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This study aims to investigate the relationship between university and industry as two major infrastructures of national innovation system in all leading scientific and industrial settings.

Large complex organizations with high technology that follow non-linear dynamic rules need to define concepts and adopt new approaches to achieve organizational efficiency and effectiveness. Among various models, a dynamic model of innovation was developed based on a joint investment between industry and university. Hence, the concepts of systems thinking and system dynamics were used.

The results reveal three levels of industry and university communication from the lower levels to the higher levels.

The value of this paper lies in adding two axes of “type of relationship” and “form of relationship” to the axis of “strength of relationship”, and a static three-dimensional space as a spatial capacity of the relationship between the industry and the university has been organized. Further, this is the first study that investigates the dynamic relationship between industry and university based on the self-organization theory and system thinking.

This study aims to explore the factors undergirding knowledge creation in the university-industry complex inter-organizational arrangement. It builds upon social capital and relationship marketing theories.

This study uses a qualitative research design. In total, 36 innovation champions involved in knowledge creation were interviewed to provide detailed insights into the process. A thematic analysis of the in-depth interviews was conducted.

The principal finding was that opportunistic behavior was a significant barrier to knowledge creation. In severe cases, the knowledge creation process was destroyed, resulting in lost investment. Principled behavior and investment in affect-based and cognition-based trust, through five critical trust development activities, provided the best path to successful knowledge creation.

This study contributes to the knowledge management literature by providing insights into the enablers and barriers to the formation of cooperation, a crucial antecedent to knowledge creation literature. It also affords practical implications for innovation managers and policymakers on how they can improve knowledge creation by using social capital and relationship marketing theory in complex inter-organizational arrangements.

This paper seeks to redress the lack of academic attention being given to the relationship between academia and industry in the competitive environment of international higher education‐related service delivery and use. It adopts a relational marketing approach to value creation in service interactions and explore the expectations and perceptions of employers and university academics and practitioners. The research focuses on a comprehensive mapping of gaps in the international higher education service at large, and specifically in the context of degree programme development, recruiting international students and associated service interactions with industry. It highlights some of the issues pertaining to service quality, customer orientation and sustainability in international higher education and associated service delivery.

This paper is an exploratory case study set in the context of Finnish higher education. It relies on qualitative research methods and applies the GAP model for the analysis of the empirical data.

Five gaps and a surplus gap are identified in the delivery of high‐quality customer service in the context of university‐industry interactions in international higher education. To close the gaps between academia and industry, the relationship marketing approach is proposed.

The paper contributes to the research stream on university‐industry relationships. The results should help to enhance understanding of customer service delivery in the linkage between academia and industry. The novelty of the paper lies in the integration of the language and logic associated with customer‐oriented service delivery into the operational context of higher education service interaction between academia and industry.

This paper attempts to propose an integrated model for measuring the knowledge transfer effectiveness in university‐industry alliances. The so‐called “RDCE” model is thereby proposed as an integrated model for measuring the knowledge transfer effectiveness. By combining inter‐organizational relations (IORs), knowledge‐based view (KBV) and resource‐based view (RBV) of firms, this paper aims to focus on the influence of determinant factors such as partner complementarities, partner attributes, the characteristics of the coordination and relationship quality between industrial companies and universities that may lead to the effectiveness of knowledge transfer.

This framework thereby clarifies how mediating variables influenced the paths that constitute the direct, indirect and total effects of mediated models by integrating moderated regression analysis together with bootstrap resampling methods to ensure the precision in estimating confidence intervals of indirect effects and path analysis using structural equation models to test all the hypotheses simultaneously for the robustness of the results and conclusions.

The statistical results reveal that the proposed model has a significant mediating effect that contributes to knowledge transfer effectiveness. Only partner attributes and relationship factors have a direct impact on the effectiveness of knowledge transfer. This appears plausible since mere complementarities and coordination between partners may not lead to learning or knowledge transfer, which requires a certain depth of the partner interaction in terms of the specific attributes of partners, coordination and relationship quality.

The authors assumed that the alliance constitutes partnerships between firms of roughly equal size and market power. Therefore, this study provided only broad perspectives of collaboration among alliance partners, and did not capitalize on different degree of alliance integration and different types of collaboration.

Managerial suggestions on how to improve their knowledge transfer effectiveness are also provided at the end of the text.

There are numerous studies examining alliance network performance. Very few studies, however, have examined detailed collaborative activities in dyadic university‐industry partnerships and potential constructs for measuring knowledge transfer and commercialization in the research and development alliance between industrial firms and university context.

U.S. industry–university (I–U) relations around intellectual property (IP) have become increasingly contentious since the Bayh-Dole Act of 1980, while especially lucrative patents and licenses resulting from biomedical and pharmaceutical discoveries capture the headlines. Some assert that I–U relations around IP are in crisis, others suggest that no such problem exists, and still others bemoan the “increasing commercialization” of U.S. education. This chapter develops a multi-level model of I–U IP dynamics, drawing on pluralistic, multi-theory perspectives, field interviews, and secondary data. The model includes three levels: the institutional (economy) level, I–U (sector) level, and the organizational level. These levels jointly affect the immediate context of any deal. The chapter closes with a discussion of this model's implications for further research and some theoretical speculations.

Background and objective: The lack of human and intellectual resources and capital has affected the survival of different industries and organizations in this globally competitive world. Universities have failed to provide necessary human resources to these organizations. The coordination between industries and universities is not optimal. Such challenges are being faced in the United Arab Emirates (UAE); therefore, there is a need to investigate the reasons behind these challenges to develop an ideal university-industry relationship in UAE. The present study aims to evaluate the missing links in the relationships between universities and industries of UAE.

Methods: A quantitative research design has been used to recruit 100 department heads and senior professors from 20 public and private universities in the UAE. Descriptive statistics, regression analysis and factor analysis have been used to analyze the data collected through SPSS v.20.

Results: The results have shown a significant and positive impact of intellectual property (IP) policies (p=0.045) and scientific knowledge (p=0.023) on knowledge transfer; IP policies (p=0.067), shared governance (p=0.018) and scientific knowledge (p=0.017) on trust; IP policies (p=0.069), shared governance (p=0.034) and scientific knowledge (p=0.018) on innovation performance.

Conclusion: The findings have suggested that the role of interorganizational governance mechanisms is important in university industry collaborations to increase trust, innovation, and shared governance.

The purpose of the paper is to analyse empirically research‐oriented university‐industry relationships based on the incorporation of relationship marketing (RM) and technology transfer theory.

This paper is based on an extensive literature review and initial qualitative research, a conceptual model is presented and tested using structural equation modelling methods. Analysis was conducted, and is reported, in three steps, including path analysis and hypothesis testing, model re‐specification and a multi‐group analysis comparing university and industry respondents.

Trust, commitment and integration were found to positively influence satisfaction and were confirmed as key drivers of successful university‐industry relationships. While trust was the strongest driver of satisfaction, commitment emerged as the strongest predictor of intention to renew. The results also confirmed the proposed interrelationships between the relationship characteristics. Organisational compatibility emerged as positively influencing all relationship characteristics, indicating its relevance for university‐industry relationships and suggesting its potential importance for other relationships crossing essentially different organisational environments. Surprisingly, only a weak influence of staff personal experience on commitment was found.

The results are limited to Australian relationships and by their cross‐disciplinary nature. Furthermore, a potential bias towards positive relationships might exist in the data.

The primary contribution of this paper lies in the development of a foundation for research in a new services business context by combining the established theory of RM with the emerging area of technology transfer. Building a thorough empirical basis for future research, the researchers anticipate the development of a comprehensive university‐industry relationship research stream.

This study aims to identify the effect of demographic factors on the relationship between academic contribution and university–industry knowledge exchange in Sri Lanka.

This study is of quantitative and explanatory type , which applied the deductive research method, and is conducted with minimum interference of researcher taking individual academics as the unit of analysis. The study collected data from 178 academics randomly through a structured questionnaire designed to analyze through statistical package for the social sciences and analysis of a moment structure statistical software. A structural equation model is applied to collected data to explore the moderating impact of the demographic factor on the university–industry knowledge exchange.

Overall involvement of academic staff in joint research, contract research, human resource mobility and the training with industry were was low in Sri Lanka. However, all four independent variables significantly associated with the knowledge exchange process from which only joint research and training had a statistically significant effect on university–industry knowledge exchange . Concerning demographic factors, only the quality of academic research significantly moderated the relationship between academic contribution and university–industry knowledge exchange process in Sri Lanka.

This study considered only the university side of the university–industry knowledge exchange process.

This paper implies that gender, age and area of specialization did not have significant power to moderate the relationship between academic contribution and university–industry knowledge exchange process.

There is a lack of research literature discussing the moderating effect of demographic factors on the university–industry knowledge exchange process. In Sri Lanka, money and commercial benefits that received through industry partnerships had not been valued by academics. The majority considered the connection with industry and exchange knowledge as a responsibility that they should perform in return to free education received from grade one to graduation.

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.

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.