The Impact of Digital Transformation on Economic Integration in ASEAN-6: Evidence from a Generalized Least Squares (GLS) Model

Abstract

This study analyzes the impact of digital transformation on the international economic integration of ASEAN-6 countries during the period of 2000–2023 using the Generalized Least Squares (GLS) estimation method. The findings indicate that factors such as fixed broadband subscriptions (FixB), fixed telephone subscriptions (FixT), and the value added from medium- and high-tech manufacturing (MHT) have a positive and statistically significant effect on trade openness (TO). Conversely, mobile cellular subscriptions (MB) and the percentage of individuals using the Internet (IU) exhibit a negative impact on economic integration, reflecting the uneven development of digital infrastructure across countries. Based on these results, the study suggests policy implications, including substantial investment in digital infrastructure, technological advancement in production, and improved accessibility to digital services to foster more effective economic integration. ASEAN-6 countries should adopt tailored development strategies that emphasize innovation and the development of a skilled digital workforce to enhance their competitiveness both regionally and globally.

1. Introduction

Digital transformation has emerged as a key driver of international economic integration, particularly in the ASEAN-6 region, which includes Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. While these countries exhibit varying levels of economic development, they are all prioritizing digital transformation to enhance competitiveness and strengthen their integration into the global economy.

As globalization deepens, digital transformation has become a fundamental factor in enhancing connectivity, reducing transaction costs, and expanding the market reach. For ASEAN-6 countries, digital transformation not only creates opportunities for economic growth but also enhances their capacity to integrate with major global economies. However, these countries also face significant challenges, including disparities in digital development, cybersecurity risks, and firms’ adaptability to technological changes. Therefore, investigating the relationship between digital transformation and international economic integration is essential to proposing effective and sustainable solutions to accelerate this process.

While existing studies have extensively explored the role of digital transformation in enhancing labor productivity, optimizing supply chains, and facilitating firms’ expansion into international markets (Baldwin, 2016), there remains a gap in understanding its direct impact on economic integration at the regional level. Specifically, prior research has not sufficiently addressed how digital transformation mitigates trade barriers, strengthens economic connectivity, and fosters deeper integration between ASEAN economies and global markets (Wysokińska, 2021).

This study seeks to bridge this gap by providing a comprehensive analysis of the role of digital transformation in accelerating the international economic integration of ASEAN-6 countries. While digital transformation presents significant opportunities for enhancing regional and global economic connectivity, challenges such as digital disparities, cybersecurity risks, and firms’ adaptability to technological changes persist. Unlike previous research that primarily examines digital transformation from a national or sectoral perspective, this study positions itself at the intersection of digitalization and economic integration, offering a novel contribution by mapping the mechanisms through which digital adoption influences regional connectivity.

By addressing this research gap, this study aims to analyze the impact of digital transformation on the international economic integration of ASEAN-6 countries, identifying both opportunities and challenges faced by the region. Additionally, the research provides policy recommendations to maximize the benefits of digital transformation, enhance national competitiveness, and promote regional and global economic integration.

2. Theoretical Basis

The relationship between digital transformation and international integration can be analyzed through the lenses of several key economic theories. First, according to the Global Value Chains (GVCs) theory proposed by Baldwin (2016), digital transformation plays a pivotal role in restructuring global production and trade models. Digital technologies reduce transaction costs, enhance connectivity among businesses, and enable more countries to participate in global production without requiring a traditional industrial base. Sundaram (2018) further emphasized that advancements in information technology have accelerated a new wave of globalization, expanding market access through digital trade and online services.

Moreover, based on Ricardo’s (1821) theory of comparative advantage, later extended in the modern context by Baldwin (2016), digital transformation is reshaping how nations compete in the global economy. Traditionally, comparative advantage was largely determined by natural resources and cheap labor. However, in the digital era, countries can leverage technology and innovation to gain a competitive edge, regardless of their natural endowments. Wysokińska (2021) highlighted that digital technology and high-tech industries not only enhance production efficiency but also create new competitive advantages, particularly in sectors such as artificial intelligence, software development, and e-commerce.

Additionally, Romer’s (1986) endogenous growth theory, as applied in the modern context by Wysokińska (2021), suggests that digital transformation not only boosts labor productivity but also drives innovation, ultimately fostering long-term economic growth. Economies that rapidly adopt digital technologies are likely to gain a competitive advantage in an increasingly interconnected world. Inshakova et al. (2020) pointed out that digitalization policies within the Eurasian Economic Union (EAEU) have generated positive spillover effects, leading to GDP growth and deeper regional integration.

From a financial perspective, theories of financial integration explain how digital transformation enhances cross-border capital flows. Obstfeld and Rogoff (1996) argue that reduced information asymmetry and improved financial technologies facilitate global investment mobility. Digital payment systems, blockchain-based transactions, and fintech solutions enable seamless international capital movement, fostering deeper economic integration. Lane and Milesi-Ferretti (2008) further suggest that financial openness, supported by digital innovations, enhances macroeconomic stability and resilience in emerging markets.

Additionally, the international diversification theory (Markowitz, 1952) sheds light on how digital transformation enables firms to expand into global markets with lower risks. By digitalizing business operations, companies can diversify revenue streams across multiple markets, reducing dependency on domestic demand fluctuations. Karimov et al. (2021) highlight that digitalization allows businesses to scale internationally through e-commerce and cloud-based services, thereby enhancing economic integration.

Despite its benefits, digital transformation also presents challenges related to economic security, especially as data and technological infrastructure become critical components of international trade. Karimov et al. (2021) argued that countries must develop strategies to protect their information systems and ensure digital economic security to mitigate risks in the digitalization era. This is particularly crucial as major technology powers, such as the U.S., China, and the EU, increasingly assert control over technological infrastructures to safeguard their economic and political interests.

Several studies have shown that digital transformation not only enhances productivity and optimizes supply chains but also facilitates businesses’ expansion into international markets through e-commerce and the digitalization of business operations (Baldwin, 2016). In the context of economic integration, digital transformation plays a vital role in lowering trade barriers and strengthening economic connectivity between ASEAN economies and the global market (Wysokińska, 2021).

Furthermore, the Communist Review (2022) identified digital transformation as a breakthrough mechanism that enables nations, particularly developing economies, to accelerate industrialization and modernization. The adoption of digital technologies enhances production efficiency and expands access to international markets via e-commerce platforms and digital services, thereby fostering economic integration.

A report by Economica Vietnam (2020) highlighted the rapid expansion of Vietnam’s digital economy, which was valued at approximately $12 billion in 2019 and is projected to reach $43 billion by 2025. This growth is driven by digital transformation, particularly in the e-commerce sector, which enables businesses to scale their market presence and integrate more deeply into global value chains.

Karimov et al. (2021) also discussed the role of digital transformation in enhancing national competitiveness, stressing the need for investments in technological infrastructure, workforce training, and policy improvements to support enterprises in leveraging digital opportunities for economic integration.

In summary, digital transformation has far-reaching implications for international integration by restructuring global value chains (Baldwin, 2016), redefining comparative advantage (Wysokińska, 2021), fostering endogenous economic growth (Romer, 1986; Inshakova et al., 2020), and introducing challenges related to economic security (Karimov et al., 2021). In this context, countries and businesses must adopt adaptive strategies to capitalize on digital opportunities while mitigating associated risks in the digital era.

3. Research Methods

The endogenous growth theory emphasizes the role of technology and innovation in driving economic growth, where digital transformation enhances productivity and national competitiveness through the development of information technology infrastructure, e-commerce, and production innovation. Additionally, the theory of comparative advantage suggests that countries can optimize their advantages by specializing based on available resources. Digital transformation enables ASEAN-6 countries to capitalize on their strengths in high-tech industries, e-commerce, and digital services, thereby improving their position in the global market.

Furthermore, the theory of international economic integration highlights that integration occurs at different levels, ranging from free trade areas to economic unions. In this context, digital transformation plays a crucial role in reducing trade barriers, strengthening supply chain connectivity, and facilitating cross-border e-commerce transactions. Moreover, transaction cost theory posits that reducing transaction costs fosters international trade and investment. Digital transformation supports this process by automating procedures, enabling electronic payments, and improving transparency in international trade.

Based on these theoretical foundations, this study proposes a model to assess the impact of digital transformation on the international economic integration of ASEAN-6. The degree of economic integration is measured by the trade-to-GDP ratio, while digital transformation is represented by indicators such as the proportion of Internet users, the number of mobile subscriptions, fixed broadband subscriptions, and the share of ICT goods in total trade. Additionally, the model considers macroeconomic factors, including inflation rate, population growth, fixed capital investment, as well as supporting infrastructure, such as urbanization rate and electricity access.

To validate the model, this study utilizes panel data from ASEAN-6 for the period of 2010–2023 and applies panel regression techniques, including the Fixed Effects Model (FEM) and the Random Effects Model (REM), while employing the Hausman test to determine the most appropriate model.

Therefore, the research model has the following form:

gTO = f(INF, INV, POP, FixB, FixT, MB, IU, MHT, ICTE, ICTI, Electric, Urban)

With the baseline regression equation:

TO_t = β₀ + β₁INF_t + β₂INV_t + β₃POP_t + β₄FixB_t + β₅FixT_t + β₆ × MB_t + β₇ × IU_t
+ β₈ × MHT_t + β₉ × ICTE_t + β₁₀ × ICTI_t + β₁₁ × Electric_t + β₁₂ × Urban_t

The analytical model assessing the impact of digital transformation on international economic integration in ASEAN-6 countries is represented through the dependent variable TO, which denotes the total annual trade value of the region from 2000 to 2023 as a percentage of GDP. This model examines the influence of digital infrastructure, technology, macroeconomic conditions, and demographic factors on the level of economic integration among ASEAN-6 nations.

Economic integration in ASEAN-6 refers to strengthening economic linkages among member countries to promote trade liberalization, investment flows, and policy coordination, ultimately aiming for a unified market and regional supply chains. This concept is grounded in international economic theories, particularly the stages of economic integration, which outline progressive levels from preferential trade agreements to full economic and monetary union (Balassa, 2013). ASEAN-6 aligns with the new regionalism framework (Hettne, 1999), emphasizing deeper economic and institutional cooperation beyond mere tariff reductions. The integration process is reflected in agreements such as the ASEAN Trade in Goods Agreement (ATIGA), which facilitates trade liberalization, and the ASEAN Comprehensive Investment Agreement (ACIA), which promotes intra-regional investments (ASEAN, Secretariat, 2015).

Financial integration, a crucial aspect of economic integration, represents the interconnectedness of financial markets, cross-border capital flows, and monetary and financial policy coordination within the region. Theoretical foundations for financial integration include the optimal currency area theory, which highlights the benefits of coordinated financial policies (Mundell, 1961), and the financial liberalization hypothesis, which underscores the role of capital mobility in economic development (McKinnon, 2010).

In this study, trade openness (TO), measured as the ratio of total trade (exports plus imports) to GDP, is selected as the primary indicator of economic integration. This choice is justified by several factors. First, trade is the cornerstone of ASEAN-6 economic integration, as regional economies heavily rely on exports and imports to drive growth and enhance cross-border economic linkages. Second, TO effectively captures a country’s degree of participation in the global and regional economy, reflecting its openness to trade policies and economic cooperation (Frankel & Romer, 2017). Third, TO is a widely used, standardized metric with consistent data availability, ensuring comparability across countries and over time (Seetanah & Matadeen, 2012). While TO may not fully encompass financial and institutional dimensions of integration, it remains the most appropriate measure within the scope of this research, given the central role of trade in ASEAN-6’s economic development.

Additionally, TO is widely recognized as a key indicator of a country’s economic integration, reflecting the extent of trade engagement with global markets. The most commonly used measure of TO is the trade-to-GDP ratio, calculated as the sum of exports and imports divided by GDP, which provides a straightforward assessment of trade intensity relative to economic size (Sachs et al., 1995; Frankel & Romer, 2017). Alternative measures include tariff and non-tariff barriers (Edwards, 1998) or composite indices (Squalli & Wilson, 2011). Still, due to data limitations, the trade-to-GDP ratio remains the most widely adopted approach. Empirical studies support variable TO as a reliable proxy for economic integration, as higher trade openness is associated with increased capital inflows, technology transfer, and participation in global supply chains (Dollar & Kraay, 2004; Balassa, 2013). Given this theoretical and empirical foundation, this study adopts the trade-to-GDP ratio as the primary measure of TO to analyze the impact of digital transformation on economic integration in ASEAN-6.

First and foremost, telecommunication infrastructure and digital connectivity play a crucial role in facilitating international trade. FixB represents the number of fixed broadband subscriptions per 100 people per year, reflecting access to high-speed Internet—an essential factor in e-commerce transactions and the digital economy. Meanwhile, FixT denotes the number of fixed telephone subscriptions, which, despite declining over time due to the rise of mobile technology, still retains a certain role in traditional commercial activities. On the other hand, MB measures the number of mobile subscriptions per 100 people, capturing the extent of access to mobile services that drive cross-border trade through digital platforms. Simultaneously, IU represents the percentage of the population using the Internet, serving as a critical indicator of information connectivity and online transactions across countries.

Beyond telecommunication infrastructure, technological capabilities and industrial production significantly influence international economic integration. MHT measures the value-added contribution of medium- and high-tech manufacturing industries, reflecting the region’s technological competitiveness. The trade of information and communication technology (ICT) products is another key indicator, with ICTE representing the share of ICT goods exports in total exports, while ICTI indicates the proportion of ICT goods imports, illustrating the region’s reliance on foreign technology.

In addition to technological factors, socio-economic variables also shape ASEAN-6’s trade integration. Urban measures the percentage of the urban population, indicating the concentration of economic activities in major cities, which serve as hubs for trade and production. Electric quantifies the percentage of the population with access to electricity, reflecting the development of essential infrastructure that supports digital transformation and economic activities. Meanwhile, INF captures the annual inflation rate, which affects transaction costs and overall economic stability. INV represents the total investment-to-GDP ratio, indicating the level of investment in infrastructure and digital technology, thereby influencing trade integration. Lastly, POP measures the population growth rate, which plays a crucial role in expanding the labor market and domestic consumption, contributing to the region’s international trade.

Thus, this model provides a comprehensive framework for evaluating the impact of digital transformation on ASEAN-6’s international economic integration over the past two decades. Key factors such as telecommunication infrastructure, industrial technology, urbanization, and investment play a pivotal role in fostering trade and enhancing the region’s competitiveness in the global market.

In this study, panel regression techniques are employed to analyze the impact of economic factors, with the following three primary models considered: Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM).

The Pooled OLS model assumes no individual-specific effects across the observed units, meaning that all countries or firms in the sample share a common regression equation. This approach does not account for unobserved heterogeneity across entities, which may result in biased estimates due to heteroskedasticity and autocorrelation issues. In contrast, the Fixed Effects Model (FEM) controls for individual-specific effects by incorporating entity-specific dummy variables, thereby eliminating unobserved confounding factors. However, FEM may lead to the loss of time-invariant variables in the analysis. Meanwhile, the Random Effects Model (REM) assumes that the differences across entities are random and uncorrelated with the independent variables, allowing for more efficient parameter estimation but requiring strong assumptions about the homogeneity of individual effects.

To determine the most appropriate model, several statistical tests are conducted. The F-test is employed to compare the Pooled OLS and FEM; a significant test result indicates that FEM is preferable, as it accounts for individual heterogeneity. Next, the Breusch–Pagan Lagrange Multiplier (LM) test assesses whether REM is a better alternative to the Pooled OLS. Finally, the Hausman test is performed to distinguish between FEM and REM; if individual effects are correlated with the independent variables, FEM is the preferred model. Conversely, if no correlation is detected, REM provides more efficient and consistent estimates.

Based on the outcomes of these tests, the most suitable model is selected to ensure the reliability and validity of the analysis. If the Hausman test favors FEM, this model is applied to control for country- or firm-specific effects. However, if the individual effects are found to be uncorrelated with the explanatory variables, REM is considered a more efficient approach for estimating the impact of economic factors in this study.

4. Empirical Results and Discussion

4.1. Descriptive Statistics of Variables in the Research Model

The descriptive statistics in

Table 1. Description of research variables and hypotheses.

Variable	Obs	Mean	Std. Dev.	Min	Max
TO	144	151.4903	103.8191	32.97218	437.3267
INF	144	3.613194	3.43579	−1.7	23.1
INV	144	26.26458	5.223568	15.7	39.6
POP	144	1.343056	0.9641586	−4.2	5.3
FixB	144	7.73349	8.574489	0.0011	28.5393
FixT	144	14.20361	12.36937	2.434818	49.71947
MB	144	101.3633	51.08577	0.9981615	181.767
IU	144	43.19688	29.55023	0.254248	97.6927
MHT	144	44.49372	16.2638	19.08404	83.72707
ICTE	144	25.47167	15.00655	2.66	54.97
ICTI	144	21.17208	11.84956	3.5	51.48
Electric	144	95.56736	6.107748	74.7	100
Urban	144	57.27944	22.96956	24.374	100

Source: Result from the Stata 14 software.

provide an overview of the degree of economic integration and the impact of digital transformation in ASEAN-6 from 2000 to 2023. The average level of economic integration (TO) reached 151.49% of GDP, though there was a significant disparity among countries, with Singapore exhibiting the highest level. Inflation (INF) fluctuated considerably, ranging from −1.7% to 23.1%, reflecting economic volatility. Investment (INV) averaged 26.26% of GDP, indicating the level of infrastructure and technological development. Population growth (POP) averaged 1.34%, though some countries experienced a population decline.

Digital infrastructure displayed substantial variations across the region. Fixed broadband subscriptions (FixB) averaged 7.73 per 100 people, while mobile subscriptions (MB) exceeded 100 per 100 people, reflecting the widespread use of multiple SIM cards. Internet usage (IU) reached 43.19% of the population, though there was significant divergence among countries. Indicators of industrial technology and trade, such as medium- and high-tech manufacturing (MHT) and ICT goods exports and imports (ICTE, ICTI), highlighted the region’s reliance on foreign technology.

Access to electricity (Electric) averaged 95.57%, and the urbanization rate (Urban) stood at 57.28%, reflecting the development of essential infrastructure. The findings suggest that digital transformation and technological infrastructure have played a significant role in shaping the economic integration of ASEAN-6.

4.2. Correlation Analysis

The correlation analysis in

Table 2. Correlation of variables in the model.

	TO	INF	INV	POP	FixB	FixT	MB	IU	MHT	ICTE	ICTI	Electric	Urban
TO	1.0
INF	−0.26	1.0
INV	−0.03	0.28	1.0
TO	0.22	0.11	−0.13
POP	0.22	0.11	−0.13	1.0
FixB	0.70	−0.29	0.00	−0.03	1.0
FixT	0.89	−0.22	−0.03	0.22	0.57	1.0
MB	0.21	−0.21	0.11	−0.16	0.66	0.20	1.0
IU	0.51	−0.35	−0.01	−0.05	0.81	0.46	0.77	1.0
MHT	0.85	−0.41	−0.28	0.17	0.71	0.83	0.30	0.53	1.0
ICTE	0.38	−0.35	−0.51	0.21	0.22	0.30	0.08	0.29	0.46	1.0
ICTI	0.38	−0.34	−0.52	0.26	0.19	0.31	−0.0	0.23	0.48	0.88	1.0
Electric	0.46	−0.26	0.34	−0.13	0.55	0.46	0.69	0.71	0.35	0.02	0.10	1.0
Urban	0.77	−0.35	−0.24	0.22	0.67	0.86	0.38	0.88	0.37	0.40	0.40	0.46	1.0

Source: Result from Stata 14 software.

indicates that digital transformation and telecommunication infrastructure play a crucial role in fostering the economic integration of ASEAN-6. Specifically, fixed broadband penetration (r = 0.70), Internet usage (r = 0.51), and high-tech manufacturing (r = 0.85) exhibit a positive impact on international trade. Additionally, urbanization (r = 0.77) and access to electricity (r = 0.46) contribute significantly to economic integration.

Conversely, inflation (r = −0.26) has a negative effect, while investment (r = −0.03) and population growth (r = 0.22) do not demonstrate significant influence. These findings underscore the importance of digital infrastructure and technological advancements in enhancing regional economic integration.

4.3. Estimation by Regression Model Using Least Squares Method (POOL OLS)

The regression model results in

Table 3. Estimation results by regression model using the Least Squares Method (POOL OLS).

TO	Coef.	Std. Err.	t	P > |t|	Beta
INF	0.3110046	0.8303733	0.37	0.709	0.0102924
INV	0.3653174	0.660097	0.55	0.581	0.0183806
POP	8.062563	2.664899	3.03	0.003	0.0748763
FixB	6.288771	0.6791511	9.26	0.000	0.5193938
FixT	4.817599	0.5220158	9.23	0.000	0.5739856
MB	−0.5949385	0.0860151	−6.92	0.000	−0.2927485
IU	−0.4116374	0.2092921	−1.97	0.051	−0.1171651
MHT	1.587947	0.437787	3.63	0.000	−0.2927485
ICTE	0.3977576	0.3507592	1.13	0.259	0.0574939
ICTI	0.7845233	0.4636717	1.69	0.093	0.0895428
Electric	4.150543	0.808097	5.14	0.000	0.2441793
Urban	−1.276049	0.3078265	−4.15	0.000	−0.2823207
_cons	−329.9921	71.21763	−4.63	0.000

Source: Result from Stata 14 software.

indicate that telecommunication infrastructure, technology, and electrification play a crucial role in promoting the economic integration of ASEAN-6. Specifically, fixed broadband penetration (β = 0.52, p < 0.001) and fixed telephone subscriptions (β = 0.57, p < 0.001) exhibit the strongest positive impact, highlighting the significant role of telecommunication infrastructure in facilitating international trade. Additionally, high-tech manufacturing (β = 0.29, p < 0.001) and electricity access (β = 0.24, p < 0.001) contribute positively, indicating that advanced industries and energy infrastructure are critical enablers of economic integration. Conversely, mobile penetration (β = −0.29, p < 0.001) has a negative effect, possibly due to its widespread usage not being effectively leveraged for trade and production purposes. Similarly, urbanization (β = −0.28, p < 0.001) negatively impacts integration, reflecting disparities between urban and rural development. Meanwhile, Internet usage (β = −0.11, p = 0.051) has a slightly negative but statistically weak effect, likely due to infrastructure quality disparities across ASEAN-6 countries.

Furthermore, macroeconomic factors, such as inflation (p = 0.709), investment (p = 0.581), and technology trade (p > 0.05), do not exhibit a significant influence on economic integration in this model. This suggests that ASEAN-6 integration is primarily driven by digital infrastructure and technology development, while traditional economic factors do not show a clear impact. However, diagnostic tests reveal two critical issues in the current model, multicollinearity and autocorrelation, which affect the accuracy of the estimates. The average Variance Inflation Factor (VIF) is 5.53, with several variables showing high VIF values, including MHT (9.82), Urban (9.68), FixT (8.08), IU (7.41), and FixB (6.57). This indicates strong correlations among explanatory variables, leading to multicollinearity. To address this issue, potential solutions include eliminating highly correlated variables, combining variables with similar conceptual meanings, or applying a Principal Component Analysis (PCA) to reduce the multicollinearity effects.

Additionally, the Wooldridge test (p = 0.0032) confirms the presence of first-order autocorrelation, making OLS estimates inefficient. To address this, several approaches are recommended, including the use of a Fixed Effects Model (FE) if unobserved factors vary over time but remain constant across ASEAN-6 countries or a Random Effects Model (RE) if unobserved factors are random and uncorrelated with explanatory variables. The Hausman test can be applied to determine the appropriate choice between FE and RE. Furthermore, Feasible Generalized Least Squares (FGLS) or the Generalized Method of Moments (GMM) can be employed to simultaneously address autocorrelation and heteroskedasticity, particularly in the context of time-series and dynamic panel data. Therefore, to improve the model’s robustness, it is essential to conduct the Hausman test to determine whether FE or RE is more appropriate and to implement FGLS or GMM to correct autocorrelation and heteroskedasticity. These adjustments will enhance the efficiency and reliability of the research findings.

4.4. Estimation According to Fixed Effects and Random Effects Models

The comparison between the Fixed Effects Model (FEM) and the Random Effects Model (REM) highlights notable differences in their estimations in

Table 4. Estimation results by the Fixed Effects Model (FEM) and Random Effects Model (REM).

TO	FEM Model	REM Model
INF	1.889115 *** (3.21)	0.3110046 (0.37)
INV	0.0531296 (0.10)	0. 3653174 (0.55)
POP	7.430915 *** (3.97)	8.062563 *** (3.03)
FixB	1.20987 ** (1.94)	6.288771 *** (9.26)
FixT	−0.5466801 (−1.01)	4.817599 *** (9.23)
MB	−0.3953586 *** (−3.41)	−0.5949385 *** (−6.92)
IU	−0.5933004 *** (−3.41)	−0.4116374 ** (−1.97)
MHT	1.302598 *** (3.18)	1.587947 *** (3.63)
ICTE	0.7161764 *** (2.40)	0.3977576 (1.13)
ICTI	−0.1036885 (−0.27)	0.7845233 * (1.69)
Electric	2.754107 *** (3.15)	4.150543 *** (5.14)
Urban	2.70306 *** (3.15)	−1.276049 *** (−4.15)
Số quan sát	144
Hausman test results: chi2(13) = (b-B)’[(V_b-V_B)^(−1)](b-B) = 237.44 Prob > chi2 = 0.0000

Notes: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

. The results of the Hausman test show a chi-squared value of 237.44 with a p-value of 0.0000, leading to the rejection of the null hypothesis, which posits no correlation between the independent variables and individual effects. This outcome indicates that FEM is more suitable than REM for this analysis.

A comparison of the two models reveals that certain variables—namely, inflation (INF), fixed broadband (FixB), ICT exports (ICTE), and urbanization (Urban)—are statistically significant in the Fixed Effects Model (FEM) but not in the Random Effects Model (REM). This indicates that, when controlling for fixed effects, these variables demonstrate a clearer relationship with the level of international economic integration (TO). Conversely, fixed telephone subscriptions (FixT) are insignificant in FEM but highly significant in REM, likely because the Fixed Effects Model accounts for country-specific differences, thereby diminishing the influence of fixed-line telephony on international trade.

Notably, mobile penetration (MB) and Internet usage (IU) display significant negative effects in both models, with a more pronounced impact observed in FEM. This reflects the structural shifts in trade as mobile and Internet technologies continue to evolve. Interestingly, urbanization (Urban) has a positive effect in FEM but a negative one in REM, suggesting that the influence of urbanization on economic integration is contingent on the specific characteristics of individual countries.

Based on the test results, the Fixed Effects Model (FEM) is recommended, as it effectively accounts for country-specific heterogeneity within the ASEAN-6, resulting in more accurate estimations. In contrast, employing the Random Effects Model (REM) may yield biased outcomes due to the violation of the independence assumption between individual effects and independent variables. To mitigate potential issues related to autocorrelation or heteroskedasticity, it is advisable to combine FEM with Feasible Generalized Least Squares (FGLS) to enhance estimation efficiency.

4.5. Estimated Results According to the GLS Model

The research findings in

Table 5. Estimated results according to the GLS model.

TO	Coef.	Std. Err.	z	P > |z|	95% Conf. Interval
					Min	Max
INF	0.9040998	0.4572452	1.98	0.048	0.0079157	1.800284
INV	1.818976	0.5184259	3.51	0.000	0.8028801	2.835072
POP	3.899428	2.491026	1.57	0.117	−0.9828939	8.78175
FixB	5.903205	0.690607	8.55	0.000	4.54964	7.25677
FixT	3.873947	0.534095	7.25	0.000	2.82714	4.920754
MB	−0.4673889	0.0854305	−5.47	0.000	0.000	−0.6348297
IU	−0.3124981	0.1409459	−2.22	0.027	−0.588747	−0.0362491
MHT	0.9454608	0.2993551	3.16	0.002	0.3587355	1.532186
ICTE	0.4443421	0.2761294	1.61	0.108	−0.0968617	0.9855458
ICTI	0.2430418	0.3565675	0.68	0.495	−0.4558175	0.9419012
Electric	1.387201	0.5296185	2.62	0.009	0.349168	2.425234
Urban	−0.2032056	0.3241908	−0.63	0.531	−0.8386079	0.4321967
_cons	−124.9585	49.88983	−2.50	0.012	−222.7408	−27.17622

highlight the key factors influencing the international economic integration of ASEAN-6 countries, with several variables exhibiting strong statistical significance and substantial impact.

Specifically, inflation (INF), investment (INV), fixed broadband (FixB), fixed telephone subscriptions (FixT), medium- and high-tech manufacturing (MHT), and electricity access (Electric) have a positive and highly significant effect (p < 0.05). This underscores the critical role of investment, digital infrastructure, and energy accessibility in promoting international trade. Notably, FixB (5.90) and FixT (3.87) have large coefficients, indicating that the expansion of telecommunication infrastructure significantly enhances economic integration.

Conversely, mobile subscriptions (MB) and Internet usage (IU) have negative and statistically significant effects, with MB showing a coefficient of −0.467 and IU −0.312. This suggests that, within the ASEAN-6 context, an increase in mobile subscriptions and Internet access does not necessarily translate into higher international trade volumes. This may be attributed to variations in digital infrastructure quality or the ineffective utilization of technology in economic activities.

Meanwhile, population growth (POP), ICT exports (ICTE), ICT imports (ICTI), and urbanization (Urban) are not statistically significant (p > 0.1), indicating that these factors do not play a decisive role in determining the level of international economic integration within the model.

The results also reveal a negative and significant constant term (_cons = −124.96, p = 0.012), suggesting that, in the absence of other influencing factors, economic integration would be considerably limited.

In summary, this study reaffirms that investment, telecommunication infrastructure, and high-tech manufacturing are key drivers of ASEAN-6’s international economic integration. Additionally, further examination of the role of mobile subscriptions and Internet access is necessary to optimize the impact of digital transformation on international trade within the region.

5. Discussion of Research Results

The research findings provide critical insights into the key determinants of international economic integration within ASEAN-6, encompassing Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. The results emphasize the significant role of investment, telecommunication infrastructure, high-tech manufacturing, and electricity access in fostering regional trade integration. These findings are consistent with the theory of economic integration, which identifies infrastructure and investment as fundamental drivers of regional economic convergence (Balassa, 2013). Additionally, the results align with Sachs et al. (1995), who argue that economic reforms, particularly in trade liberalization and technological advancement, play a crucial role in facilitating global integration.

Notably, the study highlights the positive effects of fixed broadband (FixB = 5.90) and fixed telephone subscriptions (FixT = 3.87), suggesting that countries with advanced digital infrastructure, such as Singapore and Malaysia, benefit significantly from robust telecommunication networks in trade facilitation. This supports the arguments of Baldwin (2016) regarding the role of digital transformation in reshaping globalization patterns, where economies with strong ICT infrastructure experience greater trade integration. Moreover, findings from Economica Vietnam (2020) and Communist Review (2022) reinforce this view by emphasizing how digital trade agreements, such as the EVFTA, enhance Vietnam’s integration into global value chains.

However, the study also uncovers a paradox: mobile subscriptions (MB = −0.467) and Internet usage (IU = −0.312) have negative and statistically significant effects on trade integration. This contradicts the conventional belief that digital connectivity inherently promotes trade. Possible explanations include disparities in digital infrastructure quality (Dollar & Kraay, 2004), digital literacy gaps (Wysokińska, 2021), and the ineffective utilization of technology for economic activities (Karimov et al., 2021). In particular, while Indonesia and the Philippines report high mobile penetration rates, digital trade remains constrained due to weak cybersecurity, fragmented e-commerce platforms, and inconsistent digital payment adoption. This supports the argument by Inshakova et al. (2020) that digital transformation alone is insufficient for economic integration unless accompanied by comprehensive policies addressing regulatory, security, and institutional barriers.

From a broader perspective, the findings contribute to the ongoing debate on the heterogeneous impact of digital transformation on economic integration. While some studies (e.g., Lane & Milesi-Ferretti, 2008) emphasize financial openness and capital mobility as primary drivers of integration, this study highlights the critical role of ICT infrastructure and digital capabilities. Furthermore, the results resonate with the work of Frankel and Romer (2017), who argue that trade and digital connectivity are interdependent but require strong institutional frameworks to maximize their benefits.

These findings underscore the importance of country-specific policies to enhance the effectiveness of digital transformation in trade expansion. Moving forward, policymakers must focus not only on improving infrastructure but also on strengthening digital regulations, harmonizing trade policies, and fostering digital literacy to bridge the digital divide and enhance regional economic integration.

6. Conclusions

6.1. Policy Implications

This study reaffirms that digital transformation plays a crucial role in advancing the international economic integration of ASEAN-6, though its impact varies across countries. Singapore and Malaysia are at the forefront due to their advanced digital infrastructure and high-tech industries, while Indonesia, Thailand, Vietnam, and the Philippines are still leveraging digitalization to enhance integration. Despite notable progress, several challenges remain, including digital infrastructure disparities between urban and rural areas, limitations in technology adoption in trade and manufacturing, and gaps in digital capabilities among enterprises.

Four key policy directions are recommended to foster digital-driven economic integration in ASEAN-6: (1) expanding digital infrastructure, particularly broadband coverage in underserved areas; (2) accelerating digitalization in trade and manufacturing by encouraging traditional businesses to adopt e-commerce; (3) strengthening regional cooperation in digital payments, data standardization, and e-commerce regulations; and (4) supporting small and medium-sized enterprises (SMEs) in accessing digital technologies through initiatives promoting artificial intelligence, big data analytics, and e-commerce platforms.

6.2. Study Limitations and Future Research Agendas

However, this study has certain limitations. Firstly, the model primarily relies on quantitative data and does not fully account for institutional and policy-specific factors in each country. Secondly, it does not provide an in-depth analysis of the impact of emerging technologies, such as blockchain, artificial intelligence, and cloud computing, on economic integration. Thirdly, the influence of social factors, such as workforce readiness and access to digital education, has not been thoroughly examined.

Future research should expand its scope by integrating advanced econometric techniques, such as the Vector Autoregressive (VAR) model, to examine the dynamic interrelationships between digital infrastructure, policy interventions, and economic integration over time. The application of VAR models would allow for a deeper understanding of how shocks in digital transformation propagate through the economy and influence trade flows, investment patterns, and productivity across ASEAN-6.

Additionally, sector-specific studies are needed to evaluate the impact of digital transformation on key industries such as finance, manufacturing, and services, considering both short-term fluctuations and long-term structural changes. Moreover, incorporating qualitative analysis to assess the role of public policies and social dynamics in digital economic integration would provide a more holistic perspective.

These future research directions will contribute to a more comprehensive analytical framework, enabling policymakers to design data-driven digital transformation strategies that maximize the benefits of international economic integration in an increasingly digitalized global economy.