Introduction

In the contemporary global landscape, profound transformations are reshaping the fabric of international trade and economics. A notable trend in this era is the burgeoning skepticism towards globalization, compelling nations to reassess the intricate interplay between their international trade strategies and domestic economic imperatives. This evolving stance has precipitated increased trade tensions and barriers, leading to significant disruptions in global value chains (GVCs) [1]. This is evidenced by a steady decline in GVCs participation since 2012, according to World Bank data [2], reflecting challenges to the efficacy and advancement of value chain processes.

Central to understanding these global shifts is the interplay between GVCs and domestic value chains (DVCs) [3]. DVCs, encompassing the distribution of labor and the exchange of goods within a country [4, 5], are pivotal for a country’s internal economic dynamics. The stability of DVCs referring to their capacity to adapt and maintain efficiency despite external disruptions, is now more important than ever in fostering higher stability in global trade, particularly considering the diverse impacts on countries at different stages of development. The impact of globalization, digital trade, and the RCDF on GVCs and DVCs can vary considerably among countries with varying levels of economic structure and technological advancement. For example, developing countries may face unique challenges in integrating into GVCs [6], while developed countries may be more focused on technological innovation and digitalization. Understanding these nuances is crucial for the formulation of targeted policies that address the specific needs and constraints of countries at different stages of development.

This study situates these developments within a broader theoretical framework, tracing the evolution of the value chain concept from its inception [7] to the contemporary notions of the “global commodity chain” [8] and “global value chain”[9]. Our focus extends from key factors affecting the position of GVCs, such as factor endowment [10–12], financial development [13], and institutional stability [14], to innovation and RCDF, which underline the increasing complexity and interconnectedness in global trade dynamics, especially under the influence of digitalization.

We explore the context of the burgeoning trade in digital trade and trade in digital services, collectively referred to as the exchange of goods and services through digital means [15], as well as those provided via digital platforms and technologies [16]. This exploration is crucial as it sheds light on how data factor flows—a key component of digital trade—affect the dynamics of the value chain. The boom of digital services in international trade is not only a trend, but also a transformative force [17, 18], making RCDF a critical factor in affecting the stability of DVCs [15].

In response to these global economic shifts, numerous nations are recalibrating their trade strategies, with a particular emphasis on bolstering their DVCs to mitigate risks associated with participation in GVCs. The intricate interplay between RCDF and the stability of DVCs significantly impacts the progression of digital trade, subsequently influencing the dynamics of GVCs [19]. This strategic recalibration necessitates a critical examination of cross-border data flow restrictions. Central to this inquiry are pivotal questions: Does limiting these data flows enhance the stability of DVCs, or does it, in contrast, hinder their integration with GVCs? Furthermore, assessing whether the effects of data flow restrictions on the stability of DVCs are consistent across economies at different developmental stages is crucial. Considering the significant repercussions of these issues on both regional collaboration and the broader spectrum of global trade dynamics, an in-depth and nuanced exploration is indeed imperative.

This paper sets out to comprehensively address the complexities and dynamics of digital trade policies, aiming to contribute significantly both in theoretical frameworks and practical applications.

• Theoretically, central to this study is the thesis that prioritizing “stability” over “efficiency” in value chain research is essential. The study endeavors to elucidate the principles underpinning shifts in digital services trade policies and their effects on DVCs stability, particularly highlighting the role of innovation. It introduces a novel perspective by examining how RCDF influences the robustness and resilience of DVCs, employing a methodology that utilizes each country’s input-output table—a technique not extensively explored in previous literature.
• Practically, the study offers insightful policy recommendations for countries to effectively leverage industrial digitalization and maximize digital trade benefits. It aims to lay a foundational basis for broader international economic and trade cooperation, potentially contributing to a “re-globalization” movement.
• Empirically, employing a panel data fixed effect model, the study probes the effects of RCDF on the operational dynamics of DVCs. This includes analyzing mechanisms from an innovation perspective and providing a reference for establishing diverse and stable international economic and trade relations. The study also examines the differentiated consequences of RCDF on countries at various income levels, aiming to provide insights that could support and enhance international economic and trade cooperation.

Ultimately, the research aims to empower nations with essential insights for effectively navigating the digital era. It fosters global collaboration across various sectors, contributing to the development of stable international economic and trade relations, especially in the context of advancing “anti-globalization”.

Literature review

The ongoing digital transformation of the global economy, a key feature of the Fourth Industrial Revolution as described by Schwab (2017) [20], has significant implications for international trade and global commerce [21–23]. Vial (2019) indicates the importance of digital transformation as a process in which the use of digital technologies causes disruptions, leading organizations to respond strategically by changing their methods of creating value [23]. ElMassah and Mohieldin (2020) highlight this transformation through the lens of localizing sustainable development goals, emphasizing the digitization of production and business processes as a pivotal aspect [21]. This shift is leading to a reconfiguration of international trade, characterized by the migration of many operations to the digital realm and a trend towards localized trade due to distributed manufacturing and decentralized value chains [1]. Jouanjean et al. (2019) delves into the effects of digitalization on the agriculture and food sectors, demonstrating how it influences the distribution among value chain participants and reduces trade and transaction costs [24]. While these developments mark significant progress, they are not without challenges. Aaronson (2023) introduces a critical perspective, pointing out the regulatory challenges presented by this digital revolution. Particularly, governments are imposing restrictions on global data flows, undermining the economic benefits of digital trade [25]. This juxtaposition of advancement and regulation underscores the complex nature of the digital transformation in the global economy.

Focusing on digital trade barriers, recent studies emphasize their unique challenges to global commerce [26–28]. Meltzer (2019) explores the governance aspects of digital trade, emphasizing the complex nature of these emerging barriers [29]. Potluri et al. (2020) discuss the effects of data localization on digital trade, arguing that such barriers often protect established firms at the expense of consumers [28]. Jiang et al. (2022) further provide empirical evidence on how digital trade barriers, specifically in the context of China, impact export performance by constraining service inputs and hindering foreign direct investment (FDI) entry [26]. In the broader context of GVCs, the impact of trade barriers is a critical area of investigation [16, 30–32]. Hufbauer et al. (2013) study the impact of trade barriers on the participation of exporting countries, highlighting that these barriers hinder the smooth functioning of the value chain [30]. Biryukova and Vorobjeva (2017) examine the impact of service liberalization on the participation of BRICS countries in GVCs, revealing that service trade restrictions significantly hinder this process [32]. Contrary to the expectation that trade restrictions lead to GVCs’ shrinkage or disruption, Gereffi et al. (2021) posit that the unintended consequences of trade policies, both restrictive and liberal, are exacerbated by the complexity and prevalence of GVCs [31]. Wang et al.(2023) further demonstrate empirically that digital trade rules significantly influence forward GVCs [16].

Further extending the discussion to RCDF, their far-reaching effects on GVCs are explored in recent studies. Studies such as those by Ferracane et al. (2018) have explored the extensive ramifications of RCDF on GVCs [33]. These studies predominantly focus on how such restrictions influence international trade patterns, alter global production and service distribution within GVCs, and affect market entry strategies and innovation capacities across different economies. Lastly, Gereffi et al. (2021) discuss the effects of anti-globalization policies, including various barriers, on value-added activities in GVCs. They argue that these policies can disrupt GVCs by restricting the flow of knowledge, thereby affecting the overall dynamics of GVCs [31].

The preceding discussion reveals two primary research gaps. Firstly, there is a noticeable absence of studies that specifically address the impact of RCDF on the stability of DVCs across various countries. Given the pivotal role of DVCs in an increasingly anti-globalizations economy, particularly for low-income countries, it is imperative to explore whether the effects of RCDF mirror those of traditional barriers and if these impacts are consistent across different economic contexts. Furthermore, it is equally important, yet less explored, how RCDF affect DVCs, considering their intricate interconnections with GVCs. This aspect is crucial for understanding the full spectrum of the digital trade barriers’ impact. Secondly, while the current body of research has provided valuable insights into the effects of digital trade barriers at a bilateral or regional level, there is a significant gap in understanding these dynamics from a more global perspective, encompassing a diverse range of countries and industries. Unraveling the nuances of how RCDF influences DVCs on a global scale, especially in terms of value-added production, is crucial for comprehensively understanding the broader implications of digital trade barriers.

Theoretical analysis

RCDF, conceptualized as trade costs, directly impede the free movement of digital factors, which are integral to the efficiency of GVCs [34, 35]. These barriers engender a rise in operational costs for businesses, exemplified by increased search costs for trade intermediaries, heightened transaction costs in production’s division, and the added burden of navigating bidirectional regulatory compliance​ [36, 37], The ripple effect of these increased costs may prompt industries to consider domestic relocation, thereby disrupting the established synergies between domestic and foreign digital factors within GVCs, which in turn jeopardizes the stability of DVCs.

The ramifications of RCDF are multifaceted. They not only inflate operational costs and disturb existing interdependencies but also provoke potential industry relocations within GVCs [38]. This disturbance diminishes market share and the capacity for rapid adaptation in dynamic markets, further constraining decision-making agility and the dynamics of relationships—both of which are indispensable for maintaining the viability and strategic flexibility of service systems [39].

Moreover, the role of digital technologies as specialized factors in production division is integral to both production efficiency and management acumen [40, 41], When RCDF interrupt the digital empowerment of DVCs, the synergy and collaboration between upstream and downstream enterprises deteriorate. This breakdown leads to a compromised supply of intermediate goods and a consequent dip in DVCs stability [33, 42, 43]. Such an inhibition of technological innovation sends shockwaves throughout the system [44], indirectly crippling a country’s capacity for innovation and, as a result, the enhancement of processes within DVCs. The domino effect continues as this leads to a diminished production and service capacity within enterprises [33, 45], eroding the stability of intermediate goods supply. Furthermore, RCDF thwart the progression of DVCs into sectors that yield higher value-added products [46, 47], thereby diluting inter-industry production linkages [48] and further destabilizing DVCs.

Based on this nuanced analysis, the paper firmly posits the following hypothesis:

1. Hypothesis 1: RCDF inhibit the stability of DVCs.
2. Hypothesis 2: The impact of RCDF on DVCs stability is mediated by their stifling effect on technological innovation, which is essential for the continuous improvement and value addition within these chains.

According to the Hicks Theorem, which posits that technology improvements in industries where a country lacks a comparative advantage lead to welfare reductions in foreign nations [49], advancements in digital technology have, however, narrowed global labor cost disparities [50]. A critical factor influencing firms’ participation in DVCs in international markets is the economic disparity between home and host countries. Nations at varying economic levels exhibit significant differences in resource quality and cost [51], impacting their roles within DVCs. impacting their roles within DVCs. Developed economies, such as the United States, Japan, and Germany, with their advanced digital technologies, provide digital intermediates and services globally [52]. This positions them at the higher-value segments of DVCs, such as design, innovation, and analytics. In contrast, low-income countries, constrained by their lesser digital capabilities and infrastructure, often participate in lower-value segments, such as assembly or basic data processing.

On one hand, changes in factor endowments, spurred by digital service trade, can transform value chain logic [53], allowing affluent nations to enhance their trade terms and facilitate DVCs’ upgrading. Paradoxically, RCDF enforced by these nations may hinder the enhancement of their own DVCs stability. Conversely, these countries could use their digital dominance to expand the digital divide, erecting developmental barriers for other nations [54]. Meanwhile, low-income countries, grappling with the impacts on both traditional and digital sectors, may find increasing RCDF instrumental in protecting their comparative advantages and stabilizing their DVCs. This observation leads to Hypothesis 3:

1. Hypothesis 3: The impact of RCDF on DVCs varies among countries (regions) with different income levels.

In summary, RCDF obstruct transaction efficiency, impede the formation and integration of DVCs, and hinder the transition from traditional to innovative industry dynamics within GVCs. These limitations directly erode the confidence and willingness of traders to export, disrupt the acquisition of intermediate goods in deeply involved industries, and challenge DVCs stability. Additionally, by curtailing technological innovation within value chains and escalating trade costs, RCDF adversely affect intermediate goods supply levels, further compromising DVCs stability. Consequently, the paper’s theoretical analysis framework, refer to Fig 1, emerges from these findings.

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Fig 1. Theoretical analysis framework.

https://doi.org/10.1371/journal.pone.0308716.g001

Material and methods

Data sources

This study utilizes a comprehensive dataset spanning from 2014 to 2020, drawing from multiple databases and sources. The WIOD-SEA database provided the sub-industry employment data for 2014, while data from 2015 onwards was segmented and processed based on the input-output share. The total labor force data was sourced from the WDI database.

Various other databases were instrumental in gathering specific information. The UIBE GVCs database and the ADB input-output database were used to derive data on industry value added, final product value added, industry production consumption added, and backward average length of production. The OECD database supplied the digital cross-border flow restriction index and the industry FDI regulation index. The SPI database contributed the employment economic development index and the digital product index. To measure the level of trade openness and financial openness, data was extracted from the KOF database and the World Bank. These databases provided detailed information on these specific variables. For further details on these variables, refer to Table 1.

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Table 1. Variable description.

https://doi.org/10.1371/journal.pone.0308716.t001

Descriptive statistics

Table 2 shows the descriptive statistics of all variables including sample size, mean, variance and maximum and minimum values.

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Table 2. Descriptive statistics.

https://doi.org/10.1371/journal.pone.0308716.t002

As depicted in Table 2, it can be seen that the mean value of the explanatory variables is 3.464, the maximum value is 39.777, the minimum value is -32.919, and the standard deviation is 2.770, which is not much different from the mean value, indicating that there is no over-dispersion after taking the logarithm. The core explanatory variable has a standard deviation of 1.681 and a mean of -2.132, indicating that the distribution of RCDF vary greatly across countries. This may lead to a repositioning of firms’ production activities to avoid the efficiency impacts of production and trade caused by different barriers [62], which in turn leads to the segmentation of the value chain.

Results

Benchmark regression analysis

In Table 3, the baseline regression results (M1) and the post-shrinkage results (M2) illustrate the effect of RCDF on the stability of DVCs. In M1, the negative coefficient of RCDF at -0.0385, significant at the 1% level, indicates a detrimental impact on DVCs stability. This finding is aligned with Hypothesis 1, suggesting that RCDF may act as a barrier at the interface of GVCs and DVCs. It appears to disrupt the stable supply of intermediate products and the cohesion of production linkages within the value chain, ultimately impeding the enhancement of the DVCs

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Table 3. Regression results.

https://doi.org/10.1371/journal.pone.0308716.t003

This interpretation of RCDF’s impact is more profound when contrasted with earlier studies. For instance, Meltzer (2015) observed a more moderate impact of RCDF on international trade [47], indicating a potential evolution in the nature and effect of RCDF over time. This evolution could be attributed to the increasing complexity and integration of digital factors in global trade.

Regarding the coefficients of other continuous variables, the negative coefficient of industry FDI regulation in M1 (-0.0163) at a significant level suggests that more relaxed FDI regulations are conducive to creating value chains with higher value-added, likely due to enhanced technological spillovers and capital accumulation from FDI. This observation is consistent with the findings of Martínez-Galán and Fontoura (2015), which also underscored the positive influence of liberal FDI policies on value chain enhancement [63].

Economic development (lnED), showing a positive and significant coefficient (0.624) in M1, reaffirms that higher economic development is a crucial factor in augmenting DVCs stability. Furthermore, the positive coefficients for trade openness (lnTO) (1.978) and financial openness (lnFO) (2.339) underscore the importance of global integration and diverse capital supply in enhancing DVCs production levels, echoing the conclusions of Gereffi (2015) on the benefits of globalization and financial liberalization for value chain division [64].

Interestingly, the negative coefficient of digital technology development (lnDTD) suggests a counterintuitive impact on DVCs stability. This may imply that while digital technologies offer numerous advantages, their rapid development and integration might lead to a restructuring of value chains that temporarily destabilizes existing setups. This finding echoes Kehoe and Mateer (2015), which concluded that digital technology negatively affects the distribution segment of an industry-specific value chain [65, 66], indicating a more nuanced relationship that warrants further investigation.

In the segmented analysis for high-income (M3) and low-income (M4) countries, the contrasting coefficients of RCDF reveal distinct impacts based on economic status. In high-income regions, the negative coefficient of RCDF (-0.0358) at the 1% significant level suggests that RCDF significantly suppresses the stability of DVCs. This could be attributed to the advanced digital infrastructures in these regions, where RCDF may hinder the optimal utilization of digital technologies in value chains, thus reducing labor’s relative contribution and possibly leading to a decrease in labor productivity. This finding supports from the assertions of studies like Meltzer (2019), who observed that RCDF undermined the economic benefits of digital trade [29].

Contrastingly, in low-income regions, the positive coefficient of RCDF (0.678) at the 5% significant level presents a different scenario. The increase in RCDF appears to contribute positively to the stability of DVCs. This can be interpreted as RCDF acting as a protective measure for domestic industries, particularly in economies where digital infrastructure is less developed. By limiting the influx of advanced digital services and products, RCDF allows local industries more time to adapt and integrate into the evolving digital global economy. This finding diverges from the assertions of studies like Dong et al. (2020), which hypothesized a negative impact of RCDF on the competitiveness of digital trade in all economies [67].

This nuanced understanding of RCDF’s impact is crucial, as it highlights the divergent pathways through which digital trade policies affect economies at different stages of development. In high-income countries, where digital services and technologies are deeply embedded in the value chain, restrictions can disrupt established patterns and impede efficiency. However, in low-income countries, where the integration of digital technologies is still in nascent stages, these restrictions can provide a buffer, allowing for a more gradual integration into the global digital economy. The implications of these findings are significant, particularly in the context of policy formulation. For policymakers in high-income countries, there may be a need to reassess the approach towards digital trade barriers, ensuring that they do not stifle the potential benefits of digital integration in value chains. Conversely, in low-income countries, a cautious approach towards rapidly lifting such barriers might be advisable, to allow for the organic development of local industries and their eventual integration into the global digital economy. This comparative analysis suggests a paradigm shift in the understanding of RCDF’s role in global trade, challenging the one-size-fits-all approach and advocating for a more nuanced, context-specific understanding of trade policies.

Robustness tests

Shrinkage procedure.

In this study, we’ve applied a 1% tail trimming to all variables, from both ends, to mitigate the impact of outliers. The outcomes of the regression are presented in Table 3. M2 represents a regression conducted for the stability of DVCs. The regression coefficient of RCDF is -0.0302, the findings from the regression analysis indicate that the RCDF coefficient remains significantly negative, thus affirming the robustness of our benchmark regression.

Addressing endogeneity.

To tackle the endogeneity bias that arises from issues like omitted variables and reverse causality, we’ve employed the instrumental variable technique. This helps to counter potential endogeneity problems within the econometric model. We’ve chosen the count of e-commerce and digital trade rules within all preferential trade agreements (PTAs) signed by each country as our instrumental variable. Drawing from existing studies, this instrumental variable was chosen because countries (or regions) operating under identical trade rules tend to have more aligned digital trade policies. Furthermore, the more agreements containing digital trade rules a country signs, the higher its level of digital trade openness and the lower its RCDF level, satisfying the relevance assumption. However, the presence of digital trade clauses in the signed PTAs does not affect the stability of DVCs, which satisfies the exogeneity assumption. We’ve constructed the instrumental variable in a specific manner to enhance its effectiveness.

(5)

In Eq (5), IV_θjt denotes the RCDF instrumental variable for country θ, while DTR_θjt represents the count of PTAs that country θ has signed, which include digital trade rules. The data is derived from the statistics on specific agreement terms of PTAs as reported in the TAPED database [68] and the official RTA database of the WTO.

Table 4 displays the outcomes of the endogeneity treatment post tail trimming. The Kleibergen-Paap rk LM statistics stand at 233.799 and 241.421 respectively, and are significant at the 1% level, thereby rejecting the null hypothesis of insufficient identification of the instrumental variable. The Cragg-Donald Wald F statistic surpasses the critical value at the 10% level, confirming the absence of a weak instrumental variable. Hence, we can conclude that the instrumental variable is effective. The regression results reveal that the RCDF coefficient is significantly negative, suggesting that even after employing the instrumental variable to address the endogeneity issue, RCDF continues to significantly suppress the stability of DVCs. This is in line with the findings from the benchmark regression.

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Table 4. Endogenous treatment: instrumental variables approach.

https://doi.org/10.1371/journal.pone.0308716.t004

As the results in Table 4 show, the coefficient of the RCDF for high-income countries in M6 is -0.503, which is significantly negative and consistent with the results of the heterogeneity test. This result proves that after dealing with endogeneity, the test results for high-income countries remain robust.

Mechanism testing

This paper constructs a mediation model with innovation capacity as the mechanism variable to examine the pathways through which digital services trade affects domestic value chain quality. Following the steps proposed by [69] to test mediation effects, the models are set as follows: (6) (7) (8) where, Innov_θit is the mediating variable representing country i’s innovation level in year t. The proxy variable is from the Global Innovation Index (GII). Since the index contains 7 sub-indices, the 6th and 7th sub-indices on knowledge and technology outputs (Tech) and creative outputs (Crea) directly measure a country’s technological innovation. Thus, this paper uses the ratio of knowledge, technology and creative outputs to total innovation outputs (Innout) as a proxy indicator of technological innovation level, calculated as: (9)

The regression analysis for Eqs (6), (7), and (8) is methodically explored, with the mediation effect model’s results detailed in Table 5. Specifically, in M7, the regression coefficient is -0.00375, which is statistically significant at the 1% level. This significant negative coefficient indicates that digital service trade barriers have a considerable inhibitory effect on innovation capacity. Moving to M8, the mediating variable’s coefficient stands at 0.219, which is also significant but in a positive direction. This suggests that RCDF negatively impact the stability of DVCs by curbing innovation, echoing the effects observed with localization trade barriers in traditional trade contexts [70].

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Table 5. Mechanism test: mediating effect.

https://doi.org/10.1371/journal.pone.0308716.t005

The coherence in the signs of these coefficients is noteworthy. It implies that technological innovation acts as a partial mediator between RCDF and DVCs, thus lending support to Hypothesis 3. The implementation of RCDF within a country can obstruct the domestic spillover of new technologies, thereby suppressing domestic innovation. This suppression extends to various facets of innovation within the value chain, including product, process, technological, and service innovation. Consequently, this leads to a negative impact on the production efficiency and the value enhancement of intermediate goods. Such an outcome counteracts the potential improvements in DVCs stability that could otherwise be realized through enhanced innovation capacity.

This study also aims to dissect the mechanism of action of RCDF in high-income versus low-income countries. In M9, which focuses on high-income countries, the regression coefficients for β1 and δ2 of -0.0352 and 0.337, respectively, and both are significant at the 1% level. These findings indicate that an increase in RCDF in high-income countries suppresses domestic innovation capacity and therefore affects the stability of DVCs. In contrast, when examining low-income countries, the mechanism of action appears to be insignificant. This suggests that the role of digital factors in facilitating technological innovation varies considerably depending on the country’s stage of technological development, as well as the extent of digital segregation and integration adopted by different countries.

For high-income countries, digital technologies can assist in building national systems of innovation and are a natural element in consolidating technological and innovation advantages within the country. Therefore, RCDF can hinder the healthy and stable development of their value chains. This aligns with the observations made by Baldwin and Tomiura (2020) [1]. Conversely, in low-income countries, the potential enabling role of digital technologies and their transmission are constrained by the limitations in innovation capacity. Thus, the impact of RCDF in these countries does not follow the same pattern as in their high-income counterparts.

Discussion

This scholarly investigation thoroughly explores the functional mechanism of RCDF on DVC stability and conducts a heterogeneity analysis across various income levels. It is discerned that RCDF generally reduces the stability of DVCs, thereby increasing industrial correlation risk. This finding extends the research scope of Saberi et al. (2019) [71] into digital technology’s role in supply chain management.

The income level is identified as a critical factor in the disparate impacts of digital trade regulatory policies. In affluent regions, RCDF notably suppresses DVC stability, whereas in less affluent areas, its impact is less pronounced. Interestingly, lower-income regions may use RCDF to protect domestic industries and data security, potentially enhancing efficiency within their value chains.

Mechanism analysis shows that RCDF hampers technological innovation. Wealthier nations, with advanced digital technologies, face greater challenges from RCDF, while less affluent countries need to bolster innovation to utilize digital technology effectively.

Despite the insights offered, this study acknowledges room for further enhancement, primarily due to constraints in data availability and associated index measurement methodologies. Firstly, this study has not managed to strike a balance between considerations of data security and the extent of digital trade openness. There is a lack of consensus on the measurement standards for data security, and this study hypothesizes that as the momentum of digital economy development quickens, the repercussions of digital trade openness will also amplify. Secondly, due to data accessibility limitations, RCDF has not been further dissected by industry. The analysis of industry heterogeneity is instrumental for a profound understanding of the operational mechanism of digital trade policies. As data accessibility improves, more comprehensive and expansive research perspectives will gain increasing significance, marking the future research trajectory of this study.

Conclusion and implication

Supporting information

Acknowledgments

The authors would like to acknowledge the valuable comments and suggestions from the Editor and anonymous reviewers, which greatly contributed to improving the quality of this paper. Of course, all remaining errors are our own responsibility. The authors also would like to acknowledge the support provided by the First Class Discipline Construction Project in Yunnan Province for ’Cross-border Digital Economy’, as well as the Yunnan Philosophy and Social Sciences Innovation Team for ’Cross-border Digital Economy for South and Southeast Asia’ for their valuable contributions and support.