From Click to Cargo: The Role of Digitalization, Cross-Border E-Commerce, and Logistics in Deepening the China–Africa Trade

Abstract

Enhancing digital connectivity, advancing cross-border e-commerce (CE), and optimizing logistics performance (LP) are fundamental pillars for boosting trade flows between trading partners. However, the multifaceted impacts of digitalization, CE, and LP on China–Africa (C-A) trade remain underexplored. Therefore, employing the gravity model, this study examines the impacts of digitalization, CE, and LP on C-A trade using data on Chinese trade flow to 53 African countries from 2007 to 2023. Further analysis is conducted by accounting for African countries’ income, population, resources, and institutional heterogeneity. We also control for the impact of digitalization and logistics performance distance between China and African countries on C-A trade. The findings provide evidence that the digitalization of African countries and China significantly enhances C-A trade. Moreover, CE and LP improvements in China positively affect C-A trade, revealing that promoting the sustainable development of CE and LP can lead to increased trade integration between China and African countries. Furthermore, the effects of digitalization, CE, and LP on C-A trade are influenced by heterogeneity in the income level, population size, and institutional performance of African countries, revealing more beneficial effects in middle-income countries, more populated countries, and countries with better institutional quality. Policy suggestions are forwarded based on the findings.

1. Introduction

China has emerged as Africa’s largest trading partner, surpassing the continent’s traditional trade allies (M. Miao et al., 2020; Usman & Tang, 2024; Shao et al., 2025). Although trade relations between Africa and China date back many years, the past two decades have witnessed unprecedented growth in their trade exchanges (M. Miao et al., 2020). Trade between China and Africa has expanded at the most rapid pace since 2000 (M. Miao et al., 2020). More specifically, the speed and scale of China–Africa (C-A) trade growth have outpaced those of the United States (US) and most of the European Union (EU) economies, reshaping the continent’s economic landscape. The volume of C-A trade outpaced US–Africa (US-A) trade and is approaching surpassing Africa’s trade with the EU member countries (Figure 1). Consequently, the C-A trade relationship has achieved a remarkable compound annual growth rate of 24.7% over this period, exceeding USD 260 billion in total bilateral trade. More specifically, Chinese exports to Africa registered USD 172.45 billion, imports to African countries registered USD 89.77 billion, and total trade exceeded USD 261 billion in 2023 (Figure A1). Moreover, China has progressively established trade relationships with over 50 African nations and regions, formalizing bilateral trade agreements with more than 40 countries (Ya & Pei, 2022).

While Africa’s exports to China remain largely dominated by raw commodities, the composition of these exports is undergoing a notable shift. For instance, China’s oil imports from Africa are decreasing as the country increasingly sources crude oil from Gulf Cooperation Council states, Russia, and other Asian nations. Additionally, a growing trend that could significantly influence the A-C trade dynamic is the rising export of semi-processed agricultural products from African countries (Usman & Tang, 2024).

Moreover, existing studies argued that C-A trade dynamics and increased growth of C-A trade are explained by resources, institutional, geographical, economic size, and other socio-economic determinants (Ya & Pei, 2022; Gold & Rasiah, 2022; Landry, 2024). However, the dynamics of C-A trade flow can also be determined by recent technological developments such as digitalization, cross-border e-commerce (CE) development, and trading countries’ logistics performance (LP). Therefore, C-A trade flow can be explained by these variables, as international trade is mainly reliant on the variables that facilitate trade and lower trade costs, including communication technology, e-commerce, and LP (Bugarčić et al., 2020).

Empirical research contended that the increase and adoption of digital technologies (hereafter digitalization) is one of the most vital technological transformations in history, impacting economic activities worldwide (Labhard & Lehtimäki, 2022). It refers to applying digital technologies, including mobile phones, laptops, computers, and other digital devices, across various domains such as economic and commercial activities, financial and banking operations, public administration, and service delivery (Kere & Zongo, 2023). Therefore, the rapid advancement of information and communication technology (ICT) and the progression of digitalization have significantly influenced trade relationships between countries, promoting access to foreign markets (Bellucci et al., 2023; Herman & Oliver, 2023).

Furthermore, as a specific form of digitalization, CE has been a significant development trend in international trade. CE refers to cross-border logistics transactions involving multiple parties from different customs regions facilitated through e-commerce platforms and represents a contemporary model of international trade (Ai et al., 2016; Pejić-Bach, 2021). Global CE transactions have experienced significant growth in recent years (Li & Li, 2024). The shift toward borderless trade will also have a significant impact on global trade patterns (Y. Chen et al., 2022). Thus, with the continuous development of digitalization, trade between countries has grown closer and contributed to the development of countries (Y. Chen et al., 2022).

From a practical standpoint, digitalization is advancing rapidly in China and Africa, with Africa making remarkable progress over the past decade. Hundreds of millions have gained internet access and embraced digital services like mobile payments (WB, 2024). This transformation began with the introduction of M-PESA in Kenya in 2007 (Kere & Zongo, 2023) and has been fueled by widespread smartphone adoption, which reached 64% in 2021, alongside other mixed technologies (Kere & Zongo, 2023). These advancements significantly bolster digitalization and facilitate trade flows. Internet usage in Africa has also risen dramatically, from less than 0.1% in 2005 to 40% in 2022 (ITU, 2022), positively influencing China–Africa trade. Consequently, digitalization is recognized as a catalyst for economic growth and a significant opportunity for African trade (Kere & Zongo, 2023). China, the world’s largest e-commerce market, has seen annual transaction growth of 32% (H. Wang & Liu, 2022; C. Wang, 2021), playing a vital role in expanding foreign trade (Mou et al., 2019; Holslag, 2017). From 2012 to 2023, China’s CE market grew from 2.1 trillion to CNY 16.8 trillion (K. Chen et al., 2024). Advances in Internet and information technology have enabled businesses to access global markets, driving economic and trade integration (Lee & Shen, 2020). Platforms like Kilimall, Alibaba, JD.com, and Kikuu are transforming China–Africa trade, providing African suppliers access to a vast market. Notably, sales of African products, including Kenyan black tea and Ethiopian coffee, surged by 409% and 143% during Chinese shopping festivals in 2022 (Usman & Tang, 2024).

Empirically, several research works addressed the effects of digitalization on the trade performance of countries (López González et al., 2023; Herman & Oliver, 2023; Bellucci et al., 2023; Añón Higón & Bonvin, 2024; Yin & Choi, 2024; Fan, 2021) and CE on the trade performance of a group of countries or individual countries (Xing, 2018; Liang et al., 2021; Yin & Choi, 2023; He, 2024; H. Wu et al., 2024). However, empirical studies focusing on the role of digitalization and CE in C-A trade are scarce. Consequently, the precise impact of digitalization and CE on C-A trade remains largely unexplored despite their critical importance for promoting trade flow.

Furthermore, LP is crucial for supporting trade growth and is a primary determinant of a country’s economic development (Cui et al., 2022; Bhukiya & Patel, 2023). LP reflects a country’s connectivity to international logistics networks by evaluating the overall logistics conditions, which represent the average of six sub-dimensions (Chakamera & Pisa, 2021; Song & Lee, 2022). It is used to analyze variations between countries regarding logistics costs and the quality of infrastructure for both overland and maritime transport (Martí et al., 2014). Thus, enhancements in LP contribute to promoting trade openness and the reduction in trade costs (Barakat et al., 2023; Jayathilaka et al., 2022). Conversely, inadequate logistics infrastructure can significantly hinder global trade integration (Gani, 2017). From a theoretical standpoint, highly efficient logistics services reduce trade costs, promote product movement, and ensure product safety and delivery speed (Jayathilaka et al., 2022). Consistent with the theoretical underpinnings from empirical research prospects, several studies have been conducted on the effects of LP on trade flow (Jayathilaka et al., 2022; Barakat et al., 2023; Bugarčić et al., 2020; Górecka et al., 2022; Mishrif et al., 2024; W. Wang et al., 2024; Cui et al., 2022; Bhukiya & Patel, 2023) and found that promoting LP positively influences trade flow among countries. However, there is limited empirical literature on the C-A trade and LP nexus, so this study investigates the impact of LP on C-A trade.

Moreover, the impact of digitalization, CE, and LP on C-A trade performance can be shaped by factors such as income heterogeneity, demographics, and resources, as well as the institutional quality of African countries. The impacts of these factors vary with diverse income levels, governance, and population sizes of trading countries (Labhard & Lehtimäki, 2022; Yin & Choi, 2024). Therefore, demographic shifts, income variation, resources, and governance heterogeneity influence trade patterns, as population growth in partner countries boosts demand for imports, shaping the role of digitalization, CE, and LP in trade.

Therefore, this research addresses the following questions, drawing upon the aforementioned analytical discourse, theoretical base, and practical insights. First, what are the roles of digitalization in China and trade partner African countries in C-A trade? Second, what are the impacts of CE dynamics in China on C-A trade? Third, what are the roles of the LP of China and the trade destination of African countries in C-A trade? Finally, how does the heterogeneity in income, demographics, resources, and institutional quality of trade partner countries affect C-A trade? Based on these guiding research questions, this study aims to examine the complex impact of digitalization, CE, and LP on trade between China and 53 African countries for the periods between 2007 and 2023, applying the robust techniques of random effect (RE), Hausman–Tylor (H-T), and the Poisson pseudo maximum likelihood (PPML) strategies. Moreover, the analysis is repeated, controlling for income, population, resources, and institutional heterogeneity. Further analysis is conducted to incorporate the effects of digitalization and LP distance between China and African countries on C-A trade. Our findings demonstrate that digitalization, CE, and LP consistently positively influence C-A trade. Moreover, these outcomes differ subject to income, governance, and demographic heterogeneity among African countries.

We focus on the role of digitalization, CE, and LP on C-A trade for the following reasons. First, the C-A trade pattern differs from Africa’s trade with its conventional trade partners, such as the US and EU economies. China’s engagement with Africa is characterized by a focus on infrastructure development and resource-driven partnerships, differing from the colonial legacies and development aid frameworks that historically shaped Africa’s trade with the US and EU. Moreover, compared to other trade allies of Africa, China’s trade with Africa has a pronounced upward trajectory and is more dynamic. C-A trade has experienced the most rapid growth compared to the US-A and EU-A trade (Figure 1). Furthermore, the EU remains Africa’s largest traditional trade partner, and China is closing the gap quickly due to the increasing two-way trade flow. US-A trade is declining and obtaining a more limited role in recent African trade growth trends compared to C-A trade (see Figure 1).

Second, a distinctive feature of China–Africa trade is its strong foundation in south–south cooperation, reinforced by China’s Belt and Road Initiative (BRI) and the Digital Silk Road. These strategic initiatives have significantly enhanced connectivity and trade flows, integrating Africa more deeply into emerging global value chains (Asante, 2018; Y. Wu et al., 2024). This unique partnership has driven substantial growth in two-way trade, propelled by a surge in Chinese private investment and the implementation of diverse projects, including export diversification efforts and expansive infrastructure programs, particularly under the framework of the BRI (Asante, 2018; Y. Wu et al., 2024).

Third, China’s technology level is closer to that of African countries than that of Western countries and traditional trade allies (Xu et al., 2016), and its economic model and foreign policy framework attract most developing economies. Unlike others, China’s model of cooperation emphasizes economic development over politics (Zhang & Huang, 2023). As a result, China’s policy approach is more attractive to African leaders than that of other countries, particularly Western countries, leading to increased economic cooperation (Huang & Cao, 2023).

Fourth, the C-A trade relationship is accompanied by investments in economic and trade cooperation zones, as China has successfully established a total of 25 economic and trade cooperation zones across 16 African nations, with investment flows and financial aid supporting infrastructure development (Bo et al., 2024). Trade flow, complemented by investment, financial aid, and contracted projects, can differ from Africa’s trade with others in terms of its efficiency and flow (W. Chen et al., 2024).

Finally, unlike other conventional trade partners, digital transformation plays a crucial role in China’s innovation model and transformation, including trade flows (Hardi et al., 2024). Rapidly expanding digital infrastructure is enhancing supply chain efficiency and making CE more streamlined. China, the world’s largest e-commerce market (H. Wang & Liu, 2022), plays a vital role in expanding foreign trade by leveraging its advanced digital infrastructure and extensive online platforms. Similarly, Internet usage in Africa has also risen dramatically (ITU, 2022), which can affect C-A trade dynamics.

Therefore, this study makes several valuable contributions to the existing literature and the current academic understanding of the multifaceted connection between digitalization, CE, LP, and trade performance in C-A trade. First, our research highlights the role of digitalization in China and trade destination African countries as a key component of factors affecting C-A trade. Subsequently, this study is the first attempt to assess the role of digitalization in C-A trade, which is an underexplored area in the context of trade flow between China and Africa. Second, this study synthesizes the roles of CE in C-A trade, offering fresh insights into the collective influence of e-commerce on China’s exports to African countries. Thus, our study extends this perspective by explicitly linking CE to trade performance in the C-A trade context. Third, it serves as a valuable addition to theoretical research and empirical works on the logistics trade nexus by examining the impact of LP on C-A trade. Therefore, this provides a comprehensive understanding of how LP facilitates trade flows between China and Africa, which is unexplored in the prior empirical works.

Fourth, complementing the existing literature, this study’s results seem to provide further evidence of the important role of heterogeneity of governance, income, demographic, and resource aspects of African countries in the roles of digitalization, CE, and LP in C-A trade. Therefore, the findings of this study can serve as a reference for countries with different income, demographic, resources, and institutional levels to develop tailored trade development strategies to promote digitalization, CE, and LP. Finally, robust trade gravity model specification approaches such as H-T and PPML strategies are applied to control for endogeneity concerns, heterogeneity, and zero-valued observations concerns in C-A trade data.

Therefore, this paper serves as a primary step in examining the effects of digitalization, CE, and LP on C-A trade across the aforementioned dimensions. Collectively, these elements of the research provide fresh insights into the complex interplay between trade, digitalization, CE, and LP within the context of C-A trade dynamics. These findings are especially valuable for policymakers and business leaders seeking to understand and navigate the challenges and opportunities emerging from China’s changing trade landscape and the evolving pattern of C-A trade.

The structure of the paper is as follows: Section 2 reviews the relevant literature; Section 3 outlines the theoretical framework of model construction, data, and method of analysis; Section 4 presents the findings and results; and Section 5 provides discussions and policy implications.

2. Literature Review

2.1. Digital Connectivity, CE, and Trade Flow

Adopting and advancing digital technology can lower both fixed and variable costs in trade, thereby fostering trade growth (Fan, 2021). The theoretical relationship between digital connectivity, CE, and trade flow is typically carried out from the standpoint of trade costs. It can reduce the cost of trade through several mechanisms. First, digitalization diminishes the need for physical proximity and in-person interactions in business relationships. Second, digitalization leads to lower communication costs, with automatic translation tools helping to bridge language gaps and further reduce these costs (Bellucci et al., 2023). Third, digital technologies enhance trade efficiency by improving logistics and customs processes. For instance, tracking systems and the automation of airport and port operations shorten transit times, while digitalized customs procedures minimize the time goods spend at borders and reduce administrative costs (Bellucci et al., 2023). Additionally, the rise of e-payments and e-commerce platforms has lowered the costs of business transactions, especially when acquiring products from abroad (Bellucci et al., 2023). Similarly, Freund and Weinhold (2004) provided a foundation for the theoretical relationship between trade and digitalization, arguing that internet access has a positive influence on trade flows.

Based on these theoretical foundations, several empirical studies have examined the impact of digitalization on trade costs and trade flows, utilizing various digitalization measures. For example, using data from 37 countries, Rubínová and Sebti (2021) found that ICT, proxied by the proportion of the population with mobile phone subscriptions and the share of the population with access to the Internet, significantly reduces trade costs across trade partners. Aligned with the findings of Herman and Oliver (2023), López González et al. (2023) and Rubínová and Sebti (2021) provided reliable evidence that digitalization and the increasing share of the population using the Internet for business boost trade flow. Additionally, Bellucci et al. (2023) investigated the impact of digitalization, measured by the amount of active mobile broadband subscriptions per capita, on a comprehensive measure of costs of trade. Their findings suggest that advancements in digitalization can lower trade costs through various channels, including improved access to information, reduced transaction costs, less reliance on business travel, more efficient customs and logistics processes, and enhanced communication capabilities.

Moreover, Rodríguez-Crespo and Martínez-Zarzoso (2019) analyzed the role of the Internet on trade flow using bilateral goods export data from 120 countries. The findings showed that utilizing the Internet promotes bilateral goods exports. Likewise, Fan (2021) analyzed the impact of digital economy development in importing countries on China’s exports. The findings indicate that developing a digital economy in importing countries can substantially mitigate China’s export efficiency losses and enhance the overall efficiency of its export trade. This effect is particularly pronounced in China’s exports to low- and middle-income countries. Besides using Spanish manufacturing firms, Añón Higón and Bonvin (2024) examined the effects of digitalization on enterprises’ participation in export and import activities. The results suggest that firms’ digitalization positively impacts their likelihood of engaging in both exporting and importing activities, either directly or by enhancing productivity. Additionally, some studies have linked digital connectivity with service trade. For example, a study by Kong et al. (2024) examined the role of digital technology in service exports and found that digital technology significantly promotes service exports. Also, applying the gravity model, Yin and Choi (2024) analyzed the role of digitalization in the trade of services and goods in the G-20 countries. The results revealed that digitalization exerts a more substantial positive impact on trade in services than in goods.

Regarding African countries, applying the gravity model, Sawadogo and Wandaogo (2020) analyzed the impact of mobile money service adoption on trade among 48 African countries. Their findings indicate that countries adopting mobile money services experience a significant increase in the share of goods trade as a percentage of GDP. Similarly, Abendin et al. (2022) explored the role of digitalization in Africa’s trade, with a specific focus on intra-ECOWAS trade. Using data from ECOWAS countries spanning their study, they demonstrate that digitalization has a positive and significant effect on bilateral trade within the ECOWAS region. Enhanced digitalization of the economy notably boosts trade among ECOWAS member states. Additionally, Kere and Zongo (2023) investigated the impact of digitalization on intra-African trade for 48 African countries. The results indicate that the use of ICT, particularly the Internet, has a positive and significant impact on exports. Additionally, the introduction of mobile payment services in Sub-Saharan African countries stimulates product exports.

Furthermore, as consumer demand in the international market becomes increasingly diversified and the Internet continues to evolve rapidly, the numerous advantages of CE as an emerging trade model have become increasingly evident (C. Wang, 2021). Several studies have assessed the influences of CE on the trade performance of various economies. For example, Xing (2018) assessed the impact of the Internet and e-commerce adoption on trade flows among 21 developing and 30 OECD countries. The findings indicated that better access to modern ICT and the adoption of e-commerce stimulate trade flows. Similarly, Gregory et al. (2019) argued that e-commerce is crucial in reducing information asymmetry during trade processes, enabling the optimal allocation of resources across countries.

As China is at the forefront of e-commerce technology, several studies have focused on the effects of CE on Chinese trade with various countries. For example, Y. Miao et al. (2019) highlighted that CE plays a significant role in the Belt and Road (B&R) initiative. Special CE policies have encouraged Chinese enterprises to expand their product exports to international markets. Liang et al. (2021) investigated the role of CE transactions between China and countries along the RBI as the core of the study using the GMM method and provided evidence that marine and land transport infrastructure have the most decisive impact on trade. Moreover, C. Wang et al. (2021) investigated the effects of international electronic commerce on the export trade in China. The findings indicated that international e-commerce development has significantly promoted China’s export trade expansion. Additionally, Yin and Choi (2023) investigated the roles of China’s CE in its exports to B&R countries and found that CE has a more substantial positive impact on trade in services than on goods. Also, He (2024) explored the dynamic interaction between e-commerce and China’s export growth, and the findings highlighted the substantial role of e-commerce in trade performance. H. Wu et al. (2024) analyzed the link between China’s CE, trade digitization, and enterprise export resilience. They found that the progression of CE can improve enterprise export resilience.

Additionally, the role of digitalization and CE on countries’ trade performance can be affected by several factors. For example, appropriate governance policies lead to transparent institutions supporting digital connectivity and CE activities and lowering the risks of participating in digital connectivity and e-commerce (OECD, 2019). In contrast, weak institutional quality and policy frameworks underpinned by inefficient regulatory and legal enforcement can adversely affect economic activity (Doh et al., 2017) and supplement the risks of digitalization and e-commerce (Jean et al., 2020). This suggests that the growth benefits of digitalization are likely to be more substantial in environments where institutions and governance are of high quality (Labhard & Lehtimäki, 2022).

Similarly, the effects of digitalization and CE will be subject to income heterogeneity. In this regard, Yin and Choi (2024) argued that the role of digitalization is income-dependent; high-income economies tend to positively impact services trade. Also, Kong et al. (2024) found that the trade impact of digital technology is more pronounced in developed countries. Likewise, the demographic structure can affect the pattern of trade between economies through the market change in demand for goods and services (Tao et al., 2021). The large population increase in trade partner countries can increase the demand for the import of goods and services. It can influence the role of digitalization and CE on trade flow. Therefore, the following hypotheses are developed.

Hypothesis 1 (H1). 

Digitalization in China and its trade destination countries has a positive but heterogeneous impact on C-A trade.

Hypothesis 2 (H2). 

CE development in China has positive and varied impacts on C-A trade.

2.2. LP and Trade Flow

LP is a vital instrument for driving trade growth and serves as a critical determinant of a country’s economic development (Cui et al., 2022). Theoretically, LP is conceptualized as capital resources and technology that lower the cost of logistics services, promoting trade flow among countries. LP significantly promotes international trade (Bhukiya & Patel, 2023). Indeed, the volume of goods traded between two countries is primarily determined by the availability and quality of logistics services and other factors in exporting and importing countries. Therefore, the quality and efficiency of logistics services can matter for trade flow among countries, as a weak logistics infrastructure and operational processes can be a significant obstacle to trade integration (Gani, 2017).

Based on the aforementioned theoretical underpinnings, Gani (2017) explored the effect of LP on trade flow. The findings implied that LP is positively correlated with both exports and imports. Besides using the gravity model, Bugarčić et al. (2020) investigated the effects of LP on trade flow in some European countries and the Western Balkans, and the findings indicated that logistics has a positive impact on bilateral trade between countries. Moreover, Jayathilaka et al. (2022) examined the impacts of LP on trade and indicated that LP positively promotes trade flow. Likewise, Górecka et al. (2022) investigated the impacts of LP on the trade of energy products in EU countries and found that LP positively affects the export of energy products. Additionally, Barakat et al. (2023) investigated the role of LP in trade openness in European countries. The results showed that the LP significantly affects trade performance. Also, Lu et al. (2024) used the meta-analysis to integrate the impact of LP on trade. The findings show that promoting logistics development, especially in the B&R countries, has a meaningful impact on trade.

Furthermore, a recent study by Mishrif et al. (2024) assessed the role of logistics and supply chains in both international and domestic trade within a developing country. The results proved that developing countries should enhance logistics and promote supply chain infrastructure to enhance trading activities. Additionally, using the gravity model, W. Wang et al. (2024) analyzed the impact of LP in countries participating in the BRI on China’s trade. Their empirical findings indicate that enhancing the LP of countries along the B&R route significantly boosts China’s trade volume with these nations.

Moreover, the effects of LP on trade flow can be affected by the economic size of African countries, including differences in income level and demographic heterogeneity. This argument is valid, as the impact of LP varies across countries with diverse income levels and population sizes (Fan & Yu, 2015). For instance, Kumari and Bharti (2021) explored the role of country size in the relationship between trade and LP, focusing on population size differences. Their findings revealed that the effect of LP on trade volume is most pronounced in medium-sized countries.

Moreover, Çelebi (2019) argued that the magnitude of the effect of logistics systems may vary according to the economic and geographical characteristics of the trading countries. More specifically, the study argued that income heterogeneity is a vital aspect of the impact of LP on trade volumes. Specifically, the results suggest that low-income economies derive the greatest benefits from their logistics performance. For these economies, logistics excellence boosts exports more than imports. In contrast, higher-income economies, particularly upper-middle-income and high-income countries, tend to see greater benefits from improved LP in terms of imports, as better-income nations generally have superior logistics capabilities (See et al., 2024). Similarly, soft infrastructure, such as institutional quality, can support the quality of logistics and policies on border and customs clearance (Portugal-Perez & Wilson, 2009). Based on this notion, we articulate the following hypothesis.

Hypothesis 3 (H3). 

Improving the LP of China and its trade partner African countries positively affects C-A trade, with heterogeneous effects across countries.

2.3. Summary and Research Gap

Based on the review of the existing literature, we identify several areas that are worth further investigation. First, previous empirical works related to the impacts of CE and digitalization are reliant on advanced economies or an individual country sample, skipping the association between digitalization, CE, and C-A trade. Therefore, to the best of our knowledge, no study has focused on the role of CE and digitalization in C-A trade. As mentioned, China is the principal trade partner of African countries and a leading nation in terms of CE and digital connectivity. The rapid advancement of digitalization technology and CE is reshaping the trade structure of global economies, including the dynamics of C-A trade. In this aspect, numerous policies related to digitalization and digital business have been continuously introduced, leading to significant changes in the C-A trade environment, which requires a comprehensive investigation of their impacts on trade flow.

Second, although there is a substantial body of literature exploring the relationship between LP and trade flows, there is relatively limited empirical and analytical research focused specifically on the effects of LP on C-A trade. Existing empirical works have emphasized the effects of LP on trade in advanced economies or China’s trade with other economies, leaving a research gap on the role of LP in the dynamics of C-A trade. Thus, the study on the effects of LP on C-A trade is mainly absent and deserves more attention, which this study closes the gap.

Third, unlike existing empirical works, this study provides a new comprehensive approach to incorporate the role of heterogeneity of the trade partner countries. In this regard, it provides detailed evidence of the role of African countries’ income, demographic, institutional, and resource heterogeneity on the effects of CE, digitalization, and LP on C-A trade. Consequently, this study offers new perspectives on the complex relationships between trade, digitalization, CE, and LP within the context of C-A trade dynamics. These understandings are predominantly helpful for policymakers aiming to investigate the challenges and leverage the prospects provided by China’s changing trade trends and the evolving patterns of C-A trade.

Finally, this research contributes to developing a conceptual structure incorporating digitalization, CE, and LP into a single theoretical model to impact C-A trade as they are interconnected (Figure A3). More specifically, digitalization serves as the foundational backbone that enables the seamless functioning of cross-border e-commerce and logistics performance. The growth of internet technology has created opportunities for the development of EC and greatly promoted business process reengineering (Qi et al., 2024; K. Chen et al., 2024). Robust digital infrastructure facilitates real-time communication, secure transactions, and efficient information exchange and empowers CE platforms to expand market reach by connecting buyers and sellers across countries, breaking down traditional geographical and regulatory barriers. In turn, enhanced LP supports digital trade by ensuring timely delivery, which fuels further growth of e-commerce. This synergy reduces transaction costs, shortens delivery times, and broadens market access, making trade flows more dynamic and resilient. Also, CE has deep integration with supply chain digitization (Ma et al., 2024). This integrated approach not only enhances efficiencies and reduces trade costs but also promotes volume trade flow, ultimately affecting C-A trade.

Therefore, exploring the impact of CE and digital technologies on C-A trade is worthwhile from theoretical, empirical, and practical aspects. This study will enhance understanding of the structural changes in C-A trade, along with the dynamics and emerging trends in C-A trade development within the framework of CE and digitalization.

3. Methodology and Data

3.1. Theoretical Underpinnings of Model Construction

The theoretical basis for investigating the effects of digitalization, CE, and LP on C-A trade is the standard gravity model proposed by Tinbergen (1962), which aimed to examine trade flow among countries, which is indicated in Equation (1). This model posits that trade between countries depends on the economic size (mass) of the exporting and importing nations and the physical distance between them.

$$T_{ijt}=M_{it}^{\alpha }{M}_{jt}^{\delta }/d_{ij}^{\beta }$$

(1)

where T_ijt is the trade from exporter i country to trade destination j country. M_i and M_j represent the mass of the origin (China) and destination (African countries) at time t, respectively. The mass of the exporting and importing countries can be represented by their GDP and other relevant factors. d_ij is the physical distance between trading countries. α, δ, and $\beta$ represent parameters. In addition to distance, a gravity model often incorporates dummy variables to account for trade-related costs. More broadly, by applying a logarithmic transformation, Equation (1) can be re-expressed as follows:

$$\ln T_{ijt}={\alpha }_0+\alpha \ln M_{it}+\delta \ln M_{jt}+\beta \ln d_{ij}+{\epsilon }_{ijt}$$

(2)

We extended Equation (2) to include our target variables (digitalization, CE, and LP) on top of the control variables using the following model:

$$\ln T_{ijt}={\alpha }_0+{\alpha }_1\ln D_{it}+{\alpha }_2\ln D_{jt}+{\alpha }_3\ln C{E}_{it}+{\alpha }_4\ln L{P}_{it}+{\alpha }_5\ln L{P}_{jt}+{\alpha }_n{X}_{ijt}+{\epsilon }_{ijt}$$

(3)

where T_ijt is the trade flow between China and African countries (dependent variable), D_it, EC_it, and LP_it represent China’s digitalization, CE, and LP; and D_jt and LP_jt represent the digitalization and LP of destination countries. X depicts control variables such as the average GDP of origin and destination countries, the distance between countries (D_ij), diplomatic relationships (DD_ijt), the institutional quality of origin and destination countries (IQ_it and IQ_jt), and WTO joint membership.

Based on the existing literature, digitalization can play a significant role in reducing trade costs and improving trade efficiency, positively influencing trade flow among countries. Similarly, the adoption of electronic payment systems and e-commerce platforms further simplifies transactions, cutting costs when sourcing products internationally (Bellucci et al., 2023). Based on this background, T_ijt, defined in Equation (3), is assumed to be positively influenced by D_it (${\alpha }_1=\partial \ln T_{ijt}/\partial \ln D_{it}>0$), D_jt (${\alpha }_2=\partial \ln T_{ijt}/\partial \ln D_{jt}>0$) and CE_it (${\alpha }_3=\partial \ln T_{ijt}/\partial {\mathrm{lnCE}}_{it}>0$).

Likewise, theoretically, LP influences trade flow among countries by minimizing trade costs because it is conceptualized as capital resources and technology that lower the cost of logistics services. Therefore, it is supposed that C-A trade (T_ijt), defined in Equation (3), will be positively affected by the LP of destination African countries and China (${\alpha }_4=\partial \ln T_{ijt}/\partial {\mathrm{lnLP}}_{it}>0$ and ${\alpha }_5=\partial \ln T_{ijt}/\partial {\mathrm{lnLP}}_{jt}>0$).

3.2. Variables Measurement and Data

This study investigates the role of digitalization, CE, and LP in C-A trade using Chinese trade with 53 African countries from 2007 to 2023. The list of countries is reported in Appendix B. The scope of the data is limited to 2007–2023 based on the data availability of some of the target variables. The variables are discussed below in detail.

Dependent variable: C-A trade: C-A unadjusted total trade (sum of exports and imports in USD million) represents C-A trade performance. The data are borrowed from the C-A research initiative of the Johns Hopkins School of Advanced International Studies.

Target variables: Digitalization of origin and destination, CE of origin, and LP of origin and destination countries are our target variables. Digitalization: The digitalization level of African countries and China is proxied by aggregate indices driven by mobile subscriptions per 100 persons, fixed broadband subscriptions per 100, and internet access using principal component analysis. The results are depicted in

Table A6. Aggregate digitalization indicators.

Aggregate Digitalization Indicator for China
Component	Eigenvalue	Proportion	Cumulative
Comp1	2.929	0.976	0.976
Comp2	0.057	0.019	0.996
Comp3	0.013	0.004	1.000
Variable	Comp1	Comp2	Comp3
Individuals using the Internet (% of the population)	0.581	−0.220	−0.784
Mobile cellular subscriptions (per 100 people)	0.578	−0.567	0.587
Fixed telephone subscriptions (per 100 people)	0.574	0.794	0.202
Aggregate digitalization indicator for African countries
Component	Eigenvalue	Proportion	Cumulative
Comp1	2.061	0.687	0.687
Comp2	0.670	0.224	0.911
Comp3	0.269	0.090	1.000
Variable	Comp1	Comp2	Comp3
Fixed telephone subscriptions (per 100 people)	0.494	0.855	0.158
Mobile cellular subscriptions (per 100 people)	0.599	−0.466	0.651
Individuals using the Internet (% of the population)	0.630	−0.227	−0.743

. Fixed broadband subscriptions represent a critical foundation for establishing advanced infrastructure, which is essential for the ongoing phase of digitalization. Both internet usage and broadband subscriptions serve as pivotal digital technologies, facilitating access to and utilization of digital goods and services (Labhard & Lehtimäki, 2022). Data for these variables are taken from the WDI of the World Bank (WB) database.

Cross-border E-commerce (CE): We utilized CE trade volume per capita as a proxy, following the approach of Y. Wang et al. (2017) and Yin and Choi (2023). Data on the total CE trade volume were sourced from the e-commerce research center of China and reported in the local currency. To standardize it, we converted the figures into USD using the China (RMB) to USD exchange rate obtained from the Federal Reserve Economic Database maintained by the Federal Reserve Bank of St. Louis. Finally, the converted trade volume was divided by China’s total population to calculate the per capita value (Yin & Choi, 2023). The pattern of C-A trade, digitalization, and CE is depicted in Figure 2.

Logistics performance (LP): We used the LP index to represent LP in China and other African countries. The LP evaluates a country’s connection to international logistics networks by scoring its overall logistics conditions on a five-point scale. It reflects the average performance across six sub-dimensions: customs, infrastructure, ease of arranging shipments, quality of logistics services, tracking and tracing, and timeliness (Song & Lee, 2022). This index provides a comprehensive insight into national logistics achievements across various countries worldwide (Górecka et al., 2022). From a supply chain perspective, LP refers to cost, time, and complexity in accomplishing import and export (Hausman et al., 2013). We used LP data published by the WB. The WB publishes the LP index every two years. To maintain data continuity, any missing values are imputed using linear prediction, following the works of W. Wang et al. (2024).

Control variables: To include factors determining C-A trade, we control the average of real GDP of origin and destination countries to proxy the effects of the economic size of trading countries. A larger market potential typically leads to a higher trade volume. Thus, this research supposes that the economic size is positively linked with the C-A trade (C. Wang et al., 2021). Furthermore, destination and origin countries’ membership in the WTO, diplomatic disagreement, and the geographical distance between China and trade partner African countries are included in the analysis using data from the CPII database.

Moreover, we include aggregate indicators of the quality of governance in African countries and China following the works of Gold and Rasiah (2022), who argued that institutional structure matters for C-A trade. The level of institutional quality of trade destination African countries is the world governance indicator of the WB, respectively. We utilized principal component analysis to derive aggregate institutional quality indicators from six governance quality components provided in the WB database, incorporating them into our analysis.1 Finally, to represent diplomatic relationships, diplomatic disagreement from CPII is included in the analysis following the works of Visser (2019).

Table A2. Statistical summary of variables.

Variable	Obs	Mean	Std. Dev.	Min	Max
Tijt	901	1855.085	3354.666	0.000	24,223.770
GDPCjjt	874	5281.240	1981.491	2308.984	14,818.580
Dij	897	11,250.32	1558.757	8250	13682
DDijt	738	0.301	0.252	0.000	1.605
WTOij	901	0.811	0.391	0.000	1.000
IQjt	897	−0.040	2.225	−5.940	5.610
IQit	901	0.529	1.581	−1.519	3.394
CBit	901	568.833	427.896	59.367	1324.865
DGit	848	0.874	1.295	−1.297	2.961
DGjt	768	0.468	1.472	−1.361	7.286
LPit	848	3.554	0.108	3.320	3.700
LPjt	834	2.476	0.327	1.340	3.775

presents a statistical summary of the variables.

3.3. Method of Analysis

The gravity strategy using panel data can be analyzed through FE, RE, and H-T estimation approaches. The FE model assumes that variations between entities, such as importing and exporting country pairs, are reflected in intercept changes, which remain unchanged over the period (Yin & Choi, 2023). However, a key limitation of the FE method is that time-invariant variables, like distance or WTO membership in this context, are absorbed into the country-specific fixed effects, preventing the estimation of their coefficients (Yin & Choi, 2023).

The alternative to the FE model is the RE estimation strategy. The RE method is based on the supposition that the intercepts for individual units are randomly distributed (Yin & Choi, 2023). Unlike the FE model, the RE approach enables the execution of estimates for time-varying variables and variables that are constant over time. Thus, it contains observed time-invariant characteristics not captured in the FE model. However, a significant limitation of the RE strategy is the restrictive homogeneity assumption, which assumes no link between the explanatory variables and the random components. Therefore, the following model is specified to run either the FE or RE based on the Hausman model selection test.

$$T_{ijt}={\alpha }_0+{\alpha }_{n}T{V}_{ijt}+{\beta }_n{X}_{ijt}+c_i+c_j+{\epsilon }_{ijt}$$

(4)

where TV represents the target variables (digitalization, CE, and LP) and X represents the control variables.

Both the FE and RE methods in a gravity model may suffer from bias, mainly due to the endogeneity of variables (Yin & Choi, 2023). There will be potential reverse causality between target variables (digitalization, CE, and LP) and C-A trade, possibly due to their bidirectional causality relationship, the omitted variables, or measurement errors. For example, digitalization affects the trade performance and trade participation of countries (Bellucci et al., 2023; Herman & Oliver, 2023; Bellucci et al., 2023; Añón Higón & Bonvin, 2024). Conversely, trade openness is a crucial channel for the diffusion of technology, including digital technology, to trade partner countries (Almeida & Margarida, 2006; Clarke & Wallsten, 2006). Additionally, the level of digitalization and internet adoption in trading countries can be influenced by foreign trade, as countries with higher trade volumes can afford more ICT infrastructure and promote greater internet adoption, thereby promoting digitalization and CE (Osnago & Tan, 2016; Yin & Choi, 2023). Thus, digitalization and CE not only affect trade, but the relationship is reciprocal; trade also influences digital technology and the level of CE, resulting in reverse causality concerns. Similarly, there will be potential endogeneity between LP and C-A trade, as there will be bidirectional impacts between LP and C-A trade. On the one hand, LP reduces trade costs and improves efficiency, promoting trade flow among countries (Barakat et al., 2023; W. Wang et al., 2024). On the other hand, trade openness influences logistics performance, as trade integration fosters efficient logistics systems (Ohakwe & Wu, 2025).

Therefore, the H-T estimator offers a solution to address the limitations of the RE model while still allowing for the potential use of the FE model. This approach is commonly employed in gravity models within international trade to tackle endogeneity issues (Rodríguez-Crespo & Martínez-Zarzoso, 2019; Cantore & Cheng, 2018). Specifically, the H-T estimation involves a three-step IV regression. The first step estimates the FE model for time-varying variables and constructs the group mean of the within-group residuals based on the first-step results. These groups’ mean is IV for the explanatory variables (Rodríguez-Crespo & Martínez-Zarzoso, 2019; Greene, 2012).

$$\ln T_{ijt}=Y_{1ijt}^{\prime }{\beta }_1+Y_{2ijt}^{\prime }{\beta }_2+Z_{1ij}^{\prime }{\theta }_1+Z_{2ij}^{\prime }{\theta }_2+{\epsilon }_{ijt}+{\mu }_{ij}$$

(5)

In the Hausman–Taylor (H-T) estimator, the model defines four sets of observed variables as follows: Y_1ijt is a time-varying variable that is uncorrelated with μ_ij; Z_1ij is a time-invariant variable that is also uncorrelated with μ_ij; Y_2ijt is a time-varying variable that is correlated with μ_ij; Z_2ij is a time-invariant variable that is correlated with μ_ij.

Therefore, we use the H-T strategy as an alternative model to examine the effects of digitalization, CE, and LP on C-A trade. We consider digitalization, CE, and LP as endogenous variables in the H-T estimation.

Furthermore, for robustness purposes, we used the PPML model to control heterogeneity concerns and zero-valued trade observations. Alternatively, we apply PPML estimation for the gravity model. This approach offers an effective solution for handling zero trade flow observations and heterogeneity by estimating the gravity model in its multiplicative form rather than the logarithmic form (Silva & Tenreyro, 2006). Consequently, we apply the PPML estimator to assess the impact of digitalization, the CE, and LP on C-A trade performance, using the following Equation (6).

$$T_{ijt}=\exp [{\alpha }_0+{\alpha }_{n}T{V}_{ijt}+{\beta }_n{X}_{ijt}+MR{T}_{ijt}+{\delta }_{it}+{\delta }_{jt}]\times {\gamma }_{ijt}$$

(6)

In the model specification, the dependent variable (C-A trade) is included in its level form rather than in logarithmic terms.

4. Results and Findings

4.1. Correlation Analysis

Before performing the formal regression analysis, we conducted a correlation analysis to examine the relationships between the explanatory variables. The findings are reported in

Table A3. Correlation coefficients.

Variables	a	b	c	d	e	f	g	h	i	j	k	l
lnTijt (a)	1
lnGDPCijt (b)	0.23	1
lnDDij (c)	−0.37	−0.07	1
Dijt (d)	0.09	−0.02	−0.17	1
WTOij (e)	0.14	−0.09	0.34	−0.04	1
lnIQjt (f)	−0.08	0.03	0.13	−0.05	0.14	1
lnLPjt (g)	0.17	0.13	−0.11	0.08	0.12	0.44	1
lnDGjt (h)	0.32	0.57	−0.08	0.04	0.06	0.35	0.40	1
lnIQit (i)	0.20	0.59	0.05	0.01	0.04	0.00	0.17	0.54	1
lnLPit (j)	0.22	0.59	0.05	0.04	0.04	0.01	0.20	0.57	0.88	1
lnCEit (k)	0.23	0.53	0.05	0.03	0.04	0.01	0.21	0.60	0.93	0.95	1
lnDGit (l)	0.23	0.53	0.06	0.02	0.04	0.01	0.21	0.60	0.90	0.97	0.98	1

in Appendix A. Based on the findings, we controlled CE, digital connectivity, and LP indicators separately because they are highly correlated (Table A3).

4.2. Baseline Results

We controlled digitalization indicators of China and African countries, CE of China, and LP indicators of China and African countries separately in the analysis based on the correlation coefficients of variables reported in Table A3. Moreover, the institutional quality indicator of China is highly correlated with some target variables, and it is controlled separately. Before we run the baseline analysis, the Hausman test is conducted to select between the RE and FE models. The results supported the RE model (see

Table A1. Model selection test.

	RE vs. FE
Hausman test	H0: RE is preferred. H1: FE is preferred.
p-Value	0.680
Decision	RE is preferred.

in Appendix A). Thus, we run baseline analysis using the RE model, and the results are reported in

Table 1. The impacts of digitalization, CE, and LP on C-A trade.

Variables	(I)	(II)	(III)	(IV)	(V)	(VI)
lnGDPCijt	1.840 *** (0.201)	1.627 *** (0.235)	1.762 *** (0.211)	1.654 *** (0.235)	1.644 *** (0.235)	2.041 *** (0.201)
lnDDij	−0.261 *** (0.085)	−0.299 *** (0.088)	−0.252 *** (0.086)	−0.294 *** (0.088)	−0.296 *** (0.088)	−0.197 ** (0.088)
lnDijt	−0.133 * (0.078)	−0.141 * (0.079)	−0.042 (0.082)	−0.141 * (0.079)	−0.141 * (0.079)	−0.145 * (0.082)
WTOij	1.351 ** (0.569)	1.396 ** (0.569)	1.304 ** (0.548)	1.390 ** (0.569)	1.392 ** (0.569)	1.602 *** (0.597)
lnIQjt	0.188 * (0.097)	0.193 ** (0.098)	0.082 (0.108)	0.189 * (0.098)	0.190 * (0.098)	0.140 (0.107)
lnIQit	0.771 *** (0.232)
lnDGit		0.603 *** (0.201)
lnDGjt			0.423 *** (0.124)
lnCEit				0.393 *** (0.140)
lnLPit					1.500 *** (0.524)
lnLPjt						0.014 (0.081)
Cons	25.482 *** (7.795)	29.141 *** (8.052)	25.261 *** (7.873)	27.395 *** (7.941)	24.184 *** (7.770)	19.630 ** (8.056)
Obs.	714	714	714	714	714	676
R2	0.602	0.599	0.604	0.598	0.599	0.589

Note: *** significance at 1%, ** significance at 5%, * significance at 10%; standard errors are reported in parentheses; time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries.

below. In Column (I), we run the exercises, including the institutional quality of China and African countries, and we exclude our target variables as the correlation coefficient of some of the target variables and the institutional quality of China is high. Each target variable is included in the analysis separately in Column (II) to Column (VI).

The findings imply that the effects of the control variables on China–Africa trade are aligned with both theoretical and empirical works. Economic size has a robust positive effect on China–Africa trade, implying that economic expansion in both China and trade partner African countries positively influences trade flow between African countries and China. Therefore, economic transformation and advancement positively contribute to C-A trade, among others (Omonijo & Zhang, 2025). Similarly, the coefficients of institutional quality of China and African countries are positive and statistically significant, implying that improving governance performance indicated by the aggregate index in both China and trade partner African countries enhances trade flow between China and Africa. Also, the WTO membership of China and its trade destination, African countries, has a significant positive effect on the C-A trade.

In contrast, the effect of physical distance between China and its trade partners in Africa on C-A trade is statistically significant and negative, indicating that greater physical distance hinders trade flows between China and African countries. Moreover, diplomatic disagreements negatively affect trade flows between China and African countries. The findings are consistent with Visser’s (2019) findings that diplomatic representation significantly influences the trade performance of countries.

Regarding variables of our interest, digitalization in both China and its trade destination, African countries, positively influences C-A trade performance. More specifically, a percent increase in the digitalization index in China and African countries increases C-A trade by 0.603% and 0.423%, respectively. The results aligned with Hypothesis 1 (H1). These findings are logical, as they support the view that improvements in digital technology enhance trade flows among countries. The results support the findings of Kere and Zongo (2023), Yin and Choi (2024), Sawadogo and Wandaogo (2020), and Abendin et al. (2022), which provide evidence that an increase in digitalization among countries leads to a significant rise in trade among trading partner countries.

Likewise, China’s CE coefficient is positive and statistically significant, implying that an increase in China’s CE trade robustly enhances C-A trade. The coefficient implies that a percent improvement in CE trade increases C-A trade by 0.393% (Table 1). The results support Hypothesis 2 (H2), articulating a positive link between CE and C-A trade. These findings are logical and justified, as promoting CE supports firms in avoiding high competition in their local markets and opening opportunities to access foreign markets (Guo et al., 2018).

Furthermore, China’s LP coefficient is positive and robustly significant at a substantial level of significance, implying that the LP of China positively impacts the C-A trade. Notably, the corresponding coefficients of the LP of China display the anticipated signs and are consistent with Hypothesis 3 (H3). Therefore, availability and improvement in the quality of logistics performance of trading countries matter for promoting trade flow. The findings are congruent with the findings of Bugarčić et al. (2020) and W. Wang et al. (2024) in the case of trade flow of Central and Eastern European countries and the Western Balkans and Chinese trade with the B&R initiative countries, respectively.

Furthermore, the benchmark analysis is repeated using the H-T model to control for endogeneity concerns, which is an appropriate alternative to the RE model to run a gravity model. In the H-T method, we treated digitalization indicators, CE, and LP indicators as endogenous variables, as the associations between digital technology and trade will be endogenous because of the potential existence of reverse causality (Yin & Choi, 2023). The results are reported in

Table 2. The effects of digitalization, CE, and LP on C-A trade.

Variables	(I)	(II)	(III)	(IV)	(V)
lnGDPCijt	1.308 *** (0.231)	1.317 *** (0.157)	1.380 *** (0.225)	1.557 *** (0.165)	1.796 *** (0.070)
lnDDij	−0.252 ** (0.108)	−0.182 * (0.105)	−0.234 ** (0.108)	−0.193 * (0.102)	−0.097 (0.098)
lnDijt	−0.013 (0.077)	0.053 (0.079)	−0.020 (0.077)	−0.033 (0.077)	−0.031 (0.080)
lnIQjt	0.254 ** (0.102)	0.148 (0.111)	0.247 ** (0.102)	0.228 ** (0.100)	0.163 (0.109)
WTOij	1.254 ** (0.602)	1.047 * (0.619)	1.229 ** (0.606)	1.177 * (0.613)	1.391 ** (0.647)
lnDGit	0.379 ** (0.165)
lnDGjt		0.385 *** (0.114)
lnCEit			0.120 ** (0.059)
lnLPit				0.686 * (0.396)
lnLPjt					0.035 (0.083)
_cons	25.428 ** (9.866)	19.477 ** (9.547)	23.456 ** (9.725)	17.785 ** (8.831)	10.814 (8.814)
Obs.	714	714	714	714	714

Note: *** significance at 1%, ** significance at 5%, * significance at 10%; standard errors are reported in parentheses; time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries.

below.

The results generally align with the baseline findings, with only minor variations in the magnitude and significance of some estimates. Digitalization of China and trade partner countries in Africa has a robust positive effect on C-A trade. Additionally, the effects of China’s CE have a positive influence on trade flows between China and African countries. Finally, the LP of China has a positive effect on C-A trade.

4.3. Robustness Analysis

4.3.1. Heteroscedasticity Issue

Another important issue that should be considered in gravity model specification is heterogeneity and zero-valued observations concerns. A practical approach to addressing zero trade flows is to estimate the gravity model in its multiplicative form rather than the logarithmic form. Additionally, the presence of heteroscedasticity in the original non-linear gravity model specification arises from the assumption of a multiplicative error term, which requires the adoption of an alternative estimation method. Silva and Tenreyro (2006) suggest an easy solution for handling concerns related to heteroscedasticity using PPML. Thus, we repeat the exercises using the PPML method to run the gravity specification of the impacts of digitalization, CE, and LP on C-A trade performance. It can solve heterogeneity and zero values in the C-A trade data. The results are given in

Table 3. The role of digitalization, CE, and LP on C-A trade.

Variables	(I)	(II)	(III)	(IV)	(V)
Control variables	Yes	Yes		Yes	Yes
lnDGit	1.100 *** (0.217)
lnDGjt		0.782 ** (0.206)
lnCEit			0.732 *** (0.152)
lnLPit				2.774 *** (0.565)
lnLPjt					0.335 ** (0.146)
Obs.	714	656	714	714	676
R2	0.954	0.951	0.953	0.948	0.852
Ramsey (p-value)	0.293	0.495	0.428	0.188	0.540

Note: *** significance at 1%, ** significance at 5%; country-pair clustered standard errors are reported in parentheses; countries and time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries. Ramsey is the test for model specification.

.

The findings are congruent with those presented in Table 1 and Table 2. The effects of digitalization of origin and destination are robustly positive, strengthening the baseline results. Likewise, the coefficient of CE of origin is significant at a 1% level of significance. Also, the LP of trade origin and destination African countries is strongly positive.

4.3.2. Further Robustness Analysis

Moreover, we excluded African countries with the highest trade volume with China. More specifically, Angola, South Africa, Nigeria, and Egypt have been excluded to assess sensitivity to outliers (see Figure A2). The outcomes are presented in

Table A4. Effects of digitalization, CE, and LP on C-A trade.

Variables	I	II	III	IV	V
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	0.429 ** (0.184)
lnDGjt		0.379 *** (0.119)
lnCEit			0.133 ** (0.066)
lnLPit				0.793 * (0.430)
lnLPjt					0.020 (0.095)
Obs.	658	604	658	658	550
R2	0.603	0.581	0.626	0.558	0.510

Note: *** significance at 1%, ** significance at 5%, * significance at 10%; country-pair clustered standard errors are reported in parentheses; countries and time fixed effects are controlled; i represents exporting country (China) and j depicts trade partner African countries. Ramsey is the test for model specification.

in Appendix A. The findings are consistent with the baseline results in Table 1.

Moreover, we utilized time series data for LP, employing data interpolation in the baseline analysis. To check the sensitivity of the results, we repeated the analysis using data with intervals instead of data pooled over consecutive years because data for LP are available for 2007, 2010, 2012, 2014, 2016, 2018, and 2023. The results are presented in

Table A5. Impacts of digitalization, CE, and LP on C-A trade.

Variables	I	II	III	IV	V
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	0.876 *** (0.236)
lnDGjt		0.521 *** (0.186)
lnCEit			0.619 *** (0.167)
lnLPit				0.867 * (0.500)
lnLPjt					0.144 (0.108)
Obs.	305	275	305	305	276
R2	0.600	0.611	0.609	0.577	0.590

Note: *** significance at 1%, * significance at 10%; country-pair clustered standard errors are reported in parentheses; countries and time-fixed effects are controlled; i represents the exporting country (China) and j depicts the trade partner African countries. Ramsey is the test for model specification.

in Appendix A and are not significantly different from the baseline results.

4.4. Heterogeneity Analysis

4.4.1. The Role of Income and Demographic Heterogeneity

We re-examined the role of digitalization, CE, and LP on C-A trade on the notion that their impact on C-A trade flow may vary depending on the economic size of African countries, including variations in income levels and demographic heterogeneity. Hence, the sample of African countries is categorized as middle-income and low-income economies based on their income level following WB countries’ classifications (see Appendix B).2 Besides, highly populated and less populated African countries are separately treated to examine the role of demographic variation in the effects of digitalization, CE, and LP on C-A trade (see Appendix B). The results are reported in

Table 4. The role of income heterogeneity.

Panel A: Middle-Income African Countries
Variables	(I)	(II)	(III)	(IV)	(V)
Control variables	Yes	Yes		Yes	Yes
lnDGit	0.547 ** (0.249)
lnDGjt		0.633 *** (0.229)
lnCEit			0.353 ** (0.174)
lnLPit				1.355 ** (0.647)
lnLPjt					−0.006 (0.109)
Obs.	392	392	419	419	391
R2	0.601	0.535	0.590	0.590	0.574
Panel B: Low-Income African Countries
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	0.824 (0.996)
lnDGjt		0.275 * (0.142)
lnCEit			−0.904 (0.717)
lnLPit				0.473 (0.787)
lnLPjt					0.089 (0.126)
Obs.	295	264	295	295	285
R2	0.646	0.643	0.647	0.608	0.610

Note: *** significance at 1%, ** significance at 5%, * significance at 10%; standard errors are reported in parentheses; time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries.

and

Table 5. The role of population heterogeneity.

Panel A: C-A Trade (Highly Populated African Countries)
Variables	I	II	III	IV	V
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	0.960 ** (0.490)
lnDGjt		0.547 *** (0.241)
lnCEit			0.627 *** (0.169)
lnLPit				0.701 (0.816)
lnLPjt					0.423 *** (0.149)
Obs.	271	250	271	435	375
R2	0.566	0.564	0.600	0.588	0.500
Panel B: C-A trade (Less-populated African countries)
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	0.473 *** (0.182)
lnDGjt		0.371 *** (0.131)
lnCEit			0.100 (0.066)
lnLPit				0.959 *** (0.463)
lnLPjt					−0.106 (0.108)
Obs.	443	406	443	443	360
Wald test	0.540	0.543	0.542	0.505	0.530

Note: *** significance at 1%, ** significance at 5%; standard errors are reported in parentheses; time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries.

, respectively.

The results show that the effects of digitalization in China and African countries, CE of China, and LP index of China on LC are robustly positive at substantial levels for middle-income African countries (Table 4). However, except for China’s digitalization level, the rest of the target variables are insignificant for African countries with low incomes. Moreover, the effects of digitalization, CE, and LP on C-A trade are population size dependent, having a higher magnitude of the coefficients of each target variable for African countries with a high population size compared to less populated African countries (Table 5). Hence, the findings are consistent with the hypotheses postulated.

4.4.2. The Role of IQ and Resources Heterogeneity

The study further considers the institutional and resource heterogeneity of sample African countries to investigate the role of digitalization, CE, and LP on C-A trade flow on the proposition that effective governance supports digitalization and CE activities. Additionally, African countries exhibit significant heterogeneity in terms of resources, and variations in these resources can impact the effects of digitalization, CE, and LP on C-A trade dynamics. The IMF classifies a country as ‘resource-rich’ when exports of non-renewable natural resources, such as oil, minerals, and metals, comprise more than 25% of the country’s total export value. Based on these backgrounds, we re-evaluate the effects of digitalization, CE, and LP on C-A trade, dividing countries into countries with weak institutional quality (below the average) and strong institutional quality (greater than the sample average) and based on their value of resources export. The results are presented in

Table 6. The effects of IQ heterogeneity.

Panel A: African Countries with IQ Greater than the Average
Variables	I	II	III	IV	V
Control variables	Yes	Yes		Yes	Yes
lnDGit	1.143 *** (0.316)
lnDGjt		0.349 *** (0.175)
lnCEit			0.764 ** (0.221)
lnLPit				2.896 *** (0.824)
lnLPjt					0.033 (0.111)
Obs.	426	410	426	714	398
R2	0.589	0.582	0.568	0.589	0.565
Panel B: African Countries with IQ Less than the Average
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	0.435 (0.275)
lnDGjt		0.627 *** (0.185)
lnCEit			0.280 (0.191)
lnLPit				1.075 (0.715)
lnLPjt					−0.016 (0.137)
Obs.	288	246	288	288	278
R2	0.633	0.661	0.632	0.632	0.631

Note: *** significance at 1%, ** significance at 5%; standard errors are reported in parentheses; time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries.

and

Table 7. The influence of resource heterogeneity.

Panel A: Resource-Rich African Countries
Variables	II	II	III	IV	V
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	0.728 *** (0.261)
lnDGjt		0.617 *** (0.173)
lnCEit			0.485 *** (0.183)
lnLPit				1.839 *** (0.681)
lnLPjt					0.074 (0.127)
Obs.	435	392	435	435	375
R2	0.589	0.595	0.588	0.588	0.500
Panel B: Non-Resource African Countries
Control variables	Yes	Yes	Yes	Yes	Yes
lnDGit	1.377 *** (0.378)
lnDGjt		0.262 (0.176)
lnCEit			0.901 *** (0.265)
lnLPit				3.443 *** (0.989)
lnLPjt					0.109 (0.112)
Obs.	279	264	279	279	231
R2	0.683	0.668	0.680	0.681	0.641

Note: *** significance at 1%, standard errors are reported in parentheses; time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries.

.

The findings implied that the effects of digitalization in China and African countries, CE of China, and the LP indicator of China have a robust positive effect on C-A trade for African countries with IQ greater than the average quality of institutions (Table 6). Conversely, except for the digitalization level of China, all target variables are insignificant when African countries with institutions with a quality less than the average are considered. Therefore, the findings imply that the effects of digitalization, CE, and LP are subject to the level of institutional quality of trade destination countries. Moreover, there is no significant variation in the effects of digitalization, CE, and LP on C-A trade when resource heterogeneity is considered, except for slight variations in the magnitude and level of significance of the variables (Table 7).

4.5. The Role of Digitalization and LP Distance

The distance between two countries is much greater than the distance between geographies; it can be due to technological differences (Bellucci et al., 2023). Thus, differences in digital connectivity and logistics innovation among trading partners may indirectly affect trade between countries. Moreover, relatively better connectivity in one of the trade partners does not reduce trade costs with a poorly connected partner, which can be justified in the case of C-A trade, as China is relatively better than African countries in terms of digitalization and LP. China stands 19th in the LP rank of 2023 among world countries, and African countries stand significantly lower compared to China, except South Africa, which is ranked 19th. Therefore, we repeat our analysis controlling for digital connectivity and LP differences between China and trade destination African countries. The results are reported in

Table 8. The role of digitalization and LP distance on C-A trade.

Variables	I	II
Control variables	Yes	Yes
Digital_distanceijt	−0.302 ** (0.147)
LP_distanceijt		−0.055 (0.087)
Obs.	656	606
R2	0.557	0.529

Note: ** significance at 5%; standard errors are reported in parentheses; time-fixed effects are controlled; i represents the exporting country (China), and j depicts trade partner African countries.

below.

The findings implied that the digitalization distance between China and African countries negatively and significantly affects C-A trade. Therefore, the results show that the difference between digitalization levels in the sample African trading partners and China can promote C-A trade, whereas a higher difference in digitalization between China and African countries negatively affects trade flow between countries.

5. Discussions and Theoretical and Practical Implications

5.1. Discussions

Among key pillars to promote trade flow among trading partners are scaling up digital connectivity, development in e-commerce, and logistics and supply chain improvements in the countries. Given the fast growth of trade between China and African countries, as well as the demographic, income, institutional performance, and resource heterogeneity within African countries, this study examines the impacts of the digitalization of China and trade partner African countries, the CE of China, and the LP of China and African countries on C-A trade, taking data from Chinese trade to 53 African countries for the periods 2007 to 2023. Several econometric approaches are applied.

The findings of this study reveal a positive association between digitalization in trade partner African countries and China, suggesting that a higher degree of investment in digitalization in both trade origin and destination countries is ultimately more beneficial to promoting C-A trade. These findings can be loosely explained by the fact that the application and development of digital technology can reduce costs in trade and promote trade growth. Moreover, the findings underscore that CE improvement in China positively influenced its trade relations with African countries. Therefore, the findings provide evidence that better access to modern digital technology and the adoption of e-commerce applications stimulate bilateral trade flows between China and Africa.

Theoretically, the findings are aligned with the theoretical justification that improving the digitalization level of trade origin and destination countries serves as a transformative force in reducing trade costs through diverse channels. For example, it decreases the need for physical proximity and in-person interactions in commercial activities, fostering greater flexibility. Furthermore, digital innovations significantly cut communication costs, with tools like automated translation services bridging language divides and easing the financial and logistical challenges of cross-border communication. Additionally, digital technologies revolutionize the efficiency of goods trade by optimizing logistics and customs procedures. Advanced tracking systems and automated operations at ports and airports reduce transit times, while digitalized customs processes minimize border delays and bureaucratic hurdles. Likewise, the rise of electronic payment systems and e-commerce platforms has simplified transactions, enabling cost-effective procurement from international suppliers (Bellucci et al., 2023).

Additionally, the study’s findings align with the digitalization-trade empirical literature, such as Kere and Zongo (2023), Bellucci et al. (2023), Herman and Oliver (2023), López González et al. (2023), Rubínová and Sebti (2021), and Abendin et al. (2022), which argued that improving digital technology significantly reduces trade costs across trade partners and boosts international trade among countries. Similarly, the findings of this study are supported by some empirical works on the link between CE and trade flow. More specifically, the findings are congruent with the results of Yin and Choi (2023), C. Wang (2021), He (2024), and H. Wu et al. (2024) in the case of China and Xing (2018) in the case of OECD and some developing countries.

From a practical perspective, the findings of this study are justified, as improving digitalization in African countries and China can play a crucial role in guaranteeing the cut of trade costs and the promotion of trade flow. More specifically, the findings of this study are justifiable as digitalization is advancing rapidly in China and Africa. For instance, over the past decade, Africa has achieved significant progress, with millions gaining internet access and adopting digital services like mobile payments (WB, 2024). Since 2007, initiatives such as Kenya’s M-PESA have driven the use of ICT, mobile money, and digital payments (Kere & Zongo, 2023). Smartphone penetration reached 64% in 2021, and internet users rose from 0.1% in 2005 to 40% in 2022 (ITU, 2022). Moreover, China, the world’s largest e-commerce market, has seen transactions grow 32% annually, fostering foreign trade and global market access (H. Wang & Liu, 2022; Mou et al., 2019). Between 2012 and 2023, its CE scale surged to CNY 16.8 trillion (K. Chen et al., 2024). Platforms like Kilimall, Tmall, JD.com, and Kikuu are now enabling African suppliers to reach Chinese consumers, boosting sales of products such as Kenyan tea and Ethiopian coffee (Usman & Tang, 2024). Therefore, the findings support the notion that information and transaction costs in goods flows are the most significant factors after transport costs. They reshape the economics of cross-border business by reducing the costs associated with communications and transactions across borders (WTO, 2018).

Furthermore, the findings provide evidence that the LP of China is an important determinant of C-A trade. It positively influences the C-A trade at a substantial level. Therefore, the improving quality and efficiency of logistics services matter for trade flow between China and African countries, as a weak LP and operational processes can be a significant obstacle to trade integration (Gani, 2017). The results provided evidence that developing countries should develop logistics and supply chain infrastructure to enhance trading and commercial activities.

The findings are consistent and in line with the justification that logistics technology offers a competitive edge by enhancing a country’s economic efficiency in managing product movement and storage with greater effectiveness. This outcome is expected to contribute to understanding the potential effects of LP on C-A trade dynamics, as logistics costs are also crucial to the trade of goods and services, representing 11% of overall trade costs (WTO, 2018). It improves delivery capabilities and provides increased visibility throughout the global supply chain (Shikur, 2022). Therefore, the findings provide insights that it is crucial to enhance LP in order to promote trade integration, implying that improving LP is one of the effective approaches for emerging economies to enhance economic activities (Yeo et al., 2020).

However, the results implied that the role of LP of African countries in C-A trade is negligible. It can be argued that even though logistic infrastructure has improved significantly across some African countries (Kuteyi & Winkler, 2022), the continent still lags in terms of LP compared to other regions of the world. Most African countries are facing trade challenges due to weak LP. Except for South Africa, the top-ranked African country that is placed 19th globally, other African nations rank much lower. Many of these countries face cumbersome and inefficient customs clearing procedures that add to costs, along with poorly maintained road networks that cause severe traffic congestion at ports and further delays, increasing the overall cost of trade (Kuteyi & Winkler, 2022).

The findings of the heterogeneity analysis further implied that the effects of digitalization, CE, and LP on C-A trade are heterogeneous and subject to income and demographic variation. The findings provide evidence that the effects of digitalization, CE, and LP on C-A trade depend on the economic size of African countries, having a more robust effect in middle-income African countries and African countries with higher population sizes. These results can be aligned with the justification of Fan and Yu (2015) and Kumari and Bharti (2021), who found the influence of country size on the trade-LP relationship by considering population size and found that LP’s effectiveness in boosting trade growth is higher in medium-income countries.

Moreover, the findings implied that the effects of digitalization, CE, and LP on C-A trade are conditional on differences in the heterogeneity of institutional quality in African countries. The findings are logical because effective governance policies foster transparent institutions that enhance digital connectivity and CE activities while reducing the risks associated with digital connectivity and e-commerce participation (OECD, 2019). Conversely, poor institutional quality and weak policy frameworks, characterized by ineffective legal and regulatory enforcement, can hinder economic activities and exacerbate the risks associated with digital connectivity and e-commerce (Jean et al., 2020; Doh et al., 2017). This suggests that the growth benefits of digitalization and CE are likely to be more pronounced when institutions and governance are robust (Labhard & Lehtimäki, 2022). Additionally, strong institutions play a crucial role in improving logistics and streamlining policies related to border and customs clearance (Portugal-Perez & Wilson, 2009), positively influencing the role of LP in the C-A trade.

5.2. Theoretical Contributions

This study makes significant theoretical contributions to understanding the dynamics of C-A trade by investigating the interaction of digitalization, CE, and LP while accounting for African countries’ heterogeneity in income, demographics, institutional frameworks, and resource endowments. Therefore, the findings of this study bridge the gap in the existing literature and contribute to the theoretical framework by demonstrating how digitalization, CE, and LP serve as transformative tools that reduce trade costs and facilitate market access among developing countries. Digital technology, such as CE platforms and ICT technology, allows trading countries to better exploit their comparative advantage by accessing global markets more efficiently, optimizing production processes, and matching suppliers with the demand for the goods and services they produce, aligned with comparative advantage theory. Additionally, the research findings contribute to global value chain theory by showing how CE and LP enable deeper trade integration of China and African economies. Therefore, by incorporating these modern trade determinants, the research refines existing theories and provides a robust framework to explain evolving trade patterns, particularly in developing and emerging market contexts.

Moreover, by integrating the heterogeneity of trade partner countries, the study enriches theories on international trade, offering a more nuanced understanding of trade dynamics under varying economic, demographic, and institutional conditions. The theoretical contribution of the results is to provide a comprehensive framework that integrates digitalization, CB, and LP with socio-economic factors and the absorptive capacity of trading partner African countries, enhancing our understanding of C-A trade dynamics and offering theoretical insights tailored to diverse domestic contexts of African countries. To sum up, this study offers a novel framework to evaluate how digital technological advancements, CE, and efficient LP reduce trade costs, enhance market access, and optimize supply chain management, promoting trade flow among trading countries subject to their socio-economic contexts.

5.3. Practical Implications

The outcomes of this research offer valuable insights for governments and policymakers in China and African countries and underscore practical policies and strategies for progressing sustainable digital innovation, CE, and LP to promote C-A trade. First, the findings of the study highlight the critical role of digitalization of China and its trade partner African countries in improving C-A trade performance, suggesting the need for robust digital infrastructure, including mobile and broadband penetration, high-speed Internet, and secure digital platforms in both trade origin and destination countries. Therefore, governments and policymakers in China and African countries should collaborate on initiatives to develop shared digital ecosystems aligned with the Digital Silk Road Initiative, which aims to foster digital connectivity, enhance digital infrastructure, and promote CE across countries.

Second, despite notable advancements in digital technology across African countries, digital connectivity and ICT infrastructure in many parts of Africa remain noticeably underdeveloped compared to global standards and China. This disparity reflects a significant digital divide, with a large portion of the African population having limited access to even basic telephone services, while urban areas are saturated with high-speed Internet and advanced digital telecommunications systems (Kuteyi & Winkler, 2022). Hence, Africa’s digitalization requires a holistic approach combining investments, skills, policies, and partnerships. Therefore, governments should prioritize policies to expand digital infrastructure, promote affordable mobile Internet, enhance digital literacy through educational programs, and encourage investment in telecommunications and digital services to boost C-A trade.

Third, the findings implied that China’s CE significantly and positively influences C-A trade, implying that promoting CE can significantly reduce barriers to trade flow and enhance trade participation. Therefore, governments and policymakers should develop vital policies and strategies to promote investment in scalable e-commerce platforms that connect African suppliers with Chinese buyers. In this regard, policymakers should incentivize partnerships between Chinese fintech firms and local African payment service providers. For instance, integrating Chinese digital finance platforms like Alipay or WeChat Pay with widely used African mobile money systems can simplify payments for CE transactions and promote C-A trade.

Fourth, the study highlights the role of LP in promoting C-A trade by reducing delays and enhancing supply chain reliability. To improve LP, investments in infrastructure such as roads, railways, ports, and airports are crucial for reducing transit times and boosting connectivity. This includes enhancing logistics competitiveness through capital investment to lower trade costs. Therefore, this study calls for investing in both physical and logistics infrastructure to promote LP in African countries. Moreover, collaborative investments in ports, transport networks, and warehousing are especially important for landlocked African countries. Additionally, digital customs clearance systems can further streamline trade. Governments should prioritize upgrading key transport corridors, integrating smart logistics solutions, and partnering with technology providers to improve efficiency. Simplified regulations and automated customs will enhance trade flow between China and Africa.

Finally, the effects of digitalization, CE, and LP on C-A trade depend on income and institutional quality heterogeneity. Therefore, considering the unique socio-economic and institutional contexts of each country to maximize the impact of these technologies on C-A trade is worthwhile. Collectively, enhancing income levels and regulatory performance in African countries is crucial for maximizing digitalization, CE, and LP contributions to C-A trade. Thus, African countries should focus on improving institutional quality and formulating effective economic policies to promote sustainable economic growth.

5.4. Limitations and Future Research

Although this research offers valuable theoretical, empirical, and practical insights, it does have some limitations that future studies can explore and address. First, this study is constrained by its focus solely on C-A trade. Consequently, the findings may not be fully generalizable to China’s trade with other regions or developing countries, which may experience digitalization and its impact on international trade differently. Future research could address this limitation by examining a broader or alternative set of country samples. Second, this study incorporates a limited range of digitalization indicators (mobile and broadband subscriptions and internet penetration) that influence trade due to data availability. Expanding the scope to include other indicators, such as artificial intelligence, the Internet of Things, and others, in future studies could offer a more comprehensive understanding of the subject whenever the data for these indicators are available. Finally, future works should extend the vulnerability of African countries to shifts in Chinese economic policy dynamics or structural change in the Chinese market.