Study on the Characteristics and Operational Mechanisms of Industry–University–Research Collaborative Innovation in Megaprojects: The Case from China

Abstract

Megaproject construction endeavors and technological innovation activities, led by industry–university–research (IUR) collaboration, demonstrate marked disparities in value orientations, implementing entities, and constituent components. These discrepancies lead to a mismatch between innovation demands and actual activities, as well as insufficient innovation motivation among construction entities, subsequently impacting innovation effectiveness and the commercialization of outcomes and failing to adequately support engineering construction needs. In response to this predicament, the academic community widely acknowledges IUR collaborative innovation as a solution. This research integrates fundamental theoretical analysis with a multi-case study approach and systematically dissects the distinctive features at the micro, meso, and macro levels, grounded in the collaborative innovation practices of IUR in three iconic railway engineering projects in China. Subsequently, it unravels the inherent operational mechanics of the IUR collaborative innovation system within large-scale projects. Specifically, at the micro level, the profound engagement of governments and project owners fosters a solid supportive environment and collaborative platform for IUR collaboration, while past successful cooperation experiences among key innovation entities enhance their technological and knowledge interactions. At the meso level, shared industry cognitions and values, hierarchical organizational structures, flexible institutional designs, and resource allocation strategies based on balancing risks and benefits collectively constitute the supporting system for megaproject collaborative innovation. At the macro level, the tight integration of the innovation chain and industrial chain promotes the formation of an open cooperation ecosystem, ensuring the continuity and systematic nature of innovation activities and accelerating the rapid commercialization and efficient utilization of innovation outcomes. This study not only enriches the theoretical connotations of IUR collaborative innovation in the context of major engineering projects but also provides theoretical foundations for strategy formulation and management practices for major project managers, holding significant value in guiding the innovation management of future major engineering projects.

1. Introduction

Megaprojects are known for their large scale, complex and varied natural environments, severe technological challenges, and arduous construction tasks [1]. During the implementation of such projects, technological innovation becomes a key factor in supporting construction and ensuring the successful realization of project objectives. Technological innovation not only plays a decisive role in overcoming technical difficulties but also plays a central role in achieving safety, reliability, efficiency, and sustainability goals across all phases of project design and construction [2,3,4,5]. In the course of constructing the Qinghai-Tibet Railway, technological innovation successfully yielded a durable concrete formulation, and its corresponding construction technique was tailored to the permafrost environment. This breakthrough effectively addressed the challenge of safely executing concrete pouring while ensuring the long-term stability of the permafrost layer. Meanwhile, within the Beijing-Shanghai High-Speed Railway project, advancements in high-speed train technology resulted in the establishment of a comprehensive technical framework encompassing vehicle design to optimization. Significant breakthroughs were achieved in crucial technical domains, including the aerodynamic performance of the entire train and bogie design, ultimately leading to the formulation of high-speed train technical standards.

Despite demonstrating considerable strengths in assembling the industry’s top construction and innovation capabilities, as well as integrating industrial and societal resources [6,7], megaprojects continue to encounter substantial challenges in fostering efficient collaboration among industry, academia, and research institutions. These challenges are particularly evident in fostering effective interactions and communications among personnel from diverse disciplines, optimizing the utilization of synergistic elements, and achieving deep integration between the innovation and industrial chains, all of which significantly impede technological advancements on construction sites and the fulfilment of research requirements [8]. The underlying cause of these issues lies in the pronounced disparities in value orientations, organizational structures, and operational mechanisms among the stakeholders in industry, academia, and research. Specifically, research institutes and universities, as the primary innovators, tend to emphasize theoretical breakthroughs, whereas construction companies, as the end-users, prioritize addressing practical technical challenges on-site. This divergence in priorities results in a mismatch between the research endeavours of innovators and the actual needs of construction projects. Furthermore, communication barriers stemming from knowledge discrepancies between innovators and end-users exacerbate this demand misalignment. Additionally, the contrasting attitudes towards risk—with end-users inclined towards risk aversion and innovators more willing to embrace risk—lead to a reluctance on the part of end-users to adopt new technologies, often favouring conservative approaches and avoiding risk. Collectively, these issues of demand mismatch, inadequate innovation incentives, and intertwined interests among industry, academia, and research stakeholders pose significant barriers to the commercialization of research outcomes, making it difficult for innovations to traverse the ‘valley of death’ and effectively meet the practical needs of construction projects.

It is precisely the disparities among IUR institutions that pose challenges to the commercialization of research outcomes, thereby catalysing an unprecedented urgency for collaborative innovation across all sectors of the IUR nexus [9]. Currently, collaborative innovation is widely acknowledged as the pivotal pathway to surmounting such barriers and fostering the effective translation of research into practical applications. Collaborative innovation represents an organic integration of IUR cooperation and the collaborative innovation paradigm, emphasizing mutual collaboration, joint endeavours, and collective efficacy among system components, striving for the synergistic effect of ‘1 + 1 > 2’ and prioritizing interactivity and cooperation among systems. Since the 1970s, research on IUR collaborative innovation has thrived, with scholars producing a plethora of findings in concept delineation, entity analysis, framework establishment, and mechanism design. Nonetheless, the majority of existing studies tend to generalize at the industry or sector level or fail to specify contextual settings, particularly lacking a comprehensive and systematic theoretical framework for IUR collaborative innovation tailored to the unique context of megaprojects. Consequently, current research outcomes fall short in effectively guiding and elucidating how to facilitate efficient IUR collaborative innovation in the context of major engineering endeavours. This research gap impedes the academic community’s deep exploration and understanding of how technological innovation can effectively bolster engineering construction, especially concerning the underlying mechanisms and support systems of technology-intensive large-scale engineering projects.

In light of this, this study selects three representative major railway engineering projects in China as research samples and systematically explores and summarizes the typical characteristics and operational mechanisms of IUR collaborative innovation using the grounded theory approach. This study aims to elucidate two core research questions: first, the typical characteristics of IUR collaborative innovation in the context of major engineering projects, and, second, the operational logic of the IUR collaborative innovation system. The primary academic contributions of this study are manifested in the following two dimensions: First, through an in-depth analysis of IUR collaborative innovation practices in megaprojects, this study endeavours to construct a multi-layered and systematic theoretical framework to comprehensively understand the complex nature of IUR collaborative innovation within the context of megaprojects. This framework provides new perspectives and depths for theoretical research on IUR collaborative innovation in specific contexts, thereby fostering further enrichment and development of the theory in this field. Second, by conducting an in-depth analysis and synthesis of the operational logic of the IUR collaborative innovation system in major engineering projects, this study not only deepens the theoretical understanding of IUR collaborative innovation but also offers a solid theoretical foundation and clear directional guidance for planning, management optimization, and practical implementation of IUR collaboration in megaprojects. This promotes the effective integration of theory and practice.

2. Theoretical Background

2.1. Technological Innovation in Megaprojects

The unprecedented complex engineering environment and arduous tasks faced by megaprojects have shown an unprecedented dependence on scientific and technological innovation and significant advancements in technological capabilities. With the proliferation of megaprojects, the significance of collaborative innovation among industry, academia, and research has become increasingly prominent, serving as a pivotal pathway to addressing these challenges.

Currently, research on innovation in megaprojects often involves the analysis and summarization of one or several completed megaprojects [5,10,11,12,13], extracting the innovation paradigms of megaprojects. Given the characteristics of innovation in megaprojects, scholars have proposed several typical innovation paradigms. Among these, the multi-entity collaborative innovation paradigm emphasizes close interaction and deep cooperation among various entities, such as industry, academia, research, and application [14,15,16]. By building innovation networks [17,18] and ecosystems [19,20], this paradigm forms effective innovation linkages, helping to gather wisdom and resources from all parties, thereby promoting breakthrough advancements in engineering innovation. The integrated innovation paradigm focuses on interdisciplinary and cross-phase collaboration. Through the deep convergence of multiple disciplines and entities, it breaks the traditional boundaries of the engineering field, fostering cross-disciplinary synergy and providing new ideas and methods for engineering innovation [21,22,23]. The open innovation management paradigm emphasizes flexible mechanism design and policy guidance. By combining policy support with market mechanisms, it provides a more open and inclusive environment for engineering innovation, effectively encouraging the practice and promotion of innovative applications [24,25,26].

The study of technological innovation factors in megaprojects is a key research focus in this field. Research primarily centres on two aspects: first, exploring the core elements that drive technological innovation in megaprojects; second, identifying the key factors that hinder such innovation. Regarding the driving factors of innovation in megaprojects, the main innovation drivers often stem from the strong willingness of a construction entity to resolve critical “bottleneck” technologies or sudden technical challenges. These challenges often involve technical problems or resource bottlenecks that directly affect the smooth progress of projects and even determine whether projects can be successfully completed [27,28]. These factors include a series of technological capability enhancements that must be adopted to overcome technical challenges [29], adjustments to project–industry–research organizational structures to expedite the support of research results for engineering construction activities [30,31,32,33], and the establishment of innovative collaboration mechanisms among project–industry–research [34,35], among others. Additionally, the driving factors of innovation in major infrastructure engineering are also influenced by external environmental pressures, including innovative policies proposed to seek efficient construction, high-quality engineering, and project performance improvement [12,36], systematic processes and organizational arrangements set by the state and government [5,27], or necessary innovations due to social and environmental risks caused by engineering construction.

Regarding the inhibiting factors of innovation in major infrastructure engineering, the project’s temporality and the internal organization’s discreteness significantly limit its innovation potential [27,37]. Temporality is manifested in the project’s clear time frame and specific goal positioning, leading to knowledge loss after project completion and obstacles to cross-project knowledge accumulation, thereby hindering the formation of a culture of continuous innovation and the deepening of technological development [4,38,39]. The discreteness of organizations is reflected in the geographic, cultural, and professional background differences among participating parties and their internal members, increasing communication costs and the complexity of collaboration and reducing the sharing and application efficiency of innovative solutions [40]. The combined effect of these two project characteristics not only exacerbates the uncertainty of innovation activities but also prompts governments as investors and construction units as managers to adopt a more conservative attitude, thus forming a cycle that restricts innovation development [41].

The in-depth investigation of the technological innovation paradigm of megaprojects and its influencing factors has markedly enhanced our understanding of the nature of megaproject technological innovation and gradually revealed its unique characteristics. Building upon this solid theoretical foundation, there has been a growing interest among scholars in the field regarding how to effectively improve the technological innovation performance of megaprojects, spurring a strong motivation to explore. Particularly noteworthy is the fact that collaborative innovation among industry, academia, and research emerges as a pivotal pathway for promoting the technological innovation of megaprojects, which has become a hot direction of current research. This transition not only underscores the core position of IUR collaborative innovation in megaproject technological innovation but also mirrors the continual broadening and deepening of academic research in this domain.

2.2. Collaborative Innovation Between Industry, Academia and Research in Megaprojects

The concept of IUR collaborative innovation can be traced back to the Wisconsin Idea of the early 20th century, which emphasized the close integration of university research and social practice, laying the foundation for the later IUR collaboration model [42]. Rogers’ “Diffusion of Innovations” theory, on the other hand, provides a theoretical framework for understanding the spread of innovation among different entities [43]. In the 1980s, IUR collaborative innovation entered a stage of comprehensive development, with Nelson and Winter’s evolutionary theoretical framework and Etzkowitz and Leydesdorff’s Triple Helix model offering important theoretical support for understanding the mechanisms of IUR collaborative innovation [44,45]. Furthermore, Motohashi’s analysis of Japan’s IUR collaboration highlights the significant role of such collaboration in driving the development of national innovation systems [46].

With the growing complexity and uncertainty of technological innovation in megaprojects, innovative entities are increasingly reliant on external collaborations to acquire essential innovation resources. In this context, the collaborative innovation model among IUR has gradually emerged as a crucial approach for tackling technological challenges in megaprojects [47,48]. IUR collaborative innovation not only facilitates the integration of resources and advantages from all parties but also expedites the creation, dissemination, and application of knowledge, thereby driving technological innovation and the commercialization of scientific and technological achievements.

In recent years, IUR collaborative innovation research has attracted extensive attention from scholars, both domestically and internationally. Early research hotspots primarily focused on the construction of theoretical frameworks and models, with the Triple Helix model being widely applied in subsequent studies [44]. As research deepened, knowledge transfer and creation in IUR collaborative innovation gradually became a prominent direction. Scholars such as Perkmann and Walsh unveiled the intrinsic mechanisms of IUR collaborative innovation and emphasized the pivotal role of knowledge transfer and creation [49]. Subsequently, IUR collaborative innovation research evolved from single-mode cooperation to collaborative innovation networks. Rycroft et al. delved into the concept of self-organizing innovation networks from a dynamic evolutionary perspective, highlighting the networks’ ability to self-organize and adapt to change [50]. Guan and Zhao, meanwhile, investigated the impact of university–industry collaborative networks on innovation in the nanobiopharmaceutical field, underscoring the significance of network building in facilitating cross-disciplinary knowledge transfer and innovation [51]. With a deeper understanding of the conceptual definition and underlying mechanisms of collaborative innovation, scholars began to concentrate on how to measure its effectiveness. Early studies, such as George et al., provided empirical support for evaluating the efficiency of IUR collaborative innovation from an economic performance perspective [52]. Bai et al. used the Dynamic Network Relaxation Measurement Model (DNSBM) to analyse the dynamics of IUR collaborations in China and to analyse in depth the various aspects of the collaboration process [53]. Recently, IUR collaborative innovation research has increasingly centred on the participation modes of multiple subjects within specific industries or fields. Research emphases include cooperation mechanisms and modes in various scenarios [54], as well as the construction of innovation ecosystems [55]. Notably, IUR collaboration within megaproject innovation ecosystems is a prevalent research direction today [56,57]. This ecosystem urges all stakeholders to innovate, emphasizes resource globalization and comprehensive innovation with interdisciplinary thinking [58], and strives to overcome key technological challenges in megaprojects through deep collaboration and knowledge sharing [59].

3. Methodology

3.1. Case Studies

Case studies show significant advantages in revealing the inner mechanism of complex events in real situations, describing their details and promoting improvements in the theoretical system. We adopt the exploratory case study method and integrate the qualitative research tool of grounded theory, mainly based on the following two considerations: Firstly, in terms of the research object, the IUR collaborative innovation of megaprojects is an abstract concept with rich situational characteristics. Case studies are particularly suitable for answering mechanistic questions, which can broaden the basic framework of the theory, especially when exploring research areas that are still in the initial development stage and the theoretical construction is not yet perfect; the novelty, verifiability, and empirical validity are particularly outstanding [60]. Secondly, from the specific dimension of the research, this study uses grounded theory to explore the interaction mechanism between the innovation subjects of IUR, the coupling mode of co-innovation elements, and the articulation logic between the co-innovation chain and the industrial chain, aiming to unveil the ‘black box’ in the process of IUR co-innovation in megaprojects. The grounded theory provides a powerful research path for issues that have not been studied much in the past or lack a unified theoretical framework. On the basis of clearly defining the scope of research, grounded theory can efficiently classify and organize the case materials, explore the intrinsic links and laws between categories, construct new concepts and theories, and, finally, form a set of systematic theoretical system.

On the basis of the principles of typicality, diversity, and comparability, this study draws on the multi-case study paradigm [61] and carefully selects a number of cases according to the following strict criteria: firstly, the selected cases must be railway projects that face major technical problems in the construction process and have urgent needs for technological innovations; secondly, these cases need to have achieved significant technological breakthroughs or important scientific research results to reflect the effectiveness of collaborative innovation between industry, academia, and research. Further, these cases should have achieved significant technological breakthroughs or important scientific research results to reflect the effectiveness of IUR collaborative innovation. Based on the above rigorous selection criteria, this study finally identifies three representative Chinese railway projects as the object of in-depth study, and the detailed information is shown in

Table 1. Typical cases of railway projects.

No.	Project Name	Major Issues Faced in Engineering Construction	Landmark Technological Innovation Achievements	Features of Collaborative Innovation
1	Qinghai–Xizang Railway	Three global technological challenges: permafrost, extreme cold and hypoxia, and fragile ecology.	A comprehensive set of innovative technologies has been developed for railway construction in high-altitude regions, tackling unique challenges like track laying, train operation, and high-altitude passenger service. Additionally, the application of heat pipe technology represents a significant breakthrough in managing permafrost.	High technical difficulty and complexity, as well as more stringent requirements for environmental protection, require government-led higher level of collaborative innovation between industry, academia and research.
2	Beijing-Shanghai High-Speed Railway	The high-speed railway with the highest technical standards, the largest scale, and the longest construction mileage in one project at that time in the world.	The project has pioneered construction technologies for complex bridge structures, innovated seamless track construction on ballastless systems over ultra-long viaducts, and established a standardized system and comprehensive technology set for 350 km/h high-speed railway construction.	The level of technology integration is much higher than in a typical railway project, and the process of co-innovation also pays more attention to market needs.
3	Beijing-Xiong’an Intercity Railway	There are more than 20 bridges along the railway that cross major interchange projects, posing significant construction safety risks. Additionally, the regions through which the railway passes have extremely high requirements for environmental protection.	The project utilized BIM technology to achieve 3D digital intelligent management across design, construction, and operation, creating a “digital twin” of the intelligent high-speed railway. This significantly improved construction efficiency and accuracy, supporting the railway’s lifecycle management.	Intelligent, digital level features outstanding, the scale of IUR collaboration due to digital means and larger, more obvious network characteristics.

.

3.2. Data Collection

In order to effectively identify the correlations and influencing elements of IUR collaborative innovation in megaprojects, in accordance with the principle of selecting the largest sample and providing the most information, the selection of interview subjects must meet the following requirements. (1) The interviewees were all managers with experience in megaprojects, including department heads, project managers, and research project leaders. (2) To ensure the quality of the interviews, the interviewees were all members of the subject group. (3) The interviews were conducted anonymously for academic research only, and the interviewees did not have to worry about revealing project secrets or deliberately avoiding questions. The basic information provided by the interviewees is shown in

Table 2. Statistics on the background information of the sample.

Sampling Classification of Samples	Categories	Number of People	Proportion (%)
Type of unit	Employers	59	32.6
	Contractors	59	32.6
	Scientific research institution	63	34.8
Job position	Departmental leaders	40	22.1
	Project managers	88	48.6
	Heads of research projects	53	29.3
Education level	Undergraduate	62	34.3
	Master’s degree	93	51.2
	Ph.D.	26	14.5
Years of experience	4–6	70	38.7
	7–9	69	38.1
	≥10	42	23.2

.

This research group has investigated the site of railway engineering projects many times, adopted semi-structured interviews as the data collection method, and tightly focused on the theme of the ‘IUR Collaborative Innovation Mechanism’, following the logic of context introduction, core interview, and in-depth interview, to explore the interviewees’ cognition and understanding of IUR collaborative innovation. The data collection took two years and six months. During this period, the group conducted several railway engineering construction project-related talks with the interviewees according to the questions listed in the interview outline, combined with the actual situation on the site, and collated the audio and video recordings into written materials, resulting in a total of 80 interview memos. Among them, 70 were randomly selected for coding, and the remaining 10 were used for theoretical saturation testing. Based on the ‘open coding—axial coding—selective coding’ procedural grounded theory methodological steps proposed by Strauss et al., we used Nvivo 11 software to code and analyse the compiled interview transcripts. Theoretical saturation was rigorously tested to ensure the reliability and validity of the study. The terms and definitions used in the existing literature on the relevance of university–industry co-innovation and the factors influencing megaprojects were also taken into account.

3.3. Data Analysis

3.3.1. Open Coding

The open coding process consists of primary data collection, labelling, conceptualization, and categorization, in turn. Firstly, key words and phrases related to the IUR collaborative innovation mechanism of megaprojects were tagged from the case materials, and 170 tag nodes were established by differentiating different phenomena for preliminary refinement and assigning the coding prefix ‘a’ to these refinements. Subsequently, by repeatedly comparing the newly collected data with the original data, the tagged nodes with similar descriptions and belonging to the same phenomenon were grouped under the same tree node, and these nodes were assigned the coding prefix ‘A’, with each node representing a concept, and a total of 49 concept nodes were obtained. Finally, the refined concepts were further filtered and merged to construct different categories, which were assigned the coding prefix ‘B’, and, finally, 10 category nodes were obtained. In summary, through this series of processes, we finally obtained 170 labels, 49 concepts, and 10 categories related to the IUR collaborative innovation mechanism of mega projects, as shown in

Table 3. Open coding (partial).

Original Case Material	Labelling	Conceptualization	Categorizing
The government has issued a number of supporting policies for innovation, which guarantees the reasonable compliance of technological innovation activities; the owner actively coordinates all units and deeply participates in technological innovation activities, which greatly promotes the technological research work.	$a_8$ Innovation supporting policies and coordinates	$A_5$ Supporting and coordination	$B_1$ Deep integration of the government and project owners into the innovation process
There is already good experience of cooperation between most of the core subjects in railway engineering and construction projects.	$a_{22}$ Foundation for scientific research cooperation	$A_{14}$ Smooth communication	$B_2$ Frequent knowledge and technology interactions among key innovation entities
The key collaborative innovation participants, primarily from the railway industry, work closely and communicate effectively to keep abreast of industry trends, share innovation achievements, and jointly address challenges.	$a_{31}$ Key collaborative innovation entities related to the railway industry	$A_{18}$ Effective synchronization	$B_3$ Collaborative ideology
The Ministry of Science and Technology and the Ministry of Railways, relying on the Beijing-Shanghai High-Speed Railway project, jointly signed the “Joint Action Plan for Independent Innovation of China’s High-Speed Trains” and established a deeply integrated innovation consortium.	$a_{47}$ Innovation consortium	$A_{21}$ Novel mode of collaborative innovation organization	$B_4$ Collaborative organization
In order to promptly verify scientific research achievements, the Beijing-Shanghai High-Speed Railway took into account the construction of experimental sections and test sites in its project implementation plan, thereby providing institutional and mechanistic support for technological innovation.	$a_{55}$ Experimental sections and test sites	$A_{24}$ Institutional and mechanistic safeguards	$B_5$ Collaborative institution
China Railway Bridge Bureau and Wuhan Iron and Steel Corporation jointly leveraged their respective strengths in innovation capabilities and resources to collaboratively develop a new steel grade with high strength, high toughness, and excellent welding performance for application in the Dashengguan Yangtze River Bridge.	$a_{74}$ Collaboratively develop	$A_{29}$ Complementary resources	$B_6$ Collaborative resources
At the inception of the development of the “Fuxing” high-speed train, its purpose was clearly defined: upon successful development, it would be widely used in high-speed rail, with those who invest reaping the benefits.	$a_{85}$ Benefit-sharing mechanism	$A_{32}$ Benefit sharing	$B_7$ Innovation benefits
For the Beijing-Shanghai High-Speed Railway project, managers are required to possess not only a rigorous scientific attitude but also the courage to bear responsibilities and boldly arrange experiments.	$a_{115}$ Dare to bear responsibilities	$A_{36}$ Risk sharing	$B_8$ Innovation risk
Based on the scientific research needs of major railway engineering projects, the project owner organizes key innovation entities to carry out goal-oriented and application scenario-driven technological innovation activities.	$a_{131}$ Goal-oriented technological innovation	$A_{40}$ Efficient model for the transformation of research outcomes	$B_9$ Innovation chain
As the “chain leader”, CRRC (China Railway Rolling Stock Corporation) has facilitated the coordinated development of over 6900 “chain enterprises” spanning various sectors, including raw material suppliers, electronic and electrical manufacturers, information system integrators, and more, collectively driving the advancement of the industrial chain for the Fuxing EMU (Electric Multiple Unit) project.	$a_{163}$ Full-chain upstream and downstream enterprise collaboration	$A_{45}$ Value creation	$B_{10}$ Industrial chain

.

3.3.2. Spindle Coding

Axis coding is a stage of in-depth analysis that builds on the results of open coding and is centred on the systematic interconnection of identified categories (or conceptual categories) using the logical framework of antecedent conditions—action/interaction strategies—outcome. This process allows us to reveal the extent of the problem. Through this process, we were able to reveal the interconnections and interactions between the categories, further refine the main categories and sub-categories, and ensure that all the research data could be integrated in an orderly manner to form a coherent and strong chain of evidence, which provided a solid foundation for the theoretical research. The specific coding results and logical relationships are shown in

Table 4. Axial coding results of typical cases of “IUR co-Innovation”.

Core Category	Subcategory	Category Definition
Key innovation entities	$B_1$ Deep integration of the government and project owner into the innovation process	The government creates a favourable environment for collaborative innovation, and the owner actively coordinates engineering construction activities and technological innovation activities to promote the deep integration of industry, academia and research.
	$B_2$ Frequent knowledge and technology interactions among key innovation entities	Based on the good experience of past cooperation and the innovative atmosphere of the project, the frequent flow of engineering and construction experience, knowledge and technology between the core subjects such as the industry, academia and research institutes stimulates more opportunities for innovation.
Collaborative elements	$B_3$ Collaborative ideology	The participants in collaborative innovation of industry, academia, and research should shift their paradigms, establish a shared goal orientation, and thereby drive the rational allocation of other resources, promoting the improvement of technological innovation performance.
	$B_4$ Collaborative organization	Led by megaprojects, industry-leading enterprises, research institutes, and universities form a multi-level innovation organization system that emphasizes deep integration and close cooperation. This system encompasses collaborative innovation departments, teams, and consortia, aiming to accelerate technological innovation, achievement transformation, and industrial upgrading through resource integration, complementary advantages, and collaborative research.
	$B_5$ Collaborative institution	Given the urgency of technological innovation and problem-solving in megaprojects, establish special interim measures to facilitate the smooth implementation of collaborative innovation activities.
	$B_6$ Collaborative resources	During the implementation of megaprojects, various resource elements are collaboratively utilized to achieve common technological innovation goals, including tangible physical resources such as equipment and funds, as well as intangible resources such as knowledge and technology.
	$B_7$ Innovation benefits	In the process of technological innovation, achievement transformation, and industrial upgrading jointly carried out by industry, academia, and research institutions, the sum of economic benefits, social benefits, and other related interests generated constitutes the comprehensive benefits of collaborative innovation among industry, academia, and research.
	$B_8$ Innovation risk	In the process of technological innovation, achievement transformation, and industrial upgrading jointly carried out by industry, academia, and research, there is a possibility of losses or adverse impacts on various parties due to various uncertainties that may prevent the achievement of innovation goals or the expected cooperation effects.
Collaborative innovation value chain	$B_9$ Innovation chain	The innovation chain serves as a bridge between basic research and industrial application, encompassing crucial stages such as applied research, technological development, product design, and manufacturing. Guided by the construction demands of megaprojects, enterprises identify technological needs and research directions. Leveraging their scientific research strengths, universities and research institutions conduct applied research and technological development to generate innovative outcomes with market competitiveness.
	$B_{10}$ Industrial chain	The industrial chain is an extension and expansion of the innovation chain, transforming innovative outcomes into real productive forces and realizing economic value and social benefits. Within the industrial chain, enterprises serve as the main body, responsible for converting innovative achievements into technological systems, processes, equipment, facilities, and materials, and supporting engineering construction through demonstration applications in megaprojects.

.

3.3.3. Selective Encoding

In the selective coding stage of grounded theory, the core work is to identify the ‘core category’, which serves as the centre of theory construction and around which the logical framework is built, forming the ‘story line’ of the theoretical model. This stage also emphasizes the continuous incorporation of new empirical information and the interactive verification of the preliminary theoretical framework, in order to improve the linkage between the categories and ultimately to construct a full and comprehensive theoretical model, which can explain in depth the mechanism of IUR cooperation and innovation in major railway engineering projects.

Through tertiary coding analysis of typical cases, this study reveals the core story line of the megaproject’s industry–academia–research collaborative innovation development; multiple entities achieve efficient synergy and interaction under the strong support provided by the government and owners. The construction demand of the megaproject became the driving force for the unification of the project vision, which prompted the clustering of advantageous resources, the adjustment of organizational modes and systems, as well as the unification of benefits and risks, so as to build a targeted, organised, and planned system of scientific research and technological research. In addition, the key supporting role of the industry’s leading enterprises should not be ignored, as they have independently formed an innovation ecosystem, stimulated the synergistic innovation dynamics of upstream and downstream enterprises, greatly facilitated the scientific research results to cross the ‘valley of death’, rapidly transformed into actual productivity, and provided strong support for the project construction. Based on this story line, this study identifies ‘megaprojects, IUR collaborative innovation’ as the core category, as shown in

Table 5. Paradigm patterns of axial coding.

Core Category		Sub-Categories
	Causal Conditions	Action/Interaction Strategies	Consequences
Key innovation entities	Strong support provided by the government and owners.	Unified goals and long-term collaboration among government, industry, academia, research, and application.	Efficient multi-entity collaboration and interaction.
Collaborative elements	Unified vision, Support from a joint organizational structure, Adaptation of innovative institutional frameworks, Concentration of resources for large-scale projects, Unification of innovation benefits and risks	Major scientific research needs and key common technology research and development to attract advantageous resources gathering.	A targeted, organised and planned system of scientific research and technological research.
Collaborative innovation value chain	The pivotal supporting role of industry-leading enterprises.	Forming innovation ecosystem independently, stimulating upstream and downstream synergistic innovation dynamics	The scientific research results crossed the “valley of death” and were rapidly transformed into productivity to support the construction of the project.

. Through in-depth analysis of the intrinsic connection between the three main categories and the sub-categories and combined with the original data for comparison and verification, this study constructs a model of collaborative innovation mechanism embedded in each category, as shown in Figure 1. Figure 1 elucidates the characteristics of IUR collaborative innovation in major engineering, categorized into three levels: micro, meso, and macro. Micro-level characteristics primarily manifest in the key entities comprising government, owners, enterprises, research institutes, and universities. Meso-level characteristics are predominantly embodied in core synergistic factors, including concept, organization, system, and resources. Macro-level characteristics are manifested in the synergistic innovation value chain resulting from the integration of the innovation and industrial chains. Notably, the micro- and macro-level characteristics of collaborative innovation are not isolated entities; rather, they are intricately intertwined with the support provided by meso-level synergistic factors. These factors, in the form of factor flows, facilitate flexible circulation and optimal allocation, thereby creating the requisite conditions and environment for the innovative endeavours of micro-entities, and, concurrently, they establish a robust foundation for the formation and extension of the macro-level innovation value chain.

3.4. Theoretical Saturation Validation

In constructing a theoretical framework for IUR collaborative innovation in major railway projects, the implementation of a theoretical saturation test is a crucial part of the process, which aims to investigate whether new core concepts or categories emerge from the additional information. This test builds on existing concepts and categories to guide the coding of subsequent additions and iteratively optimises the coding system based on the newly discovered concepts and categories. Coding analyses of the 10 interview memos set aside for this study did not reveal the emergence of new concepts or categories. The results of this series of analyses indicate that the megaproject IUR collaborative innovation mechanism model proposed in this study has a high degree of theoretical saturation.

This study employs the following methodologies to validate the theoretical saturation: (1) Following an iterative process of data collection and analysis, and after establishing the influencing factors and framework model of collaborative innovation among industry, universities, and research institutions in megaprojects, this study further engaged two experts in in-depth interviews, posing the same questions as those asked of previous interviewees. The interview outcomes indicated that these experts did not introduce any new concepts or categories that deviated significantly from the existing model, thereby providing initial validation of the model’s saturation. (2) To further assess saturation, this study conducted a comprehensive coding analysis of 10 interview transcripts that were deliberately reserved for this purpose. The analysis results revealed that no novel concepts or classifications emerged from these transcripts, reinforcing the model’s high saturation. (3) Additionally, this study adopted a peer review approach to assess reliability. Specifically, two team members independently coded the results, and their coding outcomes were randomly selected for pairwise comparisons and subsequent recoding. The consistency of the coding results was evaluated by calculating the number of categories on which both coders agreed. The reliability coefficient, calculated using the appropriate formula, was 0.946, indicating a high degree of reliability in the coding process.

4. Case Analysis and Findings

4.1. Analysis of the Prominent Characteristics of IUR Collaborative Innovation in Megaprojects at the Micro Level

Unlike typical construction projects, the deep integration of governments and project owners has emerged as a distinctive feature of IUR collaborative innovation in major engineering projects [6], as exemplified by the cases presented in

Table 6. Typical cases exemplifying the characteristics of deep integration collaborative innovation involving governments and project owner.

Subcategory	Conceptualization	NO.	Examples of Typical Events
$B_1$ Deep integration of the government and project owner into the innovation process	$A_5$ Supporting and coordination	1	The Qinghai-Tibet Railway Company, representing the Ministry of Railways, deeply engaged in the project’s construction. From the initial planning stage, the company organized research endeavours, carefully selected innovation entities, coordinated resources, and rigorously approved technical proposals. Leveraging extensive experience in railway construction, it successfully addressed the challenge of permafrost thaw and settlement through innovative technologies such as active cooling and heat pipes, ensuring the railway’s safety and stability.
		2	During the Beijing-Shanghai High-Speed Railway project, the Ministries of Railways and Science & Technology jointly launched the “High-Speed Train Innovation Program”. The Beijing-Shanghai High-Speed Railway Company, in response, formed an industry–academia–research alliance. With meticulous coordination, participants conducted in-depth research and regular technical checks, ensuring effective innovation integration. This led to successful autonomous design and manufacturing of high-speed trains, markedly improving their performance and competitiveness.
		3	As the project owner, Xiong’an High-Speed Railway Co., Ltd. profoundly recognized the importance of BIM technology in managing complex engineering projects. Therefore, it took the lead in BIM implementation, planned the application path for the entire project lifecycle, closely connected all parties, ensured smooth BIM data sharing, and significantly enhanced project management efficiency.

. This deep integration is not only manifested in the precise grasp and responsive action to project innovation needs but, also, through a series of policy adjustments and efficient communication mechanisms, providing solid guarantees and support for the smooth progress of innovation activities. The deep integration of governments and project owners first manifests in their deep understanding and identification of innovation needs. As policy makers and resource allocators, governments, through thorough research and expert consultation, can accurately grasp the overall direction and potential challenges of technological breakthroughs in major engineering projects. On this basis, governments construct a favourable institutional environment and financial support system for innovation activities by adjusting science and technology policies, providing financial support, optimizing tax incentives, and other measures [62], for instance, establishing special research and development (R&D) funds to support breakthroughs in key technologies, formulating industry standards to promote the transformation and application of technological achievements, and strengthening intellectual property protection to incentivize the generation and protection of innovative outcomes. These policy adjustments not only provide necessary resource guarantees for innovation activities but also promote the healthy development of the innovation ecosystem through institutional guidance. The project owner, as the direct initiator and ultimate user of the engineering project, demonstrates its deep integration primarily through refined project management and efficient communication mechanisms. By establishing cross-organizational communication platforms such as project coordination meetings and joint R&D teams, the project owner ensures the flow of information and knowledge sharing among various innovation entities [63]. This deep integration not only facilitates the prompt resolution of technical challenges during project implementation but also promotes the collision and integration of innovative ideas, driving engineering innovation towards deeper levels and broader domains. The active participation and deep integration of the project owner not only enhance the quality and progress of the project but also mitigate project risks and elevate the overall value of the project.

There are profound motivations underlying the deep integration of governments and project owners in major engineering projects. From the government’s perspective, major projects often have significant implications for national economic development, social progress, and improvements in people’s livelihoods. Their successful implementation plays a pivotal role in enhancing national competitiveness and fostering regional economic growth. Consequently, the government is motivated to ensure the seamless progression of engineering innovation activities through deep integration, aiming to maximize both economic and social benefits. For project owners, deep integration is an inevitable choice to guarantee project quality, adhere to schedules, mitigate risks, and enhance the overall project value. In the context of highly uncertain engineering environments, project owners can more accurately gauge market demands and technological trends through deep involvement in the innovation process, enabling them to adjust project strategies promptly and ensure that project outcomes meet or even surpass anticipated objectives.

Another notable characteristic at the micro level of major projects is that key innovative entities within these projects often possess extensive and favourable prior collaboration experience, laying a solid foundation for their interactive integration [64], as exemplified by the cases presented in

Table 7. Typical cases exemplifying the characteristics of frequent knowledge and technology interactions among key innovation entities.

Subcategory	Conceptualization	NO.	Examples of Typical Events
$B_2$ Frequent knowledge and technology interactions among key innovation entities	$A_{14}$ Smooth communication	1	In the Qinghai-Tibet Railway project, confronted with the core challenge of plateau permafrost, the project was able to swiftly integrate the advantageous resources of various innovation entities, particularly the extensive research accumulations of the China Academy of Railway Sciences and the Chinese Academy of Sciences in plateau permafrost, by relying on the long-standing and excellent cooperation experience among participating construction and research units. This greatly promoted innovations in the treatment of plateau permafrost for the Qinghai-Tibet Railway.
		2	In the early stages of the project, the China Academy of Railway Sciences collaborated extensively with participating units to conduct numerous technical studies and experiments. These cooperative experiences fostered close technical ties and a sharing mechanism among the units.
		3	In the Beijing-Xiong’an Intercity Railway construction project, China Railway Construction Group and China Railway Design Group, leveraging their extensive experience in Building Information Modelling technology accumulated from previous collaborations, successfully established and operated a more efficient three-dimensional digital intelligent management system.

. Firstly, the history of past collaborations has accumulated valuable trust capital among the participants. Through numerous instances of collaborative work, where they have confronted various challenges and issues together, they have gradually forged a deep trust in each other’s capabilities and credibility [65]. This trust forms a cornerstone that is particularly crucial in major construction projects, as it motivates participants to engage in more open information exchange, resource sharing, and risk sharing, thereby significantly enhancing the depth and scope of cooperation [66,67]. Concurrently, an efficient and optimized communication mechanism has emerged, ensuring the timely flow of information and swift responses to problems, thus providing a robust guarantee for the steady progression of the project. Secondly, past cooperation experiences have greatly facilitated the exchange and sharing of technologies and knowledge, as well as stimulating innovation. The technical expertise and successful practices accumulated by each participant in their respective fields have been effectively transferred and applied in new projects. Through the organization of technical seminars, professional training, and other activities, participants have been able to rapidly absorb the core technologies of others and integrate them into project implementation. This bidirectional flow of technical knowledge not only accelerates the pace of technological innovation but also markedly improves the pertinence and efficacy of technical solutions. Furthermore, a collaborative paradigm characterized by joint R&D and concentrated problem-solving efforts has gradually emerged. Participants are able to swiftly consolidate their resources, assemble interdisciplinary R&D teams, and collectively tackle technical bottlenecks, thereby establishing a robust technical foundation for the successful accomplishment of the project. Lastly, the cooperation experiences from the past hold immense value in optimizing management procedures and enhancing collaborative efficiency. The project management knowledge accumulated by all parties through previous collaborations has been effectively referenced and integrated into new projects. Through continual refinement and innovation of management processes, participants have achieved heightened collaborative efficiency, effectively mitigating redundancies and delays within the workflow. Additionally, a range of innovative collaborative work modes have been actively explored and implemented, including digital and intelligent management tools and platforms. These not only facilitate seamless remote collaboration but also enable real-time monitoring and dynamic adjustment of project progress, further augmenting the efficiency and flexibility of collaborative endeavours.

4.2. Analysis of the Prominent Characteristics of IUR Collaborative Innovation in Megaprojects at the Meso Level

The characteristics of the meso-level elements of IUR collaborative innovation in major engineering projects are primarily manifested in the four dimensions of ideology, organization, system, and resources.

A notable feature of major projects at the ideology level is the concentration of participating construction and research entities within the same industry, as exemplified by the cases presented in

Table 8. Typical cases exemplifying the characteristics of collaborative ideology.

Subcategory	Conceptualization	NO.	Examples of Typical Events
$B_3$ Collaborative ideology	$A_{18}$ Effective synchronization	1	The construction entities of the Qinghai-Tibet Railway primarily comprise China Railway Engineering Group Co., Ltd. and China Railway Construction Corporation. The research entities are mainly led by the Northwest Research Institute of the China Academy of Railway Sciences, the First Railway Survey and Design Institute of the Ministry of Railways, and Shijiazhuang Tiedao University.
		2	The entities involved in the construction of the Beijing-Shanghai High-Speed Railway primarily comprise China Railway Engineering Group Co., Ltd., China Railway Construction Corporation, and Sinohydro Corporation. The research entities primarily include the China Academy of Railway Sciences, the Third Railway Survey and Design Institute Group Corporation, the Fourth Railway Survey and Design Institute Group Corporation, CSR Qingdao Sifang Co., Ltd., CRRC Changchun Railway Vehicles Co., Ltd., as well as academic institutions such as Shijiazhuang Tiedao University and Beijing Jiaotong University.
		3	The entities involved in the construction of the Beijing-Xiong’an Intercity Railway primarily comprise China Railway Engineering Corporation, China Railway Construction Corporation, China Railway Rolling Stock Corporation, China Railway Signal & Communication Corporation, among others. The research entities primarily include the China Academy of Railway Sciences and China Railway Design Group Co., Ltd.

. This industry-focused nature fosters a profound industry cognitive resonance and a shared values system, providing fertile ground for the cultivation of a collaborative innovation culture [68]. Taking major railway engineering projects as an example, the core construction forces typically originate from leading enterprises in the railway industry, such as China Railway Group, China Railway Construction Corporation, and China Railway Design Corporation. Research support, on the other hand, is primarily provided by the China Academy of Railway Sciences and universities closely affiliated with the railway sector, including Beijing Jiaotong University, Southwest Jiaotong University, and Shijiazhuang Tiedao University.

There are several main reasons for this characterisation. Firstly, the railway industry, as a paradigm of high specialization and technology intensity, has allowed its internal enterprises to accumulate deep industry insights and professional knowledge through long-term practice. When these enterprises converge around the same major railway project, their professional knowledge and experiences intertwine deeply, not only demonstrating precise mastery of railway engineering technologies but, also, more importantly, achieving a high level of consensus on project objectives, challenges faced, and solutions proposed. This industry cognitive resonance forms a solid cognitive foundation for industry–academia–research collaborative innovation, facilitating rapid engagement among partners, reducing communication costs, and accelerating the collaborative process. Secondly, enterprises within the railway industry generally adhere to a comparable value system, emphasizing core principles, such as safety primacy, quality supremacy, and innovation-driven development. In major railway engineering projects, these values are not merely reflected but are further reinforced and aligned through project implementation. While striving for economic benefits, all stakeholders prioritize the societal contributions and long-term impacts of the projects, deeply embedding the core values of the railway industry into the entire lifecycle, from design and construction to operation and maintenance. This integration underscores the industry’s unique characteristics and competitive advantages. The resonance and reinforcement of these values not only elevate the overall quality of the projects but also significantly bolster the sense of responsibility and mission among team members, providing a potent spiritual impetus for the smooth progression and high-quality completion of the projects [69]. Lastly, successful instances of IUR collaborative innovation in major railway projects have not only attained breakthroughs at the technical level but have also exerted a leading and exemplary influence at the ideology level. These cases have illustrated to the railway industry and broader domains the pivotal role of collaborative innovation in advancing technological progress and enhancing industry competitiveness. Through the successful implementation of these projects, enterprises in the railway industry have developed a more profound understanding of the value and significance of collaborative innovation. This realization is expected to motivate more enterprises to actively engage in IUR collaboration, fostering a more extensive and in-depth cooperation network to jointly propel the continuous innovation and upgrading of the railway industry.

Hierarchy is a distinctive characteristic of the organizational structure in collaborative innovation among industry, universities, and research institutions in major projects [70], particularly evident in the multi-layered organizational model established between “governmental regulatory authorities, project owners, and innovation entities”. The cases presented in

Table 9. Typical cases exemplifying the characteristics of hierarchical organizational model.

Subcategory	Conceptualization	NO.	Examples of Typical Events
$B_4$ Collaborative organization	$A_{21}$ Novel mode of collaborative innovation organization	1	In the Qinghai-Tibet Railway project, the Ministry of Railways and Qinghai-Tibet Railway Company collaboratively established a scientific and technological leadership body, working teams, an expert advisory group, and a research coordination department, effectively integrating multidisciplinary resources and constructing a joint innovation system cantered on the China Railway First Survey and Design Institute and the China Academy of Railway Sciences.
		2	In the Beijing-Shanghai High-Speed Railway project, the Ministry of Science and Technology and the Ministry of Railways jointly signed the “China High-Speed Train Independent Innovation Joint Action Plan”, and subsequently established a dual-entity leadership team, a comprehensive expert group, and a planning and management office, providing a solid organizational and institutional foundation for the smooth advancement of the project.
		3	In the Jing-Xiong Railway project, the Xiong’an County Government initiated the formation of a high-level coordination leadership team, responsible for comprehensive oversight and coordination, with a particular focus on critical decisions related to land acquisition and demolition to ensure efficient execution. Xiong’an High-Speed Railway Co., Ltd. assumed the key roles of overall planning, organization, coordination, and management, ensuring smooth implementation and deep integration of collaborative innovation activities among industry, academia, and research institutions.

illustrate that this hierarchical structure not only ensures a clear delineation of roles among participants and the efficient execution of their respective functions but also offers a robust institutional framework and mechanistic support for the profound advancement of IUR collaborative innovation [71]. At the apex of this hierarchical structure lies the decision-making level, which is predominantly occupied by governmental regulatory authorities. The primary responsibility of this level is to provide macro-level guidance and strategic decision making, outlining a clear direction and framework for IUR collaborative innovation activities through the formulation of long-term development plans, policies, regulations, and industry standards. Leveraging their authority and credibility, governmental regulatory authorities can effectively mediate the interests of all stakeholders, resolve significant disputes and conflicts during the cooperation process, and ensure consistency in project objectives and visions. For instance, in the Beijing-Xiong’an Intercity Railway project, the senior leadership group led by the Xiong’an New Area government played a pivotal role in advancing critical tasks such as land acquisition and relocation.

The management tier, steered by the project owner, serves as the pivotal linkage between the decision-making tier and the execution tier. The project owner bears not only the responsibility for specific construction management tasks, encompassing financing, schedule control, quality assurance, and the like, but also the crucial mission of directing technological research endeavours and fostering collaboration among industry, academia, and research institutions. Through the establishment of specialized funds, the organization of technical symposia, and the creation of communication platforms, the project owner stimulates the dynamism of innovation entities and facilitates the rapid commercialization and application of scientific and technological advancements. The efficient functioning of the management tier ensures that the project progresses efficiently within the established objectives and frameworks while simultaneously furnishing innovation entities with essential resource support and market demand orientation, thereby enhancing the relevance and practicality of technological innovations.

The executive tier, comprising pivotal innovation entities such as universities, research institutions, and high-tech enterprises, serves as the direct implementer of collaborative innovation activities among industry, academia, and research institutions. In this tier, innovation units forge close cooperative relationships grounded in shared objectives and interests, engaging in targeted collaborative innovation endeavours through resource sharing, personnel exchanges, joint R&D, and other modalities. The dynamism and efficiency of the executive tier are intricately linked to the quality and speed of the translation of scientific and technological achievements. For instance, in the Jingxiong Railway project, core innovation units including the First Survey and Design Institute of China Railway and the China Academy of Railway Sciences, under the stewardship of the project owner, collaborated closely to not only resolve technical challenges encountered during construction but also to propel continuous innovation and advancement in high-speed railway technology.

The layered organizational structure achieves the systematization and efficiency of collaborative innovation activities among industry, academia, and research institutions through the explicit delineation and synergistic integration of the decision-making tier, management tier, and execution tier. The strategic guidance provided by the decision-making tier sets the direction and impetus for the entire project; the efficient management and technological orientation of the management tier ensure the seamless execution of the project and steer the course of technological innovation, whereas the close collaboration and commercialization of outputs at the execution tier are pivotal to unlocking the value of technological innovations. These three tiers complement one another, constituting a closed-loop collaborative innovation system that not only guarantees the scientific rigor and forward thinking of decision making but also ensures efficient and innovative implementation, thereby significantly boosting the success rate and impact of collaborative innovation endeavours in major industry–academia–research projects.

Institutional adaptability, a salient characteristic of the collaborative innovation system for industry–academia–research in major projects, centres on a meticulously crafted array of policies and institutions tailored to foster the development of major projects and ensure the legitimacy and compliance of technological innovation endeavours. These policies and institutions establish a robust yet adaptable legal framework and operational protocols, which not only lay a solid groundwork for industry–academia–research collaboration but also, through ongoing refinement and innovation, facilitate the optimal allocation and deep integration of resources, thereby accelerating the pace of technological innovation and markedly enhancing the overall performance of major projects [72].

The innovativeness and flexibility inherent in institutional adaptability are pivotal to the efficacy of this attribute [73], as exemplified by the cases presented in

Table 10. Typical cases exemplifying the characteristics of collaborative institution.

Subcategory	Conceptualization	NO.	Examples of Typical Events
$B_5$ Collaborative institution	$A_{24}$ Institutional and mechanistic safeguards	1	In the construction of the Qinghai-Tibet Railway, addressing the technical challenges posed by plateau permafrost, the Ministry of Railways meticulously planned and executed a series of major research projects, while also introducing relevant policies and systems to support the establishment of permafrost research test sections. These test sections significantly facilitated the rapid validation and efficient application of permafrost research findings in actual construction environments, markedly shortening the time span from scientific research to practical application.
		2	In the Beijing-Shanghai High-Speed Railway project, facing the limitations of traditional construction systems, administrative personnel were innovatively integrated into the expert team, leveraging their dual expertise in technology and management. Additionally, the project adopted an innovative management system oriented towards preliminary testing, accelerating the translation of technological innovations into on-site applications and significantly enhancing the efficiency and effectiveness of industry–academia–research collaboration.

. In the face of emerging challenges and issues that continually arise during the construction of major projects, governments and industry regulatory bodies can swiftly respond by agilely adjusting policy orientations and optimizing institutional designs, aligning them with project progress, technological advancements, and actual market demands. For instance, the establishment of the Qinghai-Tibet Plateau permafrost research and test site, as well as the breakthrough in traditional management practices within the Beijing-Shanghai High-Speed Rail project, exemplify the crucial role of institutional adaptability in addressing the complexities of major engineering endeavours. These instances not only showcase the practical capabilities of institutional adaptability but also validate its effectiveness in steering industry–academia–research collaborative innovation endeavours in the right direction.

Policy guidance, constituting another vital facet of institutional adaptability, actively incentivizes participation from industry, academia, and research entities in R&D and innovation activities related to major engineering projects through diverse measures, such as financial subsidies, tax incentives, specialized funds, and research awards. These policies not only diminish the costs associated with innovation and augment the returns on innovation but also facilitate the circulation and integration of innovative elements, including knowledge, technology, and talent, through the initiation of research projects, the establishment of cooperation platforms, and the organization of technological exchange events. For example, numerous key R&D programs and projects funded by the National Natural Science Foundation of China, as initiated by the Ministry of Science and Technology, have provided robust policy support for fostering deep-seated industry–academia–research collaboration and technological innovation. These policies not only underscore the government’s emphasis on industry–academia–research collaboration and innovation but also underscore the central role of institutional adaptability in propelling technological innovation within major projects.

In examining the resource characteristics of IUR collaborative innovation in major engineering projects, the precise matching of benefits and risks undoubtedly stands as the most prominent and central aspect. This primarily stems from the inherent complexity and uncertainty of major engineering projects. Such projects typically involve substantial investments, prolonged R&D periods, participation from multiple stakeholders, and a high degree of technological integration. These factors collectively contribute to the diversity of interest demands and the complexity of risk factors within the cooperative process [74]. In this context, ensuring the equitable distribution of benefits and effective sharing of risks among all parties becomes crucial for the smooth progression and sustained development of the collaboration, as exemplified by the cases presented in

Table 11. Typical cases exemplifying the characteristics of collaborative resources.

Subcategory	Conceptualization	NO.	Examples of Typical Events
$B_6$ Collaborative resources	$A_{29}$ Complementary resources	1	Taking the “Fuxing” high-speed train, which entered commercial operation in 2017, as an example, this project marks a significant enhancement in China’s independent R&D capabilities and demonstrates the international leadership of China’s high-speed rail technology. In consideration of the immense challenges and innovative risks involved in its development, the principle of “who invests, who benefits” was established at the outset of the project, attracting multiple parties to participate and share the risks. Under the leadership of China Railway Corporation, through diversified investments totalling approximately CNY 4.5 billion, the project was ultimately successfully implemented. This not only promoted the development of the industry chain but also brought substantial returns to investors, highlighting the crucial role of aligning interests with risks in IUR collaborative innovation in major engineering projects.
$B_7$ Innovation benefits	$A_{32}$ Benefit sharing
$B_8$ Innovation risk	$A_{36}$ Risk sharing

. Consequently, emphasizing the alignment of benefits and risks not only addresses the practical needs of cooperation but also represents an inherent requirement for promoting the in-depth development of IUR collaborative innovation [75,76].

The primary impacts of resource allocation, grounded in the principle of aligning interests and risks, on IUR collaborative innovation are manifested in the following aspects. First, by delineating the interest demands and risk appetites of all parties involved and establishing a scientifically sound and rational mechanism for benefit distribution and risk sharing, the trust and sense of belonging among the collaborators can be bolstered, thereby fostering the stability and longevity of the partnership. This, in turn, aids in mitigating conflicts and disagreements during the cooperative process and ensures collective dedication towards the accomplishment of innovation objectives. Second, when the interests of collaborating entities are adequately protected and risks are effectively shared, they are more inclined to invest resources, share knowledge, and exchange technologies, thereby igniting the spark of innovation. The unleashing of this vitality facilitates technological breakthroughs and industrial upgrading in major projects, ultimately providing a robust impetus for economic and social development. Third, the emphasis on aligning interests and risks also contributes to enhancing cooperative efficiency. Through optimizing resource allocation and minimizing unnecessary redundancy and wastage, the collaborating parties can attain their innovation goals with greater efficiency. This not only aids in shortening the R&D cycle and reducing R&D expenditures but also elevates the quality and market competitiveness of the innovation outcomes. Fourth, in the context of IUR collaborative innovation in major engineering endeavours, risks are inherent. Nonetheless, by prioritizing the alignment of interests and risks, all parties involved can collaboratively identify and address potential risks, thereby reinforcing the resilience of the entire cooperative framework. This ensures that the collaboration remains stable and sustainable in the face of adversity.

4.3. Analysis of the Prominent Characteristics of IUR Collaborative Innovation in Megaprojects at the Macro Level

In major engineering projects, the horizontal and vertical integration of the innovation chain and the industrial chain constitutes a distinctive characteristic of the macro-level innovation value chain. This integration permeates every stage, from basic research and technology development to product application and market promotion, embodying a strategic intention of whole-chain collaboration [77,78]. The innovation chain, serving as the core driving force of these projects, is guided by market demand and emphasizes the progressive process of value creation and appreciation. It converges diverse innovation actors and aims to facilitate the industrialization of innovative outcomes through optimal resource allocation and integration, thereby forming a network-like structural system. This chain prominently exhibits high-end attributes, such as market orientation, value creativity, actor diversity, and resource integration. Conversely, the industrial chain represents the physical pathway for realizing the product lifecycle [79,80], encompassing every stage from raw material procurement, manufacturing, and production to sales and after-sales service, thus constituting a comprehensive closed loop for product value realization.

In the context of a horizontal layout, major engineering projects emphasize the collaboration among cross-sectional innovation entities, encompassing universities, research institutions, and enterprises. Through deep cooperation and resource sharing, these entities broaden the scope of innovation, facilitate the flow and complementarity of innovation elements such as technology, knowledge, and talent, and foster an open and collaborative innovation ecosystem. The vertical layout, on the other hand, centres on the continuity and systematic development of innovation activities. It starts with basic research for theoretical exploration, progresses through technology development and product prototype testing, and culminates in market validation and promotion, thereby forming a seamless innovation chain. This layout not only ensures the coherence of the innovation process but also significantly expedites the translation of scientific and technological advancements into practical applications. The organic fusion of horizontal and vertical layouts achieves efficient allocation and deep integration of innovation resources, as exemplified by the cases presented in

Table 12. Typical cases exemplifying the characteristics of innovation value chain.

Subcategory	Conceptualization	NO.	Examples of Typical Events
$B_9$ Innovation chain	$A_{40}$ Efficient model for the transformation of research outcomes	1	In the R&D process of high-speed rail technology, the innovation chain and industry chain are deeply integrated, with close collaboration among industry, academia, research institutions, and end-users, jointly driving technological innovation and standard formulation. China Railway Rolling Stock Corporation (CRRC) and its supply chain partners have established a comprehensive industrial ecosystem, achieving efficient integration of resources. This collaborative innovation model has facilitated the continuous upgrading of high-speed rail technology and successfully produced the “Fuxing” high-speed train, demonstrating China’s international leadership in high-speed rail technology.
$B_{10}$ Industrial chain	$A_{45}$ Value creation

. The strengths of universities and research institutions in basic research and cutting-edge technology exploration complement the enterprises’ proficiency in precisely grasping market demands and implementing industrialization. Through the deep cooperation mechanism between industry, academia, and research institutions, this fusion promotes the rapid transformation and efficient application of innovation outcomes, markedly shortening the innovation cycle, enhancing innovation efficiency, and accelerating the conversion of scientific and technological achievements into real productivity.

4.4. Analysis of the Overall Mechanism of IUR Collaborative Innovation in Megaprojects

This study delves into the distinctive characteristics of IUR collaborative innovation in major projects at the micro, meso, and macro levels. Based on this analysis, a comprehensive framework for the operation mechanism of IUR collaborative innovation in super-large projects is proposed (as illustrated in Figure 2). This framework is structured in progressively outer layers, collectively supporting and facilitating the efficient execution of collaborative innovation.

At the micro level, which constitutes the innermost layer of the framework, this study centres on the interaction mechanism oriented towards the commercialization of outcomes between users and innovators. Within the context of major engineering projects, users, based on field observations and project demands, articulate scientific challenges and technical requirements, thereby delineating the research trajectory for innovators. Innovators, leveraging their professional expertise, distil scientific issues and delineate key technological hurdles in response to these practical needs. This iterative process concurrently deepens users’ comprehension of the project. By implementing a rational distribution of benefit sharing and risk sharing, innovators proactively drive technological innovation and the commercialization of outcomes, while users actively engage in the research and commercialization processes. This fosters profound collaboration between the two parties, markedly enhancing the overall efficacy of collaborative innovation.

The intermediate layer constitutes a supporting mechanism for synergistic elements, aimed at enhancing interactions among micro-entities and fostering innovation value creation at the macro level. This mechanism encompasses four key components: Firstly, guided by the principle of synergy, shared objectives are established within projects to facilitate cultural identification and effective communication, thereby laying a solid foundation for collaborative endeavours. Secondly, a multi-tiered organizational structure is constructed, encompassing government regulatory bodies, industry stakeholders, project owners, and innovative entities. By leveraging the deep embedding of these parties, the efficiency of aggregating technical challenges and research demands is enhanced, fostering frequent exchanges of knowledge and technology. Thirdly, a comprehensive system adaptation framework, centred around innovation management, innovation teams, and outcome experimentation, is established. This ensures prompt responses to challenges in critical areas, facilitates timely system optimization, and guarantees the efficiency and compliance of collaborative innovation activities. Lastly, a resource allocation mechanism that aligns benefits with risks is formulated, reinforcing the principles of benefit sharing and risk sharing. This mechanism continuously stimulates on-site innovation momentum.

The outermost layer comprises the innovation value creation mechanism, jointly constituted by the innovation chain and the industrial chain, embodying the distinctive features of the holistic layout of collaborative innovation in major projects at the macro level. In the context of the innovation chain, this study underscores the importance of systematically planning collaborative innovation themes within large-scale projects, establishing a vertically and horizontally integrated innovation chain system and ensuring seamless connectivity and efficient synergy among various stages. Additionally, it highlights the pivotal role of project leaders in integrating basic research, technology development, and the application of technological innovations into a cohesive framework, thereby optimizing resource allocation and enhancing innovation efficiency. Regarding the industrial chain, this study proposes the establishment of a comprehensive industrial chain support system, grounded in innovation chain planning. This system spans from initial support for the production of innovative outcomes to the subsequent facilitation of industrialization and market penetration. It encompasses multiple facets, including production line design and installation, personnel training, technical preparedness, quality assurance, application refinement, and the establishment of commercialization channels. The objective is to ensure that innovative outcomes not only cater to the immediate project needs but also exert a broad impact across other industries and domains, fostering overall technological advancements and industrial upgrading.

The framework of the operation mechanism for IUR collaborative innovation in megaprojects collectively forms a vital support system for collaborative innovation in major engineering projects through deep interactions at the micro level, essential element support at the meso level, and value creation at the macro level. This framework offers a robust guarantee for enhancing the efficacy of collaborative innovation and fostering advancements in science and technology as well as industrial upgrading.

5. Conclusions and Prospects

5.1. Research Conclusions

Utilizing the Qinghai-Tibet Railway, Beijing-Shanghai High-Speed Railway, and Beijing-Xiong’an Intercity Railway as exemplary cases, this study systematically delves into the intrinsic characteristics of IUR collaborative innovation in megaprojects through grounded theory analysis and subsequently uncovers the overarching operational mechanism of such collaboration. The findings reveal that the characteristics of collaborative innovation in major engineering projects can be categorized into three levels: micro, meso, and macro. At the micro level, the characteristics centre on the stakeholders, embodying the deep involvement of governments and project owners, as well as the favourable past collaboration experiences of key innovative entities. The meso-level characteristics concentrate on the elemental aspects, manifesting in the industry’s shared cognitive resonance and value systems, hierarchical organizational structures, highly adaptable institutional frameworks, and resource allocation that aligns benefits with risks. The macro-level characteristics focus on the innovation value chain, reflecting the holistic layout of the innovation and industrial chains. Building upon these three-dimensional characteristics, collaborative innovation in major engineering projects fosters a unique operational mechanism, encompassing the interaction mechanism between result users and innovators, the synergistic element support mechanism, and the innovation value creation mechanism. These mechanisms collectively operate throughout the entire process of industry–academia–research collaboration, facilitating the effective integration and optimal allocation of knowledge, technology, capital, and other resources.

5.2. Implications

At the theoretical level, this study delves deeply into the micro-interaction mechanisms among the government, project owners, and industry–academia–research collaborative entities, elucidating the pivotal roles of deep embeddedness and the accumulation of cooperative experience in establishing the foundation of trust and enhancing synergistic efficiency. This finding not only enriches the extant literature on IUR collaboration but also offers a novel lens for comprehending the origins of cooperative dynamics within complex engineering contexts. Through a meso-level analysis, this study unveils the significance of industry consensus and value frameworks in fostering collaborative cohesion, as well as the manner in which hierarchical organizational structures and highly adaptable institutional systems ensure the orderly and efficient progression of collaboration. Notably, the resource allocation mechanism, characterized by a balanced distribution of benefits and risks, emerges as a core innovation. It not only supplements the traditional theories of IUR collaboration but also provides crucial guidance for the design of collaborative incentive structures. At the macro level, the strategic blueprint for the deep integration of the innovation chain and the industrial chain, as proposed in this study, presents a novel theoretical framework for understanding the process of converting scientific and technological advancements into tangible productive forces. This, in turn, reinforces the pivotal role of IUR collaboration in driving the optimization of industrial structures and sustaining economic growth.

In terms of practical applications, this study offers specific operational guidelines for governments, enterprises, and research institutions. For governments and stakeholders, the research underscores the significance of policy guidance and financial support and presents strategies for fostering effective resource allocation through meticulous coordination. For enterprises and research entities, this study delineates pathways to establish a profound foundation of trust and seamless collaboration, as well as methods for coalescing cooperative efforts through shared values and objectives. Furthermore, this study introduces design principles for a well-defined organizational structure and a highly adaptable institutional framework, providing a practical scaffold for constructing efficient collaborative processes. Most notably, the proposed mechanism for the balanced allocation of benefits and risks presents a viable practical approach to stimulating participants’ motivation and sense of responsibility and to fostering risk sharing and outcome sharing. At the macro level, this study furnishes the strategic direction on how governments and enterprises can synergistically plan the innovation and industrial chains and expedite the commercialization of scientific and technological advancements, holding profound practical implications for promoting industrial structural upgrading and economic growth.

In conclusion, this study enhances the academic comprehension of the inherent logic and extrinsic operational mechanisms underlying IUR collaborative innovation in the context of major engineering projects, thereby establishing a bridge between theory and practice. By augmenting the research on IUR collaborative innovation within the specific scenario of megaprojects, this study not only enriches the theoretical framework of such innovation but also offers theoretical underpinnings and practical pathways to address the pressing issues of mismatched actual needs, inadequate innovation impetus, and the conversion of research outcomes in major engineering endeavours. Furthermore, it provides robust intellectual backing for fostering an in-depth integration of science and technology with the economic and social spheres.

5.3. Limitations

This study adheres rigorously to the standard methodological framework of case studies, aiming to uncover the characteristics and intricate operational mechanisms of IUR collaborative innovation in megaprojects through in-depth analyses of exemplary cases. Nevertheless, this study acknowledges the inherent limitations of the case study method. First, this study is relatively deficient in the longitudinal dimension, potentially restricting our understanding of the dynamic evolution of the IUR collaborative innovation process, as well as an in-depth exploration of its long-term consequences and sustainability. Second, this study heavily relies on qualitative data during the data collection and analysis phases. While qualitative data possess unique advantages in unveiling underlying mechanisms and comprehending complex phenomena, their subjective and interpretive nature may introduce biases. Lastly, it is crucial to emphasize that this study is situated within a unique context, specifically focusing on the railway industry. The peculiarities of the railway industry, including its typical linear engineering characteristics, highly complex system structure, stringent regulatory environment, and substantial investment scale, may exert a distinctive influence on the mode and efficacy of IUR collaborative innovation. Consequently, the conclusions derived from this study should be cautiously considered for their applicability and generalizability when applied to other industries or domains.

5.4. Future Research

In terms of research data, future explorations urgently necessitate the reinforcement of the integration and analysis of time-series data. By incorporating this dimension, research can not only delineate the dynamic evolution of collaborative innovation activities over time with greater precision but also conduct an in-depth examination of the underlying long-term trends and cyclical fluctuations, thereby furnishing more robust data support for the construction and empirical validation of theoretical models. Simultaneously, to effectively mitigate the potential for subjective interpretation bias inherent in relying solely on qualitative data, future research endeavours should strive to establish a multivariate research framework that integrates both quantitative and qualitative data. Leveraging quantitative tools such as statistical analysis and econometric models, in conjunction with qualitative methods like in-depth interviews and case studies, can facilitate cross-validation and the deep integration of data, ultimately enhancing the reliability and validity of the research. In the realm of expanding research objects, future studies should transcend industry boundaries and comprehensively investigate collaborative innovation practices across diverse fields and scales of IUR collaboration. Through cross-industry comparative analysis, studies should delve into the commonalities and unique characteristics of collaborative innovation activities within different industry contexts, uncovering the universal principles and specific mechanisms at play. This endeavour not only aids in the development of a more universally applicable theoretical framework but also provides tailored practical guidance and strategic recommendations for various industries, fostering efficient operation and sustainable growth of IUR collaborative innovation across a broader spectrum of domains.

Specifically, future research can further concentrate on the following pivotal domains: firstly, investigating the similarities and differences between high-tech industries and traditional industries in terms of the collaborative innovation models among industry, academia, and research institutions, and analysing the impact of technological advancements and industrial upgrading on cooperation patterns; secondly, examining the challenges and opportunities presented by transnational industry–academia–research collaboration in the globalization context and exploring how factors such as cultural disparities and policy environments mould novel forms of cooperation; and, thirdly, focusing on how the digital economy and intelligent technologies can facilitate collaborative innovation among industry, academia, and research, analysing the manner in which technologies like big data and artificial intelligence can foster the optimal allocation and efficient utilization of innovation resources. Through these profound explorations, the research findings will not only enrich the theoretical framework of industry–academia–research collaborative innovation but also offer more precise directional guidance and pathway selections for collaborative innovation in practice.