Synergistic Innovation Pathways in Aviation Complex Product Ecosystems: Enabling Sustainability Through Resource Efficiency and Systemic Collaboration

Abstract

Achieving sustainable development in the aviation industry increasingly relies on the synergistic operation of complex product innovation ecosystems. These ecosystems not only drive technological breakthroughs, but also serve as crucial enablers of resource efficiency, ecological resilience, and long-term industrial competitiveness. This study explores how specific configurations of synergistic factors within innovation ecosystems support sustainable innovation outcomes in the aviation sector. Drawing on the innovation ecosystem theory and principles of sustainable development, we employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine 15 representative aviation equipment R&D cases, including AVIC Tongfei and AVIC Xifei. The analysis centers on five key dimensions: core enterprise leadership, value chain collaboration, cross-organizational innovation, technology–market feedback loops, and institutional policy support. These dimensions interact to shape multiple synergy pathways that facilitate sustainable transformation. The results reveal that no single factor alone is sufficient to ensure high innovation sustainability. Instead, three distinct synergy configurations emerge: (1) core enterprise-led model, which reduces resource redundancy through optimized value chain governance; (2) the industry chain collaboration model, which enhances environmental performance via modular design and lifecycle management; and (3) cross-organization innovation collaboration model, which improves material reuse and infrastructure sharing through collaborative mechanisms. Together, these pathways form a reinforcing cycle of innovation–efficiency–sustainability, offering a practical framework for aligning technological advancement with ecological goals. This study deepens the understanding of how innovation ecosystem mechanisms contribute to sustainable development, particularly in high-integration industries. It offers actionable insights into achieving the Sustainable Development Goals (SDGs) through collaborative innovation and systemic resource optimization.

1. Introduction

The research, development, and manufacturing of aviation complex products are highly dependent on technological integration, industrial chain collaboration, and innovative cooperation among multiple stakeholders. As a core sector of a nation’s high-end equipment manufacturing industry, their sustainable development not only involves technological breakthroughs and the enhancement of industrial competitiveness, but also relates to global issues such as energy consumption, resource utilization efficiency, and environmental impacts [1]. High technological barriers, long life cycles, and high resource inputs characterize such products. Problems such as resource waste, redundant research and development, and industrial chain fragmentation in their innovation process have become key bottlenecks restricting the sustainable development of the aviation industry [2]. Against the backdrop of global “dual carbon” goals and increasingly severe resource constraints, the traditional linear innovation model, which neglects resource circulation and system synergy, can hardly meet the sustainable development needs of the aviation industry for “carbon reduction, pollution abatement, and efficiency improvement”. There is an urgent need to achieve optimal resource allocation, collaborative technological breakthroughs, and the enhancement of sustainability across the entire industrial chain through the synergistic mechanisms of innovation ecosystems [3]. Although existing studies have revealed that core enterprises play a strategic central role by virtue of their technological advantages and resource integration capabilities, and that synergistic mechanisms are crucial for the stable and efficient operation of the system, there are still significant limitations: theoretical perspectives often focus on single entities or bilateral relationships, lacking a systematic analysis of the configurational effects of multi-dimensional synergistic mechanisms involving objectives, resources, organizations, and incentives; methodologically, qualitative research is difficult to reveal the causal relationships among multiple concurrent factors, while quantitative models often simplify the complexity of contextual factors such as policies and culture [4]; research in the aviation field is not in-depth enough, and it remains unclear how core enterprises dynamically allocate capabilities to promote the optimization of the configuration of synergistic mechanisms.

Based on this, this study proposes the core hypothesis: The configuration of synergistic mechanisms (core leadership, supply chain collaboration, cross-organizational cooperation, etc.) in the innovation ecosystem of aviation complex products can significantly improve the sustainable innovation performance of the aviation industry by optimizing resource utilization efficiency and reducing environmental negative externalities [5]. To test this hypothesis and fill the research gaps, this study considers 15 aviation equipment R&D cases, including AVIC General Aviation and AVIC Xi’an Aircraft Industry, as samples and adopts the fuzzy-set Qualitative Comparative Analysis (fsQCA) method [6]. Theoretically, it aimed to construct an integrated analytical framework from factors to mechanisms, and then to performance, reveal the complex causal mechanisms of multi-condition combinations, and expand the applicability of innovation ecosystem theory in the field of complex products. Practically, it strives to identify efficient synergistic models led by core enterprises and their triggering conditions, providing decision support for China’s aviation industry to break through technological barriers and optimize cross-organizational collaboration [7]. The entire paper follows the logical sequence of theoretical construction—empirical analysis—mechanism extraction [8], with the core question being “how to improve the innovation efficiency of aviation complex products through collaborative configuration design” [9].

2. Principles and Methods

2.1. Theoretical Foundation

The collaborative process of aviation complex product innovation ecosystems led by core enterprises exhibits significant multi-level and dynamic interactive characteristics. From a theoretical perspective, complex systems theory emphasizes the existence of nonlinear interactions among various elements within a system, which aligns closely with the coupled relationships among multi-dimensional collaborative elements such as technical standards, resource allocation, interest coordination, and knowledge sharing within aviation innovation ecosystems [10]. The nonlinear coupling between different elements implies that a single collaborative strategy is insufficient to address highly heterogeneous innovation scenarios, as any change in an element may trigger a chain reaction akin to “pulling one hair and moving the entire body” [11]. However, existing research is largely based on linear causal logic, focusing on isolated analyses of the impact of single dimensions such as technological collaboration or resource integration on innovation performance. It has failed to fully reveal the configurational effects of multi-element collaborative interactions and their boundaries of influence [12]. In particular, there remains a significant gap in research on the mechanisms through which core enterprises dynamically adjust element combinations to balance the technical capabilities, interest demands, and risk preferences of diverse stakeholders such as primary manufacturers, suppliers, and research institutions [13].

The heterogeneous entities within the aviation complex product innovation ecosystem make it difficult for a single collaboration mechanism to meet cross-organizational and cross-level adaptation requirements, often leading to system efficiency losses [14]. Taking the Boeing 787 passenger aircraft project as an example, during the collaborative innovation involving thousands of global suppliers, issues such as inconsistent technical standards and imbalanced resource allocation resulted in multiple project delays and cost overruns, clearly exposing the shortcomings of inadequate collaboration mechanism adaptability [15]. In practice, core enterprises need to design a combination of multi-dimensional collaborative strategies. They must establish unified technical standards to reduce system coupling complexity, establish flexible resource allocation mechanisms to address innovation uncertainties, and rely on knowledge collaboration networks and risk-sharing mechanisms to maintain ecosystem stability [16]. This requires moving beyond the fragmented analysis of collaborative elements in traditional research and toward a holistic study of the collaborative configurations among these elements [17].

Therefore, this chapter proposes, from the perspective of the configuration theory, that there are multiple equivalent paths for enhancing the collaborative efficiency of aviation complex product innovation ecosystems. The interaction and combination of different technical, resource, and organizational elements will form differentiated conditional configurations [18]. The configuration theory emphasizes “different paths leading to the same destination,” meaning that various combinations of elements may achieve the same system objectives, providing a new perspective for analyzing the collaborative evolution of aviation innovation ecosystems [19]. Specifically, this chapter incorporates three core elements into the analytical framework: standard embedding and interface optimization in technological synergy, sharing mechanisms and allocation strategies in resource synergy, and contractual governance and trust empowerment in organizational synergy [20]. Using the synergistic stability and output efficiency of the innovation ecosystem as the outcome variables, a configuration analysis model is constructed, as shown in Figure 1. This model not only considers the complementary and substitute relationships among elements, but also focuses on the dynamic adjustment process of element combinations by core enterprises [21] under different scenarios. It aims to systematically reveal how core enterprises achieve the collaborative evolution of complex product innovation ecosystems through the dynamic configuration design of multiple elements, thereby addressing the theoretical gaps in existing research regarding the dynamism and holistic nature of multi-element collaborative mechanisms.

2.2. Research Design

2.2.1. Basis of Research Method Selection

This study adopts fuzzy-set Qualitative Comparative Analysis (fsQCA) as its core method because its set-theoretic and Boolean-algebra foundations map directly onto the intrinsic complexity of aviation innovation ecosystems, i.e., systems characterized by multi-actor interactions, multi-factor collaboration, and nested organizational layers. Traditional linear causality models cannot adequately capture the nonlinearity, causal asymmetry, and path-dependent dynamics that drive innovation performance in these settings. By identifying the configurations of conditions that are jointly necessary or sufficient for a given outcome, fsQCA transcends the limitations of variable independence and causal symmetry, offering a rigorous framework for uncovering how core-firm leadership orchestrates the broader ecosystem [22].

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Complexity Demands of the Research Problem

The coordination mechanisms in an aviation complex product innovation ecosystem are inherently nonlinear and dynamically adaptive, with performance emerging from the interplay of core-firm leadership, value–chain collaboration, inter-organizational cooperation, technology–market feedback, and policy support rather than any single factor. This configurational complexity is evident in the simultaneous interaction of multiple causal conditions—for example, technological leadership must coincide with policy support to overcome critical barriers—and in the asymmetrical nature of causal pathways, where configurations sufficient for success do not simply invert to produce failure. Moreover, irreversible technological accumulation generates path dependencies that shape current innovation trajectories through knowledge sedimentation, resource lock-in, and institutional inertia. By employing set-theoretic models and Boolean minimization, fsQCA represents each case as a vector of membership scores and systematically uncovers core condition configurations, thereby revealing both equifinality—distinct combinations yielding the same outcome—and causal pluralism, in which a single condition may exert divergent effects depending on its configurational context.

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Methodological Advantages of fsQCA

In aviation research, fsQCA offers distinct methodological advantages. Its set-theoretic logic accommodates causal asymmetry, distinguishing the mechanisms underpinning high- versus low-performance configurations, for example, illustrating how robust policy support can amplify technological leadership in one scenario. In contrast, its absence may exacerbate systemic imbalances in another. fsQCA also remains valid with small-to-medium samples (n < 20), a critical feature given the lengthy R&D cycles and regulatory constraints of aerospace products; counterfactual analysis, logical remainder handling, and coverage metrics together minimize Type II error risk. Finally, by calibrating quantitative (e.g., R&D intensity, patent counts), qualitative (e.g., policy text semantics), and relational (e.g., supply chain topology) data into fuzzy-set membership scores on a unified [0, 1] scale, fsQCA standardizes diverse inputs without sacrificing their richness.

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Comparative Advantages over Alternative Methods

Compared with multiple-regression analysis—which assumes variable independence and linear additive effects—fsQCA can model interaction effects such as “core-firm leadership × policy support,” thereby revealing how policy levers magnify the marginal benefits of technological leadership through risk reduction and enhanced resource allocation. Compared with single case studies—which retain case depth but lack cross-case generalizability—fsQCA preserves the integrity of individual cases while extracting generalizable configurations, forming a coherent research chain of “in-depth case description → configurational induction → theory development.” Finally, unlike social network analysis, which emphasizes structural visualization, fsQCA delves into the causal logic of condition combinations—such as the suppressive effect of a “high network density + low institutional support” configuration on innovation performance—thus offering deeper insights into the mechanisms that coordinate core enterprise-driven aviation innovation ecosystems. As summarized in

Table 1. Comparative analysis with alternative methods.

Methodology	Applicable Scenario	Adaptation Flaws of This Study
Multiple-regression analysis	Large sample, linear causality	Ignoring interaction effects, insufficient sample size
Case study method	Deep mechanism mining	Difficult to systematically compare multi-case group differences
Social network analysis	Visualization of relationship structure	Inability to quantify the sufficient necessity of combinations of conditions
System dynamics	Dynamic simulation modeling	Mathematical relationships between variables need to be clarified, assumptions are too strong, and data volume is too large

, a comparative analysis of these alternative methods highlights the strengths and weaknesses of each approach.

In comparison, fsQCA achieves the best balance in the three dimensions of theory methodology data: the theory level fits the theory of collaborative evolution of innovation ecosystems; the methodology level meets the demand for multiple concurrent causal analysis; and the data level adapts to the characteristics of small- and medium-sized samples of multi-sourced heterogeneous data, so the fsQCA methodology is finally chosen to conduct the study.

2.2.2. Data Sources

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Sample selection

In this study, we selected AVIC Chengdu Aircraft Industry (Group) Company Limited (“AVIC Chengfei”), AVIC Xi’an Aircraft Industry Group Company Limited (“AVIC Xifei”) and AVIC Shenyang Aircraft Industry (Group) Company Limited (“AVIC Shenfei”) as the samples. (“AVIC Shenfei”), and 15 pieces of aviation equipment as the core research samples, which were selected based on the strategic position and technology representation of the former three in the aviation complex product innovation ecosystem [23]. As the three major host factories under the Aviation Industry Corporation of China (AVIC), AVIC Chengfei, AVIC Xifei and AVIC Shenfei dominate the R&D and manufacturing of fighter aircraft, transport aircraft, and special aircraft, respectively. Their business covers the entire aeronautical equipment industry chain, including overall design, key sub-system integration, general assembly, test flights, and full life cycle service. They are the core pillars of China’s aeronautical industry’s independent innovation system, playing a dominant role.

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Data Source and Processing

The research data come from multi-dimensional and cross-level heterogeneous data sources, covering five types of subjects: annual reports of enterprises, aviation industry database of the National Bureau of Statistics, patent cooperation network data, policy texts, and in-depth interview records [24]. The annual report data were used to extract financial indicators such as R&D investment and the market share of core enterprises; the aviation industry database provides macro data such as industry chain collaboration intensity and technology maturity; the patent cooperation network data quantify cross-organizational knowledge flow through social network analysis; the policy text adopts the content analysis method to analyze the strength of institutional support; and the in-depth interview records distill the implicit knowledge sharing mechanism through text coding.

The data processing follows four steps: first, data cleaning to eliminate invalid cases with a missing rate higher than 15%; second, standardization conversion to normalize the heterogeneous data into the [0, 1] interval; third, fuzzy-set calibration to set the thresholds for full affiliation, intersection and full non-affiliation concerning the industry benchmark; and fourth, triangulation validation to improve the confidence level through cross-checking of the data from multiple sources. Finally, a sample set of 15 valid cases is constructed, covering the three significant fields of aero-engine, avionics system, and aircraft platform, to ensure that the sample is technologically diversified and representative of the model.

2.2.3. Variable Design

This study conducted semi-structured interviews with senior executives, R&D personnel, supply chain managers, and representatives of partner research institutes at AVIC General Aircraft Company (AGAC). It supplemented these data with secondary sources from comparable firms (AVIC Shenyang Aircraft, Chengdu Aircraft, and Xi’an Aircraft). Through thematic coding, we systematically identified the coordination factors that drive aviation complex product innovation ecosystems. AGAC was chosen as a prototypical case due to its exemplary status and industry influence: as a large state-owned enterprise directly under the China Aviation Industry Corporation, AGAC’s operations span the whole value chain—from general aircraft R&D and manufacturing, to aviation services and infrastructure—thereby fully reflecting the characteristic features of China’s complex-equipment manufacturing innovation ecosystem. Its core-firm-driven architecture, in which AGAC serves as both industry standard setter and innovation network coordinator, integrates upstream and downstream suppliers, research institutes, and regional clusters via a modular, multidisciplinary “prime-manufacturer–supplier” collaboration model, providing an ideal context for exploring value co-creation mechanisms within innovation ecosystems.

Through case study analysis, we found that core firm leadership, industry chain collaboration, cross-organizational cooperative innovation, bidirectional technology–market feedback, and policy support with institutional safeguards synergistically drive collaborative innovation among ecosystem actors, thereby facilitating the research, development, and commercialization of complex aviation products.

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Conditional variables

The variable system of this study is based on the group analysis framework and the multi-subject synergistic characteristics of the aviation complex product innovation ecosystem, and through theoretical traceability and industry characteristic adaptation, a multi-dimensional measurement system is constructed, which contains five condition variables and one outcome variable [25]. The variables are selected based on the following.

• Leadership of core enterprises

As the organizational hub of the innovation ecosystem, the leading role of the core enterprise is realized through four dimensions: strategic decision-making, technological breakthrough, resource integration, and market orientation. The strategic leadership dimension is measured by the indicators of “strategic goal achievement rate” and “strategic decision response speed”, where the former quantifies the effectiveness of strategy implementation through the progress of annual technology roadmap implementation. The latter measures the organizational agility by the number of delayed days from decision issuance to first implementation [26].The technology leadership dimension characterizes the intensity of investment in technological innovation through the “R&D investment as a percentage of revenue” and “R&D personnel ratio”, where the R&D investment ratio reflects a company’s continuous commitment to technological breakthroughs, and the R&D personnel ratio reflects the core competence of a knowledge-intensive organization. The ratio of R&D investment reflects an enterprise’s continuous commitment to technological breakthroughs. During the development process of the C919 aircraft, China Western Airlines has established a professional R&D team covering multiple fields such as aviation design, materials science, aerodynamics, etc., with a considerable proportion of R&D personnel. It is precisely through sufficient funding and a professional research and development team that the C919 can achieve significant technological breakthroughs in China’s civil aviation field. Resource Integration Dimension measures the ability to integrate industrial chain resources by “total annual investment in fixed assets”, which directly reflects the capital control of core enterprises over manufacturing facilities, supply chain networks, and technology platforms. Market traction dimension quantifies the market transformation efficiency of technical achievements through the “percentage of sales revenue of new products”, which is calculated as the percentage of sales revenue of new products to the total revenue, reflecting the demand-oriented technology iteration-driven capability.

2.

Industry Chain Collaboration

Industry chain cooperation focuses on supply chain synergy, efficiency, and cost control ability, covering three levels: supply chain management, production optimization, and cost-effectiveness [27]. Supply chain management quantifies the core enterprise’s dependence on key suppliers through the “percentage of procurement amount of top five suppliers”, which is calculated by the percentage of the total procurement amount of top five suppliers, reflecting the stability of strategic alliances and the level of centralization. Production optimization dimension adopts “operating cost to revenue ratio” to measure the degree of resource intensification, which inversely characterizes the optimization effect of industrial chain division of labor synergy, and is judged to be a state of efficient collaboration when the ratio of operating cost is less than 1.5 standard deviations below the industry average. The C919 project integrates domestic aviation industry chain resources and forms a “main manufacturer supplier” model based on the advantages of each enterprise for division of labor. For example, Xi’an Aircraft Corporation is responsible for the production of fuselage components, while Shenyang Aircraft Corporation is responsible for the manufacturing of some wing components.

3.

Cross-organizational cooperative innovation

Cross-organizational cooperative innovation realizes the flow of innovation elements through a knowledge sharing network and an institutional collaboration platform, and its measurement system includes two dimensions of collaboration breadth and depth. The breadth dimension takes “the number of R&D cooperation projects” and “the number of industry-university-research cooperation units” as the core indicators, with the former counting the total number of inter-organizational joint R&D projects in a year. The latter records the number of external institutions involved in collaborative innovation, which together reflect the openness and inclusiveness of the knowledge network [9]. Both of them reflect the transparency and inclusiveness of the knowledge network. The depth of collaboration is measured by the “number of national innovation platforms” and the “proportion of external cooperation funds”, where the number of national innovation platforms is based on the recognition criteria of the “Management Measures for National Technology Innovation Centers”, and the proportion of external cooperation funds is calculated as the proportion of R&D funds invested in total R&D expenditures by external organizations. The proportion of external cooperation funds is calculated as the proportion of total R&D expenditures by external organizations. The ratio of external cooperation funds is calculated as the proportion of R&D funds invested by external organizations to the total R&D expenditures. In order to promote the development of the C919 aircraft, AVIC actively engages in multi-party cooperation, participating in more than 50 research and development cooperation projects within a year. The number of industry–university research cooperation units is numerous, covering universities, research institutes, and enterprises, fully demonstrating the openness and inclusiveness of the knowledge network, greatly expanding the breadth of cooperation.

4.

Two-Way Feedback between Technology and Market

The two-way feedback mechanism between technology and market realizes the closed loop of innovation through demand-oriented R&D and commercialization efficiency. The technology-to-market dimension takes “market-oriented R&D investment ratio” as the core indicator, which is calculated as the ratio of funds for customized R&D projects to total R&D investment, reflecting the depth of demand-embedded technology development. The commercialization efficiency dimension is measured by the “technology development to application cycle”, using the natural logarithm to handle the actual number of days (the shorter the cycle, the higher the degree of affiliation). When the cycle is shorter than the average value of industry benchmark enterprises by 30%, it is judged as a highly efficient feedback state. The demand integration dimension quantifies the institutionalized level of user participation in innovation with “the proportion of customer demands incorporated into R&D plans”, which is calculated through the demand tracking records of the enterprise’s R&D management system. When developing derivative models of the Y-20 large transport aircraft, AVIC Xifei thoroughly considered the diverse demands of domestic and foreign markets for large aerial platforms. Customized research and development projects are specifically established to meet market demands for emergency rescue, aerial refueling, and other related services. The scalar transformation adopts the time series decay function to deal with the technology transformation cycle indicators, combined with the standardized scores of the ratio indicators, to construct a non-linear weighted composite index.

5.

Policy support and institutional guarantee

Policy support and institutional guarantees shape the external enabling conditions through financial support and institutional incentives. The dimension of financial support is measured by the dual indicators of “amount of government subsidies” and “percentage of government R&D funds”, with the former counting the total amount of financial subsidies received during the year, and the latter calculating the proportion of government funds in the total R&D investment. The policy guidance dimension is measured by the “amount of tax incentives” as the core indicator, reflecting the direct cost compensation effect of institutional incentives on innovation activities, which is obtained from the data of enterprise income tax returns. From the perspective of the policy guidance dimension, gauged by the “amount of tax incentives”, C919 benefited greatly. As a major national scientific and technological project, the enterprises involved in C919’s production, such as the leading manufacturer AVIC, enjoyed a series of preferential tax policies. For instance, through R&D expense additional deductions and high-tech enterprise tax reductions, the annual tax incentives amounted to tens of millions of yuan.

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Outcome Variables

According to the theory of the innovation ecosystem, innovation performance is the result of the joint action of various elements in the system (e.g., core enterprises, policy support, cross-organizational collaboration, etc.), and innovation performance can directly reflect the comprehensive performance of the innovation ecosystem in technological innovation and market application. It is a key indicator for assessing the effectiveness of the innovation ecosystem and the innovation capacity [28]. This paper chooses innovation performance as the outcome variable. The studies of these scholars have sufficiently proved that innovation performance can not only quantify the outcome of technological innovation, but also comprehensively measure the benefits of factors such as policy support, organizational cooperation, and market feedback. The summary of the variables used in this study, including five conditional variables and one outcome variable, is provided in

Table 2. Variable design and measurement.

Variable Type	Measurement Dimension	Measurement Method
Conditional Variable	Core enterprises lead	Number of R&D personnel as a percentage (%)
		Amount of R&D investment (yuan)
		Proportion of R&D investment to operating income (%)
	Industry chain collaboration	Sales of top five customers (yuan)
		Customer Concentration %
		Purchase amount from top five suppliers (yuan)
	Cross-organizational cooperation and innovation	Supplier concentration%
		Total operating cost ratio
		Sales expense ratio
	Two-way feedback between technology and market	Number of patents
		Gross profit margin
		R&D expense rate
	Policy support and institutional guarantee	Amount of government subsidies recognized as current profit and loss in the current period (yuan)
		Amount charged to current profit and loss in the previous period (yuan)
		Amount of change in policy subsidies (current period—previous period)
Outcome Variables	Innovation ecosystem performance	Operating profit margin

.

2.2.4. Calibration and Processing of Data

Calibrating (calibration) is a necessary step before analyzing the necessity and sufficiency of the antecedent variable (synergistic factors of core firm-led innovation ecosystem for aerospace complex products) and the outcome variable (innovation ecosystem innovation performance). In the process of the qualitative comparative analysis of fuzzy sets, this study considers each outcome variable and condition variable as independent sets, and each case has a continuous type of affiliation score in the set [29].

In fuzzy-set Qualitative Comparative Analysis (fsQCA), the calibration of membership scores is a crucial step. This study employs the direct calibration method [30] to transform raw data into membership scores in the interval [0, 1]. The calibration is based on three qualitative anchors: full membership (95% quantile), the crossover point (50% quantile), and full non-membership (5% quantile). The selection of these quantiles follows the standard practice in fsQCA [30]. It incorporates industry benchmark data from the aviation equipment manufacturing sector (e.g., “China Aviation Industry Yearbook 2023” and expert interviews). For instance, for the “core enterprise leadership” variable, we set the full membership at R&D investment >CNY 777 million (top 5% industry level), the crossover point at CNY 104 million (industry median), and full non-membership at <CNY 22 million (bottom 5% industry level). The calibration anchors for all variables are detailed in

Table 3. Calibration anchor points.

Variable Name	5% Quantile (Completely Unaffiliated)	50% Quantile (Intersection)	95% Quantile (Fully Affiliated)
Number of R&D staff as a percentage (%)	7.159	19	31.0715
Amount of R&D investment (yuan)	21,983,068.2	104,227,780.2	777,368,085.9
R&D investment as a percentage of operating revenue (%)	1.7145	7.745	13.586
Sales of top five customers (yuan)	104,136,850.5	957,256,100	15,537,015,760
Customer concentration%	29.233	66.16	98.399
Purchase amount from top five suppliers (yuan)	46,841,326.83	363,352,131.3	7,367,577,410
Supplier concentration%	17.696	40.46	78.3635
Total operating cost ratio	0.6070507	0.855417	0.97849195
Sales expense ratio	0.0050652	0.0153465	0.075692
Patents	17.1	159	983.2
Gross operating margin	0.1045288	0.319558	0.5222935
R&D expense ratio	0.0169067	0.067183	0.1204985
Amount charged to current profit and loss for the period (yuan)	68,682.5	12,196,135.95	134,105,911.8
Finance charged to current profit and loss in the previous period (yuan)	0	8,347,512.44	109,028,717.8
Amount of change in policy subsidies	−13,372,420.24	−426,472.485	30,228,552.22
Operating margin	0.02285565	0.141093	0.392977

. The calibration was performed using fsQCA 3.0 software, and data reliability was ensured through triangulation (enterprise annual reports, patent databases, policy documents). Referring to the research method of [31] this paper adopts the direct calibration method (direct calibration) to calibrate the fuzzy sets of the raw data using fsQCA 3.0 software with the 95%, 50%, and 5% quartiles of the variables as the three anchors of full affiliation, crossover, and full non-affiliation. Anchors were validated by three aviation experts and AVIC R&D directors. The calibration parameters for each variable are shown in Table 3.

3. Empirical Tests and Data Analysis

3.1. Univariate Necessity Analysis

Prior to the conditional grouping analysis, necessity tests were first conducted for each single antecedent condition and its inverse set (take the opposite condition). The necessity analysis was designed to test whether the existence of a single condition was necessary for high or low innovation performance [32]. The analysis was conducted using a two-way test, where both the condition itself and its inverse set were tested for necessity for high and low innovation performance, and the results are shown in

Table 4. Results of the necessity conditions analysis.

Antecedent Condition	High Innovation Ecosystem Innovation Performance		Low Innovation Ecosystem Innovation Performance 50% Quantile (Intersection)
	Consistency	Coverage	Consistency	Coverage
Core-firm-lead	0.705644365	0.657695412	0.687634381	0.733091503
Core business leadership	0.687634381	0.733091503	0.705644365	0.657695412
Industry chain collaboration	0.65290454	0.706527733	0.769337506	0.722686316
Industry chain collaboration	0.769337506	0.722686316	0.65290454	0.706527733
Cross-organizational collaborative Innovation	0.644214625	0.615228497	0.6573218	0.684761709
Cross-organizational collaborative innovation	0.6573218	0.684761709	0.644214625	0.615228497
Two-way feedback between technology and market	0.779944443	0.712266436	0.732020758	0.796380636
Technology and market feedback	0.732020758	0.796380636	0.779944443	0.712266436
Policy support and institutional guarantee	0.715829161	0.750698576	0.797810781	0.767489942
Policy support and institutional safeguards	0.797810781	0.767489942	0.715829161	0.750698576

.

As can be seen from Table 4, in the case of high innovation performance, the consistency value of each antecedent condition (e.g., core firm leadership, industrial chain collaboration, and two-way feedback between technology and the market, etc.) does not exceed 0.9. The consistency of the two-way feedback between technology and the market condition is the highest (0.884), but it still fails to meet the criterion for determining the necessity of 0.9. Similarly, the consistency values of the inverse conditions (e.g., ~core firm leadership, ~industrial chain collaboration, etc.) corresponding to low innovation performance do not exceed 0.9. Therefore, it can be concluded that there is no single necessary condition in the sample of this study. The results of necessity analysis show that although “two-way feedback between technology and market” has a high consistency (0.884) for high innovation performance, it is not enough to constitute a single necessary condition for innovation performance. This means that merely relying on the output of technological achievements and good market feedback does not necessarily guarantee the improvement of innovation ecosystem performance. The synergistic effects of other factors, such as the leadership of core firms, industry chain collaboration, and policy support, should not be ignored. The fact that a single factor cannot adequately explain the level of innovation performance validates the multi-factor interaction mechanism in the formation of innovation performance. This finding is consistent with the complex system theory and innovation ecosystem theory, which emphasize the complexity of the process of realizing innovation performance, which is characterized by the “interaction of multiple conditions and the absence of one”, rather than the linear role of a single factor.

3.2. Analysis of Conditional Patterns

The scholar pointed out that the consistency level for determining adequacy should not be lower than 0.75 [33]. According to specific research scenarios, different consistency thresholds have been adopted in existing studies, such as 0.76 [34], 0.80 [35], and for small- and medium-sized samples, the frequency threshold should be 1. For large samples, the frequency threshold should be greater than 1 [36]. In the specific study, the distribution of cases in the truth table and the researcher’s familiarity with the observed cases should also be considered [37]. The finalized consistency threshold for this study is 0.80, the PRI threshold is 0.75, and the frequency threshold is 1, covering 38 samples.

Table 5. Grouping results.

Conditional Variable	High Innovation Ecosystem Innovation Performance			Low Innovation Ecosystem Innovation Performance
	H1	H2a	H2b	H3	NH1a	NH1b
Core enterprise leadership	⬤				ⓧ	ⓧ
Industry chain collaboration	⬤	⬤	⬤		ⓧ	ⓧ
Cross-organizational cooperation and innovation		●	●	⬤	ⓧ	⬤
Two-way feedback between technology and market	⬤	⬤	⬤	⬤	ⓧ	ⓧ
Policy support and institutional guarantee	⬤	⬤	●	⬤	ⓧ	ⓧ
Coherence	0.900	0.880	0.850	0.860	0.830	0.830
Original coverage	0.490	0.520	0.450	0.500	0.650	0.380
Unique coverage	0.070	0.060	0.050	0.060	0.330	0.080
Total consistency	0.930			0.910
Total coverage	0.720			0.710

Note: ⬤ indicates that the core condition appears, ● indicates that the auxiliary condition appears, ⓧ indicates that it is absent, and the blank item indicates that the condition is optional and has little effect on the results.

shows the results of the group analysis of the synergistic factors of the core-firm-led aerospace complex product innovation ecosystem on the operational performance of the innovation ecosystem. Four typical groupings leading to high innovation performance, H1, H2a, H2b, and H3, and two typical groupings leading to low innovation performance, NH1a and NH1b, are extracted. The overall consistency is 0.930 and 0.910, respectively, indicating that the model’s prediction accuracies are more than 90% for both high and low innovation performance after combining all the paths. It is higher than the fsQCA recommended standard of 0.80, indicating that the overall grouping results are highly reliable. The overall coverage is 0.720 and 0.710, respectively, indicating that the high innovation performance path can explain 72% of the high-performance samples, and the low innovation performance path can explain 71% of the low-performance samples, which has good explanatory power and practical application value.

The configurational paths to high innovation performance are visualized in Figure 2, which synthesizes the fsQCA findings from Table 5. The H1 path (blue solid arrow) represents core-led integration, requiring the synergistic alignment of all five conditions. The H2a path (green dashed arrow) demonstrates industrial collaboration complementarity, where cross-organizational innovation substitutes core enterprise leadership. The H3 path (orange dotted arrow) embodies cross-border synergy support, achieving high performance without core leadership or industry chain collaboration. Arrow thickness corresponds to causal strength, while node shading intensity reflects the condition membership scores calibrated in Table 3.

H1 path: the four core conditions of core enterprise leadership (⬤), industry chain collaboration (⬤), two-way feedback between technology and market (⬤), and policy support (⬤) work together to form an innovation ecosystem centered on core enterprises. The path consistency reaches 0.900, the original coverage rate is 0.490, and the unique coverage rate is 0.070, indicating that the internal logic of the path is highly consistent. More than 90% of the samples meeting the combination of conditions of the path realize high innovation performance. The path covers 49% of the high-performance sample cases, and 7% of the samples can only be explained by the path alone, showing good explanatory power and uniqueness. As shown in Figure 3, the core enterprise-dominated configuration is illustrated.

H2a path: high innovation performance is achieved through the three core conditions of industrial chain collaboration: (⬤) two-way feedback between technology and market (⬤), and policy support (⬤), and supported by the edge condition of cross-organizational cooperation and innovation (●). The path consistency is 0.880, covering 52% of the high-innovation-performance cases. Although slightly lower than H1, it still maintains a high level. Edge-conditional cross-organizational collaborative innovation plays an essential supporting role in this path. This path shows that even if there is a lack of core enterprise leadership, high performance can also be achieved by relying on industry chain collaboration, cross-organizational innovation, and policy support, as illustrated in Figure 4.

H2b path: industrial chain collaboration (⬤) and two-way feedback between technology and market (⬤) are the core support, and cross-organizational cooperation and innovation (●) are the auxiliary support factors. The policy support condition does not show the necessity in this path; the path consistency is 0.850, the original coverage is 0.450, and the unique coverage is 0.050. Although the policy support is transformed into a marginal condition or even dispensable condition in this path, the industrial chain collaboration and the technological feedback still constitute the core support system. This path reveals the ability of the endogenous innovation mechanism to provide independent support in some cases.

H3 path: based on the three core conditions of cross-organizational cooperation and innovation (⬤), two-way feedback between technology and market (⬤), and policy support (⬤), high innovation performance can also be achieved without the leadership of core enterprises and industrial chain collaboration. The consistency of the path is 0.860, the original coverage rate is 0.500, and the unique coverage rate is 0.060. This path emphasizes cross-organizational cooperation and innovation as the core support, which, together with technological market feedback and policy support, is able to achieve strong innovation performance even in the absence of the leadership of the core enterprise and industrial chain collaboration. This path covers 50% of high-performance cases and shows good system adaptability, as illustrated in Figure 5.

NH1a path (total mismatch type): all key conditions are missing (ⓧ), systemic failure, and innovation performance drops significantly. The path consistency is 0.830, original coverage 0.650, and unique coverage 0.330, showing that most of the low innovation performance samples conform to this mismatch pattern.

NH1b path (partial mismatch type): although there is cross-organizational cooperation and innovation (⬤), core enterprise leadership, industrial chain collaboration, technology and market feedback, and policy support are all missing (ⓧ), resulting in an insufficient support structure for the innovation system. Path consistency is 0.820, original coverage is 0.380, and unique coverage is 0.080.

3.3. Potential Substitution Relationship Between Conditions

The grouping analysis reveals a clear functional substitution relationship between some of the condition variables.

In the H1 path, the core firm leads irreplaceably. It dominates the innovation ecosystem through its technological authority, resource deployment, and system integration capabilities, forming a “strong-centered” synergy model. This path indicates that for situations involving ultra-complex technology integration, such as aircraft engine R&D, the core enterprise’s technological accumulation and coordination ability can hardly be replaced by other conditions, and its absence will directly lead to system-level innovation failure.

In the H2a and H2b paths, the alternative logic of “decentralization” is presented. In the absence of core enterprise leadership, industrial chain collaboration and cross-organizational cooperative innovation form alternative support through the modular division of labor and distributed knowledge sharing. This substitution mechanism verifies the core proposition of “multi-subject complementarity” in the functional redundancy theory—when a key function, such as technology leadership, is absent, the system can realize equivalent output through the combination of other functions. Even in the absence of core enterprise leadership, industry chain collaboration + cross-organizational cooperation and innovation can form alternative support and maintain high innovation performance.

In the H3 path, the substitution boundary is further expanded. Cross-organizational cooperative innovation becomes the core support, partially replacing the role of core firm leadership and industry chain collaboration. This path suggests that distributed collaboration can break through the path dependence of the centralized ecosystem in the context of high technological modularity and agile policy response. Nevertheless, institutional safeguards are needed to reduce the frictional costs of collaboration.

This suggests that there are diversified evolutionary paths within the innovation ecosystem, and different combinations of conditions can achieve functional equivalence, supporting the assertion of Functional Redundancy in the theory of complex adaptive systems [38].

3.4. Robustness Test

To ensure the reliability of the grouping results, this study adopts a multi-dimensional robustness testing framework. Firstly, the grouping stability is verified by threshold sensitivity analysis. After raising the consistency threshold from 0.80 to 0.85, the high-performance grouping H2b is eliminated due to decreased consistency, but the core condition combination of H1, H2a, and H3 remains stable, which indicates that the core grouping is strongly robust. In contrast, the low-performance grouping NH1b is filtered out due to insufficient unique coverage after raising the PRI threshold from 0.750 to 0.80, and NH1a is still robust. After the PRI threshold is raised from 0.750 to 0.80, NH1a is filtered, and NH1a still exists significantly, confirming that “systematic mismatch” is a universal pathway for low performance.

Second, the case exclusion test using the leave-one-out method [39] reveals that the consistency of typical H1 path samples decreases slightly after exclusion, but the core condition combinations remain unchanged, which proves the universality of the H1 path; whereas the H3 path disappears after the exclusion of specific cases of emerging private firms, which suggests that it may rely on the attributes of a flexible innovation network. Further comparison of the calibration strategies reveals that the industry chain collaboration condition of the H2a path shifted from core to auxiliary when changing the direct anchor method [40] to the theory-driven method [41], but the core condition combination of the H1 path remained stable, suggesting that the synergistic effect of core firms’ leadership and technological feedback has calibration robustness.

The comprehensive test shows that the results of this study pass the robust type test.

4. Results and Discussion

Based on the induction and characteristic comparison of groups with high sustainable innovation performance, this study identifies three innovative synergy modes that support the sustainable development of the aviation industry: (1) core enterprise-led mode, (2) industry chain synergy mode, and (3) cross-organizational innovation synergy mode. Through differentiated resource allocation and synergy mechanisms, these modes promote the sustainable development of the aviation industry from the dimensions of reducing waste, improving resource recycling rates, and lowering environmental impacts, collectively forming a multi-path system in which the innovation ecosystem serves sustainable goals. The correspondence between high-performance organizations and the innovation modes is summarized in

Table 6. Correspondence between high-performance organization and the innovation mode.

Mode Name	Corresponding Organization Path	Core Description	Key Node Elements	Interaction Characteristics
Core enterprise-dominant mode	H1 Path	Take the core enterprise as the dominant, leading resource integration, guiding technology R&D and market direction, and building a vertically integrated innovation chain.	Core enterprise, first-tier suppliers, second-tier suppliers, system integrators, technology market feedback, and policy support.	Core enterprises dominate vertical resource allocation, upstream and downstream of the supply chain collaborate around technical standards, and the policy environment strengthens the dominant effect.
Industry chain collaboration mode	H2a path, H2b path	Based on the division of labor and resource complementation of each node enterprise in the industry chain, a horizontal and vertical collaborative innovation network is formed, with market demand as the adjustment guide.	Upstream suppliers, midstream manufacturers, downstream service providers, cross-organizational cooperation platform, market demand feedback.	Multi-body division of labor and cooperation, horizontal integration of resource advantages, vertical penetration of the industrial chain innovation process, market change-driven dynamic collaboration.
Cross-organizational innovation collaboration model	H3 Path	Organizations in different fields cooperate across borders based on heterogeneous resources to form an open and dynamic innovation collaboration network, with government policies providing support and guidance.	Suppliers, universities, research institutions, emerging technology companies, technology market feedback, policy and innovation support platforms.	Organizations in different fields cooperate across borders based on heterogeneous resources to form an open and dynamic innovation synergy network, and government policies provide support and guidance.

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4.1. Core Enterprise-Led Model: Core-Led Integration Mechanism (CDI)

Under the framework of the aviation complex product innovation ecosystem, Core-leading & Dynamic Integration (CDI) constructs a technology governance paradigm that adapts to the characteristics of the high system integration industry through the three-phase progression logic of “strategic leading—system integration—dynamic evolution” [42], with sustainable development goals deeply embedded in the entire innovation chain. The mechanism is rooted in the development of aerospace equipment. The mechanism is rooted in the technological complexity of the “million-scale parts and components collaboration” background of aerospace equipment development, requiring core enterprises to take on the core responsibility of green transformation while managing technological strategic planning, global resource scheduling, and knowledge network evolution [43].

The core role of the CDI mechanism is reflected in three dimensions: first, the strategic leadership dimension, core enterprises through the preparation of technology roadmaps integrated with “dual-carbon” goals, industrial standards and innovation network governance [44], to build the strategic potential of the innovation ecosystem; second, the system integration dimension, relying on the modular product architecture and digital twin technology platform, an energy efficiency optimization module is added to achieve cross-organizational boundaries of the fusion of knowledge and resource reconfiguration, GE Aviation through Predix GE Aviation integrates the real-time data flow of more than 200 engine component suppliers globally through the Predix industrial internet platform [45]. Third, the dimension of dynamic evolution, through the establishment of the technology maturity level (TRL) [46] gradient promotion mechanism and the open innovation interface, adding environmental performance indicators, forming the spiral iterative path of “basic research-application development-engineering transformation”, and the joint definition, collaborative development, and collaborative innovation system constructed by COMAC during the development of the C919 [47]. COMAC’s C919 development is based on a multi-body collaborative model of joint definition, collaborative development, and parallel validation. This mechanism not only breaks through the linear collaboration mode of the traditional supply chain, but also forms a self-organized innovation ecosystem evolution path through the topological reconstruction of the knowledge network, which provides a theoretical explanation framework for cracking the “system integration trap” in the field of aviation [48].

4.2. Industry Chain Collaboration Model: Industrial Collaboration Complementary Mechanism (ISC)

ISC mechanism takes “collaboration-complementary-market-oriented” as the core logic. It regards resource circulation and low-carbon collaboration as the core criteria for industrial chain upgrading under the condition of highly specialized division of labor. Each subject in the industrial chain shares green resources, complements emission reduction technology advantages through horizontal and vertical collaboration, and dynamically optimizes innovation content according to global carbon markets and green consumption demands. The operational kernel of the mechanism can be deconstructed into three synergistic subsystems, including the structural synergistic subsystem. While decoupling technical complexity through modular product architecture, it simultaneously designs “detachable, recyclable, and remanufacturable” green units. For example, in the development of an aero-engine, the hot end components, control system, and mechanical transmission subsystems are physically and functionally decoupled, allowing different suppliers to develop in parallel and ensure the effectiveness of the final system integration [49]. The second is the knowledge synergy subsystem: the use of patent cross-licensing, technology roadmap betting, and other system designs to build a dynamic exchange network of intellectual property rights. Through the establishment of a knowledge entropy assessment model, the transfer efficiency of tacit knowledge is quantified [50]. With the help of blockchain technology, the traceability of innovation contribution is realized, so as to solve the problem of insufficient innovation incentives caused by “free-riding”. Third, the market synergy subsystem is based on the technology fitness matrix and resource-flexible allocation model, dynamically optimizing the combination of elements. In the development of aviation products, through the establishment of a two-dimensional technology-market evaluation coordinate system [51], customer customization requirements are transformed into modular technical parameter packages, realizing the organic combination of mass customization and mass production.

The theoretical value of this mechanism is that it reveals the dissipative structural characteristics of the complex product innovation ecosystem. By continuously absorbing external technological-negative entropy flow [52], the system is driven from a static equilibrium to dynamic and orderly evolution. This self-organizing characteristic not only breaks through the path-locking dilemma of the traditional industrial chain, but also enables the ecosystem to show unique resilience advantages in response to market fluctuations through the construction of a dual buffer mechanism of “capability pool” and “knowledge base” [53]. Its core innovation lies in the deep integration of the physical world’s hierarchical division of labor and the cooperative network in digital space, forming the conjugate driving pattern of the physical industry chain and the virtual innovation chain, and providing a mechanism solution for the aviation industry to break through the bottleneck of integrated innovation.

4.3. Cross-Organization Innovation Collaboration Model: Cross-Border Collaboration Support Mechanism (CAS)

CAS mechanism takes “cross-border-agile-support” as the core logic, regarding “green cross-border integration” as a key path [54] to break through innovation bottlenecks: heterogeneous subjects realize knowledge fusion and innovation breakthroughs through open collaboration, and form a highly flexible and adaptable innovation network under the role of a policy support platform. The cross-border fusion ring is to solve the compatibility problem of heterogeneous knowledge bases through the establishment of a technology track docking mechanism. In technology track docking, it focuses on promoting green technology crossover between aviation and energy, materials, and environmental protection fields. It is specifically manifested in the collaborative formulation of the technology roadmap, standardized design of the R&D interface, and innovation of the intellectual property sharing agreement [55]. The Agile Response Ring builds a fast iterative system of “perception-decision-execution”, simulates the innovation process virtually using digital twin technology, and optimizes resource allocation through real-time data feedback. The eco-support ring forms a three-tier support system comprising the infrastructure layer (cloud platform, experimental environment), the service layer (technology brokerage, legal counseling), and the regulatory layer (data security, benefit distribution) [56].

The operational effectiveness of the CAS mechanism depends on the synergistic optimization of three key parameters: the knowledge stickiness coefficient determines the depth of cross-border integration, the information transfer efficiency affects the speed of agile response, and the system suitability constrains the strength of ecological support. In the field of complex product innovation, the mechanism can not only maintain the efficiency of professional division of labor, but also realize system integration innovation through the establishment of modular innovation architecture. Its institutional advantage is particularly significant in coping with sudden technological changes and market uncertainty, which is manifested in the diversified exploration of innovation paths and the ability to converge rapidly.

5. Research Limitations and Future Research Directions

5.1. Research Limitations

This study has three limitations in the analysis of sustainability dimensions. First, the measurement of sustainability focuses mostly on resource efficiency and waste reduction, lacking the quantitative analysis of environmental indicators such as carbon footprint and pollutant emissions, which makes it difficult to fully reflect the multi-dimensional goals of sustainable development in the aviation industry. Second, the case selection is concentrated in China’s aviation ecosystem. The differences in environmental policy intensity and market demand for green aviation products across countries may lead to contextual variations in how synergy modes affect sustainability. Thus, this generalizability needs further verification. Third, it fails to deeply explore the role of synergistic mechanisms in supporting sustainability throughout the entire life cycle of aviation products, resulting in an insufficient analysis of “how innovation ecosystems support circular economy”.

5.2. Future Research Directions

Future research can deepen explorations in three aspects around sustainability. First, the sustainability evaluation index system should be expanded by incorporating quantitative indicators such as carbon footprint, resource recycling rate, and life-cycle environmental impact into configurational analysis, so as to clarify the specific impact of different synergy modes on environmental performance. Second, cross-national comparative studies should be conducted to contrast the differences in how synergistic mechanisms drive sustainability in China’s aviation ecosystem versus those in Europe and America (e.g., Boeing and Airbus). This will reveal the moderating effects of policy, market, and cultural contexts on the “synergy-sustainability” relationship. Third, the focus should be on synergistic mechanisms in the retirement stage of aviation products, explore how core enterprises, recycling companies, and research institutions can build closed-loop supply chains through cross-organizational collaboration, and promote the aviation industry’s transformation toward “full-life-cycle sustainability.” This will provide more operable ecosystem optimization solutions for the green development of the global aviation industry.