A conceptual review of the higher education system based on open innovation (OI) perspectives

In a global society, knowledge networks have become a core source of ideas and innovation. While the business sector has evolved rapidly through knowledge transfer generating innovation at process and human-resource levels, higher education has not kept pace. This paper underlines areas where higher education can apply business-like strategies to generate innovation and access open innovation (OI). Situated within the Triple Helix (Government–Industry–University) paradigm and the broader OI view—where ideas may be implemented by entities other than their originators—the study examines how industry–university cooperation and OI forms can create meaningful results for society. The aim is to present the evolution of the innovation concept from a higher education perspective and to propose a framework for academics aligned with needs for innovation, accessibility, and global integration.

The review traces innovation’s evolution from Schumpeter (1912) to contemporary frameworks. It distinguishes incremental, disruptive, and radical innovation (Fasnacht, 2018; Christensen and Bower, 1996), noting services’ central role in modern economies and the growing relevance of OI in services, including higher education. The paper outlines a paradigm shift from closed, R&D-centric innovation to OI focused on knowledge creation, networks, and value co-creation across value chains. It highlights disruptive trends in higher education driven by globalization, regulation, digitalization, demographics, connectivity, and evolving user expectations. Sarma’s (2022) “new educational institution” (NEI) proposes multidisciplinary, modular, online-first curricula, global partnerships, and industry experts’ involvement to address skill gaps and continuous reskilling needs. Emerging technologies such as AR/VR, online learning, and ICT integration (Al‑Ansi, 2022; Al‑Ansi and Fatmawati, 2023; Al‑Ansi et al., 2023) are identified as facilitators of learning and OI in education. The review also synthesizes ecosystem-wide impacts of innovation types (Table 2) and emphasizes the need to focus on service innovation pathways and knowledge networks for higher education.

The study employs a qualitative discourse analysis of scholarly work on open innovation and its application to higher education. An initial pool of 48 sources (primarily 2020–2023) was randomly selected based on keywords/titles (e.g., open innovation, higher education, knowledge transfer, knowledge networks, disruptive trends in education). From these, 21 relevant articles were retained after content screening. Sources were categorized using the Quadruple Helix (QH) map of innovation (Wardyn‑Runiewicz, 2022): market and social trends (society), managerial knowledge (companies), technological knowledge (industry), and scientific knowledge (universities/R&D). Frequency analysis indicated a balanced distribution across categories: market/social trends 19% (4), managerial knowledge 24% (5), technological knowledge 28.5% (6), scientific knowledge 28.5% (6). The research question asks which OI dimensions can be introduced into higher education to generate radical change and align universities with evolving industry and government perspectives. The method integrates historical and conceptual analysis (innovation, OI, knowledge networks) with pattern identification (strategic partnerships, OI ecosystem), culminating in a proposed higher education services life cycle framework.

• Innovation dimensions and models: The paper revisits the evolution from linear, closed models to systems integration and extensive networking, emphasizing knowledge flows and OI (Chesbrough, 2003; Rothwell’s models extended to a sixth OI-centric stage). Innovation is framed as collective, cross-functional, and network-driven. • Strategic partnerships: University–industry collaborations span formal (R&D partnerships, joint centers, clusters) and informal mechanisms (conferences, training, joint supervision/publications, facility sharing, spin-offs, IP transfer). Examples include industry-sponsored research (e.g., GlaxoSmithKline, British Nuclear Fuels at Cambridge; Rolls‑Royce University Technology Centres). Such interactions improve organizational performance and competitive advantage; HR mobility yields long-term benefits. • Policy and infrastructure: EU programs (e.g., Interreg Europe) promote regional development via technology/science parks, innovation centers, and smart specialization. Organizational forms vary (pure science vs. pure technology parks) with differing performance profiles (science parks excel in technology generation/patenting; technology parks in product innovation sales). Academic institutions are primary knowledge sources in such ecosystems. • Quadruple Helix (QH) and OI ecosystem: The TH model (university–industry–government) has evolved to QH by adding users/citizens. Living labs and user-centered processes embed iterative feedback (prepare > explore > understand > improve > implement). Universities act as hubs for knowledge creation/transfer and entrepreneurship, enabling spillovers and co-creation with companies and users. • Geography and knowledge flows: Proximity facilitates exchange and cluster growth, while digital communications enable long-distance collaboration. User involvement and accessibility are vital, requiring synergy between users and technology. • Human capital and EU snapshot: Human capital is central to OI; synergy between open science and OI supports commercialization. At EU level, the scientific R&D, professional, and technical activity sector accounts for ~9% of total employees and ~20% of companies, with notable cross-country differences. • Evidence synthesis balance: Frequency analysis confirms balanced representation across QH dimensions—market/social (19%), managerial (24%), technological (28.5%), and scientific (28.5%)—supporting a comprehensive OI perspective. • Proposed framework: A Higher Education Service Life Cycle model aligns service/process innovation with PLC phases—Introduction (service innovation radical; process incremental; competing designs), Growth (service incremental; process radical; dominant design diffusion), Maturity (both incremental), and Decline/Introduction (renewal via new/updated services)—guided by continuous market monitoring and R&D bridging between cycles.

Findings address the research question by showing how OI dimensions—networked knowledge flows, strategic partnerships, user engagement (QH), and policy-enabled infrastructures—can be embedded into higher education to catalyze radical change. Given the high idea-to-innovation ratio (estimated 2000:1), robust innovation processes and ecosystem orchestration are essential. Translating product life cycle logic to higher education services clarifies when to pursue radical vs. incremental innovation in both service design and delivery processes, enabling timely renewal and alignment with market and societal needs. The proposed life cycle framework operationalizes OI in higher education by integrating industry and government partners in curricula design, practical modules, and evaluative feedback loops, thereby enhancing relevance, scalability, and impact.

Higher education can no longer rely on a closed, vertically integrated model. Embracing open innovation—through purposive inflows and outflows of knowledge, partnerships with industry and government, and active user involvement—enables more relevant, accessible, and globally integrated services. The paper contributes a Higher Education Service Life Cycle framework that sequences radical and incremental innovations across introduction, growth, maturity, and renewal phases, with OI networks actively engaged throughout. This cyclical, feedback-driven approach helps institutions maintain alignment with evolving societal and market needs and sustain value creation over time. Future work should quantitatively assess the position and performance of higher education services globally within OI ecosystems to validate and refine the proposed framework.

The study is based on qualitative discourse analysis without primary data collection, relying on subjective interpretation of existing literature. The limited number of recent, directly relevant studies constrains generalizability. Future research should incorporate quantitative measures and primary data to test and extend the proposed framework.