**1. Introduction**

The sustainability of China's hitherto economic miracle is in question. As may come to pass for some developing nations, China also stands at a critical juncture between its catch-up phase that has relied on technology adaptation and one that springs from its capacity for knowledge generation and technology innovation. The national system of innovation (NSI) of a country in catch-up mode is different from one at the technology frontier. Different domestic economic and social contexts also mean that what works in the national system of innovation, synergy mechanism among university-industry-government in one country may not work in another. Sustainable economic growth is closely linked to the adaptability of NIS and the synergy of Triple Helix (TH) in NSI.

According to *the National Science and Technology Investment Statistics Bulletin 2018* issued by the National Bureau of Statistics of China on August 30 in 2019, the total national R&D expenditure in 2018 reached 19,666.7 billion yuan RMB, ranking the second in the world after the United States, which accounted for 2.19% of GDP with a total of 4.18 million R&D personnel, exceeding the average level of EU-15 countries. Among them, the expenditure of research institutions affiliated to the government is 269.17 billion yuan (accounting for 13.7%), the expenditure of colleges and universities is 145.79 billion yuan (accounting for 7.4%), the expenditure of state-owned and private enterprises is 1523.37 billion yuan (accounting for 77.4%); meanwhile, the expenditure of basic research is 109.04 billion yuan (accounting for 5.5%), the expenditure of applied research is 219.09 billion yuan (accounting for 11.1%), and the expenditure of experimental development is 163.96 billion yuan (accounting for 83.3%). Although the financial expenditure on science and technology in 2018 reached 951.8 billion yuan, considering the tax preferential policies such as additional deduction of firm's R&D expenditure and reduction of income tax of high-tech enterprises, the proportion of the government's actual investment in science and technology in the whole society has far exceeded 50% in China.

Driven by the continuous R&D investment mainly dominated by government, the number of international scientific papers and citations ranked second in the world and 244.75 million patents were authorized in 2018, however, private enterprises with less government funding have contributed 70% of the innovation outcomes. According to the latest global innovation ranking released by the World Intellectual Property Organization and Cornell University, China's innovation index in 2019, despite rising three places, still ranks 14th in the comprehensive ranking. This innovation index ranking of China is roughly in the same range as which published by China Academy of science and technology strategy, or Lausanne International School of management in Switzerland. The overall quality of NSI in China is far from meeting the requirements of building an innovative national strategy. The announcement of the 2019 National Conference on Science and Technology points out that there are some shortcomings of Chinese NSI, such as the research mechanism of key core technologies, the construction of innovation capacity, the cultivation of high-end talents, the allocation of resources, and refinement of the innovation ecology. As can be seen from the announcement, there are still some serious problems of asymmetric structure between the knowledge network and innovation network in the NSI in China. How to break down asymmetry between knowledge network and innovation network to realize higher-level equilibrium, it becomes the key point of sequential 2021–2035 National Medium and Long-Term Science and Technology Development Plan.

The synergy of NSI is closely linked to the function of TH in NSI, which is essential to knowledge generation and technology innovation of a country. Although building on the evolutionary theorizing by Nelson and Winter [1], the metaphor of NSI emerged in the late 1980s. Knowledge-Based Economy (KBE) has elaborated on NSI from an evolutionary perspective [2] since the mid-1990s, whereas TH can only be considered as an institutional elaboration [3], Due to limited research methodology to study the TH dynamic evolution mechanism of academic-industry-government relations, or to estimate accurately the synergy effect among them. This paper introduces a new approach in non-linear complex systems theory to offer steps towards a possible solution to this conundrum. Based on the pattern formation of the Belousov-Zhabotinsky (BZ) reaction, the paper constructs a simulation equation to explore the synergistic evolution mechanisms by comparing the ideal state with the current state of TH in China.

The simulation results demonstrate that (1) under the ideal balanced condition of industrial absorptive capacity and academic knowledge transfer capability, the stronger incentive policies would play much more important roles than weak policies; (2) current situation in China, the performance of collaborative innovation remains dismal at best, but the industrial absorptive capacity, especially in private enterprises, has exceeded the capability of knowledge transfer in academia, and it has become the main driving force to promote international merger and acquisition and global open innovation. If the innovation policy can be focused on the high-level balance between the domestic knowledge network and innovation network in NSI of China, the innovation performance will be accelerated more efficiently.

The subsequent parts of the paper are organized as follows. Section 2 reviews the literature on the TH in NSI. Research methods and variable refinement are discussed in Section 3, followed by Section 4 that presents the findings and their analysis. The conclusion is proposed in Section 5. The main contribution of this paper is to introduce a simulation method in non-linear complex systems theory into the research field of TH synergy in NSI, by which reveals the dynamic evolutional mechanism among TH in Chinese NSI.

#### **2. Literature Review**

Since the 1980s, market competition has become increasingly fierce, uncertainty has increased significantly, and product innovation and process innovation have shown a trend of systematization and complexity. Sahal distinguished among (i) material innovations "which are necessitated in an attempt to meet the requisite changes in the criteria of technological construction as a consequence of changes in the scale of the object", (ii) structural innovations "that arise out of the process of differential growth whereby the parts and the whole of a system do not grow at the same rate", and (iii) systemic innovations "that arise from integration of two or more symbiotic technologies" [4]. The resources and capabilities owned by a single enterprise are often unable to meet the minimum threshold requirements of complex product system. Open innovation at the micro-level and collaborative innovation at the macro level have gradually replaced the traditional closed standalone model.

After a visit to Japan, Freeman noted that Japan could be considered as NSI [5]. Lundvall further argued that interactions within national contexts might be more effective than cooperation within industry or standalone within one firm [6]. NSI combines the claims that innovation is systemic [6], that innovation systems are evolving [7] and organized institutionally, and therefore influenced by and susceptible to government policies at national or regional levels [8,9]. NSI thus seeks to combine the perspectives of policy analysis, institutional analysis, and (neo-) evolutionary theorizing [10]. In a national system of innovation, redundancy plus uncertainty (information) constitutes its maximum entropy. Redundancy can be considered as options that have not (yet) been realized, whereas uncertainty provides a measure of the options that have already been realized [11]. Increased redundancy reduces relative uncertainty [12]. Redundancy is generated in triple-helix relations because of partial overlaps in providing different meanings to the events from political, managerial, and technological perspectives [13].

The TH was first defined by Etzkowitz & Leydesdorff, in terms of links among universities, industries, and government(s) as institutional relations [3]. Etzkowitz argued that systems are innovative insofar as they generate new options from synergies among geographical, technological, and organizational factors [10]. The relations among academia, industry and government can be redefined in the light of new technological options, and institutions can substitute for each other's functions to a certain extent. Universities can take on entrepreneurial roles to engage in the wider society on all scales in order to contribute to social and economic development [14], industry can organize academic education and research, The resulting overlay of relations and communications can develop a dynamic of its own [15]. The TH perspective becomes functional because it makes the synergetic pattern of university-industry-government relations in NSI clear.

Regarding the application of TH model in China, a large number of studies have emerged for analyzing the innovation system since Zhou introduced the concept of the TH relations in NSI into China [16]. These studies include, for example, the development of the Triple Helix model in a specific industrial field [17] or a specific region [18], the technology transfer between university and industry [19]. However, few of them have tried to provide a systematic evaluation of the implementation of the Triple Helix model in China [20], hitherto, the research methodologies to study or estimate accurately the synergy effect is still limited. Leydesdorff pointed out that the three main functionalities in the TH-triangle can be considered as (i) knowledge production (carried primarily by academia), (ii) wealth generation (industry), and (iii) normative control (governance) [21]. In order to build TH indicators of synergy, academic knowledge transfer capacity, industrial knowledge absorption capacity and innovation policy are reviewed as below.

#### *2.1. Academic Knowledge Transfer Capabilities*

Knowledge transfer between academia and industry is considered an important driver of innovation and economic growth as it eases the commercialization of new scientific knowledge within firms [22]. Bloedon and Stokes defined the concept of knowledge transfer as a process, by which knowledge concerning the making or doing of useful things contained within one organized setting is brought into use within another organizational context [23]. The capabilities of knowledge generation and transfer capabilities in academia, are progressively being recognized as an important factor for structural economic growth especially in contemporary knowledge economies, and higher education institutions (HEIs) and public research institution (PRIs) are generally accepted as places for science and knowledge creation [24], as a society improves its knowledge base by creating more efficient and effective ways of functioning. The flow of information and knowledge from researchers to the wider practice community through lectures, papers, patenting, licensing, joint ventures, spin-offs and other forms of knowledge dissemination and transfer, is often ineffective and problematic, therefore leaving what is commonly referred to as the research-practice gap [25].

The gap occurs when the research undertaken by academia is thought to have little or no relevance (usefulness) to the practice or profession it is portrayed to be assisting. Issues surrounding the 'appliedness' of research have been discussed over a long period of time [26]. Biglan categorized academic fields into 'applied' disciplines, which are generally linked to theory and knowledge being applied in a practical sense, as opposed to 'basic' or 'pure' research fields, which focus on developing theoretical and conceptual understanding [27]. In an empirical investigation on maintenance management models, Fraser et al. found that a leading engineering journal had empirical evidence rates as low as 1.5%, or put another way, out of 100 published articles on the topic, only 1.5 articles presented any form of links to practice [28].

Another issue which is believed to be intertwined with the increasing problems associated with the transfer of knowledge and the research-practice gap is the ambiguity of knowledge that transferred by academics. Tacitness and explicitness (related to knowledge ambiguity) moderated knowledge transfer negatively [29]. Although most researchers feel their work has clear relevance to decision-makers, but most decisionmakers think the research community is not helpful to them [30]. Fraser et al. discuss how the manufacturing and engineering literature is saturated with sophisticated mathematical/theoretical models [31]. While the criticism is mainly anecdotal, it is argued the many academics lack practical, industry-based experience, and are training engineering students and researching innovation problems, having never worked in the industry themselves [32]. The poor-level relevance of academic research to make a difference in solving societal problems and suggest some changes which need to occur, such as the closeness of a partnership relationship, consensus of goals, tolerance of cultural differences, and so on. Therefore, this paper characterizes academic knowledge transfer capabilities with three indicators, which are useful knowledge generation ability, knowledge interpretation ability and knowledge dissemination ability.

#### *2.2. Industrial Knowledge Absorbing Capabilities*

Grant confirms the importance of knowledge as the most strategically important resources of the firm [33], Kogut and Zander maintain knowledge is the main determinant of competitive advantage [34]. Accordingly, the strategic importance of knowledge strongly reinforces the relevance of absorptive capacity as a key resource in developing and increasing a firm's knowledge [35]. The knowledge-based view of absorptive capacity is an outgrowth of the resource-based view of the firm proposed by Barney which highlights the impact of partner contributions and outward knowledge transfer to absorptive capacity. According to Barney, firm resources are all capabilities, processes, attributes, assets, information, and knowledge controlled by a firm, which can be strategically manipulated to gain competitive advantage [36]. Organizational level absorptive capacity was introduced by economists Cohen and Levinthal seminal work explaining why organizations invest in research and development [37]. Building on the concept of dynamic capability proposed by Barney, Zahra and George furthered the theoretical base of absorptive capacity as a dynamic capability related to the management and successful exploitation of knowledge [38]. The knowledge-based view of absorptive capacity stresses the importance of promoting organizational learning, developing knowledge, enhancing open innovation, managing alliances, creating strategic variety, and impacting financial performance.

The relevance of ambiguity and absorptive capacity in the context of the research-practice collaboration was confirmed by Santoro and Bierly [29]. They showed that technological relatedness and technological capability (which increases absorptive capacity) were the most important facilitators of knowledge transfer in the process of collaborations. Volberda et al. highlight the impact other factors such as a dynamic environment have on the level of absorptive capacity [35].

Absorptive capacity conceptualized an organization's ability to exploit external knowledge through a sequential process to recognize the value of external knowledge, assimilate this new knowledge through exploratory learning, and apply assimilated knowledge to create new knowledge and value. Thus, this paper uses three indicators of "knowledge exploratory learning ability, knowledge-sharing ability and knowledge application ability" to characterize the industrial absorption ability.
