Fertilizing the soil: FinTech development and corporate digital transformation

The FinTech development is expected to provide fertile soil for corporate digital transformation. Using the corporate digital transformation index creatively constructed in this paper, we verify that the development of regional financial technology can significantly promote corporate digital transformation, including digital technology and digital application. We find that the mechanisms mainly include easing financing constraints, improving the number and proportion of high-tech talents, and promoting enterprises to carry out more R&D activities. Further analysis shows that, the positive effect is more pronounced in private enterprises with better performance and lower capital expenditure. Our results provide evidence for the relationship between the financial sector and digital transformation.


Introduction
Digital transformation has become an irreversible global trend. In the past 10 years, a series of major digital infrastructures and digital technologies have been developed and put into use on a large scale since the information technology revolution (Nambisan et al. 2019). Cruelly, if firms with traditional business models do not undergo digital transformation, they will face the inevitable situation of being replaced by new entrants, such as Alibaba, Amazon, Spotify, Airbnb, etc. (Verhoef et al. 2021). A natural problem arises, that is, how to promote the digital transformation of enterprises to avoid being eliminated. Hsu et al. (2014) found that financial development can significantly stimulate high-tech innovation. Thus, in this paper, we hold the view that financial development could provide fertile soil for firms' innovation (Ding et al. 2022), and we examine the impact of FinTech development on corporate digital transformation and identify the economic mechanisms through which it occurs.
Indeed, the phrase digital transformation has come into wide use in contemporary business media to signify the transformational or disruptive implications of digital technologies for businesses, including new business models, new types of products or services, and new types of customer experiences (Nambisan et al. 2019). Therefore, when discussing corporate digital transformation, scholars mainly focus on analyzing the strategies or specific measures taken by enterprises, but there are few pieces of literature on measuring the degree of digital transformation. Unlike existing studies, we use textual mining to quantify firm-level digital transformation, including the underlying architecture technology and digital technology application. The quantitative results enable us to deeply analyze the causes and consequences of digital transformation and stimulate the cumulativeness of future research in the multiple domains on this critical topic.
Several pieces of literature have proved that the firm size, employees' digital thinking mode, senior managers' perspective, and capital ownership will affect the process of corporate digital transformation (Singh and Hess 2017; Page 2 of 11 Chen et al. Digital Economy and Sustainable Development (2023)  Porf írio et al. 2021). Critically, the foundation of innovation is partially rooted in the financial sector, and digital transformation as a form of innovation is no exception. Digital transformation, like other types of innovation, requires a large amount of capital and human investment. It has been proved that the liberalization of the financial market and arm's length financing can promote innovation (Atanassov 2016;Moshirian et al. 2021). However, this paper does not focus on general financial development, but more carefully studies whether the development of financial technology in the new era can provide the soil for digital transformation. FinTech, as the name suggests, is the fusion of finance and technology (Goldstein et al. 2019). Introducing new technologies into the financial field has many functions (Chen et al. 2022), such as mitigating information asymmetry (Cong and He 2019), reducing transaction costs (Cai 2018), improving transaction speed and operational efficiency (Fuster et al. 2019). Consequently, a branch of literature has proved the impact of FinTech on macroeconomy or micro sectors. First of all, FinTech development increases economic efficiency, employment, and household resilience (Suri et al. 2021), and also supports entrepreneurship (Hua and Huang 2021), narrows the gender wage gap (Guo et al. 2021). Second, FinTech reduces its profitability and asset quality for the bank sector, but it improves its management efficiency and decreases the credit risk (Cheng and Qu 2020;Zhao et al. 2022). Third, FinTech will also provide start-up funds to support their development (Bollaert et al. 2021).
To identify the causal impact of FinTech on the digital transformation of enterprises at the firm level, this paper introduces an index provided by a research team from the Institute of Digital Finance at Peking University and Ant Financial Services Group as the firm's exposure to FinTech development (Ding et al. 2022;Wang et al. 2022;Zhao et al. 2022). Briefly, this study examines the positive impact of city-level FinTech development on 2,841 listed firms' digital transformation in China from 2011 to 2020. In addition, we confirm two potential mechanisms of FinTech development on the digital transformation level, i.e., alleviating financial constraints and improving the structure of labor capital. Further, by dividing Fin-Tech development into two dimensions, we find that the depth of FinTech development, instead of the breadth, significantly affect digital transformation. Meanwhile, FinTech development has significant heterogeneous effects among different types of firms along with a variety of dimensions.
This paper contributes to the existing research literature in the following two ways. First, our paper extends the existing literature on measuring or quantifying firmlevel digital transformation. Extant literature mainly focuses on the points of the digital organization (Braña 2019), digital business model (Andal-Ancion et al. 2003;Westerman and Bonnet 2015), and digital strategies (Vial 2019;Hess et al. 2016). We innovatively introduce the textual analysis method in measuring the degree of firm-level digital transformation, which allows us to test the causes and consequences of digital transformation empirically.
Second, our research validates the literature on the role of the financial sector in digital transformation. Porf írio et al. (2021) analyzed how firms' characteristics, associated with management characteristics, promote digital transformation in Portuguese firms. Ding et al. (2022) showed that financially-constrained firms exhibit a disproportionally higher innovation level in cities with better-developed FinTech services. Our paper proves that the characteristics of the financial sector, especially Fin-Tech development, are crucial to digital transformation.
The remainder of the paper proceeds as follows. In Section 2, we review the related literature and correspondingly propose our hypothesis. In Section 3, we introduce our sample construction and the procedure to construct the firm-level digital transformation index. In Section 4, we report our main empirical results and mechanism tests. In Section 5, we incorporate the results of heterogeneity analysis and robustness tests. Finally, we conclude this paper in Section 6.

Literature, mechanism and hypothesis
It enjoys exceptional advantages to study FinTech development and digital transformation by using Chinese company samples. As early as 2019, China's adoption rate of FinTech was the highest in the world, at 87%, while the average adoption rate of the 27 largest markets in the world was only 64%. 1 At a practical level, FinTech in China has gradually expanded from microfinance at the preliminary stage to integrated financial services (Ding et al. 2022 Digital transformation is a company-wide phenomenon with broad organizational implications in which, most notably, the firm's core business model is subject to change through digital technology (Agarwal et al. 2010;Verhoef et al. 2021). Therefore, owning sufficient financial support and high-tech human resources is a necessary condition for the company's digital transformation, and FinTech development can precisely meet the needs.
First, FinTech can support the digital transformation goal by alleviating enterprises' financing constraints (Ling et al. 2021). Most directly, the FinTech development, with the help of advanced technology, has broadened the service scope and business boundary of traditional finance and can also provide more diversified and high-quality financial products (Cole et al. 2019;Erel and Liebersohn 2022). Therefore, Fin-Tech provides new financing channels for enterprises, and improves the quality of financial services and the accessibility of digital transformation financing. In addition, FinTech can establish the credit account of enterprises at a lower cost, reduce the information asymmetry between financial departments and enterprises, effectively reduce the financing cost of enterprises, and finally ease the financing constraints (Cheng et al. 2014). After obtaining adequate financial service supply and lower financing costs, enterprises will be able to carry out digital transformation (Ding et al. 2022).
Second, FinTech development is closely related to the transfer of labor force, and regions with a high degree of FinTech development will have more human resources to support the digital transformation of enterprises. Fundamentally, a well-functioning FinTech ecosystem is based on several core ecosystem attributes, of which talent is an essential factor (Haddad and Hornuf 2019). Arner et al. (2015) believed that skilled financial practitioners and highlevel scientific and technological personnel have jointly created the prosperity of regional FinTech (Cheng and Qu 2020). Therefore, when firms are located in cities with higher FinTech development, the prominent local talents will provide the soil for firms' digital transformation.
The arguments above suggest that FinTech development could motivate the digital transformation of enterprises. The above discussion leads to our hypothesis, and we show the underlying logic in Fig. 1.
Hypothesis: FinTech development has a positive impact on firms' digital transformation mainly through two channels, that is, alleviating financial constraints and providing prominent talents.

Sample construction
We begin with all Chinese public firms. A research team from the Institute of Digital Finance at Peking University and Ant Financial Services Group provide us with the FinTech development measures. Our sample period spans 2011 to 2020 due to the restricted sample period of city-level FinTech data. The Wind database contains the financial data of firms used in this paper. We glean the digital transformation metrics of firms from the China Economic News Database. 3 Besides, we utilize data from the National Bureau of Statistics of China at the city level.
We select our sample firms according to the procedures outlined below. First, if a company had ST, ST*, PT, etc., the company would be in an abnormal financial status. Thus, we exclude firms that received special treatment during the sample period. Second, we remove the financial-industry firms since their financial statement structure is significantly different from that of other industries. Third, to eliminate bias caused by insufficient data, we exclude firms with trading days for fewer than three trading years, i.e., firms with IPO dates after 2017. Following the sample selection methods above, the final sample for our study includes 27,959 firm-year observations from 2,841 unique firms.

FinTech development variables
Following Ding et al. (2022), we match each listed firm with the FinTech development index of the city where Page 4 of 11 Chen et al. Digital Economy and Sustainable Development (2023)  the firm's headquarter is located. About the data sources, we use the FinTech index constructed by the Institute of Digital Finance at Peking University using data from Ant Group mentioned above. We focus on the Total Digital Inclusive Finance Index (FinTech), the depth of the Digital Inclusive Finance Usage Index (FinTech_Depth), and the breadth of the Digital Inclusive Finance Usage Index (Fin-Tech_Breadth) in terms of regional FinTech development.

Corporate digital transformation variables
Digital transformation is a relatively new idea, making it challenging to quantify the degree of digital progress, particularly at the firm level. The definition of "digital transformation" is still up for debate in extant literature. In this paper, we separate the two facets of digital transformation into the digital application and digital technology.
After reviewing the current literature on the topic of digital transformation, as well as research reports, we set and select two distinct word sets. Four kinds of techniques, including artificial intelligence, big data, blockchain, and cloud computing, are included in the term set of digital technology. When creating the digital application word set, we consider digital operation, digital production, digital investment, and digital talent. It should be noted that the two kinds of word sets are distinct from one another. Specifically, the set of AI terms mainly includes {artificial intelligence, AI, AR, VR, robot, image identification, biometrics, face recognition, etc.}. The set of big data terms includes {big data, data center, data control, data mining, data assets, intelligent algorithm, etc.}. The set of blockchain terms includes {blockchain, digital currency, smart contract, timestamp, distributed ledger, etc.}. The set of cloud computing terms includes {cloud computing, graph computation, computer, 5G technology, cloud service, etc.}.
In terms of digital application, the set of digital operation terms mainly includes {O2O, online business, self-service retail, internet retail, e-commerce, digital solutions, etc.}. The set of digital production terms mainly includes {smart workshop, smart production, smart logistics, 3D printing, personalization, automatic control, integrated control, etc.}. The set of digital investment terms mainly includes {robot-advisor, digital finance, fintech, internet banking, smart financial contract, etc.}. The set of digital talent terms mainly includes {algorithm engineer, data mining engineer, data analyst, software engineer, etc.}.
After creating a relatively comprehensive term set for "digital transformation", we utilize it to crawl news articles about the sample listed companies from China Economic News Database following An et al. (2022). We conduct a full-text search for each news article using a full-part search via Python software to crawl firm-level digital-related news to see if it contains the abbreviated company name and corpus terms simultaneously. After that, we count the number of media articles about digital transformation for each company in each period. The variable DT, short for the digital transformation, denotes the natural logarithm of the total number of DT-related media news plus one. DT_Application and DT_Technology denote the natural logarithm of the number of a firm's media news related to digital application and digital technology plus one, respectively. The data on the DT-related variables are collected daily. To match the data frequencies of the explained and control variables, we sum the daily data to the annual data.

Control variables
We include a broad set of control variables in our various specifications, mainly consisting of firm-level controls and fixed effects. Following Ding et al. (2022), we choose the firm age (Age), the natural logarithm of total operating revenue (Revenue), return on assets (ROA), the leverage ratio (Lev), the ratio of capital expenditure to total assets (Capex), and Tobin Q (TobinQ) as firm-specific control variables. Along with firm-level controls, we include a set of fixed effects to absorb the influence of unobserved factors. We cluster standard errors at the firm level to account for the potential serial correlation within each individual. In addition, we also introduce some additional city-level control variables in the robustness test, mainly including city-level GDP (GDP), city-level fixed asset investment (Invest), city-level total population (Population), city-level financial development status (FD), and city-level foreign direct investment (FDI). We provide the definitions of all variables in our model in Table 1.

Empirical results
To test the effect of FinTech development on firms' digital transformation, we use the following two-way fixed effects specification, where DT i,t represents the degree of digital transformation measured by the number of corpus-related media news for firm i in year t . FinTech c,t denotes the FinTech development for city c where a firm is located in year t . X ′ is a set of firm-level control variables as listed below measured in year t , and θ i and δ t are cohort-firm and

TobinQ
Market value of equity divided by total assets  cohort-year fixed effects. All continuous variables are winsorized at the 1 st and 99 th percentiles. The coefficient of FinTech c,t , which captures the effect of FinTech development on corporate digital transformation, is expected to be significantly positive. Robust standard errors are clustered at the firm level for all estimates. Table 3 reports the regression results for the baseline analysis. Column (1) shows that the coefficient of FinTech is positive and statistically significant at the 1% level (coefficient = 0.0032 with t-statistic = 2.6508) for the DT regression, suggesting that the FinTech development measured by the Total Digital Inclusive Finance Index would boost firms' digital transformation. In columns (2) and (3), we replace the independent variable with the depth indicator of the Digital Inclusive Finance Usage Index (FinTech_Depth) and the breadth indicator of Digital Inclusive Finance Usage Index (FinTech_ Breadth). In column (3), the coefficient of FinTech_Depth is positive and statistically significant, suggesting that the depth of FinTech development is positively associated with corporate digital transformation. However, the Fin-Tech_Breadth indicator in column (2) does not significantly affect the digital transformation of firms.
Although both exist under the enterprise digital transformation framework, digital technology and digital application are fundamentally distinct. The development of digital technologies places a greater emphasis on technological advancement within a firm. By introducing or purchasing digital technologies, firms can rapidly implement digital applications. To further examine the impact of regional FinTech growth on the two aspects of digital application and digital technology of enterprises, we replace the dependent variables in Table 4 and present the results.
Columns (1)-(3) of Table 4 demonstrate the impact of FinTech development on the digital application by firms. Columns (4)-(6) of Table 4 show the role of FinTech development on firms' digital technology. The coefficients of FinTech in columns (1) and (4) of Table 4 are 0.0022 and 0.0037 and significant at the 5% and 1% levels, respectively, indicating that the development of FinTech can not only improve the application of outsourced digital technology in enterprises but also promote the digital technology itself. Table 4 further verifies the effects of depth and breadth of FinTech development on the digital transformation of enterprises in two aspects, and the results are consistent with Table 3, which demonstrates that the depth of FinTech development can significantly promote the digital application and digital technology, whereas the breadth of FinTech development has no such effect.

Mechanism tests
This section conducts the mechanism tests to determine which characteristics of regional FinTech development boost the degree of digital transformation among businesses. In Section 3, we propose that the emergence of FinTech can promote the degree of digital transformation through two mechanisms: relieving enterprises' financing restrictions and optimizing high-tech human resources. Below, we empirically test these two mechanisms independently with the following model.
where the dependent variable Mechanism contains several proxy variables for our possible channels, and other settings are consistent with those in Eq. (1).
Due to the fact that the KZ index and WW index, which measure financing constraints, tend to incorporate endogenous financial indicators, while the development of FinTech does not impact objective parameters such as firm size and firm age, we refer to Hadlock and Pierce (2010) to develop corporate financing constraints FC index. The impact of regional FinTech development on the financing restrictions of enterprises is depicted in column (1) of Table 5. It can be seen that enterprises (2) will be subject to lower levels of financing constraints if they are located in cities with a higher degree of Fin-Tech development. The results in column (1) also verify the mechanism of FinTech development's involvement in facilitating the digital transformation of businesses by reducing the financing constraint. Second, we attempt to verify the other possible mechanism primarily through the structure of firms' technology personnel and technicians, i.e., regions with advanced FinTech will attract more technology and R&D talent, which will become fertile ground for firms to accelerate their digital transformation. Columns (2)-(4) in Table 5 show how the development of FinTech affects the number of technicians (Tech_Num), the proportion of technicians (Tech_Ratio), and the proportion of R&D personnel (RD_Ratio), accordingly. In column (5) of Table 5, we replace the dependent variable with the firm's total R&D capital investment (RD_Cost), and the results indicate that firms in regions with stronger FinTech development will also increase their R&D capital investment, which      serves as a factor source of digital transformation alongside the significant growth of highly skilled personnel. The findings in Table 5, to some extent, can validate the research hypothesis of this paper, that is, the development of regional FinTech can provide the necessary conditions and thereby facilitate the digital transformation of enterprises by alleviating corporate financial constraints and attracting highly specialized talents.

Heterogeneity analysis
Despite the fact that local FinTech development can assist the digital transformation of firms by alleviating financial limitations and attracting highly trained talents, this effect could vary based on the nature of the businesses. In this subsection, we study if the facilitation effect of FinTech growth differs for undergoing digital transformation based on firms' specific characteristics. Table 6 contains interaction terms for the regional Fin-Tech development index and operating circumstances (Revenue), profitability (ROA), valuation (TobinQ), capital expenditure share (Capex), property rights attributes (SOE), and firm size (Size).
Columns (1)-(3) in Table 6 reveal that the impact of regional FinTech development on corporate digital transformation is more pronounced for firms with superior fundamental performance and higher secondary market valuation. These businesses with superior business (Revenue), profitability (ROA), and secondary market valuation (TobinQ) are better positioned to capitalize on the regional FinTech development and rapidly implement and accelerate their digital transformation.
The significantly negative coefficient estimate of the interaction term in column (4) of Table 6 demonstrates that the effect of FinTech development on digital transformation is greater for firms with a smaller proportion of capital expenditures. In column (5), the significant positive association between FinTech development and digital transformation is especially pronounced for non-SOEs. In column (6) of Table 6, we further analyze the effect of firm size. The results indicate that larger firms with higher FinTech development levels would stimulate digital transformation, which proves that large firms are better positioned for digital transformation.

Robustness tests
In this section, we conduct a series of tests to validate the robustness of FinTech development in facilitating the digital transformation of businesses.
First, since it may take some time for regional FinTech development to reach the firm level, we lag the independent variable and controls by one period. The findings of lagging these factors by one period are presented in column (1) of Table 7. The results are consistent with the main results, which can also partly alleviate the endogeneity concern. Second, the frequency of digital transformationrelated phrases in corporate annual reports has increasingly been utilized as a proxy variable for corporate digital transformation. Based on this, we also utilize the frequency of digital-transformation-related terms in corporate annual reports as an alternative measure of the degree of digital transformation among businesses and show the results in column (2) of Table 7. Specifically, data on the frequency of digital-transformation-related phrases in the annual reports of sample firms are acquired from the China Stock Market and Accounting Research Database (CSMAR). It should be highlighted that there are possible flaws in how companies' digital transformations are measured in the text form of their annual reports, which were gathered from the CSMAR database, for reasons such as missing data. We can conclude that the digital transformation indicators based on news media built in this paper have better advantages in terms of data volume than the digital transformation under the existing annual report measures by comparing the sample sizes in columns (2) and (3) of Table 7.
Third, there may exist the concern that many time series variables include a shared trend component that makes correlations look stronger than they actually are. For example, Fintech and digital transformation all move together over time or industry on average, regardless of a causal relationship. Thus, to alleviate the concern, we introduce industry-year fixed effects into the regression. The results are reported in column (3) of Table 7, and we can find that the coefficient estimate of interest is still significantly positive at the 10% level, which is consistent with our main results.
Fourth, considering FinTech development as citylevel data, we additionally control for city-level control variables that may affect the experimental results in column (4) of Table 7, which include city-level GDP (GDP), city-level fixed asset investment (Invest), citylevel total population (Population), city-level financial development status (FD), and city-level foreign direct investment (FDI). Results in Table 7 corroborate the validity of earlier findings that FinTech development can facilitate the degree of digital transformation.

Conclusions
Digital transformation is a trend that firms must cater to in the new era. This paper mainly studies whether the frontier development of the financial sector can provide fertile soil for this trend. In order to conduct the quantitative test, we creatively construct the corporate digital transformation index, which overcomes the obstacle of quantifying the digital transformation degree in the previous literature.  (1), we lag the explanatory variables and controls by one period. In column (2), we utilize the frequency of digital transformation-related terms in corporate annual reports as an alternative measure of the degree of digital transformation among businesses. In column (3), we additionally control for industry-year fixed effects. In column (4), we additionally control for city-level control variables that may affect the experimental results. Standard errors are clustered at the firm level. Significance at the 1%, 5%, and 10% levels is indicated by a , b , and c , respectively On this basis, our empirical results prove that Fin-Tech development plays a positive role in the digital transformation process of enterprises, and its depth is more effective, not its breadth. When we divide digital transformation into application level and technology level, this significant positive effect still holds. The plausible channel is that the FinTech development has eased the financial constraints, increased the number and proportion of high-tech talents in enterprises, encouraged enterprises to invest more in R&D expenses, and finally realized the promotion of digital transformation. Further analysis also shows that the positive impact of FinTech is more pronounced for enterprises with better performance. On the contrary, for state-owned enterprises or enterprises with more capital expenditure, the promotion effect of FinTech on digital transformation is weaker. Our results are robust after a battery of tests.
Our paper complements the solutions of the financial sector to promote the digital transformation of enterprises, that is, to vigorously promote FinTech development. The conclusion of this paper fills the gap in measuring the digital transformation of enterprises, and confirms the positive role of FinTech development on digital transformation and its potential mechanism.
Our results indicate that the government can promote the digital transformation of enterprises by promoting the development of the financial sector.