TRACK CHAIRS

Portrait image of Christer Carlsson.

Christer Carlsson

Institute for Advanced Management Systems Research
Abo Akademi University
Auriga Business Center
20100 Turku, Finland
Tel. +358 400 520 346
christer.carlsson@abo.fi

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
haluk@uw.edu

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.

We would like to invite papers, which would help us to understand how far we are from designing new computational models, which would evaluate past, current and future algorithms, which currently shape AI.  In academia and industry, extensive discussions on biased data sets and algorithms triggered talks on achieving fairness, non-biased, risk-averse and trustworthy AI.  We would be interested in learning how to synchronize technology advances, principles and practices of Data Science, abundance of data processed through AI, and automatic generation of algorithmic predictions through various software tools.  Whether we are waiting for a new way of looking at the problem of transparency of AI or creating a new computational paradigm, which would automatically detect hidden dangers in data sets and associated algorithms, we need to engage in wider talks across communities which have impact on or are affected by the current status of AI.

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.  Discussions at the HICSS conference would answer many AI questions we may have and show a vision where the world of algorithmic computing in AI might be heading.  We are particularly interested in the impact the Data Science.  At the time of writing this proposal, we could not find a conference, which comprehensively addresses the way we create algorithmic computations, and thus would like to debate if Data Science practices might be contributing towards creating more obscurity of AI algorithms. The co-relation between Data Science practices and AI algorithms is obvious, but we still look at the two of them separately without evaluating the impact they may have on each other.

We would also welcome the applications of these novel research results in cyber physical spaces, pervasive and intelligent healthcare, medicine, robotics, smart cities and autonomous transport, automation, IoT and many more.

Indicative Topics are:

Evaluation of AI Algorithms

  • Multidisciplinary nature of AI evaluations
  • Methods of evaluating and explaining AI algorithms and their complexities
  • Computational paradigm and frameworks for evaluating ML and AI Algorithms
  • Reconstructing AI algorithms from their descriptions and explanations
  • Automatic creation of transparent AI models and algorithms
  • Software testing and AI: Testing AI and its algorithms using software testing principles and methods
  • Adaptation of software testing to specificity of AI-based software solutions
  • Interpretability of algorithms through models and criteria for decision making

The pace of AI innovation and responses of society

  • Social and legal outcome of fair algorithms
  • Intentional and unintentional consequences of using AI
  • potential harm to people resulting from algorithmic opacity
  • Human centered AI
  • Human AI interaction for achieving fair, responsible, and acceptable outcome of using AI
  • AI for understanding humans and vice versa
  • Ethics, legal and socio-technical issues in AI based systems
  • Ethical issues and sharing principles in designing AI algorithms
  • AI monitoring and regulations
  • AI Algorithmic harm: dishonesty, fairness and lack of awareness in AI
  • The risk of NOT using AI

Obscurity of AI Algorithms

  • Impenetrable obscurity of AI
  • AI evaluation and seeing “what is inside the black box”
  • Black-box functions in AI and decision making
  • Human control in AI based systems versus black box algorithms versus transparency in AI
  • Trade-off between algorithmic decision making in and transparency of AI-based systems
  • AI accountability regime and the level of human trusts built by algorithms
  • Accountability versus responsibility in AI based systems
  • Performance of transparent AI models and algorithms

AI Algorithms, Fairness and Data Science

  • Identifying BIAS which matters
  • BIASED AI and automatic decision making
  • BIASED semantic representation of data and human BIAS
  • Abstractions in machine learning algorithms and fairness
  • Impact of Data Science practices on semantic of data in training/learning algorithms
  • Feature selection and engineering in machine learning versus fairness of algorithms where it is used
  • Impact of feature selection on the testing/ training data sets which may affects fairness and discover harms of AI
  • Co-relation between Data Science practices and AI computational models
  • Discovering fairness in AI algorithms through the semantic stored in data sets
  • Impact of statistical methods on Data Science and on the evaluation/transparency/obscurity of AI
Minitrack Co-Chairs:

Radmila Juric (Primary Contact)
ALMAIS Consultancy
radjur3@gmail.com

Robert Steele
Capitol Technology University
robertjsteele@gmail.com

The minitrack welcomes research articles and practitioner reports exploring technical and organizational issues pertaining to innovative ways for leveraging information systems and technologies for addressing sustainability issues and research that aim to mitigate the impact of economic development and information technologies on the environment. The mini-track encompasses Green IS, environmental informatics and analytics, sustainable computing, and Green IT. Theoretically founded papers that illustrate the application of analytics, artificial intelligence, deep learning, Internet of Things (IoT), cloud computing, information systems and decision technologies in environmental management for sustainability are particularly welcomed. Possible topics include, but are not limited to:

  • Agriculture 4.0
  • Analytics and decision technologies
  • Artificial intelligence and deep learning
  • Environmental sustainability and decision making
  • Environmental knowledge acquisition and management
  • Environmental Management Information Systems (EMIS)
  • Environmental Decision Support Systems (EDSS)
  • Geographic Information Systems (GIS) for Environmental Management
  • Green IS and Green IT
  • Environmental cyberinfrastructure
  • Environmental communication
  • Energy informatics
  • Smart agriculture, aquaculture and fisheries
  • 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
Omar.El-Gayar@dsu.edu

PingSun Leung
University of Hawaii at Manoa
psleung@hawaii.edu

Arno Scharl
MODUL University Vienna
scharl@ecoresearch.net

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

Minitrack Co-Chairs:

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

Frank Armour
American University
fjarmour@gmail.com

J. Alberto Espinosa
American University
alberto@american.edu

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 works 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 impart an incorrect belief in the attacker, and effects on the decision-making process 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. With such understanding, we can effectively and strategically induce cognitive biases and increase cognitive load, making our systems more difficult to attack.

Cyberpsychology research advances the science of human behavior and decision making in cyberspace to understand, anticipate, and influence attacker behavior. It also seeks to ensure scientific rigor and quantify the effectiveness of our defensive methods. These research efforts require an interdisciplinary approach and this minitrack is therefore soliciting papers across multiple disciplines.

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);
  • Psychological and social-cultural adversarial mental models that can be used to estimate and predict adversarial mental states and decision processes;
  • Cognitive Modeling of cyber tasks;
  • Adversary observation/learning schemes 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 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; and
  • Models and algorithms for decision analytics for autonomous or adaptive cyber defense.
Minitrack Co-Chairs:

Kimberly Ferguson-Walter (Primary Contact)
Department of Defense
kjfergu@spawar.navy.mil

Sunny Fugate
Naval Information Warfare Center Pacific
fugate@niwc.navy.mil

Cliff Wang
Army Research Office
xiaogang.wang.civ@army.mil

Matt Bishop
University of California Davis
mabishop@ucdavis.edu

The service business of manufacturing companies is traditionally structured around their core products and focuses on personnel-intensive services such as consulting or maintenance. Along with this, capital intensive products in particular (e.g. tooling machines) are increasingly permeated by information and communication technologies. This enables them to be globally networked and to provide intelligent functions linking digital and physical world. Such products can also be referred to as cyber-physical systems or smart products. They are a valuable asset to improve service efficiency and to drive service innovation. But, as of today, manufacturing companies still struggle to establish data-driven service systems based on cyber-physical systems.

These kinds of service systems differ strongly from the established businesses in manufacturing – from the service businesses as well as the product businesses. Manufacturing companies need new tools and approaches to create and manage data-driven service systems. Furthermore, new processes, structures, and competences are needed to provide them. Therefore a transformation of the whole business is necessary. Recognizing these challenges, this minitrack aims to explore insights on multiple facets of service systems in manufacturing, that are built around intelligent, technical systems and integrate their data. Topics may span applied, empirical, design science and conceptual research. Typical themes that are expected for contributions to the minitrack include (but are not limited to):

  • Considerations regarding strategic position, competences, and/or organizational structures of manufacturing firms
  • Ideation and portfolio management
  • Conceptual design of service systems (e.g., modelling languages, architectures 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 reengineering, business model innovation)
  • Performance assessment of digital services in manufacturing
  • 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
Minitrack Co-chairs:

Christian Koldewey (Primary Contact)
University of Paderborn
christian.koldewey@hni.upb.de

Martin Rabe
Fraunhofer Institute for Mechatronics Systems Design IEM
martin.rabe@iem.fraunhofer.de

Roman Dumitrescu
University of Paderborn & Fraunhofer Institute for Mechatronics Systems Design IEM
roman.dumitrescu@hni.upb.de

Johannes Winter
acatech – National Academy of Science and Engineering
winter@acatech.de

Data mining is the process of discovering valid, novel, potentially useful, and ultimately understandable patterns (i.e., knowledge nuggets) in data stored in structured databases, where the data is organized in records populated by categorical, ordinal and continuous variables. Text mining, on the other hand, refers to the very same discovery process as it applies to unstructured data sources including business documents, customer comments, Web pages, and XML files.

This minitrack focuses on decision support aspects of business analytics, with specific emphasis on data, text, and Web mining for 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 methods and algorithms of data/text/Web mining
  • New and improved processes and methodologies of conducting data/text/Web mining
  • Data acquisition, integration and pre-processing related research topics of data/text/Web mining, such as new and novel ways of data integration/transformation/characterization, data cleaning/scrubbing, data sampling, data reduction, data visualization, etc.
  • Novel, interesting, and impactful applications of data/text/Web mining for better managerial decision making
  • Ethical and privacy issues in data/text/Web mining
  • Futuristic directions for data/text/Web mining in the era of Big Data analytics, Deep Learning and Cognitive Computing.

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
dursun.delen@okstate.edu

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

This minitrack will address how smart contracts programmed on blockchain platforms 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. 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. Financial services, supply chain systems, healthcare delivery, manufacturing systems, and agriculture are all being impacted by the emergence of distributed ledger technologies (DLTs) that are enabled with smart contracts. Smart contracts are executable code that run on blockchains such as Ethereum or Hyperledger to enable, monitor, and execute transactions and agreements between parties without the use of traditional trusted-third parties. These smart contracts essentially automate the decision analytics required for commerce to be conducted between two or more parties. If blockchain commerce is to become widespread it is important to understand the characteristics and best practices needed for effective and efficient smart contracts.

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 theory using smart contracts. We give special consideration to research submissions when the author(s) commit to include 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 and smart contracts
  • Blockchain utilization to enhance IoT-enabled services
  • Business models for services using blockchains and smart contracts
  • Frameworks on how smart contracts function in a legal setting
  • The role of blockchains and smart contracts 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 and smart contracts to other larger systems
  • Cryptoeconomics and smart contracts
  • Frameworks on measuring the value of blockchains and smart contracts
  • Designing, planning, building and managing smart contracts
  • Safeguarding security and privacy using smart contracts
Minitrack Co-Chairs:

Fred Riggins (Primary Contact)
North Dakota State University
fred.riggins@ndsu.edu

Samuel Fosso Wamba
Toulouse Business School
s.fosso-wamba@tbs-education.fr

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

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

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

Wei Xu (Primary Contact)
Renmin University of China
weixu@ruc.edu.cn

Jian Ma
City University of Hong Kong
isjian@cityu.edu.hk

Jianshan Sun
Hefei University of Technology
sunjs9413@gmail.com

During the last 30 years, the advancements of digital technologies have enhanced our lives in a myriad of ways. Services deploying various types of mobile and other digital technologies are developed predominantly first to consumer markets all over the world to improve the quality of consumers’ everyday life. The worldwide number of mobile subscriptions alone – over 8.6 billion – means that there is more than one subscription per each adult.

Our understanding of business models, platforms and their governance, ecosystems, privacy and the value creation mechanisms of digital mobile services has not grown fast enough to fully cover the influences of all new technological developments. Further knowledge and theory building are needed to establish sound dynamic models over the phenomena, to derive theoretical explanations, or to provide solid guidance to the users, developers, and regulators of digital services. It is vital to also advance our understanding of the individual technology user; the acceptance, adoption, use and sustained use of technology within the sphere of everyday life. Within this area, there is also a necessity to encourage research involving less studied consumer groups, such as age groups above and beyond working age, users of assistive technologies, users with disabilities, technology non-users and minorities.

In this minitrack, we encourage methodological diversity and novel research approaches and models to study this multifaceted phenomenon and to offer research contributions that open new and innovative perspectives as well as insights for the better deployment and use of mobile and other digital technologies, services, and applications 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 business platforms and architectures
    • Mobile and other digital business apps for commerce, marketing and business operations
    • Mobile services used to augment business applications, for example status, location or flow of goods, money or data
  • Digital Mobile Services in consumer settings
    • Consumer initiated and co-created digital services, consumer feedback
    • Usage patterns of digital technologies, services, and applications
  • Mobile payments, virtual payments, mobile banking, comparisons of traditional mobile payment services with cryptocurrency/virtual currency payment services
    • Competition of mobile and virtual payment platforms for consumer, merchant, identity service provider and other stakeholder attention
    • Business architectures of mobile and virtual payment services, platforms, recognition and other technologies, mobile use of virtual currencies and tokens
  • Digital services for young elderly (60 +)
    • Drivers for acceptance and use of digital services
    • Self-efficacy and 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 services
Minitrack Co-Chairs:

Pirkko Walden (Primary Contact)
Institute for Advanced Management Systems Research
Åbo Akademi University
pirkko.walden@abo.fi

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

The purpose of the minitrack is to attract research focusing on theoretical and practical problems related to the innovation, design, development, management, and use of digital services and the digitalization of services. The key drivers in this area of research are the multiplying technological opportunities for digital services, such as ubiquitous connectivity, artificial intelligence, wearable devices, cyber-physical systems, Internet of Things (IoT), virtual/augmented reality, web3, and so on. 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 novel 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 services
  • Socio-psychological aspects of ICT enabled service use
  • Understanding social and cultural contexts

Cyber-Physical and IoT enabled services, Cybernized Services:

  • 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 Cyber-Physical and IoT enabled services
  • Cyber-Physical and IoT service ecosystems, platforms and novel architecture
  • Theoretical aspects of Cyber-Physical and IoT enabled services research
  • Cyber-Physical and IoT enabled services as artifacts
  • Use and adoption of Cyber-Physical and IoT enabled services

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.

Web3 enabled services:

  • New decentralized service innovations
  • Service automation with Web3 technologies
Minitrack Co-Chairs:

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

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

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

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”.

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
Minitrack Co-Chairs:

Christian Meske (Primary Contact)
Ruhr-Universität Bochum
christian.meske@rub.de

Babak Abedin
University of Technology Sydney
Babak.Abedin@uts.edu.au

Mathias Klier
University of Ulm
Mathias.Klier@uni-ulm.de

Fethi Rabhi
University of New South Wales
f.rabhi@unsw.edu.au

Gamification refers to a “process of transforming any activity, system, service, product or organizational structure into one which affords positive experiences, skills and practices similar to those afforded by games, and is often referred to as the gameful experience. This is commonly but optionally done with an intention to facilitate changes in behaviors or cognitive processes. As the main inspirations of gamification are games and play, gamification is commonly pursued by employing game design.”

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.

Minitrack Co-Chairs:

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

Nikoletta-Zampeta Legaki
Tampere University
zampeta.legaki@tuni.fi

Nannan Xi
Tampere University
Nannan.xi@tuni.fi

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

In the AI and Digital era where most future jobs will be knowledge/tech-intensive, there will be an increasing need for T-shaped upskilling – with depth and breadth. There is thus a need within higher education to turn to innovative, quick and scalable solutions to close the skills gap across a variety of curricula.

Recognizing considerable challenges in collaboration between industry and the academia and underutilized potential in industry-university collaboration for upskilling and reskilling, this minitrack invites studies on applications of technologies (such as AI and analytics) in the domain of industry-university collaboration to meet emerging requirements of future work and promote continuous learning.

 

We encourage papers that report on lessons learned, on topics which include, but are not limited to, the following:

 

  • Novel solutions (AI, analytics, digital platforms) in university-industry collaboration for upskilling
  • Addressing different challenges with university and industry collaboration
  • Applying AI, BI and analytics in education in innovative and novel ways; with particular focus on university-industry collaboration
  • Use case examples where universities and industry have worked together for education impact and digital transformation
  • How knowledge and skill development can be formally recognised among education institutions and employers amidst the rapidly expanding area of microcredentials
  • Innovative use of enabling technologies in upskilling the workforce
  • How university can support industry (e.g. in the context of continuous education in non-technology-based organisations) closing the skills gap, as well as how industry can support university (e.g. technical expertise, platforms, tools and co-creation of curricula) in closing the skills gap
  • Sophisticated metrics and ROI examples relevant in developing industry-university collaboration
  • Next generation of strategic innovative partnerships with universities and industry
Minitrack Co-Chairs:

Maarit Palo (Primary Contact)
IBM Research
maarit.palo@fi.ibm.com

Taina Eriksson
University of Turku
taina.eriksson@utu.fi

Adam Smale
University of Vaasa
adam.smale@uwasa.fi

James Spohrer
International Society of Service Innovation Professionals
spohrer@issip.org

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. 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
julp@iti.sdu.dk

Stefan Voß
University of Hamburg
stefan.voss@uni-hamburg.de

Interactive Visual Analytics and Visualization for Decision Making supports human decision making through interaction with data and statistical and machine learning processes, with applications in a broad range of situations where human expertise must be brought to bear on problems characterized by massive datasets and data that are uncertain in fact, relevance, location in space and position in time. Current applications include environmental science and technologies, natural resources and energy, health and related life sciences, precision medicine, safety and security and business processes.

Submissions are encouraged that extend the areas of use to new analytic tasks in science and technology, public health, business intelligence, financial analysis, social sciences, and other domains. Particular emphasis will be given to submissions that use visual analytics for social change discovery, analysis, communication, and focus on mixed initiative analysis recognizing that human and machine have a distinct division of labor in the problem-solving process. Submissions may include studies of visual analytics and decision support in the context of an organization (e.g., communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, Interactive Machine Learning, and collaborative analysis using visual information systems. Additionally, submissions may include understandable, trustable AI as well as human-guided AI to round out the problem-solving process.

This minitrack seeks to define analytical methods and technologies that use interactive visualization to meet challenges posed by data, platforms, and applications for decision making and risk-based decision making. Authors are encouraged to addressed the following themes from their own research perspectives as well as bring the lens of their own background and expertise to focus on the analytics of the data itself and coordination of multiple levels of analysis, decision-making and operations to the design and evaluation of effective presentations for stakeholders.

  • Visual analytics and visualization in digital economies
  • Visual analytics and visualization in “wicked” problems
  • Visualization in organizational analytics
  • Visualization and 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

For this minitrack, we invite computational, cognitive, and organizational perspectives on advanced data processing and interactive visualization 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
ebert@ou.edu

Brian Fisher
Simon Fraser University
bfisher@sfu.ca

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

Machine learning (ML) has garnered significant interest in recent years due to its applicability to a wide range of complicated problems. There is increasing realization that ML models, in addition to making predictions, reveal information about relationships between domain data items, commonly referred to as interpretability of the model. A similar situation is occurring in the artificial intelligence (AI) scientific community, which has concentrated on explainable AI (XAI) along the dimensions of algorithmic interpretability, explainability, transparency, and accountability of algorithmic judgments. ML approaches may be classified as white-box or black-box; while white-box techniques like rule learners and inductive logic programming provide explicit models that are intrinsically interpretable, black-box techniques, such as (deep) neural networks, provide opaque models. With the growing use of ML, there have been significant social concerns about implementing black-box models for decisions requiring the explanation of domain relationships. The ability to express information obtained from ML models in human-comprehensible language -aka interpretability- has sparked considerable attention in academics and industry. These interpretations have found applications in healthcare, transportation, finance, education, policymaking, criminal justice, etc. As it evolves, one aim in ML is the development of interpretable techniques and models that explain themselves and their output.

This minitrack invites papers on advancements in interpretable ML from the modeling and learning perspectives. We are looking for high-quality, original articles presenting work on the following (not exhaustive) topics:

  • Probabilistic graphical model applications
  • Rule learning for interpretable machine learning
  • Interpretation of black-box models
  • Interpretability in reinforcement learning
  • Interpretable supervised and unsupervised models
  • Interpretation of neural networks and ensemble-based methods
  • Interpretations of random forests and other ensemble models
  • Causality of machine learning models
  • Novel applications requiring interpretability
  • Methodologies for measuring interpretability of machine learning models
  • Interpretability-accuracy trade-off and its benchmarks
Minitrack Co-Chairs:

Kazim Topuz (Primary Contact)
University of Tulsa
kazim-topuz@utulsa.edu

Akhilesh Bajaj
University of Tulsa
akhilesh-bajaj@utulsa.edu

Ismail Abdulrashid
University of Tulsa
ismail-abdulrashid@utulsa.edu

The use of technology to support students’ learning has become an increasingly prominent feature of educational settings in general. The use of learning management systems to support educational settings is not new, but the vast amount of data being gathered through both students’ and teachers’ use of them, is however understudied. Because of the vast amounts of data that is now being generated and gathered through the use of learning platforms within educational settings, the learning behavior of students, derived from the data, can now be analyzed in new ways. Analyzing the learning behaviors of students can then be used to improve teaching practices.

The minitrack is interested in papers that take on analyzing teaching and learning behaviors through learning management systems (or learning platforms) through data-driven approaches. 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 and how data can be used to better understand, and improve, the educational environment. The interest thereby extends to papers discussing different approaches in data analytics (for instance by opening up the box of how machine learning techniques to discover relevant factors that can be used to improve teaching and learning and discussing the outcome) 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 from the student’s perspective, or even be written from the intersection between the teachers’ and the students’ practices.

We welcome papers that reflect on, and relate to, learning analytics and datafication in educational settings alongside papers that reflect on learning platforms, teaching practices, learning practices, student profiling, use of third-party applications (for instance for lecture capture) to support education, and everything from small apps to large systems and infrastructures in educational contexts that generate data that can be analyzed and used to impact educational outcomes. We encourage papers based on both qualitative and quantitative methods and on machine learning approaches, as well as empirical, methodological, and theoretical papers that inspire a dialog with the growing literature on learning analytics and datafication of educational settings. The interest extends to all educational levels where the partnership between students and teachers is important.

Minitrack Co-Chairs:

Sara Willermark (Primary Contact)
University West
sara.willermark@hv.se

Anna Sigridur Islind
Reykjavik University
islind@ru.is

María Óskarsdóttir 
Reykjavik University
mariaoskars@ru.is

Galina Deeva
KU Leuven
galina.deeva@kuleuven.be

The pandemic has emphasized the demand for Mixed Reality (MR) and related immersive visualization technologies for customer interactions. However, most of these services and applications 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 do so, more literature reviews, conceptual papers, field and user studies as well as laboratory experiments are needed. The objective 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.

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, metaverse systems). Research regarding these new systems calls for generalizability due to the constantly changing environment. But as the saying goes “exception proves the rule” — it is also important to showcase notable exceptions and outliers and create new research avenues.

This minitrack welcomes all entries related to:

  • Mixed, Virtual and Augmented Reality
  • 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:

  • Consumers, customers, services, marketers and students
  • Marketing models and strategies
  • Technology features and system designs
  • Psychological or behavior patterns
Minitrack Co-Chairs:

Jani Holopainen (Primary Contact)
University of Eastern Finland
jani.m.holopainen@helsinki.fi

Petri Parvinen
University of Helsinki
petri.parvinen@helsinki.fi

Essi Pöyry
University of Helsinki
essi.poyry@helsinki.fi

Tommi Laukkanen
University of Eastern Finland
tommi.laukkanen@uef.fi

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

  1. Create a forum for “colleagues from the industry” to share their insights such that they can be incorporated into future teaching and research. This is a practical step in the process to upskill our workforce.
  2. Engage industry and academic colleagues to find collaboration opportunities.

The goal of this minitrack is to explore applications of science and technology to real-world innovations through practitioner reports, case studies, best practice examples, tutorials, challenges, issues, opportunities, tools, techniques, and methodologies of emerging digital technologies. In many cases, practice is ahead of academic research contributions.

Possible themes/topics of this minitrack include, but are not limited to:

  • Data 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
    • Next generation social media & networking
    • Application of search
    • Digital marketing
Minitrack Co-Chairs:

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

Utpal Mangla
IBM
utpal.mangla@ca.ibm.com

Ammar Rayes
Cisco Systems
rayes@cisco.com

Heather Yurko
Facebook
hyurko@fb.com

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
  • Analysis of Smart Services, Mobile Services, IoT-based Services
  • Web Usage Mining and Web Personalization
  • Recommender Systems for Services
  • Social Network Analytics applied to Services
  • Privacy Issues resulting from Service Analytics
  • Fraud Analytics for Service Systems
  • Analysis and Prediction of User Behavior in Mobile Phone Systems
  • Analysis and Prediction of Driver Behavior in Traffic Situations
  • Analysis and Exploitation of Floating Car Data
  • Electricity Consumption Analysis using Smart Meter Data
  • Analytics for Healthcare Services
  • 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
hansjoerg.fromm@kit.edu

Niklas Kühl
Karlsruhe Institute of Technology
niklas.kuehl@kit.edu

Gerhard Satzger
IBM Germany
satzger@de.ibm.com

Thomas Setzer
Catholic University of Eichstätt-Ingolstadt
thomas.setzer@ku.de

Service science deals with the design, development, and managerial issues concerning “service systems,” integrated, value-creating configurations of service providers, their clients, their 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.

This minitrack seeks papers that connect rigorous disciplinary research with the emerging interdisciplinary framework of value creation in service systems, focusing particularly on innovation, technology, and the digital economy. We are interested in the use of information technology and the effects of the digital economy on services, including but not limited to:

  • The increasing capabilities of technologies in service, such as autonomous technologies, and the roles of people and technologies in creating autonomous service systems;
  • The increasingly large role played by data and information in complex service systems, including the ways sensing and analytics influence value creation in the digital economy and potential ethical challenges created by the sorts of data collection and analyses that can now be conducted in real-time; and
  • The potential for computational modeling techniques, such as agent-based simulation, to inform the theory and design of complex, human-centered service systems, especially in the digital context, such as blockchain technologies, internet of things, etc.

We encourage submission of research papers from a variety of disciplines and a variety of participating communities to address issues in service policies, service process modeling, service delivery management, innovative service technologies, and the role of the Internet, the digital economy, and information technology. We also encourage submissions related to autonomous service systems, the use of data and information for value creation, and computational modeling of complex, human-centered service systems, particularly with applications for the digital economy.

Minitrack Co-Chairs:

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

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

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. The uses of simulation models and digital twins are manifold, from planning to virtual commission and real-time operational decision support. Cyber-physical systems integrate the real world and the virtual world to enable decision making in the age of Industry 4.0. Methods 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. 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:

Tobias Reggelin (Primary Contact)
Otto von Guericke University Magdeburg
tobias.reggelin@ovgu.de

Steffen Strassburger
Technische Universität Ilmenau
steffen.strassburger@tu-ilmenau.de

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

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

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 sustainable smart mobility ecosystems and related services: data that represents accurately, for instance, 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 all build on platform thinking, as do many services reducing the need for travel to begin with. For these services to work, there is a need to analyze the ecosystems emerging around these services, as the services and platforms form complex webs.

In this minitrack, we seek new research describing smart mobility ecosystems and novel digital services for mobility. The submissions can be research papers, case studies, or practitioner reports related to service development and their implications.

In addition to usage of 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 in the near future. 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, see, air) business models
  • User issues in different smart traffic services
  • Location-based services and business models related to mobility
  • 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, see, air)
  • Technological challenges of adaptivity of services
Minitrack Co-Chairs:

Juho Lindman (Primary Contact)
University of Gothenburg
juho.lindman@ait.gu.se

Matti Rossi
Aalto University
matti.rossi@aalto.fi

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

This minitrack invites research and industry papers related to these specific challenges and others driving innovation in robotics and toy computing for social robots. In addition to novel and industrial solutions to challenging technical issues as well as compelling use cases, we are interested in an ever-increasing essential and critical role that robotic and toy computing technologies play 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
patrick.hung@ontariotechu.ca

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

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

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 particular, in the fields of digital world, digital coaching, digital health, digital economy, and cognitive computing. 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 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
  • Evolutionary algorithms
  • Genetic algorithms
  • Differential evolution
  • Swarm intelligence
  • Bio-inspired systems
  • Ant colony optimization
  • Particle swarm optimization
  • Bayesian networks
  • Software for soft computing
  • Natural language processing based on soft computing techniques

Selected outstanding manuscripts from this minitrack will be recommended to the editors of Frontiers in Artificial Intelligence to be fast-tracked for the review process.

Minitrack Co-Chairs:

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

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

Ignacio Javier Pérez
University of Cádiz
ignaciojavier.perez@uca.es

The main purpose of this minitrack is to bring business analytics researchers and industry practitioners together to discuss future directions on how technology and analytics will reshape emerging markets and the global economy. Topics of interest include, but are not limited to:

  • Scalable analytics methodologies for emerging market data
  • Data integrity issues in emerging market data
  • Technology-enabled online platforms in emerging markets
  • Mobile and social media analytics in emerging markets
  • Image, video, text data analytics in emerging businesses
  • Empirical studies in AI and machine learning-based startups
  • AI-powered bots and fight for fake news and misinformation in emerging economies
  • Internet of things (IoT) and sensor data analytics in emerging markets
  • Economics of blockchain and cryptocurrency technologies in emerging markets
  • Healthcare analytics in emerging countries
  • Quantitative marketing analytics in emerging markets
  • AI-empowered digital marketing in emerging markets
  • AI and analytics in personalized K-12 and higher educations in emerging countries
  • Experimental studies in digital advertising in emerging markets
Minitrack Co-Chairs:

Sungho Park (Primary Contact)
Seoul National University
spark104@snu.ac.kr

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

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

Wonseok Oh
Korea Advanced Institute of Science and Technology
wonseok.oh@kaist.ac.kr

A part of grounded theory methodology (GTM) is the coding of qualitative data. Much of this occurs implicitly, initially, in the mind of the researcher interacting with subjects during interviews. However, even when using available software assistance technologies characterizes as CAQDAS (Computer Aided Qualitative Data Analysis Software), it still remains difficult to convey the meaning of an emergent mid-range theory arising from GTM studies without a good way to telegraphically and pictorially demonstrate key aspects of the theory and its operational mechanisms.

This minitrack seeks to recruit papers that demonstrate practical and compelling techniques for the presentation of Grounded Theories to readers and audiences. It intends to provide guidance for GTM researchers on the mechanism of visual representation of their inductive theorizing, with an eye toward making the process vastly more discernible and accessible by readers of research. Papers with a specific methodological demonstration of the visualization process are as welcome as papers that induce and explain specific theories, using visual metaphors and representations.

Selected papers will be invited after further revision for submission to a special section of The DATA BASE for Advances in Information Systems.

Minitrack Co-Chair:

Tom Stafford (Primary Contact)
Louisiana Tech University
Stafford@latech.edu