1. bookAHEAD OF PRINT
Journal Details
License
Format
Journal
eISSN
2444-8656
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
access type Open Access

Modelling and Simulation of Collaborative Innovation System in Colleges and Universities Based on Interpreted Structural Equation Model

Published Online: 30 Dec 2021
Volume & Issue: AHEAD OF PRINT
Page range: -
Received: 17 Jun 2021
Accepted: 24 Sep 2021
Journal Details
License
Format
Journal
eISSN
2444-8656
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Abstract

This article summarises 15 factors that affect the collaborative innovation of industry, university and research through literature research and establishes the innovation entity and innovation environment subsystem of the collaborative innovation system of colleges and universities based on the theory of synergy. By setting system boundaries and causal relationship analysis, the dynamic model of the collaborative innovation system in colleges and universities is established, and the model's validity is verified. Combining the actual work, the enlightenment of collaborative innovation management in colleges and universities is as follows: Develop collaborative innovation led by scientific and technological talents strategy. Strengthen the policy and system support for collaborative innovation of colleges and universities. Improve the collaborative innovation input mechanism of institutions of higher learning.

Keywords

MSC 2010

Introduction

China, which has entered the new economic normal, has put forward higher requirements for innovation. However, a phenomenon that cannot be ignored is that various innovative organisations still focus on allocating resources or competition. Still, they do not pay much attention to the coordination between the main body, departments and regions. Innovation is a complex process that covers three levels such as micro, meso (medium) and macro levels. Micro-level innovation emphasises that innovation is human self-realisation and that innovation requires recombining production factors and conditions to generate new technologies and products [1]. At the meso (medium) and macro levels, innovation is centred on industries and rises to national and regional innovation capabilities. The multi-level characteristics of innovation determine the diversity of its influencing factors. Innovative subjects are generally in a state of division in space, making the innovation process more complicated, leading to non-coordination becoming a constraint on development.

Collaborative innovation combines collaboration and innovation, which strengthens the feature of ‘synergy’ in innovation. Most scholars agree that collaborative innovation is a process from communication to coordination to cooperation and finally achieving synergy [2]. Under the intervention of the external environment, multiple collaborative innovation subjects produce a process of synergistic effects that a single subject cannot achieve innovation through complex, non-linear and autonomous interactions. In short, collaborative innovation guides the entire process of coordinated operation of various elements within the system for ‘innovation’.

The academic research on the collaborative innovation system roughly follows the ‘enterprise→ industry→ government-industry-university-research→ region.’ Early scholars focused their attention on companies with high innovation performance and thus sought entry points for synergy. Some scholars discussed the degree of synergy between technological innovation and organisational structure innovation and proposed that with the continuous emergence of high technology, organisations should pay more attention to organisational structure innovation rather than technological innovation [3]. Some scholars believe that the innovation performance of enterprises in the stage of organisational structure innovation is higher than that of technological innovation. Under the guidance of the spirit of open innovation, scholars’ thinking on collaborative innovation broke through corporate barriers and began to expand to universities, governments and industries.

From a domestic perspective, the research on the collaborative innovation system shows four obvious trends: First, research on the factors that influence collaborative innovation, such as enterprises, governments, industry, academia, regions, technologies and markets. The second is to discuss collaborative innovation methods, such as paths, models and mechanisms [4]. The third is to link the elements and their operating mechanisms. We can attribute these studies to the field of ‘innovation clusters.’ The fourth is the study of collaborative innovation ability or degree of synergy.

In summary, the previous results have laid a good foundation for this article to think deeply about collaborative innovation. But there are still shortcomings: First, the preliminary research is mostly fragmented with only partial discussions, which do not help the systematic understanding of collaborative innovation. The second is that the research mostly focuses on the subject of innovation, while other elements are not taken into consideration. The third is the incomplete analysis of the composition of the ‘innovation synergy system’ elements and the relationship between elements. Fourth, the proposed construction path of the ‘collaborative innovation system’ is too universal and not highly targeted. To further deepen the understanding of the ‘collaborative innovation system’, this article uses the explanatory structure model to establish a ‘collaborative innovation system structure model.’ This model analyses the structural characteristics of the collaborative innovation system and proposes the implementation path to promote the construction of China's ‘collaborative innovation system’.

Collaborative innovation architecture model

The collaborative innovation system has specific functions formed by combining several interconnected things in a certain order and internal relations. It emphasises the relationship between the various elements to achieve the same goal. It is well known that when developing or transforming a system, we must first understand the relationship between the various elements in the system. Only in this way can we develop or transform the system fully in a better way [5]. There are three reasons why this article uses the explanatory structural model method to model: First, the explanatory structural model uses a directed graph to express the relationship between the elements. The structure is very intuitive and easy for managers to understand. The second is that the explanatory structure model is a conceptual model that can transform ambiguous thoughts and opinions into intuitive and well-structured models. Existing research involves many elements. It is difficult to clarify hundreds of relationships with the human brain. We need to use computers. Third, this article must construct a multi-level hierarchical structure model based on prior knowledge and using a computer.

Element selection and relationship determination

Our selection of the elements required for modelling and determining the relationship is based on related models and related research. There are many structural models proposed by scholars around the theme of the collaborative innovation system. Some scholars position universities, industry and government as a three-in-one innovation model. Some scholars believe that enterprises, universities, research institutes and the government are the four executive bodies of the innovation system, and there is a two-way connection between the four. Some scholars proposed that the regional innovation system environment includes a soothing environment and a hard environment. The peaceful environment includes the market environment, legal protection and innovation culture [6]. The hard environment includes the basic conditions of the city, such as information access, road traffic, economic aggregates and innovative talents. Some scholars place small and medium-sized enterprises in the centre of innovation subjects. The government, universities, scientific research institutions and social service systems provide resources, funds, services and innovation needs for small and medium-sized enterprises to innovate. Some scholars believe that enterprises are the core of technological innovation. Governments, universities, scientific research institutions and social intermediary service organisations provide enterprises with platform construction, technical support, service support and needs. Some scholars have proposed a framework for analysing industry-university-research collaborative innovation from the three levels: strategic collaboration, knowledge collaboration and organisational collaboration. For the first time, his analysis jumped out of the ‘main body’ thinking mode, putting the main content strategy, organisation and knowledge of the collaborative process in the first place. From his ‘Theoretical Framework of Industry-University-Research Collaborative Innovation’, the government's policies, projects and institutional mechanisms play a supporting role.

In contrast, the financial institutions of intermediary organisations play a supporting role. Some scholars believe that universities and scientific research institutions, enterprises, industries and governments form a ‘four-wheel’ driving model connected through trust, convergence, platform and extension mechanisms. Some scholars attribute the cooperation motivation of collaborative innovation to market motivation, technology motivation, organisation motivation and learning motivation. In the triple helix model, some scholars divide the enterprise dimension into two sub-categories: producer service industry and manufacturing industry. He believes that regional industrial collaborative innovation is accomplished through government, university, productive service industry, manufacturing and industrial interaction. In the ‘Double Diamond Model,’ enterprises, governments, universities, scientific research institutions and intermediary institutions are the main innovation entities [7]. The market, technological, policy system and social-cultural environments are the external environments of collaborative innovation, which play an intervening role in collaborative innovation. One has to summarise and sort out the elements and sources of the collaborative innovation system obtained from the previous research. The content includes the elements of the triple helix theory of mainstream research (industry-university-research), four main bodies (government-industry-university-research), five main bodies (government-industry-university-research funds), and six main elements (government-industry-university-research finance), as well as those in the field of research. Elements are related to the collaborative innovation system. According to the element relationship identified in the literature, we sorted out 39 elements of the collaborative innovation system (see Table 1).

Relationship between elements of the regional collaborative innovation system

Code Feature name Code Feature name Code Feature name Code Feature name

S1 Innovation S11 organise S21 Agency S31 Creativity
S2 need S12 Knowledge S22 NGO S32 Capital investment
S3 customer S13 Culture S23 Service S33 Innovation platform
S4 market S14 Technological innovation S24 Cooperation motivation S34 Financial institution
S5 government S15 Technology diffusion S25 Learn S35 Innovation cluster
S6 industry S16 System S26 Innovation environment S36 Agglomeration
S7 University S17 information S27 Innovation network S37 Innovation performance
S8 Research institute S18 Innovation risk S28 mutual benefit S38 Economic Growth
S9 enterprise S19 competitor S29 Innovation resources S39 Economic space
S10 Division of labor S20 Policy S30 Human Resources

NGO, non-governmental organizations

Establish an explanatory structure model

First, establish the adjacency matrix. Establish the upper triangular relationship matrix based on the relationship of the element Si, Sj(i, j = 1, 2,···, 39). See formula (1). Write the adjacency matrix A39×39 according to the upper triangular relation matrix. {(1)Si×Sj,SiandSjarerelatedtoeachother;(2)SiSj,SiandSjarenotrelated;(3)SiSj,SiisrelatedtoSj,andSjhasnothingtodowithSi;(4)SiSj,SjisrelatedtoSi,andSihasnothingtodowithSj; \left\{ {\matrix{ {(1){S_i} \times {S_j},{S_i}{\kern 1pt} {\rm{and}}{\kern 1pt} {S_j}{\kern 1pt} {\rm{are}}\;{\rm{related}}\;{\rm{to}}\;{\rm{each}}\;{\rm{other}};} \hfill \cr {(2){S_i} \circ {S_j},{S_i}{\kern 1pt} {\rm{and}}{\kern 1pt} {S_j}{\kern 1pt} {\rm{are}}\;{\rm{not}}\;{\rm{related;}}} \hfill \cr {(3){S_i} \wedge {S_j},{S_i}{\kern 1pt} {\rm{is}}\;{\rm{related}}\;{\rm{to}}{\kern 1pt} {S_j},{\kern 1pt} {\rm{and}}{\kern 1pt} {S_j}{\kern 1pt} {\rm{has}}\;{\rm{nothing}}\;{\rm{to}}\;{\rm{do}}\;{\rm{with}}{\kern 1pt} \;{S_i};} \hfill \cr {(4){S_i} \vee {S_j},{S_j}{\kern 1pt} {\rm{is}}\;{\rm{related}}\;{\rm{to}}{\kern 1pt} {S_i},{\kern 1pt} {\rm{and}}{\kern 1pt} {S_i}{\kern 1pt} {\rm{has}}\;{\rm{nothing}}\;{\rm{to}}\;{\rm{do}}\;{\rm{with}}\;{\kern 1pt} {S_j};} \hfill \cr } } \right. Second, calculate the reachable matrix. The reachable matrix R is obtained by adding the adjacency matrix to the identity matrix after n − 1 times of operations. The reachable matrix conforms to the transition characteristics: A1A2Ar1=Ar,rn1 {A_1} \ne {A_2} \ne \ldots \ne {A_{r - 1}} = {A_r},\quad r \le n - 1 The formula n is the order of the matrix. Ar−1 = (A + I)r−1 = R is calculated as: A1A2A10=A11=R {A_1} \ne {A_2} \ne \ldots \ne {A_{10}} = {A_{11}} = R The third is regional division. When dividing the area, it is necessary to divide the relationship between the elements into reachable and unreachable. We need to determine which elements are connected, that is, divide the system into several parts or sub-parts that are related. Determine the underlying elements through the operations on the lookahead set and the reachable set, and then determine the connectivity of these elements [8]. If the intersection of the reachable sets of the elements is empty, the elements belong to different connected domains, otherwise the same connected domain. Find the lowest element: T={S3,S18,S19} T = \{ {S_3},{S_{18}},{S_{19}}\} Because of R(S3) ∩ R(S18) ≠ Φ. So S3, S18 belongs to the same connected domain. Similarly, S3, S18, S19 belongs to the same connected domain. Then divide between levels. We divide all the elements in the system into different levels based on the reachability matrix. The different layer elements are obtained in turn as follows: T={S3,S18,S19};L1={S2,S11};L2={S5,S7,S8,S9,S22};L3={S13,S16};L4={S25};L5={S20,S24};L6={S7,S28};L7={S21,S29,S33,S34};L8={S23};L9={S6,S26};L10={S36};L11={S10,S12,S17};L12={S14,S15,S27,S30,S32,S35};L13={S31};L14={S1};L15={S37,S38,S39}; \matrix{ {\,\;\;T = \{ {S_3},{S_{18}},{S_{19}}\} ;\quad {L_1} = \{ {S_2},{S_{11}}\} ;\quad {L_2} = \{ {S_5},{S_7},{S_8},{S_9},{S_{22}}\} ;} \hfill \cr {\;{L_3} = \{ {S_{13}},{S_{16}}\} ;\quad {L_4} = \{ {S_{25}}\} ;{L_5} = \{ {S_{20}},{S_{24}}\} ;\quad {L_6} = \{ {S_7},{S_{28}}\} ;} \hfill \cr {\;{L_7} = \{ {S_{21}},{S_{29}},{S_{33}},{S_{34}}\} ;\quad {L_8} = \{ {S_{23}}\} ;\quad {L_9} = \{ {S_6},{S_{26}}\} ;\quad {L_{10}} = \{ {S_{36}}\} ;} \hfill \cr {{L_{11}} = \{ {S_{10}},{S_{12}},{S_{17}}\} ;\quad {L_{12}} = \{ {S_{14}},{S_{15}},{S_{27}},{S_{30}},{S_{32}},{S_{35}}\} ;\quad {L_{13}} = \{ {S_{31}}\} ;} \hfill \cr {{L_{14}} = \{ {S_1}\} ;\quad {L_{15}} = \{ {S_{37}},{S_{38}},{S_{39}}\} ;} \hfill \cr } According to the calculation results, draw a hierarchical directed graph and then build a ‘collaborative innovation system structure model’ according to the system's hierarchical structure model (see Figure 1). Considering a feedback mechanism in the system, we added a feedback line (dotted line).

Fig. 1

Collaborative innovation architecture model

The ‘collaborative innovation system structure model’ has the following two characteristics: First, it breaks through the subject constraints of the three-helix, four-subject, five-subject and six-subject conceptual models. It describes the collaborative innovation system more comprehensively. Second, it has a clear hierarchical structure, which is intuitive and easy for decision-makers to understand.

Structural characteristics of the collaborative innovation system
Structural characteristics of the collaborative innovation system

The collaborative innovation system is complex. The system includes 39 elements, divided into 17 layers. The layer with the most elements is the layer L13, which has 6 elements. The layer with the fewest elements is the layer L4, L7, L9, L11, L14, L15, each with 1 element. There is a direct or indirect relationship between these elements and other elements. Factors such as the government, intermediaries, innovation resources, innovation platforms, financial institutions, etc., have a direct impact on ‘services’, and the direct influence factors of these factors become the indirect influence factors of ‘services’.

The collaborative innovation system has two characteristics: closed and open. From a static perspective, the ‘collaborative innovation system’ is an independent and closed system with innovation as the goal [9]. The constituent elements of the ‘collaborative innovation system’ originate from the interior of a certain region (organisation or department). From a dynamic perspective, developing the ‘collaborative innovation system’ is a process from small to large and weak to strong. A region (organisation or department) collaborative innovation system and B region (organisation or department) collaborative innovation system have energy exchange, information exchange and resource exchange, which restrict and influence each other. Therefore, in the context of economic integration, the construction of a ‘collaborative innovation system’ will inevitably take ‘openness’ as its main feature. It is necessary to achieve dynamic synergy in fields such as the world and the entire industry chain.

The collaborative innovation system includes seven functional modules (see Figure 2). They are innovation orientation, innovation subject, innovation resources, innovation environment, government functions, innovation capabilities and economic influence from bottom to top. These seven functional modules run around their respective centres. Still, there is a direct or indirect relationship with each other, which can drive the delicate operation of the large-scale system. The elements contained in each module are shown in Table 2.

Fig. 2

Seven functional modules of the collaborative innovation system

Seven functional modules and their elements

Function classification Contains elements

Innovation-oriented Customers, competitors, innovation risks
Innovation subject Enterprises, research institutions, governments, universities, NGO, intermediary organisations
Creativity Technological innovation, technological diffusion, capital investment, human resources, innovation networks, innovation clusters, innovation capabilities
Innovation environment Culture, systems, policies, markets, financial institutions, competitors, innovation platforms, services
Innovation resources Human resources, capital investment, technological innovation, service
Government function System, culture, policy, service, market, innovation network
Affect the economy Economic growth, economic space, innovation performance

NGO, non-governmental organizations

The innovation-oriented functional module is at the bottom layer of the diagram, and the content includes three elements: customers, competitors and innovation risks. Different customer groups have different needs at different stages, and they guide innovation subjects. To obtain a price advantage, SMEs will focus on innovation. The two grow together in the game. Innovation risk is a negative element that hinders innovation behaviour. The smaller the risk, the greater the motivation for innovation entities to innovate [10]. The main functional innovation modules are distributed in the figure's second, third, and eighth layers. The content includes six elements: organisations, enterprises, universities, research institutions, intermediary agencies and non-governmental organisations (NGO). ‘Innovation risk’ directly impacts companies, universities and research institutions, while the ‘demand’ generated by customers directly impacts companies, governments, universities and research institutions. The functional module of innovation capability is in the 13th layer of the figure. The content includes six elements: technological innovation, technological diffusion, capital investment, human resources, innovation network and innovation clusters. The innovation resource modules are distributed in the 8th, 9th and 13th layers of the figure, and the content includes the four elements of human resources, capital investment, technological innovation and service.

Innovation resources are an important source of regional innovation capabilities, but they are not the only source. Government function modules are distributed in the 4th, 6th, 7th, 9th and 13th layers, including six systems, culture, policy, service, market and innovation network elements. The government's influence on the industry is realised through the market as an element; the government's influence on the ‘innovation environment’ is realised through the ‘innovation platform’ as ‘service’ and then the ‘service’ as the ‘innovation environment’. The innovation environment modules are distributed in the first layer, the fourth layer, the sixth layer, the seventh layer, the eighth layer, the ninth layer and the tenth layer. The content includes culture, system, policy, market, financial institutions, competitors and innovation. The eight elements of platform and service. These are the soft elements of the innovation environment. The distribution of influencing economic modules is at the top layer (layer 16) of the graph, including three elements: economic growth, innovation performance and economic space. In addition, elements such as culture, learning, systems, cooperation motivation and common interests constitute a ‘synergy chain’ that guides collaborative innovation. Market, industry and division of labour have a relatively direct impact on innovation networks, technological innovation and human resources.

Important nodes in system operation

In addition to focusing on the main body of collaborative innovation, the operation of the guarantee system also needs to pay attention to nodes such as culture, environment, knowledge, resources and government.

Cultural node

Most innovative regions or organisations have a good innovation culture, and the culture expresses values and visions. Over the past 30 years of China's reform and opening up, the rapid economic development has formed a successful culture of ‘emphasis on economy, the light of spirit’, ‘results, light process’, and ‘success, light struggle’ in the whole society [11]. This culture is contrary to the growth environment with a relaxed preference for innovation. Getting through cultural nodes requires correcting wrong values and changing the value orientation of the organisation and society of ‘using success as heroes.’

Environment node

‘Innovation environment synergy’ is another important point besides ‘subject synergy.’ ‘Innovation environment synergy’ promotes integration with a more consistent code of conduct, system, and culture and reduces the obstacles to collaboration between subjects. As far as the service environment is concerned, standardisation can easily achieve the goal, but the coordination of deep-level elements such as culture and system requires a long wait. Opening up environmental nodes requires reducing the gap in the system and driving environmental balance through service standardisation.

Knowledge node

The knowledge base is the foundation of the regional innovation system. The regional nature of the knowledge base is the main reason the innovation system is difficult to transplant. Enterprises gather around universities and research institutes. The spatial gathering of high-tech enterprises in Zhongguancun, China, Silicon Valley in the United States, Hsinchu Science and Technology Park in Taiwan, China, and Bangalore in India eliminates the regional knowledge.

Resource node. For most regions, the elements of innovation resources are not lacking but unreasonable in structure. Some resources are too large, and some resources are too few. Because different regions have the potential to protect their resource advantages, the subjects have essential differences in resource use, management decision-making, key tasks, and personnel deployment [12]. Therefore, in the process of collaborative innovation, it often shows the competition for resources in different regions and different departments, rather than sharing. To open up resource nodes, it is necessary to enhance the awareness of resource complementarity.

Government nodes

Collaborative innovation requires effective collaboration among government departments. Because some of the ‘coordination’ responsibilities are not included in departmental responsibilities, some are not included in the evaluation index system of the leadership team or included in the evaluation of employees. These tasks beyond the responsibilities of the department and employees are likely to be deliberately forgotten or ignored. Therefore, it is fundamental to drive joint stability with the system. Getting through government nodes requires strengthening the department's prior agreement.

System path to promote collaborative innovation
Create a culture of innovation and advocate failure studies

To create an innovative cultural atmosphere, the concepts of encouraging innovation and tolerating failure must be placed in the same dimension, so we proceed from the following four aspects. The first is to give play to the role of the new and old media, set up a ‘civilian (grassroots) innovation model’ and increase the intensity of positive media coverage. The second is building a ‘crowd creation space’ network and physical platforms [13]. Establish a ‘Crowd Creation Space’ website, link it with national and provincial science and technology information and service platforms, and encourage small and micro technological innovation enterprises, innovative individuals and innovative teams to become platform members. The third is establishing a ‘real-name system idea bank’ and encouraging enterprises to write ‘war books.’ Welcome innovative individuals and innovative teams to actively prepare for the battle. Innovative, collaborative services are provided by technology exchanges, innovation stations, productivity centres, etc. The fourth is to promote the study of failure and establish a database of failure knowledge. Learn from the experience of the Japan Agency for the Promotion of Science and Technology (JST) in establishing a failed knowledge database. While encouraging failure studies, it collects failure cases in organisational innovation and conducts event descriptions and context analysis.

Innovate the collaborative model and leverage the advantages of KICs/PP

First, learn from the practice of establishing innovative engineering colleges in Europe and effectively integrate higher education, enterprises and research institutions around important areas of future development. Aiming at the key areas of China's next innovation and development stage, relying on the well-known university platform to establish the ‘China Institute of Innovation and Technology’ to form 10 knowledge and innovation communities (KICs). We need to achieve two goals: to gather global talents and use national and departmental projects to support mobile projects. This rapidly improves China's level in these key research areas. The second is to cultivate top entrepreneurial talents through entrepreneurship education and reform higher education courses and teaching models to drive higher education reform. In this way, it promotes the combination of theory and practice. Also play the role of the patent alliance as a link.

Focus on environmental collaboration and promote tacit knowledge sharing

We need to use environmental synergy to drive the sharing of tacit knowledge. First, start the apprenticeship model among the innovative subjects, and promote the dissemination of knowledge among individuals by ‘passing and leading.’ Second, pay attention to the impact of uncoded knowledge on the collaborative innovation system, and create a soft and hard environment conducive to spreading tacit knowledge. In terms of the hard environment, it is necessary to create a good production and living environment for innovative people and provide more room for development. Create a slow culture in innovation resource clusters such as industrial, innovation and technology parks. High-end service carriers such as water bars, book bars, tea rooms, coffee houses, Internet bars and cultural salons provide innovative groups convenient for communication and leisure. A peaceful environment, industry associations, technology alliances, seminars, etc., can provide in-depth communication opportunities for innovative groups.

Conclusion

The collaborative innovation system is a large and complex system that includes seven functional modules: innovation orientation, innovation subject, innovation resources, innovation environment, innovation capabilities, government functions and economic impact. In addition to the main body, the coordination of elements such as culture, environment, knowledge, resources and the government is the focus of system operation. The path to systematically promote collaborative innovation is to create an innovation culture and advocate failure studies. Innovate the collaborative model and give full play to the advantages of the knowledge and innovation community and patent alliance. Pay attention to environmental collaboration and promote tacit knowledge sharing. Cultivate innovation clusters and upgrade industrial and technological clusters. Develop a sharing plan to realise the synergy of the main body's interests.

Fig. 1

Collaborative innovation architecture model
Collaborative innovation architecture model

Fig. 2

Seven functional modules of the collaborative innovation system
Seven functional modules of the collaborative innovation system

Relationship between elements of the regional collaborative innovation system

Code Feature name Code Feature name Code Feature name Code Feature name

S1 Innovation S11 organise S21 Agency S31 Creativity
S2 need S12 Knowledge S22 NGO S32 Capital investment
S3 customer S13 Culture S23 Service S33 Innovation platform
S4 market S14 Technological innovation S24 Cooperation motivation S34 Financial institution
S5 government S15 Technology diffusion S25 Learn S35 Innovation cluster
S6 industry S16 System S26 Innovation environment S36 Agglomeration
S7 University S17 information S27 Innovation network S37 Innovation performance
S8 Research institute S18 Innovation risk S28 mutual benefit S38 Economic Growth
S9 enterprise S19 competitor S29 Innovation resources S39 Economic space
S10 Division of labor S20 Policy S30 Human Resources

Seven functional modules and their elements

Function classification Contains elements

Innovation-oriented Customers, competitors, innovation risks
Innovation subject Enterprises, research institutions, governments, universities, NGO, intermediary organisations
Creativity Technological innovation, technological diffusion, capital investment, human resources, innovation networks, innovation clusters, innovation capabilities
Innovation environment Culture, systems, policies, markets, financial institutions, competitors, innovation platforms, services
Innovation resources Human resources, capital investment, technological innovation, service
Government function System, culture, policy, service, market, innovation network
Affect the economy Economic growth, economic space, innovation performance

Tseng, F. C., Huang, M. H., & Chen, D. Z. Factors of university–industry collaboration affecting university innovation performance. The Journal of Technology Transfer., 2020; 45(2): 560–577 TsengF. C. HuangM. H. ChenD. Z. Factors of university–industry collaboration affecting university innovation performance The Journal of Technology Transfer 2020 45 2 560 577 10.1007/s10961-018-9656-6 Search in Google Scholar

Aghili, A. Complete Solution for The Time Fractional Diffusion Problem with Mixed Boundary Conditions by Operational Method. Applied Mathematics and Nonlinear Sciences., 2021; 6(1): 9–20 AghiliA. Complete Solution for The Time Fractional Diffusion Problem with Mixed Boundary Conditions by Operational Method Applied Mathematics and Nonlinear Sciences 2021 6 1 9 20 10.2478/amns.2020.2.00002 Search in Google Scholar

Martínez-Costa, M., Jiménez-Jiménez, D., & Dine Rabeh, H. A. The effect of organisational learning on interorganisational collaborations in innovation: an empirical study in SMEs. Knowledge Management Research & Practice., 2019; 17(2): 137–150 Martínez-CostaM. Jiménez-JiménezD. Dine RabehH. A. The effect of organisational learning on interorganisational collaborations in innovation: an empirical study in SMEs Knowledge Management Research & Practice 2019 17 2 137 150 10.1080/14778238.2018.1538601 Search in Google Scholar

Distanont, A., & Khongmalai, O. The role of innovation in creating a competitive advantage. Kasetsart Journal of Social Sciences., 2020; 41(1): 15–21 DistanontA. KhongmalaiO. The role of innovation in creating a competitive advantage Kasetsart Journal of Social Sciences 2020 41 1 15 21 10.1016/j.kjss.2018.07.009 Search in Google Scholar

de Zubielqui, G. C., Lindsay, N., Lindsay, W., & Jones, J. Knowledge quality, innovation and firm performance: a study of knowledge transfer in SMEs. Small Business Economics., 2019; 53(1): 145–164 de ZubielquiG. C. LindsayN. LindsayW. JonesJ. Knowledge quality, innovation and firm performance: a study of knowledge transfer in SMEs Small Business Economics 2019 53 1 145 164 10.1007/s11187-018-0046-0 Search in Google Scholar

Ansari, J. A. N., & Khan, N. A. Exploring the role of social media in collaborative learning the new domain of learning. Smart Learning Environments., 2020;7(1): 1–16 AnsariJ. A. N. KhanN. A. Exploring the role of social media in collaborative learning the new domain of learning Smart Learning Environments 2020 7 1 1 16 10.1186/s40561-020-00118-7 Search in Google Scholar

Phong, L. B., & Hui, L. Creating competitive advantage for vietnamese manufacturing and service firms: the role of collaborative culture and innovation capability. International Journal of Business Administration., 2019; 10(2): 32–42 PhongL. B. HuiL Creating competitive advantage for vietnamese manufacturing and service firms: the role of collaborative culture and innovation capability International Journal of Business Administration 2019 10 2 32 42 10.5430/ijba.v10n2p32 Search in Google Scholar

Asada, A., Basheerb, M. F., Irfanc, M., Jiangd, J., & Tahir, R. Open-Innovation and Knowledge Management in Small and Medium-Sized Enterprises (SMEs): The role of External Knowledge and Internal Innovation. Revista Argentina de Clínica Psicológica., 2020; 29(4): 80–90 AsadaA. BasheerbM. F. IrfancM. JiangdJ. TahirR. Open-Innovation and Knowledge Management in Small and Medium-Sized Enterprises (SMEs): The role of External Knowledge and Internal Innovation Revista Argentina de Clínica Psicológica 2020 29 4 80 90 Search in Google Scholar

Bustinza, O. F., Gomes, E., Vendrell-Herrero, F., & Baines, T.Product–service innovation and performance: the role of collaborative partnerships and R&D intensity. R&D Management, 2019; 49(1): 33–45. BustinzaO. F. GomesE. Vendrell-HerreroF. BainesT .Product–service innovation and performance: the role of collaborative partnerships and R&D intensity R&D Management 2019 49 1 33 45 10.1111/radm.12269 Search in Google Scholar

Audretsch, D. B., & Belitski, M.The limits to collaboration across four of the most innovative UK industries. British Journal of Management., 2020; 31(4): 830–855 AudretschD. B. BelitskiM. The limits to collaboration across four of the most innovative UK industries British Journal of Management 2020 31 4 830 855 10.1111/1467-8551.12353 Search in Google Scholar

Shahzad, F., Xiu, G., Khan, I., Shahbaz, M., Riaz, M. U., & Abbas, A. The moderating role of intrinsic motivation in cloud computing adoption in online education in a developing country: a structural equation model. Asia Pacific Education Review., 2020; 21(1): 121–141 ShahzadF. XiuG. KhanI. ShahbazM. RiazM. U. AbbasA. The moderating role of intrinsic motivation in cloud computing adoption in online education in a developing country: a structural equation model Asia Pacific Education Review 2020 21 1 121 141 10.1007/s12564-019-09611-2 Search in Google Scholar

Scarpellini, S., Valero-Gil, J., Moneva, J. M., & Andreaus, M. Environmental management capabilities for a “circular eco-innovation”. Business Strategy and the Environment., 2020; 29(5): 1850–1864 ScarpelliniS. Valero-GilJ. MonevaJ. M. AndreausM. Environmental management capabilities for a “circular eco-innovation” Business Strategy and the Environment 2020 29 5 1850 1864 10.1002/bse.2472 Search in Google Scholar

Lee, T. D., Park, H., & Lee, J. Collaborative accountability for sustainable public health: A Korean perspective on the effective use of ICT-based health risk communication. Government information quarterly., 2019; 36(2): 226–236 LeeT. D. ParkH. LeeJ. Collaborative accountability for sustainable public health: A Korean perspective on the effective use of ICT-based health risk communication Government information quarterly 2019 36 2 226 236 10.1016/j.giq.2018.12.008712560832288166 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo