Collaborative Innovation Network Dynamics and Technological Innovation Diffusion: A Study Based on Stochastic Actor-Oriented Models

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. Based on the patent application data of listed enterprises in Jiangsu, Zhejiang, and Shanghai in China, this study constructs a cooperation matrix, describes the characteristics of collaborative innovation network from two dimensions of network structure and network relationship, introduces the breadth of the knowledge base as a moderating variable, and analyzes the nexus between characteristics of a collaborative innovation network and technological innovation performance. Based on the panel data of 193 listed companies in Jiangsu, Zhejiang, and Shanghai, this study uses a multiple linear regression model for empirical analysis. The results show a U-shaped relationship between clustering coefficient and technological innovation performance. The breadth of knowledge base strengthens the positive relationship between the structural hole and technological innovation performance. In contrast, the breadth of knowledge base weakens the positive relationship between network relationships strength and technological innovation performance. The study findings will enhance enterprises’ participation in a suitable collaborative innovation according to their knowledge-based characteristics and improve the technological innovation performance.

This paper describes lessons learned through participation in the collaborative online innovation networks (COINs) seminar, a multidisciplinary collaboration involving global virtual team (GVT) projects. The paper is a narrative case study based on insights gained through four COINs seminars. Lessons learned suggest a need for explicit learning objectives that prepare students beyond the use of technical platforms and tools. Significant opportunities for learning are rooted in the multidisciplinary and multicultural context of the seminar and the interactions that occur between GVT members. The case underscores the importance of social learning as a means of identifying and understanding the mechanisms that facilitate or inhibit collaboration at the team level. Insights from the seminar have led us to think about how the linkage between social learning, collaboration, and the knowledge sharing challenges encountered by multidisciplinary global virtual teams might be integrated into a theoretical framework to guide the development of explicit learning objectives.

In light of the crucial role of collaborative R&D in advancing technology within the new energy vehicle industry, this study seeks to explore ways to overcome the barriers to technological innovation by establishing an effective collaborative innovation network. Utilizing joint patent-authorized data from China’s new energy vehicles between 2005 and 2019, the collaborative innovation network was developed, and the Exponential Random Graph Model (ERGM) was employed to analyze its formation and evolution mechanisms. The results indicate that the network has undergone significant expansion, closely linked to strong national policy support and the active involvement of innovation participants. The network exhibits effects of expansion, transfer, and closure. External attribute analysis revealed the Matthew effect and geographical compatibility effect and found that organizational compatibility tends to foster complementary cooperation. The findings offer insights into optimizing collaborative innovation networks in the NEVs industry and suggest strategies for policymakers and industry players to promote collaborative innovation.

The evolution of a collaborative innovation network depends on the interrelationships among the innovation subjects. Every single small change affects the network topology, which leads to different evolution results. A logical relationship exists between network evolution and innovative behaviors. An accurate understanding of the characteristics of the network structure can help the innovative subjects to adopt appropriate innovative behaviors. Based on the resource-based theory, complex network theory and exploratory analysis of two typical cases, this paper analyzes the behavior rules of collaborative innovation network, and establishes the connection rule based on resource priority. Fully combining the stage-cooperation characteristic of collaborative innovation, this paper builds a network evolution model. Simulation experiments are carried out on network characteristics and evolution rules. The results show that resource priority connection conforms to the real situation, and is conducive to maintaining the continuous innovation network vitality, and thus has certain management practical significance for behavioral decisions of enterprise, government and so on.

Innovation and knowledge creation are tightly related concepts. Collaborative innovation networks (COINs) are very productive ecosystems for knowledge generation. Acknowledging their relevance for innovation, we propose a novel ontology for knowledge management (KM) in COINs, which builds on COINs related literature and is partially derived from topic-related and enterprise ontologies. The utility of the ontology is illustrated through an application case in a software house. The ontology can be applied in different cases involving KM in COINs, such as support the development of IT-based KM services for knowledge co-creation in COINs or, be used as an auditing tool in collaborative innovation processes.

PurposeThe purpose of this study is to examine the effect of collaborative innovation networks on new product development (NPD) performance in small and medium-sized enterprises (SMEs). It also investigates the mediating role of business model innovation and moderating role of collaboration experience and external information technology (IT) capability in the above relationship.Design/methodology/approachTo test the research hypotheses about the relationships above, survey data were collected from 209 Chinese manufacturing SMEs. Multiple hierarchical regressions analysis was used to examine the hypotheses.FindingsResults reveal that collaborative innovation networks have positive impacts on NPD performance in SMEs. Moreover, business model innovation mediates and collaboration experience and external IT capability positively moderate the relationship between collaborative innovation networks and NPD performance in SMEs.Originality/valueThis study paints a more complete picture of the relationship between collaborative innovation networks and NPD performance in SMEs and advances the understanding of how and when SMEs enhance their NPD performance through collaborative innovation networks.

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 key driving force for high-quality development is innovation, and collaborative innovation is an important form of organizing and realizing innovation. However, the impact of collaborative innovation networks on high-quality regional development remains unclear. At the city cluster level, this study analyzes more than 300,000 patent data based on the data of prefecture-level cities in China from 2012 to 2020 using the crawler method and social network analysis. The results show that, first, collaborative innovation in China is characterized by growth, network, and structural stability. Second, collaborative innovation can significantly improve the high-quality development of urban economies by reducing human resource mismatch and increasing the “intensive margin” and “expansive margin” of innovation. Third, at the national level, increased urban collaboration in terms of width and depth has contributed to the economy’s high-quality development. However, the two have not yet demonstrated complementarity, although at the level of urban agglomerations, the two are significantly complementary. Fourth, heterogeneity analysis shows that collaborative innovation is more effective in promoting high-quality development for highly matured city clusters and cities with robust innovation capacity. It can considerably overcome geographical constraints. From the regional heterogeneity perspective, the promotion of high-quality development through collaborative innovation is stronger in southern and central China. It is recommended that emerging market countries and city clusters focus on constructing and developing collaborative innovation networks and promoting high-quality economic development through measures such as increasing network density, enhancing the breadth and depth of synergies among cities, and developing differentiated policies.

It is challenging for a single development subject to complete complex scientific research tasks due to the peculiarities of saline–alkali land. Collaborative innovation and cooperation can help break through the common critical technology R&D problems in the development process by sharing information, technology, knowledge, and resources. However, the process of saline–alkali land development in China is slow and the transformation rate of research results is low. Few articles analyze the current situation and driving mechanism of the collaborative innovation network for saline–alkali land development from the perspective of the innovation chain. This paper first constructs an undirected weighted collaborative innovation network from the upstream, midstream, and downstream levels of technological innovation for saline–alkali land development, analyzing the network's structural characteristics and spatial distribution features. Then uses ERGM to explore the internal and external driving mechanism for network formation from network self‐organization, subject characteristics, and exogenous environmental factors. The results demonstrate that the distribution of the collaborative innovation network is relatively uniform. However, there are also clusters, and the clusters are mostly centered on universities and scientific research institutions. Both the development subjects and clusters present regional features. Centrality and transitivity are crucial to the internal driving mechanism. In the external driving mechanism, the Matthew effect is modest, and the homogeneity effect is considerable; Organizational and technical proximity play a positive role; Geographical and institutional proximity play a blocking role. This study also provides practical enlightenment for encouraging horizontal and vertical collaborative innovation of sustainable development of saline–alkali land.

The Collaborative Innovation and Improvement Network (COIIN): Effect on donor yield, waitlist mortality, transplant rates, and offer acceptance.

Identifying Collaborative Innovation Networks: At the Inter-Departmental Level

This paper presents survey results validating the theory of diffusion of innovation (DoI) (Rogers, 1962, 2003). We find that complexity, trialability, and observability exhibited correlation with innovation diffusion while the others do not. Innovation and technology have been long considered important drivers of organisational success. They are examined in the US Oil and Gas (O&G) industry. Rogers' (1962, 2003) theory of DoI is used in identifying and operationalising key variables that affect DoI. It broadens understanding of how the industry has responded to the presence of multiple, important new technologies and how the diffusion of these technologies can be explained by the application of the DoI model. Using primary data, the current study empirically tests DoI in the context of today's US O&G industry. The contribution of the current study to both researchers and practitioners, limitations, as well as suggestions for future studies are also provided.

How can you leverage the swarm creativity of Collaborative Innovation Networks? How can you recognize them? How can you join them? This talk introduces Collaborative Innovation Networks, or COINs. COINs are cyberteams of self- motivated people with a collective vision, enabled by technology to collaborate in creating a cool trend - an innovation - by sharing ideas, information, and work. COINs have been around for hundreds of years. Many of us have already been a part of one without knowing it. What makes COINs so relevant today is that the concept has reached its tipping point - thanks to the Internet and the World Wide Web. COINs are powered by swarm creativity. People work together in a structure that enables a fluid creation and exchange of ideas. It may look chaotic from the outside, but the structure of a COIN is like a beehive or ant colony, immensely productive because each team member knows intuitively what she or he needs to do.

The more cooperation, the better? Optimizing enterprise cooperative strategy in collaborative innovation networks

Collaboration networks play a crucial role in fostering innovation within the artificial intelligence (AI) industry. This study investigates the evolutionary mechanisms underlying collaboration networks within China’s AI industry. Utilizing a stochastic actor-oriented model (SAOM) framework, we integrate endogenous structural effects, exogenous organizational attributes, and dyadic proximity characteristics to analyze longitudinal patent data from 2013 to 2024. The results reveal that key network dynamics, such as transitivity and preferential attachment, actively shape tie formation. Importantly, universities and research institutions play a more central role in driving network evolution than firms, and organizations with higher innovativeness attract more collaborative partners. While geographical, cultural, and institutional proximities facilitate collaboration, technological proximity has a noteworthy negative effect, underscoring the importance of complementary knowledge in AI innovation. Our findings enhance the understanding of the differences in the different dimensional factors that drive the evolution of AI industry collaboration networks and advance the research on network dynamics.