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The purpose of this paper is to systematically review the evolution, characteristics, motivations, entry patterns, organizational structure and effectiveness of the internationalization of Chinese research institutions in the past 40 years of reform and opening-up.

This paper describes the evolution and practice of Chinese research institutions “going out” by constructing a theoretical framework diagram and uses official statistics and existing research to explain the authors’ points.

The research results show that the internationalization of research institutions has undergone four phases: sprout period, starting period, adjustment period and accelerating period. It shows a rapid growth of investment scale, diversification of investment entities, rich and varied forms, and transition to major countries along the “One Belt and One Road.” Expanding the international market, tracking and acquiring technological frontiers, nurturing domestic R&D talents, and evading the risks of political, economic, cultural and scientific differences between home and host countries are the main motivations for Chinese research institutions to “go global.” Multinational corporations have entered the host country with modes such as M&A, greenfield investment and joint R&D alliances in their own strengths and also presented a variety of organizational structures such as integrated R&D networks.

This paper systematically summarizes the internationalized experience model of research institutions with Chinese characteristics since the reform and opening-up. From the perspective of internationalization model transformation, policy integration and cooperation among emerging economies, it presents the opportunities and challenges faced by the research institutions in the process of internationalization and provides a theoretical basis for improving the internationalization ability of research institutions.

The high uncertainty of technological innovation in megaprojects brings great challenges to the R&D institution and also acts as a trigger for moral hazard. The incentive and supervision are effective means to improve the performance of innovation. The purpose of this paper is to propose appropriate incentive and supervision mechanisms to reduce information asymmetry and improve the efficiency of incentives. Suggestions on technological innovation are put forward to megaprojects management.

According to the principal-agent theory, the research develops incentive models under three states, i.e. information symmetry, information asymmetry and information asymmetry based on supervision mechanism. The Bayesian theory is employed to prove the effectiveness of the novel supervision method based on risk assessment.

The results indicate that under the information asymmetry, the incentive intensity is positively correlated with the social benefits coefficient, and negatively correlated with the patent benefits coefficient. The R&D effort and the owner's incentive intensity decline with the increase of information asymmetry. The supervision of risks can effectively reduce the degree of information asymmetry, and the higher the uncertainty of innovations, the more significant the effect of supervision is. As the supervision intensity increases, the incentive intensity, the R&D effort and the innovation output will increase. In addition, the R&D institutions with high innovation capability, low unit cost of R&D and low risk-aversion are more willing to make efforts to innovate.

This study fills the research gap on incentive and supervision of technological innovation in megaprojects. The externality of innovation benefits is considered in the model. The traditional incentive model is extended through the introduction of supervision. Furthermore, a novel supervision method based on risk assessment is proposed. The results validate the importance of risk management in technological innovation and provide a new insight for project management.

The purpose of this study is to make a prediction of the R&D output of China from the perspective of R&D institutions and put forward a set of policy recommendations for further development of the science and technology in China.

In this paper, an improved discrete grey multivariable model is proposed, which takes both the interaction effects and the accumulative effects into account. As the current research on China's R&D activities is generally based on the perspective of universities or industrial enterprises above designated size while few studies put their focus on R&D institutions, this paper applies the proposed model to carry out an empirical analysis based on the data of China's R&D institutions from 2009 to 2019. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of the R&D output in China's R&D institutions is conducted into a future horizon from 2020 to 2023 by using the model.

The results indicate that China's R&D institutions have a good development trend and broad prospects, which is closely related to China's long-term investment in science and technology. Additionally, the R&D inputs of China possess obvious interaction effects and accumulative effects.

The paper considers the interaction effects and the accumulative effects of R&D inputs simultaneously and proposes an improved discrete grey multivariable model, which fills the gap in previous studies.

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.

It is widely accepted that enterprises obtaining academic discoveries through R&D collaboration improve their innovation performance. However, it is not necessarily true in emerging economies, such as China and post-socialist countries in Europe. The purpose of this paper is to fill the gap by investigating how R&D collaboration between industry and academia (i.e. universities and research institutes) affects the industrial innovation performance; and whether and how intermediaries moderate their relationships.

This paper constructs the research model according to the knowledge production function, and the pooled ordinary least square regression is used to verify our hypotheses.

Evidence from a sample of Chinese industrial enterprises in thirty provinces spanning from 2009 to 2014 suggests that R&D collaboration with research institutes (CWR) is positively related to innovation output, while R&D collaboration with universities (CWU) exerts negative effect on innovation output measured by sales revenue of new product (NPSR). The significant moderating role of technology transfer institutions is confirmed in the negative relationship between CWU and NPSR.

This paper empirically examines the moderating role of intermediary organisations in academia–industry cooperation and industrial innovation, and has practical implications for the government to formulate policies to improve the quality and effectiveness of cooperation between academic and industrial sectors. These results vary in inland and coastal areas, which suggest the policy makers to formulate policies according to local conditions not only in China but also in other countries, like European countries.

The outcomes of public research institutions (PRIs), also known as research and development (R&D) institutions, in developing countries, including Indonesia, are still dubious. This study aims to measure the efficiency of R&D institutions using the case of the Indonesian Institute of Sciences, with and without an assessment of the role of scientific publication.

A panel data envelopment analysis (DEA) model is used to estimate the research efficiency of Indonesian R&D institutions during the period 2016–2019 based on the relationship between intellectual property (IP), research budgets and number of active researchers. The Tobit model is subsequently applied to analyze the factors that affect efficiency.

The DEA analysis shows an average efficiency value of 0.361, implying that 42% of the decision-making units (DMUs) have above-average efficiency scores. When scientific publication is added as an output variable, the efficiency increases to an average of 0.545, resulting in 53% of the DMUs with above-average efficiency.

The main implication is that scientific publications can increase the output of R&D institutions in Indonesia. This study recommends strengthening the research group establishment led by research professors along with setting acceptable high output targets. Researcher competence must be improved together with support for research collaboration among the different fields of science. Scientific publications should be considered part of IP measurement along with the type of mandate of each PRI.

This study offers a method of evaluation of research efficiency that can be applied to institutions outside Indonesia, thus furthering the dialogue on science and technology policy management.

This paper contributes to the literature by using a new and comprehensive method to measure research output – that of IP measurement, including new scientific publication. The implications provide action points for the governments to support R&D institutions and for research practitioners to augment research output.

The purpose of this paper is to highlight the use of big data through patentometric insights for R&D decision-making.

This study assesses the inventive activity through ‘big data’ patents, registered by inventors worldwide, using WIPO Patentscope database. The objective is to use the insights from patentometrics for R&D decision-making. The data from WIPO PatentScope (https://patentscope.wipo.int/search/en/search.jsf) was searched for current patent scenario in area of ‘big data’. The data was further organized and cleaned using the Google ‘OpenRefine’. Data was pre-processed to remove all null values. Cleaned data was analyzed using programming language ‘R’, MS Excel (charts and Pivot tables) and free data visualization tool called ‘Tableau Public’, to get insights for R&D decision-making.

The key insights included trends (patents with years of publication), top technologies trending the current space, top organizations leading in these technologies and the top inventors who are publishing patents in these technologies through leading organizations were drawn. Details in Section 5 in the paper.

Global patent data is multi-lingual and spreads across a set of multiple databases. Domain experts may be required to assess, identify and extract the relevant information for analysis and visualization of multi-lingual distributed data sets. Government organizations generally have multi-dimensional goals that may be more toward societal benefits. On the other hand, the commercial companies are more focused on profit. Therefore, the performance management process has to be really effective because it is critical for getting value in the government sector.

Insights from patent analytics serve as the important input to R&D managers as well as policymakers to assess the global needs to plan the national orientation according to the global market. This will help further for R&D projects prioritization, planning, budget allocations, human capital planning and other gamut of R&D management and decision-making.

Facilitation for R&D institutions (government as well as private) to formulate the research strategy for the domains or research areas to delve into. R&D decisions will be completely data-driven making them more accurate, reliable, valid and informed. These insights are very relevant for policymakers as well to facilitate the need assessment to determine the National priorities, make improvements in meeting societal country-level challenges during the resource allocation at top and subsequently at all other levels.

Data analytics of global patents in “big data” till 2019 to get insights to facilitate R&D decision-making.

The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

Although many studies explored the drivers of innovative development and the innovation performance of different countries, very few studies looked at the association of the country’s GII score with the qualitative indicators of innovation performance. The purpose of this paper is to contribute such an investigation by looking at the Republic of Kazakhstan (79th in 2019 GII ranking).

This study looks at eight dynamic variables, among which one dependent (the GII score) and seven independent (R&D spending, innovation grants, the total cost of innovative goods and services, the percentage of innovative organizations, the share of innovative goods and services in gross domestic product (GDP) and the number of R&D staff and R&D institutions) variables associated with innovation performance. Changes in variables were tracked over the period from 2010 to 2018..

The study found that the Kazakhstan’s GII score was reliant on variables, such as the percentage of innovative organizations, the value of innovative goods and services as a share of GDP, R&D spending and the cost of innovative goods and services. At the same time, the number of R&D institutions, innovation grants and number of R&D staff had no substantial impact on the GII score of Kazakhstan.

Using the proposed approach, this study proved that factors, which have no direct association with the country’s level of innovative development expressed in GII, could have a significant synergistic impact on this indicator.