While the Triple Helix and Quadruple Helix models are popular in innovation studies, the relations between them have not been addressed extensively in the literature. There are diverse interpretations of helix models in empirical studies that apply them, but these sometimes deviate from the original theses of the models. Such a situation can confuse newcomers to the field in terms of which helix model to apply in their empirical research. We discern that the cause of this research challenge is a lack of systematic comparison of the two models. To bridge the research gap, this paper compares the models from the perspectives of how they were introduced and discussed in the literature and improved and how useful they are in addressing the innovation processes in contemporary society. Our major findings are as follows: First, reviewing the extant literature applying the two helix models for identifying research gaps, we discover that these studies were influenced by three views on the relations between the two models that were located on a continuum between two extreme ends—namely, isolation versus integration of the two models. Second, we provide a systematic comparison of both the advantages and weaknesses of the two models, and this may help researchers choose suitable helix models as conceptual/analytical tools in their empirical innovation studies. Third, our comparison of the two models shows that they are largely supplementary to each other when analysing innovation processes in contemporary society, providing a ground for potential synergy building between the two helix models.

This paper aims to understand the roles universities play in regional innovation systems in China. Specifically, it examines how Chinese practice concerning the engagement of the universities with society for promoting regional innovation systems differs from Western practices from the perspective of the Triple Helix. It focuses on China’s leading metropolis, Shanghai, and takes as a case study the Tongji Creative Cluster which, unlike most other clusters in China, is based on knowledge-intensive services rather than high-tech manufacturing. We find that although it is commonly assumed that the statist Triple Helix model characterises the development of the innovation system in China, the practices of the Tongji Creative Cluster take a different approach, combining both bottom-up initiatives in the initial stage and top-down coordination in later developments. We argue that this model is more useful for China’s regional innovation systems as it can overcome many challenges in the statist model.

While institutional logics theory has increasingly been applied in higher education research, especially in the past five years, agreement is lacking on how to approach institutional logics analysis. This results in proliferating institutional logics in higher education studies and often confuses newcomers to the field as to how to use institutional logics in their empirical research. As a response to this situation, our study outlines the state-of-the-art application of institutional logics in higher education studies through scrutinising 59 articles that apply institutional logics in organisation studies in the field of higher education. Specifically, we ask the following research questions: What approaches to institutional logics analysis are used in higher education studies? What institutional logics are identified/applied in higher education studies? What challenges are evident in applying institutional logics in higher education studies? How does the use of institutional logics in higher education research contribute to institutional logics theory? The most profound outcomes of our literature analysis are: First, we construct a novel typology of approaches to institutional logics analysis that is positioned on two-dimensions: the reasoning applied (deductive vs. inductive), and the level at which the logic is examined (societal vs. field/local); Second, we create an exhaustive list of institutional logics (over 50) applied and identified in these studies; Third, we discover major challenges in using institutional logics in higher education research. Finally, we clearly define societal-level and field-level logics and suggest a rationalisation of institutional logics approaches in order to fully utilise the explanatory power of institutional logics.

While higher education has been considered as both an ‘engine’ for innovation and a ‘catalyst’ for sustainability development, the integration of both the ‘innovation engine’ and ‘sustainability catalyst’ roles is best reflected in higher education’s engagement in innovation ecosystems—the theme of this special issue, including 16 articles dealing with the topic from various perspectives. In this editorial, we outline an overarching framework about the relations between higher education and innovation ecosystem. When elaborating the framework, we provide a new definition of innovation ecosystem and identify three roles of university in innovation ecosystems, based on synthesizing relevant literature. The framework could facilitate readers to comprehend each of the collected articles and find synergy among them.

This paper presents a potential solution to fill a gap in both research and practice that there are few interactions between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems. To strengthen the synergies between TIC and TUC for innovation, the first step is to match suitable industrial firms from two countries for collaboration through their common connections to transnational university/academic partnerships. Our proposed matching solution is based on the integration of social science theories and specific artificial intelligence (AI) techniques. While the insights of social sciences, e.g., innovation studies and social network theory, have potential to answer the question of why TIC and TUC should be looked at as synergetic entities with elaborated conceptualization, the method of machine learning, as one specific technic off AI, can help answer the question of how to realize that synergy. On the way towards a transdisciplinary approach to TIC and TUC synergy building, or creating transnational university-industry co-innovation networks, the paper takes an initial step by examining what the supports and gaps of existing studies on the topic are, and using the context of EU–China science, technology and innovation cooperation as a testbed. This is followed by the introduction of our proposed approach and our suggestions for future research.