Introduction and Research Purpose

In the contemporary knowledge-based economy, innovation ecosystems are recognized as dynamic, multi-actor networks grounded in continuous interactions among universities, firms, supporting institutions (such as incubators, accelerators, and technology transfer offices), investors (including venture capitalists and angel investors), and policymakers. These ecosystems play a decisive role in value creation, technological development, and the enhancement of competitiveness at both regional and national levels. Within such ecosystems, knowledge is not merely a strategic resource but also the most critical factor shaping interactions, collective learning, and innovation processes. Indeed, the effective functioning of an ecosystem is virtually impossible without continuous and purposeful knowledge flows.Nevertheless, the majority of previous studies have primarily focused on knowledge flows, knowledge transfer, and knowledge management as static or linear processes. Far less attention has been devoted to the systematic identification and explanation of the diverse knowledge needs of different ecosystem actors—ranging from start-ups to mature firms, and from research laboratories to financial institutions. This knowledge gap has led to the design of many policies, support programs, and development initiatives for innovation ecosystems without a precise, comprehensive, and actor-centered understanding of stakeholders' knowledge needs. Consequently, these interventions have often failed to address the root causes of inefficiencies and fundamental knowledge gaps, thereby achieving limited effectiveness. In some cases, such policies have even exacerbated knowledge mismatches and the fragmentation of knowledge into isolated silos.Therefore, the present study aims to systematically identify and explain the knowledge needs within innovation ecosystems. By developing a comprehensive framework of various types of knowledge needs, this research seeks to provide the necessary foundation for strengthening synergies among actors, reducing knowledge gaps (including the gap between existing and required knowledge), improving collective learning flows, and ultimately enhancing the performance, resilience, and long-term sustainability of the ecosystem.

Research Methodology:

This study is fundamental-applied in terms of its objective, meaning it both contributes to the theoretical development of knowledge needs and offers results directly applicable to the design of policies and operational programs for innovation ecosystems. In terms of approach, the research adopts a qualitative, exploratory, and interpretive stance—an approach particularly suitable for investigating complex, context-dependent, and under-theorized phenomena such as knowledge needs. Data collection was conducted in two complementary stages. In the first stage, a systematic literature review was carried out to examine the theoretical and empirical foundations related to knowledge needs, innovation ecosystems, knowledge governance, and collective learning. This stage involved reviewing peer-reviewed articles, specialized books, policy reports, and conference proceedings from the past two decades. In the second stage, field data were collected through in-depth semi-structured interviews with 15 experts from the fields of science and technology parks and knowledge-based firms. Each interview lasted between 60 and 90 minutes. All interviews were recorded, transcribed, and then analyzed while maintaining confidentiality. Participants were selected using purposive sampling combined with a snowball strategy. Initially, individuals with specialized knowledge, professional experience (at least ten years of work within the ecosystem), and deep familiarity with the research topic were identified and invited to participate. Subsequently, other experts were selected through referrals from the initial participants, thereby facilitating access to key and knowledgeable actors. The sampling and interview process continued until theoretical saturation was achieved—that is, until no new concepts or themes emerged from the data, and further interviews produced only repetitive information. Data analysis was conducted using thematic analysis with an inductive approach, following the six-phase process proposed by Braun and Clarke. The analytical process involved initial coding, concept extraction, identification of sub-themes, and finally organization into main themes. To ensure the quality of the findings, the criteria of credibility (through member checking), dependability (via an audit trail of analytical decisions), confirmability (by bracketing the researcher's prior assumptions), and transferability (through thick descriptions of the research context) were all taken into account.

Findings:

Data analysis led to the identification of sixteen major categories of knowledge needs within innovation ecosystems. These needs include: (1) foundational and advanced technological knowledge (encompassing engineering principles, emerging technologies, and specialized technical skills); (2) managerial knowledge (including strategic planning, leadership, project management, and decision-making under uncertainty); (3) market and customer knowledge (innovation-oriented market knowledge, including market segmentation, customer discovery, go-to-market strategies, and demand forecasting); (4) legal and regulatory knowledge (including intellectual property rights, patent registration, regulatory compliance, and tax incentives); (5) networking and relational knowledge (including building and maintaining trust-based relationships, coalition formation, and collaborative governance); (6) policy and support-related knowledge (including understanding public procurement, innovation vouchers, and R&D subsidies); (7) investment and financial knowledge (including venture capital, financial modeling, valuation, exit strategies, and risk management); (8) institutional and structural knowledge (including understanding formal and informal rules, norms, and power structures within the ecosystem); (9) operational and implementation knowledge (including production processes, supply chain logistics, and day-to-day problem-solving); (10) digital literacy and competency knowledge (ranging from basic to advanced digital skills, cybersecurity, and cloud computing); (11) research and development knowledge (including basic and applied research methodologies and technology readiness levels); (12) cultural and social knowledge (including organizational culture, regional innovation culture, and social capital); (13) environmental and sustainability knowledge (including circular economy principles, ESG criteria, and green innovation); (14) international and global knowledge (including cross-border collaboration, global value chains, and geopolitical risk awareness); (15) artificial intelligence and machine learning knowledge (including data analytics, algorithm development, model training, and ethical AI implementation); and (16) education and human resource development knowledge (including talent acquisition, upskilling, reskilling, mentorship design, and lifelong learning systems). The results indicate that these knowledge needs function as an interconnected and multi-layered system in which the effectiveness of each category depends on its integration and synergy with other dimensions of knowledge. For example, technological knowledge without market knowledge leads to solutions in search of problems, while financial knowledge without legal knowledge can result in non-compliant investment deals. The findings clearly demonstrate that the success and sustainability of innovation ecosystems cannot be achieved through technological knowledge alone. Rather, they require a balanced combination of managerial, market, legal, institutional, financial, and networking knowledge that collectively facilitates the creation, sharing, and utilization of knowledge among all actors. Furthermore, continuous knowledge flows, network interactions, institutional trust, collective learning, and value co-creation emerged as key determinants of effective ecosystem functioning. The findings also suggest that a gap in any of these knowledge domains—for instance, poor digital literacy or insufficient international knowledge—can reduce innovation capacity, weaken cross-sectoral collaboration, and diminish ecosystem resilience.

Discussion and Conclusion:

The results of this study clearly indicate that a successful innovation ecosystem is not built upon the fragmented and siloed accumulation of knowledge and technology. Instead, it requires a coherent, multi-layered, and synergistic knowledge architecture. Within this architecture, technological knowledge serves as the backbone of the ecosystem; managerial, market, legal, and networking knowledge function as structural enablers and facilitators; and policy, financial, operational, digital, research, cultural, international, AI, and educational knowledge respectively play the roles of guiding, supporting, operationalizing, empowering, and regenerating ecosystem capacities. From a theoretical perspective, by moving beyond traditional approaches focused on "knowledge transfer" and "knowledge management" toward a "knowledge needs" framework, this study enriches the literature on knowledge governance and innovation ecosystem development. From a practical perspective, the proposed framework can serve as a diagnostic tool for policymakers, science and technology park managers, and business leaders to identify priority knowledge gaps and design targeted capacity-building programs. Accordingly, the failure of many innovation ecosystems cannot be attributed solely to an absolute lack of knowledge. Rather, the root cause lies in the absence of effective linkages among different types of knowledge. In other words, many ecosystems suffer from weak integration and knowledge silos, not from an absence of knowledge. From this perspective, the maturity of an innovation ecosystem depends on the integration and coordinated configuration of knowledge needs—a condition that elevates knowledge from a mere operational resource to a strategic logic for systemic capacity building. By providing a comprehensive, systematic, and empirically grounded framework of knowledge needs in innovation ecosystems, this study offers a foundation for designing intelligent policies, formulating knowledge-driven development strategies, reducing knowledge bottlenecks, and ultimately enhancing the competitiveness, resilience, and long-term sustainability of innovation ecosystems.