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Abstract

Enterprise digital transformation is critical for enterprises to remain competitive in the fast-paced business environment. To explore the impact mechanisms of enterprise digital transformation on industrial structural adjustment, this paper analyzed data from 3,608 publicly listed enterprises in China from 2007 to 2020. A baseline panel model, a widely used statistical approach that is simple yet maintains generality, was adopted to analyze the data. The indicators for industrial structural adjustment, including production efficiency, production investment, and the efficiency-investment interaction, were considered dependent variables in the model. Nine control variables related to the degree of digital transformation, industry and enterprise attributes, policy, and macroeconomic conditions were then analyzed. The results show that the effective promotion of industrial structural adjustments occurs through the implementation of digital transformation. This positive impact primarily results from its ability to enhance enterprise production efficiency, rather than attracting increased production investment. High-tech enterprises are the initial adopters of digital transformation, and they, in turn, encourage its adoption among other enterprises, including traditional manufacturing enterprises. Additionally, we have observed that well-designed digital transformation-related policies can significantly expedite the digital transformation process for enterprises and contribute to industrial structural adjustments. Lastly, specific digital technologies such as artificial intelligence, cloud computing, and big data play a crucial role in supporting industrial structural adjustments. These findings contribute to a deeper theoretical understanding of digital transformation’s role in industrial structural adjustments and carry significant policy implications.

Keywords

digital transformation industrial structural adjustment digital empowerment innovation efficiency

Introduction

With the emergence and application of digital technologies such as the Internet of Things, cloud computing and big data, the world is stepping into a new era of digital economy. For example, according to the white paper “China Academy of Information and Communications Technology (2023),” the growth rate of China’s digital economy has been significantly higher than the nominal GDP growth rate in the same period for 11 consecutive years, and the proportion of digital economy in GDP is equivalent to the proportion of secondary industry in the national economy, reaching 41.5%. To adapt to the development of the digital economy, more and more enterprises are experiencing digital transformation all over the world. Enterprises that embrace digital tools and methodologies are finding themselves better positioned to respond to market demands, optimize supply chains, and offer value-added services to their customers (AlNuaimi et al., 2022; Feliciano-Cestero et al., 2023; George & Schillebeeckx, 2022; Li, 2022; Li, Pan, & Yuan, 2022; Liu, Zhang, & Zhu, 2022; Sara & Koichi, 2021; Zhao & Liu, 2024). Especially, the traditional manufacturing enterprises of digital transformation are becoming more efficient, smart and environmentally friendly by introducing new technologies and business models (Peng et al., 2022; Shang et al., 2023; Shanyong et al., 2023). Nevertheless, several pioneering enterprises that have adopted digital transformation strategies report encountering numerous challenges. A common issue is the high initial investment and extended payback periods associated with digital transformation. This often results in many enterprises abandoning their implementation efforts (Ancillai et al., 2023). Therefore, understanding and adapting to digital transformation is becoming increasingly important for both enterprises and economies to stay relevant and competitive.

Existing studies on enterprise digital transformation primarily fall into two categories. Some literature explores the driving mechanisms and evolutionary characteristics of digital transformation in enterprises through practical cases (AlNuaimi et al., 2022; N. Chen & Yang, 2022; Chen, Song, & Huang, 2022; Jia, Guo, & Liu, 2023; Li, 2022; Yu et al., 2023). They identified internal factors such as dependent upgrading strategies, decision-makers’ cognition and external factors such as sustainable development and institutional environments (Chen, Xiao, & Jiang, 2023; C. Liu et al., 2022; Peng et al., 2022; S. Zhao et al., 2023; Zhou & Li, 2023). These factors will induce enterprises to implement digital transformation strategies. Other studies investigate the economic consequences of enterprise digital transformation. They found that digitally transformed enterprises perform better in capital markets (F. Wu et al., 2021; Zhao & Liu, 2024), exhibit higher enterprise performance and productivity (Peng et al., 2022), achieve higher innovation levels and total factor productivity (Li, Rao, & Wan, 2022; Li, Wen, Zeng, et al., 2022; Lu & Hu, 2024; Wang, 2023; Yang et al., 2022; Zhuo & Chen, 2023), and have greater cash-holding motivations, supporting the high-quality development of regional economies (Feng et al., 2022; Gaglio et al., 2022; Peng et al., 2022; Shang et al., 2023; Yang et al., 2022). Despite the growing richness of research in enterprise digital transformation and the increasing clarity of impact mechanisms, there are still several areas worth expanding upon:

First, there is a lack of literature examining the impact of enterprise digital transformation on industrial structural adjustments. Most existing studies focus on modern economic characteristics such as the digital economy and artificial intelligence (Lu & Hu, 2024; R. Zhao, Peng, et al., 2022). Digital transformation, while being an integral part of the digital economy, has not attracted sufficient attention. Tian and Li (2022a) investigated how digital technology can promote the adjustment of industrial structure and its impact on economic development. This study reveals that the application of digital technology can promote the development of enterprises and optimize industrial structure through resource redistribution. These findings provide a baseline and motivate the research studied in this article. Despite its achievements, the article studied only a limited number of influence factors, limiting the depth of insights into the impact of enterprise digital transformation on industrial structural adjustments. Furthermore, it lacks empirical analysis to substantiate its findings.

Secondly, the mechanisms by which digital transformation impacts the real economy require further clarification. A review of the literature indicates a lack of a clear, logical mechanism explaining the impacts of digital transformation on enterprises (Ancillai et al., 2023). For example, the question of whether digital transformation enhances enterprises’ innovation investment and/or boosts production efficiency remains unclear. Some studies suggest that digital transformation can guide capital inflow and enhance employee quality (S. Liu et al., 2023; Tian & Li, 2022b; F. Wu et al., 2021; Zhao, 2023), indicating that digital transformation can promote the development of the real economy by increasing innovation investment. Other studies find that digital transformation enhances enterprises through the improvement of production efficiency (Gaglio et al., 2022; Li, Wen, Zeng, et al., 2022; Yang et al., 2022; T. Zhang et al., 2022; Zhuo & Chen, 2023). Therefore, there is a need to study the impact mechanism of digital transformation on the real economy by exploring whether it affects innovation investment and/or production efficiency.

Hence, to identify the mechanisms of digital transformation’s impacts on industrial structural adjustments and provide more precise policy recommendations for enterprise digital transformation practices and decision-making, this paper collected digital transformation data of all Chinese listed enterprises from 2007 to 2020. Based on the theory of digital empowerment, we empirically investigated the impacts of digital transformation on industrial structural adjustments and constructed three variables such as production efficiency, production investment, and efficiency-investment interaction to examine the fundamental impact mechanisms.

Compared to existing studies in the literature, this article makes contributions in two aspects:

This article, for the first time, studied the impacts of digital transformation on industrial structural adjustments using empirical data from publicly listed enterprises in China from 2007 to 2020.

This article offers unique insights into how digital transformation influences the real economy, which enriches the theoretical understanding of digital transformation’s role in industrial structural adjustments and has significant implications for different stakeholders.

The rest of this article is organized as follows: Section “Literature Review and Research Hypotheses” reviews the relevant studies in the literature and makes the research hypotheses for this study. Section “Data and Methods” outlines the research design, including the studied data, the adopted model, and key variables. Section “Results and Discussion” presents estimation results and corresponding analysis. Section “Findings” summarizes the findings of the research and provides implications to different stakeholders. Finally, section “Conclusions” concludes the article and identifies future research directions.

Literature Review and Research Hypotheses

Industrial structural adjustment refers to the phenomenon where production factors are reallocated among different sectors, leading to the expansion or contraction of industries (Li & Shen, 2015). Factors such as financial development and capital market characteristics (Lu et al., 2017) can influence industrial structural adjustment. To understand the impacts of enterprise digital transformation on industrial structure adjustment, this section, based on the theory of digital empowerment and related research in the literature, conducts a theoretical analysis and proposes corresponding research hypotheses.

Literature Review

Digital transformation: Current research on digital transformation focuses on its causes and economic consequences. Some scholars have explored the driving factors of enterprise digital transformation. Hu et al. (2023) found that the overseas study and work experience of executives can significantly promote the digital transformation of enterprises. Zhu et al. (2024) found that CEO’s digital technology background can promote enterprise digital transformation in China. And managerial capabilities also have a positive impact on the digital transformation (Fu et al., 2023). Meanwhile, some researchers have also found that macroeconomic policies, judicial informatization, urban low-carbon transitions have promoted digital transformation (Chen, Ho, & Huang, 2023; Chen, Wan, Liu, et al., 2024; Zheng et al., 2024). However, organizational commitment, value creation, value proposition, value delivery, value capture, information and technology infrastructure, and data security pose challenges to digital transformation in a firm (Favoretto et al., 2022).

Other researchers have studied the economic consequences of digital transformation. More and more scholars have found that digital transformation improves the financial performance of small and medium-sized enterprises (Eller et al., 2020; Wang et al., 2022; Zhao et al., 2023; Cheng et al., 2024). Also, Cheng et al. (2024) found that digital transformation promotes internal control of enterprises. Chen and Xu (2023) found that digital transformation can significantly inhibit cost stickiness by reducing adjustment costs and optimistic management expectations.

Industrial structure: We focus on technological innovations and capital’s effects on industrial structure. Tu et al. (2021) found that there are self-promoting mechanisms for science and technology innovation and industrial structure upgrading among regions. Wang and Zheng (2020) further study the scientific and technological innovation that promotes the formation and upgrading of industrial chains. Zhang et al. (2021) focus on the impact of capital on financial development, which found that FDI has a significant promotional effect on industrial structure rationalization only when the level of financial development is greater than the threshold value.

Relationship between digital transformation and industrial structure: Some scholars have studied the impact of the digital economy on industrial structure and the influencing mechanism between them, and Table 1 demonstrates the current literature using Chinese enterprises. Firstly, digital transformation can promote enterprise innovation. Niu et al. (2023) found that digital transformation has significantly promoted corporate innovation by alleviating financial constraints and improving corporate governance. Zhao, Zhang, An, et al. (2023) found that digital transformation is conducive to improving the quality of enterprise innovation, and the influence mechanism is to improve the quality of corporate innovation by improving the quality of internal control and analytical attention. Some scholars have also found that digital technology innovation can improve the total factor productivity (TFP) of enterprises (Wang, 2023; Wu et al., 2023) and Wu et al. (2023) considered that there are three channels, information transparency, innovation ability, and financial stability.

Table 1.

Summary of Relevant Studies in Literature.

Topics	Literature
Digital transformation and innovations	Niu et al. (2023), Wang (2023), Wu et al. (2023), Zhao, Yang, Gao, et al. (2023)
Digital transformation and labor	Mao and Yang (2023)
Digital transformation and capital	He et al. (2024), Liu and Wang (2023), Zhai et al. (2023)
Digital transformation and industrial structure	Sun et al. (2022), Zhou and Ge (2023)

Secondly, digital transformation has a job creation effect and expands the scale of labor employment. Mao and Yang (2023) found that digital transformation can enable enterprises to hire more highly skilled employees and the level of internal governance, expand the employment scale, widen the internal pay gap, and finally optimize the employment structure.

Thirdly, digital transformation promotes corporate financing, cash holding, and investment efficiency. He et al. (2023) found that digital transformation improved corporate financing by reducing information asymmetry and transaction costs between financing parties. Liu and Wang (2023) found that digital transformation increases trade credit supply. The mechanism of this relationship is an increase in short-term bank credit. Sun et al. (2022) revealed that digital transformation can significantly reduce corporate cash holdings by mitigating the precautionary, agency, and transactional motivations of cash holdings. Zhai et al. (2023) found that firms undergoing digital transformation or having a higher degree of digital transformation show a lower probability and degree of overinvestment, and Zhou and Ge (2023) found the same phenomenon.

Finally, Lu and Hu (2024) found that the improvement of the digital economy in the region can directly promote the upgrading of local industrial structure and make the spatial spillover effect negative and significant.

Thus, some scholars have paid attention to the impact of the digital economy on industrial structure, and others have studied the impact of digital transformation on enterprise innovation, capital holding, and labor employment. However, it does not provide more evidence from the macro level for digital transformation and industrial structure. We examine the mechanism that influences digital transformation and industrial structure on enterprise innovation, capital holding, and labor employment based on the level of Chinese industry data.

Research Hypotheses

The theory of digital empowerment posits that digital technology can empower economic entities through production factors such as labor, capital, and innovation, subsequently affecting their impacts on economic entities (Chi et al., 2020; Tian & Li, 2022a). The impacts of digital technology on industrial structure adjustment may occur at two levels:

Firstly, at the enterprise level, digital transformation can adjust the allocation of production factors (e.g., labor, capital, and technology) towards enterprises with a higher level of digitalization. For example, enterprises with higher digitalization levels can enhance information aggregation (Yi et al., 2021), optimize human capital structures (Ye et al., 2021; Zhao, 2023), increase capital investment (F. Wu et al., 2021; K. Wu et al., 2022), and improve innovation efficiency (Du et al., 2023; Feng et al., 2022; Gaglio et al., 2022; Zhao, Wang, & Li, 2021). Consequently, these enterprises can support the growth and strengthening of their respective industries, leading to the optimization and upgrading of industrial structures.

Secondly, within industries, digital technology can promote the interconnection of industry chains and supply chains, dynamically coordinating the production factors (labor, capital, and innovation) among enterprises, transforming the industry into an interconnected network of production, sales, and financial resources (Guo et al., 2023; Q. Li et al., 2021). This facilitates industrial expansion and structural adjustment. For instance, digital transformation promotes industrial expansion by enhancing the industrial division of labor (Yuan et al., 2021) and supply chain integration (Q. Li et al., 2021; Zhang, Gao, & Han, 2023).

Based on the above two perspectives, this section elaborates on the roles of innovation, labor, and capital factors:

Innovation: Digital transformation involves innovation in business models, leading to the creation of more advanced and intelligent production modes, such as intelligent manufacturing and personalized customization (Liu, Zhang, Lei, & Chen, 2022). This innovation enhances transaction convenience and supply efficiency within the industry, driving industrial expansion. The digital technology infrastructure also fosters a conducive environment for technological innovation, increasing the willingness of economic entities to innovate (Zhao et al., 2021). Moreover, a higher level of digitalization improves the efficiency of technological innovation by integrating innovative resources, mobilizing more funds, talents, and materials, breaking down industry barriers regarding space, organization, and technology, and optimizing the allocation of resources for high-tech industries. Therefore, digital transformation can empower industrial structural adjustment through innovation.

Labor: Digital transformation entails embedding digital technology into various facets of enterprise production and operation across the entire industrial chain. This leads to improved synergy among different departments within organizations and enhances the efficiency of resource allocation (Tian & Li, 2022a). As a result, digital transformation can enhance both the quality and productivity of labor forces. On the one hand, digital technology application enhances the level of informatization within the industry, reducing job search costs for employees and improving the matching efficiency between labor and job positions within the industry (Zhao et al., 2021). This enhances the intra-industry production synergy, promoting the expansion of the industry. On the other hand, digital transformation leads to industry-level intelligence. High-tech production modes require a higher-quality labor force and specialized knowledge. As the demand for high-end labor replaces the demand for low-end labor, the industry’s human capital structure is optimized (Shu et al., 2023; Ye et al., 2021; Zhao, 2023). Therefore, industries with higher levels of digitalization are more likely to attract labor force productivity rather than quantity, driving industrial expansion.

Capital: Enterprises with higher levels of digitalization are more likely to attract market favor and external investors (F. Wu et al., 2021). Consequently, digitally transformed enterprises can obtain more funding. Industries with higher levels of digitalization are also likely to experience an improvement in capital quality and financing efficiency (R. Lu et al., 2017). Additionally, digital technology can enhance the network level within the industry, thereby creating network trust relationships through supply chain finance (Zhang et al., 2023), providing more abundant financial services. Therefore, digital transformation empowers industrial structural optimization by increasing industrial capital.

With the above analysis from three perspectives, we draw Hypotheses 1 as follows:

Hypothesis 1. Digital transformation can promote industrial structural adjustment.

Production Efficiency and Production Investment

The second core question addressed by this study is how digital transformation drives industrial structural adjustment. Historically, many countries around the world have adhered to a growth model that primarily emphasizes increasing investment, characterized as extensive growth. This model relies heavily on expanding fixed asset investment and increasing the quantity of labor. In the long term, this form of economic growth is not sustainable. Can digital transformation reverse this economic growth model? Can it transform the increase in investment into improvements in production efficiency? To investigate these hypotheses, this study decomposes the industrial structural adjustment index into three components: “Production Efficiency” (SC1), “Production Investment” (SC2), and “Efficiency-Investment Interaction” (SC3). Specifically, “Production efficiency” represents the industrial structural adjustment caused by changes in production efficiency, “Production Investment” represents the industrial structural adjustment caused by the investment changes of production factor, and “Efficiency-Investment Interaction” represents the industrial structural adjustment resulting from the interaction between production efficiency and investment of production factor.

Regarding the “Production efficiency” and “Production Investment,” a review of the literature suggests that digital transformation is more likely to promote production efficiency rather than production investment, with heterogeneity existing among different factors. In the realm of innovation, digital transformation is associated with improved enterprise productivity (Cheng, Zhou, & Li, 2023; Zhang & Zhang, 2023), production efficiency (Feng et al., 2022; Gaglio et al., 2022; Li, Rao, & Wan, 2022), and total factor productivity (Cheng et al., 2023; Y. Wu et al., 2023; Zhuo & Chen, 2023). More specifically, digital transformation enhances the industry’s information level, reducing employee search costs and enhancing labor-to-position matching efficiency within the industry (Wu & Yang, 2022; Zhao, 2023). In terms of labor, digital transformation promotes enterprise division of labor, attracting higher-skilled labor and specialized knowledge, ultimately improving labor productivity (Gaglio et al., 2022; Wu & Yang, 2022; Zhao, 2023). For capital, digital transformation can attract capital investment, but its impact on capital production efficiency remains uncertain. Therefore, industries with higher levels of digital transformation can attract more capital input but may not necessarily improve capital production efficiency.

For the Efficiency-Investment Interaction, if digital transformation leads to both increased production efficiency and innovation investment, subsequently driving industry expansion, then the Efficiency-Investment Interaction is established. Digital transformation can enhance production efficiency in an industry but may not necessarily affect the innovation investment. Thus, the impacts of digital transformation on innovation investment are contingent upon management decisions and not necessarily, on whether digital transformation takes place. Consequently, industries with higher levels of digitalization are more likely to enhance labor force productivity rather than labor force quantity, thus promoting industrial expansion. Regarding capital factors, digital transformation can attract more capital input, but its impact on capital production efficiency remains uncertain. Therefore, the relationship between digital transformation and capital input is not straightforward.

With the above analysis, we draw Hypotheses 2 as follows:

Hypothesis 2: Digital transformation can facilitate industrial structural adjustment through the “Production efficiency” for innovation and labor, the “Production Investment” for capital.

Data and Methods

Sample Data and Sources

To verify the two hypotheses proposed in the previous section and investigate the impacts of enterprise digital transformation on industrial structural adjustment, this study collected data from all Chinese listed enterprises from 2007 to 2020 in China. A two-tailed 1% trimming was applied to continuous variables to mitigate the influence of extreme values.

The resulting dataset consisted of 3,608 listed enterprises with 34,752 annual enterprise observations fitted into 74 industries, resulting in 1,036 industry-year data points. Industry data were classified according to the “Guidelines for the Classification of Industries for Listed Enterprises” published by the China Securities Regulatory Commission in 2012. Financial data and transformation data of listed enterprises were sourced from the GuotaiAn database (CSMAR), while macroeconomic data were obtained from the China Economic Network database, the official website of the People’s Bank of China, and the EPU Index website.

Regression Model

This study employed a baseline panel model, as shown in Equation 1, to examine the impacts of enterprise digital transformation on industrial structural adjustment:

${SC}_{j, t} = α_{0} + β_{1} Digita l_{j, t - 1} + β_{i} \sum control s_{j, t} + η_{j} + κ_{t} + ε_{j, t},$ (1)

where $j$ and $t$ represent industries and years, respectively. $S C_{j, t}$ denotes the dependent variable, representing indicators for industrial structural adjustment ( $SC$ ), production efficiency ( $SC 1$ ), production investment ( $SC 2$ ), and efficiency-investment interaction ( $SC 3$ ). $Digita l_{j, t}$ represents the lagged one-year digital transformation indicator for the industry, which is represented by two metrics: the number of enterprises in the industry undergoing digital transformation (Digital Num) and the degree of digital transformation (Digital Level). Control variables ( $controls$ ) include industry return on assets ( $Roa$ ), industry earnings per share ( $Eps$ ), industry debt-to-asset ratio ( $Lev$ ), industry cash flow ( $Cash$ ), industry labor productivity ( $Labor$ ), state-owned enterprise ratio ( $SOE$ ), economic policy uncertainty ( $EPU$ ), macroeconomic conditions ( $GDP$ ), and monetary conditions ( $M 2$ ). $η$ and $κ$ represent industry fixed effects and year fixed effects controlled for in the regression, and $ε$ represents the residual. Moreover, a nomenclature table is provided in Appendix A to facilitate the understanding of all variables used in the article.

Variable Construction

Industrial Structural Adjustment (SC)

To measure industrial structural adjustment, this study employed the method outlined by (R. Lu et al., 2017). The indicator for industrial structural adjustment is constructed as the rate of change in the proportion of each industry within the total, as shown in Equation 2:

${SC}_{j, t} = Y_{t} - Y_{t - 1} = \frac{sale s_{j, t}}{\sum_{i = 1}^{74} sale s_{j, t}} - \frac{sale s_{j, t - 1}}{\sum_{i = 1}^{74} sale s_{j, t - 1}}$ (2)

where $Y$ represents the total operating revenue of industry $j$ in year $t$ . This value is obtained by summing the operating revenue of listed enterprises in industry $j$ at the end of year $t$ . To ensure reasonable regression results due to the small values of $SC$ , this value was multiplied by 1,000 in empirical tests.

To examine the mechanisms through which digital transformation affects industrial structural adjustment, again, this study further constructed three sub-indicators for Industrial Structural Adjustment ( $SC$ ). They are industrial structural adjustment from the change of production efficiency, denoted by $SC 1$ , industrial structural adjustment from the change of production investment, denoted by $SC 2$ , and “efficiency-investment interaction” denoted by $SC 3$ . The construction process for these three sub-indicators is outlined in Equations 3 to 4:

$Y_{j, t} = L P_{j, t} \times L_{j, t}$ (3)

where Equation 3 represents the decomposition of total output $Y$ into the product of factor input quantity $L$ and the factor production efficiency $LP$ for industry $j$ in year $t$ . The industrial structural adjustment indicator ( $SC$ ) is further decomposed as shown in Equation 4:

$\begin{matrix} {SC}_{j, t} = Y_{j, t} - Y_{j, t - 1} = L P_{j, t} \times L_{j, t} - L P_{j, t - 1} \times L_{j, t - 1} \\ = \underset{SC 1}{\underset{︸}{(L P_{j, t} - L P_{j, t - 1}) \times L_{j, t - 1}}} + \underset{SC 2}{\underset{︸}{(L_{j, t} - L_{j, t - 1}) \times L P_{j, t - 1}}} \\ + \underset{SC 3}{\underset{︸}{(L P_{j, t} - L P_{j, t - 1}) \times (L_{j, t} - L_{j, t - 1})}} \\ = SC 1_{j, t} + SC 2_{j, t} + SC 3_{j, t} \end{matrix}$ (4)

This study adhered to the theory of digital empowerment and selected innovation, labor, and capital factors for the selection of factors. The factor investment quantity ( $L$ ) was constructed as follows: innovation factor input utilized the total R&D expenditure of all listed enterprises in the industry divided by the number of enterprises in that year, labor factor input used the total employee compensation of all listed enterprises in the industry divided by the number of enterprises in that year, and capital factor input used the total operating expenditure (operating costs + selling expenses + management expenses—R&D expenditure—employee compensation—depreciation) of all listed enterprises in the industry, excluding R&D and employee compensation, divided by the number of enterprises in that year. The factor production efficiency indicator ( $LP$ ) was calculated as the industry’s proportion of total revenue ( $Y$ ) to the logarithm of factor input ( $L$ ) for that year.

Digital Transformation (Digital)

Currently, the challenge in constructing industry-level digital transformation indicators is the lack of such data at the industry level. Therefore, this study employed digital transformation data from listed enterprises and fitted them into industries to obtain an industry-level digital transformation indicator.

In the first step, motivated by the method used by (F. Wu et al., 2021) to construct digital transformation characteristics, this study constructed two indicators based on specific keywords related to digital technologies (e.g., artificial intelligence technology, blockchain technology, cloud computing technology, big data technology, and digital technology applications) obtained from annual reports of listed enterprises. Two metrics were created: whether a listed company has undergone digital transformation (digital firms), with a value of 1 indicating digital transformation and 0 indicating otherwise; and the degree of digital transformation (digital levels), determined based on the frequency of occurrence of specific digital technology keywords for that listed company.

In the second step, company-level data were further fitted into industries. The industry-level digital transformation quantity indicator (Digital Num) was constructed based on the number of listed enterprises in industry $j$ undergoing digital transformation in year $t$ , revealing the number of enterprises in the industry undergoing digital transformation that year. A higher value indicates a higher level of digital transformation in the industry. The industry-level digital transformation degree indicator (Digital Level) was obtained by summing the frequencies of specific digital technology keywords appearing in all listed enterprises in industry $j$ in year $t$ . A higher value indicates a higher degree of digital transformation in the industry.

In the third step, to address the right-skewed nature of the frequency data on digital technology, a logarithmic transformation was applied to both of these indicators, as shown in Equations 5 and 6.

$Digital Nu m_{j, t} = \log (1 + \sum digital firm s_{i, t})$ (5)

$Digital Leve l_{j, t} = \log (1 + \sum digital level s_{i, t})$ (6)

Other Variables

To control endogeneity in the tests, this study constructed economic expectation and industrial policy indicators as potential omitted variables. The former used the Entrepreneur Confidence Index ( $MCI$ ) from the National Bureau of Statistics in China, which reflects entrepreneurs’ confidence in future economic expectations. The values range from 0 to 200, with values above 100 indicating improving economic conditions, and values below 100 indicating economic deterioration. The latter indicated whether the industry received policy support in the national “5-Year Industry Plan,” with a value of 1 indicating support and 0 indicating otherwise.

As for instrumental variables, this study constructed two instrumental variables: the degree of development in the software and communication industry (Soft) and the growth rate of internet access ports (Web). The former was represented by the ratio of business revenue to the number of employees in the software and communication industry, while the latter was represented by the growth rate of internet access ports. A higher value for both variables promotes the digital transformation of physical enterprises but does not directly affect industrial structural adjustment.

Additionally, nine control variables were constructed:

Ownership ratio ( $SOE$ ): The proportion of state-owned and collective ownership in an industry.

Industry return on assets ( $Roa$ ): The ratio of total industry profits to total industry assets.

Industry earnings per share ( $Eps$ ): The ratio of after-tax profits in the industry to the total number of industry shares.

Industry debt-to-asset ratio ( $Lev$ ): The ratio of total industry debt to total industry assets, reflecting the leverage level of an industry.

Industry cash flow ( $Cash$ ): The ratio of total year-end cash flow to total industry assets.

Industry labor productivity ( $Labor$ ): The logarithm of the ratio of total industry operating revenue to the total number of industry employees.

Economic policy uncertainty ( $EPU$ ): Measured using the Economic Policy Uncertainty Index created by Baker et al. (2016), this index reflects uncertainty in economic policies. For monthly data, this study calculated the annual data by averaging the index for the 12 months of the year and dividing it by 100.

Macroeconomic conditions ( $GDP$ ): Represented by the annual growth rate of China’s GDP.

Money supply growth ( $M 2$ ): Represented by the annual growth rate of M2.

Table 2 provides descriptive statistics for the main variables. The mean of industrial structural adjustment ( $SC$ ) is 1.351, with a median of .327, indicating frequent changes in industrial structure. The mean values for digital transformation quantity and digital transformation degree are .821 and 1.949, respectively, with medians of .590 and 1.792, demonstrating a widespread phenomenon of digital transformation in China’s real economy, which is gradually becoming a driving force for high-quality economic development. Additionally, the descriptive statistics for other variables generally align with existing literature.

Table 2.

Descriptive Statistics for the Main Variables.

Variables	Obs.	Mean	SD	Min	Max
SC	1,036	1.351	9.935	−20.19	27.14
Digital num	1,036	.821	.845	0	4.420
Digital level	1,036	1.949	1.361	0	5.846
Eco	1,036	1.015	.0170	.984	1.049
Policy	1,036	.220	.414	0	1
Soft	1,036	8.302	4.364	0	11.31
Web	1,036	10.49	.798	9.053	11.46
SOE	1,036	.095	.135	0	.795
Roa	1,036	.020	.268	−5.169	4.376
Eps	1,036	.313	.339	−3.534	2.641
Lev	1,036	.442	.138	0	1.032
Cash	1,036	.025	.0650	−.419	.588
Labor	1,036	16.68	63.12	0	1,250
EPU	1,036	2.853	2.281	0	7.919
GDP	1,036	7.772	2.859	0	14.20
M2	1,036	.138	.0550	0	.276

Results and Discussion

Estimention Results

Table 3 presents the regression results of the impacts of digital transformation on industrial structural adjustment, examining it from four perspectives: industrial structural adjustment ( $SC$ ), industrial structure adjustment from the change of production efficiency ( $SC 1$ ), adjustment from the change of production investment ( $SC 2$ ), and adjustment from efficiency-investment interaction ( $SC 3$ ). The results in the first two rows show that the regression coefficients of the number of digital transformation enterprises (Digital Num) and the degree of digitization (Digital Level) of industry on industrial structural adjustment are 2.928 and 2.178, respectively, and both are significant at the 1% level. This indicates that digital transformation within an industry significantly enhances its expansion. Holding other conditions constant, a 1-unit increase in the degree of digital transformation in an industry leads to a 2.178-unit increase in industry expansion, confirming Hypothesis 1 as proposed earlier.

Table 3.

Estimation Results of the Proposed Model.

Variables	SC		SC1_R&D		SC2_R&D		SC3_R&D		SC1_labor	SC2_labor	SC3_labor	SC1_cap	SC2_cap	SC3_cap
Digital num	2.928*** (5.06)		2.748*** (2.92)		−1.444 (−1.51)		3.341*** (3.65)
Digital level		2.178*** (3.25)		2.283** (2.11)		−1.416 (−1.29)		2.185** (2.06)	−.110 (−.15)	.860 (1.03)	−.336 (−1.24)	−.310 (−.36)	1.061 (1.41)	−.151 (−.39)
SOE	13.618*** (4.54)	12.483*** (4.15)	19.667*** (4.03)	18.645*** (3.83)	−2.855 (−.58)	−2.356 (−.48)	11.963** (2.52)	10..612** (2.23)	.075 (.06)	−.772 (−.55)	.140 (.31)	−.924 (−.64)	.227 (.18)	.001 (.00)
Roa	−.832 (−.68)	−.697 (−.56)	−.646 (−.32)	−.510 (−.26)	.394 (.19)	.314 (.16)	−.648 (−.33)	−.505 (−.26)	.445 (.39)	1.888 (1.40)	−.249 (−.57)	3.296** (2.36)	−.963 (−.79)	.225 (.36)
Eps	2.007* (1.80)	1.767 (1.55)	1.562 (.86)	1.266 (.69)	−.110 (−.06)	.109 (.06)	.666 (.38)	.481 (.27)	2.230 (.55)	−3.896 (−.82)	2.387 (1.54)	.868 (.18)	−2.533 (−.59)	−.622 (−.28)
Lev	2.089 (.52)	1.497 (.37)	−7.298 (−1.11)	−8.061 (−1.22)	4.428 (.67)	5.016 (.75)	−7.073 (−1.11)	−7.489 (−1.16)	−2.531 (−.51)	1.103 (.19)	−1.645 (−.88)	−1.983 (−.33)	.555 (.11)	−1.548 (−.57)
Cash	5.813 (1.18)	4.739 (.96)	5.949 (.75)	4.796 (.60)	−4.059 (−.50)	−3.322 (−.41)	4.151 (.53)	3.109 (.40)	.000 (.03)	−.000 (−.08)	.000 (.20)	.003 (.53)	−.003 (−.66)	.002 (.91)
Labor	−.002 (−.36)	−.001 (−.13)	−.003 (−.42)	−.002 (−.27)	.003 (.33)	.002 (.24)	−.002 (−.19)	−.000 (−.04)	6.561** (2.25)	1.334 (.39)	−.975 (−.88)	5.734 (1.62)	2.161 (.70)	4.507*** (2.82)
EPU	−.349* (−1.84)	−.340* (−1.72)	−.227 (−.74)	−.243 (−.76)	.118 (.38)	.149 (.46)	−.277 (−.92)	−.236 (−.75)	.037 (.18)	−.201 (−.84)	.035 (.45)	−.035 (−.14)	−.129 (−.60)	−.018 (−.16)
GDP	−.047 (−.25)	−.203 (−1.10)	−.279 (−.92)	−.413 (−1.39)	−.285 (−.92)	−.227 (−.75)	.133 (.45)	−.060 (−.21)	−.331* (−1.75)	.192 (.87)	.052 (.73)	−.030 (−.13)	−.109 (−.54)	−.099 (−.96)
M2	15.093* (1.78)	4.014 (.50)	31.430** (2.28)	21.923* (1.69)	−4.130 (−.30)	.063 (.00)	34.261** (2.55)	2.493 (1.62)	7.101 (.88)	−4.923 (−.52)	−8.336*** (−2.72)	−.596 (−.06)	2.774 (.32)	−1.329 (−.30)
_cons.	4.142 (1.53)	11.224*** (5.31)	−6.561 (−1.49)	−.151 (−.04)	4.372 (.98)	1.215 (.35)	−9.683** (−2.26)	−1.303 (−.39)	−1.545 (−.67)	1.477 (.54)	−.751 (−.85)	−3.299 (−1.17)	3.231 (1.31)	−.117 (−.09)
FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	1,036	1,036	1,036	1,036	1,036	1,036	1,036	1,036	1,036	1,036	1,036	1,036	1,036	1,036
R ²	.048	.033	.029	.025	.004	.004	.023	.014	.084	.085	.078	.082	.088	.082

Note. *, ** and *** respectively denote significant at the 1%, 5% and 10% levels.

Columns (3) to (14) in Table 3 respectively provide regression results for the impacts of digital transformation on industry expansion triggered by innovation, labor, and capital factors, with the latter two using the degree of digital transformation (digital level) as the main explanatory variable. The results show that digital transformation has significant positive impacts on the change of production efficiency ( $SC 1$ ) and the efficiency-investment interaction ( $SC 3$ ) for innovation, with all regression coefficients being positive and significant at the 1% level. This suggests that digital transformation can promote industry expansion by improving production efficiency within the industry. Furthermore, digital transformation can increase investment while improving production efficiency, leading to additional positive effects on industrial expansion. However, digital transformation does not significantly affect the change of innovation investment ( $SC 2$ ), labor, or capital, as the corresponding regression coefficients are not significant. Therefore, enterprise digital transformation can support industrial structural adjustment through production efficiency and efficiency-investment interaction effects, but it does not significantly affect labor and capital factors’ role in supporting digital transformation’s impacts on industrial structural adjustment, partially confirming Hypothesis 2.

In regard to the differences between the regression results and theoretical hypotheses, this paper conducts further analysis as follows:

Why can digital transformation promote industrial structural adjustment through innovation rather than capital and labor?

The digital transformation itself is a form of business model innovation. Therefore, industries with higher levels of digitization naturally have an advantage in research and development (R&D) innovation. Digital transformation can enhance decision-makers’ willingness to innovate, improve the innovation environment, and increase industry production efficiency, thereby promoting industry expansion. However, from the current practice of digital transformation, the degree of digital transformation in Chinese enterprises is relatively low, with most of them limited to using information management systems to facilitate internal communication. Few enterprises are engaged in digital transformation at the levels of production processes, marketing, supply chains, and industry chains. Therefore, the ability to drive capital and labor within the industry is weak, and the overall digital transformation in the industry has not led to the expected replacement of low-end labor by high-end labor. Additionally, the phenomenon of supply chain financing within the industry has not been widely implemented. Therefore, digital transformation can promote industrial structural adjustment through innovation factors rather than capital and labor factors.

Why can digital transformation promote industrial structural adjustment through production efficiency rather than production investment?

According to the theoretical analysis, industries with high levels of digitization have advantages in R&D innovation in two aspects: first, digital transformation can enhance decision-makers’ and managers’ willingness to innovate, making them more willing to promote R&D innovation within enterprises or industries, allocating more resources and efforts to innovation, thereby improving production efficiency. Second, digital transformation can improve the innovation environment. The higher the level of digitalization in the industry, the lower the innovation cost and the more innovation outcomes, ultimately enhancing the overall production efficiency of the industry and promoting industrial expansion. However, whether digital transformation can increase innovation investment may depend on management decisions rather than digital transformation itself. Therefore, digital transformation can promote industrial structural adjustment through production efficiency rather than investment.

Endogeneity Issue Handling

To accurately reveal the relationship between digital transformation and industrial structural adjustment, various industry and macroeconomic variables were controlled in the tests. However, it cannot be determined whether potential channels affect the regression results. To address the potential problem of omitted variables, this section further analyzes a few macroeconomic indicators.

(a) Economic expectations: Economic expectations are an important factor affecting industrial expansion. Higher market expectations for an industry lead to more policy support, funds, and labor force, strengthening its expansion capacity. This section used the Entrepreneur Confidence Index (MCI) from the National Bureau of Statistics to represent economic expectations. Columns 1 and 2 in Table 4 provide the regression results. After adding this variable, the coefficients of the two digital transformation indicators on industrial structural adjustment remain significantly positive at the 1% level.

(b) Industrial policy: Industrial policy has been an important means by which the Chinese government supports industrial upgrading. Based on previous studies, this section conducted text analysis of the national 5-year plans and identified industries mentioned in the plans with phrases such as “strong development,”“active development,” and “support.” These industries were assigned a value of 1 for the respective planning year, while others were assigned 0. Columns 3 and 4 in Table 4 provide the corresponding regression results. It is found that after including this variable, the regression coefficients of the two digital transformation indicators on industrial structural adjustment are all significantly positive at the 1% level. These regression results further support Research Hypothesis 1.

Table 4.

Results of Endogenesis Test.

					2SLS test
	Add omitted variables				Stage 1	Stage 2	Stage 1	Stage 2
Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Digital num	3.417*** (5.79)		3.389*** (5.73)
Digital level		2.880*** (4.26)		2.933*** (4.33)		15.933*** (2.68)		6.835** (2.26)
iv: soft					.037*** (4.31)
iv: web							.738*** (15.11)
Roa	−.633 (−.57)	−.551 (−.49)	−.699 (−.62)	−.670 (−.59)	−.184** (−2.09)	1.265 (.34)	.027 (.37)	1.338 (.39)
Eps	1.475 (1.41)	1.292 (1.22)	1.511 (1.44)	1.342 (1.26)	.403*** (5.60)	1.262*** (7.63)	.530*** (3.17)	.816*** (4.68)
Lev	.627 (.16)	.192 (.05)	.694 (.18)	.262 (.07)	.679*** (3.43)	.054 (.32)	1.188*** (7.24)	.103 (.63)
Cash	6.255 (1.35)	4.017 (.86)	6.289 (1.36)	4.097 (.88)	−.222 (−.62)	8.128 (.51)	.005 (.02)	8.838 (.59)
Labor	−.004 (−.66)	−.002 (−.38)	−.004 (−.67)	−.002 (−.40)	.0004 (1.12)	.014 (.74)	.0004 (1.20)	.018 (1.00)
SOE	11.915*** (4.00)	1.519*** (3.51)	12.192*** (4.06)	11.058*** (3.66)	−.162 (−.78)	42.656*** (4.52)	.096 (.51)	38.31*** (4.48)
EPU	−.518** (−2.31)	−.432* (−1.88)	−.532** (−2.36)	−.472** (−2.04)	.109*** (12.31)	−.699*** (−3.76)	.597*** (9.73)	−.204 (−1.22)
GDP	−.032 (−.12)	−.105 (−.40)	−.009 (−.03)	−.058 (−.22)	−.087*** (−5.67)	1.173 (1.48)	−.015 (−1.05)	.546 (.81)
M2	19.171** (2.21)	5.940 (.73)	18.695** (2.15)	5.608 (.69)	−1.261 (−1.63)	39.632 (1.14)	1.656** (2.48)	7.498 (.27)
Eco	17.778 (.94)	7.628 (.40)	14.894 (.77)	2.809 (.15)
Policy			−.763 (−.72)	−1.422 (−1.34)
_cons.	−13.587 (−.72)	3.428 (.19)	−10.552 (−.55)	8.353 (.44)	1.119*** (7.72)	−16.541 (−1.49)	−7.205*** (−12.04)	−.385 (−.06)
FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	962	962	962	962	962	962	962	962
R ²	.054	.038	.055	.040	.2449	.0009	.3540	.0007

Note. *, ** and *** respectively denote significant at the 1%, 5% and 10% levels.

The lagged digital transformation variables were used as controls in the previous analysis to control the potential reverse causality between digital transformation and industrial structural adjustment. However, as the industry-level digital transformation variable originates from enterprise-level digital transformation, the reasons for an enterprise’s digital transformation may be to cater to its industry’s rapid expansion, thereby driving digital transformation development. Two-Stage Least Squares (2SLS) was used as a classic instrumental variable test to mitigate the reverse causality problem. This section selected two instrumental variables for the study:

$Digita l_{j, t} = α + β_{1} i v_{j, t - 1} + \sum β_{i} Control s_{i, t} + ε_{j, t}$ (7)

${SC}_{i, t} = α + β_{1} \bar{Digita l_{j, t - 1}} + \sum β_{i} Control s_{i, t} + ς_{i, t}$ (8)

(a) Software and Communication Industry Development Capability (Soft), which reflects the degree of development in the software and communication industry. The ratio of business income to the number of employees in the software and communication industry was used to represent development capability (Soft). This variable can promote the digital transformation of the real economy but does not directly impact industrial structural adjustment, making it a suitable instrumental variable. Columns 5 and 6 in Table 4 provide the 2SLS regression results. In the first stage (Equation 7), the results indicate that the development degree of the software and communication industry (Soft) has a significantly positive impact on industry digital transformation. This suggests that the software and communication industry can promote the digital transformation of the industry. In the second stage (Equation 8), the results show that the digital transformation under the influence of the development degree of the software and communication industry has a significantly positive effect on industrial structural adjustment at the 1% level, confirming the validity of the previous results.

(b) Number of Internet Access Ports (Web): Industries with higher levels of digitization can establish connections with the upstream and downstream of the industrial chain and the economic society through channels such as blockchain, cloud computing, intelligentization, and big data, but these channels are limited by the number of Internet access ports. Therefore, this section used the growth rate of Internet access port numbers (Web) as an instrumental variable. Columns 7 and 8 in Table 4 provide the regression results for this instrumental variable. In the first stage, the results indicate that the number of Internet access ports (Web) has a significantly positive impact on the digital transformation of the industry. This suggests that the number of Internet access ports has a positive effect on digital transformation. In the second stage, the results show that digital transformation under the influence of the number of Internet access ports has a significantly positive effect on industrial structural adjustment, further confirming that digital transformation can promote industrial structural adjustment.

Robustness Test

In this section, robustness tests were conducted from three aspects: dependent variables substitute, controlling the strategic hype behavior, and excluding the special years.

The measure of industrial structural adjustment in this study was constructed based on the method used by R. Lu et al. (2017). However, this indicator has no alternative measurement methods. In this section, the total assets were used as a proxy for total revenue to construct the industrial structural adjustment index. The change in the proportion of total assets in an industry reflects the level of industry change. Columns 1 and 2 in Table 5 provide the robustness test results. The coefficients of digital transformation are 2.879 and 1.780, respectively, and both are significant at the 1% level. This indicates that the higher the level of digital transformation, the greater the change in the industry’s total asset structure, further confirming the robustness of the previous results.

Table 5.

Results of Robustness Test.

Variables	Dependent variable substitute		Control the strategic hype		Exclusion of special years
Digital num	2.879*** (5.53)		3.342*** (5.44)		2.184*** (2.82)
Digital level		1.780*** (2.95)		2.940*** (3.84)		2.567*** (3.26)
ROA	−.484 (−.48)	−.432 (−.42)	−.696 (−.60)	−.557 (−.48)	−.163 (−.14)	−.049 (−.04)
EPS	.839 (.89)	.828 (.86)	1.473 (1.35)	1.188 (1.07)	1.144 (1.04)	.749 (.68)
LEV	.904 (.26)	1.267 (.36)	−.374 (−.09)	−.469 (−.11)	14.427*** (2.67)	14.545*** (2.70)
CASH	4.723 (1.14)	3.207 (.76)	6.157 (1.27)	4.331 (.89)	5.351 (1.08)	5.142 (1.04)
LAB	−.003 (−.65)	−.002 (−.43)	−.004 (−.66)	−.002 (−.36)	−.746* (−1.95)	−.741* (−1.96)
SOE	11.238*** (4.20)	9.876*** (3.66)	11.926*** (3.82)	10.829*** (3.45)	−.665 (−.16)	−1.093 (−.27)
UNP	−.350** (−2.12)	−.261 (−1.52)	−.462** (−2.36)	−.428** (−2.10)	−.090 (−.49)	−.138 (−.73)
GDP	.065 (.32)	−.090 (−.44)	.106 (.44)	−.032 (−.13)	.504 (.79)	.300 (.49)
M2	14.370* (1.88)	1.385 (.19)	18.015** (1.98)	4.817 (.56)	12.069 (.57)	11.439 (.55)
_cons.	5.077** (2.16)	11.750*** (6.12)	4.556* (1.68)	11.464*** (5.13)	7.591** (2.02)	11.645*** (3.57)
FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	1,036	1,036	884	884	814	814
R ²	.050	.027	.054	.037	.030	.034

Note. *, ** and *** respectively denote significant at the 1%, 5% and 10% levels.

Some researchers found that some enterprises may deliberately engage in strategic hype or ride the hype wave during information disclosure to gain more attention, easier financing, and more policy subsidies (Yuan et al., 2021; Zhao, Chen, & Cao, 2020). To address this issue, this study excluded certain enterprises that are prone to exaggerated information disclosure, such as high-tech and internet enterprises, from the sample. These enterprises were re-fitted into the industry, resulting in data for 884 industry-year periods. Columns 3 and 4 in Table 5 provide the respective test results. The coefficients of digital transformation are 3.342 and 2.940, and all are significant at the 1% level. This shows that the higher the level of digital transformation, the greater the change in industrial structure, further confirming the robustness of the previous results.

Although time-fixed effects were controlled in the tests, certain special time periods may still affect the regression results, such as the global economic crisis in 2008 to 2009 and the COVID-19 pandemic in 2020, which had a significant impact on industrial development. To control for these factors, data for the years 2008, 2009, and 2020 were removed from the regression data, resulting in data for 814 industry-year periods. Columns 5 and 6 in Table 5 provide the regression results. The coefficients of digital transformation are 2.184 and 2.567, and both are significant at the 1% level. This indicates that controlling time characteristics, a higher level of digital transformation leads to greater changes in industrial structure.

Further Research and Discoveries

Currently, China’s economic digital transformation faces a series of challenges that need to be addressed urgently. There is a lack of targeted enterprise digital models in the transformation practice. The digitalization level of most enterprises is still in its early stages, hindering the upgrade of traditional manufacturing enterprises. Some enterprises exaggerate and hype the impacts of digital transformation, leading to controversies in academia and the industry regarding digital transformation. The effectiveness of policy support is also subject to debate. Therefore, to explore the objective of digital transformation in advancing industrial structural adjustments, this section undertakes further research into the effects of digital transformation on various enterprises, policies, and digital technology types.

Enterprises

How can enterprises establish an effective digital model? How can digital transformation leverage its advantages in economic growth? Statistical analysis of the data samples in this paper reveals that the highest degree of digitalization in industries is in the high-tech sector. High-tech industries, characterized by high production efficiency and widespread use of digital technology, provide an effective example of a digital model for other industries. This section further explores whether digital technologies in high-tech enterprises can have a transmission effect on other enterprises. To do this, the samples are divided into four categories: high-tech industries (6 sectors), strategic emerging industries (14 sectors), traditional manufacturing industries (30 sectors), and other industries (14 sectors). The high-tech industry’s digital transformation index (HT Digital) is calculated as the weighted average of the digital transformation indices of six high-tech sectors. Equation 9 illustrates the calculation, where ω represents the weight of each sector’s total revenue. Finally, this index is applied to strategic emerging industries, traditional manufacturing industries, and other industries. Table 6 presents the regression results.

$HT Digita l_{t} = \sum_{j = 1}^{6} Digita l_{j, t} \times ω_{j, t}$ (9)

Table 6.

Regression Results of Digital Transformation on Different Enterprises.

	Strategic emerging enterprises		Traditional manufacturing enterprises		Others
Variables	(1)	(2)	(3)	(4)	(5)	(6)
HT digital num	1.451** (2.36)		.198* (1.76)		.424 (.39)
HT digital level		2.213*** (2.93)		−.670 (−.30)		1.133 (.89)
ROA	−.251 (−.41)	−.181 (−.30)	−1.548 (−.68)	−1.459 (−.64)	3.906 (.50)	4.264 (.55)
EPS	4.266** (2.44)	3.753** (2.14)	2.136 (.82)	1.968 (.77)	1.186 (.80)	1.091 (.74)
LEV	4.774 (1.64)	5.654* (1.97)	−6.671 (−.54)	−7.851 (−.64)	3.777 (.62)	3.855 (.64)
CASH	−4.492 (−1.31)	−4.863 (−1.44)	18.121 (1.41)	18.422 (1.43)	−6.276 (−.94)	−6.508 (−.97)
LAB	.000 (.06)	.000 (.19)	.162 (.19)	.377 (.51)	.311 (.63)	.234 (.53)
SOE	9.848*** (2.96)	11.091*** (3.29)	31.807*** (4.42)	30.980*** (4.28)	.088 (.02)	.613 (.17)
UNP	.064 (.42)	−.174 (−.94)	−.604 (−1.30)	−.536 (−1.04)	−.011 (−.04)	−.120 (−.41)
GDP	.078 (.43)	.105 (.60)	−.155 (−.27)	−.290 (−.53)	−.433 (−1.30)	−.344 (−1.10)
M2	−5.193 (−.63)	−9.526 (−1.43)	1.343 (.05)	−3.365 (−.16)	.715 (.05)	2.412 (.20)
_cons.	6.348* (1.84)	6.902** (2.49)	16.523** (2.53)	17.515*** (2.92)	6.014 (1.27)	5.456 (1.21)
FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	196	196	420	420	196	196
R ²	.256	.272	.112	.112	.053	.054

Note. *, ** and *** respectively denote significant at the 1%, 5% and 10% levels.

Results indicate that in the first two columns, the regression coefficients of the high-tech digital transformation index (HT Digital) on the expansion of strategic emerging industries are 1.451 and 2.213, both significant at the 5% and 1% levels. This demonstrates that digital transformation in high-tech industries can be transmitted to strategic emerging industries, enabling the latter to learn from the effective and mature digital transformation models of high-tech industries. In the third and fourth columns, only the high-tech digital transformation quantity (HT Digital Num) has a significant positive effect on the industrial structural adjustment of traditional manufacturing industries at the 10% significance level, while the coefficient for high-tech digital transformation level (HT Digital Level) is not significant. In the last two columns, the high-tech digital transformation index (HT Digital) does not influence the expansion of other industries.

These results indicate that the digital transformation model of high-tech industries can be transmitted to other industries, with the transmission order being high-tech industries, strategic emerging industries, traditional manufacturing industries, and other industries. The digital transformation model can gradually form through mutual learning among different industries. High-tech industries have widely applied digital technology, which allows strategic emerging industries to borrow digital transformation models, and traditional manufacturing enterprises can learn from the digital transformation models of strategic emerging enterprises. This result can effectively address the current dilemma of insufficient enterprise digital models.

Policies

Currently, China is in a crucial stage of rapid development in the new industrialization. To promote the integration of informatization and industrialization, the central and local governments have issued a series of digital transformation policies. Can such policies support digital transformation in promoting industrial structural adjustment? This article reviews relevant policies for promoting digital transformation by the central and state departments since 2015. It identifies two key characteristics of digital transformation support policies: first, the implementation time of digital transformation policies mainly concentrated in two phases in 2015 and 2020, with further refinement in other years; second, these policies can be categorized into two main types, innovation-driven industrial transformation policies and “Digital China” policies. The former includes policies such as “Key Common Technology for Industries” and “National Strategic Emerging Industries Plan,” emphasizing that digital transformation is part of innovation-driven efforts, using digital thinking, means, and platforms to promote the transformation and upgrading of traditional manufacturing industries, primarily targeting the manufacturing sector. The latter includes policies like “Guiding Opinions on ‘Internet Plus’,”“New Generation of Artificial Intelligence,” and “Cloud and Big Data Empowerment,” highlighting the macroeconomic transformation through digitalization, networking, and intelligence to create new economic forms. These policies are applicable to all industries.

Using these policies as time nodes, this section uses a multi-time point DID model to examine the impacts of digital transformation policies on industrial structural adjustment. Policies are categorized into two types: industrial transformation policies and Digital China policies. Treatment and control groups are constructed based on the industries affected by these policies. If an industry belongs to a policy-affected sector, Treat = 1; if not, Treat = 0. Time variables are constructed based on policy implementation times. If it exceeds the execution time, Post = 1; otherwise, Post = 0, as shown in Equation 10:

${SC}_{i, t} = α + β_{1} Trea t_{j} * Pos t_{t} + \sum β_{i} Control s_{j, t} + ε_{j, t}$ (10)

Table 7 presents the regression results. The coefficients for industrial transformation policies are 4.330, all significant at the 1% level, indicating that the implementation of industrial transformation policies positively contributes to industrial structural adjustment. Similarly, the coefficients for Digital China policies are 3.063, which is significant at the 1% level. This suggests that both industrial policies aiming to innovate and the Digital China initiative by the Chinese government can have positive impacts on digital transformation in the real economy. However, it’s worth noting that these policies have some limitations in the research process, such as the challenge of distinguishing treatment groups from control groups, especially when Digital China policies apply to all industries.

Table 7.

DID Test Result of Digital Transformation Policies on Industrial Adjustment.

Variables	Industrial transformation policies		“Digital China” policies
Treat1 * post1	1.628*** (4.37)	4.330*** (3.99)
Treat2 * post2			1.002*** (2.58)	3.063*** (2.76)
ROA		−1.105 (−.90)		−.917 (−.74)
EPS		2.358** (2.11)		2.290** (2.04)
LEV		4.319 (1.07)		3.645 (.90)
CASH		6.549 (1.33)		5.223 (1.05)
LAB		−.001 (−.25)		−.002 (−.30)
SOE		12.605*** (4.20)		12.272*** (4.07)
UNP		−.618*** (−2.76)		−.538** (−2.23)
GDP		−.334* (−1.85)		−.342* (−1.89)
M2		3.727 (.47)		2.683 (.33)
_cons.	13.078*** (38.33)	12.237*** (6.06)	13.200*** (37.79)	12.797*** (6.34)
FE	Yes	Yes	Yes	Yes
Obs.	1,036	1,036	1,036	1,036
R ²	.006	.038	.003	.030

Note. *, ** and *** respectively denote significant at the 1%, 5% and 10% levels.

Digital Technologies

Different digital technologies have varying impacts on the real economy. Specifically, artificial intelligence technology can achieve intelligentization of business processes and automation of knowledge management. Big data technology can predict market demands and improve marketing management. Cloud computing enhances data-sharing capabilities among enterprises, while blockchain technology reduces business default risks by improving the traceability of the entire industry chain. They increase the efficiency of production and improve industrial expansion, which promotes industrial structural adjustment. When promoting industrial structural adjustment, how to choose the appropriate digital technology? In this section, an analysis is conducted from the perspective of the heterogeneity of digital technologies. Four types of digital technology indices are constructed at the industry level: artificial intelligence technology (Digital 1), blockchain technology (Digital 2), cloud computing technology (Digital 3), and big data technology (Digital 4). These indices are used as core explanatory variables in regression analysis. Table 8 provides the regression results.

Table 8.

Regression Results of Different Digital Technologies on Industrial Adjustment.

Variables	AI	Blockchain	Cloud computing	Big data
Digital 1	4.133*** (2.74)
Digital 2		18.588 (1.30)
Digital 3			3.762*** (3.22)
Digital 4				2.439** (2.21)
ROA	−.716 (−.58)	−.757 (−.61)	−.680 (−.55)	−.741 (−.60)
EPS	1.906* (1.67)	2.280** (2.01)	1.909* (1.68)	2.018* (1.77)
LEV	2.604 (.61)	3.364 (.78)	2.136 (.50)	2.477 (.57)
CASH	6.242 (1.25)	6.285 (1.26)	5.836 (1.18)	5.638 (1.13)
LAB	−.002 (−.34)	−.002 (−.31)	−.002 (−.32)	−.001 (−.28)
SOE	11.486*** (3.78)	11.944*** (3.93)	12.706*** (4.20)	11.755*** (3.87)
UNP	−.289 (−1.45)	−.140 (−.73)	−.247 (−1.28)	−.241 (−1.22)
GDP	−.260 (−1.41)	−.303 (−1.64)	−.230 (−1.24)	−.247 (−1.32)
M2	−1.698 (−.22)	−3.596 (−.47)	.792 (.10)	.142 (.02)
_cons.	13.178*** (6.06)	13.257*** (6.08)	12.187*** (5.55)	12.608*** (5.74)
FE	Yes	Yes	Yes	Yes
Obs.	1,036	1,036	1,036	1,036
R ²	.030	.024	.033	.027

Note. *, ** and *** respectively denote significant at the 1%, 5% and 10% levels.

Results show that, except for blockchain technology, the regression coefficients for the other three types of digital technology are all positive and significant at the 5% level. This indicates that at the industry level, artificial intelligence technology, cloud computing technology, and big data technology can support industrial structural adjustment. However, blockchain technology does not have significant impacts on industrial structural adjustment. The reason for this result is that the first three types of digital technology can promote production efficiency, thus supporting industrial structural adjustment. However, blockchain technology mainly records various processes within enterprises, and its impacts on production efficiency is not significant, which in turn cannot promote industrial structural adjustment.

Findings

The main theoretical findings of the research are concluded as follows: (1) Enterprise digital transformation effectively promotes industrial structural adjustment. Digital transformation within an industry significantly enhances its expansion. Holding other conditions constant, a one-unit increase in the degree of digital transformation in an industry leads to a 2.178-unit increase in industry expansion; (2) The promotion of industrial structural adjustment through enterprise digital transformation is primarily attributed to the positive influences of production efficiency and the efficiency-investment interaction from innovations. However, the effects of labor and capital (i.e., production investment) on industrial structural adjustment are found to be limited; (3) The digital transformation model of high-tech industries can be transmitted to other industries, with the transmission order being high-tech industries, strategic emerging industries, traditional manufacturing industries, and other industries; (4) Well-designed digital transformation-related policies can significantly expedite the digital transformation process for enterprises and contribute to industrial structural adjustments; (5) Specific digital technologies, such as AI, cloud computing, and big data, play a crucial role in supporting industrial structural adjustments. They increase production efficiency and improve industrial expansion.

Managerial and policy implications are revealed by the theoretical findings for different stakeholders. For policymakers, the findings suggest that digital transformation significantly enhances industry expansion and promotes industrial structural adjustment through innovation rather than capital and labor factors. Therefore, support for industry-specific digital transformation initiatives, promotion of innovation-driven policies, and encouragement of technology transfer from high-tech to traditional industries are crucial. For enterprise owners, innovative technologies can significantly support structural adjustments and competitive positioning in the market. Thus, focusing on enhancing production efficiency, leveraging innovative technologies like AI, cloud computing, and big data, adopting industry-specific digital strategies, and collaborating with policymakers are essential steps. For technology providers, our findings help identify market needs and opportunities for developing digital solutions. By understanding how digital transformation affects various industries, technology companies can tailor their products and services to meet the specific requirements of different sectors. For researchers, further investigation into the mechanisms of digital transformation, evaluating the impact of various technologies across industries, and analyzing the diffusion of digital transformation models from high-tech sectors to others are important areas of focus.

Conclusions

To understand how enterprise digital transformation impacts industrial structural adjustment, we analyzed data from Chinese-listed enterprises from 2007 to 2020. The indicators for industrial structural adjustment, including production efficiency, production investment, and the efficiency-investment interaction, were considered dependent variables in the model. Nine control variables related to the degree of digital transformation, industry and enterprise attributes, policy, and macroeconomic conditions were then analyzed. In addition, we explored whether enterprise digital transformation has different structural adjustment effects across various industrial sectors, the impact of different digital transformation policies, and the effects of different digital technologies.

Future research can be further extended in several directions. First, the study analyzed data from all Chinese-listed enterprises from 2007 to 2020. While comprehensive, the findings reflect only the period studied. Periodically re-conducting similar research with the most up-to-date data would be valuable, utilizing the same methodology and data collection procedures. Additionally, this study employed a baseline panel model, a commonly used statistical approach that is simple yet maintains generality, to examine the impacts of enterprise digital transformation on industrial structural adjustment. Future studies could adopt state-of-the-art models, such as machine learning-based methods like deep learning, to analyze the data. Moreover, while this study considered nine control variables based on the literature, there may be other relevant factors that were not covered. Further research could explore additional variables to provide a more comprehensive analysis.

Footnotes

None.

ORCID iD

Guoxia Wang

Ethical Approval

The study in this paper does not include any ethical issues.

Consent to Participate

This study did not involve human participants directly. All data analyzed were publicly available;therefore,informed consent was not required.

Funding

The authors disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This study was supported by the Humanities and Social Science Fund of Ministry of Education of China Project,“Study on fin-tech embeddedness,capital market regulation and enterprise quality improvement” (grant number: 22YJC790058).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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