Portrait image of Christer Carlsson.

Christer Carlsson

Institute for Advanced Management Systems Research
Abo Akademi University
Gezeliusgatan 2
20500 Turku, Finland
Tel. +358 400 520 346

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

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.

The rise and relevance of artificial intelligence (AI) for organizational processes created new challenges and chances for more automated, efficient, and innovative knowledge transformation to support or improve decision-making. The involvement of AI within organizational processes leads to the massive production of data, which raises the complexity of decision-making. AI can be trained and enabled to make certain decisions if defined criteria are met. In other cases, human decision- makers are still involved, and face the challenge to trace, process, and analyze data to predict and calculate various directions of different possible decisions. Hereby, decision support systems provide support for human decision-makers. With the technological advances of AI, the functionalities of decision-support systems have extended drastically, providing potentials and risks. Thus, we are interested in research that investigates AI to support human decision-making.

This minitrack seeks research that advances, integrates, or augments processes with AI to guide organizational decision-making. We encourage submissions with an organizational process perspective from any industry. We invite contributions with different methodological approaches (quantitative, qualitative, design-oriented), as well as conceptual papers. Possible relevant topics for this minitrack might include, but are not limited to:

  • What are theoretical foundations and design methodologies for hybrid-intelligent decision-making (working with an AI towards a decision)?
  • How to design and build automated decision-making processes (e.g. AI integration for process mining, etc.)
  • How to implement tools for AI-enabled decision-making and facilitate their use?
  • How does AI-enabled decision-making change the nature of work and the role of managers?
  • How does AI impact HR processes, e.g. decision-making in the recruiting process?
  • How does AI facilitate organizational innovation processes and the management of innovation?
  • To which extend can AI-enabled decision support systems shape decisions leading toward sustainable organizations?
Minitrack Co-Chairs:

Navid Tavanapour (Primary Contact):
University of Hamburg

Maren Gierlich-Joas
University of Hamburg

Leveraging advanced technologies such as artificial intelligence, analytics, and decision support can play a significant role in advancing sustainable practices, addressing sustainability issues, and research that aim to mitigate the impact of economic development and information technologies on the environment. 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 mini-track encompasses Green AI, IS, and IT, environmental informatics and analytics, and sustainable computing. The minitrack encourages the submission of theoretically founded papers that focuses on artificial intelligence, deep learning, analytics, decision support, Internet of Things (IoT), cloud computing, information systems and decision technologies in environmental management for sustainability. Possible topics include, but are not limited to:

  • Agriculture 4.0
  • Smart agriculture, aquaculture and fisheries
  • Analytics and decision technologies
  • Artificial intelligence and deep learning
  • Artificial intelligence for green transition and sustainability
  • Green IS and sustainability applications of artificial intelligence
  • Artificial intelligence and environmental challenges
  • 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 artificial intelligence, IS, and IT
  • Environmental cyberinfrastructure
  • Environmental communication
  • Energy informatics
  • Technologies for DSS development and environmental applications (e.g., Artificial intelligence, Information visualization, Web intelligence, IoT, agent-based computing, and Multiple-criteria decision making)
Minitrack Co-Chairs:

Omar El-Gayar (Primary Contact)
Dakota State University

PingSun Leung
University of Hawaii at Manoa

Abdullah Wahbeh
Slippery Rock University of Pennsylvania

Arno Scharl
MODUL University Vienna

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, metrics for assessing big data value, enabling technologies, and tools to solve complex problems using big data such as text mining and analytics. We also 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.

In a new development this year, we are integrating the longstanding work of the “Text Mining in Big Data Analytics” minitrack. This integration enhances our ability to seek papers using text mining and analytics, ranging from statistical bag-of-words and rule-based approaches to syntactic parsing and natural language processing, such as Named Entity Recognition (NER). We are also looking for text analytics papers using unsupervised machine learning, including, topic modeling, and k-means clustering; supervised machine learning models and deep learning approaches, including predictive regression and classification models;  DNN, LSTM, CNN, word embeddings, LLMs; and transformer approaches, such as BERT, GPT, and T5. 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. Additionally, 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. The addition of the Text Mining and Analytics minitrack also brings the HICSS Advanced Text Analytics Tutorial and Textathon.

A fast track publication opportunity with Data & Policy published by Cambridge University Press has been secured for selected papers accepted to this minitrack. The HICSS special issue will be entitled Text Analytics and Big Data for Policy. Data & Policy is a peer-reviewed, open access journal dedicated to data science and governance.

Minitrack Co-Chairs:

Stephen Kaisler (Primary Contact)
SHK & Associates

Frank Armour
American University

J. Alberto Espinosa
American University

Derrick Cogburn
American University

The use of biometric data in user research is an important trend in recent years, yet its development was significantly impacted by the complexity of analysis and calculations and the time-consuming procedure of translating the user experience into quantifiable data. Last five years have brought a rapid development in analytical methods and data gathering strategies (based on automatization of data description and use of neural networks) which promise a significant advance in the field.

This minitrack aim is to present and discuss the current state-of-the-art research related to biometric and behavioral data analytics in product and service design. We welcome research using various measurement methods, including classical biometric data (eye-tracking, GSR, ECG, EEG, face tracking) as well as behavioral data (automatic analysis of user inputs, data from digital controllers, parametric data from the application environment, real-world data from counter devices, Bluetooth tokens, wearable devices or IoT devices). Additionally, the minitrack is open to academic and applied/business analytics, focusing on the methodology of data gathering and data integration. We also focus on the on the analytical approaches leading to the improvement of products, services, and processes and the conceptualization of research questions, which are usually innovative and exploratory, as this area of research is still under intense development. Possible topics include, but are not limited to:

  • Mobile applications design
  • Digital games for various platforms
  • Digital service design (i.e., financial, medical, educational)
  • Enterprise software design (i.e., CRM systems, project management software)
  • Advertising and content marketing reception
  • User interface design
  • User behavior in virtual reality
  • Customer behavior analytics (both online and offline)
  • Automatic gameplay analytics in esports
Minitrack Co-Chairs:

Tomasz Gackowski (Primary Contact)
University of Warsaw

Karolina Brylska
University of Warsaw

Caja Thimm
University of Bonn

Piotr Siuda
Kazimierz Wielki University in Bydgoszcz

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

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 that helps 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

Dana LaFon
National Security Agency

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 decision 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, for 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, expatiation of existing methods or models to emerging fields, and 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 or machine learning.
  • New or improved processes and methodologies.
  • Novel ways of data integration, transformation, cleaning, sampling, reduction, and visualization.
  • Novel, interesting, and impactful applications of data science and machine learning for better managerial decision making.
  • Security, privacy, and ethical issues in data science and machine learning.
  • Futuristic directions of the use of data science and machine learning in business decision-making.
  • Explainability of machine learning models, and pros and cons of explainability in machine learning.
  • Natural language processing and AI-based language models (methods and applications in business).
  • Automated machine learning methods and methodologies and their pros and cons.
  • Deep learning methods and methodologies as enabler of managerial decision-making

Extended versions of the papers accepted for presentation in the Data, Text, and Web Mining for Business Analytics minitrack at HICSS will be invited for a fast-track review and publication consideration on the Journal of Business Analytics and the Journal of AI in Business.

Minitrack Co-chairs:

Dursun Delen (Primary Contact)
Oklahoma State University

Behrooz Davazdahemami
University of Wisconsin – Whitewater

Hamed Majidi Zolbanin
University of Dayton

Manufacturing is a mainstay of the global economy, which is increasingly confronted with (digital) servitization. This trend is a topic at the intersection of digitalization and growing sustainability demands. Traditional manufacturing companies are striving to complement their products with digital, data-driven, and AI-based services for their internal and/or external customers, and are consequently needing to position themselves in emerging service ecosystems. This affects organizational structures, resources, business processes, capabilities as well as offerings. 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 service systems in manufacturing, that are built around cyber-physical systems and integrate their data. Topics may span conceptual, empirical, design science, applied, and theoretical research. Hereby any research methodology (reviews, qualitative, quantitative, mixed methods, etc.) on any level of analysis is welcome. Typical themes that are expected for contributions to the minitrack include, but are not limited to:

  • Digital Servitization of manufacturing
  • Considerations regarding strategic position, competencies, and/or organizational structures for digital servitization
  • Ideation and prototyping of data-driven service systems
  • Creating and managing a portfolio of data-driven service systems
  • Design knowledge and artifacts for data-driven service systems (e.g., theories, modeling languages, design principles, methods, evaluation of methods, etc.)
  • Business models for smart services, digital services, and/or digital product-service- systems (e.g., business model patterns, archetypes, revenue models, etc.)
  • Organizational transformation (e.g., management of change processes, business process optimization, business process reengineering, business model innovation)
  • Performance assessment of data-driven service systems and respective organizations
  • Realized data-driven service systems and their implications for theory
  • Studies on the actual state of the art in practice, associated challenges, and future research needs
  • Studies on the transition into a more sustainable (green) manufacturing (e.g., circularity) due to data-driven services and servitization
  • Contributions to better understand new occurring phenomena and paradoxes within
    the transformation (rebound effects, backfire effects, service paradox, deservitization)
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

Roman Dumitrescu
University of Paderborn and Fraunhofer Institute for Mechatronics Systems Design IEM

This minitrack addresses how smart contracts and AI on blockchains are being used to automate decision analytics to provide new types of services. 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.

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 distributed 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

This minitrack seeks submissions that discuss how to augment human reasoning and decision making through interactive data visualization coupled with statistical and machine learning processes. This Hybrid Intelligence approach has applications in a broad range of situations where human expertise must be brought to bear on problems characterized by complex causal models, massive datasets, and data that are uncertain in fact, relevance, location in space and position in time. Current applications include environmental science and technologies, natural resources and energy, health and related life sciences, precision medicine, safety and security and business processes. Visual analytic environments have been widely used for pandemic policy making, response planning and execution. There has also been a growth in visual decision making environments to analyze supply chain risks, climate resiliency and adaptation.

Submissions are encouraged that focus on technological and sociotechnical approaches to support individual and collaborative analysis and decision making in organizations. Core issues of theory and methods for decision intelligence, collaborative work, data visualization, analytics, and knowledge integration 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 intelligence in the context of an organization (e.g., business planning, communication with analysts and policy-makers), perceptual and cognitive aspects of graphical visualization environments in the context of cognitive tasks, Interactive Machine Learning, and collaborative analysis using visual information systems. Additionally, submissions may include methods for 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. Topics for this minitrack include, but are not limited to:

  • Decision Intelligence approaches to computer augmented decision-making.
  • Use of interactive visualization and visual analytics in in organizations.
  • Applications of visual analytics.
  • 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

Authors are encouraged to bring the lens of their own background and expertise to discuss the complexities of advanced analytic and decision intelligence systems in organizations, coordination of multiple levels of analysis, decision-making and operations and design and evaluation of effective communication with and among diverse stakeholders. We invite computational, cognitive, 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.

Minitrack Co-Chairs:

David Ebert (Primary Contact)
University of Oklahoma

Brian Fisher
Simon Fraser University

Kelly Gaither
University of Texas at Austin

We would like to invite papers, which would help us to understand how far we are from creating a new era of decision making, based on sustainable, fair, and trustworthy AI.  We are all focused on the evaluation of algorithms, which currently shape AI.  We also talk about their roles and responsibilities in achieving non-biased and risk-averse AI.  However, AI algorithms are not the only factors which could affect the outcome of using AI in decision analytics.  It is the type of data, its semantics/interpreted meaning and its sources that may influence both: the precision/usefulness of algorithms and reliable decision making.  This is a very complex picture in which we see principles and practices of Data Science playing a dominant role and the abundance of data processed through AI, with the automatic generation of algorithmic predictions through various software tools.  The complexity of data collection and its processing through AI and data science practices may have a serious impact on decision analytics, particularly if we consider that decision making, based on predictive inference, may pose risks.  Do we need to supplement current inference predictions with logic reasoning, which dominated in decision science for decades, and how would it affect the deployment of current AI and decision analytics?  Does decision analytics have future based solely on algorithmic predictions? Is the explainable, fair, and trustworthy AI a final answer for modern decision making?

We invite a spectrum of papers, which highlight any of our concerns and the topics below, and thus help us to cut through the current hype of AI and its impact on decision analytics.  Discussions at the HICSS conference would answer many AI questions we may have and show a vision where the world of algorithmic predictions might be heading in order to support decision making.  We are also interested in data science practices, their co-relation with AI algorithms and obvious impact of both on decision making.

We would also welcome the applications of these ideas and decision analytics in automation, transport, pervasive healthcare, medicine, robotics, smart cities, IoT, management and governance. Topics of interests include:

  • The Roles of Algorithmic Predictions and AI in Decision Analytics
    • Drawbacks and benefits of algorithmic decision making
    • Human control in making decision based on AI: black box algorithms versus transparency in choosing data sets and algorithms for decision making
    • Trade-off between algorithmic decision making and transparency, explainability and fairness of AI
    • AI trustworthy regime, the level of human trusts built by AI algorithms
    • Decision making based on algorithms we cannot explain: do we have to see what is in “the black box” which house AI algorithms?
    • Choosing the importance and level of prediction in algorithmic decision making?
    • AI algorithms with their predictive inference versus logic inference in computational models, applicable in decision analytics.
    • Adapting data science principles in decision science, based on fair and trustworthy AI
  •  Data Science in Decision Making
    • Biased data and algorithms in automated decision making.
    • Biased semantic representation of data, human bias, bias hidden in software tools used in running predictions in decision analytics
    • Abstractions in machine learning models, feature selections, training and testing data sets which bring forward fair AI and reliable decision analytics.
    • Discovering (un)fairness in decision making through human intervention when choosing data set and running predictions
    • Impact of statistical methods on creating sustainable, fair, and trustworthy decision making with AI
    • Complementing data science practices with logic inference in decision analytics
  • AI and Decision Making in Social Domain
    • Social AI in decision making
    • Opinions, interactions and relationships between humans in decision analytics
    • Benefits and drawback of management, governance and community decisions with AI
    • Social and legal outcome as intentional and unintentional consequences of using AI in decision making.
    • Human AI for achieving fair and trustworthy AI which understands humans and vice versa.
    • Ethics, legal and socio-technical issues in decision analytics
    • AI Algorithmic harm: sources of dishonesty, the lack of fairness and biased views within decision analytics
    • The risk of NOT using AI in decision making
Minitrack Co-Chairs:

Radmila Juric (Primary Contact)
ALMAIS Consultancy

Robert Steele
Quantic School of Business and Technology

Smart farming is a management concept that focuses on providing the agricultural business with the infrastructure to harness sophisticated technology, such as big data, the cloud, and the internet of things (IoT), for tracking, monitoring, automating, and analyzing processes. Precision agriculture is another term for precision farming. The combination of the expanding global population, the increasing demand for higher crop yield, the need to use natural resources efficiently, the rising use and sophistication of information and communication technology, and the growing need for climate-smart agriculture is increasing the importance of smart farming.

According to the United Nations population projection, the global population could reach approximately 8.5 billion in 2030 and 9.7 billion in 2050. It is anticipated that the population will reach a peak of approximately 10,4 billion people during the 2080s and remain at that level until 2100. All of this will place agriculture at the center of the global stage. Population growth is anticipated to place demand-side pressure on global agriculture and food production. It is comprehensible. Demand will not be the only obstacle. The climate will continue to be the “X” element in agricultural output.

Will smart farming and related technologies in agriculture help farmers make good decisions?

The objective of this minitrack is to encourage and attract research in Internet of Things, Drones, Smart remote sensing, Computer imaging, Data analysis, Machine learning and deep learning in smart farming context. Typical themes that are expected for contributions to the minitrack include, but are not limited to:

  • Precision irrigation
  • Fertilization
  • Irrigation
  • Early disease detection
  • Automation of farmer’s tasks
  • IoT-based solutions, robotics and automation for farmers
  • 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:

Khouloud Boukadi (Primary Contact)
Sfax Tunisia

Rima Grati
Zayed University

During the last 30 years, the development of mobile digital technologies have improved our lives profoundly. People use smartphones, mobile internet-based services, and a variety of mobile solutions in their diverse everyday activities everywhere and all the time. Many of us have become extremely dependent on these services or even addicted to them. Mobile services are developed predominantly first to consumer markets all over the world. The worldwide number of smartphones and mobile-connected devices – 7 and 15 billion – means that most adults have smartphones and more than one mobile-connected device, even though 775 million people do not have access to neither electricity nor mobile services.

Our understanding of development strategies, business models, platforms and their governance, ecosystems, and the value creation mechanisms of digital mobile services has not grown fast enough to fully cover the influences of all mobile technological developments. The more we rely on mobile-connected devices and mobile services the more important their business continuity, information security, privacy protection, and cyber threat mitigation become. The same goes for recognizing and mitigating the potential dark side consequences of using digital mobile services in everyday life. Thus, knowledge and further theory building are needed to establish sound dynamic models over the phenomena, to find theoretical explanations, or to provide solid guidance to the users, developers, and regulators of digital mobile services.

In this mini-track, we have followed the development of digital payment services – mobile payments – that are used to pay for everyday life purchases. Mobile payments and mobile banking, fintech included, continue to be active research streams with lots of unsolved and new research questions. Recently, virtual currencies, virtual tokens, and central bank virtual currencies (CBVC) have emerged as payment alternatives for everyday purchases of digital and other services, typically with a tight connection to the underlying service. In some countries, we have also witnessed the spreading of mobile payment services to electronic receipts, invoices, and other business documents, sometimes combined with digital wallets. Research on contemporary mobile payment, wallet, and banking services, their platforms and ecosystems, comparisons between developed and emerging countries’ use of mobile and virtual payments, as well as contributions on other mobile and virtual payment issues are very relevant for the minitrack.

We encourage methodological diversity and novel research approaches and models to study the multifaceted phenomenon. For example, user and service usage data, or usage pattern sequences, can be used, e.g., for customer profiling, marketing, and to develop new value-adding services, but it also gives researchers an objective view on individuals’ technology usage and the means to investigate usage patterns, rhythms, and configurations as well as the possibility to discover unexpected phenomena. Qualitative work can yield rich insights into individuals’ motivations and decision making. Creative, well-designed research, regardless of the chosen method, is needed to achieve the goal of the minitrack since its very start in 2002: to offer research contributions that open new perspectives and insights for the better deployment and use of mobile and other digital technologies, services, and applications.

It appears that research will have to tackle an increasing number of challenges, and we invite research contributions that open up new and innovative perspectives and, thus, offer insight for a better understanding on mobile and other digital services in any area of everyday life. The research may focus on the design, implementation, usage, regulation, and/or evaluation of digital technologies, services, and applications. Relevant topics for this minitrack include, but are not limited to:

  • Digital Mobile Services in a business/organizational setting
    • Design, development, implementation, and/or continued use of business apps, integration with IT infrastructure, data storage, business platforms and architectures
    • Mobile and other digital business apps for commerce, marketing and business operations
    • Mobile services used to access and augment business applications, for example status and location data, the use of such data in the monitoring of cargo transport and traffic, money or data and documents
    • Governance and of management of IT infrastructure, data storages, and digital applications used to design, develop, implement, roll-out, and use mobile services
  • Digital Mobile Services cybersecurity issues
    • Business continuity issues of mobile services and mobile-connected devices
    • Information security issues of mobile services and mobile-connected devices
    • Privacy protection issues of mobile services and mobile-connected devices
    • Cyber threat issues of mobile services and mobile-connected devices: viruses, malware, phishing, ransom, etc.
  • Digital Mobile Services in consumer settings
    • Consumer initiated and co-created digital services, consumer feedback
    • Usage patterns of digital technologies, services, and applications
    • Dark sides of digital mobile service use in everyday life (e.g., addiction and technostress)
  • Mobile payments, virtual payments, mobile banking, comparisons of traditional mobile payment services with cryptocurrency/virtual currency payment services
    • Mobile payment strategies, business models, regulatory issues, inter- and cross industry competition, adoption and continued use
    • Mobile payment ecosystems
    • Competition of mobile and virtual payment platforms for consumer, merchant, payment service provider, identity service provider, and other stakeholder attention
    • Business architectures of mobile and virtual payment services, platforms, recognition and other technologies
    • Mobile banking and fintech architectures, platforms, services
    • Mobile use of virtual currencies and tokens
    • Mobile receipt, invoice, and other electronic business document services
  • Digital Mobile Services for young elderly (60+)
    • Drivers and barriers for the acceptance and use of digital mobile services
    • Self-efficacy and the forming of daily routines for physical activities
  • Wearable devices and digital coaching
    • Understanding wearable device usage
    • Digital coaching apps
    • Sports watches as drivers of systematic physical activities
  • Integrated location-based services
  • Adoption, acceptance, and diffusion of digital mobile services
Minitrack Co-Chairs:

Tomi Dahlberg (Primary Contact)
University of Turku

Anna Sell
Åbo Akademi University

Markus Makkonen
University of Jyväskylä

Pirkko Walden
Institute for Advanced Management Systems Research
Åbo Akademi 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 and transforming their personal lives and making it more sustainable. Furthermore, the utilization of Personal Decision Analytics (PDA) and Artificial Intelligence (AI) has become a crucial aspect in the Digitization of Individuals. For example, personal finance management for budgeting and investment advice, health information systems for health risk predictions and personalized recommendations, and productivity systems 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 development of systems that leverage Personal Decision Analytics (PDA) to measure and track individuals’ personal data, such as health and financial information.
  • The use of data analytics 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 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.
  • Integration of DI with emerging technologies or digital environment like natural language processing, blockchain, and metaverse.

We welcome submissions from a wide range of disciplines and inter-disciplinary 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

The purpose of the minitrack is to attract research on the innovation, design, development, management, and use of digital service innovations and the new technological opportunities. The key drivers in this area of research are the multiplying technological opportunities for digital services stemming from generative AI (such as ChatGPT, and the metaverse the Internet of Things (IoT), virtual/augmented reality, web3, and so on. The minitrack provides a discussion forum for researchers interested theoretical and practical problems related to such service innovations.

This emerging area of research raises interesting questions. For example, traditional development approaches focus on improving the efficiency and effectiveness of organizational processes. The design of such services may, however, require an emphasis on the socio-psychological aspects, such as the value-in-use and user/consumer/co-creator experiences. Digital services create novel ways of engaging customers and other actors in service ecosystems, raising the question of effective patterns of such digital actor engagement. Moreover, digital services facilitate data-driven and analytics-based service design and development, particularly if the service is linked to the physical world through sensors and/or people’s interactions.

The shift of consumer and enterprise personnel from users to co-creators and co-destructors of value, calls for a significant re-appraisal of our current design and development approaches. Relevant topics for this minitrack include, but are not limited to:

  • Digital and ICT enabled services, industrial services, mobile services, and consumer information services and systems
  • Discovery, fuzzy-front end, and innovation processes
  • Continuous and experimental service design, and development processes and methodologies
  • 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 ICT enabled service innovations
  • Socio-psychological aspects of ICT 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

Educational Data Mining (EDM) is concerned with exploring large-scale educational data using computational and statistical methods in order to gain a better understanding of the educational process. This minitrack invites research articles and practitioner reports that explore educational data mining in the context of decision analytics and decision making. Topics that cover learning analytics, which is a major component of  EDM, are not the focus of this minitrack.

Topics of interest include but are not limited to:

  • Tools and decision support systems for education
    • Admissions decision-making
    • Enrollment projections
    • Data visualization tools and methods for decision support
    • Design, deploy, and evaluation of human-AI hybrid systems
  • Course and program evaluation
    • Personalized course and major recommendations
    • Algorithms for discovering relationships, associations, and prerequisite structures between course sequences and learning resources
  • Prediction of student performance (grades, completion rates, etc.)
    • Machine learning and statistical methods
    • Impact of student psychology and non-academic factors on student performance
    • Student cognitive and behavior modeling and its association with academic achievements
  • Ethics and AI in Education
    • Detection of bias in admissions or other aspects of the educational process and strategies to address and minimize such biases.
    • Equity, Transparency, and Inclusion in Education
Minitrack Co-Chairs:

Yijun Zhao (Primary Contact)
Fordham University

Gary Weiss
Fordham University

Daniel Leeds
Fordham University

Yi Ding
Fordham University

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—machine learning-based or not. We especially look for contributions that investigate XAI from either a developer’s or user’s perspective. 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
    • 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
    • Impact of explainability on AI-based decision support systems use and adoption
    • Explainability of AI in crisis situations
    • Potential harm of explainability in AI
    • Identifying user-centric requirements for XAI systems
  • The developers’ perspective on XAI
    • XAI to open, control and evaluate black box algorithms
    • Using XAI to identify bias in data
    • 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
    • Addressing user-centric requirements for XAI systems
  • The governments’ perspective on XAI
    • XAI and compliance
    • Explainability and transparency policy guidelines
    • Evidence base benefits and challenges of XAI expectations and implementations

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
Macquarie University

Mathias Klier
University of Ulm

Fethi Rabhi
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 behaviours or cognitive processes. As the main inspirations of gamification are games and play, gamification is commonly pursued by employing game design.”

Gamification has become an umbrella concept that, to varying degrees, includes and encompasses other related technological developments such as serious games, game-based learning, exergames & quantified- self, games with a purpose/human-based computation games, and persuasive technology.

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

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

Authors of accepted papers have the option to fast-track extended versions of their HICSS papers to Internet Research or AIS Transactions on Human-Computer Interaction. The Gamification minitrack is also part of the Gamification Publication Track aimed at persistent development of gamification research.

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

Computer scientists have been exploring the use of Artificial Intelligence (AI) and Machine Learning (ML) in Information and Cybersecurity (ISEC) technology such as anti-malware, firewalls, and IDS. These are early attempts, and much is needed to improve not only the algorithms but also the organizational implications of these approaches such as balancing type I and type II errors with work processes and user behavior.

Recent work also focused on the use of AI and ML to improve fraud detection. For example, some studies have employed existing machine learning techniques (e.g., support vector machine, logit, genetic algorithm, and associations) to decern fraudulent activities in financial transactions while others have employed deep neural networks by incorporating artificial neural network, autoencoder, long short-term memory, and gated recurrent units. More recently, machine learning techniques have been used to increase the rigor and credibility of fraud detection.

In addition, there has been some work on using ML to better quantify cyber-risk and to optimize investments in ISEC. For example, recent work has developed a set of machine learning methods to identify a benchmarking peer for establishing optimal information security policies. ISEC studies used machine learning to develop efficient and autonomous information security systems such as Intrusion detection systems, mobile transaction and signal security, and federated machine learning.

This minitrack goal is two folds. First, we would like to explore ways that AI and ML could improve ISEC for example by:

  • Exploring ways to align advanced security algorithms with organizational constraints.
  • Developing mechanisms to better quantify ISEC risk
  • Enabling organizations to optimize their investments in ISEC
  • Investigating misuse behavior patterns
  • Proposing measures to prevent misuse behavior by insiders
  • Investigating attack patterns
  • Combating zero-day attacks

Second, as technology progresses from traditional AI to Artificial General Intelligence (AGI), society is going to rely on AGI-type services (robotic physicians, lawyers, education). In this mini track, we would like to explore the unique ISEC challenges of these trends, such as:

  • Do data warehouses require different ISEC approaches than traditional databases?
  • What are the risks of reusable APIs?
  • Are AI-based security appliances easier to attack?
  • What would an attack on an AI-based appliance (i.e., a robot) look like?
  • What security mechanisms are used in AGI-based applications?
Minitrack Co-Chairs:

Martin Kang (Primary Chair)
Loyola Marymount University

Anat Zeelim-Hovav
Korea University, retired

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 minitrack invites contributions in the area of intelligent 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 and innovative techniques or new interactions among existing approaches with a particular focus on supply chains and hybrid network analysis. Topics related to this minitrack include, but are not limited to:

  • Explainable Reinforcement Learning for safe decision making
  • Supply chain data-driven optimization
  • Self-explanatory agents and decision support systems
  • Explainable, reliable, safe, transparent, and fair AI
  • Causal explanations, and causal inference
  • Smart 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
Minitrack Co-Chairs:

Stefan Pickl (Primary Contact)
University of Bundeswehr Munich, Germany

Alex Bordetsky
Naval Postgraduate School

Wolfgang Bein
University of Nevada, Las Vegas

Learning analytics (LA) is a rapidly growing research field with a substantial and lasting impact on learning and teaching practices. Given the unprecedentedly fast development of data storage and data analysis capacities, more opportunities are emerging to gain interesting insights from educational data and to develop data-driven understanding of learning and teaching processes. More recently, the need for direct involvement of educational research in learning analytics has been acknowledged as a first fundamental pillar of learning analytics to scaffold the research, i.e. to determine which learning theories should be investigated so that appropriate data can be collected and analyzed. The second pillar, capturing, refers to finding evidence of learning, by identifying and explaining useful data for analyzing and understanding teaching, learning, and developing methods that capture and model learning. Then, understanding is associated with how learning theory is informed by large-scaled data analysis, as well as the use of data science techniques to understand specific aspects of teaching and learning. The final pillar is the impact on learning and teaching by providing decision support and feedback based on LA, such as through dashboards and early-alert systems, and personalized and adaptive learning.

Recently we have witnessed new promising trends both in the field of education and in Artificial Intelligence (AI), which open new directions in the field of learning analytics. After the recent pandemics, blended, hybrid and distance learning have first become an emergency measure and then gradually shifted towards a “new normal”. Currently, blended and hybrid approaches in education have significantly matured and transformed the learning analytics research, by providing new tools and raising new challenges. In addition, rapidly developing AI applications have become omnipresent in various domains, with no exception for education. One example is educational chatbots, which have gone through the evolution from simple keywords matching systems towards fully autonomous and intelligent agents such as the most recent ChatGPT. Another example is automatic grading systems, which, thanks to the development of better natural language processing (NLP) techniques, allow to grade student essays and other complex educational tasks as good as a human tutor would. Such educational chatbots and grading systems, alongside other AI powered educational tools, not only support the learning and teaching processes, but also allow to collect vast amounts of data for learning analytics.

In this minitrack, we welcome papers that address, reflect on, and relate to, the four pillars of learning analytics and datafication in educational settings alongside papers that reflect on recent trends in technology enhanced learning, AI powered education, intelligent tutoring systems, teaching and learning practices, and student profiling. Furthermore, analyzing teaching and learning behavior through learning management systems (or learning platforms) through data-driven approaches or through qualitative approaches, or even mixed methods. The interest extends to papers that shed light on the type of data that is required to improve teaching and learning in different levels of education, how data can be used to better understand, and improve, the educational environment, as well as to papers discussing qualitative research on teaching and learning, where data is used to support these processes. The interest is therefore not merely in big data and grand projects but also extends to the use of small data that can support teaching or learning. The papers can take the point of departure from the teacher’s side, or a student perspective, or even be written from the intersection between the teachers’ and the students’ practices. In addition to that, the papers can take on challenges and benefits for management, operations, practice or research.

Minitrack Co-Chairs:

Galina Deeva (Primary Contact)
KU Leuven

María Óskarsdóttir 
Reykjavik University

Anna Sigridur Islind
Reykjavik University

Sara Willermark
University West

The concept of ’Mixed Reality’ (MR) refers to an integration of various types and levels of virtual and real environments. Mixed reality technologies and systems have increasingly merged with other contemporary media technologies (e.g., video content, second screens, visualization technologies, companion apps, motion tracking, avatars, digital twins, 360 environments, virtual worlds). More recently, this integration of digital and physical platforms is being referred to metaverse platforms (MPs). The metaverse is also a multiuser platform ecosystem, that is, a combination of various platforms.

MPs integrate computational and physical capabilities and expand the capabilities of physical world entities through computation, communication and control. Consequently, we contend that the increasing prevalence of these kind of systems in combination with techniques such as AI undeniably heralds an era of MPs as new ways to create value for organizations.

Investigating MPs is important for several reasons: (1) Digital and physical platforms have different characteristics and thus, need different design principles. (2) Integrating the two or several platforms requires taking digital experience into account when designing for the physical experience (and vice versa). (3) There is substantially increased demand for such hybrid experiences in various institutional and business contexts. (4) Previous studies have investigated various platforms separately, but much less is known about the integration and combination of the two or several platforms.

Therefore, we still have limited knowledge of even single MR technologies and MPs and especially their opportunities and challenges for organizations, which should be one crucial area for the current research. As MPs are evolving and transitioning to this new phase of multiple physical and digital platforms, we argue that the ways of managing these platforms are subject to changes. Specifically, we posit that MPs are most likely evolving in novel ways in areas such as strategic posture, business models, and innovation, and establishing new ways for designing and delivering new types of applications and services. Accordingly, this minitrack presents a promising avenue for various information systems (IS) research and management research streams to come together to generate new knowledge as well as equip practitioners with new insights into identifying new value-creation opportunities through MPs.

The pandemic emphasized the demand for 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. That is the goal of this minitrack.

To summarize, this minitrack welcomes all entries related to:

  • Mixed, Virtual and Augmented Reality and Metaverse platforms
  • Multiple virtual technologies and multimedia
  • 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

Petri Parvinen
University of Helsinki

Essi Pöyry
University of Helsinki

Tommi Laukkanen
University of Eastern Finland

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 needs to be a practitioner. If accepted, the paper will be presented by the practitioner as well. Possible themes/topics of this minitrack include, but are not limited to:

  • Data and Analytics
    • Descriptive, diagnostic, predictive, prescriptive analytics
    • Machine, deep, 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
    • Hyperautomation
    • Robotics/automation
    • Trusted/responsible AI
    • Intelligence augmentation
    • AI Bias
    • Networking & AI
  • Cybersecurity
    • Blockchain & distributed ledger, federated learning
    • Protecting endpoint/consumer devices
    • Privatization, legal, risk, and compliance solutions
    • Intelligence, response, and orchestration with data and analytics
    • Data, network security products, and strategies
  • Internet of Things
    • Mobile services & technologies
    • Data processing with edge computing
    • Personalization of the retail, financial, healthcare e-commerce experience
    • Energy and resource management
    • Reliable, scalable, and secure wireless access
  • Service-Oriented Technology & Management
    • Cloud computing
    • Service computing
    • Platforms & service ecosystems
    • Collaboration systems and technologies
    • Serverless computation
    • Network-as-a-Service
    • Data-, analytics, and -information-as-a-service
    • X-as-a-service
    • Responsible innovation
  • Digital government
    • Smart government: Smart city solutions for the public service landscape
    • Multichannel citizen engagement
    • Improving end-to-end public service delivery via unique digital identity
    • Ethical technology and trust
  • Intelligent Augmentation & Virtual Reality
    • Science and application of virtual reality and gaming
    • Augmented reality for education, shopping, healthcare, tourism, etc.
    • Augmented reality for navigating solutions
  • Logistics & Supply Chain Management
    • Autonomous trucking
    • Smart warehouse management and demand management solutions
    • Robotic automated storage and retrieval
    • IoT for transportation
    • Location analytics
  • 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
  • Web
    • Nest generation social media & networking
    • Application of search
    • Digital marketing
Minitrack Co-Chairs:

Tayfun Keskin (Primary Contact)
University of Washington, Seattle

Utpal Mangla

Ammar Rayes
Cisco Systems

Heather Yurko

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 in Information Systems research, with contributions increasing in particular during the last decade. As these contributions in the recent years have shown, QCA can advance theory building in IS research by going beyond the variance-oriented logic to embrace conjunctural causation and equifinality. The logics of conjunctural causation and equifinality advocated in QCA, can aid in the development and validation of configurational theories for explaining increasingly complex digital phenomena, in particular in business and management problems. The results of QCA provide novel theoretical contributions by focusing on causal pathways leading to an outcome of interest, and at the same time deliver explainable and actionable insights to be used by decision makers when dealing with complex digital systems.

QCA is a technique employed in information systems research to analyse complex causal relathionships. It combines the benefits of qualitative and quantitative methods by utilising Boolean logic to determine the necessary and sufficient conditions for an outcome to occur. In IS research, QCA can be utilised to examine the factors that contribute to a specific outcome, like the successful IT adoption or organisational performance. It offers a more nuanced comprehension of complex causal relationships compared to conventional regression analysis and enables the examination of multiple causes and outcomes.

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, 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
  • Extensions of QCA with various (fuzzy) logical foundations
  • QCA and big data
  • Advances in QCA methods, techniques and software tools
  • Best practices and opportunities of QCA
  • Analyzing complex causal relationships in IS adoption and implementation
  • Exploring factors affecting technology acceptance and usage behavior
  • Studying the success factors of IS projects
  • Examining the antecedents and outcomes of IS innovations
  • Evaluating the impact of IS on organisational 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
Åbo Akademi University and Stockholm 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
  • 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 the design, development, and managerial issues of “service systems,” integrated, value-creating configurations of service providers, clients, partners, and others. The best-performing service systems are IT-enabled, customer-centered, relationship- focused, and knowledge-intensive – yet span multiple formal and informal organizations. Because of this multidisciplinary context, researchers and practitioners in management, social sciences, and computer sciences are all working to increase service innovation. 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.

Though the minitrack will consider papers from all areas related to service science, particular priority will on service design, service engineering, and service technologies. In 2024, the Service Science minitrack will focus on the use of computational technology in services, including but not limited to:

  • The increasing capabilities of technologies in service, such as artificial intelligence and service robots
  • The increasingly large role played by data in complex service systems, including sensing, analytics, and potential ethical challenges
  • The potential for technologies to support design of human-centered service systems, especially in a digital context, including blockchain, internet of things, and artificial intelligence;
  • The initiatives in service innovation to enhance the resilience and sustainability in the post-covid era
Minitrack Co-Chairs:

Paul Maglio (Primary Contact)
University of California, Merced

Fu-ren Lin
National Tsing Hua University, Taiwan

The concepts of design for all, universal design, accessibility, inclusion, diversity, and many other related terms, all share a similar core notion. They signify the importance of designing communities, experiences, services, and artifacts that are usable by everyone, regardless of their (dis)ability, age, skills, gender, race, ethnicity, religion, income, or any other such factors. Design for all is essential to ensure justice, equity, and human rights. Given the importance services have in our society, they would especially benefit from design for all practices that ensures equity and equal access. Services, in this context, are defined malleably as: processes, actions, application of competence, or activities, performed by an entity to fulfill a need for another.

The importance of services and their target groups varies. It has been argued that public services are of higher significance to society, and hence, should be accessible to and usable by everyone in society. Different governmental agencies have issued numerous directives to ensure equal access to public services, yet, in practice, many services still remain largely inaccessible. On the other hand, in the private sector, there have long been debates on whether private services should comply with accessibility and inclusion standards, or if the market should regulate the matter. With privatization and the increasing significance of private services, the stakes have been raised for these sectors. Private sector services have become akin to essential services that arguably need to be accessible to ensure equitable society. Financial accessibility is similarly a subject of contention between services providers needing to maintain their viability and unfolding international economic crises that significantly impede access to even the most basic services. Regionally, there are divides between countries and populations in access to services based on location and geopolitics, the latest of which is around AI-based services, such as ChatGPT, which have been blocked in several regions, leaving their populations behind in technological capabilities.

When it comes to services deemed by some as non-essential, the debate on inclusion becomes even more complex. Disagreements exist over whether games, VR, AR, serious games, gamification, and such services that combine utility and entertainment are required to be accessible and inclusive. Nonetheless, we see streaming services not only ensuring the accessibility of their services, but becoming pioneers in it. Similarly, we see arguments that services are not meant to be accessible or inclusive to everyone, e.g., for example games require high utilization of different abilities and senses and are not compatible with disabilities. Yet, we also see the release of critically acclaimed games, playable by a wide range of individual and inclusive of many diversity aspects.

Overall, we see disagreements on what inclusion and design for all mean, how to design, implement, and evaluate it and what benefits can be drawn from it and for whom. We encourage a wide range of submissions from any disciplinary backgrounds: empirical and conceptual research papers, case studies, and reviews that investigate design for all in the services context and push it forwards. Relevant topics include (but are not limited to:

  • Designing, implementing, or evaluating accessible, inclusive, and socially sustainable services
  • Understanding the needs of new and diverse user groups
  • Understanding developer, designer, and decision-maker attitudes toward design for all in services
  • Investigating the barrier to and facilitators of design for all approaches
  • Investigating regional and financial barriers in service access and the consequences of inequities
  • Examinations of the inclusivity and accessibility of public, private, essential, non-essential, and hedonic services
  • Examinations of accessibility and inclusivity of entertainment-based services, such games, gamification, gamefulness, playfulness, simulations, serious games, games with a purpose, gamebased learning, VR, AR, and the metaverse
  • The use of design for all as PR moves or CSR initiatives
  • Quantifying the return from design for all and inclusion
  • Impact of AI, automated decision tools, and similar service analytics, on access to services
Minitrack Co-Chairs:

Lobna Hassan (Primary Contact)
LUT University

Kat Schrier
Marist College

Dominik Siemon
LUT University

Sami Hyrynsalmi
LUT University

Simulation models are indispensable for the planning, control and optimization of processes and systems in all areas, for instance manufacturing, production, logistics, traffic, and many more. 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 and digital twins and decision making in the context of Industry 4.0. 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, and adaptive systems. Topics of interest also include strategies for the successful application of simulation modeling 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 simulation education 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:

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

Steffen Strassburger
Technische Universität Ilmenau

Stefan Galka
Ostbayerische Technische Hochschule Regensburg

Tobias Reggelin
Otto von Guericke University Magdeburg

Transportation is changing, and this change is driven by technology-driven trends: autonomy, connectivity, electrification, and diverse mobility business models of shared vehicles. At the same time, there is a drive towards more sustainable transportation. Digital platforms and services are essential for orchestrating smart and sustainable mobility ecosystems and related services: data that accurately represents, for instance, the location of passengers and service providers, weather, usage, and maintenance is a cornerstone for service design. Identification and communication systems that link specific physical things to specific digital addresses offer possibilities to communicate, transact, pay, build trust, sense and activate “things” from the internet – and design novel services based on this generated data. Autonomous vehicles, electric cars, and mobility-related sharing economy services build on platform thinking, as do many services reducing the need for travel. 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.

More acutely, electric cars are increasingly embraced by car owners, and they require their own set of infrastructure and services. Full-scale charging infrastructure needs building, and it involves a lot of mobile services to support it. Some are simple, like applications that show the location and availability of charging stations, and some more complex, like payment systems for charging and – in the near future – for trading electricity stored in car batteries, which can be used in smart grids to balance peaks of consumption. Societal benefits of moving from fossil fuels to electricity in terms of reduction of pollution are pretty evident, but in addition to insufficient charging infrastructure, limited driving range, high costs and battery issues still act as barriers to wider acceptance.

At the same time, there is growing concerns about the sustainability of current practices of mobility and travel. Mobility-related sharing economy services as well as different types of fleet services, are seen as viable options for privately owned cars. Still, they have their own challenges, such as added congestion in cities, reconfiguring existing modes of (especially public) transportation, disrupting incumbent industries, and widening the power imbalance between the platform owners and “independent contractors.” These services require the ability to connect to these specific platforms and seem to be prone to solid location and availability-based network effects. Examples of these services are on one hand, Uber and Lyft type of services and, on the other hand, in the near future, autonomous transportation of people and cargo through fleets of autonomous vehicles on land, water and air.

At the same time concerns about business travel have become acute in many countries, and there are movements to limit work and leisure travel when possible. Could smart digital services and apps offer alternatives for travel, or could some services propose the best ways to limit the carbon footprint of travel?

In addition to using mobility value services, we are also interested in their development, design and service innovation. Furthermore, social, societal, and potential customer segmentation issues are of great interest. In this proposed minitrack, we take stock of what is the state of the art in current mobility services and service ecosystems and what is coming shortly.

As HICSS is addressing leading edge developments, we especially encourage submissions on new subareas, such as sustainable travel services, autonomous transportation services, and privacy and security concepts. 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:

Juho Lindman (Primary Contact)
University of Gothenburg

Matti Rossi
Aalto University

Virpi Kristiina Tuunainen
Aalto University

Advancement in information and communications technology has made valuable data from infrastructure, social connections, human behavior, and organizations available. Smart tourism uses such data generated from destinations, i.e., smart cities, and tourists to help destination management organizations to make informed decisions; visitors create more sustainable and enriched experiences; and residents improve their quality of life and coexist with visitors. Data science and data analytics can be used to achieve smart tourism by extracting insights and making decisions on tourist behaviors, residents’ quality of life, and destination management.

Using technologies such as cloud computing and the IoT, we can now collect rich data like demographics and psychographics of tourists, their behavior and experiences, and their travel patterns. Data analytics and data science can be used to process that data for practical use. Insights extracted from data can be used for contextual marketing and personalized services for tourists, improving tourists’ experiences at destinations and increasing their satisfaction. Another aspect where the data can be used is to make the destinations more sustainable through the resident quality of life management and sustainable tourism product development.

This minitrack calls for original research that applies data science and data analytics to promote and investigate smart tourism. In particular, we are interested in studies that discuss the most recent innovations, trends, and concerns, as well as practical challenges encountered, and solutions adopted in the fields of Smart Tourism and Decision Analytics. Co-authored papers with practitioners are also encouraged. Topics of interest include, but are not limited to:

  • Data analytics for tourist decision-making
  • Social media data analytics for destination management
  • Big data analytics for destination management
  • Tourism intelligence and visual data analytics for destination management
  • Travel demand modeling with behavioral data
  • GIS monitoring of traveler flows with big data
  • Tourism intelligence and visual media analytics for destination management organizations
  • Evaluating destination communications on the Internet
  • Analytics for tourism planning, management, and marketing
  • Data mining with social networks
  • Tourism information management and advanced analytics
  • Big data analytics for knowledge generation in tourism destinations
  • Spatial analysis of social data
  • City monitoring and tourism analytics
  • Social networks monitoring
  • Crowd monitoring in events
Minitrack Co-Chairs:

Soyoung Park (Primary Contact)
Florida Atlantic University

Jahyun Goo
Florida Atlantic University

C. Derrick Huang
Florida Atlantic University

Chul Woo Yoo
Florida Atlantic 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). These data’s richness and vastness 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 usage 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 robotic and toy computing. Robotic computing is one branch of AI technologies, and their synergistic interactions enable by robots. Social robots can now easily capture a user’s physical activity state (e.g., walking, standing, running, etc.) and store personalized information (e.g., face, voice, location, activity pattern, etc.) through the camera, microphone, and sensors AI technologies. 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 minitrack covers related 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
  • Case Studies (e.g., smart toys, healthcare, financial, aviation, 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:

  • Machine learning
  • 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 solutions

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

Minitrack Co-Chairs:

Enrique Herrera-Viedma (Primary Chair)
University of Granada

Francisco Javier Cabrerizo
University of Granada

Ignacio Javier Pérez
University of Granada

Technology and analytics are rapidly 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 Tencent, Alibaba, Samsung, 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 ChatGPT—a revolutionary AI-enabled conversational bot. 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 econometrics and machine learning, along with the availability of powerful computational resources, such as cloud computing and GPU.

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 rapidly building technological and analytics 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., 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 analytics 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 ChatGPT 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:

Wonseok Oh (Primary Contact)
Korea Advanced Institute of Science and Technology

Gene Moo Lee
University of British Columbia

Sang-Pil Han
Arizona State University

Sungho Park
Seoul National University