Next Article in Journal
Systematic Bibliometric Analysis of Entrepreneurial Intention and Behavior Research
Previous Article in Journal
Unlocking a Pathway to Fashion Circularity: Insights into Fashion Rental Consumption and Business Practices
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Administrative Sciences
Volume 15
Issue 8
10.3390/admsci15080289
admsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Dual Policy–Market Orchestration: New R&D Institutions Bridging Innovation and Entrepreneurship

by
Yinhai Fang
1,2 and
Xinping Qiu
1,*
1
College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China
2
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Adm. Sci. 2025, 15(8), 289; https://doi.org/10.3390/admsci15080289
Submission received: 4 May 2025 / Revised: 14 July 2025 / Accepted: 21 July 2025 / Published: 24 July 2025
(This article belongs to the Section International Entrepreneurship)
Browse Figures
Review Reports Versions Notes

Abstract

This study investigates how new R&D institutions mediate policy–market disjunctures to foster integrated innovation and entrepreneurship ecosystems. Employing a longitudinal case analysis (2013–2023) of the Jiangsu Industrial Technology Research Institute (JITRI), we delineate a three-phase evolutionary process: (1) an initial government-dominated phase, stimulating foundational capability development through contract R&D; (2) a subsequent marketization phase, enabling systemic resource integration via co-creation centers and global networks; and (3) a culminating synergy phase, where policy–market alignment facilitates ecosystem optimization through crowdsourced R&D and cross-domain collaboration. Three core mechanisms underpin this adaptation: policy–market coupling (providing external momentum), endogenous capability development (absorption to innovation), and dynamic resource orchestration (acquisition to optimization). JITRI’s hybrid governance model demonstrates that stage-contingent interventions—specifically, policy anchoring in early stages followed by market-responsive resource allocation—effectively transmute inherent tensions into productive synergies. These findings yield implementable frameworks for structuring innovative ecosystems and underscore the necessity for comparative studies to establish broader theoretical generalizability.

1. Introduction

Amidst high-quality development, the deep integration of scientific and technological innovations with advanced factors (digitalization, intelligence, and green development) fosters new quality productive forces—an innovation-driven productivity paradigm emerging from technological breakthroughs, optimized factor allocation, and industrial transformation (Wang et al., 2024). The development of such productive forces depends on collaborative mechanisms formed by the symbiosis between innovation and entrepreneurship (Wang et al., 2024). Innovation, which is the foundation of high-quality entrepreneurship, stimulates latent market demand and generates business opportunities through technological breakthroughs and business model transformations. Entrepreneurship translates innovation into practical applications (Zhou & Wang, 2023), yet integration faces challenges from environmental volatility and policy–market tensions—a duality reflecting misalignment between state interventions (e.g., subsidies) and market mechanisms (e.g., venture capital) that creates institutional voids (Zhao, 2024).High uncertainty undermines entrepreneurs’ stable expectations and reduces their innovation motivation (Crnogaj & Rus, 2023).
To reconcile these constraints, extant research proposes phased policy calibration—leveraging technology-push instruments (e.g., public R&D funding) to mitigate early-stage uncertainties and demand-pull measures (e.g., procurement policies) to accelerate mature-phase diffusion—alongside hybrid governance models that dynamically integrate policy and market tools (Gamidullaeva et al., 2021; Laatsit et al., 2025). However, despite offering theoretical paradigms, the practical efficacy of phased calibration and hybrid governance remains constrained by three structural limitations: First, while open innovation ecosystems enhance resource integration efficiency through multi-actor collaboration (Moradi et al., 2024), fundamental policy–market institutional disconnect persists, causing systemic obstruction in technology translation. In China, this is evident in low university patent conversion, which remains below 30% (Shi et al., 2020)—revealing governance instruments’ ineffectiveness against deep-seated institutional fractures. Second, the contextual dependency of institutional innovation further undermines governance generalizability. Recent studies highlight regional divergence: policy experimentation (e.g., visa facilitation for cross-border collaboration) catalyzes innovation in certain Asian economies through government-led institutional void-filling (Liu, 2024), whereas enterprises in contexts like Nigeria rely on self-organization to address systemic institutional gaps (Andrews & Luiz, 2024). Such specificity fundamentally conflicts with new R&D institutions’ inherent need for standardized operational frameworks. Most critically, the absence of dynamic response mechanisms remains unaddressed. Prevailing frameworks over-rely on predefined stage-specific tools (e.g., Germany’s demand-side subsidies), neglecting the real-time remedial function of resource reconfiguration capabilities toward institutional voids (Lamberova, 2024; Rogge & Reichardt, 2016).
This exposes the core research gap: how can new R&D institutions dynamically bridge policy–market disconnect through resource orchestration actions? Addressing this imperative, we select Jiangsu Industrial Technology Research Institute (JITRI) as our empirical case to investigate:
RQ1: 
How do new R&D institutions deploy differentiated resource orchestration actions across developmental phases to bridge policy–market disconnect?
RQ2: 
How do such actions dynamically optimize innovation–entrepreneurship synergy to cultivate new quality productive forces?

2. Literature Review

2.1. Policy–Market Dual System Contexts

In China’s specific regulatory context, the intertwining of policy and market contexts, also termed policy–market duality, constitutes a key institutional context that influences innovation and entrepreneurship activities (Zhou & Wang, 2023). Policy–market duality is defined as the co-existing, interdependent, and dynamically interacting relationship between governmental policy interventions and market forces within a specific institutional context (Su et al., 2020; P. Zhang et al., 2023). In China’s evolving market economy, this duality manifests in the government-led market structure and the complex interplay between the fundamental orientation of competition policy and the market’s determining force (Zhao, 2024).
Policy context significantly shapes the quality and allocation efficiency of resources for innovation and entrepreneurship. Through instruments like R&D subsidies, tax incentives, and innovation funds, policy interventions directly steer innovation resource allocation while simultaneously cultivating indirect enablers—including infrastructure, knowledge diffusion, and human capital—for entrepreneurial ecosystems (Kern et al., 2019; P. Zhang et al., 2023; Y. Zhang, 2024). Governments deploy supply-side (e.g., R&D subsidies and innovation funds to reduce costs) (Nuñez-Jimenez et al., 2022), environmental (e.g., strengthened IP protection to incentivize early-stage innovation) (Lamberova, 2024), and demand-side policy tools (e.g., public procurement to create markets) (Yu et al., 2024) based on developmental phases of innovation. Empirical evidence quantifies this effect: non-funded firms exposed to demand-side innovation policies achieve 29% higher growth than supply-driven cohorts, validating the efficacy of dynamic policy calibration (Moradi et al., 2024).
Simultaneously, the market context is indispensable. The degree of marketization reflects the perfection of the market environment, including the soundness of market mechanisms and the development level of factor markets (Song et al., 2023). While a highly marketized environment can benefit new R&D institutions in promoting technological innovation and commercialization through venture capital and government subsidies, the market’s inherent profit-seeking nature poses challenges (Ao, 2024), Resources tend to converge towards large enterprises promising high short-term returns, often neglecting SMEs due to their longer R&D cycles and higher risks (Su et al., 2020). This underscores the need for market supervision and targeted government support. Research further indicates a nuanced relationship: moderate market openness enhances resource integration, but excessive openness (e.g., high market concentration) can trigger dispersion and path dependence (Laursen & Salter, 2006; Liu, 2024).While large firms in concentrated markets can leverage resources for technological advancement, they may also suffer from innovation inertia due to monopoly positions. Conversely, low concentration increases competition but may disperse resources, potentially affecting overall innovation efficiency (Kong et al., 2024).
Crucially, policy and market contexts do not operate in isolation. This interdependence is evidenced by X. Yin et al. (2023), whose research reveals that the innovation spirit of micro and small enterprises (MSEs)—defined as firms with fewer than 50 employees and annual turnover below EUR 10 million—drives R&D investment only in moderately regulated business environments. Their findings underscore that a delicate equilibrium between policy and market forces is essential to spur innovation, particularly among smaller actors. However, a critical gap remains: the mechanisms through which hybrid governance models (e.g., healthcare FDI reforms) transform policy–market tensions into coadaptive synergies—specifically by mediating resource allocation for new R&D institutions—are still unexplored (Liu, 2024).

2.2. Dynamic Capabilities and New R&D Institutions

Dynamic capabilities theory has evolved substantially since Collis (1995)’ foundational work on strategic routines for sustaining competitive advantage. Dynamic capabilities—defined as integrating/reconfiguring competencies to address change (Eisenhardt & Martin, 2000; Teece, 2007)—evolve toward ecosystem orchestration in innovation contexts (Teece, 2018). This framework crystallized into three core dimensions—sensing opportunities, seizing value pathways, and reconfiguring assets—which continue to shape scholarly discourse (Eisenhardt & Martin, 2000; Helfat & Peteraf, 2015). Recent refinements by Teece further emphasize meta-capabilities for ecosystem orchestration, particularly relevant to innovation-intensive contexts.
Critical theoretical tensions persist with alternative paradigms. Most notably, the resource-based view (RBV) (Barney, 1991) prioritizes static resource endowments as sources of competitive advantage, while dynamic capabilities foreground resource fluidity in volatile environments—a dichotomy acutely relevant for hybrid organizations operating in institutionally complex settings. Compounding this divide, empirical applications remain disproportionately centered on for-profit enterprises (Franco & Landini, 2022) or mature innovation ecosystems (Heaton et al., 2019), creating significant contextual blindspots. As Krishnan et al. (2022) demonstrate, “institutional voids fundamentally alter capability deployment logic.” However, scant research has explored how policy–market dualities reconfigure capability dynamics within R&D institutions.
Domain-specific advancements reveal nuanced mechanisms when dynamic capabilities operate within innovation systems. Absorptive capacity demonstrates enhanced efficacy when R&D team heterogeneity interacts with open innovation mediation, facilitating cross-disciplinary knowledge integration (Leskovec et al., 2025). Integrative capacity increasingly depends on workforce agility to enable boundary-spanning collaboration through relational trust (Franco & Landini, 2022), while transformative capacity counters environmental turbulence via strategic capital restructuring (Xiong et al., 2020). Despite these insights, four interconnected limitations hinder theoretical progress: First, prevailing frameworks presuppose market-driven institutional contexts (Teece, 2018), neglecting policy-dominated environments where entities like new R&D institutions operate. Second, capability studies impose linear developmental models (Helfat & Peteraf, 2015), overlooking nonlinear transitions across policy–market phases. Third, measurement methodologies rely excessively on output proxies such as patent metrics, inadequately capturing processual resource orchestration (Zahra et al., 2006). Finally, theoretical isolation persists between dynamic capabilities and institutional entrepreneurship scholarship, particularly regarding actor strategies in policy–market tensions (Hoogstraaten et al., 2020).

2.3. Resource Action for New R&D Institutions

Resource action theory, extending the resource-based view (RBV), posits that strategic resource management underpins competitive advantage through dynamic orchestration—the continuous reconfiguration of resources in volatile contexts (Zahra et al., 2006). This paradigm shifts focus from static resource endowments (Barney, 1991) toward adaptive processes, yet remains inadequately applied to institutional settings. Prevailing firm-centric models, exemplified by resource bricolage (Baker et al., 2003), dominate the literature but fail to address how hybrid institutions navigate policy–market disconnects. Concurrently, institutional entrepreneurship frameworks emphasize resource mobilization (Hoogstraaten et al., 2020) while overlooking processual sequencing across developmental phases.
Domain-specific research reveals contextual mechanisms: resource acquisition efficiency depends on transparent intellectual property governance (Moradi et al., 2024), integration aligns with alliance-driven knowledge flows (Yang et al., 2024), and optimization leverages financial instrumentality (Paasi et al., 2023). Nevertheless, critical gaps persist. Enterprise-level theories ignore multi-scalar orchestration in institutional platforms; input–output metrics obscure adaptive action sequencing; and theoretical isolation from dynamic capabilities impedes the understanding of coevolution with policy–market bridging.

3. Research Design

3.1. Research Methods

This study employs a longitudinal single-case design, an approach particularly suited to investigating complex, dynamic processes and uncovering underlying mechanisms in real-world contexts (R. K. Yin, 1981). This methodology aligns with our dual research objectives: (1) to reveal stage-dependent evolutionary characteristics of new R&D organizations, and (2) to explicate mechanisms driving deep innovation–entrepreneurship integration. Longitudinal analysis of a single case enables tracing intricate event sequences and interactions as they unfold, providing essential context for understanding causal dynamics in temporal evolution (Eisenhardt, 1989a). Given the inherent complexity of new R&D institutions—involving multi-stakeholder collaboration (government, industry, and academia) during innovation–entrepreneurship integration—an embedded case design was adopted (R. K. Yin, 1981). This framework examines the primary institution through its key collaborative relationships and projects (sub-units). Analysis of these embedded units facilitates within-case comparison and data triangulation, strengthening internal validity and establishing robust foundations for analytical generalization (Gerring, 2004; Siggelkow, 2007).

3.2. Selection of Case Objects and Stages

The Jiangsu Industrial Technology Research Institute (hereinafter referred to as “JITRI”) is selected for this longitudinal study based on two compelling considerations: (1) Institutional Typicality: Established in 2013 to address science–economy decoupling, JITRI pioneered China’s Team–Equity–Holding hybrid governance model (Jiangsu Industrial Technology Research Institute, 2013). Its distinctive three-tier architecture (Headquarters → Specialized Institutes → Enterprise Innovation Centers) implements market-adaptive mechanisms including contract research and appropriation-to-investment transformation (Zhou et al., 2024)—a framework subsequently codified in China’s 2021 National Innovation Reform Agenda (Jiangsu Provincial Department of Science and Technology, 2021). (2) Demonstrated Impact: By 2023, JITRI had incubated >1200 technology ventures, commercialized >7000 patents with a 37% reduction in low-value outputs, and served >20,000 enterprises, empirically demonstrating exceptional innovation-to-entrepreneurship conversion efficacy (Jiangsu Industrial Technology Research Institute, 2024).
Examining JITRI’s complete development trajectory (2013–2023) through Cai et al. (2021)’s innovation value transformation lens reveals three evolutionary phases (see Figure 1):
Trigger Phase (2013–2015): Institutional formation focused on activating entrepreneurial opportunities through foundational mechanisms: contract research and project manager systems.
Catalytic Phase (2016–2018): Strategic expansion via the World Association of Industrial and Technological Research Organizations (WAITRO) accession, industrial platforms, and provincial adoption of the equity model amplified opportunities through multi-stakeholder collaboration.
Fusion Phase (2019–2023): Advanced open innovation models (e.g., Jacua Program) strengthened innovation–entrepreneurship synergies and cross-stakeholder opportunity development capabilities.

3.3. Data Analysis

3.3.1. Data Collection and Processing

The study employs NVivo 14.0 for secondary data collation and analysis. Case materials were systematically collected from four primary sources:
Official Websites: Approximately 800,000 Chinese characters of innovation- and entrepreneurship-related content were extracted from the official website of JITRI.
Institutional Reports: Archival data spanning 2019–2023 were compiled from publicly available annual reports published by JITRI (specifically including, for example, the 2019 and 2023 reports), totaling roughly 360,000 Chinese characters.News Media: A carefully compiled corpus of 40,000 Chinese characters was collected from mainstream news portals, including the online edition of People’s Daily and Sohu.
Academic Publications: Twenty peer-reviewed articles (~24,000 Chinese characters) were retrieved from the China National Knowledge Infrastructure (CNKI) database.
Our multi-source data collection yielded approximately 1.22 million characters (Chinese characters, the same as below). To ensure the validity and reliability of the data through triangulation (Eisenhardt, 1989b), we implemented a three-stage analysis protocol: (1) Automated data collection. We utilized the Python Selenium framework (v4.11.2) (Rao et al., 2018) to dynamically collect web content via Boolean queries: (“innovation” or “entrepreneurship”) and (“technology transfer” or “technology commercialization”). (2) Data cleansing. The raw data underwent systematic cleaning: the removal of non-text elements, normalization of spaces, correction of encoding errors, and verification of completeness through comparison with the source documents. This process reduced the corpus from 1.22 million characters to 920,000 characters through precise filtering. (3) Analysis processing. The refined dataset was subjected to double-loop coding in NVivo 14: first, time-series automatic coding was performed, followed by manual refinement based on the automatic coding results as a classification framework.
The period of data information is 2013–2023, and the specific data coding process is as follows:
(1)
Open coding
The open coding phase commenced with independent analyst coding to generate primary codes, followed by consensus discussions to resolve discrepancies—a protocol enhancing reliability through investigator triangulation. The formation process of the dynamic capability category can be taken as an example (see Table 1): First, labeling was undertaken—marking phrases related to perception and opportunity detection in the data, then simplifying and preliminarily refining them (decoding prefix “a”), such as “a2 Demand for high-performance computing technology, network security technology, and sensing and perception technology due to intensified competition in the international information field”. Second, conceptualization was done—grouping free nodes belonging to the same phenomenon under the same tree (decoding prefix “A”) and developing a complete conceptual definition for this node, for example, categorizing “a2 Demand for high-performance computing technology, network security technology, and sensing and perception technology due to intensified competition in the international information field” and “a3 Beijing Moscow Online Linkage, China-Russia Science and Technology Innovation Co-operation and Exchange Meeting in the Field of New Materials Held in Ningxia” as “A2 Perception technology emerges” and “A3 Two-way knowledge flows”. Third, categorization was done: tree nodes that appeared to be related to the same phenomenon were grouped into a category to form new tree nodes (decoding prefix “B”), such as “A1 Sensing market opportunities” and “A4 Accumulating experience in institutional innovation” being categorized as “B1 Sensing capability” and “B2 Absorptive Capacity”, respectively.
(2)
Axial coding
Axial Coding further classifies and analyzes the categories developed in open coding by connecting the relationships between categories. For example, “B1 Sensing capability,” “B2 Absorptive capacity,” “B3 Integrating capability,” and “B4 Innovative capability” are all summarized as “Dynamic capability” (see Table 2).
(3)
Selective coding
The purpose of selective coding is to identify the interrelationships between core categories and other categories, forming a “narrative thread” to elucidate the underlying meaning of the entire study, thereby establishing a “grounded” theory. Through selective coding, we found that sensing capability, absorptive capacity, integrating capability, and innovative capability all reflect the core category of “dynamic capability”. Ultimately, the 15 subcategories were condensed into 5 main categories, and the 5 main categories were condensed into 3 core categories (see Table 3).

3.3.2. Model Framework Construction

This study draws on Clarke (2003)’s research to construct a “occasion–process–result” analytical framework. Figure 2 is the core evidence diagram of the theoretical model constructed through three-level coding in this study, which visually demonstrates how raw data is progressively refined into a theoretical framework to explain phenomena. Taking the trigger phase (2013–2015) as an example, the construction process of the “occasion–process–result” logical chain is as follows:
Context layer (left side): Labels (e.g., tax incentives) are extracted from policy and market events (e.g., ‘tax exemptions of CNY 26 billion’ and ‘delayed technology transactions’), clustered to form the main categories of policy context (environment-based policy) and market situation (low marketisation level), revealing the government-led initial environment.
Process layer (middle): Institutional action events (e.g., ‘learning from Stanford’s experience’) are coded as capability tags (e.g., absorptive capacity) and grouped into the main category of dynamic capability; resource strategies (e.g., ‘introducing the Danish Institute’) are coded as action tags (e.g., seeking external cooperation) and grouped into the main category of resource action, reflecting the core mechanism of ‘capability driving resources’.
Outcome layer (right side): Outcome events (e.g., ‘converting 1269 outcomes’) are coded as integration labels (e.g., innovation and entrepreneurship platform construction) and assigned to the main category of innovation and entrepreneurship integration, signifying the completion of the foundational platform.

4. Case Studies

4.1. Trigger Phase (2013–2015): Innovation and Entrepreneurship Platform Building

JITRI was unable to break through the bottleneck of the resource pool in the initial stage to fall into the predicament of reform and development and needed to be guided by policies to complete the acquisition of resources.
Contextual motivation: JITRI’s initial resource acquisition bottleneck stemmed from dual systemic constraints. First, underdeveloped market conditions manifested through inefficient IP transactions, technologically deficient competition (e.g., Qualcomm–Samsung duopoly controlling >90% of premium semiconductor markets), and immature venture capital ecosystems. Second, traditional R&D models exhibited acute science–market decoupling due to weak commercialization pathways and academic-biased evaluation systems—evidenced by Jiangsu universities’ 10% technology transfer rate in 2013. These conditions necessitated policy intervention: provincial technology initiatives incentivized R&D investment while state-mediated university–JITRI partnerships established industry–academia–research alliances. Consequently, JITRI adopted a policy-guided marketization framework to harmonize institutional reform with resource mobilization.
Evolutionary process: Institutional innovations propelled JITRI’s structural transformation. The implementation of a two-tier governance architecture—pairing university research centers (focused on basic/applied research) with legally independent corporate entities—enabled operational flexibility. The biomedical sector development exemplifies this strategy: collaboration with Suzhou Industrial Park established the Chinese Academy of Medical Sciences’ Institute of Systemic Medicine, catalyzing biopharmaceutical innovation clusters. Concurrently, enhanced absorptive capacity through global knowledge transfer proved critical. By adapting the U.S. Defense Advanced Research Projects Agency (DARPA)’s mission-driven model (characterized by high-risk/high-reward projects, project manager autonomy, and top-down coordination), JITRI recruited international talent to implement its Project Manager System, accelerating resource integration.
Evolutionary results: This phase established JITRI as a triple-helix nucleus (F. Zhang et al., 2021) through three institutional pillars: (1) dual-track governance separating research and commercialization; (2) a Project Manager System enabling agile resource deployment; and (3) membership models fostering stakeholder cohesion. This framework channeled academic research into enterprise-aligned applications, spawning specialized institutes and university–industry incubators. The emergent ecosystem attracted international partners (e.g., Technical University of Denmark) for joint R&D facilities, consolidating foundational resources for subsequent growth. Figure 3 details this platform-building mechanism.

4.2. Catalytic Phase (2016–2018): Innovation and Entrepreneurship Networking

At this stage, JITRI further strengthens the completeness and relevance of resources on the basis of the elements and resources gathered in the previous stage and creates industrial clusters to form an innovation and entrepreneurship network of “diversified linkage, in-depth expansion, and international integration.”
Contextual Motivation: During the catalytic phase, JITRI confronted systemic constraints, including limited technological capabilities, that impeded responsiveness to diverse entrepreneurial demands. Illustratively, medical data transmission inefficiencies compromised diagnostic accuracy on existing platforms. Concurrent market pressures compelled enterprises to adopt cutting-edge technologies for competitive differentiation, while policy shifts accelerated institutional transformation. China’s Outline of National Innovation-Driven Development Strategy (Xinhua News Agency, 2016) and Jiangsu’s tripartite innovation policies—tax incentives, R&D funding, and IP reforms (e.g., “18 Articles of Intellectual Property Rights”)—established a supportive framework stimulating innovation-capacity upgrading.
Evolutionary process: To overcome fragmented collaborations constraining resource efficacy, JITRI executed three strategic initiatives: (1) Platform-Based Resource Leverage: Co-created innovation centers included enterprises (e.g., Suzhou Xutron Technology’s intelligent systems’ R&D combining algorithm–hardware synergies) and research institutions (e.g., Sichuan University’s polymer materials industrialization). (2) Global Talent Integration: Academic–industry talent programs were launched and initiated overseas recruitment (e.g., Houston-based Gu Xing team recruitment enabling the Suzhou Hanhua Semiconductor). (3) Dynamic Capability Institutionalization: Multidisciplinary R&D strengths (e.g., advanced materials) were consolidated, stakeholder coordination protocols were established (e.g., intelligent manufacturing consortia with clear governance), and co-creation through international platforms was catalyzed (e.g., the US–China Innovation & Investment Conference). This systematic approach transformed resource aggregation into synergistic orchestration, optimizing ecosystem foundations.
Evolutionary results: Enhanced capabilities yielded a multi-scalar innovation architecture: (1) Sectoral Specialization: Established dedicated structures across five priority domains (biomedicine, advanced manufacturing, materials, environmental tech, and ICT) were exemplified by Nanjing/Suzhou biomedical clusters integrating fragmented technological assets. (2) Global Integration: The World Association of Industrial Technology Research Organizations (WAITRO) membership (2017) bridged domestic R&D with global frontiers. (3) Network Amplification: Network density and dimensionality were expanded through dual mechanisms—sectoral clustering (fostering resource integration/competition) and international coupling (see Figure 4). This architecture catalyzed innovation output leaps by aligning institutional design with regional industrial imperatives. Enhanced capabilities yielded a multi-scalar innovation architecture.

4.3. Fusion Phase (2019–2023): Innovation and Entrepreneurship Ecosystem Building

Although resource integration advanced in prior phases, persistent complementarity gaps (e.g., 23% R&D projects lacking commercialization pathways per (Jiangsu Industrial Technology Research Institute, 2024)) and incompatible incentive structures (e.g., academia’s publication-first vs. industry’s profit-driven goals) hindered innovation–entrepreneurship synergy. This necessitated dynamic coupling mechanisms to align resources.
Contextual motivation: Operating as an independent legal entity within a mature market ecosystem, JITRI leverages institutionalized risk–reward mechanisms—notably team equity incentives granting researchers 30% spin-off ownership—to enhance governance efficiency. Specialized intellectual property courts and standardized technology transaction platforms provide robust rights protection (Jiangsu Industrial Technology Research Institute, 2024). Concurrently, as a national innovation system pillar, JITRI advances structural reforms through a dual policy framework: (1) supply-side interventions directly bridge resource gaps (e.g., targeted R&D subsidies and overseas talent residency permits); (2) environmental enablers build institutional infrastructure (e.g., expedited IP adjudication and tiered R&D tax credits); and (3) within this supportive milieu, JITRI deploys core innovations: demand-driven contract research, project manager systems (80% budgetary autonomy), and integrated graduate programs, complemented by strategic municipal partnerships. This policy–market symbiosis creates institutional safeguards for deep innovation–entrepreneurship integration.
Evolutionary process: To strengthen innovation–entrepreneurship coupling, JITRI executed a triaxial resource optimization strategy initiating with horizontal scaling through cross-sector alliances—exemplified by accelerating Nanjing Mulam Laser Technology’s global market penetration—followed by vertical deepening via innovation–integration dual capabilities that constructed an integrated triad architecture: strategic alliances aggregating critical resources, specialized institutes executing mission-driven R&D, and enterprise joint centers articulating market demands. This architectural integration culminated in systemic fluidization prioritizing resource mobility, operationalized through crowdsourced R&D mechanisms as demonstrated in the Anhui University of Technology collaboration, enhancing titanium alloy durability. By 2023, this three-dimensional strategy will have supported 63 strategic projects across priority areas, established 356 corporate partnerships, and established approximately 100 professional R&D platforms, systematically transforming resource operations from decentralized to coordinated.
Evolutionary result: Resource recalibration yielded a multidimensional ecosystem wherein talent architectures materialized through project manager systems and postgraduate programs cultivating applied innovation teams; spatial consolidation manifested via strategic R&D hubs in Nanjing, Jiangbei and Suzhou, Xiangcheng, concentrating innovation assets; and financial scaffolding emerged through JITRI’s equity investments and overseas incubators forging capital-linked value networks. This institutionalized tripartite synergy (see Figure 5) drives factor mobility across domains, reinforces industrial-chain resilience through resource-channeling mechanisms, and accelerates distinctive ecosystem maturation characterized by self-sustaining innovation loops.

5. Mechanism for Deep Integration of Innovation and Entrepreneurship Driven by New R&D Institutions

This longitudinal examination of JITRI employs its “trigger–catalysis–fusion” evolutionary model to theorize innovation–entrepreneurship integration mechanisms (Figure 6). The analysis establishes that JITRI’s advancement demonstrates distinct stage-contingent properties. Policy–market institutional coupling functions as the primary external driver for cultivating new productive forces, while dynamic capability–resource pathway synergy constitutes the internal catalytic engine.
The external policy–market dynamic operates through sequential institutional phases. Initial environmental policies establish innovation-conducive conditions, succeeded by mid-term supply-side interventions that accelerate factor accumulation through targeted resource infusion. Late-stage integration of environmental and supply policies systemically optimizes cultivation frameworks for industrial diffusion. Concurrently, market maturation evolves from early constraints necessitating institutional innovation, through mid-phase efficiency gains in resource allocation, toward late-stage concentration-driven upgrading of productive forces. This dialectical progression creates institutional safeguards where policy instruments and market maturation reciprocally reinforce developmental imperatives specific to each evolutionary stage.
Internally, capability–resource coevolution manifests through three nonlinear transitional phases. During the trigger phase, absorptive and perceptual capabilities facilitate unilateral knowledge acquisition. The catalytic phase witnesses integrative capabilities constructing resource networks via relational expansion. Ultimately, the fusion phase deploys innovation capabilities to optimize frontier technology breakthroughs. This progression transitions resource operations from initial accumulation through strategic orchestration toward advanced reconfiguration, directly catalyzing the formation and refinement of new productive forces.
The integration process exhibits three constitutive attributes with theoretical significance. First, cross-domain transcendence describes entity transitions from organizational to ecosystem engagement, actualized through entrepreneurial opportunity development that enables boundary-spanning innovation(Chesbrough, 2012). Second, heterogeneous diversity features collaborative governance among governments, universities, and cross-industry enterprises, where value-network nodes trigger opportunity-driven recombinant aggregation. This dynamic extends innovation ecosystem theory through “opportunity–entity–value” coevolution (Adner, 2016). Third, recursive interactivity generates bidirectional reinforcement between innovation and entrepreneurship: innovation empowers entrepreneurial value creation while entrepreneurship fuels innovative vitality. Behaviorally, multi-actor collaboration evolves through capability–network coadaptation, validating symbiotic frameworks (Nambisan & Baron, 2012).

6. Conclusions

6.1. Findings

This longitudinal single-case study examines JITRI to theorize how new R&D institutions drive innovation–entrepreneurship integration within China’s dual policy–market contexts. Grounded in a “context–process–outcome” analytical framework, the research identifies three interconnected mechanisms governing this integration process.
Policy–market institutional coupling constitutes the primary external driver, where governmental empowerment operates through phased policy interventions. Initial environmental policies establish institutional foundations via intellectual property reforms, exemplified by dual-track mechanisms. Subsequent supply-side interventions activate resource flows through targeted R&D subsidies, while late-stage policy coupling integrates the environmental and supply dimensions. Concurrently, market maturity progresses from constrained to advanced stages, expanding innovation networks through enhanced marketization. The synergistic alignment between policy instruments and market evolution forms strategic–institutional coupling, creating essential external conditions for systemic integration.
Endogenous dynamic capability advancement emerges as the core internal mechanism. A spiral progression of organizational capabilities enables cross-phase resource orchestration: absorptive capacity breakthroughs facilitate critical knowledge acquisition through elite recruitment; integrative capacity upgrades optimize resource deployment via institutional collaborations such as the Sichuan University joint laboratory; and innovative capacity reconstruction drives technological frontier advancement. This capability triad operates as the microfoundational engine for sustained integration.
The integration process further demonstrates nonlinear coevolutionary complexity through three constitutive characteristics. Cross-domain dynamics manifest as actor transitions from isolated entities to multi-actor ecosystems, illustrated by JITRI’s crowdsourced R&D initiatives. Multidimensional interaction enables symbiotic value reconfiguration among governments, universities, and cross-industry enterprises. Recursive feedback loops create bidirectional reinforcement: innovation outputs propel entrepreneurial opportunities while entrepreneurial practices generate new technological demands, as evidenced in medical technology iterations responding to diagnostic accuracy requirements. These characteristics collectively reveal the lifecycle’s nonlinear trajectory from unilateral triggering through systematic catalysis toward ecological fusion.

6.2. Theoretical Contribution

This study significantly extends dynamic capability theory through the JITRI case analysis, contextualizing Teece (2018)’s framework within hybrid R&D institutions operating under policy–market duality. We demonstrate three essential capability reconstructions: absorption capability (Section 4.1) manifests as knowledge barrier breakthrough through recruitment of 21 global academicians; integration capability (Section 4.2) reorganizes fragmented resources via cross-domain laboratories; and innovation capability (Section 4.3) achieves ecosystem-level collaboration through crowdsourced R&D. This tripartite reconstruction empirically validates how institutional tension transforms into collaborative leverage (Franco & Landini, 2022).
Concurrently, we establish a dual-context policy–market model where environmental policies (Section 4.1) reduce low-quality patents by 37% while expanding innovation networks, supply-side policies (Section 4.2) address SME resource gaps through targeted R&D subsidies, and coupling policies (Section 4.3) enable adaptive governance via equity incubators. This model system elucidates the dynamic scenario overlap mechanisms driving value spillovers. Furthermore, transcending enterprise-centric resource paradigms (Lin et al., 2021), we propose an institutional platform resource orchestration path progressing from policy-enabled acquisition through university alliances (trigger phase, Section 4.1), to market-driven integration via WAITRO networks (catalytic phase, Section 4.2), culminating in ecosystem co-creation optimization through crowdsourced R&D (fusion phase, Section 4.3). The “context–process–outcome” framework thereby decodes deep innovation–entrepreneurship integration mechanisms from a dynamic evolutionary perspective.

6.3. Practical Implications

Policymakers should establish a phased intervention pathway: during institutional startup, prioritize environmental policies such as intellectual property reforms and institutional audits; transition to supply-side tools including targeted R&D subsidies and cross-border talent visas during growth phases; and adopt coupling policies like contract R&D with equity crowdfunding at maturity to activate ecosystem adaptability. Concurrently, R&D institution managers ought to develop dynamic capability-driven resource systems by systematically enhancing absorptive capacity through cross-border talent networks (e.g., global academician recruitment paradigms), upgrading integrative capacity via cross-domain laboratories (exemplified by the Sichuan University joint model), and unleashing innovative capacity using open innovation paradigms such as crowdsourced R&D.

6.4. Limitations and Future Research Directions

This study’s longitudinal single-case design, while providing depth, constrains generalizability due to JITRI’s distinctive governmental attributes. Limited inclusion of external stakeholders like partner enterprises may affect understanding of institution–environment interactions. Future research should therefore pursue three critical directions: first, multi-case comparisons across heterogeneous R&D institutions to examine how governance structures and resource dependencies shape fusion pathways; second, systematic integration of external stakeholder perspectives to deepen analysis of ecological network dynamics (e.g., partnership configurations and market demands) on institutional evolution; and third, dedicated investigation into how digital technologies reconfigure R&D processes, incubation models, and core capabilities within digital innovation ecosystems, requiring development of context-specific theoretical frameworks.

Author Contributions

Conceptualization, Y.F. and X.Q.; methodology, Y.F. and X.Q.; software, X.Q.; validation, Y.F. and X.Q.; formal analysis, X.Q.; investigation, X.Q.; resources, Y.F.; data curation, X.Q.; writing—original draft preparation, X.Q.; writing—review and editing, Y.F.; visualization, Y.F. and X.Q.; supervision, Y.F.; project administration, Y.F..; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study utilizes publicly available secondary data obtained from open-access corporate websites. Key features: Non-personal: Contains no identifiable individual information; Public nature: Acquired through unrestricted public platforms; Compliance: Adheres to robots.txt protocols and platform terms of service.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Adner, R. (2016). Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1), 39–58. [Google Scholar] [CrossRef]
  2. Andrews, L. R. J., & Luiz, J. M. (2024). Dynamic capabilities and the management of institutional voids: A case study of intra-African internationalization. Thunderbird International Business Review, 67(3), 313–327. [Google Scholar] [CrossRef]
  3. Ao, S. (2024). The impact of government subsidy policies on industrial innovation. Open Journal of Business and Management, 13(1), 502–513. [Google Scholar] [CrossRef]
  4. Baker, T., Miner, A. S., & Eesley, D. T. (2003). Improvising firms: Bricolage, account giving and improvisational competencies in the founding process. Research Policy, 32(2 SPEC), 255–276. [Google Scholar] [CrossRef]
  5. Barney, J. B. (1991). Firm resources and sustained competitive advantage. Advances in Strategic Management, 17(1), 99–120. [Google Scholar] [CrossRef]
  6. Cai, L., Zhang, Y., Cai, Y., & Yang, Y. (2021). Innovation-driven entrepreneurship: A core academic construct of innovation and entrepreneurship research in the New Era. Nankai Business Review, 24(4), 217–226. [Google Scholar]
  7. Chesbrough, H. (2012). Open innovation: Where we’ve been and where we’re going. Research Technology Management, 55(4), 20–27. [Google Scholar] [CrossRef]
  8. Clarke, A. E. (2003). Situational analyses: Grounded theory mapping after the postmodern turn. Symbolic Interaction, 26(4), 553–576. [Google Scholar] [CrossRef]
  9. Collis, D. J. (1995). Research note: How valuable are organizational capabilities. Long Range Planning, 28(4), 129. [Google Scholar] [CrossRef]
  10. Crnogaj, K., & Rus, M. (2023). From start to scale: Navigating innovation, entrepreneurial ecosystem, and strategic evolution. Administrative Sciences, 13(12), 254. [Google Scholar] [CrossRef]
  11. Eisenhardt, K. M. (1989a). Building theories from case study research. Academy of Management Review, 14(4), 532–550. [Google Scholar] [CrossRef]
  12. Eisenhardt, K. M. (1989b). Making fast strategic decisions in high-velocity environments. Academy of Management Journal, 32(3), 543–576. [Google Scholar] [CrossRef]
  13. Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10–11), 1105–1121. [Google Scholar] [CrossRef]
  14. Franco, C., & Landini, F. (2022). Organizational drivers of innovation: The role of workforce agility. Research Policy, 51(2), 104423. [Google Scholar] [CrossRef]
  15. Gamidullaeva, L., Tolstykh, T., Bystrov, A., Radaykin, A., & Shmeleva, N. (2021). Cross-sectoral digital platform as a tool for innovation ecosystem development. Sustainability, 13(21), 11686. [Google Scholar] [CrossRef]
  16. Gerring, J. (2004). What is a case study and what is it good for? The American Political Science Review, 98(2), 341–354. [Google Scholar] [CrossRef]
  17. Heaton, S., Siegel, D. S., & Teece, D. J. (2019). Universities and innovation ecosystems: A dynamic capabilities perspective. Industrial and Corporate Change, 28(4), 921–939. [Google Scholar] [CrossRef]
  18. Helfat, C. E., & Peteraf, M. A. (2015). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831–850. [Google Scholar] [CrossRef]
  19. Hoogstraaten, M. J., Frenken, K., & Boon, W. P. C. (2020). The study of institutional entrepreneurship and its implications for transition studies. Environmental Innovation and Societal Transitions, 36, 114–136. [Google Scholar] [CrossRef]
  20. Jiangsu Industrial Technology Research Institute. (2013). The first meeting of the first council of Jiangsu Industrial Technology Research Institute was convened. Available online: http://www.jitri.cn/news?id=581 (accessed on 10 October 2023).
  21. Jiangsu Industrial Technology Research Institute. (2024). Annual report 2023 of Jiangsu Industrial Technology Research Institute (Chinese version). Available online: http://www.jitri.cn/static/custom/jitri/%E9%9B%86%E8%90%83%E5%B9%B4%E6%8A%A5/2023/JITRI-2023%E5%B9%B4%E5%B9%B4%E6%8A%A5.pdf (accessed on 3 June 2024).
  22. Jiangsu Provincial Department of Science and Technology. (2021). The Jiangsu Industrial Technology Research Institute was approved for 2 national comprehensive innovation and reform tasks in 2021. Available online: https://kxjst.jiangsu.gov.cn/art/2021/11/1/art_82539_10093538.html (accessed on 15 March 2024).
  23. Kern, F., Rogge, K. S., & Howlett, M. (2019). Policy mixes for sustainability transitions: New approaches and insights through bridging innovation and policy studies. Research Policy, 48(10), 103832. [Google Scholar] [CrossRef]
  24. Kong, H., Sun, L., & Zhang, W. (2024). Digitization and green technology innovation of Chinese firms under government subsidy policies. Systems, 12(11), 447. [Google Scholar] [CrossRef]
  25. Krishnan, C. S. N., Ganesh, L. S., & Rajendran, C. (2022). Entrepreneurial interventions for crisis management: Lessons from the Covid-19 Pandemic’s impact on entrepreneurial ventures. International Journal of Disaster Risk Reduction, 72, 102830. [Google Scholar] [CrossRef] [PubMed]
  26. Laatsit, M., Grillitsch, M., & Fünfschilling, L. (2025). Great expectations: The promises and limits of innovation policy in addressing societal challenges. Research Policy, 54(3), 105184. [Google Scholar] [CrossRef]
  27. Lamberova, N. (2024). The paradox of government-funded innovation in weakly institutionalized environments. Journal of Innovation & Knowledge, 9(4), 100536. [Google Scholar] [CrossRef]
  28. Laursen, K., & Salter, A. (2006). Open for innovation: The role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27(2), 131–150. [Google Scholar] [CrossRef]
  29. Leskovec, F., Černe, M., & Peljhan, D. (2025). Open innovation as the missing link in the mediated model among R&D educational heterogeneity, innovation and performance. Journal of Innovation & Knowledge, 10(1), 100646. [Google Scholar] [CrossRef]
  30. Lin, J., Zhang, Y., & Su, J. (2021). From resource bricolage to resource orchestration—An explanation from the institutional context perspective. Management Review, 33(10), 249–262. [Google Scholar] [CrossRef]
  31. Liu, Y. (2024). Asia’s institutional innovation, cross-boundary learning, and resilience in business and society fostering international collaborations around the world. Asian Business & Management, 23(5), 651–659. [Google Scholar] [CrossRef]
  32. Moradi, M., Hepsø, V., & Schiefloe, P. M. (2024). Institutional complexity and governance in open-source ecosystems: A case study of the oil and gas industry. Journal of Innovation & Knowledge, 9(3), 100523. [Google Scholar] [CrossRef]
  33. Nambisan, S., & Baron, R. A. (2012). Entrepreneurship in innovation ecosystems: Entrepreneurs’ self-regulatory processes and their implications for new venture success. Entrepreneurship Theory and Practice, 37(5), 1071–1097. [Google Scholar] [CrossRef]
  34. Nuñez-Jimenez, A., Knoeri, C., Hoppmann, J., & Hoffmann, V. H. (2022). Beyond innovation and deployment: Modeling the impact of technology-push and demand-pull policies in Germany’s solar policy mix. Research Policy, 51(10), 104585. [Google Scholar] [CrossRef]
  35. Paasi, J., Wiman, H., Apilo, T., & Valkokari, K. (2023). Modeling the dynamics of innovation ecosystems. International Journal of Innovation Studies, 7(2), 142–158. [Google Scholar] [CrossRef]
  36. Rao, J., Yang, Y., Ma, R., & Du, C. (2018). Research and analysis of digital city based on web crawler. Computer Science and Application, 8(8), 1172–1182. [Google Scholar] [CrossRef]
  37. Rogge, K. S., & Reichardt, K. (2016). Policy mixes for sustainability transitions: An extended concept and framework for analysis. Research Policy, 45(8), 1620–1635. [Google Scholar] [CrossRef]
  38. Shi, X., Wu, Y., & Fu, D. (2020). Does University-Industry collaboration improve innovation efficiency? Evidence from Chinese Firms. Economic Modelling, 86, 39–53. [Google Scholar] [CrossRef]
  39. Siggelkow, N. (2007). Persuasion with case studies. Academy of Management Journal, 50(1), 20–24. [Google Scholar] [CrossRef]
  40. Song, W., Meng, L., & Zang, D. (2023). Exploring the impact of human capital development and environmental regulations on green innovation efficiency. Environmental Science and Pollution Research, 30(25), 67525–67538. [Google Scholar] [CrossRef] [PubMed]
  41. Su, J., Zhang, Y., & Lin, J. (2020). Context identification and mechanism analysis of why enterprises choose specialization strategy in emerging countries: Based on a multiple-case study of the enterprises in Shenzhen. Management Review, 32(1), 309–323. [Google Scholar] [CrossRef]
  42. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. [Google Scholar] [CrossRef]
  43. Teece, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47(8), 1367–1387. [Google Scholar] [CrossRef]
  44. Wang, L., Tao, H., & Cui, L. (2024). Research on the inner logic, promotion mechanism, and path of new R&D institutions empowering new quality productivity. Academic Journal of Zhongzhou, (05), 55–62. [Google Scholar]
  45. Xinhua News Agency. (2016). Outline of the national innovation-driven development strategy outline of the strategy. Available online: https://www.gov.cn/gongbao/content/2016/content_5076961.htm (accessed on 10 March 2024).
  46. Xiong, X., Yang, G.-l., & Guan, Z.-c. (2020). Estimating the multi-period efficiency of high-tech research institutes of the Chinese Academy of Sciences: A dynamic slacks-based measure. Socio-Economic Planning Sciences, 71, 100855. [Google Scholar] [CrossRef]
  47. Yang, H., Liu, L., & Wang, G. (2024). Does large-scale research infrastructure affect regional knowledge innovation, and how? A case study of the National Supercomputing Center in China. Humanities and Social Sciences Communications, 11(1), 338. [Google Scholar] [CrossRef]
  48. Yin, R. K. (1981). The case study crisis: Some answers. Administrative Science Quarterly, 26(1), 58–65. [Google Scholar] [CrossRef]
  49. Yin, X., Qi, L., Ji, J., & Zhou, J. (2023). How does innovation spirit affect R&D investment and innovation performance? The moderating role of business environment. Journal of Innovation & Knowledge, 8(3), 100398. [Google Scholar] [CrossRef]
  50. Yu, R., Xia, X., Huang, T., Zhang, S., & Zhou, W. (2024). Has the establishment of high-tech zones improved urban economic resilience? Evidence from prefecture-level cities in China. Land, 13(2), 241. [Google Scholar] [CrossRef]
  51. Zahra, S. A., Sapienza, H. J., & Davidsson, P. (2006). Entrepreneurship and dynamic capabilities: A review, model and research agenda. Journal of Management Studies, 43(4), 917–955. [Google Scholar] [CrossRef]
  52. Zhang, F., Yuan, C., & Guo, J. (2021). Deep integration of industry, university and research in new R&D institutions: The password of institutional mechanism innovation. Science Research Management, 42(11), 43–53. [Google Scholar] [CrossRef]
  53. Zhang, P., Zhou, D., & Guo, J. (2023). Policy complementary or policy crowding-out? Effects of cross-instrumental policy mix on green innovation in China. Technological Forecasting and Social Change, 192, 122530. [Google Scholar] [CrossRef]
  54. Zhang, Y. (2024). The influence of “industry policy” and “financial institution” configuration effect on innovation performance of China’s biomedical industry-based on necessary condition analysis and qualitative comparative analysis. Frontiers in Medicine, 10, 1297495. [Google Scholar] [CrossRef] [PubMed]
  55. Zhao, J. (2024). How do innovation factor allocation and institutional environment affect high-quality economic development? Evidence from China. Journal of Innovation & Knowledge, 9(2), 100475. [Google Scholar] [CrossRef]
  56. Zhou, J., & Wang, M. (2023). The role of government-industry-academia partnership in business incubation: Evidence from new R&D institutions in China. Technology in Society, 72, 102194. [Google Scholar] [CrossRef]
  57. Zhou, J., Wang, M., & Ren, J. (2024). Organizational perspective of new R&D institutions: An SCGP theoretical framework. Science of Science and Management of S.& T., 45(9), 3–14. [Google Scholar] [CrossRef]
Admsci 15 00289 g001
Figure 1. Key events in the Jiangsu Industrial Research Institute.
Figure 1. Key events in the Jiangsu Industrial Research Institute.
Admsci 15 00289 g001
Admsci 15 00289 g002
Figure 2. Data structure of the Jiangsu Industrial Research Institute.
Figure 2. Data structure of the Jiangsu Industrial Research Institute.
Admsci 15 00289 g002
Admsci 15 00289 g003
Figure 3. Trigger phase innovation and entrepreneurship integration processes.
Figure 3. Trigger phase innovation and entrepreneurship integration processes.
Admsci 15 00289 g003
Admsci 15 00289 g004
Figure 4. Innovation and entrepreneurship integration processes in the catalytic phase.
Figure 4. Innovation and entrepreneurship integration processes in the catalytic phase.
Admsci 15 00289 g004
Admsci 15 00289 g005
Figure 5. Fusion stage innovation and entrepreneurship integration processes.
Figure 5. Fusion stage innovation and entrepreneurship integration processes.
Admsci 15 00289 g005
Admsci 15 00289 g006
Figure 6. Mechanisms for deeper integration of innovation and entrepreneurship.
Figure 6. Mechanisms for deeper integration of innovation and entrepreneurship.
Admsci 15 00289 g006
Table 1. Examples of open codes.
Table 1. Examples of open codes.
LabelConceptualizationCategorization
a1 The construction of the Joint Venture Centre will focus on the application field of an automotive intelligent cockpit, based on the existing intelligent cockpit products and software solution systems, with the main direction of expanding and improving the performance of the products in the early stage, and the innovation projects of new technologies and products will be determined in the later stage following the technical reserve situation and market demand.A1 Sensing market opportunitiesB1 Sensing capability
a2 Demand for high-performance computing technology, network security technology, and sensing and perception technology due to intensified competition in the international information field.A2 Perception technology emerges
a3 Beijing+Moscow Online Linkage, China–Russia Science and Technology Innovation cooperation and exchange meeting in the field of new materials held in Ningxia.A3 Two-way knowledge flowsB2 Absorptive capacity
a4 The event invited Mr. Lin ChuiZhu, former president of the Taiwan Industrial Technology Research Institute, to give a keynote speech, and Mr. Cao SuMin, executive vice president of the Provincial Industrial Research Institute, and Mr. Hu Yidong, vice president of Provincial Industrial Research Institute, attended the event and carried out exchanges.A4 Accumulating experience in institutional innovation
a5 JITRI-Key Egg Bio Joint Innovation Centre will be guided by the enterprise’s needs and problems, and will focus on the areas of microfluidics, molecular diagnostics, chemiluminescence technology, etc., and will actively promote the development of in vitro diagnostic prospective common key technologies and conduct industrial application technology research.A5 Key technology learning
a6 The Food Biotechnology Research Institute of the Jiangsu Provincial Industrial Technology Research Institute (Rugao) held the first Rugao Longevity Food Biotechnology Industry Forum and Innovation Alliance Establishment Ceremony in the conference room on the ground floor of the Times Building in the Rugao Economic and Technological Development Zone.A6 Building strategic
alliances for innovation		B3 Integrating capability
a7 “Through the provincial scientific and technological achievements transformation project matchmaking, we found Southeast University and Huazhong University of Science and Technology to jointly carry out robot research and development”.A7 Integration of superior disciplinary resources
a8 At present, 30 leading talents with a first-class level have been selected and recruited globally as project managers, and 250 high-level experts at home and abroad have been gathered, including 21 academicians from developed countries.A8 Enrichment of quality human resources
a9 The Institute of Biomedical Engineering Technology of the Provincial Industry Research Institute, the Institute of Translational Medicine and Innovative Drug Technology, the Provincial Medical Device Industry Technology Innovation Centre, and other enterprises and institutions are actively striving for cooperation with the “coronary artery detector” project.A9 Optimizing cooperative resourcing
a10 JITRI Digital Manufacturing Equipment and Technology Research Institute’s “large components multi-robot intelligent grinding and polishing processing technology” was selected as “2018 China’s top ten scientific and technological advances in intelligent manufacturing”.A10 Technological innovation breakthroughB4 Innovative capability
a11 In 2020, we applied the “allocation and investment” process to implement new major projects. “In 2020, we organized and implemented seven major projects such as “flexible customized roll pressing technology” and “sic silicon carbide epitaxial equipment” with a total investment of 300 million yuan, filling a number of domestic gaps and promoting major innovations. The total investment of the projects reached 300 million yuan, filling a number of domestic gaps and promoting the industrialization of major innovations” (A11).A11 Innovation R&D investment
Table 2. Axial coding.
Table 2. Axial coding.
Main CategoryCategoryConnotation
Policy contextEnvironment-based policyGovernment measures that indirectly support innovation by shaping institutional environments (e.g., regulations, intellectual property protection, and infrastructure development).
Supply-based policyGovernment initiatives that directly provide innovation resources (e.g., R&D subsidies, innovation funds, and talent recruitment programs).
Coupled environment–supply policyPolicy portfolios that simultaneously optimize institutional environments and resource provision (e.g., “Grant–Investment Hybrid” mechanisms and industrial innovation alliance support policies).
Market situationMarket concentrationThe degree to which leading companies in the industry control key resources and market share directly affects the competitive landscape of innovation.
Degree of marketisationThe maturity level of the free flow and efficient allocation of factors such as technology, talent, and capital through market mechanisms.
Dynamic capabilitySensing capabilityIdentify technology trends and market demand (such as identifying emerging technology areas and predicting industry demand).
Absorptive capacityAcquire and internalize external knowledge (such as learning from international experience, introducing technology, and two-way knowledge exchange).
Integrating capabilityBreak down organizational boundaries to coordinate multiple resources (technology, talent, and capital) and build strategic connections within the innovation community.
Innovative capabilityAchieving technological breakthroughs and institutional reforms (such as developing disruptive technologies and designing a “combined investment and allocation” model).
Resource actionResource acquisitionFundamental actions to break through initial resource constraints and introduce key technologies, capital, and core talent from external sources.
Resource integrationReorganize fragmented resources into an organic system (such as industry–academia–research alliances) to achieve synergistic value-added effects greater than the sum of their parts.
Resource optimizationAdvanced allocation behavior that improves resource combination efficiency and marginal returns through cross-border allocation, financial leverage, and other strategies.
Innovation and entrepreneurship integrationInnovation and entrepreneurship platform constructionBasic infrastructure construction (such as the establishment of research institutes and incubators).
Innovation and entrepreneurship network constructionBuild a functional connection system for specialized collaboration between multiple nodes (enterprises/universities/research institutes) based on industry demand.
Innovation and entrepreneurship ecological constructionCreate a self-sustaining system environment supported by a “talent–capital–space” cycle to achieve the organic reproduction of innovative elements and value symbiosis.
Table 3. Selective coding results.
Table 3. Selective coding results.
Core CategoryMain CategorySubcategory
Dualist contextPolicy contextEnvironment-based policy, supply-based policy, coupled environment–supply policy
Market situationMarket concentration, degree of marketisation
Course of actionDynamic capabilitySensing capability, absorptive capacity, integrating capability, innovative capability
Resource actionResource acquisition, resource integration, resource optimization
ResultsInnovation and entrepreneurship integrationInnovation and entrepreneurship platform construction, innovation and entrepreneurship network construction, innovation and entrepreneurship ecological construction
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Fang, Y.; Qiu, X. Dual Policy–Market Orchestration: New R&D Institutions Bridging Innovation and Entrepreneurship. Adm. Sci. 2025, 15, 289. https://doi.org/10.3390/admsci15080289

AMA Style

Fang Y, Qiu X. Dual Policy–Market Orchestration: New R&D Institutions Bridging Innovation and Entrepreneurship. Administrative Sciences. 2025; 15(8):289. https://doi.org/10.3390/admsci15080289

Chicago/Turabian Style

Fang, Yinhai, and Xinping Qiu. 2025. "Dual Policy–Market Orchestration: New R&D Institutions Bridging Innovation and Entrepreneurship" Administrative Sciences 15, no. 8: 289. https://doi.org/10.3390/admsci15080289

APA Style

Fang, Y., & Qiu, X. (2025). Dual Policy–Market Orchestration: New R&D Institutions Bridging Innovation and Entrepreneurship. Administrative Sciences, 15(8), 289. https://doi.org/10.3390/admsci15080289

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop