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Collaboration with universities is an important innovation strategy for enterprises. However, currently very little research has focused on how such university-industry collaborative innovation activities should be managed. The paper aims to discuss this issue.

This paper introduces the university-industry collaborative innovation practices of Zhejiang NHU Company in China. By using a case study as the method, this paper aims to illustrate the mechanism of university-industry collaborative innovation and how to manage the collaborative innovation activities efficiently.

Zhejiang NHU Company established a university-industry collaborative innovation link through three innovation platforms: the technology R&D center, the ZJU-NHU joint-research center, and the national engineer center. Zhejiang NHU Company manages its collaborative relationships with universities through this innovation network.

NHU Company managed the collaborative relationship efficiently with the institutions, representing an effective degree of university-industry collaborative innovation management.

The purpose of this paper is to explore recent national human resource development (NHRD) practices in China through a literature review focusing on programs and activities that represent the roles and interactions among the government, industry and universities.

To effectively consolidate previous work and conceptualize the recent development of the NHRD practices in China, a semi-narrative literature review was used to explore and analyze NHRD-related functions and activities.

Findings from the literature review showed that although the central government still plays a predominant role in China, universities and corporations are increasingly playing a critical role in developing an innovative and skilled workforce. At the regional level, NHRD initiatives in China have been increasingly undertaken by universities, industry and government–industry–university collaborations. The authors also found a disparity between developed and underdeveloped regions in terms of NHRD in China.

This study used the triple helix model as a framework that provides an insightful lens for researchers to examine how various social entities interact with each other and jointly contribute to NHRD. Further case studies are needed to generate evidence-based knowledge to the NHRD literature.

A more systematic NHRD leadership structure at both the national and local level is desired to unleash the potential of bottom-up development and active government–industry–university collaboration. To counter regional divergence in NHRD in China, intra- and cross-regional collaborations are helpful in improving resources distribution and workforce development.

Based on open system theory, this study focused on programs and activities that represent the roles and interactions among the government, industry and university in Chinese NHRD through the lens of the triple helix model. In addition, this study offers a conceptual model of Chinese NHRD to help scholars and practitioners understand the transitional efforts in NHRD.

The study aims at disclosing the evolution process to an entrepreneurial university in the government‐pulled triple helix in China through the analysis of MIT and Stanford model of “university‐pushed triple helix” in which academic institutions take the lead in regional innovation.

The paper is based on a case study of the Northeastern University (NEU), which is located in the Northeast China where there is a dominant government‐pulled triple helix and with the establishment of China's first science park in which a highly successful software company (Neusoft) was created.

The pathway to an entrepreneurial university begins with government‐pulled + industry‐university collaboration, to university‐industry collaboration + interaction triple helix. This may be followed by a gradually developing “university‐industry collaboration” in which companies fund academic research with potential industrial use, the beginnings of a university‐pushed triple helix.

The analysis of NEU exemplifies the emergence of the entrepreneurial university in China and provides strategic implications for policy makers in terms of designing the appropriate policy to support university enterprising strategy.

This 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.

Mix 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.

The 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.

The 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.

The purpose of this paper is as follows: studying a case for technological innovation in Iran; studying the process of network creation; studying different challenges in finding, forming and performing of a collaboration network; studying comprehensive identification and analysis of the challenges in the network creation process; studying an active collaboration network in the field of medical equipment; and studying a network with different partners (consisting of a large company, a small- and medium-sized company, two subcontractors, three universities, a start-up, an accelerator and a venture capital fund).

The qualitative research method has been used because of a deep and rich analysis of the studied network. The study also provides an overview of finding and forming of partners and details of collaborations in the network, including partner’s goals from network entry, initial negotiations between members, business plan, shared resources and final results are expressed. The data used in this study has been collected by a totally seven semi-structured interviews. To analyze the collected data by open coding, the MAXQDA12 software was used and the basic concepts were identified and were categorized based on their similarities.

The studied network was identified as a strategic alliance. Details of finding, forming and performing of the studied network were explained. In total, 4 collaboration models and 28 different challenges in the network creation process were identified. In total, 28 different challenges were divided into 7 categories (based on their existence or absence in each of the 4 identified collaboration models). In total, seven categories of challenges were analyzed completely and the relations between challenges and partners of collaboration models were studied.

The collaboration network studied in this research involves several technological collaborations among different actors. Saadat, as the hub of this network, has been involved in all these collaborations, and other members have joined the network in accordance with network requirements and their capabilities. This network has been formed and developed with the aim of developing and producing various modules of vital signs monitoring since 2009. Some of the collaborations in this network, which are for production of a specific module and require long-term collaboration between the partners (such as the production of electrocardiograph or heart attack rapid diagnostic module), continue to be ongoing and some of them that focus on research and development or acquisition of technical knowledge (such as academic collaboration) after the specified result has been ended.

This paper is a response to a frequently asked question from prospective industry partners searching for opportunities to collaborate with the Computer Science Department of Swansea University, UK. This paper seeks to provide structured guidance in the form of what is titled the Industry Engagement Ladder.

The Industry Engagement Ladder presents a selection of collaboration opportunities, outlining the possible cooperation mechanisms between an industry partner and the academic institution.

Opportunities are described and ranked according to the amount of investment required by the industrial partner, and therefore risk, from low to high.

This concise paper provides a quick reference for perspective industry academia collaboration opportunities, the risks involved with each, the possible funding available to help foster these partnerships and the benefits to industry. These collaborative activities (and variations thereof) can be incorporated by any university department. The manuscript offers an ideal starting point for industry–university collaboration.

The purpose of this paper is to present an approach to start industry–university (I-U) collaboration through a stepped process aimed at building a portfolio of research and development (R&D) projects.

It devises from an 18-month action-research program held between a multinational automotive manufacturer and the a top-ranked Brazilian university.

The three-stage R&D shared portfolio methodology results from a combined application of quality function deployment-like correlation matrices and roadmapping. A first matrix tackles industry interests and correlates product performance dimensions and components to reveal broad research areas of interest. A second matrix correlates research areas and engineering competences, highlighting the types of the required know-how from the university standpoint. Thirdly, academic experts help to fill a roadmap-like layer with possible collaborative R&D deliverables over time.

Since the study lies on a single experience, extensions to other contexts should be made with care. However, the proposal offers robust rationale and a set of supporting tools to nurture new applications.

Theoretical and methodological reflections help managers tackling the long-standing problem of setting a shared R&D agenda.

Literature on I-U collaboration tends or to over-emphasize the role of technology transfer offices in promoting the partnerships or to seek implications for public policy. This research offers a valuable approach to build shared R&D project portfolio from a managerial viewpoint, filling an academic gap and offering guidance for managers in both sides.

The purpose of this study is to examine the impact of government support policies and research and development (R&D) activities on product innovation under market uncertainty.

This study applies logistic regression analysis to a sample of 4,000 South Korean manufacturing firms in order to investigate the impact of government policies and R&D activities of the firm on firm innovation performance, with particular interest in the moderating role of the firm's perceived market uncertainty (PMU).

Policies supporting industry/university/institute/local collaboration are found to have greater benefit under high PMU. Surprisingly, support for a consortium among different-sized firms has a negative effect on product innovation, although this negative effect disappears under high PMU. Both support for the protection of intellectual property (IP) and support for the resolution of manpower shortages have strong positive effects on the propensity to innovate products, but in both cases the moderating effects of uncertainty are negative. Finally, all types of R&D activities have positive effects on the propensity to innovate, more so for new product innovation than for improved product innovation.

To the best of the authors' knowledge, this is the first study to examine analytically the moderating effect of PMU in the effectiveness of government policies promoting innovation in the manufacturing sector. The study is potentially useful both for policymakers in deciding which policies to implement under prevailing market conditions; and for entrepreneurs choosing between different forms of government support, particularly given the abnormal levels of market uncertainty prevailing in the Covid-19 era.

The purpose of this paper is to explore how collaborative entrepreneurial learning (EL) processes between entrepreneurs and university students can enhance the entrepreneurial practices in the context of knowledge-intensive enterprises. These learning processes represent a valuable source for entrepreneurship development in incumbent enterprises in the forms of innovative products, services, processes or organizational renewal.

An extreme case study is the project “Mimprendo” (www.mimprendo.it), an initiative promoted by the Italian Conference of the University Colleges and the Italian Association of Young Entrepreneurs in collaboration with Italian universities. This is analyzed in the period 2009-2015, during which seven editions were developed.

A framework is presented based on collaborative EL processes to perform relevant entrepreneurial projects in knowledge-intensive enterprises. The framework provides a coherent and systematic approach to generate, select and implement entrepreneurial practices in incumbent companies starting from a project competition involving creative students and innovative entrepreneurs. EL processes in the community composed of entrepreneurs, experts and university students are grouped into the entrepreneurial phases of inspiration, exploration, exploitation, acceleration and growth, and include the learning processes of “intuition and sensing,” “contamination,” “experiential and contextual learning,” “experimenting and acting” and finally “thinking and reflecting.”

Implications for research can be identified according to many perspectives to deepen the centrality of the learning process in the research on knowledge-intensive entrepreneurship.

The framework results to be a promising approach to diffuse an entrepreneurial culture both in incumbent enterprises and in university students through a synergic collaboration among industry, university and institution. Practical implications could be derived for enterprise, students and educators involved in the design of innovative learning initiatives to sustain the development of an entrepreneurial mind-set.

The framework contributes to extending an emerging research area exploring entrepreneurship as a never-ending dynamic learning process. The involvement of brilliant university students in activating EL process with entrepreneurs in incumbent enterprises represents a novel aspect in the field of entrepreneurship and innovation.

University–industry (UI) collaboration is essential for knowledge and technology exchange between higher education institutions and industries, enabling enterprises to accelerate innovation. However, few studies have investigated the collaborative innovation mechanism through which UI collaboration can enhance the accumulation of firms' intellectual capital (IC) and how this, in turn, affects their innovation-driven development.

Drawing from the knowledge management and collaborative innovation theory, this research proposes a theoretical framework of the inter-organization relationship between enterprises and universities to investigate the influence mechanism of UI collaboration, including academic engagement and commercialization, on corporate performance as well as the mediating role of IC by employing survey that covers 177 UI collaborations.

Empirical results show that human capital and relational capital fully mediate the relationship between academic engagement UI collaboration and corporate economic performance, while human capital partially mediates the relationship between commercialization UI collaboration and corporate economic performance. Additionally, structural capital and relational capital partially mediate the relationship between academic engagement and corporate innovation performance, while structural capital fully mediates the relationship between commercialization and corporate innovation performance.

This study empirically investigates how academic engagement and commercialization impact corporate performance (i.e. innovation dimension or economic dimension). It uncovers this relationship's underlying mechanism by documenting the IC's mediating impact.