Capital or Technology? Which Is Better at Promoting the Value of AI Companies—Theoretical Analysis and Empirical Test

Abstract

Capital and technology gradually replace land and labor factors to become the important production factors of enterprises, especially for artificial intelligence enterprises. How to use capital and technology factors to enhance enterprise value becomes an important topic when artificial intelligence enterprises are affected by internal and external factors. Using data from 55 AI concept companies, a multiple regression model was constructed to verify the mediating role of capital and technology, judge the dynamics of the two elements dialectically, and explore the paths that shape the value enhancement of AI companies. The study found that both degree of enterprise attention and government support positively affect firm value, and that the mediating role of both capital and technology is more pronounced. However, compared to the technology element, the capital element plays a leading role in the value enhancement mechanism of AI companies, with physical capital being the most effective, and strengthening the capital element is a more sensible path choice to increase the value of companies in the short term. However, in the long term, as the business cycle and the external environment change, the technology element is a driving force that cannot be underestimated.

1. Introduction

The traditional system of factors of production includes land, labor, and capital. With the development of computer technology since the 21st century, the impact of technology as a new factor of production on the development of enterprise products has been deepening, while the marginal contribution of land and labor to total factor productivity has been gradually decreasing. Furthermore, the new factor of the production system led by technology and capital as the core has gradually replaced the traditional factor of a production system. Influenced by the new production factor system, the two factors of capital and technology play a vital role in the development of enterprises. Capital provides material security for the development of enterprises, while technology provides new growth points for the development of enterprises and promotes innovation and transformation, and upgrading of enterprises. With technology and capital as the main drivers of the growing ranks of high-tech enterprises, capital and technology complement each other in developing high-tech enterprises, with capital investment helping enterprises develop technology and technological advances attracting more investment.

As information technology improves, data become the new factor driving total factor productivity in business [1]. Data-based artificial intelligence technology is gradually empowering production, distribution, exchange, consumption, and other aspects of economic activity, and the representative high-tech enterprises that promote the integration of artificial intelligence technology and society are artificial intelligence enterprises. The iResearch estimates that large Chinese companies are already planning and implementing AI projects on an ongoing basis, and that more than 10% of all companies above a specific size have integrated AI into their primary business [2]. With companies receiving internal and external support, there has been an influx of capital into the AI sector, and the value of companies creating or using AI technology has risen rapidly on the stock market. However, due to the lack of funds and projects, long cycles, and difficult revenue, the transition from traditional industries to the artificial intelligence industry is relatively slow, the newly created artificial intelligence companies continue to be acquired, and some companies still face the risk of collapse even after receiving several rounds of financing. Imbalance in the investment of funds and technology has led to a bubbling trend in the development of artificial intelligence. The importance of capital and technology in the development of AI companies cannot be overstated, but it is not yet known what elements rise to the top as the main drivers of corporate value in a complex external context.

Some scholars have analyzed the influence of a certain element of capital or technology on the value of high-tech enterprises. This element is often an internal element with advantages owned by enterprises, which can be easily absorbed and transformed by enterprises to promote the improvement of enterprise benefits [3]. From the perspective of practice, Ran and Bai proved the impact of capital on enterprises [4]. Ye and Wang proved the impact of technology on enterprises’ performance from the perspective of practice [5]. Burgelman [6] believes that an enterprise’s innovation capability is not only affected by its technological capability, but also related to its manufacturing, marketing, and human resource management capabilities. In other words, enterprises need to acquire knowledge of both the market and technology in the process of improving innovation ability, and then integrate this acquired knowledge into creative ideas and create new products by integrating it. However, few studies use system theory to comprehensively analyze the impact of capital and technology on AI enterprise value. Based on the research of predecessors, this paper studies artificial intelligence enterprise internal value and the function of government support for the improvement of enterprise value. Following high-level attention and government support, the enterprise converts capital and technology elements to improve the enterprise value of direct factors, ultimately promoting the enterprise value enhancement of artificial intelligence through internal technology absorption systems and capital absorbing systems. Therefore, this paper introduces capital and technology as mediating variables based on the system theory, discusses which elements of capital and technology play a more important role in the improvement of enterprise value, and finally provides suggestions for how to use capital and technology to improve the value of artificial intelligence enterprises. The following is the section arrangement of this paper: The second section is the theoretical analysis. The whole section theoretically analyzes the factors affecting the value of AI enterprises and puts forwards the corresponding hypotheses. The third section is the research design. This section determines the research samples of AI enterprises, collects data, and determines the measurement method of the final variables. The fourth section is the empirical analysis. The hypotheses put forward in the third section are empirically tested, and the conclusions are theoretically analyzed while the hypotheses are verified. The fifth section contains the conclusions. The sixth section is about suggestions, and research outlooks. According to the empirical results of the fourth section, the important conclusions of this paper are summarized, and the corresponding suggestions are put forward for enterprises. Finally, further research directions are proposed for the future.

2. Theoretical Analysis

2.1. Framework Analysis

Enterprise value is very important for listed enterprises. It not only reflects the overall situation of an enterprise at the present stage, such as the asset status, equity structure, and operational status of the enterprise, but also the future development potential of the enterprise, such as the company’s growth indicators and risk resistance indicators, which provide a reference for potential investors to make investment decisions. For AI companies, the study of firm value is even more critical because AI companies are more dependent on capital and technology than traditional companies, and the primary way to obtain capital and technology is financing. To raise sufficient funds, companies need to focus on improving the enterprise value to attract investment from potential investors. Improving enterprise value requires a focus on the factors that influence enterprise value, and existing literature on the factors that influence enterprise value is divided into two main research areas.

On the one hand, there are factors within the enterprise. From the perspective of the management level of the enterprise, the internal influencing factors originate from different levels of the enterprise. Firstly, there is a company’s strategic level, the main task of which is to exercise a unified command and integrated management of the entire organization in the organization’s interests as a whole, and set organizational objectives. The corporate strategy developed in the strategic layer can significantly impact the value of the business, as it determines the future direction and development of the company. For AI companies, significant decisions, such as whether a company is selected for the government’s list of AI pilot demonstration projects [7] and whether to transform from a traditional company to an AI company [8], are a reflection of the company’s strategy and all significantly affect the value of the company.

The overall quality of leadership at the strategic level also directly impacts the quality of corporate strategy and, therefore, the company’s future value [9]. Management is the core role of the business managers, and includes developing sub-objectives and implementation programs, allocating resources by department, and coordinating activities at lower levels. The managers’ ability in the management team directly impacts the company’s performance [10]. In AI companies, managers with an overall solid ability will develop corresponding implementation plans according to the AI development strategy formulated by the company and lead the company to achieve intelligent transformation gradually. The support for AI, whether from leadership or management, reflects the importance placed on AI within the business and the fact that the importance placed on AI within the business affects the value of the business.

On the other hand, there are factors external to the company, which are influenced by various external factors in the process of development, including economic, social, and political factors that affect the value of the company. The economic factors that scholars have studied are mainly the state of the economic environment in which the company operates. For AI companies, the economic factors that affect the company’s value are the technological dividends of the transition from traditional forms of the economy to the digital economy [11], and the harmful effects of socio-economic fluctuations such as the economic crisis [12]. Research on social aspects has focused on the impact of major public health events on business value [13]. Compared to economic and social factors, more literature examines the impact of political factors on corporate value. Political factors include the impact of the political environment and government measures on corporate value, and an excellent political environment provides good political security for corporate development, thus promoting corporate value [14]. Government support is crucial for new businesses such as AI companies, as it can provide direct financial support or indirectly help companies obtain financing, thereby increasing the value of the business. Therefore, this paper examines the external factors that have the most significant impact on firm value in government support.

The degree of enterprise attention and government support does not directly increase the value of the enterprise, but there is a process to increase the value of the enterprise. These activities include investment and financing, improvement of business practices, human resources adjustment, R&D investment activities, independent R&D activities, etc. These activities mainly revolve around capital and technology, so it is crucial to study the influence of capital and technology on enterprise value. There is a large body of literature on the impact of capital and technology on firm value, with studies on capital including physical, structural, human, financial, and relational capital [15,16], and studies on technology including R&D investment and the ability to innovate [17,18]. Existing research has focused on the impact of individual capital or technology factors on firm value, and while all of these factors play a role in a firm’s value enhancement, which plays a more significant role has not been discussed. Artificial intelligence companies are still in the initial stages of development and require capital and technology investment in particular. However, it is unlikely that both capital and technology will be adequately replenished in a short period for such emerging start-ups, so there is a need to compare the importance of both in the company’s development process to help them make a focused investment. This paper differs from previous studies in that it considers both capital and technology factors when considering the impact of internal and external factors on the value of an AI firm, compares the two, and finally provides recommendations for the firm’s development.

2.2. Specific Analysis of Variable Relation

2.2.1. Degree of Enterprise Attention and Firm Value

The degree of enterprise attention is essential for developing a business or introducing new technology [19]. The emphasis within the company is manifested in several ways, not least in the support from the strategic level [20]. The main body that develops corporate strategy is the company’s leadership, which, when faced with new opportunities, develops a new corporate strategy to lead the company into new areas and thus inspire it to improve its performance [21]. Moreover, in addition to leadership attention, the competence of corporate management also affects corporate value. Competent managers can reduce errors in business decisions and facilitate the implementation of corporate strategies, thereby enhancing corporate value [22].

The support of leaders and managers plays an essential role in the development of traditional companies and even more so in the development of high-tech companies. The main driver for the development of high-tech enterprises is new technology, and the most critical factor influencing the adoption of new technology is the willingness of top management, who are more inclined to adopt high technology if they are innovative [23]. In the age of intelligence, high technology, represented by artificial intelligence technology, is increasingly favored by senior managers in enterprises. The adoption of AI technology by senior management is often through leading companies applying for intelligent manufacturing pilot demonstration projects, and studies have shown that companies participating in intelligent manufacturing pilot demonstration projects can increase the corporate value [5].

Not much of the existing literature directly examines the degree of enterprise attention, and most of the measures of the degree of enterprise attention are based on leadership and management willingness. Some studies examine whether companies implement AI technologies or participate in intelligent manufacturing pilot demonstration projects as a single, unquantifiable measure that makes it challenging to reflect the specific degree of enterprise attention placed on AI. This paper breaks through the limitations of previous studies and, based on Zhao et al.’s research [5], proposes quantifying the importance companies attach to AI by the number of companies selected as AI concept stocks.

2.2.2. Government Support and Firm Value

Internal support is the intrinsic driver of a company’s value, while external support is the objective condition that drives its development. External support for enterprises includes economic, cultural, social, and government support, of which government support is a crucial factor affecting the development of high-tech enterprises [24]. Existing studies have examined the impact of government support on firm value from various perspectives and have reached different conclusions. Government support for enterprises includes policy funding subsidies and the formulation of related policies, and the most direct support is policy funding subsidies, of which policy funding subsidies include subsidies for emerging industries of enterprises and subsidies for R&D funding. Moreover, most studies conclude that the higher the policy funding subsidies are, the more they can promote the value of enterprises [25,26]. Some studies have also found that government subsidies have a significant value-enhancing effect on high-tech firms in the formative years, but the effect of government subsidies diminishes as firms become more mature [27]. As there are conflicting views in the academic community on the impact of government support on firm value.

2.2.3. Capital and Firm Value

Capital provides the economic foundation for the development of a business. The elements of capital that traditional businesses focus on are divided into tangible and intangible capital, where tangible capital includes fixed assets, inventory, and valuable items, and intangible capital includes land rights, concessions, and trademarks, among others [28]. In the age of intelligence, high-tech enterprises represented by artificial intelligence companies no longer focus only on tangible and intangible capital but also on the impact of intellectual capital on enterprise value [29]. Intellectual capital is essentially intangible, consisting of the added value created by the company’s employers, the organization in which they work, and the employees [30], and it is a strategic asset that provides a company with a sustainable competitive advantage [31]. Existing studies use different approaches to classify intellectual capital and produce inconsistent results, including human capital, structural capital, and relational capital [32,33]. It has also been argued that human capital affects structural and relational capital, and ultimately the firm’s financial capital, so financial capital is included in the intellectual capital structure [34]. While intellectual capital is ultimately an intangible capital of an enterprise, the development of an AI enterprise also requires tangible capital investment, physical capital being a tangible capital as distinct from human, structural, and relational capital [13]. Based on previous research, this paper examines the impact of four capital elements—physical capital, structural capital, human capital, and financial capital—on the value of artificial intelligence firms. As relational capital is a valuable network of relationships built between a firm and its external stakeholders, a social network formed by the firm over time, rather than just a capital input to the firm, this paper does not include relational capital in the capital framework.

(1)

The impact of physical capital on enterprise value

For AI companies, physical capital is the machinery and equipment needed to develop AI, intelligent products, production plants, etc. It is the most direct and rapid type of capital investment that companies can achieve. More attention is paid to the development of AI within and outside the company, and more physical capital will be invested in the first place, thus contributing to the rapid increase in enterprise value [13]. The following hypotheses are therefore proposed in this paper:

H1: 

Physical capital mediates the effect of the degree of enterprise attention on firm value.

H2: 

Physical capital mediates the effect of government support on firm value.

(2)

The impact of structural capital on firm value

Structural capital is the organizational intangible assets of a company, including the leadership of its management, corporate culture, information systems, organizational systems and processes, the extent to which databases and information technology are used, etc. [27]. The more attention companies and governments pay to the development of AI, the more they will increase the support for information systems and improve organizational processes and systems to facilitate the integration of AI technology with the rest of the company’s business, thus contributing to the enhancement of corporate value. This paper, therefore, proposes the following hypotheses:

H3: 

Structural capital mediates the effect of the degree of enterprise attention on firm value.

H4: 

Structural capital mediates the effect of government support on firm value.

(3)

The impact of human capital on firm value

Human capital is one of the most important intangible assets of an enterprise, including the knowledge base, work experience, education level, and years of experience. For AI companies, human capital is a key factor in the successful implementation of AI policies. Moreover, companies mainly improve their human capital by improving their compensation and benefits systems to promote firm value [35]. The following hypotheses are therefore proposed in this paper:

H5: 

Human capital mediates the effect of the degree of enterprise attention on firm value.

H6: 

Human capital mediates the effect of government support on firm value.

(4)

The impact of financial capital on firm value

Financial capital is the monetary capital invested in a business by the business owners and is the source of funding for the business. Developing AI requires a business to invest monetary capital. If a business invests enough financial capital, it can bear the cost of other capital elements to acquire other types of capital and invest them in the development of AI, ultimately increasing the value of the business. The following hypotheses are therefore proposed in this paper:

H7: 

Financial capital mediates the effect of the degree of enterprise attention on firm value.

H8: 

Financial capital mediates the effect of government support on firm value.

2.2.4. Technology and Firm Value

In the Cobb Douglas production function, technology can provide new growth points for the development of a company. The development of artificial intelligence companies is more dependent on technological advances and innovation than on traditional companies. Sun et al. studied the impact of companies’ use of digital technology, such as artificial intelligence technology, on firm value and found that investment in digital technology can significantly increase firm value [36]. Furthermore, to transform into an AI company, companies must invest more in the research and development of technology, improve their innovation capabilities, and apply for more AI-related patented technologies. Previous research on technology has primarily focused on the direct impact of technology R&D investment on firm value, without examining the factors that affect a firm’s technology investment. Not all companies are committed to investing in and developing AI technology. Only when companies take AI technology seriously enough, both internally and externally, will they invest more in R&D accordingly [37], thus contributing to the company’s value. This paper, therefore, examines technology-related variables as mediating variables by referring to the study by Wang et al. [38].

(1)

The impact of technology inputs on firm value

Technology investment includes investment in R&D staff and R&D funding [39]. Companies attach importance to the development of AI and will invest in corresponding professional staff and special funds. The special funds are used to recruit professional talents and purchase professional equipment to provide the foundation for the company’s R&D activities. In contrast, professional R&D staff can help companies integrate AI technology with their business, thus contributing to enhancing corporate value. The following hypotheses are therefore proposed in this paper:

H9: 

R&D staff input mediates the effect of the degree of enterprise attention on firm value.

H10: 

R&D fund input mediates the effect of the degree of enterprise attention on firm value.

H11: 

R&D staff input mediates the effect of government support on firm value.

H12: 

R&D fund input mediates the effect of government support on firm value.

(2)

The impact of independent innovation capability on firm value

The independent innovation capability can continuously provide new ideas, methods, and inventions of economic and social value in technology and various practical activities. Han et al. [40] found that the stronger the sense of corporate social responsibility, the more willing a company is to take the initiative to improve its innovation capabilities, thereby contributing to the enhancement of firm value. Similarly, companies that attach importance to the development of artificial intelligence enterprises will take the initiative to improve their innovation capabilities and develop more patented artificial intelligence technologies, thus contributing to the enhancement of enterprise value. The following hypotheses are therefore proposed in this paper:

H13: 

Independent innovation capability mediates the effect of the degree of enterprise attention on firm value.

H14: 

Independent innovation capability mediates the effect of government support on firm value.

2.3. Theoretical Model

According to the previous theoretical analysis, this paper takes the degree of enterprise attention and government support as independent variables, capital and technology as intermediary variables, and enterprise value as a dependent variable to investigate the role of capital and technology in the process of enterprise importance and how government support affects enterprise value. Finally, it compares which elements of capital and technology play a larger role and which elements play a smaller role, so as to provide suggestions for enterprises to make decisions. Figure 1. is the theoretical model of this paper:

3. Research Design

3.1. Sample Selection and Source of Data

(1)

Sample Selection

The sample used in this study is companies using artificial intelligence technology. The selection of listed companies with the concept of “artificial intelligence” published on the website Flush was determined based on whether the business, operating activities, and financial reports of the companies involve the concept of artificial intelligence. Thus, this paper selected listed companies with the concept of “artificial intelligence” on the website of Flush as the research sample. Due to the different methods of calculating the market value of B shares and H shares, only A-share listed companies were selected as samples in this paper. Listed companies of ST shares and PT shares would lead to missing financial data due to various abnormal conditions, so ST-share and PT-share listed companies were excluded from the original sample in this paper. Considering that artificial intelligence in China started to emerge in 2015, and considering the huge impact of COVID-19 on listed enterprises, the data of enterprises would be relatively unstable. This paper did not collect data after 2019. The time range of sample data selection was from 2016 to 2019.

(2)

Source of data

The financial data in this paper originate from the annual reports published by the companies, where the source of downloading the annual reports is the website of Flush, and the patent data of listed companies come from the State Intellectual Property Office. The data of 55 AI A-share listed companies were finally obtained after excluding the companies with missing data. This paper collates the obtained data into panel data analysis according to the high volatility of time series data.

3.2. Variable Measurements

(1)

Explained variables

The indicators used to measure firm value in existing studies include return on assets and Tobin’s Q. Tobin’s Q is calculated in different ways. This paper refers to the method of Zhao et al. [41] to calculate Tobin’s Q (TBQ) by using the ratio of the company’s market value at the end of the period to its total assets as the measurement standard of the enterprise value of listed companies.

(2)

Explanatory variables

The degree of enterprise attention indicates the degree to which companies value the use of AI technology. The number of “AI” concept stocks was selected as an indicator of the importance companies attach to AI (FI), as the sample was drawn from the list of “AI” concept stocks on the Flush website. Moreover, the number of “artificial intelligence” concepts is based on the number of times the concept of artificial intelligence is mentioned in the business, operating activities, and financial reports of the related companies, which is a good indicator of how much attention companies pay to artificial intelligence and how much importance they attach to artificial intelligence technology. Government support indicates the strength of government support for AI development, and the “financial subsidies” in the annual reports of listed enterprises can reflect the degree of government support to enterprises. Therefore, this paper refers to the study of Liu et al. [42] and measures government support (GS) by the proportion of government subsidies to operating income in the annual reports.

(3)

Intermediary variables

The capital variables were selected as human capital (HC), physical capital (MC), structural capital (SC), and financial capital (FC). In this paper, human capital is measured using the “compensation payable to employees” in the consolidated balance sheet in the annual report. Financial capital is measured by the difference between total current assets and human capital, following Chen et al. [13] Physical capital is measured by the total corporate assets, excluding intangible assets, following Manning et al. [43] Structural capital is measured using the sum of the firm’s net profit, employee compensation payable, interest expense, and income tax expense.

The technical variables were selected as the R&D staff input (PI), R&D fund input (CI), and independent innovation capability (IA). R&D staff input and R&D fund input variables were measured by referring to Liu et al. [44] and using logarithmically processed data on the number of R&D personnel and R&D input in annual reports. The independent innovation capability of enterprises is referred to as the measure used by Zhou et al. [45] using the number of patents applied for by enterprises as a measure.

(4)

Control variables

The following variables that may affect the value of a business were selected as control variables in this paper. The size of a business (Size) affects the total assets of the business and therefore the market value of the business, measured as a logarithmic total of the business assets. The gearing ratio (lev) is measured as the ratio of total liabilities to total assets in the annual report. The age of the business (age) is measured by the number of years the company has been in existence. The growth of an enterprise (growth) affects the stock market value of the enterprise and thus the value of the enterprise, which is measured by the growth rate of operating income. Business performance (manage) is measured as the logged business revenue. The measurement methods of all variables are shown in

Table 1. Variable names and measures.

Variable Type	Variable	Variable Measurement
Independent variable	Degree of enterprise attention (FI)	Number of concepts to which artificial intelligence belongs
	Government support (GS)	Government grants/Operating income
Dependent variable	Firm value (TBQ)	Price per share multiplied by shares/Total assets
Control variable	Enterprise size (size)	Total assets are taken as a logarithm
	Gearing ratio (lev)	Total liabilities/Total assets
	Business age (age)	Year of establishment
	Firm nature (property)	Dummy variable, state takes the value 1, private takes the value 0
	Firm growth (growth)	Operating income growth rate
	Business conditions (manage)	Operating income taken as a logarithm
Capital intermediary variable	Human capital (HC)	Employee remuneration is taken as a logarithm
	Physical capital (MC)	Total assets excluding intangible assets and logarithm
	Structural capital (SC)	Net profit, salaries, interest expense and income tax are added together and subtracted from human capital and then logged
	Financial capital (FC)	Current assets less human capital taken as a logarithm
Technical intermediary variable	R&D staff input (PI)	Number of R&D staff
	R&D fund input (CI)	Logarithm of the amount invested in R&D
	Independent innovation capability (IA)	Number of corporate patents

.

3.3. Research Method

In this paper, the least square method was used for panel data regression. In order to test the intermediary effect of capital and technology, this study used a three-step regression method with reference to Zhao et al. [39]. The three-step regression method performs three regressions, respectively, when testing the intermediary effect. The results of each regression should meet the following conditions: First, when testing the influence of the explanatory variable on the intermediary variable, the coefficient value of the explanatory variable should pass the significance test; second, when testing the influence of explanatory variables on the explained variables, the coefficient value of explanatory variables should pass the significance test; third, when testing the common influence of explanatory variables and intermediate variables on the explained variables, the coefficient value of intermediate variables should pass the significance test, and the influence of explanatory variables on the explained variables should be small (compared with the regression results in the second step). If the above three conditions are met, then the intermediary effect exists. In this paper, stata15.0 software was used for regression analysis.

4. Empirical Analysis

4.1. Descriptive Statistics and Correlation Analysis

The correlation coefficients between the variables in this study and the means, and the standard deviations of the variables are shown in

Table 2. Descriptive statistics.

Variable	age	property	lev	size	growth	manage	FI	GS	MC	SC	HC	FC	PI	CI	IA	TBQ
age	1.000
property	−0.088 **	1.000
lev	0.036 **	0.032	1.000
size	0.017 **	0.247	0.357 **	1.000
growth	−0.138 **	−0.088	0.083 **	0.006 **	1.000
manage	0.031 **	0.181	0.473 **	0.920 **	0.044	1.000
FI	−0.094 **	0.227	0.257 **	0.715 **	−0.045	0.699 **	1.000
GS	−0.135 **	0.040	−0.291 **	−0.243 **	−0.048	−0.383 **	0.165 **	1.000
MC	0.016 **	0.250	0.356 **	0.999 **	0.002	0.920 **	0.718 **	0.243 **	1.000
SC	−0.034 **	0.181	0.294 **	0.858 **	0.011	0.816 **	0.662 **	0.285 **	0.860 **	1.000
HC	0.000	0.082	0.308	0.629	0.044	0.732	0.373	0.358	0.630	0.630	1.000
FC	0.064 **	0.291	0.348 **	0.967 **	−0.006	0.921 **	0.723 **	0.256 **	0.968 **	0.825 **	0.600 **	1.000
PI	0.010 **	0.078	0.319 **	0.664 **	0.031	0.751 **	0.559 **	0.216 **	0.665 **	0.630 **	0.660 **	0.647 **	1.000
CI	−0.075 **	0.150	0.300 **	0.770 **	−0.034	0.831 **	0.677 **	0.231 **	0.772 **	0.730 **	0.716 **	0.767 **	0.866 **	1.000
IA	−0.100 **	0.202	0.060 **	0.456 **	0.020	0.492 **	0.370 **	0.094 **	0.457 **	0.502 **	0.449 **	0.522 **	0.490 **	0.559 **	1.000
TBQ	−0.208 **	−0.026	−0.408 **	−0.371 **	0.089	−0.288 **	0.140 **	0.223 **	0.365 **	0.173 **	0.152 **	0.312 **	0.106 **	0.107 **	0.140 **	1.000
Average	21.182	0.327	0.319	22.064	0.196	21.180	20.155	0.016	22.029	19.233	17.586	21.571	6.406	18.788	99.005	2.885
Std	4.951	0.470	0.159	1.076	0.508	1.341	7.999	0.017	1.079	1.512	1.466	1.074	1.246	1.260	232.275	2.288

Note: (1) Standard deviation = Std, (2) ** = p < 0.05.

. Except for the two variables of firm nature and firm growth, all the variables showed a significant correlation with each other, and the correlation coefficients between the variables were overwhelmingly less than 0.5 so that the problem of multicollinearity between the variables could be ruled out and the data were suitable for further research.

4.2. Hypothesis Test

(1)

Direct effects test

The results of the firm’s internal and external support and the financial and technology-related variables on firm value are shown in

Table 3. Effect of variables on firm value.

Variable	Model1	Model2	Model3	Model4	Model5	Model6	Model7	Model8	Model9	Model10
age	−0.085 ***	−0.076 ***	−0.082 ***	−0.074 ***	−0.085 ***	−0.098 ***	−0.075 ***	−0.083 ***	−0.069 ***	−0.073 ***
property	0.345	0.305	0.267	0.414	0.345	0.148	0.307	0.387	0.379	0.210
lev	−6.357 ***	−6.254 ***	−6.195 ***	−6.037 ***	−6.358 ***	−6.054 ***	−6.172 ***	−6.193 ***	−5.854 ***	−5.509 ***
size	−1.994 ***	−2.135 ***	−14.433 ***	−2.630 ***	−1.995 ***	−2.881 ***	−2.241 ***	−1.929 ***	−1.972 ***	−1.909 ***
growth	0.352	0.405	0.446	0.369	0.352	0.367	0.366	0.361	0.464	0.349
manage	1.317 ***	1.219 ***	1.251 ***	1.152 ***	1.319 ***	1.064 ***	1.610 ***	1.042 ***	0.838 ***	0.998 ***
FI		0.047 ***
MC			12.481 ***
SC				0.664 ***
HC					0.002
FC						1.225 **
GS							24.151 ***
PI								0.307 **
CI									0.550 ***
IA										0.003 ***
R2	0.348	0.361	0.373	0.397	0.348	0.365	0.374	0.360	0.374	0.395
Adjust-R2	0.330	0.340	0.353	0.377	0.327	0.344	0.353	0.339	0.354	0.375
△R2	0.018	0.021	0.021	0.020	0.022	0.021	0.021	0.021	0.021	0.020
F	18.970	17.090	18.030	19.930	16.180	17.410	18.060	17.050	18.100	19.750

Note: ** = p < 0.05, *** = p < 0.01.

. Models 1–10 are the results of the regression of firm value on all variables separately. There was a significant positive effect of the degree of enterprise attention on the firm value in model 2 (β = 0.047, p < 0.01), which suggested that the more importance is attached to the development of AI within the firm, the more the value of the firm can be enhanced. There was a significant positive effect of physical capital on firm value in model 3 (β = 12.481, p < 0.01). There was a significant positive effect of structural capital on firm value in model 4 (β = 0.664, p < 0.01). There was no significant effect of human capital on firm value in model 5 (β = 0.002, p > 0.1). On the one hand, human capital is an essential productive asset and factor as far as enterprises are concerned. However, differences in the quality of internal personnel and the differentiation of knowledge levels can lead to the alienation of the quality of this asset, and some enterprises consider poor-quality human capital to be a heavy or inefficient asset and include it in their divestment strategies [46]. On the other hand, the path of human capital to firm value in R&D companies is influenced by the external environment. At the beginning of a new industry or technology, human capital is essential to drive firm value through the mediated path of R&D investment [47], but when R&D companies are marketed, redundant human capital or divided human teams can lead to barriers to the firm value. Model 6 had a significant positive effect of financial capital on firm value (β = 1.225, p < 0.01). Model 7 had a significant positive effect of government support on firm value with a strong effect (β = 24.151, p < 0.01). Model 8 had a significant positive effect of investment in R&D personnel on firm value (β = 0.307, p < 0.01). Model 9 had a significant positive effect of R&D capital investment on firm value (β = 0.550, p < 0.01). The investment of R&D personnel can bring sufficient nutrients to the enterprise’s technology research and development and provide talent reserves for the enterprise to develop various new technologies and products. In contrast, the investment of R&D funds can provide fertile soil for the enterprise’s technology research and development, support various R&D activities, improve the welfare treatment of R&D personnel, and motivate the R&D enthusiasm of R&D personnel. The two complement each other, laying a solid foundation for the enterprise’s technology research and development and innovation, thus promoting the enterprise’s value. The results of model 10 showed that the ability to innovate positively affects firm value (β = 0.003, p < 0.01). A comparison of the regression coefficients shows that government support and physical capital investment have a more significant impact on the value of AI firms, while the impact of independent innovation capability on firm value is less prominent. The reason for this is that AI companies belong to strategic emerging industries. Therefore, the marginal contribution of independent innovation capability at the beginning of the enterprise life cycle is relatively weak. The turnover of innovation results is complex, the R&D breakthroughs are significantly blocked, and the transformation of innovation results on the ground is complex, making it difficult for enterprises to form a rapid value path through innovation power in the short term [48]. For the private sector AI industry, political connections and government subsidies are important ways to obtain value in the early stage of the enterprise, which is also the beginning of forming a “new type of government–business relationship.” With the multi-dimensional integration of government, industry, academia, and research, the enterprise realizes the construction of a value network, laying a good foundation for later development and value enhancement [26]. In general, the impact of capital variables on the value of AI firms is stronger than the impact of technology variables, as capital is the basis for rapid growth in the early stages of a firm, and AI firms, especially start-ups, require large amounts of capital to purchase the resources needed for the business, including the plant and various equipment needed to develop the technology.

The results of the regression analysis of the degree of enterprise attention on capital and technology-related variables are shown in

Table 4. Direct effects of degree of enterprise attention on each of the capital and technology variables.

Variable	Model11	Model12	Model13	Model14	Model15	Model16	Model17
age	−0.076 ***	−0.074 ***	−0.075 ***	−0.066 ***	−0.069 ***	−0.064 ***	−0.072 ***
property	0.306	0.308	0.312	0.322	0.314	0.301	0.307
lev	−6.162 ***	−6.102 ***	−6.132 ***	−6.163 ***	−6.135 ***	−6.121 ***	−6.122 ***
size	−2.245 ***	−2.234 ***	−2.321 ***	−2.210 ***	−2.254 ***	−2.133 ***	−2.223 ***
growth	0.366	0.356	0.374	0.371	0.376	0.357	0.368
manage	1.623 ***	1.588 ***	1.594 ***	1.602 ***	1.609 ***	1.625 ***	1.621 ***
FI	24.151 ***	0.125 ***	0.068 ***	0.097 ***	0.870 ***	0.107 ***	10.753 ***
R2	0.515	0.439	0.139	0.522	0.312	0.458	0.137
Adjust-R2	0.513	0.436	0.135	0.520	0.309	0.456	0.133
△R2	0.002	0.003	0.004	0.002	0.003	0.002	0.004
F	231.350	170.230	35.150	238.140	98.910	184.550	34.640

Note: *** = p < 0.01.

. The independent variable in models 11–17 is the degree of enterprise attention, and the dependent variables are physical capital, structural capital, human capital, financial capital, R&D staff input, R&D fund input, and independent innovation capability. The results of models 11 to 14 show that the coefficients on the degree of enterprise attention were positive and had p-values less than 0.01 (β > 0, p < 0.01). The degree of enterprise attention significantly and positively affects the variables of each dimension of capital. Companies that attach sufficient importance to AI invest sufficient physical, structural, human, and financial capital to support AI development and use the capital investment to build a solid material foundation for the development and application of AI. The regression coefficients of the degree of enterprise attention in models 15 to 17 are positive, and the p-values are less than 0.01 (β > 0, p < 0.01). The degree of enterprise attention significantly and positively affects the variables of each dimension of technology. Companies that attach importance to AI also show their investment in technology. Companies that attach importance to AI strive to improve their soft power, invest in professional R&D staff and sufficient R&D funds in the field of AI, and at the same time improve their ability to innovate independently, develop their own patented technology, achieve independent development, and reduce the costs incurred by companies using external patents.

The results of the regression analysis of government support on capital and technology-related variables are shown in

Table 5. Direct impact of government support on each of the capital and technology variables.

Variable	Model18	Model19	Model20	Model21	Model22	Model23	Model24
age	−0.075 ***	−0.068 ***	−0.067 ***	−0.076 ***	−0.072 ***	−0.068 ***	−0.078 ***
property	0.307	0.307	0.323	0.309	0.322	0.321	0.309
lev	−6.163 ***	−6.108 ***	−6.133 ***	−6.145 ***	−6.142 ***	−6.123 ***	−6.156 ***
size	−2.235 ***	−2.245 ***	−2.301 ***	−2.223 ***	−2.241 ***	−2.112 ***	−2.221 ***
growth	0.356	0.366	0.375	0.362	0.365	0.369	0.382
manage	1.621 ***	1.592 ***	1.587 ***	1.605 ***	1.623 ***	1.626 ***	1.566 ***
GS	15.110 ***	24.851 ***	30.243 ***	15.865 ***	15.552 ***	16.814 ***	18.223
R2	0.375	0.386	0.367	0.387	0.377	0.384	0.382
Adjust-R2	0.352	0.364	0.346	0.363	0.355	0.363	0.361
△R2	0.023	0.022	0.021	0.024	0.022	0.021	0.021
F	13.640	19.250	31.950	15.280	16.710	17.320	1.930

Note: *** = p < 0.01.

. The independent variable in models 18–23 is government support, and the dependent variables are physical capital, structural capital, human capital, financial capital, R&D staff input, R&D fund input, and independent innovation capability. The coefficients of government support in models 18 to 23 were above 15, and the p-values were less than 0.01 (β > 15, p < 0.01). Government support positively affected physical capital, structural capital, human capital, financial capital, R&D staff input, and R&D fund input. With government funding support and a favorable policy environment during AI development, companies can strengthen their determination to develop AI and invest more in capital and technology to provide comprehensive support for AI development. The regression coefficient of government support in model 24 was enormous, but the p-value was less than 0.01, indicating no direct effect of government support on independent innovation capability. This is because external forces do not achieve the improvement of an enterprise’s independent innovation capability except through the training of R&D personnel, the development of R&D activities, the improvement of the quality of business operators, etc. Therefore, government support does not have a direct impact on an enterprise’s independent innovation capability. The coefficients for government support in each model in Table 5 are more significant than the coefficients for the degree of enterprise attention in each model in Table 4. Artificial intelligence in China is still in the early stages, relying on government support. Enterprises for developing artificial intelligence are always in a wait-and-see state, and only the government strongly supports the development of artificial intelligence. Artificial intelligence can cause enough attention to enterprises, and enterprises have the confidence to develop artificial intelligence, so the government support for the development of artificial intelligence is stronger than the degree of enterprise attention.

(2)

Intermediary effects test

The mediating role of capital variables in the impact of the degree of enterprise attention on firm value is shown in

Table 6. Tests for mediating effects of capital variables on degree of enterprise attention.

Variable	Model25	Model26	Model27	Model28	Model29	Model30	Model31	Model32	Model33	Model34	Model35	Model36
age	−0.076 ***	−0.082 ***	−0.074 ***	−0.076	−0.074	−0.067 ***	−0.076 ***	−0.085 ***	−0.075 ***	−0.076	−0.098 ***	−0.089 ***
property	0.305	0.267	0.236	0.305	0.414	0.379	0.305	0.345	0.310	0.305	0.148	0.134
lev	−6.254 ***	−6.195 ***	−6.115 ***	−6.254 ***	−6.037 ***	−5.968 ***	−6.254 ***	−6.358 ***	−6.190 ***	−6.254	−6.054 ***	−5.999 ***
size	−2.135 ***	−14.433 ***	−13.849 ***	−2.135 ***	−2.630 ***	−2.717 ***	−2.135 ***	−1.995 ***	−2.114 ***	−2.135	−2.881 ***	−2.907 ***
growth	0.405	0.446	0.487	0.405	0.369	0.411	0.405	0.352	0.409	0.405	0.367	0.410
manage	1.219 ***	1.251 ***	1.168 ***	1.219 ***	1.152 ***	1.080 ***	1.219 ***	1.319 ***	1.125 ***	1.219	1.064 ***	1.007 ***
FI	0.047 ***		0.412	0.047 ***		0.038	0.047 ***		0.051 **	0.047		0.040
MC		12.481 ***	11.770 ***
SC					0.664 ***	0.636 ***
HC								0.002	0.074
FC											1.225 **	1.096 **
R2	0.361	0.373	0.383	0.361	0.397	0.405	0.361	0.348	0.362	0.361	0.365	0.374
Adjust-R2	0.340	0.353	0.359	0.340	0.377	0.382	0.340	0.327	0.338	0.340	0.344	0.350
△R2	0.021	0.021	0.023	0.021	0.020	0.023	0.021	0.022	0.024	0.021	0.021	0.024
F	17.090	18.030	16.350	17.090	19.930	17.950	17.090	16.180	14.940	17.090	17.410	15.750

Note: ** = p < 0.05, *** = p < 0.01.

. A stepwise regression test revealed that the p-value for the degree of enterprise attention was more significant than 0.1 and became insignificant when physical capital was added to model 27. The physical capital variable remained significant and physical capital produced a full mediation effect, and hypothesis H1 was tested. With the addition of structural capital in models 28 to 29, the degree of enterprise attention became insignificant, while structural capital was significant and structural capital produced a full mediation effect, with hypothesis H3 being tested. The p-values for the human capital variables in models 31 to 33 were all greater than 0.1, and none of the human capital variables was significant, indicating that human capital does not have a mediating effect, and with hypothesis H5 not being tested. With the inclusion of financial capital in models 34 to 36, the degree of enterprise attention was insignificant but financial capital was significant: Financial capital produced a full mediation effect, and hypothesis H7 was tested. The results of the mediating role of capital variables in the process of the impact of government support on firm value are shown in

Table 7. Tests of mediating effects of funding variables on government support.

Variable	Model46	Model47	Model48	Model49	Model50	Model51	Model52	Model53	Model54	Model55	Model56	Model57
age	−0.075 ***	−0.082 ***	−0.72 **	−0.075 ***	−0.074	−0.075 **	−0.075 ***	−0.085 ***	−0.075 ***	−0.075 ***	−0.098 ***	−0.088 ***
property	0.307	0.267	0.230	0.307	0.414	0.343	0.307	0.345	0.310	0.307	0.148	0.125
lev	−6.172 ***	−6.195 ***	−6.014 ***	−6.172 ***	−6.037 ***	−6.056 ***	−6.172 ***	−6.358 ***	−6.146 ***	−6.172 ***	−6.054 ***	−5.899 ***
size	−2.241 **	−14.433 ***	−14.527 **	−2.241 **	−2.630 ***	−2.169 ***	−2.241 ***	−1.995 ***	−2.230 ***	−2.241 ***	−2.881 ***	−3.053 ***
growth	0.366	0.446	0.458	0.366	0.369	0.372	0.366	0.352	0.366	0.366	0.367	0.379
manage	1.610 ***	1.251 ***	1.541 ***	1.610 ***	1.152 ***	1.367 ***	1.610 ***	1.319 ***	1.575 ***	1.610 ***	1.064 ***	1.362 ***
GS	24.151 ***		23.864 **	24.151 ***		23.041 ***	24.151 ***		24.333 **	24.151 ***		23.041 ***
MC		12.481 ***	12.330 **
SC					0.664 ***	0.737 ***
HC								0.002	0.031
FC											1.225 **	1.138 **
R2	0.374	0.373	0.398	0.374	0.397	0.381	0.374	0.348	0.374	0.374	0.365	0.388
Adjust-R2	0.353	0.353	0.375	0.353	0.377	0.358	0.353	0.327	0.350	0.353	0.344	0.365
△R2	0.021	0.021	0.023	0.021	0.020	0.023	0.021	0.022	0.024	0.021	0.021	0.023
F	18.060	18.030	17.430	18.060	19.930	16.230	18.060	16.180	15.740	18.060	17.410	16.720

Note: ** = p < 0.05, *** = p < 0.01.

. After adding physical capital and structural capital to models 46 to 51, respectively, the regression coefficients of government support became smaller but still significantly affected firm value (p < 0.01).

Moreover, physical capital and structural capital also had a significant effect on firm value (p < 0.01), indicating that physical capital and structural capital play a partially mediating role in the effect of government support on firm value, and hypotheses H2 and H4 were tested. The coefficients on the human capital variables in models 52 to 54 were insignificant: human capital did not mediate government support, and hypothesis H6 was not tested. The coefficients of financial capital and government support in models 55 to 57 were significant (p < 0.01), with financial capital playing a partially mediating role, and hypothesis H8 was tested.

In general, capital had a solid mediating effect in the interaction between the degree of enterprise attention, government support, and corporate value enhancement. It is a practical manifestation of the degree of enterprise attention and government support, and a crucial mediating resource for value transformation. In terms of the internal structure of capital, the role of human capital in driving value to AI firms is minimal. On the one hand, artificial intelligence enterprises are high-tech industries. The demand for human resources makes the cost of capital investment rise, and the marginal utility of human capital decreases rapidly, making it less creative in radiating enterprise value and driving innovation.

On the other hand, the late start of the AI industry and the small size of the average company’s workforce and its entire sector make it difficult to demonstrate the human capital effect. Moreover, this paper refers to existing research paradigms that portray human capital in terms of payroll size, where redundant capital undoubtedly becomes a burden on firm value enhancement. Of the remaining categories of capital elements, physical capital is the most vital driver, followed by the utility of financial capital and the limited contribution of structural capital. Indicating a solid reliance on natural physical capital, the combination of descriptive statistics of the data reveals a lower demand for structured capital and a correspondingly smaller scale of structured capital in the early stages of development. As development evolves and the company matures, structural capital gradually adjusts to the company’s internal environment, and its share increases year by year, providing support for the company’s ability to plan and transform itself later on, and its usefulness increases.

The mediating role of technology variables in the impact of the degree of enterprise attention on firm value is shown in

Table 8. Tests for mediating effects of technology variables on degree of enterprise attention.

Variable	Model37	Model38	Model39	Model40	Model41	Model42	Model43	Model44	Model45
age	−0.076 ***	−0.069 ***	−0.064 ***	−0.076 ***	−0.069 ***	−0.064 **	−0.076 ***	−0.073 ***	−0.064 **
property	0.305	0.379	0.346	0.305	0.379	0.346	0.305	0.210	0.170
lev	−6.254 ***	−5.854 ***	−5.831 ***	−6.254 ***	−5.854 ***	−5.831 ***	−6.254 ***	−5.509 ***	−5.405 ***
size	−2.135 ***	−1.972 ***	−2.078 ***	−2.135 ***	−1.972 ***	−2.078 ***	−2.135 ***	−1.909 ***	−2.050 ***
growth	0.405	0.464	0.491	0.405	0.464	0.491	0.405	0.349	0.402
manage	1.219 ***	0.838 ***	0.816 ***	1.219 ***	0.838 ***	0.816 ***	1.219 ***	0.998 ***	0.899 ***
FI	0.047 ***		0.035	0.047 ***		0.035	0.047 ***		0.047 **
PI		0.307 **	0.279 **
CI					0.550 ***	0.492 **
IA								0.003 ***	0.003 ***
R2	0.361	0.360	0.370	0.361	0.374	0.381	0.361	0.395	0.407
Adjust-R2	0.340	0.339	0.347	0.340	0.354	0.357	0.340	0.375	0.385
△R2	0.021	0.021	0.024	0.021	0.021	0.024	0.021	0.020	0.022
F	17.090	17.050	15.520	17.090	18.100	16.210	17.090	19.750	18.120

Note: ** = p < 0.05, *** = p < 0.01.

. The coefficients on the degree of enterprise attention became insignificant (p > 0.1) with the addition of R&D staff input and R&D fund input in models 37 to 42. In contrast, R&D staff input and R&D fund input remained significant, suggesting that R&D staff input and R&D fund input produced fully mediating effects in the influence of the degree of enterprise attention on firm value, and hypotheses H9 and H10 were tested. Models 43 to 45 show different results from the other two variables, with a partial mediating effect of the independent innovation capability in the impact of the degree of enterprise attention on firm value, testing hypothesis H10. The results of the mediating role of technology variables in the process of the impact of government support on firm value are shown in

Table 9. Tests for mediating effects of technology variables on government support.

Variable	Model58	Model59	Model60	Model61	Model62	Model63	Model64	Model65	Model66
age	−0.075 ***	−0.069 ***	−0.075 ***	−0.075 ***	−0.069 ***	−0.063 **	−0.075 ***	−0.073 ***	−0.065 **
property	0.307	0.379	0.343	0.307	0.379	0.341	0.307	0.210	0.183
lev	−6.172 ***	−5.854 ***	−6.056 ***	−6.172 ***	−5.854 ***	−5.752 ***	−6.172 ***	−5.509 ***	−5.392 ***
size	−2.241 ***	−1.972 ***	−2.169 ***	−2.241 ***	−1.972 ***	−2.192 ***	−2.241 ***	−1.909 ***	−2.134 ***
growth	0.366	0.464	0.372	0.366	0.464	0.463	0.366	0.349	0.361
manage	1.610 ***	0.838 ***	1.367 ***	1.610 ***	0.838 ***	1.152 ***	1.610 ***	0.998 ***	1.276 ***
GS	24.151 ***		22.141 ***	24.151 ***		21.178 **	24.151 ***		21.509 ***
PI		0.307 **	0.245 ***
CI					0.550 ***	0.484 ***
IA								0.003 ***	0.002 ***
R2	0.374	0.360	0.381	0.374	0.374	0.393	0.374	0.395	0.415
Adjust-R2	0.353	0.339	0.358	0.353	0.354	0.370	0.353	0.375	0.392
△R2	0.021	0.021	0.023	0.021	0.021	0.023	0.021	0.020	0.023
F	18.060	17.050	16.230	18.060	18.100	17.090	18.060	19.750	18.680

Note: ** = p < 0.05, *** = p < 0.01.

. The coefficients of government support and the coefficients of the three mediating variables were significant when the three variables of R&D staff input, R&D fund input, and independent innovation capability were added to models 58 to 66, indicating that the technology variables play a partially mediating role in the influence of government support on firm value, and hypotheses H11, H12, H13, and H14 were tested. By comparing the coefficients of the models, it can be found that the coefficients of R&D staff input and R&D fund input were 0.245 and 0.484, respectively, while the coefficient of independent innovation capability was only 0.002, indicating that the mediating effect of both R&D staff and R&D fund input was significantly greater than that of independent innovation capability.

Technology is the primary resource for building the core competitiveness of an AI enterprise. An enterprise’s innovative technology is a significant mark that distinguishes it from other enterprises. Innovative technology that is difficult to imitate and replicate can help an enterprise form a unique competitive advantage in the industry, which plays an essential role in enhancing its value. Investment in R&D staff and R&D fund, as well as the development of independent innovation capabilities, are concrete signs of the importance companies attach to technology and the government’s focus on developing artificial intelligence in companies. The investment of R&D staff and funds are the necessary innovation input resources for enterprises to carry out R&D activities, and are the essential elements of an enterprise’s technological innovation system. The investment in R&D by enterprises is mainly used to support enterprises to carry out technological research and development, product design, and transformation of results, which in turn effectively contribute to the enhancement of enterprise value. Independent innovation capability is the catalyst for enterprises to develop new technologies. The research and development cycle of an enterprise’s independent patent technology depends on its independent innovation capability, and enterprises with solid capability can develop more competitive new patents in a shorter period, thus occupying more market share in the artificial intelligence industry. The market value of the enterprise can be substantially increased. The reason why the effect of the innovation capability is weaker than that of the investment in R&D staff and fund is that the innovation capability is the result of the long-term cultivation and accumulation of the enterprise, while the investment in R&D staff and the fund can be replenished in a short period. Thus, the effect of the enterprise’s investment in R&D is better than that of the innovation capability in only four years of data.

A comparison of the models in Table 7 and Table 8 reveals that the coefficients of all the capital dimension variables were above 0.5, while the coefficients of all the technology dimension variables were at the level of 0.5 and below with a mediating effect arising from the capital variables being more significant than that arising from the technology variables. The effect of capital investment in the short term was significantly better than that of technology investment. The physical, structural, and financial capital invested by companies in the short term can be used directly to purchase the infrastructure and facilities for the development of AI and a range of infrastructure and equipment can provide a solid physical foundation for companies to enter the AI industry and address the urgent need for companies to develop AI technology in the short term rapidly. However, in the long run, the effect of capital is diminishing, and companies need to rely on technology rather than capital to achieve long-term sustainable development in the field of artificial intelligence. A higher level of investment in research and development is conducive to forming a robust technological innovation capability in manufacturing companies. Constantly innovative technology can bring a constant stream of innovative breakthrough points for companies, breaking the spell of diminishing marginal effects. Technological innovation results from a firm’s accumulation over a long period, and therefore technology is not visible in the short term compared to capital.

After empirical analysis, the hypothesis test results proposed in this paper are as follows in

Table 10. Summary of hypotheses tests.

H1: Physical capital mediates the effect of the degree of enterprise attention on firm value.	Yes
H2: Physical capital mediates the effect of government support on firm value.	Yes
H3: Structural capital mediates the effect of the degree of enterprise attention on firm value.	Yes
H4: Structural capital mediates the effect of government support on firm value.	Yes
H5: Human capital mediates the effect of the degree of enterprise attention on firm value.	No
H6: Human capital mediates the effect of government support on firm value.	No
H7: Financial capital mediates the effect of the degree of enterprise attention on firm value.	Yes
H8: Financial capital mediates the effect of government support on firm value.	Yes
H9: R&D staff input mediates the effect of the degree of enterprise attention on firm value.	Yes
H10: R&D fund input mediates the effect of the degree of enterprise attention on firm value.	Yes
H11: R&D staff input mediates the effect of government support on firm value.	Yes
H12: R&D fund input mediates the effect of government support on firm value.	Yes
H13: Independent innovation capability mediates the effect of the degree of enterprise attention on firm value.	Yes
H14: Independent innovation capability mediates the effect of government support on firm value.	Yes

.

5. Conclusions

By examining the mechanisms by which the degree of enterprise attention and government support affect the value of AI firms, the following conclusions were drawn from an analysis of data from 55 AI firms:

(1)

The degree of enterprise attention and government support contribute positively to firm value, capital, and technology factors. The more importance companies place on their development in AI, the more they will invest significant capital or technology, thus contributing to the company’s value. Strong government support for enterprises can increase their motivation to develop, and they will boldly invest capital or technology in developing AI, thus increasing their enterprise value. The regression results comparing the degree of enterprise attention and government support found that the impact of government support on firm value was more significant than the impact generated by its emphasis on AI technology. AI companies need not only internal attention from the company but also support from the government in order to grow faster and better;

(2)

While emerging companies such as artificial intelligence companies need to rely on technology and their ability to innovate independently to add value, at the current level of economic development, the development of artificial intelligence companies is still dependent on physical capital support. The effects of degree of enterprise attention on each of the capital and technology variables are, in descending order, physical capital (β = 24.151), independent innovation capability (β = 10.753), R&D staff input (β = 0.870), structural capital (β = 0.125), R&D fund input (β = 0.107), and financial capital (β = 0.097). The larger coefficients for physical capital, independent innovation capacity, and R&D staff input suggest that the more importance companies attach to the development of AI technology, the quicker and more direct investment in the physical capital will be, such as equipment and production plants used to develop the technology. Compared with general technology, the research and development of artificial intelligence technology are more difficult. They require more independent innovation ability and a higher quality of research and development personnel for enterprises, which need to invest many professional talents to cope with the demand for artificial intelligence development;

(3)

In addition to human capital, physical capital, structural capital, financial capital, R&D investment, and independent innovation capability significantly influence AI firm value. In the pathway of the impact of the degree of enterprise attention on firm value, the independent innovation capability plays a partially mediating role, with physical capital, structural capital, financial capital, R&D staff, and fund input all playing a fully mediating role. The mediating effect of physical capital is significantly greater than that of the other variables, which is consistent with the fact that physical capital is a capital that can be replenished directly and in large amounts in a short period compared to other capitals. In contrast, structural and financial capital are both manifestations of a company’s operating capacity that cannot be improved quickly in a short period and whose effects can only be demonstrated in the long term;

(4)

The independent innovation capability does not play a mediating role in the path of government support on firm value: Physical capital, structural capital, financial capital, R&D staff input, and fund input all play a partially mediating role. Moreover, capital-related variables play a more significant mediating role than technology-related variables. The government’s support for enterprises is more in the form of policy support or indirect policies to help enterprises obtain funds, but not directly to help enterprises improve their technology. Enterprises are more likely to improve their technological capabilities through independent research and development, and the introduction of external technology, so the mediating role of capital variables is significantly more significant than that of technology variables.

6. Suggestion and Outlook

6.1. Suggestion for Enterprises

This paper can help AI companies understand what resources are better for driving value in their development process, capital, and technology. Based on the results of the data analysis, the following recommendations can be made for AI companies:

(1)

Government support has a significantly higher effect on firm value enhancement than the degree of enterprise attention, i.e., external support from firms has a more significant impact on AI firm value than internal support from firms. Therefore, AI companies should not only be self-reliant in the process of development but should also actively obtain external government support, thoroughly understand government documents on AI development, actively participate in government pilot demonstration projects on AI technology, and establish an active partnership with the government to create a favorable external environment for the development of AI;

(2)

The effect of the capital variable is greater than the effect produced by the technology variable in both the paths of influence of the degree of enterprise attention and government support on firm value. At this stage of AI development, capital is still the most critical resource for the development of AI companies. Moreover, AI companies need to focus on capital in the early stages of the development process. Companies should develop competitive products and applications and seek investment from capital groups or raise funds through equity offerings to promote AI after obtaining sufficient capital support to focus on developing artificial intelligence technology to achieve sustainable business development;

(3)

Among the mechanisms influencing the value of a business, physical capital generates the most significant role, followed by structural capital, financial capital, and the role of technological inputs. Therefore, the first task for enterprises to develop AI is to invest in physical capital, i.e., infrastructure such as R&D equipment and plants required for AI development, as well as to increase R&D staff input for AI technologies, improve the enterprise human resource system and bring in more professionals in the field of AI. After introducing talents, enterprises should increase the R&D fund input. Moreover, improve the welfare treatment of R&D personnel, stimulate the enthusiasm of R&D personnel and retain professional and technical talents. In terms of investment in structural capital, enterprises should increase their investment in information systems, improve their information operation capabilities, improve their business processes, and build a more rational organizational system. In terms of financial capital, companies should also improve their ability to operate capital to ensure smooth capital flow and prevent AI development from being affected by a break in the capital chain;

(4)

This paper proved that both capital and technology have an impact on enterprise value. From a system point of view, these financial and technical elements come from inside the enterprise, on the one hand, and from outside the enterprise, on the other hand. For example, enterprises in the cluster industry can promote enterprise performance through knowledge spillovers, technology sharing, and financial mutual assistance [49]. The elements from inside the enterprise can be better utilized and absorbed by the enterprise so as to promote the improvement of enterprise performance, and factors from external clusters will also have an impact on enterprise performance. Therefore, enterprises not only need to plan and adjust their existing internal factor resources, but also need to make good use of the industrial cluster effect or enterprise cluster effect to obtain capital or technology-related factors from the outside to improve enterprise value.

6.2. Research Outlook

The research in this paper has some limitations, and there are directions for further research. Firstly, in this paper, capital and technology variables are studied as mediating variables in studying the impact of internal and external support on firm value. The analysis compares the magnitude of the role played by both in a short period, but the role played by capital and technology may differ in different stages of AI development. Future research can track sample firms and obtain data over a more extended period to examine the changes in the role of capital and technology variables during their development, and provide suggestions for the subsequent development of firms. Secondly, the sample of this paper is the AI concept stock companies. The sample scope is still tiny, and future research can also consider expanding the sample scope to make the research results more generalized. Thirdly, in future research, we can consider and discuss “cluster” and external validity in depth.