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# Digital transformation and corporate financialization: evidence against the tech bubble hypothesis

Mingyan Yang, Zhengning Pu, Christophe Tavera, G Liu

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Mingyan Yang, Zhengning Pu, Christophe Tavera, G Liu. Digital transformation and corporate financialization: evidence against the tech bubble hypothesis. Financial Innovation, 2026, 12, pp.7. 10.1186/s40854-025-00800-0. hal-05504964

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# Digital transformation and corporate financialization: evidence against the tech bubble hypothesis

Mingyan Yang<sup>1,2,3</sup>, Zhengning Pu<sup>2*</sup> ◐, Christophe Tavera<sup>3</sup> and Guanyu Liu<sup>4</sup>

**Abstract**

The inquiry into whether the current surge in digital technology constitutes another emerging technological bubble holds important significance for both the progression of digital technology and the soundness of the financial sector. This study uses Chinese listed companies in the Shanghai and Shenzhen stock markets as samples to investigate the effect of digital transformation on the corporate financialization of entity enterprises and to explore its impact mechanism to elucidate whether ongoing digital technology advancement may be indicative of a potential technology bubble. The study revealed that digital transformation has a negative effect on corporate financialization. Specifically, digital transformation restricts corporate financialization by enhancing primary business operations, improving corporate R&D, and reducing corporate agency costs. Moreover, the study identifies property rights heterogeneity, policy support heterogeneity, regional heterogeneity and period heterogeneity in the impact of digital transformation on corporate financialization. The findings demonstrate that the current surge in digital technology significantly facilitates enterprise development, which is not a technology bubble.

**Keywords:** Technology bubble, Digital transformation, Corporate financialization

**Introduction**

The blind pursuit of new technologies by the financial system has induced financial bubbles and triggered terrible incidents of financial risk. Since the early 1990s, internet technology has gradually emerged as a major innovation, attracting substantial venture capital investment in internet companies. A large amount of capital entered the internet industry, forming a capital bubble. With investment distortions and capital wasted, the internet bubble ultimately burst in 2001, imparting a dramatic and long-lasting impact on the global economic landscape. This lesson is still being learned, underscoring the importance of heeding the potential risks posed by technology bubbles accompanying new technological advancements.

In recent years, digital technologies, exemplified by artificial intelligence and big data, have launched a new wave of technological revolution. Corporate transformation led by digital technologies (i.e., digital transformation) has brought new dynamism to

<figure><img src="image_3.png" /></figure>

<footnote>*Correspondence:
puzhengning@seu.edu.cn</footnote>

<footnote n="1">¹ School of Economics,
Shandong Women's University,
Jinan 250300, Shandong, China</footnote>

<footnote n="2">² School of Economics
and Management, Southeast
University, Nanjing 210000,
Jiangsu, China</footnote>

<footnote n="3">³ Faculty of Economics,
Université de Rennes and CREM,
35000 Rennes, France</footnote>

<footnote n="4">⁴ School of Management
and Engineering, Capital
University of Economics
and Business, Beijing 100070,
China</footnote>
the economic landscape. Digital transformation has fostered the emergence of innovative business models, such as the platform economy, driven by digital industrialization, while also revitalizing traditional industries through industrial digitization. These developments underscore the critical role of digital technologies in driving industrial restructuring and fostering economic growth (Vial 2019; Zhang et al. 2021; Li et al. 2022). Governments have also encouraged corporate digital transformation by introducing relevant policies. In 2019, the President of America signed the Executive Order on *Maintaining American Leadership in Artificial Intelligence*, and in 2020, the United States Agency for International Development (USAID) unveiled *the Digital Strategy* (2020–2024). Concurrently, Japan’s cabinet endorsed *the Basic Policy on Digital Reform* in 2020. In the same year, the European Commission also released *Shaping Europe’s digital future*. In 2022, the United Kingdom unveiled its *UK Digital Strategy*. China has also unveiled a suite of policies, including *Made in China 2025*, a three-year action plan to boost “core manufacturing industries” (2018–2020), and guidelines to facilitate the digital transformation of small and medium-sized enterprises, all with the objective of promoting digital transformation. Consequently, digital technology has gained popularity from governments, venture capital entities, and corporate enterprises worldwide, promoting an unprecedented surge in investments in emerging technologies. For example, the scale of China’s burgeoning big data industry surged to CNY 1.3 trillion<fnref n="1" /> in 2021, while the artificial intelligence industry reached an impressive CNY 404.1 billion, with investment and financing inflows amounting to USD 20.12 billion.<fnref n="2" /> The similarly large amount of capital investment inevitably raises concerns about whether digital transformation is a practical use of digital technology to achieve industrial development or whether it potentially metamorphoses into yet another transient fad, ultimately evolving into another technology bubble in the financial market.

This concern has garnered scholarly attention as well. Banji-Owoka (2021) delves into the impact of the burgeoning digital economy on the valuation of technology stocks within the National Association of Securities Dealers Automated Quotations (NASDAQ) index, employing both single-stage and multistage Gordon growth valuation models. On the basis of the empirical findings derived from this investigation, Banji-Owoka (2021) deduces that, presently, there is no discernible asset bubble within the technology sector. Turning to the realm of digital art, Boido and Aliano (2023) focus their inquiry on nonfungible tokens, conducting an investigation into this nascent area. The outcomes of their research posit that the trajectory of investing in nonfungible tokens within the art domain constitutes an investment opportunity emblematic of a paradigmatic shift within the art sphere. However, studies by Lagoarde-Segot (2017) and Currie (2017) show that digital technology drives financialization, which adds to concerns about tech bubbles. While these studies provide valuable insights, a comprehensive analysis of whether digital transformation constitutes a technology bubble—particularly at the intersection of corporate operations and digital technology—remains lacking. Specifically, digital transformation may yield two outcomes for firms: it can either strengthen core business and

<footnote n="1">¹ Data source: the Big Data White Paper (2022) published by the China Information and Communications Technology Agency (CICTA).</footnote>

<footnote n="2">² Data source: Artificial Intelligence Industry Development Plenary at the World Conference on 2022.</footnote>
create new growth opportunities, or it can lead to the allocation of resources to finan-
cial markets, promoting corporate financialization. Excessive financialization heightens
the risk of bubble formation, making it essential to assess whether digital transforma-
tion encourages corporate financialization. If so, digital transformation diverts resources
from core business activities, increasing financial fragility and bubble risk. Conversely, if
digital transformation reduces corporate financialization, it supports the view that digi-
tal transformation promotes business growth and is not a technology bubble. However,
few studies address this issue, and the underlying mechanisms remain underexplored.

To bridge these research gaps, this study investigates the effects of digital transforma-
tion on corporate financialization and its underlying mechanisms using Chinese data to
determine whether digital transformation constitutes another technology bubble. First,
the study empirically assesses the impact of digital transformation on corporate finan-
cialization, utilizing data from A-share companies listed on the Shanghai and Shenzhen
Stock Exchanges between 2007 and 2023. Second, it examines the implication channels
through which digital transformation affects corporate financialization, focusing on
three dimensions: core business operations, research and development (R&D) activities,
and agency costs. Finally, the study analyzes various heterogeneities in the impact of digi-
tal transformation on corporate financialization.

The contributions of this study are reflected in three main aspects. First, it advances the debate on whether popular digital technologies represent a potential technology bubble by examining the microlevel impact of digital transformation on corporate financialization. Second, it enriches the literature on the determinants of corporate financialization by introducing digital transformation as a critical factor. This facilitates a more comprehensive understanding of how to manage and mitigate corporate financialization. Third, the conclusions deepen the understanding of the impact of the digital economy on the growth of the real economy, highlighting how digital transformation influences core business operations, R&D activities, and agency costs. These findings offer valuable guidance for directing digital transformation efforts toward supporting the real economy.

The remainder of this paper is organized as follows. "Literature review and research hypotheses" section presents a literature review and theoretical analysis to discuss our research hypotheses. "Data and methodology" section outlines our research design, including model construction, variable selection, and data sources. In "Empirical results and analysis" section, we empirically analyze the effect of digital transformation on corporate financialization and explore its impact mechanism and heterogeneity. Finally, "Discussions" section concludes our findings and provides recommendations.

**Literature review and research hypotheses**

Literature review

The phenomenon of excessive financialization has been the subject of considerable
academic scrutiny because of its propensity to give rise to economic bubbles that can
precipitate severe financial crises (Lucarelli 2012). For business operations, corporate
financialization can result in the misallocation of a company's finite resources toward
the financial sector, thereby hampering the company's primary productive functions and
detrimentally impacting its long-term growth and development (Barradas and Lagoa
2017). Existing studies have analyzed the motives, consequences and factors influencing corporate financialization. The motives for corporate financialization include the “reservation” motive of reserving capital to prevent capital shortages and the “investment substitution” motive of pursuing high profits by making financial investments in the context of a sluggish real economy (Orhangazi 2008). Indeed, corporate financialization means that capital enters more into unproductive activities, and excessive financialization of firms has negative effects on real investment, innovation activities, mergers and acquisitions, and total factor productivity (Barradas and Lagoa 2017; Trivedi 2014; Li et al. 2022). With respect to the factors influencing corporate financialization, the literature explores two main dimensions: the external environment of the firm and its internal governance. The institutional environment, economic policy uncertainty and climate policy uncertainty are examples of external factors that can exacerbate corporate financialization (Zhao and Su 2022; Qi et al. 2021; Ren et al. 2024). On the other hand, internal factors such as firms’ shareholder value, equity structure, corporate social responsibility and customer concentration play a significant role in shaping corporate financialization trends (Lazonick and O’Sullivan 2000; Alexiou et al. 2021; Jiang et al. 2022; Zheng et al. 2024; Su et al. 2024).

In recent years, digital technologies have boomed. Firms have applied digital technologies to reduce the duplication of labor or have replaced traditional digital technologies with advanced technologies to achieve digital transformation (Ebert and Duarte 2018; Hess et al. 2016; Vial 2019). Digital transformation improves the operational efficiency and automation of organizations, which has a positive effect on firms’ innovation activities, business performance, productivity, and pollution reduction (Li et al. 2022; Zhang et al. 2021; Tsou and Chen 2023; Xiong et al. 2022; Zhao et al. 2024). Moreover, studies have focused on the role of enterprise digital transformation in enterprise financial resource allocation. For example, studies by Li et al. (2023) and Zhang et al. (2023) have demonstrated that digital transformation can effectively mitigate corporate financing constraints. Chang et al. (2024) have shown that digital transformation significantly reduces long-term investment in business. Sun et al. (2022) highlighted how digital transformation can lead to a reduction in the cash reserves held by firms. Nonetheless, relatively few scholars have focused their attention on the role of digital transformation in shaping the financialization trends of firms.

Macroeconomic studies have argued that the rapid development of information technology has laid the technological foundation for economic financialization. Whether through enhancements in transaction efficiency (Lagoarde-Segot 2018) or the introduction of novel financial instruments (Ma and McGroarty 2017), it is undeniable that information technology has assumed a critical role as a facilitator of economic financialization (Haferkorn et al. 2017). At the micro level, the integration of corporate digital transformation and business operations has far-reaching and intricate impacts on financialization. Sui and Yao (2023) reported that digital transformation inhibits the financialization of firms, with green technological innovation playing a mediating role. Fang and Ju (2024) also reveal the negative impact of digital transformation on corporate financialization and explore the impact channels in terms of information flow and operational capabilities. Wu and Lu (2023), on the other hand, reported a positive correlation between digital transformation and corporate financialization. These divergent findings
underscore the importance of a more in-depth and comprehensive analysis of this issue. The influence of digital transformation on corporate financialization is multifaceted, and its underlying mechanisms are yet to be thoroughly explored and delineated.

## Research hypotheses

Figure 1 provides a framework for analyzing the impact of digital transformation on corporate financialization. Specifically, digital transformation can exert a direct effect on corporate financialization through what is known as the “crowding-out effect”. As businesses undergo digital transformation, their operations become more digitalized and intelligent, which results in improvements in operational efficiency and automation (Craglia et al. 2008). The process of digital transformation necessitates investment in technology and hardware, such as intelligent financial software, management systems, cloud storage technology, and intelligent production equipment. This investment in digital technology may reduce the capital available for other financial investments, leading to a “crowding-out effect” on corporate financialization and ultimately inhibiting corporate financialization.

The process of digital transformation can potentially impact corporate financialization indirectly through various channels, such as main business operations, R&D activities, and agency costs. Specifically, digital transformation can have a positive effect on the development of enterprises’ main business operations by enhancing their data collection, analysis, and integration capabilities. By leveraging digital technologies, companies can obtain high-quality information that can aid in making better business decisions and developing products that better meet consumer needs (Xiao et al. 2015). Moreover, digital transformation can assist enterprises in optimizing production processes by utilizing digital technologies to achieve fine-grained management of the entire process and enhance production efficiency (Gölzer and Fritzsche 2017; Vial 2019; Meng and Wang 2020). This can lead to the development of more market-compatible products, which in turn can reduce business risk and promote the main business operations of enterprises.

Digital transformation can also have a positive effect on enterprise R&D. By leveraging digital technologies such as the internet, enterprises can increase their ability to acquire knowledge or technology and access expertise from various sources (Abouzeedan et al. 2013; Parida and Ortvist 2015). Digital technologies can facilitate the absorption of external knowledge and technology, thereby accelerating the development of new products and promoting innovation (Li et al. 2022). In addition, new generation information technology can enhance communication with other enterprises, promoting collaborative innovation capability. Both improvements in the main business operations and R&D activities can enhance enterprise performance. Enterprises typically invest in financial projects for profit maximization. As the level of business operations in an enterprise

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Analysis framework of digital transformation affecting corporate financialization</figcaption></figure>
improves through digital transformation, the incentive for corporate financialization is weakened, which can dampen corporate financialization.

The impact of digital transformation on corporate financialization can be mediated through its effects on agency costs, as the conflict between shareholders and management is an intrinsic factor in corporate financialization. Digital transformation can play a significant role in alleviating agency conflict and reducing agency costs. First, digital transformation can promote more transparent business processes within a firm. By facilitating efficient and seamless information flow, digitalization can enhance the transparency of corporate processes, thus reducing the potential for management opportunism and mitigating the agency conflict between shareholders and management. Second, digital transformation can support external corporate disclosure and regulation. As companies undertake digital transformation, management tends to be more forthcoming in disclosing information to underscore their digital business advantages, thus decreasing internal and external information asymmetry and reducing agency costs (Chen et al. 2021).

The promotion of core business operations, advancements in research and development, and the mitigation of agency conflicts can serve as deterrents to corporate financialization. Corporate financialization is driven primarily by the pursuit of higher profits. The promotion of core business operations and improvements in R&D capabilities can enhance the competitiveness and profitability of firms, thereby impeding the tendency toward financial investment and reducing the degree of corporate financialization. In addition, principal-agent problems often result in corporate managers investing limited funds in financial projects to obtain short-term excess returns. Thus, alleviating agency conflicts between management and shareholders can enable corporate management to better fulfill the principal-agent relationship, ultimately reducing the incidence of corporate financialization.

On the basis of the above analysis, the following hypotheses are formulated.

H1 Digital transformation can inhibit the financialization of enterprises.

H2 Digital transformation inhibits corporate financialization by promoting the main business operations.

H3 Digital transformation inhibits corporate financialization by improving corporate R&D activities.

H4 Digital transformation inhibits corporate financialization by reducing agency costs.

# Data and methodology

## Model

To verify the impact of digital transformation on corporate financialization, the following model is constructed:

$$ Fin_{it} = \alpha_0 + \alpha_1 DT_{it} + \alpha Controls_{it} + \sum Firmfe + \sum Yearfe + \varepsilon_{it} \quad (1) $$

In Model (1), Fin denotes the degree of corporate financialization, using the ratio of corporate financial assets to total assets. DT denotes the degree of digital transformation,
using the ratio of digital assets to total assets or the textual information of corporate annual reports. Controls are control variables, including information on firm characteristics such as firm size and regional variables such as level of economic development. Detailed measurements of the above variables are shown in "Data and variables" section. This paper also controls for individual firm fixed effects (Firmfe) and year fixed effects (Yearfe). εit denotes the error term.

Fixed-effects models account for both individual and time effects, effectively controlling for unobserved heterogeneity across individuals and over time, thereby ensuring that the estimated effects are not biased by these factors. To validate the appropriateness of using a fixed-effects model, we conducted both the F test and the Hausman test. The F test assesses whether the fixed-effects model is preferable to the mixed-effects model by determining whether the differences in intercepts across individuals are significant. Meanwhile, the Hausman test compares the fixed-effects and random-effects models to determine if the fixed-effects model provides more reliable estimates by checking the consistency of the estimates between the two models.

If α₁ is significantly positive, digital transformation has a facilitating effect on the financialization of enterprises, and if α₁ is significantly negative, digital transformation has a suppressing effect on the financialization of enterprises.

We examine potential mechanisms by which the digital transformation of enterprises affects corporate financialization via the following model.

$$M_{it} = \alpha_0 + \alpha_1 DT_{it} + \alpha\text{Controls}_{it} + \sum Firmfe + \sum Yearfe + \varepsilon_{it} \quad (2)$$

$$Fin_{it} = \alpha_0 + \alpha_1 DT_{it} + \alpha_2 M_{it} + \alpha\text{Controls}_{it} + \sum Firmfe + \sum Yearfe + \varepsilon_{it} \quad (3)$$

Model (2) is used to examine the impact of digital transformation on the mechanism variables, and model (3) is used to examine the impact of the mechanism variables on corporate financialization. Mᵢₗ denotes mechanism variables, including the level of main business operations (MB), the level of corporate R&D (R&D) and agency costs (AC), and the other variables have the same meaning as in Model (1). Model (1)–Model (3) are conducted via the linear regression command in Stata16 software.

## Data and variables

A research sample comprising 4983 companies listed on the Shanghai and Shenzhen Stock Exchanges in China was selected for analysis spanning the years 2007–2023. The data are cleaned according to the following steps: (1) Listed companies belonging to the financial industry and real estate industry are excluded because of the specificity of the investment data. (2) On the basis of the special characteristics of the financial data, listed companies in ST and *ST status<fnref n="3" /> are excluded. (3) On the basis of the availability of data, listed companies with incomplete or missing data are excluded. (4) To mitigate the effect of extreme values, we winsorize all continuous variables at the enterprise level at

<footnote n="3">³ ST is a special treatment to listed companies that has two consecutive years of losses by China Securities Regulatory Commission (CSRC). *ST companies are listed companies with losses for three consecutive years and are given delisting warnings by CSRC.</footnote>
1% and 99%. The corporate data are obtained from the China Stock Market & Accounting Research (CSMAR) database. The data on region and industry are obtained from the Chinese Research Data Services Platform (CNRDS) database.

The specific variables are as follows.

### (1) Explained variables

Corporate Financialization (Fin). The methods used to measure the financialization of listed companies include mainly asset measures and earnings measures. This paper focuses on revealing the impact of the digital economy on corporate investment behavior. Financial earnings are uncertain, and corporate financialization calculated using assets can more directly reflect corporate investment in financial assets. We use the proportion of financial assets to total assets to measure corporate financialization (Fin) following previous studies (Jiang et al. 2022; Dai et al. 2018). Financial assets specifically include monetary funds, trading financial assets, available-for-sale financial assets, held-to-maturity investments, long-term equity investments, dividends receivable, interest receivable and investment properties.

### (2) Explanatory variables

Digital Transformation (DT). This study uses two methods to measure the degree of digital transformation of an enterprise. First, we use the proportion of a firm's digital technology assets to its total assets to indicate the degree of digital transformation (Li et al. 2022; Qi et al. 2020). The digital technology-related portion of an enterprise's intangible assets is considered the enterprise's digital technology assets. Specifically, an intangible asset is considered a digital technology asset if it contains keywords related to the digital economy, such as "software", "cloud storage", "network", "intelligent platform" and "QR code". The amount of digital technology assets owned by an enterprise is obtained by summing all the digital technology assets of the same enterprise in the same year.

Second, we use the text content of the annual reports of listed companies to count the frequency of key words related to digital transformation to measure the degree of digital transformation. For keyword selection, referring to Wu et al. (2021) and Zhai et al. (2022), we select keywords from key digital technologies, including artificial intelligence, big data, cloud computing and blockchain, and digital technology applications. The specific keywords are shown in Fig. 2.

### (3) Mechanistic variables

Level of main business operations (MB). We use the logarithm of gross operating income to measure the level of the main business operation of a company.

The level of corporate R&D (R&D). We use the proportion of R&D expenditure on corporate total assets to measure the level of corporate R&D.

Agency costs (AC). We use the proportion of administrative expenses in total operating revenue to measure agency costs.

### (4) Control variables
<figure><img src="image_5.png" /><figcaption>**Fig. 2** Digital transformation keyword phraseology</figcaption></figure>

The control variables at the firm level include firm size (Size), the gearing ratio (Lev), the asset margin (ROA), growth capacity (Growth), Tobin's Q (Tq), liquidity position (Liq), board size (Board), CEO Duality (Dual), and the percentage of top shareholders (Top1). The control variables at the regional level include economic level (Eco) and real estate development (RE). The details about the variables are presented in Table 1. The descriptive statistics of each variable are shown in Table 2. Table 3 presents the results of the Pearson correlation analysis between the variables. The absolute value of the Pearson correlation coefficient is not greater than 0.8 for almost all the variables,<fnref n="4" /> which indicates that the study variables are not highly correlated with each other and are ready for further regression analysis.

# Empirical results and analysis

## Baseline regression

Table 4 reports the results of Model (1). As shown in column (1) of Table 4, the regression coefficient of digital transformation (DT) is – 3.0513, which is statistically significant and negative at the 1% level, indicating that the higher the degree of digital transformation is, the lower the degree of corporate financialization.<fnref n="5" /> Digital transformation

<footnote n="4">⁴ The correlation coefficient between the variable *Size* and the variable *MB* exceeds 0.8, which may lead to the distortion of implication channels analysis model estimation about main business operations. Therefore, in the empirical analysis of implication channels about main business operations, we exclude *Size* from the model. Specifically, the control variables in columns (1) and (2) in Table 7 below exclude the *Size* variable.</footnote>

<footnote n="5">⁵ To ensure the scientific validity of the selected fixed effects model for estimation, a test is incorporated for the selection of mixed effects, fixed effects, and random effects models. The F test compares the residual sum of squares (RSS) between the fixed effects model and the pooled OLS model to test whether individual fixed effects are significant. If the F-statistic is significant, it indicates that introducing fixed effects significantly improves model fit, and the fixed effects model should be selected. The Hausman test compares the coefficient estimates of the fixed effects model and the random effects model to examine whether the key assumption of the random effects model—that individual</footnote>
<table><caption>**Table 1** Variable definitions</caption><thead><tr><th>Variables</th><th>Meaning</th><th>Measurement method</th></tr></thead><tbody><tr><td>Fin</td><td>Corporate Financialization</td><td>Financial assets/total assets</td></tr><tr><td>DT</td><td>Digital Transformation</td><td>Digital assets/total assets, text analysis of company annual reports</td></tr><tr><td>Size</td><td>Enterprise size</td><td>Logarithm of total corporate assets</td></tr><tr><td>Lev</td><td>Gearing ratio</td><td>Total liabilities/assets</td></tr><tr><td>ROA</td><td>Asset Margin</td><td>Net profit/total assets</td></tr><tr><td>Growth</td><td>Growth Capacity</td><td>Operating income growth rate</td></tr><tr><td>Tq</td><td>Tobin Q</td><td>Total market capitalization/assets</td></tr><tr><td>Liq</td><td>Current asset position</td><td>Current assets/total assets</td></tr><tr><td>Board</td><td>Board Size</td><td>The number of board members is taken as the natural logarithm</td></tr><tr><td>Dual</td><td>CEO Duality</td><td>The chairperson and general manager are the same as 1, otherwise 0</td></tr><tr><td>Top1</td><td>Percentage of the first largest shareholder</td><td>Shareholding ratio of the first largest shareholder</td></tr><tr><td>Eco</td><td>Economic Development Level</td><td>GDP rate of increase</td></tr><tr><td>RE</td><td>Real Estate Development</td><td>Completed real estate development investment/GDP</td></tr><tr><td>MB</td><td>Main Business Operation</td><td>Logarithm of total operating income</td></tr><tr><td>R&D</td><td>R&D level</td><td>R&D Investment/total assets</td></tr><tr><td>AC</td><td>Agency Costs</td><td>Administrative expenses/total operating income</td></tr></tbody></table>

<table><caption>**Table 2** Descriptive statistics of the variables</caption><thead><tr><th>Variable</th><th>N</th><th>Mean</th><th>SD</th><th>Max</th><th>Min</th></tr></thead><tbody><tr><td>Fin</td><td>41,791</td><td>0.268</td><td>0.168</td><td>0.788</td><td>0.031</td></tr><tr><td>DT1</td><td>41,791</td><td>0.002</td><td>0.004</td><td>0.03</td><td>0</td></tr><tr><td>DT2</td><td>41,774</td><td>1.269</td><td>1.382</td><td>5.017</td><td>0</td></tr><tr><td>Size</td><td>41,791</td><td>22.07</td><td>1.264</td><td>26.03</td><td>19.80</td></tr><tr><td>Lev</td><td>41,791</td><td>0.414</td><td>0.204</td><td>0.888</td><td>0.05</td></tr><tr><td>ROA</td><td>41,791</td><td>0.039</td><td>0.063</td><td>0.199</td><td>-0.251</td></tr><tr><td>Growth</td><td>41,605</td><td>0.312</td><td>0.819</td><td>5.662</td><td>-0.709</td></tr><tr><td>Tq</td><td>41,186</td><td>2.030</td><td>1.266</td><td>8.344</td><td>0.859</td></tr><tr><td>Liq</td><td>41,791</td><td>0.573</td><td>0.205</td><td>0.957</td><td>0.096</td></tr><tr><td>Board</td><td>41,747</td><td>2.128</td><td>0.201</td><td>2.708</td><td>1.609</td></tr><tr><td>Dual</td><td>41,138</td><td>0.292</td><td>0.455</td><td>1</td><td>0</td></tr><tr><td>Top1</td><td>41,750</td><td>0.344</td><td>0.149</td><td>0.741</td><td>0.086</td></tr><tr><td>Eco</td><td>41,456</td><td>8.195</td><td>3.417</td><td>32.90</td><td>-20.63</td></tr><tr><td>RE</td><td>41,412</td><td>0.138</td><td>0.062</td><td>1.071</td><td>0.003</td></tr><tr><td>MB</td><td>41,791</td><td>21.41</td><td>1.444</td><td>25.55</td><td>18.45</td></tr><tr><td>R&D</td><td>33,311</td><td>0.024</td><td>0.021</td><td>0.113</td><td>0</td></tr><tr><td>AC</td><td>41,786</td><td>0.087</td><td>0.07</td><td>0.427</td><td>0.008</td></tr></tbody></table>

inhibits corporate financialization. To make the test more accurate, this paper controls

effects are uncorrelated with explanatory variables—holds. If the test is significant, it indicates that the assumption does not hold, and the fixed effects model should be chosen. According to the F value test (F=11.69***), the fixed effect is preferred over the mixed effect for panel data estimation. Additionally, based on the results of the Hausman test (Prob > chi2 = 0.0000), the choice of fixed effects for panel data estimation is superior to that of random effects.

<footnote>Footnote 5 (continued)</footnote>
<table><caption>**Table 3** Correlation matrix</caption><thead><tr><th></th><th>Fin</th><th>DT</th><th>Size</th><th>Lev</th><th>ROA</th><th>Growth</th><th>Tq</th><th>Liq</th></tr></thead><tbody><tr><td>Fin</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DT</td><td>0.076***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Size</td><td>-0.191***</td><td>-0.062***</td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lev</td><td>-0.449***</td><td>-0.056***</td><td>0.510***</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>ROA</td><td>0.231***</td><td>-0.083***</td><td>-0.022***</td><td>-0.378***</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Growth</td><td>0.054***</td><td>0.103***</td><td>-0.055***</td><td>-0.008</td><td>0.005</td><td>1</td><td></td><td></td></tr><tr><td>Tq</td><td>0.112***</td><td>0.094***</td><td>-0.297***</td><td>-0.226***</td><td>0.172***</td><td>0.044***</td><td>1</td><td></td></tr><tr><td>Liq</td><td>0.400***</td><td>0.049***</td><td>-0.302***</td><td>-0.215***</td><td>0.172***</td><td>0.128***</td><td>0.095***</td><td>1</td></tr><tr><td>Board</td><td>-0.064***</td><td>-0.045***</td><td>0.252***</td><td>0.155***</td><td>0.006</td><td>-0.001</td><td>-0.095***</td><td>-0.119***</td></tr><tr><td>Dual</td><td>0.097***</td><td>0.051***</td><td>-0.186***</td><td>-0.155***</td><td>0.041***</td><td>-0.003</td><td>0.053***</td><td>0.109***</td></tr><tr><td>Top1</td><td>0.009*</td><td>-0.096***</td><td>0.153***</td><td>0.027***</td><td>0.142***</td><td>-0.029***</td><td>-0.098***</td><td>-0.011**</td></tr><tr><td>Eco</td><td>0.013**</td><td>-0.066***</td><td>-0.151***</td><td>-0.017***</td><td>0.063***</td><td>-0.008</td><td>0.020***</td><td>0.036***</td></tr><tr><td>RE</td><td>-0.026***</td><td>0.029***</td><td>0.025***</td><td>0.037***</td><td>-0.010*</td><td>0.053***</td><td>0.019***</td><td>0.003</td></tr><tr><td>MB</td><td>-0.248***</td><td>-0.073***</td><td>0.909***</td><td>0.536***</td><td>0.059***</td><td>-0.111***</td><td>-0.273***</td><td>-0.194***</td></tr><tr><td>R&D</td><td>0.136***</td><td>0.285***</td><td>-0.182***</td><td>-0.191***</td><td>0.093***</td><td>0.060***</td><td>0.233***</td><td>0.261***</td></tr><tr><td>AC</td><td>0.173***</td><td>0.186***</td><td>-0.321***</td><td>-0.251***</td><td>-0.202***</td><td>0.132***</td><td>0.265***</td><td>-0.030***</td></tr><tr><td></td><td><strong>Board</strong></td><td><strong>Dual</strong></td><td><strong>Top1</strong></td><td><strong>Eco</strong></td><td><strong>RE</strong></td><td><strong>MB</strong></td><td><strong>R&D</strong></td><td><strong>AC</strong></td></tr><tr><td>Board</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dual</td><td>-0.183***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Top1</td><td>0.012**</td><td>-0.042***</td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Eco</td><td>0.095***</td><td>-0.039***</td><td>0.060***</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>RE</td><td>0.023***</td><td>-0.012**</td><td>0.006</td><td>0.027***</td><td>1</td><td></td><td></td><td></td></tr><tr><td>MB</td><td>0.240***</td><td>-0.191***</td><td>0.179***</td><td>-0.102***</td><td>0.011*</td><td>1</td><td></td><td></td></tr><tr><td>R&D</td><td>-0.106***</td><td>0.122***</td><td>-0.135***</td><td>-0.097***</td><td>-0.004</td><td>-0.109***</td><td>1</td><td></td></tr><tr><td>AC</td><td>-0.072***</td><td>0.057****</td><td>-0.128***</td><td>0.081***</td><td>0.063***</td><td>-0.505***</td><td>0.181***</td><td>1</td></tr></tbody></table>

a series of firm-level variables, the results are shown in column (2), and the estimated coefficient of *DT* is still significantly negative at the 1% level. We further add the control variables at the regional level, and the regression results are shown in column (3). The estimated coefficient of *DT* is still significantly negative at the 1% level. The above results suggest that the inhibitory effect of digital transformation on corporate financialization is relatively robust. The results confirm Hypothesis H1.

## Robustness test

### Changing measurement of the explained variable

Money funds are considered special financial assets because they are mostly used in the daily production and operation of enterprises. Money funds are excluded from financial assets, and the financialization indicators of enterprises are recalculated for robustness testing. The specific regression results are displayed in column (1) of Table 5 after the calculation of the explained variable is changed. The regression coefficient of digital transformation (*DT*) is significantly negative, and the results are consistent with the results of the benchmark regression, indicating the robustness of the previous study findings. Long-term equity investments may be the result of operational investments, so we further exclude long-term equity investments from financial assets, recalculate

<footnote>***p < 0.01, **p < 0.05, *p < 0.1</footnote>
<table><caption>**Table 4** Baseline regression results</caption><thead><tr><th>Variables</th><th>(1)<br>Fin</th><th>(2)<br>Fin</th><th>(3)<br>Fin</th></tr></thead><tbody><tr><td>DT</td><td>– 3.0513***<br>(– 12.280)</td><td>– 1.5930***<br>(– 7.600)</td><td>– 1.5944***<br>(– 7.608)</td></tr><tr><td>Size</td><td></td><td>0.00002<br>(0.010)</td><td>– 0.0004<br>(– 0.237)</td></tr><tr><td>Lev</td><td></td><td>– 0.2728***<br>(– 39.167)</td><td>– 0.2729***<br>(– 38.900)</td></tr><tr><td>ROA</td><td></td><td>0.0132<br>(0.989)</td><td>0.0137<br>(1.022)</td></tr><tr><td>Growth</td><td></td><td>– 0.0028***<br>(– 2.866)</td><td>– 0.0029***<br>(– 2.968)</td></tr><tr><td>Tq</td><td></td><td>– 0.0022***<br>(– 2.882)</td><td>– 0.0022***<br>(– 2.824)</td></tr><tr><td>Liq</td><td></td><td>0.3517***<br>(46.498)</td><td>0.3519***<br>(46.246)</td></tr><tr><td>Board</td><td></td><td>0.0099*<br>(1.910)</td><td>0.0097*<br>(1.872)</td></tr><tr><td>Dual</td><td></td><td>0.0069***<br>(3.710)</td><td>0.0068***<br>(3.613)</td></tr><tr><td>Top1</td><td></td><td>0.0177*<br>(1.788)</td><td>0.0183*<br>(1.840)</td></tr><tr><td>Eco</td><td></td><td></td><td>– 0.0004<br>(– 1.395)</td></tr><tr><td>RE</td><td></td><td></td><td>0.0198<br>(1.226)</td></tr><tr><td>Constant</td><td>0.2714***<br>(389.910)</td><td>0.1563***<br>(4.366)</td><td>0.1658***<br>(4.584)</td></tr><tr><td>Observations</td><td>41,360</td><td>39,930</td><td>39,575</td></tr><tr><td>R-squared</td><td>0.635</td><td>0.720</td><td>0.721</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Robust t statistics in parentheses

***p<0.01, **p<0.05, *p<0.1

the degree of financialization and perform regression analysis. The regression results are shown in column (2) of Table 5, which reveals that the coefficient of *DT* is significantly negative, indicating that the negative effect of digital transformation on the financialization of firms is robust.

### Changing measurement of explanatory variables

We use the frequency of keywords related to digital transformation in the annual reports of enterprises to measure the degree of digital transformation and regress Model (1). The regression results are shown in column (3) of Table 5. The regression coefficient of *DT* is significantly negative, which is consistent with previous findings.
<table><caption>**Table 5** Robustness tests</caption><thead><tr><th>Variables</th><th>(1)<br>Fin</th><th>(2)<br>Fin</th><th>(3)<br>Fin</th><th>(4)<br>Fin</th><th>(5)<br>Fin</th></tr></thead><tbody><tr><td>DT</td><td>-0.3169***<br>(-3.018)</td><td>-0.6162***<br>(-4.735)</td><td>-0.0025***<br>(-3.024)</td><td>-1.0982***<br>(-3.331)</td><td>-1.7439***<br>(-6.295)</td></tr><tr><td>Firm-level control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-level control variable</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>39,575</td><td>39,575</td><td>39,558</td><td>26,539</td><td>30,951</td></tr><tr><td>R-squared</td><td>0.617</td><td>0.684</td><td>0.720</td><td>0.719</td><td>0.717</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Robust t statistics in parentheses

***p < 0.01, **p < 0.05, *p < 0.1

## Using only manufacturing industry samples

The manufacturing industry is the core part of the real economy, and this study uses listed manufacturing companies as the sample for robustness testing. The regression results are shown in column (4) of Table 5. Digital transformation has a significant negative effect on the financialization of enterprises, which is consistent with previous findings.

## Excluding municipalities

Considering that municipalities are at the same administrative level as provincial administrative regions are, they tend to have special characteristics compared with other prefecture-level cities. In this paper, four municipalities, Beijing, Tianjin, Shanghai and Chongqing, are excluded to reestimate the benchmark model for robustness testing. The empirical results are shown in column (5) of Table 5. The direction and significance of the regression coefficients of digital transformation are consistent with those of the benchmark regression, indicating that the findings of this paper are robust.

## Endogeneity problem

The explanatory and explained variables may be mutually causal, which is the main source of endogeneity in this study. Specifically, digital transformation affects corporate financialization, and in turn, corporate financialization may impact digital transformation by affecting corporate funding. This paper eliminates possible endogeneity problems with the help of the PSM-DID method, thus further testing the rigor of the findings.

To explore the smart manufacturing model and promote the transformation and upgrading of China's manufacturing industry to smart manufacturing, China's Ministry of Industry and Information Technology (MIIT) has selected a group of enterprises as pilot enterprises for smart manufacturing projects every year from 2015 to 2018.<fnref n="6" /> Smart manufacturing is an innovative manufacturing model that integrates new generation information technology into the traditional manufacturing industry and promotes the digital transformation of the manufacturing industry. Therefore, this paper selects

<footnote n="6">⁶ The number of smart manufacturing project pilot enterprises from 2015 to 2018 is 46, 64, 97, and 99, respectively.</footnote>
“smart manufacturing pilot demonstration projects” in China as an exogenous policy shock for the digital transformation of manufacturing enterprises. First, we use the PSM method to match the companies in the “experimental group” that successfully obtained the smart manufacturing demonstration projects with the companies in the “control group” that did not successfully obtain the demonstration projects. We then introduce the time point at which the demonstration project was declared successfully and use the DID model to examine the impact of digital transformation on the financialization of enterprises. The specific model is as follows.

$$ Fin_{it} = \alpha_0 + \alpha_1 IMPolicy_{it} + \alpha Controls_{it} + \sum Firmfe + \sum Yearfe + \varepsilon_{it} \quad (4) $$

IMPolicy indicates whether the enterprise was successfully selected as a pilot demonstration enterprise for smart manufacturing in that year. IMPolicy is set to 1 if the enterprise is selected as a pilot demonstration enterprise; otherwise, IMPolicy is set to 0. The meanings of the other variables are the same as those in Model (1). Concurrent with the implementation of smart manufacturing projects, China's Ministry of Industry and Information Technology, in collaboration with the National Development and Reform Commission, launched the “Broadband China” policy. This policy, executed in three phases across 120 cities or city clusters in 2014, 2025, and 2016, was designed to strengthen broadband infrastructure in these regions. The policy is anticipated to significantly enhance urban informatization, potentially influencing the financial investments of local enterprises. To more accurately capture the isolated effects of smart manufacturing projects and mitigate the confounding influence of this macroeconomic policy shock, this study includes broadband policy as a dummy variable for the control variables.

Prior to the regression, the parallel trend hypothesis is verified via an event test with Model (5):

$$ Fin_{it} = \alpha_0 + \sum_{t=-10}^{5} \alpha_t Period_t \times Treat_t + \alpha Controls_{it} + \sum Firmfe + \sum Yearfe + \varepsilon_{it} \quad (5) $$

where Period_t is a year dummy variable used to test for policy effects before and after its implementation. Figure 3 illustrates the results of the parallel trend test. The regression coefficients for the policy changes in each period, denoted as αt, were obtained and plotted along with their 95% confidence intervals. When the period before the policy implementation (t=-1) is used as the baseline, there are no significant differences between the experimental and control groups prior to the policy's implementation. The results indicate that the premise hypothesis of a parallel trend is passed.

The regression results of PSM-DID are shown in column (1) in Table 6. The results indicate that the smart manufacturing pilot has a negative and significant effect on the financialization of firms. Multiperiod DID estimates from two-way fixed effects models may be subject to bias (Goodman-Bacon 2021). To further enhance the robustness of the conclusions, this paper performs the estimation via the heterogeneity-robust DID estimator proposed by Callaway and Sant' Anna (2021), and the results are displayed in columns (2) and (3) of Table 6. Specifically, column (2) reports the results when a control
<figure><img src="image_6.png" /><figcaption>Fig. 3 Parallel trend test results</figcaption></figure>

<table><caption>**Table 6** DID estimation results</caption><thead><tr><th>Variables</th><th>(1)<br>PSM-DID</th><th>(2)<br>CSDID-1</th><th>(3)<br>CSDID-2</th></tr></thead><tbody><tr><td>DT-Policy</td><td>-0.0138**<br>(-2.053)</td><td>-0.0114**<br>(0.0055)</td><td>-0.0103**<br>(0.005)</td></tr><tr><td>Firm-level control variables</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-level control variable</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>6646</td><td>19,293</td><td>19,293</td></tr><tr><td>R-squared</td><td>0.705</td><td>-</td><td>-</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

t statistics are in parentheses in column (1), and standard errors are in parentheses in columns (2) and (3)

***p < 0.01, **p < 0.05, *p < 0.1

group of firms that have never been exposed to a policy shock is used, whereas column (3) includes firms that have not yet experienced a policy shock in the control group. As shown in columns (2) and (3), the results from this more robust estimation approach align with the benchmark regression findings, confirming the negative impact of digital transformation on corporate financialization. Again, the robustness of the findings is illustrated.

## Implication channels

First, we examine the mechanism by which digital transformation promotes main business operations, thus inhibiting corporate financialization. The regression results are shown in columns (1) and (2) in Table 7. Column (1) demonstrates the effect of digital
<table><caption>**Table 7** Mechanistic examination</caption><thead><tr><th>Variables</th><th>(1)<br>MB</th><th>(2)<br>Fin</th><th>(3)<br>R&D</th><th>(4)<br>Fin</th><th>(5)<br>AC</th><th>(6)<br>Fin</th></tr></thead><tbody><tr><td>DT</td><td>7.5413***<br>(6.955)</td><td>-1.3975***<br>(-6.812)</td><td>0.4299***<br>(13.879)</td><td>-0.8750***<br>(-4.010)</td><td>-0.0248***<br>(-11.595)</td><td>-0.0003***<br>(-6.659)</td></tr><tr><td>MB</td><td></td><td>-0.0263***<br>(-18.817)</td><td></td><td></td><td></td><td></td></tr><tr><td>R&D</td><td></td><td></td><td></td><td>-0.9184***<br>(-12.591)</td><td></td><td></td></tr><tr><td>AC</td><td></td><td></td><td></td><td></td><td></td><td>0.0022***<br>(10.988)</td></tr><tr><td>Firm-level control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-level control variable</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>39,575</td><td>39,575</td><td>31,501</td><td>31,501</td><td>39,555</td><td>39,555</td></tr><tr><td>R-squared</td><td>0.907</td><td>0.725</td><td>0.837</td><td>0.759</td><td>0.726</td><td>0.723</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Robust t statistics in parentheses

***p < 0.01, **p < 0.05, *p < 0.1

transformation on corporate financialization. The regression coefficient of *DT* is 3.7902, which is significantly positive at the 1% level, indicating that digital transformation helps enhance main business operations. Column (2) demonstrates the impact of the main business operation on the financialization of the enterprise. The regression coefficient of *MB* is -0.0571, which is statistically significant at the 1% level. Thus, the higher the level of main business operation is, the lower the degree of financialization of enterprises. We have verified that digital transformation suppresses the financialization of enterprises by promoting their main business operations, and Hypothesis H2 is confirmed.

Second, we verify the mechanism by which firm digitization enhances *R&D* and thus inhibits firm financialization. The regression results are shown in columns (3) and (4) in Table 7. Column (3) shows the effect of digital transformation on corporate R&D, and column (4) shows the effect of the corporate R&D level on corporate financialization. The regression coefficient of *DT* in column (3) is significantly positive, and the regression coefficient of *R&D* in column (4) is significantly negative. These results indicate that the digital transformation of enterprises can significantly increase the level of enterprise R&D, which in turn has a suppressive effect on enterprise financialization, and Hypothesis H3 is confirmed.

Finally, we explore the mechanism by which digital transformation reduces agency costs and thus inhibits financialization. The regression results are shown in columns (5) and (6) of Table 7. The regression coefficient of *DT* in column (5) is significantly negative, indicating that digital transformation significantly reduces the agency costs of firms. The regression coefficient of *AC* in column (6) is significantly positive, indicating that the increase in agency cost significantly promotes the financialization of the firm. The above results indicate that digital transformation significantly reduces the agency costs of firms, which in turn inhibits their financialization, and Hypothesis H4 is confirmed.
## Heterogeneity analysis

We have revealed the inhibitory effect of digital transformation on financialization. Referring to the differences in the nature of enterprises and the environment in which they operate, we provide an in-depth examination of the differences in the impact of digital transformation on corporate financialization from the perspectives of the nature of corporate property rights, policy support, operating regions and periods.

## Nature of corporate property

State-owned enterprises (SOEs) have close government-business relationships and good bank-business relationships, which provide them with easy access to key resources such as capital. In addition, SOEs have many social responsibilities and have less incentive to capture private interests. In contrast, non-SOEs often face more severe financing constraints and have a stronger profit motive.

To analyze the heterogeneity of property rights in the impact of the digital economy on financialization, this study divides the full sample into two groups of state-owned enterprises and non-state-owned enterprises according to whether they are state-owned enterprises and then conducts group regressions to obtain the baseline model estimation results for the two groups of samples. In addition, we tested for differences in coefficients between groups. The specific regression results are shown in columns (1) and (2) of Table 8. The results show that the regression coefficient of *DIG* in the non-SOE group is -0.343 and passes the significance test at the 1% level. The regression coefficient of *DIG* in the SOE group does not pass the significance test at the 10% level, which indicates that digital transformation has a more significant inhibitory effect on the financialization of non-SOEs.

<table><caption>**Table 8** Heterogeneity examination</caption><thead><tr><th></th><th>(1) Non-SOEs</th><th>(2) SOEs</th><th>(3) No industrial policy</th><th>(4) With policy support</th><th>(5) Central and Western Region</th><th>(6) Eastern Region</th><th>(7) Before the pandemic</th><th>(8) During the pandemic</th></tr></thead><tbody><tr><td>DT</td><td>-0.343***<br>(-2.845)</td><td>0.034<br>(0.184)</td><td>-1.302**<br>(-2.520)</td><td>-0.134<br>(-1.084)</td><td>-0.343<br>(-1.618)</td><td>-0.391***<br>(-3.262)</td><td>-1.380***<br>(-5.495)</td><td>-1.980***<br>(-4.740)</td></tr><tr><td>control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>25,427</td><td>14,075</td><td>7190</td><td>23,700</td><td>10,934</td><td>28,615</td><td>27,693</td><td>11,611</td></tr><tr><td>R-squared</td><td>0.609</td><td>0.697</td><td>0.725</td><td>0.568</td><td>0.645</td><td>0.614</td><td>0.713</td><td>0.937</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Comparison test of coefficient between groups</td><td>-0.377***</td><td>-1.169**</td><td>1.047**</td><td>0.6***</td><td></td><td></td><td></td><td></td></tr></tbody></table>

t statistics in parentheses

**p**<0.01, **p**<0.05, *p*<0.1
## Industrial policy support

Governments commonly use industrial policy as a tool to guide industrial development. Industrial policies are often accompanied by supportive resources such as tax subsidies, thus creating a favorable development environment for industrial development. To examine the heterogeneity of policy support, this study divides the research sample into groups with and without policy support to examine the impact of digital transformation on corporate financialization. Specifically, this study uses the industrial policy database in the *CNRDS* database, which extracts the relevant industries and planning contents mentioned in the five-year plan outline, and summarizes the policy attitudes of each province toward the mentioned relevant industries on the basis of the descriptive tone of the mentioned industries in the text, which is divided into three categories: encouraging, neutral, and inhibiting. We classify the enterprises belonging to the encouragement group with policy support, otherwise, as the group without policy support.

The baseline model is estimated separately for the group with policy support and the group without policy support, and the results are shown in columns (3) and (4) of Table 8. The regression coefficient of *DT* in the group without policy support is – 1.302 and passes the significance test at the 5% level, whereas the regression coefficient of *DT* in the group with policy support does not pass the significance test at the 10% level. This result indicates that digital transformation has a weaker inhibitory effect on the degree of financialization of enterprises with industrial policy support.

## Regional heterogeneity

The economic environments of different regions vary greatly in China. To investigate the regional differences in the impact of digital transformation on the financialization of enterprises, this study divides the full sample into two subsamples, the eastern region<fnref n="7" /> and the central and western regions, according to the provinces where the enterprises are located. The specific regression results of the groups are shown in columns (5) and (6) in Table 8. The negative effect of digital transformation on enterprise financialization in the central and western regions is not significant at the 10% level, whereas the regression coefficient of digital transformation on enterprise financialization in the eastern region is – 0.391, which passes the significance test at the 1% level. The results indicate that digital transformation has a significant negative effect on financialization in the eastern region and does not have a significant effect on financialization in the central and western regions.

## Period heterogeneity

During the COVID-19 pandemic, industries experienced substantial digital transformation. This was necessitated by the need for consumers and employees to adhere to stay-at-home measures. Consequently, companies have found it imperative to establish online marketing channels and facilitate remote work conditions. Hence, the impact of digital transformation on firms during the pandemic appears to have been more pronounced. This study delves into the temporal dynamics of the impact of digital transformation on

<footnote n="7">⁷ The eastern region includes Beijing, Fujian Province, Guangdong Province, Hainan Province, Hebei Province, Jiangsu Province, Liaoning Province, Shandong Province, Shanghai, Tianjin and Zhejiang Province, a total of 11 provinces.</footnote>
firms' financialization by dividing the examination period into two distinct phases: the prepandemic period (2007–2019) and the pandemic period (2020–2023). The results, depicted in columns (7) and (8) of Table 8, reveal regression coefficients of -1.98 for DT during the pandemic and -1.38 for DT before the pandemic, both passing significance tests at the 1% level. These findings highlight the heightened inhibitory effect of digital transformation on corporate financialization during the pandemic.

## Discussions

This study investigates the effects of digital transformation on corporate financialization based on a sample of Chinese listed companies in the Shanghai and Shenzhen Stock Exchanges from 2007 to 2023. The findings contribute to answering the question of whether digital transformation represents another tech bubble.

The empirical results demonstrate that digital transformation inhibits corporate financialization, which is consistent with the findings of Sui and Yao (2023) and Fang and Ju (2024). This conclusion highlights that digital transformation, unlike previous technological bubbles, plays a positive role in promoting real economic progress. It serves as compelling evidence against the view that digital transformation is merely another bubble, alleviating concerns about the rapid expansion of digital technology, as similarly suggested by Banji-Owoka (2021) and Boido and Aliano (2023). These studies offer strong support for the continued development and application of digital technologies, providing reassurance against potential bubble risks.

The mechanism analysis reveals that digital transformation inhibits corporate financialization by promoting main business operations, enhancing R&D efforts, and reducing agency costs. These results indicate that digital transformation helps companies focus on their main business, improve productivity, and foster innovation. These conclusions align with the findings of Gaglio et al. (2022) and Fang and Ju (2024). Gaglio et al. (2022) suggest that digital transformation optimizes production processes and improves product quality, thereby increasing productivity. Additionally, the enhanced transparency and accountability facilitated by digital technologies align managerial and shareholder interests, thereby reducing agency costs (Johl et al. 2015). Similarly, Fang and Ju (2024) underscore the role of digital transformation in improving information flows, further emphasizing its benefits for corporate operations. Our research supplements previous studies by examining different channels through which digital transformation affects financialization. While Sui and Yao (2023) explore the impact through green technological innovation and Fang and Ju (2024) focus on information flows and operational capabilities, this study extends the discussion by considering core business operations, R&D, and agency costs, enriching the understanding of the mechanisms at play.

The study explores the heterogeneity in the impact of digital transformation on corporate financialization across various contexts. We find that digital transformation has a stronger inhibitory effect on financialization in non-state-owned enterprises (SOEs) than in SOEs. This is consistent with Tian et al. (2022), who argue that non-SOEs face more stringent financial constraints, as they have less access to credit markets and are more affected by capital limitations. In such circumstances, digital transformation can help non-SOEs divert resources from financial activities toward core business investments, thereby reducing financialization. In contrast, SOEs, with their planned investments and
less market influence, tend to rely less on digital transformation to curb financialization. Furthermore, firms without industrial policy support benefit more from digital transformation in reducing financialization than do those with such support. Industrial policies, while easing financial constraints through subsidies or tax incentives, may inadvertently encourage financialization by providing firms with easier access to capital. These findings suggest that policymakers should carefully balance financial incentives to avoid unintended consequences. Geographically, firms in the eastern regions of China benefit more from digital transformation to reduce financialization than those in the central and western regions do. This disparity likely stems from the more advanced economic development, higher levels of technological innovation, and superior human capital in the eastern regions, which provide favorable conditions for digital transformation. As a result, firms in Eastern China are better positioned to leverage digital tools to increase their operational efficiency, reducing their reliance on financial markets. These findings are consistent with those of Tian et al. (2022), who observe a stronger impact of digital transformation in economically developed regions.

Additionally, the more advanced digital infrastructure and heightened market competition in the East further drive firms to focus on operational efficiency rather than financial strategies. Finally, during the COVID-19 pandemic, the inhibitory effect of digital transformation on corporate financialization intensified. This is closely tied to the increased application of digital transformation in operations during this period. This trend aligns with that of Klein and Todesco (2021), who noted that digital transformation played a pivotal role in helping companies navigate the crisis by maintaining operational continuity, further reducing reliance on financial markets.

# Conclusions, policy implications, and limitations

## Conclusions

The findings indicate that in the current landscape of rapid digital technology advance-
ment, digital transformation can stimulate core business operations, enhance R&D capa-
bilities, reduce agency costs, and consequently inhibit corporate financialization. These
results help alleviate concerns that digital transformation may lead to another technol-
ogy bubble. The inhibitory effect of digital transformation on financialization is more
pronounced for nonstate-owned enterprises, firms without industrial policy support,
and companies located in economically developed eastern regions during the COVID-
19 pandemic. These insights provide valuable guidance for government policy formula-
tion and corporate decision-making.

## Policy implications

First, the government should foster a conducive environment for enterprise digital trans-
formation and incentivize enterprises to embark on this journey. Enterprises can aid in
overcoming barriers to digital transformation through the introduction of supportive
policies tailored to digital transformation endeavors. However, it is imperative for the
government to take into account the unique characteristics of enterprises when crafting
policies. This study identifies instances of policy inefficacy, underscoring the importance
of policy relevance and enhancing incentive efficiency.
Second, enterprises should seize the opportunities afforded by digital transformation to drive their growth and competitiveness. Digital transformation serves as a powerful tool for optimizing operations, enhancing innovation capabilities, and ensuring resilience under adverse conditions, such as those experienced during the COVID-19 pandemic. By leveraging their unique capabilities, enterprises should proactively develop and implement digital transformation strategies that integrate advanced technologies such as AI, big data, and cloud computing. This will not only enhance their operational efficiency but also strengthen their capacity for innovation, ultimately supporting long-term sustainable development.

Finally, the regulatory framework should be refined to mitigate excessive financialization. This study reveals that diminishing agency costs negatively impacts enterprise financialization. Firms can achieve this by improving internal governance mechanisms, enhancing transparency in information disclosure, and minimizing information asymmetry between shareholders and executives. Regulatory bodies should also establish clearer guidelines to monitor and control financial activities, ensuring that resources are channeled into productive and innovative business ventures.

## Limitations and future research

There are two limitations to this study. First, the impact of digital transformation on the financialization of firms may be influenced by various other variables, such as customer satisfaction, marketing capabilities and environmental performance (Vorhies et al. 2011; Ma, et al. 2024). Future research could explore the role that firm characteristics play in the process of digital transformation affecting firm financialization. Second, although the proxy variables used in this paper are supported by the literature (Li et al. 2022; Qi et al. 2020; Wu et al. 2021; Zhai et al. 2022), there is still room for improvement in future research regarding the measurement of the variables, especially the digital transformation indicators. For example, attempts can be made to construct comprehensive indicators to measure digital transformation, and richer research methods, such as case studies, can be employed to explore the question of whether digital transformation constitutes a potential technology bubble.

## Abbreviations

<table><tr><td>R&D</td><td>Research and development</td></tr><tr><td>USAID</td><td>The United States Agency for International Development</td></tr><tr><td>CNY</td><td>Chinese Yuan</td></tr><tr><td>USD</td><td>United States Dollar</td></tr><tr><td>NASDAQ</td><td>The National Association of Securities Dealers Automated Quotations</td></tr><tr><td>CSMAR</td><td>China Stock Market & Accounting Research</td></tr><tr><td>PSM</td><td>Propensity score matching</td></tr><tr><td>DID</td><td>Differences-in-differences</td></tr><tr><td>MIIT</td><td>Ministry of Industry and Information Technology</td></tr><tr><td>SOEs</td><td>State-owned enterprises</td></tr></table>

## Acknowledgements

We thank all funding supporters for this research.

## Author contributions

YANG Mingyan conceived the research structure, ran data experiment and wrote the original draft; PU Zhengning collected funding support, and made final article modification; Christophe Tavera complete research structure and modified "Literature review and research hypotheses" and "Data and methodology" sections for the article. LIU Guanyu is responsible for reviewing and editing.
**Funding**

This research is supported by Jiangsu Social Science Fund (No. 22EYA003) and The National Social Science Fund of China (No. 22BJY099).

**Availability of data and materials**

All data for this research could provide by corresponding author under requirement.

**Declarations**

**Competing interests**

All authors claim no conflicts in interest for this research.

Received: 13 November 2023 Accepted: 11 October 2025

Published online: 11 January 2026

**References**

Abouzeedan A, Klofsten M, Hedner T (2013) Internetization management as a facilitator for managing innovation in high-technology smaller firms. Glob Bus Rev 14(1):121–136

Alexiou C, Mohamed A, Nellis J (2021) The impact of institutional investors on firms’ performance in the context of financialization. Int J Finance Econ 26(1):290–309

Banji-Owoka IF (2021) The Impact of the modern boom of the digital economy on the Price of Technology stocks, Are we in another tech bubble?. Dublin, National College of Ireland

Barradas R, Lagoa S (2017) Financialization and Portuguese real investment: a supportive or disruptive relationship? J Post Keynes Econ 40(3):413–439

Boido C, Aliano M (2023) Digital art and non-fungible-token: bubble or revolution? Financ Res Lett 52:103380

Callaway B, Sant'Anna PHC (2021) Difference-in-differences with multiple time periods. J Econom 225(2):200–230

Chang K, Li J, Xiao L (2024) Hear all parties: peer effect of digital transformation on long-term firm investment in China. Manag Decis Econ 45(3):1242–1258

Chen W, Zhang L, Jiang P et al (2021) Can digital transformation improve the information environment of the capital market? Evidence from the analysts’ prediction behaviour. Acc Finance. https://doi.org/10.1111/acfi.12873

Craglia M, Goodchild MF, Annoni A et al (2008) Next-generation digital earth. Int J 3:146–167

Currie WL, Lagoarde-Segot T (2017) Financialization and information technology: themes, issues and critical debates–part I. J Inf Technol 32(3):211–217

Dai B, Peng Y, Ma S (2018) Understanding the economic shifting "from real to virtual" from the micro perspective: a literature review of corporate financialization. Foreign Econ Manag 40(11):31–43

Ebert C, Duarte CHC (2018) Digital transformation. IEEE Softw 35(4):16–21

Fang X, Ju C (2024) Digital transformation and corporate financialization in emerging markets: evidence from China. Heliyon 10(2):24616

Gaglio C, Kraemer-Mbula E, Lorenz E (2022) The effects of digital transformation on innovation and productivity: firm-level evidence of South African manufacturing micro and small enterprises. Technol Forecast Soc Change 182:121785

Gölzer P, Fritzsche A (2017) Data-driven operations management: organisational implications of the digital transformation in industrial practice. Prod Plan Control 28(16):1332–1343

Goodman-Bacon A (2021) Difference-in-differences with variation in treatment timing. J Econom 225(2):254–277

Haferkorn M (2017) High-frequency trading and its role in fragmented markets. J Inf Technol 32(3):283–296

Hess T, Matt C, Benlian A et al (2016) Options for formulating a digital transformation strategy. MIS Q Exec 15(2):123–139

Jiang F, Shen Y, Cai X (2022) Can multiple blockholders restrain corporate financialization? Pac Basin Financ J. https://doi.org/10.1016/j.pacfin.2022.101827

Johl SK, Kaur S, Cooper BJ (2015) Board characteristics and firm performance: evidence from Malaysian public listed firms. J Econ Bus Manag 3(2):239–243

Klein VB, Todesco JL (2021) COVID-19 crisis and SMEs responses: the role of digital transformation. Knowl Process Manag 28(2):117–133

Lagoarde-Segot T, Currie WL (2018) Financialization and information technology: a multi-paradigmatic view of IT and finance-Part II. J Inf Technol 33(1):1–8

Lagoarde-Segot T (2017) Financialization: towards a new research agenda. Int Rev Financ Anal 51:113–123

Lazonick W, O'sullivan M (2000) Maximizing shareholder value: a new ideology for corporate governance. Econ Soc 29(1):13–35

Li C, Huo P, Wang Z et al (2023) Digitalization generates equality? Enterprises’ digital transformation, financing constraints, and labor share in China. J Bus Res 163:113924

Li R, Rao J, Wan L (2022) The digital economy, enterprise digital transformation, and enterprise innovation. Manag Decis Econ. https://doi.org/10.1002/mde.3569

Lucarelli BD (2012) Financialization and global imbalances: prelude to crisis. Rev Radic Polit Econ 44(4):429–447

Ma T, McGroarty F (2017) Social Machines: how recent technological advances have aided financialisation. J Inf Technol 32(3):234–250

Ma D, Zhai P, Zhang D et al (2024) Excess stock returns and corporate environmental performance in China. Financ Innov 10(1):41
Meng F, Wang W (2020) Research on the mechanism of digitalization to the improvement of manufacturing enterprises performance based on mediating effect. In: 2020 6th international conference on information management (ICIM). IEEE, pp 122–126

Orhangazi Ö (2008) Financialisation and capital accumulation in the non-financial corporate sector: a theoretical and empirical investigation on the US economy: 1973–2003. Camb J Econ 32(6):863–886

Parida V, Örtqvist D (2015) Interactive effects of network capability, ICT capability, and financial slack on technology-based small firm innovation performance. J Small Bus Manage 53:278–298

Qi H, Cao X, Liu Y (2020) The impact of digital economy on corporate governance - based on the perspective of information asymmetry and irrational behavior of managers. Reform 04:50–64

Qi Y, Yang Y, Yang S et al (2021) Does government funding promote or inhibit the financialization of manufacturing enterprises? Evidence from listed Chinese enterprises. N Am J Econ Finance 58:101463

Ren X, Li W, Duan K et al (2024) Does climate policy uncertainty really affect corporate financialization? Environ Dev Sustain 26(2):4705–4723

Su K, Zhao Y, Wang Y (2024) Customer concentration and corporate financialization: evidence from non-financial firms in China. Res Int Bus Finance 68:102159

Sui B, Yao L (2023) The impact of digital transformation on corporate financialization: the mediating effect of green technology innovation. Innov Green Dev 2(1):100032

Sun C, Lin Z, Vochozka M et al (2022) Digital transformation and corporate cash holdings in China's A-share listed companies. Oecon Copernic 13(4):1081–1116

Tian G, Li B, Cheng Y (2022) Does digital transformation matter for corporate risk-taking? Finance Res Lett 49:103107

Trivedi SR (2014) Financialisation and accumulation: a firm-level study in the Indian context. Procedia Econ Finance 11:348–359

Tsou HT, Chen JS (2023) How does digital technology usage benefit firm performance? Digital transformation strategy and organisational innovation as mediators. Technol Anal Strat Manag 35(9):1114–1127

Vial G (2019) Understanding digital transformation: a review and a research agenda. J Strateg Inf Syst 28(2):118–144

Vorhies DW, Orr LM, Bush VD (2011) Improving customer-focused marketing capabilities and firm financial performance via marketing exploration and exploitation. J Acad Mark Sci 39:736–756

Wu F, Hu H, Lin H et al (2021) Digital transformation and capital market performance: empirical evidence from stock liquidity. Manage World 37(07):130–144 (in Chinese)

Wu K, Lu Y (2023) Corporate digital transformation and financialization: evidence from Chinese listed firms. Financ Res Lett 57:104229

Xiao JH, Xie K, Wu Y et al (2015) The supply chain transformation from being partner from being partner-oriented to being customer oriented: a double-case study on the supply chains in ecommerce enterprises. Manage World 4:137–154

Xiong L, Ning J, Dong Y (2022) Pollution reduction effect of the digital transformation of heavy metal enterprises under the agglomeration effect. J Clean Prod 330:129864

Zhai H, Yang M, Chan KC (2022) Does digital transformation enhance a firm's performance? Evidence from China. Technol Soc 68:101841

Zhang T, Shi ZZ, Shi YR et al (2021) Enterprise digital transformation and production efficiency: mechanism analysis and empirical research. Econ Res-Ekonomska Istraživanja. https://doi.org/10.1080/1331677X.2021.1980731

Zhang X, Li J, Xiang D et al (2023) Digitalization, financial inclusion, and small and medium-sized enterprise financing: evidence from China. Econ Model 126:106410

Zhao S, Zhang L, Peng L et al (2024) Enterprise pollution reduction through digital transformation? Evidence from Chinese manufacturing enterprises. Technol Soc 77:102520

Zhao Y, Su K (2022) Economic policy uncertainty and corporate financialization: evidence from China. Int Rev Financ Anal 82:102182

Zheng D, Lei L, Wang L et al (2024) Inhibiting or promoting: the impact of corporate social responsibility on corporate financialization. Int Rev Econ Finance 89:1404–1421

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