New quality productivity, science and technology financial development and changes in the investment and financing system in the context of information technology

With the rapid development of science and technology, the financial industry, as the core of the modern economy, is also undergoing continuous innovation and change. As an emerging financial model, science and technology finance combines scientific and technological innovation with financial services to provide a more efficient and convenient financing channel for the real economy and promote the development of the financial industry [1-4]. The development of science and technology finance also faces many challenges, such as information security, regulatory lag and other issues. It is of great theoretical and practical significance to study the effect and mechanism of generating new quality productivity in the development of science and technology finance [5-6].

Cultivating new productivity requires the formation of a new type of production relations, and deepening the reform of the financial system to form a science and technology financial system that is compatible with scientific and technological innovation is especially critical. At present, China’s science and technology financial development faces many challenges, including uneven distribution of financial resources, shortage of patient capital, and insufficient innovation in financial services [7-10]. It is necessary to give full play to the advantages of China’s socialist system, find the best fit between government guidance, market operation and innovation of science and technology enterprises, focus on the growth of patient capital, focus on the service of start-ups and small and medium-sized science and technology enterprises, and deepen the change of investment and financing system of science and technology enterprises relying on a new type of national system and the socialist market economic system [11-14]. In the future, we should focus on building a science and technology financial system that links “stock, loan, debt and insurance”, covers the whole life cycle, the whole chain, and the relay type, so as to provide solid financial support for the development of new quality productivity [15-16].

In this paper, talent, technology, and knowledge in the context of informationization are used as explanatory variables to regressively predict the development of new quality productivity.By collecting data related to science and technology finance, measuring the development index, and using the kernel density algorithm, it analyzes the temporal convergence trend of its development. Six indicators, including the effectiveness of project delineation, project management, government investment drive, capital financing and mobilization, investment regulation and management, and the adaptability of the investment environment, are used to make a comprehensive evaluation of the changes in the investment and financing system.

2

Mechanisms for influencing the development of new qualitative productivity enabled by digital finance

2.1

New quality productivity under information technology

New-quality productivity is innovation-led, free from the traditional mode of economic growth and path of productivity development, characterized by high technology, high efficiency and high quality, and in line with the new development concept of productivity quality. Revolutionary breakthroughs in technology, innovative allocation of production factors, industrial transformation and upgrading in depth, to enhance total factor productivity as the core mark, characterized by innovation, the key is quality.The development of intelligent manufacturing can enable the construction of a modernized industrial system and provide a key material and technological foundation for new quality productivity. In the high-tech industry, its talent-intensive, technology-intensive and knowledge-intensive characteristics satisfy the demand for talent-technology-education for the development of new-quality productivity and help to open up the blind spots that bind new-quality productivity.

2.2

Indicators of impacts on the productivity of new matter

1)

Industrial Structure

The core of new quality productivity is to develop advanced and high-quality industries, especially strategic emerging industries and future industries. In this paper, the industrial structure is divided into three items: industrial diversification, industrial sophistication, and industrial external dependence. The more diversified the industry, the more advanced the industry, and the lower the industry’s external dependence, the better the industrial structure, and the more promising the development of new-quality productivity.

According to the established experience, industrial diversification is expressed by the inverse of the HHI index (Hirschmann-Heffendahl Index): (1) $Industrial structure diversification index = \frac{1}{H H I} = \frac{1}{\sum_{i = 1}^{3} S_{i}^{2}}$ (2) $Industrial structure advanced index = \sum_{i = 1}^{3} i \times S_{i}$ Where i denotes industry i of the three major industries that make up GDP, and S_i denotes the weight of industry i in relation to GDP.

2)

Innovation capacity

The dimension of innovation ability is measured by the number of invention patents authorized in the year.Invention patents include product invention and method invention, which have a higher technical level than utility model patents and appearance patents, and better represent the advancement of research results.Meanwhile, the number of patents granted in the year is more result-oriented and better reflects scientific and technological strength than the amount of R&D investment in the past.

3)

Enterprise Operation

Development of new quality productivity of the most basic market competition is the main body of the enterprise, the starting point is also the enterprise, the enterprise should have the ability to have confidence in the investment of funds, labor, so the enterprise operation dimension with the enterprise profitability and confidence in the enterprise agent. Enterprise profitability is expressed by the total profit of enterprises above the designated size. The higher the enterprise profit, the stronger the ability to bear losses, and the more capable of developing new quality productivity.Enterprise confidence index includes revenue capacity, R&D expenditures, cost control, industry potential, and other aspects.

4)

Employee Maintenance

The development of new quality productivity requires a significant number of stable frontline skilled workers, so employee maintenance starts from two dimensions: industrial worker reserve and employee security.Reserve indicates a substantial number, and employee security indicates stability.Unemployment insurance can help industrial workers during the difficult period of unemployment. The market for advanced technology applications has a certain degree of risk, and fluctuations in demand are transmitted to front-line production manifested in changes in the number of front-line employees, i.e., the transition between unemployment and employment. Unemployment insurance keeps a certain group of skilled industrial workers, which provides protection for the labor force and the development of new quality productivity.

5)

Government Support

The development of new quality productivity can not be separated from the strong support of the government, government support includes education and science and technology, respectively, with the proportion of fiscal expenditure in these two aspects of the total fiscal expenditure on behalf of the education is mainly through the improvement of the quality of the labor force, the training of industrial workers, to enhance the competitiveness of the industry and resilience. Science and technology mainly through financial investment in science and technology to leverage social investment, cultivate a social atmosphere of love for science, enhance the city’s scientific and technological innovation environment and support for enterprise innovation efforts, the development of advanced industries.

2.3

Regression analysis of new quality productivity development based on informatization

The above section provides a comprehensive evaluation of the indicators affecting the development of new quality productivity, and this section utilizes a regression model to predict and analyze the level of future development of new quality productivity in the context of information technology. This section analyzes the development of new quality productivity, i.e., the explanatory variables, by taking industrial structure, innovation capability, business operation, employee maintenance, and government support as the control variables, and talent, technology, and knowledge in the context of information technology as the explanatory variables. In order to contribute to the research on information technology in developing new methods for improving productivity. Table 1 shows the results of regression analysis regarding the development of new quality productivity in the context of information technology.

Table 1.

Regression analysis of new quality productivity development

Variable	New quality productivity development
Variable	Regression coefficient	Relative error	Significance
Industrial structure	0.159*	0.017	0.032
Innovative ability	0.108*	0.019	0.022
Business operation	0.143*	0.009	0.043
Employee maintenance	0.103*	0.014	0.013
Government support	0.172*	0.008	0.011
Talent	0.347***	0.056	0.000
Technology	0.421***	0.037	0.000
Knowledge	0.291***	0.026	0.000
R²	0.196
F	35.513

*, **, *** represent p significant at 0.05, 0.01, 0.001 level respectively

As can be seen from the table, both control variables and explanatory variables have positive effects on the development of new quality productivity, in which the regression coefficients between industrial structure, innovation capacity, business operation, employee maintenance and government support and new quality productivity range from 0.103 to 0.172, which passes the test of significance at the level of 0.05, i.e., when the control variables are increased by one unit, the new quality productivity can be increased by 0.103 to 0.172 at the level of 0.05. In addition, the effect of information technology background on new quality productivity is more significant, the lowest regression coefficient between the two is 0.291, and the significance coefficient is 0.000, that is, the information technology background can promote the development of new quality productivity at the 0.001 level. Among them, information technology has the greatest impact on new quality productivity, and a one-unit increase in it leads to a corresponding 0.421 increase in new quality productivity.

3

Construction of the FinTech Development Index and data sources

Financial technology for, Internet-based information technology and the organic integration of traditional finance, financial technology is more focused on science and technology, science and technology technology continues to drive financial innovation, financial innovation into the ability to provide financial services, which in turn reduces the transaction costs in financial services, to solve the problem of asymmetric information and weak risk control, and to improve the quality of service to customers and efficiency.

3.1

Development index system and data sources

3.1.1

Construction of the index system

Fintech is in the stage of rapid development, and the boundaries of the fintech industry have not been clearly delineated yet, while the research that can authoritatively and completely, meticulously and comprehensively measure the level of development of fintech has yet to be enriched. With the help of text mining method, we extract the hot words related to FinTech in the Internet environment and synthesize an evaluation index for FinTech.

This paper refers to the functional classification of FinTech by the Financial Stability Board, and extracts hot words from four dimensions, namely, payment and clearing, financing, intelligent investment, and technological foundation, to synthesize the initial keyword database of FinTech, and the original thesaurus of FinTech is shown in Table 2.

Table 2.

Financial technology primitive library

Dimension	Key words
Payment liquidation	NFC payment, third party payment, mobile payment, online payment
Raise money	Equity crowdfunding, net lending, crowdfunding
Intelligent investment	Network investment, internet finance, internet insurance, business intelligence
Technical basis	Intelligent data analysis, cloud computing, large number, block chain, mobile interconnection, virtual reality, integrated circuit

3.1.2

Data sources

Following the principle of data comparability, the Baidu index hot word data from 2020-2023 are selected. Considering the research meaning and data availability principle, this paper selects the data for 30 provincial capitals and municipalities in China, except for Hong Kong, Macao, Taiwan, and Tibet Autonomous Region.The data is mostly from Baidu Index and hand-arranged.

3.1.3

FinTech Development Index Measurement

The general idea of the process of constructing the fintech index in this paper is that the word frequency of Baidu index in 2020-2023 is used as the basis for constructing the index, and the entropy weighting method is used first to determine the objective weight of each index, and then the fintech development index is constructed according to the weights. Because of the different orders of magnitude of the collected data indicators, a certain degree of error may occur, so the data should be standardized for polar deviation, so that the standardized values are mapped in the [0,1] interval: (3) $X_{i j} = \frac{x_{i j} - {min}_{i j}}{{max}_{i j} - {min}_{i j}}$

In equation (3), X_ij represents the sample value after normalization of the jrd indicator in the i region, x_ij represents the sample value of the j th indicator in the i th region, and max_ij and min_ij represent the maximum and minimum values of the sample, respectively.

Normalization of evaluation indicators: (4) $d_{i j} = \frac{X_{i j}}{\sum_{i = 1}^{30} X_{i j}}$

The entropy value in information theory measures the uncertainty of information, and the amount of information is negatively correlated with the entropy value, and the information entropy of the jst index is calculated with the formula: (5) $e_{j} = \frac{1}{ln m n} \sum_{i = 1}^{m} d_{i j} ln d_{i j}$

In equation (5), m is the number of evaluation objects, n the number of specific indicators.

The entropy value is utilized to calculate the weight of each indicator: (6) $W_{j} = \frac{1 - e_{j}}{\sum_{j = 1}^{n} (1 - e_{j})}$

Finally, the weights calculated by entropy weights are multiplied by the dimensionless processed data to get the final fintech development index of each province: (7) $F T_{i} = \sum_{j = 1}^{n} W_{j} d_{i j}$

3.2

Chronological characterization of the development of science and technology finance

3.2.1

Kernel density estimation methods

This study adopts the method of kernel density estimation [17-19] to reveal the dynamic evolution law of the distribution of fintech development, to explore the distribution location, trend, peaks, and extensibility characteristics, and then to illustrate the evolution trend of fintech development. The height and width of the peaks of the kernel density curve of the sample data can reflect the degree of aggregation difference of FinTech development, the number of peaks can reflect the degree of polarization of the sample data, and the degree of trailing of the curve can describe the gap between the region with the highest or the lowest degree of FinTech development and the other regions, and if the trailing is lengthened, it means that the degree of difference within the region is higher.

Assuming that the random variable X₁,X₂,X₃,…,X_n is independently and identically distributed, and the distribution function of X is f(x) and unknown, then the kernel density estimator can be expressed as follows: (8) $f (x) = \frac{1}{N h} \sum_{j = 1}^{k} K (\frac{X_{i} - x}{h})$

Equation (8), n is the sample size h is the bandwidth. Both satisfy $lim_{N} \to \infty h (N) = 0, lim_{N \to \infty} N h (N) = N \to \infty$ . x_i is the value of FinTech development index taken. K(*) is the kernel function and satisfies $\int_{- \infty}^{+ \infty} $ K (x) d x = 1$ . The kernel function generally chooses Gaussian kernel function, i.e. $K (x) = \frac{1}{\sqrt{2 π}} exp (- \frac{x^{2}}{2})$ .

Calculated by the entropy weight method, the weights of each index in 30 provinces are shown in Table 3. It can be seen that among the four levels of the FinTech Development Index, the weight of the technical foundation accounts for the largest proportion, reaching 36.83%, followed by smart investment accounting for 31.25%, payment and clearing accounting for 21.43%, and raising financing accounting for 10.49%.

Table 3.

Financial technology index weight

Index	Payment liquidation	Raise money	Intelligent investment	Technical basis
Weighting	21.43%	10.49%	31.25%	36.83%

3.2.2

Convergence analysis of the dynamic evolution of science and technology finance

Dynamic evolution convergence state analysis using the kernel density function estimation method for research, the method of science and technology financial development efficiency as a continuous state for processing, used to portray the overall shape of the distribution of efficiency of science and technology financial development and efficiency distribution over time changes in the state. In this paper, the Gaussian kernel function is selected for estimation, and the kernel density curve of science and technology’s financial development efficiency is shown in Figure 1.

As can be seen from the figure, the dynamic evolution of the efficiency distribution of science and technology financial development in the context of information technology presents the following characteristics:

1) From the point of view of the number of wave peaks, with the development of information technology, the period from 2014 to 2023 has experienced an obvious transition from a “single-peak” distribution to a “bimodal” distribution. 2014-2020 is an obvious single-peak distribution, and by 2023 it has shown an obvious bimodal distribution, which indicates that the distribution of science and technology financial development efficiency in the context of information technology has changed. The obvious bimodal distribution in 2014-2020 shows that the gap in the efficiency of science and technology financial development between 2014 and 2020 is widening, and there is a bimodal convergence phenomenon in the efficiency of science and technology financial development in various provinces.

Meanwhile, it is noted that in the bimodal distribution of this period, the main peak is located in the interval of higher efficiency, which indicates that, except for a few regions where the efficiency of scientific and technological financial development is at a lower level, the efficiency of scientific and technological financial development in most regions is developing towards a higher level.

2) From the position of the distribution graph, the distribution graph of the kernel density function has been shifted to the right with the passage of time, which indicates that the efficiency of scientific and technological financial development in the context of information technology has shown a rapid growth from the overall situation. At the same time, it can be seen from the figure that the mean position of the distribution graph gradually converges to a more stable level of roughly 0.70 from 2014 to 2023, which once again indicates from the position that the efficiency of the development of science and technology finance in China’s provinces is characterized by a faster rate of convergence.

3.3

Assessment of the contribution of science and technology finance based on variance decomposition

The variance decomposition understands the relative roles of various types of factors by solving the contribution of the perturbation term to the mean squared error of the vector autoregressive model prediction, and like the time series data, the error variance of the panel data prediction is the result of the combination of its own perturbation and the systematic perturbation. Figure 2 shows the variance analysis of information technology and technology finance.

The variance decomposition of tech finance shows that the contribution of information technology (INFO) to the development of tech finance decreases with the increment of the period, while tech finance (FIR) itself is the opposite. The greatest impact on FIR is its own, with 89.29% of FIR itself at the end of the period, while information technology (IT) accounts for 10.41%, and the level of regional economic development (GDP) and the level of science and technology (ST) have almost no impact on FIR. This shows the important role of information technology on the development of ST finance.

4

Establishment of an investment and financing system in the context of information technology

4.1

General framework

On the basis of analyzing the current situation of investment and financing, and according to the inspiration of various experiences, this paper constructs a framework for the development of investment and financing system suitable for infrastructure construction, and the specific investment and financing framework is shown in Figure 3.

4.2

Differentiation of construction projects based on different investment and financing models

4.2.1

Project differentiation

With the development of neoclassical economics and its theory of the public sector, researchers have rethought the operability of various types of construction projects, the efficiency and the role of the Government in the field of public investment, and have put forward a variety of new theories and systems of practice for public administration reform. Among them, the theory of project differentiation is a benchmark for the demarcation and separation of government and private economic forces in the field of micro-investment. In essence, the theory of project differentiation conforms to the requirements of the era of “small government, big market”, and advocates and promotes the democratization of governmental decision-making, the marketization of governmental functions, and the legalization of governmental behavior.

The so-called project differentiation is according to whether there is a charging mechanism, the public construction projects will be differentiated into non-operational and operational projects, so as to determine the project according to the attributes of the project’s main body of investment, mode of operation, funding channels and rights and interests belonging to the project.

For non-operational projects, the main body of investment is borne by the government, according to the government’s investment operation mode, the source of funds should be the government’s financial input, and with a fixed tax or fee can be guaranteed. In the operation of the investment process, the introduction of competition mechanism, according to the bidding system, and strive to improve the scientific and standardized investment decision-making, and promote the further improvement of investment efficiency.

The operational project is a social investment, the premise is that the project must be in line with urban development planning and industry-oriented policies, the main body of the investment can be a state-owned enterprises, private enterprises, including foreign-funded enterprises, etc., through open, fair and competitive bidding, its financing, construction, management and operation of the investor’s own decision-making, and the rights and interests should be entitled to the investor all.

4.2.2

Quantitative analysis of project differentiation

According to whether the project itself has a charging mechanism and net income and other indicators can be used as the project’s operating coefficient as a quantitative classification index of the attributes of the town’s construction projects, as follows:

Definitional equation: a = V/C, where C is the construction cost of the project.V is the market value of the project.V = H/I, where H is the annual net project revenue that could be measured if the project had a fee-based mechanism.I is the acceptable rate of return on investment in the market. Substituting V into the above equation gives the formula: a = (H/I)/C.

4.2.3

Criteria for categorizing construction projects

According to the theory of project differentiation, construction projects can be categorized according to the perspective of whether the market can be allowed to play a role in the investment project with or without a charging mechanism, i.e., the inflow of funds and with a charging mechanism, whether there is a return into three categories.

1)

Non-operational construction projects

This kind of project without fee mechanism, no capital inflow, this is the market failure and the government effective part, its purpose is to obtain social and environmental benefits, market regulation is difficult to play a role in this, this kind of investment can only be represented by the public interest of the government finances to undertake.

2)

Operational construction projects

This type of project has a charging mechanism, which results in an inflow of funds.Can be effectively configured through the market. Its motive and purpose is to maximize profits. The formation of its investment is the value of the value-added process, which can be achieved through social investment.

3)

Quasi-operational construction projects

There are charging mechanism and capital inflow, with potential profits, but because of its policy and charging price is not in place and other objective factors, can not recover the cost of the project, with some public welfare, is the market failure or inefficient part, because it is not obvious enough to the economic benefits of the market will inevitably result in the formation of many gaps in the supply of funds to the government through the appropriate interest rate subsidies or policy incentives to maintain the operation, to wait for their When prices are gradually put in place and conditions are ripe, they can be transformed into purely operational projects.

4.2.4

Interconversion of operational and non-operational projects

Construction projects can be categorized into operational and non-operational projects according to the perspective of whether or not the market can be allowed to function, but they are subject to the influence of government policies and are subject to variation, and can even be transformed into each other. The government through the formulation of policies or increase its price and so on to make its business index increased, that is, quasi-operational projects can become purely operational projects.Non-operational projects can also become quasi-operational or even purely operational projects. For example, once a toll mechanism is set for an open road, the non-operational project becomes an operational project. And the operating project once canceled charges that became non-operating projects.Operational and non-operational projects are transformed into each other as shown in Figure 4.

4.3

Analysis of changes in the investment and financing system in the context of information technology

This section establishes evaluation indicators for the investment and financing system, including the effectiveness of project classification, the effectiveness of project management, the effectiveness of government investment promotion, the effectiveness of capital financing and mobilization, the effectiveness of investment regulation and management, and the adaptability of the investment environment.

In order to more comprehensively summarize the effectiveness of the reform of the investment and financing system in the context of information technology, and objectively reflect the deficiencies and gaps in the reform process, the evaluation of the indicators adopts a combination of questionnaires and expert scoring, and conducts the overall evaluation of the “five forces” indicators and the individual evaluation of the indicators of each level. Among them, the overall evaluation is measured by the average score of the first-level indicators and the percentage of the scores of the evaluation indicators, while the evaluation of the indicators at all levels is measured by the average score of the first-level indicators and the total score of the second-level indicators. The average scores of the evaluation indicators at each stage are shown in Figure 5.

From the analysis of the scores of the evaluation indicators, with the development of information technology, the scores of the indicators have shown different degrees of growth trend, until after 2023, the scores of the indicators have exceeded 90.Specifically, from 2023 to today, the scores of the effectiveness of project delineation, the effectiveness of project management, the effectiveness of the government’s investment drive, the effectiveness of the mobilization of capital financing, the effectiveness of the management of the investment regulation and control and the effectiveness of the investment environment are in the order of 95%. Adaptability scores are 95.36, 96.59, 97.16, 92.77, 91.39, and 96.04 in that order.The average annual increase from 2014 to 2023 ranges from 3.32 to 5.26 points. This trend is due to the development of information technology, which improves the efficiency and transparency of investment and financing decisions and reduces transaction and operational costs, promoting the emergence of new investment and financing models.

5

Conclusion

This paper analyzes the development of new quality productivity and science and technology finance in the context of information technology in various aspects using various methods, such as kernel density estimation. It also provides comprehensive scores on the effectiveness of project division, project management, government investment drive, capital financing and mobilization, investment regulation and management, as well as the adaptability of the investment environment in different eras, aiming at evaluating the effectiveness of the change of the investment and financing system in the context of information technology.

The regression analysis concludes that information technology has the greatest impact on the new quality productivity, and a unit increase in the new quality productivity can rise by 0.421. Information technology promotes the efficiency of science and technology financial development around the world to gradually improve, and gradually converge to about 0.70 in the period of 2014 to 2023. The contribution of information technology to the development of science and technology finance at the end of the period is 10.41%, that is, information technology promotes the development of science and technology finance.The effectiveness of investment and financing systems increases with the development of information technology.

Funding:

The Education Department of Jilin Province Scientific Research Project (JJKH20251766SK).

Język:: Angielski

Częstotliwość wydawania:: 1 razy w roku
Dziedziny czasopisma:: Nauki biologiczne, Nauki biologiczne, inne, Matematyka, Matematyka stosowana, Matematyka ogólna, Fizyka, Fizyka, inne

Kanał RSS czasopisma

New quality productivity, science and technology financial development and changes in the investment and financing system in the context of information technology

Shuai Yuan

Data publikacji: 21 mar 2025

Otrzymano: 16 paź 2024

Przyjęty: 02 lut 2025

DOI: https://doi.org/10.2478/amns-2025-0643

Słowa kluczoweInformation technology, Regression analysis, Kernel density estimation, New quality productivity

© 2025 Shuai Yuan, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Information technology, Regression analysis, Kernel density estimation, New quality productivity