Dynamics of collaboration network community and exploratory innovation: the moderation of knowledge networks

Partners' centrality in scientific collaboration and exploratory innovation: Roles of patent stock and structural holes

Studies adopting a first-order social-capital perspective have found that firms' innovation is influenced by network embeddedness in scientific collaboration. The present study adopts a second-order social-capital perspective to examine the effect of partners' centrality on exploratory innovation. We further investigate the boundaries of this effect via patent stock and structural holes (SHs), individually and jointly. Our theoretical framework is validated using negative binomial regression models and a sample of patents and joint publications by 194 Chinese pharmaceutical firms between 2007 and 2021. The results indicate that a focal firm's exploratory innovation is boosted by partners' centrality in scientific collaboration, but this effect is weakened by patent stock and SHs. Moreover, patent stock and SHs jointly influence this relationship-that is, partners' centrality has a strong positive effect on exploratory innovation when the firm holds low patent stock and occupies fewer SHs. This study offers insights into how businesses might profit from the innovation advantages of influential partners by exploring the contingencies of patent stock and SHs. Extending the framework to second-order aspects, we further illuminate the scope of the scientific collaboration-innovation issue.

Identifying the perceived local properties of networks reconstructed from biased random walks

Many real-world systems give rise to a time series of symbols. The elements in a sequence can be generated by agents walking over a networked space so that whenever a node is visited the corresponding symbol is generated. In many situations the underlying network is hidden, and one aims to recover its original structure and/or properties. For example, when analyzing texts, the underlying network structure generating a particular sequence of words is not available. In this paper, we analyze whether one can recover the underlying local properties of networks generating sequences of symbols for different combinations of random walks and network topologies. We found that the reconstruction performance is influenced by the bias of the agent dynamics. When the walker is biased toward high-degree neighbors, the best performance was obtained for most of the network models and properties. Surprisingly, this same effect is not observed for the clustering coefficient and eccentric, even when large sequences are considered. We also found that the true self-avoiding displayed similar performance as the one preferring highly-connected nodes, with the advantage of yielding competitive performance to recover the clustering coefficient. Our results may have implications for the construction and interpretation of networks generated from sequences.

Partners' knowledge utilization and exploratory innovation: the moderating effect of competitive and collaborative relationships

Purpose

Partners' knowledge is an essential source of knowledge for organizations in the collaborative innovation. Exploratory innovation is an innovation strategy that is significant to the organization and is characterized by learning and absorbing new knowledge for new products and technology. The purpose of this study is to investigate the impact of partners' knowledge utilization on exploratory innovation and to analyze further the moderating effect of previous competitive and collaborative relationships between organizations on the above relationships.

Design/methodology/approach

In this study, the patent data of 1,636 organizations in the nano-biopharmaceutical field collaboratively from 1998 to 2020 are used as a sample. The knowledge networks and collaboration networks are constructed through the patent data. A negative binomial regression method is used to conduct the empirical study.

Findings

The results indicate that partners' knowledge utilization has an inverted U-shaped effect on exploratory innovation. The competitive relationship steepens the inverted U-shape effect of partners' knowledge utilization on exploratory innovation and makes the turning point to the left. The collaborative relationship steepens the inverted U-shape effect of partners' knowledge utilization on exploratory innovation and makes the turning point to the right.

Originality/value

This study is the first to examine the impact of partners' knowledge utilization on exploratory innovation and to explore the role of the competitive and collaborative relationships between organizations. It extends current research in organizational innovation and knowledge management by providing insight into partners' knowledge utilization.

Managing the dynamic change of organizational knowledge base in collaboration networks

Purpose

Previous studies have addressed the importance of knowledge base and its effect on innovation outputs. However, few studies have focused on the antecedents of dynamic changes of the organizational knowledge base. This study aims to shed light on the antecedents of dynamic change of the organizational knowledge base by examining how network centrality in an organization's collaboration network impacts this change and the moderating role of knowledge network cohesion.

Design/methodology/approach

The empirical setting of this study is the smartphone collaboration network. The authors selected patent data from the Derwent Innovation Database. A negative binomial model was used to test the hypotheses.

Findings

The results verified that network centrality has a positive effect on the change in coupling among existing knowledge domains and has an inverted U-shaped relationship with the coupling between new and existing knowledge domains. Furthermore, when local cohesion is high, network centrality has a stronger positive effect on the change in coupling among existing knowledge domains. Global cohesion moderates this process in such a way that when it is at a high level, the coupling between new and existing knowledge domains can benefit more from a moderate level of network centrality.

Originality/value

This study sheds light on the antecedents of dynamic change of the organizational knowledge base and links the literature on collaboration and knowledge networks by providing novel insights to match collaboration network centrality with knowledge network cohesion for successful improvement of the organizational knowledge base.

The Internal Connection Analysis of Information Sharing and Investment Performance in the Venture Capital Network Community

In order to explore the internal connection between information sharing and investment performance in the venture capital network community, this study took environmental-governance start-ups as the research object and used the 2009–2020 environmental-social enterprise start-up venture capital investment events as a data sample. The successful exit rate of the venture capital portfolio and the successful listing rate of investment ventures were used as the measurement criteria. Combined with regression analysis, the relationship between information sharing and investment performance in the venture capital network community was analyzed in detail. Research shows that there are differences between the ways of information sharing in the venture capital network communities. In the regression results, all coefficients are less than 0.01. There is a positive correlation between information sharing and investment performance in the venture capital network community. With the increase in enterprise characteristic variables, the degree of enterprise risk information sharing is getting higher and higher. This ultimately leads to more and more frequent corporate investment performance and a higher possibility of acquisition. Among them, the degree of information sharing in the venture capital network community is relatively high, and venture capital companies that are supported by corporate venture capital institutions will benefit even more from listed capital. Not only was the analysis of the relationship between finance and investment in the venture capital network community pointed out in this research, but also the investment development of entrepreneurial enterprises was also provided with feasible suggestions.

Effects of Circular Economy Policies on the Environment and Sustainable Growth: Worldwide Research

The challenge of transitioning from a linear to a circular economy model is still ongoing and requires the development and application of new knowledge, leading to innovative, technological, and sustainable processes, products, and services. The evolution of global research on this topic from 2004 to 2019 was studied in this work. For this purpose, a bibliometric analysis of 1366 articles was applied, producing results on the scientific productivity of the driving agents that most contribute to this theme. The findings show a growing interest, especially in the last four years, in the study of circular economy (CE) policies on the environment. The main subject area that articles address is environmental science. Five research lines have been identified, which mainly study the efficient management of energy resources and the economic factors that make the CE model possible: Environmental pollution and agricultural activity; waste management to avoid a negative environmental impact; improvement of the environmental impact through ecological products; the product life cycle, and the consequences of climate change. The implementation of CE policies will contribute to making economies less dependent and unprotected and more competitive. Global research has exhibited an upward trend during the period analyzed and has grown exponentially since 2015, coinciding with the publication of the Sustainable Development Goals (SDGs) of the 2030 Agenda.