The BRI was launched in 2013 as a major initiative to increase China’s economic links to Southeast Asia, Central Asia, Russia and Central and Eastern Europe (Panibratov et al., 2022). It reflects the aspirations of China to become a dominant player in the international financial system and to exert its soft power (Taliga, 2021). The proponents of the initiative emphasize the trade advantages and mutual benefits it offers both to China and participating countries. Potentially, its implementation should influence not only trade and finance but also sectors such as energy and technology markets, health, tourism, education, and culture (Ohashi, 2018; Dong & Pan, 2020).

By December 2023, 151 countries had joined the BRI by signing a Memorandum of Understanding (MoU) with China; most of them were emerging markets (Green Finance and Development Center, 2023). They had different motivations for participation, including the opportunity to access capital markets and financial resources through Chinese investments and loans (Lai et al., 2023). The promise of financial support particularly encourages participation, especially among smaller economies that may lack alternative funding sources. The BRI also advances a political agenda by inviting emerging markets to forge closer ties with China through political support and economic partnerships (Lu et al., 2021). Besides, BRI aims to reduce the trade costs globally by providing the necessary infrastructure. This reduction can lead to increased trade between China and other participating countries and incentivize new membership in the initiative (European Bank for Reconstruction and Development, 2024).

The BRI, as a global trade initiative, presents not only opportunities but also considerable risks. Despite the positive narrative consistently promoted by the Chinese government, around 35% of BRI projects have been classified as failures for various reasons (Nelson, 2023). Alongside financial burdens imposed on emerging economies through these projects, the BRI could have exacerbated economic challenges for participating countries (Lai et al., 2020). Analyzing the progress towards the goals of ‘Made in China 2025’, another ambitious government initiative to promote international collaboration, Li & Branstetter (2021) indicates that Chinese firms still have insufficient innovation capability. Other authors refer to the irrelevant and redundant policies and multi-vectored goals that are hard to achieve simultaneously (Wang et al., 2020).

In addition to economic challenges, the participant countries are facing political risks. Numerous sources point out that Chinese companies, especially state-owned enterprises, are not always welcome in host countries because of their substantial bargaining power and expansion threats (Li et al., 2021). These companies are also frequently favored with government subsidies and financial loans as part of their involvement in BRI projects (Wen & Zhao, 2021). Besides, global political and economic climate are important moderators of BRI, causing fluctuations in the conditions, under which the BRI projects operate (Schulhof et al., 2022). This volatility can propagate shocks in stock markets, currency values, and commodity prices, which may adversely affect investment opportunities (Lee, Karim et al., 2022).

Silk Road e-commerce is another constituent of the BRI which serves as a platform for international cooperation facilitating two-way trade between China and countries along the route. In 2021, the Covid-19 pandemics caused disruption of the global industrial and supply chains, which resulted in reduction or cancellation of many maritime and aerial transportation streams (Atayah et al., 2022). To maintain the speed of cross-border e-commerce logistics between China and the countries involved in the BRI, China began to set up logistics bases, overseas warehouses and cross-border digital industrial parks. One of such these is Lianyungang logistics base that was jointly funded and constructed by China and Kazakhstan (People’s Daily, 2022). The analytical reports claim that e-commerce will keep expanding globally (TMO Group, 2024) and especially in China, where the e-commerce transactions account for 47% of total sales compared to a global average of 25%, underscoring the important role of China in global economy and significance of BRI for the country’s economic growth (Statista, 2023).

Since the time of initiating bilateral agreements on e-commerce, China has established cooperation mechanisms with over 20 countries, enhancing policy exchange, industry promotion, and capacity building (National Development and Reform Commission, 2022). Logistics plays a crucial role in establishing trade networks with the participant countries. The impact of the trade regulations of developed countries on China’s CBEC exports is second only to that of the logistics environment, and the impact of the e-commerce environment in developing countries is greater than that of logistics (Guo et al., 2024). This indicates certain overlaps between different layers of business environment.

China’s logistics industry started to develop comparatively late but it is growing rapidly and over the past decade it has achieved impressive results. The major determinant of success has been maritime transport logistics (Wang et al., 2021). In 2019, the total social logistics in China was 298 trillion yuan, a year-on-year increase of 5.9%; its total cost was 14.6 trillion yuan, a year-on-year increase of 7.3%. Covid-19 pandemics led to serious disruptions in global supply chains, with severe downfall of Chinese logistics, which has not recovered entirely even now. At the same time, the pandemic catalyzed innovation in Chinese firms and prompted them to adopt more digitalized and resilient logistic models. This shift included the use of green supply chain practices spurred by both global and national sustainability initiatives (Li et al., 2021).

Logistics business models mostly operate within a bottom-up hierarchy but they fundamentally differ in the range of services and functionalities. 1PL and 2PL are better suited for manufacturers and small enterprises respectively, whereas 3PL and 4PL are both meant for larger companies but differ in functionality. In the 4PL model, all supply chain processes are outsourced to external parties. Compared to the 3PL, 4PL offers more sophisticated ways of outsourcing logistics as it integrates all activities of a company and provides customers with integrated logistics operation solutions. It attaches great importance to the integration and optimization of resources for the entire logistics system, rather than focusing on specific logistics operations, such as shipping or warehousing (Gruchmann et al, 2020; Feng et al., 2023).

Russia and China have announced ambitious cooperation agreements on the development of transport infrastructure and logistics. The two countries’ firms have much to improve in the technological sphere. They need to establish information exchange through satellite navigation systems of both countries, construct 5G mobile communication networks and build infrastructure (Liu et al., 2022). Concerning the development of 4PL logistics, Russia faces challenges such as insufficient technological adoption and less developed infrastructure compared to China. Besides, the Russian e-commerce sector may experience skilled labour shortages because of low unemployment rate and persistent outflow of talent (Latukha et al., 2023; Oreanda News, 2024), which can affect logistics in some regions.

Government policies are also very important for the BRI logistics. In China this sector is heavily regulated, with multiple layers of regulations imposed by national, regional, and local authorities. This regulatory environment can differ significantly at the region and city levels, complicating efforts to establish a national logistics network and hindering international collaboration (C.I. Process, 2024). Since China’s accession to the WTO in 2001, some restrictions have been loosened but local governments still maintain bureaucratic barriers that protect local businesses, making it difficult for the new players to enter the industry and compete effectively. Indeed, the national and local industrial policies sometimes present thorny issues for 4PL activities (Yu, 2018).

Sustaining the VC is one of the strategies to achieve competitive advantage that involves the full range of activities a business undertakes to deliver a product or service, from initial conception to final delivery and beyond (Figure 1). VCs are essential for firms engaging in BRI, as they enable successful collaboration under rapidly changing international business environment with the potential to become transformed into the global VCs. This is a development (Porter, 2001) that enjoys the support of the BRI. While policies, infrastructure, and industrial standards are crucial exogenous factors, the firm-level ones also significantly impact the success of 4PL e-commerce. The present study employs the VC theory to explore these determinants.

Figure 1. 

Porter’s value chain (Porter, 2001)

VC-related activities include primary and support activities. Primary activities involve tasks that directly contribute to value creation through operations and products. 4PL as a business model deals with the other type: it outsources logistic activities of customer enterprises. It differs from 3PL in that it can handle a much larger scope of tasks.

At the level of primary activities, BRI can drive demand for various logistic services, thus creating growth factors for enterprises. The development of the railway Land bridge between China and Europe is an example of how service companies seize a lucrative business opportunity. This initiative created a faster, more reliable and cost-effective transport alternative to traditional maritime routes, connecting 27 Chinese cities with eleven EU destinations by May 2017. Enhanced service (improved tracking, less-than-container-load services and refrigerated containers) made the project more attractive to traders and reduced the trade costs (Pomfret, 2019).

BRI significantly influences marketing and service strategies of partnering enterprises. Respective market-oriented activities become a key instrument for cultivating knowledge about partners which improves the use of information within collaboration and helps to secure the necessary assets (Ahmat et al., 2021). Furthermore, the BRI positively impacts the image of contributing countries, thereby increasing marketing effectiveness of firms belonging to them (Deng et al., 2024; Liu & Ding, 2024). As for the service, which is one of the primary activities in the VC, digitalization facilitates service delivery and contributes to general satisfaction. It also provides opportunities for development of more innovative services, suggesting possibilities for diversification (Agostino et al., 2021; Ambalov & Heim, 2020).

The support activities are secondary component of VC aimed to enhance its primary activities. They can support the entire VC or be associated with certain primary activities (Porter, 2001). They are normally linked with a particular functional division of an enterprise and encompass HRM, procurement, and firm infrastructure and technologies. Human resources in BRI are regarded as a form of intellectual capital that helps evaluate the strengths and weaknesses of strategies employed by the Chinese companies (Kong et al., 2020). Consequently, the Chinese government expects that the BRI will not only foster the development of partnerships but also increase the workforce supply through technical and vocational training (Johston, 2023).

Infrastructure and technologies are critical components of VC everywhere and the BRI is no exception. The development of logistics infrastructure is prioritized as its key objective, often overshadowing social projects. Infrastructure has become central to the BRI, with the Chinese government actively providing funds to support these developments. However, there are concerns about the possible underestimation of the project-related risks that can result in costly failures. It is therefore essential that clear infrastructural policy programs are implemented to meet the needs of the participant enterprises. Such programs could greatly improve collaboration within the BRI framework.

The interplay between firm-level determinants and external factors is crucial for the success of 4PL and e-commerce, particularly for the VCs exposed to dynamic and international business environments. Drawing upon the insights from the literature, we have identified the items that appear to be directly and indirectly linked to specific VC activities and analyzed their connection with the development stage of logistics. Table 1 below presents a summary of the factors which constitute the core of a priori knowledge required for quantitative analysis.

These 13 factors can also be divided into external and internal ones. To understand how firms can effectively navigate challenges and seize opportunities in a rapidly evolving marketplace, it is necessary to analyze the significance of these factors. In the next section we explain the methodology and present the outcomes of our analysis.

The data for this study were collected through a survey conducted in 2021-2022 in China and Russia. The purpose of the survey was to gather the data that characterize the factors influencing 4PL industry in Sino-Russian e-commerce. Respondents were selected according to the two criteria: they were to work in a Sino-Russian e-commerce logistics company and its affiliated logistics companies were to have joined the BRI support plan. The survey consisted of 55 questions; it was divided into three parts in order to collect the demographic data of respondents, find out the 4PL development level of their company, and identify the factors that influence the development of 4PL firms. All the questions were in the Chinese language so the Russian respondents had to possess the Chinese reading skills to participate in the survey. The limitations of the study were determined by the following facts: first, the Chinese respondents generally have greater knowledge of the BRI and their influence is comparatively more significant; second, the language barrier prevented some of the potential Russian respondents from participating. To create the questionnaire and access the respondents we used the WIX¹ online survey platform. To ensure that the sample is large enough, we collaborated with professional companies for the distribution and collection of questionnaires. Ultimately, 152 questionnaires were filled out by representatives of the two countries’ e-commerce logistics companies.

After collecting all the necessary data and encoding them, we conducted a quantitative analysis. The primary method employed was MLR, which is a type of regression model that examines the relationship between one dependent variable and multiple independent variables. The MLR model assumes a linear relationship among the variables, enabling the simultaneous analysis of the impact of various independent variables on the dependent variable. This approach provides insights into complex relationships that simpler models may overlook, making it well-suited for the objectives of our study (Uyanik & Güler, 2013).

To enhance the robustness of our regression results, we also applied ANOVA. This method serves to test hypotheses allowing researchers to determine whether observed differences in the sample means are attributable to actual differences in the populations or merely the result of random sampling variability. In our study, ANOVA was employed to validate the findings from our regression analysis (Ahmand & Harison, 2015).

Prior to conducting MLR, we performed Exploratory Factor Analysis (EFA), which included assessments such as Cronbach’s Alpha, the Kaiser-Meyer-Olkin (KMO) Test, and Bartlett’s Test of Sphericity (BTS). For these analyses, we utilized SPSS analytical software as our primary tool.

To control for bias and validity of the results obtained, we asked the respondents about their professional qualities. The descriptive statistics allowed us to create a profile of a typical respondent. Most respondents are ordinary employees of the company, aged 31-50, with a bachelor’s degree or college education and a salary of 3001-6000 RMB. The majority of respondents have certain industry experience and about 50% of them are middle and senior managers who have full understanding of the industry. The collected data are consistent with our expectations and suitable for further analysis.

Most respondents work for medium or large logistics companies (see Figure 2) characterized by relatively high level of informatization that have major influence in the logistics industry and sufficient talent reserves (Latukha et al., 2023). We also addressed a considerable number of respondents from small and medium-sized companies. It should be noted that, because of the vast employee numbers in large companies, the possibility of several groups of samples from the same company cannot be ruled out. That is why samples from small and medium-sized companies also need to be considered.

Figure 2. 

Company Size

Having processed the data, we moved on to the analysis itself, beginning with EFA. We applied KMO test and BTS. These statistical tests, that are essential parts of EFA, are used primarily in factor analysis. They help assess the appropriateness of the relationships between variables. We observe that KMO statistic value is 0.789. A higher KMO value suggests that the variables share a high degree of common variance, which makes them suitable for factor analysis (Zhang et al., 2024). The BTS determines whether the correlation matrix of variables is an identity matrix, which would indicate that the variables are uncorrelated and unsuitable for factor analysis. The null hypothesis states that there are no significant correlations among the variables (Tobias & Carlson, 1969). In our cases the significant result is 0.000, which is less than 0.05, indicating that correlations exist in the data. It means that both tests support the appropriateness of proceeding with factor analysis.

Further, we apply the KMO and BTS to each analyzed factor (Table 2). The values of KMO range from 0.7 to 0.75 which is slightly less than overall consistency of data but allows to conclude that factors have good consistency when taken separately. BTS significance result of all the indicators is 0.000, which is less than 0.05, indicating that the reliability of the research data is high, and the next step can be taken.

Having prepared the data and ensured their reliability and consistency, we proceeded to the quantitative analysis in the form of linear regression and ANOVA modelling.

Given the multifaceted nature of the capabilities that companies need to ensure international success of 4PL model, we codified the data into factors to test their influence on the development of 4P logistics in Sino-Russian companies that participate in BRI. To take account of multiple factors, we decided to use multiple linear regression that included 13 factors.

The theoretical formula for our regression

y_i = β₀ + β₁x_i1 + β₂x_i2 + β₃x_i3… + β₁₃x_i13 + ∈

where y_i is the predicted value of the dependent variable, β₀ is the y-intercept (constant), β_i are the coefficients for each independent variable and x_ij are the independent variables themselves. ∈ represents the residuals (Uyanik & Güler, 2013).

In accordance with the aim of our study, we selected the development level of 4PL as dependent variable in our model. We assessed the model using the least squares estimation method to acquire the coefficient weights and saw that several factors related to logistics significantly influence the dependent variable, with strong evidence supporting their effects (as indicated by p-values). Table 3 features the key indicators of the model that point to its properties.

The significant F variation indicates a statistically meaningful relationship between at least one independent variable and the dependent variable at conventional significance levels (e.g., p < 0.05). This suggests that the model is statistically significant.

According to the Adjusted R², the predictors account for approximately 52% of the variance in the dependent variable, demonstrating a relatively strong explanatory power. The F-statistic value of 13.651 further confirms the significant relationship between the predictors and the response variable.

Overall, this regression analysis suggests a strong relationship between the predictors and the dependent variable, with moderate explanatory power and statistical significance indicating that the model is likely to capture the meaningful patterns of data.

Next, we used ANOVA model to verify these results (Table 4). P-value of 0.0 indicates that the results are statistically significant, which means that there are no differences between the group means. Also, the calculated regression sum of squares is significantly larger than the residual sum which suggests that the model explains a substantial amount of variability in the outcome variable. Overall, ANOVA results suggest that regression model is statistically significant and effectively explains variability in the dependent variable.

Next, we analyzed the distribution of the regression residuals. From the histogram (Figure 3) we can see that the distribution of regression residuals is normal. Its mean comprises -4.69E-15, standard deviation makes 0.956.

Figure 3. 

Sample distribution

The individual analysis of the variables helped identify five insignificant variables, the p-values of which are greater than 0.05 confidence level (Table 5). These are Logistics Service Scope, Logistics Professionals, Supply Chain Resource Integration Capabilities, Resource Development Capabilities, Customer Recognition and Trust. These results will be interpreted and discussed in the next part of the paper.