), and identity verification (on-chain and off-chain)
• Algorithmic Decision Making: The role of AI in high-frequency trading, portfolio optimization, and roboadvisory services
• Credit and Lending Analytics: Machine learning and deep learning for credit scoring, underwriting, and bias mitigation in lending
• Financial Service Innovation: Digital banking ecosystems, smart contracts, and programmable assets as service systems
• Open Data & Reproducibility: Research utilizing public datasets (e.g., SEC EDGAR, XBRL), open banking APIs, public ledger data, and comparative studies of proprietary vs. open data
• Explainable AI (XAI): Methodologies for ensuring transparency, auditability, and trust in AI-driven financial decisions.
• Regulatory Technology (RegTech): Using AI to automate compliance, monitor market abuse, and interpret regulatory frameworks for traditional and crypto markets
• Human-AI Collaboration: How financial professionals (analysts, traders, advisors) collaborate with AI tools to enhance decision-making

Minitrack Co-Chairs:

Feng Mai (Primary Contact)
University of Iowa
feng-mai@uiowa.edu

Zonghao Yang
Stevens Institute of Technology
zyang99@stevens.edu

Minitrack Co-Chairs:

Torrey Wagner (Primary Contact)
Air Force Institute of Technology
tjw.1808@gmail.com

Brent Langhals
Air Force Institute of Technology
brent.langhals.1@us.af.mil

Neil Ranly
Air Force Institute of Technology
neil.ranly@afit.edu

Winston Wu
University of Hawaii at Hilo
wswu@hawaii.edu

Practitioner Research Insights are three-page executive summaries and 10-minute presentations. Our goals are two-fold:

• Create a forum for industry colleagues to share their insights that can be incorporated into future teaching and research, a practical step in upskilling our workforce
• Engage industry and academic colleagues to find collaboration opportunities

Some people call the current wave of transformation “digital transformation.” We call it “service transformation enabled by digital technologies.” This minitrack aims to explore applications of science and technology to real-world innovations through practitioner reports, case studies, best practice examples, tutorials, challenges, issues, opportunities, tools, techniques, and methodologies of emerging digital technologies. In many cases, practice is ahead of academic research contributions.

A central purpose of this minitrack is to align questions that matter in organizational practice with the concepts, methods, and evidence that shape academic work in information systems. The structure of the minitrack reflects this aim: a requirement for practitioner co-authors, a concise executive summary format, and a review process that values both analytic clarity and practical consequences. Together, these elements leverage the broader HICSS ecosystem into a setting where practitioners surface practice-based problems for academics and where academic theories confront the realities of implementation.

Advanced technical capabilities are revolutionizing business activities, processes, and business models. The overall goal of any transformation, including service transformation, is to increase individual and organization productivity and creativity (decision-making, connectivity, innovation, and augmentation).

Practitioner research offers more directly applicable results and an opportunity to focus academic attention and teaching where needed most. This focus is aligned with the ideals promoted by the Responsible Research in Business and Management Network (RRBM), The HIBAR Research Alliance, and calls by the World Economic Forum and others. By offering a venue to highlight practice-based innovation, we hope to increase the pace of discovery and application overall. Practitioner research serves as a two-way bridge between academic research and the organizations on the front lines. We need to link robust research findings to practitioner experiences across management, organizational contexts, architecture, and design related to applications of science and technology.

Minitrack Co-Chairs:

Tayfun Keskin (Primary Contact)
University of Washington
keskin@uw.edu

Terri Griffith
Simon Fraser University
t@terrigriffith.com

David Ing
Creative Systemic Research Platform Institute
coevolving@gmail.com

Maggie Qian
Dell Technologies
maggiemq.ux@gmail.com

The pervasive nature of digital technologies, as witnessed in industry, services, and everyday life, has given rise to an emergent, data-focused economy driven by many aspects of human individuals and the Internet of Things (IoT). The richness and vastness of these data create unprecedented research opportunities across many fields, including urban studies, geography, economics, finance, entertainment, social science, physics, biology and genetics, public health, and many others. As businesses build out emerging hardware and software infrastructure, it becomes increasingly important to anticipate technical and practical challenges and to identify best practices learned through experience in this research area. A social (companion) robot, such as SoftBank Robotics’ Pepper and ASUS’s Zenbo, consists of a physical humanoid robot component that connects through a network infrastructure to online services that enhance traditional robot functionality. Humanoid robots usually behave like natural social interaction partners for human users, with features such as speech, gestures, and eye gaze, and they refer to users’ data and social background. The behaviour of anthropomorphic robots indicates that users are more open to robots. For example, prior research shows that it is much easier for an embodied humanoid robot to trust users to release their personal information than for a disembodied interactive kiosk to do so. Human- Robot Interaction (HRI) is a research area that involves understanding, designing, and evaluating robots for use by or with humans from social-technical perspectives.

Recently, Artificial Intelligence (AI) technologies have been applied to robotics and toy computing. Robotic computing is a branch of AI that enables synergistic interactions. Social robots can now easily capture a user’s physical activity state (e.g., walking, standing, running, etc.), store personalized information (e.g., face, voice, location, activity pattern, etc.) through the camera, microphone, and sensors AI technologies, or even take advantage of text-to-speech and speech-to-text services coupled with generative AI capabilities to generate more natural interactions with the user. Toy computing is a relatively new concept that extends the traditional toy into a new research area in computer science using AI technologies. In this context, a toy can be considered a computing device or peripheral called a Smart Toy.

We invite research and industry papers on these specific challenges and on other challenges that drive innovation in robotics and toy computing for social robots.

Minitrack Co-Chairs:

Patrick Hung (Primary Contact)
Ontario Tech University
patrick.hung@ontariotechu.ca

Shih-Chia Huang
National Taipei University of Technology
schuang@ntut.edu.tw

Sarajane Marques Peres
University of São Paulo
sarajane@usp.br

Soft computing encompasses a range of established techniques, including fuzzy logic, neuro-computing, probabilistic reasoning, and evolutionary computation. By capitalizing on the distinct advantages of each technique, these methodologies can collaborate effectively to tackle myriad complex real-world challenges. Such problems often elude conventional methods, which typically fail to deliver low-cost, analytical, and comprehensive solutions. Historically, computational approaches have been confined to the modeling and analysis of relatively simple systems. However, the growing complexity of systems in fields such as biology, health, economics, and the digital world has rendered conventional mathematical and analytical methods insufficient. Consequently, advancements in soft computing techniques have emerged as essential for the analysis and modeling of more complex systems. Soft computing effectively addresses challenges associated with imprecision, uncertainty, partial truth, and approximation, thereby facilitating enhanced computability, robustness, and cost-effectiveness in solutions. This methodology is particularly adept at managing large-scale, rapid, and unstructured changes intrinsic to the digital environment.

This minitrack is designed to engage researchers with an interest in the outlined research area. We welcome submissions that encompass not only theoretical advancements but also practical applications that illustrate the problem-solving advantages of utilizing soft computing-based methodologies. Relevant fields of interest include the digital world, digital coaching, digital health, digital economy, cognitive computing, and the design and management of digital services and service systems. We invite submissions that employ either analysis-oriented or systems-oriented methodologies. Submissions may focus on experimental or empirical research. We particularly encourage innovative studies that utilize explainable methods, integrating advanced theoretical results with rigorous empirical verification or effective empirical problem-solving, planning, and decision-making in conjunction with innovative theory development. A fundamental aspect of all submissions is the construction and application of models based on soft computing principles. Topics suitable for this minitrack include, but are not limited to:

• Neural networks
• Fuzzy large language models
• Fuzzy logic
• Fuzzy decision-making
• Evolutionary computation and evolutionary algorithms
• Hybrid intelligent systems
• Software for soft computing
• Natural language processing based on soft computing techniques
• Explainable decision-making
• Soft computing and its application to design and manage of digital services
• Applications of soft computing in digital world
• Soft computing and its application to digital coaching solutions

High quality and relevant papers from this minitrack will be selected for fast-tracked development towards Frontiers in Artificial Intelligence. Selected papers will need to expand in content and length in line with the requirements for standard research articles published in the journal. Although the minitrack co-chairs are committed to guiding the selected papers towards final publication, further reviews may be needed before a final publication decision can be made.

Minitrack Co-Chairs:

Francisco Javier Cabrerizo (Primary Chair)
University of Granada
cabrerizo@decsai.ugr.es

Enrique Herrera-Viedma
University of Granada
viedma@decsai.ugr.es

Ignacio Javier Pérez
University of Granada
ijperez@decsai.ugr.es

Technology is transforming the global economy at an unprecedented pace. Artificial Intelligence (AI) and digital platforms are at the center of this transformation, driving fundamental changes in how firms compete, how markets operate, and how societies function. Massive investments in AI infrastructure and foundation models—illustrated by large-scale initiatives in the United States and rapid advances by innovative firms in China and Korea—underscore the intensifying global competition in AI development and deployment. Supported by national policies and industry leadership, firms and governments worldwide are racing to build AI capabilities that will shape future economic growth and strategic advantage.

These developments have generated a vast volume of data on individuals, organizations, and markets. Advances in machine learning, agentic workflows, causal inference, and economics—combined with powerful computational resources such as GPUs and cloud computing—now enable researchers to analyze such data at scale and extract actionable insights. At the same time, industry practitioners are building AI capabilities to gain a competitive advantage, while entrepreneurs are leveraging these technologies to create new business models and automated service ecosystems.

Despite these opportunities, analytics-based technology research faces important challenges. Researchers must address issues of scalability and data integrity, develop sensitivity to contextual factors such as institutions, culture, and regulation, and ground empirical analyses in strong theoretical foundations from disciplines including economics, psychology, and sociology to ensure generalizability and cumulative knowledge.

The purpose of this minitrack is to bring together AI and IT researchers, economists, industry practitioners, and policymakers to examine how AI-driven technologies are reshaping marketplaces and the global  economy. It aims to advance analytics research while stimulating dialogue and collaboration between academia and industry.

The minitrack is co-chaired by scholars who serve as editors and reviewers at premier journals such as Information Systems Research, Decision Support Systems, and Decision Sciences Journal. Drawing on this editorial experience, the minitrack aims to foster rigorous, theory-grounded, and methodologically innovative research. Authors of accepted papers will be strongly encouraged to further develop their work for submission to leading journals, and the minitrack will provide a constructive forum for early feedback aligned with top-tier publication standards. Topics of interest include, but are not limited to:

• AI infrastructure development
• AI economic impact
• Development of foundation models
• Global AI strategies
• AI regulatory policies
• AI market disruption
• AI-driven consumer behavior
• Personalized marketing through AI
• Generative AI and large language model services
• AI-powered bots and the fight against fake news and misinformation in emerging economies
• Consumer trust in generative AI systems
• AI-driven healthcare decision support
• AI-empowered digital entrepreneurship
• AI and analytics in personalized K-12 and higher education in emerging countries
• Scalable analytics methodologies
• AI ethics and data integrity

Minitrack Co-Chairs:

Sang-Pil Han (Primary Contact)
Arizona State University
shan73@asu.edu

Gene Moo Lee
University of British Columbia
gene.lee@sauder.ubc.ca

Keeheon Lee
Yonsei University
keeheon@yonsei.ac.kr

Donghyuk Shin
Korea Advanced Institute of Science and Technology
dhs@kaist.ac.kr

Organizations increasingly operate in environments marked by accelerated changes, uncertainty, and complexity. Strategic foresight offers structured ways to anticipate and shape future developments, while digital technologies – including AI, immersive environments, digital twins, and simulation tools – provide new opportunities to enhance these capabilities to improve decision-making.

This minitrack focuses on the two-way relationship between technology and strategic foresight in the context of decision-making. It examines how new technologies influence and enable foresight in decision‑making contexts, and how foresight, in turn, shapes the development, governance, and adoption of technological innovations.

We welcome conceptual, empirical, methodological, and design science submissions that explore how the relationships between digital technologies and strategic foresight can impact organisational decisionmaking. Submissions should clearly outline their implications for decision-making strategies, processes, practices, or tools in organisations. Topics of interest include, but are not limited to:

• Technological innovations for strategic foresight and decision‑making
  1. Digitally‑enabled environmental and horizon scanning, scenario-building, and evaluation of alternatives
  2. Decision and strategy simulations
  3. Development and application of, for example, AI, machine learning, large language models, and multi‑agent systems for strategic foresight and decision-making
  4. Use of AI, digital twins, simulations, and immersive environments for strategic exploration
  5. Data‑driven modelling and insights for long‑term strategic planning
  6. Human-AI collaboration for future‑oriented decision‑making
• Methods, approaches and methodological innovations
  1. New methods, techniques, and frameworks for integrating technology and strategic foresight
  2. Hybrid foresight methods combining quantitative and qualitative data
  3. Integration of human and artificial intelligence for decision‑making
  4. Design science research on anticipatory decision‑making
  5. Integration of multidisciplinary methodologies
• Organisational practices and capabilities
  1. Case studies on using emerging technologies in strategic foresight and decision‑making
  2. Impacts of organisational practices, capabilities and culture on the adoption of new technologies for decision-making
  3. Organisational responses to technologically enhanced foresight and decision-making
  4. The coevolution of organizational foresight capabilities and emerging technologies
  5. Collaborative foresight and sensemaking practices for improved decision‑making
  6. Decision-making strategies for navigating digital transformation

Cross‑cutting considerations at the intersection of technology and strategic foresight for decision‑making can include epistemic and methodological limits in different decision-making contexts, ethical and normative considerations, human-machine collaboration, systems complexity, organisational learning, data and infrastructural issues, temporal dynamics, and the translation of anticipatory insights into actionable decisions.

Minitrack Co-Chairs:

Tero Villman (Primary Contact)
University of Turku
tero.villman@utu.fi

Toni Ahlqvist
University of Turku
toni.ahlqvist@utu.fi

Arho Suominen
VTT Technical Research Centre of Finland and Tampere University
arho.suominen@vtt.fi

Nicholas J. Rowland
Pennsylvania State University
njr12@psu.edu

Across organizations, governments, and societies, critical decisions increasingly rely on insights derived from policies, reports, regulatory comments, logs, communications, narratives, social media, vulnerability disclosures, audits, and other text-heavy artifacts. Yet these sources remain underleveraged relative to structured data in many decision support contexts.

This minitrack focuses explicitly on how natural language processing (NLP), text mining, and large language models (LLMs) transform text into decision-relevant knowledge. Rather than centering on a single domain, the minitrack is intentionally text-centric and cross-domain, welcoming contributions from cybersecurity, AI governance and safety, public policy, organizational studies, inclusion, healthcare, digital government, social media, and related areas—provided that the analytical treatment of text data is central to the research design.

The minitrack contributes to the Decision Analytics and Service Science track by emphasizing theoretical, methodological, and empirical work that places text data at the center of decision making under uncertainty. Illustrative topic areas include:

• General Text-Centric Decision Analytics
  1. NLP and LLM applications to organizational documents, communications, reports, and narratives
  2. Text analytics for sensemaking, forecasting, evaluation, and strategic decision support
  3. Mixed-method, qualitative-computational, and design-oriented approaches
• Text Analytics for Risk, AI Safety, and Cybersecurity
  1. Analysis of incident reports, vulnerability disclosures, threat intelligence narratives, and security alerts
  2. NLP- and LLM-based approaches to cyber risk assessment, detection, response, and recovery
  3. Decision support systems built on text-heavy cyber data
• Text Analytics for AI, Governance, and Policy
  1. Computational analysis of AI governance frameworks, standards, and regulations
  2. Text mining of audits, model documentation, risk assessments, and regulatory comments
  3. Decision implications of explainability, accountability, fairness, and transparency as articulated in text
• Text, Inclusion, and Human-Centered Decision Making
  1. Detection and mitigation of bias in text data, prompts, and model outputs
  2. Accessibility and linguistic diversity in text-based decision systems
  3. Analysis of discourse, narratives, and power in organizational and socio-technical contexts

Methodologies may include classical statistical text analytics, rule-based approaches, embeddings and representation learning, transformer-based and generative models (e.g., BERT, GPT-style architectures), unsupervised learning, supervised machine learning and deep learning, retrieval-augmented generation, explainable AI (XAI), and research addressing evaluation, reproducibility, scalability, and responsible deployment.

Papers that span multiple areas or introduce new text-centric domains are especially encouraged. We look forward to welcoming both returning contributors and new voices as this minitrack continues to evolve.

Papers accepted to this minitrack are eligible for HICSS expedited review consideration at Data & Policy (Cambridge University Press), as well as book and pivot-style publication opportunities in the Information Technology and Global Governance series (Palgrave Macmillan)

Minitrack Co-Chairs:

Tahir Ekin (Primary Contact)
Texas State University
tahirekin@txstate.edu

Derrick Cogburn 
American University
dcogburn@american.edu

Haiman Wong
Purdue University
wong424@purdue.edu

In response to the development of artificial intelligence technologies across service domains, we propose to further refocus the minitrack toward Transformative AI Applications in Service Systems. This renewed focus maintains the core service system perspective of value co-creation among multiple actors while foregrounding the role of AI-enabled technologies, particularly generative AI, data-driven decision systems, and human–AI collaboration, in addressing complex societal challenges.

Transformative service emphasizes service innovations that improve individual and collective well-being by reconfiguring service systems at organizational, inter-organizational, and ecosystem levels. In this minitrack, AI is not treated merely as a technical artifact, but as an active resource integrated into service systems that reshape roles, governance mechanisms, capabilities, and value propositions among service actors, including citizens, professionals, firms, public institutions, and intelligent agents.

This minitrack explicitly encourages research that adopts a service system and service ecosystem perspective to examine how AI-enabled services contribute to resilience, inclusion, and sustainability across various societal domains, such as healthcare, education, disaster risk reduction, smart cities, and public administration. Contributions may span conceptual, empirical, analytical, and design-oriented research, provided they demonstrate clear implications for service system transformation and societal impact. By aligning advances in artificial intelligence with the theoretical foundations of service science and transformative service research, this minitrack aims to reposition HICSS as a leading venue for interdisciplinary scholarship on AI-enabled service systems that generate sustainable value for society.

Relevant papers may focus on the application of artificial intelligence, especially generative AI, as a driver of service system transformation, influencing service governance structures, process design, and service delivery management. Of particular interest are studies that examine human–AI collaboration in real-world service contexts and that demonstrate how AI-enabled services address societal challenges and improve human and social well-being.

Outstanding papers accepted by the minitrack Transformative Service Systems for a Sustainable World will be fast-tracked for review and potential publication in the Pacific Asia Journal of the Association of Information Systems (PAJAIS).

Minitrack Co-Chairs:

Paul Maglio (Primary Contact)
University of California, Merced
pmaglio@ucmerced.edu

Fu-ren Lin
National Tsinghua University
frlin@iss.nthu.edu.tw

Nila Armelia Windasari
Bandung Institute of Technology
nila.armelia@itb.ac.id