Decision Analytics and Service Science

The rapid development and widespread diffusion of AI—especially large language models and generative AI—introduces new challenges to the study of technology acceptance and use, giving age-related questions a new twist. AI has the potential to act as an equalizer by bridging generational technology gaps through adaptive and intuitive interfaces. However, unequal access and varying levels of AI literacy may instead contribute to widening 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 age groups. Furthermore, AI applications trained on biased datasets have already been shown to exacerbate age-related discrimination in areas such as job recruitment and healthcare, raising critical ethical and societal concerns. In summary, age-related questions are central to understanding technology use, yet they have often been studied in a superficial or overly simplistic manner in prior information systems research.

A frequent issue in many prior studies is that the role of age has been reduced to a mere control variable, with studied samples typically limited in terms of age range, most often focusing mainly on working-age individuals. Similarly, when potential age effects have been found, few studies provide in-depth explanations for why these effects exist in the first place, what are their broader implications for research and practice, and whether they are indeed age effects that apply to all individuals of a certain age or whether they are actually generational effects that only apply to individuals born in a certain time period. Because of this, our current understanding of the potential age and generational aspects in technology acceptance and use remains limited, resulting in age and generational stereotypes and the potential deepening of the digital divide between individuals of different ages and generations. For example, healthcare self-service solutions aimed at aging populations may face resistance if they fail to address distinctive technology acceptance patterns in older adults. Similarly, concerns about younger generations’ social media use require a more nuanced understanding of their actual behaviors rather than broad assumptions.

In this minitrack, we call for multidisciplinary and multimethodological studies that dive deeper into the potential age and generational aspects of technology acceptance and use. This includes studies that focus on the differences and similarities between multiple age groups and generational cohorts, studies that focus on the distinctive features of a single age group or generational cohort, and other kinds of studies in which age and/or generation acts as a central research construct. We warmly welcome more critical and controversial studies that aim at challenging the prevailing age and generational stereotypes related to technology acceptance and use in our society. Although such stereotypes may not be completely without merit, the increasing exposure to various technologies and the fading differences in factors like technology readiness in most modern societies may have rendered many of them invalid, thus making them a dangerous foundation to build any future research on and a potential source of missed business opportunities. We also invite innovative studies that introduce novel theoretical mechanisms and insights for explaining the potential differences or similarities in technology acceptance and use as well as their antecedents (e.g., beliefs and attitudes) between age groups. These are needed to advance the study of technology acceptance and use from its current stagnate state, to establish a deeper understanding of the phenomena, and to provide better practical guidance to the users, developers, and regulators of digital services.

To achieve this, we encourage creativity and curiosity, methodological diversity, as well as cross-disciplinary cooperation and cross-fertilization. On one hand, qualitative studies have the potential to yield rich insights into the decision-making and behaviors of individuals. On the other hand, quantitative studies (especially studies that employ not only the traditional variable-oriented methods like structural equation modeling but also more person-oriented methods like qualitative comparative analysis and latent class/profile analysis) have the potential to uncover previously undiscovered configurations of variables that affect technology acceptance and use or that can be used as a basis of user segmentation. Similarly, usage data can provide researchers with a more accurate view of technology use patterns and behaviors.

Relevant topics for this minitrack include (but are not limited to):

1. Differences and similarities in technology acceptance and use between various age groups and generational cohorts
2. Distinctive features of technology acceptance and use in specific age groups (e.g., young elderly) and generational cohorts (e.g., Baby Boomers, Generation X, Generation Y, Generation Z, Digital Immigrants, or Digital Natives)
3. Age and generational stereotypes related to technology acceptance and use
4. Age and generational discrimination (e.g., ageism) related to technology acceptance and use
5. Age and generational aspects in the context of digital divide and digital exclusion
6. Age and generational effects in the antecedents of technology acceptance and use (e.g., technology beliefs, technology attitudes, and technology readiness)
7. Age and generational effects in various dark side of IT/IS use phenomena (e.g., technostress and technology addiction)
8. Examining how major life events (e.g., entering the workforce, retirement, health changes) shape technology acceptance and use across the life span
9. Multi-dimensional approaches to exploring variations in technology use within and across generations, considering factors such as culture, access, daily activities, personality, values and personal interests alongside age
10. Digital habit formation, habit-driven technology use; exploring how repeated interactions, contextual cues, and reinforcement shape technology habits across different age groups
11. AI as an equalizer or divider: examining the potential of AI to bridge generational technology gaps through novel interfaces, while also posing risks for widening digital divides due to unequal access and varying levels of AI literacy

Minitrack Co-Chairs:

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

Markus Makkonen
Tampere University
markus.makkonen@tuni.fi

Pirkko Walden
Institute for Advanced Management Systems Research and Åbo Akademi University
pirkko.walden@abo.fi

Tomi Dahlberg
University of Turku
tomi.dahlberg@utu.fi

This minitrack seeks a wide range of theoretical and empirical papers that employ natural language processing (NLP), text mining/analytics, and large language models (LLMs) to better understanding decision making in AI safety, cybersecurity, and inclusion. These papers could include, but are certainly not limited to, large scale analysis of AI risk management frameworks, such as those developed in the United States by the National Institute of Standards and Technology (NIST) designed to regulate the development of artificial intelligence, frameworks for the development of trustworthy AI. Other international frameworks for the development of responsible AI include those developed by the EU, Australia, Japan, Singapore, G20, China, Africa, and the OECD. These frameworks could be evaluated on their level of human-centeredness, how they mitigate risks and promote the benefits of AI, how they approach “fairness”, their explainability, privacy and security, safety, reliability, and accountability of AI. Also, how they consider context-specific risk management techniques; how stakeholders are involved, and how they promote innovation. Understanding the use of AI for cyberthreat detection, and other applications of ML/DL/AI related to any aspect of cybersecurity.

Potential papers for this minitrack may deploy any number of text analytics techniques, ranging from statistical bag-of-words and rule-based approaches to syntactic parsing and natural language processing approaches, including Named Entity Recognition (NER), text embeddings, and Bidirectional Encoder Representation Transformers (BERT). Papers may also use unsupervised machine learning (ML) approaches, including topic modeling (using Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), or other topic modelling techniques; k-means clustering, as well as supervised machine and deep learning approaches, including predictive regression and classification models, Deep Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs). Other papers may address methodological challenges such as text summarization, classification, and clustering, using generative large language models (e.g. ChatGPT, Gemini/Bard) to create synthetic data, overcoming API limitations, and working on distributed, high-performance computers. We also seek papers on enhanced explainability in text analytics (particularly AI/ML) relative to results and the detection and mitigation of bias in analytics.

In this minitrack we plan to create a highly engaging interactive forum and community for researchers and practitioners to discuss the critical issues related to text mining and analytics and contribute to the ongoing big data focus and emerging AI concentrations at HICSS. We also encourage cyber analytics and AI experts to contribute their perspectives.

This minitrack invites papers using both theoretical and applied NLP, text mining, and LLM-based approaches to analyze various genres of text data, including, but not limited to:

1. Security alerts
2. Threat intelligence feeds
3. Computer logs
4. Email archives (including phishing emails)
5. Incident and maintenance reports
6. Legal documents (patents, contracts, etc.)
7. Public policies and public comments
8. Twitter and social media
9. Online communities and discussion forums (Reddit, Discord, etc.)
10. Blog posts
11. Published articles
12. Websites
13. Meeting and call center transcripts
14. Speeches
15. News transcripts
16. Customer feedback
17. Resumes and CVs
18. Job Postings and Descriptions
19. Employee evaluations
20. Insurance claims (cyber insurance, etc.)
21. Annual reports
22. Case studies

Fast track publication opportunities with Data & Policy published by Cambridge University Press and book publishing opportunities in the Information Technology and Global Governance Series by Palgrave Macmillan have been secured for papers accepted to this minitrack. Papers accepted to other HICSS minitracks using NLP and text analytics to address policy related problems may also be invited to submit.

Minitrack Co-Chairs:

Derrick Cogburn (Primary Contact)
American University
dcogburn@american.edu

Haiman Wong
Purdue University – West Lafayette
wong424@purdue.edu

Tahir Ekin
Texas State University
tahirekin@txstate.edu

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

1. Evaluation of AI systems, systems built on top of AI foundation models
2. Empirical studies evaluating a proprietary or open source model’s performance holistically or on a specific task or benchmark
3. Comparative evaluation of two or more systems or models for particular tasks
4. Comparative evaluations of AI systems against human performance on particular tasks
5. Proposal of new evaluation approaches and/or task-specific benchmarks
6. Evaluation of prompting and prompt engineering approaches
7. Proposal and evaluation of novel prompting approaches and techniques
8. Proposals for evaluating new aspects of AI models or systems not yet commonly measured
9. Evaluation of AI agents or applications
10. Case studies of the performance of AI models and systems when deployed in real-world settings or use cases
11. Evaluation of the computational performance of AI systems or models
12. Evaluation of the social impact of AI systems
13. Novel frameworks for AI system evaluation

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:

1. Evaluation of AI Systems
2. Empirical evaluations of AI systems
3. Evaluation of publicly available foundation models or model fine-tunes
4. Comparative evaluations of models for specific cognitive capabilities e.g. reasoning, code generation
5. Novel or improved approaches to AI system evaluation
6. Evaluation of prompting techniques and prompt engineering strategies
7. Evaluation of the limits of prompt engineering
8. AI system evaluation metrics
9. Defining and tracking performance metrics of AI systems and models
10. Human in the loop systems and model evaluation
11. Evaluation of AI agents
12. Benchmarks for evaluating AI agents in given domains
13. The use of benchmarks in AI evaluation
14. The proposal of new benchmarks
15. Evaluation and quantification of AI system risk
16. Evaluation of efficiency and scalability of models
17. Evaluation of the performance benefits of inference-time scaling of compute or thinking time
18. Evaluation of how AI systems can augment human performance in given tasks
19. Theoretical results in relation to AI system evaluation
20. Theoretical results in relation to predicting model capabilities
21. Computational paradigms and frameworks for evaluating AI systems
22. Ethics, legal and socio-technical issues in AI-based systems
23. Frameworks for evaluating AI system alignment and super alignment
24. Evaluation of model code generation and programming assistance capabilities
25. Evaluation of negative AI model characteristics: dishonesty, deceptiveness and inaccuracy
26. Multimodal model evaluation
27. Evaluating societal impact of AI systems

Minitrack Co-Chairs:

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

Radmila Juric 
ALMAIS Consultancy
radjur3@gmail.com

Julia Rayz
Purdue University
jtaylor1@purdue.edu

This minitrack explores the impact and role of digitalization in terms of processes, tools, infrastructures, and ecosystems used at groups, firms, institutions, and organizations-levels. The analysis of digitalization and digital artifact characteristics is crucial for instance for digital service firms, as the shift towards digitalization enables a more efficient and effective knowledge exchange that is pivotal for business practice and sustained performance in service business. This is particularly relevant for firms developing and scaling their business through digital infrastructures and ecosystems. Success of firms’ activities, business operations, marketing and selling depends on effective use of decision-making analytics and service management. This requires changes through adaptation of the digital business model to environmental changes, geopolitical turbulence and disruptions (e.g. crises situations, geographical, cross-cultural, and information misuse and mistrust). Furthermore, the encompassing of decisions about sustainability needs into existing business models and their integration through digitalized services deserves further academic attention. Solutions like AI, digital platforms and ecosystems can be helpful in tackling such challenges and offer pivotal tools for digital businesses.

Our minitrack offers multidisciplinary view and expertise covering for instance decision analytics, service science, and their applications in marketing and management. We invite papers that have theoretical and practical relevance, which can appeal a broad audience of academics and practitioners. Our methodological and theoretical standpoints contemplate qualitative, quantitative, and mixed method research approaches, as well as advanced conceptual pieces, and where cross-pollination of fields of research is particularly relevant. We welcome papers from researchers as well as practitioners.

Potential topics in our minitrack may include, but are not limited to:

1. AI, Digital platforms and ecosystems in the context of digital service business
2. Digitalization in the context of service firms and/or organizations
3. The role of digital artifacts and AI in decision analytics
4. AI-Driven Digital Platforms and how AI technologies are central to the operation of digital platforms and digital services.
5. The transformative potential of AI and digital platforms in reshaping business models and decision analytics, leading to enhanced collaboration, improved value co-creation, and disruptive innovations.
6. AI and digital platform for scaling of digital services.
7. Geopolitical turbulence in uses of digital infrastructure like AI and related decision-analytics.
8. Predictive Analytics investigating how AI algorithms can analyze historical data to predict future market trends, decision analytics, and foster digital services.
9. Decision analytics and the role of information misuse and mistrust
10. The role of AI and digital tools to solve or generate sustainability challenges and needs in existing business model
11. Practitioner papers and field studies in topics above
12. Changes spurring from crisis situations impacting the marketing and management activities of digital service firms

Minitrack Co-Chairs:

Arto Ojala (Primary Contact)
University of Vaasa
arto.ojala@uwasa.fi

Sara Fraccastoro
University of Eastern Finland
sara.fraccastoro@uef.fi

Mika Gabrielsson
Hanken School of Economics
mika.gabrielsson@hanken.fi

Artificial Intelligence (AI) has revolutionized the management of connectivity between devices and sensors, enabling more effective problem analysis, risk mitigation, and deeper insights into complex systems. The integration of AI into manufacturing and infrastructure enhances decision-making by detecting anomalies, assessing risks, and triggering appropriate responses based on the risk level. Significant progress has been made in developing AI-driven methods and models to improve the accuracy of problem identification within large datasets, empowering experts with actionable insights.

This minitrack focuses on pioneering approaches that extract valuable knowledge from high-frequency data sources—such as IT data centers, energy service companies, and medical diagnostics—to enhance productivity, reduce downtime, and personalize treatment. General Topics Covered in this minitrack:

1. AI-driven natural language processing (NLP) for short-text analysis
2. Advanced AI-powered anomaly detection techniques
3. Enhanced AI-based filtered selection approaches
4. Machine learning methodologies for rare-event detection
5. AI-driven risk assessment frameworks
6. Novel AI methodologies for event-triggering mechanisms
7. Strategies for handling unbalanced data in data science
8. Generative AI models for field-support chat applications
9. AI algorithms for detecting environmental changes

Potential Application Fields:

1. IT data centers
2. Transmission lines
3. Electrical power plants
4. Cooling plants
5. Cybersecurity and malware detection
6. Oil refineries
7. Healthcare systems
8. Medical diagnostics and treatments
9. Biosensors systems

This minitrack aims to foster innovation in AI applications, providing cutting-edge solutions to industries that generate vast amounts of data and require intelligent systems for optimized decision-making.

Minitrack Co-Chairs:

Elisabetta Ronchieri (Primary Contact)
INFN CNAF
elisabetta.ronchieri@cnaf.infn.it

Daniele Cesini
INFN CNAF
daniele.cesini@cnaf.infn.it

Luca dell’Agnello
INFN CNAF
luca.dellagnello@cnaf.infn.it

This minitrack focuses on research related to big data and analytics, and the incorporation and use of Large Language Models (LLMs) that facilitate and support the processing and analysis of vast amounts of unstructured data, particularly text. Empirical and theory based papers illustrating the new insights derived from such systems, and how they enable businesses and organizations to optimize or support their operational practices, improve and automate their decision-making, and more fully understand and provide services tailored to their customers, clients and stakeholders. This minitrack seeks papers in all business and technical areas of LLMs addressing big data and analytics, including: technology and infrastructure, storage, LLM model development, testing, risks, curation, governance and configuration management, agentics and usage case studies, innovative agentic applications, supportive tools, and metrics incorporated into these systems to solve complex problems.

We seek empirical, theoretic, papers and case studies in relevant organizational and management areas associated with effective LLM and big data and analytics practices, including: strategic application of these systems, governance and control, security, human resources demand, task coordination, business process and LLM uses of combination with robotic process automation (RPA), documented organizational impact, information systems success, and assessment of business value. We especially seek papers on enhanced explainability in LLMs relative to results and the detection and mitigation of bias in analytics.

We seek relevant papers on the development of strategy for deploying big data and analytics with LLMs in unique or distributed organizations, including: geographic and virtual entities; the effects of LLMs combined with big data and analytics on organizational behavior.

We seek papers on developing an LLM and big data analytic staff or cadre, necessary educational frameworks, critical body of knowledge, in-house training practices, and skills development and measurement in any of the areas above. Papers will be solicited in several areas, including, but not limited to the following:

1. Challenges in managing big data repositories and projects with LLMs – exabytes and beyond.
2. Analytics Enterprise Governance for Big Data and associated LLMs: Including data standardization, privacy and security
3. Graph analytics and knowledge graphs for LLMs– both syntactic and semantic – that play a big role in the exploitation of social media data.
4. Advanced analytics and LLM model, – emphasizing visual analytics and non-numeric analysis models and their implementation as applied to complex problems in different domains.
5. Application of LMs to scalable semantic annotation and reasoning across big data stores.
6. Metrics for assessing the impact of Big Data and LLMs in business, scientific, and governmental decision-making.
7. Evaluation, analysis and comparison of explainable vs. black-box models.
8. Organizational and business aspects of big data, analytics and LLMs, data science, and data governance.
9. Educational and body of knowledge frameworks combining LLMs and Big Data, analytics, data science and analytic science.
10. Analytics for large-scale, fast, and unstructured changes in very large data sets – petabytes and beyond, and the relevancy of LLMs in these conditions
11. The changing nature of analytics from deterministic precise modeling to fast, adaptive computational and non-deterministic intelligence for handling imprecise knowledge.
12. Big data and analytics combined with LLMs to enable technologies for a smart society – smart cities, aging populations, and responses to critical environmental and geopolitical problems.
13. New analytics combined with LLMs as the basis for cognitive computing and connected (possibly distributed) intelligence that augments (or even replaces) human decision-making.
14. Explainability of quantitative and qualitative analytic results Big Data and LLM results, and
    automatic detection and mitigation of bias in Big Data sets and analytics, and the evaluation of bias vs. variance in models.

Minitrack Co-Chairs:

Stephen Kaisler (Primary Contact)
SHK & Associates
skaisler@comcast.net

Frank Armour
American University
fjarmour@gmail.com

William Money
The Citadel
wmoney@citadel.edu

Humans have the ability to be conscious and self-awareness of the environment around them. Humans are electrochemical engines that possess to varying degrees these abilities. It is conjectured that some animals also possess these abilities. We suggest that consciousness and awareness (C&A) in part are electrochemical processes, but also computational and cognitive processes, as postulated by Daniel Dennett, George Lakoff, and others. We believe a reasonable question to ask is: Can human C&A can be emulated, e.g., can we mechanize some aspects of C&A in a computer system? This minitrack seeks to address this topic, but with no certainty as to the answer on the horizon.

Our research question is, how to create, possibly, a smaller and more abstract version of the World in computer system, such that a computer program can emulate the mechanisms to experience it. What should the level of abstraction be taking into account the level and complexity of intelligence, consciousness, and awareness mechanisms emulated in the system. We seek to understand, given the appropriate emulation mechanisms, how scale will affect the rise of machine consciousness and awareness.

Numerous theories and models have been developed to explain what C&A  how it arose, how it manifests itself, and how it works to generally keep us out of trouble and progress in an ever-challenging environment. No model or theory seems to fully explain what C&A is, how it arises, and how it works.

To this end, then, we believe it is time not to just construct, but to begin developing experimental models (systems) that can yield insights into C&A structures and the essential computational and cognitive (reasoning) processes.
This minitrack is about qualitative (symbolic) and quantitative mechanisms that can yield insights into a (semi-)automated C&A capability. We seek, but are not limited to, papers relative to:

1. What are the properties of “Consciousness” and Awareness”? At a minimum this will require a good survey of the published literature to understand the state of the domain.
2. Software Architectures for developing C&A capabilities in a computer system, including both storage (memory) and reasoning systems.
3. Temporal and Spatial aspects of C&A represented as qualitative and quantitative AI programs. Our reasoning about time and space is imperfect in many cases, yet we exist and operate adequately in a 3-dimensional + time world. What cognitive mechanism allow us to do this?
4. Programs that exhibit C&A at both the subconscious (autonomic) and conscious (e.g., focused, intentional) level. How do these coexist? And interoperate, if they do. This implies, in human beings, that there is a concurrent set of mechanisms working simultaneously.
5. Mechanisms for implementing short-term and long-term memories as one of key constraints for the learning process.
6. Mechanisms for “forgetting” in a large {data, knowledge, information} base.
7. Self-adaptive, self-modifying mechanisms that increase knowledge, improve reasoning, and develop new structures of the internalized and externalized environment. We know that human beings “learn as they go”, but the neurological mechanisms elude us. However, if we can emulate understanding of his mechanism, we can improve our human reasoning, computer-based reasoning, and incremental learning from non-stationary information streams instead of fragmental learning from batch data.
8. Metrics and methods for evaluating the C&A capabilities of programs. If a computer became conscious and aware, how would we recognize it? What metrics would be used to assess it? This raises the question of what is the “unit of consciousness” and how can it be measured?
9. Perhaps, this suggests that there are levels of C&A in a human being. How to represent those levels and  how they interact seems essential to emulating C&A in a computer system.
10. Open issues in AI/ML involve explainability of results. Within the C&A domain, this process will be several orders of magnitude more complex.
11. How to design a computer system that possesses C&A and ,at the same time, demonstrates compute and energy efficiency. Once consideration might be specialized software stacks with layers optimized for C&A domain levels.
12. What kind of mechanism can be used to divide conscious state from unconscious and conscious actions from unconscious (automatic like walking, talking, driving, etc.) actions?

These types of questions are open-ended, at least for the foreseeable future, but are critical to really understand C&A. We believe that such understanding cannot arise from theory alone, nor from observation, but must arise through experience as implemented in demonstrable applications.

Minitrack Co-Chairs:

Stephen Kaisler (Primary Contact)
SHK & Associates
skaisler1@comcast.net

Brittany Davidson
University of Bath
bid23@bath.ac.uk

Abzatdin Adamov
ADA University
aadamov@ada.edu.az

Creating a system that is always protected and secure in all situations against all attackers is a far-reaching and likely impossible goal. It is important for researchers to continue to move systems closer to guarantees of security, but it is also essential to create techniques to adaptively defend against an attacker who circumvents the current security or has insider knowledge of system properties or security practices. Deception for cyber defense and cyberpsychology research work towards that goal—to rebalance the asymmetric nature of computer defense by increasing attacker workload and risk while decreasing that of the defender.

Cyber deception is one defensive technique that considers the human component of a cyber attack. Deception holds promise as a successful tactic for making an attacker’s job harder because it does more than just block access: it can also cause the attacker to waste both time and effort. Moreover, deception can be used by a defender to leverage an incorrect belief in the attacker—altering the attacker’s decision-making process– the effects of which can go beyond any static defense.

Understanding the human cognition and behavior of both the cyber defender and cyber attacker is a critical component of cybersecurity. Cyberpsychology research advances the science of human behavior and decision making in cyberspace to understand, anticipate, and influence cyber operators behavior (i.e., improve defender success and minimize attacker success). It also seeks to ensure scientific rigor and quantify the effectiveness of our defensive methods.

In the cyber world, an attacker only knows what is perceived through observation of the target network. The intruder is often thousands of miles away from the network to which he or she is attempting to gain entry. Unfortunately, modern networks and systems often unintentionally provide more information to an attacker than defenders would like. However, the network owner also has the opportunity to reveal information he or she desires the attacker to know—including deceptive information. Because network information is often complex and incomplete, it provides a natural environment in which to embed deception since, in chaos, there is opportunity. Deception, and other cyberpsychology techniques, can alter the mindset, confidence, and decision-making process of an attacker, which can have more significant effects than traditional defenses. Furthermore, using deception for defensive purposes gives the defender at least partial control of what an attacker knows, which can provide opportunities for strategic interaction with an attacker.

These research efforts require an interdisciplinary approach and the mini-track is soliciting papers across multiple disciplines. It is essential to understand attacker cognition and behavior to effectively and strategically induce cognitive biases, increase cognitive load, and leverage heuristic thinking to make our systems more difficult to attack. A greater understanding of cyber defenders will aid in fortifying the cognitive gates of cyber defense. Researching both attackers and defenders supports improved decision analytics for cybersecurity. Topics of interest include (but are not limited to):

1. Science of Deception (e.g., evaluation techniques, deception frameworks applied to cyber);
2. Practice of Cyber Deception (e.g., case studies, deception technology, deception detection);
3. Understanding/influencing the cyber adversary (e.g., adversary emulation, measures of effectiveness, decision analytics for cyber operators);
4. Leveraging influence principles and cognitive heuristics in cyber defense systems;
5. AI as applied to cyber deception and psychological-based mitigations for defense;
6. Psychological and social-cultural adversarial mental models that can be used to estimate and anticipate adversarial mental states and decision processes;
7. Cognitive Modeling of cyber tasks;
8. 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;
9. Oppositional Human Factors to induce cognitive biases and increase cognitive load for cyber attackers;
10. Metrics for quantifying deception and other cognitive altering techniques’ effectiveness in driving adversary mental state and in determining optimized deception information composition and projection;
11. Experimental Design, approaches, and results;
12. Theoretical formulation for a one-shot or multiple rounds of attacker/defender interaction models;
13. Identification of social/cultural factors in mental state estimation and decision manipulation process;
14. Cyber maneuver and adaptive defenses;
15. Cyber defense teaming;
16. Protecting our autonomous systems from being deceived;
17. Policy hurdles, solutions, and case studies in adoption of cyber deception and similar technologies;
18. Predicting, understanding, and protecting against insider threats;
19. Analyzing the effects of insider attacks;
20. Human factors and the insider threat problem;
21. Examining the causes of an insider threat from a behavioral science perspective;\
22. Measuring the effectiveness of mitigation technologies and methodologies;
23. Models and algorithms for decision analytics for autonomous or adaptive cyber defense;
24. Decision analytics to analyze various deceptions and psychological approaches and how to improve them; and
25. Decision analytics related to improved cyber defense operations.

Minitrack Co-Chairs:

Perry Sherouse (Primary Contact)
IARPA
perry.sherouse@iarpa.gov

Kimberly Ferguson-Walter
IARPA
Kimberly.ferguson-walter@iarpa.gov

Paul Yu
DEVCOM Army Research Lab
paul.l.yu.civ@army.mil

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

Data science refers to the processing and analysis of data – in all its structured or unstructured varieties – to extract meaningful insights for business. Such insights could be obtained via the use of statistical procedures, scientific methods, computing techniques, experiments, and machine learning algorithms. Machine learning, specifically, has become so widespread that many business decisions are now improved via the development and application of models that learn from historical data to enable reliable predictions for new or prospective data. 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 novel and emerging concepts or fields, for enabling the development of new products or services, or for modifying the methods to improve their transparency and explainability.

This minitrack focuses on decision support aspects of data science and machine learning, with specific emphasis on the development of novel methods or models, the exaptation of existing methods or models to emerging fields, and the discovery of knowledge and actionable insight. A representative list of general topic areas covered in this minitrack (which is not meant to be complete or comprehensive) is given below.

1. New or improved methods and algorithms in data science and/or machine learning.
2. New or improved standardized processes and methodologies in data science.
3. Novel ways of data blending, cleaning, transformation, reduction, and visualization.
4. Novel, interesting, and impactful applications of data science and machine learning for supporting better managerial decision-making processes.
5. Security, ethical, and privacy issues in data science and machine learning.
6. Futuristic directions of the use of data science and machine learning in decision-making.
7. Explainability, interpretability, and transparency of machine learning models.
8. Natural language processing methodologies and innovative applications in business decision-making
9. Applications of Large Language Models (LLMs) in improving business processes and decision-making”

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 generative AI (such as ChatGPT), Internet of Things (IoT), virtual/augmented reality, web3, cyber-physical systems, 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.

Relevant topics for this minitrack include (but are not limited to) digital service design, innovation, and management of:

1. AI-enabled services (such as ChatGPT, etc.)
2. Algorithmic services
3. Analytics-supported services
4. Cyber-physical and IoT-enabled services
5. Generative AI-based services
6. IT-enabled service innovations
7. Metaverse based services
8. Service ecosystems, platforms, and novel architectures
9. Service Robots and Service Robot enabled services
10. Services enabled by natural language assistants
11. Services using smart television, smart watches, wearables, mobile devices, phones, or other technologies like blockchain, Web3, etc.

We will consider papers from the minitrack that advance knowledge in these areas, subject to another round of review(s) and some additional contribution following the Communications of the Association of Information Systems (CAIS) norms and standards to be considered for the Digital Design department of the journal.

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

Tilo Böhmann
University of Hamburg
tilo.boehmann@uni-hamburg.de

The integration of information systems and artificial intelligence (AI) into daily life is transforming how individuals make data-driven decisions for personal well-being and sustainability. Smart technologies, wearable devices, and digital decision-support systems are revolutionizing personal data collection, analysis, and application, enabling more informed choices across various life domains.

This minitrack explores how Personal Decision Analytics (PDA) and AI-powered decision intelligence empower individuals to enhance their quality of life and maintain long-term well-being. Examples include personal finance management (budgeting and investment guidance), health decision support (risk prediction and personalized recommendations), and productivity tools (task prioritization and time management).

We invite researchers, practitioners, and educators to discuss the design, implementation, and impact of AI-driven decision analytics for personal sustainability. Topics of interest include, but are not limited to:

1. Modeling or Prediction: AI and data analytics for personalized health, finance, and education recommendations.
2. Decision Analytics & Digital Intelligence (DADI): Development of adaptive and dynamic decision companions for everyday life.
3. Ethical & Privacy Considerations: The dark side of DADI, including responsible data use and privacy protection.
4. Applications Across Life Domains: Integration of PDA with Quantified Self, health, finance, and beyond.
5. Gamification for Self-Management: How games and gamification enhance personalized decision-making.
6. Emerging Technologies: Integration of DADI with NLP, blockchain, metaverse, and other digital environments.

This minitrack welcomes interdisciplinary contributions from computer science, psychology, education, health informatics, and related fields, fostering a diverse discussion on the future of personal decision analytics.

Minitrack Co-Chairs:

Claris Chung (Primary Contact)
University of Canterbury
claris.chung@canterbury.ac.nz

Yvonne Hong
Victoria University of Wellington
yvonne.hong@vuw.ac.nz

Ghazwan Hassna
Hawaii Pacific University
ghassna@hpu.edu

David Sundaram
University of Auckland
d.sundaram@auckland.ac.nz

Gamification and artificial intelligence may well be trends that revolutionize user engagement and personalization, leading to behavior shifts in social environments such as education, health, sustainability, and business development. However, these reap ethical challenges: transparency, accountability, and user trust. This minitrack is dedicated to the intersection of gamification and responsible AI moving toward designing explainable, ethical, user-centric systems, and striking a balance between innovation to accountability. Companies like Roblox have been investigating similar issues and with the growing policy implications of AI use by countries, the mini track offers a platform to have debate and discuss the future of ethical and responsible gamification in the growing nature of AI.

Important topics include:

1. Exploratory applications of Explainable AI (XAI) in gamified systems for boosting user trust and understanding.
2. Ethical aspects on AI-based gamification, for instance fairness, data privacy, and lack of manipulation or exclusion prevention.
3. Responsible gamification design strategies in line with ethical AI offering positive experiences to users.
4. Ethical paradoxes in design, use and application of AI powered gamified systems
5. AI and manipulated user behaviour using gamification
6. Research cases on gamification with explainable and responsible AI that deliver value against organizational goals ranging from customer engagement to social impact initiatives.
7. Business implications for responsible AI under gamification, some of which may include brand trust, scalability, and adoption.

Besides these, this minitrack is meant to bring together an interdisciplinary crowd, especially researchers and practitioners, about but not exclusively limited to these areas: AI ethics, gamification design, business development, and HCI. This minitrack is intended to improve further understanding and implementation of responsible, and ethical AI technologies in gamified systems through minitracks by bringing scholars and practitioners together in related fields around addressing the unique challenges and openings that contribute to the intersection of HCI and AI.

Minitrack Co-Chairs:

Abhishek Behl (Primary Contact)
Keele University
a.behl@keele.ac.uk

Connie Barber
University of Arkansas at Little Rock
csbarber@ualr.edu

Marc Schmalz
Boise State University
marcschmalz@boisestate.edu

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 responsible use of AI systems is their opacity. Many AI systems are “black boxes” that are difficult to comprehend – 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. XAI hence refers to “the movement, initiatives, and efforts made in response to AI transparency and trust concerns, more than to a formal technical concept.” It comprises both post-hoc explainability methods as well as 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 – may they be generative or predictive, machine learning-based or not. We especially look for contributions that investigate XAI 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:

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

Minitrack Co-Chairs:

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

Babak Abedin
Macquarie University
Babak.Abedin@mq.edu.au

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

Elisa Gagnon
Bishop’s University
egagnon@ubishops.ca

This minitrack will include talks featuring the cutting-edge applications of data-driven decision-making in both the performance and business aspects of sports. In the performance area, talks will focus on how analytics are revolutionizing player evaluation, game strategy, injury prevention, and training methodologies across various sports. Presentations will showcase advanced statistical models, machine learning algorithms, and visualization techniques that are helping coaches and athletes gain competitive advantages on the field, court, or track. The business side of the track examines how sports organizations are leveraging analytics to optimize ticket pricing, enhance fan engagement, improve marketing strategies, and maximize revenue streams. From predicting attendance patterns to personalizing fan experiences, speakers will demonstrate how data analytics is transforming the sports industry’s operational efficiency and financial performance. This track offers a comprehensive look at how analytics is shaping the future of sports, bridging the gap between on-field performance and off-field business success.

Minitrack Co-Chairs:

Scott Nestler (Primary Contact)
University of Florida
nestler.scott@ufl.edu

Yonghwan Chang
University of Florida
yhchang@ufl.edu

Jonathan A. Jensen
Texas A&M University
jajensen@tamu.edu

Brian Macdonald
Yale University
brian.macdonald@yale.edu

Interaction with games is considered to have positive effects on our cognitive, emotional, social and motivational abilities. It isn’t surprising, then, that our reality and lives are increasingly becoming game-like. This is not limited to the fact that digital games have become a pervasive part of our lives, but perhaps most prominently with the fact that activities, systems and services that are not traditionally perceived as game-like are becoming either intentionally or emergently gameful.

Gamification refers to a “process of transforming any activity, system, service, product or organizational structure into one which affords positive experiences, skills and practices similar to those afforded by games, and is often referred to as the gameful experience. This is commonly but optionally done with an intention to facilitate changes in behaviours or cognitive processes. As the main inspirations of gamification are games and play, gamification is commonly pursued by employing game design.” Gamification has become an umbrella concept that, to varying degrees, includes and encompasses other related technological developments such as serious games, game-based learning, exergames & quantified-self, games with a purpose/human-based computation games, and persuasive technology.

Secondly, gamification also manifests in a gradual, albeit unintentional, cultural, organizational and societal transformation stemming from the increased pervasive engagement with games, gameful interactions, game communities and player practices. For example, recently we have witnessed the popular emergence of augmented reality games and virtual reality technologies that enable a more seamless integration of games into our physical reality. Case in point are urban spaces that are increasingly becoming playgrounds for different games and -play activities. While location-based games such as Pokémon Go were able to attract millions of players, concepts such as Playable Cities and Urban Gamification highlight the large-scale changes that games are bringing about in the smart cities of the future. Moreover, the media ecosystem has also experienced a degree of ludic transformation: with user generated content becoming an important competitor for large media corporations. This transformation has led to the development of several emerging phenomena such as the Youtube and modding cultures, esports, or the ‘metaverse’, that have penetrated the cultural membrane allowing games to seep into domains hitherto dominated by traditional media.

We encourage a wide range of submissions from any disciplinary backgrounds: empirical and conceptual research papers, case studies, and reviews. Relevant topics include (but are not limited to):

1. Users: e.g. Engagement, experience, motivations, user/player types
2. Education: e.g. Serious games, game-based learning, simulation games
3. Media: e.g. eSports, streaming, Web 3.0 / metaverse
4. Commerce: e.g. Game business models, free-to-play, gamification as marketing, adoption
5. Work: e.g. Organizational gamification, gameful work, games-with-a-purpose, playbour
6. Technology: e.g. VR, AR, MR, gameful wearables, gamified AI, and IoT
7. Toys & playfulness: e.g. smart toys, serious toys
8. Health: e.g. Quantified-self, games for health, health benefits
9. Cities: e.g. smart cities, urban gamification, playable cities, community engagement, governance
10. Theories/concepts/methods: Contributions to science around gamification

The Gamification minitrack at HICSS is part of the Gamification Publication Track aimed at persistent development of gamification research: http://gamifinconference.com/gamification-track/

High quality and relevant papers from this minitrack will be selected for fast-tracked development towards Internet Research. 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:

Juho Hamari (Primary Contact)
Tampere University
juho.hamari@tuni.fi

Nannan Xi
Tampere University
Nannan.xi@tuni.fi

Benedikt Morschheuser
Friedrich-Alexander-Universität Erlangen-Nürnberg
Benedikt.Morschheuser@fau.de

Scholarly research papers are sought that apply a variety of theories, methods (qualitative and quantitative), and empirical techniques from various disciplines including Geography, Geographic Information Science, Information Systems, Decision Sciences, Statistics, etc., to understand the importance of incorporating location, geography, and related data into the system sciences.

There is a need to develop new theories and amend existing ones for the systems sciences that incorporate spatial data, locational intelligence, and geographical concepts. Current concepts of data science, big data, trust, and privacy need attention in addressing locational intelligence research questions.
Research papers focusing on theory development, methodological innovations, empirical contributions, qualitative studies, and case studies are solicited across a broad spectrum of topics, including but not limited to:

Theoretical Advancement and Methodological Innovations

1. FAIR principles in GeoAI: Interpretations of Findability, Accessibility, Interoperability, and Reusability (FAIR) principles in GeoAI Research.
2. Methodological Innovations: Theories and methods that enhance space-time modeling, geospatial generative AI, spatial data science (spatial statistics, spatial data mining, spatial machine learning, spatial deep learning), and social media analytics.
3. Novel Theories and Applications of Geo-Blockchain technology, Indoor Positioning Systems, Location Tracking, Wayfinding, and Geospatial Digital Twins
4. Location Data Privacy and Security: Research on concepts and problems of locational data privacy, security, reliability, transparency, and trustworthiness of GeoAI.
5. Innovative Governance and policymaking for GeoAI, Location Analytics, and GIS.
   Applications of GeoAI, Location Analytics, and GIS
6. Spatial Business: Analyses that address a range of business functions, including marketing, customer relations, operations, logistics, supply chain, asset and risk management, corporate social responsibility.
7. Geospatial Big Data and Commercial Services: Analyses that explore the advancements in geospatial big data for business functions, including analysis of customer mobility, consumer preferences, change detection, in store behavior analysis, variety and price optimization, product placement design, improve performance, labor inputs optimization, and distribution and logistics optimization.
8. Geospatial Big Data and the Public Sector: Analyses that explore the use of geospatial big data for improving transportation, accessibility, social services, service delivery, and policy decision-making.
9. Geospatial Big Data and Personal Location Data: Research related to indoor and outdoor individual location tracking including massive mobile data (MMD).
10. Imagery Analysis: Analysis of imagery using GeoAI for pattern recognition, change detection, and decision-making in a variety of scenarios.
    Societal Impact of GeoAI, Location Analytics, and GIS
11. Public Health and Healthcare: Analyses that address locational dimensions of
    health, healthcare delivery, healthcare accessibility and equity issues, patient profiling, personalized medicine, and disease pattern identification.
12. Climate Action: Analyses that address climate change, resilience, adaptation, environmental sustainability (climate, water, energy, and agriculture, as described by the United Nations SDGs), and environmental justice issues.
13. Smart Cities: Urban issues including patterns of urban mobility, change detection, and integrating GIS in smart cities for sustainable and resilient infrastructure development
14. Bridging Digital Divides: Study and analysis of geographic patterns and disparities in adoption, diffusion, use, and impacts of the internet and information and communication technologies (ICTs), including the internet.
15. Industry Clusters and Economic Development: Analyses that address spatial aspects of economic development and community impacts, including infrastructure, workforce, automation, environmental impacts, and social inequalities.
16. Gig Economy: Location patterns of the Gig Economy, including geospatial analysis of collaborative consumption-based platforms, markets, and models.
17. Public Safety and Disaster Management: Studies of systems using GIS, GeoAI, and
    locational analytics for disaster mitigation, crisis management, crime analysis, and community risk and resilience.

Minitrack Co-Chairs:

Avijit Sarkar (Primary Contact)
University of Redlands
avijit_sarkar@redlands.edu

James Pick
University of Redlands
james_pick@redlands.edu

Joseph Aversa
Toronto Metropolitan University
javersa@torontomu.ca

Namchul Shin
Pace University
nshin@pace.edu

In this minitrack, we seek new research exploring the approaches for and consequences of implementing artificial intelligence (AI) in service work contexts. Our interest lies in how service work is being fundamentally reimagined as artificial intelligence reshapes service design and delivery, and how human capabilities are evolving, expanding, and being redefined in response to AI. The submissions can be empirical studies, conceptual papers, or practitioner reports on service development and its implications.

AI is transforming service delivery by augmenting human employees and automating entire service processes, even fundamentally reimagining what human capabilities mean in service contexts. Machine-learning-powered service technologies such as service robots, LLM chatbots, diagnostic AI tools, and algorithmic decision-making and management systems have become integral parts of many service processes. This directly impacts frontline service employees who engage with customers –physically or virtually. By taking over employees’ old tasks, creating new tasks for them, and transforming their roles, AI changes the nature of humans’ work, for better or for worse. Furthermore, this transformation can extend beyond simple task automation to reshape how human workers leverage their uniquely human capabilities like emotional intelligence, complex problem-solving, and adaptive thinking in collaboration with AI systems.

On the one hand, AI can benefit service work by yielding cost savings, ensuring quality control, identifying novel sources of revenue, and providing personalized services, which all can contribute to better customer experiences. If services are redesigned appropriately, human workers can offload repetitive and onerous tasks to AI and focus on meaningful customer interactions (e.g., chatbots helping customers with simple routine questions). Moreover, leveraging AI’s analytical abilities can help workers improve their technical expertise and mastery of service tasks. For instance, AI decision aids can help medical professionals gain novel insights into different health conditions and potential treatments, improving patient outcomes.

On the other hand, AI can cause unintended effects, such as reinforced bias, disrupted employee role identity, and dehumanization of work. The introduction of AI systems sometimes shifts decision-making agency and expertise from the human to the algorithm (e.g., when AI makes final decisions about whether a hotel guest should be upgraded to a better room or whether a bank customer should be granted a loan), rendering human workers as mere assistants of the AI. This may not only create feelings of powerlessness and meaninglessness among service workers but also result in a less skilled workforce over time. Furthermore, AI’s increasing capabilities cause concerns over job displacement as AI systems are increasingly replacing human workers.

Furthermore, in a service work context, the perceptions and attitudes of the customers ultimately determine whether the technologies can be successfully integrated. For example, the usefulness of self-service technologies or service robots depends on how widely they are accepted.

The dynamic nature of AI technology suggests that its impacts on workers and customers can be multifaceted. Its disruptions fall unevenly over service workers with different skills, roles, and tenure across industries. For instance, research finds that while seasoned financial service experts have lamented losing their autonomy and expertise to AI, less experienced bank employees have found the same as AI empowering. In either case, workers and their organizations tend to respond to AI’s disruptive impacts in various, often creative ways, such as investing in the human element in the service and gaming the AI system through workarounds like data manipulation.

In this minitrack, we invite authors working in the intersection of AI and service research to submit their best work on the topic. Empirical analyses can be particularly valuable but we also welcome conceptual papers attempting to provide much-needed theoretical organization on this topic. As HICSS addresses leading-edge developments, we especially encourage submissions discussing recently emerged or potential future impacts on service ecosystems and service workers.

Relevant topics for this minitrack include (but are not limited to):

1. The incorporation of AI into service delivery
2. Work design for AI-based services
3. New/emerging/future service roles and skills enabled by AI
4. Service job displacement due to AI
5. How AI transforms existing service roles and skills
6. Upskilling or deskilling of service workers
7. The effect of AI on service workers’ wellbeing
8. How service employees cope with the incorporation of AI into their work
9. How service organizations adapt and change in response to AI
10. How service business models adapt and change in response to AI
11. Unintended consequences of incorporating AI into service delivery
12. Future of humans’ role in service delivery in the age of AI
13. AI-facilitated customer interaction
14. Customer perceptions of, and attitudes toward, AI-based services
15. AI-based self-service technologies (in, e.g. retail, hospitality)
16. Development of capabilities in managing AI-powered service systems
17. Development of complementary human-AI capabilities
18. New capabilities required for human-AI collaboration in service delivery”

Minitrack Co-Chairs:

Tapani Rinta-Kahila (Primary Contact)
University of Queensland
t.rintakahila@uq.edu.au

Juho Lindman
University of Gothenburg
juho.lindman@ait.gu.se

Virpi Kristiina Tuunainen
Aalto University
virpi.tuunainen@aalto.fi

The prevailing ISR methodology has been built on decision analysis and design science for the creation, development and use of decision support systems. The common denominator in most research inquiries is the accumulation of human cognitive power by using as support the analytic capabilities of computers.
The rapid development of artificial and computational intelligence is challenging the assumption that only human cognitive power matters as these technologies can learn and build new cognitive skills. Decision makers still carry full responsibility for the decisions and the consequences they bring. Decision analytics was developed as a partial response to the emergence of intelligent, analytic technologies. It is now clear that such pursuit is not enough: new principles and guidelines are needed for ISR methodologies in the 2030’s as the cognitive capabilities of AI technologies grow.

This minitrack invites papers on expected novel and speculative methodological forms, methods and elements that can serve as foundational elements of new methodologies for IS research. The papers can build on descriptions and evaluations of ongoing business innovations, assessments of industrial best practises and corporate cases that introduce novel problems. The papers will show management decision approaches that could be better and more productively handled if they were guided by innovative and enhanced ISR methodologies which are adaptive to the new organizational challenges and tasks.

The current principles of decision analysis (DA) have been reinterpreted, enhanced, and adjusted over 3-4 decades to meet the needs from growing complexities of large, multinational, dynamically interdependent industries, and corporations that in ever growing competition adapt to dynamically evolving innovations.

Information systems research aims at using information technology to handle and solve organizational problems and improve or change organizational tasks and related human activities. Design science has become a key part of this process as it creates novel artifacts (algorithms, human/computer interfaces, process models, languages, etc.) which extend the boundaries of human capabilities to address problems and tasks that so far have not been tractable. The design-science paradigm has guided problem-solving with research informed innovations that define theoretical ideas, new practices and create visions through which analysis, design, implementation, management and use of information systems can be more effective and productive.

The Decision Support Systems (DSS) movement (first), methodology (rather soon after) and paradigm (gradually) started in the 1980’es. Managerial tasks are not routine, and managers asked for “support to do a better job”. DSS methods were shown to offer both simpler and better guidance than methods developed within Decision Analysis – they offered service, fast delivery, ease of use, benefit focused more than cost, imprecision allowed for timely delivery and user control; these were messages that got support in management. DSS builders focused on the users’ priorities, they developed systems linked to key business activities and they viewed the quality of a system from the value it gives to users rather than how advanced the offered systems technology was.

Decision analytics represents a shift from both DA and DSS towards developing and delivering critical data, information and knowledge for management, decision-making, negotiations, planning, operations, (public, private sector) administration, etc. Analytics builds on theory and advanced algorithms as part of information systems. Research used for decision analytics in the 2020’s shows themes like big data, machine learning, business and service analytics, gamification, virtual and augmented reality, visual decision analytics, soft computing, computational logistics, explainable AI, etc. These all are described as “hot topics”, which offer stronger results than DA and DSS, and that are relevant for contemporary, competitively effective management. ISR methodologies for the 2030’’s will go beyond these and are expected to offer frameworks and conceptual models and approaches that will make full use of insights and contributions achieved with advances in decision analytics and AI.
Digital coaching requires that we master the transition from data to information, and on to knowledge, now known as digital fusion. A new ISR methodology will probably take form as a synthesis of the successful elements of decision analysis, design science, decision support systems and decision analytics but with additional elements (both cognitive and digital) from digital coaching, which probably will use digital platforms to make artificial and computational intelligence methods operational for practical problem-solving and decision making.
With this new mini track, we seek contributions from researchers with experience and interest in theoretical ISR issues and challenges, but also with experience of finding practical solutions for information systems. We are interested in papers reporting on the “what and how” and why the users accept and adopt the methods. We are interested in contributions where the applied/defined ISR methodologies become visible with either an experimental or empirical focus. We look for contributions which combine innovative theoretical results with sufficient empirical verification. Topics that are appropriate for this mini track include, but are not limited, to:

1. Acceptance and use of ISR methodologies for solving contemporary large problems
2. Artificial intelligence and large language models in management
3. Delegation and monitoring of AI during decision making
4. Automation and augmentation in organizational decision making
5. Risk associated with machine learning based decision making
6. Explainable algorithmic and heuristic decision making for large problems
7. Natural language processing for information and knowledge support
8. Cognitive computing for design and management of digital services
9. Soft computing for digital coaching
10. Fuzzy logic and fuzzy decision making (precision vs. relevance issues)
11. Digital fusion for decision support
12. AI and machine learning for effective operational management
13. Machine learning and fast online problem solving
14. Deep learning approaches for handling large, complex planning problems
15. Digital fusion of data, information and knowledge
16. Digital coaching with support from automated joint computing systems
17. Testing/evaluations of ISR methodology impacts on organizational performance
18. ISR methodology enhancement with interactive visualization and visual analytics

Minitrack Co-Chairs:

Christer Carlsson (Primary Contact)
Institute for Advanced Management Systems Research, Abo Akademi University
christer.carlsson@abo.fi

Kalle Lyytinen
Case Western Reserve University
kjl13@case.edu

Yong Liu
Aalto University
yong.liu@aalto.fi

Jozsef Mezei
Abo Akademi University
jozsef.mezei@abo.fi

Minitrack Co-Chairs:

David Ebert (Primary Contact)
University of Oklahoma
ebert@ou.edu

Kelly Gaither
University of Texas at Austin
kelly@tacc.utexas.edu

Brian Fisher
Simon Fraser University
bfisher@sfu.ca

Financial markets have a long history of regulation, requiring public companies to disclose information to government agencies. Over the past two decades, regulators have increased measures to democratize financial information and adopted standardized data reporting formats such as XBRL to make it easier for the average investor to analyze company data. In the United States, the Securities and Exchange Commission (SEC) provides access to these structured datasets on its website (SEC Markets Data).

One of the largest and most detailed datasets available is the SEC’s Structured Financial Statements and Notes Data Set (link), which exceeds 230 GB of .tsv files and is also accessible via the EDGAR API. However, due to its size and the complexity of XBRL tags, extracting meaningful insights from this dataset presents significant challenges. As a result, many researchers still rely on proprietary financial databases such as Compustat and Wharton Research Data Services (WRDS). While proprietary databases offer convenience, they lack transparency regarding the source of financial figures, making it difficult to audit and replicate research findings. In contrast, publicly available datasets provide researchers with auditable data, fostering reproducibility and open inquiry.

Over the past decade, advancements in big data tools (e.g., Pandas, R, DuckDB, Malloy) and generative AI (e.g., ChatGPT) have made it easier to analyze large datasets, such as the SEC’s Structured Financial Statements and Notes Data Set. Artificial intelligence (AI) has advanced rapidly, driven by sharp increases in commercial investment. A striking example is the swift development and deployment of large language models (LLMs). AI is already transforming financial services, presenting both vast opportunities and potential risks to economic and financial stability. Recent debates highlight concerns such as existential threats and widening societal disparities. However, these tools can help level the playing field for individual investors.

We invite empirical, theoretical, and experimental papers exploring AI’s opportunities and risks in finance, accounting, and fin-tech. We encourage researchers to explore publicly available datasets and leverage modern analytical tools to generate novel and reproducible insights into financial markets and regulation. Our goal is to enhance understanding of how firms, investors, and other market participants use—or could use—AI and big data techniques, as well as the broader societal and regulatory implications. Potential issues and topics include, but are not limited to:

1. Use of LLMs in financial statement analysis
2. AI for understanding economic data
3. Survey of AI techniques used by financial professionals
4. Quantitative analysis of risk factors or litigation disclosures
5. Comparative analysis of different data analysis tools
6. Analyzing financial restatements with AI
7. Using LLMs to understand board characteristics
8. ESG-related disclosures
9. Replacing proprietary data sources with free alternatives
10. AI in corporate finance
11. AI in trading and asset management
12. AI in banking and credit
13. AI in financial forecasting
14. AI in consumer finance
15. AI in fraud detection
16. Macroeconomic and market effects
17. Regulatory challenges: frictions, market failures, and policy solutions
18. Exploratory data analysis using SQL or other tools
19. Using data pipelines in research
20. Tools for cleaning and processing XBRL data
21. AI for social impact

Minitrack Co-Chairs:

Tim Olsen (Primary Contact)
Gonzaga University
olsent@gonzaga.edu

Joseph Johnston
Illinois State University
jajohn6@ilstu.edu

Minitrack Co-Chairs:

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

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

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

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

Cities and organizations worldwide are increasingly shaped by complex interconnected networks of people, infrastructure, and technology. Rapid urbanization, coupled with accelerating technological advancements and evolving user needs creates highly complex interdependencies that demand more intelligent, adaptable, and secure systems.

Digital Twins have emerged as powerful platforms for simulating these complex systems, improving strategic decision-making and enabling stakeholders to predict, test, and refine policies or interventions under normal and extreme conditions. The complexity of decision-making processes, however, is further compounded by the lack of scalable data-integration approaches that effectively capture space-time fluctuations and variations in system behavior at human-infrastructure intersections. Moreover, the surge in data size, diversity, and complexity—along with its sporadic spatiotemporal generation—demands a paradigm shift in how we comprehend, influence, and ultimately manage these complex systems across scales. AI-enabled digital twin intelligence—including Generative AI and Large Language Models (LLMs)—open new avenues for scenario generation, adaptive, management and innovation, and human-machine collaboration. This augments the intelligence and responsiveness of systems, enabling them to better anticipate and respond to the dynamics of complex sociotechnical systems.

We invite theoretical, methodological, and practical contributions exploring how networks, digital twins, and intelligent systems can transform future cities. Submissions are encouraged to explore networked interactions involving people, devices, data flows, services, and systems—particularly where these interactions lead to emergent, complex behaviors. Topics of interest include (but are not limited to):

1. Network-Centric Theories, Models, and Architectures for Digital Twins
2. Generative AI for Scenario Creation, Risk Assessment, and Dynamic Modeling
3. Scaling Digital Twins across Interconnected Cyber-Physical Systems
4. Human-Infrastructure Interaction Modelingand Uncertainty Quantification
5. Language Model Interfaces for Digital Twin Stakeholder Engagement
6. Scalable Synthetic Data Generation and Fusion for training AI-driven Digital Twins
7. Digital Twin Virtualization and Interactivity (Virtual Reality / Augmented Reality / Mixed Reality)

Minitrack Co-Chairs:

Neda Mohammadi (Primary Contact)
University of Sydney
neda.mohammadi@sydney.edu.au

John Taylor
Georgia Tech
jet@gatech.edu

This minitrack serves as a two-way bridge between academic research and the organizations on the front lines. Our goals are two-fold:

1. 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.
2. Engage industry and academic colleagues to find collaboration opportunities.

This minitrack solicits 3-page executive summaries and 10-minute presentations. It 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. Possible themes/topics of this minitrack include, but are not limited to:

1. Data and Analytics
   ● Descriptive, diagnostic, predictive, prescriptive analytics
   ● Machine learning, deep learning, quantum computing
   ● Data, text, web, and social media mining
   ● Data, information & knowledge management
   ● Design science, storytelling, and visual analytics
2. Artificial Intelligence and Cognitive Computing
   ● Smart machines and computerization
   ● Hyperautomation and robotic process automation
   ● Ethical AI and responsible AI practices
   ● AI-driven decision-making and augmentation
   ● Networking & AI
3. Cybersecurity
   ● Blockchain & distributed ledger technologies
   ● Federated learning and secure data sharing
   ● Endpoint security and consumer device protection
   ● Risk management, compliance, and legal solutions
   ● Data, network security products, and strategies
4. Internet of Things
   ● IoT applications in various industries
   ● Edge computing and real-time data processing
   ● Personalization in retail, finance, healthcare, and e-commerce
5. Service-Oriented Technology & Management
   ● Cloud computing and hybrid cloud solutions
   ● Service ecosystems and platform economies
   ● Collaboration systems and technologies
   ● Serverless computation and network-as-a-service models
   ● Data-, analytics, and -information-as-a-service
   ● X-as-a-service
6. Responsible innovation
   ● Digital government and smart city solutions
   ● Multichannel citizen engagement
   ● Ethical technology, trust, and data privacy
7. Intelligent Augmentation & Virtual Reality
   ● Virtual reality and gaming applications
   ● Augmented reality for education, healthcare, and tourism
   ● AR/VR for enhancing navigation and user experiences
8. Future of Work
   ● Human-machine partnership
   ● Real-time and immersive collaboration
   ● Digital workplace operations
   ● Optimizing the employee experience
   ● New roles and jobs for the future
   ● Lexicon of technology for diversity, inclusion, belonging
9. Emerging Information Technologies
   ● Quantum computing applications and advancements
   ● Edge AI and edge computing innovations
   ● Next-generation networking technologies
   ● Bioinformatics and computational biology

Minitrack Co-Chairs:

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

Terri L. 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

Decision support for the planning, control and operation of systems and processes in manufacturing, production and logistics requires powerful methods. Simulation modeling has traditionally been at the core of decision support systems in this application domain. These methods are more and more supplemented, combined and sometimes even substituted with data science methods. Key concepts of Industry 4.0 and smart manufacturing, such as the digital twin, are frequently based on a combination of simulation modeling and data-driven approaches.

The benefits of simulation models are numerous and reach from classical verification of planning results up to data synthesis for AI methods or knowledge discovery through data or process mining. Furthermore, simulation modeling has become essential for the application of powerful optimization methods, such as genetic or swarm-based algorithms, or for the deployment of advanced machine learning techniques, in particular reinforcement learning.

The minitrack aims at attracting contributions with a focus on simulation modeling, AI methods and digital twins for decision making in production and logistics. Methods of interest include discrete-event simulation, system dynamics, hybrid simulation, and hybrid systems modeling, where simulation modeling is used in combination with methods from other disciplines, such as AI, data science, machine learning or optimization heuristics.
Topics of interest also include strategies for the successful application of simulation modeling, AI and digital twins to real-world problems as well as reports on the successful construction of digital twins for real-world production and logistics systems. Aspects of higher education related to the minitrack topics can also be addressed.

Selected authors of accepted conference papers will be invited to submit an extended version of their paper to the Journal of Simulation.

Minitrack Co-Chairs:

Steffen Strassburger (Primary Contact)
Technische Universität Ilmenau
steffen.strassburger@tu-ilmenau.de

Tobias Reggelin 
Otto von Guericke University Magdeburg
tobias.reggelin@ovgu.de

Stefan Galka
Ostbayerische Technische Hochschule Regensburg
stefan.galka@oth-regensburg.de

Sebastian Lang
Otto von Guericke University Magdeburg
sebastian.lang@ovgu.de

The pervasive nature of digital technologies, as witnessed in the industry, services, and everyday life, has given rise to an emergent, data-focused economy stemming from many aspects of human individuals and the Internet of Things (IoT). The richness and vastness of these data create unprecedented research opportunities in many fields, including urban studies, geography, economics, finance, entertainment, social science, physics, biology and genetics, public health, and many other smart devices. 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’ 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, referring to the users’ data and social background. The behavior 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 a disembodied interactive kiosk. 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, AI technologies have been applied to robotics and toy computing. Robotic computing is one branch of AI technologies, and robots enable their 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 recently developing concept that transcends the traditional toy into a new computer research area using AI technologies. A toy in this context can be effectively considered a computing device or peripheral called Smart Toys. We invite research and industry papers related to these specific challenges and others driving innovation in robotics and toy computing for social robots. This mini-track covers associated topics such as:

1. Social Technical Issues
2. Human Behavior Study
3. Human-Robot Interaction
4. Business Models
5. Conceptual and Technical Architecture
6. Visualization Technologies
7. Modeling and Implementation
8. Security, Privacy, and Trust
9. Industry Standards and Solution Stacks
10. Provenance Tracking Frameworks and Tools
11. Accountability, ethics, and transparency
12. Case Studies (e.g., smart toys, healthcare, financial, aviation, education, etc.)

This proposed minitrack aims to crystallize this emerging research domain and trends into positive efforts to focus on the most promising research and industrial solutions. The papers will prove that social robots and AI technologies are more critical in supporting robotic and toy computing applications. The papers are expected to research further new best practices and directions in this research domain.

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:

1. Neural networks
2. Fuzzy large language models
3. Fuzzy logic
4. Fuzzy decision-making
5. Evolutionary computation and evolutionary algorithms
6. Hybrid intelligent systems
7. Software for soft computing
8. Natural language processing based on soft computing techniques
9. Explainable decision-making
10. Soft computing and its application to design and manage of digital services
11. Applications of soft computing in digital world
12. 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:

Enrique Herrera-Viedma (Primary Chair)
University of Granada
viedma@decsai.ugr.es

Francisco Javier Cabrerizo
University of Granada
cabrerizo@decsai.ugr.es

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

Technology is revolutionizing the global economy, driving transformational changes that are reshaping industries and societies at an unprecedented scale and pace. Artificial intelligence (AI) and the growth of digital platforms are at the forefront of this transformation, with investments in advanced infrastructures and cutting-edge models fueling the fourth industrial revolution. The Stargate Project exemplifies this trend, with plans to invest $500 billion over the next four years to build AI infrastructure in the United States, reinforcing American leadership in AI while creating hundreds of thousands of jobs and securing strategic national capabilities. Similarly, the rise of innovative players like DeepSeek highlights the global competition in AI development. DeepSeek’s recent breakthrough with its cost-efficient and highly capable DeepSeek-V3 model has disrupted the market, challenging assumptions about U. S. primacy in AI and the effectiveness of export control. This surge in technological innovation is driven by key industry leaders and supported by national policies aimed at fostering growth in AI capabilities. Companies continue to race to develop foundation models, while governments worldwide prioritize strategic investments to remain competitive in this rapidly evolving landscape.

These developments mean that an unprecedented amount of data on individuals and business operations is now available. The effective and efficient analysis of such data and the extraction of actionable insights are made possible by innovations in machine learning, causal inference, and economics, along with the availability of powerful computational resources, such as GPUs and cloud computing. These advancements provide researchers with unique opportunities to conduct analytics-based research. Industry practitioners in these countries are also building technological and AI capabilities for competitive advantage, and entrepreneurs are creating new business models in this technology-enabled ecosystem. At the same time, there are many interesting challenges to technology and analytics research. First, the sheer volume of data necessitates the development of a scalable analytics framework. Second, data integrity needs to be carefully examined to ensure quality research. Third, a deep understanding of the contextual features (e.g., business practices, languages, cultures, social norms, legal systems) is important. Finally, to ensure generalizability, analytics-based perspectives require support from sound theories from disciplines such as economics, psychology, and sociology.

The main purpose of this minitrack is to bring AI/IT researchers, economists, industry practitioners, and policymakers together to discuss future directions on how AI will reshape various marketplaces and the global economy. It is anticipated to offer a pathway to initiating and strengthening collaborations between academia and industry. Topics of interest include, but are not limited to:

1. AI infrastructure development
2. AI economic impact
3. Development of foundation models
4. Global AI strategies
5. AI regulatory policies
6. AI market disruption
7. AI-driven consumer behavior
8. Personalized marketing through AI
9. Generative AI and large language model services
10. AI-powered bots and the fight against fake news and misinformation in emerging economies
11. Consumer trust in generative AI systems
12. AI-driven healthcare decision support
13. Marketing automation and AI
14. AI-empowered digital entrepreneurship
15. AI and analytics in personalized K-12 and higher education in emerging countries
16. Scalable analytics methodologies
17. AI ethics and data integrity

Minitrack Co-Chairs:

Gene Moo Lee (Primary Contact)
University of British Columbia
gene.lee@sauder.ubc.ca

Sang-Pil Han
Arizona State University
shan73@asu.edu

Huaxia Rui
University of Rochester
huaxia.rui@simon.rochester.edu

Service science deals with issues of value creation in service systems, which may involve knowledge from various disciplines, such as social science, management, design, and engineering, especially information technologies, including computer science and information management. These multiple perspectives can be unified using the theoretical construct of the service system, in which entities (people, businesses, government agencies, etc.) interact to co-create value via value propositions that describe dynamic re-configurations of resources. As ever more people in academia and industry recognize the need for service innovation, HICSS remains an excellent venue for exploring all aspects of service science and its application areas. The HICSS community can continue to play a leadership role.

The challenges of achieving environmental, social, and cultural sustainability while maintaining prosperity demand a social-ecological system view to analyze the causes of the problems and tackle them via interdisciplinary solutions.  To that end, this minitrack will encourage submission of research papers from a variety of disciplines and a variety of participating communities that focus on service innovations and business transformations that aim to achieve resilience and sustainability, ultimately improving human well-being by transforming service systems through technological and other interventions on organizational structure, inter-organizational value chain, and business models. Relevant papers may focus specifically on issues in service system governance, service process modeling, service delivery management, innovative service technologies, and the relationship between humans and artificial intelligence (AI), especially Generative AI to collaborate for greater well-being. Throughout, we welcome service research connected with practical societal-level problem domains to demonstrate the impact of service innovation on human and social well-being.

The trend of increasing contributions to economic outputs from services-related activities in major countries pushes the focus on service innovation to be a major part of most business models. Even in traditionally manufacturing-driven industries, such as IT and related industries, the importance of service has surpassed most other corporate competencies. From the outset, efforts in creating, composing, and delivering services call for systematic studies of managerial, technical, and social issues. Pioneers in service research have moved service research up to the inflection point, and now there is a great need of a wider range of service research – and the opportunity to focus work on more meaningful human and societal problems. Combining managerial, organizational, and technical perspectives, service science and transformative service research and education aims to create service professionals with technological, business, and social-organizational abilities who aim to improve human and social well-being.

This minitrack will consider papers from technology-led service innovations and transformations that create sustainable solutions for societal problems with a variety of research methodologies, such as qualitative, quantitative, and design science approaches. It focuses on important issues faced in service system transformation resulting from advances in new age technologies for services, including but not limited to:

1. Application of technologies in service, such as artificial intelligence, metaverse and robotics, for greater wellbeing
2. Role of data in complex service systems, including sensing, analytics, and potential ethical challenges
3. The adoption of digital technologies, such as Generative AI, blockchain, and internet of things, to support service innovation
4. Human-AI collaborative knowledge management to enhance resilience and sustainability
5. Initiatives in service innovation to enhance resilience and sustainability
6. Digital transformation of service systems in different industries or sectors
   Mid-range theory in service based on service-dominant logic or other paradigmatic theories;
7. Conceptualization of service ecosystem design to increase value co-creation in service and uplift human wellbeing

Minitrack Co-Chairs:

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

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

Nila Windasari
Institut Teknologi Bandung
nila.armelia@itb.ac.id