Strengthening urban innovation systems and optimizing the efficiency of innovation factor allocation are critical for overcoming the "middle-technology trap" and realizing a Chinese-style modernization. Against this backdrop, the digital revolution has positioned data as a crucial production factor. In China, public data accounts for over 80% of the national total data resources, with an estimated value of 10-15 trillion yuan in 2020. The nationwide open datasets have exceeded 2 million items, covering livelihood areas such as transportation, healthcare, education, and environmental protection. Open government data policies can break down information silos, enhance inter-departmental coordinated development, and expand socio-economic benefits. Both domestic and international research consensus holds that enhancing innovation capacity is a key impact of public data openness, though systematic empirical verification remains insufficient. Domestic research mostly focuses on the enterprise perspective, with less research on urban innovation.Additionally, existing literature varies significantly in its research angles, resulting in no unified conclusions regarding how public data openness specifically influences urban innovation. Therefore, it is essential to explore their mechanisms in promoting urban innovation.
This study utilizes annual panel data from 271 cities in China from 2009 to 2023 to construct a multi-period difference-in-differences model to explore the impact of open public data on urban innovation. The establishment of open government data platforms in cities is treated as a quasi-natural experiment, with the number of patent grants per city as the dependent variable. Mechanism tests are conducted via effective market and proactive government paths. In addition, the paper studies the moderating role of digital inclusive finance and human capital stock, and finally uses a spatial Durbin model to investigate whether the positive effect of open government data on urban innovation has spatial spillovers. The study tests the spatial autocorrelation of the dependent variable—urban innovation capacity. Following the existing literature, it computes the global Moran’s I for each year under three weighting schemes: the contiguity matrix (W1), the economic-distance matrix (W2), and the nested economic-geographical matrix (W3).
Empirical results indicate that the implementation of open government data policies significantly promotes urban innovation by stimulating market vitality and enhancing marketization levels to make the market effective, reducing government intervention, optimizing government-business relations, and improving the business environment to promote proactive government action, thus fostering urban innovation through the dual pathways of an effective market and a proactive government. Digital inclusive finance plays a positive moderating role by alleviating financing constraints and providing funding support, while human capital stock amplifies the innovative advantages of open government data by strengthening knowledge spillover effects. Moreover, there is heterogeneity among cities: cities southeast of Hu Huanyong’s line, cities with a higher level of industrial structure upgrading, and large cities exhibit a stronger innovation promotion effect, likely due to more complete digital infrastructure, a more advantageous industrial structure, and stronger human capital, which further confirms the positive moderating effect of human capital stock and digital inclusive finance. In addition, open government data has a significant spatial spillover effect on urban innovation, positively impacting other cities through spatial interactions, and this effect is jointly driven by geographic proximity, economic similarity, and the interaction between the two. Lastly, in the transmission mechanism of public data openness promoting urban innovation capacity, the synergy of an effective market and a proactive government has produced a significant promotion effect.
The theoretical contributions are threefold. First, by conceptualizing open government data as a quasi-experiment, we extend existing innovation theory to encompass data governance frameworks. Second, the integration of effective market and proactive government mechanisms addresses a critical gap in understanding state-market synergies within innovation ecosystems. Third, our spatial and moderating analyses provide nuanced insights into cross-city disparities and multiplier effects. Practically speaking, this study offers valuable guidance for policies related to data openness roadmaps, digital finance integration, and intercity innovation networks, providing an actionable blueprint for fostering high-quality urban development.

The report of the 20th National Congress of the Communist Party of China underscores the imperative to promote the development of “specialized, sophisticated, differentiated, and innovative”(SSDI) small and medium-sized enterprises (SMEs). As a key part of the innovation-driven development strategy, SSDI SMEs have gained new attention for their innovation capabilities. Yet, innovation requires heavy upfront investment and brings uncertain results. For SSDI enterprises—typically constrained by limited scale—technological innovation faces compounded challenges. They often face serious R&D funding shortages, structural talent deficits, and a common leaning toward imitative innovation. Consequently, many SMEs are compelled to adopt low-value-added innovation models that prioritize quantity over quality, creating a critical bottleneck in their innovative advancement.Thus, addressing financing constraints, optimizing talent allocation, and refining innovation mechanisms are urgent tasks for promoting the innovative growth of SSDI SMEs.
Against the backdrop of digital-real economy integration, the disclosure of data assets can effectively address the aforementioned challenges. As a form of voluntary corporate disclosure, data asset disclosure serves as a critical channel for the public to understand value creation from data assets, big data applications, digital transformation, and related corporate activities. It also holds significant implications for firms' own innovation development. Given the exponential growth of data as an “asset class” and the pressing need to enhance the innovation capabilities of SSDI SMEs, academia urgently needs to investigate the innovation effects of data asset disclosure. Such research would provide valuable insights for China to seize new opportunities in digital development and expand new frontiers for economic growth.
Existing literature has empirically demonstrated the positive effects of data asset disclosure, including lowering external financing costs, boosting market reactions, enhancing analyst forecasts, and increasing firm value.Yet, its impact on SME innovation quality is still unclear. This research gap takes on heightened significance when examining SSDI SMEs. Given their advanced digital maturity, richer data asset portfolios, and more comprehensive disclosure practices compared to conventional SMEs, SSDI enterprises present an ideal context to investigate whether data asset disclosure exerts a disproportionately stronger effect on innovation quality enhancement. Therefore exploring this is crucial for innovation policies and corporate digital strategies.
Adopting a micro-level perspective, this study examines the impact of data asset disclosure on innovation quality in SSDI SMEs, using a sample of China's “Little Giant” listed enterprises from 2011 to 2023. The empirical results demonstrate that data asset disclosure significantly enhances innovation quality among SSDI SMEs, with findings remaining robust across a series of sensitivity tests. Mechanism analysis reveals that data asset disclosure primarily improves innovation quality through three channels: alleviating financing constraints, optimizing human capital allocation, and reducing innovation conformity behavior. Furthermore, the study identifies significant moderating effects, showing that stronger intellectual property protection, intensified industry competition, and increased analyst coverage can amplify the positive influence of data asset disclosure on SMEs’ innovation quality.
Compared with existing literature, this study makes three key marginal contributions: First, it enriches micro-level research on data assets. Prior studies focused on macro-level aspects, neglecting micro-level analyses of how data asset disclosure enhances innovation quality. This work bridges the gap between information and innovation economics. Second, this study elucidates the mechanisms through which data asset disclosure improves innovation quality. Previous research has largely treated the relationship between data disclosure and SME innovation as a “black box”. Departing from conventional approaches, this study adopts an information transparency perspective to demonstrate how SSDI SMEs leverage data asset disclosure to enhance innovation quality along three dimensions: (1) improving capital accessibility, (2) optimizing labor structure, and (3) strengthening governance capacity. Third, existing studies predominantly rely on annual reports as the primary source of data asset information. Recognizing that corporate news coverage serves as an equally critical channel for information dissemination,and is indispensable for a comprehensive evaluation of data asset disclosure,this study systematically analyzes data-related disclosures in both annual reports and news articles. Methodologically, it employs Python-based text mining techniques and constructs a specialized data asset lexicon using Word2Vec neural network modeling. This dual-source approach not only enhances measurement accuracy but also pioneers a novel methodological pathway for future data asset research.

Innovation plays a central role in driving China's high-quality development. However, China remains heavily reliant on international sources for key core technologies and components. In recent years, global economic fluctuations and geopolitical tensions have constrained technological innovation activities, with the risk of innovation disruption persisting and potentially increasing. It is urgent to optimize innovation practices and activate innovation resilience. Technological innovation often has high risks due to its large cost scale and high output uncertainty, and is easily constrained by corporate resources. Patient capital can provide long-term and stable financial support for corporate technological innovation. Furthermore, through its risk diversification function, patient capital can effectively mitigate the high-risk nature of technological innovation. Therefore, patient capital has the typical characteristics of highly matching with the intrinsic needs of corporate technological innovation, and is gradually becoming a new quality driving force to enhance corporate innovation resilience.
However, due to the inherent profit-seeking and risk-taking nature of capital, the excessive penetration of patient capital may intensify the “goal conflict” caused by investors chasing short-term self-interest. This could lead to an imbalance of interests between investors and innovative subjects and ultimately weaken the sustainability of the innovation ecosystem. Additionally, patient capital may also lead to the development of a comfortable mentality and innovation inertia among managers. These issues raise a series of key questions that deserve deeper inquiry. Can patient capital become the core element to stimulate corporate innovation resilience And how to effectively ensure that patient capital can continue to promote corporate innovation resilience and help enterprises overcome technological blockades and continuously realize technological breakthroughs.
Using the data of A-share listed companies in Shanghai and Shenzhen from 2009 to 2023, this study empirically examines the influence of patient capital on innovation resilience. Meanwhile, from the horizontal strategy dimension and vertical time dimension, this study constructs a framework for the transmission path of patient capital's influence on innovation resilience. Additionally, by analyzing the characteristics of internal innovation decision-makers within enterprises and the characteristics of the external innovation environment, this study explores how patient capital influences innovation resilience under different circumstances.
The results show that the influence of patient capital on corporate innovation resilience presents an inverted U-shaped relationship and the optimal allocation for patient capital is 34.36%. Mechanism tests reveal that patient capital affects corporate innovation resilience through three dimensions: innovation cooperativeness, innovation ambidexterity, and innovation sustainability. Further research based on innovation decision-makers and environmental characteristics shows that the inverted U-shaped impact of patient capital on corporate innovation resilience is further strengthened when managers exhibit higher levels of patience, innovation decision-making power is more concentrated, and intellectual property protection is higher.
Compared to existing research, the possible marginal contribution of this study is three-fold. First, this study reveals the inverted U-shaped relationship between patient capital and corporate innovation resilience from a nonlinear dual perspective, which not only deepens the theoretical understanding of the duality of patient capital, but also provides a scientific basis for corporate capital allocation by identifying the “optimal” threshold. Second,this study explores the role of patient capital in enterprise innovation resilience from three dimensions: innovation cooperativeness, innovation ambidexterity, and innovation continuity. It not only expands the connotation of innovation resilience research at the theoretical level, but also provides new ideas for enterprises to optimize capital allocation and enhance innovation effectiveness in practical application. Third, the study characterizes the innovation decision subjects within the firm in terms of the degree of managerial patience and the allocation of innovation decision-making power, and applies the degree of intellectual property protection to characterize the external innovation environment. From the perspectives of internal decision-making subjects and external environment, it can provide new perspectives and insights for academics to understand the role of the boundary of patient capital, and help promote the in-depth integration of relevant theoretical research and corporate practice.

There is a close interactive relationship between sci-tech service ecosystem of maker spaces and their multi-dimensional performance, promoting the interdependence, mutual driving, iterative reinforcement, and "spiral rise" of incubation performance, social performance, and innovation performance. However, domestic studies focus on the operational management of maker spaces and the short-term performance of incubated enterprises, while neglecting the systematic role of the full-chain service ecosystem in driving multi-dimensional outcomes.Foreign research has shifted toward sustainability and value co-creation, yet it still fails to clarify how maker spaces integrate human, cultural, and industrial resources and embed themselves in regional innovation systems.Existing literature emphasizes quantifiable explicit outputs but downplays the cultivation of high-growth enterprises like unicorns, lacking in-depth analysis of the collaborative mechanism among incubation, social, and innovation performance. Thus, it is necessary to adopt a multi-stage and whole-process perspective to systematically examine the collaborative paths of resource integration, knowledge sharing, and multi-dimensional performance so as to achieve mutual empowerment between maker spaces and the sustainable development of regions.
From the perspective of the sci-tech service ecosystem, this study selects 13 provincial-level administrative regions across 5 major urban agglomerations as research cases. It draws on the average values of data concerning the multi-dimensional performance of maker spaces from 2017 to 2023, and employs configuration thinking and the fsQCA method to explore the complex synergistic paths through which the human resource ecosystem, financial capital ecosystem, infrastructure ecosystem, technology service ecosystem, and industrial innovation ecosystem drive the sustained improvement of maker spaces' multi-dimensional performance.
The research findings reveal that the achievement of maker spaces' multi-dimensional performance relies on the collaborative coupling of multiple elements within the sci-tech service ecosystem. For maker spaces, the collaborative coupling of various elements follows a consistent path in realizing high incubation performance, high social performance, and high innovation performance,specifically, the driving path of "financial capital ecosystem - human resource ecosystem - infrastructure ecosystem - industrial innovation ecosystem" serves as the core pathway to attain their multi-dimensional performance. No single element constitutes a necessary condition for maker spaces to achieve high incubation, social, or innovation performance; nevertheless, a robust financial capital ecosystem emerges as a key antecedent condition for their high performance.
This study frames a "technology-economy-society" coupling path for maker-space performance that is woven through the sci-tech service ecosystem. First, under a regional-innovation integration strategy, maker spaces are embedded in the local innovation ecosystem to address national missions and core technological bottlenecks, thereby completing a full-chain incubation and growth pipeline for technology-based enterprises. Second, an ecological-governance trajectory is followed vertically,i.e.,resource foundations strengthen incubation performance, breakthrough outputs lift innovation performance, and widened application scenarios amplify social performance,while horizontally expanding social networks, feeding achievements back into incubation capacity, and steering resource flows to cultivate unicorn enterprises to enhance social contributions.. Third, a collaborative path is forged with the sci-tech financial ecology as the pivotal condition, complemented at the margin by talent, large-scale facilities, and industrial-innovation ecologies, to propel multi-dimensional performance efficiently. Patient state capital is cultivated and enlarged within an investment and financing mechanism led by government funds, dominated by corporate finance, with enterprise investment as the main body, and followed by the Science and Technology Innovation Board and private capital.
The research conclusions can provide effective support for the deep integration of the innova-tion chain, industrial chain, capital chain, and talent chain through entrepreneurship incubation services, and provide important implications for the accelerated growth of unicorn companies in China. Future research should incorporate government policies like digital transformation into the analytical framework, examine their impacts on the performance of maker spaces, thereby enhancing the explanatory power of research conclusions and facilitating an understanding of the internal logic behind maker spaces transitioning from the "performance improvement" stage to the new "ecological intelligence" stage. Meanwhile, with in-depth digital transformation and upgrading driving the evolution of their models from "physical incubation" to "digital incubation", future research also needs to further focus on and explore the specific paths for high-quality development after this model transformation, so as to fill the gaps in current research in this field.

Investigating the science-technology coupling is essential for deepening the scientific understanding of technology transfer and synergistic innovation pathways in emerging industries, thereby enhancing the practical impact of research outcomes. Existing studies on strategic emerging industries often focus on single innovation elements from either the scientific or technological domain, rarely exploring the coupling phenomena and the associated feedback effects within the science-technology system from a parallel observation perspective. Drawing upon industrial growth theory, this study attempts to investigate the impact mechanisms and pathways of science-technology coupling on the growth of emerging industries, as well as the moderating role of regional government intervention, by matching and utilizing multi-source heterogeneous data. It designs a method to detect science-technology coupling at the knowledge content level through the coupling of knowledge networks, aiming to explore the impact of this internal techno-scientific synergy on industrial growth characteristics. Concurrently, considering government intervention as a key external driver in China’s industrial innovation system, the study examines its mechanisms, specifically investigating the moderating effects of regional industrial policy intensity and adoption speed on the relationship between science-technology coupling and industrial growth.
Then the study proposes two hypotheses. H1: A non-linear, inverted U-shaped relationship exists between science-technology coupling and the growth of emerging industries. H2: Government intervention has a positive moderating effect on the inverted U-shaped relationship between science-technology coupling and industrial growth. First, using scientific literature and technology patents as primary representations of scientific and technological outputs, the study constructs time-series science-technology knowledge networks and calculates the degree of science-technology coupling based on a network portrait projection method. Second, it employs a panel data model to empirically test the mechanism through which science-technology coupling affects industrial agglomeration, activity, and growth. Finally, by quantifying the industrial policy intensity of different regional governments, it analyzes the moderating role of government intervention to identify effective development paths.
Using the information and communication technology (ICT) industry as a case study, the study concludes that the coupling of science and technology and the growth characteristics of the ICT industry have progressively strengthened over time, exhibiting significant spatial heterogeneity. A non-linear, inverted U-shaped relationship exists between the degree of science-technology coupling and the growth characteristics of the ICT industry. Notably, government intervention positively and significantly moderates this inverted U-shaped relationship. These findings yield several policy implications. First, it is essential to formulate regionally differentiated strategies for ICT development by dynamically adapting policy objectives to the synergy between science, technology, and policy. This requires tailoring the ICT industrial policy toolkit to meet regional, stage-specific growth demands and constructing a resilience governance framework based on “precise identification of differences, dynamic adaptation of tools, and multi-level risk hedging.” Second, an optimal degree of coupling between the scientific and technological knowledge systems must be maintained. Over-coupling which is caused by factors such as cognitive lock-in, path dependency, profit-driven resource concentration, or institutional constraints should be avoided, as it leads to monolithic resource allocation and innovation pathway homogenization. This negative impact can be mitigated by optimizing resource allocation and benefit distribution mechanisms and strengthening market-oriented, demand-driven innovation. Third, moderate policy intervention can offset technology coupling’s negative effects and boost healthy industrial development. Regional governments should optimize basic research layouts, strengthen industry-academia-research collaboration, and improve technology transfer mechanisms to achieve synergistic science-technology-driven growth, supporting national high-quality economic development and sustainable social progress.
The contributions of this study are primarily reflected in three aspects: (1) By integrating and aligning multi-source heterogeneous data, it establishes an “industry-science-technology” connection, and explores the dynamic coupling characteristics of science and technology in emerging industries across diverse regions; (2) This study integrates science-technology relationships into an econometric model to explore technological coupling’s link with industrial growth theoretically, and empirically reveals the synergistic innovation effects and mechanisms of science-technology coupling; (3) It constructs an analysis model for emerging industry growth within the “science-technology-policy” framework, elucidating the dual driving forces of technology and government in achieving emerging industry outcomes.

Key industrial technologies are the core areas for cultivating technological competitive advantages and seizing opportunities for industrial development, playing a pivotal role in driving breakthroughs in industrial technology. Strategic emerging industries are knowledge- and technology-intensive sectors rooted in major technological breakthroughs and evolving development needs. Their key technologies are the essential and irreplaceable technologies (or links) that play an important role in the industry, reflecting current technological hotspots, difficulties, or future technological breakthroughs. As China’s technological and industrial competitiveness advances rapidly, Western countries have intensified their technological blockades against China. Strategic emerging industries now face the "small courtyard, high fence" technological dilemma, resulting in unbalanced and inadequate development of key technologies. Against this backdrop, it is essential for China's strategic emerging industries to focus on enhancing their independent innovation capabilities and breaking technological barriers. Therefore, in the new round of technological revolution and industrial transformation, identifying and clarifying the development direction of key technologies, and making forward-looking layouts are of great significance for enhancing independent innovation capabilities, achieving high-level technological self-reliance and self-improvement, and accelerating the development of new quality productive forces.
Traditional technology identification methods mostly rely on single-source data or static analysis, making it difficult to reveal the cross-disciplinary relevance and dynamic evolution process of key technologies. Furthermore, they lack systematic consideration in data source construction and dynamic analysis, resulting in insufficient accuracy and agility in technology identification. The methods of multi-source data fusion and knowledge association aggregation provide a new direction for solving the above problems. The former can integrate multi-source data and statistical methods into a unified framework, adapting to the intelligence analysis needs for addressing uncertainty and complexity. The latter functions through the reorganization, association, aggregation and presentation of multi-dimensional and multi-granularity information objects (including knowledge association, knowledge aggregation, etc.).
Drawing on a three-stage intelligence analysis model, this study integrates multi-source data fusion, knowledge association and aggregation methods to construct a key technology identification model for strategic emerging industries.Specifically, first, multi-source data fusion provides a comprehensive data foundation for key technology identification; second, knowledge association is applied to reveal potential correlations between technical topics in multi-source data, which in turn helps identify key technological hotspots; and finally, guided by the theme of knowledge aggregation and integration technology, core themes in the development of generic technologies and industrial-specific technologies are extracted, and on this basis, the development direction of key technologies in strategic emerging industries is further explored.
To verify the scientific validity of the key technology identification method, this study conducts a horizontal analysis by selecting the BERT terminology model and the Gompertz patent model. Three quantitative indicators for knowledge network topology are adopted: Technology Potential Index (TPI), Technology Influence Degree (TID), and Cross-domain Relevance (CDR). The study finds that incorporating multi-source data fusion and knowledge association aggregation into a unified framework to construct an industry key technology identification model can help identify strategic emerging industry key technologies with uncertainty and complexity. In addition, the effectiveness of the proposed method is verified by comparing it with relevant authoritative documents.
The contribution of this paper includes two aspects: Firstly, it enriches the identification methods of emerging technologies. In response to the uncertainty and complexity of key technologies in strategic emerging industries, it integrates multi-source data fusion and knowledge association aggregation into the intelligence analysis model, reduces the difficulty of identifying key technologies in strategic emerging industries, expands the ideas of emerging technology identification, and deepens the identification methods of emerging technologies. Secondly, it reveals key industrial technologies with higher granularity. The study conducts a deep analysis of common and hot technologies in the industry, and compares them with authoritative and public literature to obtain finer grained key technologies. The insights offer theoretical support for government authorities in formulating targeted industrial policies and provide valuable guidance for enterprises to advance their technological innovation strategies.

Amid the rise of economic anti-globalization and intensifying competition along international industrial chains, the Chinese government has introduced the "chain leader system". This policy is designed to enhance the stability and security of industrial chains, addressing governance gaps in market and administrative mechanisms. As the third key governance mechanism—alongside market and administrative approaches—it plays a crucial role in promoting collaboration across the industrial chain. Thus, it's worth deeply analyzing whether the "chain leader system" can effectively promote the collaboration of industrial and innovation chains in Chinese urban clusters from a policy perspective. Existing studies have thoroughly examined the “chain leader” system and the collaboration between industrial and innovation chains. Nevertheless, the policy effects of the “chain leader” system in enhancing the collaboration are still under-explored. In reality, the “chain leader” system, as an effective and innovative management system, is expected to boost industrial-innovation chain collaboration by integrating an efficient market with proactive government actions.
Thus, this study theoretically analyzes the internal mechanism of the "chain leader system" on the collaboration of industrial chain innovation chain. This study selects data from cities within 19 city clusters and 210 prefecture-level cities in China, covering the four years before and after the initial implementation of the policy (2013—2021). Data from 2013—2016 serve as the control group for the quasi-natural experiment using the DID model to test for parallel trends. The findings show that the "chain leader system" of industrial chain has significantly promoted the collaboration of industrial and chain innovation chain in Chinese urban clusters.The policy effect is more significant in the higher-level urban clusters,particularly major metropolitan clusters, core cities, resource-dependent cities, transportation hubs, and cities located in central regions. The mechanism test shows that the "chain leader system" of industrial chain has an impact on the collaboration of industrial chain and innovation chain through market effect, technology agglomeration effect and policy support effect. This study offers theoretical and empirical support for refining relevant policies, aiming to enhance the collaborative development of the industrial chain and innovation chain and boost the high-quality development of China's economy.
The policy enlightenment of the paper is as follows: First, the government should give full play to the market effect, technology agglomeration effect and policy support effect through the combination of "efficient market and proactive government". On the one hand, by relying on an "efficient market", the government can boost the innovation vitality and competitiveness of enterprises, encouraging them to continuously optimize their product structures and enhance technological innovation. This also promotes cooperation and exchange among upstream and downstream enterprises in the industrial chain, creating a virtuous cycle of technology sharing and innovation, and thereby driving internal innovation activities and technological progress within the industrial chain. On the other hand, through the role of an "proactive government" the government can address market failures and information asymmetry in market operations. Second, the paper emphasizes the need to focus on the differences in urban resource endowments and to implement policy classification and precision targeting. Third, it highlights the importance of considering urban geographical locations. Specifically, it advocates for strengthening the "chain leader system" in the industrial chains of central cities to support the strategy for the rise of central China.
The possible marginal contribution of the paper is as follows: first, it systematically assesses the impact of the "chain leader system" on the collaboration of industrial chain-innovation chain, expanding related research and enriching the policy-level collaboration studies; second, it reveals the internal mechanisms of this synergy through market, technology agglomeration, and policy effects, using effective market and active government theories to enrich empirical research; third, it analyzes urban status, agglomeration levels, resource cities, transportation hubs, and geographical heterogeneity, providing in-depth theoretical support and policy insights for industrial chain-innovation chain collaboration.

In the context of escalating environmental challenges and the global imperative for sustainable development, green technological innovation (GTI) has emerged as a strategic pathway for firms to reconcile economic growth with ecological responsibility. While existing studies acknowledge the potential of GTI in fostering environmental and economic outcomes, critical gaps persist in understanding how GTI translates into firm-level sustainable development performance (SDP), particularly under the moderating effects of value network dynamics. This study addresses two pivotal questions: (1) Does GTI systematically enhance SDP across firms, especially in heavily polluting industries? (2) How do value network attributes—specifically, resource heterogeneity, resource coupling, and total-benefit structural holes—amplify or constrain this relationship? By integrating value network theory with innovation and sustainability literature, this study provides a nuanced framework to decode the mechanisms through which GTI drives SDP, offering actionable insights for firms navigating the dual pressures of regulatory compliance and market competitiveness.
The study adopts a multi-method approach, combining theoretical modeling with empirical validation. A conceptual framework is developed to delineate the direct effect of GTI on SDP and the moderating roles of three value network dimensions: total-benefit structural holes (network positions balancing information brokerage and mutual benefit), resource heterogeneity (diversity of complementary resources), and resource coupling (synergistic integration of resources). Data collected from 367 Chinese firms in heavily polluting industries (e.g., steel, chemicals, energy) is selected due to their high environmental footprint and regulatory exposure. Firm-level GTI activities are measured using patent data (IPC green technology classifications), R&D expenditure ratios, and internal environmental management audits. SDP is operationalized through a composite index encompassing environmental performance (e.g., emission reduction rates), economic performance (e.g., green product revenue share), and social performance (e.g., stakeholder satisfaction scores). Value network variables are quantified via social network analysis (SNA) of inter-firm collaboration networks, resource inventories, and survey-based assessments of resource integration efficiency. Hierarchical regression analysis and moderated mediation models are employed to test hypotheses, with robustness checks addressing endogeneity and industry heterogeneity.
Firms actively implementing GTI exhibit a 23.7% higher SDP index compared to non-adopters, confirming that GTI serves as a critical driver of sustainability outcomes. This effect is particularly pronounced in industries with stringent environmental regulations. Firms occupying structural holes that prioritize mutual benefit (vs. mere information control) amplify the GTI-SDP link by 18.2%. Such positions enable firms to reconcile conflicting stakeholder interests (e.g., balancing cost-intensive green R&D with shareholder returns). A one-standard-deviation increase in resource diversity (e.g., combining clean energy patents with circular economy expertise) enhances GTI’s impact on SDP by 14.5%, underscoring the combinatorial advantage of heterogeneous knowledge assets. High coupling efficiency (measured as resource integration speed/quality) boosts GTI’s SDP returns by 21.3%, indicating that systemic alignment of technical, financial, and human resources is pivotal for scaling green innovations. Resource heterogeneity and coupling exert stronger effects when mediated by total-benefit structural holes. For instance, firms with both high resource diversity and central network positions achieve 31% faster green product commercialization cycles.
This study extends value network theory by introducing total-benefit structural holes as a novel construct. It bridges the focus on information control in structural hole theory with the emphasis on value co-creation in stakeholder theory. A tripartite moderating framework (structure-resource-synergy) is established to elucidate how network embeddedness and resource orchestration jointly shape GTI outcomes, thereby addressing prior oversights in the innovation literature. The study highlights the need for regionally differentiated GTI incentives. For example, it suggests fostering structural hole brokers in underperforming networks (e.g., Western China) through consortium-building initiatives. It also provides a roadmap for leveraging value networks, such as prioritizing “bridging” partnerships with ESG-aligned stakeholders. This approach enables firms to access heterogeneous resources while mitigating collaboration risks. Additionally, the study identifies SDP-enabling metrics (e.g., resource coupling efficiency scores) that can be used for ESG portfolio optimization.
This study demystifies the “black box” of GTI-driven sustainability transitions, demonstrating that value networks are not mere backdrops but active enablers of green innovation efficacy. For firms in polluting industries, strategic navigation of network structures and resource flows,rather than isolated technological prowess,emerges as the linchpin of sustainable competitiveness. Future studies could explore temporal dynamics, such as how digital platforms reshape value networks in accelerating GTI diffusion.

The issue of "who should confront key technological challenges" has led policymakers and scholars to increasingly acknowledge that, alongside national strategic S&T pillars such as national laboratories, premier research institutions, elite universities, and industry leaders,small and medium-sized enterprises (SMEs) serve as an important force. Specifically, SMEs possessing distinctive technical advantages in niche domains can play a crucial role ;however, in practice, SMEs often exhibit limited willingness to engage in such efforts due to the inherent characteristics of key technology R&D: long development cycles, substantial capital requirements, and high levels of uncertainty. Under the dominant market logic of profit maximization, motivating and empowering SMEs to actively participate in key technology innovation has thus become an urgent imperative, necessitating synergistic efforts between the government and strategic S&T forces such as the leading enterprises.
Although existing literature has affirmed the positive role of SMEs in key core technology research, it mainly focuses on strategic scientific and technological forces including core enterprises and scientific research institutions. The behavior mechanism of SMEs participating in key core technology research has not been thoroughly analyzed, which cannot provide sufficient theoretical support for solving the above practical problems. At the same time, most of the existing studies are carried out by inductive deduction and case studies, based on limited sample data and resulting in a weak generalization strength in statistical significance. In view of the practical needs and theoretical gap, employing Meta-UTAUT model integrating perception trust variable and questionnaire data from 283 SMEs, this study integrates PLS-SEM and fsQCA methods to explore the factors influencing the behavioral willingness of SMEs to participate in key technology research under the organizational model of an innovation ecosystem, because innovation ecosystem is an important carrier for SMEs to participate in key technology research.
The findings show that antecedent variables including expected performance, effort expectation, social influence, convenience and perceived trust do not directly affect behavioral intention but are fully mediated by attitude. Although attitude exerts a strong linear influence, its absence in one path confirms it is not always necessary. This demonstrates that no single factor alone dictates SME participation. Acknowledging the heterogeneity of SMEs, the study recommends moving beyond a "one-size-fits-all" model and advocates for customized incentive strategies that are tailored to the specific realities and needs of different firms.
The theoretical contributions lie in three aspects. First, it shifts the focus from traditional leaders of innovation ecosystems to the pivotal yet underexplored role of SMEs as complementary actors. By constructing an integrated theoretical framework based on an extended Meta-UTAUT model (incorporating trust), we systematically identify the factors influencing SMEs' willingness to participate in key core technology research. A central finding is the core mediating role of attitude, through which all other antecedent variables exert their influence. This not only validates but also contextually refines the Meta-UTAUT model, extending its application to the high-stakes domain of key technology breakthroughs and providing novel insights into the complex decision-making mechanisms of SMEs in this critical context. Second, existing studies mainly adopt case analysis or induction and deduction method, thus the generalization power of statistical significance was weak. This study is based on the analysis of 283 valid questionnaire data, which enhances the universality of the study conclusions. Third, this study integrates PLS-SEM with fsQCA method, exploring not only how each factor independently affects the behavioral willingness of SMEs, but also the complex "synergies" and "interactive relationships" among these factors. It is emphasized that these factors can better stimulate the participation willingness of SMEs through different matching.

In the era of rapid digitalization, digital technology has accelerated the innovation process at an unprecedented pace, offering enterprises new opportunities and momentum. With advanced tools such as big data analytics and artificial intelligence, firms' can quickly capture market dynamics, consumer demands, and technological trends, accurately identify innovation directions, and significantly shorten the product development cycle from concept to market through intelligent and automated processes. The introduction of digital technology has fundamentally transformed business models and innovation pathways. The application of digital technology in innovation and entrepreneurship activities has become a central topic of academic research. This study explores how digital technology affordances influence new product development performance, and examines the moderating role of managerial digital literacy in this relationship. Although existing literature has discussed the impact of digital technology affordances, most studies approached this from the perspectives of organizational capabilities or digital transformation. There remains a lack of in-depth exploration regarding the mechanism through which the dual-dimensional affordances of digital technology affect new product development performance.
　Drawing on technology affordance theory, this study selects enterprises from different industries with significant differences in the level of digital technology application as samples, and targets front-line, middle and senior managers as respondents, for they have a thorough understanding of their enterprises' digital technology application and new product development activities. The data was collected through questionnaire surveys from two phases. A total of 372 questionnaires were distributed,and 331 valid questionnaires were recovered, with an effective response rate of 88.98%. Variable measurements are primarily based on established, validated scales, with appropriate modifications tailored to the research context. Scale reliability and validity were assessed with SPSS 27.0 and AMOS 26.0. Model fit was further evaluated by comparing five nested structures:(1) the hypothesised five-factor model (accumulative affordance, variational affordance, opportunity iteration, NPD performance, and managers′ digital literacy); (2) a four-factor model that merged managers′ digital literacy with NPD performance; (3) a three-factor model that additionally merged opportunity iteration with NPD performance; (4) a two-factor model combining all affordance constructs; and (5) a single-factor model.
The research findings are as follows: Firstly, digital technology affordances significantly enhance enterprise new product development performance. Digital technology affordances comprise two dimensions,cumulative affordance and variant affordance,both of which have a significant positive impact on new product development performance. This indicates that the cumulative and variant characteristics of digital technology are crucial driving forces in the product development process. Secondly, opportunity iteration mediates the relationship between digital technology affordances and new product development performance. Digital technology affordances facilitate opportunity iteration, which in turn enhances new product development performance, revealing the internal mechanism of how digital technology affordances influence new product development performance. Finally, managerial digital literacy positively moderates the relationships between digital technology affordances and opportunity iteration, as well as between digital technology affordances and new product development performance. Higher levels of managerial digital literacy strengthen the positive impact of digital technology affordances on opportunity iteration and new product development performance, while enhancing the mediating effect of opportunity iteration, highlighting the critical role of managerial digital literacy in digital technology-driven new product development.
This study, combining technology affordance theory, with research on digital technology affordances, opportunity iteration, new product development performance, and managerial digital literacy, constructs a model of the influence mechanism of digital technology affordances on new product development performance from the perspective of the essential characteristics of digital technology. It contributes to the integration of technology affordance theory with innovation and entrepreneurship research from an opportunity perspective, providing valuable insights for enterprises to enhance new product development performance using digital technology affordances. Given the limitations in dimensionality, methodology, context, and industry sample, future research could expand the dimensional scope to include additional technological characteristics such as specificity and adaptability, adopt case studies or QCA to trace the long-term effects of digital affordances across distinct developmental stages (start-up, exploration, maturity), introduce contextual variables like top-management-team heterogeneity and technological complexity, and conduct in-depth investigations within industries that are highly dependent on digital technologies.

Core technologies are crucial for national development, leading China's innovation and industrial upgrade. The role of R&D personnel is vital in the process of core technology breakthrough. The dynamic changes of R&D personnel within the enterprises affect knowledge flow and the integration of technology breakthrough resources. Research on the role of cooperation networks in breakthroughs of core technology has garnered attention in the academic community, primarily focusing on the impact of static cooperative networks on technological breakthroughs. However, the internal structure of the R&D personnel cooperative network is dynamic, evolving over time and across tasks. This dynamic is reflected in changes in the cooperative relationship, information transmission methods, and the distribution patterns of knowledge resources. Compared to the static network, dynamic network better captures the interactions and cooperation of R&D personnel at different stages, which is critical for identifying and grasping the core technology breakthrough opportunities. Therefore, this study investigates the impact and mechanism of the dynamics of R&D personnel cooperative networks on core technology breakthroughs in new energy vehicle enterprises, starting from the internal dynamics of the enterprises. The study introduces the depth and breadth of technology integration as mediating variables. Furthermore, it investigates the heterogeneity of the impact of R&D personnel cooperative network dynamics on core technology breakthroughs across enterprises of different natures and scales.
For this study, A-share listed companies in the new energy vehicle sector are selected as the initial sample to analyze the dynamics of R&D personnel cooperative network and core technologies breakthroughs. The core technologies in the new energy vehicle sector (including power battery, electric drive motor and electronic control system ) are identified based on official documents, and the relevant patent data from 2010 to 2023 is obtained from the Incopat database. Hypotheses are verified by defining variables and constructing multiple linear regression models. In addition, to further validate the hypothesis, the study divides the data according to the nature and scale of the enterprise and conducts heterogeneity analysis based on the nature and scale of the enterprises. The results show that, first, the dynamics of the R&D personnel cooperative network has an inverted U-shaped effect on core technology breakthroughs. Specifically, as the dynamics of the R&D personnel cooperative network gradually increases, the degree of technological breakthrough shows a significant trend of rising and then falling. In the early stage, an increase in network dynamics allows enterprises to obtain more external resources and innovation elements through diversified R&D personnel cooperative networks, thus promoting breakthroughs in core technologies. However, when the dynamics surpasses a certain threshold, frequent changes in cooperation may lead to instability of cooperative relationships, increased management complexity, and decreased resource utilization efficiency, thereby inhibiting the effect of core technology breakthroughs. Secondly, the depth of technology integration plays an inverted U-shaped mediating role between R&D personnel cooperative network dynamics and core technology breakthroughs, while the breadth of technology integration does not have a significant impact on this relationship. Third, further heterogeneity analysis reveals that the impact of R&D personnel cooperative network dynamics on core technology breakthroughs varies depending on the nature and scale of the enterprises.
Different from previous studies, this paper introduces the dynamic change of R&D personnel cooperative network into the research of core technology breakthroughs from the perspective of innovation. Through theoretical and empirical analysis, the study supports the original hypothesis and expands the classical theory of resource flow and cooperative network. The research findings provide valuable guidance for the dynamic configuration and integration of R&D personnel and technical resources within the enterprises. Enterprises need to maintain stable core teams and ensure long-term cooperation. They should regularly evaluate R&D collaboration networks and introduce new partners to acquire diverse knowledge and innovative thinking. To ensure efficient communication, excessive turnover should be avoided. Encouraging cross-departmental and cross-domain collaboration, providing interdisciplinary training, and establishing multi-field projects can prevent over-reliance on specific tech paths. Strengthening dynamic resource allocation enables enterprises to quickly respond to external changes and optimize resource use, thereby enhancing competitiveness and facilitating breakthroughs in core technologies.

Global technological competition among major powers is shifting toward the frontier of the innovation chain, which is increasingly reliant on the support of basic research with the deepening of the new round of technological revolution and industrial transformation. As key actors driving high-quality development in China's critical industrial chains, leading enterprises in advanced manufacturing should exhibit heightened sensitivity to opportunities for leveraging basic research to overcome core technological bottlenecks. Therefore, identifying new driving forces to propel leading enterprises toward basic research has become a focal point for academia and policymakers in the big science era. In a transitional stage of Chinese enterprises’ basic research, existing research has failed to reach a consensus on how policy support, market drivers, and organizational behavior factors influence it, and few studies have directly examined the relationship among executive cognition, organizational behavior and capability, and enterprises′ basic research. Specialized research on advanced manufacturing leading enterprises, which are the vanguard forces in basic research, is even more scarce. Therefore, this study focuses on leading advanced manufacturing enterprises to investigate three core relationships: (1) the impact of executive technological imprint on basic research in leading enterprises; (2) the mediating roles of Industry-university-research collaboration and exploratory innovation between this impact; (3) the moderating effects of management power, marketization degree and government subsidies on the relationship between executive technological imprint and basic research.
　Addressing the above core issues, this study first constructs a "Cognition-Behavior-Outcome" chain pathway grounded in imprinting theory, resource dependence theory and dual innovation theory. It further combines the perspective of the regulatory focus theory to design the moderating effects of the management power, marketization degree and government subsidies. Then, the study establishes an initial sample of China's A-share listed leading advanced manufacturing enterprises from 2010 to 2023, with a final sample of 370 firms and 4424 valid observations. It uses descriptive statistical analysis and regression modeling to test the hypotheses. Additionally, through instrumental variable methods, PSM, and placebo tests are employed to solve these potential endogeneity and robustness problems. Furthermore, heterogeneity analyses are conducted across three dimensions: (1) ownership structure, distinguishing between state-owned and non-state-owned enterprises, and grouping by the shareholding ratio of institutional investors; (2) industry characteristics, categorizing firms into "bottleneck" industries (e.g., semiconductor, high-end equipment) and non-"bottleneck" industries, and by the degree of market competition (measured by the proportion of the top 5 firms' main business revenue in the total main business revenue of the industry); (3) regional endowments, classified by the level of regional openness (measured by the ratio of total import and export volume of the region where the business unit is located to the region's gross domestic product (GDP)).
　Through empirical analysis, the following three main conclusions are drawn: (1)Executive technological imprint significantly promotes basic research in leading enterprises; the reform-oriented mindset and long-term value orientation of executives with technological imprints are conducive to leading enterprises' engagement in basic research, accelerating the shift from the "comparative advantage" logic to the "first-mover advantage" logic. This impact is more pronounced in non-state-owned enterprises and those with low institutional investor ownership, as well as in enterprises operating in non-"bottleneck" industries and highly competitive sectors, and in firms located in regions with a high level of opening-up.(2)Industry-university-research collaboration and exploratory innovation exhibit significant mediating effects, revealing a path mechanism of "executive cognition-organizational behavior-basic research"; (3) Management power, marketization degree, and government subsidies exert a positive moderating effect.
　This study has three main theoretical contributions: (1) revealing the impact of executive technology imprint on enterprise basic research by identifying it as an endogenous driver, which provides new empirical insights for driving basic research in leading enterprises from a contingency perspective; (2) disclosing the mechanism of integrating industry-university-research cooperation and exploratory innovation from the perspectives of resource integration and innovative exploration, which provides a new path for promoting basic research in leading enterprises through open and exploratory cross-border transformation; (3) unravelling the complex moderating effects of management power, marketization degree, and government subsidies from the perspective of integrating the imprinting theory and the regulatory focus theory, which provides new empirical insights for contingency-based approaches to driving basic research in leading enterprises.

Interdisciplinary knowledge integration is a crucial pathway for driving innovation. While previous research has extensively explored the positive impact of interdisciplinarity on the innovative outcomes of knowledge, limited attention has been given to how enterprises strategically configure interdisciplinary knowledge in the technological domain, and the specific mechanisms through which this configuration influences innovation in core technologies. The impact of interdisciplinary knowledge on corporate innovation is dual in nature. On the one hand, interdisciplinary research promotes the exchange of heterogeneous knowledge, providing multi-dimensional solutions to key technological challenges and often resulting in original, disruptive breakthroughs. On the other hand, excessive reliance on interdisciplinary knowledge or improper focus can divert an enterprise from its core development path, weaken its technological focus, and disrupt technological accumulation, inhibiting innovation. Therefore, it is crucial to explore how to effectively allocate interdisciplinary knowledge resources and encourage collaboration in core technology innovation.
Drawing on the knowledge-based view, this study constructs a measurement of corporate interdisciplinary knowledge from three dimensions: knowledge diversity, knowledge balance, and knowledge difference. This study utilizes data from 400 listed companies in China′s new energy vehicle industry spanning 2010 to 2023. Applying a fixed-effects negative binomial regression model, it explores the relationship between interdisciplinary knowledge and core technology innovation. Furthermore, the study delves into the moderating effect of industry technological turbulence on this relationship. The following conclusions are drawn: (1) There is an inverted U-shaped relationship between technological knowledge diversity and core technology innovation, indicating that a moderate number of interdisciplinary knowledge categories is most conducive to innovation in core technologies;(2)Knowledge balance has a positive effect on core technology innovation, indicating that a more balanced distribution of “interdisciplinary” knowledge is beneficial for innovation in core technologies; (3) Knowledge difference has a positive effect on core technology innovation, suggesting that “long-distance” knowledge experience can facilitate breakthroughs in core technologies; (4)Industry technological turbulence reduces the inhibitory effect of knowledge diversity on core technological innovation, thereby weakening the inverted U-shaped relationship between interdisciplinary knowledge diversity and core technological innovation. The relationship between knowledge balance and core technological innovation depends more on a firm′s internal knowledge management mechanisms and organizational capabilities and is not significantly affected by a dynamic technological environment. Meanwhile, the positive role of differentiated knowledge reserves in core technological innovation is amplified in industries with high technological turbulence.
Theoretical contributions of this study are threefold: First, it enterprises the scope of the “interdisciplinarity” concept by shifting the focus from researchers or technical teams to the enterprise perspective, systematically exploring the interdisciplinary characteristics of corporate technological knowledge. Second, it examines the relationship between corporate knowledge interdisciplinarity and core technology innovation, offering a theoretical model and empirical analysis to highlight the role of interdisciplinary knowledge layout in achieving technological breakthroughs, providing both theoretical insight and practical guidance for enterprises. Third, the study analyzes the moderating role of industry technological turbulence in the relationship between interdisciplinary knowledge and core technology innovation, clarifying the resonance mechanism between interdisciplinary knowledge restructuring and dynamic technological environments, and enriching related contextual research.
The management insights are presented. First, enterprises need to keep interdisciplinary knowledge diversity at a proper level for optimal critical core technology innovation. Excessive diversity can cause resource dispersion and higher coordination costs, while insufficient diversity can stifle innovation. Second, managers should ensure a balanced distribution of knowledge resources across different disciplines. This not only improves knowledge utilization efficiency and flexibility in core technology innovation but also prevents "inertia" from path dependence. Enhancing knowledge-sharing mechanisms and creating interdisciplinary communication platforms are essential for this balance and overall technological innovation capability. Third, enterprises should broaden their knowledge boundaries as much as possible within their capabilities, actively absorbing "distant" knowledge to address the complexity challenges in critical core technology innovation. This can be achieved through cross-industry collaboration, technology alliances, and external technology acquisitions, introducing heterogeneous knowledge to drive technological innovation breakthroughs. Fourth, in a volatile industry technology environment, enterprises should keenly seize the opportunity window of technological change, strategically expand diverse knowledge reserves, and accumulate practical experience in integrating high-span disciplinary knowledge. This approach enhances the ability to adapt and innovate critical core technologies in rapidly changing technological contexts.

Knowledge has gradually become a competitive strategic resource of the organization, and efficient transfer of knowledge among members is an effective practice for the organization to obtain competitive advantages. Due to the negative evaluation of knowledge value and negative emotional experience of participating in knowledge activities, members generally lack enthusiasm for participating in organizational knowledge activities. In some cases, they may even actively resist involvement or engage in pretense when taking part. , showing a bad situation of knowledge aversion which seriously threatens the competitive development of the organization. Knowledge aversion in the organization interferes with the productive knowledge behavior of members and accelerates the deterioration of various counterproductive knowledge behaviors. It has a hidden destructive effect on the innovation and development of organizations, and thus it is important to promote the value and practice of organizational knowledge by exploring the formation mechanism and governance framework of knowledge aversion.
The subjective psychological factors such as individual are included in the interaction process with the object in the theory of affordances, which is conducive to analyzing the formation basis of the emotional behavior of knowledge aversion from the perception level. COM-B model provides a framework to comprehensively capture all the factors affecting behavior change, which aids in exploring the formation process of knowledge aversion from the driver level. Therefore, on the basis of the affordances theory and COM-B model, the formation mechanism of knowledge aversion is analyzed around perception layer, driver layer and behavior layer. The multi-dimensional affordances mismatch in the perceptual layer interferes with the value perception of members to the organization's knowledge activities, the personal-knowledge affordances mismatch in the perceptual layer causes cognitive paralysis of members, the knowledge-situation affordances split passifies the value perception of knowledge, and the context-personality affordances conflict aggravates the emotional exhaustion of members. The synergistic imbalance of multi-factor driving forces in the driver layer arouses the tendency of knowledge aversion of members: ability contradiction initiates endogenous knowledge aversion, lack of opportunity catalyzes exogenous knowledge aversion, and motivation exhaustion solidifies antagonistic knowledge aversion. At the behavior level, the endogenous knowledge aversion of members is manifested as adaptive resistance or resistance to knowledge activities, the exogenous knowledge aversion is manifested as defensive evasion or procrastination, and the antagonistic knowledge aversion is manifested as destructive pander or camouflage, which further spreads from the individual member level to the organizational group level.
Knowledge activities need to be resilient, dynamic, embedded and emergent, so that organizations can maintain their knowledge competitiveness in the complex living environment. However, the destructive, complex, infectious and hidden characteristics of knowledge aversion in organizations seriously interfere with the practice of knowledge competitiveness in organizations. The contradiction between organizational goal and the nature of knowledge aversion is the core problem that organizations face in managing knowledge aversion. Therefore, it is necessary to take systematic thinking as a guiding principle. This approach entails comprehensively considering the interplay of multi-level factors within and outside the organization, using the developmental trends of knowledge aversion as a foundation. It also involves exploring a dynamic adaptive systematic governance model that incorporates both bottom-up and top-down perspectives and implementing governance according to the hierarchical governance logic of perception, motivation, and behavior layers. Within the framework of systematic governance thought, hierarchical governance logic is adopted according to the formation mechanism of knowledge aversion, positive coupling governance is adopted for the perception layer with the purpose of reconstruction of multi-dimensional affordances adaptation order, collaborative governance is adopted for the driver layer with the positive synergy of multi-factor driving forces, and differential governance is applied to curb the chaotic spread of knowledge aversion.
At the practical level, this paper offers organizations an effective paradigm for implementing knowledge-aversion governance. It expedites the realization of organizational knowledge - management effectiveness value and enhances the fluidity and value-adding potential of organizational knowledge. Ultimately, it can invigorate the organization's knowledge activities, ensuring the sustainability and value of organizational knowledge efficiency and perceived benefits. This, in turn, helps the organization attain greater knowledge-based value benefits.