University-industry interaction and product innovation performance of Guangdong manufacturing firms: the roles of regional proximity and research quality of universities

Exploring influential factors in Chinese engineering professors' industrial experience: A big data and CV analysis approach

There is a long-standing criticism in academic circles about the inadequacy of engineering industrial experience for professors in Chinese research universities, but there is a lack of analysis of the manifestations and root causes of the problem. The research aims to contribute meaningful insights into the factors influencing the industrial experience of engineering professors in Chinese research universities, facilitating informed policymaking and fostering collaborative efforts between academia and industry. This paper examines Chinese engineering professors' industrial experience using big data mining, computing, modeling, and image rendering. The data was collected from the publicly available CVs from the websites of various Chinese universities which contains data on colleges and universities across China and analyzed engineering professors' professional trajectories and industrial experience using the method of resume analysis. A nonlinear logistic regression model is fitted to determine the impact of multiple independent variables on the probability of engineering industrial experience among professors, with logit transformation and maximum likelihood estimation. The results show that a low percentage of engineering university professors in Chinese research universities have had industry employment after obtaining their Ph.D. degrees. The regression model indicates that gender, level of inbreeding, overseas study experience, university level, and birth age significantly affect the engineering experience of professors. This study proposes several policy recommendations based on comparative analysis and its research. The study's findings are critical for policymakers to create policies that promote industrial partnerships as a fundamental aspect of the professional development of engineering faculty and for further research in the field.

Educational cooperation in the perspective of tripartite evolutionary game among government, enterprises and universities

Government-enterprise-university synergy (GEUS) is an effective way to mobilize government, enterprises, and universities to collaborate on education, but these three parties involved in GEUS may, out of bounded rationality, choose to collaborate in ways that benefit themselves and harm others. To guide the three parties to better cooperation, this study creates an evolutionary game model among the three parties and evaluates the applicability and validity of the model by selecting the educational cooperation data in Beijing. It is shown that participation in education cooperation is the best course of action for all three parties. The intensity of willingness to participate in the GEUS is on the order of high to low for universities, enterprises, and the government. If the three parties wish to accomplish education collaboration sooner, they can increase default payments, boost government revenues, raise corporate participation in distribution, and reduce government and government spending. These results highlight the inherent regularities of GEUS and provide concrete implementation strategies to improve the efficiency of education cooperation.

Impact of local living environment on innovation efficiency of high-tech industries in China: a spatial analysis

Environmental problems not only relate to residents' happiness but also challenge the innovation development of industries. This study first measures the innovation efficiency of China's high-tech industries using the super-efficiency data envelopment analysis model and portrays its spatial characteristics through the Moran's I index and the local indicators of spatial association map. Second, we use the entropy weight method to construct the local living environment index from both natural and social environments. Finally, we utilize spatial econometric models to analyze the impact of local living environment on high-tech industries' innovation efficiency. The results reveal that, first, the spatial variation of innovation efficiency in China's high-tech industries is significant, with efficiency being higher in the east than the Midwest, and higher in the south than the north. Second, innovation efficiency has a positive adjacent and geographical spatial autocorrelation, and low-low agglomeration and low-high agglomeration dominate the types of spatial correlation. Finally, the contribution of the local living environment to the innovation efficiency of high-tech industries is positive and significant. This contribution has an obvious spatial spillover effect and regional heterogeneity. This study can help regional governments to improve local living environments and promote industrial innovation and development.

The geographic proximity effect on domestic cross-sector vis-à-vis intra-sector research collaborations

Geographic proximity is acknowledged to be a key factor in research collaborations. Specifically, it can work as a possible substitute for institutional proximity. The present study investigates the relevance of the “proximity” effect for different types of national research collaborations. We apply a bibliometric approach based on the Italian 2010–2017 scientific production indexed in the Web of Science. On such dataset, we apply statistical tools for analyzing if and to what extent geographical distance between co-authors in the byline of a publication varies across collaboration types, scientific disciplines, and along time. Results can inform policies aimed at effectively stimulating cross-sector collaborations, and also bear direct practical implications for research performance assessments.

Barriers to university–industry collaboration in an emerging market: Firm-level evidence from Turkey

University-industry collaborations (UICs) are one of the main sources of external knowledge and technologies for industrial firms, particularly in the context of emerging markets (EMs) and firm development. It is thus highly relevant to identify potential barriers internal to the firm as well as in the regional innovation system that might prevent firms from using UICs for innovation, in particular in an EM context. In order to address this issue, we conduct a firm-level study of the R&D-related segment of the manufacturing industry in Istanbul. Logistic regression analysis is used to test the effect of potential barriers on using UICs for innovative activities. With this approach, we are able to identify barriers that prevent innovation-related UICs and thus form a bottleneck to collaborations in the first place. Our findings show that lack of information about UIC opportunities as well as lack of financial support for UICs are the most relevant barriers that inhibit firms’ usage of UICs for innovation. This firm-level evidence points out the importance of university technology transfer offices in regional innovation systems and for fruitful UICs. We further find that administrative barriers have no significant effect, while barriers related to trust and skill matching with scientific partners even have a reverse effect to what we would have expected from the literature. This finding might point towards an effect of perceived versus deterring barriers that has been observed in innovation studies before and might be relevant for studying UICs as well.

Innovation in emerging economies: How do university-industry linkages and public procurement matter for small businesses?

Small businesses face major challenges to becoming more innovative. These challenges are particularly prevalent in emerging economies where high uncertainties are a barrier to innovation. We know from previous studies that linkages to universities, on the one hand, and public procurement, on the other, support large and innovative firms in their efforts to become more innovative. However, we do not know whether these positive effects also hold true for small businesses. In this paper, we focus on how policy strategies reducing information, market and financial uncertainties shape small businesses’ innovation in China. Based on a sample of 926 small businesses derived from the World Bank Enterprises Survey in China (2012), we find that university-industry linkages enhance innovation, though only when it comes to minor forms of innovation. In line with the resource-based view of the firm, this effect is stronger for small businesses with higher capabilities. Moreover, we show that bidding for or delivering contracts to public sector clients has a positive effect on innovation, and in particular of major forms of innovation. In the bidding selection process, private firms and firms with higher capabilities are selected. Our findings show that both policy strategies have enhanced innovation, though with different effects on the degree of novelty. We attribute this finding to the different degrees of uncertainties they address.

Improving the Effectiveness of Multi-Agent Cooperation for Green Manufacturing in China: A Theoretical Framework to Measure the Performance of Green Technology Innovation

A green manufacturing system is an important tool to realize green transformation of the manufacturing industry. The systematicness of green technology innovation as the key foundation of green manufacturing supports the entire huge green manufacturing system. In order to improve the effectiveness of multi-agent cooperation, it is necessary to analyze a series of green technology innovation achievements of manufacturing enterprises under multi-agent cooperation. First of all, inter-indicator correlation analysis and exploratory factor analysis were used to construct the evaluation index system of the green technology innovation performance of manufacturing enterprises under multi-agent cooperation. Then, a secondary combined evaluation model was constructed based on the evaluation conclusions. Finally, a theoretical framework was constructed to measure the performance of the green technology innovation of manufacturing enterprises under multi-agent cooperation. The results of this study are as follows: The evaluation index system of the green technology innovation performance of manufacturing enterprises under multi-agent cooperation is composed of the technology output, economic output, and social effect of green technology innovation. The key factors that influence the green technology innovation performance of manufacturing enterprises under multi-agent cooperation are the proportion of green technology transformation in traditional technology, the number of papers published jointly by multi-agent cooperation, the user acceptance of green technology products, and the degree of improvement of public environmental preference and consciousness. A fusion of technology of subjective and objective methods is an effective evaluation technique and can be applied to evaluate the performance of green technology innovation. The secondary combined evaluation combines the evaluation conclusions obtained by each single evaluation method in a certain form.