Influencing Factors Analysis in Railway Engineering Technological Innovation under Complex and Difficult Areas: A System Dynamics Approach

Abstract

The geological complexity, environmental sensitivity, and ecological fragility inherent in complex and difficult areas (CDAs) present new opportunities and challenges for technological innovation in railway engineering development in China. At the current stage in China, the process of technological innovation in railway engineering within CDAs still faces a series of pressing issues that need addressing. The paper identifies and determines 22 influencing factors for technological innovation in railway engineering within CDAs across five dimensions. Subsequently, a technological innovation model for railway engineering in such areas is constructed based on system dynamics (SD), which is followed by simulation and sensitivity analysis to identify the key influencing factors. The results indicate that key influencing factors for technological innovation in railway engineering within CDAs include technological innovation capability, the adaptability of technology to the environment, R&D funding investment, technological product requirements, technological innovation incentive mechanisms, and the level of technological development. The importance ranking of each dimension is as follows: technological factors > technical factors > management factors > resource factors > environmental factors. The paper provides new insights for promoting technological innovation and management development in complex and challenging railway engineering projects. It offers a fresh perspective to enhance the technological innovation efficiency of railway projects in complex and challenging areas.

1. Introduction

With the implementation of the China’s western development and the strategy of “the Belt and Road” initiative, the demand for railway engineering in CDAs is increasing [1]. The major railway projects in CDAs are key national construction projects, holding significant importance in maintaining national unity, consolidating border stability, and promoting economic and social development in western regions. In recent years, China has conducted in-depth research and exploration in railway engineering management in CDAs, achieving a series of important results. Taking a railway as an example, Bombelli et al. explored the evolution law of innovation behavior of major engineering innovation teams under deep uncertainty based on network game theory and the multi-agent modeling tool Netlogo [2]. Bai [3] established a risk warning model for railway construction in difficult mountainous areas based on extension theory and applied it to the railway to verify the applicability of the proposed risk warning model. Sun [4] applied the theory of comprehensive evaluation to construct a comprehensive evaluation index system for railway construction projects from the three dimensions of economic, social, and environmental benefits, and applied it to the railway. Wang [5] designed indicators based on the stability connotation of railway planning, construction, and operation to evaluate the layout stability of the railway, providing a practical basis for the planning of the railway.

The diverse and complex geological conditions, as well as the harsh climate in CDAs, increase the difficulty of railway construction projects and prolong the construction period, posing significant challenges to engineering construction and management [6]. With the advancement of science and technology, railway engineering technology has evolved from a single technology to a diverse range of technologies, gradually forming a relatively mature technical system. The application of new technologies not only reduces the costs of railway construction, operation, and management but also significantly enhances engineering efficiency. Moreover, it effectively reduces project risks and improves engineering safety. Therefore, the academic community has conducted extensive research in the field of railway engineering technology innovation management, covering areas such as dynamic mechanisms [7,8], risk management [9,10,11,12], and collaborative mechanisms [13,14,15]. However, there is relatively little research on the factors influencing technological innovation, with most focusing on the efficiency of enterprise technological innovation. Chen [16] conducted a study on the regional differences and influencing factors of innovation efficiency in China’s high-tech industries based on network data envelopment analysis. Bai [17], using data from listed companies in China from 2010 to 2019, empirically analyzed the impact of technological innovation enthusiasm on innovation performance from a nonlinear perspective. Li [18], with 436 high-end equipment manufacturing enterprises as the research subjects, constructed a conceptual model of different dimensions of digital empowerment, technology-embedding adaptability, and technological innovation performance, revealing a nonlinear relationship between digital empowerment and enterprise technological innovation performance. Chen [19] used the DEA model to measure the green technology innovation efficiency of 35 industrial sectors in China and compared the efficiency of green technology research and development with green technology transformation. Finally, they used the Tobit model to empirically study the influencing factors of green technology innovation efficiency. Furthermore, scholars have also focused on the impact of certain single aspects such as key core technologies [20,21], leader behavior [22,23], and government subsidies [24] on technological innovation. However, railway engineering technological innovation possesses characteristics such as organizational synergy and environmental complexity [25,26,27]; the subjects of technological innovation are influenced by factors at different levels during the execution process. The aforementioned studies overlook this aspect and lack exploration of the interaction among technological innovation influencing factors and the investigation of key influencing factors. This hinders the improvement of the technological innovation management level in complex and challenging railway engineering projects. Therefore, further research is needed on how to scientifically identify key influencing factors and analyze the mechanisms of interaction among influencing factors.

System dynamics (SD) is an interdisciplinary and comprehensive discipline aimed at understanding and addressing systemic issues [28]. This approach is used to study and analyze the structure of complex systems and the interactions among variables. By focusing on causal relationships among variables and their impact on the system, SD assists researchers in better understanding the complex causal relationships and dynamic behavior of systems over time. Currently, SD is widely employed in the field of technological innovation in construction projects. For example, Liu [29] conducted research using SD to study the interactions and feedback mechanisms among various elements influencing innovation activities in engineering technology projects. Gao [30] developed an SD evaluation model for the technological innovation capability of construction engineering projects, proposing a dynamic evaluation method for technological innovation efficiency based on DEA (Data Envelopment Analysis). An [31], considering factors at both the project and enterprise organization levels, proposed a comprehensive evaluation method for the efficiency of enterprise technology innovation projects based on SD and DEA. Park [32] and Bajracharya [33] analyzed the dynamic processes of innovation in construction using SD, providing new perspectives and methods for innovation research in the construction field. The above studies have demonstrated the significant role of SD in this field, but there is still a lack of exploration into the key influencing factors.

Although the current literature includes some studies on the interaction of risks affecting railway construction and technological innovation, as well as the mechanisms of various influencing factors, there remain issues that require further refinement and in-depth study.

• In railway engineering management, the focus is mainly on research related to project quality, progress, cost, and safety risks. Comparatively, there is less research on the management of technological innovation in railway engineering.
• Current research on technological innovation primarily focuses on how enterprises can enhance their innovation capabilities and how industries can improve the efficiency of technological innovation. There is relatively insufficient research on the management of technological innovation in engineering projects, and the impact of the environment on technological innovation is rarely considered.

Railway engineering technological innovation in complex and difficult areas, as a complex system, is explored in this paper using the SD theoretical approach. System dynamics in innovation-related research management can be used to study and analyze the structure of complex systems and the interaction between variables, through the analysis of the causal relationship between variables and the relationships within the systems, to help researchers better understand the complex causality of the system. In the specific scenario of railway engineering technology innovation in complex and dangerous areas, the application of system dynamics is particularly important. Railway projects in these areas often face extreme natural conditions, complex geological structures, limited resource supply, and stringent environmental requirements. Technological innovation is the key to overcoming these challenges and ensuring the smooth implementation of projects. System dynamics can not only reveal the key factors in the process of railway engineering technology innovation and its mechanism but also help to predict the potential impact of different management measures on technology innovation, provide scientific decision support for decision makers, and promote the efficient advancement of technological innovation and the success of the project goal.

The rest of this article is organized as follows. Section 2 introduces the process of identifying influencing factors. Section 3 presents the research methodology, describing the variables and parameters that need to be confirmed. Section 4 provides the analysis results. Finally, Section 5 summarizes the findings of this study.

2. Identification of Influencing Factors

As a critical infrastructure and a major project benefiting people’s livelihoods in China, railways play a crucial role in stabilizing the economy, promoting development, and improving the well-being of the population. With the gradual extension of China’s railway network to the western regions, there has been increasing attention on railway projects and technological innovation initiatives in CDAs. However, in CDAs characterized by harsh environmental conditions and intricate topography, innovation entities engaged in technological advancements need to validate the feasibility of new technologies and their applicability under specific geological and meteorological conditions. Moreover, the innovation process also faces challenges such as long construction cycles, high costs, and difficulties in resource allocation [34]. Therefore, the success or failure of technological innovation in CDAs railway projects depends not only on the technological and innovation environment but also on the close correlation with organizational management during the technological innovation process. At the corporate level, the success or failure of technological innovation is influenced by non-technical factors such as the market, corporate culture, managerial approaches, organization, and information. Meanwhile, at the engineering level, technological innovation is reflected in factors such as technology and risk [35]. Based on the above analysis, this paper, through an extensive literature review and on-site investigations, has identified and summarized a list of 22 factors influencing technological innovation in railway engineering. These factors are categorized into five classes: environmental factors, technical factors, resource factors, technological factors, and management factors.

2.1. Environmental Factors

In the context of discussing technological innovation in railway engineering, environmental factors, especially the macro-policy framework and the dynamically changing market environment, constitute key external variables that affect the technological innovation process. Macroeconomic policies, viewed from an overall perspective, are important levers to guide and regulate the development of the railway industry. They have a direct and far-reaching impact on technological innovation activities and directly affect railway engineering technological innovation in CDAs [7]. The government aims to create a favorable ecological environment for technological innovation in railway engineering by carefully designing and implementing a series of policy measures. Policies not only promote the original innovation and application transformation of technology but also enhance the overall innovation awareness and capabilities of the industry, provide indispensable policy support and legal protection for the progress of railway engineering technology, and are the basic environmental driver for promoting the development of the industry. At the same time, as global competition intensifies, customer demands diversify, and technology iteration accelerates, dynamic changes in the market environment force all stakeholders in the railway construction field to actively seek technological innovation to adapt to the new requirements of the market. This external pressure is transformed into internal motivation, driving companies to increase investment in R&D and explore new technologies, new materials, and new processes to improve project efficiency, reduce costs, and enhance service quality and safety, thereby standing out in the fierce market competition, consolidating, and enhancing its market position and core competitiveness. The specific list of influencing factors is shown in

Table 1. List of environmental factors.

	Influencing Factors	Definition	References
Environmental factors	Technological innovation policy	In order to promote scientific and technological progress and innovation activities, the government or relevant decision-making agencies carefully design and implement a series of strategies and action plans.	[36,37]
	Intellectual property protection	Legal recognition and maintenance of the fruits of the intellectual labor of innovators and creators.	[38]
	Market competition intensity	In the railway field, the degree of competition between enterprises to compete for market share, customer resources and profits.	[36]

.

2.2. Technical Factors

Technical factors occupy a core position in the field of technological innovation in civil aviation and railway engineering. It covers all elements related to technological progress, technological application, technological integration, and technological management, and it plays a decisive role in the formation and promotion of innovation results. These technical factors are directly related to whether the innovation subject has sufficient capabilities to promote technological innovation, and they are key indicators to measure the competitiveness of an organization or country in the field of civil aviation and railway engineering technology. Strengthening technical capabilities and ensuring that innovative entities can quickly adopt and master new technologies provides substantial support for the realization of CDAs railway engineering technology innovation. The specific list of influencing factors is shown in

Table 2. List of technical factors.

	Influencing Factors	Definition	References
Technical factors	Technological development level	The level of technology development is a comprehensive reflection of technology maturity, innovation ability, and application practice in the railway engineering industry.	[38]
	Technological product requirements	The sum of the market’s desire and purchase desire for a particular technology product or service.	[39,40]
	Technological innovation benefits	A series of positive effects achieved through the introduction and application of new technologies are not only reflected in the economic level but also include positive social and environmental effects.	[41]
	Green environmental protection level	Minimizing the environmental impact of new technologies or products and the sustainable use of natural resources in production, consumption and waste.	[42,43]
	Technological environment adaptability	Combining the adoption, application, development and innovation of technology, it affects the influence of the adaptability, growth, and innovation potential of technology in a specific environment.	[39]

.

2.3. Resource Factors

Resource factors play a vital role in the innovation of railway engineering and technology, among which the two most critical resources are human resources and financial resources, which are the cornerstone to support the smooth development of technological innovation activities. Technological innovation itself is a complex process highly dependent on resource integration, involving the whole chain from the germination of creativity to the commercial application of technology. Each step requires the precise input and efficient allocation of corresponding resources. Professional talents with high technical levels and innovative thinking are the core driving force to promote the innovation of railway engineering technology. The ability and quantity of technical personnel can promote the development and realization of technological innovation [44]. Financial resources are the material basis for the continuous development of technological innovation activities. Sufficient capital investment can enhance the willingness of innovative entities to engage in innovative activities and ensure the smooth progress of technological innovation [45]. Therefore, the successful implementation of CDAs railway engineering technology innovation is inseparable from the efficient integration and optimal allocation of the two key resources of talent and capital. A specific list of the influencing factors is shown in

Table 3. List of resource factors.

	Influencing Factors	Definition	References
Resource factors	R&D funding investment	In order to promote technological innovation and scientific and technological progress, the funds are specially allocated to support the technical research, development, and experimental activities of railway engineering projects.	[39]
	Organizational scale	It refers to the number of organizations involved in railway engineering technology innovation, and it also covers multiple dimensions such as the ability, structure, and influence of the organization	[46,47]
	Number of technical personnel	The number of research and development personnel involved in technological innovation in railway engineering projects.	[40]
	Worker capability	The skills, knowledge, and professional qualities of the front-line staff who directly use or operate the innovative technical products.	[39]

.

2.4. Technological Factors

Technical factors refer to the factors that carry out the research and application of technological innovation, which covers the whole process from theoretical research, technology research and development to practical application. It is the direct driving force to promote technological innovation and industrial upgrading. Its depth and breadth are directly related to the speed and quality of the industry development. The advanced nature, applicability, and integration ability of technical factors not only determine the feasibility and efficiency of technological innovation but also directly affect whether the innovation results can be successfully transformed into actual productivity, which brings substantial progress and improvement for the development of the industry. A specific list of the influencing factors is shown in

Table 4. List of technological factors.

	Influencing Factors	Definition	References
Technological factors	Supporting industrial base	A comprehensive industrial cluster that provides comprehensive support for innovative railway engineering technologies, which not only covers key links such as design, production, and maintenance but also includes a range of related services and facilities.	[39,48]
	Technological innovation capability	Organizations or individuals rely on existing resources in the field of railway engineering to carry out innovative activities, research and develop core technologies, and transform them into comprehensive capabilities for practical application.	[40,49]
	Technological innovation strategy	Comprehensive planning, deployment, and arrangement of technological innovation activities under the guidance of the overall development goals and visions of the organization or the enterprise.	[48]
	Theoretical foundation research	It reveals its intrinsic laws and essential characteristics through systematic experiment or theoretical derivation.	[39]

.

2.5. Management Factors

As the most dynamic and flexible dimension in the field of technological innovation, the management factor involves how to use efficient management strategies and systematic methods to promote the innovation, application, and diffusion of technology. In the complex ecosystem of railway engineering technology innovation, management factors play the role of the baton. Effective management measures play a role in motivation and coordination, ensuring the orderly integration of various forces to collectively address the challenges of technological innovation in CDAs railway engineering. This takes place primarily through benefit distribution [40], incentive promotion [45], and organizational coordination [29], increasing the motivation of innovation subject and integrating the resources from various parties to form a concerted effort, promoting technological innovation. The specific list of influencing factors is presented in

Table 5. List of management factors.

	Influencing Factors	Definition	References
Management factors	Information management level	The ability to effectively acquire, process, share, and protect knowledge resources in the process of railway engineering technology innovation.	[50]
	Organizational coordination level	Within or across organizations, through effective communication, cooperation and resource integration, the synergy of all forces and resources can be realized to improve the overall operational efficiency and efficiency.	[29,51]
	Innovation culture	In the field of railway engineering, innovation culture refers to a unique and distinct spirit of innovation nurtured and shaped in the complex engineering technology innovation and project management practice.	[48]
	Leader support	Senior managers provide the necessary conditions, resource guarantees, and incentives to ensure that innovation activities can proceed smoothly and achieve the expected results.	[29,52]
	Technological innovation incentive mechanisms	A management system that aims to stimulate the innovation potential and enthusiasm of individuals or teams in technology research and development, product design, and service improvement through systematic and diversified incentive strategies.	[46,51]
	Benefit/Responsibility allocation mechanisms	Through clarifying the rules and procedures, an institutional arrangement to reasonably define the responsibilities and benefits of the parties involved in the innovation project.	[40,51]

.

3. System Dynamics Model Construction

3.1. System Boundaries and Model Assumptions

System dynamics is characterized by its comprehensiveness, nonlinear dynamics, multiple feedback loops, long-term orientation, and practicality [53]. Research applications of this method primarily focus on simulation forecasting [54,55], optimization control [56,57], and other related areas. Therefore, it is suitable for analyzing the behavior and evolution of the complex system of technological innovation in CDAs in railway engineering. It allows exploring causal relationships, establishing causal pathways, and identifying key influencing factors. Simulation models can to a certain extent reflect the changes in the success rate of technological innovation in real situations. Additionally, they facilitate the analysis of how various factors influence the trend of technological innovation success rates. SD research focuses on closed social systems, and the selection of the system boundary is a critical step that directly affects the success of the model [58]. Therefore, to avoid incorporating less significant factors into the model, the variables in Figure 1 are set as the system boundary for this study.

In addition, this study also proposes the following hypotheses:

H1: 

The construction and operation of the technological innovation system for CDAs railway engineering is a continuous and progressive process, where the various elements within the system interact to jointly promote the stable development of the system.

H2: 

All influencing factors will only vary within a certain range during the specified time frame without considering abrupt changes caused by abnormal factors.

H3: 

Based on the actual situation, it is assumed that the dynamic model of the technological innovation system for CDAs railway engineering projects has a cycle of 2 years, consisting of eight quarters.

3.2. Causal Relationship Diagram and Stock Flow Diagram

The causal relationship diagram can intuitively depict the various influencing factors of the success rate of technological innovation and the complex relationships among them, providing a realistic reflection of the internal operation of the system. Based on the theory of SD, a causal relationship diagram of the technological innovation system is drawn, as shown in Figure 1.

Based on the causal relationship diagram, we drew the stock and flow diagram of the technology innovation success rate, as shown in Figure 2. The stock and flow diagram focuses on the success rate of technological innovation and establishes five state variables around the center: completeness of management system, intensity of technological support, degree of resource adequacy, degree of technical completeness, and environmental suitability. This configuration illustrates the collective impact of these five dimensions on the success rate of technological innovation. In addition, the 22 influencing factors, including technological innovation policy, technological development level, and R&D funding investment, are transformed into various constants and auxiliary variables.

3.3. Design of Main Equations

The paper focuses on the technological innovation in the CDAs railway engineering sector, identifying and confirming a total of 22 influencing factors across five dimensions. In order to clarify the interactive mechanisms among influencing factors, a CDAs railway engineering technological innovation analysis matrix was established, as shown in Equation (1).

$$R_i=\left[\begin{array}{c}{R}_1\\ R_2\\ ⋮\\ R_n\end{array}\right],R_1=\left[\begin{array}{c}{R}_{11}\\ R_{12}\\ ⋮\\ R_{1m}\end{array}\right],R_2=\left[\begin{array}{c}{R}_{21}\\ R_{22}\\ ⋮\\ R_{2k}\end{array}\right],R_3=\left[\begin{array}{c}{R}_{n1}\\ R_{n2}\\ ⋮\\ R_{nz}\end{array}\right]R_4=\left[\begin{array}{c}{R}_{n1}\\ R_{n2}\\ ⋮\\ R_{nu}\end{array}\right],R_5=\left[\begin{array}{c}{R}_{n1}\\ R_{n2}\\ ⋮\\ R_{nv}\end{array}\right]$$

(1)

where $R_i$ (where $i=1,2,\cdots ,$n) represents the various dimensions of the influencing factors of technological innovation, and $R_{ij}$ (where $j=m,k,z,u,v$) represents the technological innovation influencing factors.

The SD model can provide a comprehensive and dynamic perspective, capturing the evolutionary process of technological innovation success rate and the interrelationships among various factors. Establishing SD equations can quantify and describe the time-varying relationships between variables. In order to deeply understand the dynamic evolution process of the technological innovation success rate, Equation (1) is introduced into the SD model, providing a comprehensive analytical framework for research. The main equation form of the SD model for technological innovation in CDAs railway engineering projects is as follows:

Calculate the values of five state variables in the technological innovation system, including environmental factors, technical factors, resource factors, technological factors, and management factors. At the current time, the values of the five dimensions of technological innovation impact factors are determined by the sum of their initial values and the rate of change per unit time. The equation is formulated in the following expression [59].

$$R_i^t=R_i^{t_0}+{\int }_{t_0}^x(inflow\left(R_i\right)-outflow(R_i\left)\right)dt$$

(2)

where $R_i^t$ (i = 1, 2, …, n) represents the values of the various dimensions $R_i$ of the influencing factors of technological innovation impact factors at time $t$ within the technological innovation system. Here, t denotes the temporal change from the current moment to the initial moment. “Inflow” ($R_i$) indicates the input values of each dimension $R_i$ from the initial moment to the current time t, while “outflow” ($R_i$) represents the output values of each dimension $R_i$ from the initial moment to the current time t [59].

$$outflow(R_i)={\sum }_{j=1}^n{R}_{ij}\times {\omega }_j$$

(3)

where ${\omega }_j$ represents the weight of the influencing factors.

Calculate the values of auxiliary variables in 22 influencing factors such as technological innovation policy, market competition intensity, and others in the technological innovation system. It is determined based on the structure of the system and can be calculated from other variables at the present moment. Typically, it is determined by the initial values, the rate of change per unit time, and the coefficient of influence. The equation form is as follows [60].

$$R_{ij}=\left(N_j+t\times V_j\right)\times {\phi }_j$$

(4)

where $V_j$ represents the rate of change in influencing factors, $N_j$ (j = 1, 2, …, m) represents the initial value of the influencing factor $R_{ij}$, and ${\phi }_j$ represents the coefficient of influence of other factors on this particular factor.

Solving Equations (3) and (4) and substituting them into Equation (2), the final resulting equation is formulated as follows [60].

$$R_i^t=R_i^{t_0}+\sum _{j=1}^n{\phi }_j\times {\omega }_j\times V_j\times (t-t_0)$$

(5)

3.4. Model Parameters Determination

For solving the technical innovation model for railway engineering in CDAs first requires determining the weight and influence coefficients of the influencing factors. To ensure the feasibility and effectiveness of the research, a total of 20 academic and industry experts were invited to participate in this study. We distributed two sets of survey questionnaires to the experts, utilizing different scoring formats and data processing methods. The specific procedures are as follows.

(1)

Determining the weight of influencing factors ${\omega }_j$

Apply the Analytic Hierarchy Process (AHP) to calculate the weight values for the five dimensions and 22 influencing factors. The weight of each factor has successfully passed the consistency check. The magnitudes of these values can offer an initial reflection of the relative importance of influencing factors at different levels. For detailed weight values of influencing factors at each level, refer to

Table 6. Weights of influencing factors.

Name	Weight	Name	Weight
Environmental factors	0.074	Technological innovation policy	0.539
		Intellectual property protection	0.297
		Market competition intensity	0.164
Technical factors	0.216	Technological development level	0.134
		Technological product requirements	0.397
		Technological innovation benefits	0.207
		Green environmental protection level	0.099
		Technological environment adaptability	0.163
Resource factors	0.129	R&D funding investment	0.376
		Organizational scale	0.189
		Number of technical personnel	0.326
		Worker capability	0.109
Technological factors	0.376	Supporting industrial base	0.345
		Technological innovation capability	0.175
		Technological innovation strategy	0.404
		Theoretical foundation research	0.075
Management factors	0.215	Information management level	0.118
		Organizational coordination level	0.176
		Innovation culture	0.061
		Leader support	0.068
		Technological innovation incentive mechanisms	0.338
		Benefit/Responsibility allocation mechanisms	0.238

.

(2)

Determining the Coefficients of Influence ${\phi }_j$

The influence coefficient represents the strength of the impact that an actor has on the recipient in the relationship between two interrelated factors. By drawing on the research findings of numerous scholars, we have taken the range of influence coefficients between 1 and 1.5. Within this range, values from 1.0 to 1.1 denote low impact, while values from 1.4 to 1.5 indicate high impact. The determination of the influence coefficient for various factors in this paper relies on a questionnaire survey conducted by experts and scholars, and the mean is calculated after organizing the responses of experts.

The summary results of the survey are shown in

Table 7. Investigation results of impact coefficient.

Number	Type of Influencing Factor	Average Value
1	The impact coefficient of technological innovation policy on intellectual property protection	1.16
2	The impact coefficient of technological innovation policy on state of the technology	1.13
3	The impact coefficient of technological innovation policy on technical product requirements	1.26
4	The impact coefficient of technological innovation policy on technological innovation strategy	1.30
5	The impact coefficient of technological development level on technological environmental adaptability	1.31
6	The impact coefficient of technological development level on technological innovation benefits	1.27
7	The impact coefficient of technological development level on green environmental protection level	1.23
8	The impact coefficient of market competition intensity on R&D funding investment	1.24
9	The impact coefficient of green environmental protection level on technical product requirements	1.16
10	The impact coefficient of technical product requirements on R&D funding investment	1.15
11	The impact coefficient of R&D funding investment on supporting industrial base	1.16
12	The impact coefficient of R&D funding investment on worker capability	1.24
13	The impact coefficient of R&D funding investment on organizational scale	1.26
14	The impact coefficient of R&D funding investment on number of technical personnel	1.22
15	The impact coefficient of R&D funding investment on technological innovation capability	1.34
16	The impact coefficient of organizational scale on number of technical personnel	1.16
17	The impact coefficient of organizational scale on organizational coordination level	1.38
18	The impact coefficient of number of technical personnel on technological innovation capability	1.27
19	The impact coefficient of theoretical foundation research on technological development level	1.24
20	The impact coefficient of technological innovation strategy on theoretical foundation research	1.13
21	The impact coefficient of incentive mechanism for technological innovation on innovation culture	1.16
22	The impact coefficient of leader support on innovation culture	1.27
23	The impact coefficient of leader support on information management level	1.12
24	The impact coefficient of leader support on incentive mechanism for technological innovation	1.29
25	The impact coefficient of leader support on R&D funding investment	1.16

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4. System Dynamics Simulation and Result Analysis

4.1. Analysis of Simulation Result

This paper uses Vensim PLE software for simulation analysis. Given the complexity and abstraction of the technological innovation system for railway engineering within CDAs, it is difficult to obtain the values of some variables. Therefore, the setting of initial values for variables, which may be challenging to obtain, does not compromise the correctness and scientific integrity of the simulation results. Values for such variables are assigned based on the opinions of experts in the relevant field. The simulation is conducted over a period of eight quarters, with values set at 20 for the benefit/responsibility allocation mechanism and market competition intensity, and 50 for technological innovation policy and leader support. When the system maintains its initial state, the trend of the technological innovation success rate is illustrated by the curve in Figure 3.

The simulation results indicate that under established parameters, the technological innovation success rate for railway engineering in CDAs shows a growing trend over time. The rapid growth of the technological innovation success rate in the early stages of technological innovation is primarily attributed to the crucial role of the learning effect. During this period, various innovation entities can continuously accumulate experience, optimize processes, and achieve rapid growth. Subsequently, the growth rate begins to stabilize with a slight mid-term decline. As technology advances, further innovation may become more complex. Consequently, the growth rate of the technological innovation success rate tends to slow down. During this phase, innovation entities may encounter factors such as resource constraints, leading to a slight decrease in the innovation success rate. However, once faced with bottlenecks, innovation entities swiftly adjust their strategies, seek new breakthroughs, and eventually, the technological innovation success rate starts to rebound, entering a phase of slow growth. The above simulation accurately reflects the primary development trends in the evolution of technological innovation systems for railway engineering in CDAs.

4.2. Sensitivity Analysis

The paper employs a single-factor sensitivity analysis method to analyze the degree to which each influencing factor affects the entire system. In the scenario where other influencing factors remain constant, changing the value of a specific variable in the model allows an assessment of the extent to which that factor affects the system. This approach provides a theoretical basis and data support for practical work. The greater the sensitivity of the system to changes in the influencing factor, the more significant its impact on the entire system [61]. The sensitivity value is calculated by dividing the percentage change in the success rate of technological innovation by the percentage change in the influencing factors.

While keeping other influencing factors constant, the values of information management level and technological environment adaptability in the system are simulated by adjusting them reductions of 40% and 20% as well as increases of 20% and 40%. Figure 4 illustrates the trend of their changes.

From Figure 4., it can be observed that at the initial state, both the curves for the information management level and the technical environment suitability show an upward trend with noticeable variations midway and a subsequent decrease in the growth rate. After adjusting the values of the information management level and technological environment adaptability by reductions of 40% and 20%, and increases of 20% and 40%, the overall trend remains unchanged. With corresponding variations observed in the overall numerical values, the success rate of technological innovation also undergoes corresponding changes overall based on the initial values. Subsequently, other influencing factors are adjusted in the same way and simulated with the sensitivity values calculated and ranked. The results are presented in

Table 8. Changes in the success rate of technological innovation caused by influencing factors.

Influencing Factors	Success Rate of Technological Innovation
	Changes
	Reduce 40%	Reduce 20%	Initial Value	Increase 20%	Increase 40%
Technological innovation policy	87.1133	87.8091	88.5049	89.2006	89.8964
Intellectual property protection	88.0301	88.2675	88.5049	88.7422	88.9796
Market competition intensity	88.3786	88.4417	88.5049	88.5680	88.6311
Technological development level	86.8628	87.6838	88.5049	89.3259	90.1469
Technological product requirements	86.5505	87.5276	88.5049	89.4821	90.4593
Technological innovation benefits	87.4782	87.9915	88.5049	89.0182	89.5315
Green environmental protection level	87.9439	88.2244	88.5049	88.7853	89.0658
Technological environment adaptability	82.9189	85.7119	88.5049	91.2978	94.0908
R&D funding investment	86.5519	87.5284	88.5049	89.4813	90.4578
Organizational scale	87.7379	88.1214	88.5049	88.8883	89.2718
Number of technical personnel	87.3494	87.9271	88.5049	89.0826	89.6603
Worker capability	87.3195	87.9122	88.5049	89.0975	89.6902
Supporting industrial base	85.4724	86.9886	88.5049	90.0211	91.5373
Technological innovation capability	84.2188	86.3618	88.5049	90.6479	92.7909
Technological innovation strategy	84.9639	86.7344	88.5049	90.2754	92.0458
Theoretical foundation research	87.8022	88.1535	88.5049	88.8562	89.2075
Information management level	84.6324	86.5686	88.5049	90.4411	92.3773
Organizational coordination level	87.5843	88.0446	88.5049	88.9652	89.4255
Innovation culture	86.3894	87.4471	88.5049	89.5626	90.6203
Leader support	87.6392	88.072	88.5049	88.9377	89.3706
Technological innovation incentive mechanisms	86.6893	87.5971	88.5049	89.4126	90.3204
Benefit/Responsibility allocation mechanisms	88.0955	88.3002	88.5049	88.7095	88.9142

and

Table 9. Sensitivity in influencing factors.

Influencing Factors	Sensitivity
	Changes
	Reduce 40%	Reduce 20%	Increase 20%	Increase 40%	Sort
Technological innovation policy	−0.06958	−0.06958	0.06957	0.06957	7
Intellectual property protection	−0.02198	−0.02198	0.02197	0.02198	19
Market competition intensity	−0.01579	−0.01580	0.01577	0.01577	20
Technological development level	−0.07485	−0.07485	0.07484	0.07484	6
Technological product requirements	−0.08803	−0.08804	0.08803	0.08803	4
Technological innovation benefits	−0.04471	−0.04472	0.04471	0.04471	10
Green environmental protection level	−0.02491	−0.02491	0.02490	0.02490	16
Technological environment adaptability	−0.02911	−0.02911	0.02911	0.02911	15
R&D funding investment	−0.09123	−0.09123	0.09122	0.09122	3
Organizational scale	−0.03367	−0.03367	0.03366	0.03366	13
Number of technical personnel	−0.05231	−0.05232	0.05231	0.05231	8
Worker capability	−0.00635	−0.00635	0.00634	0.00634	22
Supporting industrial base	−0.13185	−0.13185	0.13184	0.13184	2
Technological innovation capability	−0.02407	−0.02407	0.02407	0.02407	17
Technological innovation strategy	−0.15190	−0.15190	0.15190	0.15190	1
Theoretical foundation research	−0.03221	−0.03221	0.03220	0.03220	14
Information management level	−0.02228	−0.02228	0.02228	0.02228	18
Organizational coordination level	−0.03784	−0.03784	0.03784	0.03784	12
Innovation culture	−0.01152	−0.01152	0.01152	0.01152	21
Leader support	−0.04329	−0.04329	0.04328	0.04328	11
Technological innovation incentive mechanisms	−0.07928	−0.07928	0.07928	0.07928	5
Benefit/Responsibility allocation mechanisms	−0.05118	−0.05118	0.05115	0.05116	9

.

The analysis results from Table 9 indicate that the sensitivity corresponding to different degrees of change in influencing factors is essentially the same. Therefore, the initial values of influencing factors do not impact the results. At the same time, based on the results, the six factors with the greatest impact on technological innovation research and application, ranked from largest to smallest, are technological innovation capability, technological innovation strategy, R&D funding investment, technological product requirements, incentive mechanism for technological innovation, and technological development level.

While keeping other factors constant, simulation was conducted by adjusting the values of technical factors, technological factors, resource factors, management factors, and environmental factors by reductions of 40% and 20%, and increases of 20% and 40%, respectively. The simulation results are shown in Figure 5.

From Figure 5, it can be observed that the overall trend of changes in the completeness of management system (a) is consistent with the degree of technical completeness (d), exhibiting a rapid growth rate in the early stage of technological innovation, which is followed by a decrease in the growth rate. The intensity of technological support (b) also shows a rapid growth rate in the initial stage, which is followed by a decrease in the growth rate. There is a slight decline midway, which is followed by a subsequent upward trend. However, the growth rate is the lowest compared to other quarters. The degree of resource adequacy (c) shows a rapid growth rate in the initial stage. As time progresses, the growth rate of technological innovation decreases, which is followed by a noticeable increase in the growth rate compared to the previous stage, but it remains lower than the initial growth rate. The environmental suitability (e) also shows a rapid growth rate in the initial stage, which is followed by a significant turning point, and the value of environmental suitability remains unchanged in the later stage. After adjusting the values by reductions of 40% and 20%, and increases of 20% and 40%, the overall trend remains unchanged with corresponding variations observed in the overall numerical values.

Accordingly, the sensitivity for each of the five levels is computed, and the results are summarized in

Table 10. Changes in the success rate of technological innovation caused by different dimensions.

Dimensions	Success Rate of Technological Innovation
	Changes
	Reduce40%	Reduce20%	Initial value	Increase 20%	Increase 40%
Completeness of management system	79.2304	83.8676	88.5049	93.1421	97.7793
Intensity of technological support	77.0881	82.7965	88.5049	94.2132	99.9216
Degree of resource adequacy	84.7968	86.6508	88.5049	90.3589	92.2129
Degree of technical completeness	78.8718	83.6883	88.5049	93.3214	98.1379
Environmental suitability	87.1353	87.8201	88.5049	89.1896	89.8744

and

Table 11. Sensitivity in different dimensions.

Dimensions	Sensitivity
	Changes
	Reduce 40%	Reduce 20%	Increase 20%	Increase 40%	Sort
Completeness of management system	−0.21500	−0.21501	0.21499	0.21500	3
Intensity of technological support	−0.37600	−0.37600	0.37599	0.37600	1
Degree of resource adequacy	−0.12900	−0.12900	0.12900	0.12900	4
Degree of technical completeness	−0.21600	−0.21600	0.21600	0.21600	2
Environmental suitability	−0.07400	−0.07400	0.07399	0.07400	5

.

As shown in Table 11, the sensitivity change in the completeness of the management system is 0.215, the sensitivity change in the intensity of technological support is 0.376, the sensitivity change in the degree of resource adequacy is 0.129, the sensitivity change in the degree of technical completeness is 0.216, and the sensitivity change in the environmental suitability is 0.074. According to the results, it can be concluded that the ranking of the five dimensions influencing technological innovation research and application from largest to smallest is technological factors > technical factors > management factors > resource factors > environmental factors.

5. Conclusions

In this study, we used the systematic dynamics method to deeply explore the influencing factors of railway engineering technology innovation in complex and difficult areas, and we also construct the corresponding SD model. Through a literature review, questionnaires and expert consultation, we identified and confirmed 22 influencing factors under five dimensions. Combined with data analysis and expert opinion, we determined the model parameters, conducted the simulation and sensitivity analysis, and finally reached the following conclusions:

(1)

Technological innovation ability, the adaptability of the technological environment, research and development capital investment, technological product demand, the incentive mechanism of technological innovation, and the technological development level are the key factors affecting the success of railway engineering technological innovation. The interaction and extent of the influence of these factors were quantified and visualized through our model. The analysis results at the dimensional level show that technical factors and management factors have the greatest influence on technological innovation, followed by resource factors, and environmental factors are relatively small. Among all the influencing factors, technological innovation ability and technological innovation strategy have the greatest impact on railway engineering technology innovation, which is followed by R&D investment, technology product demand, incentive mechanism, and the technology development level. This provides us with clear priorities to guide resource allocation and policy making.

(2)

The establishment and application of the system dynamics model help us to understand the interaction of various factors in the complex system, and it provides a powerful analytical tool for the management of railway engineering technology innovation. Based on the case analysis, the results highlight the need for policy makers and project managers to consider various factors comprehensively and develop effective strategies and measures to promote technological innovation in railway engineering in complex and difficult areas.

This paper provides a new perspective and method for understanding and promoting the technological innovation of railway engineering in complex and difficult areas, and it has important theoretical and practical value for promoting the technological progress and sustainable development in related fields.