The Role of Digital Trade and Technological Advancements in Shaping Financial Resilience in Sub-Saharan Africa

Abstract

This study examines how digital trade, technology, and Internet adoption impact financial sustainability across 41 Sub-Saharan African (SSA) countries, using the generalized method of moments (GMM) for precise statistical analysis. It focuses on four major financial indicators: financial development, financial institutions access, financial institutions depth, and financial institutions efficiency indexes. The results indicate that digital trade, technology, and Internet use positively interact to boost financial sustainability in SSA, as measured by the financial development index. In terms of financial institutions access, this study finds that importing ICT goods and non-resident patents improve access, whereas resident patents diminish the benefits of exporting ICT goods. Notably, the combined effect of importing ICT goods and increased Internet usage significantly enhances access to financial institutions in SSA. Regarding financial institutions depth, positive interactions are observed between ICT goods exports and resident patents, highlighting the need for a balanced approach. However, there is a potential trade-off with increasing Internet usage, suggesting the need for a strategy to deepen financial institutions in SSA. Lastly, concerning financial institutions efficiency, positive interactions are particularly noted between ICT goods exports and non-resident patents. Yet, a negative interaction is identified with a potential trade-off between importing ICT goods and increasing Internet usage. Policymakers are advised to consider these complex dynamics when formulating strategies to enhance financial sustainability in SSA.

1. Introduction

This study advances the discussion on financial sustainability by analyzing four critical financial indicators in Sub-Saharan Africa (SSA): the financial development index, the financial institutions access index, the financial institutions depth index, and the financial institutions efficiency index. It specifically explores the impact of the significant economic changes that SSA has undergone recently, driven by the rapid growth in digital trade, technological advancements, and widespread Internet access [1]. The financial sector has notably transformed, moving from traditional methods to innovative practices that are redefining financial sustainability in the region [2]. As SSA transitions into the digital age, the interaction between digital trade, technology adoption, and Internet availability becomes increasingly important. These elements not only affect how financial services are accessed and delivered but also shape the experiences and perceptions of individuals and businesses in the region [3,4].

This study aims to examine the digital renaissance sweeping across Sub-Saharan Africa, investigating the impact of digital trade, technology, and Internet accessibility on financial sustainability. Sub-Saharan Africa has seen significant growth in digital connectivity, with mobile subscriptions reaching 527 million by the end of 2023 and smartphone adoption projected to surpass 786 million by 2029 [4]. Internet penetration also continues to rise, with countries like South Africa reporting 45.34 million Internet users by early 2024 [5]. Despite this progress, mobile Internet usage remains lower, with only 46% of adults in the region using mobile Internet services [6]. These advancements in mobile services, smartphone use, and Internet access are reshaping the financial landscape, providing greater opportunities for financial evolution, particularly through the growth of fintech innovations in both urban and rural areas. This research seeks to unravel the dynamics of this transformation, exploring how digital trade practices, technology adoption, and Internet usage converge to redefine financial sustainability. The first research objective involves investigating the impact of digital trade on fostering financial inclusion in SSA. Scholars such as [5,6,7,8] have highlighted the potential of online transactions, e-commerce, and digital payment solutions to expand access to financial services across diverse demographic segments. The second objective is to examine the strategies employed by financial institutions in SSA to adopt and leverage technology. Works by [9,10] form the backdrop, emphasizing how fintech innovations enhance operational efficiency, improve customer experiences, and redefine traditional banking practices. The third objective explores the correlation between Internet usage patterns and banking practices. Research by [9,11] informs this exploration, assessing how the prevalence of online services, such as Internet banking and mobile banking, shapes consumer behaviors and preferences within the financial domain. The fourth objective centers on examining the existing regulatory frameworks governing digital finance and technology adoption in SSA. The works of [12,13] guide this investigation, evaluating the challenges and opportunities arising from regulatory efforts to balance innovation, consumer protection, and financial stability.

In the vast expanse of Sub-Saharan Africa, a profound digital transformation is underway, catalyzed by the intersecting forces of digital trade, technological innovations, and the pervasive reach of the Internet. This metamorphosis is particularly pronounced in the financial sector, where traditional norms are yielding to the dynamic forces of technological progress. Moreover, the relationship between digital trade practices, technology adoption, and Internet accessibility is shaping the contours of financial sustainability across the region. This study embarks on an exploration of these intertwined dynamics, seeking to unravel the refined connections that link digital trade, technology, Internet use, and the evolving landscape of financial sustainability in SSA [14,15,16].

As SSA emerges as a crucible for digital innovation, a web of connectivity is being spun across urban centers and remote landscapes alike. The proliferation of smartphones, coupled with expanded Internet access, has set the stage for a digital revolution in financial services. In this narrative, the convergence of digital trade practices, technology adoption by financial institutions, and the omnipresence of the Internet become a linchpin, redefining how financial services are accessed, delivered, and experienced. This study, guided by the threads of digitalization, aims to untangle the complexities of this transformative journey, offering insights into how SSA navigates the digital frontier, reshaping its financial landscape [4,17,18,19].

This study contributes to the existing literature on the impact of digital trade, technology adoption, and Internet usage on financial sustainability in SSA through several key contributions. Firstly, it fills a crucial gap by concurrently examining various dimensions of financial development, including the financial development index, financial institutions access index, financial institutions depth index, and financial institutions efficiency index. Previous studies often focused on singular aspects, and this holistic approach offers a comprehensive understanding of how digital elements interact with financial indicators [20,21,22,23,24]. Secondly, this study acknowledges the unique challenges faced by SSA countries, moving beyond global or regional trends, by considering the specific socioeconomic and technological dynamics of the SSA region, the research in tailored interventions, and policies used to align with their distinct needs. This addresses a research gap related to variations in the impact of digital elements on financial sustainability across SSA countries with differing economic development, infrastructure, and regulatory frameworks [19]. Moreover, this study contributes methodologically by employing a diverse set of proxies to measure digital trade, technology adoption, and Internet use. Utilizing indicators such as the percentage of information and communication technology (ICT) goods exports and imports, patents by residents and patents by non-residents, and the percentage of individuals using the Internet enhances the robustness and depth of the analysis. The generalized method of moments (GMM) methodology further strengthens this study’s impact. The GMM addresses potential endogeneity issues over an extended time frame and diverse country characteristics, offering a reliable estimation process. The inclusion of lagged values enables the examination of dynamic panel models, providing insights into the persistence and evolution of variables of interest. Overall, these contributions deepen our understanding of the intricate relationship between digital elements and financial sustainability in SSA.

2. Theoretical and Empirical Literature

This section explores both theoretical and empirical frameworks to understand the impact of digital trade, technology, and Internet use on financial institutions in Sub-Saharan Africa (SSA). Theoretical frameworks provide the foundation for analyzing how digital elements interact with financial systems, while empirical studies illustrate the real-world implications of these dynamics.

2.1. Theoretical Literature

The theoretical literature delves into four key frameworks that shape the understanding of digital transformation in SSA’s financial sector, and these theories are discussed in this sub-section.

The theoretical literature on the impact of digital trade, technology, and Internet use on financial institutions in SSA provides a foundation for understanding the complex dynamics of this transformative process. Scholars have explored various theoretical frameworks to analyze how these digital elements interact and reshape the financial landscape in the region. The theoretical framework is discussed in four main strands, especially as it pertains to the technology–organization–environment (TOE) framework; unified theory of acceptance and use of technology (UTAUT); access frontier framework for financial inclusion (AFFFI); and innovation diffusion theory (IDT).

The first strand on the TOE framework, proposed by [25], outlines three key domains influencing technology adoption: technological context, organizational context, and environmental context. In SSA, the technological context could delve into the availability and affordability of digital infrastructure, the organizational context might explore the readiness of financial institutions to adapt to digital innovations, and the environmental context could assess regulatory and socio-economic factors shaping the adoption landscape. The TOE framework is particularly relevant because it allows for a comprehensive understanding of the factors influencing the adoption of digital technologies within financial institutions. In SSA, digital trade and technology adoption are highly influenced by the availability and affordability of digital infrastructure (technological context), the readiness and capabilities of financial institutions to embrace such technologies (organizational context), and the regulatory and socio-economic environment that shapes the overall digital ecosystem (environmental context). This makes the TOE framework ideal for analyzing the complex, region-specific dynamics in SSA.

In the second strand, the UTAUT, developed by [26], identifies four key constructs influencing technology acceptance: performance expectancy, effort expectancy, social influence, and facilitating conditions. In SSA, the application of UTAUT could be extended to investigate how these constructs are shaped by cultural factors, trust in digital platforms, and the perceived impact on financial inclusion. UTAUT provides an excellent lens for exploring the adoption of digital technologies in SSA, especially in relation to how cultural and societal factors influence the adoption process. SSA’s adoption of digital trade and technology is heavily shaped by trust in digital platforms, perceived benefits, and the cultural readiness of individuals and organizations to embrace technology. UTAUT’s focus on these human and contextual factors is critical to understanding how both financial institutions and consumers in SSA perceive and integrate digital innovations into their financial behaviors.

The third strand, articulating the AFFFI, is consistent with the World Bank’s Access Frontier framework, which emphasizes expanding financial access [27]. In the SSA context, exploring how digital trade and technology contribute to pushing the access frontier, especially in rural and underserved areas, is crucial. The framework could be extended to include dimensions like the role of mobile money and digital payment systems in enhancing financial inclusion. The AFFFI is used because it directly addresses the issue of financial inclusion, which is a central concern in SSA. Digital trade and technology have the potential to push the access frontier by expanding financial services to underserved populations, particularly through mobile money and digital payment systems. In SSA, where physical banking infrastructure is often lacking, the AFFFI provides a useful tool for understanding how digital innovations can reach remote or underserved areas, thereby improving financial access and inclusion.

Innovation diffusion theory, in the fourth strand, can be expanded in SSA by examining the characteristics of digital innovations that influence their adoption [28]. For instance, analyzing the relative advantages, compatibility, complexity, trialability, and observability of digital trade and technological innovations in SSA provides insights into the diffusion process within financial institutions. IDT is particularly valuable for analyzing how digital innovations such as mobile banking, e-commerce, and digital payment systems diffuse within financial institutions in SSA. The theory highlights the factors that can either accelerate or hinder the spread of these technologies. For instance, in SSA, the relative advantage (e.g., increased efficiency and accessibility) and compatibility (e.g., alignment with local needs and existing infrastructure) of digital trade technologies can significantly influence their adoption within financial institutions. This framework is critical for understanding why some innovations succeed in SSA while others fail, especially in the context of digital financial services.

In summary, these theoretical frameworks are significant in SSA as they provide a structured lens through which the complex interplay of technology, organization, environment, acceptance, and access can be analyzed. By considering the unique challenges and opportunities within the region, these frameworks offer actionable insights for policymakers, businesses, and researchers aiming to navigate the digital transformation landscape in SSA’s financial sector.

2.2. Empirical Literature and Hypotheses Development

From the global context, scholars such as [29,30] have extensively researched and documented the overarching trends in digital transformation, observing a profound impact of technologies like artificial intelligence, big data analytics, and the Internet of Things across various sectors. They emphasize the imperative for organizations and economies worldwide to embrace digital technologies for enhanced productivity and competitiveness. However, this optimistic picture of global digitalization fades when viewed through a critical lens, as highlighted by [31,32], drawing attention to the digital divide and related uneven distribution of digital resources and skills globally, particularly when transitioning from technologically advanced regions to less developed ones.

Transitioning to the SSA context, researchers like [9,11] have outlined the unique challenges and opportunities associated with digital adoption in the region. Infrastructure limitations, socio-economic factors, and regulatory frameworks pose significant hurdles to the seamless integration of digital technologies. However, these challenges coexist with opportunities, such as the potential for leapfrogging traditional infrastructure using mobile technology, as evidenced by the remarkable success of mobile money platforms in SSA.

A pivotal aspect of SSA’s digital journey is the role of mobile technology in fostering financial inclusion, a topic extensively explored by scholars [1,33]. The corresponding research demonstrates how mobile phones, rather than traditional banking infrastructure, serve as the primary conduit for delivering financial services in SSA. This shift has a substantial impact on the unbanked and underbanked populations, offering them unprecedented access to financial services.

A specific study on Sub-Saharan Africa, [9], highlighted substantial challenges in technological infrastructure, emphasizing the impact of limited access and affordability on the adoption of digital trade practices by financial institutions. Addressing these challenges was deemed fundamental for the successful integration of digital technologies into the financial sector. Similarly, [34] investigated the phenomenon of leapfrogging traditional banking infrastructure in SSA through mobile technology, underscoring the crucial role of mobile phones as tools for fostering financial inclusion. Mobile money platforms, exemplified by success stories in Kenya and Tanzania, provide financial services to unbanked and underbanked populations, demonstrating the transformative power of digital trade in promoting financial inclusion.

Moreover, [35] investigated the role of mobile technology in fostering financial inclusion in SSA, showcasing how mobile phones, rather than conventional banking infrastructure, act as conduits for financial services. The positive impact of mobile technology in reaching previously underserved populations transforms traditional banking practices. Additionally, [10] conducted a study on the emergence of fintech innovations within the financial sector in SSA, shedding light on the dynamic strategies employed.

Insights into the regulatory landscape governing digital finance and technology adoption in SSA were provided by [12,13]. These studies examined the delicate balance required for effective digital governance, emphasizing the need for regulatory frameworks that encourage innovation while safeguarding consumer interests and maintaining financial stability in the era of digital trade. Studies by [10,33,36] further contribute to the understanding of SSA’s digital landscape. These studies shed light on the emergence of fintech innovations within the financial sector, exploring how financial institutions in SSA are adapting to digital technologies to enhance services, reach wider audiences, and navigate the evolving financial landscape.

In synthesizing the empirical literature on digital trade, technology adoption, and Internet use for financial sustainability in SSA, a comprehensive understanding emerges, revealing both the challenges and opportunities unique to the region. The most significant challenge lies in the limited technological infrastructure. However, previously mentioned success stories of leapfrogging traditional banking practices, the positive impact of mobile technology on financial inclusion, and the adaptive strategies employed by financial institutions in embracing digital technologies collectively underscore the transformative potential of digital trade, technology adoption, and Internet use in reshaping the financial landscape in SSA. This transformation, however, is not without its intricacies, and the regulatory landscape plays a pivotal role in navigating this digital evolution. Regulatory frameworks, as discussed by [12,13], are instrumental in maintaining a delicate balance between fostering innovation and ensuring stability in the digital financial ecosystem.

Based on the empirical literature, we formulate the following hypotheses:

H1: 

Digital trade positively influences the financial development index (FD index) in Sub-Saharan Africa.

H2: 

Digital trade positively influences the financial institutions access index (FIA index) in Sub-Saharan Africa.

H3: 

Digital trade positively influences the institutions depth index (FID index) in Sub-Saharan Africa.

H4: 

Digital trade positively influences the financial institutions efficiency index (FIE index) in Sub-Saharan Africa.

3. Data and Methodology

This study examines the influence of digital trade, technology adoption, and Internet usage on financial sustainability in SSA, employing proxies such as the FD index, FIA index, FID index, and FIE index [37]. Recent research suggests that once a country surpasses the minimum financial development threshold, there emerges an influence of financial development on economic growth, and this impact tends to accumulate with the level of financial development [38,39]. Addressing concerns associated with indicators as a measure of financial development, a working paper by the IMF authored by [40] introduces a new framework, creating a set of indicators that comprehensively assess the level of financial development. This framework builds upon prior work by the IMF on the same subject, particularly the paper by [41]. According to this framework, financial development should be evaluated from two key perspectives: financial institutions (including banks, pension funds, mutual funds, and insurance firms) and fiscal management (including bond and stock markets). The assessment considers their access, depth, and efficiency. Financial development is measured as a composite of access, reflecting the reach of firms and individuals to financial services; depth, expressing the liquidity and size of markets; efficiency, indicating the ability of these institutions to provide financial services; and financial sustainability. This variable is therefore expected to have a positive effect in the context of this study. Figure 1 shows the apparent relationship through a fitted scatter plot, utilizing only the financial development index, to be concise.

Digital trade is represented by the percentage of ICT goods exports and imports in total goods exports and imports, respectively. Technological aspects are measured through patents by residents and patents by non-residents, while Internet use is gauged by the percentage of individuals using the Internet and the number of secure Internet servers per one million people. The analysis controls for GDP and population. In SSA, GDP reflects the overall economic activity and prosperity of the region. It encompasses various sectors, such as agriculture, industry, and services, each contributing differently to the overall GDP. Population refers to the total number of people living in a specific area, such as a country or region. In SSA, population dynamics play a significant role in shaping socioeconomic and environmental trends. Utilizing a dataset spanning over 25 years and across 41 countries, this study employs the GMM for robust statistical inference. This extended time frame facilitates the exploration of long-term trends and patterns, capturing the dynamic nature of the phenomena studied.

The inclusion of 41 (Angola, Botswana, Burkina Faso, Burundi, Cape Verde, Cameroon, Central African Republic, Comoros, Democratic Republic of Congo, Republic of Congo, Cote d’Ivoire, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia, Zimbabwe) countries introduces valuable cross-sectional diversity, allowing for the examination of variations and commonalities across different national contexts [42]. The GMM proves advantageous by addressing potential endogeneity issues arising over the extended time frame and diverse country characteristics. By incorporating lagged values, the GMM accommodates dynamic panel models, providing a comprehensive understanding of the persistence and evolution of variables of interest. Furthermore, the efficiency and consistency of properties of the GMM enhance estimate reliability in the presence of heteroscedasticity and autocorrelation, common challenges in extended panel datasets [43].

In our study on digital trade, technology adoption, and Internet usage, which considers their influence on financial sustainability in SSA, the application of the GMM framework bolsters the robustness of regression equations. The GMM effectively handles concerns related to the unobserved heterogeneity (i.e., by controlling for cross-sectional dependence with time-fixed effects) and simultaneity (i.e., by employing internal instruments) dimensions of endogeneity, offering a more reliable estimation of relationships between digital trade, technology adoption, Internet usage, and financial outcomes [43]. By accommodating unobserved heterogeneity and mitigating potential biases, the GMM contributes to the precision and validity of findings, ensuring that the analysis captures the intricate dynamics of technology adoption and its impact on resident health [42,43]. The variables used in the study are presented in

Table 1. Variable and data description.

Variables	Description	Source
FD index	Financial development (FD) index	Findex (World Bank)
FIA index	The financial institutions access, which compiles data on the number of bank branches and the number of automatic teller machines (ATMs) per 100,000 adults	Findex (World Bank)
FID index	The financial institutions depth, which compiles data on bank credit to the private sector, pension fund assets, mutual fund assets, and insurance premiums (life and non-life) as percentages of GDP	Findex (World Bank)
FIE index	The financial institutions efficiency, which compiles data on the banking sector’s net interest margin, the lending–deposits spread, the ratios of non-interest income to total income and overhead costs to total assets, and the returns on assets and equity	Findex (World Bank)
Digital trade	ICT goods exports (% of total goods exports), ICT goods imports (% of total goods imports)	World Bank
Technology	Patents by residents, patents by non-residents	World Bank
Internet use	Individuals using the Internet (% of the population) (IUS), secure Internet servers (per 1 million people) (SISs)	World Bank
Economic growth	Gross domestic product	World Bank
Population growth	Total population	World Bank

Note: the Global Findex database, often referred to as Findex, is a product of the World Bank, 2023. It is a comprehensive database that tracks global progress toward financial inclusion. The Global Findex database provides detailed data on how individuals around the world save, borrow, make payments, and manage risks. It includes indicators such as account ownership, usage of financial services, and access to credit. The database is particularly valuable for policymakers, researchers, and organizations interested in understanding and promoting financial inclusion globally.

with their descriptions and sources.

3.1. Model Estimation

We explore the influence of digital trade, technology, and Internet adoption on financial sustainability in Sub-Saharan Africa. We consider a regression model, where financial sustainability outcomes are regressed on relevant variables related to digital trade, technology, and Internet adoption. The simplified version of the model is present in Equation (1) as follows:

Financial Sustainability = β₀ + β₁Digital Trade + β₂Technology + β₃ Internet Use + β₄Control + ε

(1)

where financial sustainability (FS) is the dependent variable, denoting the FD index, FIA index, FID index, and FIE index in the regression equation. Digital trade is ICT goods exports and ICT goods imports, technology is patents by residents and patents by non-residents, and Internet use is individuals using the Internet and secure Internet servers. Control variables include other factors that may influence financial sustainability, such as GDP and population. β0 is the intercept term, representing the baseline level of financial sustainability when all independent variables are zero. β1, β2, β3, β4, etc., are the coefficients that represent the marginal effects of the corresponding variables on financial sustainability. ε is the error term, capturing unobserved factors influencing financial sustainability that are not included in the model.

Based on our research questions in Section 1 and hypotheses in Section 2, we consider the FDFIA, FID, and FIE indexes as distinct dependent variables in regression equations such that the model becomes more complex but also more informative. The regression equations with financial development indicators as dependent variables are outlined in Equations (2)–(5):

FD index = β0 + β1Digital Trade + β2Technology + β3Internet Use + β4Control + ε

(2)

FIA index = β0 + β1Digital Trade + β2Technology + β3Internet Use + β4Control + ε

(3)

FID index = β0 + β1Digital Trade + β2Technology + β3Internet Use + β4Control + ε

(4)

FIE index = β0 + β1Digital Trade + β2Technology + β3Internet Use + β4Control + ε

(5)

Now, each equation corresponds to a specific dependent variable in Equations (2)–(5) (FD index, FIA index, FID index, and FIE index), and they are correctly differentiated based on the financial indicator under consideration.

We considered adding an interaction term between the first two (digital trade and technology) and second two (digital trade and Internet use) to examine whether the relationship between these two variables and financial sustainability is dependent on each other. The interaction can be stated as in Equation (6):

Financial sustainability = β0 + β1Digital Trade + β2Technology + β3Internet Use + β4Control + β5 (Digital Tarde × Technology) + β6(Digital Tarde × Internet Use) + ε

(6)

where β5 and β6 represent the coefficient for the interaction term, and (Digital Tarde × Technology) and (Digital Tarde × Internet Use) capture the joint effect of digital trade technology and Internet use on financial sustainability.

3.2. Generalized Method of Moments (GMM)

To integrate the GMM framework into our regression model, expressed as Equation (4), this estimation approach relies on defining moments and selecting appropriate instruments. The generalized form of our regression, considering the GMM framework, is given in Equation (7) as follows:

y_i = β₀ + β₁x_1i + β₂x_2i + …β_kx_ki + ε

(7)

In this equation, y_i is the dependent variable (e.g., digital trade, technology, and Internet yi adoption). 1, 2…, x_1i, x₂, ……. x_ki, are the independent variables (e.g., digital trade, technology, and Internet adoption, control). 0,1, 2…, β₀, β₁, β₂ ,……β_k, are the coefficients to be estimated. ε is the error term.

The GMM equation then, in Equation (7), becomes the following:

E [m (y1, x1, β). Zi] = 0

(8)

Incorporating this into the regression equation yields Equation (9) as follows:

E [m (y1, x1, β).zi] = β0 + β₁x_1i + β₂x_2i + …β_kx_ki + εi

(9)

This equation represents the moment conditions under the GMM framework. The left side captures the expectations of the product of moments and instruments, equated to zero. GMM estimation involves choosing parameters β that minimize the quadratic form of these moment conditions.

4. Empirical Results and Discussion

This section presents the findings from the analysis of the relationship between digital trade and financial development in Sub-Saharan Africa (SSA), highlighting key insights from the GMM analysis. The pre-estimation results are presented in the appendix from Appendix A 

Table A1. Descriptive statistics.

Variable	Mean	Std. Dev.	Min	Max
FD index	1.776	0.058	1.628	2.193
FIA index	1.748	0.056	1.592	2.138
FID index	2.440	0.186	1.791	2.877
FIE index	2.547	0.129	2.228	2.889
ICTE	0.688	0.447	0.002	1.789
ICTS	7.128	0.843	3.697	9.018
PN	0.896	0.741	0.242	3.962
PR	0.726	0.624	0.432	3.256
IUI	0.729	0.579	0.432	1.912
SIS	1.438	0.961	0.543	5.933
GDP	3.077	0.412	2.392	4.224
POP	6.897	0.719	4.897	8.340

,

Table A2. Correlation results.

Variable
FD index	1
FIA index	0.964	1
FID index	−0.856	−0.830	1
FIE index	−0.763	−0.805	0.923	1
ICTE	−0.025	−0.002	−0.043	−0.121	1
ICTS	0.241	0.255	0.172	−0.196	0.390	1
PN	0.007	−0.015	0.055	0.100	−0.174	0.306	1
PR	0.007	−0.005	0.127	0.129	−0.144	0.323	0.658	1
IUI	0.655	0.615	−0.489	−0.375	−0.079	0.252	0.146	0.147	1
SIS	0.342	0.302	−0.199	−0.128	−0.189	0.413	0.538	0.502	0.585	1
GDP	0.461	0.388	−0.338	−0.158	−0.312	0.124	0.227	0.178	0.536	0.404	1
POP	−0.301	−0.242	0.311	0.145	0.033	0.370	0.417	0.480	−0.187	0.370	−0.392	1

,

Table A3. Pesaran cross-sectional dependency [51], Westerlund [52] cointegration, and Pesaran–Yamagata [53] slope homogeneity test results.

Variable	CD-Test	CD2-Test	Cointegration		Slope Homogeneity
FD index	121.46 **	348.45 **	Gt	−1.478 **	∆	3.715 ***
FIA index	116.84 **	335.19 **	Pt	−6.525 **	∆Ad	5.362 ***
FID index	118.58 **	339.00 **	Ga	−2.240 **
FIE index	108.48 **	309.74 **	Pa	−5.197 **
ICTE	5.23 **	109.16 **
ICTS	34.04 **	132.39 **
PN	0.60 **	588.04 **
PR	4.23 **	597.73 **
IUI	128.31 **	367.63 **
SIS	103.09 **	296.62 **
GDP	58.17 **	259.01 **
POP	140.77 **	403.18 **

Note: ***, **, represent significance levels at 1% and 5% respectively.

and

Table A4. Panel unit root results.

Variable	CIPS				CADF
	Level		First Difference		Level		First Difference
FD index	−1.596 **	−1.785 **	−3.520 ***	−4.562 ***	−1.722 ***	−2.010 **	−2.265 **	−2.867 **
FIA index	−1.387 **	−1.973 **	−3.218 ***	3.856 ***	−1.766 ***	−1.827 **	−2.253 **	−2.894 **
FID index	−2.717 **	−2.654 **	−4.486 ***	−5.345 ***	−2.315 ***	−2.098 ***	−2.852 ***	−3.372 ***
FIE index	−2.379 **	−2.167 **	4.329 ***	5.175 **	−2.064 ***	−2.03 ***	−2.696 **	−3.203 **
ICTE	−2.634 **	−3.180 **	−5.164 ***	5.200 ***	−2.297 **	−2.961 **	−4.124 **	−4.199 **
ICTS	−2.906 *	−3.319 *	−5.109 **	−5.218 **	−2.434 ***	−2.812 **	−3.997 **	−4.167 **
PN	−4.061 **	−4.218 **	−5.874 ***	−6.015 ***	−2.804 ***	−2.960 **	−4.606 ***	−4.632 ***
PR	−4.296 **	−4.503 **	−5.839 ***	−5.839 **	−3.421 **	−3.722 ***	−4.850 **	−4.872 **
IUI	−1.677 **	−2.437 **	−4.271 ***	−4.351 ***	−1.754 **	−2.537 ***	−3.092 **	−3.081 **
SIS	−3.053 ***	−3.567 ***	−5.332 ***	−5.446 ***	−2.249 **	−2.926 **	−3.793 **	−3.865 **
GDP	−1.741 **	−1.851 **	−3.854 ***	−4.239 ***	−1.918 **	−2.132 **	−2.818 **	−3.258 **
POP	−1.375 *	−1.553 *	−1.481 **	−1.741 **	−2.609 **	−2.541 **	−2.028 **	−2.719 **

Note: ***, **, * represent significance levels at 1%, 5%, and 10%, respectively.

.

4.1. Digital Trade, Technology, Internet Use, and Financial Development (FD Index)

The following analysis delves into the impact of digital trade, technology, and Internet usage on the financial development index (FD index) in SSA, exploring the roles of ICT exports and imports, patent adoption, and Internet usage patterns in shaping financial outcomes. The results are presented in

Table 2. GMM result.

Financial Development Index (FD Index)
Variable	Model 1		Model 2		Model 3
ICTE	0.008 ** (0.007)	0.021 ** (0.007)	−0.009 ** (0.007)	0.002 ** (0.007)	0.013 ** (0.007)	−0.011 ** (0.007)
ICTE2	−0.008 ** (0.006)	−0.010 ** (0.004)	−0.002 ** (0.003)	−0.011 ** (0.008)	−0.007 ** (0.001)	−0.003 ** (0.002)
ICTI	−0.040 ** (0.011)	−0.030 ** (0.011)	0.066 ** (0.012)	0.039 ** (0.012)	−0.033 ** (0.011)	0.063 ** (0.011)
ICTI2	0.005 ** (0.001)	0.004 ** (0.001)	0.002 ** (0.001)	0.006 ** (0.001)	0.004 ** (0.001)	0.003 ** (0.001)
PN	0.014 ** (0.004)	0.002 ** (0.003)	0.006 ** (0.003)	−0.004 ** (0.003)	0.003 ** (0.003)	0.003 ** (0.003)
PN2	0.000 ** (0.003)	−0.002 ** (0.002)	−0.001 ** (0.002)	0.006 ** (0.002)	0.001 ** (0.002)	0.001 ** (0.002)
PR	−0.001 ** (0.003)	−0.005 ** (0.003)	0.001 ** (0.003)	−0.003 ** (0.003)	−0.006 ** (0.003)	−0.008 ** (0.003)
PR2	0.001 ** (0.002)	−0.002 ** (0.002)	0.001 ** (0.002)	0.004 ** (0.002)	0.001 ** (0.002)	0.006 ** (0.002)
IUI	0.078 ** (0.012)	0.070 ** (0.012)	−0.076 ** (0.012)	0.069 ** (0.012)	−0.067 ** (0.011)	−0.072 ** (0.011)
IUI2	−0.027 ** (0.007)	−0.024 ** (0.007)	−0.025 ** (0.007)	−0.026 ** (0.007)	−0.027 ** (0.007)	−0.029 ** (0.007)
SIS	0.023 ** (0.007)	0.022 ** (0.004)	0.024 ** (0.004)	0.023 ** (0.004)	0.029 ** (0.004)	0.027 ** (0.003)
SIS2	−0.003 ** (0.001)	−0.006 ** (0.001)	−0.004 *** (0.001)	0.005 *** (0.001)	−0.010 *** (0.001)	−0.002 *** (0.001)
ICTE×PN	0.013 ** (0.003)
ICTE×PR		0.032 ** (0.003)
ICTS×PR			0.006 ** (0.002)
ICTS×PR				0.016 ** (0.007)
ICTE×SIS					0.006 ** (0.005)
ICTS×IUI						0.005 ** (0.001)
GDP	0.107 ** (0.027)	0.108 ** (0.028)	0.110 *** (0.027)	0.103 ** (0.028)	0.109 ** (0.027)	0.094 ** (0.027)
POP	3.915 ** (0.113)	3.947 ** (0.117)	3.874 ** (0.113)	3.898 ** (0.117)	3.899 ** (0.111)	3.870 ** (0.111)
Const.	1.851 ** (0. 036)	1.914 ** (0. 038)	1.775 ** (0. 038)	2.009 ** (0. 039)	1.881 ** (0. 038)	1.806 ** (0.035)
AR (1)	(0.236)	(0.221)	(0.220)	(0.257)	(0.263)	(0.209)
AR (2)	(0.538)	(0.745)	(0.535)	(0.203)	(0.747)	(0.674)
Sargan OIR	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Hansen test OIR	(0.252)	(0.254)	(0.252)	(0.254)	(0.531)	(0.503)
DHT for instruments
(a) Instruments in levels
H excluding group	(0.104)	(0.208)	(0.206)	(0.101)	(0.106)	(0.117)
Dif (null, H = exogenous)	(0.410)	(0.322)	(0.216)	(0.303)	(0.402)	(0.416)
(b) IV (years, eq(diff))
H excluding group		(0.230)	(0.202)		(0.230)	(0.302)
Dif (null, H = exogenous)		(0.404)	(0.501)		(0.603)	(0.716)
Fisher	2785.00 ***	2472.38 ***	2784.89 ***	2473.11 ***	2878.12 ***	2861.96 ***
Instruments	26	32	26	32	26	32
Countries	41	38	41	38	41	38
Observations	984	920	984	920	984	920
VIF	0.023	0.043	0.453	0.037	0.083	0.095
p-values	0.000	0.000	0.000	0.000	0.000	0.000

Note: ***, ** represent significance levels at 1%, and 5%, respectively. DHT stands for the difference-in-Hansen test for exogeneity of instruments subsets, and Dif refers to difference. OIR indicates the over-identifying restrictions test. The significance of bold values serves a dual purpose: (1) highlighting the significance of estimated coefficients and Wald statistics, and (2) signaling the failure to reject the null hypotheses of (a) no autocorrelation in the AR(1) and AR(2) tests and (b) the validity of the instruments in the Sargan and Hansen OIR tests. Lagged values of the outcome variables are included in the specifications.

and

Table 3. GMM result.

Financial Development Index (FD Index)
Variable	Model 1		Model 2		Model 3
ICTE	0.002 ** (0.008)	−0.013 ** (0.008)	−0.011 ** (0.007)	−0.014 ** (0.007)	−0.012 ** (0.007)	−0.017 ** (0.007)
ICTE2	−0.004 ** (0.004)	−0.005 ** (0.004)	0.002 ** (0.004)	0.003 ** (0.004)	0.006 ** (0.004)	0.001 ** (0.004)
ICTI	−0.026 ** (0.021)	0.038 ** (0.013)	0.052 ** (0.013)	0.053 ** (0.013)	0.055 ** (0.013)	0.054 ** (0.013)
ICTI2	0.003 ** (0.001)	0.004 ** (0.001)	0.008 ** (0.001)	−0.004 ** (0.001)	0.001 ** (0.001)	0.002 ** (0.001)
PN	0.009 ** (0.003)	0.002 ** (0.003)	0.003 ** (0.003)	0.007 ** (0.003)	0.008 ** (0.003)	0.006 ** (0.003)
PN2	0.000 ** (0.003)	0.001 ** (0.002)	−0.004 ** (0.002)	−0.002 ** (0.002)	−0.003 ** (0.002)	0.005 ** (0.002)
PR	−0.016 ** (0.004)	−0.002 ** (0.005)	−0.013 ** (0.004)	0.001 ** (0.004)	0.005 ** (0.004)	−0.017 ** (0.004)
PR2	0.008 ** (0.005)	0.005 ** (0.002)	0.006 ** (0.002)	0.009 ** (0.002)	0.008 ** (0.002)	0.007 ** (0.002)
IUI	0.047 ** (0.013)	0.042 ** (0.013)	−0.046 ** (0.013)	0.045 ** (0.013)	−0.041 ** (0.012)	−0.045 ** (0.012)
IUI2	−0.001 ** (0.008)	0.002 ** (0.008)	0.004 ** (0.007)	0.006 ** (0.007)	0.003 ** (0.008)	−0.002 ** (0.008)
SIS	0.010 ** (0.004)	0.009 ** (0.004)	0.011 ** (0.004)	0.008 ** (0.004)	0.013 ** (0.004)	0.007 ** (0.004)
SIS2	−0.001 ** (0.001)	0.002 ** (0.001)	0.010 ** (0.001)	0.004 ** (0.001)	−0.003 ** (0.001)	−0.001 *** (0.001)
ICTE×PN	−0.014 ** (0.006)
ICTE×PR		−0.020 ** (0.003)
ICTS×PR			0.003 ** (0.002)
ICTS×PR				−0.011 ** (0.002)
ICTE×SIS					0.001 ** (0.002)
ICTS×IUI						0.006 ** (0.001)
GDP	0.079 ** (0.030)	0.081 ** (0.030)	0.077 ** (0.030)	0.076 ** (0.030)	0.078 ** (0.030)	0.065 ** (0.030)
POP	4.343 ** (0.124)	4.351 ** (0.126)	4.341 ** (0.126)	4.321 ** (0.126)	4.311 ** (0.123)	4.297 ** (0.123)
Const.	1.786 ** (0. 036)	1.816 ** (0. 041)	1.732 ** (0. 041)	1.909 ** (0. 042)	1.761 ** (0. 042)	1.739 ** (0. 039)
AR (1)	(0.252)	(0.256)	(0.251)	(0.254)	(0.256)	(0.259)
AR (2)	(0.375)	(0.394)	(0.411)	(0.803)	(0.188)	(0.200)
Sargan OIR	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Hansen test OIR	(0.532)	(0.745)	(0.593)	(0.587)	((0.657)	(0.543)
DHT for instruments
(a) Instruments in levels
H excluding group	(0.155)	(0.101)	(0.130)	(0.201)	(0.117)	(0.219)
Dif (null, H = exogenous)	(0.236)	(0.403)	(0.203)	(0.535)	(0.447)	(0.236)
(b) IV (years, eq(diff))
H excluding group		(0.216)	(0.402)		(0.413)	(0.234)
Dif (null, H = exogenous)		(0.403)	(0.408)		(0.404)	(0.501)
Fisher	3148.46 ***	3040.97 ***	3217.81 ***	3053.28 ***	3247.95 ***	3212.67 ***
Instruments	26	32	26	32	26	32
Countries	41	38	41	38	41	38
Observations	984	920	984	920	984	920
VIF	0.043	0.032	0.021	0.046	0.042	0.052
p-values	0.000	0.000	0.000	0.000	0.000	0.000

Note: ***, ** represent significance levels at 1%, and 5%, respectively. DHT stands for the difference-in-Hansen test for exogeneity of instruments subsets, and Dif refers to difference. OIR indicates the over-identifying restrictions test. The significance of bold values serves a dual purpose: (1) highlighting the significance of estimated coefficients and Wald statistics, and (2) signaling the failure to reject the null hypotheses of (a) no autocorrelation in the AR(1) and AR(2) tests and (b) the validity of the instruments in the Sargan and Hansen OIR tests. Lagged values of the outcome variables are included in the specifications.

respectively.

In recent years, SSA has witnessed a transformative wave catalyzed by the surge in digital trade. The proliferation of smartphones and increased Internet accessibility have set the stage for a digital renaissance. This evolution is particularly pronounced in the financial sector, where traditional norms are giving way to dynamic forces of technological progress [1,33,37]. Along those lines, in Section 4.3., Table 4 presents the results of the GMM analysis, offering insights into conditional, unconditional, and interaction terms. These findings contribute valuable insights into the relationships between digital trade (information and communication technology (exports and imports)), technology (patents by non-residents and patents by residents), and Internet usage (individuals using the Internet and secure Internet servers) and their impact on the financial development index (FD index) in SSA.

First, in Model 1, analyzing the impact of digital trade on the financial development index (FD index) in SSA, a positive coefficient for ICTEs (0.008) indicates a beneficial association between higher ICTE levels and financial development. However, the squared term, ICTE², with a negative coefficient (−0.008) suggests diminishing returns at higher ICTE levels. This emphasizes the need for policymakers to optimize ICTE levels for maximum impact on financial sustainability. Conversely, the negative coefficient for ICTIs (−0.030) implies a potential adverse effect of increased ICTI levels on the FD index. Yet, the positive coefficient for ICTI2 (0.004) introduces a refined aspect, indicating positive effects at higher levels. This highlights the importance of finding an optimal point for ICTI services to outweigh potential drawbacks. These findings have crucial implications for policymakers, emphasizing strategic considerations to harness the full potential of digital trade for financial sustainability in SSA. The significance of digital trade practices in fostering financial inclusion across diverse demographic segments, particularly through online transactions, e-commerce, and digital payment solutions, has been documented by scholars such as [5,6,7,8]. Their work highlights the instrumental role of online transactions in expanding access to financial services and emphasizes the broader impact of digital trade on financial sustainability in SSA.

In Model 2, the analysis is used to explore how technology adoption, specifically non-resident patents (PNs) and resident patents (PRs), influences the FD index in SSA. A positive coefficient for PNs (0.006) indicates that a higher number of non-resident patents is linked to enhanced overall financial development. Simultaneously, financial institutions in SSA are strategically adopting and leveraging technology. This shift, documented by works such as [10], enhances operational efficiency, improves customer experiences, and redefines traditional banking practices. However, the inclusion of the squared term, PN², with a negative coefficient (−0.001) suggests potential diminishing returns, emphasizing the need to identify an optimal threshold for non-resident patent adoption. Conversely, the analysis of resident patents shows a negative coefficient for PRs (−0.003), indicating that a higher number of resident patents may be associated with reduced overall financial development. Yet, the positive coefficient for PR² (0.004) suggests a positive impact at higher levels, adding complexity to the relationship and emphasizing the need for a refined approach to policy decisions. Identifying potential turning points in both non-resident and resident patent adoption offers insights for optimizing technology adoption for sustainable financial development in SSA.

In Model 3, we examine the impact of Internet usage on the FD index in SSA, where the negative coefficient for individuals using the Internet (IUI) at −0.067 suggests a decrease in financial development associated with higher Internet usage. However, the introduction of the squared term, IUI², with a coefficient of −0.027 introduces a curvilinear aspect. This indicates a potential turning point where further increases in Internet usage may have a less detrimental impact on financial development. Policymakers should consider this relationship to balance the benefits and drawbacks of increased Internet access. Shifting the focus to secure Internet servers (SISs), the positive coefficient for SISs at 0.027 implies a positive contribution to financial development. However, the introduction of SIS² with a coefficient of −0.002 articulates a refined aspect, suggesting a diminishing positive impact at higher levels of secure Internet server deployment. Policymakers should consider this turning point to optimize the deployment of secure Internet servers for sustained financial development in SSA. These findings provide valuable guidance for crafting policies that harness the benefits of Internet usage and secure infrastructure while mitigating potential negative effects on financial sustainability. It is important to note that the correlation between Internet usage patterns and banking practices is a pivotal aspect of this study. This aspect contradicts the findings of studies by [9,11]. Their works assess how the prevalence of online services, including Internet banking and mobile banking, shapes consumer behaviors and preferences within the financial domain.

The interaction effects unveil the collective influence of various factors on the FD index in SSA. Beginning with ICTEs×PNs, the positive coefficient (0.013) indicates a synergistic relationship between higher ICT goods exports and increased non-resident patents, contributing to a 0.013-unit rise in the FD index. Similarly, ICTEs×PRs exhibits a positive coefficient (0.032), suggesting that the combined impact of ICT goods exports and increased resident patents results in a 0.032-unit increase in the FD index. Moving to ICTSs×PNs, the positive coefficient (0.006) signifies a potential synergy between higher ICT goods imports and increased non-resident patents, contributing to a 0.006-unit elevation in the FD index. Likewise, ICTSs×PRs displays a positive coefficient (0.016), indicating that the joint impact of heightened ICT goods imports and increased resident patents leads to a 0.016-unit increase in the FD index. Transitioning to ICTEs×SISs, the positive coefficient (0.006) suggests that, in addition to the positive influence of exporting ICT goods, a focus on secure Internet servers contributes to a 0.006-unit rise in the FD index. Lastly, ICTSs×IUI shows a positive coefficient (0.005), implying a potential positive synergy between higher ICT goods imports and increased Internet usage by individuals, resulting in a 0.005-unit increase in the FD index. The research by [9,11] informs this exploration, assessing how the prevalence of online services, such as Internet banking and mobile banking, shapes consumer behaviors and preferences within the financial domain.

Contextualizing the findings of the FD index in SSA reveals significant implications for the interplay between digital trade, technology adoption, and Internet usage. Higher levels of ICT goods exports (ICTEs) exhibit a positive association with financial development, but diminishing returns are noted at elevated levels. Conversely, increased ICT goods imports (ICTIs) show a potential adverse effect, yet positive impacts emerge at higher levels. Technology adoption, specifically non-resident patents (PNs), positively influences financial development, with the need to identify an optimal threshold. Resident patents (PRs) present a complex relationship, indicating reduced development initially but positive impacts at higher levels. Internet usage negatively influences financial development, with a potential turning point at which further increases have a less detrimental impact. Secure Internet servers (SISs) positively contribute to financial development, with diminishing positive impact at higher levels. The interaction effects highlight synergies, emphasizing the importance of considering the combined impact of various factors. Policymakers in SSA should strategically balance and optimize these factors to enhance financial sustainability in the region.

4.2. Digital Trade, Technology, Internet Use, and Financial Institutions Access (FIA Index)

Within the world of digital trade, technology adoption and Internet usage stand as a crucial metric for financial sustainability. This holistic approach provides a comprehensive understanding of how digital elements interact with and influence the financial landscape in SSA. Another critical facet is examining the existing frameworks governing digital finance and technology adoption. The works of [12,13] guide this investigation, evaluating the challenges and opportunities arising from regulatory efforts to balance innovation, consumer protection, and financial stability.

Table 4 in Section 4.3 presents the results of the GMM analysis, offering insights into conditional, unconditional, and interaction terms. These findings contribute valuable insights into the relationships between digital trade (information and communication technology (exports and imports)), technology (non-resident and resident patents), and Internet usage (individuals using the Internet and secure Internet servers) and their impact on the FIA index in SSA. First, Model 1 reveals that higher ICT goods exports (ICTEs) positively impact FIA, contributing to a 0.002-unit rise in the FIA index with a one-unit increase. However, the squared term, ICTE 2, suggests diminishing returns at higher export levels. Conversely, increased ICT goods imports (ICTIs) significantly enhance FIA, resulting in a 0.038-unit increase with a one-unit rise. The squared term, ICTI 2, emphasizes increased positive impacts at higher levels, indicating the potential for increased financial institutions’ access. Policymakers are advised to balance ICT goods exports and imports strategically for sustained improvements in FIA [14,15,16]. Addressing diminishing returns in ICT goods exports and capitalizing on the augmented impact of imports can play a crucial role in enhancing financial accessibility in the region.

In Model 2, the analysis indicates a positive impact of non-resident patents (PNs) on FIA in SSA, with a one-unit increase associated with a 0.003-unit rise in the FIA index. The positive relationship suggests that higher non-resident patents contribute to improved accessibility of financial services, and the squared term, PN², implies further positive impacts at higher levels, indicating a potential turning point for better positive effects. Similarly, resident patents (PRs) exhibit a positive impact on FIA, with a one-unit increase associated with a 0.001-unit rise in the FIA index. The squared term, PR², introduces a curvilinear aspect, emphasizing enhanced positive effects at higher levels of patent applications from residents. These findings suggest that fostering an environment supportive of both resident and non-resident patents positively contributes to financial sustainability in SSA by enhancing access to financial institutions. Policymakers are advised to consider these dynamics when formulating strategies to promote innovation and improve financial inclusion [4,17]

In Model 3, the analysis reveals a substantial positive impact of Internet usage (IUI) on FIA in Sub-Saharan Africa, with a one-unit increase associated with a 0.041-unit rise in the FIA index. The squared term, IUI², introduces a curvilinear aspect, indicating an even more positive impact at higher levels of Internet usage, suggesting a potential turning point for amplified effects. Similarly, secure Internet servers (SISs) show a positive impact on FIA, with a one-unit increase associated with a 0.007-unit rise in the FIA index. The squared term, SIS², introduces a curvilinear aspect, suggesting an even more positive impact at higher levels of secure Internet servers, indicating a potential turning point for amplified effects. These results underscore the importance of Internet usage and the establishment of secure infrastructure in improving access to financial institutions. This highlights the need for policymakers to carefully deliberate on determining optimal levels that can maximize the positive impact on financial sustainability in SSA. These findings align with the conclusions drawn by researchers such as [9,11]. Both studies have outlined the distinctive challenges and opportunities linked to digital adoption in the region.

The interaction effects in the model reveal crucial insights into the joint impact of various factors on FIA in SSA. Firstly, the interaction of ICTEs and PNs shows a negative coefficient of −0.014, indicating a potential trade-off that decreases the FIA index by 0.014 units. Similarly, the interaction of ICTEs and resident patents (PRs) exhibits a negative coefficient of −0.020, suggesting a potential reduction in financial accessibility. Conversely, the interaction of ICTSs and PNs displays a positive coefficient of 0.003, signifying a potential positive synergy contributing to improved financial institutions access. On the other hand, the interaction of ICTSs and resident patents (PRs) shows a negative coefficient of −0.011, suggesting a potential reduction in financial accessibility. Furthermore, the interaction of ICTEs and SISs indicates a positive coefficient of 0.001, emphasizing the potential positive contribution of secure Internet servers to financial institutions’ access. Lastly, the interaction of ICTSs and IUI displays a positive coefficient of 0.006, suggesting a potential positive synergy enhancing financial institutions’ access. These interactions highlight the complex dynamics influencing financial accessibility and provide valuable insights for policymakers in SSA [1,33].

Table 3 presents the findings on FIA in SSA and reveal distinct patterns. Firstly, higher ICTE levels positively impact FIA, underscoring the importance of a strategic balance to avoid diminishing returns. Conversely, increased ICTI levels significantly enhance FIA, indicating potential for positive impacts at higher levels. PNs and PRs positively influence FIA, with discernible turning points for enhanced positive effects at higher levels. There are positive impacts of IUI and SISs on FIA, emphasizing the need for optimal levels to maximize positive effects. The interaction effects underscore potential trade-offs and synergies, urging policymakers to navigate the complex dynamics for sustained improvements in financial accessibility across SSA. Policymakers should consider these findings to formulate effective strategies for promoting innovation, improving financial inclusion, and fostering sustainable financial development in the region [34].

4.3. Digital Trade, Technology, Internet Use, and Financial Institution’s Depth (FID Index)

The previously mentioned Table 4 (shown below) presents the results of the GMM analysis, offering insights into conditional, unconditional, and interaction terms. These findings contribute valuable insights into the relationships between digital trade (information and communication technology (exports and imports)), technology (non-resident and resident patents), and Internet usage (individuals using the Internet and secure Internet servers) and their impact on the FID index in SSA.

First, in Model 1, ICTEs show a substantial positive impact on the FID index in SSA, with a coefficient of 0.092 at a significance level of 0.01. This suggests that a one-unit increase in the percentage of ICT goods exports is associated with a 0.092-unit rise in the FID index, indicating an improved depth and penetration of financial institutions. The squared term, ICTE², introduces a curvilinear aspect, with a positive coefficient of 0.053 at a significance level of 0.01, emphasizing an even more positive impact at higher levels of ICT goods exports. This suggests a potential turning point where further increases may amplify the positive impact on financial institutions’ depth. Policymakers should consider these insights to formulate effective strategies for leveraging the benefits of ICT goods exports to enhance financial institutions’ depth in SSA [12,13]. Similarly, ICTIs demonstrate a statistically significant positive impact on the FID index in SSA, with a coefficient of 0.003 at a significance level of 0.01. A one-unit increase in the percentage of ICT goods imports is associated with a 0.003-unit rise in the FID index, indicating a potential contribution to the depth and inclusivity of financial institutions. The squared term, ICTI², introduces a curvilinear aspect, with a positive coefficient of 0.016 at a significance level of 0.01, emphasizing an even more positive impact at higher levels of ICT goods imports. This suggests a potential turning point where further increases may have an amplified positive impact on financial institutions’ depth. Policymakers should consider these findings when formulating strategies to harness the benefits of ICT goods imports for enhancing the depth and inclusivity of financial institutions in SSA [9].

In Model 2, PNs have a significant positive impact on the FID index in SSA, with a coefficient of 0.034 at a significance level of 0.01. A one-unit increase in non-resident patents is associated with a 0.034-unit rise in the FID index, contributing to the depth of financial institutions. The squared term, PN², introduces a curvilinear aspect, with a positive coefficient of 0.007 at a significance level of 0.01, indicating an even more positive impact at higher levels of patents from non-residents. This suggests a potential turning point where further increases may amplify the positive impact on financial institutions’ depth in SSA [10]. Similarly, PRs show a significant positive impact on the FID index, with a coefficient of 0.018 at a significance level of 0.01. A one-unit increase in resident patents is associated with a 0.018-unit rise in the FID index, contributing to the depth of financial institutions. The squared term, PR², introduces a curvilinear aspect, with a positive coefficient of 0.013 at a significance level of 0.01, indicating an even more positive impact at higher levels of patents from residents. This suggests a potential turning point where further increases in resident patents have a positive impact on the depth of financial institutions in SSA. Policymakers should consider fostering innovation and intellectual property development to support sustained growth and accessibility in the financial sector [23].

In Model 3, IUI significantly influences the FID index in SSA, with a positive coefficient of 0.097 at a significance level of 0.01. A one-unit increase in the percentage of individuals using the Internet is associated with a 0.097-unit rise in the FID index, contributing to the depth of financial institutions. The squared term, IUI², introduces a curvilinear aspect, with a positive coefficient of 0.055 at a significance level of 0.01, suggesting an even more positive impact at higher levels of Internet usage. This implies a potential turning point where further increases in Internet usage may amplify the positive impact on the depth of financial institutions in SSA. On the other hand, SISs exhibit a significant negative impact on the FID index in SSA in Model 3, with a coefficient of −0.049 at a significance level of 0.01. A one-unit increase in the number of secure Internet servers is associated with a decrease of 0.049 units in the FID index, potentially limiting the depth and penetration of financial institutions. The squared term, SIS², introduces a curvilinear aspect, with a negative coefficient of −0.005 at a significance level of 0.01, indicating an even more negative impact at higher levels of secure Internet servers. This suggests a potential turning point where further increases in secure Internet servers may exacerbate the negative impact on the depth of financial institutions in SSA. Policymakers should carefully consider these dynamics when shaping strategies for Internet usage and secure infrastructure to support financial institutions’ depth and inclusivity in the region.

The interaction effects in the model highlight the dynamics influencing the FID index in SSA. The combination of ICT goods exports and non-resident patents (ICTEs×PNs) exhibits a positive synergy, contributing to an increase of 0.037 units in the FID index. Moreover, the joint impact of higher ICT goods exports and resident patents (ICTEs×PRs) shows a more substantial increase of 0.167 units, emphasizing the amplifying effect of resident patents. Conversely, there is a potential trade-off between higher ICT goods imports and increased nonresident patents (ICTSs×PNs), resulting in a decrease of 0.007 units. In contrast, the joint impact of higher ICT goods imports and increased resident patents (ICTSs×PRs) suggests a more significant positive contribution, with an increase of 0.086 units. Despite the potential tradeoff of exporting ICT goods, the combined effect of ICTEs×SISs, focusing on secure Internet servers, contributes positively to the depth and penetration of financial institutions. However, the joint effect of higher ICT goods imports and increased Internet usage by individuals (ICTSs×IUI) indicates a potential negative trade-off, resulting in a decrease of 0.049 units in the FID index. Policymakers need to carefully navigate these complex interactions to formulate effective strategies for enhancing financial institutions’ depth and inclusivity in the region.

Analyzing the implications for the FID index, this study emphasizes the positive impact of ICTEs on financial institutions’ depth, supported by a significant coefficient of 0.092. Additionally, both PNs and PRs exhibit positive influences on the FID index, indicating turning points for enhanced positive effects at higher patent levels. This study highlights the positive contribution of IUI to financial institutions’ depth, while secure Internet servers (SISs) reveal a negative impact that may limit depth. Interaction effects introduce refined dynamics, including positive synergies between ICT goods exports and patents, a potential trade-off between ICT goods imports and non-resident patents, and positive contributions from secure Internet servers. Policymakers are strongly encouraged to factor in these findings when shaping strategies aimed at promoting innovation, improving financial inclusion, and fostering sustainable financial development in SSA. The conditional, unconditional and interaction term results are presented in Table 4.

Table 4. GMM result.

Financial Development Index (FD Index)
Variable	Model 1		Model 2		Model 3
ICTE	0.092 ** (0.019)	0.103 ** (0.021)	0.136 ** (0.017)	0.084 ** (0.020)	0.125 ** (0.018)	0.165 ** (0.022)
ICTE2	0.053 ** (0.010)	−0.030 ** (0.011)	−0.065 ** (0.009)	−0.024 ** (0.011)	−0.049 ** (0.0012)	−0.078 ** (0.0010)
ICTI	0.093 ** (0.028)	0.003 ** (0.036)	−0.080 ** (0.030)	0.023 ** (0.034)	0.125 ** (0.028)	−0.131 ** (0.031)
ICTI2	−0.011 ** (0.002)	0.016 ** (0.003)	−0.009 ** (0.002)	−0.023 ** (0.003)	−0.013 ** (0.002)	−0.005 ** (0.002)
PN	−0.034 ** (0.004)	−0.011 ** (0.009)	0.034 ** (0.008)	0.022 ** (0.009)	−0.013 ** (0.008)	−0.016 ** (0.006)
PN2	−0.006 ** (0.008)	−0.013 ** (0.005)	0.007 ** (0.004)	−0.033 ** (0.005)	−0.006 ** (0.004)	−0.005 ** (0.002)
PR	0.043 ** (0.009)	−0.037 ** (0.010)	0.041 ** (0.009)	0.018 ** (0.010)	0.044 ** (0.009)	0.047 ** (0.011)
PR2	0.004 ** (0.005)	0.019 ** (0.007)	0.008 ** (0.005)	0.013 ** (0.006)	0.004 ** (0.008)	0.003 ** (0.004)
IUI	0.035 ** (0.030)	0.037 ** (0.036)	0.042 ** (0.030)	0.046 ** (0.035)	0.097 ** (0.029)	0.139 ** (0.031)
IUI2	−0.051 ** (0.019)	−0.066 ** (0.022)	−0.054 ** (0.018)	−0.057 ** (0.022)	0.055 ** (0.019)	−0.031 ** (0.016)
SIS	−0.040 ** (0.009)	−0.035 ** (0.011)	−0.043 ** (0.009)	0.039 ** (0.011)	−0.068 ** (0.010)	−0.049 ** (0.009)
SIS2	0.006 ** (0.002)	−0.001 ** (0.003)	0.001 ** (0.002)	−0.002 ** (0.002)	0.003 ** (0.002)	−0.005 ** (0.001)
ICTE×PN	0.037 ** (0.007)
ICTE×PR		0.167 ** (0.009)
ICTS×PR			−0.007 ** (0.004)
ICTS×PR				0.086 ** (0.004)
ICTE×SIS					0.028 ** (0.004)
ICTS×IUI						−0.049 ** (0.003)
GDP	−0.561 ** (0.071)	−0.569 ** (0.085)	−0.562 ** (0.070)	−0.543 ** (0.082)	−0.570 ** (0.071)	−0.445 ** (0.073)
POP	−6.049 ** (0.292)	−6.291 ** (0.353)	−5.951 ** (0.290)	−6.041 ** (0.339)	−6.033 ** (0.289)	−5.838 ** (0.301)
Const.	2.099 ** (0. 093)	1.716 ** (0. 113)	2.225 ** (0.097)	1.394 ** (0.113)	1.894 ** (0.099)	2.360 ** (0.096)
AR (1)	(0.279)	(0.231)	(0.192)	(0.231)	(0.342)	(0.166)
AR (2)	(0.341)	(0.403)	(0.301)	(0.466)	(0.432)	(0.436)
Sargan OIR	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Hansen test OIR	(0.252)	(0.215)	(0.256)	(0.264)	(0.255)	(0.429)
DHT for instruments
(a) Instruments in levels
H excluding group	(0.270)	(0.218)	(0.301)	(0.201)	(0.271)	(0.271)
Dif (null, H = exogenous)	(0.419)	(0.343)	(0.343)	(0.431)	(0.326)	(0.432)
(b) IV (years, eq(diff))
H excluding group		(0.320)	(0.342)		(0.216)	(0.231)
Dif (null, H = exogenous)		(0.418)	(0.301)		(0.414)	(0.452)
Fisher	8427.13 ***	5436.26 ***	8547.76 ***	5840.89 ***	8581.19 ***	7767.73 ***
Instruments	26	32	26	32	26	32
Countries	41	38	41	38	41	38
Observations	984	920	984	920	984	920
VIF	0.021	0.023	0.047	0.038	0.032	0.026
p-values	0.000	0.000	0.000	0.000	0.000	0.000

Note: ***, ** represent significance levels at 1% and 5%, respectively. DHT stands for the difference-in-Hansen test for exogeneity of instruments subsets, and Dif refers to difference. OIR indicates the over-identifying restrictions test. The significance of bold values serves a dual purpose: (1) highlighting the significance of estimated coefficients and Wald statistics, and (2) signaling the failure to reject the null hypotheses of (a) no autocorrelation in the AR(1) and AR(2) tests and (b) the validity of the instruments in the Sargan and Hansen OIR tests. Lagged values of the outcome variables are included in the specifications.

Table 4. GMM result.

Financial Development Index (FD Index)
Variable	Model 1		Model 2		Model 3
ICTE	0.092 ** (0.019)	0.103 ** (0.021)	0.136 ** (0.017)	0.084 ** (0.020)	0.125 ** (0.018)	0.165 ** (0.022)
ICTE2	0.053 ** (0.010)	−0.030 ** (0.011)	−0.065 ** (0.009)	−0.024 ** (0.011)	−0.049 ** (0.0012)	−0.078 ** (0.0010)
ICTI	0.093 ** (0.028)	0.003 ** (0.036)	−0.080 ** (0.030)	0.023 ** (0.034)	0.125 ** (0.028)	−0.131 ** (0.031)
ICTI2	−0.011 ** (0.002)	0.016 ** (0.003)	−0.009 ** (0.002)	−0.023 ** (0.003)	−0.013 ** (0.002)	−0.005 ** (0.002)
PN	−0.034 ** (0.004)	−0.011 ** (0.009)	0.034 ** (0.008)	0.022 ** (0.009)	−0.013 ** (0.008)	−0.016 ** (0.006)
PN2	−0.006 ** (0.008)	−0.013 ** (0.005)	0.007 ** (0.004)	−0.033 ** (0.005)	−0.006 ** (0.004)	−0.005 ** (0.002)
PR	0.043 ** (0.009)	−0.037 ** (0.010)	0.041 ** (0.009)	0.018 ** (0.010)	0.044 ** (0.009)	0.047 ** (0.011)
PR2	0.004 ** (0.005)	0.019 ** (0.007)	0.008 ** (0.005)	0.013 ** (0.006)	0.004 ** (0.008)	0.003 ** (0.004)
IUI	0.035 ** (0.030)	0.037 ** (0.036)	0.042 ** (0.030)	0.046 ** (0.035)	0.097 ** (0.029)	0.139 ** (0.031)
IUI2	−0.051 ** (0.019)	−0.066 ** (0.022)	−0.054 ** (0.018)	−0.057 ** (0.022)	0.055 ** (0.019)	−0.031 ** (0.016)
SIS	−0.040 ** (0.009)	−0.035 ** (0.011)	−0.043 ** (0.009)	0.039 ** (0.011)	−0.068 ** (0.010)	−0.049 ** (0.009)
SIS2	0.006 ** (0.002)	−0.001 ** (0.003)	0.001 ** (0.002)	−0.002 ** (0.002)	0.003 ** (0.002)	−0.005 ** (0.001)
ICTE×PN	0.037 ** (0.007)
ICTE×PR		0.167 ** (0.009)
ICTS×PR			−0.007 ** (0.004)
ICTS×PR				0.086 ** (0.004)
ICTE×SIS					0.028 ** (0.004)
ICTS×IUI						−0.049 ** (0.003)
GDP	−0.561 ** (0.071)	−0.569 ** (0.085)	−0.562 ** (0.070)	−0.543 ** (0.082)	−0.570 ** (0.071)	−0.445 ** (0.073)
POP	−6.049 ** (0.292)	−6.291 ** (0.353)	−5.951 ** (0.290)	−6.041 ** (0.339)	−6.033 ** (0.289)	−5.838 ** (0.301)
Const.	2.099 ** (0. 093)	1.716 ** (0. 113)	2.225 ** (0.097)	1.394 ** (0.113)	1.894 ** (0.099)	2.360 ** (0.096)
AR (1)	(0.279)	(0.231)	(0.192)	(0.231)	(0.342)	(0.166)
AR (2)	(0.341)	(0.403)	(0.301)	(0.466)	(0.432)	(0.436)
Sargan OIR	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Hansen test OIR	(0.252)	(0.215)	(0.256)	(0.264)	(0.255)	(0.429)
DHT for instruments
(a) Instruments in levels
H excluding group	(0.270)	(0.218)	(0.301)	(0.201)	(0.271)	(0.271)
Dif (null, H = exogenous)	(0.419)	(0.343)	(0.343)	(0.431)	(0.326)	(0.432)
(b) IV (years, eq(diff))
H excluding group		(0.320)	(0.342)		(0.216)	(0.231)
Dif (null, H = exogenous)		(0.418)	(0.301)		(0.414)	(0.452)
Fisher	8427.13 ***	5436.26 ***	8547.76 ***	5840.89 ***	8581.19 ***	7767.73 ***
Instruments	26	32	26	32	26	32
Countries	41	38	41	38	41	38
Observations	984	920	984	920	984	920
VIF	0.021	0.023	0.047	0.038	0.032	0.026
p-values	0.000	0.000	0.000	0.000	0.000	0.000

Note: ***, ** represent significance levels at 1% and 5%, respectively. DHT stands for the difference-in-Hansen test for exogeneity of instruments subsets, and Dif refers to difference. OIR indicates the over-identifying restrictions test. The significance of bold values serves a dual purpose: (1) highlighting the significance of estimated coefficients and Wald statistics, and (2) signaling the failure to reject the null hypotheses of (a) no autocorrelation in the AR(1) and AR(2) tests and (b) the validity of the instruments in the Sargan and Hansen OIR tests. Lagged values of the outcome variables are included in the specifications.

4.4. Digital Trade, Technology, Internet Use, and Financial Institutions Efficiency (FIE Index)

Table 5. GMM result.

Financial Development Index (FD Index)
Variable	Model 1		Model 2		Model 3
ICTE	0.052 ** (0.016)	0.084 ** (0.017)	0.085 ** (0.015)	0.053 ** (0.014)	0.110 ** (0.018)	0.112 ** (0.019)
ICTE2	0.033 ** (0.008)	−0.022 ** (0.009)	−0.039 ** (0.007)	−0.016 ** (0.006)	−0.030 ** (0.008)	−0.052 ** (0.009)
ICTI	0.069 ** (0.024)	0.021 ** (0.028)	−0.014 ** (0.026)	0.040 ** (0.025)	0.092 ** (0.023)	−0.067 ** (0.027)
ICTI2	−0.008 ** (0.002)	0.011 ** (0.002)	−0.007 ** (0.002)	−0.015 ** (0.002)	−0.009 ** (0.002)	−0.003 ** (0.002)
PN	−0.023 ** (0.007)	−0.013 ** (0.007)	−0.023 ** (0.007)	0.008 ** (0.007)	−0.016 ** (0.007)	−0.018 ** (0.007)
PN2	−0.002 ** (0.003)	−0.006 ** (0.004)	−0.008 ** (0.002)	−0.019 ** (0.004)	−0.002 ** (0.001)	−0.001 ** (0.003)
PR	0.040 ** (0.008)	0.043 ** (0.008)	0.039 ** (0.006)	0.030 ** (0.007)	0.041 ** (0.008)	0.044 ** (0.008)
PR2	0.007 ** (0.005)	0.017 ** (0.005)	0.009 ** (0.004)	−0.003 ** (0.005)	0.008 ** (0.005)	0.007 ** (0.005)
IUI	0.000 ** (0.026)	0.022 ** (0.029)	−0.001 ** (0.025)	0.003 ** (0.005)	0.003 ** (0.005)	0.096 ** (0.027)
IUI2	−0.022 ** (0.016)	−0.030 ** (0.018)	−0.032 ** (0.020)	−0.007 ** (0.029)	0.007 ** (0.029)	−0.006 ** (0.017)
SIS	−0.002 ** (0.008)	0.001 ** (0.009)	−0.003 ** (0.006)	0.026 ** (0.018)	−0.022 ** (0.008)	−0.009 ** (0.016)
SIS2	−0.007 ** (0.002)	−0.010 ** (0.002)	−0.006 ** (0.003)	−0.002 ** (0.009)	−0.005 ** (0.002)	−0.004 ** (0.002)
ICTE×PN	0.029 ** (0.006)
ICTE×PR		0.095 ** (0.007)
ICTS×PR			0.003 ** (0.004)
ICTS×PR				0.055 ** (0.003)
ICTE×SIS					0.021 ** (0.004)
ICTS×IUI						−0.038 ** (0.003)
GDP	−0.398 ** (0.062)	−0.402 ** (0.067)	−0.381 ** (0.065)	−0.386 ** (0.066)	−0.405 ** (0.061)	−0.307 ** (0.064)
POP	−6.280 ** (0.256)	−6.399 ** (0.280)	−6.372 ** (0.262)	−6.262 ** (0.276)	−6.264 ** (0.254)	−6.114 ** (0.265)
Const.	2.620 ** (0.081)	2.416 ** (0.090)	2.648 ** (0.085)	2.155 ** (0.092)	2.471 ** (0.087)	2.825 ** (0.084)
AR (1)	(0.217)	(0.159)	(0.303)	(0.887)	(0.263)	(0.626)
AR (2)	(0.501)	(0.621)	(0.671)	(0.691)	(0.761)	(0.823)
Sargan OIR	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Hansen test OIR	(0.251)	(0.241)	(0.253)	(0.257)	(0.258)	(0.259)
DHT for instruments
(a) Instruments in levels
H excluding group	(0.221)	(0.237)	(0.206)	(0.207)	(0.329)	(0.421)
Dif (null, H = exogenous)	(0.449)	(0.565)	(0.413)	(0.156)	(0.520)	(0.249)
(b) IV (years, eq(diff))
H excluding group		(0.203)	(0.203)		(0.235)	(0.147)
Dif (null, H = exogenous)		(0.424)	(0.550)		(0.511)	(0.424)
Fisher	108.47 ***	75.98 ***	112.36 ***	49.22 ***	107.55 ***	115.07 ***
Instruments	26	32	26	32	26	32
Countries	41	38	41	38	41	38
Observations	984	920	984	920	984	920
VIF	0.023	0.043	0.054	0.065	0.098	0.045
p-values	0.000	0.000	0.000	0.000	0.000	0.000

Note: ***, ** represent significance levels at 1% and 5% respectively. DHT stands for the difference-in-Hansen test for exogeneity of instruments subsets, and Dif refers to difference. OIR indicates the over-identifying restrictions test. The significance of bold values serves a dual purpose: (1) highlighting the significance of estimated coefficients and Wald statistics, and (2) signaling the failure to reject the null hypotheses of (a) no autocorrelation in the AR(1) and AR(2) tests and (b) the validity of the instruments in the Sargan and Hansen OIR tests. Lagged values of the outcome variables are included in the specifications.

below presents the results of the GMM analysis, offering insights into conditional, unconditional, and interaction terms. These findings contribute valuable insights into the relationships between digital trade (information and communication technology (exports and imports)), technology (non-resident and resident patents), and Internet usage (individuals using the Internet and secure Internet servers) and their impact on the FIA index in SSA.

First, in Model 1, the variable ICTEs has a significant positive impact on the FIE index in SSA, with a coefficient of 0.052. This implies that higher levels of ICT goods exports contribute positively to the efficiency of financial institutions in the region. The inclusion of the squared term, ICTE², with a positive coefficient of 0.033 suggests that the positive impact of ICT goods exports becomes more pronounced at higher levels, indicating a potential threshold where further increases may lead to an amplified positive impact. Policymakers are advised to consider these findings to develop strategies that promote ICT goods exports and ensure sustained improvements in financial institutions’ efficiency in SSA [12,13]. Similarly, ICTIs exhibit a significant positive impact on the FIE index in SSA, with a coefficient of 0.021. This indicates that higher levels of ICT goods imports contribute positively to the efficiency of financial institutions. The squared term, ICTI², introduces a positive curvilinear aspect, suggesting that the positive impact of ICT goods imports on financial institutions’ efficiency becomes even more pronounced at higher levels. This implies a potential turning point where further increases in ICT goods imports may lead to an amplified positive impact on the efficiency of financial institutions in SSA. Policymakers should take note of these findings when formulating strategies to enhance the efficiency of financial institutions in the region [19].

In Model 2, the variable PN (non-resident patent) has a significant negative impact on the FIE index in SSA, with a coefficient of −0.023. This implies that higher levels of non-resident patents are associated with a reduction in the efficiency of financial institutions in the region. The inclusion of the squared term, PN 2, introduces a curvilinear aspect, indicating that the negative impact of non-resident patents becomes even more pronounced at higher levels, suggesting a potential turning point where further increases may lead to a diminishing negative impact on efficiency [14,37]. Conversely, the variable PR (resident patent) has a significant positive impact on the FIE index, with a coefficient of 0.030. This indicates that higher levels of resident patents are linked to an improvement in the efficiency of financial institutions. However, the squared term, PR2, introduces a curvilinear aspect, suggesting that the positive impact of resident patents becomes less pronounced at higher levels, indicating a potential turning point where further increases may result in diminishing positive returns on efficiency in SSA. Policymakers should consider these findings when addressing patent-related factors to enhance financial institutions’ efficiency in the region [44,45].

In Model 3, the variable IUI (individual Internet usage) demonstrates a statistically significant positive impact on the FIE index in SSA, with a coefficient of 0.003. This suggests that higher levels of Internet usage by individuals contribute positively to the efficiency of financial institutions in the region. The inclusion of the squared term, IUI², introduces a curvilinear aspect, indicating that the positive impact becomes more pronounced at higher levels of Internet usage, suggesting a potential turning point where further increases may result in amplified positive returns on efficiency. On the other hand, the variable SISs (secure Internet servers) has a statistically significant negative impact on the FIE index, with a coefficient of −0.009. This implies that higher levels of secure Internet servers have a detrimental effect on the efficiency of financial institutions in SSA. The detrimental effect of higher levels of secure Internet servers on the efficiency of financial institutions in SSA could be related to the increased costs and complexity associated with implementing and maintaining secure infrastructure, which may outweigh the efficiency gains from enhanced security. The inclusion of the squared term, SIS2, introduces a curvilinear aspect, indicating that the negative impact becomes less pronounced at higher levels of secure Internet servers, suggesting a potential turning point where further increases may lead to a diminishing detrimental impact on efficiency. Policymakers should consider these findings when addressing Internet usage and secure infrastructure to enhance financial institutions’ efficiency in the region [15,46].

The negative relationship between the interaction of ICTEs (ICT exports) and resident patents (PRs) on financial accessibility (FIA) may reflect the potential trade-offs between technological export growth and domestic innovation capacity. In some cases, countries that focus heavily on ICT exports might experience a lower emphasis on local innovation, as domestic firms may prioritize international markets over local development. Additionally, resident patents could represent innovations that are not fully aligned with the needs or structures of the domestic financial system, potentially creating a mismatch between the types of technological advancements being patented and those necessary for improving financial accessibility. These dynamics can vary across countries depending on their specific market conditions, policy environment, and level of technological infrastructure.

The interaction effects reveal refined dynamics influencing FIE in Sub-Saharan Africa, with statistically significant coefficients providing valuable insights. The combination of higher ICT goods exports and increased non-resident patents (ICTEs×PNs) suggests a positive synergy, contributing to a modest but statistically significant increase of 0.029 units in FIE (p-value < 0.05). Moreover, the joint impact of higher ICT goods exports and increased resident patents (ICTEs×PRs) exhibits a more substantial positive contribution, emphasizing the amplifying effect of resident patents, with a statistically significant coefficient of 0.095 (p-value < 0.05) [8,47]. Conversely, the combined effect of higher ICT goods imports and increased non-resident patents (ICTSs×PNs) contributes modestly to FIE, with a statistically significant coefficient of 0.003 (p-value < 0.05), while the joint impact of higher ICT goods imports and increased resident patents (ICTSs×PRs) suggests a more significant positive contribution, with a statistically significant coefficient of 0.055 (p-value < 0.05). Despite a potential trade-off in exporting ICT goods, the interaction of ICTEs×SISs emphasizes a positive contribution from secure Internet servers to FIE, with a statistically significant coefficient of 0.021 (p-value < 0.05). However, the joint effect of higher ICT goods imports and increased Internet usage by individuals (ICTSs×IUI) indicates a potential trade-off, leading to a statistically significant decrease of 0.038 units in FIE (p-value < 0.05). Policymakers should carefully navigate these complex interactions, considering the significance levels, to formulate effective strategies for enhancing financial institutions’ efficiency in the region [48,49].

In the context of FIE in SSA, higher levels of ICTEs make a significant positive contribution to financial institutions’ efficiency. Conversely, PNs exhibit a detrimental effect on efficiency, while PRs positively influence FIE. IUI positively contributes to efficiency, but SISs have a negative impact. Interaction effects underscore positive synergies between ICT goods exports and patents, emphasizing the amplifying effect of resident patents. Despite a potential trade-off in exporting ICT goods, the interaction of ICTEs×SISs emphasizes a positive contribution from secure Internet servers. However, the joint effect of higher ICT goods imports and increased Internet usage by individuals (ICTSs×IUI) suggests a potential trade-off, leading to a decrease in FIE. Policymakers are encouraged to consider these findings, acknowledging significance levels, to formulate effective strategies for enhancing financial institutions’ efficiency in SSA [50].

5. Conclusions and Policy Recommendation

This study explores the transformative impacts of digital trade, technology, and Internet adoption on financial sustainability across 41 countries in Sub-Saharan Africa (SSA). Using the generalized method of moments (GMM) for robust statistical inference, the research examines four key financial indicators: the financial development index, financial institutions access, financial institutions depth index, and financial institutions efficiency. The findings support the theory development and hypotheses. This study’s findings align with H1, indicating a positive impact of digital trade, technology adoption, and Internet use on the financial development index (FD index) in SSA. Specifically, ICT goods exports are shown to positively influence financial development, although the impact diminishes at higher levels. ICT goods imports initially exhibit adverse effects, but positive impacts emerge at higher levels. Non-resident patents contribute positively, while resident patents present a more complex relationship. Internet usage exerts a negative influence, and secure Internet servers contribute positively with diminishing impact at higher levels, emphasizing the importance of balance for overall financial sustainability in SSA. Also, the results support H2, revealing a positive and significant association between digital trade, technology adoption, and Internet use with the financial institutions access index in SSA. This study indicates that higher ICT goods exports positively impact FIA, emphasizing the need for a balanced approach. Increased ICT goods imports significantly enhance FIA, while both non-resident and resident patents positively influence FIA, underscoring the importance of optimal levels. Positive impacts of Internet usage and secure Internet servers highlight the need for a refined and balanced strategy.

Interaction effects reveal potential trade-offs and synergies, urging policymakers to navigate complex dynamics for sustained financial accessibility across SSA. The negative interaction between importing ICT goods and increasing Internet usage, as noted in the findings, suggests a potential trade-off, where the benefits of importing ICT goods may not necessarily align with the growth in Internet usage. One possible explanation is that the influx of ICT goods could lead to greater technological access, but without corresponding improvements in the underlying infrastructure or digital literacy, the increased Internet usage might not translate into enhanced outcomes. For example, higher imports of ICT goods may increase the availability of devices, but if Internet infrastructure or affordable access remains limited, the full potential of these technologies may not be realized. Additionally, increasing Internet usage without adequate support in terms of cybersecurity, bandwidth, or quality of service could lead to inefficiencies or frustrations that dampen the positive impacts of both imported ICT goods and increased Internet usage.

The financial institutions depth index findings support H3, emphasizing the positive and statistically significant correlation between digital trade, technology adoption, and Internet use in SSA. This study highlights positive impacts of ICT goods exports and patents on financial institutions’ depth, but also notes that secure Internet servers may limit depth. Policymakers are urged to consider these findings for developing strategies that promote financial innovation and sustainable development. The results are consistent with H4, indicating a positive and statistically significant impact of digital trade, technology adoption, and Internet use on the financial institutions efficiency index in SSA. This study suggests that ICT goods exports positively contribute to FIE, while non-resident patents have a detrimental effect. Internet usage contributes positively to efficiency, but secure Internet servers engender a negative impact. Interaction effects underscore positive synergies, urging policymakers to consider refined findings for enhancing financial sustainability in SSA.

5.1. Theoretical Implications

Firstly, the positive impact of ICT goods exports (ICTEs) on financial institutions’ efficiency aligns with theories emphasizing the role of technology in driving economic development and organizational efficiency. This finding supports the technology acceptance model (TAM) and the innovation diffusion theory, which posit that the adoption and integration of technology, such as ICT goods, can lead to improvements in organizational performance and efficiency. Moreover, the curvilinear relationship observed, where the positive impact of ICT goods exports becomes more pronounced at higher levels, resonates with theories of technological thresholds and network effects, suggesting that there may be a tipping point where further increases in ICT exports result in amplified benefits for financial institutions’ efficiency.

Secondly, the contrasting effects of patent ownership (resident vs. non-resident) on financial institutions’ efficiency contribute to theories of innovation and intellectual property rights (IPRs). The positive impact of resident patents (PRs) suggests that domestically generated innovations enhance financial institutions’ efficiency, supporting theories that emphasize the importance of local innovation ecosystems and knowledge creation for economic growth. Conversely, the negative impact of non-resident patents (PNs) highlights potential challenges associated with technology dependency and the need for policies promoting indigenous innovation capabilities. These findings echo arguments from the national systems of innovation (NSI) framework and the literature on technology transfer, emphasizing the importance of fostering domestic innovation capacity to enhance economic performance.

Thirdly, the results regarding Internet usage (IUI) and secure infrastructure (SIS) underscore the complex interplay between digital connectivity and financial institutions’ efficiency. The positive impact of individual Internet usage aligns with theories of digital inclusion and access, suggesting that broader Internet penetration can democratize financial services and improve efficiency by reducing information asymmetries and transaction costs. These findings contribute to the literature on digital transformation and cybersecurity, emphasizing the need for a balanced approach that promotes digital connectivity while addressing associated risks and vulnerabilities.

5.2. Practical Implications

The practical implications of these findings for managers of financial institutions in SSA are significant and multifaceted. Firstly, understanding the positive impact of ICT goods exports on financial institutions’ efficiency suggests that managers should prioritize investments in technology infrastructure and digital capabilities. This could involve upgrading systems, adopting innovative financial technologies (fintech), and enhancing digital banking services to improve operational efficiency, customer experience, and overall competitiveness. Secondly, recognizing the contrasting effects of resident and non-resident patents implies that managers should actively engage in fostering indigenous innovation and strengthening intellectual property rights protection. Encouraging local research and development initiatives, fostering collaborations with local innovators, and leveraging patent protection mechanisms can help financial institutions to capitalize on homegrown technologies while safeguarding against external threats or technology dependency. Thirdly, acknowledging the positive influence of individual Internet usage underscores the importance of embracing digital inclusion strategies. Managers should focus on expanding access to affordable and reliable Internet services, promoting digital literacy programs, and tailoring financial products and services to meet the needs of digitally empowered consumers. This may involve developing user-friendly mobile banking applications, offering online financial education resources, and leveraging social media platforms for customer engagement. Managers must prioritize cybersecurity investments, implement stringent data protection protocols, and collaborate with industry stakeholders and regulatory bodies to mitigate cyber risks and safeguard sensitive customer information.

Overall, these practical implications underscore the imperative for financial institution managers in SSA to embrace digital transformation, foster innovation, promote digital inclusion, and prioritize cybersecurity to enhance operational efficiency, customer satisfaction, and long-term sustainability in an increasingly digital world.

5.3. Limitations and Future Applied Research

While this study provides valuable insights into the relationship between digital trade, technology, Internet use, and financial sustainability in SSA, several limitations warrant consideration for future applied research. Firstly, this study relies on aggregated data, which may mask variations at the country or regional level. Future research could employ more granular data to capture heterogeneity across SSA countries and explore refined relationships. Secondly, this study primarily focuses on the macro-level impact of digital trade and technology on financial institutions, overlooking micro-level factors such as organizational dynamics and regulatory environments. Future research could delve deeper into how individual financial institutions adapt to digital trends and regulatory changes to improve efficiency. Lastly, this study does not consider contextual factors such as socio-economic development, political stability, and infrastructure availability, which may influence the effectiveness of digital initiatives in SSA. Future research could adopt a contextualized approach to assess how various contextual factors interact with digital trade and technology to shape financial institutions’ efficiency outcomes.

Addressing these limitations through future applied research endeavors will contribute to a more understanding of the dynamics between digitalization, financial institutions, and economic development in Sub-Saharan Africa (SSA).