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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.

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.

Although the theoretical arguments provide several channels through which innovation affects export, empirical validation of this relationship is scarce. Further, the impact of the diverse channels of domestic and foreign research and development (R&D) on export is assessed in isolation by previous studies. This paper empirically investigates the impact of technological innovation on export capacity and intensity of industrial enterprises in emerging countries by considering three channels of domestic innovation and foreign R&D spillovers, namely internal R&D, embodied knowledge and disembodied knowledge in a unified framework.

Data on China's industrial enterprises in the manufacturing sector are extracted from the China National Bureau of Statistics (NBSC), the Ministry of Science and Technology of China (MST) and the UN Comtrade database for the period from 1998 to 2020. The instrumental variables two-stage least squares (IV-2SLS) and three-stage least squares (IV-3SLS) methods are used to control for the possible endogeneity bias and the problem of cross-equation correlation between residuals.

The results show that internal R&D is a critical factor to enhance the export performance of enterprises in emerging countries, while the effect of embodied spillovers and public–private collaboration on export capacity and intensity of industrial enterprises is substantial. Further, disembodied knowledge that is acquired through licensing of technology from advanced countries does not directly contribute to the export performance of enterprises but requires a threshold level of internal R&D capability. This study’s results also report a greater effect of embodied knowledge spillovers on export capacity and export intensity than internal R&D in emerging countries. The results are consistent to changes in the sample period and the estimation methods. The findings of the paper suggest that developing countries can speed up the process of export upgrading by relying on both domestic and foreign R&D efforts.

The findings would help policymakers to keep in mind the relative importance of internal R&D and embodied and disembodied knowledge spillovers for export performance before formulating a catch-up strategy and the outcome would encourage them to consider prior related knowledge in terms of internal R&D capability while acquiring external technology.

This study fills the gap in the existing literature by providing empirical validation of the innovation–export interplay and simultaneously assessing the effect of three diverse channels of technological innovation on the export performance of industrial enterprises. This paper enunciates important policy lessons for emerging countries' smooth transition to a knowledge-based economy.

This paper aims to examine the effect of R&D laboratories on the perceived performance of MNE subsidiaries during recession.

Employing resource-based view and knowledge-based theory, the authors investigate a unique sample of 171 technologically heterogenous foreign MNE subsidiaries located in Greece over the period of recession 2009–2016. The sample subsidiaries operate different types of R&D laboratories.

The authors find that MNE subsidiaries with advanced R&D laboratories such as locally integrated laboratories (LILs) and internationally interdependent laboratories (IILs) perform better in recession than subsidiaries with support laboratories (SLs) or subsidiaries without R&D laboratories. Overall, the authors find an asymmetric performance contribution of R&D laboratories at subsidiary level.

The study provides useful insights into the environmentally derived “knowledge-based - performance” context, so filling an important research gap, since little is known about the performance impact of the input-side of technological activity at MNE subsidiary level, especially as regards R&D facilities/infrastructure. Based on the findings the authors identify important managerial implications.

This research outlines the technological structure of the entire Japanese manufacturing and service industry using the patent information from research and development (R&D) activities to set R&D goals.

By analyzing the technological development capability of individual companies, the direction of the companies' R&D activities and current state of technological fusion between them can be understood. A group of companies participating in a particular product/service market must have the same technological development capabilities. As a result, the ratio of patent applications by a company to the total number of applications in a technical field will be similar across companies. This study uses the inter-company correlation coefficient of the ratio of patent applications by technical field as an index of technological development capability. A total of 167 major companies covering the major industries of Japan were analyzed. The analysis period was 15 years from 2004 to 2018, and the technical fields were rearranged to 42 fields with reference to the International Patent Classification (IPC)-Technology Concordance used by the World Intellectual Property Organization (WIPO). Considering the fluctuation in patent application opportunities, the number of patent applications was collected for at least three years for the analysis of patent applications by technical field, company and industry.

Examining the entire Japanese industry, the research found that chemicals, ceramics, non-ferrous metals and electrical/electronic equipment act as intermediaries between the respective groups and are linked to the transportation equipment, electrical/electronic equipment and information and communication services industries that are currently driving the Japanese economy. However, the technical connections between these groups are relatively loose. Over the last 15 years, the propagation structure of technical knowledge information has not changed. The progress of technological fusion remains within the scope of commerce and is conditioned by commerce.

Studies focusing on the technological development capability between companies and the technological structure of the Japanese manufacturing and service industries are almost non-existent since 2000 when Japan's economic growth slowed. The analytical methods presented in this research can be applied to individual companies to gain an understanding of technical positions of companies and can be useful for planning a technical environment, business or R&D strategy.

This study aims to investigate the association of FinTech innovation (FinTechINN) and firm performance (FP) by considering the role of knowledge assets (KA) as a causal mechanism underlying the FinTechINN – FP association.

In this study, the authors consider panel data of 1,049 Chinese A-listed firm and construct a structural model for corporate FinTech innovation, knowledge assets and firm performance while considering endogeneity issues in analyses over the period of 2014–2022. The modified value added intellectual capital (VAIC) and research and development (R&D) expenses are used as a proxy measure for knowledge assets, considering governance and corporate performance measures.

According to the findings of this study FinTech innovation (FinTechINN) has a positive significant effect on firm performance. Particularly; the findings disclose that FinTech innovations has a link with knowledge assets, FinTech innovations indirectly affects firm performance, and the association between FinTech innovation and firm performance is partially mediated by knowledge assets (MVAIC and R&D expenses).

Rooted in the dynamic capability and resource-based view, this study pioneers an empirical exploration of the association of FinTech innovation with firm performance. Moreover, it introduces the novel dimension of knowledge assets (on firm-level), acting as a mediating factor with in this relationship.

Going beyond the traditional approach of formal and informal institutions as antecedents of entrepreneurship (directly) and development (indirectly), this paper seeks to explore knowledge institutions as a necessary input for entrepreneurship and the development of societies.

Institutional economics lenses are utilized to observe other factors (e.g. the number of R&D staff and researchers from the public sector) that involve laws and socialization processes, which at the same time create knowledge useful for entrepreneurs and society. These ideas are tested through a sample of 281 observations from 17 autonomous communities and two autonomous cities in Spain. The information coming from the Global Entrepreneurship Monitor (GEM), Ministry of Economics, Industry, and Competitiveness, and INE (Instituto Nacional de Estadística), was analyzed through 3SLS, which is useful for a simultaneous equation strategy.

Knowledge institutions such as the number of R&D staff and researchers from the public sector are found positively associated with entrepreneurship, which is a factor directly and positively linked to economic development across Spanish regions.

The findings help the operationalization of other institutions considered in institutional economics theory and its application to entrepreneurship research. Moreover, the results bring new insights into the knowledge spillover theory of entrepreneurship in the public sector, in which the institutional analysis is implicit.

Small and medium-sized enterprises (SMEs) are currently showing an increasingly open innovation (OI) approach. Public policies supporting the adoption of OI by SMEs are becoming a priority for policymakers. Therefore, the aim of this article is to contribute to the literature by mapping scholars' policy recommendations for implementing OI among SMEs.

The authors conducted a systematic review of the literature (SRL) on the topic to achieve this purpose. A total of 99 academic articles were selected from the Web of Science and Scopus databases to suggest the main scholars' policy recommendations to implement OI among SMEs.

Results indicated that scholars' policy recommendations for OI adoption in SMEs can be organized into: research and development (R&D), networking, collaboration, knowledge and intellectual property rights (IPR), ecosystem, managerial capabilities, funding and incentives and sustainability policies.

Only relevant articles about this topic have been included due to the reliance on the interpretations of the authors. The analysis of the literature revealed that the authors did not always distinguish policies dedicated to SMEs and those dedicated to large companies. Moreover, policies are not matched according to each OI dimensions (e.g. inbound, outbound and coupled OI).

The article uses a systematic literature review method that combines qualitative and quantitative analyses. This method contributes to theoretical development of OI policies dedicated, in particular to SMEs. This paper also provides policymakers and researchers with insights on the scope of OI policies that could support economic growth.

This study contributes to the literature on eco-innovation (EI) and the circular economy (CE) by providing insights into the factors of external knowledge absorption that facilitate the adoption of a subset of EIss called proactive-EIs. Proactive-EIs involve collaborations among multiple stakeholders, the use of technical knowledge and a greater level of investment than other innovations. In this study, the environmental actions taken by small and medium-sized enterprises (SMEs) in resource-intensive sectors in the European Union (EU) were observed, and elements related to the national context were compared.

The national innovation system (NIS) perspective was adopted using a multilevel framework to assess the determinants of proactive-EIs among SMEs in the EU. The framework involves three levels: micro- (environmental awareness), meso- (external collaboration and intra-industry agglomeration) and macro- (economic complexity, trade openness and government research and development [R&D]). The survey data of 6,188 SMEs in resource-intensive sectors were analysed using a binary logistic regression.

The results showed that public awareness, economic complexity and public sector R&D positively influenced SMEs' adoption of proactive-EIs, whereas external collaboration and sectoral agglomeration negatively influenced adoption.

Among the existing empirical studies on EI, areas related to external knowledge-based innovations and systematic assessments of heterogeneity among EU member states remain underexplored. This study contributed to the literature by assessing the conditions surrounding external knowledge absorption. The findings contribute to the green entrepreneurship literature in the context of developed economies and offer insights for managers and policymakers seeking to promote EIs.

Expansion of the consumption of renewable energy is a significant issue for reducing global warming, to cope with climate change and achieve sustainable development. This study aims to examine how research and development expenditure (R&D) affects renewable energy development in developed G-7 countries over the period from 2000 to 2019. Variables of trade liberalization and CO₂ emissions are considered control variables.

This study has adopted a panel quantile regression. The impact of the variables on renewable development has been examined in quantiles of 0.1, 0.25, 0.5, 0.75 and 0.9. Also, a robust examination is accomplished by applying generalized quantile regression (GQR).

The empirical findings reveal a positive and significant relationship between R&D and the consumption of renewable energy in 0.1, 0.25, 0.5 and 0.75 quantiles. Also, the findings describe that the expansion of trade liberalization and CO₂ emissions can significantly increase the development of renewable energy in G-7 countries. Furthermore, GQR verifies the main outcomes.

These results have very momentous policy consequences for the governments of G-7 countries. Therefore, investment and support for the R&D section to promote the development of renewable energy are recommended.

This paper, in comparison to other research, used panel quantile regression to investigate the impact of factors affecting renewable energy consumption. Also, to the best of the authors’ knowledge, no study has perused the effect of R&D along with trade liberalization and carbon emissions on renewable energy consumption in G-7 countries. Also, in this paper, as a robustness check for panel quantile regression, the GQR has been used.