While there is growing awareness of the role of digital technologies in transforming organizations and social relationships [1
], the new economy (called platform economy or digital platform economy) based on digital technologies is changing the nature of globalization [2
], in which the focus has been shifted from countries (globalization 1.0) and companies (globalization 2.0) to individuals and groups (globalization 3.0) [3
]. The involvement of individuals or citizens in multi-faced social life is crucial in sustainable development [4
] in ecological, social, and economic dimensions [7
]. In this paper, we try to bring interactions of key actors in transnational innovation processes, previously mainly explored through social science approaches, into the digital domain, applying some specific artificial intelligence (AI) technologies, i.e., machine learning and knowledge representation and reasoning. Our focus is on the relations between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems (see more descriptions about the concept of transnational innovation ecosystem in the following section), which share basic assumptions of sustainability and globalization 3.0. TIC is concentrated on joint research and development (R&D) and co-production; TUC is mainly concerned with academic research collaboration. The research problem of TIC and TUC synergy building, as described below, has emerged in the context of innovation studies and their adjacent fields. We try to resolve the problem by integrating relevant studies in social sciences (in the fields of innovation studies, higher education research and international relations with respect to EU and China) and AI technologies (i.e., machine learning and knowledge representation and reasoning).
The literature of innovation studies, which conceptualizes reciprocal relations and proactive interactions between universities, industry, government and citizens for promoting innovation [8
], stresses the importance of synergy building between cross-sector organizations. Particularly, university and industry (U–I) interactions constitute a core area in innovation studies [10
]. While the innovation processes are becoming globally interconnected [11
], there is also an urgent demand to extend cross-sectoral organizational synergy building to the transnational context. For instance, it has been evidenced, though mainly in the industrial context, that technological capabilities for innovation are dispersed in international innovation networks [15
] and the degree of R&D internationalization is increasing [16
]. Thus, actors from both university and industry sectors, as key contributors to technological capabilities and R&D, are likely to become international [13
However, academic research is far behind the needs for further integration between TIC and TUC arising from the practices of transnational STI cooperation. Although one widely shared view in innovation studies is that universities have a prominent role in the national or regional innovation system, e.g., through co-creation partnerships with industry [8
], the research of U–I cooperation in transnational contexts is rare in the literature. For instance, in their literature review of U–I international knowledge transfer, Govind and Küttim [20
] only found 26 articles (mainly in the fields of innovation studies and higher education research) meeting their search criteria in the Scopus and Web of Science databases. Amongst the 26 articles, U–I interactions in the transnational context are mainly seen in the form of universities from one country interacting with enterprises from another country, which is echoed in other literature e.g., [21
]. Other studies deal with joint R&D between universities and branches of international companies e.g., [24
]. The patterns of transnational U–I interactions examined in the existing literature can be illustrated in Figure 1
When innovation systems become global [25
] or internationalized [26
], international university cooperation could added value to the process [27
]. Some rare studies e.g., [23
] do touch upon the issues of how TUC could be aligned with and supportive of broader societal priorities and development goals in transnational contexts. However, there is a lack of profound exploration in this area, particularly from theoretical and methodological perspectives.
In the context of transnational innovation networks, the interactions between TIC and TUC can be understood as synergetic entities or transnational U–I co-innovation networks (Figure 2
). In such networks, new ideas and approaches from various internal and external sources are integrated in a platform to generate shared values [31
]. The core elements of co-innovation include “collaboration, coordination, co-creation, convergence, and complementary” [32
] (p. 361).
demonstrates that two industrial firms that are unfamiliar with each other, respectively from Country A and Country B, could be connected for potential collaboration by utilizing their common connections to existing university collaborations (especially individual collaborators) between the two countries. Although our focus is on how TUC can support TIC, once TIC and TUC synergetic entities are formed, the TIC would enhance the quality and value of TUC.
Compared to existing studies on U–I interactions in transnational context (Figure 1
), our investigation of U–I co-innovation networks (Figure 2
) is novel in two aspects. First, although the subject of university and industry interactions in transnational context is not new in research, our study is different to existing approaches by discovering unobvious/hidden links between TUC and TIC. When links are hidden, it means that the links are unobvious or the data revealing the links are scarce or not apparent. Second, when it comes TIC, we not only look at existing collaboration of industrial firms across countries as examined by current studies, but specifically explore how missing links between potential transnational industrial partners could be bridged through utilizing the hidden links. The dash lines in Figure 2
represent either hidden or missing links that are rarely examined in the existing studies but will be the focus of our proposed approach. Missing links demonstrate that the actors with potential for reciprocal collaboration are not connected. However, the actors can be connected by leveraging the hidden links. Third, while current research related to the topic are mainly in the field social science studies, we seek an approach integrating insights of both social sciences and AI technology.
The lack of interaction between TIC and TUC is also seen in the practices of transnational science, technology and innovation (STI) cooperation. For instance, as indicated by the first author’s recent interviews of actors involved in EU–China STI cooperation, in the EU–China context, when European high-tech firms, especially small- and medium-sized enterprises (SMEs), seek Chinese counterparts for cooperation, they normally go directly to the industry sector, sometimes through business brokers. As a result, the Chinese partners that first appear to them are not necessarily the most suitable ones. This could be explained by three causes. First, the business brokers, e.g., consulting companies or governmental business promotion agencies, have a limited pool of Chinese companies as candidates to be matched with European SMEs. Second, when matching collaborators, human decisions are often subject to the “homophily principle”—a tendency in which people form ties with similar others [33
]. Selecting a “friend of a friend” helps strengthen existing social clusters [33
], but does not help to reach those unknown communities and individuals [34
], who could potentially contribute to innovation [35
]. Third, although one may notice the limitation of professional social matching based on human decisions, it is difficult to find suitable tools and data to resolve the problem [34
Our proposition is that European and Chinese industrial firms can be best matched through their connections to existing university cooperation between both sides. In so doing, transnational U–I co-innovation networks are formed, which are crucial for enhancing the performance of EU–China STI cooperation. This has also been implied by the interviews mentioned above.
Now there is a great opportunity to exploit EU–China university cooperation to support EU–China industry cooperation, because EU–China higher education cooperation has reached an unprecedented level, demonstrated by not only a large scale of student/staff exchange but also increasingly deeper research collaboration and joint education programs [30
]. For instance, there were 383 participating institutions from China in 274 projects in the EU’s Seventh Framework Program (2007–2013). Although China is no longer considered to be a beneficiary country in the Horizon 2020, the Chinese Ministry of Science and Technology provides co-funding to support Chinese research organizations involved in the EU’s Horizon 2020 projects. In the Web of Science database, the number of co-authored publications between Chinese and European authors has increased from 12,669 in 2011 to 35,218 in 2018.
The EU–China context is used as a testbed for three reasons. First, there is huge potential for STI cooperation between the EU and China. Second, the scales of both higher education and industry in the EU and China are tremendous, which provide abundant data for testing and application. Third, since the EU and China have different, sometimes contrasting, social structures and value systems, if we can find effective solutions to build TIC and TUC synergies in EU–China STI cooperation, the approach is likely to be applied (with possible adjustment) in other transnational contexts.
While using a specific EU–China context helps illustrate our research problem, the underlying research gaps concerning TIC and TUC synergy building are general. To narrow the gaps, we must first clearly see what the gaps are. Thus, this paper will analyze how the state-of-the-art studies have shed light on the following questions as well as limitations in answering these questions:
Why should TIC and TUC be looked at as synergetic entities?
How can the synergy building be theoretically elucidated?
How can the synergy building be methodologically realized?
After that, we will present our proposed approach in brief, and discuss how it could help advance the frontier of research on the topic. It should be mentioned that the paper is primarily on the conceptual level. When it comes to our proposed AI-based approach we focus on developing operating principles rather than final technological solutions. The method in this paper is primarily based on reviewing, analyzing and synthesizing relevant literature.
This paper has demonstrated how social sciences and AI could be integrated to develop a transdisciplinary approach to TIC and TUC synergy building, thus contributing to the knowledge pool in which studies on TIC and TUC have been separately reported in spite of a growing awareness on necessary of synergy building between TIC and TUC. Specifically, the originality of our research lies in four aspects.
First, while the research on TIC and TUC synergy building is almost an uncharted field, our research maps a landscape of the research area with identification of specific research gaps through extensive analysis of relevant literature. Our efforts are around discussions on how existing research may offer useful insights and have limits in answering three questions: Why should TIC and TUC be looked at as synergetic entities? How can the synergy building be theoretically elucidated? How can the synergy building be methodologically realized? The diagnose of advantages and weaknesses of state-of-the-art research in the field not only serves as clear point of departure of our research on TIC and TUC synergy building, but also helps guide more scholars to plunge into the field.
Second, we propose a transdisciplinary approach to TIC and TUC synergy building by integrating insights of social sciences, such as Helix Model of innovation, institutional theory and social network theory, and AI, such as ML and KR and R. While the insights from social sciences have the potential to answer the question of why TIC and TUC should be regarded as synergetic entities, AI technologies can help answer the question of how such synergies can be realized. Current methods using AI (i.e., ML) in social science research tend towards two extremes: they are used either for verifying assumptions about human intelligence or for independent prediction, thus tending to replace human intelligence. We try to integrate between social-science-based theoretical modelling and data-based computational modelling, particularly regarding the understanding of TIC–TUC interactions. Empirical findings guided by social sciences theories will help improve ML algorithm by providing validation and test data. The models learnt by machine will be useful input for advancing understanding of TIC and TUC synergy building in social sciences studies.
Third, our proposed approach will specifically identify/predict hidden/missing connections between actors in TIC and TUC co-innovation networks by analyzing unstructured data from various sources, such as public databases (e.g., research projects, co-publications, patents), website text (on cooperation activities) and auto-generated survey data. In doing so, on one hand we try to advance current social network analyses, which mainly map out anticipated networks by processing classified data [107
]. On the other hand, we will open new horizons for studies on professional social matching [34
] from both cross-sectoral and transnational perspectives.
Finally, our approach of AI-based matching system will help realize the potential role of university for institutional change and trust building, which are important to the sustainable dimension of innovation ecosystem development.
Nevertheless, our paper is primarily on the conceptual level discussions. Despite promising potential of our approach, it must be first tested and verified with sample data. This will be our next research task. It should also be noted that matching TIC and TUC, e.g., in the EU and China context, is just the first step to building EU–China transnational co-innovation networks. To achieve full synergy of the networks, there are also other important issues that need to be deeply explored, such as research and innovation policies, entrepreneurship, knowledge management, intellectual property rights, and inter-cultural communications. This will indeed open a new area of multidisciplinary research. Studying the TIC and TUC synergy building in the EU and China context also propels an urgent demand for closer communication between two separate research communities, namely international researchers and Chinese researchers both conducting research on the topic [127