Does Digital Transformation Affect Outward Foreign Direct Investment Performance? Evidence from China

Abstract

Digital transformation has become a crucial strategic decision for enterprises to strengthen international competitiveness and achieve sustainable development. This study aims to investigate the impact of digital transformation on outward foreign direct investment (OFDI) performance and the conditions that influence this relationship using the ordinary least-squares regression estimation method and the data of Chinese A-share listed enterprises. The results show that digital transformation improves OFDI performance. The mechanism analysis verifies that digital transformation enhances OFDI performance by promoting corporate reputation and innovation. The moderating analysis demonstrates that the host country’s digital infrastructure negatively moderates the positive relationship between digital transformation and OFDI performance, while diplomatic relations between home and host countries play a positive moderating role. The heterogeneity analysis reveals that state-owned, labor-intensive, and technology-intensive enterprises and enterprises investing in non-Belt-and-Road countries benefit more from digital transformation to promote OFDI performance. This study extends the OFDI theory of emerging market enterprises in the context of digital transformation and provide practical implications for improving the OFDI performance of multinational enterprises.

1. Introduction

Major countries around the world have formulated digital economy strategies to strengthen international competitiveness and harmonize economic growth and environmental protection through the adoption of digital technologies. The international community’s growing focus on environmental performance makes multinational companies face a sharp increase in environmental risks and stakeholder pressure. In the 14th Five-Year Plan, China proposes to accelerate the digital transformation of economic development in order to achieve the dual-carbon goals of “peaking carbon by 2030” and “achieving carbon neutrality by 2060”. Digital transformation can be regarded as a strategic social responsibility tool to mitigate host countries’ concerns about the environmental performance of enterprises. Enterprise digital transformation refers to the application of digital technologies, including artificial intelligence, the Internet of Things, cloud computing, big data analytics, and blockchain, as well as mining, monitoring, and analyzing the data generated at every stage in response to environmental changes to assist the strategic decision-making of firms [1,2,3]. With the potential to revolutionize existing business models and create novel revenue streams [4], digital transformation improves internal operational efficiency and reduces overseas operational costs, which enhances firms’ ability to undertake outward foreign direct investment (OFDI) [5].

Although the potential benefits of digital transformation have been widely recognized, whether digital transformation affects the OFDI performance remains to be explored. As the largest emerging market country and the second largest country in terms of digital economy scale, China has ranked among the world’s top three countries in terms of OFDI flows for eleven years since 2012. The profitability of Chinese multinationals’ OFDI has been much lower than that of companies in major OFDI countries. The resource-based view suggests that a firm’s competitive advantage derives from its rare, valuable, imperfectly imitable, and irreplaceable resources [6]. The superior OFDI performance depends on the resources that they possess and the fit between these resources and the market [7]. As latecomers to the global market, emerging market enterprises (EMEs) encounter the liabilities of emergingness and country of origin in their pursuit of OFDI due to fewer firm-specific advantages (FSAs) than developed-market multinational enterprises (DMNEs) and home-country institutional hazards like state intervention [8]. These unfavorable factors incur credibility and legitimacy deficits in EMEs among the host country’s stakeholders, creating extra costs for conducting OFDI [9]. Digital transformation has triggered a radical revolution of technological paradigms, and the application of digital technologies to production processes is also complex, bringing both opportunities and challenges. Therefore, exploring whether and how digital transformation affects the OFDI performance in the context of China is of great significance for emerging market countries.

Existing studies on the economic consequences of digital transformation draw inconsistent conclusions. Some studies affirm the positive role of digital transformation in enterprise knowledge creation [10], innovation investment [11], total factor productivity [12], and financial performance [13]. Conversely, others argue that digital transformation decreases financial performance by increasing operation and management costs and reducing total asset turnover [14]. Feliciano-Cestero et al. [1] provide a comprehensive review to reveal that digital transformation can positively and negatively affect firm internationalization, and four critical factors (knowledge, technology, leadership, and digital servitization factors) affect the adoption of digital transformation as a strategy for firm internationalization. OFDI is an important means of corporate internationalization, and the impact of digital transformation on OFDI outcomes should be explored further.

Previous research focuses on whether digital transformation affects OFDI decision-making and ignores its impact on OFDI performance. Some scholars theoretically analyze that digital transformation decreases corporate OFDI by creating digital channels to serve foreign markets without establishing physical facilities in the host country [15,16,17]. However, empirical studies have reached a consensus on the positive effect of digital transformation on OFDI decision-making. Digital transformation enhances dynamic capabilities [18], decreases overseas operational costs [5], lowers transaction costs [19], promotes total factor productivity, and mitigates financing constraints [20], thus improving OFDI propensity and scale. Stallkamp et al. [21] empirically affirm that born-digital firms deploy OFDI to acquire complementary and fungible resources across borders that are specific to local contexts. Whether digital transformation affects OFDI performance, which highlights the economic performance of profitability, remains unexplored.

This study aims to explore the impact and boundary conditions of digital transformation on OFDI performance in the context of China. It employs data from listed Chinese companies using the quantitative analysis method to examine the relationship between digital transformation and OFDI performance. The empirical findings reveal the positive impact of digital transformation on the OFDI performance of CMNEs. Digital transformation improves the OFDI performance of CMNEs by promoting reputation and innovation. The host country’s digital infrastructure weakens the positive relationship between digital transformation and OFDI performance of CMNEs, while diplomatic relations between home and host countries play a positive moderating role. The positive impact of digital transformation on OFDI performance is more pronounced in state-owned CMNEs, labor-intensive and technology-intensive CMNEs, and CMNEs investing in non-Belt-and-Road countries.

The contributions of this study are as follows. First, it links digital transformation and OFDI performance that emphasizes profitability from conducting OFDI for the first time based on the resource-based view, offering a novel perspective and evidence to understand the intrinsic link. It enriches the literature on the economic consequences of digital transformation and determinants of OFDI performance, which extends the OFDI theory of EMEs. The empirical findings provide practical insights and references for EMEs to efficiently implement digital transformation for the augmentation of strategic resources to improve OFDI performance. Second, this paper identifies the mechanisms through which digital transformation affects OFDI performance, including corporate reputation and innovation. Third, it examines the boundary conditions under which digital transformation affects OFDI performance from the perspective of the host country’s digital infrastructure and bilateral diplomatic relations. Bu et al. [22] reveal that soft and hard infrastructure deficiencies, where the former refers to intangible components like institutions and the latter represents physical components such as transportation, act as resource constraints that affect the business operation of foreign firms in host countries. The digital infrastructure and bilateral diplomatic relations reflect exactly the hard and soft infrastructure of the host country, respectively. Furthermore, this study conducts a heterogeneity analysis to investigate how the impact of digital transformation on the OFDI performance of CMNEs varies across different ownerships, industries, and regions.

The rest of this paper is structured as follows. Section 2 presents the theoretical analysis and hypotheses. In Section 3, the research design is outlined. Section 4 reports the empirical results. Finally, Section 5 provides conclusions, managerial implications, and future research directions.

2. Theoretical Analysis and Hypotheses

2.1. Digital Transformation and OFDI Performance of CMNEs

Digital transformation facilitates the augmentation and deployment of FSAs across national boundaries, thus improving the OFDI performance of CMNEs. Unlike multinational enterprises from developed countries, CMNEs usually lack strategic resources and tend to augment strategic assets to strengthen their capabilities instead of exploiting the existing resource base [23]. Augmentation of strategic resources enables CMNEs to overcome latecomer disadvantages and build a market position in the host country. Technological and marketing resources represent two types of core FSAs [24]. Technological FSAs encompass technological knowledge and patents while marketing FSAs comprise brands and reputation for quality [7]. Digital transformation augments digital technological-based FSAs with propriety knowledge attached. Specifically, CMNEs can apply digital technologies to obtain digital assets such as production information and user demand data that can be created, stored, and transmitted in different forms like bits and bytes. Digital transformation also achieves technological modularization and connection of separate parts by providing a standard interface to acquire complementary knowledge and integrate advanced technologies from partners in host countries [18]. This easier and less costly way than utilizing physical assets allows CMNEs to accumulate more technological FSAs rapidly and leverage them across borders [15]. Meanwhile, digital technologies create a multitude of virtual channels like social media and search engines, which are characterized by low cost, extensive reach, and high interactivity. Digital transformation can analyze consumer behavior and forecast market trends for CMNEs to develop targeted marketing strategies. These multi-channel precision marketing activities enhance recognition and trust among the host country’s stakeholders, thereby attracting more partners and customers. The greater the marketing and technological FSAs, the easier it is for CMNEs to develop high-value-added and competitive products that meet market demand [23]. Therefore, this study hypothesizes the following:

Hypothesis 1.

Digital transformation improves the OFDI performance of CMNEs.

2.2. The Mediating Role of Reputation

Digital transformation enhances a reliable reputation, thereby improving the OFDI performance of CMNEs. Reputation refers to stakeholders’ perceptions of an organization’s capacity to create value compared to its competitors and distinctiveness in situations of asymmetric information [25]. It is a crucial “social approval asset” that generates economic value through a favorable collective perception that is difficult for competitors to replicate [26]. On the one hand, digital transformation can optimize customer experience by accurately analyzing consumer behavior, forecasting market trends based on large amounts of data, and providing customized services. Such satisfied customers are inclined to share positive reviews, which in turn enhances the reputation of CMNEs. On the other hand, the implementation of digital technologies throughout all business sectors can improve productivity and reduce production costs, which enhances the reputation of CMNEs for reliability and competence [10]. Furthermore, digital technologies can serve as appropriate tools for CMNEs to distribute corporate progress and development prospects and interact with stakeholders in real time to demonstrate their competitiveness [27]. Digital technologies create a multitude of virtual channels like social media and search engines, which are characterized by low cost, extensive reach, and high interactivity. These favorable mandatory and voluntary information disclosures guarantee a trustworthy brand reputation for CMNEs. Emerging markets typically lack capable third-party regulators to monitor enterprises’ commitments to the stakeholders [8]. In such circumstances, the reputation of CMNEs, which are full of promises and credible commitments, can be advantageous in motivating stakeholders to contribute to their success. A favorable reputation sends credible signals to the host country’s stakeholders regarding the competencies and product quality of CMNEs. It provides greater access to other resources such as foreign capital and future alliance partners and confers informational advantages for CMNEs to better meet the market needs of the host country.

Hypothesis 2.

Digital transformation promotes the OFDI performance of CMNEs by improving reputation.

2.3. The Mediating Role of Innovation

Digital transformation promotes corporate innovation, thereby improving the OFDI performance of CMNEs. Innovation involves the development of new products, processes, and services that allow firms to provide potentially superior offerings to customers and differentiate themselves from rivals [28]. Digital technologies create highly interactive platforms for CMNEs to co-create and modify user-generated content with stakeholders and gather innovative ideas from online user comments, thus generating a wealth of data and business intelligence to spark innovation. Moreover, digital transformation creates resource superiority for innovation [29]. A complex information environment hinders investors from understanding the business risks and operating conditions of the enterprise and damages their trust in firms. Digital transformation improves information transparency and decreases the information process cost for investors. Digital transformation is highly aligned with national strategies and signals positive growth prospects of CMNEs to the host country’s stakeholders, which makes it easier for CMNEs to gain international cooperation and financial support from governments and investors [11]. Furthermore, digital transformation mitigates the information asymmetry between managers and shareholders by optimizing risk management and rendering business processes transparent [30], which reduces agency problems and further improves the innovation willingness of managers. Innovation improves international competitiveness and the bargaining power of CMNEs by increasing the added value of products and services. CMNEs with strong innovation can speed up product development to improve the rate of the introduction of new products and develop products that are more appealing and higher performing. Therefore, this study proposes:

Hypothesis 3.

Digital transformation promotes the OFDI performance of CMNEs by promoting innovation.

2.4. The Moderating Role of Host Country Digital Infrastructure

The implementation of digital technologies depends on local digital infrastructure, which varies across countries. Digital infrastructure denotes fundamental information and communication technologies (ICT) facilities serving enterprises, which may be reflected by the number of Internet accesses [31]. The host country’s digital infrastructure plays an indispensable role in providing accessible and reliable services for international business participants [32].

Developed digital infrastructure in host countries weakens the economic benefits of FSAs derived from digital transformation, ultimately reducing the contribution of digital transformation to the OFDI performance of CMNEs. The well-developed digital infrastructure in host countries may cause undesirable over-competitive effects and market saturation, which lowers the return on investment [33]. The mature digital infrastructure enhances transparency concerning product specifications, prices, and new technologies, enabling competitors to readily imitate and replicate offerings. This challenge hinders CMNEs’ ability to sustain the competitive advantages stemming from digital transformation [31]. As competition intensifies, particularly for homogeneous products, it becomes increasingly overstretching for CMNEs to secure a larger market share and achieve superior financial performance. Meanwhile, well-developed digital infrastructure in host countries may elevate compliance costs and legal risks, which increases the operating costs of CMNEs. CMNEs tend to devote more resources and effort to address compliance requirements in host countries with advanced digital infrastructure, where governments and regulators exercise stricter oversight of digital technology and data privacy. Therefore, this study proposes:

Hypothesis 4.

The host country’s digital infrastructure negatively moderates the relationship between digital transformation and the OFDI performance of CMNEs.

2.5. The Moderating Role of Diplomatic Relations

As institutional arrangements deliberately constructed between home and host governments [34], bilateral diplomatic relations are a kind of soft infrastructure involving governmental policies and operational permission that affect the daily operations of CMNEs in the host country. Diplomatic relations refer to the alignment of the national interests between two governments in global affairs [35]. Friendly diplomatic relations create a stable business environment and enhance the credible brand image, thus facilitating the positive impact of digital transformation on the OFDI performance of CMNEs.

Amicable diplomatic relations reduce political risk and uncertainty in the host country as the local government is less likely to carry out rigorous entry reviews and policy interventions for CMNEs to safeguard national political security and economic interests [36]. This guarantees legal protection and resource support for CMNEs that conduct digital transformation to strengthen technological FSAs since host governments tend to provide preferential policies and confidential information for enterprises from countries with amicable diplomatic relations [35]. Moreover, friendly diplomatic relations provide CMNEs with trust endorsement and lower the threshold for market access [34]. This makes it easier for CMNEs to interact with and gain support from the host country’s stakeholders through digital platforms, utilizing better marketing FSAs of digital transformation to improve OFDI performance. This study proposes:

Hypothesis 5.

Diplomatic relations positively moderate the relationship between digital transformation and the OFDI performance of CMNEs.

The logical framework diagram of this paper is shown in Figure 1.

3. Research Design

3.1. Data and Sample

The analyzed data in this paper are obtained from Chinese A-share companies listed on both the Shanghai Stock Exchange and the Shenzhen Stock Exchange. There are several reasons why Chinese firms are a suitable empirical context to test hypotheses: (1) China is the largest emerging market country and has ranked among the world’s top three countries in terms of OFDI flows for eleven consecutive years since 2012; (2) over the past decade, Chinese enterprises have simultaneously adopted traditional information and communication technologies (ICT), Internet technologies, and the latest generation digital technologies to catch up with the advanced technologies in developed countries and China’s digital economy scale ranks second in the world in 2021. Given the rapid development of digital transformation of Chinese enterprises after 2010, this study focuses on the time period from 2011 to 2021. Data at the enterprise level are from the China Stock Market and Accounting Research (CSMAR) database, while data at the national level are from the World Bank database. The China Research Data Service Platform (CNRDS) database is used to supplement some missing data. This study has excluded certain firms from the sample to improve data validity: (1) firms in the information technology services industry, as their industry characteristics may lead to a miscarriage of digital transformation carve-outs; (2) firms with missing values for the primary and control variables; (3) firms with abnormal financial systems (ST, ST*); (4) firms whose overseas subsidiaries are located in tax haven countries such as Cayman Islands, Luxembourg, Bermuda Islands, the British Virgin Islands and Jersey. The final sample comprises 3344 firm-year observations. The software used for the analyses is STATA17.

3.2. Measures

3.2.1. The Dependent Variable

This paper focuses on profitability performance using return on assets (ROA) that has been tailored at the 1% and 99% levels to measure the OFDI performance of CMNEs [37,38]. It is a profitability ratio that indicates a firm’s effectiveness in transforming its assets into net profit.

3.2.2. The Independent Variable

As a major strategy for development, digital transformation refers to the application of digital technologies throughout the enterprise. This process involves various aspects such as updating modern information systems, upgrading business models, smart manufacturing, etc., which are reflected in annual reports of firms. The vocabulary in annual reports describes the strategic characteristics and development path of the enterprise, and the frequency of keywords related to digital transformation reflects the importance and implementation of digital transformation. Therefore, it is reasonable to measure digital transformation using the related keyword frequency in annual reports.

This study measures digital transformation by adopting the textual analysis method to count the frequency of related keywords from annual corporate reports [10,39]. The specific steps are as follows: First, collect the annual reports of listed firms and extract the “Report of the Board of Directors” and “Managerial Discussion and Analysis” sections from the annual reports by Python 3.12.8. Second, extract a certain number of enterprises with more successful digital transformation by manual judgment. The judgment criteria are derived from the analysis of the theoretical part, i.e., whether the enterprise adopts new digital technology, implements an Internet business model, realizes intelligent manufacturing, and builds a modern information system in production and operation. Third, based on Python’s Jieba Chinese Segmentation function, the selected samples are subjected to segmentation processing and keyword frequency statistics to screen out high-frequency keywords related to digital transformation. Fourth, further narrow down the keywords. Based on the vocabulary formed in the third step, extract the texts before and after them from the total sample of listed companies and look for text combinations with a higher frequency of occurrence. Fifth, the keywords are supplemented on the basis of the existing literature to form the final keyword dictionary. Sixth, based on the self-constructed participle dictionary, count the number of disclosures of keywords from four aspects, namely, digital technology application, Internet business model, intelligent manufacturing, and modern information system, to reflect the degree of development of the enterprises in each aspect. Finally, the natural logarithm of the keyword frequency plus one is obtained to reflect the degree of digital transformation (lndigital) of each firm.

3.2.3. Mediating Variables

This study utilizes the factor analysis method to construct the reputation evaluation indicator (Reputat), including twelve dimensions: the company’s assets, operating receipt, net profit and market value from the consumer and social perspective; asset-liability ratio, current ratio, long-term debt ratio from the creditor perspective; earnings per share, dividends per share, and whether or not it is audited by the Big Four accounting firms from the shareholder perspective; and sustainable growth rate, and the percentage of independent directors from the enterprise perspective [40]. The specific constructs of corporate reputation and the factor loadings of the sub-dimension variables are displayed in Appendix A,

Table A1. Factor analysis for measuring corporate reputation.

Variable	Sub-Dimension Variables	Factor Loadings
Reputat	Assets	0.9337
	Operating receipt	0.8814
	Net profit	0.9152
	Market value	0.9567
	Asset-liability ratio	0.0671
	Current ratio	−0.0575
	Long-term debt ratio	0.0384
	Whether or not it is audited by the Big Four accounting firms	0.3659
	Earnings per share	0.2985
	Dividends per share	0.3308
	The percentage of independent directors	0.1135
	Sustainable growth rate	0.0129

.

Innovation is typically measured in the following ways: (1) the level of input devoted by firms, such as research and development expenditures; (2) intermediate output like patents; (3) the value of innovative activities [41]. Considering that innovation is regarded as the intermediate good to be exploited across borders in this research scenario, this study uses corporate patents to measure innovation. Specifically, the natural logarithm of one plus firm i’s patents application is adopted to measure corporate innovation (Innovat) [29]. Data on corporate patents are from the China Stock Market and Accounting Research (CSMAR) database and the China Research Data Service Platform (CNRDS) database.

3.2.4. Moderating Variables

This paper utilizes secure Internet servers (per 100 people) from the World Bank database to measure the host country’s digital infrastructure (DigitInf) [42]. Diplomatic relations between home and host countries (DipRe) are measured by the similarity of the votes between countries at the UN General Assembly since all countries are free to express their public positions on issues in different areas such as economic, political, security, social, and military [43]. Countries that share the same attitudes towards world-related issues and have similar international political tendencies are expected to have friendly bilateral relations [44]. Bailey et al. [45] process UN voting data to measure the ideal point of each country’s political position. This study uses the absolute distance from the mean of the ideal point estimate between China and the host country to measure diplomatic relations (Relation), where smaller distances indicate better diplomatic relations.

3.2.5. Control Variables

This paper controls for several firm characteristics and the host country level variables: (1) Firm age (Age), which is expressed by subtracting the year of establishment from the current year. (2) The asset-liability ratio (Lev), which is represented by the ratio of total liabilities to total assets. (3) Corporate equity concentration (Concen), measured by the ratio of shares held by the largest shareholder. (4) The host country’s market potential, measured by the GDP growth rate (GDPgrowth) of the host country. (5) The host country’s economic stability, measured by the inflation rate (Inflation) of the host country. (6) Openness, which is represented by the logarithm of merchandise trade (percentage of GDP) of the host country (Trade). (7) The level of governance of host countries, which is represented by Worldwide Governance Indicators (WGI), consists of voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rules of law, and control of corruption. (8) Overseas investment experience (Exp), where the value is 1 if the company has experience in OFDI; otherwise, it is 0. The variables are defined as shown in

Table 1. Variable definitions.

Variable Type	Variable Name	Variable Description	Data Resource
Dependent variable	OFDI performance	Return on assets (ROA) that has been tailored at the 1% and 99% levels	CSMAR database
Independent variable	Digital transformation	Natural logarithm of frequency plus one for keywords related to digital transformation in the annual reports of listed firms	CSMAR database
Mediating variables	Innovation	Natural logarithm of one plus the number of corporate patents application	CSMAR database and CNRDS database
	Reputation	A composite indicator including twelve dimensions, the company’s assets, operating receipt, net profit, and market value from the consumer and social perspective; asset-liability ratio, current ratio, long-term debt ratio from the creditor perspective; earnings per share, dividends per share, and whether or not it is audited by the Big Four accounting firms from the shareholder perspective; and sustainable growth rate, and the percentage of independent directors from the enterprise perspective	CSMAR database
Moderating variables	Host country digital infrastructure	Secure Internet servers (per 100 people)	World Bank
	Diplomatic relations between home and host countries	The similarity of the votes between home and host countries at the UN General Assembly	United Nations General Assembly Voting Data https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LEJUQZ (accessed on 24 June 2024)
Control variables	Firm age	Subtracting the year of establishment from the current year	CSMAR database
	Asset-liability ratio	The ratio of total liabilities to total assets	CSMAR database
	Corporate equity concentration	The ratio of shares held by the largest shareholder	CSMAR database
	Host country market potential	GDP growth rate of the host country	World Bank
	Host country economic stability	Inflation rate of the host country	World Bank
	Openness	Natural logarithm of merchandise trade (percentage of GDP) of the host country	World Bank
	Overseas investment experience	An indicator variable that equals 1 if the company has experience in OFDI and 0 otherwise.	CSMAR database
	The level of governance of host countries	Worldwide Governance Indicators (WGI) consist of voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rules of law, and control of corruption	World Bank

.

3.3. Model Specification

To test the impact of digital transformation on the OFDI performance of CMNEs, this study constructs the following fixed effect model based on the ordinary least-squares regression estimation method:

$${\mathit{Performance}}_{\mathit{ijpt}}={\beta }_0+{\beta }_1{\mathit{lndigital}}_{\mathit{ijpt}}+\mathrm{\Sigma }{{\beta }_c\mathit{Control}}_{\mathit{ijpt}}+{\alpha }_j+{\delta }_p+{\gamma }_t+{ϵ}_{\mathit{ijpt}}$$

(1)

Performance_ijpt represents the OFDI performance of firm i in industry j in making a single overseas investment in host country p in the year t. lndigital_ijpt denotes the degree of digital transformation of firm i in year t of host country p and industry j. Control_ijpt represents a series of control variables. α_j represents the industry fixed effect. δ_p represents the country fixed effect, and γ_t represents the time fixed effect. ϵ_ijpt represents the error term. This study conducts the Hausmann test to determine whether to apply the fixed or random effects models. The results indicate that the individual effects are related to the explanatory variables, and the fixed effects model should be selected. Based on the previous theoretical analysis, if the β₁ is significantly positive, it indicates that digital transformation can significantly improve the OFDI performance of CMNEs.

4. Results

4.1. Descriptive Statistics

Table 2. Summary statistics.

Variables	N	Mean	Sd	Min	Max
ROA	3340	4.786	5.920	−20.57	23.52
lndigital	3340	3.181	1.105	0	6.909
Age	3340	17.98	5.904	2	52
Lev	3340	0.481	0.195	0.0246	1.354
Concen	3340	34.63	15.12	3	89.99
GDPgrowth	3340	2.273	3.292	−20.49	17.29
Inflation	3340	2.849	5.880	−17.59	225.4
Trade	3340	3.936	0.777	2.722	5.626
Exp	3340	0.636	0.481	0	1
WGI	3340	0.781	0.837	−1.667	1.859
Innovat	2853	4.094	1.797	0	10.86
Reputat	2975	0.660	0.954	−2.282	4.569
DigitInf	3320	3.012	4.515	7.28 × 10−6	27.73
DipRe	3254	1.430	1.090	0.00329	3.494

reports the descriptive statistics of the main and control variables. The firms in the sample exhibit a substantial difference in digital transformation, with an average value of 3.181, a standard deviation of 1.2801, and a maximum and a minimum value of 6.909 and 0, respectively.

Table 3. Correlation statistics.

	ROA	Lndigital	Lev	Concen	Age	GDPgrowth	Trade	WGI	Exp	Inflation	Reputat	Innovat	DigitInf	DipRe
ROA	1
lndigital	0.051 ***	1
Lev	−0.401 ***	−0.00500	1
Concen	0.105 ***	−0.086 ***	0.047 ***	1
Age	−0.071 ***	0.075 ***	0.168 ***	−0.113 ***	1
GDPgrowth	0.0230	−0.097 ***	0.043 **	0.043 **	−0.034 **	1
Trade	−0.0160	0.038 **	0.072 ***	0.051 ***	0.00700	0.145 ***	1
WGI	−0.0100	0	−0.051 ***	−0.032 *	−0.057 ***	−0.226 ***	0.00800	1
Exp	−0.075 ***	0.112 ***	0.186 ***	−0.0200	0.144 ***	−0.037 **	0.0130	−0.074 ***	1
Inflation	0.0260	−0.00500	0.0280	0.00100	0.0250	0.096 ***	−0.068 ***	−0.347 ***	0.044 **	1
Reputat	0.179 ***	0.087 ***	0.408 ***	0.140 ***	0.107 ***	0.033 *	0.0270	−0.0210	0.286 ***	0.0280	1
Innovat	0.073 ***	0.253 ***	0.074 ***	−0.037 **	0.086 ***	−0.047 **	−0.0200	−0.096 ***	0.137 ***	0.0110	0.220 ***	1
DigitInf	−0.0110	0.118 ***	−0.056 ***	−0.086 ***	0.123 ***	−0.293 ***	−0.041 **	0.398 ***	0.057 ***	−0.132 ***	−0.038 **	−0.00600	1
DipRe	0.0240	−0.050 ***	−0.125 ***	−0.047 ***	−0.066 ***	−0.186 ***	−0.643 ***	0.467 ***	−0.090 ***	−0.168 ***	−0.069 ***	−0.048 **	0.303 ***	1

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

displays the correlation matrix between the variables. Digital transformation (lndigital) is positively correlated with OFDI performance (ROA). All correlation coefficients are less than 0.5, and the variance inflation factors (VIF) are far below the threshold levels of 10, indicating that multicollinearity is not a concern.

4.2. Benchmark Estimation

Table 4. Benchmark estimation test.

Variables	(1)	(2)	(3)	(4)
lndigital	0.272 ***	0.330 ***	0.376 ***	0.372 ***
	(0.0925)	(0.104)	(0.0952)	(0.0954)
Lev			−12.84 ***	−12.88 ***
			(0.552)	(0.561)
Concen			0.0485 ***	0.0482 ***
			(0.00635)	(0.00636)
Age			−0.00214	−0.00239
			(0.0178)	(0.0178)
GDPgrowth				−0.0151
				(0.0609)
Trade				−1.611
				(1.190)
WGI				1.281
				(1.379)
Exp				0.102
				(0.204)
Inflation				0.0154
				(0.0215)
Constant	3.920 ***	3.747 ***	8.135 ***	13.43 ***
	(0.312)	(0.345)	(0.570)	(4.928)
Observations	3340	3316	3316	3316
R-squared	0.003	0.099	0.244	0.245
Year FE	NO	YES	YES	YES
Industry FE	NO	YES	YES	YES
Country FE	NO	YES	YES	YES

Note: Standard errors in parentheses, *** p < 0.01.

reports the estimated results of the impact of digital transformation on the OFDI performance of CMNEs. Column (1) includes only the independent and dependent variables, and the results show that digital transformation improves the OFDI performance of CMNEs. After controlling for fixed effects in column (2), the results are still significant. Column (3) adds firm-level control variables, and the coefficient of lndigital is significantly positive. Column (4) shows the regression results of the baseline model. The estimated coefficient of digital transformation is significantly positive at the 1% level, which reflects that digital transformation does improve the OFDI performance of CMNEs, supporting Hypothesis 1.

4.3. Mediating Effect Test

This study tests the mediating role of corporate reputation and innovation in

Table 5. Mediating effect test.

	(1)	(2)	(3)	(4)
Variables	Reputat	ROA	Innovat	ROA
lndigital	0.101 ***	0.122 *	0.366 ***	0.268 **
	(0.0155)	(0.0703)	(0.0310)	(0.109)
Reputat		2.153 ***
		(0.0850)
Innovat				0.458 ***
				(0.0656)
Constant	−1.374 *	17.53 ***	5.101 ***	11.12 **
	(0.790)	(3.571)	(1.631)	(5.572)
Observations	2950	2950	2825	2825
Control	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES
Country FE	YES	YES	YES	YES
R-squared	0.319	0.403	0.270	0.250

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

. The results in column (1) show that the coefficient of lndigital is significantly positive, suggesting that digital transformation contributes to corporate reputation. The estimation results in column (2) show that the coefficients of both corporate reputation and digital transformation are significantly positive. These findings verify that digital transformation improves the OFDI performance of CMNEs by promoting reputation. The results in column (3) and column (4) reflect the similar results. These findings suggest that digital transformation improves the OFDI performance of CMNEs by promoting innovation.

4.4. Moderating Effect Test

This study tests the moderating role of the host country’s digital infrastructure and diplomatic relations between home and host countries in

Table 6. Moderating effect test.

Variables	(1)	(2)
lndigital	0.491 ***	0.579 ***
	(0.113)	(0.151)
DigitInf	0.112
	(0.0821)
Lndigital × DigitInf	−0.0403 **
	(0.0200)
DipRe		0.339
		(0.712)
Lndigital × DipRe		−0.143 *
		(0.0799)
Constant	14.44 **	13.61 **
	(5.338)	(5.332)
Observations	3320	3232
Control	YES	YES
Year FE	YES	YES
Industry FE	YES	YES
Country FE	YES	YES
R-squared	0.246	0.241

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

. The coefficient of interaction term of digital transformation and the host country’s digital infrastructure in Table 6 are positively negative. This indicates that the host country’s digital infrastructure weakens the positive impact of digital transformation on the OFDI performance of CMNEs, supporting Hypothesis 4. The coefficient of the interaction term between digital transformation and diplomatic relations is significantly negative, implying that friendly diplomatic relations strengthen the positive impact of digital transformation on the OFDI performance of CMNEs.

4.5. Robustness Tests

4.5.1. Sobel Test

This study further uses the Sobel test to enhance the reliability of the mediating effect. The result in column (1) of

Table 7. Sobel test.

	(1)	(2)
Variables	Reputat	Innovat
Sobel	0.2170 ***	0.1717 ***
	(0.0344)	(0.0280)
Goodman-1	0.2170 ***	0.1717 ***
	(0.0344)	(0.0281)
Goodman-2	0.2170 ***	0.1717 ***
	(0.0344)	(0.0280)
Indirect effect	0.2170 ***	0.1717 ***
	(0.0344)	(0.0280)
Direct effect	0.1267 *	0.2756 *
	(0.0704)	(0.1081)
Total effect	0.3437 ***	0.4473 ***
	(0.0774)	(0.1064)
Proportion of total effect	0.6314	0.3839
Observations	2975	2853

Note: Standard errors in parentheses, *** p < 0.01, * p < 0.1.

shows the indirect effect of reputation on OFDI performance is statistically significant (Z = 6.3064), indicating the mediating role of reputation on the relationship between digital transformation and OFDI performance. Meanwhile, the coefficients for a and b have the same direction, suggesting that the indirect mediation effect of reputation is greater than the direct mediation effect. In this regard, the proportion of the total effect that is mediated is 63.14%.

Column (2) in Table 7 likewise reports similar results for innovation as well. The indirect effect of innovation on OFDI performance is statistically significant (Z = 6.1257), which indicates that the mediating effect of innovation on the relationship between digital transformation and OFDI performance is significant. In this regard, the proportion of the total effect that is mediated is 38.39%. Moreover, the Goodman-1 and Goodman-2 tests support the significant indirect effect of innovation on OFDI performance.

4.5.2. Bootstrap Test

Table 8. Bootstrap test.

	(1)	(2)
Variables	Reputat	Innovat
Indirect effect (P)	0.1596 ***	0.1283 ***
	(0.0328)	(0.0248)
Direct effect (P)	0.2073 **	0.2372 **
	(0.0688)	(0.1090)
Bias of Indirect effect	0.0006	−0.0007
Bias of Direct effect	0.0018	0.0073
95% Conf. Interval of Indirect effect	(0.0917, 0.2279)	(0.0828, 0.1796)
95% Conf. Interval of Direct effect	(0.0760, 0.3406)	(0.0336, 0.4693)
Proportion of total effect	0.6314	0.3839
Observations	2975	2853

Note: Sampling number = 500. Standard errors in parentheses, *** p < 0.01, ** p < 0.05.

reports the results of the bootstrap test further to the Sobel test. Column (1) shows that the indirect effect of reputation on OFDI performance has a coefficient of 0.1596, with a standard error of 0.0328 and a 95% confidence interval of (0.0917, 0.2279). As for the direct effect, it has a coefficient of 0.2073, with a standard error of 0.0688 and a 95% confidence interval of (0.0760, 0.3406). Column (2) shows that the indirect effect of innovation on OFDI performance has a coefficient of 0.1283, with a standard error of 0.0248 and a 95% confidence interval of (0.0828, 0.1796). As for the direct effect, it has a coefficient of 0.2372, with a standard error of 0.1090 and a 95% confidence interval of (0.0336, 0.4693).

These findings suggest that the indirect effect on the association between digital transformation and OFDI performance is strong and that the mediation role of reputation and innovation in this relationship is partially established.

4.5.3. Alternative Measures of Variables

Replacing the dependent variable. This study uses the return on equity (ROE) as an alternative dependent variable. Results in column (1) of

Table 9. Alternative measures of variables.

	(1)	(2)	(3)
Variables	ROE	ROA	ROA
lndigital	0.00796 ***
	(0.00195)
lndigi2		0.305 **
		(0.124)
lnDTIA_pro			0.151 **
			(0.0657)
Constant	0.123	14.17 ***	11.56 **
	(0.101)	(4.927)	(5.490)
Observations	3313	3312	2753
Control	YES	YES	YES
Year FE	YES	YES	YES
Industry FE	YES	YES	YES
Country FE	YES	YES	YES
R-squared	0.116	0.242	0.246

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05.

show that the coefficient is still significantly positive, further supporting Hypothesis 1.

Replacing the independent variable. On the one hand, this study obtains the relevant word frequency in the annual reports of firms by textual analysis, including five dimensions of artificial intelligence, blockchain, cloud computing, big data, and digital technology applications to measure digital transformation (lndigi2) [46]. On the other hand, considering that the business philosophy and strategic intent embodied in the vocabulary of the annual report may not necessarily be translated into actual actions, this study uses the percentage of digital intangible assets as an alternative independent variable (lnDTIA_pro) [10]. The coefficients of digital transformation in columns (2) and (3) of Table 9 are significantly positive, indicating the robustness of the benchmark regression results.

4.5.4. Endogeneity Analysis

Firstly, this study employs the Heckman-type two-stage estimation to mitigate potential sample selection bias due to non-random matching [47]. In the first stage, it constructs a dummy variable OFDI_dum that equals one if the firm is involved in OFDI and zero otherwise, and an exogenous variable (scores of Environmental, Social, and Governance, ESG) which may improve the possibility of OFDI [48]. This paper uses the mean value of HuaZheng ESG score over the sample period as a measure of ESG and estimates a probit model regression of OFDI_dum on the control variables at the firm level. In the second stage, it re-conducts the analyses in Table 4 but includes the inverse Mills ratio (Imr) derived from the first stage to correct for the sample selection bias. The coefficient of ESG on OFDI decision in column (1) of

Table 10. Heckman’s two-stage model.

	(1)	(2)
Variables	OFDI_dum	ROA
lndigital		0.251 ***
		(0.0942)
ESG	0.127 ***
	(0.00941)
Lev	0.0160 ***	−12.82 ***
	(0.00602)	(0.551)
Concen	7.38 × 10−6	0.0419 ***
	(0.000657)	(0.00626)
Age	−0.00771 ***	0.0538 ***
	(0.00182)	(0.0182)
Imr		−9.822 ***
		(0.881)
Exp		−0.0452
		(0.200)
WGI		0.899
		(1.127)
Trade		−1.890
		(1.167)
Inflation		0.0130
		(0.0211)
GDPgrowth		−0.00381
		(0.0598)
Constant	−2.440 ***	30.29 ***
	(0.122)	(5.066)
Observations	3134	3316
Year FE	YES	YES
Industry FE	YES	YES
Country FE		YES
R-squared	0.0393	0.273

Note: Standard errors in parentheses, *** p < 0.01.

is significantly positive, indicating that the choice of exogenous variables is reasonable and in line with theoretical expectations. The coefficient of Imr in column (2) is significantly negative, confirming the existence of sample selection bias, and the coefficient of digital transformation remains significantly positive.

Secondly, as OFDI performance improves, CMNEs may be better able to enhance the degree of digital transformation, resulting in reverse causality. This paper adopts the instrumental variable method to solve the possible endogeneity. Given that the sample data are panel data, this paper uses the interaction term between the number of post offices per million people in 1984 and the national Internet penetration rate one year lagged (IV) as an instrumental variable [49]. On the one hand, the distribution of post offices affects the distribution of fixed telephones, and the fiber broadband access technology that underpins digital transformation begins after the public switched telephone network (PSTN), which meets the relevance requirement for instrumental variables [50]. On the other hand, the impact of the historical number of post offices on the digital transformation of enterprises is disappearing relative to the development of information technologies, which satisfies the exclusivity requirement for instrumental variables [50]. The coefficients of IV in

Table 11. Instrumental variable regressions.

Variables	(1)	(2)
lndigital		1.792 ** (0.824)
IV	7.25 × 10−5 ***
	(1.10 × 10−5)
Constant	2.082 *	9.306
	(1.114)	(6.361)
F-value	46.37
Observations	2906	2906
Control	YES	YES
Year FE	YES	YES
Industry FE	YES	YES
Country FE	YES	YES
R-squared	0.273	0.202

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

are significantly negative, and the F value is greater than 10, indicating that the instrumental variable is reasonable. The coefficient of digital transformation in column (2) remains significantly positive, indicating that the benchmark regression result is robust.

4.6. Heterogeneity Analysis

4.6.1. Ownership Structure

State-owned enterprises (SOEs) enjoy more financial resources and preferential policies from the home government to support digital transformation [51]. That suggests the impact of digital transformation on the OFDI performance of EMFs may differ between SOEs and non-state-owned enterprises (non-SOEs). This study divides the sample into State-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) based on firm ownership structure. The coefficients of lndigital in

Table 12. Heterogeneity test results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Variables	SOEs	Non-SOEs	Labor-Intensive	Capital-Intensive	Technology-Intensive	B&R	Non-B&R
lndigital	0.551 ***	0.310 ***	0.479 ***	0.308	0.387 **	0.325 *	0.339 ***
	(0.171)	(0.119)	(0.168)	(0.275)	(0.151)	(0.173)	(0.123)
Constant	19.51 **	13.31 **	18.40 **	22.05	12.42	29.80 ***	7.003
	(7.915)	(6.500)	(8.510)	(16.17)	(7.549)	(9.413)	(7.465)
Observations	893	2395	1008	432	1713	988	2095
Control	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES	YES
Country FE	YES	YES	YES	YES	YES	YES	YES
R-squared	0.300	0.231	0.304	0.366	0.239	0.293	0.231

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

indicate that digital transformation significantly improves OFDI performance for both state-owned CMNEs and non-state-owned CMNEs, while this positive impact is more pronounced than that of non-state-owned CMNEs. Possible reasons for this are that SOEs enjoy more financial resources and preferential policies from the home government to support digital transformation [19].

4.6.2. Industrial Differences

CMNEs of different sub-sectors vary significantly in the application scenarios and implementation difficulties of digital technology. This paper classifies sample industries as labor-intensive, capital-intensive, and technology-intensive industries based on the proportion of fixed assets and remuneration for research and development (R&D) expenditures [52]. The coefficients of lndigital for labor-intensive CMNEs and technology-intensive CMNEs in Table 12 are significantly positive, while for capital-intensive CMNEs, they are not significant. Possible explanations for this could be that capital-intensive CMNEs with a high proportion of fixed assets face higher capital requirements and resource dependence for digital transformation, resulting in lower returns. Furthermore, the contribution of digital transformation to OFDI performance is more pronounced for labor-intensive CMNEs than for technology-intensive CMNEs. Possible reasons are that technology-intensive CMNEs have higher levels of digital transformation, and the marginal performance from further digital transformation is relatively weaker.

4.6.3. Regional Differences

Relying on the Belt and Road Initiative, China is transforming its production capacity into market advantages, which may mitigate the liability of origin and thus affect the OFDI performance of enterprises [53]. This study divides all sample enterprises into two categories according to the host countries: those investing in countries along the Belt and Road (B&R) and those investing in countries not along the Belt and Road (non-B&R). The results in columns (6) and (7) of Table 12 show that digital transformation improves the OFDI performance of CMNEs in the two regions, while the promotion effect for non-B&R countries is more pronounced. Possible reasons could be that most countries along the B&R are relatively developing countries, and political instability in these countries is not conducive to the development of digital transformation.

5. Conclusion and Discussion

5.1. Theoretical Contributions and Conclusion

As global competition intensifies, digital transformation has emerged as a pivotal strategy for EMEs to enhance international competitiveness and overcome latecomer disadvantages. As the largest emerging country, China is in an urgent period of transformation, and digital technologies have been increasingly employed by CMNEs for international expansion. This study empirically investigates the impact and boundary conditions of digital transformation on OFDI performance using a sample of Chinese A-share listed firms from 2011 to 2021. The findings verify that digital transformation significantly enhances the OFDI performance of CMNEs, which remains robust after a series of robustness tests and supports Hypothesis 1. Mechanism analysis reveals that digital transformation improves the OFDI performance of CMNEs by promoting corporate reputation and innovation, supporting Hypothesis 2 and Hypothesis 3. The moderating analysis indicates that the host country’s digital infrastructure negatively moderates the positive relationship between digital transformation and OFDI performance of CMNEs, while diplomatic relations play a positive moderating role, confirming Hypothesis 4 and Hypothesis 5. The heterogeneity analysis shows that the contribution of digital transformation to OFDI performance is more pronounced for state-owned CMNEs, labor-intensive and technology-intensive CMNEs, and CMNEs investing in non-Belt-and-Road countries.

This study contributes to the theoretical and empirical analysis of the impact of digital transformation on OFDI performance. First, it links digital transformation with OFDI performance for the first time in the context of China, extending the OFDI theory of EMEs. Prior research draws inconsistent conclusions with regard to the economic consequences of digital transformation and the impact of digital transformation on OFDI decision-making involving investment propensity and scale. There is a lack of research on the impact of digital transformation on OFDI performance that emphasizes the economic performance of profitability from conducting OFDI. This study reveals that digital transformation is conducive to the OFDI performance of CMNEs, providing a reference for EMEs to employ digital transformation as a means of overcoming latecomer disadvantages and augmenting strategic resources. Second, by clarifying the mechanisms through which digital transformation affects OFDI performance, it offers novel perspectives that digital transformation facilitates the augmentation of reputation and innovation that represent two types of core strategic resources, thus enhancing OFDI performance. Third, it demonstrates that the extent to which digital transformation facilitates OFDI performance is contingent upon contextual factors. Considering that the deficiencies in both hard and soft infrastructure provided by the host country’s governments act as resource constraints that affect the business operations of foreign firms, the host country’s digital infrastructure and diplomatic relations between home and host countries are critical factors that form the boundary conditions for the impact of digital transformation on the OFDI performance of CMNEs. Finally, it further conducts a heterogeneity analysis on the impact of digital transformation on OFDI performance in terms of corporate ownership, industry factor intensity, and the host country’s heterogeneity.

5.2. Implications for Policy and Practice

Managers should embrace digital transformation to strengthen technological and marketing FSAs and improve OFDI performance. Digital transformation can boost the augmentation and deployment of technological and market FSAs across national boundaries to better cater to the customer demand of the international market. It is necessary for CMNEs to adopt digital technologies in the production and sales of their businesses, especially the various stages of innovation, to stimulate innovation. Managers can proactively disclose the implementation of digital transformation to enhance a credible and competitive corporate reputation for legitimacy and resource support from the host country’s stakeholders. Managers are expected to assess whether the host country’s digital infrastructure would trigger potential over-competition and elevate compliance costs and legal risks, as well as invest in countries with friendly bilateral diplomatic relations to maximize profitability when conducting OFDI.

Policymakers should formulate preferential credit policies for overseas investment and relevant laws to actively guide enterprises to engage in digital transformation for superior OFDI performance. Feasible measures should be taken to prioritize both the hard and soft infrastructures to create a favorable and stable business environment. Host governments can improve digital infrastructure while avoiding excessive competition and compliance costs to attract foreign investment. Moreover, policymakers are supposed to maintain friendly diplomatic relations with foreign countries to strengthen cooperation and interaction and empower the role of digital transformation as a catalyst for OFDI performance.

5.3. Limitations and Future Work

This study has several limitations that should be addressed in future research. First, it focuses on the impact of digital transformation on OFDI performance. Future research can further expand the impact of digital transformation on other international business strategies, such as entry mode and location choices, to enrich the internationalization theory of CMNEs. Second, this research uses a dataset of Chinese listed firms based on contextual features of China, which may limit the generalizability of conclusions. Future scholars can explore whether and how digital transformation affects OFDI performance in the context of other emerging markets and developed countries. Third, it is reasonable to investigate other factors that moderate the relationship between digital transformation and OFDI performance. Finally, there may be certain measurement biases associated with using word frequencies in annual reports as a measure of digital transformation. Alternative measure methods, such as questionnaire surveys and increased construction of more comprehensive indicator systems for digital transformation, could be employed to enhance the findings of this study in future research.