Knowledge Innovation and Entrepreneurial Systems Track

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, teachers, school administrators, policymakers, and other actors involved with CE:

• 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.
• Educational science: e.g., educational theories behind CE and their application
• Pedagogy: pedagogical aspects (e.g., collaborative learning, blended learning, cognitive processes, intellectual skills, edutainment, and others) in CE
• Learning analytics: e.g., tools for measuring skills behind computing, adaptivity and personalization in CE
• Teaching strategies: e.g., unplugged computing, robotics, visual languages, innovative didactic materials/techniques, new courses, metacognition, etc
• Theories/concepts/methods: e.g., contributions to the science of CE
• 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
• Computational Thinking: e.g., general aspects of computational thinking
• 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

Entrepreneurial activities are increasingly organized through digital systems that extend beyond the boundaries of individual firms and centralized platforms. Advances in emerging technologies such as artificial intelligence, blockchain, and Web3 are enabling decentralized digital architectures, including platforms, protocols, and shared infrastructures, that support startup crowdfunding (e.g., initial coin offerings), entrepreneurial coordination (i.e., Decentralized Autonomous Organizations, Decentralized AI marketplaces), knowledge exchange (e.g., Decentralized science), and value creation (e.g., Uniswap, NFT creator economies) across distributed actors and organizations. These developments reflect a broader shift toward ecosystem-based entrepreneurship shaped not only by individual entrepreneurs or firms, but also by the architectural design of digital systems that enable friction-free interactions among diverse participants.

This minitrack focuses on decentralized digital architectures and entrepreneurial ecosystems as a parallel organizational form to centralized, platform-mediated ecosystems. While prior research has extensively examined entrepreneurial activity within centralized organizational and platform contexts, decentralized entrepreneurial ecosystems – characterized by distributed control, non-hierarchical coordination, and system-level governance embedded in digital architectures – remained underexplored. a deeper understanding requires analytical attention to the design, structure, and evolution of digital architectures that enable entrepreneurial activities across ecosystem boundaries.

A central interest of this minitrack is how architectural features of digital systems shape knowledge creation and transfer, coordination mechanisms, and ecosystem dynamics in decentralized entrepreneurial contexts. Digital architectures may embed coordination and governance mechanisms through protocols, standards, algorithms, and infrastructural constraints, thereby structuring entrepreneurial interactions and knowledge sharing without relying on centralized managerial authority. Such architectural choices influence how distributed actors collaborate and learn; how entrepreneurial ventures interoperate within ecosystems; and how innovation is sustained under conditions of uncertainty, remote collaboration, and rapid technological change. Relevant contributions may explore AI-enabled platforms as shared entrepreneurial infrastructures, blockchain- and Web3-based systems for decentralized coordination and value creation, modular architectures that support or constrain ecosystem scaling, and comparative analyses of centralized versus decentralized ecosystem forms. Integrating insights from information systems, knowledge management, entrepreneurship, and innovation research, this minitrack aims to deepen understanding of how decentralized digital architectures function as critical infrastructures for knowledge creation, coordination, and innovation in entrepreneurial ecosystems.

This minitrack welcomes a broad range of methodological approaches, including conceptual and theoretical work, analytical and computational models, empirical studies, case-based research, and design-oriented investigations. Topics of interest include, but are not limited to:

• Knowledge creation, storage, and transfer mechanisms in decentralized entrepreneurial ecosystems
• Architectural modularity and its effects on ecosystem collaboration, interoperability, and knowledge flow
• Protocol-level governance and algorithmic coordination in distributed entrepreneurial networks
• Token-based incentive structures and their influence on ecosystem participation and value distribution
• Smart contracts and DAOs as governance mechanisms for distributed collaboration and organizational learning
• Trust formation, reputation systems, and social identity in decentralized entrepreneurial communities
• AI agents and autonomous systems operating within decentralized entrepreneurial infrastructures
• Decentralized AI platforms and marketplaces as shared knowledge infrastructures for entrepreneurship 
• Scaling dynamics and network effects in decentralized versus centralized ecosystem architectures
• Hybrid architectures balancing centralized coordination with decentralized knowledge exchange
• Innovation diffusion, organizational learning, and continuous improvement in protocol-based ecosystems 
• Information quality and verification challenges in decentralized knowledge systems
• Decision-making processes—human, augmented, and automated—in decentralized entrepreneurial contexts 
• Societal implications of decentralized ecosystems including access, equity, and resource allocation 
• Cross-ecosystem entrepreneurial activity and venture emergence enabled by decentralized architectures

This minitrack is intended for researchers, scholars, and practitioners interested in the intersection of digital architecture, decentralized systems, and entrepreneurship. We welcome contributions from information systems, entrepreneurship, innovation management, organizational studies, and related disciplines. The minitrack will appeal to those studying blockchain and Web3 ecosystems, platform and protocol economics, distributed governance, and knowledge management in networked contexts. We also encourage submissions from researchers examining emerging phenomena such as DAOs, token economies, decentralized AI systems, and open-source entrepreneurial communities. Practitioners and policymakers engaged in designing, governing, or regulating decentralized infrastructures will find the minitrack relevant to understanding how architectural choices shape entrepreneurial outcomes and ecosystem evolution.

Minitrack Co-Chairs:

Ziyi Xiong (Primary Contact)
Kennesaw State University
zxiong@kennesaw.edu

Rong Liu
Florida International University
roliu@fiu.edu

Soo Il Shin
Kennesaw State
University sshin12@kennesaw.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 platform, 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 of interest include (but are not limited to):

• Futuring as (future) core topic of KM?
  1. How can futuring and/or design methodologies be integrated as a component of Knowledge Management (systems) to enhance organizational strategic innovation and resilience?
  2. What role can the different approaches of Learning from the Future play in Knowledge Management?
  3. What are the critical success factors and barriers for incorporating futuring and/or design techniques within traditional Knowledge Management frameworks?
  4. How can the synergy between futuring and Knowledge Management drive innovation, and which metrics can best capture its impact on organizational performance?
  5. What cultural and structural capabilities are necessary to embed futuring as a core element in Knowledge Management systems?
  6. 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?
• The future role of KM in Artificial Intelligence
  1. KM as the foundation for Artificial Intelligence – or Artificial Intelligence as enabler for KM, or both directions?
  2. How should we integrate AI-based systems into KM initiatives as they possess increasing processing capabilities and degrees of agency?
  3. What is the role of human knowledge, competence and expertise in hybrid work systems including AI?
  4. What is the role of wisdom in AI?
  5. How does AI enable new forms of learning processes between humans?
  6. How do training data sets for AI-based systems imply organizational biases and thus influence future learning processes?
• The further development of new approaches and ideas currently emerging in KM
  1. How can new approaches to KM, such as Responsible KM, be realized by means of concrete tools, techniques, methods?
  2. KM and Spirituality – what is their link? What role could Spiritual KM play in the future of KM?
  3. How to enable the knowledge flow of non-rational knowledge for individuals and/or in organizations?
  4. What is the role of practical wisdom (phronesis) in managing organizations?
  5. How can the realization of an organization’s purpose and KM be connected?
  6. How can KM support topics such as Organizational Becoming or Organizational Self-Enactment?
  7. 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?
• The future role of KM in society
  1. What role does/will KM play in future organizations? And (why) should organizations still invest in KM issues?
  2. In what ways do new digital technologies change how people and organizations communicate and collaborate, and how does this change KM?
  3. How and to what extent should we expand established KM frameworks to account for new digital technologies?
  4. Business Ethics and KM – what can KM contribute to doing well by doing good?

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
Ernst.wageneder@eds.at

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

This minitrack aims to present research on how organizations truly utilize IT advancements to foster growth in revenue, market share, customer satisfaction, and many other metrics utilized for measuring success. In particular, the minitrack welcomes submissions that display novel technologies and protocols for fostering innovation from both the top down and bottom up in an organization. In order words, the minitrack welcomes work that focuses on every layer of the managerial structure of a company in terms of applying IT to improve efficiency and promote positive change. Included in this scope is the education of employees and students on the application of IT to bring about innovation in an organization. Topics relevant to the minitrack include the following:

• Knowledge creation and management in innovation and entrepreneurship
• Information systems and knowledge management in:
  1. Digital entrepreneurship
  2. Intrapreneurship (corporate entrepreneurship)
  3. Research translation (academia)
  4. Social entrepreneurship
• Knowledge management and ideation, opportunity discovery, and design thinking
• Artificial intelligence in innovation and entrepreneurship: technology and policy
• Knowledge management and innovation in entrepreneurial ecosystems
• Open, collaborative, and visualization systems in entrepreneurship
• Digital entrepreneurship: digital products, services, tools and business models
• Success and failure cases and lessons learned
• Innovation and entrepreneurship education in the classroom and the field
• Incubators, accelerators, and maker spaces as hubs for knowledge creation
• Regulating the risks of innovation and entrepreneurship
• Sustainability in innovation and entrepreneurship
• Emerging trends

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:

Michael Bartolacci (Primary Contact)
Pennsylvania State University – Berks
mrb24@psu.edu

Sadan Kulturel-Konak
Pennsylvania State University – Berks
sxk70@psu.edu

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

Katia Passerini
Gonzaga University
pkatia@gmail.com

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.

Minitrack Co-Chairs:

Paul Shigley (Primary Contact)
Naval Information Warfare Center Pacific
paul.r.shigley.civ@us.navy.mil

Clare Morton
ServiceNow Inc
clare.e.morton@gmail.com

Jon Brewster
Naval Information Warfare Center Pacific
jon.m.brewster.civ@us.navy.mil

Mika Yasuoka
Roskilde University
mikaj@ruc.dk

This minitrack focuses on neuro-symbolic (NeSy) AI and its impact on knowledge management with GenAI technologies for defining, extracting and disseminating knowledge across diverse, complex and knowledge-intensive domains. The NeSy paradigm is a rather new initiative which addresses the widening gap between learning technologies, accompanied by Generative AI, and traditional symbolic computing, enhanced with human like reasoning. Initially aimed at resolving the problem of the accuracy of and trust in current AI technologies, the NeSy paradigm has now become a new AI pathway, with a potential positive impact on knowledge manipulation and content generation. It has shown itself to be effective in addressing shortcomings of learning technologies and symbolic computing encompassed by current AI.

We would like to invite research and position papers which address excerpts of the complex problem of accommodating symbolic computing with logic reasoning and predictive inference within NeSy AI, applicable to knowledge management. The topics of interest are listed below.

• Key aspects of NeSyAI and knowledge manipulation
  1. Neurosymbolic, subsymbolic, and symbolic AI for knowledge management
  2. Overcoming limitations of LLM: Retrieval-Augmented Generation (RAG) for knowledge discoveries
  3. Automating knowledge graph (KG) creation across domain knowledge
  4. Knowledge Graph Foundation Models (KGFMs)
  5. Semantically enriched KG with textural relational graphs
  6. Neurosymbolic RAG: Symbolic reasoning over KG
  7. Updating KG ontologies and taxonomies using predictive inference
  8. Structuring knowledge and verified facts for addressing shortcomings of statistical AI and its interpretability
  9. Logic reasoning and symbolic knowledge for enabling learning from fewer examples and thus reducing computational complexities of NeSy AI
  10. Validating knowledge created by GenAI tools with logic reasoning rules
  11. Improving knowledge extraction across diverse, knowledge-intensive domains
• Ontologies for NeSy AI
  1. NeSy AI ready ontologies: static vocabularies versus computational reasoning
  2. Learning ontologies: GenAI knowledge discoveries for restructuring and populating ontologies
  3. Predicate logic and logic programming for NeSy AI
  4. Semantic prompting in NeSy question/answer systems versus ontology competency questions
  5. Deriving semantic prompts for knowledge extraction from logic reasoning upon ontological concepts
  6. Impact of ontology competency questions, generated by ChatGPT, on knowledge management and discoveries.
  7. Reasoning with first order logic and symbolic rules upon ontological concepts for creating more interpretable and traceable AI generated content
  8. Ontological triplets for knowledge intensive NLP tasks: injecting symbolic reasoning within LLMs.
• Knowledge-intensive content generation with NeSy AI
  1. Knowledge intensive and knowledge-based question/answer system
  2. Knowledge generation versus responses in question/answer system
  3. Semantic searches and knowledge representations with NeSy AI
  4. Knowledge-powered AI/GenAI and conversational agents
  5. Logic reasoning and knowledge based semantic for explaining agentic AI behaviour
  6. LLM based versus ontology and taxonomy aware knowledge generation/extraction
  7. Examples of using ontological reasoning within non-symbolic computing for enhancing knowledge extractions
  8. Industry trends for accommodating NeSy AI
  9. NeSy AI enabled content/metadata/knowledge for enterprises

Minitrack Co-Chairs:

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

Trevor Bihl
Ohio University
bihlt@ohio.edu

Viktor Dorfler
Strathclyde University
viktor.dorfler@strath.ac.uk

As entrepreneurial systems continue to transform through digital technologies, Deep Tech entrepreneurship (including startups in industries like artificial Intelligence, robotics, space tech, quantum, semiconductors, and biotechnology) is gaining in importance, standing out as one of the most transformative forces shaping the next wave of innovation. Deep Tech, frequently driven by new ventures, redefines fundamental technological and interorganizational knowledge capabilities and increasingly serves as a critical source of competitive advantage for incumbents and other ecosystem actors seeking to remain innovative in a rapidly changing technological landscape. Hereby, Deep Tech substantially differs from conventional digital innovation due to its operation at the intersection of scientific discovery, technical feasibility, and cross-boundary knowledge creation, transfer, and appropriation in entrepreneurial systems. These features make collaboration with Deep Tech ventures and the knowledge systems and governance mechanisms that enable it fundamentally different from traditional corporate-startup partnerships.

As a result, while we already understand how established ecosystem actors can cooperate with ventures, we have little knowledge about how they create, translate, evaluate, and integrate science-based technologies under high uncertainty, how knowledge capabilities, boundary-spanning roles, and governance arrangements need to adapt to collaborate effectively with Deep Tech ventures, and how such collaborations reshape ecosystem innovation trajectories. Complementary research suggests that the interplay of self-organization and governance mechanisms is central to explaining how ecosystems coordinate under uncertainty.

With Deep Tech evolving from scientific research into scalable commercial applications, established ecosystem actors increasingly collaborate with ventures, research institutions, public agencies, and ecosystem intermediaries to explore, test, and commercialize such frontier technologies. These collaborations are reshaping the boundaries between research, entrepreneurship, and corporate innovation, creating new models for how knowledge is created, captured, transferred, and governed across organizational boundaries. Such boundary shifts can trigger renegotiations of resource control and coordination among ecosystem participants. However, existing research has primarily focused on organizational change in other technological contexts, such as digital transformation, and the organizational dynamics of Deep Tech adoption and collaboration remain largely underexplored. Further, studies on innovation ecosystems and corporate–startup collaboration emphasize the organizational and governance-related challenges faced in such collaborations, which are expected to further intensify in complex Deep Tech environments. Finally, research on technological innovation and capability development likewise highlights difficulties associated with emerging technologies characterized by high uncertainty and specialized knowledge. Taken together, this reveals substantial conceptual and methodological gaps in understanding how Deep Tech ventures and established ecosystem actors engage in knowledge-intensive collaboration.

To address these gaps, this minitrack welcomes research that examines these emerging research questions to enable research about how Deep Tech ventures innovate entrepreneurial ecosystems and drive ecosystem transformation through knowledge-intensive collaboration. We invite conceptual and empirical contributions that investigate the strategic (e.g., collaboration models, ecosystem orchestration and knowledge governance structures), organizational (e.g., knowledge capability development, boundary-spanning roles, structural and cultural adaptations), and technological (e.g., knowledge systems and digital infrastructures, technology transfer, scaling of science-based technologies) dimensions. Moreover, to close the aforementioned gap, we invite methodological work that applies novel research designs or tools to understand interactions between Deep Tech ventures, incumbents, research institutions, and other ecosystem actors. Research may address firms, inter-organizational networks, or broader innovation ecosystems, opening this track to both information systems and management science communities to reflect their increasing interdependence. Topics of interest include, but are not limited to:

• Emerging trends, definitions, and taxonomies of Deep Tech innovation 
• Corporate venturing and Deep Tech collaboration models for knowledge transfer 
• Ecosystem transformation through Deep Tech adoption 
• Governance, resource dependence and success factors of multi-actor collaborations
• Ecosystem Actor Capability and Attribute Developments
• Cultural and structural enablers of corporate–venture collaboration 
• Scaling technology transfer in hardware-intensive Deep Tech ecosystems 
• Business model innovation and value-chain transformation through science-based technologies 
• Innovation ecosystems and platform approaches for frontier technologies
• Metrics and performance assessment of Deep Tech initiatives 
• Regulatory, strategic, and implementation challenges in Deep Tech entrepreneurship
• Information systems perspectives on data, infrastructure, and governance for Deep Tech adoption

Minitrack Co-Chairs:

Isabell Welpe (Primary Contact)
Technical University of Munich
welpe@tum.de

Tung Bui
University of Hawaii at Manoa
tungb@hawaii.edu

Stan Karanasios
University of Queensland
s.karanasios@business.uq.edu.au

Jason Shaw
Nanyang Technological University Singapore
jdshaw@ntu.edu.sg