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The purpose of this research is to explore how an innovation organization's orientation toward the digital economy influences its position within R&D networks. By using institutional theory, the study aims to forecast market changes and understand how organizations can navigate the digital economy to secure essential resources and minimize dependencies.

This study employs a longitudinal panel dataset with 11,763 entries from 1995 to 2018, covering strategic emerging industries in China to analyze the impact of digital economy orientation on R&D networks. Utilizing advanced statistical models, it assesses the role of the legal environment as a moderator. This methodological approach facilitates a robust examination of the nexus between digital orientation and network dynamics within the context of institutional theory.

The study reveals that an organization's digital economy orientation enhances its centrality in R&D networks but reduces its control over structural holes. The legal environment negatively moderates the impact of digital economy orientation on network centrality, while positively influencing the relationship with network structural holes. These findings offer new insights into how institutional forces shape the strategic positioning of organizations in R&D collaborations.

This research offers a fresh perspective on the digital economy's impact on R&D networks, particularly in the Industry-University-Research (IUR) context. It extends the discourse by integrating institutional theory to elucidate the adaptation of R&D networks in the digital era. By identifying the legal environment as a moderator, the study provides a nuanced understanding of the strategic alignment within networks influenced by digital advancements. The unique focus on China's R&D networks presents a valuable contribution to the global discussion on digital integration and innovation ecosystems, highlighting the intersection of policy, academia, and industry in shaping research and development trajectories.

Previous research has analyzed the consequence of network stability; however, little is known about how partner type diversity influence network stability in R&D network. Based on knowledge-based view and social network theory, the purpose of this paper is to unravel the internal mechanisms between partner type diversity and network stability through the mediating role of knowledge recombination in R&D network.

The authors collected an unbalanced panel patent data set from information communication technology industry for the period 1994–2016. Then, the authors tested the different dimensions of partner type variety and its relevance in the R&D network and the mediating role of knowledge recombination through adopting the multiple linear regression.

Results indicate an inverted U-shaped relationship between partner type diversity (variety and relevance) and network stability, whereas knowledge recombination partially mediate these relationships.

From the perspective of R&D networks, this paper explores that there are the under-researched phenomena the antecedent of network stability through nodal attributes (i.e. partner type variety and partner type relevance). Moreover, this paper empirically examined the mediating role of knowledge recombination in the partner type diversity–network stability relationships. The novel perspective allows focal firm to recognize importance of nodal attributes, which are critical to fully excavate the potential capabilities of cooperating partners in R&D network.

The purpose of this paper is to verify the impacts of the transaction costs rationale on economic agents’ innovative results when they engage in European R & D networks, supplying both firms and policymakers with empirical support for improved decision making toward economic competitiveness and construction of the European research area. Furthermore, unlike many transaction cost economics assessments, the authors evaluate the existence of transaction costs following a dynamic framework of analysis (instead of using solely ex ante governance choice as a driver of inter-firm “friction” management), offering a novel perspective on these phenomena.

Data consist of firm-level information from Eureka’s Final Reports (1995-2006) for Spanish, Italian, French, British and German firms. Empirical assessments were performed through a two-step approach of direct and indirect effects of network management and potential sources of disturbances. Ordinal regressions were applied in order to identify transaction costs’ relevance as drivers of firms’ technological and commercial outcomes, as well as on managerial quality of alliances. Statistical controls include microeconomic and project-specific variables.

Results highlight the role played by transactional aspects as drivers of companies’ outcomes and managerial complexity. Furthermore, the authors find robust evidence that formal ex ante governance structures are incapable of satisfactorily addressing dynamic disturbances that take place within R & D networks. Whereas such findings are directly related to existing transaction costs, the authors find no support for the usual variables attributed to increased complexity in international inter-firm relationships.

Self-selection issues are inherently related to the research instrument (i.e. Eureka’s Reports), while further firm-level data could not be obtained since confidentiality issues protected companies’ names and sectors. Also, network-level data are not available, allowing the evaluation of individual perceptions only.

While literature addresses the issue of transaction costs in R & D networks via theoretical assumptions and rough proxies, this assessment offers an in-depth evaluation of a set of valuable indicators with direct implications for researchers, managers and policymakers. Main contributions concern the identification of dynamic interactions (and their respective disturbances) as a key feature of the overall performance of R & D networks, stressing the non-linearity of economic processes in these hybrid relationships, an issue that has been poorly tackled by previous empirical investigations.

The purpose of this paper is to explore the relationship between prior experience of organisations and their “co‐opetitive” behaviours in forming research networks when a R&D programme is launched in order to fund future research projects.

Drawing on both resource‐based writings and social embeddedness studies, the paper posits that two types of co‐opetition – namely the intra‐network and inter‐network co‐opetition – should be clearly distinguished in order to understand co‐opetitive behaviours of organisations. These two types of co‐opetition arise as a consequence of the different levels of firms' prior experience in forming successful co‐opetitive networks with their competitors.

A reverse U‐shape trade‐off between the two types of co‐opetition is hypothesised: the maximum level of intra‐network co‐opetition and the lowest of inter‐network co‐opetition are expected for low and highlevels of prior experience; the minimum level of intra‐network co‐opetition and the highest of inter‐network co‐opetition are expected for moderate levels of prior experience.

This paper sheds light on how co‐opetitive relationships emerge in R&D networks and under which circumstances competition prevails on cooperation.

The purpose of this paper is to explore that relational capability plays a mediator role in the relationship between focal enterprises' control on knowledge transfer risks in R&D network.

The paper reviews the related literature from which it proposes a theoretical hypothesis and conceptual framework which is tested empirically adopting regression methodology by mathematical statistics software.

The results reveal that focal enterprises' control (core technological capability and network position) have a positive effect on knowledge transfer risks without the mediation of relational capability. Moreover, relational capability mediates the positive relationship between core technological capability and knowledge transfer risks.

The cross-sectional data of this research has difficult in testing causality between the variables, and future research should take account of the potential factors that can impact on knowledge transfer risks.

The results provide more valuable information to managers in alleviating knowledge transfer risks in R&D networks. For one thing, the study indicates how focal firms' control (core technological capability and network position) can be taken advantage of improving R&D efficiency and reducing the knowledge transfer risks. For another, the results emphasize that relational capability plays an important role in interacting with cooperative enterprises in the process of R&D activities.

Few studies present a theoretical discussion on focal firms control as the important determinants of knowledge transfer risks especially in the R&D network. Moreover it expands the related risk management theory by relational capability that mediates the relationship between focal firms control and knowledge transfer risks.

The relationship between cooperative R&D network embeddedness and firm innovation resilience is understudied. This paper seeks to answer the questions of whether and how embedding in cooperative R&D networks improve digital firms’ innovation resilience.

Based upon social capital theory, this paper proposes a conceptual model with several hypotheses. The empirical analysis is based on a sample of 2,908 observations from 2005 to 2022. We measure firm innovation resilience by drawing on economic resilience and use LSM tests to assess the effect of cooperative R&D network position on innovation resilience.

The results indicate that cooperative R&D network centrality has a positive impact on firm innovation resilience and that the structural holes of the cooperative R&D network have an inverted U-shaped relationship with firm innovation resilience. Moreover, technological turbulence negatively moderates the relationship between centrality and firm innovation resilience while also steepening the inverted U-shaped relationship between structural holes and firm innovation resilience. Market turbulence positively moderates the relationship between centrality and firm innovation resilience. However, the moderating effect of market turbulence on the inverted U-shaped relationship between structural holes and firm innovation resilience is not significant.

Innovators' knowledge needs, bounded rationality, interests, and even organizational environments change over time, thus prompting them to constantly seek new opportunities to exchange and integrate knowledge, meet new beneficial partners, maintain beneficial cooperation, or terminate unhelpful cooperation. The utility of the network structure has dynamic characteristics. Only by considering the dynamics of the network can research on the mechanism of network structure be more complete, accurate and convincing. Therefore, future research can pay more attention to the relationship between dynamic networks and firm innovation resilience.

Firms should actively embed themselves in the cooperative R&D network and occupy a beneficial network position. By joining the cooperative R&D network, firms can gain resource advantages and enhance their ability to resist external shocks and improve innovation resilience.

This research advances our understanding of the antecedents of firm innovation resilience through the lens of organizational cooperation and uncovers the boundary conditions under which network embeddedness influences innovation resilience, thereby further enriching the theoretical framework of innovation resilience.

We argue that a foreign-based R&D subsidiary of a multinational enterprise (MNE) can potentially source knowledge from three diverse knowledge networks, namely (i) external knowledge network of the home country, (ii) external knowledge network of the host country, and (iii) internal (MNE) knowledge network. Drawing on the relative costs and benefits associated with the process of synergistic knowledge, this study examines whether a substitutive or a complementary relationship exists when two of the aforementioned networks collaborate in order to generate new knowledge at the subsidiary level. Our study’s sample is based on a survey questionnaire addressed to foreign-based R&D subsidiaries of Fortune 500 companies. We assess the existence of complementarity/substitutability using the “production function approach.” Our results indicate that a complementary relationship exists between external knowledge network of the host and the home country, as well as between external knowledge network of the host country and internal knowledge network. On the other hand, external knowledge network of the home country and internal knowledge network form a substitutive relationship. Our study offers a more comprehensive view of the diverse sources/knowledge networks that R&D subsidiaries are sourcing knowledge from when compared to existing research. We also specify and account for the costs/benefits involved in knowledge sourcing and thereby detect possible substitution/complementarity between different sources of knowledge. So far, there has been limited to nonexistent research into the diversity of knowledge networks of R&D subsidiaries and the examination of potential substitutabilities and complementarities. Hence our empirical study contributes to the development of this particular research stream.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

This paper aims to shed light on the linkage between research and development (R&D) networks and public funding presented in a given period by using network-based evaluation tools as a means of exploring the relational dimension in public projects designed to foster technology R&D activities.

This research uses co-occurrence network analysis of relevant public projects to assess how technological associations might occur within the R&D activities of given publicly funded projects as well as conducts correlation analysis to understand the extent to which linkages of R&D activity in technology fields are related to public expenditure.

Core technology fields, regarded as eligible to receive continued public funding, are critical for enhancing competitiveness and sustainable growth at the nationally strategic technology level. Thus, the relationship between R&D and the level of government funding for these fields is generally perceived as strong. However, a few technology fields, which did not actively form specific network relationships with other technology fields, are considered to exceptionally drive the largest government support. This trend indicates that the government-funded R&D should be designed and managed not only to curb the inefficiencies existing in the current funding programs but also to achieve the appropriateness for further technology development.

Despite the comprehensive findings, this study has several limitations. First, it is difficult to control any confounding factors, such as the determinants and constraints of the government budget allocation and expenditure decisions over S&T areas, strategic frameworks for public investment and evolving policy landscapes in technology sectors, which lead to bias in the study results. Second, this study is based on a narrow, single-year data set of a specific field of projects supported by the Korean government’s R&D program. Therefore, the generalization of findings may be limited. The authors assumed that influences caused by confounding variables during the initial phase of the public funding schemes would not be significant, but they did not take into account possible factors that might arise coincident with the subsequent phase changes. As such, the issue of confounding variables needs to be carefully considered in research design to provide alternative explanations for the results that have been ruled out. The limitations of this study, therefore, could be overcome by comparing the outcome difference between subsidized and non-subsidized R&D projects or evaluating targeted funding schemes or tax incentives that support and promote various areas of R&D with sufficiently large, evidence-based data sets. Also, future research must identify and analyze the R&D activities concerning public support programs performed in other countries associated with strategic priorities to provide more profound insight into how they differ. Third, there are some drawbacks to using these principal investigators-provided classification codes, such as subjectivity, inaccuracy and non-representation. These limitations may be addressed by using content-based representations of the projects rather than using pre-defined codes. Finally, the role that government investment in R&D has played in developing new science and manufacturing technologies of materials and components through network relationships could be better examined using longitudinal analysis. Furthermore, the findings suggest the need for further research to integrate econometric models of performance outcomes such as input–output relations into the network analysis for analyzing the flow of resources and activities between R&D sectors in a national economy. Therefore, future research would be helpful in developing a methodological strategy that could analyze temporal trends in the identification of the effects of public funding on the performance of R&D activity and demand.

Public funding schemes and their intended R&D relationships still depend on a framework to generate the right circumstances for leading and promoting coordinated R&D activities while strengthening research capacity to enhance the competitiveness of technologies. Each technology field has a relatively important role in R&D development that should be effectively managed and supervised to accomplish its intended goals of R&D budgeting. Thus, when designing and managing R&D funding schemes and strategy-driven R&D relations, potential benefits and costs of using resources from each technology field should be defined and measured. In this regard, government-funded R&D activities should be designed to develop or accommodate a coordinated program evaluation, to be able to examine the extent to which public funding is achieving its objectives of fostering R&D networks, balancing the purpose of government funding against the needs of researchers and technology sectors. In this sense, the examination of public R&D relations provides a platform for discussion of relational network structures characterizing R&D activities, the strategic direction and priorities for budget allocation of the R&D projects. It also indicates the methodological basis for addressing the impact of public funding for R&D activities on the overall performance of technology fields.

The value of this work lies in a preliminary exploratory analysis that provides a high-level snapshot of the areas of metallurgy, polymers/chemistry/fibers and ceramics, funded by the Korean Government in 2016 to promote technological competitiveness by encouraging industries to maintain and expand their competencies.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.