Collaborative Innovation Networks Research Articles

A relations-based evaluation framework to support collaborative research and innovation (R&I) networks

The majority of the projects funded under the European Research Framework Programme (FP) require a collaborative effort in order to tackle complex societal challenges. The underlying assumption is that collaborative networks boost research productivity and the attainment of the goals set out by the European Commission's agenda. In general, the production of scientific knowledge increasingly takes place within networks and consortia with a wide diversity and interlinkages of disciplines, expertise, backgrounds and approaches. This relational dimension has become even more important in the Horizon Europe FP, which has moved from a project-focused to a mission-oriented approach whereby a portfolio of projects is managed in coordination in order to stimulate cross-learning and experimentation. Starting from key concepts and theories of evaluation research, including ten years of experience for the Italian National Research Council (CNR) team as an internal evaluator, we are proposing a new approach, namely a Relations-based Evaluation (RE) approach, that analyses, assesses and builds relationships, both internal and external, with the ultimate aim of supporting collaborative research and innovation (R&I) projects. The framework originated within the context of EU projects and deals with the specific case of consortia funded under the EU FPs. However, it could be easily applied to other types of research consortia and project networks with similar features.

Stability Analysis of Collaborative Product Innovation Network Exposed to Knowledge Resource Loss

Stability Analysis of Collaborative Product Innovation Network Exposed to Knowledge Resource Loss

Simulation research on knowledge flow in a collaborative innovation network

AbstractThis study takes diffusion capacity, absorptive capacity, and relationship strength as the main influencing factors, constructs models of knowledge flow in small‐world networks and scale‐free networks, and uses numerical simulation to observe the flow characteristics of explicit knowledge and tacit knowledge in different networks. The knowledge of explicit flow in different networks exhibits the phenomenon of knowledge emergence, but this phenomenon is more obvious in a small‐world network. The flow of tacit knowledge in a small‐world network has a better effect. In a scale‐free network, the quantity and frequency of knowledge flow are significantly higher than those in a small‐world network. The reason for this phenomenon is the differences in the response, connection and structure of different networks. The quantity and frequency of the flow of explicit knowledge in the same network are significantly higher than those of tacit knowledge. The reason for this phenomenon is the different types of knowledge flow in different modes, with different levels of flow difficulty and flow sustainability. First, this study visually compares the differences in flow between the two types of knowledge. Second, flow models of the two types of knowledge are constructed, and the flow characteristics of the two types of knowledge in different networks are simulated. Finally, the reasons for the differences in flow between the two types of knowledge are explained by using loosely coupled theory.

The Dynamics of China’s Collaborative Innovation Network in Agricultural Biotechnology: A Spatial-Topological Perspective

This paper explores the dynamics of the collaborative innovation network of China’s agricultural biotechnology, from a spatial-topological perspective. The data pertain to a collection of patent applications jointly filed by universities, research institutes and enterprises on the mainland of China during 1985–2017. Using the logistic model, we first identify the developing phases of China’s agricultural biotechnology. By dismantling the collaborative innovation network into spatial and topological networks, the dynamics are analyzed from these two dimensions at the three levels of nodes, edges and whole network. The results indicate that with the technology developing from the introduction to the growth-to-maturity phase, the collaborative innovation network exhibits dynamics as follows: as the scale expands, collaborations in the network are concentrated core cities, while dispersing to more innovators; enterprises replace universities and become the main innovation forces; the network attributes of small-world, scale-free and core-edge structures are apparent. Multi proximity factors including geographical, cognitive and organizational, play key roles in driving the dynamics, and the main factor evolves from geographical proximity to cognitive as well as organizational proximity.

The influence of collaborative innovation network characteristics on firm innovation performance from the perspective of innovation ecosystem

PurposeThis study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.Design/methodology/approachThis study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.FindingsThe results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.Research limitations/implicationsThere are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.Practical implicationsThis analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.Originality/valueThe study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.

Collaborative relationship discovery in green building technology innovation: Evidence from patents in China's construction industry

Green building technologies (GBTs) have grown rapidly due to accelerating climate change and environmental pollution. Under the background of double carbon, innovation in GBTs have become the general trend of sustainable development in the construction industry. Inter-organizational collaboration can not only of great significance to take advantage of innovation resources of the stakeholders involved, but also has a pivotal role in reducing costs and containing associated risks. However, little research has shed light on the underlying patterns and complex mechanisms of collaborative relationships in GBTs innovation. The aim of this study is to explore the structural characteristics, evolution laws and patterns of GBTs collaborative innovation through longitudinally tracking research on China's construction industry, and in turn, reveal the complicated collaboration mechanism of stakeholders in inter-organization. Therefore, this study establishes the GBTs collaborative innovation network model via patents in DII database. Combined with social network analysis method, the temporal evolution characteristic and spatial evolution pattern of the GBTs collaborative innovation network are quantitatively revealed. The results demonstrate that GBTs collaborative innovation gradually expands with a three-stage trend in 2006–2021. State Grid Corp. China in particular exerts a significant influence and collaborative innovation among various stakeholder, such as the general contractors and research institutions becomes the dominant force. In addition, the geospatial distribution is quite different. Beijing has a central position and is continuously strengthening. In terms of spatial pattern evolution, most of the eastern regions with high breadth-high depth are not only the center of information exchange, but also the bridge for inter-provincial resource exchanges. This study enriches the existing literature on the innovation in GBTs and helps us better understand the evolutionary mechanism of GBTs collaborative innovation and provides a useful reference for stakeholders to establish relational contracts in innovation in GBTs. It is of great significance to allocate innovation resources more efficiently and promote the sustainable development of the construction industry.

Change and driving forces of intercity collaborative innovation networks in the Yangtze River Delta region

Change and driving forces of intercity collaborative innovation networks in the Yangtze River Delta region

Reward-based crowdfunding for building a valuable circular business model

Reward-based crowdfunding is a source of funds for innovative start-ups. However, prior literature paid little attention to how this phenomenon occurs when such micro-firms market products and services within the context of the so-called circular economy (CE) and for more sustainable innovation. We aim to explore the potential of reward-based crowdfunding in designing and orchestrating a circular business model for micro-firms. Drawing on this evidence, the present study performs a multiple case study analysis of selected best practices by sustainable projects funded on the Kickstarter platform. Results show that reward-based crowdfunding shapes circular business models (CBM) in informational mechanisms, collaborative innovation networks, and marketing aspects. Furthermore, reward-based crowdfunding reduces risks connected to the CBM, such as uncertainty and the long-range perspective for campaign creators and customers.

Heterogeneous venture capital and technological innovation network evolution: Corporate reputation as mediating variable

Amid the pandemic of COVID-19, the collaborative innovation network of enterprises is conducive to the sharing of innovation resources, knowledge transfer, and technology diffusion, which is closely related to the improvement of corporate technological innovation performance. From the perspective of corporate reputation in the field of venture capital investment, and based on transaction cost theory, basic resource theory and reputation effect theory, this paper discusses the evolution mechanism of corporate technological innovation network, constructs an integrated model for the relationships between heterogeneous venture capital investment, corporate reputation and technological innovation network evolution, and then examines the impact of different types of heterogeneous venture capital investment on the evolution mechanism of technological innovation network, as well as the mediating effects of corporate reputation. Through the empirical analysis of the data obtained from the survey of 500 enterprises, the research shows that that independent venture capital is more conducive to enhancing the internal corporate reputation and promoting the evolution of technological innovation networks towards being self-centered; corporate venture capital is more conducive to consolidating the external reputation of enterprises and promoting the evolution of technological innovation networks towards being holistic. Corporate reputation has some mediating effects on the relationship between heterogeneous venture capital and technological innovation network evolution.

Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

Artificial intelligence (AI), as a rapidly developing interdisciplinary field, is a key driver of future economic development. The Yangtze River Delta (YRD) is one of the most significant economic regions of China, which also has a leading role in the AI industry. In this study, based on the patent cooperation data of YRD in the past decade, we focus on studying the collaborative innovation relationship in the AI field of the YRD from the perspective of complex networks. In order to investigate the interprovincial, intra-city and inter-city collaborative innovation relationships, we construct the Yangtze River Delta AI collaborative innovation (YRD-AICI) network. Subsequently, to analyze the development status and collaborative innovation relationship of innovation bodies in the AI field of YRD, we construct the Yangtze River Delta AI patent cooperation (YRD-AIPC) network. Next, the basic characteristics and spatio-temporal evolution of these two networks are explored, and the research results are presented that: (1) Shanghai, Jiangsu Province, and Zhejiang Province have obvious leading advantages in the AI field of the YRD, and the development gap between cities is significant; (2) the pioneering innovation bodies in the AI industry of the YRD are identified using centrality measures, and their cooperative innovation relationship is revealed; (3) based on link prediction methods, future partnerships between cities and innovation bodies are predicted to provide the future development trend of the YRD. The results provide theoretical support for exploring the cooperation mechanism of collaborative innovation in the AI field of YRD and inspire future development planning.

Interactive Mapping: A Method for Engaged Community Assessment

The Florida Early Childhood Comprehensive Systems (ECCS) Impact Project focuses on improving the developmental health of infants and young children by supporting place-based community initiatives through a Collaborative Improvement and Innovation Network (CoIIN) approach. The CoIIN is a multidisciplinary team that fosters collaboration and coordination among agencies and community residents. A team at the University of South Florida conducted an independent evaluation of the ECCS Impact Project in Liberty City (LC), Miami, Florida, starting with a community assessment to gain a better understanding of local assets, needs and priorities. This community assessment was completed in collaboration with LC ECCS. Across the first two years of the initiative, the community assessment included methods such as geographic information system mapping; attending and planning meetings; conducting focus groups with parents, residents, and community providers; engaging in virtual and walking tours; and hosting interactive mapping workshops. The multimethod approach contributed to a community assessment that was comprehensive and holistic and that encompassed multiple perspectives. Results of the process included authentic engagement across community sectors and levels of influence and learning and reflection on community strengths, resources, and priorities to facilitate organization for positive change.

RESEARCH ON VULNERABILITY RISK ASSESSMENT OF COLLABORATIVE INNOVATION NETWORK DRIVEN BY MULTI-AGENT EMOTIONAL BEHAVIOR

BackgroundIn the increasingly competitive market environment, the high complexity of innovation activities makes enterprises gradually combine with other institutions or groups to form a collaborative innovation network and jointly participate in the completion of innovation activities. Aiming at the vulnerability risk of collaborative innovation network caused by a series of psychological behaviors of each subject under the influence of internal and external environment, a vulnerability risk assessment method of collaborative innovation network based on cloud model is proposed. At the same time, the emotional behavior of multiple subjects is analyzed and studied.Subjects and MethodsFirstly, by systematically combing the existing research results, the influencing factors of collaborative innovation network vulnerability risk are analyzed and identified, and the collaborative innovation network vulnerability risk evaluation index is constructed. Secondly, in order to prevent the weight difference of individual indicators from being too large and reduce the correlation degree between indicators, the anti entropy weight method is used to determine the weight of evaluation indicators. Then, considering the fuzziness and uncertainty of qualitative and quantitative description of evaluation indicators, the comprehensive evaluation method based on cloud model is used to evaluate the vulnerability risk of collaborative innovation network. Finally, taking the vulnerability risk analysis of project product collaborative innovation network of enterprise a as an example, the risk assessment analysis is carried out. The modified Lin Shangping's “innovative network Emotion Scale” and “Minnesota Satisfaction Scale (MSQ)” were used in the study. Using the convenient sampling method, 200 questionnaires were distributed to the empirical objects, and 200 were recovered. The sample structure distribution is that men account for 49.7%, the proportion under the age of 25 (including) is the lowest, accounting for 2.5%, and the proportion over the age of 36 is the highest, accounting for 52.6%. As for the education level, universities (colleges and universities) the proportion is the highest, accounting for 64.0%. In terms of service years, the proportion of 1-3 years is the lowest, accounting for 4.4%, and the proportion of more than 7 years is the highest, accounting for 84.2%. The total correlation coefficient and total reliability of the modified items were analyzed to delete the items without internal consistency in the questionnaire, so as to improve the reliability of the questionnaire and the quality of measurement tools. Regression analysis was used to verify the three hypothesesResultsOur research results show that the collaborative innovation network vulnerability risk of the product is generally at a low level. In order to specifically understand the influencing factors of collaborative innovation network vulnerability risk, the cloud chart analysis of secondary indicators shows that the equity index of benefit distribution and the index of risk prevention mechanism are at medium risk.ConclusionUsing the comprehensive evaluation method of anti entropy weight method and cloud model to analyze the vulnerability risk of collaborative innovation network can help enterprise managers effectively understand the weak links of collaborative innovation network, formulate and implement feasible countermeasures, and provide decision support for the risk management and sustainable operation of collaborative innovation network. Of course, there are still some deficiencies in this study: in the composition of indicators, qualitative indicators are the main indicators; It mainly evaluates and analyzes the indicators, and then considers the correlation between risk indicators and network risk management. Multi-agent collaborative innovation is an important mode to promote enterprise innovation and development and obtain competitive advantage. As an important part of collaborative innovation network, risk management is an important guarantee to avoid risks and carry out collaborative innovation activities smoothly. Therefore, this paper analyzes the vulnerability risk of collaborative innovation network, which helps to enrich the collaborative innovation network and risk management system, improve the efficiency of enterprise product innovation, reduce the risk of collaborative innovation, and maintain the sustainable operation and development of collaborative innovation network.AcknowledgementsThe authors acknowledge the collective support granted by National Natural Science Youth Foundation of China (Grant No 71701027); Chongqing Municipal Education Commission Humanities and Social Sciences Research General Project (Grant No 20SKGH116); Chongqing Natural Science Foundation of the General Project (Grant No cstc2020jcyj-msxmX0562); China Postdoctoral Science Foundation Funded Project (2018M643456); Chongqing Natural Science Foundation Project (Grant No cstc2020jcyj-msxmX0864) and Chongqing Social Science Planning and Cultivation Project (Grant No 2020PY51).

Formation and Evolution of Ideal Interfirm Collaborative Innovation Networks Based on Decision-Making Rules for Partner Selection

On the basis of an external and static perspective on the topological structure of collaborative innovation networks, it is extremely difficult to answer the two most important concerns, namely, which structure is ideal and how to develop it in practice. By contrast, this study transfers to internal and dynamic perspectives, and then proposes that the essence of developing the ideal network lies in choosing the best partners. Therefore, we firstly propose the basic decision-making rules for selecting partners. In order of priority: knowledge distance, knowledge complementarity and barter exchange. Secondly, a model is constructed to describe this process of selecting partners and exchanging knowledge. Thirdly, the simulation results show that a small-world network is ideal in the initial stage of collaborative innovation. However, a random network is ideal in the mature periods. This result shows that the ideal network structure is not fixed, but affected by the life cycle of collaborative innovation alliance. Furthermore, this supports the notion that a small world is spontaneously generated in the real world, and also confirms that the formation of a small-world network will be driven intrinsically by a firm’s demand for external knowledge, and not necessarily by the external driving force of social capital. Finally, these findings solve the above two most important questions.

A Study on Conformity Appeal Attributes and Social Contagion of Beauty-Focused One-Person Media in Sustainable E-Commerce

In the beauty industry, the use of one-person media is making new, more active relationships that differ from those of past online services and information sharing. Therefore, in this study, we developed conformity appeal attributes in one-person media, identifying their effects on information diffusion behavior through social conformity and collaborative innovation networks, to investigate the social contagion effect of information in beauty-focused one-person media and develop a model by connecting them to the conformity threshold of individuals influenced by others. In this study, a total of 694 complete samples of experienced consumers in the beauty-focused one-person media category were selected, and research questions were verified through reliability and validity tests, path analysis, and measurement model analysis. The research results are as follows. First, conformity appeal attributes of beauty-focused one-person media in terms of information cascade, utility value efficiency, reference group influence, and subnetwork structure significantly affected social imitation conformity. However, only subnetwork structure significantly affected social connection conformity. Similarly, only reference group influence and subnetwork structure significantly affected social comparative conformity. Second, social imitation conformity, social connection conformity, and social comparative conformity in beauty-focused one-person media significantly affected cocreation. Third, cocreation in beauty-focused one-person media significantly affected information diffusion behavior. The detailed use of the conformity appeal attribute factors shown in the above results will be combined with functional changes for online and mobile services of beauty companies, and it will be a driving force to create new value for the network. Moreover, the results of this study not only enable social connection among members within the reference group of beauty-focused one-person media but are also applied as an effective phenomenon to explain the continuous maintenance, reinforcement, and expansion of these relationships, thereby enabling researchers to achieve theoretical expansion and evaluation of relevant variables.

From Psychology Management Innovation and Education Reform in the Digital Age: Role of Disruptive Technologies.

In the main body of colleges and universities, the effect of college teaching psychology management is an important standard to test the quality of college teaching psychology management and its effects on the development of college teaching psychology management. However, the psychology management system used by traditional colleges and universities is challenging to meet the needs of the innovation of the new talent training model of higher vocational education. The construction of the new micro-level teaching organization inevitably requires the psychology management practice, structure, process, and technology at the medium and macro-levels to adapt to it. However, there are many mismatches in Higher Vocational Colleges’ internal and external administrative systems in China, such as low administrative efficiency, generalization of administrative power, low degree of specialization in administrative psychology management, and unreasonable administrative and organizational structure. This paper innovates the psychology management of colleges and universities reforms in combination with the characteristics of the digital age. Based on the collaborative innovation theory of colleges and universities, this paper puts forward the collaborative psychology management innovation network model of colleges and universities. The model combines the psychology management theory with the practical research of collaborative innovation in colleges and universities, expands the application and research field of innovation network theory, constructs the evaluation index system of students’ satisfaction in teaching psychology management in colleges and universities, and discusses it through reading and consulting materials and communication with teachers and students. A questionnaire of college teaching psychology management based on student satisfaction is formed. The experiment shows that teaching satisfaction is improved by nearly 31% compared with before innovation and reform. It can initially get the effect of innovation and reform, meet students’ needs, and promote the improvement of teaching psychology management quality.

Research on the Influence of Collaborative Innovation Network Structure and Innovation Capability in the Yangtze River Economic Belt

This paper uses the modified gravity model to establish the collaborative innovation network among 110 cities in the Yangtze River Economic Belt from 2009 to 2018, and uses social network analysis and dynamic panel model to study the impact of innovation network structure on the level of collaborative innovation. The results show that: (1) The collaborative innovation network connection level of the Yangtze River Economic Belt is relatively low, showing a spatial distribution pattern of “high in the east and low in the west”, and the degree of network connection is not high, and the network structure is relatively loose. (2) The power of network structure significantly promotes the improvement of the level of collaborative innovation, but the improvement effect has certain differences, which is embodied in the promotion effect of proximity centrality is greater than that of degree centrality is greater than that of intermediate centrality. The degree of influence of network structure power varies.

The drivers of collaborative innovation of the comprehensive utilization technologies of coal fly ash in China: a network analysis.

Coal consumption brings a lot of coal fly ash (CFA). It requires interdisciplinary efforts in research, policy, and practice to improve the utilization of CFA. Although there have been a lot of achievements in technological innovation, the utilization of CFA is still difficult to match its output. So, it is urgent to explore how to guide its effective innovation. This paper uses social network analysis to discuss the characteristics of the collaborative innovation network of CFA comprehensive utilization technology in China. Then, this paper uses regression analysis to explore the differences in innovation performance under different research and development (R&D) backgrounds. The results show that (1) based on the network-level indicators, the collaborative innovation scale has an obvious trend of expanding. Partnerships increased from 20 to 574. Meanwhile, the network shows obvious scale-free and “small-world” characteristics, indicating that innovation resources are concentrated in a few organizations. (2) Based on the node-level indicators, the major contributor has shifted from universities and research institutions to enterprises. Enterprises account for the highest proportion (73%) and have the highest centrality (8.3). The betweenness centrality of the universities is 265, and only 14% of the organizations are universities which means universities play an important role in connecting different organizations in the network, but their participation in the collaborative innovation is insufficient. (3) Based on the collaborative relationship-level indicators, the cooperation is lack of depth. Only a small number of organizations, especially enterprises, have stable partners, showing the characteristic of “low cooperation width and high cooperation depth,” which means fewer partners but more frequently collaborative innovation. (4) Based on the innovation performance, the innovation performance under the category of cooperative R&D, especially industry-academy cooperation, is better than that of independent R&D. But, industry-academy cooperation only occupied 43% of collaborative relationships in the network. Finally, this paper puts forward suggestions for governments from five aspects: decentralization, defining roles of enterprise and university, encouraging collaboration, changing the idea of the patent application, and promoting deeper cooperation.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-19816-5.

Collaborative innovation relationship in Yangtze River Delta of China: Subjects collaboration and spatial correlation

In the regional innovation system (RIS), the triple helix (TH) relations of university, industry, government (UIG) have received wide attention, but the spatial correlation involving various innovation subjects has rarely been deeply analyzed, especially in urban agglomeration. Based on the triple helix perspective, this paper integrates the collaborative coupling relationship between innovation subjects and the spatial correlation between regions in the RIS under a theoretical framework. Two types of collaborative innovation networks are constructed: one is the collaborative coupling network between UIG, one is the spatial correlation network between different areas. Using the invention patent data of provinces in the Yangtze River Delta (YRD) from 2014 to 2018, and on account of the TH algorithm based on mutual information, to explore the collaborative innovation relationship from the two dimensions of collaborative coupling among innovation subjects and spatial correlation among regions. The results show that:(1) Jiangsu has an obvious leading advantage in the total amount of invention patents, Zhejiang and Anhui have witnessed a rapid growth of patent output. (2) In the YRD region, the collaboration between university and industry is the closest, the UIG collaborative relationship is close in Shanghai, the collaborative relationship between industry and university in Jiangsu is close. (3) The bilateral correlation between Jiangsu and the other three provinces is relatively strong; the bilateral correlation between Zhejiang and the other three provinces tends to be close. The trilateral correlation among Jiangsu, Zhejiang, and Anhui is relatively close. This study expands the application fields of the TH theory and provides a new perspective and method for RIS research. The research results offer helpful theoretical support and policy inspiration for accurately grasping the development law of the regional innovation in the YRD and coordinating the relationship between different subjects and different regions in the innovation network.

Evolution of New Energy Vehicle Collaborative Innovation Networks in China

Evolution of New Energy Vehicle Collaborative Innovation Networks in China

The role of collaborative innovation networks in the relationship between firms innovation capabilities and firm performance

The role of collaborative innovation networks in the relationship between firms innovation capabilities and firm performance