University-industry Collaborative Research Research Articles

The triple helix collaboration: lessons from a Chinese provincial innovation ecosystem

PurposeInnovation is vital for sustainable regional development. Government is generally not a source of innovation per se, but the authors shows that by establishing and funding an industrial innovation ecosystem, it can create fertile conditions for research institutions to introduce innovative technologies into industry and for industry to introduce its gaps and needs to motivate and direct academic research. This paper aims to present a case study of the Jiangsu Industrial Technology Research Institute (JITRI) as a “triple helix” collaboration model engaging Industry-University-Research (IUR) institutions. The authors aim to provide a fresh perspective on constructing innovation ecosystems, promoting value creation and advancing the adoption of industrial technology.Design/methodology/approachTaking JITRI as a case study, this research examines its innovative approaches to interfacing with IUR organizations within China’s Yangtze Delta region. The authors propose a multilayered framework where JITRI serves as the core, facilitating the formation of a robust innovation ecosystem with diverse IUR entities.FindingsThis framework comprises three layers: core, platform and support. JITRI acts as the central nexus, binding these elements together and fostering a cohesive innovation ecosystem. By analyzing the operational mechanisms of the Jiangsu Provincial innovation system, the authors gain valuable insights into how JITRI bridges the gap between academia and industry. This facilitates the efficient commercialization of technologies. The success of the JITRI approach presents a compelling model for emulation by other provincial innovation ecosystems seeking to enhance their innovation capabilities.Research limitations/implicationsThis study enriches the ongoing conversation on innovation ecosystems, proposing novel avenues for further research. However, the field requires continuous monitoring through longitudinal studies. Given the long-term nature of such initiatives, the full impact of this approach might only be realized over several decades.Practical implicationsJITRI aims to create a robust and innovative provincial innovation ecosystem through its focus on IUR collaboration. This unique approach offers valuable insights and ideas for fostering the development of thriving innovation ecosystems in other provinces. These insights encompass new perspectives on innovation ecosystem development, value creation and industrial advancement.Originality/valueThis study pioneers the use of the JITRI system as a case study, offering valuable insights with potential application to innovation ecosystems across China and globally.

Research on the mechanism of government subsidy on enterprise innovation based on industry-university-research collaboration

This paper provides a theoretical and empirical examination of the relationship between government subsidies, enterprise innovation, and Industry-University-Research (IUR) collaboration. The study begins with a theoretical analysis of the impact of government subsidies on IUR collaboration and enterprise innovation, employing a dynamic game model. Secondly, the research sample comprises A-share listed enterprises in China, and the theoretical findings are validated through empirical methods. Moreover, this study investigates the mediating influence of IUR collaboration and the heterogenous impacts of government subsidies. Research has shown that government subsidies can facilitate enterprises in establishing IUR collaboration, enhancing the extent of IUR collaboration, and effectively stimulating enterprises to participate in innovation. Government subsidies have a more pronounced impact on non-state-owned enterprises, high-tech enterprises, and enterprises in eastern regions in terms of incentivization. By enhancing IUR upgrade, government subsidies can offset the shortcomings in limited Research and Experimental Development (R&D) funding and inadequate technical reserves of non-high-technology enterprises. This could result in an improvement in the quality of innovation and the performance of the subsidized enterprises.

Effect of digital transformation on labor income share in manufacturing enterprises: insights from technological innovation and industry–university–research collaborations

PurposeThis study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.Design/methodology/approachThe relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.FindingsThe results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.Originality/valueThis study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Early-career academic engagement in university–industry collaborative PhDs: Research orientation and project performance

Academic engagement with industry is a well-researched topic. However, research has focused on scholars in traditional university departments, overlooking more hybrid research contexts such as the growing trend of PhD students and their supervisors engaging in collaborative research projects with industry during their training. To address this gap, this study explores the early-career academic engagement of PhD students in university-industry collaborative research projects. It particularly focuses on research orientation and its association with scientific and inventive yield. Drawing on Stokes' (1997) framework, the study investigates the extent to which the research orientation (basic research, applied research, and user-inspired fundamental research) is associated with scientific and inventive yield. The study is based on a review of 631 collaborative PhD projects with industry completed in France in 2018 through a national programme (CIFRE). It finds that the three research orientations are associated with different levels of scientific and inventive yields. While basic research is the most common orientation across the sample, the rarest, user-inspired fundamental research, is associated with the highest scientific and inventive yield. Moreover, the study highlights the critical role of PhD students' taste for science or industry (and the potential change in this taste during the project) in the association between research orientation and projects' yields. Overall, this study sheds light on an understudied area of academic engagement and highlights the significance of considering research orientation and students' taste for science or industry when developing university-industry collaborations. This has implications for PhD supervisors, PhD students, their industrial partners, and policymakers.

Energy-aware enterprise-wide optimization and clean energy in the industrial gas industry

Industrial gases, such as nitrogen, oxygen, and hydrogen, play an essential role in a wide range of sectors, including chemicals, metals, energy, and healthcare. Due to the high energy intensity of the production processes, the industrial gas industry is constantly exploring opportunities to reduce its energy cost. In addition, with the urgent need for decarbonization, there has been a strong push for clean technologies, especially in the hydrogen business. In this work, we review the existing literature on energy-aware optimization for the industrial gas industry, focusing on decision-making at the enterprise level in atmospheric gases and hydrogen supply chains. Case studies drawn from previous industry-university research collaborations are presented to highlight the potential benefits of demand response. Moreover, we provide a perspective on opportunities and challenges in clean energy for this industry, emphasizing the need to transition to more sustainable and agile industrial gas supply chains.

The Spatial and Temporal Characteristics of Industry–University Research Collaboration Efficiency in Chinese Mainland Universities

The aim of this study was to investigate the spatio-temporal characteristics of the industry–university research (IUR) collaboration efficiency of Chinese mainland colleges and universities, from 2008 to 2018. A comparative analysis method was used to analyze the data from the Statistical Yearbook of China’s Education Funds, the Compilation of Science and Technology Statistics of Colleges and Universities, and the China Statistical Yearbook. The principal components were extracted from relevant indicators of IUR capability in colleges and universities, with a principal component analysis (PCA) method. The principal component scores and comprehensive scores of 31 provinces in mainland China were calculated. The results showed that the efficiency of IUR collaboration in Chinese colleges and universities has increased rapidly within the 11 years studied. The efficiency in the eastern region has grown faster than that in the western region, and the gap between the southern region and the northern region has also continued to widen. The results also showed that the development of IUR collaboration efficiency of colleges and universities in mainland China is unbalanced. Scientific and technological funds, and scientific and technological manpower, were excessively concentrated in the southeast. Therefore, there is large room for improvement in the overall development of IUR collaboration in Chinese colleges and universities.

Impact of university-industry collaborative research with different dimensions on university patent commercialisation

ABSTRACT The purpose of the paper is to investigate the impact of university-industry collaborative research with different dimensions on university patent commercialisation and offer suggestions to promote patent commercialisation. University-industry collaborative research is measured by the university-industry collaborative patent application. Collaboration breadth and depth are defined. The theories about the impacts of the collaboration breadth and depth on university patent commercialisation are analysed and the hypotheses are proposed accordingly. We explore the impacts by negative binomial regression with the panel data set constructed from the patent data of the ‘double first-class’ universities in China during 2015–2017. Results show that there is an inverted U-shaped relationship between the collaboration breadth and the university patent commercialisation. Nevertheless, the collaboration depth has no impact on university patent commercialisation. And surprisingly, the interaction of breadth and depth shows a negative impact on it. Suggestions are offered that universities should avoid collaborating with industry inordinately and pay attention to balance breadth and depth of the collaboration with industry, improving the quality of the collaboration and promoting patent commercialisation.

Fifty years of University-industry collaboration: a global bibliometrics overview

Abstract This article presents a bibliometric overview of publications on university-industry collaboration (UIC) from the last fifty years (1969–2018). Among the results that were obtained, three main ones come to light. First, this article presents a complete panorama of UIC research from the past fifty years. Secondly, we were able to note that the UIC research trajectory can be divided into four generations which are represented by different phases: the discovery phase (1969–1979), the development phase (1980–1999), the expansion phase (2000–2010) and the consolidation phase (2011–2018). Furthermore, the connections and evolution pertaining to these four generations were identified. The third result was the identification of eight research trends within the UIC context: ‘UIC topics’ (especially: motivation, channels, barriers and results), ‘engineering education’, ‘societies and institutions’, ‘knowledge transfer’, ‘innovation’, ‘entrepreneurial university’, ‘sustainability’ and ‘developing countries’.

Developing a predictive model of construction industry-university research collaboration

PurposeThis paper aims to identify and examine the factors that influence construction industry-university (IU) collaboration and develop the likelihood model of a potential industry partner within the construction industry to collaborate with universities.Design/methodology/approachMix method data collection including questionnaire survey and focus groups were used for data collection. The collected data were analysed using descriptive and inferential statistical methods to identify and examine factors. These findings were then used to develop the likelihood predictive model of IU collaboration. A well-known artificial neural network (ANN) model, was trained and cross-validated to develop the predictive model.FindingsThe study identified company size (number of employees and approximate annual turnover), the length of experience in the construction industry, previous IU collaboration, the importance of innovation and motivation of innovation for short term showed statistically significant influence on the likelihood of collaboration. The study also revealed there was an increase in interest amongst companies to engage the university in collaborative research. The ANN model successfully predicted the likelihood of a potential construction partner to collaborate with universities at the accuracy of 85.5%, which was considered as a reasonably good model.Originality/valueThe study investigated the nature of collaboration and the factors that can have an impact on the potential IU collaborations and based on that, introduced the implementation of machine learning approach to examine the likelihood of IU collaboration. While the developed model was derived from analysing data set from Western Australian construction industry, the methodology proposed here can be used as the basis of predictive developing models for construction industry elsewhere to help universities in assessing the likelihood for collaborating and partnering with the targeted construction companies.

An Evolutionary Game Analysis of Synergetic Innovation behaviour in regional innovation networks

Regional innovation network has become an important way to improve national independent innovation ability and promote regional and social development. This paper studies the evolution of the synergetic innovation system of industry-university-Research Collaboration under the government behaviour, constructs an asymmetric evolutionary game model between the government and industry-university Research collaboration, and analyses the influence of parameter changes on synergetic innovation decisionmaking behaviour. The results showed that the government’s regulatory costs, subsidies from the government, the production, will take the profit resulting from the synergetic innovation behavior, coordination costs and government punishment, and one party to the cooperative behaviour decision to bring the other side of the loss factors such as size, to participate in Innovation decision-making behaviour of the subject of dynamic evolution path and the results have important influence to the use of MATLAB software evolution paths of related factors under different conditions and the trend of simulation test and verify.

Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis

In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.

Sustaining Corporate Innovation Through University–Industry Collaborative Research: Evidence from the Jiangsu University of China

Based on the perspective of collaborative innovation, this study employed the social network analysis method to empirically analyze the evolution of knowledge diffusion networks between research institutions and industry in China. The study is centered on the Jiangsu University of China, an institution noted for its innovative activities for a long time. The results show that since 2008 the extent and depth of explicit knowledge diffusion of Jiangsu University have increased significantly. The regional knowledge diffusion network presents a core-edge spatial pattern, but the core-edge structure is weakening; indicating that while the number of partners in the collaboration effort is increasing, the strength of ties between groups is weakening. The tacit knowledge diffusion network presents a small-world effect; small groups of partners clustered around one or a few influential inventors have gradually emerged. It is also evident that enterprises that are located close to the university benefit more from such collaboration than those located in farther provinces. We recommend that universities in such collaborations should reach out to diverse industry players using their specialized expertise.

Analysis of the Determinants of Innovation in Developing Countries: A Vector Auto-regression Approach

Several factors account for innovation output in developed economies as documented in extant literature. The objective of this study is to make a case for a multi-faceted approach to innovation in developing countries. To analyze various factors affecting innovation, a sample of 43 developing countries is taken and the period of study is between 2009 and 2018. A vector auto-regression model is used in a panel data setting to test the effect of various variables on innovation at country level, a granger causality test is also conducted to determine the causal relationship among these factors that together can spur innovation activities. Findings suggest a strong influence of R&D and government procurement of technology on the advancement of innovation. R&D is largely affected by the quality of research institutions and university-industry collaborative research, thus showing the strength of the effect of these variables on innovation. Scientists and engineers with requisite expertise greatly improve a country’s innovation efforts, however, developing countries do not benefit fully from the economic value of these experts. These results show that an integration of all these factors is a good approach to enhancing innovation in developing economies.

Industry–university–research alliance portfolio size and firm performance: the contingent role of political connections

Nowadays firms often collaborate with multiple universities and research institutes simultaneously, thereby forming portfolios of industry–university–research (IUR) alliances. To study this, we go beyond the dyadic level that has been the dominant theme in prior research on IUR alliances to examine IUR alliances at the alliance portfolio level. Using the dataset of Chinese manufacturing firms, we investigate the influence of IUR alliance portfolio size on focal firms’ innovation and financial performance. Our findings indicate that IUR alliance portfolio size has an inverted U-shaped effect on firm innovation performance, while political connections weaken the negative effect of IUR alliance portfolio size, thus making the inverted U-shaped relationship become flattened. In addition, we find that IUR alliance portfolio size exerts a negative effect on firm financial performance, while political connections weaken the negative effect of IUR alliance portfolio size. This study contributes to previous research on the relationships between IUR alliances and firm performance, extends prior research on alliance portfolios, and adds to the ongoing debate on the value of political connections to firms. Besides, this study suggests firm managers should consider IUR alliance portfolio size and actively seek government support, that academic institute managers should strengthen IUR collaborations and reduce conflicts and costs in cooperation, and that policymakers should offer more support for firms and shape a better market environment.

Exploring the effect of political borders on university-industry collaborative research performance: Evidence from China’s Guangdong province

This paper investigates how political borders affect university-industry (U-I) collaborative research performance. It is hypothesised that cross- and within-regional political borders have an inhibitory effect on cross-border U-I collaborative research performance. The empirical research considers political borders and geographic distance to disentangle the border effect from the spatial distance effect based on a novel dataset comprising 681 U-I collaborative research projects in China’s Guangdong province. The results confirm the hypothesis that within-regional political border has a negative impact on U-I collaborative research performance, while an inhibitory effect of cross-regional political border was not observed. The results are robust for alternative dependent variables and different regression methods. Theoretical contributions and practical implications for cross-border U-I collaborative research management and policies are discussed.

A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory

Abstract Purpose This study aims at identifying potential industry-university-research collaboration (IURC) partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory. Design/methodology/approach The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors. Findings Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations. Research limitations In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process. Practical implications Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements. Originality/value Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.

The design speculation and action research assemblage: ‘transit for all’ and the transformation of Melbourne's passenger rail system

ABSTRACTThe planning problems of neo-liberal cities require theories and methods that are adequate to the task of engaging as well as understanding their fragmented opacity. This paper reports on research that combines well-established processes of action research and design studio teaching with more recent interest in assemblage theory. ‘Transit for All’ was a recent industry–university research collaboration investigating railway station design in Melbourne. The project illustrates ‘the design speculation action research assemblage’ as a tool for creative as well as critical thinking. Framed by the need to find ways to achieve significant modal shift toward active transport in a car-dependent city, the project brought 15 industry partners from state and local government, transport companies and the design sector together around a series of architectural and urban design studios for real sites. The outcomes of the project allow a deeper understanding of how transformative the focus on the relationship between network planning, urban renewal, interchange design and the removal of level crossings could be in bringing Melbourne's 19th century rail system into the 21st century.

Internationalization of Higher Education: Industry-University Research Collaborations for Energy Sector Development

Higher education is pursuit of excellence within a specified field of study. With internationalization of higher education, a broad based arena is provided to scholars to exchange their ideas and research interests. This process can be built for any subject matter from physics, computers, money and markets to arts, culture and theological studies. Energy and Renewable Energy are some of the topics that have emerged as new fields of focus. Energy sector educational development engulfs some of resources in the fields of collaborative studies for industry-university link. To understand how this common interest links local and over-seas universities; this study is conducted, where examples from universities with well-established energy and renewable energy programs is analyzed. Starting from examples from USA, UK and Asian Universities, a discussion is presented where outcome of this collaboration is tangible in the form of successful degree programs preparing students for Energy Sector Development.

The Research on the Long-Effect Mechanism of Industry-University-Research Cooperation Based on Swarm

The paper regards the speculative behavior in Industry-University-Research collaboration (IURC) as a process of principal-agent game with asymmetric information; establishes a dynamic model of the IURC with asymmetric information. And on this base we conduct simulation research combined with the reputation mechanism to reveal the forming process of mixed strategy between the two parities.

Vocationalization in the Research Intensive University

This paper analyses the trends in program creation, modification and abolition/suspension, during a ten year period, of three research intensive universities in Canada: Universite de Montreal, Universite Laval, and McGill University. The trends observed, by analyzing the Minutes of the Commission des etudes in the case of Francophone universities and the Senate Subcommittee on Teaching and Programs for McGill University, reveal a tendency toward vocationalization when changes are introduced in the academic programs supply. Data from 25 interviews of professors involved in university-industry collaborative research projects, conducted between April and June of 2007 in McGill University and University of British Columbia show also a tendency towards an utilitarian graduate training. Values of practicality, collaboration, interdisciplinarity and entrepreneurship, resulting from professors’ bisectorial and multisectorial collaborations with industry, are passed on to students, particularly graduate students.