Portrait image of Haluk Demirkan.

Haluk Demirkan

Milgard School of Business
Center for Business Analytics
University of Washington – Tacoma
1900 Commerce Street, Box 358420
Tacoma, WA 98402-3100
Tel: +1-253-692-5751

Matti Rossi

School of Business
Aalto University
Tel: +358-50-383-5503

The DA/SS Track works out emerging managerial and organizational decision-making strategies, processes, tools, technologies, services and solutions in the Digital Age. This is done in 2 interrelated themes. The first theme, Decision Analytics, focuses on decision making processes, analytics tools and supporting technologies which has collected papers on big data and analytics, machine learning, business and service analytics, gamification, virtual and augmented reality, visual decision analytics, soft computing, logistics and supply chain management, explainable AI, etc., which now are core research themes in analytics. Challenges and issues of service industries, service science, digitalization of services, digital mobile services, smart service systems, smart cities and communities, smart mobility services, social robots, etc. form the Service Science.

Our current understanding of the potential age and generational aspects in technology acceptance and use remains limited, resulting in age and generational stereotypes (or even age and generational discrimination), the potential deepening of the digital divide between individuals of different ages and generations, and numerous other issues in technology acceptance and use. For example, many modern societies aim at addressing their rising healthcare costs through the diffusion of different kinds of self-service solutions targeted particularly at the aging population, but the adoption of such solutions risks meet considerable resistance if they are developed without fully understanding the distinctive features of technology acceptance and use of their target users. Similarly, many studies have warned us about the numerous perils that technologies like social media pose particularly for younger generations, but properly addressing these problems may prove impossible without first gaining a more in-depth insight into what these technologies actually mean and how are they actually being used by the members of these generational cohorts.

In this minitrack, we call for multidisciplinary and multi-methodological studies that dive deeper into the potential age and generational aspects of technology acceptance and use in order to advance our understanding of these phenomena. This includes studies that focus on the differences and similarities between multiple age groups and generational cohorts, studies that focus more specifically 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 in their research models and research settings. We also 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 many of such stereotypes may not be completely without merit, the increasing exposure to various technological innovations 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 many business failures and missed business opportunities. One good example of the laeer is the ever-growing segment of young elderly consisting of individuals aged around 60–75 years, which has traditionally been overlooked by many companies based on the stereotypical view of its members as individuals with low technology readiness and low interest in technology, which may not necessarily be valid. We also invite innovative and ground-breaking studies that introduce novel theoretical mechanisms and more in-depth 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 and generational cohorts. 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, real user and use data can not only be used for customer profiling, marketing, and developing new value-adding digital services but can also provide researchers with a more realistic view of the technology acceptance and use intentions and behaviors of individuals as well as act as a means for more in-depth investigation of their use patterns and rhythms. Relevant topics for this minitrack include (but are not limited to):

  • Differences and similarities in technology acceptance and use between various age groups and generational cohorts
  • 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)
  • Age and generational stereotypes related to technology acceptance and use
  • Age and generational discrimination (e.g., ageism) related to technology acceptance and use
  • Digital divide between individuals of different ages and generations
  • Age and generational effects in the antecedents of technology acceptance and use (e.g., technology beliefs, technology attitudes, and technology readiness)
  • Age and generational effects in various dark side of IT/IS use phenomena (e.g., technostress and technology addiction)
Minitrack Co-Chairs:

Anna Sell (Primary Contact)
Åbo Akademi University

Markus Makkonen
Tampere University

Pirkko Walden
Åbo Akademi University

Tomi Dahlberg
University of Turku

This minitrack solicits research contributions that address topics related to artificial intelligence model evaluation – one of the most significant areas with significantly increased research inquiry in the post-ChatGPT world. Topics of interest include, but are not limited to:

  • Empirical evaluations of models
  • Evaluation of publicly available foundation models or model fine-tunes
  • Comparative evaluations of models for specific cognitive capabilities e.g. reasoning, code generation
  • Novel or improved approaches to model evaluation
  • Evaluation of prompting techniques and prompt engineering strategies
  • Evaluation of the limits of prompt engineering
  • Evaluation of AI models through training and tuning
  • AI model evaluation metrics
  • Defining and tracking performance metrics of AI models
  • Human in the loop systems and model evaluation
  • The use of benchmarks in model evaluation
  • The proposal of new benchmarks
  • Evaluation and quantification of model risk
  • Evaluation of efficiency and scalability of models
  • Evaluation of the relationship between model size and performance characteristics
  • Evaluation of how AI models can augment human performance in given tasks
  • Theoretical results in relation to model evaluation
  • Theoretical results in relation to predicting model capabilities
  • Proposed new methods for evaluating AI models and their complexities
  • Computational paradigms and frameworks for evaluating AI models
  • Explainability and interpretability of models
  • Ethics, legal and socio-technical issues in AI based systems
  • Frameworks for evaluating AI model alignment and super alignment
  • Evaluation of model code generation and programming assistance capabilities
  • Evaluation of negative AI model characteristics: dishonesty, deceptiveness and inaccuracy
  • Multimodal model evaluation
  • Evaluating societal impact of AI models
Minitrack Co-Chairs:

Robert Steele (Primary Contact)
Quantic School of Business and Technology

Radmila Juric 
ALMAIS Consultancy

Julia Rayz
Purdue University

This minitrack seeks a wide range of theoretical and empirical papers that employ natural language processing (NLP), text mining, and text analytics techniques to better understand and improve 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: (1) those developed in the United States by the National Institute of Standards and Technology (NIST) designed to regulate the development of AI; (2) frameworks for the development of trustworthy AI; (3) other national and international organization frameworks for the development of responsible AI which 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 approaches to AI explainability, privacy and security, safety, reliability, and accountability
  • How they consider context-specific risk management techniques
  • How stakeholders are involved
  • How they promote innovation
  • How they promote inclusion

These papers could also explore the use of AI for cyberthreat detection, and other applications of Machine Learning (ML)/Deep Learning (DL)/AI related to any aspect of cybersecurity. Other papers may address methodological challenges such as: text summarization, classification, and clustering; using large language models (e.g. BERT, GPT, PaLM) and generative AI products (such as ChatGPT, Google Gemini) to create large scale synthetic data; overcoming API limitations; and working on distributed, high-performance computers. We also seek papers on enhanced explainability in AI and text analytics (particularly AI/ML) relative to results and the detection and mitigation of bias in analytics.

Potential papers for this minitrack may deploy any number of text analytics techniques, such as:

  • Statistical bag-of-words (BoW), term frequency by inverted document frequency (TF-IDF), and rule-based approaches.
  • Categorization/dictionary/lexicons approaches and sentiment analysis models.
  • Syntactic parsing and NLP approaches, including Named Entity Recognition (NER), text embeddings.
  • Large Language Models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformers (GPT).
  • Unsupervised ML approaches, including topic modeling (using Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), or other techniques); and k-means clustering.
  • Supervised ML/DL approaches, including predictive regression and classification models, Deep Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs).

Papers submitted to this minitrack may analyze various genres of text data, including, but not limited to:

  • Security alerts
  • Reports from Computer Emergency Response Teams (CERT)
  • Common Vulnerabilities and Exposures (CVE) listings
  • Threat intelligence feeds
  • Computer logs
  • Email archives (including phishing emails)
  • Incident and maintenance reports
  • Legal documents (patents, contracts, etc.)
  • Public policies and public comments
  • Social media feeds and online communities and discussion fora (Reddit, Discord, etc.)
  • Websites and Blog posts
  • Published articles
  • Meeting and call center transcripts
  • Speeches
  • News transcripts
  • Customer feedback
  • Resumes and CVs
  • Job Postings and Descriptions
  • Employee evaluations
  • Insurance claims (cyber insurance, etc.)
  • Annual reports
  • Case studies

A fast track publication opportunity with Data & Policy published by Cambridge University Press has been secured for selected papers accepted to this minitrack.

Minitrack Co-Chairs:

Derrick L. Cogburn (Primary Contact)
American University

Haiman Wong
Purdue University – West Lafayette

Theodore A. Ochieng
American University

Leveraging emerging technologies in artificial intelligence, data science, analytics, and decision support can play a significant role in addressing sustainability issues and advancing research to achieve Sustainable Development Goals (SDGs). The minitrack welcomes research articles and practitioner reports that explore the challenges, applications, systems, and methodologies related to the use of advanced technologies for green IS and environmental sustainability. The minitrack encompasses Green AI, IS, and IT, environmental informatics and analytics, and sustainable computing. It encourages the submission of theory-based papers that focus on (generative) artificial intelligence, large language models, visual analytics, decision support, the Internet of Things (IoT), cloud and edge computing, as well as conceptual work on information systems design and decision technologies in sustainability applications. Possible topics include, but are not limited to:

  • Analytics and decision technologies to enable the green transition
  • Artificial intelligence and large language models for supporting the Sustainable Development Goals (SDGs)
  • Smart agriculture and circular economy
  • Energy informatics
  • Environmental intelligence and decision support systems
  • Environmental knowledge acquisition and management
  • Environmental Management Information Systems (EMIS)
  • Environmental Decision Support Systems (EDSS)
  • Geographic Information Systems (GIS) for Environmental Management
  • Green IS and IT
  • Environmental cyberinfrastructure
  • Environmental communication
Minitrack Co-Chairs:

Omar El-Gayar (Primary Contact)
Dakota State University

Abdullah Wahbeh
Slippery Rock University of Pennsylvania

Arno Scharl
MODUL University Vienna

Artificial Intelligence (AI) has significantly advanced the management of connectivity between devices and sensors, thereby improving problem analysis, mitigating risks, and enhancing understanding of complex systems. The integration of AI into manufacturing and infrastructure systems facilitates decision-making regarding events that deviate from the norm, prompting tailored actions according to the risk level. Various methods and models have been developed so far to enhance the accuracy of problem identification within data sets, thereby helping experts in making informed decisions.

The minitrack focuses on the development of novel methods and models to fields that produce wealth of information with a high frequency (such as IT data centers and energy service company), and require to extract knowledge and actionable insights from them in order to e.g. enhance productivity and reduce downtime.
Topics of interest include:

  • Natural language processing and AI-based language models applied to short text
  • New or improved AI-powered anomaly detection approaches
  • New or improved AI-powered filtered selection approaches
  • Novel methodologies for better identification of rare events through machine learning
  • Novel methodologies for risk definition through machine learning
  • Novel methodologies for triggering events
  • Novel methodologies for supporting unbalanced data in data science
  • Generative AI models for field-support chat
Minitrack Co-Chairs:

Elisabetta Ronchieri (Primary Contact)
INFN-CNAF Bologna and University of Bologna

Daniele Cesini
INFN-CNAF Bologna and University of Bologna

Luca dell’Agnello

This minitrack focuses on research related to big data and analytics, and how they enable businesses and organizations to optimize their operational practices, improve their decision-making, and better understand and provide services to their customers, clients and stakeholders. This minitrack seeks papers in all business and technical areas of big data and analytics, including: technology and infrastructure, storage, governance and management, usage case studies, innovative applications, and tools to solve complex problems using big data, metrics for assessing big data value, and enabling technologies

We seek papers and case studies in relevant organizational and management areas associated with effective big data and analytics practices, including: strategy, governance, security, human resources, task coordination, business process, organizational impact, information systems success, and business value, among others. We especially seek papers on enhanced explainability in analytics (particularly AI/ML) 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 in distributed organizations, including: geographic and virtual entities; the effects of big data and analytics on organizational behavior; and the development of big data analytics.

We seek papers on developing an analytic cadre, educational frameworks, a body of knowledge, in-house training, and skills development and measurement in any of the areas above.

We seek papers on text mining and analytics, ranging from statistical bag-of-words and rule-based approaches, as well as syntactic parsing and natural language processing approaches, including Named Entity Recognition (NER), word embedding, and Bidirectional Encoder Representation Transformers (BERT). Also, unsupervised machine learning approaches, including, topic modeling, k-means clustering, word embeddings, as well as supervised machine learning models and deep learning approaches, including predictive regression and classification models, Dense Neural Networks (DNN), Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs).

Minitrack Co-Chairs:

Stephen Kaisler (Primary Contact)
SHK & Associates

Frank Armour
American University

William Money
The Citadel

Human cognition has been a key component of Information Systems research. Considering the fast-developing technologies that enable us to better understand human cognition, this minitrack provides a research forum at the intersections of brain, mind, and body in the shaping of human experience as it is manifested in information technology design and applications. Through the latest developments in neuroscience, psychology, and cognitive science, we seek a deeper understanding of the interplay of biophysiological, emotional, and cognitive factors that affect our digital technology-enabled experiences. Such understanding will provide us with a fuller picture of the human-technology experience and benefit system design, application, and use in many domains. We encourage papers associated with topics including, but not limited to:

  • Use of neurophysiological or biofeedback tools and methods in the study of IS phenomena
  • Affective decision making
  • Cognitive flow
  • Emotions and decision making
  • Embodied cognition
  • Social cognition
  • Human computer interaction
  • Digital habits
  • Decision making
  • Attention, awareness, and consciousness
  • Creativity
  • Mindfulness

The best manuscripts will be invited for fast tracking publication considerations in AIS Transaction on Human-Computer Interaction.

Minitrack Co-Chairs:

Jia Shen (Primary Contact)
Rider University

Jerry Fjermestad
New Jersey Institute of Technology

Jordan Suchow
Stevens Institute of Technology

The minitrack invites contributions in the area of complex decision support under multiple-criteria and where a multitude of variables have to be considered under great uncertainty. Typically, such systems are used for crisis and risk management in diverse contexts, such as health care, homeland security, aviation, transportation, energy grids, and defense to name a few.

Due to the availability of faster computers and the opportunity to analyze a large amount of data, a renewed interest in the application of Artificial Intelligence and, in particular, Machine Learning (ML) techniques for the solution of these problems was manifested. However, ML algorithms sometimes lack explainability and transparency, which is fundamental for being trusted by humans in high-risk environments. Therefore, the focus of research in this field was centered on Explainable Artificial Intelligence (XAI) methods, i.e., algorithms that are interpretable or understandable to users. Moreover, these approaches were complemented with well-known techniques from a gamut of different fields, e.g., game theory, mathematical programming, heuristics, supply chain, and statistical methods to big data, to solve efficiently practical logistics problems.

The minitrack seeks to showcase novel techniques or new interactions among existing approaches with a particular focus on supply chains and network analysis. Topics related to this minitrack include, but are not limited, to:

  • Supply chain optimization
  • Explainable Reinforcement Learning for safe decision making
  • Self-explanatory agents and decision support systems
  • Explainable, reliable, safe, transparent, and fair AI
  • Causal explanations, and causal inference
  • Planning for intelligent and transparent production
  • Risk assessment through Explainable AI
  • Supply chain in crisis situations
  • Data driven network analysis
  • Explainable AI for Intelligent Transportation Systems
  • Multimodal Decision Support
  • Cost optimization on complex structures
Minitrack Co-Chairs:

Maximilian Moll (Primary Contact)
University of Bundeswehr Munich

Wolfgang Bein
University of Nevada, Las Vegas

Alex Bordetsky
Naval Postgraduate School

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.

This minitrack 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:

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

Kimberly Ferguson-Walter (Primary Contact)

Sunny Fugate
Naval Information Warfare Center Pacific

Cliff Wang
National Science Foundation

Tejas Patel

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.

  • New or improved methods and algorithms in data science and/or machine learning.
  • New or improved standardized processes and methodologies in data science.
  • Novel ways of data blending, cleaning, transformation, reduction, and visualization.
  •  Novel, interesting, and impactful applications of data science and machine learning for supporting better managerial decision-making processes.
  • Security, ethical, and privacy issues in data science and machine learning.
  • Futuristic directions of the use of data science and machine learning in decision-making.
  • Explainability, interpretability, and transparency of machine learning models.
  • Natural language processing and new AI-based language model (methods, methodologies, and innovative applications in business 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

Behrooz Davazdahemami
University of Wisconsin – Whitewater

Hamed Majidi Zolbanin
University of Dayton

Smart contracts can be used to track changes and automate decision-making in supply chain systems, ledgers recording ownership transfers, and governance in decentralized autonomous organizations (DAOs). The rapid development of blockchain technology and smart contracts in the last ten years has fueled a dramatic increase in commerce in the crypto space. Decentralized financial services (DeFi), supply chain systems, healthcare delivery, manufacturing systems, and agriculture are all being impacted by the emergence of distributed ledger technologies (DLTs) enabled by smart contracts. Smart contracts are executable code that run on blockchains such as Ethereum to enable, monitor, and execute transactions and agreements between parties without using traditional trusted third parties. These smart contracts automate the decision analytics required for commerce to be conducted between two or more parties. Artificial intelligence plays an important role in detecting fraud and misuse, allowing these systems and organizations to run without human intervention. If blockchain commerce is to become widespread it is important to understand the characteristics and best practices needed for effective and efficient smart contracts and how AI enables these systems to operate autonomously.

This minitrack addresses how smart contracts and AI on blockchains are being used to automate decision analytics to provide new types of services. We encourage authors to share new and interesting theoretical and methodological perspectives on topics relevant to both academic researchers and practitioners. We welcome work-in-progress that examines existing and extended theories using smart contracts and AI in blockchain autonomous systems and organizations. We give special consideration to research submissions when the author(s) commit to including an industry partner in their presentation. We welcome research that reflects a range of current research methods, including case studies, analytical models, conceptual studies, econometrics, and frameworks. The following areas are suggestive of the range of topics that are considered suitable:

  • Case studies on supply chains using blockchains, smart contracts, and AI
  • Blockchain utilization and smart systems to enhance IoT-enabled services
  • Effective governance and operations in decentralized autonomous organizations (DAOs)
  • Business models for services using blockchains, smart contracts, and AI
  • Frameworks on how smart contracts function in a legal setting
  • The role of blockchains, smart contracts, and AI in smart services
  • The role and integration of oracles into blockchain systems
  • Technical evolution, including best practices for writing and testing smart contracts
  • Application of service science and service-dominant logic to smart contracts
  • Integrating blockchains, smart contracts, and AI to other larger systems
  • Cryptoeconomics and smart contracts
  • Frameworks on measuring the value of blockchains, smart contracts, and AI
  • Designing, planning, building and managing smart contracts
  • Safeguarding security and privacy using smart contracts and AI
Minitrack Co-Chairs:

Fred Riggins (Primary Contact)
North Dakota State University

Samuel Fosso Wamba
TBS Education

Developing smart city and enhancing digital services are the critical important to urbanization process for improving the effectiveness and efficiency of traditional cities. With the massive applications of Internet of things (IoT), mobile networks, and social networks, unprecedentedly large amount of various heterogeneous data can be gathered and processed in terms of advanced analytics to support smart applications and digital services. Furthermore, decision support tools and soft computing models can be employed to speed up the whole process.

This minitrack addresses issues that focus on the applications of various decision support tools, such as big data analytics, decision analysis, and soft computing, to develop smart city applications and digital services. We also encourage papers to report on system level research and case studies related to smart city and digital services. Topics of interest include, but are not limited to:

  • Advanced analytics for smart city planning and digital services
  • Case study and best practices for smart cities and digital services
  • Decision support models and tools for smart city and digital services
  • Design and implementation of intelligent systems for smart city applications
  • Innovative applications in smart cities, such as smart traffic, and smart travel
  • Novel applications in digital services, such as social networks, social media analytics, and social recommendation
  • Soft computing for smart city and digital services
Minitrack Co-Chairs:

Wei Xu (Primary Contact)
Renmin University of China

Jian Ma
City University of Hong Kong

Jianshan Sun
Hefei University of Technology

Smart farming is a management approach that uses advanced technologies like big data analytics, cloud computing, and the Internet of Things (IoT) to transform operations related to tracking, monitoring, automation, and analysis. Precision agriculture, also known as this approach, is driven by a combination of factors: the growing global population, increasing need for higher crop yields, the necessity to use resources wisely, advancements in information and communication technology, and the requirements of climate-smart agriculture.

The primary objective of this minitrack is to stimulate and foster research endeavors spanning the domains of Internet of Things (IoT), drones, smart remote sensing, computer imaging, data analysis, as well as machine learning and deep learning within the context of smart farming. By encouraging scholarly contributions in these areas, we aim to cultivate a robust knowledge base and facilitate innovative solutions geared towards enhancing decision-making capabilities in modern agricultural practices. Typical themes that are expected for contributions to the minitrack include (but are not limited to):

  • Precision irrigation decision system
  • Fertilization decision system
  • Early disease detection
  • Semantic technologies for smart agriculture/farming
  • Machine learning and deep learning models for smart agriculture
  • Blockchain based solution for smart agriculture
  • IoT-based solutions, robotics and automation for farmers
  • Explainable artificial intelligence models for smart farming
  • Smart Farming for Food Safety

High quality and relevant papers will be fast tracked for consideration of publication in the International Journal of Neural Computing and Applications. Authors will be notified during or shortly after the HICSS conference about this opportunity. If interested, they will need to extend the manuscript in content and length by at least 30%, change the paper’s title, and make sure that the manuscript is in line with the requirements for standard research articles published in this journal. The invited authors, in addition, need to declare their acceptance to the invitation to participate in this fast-track opportunity in the cover letter to the editor-in-chief and provide a table comparing their original manuscript with the extended one.

The minitrack chairs will function as guest editors and invite 2-3 reviewers for each manuscript. These reviewers will not be the same as the reviewers from the HICSS conference. Although the minitrack co-chairs are committed to guiding the selected papers towards final publication, further reviews are needed before a final publication decision can be made.

The submission will run as a regular submission. However, guest editors and reviewers will commit to a speedy review process while keeping the quality of the journal in mind. Hence, the invitation to the fast-track is no guarantee for publication.

Minitrack Co-Chairs:

Rima Grati (Primary Contact)
Zayed University

Khouloud Boukadi
Faculty of Economics and Management of Sfax-Tunisia

The challenge to limit global warming and extreme weather events has become even greater due to continuously rising greenhouse gas emissions. One major action to reduce emissions is to shift from fossil to renewable energies. The share of renewable energy in electricity generation has globally increased to 28.3%, however, an acceleration of the sustainable energy transition is required to limit worldwide temperature rise. Digital energy services and analytics (DESA) facilitate the automation of electricity infrastructures and decision support by dynamically deploying options to manage energy supply and demand for optimal and efficient renewable energy usage. We invite submissions that include – but are not limited to – the following topics:

  • Cyber-physical energy systems and Internet of Things (IoT), including various sensors and devices
  • Artificial Intelligence (AI) and Cognitive Computing Systems (CCS) to optimize energy usage, distribution, decision support, and management
  • Digital transformation of energy resources management (e.g., electric vehicles, battery storage solutions, photovoltaics, or wind energy)
  • Design of future energy applications and ecosystems
  • Consumer behavior and motivation, renewable energy technology adoption, and privacy and trust in digital energy services
  • Customer messaging and digital nudging to encourage pro-environmental engagement and behavior, e.g., in smart homes
  • Co-creating system values (e.g., grid resilience, cybersecurity, and efficiency) and customer values (e.g., cost savings and customer experience)
  • Digitally informed energy policies to manage intermittency (e.g., deferrable devices, rights markets and regulation, dynamic pricing, or energy storage)
  • Real-time energy supply and demand predictions and forecasts, e.g., to balance fluctuation of intermittent renewable energy sources
  • (Big) data analytics of energy consumption and production
  • Energy simulations, e.g., with system dynamics or agent-based modelling
  • Transitioning electricity providers from selling electrons to marketing energy services (e.g., Thermal Comfort as a Service)

Selected papers from this minitrack will be recommended to the editors of two journals: (1) MISQ Executive sustainability section, and (2) Energy Informatics, for fast-track review and publication of an extended version of the HICSS paper. The extended version must include at least 30% new material, cite the HICSS paper, and include an explicit statement about the increment (e.g., new results and findings, better description of materials, etc.) in the cover letter.

Minitrack Co-Chairs:

Kenan Degirmenci (Primary Contact)
Queensland University of Technology

Alistair Barros
Queensland University of Technology

Michael H. Breitner
Leibniz Universität Hannover

Richard T. Watson
Digital Frontier Partners

There are substantial opportunities for information technology (IT) and digitalization-driven service innovation in industrial and business-to-business settings and in 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 the development and implementation of 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 service 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 minitrack aims to attract research on digital service innovation and design and new technological opportunities. The key drivers in this area of study are the multiplying technological opportunities for digital services stemming from generative AI (such as ChatGPT), Internet of Things (IoT), virtual/augmented reality, web3, cyber-physical systems, and so on. The minitrack provides a discussion forum for researchers interested in theoretical and practical problems related to digital service innovations and their design. Relevant topics for this minitrack include, but are not limited to:

  • Generative AI-based service innovations
  • Discovery, fuzzy-front end, and innovation processes
  • Continuous and experimental service design and development processes and methodologies
  • Generative AI in service design and service innovation processes
  • Analytics-supported service design and development
  • Design and evaluation of innovative digital services
  • Service ecosystems, platforms, and novel architectures
  • Consumer and enterprise user aspects, novel forms of actor engagement
  • Service ecosystems and effective patterns of actor engagement in digital services
  • Hedonic IT-enabled service innovations
  • Socio-psychological aspects of IT-enabled service use
  • Understanding social and cultural contexts
  • Cyber-physical and IoT-enabled service innovations
  • Metaverse, Generative AI, Cyber-Physical, and IoT-enabled services from different disciplinary perspectives, such as information systems, operations research, software engineering, service science, and service research
  • Service innovation based on generative AI-enabled services (such as ChatGPT, etc.)
  • Metaverse and IoT service ecosystems, platforms, and novel architecture for service innovation
  • Theoretical aspects of Metaverse, Cyber-physical, and IoT-enabled services research
  • Metaverse, Generative AI-augmented, Cyber-physical, and IoT-enabled services as artifacts
  • Generative Artificial-intelligence enabled services:
  • Service Robots and Service Robot enabled services
  • Services enabled by natural language assistants
  • Human-machine interaction in AI-enabled services
  • Operational aspects of AI-enabled services, e.g., monitoring and support
  • Ethical and regulatory considerations in designing AI-enabled services
  • New technology-enabled services, e.g., services using smart television, smart watches, wearables, mobile devices, and phones, or other technologies like augmented/virtual reality, blockchain, IoT, etc.
  • Metaverse and Web3-enabled services:
  • New decentralized service innovations
  • Service automation with Web3 technologies
Minitrack Co-Chairs:

Tuure Tuunanen (Primary Contact)
University of Jyväskylä

Tilo Böhmann
University of Hamburg

Jan Marco Leimeister
University of St.Gallen

Digital servitization represents a fundamental shift for product-based companies arising from the convergence of digitalization and servitization trends. Additionally, it’s increasingly influenced by the growing demand for sustainability. Advanced digital technologies are permeating capital-intensive complex products (e.g., tooling machines) as well as their integrated components (e.g., drives). This enables the products to connect globally and offer intelligent features bridging digital and physical words. Such products can also be referred to as cyber-physical systems or smart products. As boundary objects in complex service systems, they improve service efficiency, enable sustainable life cycle considerations, and drive service innovation. Companies are attempting to benefit from these developments by means of data-driven services, product-service-software systems, and innovative business models.

The emergence of these service systems marks a departure from traditional manufacturing business models, presenting a socio-technical challenge that requires the integration of humans, organizations, and technologies. Consequently, organizations are under significant pressure to undergo substantial transformation as they aim to incorporate digital, data-driven, and AI-based services for both internal and external customers. This necessitates navigating increasingly complex service ecosystems, where organizational strategies, structures, resources, business processes, capabilities, and offerings undergo substantial transformation.

Therefore, manufacturing companies need well-grounded strategic guidance, models, and methods to design and implement data-driven service systems. Recognizing these challenges, this minitrack aims to explore insights on multiple facets of digital servitization. Topics may span conceptual, empirical, design science, applied, and theoretical research. Any research methodology (conceptual, reviews, qualitative, quantitative, mixed methods, etc.) at any level of analysis is welcome. Typical themes that are expected for contributions to the minitrack include (but are not limited to):

  • (Digital) Servitization in manufacturing and service industries
  • Enabling technologies and affordances in (digital) servitization
  • Strategies, tools, and frameworks in service ecosystem design and management
  • Considerations regarding strategic position, competences, and/or organizational structures for digital servitization
  • Ideation and prototyping of service business models and product-service-software-systems
  • Creating and managing a portfolio of data-driven services
  • Design knowledge and artifacts (e.g., theories, modeling languages, design principles, methods, evaluation of methods, etc.) for digital servitization in manufacturing
  • Business models for smart services, digital services and/or product-service-software-systems (e.g., business model patterns, archetypes, revenue models, etc.)
  • Organizational transformation in the given context (e.g., change management, business process optimization, business process reengineering, business model innovation)
  • Performance assessment of data-driven service systems and respective organizations
  • Practitioners insights and real-world implementations of data-driven service systems
  • Contributions to better understand new occurring phenomenons and paradoxes in digital servitization (rebound effects, backfire effects, service paradox, deservitization)
  • Cybersecurity challenges in digital servitization
  • Data governance, ethics, privacy, and security considerations for data-driven services
  • Customer co-creation/co-production of data-driven services
  • Integrating sustainability goals into data-driven service strategies
  • Assessing the social and environmental impact of digital servitization
Minitrack Co-chairs:

Christian Koldewey (Primary Contact)
University of Paderborn

Martin Ebel
Ruhr-Universität Bochum

Johannes Winter
University of Hannover and acatech – National Academy of Science and Engineering

Muztoba Ahmad Khan
Carroll University

The advancements in smart technologies and wearable devices have brought about a new era in collecting, analyzing, and utilizing personal data. As a result, the Digitization of Individuals (DI) is continually evolving to support individuals in making informed decisions, transforming their personal lives, and making them more sustainable. Furthermore, the utilization of Artificial Intelligence (AI) and Personal Decision Analytics (PDA) has become a crucial aspect in the Digitization of Individuals. For example, personal finance management is used for budgeting and investment advice, health information systems are used for health risk predictions and personalized recommendations, and productivity systems are used for task prioritization and time management.

This minitrack aims to bring together researchers, practitioners, and educators who are interested in investigating the digitization of individuals and personal decision analytics. Topics of interest include, but are not limited to:

  • Design and develop DI systems that leverage Artificial Intelligence (AI) and Personal Decision Analytics (PDA) to measure and track individuals’ personal data, such as health and financial information
  • Data analytics is used to model and predict personal outcomes, such as health risks and financial outcomes, and provide personalized recommendations
  • The development and evaluation of DI as an adaptive and dynamic decision companion for everyday life, including supporting individuals in making informed decisions and transforming their lives through the use of personal decision analytics
  • The dark side of DI, such as the ethical and privacy implications of DI, including the protection and responsibility of personal information
  • Application of DI in the various life dimensions such as health, financial, social, spiritual, intellectual, occupational, and physical and their integration with the Quantified Self
  • Approaches for personalized decision-making and utilization of games and gamifications for self-management
  • Investigate the integration of DI with emerging AI technologies like natural language processing and blockchain and digital environments like the metaverse to enhance personalization and effectiveness

We welcome submissions from a wide range of disciplines and interdisciplinary environments, including computer science, information systems, psychology, and health informatics.

Minitrack Co-Chairs:

Claris Chung (Primary Contact)
University of Canterbury

Yvonne Hong
Victoria University of Wellington

David Sundaram
University of Auckland

Cities, driven by digitalization and the challenges of rapid urbanization, are undergoing profound socio- technological transformations in their quest to become smarter entities. However, this rapid urbanization, coupled with unprecedented increases in human activities, is generating complex interdependencies among humans, infrastructures, and technologies. These evolving dynamics may lead to heightened uncertainties, unreliable predictions, and suboptimal management decisions. Understanding the normal and disrupted operational dynamics across human, infrastructure, policy, and technology systems collectively as an integrated entity in real-time is paramount. Developing integrated cyber-physical systems enables the simulation of infrastructure processes, environmental dynamics, and human activities, fostering adaptability to evolving conditions in real-time. Nonetheless, this undertaking demands novel technological and methodological breakthroughs, enabled by interdisciplinary collaboration. The complexity of decision-making processes is further compounded by the lack of scalable data integration approaches, failing to encapsulate space-time fluctuations and system behavior variances at human-infrastructure interactions. Moreover, the surge in data size, diversity, complexity, and its sporadic spatiotemporal generation demands a paradigm shift in comprehending, influencing, and ultimately managing urban service provisioning across scales.

Digital twins are an endeavor to create intelligent adaptive machines by generating a parallel virtual version of the system utilizing real-time data and analytics for understanding the physical system. Information connectivity, analytical, and visualization capabilities enabled by Internet of Things (IoT) and emerging virtualization technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) allow for the creation of a digital twin of a building (to include residential, commercial), a community (to include neighborhoods, corporate campuses), and a city. The predictive conduct of these digital duplicates, however, relies on real-time and aggregated historical performance of IoT-integrated human-infrastructure systems for context-aware experimentation under normal operations, as well as in extreme events.

Integration of digital twins with cutting-edge technologies such as Generative artificial intelligence (AI), and large language models (LLMs) augments the intelligence and responsiveness of smart cities, heightening their transformative potential. Digital twins, acting as virtual replicas of urban infrastructure, serve as pioneering platforms for refining resilience capacity and real-time decision-making strategies. In tandem, Generative AI enables the quest for optimal solutions, facilitating realistic simulations and synthetic data generation crucial for urban infrastructure design and optimization. This includes techniques like Generative Adversarial Networks (GANs), which can produce images, videos, and other types of data, and language models that can write text. Generative AI can learn from vast amounts of data and generate predictions or simulations based on this learning. LLMs, epitomized by advanced models like GPT-4, emerge as linguistic bridges, facilitating effective communication across engineers, managers, residents, and smart city infrastructure. This can enables more informed decision-making by providing detailed analyses of potential outcomes, innovating solutions to complex challenges, and adapting to changing conditions in real-time by generating predictive models on the fly. Such capabilities are crucial for dynamic systems like traffic management or emergency services, where conditions can change rapidly.

This Minitrack aims to establish the theoretical and scholarly foundation for a Generative-AI-Advanced Smart City Digital Twin paradigm. It aims to deepen our understanding of cyber-physical interactions/dynamics in Smart Cities, focusing on the interplay between humans, infrastructure, and technology through learning, analytics, and spatiotemporal data exchange within cities. We invite submissions that offer theoretical and/or practical contributions across a wide range of exploratory areas, including but not limited to the following:

  • Generative-AI-advanced Theories, Models, and System Architecture for Smart City Digital Twins
  • Generative-AI-advanced modeling of cyber-physical systems
  • Scaling Digital Twins from single to multiple interdependent cyber-physical infrastructure systems
  • Human-infrastructure interactions, interdependencies, and uncertainties analysis
  • Data generation, sensing, augmentation, synthesis, and realistic scenario generation for Digital Twins
  • Context-aware simulation, real-time response, and dynamic adaptation in decision support systems
  • Natural language interfaces for enhanced interaction and communication with Digital Twins
  • Digital Twin virtualization and interactivity (Virtual Reality / Augmented Reality / Mixed Reality)
Minitrack Co-Chairs:

Neda Mohammadi (Primary Contact)
University of Sydney

John E. Taylor
Georgia Institute of Technology

The use of Artificial Intelligence (AI) in the context of decision analytics and service science has received significant attention in academia and practice alike. Yet, much of the current efforts have focused on advancing underlying algorithms and not on decreasing the complexity of AI systems. AI systems are still “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 to “produce explainable models, while maintaining a high level of learning performance (prediction accuracy); and enable human users to understand, appropriately, trust, and effectively manage the emerging generation of artificially intelligent partners.” 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.” XAI is designed user-centric in that users are empowered to scrutinize and appropriately trust AI, eventually impacting task performance of users.

With a focus on decision support, this minitrack aims to explore and extend research on how to establish 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:

  • The users’ perspective on XAI
    • Organizational implications of 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 explainability of AI
  • The 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
    • Designing and deploying XAI systems
    • Neuro-symbolic learning for XAI
  • The governments’ perspective 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

The best paper from this minitrack may be selected for fast-tracked development towards Information Systems Management. A selected paper will need to change its title, and expand in content as well as length (at least +30%), in line with the requirements for standard research articles published in the journal. Although the minitrack co-chairs are committed to guiding the selected paper towards final publication, further reviews will be needed before a final decision can be made. The invitation to the fast-track is no guarantee for publication.

Minitrack Co-Chairs:

Christian Meske (Primary Contact)
Ruhr-Universität Bochum

Babak Abedin
University of Technology Sydney

Maximilian Förster
University of Ulm

Yang Song
University of New South Wales

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 behaviors 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.

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.

This minitrack encourages a wide range of submissions from any disciplinary backgrounds: empirical and conceptual research papers, case studies, and reviews. Relevant topics for this minitrack include, but not limited to:

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

Juho Hamari (Primary Contact)
Tampere University

Nannan Xi
Tampere University

Ana Tome Klock
Tampere University

Benedikt Morschheuser
Friedrich-Alexander-Universität Erlangen-Nürnberg

The surge of Generative AI (GAI) in decision analytics marks a paradigm shift toward more sophisticated and interpretable AI systems. GAI extends beyond mere predictions, weaving explainability into the fabric of decision-making processes. The explicative power of GAI models demystifies complex decisions, ensuring that stakeholders understand the “how” and “why” behind algorithmic conclusions. This mini track seeks to spotlight the innovative applications of GAI, particularly focusing on its ability to elucidate and innovate within decision analytics. We call for contributions that explore the intersection of GAI and explainable AI (XAI), and the role of Large Language Models (LLMs) as a driving force for innovation and understanding in AI systems. Submissions should not exceed one page and may include, but are not limited to, the following areas:

  • Explorations into the explainability of GAI, detailing mechanisms that shed light on AI decision processes
  • Innovative applications of GAI across domains, highlighting the transformative impact on decision-making
  • Comparative studies between GAI and traditional models, examining improvements in decision accuracy and transparency
  • Investigations into the role of LLMs within GAI, showcasing advancements and applications in natural language understanding and generation
  • Analyses of the societal and ethical implications of GAI in decision analytics, including potential threats and mitigation strategies
  • Empirical and theoretical studies on the trade-offs between model complexity, explainability, and performance in GAI systems
  • Benchmarks and novel methodologies for evaluating the interpretability of GAI models
  • Case studies that demonstrate the real-world impact of GAI on decision analytics, offering insights into successes and challenges
Minitrack Co-Chairs:

Kazim Topuz (Primary Contact)
University of Tulsa

Kristof Coussement
IÉSEG Center of Marketing Analytics

Ali Tosyali
Rochester Institute of Technology

Salih Tutun
Washington University in St. Louis

The minitrack features information systems, theoretical developments and real-world applications related to intelligent problem solving in logistics and supply chain management. This also incorporates issues related to digital transformation in this area. Methods include optimization, heuristics, metaheuristics and matheuristics, simulation, agent technologies, and descriptive as well as predictive methods. Recent advances incorporating Internet of Things, big data, cloud computing, and machine learning are especially welcome.

We seek papers dealing with decision analytics, business intelligence, big data, cloud computing, industrial engineering, and decision technologies which contribute to intelligent decision support in the whole field of logistics and in all categories of SCM. This includes but is not restricted to simulation, optimization, heuristics, metaheuristics, agent technologies, decision analytics, descriptive models, and data mining.

Recent advances in data science, machine learning and artificial intelligence successfully complement these methods. This is especially interesting within the context of supply chain resilience and uncertainties that might invoke supply chain disruptions. We are especially interested in real-world applications and in information systems and software solutions which assist in solving decision problems. This is extended towards, e.g., computational logistics, advanced planning systems, and the intelligent use of ERP systems. Also, conceptual ideas, reports on projects in progress, and case studies are welcome. Moreover, teaching cases both at the university as well as the executive level may be of interest.

Minitrack Co-Chairs:

Julia Pahl (Primary Contact)
University of Southern Denmark

Stefan Voß
University of Hamburg

The first industrial revolution used water and steam power to mechanize production. The technological revolution or the second industrial revolution used electric power to create mass production. Both of these transformed the way people worked and lived and made people wealthier and more urban. A third industrial revolution, marked by digital technologies, access and manufacturing, used electronics and information technology to automate production. The fourth industrial revolution will be characterized by a fusion of information systems that blur the lines between physical, virtual, and organizational and public spheres. This fourth industrial revolution is evolving at an exponential pace, disrupting almost every industry on a global landscape, inevitably leading to profound impact on business, government, health, and people. The demands of decision-making in an increasingly interconnected world mandates that complex real-time data analytics will combine with organizational knowledge integration, synthesis, and engineering to a key role in decision-making. Interactive Visual Analytics for Knowledge Integration and Decision Intelligence supports human decision making through interaction with data and statistical and machine learning processes, with applications in a broad range of situations where human expertise must be brought to bear on problems characterized by massive datasets and data that are uncertain in fact, relevance, location in space and position in time. In partnership with organizations in defence, health care, and business, visual analytics research methods combining laboratory studies, cognitive ethnography, and field experiments have aided the design of information systems for decision making about injuries to children, multiomic precision health, radiological diagnosis, and VR for conflict zone operations.

Submissions are encouraged that focus on the core issues of theory and methods for visualization, analytics, knowledge integration and decision intelligence in organizations. Case studies of applications of these methods to new analytic and decision making tasks in science and technology, public health, business intelligence, financial analysis, social sciences, and other domains are particularly welcome. Submissions may include studies of visual analytics and decision support in the context of an organization (e.g., communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, Interactive Machine Learning, and collaborative analysis using visual information systems. Additionally, submissions may include understandable, trustable AI as well as human-guided AI to round out the problem-solving process. Emphasis will be given to submissions that use visual analytics for social change discovery, analysis, communication, and focus on mixed-initiative human/AI analysis.

This minitrack seeks to define analytical methods and technologies that use interactive visualization to meet challenges posed by data, platforms, and applications for decision making and risk-based decision making:

  • Analysis of multi-perspective knowledge integration, synthesis and engineering in organizations.
  • Use of interactive visualization and visual analytics in digital economies
  • Visual analytics and visualization in “wicked” problem solving in organizations
  • Analysis of datasets of varying size and complexity from archives and real-time streams
  • Collaborative visual analysis and operational coordination within and across organizations.
  • Interactive and visual risk-based decision making
  • Interactive machine learning methods
  • Managing response time of complex analytical tasks
  • Effective deployment and case studies of success from deployed visualization and analytics experiences
  • Visualization and analytics for data-driven policy making and decision support
  • Issues and challenges in evaluation of visual decision making
  • Mixed-initiative analysis methods for decision making
  • Cognitive and social science aspects of visual decision-making environments
  • Visual decision-making in the context of Trustable AI or mis/disinformation
  • Theory-enhanced automated detection of fake news and fake comments (with visualization)

For HICSS-58, we extend our focus to multidisciplinary collaboration and communication among researcher from a variety of research perspectives. Authors are encouraged to bring the lens of their own background and expertise to focus on the analytics of the data itself and coordination of multiple levels of analysis, decision-making, communication, and operations to the design and evaluation of effective presentations for stakeholders and dissemination of trustful and actionable information. We invite computational, cognitive, communication, and organizational perspectives on advanced data processing and interactive visualization for analysis and decision-making across a range of human endeavors. We also invite participation from researchers who are looking at scaling issues and multiscale issues, whether these scales refer to the time of decision making, the form-factor and operational constraints of mobile devices, the number of decision makers or the more traditional notion of multiscale simulation and real-world scales of data. We are particularly interested in approaches that combine computational and interactive analytics in “mixed initiative” or Interactive Machine Learning systems, decision support in the context of an organization (e.g. communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, and collaborative analysis using visual information systems, including developing trustable AI and the challenge of dis/misinformation.

Minitrack Co-Chairs:

David Ebert (Primary Contact)
University of Oklahoma

Brian Fisher
Simon Fraser University

Kelly Gaither
University of Texas at Austin

This minitrack presents a promising avenue for various information systems research and management research streams to generate new knowledge as well as equip practitioners with new insights into identifying new value-creation opportunities through Metaverse Platforms (MP) which include a wide range of ’Mixed Reality’ (MR) technologies combining both virtual and real worlds.

The pandemic emphasized the demand for these immersive technologies for user and customer interactions. However, most of these applications and services lack generalizable models and strategies on how they are used in the interactions of firms, their customers and service beneficiaries. The existing literature introduces various affordances these technologies offer but the research lacks assessments of meaningful business outcomes such as service scalability and sales, and behavioral outcomes like user satisfaction, loyalty, and purchase behavior. Integrating research on technology affordances with meaningful business and behavioral outcomes is necessary to build both theoretical understanding and practical implications.

In order to achieve all these research objectives, more literature reviews, conceptual papers, field and user studies as well as laboratory experiments are needed. The focus of these studies should be to introduce propositions for both businesses, service personnel, users and customers as well as show the improvements (or the lack of them) compared to existing and conventional systems. These studies should also aim at scrutinizing technology features and system designs as well as psychological and behavioral patterns behind the meaningful business outcomes in order to build valid research models and related strategy implications.

This minitrack welcomes all entries related to:

  • Metaverse, Mixed, Virtual and Augmented Reality platforms
  • Multiple virtual technologies and multimedia promoting remote interactions
  • Immersive applications (including motion tracking, avatars, digital twins and 360-environments etc.) and sensory modalities
  • Moving image, second screens, visualization technologies, companion apps
  • Literature reviews, conceptual papers, empirical papers, field and user studies and laboratory experiments

In the context of:

  • User experiences among consumers, customers, users and services
  • Roles and identities of avatars and user personas
  • Changing workplace and integration of MR/Metaverse
  • Examining the implications on real life experiences
  • Changing meaning of relationships in MR/Metaverse platforms
  • Technology features and service system designs
  • Psychological or behavioral patterns
  • Marketing and managerial models and strategies
  • Sustainable businesses, supply chains, learning or healthcare
Minitrack Co-Chairs:

Jani Holopainen (Primary Contact)
University of Eastern Finland

Essi Pöyry
University of Helsinki

Triparna de Vreede
University of South Florida

Petri Parvinen
University of Helsinki

This minitrack provides a focused exploration on applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in the context of data analytics for system sciences. Aimed at the conference’s emphasis on emerging managerial and organizational decision-making strategies in the digital age, this session has an emphasis on the use of text as the primary input to a wide variety of machine-learning algorithms and applications. Presentations will discuss how NLP and LLMs can be harnessed to enhance data analytics for system sciences.

Authors are invited to submit papers that delve into the practical applications and methods surrounding NLP and LLMs within the realms of data analytics, machine learning, business intelligence, and system sciences. The session seeks to provide clarity on the relevance of proposed research to the broader landscape of decision-making processes in contemporary digital environments. Here is a general list of topic areas for this minitrack, which is not meant to be complete or comprehensive:

  • Business and Service Analytics: Showcasing practical applications of NLP and LLMs in business and service analytics, or providing insights into organizations gaining a competitive edge through intelligent data-driven decision-making.
  • NLP Tasks: Relating to traditional NLP understanding tasks, such as sentiment analysis, named entity recognition, and part-of-speech tagging, and their application to system science
  • Large Language Models: Using LLMs such as BERT, ChatGPT or Llama either to directly enable data analytics and system science applications, or in supporting roles such as dataset generation, text summarization, sentiment analysis, feature extraction, anomaly detection, or other data preprocessing tasks.
  • Logistics and Supply Chain Management: Research work illustrating the impact of language technologies on optimizing logistics and supply chain management processes, fostering efficiency or increasing resilience.
  • Other Applications: Papers focusing on the utilization of NLP and LLMs for other applications, elucidating how NLP technologies contribute to decision-making in a variety of contexts.
  • Ethical Use of NLP & LLMs: Papers that highlight ways developers can work towards creating more fair and unbiased machine learning models, including bias detection or case studies that examine bias in machine learning models.

Extended versions of the papers accepted for presentation in this minitrack will be invited for a fast-track review and publication consideration in the Algorithms journal.

Minitrack Co-Chairs:

Torrey Wagner (Primary Contact)
Air Force Institute of Technology

Kimberly Binsted
University of Hawaii at Manoa

Neil Ranly
Air Force Institute of Technology

Winston Wu
University of Hawaii at Hilo

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

This minitrack solicits 3-page executive summaries that 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. One of the co-authors must be a practitioner. Accepted and presented papers will be published in the HICSS proceedings and considered for ongoing publication in one of the Business Expert Press Book Series “Collaborative Intelligence: People, AI, and the Future of Work” or “Service Systems and Innovations in Business and Society.” Possible themes/topics of this minitrack include, but are not limited to:

  • 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
  • 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
  • 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
  • Internet of Things
    • IoT applications in various industries
    • Edge computing and real-time data processing
    • Personalization in retail, finance, healthcare, and e-commerce
  • 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
  • Responsible innovation
    • Digital government and smart city solutions
    • Multichannel citizen engagement
    • Ethical technology, trust, and data privacy
  • Intelligent Augmentation & Virtual Reality
    • Virtual reality and gaming applications
    • Augmented reality for education, healthcare, and tourism
    • AR/VR for enhancing navigation and user experiences
  • Future of Work
    • Human-machine partnership
    • Real-time and immersive collaboration
    • Digital workplace operations
    • Optimizing the employee experience
    • New roles and jobs for future
    • Lexicon of technology for diversity, inclusion, belonging
  • 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

Terri L. Griffith
Simon Fraser University

Nicole Reineke

Maggie (Ming) Qian
Dell Technologies

This minitrack covers contributions on the development and applications on the use of Qualitative Comparative Analysis (QCA) and its various extensions The use of configurational analysis, specifically QCA, has been present in the academic literature, specifically in Information Systems research, with contributions increasing in particular during the last decade. This approach moves beyond the simplistic cause-and-effect analysis, allowing researchers and practitioners to uncover patterns of causality that are more reflective of the real world’s complexity. Whether identifying the causal pathways that lead to successful IT adoption, improving organizational performance, or any other outcome of interest, QCA provides a framework for delivering explainable and actionable insights. These insights are not just academic; they are directly applicable, helping decision-makers navigate the complexities of digital systems with confidence.

In the evolving landscape of Information Systems research, this minitrack aims to delve into the intricacies of configurational causation and the multifaceted nature of digital phenomena. This minitrack is dedicated to fostering groundbreaking discussions and insights on the development, application, and extension of QCA and its role in advancing theory building within IS research. As digital complexities grow, especially in business and management, the need for nuanced, actionable insights become paramount. QCA, with its unique blend of qualitative and quantitative lenses, offers a powerful tool to unearth causal pathways, embrace equifinality, and provide explainable decision-making guidance for navigating the digital landscape.

The purpose of this minitrack is to disseminate significant results on new theoretical and methodological developments related to QCA and show the importance of configurational approaches in Information Systems research, and in particular decision analysis. The minitrack aims to bring together scholars and practitioners from various disciplines to exchange ideas, share insights, and discuss the latest developments relayed to QCA. Topics of interest include, but are not limited to:

  • QCA in business and management
  • Theoretical and methodological breakthroughs in QCA
  • Advances in QCA methods, techniques and software tools
  • Best practices and opportunities of QCA
  • Analyzing complex causal relationships in IS adoption and implementation
  • Examining the antecedents and outcomes of IS innovations
  • Evaluating the impact of IS on organizational performance
  • The role of QCA in addressing complex industry problems and real-world challenges
  • The application of QCA in industries, including but not limited to finance, healthcare and technology
  • The potential of QCA to contribute to the advancement of knowledge in various industries, as well as its limitations and future research directions
Minitrack Co-Chairs:

József Mezei (Primary Contact)
Åbo Akademi University

Shahrokh Nikou
Delft University of Technology

Yong Liu
Aalto University

Research topics addressed in this minitrack include the applicability of basic and advanced analytics to different service systems, the state-of-the-art of service analytics methodologies and tool-support, and the investigation of benefits resulting from the application of service analytics. This minitrack will serve as a forum for researchers and practitioners to share progress in the study of these and related themes. Submissions on, but not limited to, the following topics are encouraged:

  • Data Mining, Machine Learning applied to Services
  • Artificial Intelligence in Service Systems
  • Appropriate Reliance in AI Services
  • Data-centric Artificial Intelligence
  • Foundation Models for Service Analytics
  • Analysis of Smart Services, Mobile Services, IoT-based Services
  • Recommender Systems for Services
  • Social Network Analytics applied to Services
  • Privacy Issues resulting from Service Analytics
  • Fraud Analytics for Service Systems
  • Electricity Consumption Analysis using Smart Meter Data
  • Analytics for Healthcare Services
  • Human-AI Collaboration in Service Analytics
  • Analysis and Prediction of IT Service Demand Patterns
  • Analysis of Service Problem Reports
  • Industrial Service Analytics and Optimization
  • Sports Analytics
Minitrack Co-Chairs:

Hansjoerg Fromm (Primary Contact)
Karlsruhe Institute of Technology

Niklas Kühl
University of Bayreuth

Thomas Setzer
Catholic University of Eichstätt-Ingolstadt

Michael Vössing
IBM Germany and Karlsruhe Institute of Technology

Service science deals with issues of value creation in service systems, which may involve knowledge in various disciplines, such as social science, management, design, and engineering, especially the 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.

This minitrack intends to solicit papers that connect rigorous disciplinary research with the emerging interdisciplinary framework of value creation in human-centered service systems, focusing particularly on autonomous technology (including generative artificial intelligence and service robots, particularly as they operate in human settings), data in complex service systems (including sensing, analytics, and ethical challenges in the context of human-technology systems), technology support for service system design (including blockchain, internet of things, and artificial intelligence to enable or enhance service system design), and service innovation to achieve resilience and sustainability (especially related to human values in the post-covid era).

This minitrack will also encourage submission of research papers from a variety of disciplines and a variety of participating communities to address issues in service system governance, service process modeling, service delivery management, innovative service technologies, and the role of digital technology, including artificial intelligence. We will encourage submissions related to autonomous service systems, the use data analytics for value creation, and computational modeling of complex, human-centered service systems. We would also welcome those service research areas connected with practical problem domains to demonstrate the impact of service innovation on business transformation, or the lesson learned from industrial or social innovation.

For HICSS-58, this minitrack will focus on important issues faced in service system transformation resulting from advances in computational technology for services, including but not limited to:

  • Increasing capabilities of technologies in service, such as artificial intelligence and service robots
  • Increasingly large role played by data in complex service systems, including sensing, analytics, and potential ethical challenges
  • Potential for digital technologies to support design of human-centered service systems, including blockchain, internet of things, and generative AI
  • Initiatives in service innovation to enhance the resilience and sustainability in the post-covid era
  • Digital transformation of service systems in different industries or sectors, such as industry 4.0, AI-enabled service innovation, etc
  • Mid-range theory in service based on service-dominant logic or other paradigmatic theories
  • 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

Fu-ren Lin
National Tsing Hua University, Taiwan

Nila Windasari
Institut Teknologi Bandung

Simulation models are indispensable for the planning, control and optimization of processes and systems in manufacturing, production, and logistics. Key concepts of Industry 4.0 and smart manufacturing, such as the digital twin, are essentially based on simulation modelling methods. 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 models and digital twins for decision making in production and logistics.

Methods of interest include discrete-event simulation, discrete-rate simulation, hybrid simulation, system dynamics simulation, the combination of simulation modeling with machine learning or optimization heuristics, prescriptive analytics, data preparation, data prediction for simulation and adaptive systems.

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.

Furthermore, this minitrack addresses aspects of higher education related to the minitrack topics and simulation models used for education and training in manufacturing and logistics.

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:

Tobias Reggelin (Primary Contact)
Otto von Guericke University Magdeburg

Steffen Strassburger
Technische Universität Ilmenau

Stefan Galka
Ostbayerische Technische Hochschule Regensburg

Sebastian Lang
Fraunhofer Institute for Factory Operation and Automation (IFF)

Transportation is changing, and this change is driven by technology-enabled possibilities such as autonomy, connectivity, electrification, and diverse mobility business models of shared vehicles. At the same time, there is a growing push towards more sustainable transportation. Digital platforms are essential for orchestrating smart and sustainable mobility ecosystems and related services. The cornerstone of designing services on these platforms is data that accurately represents, for instance, the location of passengers and service providers, weather, usage, and maintenance. Identification and communication systems that link specific physical things to specific digital addresses offer possibilities to communicate, transact, build trust, sense, and activate “things” from the internet – paving the way for novel service designs based on the generated data. For instance, autonomous vehicles, electric cars, and ride-sharing services build on platform thinking, as do many services that reduce the need for travel altogether. Furthermore, for these services to work, there is a need to analyze the ecosystems emerging around these services, as the benefits and platforms form complex webs.

Autonomous vehicles have been hailed as harbingers of new mobility and travel services. Autonomous cars seem to be further away from wide prevalence than expected a couple of years ago. Still, the data gathered for them and made available from, e.g., smart city initiatives and mobile devices provide excellent opportunities for different kinds of smart solutions for private and public transportation. Further, the shipping industry has many ongoing advanced projects and pilots (especially in cargo shipping), and several aviation companies are developing autonomous aircraft. Finally, autonomous vehicles already carry out most operations in closed and hazardous environments, such as mines. While autonomous vehicles on land, in sea, and air (including drones) are expected to decrease costs and increase efficiency and safety, a host of regulatory, safety, legal, and security challenges are yet to be resolved.

Car owners increasingly embrace electric cars, which require their own infrastructure and services. Full-scale charging infrastructure needs building, and this process will depend on the support of various mobile services. Though some of these services are simple, like applications that show the location and availability of charging stations, others are more complex, like payment systems for charging and – shortly – for trading electricity stored in car batteries, which can be used in smart grids to balance consumption peaks. Even though the environmental benefits of moving from fossil fuels to electricity are evident,  widespread acceptance of electric vehicles is still hindered by insufficient charging infrastructure, limited driving range, and battery issues. Further, even if humans are likely to remain behind the wheel for now, the rapidly increasing level of automation in vehicles makes it increasingly difficult for drivers to maintain their skills and situational control. This may be problematic when technology malfunctions and human intervention is needed.

At the same time, there are growing concerns about the sustainability of platform-enabled transportation, mobility, and travel practices. Mobility-related sharing economy services as well as different types of fleet services, are seen as viable options for privately owned cars. Still, their growing popularity is associated with many societal problems, such as added congestion in cities, disruption of existing modes of (especially public) transportation, and the widening power imbalance between platform owners and their workforce of “independent contractors.” These services require their users to connect to specific platforms and seem prone to solid location and availability-based network effects. Concerns about business travel have also become acute in many countries, and there are movements to limit work and leisure travel when possible. Could smart digital services and apps offer considerable alternatives for travel or propose the best ways to limit the carbon footprint of travel?

In this minitrack, we seek new research describing smart mobility ecosystems and novel digital services for mobility. The submissions can be research papers, case studies, or practitioner reports on service development and its implications. We intend to take stock of state-of-the-art research on transportation/mobility/travel services and service ecosystems and provide an outlook on what is about to come. We especially encourage submissions on new subareas, such as sustainable travel services, autonomous transportation services, and privacy and security concepts. In addition to using transportation/mobility/travel services, we are also interested in their development, design, and service innovation. Furthermore, related social, societal, and potential customer segmentation issues are of great interest. Relevant topics for this minitrack include (but are not limited to):

  • Transportation ecosystems and services
  • Smart traffic services
  • Autonomous and connected vehicle development
  • Autonomous vehicle (land, sea, air) business models
  • User issues in different smart traffic services
  • Location-based services and business models related to mobility
  • The business value of transportation and mobility services
  • Data privacy and quality in mobility services
  • Data sharing and ownership issues hampering data utilization in mobility services
  • Sustainable travel services
  • Value-added services for travelers (usage, location, maintenance data)
  • Business and societal issues related to autonomous vehicles (land, sea, air)
  • Technological challenges of adaptivity of services
Minitrack Co-Chairs:

Tapani Rinta-Kahila (Primary Contact)
University of Queensland

Juho Lindman
University of Gothenburg

Virpi Kristiina Tuunainen
Aalto University

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 of understanding, designing, and evaluating robots for use by or with humans from the 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.

The goal of this minitrack is to continue presenting both novel and industrial solutions to challenging technical issues as well as compelling use cases. In addition, this minitrack will share related practical experiences to benefit the reader and provide clear proof that robotic and toy computing technologies play an ever-increasing essential and critical role in supporting social robots – a new cross-discipline research topic in social science, computer science, decision science, and information systems. With a general focus on social robots and their related robotics and toy computing, this mini-track covers associated topics such as:

  • Social Technical Issues
  • Human Behavior Study
  • Human-Robot Interaction
  • Business Models
  • Conceptual and Technical Architecture
  • Visualization Technologies
  • Modeling and Implementation
  • Security, Privacy, and Trust
  • Industry Standards and Solution Stacks
  • Provenance Tracking Frameworks and Tools
  • Accountability, ethics, and transparency
  • Case Studies (e.g., smart toys, healthcare, financial, aviation, education, etc.)
Minitrack Co-Chairs:

Patrick C. K. Hung (Primary Contact)
Ontario Tech University

Shih-Chia Huang
National Taipei University of Technology

Sarajane Marques Peres
University of São Paulo

The term soft computing is used in reference to a family of preexisting techniques, namely fuzzy logic, neuro-computing, probabilistic reasoning, evolutionary computation, and so on. Taking profit from the main advantages of each individual technique, they can work in a cooperative way to solve lots of complex real-world problems: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Earlier computational approaches could model and precisely analyze only relatively simple systems. However, more complex systems arising in biology, health, economy, digital world, and similar fields, often remained intractable to conventional mathematical and analytical methods. Therefore, the advances in soft computing techniques play an important role in analyzing and modeling more complex systems. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve computability, robustness, and low solution cost, which can better deal with large-scale, fast, and unstructured changes that occur as part of the digital world.

This minitrack aims to attract researchers with an interest in the research area described above. Specifically, not only contributions on theoretical innovations are welcome, but also those describing different problem-solving benefits by using soft computing-based methodologies in the fields of digital world, digital coaching, digital health, digital economy, cognitive computing, and design and manage of digital services and service systems. We are interested in the contributions where the applied/defined methodologies used are either analysis- or systems-oriented. They may have an experimental or empirical focus. Innovative studies based on explainable methods are favored, which combine innovative theoretical results with a careful empirical verification, or good empirical problem solving, planning or decision making with innovative theory building. A common denominator for all studies is the building and use of soft computing-based models. Topics appropriate for this minitrack include, but are not limited, to:

  • Neural networks
  • Support vector machines
  • Fuzzy reasoning
  • Fuzzy logic
  • Fuzzy decision making
  • Neuro fuzzy
  • Evolutionary computation and evolutionary algorithms
  • Bayesian networks
  • Genetic algorithms
  • Differential evolution
  • Swarm intelligence, ant colony optimization, particle swarm optimization
  • Bio-inspired systems
  • Software for soft computing
  • Natural language processing based on soft computing techniques
  • Explainable decision making
  • Soft computing and its application to design and manage of digital services
  • Applications of soft computing in digital world
  • Soft computing and its application to digital coaching solution

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

Francisco Javier Cabrerizo
University of Granada

Ignacio Javier Pérez
University of Granada

Technology and AI are revolutionizing the global economy. These transformational changes have gained a strong foothold, particularly in emerging markets (EMs) where automation driven by artificial intelligence (AI) and the growth of digital platforms are stimulating industrial development at a scale and pace never before witnessed in history. By leveraging advanced technological infrastructures and highly skilled developers, gigantic tech companies in EMs, such as Samsung, Tencent, Alibaba, and Naver are poised to be at the forefront of digital breakthroughs. Alibaba, Baidu, and Naver, for instance, have joined the race to develop and release their own versions of foundation models. Government agencies in various EM countries continue to invest heavily in technological resources and infrastructures, capitalizing on their national technology and AI strategies to drive the fourth industrial revolution.

These developments mean that an unprecedented amount of data on individuals and business operations from EM countries is now available. The effective and efficient analysis of such data and the extraction of actionable insights are made possible by computational innovations in machine learning and econometrics, along with the availability of powerful computational resources, such as GPUs and cloud computing.

In EMs, these advancements provide information systems and marketing scholars with unique opportunities to conduct analytics-based research. Industry practitioners in these countries are also building technological and AI capabilities for competitive advantage, and EM entrepreneurs are creating new business models in this technology-enabled ecosystem.

Despite the progress made in EMs, however, the unique characteristics of these regions introduce interesting challenges to technology and analytics research. First, the sheer volume of data from EM 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 of EMs (e.g., business practices, languages, cultures, social norms, legal systems) is important. Finally, analytics-based perspectives require support by sound theories to ensure the generalizability of EM-based scholarship.

The main purpose of this minitrack is aimed primarily at bringing business analytics/IT researchers and industry practitioners together to discuss future directions on how technology and AI will reshape EMs and the global economy. It is anticipated to offer a pathway to initiating and strengthening collaborations between academia and industry in EMs. Topics of interest include, but are not limited to:

  • Effects of AI and analytics on competitiveness and innovation in EMs
  • Development of foundation models in EMs
  • Technology-enabled online platforms in EMs
  • Mobile and social media analytics in EMs
  • Image, video, and text data analytics in emerging businesses
  • Empirical studies on AI and machine learning-based startups
  • Generative AI and large language model services in EMs
  • AI-powered bots and the fight against fake news and misinformation in emerging economies
  • The Internet of Things (IoT) and sensor data analytics in EMs
  • Healthcare analytics in emerging countries
  • Quantitative marketing analytics in EMs
  • AI-empowered digital entrepreneurship in EMs
  • AI and analytics in personalized K-12 and higher education in emerging countries
  • Scalable analytics methodologies for EM data
  • Data integrity issues in EM data
Minitrack Co-Chairs:

Gene Moo Lee (Primary Contact)
University of British Columbia

Wonseok Oh
Korea Advanced Institute of Science and Technology

Sang-Pil Han
Arizona State University

Sungho Park
Seoul National University