1 Introduction

The latest research report from the China Academy of Information and Communications Technology (2023) reveals that the digital economy in five major countries including the USA, China, Germany, Japan, and South Korea reached $31 trillion in 2022, representing 58% of GDP. The digital economy scale in these countries grew by 7.6% year-on-year, outpacing GDP growth by 5.4 percentage points. The report highlights industrial digitization as the primary driver of global digital economy growth, with digital technologies accelerating their increasingly penetration intone traditional industries. Despite this progress, the challenge of promoting digital transformation in traditional enterprises remains significant. For instance, recent data from Accenture (2022) show that only 11% of enterprises have embarked on their digital transformation journey, indicating widespread obstacles and challenges in the digitalization process. The digital transformation of enterprises not only impacts industrial digitization but also plays a crucial role in shaping Industry 4.0. As a result, the question of how to facilitate enterprise digital transformation has emerged as a significant area of interest for both academia and industry. It is suggested that addressing the limitations in the digital transformation process may require more than market-driven approaches, underscoring the potential impact of small-scale entity digitalization on economic development.

The digital transformation of enterprises is a change process facilitated by information technology (Sambamurthy et al. 2003; Bharadwaj et al. 2013). Information technology offers a range of technical tools and resources for digital transformation, with cloud computing, big data, artificial intelligence, and other technologies playing key roles in enabling improved data analysis, innovation, decision-making, and efficiency within enterprises. This shift also aligns with the principles of Industry 4.0, empowering enterprises to enhance their competitive edge and innovation capabilities. By leveraging information technology for digital transformation, enterprises can enhance operational efficiency and business performance, thus providing a solid foundation for strategic decision-making, and driving the advance of Industry4.0. (Westerman et al. 2014).

Existing literature predominantly examines the reasons why enterprises engage in digital transformation from their own perspective (Barrett et al. 2015; Delmond et al. 2016; Björkdahl 2020). For example, digitalization can help enterprises to generate new value (Barrett et al. 2015). Furthermore, studies have also indicated that digitization assists enterprises in shaping their value networks (Delmond et al. 2016), further incentivizing digital transformation. Additionally, integrating various aspects of enterprise management with digital technology during the digital transformation process can lead to cost reductions, enhanced efficiency and increased profits. Consequently, enterprises aiming for improved production efficiency, economic profits, and market positioning are more inclined to pursue digital transformation (Björkdahl 2020). A limited amount of literature focuses on the influence of voluntary information technology investments made by enterprises under market competition pressure on digital transformation (Verhoef et al. 2021). Digitization brings about alterations in industry competitive dynamics, market demand patterns, and value creation models, prompting corporate digital transformation (Verhoef et al. 2021). However, current research tends to neglect the exploration of how informatization construction external to the enterprise impacts enterprise digital transformation.

Since the establishment of the Industry and Information Department in 2008, the Chinese government has actively promoted the integration of information technology into industrial development. To systematically advance the integration of informatization and industrialization and establish a mature development path that can be a model for other regions, the Ministry of Industry and Information Technology launched two batches of 16 national-level 'integration of informatization and industrialization' pilot zones in late 2008. A key question in this context is whether government-led informatization initiatives can drive enterprise digital transformation and foster high-quality economic development. Given that enterprises are key players in microeconomic activities and China plays a significant role in the digital economy, further promoting digital transformation among Chinese enterprises could provide insights for other countries looking to develop their digital economies.

This study aims to investigate the impact of informatization construction on enterprise digital transformation by utilizing 'integration of informatization and industrialization' pilot zones as a quasi-natural experiment. Using a difference-in-differences (DID) model, the research thoroughly analyzes the effects of the policy on micro-level enterprise digital transformation and explores the various channels through which the policy impacts this transformation. Furthermore, the study evaluates the diversity and longevity of the policy within the framework of China's initiatives to promote the integration of informatization and industrialization. Overall, this study offers valuable insights into how informatization policies can facilitate enterprise digital transformation. By examining the distinct impacts of pilot zone policies and providing empirical evidence to illustrate the role of pilot zone construction in advancing high-quality economic development, this research contributes to the existing body of empirical research on the digital economy. Additionally, the analysis of dynamic trends underscores the continuous influence of informatization structures in propelling digital transformation within enterprises, thus enhancing our understanding of this evolving landscape.

The paper is structured as follows: Part two includes a literature review of previous research. Part three discusses the institutional background and research hypotheses. Part four introduces the data, variables, and estimation models. Part five analyzes and discusses the findings. Finally, part six presents the conclusions and policy implications.

2 Literature review

2.1 The role of informatization construction

With the rapid development of information technology, the global economy is transitioning into a digital economy era driven by information technology. Enterprise digital transformation is a way of change achieved based on information technology (Sambamurthy et al. 2003; Bharadwaj et al. 2013). The development and application of information technology provide rich technical tools and resources for digital transformation although some studies suggest that the application of information technology may not always enhance the degree of digitization for manufacturing enterprises (Zhang and Lado 2001). However, most studies indicate that information technology is beneficial for improving operational efficiency in enterprises (Goldfarb & Tucker 2019; You & Wu 2019; Tang et al. 2021). Integrating information technology into traditional industries, products, and processes can not only improve the productivity of relevant enterprises (Stolarick 1999) but also contribute to supply chain management, cost management, and control (Goldfarb and Tucker 2019). It also optimizes business processes and aids in decision-making (You and Wu 2019). Furthermore, informatization has the potential to overcome spatial barriers and promote business model innovation (Tang et al. 2021), thereby enhancing the operational efficiency of enterprises. After using information technology for digital transformation, enterprises can change operational efficiency and business performance, providing a reliable basis for company strategic decisions (Westerman et al. 2013). The scholars used the Cobb–Douglas production function and its logarithmic model to measure the impact of information technology investment on the technical efficiency of Italian manufacturing enterprises from 1995 to 2003, revealing a significantly positive effect on technical efficiency (Castiglione 2012).

2.2 The connotation and significance of enterprise digital transformation

Different scholars have different views on the definition of digitalization. Hanelt et al. (2021) believe that digitalization is the integration and transformation of various fields in society, including socioeconomics, business models, and management paradigms, based on the application of modern advanced digital technologies. Plekhanov et al. (2022) suggest that the essence of Industry 4.0 lies in digital transformation, which involves the systemic integration of embedded production technologies into the manufacturing process, tightly coupling them and impacting the industrial production value chain. Ghosh et al. (2022) defines digital transformation as a systemic change process involving the introduction of digital technologies, which broadens the systemic channels of products and serves as a significant means for value creation. Birkel and Wehrle (2022) believe that the essence of digital transformation is the process of integrating internal and external operational processes using digital technologies to gain a competitive advantage. Riasanow et al. (2019) define that digital transformation is the profound and rapid impact of advanced digital technologies on business models, production processes, and service processes, which can bring about strategic changes in the entity. Vial (2021) believes that digitalization mainly relies on advanced technologies such as information, computing, and communication. Through the combination of these technologies, it achieves the reform of entity attributes and completes the process of optimizing the entity.

From this, we can roughly see that digital transformation in the context of enterprises refers to the strategic utilization of digital technologies to reshape organizational structures, business models, collaborative methodologies, and culture. Enterprises achieve changes in the interaction modes among the three main entities (enterprise, customers, market) through the application of digital technology (Ritter and Pedersen 2020). Digitalization can help enterprises create new value (Barrett et al. 2015). Furthermore, research has shown that digitization helps enterprises to reconstruct the value network (Delmond et al. 2016). To stay relevant in the market, enterprises often undertake to take digital transformation initiatives (Vial 2021). A robust organizational preparedness and culture are crucial for successfully implementing digital transformation, starting from internal enterprise management (Halpern et al. 2021). Enterprises need to adapt to the rapidly changing digital business landscape (Warner and Wäger 2019; Li et al. 2018; Gurbaxani and Dunkle 2019).

2.3 The impact of informatization construction on enterprise digital transformation

The digital transformation of enterprises is a way of change achieved based on information technology (Sambamurthy et al. 2003; Bharadwaj et al. 2013). Cloud computing, big data, artificial intelligence, and other technologies have played an important role in digital transformation, enabling enterprises to better conduct data analysis, business innovation, intelligent decision-making, and enhancing efficiency and competitiveness. Information and communication technology are crucial in various aspects of modern society (Roztocki et al. 2019). Technological advancements can stimulate economic and productivity growth and induce industrial restructuring (Bohm and Oberfield 2020; Yan et al. 2023). Digital transformation seeks the fundamental change of an organization through the application of digital technology or transformative changes based on digital technology, leading to unique changes in business operations, business processes, and value creation (Chanias et al. 2019; Kretschmer and Khashabi 2020). Therefore, information construction lays the foundation for the digital transformation of enterprises. In the post-pandemic era, with China's accelerated reshaping of industrial chains, there is a pressing need to optimize and upgrade the industrial structure (Su et al. 2021). Proficiency in information technology has a positive impact on both process innovation performance and digital transformation (Chu et al. 2019).

2.4 Literature review

Existing literature mainly discusses the relationship between information technology construction and technological progress (Castiglione 2012), enterprise operational efficiency (Goldfarb and Tucker 2019; You and Wu 2019; Tang et al. 2021), and other aspects. However, the current literature has not yet addressed the relationship between information technology construction and enterprise digital transformation. As for relevant literature on enterprise digital transformation, it primarily focuses on the perspective of the enterprises themselves, investigating the factors influencing companies' decisions to undergo digital transformation (Barrett et al. 2015; Delmond et al. 2016; Björkdahl 2020). A small number of studies focus on the relationship between government-driven information technology construction and enterprise digital transformation from an external perspective, particularly under market competitive pressures. In this paper, we investigate the influence of informatization construction on enterprises' digital transformation, focusing on the comprehensive policy of the 'Pilot Zone for Integration of Informatization and Informatization.' The construction of information by enterprises is often confronted with uncertainties and challenges due to economic turbulence. The government has the potential to enhance the external economic environment, provide support to enterprises, and guide them toward digital transformation. However, government policies impact on enterprises' digital transformation remains to be seen. This paper aims to conduct a comprehensive examination and analysis of this mechanism.

3 Institutional background and research hypotheses

3.1 Institutional background

The integration of informatization and industrialization is a key strategic initiative proposed by the Party Central Committee and the State Council. The establishment of national pilot zones for this integration is a crucial step taken by the Ministry of Industry and Information Technology to drive the convergence of these two sectors. Strategically positioned in specific regions, these pilot zones are designed to facilitate the seamless integration of informatization and industrialization. The ultimate goal is to create a robust framework and model for modern industrial economic growth through comprehensive and systematic measures, serving as a demonstration for similar efforts in other regions.

Additionally, five aspects of safeguard measures have been established to ensure integrated development in crucial areas. Firstly, the implementation mechanism is strengthened by enhancing inter-departmental, inter-provincial, and central-local cooperation. This is achieved by leveraging the bridging role of diverse entities such as research institutions, industry organizations, and industry alliances. These measures ensure the effective implementation of policies. Secondly, financial and fiscal support can be enhanced by fully utilizing mechanisms such as significant special funds, exploring diverse social investment mechanisms, implementing tax preferential policies, and strengthening financial assistance. Thirdly, efforts are being made to accelerate the cultivation of a multi-level, systematic, and high-level talent pool. This includes fostering a talent development model that integrates industry, academia, and research.

The construction of informatization in each pilot zone has emphasized the enhancement of information infrastructure capacity. This includes standardized network construction, increased base stations, integration of the three networks, improved network broadband coverage, and service quality. Additionally, the development of informatization has enabled the deployment of next-generation core networks, big data, cloud computing, and various consumer platforms, laying the groundwork for digital transformation. The integration of data resources with traditional factor resources has been promoted, unlocking the potential of these resources and driving digital transformation. Furthermore, the industrial internet theory suggests that the industrial internet can bridge the gap between the physical and virtual worlds in terms of perception, connection, and control, facilitating a shift from unidirectional chain production to a more collaborative approach. The Ministry of Industry and Information Technology approved the first batch of national pilot zones for the integration of informatization and industrialization between October 2008 and 2009. The pilot zones for the integration of informatization and industrialization in China encompass various regions such as Shanghai, Chongqing, Baotou, Ordos, the Pearl River Delta, Qingdao, Nanjing, Tangshan, Caofeidian, Chang-Zhu-Tan Urban Agglomeration, Guangxi Zhuang Autonomous Region, Xi'an, Xianyang, Shenyang, Hefei, Lanzhou, Kunming, and Zhengzhou. These zones were established in two rounds starting from the end of 2008, totaling 29 cities across 16 zones. Each pilot city within these zones has set specific development goals and tasks focusing on technology integration, product integration, business integration, and industry diversification.

4 Research hypotheses

Digital transformation seeks the fundamental change of an organization through the application of digital technology or transformative changes based on digital technology, leading to unique changes in business operations, business processes, and value creation (Chanias et al. 2019; Kretschmer and Khashabi 2020). Digitalization mainly relies on advanced technologies such as information, computing, and communication. Through the combination of these technologies, it achieves the reform of entity attributes and completes the process of optimizing the entity (Vial 2021). The construction of informatization compels each pilot zone to focus more on enhancing the level of information infrastructure capabilities. This not only increases the number of base stations but also facilitates the widespread application of a new generation of core networks, big data, cloud computing, and various consumer platforms, laying the groundwork for enterprises to undergo digital transformation. In addition, informatization construction promotes the integration and coexistence of data resources with traditional factor resources, enabling enterprises to fully recognize the value of data, which is conducive to motivating enterprises to undertake digital transformation. Moreover, informatization construction can effectively promote the development of the industrial internet, assisting enterprises in shifting from a unidirectional chain production mode to a diverse and collaborative one, and urging enterprises to proceed with digital transformation. Based on the comprehensive analysis above, this study proposes the following research hypotheses:

Hypothesis 1: Informatization construction can facilitate the digital transformation of enterprises.

Based on existing literature, the digital transformation of enterprises can be influenced by various mechanisms involving information technology construction. In the era of informatization, enterprises can enhance their innovation capabilities through various platforms, such as technology communities and online conferences, facilitating the integration of knowledge from different domains (Kleis et al. 2012). This enables improved efficiency in information acquisition and processing, reduced information search costs, and enhanced efficiency in knowledge sharing, thereby providing better support for enterprise innovation (Spiezia 2011). In summary, the process of informatization can enhance the research and development efficiency of enterprises, reduce search costs, and foster creativity and collaborative innovation through online collaboration. This strengthens the innovation capabilities of enterprises and provides a solid hardware foundation for digital transformation, thereby driving the profound digitization of enterprises (Bouwman et al. 2019). Based on the comprehensive analysis above, this study proposes the following research hypotheses:

Hypothesis 2: Information technology development can facilitate enterprise digital transformation by enhancing innovation capabilities.

Furthermore, information technology development can mitigate operational risks for enterprises and facilitate digital transformation. Enterprises are prone to operational risks, such as the disruption of their financial supply chain due to various financial issues. Enterprises can enhance their informational management by constructing risk maps to ensure financial stability and identify potential risks, thereby reducing uncertainty (Forsythe et al. 2015; Vovchenko et al. 2017). Construction projects can implement automated risk management processes through information technology, eliminating the most significant risks (Ahmad et al. 2018). Furthermore, managing the information flow and optimizing the design process can minimize the risk associated with offsite construction projects, ensuring efficient and seamless project implementation (Sutrisna and Goulding 2019). In conclusion, information technology can effectively identify and mitigate various risks in business operations. Synthesizing the analysis as mentioned above, this study puts forth the following research hypotheses:

Hypothesis 3: Information technology development can facilitate enterprise digital transformation by mitigating operational risks.

5 Empirical models and estimated results

5.1 Data sources

In October 2008, China introduced pilot zones to respond to the ongoing Internet bubble crisis until 2002 (Zhu and Wang 2005; Goodnight and Green 2010; Dowell et al. 2011). This study examines explicitly Chinese A-share listed enterprises from 2003 to 2021, conducting an empirical analysis using panel data at the micro-enterprise level.

The explanatory variable “IIF” is based on the establishment of the ‘Pilot Zone for the Integration of Informatization and Industrialization’. This pilot zone consists of 29 prefecture-level cities, divided into two batches. The list of these cities can be found on the Ministry of Industry and Information Technology website. The annual report information of enterprises is obtained from the Wind Information website, while the data at the enterprise level are sourced from the Guotai An database. This study excludes enterprises with circumstances, such as ST, *ST, and PT, during the sample period. It also removes enterprise samples with missing data on variables, including enterprise size, enterprise age, the net profit margin on total assets, asset-liability ratio, Tobin Q value, equity nature, the proportion of independent directors, and the shareholding proportion of the largest shareholder.

Additionally, enterprises in the financial and real estate sectors are excluded. All continuous variables in this study are trimmed at the lower and upper tails beyond the 1% threshold. The final sample consists of 3901 enterprises with a total of 35,454 observations.

5.2 Variable description

Explained variable: Enterprise Digital Transformation. The digital keywords disclosed in the annual reports of enterprises serve as a micro-level manifestation of national digital economy policies. These keywords provide a comprehensive and accurate reflection of the digital transformation status of listed enterprises. This study categorizes the structured layers of enterprise digital transformation into two levels: 'Underlying Technology Adoption' and 'Technology Practice Application.' The 'Underlying Technology Adoption' level is further divided into four mainstream technology directions, namely big data, blockchain, artificial intelligence, and cloud computing.

On the other hand, the 'Technology Practice Application' level focuses on specific digital business scenario applications. To gain a comprehensive understanding of different types of digital transformation keywords, this study aims to identify and record the frequency of occurrence of these keywords in the annual reports of enterprises. The cumulative count of keyword occurrences serves as a proxy indicator for the level of digital transformation for each sample enterprise in a given year. The higher the value of this indicator, the greater the level of digital transformation in the enterprise. To account for the right-skewed nature of these data, a logarithmic transformation has been applied in this study to obtain an overall indicator that accurately represents the level of digital transformation in the enterprise. This is illustrated in Fig. 1.

Fig. 1
figure 1

Key characteristics of enterprise digital transformation

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The indicator variable \({IIF}_{jt}\) is based on the 'Integrated Development Pilot Zone for Informatization and Industrialization' policy. If city j is chosen as a pilot zone for integrated development in year t, the variable takes a value of 1 for that year and the following years. Otherwise, it takes a value of 0. Considering the approval of the 'Hubei, Baotou, and Ordos' pilot zone in Inner Mongolia in October 2008, which was close to the end of the year, this study considers the starting year for the cities of Hohhot, Baotou, and Ordos as 2009. The variable \({IIF}_{jt}\) can be further decomposed as \({IIF}_{jt}={Treat}_{j}\times {Post}_{jt}\), and the variable \({Treat}_{j}\) represents the policy treatment variable, where \({Treat}_{j}\) takes a value of 1 if city j is selected as an "Integrated Development Pilot Zone for Informatization and Industrialization" during the sample period, and 0 otherwise. The variable \({Post}_{jt}\) represents the indicator variable for the 'Integrated Development Pilot Zone for Informatization and Industrialization' policy before and after. If city j is selected as a pilot zone at time t_0, the variable \({Post}_{jt}\) takes a value of 1 for \({t\ge t}_{0}\) and 0 otherwise.

To address potential confounding effects, this study includes a comprehensive set of covariates that could impact the process of enterprise digitalization. These covariates consist of firm size (Size), firm age (Age), net asset profit ratio (Roa), debt-to-assets ratio (Lev), Tobin's Q valuation (TobinQ), equity ownership structure (Soe), proportion of non-executive directors (Indep), and ownership stake held by the primary shareholder (Top1).

5.3 Mediating variables

The number of granted invention patents can measure the innovation capability of enterprises. This number serves as an indicator of the achievements made by enterprises in technological innovation. By acquiring more invention patents, enterprises demonstrate their innovative capabilities in various areas, including products, processes, and methods. This, in turn, showcases their advanced technical expertise in relevant fields. This indicator can be used to measure the innovation capability of enterprises (Jaffe et al. 2000). A higher number of granted invention patents suggests that an enterprise has more core technologies and innovative achievements. This can help improve its market position and create more business opportunities.

Enterprise operational risk is measured using a comprehensive leverage indicator, which considers the financial risks and debt obligations that enterprises have in their operational and capital structures. A higher value of this indicator indicates that enterprises are more exposed to financial risk and debt-related pressures (Harjoto 2017; Elkhal 2019; Rujiin and Sukirman 2020). The comprehensive leverage indicator is determined by considering fixed operating and fixed financial costs during capital acquisition. A logarithmic transformation is often applied by adding 1 to the original value to analyze the right-skewed nature of the comprehensive leverage coefficient. The comprehensive leverage coefficient is calculated by summing net profit, income tax expense, financial expenses, fixed asset depreciation, depletion of oil and gas assets, depreciation of productive biological assets, amortization of intangible assets, and long-term deferred expenses. This sum is then divided by net profit and income tax expense.

The specific variable definitions are presented in Table 1.

Table 1 Variable measurement
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5.4 Descriptive statistics

Table 2 provides the descriptive statistics for the main variables. On average, the 'Digital' variable has a value of 1.09, indicating that approximately 1.09% of the vocabulary in the annual reports of listed enterprises is related to digital transformation. The minimum value is 0, while the maximum value is 4.98, showing considerable variation in the degree of digital transformation among listed enterprises. The characteristics of the other variables are consistent with previous studies and do not require further explanation.

Table 2 Descriptive statistics
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5.5 Model specification

Based on the theoretical analysis and research hypotheses presented earlier, this study aims to examine the impact of informatization policies on the digital transformation of enterprises. To achieve this, the study establishes the following DID model:

$$\begin{array}{c}{Digital}_{ijt}=\alpha +\beta {IIF}_{jt}+{X}{\prime}\Gamma +{\eta }_{i}+{\omega }_{t}+{\epsilon }_{ijt}\#(1)\end{array}$$

In this context, \({Digital}_{ijt}\) represents the dependent variable, indicating the level of digital transformation in enterprises. On the other hand, \({IIF}_{jt}\) serves as the core explanatory variable, signifying [replace with a more specific description]. \({{\text{X}}}^{\mathrm{^{\prime}}}\) refers to a series of control variables, \({\eta }_{i}\) controls for enterprise-level factors that vary over time and their impact on digital transformation in enterprises, \({\omega }_{t}\) represents annual fixed effects of accounting for economic cycle characteristics in different years, \({\epsilon }_{it}\) corresponds to the random disturbance term, and \(\beta\) stands for the estimated parameter.

6 Empirical regression results and analysis

6.1 Baseline regression results

Table 3 presents the main findings on the relationship between 'informatization construction' and 'digital transformation.' To determine if the correlation among control variables impacts the estimation results of key explanatory variables, this study utilizes a stepwise regression approach. The table provides the specific regression results to analyze this relationship. Among them, column (1) independently tests the impact of informatization construction, with a regression coefficient of 0.1 for IIF, which passes a statistical significance test at the 5% level. The economic implication of this coefficient is that the affected enterprises have a 10% higher degree of digital speaking than those not affected by the 'Pilot Zone for the Integration of Informatization and Industrialization' policy. Columns (2) to (4) sequentially introduce control variables while controlling for firm-fixed and time-fixed effects. The correlation coefficients remain significantly positive and exhibit minimal variation. These findings suggest that the pilot policy of " 'integration of the two transformations '" has significantly enhanced the degree of digital transformation in enterprises. Hypothesis 1 is validated.

Table 3 Baseline regression
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6.2 Parallel trend test

An important premise of the DID model is the assumption of parallel trends. This assumption suggests that the treatment and control groups would follow the same trend if there were no policy intervention. Since the policy impact varied across pilot cities at different times, it is necessary to establish time value dummy variables relative to implementing the "'integration of the two transformations" pilot policy for each city. This study uses Eq. (2) to conduct parallel trend tests, following the methodology outlined as follows:

$$\begin{array}{c}{Digital}_{ijt}=\alpha +{\beta }_{1}{pr{e}_{4}}_{it}+{\beta }_{2}{pr{e}_{3}}_{it}+{\beta }_{3}{pr{e}_{2}}_{it}+{\beta }_{4}{Current}_{it}+{\beta }_{5}{pos{t}_{1}}_{it}+{\beta }_{6}{pos{t}_{2}}_{it}\\ +{\beta }_{7}{pos{t}_{3}}_{it}+{\beta }_{8}{pos{t}_{4}}_{it}+{\beta }_{9}{pos{t}_{5}}_{it}+{\beta }_{10}{pos{t}_{6}}_{it}+{\beta }_{11}{pos{t}_{7}}_{it}+{\beta }_{12}{pos{t}_{8}}_{it}\\ +{\beta }_{13}{pos{t}_{9}}_{it}+{\beta }_{14}{pos{t}_{10}}_{it}+{X}{\prime}\Gamma +{\eta }_{i}+{\omega }_{t}+{\varepsilon }_{ijt}\end{array}$$
(2)

The results of the parallel trend test are illustrated in Fig. 2, which shows no significant impact before policy implementation. This suggests that both the treatment and control groups follow parallel trends. However, a significant effect emerges after policy implementation, starting from the fifth period, indicating a delayed impact of the policy. This signifies the policy's effectiveness in promoting the digital transformation of enterprises through the pilot policy of 'integration of the two transformations.'

Fig. 2
figure 2

The parallel trend test

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6.3 Robustness

Placebo test. Despite controlling for many observable factors in the quasi-natural experiment, we randomly generate pseudo-treatment group dummy variables and pseudo-policy shock dummy variables. These are then reintegrated into the model (1) for regression analysis, and this stochastic process is repeated 500 times. The outcomes are illustrated in Fig. 3. Most coefficients and t-values are concentrated around zero, with the mean deviating significantly from the actual values. Additionally, most estimated coefficients are not statistically significant. These findings suggest that the promotion effect of the 'Integration of Informatization and Industrialization' pilot policy on the digital transformation of enterprises has not been influenced by other unobserved factors. Therefore, the placebo test conducted randomly generating the experimental group demonstrates its efficacy.

Fig. 3
figure 3

Placebo test

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To improve the reliability of the benchmark regression results, we cluster standard errors at the industry level instead of the individual level. The results in column (1) of Table 4 still show a significant positive coefficient for IIF, indicating that the 'Integration of Informatization and Industrialization' pilot policy has an apparent positive effect on the digital transformation of enterprises. These findings remain robust.

Table 4 Robustness check
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To minimize the influence of other policies, we have taken steps to exclude their impact. The international financial crisis in 2008 and the stock market crash in China in 2015 could have potentially affected the assessment of the pilot policy of 'integration of the two worlds' during the period of this study. Therefore, this study has removed the samples from these two years in the benchmark regression model, as shown in column (2) of Table 4. The regression results indicate that even after excluding the potential impact of these events, the coefficient for IIF remains significantly positive. This suggests that the pilot policy of 'Integration of Informatization and Industrialization' has a noticeable facilitating effect on the digital transformation of enterprises, and these results remain robust.

Exclude samples with less than three consecutive years of data to mitigate potential sample self-selection bias. The outcomes are illustrated in column (3) of Table 4. Analyzing the regression results, it is observed that even after excluding the potential impact of these two events, the coefficient for IIF remains significantly positive. This finding suggests that the pilot policy of 'Integration of Informatization and Industrialization' has a noticeable promotion effect on the digital transformation of enterprises, with the results remaining robust.

The study employed a multi-time point propensity score matching–difference-in-differences (PSM-DID) approach. The matching procedure involved selecting matching variables such as enterprise size, enterprise age, net profit margin of total assets, debt-to-asset ratio, Tobin's Q, equity nature, independent director proportion, and proportion of shares held by the largest shareholder. Annual matching was conducted for the urban samples, and the matched data for each year were merged into a panel dataset for regression analysis. Finally, the multi-time point DID method was used to assess the impact of the pilot policy of 'Integration of Informatization and Industrialization' on the digital transformation of enterprises. The regression results of PSM-DID using the annual matching approach are presented in column (4) of Table 4. The findings indicate that the coefficient for IIF remains consistently positive, which suggests that the promotion effect of the pilot policy of 'Integration of Informatization and Industrialization' on facilitating the digital transformation of enterprises is robust. The column (5) of Table 4 reports the estimated results of the control city fixed effect and the time fixed effect, and the regression results are still robust.

6.4 Heterogeneity

Urban scale heterogeneity. According to 'Notice on Adjusting the Standards for Urban Scale Division' issued by the State Council in 2014, this study classifies cities with a population size of over 3 million in the urban area as large cities, while cities with a population size below 3 million are classified as small cities.Footnote 1 Based on the regression results presented in column (1) and column (2) of Table 5, it can be inferred that the pilot policy of 'Integration of Informatization and Industrialization' has a positive impact on the digital transformation of enterprises in cities of different scales. The coefficient of IIF in column (2) of Table 5 is 0.1285 and is statistically significant at the 1% level, indicating a more substantial promotion effect in small cities. This is because, compared to enterprises in large cities, enterprises in small cities need to catch up in information development. As a result, they have more room and potential for digital transformation. Consequently, the development of informatization can provide more digital opportunities and advantages, thus facilitating digital transformation more effectively. Additionally, enterprises in small cities, due to their smaller scale, have simpler management decision-making processes, making it easier to achieve progress in digital transformation overall.

Table 5 Urban and technological heterogeneity
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Technological heterogeneity. According to the qualification recognition information documents of listed enterprises from the GuotaiAn database, the sample is classified into high-tech enterprises and non-high-tech enterprises. The coefficient of IIF in the third column of Table 5 is 0.0437, while the regression coefficient of IIF in the fourth column is 0.1174, passing the statistical significance test at the 1% level. Non-high-tech enterprises, in comparison to high-tech enterprises, tend to have more traditional and complex business processes, which makes them more susceptible to information silos. Informatization development plays a crucial role in enabling enterprises to achieve information sharing, enhance transparency, and improve collaborative efficiency in their business processes. Non-high-tech enterprises often need help in terms of technological innovation and research and development capabilities. Informatization development offers these enterprises digital tools and technologies, which can reduce research and development costs and enhance overall efficiency. As a result, their digital transformation and overall development can be more effectively promoted. On the other hand, high-tech enterprises may already possess advanced digital technologies and tools, which reduces the significance of informatization development for them. Therefore, these enterprises should enhance their technological innovation and research and development capabilities.

Industry heterogeneity. Enterprises operating within the secondary sector often engage in complex production processes and technological innovation. Information technology can assist these enterprises in streamlining production workflows, improving productivity, and enhancing product quality, thereby increasing their competitiveness in the market (Li 1997). Moreover, enterprises in the manufacturing and construction sectors often have larger scales and more financial and human resources, allowing them to make substantial investments in information technology. By referring to coefficients from columns (2) and (3) in Table 6, it becomes clear that the 'Integration of Informatization and Industrialization' pilot policy significantly facilitates the digital transformation of enterprises in the secondary sector.

Table 6 Industry heterogeneity
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6.5 Mechanism analysis

This article emphasizes the role of informatization construction in promoting the digital transformation of enterprises. So, by what pathways does it influence firms' behavior and thus facilitate their digitalization? In view of this, this paper examines the impact of informatization on the degree of digital transformation of enterprises by analyzing the research on the innovation capability and business risk of enterprises, and points out the path of the impact and its consequences. To test the research hypotheses 2 and 3, this paper sets up the following model:

$$\begin{array}{c}{Digital}_{ijt}=\alpha +\beta {IIF}_{jt}+\gamma {Mediator}_{ijt}+\mu {IIF}_{jt}*{Mediator}_{ijt}+{X}{\prime}\Gamma +{\eta }_{i}+{\omega }_{t}+{\epsilon }_{it}\#\left(3\right)\end{array}$$

In particular,\({Mediator}_{ijt}\) contains two variables: the number of patents granted for inventions and the combined leverage ratio, which are used to characterize the firm's innovation capacity and the level of business risk, respectively.

The increase in granted invention patents is an essential indicator of both technological innovation and intellectual property protection within enterprises. The coefficient of \({Mediator}_{ijt}\) in the first column of Table 7 is 0.0617, passing the statistical significance test at the 1% level, it becomes clear that the 'Integration of Informatization and Industrialization' pilot policy significantly facilitates the digital transformation of enterprises with stronger innovation ability. This suggests that the 'Integration of Informatization and Industrialization' pilot policy can encourage enterprises to re-examine their own production mode and management structure, promote enterprise digital transformation by enhancing competitiveness, diversifying technological channels, increasing brand value, and driving industrial upgrading. Hypothesis 2 is validated.

Table 7 Mechanism analysis
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The second variable represents the operational risk (Leverage, logarithm of the comprehensive leverage plus 1). The empirical results from Table 7 indicate that firms reduce their digital transformation behavior when their business risks are on the high side. Because digital transformation itself has certain risks and uncertainties, and the benefits are long-term, thus enterprises with high business risks will take a conservative attitude toward digital transformation, a project with more uncertain risks and benefits; however, the implementation of the pilot policy has given enterprises a strong backing to take risks and accelerate the pace of industrial upgrading. This implies that the pilot policy of 'Integration of Informatization and Industrialization' can enhance efficiency and management level, reduce costs and risks, and enable enterprises to handle operational risks better, promoting digital transformation. Hypothesis 3 is validated.

6.6 Dynamic analysis

This study investigates the long-term effects of the ‘Integration of Informatization and Industrialization' pilot policy on enterprise digital transformation. It explores the dynamic trends of policy influence over time, considering the potential enduring impact of the policy. By conducting a temporal analysis of policy effects, this paper presents an investigation that offers a comprehensive exploration of the ongoing impact of the pilot policy on enterprise digital transformation, known as the 'Integration of Informatization and Industrialization.' The regression coefficients in Table 8 show the impact of the lagged treatment of the core explanatory variable (IIF) on the dependent variable. The coefficients for one, two, three, and four periods are 0.1136, 0.1074, 0.0974, and 0.0952, respectively. These coefficients pass the statistical significance test at the 5% level. Since implementing the 'Integration of Informatization and Industrialization' pilot policy, it has consistently played a positive role in promoting enterprise digital transformation.

Table 8 Dynamic analysis
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7 Conclusion

This study examines the impact of the 'Integration of Informatization and Industrialization' pilot policy on enterprise digital transformation using a quasi-natural experiment in pilot zones. The results show that informatization construction positively affects digital transformation in enterprises, expediting the process. The research also delves into the mechanisms, revealing that informatization construction boosts innovation capabilities by facilitating the acquisition of invention patents. Additionally, it reduces the overall leverage ratio, thereby decreasing operational risks for enterprises. These enhancements in innovation capabilities and risk reduction are crucial factors in driving enterprise digital transformation. The influence of the pilot policy varies based on city size, industry sector, and level of high-tech development, with a more significant impact observed in small cities, the secondary industry, and non-high-tech enterprises, highlighting contextual differences in effects.

In contrast to previous studies on the effects of policies on businesses, this paper analyzes the text data of publicly listed companies to investigate how informationization policies influence corporate digital transformation in terms of direction and channels. This approach aims to elucidate the relationship and communication pathways between pilot zone policies and corporate digital transformation. Through further investigation into the mechanisms involved, it is discovered that innovation output and leverage are significant factors.

The conclusions drawn have significant policy implications. From an enterprise standpoint, improving innovation capability can be achieved through the construction of information technology. This involves utilizing IT tools and platforms to foster collaboration and communication both internally and externally, breaking down organizational barriers, and encouraging knowledge sharing and innovation partnerships. Furthermore, creating open innovation platforms and ecosystems allows for collaboration with innovation partners, suppliers, customers, and scientific research institutions in joint R&D, product innovation, and business model innovation. On the government side, enhancing enterprise innovation through informatization can be accomplished through various measures. Firstly, the government can provide financial support, tax incentives, and technical guidance to promote innovation. Secondly, enterprises are encouraged to partner with higher education and scientific research institutions for collaborative R&D activities. Additionally, the government can establish a platform for sharing governmental data, enabling enterprises to utilize these data and resources for innovative R&D.