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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.

There is little known about investigating the importance of all proximity dimensions simultaneously as a result of geographical proximity on university-industry collaborative innovation. This paper aims to answer the question of how geographically proximate university and industry influence cognitive, social, organizational, institutional and cultural proximity within university-industry joint laboratories and finally, what is the outcome of these interplays on collaborative innovation.

The study uses an exploratory multiple-case study approach. The results are derived from 53 in-depth, semistructured interviews with laboratory directors and representatives from both the company and the university within 8 joint laboratories of Telecom Italia (TIM). The data collection was carried out in 2014 and 2015. The analysis follows a multi-grounded theory approach and relies on a mix of deductive and inductive reasoning with the final goal of theoretical elaboration.

This study finds the role of social and cultural proximity at the individual level as a result of geographical proximity as an enabler of collaborative innovation by triggering mutual learning, trust formation and frequent interactions. Cognitive proximity at the interface level could systematically influence collaborative innovation, while organizational and institutional proximity has marginal roles in facilitating collaborative innovation. The qualitative analysis offers a conceptual framework for proximity dimensions and collaborative innovation within university-industry joint laboratories.

The framework not only advances state-of-the-art university-industry collaboration and proximity dimension but also offers guidance for managers in designing collaborative innovation settings between university and industry.

With this study, the paper advances the understanding beyond solely the relationship between proximity and collaboration and shed light on the interplay between geographical proximity and other proximity dimensions in this context, which has received limited scholarly attention.

The research purpose is to analyse when and how innovation support programmes (ISPs) can affect collaboration between universities and established small and medium sized enterprises (SMEs). The paper specifically considers SME’s absorptive capacity.

A Swedish research centre is studied in the context of innovation support and two of its SME-ISPs are examined with regards to industry–university collaboration and impact on firm innovation capabilities. Data collection and analysis are performed, using interviews, survey answers, document search and reflectional analysis to evaluate processes and effects of the centre and the programmes.

A developed research centre, integrated into both academia and industry, can support translational collaboration and promote SME innovation absorptive capacity. The action learning elements and the organisational development approaches used when coaching in the ISPs contribute to the SMEs internal absorption capacity and collaborational skills. Organising collaboration into ISPs can provide a relational path to future collaboration with universities, which, for example start with student projects.

The study, though limited to one Swedish region, adds to empirical innovation research as it connects industry–university collaboration and absorptive capacity to organisational learning.

The empirical results indicate possible long-term gains for industry and universities in building collaborative innovation into SME-ISPs.

The contribution of this study pertains to the practice of innovation support for established SMEs with the inclusion of absorption capacity and collaborative innovation development.

To increase university–industry collaboration and research commercialisation, the Australian government recently introduced the Intellectual Property (IP) Framework, a set of online standard contracts. This follows a predecessor standard contract initiative, the IP Toolkit, which has not previously been evaluated. This paper aims to examine standard contracting in the innovation sector, tracing the policymaking behind the IP Toolkit using the lens of Macneil’s relational contract theory, to assess prospects of success for the new IP Framework, and similar initiatives in other jurisdictions.

This is a disciplined-configurative case study, drawing on qualitative secondary data analysis and applying Macneil’s relational contracting theory to guide case construction and generate hypotheses around likely success of standard contracting initiatives (stakeholder sentiment, stakeholder adoption). Within-case analysis process-traces development of the IP Toolkit, to discover what the policymakers wanted, knew and computed – and to detail observable implications Macneil’s theory predicts. Its themes are triangulated with multiple sources.

The case study, via Macneil’s theory, confirms the first hypothesis (resistant stakeholder sentiment) and partly validates the second hypothesis (low levels of adoption), demonstrating limited suitability of standard contracting in the dynamic and highly uncertain space of university–industry collaboration.

The study provides insights into the limited role that standard contracts can play in improving national collaborative research and development performance.

This is a novel theory-driven case study triangulated with previously unpublished data on the IP Toolkit’s website usage, and data from recent consultations on the new IP Framework. It has broader implications for other jurisdictions considering adoption of the standard contract model.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

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.

The purpose of this paper is to examine the influence of the innovation ability of universities (IAU) on the efficiency of University–Industry knowledge flow and investigate whether the level of provincial innovative agglomeration (PIA) moderates the relationship between IAU and the efficiency of the University–Industry knowledge flow.

This study uses the super-efficiency data envelopment analysis model to measure knowledge research efficiency (KRE) and knowledge transformation efficiency (KTE) and then studies the influencing mechanism of the two kinds of efficiency using the spatial Tobit model with panel data from 2008 to 2017.

The results show that the overall KRE in Chinese universities is higher than the KTE. IAU has a significantly positive impact on KRE and KTE. PIA has a significantly inverted U-shaped influence on KRE and KTE and positively moderates the promoting effect of IAU on KRE and KTE.

Due to the limitations of the data, this paper only selects several secondary indicators to measure KRE and KTE with reference to previous studies.

This study enriches the future research of University–Industry cooperation and knowledge flow and it is conducive to promoting the efficiency of University–Industry knowledge research and transformation from the perspective of universities, enterprises and local governments.

This study proposes the concept of University–Industry knowledge flow and divides the knowledge flow into the knowledge research stage and the knowledge transformation stage based on the knowledge supply chain theory. Moreover, the paper expands the theoretical framework of the impact of IAU on the efficiency of University–Industry knowledge flow and provides findings on the moderating effect of PIA.

The purpose of the study was to examine the factors that are important for strengthening university–industry collaboration (UIC). This study also investigates the outcome of UIC in the light of creativity, skill, knowledge, and research work.

A survey method has been used to collect data for the study. This study applied a purposive judgmental sampling technique where particular types of respondents like university faculty members and the top officials of the organizations were selected who are knowledgeable and can provide the desired information. The current study used the structural equation modeling method to analyze the data. In the first stage, this research assessed the demographic factors of the respondent. Then this study conducts confirmatory factors analysis and convergent and discriminant validity and reliability test. Finally, the hypotheses are tested by using nonparametric.

This study finds that knowledge transfer mechanism, governmental factors, organizational design factors, technology transfer and the collaborative network has a significant impact on strengthening UIC, which ultimately facilitates creativity, knowledge creation, skills development and supply of graduate according to the requirement of the industry, good research work.

The current study identified some important determinant that has a substantial influence on strengthening UIC. According to the study organizational design, government, technology, collaborative network and mechanism for knowledge transfer play very crucial roles in strengthening collaboration that ultimately increases the creativity, skills, knowledge and research capability of graduates.

With the evolution of the turbulent environment constantly triggering the emergence of a trust crisis between organizations, how can university–industry (U–I) alliances respond to the trust crisis when conducting green technology innovation (GTI) activities? This paper aims to address this issue.

The authors examined the process of trust crisis damage, including trust first suffering instantaneous impair as well as subsequently indirectly affecting GTI level, and ultimately hurting the profitability of green innovations. In this paper, a piecewise deterministic dynamic model is deployed to portray the trust and the GTI levels in GTI activities of U–I alliances.

The authors analyze the equilibrium results under decentralized and centralized decision-making modes to obtain the following conclusions: Trust levels are affected by a combination of hazard and damage (short and long term) rates, shifting from steady growth to decline in the presence of low hazard and damage rates. However, the GTI level has been growing steadily. It is essential to consider factors such as the hazard rate, the damage rate in the short and long terms, and the change in marginal profit in determining whether to pursue an efficiency- or recovery-friendly strategy in the face of a trust crisis. The authors found that two approaches can mitigate trust crisis losses: implementing a centralized decision-making mode (i.e. shared governance) and reducing pre-crisis trust-building investments. This study offers several insights for businesses and academics to respond to a trust crisis.

The present research can be extended in several directions. Instead of distinguishing attribution of trust crisis, the authors use hazard rate, short- and long-term damage rates and change in marginal profitability to distinguish the scale of trust crises. Future scholars can further add an attribution approach to enrich the classification of trust crises. Moreover, the authors only consider trust crises because of unexpected events in a turbulent environment; in fact, a trust crisis may also be a plateauing process, yet the authors do not study this situation.

First, the authors explore what factors affect the level of trust and the level of GTI when a trust crisis occurs. Second, the authors provide guidelines on how businesses and academics can coordinate their trust-building and GTI efforts when faced with a trust crisis in a turbulent environment.

First, the interaction between psychology and innovation management is explored in this paper. Although empirical studies have shown that trust in U–I alliances is related to innovation performance, and scholars have developed differential game models to portray the GTI process, building a differential game model to explore such an interaction is still scarce. Second, the authors incorporate inter-organizational trust level into the GTI level in university–industry collaboration, applying differential equations to portray the trust building and GTI processes, respectively, to reveal the importance of trust in CTI activities. Third, the authors establish a piecewise deterministic dynamic game model wherein the impact of crisis shocks is not equal to zero, which is inconsistent with most previous studies of Brownian motion.