COVID-19, Digital Transformation of Banks, and Operational Capabilities of Commercial Banks

Abstract

The development of financial technology has promoted the innovation and digital transformation of commercial banks. Through digital transformation, commercial banks can improve bank efficiency and operational capabilities. Through empirical analysis, this study explored the relationship between digital bank transformation and commercial bank operating capabilities and how COVID-19, bank categories, and enterprise life cycles affect the relationship between digital bank transformation and commercial bank operating capabilities. This study selected data from China’s commercial banks from 2011 to 2021 and used the regression method of fixed effects to conduct an empirical analysis. The research results show that the digital transformation of banks has improved the operational capabilities of commercial banks. Further analysis showed that the emergence of COVID-19 has negatively affected their relationship. At the same time, compared with rural commercial banks and commercial banks in the recession and phase-out periods, non-rural commercial banks and commercial banks in the growth and maturity stages play a more vital moderating role in the impact of the digital transformation of banks on the financial performance of commercial banks. The main research object of this study is Chinese commercial banks, and this study examines the results of banks’ digital transformation and enriches the research on digital transformation. At the same time, this study is helpful to investors who like investment banks and has good practical significance.

1. Introduction

With the rise of 5G, big data, artificial intelligence, blockchain, and other digital technologies, the wave of digitalization has swept the world, and the world has entered the era of the digital economy [1]. Residents’ consumption has turned to the online environment and presents a trend of personalization, differentiation, and diversification [2]. The emergence of COVID-19 has accelerated the pace of digital economy construction [3].

In the banking industry, new financial services such as digital renminbi, online loan issuance, intelligent wealth management, online account managers, and fund screening continue to emerge. It is digital technology that promotes the digital transformation of commercial banks and enhances the competitiveness of commercial banks and customer service capabilities. At the same time, the People’s Bank of China issued two fintech development plans in 2019 and 2022, emphasizing that financial institutions should accelerate digital transformation [4]. In 2022, the “14th Five-Year” Digital Economy Development Plan issued by the State Council of China pointed out that it is necessary to accelerate the digital transformation of the financial sector and promote the in-depth application of digital technologies such as big data, artificial intelligence, and blockchain in commercial banks and other fields [5].

As a traditional industry, commercial banks are the pioneers of informatization [6]. Facing the advent of the digital economy and the Internet economy, if the survival and development of commercial banks are to adapt to the development of the times, they need to continue to carry out digital transformation [7]. At the same time, commercial banks, as an essential part of the financial system, are actively or passively undergoing a digital transformation under the impact of the digital economy and financial technology [8]. Commercial banks mainly use digital technologies such as artificial intelligence, big data, cloud computing, and blockchain to digitally adjust their traditional business products, service methods, and organizational structures [9]. The comprehensive digital transformation of commercial banks is a problem and an ongoing task for commercial banks.

How does the digital transformation of commercial banks affect operational capabilities?

First, commercial banks’ digital transformations will reduce operating costs and management expenses. After a bank’s digital transformation, the number of customers visiting the store will decrease, and the bank will “reduce face and press counters”, the area used by business outlets, rent expenses, and compress counters, which will liberate some tellers who can then be invested in marketing lines and improving bank performance. At the same time, the number of employees in the operation line will decrease sharply, the number of employees required will decrease, and the number of new employees recruited will decrease, thereby saving human resource costs [10].

Secondly, commercial banks’ digital transformations will increase interest and non-interest income [11]. After a bank’s digital transformation, optimizing the loan process will improve the efficiency of the loan business and increase the number of loan customers, and the enrichment of loan products will increase the types of loan customers [12]. Interest income from loans will increase the interest income of commercial banks. The introduction of online customers will increase the sales of gold, insurance, and precious metals and increase the non-interest income of commercial banks [13].

Finally, the digital transformation of commercial banks will improve the efficiency of bank operations. After the digital transformation of banks, the internal efficiency of commercial banks will be improved, thereby improving the financial performance of commercial banks [14]. After digital transformation, commercial banks’ internal approval (office, human resources, etc.), business approval (loan placement, sales, wealth management, etc.), and risk control (post-loan review reminder, whether loan funds have been embezzled, etc.) will all be streamlined. This can improve work efficiency and allow employees to better invest in marketing. The digital transformation of banks can realize financial sharing [15]. When reimbursing accounts, grassroots sub-branches and outlets only need to submit the original bills, and grassroots financial personnel need to review and upload their bills. Financial approval and reimbursement can be directly handled by superior branches or provincial branches, which improves the internal financial efficiency of the bank, saves staff time costs, and improves the marketing, service to customers, and financial performance of the commercial bank. After a bank’s digital transformation, the head office can obtain the deposit status of the branches under its jurisdiction, and the assets and liabilities department of the head office can conduct lending and centralized investment to maximize the benefits of assets. Subordinate branches are priced according to the FTP of the head office, which can obtain a stable income, avoid idle funds, improve the bank’s financial management efficiency, and thus improve the financial performance of the commercial bank [16].

There are very few studies on the impact of digital bank transformation on the operational capabilities of commercial banks, and most of them are based on literature reviews, quantitative research, and case analysis. Even fewer studies have used Chinese commercial banks as research samples and conducted empirical analysis. Naimi-Sadigh, Asgari, and Rabiei [9] pointed out that in the era of the digital economy, digital technology is the primary support for the digital transformation of commercial banks. Commercial banks can improve efficiency and enhance operating capabilities and profitability through digital transformation. At the same time, their study examined the current progress and outcomes of digitization. However, few related studies are based on the financial and banking industries, and many studies still need to be based on literature reviews and quantitative research. The research described business models in the era of the digital economy. It also discussed commercial banks’ ICT vision, goals, and mission statements through the case analysis method. The position of commercial banks in the current market was determined, and a four-step model was used to draw a commercial bank capability gap matrix analysis to describe banks’ digitalization strategy. Tang and Yang [17] studied the impact of digital bank transformation and corporate performance in sustainable development and carried out a case study using behavioral integration as an intermediary variable. The bank’s efficiency, operating capacity, growth, and profitability were analyzed in the study. Banks’ digital transformations can improve bank efficiency, operating capacity, growth, and profitability, thereby improving corporate performance. However, the nature of digital transformation should cause us to question some aspects. At the same time, how should we understand the digital transformation of banks? The impact of digital transformation on commercial banks is not only at the economic and social levels. Specifically, the operational effect is not only the result of a commercial bank’s actions but also the result of the environment in which it operates. Therefore, it is necessary to introduce corresponding moderator variables for further research, such as COVID-19.

Based on the above background, this study explores the relationship between the digital transformation of banks and the operational capabilities of commercial banks through empirical analysis. At the same time, the new crown epidemic, bank category, and enterprise life cycle are introduced as adjustment variables. Empirical research was carried out with data from Chinese commercial banks.

Compared with the existing literature, this paper makes the following contributions: First, taking the digital transformation of commercial banks as an entry point, it expands the research on the operational capabilities of commercial banks. Existing studies have explored the relationship between commercial bank governance structure, bank capability models, strategic investors, etc., and commercial bank financial performance. However, few pieces of literature have focused on the impact of commercial bank digital transformation on operational capabilities. Second, this paper examines the impact of COVID-19 on the operational capabilities of commercial banks during the digital transformation of banks. Third, the impact of digital bank transformation and commercial bank operating capabilities is analyzed from the perspective of heterogeneity, which provides a helpful reference for commercial banks to formulate differentiated digital transformation strategies. Fourth, by introducing the enterprise life cycle, this study examines the impact of the digital transformation of commercial banks in different life cycles on their operational capabilities.

2. Literature Review and Theoretical Hypotheses

Operational capability is the ability of commercial banks to survive and develop, reflecting the operational, income-generating, and sustainable development capabilities of commercial bank managers [18]. Banks’ digital transformation is needed for the sustainable development of commercial banks. It is also a response to the policies of the Chinese government and a manifestation of the management capabilities of managers. Based on agency theory and signal transmission theory, after the digital transformation of banks, the impact on the financial performance of commercial banks can allow investors and shareholders to understand the results of the digital transformation of banks and the operational capabilities of commercial banks. The result of the digital transformation of banks reflects the performance of managers, and it is also related to the positions and salaries of managers, which affects their employment status. The operational capability of commercial banks reflects the operational capabilities of managers. Based on the theory of information asymmetry, shareholders and investors reduce information asymmetry through financial statements, operating conditions, and managers’ operating capabilities to understand commercial banks better.

The operational capability of a commercial bank mainly depends on how it effectively uses funds to improve and increase its business activities [19]. After the digital transformation of banks, based on big data, commercial banks can screen high-quality customers, allocate limited funds to customers with good credit and the ability to repay, and provide different loan interest rates according to qualifications to maximize the use of funds and improve their operational capabilities, thereby improving their financial performance [20]. Based on big data, commercial banks can promote different products to different customers, directly contact customers through smart outbound calls, liberate account managers, allow account managers to have time to market to critical customers, improve their operational capabilities, and thus enhance their profitability and financial performance. The digitization of commercial banks’ credit systems can simplify business processes and enable better post-loan management. For abnormal loans and customers whose loans are not used according to regulations, they can be identified in advance and tracked after the loan, effectively reducing non-performing loans and non-performing loan rates. After a bank’s digital transformation, the excess funds of the branch can be lent to other banks and other branches in the system to maximize the use of funds and improve the operational capabilities of commercial banks, thereby improving their financial performance [21]. Kwan et al. (2021) showed that banks with better IT resources experienced larger reductions in physical branch visits and larger increases in website traffic during the pandemic, implying a larger shift to digital banking [22]. Khalifaturofi‘ah et al. (2022) showed that banks with better IT resources provide a higher number of cheaper and faster guaranteed loans and lend more in areas where they have no bank branches [23].

A commercial bank’s operating capability’s strength reflects its long-term and sustainable profitability and is an essential indicator that regulators and investors value. Based on signal transmission theory, commercial banks have solid operational capabilities, are more likely to attract investors’ attention, and can improve their financing capabilities and sustainable development capabilities. Therefore, this study proposes Hypothesis 1:

Hypothesis 1 (H1):

The digital transformation of banks has improved the operational capabilities of commercial banks.

The emergence of COVID-19 at the end of 2019 has had a massive impact on the economy and travel of various countries. Jin et al. [24] showed that COVID-19 significantly impacted the tourism industry. Sohibien et al. [25] took Indonesia as a sample, and their research showed that COVID-19 had a significant impact on the financial industry’s operating performance and financing belt. Here comes the negative. With the implementation of various restrictive epidemic prevention policies, business operations and people’s travel were greatly restricted. Therefore, ample data information, contactless service models, and online Internet products developed rapidly during the COVID-19 pandemic. The development of digital technology can use its Internet technology to meet non-contact needs during the epidemic. At the same time, it can meet customers’ shopping, consumption, and financial needs. In China, COVID-19 has also significantly impacted the financial industry, especially the banking industry. Due to the emergence of third-party payment platforms such as the Internet, online banking, and Alipay, the number of commercial banks’ in-store customers has decreased, and deposits have been lost. Commercial banks have begun or have already carried out digital transformation. The emergence of COVID-19 has accelerated the process of the digital transformation of commercial banks. However, customers are restricted from traveling, causing many customers to lose their source of income. The non-performing loan ratio of commercial banks and the number of overdue credit card customers continue to increase [26]. Commercial banks have adopted measures such as lowering interest rates or delaying repayment times to reduce the non-performing loan ratio. At the same time, Phan Thi Hang [27] also proposed how commercial banks can prevent credit crises in the post-COVID-19 period. However, it is undeniable that the emergence of COVID-19 has made it difficult for commercial banks to recover funds, has affected the number of funds, and has affected the profitability of commercial banks. The decrease in customer income and purchasing power has led to a decrease in the non-interest income of commercial banks and affected their profits, thus affecting their operational ability and financial performance. Before and after the outbreak of COVID-19, there were significant differences in commercial banks’ operational capabilities, operating capabilities, and profitability.

Given the magnitude of the COVID-19 pandemic, governments and central banks around the world implemented a range of fiscal [28] and monetary policies [29,30] to mitigate the economic hardship caused by the public health crisis. China itself did a relatively good job controlling the pandemic after the first wave in 2020, but the combination of economic policies and public health interventions affected banks’ performances.

The massive liquidity injection by central banks of developed and emerging market economies generated a search-for-yield effect with abnormal capital flows to emerging markets such as China [29]. Chinese banks may have benefitted from this injection of liquidity, which coincided with the growing adoption of digital technologies by Chinese banks.

Fiscal policies implemented by the governments of developed nations (e.g., the US) were particularly aggressive. Whereas fiscal policies aiming to provide financial backing to businesses and banks can be construed as government guarantees, the associated capital outlays affect the credibility of future fiscal policies. More specifically, government guarantees affect the banking sector [31], and increased government deficits (due to fiscal stimuli) affect the real economy through their effect on fiscal multipliers [32].

Therefore, this study proposes Hypothesis 2:

Hypothesis 2 (H2):

The emergence of COVID-19 has negatively affected the ability of the digital transformation of banks to improve the operational capabilities of commercial banks.

Chinese commercial banks are classified according to different natures. They are mainly divided into the central bank, policy banks, large state-owned commercial banks, joint-stock commercial banks, city commercial banks, rural commercial banks, and foreign banks [33]. Different types of commercial banks have different advantages and characteristics.

In China, the types of banks can be divided into rural commercial banks and non-rural commercial banks [34]. Rural commercial banks are joint-stock local financial institutions formed by farmers, rural industrial and commercial households, corporate legal persons, and other economic organizations within their jurisdiction. As essential financial support for serving “Sannong,” rural commercial banks can not only open up the “last mile” of financial services after digital transformation but can also analyze the financial needs of all social strata and groups through big data and increase the added value of financial products and service stickiness.

The digital transformation of banks will increase business operating costs [35]. Commercial banks must invest a lot of technical personnel, research, and development expenses into their digital transformation. According to statistics from the China Banking and Insurance Regulatory Commission, in 2020, commercial banks’ total investment in information technology funds reached CNY 207.8 billion, a year-on-year increase of 20%. Some commercial banks’ technology investments accounted for 4% of their operating income [36]. According to data from the Kubei Research Institute, there are 42 listed banks in China’s A-share market. Among them, 22 disclosed the amount of science and technology investment in 2021, totaling CNY 168.132 billion. Furthermore, three commercial banks invested more than CNY 20 billion in technology in 2021, which were all large state-owned banks. China Merchants Bank is the joint-stock bank with the highest investment, reaching CNY 13.291 billion. According to the annual reports released by listed banks, the commercial banking industry is increasing investment in technology capital and introducing technology talents to accelerate the realization of banks’ digital transformation. The increase in scientific research personnel has increased the management expenses of commercial banks; the investment in research and development expenses has increased the operating costs of commercial banks. Rural commercial banks have the advantages of localization, capital management and control, and information acquisition efficiency; they have the disadvantages of weak resistance to risks, small service areas, a single-business structure, and a lack of human resources.

Through digital transformation, the disadvantages of rural commercial banks can be effectively improved, but large amounts of scientific and technological personnel and research and development expenses are required. At the same time, rural commercial banks have the characteristics of a small scale and many outlets, leading to relatively large resistance for rural commercial banks when the digital transformation of banks affects the operational capabilities of commercial banks. Therefore, this study proposes Hypothesis 3:

Hypothesis 3 (H3):

The digital transformation of banks plays a minor role in improving the operational capabilities of rural commercial banks compared to non-rural commercial banks.

The digital transformation of banks needs to invest a lot of personnel and expenses and sometimes needs to obtain financing, e.g., from the capital market. Investment and risk are related, and financing difficulties are more concentratedly reflected in certain specific periods of commercial banks. According to enterprise life cycle theory, a commercial bank can be regarded as a living organization that needs to go through the development stages of germination, growth, maturity, decline, and elimination [37]. In different life cycle stages, there are significant differences in various aspects, such as cash flow and financing constraints. Therefore, commercial banks need to choose different solutions. When commercial banks are in different life cycle stages, the impact of bank digital transformations on commercial banks’ operational capabilities may be different. From the perspective of financing needs and banks’ digital transformation capabilities, commercial banks in the growth and maturity stages have more significant potential and financing needs than commercial banks in the recession and phase-out stages. According to the theory of financial exclusion and life cycle, investors will reduce financing support for commercial banks in the recession and phase-out stages and increase financing support for commercial banks in the growth and maturity stages. It is easier for growing and mature commercial banks that have obtained financing support from investors to realize digital transformation. At the same time, the digital transformation of banks also has different impacts on the operational capabilities of commercial banks in different life cycles. When a commercial bank is in the growth and maturity stages, it is easier to obtain the support of investors and shareholders, thereby increasing the investment support of scientific and technological personnel and research and development expenses, which is conducive to improving the commercial bank’s operational capabilities, operating capabilities, and profitability.

Therefore, this paper proposes Hypothesis 4:

Hypothesis 4 (H4):

Compared with commercial banks in the recession and phase-out periods, commercial banks in the growth and maturity stages have a more significant impact on the digital transformation of banks in improving the operational capabilities of commercial banks.

Figure 1 is the model of this study.

3. Research Design

3.1. Sample Selection and Data Sources

This study selected Chinese commercial banks from 2011 to 2021 as the research object.

Table 1. Number of commercial banks in China.

Year	Number of Banks	Number of Listed Banks
2011	180	16
2012	216	16
2013	255	16
2014	291	16
2015	301	16
2016	312	25
2017	334	26
2018	332	32
2019	350	36
2020	349	38
2021	306	42
Total	3226	279

Note: The data in the table came from the Guotaian database. Listed companies refer to companies that are listed on the A-share market in China.

lists the number of commercial banks in China. In order to avoid sample bias, we selected all commercial banks across China as samples, including listed and non-listed banks. The sample data were processed as follows: (1) Samples with missing data were eliminated. (2) Samples with outliers were eliminated. Finally, 1419 sample data were obtained. The data on bank digital transformation in this study came from the “Bank Digital Transformation Index” compiled by the research group of the Digital Finance Research Center of Peking University. The rest of the data came from the Guotaian database.

In order to eliminate the impact of outliers on this study, the sample data were shrunk by 1%. At the same time, to reduce heteroscedasticity interference, some main continuous variables were logarithmized in this study.

3.2. Variable Definition

(1)

Dependent variable

Commercial banks’ operating capacity (BC) is usually measured using the total asset turnover ratio, asset return rate, net interest rate, and net profit growth rate [38]. In order to conform to the research of this paper, after careful consideration, this research selected net profit and total asset turnover as the proxy variables of operating capacity.

(2)

Independent variable

At present, scholars mainly measure the indicators of digital bank transformation (BDT) through the number of mobile banking registrations, monthly active customers (MAU) of mobile banking [39], the frequency of “digital transformation” words in annual reports, and investment in financial technology [40]. However, this method has specific gaps and cannot fully reflect the degree of banks’ digital transformations. Therefore, this study used the Bank Digital Transformation Index developed by the Digital Finance Research Center of Peking University in 2022 as a proxy variable for digital bank transformation [41]. The index is a set of indicators for the digital transformation of banks constructed from the three dimensions of strategy, business, and management. Among them, strategic digitalization focuses on a bank’s attention on digital technology. It is specifically constructed by counting the mentions of digital-technology-related keywords in the text of the bank’s annual report: specifically, artificial intelligence, blockchain, cloud computing, big data, online, and mobile. Business digitization focuses on the extent to which banks integrate digital technology into the financial services they provide. The digitization of a banking business is measured through the three dimensions of digital channels, digital products, and digital research and development. The management of the digitalization of banks focuses on how banks integrate digital technology into governance structures and organizational management, and is measured through the three dimensions of digital architecture, digital talents, and digital cooperation. The digital transformation of Chinese commercial banks is measured from an all-round and multi-angle perspective, which can more comprehensively reflect the digital transformation of banks.

(3)

Modulating variable

The impact of COVID-19 was tested by Jin, Gao, and Xiao [24] using PSM-DID. In order to test the impact of COVID-19 in this study, the study of Sohibien, Laome, Choiruddin, and Kuswanto [25] was used for reference, and the dummy variable COVID19 was introduced as a proxy variable for COVID-19. If the year belonged to 2020–2021, it was assigned a value of 1. Otherwise, it was assigned a value of 0. The impact of digital bank transformation on the operational capabilities of commercial banks of different bank natures was grouped by bank nature. According to the bank type (BT) classification of rural and non-rural commercial banks, rural commercial banks were assigned a value of 1, and non-rural commercial banks were assigned a value of 0. Readers can refer to the method of Gao and Jin [37] for assessing the life cycle of enterprises. According to the cash flow calculation, a value of 1 was assigned to the growth and maturity periods, while a value of 0 was assigned to the other periods.

(4)

Control variables

In order to exclude the interference of other factors on the results of this study, this study drew on the research results of Liu et al. [42]. Firm size (SIZE), solvency (LEV), growth (GRO), firm age (AGE), ownership concentration (TOP1), firm nature (SOE), and annual effect (YEAR) were selected as controls for this study variable.

At the same time, to control the impact of time factors that are difficult to observe in this study, this study used time and year effects (YEAR) as its proxy variable.

Please refer to

Table 2. Variable definitions.

Variable	Variable Name	Variable Code	Variable Definitions
Dependent Variable	Commercial Bank Operating Capacity	BC	Operating income/total assets × 100%
Independent Variable	Bank Digital Transformation	BDT	Peking University Digital Finance Research Center
Moderator	COVID-19	COVID19	Dummy variable, 1 for 2020–2021, 0 for others
	Bank Type	BT	Dummy variable, 1 for rural commercial banks and 0 for others
	Enterprise Life Cycle	ELC	The dummy variable, calculated according to cash flow, takes 1 when the commercial bank is in the growth and maturity stages, and 0 otherwise
Control Variable	Bank Size	SIZE	The natural logarithm of the total assets at the end of the year
	Solvency	LEV	Total liabilities at the end of the year/total assets at the end of the year
	Growth	GRO	Operating income growth rate
	Bank Age	AGE	Ln (observation year—bank establishment year + 1)
	Concentration of Ownership	TOP1	The shareholding ratio of the largest shareholder
	Bank Nature	SOE	Dummy variable, 1 for state-owned holdings, 0 otherwise
	Individual Effect	COMPANY	Commercial bank individual dummy variables
	Annual Effect	YEAR	Year dummy variable

for the specific variables and their definitions.

3.3. Model Design

(1)

Benchmark regression model

In order to support Hypothesis 1 of this study, that is, the digital transformation of banks has improved the operational capabilities of commercial banks, referring to the practice of Gao and Jin [37], benchmark Models (1) and (2) were constructed to control the annual effect and personal effect. Among them, Model (2) is based on Model (1) with control variables added.

$$BC=\beta +{\beta }_{1}BDT+\sum \mathrm{Company}+\sum \mathrm{Year}+\epsilon$$

(1)

$$BC=\beta +{\beta }_{1}BDT+{\beta }_2\sum \mathrm{Control}+\sum \mathrm{Company}+\sum \mathrm{Year}+\epsilon$$

(2)

(2)

Moderating effect model

In order to explore the influence of moderating variables between the digital transformation of banks and the operational capabilities of commercial banks, that is, to verify assumptions 2–4, referring to the practice of Gao and Jin [37], regression Models (3)–(5) were constructed to control the annual effect and individual effect.

$$BC=\beta +{\beta }_{1}BDT+{\beta }_{2}COVID19+{\beta }_{3}BDT\times COVID19+{\beta }_4\sum \mathrm{Control}+\sum \mathrm{Company}+\sum \mathrm{Year}+\epsilon$$

(3)

$$BC=\beta +{\beta }_{1}BDT+{\beta }_{2}BT+{\beta }_{3}BDT\times BT+{\beta }_4\sum \mathrm{Control}+\sum \mathrm{Company}+\sum \mathrm{Year}+\epsilon$$

(4)

$$BC=\beta +{\beta }_{1}BDT+{\beta }_{2}ELC+{\beta }_{3}BDT\times ELC+{\beta }_4\sum \mathrm{Control}+\sum \mathrm{Company}+\sum \mathrm{Year}+\epsilon$$

(5)

Among them, BC represents the dependent variable commercial bank operating capacity, BDT represents the independent variable bank digital transformation, COVID19 represents the moderator variable COVID-19, BT represents the moderator variable bank type, ELC represents the moderator variable enterprise life cycle, Control represents the control variable, β represents the coefficient of each variable, Company represents the personal effect, Year represents the annual effect, and ε is a random disturbance term.

4. Research Results

4.1. Descriptive Statistics

The descriptive statistics of the sample data are shown in

Table 3. Descriptive statistics.

Variables	N	Mean	SD	Min	Max
BC	1419	2.197	0.616	0.540	3.800
BDT	1419	58.50	37.06	0	159.1
COVID19	1419	0.188	0.391	0	1
BT	1419	0.232	0.422	0	1
ELC	1419	0.600	0.490	0	1
SIZE	1419	25.93	1.572	23.08	30.78
LEV	1419	91.91	3.145	73.88	95.74
GRO	1419	12.86	22.45	−54.42	110.7
TOP1	1419	27.31	28.10	4.860	100
SOE	1419	0.0359	0.186	0	1
AGE	1419	2.709	0.534	1.099	4.159

. The average value, standard deviation, and minimum and maximum values of the dependent variable commercial bank operating capacity (BC) are 2.197, 0.616, 0.540, and 3.800, respectively. The data show a significant gap among the commercial banks’ operating capabilities (BC), which are relatively scattered. Some commercial banks perform better, and some perform poorly, but the overall performance is still good. The mean, standard deviation, minimum, and maximum of the independent variable bank digital transformation (BDT) are 58.50, 37.06, 0, and 159.1, respectively. The data show significant differences in digital transformation progress among commercial banks. Some commercial banks are progressing faster, and others still need to start. The overall degree of digital transformation still needs to improve. This may be related to the strategic planning of commercial banks. The average value, standard deviation, and minimum and maximum values of the adjustment variable bank type (BT) are 0.232, 0.422, 0, and 1, respectively. The data show that in China, rural commercial banks account for a large proportion; although the number is less than that of non-rural commercial banks, it is still nearly 1/4. The average value, standard deviation, and minimum and maximum values of the adjustment variable enterprise life cycle (ELC) are 0.600, 0.490, 0, and 1, respectively. The data show that most commercial banks are in the growth and maturity stages.

4.2. Related Analysis

The correlation analysis of the sample is shown in

Table 4. Correlation analysis.

Variables	BC	BDT	COVID-19	BT	ELC	SIZE	LEV	GRO	TOP1	SOE	AGE
BC	1
BDT	0.103 ***	1
COVID19	−0.144 ***	0.408 ***	1
BT	0.152 ***	−0.077 ***	0.0390	1
ELC	0.089 ***	−0.076 ***	−0.0300	−0.0250	1
SIZE	0.128 ***	0.653 ***	0.123 ***	−0.219 ***	0.0230	1
LEV	0.058 **	0.207 ***	−0.065 **	−0.0230	0.135 ***	0.362 ***	1
GRO	0.133 ***	−0.137 ***	−0.139 ***	−0.091 ***	0.0300	−0.0190	0.0160	1
TOP1	−0.180 ***	−0.099 ***	0.0370	−0.342 ***	−0.077 ***	−0.052 **	−0.521 ***	0.00800	1
SOE	0.147 ***	0.241 ***	0.00400	−0.106 ***	0.104 ***	0.548 ***	0.044 *	−0.0410	0.134 ***	1
AGE	−0.115 ***	0.495 ***	0.234 ***	−0.362 ***	−0.0310	0.507 ***	0.205 ***	−0.102 ***	−0.069 ***	0.329 ***	1

Note: “*”, “**”, and “***” in the table represent significance at the 10%, 5%, and 1% levels, respectively.

. The data show that there is a significant positive correlation between the dependent variable commercial bank operating capacity (BC) and the independent variable bank digital transformation (BDT), with a correlation coefficient of 0.103 (1% level), and to a certain extent, this supports Hypothesis 1 of this study, that is, the digital transformation of banks has improved the operational capabilities of commercial banks. The variance inflation factors (VIFs) are all less than 4, with a mean of 1.74. This means that multicollinearity is negligible for the primary outcome of this study.

4.3. Empirical Analysis Results

(1)

Benchmark regression

According to the Hausman test results, the p-value is less than 0.05. Therefore, this study used a fixed effects model that controls year and individual effects for empirical analysis. The results of the benchmark regression analysis are shown in

Table 5. Benchmark regression.

	(1)	(2)
Variables	BC	BC
BDT	0.006 ***	0.005 ***
	(7.21)	(6.95)
SIZE		−0.169 **
		(−2.45)
LEV		−0.032 ***
		(−3.94)
GRO		0.001 ***
		(2.61)
TOP1		−0.008 ***
		(−3.04)
AGE		−0.095
		(−0.92)
CONSTANT	2.412 ***	9.959 ***
	(48.49)	(5.84)
COMPANY FE	YES	YES
YEAR FE	YES	YES
OBSERVATIONS	1419	1419
R-SQUARED	0.270	0.296
NUMBER OF IDS	202	202

Note: t-statistics in parentheses; *** p < 0.01, ** p < 0.05.

. In column (1), without adding the control variable, the dependent variable commercial bank operating capacity (BC) and the independent variable bank digital transformation (BDT) have a positive and significant correlation, with a correlation coefficient of 0.006 (1% level). In column (2), control variables are added based on column (1), and the dependent variable commercial bank operating capacity (BC) is positively and significantly correlated with the independent variable bank digital transformation (BDT), with a correlation coefficient of 0.005 (1% level). Compared with the control variable, the R-squared increases from 0.270 to 0.296, which indicates that the addition of the control variable affects the relationship between the dependent variable commercial bank operating capacity (BC) and the independent variable bank digital transformation (BDT). Therefore, Hypothesis 1 is supported again, that is, banks’ digital transformation improves commercial banks’ operational capabilities.

(2)

Moderator effect analysis

The regression analysis results of the adjustment effect are shown in

Table 6. Regression analysis of moderation effect.

	(1)	(2)	(3)	(4)
Variables	BC	BC	BC	BC
BDT	0.005 ***	0.006 ***	0.006 ***	0.004 ***
	(6.95)	(7.32)	(7.37)	(5.16)
COVID19		−0.452 **
		(−2.58)
BDT × COVID19		−0.002 **
		(−2.26)
BT			−0.771 **
			(−2.47)
BDT × BT			−0.002 **
			(−2.09)
ELC				−0.158 ***
				(−3.53)
BDT × ELC				0.002 ***
				(2.63)
SIZE	−0.169 **	−0.166 **	−0.184 ***	−0.173 **
	(−2.45)	(−2.41)	(−2.68)	(−2.52)
LEV	−0.032 ***	−0.034 ***	−0.032 ***	−0.030 ***
	(−3.94)	(−4.17)	(−3.93)	(−3.69)
GRO	0.001 ***	0.001 ***	0.002 ***	0.001 **
	(2.61)	(2.64)	(2.78)	(2.47)
TOP1	−0.008 ***	−0.008***	−0.007 ***	−0.007 ***
	(−3.04)	(−3.03)	(−2.93)	(−2.91)
AGE	−0.095	−0.129	−0.003	−0.105
	(−0.92)	(−1.23)	(−0.02)	(−1.02)
CONSTANT	9.959 ***	10.134 ***	10.283 ***	10.005 ***
	(5.84)	(5.95)	(6.05)	(5.87)
COMPANY FE	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES
OBSERVATIONS	1419	1419	1419	1419
R-SQUARED	0.296	0.299	0.303	0.303
NUMBER OF IDS	202	202	202	202

Note: t-statistics in parentheses; *** p < 0.01, ** p < 0.05.

. In column (2), the dependent variable commercial bank operating capacity (BC) is positively and significantly correlated with the independent variable bank digital transformation (BDT), with a correlation coefficient of 0.006 (1% level). At the same time, the interaction term between digital bank transformation (BDT) and COVID-19 (COVID19) is significantly negatively correlated with commercial bank operating capacity (BC) at the 1% level, and the regression coefficient is −0.002. This shows that the emergence of COVID-19 has negatively affected the ability of the digital transformation of banks to improve the operational capabilities of commercial banks. Therefore, Hypothesis 2 is supported.

In column (3), the dependent variable commercial bank operating capacity (BC) is positively and significantly correlated with the independent variable bank digital transformation (BDT), with a correlation coefficient of 0.006 (1% level). At the same time, the interaction term between digital bank transformation (BDT) and bank category (BT) is significantly negatively correlated with commercial bank operating capacity (BC) at the 1% level, and the regression coefficient is −0.002. This shows that compared with rural commercial banks, non-rural commercial banks play a more vital role in the ability of the digital transformation of banks to improve the financial performance of commercial banks. Therefore, Hypothesis 3 is supported, that is, banks’ digital transformation plays a minor role in improving the operational capabilities of rural commercial banks compared to non-rural commercial banks.

In column (4), the dependent variable commercial bank operating capacity (BC) is positively and significantly correlated with the independent variable bank digital transformation (BDT), with a correlation coefficient of 0.004 (1% level). At the same time, the interaction term between digital bank transformation (BDT) and enterprise life cycle (ELC) is significantly positively correlated with commercial bank operating capacity (BC) at the 1% level, and the regression coefficient is 0.002. This shows that compared with commercial banks in the recession and phase-out periods, commercial banks in the growth and maturity stages have a more significant impact on the ability of the digital transformation of banks to improve the operational capabilities of commercial banks. Therefore, Hypothesis 4 is supported.

5. Robustness Check

In order to test the robustness of the above conclusions, this study used a test based on the two-stage least squares model (2SLS) method for the robustness test.

Considering the bias caused by omitted variables and endogenous problems, this study referred to the practice of Gao and Jin [37], selected bank digital transformation (BDT) lagged by one period (LBDT) as an instrumental variable, and used the 2SLS method to conduct a robustness test.

The regression Models (6) and (7) are the first-stage and second-stage models of 2SLS, respectively.

$$BDT=\beta +{\beta }_{1}LBDT+{\beta }_2\sum \mathrm{Control}+\sum \mathrm{Company}+\sum \mathrm{Year}+\epsilon$$

(6)

$$BC=\beta +{\beta }_{1}BDT+{\beta }_2\sum \mathrm{Control}+\sum \mathrm{Company}+\sum \mathrm{Year}+\epsilon$$

(7)

Among them, LBDT is the data of BDT lagged by one period.

The regression results of 2SLS are shown in Table 6. In the first stage (column 1), the regression coefficient between BDT and LBDT of digital bank transformation is 0.357 (1% level); in the second stage (column 2), following the simulation of BDT and LBDT, the regression coefficient of commercial bank operating capacity is 0.014 (1% level). In addition, in

Table 7. Robustness test regression analysis.

	(1)	(2)
	First-Stage	Second-Stage
Variables	BDT	BC
LBDT	0.357 ***
	(12.12)
BDT		0.014 ***
		(5.76)
SIZE	2.283	−0.291 ***
	(0.73)	(−3.11)
LEV	−0.058	−0.018 *
	(−0.16)	(−1.66)
GRO	−0.021	0.004 ***
	(−0.82)	(4.84)
TOP1	−0.086	−0.011 ***
	(−0.82)	(−3.51)
AGE	−19.435 ***	0.286 *
	(−4.37)	(1.90)
COMPANY FE	YES	YES
YEAR FE	YES	YES
OBSERVATIONS	1117	1117
R-SQUARED		0.157
NUMBER OF IDS	186	186
UNDER-IDENTIFICATION TEST (KLEIBERGEN–PAAP RK LM STATISTIC)	244.309 (CHI-SQ(1) P-VAL = 0.0000)
WEAK IDENTIFICATION TEST (CRAGG–DONALD WALD F STATISTIC)	1493.585
(KLEIBERGEN–PAAP RK WALD F STATISTIC)	1035.078
10% MAXIMAL IV SIZE	16.38

Note: t-statistics in parentheses; *** p < 0.01, * p < 0.1.

, the under-identification test (Kleibergen–Paap rk LM statistic) statistic is 244.309 (0.0000), indicating that the instrumental variable is identifiable. At the same time, the Cragg–Donald Wald statistic is 1493.585, which is greater than the critical value of the Stock–Yogo weak ID test with a 10% judgment level of 16.38, meaning there is no weak instrumental variable problem. The above results show that after considering endogenous issues, banks’ digital transformation is still significantly positively correlated with the operational capabilities of commercial banks, which, once again, verifies the correctness of Hypothesis 1.

6. Discussion and Conclusions

6.1. Discussion

Bank digital transformation refers to the use of digital technology by commercial banks to innovate and optimize their internal traditional business models, settlement management, business processes, and customer service models [43]. In the existing research, the impact of bank digital transformation on commercial banks is minimal, and most studies have explored the impact of bank digital transformation on commercial bank financial performance. Moreover, the conclusions could be more consistent [41]. Some scholars believe that banks’ digital transformation has increased costs, such as technology investment, and has hurt financial performance. Some scholars have found through empirical analysis that banks’ digital transformation does not directly bring about a significant improvement in overall performance [44]. However, it can significantly improve the profitability of banks’ deposit and loan business and the efficiency of income acquisition, indicating that digital transformation does have a specific effect on banks’ operating performance. In similar studies, it was found that banks’ digital transformation has improved the service capabilities and bank efficiency of commercial banks. This is consistent with the research results of Soesilo & Tampubolon [45]. However, little attention has been paid to the impact of the digital transformation of banks on the operational capabilities of commercial banks. Therefore, a particular gap is created. The primary purpose of examining operating capabilities is to judge the operating capabilities of commercial banks’ assets. The strength of a commercial bank’s operating ability indicates the utilization degree and efficiency of its assets, which largely determines its operating efficiency and the resulting degree of protection for debt repayment. The continuous improvement of operating capabilities is the foundation of the survival of commercial banks [46]. In today’s fierce market competition, maintaining the continuous improvement of commercial banks’ operating capabilities has become the primary task of commercial banks’ survival and development. Therefore, improving the operating ability of commercial banks has become a problem that the leaders of commercial banks must consider. Therefore, this study has good theoretical and practical significance.

At the same time, banks’ digital transformation is based on the advancement of digital technology. However, our research lists the impact of bank digital transformation on the operational capabilities of commercial banks. However, changes in operating capabilities may be affected by the general environment, not just the results of commercial banks’ actions. This also provides a new idea for our future research direction, which will explore the relationship between banks’ digital transformations and commercial banks’ environments. The emergence of COVID-19, a global public health emergency, affected not only the tourism and catering industries but also commercial banks [37]. Therefore, this paper introduced COVID-19 as a moderator variable to examine the impact of COVID-19 on the operational capabilities of commercial banks. The research results show that the digital transformation of commercial banks is affected by COVID-19, which accelerates the pace of construction but harms their operational capabilities. In addition, based on the perspective of the nature of enterprises, we introduced the moderating variables of the nature of the bank and the life cycle of the enterprise and further explored how commercial banks of different natures and in different stages of the life cycle change their operational capabilities. This study found that non-rural commercial banks and commercial banks in the growth and maturity stages have a more significant impact on the ability of the digital transformation of banks to improve the operational capabilities of commercial banks. This is related to the scale of rural commercial banks, the particularity of China’s market, and China’s economic policies. At the same time, the theory of financial exclusion shows that commercial banks in the growth and maturity stages are more likely to obtain financing support, which can support commercial banks in their digital transformation.

6.2. Conclusions

This study selected Chinese commercial banks from 2011 to 2021 as the research object and studied the relationship between the digital transformation of banks and the operational capabilities of commercial banks. The impact of COVID-19 and the business life cycle were further considered. At the same time, from the analysis of heterogeneity, the moderator variable bank nature was used to test the impact of digital bank transformation on commercial bank operation ability.

The research results show the following: (1) The digital transformation of banks has improved the operational capabilities of commercial banks. (2) The emergence of the new crown epidemic has negatively affected the ability of the digital transformation of banks to improve the operational capabilities of commercial banks. (3) The digital transformation of banks has improved the operational capabilities of rural commercial banks. The digital transformation of banks plays a minor role in improving the operational capabilities of rural commercial banks compared to non-rural commercial banks. (4) Compared with commercial banks in the recession and phase-out periods, commercial banks in the growth and maturity stages have a more significant impact on the ability of the digital transformation of banks to improve the operational capabilities of commercial banks.

6.3. Implications

Internal and external environmental factors force commercial banks to carry out digital transformation. With the advent of the digital economy era, some commercial banks have begun a digital transformation, and the effect is evident [47]. Commercial banks have improved bank efficiency, operational capabilities, and profitability through digital transformation. At the same time, it promotes the improvement of the financial performance of commercial banks [48]. As a traditional industry, in the face of an era when the Internet and data are king, the survival and development of commercial banks must continue to undergo digital transformation. Commercial banks are an essential part of the high-quality development of the digital economy. In order to conform to the trend of the digital economy and promote the sustainable development of commercial banks themselves, the digital transformation of commercial banks will become a common phenomenon in the Chinese economic system for an extended period now and in the future [49]. As stated in the study of Xie and Wang [41], banks’ digital transformation does not necessarily bring direct economic performance, but at least it eases competitive pressure. Commercial banks should continue to develop and build digitalization in future development. In addition, while accelerating digital transformation, commercial banks must strengthen risk prevention and control [50]. Based on this study, the following suggestions are put forward: ① commercial banks should continue to carry out digital transformation; ② commercial banks should carry out digital transformation in a timely and appropriate manner according to their conditions; ③ commercial banks should improve customer service levels through digital transformation; ④ commercial banks should practice good risk prevention and control measures during digital transformation.

6.4. Limitations and Future Prospects

The research object of this study was Chinese commercial banks. Although listed commercial banks and unlisted commercial banks were included, the impact of bank digital transformation on the operational capabilities of commercial banks was tested through heterogeneity analysis. However, this research still has limitations. Specifically, the digital transformation of banks is aimed at commercial banks. It may not apply to other industries in the financial industry, such as securities and insurance, nor is it applicable to manufacturing companies. At the same time, due to the small number of listed banks, Tobin’s Q value of non-listed banks cannot be obtained, and Tobin’s Q value cannot be used to test the research content. Future research can be extended to the financial industry to explore the impact of the digital transformation of the financial industry on the financial performance of financial institutions.