Next Article in Journal
Multidimensional Human Responses Under Dynamic Spectra of Daylighting and Electric Lighting
Previous Article in Journal
Thermoacoustic, Physical, and Mechanical Properties of Bio-Bricks from Agricultural Waste
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 15
Issue 13
10.3390/buildings15132185
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Unlocking the Mechanism of Technological Innovation Cooperation in Megaprojects: A 3C Theory Perspective

by
Zhenxu Guo
1,2,
Qing’e Wang
1,* and
Xiaoping Cao
1
1
School of Civil Engineering, Central South University, Changsha 410018, China
2
College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(13), 2185; https://doi.org/10.3390/buildings15132185
Submission received: 17 May 2025 / Revised: 11 June 2025 / Accepted: 20 June 2025 / Published: 23 June 2025
(This article belongs to the Special Issue A Digital Innovation Framework for Construction Management and Built Environment)
Browse Figures
Versions Notes

Abstract

In the context of green development and digital transformation, the technological innovation cooperation in megaprojects requires a spanning from policy guidance, technological breakthroughs, and localized pilot projects to driven demand, integrated innovation (i.e., collaborative innovation across sectors, stages, and stakeholders), and comprehensive promotion. Despite the potential benefits, many megaprojects face challenges related to complex relationships, behavioral uncertainties, low performance, and technological innovation risks. A question of practical and theoretical significance is how to facilitate technological innovation cooperation in megaprojects. This study proposes the 3C Theory, which integrates cooperative relationships, behaviors, and performance, and investigates how technological innovation risks moderate these interactions. Using data from 19 megaprojects, we employ a mixed-methods approach involving hypothesis testing through regression analysis. The findings reveal that strong cooperative relationships positively influence cooperative performance through cooperative behaviors and that technological innovation risks play a significant moderating role. This study offers several practical recommendations for megaproject managers, including enhancing cooperative relationships, promoting effective behaviors, managing innovation risks, and developing cooperative innovation platforms. This study introduces the 3C Theory to megaprojects and provides novel insights into how collaboration and risk management can drive sustainable innovation.

1. Introduction

Green development and digital transformation profoundly reshape megaprojects, integrating sustainability principles with innovative solutions through technological innovation cooperation. Despite the potential benefits, many megaprojects fail to achieve delivery expectations due to relationship complexities, behavioral uncertainties, low performance, and risk factors [1,2,3]. For example, inadequate information sharing on digital platforms led to frequent design changes in London’s Crossrail project [4]. Similarly, budget over-runs in the California high-speed rail project were exacerbated by inconsistencies in technical standards, green material applications, and contractor management [5]. Additionally, the Sydney Light Rail project’s attempt to integrate intelligent transportation systems with renewable energy suffered from interoperability issues, significantly reducing operational efficiency and triggering public complaints [6]. These cases highlight the urgent demands to understand the mechanisms driving technological innovation cooperation in megaprojects to enhance stakeholder relationships, improve behaviors, optimize performance, and mitigate risks.
Technological innovation cooperation involves interactions among stakeholders to compensate for the limitations of individual organizations, fostering systematic improvements in innovation capacity [7]. It is critical to align stakeholder relationships, goals, and processes [8]. Although the importance of technological innovation cooperation is widely acknowledged [3], recent studies on mechanisms in the context of green development and digital transformation remain limited. Prior studies primarily focus on the network effects of technological innovation cooperation [9] or specific mechanisms such as technological proximity [10]. Others emphasize the role of digital tools like building information modeling and systems information modeling in fostering stakeholder cooperative confidence [11]. These studies emphasize the interaction and effects of external factors while neglecting internal and systematic examination, mainly how stakeholders invest effort in collaborative innovation, manage risks, and enhance cooperation effectiveness in megaprojects.
This study investigates the mechanisms underlying technological innovation cooperation in megaprojects to address these gaps by examining relationships, behaviors, performance, and risks. Specifically, we aim to answer the following research questions:
(1)
What are the key factors influencing technological innovation cooperation in megaprojects? While prior research has explored various aspects of cooperation, a systematic framework integrating these factors remains absent.
(2)
How do technological innovation risks impact cooperation mechanisms? Given the complexity of megaprojects, understanding the interaction between risks and cooperation processes is crucial for effective management.
To answer these questions, this study follows a “hypothesis–empirical analysis–results” approach. Drawing on existing theories, we construct a theoretical model of technological innovation cooperation and propose theoretical hypotheses. Using data from 19 megaprojects, we conducted a comprehensive empirical analysis based on a structured questionnaire survey. The findings contribute to the theoretical understanding of innovation cooperation mechanisms and offer practical insights for megaprojects managers seeking to enhance collaborative efforts under green development and digital transformation. Finally, we discuss key findings, implications, limitations, and directions for future research.

2. Literature Review and Hypothesis Development

2.1. Technological Innovation Cooperation

Technological innovation cooperation is a complex and dynamic process that involves multiple stakeholders to enhance innovation capacity [12]. Various theoretical models have been proposed to explain technological innovation cooperation, with three widely recognized perspectives: Triple Helix Theory, Open Innovation Theory, and Synergy Theory.
Triple Helix Theory: Etzkowitz & Dzisah (2008) describe the interdependent relationships among government, enterprises, and universities in technological innovation [13]. It emphasizes long-term, multi-stakeholder interactions at the national and regional levels, forming a dynamic structure. However, this model primarily focuses on macro-level policy and institutional interactions, providing limited insights into firm-level cooperation dynamics [14].
Open Innovation Theory: Chesbrough & Crowther (2006) and Chesbrough et al. (2018) highlight the importance of two-way knowledge and technology flows between firms and external stakeholders [15,16]. It clarifies how organizations leverage external knowledge sources to enhance internal R&D efforts [17]. While this model explains the necessity of external collaboration, it does not sufficiently address relationships, behaviors, and performance uncertainties [18].
Synergy Theory explores how various innovation subsystems align to enhance cooperative behaviors such as knowledge sharing and joint R&D [19]. It suggests that technological innovation cooperation evolves from a disordered state to an ordered system through interaction [20]. However, this perspective lacks a detailed examination of long-term cooperative performance.
While these models offer valuable insights, they focus on specific aspects of technological innovation cooperation rather than providing an integrated framework. Furthermore, their applicability to megaprojects remains limited due to the high complexity, cross-industry collaboration, and extended lifecycle [21]. Unlike the above models, megaprojects require various coordination across stakeholders [22].
Green and digital innovation: Technological innovation cooperation in megaprojects is increasingly shaped by green development and digital transformation [23,24]. Digital technology positively impacts vertical and horizontal collaborative innovation [25], while sustainability efforts focus on resource optimization, environmental protection, and green management systems [26].
Stakeholder dynamics and strategic synergy: Friendly cooperative relationships are more likely to achieve the goal of technological innovation cooperation for megaprojects when faced with internal and external pressures [27]. Practical technological innovation cooperation requires stable, long-term relationships with mutual trust [28]. However, achieving synergy is difficult due to conflicting priorities, regulatory constraints, and risky behaviors. Scholars have emphasized the demands for adaptive contracting, collaborative networks, and innovation-driven incentives to enhance effectiveness [27,29].
Cooperative performance metrics: Cooperative performance in megaprojects extends beyond traditional measures to include green performance indicators and long-term ecosystem impacts [30,31,32]. While some studies have proposed specific metrics [33,34], an integrated performance evaluation framework is still lacking.
Despite the growing attention to technological innovation cooperation in megaprojects, the literature fails to systematically examine the interaction among cooperative relationships, behaviors, and performance. This gap necessitates a holistic theoretical framework that can capture the evolutionary mechanism of technological innovation cooperation in megaprojects. To address the research gaps, this study proposes the 3C Theory, which integrates cooperative relationships, behaviors, and performance into a theoretical framework (Figure 1).
Compared to existing models focusing on a single aspect of technological innovation cooperation, the 3C Theory provides a dynamic perspective on how innovation cooperation evolves. Unlike the macro-level focus of the Triple Helix model, the 3C Theory analyzes technological innovation cooperation of megaprojects at micro- (firm-level), meso- (industry-level), and macro- (policy-level) scales. It extends beyond traditional technological innovation cooperation performance indicators by incorporating short-term and long-term performance. Short-term performance refers to developing new products, promoting technology accumulation, enhancing knowledge acquisition ability, improving market competitiveness, and expanding industry influence. Long-term performance focuses on new markets. In addition, the 3C Theory explains how technological innovation cooperation transitions from fragmented collaboration to structured and systematic cooperation, addressing the gaps in existing models.
In addition to the Triple Helix, Open Innovation, and Synergy Theories, this study also integrates broader theoretical perspectives from inter-organizational collaboration and innovation management, thereby reinforcing the theoretical basis of the 3C Theory. For example, the Resource-Based View highlights that competitive advantage arises from leveraging valuable and rare resources. In megaprojects, effective use of shared knowledge and technological assets is essential for innovation. Dynamic Capabilities Theory emphasizes the need to explore opportunities and adapt coordination to sustain innovation in uncertain environments. Relational Contract Theory focus on that long-term inter-organizational collaboration depends on formal contracts and on trust, shared norms, and mutual expectations.

2.2. Technological Innovation Risks in Megaproject

Technological innovation risks refer to the uncertainties that hinder the successful implementation of technological innovation cooperation due to external environmental factors, technological complexity, and stakeholder limitations [35]. Therefore, technological innovation risks can be divided into three categories: technology research, application, and innovation management risks.
Technology research risks stem from bias in site conditions, stakeholder demands, and resource allocation during the research phase [36,37]. In megaprojects, failures in this phase can lead to inefficient technological solutions incompatible with sustainability goals. For instance, inadequate construction equipment adaptation can exacerbate research risks, making it challenging to align technological innovation with green development [38].
Technology application risks arise when stakeholders face difficulties implementing innovative technologies due to data overload and system incompatibility [39]. As megaprojects generate mass data, data integration challenges become a significant obstacle [40]. A recent study found that organization, management, the system, the platform, and the structure form essential parts of technological innovation cooperation, in which data governance can promote green development and reduce resistance [41].
Innovation management risks impact strategic coordination and stakeholder participation in technological innovation cooperation. Research indicates that collaborative control mechanisms can mitigate these risks by improving economic integration and strategic alignment [42]. However, differences in information disclosure policies create barriers to data sharing, limiting the potential for long-term cooperative success [43]. Addressing these risks requires a balance between transparency, trust, and regulation.
Although previous studies have identified various technological innovation risks, few have systematically examined their interaction with technological innovation cooperation mechanisms. The 3C Theory provides an integrated framework for analyzing how these risks influence cooperative relationships, behaviors, and performance in megaprojects. This study aims to fill this gap by proposing a risk analysis framework based on the 3C Theory that systematically evaluates how different technological innovation risks affect technological innovation cooperation in megaprojects. By integrating risk assessment into technological innovation cooperation, stakeholders can enhance collaborative efficiency, improve risk resilience, and optimize innovation performance.

2.3. Theoretical Hypotheses

Cooperative relationships among stakeholders facilitate resource integration, cooperative innovation, and knowledge exchange, which are crucial for enhancing cooperative performance [44]. Scholars argue that strong inter-organizational relationships improve project efficiency, innovation capacity, and sustainability outcomes [45]. A practical benefit distribution mechanism ensures long-term collaboration and innovation success [46]. Trust is another key factor that strengthens cooperative relationships by reducing transaction costs and fostering open communication [47]. Furthermore, knowledge-sharing intensity positively relates to cooperative quality, indicating that stakeholders with strong cooperative relationships tend to engage in more meaningful interactions [48]. Based on this, we propose the following hypothesis:
H1: 
Cooperative relationships will be positively related to cooperative performance.
Cooperative behaviors serve as a crucial bridge between cooperative relationships and cooperative performance. Strong cooperative relationships encourage stakeholders to participate actively in joint R&D, exchange technology, and commit to long-term cooperation [49]. Studies suggest that frequent interaction among stakeholders leads to more excellent knowledge absorption and longer cooperative duration, which is essential to sustaining competitive advantage [50]. Furthermore, cooperative behaviors such as supplier–customer collaboration may achieve a holistic impact and better overall sustainable performance [51]. A collaborative culture fosters trust and knowledge sharing, promoting organizational innovation capabilities [52]. Thus, cooperative behaviors act as a mediating mechanism that translates strong cooperative relationships into enhanced cooperative performance. Based on this, we propose the following hypotheses:
H2: 
Cooperative relationships will be positively related to cooperative behaviors.
H3: 
Cooperative behaviors will be positively related to cooperative performance.
H4: 
Cooperative behaviors will mediate the relationship between cooperative relationships and cooperative performance.
Technological innovation risks introduce uncertainty, complexity, and potential conflicts into technological innovation cooperation, influencing stakeholder interaction at multiple levels [53]. Risk appetite affects stakeholders’ willingness to engage in long-term cooperation [54], which reduces interaction frequency and results in shorter collaboration duration. For example, stakeholders may hesitate to commit resources under high-risk conditions, weakening the positive impact of cooperative relationships on cooperative performance [55]. Conversely, stable cooperative relationships can mitigate these risks by enhancing enthusiasm and maintaining a long-term connection [56]. Stakeholders emphasize improving cooperative quality through communication, trust, and commitment [57]. Scholars also highlight the role of technological distance, where stakeholders with different technological capabilities may struggle to align innovation strategies, thereby affecting cooperative performance [58]. Based on this, we propose the following hypotheses:
H5: 
Technological innovation risks will moderate the relationship between cooperative relationships and cooperative performance.
H6: 
Technological innovation risks will moderate the relationship between cooperative relationships and cooperative behaviors.
H7: 
Technological innovation risks will moderate the relationship between cooperative behaviors and cooperative performance.
Therefore, we propose the conceptual model shown in Figure 2.

3. Methodology

This study employs a mixed-methods approach to explore the mechanism of technological innovation cooperation in megaprojects. First, a comprehensive literature review was performed to examine the key factors influencing technological innovation cooperation. We constructed the 3C Theory based on previous studies, which integrates cooperative relationships, behaviors, and performance as core components. Seven research hypotheses were proposed to construct a conceptual model explaining the interaction mechanisms among these variables. Next, we designed a structured questionnaire following a rigorous Delphi method to validate the proposed model. Three rounds of Delphi expert consultation were conducted with industry professionals, project managers, and academic researchers specializing in technological innovation. The Delphi process aimed to refine measurement items, ensure content validity, and reach a consensus on the key constructs. Following this, an empirical study was carried out using data from 19 megaprojects involving technological innovation cooperation. A pilot study was conducted to test the reliability and clarity of the questionnaire before scale distribution. Further, we employed SPSS 25.0 and PROCESS macro 4.2 for statistical analysis. We selected Hayes PROCESS macro (Model 4 and Model 59) based on its ability to test mediation and moderated mediation effects within a robust regression framework. Finally, we interpreted the findings, discussing their theoretical and practical implications.

3.1. Research Design

The research design comprises four stages: variable identification, item determination, questionnaire development, and data collection. This study commenced in January 2022, with the support of the China National Railway Group and Central South University. It was conducted from July 2022 to March 2023, with detailed timelines and procedures presented in Figure 3.

3.1.1. Variable Identification

The identification of key variables was based on the 3C Theory and insights from the literature on technological innovation cooperation in megaprojects. From July 2022 to September 2022, a comprehensive review was conducted to illustrate the following critical variables:
Cooperative relationships were measured to explain the interaction among the upstream, midstream, and downstream stakeholders. Porcu et al. (2020) identified factors affecting cooperative relationships, including trust, organizational alignment, brand performance, clan culture, and hierarchy culture [59]. Yu et al. (2022) emphasized the importance of partnership stability, the number of partners, and the frequency of collaborative innovation from the cooperative width and depth perspective [60]. According to Xue et al. (2018), the relationship network indicates organizational information exchanges. This study selected four constructs to maximize the response rate [61].
Cooperative behaviors were defined as the observable actions stakeholders, such as information feedback collection, frequent communication, and the willingness to continue cooperation [62,63]. Wang and Zhang (2019) proposed an innovation network formed by the initiative of stakeholders that facilitates technology spillovers, which is usually the critical factor in improving Indigenous innovation abilities and collaboration abilities [62]. Guo et al. (2024) illustrated that the cooperative scenario is a particular variable that affects technological innovation cooperation and highlighted the effect of cooperative attitude, subjective cooperative norm, perceived cooperative behavior control, and cooperative intention [63]. Dong et al. (2021) revealed that spatial, economic, technological, and political biases significantly influence collaboration [64]. In this study, cooperative behaviors were measured based on the above.
As mentioned in the literature review and hypotheses, cooperative performance was measured as the degree to which technological innovation cooperation meets desired outcomes. For example, Wang et al. (2023) explored seven indicators involving profitability, annual sales growth, market share, labor productivity, customer satisfaction, repeat business, and reputation [65]. Xue et al. (2018) proposed that cost performance drives the stakeholders’ collaborative management [61]. Demirkesen and Ozorhon (2017) evaluated project performance levels regarding cost, time, quality, and client satisfaction. Satisfaction, achievement status, and cooperative potential were designed to measure cooperative performance [66].
Technological innovation risks are a well-developed construct. Wang et al. (2022) divided technological innovation risks into organizational synergy, market, and benefit distribution risks [53]. Klimczak et al. (2023) confirmed that trust, business performance, and environmental turbulence affect the risk assessment results [67]. This study analyzed technological innovation cases and the related literature and measured technological innovation risks with three items.

3.1.2. Item Determination and Questionnaire Design

From September 2022 to December 2022, this study was provided with the opportunity to examine internal documents, including the implementation plans, management systems, and organizational frameworks of 19 megaprojects. This study employed a three-round Delphi method to determine the measurement items, involving five experts in project technology innovation management.
Experts were selected based on rich experience of technological innovation in megaprojects over ten years. The panel included professionals from academic institutions, industry leaders, and government agencies (Appendix A). Round 1: Each variable was transformed into measurable items on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The experts reviewed an initial list of proposed measurement items and provided feedback. The focus was on refining the items to develop the key constructs. Round 2: Based on feedback from Round 1, the questionnaire was revised and redistributed to experts. At this stage, the experts were asked to rate each item for relevance and clarity. Feedback was discussed with the team to address any ambiguities. Round 3: In this round, the experts reassessed the revised items. A 75% consensus threshold was set for finalizing the measurement items, making the final list reflective of the key aspects of technological innovation cooperation in megaprojects. All expert feedback was provided anonymously to encourage honest and unbiased responses. The process allowed the experts to revise their opinions in subsequent rounds, ensuring robust consensus. The measurement items are as shown in Table 1.

3.1.3. Data Collection

From March 2023 to June 2023, the final questionnaire was distributed to stakeholders involved in the 19 selected megaprojects. They were chosen based on four criteria: (1) covering key industry scopes such as transportation infrastructure, intelligent manufacturing, and green construction; (2) involving “breakthrough innovation” (i.e., interdisciplinary collaboration and cutting-edge technology), “four small” (i.e., small invention, small creation, small design, and small innovation), and “four new” (i.e., new technology, new processes, new materials, and new equipment); (3) engaging government, enterprises, research institutions and universities, and supply chain partners to reflect different collaboration types (i.e., public private partnerships, industry alliances, and academia cooperation); and (4) emphasizing green development (i.e., low-carbon solutions and sustainable materials) and digital transformation (i.e., building information model, Internet of Things, and digital twins). These projects represent cutting-edge advancements in technological innovation and serve as successful cooperation cases in megaprojects. Furthermore, they incorporate sustainability principles and digital transformation strategies, making them highly relevant for contemporary megaprojects. We targeted stakeholders, including project managers, engineers, R&D specialists, and policymakers in the technological innovation cooperation process. A total of 202 responses were received, and 172 valid responses were retained, resulting in an effective response rate of 85.15%. Invalid responses were excluded based on criteria such as unreasonably short completion times (less than 90 s) or identical scale scores across all items.

3.2. Data Analysis

3.2.1. Descriptive Statistics and Correlation Analysis

The general characteristics of the respondents were summarized to understand the sample distribution. The majority of respondents (87%) came from stakeholders directly involved in the technological innovation cooperation process. Most respondents held junior management positions or higher (81.4%), which contributed to the credibility of the responses, as they had significant experience. The mean scores for the variables ranged from 3.80 to 4.20, with standard deviations between 0.839 and 0.987, indicating moderate to high consistency among the responses. The test of the normal distribution shows that the absolute value of the skewness coefficient by the observed variables is less than 3. The absolute values of the kurtosis coefficients are less than 8, which accords with the normal distribution. VIF is [1.073, 1.802] less than 3. In other words, there is no multicollinearity in this study.

3.2.2. Common Method Deviation Test

To evaluate potential common method bias, we performed an exploratory factor analysis. The exploratory factor analysis yielded four factors with feature roots > 1, accounting for 56.236% of the total variance. The first common factor explained 18.376% of the variance, below the 40% threshold, indicating that common method bias is not a significant concern in this study.

3.2.3. Reliability and Validity Test

To assess the reliability of the measurement scales, we tested Cronbach’s alpha. The Cronbach’s α coefficient ranged from 0.70 to 0.86, exceeding the recommended threshold of 0.7, indicating good internal consistency. The Kaiser–Meyer–Olkin value of the total scale was 0.799, and Bartlett’s Test was significant (p < 0.05), indicating that the data are suitable for factor analysis. The cumulative variance explained by the four factors was 56.236%, which is higher than the acceptable threshold of 50%, demonstrating adequate construct validity for the scales.

4. Results

4.1. Direct Effect Test

Regression analysis tests the relationship between CR and CP. The standardized path coefficients and the model test results are shown in Table 2. M1 to M3 represent the base model, direct effect model, and mediation model, respectively. According to M1 and M2, the controlling variables are types, years, and position, and CR has a significant positive effect on CP (β = 0.501, p = 0.000 < 0.01). Therefore, H1 is acceptable.

4.2. Mediating Effect Test

A three-step mediation is used to test the mediating effect. M4 and M5 incorporate the mediating variable CB, based on Hayes PROCESS Model 4. According to M4 and M5, it can be seen that CR has a significant positive effect on CB (β = 0.597, p = 0.000 < 0.01). M3 shows that the changes (β = 0.501, p = 0.000 < 0.01) after CB is added (β = 0.354, p = 0.000 < 0.01). It indicates that CB plays a mediating role in the influence of CR on CP. H2 and H3 are acceptable.
Bootstrap is adopted to determine the direct and indirect effects of CR→CB→CP. After running 5000 times through the Bootstrap, the bias-corrected and -uncorrected 95% confidence intervals are obtained, respectively. Using Model 4 (simple mediation model) of the Hayes Process program, Table 3 shows the analysis results. The coefficient of the total effect is significantly positive (β = 0.5775, p < 0.01). At the 95% confidence level, the confidence interval is [0.4214, 0.7336], excluding 0. The coefficient of direct effect is significantly positive (β = 0.4081, p < 0.01). At the 95% confidence level, the confidence interval is [0.2195, 0.5967], excluding 0. The coefficient of indirect effect is significantly positive (β = 0.1694, p < 0.01). At the 95% confidence level, the confidence interval is [0.0386, 0.3094], excluding 0. Therefore, CB partially mediates the influence of CR on CP, accounting for 29.33%.

4.3. Moderating Effect Test

To examine the moderating effect of TIR, interaction terms between TIR and CR, CB, and CP were constructed and tested using hierarchical regression analysis. Table 4 shows the test results of the moderating effect. M6 to M11 refer to hierarchical models testing moderation and moderated mediation effects, corresponding to Hayes PROCESS Models 1 and 59. It can be seen from M11 that the interaction term between CR and TIR has a significant positive impact on CP (β = 0.072, p < 0.01), indicating that TIR can significantly enhance the positive effect of CR on CP. Similarly, as shown in M7 and M9, the interaction between CR and TIR positively influences CB (β = 0.066, p < 0.10), while the interaction between CB and TIR significantly impacts CP (β = 0.064, p < 0.01). Therefore, H5, H6, and H7 are acceptable.

4.4. Moderated Mediating Effect Test

Using Model 59 of the Hayes Process program, the moderated mediating effect was tested while controlling for variables such as type, years of experience, and position. The study conducted 5000 Bootstrap resampling iterations, with the results presented in Table 5. In Model 12, CR and TIR positively predicted CP. Additionally, the interaction between CR and TIR had a significant impact on CP, with the 95% confidence interval for the interaction effect ranging from [0.018, 0.243], excluding 0. However, in Model 13, the interaction between CR and TIR, as well as between CB and TIR, was not significant, and did not predict a meaningful positive effect on CP.
To further explore the moderating effect of TIR on the relationship in the initial segment of the model, a simple slope test was conducted, as illustrated in Figure 4. The results indicated that CR had a significant positive impact on CP under low TIR. However, the effect of CR on CP was no longer significant under high TIR.

5. Discussions

5.1. The 3C Theory: Revealing the Mechanism of Technological Innovation Cooperation

This study highlights the critical role of understanding the interactions among cooperative relationships, cooperative behaviors, and cooperative performance in addressing innovation management challenges in megaprojects. Based on the Triple Helix model, the Haken model, and Open Innovation, we have developed and validated the 3C Theory. The formation of the 3C Theory is also influenced by the Structure–Conduct–Performance (SCP) Paradigm [68], which frames external conditions and contexts impacting innovation. This study extends the application of the SCP paradigm from the enterprise level to the project level, establishing a dynamic theoretical framework for understanding technological innovation cooperation in megaprojects.
Regarding cooperative relationships, we emphasize the importance of knowledge sharing, trust, and high-performing teams to maintain stable and long-lasting collaborations [69]. Notably, the mechanisms for sharing technological innovation achievements play a pivotal role in helping innovations successfully traverse the “Valley of Death,” which is the critical phase where ideas often fail to move from research to commercial viability [70]. Cooperative behaviors entail overcoming the limitations of individual capabilities, whereby stakeholders jointly create value and achieve long-term objectives. In addition, we focus on achieving short-term outcomes and emphasize the efficiency improvements that arise from long-term collaboration. The cooperative performance of this study includes concrete achievements [71] and abstract achievements [72].

5.2. Cooperative Relationships: Identifying Two Influence Paths

This study focuses on the significant advantages of the interaction between formal and informal relationships in addressing critical issues related to technological innovation cooperation in megaprojects. Formal relationships are explicitly defined through legal documents, identifying stakeholder responsibilities [73] and resource allocation [74]. These relationships ensure that resources are directed to essential areas as planned. However, when unanticipated situations arise outside the scope of contractual agreements, trust becomes the key driver of successful cooperative performance [75]. On the other hand, informal relationships offer greater flexibility, allowing for rapid adjustments in task allocations to meet the demands of green development and digital transformation. When formal and informal relationships work in synergy, the outcome will be greater than contributions.
Therefore, this study proposes the establishment of a technological innovation cooperation platform to enhance cooperative relationships. The platform comprises five modules: (1) information dissemination capabilities that allow partners to stay updated on megaprojects requirements and an intelligent matching system to connect them with appropriate collaborators; (2) project management tools, including document sharing and online meeting functionalities, to support remote collaboration and ensure real-time communication; (3) a comprehensive case repository that compiles best practices, technical documents, and research findings to foster knowledge sharing; (4) an expert advisory module offering technical consulting, solution design, and talent training services to address project-specific challenges; and (5) a dedicated platform for showing achievements and facilitating transactions, promoting the practical application and commercialization of technological innovations.

5.3. Cooperative Behaviors: Building a Bridge

This study highlights the bridging role of cooperative behaviors. Key factors that promote these behaviors include shared objectives, trust, and teamwork, which are fundamental to establishing successful cooperative relationships [76]. This study argue that cooperative scenarios can stimulate motivation and creativity, thereby facilitating the effective integration of resources. Stakeholders are more likely to reduce conflicts and misunderstandings while enhancing team cohesion. This approach differs from previous research [77], particularly in green development and digital transformation. In these areas, the sustainability of technological innovation cooperation requires a particular focus on credibility, capabilities, historical collaboration experience, and policy orientation. This study adopts a comprehensive perspective, balancing short-term demands with long-term objectives. First, it is essential to ensure that stakeholders understand and endorse the project goals. Shared objectives foster a sense of belonging and accountability. Second, creating an open and inclusive communication environment is critical, as it encourages the exchange of information, ideas, and feedback. Lastly, teams should be supported in learning and refining work methods and skills, which are vital for continuous improvement and long-term success.

5.4. Cooperative Performance: Obtaining Concrete Achievements and Abstract Achievements

The achievements of technological innovation cooperation in megaprojects can be classified into three categories: “four small,” “four new,” and “breakthrough innovation.” These achievements are discussed in terms of concrete and abstract cooperative performance measurements [78]. Concrete achievements represent the tangible outcomes of collaborative efforts, which can be quantified through specific indicators or measurable data. Although the abstract achievements may not be immediately reflected in short-term economic benefits, they contribute to long-term cooperation by fostering trust and shared goals and enhancing the potential for collaboration. Over time, stakeholders place greater value on these long-term benefits, assessing how cooperation generates sustainable value.
To enhance cooperative performance, it is essential to ensure the equitable distribution of benefits among stakeholders. Intellectual property plays a significant role in influencing stakeholders’ interests, and its rational assessment can effectively motivate transformation. Establishing a valuation mechanism requires precise specifications regarding the evaluation procedures, methodologies, and parameters. Objective intellectual property valuation methods should be developed and complemented by subjective approaches such as negotiation and consultation to assess benefits. Furthermore, personalized evaluation standards should be implemented by considering intellectual property characteristics, market conditions, and risk factors, thereby minimizing discrepancies and conflicts related to intellectual property valuation.

5.5. Technological Innovation Risks: Emphasizing the Whole Process

This study provides empirical evidence that technological innovation risks moderate the interaction among cooperative relationships, cooperative behaviors, and cooperative performance, which is consistent with the hypothesis. When the technological innovation risks are low, cooperative performance tends to increase as cooperative relationships improve, resulting in substantial changes. Conversely, when technological innovation risks are high, cooperative performance still increases but exhibits a smaller range of change. However, this does not imply a decline in cooperative performance. This study emphasizes that the consistency of responses triggered by technological innovation risks may reduce the variability of cooperative performance. This reduction potentially constrains the diversity that different stakeholders could contribute based on unique characteristics and strengths.
Risk allocation is crucial for stakeholders to identify, manage, and take risks [79]. The positive correlation between management capacity and the risk allocation ratio facilitates proactive resource distribution. Stakeholders differ in terms of risk management experience, systems, and personnel, which leads to varying preferences regarding risk assumptions [80]. Allocating risks to stakeholders with relevant management experience and a willingness to undertake risks is more appropriate. This balance is essential because if the returns generated by participants are lower than the costs associated with managing the risks, the transfer of risks becomes increasingly challenging.

6. Conclusions

This study contributes to theoretical contributions and practical recommendations by deepening the understanding of technological innovation cooperation in megaprojects through the 3C Theory. The findings offer empirical evidence that cooperative relationships, cooperative behaviors, and cooperative performance are closely related in the context of green development and digital transformation. Technological innovation risks play a significant moderating role in these interactions.

6.1. Theoretical Contributions

This study makes a significant contribution by introducing the 3C Theory into the context of megaprojects, integrating cooperative relationships, behaviors, and performance to enhance the understanding of technological innovation cooperation dynamics. Although other innovation theories have been proposed, it is the first to adapt the 3C Theory specifically for megaprojects. Further, this study extends the existing literature by systematically exploring the moderating effect of technological innovation risks. It demonstrates that risks are not static barriers but dynamic factors that significantly influence the relationships between cooperative behaviors and performance. This addresses a critical gap in risk management research.

6.2. Practical Recommendations

Enhancing cooperative relationships: In megaprojects, the government plays a dominant role. It is recommended that a dedicated coordinating subject or a third-party platform be established to ensure effective management of these interests. In infrastructure projects, formal contracts should be improved by clearly stating each party’s roles, rewards, and legal duties to build a stable cooperation framework. In contrast, digital projects demand rapid responsiveness and adaptability, making informal cooperation mechanisms, such as trust-based alliances or platform-driven collaborative models, more effective in facilitating cooperative partnerships.
Promoting effective cooperative behaviors: To achieve both short-term and long-term innovation objectives, stakeholders should prioritize shared goals and promote transparent, open communication. Creating an environment that fosters creativity and supports the efficient integration of resources is crucial. In infrastructure projects, cooperative behavior should focus on cross-disciplinary coordination, including integrated design–build models and collaborative problem-solving mechanisms throughout the project life cycle. Conversely, digital projects benefit from regular information sharing, quick testing, and flexible development methods that support fast innovation.
Managing technological innovation risks: Effective risk management strategies (i.e., risk-sharing agreements and contingency plans) is essential in high-risk environments. Assigning risk responsibilities to stakeholders with relevant capabilities can reduce uncertainty and improve overall cooperative performance. In infrastructure projects, risks arise from site uncertainties and the need for long-term operational stability. To mitigate these, simulation modeling, scenario analysis, and construction rehearsals should be employed early in the project. In contrast, digital projects frequently face challenges stemming from rapid technological evolution and non-standardized systems. In these contexts, it is vital to develop cross-platform data integration protocols and robust mechanisms for protecting digital assets.
Developing cooperative innovation platforms: Establishing digital platforms that support the exchange of best practices, technical knowledge, and research outputs can enhance collaboration, foster a culture of continuous learning, and support real-time and remote coordination, especially in geographically dispersed megaprojects. For instance, public infrastructure projects can benefit from government open innovation platforms that promote knowledge diffusion and civic participation. In contrast, private digital projects can leverage industrial alliances to ensure efficient technology transfer and facilitate the implementation of emerging solutions.

6.3. Limitations

The findings are based on cases in China, so future research should explore cross-country comparisons to gain a more comprehensive understanding of how national policies and governance structures influence innovation cooperation. This study only explored the role of technological innovation risks, but it was insufficient in researching the risk taking of technological innovation among different stakeholders. Regarding the research methods, this study has limitations. Future research could incorporate more advanced algorithms such as machine learning to enhance the objectivity.

Author Contributions

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

Funding

The authors gratefully acknowledge the funding support from the China Scholarship Council (No. 202406370135) and the National Natural Science Foundation of China (No. 72171237).

Data Availability Statement

Data for this study are available upon request to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. The details of the participants.
Table A1. The details of the participants.
MembersOrganizationPositionRoleTime
T1Central South UniversityProfessorSeminar moderator/
T2Central South UniversityProfessorSeminar interviewer/
T3Central South UniversityPhD studentSeminar interviewer/
T4Central South UniversityPhD studentSeminar interviewer/
T5Jimei UniversityAssistant professorSeminar recorder/
I1Northeastern UniversityProfessorOverall project and equipment development30 min
I2Northeastern UniversityProfessorOverall project and equipment development30 min
I3Northeastern UniversityProfessorOverall project and equipment development27 min
P1China Railway Second BureauManagerProject task 123 min
P2China Railway Second BureauManagerProject task 122 min
P3China Railway Tunnel GroupManagerProject task 634 min
P4China Railway Twelfth BureauManagerProject task 324 min
P5China Railway Twelfth BureauManagerProject task 329 min
O1Innovation CenterDepartment directorTechnological innovation cooperation platform36 min
O2Innovation CenterDirectorTechnological innovation cooperation platform47 min
O3National Railway GroupDepartment directorOrganized project1 h 5 min
O4National Railway GroupDepartment directorOrganized project1 h 27 min

References

  1. Locatelli, G.; Mikic, M.; Brookes, N.; Kovačević, M.; Ivanisevic, N. The Successful Delivery of Megaprojects: A Novel Research Method. Proj. Manag. J. 2017, 48, 78–94. [Google Scholar] [CrossRef]
  2. Zheng, X.; Lu, Y.; Chang, R.D. Governing Behavioral Relationships in Megaprojects: Examining Effect of Three Governance Mechanisms under Project Uncertainties. J. Manag. Eng. 2019, 35, 04019016. [Google Scholar] [CrossRef]
  3. Denicol, J.; Davies, A.; Pryke, S. The organisational architecture of megaprojects. Int. J. Proj. Manag. 2021, 39, 339–350. [Google Scholar] [CrossRef]
  4. Muruganandan, K.; Davies, A.; Denicol, J.; Whyte, J. The dynamics of systems integration: Balancing stability and change on London’s Crossrail project. Int. J. Proj. Manag. 2022, 40, 608–623. [Google Scholar] [CrossRef]
  5. Chester, M.; Horvath, A. Life-cycle assessment of high-speed rail: The case of California. Environ. Res. Lett. 2010, 136, 014003. [Google Scholar] [CrossRef]
  6. Currie, G.; Gruyter, C. Exploring performance outcomes and regulatory contexts of Light Rail in Australia and the US. Res. Transp. Econ. 2016, 59, 297–303. [Google Scholar] [CrossRef]
  7. Cai, H.; Wang, Z.; Wang, W. Spatiotemporal investigation and determinants of interprovincial innovation network from a multilayer network perspective. Technol. Anal. Strateg. Manag. 2022, 36, 2171–2186. [Google Scholar] [CrossRef]
  8. Khosravi, P.; Rezvani, A.; Ashkanasy, N. Emotional intelligence: A preventive strategy to manage destructive influence of conflict in large scale projects. Int. J. Proj. Manag. 2019, 38, 36–46. [Google Scholar] [CrossRef]
  9. Zhang, J.; Cao, Y.; Shen, N. Research on the evolution of disruptive technological innovation cooperation network: A case study of blockchain and automatic driving technology. Technol. Anal. Strateg. Manag. 2023, 36, 3066–3081. [Google Scholar] [CrossRef]
  10. Zhang, B.; Liu, X. Technology Proximity Mechanism and Collaborative Innovation Orientation: How to Coordinate Multiple Subsidiaries’ Innovation Strategies? J. Knowl. Econ. 2023, 15, 706–731. [Google Scholar] [CrossRef]
  11. Zhou, J.; Edwards, D.; Irani, Z.; Sing, M.C.P. Off the rails: The cost performance of infrastructure rail projects. Transp. Res. Part A Policy Pract. 2017, 99, 14–29. [Google Scholar] [CrossRef]
  12. Ozdemir, S.; Arroyabe, J.; Sena, V.; Gupta, S. Stakeholder diversity and collaborative innovation: Integrating the resource-based view with stakeholder theory. J. Bus. Res. 2023, 164, 113955. [Google Scholar] [CrossRef]
  13. Etzkowitz, H.; Dzisah, J. Rethinking development: Circulation in the triple helix. Technol. Anal. Strateg. Manag. 2008, 20, 653–666. [Google Scholar] [CrossRef]
  14. Etzkowitz, H. Triple Helix Clusters: Boundary Permeability at University—Industry—Government Interfaces as a Regional Innovation Strategy. Environ. Plan. C Gov. Policy 2012, 30, 766–779. [Google Scholar] [CrossRef]
  15. Chesbrough, H.; Crowther, A. Beyond High Tech: Early Adopters of Open Innovation in Other Industries. R&D Manag. 2006, 36, 229–236. [Google Scholar] [CrossRef]
  16. Chesbrough, H.; Lettl, C.; Ritter, T. Value Creation and Value Capture in Open Innovation. J. Prod. Innov. Manag. 2018, 35, 930–938. [Google Scholar] [CrossRef]
  17. Hervas-Oliver, J.L.; Sempere Ripoll, F.; Boronat-Moll, C. Technological innovation typologies and open innovation in SMEs: Beyond internal and external sources of knowledge. Technol. Forecast. Soc. Change 2020, 162, 120338. [Google Scholar] [CrossRef]
  18. Audretsch, B.; Belitski, M. The limits to open innovation and its impact on innovation performance. Technovation 2022, 119, 102519. [Google Scholar] [CrossRef]
  19. Lumpkin, D.; Horton, W.; Sinfield, J. Holistic synergy analysis for building subsystem performance and innovation opportunities. Build. Environ. 2020, 178, 106908. [Google Scholar] [CrossRef]
  20. Hao, B.; Ye, J.; Feng, Y.; Cai, Z. Explicit and tacit synergies between alliance firms and radical innovation: The moderating roles of interfirm technological diversity and environmental technological dynamism. R&D Manag. 2019, 50, 432–446. [Google Scholar] [CrossRef]
  21. Davies, A.; Macaulay, S.; Brady, T. Delivery Model Innovation: Insights From Infrastructure Projects. Proj. Manag. J. 2019, 50, 875697281983114. [Google Scholar] [CrossRef]
  22. Sankaran, S. Industrial Megaprojects: Concepts, strategies and practices for success. Proj. Manag. Res. Pract. 2016, 3, 5118. [Google Scholar] [CrossRef]
  23. Li, Y.; Huang, L.; Tong, Y. Cooperation with competitor or not? The strategic choice of a focal firm’s green innovation strategy. Comput. Ind. Eng. 2021, 157, 107301. [Google Scholar] [CrossRef]
  24. Pan, Y.; Zhang, L. A BIM-data mining integrated digital twin framework for advanced project management. Autom. Constr. 2021, 124, 103564. [Google Scholar] [CrossRef]
  25. Shi, J.; Jiang, Z.; Liu, Z. Digital Technology Adoption and Collaborative Innovation in Chinese High-Speed Rail Industry: Does Organizational Agility Matter? IEEE Trans. Eng. Manag. 2023, 71, 4322–4335. [Google Scholar] [CrossRef]
  26. He, G.; Mol, A. Public protests against the Beijing–Shenyang high-speed railway in China. Transp. Res. Part D Transp. Environ. 2016, 43, 1–16. [Google Scholar] [CrossRef]
  27. Hofman, E.; Halman, J.; Looy, B. Do design rules facilitate or complicate architectural innovation in innovation alliance networks? Res. Policy 2016, 45, 1436–1448. [Google Scholar] [CrossRef]
  28. Bahemia, H.; Roehrich, J. Governing open innovation projects: The relationship between the use of trust and legal bonds. Ind. Mark. Manag. 2023, 110, 17–30. [Google Scholar] [CrossRef]
  29. Huang, X.; Zhang, S.; Zhang, J.; Yang, K. Research on the impact of digital economy on Regional Green Technology Innovation: Moderating effect of digital talent Aggregation. Environ. Sci. Pollut. Res. 2023, 30, 74409–74425. [Google Scholar] [CrossRef]
  30. Bayraktar, M. Venture Capital Opportunities in Green Building Technologies: A Strategic Analysis for Emerging Entrepreneurial Companies in South Florida and Latin America. J. Manag. Eng. 2013, 29, 79–85. [Google Scholar] [CrossRef]
  31. Niu, Y.; Deng, X.; Duan, X. Understanding critical variables contributing to competitive advantages of international high-speed railway contractors. J. Civ. Eng. Manag. 2019, 25, 184–202. [Google Scholar] [CrossRef]
  32. Zeng, J. Exploring the influence of ecological relationship on knowledge interaction in green innovation cooperation. Environ. Sci. Pollut. Res. 2023, 30, 45369–45387. [Google Scholar] [CrossRef] [PubMed]
  33. Rauter, R.; Globocnik, D.; Perl-Vorbach, E.; Baumgartner, R. Open innovation and its effects on economic and sustainability innovation performance. J. Innov. Knowl. 2018, 4, 226–233. [Google Scholar] [CrossRef]
  34. Zhao, X.; Cui, H. Impact of university-industry collaborative research with different dimensions on university patent commercialisation. Technol. Anal. Strateg. Manag. 2021, 34, 1235–1248. [Google Scholar] [CrossRef]
  35. Giaccone, S.; Magnusson, M. Unveiling the role of risk-taking in innovation: Antecedents and effects. R&D Manag. 2021, 52, 93–107. [Google Scholar] [CrossRef]
  36. Hu, X.; Chen, J.; Lin, S. Influence from highways’ development on green technological innovation: The case of Yangtze River economic belt in China. Environ. Dev. Sustain. 2022, 25, 11095–11120. [Google Scholar] [CrossRef]
  37. Zhou, Y.; Xu, X.; Tao, L. The impact mechanism of high-speed railway on regional green innovation spillover under multi-dimensional paths. Environ. Impact Assess. Rev. 2022, 95, 106795. [Google Scholar] [CrossRef]
  38. Alawad, H.; Kaewunruen, S.; An, M. A Deep Learning Approach Towards Railway Safety Risk Assessment. IEEE Access 2020, 8, 102811–102832. [Google Scholar] [CrossRef]
  39. Shin, J.; Lee, H. Low-risk opportunity recognition from mature technologies for SMEs. J. Eng. Technol. Manag. 2013, 30, 402–418. [Google Scholar] [CrossRef]
  40. Saleem, H.; Li, Y.; Ali, Z.; Ayyoub, M.; Wang, Y.; Mehreen, A. Big data use and its outcomes in supply chain context: The roles of information sharing and technological innovation. J. Enterp. Inf. Manag. 2020, 34, 1121–1143. [Google Scholar] [CrossRef]
  41. Lis, D.; Arbter, M.; Spindler, M.; Otto, B. An Investigation of Antecedents for Data Governance Adoption in the Rail Industry—Findings From a Case Study at Thales. IEEE Trans. Eng. Manag. 2022, 70, 2528–2545. [Google Scholar] [CrossRef]
  42. Dong, S.; Yang, Y.; Li, F.; Hao, C.; Li, J.; Li, Z.; Li, Y. An evaluation of the economic, social, and ecological risks of China-Mongolia-Russia high-speed railway construction and policy suggestions. J. Geogr. Sci. 2018, 28, 900–918. [Google Scholar] [CrossRef]
  43. Ravesteijn, W.; He, J.; Chen, C. Responsible innovation and stakeholder management in infrastructures: The Nansha Port Railway Project. Ocean. Coast. Manag. 2014, 100, 1–9. [Google Scholar] [CrossRef]
  44. Zhang, N.; Deng, X.; Hwang, B.G.; Niu, Y. How to balance interfirm relationships? A case from high-speed railway industry. J. Bus. Ind. Mark. 2020, 35, 1785–1799. [Google Scholar] [CrossRef]
  45. Sariola, R.; Martinsuo, M. Enhancing the supplier’s non-contractual project relationships with designers. Int. J. Proj. Manag. 2016, 34, 923–936. [Google Scholar] [CrossRef]
  46. Jiang, X.; Wang, L.; Cao, B.; Fan, X. Benefit Distribution and Stability Analysis of Enterprises’ Technological Innovation Cooperation Alliance. Comput. Ind. Eng. 2021, 161, 107637. [Google Scholar] [CrossRef]
  47. Meng, X. The role of trust in relationship development and performance improvement. J. Civ. Eng. Manag. 2015, 21, 845–853. [Google Scholar] [CrossRef]
  48. Moreno, Á.; Cano, A.; Sáez-Martínez, F. Many or trusted partners for eco-innovation? The influence of breadth and depth of firms’ knowledge network in the food sector. Technol. Forecast. Soc. Change 2019, 147, 51–62. [Google Scholar] [CrossRef]
  49. Zhong, Z.; Zhang, C.; Jia, F.; Bijman, J. Vertical Coordination and Cooperative Member Benefits: Case Studies of Four Dairy Farmers’ Cooperatives in China. J. Clean. Prod. 2017, 172, 2266–2277. [Google Scholar] [CrossRef]
  50. Chen, Y.C.; Li, P.C.; Arnold, T. Effects of collaborative communication on the development of market-relating capabilities and relational performance metrics in industrial markets. Ind. Mark. Manag. 2013, 42, 1181–1191. [Google Scholar] [CrossRef]
  51. Ahmed, W.; Ashraf, M.; Khan, S.; Kusi-Sarpong, S.; Arhin, F.; Kusi-Sarpong, H.; Najmi, A. Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance. Oper. Manag. Res. 2020, 13, 4–21. [Google Scholar] [CrossRef]
  52. Yang, Z.; Nguyen, V.; Ba Phong, L. Knowledge sharing serves as a mediator between collaborative culture and innovation capability: An empirical research. J. Bus. Ind. Mark. 2018, 33, 958–969. [Google Scholar] [CrossRef]
  53. Wang, X.; Sun, Y.; Li, G. The Relationship Between Collaborative Innovation Risk and Performance of Industrial Parks. J. Glob. Inf. Manag. 2022, 30, 1–19. [Google Scholar] [CrossRef]
  54. Liu, Z.; Ding, R.; Wang, L.; Song, R.; Song, X. Cooperation in an uncertain environment: The impact of stakeholders’ concerted action on collaborative innovation projects risk management. Technol. Forecast. Soc. Change 2023, 196, 122804. [Google Scholar] [CrossRef]
  55. Avsar, A.; Grogan, P. Effects of differential risk attitudes in collaborative systems design. Syst. Eng. 2023, 26, 770–782. [Google Scholar] [CrossRef]
  56. Fanousse, R.; Nakandala, D.; Lan, Y.C. Reducing uncertainties in innovation projects through intra-organisational collaboration: A systematic literature review. Int. J. Manag. Proj. Bus. 2021, 14, 1335–1358. [Google Scholar] [CrossRef]
  57. Cardador, M.; Northcraft, G.; Rockmann, K.; Grant, B. Characteristics of Affected Third Parties and Cooperative Behavior in Social Dilemmas. J. Soc. Psychol. 2016, 156, 1140116. [Google Scholar] [CrossRef]
  58. Martinez, H.; Moreno, J.; Camacho, J. Networks of collaborative alliances: The second order interfirm technological distance and innovation performance. J. Technol. Transf. 2020, 45, 1255–1282. [Google Scholar] [CrossRef]
  59. Porcu, L.; Del, S.; Kitchen, P.; Tourky, M. The antecedent role of a collaborative vs. a controlling corporate culture on firm-wide integrated marketing communication and brand performance. J. Bus. Res. 2020, 119, 435–443. [Google Scholar] [CrossRef]
  60. Yu, X.; Cui, Y.; Chen, Y.; Chang, I.S.; Wu, J. The drivers of collaborative innovation of the comprehensive utilization technologies of coal fly ash in China: A network analysis. Environ. Sci. Pollut. Res. 2022, 29, 56291–56308. [Google Scholar] [CrossRef]
  61. Xue, H.; Zhang, S.; Su, Y.; Wu, Z.; Yang, R. Effect of stakeholder collaborative management on off-site construction cost performance. J. Clean. Prod. 2018, 184, 490–502. [Google Scholar] [CrossRef]
  62. Wang, C.; Zhang, G. Examining the moderating effect of technology spillovers embedded in the intra- and inter-regional collaborative innovation networks of China. Scientometrics 2019, 119, 561–593. [Google Scholar] [CrossRef]
  63. Guo, Z.; Wang, Q.; Peng, C.; Zhuang, S.; Yang, B. Willingness to accept metaverse safety training for construction workers based on extended UTAUT. Front. Public Health 2023, 11, 1294203. [Google Scholar] [CrossRef]
  64. Dong, Z.J.; Xu, L.; Cheng, J.H.; Sun, G.J. Major factors affecting biomedical cross-city R&D collaborations based on cooperative patents in China. Scientometrics 2021, 126, 1923–1943. [Google Scholar] [CrossRef]
  65. Wang, C.H.; Chin, T.; Chiew, Y.; Capalbo, C. How geographic diversity and collaborative breadth prevent knowledge leakage during open innovation processes. J. Knowl. Manag. 2023, 28, 743–762. [Google Scholar] [CrossRef]
  66. Demirkesen, S.; Ozorhon, B. Impact of integration management on construction project management performance. Int. J. Proj. Manag. 2017, 35, 1639–1654. [Google Scholar] [CrossRef]
  67. Klimczak, K.; Machowiak, W.; Shachmurove, Y.; Staniec, I. Perceived collaborative risk in small and medium technology enterprises. J. Small Bus. Manag. 2023, 61, 540–559. [Google Scholar] [CrossRef]
  68. Bourai, S.; Arora, R.; Yadav, N. Structure-conduct-performance (SCP) paradigm in digital platform competition: A conceptual framework. J. Strategy Manag. 2024, 17, 322–347. [Google Scholar] [CrossRef]
  69. Willem, A.; Buelens, M. Knowledge sharing in inter-unit cooperative episodes: The impact of organizational structure dimensions. Int. J. Inf. Manag. 2009, 29, 151–160. [Google Scholar] [CrossRef]
  70. Bossink, B. Managing Drivers of Innovation in Construction Networks. J. Constr. Eng. Manag. 2004, 130, 337–345. [Google Scholar] [CrossRef]
  71. Johansson, E.; Raddats, C.; Witell, L. The role of customer knowledge development for incremental and radical service innovation in servitized manufacturers. J. Bus. Res. 2019, 98, 328–338. [Google Scholar] [CrossRef]
  72. Mehralian, G.; Sheikhi, S.; Zatzick, C.; Babapour, J. The dynamic capability view in exploring the relationship between high-performance work systems and innovation performance. Int. J. Hum. Resour. Manag. 2022, 34, 3555–3584. [Google Scholar] [CrossRef]
  73. Piening, J.; Ehrmann, T.; Meiseberg, B. Competing risks for train tickets—An empirical investigation of customer behavior and performance in the railway industry. Transp. Res. Part E Logist. Transp. Rev. 2013, 51, 1–16. [Google Scholar] [CrossRef]
  74. Tsung-yi, C.; Chen, Y.M.; Wang, C.B. A Formal Virtual Enterprise Access Control Model. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2008, 38, 832–851. [Google Scholar] [CrossRef]
  75. Wu, A.; Wang, Z.; Chen, S. Impact of specific investments, governance mechanisms and behaviors on the performance of cooperative innovation projects. Int. J. Proj. Manag. 2016, 35, 504–515. [Google Scholar] [CrossRef]
  76. Deng, J.; Zhao, Y.; Li, X.; Wang, Y.; Zhou, Y. Network Embeddedness, Relationship Norms, and Cooperative Behavior: Analysis Based on Evolution of Construction Project Network. J. Constr. Eng. Manag. 2023, 149. [Google Scholar] [CrossRef]
  77. Hinojosa, M.; Mármol, A.M.; Thomas, L. Core, least core and nucleolus for multiple scenario cooperative games. Eur. J. Oper. Res. 2005, 164, 225–238. [Google Scholar] [CrossRef]
  78. Eriksson, P.E.; Westerberg, M. Effects of Cooperative Procurement Procedures on Construction Project Performance: A Conceptual Framework. Int. J. Proj. Manag. 2011, 29, 197–208. [Google Scholar] [CrossRef]
  79. Khazaeni, G.; Khanzadi, M.; Afshar, A. Fuzzy adaptive decision making model for selection balanced risk allocation. Int. J. Proj. Manag. 2012, 30, 511–522. [Google Scholar] [CrossRef]
  80. Zhang, Y.; Ye, J. Risk preference of top management team and the failure of technological innovation in firms–based on principal component analysis and probit regression. J. Intell. Fuzzy Syst. 2020, 40, 1161–1173. [Google Scholar] [CrossRef]
Buildings 15 02185 g001
Figure 1. Construction of the 3C Theory.
Figure 1. Construction of the 3C Theory.
Buildings 15 02185 g001
Buildings 15 02185 g002
Figure 2. Conceptual model.
Figure 2. Conceptual model.
Buildings 15 02185 g002
Buildings 15 02185 g003
Figure 3. Research design.
Figure 3. Research design.
Buildings 15 02185 g003
Buildings 15 02185 g004
Figure 4. TIR moderating effect.
Figure 4. TIR moderating effect.
Buildings 15 02185 g004
Table 1. Measurement items of technological innovation cooperation for megaprojects.
Table 1. Measurement items of technological innovation cooperation for megaprojects.
DimensionsItemsSource
Cooperative relationships (CRs)CR1 We think it is necessary to increase trust among stakeholders.
CR2 We are willing to promote knowledge sharing and collaboration.
CR3 We are qualified to attract and motivate high-performing groups.		I1, I2, I3, P1, P2, P3, P4, P5, O1, O2,
Cooperative behaviors (CBs)CB1 We aggressively and frequently collects other stakeholders’ feedback.
CB2 We agree that frequent communication can facilitate technological innovation cooperation.
CB3 We are willing to continue technological innovation cooperation.		I1, I2, I3, P1, P2, P3, P4, P5,
Cooperative performance (CP)CP1 We are generally satisfied with the results of cooperation so far.
CP2 Cooperation contributes to improve technology maturity, increases the number of patents granted, and reduces research costs.
CP3 Cooperation enables us to reach consensus on green awareness, the digital value concept, and social responsibility.		P1, P2, P3, P4, P5, I1, O3, O4
Technological innovation risks (TIRs)TIR1 Our uncertainties about the environment, requirements, and resources can affect technological innovation.
TIR2 There are challenges about the application of new technologies, the implementation of new processes, and the operation of new equipment.
TIR3 The failure of coordination mechanism, low cooperative efficiency, and insufficient human resources define technological innovation cooperation.		I1, I3, O1, O2, O4
Table 2. Hierarchical regression results of CR, CB, and CP.
Table 2. Hierarchical regression results of CR, CB, and CP.
VariablesCPCB
M1M2M3M4M5
Types0.1350.124 *0.0980.1170.105 *
Years0.081 *−0.023−0.0160.098−0.026
Position0.029−0.051−0.0510.0970.002
CR 0.501 ***0.354 *** 0.597 ***
CB 0.246 ***
R2 0.2620.301 0.369
Adjusted R2 0.2450.279 0.354
F1.53512.026 ***14.2642.02424.451 ***
Notes: * p < 0.10 and *** p < 0.01; n = 172.
Table 3. The mediating effect test results of CB.
Table 3. The mediating effect test results of CB.
EffectS.E.Bootstrap 95% CIRatio of Total Effect
LLCIULCI
Total effect0.57750.07910.42140.7336
Direct effect0.40810.09550.21950.5967
Indirect effect0.16940.07040.03860.309429.33%
Table 4. The moderating effect test results of TIR.
Table 4. The moderating effect test results of TIR.
VariablesCBCP
M6M7M8M9M10M11
Types0.042 **0.041 *0.0480.0460.047 **0.045 **
Years0.0570.0580.0640.0630.0640.063
Position0.0520.0520.0580.0570.0580.057
CR0.076 ***0.077 *** 0.085 ***0.085 ***
CB 0.077 ***0.076 ***
TIR0.065 **0.0740.072 ***0.077 ***0.073 ***0.081 **
CR*TIR 0.066 * 0.072 ***
CB*TIR 0.064 ***
R20.3910.4010.3190.3640.3330.374
Adjusted R20.3720.3790.2980.3410.3130.351
F21.286 ***18.428 ***15.532 ***15.759 ***16.571 ***16.437 ***
Notes: * p < 0.10, ** p < 0.05, and *** p < 0.01; n = 172.
Table 5. The moderated mediating effect test results.
Table 5. The moderated mediating effect test results.
VariablesModel 12Model 13
S.E.t95%CIS.E.t95%CI
Types0.0421.771 *[−0.008, 0.155]0.0461.724 *[−0.011, 0.169]
Years0.058−0.363[−0.135, 0.093]0.063−0.207[−0.137, 0.111]
Position0.0520.046[−0.100, 0.105]0.057−0.916[−0.164, 0.060]
CR0.2683.714 ***[0.465, 1.522]0.5931.054[−0.546, 1.795]
CB 0.5271.374[−0.316, 1.763]
TIR0.2392.302 *[0.078, 1.023]0.2643.947 ***[0.521, 1.563]
CR*TIR0.066−1.707 *[0.018, 0.243]0.143−0.623[−0.370, 0.111]
CB*TIR 0.126−1.063[−0.382, 0.115]
R20.4010.394
F18.429 ***13.223 ***
Notes: * p < 0.10 and *** p < 0.01; n = 172.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guo, Z.; Wang, Q.; Cao, X. Unlocking the Mechanism of Technological Innovation Cooperation in Megaprojects: A 3C Theory Perspective. Buildings 2025, 15, 2185. https://doi.org/10.3390/buildings15132185

AMA Style

Guo Z, Wang Q, Cao X. Unlocking the Mechanism of Technological Innovation Cooperation in Megaprojects: A 3C Theory Perspective. Buildings. 2025; 15(13):2185. https://doi.org/10.3390/buildings15132185

Chicago/Turabian Style

Guo, Zhenxu, Qing’e Wang, and Xiaoping Cao. 2025. "Unlocking the Mechanism of Technological Innovation Cooperation in Megaprojects: A 3C Theory Perspective" Buildings 15, no. 13: 2185. https://doi.org/10.3390/buildings15132185

APA Style

Guo, Z., Wang, Q., & Cao, X. (2025). Unlocking the Mechanism of Technological Innovation Cooperation in Megaprojects: A 3C Theory Perspective. Buildings, 15(13), 2185. https://doi.org/10.3390/buildings15132185

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

Article Metrics

Back to TopTop