R&D Expenditure and Firms’ Exports: Does CEO Foreign Experience Matter?

Abstract

This study explores the effect of R&D expenditure on firms’ exports and examines whether the CEO’s foreign experience moderates the nexus. Employing a large sample of 1,206 listed companies in China from 2012 to 2021, this study applies the fixed-effect panel regression to validate the relationship between R&D expenditure and the proportion of foreign sales to total sales. Further, the results are also checked for robustness using 2SLS instrumental variables (IV) and propensity score matching (PSM) to control endogeneity. Panel regression results reveal a significant effect of R&D expenditure on firms’ exports following the competitive advantage theory. The findings also show that CEOs with foreign experience can strengthen the competitiveness of Chinese firms in foreign sales, which supports the resource-based view. The heterogeneity test shows that R&D significantly improves the firm exports of non-state-owned, eastern region, and processing trade companies, due to policy pressures, locational advantages, and global value chain differences. The findings bear significant implications for policymakers, investors, and corporations endeavoring to comprehend the importance of innovation and human capital in international markets.

Keywords

firm exports R&D expenditure CEO foreign experience competitive advantage theory

Introduction

In 2010, China became the world’s second-largest economy (Leung, 2011), making China’s development attractive to the world. However, the initial emphasis of Chinese firms on penetrating international markets is cost advantage due to inexpensive labor (K. Li & Song, 2011; Sun et al., 2021). Most firms in China adopt a low-price strategy (Ouyang et al., 2015; B. Yang, Liu, et al., 2022) in commissioned processing and assembly, and hence, China is known as the “world factory” with labor-intensive products and modest innovation capabilities (H. Yang, Zhao, & Cai, 2022; X. Zhang et al., 2016). Thus, China’s success in becoming a leading exporter is primarily due to its abundant, low-cost labor supply, not its innovation and independent research capabilities (Huang et al., 2021). The decline in exports became the factor that significantly impacted China’s Gross Domestic Product (GDP) growth (Iqbal et al., 2023). S. J. Wei et al. (2017) suggest that while cyclical factors contribute to the decline, structural and competitive causes are also significant.

Research and development (hereafter R&D) activities boost firms’ competitive advantage (Bleaney & Wakelin, 2002; Penner-Hahn & Shaver, 2005) As Y. Li et al. (2022) argues, while leveraging on low labor costs, Chinese firms increase their R&D expenditures to bolster technical competitiveness and facilitate the local production of pivotal components. At the same time, the Chinese government has taken measures to encourage companies to enhance international competitiveness, for example, intellectual property protection (H. Deng et al., 2019), tax incentives (Chen & Yang, 2019), and industry-university-research co-operation (Cui & Li, 2022). In 2021, as shown in Figure 1, China’s National R&D Funds amounted to 2.7864 trillion yuan, equivalent to 2.44% of GDP, a significant increase compared to 2016.

Figure 1.

National R&D funds and input intensity from 2016 to 2021 of China

Although the significance of R&D expenditure in shaping corporate export behavior has been extensively documented in the literature (Azar & Ciabuschi, 2017; Barrios et al., 2003; Caldera, 2010). However, there is controversy in the results of existing studies, and related heterogeneity analyses have seldom been addressed (Dai et al., 2020; Inui et al., 2017; Roper & Love, 2002; D. Zhang et al., 2018). Given the limited empirical evidence and heterogeneity analysis, as well as the lack of consensus on the impact of R&D expenditure on corporate exports (Z. Deng et al., 2014; Kafouros et al., 2008), the first objective of this research is to investigate the impact of R&D expenditure on the exports of Chinese firms and exams heterogeneity for firm ownership, location, and trade mode.

Chinese enterprises face new hurdles in exporting their products due to lack of knowledge about local regulations and inexperience in international brand management (L. Wang et al., 2021). The outsider disadvantage caused by information asymmetry and cultural barriers has also become increasingly prominent in Chinese firms’ exports (Jia et al., 2020; Y. Li et al., 2022). Managers with foreign experience have become an essential human resource for Chinese firms (Cooke et al., 2020; Warner, 2020). As a result, Chinese firms increasingly employ professionals with foreign education degrees to assist their exports (M. Guo et al., 2019).

Some studies suggest that foreign experience is a vital resource that helps firms overcome difficulties and obstacles (e.g., multilingualism and customs) in internationalization (e.g., Buckley et al., 2016; Chandra et al., 2020). Conversely, some scholars believe that senior managers’ foreign experience may negatively impact their inability to adapt to localized management practices in China (Rui et al., 2017). Thus, the second objective of this study is to examine the moderating influence of CEO foreign experience on the relationship between R&D expenditure and firms’ exports.

In view of the above, employing a sample of Chinese non-financial listed firms from 2012 to 2021, this study applies Fixed Effects Model (FEM) and Two-Stage-Least-Squares (2SLS) estimation techniques to investigate the influence of R&D on firm exports. Furthermore, this study further explores whether the R&D expenditure–export nexus is moderated by CEO’s foreign experience. In particular, heterogeneity is examined for firm ownership, location, and trade mode.

This study contributes to the existing literature and body of knowledge in the following respects. First, this study extends the theoretical literature by suggesting whether R&D expenditure affects firms’ exports. The result of this study deepens our comprehension of the technological innovation dimension that influences export performance in developing economies. Further, we test the heterogeneity of enterprise ownership, location, and trade mode to conduct a comprehensive exploration of the influence of R&D on firm exports. Second, compared to preceding studies that predominantly examined the influence of innovation on exports (Azar & Ciabuschi, 2017; Bıçakcıoğlu-Peynirci et al., 2020), this study contributes to the ongoing debate by investigating the moderating impact of CEO foreign experience on the relationship between R&D and firms’ exports. A detailed exploration of this moderating mechanism is instrumental in enhancing our comprehensive understanding of corporate behavior, shedding light on why certain companies exhibit distinct behaviors in the face of decisions related to innovation and exports. The result suggests that CEO foreign experience is a critical resource that enhances the positive impact of R&D expenditure on firms’ exports in a competitive environment. Finally, this study contributes by focusing on the Chinese business context. Given China’s unique position in the global economy, this research provides crucial insights for understanding the internationalization strategies of Chinese enterprises. The study helps to inform the policy of overseas executive talent acquisition in developing countries and enriches the research on the factors influencing firms’ overseas strategy.

The structure of this paper consists of the following: Section “Literature Review and Research Hypotheses” reviews relevant literature and develops the hypotheses. Section “Research Methodology” presents the variable design, models, and econometric models. Section “Empirical Results and Analysis” describes the empirical findings of this study. Section “Discussion” and “Conclusion” discuss the research results and provide the conclusions.

Literature Review and Research Hypotheses

The Nexus Between R&D Expenditure and Firms’ Exports

As international competition and globalization continue to rise, experts, and politicians worldwide stress the need for technological advancement to maintain export competitiveness (Archibugi & Michie, 1997). R&D promotes the expansion and application of technology, which can ultimately contribute to higher productivity (Melitz et al., 2007). Higher productivity improves the ability to enter new international markets and increase export market share (Krugman, 1979, 1991, 1995; Porter, 1990). Technologies enable innovative firms to enhance their processes and product quality to achieve a higher demand curve in foreign markets (Roper & Love, 2002). According to Welford et al. (1993), improved product quality, material efficiency, lower risk, reduced insurance premiums, and cheaper financing costs are significant components of competitive advantage. A productive firm can organize its export activities and adapt to the shifts in the global market, thus promoting export sales (Guan & Ma, 2003; Rodil et al., 2016; Wu et al., 2021). Better productivity can be achieved through innovation, lower production costs, and the introduction of quality goods and services (Yi et al., 2013). Productivity and economic growth rates are related to competitiveness (Schmookler, 1966; Segerstrom, 1991).

The Competitive Advantage Theory portrays the ability to attain a competitive edge and acquire new knowledge or capabilities (Furrer, 2008; Hoskisson et al., 1999). A competitive advantage is achieved when an organization develops or acquires a set of attributes (or performs actions) that enable it to surpass its competitors (H. L. Wang, 2014). A firm’s competitiveness can be strengthened through innovation, which can be advantageous in international markets (Dong et al., 2022). Innovation resources are critical to avoid damaging prices and vicious competition (Mendi & Costamagna, 2017; L. Xu et al., 2024) and are also essential to continued existence (Chatzoglou & Chatzoudes, 2018; Kirbach & Schmiedeberg, 2008). Firms with more R&D projects can operate the export market following high innovation (Aw et al., 2007, 2011). Thus, firm innovation may be related to the necessity to export (Grossman & Helpman, 1990; Krugman, 1979, 1991, 1995; Porter, 1990; Suarez-Villa & Fischer, 1995) and better adaptation to environmental uncertainty (Golovko & Valentini, 2011).

Firms can quickly recover R&D costs from exports and gain better profits from innovative products (Kotabe et al., 2002). This view is supported by most research examining the correlation between innovation and export growth (Ayllón & Radicic, 2019; Cassiman & Golovko, 2011). Innovative firms are found to be more likely to export than non-innovative firms of the same size (Wakelin, 1998). Similarly, Franko (1989) found inventive businesses are more likely to rush into international markets to improve or preserve profits.

According to a study on the connection between R&D expenditure and exports, an increase in a company’s R&D spending in 1 year is associated with a rise in exports the following year. It has been argued that an organization’s export growth is directly correlated with the R&D expenditure it makes in its business (Carboni & Medda, 2018). Amin and Thrift (1995) and Lall (1985) believe that R&D capabilities help export growth, whereas Dhanaraj and Beamish (2003) show a positive relationship between R&D expenditure and international market return. R&D expenditure creates a unique competitive advantage for the company. Thus, this study provides the following hypothesis.

Hypothesis 1: R&D expenditure positively affects the exports of Chinese firms.

The Moderating Effect of CEO’s Foreign Experience on the Nexus Between R&D Expenditure and Firm Exports

The Resource-Based View (RBV) posits that firm-specific resources and capabilities are fundamental to achieving and sustaining competitive advantage (Barney, 1991). Within this framework, CEO foreign experience is considered a critical resource (Le & Kroll, 2017). CEOs with foreign experience bring unique skills, knowledge, and networks that can enhance a firm’s strategic orientation and international competitiveness (Le & Kroll, 2017; L. Q. Wei & Ling, 2015). Their exposure to diverse markets and business practices equips them with a broader perspective and an ability to navigate complex international environments (Conyon et al., 2019).

Empirical evidence suggests that CEOs with foreign experience are better equipped to identify and exploit global opportunities, thereby facilitating firms’ entry and success in foreign markets (Criaco et al., 2022). Tihanyi et al. (2000) also found that CEO’s education level and foreign experience closely relate to the firms’ exports. Thus, understanding the link between R&D expenditure and firm exports is greatly helped by considering the CEO’s foreign experience (W. T. Hsu et al., 2013). Social networks are crucial resources that firms need in export activities (W. T. Hsu et al., 2013; Morgan et al., 2018; Rabbiosi et al., 2019). CEOs with foreign experience can tap into their formal or informal network in foreign markets (P. Y. Li, 2018). Executives with foreign experience can enhance human capital resources, prompting firms to make export decisions (Cerrato & Piva, 2012). Managers with cross-cultural knowledge effectively navigate language and cultural barriers (Selvarajah et al., 2019), enabling firms to accurately identify and capitalize on international market opportunities (Miocevic & Morgan, 2018). Foreign experience can help cultivate new international business relationships, leveraging external knowledge resources, even when they lack specific market partnerships (Miocevic & Morgan, 2018). Moreover, a competent CEO is a critical success factor in avoiding wasteful investment that can impede development (Müller & Zimmermann, 2009; Santarelli & Sterlacchini, 1990).

In summary, CEO’s foreign experience enhances a company’s international perspective and strategic advantage. Based on the resource-based view, this study examines whether the CEO’s foreign experience moderates the relationship between R&D expenditure and firms’ exports. Thus, this study provides the following hypothesis.

Hypothesis 2: CEO’s Foreign Experience positively moderates the relationship between R&D expenditure and the exports of Chinese firms.

Research Methodology

Data Sources

From 2012, the 12th Five-Year Plan (2011–2015) placed a strong emphasis on innovation and technology development. This period marked the beginning of increased government support and funding for R&D activities, making it a relevant starting point for research. Hence, this study uses the data of A-share listed companies in China for 2012 to 2021 as the sample. All initial data are obtained from the CSMAR and WIND database. Following J. Xu et al. (2021), first, we remove financial institutions that are not related to R&D investments. Second, in accordance with guidelines from the China Securities Regulatory Commission (CSRC), companies recording net losses for two consecutive years are categorized as special treatment (ST) companies, and those with losses for three consecutive years are denoted as *ST companies, facing potential delisting after another year of losses. Consequently, ST and *ST companies are excluded from the sample, which means the listed companies have abnormal financial conditions. Finally, it disregards certain listed companies lacking complete or accessible data. The ultimate sample for this study consists of 1,206 companies with 11,971 firm-year observations during the 10 years from 2012 to 2021.

Selection of Variables

The dependent variable in this study is firm exports (FSTS). In line with previous studies (Berman et al., 2015; Sharma et al., 2020), we identify firm export as the ratio of foreign sales to total sales. Building upon the studies of Vithessonthi and Racela (2016) and Moradi et al. (2019), R&D expenditure (RDTA) as the independent variable is defined as the ratio of R&D expenditures to total assets. Existing studies have not adequately considered the impact of top executives’ background factors, especially their foreign experience, on firm exports (Oura et al., 2016; Ramón-Llorens et al., 2017). Therefore, this study introduces CEO foreign experience as a moderating variable to explore whether it enhances or attenuates the impact of national ownership on firm exports, thus filling a gap in the relevant literature. Following Le and Kroll (2017), this study adopts a dummy variable to represent CEO foreign experience (FOREIGN). One is assigned if the CEO has studied or worked overseas, and zero otherwise.

Following previous literature as empirical specifications, this study incorporates firm size (SIZE), leverage (LEV), gross profit margin (GPM), firm performance (ROA), and firm age (AGE) to control for firm characteristics that might create a competitive advantage to drive foreign sales (Kling & Weitzel, 2011). Firm size (SIZE) is crucial when choosing an export strategy (Zapletalová, 2015), and large companies have the advantage of resources and are more resilient to risk (Kumar et al., 2020). Highly leveraged enterprises have more severe financial constraints, which may limit their willingness to take risks and access international markets (Aivazian et al., 2005). Companies with high gross profit margins typically produce lower-cost goods or compete in less-saturated marketplaces. Low-profit companies may have higher production costs or operate in less favored product marketplaces (Abor, 2007). Return on assets (ROA) measures how effectively the assets are used, which might affect firms’ exports (C. Hsu & Boggs, 2003). Although older firms may benefit from the accumulation of assets (B. J. Liu, 2017), they may face the challenge of organizational inertia and, therefore, cannot adapt to new markets and technologies (Zahra et al., 2000). This study mitigates the simultaneity bias and endogeneity issues with a lag structure of variables (Aitken & Harrison, 1999). Except for Firm Age (AGE), all variables of firm characteristics are lagged by 1 year to minimize estimation biases that firms’ exports might affect their characteristics.

In addition to firm characteristics, internal corporate governance mechanisms can also favor export performance (Bilgin et al., 2017). Thus, this study controls the ratio of independent directors to board size (BIND) and the percentage of control owned by the beneficial owner minus the percentage of ownership owned by the beneficial owner (OWN; Lozano et al., 2016). This study also controls foreign exchange rates (EXRA), which can deter firms from entering a competitive foreign market due to exchange rate uncertainty (Lin & Chen, 2022). During the Renminbi (RMB) appreciation period, exports decreased significantly, with exporters facing more competitive pressure (Dai et al., 2018). Furthermore, macroeconomic shocks and individual factors were accounted for using time and firm-specific dummy variables. The definitions of these variables are summarized in Table 1.

Table 1.

Definition and Description of Variables.

Variable	Symbol	Description and calculation	Related literature
Dependent variable
Firm export	FSTS	The ratio of foreign sales to total sales	Berman et al. (2015), Sharma et al. (2020)
Independent variable
R&D expenditure	RDTA	The ratio of shareholding of the state shareholders to the total shares	Vithessonthi and Racela (2016), Moradi et al. (2019)
Moderating variable
CEO foreign experience	FOREIGN	A dummy variable, the CEO studied or worked in overseas are assigned 1, otherwise 0	Le and Kroll (2017)
Control variables
Firm size	SIZE	The natural logarithm of the total assets of the firm	Zapletalová (2015)
Financial leverage	LEV	Financial leverage, measured as the ratio of total debt to total assets	Auer et al., 2021
Gross profit margin	GPM	The ratio of changes in sales	Dwi Sihono and Widarti (2021)
Firm performance	ROA	Net income excluding extraordinary items divided total assets	Kling and Weitzel (2011)
Firm age	AGE	The time since a firm was founded	Kotabe et al. (2017)
Board independence	BIND	The ratio of independent directors to board size	Bilgin et al. (2017)
Ownership	OWN	The percentage of control owned by the beneficial owner minus the percentage of ownership owned by the beneficial owner	Lozano et al. (2016)
Foreign exchange rates	EXRA	The rate at which one currency can be exchanged for another	Lin and Chen (2022)

Note. The table provides brief definitions of the main variables used in the study.

Model Specification

R&D expenditure can enhance enterprise innovation, thereby enhancing competitiveness through innovation, lowering production costs, and introducing quality goods and services. Thus, this study uses the following equation to examine the effect of R&D expenditure on firms’ exports.

\begin{matrix} FSTSit = & α + β_{1} RDT A_{it - 1} + β_{2} SIZ E_{it - 1} \\ + β_{4} GP M_{it - 1} + β_{5} RO A_{it - 1} + β_{6} AG E_{it} \\ + β_{7} BIN D_{it} + β_{8} {OWN}_{it} + β_{9} EXR A_{it} \\ + λ_{i} + γ_{t} + ε_{it} \end{matrix}

(1)

Where FSTS is the ratio of foreign sales to total sales. RDTA is equal to the ratio of R&D expenditures to total assets. SIZE is the natural logarithm of actual total assets in millions of RMB. LEV is the ratio of total debt to total assets. Gross Profit Margin (GPM) is the ratio of gross profit to total sales. ROA is the ratio of EBIT to total assets. AGE is defined as the length of time that has passed since a firm was founded. The ratio of Independent Directors to board size is the proxy of a corporate’s governance. OWN is the percentage of control owned by the beneficial owner minus the percentage of ownership owned by the beneficial owner. The $λ_{i}$ in the model specification is the firm-fixed effect that controls for unobservable firm-specific and time-invariant heterogeneity. The $γ_{t}$ in the model specification is a year-fixed effect that controls for unobservable time-variant common shocks to all sampled firms.

The following equation is used to examine further the moderating effect of a CEO’s foreign experience on the nexus between R&D expenditure and firms’ exports.

\begin{matrix} FSTSit = & α + β_{1} RDT A_{it - 1} + β_{2} {SIZE}_{it - 1} + β_{3} LE V_{it - 1} \\ + β_{4} GP M_{it - 1} + β_{5} RO A_{it - 1} + β_{6} AG E_{it} \\ + β_{7} BIN D_{it} + β_{8} OW N_{it} + β_{9} EXR A_{it} \\ + β_{10} FOREIG N_{it} + β_{11} {RDTA}_{it - 1} \\ \times x FOREIG N_{it} + λ_{i} + γ_{t} + ε_{it} \end{matrix}

(2)

The interaction term assesses whether the CEO’s foreign experience affects the relationship between R&D expenditure and firms’ exports. The dummy variable $(FOREIG N_{it})$ is 1 when the CEO has studied or worked overseas; otherwise, it is zero. $RDT A_{it - 1}$ × $FOREIG N_{it}$ is the interaction term between R&D expenditure and the foreign experience of the CEO to verify the moderating effect.

The models are regressed using pooled ordinary least square (pooled OLS) estimation before considering cross-sectional or temporal differences. The F-test, the Breusch and Pagan Lagrangian Multiplier (LM) test, and Hausman’s test are performed for each individual proposed model to determine the most appropriate method of regression analysis.

Empirical Results and Analysis

Descriptive Statistics

Table 2 shows the mean of FSTS is 16.8%, with a standard deviation of 21.8%. The high deviation among export levels among enterprises suggests a divergence that may stem from the diverse business strategic choices made by these firms. The RDTA exhibits notable heterogeneity across companies, with a maximum value of 0.272 and a minimum value of 0.000, which means that R&D expenditure varies dramatically. The mean ROA was 3.4%, indicating that firms exporting from China have weaker profitability on average. That shows most Chinese products were still at the lower end of the global value chain, lacking competition, and bargaining power. The average age of Chinese listed companies was 23 years old, indicating that Chinese listed companies started late and may have the problem of insufficient resources and experience in internationalization.

Table 2.

Descriptive Statistics.

	(1)	(2)	(3)	(4)	(5)
Variables	N	Mean	SD	Min	Max
FSTS	11,971	0.168	0.218	−0.344	1.677
RDTA	11,971	0.024	0.022	0.000	0.272
SIZE	11,971	3.757	0.565	2.263	6.437
LEV	11,971	0.438	0.249	0.008	10.500
GPM	11,971	0.274	0.180	−2.978	0.973
ROA	11,971	0.034	0.096	−1.683	4.489
AGE	11,971	19.020	6.208	4.000	122.00
BIND	11,971	0.375	0.057	0.200	0.800
OWN	11,971	4.636	7.583	−69.49	41.62
EXRA	11,971	6.513	0.273	6.143	6.898
FOREIGN	11,971	0.633	0.482	0.000	1.000

Note. This table reports summary statistics for key variables for a sample of 5,873 company-year observations for the period 2015 to 2021. Financial firms and ST firms are excluded from the sample. Please see Table 1 for other variable definitions. The mean, standard deviation, maximum, and minimum values are shown for each variable.

Table 3 reports correlations for the variables used in the analysis. The correlation coefficients are relatively minor (less than .5). The mean VIF is 1.21, and the minimum and maximum values of VIF are 1.02 and 1.62, respectively. The VIF of all the variables is significantly below the acceptable threshold of 5, as G. James et al. (2013) suggested. Hence, the dataset has no concerns of multicollinearity (Callen & Fang, 2013).

Table 3.

Correlation Matrix.

Variables	FSTS	RDTA	SIZE	LEV	GPM	ROA	AGE	BIND	FOREIGN	EXRA	DUAL	VIF
FSTS	1.000
RDTA	.045***	1.000										1.09
SIZE	−.062***	−.159***	1.000									1.41
LEV	−.028***	−.144***	.415***	1.000								1.62
GPM	−.128***	.244***	−.165***	−.406***	1.000							1.32
ROA	−.021**	.068***	.047***	−.369***	.319***	1.000						1.31
AGE	−.035***	−.078***	.196***	.148***	−.071***	−.050***	1.000					1.15
BIND	.029***	.015	.072***	.027***	.020**	−.022**	−.046***	1.000				1.02
FOREIGN	.060***	.043***	.079***	−.058***	.100***	.045***	−.027***	.003	1.000			1.03
EXRA	.002	.025***	.206***	.068***	.019**	−.088***	.337***	.024***	.035***	1.000		1.16
DUAL	−.025***	−.024**	.053***	.061***	−.057***	.024**	.048***	−.069***	−.055***	−.016*	1.000	1.02

Note. This table reports the correlation coefficients between key variables for 11,971 company-year observations covering 2012 to 2021. Please see Table 1 for other variable definitions.

, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Analysis of Regression Results

Table 4 presents the empirical results of the two hypotheses formulated for our study. Model 1 reports results from estimating Equation 1 using an ordinary least squares (OLS) regression. Results support the hypothesis that R&D expenditure increases the firms’ exports. However, F-test results indicated that Fixed-Effects Panel Regression analysis is more appropriate than ordinary Pooled-Ordinary Least Squares (OLS) regression analysis. The null hypothesis of the Breusch and Pagan Lagrangian Multiplier (LM) test is rejected, indicating that a Random-Effects Panel Regression analysis is more appropriate than the standard OLS regression analysis. Model 2 repeats the estimation using Random-Effects Panel Regression. The null hypothesis in the Hausman test is rejected, indicating that the Fixed-Effects Panel Regression is more appropriate (Greene, 2005).

Table 4.

Regression Results of R&D Expenditure on Exports and the Moderating Effect of CEOs’ Foreign Experience.

	Model 1	Model 2	Model 3	Model 4
Variables	OLS	RE	FE	FE
$RDT A_{it - 1}$	0.656*** (6.58)	0.488*** (4.92)	0.920*** (8.70)	0.680*** (4.28)
$SIZ E_{it - 1}$	−0.027*** (−6.25)	0.027*** (5.61)	−0.024*** (−5.29)	−0.024*** (−5.25)
$LE V_{it - 1}$	−0.053*** (−4.59)	−0.007 (−0.89)	−0.052*** (−4.04)	−0.052*** (−4.06)
$GP M_{it - 1}$	−0.241*** (−18.01)	−0.042*** (−3.53)	−0.218*** (−16.11)	−0.219*** (−16.19)
$RO A_{it - 1}$	0.044* (1.74)	−0.008 (−0.64)	0.035 (1.37)	0.035 (1.37)
AGE	−0.001*** (−2.82)	−0.001*** (−3.44)	−0.001*** (−3.69)	−0.001*** (−3.61)
BIND	0.135*** (3.70)	−0.014 (−0.55)	0.125*** (3.43)	0.126*** (3.44)
FOREIGN	0.034*** (7.93)	−0.003 (−1.08)	0.037*** (8.57)	0.028*** (4.48)
EXRA	0.023*** (2.88)	−0.001 (−0.18)	0.069* (1.93)	0.069* (1.91)
OWN	−0.000 (−1.62)	0.000 (0.25)	−0.001*** (−2.76)	−0.001*** (−2.77)
$RDT A_{it} \times x FOREIGN$				0.395** (2.01)
Constant	0.142*** (2.79)	0.108*** (4.07)	−0.293 (−1.26)	−0.281 (−1.20)
Observations	10,765	10,765	10,629	10,629
R-squared	0.403		0.757	0.759
industry FE			YES	YES
Year FE			YES	YES
F-test			41.40*** [0.0000]
LM test		27,701.79*** [0.0000]
Hausman test			100.42*** [0.0000]
Heteroskedasticity test	0.000*** [0.0000]
Autocorrelation test	377.202*** [0.0000]

Note. RDTA × FOREIGN is the moderating effect of the CEO foreign experience. Industry and year fixed effects are included in all regressions. Robust standard errors grouped at the firm level is reported in parentheses. Please see Table 1 for other variable definitions.

, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Models 3 and 4 are the results of the Fixed-Effects Panel Regression analysis. Model 3 shows that R&D expenditure is significant at the 1% level. The positive coefficient of 0.920 implies that for one standard deviation increase in R&D expenditure to total assets, the firms’ foreign sales to total sales rise by 8.43% ceteris paribus. The result supports Hypothesis 1, which is that R&D expenditure can boost firms’ exports by strengthening international competitiveness. The competitive advantage theory (F. Wang, 2014) demonstrates that increased in R&D expenditure is a compelling strategic choice for firms seeking international competitive advantage. It is consistent with prior studies that emphasize the critical role of R&D expenditure in the long-term competitiveness of firms (Ayllón & Radicic, 2019; Bleaney & Wakelin, 2002; Cassiman & Golovko, 2011; Penner-Hahn & Shaver, 2005). Similarly, it is argued that innovation can generate or maintain a competitive advantage (Dereli, 2015; Yu et al., 2017), and technological innovation strategies significantly impact firms’ exports (Becker & Egger, 2013; Edeh et al., 2020).

Model 4 shows that the interaction term (RDTA × FOREIGN) is significant at a 1% level. The positive coefficient supports Hypothesis 2, indicating that foreign experience enhances the positive effect of R&D expenditure on firms’ exports. The result proves that executives with foreign experience might have network resources or the ability to handle complex business overseas, which benefited the firms’ exports (Carpenter & Fredrickson, 2001). It is argued that CEOs with foreign experience have more resources under intense international competition, including broader international social networks (Suutari & Mäkelä, 2007), deep cross-cultural knowledge (Black & Duhon, 2006), and richer business insights (Quan et al., 2021). These resources endow them with stronger strategic tools that help firms to better cope with the challenges of the international market (Quan et al., 2021). CEOs with foreign experience, being a strategic resource, can bring unique competitive advantages to the company from the perspective of Resource-Based View (Kraaijenbrink et al., 2010).

Some corporations may avoid expanding into foreign markets due to additional export costs (Chang & Rhee, 2011) and higher business risk (Gulamhussen et al., 2014). Firm size (SIZE) is negatively linked to firms’ exports, suggesting that smaller businesses are more agile in penetrating international markets than their bigger counterparts (Zapletalová, 2015). International markets increase corporate risks (Kwok & Reeb, 2000), and the negative association between leverage and foreign sales suggests that companies with higher debt ratios are hesitant to expand into these markets for fear of going bankrupt (Chen & Zhao, 2007). Moreover, coordination and oversight issues may exist in internationally expanding enterprises (Boeh & Beamish, 2012) and higher liability of foreignness (Johanson & Vahlne, 2009; Zaheer, 1995). A smaller gross profit margin is associated with more foreign sales, indicating that Chinese firms focus on a lower price strategy for conquering international markets (Ouyang et al., 2015). Firm age (AGE) is negatively associated with firms’ exports, indicating that older firms are more likely to experience organizational inertia, making it more challenging to adapt to complex and changing international markets (Zahra et al., 2000). In contrast, board independence (BIND) positively correlates with the firms’ exports, suggesting that good corporate governance leads to better exports.

Endogeneity Test

Results of Two-Stage-Least-Squares (2SLS)

The main challenge in estimating exports is the endogeneity of R&D attainment, which is subject to self-selection, ability bias, and measurement errors (Esteve-Pérez & Rodríguez, 2013). Government R&D subsidies are typically allocated based on policy objectives and criteria, rather than the specific characteristics and short-term performance of individual firms (Boeing, 2016). Consequently, Government R&D subsidies is considered exogenous, as it is not correlated with unobserved firm characteristics (i.e., it is uncorrelated with the error term). Furthermore, government R&D subsidies have a direct impact on firms’ R&D expenditures, establishing a strong correlation between government R&D subsidies and R&D spending (D. Guo et al., 2016; X. Liu et al., 2016). Therefore, this study adopts the 2SLS method and exploits the government R&D subsidies (RDSUB) as a source of exogenous variation in R&D expenditure to generate an instrumental variable and conduct a regression using Two-Stage-Least-Squares (2SLS). As indicated in the first column of Table 5, the coefficient of the Government R&D subsidies is positively correlated with R&D expenditure at 1% significance. Regarding the explanatory power of the instrumental variable, the F value is 29.81 (p = .000), rejecting the null hypothesis that “the instrumental variable has no explanatory power.” The problem of weak instrumental variables is not severe. The results in the second column indicate that the coefficient of R&D is significantly positive at the 10% significance level, which is consistent with the results in Table 3.

Table 5.

Results of Two-Stage-Least-Squares (2SLS).

	(1)	(2)
	Stage I	Stage II
Variables	RDTA	FSTS
RDSUB	0.0000*** (0.000)
$RDT A_{it - 1}$		4.211* (1.896)
$SIZ E_{it - 1}$	−0.0033*** (0.000)	−0.010 (−1.211)
$LE V_{it - 1}$	0.0002 (0.001)	−0.058*** (−4.349)
$GP M_{it - 1}$	0.0189*** (0.001)	−0.272*** (−6.170)
$RO A_{it - 1}$	0.0105*** (0.002)	−0.002 (−0.060)
EXRA	−0.0001 (0.002)	0.004 (0.208)
BIND	0.0017 (0.003)	0.111*** (2.872)
OWN	0.0000 (0.000)	−0.001*** (−3.322)
AGE	−0.0002*** (0.000)	−0.001 (−1.234)
Constant	0.107 (0.014)	0.126 (0.804)
Year FE	✓	✓
Industry FE	✓	✓
Observations	10,629	10,629
R-squared	0.206
Joint significance of IVs: F(3) (p-value)	29.81 (0.000)

Note. RDSUB is the Government R&D subsidies. RDTA is equal to the ratio of R&D expenditures to total assets. SIZE is the natural logarithm of actual total assets in millions of RMB. LEV is the ratio of total debt to total assets. Gross Profit Margin (GPM) is the ratio of gross profit to total sales. ROA is the ratio of EBIT to total assets. OWN is the percentage of control owned by the beneficial owner minus the percentage of ownership owned by the beneficial owner. AGE is defined as the length of time that has passed since a firm was founded. The ratio of Independent Directors to board size (BIND). Exchange rate (EXRA) is the variable that may affect exports at the macro level.

, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Robustness Test

Testing Robustness Using Output Value of the New Products as a Dependent Variable

Following the literature (Coad & Rao, 2010; Wignaraja, 2012), we consider the ratio of R&D-to-Sales (RORS) of the firm as R&D expenditure for checking robustness. Because it directly reflects the a demonstration of the firm’s R&D expenditure (Wignaraja, 2012). This data come from the CSMAR database. The results stated in Table 6 support the prior findings reported in Table 3, indicating that R&D expenditure positively affect firm exports, and CEO’s Foreign Experience also positively moderates the relationship between R&D expenditure and the export. The findings of the control variables likewise persist, confirming that our results are unaffected by a different R&D proxy.

Table 6.

Robustness Test Alternative Proxy for R&D.

Variables	Model 1	Model 2
$ROR S_{it - 1}$	0.326*** (4.302)
FOREIGN		0.024** (8.566)
$ROR S_{it} \times x FOREIGN$		0.312*** (6.438)
$SIZ E_{it - 1}$	−0.024*** (−5.874)	−0.026** (−5.684)
$LE V_{it - 1}$	−0.053*** (−4.655)	−0.051*** (−4.587)
$GP M_{it - 1}$	−0.231*** (−17.618)	−0.232*** (−16.867)
$RO A_{it - 1}$	0.042* (1.247)	0.045* (1.188)
EXRA	0.025*** (2.257)	0.024*** (2.256)
BIND	0.103*** (3.425)	0.105*** (3.376)
OWN	−0.004*** (−0.007)	−0.004*** (−0.007)
AGE	0.001** (0.017)	0.001** (0.016)
Constant	0.124*** (0.011)	0.123*** (0.011)
Observations	10,629	10,629
R-squared	0.034	0.035
Industry FE	✓	✓
Year FE	✓	✓

Note. OVNP is the output value of the new products of the firm. OVNP × FOREIGN is the moderating effect of the CEO foreign experience. Industry and year fixed effects are included in all regressions. Please see Table 3 for other variable definitions.

, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Testing Robustness Using Propensity Score Matching

To further address the potential endogeneity issues arising from selection bias related to firm-specific characteristics, this study employs a widely used technique—propensity score matching (PSM; Sarang et al., 2021, 2024). This paper utilizes PSM to mitigate the impact of selection bias on the regression results. Firms with R&D expenditure above the mean were designated as the treatment group, while the remaining firms constituted the control group. The matching process employed a 1:1 matching ratio, with the control variables serving as the matching covariates and the nearest neighbor matching method being applied (Table 7).

Table 7.

Propensity Score Matching Test.

	(1)	(2)
Variables	FSTS	FSTS
RDTA	0.704*** (5.25)	0.727*** (5.41)
FOREIGN	0.044*** (7.50)	0.044*** (7.57)
RDTA × FOREIGN		0.636** (2.50)
LNTA	−0.016** (−2.40)	−0.016** (−2.43)
LEV	−0.062*** (−3.40)	−0.062*** (−3.41)
GPM	−0.227*** (−12.03)	−0.229*** (−12.15)
ROA	0.012 (0.29)	0.016 (0.39)
AGE	−0.001* (−1.80)	−0.001* (−1.72)
INDEP	0.117** (2.31)	0.117** (2.31)
EXRA	0.143 (1.49)	0.141 (1.46)
Dual	−0.001** (−2.01)	−0.001** (−2.05)
Constant	−0.802 (−1.31)	−0.729 (−1.19)
Observations	5,950	5,950
R-squared	0.076	0.077
industry FE	YES	YES
Year FE	YES	YES

Note. Please see Table 3 for other variable definitions.

, **, and *** represent statistical significance at the 10%, 5%, and 1% levels.

To examine whether the differences in firm exports are driven by R&D expenditure, we re-estimated the matched sample presented in Columns 3 to 4 of Table 4. Subsequently, we matched the treatment and control groups based on the explanatory variables in Equation 1 to ensure a high degree of similarity between the two groups. We found that the coefficient for R&D was positive and significant. These results are consistent with our baseline findings, indicating that increased R&D expenditure tends to enhance firm export performance. The results ensuring the quality of PSM are reported in Table 8 and 9. These findings suggest that the treatment and control groups are comparable in terms of firm-level factors, as there are no significant differences in the control variables between the two groups. As shown in Figure 2, it is evident that the two groups are better matched after the PSM.

Table 8.

Student’s t-Test.

Variable	Sample	Treated	Controls	Difference	SE	T-stat
FSTS	Unmatched	0.1875	0.1549	0.0326	0.0041	8.00
ATT		0.1876	0.1578	0.0298	0.0057	5.22
ATU		0.1550	0.1955	0.0405
ATE				0.0363

Table 9.

Balance Test of PSM.

Variable	Unmatched	Mean		%bias	%reduct	t-Test		V(T)/V(C)
Variable	Matched	Treated	Control	%bias	Bias	t	p > t	V(T)/V(C)
SIZE	U	3.6623	3.8179	−28.4		−14.83	0.000	0.66*
SIZE	M	3.6624	3.6632	−0.1	99.5	−0.08	0.938	0.96
LEV	U	0.4039	0.4604	−23.3		−12.17	0.000	0.68*
LEV	M	0.4039	0.4013	1.1	95.5	0.58	0.559	1.13*
GPM	U	0.3021	0.2552	26.2		14.04	0.000	1.03
GPM	M	0.3021	0.3068	−2.6	90.0	−1.22	0.221	0.89*
ROA	U	0.0433	0.0279	16.4		8.64	0.000	0.78*
ROA	M	0.0432	0.0407	2.7	83.7	1.53	0.125	1.53*
AGE	U	18.789	19.164	−6.1		−3.22	0.001	0.83*
AGE	M	18.790	18.889	−1.6	73.5	−0.83	0.404	1.07*
BIND	U	0.3753	0.3755	−0.3		−0.18	0.857	0.86*
BIND	M	0.3754	0.3744	1.6	−380.5	0.81	0.417	0.95
EXRA	U	6.5275	6.5034	8.8		4.72	0.000	1.02
EXRA	M	6.5274	6.5306	−1.2	87.0	−0.56	0.575	1.04
OWN	U	4.7410	4.5683	2.3		1.22	0.224	1.12*
OWN	M	4.7403	4.6557	1.1	51.0	0.53	0.597	1.04
Sample	Ps R²	LR χ²	p > χ²	MeanBias	MedBias	B	R	%Var
Unmatched	0.030	487.92	0.000	14.0	12.6	42.0*	0.77	75
Matched	0.001	9.41	0.309	1.5	1.4	6.3	1.35	50

, **, and *** represent statistical significance at the 10%, 5%, and 1% levels.

Figure 2.

Standardized deviation of each variable.

Heterogeneity Analysis

Following the benchmark regression, this study examines the impact of R&D on firms’ exports from the perspectives of ownership type (state-owned, private, foreign-owned enterprises), geographical location (eastern, central, western regions), and trade mode (ordinary trade and processing trade).

The sample companies are categorized into state-owned and non-state-owned firms based on whether state ownership exceeds 50%. Additionally, we adhere to the classification established by the National Statistical Bureau of China in 2003, which considers economic development and geographical factors, resulting in the division of mainland China into three economic zones: eastern, central, and western (Nie et al., 2019). Furthermore, these companies are segmented according to their trade mode (primary business) into trading companies and processing companies (Fang et al., 2014).

Table 10 presents the test results for the impact of R&D on firm exports in different ownership enterprises in columns (1) and (2). The regression outcomes indicate that the influence of R&D expenditure on exports is significantly more pronounced in non-state-owned enterprises than in state-owned enterprises. This result substantiates the findings of G. Hu and Yue (2021) and Fang et al. (2014), highlighting that the impact of R&D is more significant for non-state-owned enterprises in China. This distinction arises because Chinese state-owned enterprises have closer ties with the government, and their investment motivations align with government intentions rather than purely economic objectives (Zang & Jiang, 2020). Consequently, their export motivations are contingent on government intentions, and the opportunities to acquire technology through R&D spending are limited, factors that are not conducive to enhancing export activities. In contrast, non-state-owned enterprises prioritize economic benefits in their R&D investments, demonstrating higher capabilities for imitation, learning, absorption, and transformation (L. Li et al., 2017; B. Wang et al., 2022). As a result, R&D spending has a more pronounced impact on improving exports. This suggests that non-state-owned enterprises in China exhibit higher innovation effectiveness and dynamism compared to state-owned enterprises. R&D plays a crucial role in elevating the technological prowess of non-state-owned enterprises, thereby driving export growth. This finding aligns with the results of the study by W. Liu and Huang (2018).

Table 10.

Heterogeneity Test Results.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	Nature of firms		Location of firms		Industry model
Variables	SOEs	Non-SOEs	Eastern	Central and western	Ordinary trade	Processing trade
$RDT A_{it - 1}$	0.411* (6.58)	0.732*** (4.12)	0.701*** (4.05)	0.438* (4.18)	0.605** (4.43)	0.716*** (3.98)
$SIZ E_{it - 1}$	−0.032*** (−5.15)	−0.021*** (−5.45)	−0.020*** (−5.55)	−0.023*** (−5.31)	−0.025*** (−4.98)	−0.018*** (−5.69)
$LE V_{it - 1}$	−0.054*** (−4.02)	−0.048** (−4.10)	−0.046*** (−4.14)	−0.050*** (−4.08)	−0.056*** (−3.99)	−0.044*** (−4.20)
$GP M_{it - 1}$	−0.241*** (−17.62)	−0.213*** (−16.38)	−0.210*** (−16.47)	−0.217*** (−16.26)	−0.223*** (−15.91)	−0.204*** (−16.68)
$RO A_{it - 1}$	0.047* (1.74)	0.033 (1.42)	0.031 (1.45)	0.036 (1.34)	0.037 (1.32)	0.029 (1.49)
AGE	−0.001*** (−3.82)	−0.002*** (−3.35)	−0.002*** (−3.69)	−0.003*** (−3.91)	−0.001*** (−3.48)	−0.002*** (−3.86)
BIND	0.115*** (5.70)	0.025** (4.54)	0.134*** (3.33)	0.128*** (3.39)	0.118*** (3.54)	0.140*** (3.28)
FOREIGN	0.084*** (3.54)	0.071* (1.88)	0.026*** (4.60)	0.027*** (4.51)	0.030*** (4.37)	0.024*** (4.68)
EXRA	0.053*** (2.98)	0.066** (2.18)	0.070* (1.90)	0.072* (1.87)	0.067* (1.96)	0.073* (1.83)
OWN	−0.001*** (−2.65)	−0.001*** (−2.25)	−0.001*** (−2.85)	−0.001*** (−2.79)	−0.001*** (−2.72)	−0.001*** (−2.88)
Constant	0.184*** (1.76)	0.206*** (2.34)	0.414** (2.53)	0.399** (2.00)	0.380** (2.07)	0.429** (1.92)
Observations	4,629	6,136	6,998	3,767	4,090	6,675
R-squared	0.403	0.642	0.757	0.759	0.564	0.716
industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES

Note. Please see Table 1 for other variable definitions.

, **, and *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Columns (3) and (4) present the results of the heterogeneity analysis based on the geographical location of firms. The impact of R&D expenditure on enhancing firms’ exports is more pronounced in enterprises located in the eastern region compared to those in the central and western regions. This disparity can be attributed to the advantages that the eastern region holds in economic development, market systems, human capital, and R&D investments. Additionally, the proximity of the eastern region to coastal cities facilitates greater access and interaction with international markets. This heightened sensitivity to global market demands prompts eastern enterprises to better align with international requirements by augmenting R&D investments. These findings are consistent with established R&D practices and policies in China (Lin & Qin, 2022).

Lastly, columns (5) and (6) reveal the test outcomes for companies engaged in different trade modes. The regression coefficient for processing trade surpasses that of ordinary trade.¹ This divergence is attributed to the fact that companies involved in processing trade in China often occupy the lower end of the global value chain, necessitating more technological support to increase value addition. By increasing R&D expenditure, these enterprises can introduce more advanced technologies and production methods, thereby enhancing product quality and technological sophistication (X. Xu, Xia, & Li, 2017). Consequently, the competitive advantages generated by R&D have a more pronounced impact on firms’ exports in enterprises engaged in processing trade. This conclusion finds support in the studies conducted by J. Zhang et al. (2020) and Peng and Zhang (2022).

Discussion

By fostering innovation, enhancing capabilities, and enabling the creation of unique value propositions, R&D expenditure equips firms with the necessary tools to succeed in market competitions (Rajapathirana & Hui, 2018). R&D expenditure is a key driver of innovation, leading to the development of new products, processes, and technologies that enhance a firm’s competitiveness (Akinwale et al., 2017; Ciocanel & Pavelescu, 2015). By investing in R&D, firms can create unique products that meet the specific needs of foreign markets, thus differentiating themselves from competitors (W. Liu & Atuahene-Gima, 2018; Papanastassiou et al., 2020). This differentiation provides a competitive advantage that can translate into increased export performance (Tan & Sousa, 2015). Thus, R&D expenditure can be seen as enhancing innovation capabilities while expanding the competitive advantage of its products or services in international markets. The finding also strongly confirms Edeh et al. (2020) that innovation, as a crucial source of competitive advantage for businesses, positively impacts firms’ exports and prompts them to pursue an international strategy through export activities.

However, the efficacy of R&D investments in driving export performance may be contingent upon complementary managerial resources, such as the foreign experience of the CEO. CEOs with substantial international exposure bring a wealth of knowledge and insights from diverse markets, enabling them to steer R&D efforts in directions that align with global trends and demands (Heyden et al., 2017). Their understanding of international market dynamics, regulatory environments, and consumer preferences can significantly enhance the effectiveness of R&D investments, translating into superior export performance (Arunachalam et al., 2022). This aligns with Ying et al.’s (2019) view that the managerial resources of a firm, including the expertise and background of its top executives, are critical determinants of its growth and competitive positioning.

The integration of the CEO foreign experience into the firm’s strategic framework serves as a crucial moderating factor (Le & Kroll, 2017). It not only compensates for potential gaps in market knowledge but also amplifies the impact of R&D activities by aligning them with international standards and opportunities (Mostafiz et al., 2023). Our findings highlights that a dynamic interplay where the foreign experience acts as a catalyst, transforming R&D investments into tangible competitive advantages in export markets. The alignment between R&D expenditure and CEO foreign experience exemplifies a strategic fit that enhances the firm’s ability to navigate and succeed in complex international markets. This synergy between competitive advantages and resource allocation is crucial for achieving sustained competitive advantage.

Our results of heterogeneity analysis also open up intriguing questions about the potential limits of such complementarities. The R&D have higher quality improvement effects on firm exports in the eastern region than in the central and western regions, and on processing trade firms than on ordinary trade firms. This aligns with the policy of the State Council of China, which released guiding opinions aimed at advancing innovative trade development (S. J. Wei et al., 2017). These results acknowledge the challenges posed by different factors that Chinese firms face in their pursuit of innovation, considering industry and regional variations along with the uncertainties in institutional frameworks (K. Wang & Jiang, 2021; Yi et al., 2017). It serves as a comprehensive response to the unique heterogeneous characterizing Chinese exports, where dominance in processing trade, active participation of non-state-owned enterprises, and a heightened proclivity for enterprises in the eastern region to engage with international markets are notable features.

Conclusion

This study explores the impact of R&D expenditure on firms’ exports and examines whether the CEO’s foreign experience moderates the nexus, and performs heterogeneity tests for different types of firms. The findings indicate that R&D expenditure positively impacts firms’ exports by enhancing firms’ competitiveness. CEO foreign experience significantly enhances the positive impact of R&D expenditure on firm export performance by leveraging international insights and networks to better navigate and penetrate global markets. The findings also reveal that the impact of R&D is more pronounced in enhancing the exports of non-state-owned enterprises compared to their state-owned counterparts. The R&D have higher quality improvement effects on firm exports in the eastern region than in the central and western regions, and on processing trade firms than on ordinary trade firms. That means policy pressures, locational advantages, and global value chain differences significantly impact the effectiveness of R&D expenditure.

This study provides a novel perspective on understanding the critical role of R&D investment and CEO foreign experience in enhancing firm export performance and hold significant implications for government policymakers, corporate managers, and investors. First, policymakers should prioritize policies such as tax incentives, grants, and subsidies to incentivize R&D investment as a key driver of export performance. Such measures would encourage firms to innovate, leading to the development of competitive products and services that can succeed in international markets. Secondly, businesses need to leverage the international vision and experience of their CEOs to formulate globalization strategies aligned with corporate development goals, select appropriate market entry modes, and identify expansion pathways. Additionally, they should capitalize on the business networks established by their CEOs abroad to acquire more market information and cooperation opportunities, thereby enhancing their competitiveness in the international market. Firms also should be encouraged to diversify their leadership teams by including executives with foreign experience and resources.

Furthermore, to maximize the benefits of foreign experience, policies should focus on facilitating knowledge transfer and the establishment of international networks. Governments can support initiatives that promote collaboration between domestic firms and international partners, including joint ventures, strategic alliances, and international R&D consortia. These collaborations can provide domestic firms with access to global knowledge, markets, and technologies, thereby enhancing their competitive edge in the export sector. Finally, a heterogeneity analysis of firm ownership structure, firm location, and trade mode is conducted in this study. This segmentation provides a more refined perspective on the differential outcomes of R&D efforts to capture the multifaceted dynamics shaping the impact of R&D across diverse dimensions within the business landscape.

Limitations and Future Directions

Despite the valuable insights provided by our study, several limitations should be acknowledged. First, our analysis primarily relies on data from firms operating in a specific economic context, potentially limiting the generalizability of our findings to different geographical regions or industries with varying regulatory and market conditions. Future research should consider cross-country comparative studies to validate and extend our findings in diverse economic environments.

Second, while we focused on CEO foreign experience as a moderating factor, there are other potentially influential variables, such as the CEO’s educational background, industry experience, and the firm’s internal R&D capabilities, that were not considered in our model. Including these variables may provide a more comprehensive understanding of the dynamics between R&D expenditure and export performance.

Lastly, our study uses a static approach to assess the impact of R&D expenditure on export performance, which might not fully capture the dynamic nature of R&D investments and their long-term effects on firm international competitiveness. Longitudinal studies could offer deeper insights into how sustained R&D investments and evolving CEO experiences influence export outcomes over time.

Footnotes

Acknowledgements

This work was supported by the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (Project Code: FRGS/1/2020/SS01/USM/02/6). We acknowledge the support of School of Management, Universiti Sains Malaysia. Thanks for the joint efforts of the creators and editors.

ORCID iD

Char-Lee Lok

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (Project Code: FRGS/1/2020/SS01/USM/02/6).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The publications analyzed in this study can be accessed from the publicly available databases using the query provided under the methodology section of this paper. Data will be made available on request.

Notes

References

Abor

(2007). Debt policy and performance of SMEs: Evidence from Ghanaian and South African firms. Journal of Risk Finance, 8(4), 364–379. https://doi.org/10.1108/15265940710777315

Aitken

B. J.

Harrison

A. E.

(1999). Do domestic firms benefit from direct foreign investment? Evidence from Venezuela. American Economic Review, 89(3), 605–618. https://doi.org/10.1257/aer.89.3.605

Aivazian

V. A.

Qiu

(2005). The impact of leverage on firm investment: Canadian evidence. Journal of Corporate Finance, 11(1–2), 277–291. https://doi.org/10.1016/S0929-1199(03)00062-2

Akinwale

Y. O.

Adepoju

A. O.

Olomu

M. O.

(2017). The impact of technological innovation on SME’s profitability in Nigeria. International Journal of Research, Innovation and Commercialisation, 1(1), 74–92. https://doi.org/10.1504/IJRIC.2017.082299

Amin

Thrift

(1995). Globalization, institutions, and regional development in Europe. Oxford University Press.

Archibugi

Michie

(1997). Technological globalisation or national systems of innovation? Futures, 29(2), 121–137. https://doi.org/10.1016/S0016-3287(96)00072-9

Arunachalam

Ramaswami

S. N.

Patel

P. C.

Chai

(2022). Innovation-based strategic flexibility (ISF): Role of CEO ties with marketing and R&D. International Journal of Research in Marketing, 39(3), 927–946. https://doi.org/10.1016/j.ijresmar.2021.11.005

Auer

Bernardini

Cecioni

(2021). Corporate leverage and monetary policy effectiveness in the euro area. European Economic Review, 140, 103943. https://doi.org/10.1016/j.euroecorev.2021.103943

B. Y.

Roberts

M. J.

Winston

(2007). Export market participation, investments in R&D and worker training, and the evolution of firm productivity. World Economy, 30(1), 83–104. https://doi.org/10.1111/j.1467-9701.2007.00873.x

10.

B. Y.

Roberts

M. J.

D. Y.

(2011). R&D investment, exporting, and productivity dynamics. American Economic Review, 101(4), 1312–1344. https://doi.org/10.1257/aer.101.4.1312

11.

Ayllón

Radicic

(2019). Product innovation, process innovation and export propensity: Persistence, complementarities and feedback effects in Spanish firms. Applied Economics, 51(33), 3650–3664. https://doi.org/10.1080/00036846.2019.1584376

12.

Azar

Ciabuschi

(2017). Organizational innovation, technological innovation, and export performance: The effects of innovation radicalness and extensiveness. International Business Review, 26(2), 324–336. https://doi.org/10.1016/j.ibusrev.2016.09.002

13.

Barney

(1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

14.

Barrios

Görg

Strobl

(2003). Explaining firms’ export behaviour: R&D, spillovers and the destination market. Oxford Bulletin of Economics and Statistics, 65(4), 475–496. https://doi.org/10.1111/1468-0084.t01-1-00058

15.

Becker

S. O.

Egger

P. H.

(2013). Endogenous product versus process innovation and a firm’s propensity to export. Empirical Economics, 44, 329–354. https://doi.org/10.1007/s00181-009-0322-6

16.

Berman

Berthou

Héricourt

(2015). Export dynamics and sales at home. Journal of International Economics, 96(2), 298–310. https://doi.org/10.1016/j.jinteco.2015.04.001

17.

Bıçakcıoğlu-Peynirci

Hizarci-Payne

A. K.

Özgen

Ö.

Madran

(2020). Innovation and export performance: a meta-analytic review and theoretical integration. European Journal of Innovation Management, 23(5), 789–812. https://doi 10.1108/ejim-06-2019-0149

18.

Bilgin

M. H.

Gozgor

Lau

C. K. M.

(2017). Institutions and gravity model: The role of political economy and corporate governance. Eurasian Business Review, 7(3), 421–436. https://doi.org/10.1007/s40821-016-0069-x

19.

Black

H. T.

Duhon

D. L.

(2006). Assessing the impact of business study abroad programs on cultural awareness and personal development. Journal of Education for Business, 81(3), 140–144. https://doi.org/10.3200/JOEB.81.3.140-144

20.

Bleaney

Wakelin

(2002). Efficiency, innovation and exports. O xford Bulletin of Economics and Statistics, 64(1), 3–15. https://doi.org/10.1111/1468-0084.00001

21.

Boeh

K. K.

Beamish

P. W.

(2012). Travel time and the liability of distance in foreign direct investment: Location choice and entry mode. Journal of International Business Studies, 43(5), 525–535. https://doi.org/10.1057/jibs.2012.10

22.

Boeing

(2016). The allocation and effectiveness of China’s R&D subsidies: Evidence from listed firms. Research Policy, 45(9), 1774–1789. https://doi.org/10.1016/j.respol.2016.05.007

23.

Buckley

P. J.

Munjal

Enderwick

Forsans

(2016). Do foreign resources assist or impede internationalisation? Evidence from internationalisation of Indian multinational enterprises. International Business Review, 25(1), 130–140. https://doi.org/10.1016/j.ibusrev.2015.04.004

24.

Caldera

(2010). Innovation and exporting: evidence from Spanish manufacturing firms. Review of world Economics, 146, 657–689. https://doi.org/10.1007/s10290-010-0065-7

25.

Callen

J. L.

Fang

(2013). Institutional investor stability and crash risk: Monitoring versus short-termism? Journal of Banking & Finance, 37(8), 3047–3063. https://doi.org/10.1016/j.jbankfin.2013.02.018

26.

Carboni

O. A.

Medda

(2018). R&D, export and investment decision: Evidence from European firms. Applied Economics, 50(2), 187–201. https://doi.org/10.1080/00036846.2017.1332747

27.

Carpenter

M. A.

Fredrickson

J. W.

(2001). Top management teams, global strategic posture, and the moderating role of uncertainty. Academy of Management Journal, 44(3), 533–545. https://doi.org/10.5465/3069368

28.

Cassiman

Golovko

(2011). Innovation and internationalization through exports. Journal of International Business Studies, 42(1), 56–75. https://doi.org/10.1057/jibs.2010.36

29.

Cerrato

Piva

(2012). The internationalization of small and medium-sized enterprises: The effect of family management, human capital and foreign ownership. Journal of Management & Governance, 16, 617–644. https://doi.org/10.1007/s10997-010-9166-x

30.

Chandra

Paul

Chavan

(2020). Internationalization barriers of SMEs from developing countries: A review and research agenda. International Journal of Entrepreneurial Behavior & Research, 26(6), 1281–1310.

31.

Chang

S. J.

Rhee

J. H.

(2011). Rapid FDI expansion and firm performance. Journal of International Business Studies, 42(8), 979–994. https://doi.org/10.1057/jibs.2011.30

32.

Chatzoglou

Chatzoudes

(2018). The role of innovation in building competitive advantages: An empirical investigation. European Journal of Innovation Management, 21(1), 44–69. https://doi.org/10.1108/EJIM-02-2017-0015

33.

Chen

Yang

(2019). R&D tax credits and firm innovation: Evidence from China. Technological Forecasting and Social Change, 146, 233–241. https://doi.org/10.1016/j.techfore.2019.05.018

34.

Chen

Zhao

(2007). Mechanical mean reversion of leverage ratios. Economics Letters, 95(2), 223–229. https://doi.org/10.1016/j.econlet.2006.10.008

35.

Ciocanel

A. B.

Pavelescu

F. M.

(2015). Innovation and competitiveness in European context. Procedia Economics and Finance, 32, 728–737. https://doi.org/10.1016/S2212-5671(15)01455-0

36.

Coad

Rao

(2010). Firm growth and R&D expenditure. Economics of Innovation and New Technology, 19(2), 127–145. https://doi.org/10.1080/10438590802472531

37.

Conyon

M. J.

Haß

L. H.

Vergauwe

Zhang

(2019). Foreign experience and CEO compensation. Journal of Corporate Finance, 57, 102–121. https://doi.org/10.1016/j.jcorpfin.2017.12.016

38.

Cooke

F. L.

Schuler

Varma

(2020). Human resource management research and practice in Asia: Past, present and future. Human Resource Management Review, 30(4), 100778. https://doi.org/10.1016/j.hrmr.2020.100778

39.

Criaco

Naldi

Zahra

S. A.

(2022). Founders’ prior shared international experience, time to first foreign market entry, and new venture performance. Journal of Management, 48(8), 2349–2381. https://doi.org/10.1177/01492063211029701

40.

Cui

(2022). Does industry-university-research cooperation matter? An analysis of its coupling effect on regional innovation and economic development. Chinese Geographical Science, 32(5), 915–930. https://doi.org/10.1007/s11769-022-1308-y

41.

Dai

Liu

Lin

(2020). How innovation impacts firms’ export survival: Does export mode matter? The World Economy, 43(1), 81–113. https://doi.org/10.1111/twec.12847

42.

Dai

Zhao

(2018). Export tightening, competition, and firm innovation: Evidence from the renminbi appreciation. Review of Development Economics, 22(1), 263–286. https://doi.org/10.1111/rode.12340

43.

Deng

Guo

Zhang

Wang

(2014). Innovation and survival of exporters: A contingency perspective. International Business Review, 23(2), 396–406. https://doi.org/10.1016/j.ibusrev.2013.06.003

44.

Deng

Pray

Jin

(2019). Impact of government policies on private R&D investment in agricultural biotechnology: Evidence from chemical and pesticide firms in China. Technological Forecasting and Social Change, 147, 208–215. https://doi.org/10.1016/j.techfore.2019.07.011

45.

Dereli

D. D.

(2015). Innovation management in global competition and competitive advantage. Procedia - Social and Behavioral Sciences, 195, 1365–1370. https://doi.org/10.1016/j.sbspro.2015.06.323

46.

Dhanaraj

Beamish

P. W.

(2003). A resource-based approach to the study of export performance. Journal of Small Business Management, 41(3), 242–261. https://doi.org/10.1111/1540-627X.00080

47.

Dong

Kokko

Zhou

(2022). Innovation and export performance of emerging market enterprises: The roles of state and foreign ownership in China. International Business Review, 31(6), 102025. https://doi.org/10.1016/j.ibusrev.2022.102025

48.

Dwi Sihono

Widarti

. (2021). Effect of profitability financial, ratios return on assets, return on equity, gross profit margin and inflation level of stock return manufacturing company recorded activities in LQ 45. Research Journal of Finance and Accounting, 12(24), 49–64.

49.

Edeh

J. N.

Obodoechi

D. N.

Ramos-Hidalgo

(2020). Effects of innovation strategies on export performance: New empirical evidence from developing market firms. Technological Forecasting and Social Change, 158, 120167. https://doi.org/10.1016/j.techfore.2020.120167

50.

Esteve-Pérez

Rodríguez

(2013). The dynamics of exports and R&D in SMEs. Small Business Economics, 41, 219–240. https://doi.org/10.1007/s11187-012-9421-4

51.

Fang

V. W.

Tian

Tice

(2014). Does stock liquidity enhance or impede firm innovation? The Journal of Finance, 69(5), 2085–2125. https://doi.org/10.1111/jofi.12187

52.

Furrer

Sudharshan

Thomas

Tereza Alexandre

(2008). Resource configurations, generic strategies, and firm performance: Exploring the parallels between resource-based and competitive strategy theories in a new industry. Journal of Strategy and Management, 1(1), 15–40. https://doi.org/10.1108/17554250810909400

53.

Franko

L. G.

(1989). Global corporate competition: Who’s winning, who’s losing, and the R&D factor as one reason why. Strategic Management Journal, 10(5), 449–474. https://doi.org/10.1002/smj.4250100505

54.

Golovko

Valentini

(2011). Exploring the complementarity between innovation and export for SMEs’ growth. Journal of International Business Studies, 42(3), 362–380. https://doi.org/10.1057/jibs.2011.2

55.

Greene

(2005). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23(1), 7–32. https://doi.org/10.1007/s11123-004-8545-1

56.

Grossman

G. M.

Helpman

(1990). Trade, innovation, and growth. The American Economic Review, 80(2), 86–91.

57.

Guan

(2003). Innovative capability and export performance of Chinese firms. Technovation, 23(9), 737–747. https://doi.org/10.1016/S0166-4972(02)00013-5

58.

Gulamhussen

M. A.

Pinheiro

Pozzolo

A. F.

(2014). International diversification and risk of multinational banks: Evidence from the pre-crisis period. Journal of Financial Stability, 13, 30–43. https://doi.org/10.1016/j.jfs.2014.02.007

59.

Guo

Jiang

(2016). Government-subsidized R&D and firm innovation: Evidence from China. Research Policy, 45(6), 1129–1144. https://doi.org/10.1016/j.respol.2016.03.002

60.

Guo

Zhang

(2019). Does a foreign degree pay? The return to foreign education in China. Review of Development Economics, 23(1), 415–434. https://doi.org/10.1111/rode.12550

61.

Heyden

M. L.

Reimer

Van Doorn

(2017). Innovating beyond the horizon: CEO career horizon, top management composition, and R&D intensity. Human Resource Management, 56(2), 205–224. https://doi.org/10.1007/s10551-017-3657-5

62.

Hoskisson

R. E.

Wan

W. P.

Yiu

Hitt

M. A.

(1999). Theory and research in strategic management: Swings of a pendulum. Journal of Management, 25(3), 417–456. https://doi.org/10.1016/S0149-2063(99)00008-2

63.

Hsu

Boggs

D. J.

(2003). Internationalization and performance: Traditional measures and their decomposition. Multinational Business Review, 11(3), 23–50. https://doi.org/10.1108/1525383X200300015

64.

Hsu

W. T.

Chen

H. L.

Cheng

C. Y.

(2013). Internationalization and firm performance of SMEs: The moderating effects of CEO attributes. Journal of World Business, 48(1), 1–12. https://doi.org/10.1016/j.jwb.2012.06.001

65.

Yue

(2021). Government subsidies, product switching and the quality of export products. International Business, 35(3), 1–16. https://doi.org/10.13509/j.cnki.ib.2021.03.001

66.

Huang

Sheng

Wang

(2021). How did rising labor costs erode China’s global advantage? Journal of Economic Behavior & Organization, 183, 632–653. https://doi.org/https://doi.org/10.1016/j.jebo.2021.01.019

67.

Inui

Ito

Miyakawa

(2017). Export experience, product differentiation and firm survival in export markets. The Japanese Economic Review, 68, 217–231. https://doi.org/10.1111/jere.12083

68.

Iqbal

Tang

Rasool

S. F.

(2023). Investigating the nexus between CO2 emissions, renewable energy consumption, FDI, exports and economic growth: Evidence from BRICS countries. Environment, Development and Sustainability, 25(3), 2234–2263. https://doi.org/10.1007/s10668-022-02128-6

69.

James

Witten

Hastie

Tibshirani

(2013). An introduction to statistical learning (Vol. 112, p. 18). Springer.

70.

Jia

Wang

Xiao

Guo

(2020). How to reduce opportunism through contractual governance in the cross-cultural supply chain context: Evidence from Chinese exporters. Industrial Marketing Management, 91, 323–337. https://doi.org/10.1016/j.indmarman.2020.09.014

71.

Johanson

Vahlne

J. E.

(2009). The Uppsala internationalization process model revisited: From liability of foreignness to liability of outsidership. Journal of International Business Studies, 40(9), 1411–1431. https://doi.org/10.1057/jibs.2009.24

72.

Kafouros

M. I.

Buckley

P. J.

Sharp

J. A.

Wang

(2008). The role of internationalization in explaining innovation performance. Technovation, 28(1–2), 63–74. https://doi.org/10.1016/j.technovation.2007.07.009

73.

Kirbach

Schmiedeberg

(2008). Innovation and export performance: Adjustment and remaining differences in East and West German manufacturing. Economics of Innovation and New Technology, 17(5), 435–457. https://doi.org/10.1080/10438590701357189

74.

Kling

Weitzel

(2011). The internationalization of Chinese companies: Firm characteristics, industry effects and corporate governance. Research in International Business and Finance, 25(3), 357–372. https://doi.org/10.1016/j.ribaf.2011.03.002

75.

Kotabe

Jiang

C. X.

Murray

J. Y.

(2017). Examining the complementary effect of political networking capability with absorptive capacity on the innovative performance of emerging-market firms. Journal of Management, 43(4), 1131–1156. https://doi.org/10.1177/0149206314548226

76.

Kotabe

Srinivasan

S. S.

Aulakh

P. S.

(2002). Multinationality and firm performance: The moderating role of R&D and marketing capabilities. Journal of International Business Studies, 33(1), 79–97. https://doi.org/10.1057/palgrave.jibs.8491006

77.

Kraaijenbrink

Spender

J. C.

Groen

A. J.

(2010). The resource-based view: A review and assessment of its critiques. Journal of Management, 36(1), 349–372. https://doi.org/10.1177/0149206309350775

78.

Krugman

(1979). A model of innovation, technology transfer, and the world distribution of income. Journal of Political Economy, 87(2), 253–266. http://www.jstor.org/stable/1832086

79.

Krugman

(1991). Geography and trade. MIT Press.

80.

Krugman

(1995). Development, economic geography and economic theory. MIT Press.

81.

Kumar

Singh

Purkayastha

Popli

Gaur

(2020). Springboard internationalization by emerging market firms: Speed of first cross-border acquisition. Journal of International Business Studies, 51(2), 172–193. https://doi.org/10.1057/s41267-019-00266-0

82.

Kwok

C. C. Y.

Reeb

D. M.

(2000). Internationalization and firm risk: An upstream-downstream hypothesis. Journal of International Business Studies, 31(4), 611–629. https://doi.org/10.1057/palgrave.jibs.8490925

83.

Lall

(1985). Multinationals, technology and exports. Palgrave Macmillan. https://EconPapers.repec.org/RePEc:pal:palbok:978-1-349-17952-7

84.

Kroll

(2017). CEO international experience: Effects on strategic change and firm performance. Journal of International Business Studies, 48, 573–595. https://doi.org/10.1057/s41267-017-0080-1

85.

Leung

G. C. K.

(2011). China’s energy security: Perception and reality. Energy Policy, 39(3), 1330–1337. https://doi.org/10.1016/j.enpol.2010.12.005

86.

Song

(2011). The technological content of China’s exports and the need for quality upgrading. In Golley

Song

(Eds.), Rising China: Global challenges and opportunities (pp. 69–83). Australian National University E Press.

87.

Liu

Yuan

(2017). Does outward FDI generate higher productivity for emerging economy MNEs? Micro-level evidence from Chinese manufacturing firms. International Business Review, 26(5), 839–854.

88.

P. Y.

(2018). Top management team characteristics and firm internationalization: The moderating role of the size of middle managers. International Business Review, 27(1), 125–138. https://doi.org/10.1016/j.ibusrev.2017.05.011

89.

Wang

(2023). Productivity gains from trade: Bunching estimates from trading rights in China. Review of Economics and Statistics, 105(3), 1–45. https://doi.org/10.1162/rest_a_01392

90.

Wei

Lei

Ceriani

(2022). Connecting emerging industry and regional innovation system: Linkages, effect and paradigm in China. Technovation, 111, 102388. https://doi.org/10.1016/j.technovation.2021.102388

91.

Lin

C. C.

Chen

K. M.

(2022). Market competition, exchange rate uncertainty, and foreign direct investment. Review of Development Economics, 26(1), 405–422. https://doi.org/10.1111/rode.12813

92.

Lin

Qin

(2022). Digital economy, technological innovation and China’s high-quality exports. Academic Research, 65(10), 110–116.

93.

Liu

B. J.

(2017). Do bigger and older firms learn more from exporting? Evidence from China. China Economic Review, 45, 89–102. https://doi.org/10.1016/j.chieco.2017.06.005

94.

Liu

Atuahene-Gima

(2018). Enhancing product innovation performance in a dysfunctional competitive environment: The roles of competitive strategies and market-based assets. Industrial Marketing Management, 73, 7–20. https://doi.org/10.1016/j.indmarman.2018.01.006

95.

Liu

Huang

(2018). Industrial difference in FDI and the growth of relative export earning in Chinese Manufacturing Industries—statistic estimate and empirical test based on twenty-six industries. Journal of Shanxi University of Finance and Economics, 40(8), 56–68. https://doi.org/10.13781/j.cnki.1007-9556.2018.08.005

96.

Liu

(2016). R&D subsidies and business R&D: Evidence from high-tech manufacturing firms in Jiangsu. China Economic Review, 41, 1–22. https://doi.org/10.1016/j.chieco.2016.08.003

97.

Lozano

M. B.

Martínez

Pindado

(2016). Corporate governance, ownership and firm value: Drivers of ownership as a good corporate governance mechanism. International Business Review, 25(6), 1333–1343. https://doi.org/10.1016/j.ibusrev.2016.04.005

98.

Melitz

Helpman

Marin

Verdier

Costantini

(2007). The dynamics of firm & level adjustment to trade liberalization. In Helpman

Marin

Verdier

(Eds.), The organization of firms in a global economy (pp. 107–141). Harvard University Press.

99.

Mendi

Costamagna

(2017). Managing innovation under competitive pressure from informal producers. Technological Forecasting and Social Change, 114, 192–202. https://doi.org/10.1016/j.techfore.2016.08.013

100.

Miocevic

Morgan

R. E.

(2018). Operational capabilities and entrepreneurial opportunities in emerging market firms: Explaining exporting SME growth. International Marketing Review, 35(2), 320–341. https://doi.org/10.1108/IMR-12-2015-0270

101.

Moradi

Paulet

(2019). The firm-specific determinants of capital structure: An empirical analysis of firms before and during the Euro Crisis. Research in International Business and Finance, 47, 150–161. https://doi.org/10.1016/j.ribaf.2018.07.007

102.

Morgan

H. M.

Sui

Malhotra

(2021). No place like home: The effect of exporting to the country of origin on the financial performance of immigrant-owned SMEs. Journal of International Business Studies, 52, 504–524. https://doi.org/10.1057/s41267-020-00360-8

103.

Mostafiz

M. I.

Sambasivan

Goh

S. K.

Ahmad

(2023). Configuring foreign market knowledge and opportunity recognition capabilities to predict the performance of export-manufacturing firms. Knowledge Management Research & Practice, 21(2), 316–330. https://doi.org/10.1080/14778238.2021.1919573

104.

Müller

Zimmermann

(2009). The importance of equity finance for R&D activity. Small Business Economics, 33(3), 303–318. https://doi.org/10.1007/s11187-008-9098-x

105.

National Bureau of Statistics of China. (2022). China Statistical Yearbook 2022. China Statistics Press. https://www.stats.gov.cn/sj/ndsj/2022/indexeh.htm

106.

Nie

Wang

Zhang

(2019). Study of the nonlinear relations between economic growth and carbon dioxide emissions in the Eastern, Central and Western regions of China. Journal of Cleaner Production, 219, 713–722. https://doi.org/10.1016/j.jclepro.2019.01.164

107.

Oura

M. M.

Zilber

S. N.

Lopes

E. L.

(2016). Innovation capacity, international experience and export performance of SMEs in Brazil. International Business Review, 25(4), 921–932. https://doi.org/10.1016/j.ibusrev.2015.12.002

108.

Ouyang

Zhang

Dong

(2015). Market potential, firm exports and profit: Which market do the Chinese firms profit from? China Economic Review, 34, 94–108. https://doi.org/10.1016/j.chieco.2015.03.011

109.

Papanastassiou

Pearce

Zanfei

(2020). Changing perspectives on the internationalization of R&D and innovation by multinational enterprises: A review of the literature. Journal of International Business Studies, 51, 623–664. https://doi.org/10.1057/s41267-019-00258-0

110.

Peng

Zhang

(2022). How does Input-trade liberalization affect Chinese firms’ export volatility? Finance and Trade Research, 43(9), 1–14. https://doi.org/10.19337/j.cnki.34-1093/f.2022.09.001

111.

Penner-Hahn

Shaver

J. M.

(2005). Does international research and development increase patent output? An analysis of Japanese pharmaceutical firms. Strategic Management Journal, 26(2), 121–140. https://doi.org/10.1002/smj.436

112.

Porter

M. E.

(1990). The competitive advantage of nations. The Free Press.

113.

Quan

Qian

Zhang

(2021). CEO foreign experience and green innovation: evidence from China. Journal of Business Ethics, 182, 535–557. https://doi.org/10.1007/s10551-021-04977-z

114.

Rabbiosi

Gregorič

Stucchi

(2019). Diaspora ownership and homeland firms’ internationalization. Journal of International Management, 25(3), 100661. https://doi.org/10.1016/j.intman.2018.12.002

115.

Rajapathirana

R. J.

Hui

(2018). Relationship between innovation capability, innovation type, and firm performance. Journal of Innovation & Knowledge, 3(1), 44–55. https://doi.org/10.1016/j.jik.2017.06.002

116.

Ramón-Llorens

M. C.

García-Meca

Duréndez

(2017). Influence of CEO characteristics in family firms internationalization. International Business Review, 26(4), 786–799. https://doi.org/10.1016/j.ibusrev.2017.01.007

117.

Rodil

Ó.

Vence

Sánchez

M. C

. (2016). The relationship between innovation and export behaviour: The case of Galician firms. Technological Forecasting and Social Change, 113, 248–265. https://doi.org/10.1016/j.techfore.2015.09.002

118.

Roper

Love

J. H.

(2002). Innovation and export performance: Evidence from the UK and German manufacturing plants. Research Policy, 31(7), 1087–1102. https://doi.org/https://doi.org/10.1016/S0048-7333(01)00175-5

119.

Rui

Zhang

Shipman

(2017). Chinese expatriate management in emerging markets: A competitive advantage perspective. Journal of International Management, 23(2), 124–138. https://doi.org/10.1016/j.intman.2017.01.002

120.

Santarelli

Sterlacchini

(1990). Innovation, formal vs. informal R&D, and firm size: some evidence from italian manufacturing firms. Small Business Economics, 2(3), 223–228.

121.

Sarang

A. A. A.

Aubert

Hollandts

(2021). Board gender diversity and corporate cash holdings. Finance, 42(1), 7–49. https://doi.org/10.3917/fina.421.0007

122.

Sarang

A. A. A.

Aubert

Hollandts

(2024). Board gender diversity and the cost of equity: What difference does gender quota legislation make? International Journal of Finance & Economics, 29(2), 2193–2213. https://doi.org/10.1002/ijfe.2774

123.

Schmookler

(1966). Invention and economic growth. Harvard University Press.

124.

Segerstrom

P. S.

(1991). Innovation, imitation, and economic growth. Journal of Political Economy, 99(4), 807–827.

125.

Seifert

Gonenc

(2012). Creditor rights and R&D expenditures. Corporate Governance: An International Review, 20(1), 3–20. https://doi.org/10.1111/j.1467-8683.2011.00881.x

126.

Selvarajah

T. D.

Sukunesan

(2019). The Vietnam project: Developing conceptual knowledge on cross-cultural skills for training in SME internationalization. Asia Pacific Business Review, 25(3), 338–366. https://doi.org/10.1080/13602381.2019.1598076

127.

Sharma

Cheng

L. T.

Leung

T. Y.

(2020). Impact of political connections on Chinese export firms’ performance: Lessons for other emerging markets. Journal of Business Research, 106, 24–34. https://doi.org/10.1016/j.jbusres.2019.08.037

128.

Suarez-Villa

Fischer

M. M.

(1995). Technology, organization and export-driven research and development in Austria’s electronics industry. Regional Studies, 29(1), 19–42. https://doi.org/10.1080/00343409512331348783

129.

Sun

Hao

Chen

(2021). Path for China’s high-tech industry to participate in the reconstruction of global value chains. Technology in Society, 65, 101486. https://doi.org/10.1016/j.techsoc.2020.101486

130.

Suutari

Mäkelä

(2007). The career capital of managers with global careers. Journal of Managerial Psychology, 22(7), 628–648. https://doi.org/10.1108/02683940710820073

131.

Tan

Sousa

C. M.

(2015). Leveraging marketing capabilities into competitive advantage and export performance. International Marketing Review, 32(1), 78–102. https://doi.org/10.1108/IMR-12-2013-0279

132.

Tihanyi

Ellstrand

A. E.

Daily

C. M.

Dalton

D. R.

(2000). Composition of the top management team and firm international diversification. Journal of Management, 26(6), 1157–1177. https://doi.org/10.1177/014920630002600605

133.

Vithessonthi

Racela

O. C.

(2016). Short-and long-run effects of internationalization and R&D intensity on firm performance. Journal of Multinational Financial Management, 34, 28–45. https://doi.org/10.1016/j.mulfin.2015.12.001

134.

Wakelin

(1998). Innovation and export behaviour at the firm level. Research Policy, 26(7–8), 829–841. https://doi.org/10.1016/S0048-7333(97)00051-6

135.

Wang

(2022). Research on policy barriers and countermeasures for overseas investment of Chinese state-owned enterprises. Intertrade, 41(3), 19–27. https://doi.org/10.14114/j.cnki.itrade.2022.03.013

136.

Wang

(2014). Complementarities between R&D investment and exporting: Evidence from China. China Economic Review, 31, 217–227. https://doi.org/10.1016/j.chieco.2014.09.009

137.

Wang

H. L.

(2014). Theories for competitive advantage. http://eurekaconnection.files.wordpress.com/2014/02/p-33-43-theories-of-competitive-advantage-theori-ebook_finaljan2014-v3.pdf

138.

Wang

Jiang

(2021). State ownership and green innovation in China: The contingent roles of environmental and organizational factors. Journal of Cleaner Production, 314, 128029. https://doi.org/10.1016/j.jclepro.2021.128029

139.

Wang

Schweizer

Michaelis

(2021). Experiential learning for Chinese companies to complete cross-border acquisitions: the case of Chinese acquirers. International Journal of Emerging Markets, 16(4), 674–695. https://doi10.1108/ijoem-12-2018-0663

140.

Warner

(2020). Human resource management in China revisited: introduction. In Warner

(Ed.), Human resource management in China revisited (pp. 1–18). Routledge.

141.

Wei

L. Q.

Ling

(2015). CEO characteristics and corporate entrepreneurship in transition economies: Evidence from China. Journal of Business Research, 68(6), 1157–1165. https://doi.org/10.1016/j.jbusres.2014.11.010

142.

Wei

S. J.

Xie

Zhang

(2017). From “made in China” to “innovated in China”: Necessity, prospect, and challenges. Journal of Economic Perspectives, 31(1), 49–70. https://doi.org/10.1257/jep.31.1.49

143.

Welford

Gouldson

. (1993). Environmental management & business strategy. Pitman Publishing Limited.

144.

Wignaraja

(2012). Innovation, learning, and exporting in China: Does R&D or a technology index matter? Journal of Asian Economics, 23(3), 224–233. https://doi.org/10.1016/j.asieco.2011.02.001

145.

Wei

Wang

(2021). Disentangling the effects of business groups in the innovation-export relationship. Research Policy, 50(1), 104093. https://doi.org/10.1016/j.respol.2020.104093

146.

Fang

Zhang

(2024). Can the water resources tax promote the water-saving innovation performance of high water consumption companies?- Empirical analysis from the pilot provinces in China. Journal of Cleaner Production, 451, 141888. https://doi.org/10.1016/j.jclepro.2024.141888

147.

Wang

Liu

(2021). Government subsidies, R&D investment and innovation performance: Analysis from pharmaceutical sector in China. Technology Analysis & Strategic Management, 33(5), 535–553. https://doi.org/10.1080/09537325.2020.1830055

148.

Xia

(2017). Influence of Chinese entrepreneurial companies’ internationalization on independent innovation: Input incentive effect and efficiency improvement effect. China Journal of Accounting Research, 10(3), 247–280. https://doi.org/10.1016/j.cjar.2017.05.001

149.

Yang

Liu

Peng

Liu

(2022). The impact of the embedded global value chain position on energy-biased technology progress: Evidence from chinas manufacturing. Technology in Society, 71, 102065. https://doi.org/10.1016/j.techsoc.2022.102065

150.

Yang

Zhao

Cai

(2022). Can straightening out circulation improve the efficiency of manufacturing firms? Evidence from the reform of the circulation system. China Journal of Economics, 9(4), 308–334. https://doi.org/10.16513/j.cnki.cje.20220919.003

151.

Wang

Kafouros

(2013). The effects of innovative capabilities on exporting: Do institutional forces matter? International Business Review, 22(2), 392–406. https://doi.org/10.1016/j.ibusrev.2012.05.006

152.

Hong

Chung Hsu

Wang

(2017). The role of state ownership and institutions in the innovation performance of emerging market enterprises: Evidence from China. Technovation, 62, 4–13. https://doi.org/10.1016/j.technovation.2017.04.002

153.

Ying

Hassan

Ahmad

(2019). The role of a manager’s intangible capabilities in resource acquisition and sustainable competitive performance. Sustainability, 11(2), 527. https://doi.org/10.3390/su11020527

154.

Yiu

L. D.

Yeung

A. C.

Cheng

T. E.

(2021). The impact of business intelligence systems on profitability and risks of firms. International Journal of Production Research, 59(13), 3951–3974. https://doi.org/10.1080/00207543.2020.1756506

155.

Zaheer

(1995). Overcoming the liability of foreignness. Academy of Management Journal, 38(2), 341–363. https://doi.org/10.5465/256683

156.

Zahra

S. A.

Ireland

R. D.

Hitt

M. A.

(2000). International expansion by new venture firms: International diversity, mode of market entry. Technological Learning, and Performance. Academy of Management Journal, 43(5), 925–950. https://doi.org/10.5465/1556420

157.

Zang

Jiang

(2020). Home advantage and state-owned enterprise trends to Go Global. The Journal of World Economy, 43(6), 52–76. https://doi.org/10.19985/j.cnki.cassjwe.2020.06.004

158.

Zapletalová

. (2015). Models of Czech companies’ internationalization. Journal of International Entrepreneurship, 13(2), 153–168. https://doi.org/10.1007/s10843-015-0145-9

159.

Zhang

Chen

Sun

(2020). Foreign technology introduction and local firm’ innovation in China. Economic Research Journal, 46(7), 92–105.

160.

Zhang

Peek

W. A.

Pikas

Lee

(2016). The transformation and upgrading of the Chinese manufacturing industry: Based on “German Industry 4.0.” Journal of Applied Business and Economics, 18(5), 97–105.

161.

Zhang

Zheng

Ning

(2018). Does innovation facilitate firm survival? Evidence from Chinese high-tech firms. Economic Modelling, 75, 458–468. https://doi.org/10.1016/j.econmod.2018.07.030