The effect of blockchain technology on international trade in South Africa

1 Introduction

Global interest in blockchain technology is expanding owing to its potential importance. According to Siddik et al. (2021), the term blockchain technology was pioneered by Haber and Stornetta (1991), and later popularised in the work of Nakamoto (2008). Blockchain technology can be described as a decentralised ledger comprising information of all transactions within node-to-node techniques (Nakamoto, 2008). Likewise, blockchain technology is a decentralised databank powered by the decentralised ledger concept that keeps track of the condition of electronic assets. In addition, Civelek and Özalp (2018) define it as a bill of lading and digital file that enables international trade. Blockchain technology plays an imperative role in worldwide business and is one of the various emerging smart technologies. The technology came into the spotlight as the technology supporting Bitcoin (Zheng et al., 2018).

Blockchain technology has grown in popularity since Bitcoin was invented as its first application. Blockchain-based applications contain the utilisation of cryptocurrency such as Bitcoin in the financial industry (Kutowaroo et al., 2023). Although Bitcoin is the most well-recognised blockchain application, blockchain can be utilised for a variety of reasons other than cryptocurrency. For instance, blockchain can be utilised in a variety of financial services such as online payments, remittance and digital assets, since it does not rely on any middleman or bank (Siddik et al., 2021). Moreover, blockchain technology is becoming more widely recognised as a highly favourable technological innovation for the next wave of internet interaction platforms. Given the prospective of blockchain technology, administrations, banks and regulators altogether have started adopting it to enable international trade.

The application of blockchain technology in international trade enables the formation, storage and management of information in flexible approaches, while guaranteeing the full security of these processes (Valeria et al., 2022). It is argued that blockchain is one of the most impactful and fast-growing tools for international trade. This argument is based on the view that the blockchain is used to control assets, prevent fraud, build trust, reduce paperwork, facilitate payments, avoid counterfeiting and form cost-effective systems for business (Böhme et al., 2015; Civelek and Özalp, 2018; Hooper and Holtbrügge, 2020). In addition, utilising blockchain technology for international trade transactions may eradicate or reduce the likelihood of tampering with commodities and businesses files (Derindag et al., 2020). Valeria et al. (2022) contend that while blockchain technology has significant potential, it remains insufficient to be regarded as a solution for complex issues faced by developing nations. Nonetheless, developing nations in Africa have adopted blockchain technology, particularly in the financial sector. The growing interest in blockchain technology, such as cryptocurrencies, is particularly noticeable in Sub-Saharan Africa, especially in nations such as South Africa, Nigeria, Ghana and Kenya (Distor et al., 2023).

According to Thegeya (2023), the leading innovators in blockchain technology in Africa are South Africa, Nigeria and Kenya, constituting about 80% of blockchain technology. In these nations, the key areas of innovation are in insurance and finance, telecommunications and the health sector. While blockchain technology has offered African nations benefits, the technology still faces some challenges. For instance, limited technology skills, insufficient technology infrastructure, and high costs have hampered African nations from completely capitalising on the economic benefits brought by technology (Thegeya, 2023; Yoon, 2020). Consequently, African entrepreneurs are faced with challenges because of the aforementioned limitations. Despite these limitations, blockchain technology has a great deal of potential for African entrepreneurs since it can shape how industries do business and network with larger customers and suppliers across the world.

To fully comprehend the complexities of blockchain technology, several studies have been conducted on its application in international trade. For instance, the following studies have primarily used a systematic review or conceptual approach (Belu, 2019; Civelek and Özalp, 2018; Derindag et al., 2020; Hooper and Holtbrügge, 2020). In addition, Valeria et al. (2022) have used mixed methods to investigate the influence of blockchain technology on business and international trade. A few studies have primarily focused on empirical analysis to give global perspectives (Kutowaroo et al., 2023; Siddik et al., 2021). To date, little attention has been paid to the influence of blockchain technology on international trade at the country-specific level. A country-specific study is important because institutional and economic conditions differ across countries, making inappropriate to universally generalise findings from one context to another. Therefore, this study contributes to existing literature by empirically examining the effects of blockchain technology on international trade in South Africa, thereby providing a country-specific analysis. Noteworthy, previous studies have overlooked the asymmetric effect of blockchain technology on international trade. As a result, this study also looks at the possible nonlinear effect of blockchain technology on international trade in South Africa. This study focuses on South Africa since it is one of the leading innovators in blockchain technology in Africa. This study is important since its findings aid policymakers, administrators and business leaders to make informed decisions concerning blockchain technology. In addition, this study adds to existing literature related to international trade and blockchain technology. As a result, this benefits the academic community and future researchers.

This paper is organised as follows. Section 2 explores the theoretical and empirical literature related to international trade and blockchain. Section 3 describes the data sources and methodology. Section 4 presents the results and analysis, while Section 5 concludes with key findings and policy recommendation.

2 Literature review

The core aim of this section is to explore literature on the link between trade and blockchain technology. The theoretical framework and empirical literature are covered in this section. The theoretical framework presents the theoretical notion that explains the link between trade and blockchain technology. Empirical literature provides findings from existing research on trade and blockchain technology.

2.1 Theoretical framework

The link between blockchain technology and trade can mainly be described using several economic theories, but for the purpose of this study, only the transaction cost theory and technology acceptance model are discussed. In addition, the traditional international trade model and blockchain model are provided in this section.

2.1.1 Transaction cost theory

The transaction cost theory was initially proposed by Coase (1937) and further advanced by Williamson (1975). This theory describes how economic players organise their transactions and try to minimise the costs involved in these exchanges. According to Williamson (1987), this theory regards the transaction as the essential unit of measure and concentrates on how much work, resources or cost are required for two parties to realise trade. Sarkis et al. (2011) describe transaction costs as the costs incurred in enabling the trade of goods and services between two parties, outside the actual price of goods and services. The aim is to increase transaction performance and reduce cost.

The transaction cost theory can be beneficial in studying the implementation of blockchain technology in international trade. Blockchain technology can benefit online transactions by reducing costs, saving time, improving processing efficiency, and increasing transparency etc., (Torres de Oliveira, 2017). Noteworthy, blockchain technology can offer the efficiency required to enable online trade by lowering cost and offering valuable information and guaranteeing transparency for all entities involved.

2.1.2 Technology acceptance model (TAM)

The technology acceptance model was established by Davis (1986) to clarify how users come to accept and utilise a technology. The TAM offers two aspects that are significant contributing factors of users’ acceptance: (i) perceived usefulness and (ii) perceived ease of use. The TAM is based on the concept that the more users perceive that a certain application would improve their performance and the less work it takes to utilise, the greater the adoption rate will be. The model has been updated to include elements such as social influence and cognitive instrumental processes to improve its analytical abilities (Venkatesh and Davis, 2000). It is believed that social influence can shape users’ intention to accept technology. The TAM can be applied to comprehend how new technologies are employed in business. Building on the concept of TAM, Murungu (2025) argues that industries are more likely to accept new technology if they identify it as beneficial to their operations, user-oriented, and valuable for both them and their businesses.

2.1.3 Traditional international trade model and blockchain model

Figure 1 shows the traditional international trade model and blockchain model. In the traditional model, it can be observed that the initial step entails the establishment of business order between exporter and importer and is followed by initiating related business activities with banks. This process involves paperwork flow, cashflow and logistics flow. Documents such quotations move between exporter and importer, then the letter of credit and bill of lading handled by banks circulate between exporter and importer. This process is significantly limited by la ack of transparency, paper heavy processes, high costs and the need for time-consuming authentication of cash, goods and information flows (Chang et al., 2019; Ganne, 2018). Nonetheless, this is not an issue in the blockchain model, as it can be observed that the process in this model involves a permissioned distributed ledger. This ledger is accessible only to authorised parties, guaranteeing security. The distributed ledger allows peer-to-peer transaction and provides transparency to all parties involved and information can be accessed in real-time. Moreover, blockchain allows exporters and importers to record their business transactions on the platform, resulting in less paperwork and increased transparency. Blockchain technology also uses smart contracts, which offer guarantee of payment and automatic payment when conditions determined between all parties are fulfilled. This also reduces costs and increases speed.

2.2 Empirical literature

This sub-section presents a review of existing studies that are relevant to the link between international trade and blockchain technology. Civelek and Özalp (2018), using a conceptual approach, have analysed blockchain challenges for paperless international trade. The study highlighted that the persistent use of paper-based methods shows the need for significant and complete reforms in the foreign market. Therefore, to completely eradicate paper-based methods, it is critical to form shared digital platforms that link all parties involved in trade transactions. To facilitate this, it requires legal integration between countries in areas such as trade regulation and electronic signatures. The study concluded that blockchain technology has the potential to enable this integration.

Belu (2019) explored the possible benefits of the implementation of blockchain technology in foreign trade using the systematic review approach. The study showed that blockchain technology in logistics can lead to developing more useful and reliable processes, thereby improving customers’ confidence and providing a more trustworthy operational environment for suppliers and carriers. Moreover, blockchain technology can lead to lower transaction costs. Within the context of embracing blockchain technology, Belu (2019) suggests that all participants involved in the opening, performance and controlling of trade transactions must collectively develop a procedure encompassing the new system in the programme. Simultaneously, the governing framework regulating these transactions must be implemented.

Derindag et al.(2020) explored the application of blockchain technology in foreign trade using qualitative methods by reviewing existing literature. The study uncovered that blockchain technology plays an important part in foreign trade with several benefits, such as easing trade processes, transaction record keeping and efficiency in the supply chain. However, a few issues such as legal, regulatory and policy concerns, have been noted. The study concluded that these issues should be addressed immediately for blockchain technology to benefit international trade. Likewise, Hooper and Holtbrügge (2020) highlight how the implementation of blockchain technology in international trade can enhance customer transparency and reduce transaction costs. However, blockchain technology brings challenges for global governance, and therefore, it is critical to establish future governance strategies for blockchain technology within the context of international trade.

Using a mixed method, Valeria et al. (2022) investigated the effects of blockchain technology on financial business and international trade using data spanning from 2016 to 2020. The study discovered that blockchain technology has great potential to improve financial business and international trade due to the possibility of enhancing the settlement of foreign trade, the application of smart contracts in the implementation of the transaction, enhancing the supply chain, and lessening the velocity of information and distribution mechanisms. Moreover, blockchain technology is expected to have a significant influence on the protection of intellectual property, especially by mitigating piracy and counterfeiting.

Evans (2019) explored the link between blockchain technology and financial markets for the United States (US) and China. An analysis was carried out for the period spanning 2008 to 2016 using the Toda-Yamamoto causality and fully modified least square techniques. The study discovered that blockchain technology positively influences financial markets in both nations, implying that the introduction of blockchain technology in financial markets stimulates financial growth.

Siddik et al. (2021) investigated the influence of blockchain technology on foreign trade for the rest of the world using time series data from 2009 to 2018. An analysis was conducted using the generalised linear model (GLM). The study revealed that blockchain technology positively influences international trade, suggesting that blockchain technology enables foreign trade. Similarly, Kutowaroo et al. (2023), using the GLM focusing on the rest of the world for period from 2012 to 2021, investigated the nexus between blockchain technology and foreign trade. The results show that blockchain technology plays a major beneficial role in promoting international trade.

Tuccaroglu et al. (2025) explored the need for blockchain technology in international trade in the island of Cyprus. The study employed qualitative research approach with the use of semi-structured interview questions. The study revealed that smart contracts have the potential to enhance trade between untrusted parties by offering a secure and transparent platform for executing and verifying transactions. In support, Adeoye et al. (2025) noted the potential of blockchain in minimising costs, accelerating transaction speed, and improving trust between global trade partners. Table 1 provides a summary of the literature reviewed.

TABLE 1. Summary of selected literature review.

Overall, the reviewed studies have demonstrated the role of blockchain technology in international trade, with the majority of studies confirming that blockchain technology has several benefits in international trade. The empirical studies have also shown that blockchain technology has a positive influence on international trade. However, the substantial body of existing literature has focused on theoretical/conceptual aspects, with limited literature on the data-driven empirical analysis. Moreover, empirical studies reviewed in this study have mostly focused on the rest of the world (Kutowaroo et al., 2023; Siddik et al., 2021). Therefore, there is limited empirical evidence demonstrating the influence of blockchain technology on international trade at the country-specific level. Therefore, this paper fills in the gap by empirically examining the influence of blockchain on international trade in South Africa. In addition, this study also examines the nonlinear effect of blockchain technology on international trade in South Africa. The following section describes the methodology.

3 Methodology

The key reason for this section is to describe the methodology and data sources used in this study. The methodology guides the authors on how to perform econometric analyses to explore the impact of blockchain technology on international trade in South Africa.

This study follows the model specification by Kutowaroo et al. (2023), whose study investigated how blockchain technology influences international trade. Kutowaroo et al. (2023) model specification is as follows: Equation 1 shows that international trade is a function of blockchain technology, gross domestic product, population growth, inflation, business freedom, taxation index and foreign direct investment. Where IT, BT, GDP, POP, INF, BF, TAX, and FDI denote international trade, blockchain technology, gross domestic product, population growth, inflation, business freedom, taxation index and foreign direct investment, respectively. Moreover, β represents the coefficient, t is at time and μ signifies the residual error term. Business freedom and population growth were excluded from this study to due to unavailability of quarterly data and to avoid the issue of multicollinearity as this study introduces new variable to the model, i.e. the exchange rate as additional explanatory variable. The exchange rate is included in this study as it plays an important role in an open economy like South Africa, where traditional trade exchange rates affect international trade. After excluding business freedom and population growth, as well as adding the exchange rate, the model becomes as follows:

I T t = β 0 + β 1 B T t + β 2 G D P t + β 3 INF t + β 4 P O P t + β 5 F D I t + B 6 T A X t + B 7 B F t + μ (1)

I T t = β 0 + β 1 B T t + β 2 INF t + β 3 T A X t + B 4 G D P t + B 5 E X t + B 6 F D I t + μ (2)

Equation 2 demonstrates that international trade is modelled as a function of blockchain technology, inflation, taxation, gross domestic product, exchange rate and foreign direct investment. The purpose of this study is to investigate the effects of blockchain technology on international trade. Therefore, international trade is the dependent variable in the model; it is quantified as a ratio of the sum of exports and imports to GDP. Moreover, international trade represents the exchange of goods and services across countries. As a result, many scholars have used the ratio of the sum of exports and imports to GDP as a proxy for international trade (Kutowaroo et al., 2023; Siddik et al., 2021). As a result, the authors recognise international trade as the ratio of the sum of exports and imports to GDP. Furthermore, Bitcoin (transaction volume) is used as a proxy for blockchain technology infrastructure. According to Crosby et al. (2016), Bitcoin is the most common example that is integrally associated with blockchain technology. In addition, it is used as a measure for blockchain technology in the literature (Evans, 2019; Siddik et al., 2021). Moreover, the connection between Bitcoin and blockchain technology stem from the belief that Bitcoin was the first practical application blockchain technology. As a result, Bitcoin is frequently utilised as a proxy for blockchain acceptance and usage because increased Bitcoin activity reflects greater engagement with blockchain-based system. Existing blockchain studies predominately focuses on cryptocurrencies such as Bitcoin (Akgiray, 2019). Nonetheless, cryptocurrencies only represent one of many applications enabled by blockchain technology. Equation 2, in this study, is grounded in the transaction cost theory, which describes how economic players organise their transactions and try to minimise the costs involved in these exchanges. Therefore, the blockchain technology can boost international trade transactions by reducing costs, saving time, improving processing efficiency, increased transparency etc. As a result, this increases international trade volume.

To eliminate the problem of heteroskedasticity, Equation 2 is transformed into logarithmic form. As a result, Equation 2 with natural logarithm is as follows:

L I T t = β 0 + β 1 L B T t + β 2 L I N F t + β 3 L T A X t + β 4 L G D P t + β 5 L E X t + β 6 L F D I t + μ (3)

Equation 3 denotes the fundamental model to be computed in logarithms. The study employed Bitcoin transaction volume because Bitcoin is built on blockchain technology and represents its widely adopted application. The use of blockchain technology in international trade minimises costs between trade partners (Adeoye et al., 2025). Consistent with the transaction cost theory which on how economic agents try to minimise cost in transaction exchanges. Moreover, rising Bitcoin use indicates growth acceptance of blockchain technology among organisations and individuals. In with the Technology Acceptance Model, organisations and individuals adopt new technologies when they perceive them to be valuable and beneficial.

The study employed a number of control variables in the model. Inflation is evaluated, which is described as a continuous rise the general price level. It is measured using consumer price index. Classical theories underline that inflation undermines purchasing power and weaken trade by inflating import costs and impeding export competitiveness. Kutowaroo et al. (2023) noted that inflation is unfavourable to international trade. Gross Domestic Product (GDP) is utilised to measure the size of economic activities in terms of transactions. According to the gravity model of trade, GDP is among the determinants of trade and the volume trade is proportional related to GDP of the country (Lypko, 2022).

The exchange rate can be labelled as the price of one nation’s currency in relation to another, measuring how much of one unit of currency can be traded for another. Marshall-Lerner condition asserts that variations in the exchange rate are the only factors that influence changes in a country’s imports and exports of a country (Osei-Gyebi, 2021). Taxation (Tax) serves as an indicator of the tax burden levied on by relevant authorities on economic participants. Higher tax rates adversely affect international trade by increasing the cost of conducting internation trade (Kutowaroo et al., 2023). Foreign Direct Investments (FDI) is viewed as the movement of capital investments across national borders. According to Ky (2025) FDI contributes to improved trade activity. Table 2 provides descriptions and sources of variables of this study.

TABLE 2. Description and sources of variables.

This study used quarterly time series data for South Africa for the period spanning from 2013Q1 to 2024Q4. However, Bitcoin data was original recorded monthly, then converted to quarterly intervals to align with data for other variables. This process is done using EViews 13 software. All data for other variables was in quarterly intervals. The main reason for the adopted timeframe is the availability of Bitcoin data as a proxy for blockchain technology. Bitcoin was launched and adopted between 2009 and 2013; therefore, this study is limited to the adopted timeframe.

This study investigates the impact of blockchain technology on international trade in South Africa. With the application of time series data to examine the relationship between the variables, the auto regressive distributed lags (ARDL) technique is employed. The ARDL technique was established by Pesaran and Pesaran (1997), Pesaran and Shin (1995), and Pesaran et al. (2001). It has several merits over other techniques as such vector autoregression (VAR), which requires all the variables to be integrated in identical order. Moreover, it works well with small sample size. ARDL allows variables to take an integration order of I (0), I (1) or both. The ARDL model estimated in this study is specified as follows:where Δ represents first the differencing operator, p is the lag length for unrestricted error correction model, β is the long-run coefficients, a is a constant, ∅ signifies the short-run coefficients and μ t is the residual error term. Since this study also seeks to discover the nonlinear effects of blockchain technology on international trade, Equation 4 is adjusted as it represents the linear ARDL model. The adjustments use Shin, Yu and Greenwood-Nimmo’s (2014) nonlinear ARDL framework. Within the context of this framework, the blockchain technology must be decomposed into two series variables; that is, BTPOS and BTNEG, which represent an increase and decline in blockchain technology, respectively. This is accomplished using the partial sum technique, as follows: Equations 5, 6 show the partial sum of positive and negative changes in blockchain technology, respectively. Where the BTPOS and BTNEG variables are the partial sum of positive and negative changes in blockchain technology. As a result, the nonlinear ARDL model is specified as follows:

Δ L I T t = a 0 + ∑ i = 1 p ∅ 1 Δ L I T t − i + ∑ i = 1 p ∅ 2 L B T t − i + I ∑ i = 1 p ∅ 3 L I N F t − i + ∑ i = 1 p ∅ 4 L T A X t − i + ∑ i = 1 p ∅ 5 L G D P t − i + ∑ i = 1 p ∅ 6 L E X t − i + ∑ i = 1 p ∅ 7 L F D I t − i + β 1 L I T t − 1 + β 2 L B T t − 1 + β 3 L INF t − 1 + β 4 L T A X t − 1 + β 5 L G D P t − 1 + β 6 L E X t − 1 + β 7 L F D I t − 1 + μ t (4)

L B T t = ∑ n = 1 t Δ B L T P O S t = ∑ n = 1 t ⁡ max ⁡ Δ L B T P O S t , 0 (5)

L B T t = ∑ n = 1 t Δ L B T N E G t = ∑ n = 1 t ⁡ min ⁡ Δ L B T N E G t , 0 (6)

Δ L I T t = a 0 + ∑ i = 1 p ∅ 1 Δ L I T t − i + ∑ i = 1 p ∅ 2 L B T P O S t − i + ∑ i = 1 p ∅ 3 L B T N E G t − i + ∑ i = 1 p ∅ 4 L INF t − i + ∑ i = 1 p ∅ 5 L T A X t − i + ∑ i = 1 p ∅ 6 L G D P t − i + ∑ i = 1 p ∅ 7 L E X t − i + ∑ i = 1 p ∅ 8 L F D I t − i + β 1 L I T t − 1 + β 2 L B T P O S t − 1 + β 3 L B T N E G t − 1 + β 4 L INF t − 1 + β 5 L T A X t − 1 + β 6 L G D P t − 1 + β 7 L E X t − 1 + β 8 F D I t − 1 + μ t (7)

The descriptive statistics are conducted first to provide an exhibition on the features of the data. Following that, the correlation analysis is carried out to examine the side of correlation the variables share. To avoid misleading inferences, the unit root tests such as the augmented Dickey-Fuller (ADF) and Phillip-Perron (PP) are conducted to check for the existence of unit roots in time series data. Following the confirmation of order of integration of the variables using unit root test, the optimal lag length test is conducted and the bounds cointegration test is run to assess the existence of the long-run equilibrium between the variables. Thereafter, the ARDL and nonlinear ARDL models are computed and the Wald test estimated to confirm the nonlinear effects. Lastly, the model stability, residual diagnostic and Granger causality tests are conducted. The following section provides an analysis of the results and interpretation.

4 Results analysis and interpretation

This section presents the computed results and interpretation. The results are computed by running several tests such as descriptive statistics, correlation analysis, unit root tests, cointegration and diagnostic tests.

4.1 Descriptive statistics

Descriptive statistics are computed as they give fundamental features of data and a basic summary of the sample and measures. The descriptive statistics enable the visualisation of data. Therefore, Table 3 provides the results of descriptive statistics.

TABLE 3. Descriptive statistics result.

* denotes statistical significance at 10%. Source: Author’s calculation using EViews 13.

The results given in Table 3 display that the mean of LIT, LBT, LTAX, LINF, LEX, LGDP and LFDI are 5.8347, 22.5199, 2.3259, 1.6125, 2.6523, 0.1597, and 6.7623, respectively. Moreover, the standard deviation demonstrates the flexibility in the data. As shown in Table 2, the most flexible variable is LBT with a value of 1.8030, followed by LGDP (1.0722), LTAX (0.8538), LFDI (0.7718), LIT (0.2891), LINF (0.2655), while LEX is the least flexible variable, with a value of 0.1951. It is also important to study the normality assumption; the results illustrated by the Jarque-Bera statistics show that LIT is statistically significant at 10% level. Therefore, it can be concluded that LIT is not normally disturbed. However, the rest of the variables are normally distributed.

4.2 Correlation analysis

The correlation analysis provides the nature of the relationships that the variables under consideration share. According to Wegner (2013), correlation analysis establishes the magnitude of linear relationships between two variables. Pesaran and Pesaran (1997) highlight that the correlation coefficient between two explanatory variables is regarded high if it is ±0.80, indicating the existence of multicollinearity. Therefore, estimating a regression with such variables may lead to spurious estimates. Table 4 presents the correlation coefficients.

TABLE 4. Correlation coefficients.

Source: Author’s calculation using EViews, 13.

Table 4 shows that LBT has a weak positive relationship with LTAX and LGDP, while it has a weak negative relationship with LINF, and FDI. However, it has a moderate positive relation with LEX. LGDP have a weak negative correlation with LFDI, LEX, LINF while it has a positive weak relationship with LBT and LTAX. EX shares a weak relationship with LINF, LTAX, LGDP and LFDI, while it shares a moderate positive relationship LBT. Nonetheless, the computed correlation coefficients between the explanatory variables are less than ±0.80, demonstrating a lack of multicollinearity between the explanatory variables considered in this study. Given that, the unit root tests are applied to check the stationarity conditions of the variables.

4.3 Unit root test

This section provides the results of unit root tests. In this study, two-unit root tests, namely the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) are conducted. The use of two tests is aimed at obtaining a robust result. Essentially, the unit root test is conducted to establish the order of integration of a time series. Therefore, Table 5 presents ADF and PP unit root test results.

TABLE 5. ADF and PP unit root test results.

*/ denotes statistical significance at 1% and 5%. Source: Author’s calculation using EViews 13.

As exhibited in Table 5, both the PP and ADF tests produced similar results. The results computed reveal that LIT, LBT, LINF and LEX are integrated of order one (I(1)), indicating that LIT, LBT, LINF and LEX are stationary at 1st difference. In addition, LTAX, LGDP and LFDI are found to be integrated of order zero (I(0)), indicating that these variables are stationary at 1st difference. The order of integration of the variables considered in this study is in line with the prerequisite of the ARDL model, which requires stationarity at level, 1st difference or the combination of both. The following step is to compute optimal lag length results.

4.4 Optimal lag length

In estimating the ARDL model, selecting the ideal lag length is important. As a result, each variable in the model requires to be allocated optimal lag. Table 6 presents the optimal lag length results. In this study, the Akaike Information Criterion is employed to determine the optimal lag length for the variables under consideration using the automatic model selection with a maximum of 4 lag through Eviews 12 version. The automatic model selection is used since it derives the most efficient model as far as minimising the residual error is concerned. Hence, Table 6 present optimal lag length results for ARDL model.

TABLE 6. Optimal lag length.

Source: Author’s calculation using EViews, 13.

As exhibited in Table 6, the optimal lag length for LIT and LTAX is 1. While the optimal lag length for LBT and LEX is 4. However, the lag length for LGDP and LINF. Therefore, this gives an incentive for the estimation of ARDL (1,4,4,2,3,3,1) model. The following step is to present the cointegration test results.

4.5 Cointegration test

This section presents the cointegration test results. Since the time series reveals integration of order zero and one, the bounds test is conducted. Bhatta et al. (2020) highlighted that bound cointegration tests may be employed whether time series are I(0) and/or I(1), whereas the Johansen cointegration test necessitates that time series have similar orders of integration. Therefore, Table 7 presents bound test results.

TABLE 7. Bounds cointegration test results.

*/ denotes statistical significance at 1% and 5%. Source: Author’s calculation using EViews 13.

Table 7 exhibits that the ARDL and NARDL F-statistics (4.3835 and 8.2671) surpass the upper-bound statistics, implying that the variables under consideration are cointegrated at the 1% and 5% level of significance. In other words, this suggests that there is a long-term relationship between the variables under consideration. After confirming the presence of long-term relationship between the variables under consideration, the next step is to compute the ARDL and NARDL model estimates.

4.6 ARDL and NARDL

To establish the ARDL long- and short-run coefficients of the variables under consideration, the model presented in Equation 4 was computed, and the findings are presented in Table 8.

TABLE 8. ARDL (1,4,4,2,3,3,1) coefficients.

*//* denotes statistical significance at 1, 5 and 10%, respectively. Source: Author’s calculation using EViews 13.

Table 8 reflects the ARDL long- and short-run coefficients. The findings show that blockchain technology has a positive and significant impact on international trade in South Africa in the long run. This infers that a 1% increase in blockchain technology would result in a 0.0471% increase in international, ceteris paribus. The economic implication associated with these results is that blockchain technology has a significant influence on international trade in South Africa, implying that blockchain technology is effective in facilitating international trade transactions in South Africa. Accordingly, the application of blockchain technology in the international trade process reduces tax, trade barriers, and paperwork enabling international trade. These findings are in line with those of Kutowaroo et al. (2023), Siddik et al. (2021), and Valeria et al. (2022); their studies revealed that blockchain technology has a significant positive influence on international trade. In addition, these findings align with the transaction cost theory, which suggests that economic players organise their transactions and try to minimise the costs involved in these exchanges. In other words, blockchain technology benefits international trade by reducing transactions costs and taxes, thereby accelerating international trade. The short-run results also report a positive and significant impact of blockchain technology on international trade. The main purpose of this study was to discover the impact of blockchain technology on international trade in South Africa. The results established that blockchain technology has a significant and positive impact on international trade in South Africa.

The findings also report that the exchange rate has a significant and positive influence on international trade in South Africa. An increase in the exchange rate represents a depreciation of the local currency. As a result, a 1% increase in the exchange rate would lead to a 0.5299% increase in international trade in the long run, ceteris paribus. The economic implication associated with these results is that the exchange rate has a significant impact on international trade, suggesting that the exchange rate promotes international trade. Changes in the exchange rate influence buyer and supplier countries to modify goods and services prices, thereby influencing the volume of international trade. These findings align with the Marshall-Lerner condition theory, which highlights that an increase in the exchange rate increases international trade. However, the short-run results revealed a negative and insignificant impact of the exchange rate on international trade, implying that the exchange rate exerts little influence on international trade in the short run.

In addition, the long-run coefficients reveal that foreign direct investment has a positive significant impact on international trade in South Africa. That is, a 1% increase in foreign direct investment would lead to a 0.0585% increase in international trade, ceteris paribus. The economic implication presented by this finding is that greater amount of foreign direct investment flows leads to increases in international trade volumes. This could be associated with the view that investment increases production and demand, which stimulate trade volume. This finding was also reported by Kutowaroo et al. (2023) and Siddik et al. (2021). The short-run result reveals a statistically insignificant coefficient of FDI, implying that FDI hardly encourages international trade in the short run in South Africa.

The findings also demonstrate that GDP has a significant and beneficial impact on international trade in the long run. This infers that a 1% increase in GDP would result in a 0.0448% increase in international trade, ceteris paribus. The implication is that higher GDP increases local income, which boosts consumers and firms’ demand for goods and services in international trade. This finding was also reported by Kutowaroo et al. (2023) and Siddik et al. (2021). However, in the short run, it was found that GDP has negative and statistically significant impact on international trade in South Africa.

Surprisingly, inflation was found to have positive and statistically insignificant relationship with international trade in the long run. This implies that a 1% increase in inflation would hardly lead to 0.0127% increase in international trade in the long run. This finding disagrees with that of Kutowaroo et al. (2023) and Siddik et al. (2021). Interestingly, the impact of tax was found to be insignificant. This is surprising as tax on international trade is known to have an influence on international trade. For instance, high tax on international trade increases transactions costs, thereby influencing international trade volume.

The computed error correction term (ECT) is −0.2459 and is statistically significant. This suggests that 24.59% of imbalance in the model is adjusted in each quarter, and therefore the model alters slowly to reach equilibrium state. The computed ARDL model’s goodness of fit is assessed using adjusted R-squared coefficient, which is 0.7157, implying that 71.57% of variation in international trade in South Africa is jointly explained by the variation in the explanatory variables considered in this study. The adjusted R-squared is moderately high with the significant F-statistics, implying that the selected explanatory variables provide meaningful statistical inferences over the period under consideration.

Examining the asymmetric (nonlinear) relationship is important, as symmetric (linear) approach may mask the asymmetries and fail to capture the differential effects of increase and decrease in blockchain technology. As a result, to establish the NARDL long- and short-run coefficients of the variables under consideration, the model presented in Equation 7 was computed and the findings are presented in Table 9.

TABLE 9. NARDL (1,3,4,3,0,4,3,0) coefficients.

*//* denotes statistical significance at 1, 5 and 10%, respectively.

Source: Author’s calculation using EViews 13.

The results presented in Table 9 report that a negative shock in blockchain technology has a negative and statically insignificant impact on international trade in the long run. This implies that negative changes in blockchain would hardly lead to 0.0291% decline in international trade. However, a positive shock in blockchain technology has a positive and statistically significant impact on international trade in the long run. This implies that positive changes in blockchain technology would lead to 0.0409% increase in international. In the short run, both negative and positive shocks in blockchain were found to have a positive effect, but only positive shock is statistically significant. Nonetheless, the Wald test results establish that there is asymmetric effect of these shocks in both long run. This implies that the effects of blockchain technology on international trade in South Africa are asymmetric (nonlinear) in the long run.

GDP, exchange rate and inflation were found to have a positive relationship international trade in the long run but negative in the short run. These findings are consistent with those reported in the linear model in terms of positive and negative impact in the long run and short run, respectively. Tax was found to have negative and statistically significant impact on international trade in the long run. While FDI was found to have insignificant impact. These findings of tax and FDI are inconsistent with those reported in the linear model due differing lag length selected by AIC automatic selection system.

The computed ECT NARDL model is −0.9706and statistically significant. This suggests that 97.06% of imbalance in the model is adjusted in each quarter, and therefore the model alters rapidly to reach an equilibrium state. The computed ARDL model’s goodness of fit is assessed using the adjusted R-squared coefficient, which is 0.8168, implying that 81.68% of variation in international trade in South Africa is jointly explained by the variation in the explanatory variables considered in this study. The R-squared is high with the significant F-statistics, implying that the selected explanatory variables provide meaningful statistical inferences over the period under consideration.

4.7 Diagnostic tests

Table 10 illustrates that the probability value for the Ramsey reset specification test for both models is above the significance level of 5%, implying that we fail to reject the null hypothesis which states that there is no misspecification. Therefore, it can be concluded that the estimated ARDL and NARDL models are perfectly specified. In addition, the probability of normality tests for both models are above the significance level of 5%, implying that we fail to reject the null hypothesis, which states that the residuals are normally distributed. As a result, it can be concluded that the estimated residuals are normally distributed. Moreover, the probabilities of serial correlation and heteroskedasticity tests for both models are above the significance level of 5%, implying that we fail to reject the null hypothesis of no serial correlation and homoskedasticity. However, the residuals from NARDL model suffers from serial correlation. Figures 2, 3 show the CUSUM and CUSUM square to evaluate the consistency of computed ARDL and NARDL model stability, respectively.

TABLE 10. Model stability and residual diagnostic tests.

** denotes statistical significance at 5%. Source: Author’s calculation using EViews 13.

Figures 2, 3 demonstrate that both models are stable/consistent at the 5% level of significance; all the CUSUM lines are within the 5% bound. However, the CUSUM of squares from both models demonstrate some instabilities between some periods as indicated by blue line drifting from 5% critical bound line. Nonetheless, both models were able to regain stability as evidenced by blue line drifting back within 5% critical bound line. Noteworthy, all the diagnostic tests are passed, granting for dependable economic inferences.

4.8 Granger causality test

The Granger causality test is conducted to assess the causality statues of the explanatory variables and international trade. The results of Granger causality test are presented in Table 11.

TABLE 11. Granger causality test.

*/ denotes statistical significance at 1% and 5%. Source: Author’s calculation using EViews 13.

The results reveal that there is no causality between international trade and blockchain technology. There is unidirectional causality between international trade and foreign direct investment, coming from international trade to foreign direct investment. Moreover, there is unidirectional causality between international trade and exchange rate, coming from international trade to exchange rate. This implies that changes in international trade can influence future fluctuations in exchange rate foreign direct investment. The unidirectional causality was observed between international trade and inflation, coming from inflation to international trade. This implies that changes in inflation influence changes in international trade. However, no causality was reported for exchange rate and international trade, GDP and international trade, as well as tax and international trade.

5 Conclusion

This study aims to empirically examine the impact of blockchain technology on international trade in South Africa using the ARDL and NARDL approaches. The study is guided by literature on the selection of economic variables such as international trade, blockchain technology, GDP, exchange rate, tax, FDI and inflation. Moreover, the findings of this study provide an imperative contribution to the existing literature on the relationship between blockchain technology and international trade. The major contribution of this study was to provide a data-driven empirical analysis on the impact of blockchain technology on international trade in South Africa.

The results found in this study indicate that blockchain technology has a positive and significant impact on international trade in South Africa. This implies that blockchain technology plays an important role in influencing the level of international trade in South Africa. In addition, the study found nonlinear effect of blockchain technology on international trade in both the long run. This implies that the effects of blockchain technology are nonlinear in the long run.

The study found that exchange rate, FDI and GDP have a positive and significant impact on international trade. This implies that these variables have a beneficial impact on international trade flows in South Africa. Unexpectedly, this study revealed that tax and inflation have an insignificant positive impact on international trade in South Africa. This implies that taxation and inflation in South Africa is incapable of influencing international trade flows.

6 Implication of the study

This study recommends that policymakers should promote the utilisation of blockchain technology such as cryptocurrency for international trade transactions. This could be attained by creating regulations and investing in blockchain infrastructure. Creating a supportive regulatory policy and investment in blockchain infrastructure could provide a secure, transparent and efficient trade environment that encourages global economic integration and reduces hurdles for all participants. The implementation of supportive regulatory policies and investment in blockchain infrastructure could improve the level of South African international trade. Small companies in South Africa could benefit from applying blockchain technology in their business operations, which may lead to greater participation in the international market.

Moreover, this study recommends that policymakers should implement policies that are conducive to FDI, exchange rate and economic growth, policies to encourage trade openness, macroeconomic and exchange rate stability. These policies will boost economic growth, FDI and stabilise exchange rate, leading to increased international trade volume.

This study made every attempt to ensure that its discoveries were accurate and reliable. However, this study only used cryptocurrency, namely Bitcoin as a proxy for blockchain technology which represent only one of many applications enabled blockchain technology, and therefore future studies are recommended to look at other blockchain technology factors and use different estimation techniques to validate the findings of existing literature. Moreover, this study focused on the impact of blockchain technology on international trade. Future studies could expand this area by investigating the impact of blockchain technology on export and import, respectively.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. During the preparation of this manuscript/study, the author used ChatGPT (GPT-4-turbo) for the purposes of improving spelling, grammar, language, and clarity. The author have reviewed and edited the output and take full responsibility for the content of this publication.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Tong Wang, Hubei University of Economics, China

Reviewed by: Mohd Naved Khan, Saudi Electronic University, Saudi Arabia

Miftakul Huda, Pelita Bangsa University, Indonesia