Study on the Spatial Structure and Evolution Stage of Industrial R&D Ecosystem Based on Evolutionary Biology.

The insufficient innovation ability is the biggest bottleneck for the upgrading of creative industry in China. Thus, it is an important way to structure the good R&D innovative ecosystem around perfecting the innovative chain for improving the innovation ability of creative industry. This paper discusses the theoretical connotation of the R&D innovation ecosystem of creative industry, studies the spatial structure of the R&D innovation ecosystem, and proposes the fact that the innovative population of R&D innovation ecosystem in creative industry can be divided into original innovative population, technological innovation population, innovation service population, innovation input population, and system innovation population. The research on the demand, financing, and business model of R&D breakthrough innovation in China's industry cannot be separated from the analysis of the evolution stages of various industries, because different strategies need to be formulated at different stages of development, and it is very important to accurately grasp the stage of industrial development. By introducing logistic curve equation, the breakthrough innovation of industrial R&D is divided into four stages: incubation stage, growth stage, evolution stage, and maturity stage, and the different characteristics of different stages of industry development are analyzed. Taking the breakthrough innovation of R&D in industry as an example, this paper uses the logistic empirical model based on factor analysis to study it. The results show that the breakthrough innovation of R&D in industry has entered a maturity stage.

1. Introduction

Compared with foreign countries, China's industrial R&D breakthrough innovation development is relatively late. Vernon was the first to study the stage of industrial development. On the basis of Posner's technology gap theory, he put forward the product life cycle theory, which divides products into three stages: new product introduction, growth and maturity, and standardization. Subsequently, a series of studies on the law of industrial growth were carried out by scholars. Gort Michael studied the market evolution, divided it into five stages, and established the G-K model. Based on the G-K model, Klep and Graddy divide the industrial life cycle into three stages: growth, stability, and elimination. Peres calls the first two or three decades of revolutionary change as the period of introduction and the latter two or three decades as the period of expansion. In order to further investigate the relationship between science and technology revolution and industry, Peres divides the introduction period and expansion period into two parts, namely, outbreak period, fanaticism period, synergy period, and maturity period [1].

Breakthrough innovation in R&D of China's s industry started relatively late. Domestic scholars have less research on the evolution stage of breakthrough innovation in R&D of China's s industry, focusing mainly on qualitative analysis of the evolution stage. Duan analysis on technological system, innovation mode, leading design, industrial scale, and market environment of breakthrough innovation in industrial R&D shows that most of the breakthrough innovation in industry is in the incubation or growth period. Gu pointed out that the breakthrough innovation of industrial R&D originated from general industry (seed stage) and came into being through scientific selection mechanism, while the emerging industry (cultivation stage), leading industry (evolution stage), and pillar industry (maturity stage) represented the development of breakthrough innovation of industrial R&D in three different stages of development. Zhu pointed out that, in the critical period of the transition from the introduction period to the expansion period of the fifth technological revolution, the key for cultivating the breakthrough innovation of R&D in China's industry is to grasp the inherent requirements and development trends of the new paradigm of technology and economy [2].

From the current research situation, scholars focus on qualitative analysis of the judgment of the breakthrough innovation evolution stage of industrial R&D but lack quantitative analysis. This paper attempts to use logistic curve model and factor analysis-based logistic empirical model, from a quantitative point of view, taking the breakthrough innovation of industrial R&D as an example, to analyse the evolution stage of breakthrough innovation of industrial R&D [3].

The rest of this paper is organized as follows: Section 2 discusses logistic curve model for judging the evolution stage of breakthrough innovation, followed by empirical model for judging the evolution stage of breakthrough innovation in industrial R&D in Section 3. The theoretical connotation of creative industrial R&D innovation ecosystem is discussed in Section 4, and Section 5 concludes the paper with summary and future research directions.

2. Logistic Curve Model for Judging the Evolution Stage of Breakthrough Innovation

2.1. Logistic Curve and Evolution Stage Index

Van Dyne assumes that the life cycle is S-shaped and that the general evolutionary model of life is S-shaped. Logistic curve is a typical S-shaped curve model which quantitatively identifies the growth stage of life. Because the growth of industry is similar to that of life, the development curve is presented as S-shaped. Therefore, this paper introduces logistic curve to study the stage of breakthrough innovation development of industry. The stage index of industrial development is used to measure the breakthrough innovation evolution stage of industrial R&D [4]. Assuming that the stage index Y of breakthrough innovation evolution stage of industrial R&D changes with time, it can be described by the following formula, in which α is the development potential coefficient of breakthrough innovation in industrial R&D and β is the saturation R&D of breakthrough innovation development in s industry. In theory, with the passage of time, β can only be approached and cannot be realized. For type separation of variables, integral can be obtained:

After finishing the formulas, we can get

Equation (3) is logistic equation; among them, e=α − Y1/Y1.

For (1), the following formulas can be obtained by calculating the first and second derivatives, respectively:

According to the above analysis, logistic curve has three inflection points: (t1, αβ/3), (t0.5, αβ/2), (t2, αβ/3).

The corresponding evolution stage indices of t1, t0.5, and t2 are , β/2, and , about 0.211β, 0.500β, and 0.789β, and the acceleration reaches ,0,-, respectively.

2.2. Division of Breakthrough Innovation Evolution Stages in Industrial R&D

From the above analysis, three logistic inflection points divide the curve into four sections, each representing different development periods. Therefore, this paper divides the breakthrough innovation of industry into four periods, namely, incubation period, growth period, development period, and maturity period [5]. The corresponding development index, development speed, and acceleration are shown in Figure 1.

Figure 1

Index, development speed, and acceleration curve of breakthrough innovation evolution stage in industrial R&D.

Incubation period (0 < t < t1): it is clearly evident from Figure 1 that in this period the development of industry is in the primary stage and there is a large space for development, but there are a lot of uncertainties of technological factors, various technological routes coexist, there is no clear leading design, and technological innovation is gradually active.

Growing period (t1 < t < t0.5k): it is clearly evident from Figure 1 that the breakthrough innovation of R&D in industry reaches 50% of the saturation R&D. During this period, the speed of industrial development reached the fastest, but the acceleration began to decrease, indicating that the potential of industrial development is decreasing. The growth period is the initial development period of breakthrough innovation in industry research and development. At this stage, there is no dominant technology in the industry, and enterprise innovation is very active. Competitors mainly compete for the ownership of dominant technology, because enterprises with dominant technology can obtain huge benefits that are close to monopoly and even affect the future development trend of the industry. Each competitor sells its own technology, consumers show their own consumption preferences, and each technology occupies a certain share in the market [6].

Development period (t0.5k < t < t2): it is clearly evident from Figure 1 that in this stage the industrial development tends to be saturated and the driving force for development is insufficient. In the period of industrial development, technological differences lead to differences in production costs. After fierce competition, some manufacturers have acquired dominant technology ownership because of advanced technology and low production costs, and the speed of industry innovation has slowed down. For example, in the incubation period of mobile communication industry, there are two “tracks” of cellular communication and satellite communication, but in the development period cellular communication has become the dominant technology [7].

Maturity period (t2 < t < ∞): it is clearly evident from Figure 1 that the breakthrough innovation development index of industry is close to saturation R&D, which indicates that the industry has reached saturation and the development tends to be stable. At this stage, the leading technology has been formed, the product technology and production technology are mature, the existing technology is close to the “natural limit,” the speed of technological innovation is further slowed down or even stagnated, and the national and industrial standards are basically complete, which can enter the stage of industrial production. Research on higher-level technology has begun within the industry. At this stage, the market selection mechanism has been strengthened, mergers and acquisitions have increased greatly, the scale of enterprises is in the stage of expansion, and the market structure tends to be centralized [8].

Through the above four stages of industrial development, we can see that in different stages of development the speed and acceleration of industrial development are not the same and the characteristics of industrial development are also different. On the whole, it conforms to the law of development from development to growth and tends to be saturated [9].

3. An Empirical Model for Judging the Evolution Stage of Breakthrough Innovation in Industrial R&D

3.1. Establishment and Operation of Innovation Partnership in Development Team

3.1.1. Partnership Building

If an enterprise wants to develop better and faster, it must change and upgrade its operation mode. Many enterprises do not want to change the original mode of operation, the old mode of enterprise development, leading to business stagnation, or even a large loss phenomenon. The establishment of R&D team innovation partnership is an important reflection of the upgrading of business mode. After a detailed analysis of the overall situation of enterprises, enterprises will select innovation partners according to the needs of enterprises, determine the selection procedures and principles of innovation partners, establish evaluation criteria, collect information of innovation partners, and use certain tools and technical methods to select innovation partners [10].

In today's society, the pace of the information age is constantly accelerating, and the cooperation of dynamic alliance is also developing rapidly. In order to keep up with the rhythm of the times, enterprises constantly improve their competitiveness in the market competition. In order to improve the competitive advantage, many enterprises began to make strong alliances and strengthen the win-win relationship between enterprises. Therefore, how to choose innovation partners has become a key problem. In order to solve this problem, mobile phone and organization of enterprise information is a key prerequisite. It is directly related to the choice of a good and appropriate innovation partner to achieve win-win cooperation.

Enterprises often consider efficiency when choosing innovation partners, so, in the process of screening, they tend to quickly exclude most of the options and select few high-quality partners. In this exclusion process, some scientific means and methods will be used to conduct comprehensive selection. They will consider whether they will choose the enterprise as their ultimate innovation partner in terms of advantages, satisfaction, and nature of the enterprise.

In the process of partner selection, the combination of rough and fine selection is often used to select partners efficiently. Firstly, enterprises will be screened on the nature of the enterprise, and some of the nature of the company is not compatible with their own or do not want to be directly ignored. Then, it will carefully analyse a series of meticulous aspects such as data investigation, data analysis, and operation situation [11] of the target enterprises that may remain to cooperate and consider the strength and market advantages of the enterprise comprehensively, so as to form a dynamic alliance and win-win cooperation with them eventually.

From the perspective of relationship, we need to consider the following aspects: first, we should consider the duration of the alliance; second, we should consider the frequency of links between enterprises in alliance cooperation; third, we should consider the channels of links between enterprises; fourth, we should consider the symmetry of enterprises in dynamic alliance; fifth, we should promote each other among enterprises. Consider progress and win-win cooperation. Consider and collect data from the above five aspects, so as to find the appropriate innovation partners scientifically and efficiently.

But it is not a simple matter to find the most suitable and ideal alliance object from the vast data ocean. We need to do a lot of work. We need to take more factors into full consideration in the selection of alliance object. When choosing their own alliance partners, enterprises can find the ideal partners by summarizing the past cooperation experience and comparing the partners that they want to choose. In addition, enterprises should continue to collect data and information about potential partners and constantly understand the potential partners thoroughly and comprehensively.

In the process of selecting innovation partners, we need to consider and determine the selection index system of R&D team relationship for innovation partners, which is the most basic and critical part of the selection of innovation partners and the basis for our final decision-making. This system will make enterprises more efficient and scientific in choosing innovation partners [12].

At present, there are still some biases in the research of Chinese scholars in this area. The main focus is mostly on the quality of products, market prices, profits, and so on. For the lack of research on the ability of enterprises, the most important aspect of the credibility of enterprises has not enough attention. Therefore, in the future, more attention should be paid to the ability of enterprises, the reputation of enterprises, and the attitude of cooperation, so as to provide better reference and service for the dynamic development of enterprises.

3.1.2. Operations of Partnerships

Partnership operation is a complex process which is constantly changing, and it is affected by many factors. A large number of investigations and studies have been carried out both at home and abroad. In this paper, the influencing factors will be sorted out and analyzed from the following aspects.

Communication needs to be carried out in accordance with the procedures. Communication procedures are important. Communication behavior affects whether the organization cooperates smoothly or not. There are common interests between the two sides, so effective communication is needed. Communication behavior can be discussed from three aspects: communication quality, degree of information sharing, and degree of planning and goal participation [13].

Communication quality affects the transmission of information, which is very critical. Whether the information exchange is correct, timely, and accurate is necessary; the quality of communication can affect the success of innovation partners; if there is timely, necessary, and appropriate communication, the partnership will achieve the agreed goals. The honesty and sharing of communication between innovation partners will affect the intimate relationship between them.

Information sharing is the transmission of critical and patentable information to partners to the maximum extent. The speed of accomplishing tasks is controlled by the correctness of the system information and is related to the degree of satisfaction and the expected success of the partnership [14].

Participation is a cooperative limit set by innovation partners. The activities of one partner will affect another member. With the increase in participation, there will be certain expectations for the role selection, responsibilities, and obligations in cooperation. Participation in decision-making and goal setting determines the success of cooperation [15].

Interorganizational conflicts often arise because of the lack of agreement. Partners are inherently interdependent. There will be some difference between the expected value and the actual value. If there are differences and contradictions between the expected response and the actual response, then this will happen. Conflicts will arise when the actions of other members impede the members' expectations of achieving their goals.

If we can anticipate the occurrence of conflicts, it is essential to prevent them correctly. The impact of conflict can affect the relationship between the two sides, so success is greatly affected by the conflict.

Feeling and perception are two stages of conflict. If a member finds conflict, it is the stage of indication. At the same time, it is advantageous to deal with conflicts properly. It can bring some potential benefits. It can open up the problems of conflicts and solve them in time. It can summarize the old problems and put forward new ideas according to the situation, so that members can know themselves better [16].

When partners work together to resolve conflict problems, mutually satisfactory solutions may emerge, thus increasing the success of partnerships.

Arbitration by a third party can resolve conflicts. Arbitrators will persuade the members of both parties to use specific solutions to complete a series of problems. In persuasion, the general use of positive language cannot take coercive measures. But if we adopt inappropriate solutions, it will have adverse consequences, and even the cooperation between the two sides will no longer be carried out.

In partnership, members are not all dependent, so the issue of power will be highlighted. Unbalanced power can force one party to do something they do not want to do.

In the past, it was believed that there must be a person who plays an important role in the cooperative relationship between the two sides on the premise of trust. This person holds the important power. Even if it appears vague or does not abuse power, power is also potential. Therefore, the dependent party will first think of the idea of the power and then meet its requirements.

Power is vital for fulfilling short-term commitments, and power that depends on threatening sources will accomplish them faster. But threatening power has more negative effects. Power can affect the efficiency of other members, but it can also change the behavior of members. The asymmetry of power among members of innovation partners makes the vested interests of partners different. Those who are in absolute disadvantage will take various efforts to seek their own interests. Therefore, the premise of stability is to balance the interests of innovation partners and the equivalence of power.

3.2. Analysis of the Effect of R&D Openness on Innovation Performance

Reviewing the existing literature, we can see that the current research mainly focuses on the breadth of open R&D analysis. Specifically, the more the ways through which an organization can acquire knowledge, the more open the team's R&D will be. Open R&D activities not only include the familiarity and application of knowledge already acquired by the team, including prior experience and repeated accumulation, absorption, and application (i.e., continuous learning, accumulation of experience, and professionalization), but also can reflect how much of the knowledge applied in the R&D process is acquired differently and not applied in the past. A survey of more than 2,700 industrial teams in the UK shows that the relationship between team R&D openness and team innovation performance is inverted and U-shaped. The theoretical basis is as follows: firstly, because each knowledge path has a saturation limit, the marginal benefit of exploring knowledge behaviour gradually decreases; secondly, if the team invests too much resources in a certain knowledge field and goes deep into it, it will inevitably lead to more rigid organizational capacity, resulting in a “capability trap.” After that, many empirical studies carried out by experts also confirmed the above theory.

Because knowledge creation and R&D technology occur within the team, the team is totally subject to R&D costs and R&D risks, so the relationship between R&D costs and the return of results to the market finds it difficult to achieve the intention and objectives of team R&D. In contrast, with the refinement of social division of labor and the development of technological innovation, if the team still uses the closed mode to carry out R&D activities in the current external open environment, it will inevitably lead to greater coordination costs, product R&D cycle exceeding expectations, and the risk of R&D failure increases. That is to say, by breaking through team boundaries, R&D will be more open, more heterogeneous resources will be obtained from outside, more potential opportunities for technology development will be obtained, existing knowledge systems will be broken down or even subverted, and technology will be innovated. Specifically, the existing R&D has fully proved that R&D openness can broaden the team's profit space, (the team is more deeply embedded in the external innovation network), improve the probability of commercialization of R&D results, and improve the company's profit space and ability. More importantly, the team's open R&D activities can not only promote the development of industrial teams, but also greatly promote the development of traditional teams, small and medium-sized teams, high-tech teams, and large teams.

As mentioned above, scholars are interested in open innovation topics and have carried out a lot of research. For the openness of R&D activities, the breadth is the total amount of organization and team cooperation that can provide new knowledge for the team and the outside in a specific cycle, and the depth is the level of mining applications for various R&D activities. From the definition of the concept, it is easy to know that, for introverted open innovation, the emphasis is on the extent of absorbing external resources, and the connotation is not full and perfect. There is a nonlinear relationship between the breadth and depth of team R&D and team innovation performance. When R&D openness is gradually increased, team innovation performance will gradually improve first and then gradually decline after reaching a certain level. For the team driven by technology, there is an inverted U-shaped nonlinear relationship between the breadth and depth of R&D and innovation output. At present, this conclusion does not apply to the development of team driven by experience, because of the positive impact of the breadth and depth of R&D.

The technology acquired from outside can effectively promote the effectiveness of team innovation activities. In the process of research, relationship openness is defined as the relationship between team and government, R&D team, and process openness is defined as the frequency of external joint innovation. Comparing the methods of describing and characterizing R&D openness in the existing literature and considering the team R&D openness in this paper are regarded as an important indicator of the team's overall investment; this paper defines team R&D openness as the sum of R&D activities that reflect innovation organizations, institutions, and personnel working together outside the team and organization in a specific cycle.

In this paper, the logistic curve model is used to divide the evolution stage of R&D breakthrough innovation in industry. The evolution stage and influencing factors are summarized and analyzed in general, and the macroscopic grasp of the evolution stage of industry is obtained. However, the development of R&D breakthrough innovation in industry is very complex. There are many factors affecting the development of R&D breakthrough innovation in industry. How to find out the main factors from these factors is the key to establish an empirical model. According to the existing theory and literature, this paper chooses 16 indicators from three dimensions: industrial scale, industrial technology, and industrial organization, which affect the development of emerging industries and use them as an index to measure the evolution stage of breakthrough innovation in industry [17].

3.2.1. Industrial Scale

Industrial scale is an important index to judge the evolution stage of breakthrough innovation in industry of R&D. It will show different characteristics of industrial scale in different evolution stages of the industry. It mainly includes the following five indicators.

Industrial output R&D: it refers to the total R&D of the final product or service provided by the industry in a certain period of time, reflecting the total scale and level of industrial production in a certain period of time.

Main business income: it refers to the cumulative current year of corresponding indicators in the accounting profit statement [18].

Profit: it refers to the final result of industrial production and operation activities, which is equal to operating profit plus subsidized income plus investment income plus net income outside business plus profit and loss adjustment in previous years.

Profit tax: it is the sum of sales tax, education fee surcharge, resource tax, and total profit of industrial products but does not include all taxes in production cost. It is an important index reflecting the total net income of the industry in a certain period of time and also reflects the scale of the industry.

Employees: this refers to the number of people participating in economic activities. The number of employees can also reflect the size of the industry [19].

3.2.2. Industrial Technology

Industrial technology is the main source of promoting industrial development. It mainly includes the following seven indicators.

Technology intensity: it refers to the ratio of R&D expenditure to main business income, and it is an important index to measure industrial technology investment.

R&D personnel: it refers to the number of people involved in R&D and experimental development. The technical quality of R&D personnel reflects the technical level. It is also an important index to measure the level of industrial technology investment.

R&D personnel converting into full-time equivalents: it is an internationally used index for comparing human input in science and technology. It refers to the sum of R&D full-time personnel workload and non-full-time personnel workload converted according to actual working hours.

Number of R&D institutions: R&D institutions are becoming the main carrier of scientific and technological activities. The number of R&D institutions is positively correlated with R&D investment, which reflects the level of industrial technology investment to a certain extent.

Number of patent applications: the number of patent applications can reflect the progress and innovation of science and technology, as well as the level of industrial technology output.

Industrial innovation capability: it refers to the ratio of main business income to total output R&D of new products. New products refer to new products developed and produced with new technology principles, new design concepts. The index of industrial innovation capability can reflect the output level of industrial technology.

New product output rate: it refers to the ratio of new product R&D to total output R&D, which can reflect the level of industrial innovation and output.

3.2.3. Industrial Organization

Industrial organization is an important indicator to measure the stage of industrial development. It includes the following four indicators.

Output performance: it refers to the ratio of the main business income of new products to the number of R&D personnel, which measures the efficiency level of industrial organization.

Export sales rate: it points out the ratio of export delivery R&D to gross output R&D and measures the rationality of industrial structure.

Employment rate: it refers to the ratio of gross output R&D to the number of employees and measures the level of industrial organization structure.

Profit margin: as an index to measure the efficiency of resource allocation in industrial market, it refers to the ratio of total sales profit to total output R&D in a certain period of time.

The linear expression of the principal component can be obtained by estimating the score coefficient of the factor by regression method as follows:

The logistic curve equation can be obtained as follows: ln(α × β − Y/βY)=ln  ϕ − α·t, and ϕ=e2.

Taking the proportion of variance contribution rate of each factor to total variance contribution rate as the weight, the comprehensive score of breakthrough innovation in industrial R&D over the years is obtained. The development index Y of breakthrough innovation in industrial R&D over the years is obtained by standardizing the R&D. Using the four-point method, the saturation R&D of R&D breakthrough innovation development index of industry is 0.891. From the logistic curve equation, we can get the index R&D range of four evolution stages of R&D breakthrough innovation in industry, as shown in Table 1.

Table 1

Index range of four evolution stages of breakthrough innovation in industrial R&D.

4 stages	Scope of index R&D in evolution stage
Incubation period	(0, 0.218)
Growing period	(0.218, 0.336)
Development period	(0.336, 0.712)
Maturity period	(0.712, 0.891)

Logistic regression analysis was carried out with EViews software. The regression equation predicts that the index of breakthrough innovation evolution stage of industry in 2019 is 0.799.

4. The Theoretical Connotation of Creative Industrial R&D Innovation Ecosystem

We can study independently the different factors that affect the development of innovation partnership in each stage, so that we can refine the various factors, which is more conducive to the formulation of targeted solutions. Many scholars have their own division methods in the management stage of innovation partnership of R&D team. They also have different division stages [20]. At present, there are mainly the following division methods in the global scope: the methods and contents of each stage management are different. Before choosing partners, we should evaluate and determine the strength and development direction of our own enterprises, as well as the relevant aspects of innovation partners; after choosing innovation partners, we should initially sign contracts; after signing contracts, we should basically determine the partners, and then we should make an assessment of the relevant aspects of innovation partners. It is necessary to establish internal management mechanism, which involves cultural differences, resource sharing, operation direction, and different operation styles among cooperative enterprises. On this basis, cooperative schemes should be established. Finally, internal adjustments should be made according to different stages. The management of innovation partnership management is divided into four stages; the first stage is strategic decision stage, which is the stage of enterprise's rational analysis of its overall situation, including the current environment, the amount of resources, and talent reserve. The second stage is strategic alliance form, which determines the field of cooperation and the strength of cooperation. The third stage is partner selection, which is very important. When choosing innovation partners, we should pay attention to enterprise culture and better development direction. The last stage is relationship management, which includes contract negotiation and cooperation interface. R&D team innovation partnership management is divided into four stages: identification and selection stage, negotiation and negotiation stage, monitoring and management stage, and cooperation termination stage. The first stage is basically the same as the above management stages, that is, to fully understand the overall situation of their own enterprises and cooperative enterprises, to judge the development direction and development potential of innovation partners, and to select the target of cooperation as a standard; the second stage is to negotiate mainly according to the duration and content of cooperation; the third stage is to carry out knowledge creation. The management of new partnership belongs to the dynamic relationship stage. Generally speaking, the management of R&D team's innovation partnership mainly includes four stages, that is, the analysis of the preestablishment of R&D team's innovation partnership, the establishment of R&D team's innovation partnership, the management of R&D team's innovation partnership, and the end of R&D team's innovation partnership. These four stages contain most of the content of the development of innovation partnership of R&D team. Therefore, this paper will make a thorough discussion on the influencing factors of these four stages and how to deal with the relationship of each stage.

Since the twenty-first century, the development progress of the industry has become increasingly diverse, creative industry as the most important industries with high added value, through which innovative models provide creative products for consumers. At the same time, the vigorous development of creative industries has a great role in promoting the optimization and upgrading of industrial structure. The insufficient innovation ability is the biggest bottlenecks for the upgrading of creative industry in China. Thus, it is an important way to structure the good value innovative ecosystem around perfecting the innovative chain for improving the innovation ability of creative industry. This paper discusses the theoretical connotation of the value innovation ecosystem of creative industry, studies the spatial structure of the value innovation ecosystem, and proposes the fact that the innovative population of value innovation ecosystem in creative industry can be divided into original innovative population, technological innovation population, innovation service population, innovation input population, and system innovation population.

Generally speaking, under the condition of market economy, creative industry is often an important way to understand the shortage of resources and the relative lack of professionalism in the process of government guidance. Based on this, the development of creative industry has attracted wide attention from all sectors of society; researches on creative industries are often confined to the analysis of its connotation, characteristics, and mode of development, on the ecological innovation perspective that is relatively small. From the practical point of view, as an important part of modern service industry, the research and analysis of value innovation ecosystem are particularly necessary to achieve sustainable development.

In this highly competitive market economy, innovation has become the main means of competition among enterprises, but also to improve their overall competitiveness of the main factors. So, in this situation, the construction of ecological R&D innovation system plays an important role; the system is conducive to the progress and development of the enterprise and can guarantee the advantage of fierce competition in the market economy environment. In view of the role of enterprise ecosystem, we should strengthen the study of scholars in this area and encourage the use of cases to explore the object of study.

In the process of the study of R&D innovation ecosystem, it is not difficult to find that the enterprise is no longer a single industry, it has been gradually transformed into a structured group, and the main reason is that the single enterprise's competitive ability is too low, resulting in fierce competition in the market economy gradually eliminated. Therefore, if enterprises want to occupy a dominant position in the market, only by choosing cooperation can they continuously meet the needs of customers, improve their competitiveness, and finally achieve the purpose of innovation. The main form of innovation is the harmony of ecosystem.

The so-called creative industrial R&D innovation ecosystem is the common goal of innovation based on different parts of the organization having a link, which makes relationships between organizations more close, and mutual exchanges between organizations are more frequent; the creative industry has become a specific space, stable structure, and innovative features of the overall stability, then promoting the formation of creative industrial R&D innovation ecological system.

4.1. Innovation Organization

In the creative industry, the organization mentioned that its R&D innovation ecosystem covers all involved innovation; as we have mentioned in the universities, government, research institutions, enterprises, and so on, the main innovation is composed of the basic elements of the system, with the growth and development characteristics in a certain extent, which can change according to different environmental situations.

4.2. Innovation Population

The innovative population in the R&D innovation ecosystem of creative industry mainly refers to the homogeneous organization inside the industry. The formation of innovative population has the corresponding distribution in space, and the innovative organization in the population is organically combined with each other through their own links. In general, the continuous development of innovative organizations will enable them to form a population and then multiply and expand; the homogeneous innovation population in the context of the associated with the environment is often in an overall form.

The innovative organization in this population mainly refers to two kinds of colleges and universities. Universities and research institutions are gathering high intellectual and technical personnel; product development and innovation have fully applied research facilities. In the original innovation population classification, mainly consider that the innovation organization should have strong research ability; there are more strategic researches, which can provide the original technology for building the R&D of innovation ecosystem, transportation of high-tech talent, and can better promote enterprise economic growth.

4.3. Innovation Community

The system of creative industrial R&D innovation constituting the sum biological components in the course of innovation refers to innovation of ethnic groups, which often contain multiple types of population, the population with each other at the corresponding time and space, in order to better adapt to the external environment of the formation of the community. There is diversity in the nature of innovation community. The diversity of its structure and function is due to the difference of community population. The adaptability of population to the environment directly affects the nature of community. For populations of similar species for community organizations, also to make the formation of information resources sharing between multiple populations, mutual exchange of needed products can better carry out complementary advantages, the force of knowledge and technology to maximize, and provide a better innovation environment for the development of creative industries. Innovation community covers the creative industries, as well as the above populations which consists of five types of population; these communities have formed their own unique structure in the R&D of innovation ecosystem, playing the function and role of their own, as shown in Figure 2.

Figure 2

The spatial structure of innovation community in creative industry.

5. Conclusions

In order to make the industry transition arrive to the later stage of maturity smoothly, this paper puts forward two policy suggestions: firstly, we should improve the policy of intellectual property protection. Emerging industries that have entered the initial maturity stage mean that their own technology has been formed. The government should encourage enterprises to strengthen patent applications, such as exempting enterprises from related fees for patent application for breakthrough innovation in industry, awarding patent acquisition, and taking patent acquisition as an important indicator for application for innovation funds and project declaration and acceptance. Secondly, we should guide the large-scale development of industry. At the early stage of maturity, the market structure of emerging industries tends to be stable and centralized. The government should guide the development of industrial scale, such as reducing the cost of industrial transactions, improving industrial efficiency, building supporting systems for industries, and expanding industrial scale.

By introducing logistic curve equation and using its derivative principle, the evolution stage of R&D breakthrough innovation in industry is divided into four stages: incubation stage, growth stage, evolution stage, and maturity stage. Taking the breakthrough innovation of R&D in industry as an example, this paper uses the logistic empirical model based on factor analysis to study it. By comparing and analysing the index of industrial evolution stage, the results show that the breakthrough innovation of R&D in industry has entered the maturity stage. During this period, the breakthrough innovation development index of industrial R&D was relatively stable, the speed and acceleration of industry development tended to zero, and the industry development tended to be saturated. Industry shows that technology paradigm has been formed, product technology and production technology are mature, industry development is relatively stable, and market structure tends to be centralized.

It is not difficult to find that the formation of the framework is based on long-term exchanges and cooperation between different innovation organizations; then it is built to the industrial chain, between universities, research institutions, enterprises, government, intermediary, and financial institutions to constitute a stable network; all innovation organizations through innovative activities of enterprises will be the main organic together with the integration between different function, promoting the establishment of the system of R&D innovation ecosystem in creative industry.