Research on Synergy between Entrepreneurial Service and Financial Support in Crowd Innovation Space Ecosystem

Abstract

Crowd innovation space ecosystem (CISE) can realize the combination of innovation and entrepreneurship and that of incubation and investment. Based on synergetics, this paper defines the agent interactions and subsystem synergy of the CISE, explores the co-evolution mechanism of the CISE and the development trend of the agents in the CISE through simulation experiments, and studies the impact of synergy on the operation scale of the CISE through empirical analysis. The research findings reveal the following: (1) The synergy between financial support and entrepreneurial services affects the evolution and development of the CISE, and the synergy has a positive impact on the operating scale of the CISE. (2) The CISE should focus on improving the quality of financial support and the efficiency of entrepreneurial services, ensuring the coordinated development of financial support and entrepreneurial service, optimizing internal operating mechanisms, and achieving high-quality development. (3) Small and micro startups can rely on the CISE, with the help of entrepreneurial services provided by MMS service platforms and financial support provided by financial institutions, to optimize the external environment, integrate resource endowments, and enhance core values, thereby promoting business model innovation and sustainable development.

1. Introduction

With the development of the global economy, innovation and entrepreneurship are becoming new driving forces for sustained economic growth. The Chinese government encourages “mass entrepreneurship and innovation”, and entrepreneurial service is an important part of the entrepreneurial activities. Therefore, mass maker space (MMS) is booming as a new type of incubator providing entrepreneurial services. MMS can be considered as a new type of entrepreneurial service platform built for mass entrepreneurship, which can provide a new service model for innovation and entrepreneurship and has the characteristics of low cost, convenience, all elements, and openness [1]. China has established many MMS platforms to provide convenience for entrepreneurs. As of December 2021, there are more than 8500 MMS platforms in China [2]. These MMS platforms provide a lot of convenience for entrepreneurs, technology innovators, and startup enterprises and thus promote the country’s economic transformation and industrial structure optimization.

Financial support also plays a crucial role in entrepreneurial activities. However, small and micro startups are faced with serious financial exclusion and financing difficulties. This is mainly due to the information asymmetry between startups and financial institutions, which may lead to adverse selection and moral hazard problems for financial institutions. In recent years, the Chinese government has encouraged financial institutions to cooperate with MMS platforms to explore diversified financial service models, including bank loans, venture capital, equity financing, bond issuance, and equipment financial leasing. As of December 2021, the balance of loans used by banking financial institutions for small and micro enterprises has reached RMB 50 trillion. These financial support measures have increased the enthusiasm of entrepreneurs, eased the difficulty of obtaining funds for startups, and greatly stimulated market vitality and social creativity.

Crowd innovation space ecosystem (CISE) is a complex ecosystem gathering innovative activities and entrepreneurial activities, which includes MMSs, startup enterprises, financial institutions, and other agents. CISE can give full play to the integration effect of the policies and realize the combination of innovation and entrepreneurship and that of incubation and investment. CISE is essentially an entrepreneurial ecosystem. Some scholars combined sustainability with the entrepreneurial ecosystem, defined the connotation and goals of the sustainable entrepreneurial ecosystem [3], explored the important factors affecting the development of the system [4], and studied the evolutionary mechanism of the system [5]. However, due to the complexity of CISE and the non-linear interactions between the agents in the system, a series of problems, such as low resource utilization of MMS platforms, low incubation success rate of startup enterprises, and low investment return of financial institutions, are not conducive to the sustainable development of CISE. Therefore, it is of great significance to analyze the agents’ interactions and the subsystems’ synergy, study the internal evolution mechanism of the system, and explore the sustainable development law of the entrepreneurial ecosystem.

This paper studies the impact of synergy on the CISE. Different from existing studies, we focus on the following three aspects: (1) Research perspectives: existing studies mostly focused on the impact of a single factor on entrepreneurial activities, such as the impact of financial support on entrepreneurial activities, the impact of entrepreneurial services on entrepreneurial activities, etc., while this paper discusses the impact of multi-factor synergy on entrepreneurial activities from the perspective of the entrepreneurial ecosystem. (2) Research content: existing studies have paid more attention to “hardware” services such as office space provided by traditional incubators, while this paper studies “software” services such as entrepreneurial training and entrepreneurial education provided by MMS service platforms. (3) Research results: existing studies mostly focused on the operating mechanism and operating efficiency of the system, while this paper focuses on the interactions of the agents in the entrepreneurial ecosystem and the evolution mechanism of the system.

Based on synergetics, this paper defines CISE as a financial support subsystem (FSS) and entrepreneurial service subsystem (ESS) and sets the parameters of the system. Then, based on the Haken model, it explores the synergy in the system. Finally, the synergy between subsystems and the development trend of the agents in the system are explored through a simulation experiment, and the impact of synergy on the operation scale of CISE is studied through empirical analysis. The marginal contributions are as follows: (1) Theoretically, this paper applies synergetics to analyze CISE. Moreover, it explores the synergy of FSS and ESS and expands the research perspective of CISE’s internal operation mechanism. (2) Practically, by combining simulation experiments and empirical analyses, it examines the synergy in the system. It also provides feasible suggestions for the resource allocation of CISE, the operation mechanism of MMS, and the development model of small and micro startups. The subsequent sections are as follows: the second part is research status; the third part is model design; the fourth part is simulation experiment; the fifth part is empirical analysis; and the sixth part is summary.

2. Research Status

CISE is an entrepreneurial ecosystem that takes the MMS platform as the agent of entrepreneurial services. Many scholars have carried out a series of studies on MMS (also known as “makerspaces”) and CISE. Troxler (2010) defined makerspace as a new model of innovation based on “mass production” [6]. Wu (2018) defined CISE as an innovation and entrepreneurship activity space that provides settled enterprises with functions such as communication, R&D, and incubation [7]. Bauwens (2012) explored the behavioral patterns of makerspaces from the perspective of the sharing economy [8]. Hu (2020) studied the realization mechanism of value co-creation of different types of MMSs from the perspective of platform economy [9]. Further, based on the concept of MMS and the theory of entrepreneurial ecosystem, Li (2017) clarified the agent participation mechanism and operation mechanism of CISE [10]. Hou (2016) put forward suggestions for optimizing CISE based on CAS theory [1]. Pei (2018) proposed that the role of MMS in the multi-level innovation ecosystem is the interface function between makers and other innovative agents [11]. Further, scholars’ research on the entrepreneurial ecosystem mainly includes three aspects: concept definition, evolution mechanism, and sustainable development.

Many scholars have carried out a lot of research on the conceptual definition of an entrepreneurial ecosystem, which can be divided into two aspects. The first is the definition of the characteristics and connotation of the entrepreneurial ecosystem. Cohen (2006) proposed that an entrepreneurial ecosystem is a community formed by interacting subjects in a specific area to achieve sustainable development and create social and economic value by supporting and promoting the creation and growth of new enterprises [3]. Isenberg (2011) proposed the gold standard of the entrepreneurial ecosystem: market, policy, capital, talent, culture, and professional support through case studies of Silicon Valley [12]. Lin (2011) believed that the entrepreneurial ecosystem is a dynamic balance system composed of startups and the entrepreneurial ecological environment, and the startups and the environment are interdependent and influence and develop together [13]. Jiao (2022) proposed that the entrepreneurial ecosystem is composed of a series of interacting subjects, and the interaction between the agents realizes the flow of resources and information, provides support services for entrepreneurs, and promotes the development of entrepreneurial activities [14]. The second is the study of the internal structure and constituent bodies of the entrepreneurial ecosystem. Fernández (2015) proposed that the agent of the entrepreneurial ecosystem includes the government, entrepreneurial organizations, educational institutions, entrepreneur networks, etc. [15]. Brown (2014) believed that the agent of the entrepreneurial ecosystem includes the public sector, financial institutions, academic institutions, private sector, culture, infrastructure, etc. [16]. Kuratko (2017) pointed out that the participants in the entrepreneurial ecosystem include startups, banks, venture capital, incubators, accelerators, universities, professional service providers, government agencies, etc. [17].

Many scholars have conducted extensive research on the evolution mechanism of entrepreneurial ecosystems. Based on field theory, Thompson (2018) proposed a two-stage model for the formation of entrepreneurial ecosystems, which expanded the theoretical perspective of research on the evolutionary mechanisms of entrepreneurial ecosystems [18]. Based on the industry life cycle theory, Cantner (2021) built a dynamic entrepreneurial ecosystem life cycle model, which mainly includes stages of birth, growth, maturity, decline, and reemergence [19]. Colombelli (2019) summarized the evolution of the entrepreneurial ecosystem into stages of birth, transition, and consolidation [20].

In recent years, scholars’ attention to sustainability has gradually increased, and sustainable entrepreneurial ecosystems have become a research hotspot [21]. The core of the sustainable entrepreneurial ecosystem is the sustainability of enterprise growth, which has been studied by scholars. Eisenhardt (1990) believed that financial resources are the most basic resources of SMEs, and financing capacity is an important factor affecting the growth of SMEs [22]. Marris (1963) proposed that for small and medium-sized enterprises lacking financing ability, the shortage of funds will cause the growth of enterprises to stagnate or even fail due to insufficient “fuel” [23]. Zhu (2015) found that there is a significant spatial interaction effect between innovation drive and financial support from the perspective of different financial industries, but the degree of regional coordinated development is low, and then analyzed the factors that affect the coordinated development of the two [24]. Liu (2018) explored the coupling mechanism of scientific and technological innovation and financial innovation groups based on the perspective of group dynamics and believed that the deep integration of the two would drive the efficient allocation of social resources and economic fusion [25].

Although research on the CISE and synergetics has made some achievements, it still needs to be further expanded. First, although existing studies have analyzed the internal structure and constituent agents of the system based on the concept definition of an entrepreneurial ecosystem, the interaction between agents and the interaction between agents and systems still need to be further clarified. Second, although existing studies have analyzed the evolution mechanism of the entrepreneurial ecosystem, they mainly focus on the impact of a single factor on the ecosystem while ignoring the impact of multiple factors in the process of system evolution. Third, although the existing studies have analyzed and explored the sustainable entrepreneurial ecosystem, they are mostly analyzed at the theoretical level.

This paper studies the impact of multi-factor synergy on the CISE from the perspective of the entrepreneurial ecosystem, considering that the agents in the entrepreneurial ecosystem do not exist in isolation but interact and influence each other, and this joint action affects the development and evolution of the system. Scholars have studied this interaction based on synergetics. Synergetics holds that there are a large number of subsystems in a complex system, the subsystems are dominated by order parameters to produce synergy, and the synergy makes the system evolve from a disordered state to an ordered structure [26]. Synergetics has been widely used in the study of the internal operating mechanism of complex systems. Stank (2001) proposed that synergy is a process in which subsystems interact and develop together [27]. Jin (2006) studied the dynamic mechanism of venture capital city alliance under synergetics [28]. Wu (1990) proposed the typical equation of a venture capital self-organizing system and constructed the synergy model of a venture capital system by analyzing the interaction of subsystems through order parameters [29]. Xu (2016) analyzed the synergy of venture capital, banks, and government support in CISE from the perspective of capital [30]. Zhang (2017) used the dissipative structure theory in synergetics to construct the evolution model of CISE, which showed the formation and evolution path of CISE [31]. Therefore, referring to the existing research, this paper also uses synergetics to study the synergy in the CISE, that is, synergetics is used as the theoretical basis of this paper.

The research procedures are as follows: Firstly, we define the scope and agent of CISE and analyze the synergy between financial support and entrepreneurial services based on synergetics. Then, through simulation experiments, we study the co-evolution mechanism of CISE and the development trend of each agent. Finally, through empirical analysis, we study the quantitative relationship between synergy and the operating scale of the CISE, that is, the impact of financial support and entrepreneurial services on the level of entrepreneurial activities (i.e., the number and success rate of startups). Our findings can provide feasible suggestions for the sustainable development of the CISE.

3. Model Construction

3.1. Synergetics

Synergetics was proposed by German physicist Haken in the 1970s. It mainly studies how an open system spontaneously evolves from a disordered state to an ordered state through its internal synergy when material or energy exchange exists with the outside world [26]. Based on synergetics, natural science methods can be used to study social science problems, which open up ideas for interdisciplinary research. The following is a brief introduction to the main concepts involved in synergetics.

• Synergy: Synergetics holds that there are many subsystems in complex systems, and synergy exists among these subsystems, which will affect the evolution of the whole system [26,29]. Therefore, under external energy, synergy will exist between subsystems in every complex system. This synergy can make the system qualitatively change at the critical point from disordered to ordered.
• Order parameter: Synergetics holds that a complex system is composed of many subsystems, each of which can be described by one parameter or multiple parameters [26,29]. Therefore, a complex system contains many parameters, which are non-independent, interrelated, mutually restrictive, and interacting. Order parameters can be the most important, the most effective, and the most decisive parameters in the system, which can usually be one or several. Order parameters can be used to describe the order degree of the system. It is the core force controlling the evolution and development of the system and determines the behavior state and evolution direction of the whole system.
• Convergence force and dissipation force: Synergetics holds that some unstable factors will be produced when the system is disturbed by the outside world. These factors can be divided into convergence force and dissipation force [31], and the interaction between the convergence force and dissipation force determines the direction and degree of system evolution and development. Convergence force can promote the subsystems to integrate and develop together, which makes the value of complex systems increase. Dissipation force is dominated by convergence force, but its action direction is opposite to the convergence force. That is, the dissipation force is opposite to the direction of the increase of the complex system.
• Self-organization principle: The self-organization principle holds that the internal subsystems of a complex system can spontaneously evolve and develop in a certain direction [31]. That is, a complex system spontaneously evolves from a disordered state to an ordered state under the control of the system’s order parameters.

3.2. CISE Model

CISE can be regarded as an open innovation and entrepreneurship ecosystem. Some scholars believe that it can be embedded in the economic and cultural environment of the whole society and exchange material, energy, and information with the external environment [32]; thus the support for entrepreneurial activities should adopt a more systematic form [33]. Referring to Jiao’s definition of an entrepreneurial ecosystem [14], this paper believes that the CISE is composed of a series of interacting participants who realize the flow of resources and information through interaction, provide support services for entrepreneurs, and promote entrepreneurial activities. Drawing on Brown and Kuratko’s definition of a system agent [16,17], this paper believes that CISE is composed of a series of interdependent entrepreneurial agents, mainly including startups, makerspaces, financial institutions, scientific research institutions, and government departments. Drawing on the research methods of Hou (2016), Pei (2018), Xu (2016), and Yang (2014) [1,11,30,32], this paper chooses the complex adaptive systems theory to study CISE. Complex adaptive system (CAS) theory holds that the power of system evolution essentially originates from the interior of the system, and the interaction of subjects generates macroscopic complexity phenomena. Its research ideas focus on the interaction of the internal subjects of the system and focus on revealing the reasons for the formation of objective things and the process of their evolution.

Based on the CAS theory and synergetics, this paper establishes the CISE model (Figure 1). The model determines the agents of the system, simplifies the interaction process of the agents, and shows the complexity and non-linearity of the system.

The CISE model has five types of agents: startup enterprise, MMS service platform, financial institution, scientific research institution, and government department. Startup enterprise is the core of the model. There is a non-linear relationship between the five agents that restrict and influence each other. Each agent plays its own role in the model and affects other agents through the resources flowing and behavior interaction.

Startup enterprises include entrepreneurial teams and small and micro enterprises, which can obtain pertinent resource support from other agents and give positive or negative feedback to other agents. MMS service platforms can provide entrepreneurial services for startup enterprises, build a platform for scientific research institutions to share knowledge and technology, and cultivate an atmosphere for government departments to implement entrepreneurial policies. Financial institutions can provide financial support for startup enterprises, provide R&D funding support for scientific research institutions, and create certain economic benefits for the society. Scientific research institutions can provide technology and knowledge support for startup enterprises by cooperating with MMS service platforms and realize the combination of production, education, and research. Government departments can support the development of other agents through a series of policy measures.

In the CISE model, MMS service platforms and financial institutions directly participate in the process of promoting the growth of startup enterprises, while scientific research institutions and government departments indirectly participate in this process by cooperating with the MMS service platform. Therefore, this paper divides the five agents into two categories: dominant agent (DA), which directly interacts with startup enterprises, and recessive agent (RA), which does not interact with startup enterprises directly but plays its role by cooperating with DAs. The details of the five agents are listed in

Table 1. Agents and actions list.

Agent	Category	Actions
Startup Enterprise	DA	Obtain resources and support from other agents; pay pertinent service fees to MMS service platform; give capital returns to financial institutions; create economic benefits for the society through their own incubation and regeneration.
MMS Service Platform	DA	Provide entrepreneurial services for startup enterprises, such as leasing office space, holding entrepreneurial activities, conducting entrepreneurial education and training, providing technical support services; cooperate with scientific research institution and government department to obtain more resource support for startup enterprises.
Financial Institution	DA	Provide financial support for startup enterprises, such as loans, venture capital, equity financing, and special funds; provide R&D funding support to scientific research institution to improve the efficiency of the use of financial resources.
Scientific Research Institution	RA	Cooperate with MMS service platform to provide technical support services; provide knowledge, technology, and talents to startup enterprises, realize the sharing of knowledge and technology, and promote the combination of production, education, and research.
Government Department	RA	Provide policy support for other agents, and form an innovation and entrepreneurship policy system.

.

3.3. Model Parameters

Synergetics holds that there are a large number of subsystems in a complex system. The interaction between subsystems determines the trend of the whole system [29]. Through the above analysis, we already know that the RAs mainly function through the DAs. In the CISE model, this paper focuses on DAs and defines two subsystems based on the interactive behavior between the dominant agents: financial support subsystem (FSS) and entrepreneurial service subsystem (ESS). ESS is generated by the interaction between the MMS service platform and startup enterprise; that is, the MMS service platform holds innovation activities and entrepreneurship education and training for startup enterprises. Meanwhile, startup enterprises feed back to the MMS service platform by cash payments and equity option deduction. FSS is generated by the interaction between financial institutions and startup enterprises; that is, financial institutions provide startup enterprises with financial support services such as venture capital, equity investment, angel investment, and financial subsidies. Simultaneously, startup enterprises also provide feedback to financial institutions through cash payments, equity option deduction, and other means.

In the CISE model, FSS and ESS realize resource sharing and information transmission through the interaction between agents to form the synergy in the system and then promote the healthy and sustainable development of CISE. We know that every system can be represented by one or more parameters. Therefore, the essence of synergy between subsystems is the interaction between parameters. This paper set ESS’s parameter as entrepreneurial service and set FSS’s parameter as financial support, and both of them determine the evolution and development of the system. The specific introduction is listed in

Table 2. Model parameters introduction list.

Parameter	Variable	Introduction
Entrepreneurial Service	$e_1$	Describe the interactive behavior of the agents in the ESS, including the number of innovation and entrepreneurship activities, the number of entrepreneurial education and training, the number of technical support services, etc.
Financial Support	$e_2$	Describe the interactive behavior of the agents in the FSS, including the number of startup enterprises and entrepreneurial teams that have obtained investment and financing, the amount of investment and financing, etc.

.

The $e_1$ comes from ESS, which can be mainly reflected in the activities, such as entrepreneurship education and training held by the MMS service platform to improve the entrepreneurship awareness of startup enterprises. Meanwhile, the $e_2$ comes from FSS, which can be mainly reflected in the activities, such as venture capital, equity financing, and other financial support provided by financial institutions to solve the most important financial problems for startup enterprises.

3.4. Model Coefficients

Synergetics holds that the system’s development process will be affected by two factors: convergence force and dissipation force. Convergence force dominates the evolution direction of the whole system and dominates dissipation force, that is, the result of the interaction between convergence force and dissipation force determines the evolution direction and development process of the system. Therefore, this paper defines the result of the interaction between convergence force and dissipation force as the model coefficients.

In the CISE model, convergence force and dissipation force are recorded, respectively, as $F_i$ and $f_i$. Each subsystem has its own convergence force and dissipation force, which together constitute the set of convergence forces and dissipation force of the whole system.

Convergence force can be divided into three categories: The first is technological progress. Startup enterprises improve their management and innovation capabilities through the entrepreneurial services provided by the MMS service platform, thereby optimizing operational efficiency and technical level. The second is economic benefits. Financial institutions help startup enterprises to industrialize and market their R&D results through financially supported services, thereby obtaining more investment returns and capital appreciation. The third is policy support. Government departments promote mass entrepreneurship and innovation through a series of policy support, thereby promoting the optimization of industrial structure and stimulating economic growth. Convergence force can make subsystems integrate and restrict each other and increase the value of startup enterprises and financial institutions. Convergence force is consistent with the promotion direction of startup enterprises and financial institutions, and the better the convergence of startup enterprises and financial institutions, the greater the convergence force.

Dissipation force can be divided into three categories: The first is extensive information asymmetry. This may make it difficult for good startup enterprises to obtain funds, financial institutions to make more accurate investment decisions, and government departments to provide more effective support policies. The second is the difference between technology and finance. The technical field is unclear about the best financing channels and financing instruments, while the financial field lacks accurate judgment on technical feasibility and risk. This also may make it difficult to achieve the integration of the two fields at the same time. The third is high uncertainty in innovation and emerging entrepreneurship. The high risk of the development and survival of a startup enterprise makes it difficult for financial institutions to provide greater support, and the high risk of innovative projects will reduce the enthusiasm of the innovative agents. The direction of dissipative force is opposite to the direction of value promotion of startup enterprises and financial institutions, increasing the possibility of financial institutions’ investment failure and reducing the value of startup enterprises and financial institutions.

3.5. Model Synergy Analysis

Synergetics holds that the synergy between the subsystems is essentially the interaction between the parameters. A complex system may contain a lot of parameters that have different effects on the system, but the core force governing the evolution and development of the system is the order parameter. The order parameter can be used to describe the order degree of the system. It is the core force controlling the evolution and development of the system and determines the behavior state and evolution direction of the whole system. Therefore, the most important work of studying the synergy in the model is to analyze the order parameter of the model.

At present, the Haken model is widely used in the study of system evolution mechanisms in the field of economics and management [34]. Referring to existing research [35,36], this paper carries out the mathematical derivation and analysis of the order parameter based on the Haken model. The Haken model evaluates the level of synergy of the system by analyzing the order parameters. The Haken model performs mathematical processing of system parameters and uses the adiabatic approximation principle. Assuming that $q_1$ is a parameter of a subsystem, and $q_2$ is a parameter controlled by $q_1$, the system needs to satisfy the following dynamic equations:

$$\dot{q_1}=\frac{d{q}_1}{dt}=-\left(F_1-f_1\right)q_1-a{q}_1{q}_2$$

(1)

$$\dot{q_2}=\frac{d{q}_2}{dt}=-\left(F_2-f_2\right)q_2+b{q}_1{}^2$$

(2)

In the formula, $F_1$ and $f_1$ stand for convergence force and dissipation force of the subsystem that can produce $q_1$; $F_2$ and $f_2$ stand for convergence force and dissipation force of the subsystem that can produce $q_2$; and $a$ and $b$ represent the intensity of the interaction between the two subsystems.

The “adiabatic approximation hypothesis” is defined as $\left|F_2-f_2\right|\gg \left|F_1-f_1\right|$, and $F_2-f_2>0$. In practical applications, the difference between $\left|F_2-f_2\right|$ and $\left|F_1-f_1\right|$ is at least one order of magnitude [26,29]. If the “adiabatic approximation hypothesis” is established, and $q_2$ is suddenly removed, $q_1$ has no time to change. Let ${\dot{q}}_2=0$; we get:

$$q_2=\frac{b}{F_2-f_2}{q}_1{}^2$$

(3)

$q_1$ is the order parameter, which determines the evolution process of the system from a disordered state to an ordered state. This process is manifested as the self-organization process of the system; that is, it is completed spontaneously by the internal synergy of the system and is not disturbed by external forces. Then, to solve the self-organizing motion equation of the system, that is, the order parameter evolution equation:

$${\dot{q}}_1=-\left(F_1-f_1\right)q_1-\frac{ab}{F_2-f_2}{q}_1{}^3$$

(4)

Integrating the inverse number of $q_1$ can obtain the system potential function, which can effectively judge the state of the entire system:

$$\mathrm{v}=\frac{1}{2}\left(F_1-f_1\right)q_1{}^2+\frac{ab}{4(F_2-f_2)}{q}_1{}^4$$

(5)

Generally speaking, there may be two relationships between the two subsystems, and correspondingly, there may be two relationships between two parameters. One is competition and antagonism. The premise of this situation is that the two subsystems are independent of each other, which is inconsistent with the content of the CISE model. The second is synergy and symbiosis, that is, the coordinated development and cooperative symbiosis of two subsystems. Both parameters are order parameters of the system and have the same impact on the evolution and development of the system. Introduce the coupling terms to the dynamic equations of $e_1$ and $e_2$; we get:

$$\dot{e_1}=\frac{\partial e_1}{\partial t}=\left({\alpha }_1-k_1{e}_2\right)e_1-{\gamma }_1{e}_1$$

(6)

$$\dot{e_2}=\frac{\partial e_2}{\partial t}=\left({\alpha }_2-k_2{e}_1\right)e_2-{\gamma }_2{e}_2$$

(7)

In the formula, $k_1$ and $k_2$ represent the coupling coefficient, ${\alpha }_i$ and ${\gamma }_i$ are determined by external factors, and ${\alpha }_i>{\gamma }_i$. When the system reaches a steady state, let ${\dot{e}}_1=0$ and ${\dot{e}}_2=0$, we get two sets of solutions by solving the equations:

First solution:

$$e_1=e_2=0.$$

(8)

It means that entrepreneurial services and financial support do not exist, which is meaningless.

Second solution:

$$e_1=\frac{\left({\alpha }_2-{\gamma }_2\right)}{k_2}$$

(9)

$$e_2=\frac{\left({\alpha }_1-{\gamma }_1\right)}{k_1}$$

(10)

This is the most meaningful result. It means that entrepreneurial service and financial support show an increasing trend and reach a steady state in the interaction process. This also reflects synergy in the system. Therefore, synergy exists between ESS and FSS. However, due to the resistance factors in collaborative development and the limitation of real production factors, entrepreneurial service and financial support cannot be increased indefinitely, and a certain upper limit exists.

3.6. Model Synergy Effect

CISE analyzes the impact of factors such as financial support and entrepreneurial services on the system from a systematic and integrated perspective. Some scholars have pointed out that the entrepreneurial ecosystem is an organic whole formed by a variety of participants (including entrepreneurs, entrepreneurial enterprises, and related organizations and institutions) and the system, market, culture, and natural environment in which they are located through the interaction that improve the level of regional entrepreneurial activities (i.e., the number of entrepreneurial enterprises and entrepreneurial success rate, etc.) [33]. Therefore, this paper studies the impact of synergies within the CISE on the development and incubation of startups.

Synergy effect is the result of the synergy process. In the CISE model, startup enterprise is the core of the model, and synergy between ESS and FSS will produce many synergy effects. First, in FSS, financial institutions provide financial support for the process of knowledge innovation, technological innovation, and industrialization of startup enterprises. Moreover, financial institutions give full play to the management mechanism of pre-screening and post-supervision so as to improve the capital allocation and utilization efficiency of startup enterprises. Second, in ESS, the MMS service platform provides innovation support, entrepreneurship training, and other innovation and entrepreneurship services for startup enterprises. Moreover, the MMS service platform gives full play to the resource accumulation effect of the platform, thereby improving the operation and management level of the startup enterprise. Third, in CISE, the synergy between the subsystems makes the whole system develop smoothly and orderly. The MMS service platform optimizes service quality, financial institutions improve return of capital, startups increase the survival rate and incubation rate, and CISE improves the operational scale. Therefore, based on the above theoretical analysis, this paper proposes hypothesis 1:

Hypothesis 1.

In CISE, the synergy between ESS and FSS positively affects the operation scale of the CISE.

4. Simulation Experiment

This section conducts simulation experiment based on the CISE model. In the experiment, the system starts from a disordered state. With the exchange of information and resources, the agents in the system continuously adjust their behaviors according to the principles of the CISE model, the subsystems develop collaboratively, and the entire system finally reaches a stable state.

4.1. Experiment Design

Based on synergetics and CISE model, this paper uses Netlogo to conduct a co-evolution simulation experiment of the ecosystem, trying to explore the impact of the synergy of financial support and entrepreneurial services on the level of entrepreneurial activities in the system (i.e., the number of enterprises and entrepreneurial success rate). Netlogo is a programmable simulating software that is used to simulate natural and social phenomena [37,38]. Netlogo is often used for simulation experiments on complex systems and has significant advantages in multi-agent modeling, especially for the analysis of co-evolutionary mechanisms of complex systems [39].

4.1.1. Agent Definition

In the experiment, we set up 3 types of agents: startup enterprise, MMS service platform, and financial institution. These agents are all DAs that directly interact with startup enterprises. For RAs (i.e., scientific research institution and government department), they are expressed as the attributes of the DAs. The agents are represented in different graphs in the experiment (

Table 3. Agent graph list.

Agent	Category	Graph
Startup Enterprise	DA
Financial Institution	DA
MMS Service Platform	DA

).

4.1.2. Variable Definition

In the experiment, it is assumed that the initial state of the system is disordered, that the state of agents is random. The categories and definitions of the experimental variables are listed, that is, the attributes of the 3 types of agents are defined in

Table 4. Agent attributes, categories, and definitions list.

Agent	Attribute	Category	Definition
Startup Enterprise	Funds amount	CF	Indicates the amount of money owned by enterprises.
	Ability index	CF	Indicates the comprehensive ability of the enterprise.
	Operating revenue	CF	Indicates the total revenue of the enterprise.
	Operating cost	DF	Indicates the operation expenses of the enterprise.
	Searching cost	DF	Indicates the expenses paid by enterprises to seek financial support and entrepreneurial services.
	Regeneration threshold	DF	Indicates the marginal value of the amount of funds that the enterprise regenerates.
Financial Institution	Funds amount	CF	Indicates the amount of money owned by financial institutions.
	Operating revenue	CF	Indicates the operation expenses, mainly capital return.
	Operating costs	DF	Indicates the operation expenses of the financial institutions.
	Investment probability	DF	Indicates the possibility of financial institution giving financial support to startup enterprises.
	Regeneration threshold	DF	Indicates the marginal value of the amount of funds that the financial institution regenerates.
MMS Service Platform	Funds amount	CF	Indicates the amount of money owned by MMS service platform.
	Operating costs	DF	Indicates the operation expenses of the MMS service platform.

. The attributes of the agents can be divided into convergence force (CF) and dissipative force (DF). Referring to the simulation experiment of the existing research, the initial values of the experimental variables are set, but those values do not represent the actual situation but only the relative size between the variables.

Given an initial number of 3 types of agents, the number of startup enterprises is 300, the number of financial institutions is 30, and the number of MMS service platforms is 40. The initial numbers of startup enterprises, financial institutions, and MMS platforms are based on real statistical data, and they are set after being reduced in the same proportion so as to ensure the visualization and running speed of the simulation experiment. Note that the initial numbers do not change the trend of the system evolution but only speed up or slow down the system to reach a steady state.

4.1.3. Function Definition

This paper refers to the classic wolf–sheep predation model in Netlogo [40] and related model frameworks [41] and modeling rules [42] and constructs a synergy model composed of MMS service platforms, startup enterprises, and financial institutions. With reference to the CAS theory and the life cycle model [19], we set up experimental functions. Experiment functions define the interaction behaviors of the agents. This paper designs six experiment functions: Search, Invest, Entrepreneurship, Distribute, Reproduce, and Death. The results of function operation are shown in the next section. The experimental function descriptions are listed in

Table 5. Functions and interaction behaviors list.

Function	Interaction Behaviors
Search	Startup enterprises seek financial support and generate searching cost at the same time.
Invest	Financial institutions provide financial support to startup enterprises.
Entrepreneurship	MMS service platforms provide entrepreneurial services to startup enterprises.
Distribute	Startup enterprises distribute the profits if the enterprise is profitable.
Reproduce	Startup enterprises or financial institutions reproduce if the funds amount of the startup enterprise or financial institution is greater than its regeneration threshold.
Death	Startup enterprise or financial institution will disappear from the system if their funds amount is less than 0.

.

4.2. Experiment Results

After initializing the system, the system is in a disordered state. Startup enterprises, financial institutions, and MMS service platforms are randomly distributed (Figure 2a). After the system runs for 100 cycles, that is, when tick = 100, the simulation experiment stops, and the system state changes significantly (Figure 2b). To obtain more intuitive experimental results, the experiment cycle is set to 100. Because we compared some experimental results of more than 100 cycles and found that these experimental results have the same trend of change as the result of 100 cycles, that is, if the experiment runs for more than 100 cycles, the number of the agents in the system will maintain a stable growth trend in the same proportion, and the system evolves to a stable development state. Therefore, 100 cycles can reflect the changing trends of startups and financial institutions. Note that the initial numbers do not change the trend of system evolution but only accelerate or slow down the system to reach the steady state.

The experimental results reveal that the survival rate of startup enterprises around the MMS service platforms and financial institutions is significantly higher than other regions, which indicates that the growth and reproduction of startup enterprises are strongly dependent on financial support and entrepreneurial service in CISE. Financial support promotes the growth of enterprises, and entrepreneurial service ensures the incubation and regeneration of enterprises. Thus, they jointly promote the development of the entire system. That is, the synergy between ESS and FSS has influenced the evolution and development of CISE. From the perspective of the impact of a single factor on entrepreneurial activities, in the process of enterprise growth and development, financial resources are the most basic resources [22]. Solving the problem of the enterprise’s capital shortage will increase the motivation power for the enterprises [23]. Entrepreneurship services can also provide physical space and network support for startups to promote the development of startups [43]. From the perspective of the impact of multi-factor synergy on the evolution and development of the system, Liu studied the synergy of technological innovation and financial innovation, which is conducive to the efficient allocation of resources in the social system [25], and Zhu studied the synergy between innovation driving and financial support for regional economic development [24]. This paper studies the synergy of financial support and entrepreneurial services to improve the level of entrepreneurial activities in the entrepreneurial ecosystem, that is, to increase the number of enterprises in the system, which is a supplement to the co-evolution mechanism of the entrepreneurial ecosystem.

In the experiment, the number of startup enterprises varied over time (Figure 3). Apparently, the curve can be divided into two stages: the decline stage corresponding to 1–20 ticks and the slow-rise stage corresponding to 20–100 ticks. The decline stage corresponds to the elimination stage of startup enterprises. Due to the lack of effective investment and limited profitability, many low-quality startup enterprises gradually consume their own resources until the system eliminates them. The remaining few high-quality startup enterprises will gradually start to grow with the help of effective investment and strong profitability. Moreover, startup enterprises continue to reproduce new enterprises, making the number of enterprises begin to rise. Additionally, when the initial value setting of the variables in the model changes, the change trend in the number of startup enterprises is consistent, which shows that the results of startup enterprises are robust. From the perspective of synergy, startup enterprises are in a disordered state at first. With the convergence force, such as seeking financing and development, and the dissipative force, such as cost consumption and information asymmetry, startup companies can eventually reach a stable state.

In the experiment, the number of financial institutions varied over time (Figure 4). It reveals that the overall trend of the curve is rising slowly, but it will fluctuate in the short term. This may be because startup enterprises need a lot of capital investment in the initial stage, but their income will not increase immediately. A short break-even state or even a loss state occurs at this period. However, with the rapid growth of some high-quality enterprises, financial institutions will get several returns. The number of them began to rise. When the initial value setting of variables in the model changes, the change trend in the number of financial institutions is consistent, which shows that the results of financial institutions are robust. From the perspective of synergy, financial institutions are initially in a disordered state. With the convergence force of good projects and capital appreciation and the dissipative force of investment barriers, financial institutions can eventually reach a stable state.

4.3. Model Validation

Based on synergetics, the simulation experiment explores the interaction behaviors of the agents and the synergy of the subsystems in the CISE and studies the influence of the synergy on the evolution of the system. However, there may be some limitations in the CISE model setting and Netlogo software operation; for example, the simulation efficiency of Netlogo software is still not high. Thus we perform model validity justification.

The simulation experiment based on the CISE model in this paper is a multi-agent interactive simulation process, focusing on the complexity of the system, the interaction behavior of the agents, and the evolution mechanism of the system, rather than the presentation results of the numerical relationship of the system. Drawing on existing research [44], we choose the extreme condition test to verify the model validity. The extreme case test is for testing whether the constructed model conforms to the real system under extreme conditions.

This paper passes three extreme case tests. (1) When the number of financial institutions is 0 or the amount of financial support is 0, the number of startups shows a downward trend, and the simulation results conform to the actual law. (2) When the number of MMS platforms is 0 or the entrepreneurial services provided by MMS platforms is 0, the number of startups also declines. Indeed, the simulation results conform to the actual law. (3) When both financial support and entrepreneurial services are zero at the same time, the decreasing trend of the number of startups is more obvious, which is also in line with the actual law. With the increase in financial support and entrepreneurial service, the number of startups has indeed gradually increased, which is the same as the actual situation. Based on the above analysis, it can be considered that the setting of the model is reasonable and the results of the model are also valid.

There are also some uncertainties in the application of the model. Firstly, there is a gap between the simulated environment and the real environment. In the real environment, the entrepreneurial ecosystem is more complex, and entrepreneurial activities are affected by more uncertain factors. Meanwhile, the simulation experiment only gives qualitative research results, and the lack of quantitative relationship results also makes the application of the model uncertain. Next, we conduct an empirical analysis of the quantitative relationship between synergy and the CISE based on real statistical data, as a supplement to the justification of the model’s validation.

5. Empirical Analysis

This section mainly studies the synergy effect of CISE, that is, how the synergy between ESS and FSS affects the operation scale of the CISE. The results of simulation experiments show that startup enterprises have a higher survival rate near financial institutions and MMS service platforms. This reveals that synergy can promote the incubation, growth, and reproduction of startup enterprises. Through empirical analysis, the synergy effect of CISE is quantified, which provides a reference for MMS service platforms and financial institutions to improve efficiency and explores new business models for the development of startup enterprises.

5.1. Data Source

This paper takes the CISE of 30 provinces in mainland China (except Tibet) as the research sample. Due to the short development time of the CISE, the availability of data is challenged, and the dynamic analysis of it cannot obtain obvious trend changes. Simultaneously, considering the development situation in the past few years, the time span of the data is 2017–2021. In view of the lack of data, the data of Tibet province was excluded. The data comes from the “China Torch Statistical Yearbook,” which includes the basic operation, service, income, team, and enterprise information of CISEs, which objectively reflects the development of the CISEs in China [2]. Therefore, this paper uses panel data of 30 provinces in China from 2017 to 2021 for empirical analysis.

5.2. Variable Measurement

Dependent Variable: Operation scale of CISE (Ope_sca). Based on the process and results of the simulation experiments above, synergy in CISE makes the number of startup enterprises show a steady growth trend, that is, the synergy will have an impact on operation scale. Generally, the greater the number of startups and groups in the CISE, the larger the operating scale of the CISE. At the same time, as the scale of the CISE expands, the synergy will also promote the incubation and reproduction of more companies. Thus, this paper measures the operational scale from two perspectives: (1) the number of startup enterprises and entrepreneurial groups in the CISE (Sta_num); (2) the number of newly registered companies in the CISE (New_num).

Independent Variable: the synergy includes two variables, financial support (Fin_sup) and entrepreneurial service (Ent_ser). (1) The MMS service platforms will provide entrepreneurial services for startup enterprises, and this paper uses the number of entrepreneurial services as a proxy variable, that is, the sum of the entrepreneurial activities and entrepreneurial trainings held by MMS service platforms. (2) Financial institutions will provide financial support for startup enterprises, and this paper measures the amount of financial support as a proxy variable, that is, the number of startup enterprises and entrepreneurial groups that have received investment and financing from financial institutions.

Control Variables: To control the factors influencing the operation scale and avoid potential endogeneity problems caused by omitted variables. Referring to the research design of Lv (2021), Wang (2021), and Dutt (2016) [45,46,47], this paper set control variables as follows: (1) Received fiscal fund support of the CISEs (Fun_sup). The CISE with more financial support will have a higher number of startups and entrepreneurial teams in it. The unit is one thousand RMB. (2) Service income of the MMS service platforms (Ser_inc). The higher the service income, the greater the number of startups and groups that obtain entrepreneurial services, which is conducive to the growth and regeneration of enterprises. The unit is one thousand RMB. (3) Valid IPRs held by startups and tenants (Ipr_num). The valid IPRs are the core competitiveness of an enterprise and have a positive impact on the development of the enterprise. (4) The amount of financial support funds provided by financial institutions (Inv_amo). The unit is one thousand RMB. (5) Economic development level (Grp), which is measured by the gross regional product in the region where the CISE is located. The unit is 100 million RMB.

Considering the possibility of heteroscedasticity, all variables are processed by a natural logarithm, which will make the data series more stationary. Descriptive statistics of all variables are presented in

Table 6. Variable descriptive statistics list.

Variable	Obs	Mean	Std.Dev	Min	Max
Sta_num	150	9.064612	1.069033	5.736572	11.16382
New_num	150	7.485975	1.078027	4.234107	9.337678
Ent_ser	150	5.869501	1.182196	2.079442	7.793587
Fin_sup	150	8.559326	1.086801	4.330733	10.10074
Fun_sup	150	10.97622	1.250337	7.524022	13.19442
Ser_inc	150	11.45214	1.558951	6.234411	15.32211
Ipr_num	150	8.156003	1.448649	3.367296	11.57824
Inv_amo	150	13.13774	1.843157	7.473069	17.71886
Grp	150	9.985491	0.8659497	7.722323	11.61513

.

5.3. Model Setting

Referring to the research design of Lv (2021), Wang (2021), and Wang (2021) [45,46,48], this paper constructs the following model to analyze the impact of synergy on the operating scale of the CISE. The model is stated as follows:

$$Ope\_{sca}_{i,t}={\beta }_0+{\beta }_{1}Fin\_su{p}_{i,t}+{\beta }_{2}Ent\_Se{r}_{i,t}+Contro{l}_{i,t}+{\mu }_t+{\eta }_i+{\epsilon }_{i,t},$$

where the subscript i represents province, and the subscript t represents year. Ope_sca is the dependent variable, and Sta_num and New_num can be used as proxy variables of Ope_sca, respectively. That is, model 1 and model 2 are set separately for regression analysis, where model 1 represents the regression of Fin_sup and Ent_ser on the Sta_num, and model 2 represents the regression of Fin_sup and Ent_ser on the New_num. $Contro{l}_{i,t}$ stands for the control variables. ${\eta }_i$ represents the individual fixed effects, and ${\mu }_t$ is time fixed effects. ${\epsilon }_{i,t}$ is a random error term. According to the results of the Hausman test, this paper adopts a random effect model. This paper uses the OLS method to perform regression analysis.

5.4. Regression Analysis

Table 7. Basic regression results list.

	Model 1	Model 2
Ent_ser	0.4581371 *** (0.0918918)	0.4316335 *** (0.1197555)
Fin_sup	0.204742 *** (0.0640381)	0.117038 * (0.0649754)
Fun_sup	−0.0191665 (0.0347381)	0.0149321 (0.0409976)
Ser_inc	0.0366713 (0.0411465)	0.0499406 * (0.0282902)
Ipr_num	0.1647261 *** (0.0410153)	−0.0039598 (0.0464501)
Inv_amo	−0.0126801 (0.0263291)	0.0657454 * (0.0342436)
Grp	0.100904 * (0.0530999)	0.2688699 ** (0.1330584)
_cons	1.547449 *** (0.4299981)	−1.147544 * (0.5915398)
within-R2	0.8143	0.6556
N	150	150
Wald chi2(3)	805.78	2343.79

Note: Robust standard error in brackets; * p < 0.1, ** p < 0.05, *** p < 0.01.

reports the regression result. Model 1 represents the effect of synergy on the sum of startups and entrepreneurial groups in CISE, that is, the regression of Fin_sup and Ent_ser on the Sta_num. Model 2 represents the effect of synergy on the number of newly registered companies, that is, the regression of Fin_sup and Ent_ser on the New_num. The empirical results show that the coefficients of Fin_sup and Ent_ser are significantly positive in the regression of the two models, indicating that the synergy has a significant positive correlation with the operating scale of the CISE. Therefore, H1 proposed in Section 3.6 is validated.

Model 1 reports that the increase in the number of startup enterprises that have received financial support has a positive impact on the operating scale of CISE. At the same time, increasing the number of entrepreneurial services such as entrepreneurial activities and entrepreneurial training is also conducive to improving the operating scale of the CISE. The possible reason is that within CISE, the MMS service platforms have improved the soft power of startup enterprises in business management by providing entrepreneurial services, and financial institutions have improved the approval speed of financial support such as venture capital by assessing the improvement of the ability of startup enterprises. Thus, the financial services can accurately target small and micro startup enterprises and alleviate the “urgent need” of the shortage of funds in the development of enterprises.

In Model 2, the measurement of the operating scale of the CISE is changed to the number of newly registered companies. The regression results are still significantly positive, which is consistent with the conclusions of Model 1, indicating that the regression results are robust. Meanwhile, it can also be seen that although improving the quality of entrepreneurial services is difficult to affect the sum of startup enterprises and entrepreneurial groups, it can have a positive impact on the number of newly registered enterprises to a certain extent. The number of valid patents owned by an enterprise is the foundation of the company’s survival and effectively promotes the incubation and regeneration of the company. Therefore, the number of valid patents will have an impact on the total number of enterprises in CISE.

Therefore, CISE should carry out resource circulation and optimal allocation in the system based on synergy. On the one hand, the CISE can strengthen the introduction of financial institutions and make startup enterprises more closely combined with financial support. On the other hand, MMS service platforms can provide pertinent intermediary services, such as an information disclosure management system and credit rating services, so that financial institutions can better obtain the information of startup enterprises to facilitate financial institutions in identifying excellent enterprises with investment value. Both financial support and entrepreneurial services can synergistically increase the success rate of excellent projects and realize the effective growth and development of excellent enterprises.

6. Discussion

The CISE is composed of a series of interacting agents which realize the flow of resources and information through interactions, provide support services for entrepreneurs, and provide financial support services for startups. Entrepreneurial services and financial support jointly promote entrepreneurial activities. This paper analyzes the interactive behaviors of the agents and the synergy of the subsystems in the CISE and studies the internal evolution mechanism and external development law of the system. On the one hand, it expands the research perspective of the evolution mechanism of the entrepreneurial ecosystem. On the other hand, it supports the sustainable development of the CISE, including optimizing the operating efficiency of the MMS platforms, improving the investment efficiency of financial institutions, and increasing the number of startups, their development, and hatching success rate.

This paper defines the CISE as FSS and ESS and explores the synergy mechanism. Some scholars have conducted research on the constituent agents of the entrepreneurial ecosystem, but they focused more on the definition of agents and the construction of relationships while ignoring the interaction between the agents. Based on the existing studies, we pay more attention to the complex behaviors in the system and study the interactive behaviors between the agents and the synergy between the subsystems, which supplements the related research on the internal structure of the entrepreneurial ecosystem. Meanwhile, this paper studies the impact of synergy on the co-evolution of the CISE through simulation experiments and empirical analysis. Some scholars have studied the impact of financial support, technological innovation, and other factors on entrepreneurial activities. However, these studies analyze the influence of a single factor on entrepreneurial activities but ignore the impact of multi-factor synergy on entrepreneurial activities. Based on the existing research, this paper studies the influence of multi-factor interactions on the co-evolution of CISE from the perspective of an entrepreneurial ecosystem.

The simulation experiment and empirical analysis are complementary. The simulation results show that the survival rate of startups near financial institutions and MMS platforms is significantly higher than in other regions, which indicates that the growth and reproduction of startup enterprises are strongly dependent on financial support and entrepreneurial service in CISE. However, this cannot show the specific quantitative relationship between financial support, entrepreneurial services, and startups, that is, how the synergistic configuration relationship between financial support and entrepreneurial service in the CISE affects the growth and development of startups. Empirical analysis studies the quantitative relationship between financial support, entrepreneurial services, and the number of startups. The results of the empirical analysis provide support for the allocation of resources in the CISE and are conducive to further research on a sustainable entrepreneurial ecosystem. Therefore, the combination of simulation and empirical analysis can make the research results of this paper more robust and have wider applicability.

After discussing the results of simulation and empirical analysis, we yielded three management insights. First, the synergy in CISE, which is composed of the interactions between startup enterprises, financial institutions, and MMS service platforms, and the development of the whole system depend on the system’s order parameters—financial support and entrepreneurial service. Moreover, the synergy between ESS and FSS has a positive effect on the operation scale of CISE. Second, CISE can strengthen the introduction of financial institutions and make startup enterprises more closely combined with venture capital. Meanwhile, the MMS service platform can provide more entrepreneurial services in a targeted manner to help the generation and development of startups. Third, from the perspective of enterprise business model innovation, small and micro startup enterprises can rely on the CISE to optimize the external business environment, actively participate in entrepreneurial activities and entrepreneurial training to enhance the core values, and strive to seek financial support from financial institutions to ensure the sustainable development of the enterprise.

7. Conclusions

This paper studies the impact of the synergy on the entrepreneurial ecosystem, expands the research perspective of the evolution mechanism of the CISE, and provides feasible suggestions for the sustainable evolution and development of the entrepreneurial ecosystem. However, this paper also has some limitations. Firstly, this paper only studies the synergy between financial support and entrepreneurial services and does not consider the influence of other factors in the system, which is different from the actual situation. In the actual entrepreneurial ecosystem, the agents, resources, and interactions need to be further defined and improved. Secondly, this paper studies the impact of synergy on the evolution of the entrepreneurial ecosystem, focusing only on the level of entrepreneurial activities (i.e., the number of startups and the success rate of startups). Considering the comprehensiveness of the economic system, it is also necessary to consider other measurement indicators, including social welfare, ecological environment, etc.

Future research can aim at the sustainable development of the entrepreneurial ecosystem and build a more complete entrepreneurial ecosystem based on the similarities and differences between the CISE and the sustainable entrepreneurial ecosystem so as to provide theories for the support of the sustainable development of the entrepreneurial ecosystem theoretically. At the same time, future research can also combine the characteristics of the digital economy to explore the impact of multiple factors such as digital technologies, entrepreneurial services, and financial support on the evolution mechanism of the entrepreneurial ecosystem.