Knowledge Innovation and Entrepreneurial Systems

In today’s knowledge-intensive world, efficient information management and utilization are paramount for organizational (and sometimes personal) success. Artificial Intelligence (AI) has emerged as a powerful tool for knowledge management, offering solutions that streamline processes, enhance decision-making support, and facilitate collaboration. This minitrack focuses on the intersection of AI assistants (including chatbots, text-based and voice-based assistants) and generative AI techniques in the realm of organizational and personal knowledge management.

We invite submissions from researchers and practitioners that explore innovative approaches, design science and design theory, case studies, theoretical insights, and practical applications of AI-driven solutions in knowledge management for both organizational and personal life settings. Topics of interest include, but are not limited to:

1. AI-powered knowledge retrieval focusing on techniques and systems for efficient retrieval of relevant information from large knowledge bases using AI assistants, both for personal and organizational-level knowledge (including their issues and limitations).
2. Use of generative AI for content creation, including applications of generative AI models such as GPT, BERT, and transformers for generating and summarizing knowledge content (including their issues and limitations).
3. Personalized knowledge delivery with AI-driven methods for tailoring knowledge delivery to individual users’ preferences and needs, both in personal and work life
4. Collaborative knowledge sharing, including platforms and tools leveraging AI to facilitate collaborative knowledge sharing and collective intelligence within organizations.
5. Diversity, ethical aspects, risks, and challenges of designing and appropriating knowledge with AI assistants and other AI systems (e.g. information overload, ‘operator hand-off’ problems, technostress, and protection of information assets).
6. Changing organizational cultures and structures by integrating AI assistants, generative AI and other AI systems for knowledge management.
7. Design, evaluation, and/or use of knowledge management and AI systems and processes to facilitate knowledge creation and sharing as well as quick problem solving.
8. Technology-in-practice outcomes and processes across both technology-centric and socio-centric approaches to generative AI and AI systems design (as related to, but not limited to, various affordance and agency/agential frameworks, computer-supported cooperative work).

Minitrack Co-Chairs:

Alina Bockshecker (Primary Contact)
University of Hagen
alina.bockshecker@fernuni-hagen.de

Pierre Hadaya
Université du Québec à Montréal
hadaya.pierre@uqam.ca

Stefan Smolnik
University of Hagen
stefan.smolnik@fernuni-hagen.de

Computing Education (CE), also known as Computer Science Education, is best known as the field leading the conversation about what, how, and for whom Computer Science should be taught. Computational thinking has been a theme emphasized by educators and researchers in the field, but CE addresses the broader impact of computing in society. In recent years, the teaching of computing has moved from being the exclusive domain of higher and graduate education to becoming a subject of primary education. In some countries it is taught as a specific subject and in others it is taught in an interdisciplinary way.

To keep advancing the literature on CE, this minitrack encourages submissions from any disciplinary background reporting different kinds of studies: e.g., empirical studies, case studies, methods and techniques, conceptual frameworks, and literature reviews. Beyond the title of the minitrack, the minitrack covers research and practice framed as related to neighboring concepts such as: computing educators, instructional designers, teacher, school administrators, policymakers, and other actors involved with CE:

1. People: e.g., studies on the impact of different technologies (digital or not) on the use of computing, student and teacher experience, behavior, performance, etc.
2. Educational science: e.g., educational theories behind CE and their application.
3. Pedagogy: pedagogical aspects (e.g., collaborative learning, blended learning, cognitive processes, intellectual skills, edutainment, and others) in CE.
4. Learning analytics: e.g., tools for measuring skills behind computing, adaptivity and personalization in CE.
5. Teaching strategies: e.g., unplugged computing, robotics, visual languages, innovative didactic materials/techniques, new courses, metacognition, etc.
6. Theories/concepts/methods: e.g., contributions to the science of CE.
7. Digital world: e.g., ethics, equity, and civil rights and their implications concerning the interaction with digital media, socio-cultural relations related to CE, anthropology, civic potentials of being in the digital world, bodies, gender, identity, poetics, and politics in CE.
8. Computational Thinking: e.g., general aspects of computational thinking.
9. Curricula: e.g., CE for K2-K12, multidisciplinary, connected, and interdisciplinary approaches involving CE, international curricula.

Authors of accepted papers have the option to fast-track extended versions of their HICSS papers to Smart Learning Environments.

Minitrack Co-Chairs:

Wilk Oliveira (Primary Contact)
Tampere University
wilk.oliveira@tuni.fi

Pasqueline Dantas Scaico
Federal University of Paraíba
pasqueline@dcx.ufpb.br

Mirka Saarela
University of Jyväskylä
mirka.saarela@jyu.fi

Today’s classrooms have moved beyond just desktop computers that were once the norm and are now tech-infused with tablets, interactive online courses and even robots that can take notes and record lectures for students who are ill.

This influx of edtech tools are changing classrooms in a variety of ways: AI based edtech robots, machine learning based classroom models, usage of conversational AI based chatbots, are making it easy for students to stay engaged through fun forms of learning; IoT devices, that are not only providing resources over a variety of platforms, but are encouraging an ecosystem driven by the need of connectivity, are being hailed for their ability to create digital classrooms for students, whether they’re physically in school, on the bus or at home; even machine learning and blockchain tools are assisting teachers with grading tests and holding students accountable for homework.

The potential for scalable individualized learning goes beyond a classroom and has ushered an integrated environment that is seamless. It has played an important role in edtech’s ascendance. The way we learn, how we interact with classmates and teachers, and our overall enthusiasm for the same subjects is not a one-size-fits-all situation. Everyone learns at their own pace and in their own style. Edtech tools make it easier for teachers to create individualized lesson plans and learning experiences that foster a sense of inclusivity and boost the learning capabilities of all students, no matter their age or learning abilities.

This minitrack is developed to address and serve the purpose of:

1. Connecting people – Businesses, communities, government and entities who can use advancement in frameworks of education to cultivate a change for a better future.
2. Solving problems – Some of the major problems that requires methods to share knowledge and give a platform to the audience to be able to equip themselves with the right education. The minitrack helps bring partners that can find creative solutions, with the help of education, provisioned in the right possible ways.
3. Developing champions – The minitrack aims to find methods to develop the champions and leaders that shall tackle the best problems. The results should not only solve the problems we face, but also inspire others to seek educational ways to resort to solutions.
4. Creating Institutions/Entities – the societal impact we seek cannot be created using temporary solutions. We need to cultivate them over a period of time and help stay relevant to the needs to the society. These long terms entities shall provide the resources and leadership to have a purpose driven solution to our problems. This minitrack shall help bring those ideas and frameworks together that can create a lasting impact.

The value that we seek in our societies cannot be created on a one-way street. In fact the value that we seek cannot be created, but co-created. Edtech has the power of unifying these different elements in the society to create those long term and meaningful solutions through the use of technology. ‘Education beyond borders’, ‘Education for All’ are some schools of thought that have limitless benefits and the advancements in edtech can help us explore creative ways to make it a possibility. At the end of the day, the number one goal of education is to be that positive influence that helps brings solutions together. In the world of technology, edtech is that vehicle to make this happen.

This minitrack welcomes papers in all formats including empirical studies, design, theory, theoretical framework, case studies, and etc. The minitrack encourages submission of any studies from an implementation standpoint of a technical or economical model, which engages emerging technologies in the area of edtech. The submissions include, but are not limited to, the following topics:

1. Theories used in edtech
2. Virtual and distributed edtech models and technologies
3. Management of frameworks used in edtech
4. Knowledge networks and the future of edtech
5. Edtech implementation methodologies
6. Human networks in Edtech
7. Quality Metrics to measure the effectiveness of Edtech platforms
8. Best practices in edtech
9. Management and implementation frameworks and standards
10. Automation in edtech
11. Knowledge sharing and management in edtech using novel technologies
12. Repurposing current methodologies into edtech
13. Governance models in edtech
14. Software and eservices in edtech
15. Role of corporates/governments in edtech

Minitrack Co-Chairs:

Gaurav Shekhar (Primary Contact)
University of Texas at Dallas
gaurav.shekhar@utdallas.edu

Deepak Khazanchi
University of Nebraska at Omaha
khazanchi@unomaha.edu

Entrepreneurs identify and pursue opportunities to create value under conditions of uncertainty. The growth of emerging technologies such as artificial intelligence (AI), blockchain, and quantum computing in entrepreneurship has begun. For example, advances in AI bring new opportunities for value creation, enabling fewer individuals to accomplish more than ever. From entirely new business opportunities to increased efficiency and automation within business processes. Meanwhile, blockchain and Web 3.0 enables different business models creating an opportunity to put users at the center of the system and upend centralized corporate control. Finally, opportunities to leverage quantum computing are just emerging given the extreme levels of investment required. It is largely unknown how entrepreneurs will extend and apply the capabilities in more mainstream markets. This minitrack centers on understanding how these emerging technologies influence entrepreneurship at the micro, meso, and macro levels.

At the micro level, this could include perceptions of AI and individual differences that influence if and how an organization leverages AI capabilities. Are the skills and resources required to grow an entrepreneurial venture evolving in light of these technologies, and if so, how are they developed? Do users really value regaining control of their data and the value associated with it? How should individuals interact with AI, and how do those interactions influence the individuals? What potential risks are associated with using AI for startups and customers? How will AI influence our decision-making processes?

At the meso level, this includes new businesses built on the technologies along with the new business models they enable. Frameworks that understand and guide the various ways entrepreneurs can effectively leverage these technologies are needed. Blockchain relies on expansive network effects which can create a challenge to achieve initial scale. Quantum computing likely requires large investments only achievable for large corporations, government entities, and extremely well capitalized startups. Alternatively, AI enables smaller groups of individuals to expand their impact and grow larger firms, yet the influence on organizational structure, organizational culture, and fundraising remains largely unknown.

At the macro level, what is the entrepreneurial ecosystem’s role, and how can the ecosystem provide the training and infrastructure required for entrepreneurs to adopt the technology successfully? Is there a risk of too much AI adoption, especially in the ecosystem coordination process? Can ecosystems help facilitate the network scaling needed for successful blockchain businesses? Additionally, AI is already being integrated into deal vetting processes. Can AI develop intuition, which is currently viewed as essential in assessing investment opportunities? Do the cost and barriers to scale inhibit the opportunity for small startups to innovate and drive creative destruction? Will this motivate more collaboration between startups and large corporations? If so, who will have access to these opportunities, or will these technologies result in even more exclusion of historically underrepresented groups?

We encourage the submission of both theoretical and empirical papers, and all types of methods (qualitative or quantitative) are welcome. Topics of interest include, but are not limited to, the following:

1. Strategies for Implementing AI in Entrepreneurship
2. Technology – Task Fit
3. Factors Influencing Technology Adoption
4. Entrepreneurial Training and Emerging Technologies
5. Emerging Technologies and Equity Outcomes
6. Ethics and Risks Associated with Entrepreneurial AI Application
7. AI Influence on Organizational Structure
8. Emerging Technology Influence on Business Models
9. Opportunity Identification Through AI
10. Entrepreneurial Ecosystems & Emerging Technologies
11. Quantum Computing Infrastructure for Entrepreneurs
12. Technology Influence on Diversity and Inclusivity in Entrepreneurship

Entrepreneurship remains the core strategy for economic development and innovation, and artificial intelligence stands to influence all aspects of entrepreneurship. Entrepreneurial ecosystems may play a critical role in facilitating the adoption of AI. Nascent research on this intersection underscores the importance of theoretical development and empirical analysis.

Minitrack Co-Chairs:

John Sebesta (Primary Contact)
University of Denver
john.sebesta@du.edu

Hope Jensen Schau
University of California Irvine
schauh@uci.edu

Melissa Akaka
University of Denver
melissa.akaka@du.edu

Martin Key
University of Colorado Colorado Springs
tmkey@uccs.edu

In an era defined by digital transformation and rapidly evolving knowledge landscapes, traditional approaches to Knowledge Management (KM) are increasingly challenged by emerging opportunities and new requirements. The integration of future-oriented methods such as Futuring or Learning from the Future alongside innovative practices like Design Thinking offers a unique opportunity to not only adapt KM practices to current needs but also to proactively shape the future. Technological advancements—such as artificial intelligence, big data analytics, and collaborative digital platforms—are reshaping the ways in which knowledge is created, shared, and utilized. Traditional KM models are reaching their limits in this dynamic environment. By combining (new) methods for shaping the future with creative, user-centered design approaches, organizations can reinvent their KM systems and/or rethink their understanding of knowledge and knowledge management as such, to better meet the demands of the digital era.

This minitrack provides a platform to scientifically ground this paradigm shift while delivering actionable insights for practice. To achieve this, this minitrack aims to foster interdisciplinary discussions, explore innovative concepts, and critically examine methods that could define the future trajectory of KM.
We welcome submissions for this minitrack adopting different theoretical lenses and worldviews, using a variety of research methods and conceptual ideas, and exploring the topic with a visionary mindset. We are also very looking for contributions that break with well-trodden empirical and conceptual conventions to help academic and practice build novel concepts, instruments and designs by focusing on (digital) future(s). Topics covered include (but are not limited to):

1. Futuring as (future) core topic of KM?
   • How can futuring and/or design methodologies be integrated as a component of Knowledge Management (systems) to enhance organizational strategic innovation and resilience?
   • What role can the different approaches of Learning from the Future play in Knowledge Management?
   • What are the critical success factors and barriers for incorporating futuring and/or design techniques within traditional Knowledge Management frameworks?
   • In what ways do futuring and/or design practices enable organizations to effectively respond to disruptive trends impacting knowledge-intensive environments?
   • How can the synergy between futuring and Knowledge Management drive innovation, and which metrics can best capture its impact on organizational performance?
   • What cultural and structural capabilities are necessary to embed futuring as a core element in Knowledge Management systems?
   • In what ways does integrating futuring into Knowledge Management reshape strategic decision-making and risk assessment processes at various organizational levels?
   • What synergies can be achieved by combining qualitative and quantitative and “non-state-of-the-art” futuring methodologies within KM, and how do these synergies enhance long-term organizational learning and adaptability?
2. The future role of KM in Artificial Intelligence
   • KM as the foundation for Artificial Intelligence – or Artificial Intelligence as enabler for KM, or both directions?
   • How should we integrate AI-based systems into KM initiatives as they possess increasing processing capabilities and degrees of agency?
   • How much knowledge is in digital representations of knowledge, such as knowledge graphs?
   • What is the role of human knowledge, competence and expertise in hybrid work systems including AI?
   • What is the role of wisdom in AI?
   • How does AI enable new forms of learning processes between humans?
   • How do training data sets for AI-based systems imply organizational biases and thus influence future learning processes?
3. The further development of new approaches and ideas currently emerging in KM
   • How can new approaches to KM, such as Responsible KM, be realized by means of concrete tools, techniques, and methods?
   • KM and Spirituality – what is their link? What role could Spiritual KM play in the future of KM?
   • What are epistemological alternatives to the prevailing paradigm of instrumental-calculative rationality, i.e., to the reliance on rational knowledge and thinking?
   • How to enable the knowledge flow of non-rational knowledge for individuals and/or in organizations?
   • What is the role of practical wisdom (i.e., phronesis) in managing organizations?
   • How can the realization of an organization’s purpose and KM be connected?
   • How can KM support topics such as Organizational Becoming or Organizational Self-Enactment?
   • What is the role of tacit knowledge and how can the use of tacit knowledge be further improved in organizations and at the individual level?
   • What is the role of unlearning in envisioning as well as adopting new KM practices?
4. The future role of KM in society
   • What role does/will KM play in future organizations? And (why) should organizations still invest in KM issues?
   • In what ways do new digital technologies change how people and organizations communicate and collaborate, and how does this change KM?
   • How and to what extent should we expand established KM frameworks to account for new digital technologies?
   • How can insights from KM research and practice enable remote work?
   • What role should KM play in an increasingly connected world and how to ensure that role is realized?
   • How can KM research tackle and contribute to solving the grand challenges of our times?
   • What role does KM play in the transformation towards sustainable business?
   • Business Ethics and KM – what can KM contribute to doing well by doing good?

This minitrack welcomes all types of contributions, both conceptual and empirical, using a variety of methods to provide new insights for the future of KM as well as futuring as an important part of KM. Therefore, papers in this minitrack can and should explicitly provide the basis for more speculative future-leaning conceptualizations of phenomena within the discipline of KM.

Minitrack Co-Chairs:

Alexander Kaiser (Primary Contact)
Vienna University of Economics and Business
alexander.kaiser@wu.ac.at

Ernst Wageneder
Vienna University of Economics and Business and Archdiocese Salzburg
Ernst.wageneder@eds.at

Florian Kragulj
Vienna University of Economics and Business
florian.kragulj@wu.ac.at

Game-Based Learning (GBL) is an evolving field that integrates game elements, mechanics, and principles into education and training to enhance students’ experience and learning outcomes. As digital learning landscapes rapidly expand, GBL continues to evolve, incorporating cutting-edge technologies such as artificial intelligence (AI), extended reality (XR), and adaptive learning systems. This minitrack explores the latest advancements in GBL and its intersections with emerging fields, fostering discussions on the future of learning through play, interactivity, and immersive experiences.

This minitrack invites interdisciplinary contributions that explore empirical research, theoretical advancements, design methodologies, and applications of GBL in diverse contexts. We encourage submissions addressing both foundational and forward-thinking aspects of GBL, including but not limited to:

1. Artificial Intelligence & Learning Analytics: AI-driven adaptive learning in GBL, predictive analytics, dynamic game-mechanics adjustments based on learner data, and ethical considerations in AI-driven game-based learning.
2. Computational & Technological Developments: Procedural content generation, AI-powered NPCs for dynamic learning interactions, multi-agent simulations, blockchain applications in GBL, and cloud-based GBL platforms.
3. Cultural & Social Dimensions: Socio-cultural perspectives on GBL, inclusive design, accessibility, diversity in game narratives, gamification of social learning, and ethical concerns in game-based educational interventions.
4. Ethical & Societal Implications: Addressing privacy, bias, fairness, and the responsible use of gamification and game-based approaches in education and training.
5. Health & Wellbeing: GBL for mental health, physical rehabilitation, behavior change interventions, mindfulness training, and exergaming applications.
6. Human Factors & Learner Experience: Studies on user engagement, motivation, cognitive load, player typologies, adaptive learning pathways, and personalized learning experiences in GBL.
7. Immersive & Emerging Technologies: Applications of XR (VR/AR/MR), the metaverse, spatial computing, and haptic technologies in GBL; implications of generative AI in content creation and adaptive learning.
8. Pedagogical Innovation: Integration of GBL with instructional strategies, intelligent tutoring systems, competency-based education, and immersive storytelling for deeper learning.

This minitrack welcomes diverse research methodologies, including experimental studies, longitudinal research, mixed-methods approaches, case studies, design-based research, and systematic literature reviews. We seek to push the boundaries of GBL and explore how emerging technologies, interdisciplinary frameworks, and novel pedagogical models can shape the future of learning through games.

The Game-based learning minitrack is part of the Gamification Publication Track aimed at persistent development of gamification research. Authors of accepted papers have the option to fast-track extended versions of their HICSS papers to Smart Learning Environments.

Minitrack Co-Chairs:

Wilk Oliveira (Primary Contact)
Tampere University
wilk.oliveira@tuni.fi

Samuli Laato
University of Turku
samuli.laato@utu.fi

Juho Hamari
Tampere University
juho.hamari@tuni.fi

The higher education sector must constantly evolve to keep up with technological advances. In particular, the rapid deployment of new tools based on Gen AI has reawakened the challenge of adopting new tools. Investigating and understanding the implications of Gen AI in higher education is critical, as well as exploring how to adapt the educational environment to ensure that the next generation of students can benefit from Gen AI while limiting its negative consequences.

Our minitrack includes, but is not limited to, a discussion of the experience and consequences of using Gen AI in curriculum and course implementation and its impact on institutions, instructors, and students. Another important aspect concerns formulating new proposals to create a pathway for standard regulation of disruptive technologies such as Gen AI. Examples of potential topics are:

1. The institutional levels:
   • Case studies on Gen AI policy and practice within and across institutions
   • Innovations in Gen AI from higher education institutions
   • AI literacy and digital skills
2. The program/curriculum level:
   • Gen AI’s effect on assessment and accreditation
   • Implementing Gen AI into a college curriculum across disciplines
3. The course level focus:
   • Using Gen AI in classrooms, assignments, and assessments.
   • Different disciplines (Art, Mathematics, Computer Science, etc.) and Gen AI
   • Learning process and outcomes and Gen AI
4. The instructor-level focus:
   • Integrating Gen AI in the classroom activities and assignments
   • Tech skills and Gen AI
   • Pedagogy and Gen AI
5. Student-level focus:
   • Use of Gen AI and ethics
   • Case studies on student behavior with Gen AI
   • The expectations of Gen AI use in college classroom
6. Multiple stakeholders’ perspectives
   • Ethical use of Gen AI in higher education
   • Inclusivity and equality in the context of Gen AI in higher education
   • Gen AI in shaping learning and teaching
   • Aligning interests among multiple stakeholders in the use of Gen AI (Industry/Employers, University Staff, Students)

Minitrack Co-Chairs:

Minna Rollins (Primary Contact)
University of West Georgia
mrollins@westga.edu

Xin Zhao
University of Manchester
skye.zhao@manchester.ac.uk

Marco Carratù
University of Salerno
mcarratu@unisa.it

Irida Shallari
Mid Sweden University
irida.shallari@miun.se

This minitrack investigates the current state of research in measuring knowledge systems impact and knowledge-based initiatives. As artificial Intelligence (AI) and associated systems are designed to augment and replace human actors, it is becoming increasingly important to identify and explore how knowledge artifacts contribute to the successful implementations of these systems as well as how such systems impact both knowledge-based initiatives and organizational performance.

This minitrack encourages paper submissions from researchers and practitioners exploring value, performance and success measurement aspects of knowledge and AI systems. Topics of interest include but are not limited to:

1. Impact of knowledge management strategy, organization, systems, culture, and other issues on knowledge management success
2. Frameworks and models for assessing knowledge management systems
3. The role of knowledge artifacts in AI and augmented reality systems
4. The role of knowledge applications for digital transformation
5. Methodologies and processes for measuring knowledge and AI system success and performance
6. Organizational effectiveness/efficiency due to knowledge management/organizational memory/organizational learning, knowledge and organizational memory use
7. The development of metrics and key performance indicators for evaluating knowledge systems
8. Benchmarking of knowledge-based initiatives
9. Case studies of knowledge and AI system success and performance measurements
10. Measuring knowledge management performance in global organizations and globally dispersed communities
11. Effectiveness and/or efficiency of knowledge and AI systems
12. Modeling and measuring the impact of social software on knowledge management performance
13. Anecdotes and user stories and their theoretical basis to facilitate the value of knowledge-based initiatives
14. Developing grounded theory approaches to valuing knowledge-based initiatives
15. Understanding knowledge-based initiatives’ activities and output as service offerings and exploring their productivity
16. Usage, adoption and success of knowledge management methods

Minitrack Co-Chairs:

Stefan Smolnik (Primary Contact)
University of Hagen
Stefan.Smolnik@FernUni-Hagen.de

David Croasdell
University of Nevada, Reno
davec@unr.edu

Murray Jennex
West Texas A&M University
mjennex@wtamu.edu

This minitrack aims to explore the complex journey from innovation to market success. This minitrack emphasizes the critical role of information systems and knowledge management in aiding entrepreneurs and promoting innovation. By examining theoretical foundations and practical applications, we seek to understand how information collection, interpretation, and management advancements can drive innovation and entrepreneurial success.

This minitrack invites research integrating theoretical insights with empirical evidence, highlighting the impact of emerging technologies such as sensors, IoT, decision systems, deep learning, and generative artificial intelligence. Additionally, we welcome studies focused on the education of entrepreneurship students and practitioners, emphasizing the importance of knowledge management and information system technology in their training, and on innovation and entrepreneurship ecosystems and policy. Topics relevant to the minitrack include the following:

1. Knowledge creation and management in innovation and entrepreneurship
2. Information systems and knowledge management in:
   • Digital entrepreneurship
   • Intrapreneurship (corporate entrepreneurship)
   • Research translation (academia)
   • Social entrepreneurship
3. Knowledge management and ideation, opportunity discovery, and design thinking
4. Artificial intelligence in innovation and entrepreneurship: technology and policy
5. Knowledge management and innovation in entrepreneurial ecosystems
6. Open, collaborative, and visualization systems in entrepreneurship
7. Digital entrepreneurship: digital products, services, tools and business models
8. Successes and failures: cases and lessons learned
9. Innovation and entrepreneurship education in the classroom and the field
10. Incubators, accelerators, and maker spaces as hubs for knowledge creation
11. Regulating the risks of innovation and entrepreneurship
12. Emerging trends:
    • Sustainability in innovation and entrepreneurship
    • The role of data analytics in entrepreneurial decision-making
    • Generative AI and the process of invention

This minitrack offers fast track opportunities in the Journal of Small Business Management and in the Journal of the International Council for Small Business.

Minitrack Co-Chairs:

Sadan Kulturel-Konak (Primary Contact)
Pennsylvania State University – Berks
sxk70@psu.edu

Cesar Bandera
New Jersey Institute of Technology
cesar.bandera@njit.edu

Katia Passerini
Seton Hall University
Katia.Passerini@shu.edu

Michael Bartolacci
Pennsylvania State University – Berks
mrb24@psu.edu

We bring together academicians, researcher, practitioners, and scientists who work in the areas of intelligent agents and its applications in academia, industry and the real world. This minitrack intends to provide an opportunity for researchers to meet and discuss latest discoveries, solutions, results, and methods in design and architecture in intelligent agents and its real-world applications.

We invite papers that explores the transformative role of intelligent agents, particularly chatbots, in enhancing human-computer interaction. Submissions are especially invited for insights into the latest advancements in natural language processing, machine learning, and AI that enable chatbots to perform complex tasks and deliver personalized experiences for training and learning.

Special focus may be on practical applications across industries, successful integration strategies, and the ethical considerations in deploying these technologies. Case studies are also invited to discuss the harnessing of the potential of intelligent agents to improve learner engagement, streamline operations, and foster innovative education solutions.

Minitrack Chair:

Sang Suh
Texas A&M University-Commerce
Sang.Suh@tamuc.edu

This minitrack examines the nature and role of knowledge flows across people, organizations, places and times from technical, managerial, behavioral, organizational, and economic perspectives. As the nature of knowledge flows changes due to digitalization, consumerization of information technology (IT), and the integration of artificial agents into daily routines, it is increasingly important to understand the changes required in how knowledge workers conduct work, share knowledge and information, and learn. Knowledge management (KM) activities in organizations are no longer supported only by traditional information and communications technologies (ICTs; e.g., databases, data warehouses, information repositories, websites, email streams), but are also enabled through new forms of ICTs including artificial intelligence (AI; e.g., agents, robotic process automation bots, learning algorithms), social software, Web 4.0 technologies and Internet of Things (IoT). The ubiquitous and pervasive nature of these new forms of ICTs are creating flexible KM sharing environments that need to be researched more systematically.

Technical, managerial, behavioral, organizational and economic perspectives on knowledge flows will be accepted and presented in this minitrack, and both qualitative and quantitative research methods are welcome. Potential topics that this minitrack will address include:

1. Technical, managerial, behavioral, organizational and economic challenges and perspectives on knowledge flows
2. The effects on knowledge flows of the consumerization of IT (CoIT)
3. Internet of things (IoT)
4. Social media, social computing, social networks and communities, communities of practice (CoPs); information and computer technologies (ICTs)
5. Knowledge reuse; organizations
6. Artificial intelligence (AI), machine learning and robotics, Robotic Process Automation (RPA)
7. Neuroscience, brain-computer interfaces, artificial humans and other computer-based entities
8. Knowledge system analysis, design, test, evaluation, implementation, maintenance and redesign
9. Harnessing, analyzing, visualizing and measuring knowledge flows for creativity, innovation, competitive advantage

Minitrack Co-Chairs:

Clare Morton (Primary Contact)
Naval Information Warfare Center Pacific
Clare.e.morton.civ@us.navy.mil

Paul Shigley
Naval Information Warfare Center Pacific
Paul.r.shigley.civ@us.navy.mil

Mika Yasouka
Roskilde University
mikaj@ruc.dk

The integration of advanced artificial intelligence (AI) into research offers compelling advantages. Either independently or in conjunction with human researchers, it has the potential to enhance scientific productivity and improve objectivity. AI is already being adopted by some researchers. For example, academics use AI to conduct literature reviews during the study design, gather synthetic data generated by AI in the data collection phase, or identify complex relationships with AI during data analysis. AI based conversational agents or virtual avatars might also support researchers in generating new ideas or guide them. Besides the compelling advantages AI brings the risks of epistemic fallacies and also raises ethical concerns.

This minitrack is open to researchers who aim to explore new AI applications in research, guide the ethical use of AI in research, or offer theoretical insights into the impact of AI on research. The focus is on both methodological, theoretical or applied contributions. Topics of interest include (but are not limited to):

1. (Generative) AI applications in research
2. Human-AI collaboration in research
3. Application of virtual avatars in academia
4. Reevaluating scientific philosophies for AI driven research
5. Ethical guidance for AI use in research
6. Guidance to avoid epistemic risks of AI

Minitrack Co-Chairs:

Stefan Stieglitz (Primary Contact)
University of Potsdam
stefan.stieglitz@uni-potsdam.de

Shahper Richter
University of Auckland
shahper.richter@auckland.ac.nz

Alexander Richter
Victoria University of Wellington
alex.richter@vuw.ac.nz

Till Schirrmeister
University of Potsdam
till.schirrmeister@uni-potsdam.de

In a world that is exposed to constant crises and adversities and is increasingly confronted with incorrect or incomplete knowledge, responsible management and use of knowledge (rKM) is of utmost importance for organizational (and individual) success. The concept of resilience offers an interesting framework to guide organizations in their challenging efforts to implement responsible KM (rKM) in their organizations, an approach that is based on inclusion, respect, appreciation, and cooperation. This track focuses on the role and importance of resilience and its underlying notions, directions and perceptions for rKM.

This minitrack welcomes all types of contributions – theoretical, conceptual and empirical – that use a variety of methods and methodologies as well as different perspectives and worldviews to present new thoughts and ideas that make responsible KM realizable framed around a resilience perspective. Topics of interest include, but are not limited to, the following:

1. Strategies for implementing rKM in different types of organizations
2. Factors influencing the implementation of rKM
3. Training for rKM
4. Resilience capabilities and processes for rKM implementation
5. Demonstration of resilience framed rKM
6. Risks related to rKM
7. Dark sides of resilience and its consequences

Authors of selected high quality papers will be encouraged to submit their papers for the regular issue after thorough revision and improvement according to the requirements and guidelines of The Bottom Line. The papers will undergo the traditional double-anonymous peer review process. The Bottom Line journal does not offer a fast track.

Minitrack Co-Chairs:

Susanne Durst (Primary Contact)
LUT University and Reykjavik University
susanned@ru.is

Aino Kianto
LUT University
Aino.Kianto@lut.fi

Ilona Toth
LUT University
Ilona.Toth@lut.fi

Maral Amanova
LUT University
Maral.Amanova@lut.fi

This minitrack recognizes the multifaceted nature of artificial intelligence and advocates for a holistic understanding of its impact. We welcome submissions that examine cutting-edge AI methodologies or analyze their societal, economic, and ethical consequences, including workforce displacement, algorithmic bias, and the responsible development and deployment of AI systems.

Our goal is to foster interdisciplinary dialogue and encourage research that bridges the gap between technical advancements and real-world impact, ultimately promoting a more nuanced and responsible approach to AI development and governance. We welcome researchers from all disciplines, ranging from the hard sciences, such as engineering and computer science, to social sciences, economics, finance, management, and beyond. Our scope of interest spans a wide range of topics, including, but not limited to:

1. AI governance
2. AI economics
3. AI agents
4. LLM architectures
5. AI and cognitive science
6. AI psychometrics
7. Scalability and efficiency of AI
8. AI-human interaction
9. Accountability of AI
10. Explainable AI
11. Evaluation of AI and LLMs
12. AI security
13. Decentralized AI
14. Ethical issues of AI
15. Trust in AI
16. Users’ fear of AI
17. AI bias
18. Responsible development of AI

Minitrack Co-Chairs:

Yibai Li (Primary Contact)
University of Scranton
yibai.li@scranton.edu

Zhenghui Sha
University of Texas at Austin
zsha@austin.utexas.edu

Zhiye Jin
Marywood University and millionminds.co
zjin@m.marywood.edu