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Open research questions in University-Industry-Government Innovation Models

59 unresolved questions extracted from the limitations and future-work sections of 1,842 University-Industry-Government Innovation Models papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Laurence C. Espino1,*, Camille L. Espino2, Ronilo P. Antonio2, Jovita E. Villanueva2 and Lilibeth DG. Antonio3 1 College of Business Education and Accountancy, Bulacan State University, Philippines; [email protected] 2 College of Professional Teacher Education, Bulacan State University, Philippines; [email protected], [email protected], [email protected] 3 College of Information and Communications Technology, Bulacan State University, Philippines; [email protected] * Correspondence: Email: [email protected]. Academic Editor: Xuesong Zhai Abstract: This study examines the convergence of entrepreneurship and science, technology, engineering, and mathematics (STEM) education, addressing the growing need to equip learners with technical expertise and entrepreneurial competencies for innovation-driven economies. Specifically, it aims to map the intellectual structure, research trends, and emerging themes that shape this interdisciplinary field. A bibliometric research design was employed using 174 peer-reviewed journal articles retrieved from the Scopus database (2002–2024). The analysis integrated performance analysis and science mapping techniques, including bibliographic coupling and co-word analysis, to identify conceptual relationships and future research directions. VOSviewer and Biblioshiny were utilized for data visualization and network mapping. Results reveal a steady growth in publications and citations since 2015, reflecting increasing scholarly interest in integrating entrepreneurial learning with STEM education. Four thematic clusters were identified, highlighting socio-technical and sustainability-oriented teacher- and foundations, experiential curriculum competency-centered E-STEM models, and institutional mechanisms that shape entrepreneurial readiness in STEM education. Co-word analysis further emphasized engineering education, innovation, and entrepreneurship education as central research nodes, with emerging directions involving artificial intelligence (AI), sustainability, and digital transformation. The study highlights integration, 110 systems importance of education the interdisciplinary, and technology-driven pedagogies to foster entrepreneurial mindsets and sustainable innovation among STEM learners. Overall, this research contributes by offering a methodologically integrated bibliometric mapping of the convergence between entrepreneurship and STEM education, utilizing a 22-year dataset, multi-technique clustering, and an explicit query design.

    Convergence of entrepreneurship and STEM Education: Trends and perspectives · 2026 · DOI

  • Despite their recognised importance in innovation ecosystems, their po- tential contributions to sustainability-oriented innovation remain underexplored (Costa & Ma- tias, 2020). , 2021), its integration with environmental and social objectives remains insufficiently theorised (De Martino, 2021; Garcia et al.

    OPEN SUSTAINABLE INNOVATION IN PORT ECOSYSTEMS: A LITERATURE REVIEW · 2026 · DOI

  • While this study successfully establishes an 8-Factor XR Readiness Framework using Socio-Technical Grounded Theory (STGT), there are several limitations. First, while this study provides a robust, empirically grounded framework, it is delimited by the specific temporal context of the data collection. The findings reflect the state of XR hardware and the nascent stage of Generative AI integration at the time of the interviews. The rapid emergence of spatial computing hardware and the accelerating capability of AI to generate 3D assets may alter the weight of the “Technological Convergence” factor more quickly than the current framework predicts. Second, as a qualitative study rooted in Grounded Theory, the findings prioritize depth and causal explanation over statistical generalizability. Third, due to the context-specific nature and setting of this qualitative study, there is limited replicability. While the sample represents diverse geographic regions, specific national funding mandates or regulatory environments may influence the relative weight of economic or legal factors in local contexts. Furthermore, to address the complex interrelationships among the factors identified in this study, future research should move beyond simple taxonomies and instead undertake rigorous mapping to examine how different factors within a framework influence one another through feedback loops (e.g. causal loop diagram). This approach also makes complexity visible, facilitates communication, and improves decision-making, while helping to identify where institutional momentum in adopting emerging technologies is lost. Hafeth Hakami et al.

    A framework for university readiness in extended reality (XR): a socio-technical perspective · 2026 · DOI

  • The M3 Innovation Topology is proposed as a conceptual framework rather than as a fully validated empirical model. Its primary contribution is to clarify structural scale, locus, and cascading 15 dynamics across systems. This means that several important issues remain open for further theoretical development and empirical investigation. First, the framework requires further work on locus. The paper argues that locus matters because innovations introduced at structurally central points can produce disproportionate effects. Future research should examine what characteristics make a particular locus more generative. Candidate characteristics include centrality within a network, proximity to decision rights, connection to core workflows, control over interfaces, regulatory or infrastructural significance, symbolic salience, modular position, and the degree to which a locus governs multiple downstream dependencies. These characteristics may differ across technical, biological, organizational, social, sociotechnical, and conceptual systems. Second, future research should ask whether cascade potential can be predicted in advance. The present framework can retrospectively explain why some innovations remain local while others propagate, but it does not yet provide a validated predictive method. A future research program could develop indicators of cascade potential by examining the interaction between innovation characteristics, system characteristics, and locus characteristics. Such indicators might include modularity, compatibility with existing practices, dependency density, network reach, adoption friction, cost of reversal, observability of benefits, and the availability of supporting mini and micro innovations. Third, M3 should be examined in relation to Rogers' diffusion of innovations. Rogers' framework emphasizes perceived attributes such as relative advantage, compatibility, complexity, trialability, and observability, along with communication channels, time, and social systems. M3 does not replace this account, but it asks a different structural question. Rogers helps explain adoption. M3 helps explain whether adoption becomes structurally consequential. Future research could investigate whether Rogers' adoption variables interact with M3 variables such as scale, locus, and cascade potential. For example, an innovation may diffuse rapidly because it is observable and compatible, yet still remain micro if it does not alter system architecture. Conversely, an innovation with slower adoption may eventually generate a larger cascade if it occupies a high-leverage locus. Fourth, the rate of propagation or cascading remains underdeveloped. Cascade rate is likely shaped by both the nature of the innovation and the nature of the system. Innovation-level factors may include simplicity, modularity, compatibility, reversibility, visibility, cost, required learning, and the degree to which the innovation creates new affordances for further innovation. System-level factors may include centralization, network topology, professional norms, regulatory constraints, cultural readiness, path dependence, resource availability, and the maturity or instability of the system. Highly modular systems may allow faster propagation, while tightly coupled or heavily regulated systems may slow diffusion but amplify consequences once change occurs. Fifth, future work should examine status mobility across micro, mini, and macro categories. The framework already treats classification as relative to the focal system and structural impact, but further research should study when innovations change status over time. A micro innovation may become mini when repeated adoption, codification, or integration into workflows reorganizes 16 intermediate system architecture. A mini innovation may become macro when accumulated structural changes alter the governing paradigm or operating logic of the system. Conversely, macro initiatives may remain micro in effect when they fail to cascade into supporting structures and practices. Finally, the framework's relatively low value-prescriptiveness should itself be examined. M3 is less value-laden than frameworks that privilege disruption, radicality, transformation, growth, or competitive advantage. However, it is not value-free. Researchers and practitioners still decide which system to analyze, which effects matter, and whether particular cascades are desirable, harmful, ambiguous, or mixed. Future research should therefore distinguish between the structural analysis of innovation and the normative evaluation of innovation. A cascade may be powerful without being beneficial, and a micro innovation may be ethically preferable to a macro transformation depending on the telos of the system and the values used to evaluate change.

    The M3 Innovation Topology Scale, Locus, and Cascading Innovation Across Systems · 2026 · DOI

  • This study be acknowledged. has several limitations that should First, the empirical material is based on secondary qualitative analysis of focus group discussions originally conducted for a different research purpose. Although this approach enabled a theoretically informed re-interpretation of rich empirical data, in the original educationalization was not an explicit topic discussion design. Consequently, the analysis captures implicit than direct articulations of educationalization. logics rather Nevertheless, focus on academic the COMPAC project’s competence standards and career structures provided a discursively rich context in which educationalization logics naturally surface, as institutional expectations and performance criteria are central mechanisms through which educationalization operates. Second, the sample is unevenly distributed across Quintuple Helix domains, with a strong dominance of university-affiliated participants. While this reflects the institutional context of the original project, it may limit the extent to which findings can be generalized to non-academic sectors. In particular, the environmental domain is represented by only two participants (4.65%), the smallest subgroup across all Quintuple Helix domains. This restricts both the statistical power and interpretive scope of findings for this segment. For the chi-square analysis, expected values for predestination (1.67) and social welfare (0.61) in the environmental domain fall below the conventional threshold of 5, which may affect the reliability of distribution (8 meritocracy, 4 predestination, 0 social welfare references) should therefore be interpreted as indicative qualitative patterns rather than statistically robust generalizations. Future research should ensure more balanced representation, particularly through purposive recruitment of environmental sector experts. inferences.

    Rethinking educationalization in Latvia: social innovation and the quintuple helix in expert focus group discussions · 2026 · DOI

  • Abstract This paper explores the under-researched actors in Mission-Oriented Innovation Policy (MOIP) research, specifically the transformative roles of makerspaces in the MOIP implementation in response to the widespread application of digital technologies.

    Evolving scales and spaces of mission-oriented innovation policy in the digital age: digital transition of makerspace innovation in Shenzhen, China · 2025 · DOI

  • Additionally, there is insufficient information on how regions foster the involvement of the environment in fivefold helix cooperation and how this impacts the CE.

    Circular entrepreneurial ecosystems: a Quintuple Helix Model approach · 2024 · DOI

  • The originality of this study is inherent in the qualitative cases and contextualized influences in non-westernized countries that are empirically under-explored, as well as the five keys framework that is useful from a theoretical and practical standpoint for academics, policymakers, and university leadership.

    Establishing a nexus for effective university-industry collaborations in the MENA region: A multi-country comparative study · 2023 · DOI

  • But the status quo indicates identical ESG limitations due to the board of directors’ limited knowledge capacity, inconsistent and ununified ESG measurement and a lack of ESG information.

    Bibliometrics-based visualization analysis of knowledge-based economy and implications to environmental, social and governance (ESG) · 2022 · DOI

  • One key limitation of influential frameworks of RI is that they tend to neglect some key ethical issues raised by innovation, as well as major normative dimensions of the notion of responsibility.

    Synergies in Innovation: Lessons Learnt from Innovation Ethics for Responsible Innovation · 2020 · DOI

  • In these, other actors in the system should be investigated in thorough empirical studies, armed with tools from classic sociological systems theory that enhance the conceptual strength of the innovation systems framework and enable the acknowledgement of the role(s) and function(s) of several important organizational actors, not least research institutes.

    The third sector of R&D: literature review, basic analysis, and research agenda · 2017 · DOI

  • Yet, despite a recent rise in interest, there is limited knowledge about how and where innovation takes place in these two leading emerging countries and to what extent the Chinese and Indian territorial systems of innovation differ from those in the EU or the US.

    The Geography of Innovation in China and India · 2017 · DOI

  • The results demonstrate that innovation policy mixes in the BSR countries are not characterized by incoherence or overlapping, however, strong mutual reinforcement cannot be identified either.

    Innovation policy mix in a multi-level context: The case of the Baltic Sea Region countries · 2014 · DOI

  • It will point to a need to develop policy strategies in support of institutions that create and transfer knowledge on a European scale and outline open questions for the creation of the necessary institutional background for the creation and the support of knowledge and innovation networks at this level and for the conditions of its transferability to Objective 1 regions and the EU new member states and candidate countries.

    International knowledge and innovation networks for European integration, cohesion, and enlargement<sup>*</sup> · 2004 · DOI

  • The findings demonstrate that technical success alone is insufficient without alignment with commercial, social, and regulatory maturity at critical decision gates.

    Bridging Engineering and Management: A Macro-Conceptual Model for Eco-Innovation · 2026 · DOI

  • While that of learning regions possesses enough interesting elements to warrant further exploration, we did not detect the same degree of consensus over its essential components.

    Lost in knowledge and regional development terminology: literature review of knowledge-based concepts and their singularities · 2026 · DOI

  • The architecture of innovation ecosystems—the distribution of productive activities and the structure of exchanges that integrate outputs—varies widely, and it has major implications for how ecosystems create value and which participants capture value.

    Governance Structures and Coordination Trade-offs: A Discriminating Alignment Theory of Innovation Ecosystem Architectures · 2026 · DOI

  • Although Smart City transitions are typically assessed using technological and financial indicators, the underlying structural correlates remain insufficiently explored.

    Assessing the Impact of a Quintuple Helix Framework on Smart City Performance: A Country-Level Analysis of EU Capitals · 2026 · DOI

  • However, these projects are often based on student teamwork, an open challenge from real working life and a predefined intention to produce a concrete, novel, and innovative product, service, or new operating model into use.

    Discovering the effect metrics for innovation projects · 2022 · DOI

  • However, there is a lack of consensus concerning the applicability of network thinking to megaprojects and the status of network analytic methods with respect to the facilitation of megaproject management.

    Network Perspective in Megaproject Management: A Systematic Review · 2022 · DOI

  • In particular, the article draws on concepts such as ceremony, myth, and isomorphism and argues that such an institutionalist perspective can provide one of several fields of further research on the political economy of regional innovation policy.

    An institutionalist perspective on smart specialization: Towards a political economy of regional innovation policy · 2022 · DOI

  • Although the literature on accelerators, an important and newer model of entrepreneurial support, considers their performance and the definition of the form, little is known about how accelerators populate in a single ecosystem over time.

    Accelerator niches in an emerging entrepreneurial ecosystem: New York city · 2022 · DOI

  • Although currently deployed strategies frequently promote networks between innovators to diffuse educational innovations, little is known about the efficiency of these networks or whether they promote innovation diffusion.

    Promoting educational innovations and change through networks between higher education teachers · 2021 · DOI

  • Nevertheless, the existing literature is lacking in terms of studies into the spread of cooperative behaviors in infrastructure project innovations, on which project success is highly contingent.

    THE CASCADE EFFECT OF COLLABORATIVE INNOVATION IN INFRASTRUCTURE PROJECT NETWORKS · 2021 · DOI

  • I think Godin reads more coherence into [End Page 337] Ogburn’s thought than is warranted, but Godin’s interpretation is not unreasonable.

    Where do Models of Innovation Come From? Benoit Godin, Models of Innovation · 2020 · DOI

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59 open questions have been extracted from the limitations and future-work passages of 1,842 University-Industry-Government Innovation Models papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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