Nexus Between Financial Technology and Financial Inclusion in Sub-Saharan Africa: A Dynamic Panel Technique

Abstract

Before and after the COVID-19 pandemic, interest in financial inclusion identified financial technology as a significant enabler of financial inclusion in emerging economies. Using the system GMM estimation technique, this study’s main objective was to empirically examine the nexus between financial technology (fintech) and financial inclusion across 30 selected Sub-Saharan African (SSA) economies between 2004 and 2021. Firstly, we used six variables to compute a financial inclusion index and four variables to compute a financial technology index using Principal Component Analysis. The system GMM dynamic panel data estimation was employed to examine the relationship between financial technology and financial inclusion. The analysis showed that financial technology is a decisive determinant factor for financial inclusion in selected SSA countries, as illustrated by the significantly positive relationships. Furthermore, we found that accessibility and usage of the three dimensions impact financial inclusion and maintain a positive relationship with financial technology. These findings imply that financial inclusion will improve as the SSA countries intensify their efforts to enhance financial technology. To promote financial inclusion, it is recommended that Sub-Saharan African countries formulate policies that support a conducive regulatory and investment environment to reduce financial technology data costs, while improving access and usage of financial services. The scholarly significance of this study is that it is one of the first papers to use the 2021 Global Findex Report, which was recently released in 2022, to empirically investigate the impact of fintech on financial inclusion in sub-Saharan African countries, applying system GMM dynamic panel data techniques.

Plain Language Summary

The Relationship Between Financial Technology and Financial Inclusion in Sub-Saharan Africa

This paper sought to examine the effect of various financial technologies, such as mobile money accounts, on the inclusion of people from all backgrounds into the financial system. In some countries, banks do not have ATMs in remote areas, and so those populations are unable to access financial services. By making use of cellphones to facilitate banking in mobile money accounts, as well as internet-based banking, more people can be included in the financial system. For developing countries, this has the benefit of raising more money that can be channelled towards funding productive assets that can then spur economic growth.

Keywords

fintech financial inclusion financial inclusion index financial technology index sub-Saharan Africa (SSA)PCA system GMM

Introduction

At the 2010 G20 summit hosted by South Korea, member states concurred that financial inclusion is a crucial component of the global development program driven by the United Nations (UN) to meet its Sustainable Development Goals (SDGs) around the globe (Organization for Economic Cooperation and Development [OECD], 2019). Specifically, financial inclusion was identified as a significant factor in driving seven out of the 17 identified SDGs (Wallace, 2021; UN (United Nations), 2022). Several scholars advocate that financial inclusion can improve savings, investment, financial shocks and ultimately reduce poverty (Adato et al., 2006; Arun & Kamath, 2015; Beck et al., 2009; Brune et al., 2011; Burgess & Pande, 2005; Demirgüç-Kunt & Klapper, 2012; Mas & Radcliffe, 2009). As such, financial inclusion is of great interest in Sub-Saharan Africa (Zins & Weill, 2016). Following the works of Fanta et al. (2016), Anarfo et al. (2019), Ajide (2020), Anarfo, Abor, et al. (2020), Anarfo, Amewu, et al. (2020), Agwu (2021), Fanta and Makina (2019), Demir et al. (2022), Myeni et al. (2020), Kebede et al. (2021), as well as Alhassan and Yengeni (2022), this paper seeks to quantify the effect of financial technology on financial inclusion in Sub-Saharan African countries.

The economic impact of COVID-19 was not entirely negative around the world. On the positive side, COVID-19 boosted the adoption of financial technology as the use of digital financial services increased worldwide. This paradigm shift is visible in the proliferation of ATMs, offering 24/7 access to financial services and the adoption and usage of mobile phones for financial transactions, enhancing accessibility and user engagement (Jourdan et al., 2023). This highlights the growing importance of financial technology in improving financial services (Kayed et al., 2025). Demirgüç-Kunt et al. (2022) aver that more than one-third of the adult population in developing economies and about 40% of adults in developing countries, excluding China, made a digital merchant payment and paid utility bills from a bank account, respectively, for the first time after the start of the pandemic. According to Andersson-Manjang and Naghavi (2021), formal digital credit, a recent innovation in financial technology, promises to reduce the financial exclusion of women, due to the uptick in the adoption of mobile money networks, accounting for over 1.2 billion subscriptions globally.

The Sub-Saharan African region is the undisputed leader in mobile money accounts, accounting for nearly 46% of mobile money accounts worldwide (International Monetary Fund [IMF], 2022). The expansion of mobile money accounts through mobile phone usage presents opportunities to “bank the unbanked” (Maurer, 2012). Although mobile money accounts are almost double those of traditional deposit accounts, with South Africa, Kenya and Tanzania taking the lead, their adoption still lags in several emerging SSA countries (Suri, 2017). With the impressive progress recorded over the past decades in mobile money-driven activities in the SSA region, access to, and usage of financial goods and services remain relatively subdued in comparison to other areas (IMF, 2022). Demirgüç-Kunt et al. (2022) state that specifically, the SSA region represents the world’s largest share of the unbanked population, with over 50% of its population still financially excluded, with Nigeria and Egypt taking the lead.

The objective of this research is thus to empirically confirm the impacts of financial technology on financial inclusion through its enhancement of access to banking and transactional money services in Sub-Saharan African countries. The scholarly contribution of this paper lies in its dataset and methodological approach. This study is among the first to use the 2021 Global Findex Report, which was recently released in 2022, to empirically investigate the influence of financial technology on financial inclusion in SSA countries. The 2021 Global Findex Report is the most recent available database for the financial inclusion variables adopted for this article. In addition, this is one of the few papers that applies the system GMM technique to assess the fintech-financial inclusion nexus in the sub-Saharan African region. Most previous studies have used the simplified OLS model, which is often flawed and can produce biased outcomes. Our model allowed us to control for endogeneity and autocorrelation.

This study examined the key drivers of financial technology and financial inclusion in selected SSA countries by decomposing financial inclusion into three basic dimensions: accessibility, usage and availability of financial services among its citizens. The effect is that financial technology enhances accessibility of financial services in SSA countries, and the results are significant at 1%. Similarly, financial technology impact usage as a dimension of financial inclusion in the selected SSA countries. Lastly, financial technology has a significant effect on availability as a dimension of financial inclusion. This aligns with the positive relationship that has been observed by previous studies. Overall results proved that other economic and control variables impacted financial inclusion as well. From the above analysis, the impact of financial technology on the financial inclusion of the selected SSA countries shows that it is a strong determinant factor for financial inclusion. The relationships are not only positive but also significant. The results show that as the SSA countries further intensify their effort to improve financial technology, financial inclusion will improve. In this study, the variables of interest for financial technology – mobile phone penetration, internet penetration rate, population density and population size have a strong and significant impact on financial inclusion in SSA countries.

Literature Review

Theoretical Framework

The theoretical framework for the nexus between financial technology and financial inclusion is premised on the belief that many financially excluded people have a mobile phone, and these mobile phones can be used to access financial services. Digital finance can improve access to basic financial services for those who have been financially excluded, resulting in greater financial inclusion (Ozili, 2018). According to Omarini (2024), financial technology has significantly influenced financial inclusion and user engagement. This is reflected in the widespread adoption of financial technology by the growing number of adults with access to bank accounts driving the shift toward financial inclusion and modernization (Thottoli, 2024).

Iluba and Phiri (2021) outlined four major theories and models to articulate factors that have led to technology adoption in the banking sector. They are the Diffusion of Innovation Theory (DIT), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Reasoned Actions (TRA).

Rogers (1995) proposed the Diffusion of Innovation Theory to specifically investigate the behavior of technology adopters in responding to innovative technology. According to Rogers (1995), diffusion explains the process by which a new innovation becomes widely accepted through a social system over a period of time. Rogers (1995) emphasizes that although the ultimate decisions to use a new innovation lie with the specific individuals, they are nonetheless influenced by others, especially friends and families within the social system.

Davis (1989) postulated the Technology Acceptance Model (TAM) to explain the two major factors that drive the use of computer systems in the organization. The TAM was proposed during the early days of the commercialization of computer systems to improve work productivity. Davis (1989) identified two factors that determine the acceptance of a new system by its potential users: the perceived usefulness and ease of use. It is in line with this theory that most fintech products are easy to use and user-friendly.

Venkatesh et al. (2003) came up with the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The UTAUT model is anchored on the following factors: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). According to Venkatesh et al. (2003), these factors can predict a customer’s purchase and the usage intention of a new technology.

The last theory identified by Iluba and Phiri (2021) is the Theory of Reasoned Action (TRA). The TRA which was first proposed by Fishbein and Ajzen (1975), predicts that the intention to engage in certain behavior or activities is actually the best predictor of whether the person will engage in the behavior or activity.

From the above theoretical framework, examining the nexus between financial technology and financial inclusion in SSA countries is important. Furthermore, it is essential to empirically assess the causalities between fintech and financial inclusion, intending to propose a policy framework and strategies to reduce financial exclusion in the Sub-Saharan Africa region.

Despite several empirical studies on financial inclusion in Africa, a knowledge gap still exists on the impact of financial technology on financial inclusion. This is because financial technology is a contemporary phenomenon. There is a growing consensus by scholars to focus on financial technology and how it can drive financial inclusion, especially in SSA countries. This research is significant because very few studies have empirically attempted to analyze the relationships between financial technology and financial inclusion (Fanta & Makina, 2019). Similarly, limited studies have quantified the impact of financial technology on financial inclusion (Agwu, 2021; Ajide, 2020; Anarfo, Abor, et al., 2020; Fanta & Makina, 2019; Kebede et al., 2021). This study is among the few studies that have examined the effect of financial technology on financial inclusion in SSA countries.

In terms of methodology, this research applied quantitative techniques to examine the nexus between financial technology on financial inclusion in SSA countries in the period between 2004 and 2021. Previous studies that have measured the impact of financial technology on financial inclusion have done so by using the unidimensional indicators to examine financial technology on financial inclusion (Alhassan & Yengeni, 2022). This study will be one of the few studies to use multidimensional measures of financial technology on financial inclusion (Figure 1; See Alhassan & Yengeni, 2022; Demir et al., 2022; Fanta & Makina, 2019; Myeni et al., 2020).

Figure 1.

Financial technology and financial inclusion conceptual framework.

Empirical Literature

Financial inclusion refers to the availability of banking services at a market fair price, at the right time, form and time to all members of society, without discrimination (Aduda & Kalunda, 2012). According to Cámara and Tuesta (2017), financial inclusion is an inclusive financial system that maximizes access and usage, while minimizing involuntary financial exclusion. Ozili (2020) considered financial inclusion more directly as the availability and ease of access of formal financial services to the public. Recent definitions of financial inclusion have expanded beyond just conventional financial institutions to include purpose, price and quality of financial services (Pesqué-Cela et al., 2021). This diverges from the early definitions of financial inclusion centered around traditional financial institutions. However, the melting point of these definitions is owning a bank account, which is a key indicator of financial inclusion, according to the World Bank (2022). Contemporary definitions will include owning a bank account either with a bank or through financial technology products.

More literature is exploring the potential benefits of fintech on financial inclusion (Chung et al., 2023; Fanta & Makina, 2019; Kabir Md, 2022; Ozili, 2018; Senyo & Osabutey, 2020). Over the past few decades, financial technology has changed and improved how people access financial services and promote financial inclusion (Suri, 2017). The growing importance of financial technology has attracted an increasing number of researchers to study its impact on a wide variety of fields, especially to reduce financial exclusion in poorer communities (Asongu & le Roux, 2023; Avom et al., 2023; Djahini-Afawoubo et al., 2023; Johnen et al., 2023; Mogaji & Nguyen, 2022).

Financial technology comprises businesses or organizations that develops efficient financial products and services (Hu et al., 2025). The birth of M-PESA in 2007 in Kenya highlighted some of the impacts of financial technology on financial inclusion and what to expect on the continent with appropriate government policies and incentives. Asongu and le Roux's (2023) study examined how mobile money innovations reduced poverty among women in 44 SSA countries. Using interactive quantile regressions, they found that mobile money innovations transformed jobless women into entrepreneurs. Their result confirms that financial inclusion is a handy tool to meet the UN’s SDGs, which, among others, is to reduce poverty. Furthermore, Avom et al. (2023) examined how financial inclusion via mobile money adoption within African countries was improved by innovations within the financial sector. They used parametric and nonparametric methods on panel data between 2004 and 2020 from 50 African countries and found that financial innovations significantly affect financial inclusion in Africa. We can therefore conclude that improving financial inclusion effectively reduces poverty in Sub-Saharan Africa. Similarly, Djahini-Afawoubo et al. (2023) analyzed the effect of mobile money on poverty in Togo. They used Alkire and Foster's (2011) approach to develop a multidimensional poverty index and found that mobile money significantly reduced multidimensional poverty amongst rural residents, women and illiterates in Togo. Johnen et al. (2023) highlighted the importance of mobile money agents in the adoption of mobile money in Kenya. They used logit regression analysis and found that if the agents are closer to the people’s place of residence, and if these agents offer account opening services and are formally trained, then there will be an increase in the adoption of mobile money accounts and services. More recently, Ozili (2024) warned against the reliance on digital-only financial inclusion, arguing that in some cases, it can lead to higher costs of access due to increased internet expenses, and high risk due to exposure to possible online fraud. It has therefore been well documented that the goal of financial inclusion in Sub-Saharan Africa is to reduce poverty, which is more pronounced in the region (Zins & Weill, 2016).

Evans (2018) studied the nexus between the internet, cellular phones, and financial inclusion in African countries between 2000 and 2016. His study established a significant relationship between mobile cellular phones, the internet and financial inclusion. In composing our financial technology index, we also used mobile (cellular) phones and internet penetration to depict the importance of these variables in driving financial technology adoption in SSA. His study also outlined both macroeconomic and institutional factors that are fundamental to financial inclusion in Africa. Evan’s (2018) study supported the results of Mose and Thomi’s (2021) study that examined the factors impacting financial inclusion in East Africa. They found that economic growth and the number of Internet users significantly affect East Africa’s financial inclusion. Again, both studies validate the importance of mobile phones and internet users for financial technology variables.

In Ghana, Senyo et al.’s (2022) study examined how FinTech ecosystem practices are changing and shaping financial inclusion in Ghana. Their findings can be summarized in three ways. First, they found that for FinTech services to impact financial inclusion, there must be collaboration between banks, telecommunication firms and FinTech Firms. Second, regulations must be suitable for both traditional banks and FinTech companies. Lastly, local mobile money agents must build and form trust to drive the adoption and use of FinTech services. Kim’s (2022) study assessed how mobile money affects women’s financial inclusion in Nairobi, Kenya. Through her survey, Kim (2022) realized that women in Nairobi are predominantly in the informal sector of the economy and have been financially excluded because of their inability to provide the necessary documentation that traditional banking institutions require. However, she found that mobile money has significantly improved financial inclusion among women in Nairobi.

Against this literary background, we can conclude that financial technology has significantly improved financial inclusion across nations in SSA.

Research Methodology

Research Approach, Data and Sources

In line with previous studies (Asongu & le Roux, 2023; Avom et al., 2023; Djahini-Afawoubo et al., 2023; Johnen et al., 2023; Mogaji & Nguyen, 2022), this study applies a quantitative approach to consider the relationship between financial technology and financial inclusion. Our study utilized secondary data from 30 SSA countries for the period between 2004 and 2021 sourced mainly from the International Monetary Fund’s Financial Access Survey (FAS) and the World Bank’s Global Financial Inclusion Index (Global Findex) databases.

The exclusion of some countries in the datasets is not random, but rather, they follow the work of Hoogeveen and Pape (2020), who outlined the problems of data collection by World Bank staff in fragile situations, mostly in Africa. For example, Somalia, South Sudan, and Sudan have mostly been in and out of wars. The difficulties of wars are very glaring and need no further explanation. In a war situation, collecting high-quality data in fragile situations is difficult, if not impossible. Similar to previous studies, the study encountered the problem of missing values in the dataset. The study utilized regional means for countries with missing data (Alhassan & Yengeni, 2022) and a 3-year moving average for countries with missing values. This is based on the assumption that countries in the same region would have similar economic, political, social and security indicators.

This study used the min-max normalization to normalize the index obtained in my model equation to avoid negative values. The equation below will be adopted to normalize the data:

Xnormalised = \frac{Xi - \min (X)}{\max (X) - \min (X)}

This study applied the variables in Table 1 below to construct our composite index to proxy financial inclusion (FI; Tables 2 and 3).

Table 1.

Financial Inclusion Variables.

Index	Proxy	Indicator
AC1	Accessibility	Number of ATMs
AC2	Accessibility	Number of commercial banks
AV1	Availability	Number of automated teller machines per 1,000 km²
AV2	Availability	Number of commercial bank branches per 1,000 km²
US1	Usage	Number of automated teller machines per 100,000 adults
US2	Usage	Number of commercial bank branches per 100,000 adults

Source. Authors’ own compilation.

Table 2.

Financial Technology Variables.

Index	Proxy	Indicator
MOB	Mobile phone penetration rate	Mobile phone subscriptions per 100 people of the population
INT	Internet penetration rate	Internet subscription per 100 people in the population
POD	Population density	Population divided by the land area in km²
POS	Population size	The number of individuals in a geographical area

Source. Authors’ own compilation.

Table 3.

Descriptive Statistics Summary.

Variable	Obs	Mean	Std. dev.	Min	Max	Skewness	Kurtosis
AC	540	0	1.358	−0.646	6.59	3.565614	15.04866
US	540	0	1.331	−1.205	4.808	1.974561	6.235151
AV	540	0	1.408	−0.385	8.611	5.15565	28.37249
FI	540	0	1.225	−0.945	5.314	2.395016	8.372959
FT	540	0	0.96	−1.34	3.509	1.110477	4.025891
RGDP	540	1,951.976	2,404.614	134.542	11,643.5	1.93101	5.790115
OPN	540	60.864	22.001	16.352	127.204	0.931064	3.416214
INF	540	7.336	6.573	−3.233	44.357	1.669997	7.56665
HC	540	101.944	20.237	45.977	156.445	0.235864	3.179855
INT	540	7.422	3.467	0.705	22	0.828443	3.849189
PRR	540	1.027e + 09	3.491e + 09	0	2.400e + 10	5.202402	29.39726
GCE	540	13.463	5.302	4.403	36.143	1.313958	5.350975
RIR	540	7.766	9.856	−34.743	52.437	1.067945	7.684713
IQT	540	0	2.21	−4.589	5.8	0.505581	3.192839

Source. Authors’ own computations.

Composite FI Index Construction – Principal Component Analysis (PCA)

In this study, we constructed a financial inclusion index (FI) using the principal composite analysis (PCA) technique following the studies of Sarma (2008), Gupte et al. (2012), Chakravarty and Pal (2013), C. Y. Park and Mercado (2018), as well as Jima and Makoni (2024). We applied the two-stage principal components to estimate financial inclusion to avoid the biases of PCA toward the weights of indicators, which are highly correlated with each other.

Following the suggestion of Cámara and Tuesta (2017), the financial inclusion latent variable can be determined linearly with the following specification:

F I_{i} = {\underline{t}}_{1} a c_{i} + {\underline{t}}_{2} u s_{i} + {\underline{t}}_{3} a v_{i} + ε_{1}

(1)

Where FI denotes financial inclusion index; ac, us, and av presents accessibility, usage, and availability; ε denotes the error term; i represents the host country. The variable and variation in the financial inclusion index are divided into two cases on the variable. The first is the variation associated with the coefficient of the variables, which are ${\underline{t}}_{1}$ to ${\underline{t}}_{2}$ while the variation does not capture the weight of these variables due to the error term ( $ε_{1})$ .

The next step taken in the computation of the financial inclusion index was to employ the principal component analysis (PCA) to estimate the weights of accessibility, usage, and availability. We adopted the two-step PCA technique. Firstly, the three sub-indices of financial inclusion are estimated with the PCA in the following equation.

a c_{i} = β_{1} at m_{i} + β_{2} co m_{i} + μ_{i}

(2)

u s_{i} = β_{1} atm a_{i} + β_{2} com a_{i} + μ_{i}

(3)

a v_{i} = β_{1} atm k_{i} + β_{2} com k_{i} + μ_{i}

(4)

From the above, four models were constructed based on the measures of financial inclusion and its disaggregation. The model specification follows the work of Fanta and Makina (2019) and Alhassan and Yengeni (2022) with some modifications to establish the nexus between financial technology and financial inclusion. The model is different based on measures of financial inclusion used by Fanta and Makina (2019), while the study of Alhassan and Yengeni (2022) used different control variables considered to influence financial inclusion. The models are specified below:

a c_{i} = \propto_{0} + β_{1} F T_{i} + β_{2} X_{i} + μ_{i}

(5)

u s_{i} = \propto_{0} + β_{1} F T_{i} + β_{2} X_{i} + μ_{i}

(6)

a v_{i} = \propto_{0} + β_{1} F T_{i} + β_{2} X_{i} + μ_{i}

(7)

Empirical Model Specification

Equation 8 formulates an empirical model to examine the effect of financial technology on financial inclusion. The model is specified as a dynamic model equation similar to Makina and Walle (2019):

F I_{i} = \propto_{0} + β_{1} F T_{i} + β_{2} X_{i} + μ_{i}

(8)

$F I_{i}$ represents financial inclusion index, FT represents financial technology ( proxy by mob, int, pod and pos), X represents set of economic and control variables such as real economic growth per capita (RGDP), inflation (INF), trade openness (OPN), human capital (HC), deposit interest rate (INT), personal remittances received (PRR), government consumption expenditure (GCE), institutional quality (IQT), real interest rate (RIR) and e is the error term.

One major challenge for the specified models in equations (5) to (8) is that the model estimation with just ordinary least squares and static panel estimates is due to the problem of endogeneity and biased behavior (Ashley & Parmeter, 2020). To further cater for any form of such flaws in the analysis, the dynamic Generalized Method of Moments (GMM) was considered for assessing the models, as these will reduce the tendency of having a spurious result, which validates the statistical inference of this study. The estimation of the dynamic GMM has some advantages over other forms of estimating panel data, which are prone to omitted variable errors, cross-sectional estimation bias, and country-specific effects misspecification (Blundell et al., 2001). The two-step System GMM can be presented as follows:

\begin{matrix} Δ Y_{it} = (1 - α_{0}) Δ Y_{it - 1} + β_{1} Δ FINTEC H_{it} + + \sum_{n = 1}^{i} β_{n} Δ X_{it} \\ + Δ μ_{it} \end{matrix}

(9)

Where Δ denotes change and Y indicates the dependent variables covering the three dimensions of financial inclusion and the financial inclusion index, respectively. The dynamic GMM model utilizes instrumental variables in its regression and the lagged levels of the independent variable (Mehrhoff, 2009).

The main rationale for adopting the system GMM is due to the autoregressive nature of financial inclusion, which makes static models inefficient. For example, the fixed-effect model assumes strict exogeneity, which means the explanatory variables cannot depend on past or future error term values. The fixed-effect model becomes unsuitable with the introduction of lagged financial inclusion values into the model (Verbeek, 2008). With the Pooled OLS, our estimation would create an estimation bias due to the positive correlation between the unobserved country-specific effects and the pre-existing financial inclusion level, which is usually captured as the lagged value of the financial inclusion index. For the random-effects model, the inclusion of the lag value of the dependent variable in the model would violate the assumption that the explanatory variables are uncorrelated with the error term (Agyei et al., 2020).

To check the validity of the instruments, we applied both the Sargan-Hansen tests. There was no autocorrelation, which confirms that our results are reliable and conclusive.

Data Analysis and Discussion of Findings

Summary of Descriptive Statistics

The table below summarizes the descriptive statistics for our selected variables.

As shown by the index, the financial technology (FT) in SSA countries has an average value close to zero. The minimum value (−1.34) indicates that some of the SSA countries are still behind in financial technology compared to other countries within the region. The maximum value (3.509) with the value of its standard deviation (0.96) further confirms the discrepancies between the fintech levels among the SSA countries. We observed that countries in Eastern Africa have better and broader financial technology representation than any other region in the SSA. The standard deviation for accessibility, usage and availability of financial services shows a moderate gap in accessing financial services in Sub-Saharan African countries. Other economic variables show a slight but insignificant variation across the region, except for real gross domestic product.

Composite Financial Inclusion Index

The principal component analysis (PCA) was first constructed for the three measures of financial inclusion (access, usage and availability), and the outcome is presented below.

The common factors retained in our analysis are based on the selection criteria of Kaiser (1974) and Jolliffe (2002). They state that the eigenvalues must be greater than one to be considered. These are points above the point where the scree plot becomes flatter. As shown in Table 4, the number of ATMs (ATM) has a value of 1.845, which represents about 92% of the input variable. The number of Automated Teller Machines per 100,000 adults (ATMA) has an eigenvalue of 1.773, which represents 88% of the explained variation of the input variable. The number of ATMs per 1,000 km²(ATMK) dimension of financial inclusion has an eigenvalue of 1.983 and represents over 99% of the variation of the input variable. They satisfy all the PCA criteria and were selected for the financial inclusion index construction.

Table 4.

Principal Component Analysis for the Dimensions of Financial Inclusion.

Principal components	Component		Matrix		Loadings
Principal components	AC1	AC2	US1	US2	AV1	AV2	Proportion	Cumulative proportion	Eigenvalue
ATM	0.707	0.707					0.922	0.922	1.845
COM	0.707	0.707					0.078	1.000	0.155
ATMA			0.707	0.707			0.886	0.886	1.773
COMA			0.707	0.707			0.114	1.000	0.227
ATMK					0.707	0.707	0.992	0.992	1.983
COMK					0.707	0.707	0.008	1.000	0.017

Source. Authors’ own computations.

For the financial inclusion index, the PCA was conducted using the six indicators employed for the dimensions. The result is presented in Table 5.

Table 5.

PCA for Financial Inclusion.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	3.169	1.235	0.528	0.528
Comp2	1.934	1.320	0.322	0.850
Comp3	0.614	0.411	0.102	0.953
Comp4	0.203	0.135	0.034	0.987
Comp5	0.069	0.057	0.011	0.998
Comp6	0.011	.	0.002	1.000

Source. Authors’ own computation.

The Principal Component Analysis (PCA) result presented in Table 5 revealed that the first component (the number of ATMs) has an eigenvalue of 3.169, which is greater than one and explains about 52% variation in the combined variables. The second component (the number of commercial bank branches) has an eigenvalue of 1.934. Both components will be considered since their eigenvalues are greater than one. It explains 32% of the variability in the input variables. Combining the two components shows that about 85% of the explained variation is accounted for. Therefore, the number of ATMs and commercial bank branches will be considered to explain what influences financial inclusion in Sub-Saharan African countries (SSA). These two components strongly impact financial inclusion in SSA countries.

The loadings of each variable are shown in Table 6. However, since our focus is on the component that has an eigenvalue greater than one. We will focus on components 1 and 2 that passed this selection criterion. The number of commercial bank branches per 100,000 adults of the population (COMA) has the most substantial effect in the first component (Comp1). In contrast, ATM (number of ATMs) has the most substantial effect in the second component (Comp2) on financial inclusion.

Table 6.

Loadings of Financial Inclusion.

Variable	Comp1	Comp2	Comp3	Comp4	Comp5	Comp6
ATM	0.251	0.612	0.130	0.466	−0.574	0.011
COM	0.206	0.600	0.420	−0.480	0.436	0.008
ATMA	0.451	0.181	−0.646	0.303	0.493	0.107
COMA	0.508	−0.096	−0.339	−0.622	−0.464	−0.124
ATMK	0.473	−0.313	0.366	0.257	0.151	−0.675
COMK	0.458	−0.354	0.375	0.087	−0.008	0.719

Source. Authors’ own computations.

In line with the above PCA outcomes, we constructed the financial inclusion index for the selected SSA countries using the specific principal component analysis (PCA) equation:

AC = 0.707 * ATM + 0.707 * COM

(10)

US = 0.707 * ATMA + 0.707 * COMA

(11)

AV = 0.707 * ATMK + 0.707 * COMK

(12)

\begin{matrix} FI = (0.251 * ATM + 0.206 * COM + 0.451 * ATMA \\ + 0.508 * COMA + 0.473 * ATMK + 0.458 * COMK) \\ + (0.612 * ATM + 0.600 * COM + 0.181 * ATMA \\ + (- 0.096 * COMA) + (- 0.313 * ATMK) \\ + (- 0.354 * COMK)) \end{matrix}

(13)

Where:

AC = accessibility (AC); US = usage; AV = availability; FI = Financial Inclusion Index; ATM = number of ATMs; COM = number of Commercial bank branches; ATMA = number of ATMs per 100,000 adults; COMA = number of Commercial bank branches per 100,000 adults; ATMK = number of ATMs per 1,000 km²; COMK = number of Commercial bank branches per 1,000 km².

Composite Index of Financial Technology

The index for financial technology is constructed using the four variables listed in Table 7. They are mobile phone subscriptions per 100 population (MOB), Internet subscriptions per 100 population (INT), population density (POD), and the population size (POS). The results are presented in Table 7.

Table 7.

PCA of Financial Technology.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	1.886	0.872	0.471	0.471
Comp2	1.014	0.099	0.253	0.725
Comp3	0.914	0.728	0.229	0.953
Comp4	0.186	.	0.047	1.000

Source. Authors’ own computations.

The outcome revealed that the first component (mobile phone penetration rate) has an eigenvalue of 1.886 and 47% of the explained variation of the input variables. The second component (Internet penetration rate) has an eigenvalue of 1.014, having 25% of explained variation in the input variable. The remaining two components have eigenvalues that are less than one and will, therefore, not be considered. The total variation explained by the two components is 72.5% and that will serve as the financial technology measure for the SSA countries, as they have a greater impact on financial technology than both population density (POD) and population size (POS).

Table 8 shows the factor loading for the principal component analysis (PCA) of financial technology. The first component (Comp1), INT (Internet subscriptions rate), has the strongest effect, while POS (population size) has the strongest effect in the second component. Based on these results, one can conclude that these two components are critical to constructing the composite index for financial technology in SSA countries. In line with the above principal component analysis (PCA), the study constructed the financial technology index for the selected SSA countries using the specific principal component analysis (PCA) equation:

\begin{matrix} FT = (0.688 * MOB + 0.681 * INT + 0.299 * POD \\ + 0.011 * POS) + ((- 0.128 * MOB) \\ + 0.048 * INTR + 0.141 * POD + 0.981 * POS) \end{matrix}

(14)

Table 8.

Loadings for Financial Technology.

Variable	Comp1	Comp2	Comp3	Comp4
MOB	0.668	−0.128	−0.239	0.693
INT	0.681	0.048	−0.176	−0.709
POD	0.299	0.141	0.942	0.063
POS	0.011	0.981	−0.158	0.116

Source. Authors’ own computations.

Panel Unit Root Test

To further estimate the dynamic panel regression model, the stationarity of the panel series is important to guide the analysis. Previous studies have also ascertained the stationarity of data series by employing panel unit root tests. The major concern of the unit root test is to ensure that none of the variables used to explain the phenomenon under study is stationary at the second difference which is I(2; Bertelli et al., 2022; Voumik et al., 2023).

The order of integration in this panel study was conducted using the first and second-generation unit root tests to have a robust analysis. The first-generation unit root test has their hypothesis focused on cross-sectional independence and these are Levin et al. (2002) and Im and Pesaran (2003). However, the assumption of cross-sectional independence is relaxed in the second-generation unit root test and Phillips and Perron (Im & Pesaran, 2003; Levin et al., 2002; Moon et al., 2007). The null hypothesis guiding the test is that the null hypothesis is not stationary while the alternative states that the panel is stationary and has no unit root.

The results of the unit root test for the panel are presented in Table 9 and the outcome from the first and second-generation unit root test shows that the stationarity is of mixed order of integration.

Table 9.

Panel Unit Root Test.

Variables	Levin, Lin and Chu (LLC)		Im–Pesaran–Shin (IPS)		Phillips–Perron (PP)
Variables	Statistic	Order	Statistic	Order	Statistic	Order
AC	−6.094***	I(0)	−3.164***	I(1)	−10.44***	I(1)
US	−7.341***	I(0)	−1.799**	I(0)	−1.927***	I(0)
AV	−5.447***	I(0)	−3.178***	I(1)	−10.48***	I(1)
FI	−7.266***	I(0)	−2.983***	I(1)	−9.377***	I(1)
FT	−7.379***	I(1)	−3.058***	I(1)	−10.13***	I(1)
lnRGDP	−8.747***	I(0)	−3.624***	I(1)	−5.619***	I(0)
OPN	−2.008**	I(0)	−4.049***	I(1)	−16.41***	I(1)
INF	−6.658***	I(0)	−3.167***	I(0)	−10.73***	I(0)
HC	−2.697***	I(0)	−4.156***	I(1)	−16.60***	I(1)
DINT	−5.004***	I(0)	−3.601***	I(1)	−13.73***	I(1)
lnPRR	−4.066***	I(1)	−2.722**	I(0)	−4.759***	I(0)
GCE	−3.995***	I(0)	−4.431***	I(1)	−18.79***	I(1)
RIR	−4.750***	I(0)	−3.523***	I(0)	−11.50***	I(0)
IQT	−6.978***	I(0)	−4.151***	I(1)	−16.93***	I(1)

“***” and “**” indicate the level of significance of the test at 1% and 5% respectively.

The results revealed that the order of integration differs across the generation of the test for some of the variables while some are aligned in their order of integration. The key variables of interest are financial inclusion (FI) and financial technology (FT) and are all stationary at the 1% and 5% levels of significance. The results of financial inclusion (FI) show different stationarity across the generation of tests. Furthermore, other variables that serve as control variables have different orders of stationarity. Seeing that all the variables employed to explain the phenomenon of financial inclusion in the SSA countries do not have an integrated order of I(2), it can be concluded that the variables are in the right order and do not violate the assumption of the ARDL. Dynamic panel estimation was employed for the next section of the analysis.

Since the sample of this study is a short panel, considering the difference GMM over the system GMM which takes independent variables and lagged value of the dependent variable as the instrument will be inappropriate.

The main rationale for adopting the system GMM is the endogeneity problem that it addressed. The approach also adequately tackles the autocorrelation and heteroscedasticity problem due to its robustness. As the economic variable has been dynamic, the financial inclusion across the SSA countries differs from region to region and there are also country-specific factors. Hence, the use of GMM will further assist in addressing the cross-country heterogeneity that might be present in the data set. In addition, the methodology also assists with the identification of country-specific effects, controlling for missing and unobserved relationships, as well as capturing the past and current shocks.

The relationship between financial inclusion and financial technology was ascertained by employing the system general method of moments (GMM) approach. The results as presented in Table 10 showed the key variable having a positive effect while some other economic variables included in the model are identified as positive drivers of financial inclusion in the SSA countries under study.

Table 10.

GMM Estimate of Financial Inclusion.

	(1)	(2)	(3)	(4)
Variables	AC	US	AV	FI
FT	2.2170** (0.8405)	0.7438*** (0.1844)	0.3381 (0.2506)	1.2134** (0.4995)
lnRGDP	0.4526 (0.6849)	0.5524*** (0.1924)	−1.0650*** (0.3775)	0.6230 (0.3822)
OPN	−0.0980** (0.0395)	−0.0189 (0.0127)	0.0342* (0.0180)	−0.0610* (0.0312)
INF	0.0931 (0.0975)	0.0244* (0.0130)	0.0346 (0.0402)	0.0531** (0.0218)
HC	0.0332 (0.0371)	−0.0007 (0.0059)	−0.0304** (0.0117)	0.0053 (0.0141)
DINT	0.6533** (0.2936)	0.1172** (0.0441)	−0.2025*** (0.0632)	0.2992** (0.1442)
LNPRR	−0.2866 (0.3106)	−0.1679** (0.0740)	−0.0796 (0.1707)	−0.1223 (0.1964)
GCE	1.0476*** (0.3338)	0.2752** (0.1205)	−0.2955*** (0.0787)	0.5737** (0.2359)
RIR	−0.2238 (0.1592)	−0.0091 (0.0095)	0.0552** (0.0208)	−0.0237 (0.0217)
IQT	−1.2628** (0.5798)	−0.0789 (0.1867)	1.2952*** (0.2284)	−0.6042 (0.4617)
Constant	−12.8795 (8.3245)	−4.1540 (3.3082)	14.7291*** (4.6666)	−9.0861 (7.1156)
Observations	512	512	512	512
Number of C_ID	29	29	29	29
Number of groups	29	29	29	29
Number of instruments	14	14	12	14
AR1 p-value	.033	.026	.034	.058
AR2 p-value	.224	.236	.282	.065
Sargan p-value	.619	.008	.215	.544
Hansen p-value	.527	.451	.547	.964

Standard errors in parentheses.

***

p < .01, **p < .05, *p < .1.

To address possible endogeneity issues in the model, the two-step system GMM was estimated, and the result is presented in Table 10. The rationale behind the two-step system GMM is that it is robust to tackle autocorrelation and heteroscedasticity problems. The number of instruments used is one of the changes made in the experimentation, and there is no restriction on the number of instruments that can be used. However, the guide is that the number of instruments should not exceed the number of groups. In this analysis, the number of groups is 29, and the number of instruments employed, as shown in Table 10, is between 12 and 14 instruments.

Where: FT = Financial Technology Index; MOB = mobile phone subscriptions per 100 population; INT = Internet subscriptions per 100 population; POD = population density; POS = population size.

System GMM Results and Discussion

The values of the Hansen statistics in the estimated models are 0.527, 0.451, 0.547, and 0.964, respectively, indicating that our instruments are suitable for the four models. Therefore, the null hypothesis of the Hansen test of overidentification is not rejected. The value of the AR(2) statistics is of concern as it reflects whether the model suffers from second-order autocorrelation. If it suffers from second-order autocorrelation, it means some of the lagged values of the dependent variables are endogenous variables and cannot be employed as instruments. The test has the null hypothesis of no second-order autocorrelation, and the p-values of the AR(2) in the estimated models are all above the 5% level of significance.

The effect of financial technology is significant on the accessibility and usage dimension of financial inclusion. The effect is positive for both dimensions. However, availability does not have a significant impact on financial technology. The effect of financial technology is positively significant on financial inclusion as well. Our results support the findings of Kim (2022) and Senyo et al. (2022) that confirmed that financial technology impacts financial inclusion.

The effect of real GDP, which serves as a proxy for economic growth, is not a significant determinant of the accessibility dimension of financial inclusion. However, the effect of real GDP is statistically significant for usage, and it is positive, indicating that a growth in the economy increases usage. On the other hand, real GDP is inversely related to the availability dimension of financial inclusion.

Trade Openness was found to have a negative impact on the accessibility dimension of financial inclusion. The effect of trade openness was not statistically significant for usage and availability dimensions, nor on financial inclusion. This indicates that SSA countries largely rely on imports of goods, and their foreign direct investment is low (Do & Levchenko, 2007). To address this imbalance, it is important for SSA countries to attract foreign investors to invest and expand production locally. Cases of exporting raw materials and then importing the finished goods are prevalent in the region. The government must attract the needed foreign investment to manufacture this raw material locally.

Inflation does not significantly affect any of the dimensions of financial inclusion. However, the effect of inflation was positively significant on financial inclusion as a whole. This indicates that as the inflation rate rises, so does the level of financial inclusion. This should not be surprising because it supports the result of Shukayev and Ueberfeldt (2018), who argued that a moderate level of inflation can positively affect financial inclusion.

The effect of human capital was only significant for availability out of the three dimensions of financial inclusion. The relationship between human capital and accessibility, usage, and financial inclusion is insignificant. The insignificant relation can be explained since primary school enrollment was used as a proxy for human capital in SSA countries. The results might be different if we used secondary or higher education enrollment. According to Ray et al. (2022), financial literacy in developed economies provides adequate information to compare financial products and better access financial services and benefits.

The effect of deposit interest is statistically significant for all the dimensions of financial inclusion and the financial inclusion index. The deposit interest rate has a positive relationship with all dimensions of financial inclusion, except for availability. The results reflect the attractiveness of high deposit rates to influence consumers’ choice to use financial services. We have also seen some financial technology products that offer high deposit rates to their customers.

The effect of personal remittance received was not significant for the dimensions of financial inclusion, except for usage. The relationship between personal remittance received and usage is negative. Personal remittances received do not significantly affect the financial inclusion index. According to the study of Ardıç et al. (2022), remittances through formal institutions like banks are not popular among Africans due to documentation and foreign exchange conversion costs associated with most bank transfers. Remittances through travelers by families and friends are still common in SSA.

Government consumption expenditure has a significant effect on all the dimensions of financial inclusion. The effect was positive for the accessibility and usage dimension of financial inclusion, but it maintains a negative relationship with availability. The effect of government consumption expenditure is positively significant on financial inclusion because of huge government spending, such as transfers, tax incentives, and monetary subsidies, that are provided to support the financial inclusion drive (Abeka et al., 2021).

The relationship between real interest rates and the availability dimension of financial inclusion is significantly positive, but its effect is insignificant for other dimensions of financial inclusion. Institutional quality is positive and negative for accessibility and availability dimensions of financial inclusion, respectively. The impact of institutional quality on the two dimensions is significant, while its impact on financial inclusion is insignificant.

Contribution to Knowledge

The scholarly contribution of this paper is in its dataset and methodological approach. Firstly, this study is among the first to use the 2021 Global Findex Report, which was first released in 2022, to empirically investigate the impact of financial technology on financial inclusion in sub-Saharan African countries. The 2021 Global Findex Report is the most recent available database for the financial inclusion variables adopted in this study. The 2021 and 2017 edition of the Global Findex database, for the first time, provides data on mobile phone ownership and access to the Internet. The set of data is critical to any study of financial technology. These technologies can help overcome barriers that have prevented the unbanked population from accessing financial services. In addition, this is one of the few papers that applies the system GMM dynamic panel data technique to assess the financial technology-financial inclusion nexus in the sub-Saharan African region. Most previous studies have used the simplified OLS model, which is often flawed as it can produce biased outcomes. Our model allowed us to control for endogeneity and autocorrelation.

This study focuses on financial technology as a tool to improve financial inclusion in SSA countries. According to Iman and Albert (2020), financial technology is a relatively new subject in academic literature. This study has contributed significant findings to this “virgin” field of finance and to propose future research opportunities. It is of interest to know that most literature available comes from news websites, working papers, policy studies, conference papers, contributing reports and technical reports. This implies that financial technology has not yet been over-researched (Romānova & Kudinska, 2016). According to Gabor and Brooks (2017), financial technologies such as “mobile money” present new and interesting opportunities for financial inclusion.

The outcomes of this study have provided a policy direction for the government as to why it is important to establish regulatory authorities for adequate supervision of financial technology companies in Africa. This study provided better insights and policy direction for the government on financial technology to attain the Sustainable Development Goals (SDGs) of the United Nations (UN). The World Bank and the United Nations recognized financial technology as a significant player for fighting poverty (Feyen et al., 2023). The rapid development of financial technology products in SSA countries must be accompanied by the appropriate regulatory laws if the intended outcome of an all-inclusive growth must be achieved.

Conclusion and Recommendations

This study examined the key drivers of financial technology and financial inclusion in selected SSA countries with the following conclusions.

The effect of financial technology enhances accessibility of financial services in SSA countries, and the results are significant at 1%. Overall results proved that other economic and control variables impacted financial inclusion as well. The effect of real gross domestic product (RGDP) which is the first control variable has a positive and significant effect on financial inclusion. Inflation is found (INF) to be a factor that drives financial inclusion as well. The effect of inflation was positive on financial inclusion in the selected SSA countries. The deposit interest rate (DINT) is found to be a positive driver of financial interest. The positive effect found in this study showed that deposit interest rates move in the same direction as financial inclusion in the selected SSA countries. However, human capital (HC) has a significant effect on financial inclusion at a 1% level, but the effect has a negative effect on it.

The effect of financial technology on financial inclusion is positive and significant at the 1% level. This outcome is consistent with the findings of previous studies, and it indicates that as financial technology increases so does usage as a dimension of financial inclusion in the selected SSA countries. Real gross domestic product (RGDP) has a significant effect on usage as a dimension of financial inclusion at the 5% level. The relationship between the two variables is positive and it indicates they both move in the same direction. The results aligned with the findings of previous studies. The results further revealed deposit interest rate as a driving factor of usage as a dimension of financial inclusion in the selected SSA countries under consideration. The results are significant at the 1% level, and it indicates that the deposit interest rate and usage move in the same direction. Government consumption expenditure has a positive effect on usage and the impact is significant at the 1% level. The positive relationship indicates that the government consumption expenditure enhances usage of financial services. It was found that personal remittances received have a significant but negative effect on usage as a dimension of financial inclusion. As personal remittance received increases, usage as a dimension of financial inclusion reduces.

Financial technology has a significant effect on availability as a dimension of financial inclusion. This aligns with the positive relationship that has been observed by previous studies. The results are an indication that financial technology is a strong determinant of the availability dimension and they both move in the same direction. Personal remittance received is also a strong determinant of availability in financial inclusion. This indicates that as the inflow of remittances increases, so does the availability in the same direction. The effect of institutional quality on availability as a dimension of financial inclusion is significant and positive indicating that institutional quality improves the availability of financial inclusion. It was also found human capital to be a determining factor for the availability dimension of financial inclusion.

As a result of the findings, this study has a policy implication for major stakeholders- senior government officials and government advisors, legislators and policy makers, and players and regulators of financial industry in sub-Saharan Africa.

Financial technology and financial inclusion have a clear short-and-long run relationship and a bi-directional relationship. Bank regulators should increase the capital base of commercial banks for better working capital and expansion. With a better capital base to work with, commercial banks are better positioned to provide and expand the following: access to financial services by increasing the number of ATMs and commercial bank branches, usage of financial services by increasing the number of ATMS per 100,000 adults and the number of commercial bank branches per 100,000 adults, and availability of financial services by increasing the number of ATMSs per 1,000 km² and the number of commercial bank branches per 1,000 km^2. With more access, usage and availability of financial services, the masses and the unbanked can be financially included. In addition, monetary and fiscal policies should complement each other. Both monetary and fiscal policies play a significant role in shaping the development of financial markets in SSA countries. We advise that central banks should address factors such as high interest rates and inflation that are prevalent in most SSA countries in order to draw more customers into the banking sector.

The price of mobile and Internet data is among the highest in sub-Saharan countries. Although Sub-Saharan Africa has made significant progress to reduce its coverage gap over the last decades, it remains the region with the largest coverage gap and even higher cost. The mobile phone is one major tool that Sub-Saharan Africans use to access the Internet. It was confirmed that mobile phone and Internet penetration rates are the major determinants of financial technology in SSA countries. In order to drive costs down and shift demand to the right, the government can break the monopoly in the mobile industry by providing an enabling environment for free entry and exit to the telecommunication industry. The government can also give tax incentives to encourage more players into the industry. Tax holidays and reduced tax rates are specific investment incentives that can be used to attract foreign direct investment (FDI) to SSA countries. Thus, the study recommends additional incentives for telecommunication companies to provide affordable access to rural dwellers where the coverage gap is predominant.

Financial technology and financial inclusion are complementary policy objectives for the financial regulator. The study established and found that financial technology and financial inclusion have bi-directional causality. As a result, policy makers and regulators must prioritize policies and regulations that encourage adopting of financial technology while promoting financial inclusion. For example, Nigeria liberalized its telecommunications sector at the turn of the 21st century with the deregulation of the industry and the introduction of the Global System for Mobile communications (GSM) network platforms in the country. With this, sectors like information technology and banking finance significantly improved over the years paving the way for the adoption of financial technology and by extension financial inclusion. For example, this deregulation increased subscribers from around 11,400,000 in 2004 to over 180,250,00 in 2023 with over US$25 billion into the country. Other SSA countries can implement similar reforms to improve their adoption of financial technology.

To attract foreign investors to invest in FinTech companies and the financial system to help accelerate the expansion of low-cost financial products and services, policy makers should refrain from encouraging government officials from using public power for private gain and any form of grand corruption that is widespread in SSA countries. The government can create a new and separate agency dedicated to fighting economic and financial corruption in SSA countries. This will create a more stable and predictable business environment for foreign investors. In addition, the government should allow for complete judiciary independence to resolve issues between government agencies and investors. This creates confidence in the rule of law and ultimately makes SSA countries more attractive investment destinations.

The above recommendations are only possible in an atmosphere of collaboration between, private sector and civil society. A cohesive action among all stakeholders would often yield more significant results, especially in a complex environment in Sub-Saharan Africa. It is necessary to address infrastructural, physical, and bureaucratic barriers that will hinder private sector participation and partnership.

This study identified and analyzed the determinants of financial technology and financial inclusion. It is difficult if not impossible to cover all aspects of the relationship in a single study. Furthermore, this study only analyzed a few factors out of the several factors that affect the causality between the variables of interest. The focus was on the pool of countries and no country level effort was made for comparison. The study also had limitations of data availability and only selected 30 countries for the analysis. Without time and resources constraints, it is possible to increase the number of countries. Lastly, the composite index for financial technology and financial inclusion can be expanded to accommodate more individual indicators of financial technology and financial inclusion and then compare the results.

From the above limitations of this study, future studies should increase the sample size to accommodate more countries and examine the causality between financial technology and financial inclusion and undertake comparison across nations. This will enable future studies to observe the country’s peculiarities and possible reasons for variation. In addition, future research can increase the number of individual indicators in the construction of composite index in examining the relationship among the selected variables.

In terms of methodology, the data was secondary data. With adequate funding, future research can use primary data and then use other approaches to determine the same relationship and compare the findings. The use of primary data will guarantee high frequency data and will include other specific variables and constraints that were missing in previous studies since it is generally accepted that the concept of financial inclusion is broad and multidimensional in nature.

Footnotes

ORCID iDs

Ibukun Kolawole Oyedotun

Patricia Lindelwa Makoni

Ethical Considerations

This study received ethical approval from the UNISA CEMS Research Ethics Committee for Finance, Risk Management and Banking (Approval no. 0397) on March 13, 2023.

Consent to Participate

There are no human participants in this article, and informed consent is not required.

Consent for Publication

The authors consent to the publication of the article.

Author Contributions

conceptualization (IKO; PLM); literature review (IKO); methodology (IKO; PLM); data analysis (IKO); reviewing (PLM); conclusions (PLM).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data is available upon reasonable request from the corresponding author.

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