Research in International Business and Finance 67 (2024) 102156

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Research in International Business and Finance

journal homepage: www.elsevier.com/locate/ribaf

Information technology and financial development for achieving

sustainable development goals
Sabrine Dhahri a, Anis Omri b, *, Nawazish Mirza c
a
Department of Economics, Higher Institute of Management of Sousse, Sousse University, Tunisia
b
Department of Business Administration, College of Business and Economics, Qassim University, Buraidah, Saudi Arabia
c
Excelia Business School, La Rochelle, France

A R T I C L E I N F O A B S T R A C T

Keywords: The present study aims to answer the question of the effectiveness of information and commu­
ICT nication technologies (ICT) and financial development in achieving sustainable development
Financial development goals (SDGs). Specifically, it examines the joint effects of ICT (mobile phone and internet use) and
Sustainable development goals
eight financial sector development indicators on achieving economic, social, and environmental
Sub-Saharan Africa
sustainability in light of the SDGs using data from 48 Sub-Saharan African countries. Using the
system Generalized Method of Moments, we found that (i) the four dimensions of financial
development and both indicators of ICT increase economic, social, and environmental sustain­
ability, (ii) increasing the access to mobile phones and the use of Internet contributes to the
development of the financial sector, (iii) the contribution of financial sector development on the
achievement of the SDGs increases with the presence of ICT. The study calls on policymakers to
consider the diffusion of ICT and the advantages they offer in elaborating measures for achieving
sustainable development goals.

1. Introduction

In 2015, the United Nations General Assembly adopted the 17 sustainable development goals (SDGs) to be achieved by 2030 (UN
General Assembly, 2015). The SDGs are more ambitious and detailed than the Millennium Development Goals (MDGs) to improve
current and future generations’ living conditions (WCED, 1987; UN General Assembly, 2015). The 17 SDGs revolve around three main
economic, social, and environmental sustainability dimensions (Purvis et al., 2019; Dhahri et al., 2021). Therefore, achieving them
requires a partnership between the political planner, the public and private sectors, and the urban civilization that could ensure a
better planet for future generations.
Nevertheless, Sub-Saharan African (SSA) countries still face many SDG-related challenges. Income inequality has increased due to
job shortages, food price shocks, slower boom informal activity compared to informal ones, and weak social protection (ILO, 2020;
World Bank, 2020). Indeed, the labor market situation is dramatic in this region (Sumberg et al., 2021; Cieslik et al., 2022). Socially,
the latest estimates show that the poverty rate in SSA countries represents around 40.2% of people living on less than US$1.90 per day
in 2018, which is estimated to double by 2030 (World Bank, 2020). Thus, SSA is squeezing to be among the world’s poorest pop­
ulations if the current economic challenges will not be directly resolved (World Bank, 2020). Environmentally, SSA countries face
serious climate changes and global warming added to many social and economic facts. According to Edziah et al. (2022), SSA countries

* Corresponding author.
E-mail address: a.omri@qu.edu.sa (A. Omri).

https://doi.org/10.1016/j.ribaf.2023.102156
Received 10 March 2023; Received in revised form 9 August 2023; Accepted 24 October 2023
Available online 28 October 2023
0275-5319/© 2023 Elsevier B.V. All rights reserved.
S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

experienced a significant increase in CO2 emissions of 15.48% between 1995 and 2017. Leimbach et al. (2018) project that carbon
dioxide emissions in SSA may increase by 50% by 2050.
On the one hand, SSA has been marked by a massive increase in digital connectivity (International Monetary Fund, 2019). In 2019,
272 million people were connected to mobile and Internet services in SSA (International Telecommunication Union, 2021). In
addition, the region has also developed digital innovation in financial technology, causing new services and various applications to be
created (International Monetary Fund, 2019). On the other hand, SSA received 60% of the inflows from advanced economies (In­
ternational Monetary Fund, 2021). Recently, resource-backed loans, mainly used for infrastructure development, represent 164 billion
dollars: 66 billion dollars allocated to SSA and 98 billion dollars allocated to Latin American countries (World Bank, 2022). Thus,
policymakers in this region must implement “the urgently needed bold and transformative steps to shift the world onto a sustainable and
resilient path” (UN General Assembly, 2015). This is what this current study seeks to achieve.
Recently, many scholars have widely viewed the link between financial and sustainable developments as a controversial issue
(Abbasi et al., 2021; Hunjra et al., 2022). Thus, there is a growing consensus among researchers that the financial sector helps attain
economic, social, and environmental sustainability. In this context, several studies have intensely discussed the impact of financial
development on economic growth (Schumpeter, 1934; Ruiz, 2018; Cheng et al., 2021). They have proclaimed that financial devel­
opment is one of the key determinants of economic growth as it efficiently increases resource allocation for productive activities,
enabling savings allocation for people and supplying credit and insurance. For instance, using data from 47 SSA countries, Abeka et al.
(2021) asserted that the impact of financial development on economic growth would be efficient if it had strong information
communication and technologies. There is now considerable attention in the literature related to the impacts of financial development
on social sustainability (Zhang and Naceur, 2019; Alam et al., 2021; Khan et al., 2022). Recently, Khan et al. (2022) found that
financial inclusion reduces poverty and income inequality and improves financial stability in 54 African countries. The literature about
the impact of financial development and environmental sustainability is divided on the validity of the Environmental Kuznets Curve
(EKC) phenomenon, ranging from supportive to unsympathetic. Some studies found that financial development mitigates climate
change. For example, referring to data from 88 developing countries, Khan and Ozturk (2021) highlighted that financial development
reduces CO2 emissions. Nevertheless, the impact of financial development on CO2 could produce mixed results depending on the
indicators of financial development (Acheampong, 2019). Raheem et al. (2020) reveal that the interactive term between ICT and
financial development negatively affects environmental quality.
Additionally, empirical studies dealing with ICT’s role in economic, social, and environmental sustainability have drawn the
attention of many scholars. Some relevant studies have highlighted that ICT diffusion increases a nation’s wealth by developing skills
and facilitating the exchanges between public and private management (Hong, 2017; Shetewy et al., 2022). There is also considerable
literature on the link between ICT and social sustainability (Bouzguenda et al., 2019; Nandelenga and Oduor, 2020). Most of these
researches show that ICT is the key to citizens’ development. Indeed, many studies have examined the impact of ICT on pollutant
emissions (Wen et al., 2021). For instance, Wen et al. (2021) have investigated the relationship between industrial digitization and the
environmental performance of Chinese manufacturing enterprises. They found that industrial digitization penetration reduces
manufacturing emissions and encourages green product innovation. Recently, technological progress has enabled major trans­
formations in the financial and banking sectors in SSA (International Monetary Fund, 2019; Cheng et al., 2021). It has also facilitated
financial operations flexibility at the bank branch and guaranteed the banking sector’s safe functioning through banking risk disclosure
(Alshubiri et al., 2019).
As far as we know, no previous study has examined the ICT role in supporting the financial sector to achieve the 17 SDGs in SSA
countries at both aggregate and disaggregate (economic, social, and environmental sustainability) levels. This paper tries to fill this
gap using the system General Method of Moment method (system GMM) for panel data composed of 48 SSA countries during the
2000–2018 period. This study has at least three main contributions. First, this paper is devoted to how African decision-makers can
achieve sustainable development in line with the United Nations SDGs. As an extension of Das et al. (2018), who only examined the
joint effect of ICT and financial development on economic growth, we seek to extend previous literature on achieving the SDGs by
demonstrating how ICT supports financial sector development for attaining sustainable development in SSA countries at both
aggregate and disaggregate levels. Specifically, by relying on the works of Costanza et al. (2016) and Dhahri et al. (2021) related to the
theoretical classification of the 17 SGDs, we are studying the joint effects of ICT (mobile phone and Internet use) and eight financial
sector development indicators on achieving the three dimensions of sustainability (economic, social and environmental). They
especially suggested that Goals 7, 8, 9, 11, and 12 are related to economic sustainability (1); Goals 1, 2, 3, 4, 5, 10, 16, and 17 revolve
around social sustainability (2); Goals 6, 13, 14 and 15 represent environmental sustainability (3). Based on this division and using
principal component analysis (PCA), we have created three sustainability indicators corresponding to the economic, social, and
environmental dimensions. To the best of our knowledge, this inquiry is the first to examine the link between financial development,
ICT, and three dimensions of the 17 SDGs in an integrated framework. Secondly, previous studies such as Abeka et al. (2021) have
examined the impact of ICT and financial development, measured by domestic credit to the private sector, on economic growth in SSA.
However, this measure may have limited any ability to capture the financial development multidimensional aspect. This study tries to
fill this gap by proposing three composite indexes of financial sector development considering its four dimensions. Third, in terms of
results, we found that the contribution of financial sector development to achieve sustainable development goals increases with the
presence of ICT diffusion.
The rest of this paper is structured as follows. Section 2 briefly reviews the related literature. Section 3 describes the econometric
methodology and data sources. Section 4 presents and discusses the empirical findings. The last section is devoted to the conclusion
and implications.

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2. Literature review

We divide this section into three subsections. The first subsection provides an overview of SDGs’ definitions and dimensions. The
other ones present the theoretical arguments and empirical evidence on the role of ICT and financial development in achieving the
SDGs.

2.1. Sustainable development goals (SDGs)

In recent decades, numerous definitions have been proposed for sustainable development and sustainability because of their
complexity (Lozano, 2008; Wuelser et al., 2012). In 1980, the International Union first used the term “sustainable development” for
Nature Conservation (IUCN, 1980). The World Commission on Environment and Development (1987, p.15) has defined sustainable
development as "ensuring that [development] meets the needs of the present without compromising the ability of future generations to meet their
own needs." This definition has been developed to embrace the environmental, economic, and social dimensions, recognized as the
triple bottom line (e.g., Choi and Ng, 2011). According to the United Nations (1992), the 17 SDGs, comprised of 169 targets and 232
specific indicators, seek to eliminate poverty, promote social and economic inclusion, and mitigate environmental degradation.
Indeed, it is important to measure the SDGs to facilitate monitoring the 2030 Agenda’s effectiveness (Swain and Yang-Wallentin,
2020).
There is now considerable attention in the literature regarding SDGs measurement (Spaiser et al., 2017; Dhahri et al., 2021). In this
study, we followed Dhahri et al. (2021) to calculate the scores for each SDG using several observed indicators, then its overall index
using the principal component analysis (PCA) method. The social, economic, and environmental dimensions of sustainability help
highlight the current state of sustainability and suggest directions for policymakers to formulate policies and programs that could
stimulate countries to achieve sustainability.

2.2. Theoretical arguments

Three main theories were used to explain the relationship between ICT, financial development, and the SDGs: the environmental
Kuznets curve (EKC), Schumpeter’s Theory of Economic Development, and the Diffusion of Innovation Theory.
Regarding the EKC theory, Grossman and Krueger (1993) confirmed that economic growth increases with environmental degra­
dation; however, up to a certain turning point, economic growth reduces environmental degradation. This observation is indicated by
the environmental Kuznets curve (EKC) hypothesis, which symbolizes an inverted U-shaped relationship between economic growth
and environmental degradation. Therefore, in the early stage of economic growth, using inefficient technologies by industries with
high-energy consumption and long equipment life in production leads to environmental degradation and pollution. Besides, at the
turning point, financial development encourages companies to take action to reduce CO2 emissions by investing in environmentally
friendly technologies in the industrial sector (Adams and Klobodu, 2018; Renzhi and Baek, 2020). Thus, financial development is
useful for promoting efficient technology and sustainable development.
Moreover, Schumpeter’s Theory of Innovation (Schumpeter, 1939) discussed how innovation in the financial sector affects growth
and development. Schumpeter asserted that financiers or financial intermediaries rely on high technology to provide funds to stagnant
and risky companies to help them develop new ideas and expand their operations (Schumpeter, 1939). Hence, Schumpeter believes
that expanding corporate business through these funds will help raise employment and income, allowing people to save children’s
education and access to necessary health care. Finally, Rogers (1962) developed the diffusion of innovation theory, which states that
innovation in the financial system is accepted when it is widely diffused. This can happen when residents have more access to the
financial system. As a result, innovation is critical to achieving sustainable economic development (Kardos, 2012).
According to these theories, financial development and ICT can improve sustainability in three areas: economic growth, envi­
ronmental protection, and social sustainability.

2.3. Empirical literature and hypotheses development

2.3.1. Financial development and SDGs

We have divided this section into three strands: the first presents the impact of financial development on the economic dimension,
whereas the second focuses on the impact of financial development on the social dimension. The last one reflects the impact of financial
development on the environmental dimension. Firstly, pioneers who have worked in economic growth widely considered financial
sector development as one of the main factors for national economic growth (Schumpeter, 1911; McKinnon, 1973). Nevertheless,
many controversial results have surrounded the existing literature on the relationship between financial sector development and
economic growth, as no consensus has been reached among scholars. Accordingly, we divided the empirical results into two broad
groups. The first group has asserted that the development of the financial sector has a positive effect on economic growth (Assefa and
Mollick, 2017). In this context, Cheng et al. (2021) claimed that developing the financial markets makes it possible to accumulate
savings, direct resources towards more productive investments, reduce transaction and information costs and promote the trade
industry.
In contrast, the second group has criticized that the development of the financial sector has a positive effect on economic growth
only under threshold factors presence (Taiwo, 2021; Karim et al., 2022). For instance, Aluko and Ibrahim (2020) have examined the
effect of financial development on economic growth by considering the institution level as a threshold variable in 28 SSA. They

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revealed that the financial sector promotes growth in highly institutionalized countries. Recently, Karim et al. (2022) found that
financial inclusion is beneficial and positively affects economic growth at lower or upper-bound levels in a different system. Moreover,
they found a positive impact of financial inclusion on promoting growth among least developed and emerging countries compared to
developed countries. Secondly, many scholars, such as Kaidi et al. (2019), Tchamyou et al., 2019, among others, view the financial
sector development as a solution to several common social issues in SSA. Nonetheless, Rashid and Intartaglia (2017) claimed that the
effect of financial sector development in reducing poverty varies depending on the proxies used to measure financial sector
development.
Using data from 46 SSA countries, Chireshe and Ocran (2020) have shown that financial sector development positively affects
health expenditure. Recently, Gallego-Losada et al. (2023) consider that economic inclusion represents the cornerstone of social
development by lowering poverty, enhancing incomes, and permitting sound economic choices that contribute to growing financial
savings and reducing gender inequality. Finally, previous studies have discussed the importance of financial development to enhance
environmental quality. Tamazian et al. (2009) have argued that increasing the allocation of credit at low-cost facilitates purchasing
energy-efficient technologies, which in turn leads to reduced carbon emissions. Overall, the link between financial sector development
and environmental quality has shown three contradictory results: (i) financial sector development promotes the quality of the envi­
ronment (Nasir et al., 2019; Zaidi et al., 2019); (ii) financial sector development destroys the quality of the environment by increasing
carbon emissions (Ali et al., 2017; Baloch et al., 2019; Murshed et al., 2023); (iii) financial sector development has an insignificant
effect on environmental quality (Ozturk and Acaravci, 2013).
In sum, empirical studies on the effect of financial sector development on environmental quality remain rare and controversial in
SSA. Abid (2016) has found that the advancement of financial resources is negatively correlated with CO2 emissions in 25 SSA
countries. Adams and Klobodu (2018) have asserted that the development of the financial sector has an insignificant impact on
environmental quality in 26 African countries. More recently, Omri et al. (2021) revealed that financial sector development decreases
carbon emissions in the presence of solid institutional and political governance. Numerous studies found a positive impact of money
supply on environmental sustainability. For example, Vo and Zaman (2020) found that increasing the money supply helps reduce CO2
emissions in the long term using a large panel of 101 global economies. Additionally, Mahmood et al. (2022) found that money supply,
which represents both liquid cash and financial sector deposits, negatively affects territory-based CO2 (TBC) and consumption-based
CO2 (CBC) emissions in the long term and positively affects it in the short run. They explained this result because the money supply
caused foreign direct investment and expansion in the short term; however, it raised cleaner technologies in the long term.
The present study follows the work of Ben Youssef et al. (2020), who investigated the impact of financial development on achieving
sustainable development in14 the Middle East and North African countries (MENA), and they found that financial development has a
small impact on sustainable development in the long term. However, two large gaps characterize this study of Ben Youssef et al. (2020)
and the above-discussed studies on the relationship between financial development and sustainable development, especially in the
case of SSA countries. On the one hand, they did not specify the effects of financial sector development on the three dimensions of
sustainable development in an integrated framework and in line with the 17 SDGs. Accordingly, we divided the 17 SDGs into three
economic, social, and environmental dimensions, where each one is associated with specific goals: the economic dimension includes
goals 7, 9, 11, and 12; the social dimension incorporates goals 1, 5, 10, 16 and 17; environmental dimension encompasses the goals 6
and 13–15. On the other hand, when examining the effects of financial development, their study uses only one indicator of financial
development, whereas the nature of financial sector development is multidimensional. This study fills this second gap by considering
various indicators of financial development (including depth, inclusion, efficiency, and stability) to examine their effects on the three
dimensions of the 17 SDGs at the aggregate and disaggregated level in SSA countries.
Based on this discussion, we expect that financial sector development is relevant for achieving the SDGs in SSA countries at both
aggregate and disaggregate levels. As a result, we provide the following hypothesis:
Hypothesis 1. Financial development positively contributes to achieving the SDGs in SSA at both aggregate and disaggregate levels.

2.3.2. The role of ICT

We divide this subsection into two strands. The first one reviews the role of ICT in the three dimensions of sustainable development.
The second strand highlights the role of ICT in financial sector development. Many academics have assessed the effect of ICTs diffusion
on the economic dimension of sustainability. Accordingly, Ben Youssef et al. (2018) have contended that a higher level of innovation
and good institutional quality is a driving force for achieving a higher level of entrepreneurship and sustainability. Yet, few studies
have studied the impact of ICTs on economic growth in SSA. Donou-Adonsou et al. (2016) have examined the role of telecommuni­
cations infrastructure in SSA economic growth. They have confirmed that both mobile phones and the Internet boost economic growth.
Using information from 40 SSA countries, Haftu (2019) investigated the effect of mobile phone and Internet use on GDP per capita
from 2006 to 2015. They revealed that a 10% increase in mobile phone penetration contributed to a 1.2% rise in GDP per capita. They
claimed that boosting mobile phone service has a favorable influence on per capita income, reducing the number of impoverished
individuals in the region. They have asserted that promoting access to cell phones positively impacts per capita income, thus lowering
the number of poor people in the region. Many studies believe that ICT is crucial for promoting many social problems. First, it supports
gender empowerment (Ojo et al., 2013; Asongu et al., 2021). Second, it helps to improve health care and education (Haluza and
Jungwirth, 2018). Third, it reduces inequality (Adams and Akobeng, 2021).
Moreover, the impact of ICT on environmental sustainability has also received great attention in the literature and has come up
with mixed results. Some authors have found that ICT significantly reduces CO2 emissions (Park et al., 2018; Ozcan and Apergis, 2018).
Others have claimed that the effect of ICTs on environmental quality varies depending on the development degree of a country degree.

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(Al-Mulali et al., 2015; Danish et al., 2018). Besides, Higón et al. (2017) and Shahnazi and Shabani (2019) have confirmed a non-linear
relationship between ICT and CO2 emissions, which supports the environmental Kuznets curve. Recently, Amri et al. (2019) found an
insignificant impact of ICT on carbon emissions. In the case of SSA, Asongu et al. (2018) have evaluated the influence of rising ICT
penetration, as measured by mobile phone and Internet penetration, on CO2 emissions. They have found that Internet adoption has
increased per capita CO2 emissions from liquid fuel consumption. However, mobile phone harms it. Meanwhile, Danish et al. (2018)
successfully identified that the interaction effects between ICT and economic growth decrease CO2 emissions in SSA.
Moving to the second strand of studies about the effect of ICT on financial sector development. Since the turn of the century, the
adoption of ICT has steadily increased. Thus, several studies have examined its benefits, particularly on financial sector development.
Choi et al. (2014) have indicated that Internet service availability enhances capital flow across borders by reducing information
asymmetries and transaction costs in the financial markets. For those reasons, the World Bank (2017) has established cooperation with
its clients to support the ICT industry through technical assistance and business loans. This has underpinned many financial industries
to integrate ICT while modernizing internal processes and providing advanced mobile phone services such as bill payment, re­
mittances, and cash transfers. Pellegrina et al. (2017) have proposed that increasing the Internet prevalence allows small businesses to
access credit facilities from various financial institutions. Likewise, Boateng et al. (2018) have supported that ICT increases the
likelihood of information flows in the financial marketplace between lenders and borrowers. Recently, Gallego-Losada et al. (2023)
argued that the rapid development of ICT is the backbone of these transformations in the banking and financial sectors. However,
limited studies examined the impact of ICTs on the financial sector development in the case of SSA. Subsequently, Shamim (2007)
examined the impact of ICT on financial sector development for 61 countries over the 1990–2002 period. They found that mobile
phones and Internet subscriptions have a strong positive effect on financial depth. Using a sample of 72 countries, Cheng et al. (2021)
investigated the relationship between financial sector development, ICT, and economic growth over the 2000–2015 period. They have
demonstrated that the interplay between ICT and financial sector development positively impacts economic growth in middle and
low-income-level countries. Recently, Abeka et al. (2021) have argued that developing the financial sector stimulates economic growth
in SSA since it has a robust telecommunication infrastructure.
A closer look at the link between ICT, sustainable development, and financial development reveals certain shortcomings. On the
one hand, we observed a large body of literature about the impact of ICT on economic and environmental sustainability. Yet, its effect
on social sustainability seems rarely addressed in the literature. In addition, the results about the ICT impact on environmental sus­
tainability look inconclusive. Thus, it is important to carry out another study in this field, especially in SSA. Furthermore, the question
about the impact of ICT on the three dimensions of the 17 sustainable developments has never been addressed in any integrated study.
On the other hand, the impact of ICT on financial sector development remains limited. This could be explained by the fact that the ICT
revolution was still in its infancy when their study was conducted. The above-discussed theoretical arguments show that ICT is a
crucial correlation between financial sector development and sustainable development. To our knowledge, no prior study has analyzed
the joint effects of ICT and financial development on the three dimensions of the 17 SDGs in SSA at both aggregate and disaggregate
levels. To fill these gaps, we assume that ICT promotes financial sector development, which in turn makes sustainable development.
Hence, we propose the following hypothesis:
Hypothesis 2. ICT boosts financial sector development for achieving the SDGs in SSA at both aggregate and disaggregate levels.
In light of the above arguments and hypothesis, we propose the following conceptual model of study:

3. Data and methodological approach

3.1. Data

Based on the two hypotheses mentioned above, this research uses annual and unbalanced panel data to examine the role of ICT in
developing the financial sector to achieve the SDGs at the aggregate and disaggregate levels (economic, social, and environmental
sustainability) in 48 SSA countries during the 2000–2018 period.1 More details about the list of countries will be presented in Table A
in the appendices.
The datasets were gathered from the World Development Indicators (WDI), The World Bank’s Global Financial sector development
Database (GFD), and the Standardized World Income Inequality Database (SWIID). We have divided this subsection into three parts for
more details about the construction and the used variables. First, we describe the construction of the sustainable development indices.
Second, we outline the construction of the financial sector development indices. Finally, we present the ICT indicator and the control
variables.

3.1.1. Construction of the sustainable development index (SDI)

We calculated the three dimensions of sustainability based on the 17 SDGs using the Principal Component Analysis (PCA). Table 1
shows all of the 17 SDGs indicators’ measurements and sources.
A composite index is a group of indicators with no standard unit of measure or a clear way to assign their weights (Dhahri et al.,
2021). The construction of each composite indicator must have a well-defined objective by following a generally accepted set of

1
The choice of period and the number of countries were determined by the availability of data.

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Table 1
The 17 SDG indicators, sources, and dimensions.
Goals Indicator Source Dimension type

Goal 1: No poverty Poverty headcount ratio at $1.90 a day (2011 PPP) (% of the WDI Social dimension
population)
Goal 2: Zero hunger Prevalence of undernourishment (% of the population)
Goal 3: Good health and well-being Births attended by skilled health staff (% of total)
Goal 4: Quality education Children out of school (% of primary school age)
Goal 5: Gender equality Proportion of seats held by women in national parliaments (%)
Goal 6: Clean water and sanitation People using at least basic drinking water services (% of the Environmental
population) dimension
Goal 7: Affordable and clean energy Access to clean fuels and technologies for cooking (% of the Economic dimension
population)
Goal 8: Decent work and economic growth GDP per capita growth (annual %)
Goal 9: Industry, innovation, and infrastructure Manufacturing, value added (% of GDP)
Goal 10: Reduce inequalities GNI Coefficient SWIID Social dimension
Goal 11: Sustainable cities and communities Urban population (% of the total population) WDI Economic dimension
Goal 12: Responsible consumption and Adjusted net savings, excluding particulate emission damage (% of
production GNI)
Goal 13: Climate action CO2 emissions (metric tons per capita) Environmental
Goal 14: Life below water Total fisheries production (metric tons) dimension
Goal 15: Life on land Forest area (106 km2)
Goal 16: Peace, justice, and strong institutions Completeness of birth registration (%) Social dimension
Goal 17: Partnerships for the goals Exports of goods and services (% of GDP) (%)

Notes: WDI: World Development Indicators; SWIID: Standardized World Income Inequality Database.

measures (Dhahri et al., 2021). These steps are structured as follows:

• Theoretical framework formulation: In 2012, at the UN Rio+ 20 Summit, governments decided to develop an integrated set of SDGs
to close the gaps of the Millennium Development Goals (MDGs). In 2015, the UN General Assembly officially endorsed the “2030
Agenda for Sustainable Development, " including 17 SDGs (UN, 2015). From this perspective, Costanza et al. (2016) have classified the 17
SDGs under three dimensions to provide an overall measurement and understanding of sustainable development (see Table 1). Barbier
and Burgess (2019) argued that choosing an appropriate indicator for each objective is difficult.
• Missing data calculation: A composite indicator must have complete data to be robust. There are three possible imputation
techniques to fill the missing data: (i) omitting observations with missing data; (ii) substituting individual imputation with mean or
median or regression; and (iii) multiple assumptions. The missing data for this study were completed using a simple mean-replacement
imputation.
• Data standardization: the standardization of indicators is crucial because some of them can be positively or negatively correlated
with the measured phenomenon. There are also many data standardization methods. In this study, we applied the MIN MAX method to
solve this issue.
The data standardization (Min-Max) is presented in Eqs. (1) and (2) as follows:

X′t,i,j − X′t,min,j
Xt,i,j = (1)
X′t,max,j − X′t,min,j

X′t,i,j − X′t,min,j
Xt,i,j = 1 − (2)
X′t,max,j − X′t,min,j

where Xt,i,j is the observed value for the variable i measured on the spatial unit j in the year t, X′t,min,j , X′t,max,j , which represent the
minimum and maximum values for the variable i measured in all the spatial units j, respectively. We applied Eq. (1) to variables that
positively relate to sustainability. However, Eq. (2) was applied to variables that negatively relate to sustainability.

• Weighting and aggregation of the composite index: we used the principal component analysis to reduce a large set of correlated
variables to a small set of uncorrelated ones, known as the principal components. This process aimed to explain the variance of
observed data across linear sets of the original data (Abou-Ali and Abdelfattah, 2013).

The general form of the principal component is presented in Eq. (3) as follows:
PCp = bp1 X p1 + … + bpn X pn (3)

where PCp is the P principal component, bpn is the regression coefficient of the n variables of the Pth component, and Xpn is the value of
the n variables.
There are two steps before the PC’s storage for PCA analysis adequacy. The first one is to estimate representative data adequacy
using two statistical tests: (i) the Kaiser-Meyer-Olkin (KMO) test, which allows testing the sample adequacy with any value greater than
0.50. (ii) The test of Bartlett’s Sphericity, where the level of significance must have a value less than 0.5 (p-value < 0.5) (Abou-Ali and

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Abdelfattah, 2013). The second step is to choose the number of principal components using two criteria: (i) the cumulative variance
explained at least 60–70% of the total information; (ii) the principal components that have an eigenvalue higher than one (Kaiser,
1960).2
Table 2 presents the construction of the Sustainable Development Index (SDI). The economic dimension is calculated using two
selected PCs (economic1 and economic2), which explained 61.319% of the total variance. The social dimension is examined using four
selected PCs (social1, social2, social3, and social4), which accounted for 61.399% of the total variation. The environmental dimension
is evaluated using two PCs (envirnmt1 and envirnmt2), which explained 69.016% of the total variance. We calculated the weighted
average of the selected PCs using Eq. (4) as follows:
∑m
F ∗λ
∑m Pi i
Ip = i=1 (4)
i=1 λi

Where Ip is the sustainability index, which represents the weighted average of m PC values for the unit p. Fpi indicates the value of the
Ith PC for the unit p, while λi denotes the Eigenvalue of the Ith PC.
The SDI is calculated for each country as the simple arithmetic mean of the three indices (Sagar and Najam, 1998; UNDP, 2008).
The SDI formula of the three indices (economic, social, and environmental dimensions) is presented in Equation (5) as above:
The calculations of these indices are presented in Eqs. (6), (7), and (8), respectively, as follows:
SDI = 1/3 *economic dimension+ 1/3 * social dimention+ 1/3 *environnemental dimension (5).
Where,

Economic dimension= (1⋅530* economic1+ 1⋅036* economic2)/2⋅566 (6)

Social dimension= 1⋅982*social1+1⋅762*social2+1⋅168*social3+1⋅015*social4)/5⋅927 (7)

Environnemental dimension= (1⋅563*envirnmt1+1⋅198*envirnmt2)/2⋅761) (8)

3.1.2. Construction of the financial sector development indices

As the financial sector development has multidimensional aspects, we used eight financial indicators, reflecting the four financial
dimensions, i.e., financial depth, financial inclusion, financial efficiency, and financial stability. The financial depth is measured by
four indicators which are the domestic credit to the private sector (DC), deposit bank assets (DB), money supply (MS), and liquid
liabilities (LL). These are expressed as a share of GDP. Financial inclusion is measured by the number of automatic teller machines per
100,000 adults (AM). Financial efficiency is measured with the bank’s net interest margin as a percentage (BM). Financial stability is
measured with two indicators: the bank’s Z-score (BZ) and the bank’s ratio of non-performing loans to gross loans in percentage (BL).
These variables have been gathered from the World Bank’s Global Financial Sector Development Dataset.
We followed the same approach by Kassi et al. (2020) to construct the composite financial sector development indices.3 Their
technique has been adopted by several studies thanks to its relevance in analyzing complex, correlated, and multidimensional variables
(Asongu and Odhiambo, 2020, among others). Thus, following Cheng et al. (2021), we constructed three composite financial sector
development indices using the PCA method. The first composite index of financial sector development (FSI 1) represents the financial
depth, which is composed of the variables DC, DB, MS, and LL. The second composite index of financial sector development (FSI 2)
includes a financial depth indicator DC, a financial inclusion indicator AM, a financial efficiency indicator BM and a financial stability
indicator BL. The third composite index of financial sector development (FSI 3) comprises the bank’s Z-score financial stability in­
dicator, a DC indicator, an AM indicator and a BM indicator. The results in Table 3 about the construction of the financial sector
development indices show that the first component (PC1) is the only component retained for FSI 1, FSI 2, and FSI 3 because their
cumulative variance represents more than 60% of the total information, respectively. Moreover, following the Kaiser-Guttman rule,
the first component for the three financial development indices has an eigenvalue greater than one.4

3.1.3. ICT and control variables

In this study, we used mobile phone and Internet use subscriptions for 100 people as a proxy of ICT. We have also included a set of
control variables to reduce the concerns about the omitted variable bias. These variables are GDP growth, which is measured by annual
percentage (GDP); foreign direct investment, which is measured by net inflows as a share of GDP (FDI), remittances measured as a
share of GDP (R); trade openness, measured by total imports plus exports of goods and services as a share of GDP (TO), and rule of law
(RL). All these variables were collected from the WDI statistics except RL, gathered from World Wide Governance Indicators (WGI).
Overall, we expect a positive impact of ICT on the financial sector development to achieve the 17 SDGs. Besides, we assume that the

2
After we identified the number of components to be extracted for the three dimensions of sustainability, we rotate them using the Varimax
rotation with Kaiser Normalization to correct the cross loading only for the components 1 and 2 of economic dimension, for the components 1,2,3,4
of the social dimension, and for the components 1 and 2 of the environmental dimension.
3
For more details about the definition, justification and description of the different financial variables (see Kassi et al., 2020).
4
After we identified the number of components to be extracted for the three indexes, we rotate them using the Varimax rotation with Kaiser
Normalization to correct the cross-loading only for component 1 for the indexes 1, 2 and 3 of the financial sector development.

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Table 2
Construction of the sustainable development index (SDI).
Components Eigenvalue Proportion Cumulative KMO Bartlett test

Economic dimension 1 1.530 30.590 30.590 0.573 0.000

2 1.036 20.729 61.319
3 0.998 19.964 71.283
4 0.935 18.709 89.992
5 0.500 10.008 100.000
Social dimension 1 1.982 24.770 24.770 0.543 0.000
2 1.762 22.024 46.794
3 1.168 14.605 61.399
4 1.015 12.692 74.092
5 0.802 10.024 84.115
6 0.466 5.820 89.936
7 0.433 5.414 95.350
8 0.372 4.650 100.000
Environmental dimension 1 1.563 39.066 39.066 0.530 0.000
2 1.198 29.949 69.016
3 0.696 17.402 86.417
4 0.543 13.583 100.000

Table 3
Construction of the financial sector development indices.
Components Eigenvalue Proportion Cumulative KMO Bartlett test

FSI 1 = f (lnDC, lnDB, lnMS, lnLL) 1 2.523 63.088 63.088 570 0.000
2 0.912 22.800 85.889
3 0.472 11.804 97.693
4 0.092 2.307 100.000
FSI 2 = f(lnDC, lnBM, lnAM, lnBL) 1 1.662 64.829 64.829 0.613 0.000
2 0.932 23.289 71.540
3 0.828 20.693 85.522
4 0.579 14.478 100.000
FSI 3 = f(lnDC, lnBM, lnAM, lnBZ) 1 1.671 65.420 65.420 0.564 0.000
2 0.945 23.637 71.783
3 0.868 21.704 87.124
4 0.515 12.876 100.000

impact of the control variables on the three dimensions of sustainability could be mixed at the aggregate and disaggregate levels.
The summary statistics of the variables using the global panel are provided in Table 4. The sustainable development dimensions
statistics show that the average value of economic sustainability represents about 40%, social sustainability accounted for about 30%,
and environmental sustainability represents about 30%. The ICT statistics reveal that the average rate of mobile phone use is about 51
per 100 people. However, the use of the Internet represents about 10 per 100 people. The financial sector development statistics
indicate that the average DC represents about 22% as a share of GDP, the average DB represents about 25% as a share of GDP, the
average MS represents about 13% as a share of GDP, the average LL represents about 31% as a share of GDP, the average AM represents
about 9.4% per 100,000 adults, and the average BM represents about 6%, the average BZ represents about 10.5%, and the average BL
represents about 4.2%..
Generally, Fig. 2 shows increased ICT diffusion in SSA countries during the 2000–2018 period. Specifically, we have noticed that
mobile phone subscriptions appear superior to internet use subscriptions, where about 4.5% of 100 people have access to mobile
phones. Nevertheless, about 3% of 100 people have access to the Internet. Fig. 3 shows the evolution of financial development indices
using the same sample. Therefore, we have noticed that FSI 2 has a higher level than FSI 1 and FSI 3. FSI 2 increased by approximately
0.44% in 2000 to around 0.47% in 2018. However, financial development indexes 1 and 3 have decreased from around 0.43% in 2000
to approximately 0.35% and 0.38%, respectively, in 2018. Fig. 4 demonstrates the evolution of SDI. Accordingly, we have noticed that
SDI decreased from 0.44% in 2000 to 0.32% in 2017, but after that, it reached around 0.37% in 2018. Fig. 5 represents the evolution of
the three dimensions of sustainable development. We have observed a decrease in economic sustainability from 0.41% in 2000 to
about 0.32% in 2018, a decrease in social sustainability from 0.46% in 2000 to 27% in 2016, and a decrease in environmental sus­
tainability from 0.43% in 2000 to 0.36% in 2017. The details on the description, abbreviations, and sources of the used variables are
presented in Table B in the appendices.

3.2. Methodological approach

3.2.1. Model specifications

We have proposed the following model to investigate the role of ICT in developing the financial sector for achieving the SDGs at the
aggregate and disaggregate levels for 48 SSA countries during the 2000–2018 period.

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Table 4
Summary statistics (2000–2018).
Variable N Minimum First quartile Median Mean Second quartile Maximum Standard deviation

Economic sus 537 0.000 0.255 0.367 0.401 0.367 0.96 0.26
Social sus 520 0.000 0.17 0.358 0.333 0.357 1.000 0.246
Env sus 516 0.000 0.143 0.289 0.393 0.289 1.000 0.251
Mobile phone 522 0.000 1.784 0.347 50.807 3.465 163.875 42.213
Internet use 495 0.006 –0.07 1.228 9.809 1.222 62.000 12.775
FSI 1 525 0.000 0.104 0.301 0.351 0.301 1.000 0.276
FSI 2 546 0.000 0.147 0.385 0.405 0.385 1.000 0.284
FSI 3 523 0.000 0.14 0.391 0.391 0.349 1.000 0.287
DC 491 0.000 1.389 2.525 22.376 2.525 217.269 28.407
DB 506 0.384 1.389 2.755 24.844 2.525 121.747 22.786
MS 588 0.000 1.185 0.000 12.528 2.755 311.101 41.013
LL 516 1.531 0.000 3.084 30.096 0.000 131.583 21.782
BM 504 0.000 2.38 1.678 5.865 3.084 21.434 3.542
AM 492 0.000 1.764 0.329 9.409 1.678 73.000 15.371
BL 496 0.000 0.000 0.000 4.229 0.329 4.229 6.849
BZ 503 0.000 2.366 2.154 10.456 2.154 44.413 5.968
GDP 535 –36.392 1.213 1.476 4.466 1.476 37.999 4.812
FDI 532 –5.847 0.000 0.923 4.384 0.9228 57.838 6.368
R 518 0.000 0.000 0.000 2.92 0.000 44.585 5.207
TO 533 0.000 4.643 4.12 72.032 0.42 225.023 35.996
RL 511 –1.852 –1.545 –0.676 –0.646 –0.677 1.029 0.6581

Note: N represents the number of observations; Source: see Table B in the appendices for data descriptions and sources.

Sustainable development goals

Economic sustainability Social sustainability Environnemental sustainability

ICT
Mobile phone Internet use

Financial development
Financial depth Financial efficiency Financial inclusion Financial stability
-Domes�c credit to
the private sector -The amount of -Bank net interest -Bank Z-score.
-Deposit money automa�c teller margin.
banks’ assets machine. -Bank’s non-
-Money supply performing loans to
-Liquid liabili�es. gross loans.

Fig. 1. The interaction effects of ICT and financial development on sustainable development goals.

More formally, Eq. (9) presents the specifications of the proposed model:
∑
K
Yit = α0 + α1 FSit + α2 ICT it + α3 FSit ∗ ICT it + α4 δj X′jit + τi + εit (9)
j=1

where Y refers to the sustainable development dimensions (economic, social, and environmental sustainability). FS is the financial
sector development, represented by the financial depth (DC, DB, MS, and LL), financial efficiency (BM), financial inclusion (AM), and
financial stability (BZ and BL), respectively. ICT is represented by mobile phone and internet use subscriptions. ICT*FS is the inter­
action between ICT and the financial sector development. X is the vector of control variables (GDP, FDI, R, TO, RL). Where i = 1,.,48 for
each country in the panel, t = 2000;…;2018 refers to the period, α0 is the constant, τi is the country-specific effect, and εit represents
the white noise stochastic disturbance term. The coefficients α1, α2, α3, and α4 are the long-run estimated coefficient of FDit , ICTit ,
FDit ∗ ICTit andX′jit , respectively. We used logarithmic transformations for all the variables except for sustainable development,
financial sector development, and the rule of law. Therefore, we expect that the eight variables representing the four dimensions of FS

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Fig. 2. Share of ICT (mobile phones and internet use subscriptions per 100 people) for 48 SSA countries over 2000–2018. Source: authors using WDI
statistics (2019).

Fig. 3. Share of financial sector development index 1, 2, and 3 for 48 SSA countries over 2000–2018. Source: authors using Global Financial sector
development database in World Bank (2019).

will have opposite spillover effects on the three dimensions of sustainable development. For the ICT coefficient, we expect that mobile
phone and Internet use seem to encourage sustainable development dimensions. For the interaction effect between FS and ICT,
measured with a coefficient, we expect that mobile phone and internet use will increase FS, which, in turn, boosts economic, social, and
environmental sustainability. Furthermore, we expect that the effect of the mobile phone will promote FS more than internet use in
achieving sustainable development.

3.2.2. Estimation procedures

Before estimating our empirical mode, we must check the presence of cross-sectional dependence in our model using Breusch-­
Pagan (1980), Friedman (1937), and Pesaran (2004) tests first, which allow us to choose between the first and the second-generation
panel unit root tests. According to the results in Table C in the appendices, the null of cross-sectional independence has been accepted
for all the variables in the estimated model of SSA. In this case, we applied the first-generation panel unit root tests, LLC (Levin et al.,
2002) and IPS (Im et al., 2003). These tests are based on the ADF principle statistics. Nonetheless, LLC assumes homogeneity in the
autoregressive coefficients dynamics for all panel members. Yet, IPS admits to heterogeneity in them. The null hypothesis has drawn
that the whole series is non-stationary versus the alternative hypothesis that only a fragment of the series is stationary. The results of
these two tests are reported in Table D in the appendices showing that all the used variables are integrated at order one (I), which
allows us to test the long-run equilibrium relationships among variables using Kao (1999) and Pedroni (1999, 2004) panel

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Fig. 4. Share of Sustainable development index for 48 SSA countries over the 2000–2018 period. Source: authors used the World Development
Indicator (WDI, 2019) and the Standard World Income Inequality (SWIID, 2019) databases sources.

Fig. 5. Share of sustainable development dimensions for 48 SSA countries over the 2000–2018 period. Source: authors used the World Development
Indicator (WDI, 2019) and the Standard World Income Inequality (SWIID, 2019) databases sources.

cointegration tests. Table E in the appendices has reported the results of these no cointegration tests, which cannot be rejected,
indicating that the variables included in the models are not cointegrated, so we cannot run the long-run estimates. Therefore, we
suggest using the System Generalized Method of Moments (sys-GMM) method to test our model to demonstrate the importance of ICT
in developing the financial sector for sustainable development in SSA countries. Indeed, three main reasons lead us to use the sys-GMM
technique. First, the number of countries (N = 48) is greater than the number of years in each country (T = 18). This condition is
satisfactory in our case (N > T). Second, Sys-GMM estimation addresses the heteroscedasticity issues, while difference-GMM is
consistent with homoscedasticity. Additionally, the Sys-GMM makes combining the equations in first differences and levels possible so
that the variables will be instrumented econometrically by their first differences. Third, this technique addresses the endogeneity
problems produced by the causality of reservations between variables, especially when the independent variables are macroeconomic
and institutional. For example, two-way causality may exist between economic growth and financial sector development (Fowowe,
2011; Guptha and Rao, 2018), education and financial sector development (Hatemi-J and Shamsuddin, 2016; Zaidi et al., 2019), CO2
and financial sector development (Xu et al., 2018; Shahbaz et al., 2020), economic growth and ICT (Pradhan et al., 2021; Appiah-Otoo
and Song, 2021), health and ICT (Flick et al., 2020), CO2 and ICT (Shehzad et al., 2020), ICT and financial sector development
(Pradhan et al., 2015). Subsequently, Sys-GMM is the ideal technique to deal with these problems and ensure the reliability of our
estimate. To account for the possibility of endogeneity, we used Eq. (1). Thus, Eq. (10) of the standard Sys-GMM models in level and Eq.
(11) of the difference-GMM are represented as follows:

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

∑
K
Yit = α0 + α1 Yit− 1 + α2 FSit + α3 ICT it + α4 FSit ∗ ICT it + α5 δj X′jit + µi + τi + εit (10)
j=1

∑
K
Yit − Yit− 1 = α1 (Yit− 1 − Yit− 2 ) + α2 (FSit − FSit− 1 ) + α3 (ICT it − ICT it− 1 ) + α4 (FSit ∗ ICT it − FSit ∗ ICT it− 1 ) + α5 δj (X′jit − X′jit− 1 )
j=1 (11)
+ (τi − τi− 1 ) + εit− 1

where Yit-15 represents the lagged value of the dependent variable for country i over the period t. Still, including a lagged dependent
variable Yit-1 as an independent variable violates the orthogonality assumption between the error term and the lagged dependent
variable, both at the first difference and a level. To address this issue, Arellano and Bond (1991) have advocated using the standard or
Diff-GMM estimator. This technique proposes differencing Eqs. (10) and (11) to eliminate the country-specific effects. However, the
correlation problem between the error term and the lagged dependent variables requires instrumental variables (Arellano and Bond,
1991). Blundell and Bond (1998) have indicated that the lagged value of an independent variable is a weak equation instrument in first
differences. Furthermore, the intra-country variations were not taken into consideration in this differentiation. Blundell and Bond
(1998) have suggested using the Sys-GMM method to solve these problems.
In the GMM framework, post-estimation diagnostics require a set of tests to check the overall validity of instrumental variables and
serial correlation and examine the accuracy of the GMM estimation, especially with the lagged regression coefficient and relative to
pooled OLS and within-group (fixed effects) estimates. Therefore, to demonstrate the robustness of the system’s GMM estimates, it is
necessary to report the Pooled OLS and the fixed effect model estimates about the impact of ICT and eight financial sector development
indicators on SDI and the economic, social, and environmental sustainability of the 48 SSA countries. The parameter estimate of the
lagged dependent variable must fall between the values of the Pooled OLS and the fixed-effect model.

4. Empirical results and discussions

4.1. Results at the disaggregated level

Tables 5 and 6 present the results of the moderating effect of two ICT types (mobile phone and internet use) on the relationship
between financial sector development dimensions and economic sustainability. Hence, four main results could be derived. First, as
expected, most of the estimated models show that both types of ICT have significant positive effects on economic sustainability. For
example, economic sustainability is positively affected by mobile phone and Internet use, going from 0.037% to 0.337% for mobile
phones and from 0.169% to 0.303% for Internet use. These results align with the finding of Donou-Adonsou (2019), who argue that
mobile phones and Internet use significantly impact economic growth. Second, regarding the financial sector development indicators,
only lnDB, lnLL, and lnBZ are relevant in increasing economic sustainability in the mobile phone model. However, in the model of
internet use, lnDB, lnLL, lnBM, lnAM, and lnBZ drive the empirical association between financial sector development and economic
sustainability. These results are in line with Hussein et al. (2020), which found that liquid liabilities to the private sector have a direct
positive effect on economic growth in the long term and a negative effect in the short term. These results suggest that policymakers
need to increase liquidity by offering electronic banking services to increase substantial private investment and encourage long-term
economic growth. In addition, a positive relationship was found between economic development, digital financial inclusion, and bank
stability (Z-score) (Chinoda and Kapingura, 2023). These authors have shown that promoting digital financial inclusion and financial
stability in a country plays a crucial role in achieving the SDGs and higher economic growth. Thus, financial depth and financial
stability increase economic sustainability from 0.013% to 0.387% for the mobile phone model. However, for the Internet use model,
financial depth, efficiency, inclusion, and stability are from 0.012% to 0.623%. Similarly, Cheng et al. (2021) document that the
financial sector can gather savings, allocate resources to the most productive investments, reduce information and transaction costs,
and foster inter-industry trade. Third, we emphasize an interesting gap in the prior literature on the financial sector
development-sustainable development nexus, which examines the net impact on economic sustainability from the interplays between
ICT and economic sustainability.
On the one hand, the net effects of the interactions between mobile phones and the dimensions of financial sector development on
economic sustainability are computed. Since only the proxies lnDB, lnMS, lnAM, lnBL, and lnBZ are significant. We computed the net
effects only for these five indicators. For example, the second column of Table 5 shows that a 1% increase in lnDB marginally raises
economic sustainability around 4.131% [i.e., (0.162 ×24.844) + 0.106] at the average value of a mobile phone. In this computation,
0.162 is the conditional impact from the interaction between lnDB and the mobile phone, 24.844 is the mean value of lnDB, and 0.106
is the unconditional impact of the mobile phone. For each regression, a positive net impact means the complementarity hypothesis
between mobile phones and financial sector development in promoting economic sustainability is validated. In contrast, a negative net
impact indicates that the tested hypothesis is rejected. It is evident from Table 5 that the net impacts on economic sustainability from
the interplays between mobile phones and five out of eight indicators of financial sector development are positive. This result indicates
that increasing mobile phone boosts financial sector development (financial depth, financial inclusion, and financial stability) in SSA

5
To select the appropriate lag for our model we run the VAR model. However, we found that the financial sector development and ICT would
affect sustainable development dimensions after one year.

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Table 5
ICT (Mobile phone), financial development dimensions, and economic sustainability.
Dependent variable: Economic sustainability index (EcSI)

Variables Financial depth Financial efficiency Financial inclusion Financial stability

lnDC lnDB lnMS lnLL lnBM lnAM lnBL LnBZ
Mobile phone
EcSI(–1) 0.293 0.42 * 0.367 * 0.505 * 0.501 0.355 * * 0.301 * * 0.434 *
(0.244) (0.001) (0.001) (0.001) (0.124) (0.045) (0.013) (0.001)
Mobile phone –1.182 0.106 * 0.048 * 0.037 * * 0.337 * * 0.201 * 0.063 * 0.82
(0.120) (0.009) (0.009) (0.033) (0.022) (0.009) (0.002) (0.304)
lnDC –0.686 – – – – – – –
(0.646)
lnDB – 0.387 * * – – – – – –
(0.024)
lnMS – – 0.329 – – – – –
(0.128)
lnLL – – – 0.013 * – – – –
(0.004)
lnBM – – – – 0.006 – – –
(0.980)
lnAM – – – – – 0.294 – –
(0.526)
lnBL – – – – – – –0.019 –
(0.260)
LnBZ – – – – – – – 0.036 *
(0.001)
Mobile phone*lnDC 0.12 – – – – – – –
(0.197)
Mobile phone*lnDB – 0.162 * – – – – – –
(0.007)
Mobile phone*lnMS – – 0.133 * – – – – –
(0.005)
Mobile phone*lnLL – – – 0.004 – – – –
(0.198)
Mobile phone* lnBM – – – – –0.045 – – –
(0.409)
Mobile phone*lnAM – – – – – 0.113 * * – –
(0.031)
Mobile phone*lnBL – – – – – – 0.089 * * –
(0.041)
Mobile phone*lnBZ – – – – – – – 0.011 * *
(0.027)
GDP 0.004 0.014 * 0.012 –0.013 0.035 0.01 * 0.029 * 0.03 * *
(0.929) (0.005) (0.597) (0.391) (0.346) (0.006) (0.008) (0.036)
FDI –0.002 –0.006 –0.048 0.124 * ** –0.036 –0.001 0.007 * * 0.0123 *
(0.998) (0.929) (0.293) (0.062) (0.170) (0.898) (0.013) (0.008)
R 0.08 * –0.042 –0.015 –0.014 * * –0.018 –0.15 0.029 –0.01
(0.006) (0.453) (0.276) (0.03) (0.405) (0.126) (0.690) (0.212)
TO 0.057 * –0.002 –0.16 (0.535) 0.154 * (0.003) 0.115 * * 0.098 * ** 0.016 * * 0.02 * **
(0.003) (0.931) (0.049) (0.085) (0.028) (0.072)
RL 0.284 * * 0.278 * ** –0.072 * * 0.046 * ** –0.045 0.035 * ** 0.101 * 0.065 * *
(0.035) (0.055) (0.029) (0.052) (0.657) (0.083) (0.004) (0.012)
Net effects n.a. 4.131 1.714 n.a. n.a. 1.264 0.439 0.935
AR(1) 0.057 0.001 0.000 0.001 0.001 0.002 0.001 0.001
AR(2) 0.184 0.123 0.113 0.222 0.218 0.270 0.552 0.113
Hansen OIR test 0.408 0.230 0.304 0.329 0.103 0.413 0.484 0.376
Sargan OIR test 0.000 0.000 0.000 0.000 0.000 0.002 0.000 0.000
Fisher 33.59 * 33.91 * 36.74 * 31.88 * 34.04 * 41.32 * 36.56 * 31.27 *
Number of Countries 48 48 48 48 48 48 48 48
POLS (Lagged DV) 0.839 * 0.847 * 0.861 * 0.841 * 0.858 * 0.858 * 0.862 * 0.836 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.691 0.689 0.686 0.690 0.686 0.687 0.687 0.691
R–squared 0.687 0.686 0.683 0.687 0.683 0.684 0.683 0.687
FE (Lagged DV) 0.275 * 0.275 * 0.273 * 0.271 * 0.276 * 0.279 * 0.275 * 0.275 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.147 0.147 0.148 0.148 0.149 0.147 0.151 0.147
Adjusted R–squared 0.130 0.13 0.131 0.131 0.131 0.13 0.133 0.13

Notes: AR (1) and AR (2) represent the first– and second–order autocorrelation of residuals, respectively. n.a denote not applicable because when
calculating the net effects, at least one estimated coefficient required for calculating net impacts is insignificant.* , * *, * ** : represent statistical
significance at the 1%, 5%, and 10% levels, respectively.

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Table 6
ICT (Internet use), financial development dimensions, and economic sustainability.
Dependent variable: Economic sustainability index (EcSI)

Variables Financial depth Financial Financial Financial stability

efficiency inclusion
lnDC lnDB lnMS lnLL lnBM lnAM lnBL LnBZ
Internet use
EcSI(–1) 0.521 * 0.54 * 0.139 0.43 * * 0.485 * 0.484 0.528 0.293
(0.000) (0.000) (0.464) (0.012) (0.000) (0.151) (0.179) (0.208)
Internet use –0.05 0.303 * 0.281 * * 0.224 * –0.033 0.01 –0.023 0.169 *
(0.727) (0.009) (0.017) (0.007) (0.135) (0.455) (0.254) (0.006)
lnDC –0.314 – – – – – – –
(0.381)
lnDB – 0.012 * – – – – – –
(0.008)
lnMS – – 0.04 – – – – –
(0.854)
lnLL – – – 0.143 * * – – – –
(0.036)
lnBM – – – – 0.019 * * – – –
(0.036)
lnAM – – – – – 0.014 * – –
(0.007)
lnBL – – – – – – 0.002 –
(0.882)
LnBZ – – – – – – – 0.623 *
(0.009)
Internet use*lnDC 0.002 – – – – – – –
(0.801)
Internet use*lnDB – 0.113 * * – – – – – –
(0.022)
Internet – – 0.035 – – – – –
use*lnMS (0.593)
Internet use*lnLL – – – –0.017 – – – –
(0.671)
Internet – – – – 0.02 * * – – –
use* lnBM (0.021)
Internet – – – – – 0.007 * * – –
use*lnAM (0.038)
Internet use*lnBL – – – – – – 0.004 –
(0.511)
Internet use*lnBZ – – – – – – – 0.104 * **
(0.084)
GDP 0.006 * –0.007 0.02 0.062 * * –0.003 0.033 * ** –0.009 –0.001
(0.009) (0.701) (0.751) (0.015) (0.954) (0.067) (0.584) (0.994)
FDI 0.016 * ** 0.014 * * 0.175 * * –0.005 –0.015 –0.02 –0.11 0.04
(0.075) (0.047) (0.049) (0.862) (0.109) (0.174) (0.274) (0.685)
R 0.02 * * 0.015 * 0.131 * * –0.004 0.017 * ** 0.016 0.009 * * 0.055 * **
(0.015) (0.007) (0.029) (0.821) (0.054) (0.105) (0.017) (0.063)
TO –0.018 0.02 * ** 0.09 * 0.027 * * 0.028 * 0.026 * 0.021 * ** 0.058
(0.133) (0.071) (0.001) (0.011) (0.009) (0.007) (0.074) (0.469)
RL 0.067 * * 0.056 * ** 0.605 * * 0.371 * * 0.076 * * 0.057 * ** 0.054 * 0.234 * **
(0.022) (0.087) (0.012) (0.028) (0.04) (0.069) (0.008) (0.072)
Net effects n.a. 3.11 n.a. n.a. 0.084 0.076 n.a. 1.256
AR(1) (0.000) (0.000) (0.002) (0.002) (0.001) (0.001) (0.000) (0.014)
AR(2) (0.221) (0.121) (0.755) (0.365) (0.226) (0.327) (0.219) (0.768)
Hansen OIR test (0.382) (0.213) (0.309) (0.208) (0.421) (0.388) (0.321) (0.209)
Sargan OIR test (0.000) (0.000) (0.002) 0.006) (0.004) (0.000) (0.000) (0.000)
Fisher 43.98 * 51.24 * 28.93 * 33.61 * 27.09 * 32.99 * 44.03 * 43.07 *
Number of 48 48 48 48 48 48 48 48
Countries
POLS (Lagged 0.852 * 0.857 * 0.863 * 0.852 * 0.857 * 0.864 * 0.864 * 0.846 *
DV)
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.688 0.686 0.685 0.687 0.686 0.686 0.686 0.689
R–squared 0.685 0.683 0.682 0.684 0.683 0.683 0.683 0.686
FE (Lagged DV) 0.175 * 0.178 * 0.113 * 0.177 * 0.178 * 0.178 * 0.178 * 0.177 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.147 0.147 0.148 0.147 0.148 0.15 0.150 0.147
Adjusted 0.130 0.130 0.131 0.131 0.131 0.133 0.133 0.131
R–squared

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

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Table 7
ICT (Mobile phone), financial development dimensions, and social sustainability.
Dependent variable: Social sustainability index (SSI)

Variables Financial depth Financial Financial Financial stability

efficiency inclusion
lnDC lnDB lnMS lnLL lnBM lnAM lnBL LnBZ
Mobile phone
SSI(–1) 0.897 * 0.43 * 0.746 * 0.431 * * 0.648 * 0.633 * 0.505 * 0.613 *
(0.000) (0.006) (0.000) (0.015) (0.000) (0.001) (0.005) (0.001)
Mobile phone 0.327 * * 0.089 * 0.018 * ** 0.059 * 0.09 * * –0.184 0.309 * * –0.193
(0.027) (0.005) (0.097) (0.009) (0.021) (0.546) (0.048) (0.193)
lnDC 0.269 * ** – – – – – – –
(0.09)
lnDB – 0.093 * * – – – – – –
(0.047)
lnMS – – 0.102 – – – – –
(0.665)
lnLL – – – 0.572 – – – –
(0.253)
lnBM – – – – –0.166 – – –
(0.109)
lnAM – – – – – 0.219 * * – –
(0.019)
lnBL – – – – – – 0.045 * ** –
(0.058)
LnBZ – – – – – – – 0.25 * **
(0.052)
Mobile 0.269 * * – – – – – – –
phone*lnDC (0.044)
Mobile – 0.095 * – – – – – –
phone*lnDB (0.007)
Mobile – – –0.001 – – – – –
phone*lnMS (0.83)
Mobile – – – 0.111 – – – –
phone*lnLL (0.237)
Mobile – – – – 0.062 * * – – –
phone* lnBM (0.041)
Mobile – – – – – 0.645 * * – –
phone*lnAM (0.044)
Mobile – – – – – – 0.075 * * –
phone*lnBL (0.03)
Mobile – – – – – – – –0.091 * *
phone*lnBZ (0.029)
GDP 0.13 * 0.157 * 0.125 * ** 0.02 –0.012 0.116 * 0.054 * 0.066 * *
(0.005) (0.003) (0.082) (0.691) (0.184) (0.009) (0.001) (0.045)
FDI –0.015 –0.039 0.008 –0.005 0.043 * * 0.1456 –0.178 0.079 * **
(0.84) (0.477) (0.341) (0.863) (0.037) (0.732) (0.64) (0.087)
R –0.001 –0.019 –0.007 0.029 * 0.016 * –0.01 0.016 * ** 0.009 *
(0.996) (0.469) (0.372) (0.005) (0.008) (0.906) (0.056) (0.000)
TO –0.052 * –0.14 * ** 0.021 –0.041 0.007 –0.01 0.031 0.0159
(0.003) (0.073) (0.874) (0.731) (0.452) (0.807) (0.652) (0.503)
RL 0.077 * * 0.276 * –0.001 0.149 * ** –0.013 0.535 * ** 0.498 * * 0.21
(0.021) (0.000) (0.962) (0.059) (0.479) (0.09) (0.014) (0.659)
Net effects 6.346 2.449 n.a. n.a. 0.453 5.885 0.626 –1.144
AR(1) (0.003) (0.002) (0.000) (0.011) (0.000) (0.001) (0.011) (0.004)
AR(2) (0.347) (0.478) (0.430) (0.576) (0.784) (0.351) (0.122) (0.368)
Hansen OIR test (0.474) (0.147) (0.111) (0.250) (0.225) (0.216) (0.350) (0.152)
Sargan OIR test (0.000) (0.000) (0.000) (0.000) (0.000) (0.013) (0.060) (0.016)
Fisher 57.72 * 76.11 * 34.54 * 56.90 * 44.73 * 36.15 * 43.37 * 51.93 *
Number of 48 48 48 48 48 48 48 48
Countries
POLS (Lagged DV) 0.848 * 0.843 * 0.856 * 0.840 * 0.856 * 0.856 * 0.857 * 0.851 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.729 0.730 0.728 0.731 0.728 0.728 0.729 0.729
R–squared 0.726 0.727 0.726 0.728 0.726 0.726 0.726 0.726
FE (Lagged DV) 0.345 * 0.346 * 0.347 * 0.347 * 0.337 * 0.337 * 0.347 * 0.348 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.261 0.261 0.261 0.261 0.262 0.262 0.261 0.261
Adjusted 0.245 0.245 0.244 0.244 0.245 0.245 0.244 0.244
R–squared

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

15
S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Table 8
ICT (Internet use), financial development dimensions, and social sustainability.
Dependent variable: Social sustainability index (SSI)

Variables Financial depth Financial efficiency Financial inclusion Financial stability

lnDC lnDB lnMS lnLL lnBM lnAM lnBL LnBZ
Internet use
SSI(–1) 0.347 * * 0.724 * 0.746 * 0.756 * 0.724 * 0.688 * 0.711 * 0.731 *
(0.027) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Internet use –0.299 –0.114 * –0.017 * –0.013 * * –0.008 * –0.014 0.013 * –0.168 * *
(0.612) (0.002) (0.009) (0.018) (0.115) (0.187) (0.000) (0.015)
lnDC 0.001 – – – – – – –
(0.148)
lnDB – 0.066 * – – – – – –
(0.003)
lnMS – – 0.008 * – – – – –
(0.006)
lnLL – – – –0.012 – – – –
(0.446)
lnBM – – – – 0.028 * – – –
(0.004)
lnAM – – – – – –0.025 – –
(0.236)
lnBL – – – – – – –0.07 –
(0.391)
LnBZ – – – – – – – 0.05
(0.415)
Internet use*lnDC 0.022 – – – – – – –
(0.224)
Internet use*lnDB – –0.087 * * – – – – – –
(0.029)
Internet use*lnMS – – –0.001 – – – – –
(0.865)
Internet use*lnLL – – – –0.036 * – – – –
(0.002)
Internet use* lnBM – – – – 0.012 * * – – –
(0.024)
Internet use*lnAM – – – – – –0.001 – –
(0.697)
Internet use*lnBL – – – – – – 0.025 * * –
(0.014)
Internet use*lnBZ – – – – – – – 0.072 *
(0.004)
GDP –0.181 0.061 * * 0.12 * ** 0.13 * * 0.112 * 0.014 * ** 0.124 * 0.117 *
(0.404) (0.016) (0.095) (0.037) (0.005) (0.095) (0.003) (0.008)
FDI –0.01 0.005 0.007 –0.014 0.006 0.004 * –0.025 0.015 * **
(0.675) (0.890) (0.413) (0.799) (0.492) (0.004) (0.658) (0.052)
R 0.075 * ** –0.012 –0.01 * 0.008 0.008 * * –0.01 0.007 * * –0.006
(0.052) (0.254) (0.009) (0.890) (0.039) (0.295) (0.047) (0.532)
TO –0.253 * * 0.004 –0.004 –0.003 –0.004 –0.01 * 0.004 –0.002
(0.04) (0.865) (0.521) (0.814) (0.443) (0.005) (0.764) (0.831)
RL 0.083 0.222 * ** 0.004 0.012 * * 0.005 0.013 * * 0.194 * ** (0.088) 0.014
(0.349) (0.092) (0.866) (0.011) (0.810) (0.016) (0.675)
Net effects n.a. –0.76 n.a. –0.194 0.06 n.a. 0.13 –0.415
AR(1) (0.006) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.001)
AR(2) (0.267) (0.422) (0.472) (0.449) (0.441) (0.268) (0.385) (0.391)
Hansen OIR test (0.295) (0.334) (0.298) (0.121) (0.125) (0.242) (0.392) (0.460)
Sargan OIR test (0.000) (0.008) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Fisher 56.08 * 55.29 * 44.50 * 47.74 * 45.31 * 58.74 * 47.81 * 49.71 *
Number of Countries 48 48 48 48 48 48 48 48
POLS (Lagged DV) 0.856 * 0.852 * 0.858 * 0.845 * 0.859 * 0.858 * 0.858 * 0.859 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.728 0.730 0.728 0.73 0.729 0.728 0.728 0.728
R–squared 0.726 0.727 0.725 0.728 0.726 0.726 0.725 0.726
FE (Lagged DV) 0.316 * 0.350 * 0.358 * 0.347 * 0.362 * 0.355 * 0.357 * 0.358 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.260 0.262 0.26 0.263 0.261 0.260 0.261 0.260
Adjusted R–squared 0.244 0.246 0.243 0.247 0.243 0.243 0.243 0.243

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

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

countries (Evans, 2018; Abeka et al., 2021; Ejemeyovwi et al., 2021), which, in turn, encourages economic sustainability (Taddese
Bekele and Abebaw Degu, 2023). On the other hand, net effects from the interaction between Internet use and the dimensions of
financial sector development on economic sustainability are computed to measure the global effect of this interaction. Since only the
indicators lnDB, lnBM, lnAM, and score are significant, we computed the net effects only for these four proxies. For instance, the second
column of Table 6 shows that a 1% increase in lnDB marginally raises economic sustainability around 3.11% [i.e., (0.113 ×24.844)
+ (0.303)] at the average value of Internet use. In this computation, 0.113 is the conditional impact from the interaction between lnDB
and Internet use, 24.844 is the mean value of lnda, and 0.303 is the unconditional impact of Internet use. For each regression, a positive
net impact indicates that the complementarity hypothesis between Internet use and financial sector development in promoting eco­
nomic sustainability is validated. In contrast, a negative net impact indicates that the tested hypothesis is rejected. A positive net
impact means Internet use complements financial sector development to increase economic sustainability. It is clear from Table 6 that
the net impacts on economic sustainability from the interplays between Internet use and four out of eight proxies of financial sector
development are positive. This result indicates that increasing Internet use in SSA countries leads to more financial sector development
(depth, efficiency, inclusion, and stability) (Owusu-Agyei et al., 2020; Chien et al., 2020) which, in turn, encourages economic sus­
tainability. Finally, regarding the control variables, economic sustainability is positively influenced by the growth of GDP, FDI, R, TO,
and RL.
Tables 7 and 8 report the interaction effects of two types of ICT and financial sector development indicators on social sustainability.
Four main results could be derived from these tables. First, as expected, most of the estimated models show that both types of ICT have
contradictory effects on social sustainability, i.e., social sustainability is affected positively by mobile phones and negatively by
Internet use, going from 0.018% to 0.327% for mobile phone indicators and from − 0.114 to − 0.008% for the Internet use indicators.
These results confirm the findings of Adams and Akobeng (2021), who argue that Internet and mobile cellular subscription directly
reduce inequality in Africa and that good governance indicators can strengthen the ICT and inequality relationship. Second, regarding
the financial sector development indicators, only lnDC lnDB, lnAM, lnBL, and lnBZ are respectively pertaining. Thus, financial sector
development increases social sustainability from 0.045% to 0.269% for mobile phones and from 0.008% to 0.066% for Internet use.
Thus, lnDC, lnDB, lnAM, lnBL, and lnBZ drive/lead the empirical association between financial sector development and social sus­
tainability for the mobile phone mode. This result is consistent with the findings of Asongu et al. (2021), who found that financial
stability represented by Z-Score modulates inclusive education for women to empower female employment in industrial distribution.
Thus, financial stability appears as a policy perspective that can address gender economic exclusion and promote inclusive devel­
opment in SSA countries.
Moreover, Ozili (2023) found that non-performing loans (NPLs) are positively associated with the level of sustainability measured
with the sustainability index (SDI). They found a significant positive relationship between NLPs and the SDG10 of “reducing in­
equalities” in African countries. The authors claimed that banks lend to high-risk SDG10-related activities to signal their commitment
to the SDGs, which in turn serves to increase non-performing loans. Nonetheless, lnDB, lnMS, and lnBM drive the empirical association
between financial sector development and social sustainability for the Internet use model. Third, we emphasize an interesting gap in
the prior literature on the financial sector development-sustainable development nexus, which examines the net impact on social
sustainability from the interplays between ICT and social sustainability.
On the one hand, net effects from the interactions between mobile phones and the indicators of financial sector development on
social sustainability are computed. Since only the proxies lnDB, lnDC, lnBM, lnAM, lnBL, and lnBZ are significant, we computed the net
effects only for these indicators. For example, the fifth column of Table 7 shows that a 1% increase in lnBM marginally raises social
sustainability around 0.453% [i.e., (0.062 ×5.865) + 0.09] at the average value of mobile phones. In this computation, 0.062 is the
conditional impact from the interaction between lnBM and the mobile phone, 5.865 is the mean value of lnBM, and 0.09 is the un­
conditional impact of the mobile phone. This result is in line with the findings of Claeys and Vennet (2008), who found that a high net
interest margin reflects low banking efficiency and an uncompetitive banking market. Banking sectors in Sub-Saharan Africa remain
concentrated with limited competition, hence low bank efficiency and high net interest margins (Sarpong-Kumankoma et al., 2020).
To solve these issues, Saksonova (2014) recommended that the net interest margin may decline with great competition or financial and
technological innovations that increase banking efficiency. For each regression, a positive net impact means the complementarity
hypothesis between financial sector development and mobile phone in promoting social sustainability is validated. In contrast, a
negative net impact indicates that the tested hypothesis is rejected. It is apparent from Table 7 that the net impacts on social sus­
tainability from the interplays between mobile phones and six out of eight indicators of financial sector development are positive,
indicating that increasing mobile phone leads them to attract more financial sector development (depth, efficiency, inclusion, and
stability) in SSA countries which, in turn, encourages social sustainability (Tchamyou, 2020; Omar et al., 2020).
On the other hand, net effects from the interaction between Internet use and the indicators of financial sector development on social
sustainability are computed to measure the global effect of this interaction. Since only the indicators lnDB, lnLL, lnBM, lnBL, and lnBZ
are significant, we have computed the net effects only for these five proxies. For instance, the fifth column of Table 8 shows that a 1%
increase in lnBM marginally raises social sustainability around 0.06% [i.e., (0.012 ×5.685) + (− 0.008)] at the average value of
Internet use. In this computation, 0.012 is the conditional impact from the interaction between lnBZ and Internet use, 5.658 is the mean
value of lnBM and − 0.008 is the unconditional impact of Internet use. For each regression, a positive net impact means that the
complementarity hypothesis between financial sector development and Internet use in promoting sustainable development is vali­
dated, whereas a negative net impact indicates that the tested hypothesis is rejected. A positive net impact means Internet use
complements financial sector development to increase social sustainability. It is clear from Table 8 that the net effects of social sus­
tainability from the interplays between Internet use and five out of eight proxies of financial sector development are positive, indi­
cating that increasing Internet use significantly annihilates the harmful effects of financial sector development (depth, efficiency, and

17
 Table 9
ICT (Mobile phone), financial development dimensions, and environmental sustainability.
Dependent variable: Environmental sustainability index (ESI)

S. Dhahri et al.
Variables Financial depth Financial efficiency Financial inclusion Financial stability
lnDC lnDB lnMS lnLL lnBM lnAM lnBL LnBZ
Mobile phone
ESI(–1) 0.473 * 0.482 * (0.000) 0.572 * (0.000) 0.48 * (0.000) 0.25 * * (0.039) 0.48 * 0.275 * 0.121 * (0.000)
(0.000) (0.000) (0.000)
Mobile phone –0.053 * * (0.024) –0.367 * * (0.013) –0.024 –0.044 –0.175 * (0.004) –0.051 * * –0.034 * –0.218 * (0.005)
(0.277) (0.538) (0.022) (0.001)
lnDC –0.012 – – – – – – –
(0.487)
lnDB – 0.626 * ** (0.063) – – – – – –
lnMS – – 0.038 * ** – – – – –
(0.095)
lnLL – – –0.023 – – – –
(0.314)
lnBM – – – – 0.253 * * (0.011) – – –
lnAM – – – – – –0.019 (0.277) – –
lnBL – – – – – – 0.004 (0.742) –
LnBZ – – – – – – – 0.203 (0.661)
Mobile phone*lnDC 0.002 – – – – – – –
(0.658)
Mobile phone*lnDB – 0.142 * * (0.042) – – – – – –
Mobile phone*lnMS – – 0.011 * * (0.049) – – – – –
Mobile phone*lnLL – – – 0.003 – – – –
(0.617)
Mobile phone* lnBM – – – – 0.094 * * (0.015) – – –
Mobile phone*lnAM – – – – – 0.004 (0.196) – –
18

Mobile phone*lnBL – – – – – – 0.01 * * –

(0.012)
Mobile phone*lnBZ – – – – – – – 0.104 * *
(0.019)
GDP –0.036 (0.364) –0.055 (0.319) –0.679 –0.01 (0.386) 0.01 (0.501) –0.055 (0.373) –0.149 * –0.185
(0.485) (0.008) (0.473)
FDI –0.009 (0.357) –0.009 (0.299) –0.103 * (0.004) –0.009 –0.026 (0.185) –0.009 (0.334) –0.01 –0.013

Research in International Business and Finance 67 (2024) 102156

(0.391) (0.317) (0.736)
R –0.01 (0.464) –0.009 (0.461) 0.005 * ** (0.062) –0.01 –0.015 –0.008 (0.463) –0.015 –0.088 (0.507)
(0.388) (0.366) (0.123)
TO –0.017 (0.452) –0.013 (0.507) –0.013 (0.47) –0.019 0.003 (0.894) –0.011 (0.61) –0.029 * ** –0.082 (0.45)
(0.400) (0.088)
RL 0.036 0.022 * * (0.016) 0.014 0.023 0.02 * 0.036 (0.315) 0.005 0.28 (0.469)
(0.350) (0.598) (0.517) (0.002) (0.844)
Net effects n.a. 3.161 0.114 n.a. 0.376 n.a. 0.008 0.738
AR(1) ( 0.011) (0.013) (0.008) (0.011) (0.016) (0.017) (0.012) (0.013)
AR(2) (0.530) (0.510) (0.519) (0.503) (0.435) (0.514) (0.293) (0.541)
Hansen OIR test (0.155) (0.107) (0.124) (0.363) (0.294) (0.123) (0.175) 0.112)
Sargan OIR test (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Fisher 21.05 * 22.25 * 23.39 * 18.23 * 18.69 * 18.18 * 19.89 * 19.43 *
Number of Countries 48 48 48 48 48 48 48 48
POLS (Lagged DV) 0.911 * 0.910 * 0.918 * 0.907 * 0.915 * 0.916 * 0.916 * 0.905 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.788 0.788 0.787 0.789 0.788 0.787 0.787 0.788
R–squared 0.786 0.780 0.785 0.787 0.787 0.785 0.785 0.786
FE (Lagged DV) 0.163 * 0.163 * 0.159 * 0.164 * 0.163 * 0.151 * 0.149 * 0.102 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.117 0.117 0.115 0.117 0.117 0.116 0.117 0.116
Adjusted R–squared 0.104 0.104 0.103 0.105 0.104 0.103 0.104 0.103

Notes: * , * *, * ** : represent statistical significance at the 1%, 5%, and 10% levels, respectively.
S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Table 10
ICT (Internet use), financial development dimensions, and environmental sustainability.
Dependent variable: Environmental sustainability index (ESI)

Variables Financial depth Financial Financial Financial stability

efficiency inclusion
lnDC lnDB lnMS lnLL lnBM lnAM lnBL LnBZ
Internet use
ESI(–1) 0.541 * 0.517 * 0.493 * 0.231 * 0.205 * 0.605 * 0.453 * 0.495 *
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Internet use 0.393 * * 0.387 * ** 0.026 * ** 0.343 * ** 0.165 * * 0.053 * ** 0.043 * –0.425
(0.046) (0.064) (0.091) (0.08) (0.033) (0.074) (0.006) (0.541)
lnDC 0.466 * ** – – – – – – –
(0.051)
lnDB – –0.093 – – – – – –
(0.113)
lnMS – – 0.031 * ** – – – – –
(0.077)
lnLL – – – –0.132 – – – –
(0.521)
lnBM – – – – –0.098 – – –
(0.139)
lnAM – – – – – 0.157 * * – –
(0.031)
lnBL – – – – – – 0.251 * * –
(0.028)
LnBZ – – – – – – – 0.114 *
(0.001)
Internet use*lnDC 0.193 * * – – – – – – –
(0.03)
Internet use*lnDB – 0.127 * * – – – – – –
(0.043)
Internet use*lnMS – – 0.015 * * – – – – –
(0.011)
Internet use*lnLL – – – 0.112 * ** – – – –
(0.086)
Internet – – – – 0.105 * ** – – –
use* lnBM (0.095)
Internet use*lnAM – – – – – 0.048 * ** – –
(0.057)
Internet use*lnBL – – – – – – 0.169 * * –
(0.048)
Internet use*lnBZ – – – – – – – 0.424
(0.644)
GDP –0.109 –0.006 –0.038 –0.009 * –0.031 * * –0.012 –0.042 –0.051 * **
(0.731) (0.781) (0.461) (0.004) (0.033) (0.370) (0.429) (0.061)
FDI –0.054 * ** –0.17 –0.011 –0.003 –0.021 * 0.012 –0.01 –0.011 *
(0.081) (0.296) (0.256) (0.919) (0.004) (0.539) (0.278) (0.002)
R 0.012 –0.132 –0.013 –0.093 –0.008 0.007 * * –0.01 –0.01
(0.649) (0.430) (0.226) (0.693) (0.718) (0.028) (0.456) (0.607)
TO –0.31 –0.021 –0.02 * ** 0.003 –0.026 –0.005 * * –0.026 * ** –0.026 * *
(0.542) (0.357) (0.057) (0.964) (0.112) (0.026) (0.088) (0.045)
RL 0.046 –0.023 0.028 –0.134 0.064 * ** 0.251 0.038 * 0.037 * *
(0.645) (0.698) (0.435) (0.623) (0.062) (0.55) (0.009) (0.031)
Net effects 4.781 3.542 0.214 3.714 0.781 0.505 0.770 n.a.
AR(1) (0.009) (0.011) (0.008) (0.073) (0.009) (0.004) (0.012) (0.010)
AR(2) (0.435) (0.578) (0.571) (0.514) (0.586) (0.781) (0.534) (0.662)
Hansen OIR test (0.376) (0.109) (0.134) (0.278) (0.258) (0.308) (0.178) (0.108)
Sargan OIR test (0.000) (0.000) (0.000) (0.007) (0.000) (0.000) (0.000) (0.000)
Fisher 31.44 * 24.86 * 29.78 * 26.13 * 28.24 * 31.01 * 29.58 * 28.61 *
Number of 48 48 48 48 48 48 48 48
Countries
POLS (Lagged DV) 0.919 * 0.918 * 0.919 * 0.915 * 0.919 * 0.918 * 0.916 * 0.916 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.788 0.788 0.786 0.789 0.787 0.786 0.787 0.788
R–squared 0.786 0.786 0.784 0.787 0.785 0.784 0.785 0.786
FE (Lagged DV) 0.171 * 0.164 * 0.161 * 0.165 * 0.163 * 0.154 * 0.151 * 0.163 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.118 0.119 0.115 0.119 0.117 0.116 0.117 0.116
Adjusted 0.106 0.106 0.103 0.107 0.105 0.103 0.104 0.103
R–squared

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

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

stability) in SSA countries which, in turn, support social sustainability (Kelikume, 2021). Finally, regarding the control variables,
financial sector development is positively influenced by the growth of GDP, FDI, R, and the RL, while TO negatively impacts social
sustainability.
Tables 9 and 10 present the interaction effects of both types of ICT and the indicators of financial sector development on envi­
ronmental sustainability. Four main results could be derived from these tables. First, as expected, we found that both types of ICT exert
contradictory spillover effects on environmental sustainability, i.e., environmental sustainability is influenced negatively by mobile
phones and positively by Internet use. This finding is in line with Asongu et al. (2018), who find that environmental sustainability is
influenced positively by Internet use and negatively by mobile phones. Second, regarding the impacts of financial sector development
indicators, only lnDB, lnMS, and lnBM increases environmental sustainability, going from − 0.626 to − 0.038% for the mobile phone
model. However, for the impacts of financial sector development indicators, only lnDC, lnMS, lnAM, lnBL, and lnBZ increase envi­
ronmental sustainability, running from − 0.466 to − 0.031% for the Internet use model. Third, we concentrate on another gap in the
existing research, which analyzes the net impacts on environmental sustainability from the interplay between ICT and financial sector
development. For the mobile phone model, net effects from the interplays between mobile phones and the five indicators of financial

Table 11
ICT (Mobile phone and Internet use), financial development indexes, and SDG index in SSA countries.
Dependent variable: Sustainable development index (SDI)

Mobile phone Internet use

Variables FSI 1 FSI 2 FSI 3 FSI 1 FSI 2 FSI 3
SD (–1) 0.225 * 0.458 * * 0.431 * 0.609 * 0.557 * 0.521 *
(0.003) (0.02) (0.002) (0.000) (0.000) (0.003)
Mobile phone 0.051 * * 0.114 0.231 * * – – –
(0.034) (0.588) (0.039)
Internet use – – – 0.27 0.028 * * 0.021 * **
(0.686) (0.028) (0.091)
FSI 1 0.117 * * – – 0.183 * * – –
(0.049) (0.041)
FSI 2 – 0.694 * ** – – 0.004 * * –
(0.088) (0.015)
FSI 3 – – –0.106 – – –0.008
(0.748) (0.557)
Mobile phone *FSI 1 0.065 * – – – – –
(0.002)
Mobile phone *FSI 2 – 0.256 * – – – –
(0.003)
Mobile phone *FSI 3 – – 0.118 * * – – –
(0.015)
Internet use *FSI 1 – – – 0.246 * * – –
(0.023)
Internet use *FSI 2 – – – – 0.029 * –
(0.001)
Internet use *FSI 3 – – – – – 0.02 * *
(0.042)
GDP 0.021 * * 0.041 (0.86) 0.023 * ** 0.003 * * 0.06 * ** 0.092 * *
(0.043) (0.077) (0.05) (0.074) (0.035)
FDI –0.015 0.004 * ** (0.061) 0.027 * * 0.002 * * –0.004 0.004 *
(0.302) (0.024) (0.017) (0.489) (0.006)
R 0.027 * * –0.006 (0.534) –0.15 –0.006 0.009 * * 0.004 *
(0.043) (0.17) (0.432) (0.043) (0.006)
TO –0.013 –0.019 * (0.007) –0.065 –0.01 –0.014 * ** –0.015 * **
(0.174) (0.11) (0.534) (0.051) (0.052)
RL 0.032 –0.201 (0.64) 0.117 * 0.16 * 0.034 0.042
(0.189) (0.003) (0.003) (0.218) (0.116)
Net effects 0.074 0.218 0.277 0.356 0.04 0.029
AR(1) (0.002) (0.002) (0.006) (0.000) (0.003) (0.003)
AR(2) (0.657) (0.218) (0.756) (0.693) (0.466) (0.627)
Hansen OIR test (0.221) (0.230) (0.439) (0.209) (0.117) (0.107)
Sargan OIR test (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Fisher 32.22 * 36.65 * 34.36 * 39.80 * 29.19 * 36.88 *
Number of Countries 48 48 48 48 48 48
POLS (Lagged DV) 0.898 * 0.898 * 0.897 * 0.906 * 0.909 * 0.907 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.765 0.766 0.764 0.764 0.765 0.763
R–squared 0.763 0.764 0.762 0.762 0.763 0.761
FE (Lagged DV) 0.206 * 0.272 * 0.275 * 0.29 * 0.282 * 0.282 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.104 0.107 0.104 0.103 0.107 0.104
Adjusted R–squared 0.190 0.194 0.191 0.19 0.194 0.191

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

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

sector development on environmental sustainability are computed to measure the overall impact of these interactions. Since only the
models are respectively related to lnDB, lnMS, lnBM, lnBL, and lnBZ, we computed the net effects only for these five models. For
instance, the fifth column of Table 9 shows that a 1% increase in lnBM marginally raises environmental sustainability around 0.376%
[i.e., (0.094 ×5.865) + (− 0.175)] at the average value of the mobile phones. In this computation, 0.094 is the conditional impact from
lnBM and mobile phone, 5.865 is the mean value of lnBM, and − 0.175 is the unconditional impact of the mobile phone. For each
regression, a positive net impact signifies that the complementarity hypothesis between mobile phone and financial sector develop­
ment in promoting environmental sustainability is validated, whereas a negative net impact indicates that the tested hypothesis is
rejected. Our results show that the net impacts on environmental sustainability from the interplays between mobile phones and five out
of eight indicators of financial sector development are positive, indicating that better financial sector development (depth, efficiency,
and inclusion) wipes out the detrimental effects of mobile phone in SSA, which, in turn, encourages environmental sustainability
(Avom et al., 2020).
Regarding the Internet use model, net effects from the interplays between Internet use and the indicators of financial sector
development on environmental sustainability are computed to measure the global impact of these interplays. Since only lnDC, lnDB,
lnMS, lnLL, lnBM, lnAM, and lnBL are significant, we computed the net effects only for these seven models. For instance, the seventh
column of Table 10 shows that a 1% increase in lnBL marginally raising environmental sustainability is around 0.770% [i.e.,
(0.169 ×4.299) + (0.043)] at the average value of Internet use. A negative net impact means Internet use complements financial sector
development to increase environmental sustainability. We can see from Table 10 that the net effects from the interplay between
Internet use and seven out of eight indicators of financial sector development on sustainable development are positive, indicating that
increasing Internet use leads to increase financial sector development (depth, efficiency, inclusion, and stability) in SSA (Owusu-Agyei
et al., 2020), which, in turn, encourages environmental sustainability (Park al, 2018). Finally, regarding the control variables, the rate
of environmental sustainability decreases with the increase in the rate of GDP, FDI, and TO, while it only increases with the rise in R
and the RL.

4.2. Results at the aggregated level

The interaction impacts of two types of ICT and three financial sector development indices on the sustainable development index
are presented in Table 11. In addition, four main results could be derived. First, as expected, most of the estimated models show that
both types of ICT have positive effects on the sustainable development index, i.e., the sustainable development index is positively
affected by mobile phone and Internet use, going from 0.051% to 0.231% for mobile phone indicators and from 0.021% to 0.028% for
the Internet use indicators. In this context, scholars such as Appiah-Otoo and Song (2021) argue that ICT increases economic growth,
but poor countries benefit more from the ICT revolution than rich countries. Asongu and Odhiambo (2020) found that mobile phone
and Internet penetration rates mitigate low-quality education. Khan et al. (2020) found that ICT reduces CO2 emissions. Second,
regarding the financial sector development indicators, only financial sector development indexes 1 and 2 are pertaining, respectively.
Therefore, financial sector development increases the sustainable development index from 0.117% to 0.694% for mobile phones and
from 0.004% to 0.183% for Internet use. The authors document that financial sector development indexes 1 and 2 drive the empirical
association between financial sector development and sustainability.
Similarly, according to Tchamyou et al. (2019), financial depth and size are established to alleviate inequality dependent on ICT.
Aluko and Obalade (2020) state that increased financial development improves environmental quality (CO2 emissions reduction).
Third, we emphasize an interesting gap in the prior literature on the financial sector development and sustainable development nexus,
which examines the net impact on the sustainable development index from the interplays between ICT and financial sector devel­
opment indexes. On the one hand, the net effects of the interactions between mobile phones and the indicators of financial sector
development on the sustainable development index are computed. Since only all of the proxies FSI 1, FSI 2, and FSI 3 are significant; we
computed the net effects for these indices. For example, the first column of Table 11 shows that a 1% increase in FSI 1 raises the
sustainable development index around 0.074% [i.e., (0.065 ×0.351) + 0.051] at the average value of the mobile phone. In this
computation, 0.065 is the conditional impact from the interaction between the mobile phone and FSI 1, 0.351 is the mean value of FSI
1, and 0.051 is the unconditional impact of the mobile phone. For each regression, a positive net impact means that the comple­
mentarity hypothesis between mobile phone and financial sector development in promoting sustainable development index is vali­
dated, whereas a negative net impact indicates that the tested hypothesis is rejected. It is apparent from Table 11 that the net impacts
on the sustainable development index from the interplays between mobile phones and the three financial sector development indexes
are positive, indicating that increasing mobile phone leads them to attract more financial sector development (depth, efficiency, in­
clusion, and stability) in SSA countries (Chien et al., 2020; Owusu-Agyei et al., 2020; Asongu et al., 2021), which, in turn, encourages
sustainable development index (Kirikkaleli and Adebayo, 2021).
On the other hand, net effects from the interaction between Internet use and the indexes of financial sector development on the
sustainable development index are computed to measure the global effect of this interaction. Since all the indices of financial sector
development are significant, we computed the net effects for all of them. For instance, the fourth column of Table 11 shows that a 1%
increase in FSI 1 raises the sustainable development index around 0.356% [i.e., (0.246 ×0.351) + (0.27)] at the average value of
Internet use. In this computation, 0.246 is the conditional impact from the interaction between Internet use and FSI 1, 0.351 is the
mean value of FSI 1, and 0.27 is the unconditional impact of Internet use. In this computation, 0.246 is the conditional impact from the
interaction between Internet use and FSI 1, 0.351 is the mean value of FSI 1, and 0.27 is the unconditional impact of Internet use. For
each regression, a positive net impact means that the complementarity hypothesis between Internet use and financial sector devel­
opment in promoting sustainable development index is validated, whereas a negative net impact indicates that the tested hypothesis is

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

rejected. A positive net impact means Internet use complements financial sector development to increase the sustainable development
index. It is clear from Table 11 that the net effects from the interplays between Internet use and the three indices of financial sector
development on sustainable development are positive, indicating that increasing Internet use significantly raises financial sector
development (depth, efficiency, and stability) in SSA countries (Evans, 2018; Ejemeyovwi et al., 2021) which, in turn, encourages
sustainable development (Aluko and Obalade, 2020; Bolarinwa et al., 2022). Finally, regarding the control variables, the financial
sector development is positively influenced by the growth of GDP, FDI, R, and the RL, while TO shows a negative impact on sustainable
development.

4.3. Robustness check

As a robustness check of the obtained results and to deal with cross-country heterogeneity in our sample, we also estimated our
empirical model for two sub-samples: low- and middle-income countries.
The joint effects of two types of ICT and three financial sector development indices on the sustainable development index in low and

Table 12
ICT (Mobile phone and Internet use), financial development indexes, and SDG index in low–income SSA countries.
Dependent variable: Sustainable development index (SDI)

Mobile phone Internet use

Variables FSI 1 FSI 2 FSI 3 FSI 1 FSI 2 FSI 3
SD (–1) 0.422 * * 0.455 * * 0.441 * * 0.506 * 0.367 * 0.498 *
(0.04) (0.04) (0.037) (0.006) (0.000) (0.009)
Mobile phone 0.046 * * 0.099 * * –0.037 – – –
(0.014) (0.013) (0.105)
Internet use – – – 0.348 * ** 0.067 * * –0.031
(0.072) (0.011) (0.155)
FSI 1 –0.106 – – –0.039 – –
(0.112) (0.286)
FSI 2 – 0.171 * – – 0.09 * –
(0.006) (0.001)
FSI 3 – – 0.026 – – 0.017
(0.41) (0.553)
Mobile phone *FSI 1 0.032 – – – – –
(0.12)
Mobile phone *FSI 2 – 0.036 * ** – – – –
(0.054)
Mobile phone *FSI 3 – – –0.004 – – –
(0.818)
Internet use *FSI 1 – – – 0.028 – –
(0.186)
Internet use *FSI 2 – – – – 0.023 * * –
(0.046)
Internet use *FSI 3 – – – – – –0.013
(0.451)
GDP 0.234 * ** 0.018 0.023 * ** –0.018 0.034 * –0.02
(0.059) (0.14) (0.055) (0.142) (0.004) (0.126)
FDI 0.013 * ** –0.012 (0.144) –0.01 0.011 * ** –0.015 0.0128 * *
(0.065) (0.145) (0.077) (0.132) (0.042)
R –0.01 –0.002 (0.818) –0.01 –0.007 0.055 * * 0.007 *
(0.488) (0.368) (0.551) (0.041) (0.479)
TO –0.02 * ** –0.003 –0.024 * * –0.023 * * –0.045 * –0.027 * *
(0.089) (0.827) (0.049) (0.03) (0.000) (0.027)
RL 0.017 0.08 * * 0.022 0.022 0.067 * * 0.031
(0.469) (0.751) (0.335) (0.354) (0.011) (0.195)
Net effects n.a 0.114 n.a 0.076 n.a n.a
AR(1) (0.003) (0.002) (0.007) (0.006) (0.004) (0.005)
AR(2) (0.391) (0.451) (0.510) (0.299) (0.462) (0.440)
Hansen OIR test (0.221) (0.122) (0.216) (0.179) (0.110) (0.228)
Sargan OIR test (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Fisher 37.49 * 41.67 * 36.49 * 39.80 * 37.89 * 41.88 *
Number of Countries 23 23 23 23 23 23
POLS (Lagged DV) 0.932 * 0.935 * 0.934 * 0.939 * 0.94 * 0.94 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.827 0.827 0.827 0.827 0.827 0.827
R–squared 0.824 0.824 0.823 0.824 0.824 0.824
FE (Lagged DV) 0.215 * 0.213 * 0.223 * 0.228 * 0.217 * 0.23 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.155 0.155 0.154 0.154 0.155 0.154
Adjusted R–squared 0.143 0.144 0.142 0.142 0.143 0.142

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

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

middle-income SSA countries are reported in Tables 12 and 13. Four main results could be derived from Table 12. First, as expected,
most of the estimated models show that both types of ICT have positive effects on the sustainable development index in low-income
countries, i.e., the sustainable development index is positively affected by mobile phone and Internet use, going from 0.099% to
0.046% for the mobile phone indicators and from 0.348% to 0.067% for the Internet use indicators. Second, regarding the financial
sector development indicators, only financial sector development index 2 is pertaining. Thus, financial sector development increases
the sustainable development index, from 0.171% for mobile phones to 0.09% for Internet use.
On the one hand, the net effects of mobile phone contacts with financial sector development indicators on the sustainable
development index are computed. Since only all the proxy FSI 2 is significant, we computed the net effects for these indices. For
example, column 1 of Table 12 indicates that a 1% rise in FSI 2 raises the sustainable development index around 0.114% [i.e.,
(0.036 ×0.405) + 0.099] at the average value of the mobile phone. In this computation, 0.036 is the conditional impact from the
interaction between the mobile phone and FSI 1, 0.405 represents the mean value of FSI 1, and 0.099 is the mobile phone’s uncon­
ditional impact. For each regression, a positive net impact means that the complementarity hypothesis between mobile phone and
financial sector development in promoting sustainable development index is validated, whereas a negative net impact indicates that

Table 13
ICT (Mobile phone and Internet use), financial sector development indices, and SDG index in middle–income SSA countries.
Dependent variable: Sustainable development index (SDI)

Mobile phone Internet use

Variables FSI 1 FSI 2 FSI 3 FSI 1 FSI 2 FSI 3
SD (–1) 0.505 * 0.128 0.171 0.282 0.278 0.205
(0.003) (0.584) (0.431) (0.127) (0.138) (0.282)
Mobile phone 0.031 * ** 0.069 * * 0.07 * * – – –
(0.063) (0.026) (0.024)
Internet use – – – 0.03 * 0.057 * 0.049 *
(0.006) (0.004) (0.004)
FSI 1 0.102 * ** – – 0.061 * – –
(0.053) (0.008)
FSI 2 – 0.326 * * – – 0.027 * ** –
(0.022) (0.082)
FSI 3 – – 0.12 * * – – 0.048 * **
(0.037) (0.081)
Mobile phone *FSI 1 –0.012 – – – – –
(0.331)
Mobile phone *FSI 2 – –0.005 – – – –
(0.451)
Mobile phone *FSI 3 – – 0.015 * – – –
(0.008)
Internet use *FSI 1 – – – –0.019 – –
(0.142)
Internet use *FSI 2 – – – – –0.024 –
(0.102)
Internet use *FSI 3 – – – – – 0.002 *
(0.004)
GDP 0.001 0.011 (0.169) 0.001 * ** 0.006 * 0.004 0.015
(0.859) (0.073) (0.006) (0.640) (0.110)
FDI 0.006 0.006 (0.386) 0.005 0.011 * 0.008 0.108
(0.345) (0.358) (0.002) (0.194) (0.103)
R 0.009 * ** 0.021 * (0.001) 0.02 * –0.02 * 0.02 * 0.224 *
(0.084) (0.01) (0.01) (0.009) (0.006)
TO –0.003 –0.007 (0.113) –0.008 * * 0.01 0.002 0.006
(0.46) (0.02) (0.89) (0.76) (0.385)
RL –0.005 –0.021 (0.264) –0.021 0.031 * –0.21 –0.029
(0.761) (0.294) (0.006) (0.302) (0.144)
Net effects n.a n.a 0.084 n.a n.a 0.05
AR(1) (0.028) (0.013) (0.002) (0.001) (0.001) (0.004)
AR(2) (0.500) (0.510) (0.513) (0.313) (0.548) (0.231)
Hansen OIR test (0.306) (0.296) (0.319) (0.278) (0.266) (0.2015)
Sargan OIR test (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Fisher 39.86 * 31.53 * 20.41 * 26.30 * 21.51 * 36.39 *
Number of Countries 25 25 25 25 25 25
POLS (Lagged DV) 0.782 * 0.785 * 0.79 * 0.791 * 0.798 * 0.803 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000
R2 0.619 0.623 0.613 0.619 0.62 0.61
R–squared 0.612 0.617 0.606 0.613 0.613 0.603
FE (Lagged DV) 0.115 * 0.122 * 0.137 * 0.145 * 0.123 * 0.144 *
Prob 0.000 0.000 0.000 0.000 0.000 0.000
R–squared 0.167 0.167 0.167 0.168 0.168 0.167
Adjusted R-squared 0.148 0.147 0.147 0.148 0.147 0.148

Notes: * *, * ** : represent statistical significance at the 1%, 5%, and 10% levels, respectively

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S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

the tested hypothesis is rejected. It is noticeable from Table 12 that the net impact on the sustainable development index from the
interplays between mobile phones and the financial sector development index 2 is positive, indicating that increasing mobile phone
leads them to attract more financial sector development (depth, efficiency, inclusion, and stability) in low-income SSA countries,
which, in turn, encourages sustainable development index. On the other hand, net effects from the interaction between Internet use
and the indexes of financial sector development on the sustainable development index are computed to measure the global effect of this
interaction. Since financial sector development 2 is significant, we computed the net effects only for it. For instance, column four/4 of
Table 12 shows that at the average value of Internet use, a 1% rise in FSI 2 boosts the sustainable development index by 0.076%/ % [i.
e., (0.023 ×0.405) + (0.067)]. In this computation, 0.023 is the conditional impact from the interaction between Internet use and FSI
2, 0.405 represents the mean value of FSI 2, and 0.067 indicates the unconditional impact of Internet use. A positive net impact for each
regression shows that the complementarity hypothesis between Internet use and financial sector development 2 in boosting sustainable
development index has been verified, whereas a negative net impact implies that the tested hypothesis has been rejected. A positive net
impact means Internet use complements financial sector development to increase the sustainable development index. It is clear from
Table 12 that the net effects from the interplays between Internet use and the index of financial sector development 2 on sustainable
development are positive, indicating that increasing Internet use significantly raises financial sector development (depth, efficiency,
and stability) in low- income SSA countries, which, in turn, encourages sustainable development. Finally, regarding control variables,
the financial sector development is positively influenced by the growth of GDP, FDI, R, and the RL, while TO negatively impacts
sustainable development.
Four main results could be derived from Table 13. First, as expected, most of the estimated models show that both types of ICT have
positive effects on the sustainable development index in middle-income SSA countries, i.e., the sustainable development index is
positively affected by mobile phone and internet use, going from 0.031% to 0.07% for the mobile phone indicators and from 0.03% to
0.057% for the internet use. Second, regarding the financial sector development indicators, only financial sector development index 2
is pertaining. Thus, financial sector development increases the sustainable development index from 0.102% to 0.326% for mobile
phones and from 0.027% to 0.061% for internet use. On the one hand, the net effects from the interactions between mobile phones and
the indexes of financial sector development on the sustainable development index are computed. Since only all the proxy FSI 3 is
significant, we computed the net effects for these indices. Table 13’s third column shows that a 1% rise in FSI 3 boosts the sustainable
development index by 0.084%/% [i.e., (0.036 ×0.391) + 0.07] at the average mobile phone value. In this computation, 0.015 is the
conditional impact from the interaction between the mobile phone and FSI 3, 0.391 is the mean value of FSI 3, and 0.07 represents the
mobile phone’s unconditional impact. For each regression, a positive net impact means that the complementarity hypothesis between
mobile phone and FSI 3 in promoting sustainable development index is validated, whereas a negative net impact indicates that the
tested hypothesis 3 is rejected. It is apparent from Table 13 that the net impact on the sustainable development index from the in­
terplays between internet use and the financial sector development index 3 is positive, indicating that increasing mobile phone leads
them to attract more financial sector development (depth, efficiency, inclusion, and stability) in low-income SSA countries, which, in
turn, encourages sustainable development index. On the other hand, net effects from the interaction between internet use and the
indexes of financial sector development on the sustainable development index are computed to measure the global effect of this
interaction. Since financial sector development 3 is significant, we computed the net effects only for it. Table 13’s sixth column
demonstrates that a 1% rise in FSI 3 boosts the sustainable development index by 0.05% [i.e., (0.002 ×0.391) + (0.049)] at the average
value of internet use. In this computation, 0.002 is the conditional impact of the interaction between internet use and FSI 3, 0.391
represents the mean value of FSI 3, and 0.049 indicates the unconditional impact of internet use. For each regression, a positive net
impact means that the complementarity hypothesis between internet use and financial sector development 3 in promoting sustainable
development index is validated, whereas a negative net impact indicates that the tested hypothesis is rejected. A positive net impact
means Internet use complements financial sector development 3 to increase the sustainable development index. It is clear from
Table 13 that the net effects from the interplays between Internet use and the index of financial sector development 3 on sustainable
development are positive, indicating that increasing Internet use significantly raises financial sector development (depth and stability)
in low-income SSA countries, which, in turn, encourages sustainable development. Finally, regarding the control variables, financial
sector development is positively influenced by the growth of GDP, FDI, R, and RL. In contrast, TO shows a negative impact on sus­
tainable development.

5. Conclusions and implications

This study seeks to extend the previous literature on achieving the SDGs by demonstrating how ICT supports financial sector
development for achieving sustainable development in this region at both aggregate and disaggregate levels. Specifically, using data
from 48 SSA countries, we investigate the joint effects of ICT (mobile phone and internet use) and eight financial sector development
indicators on achieving economic and social sustainability in light of the 17 SDGs. We find three main results using the system GMM
method: (i) the four dimensions of financial development and both indicators of ICT increase economic, social, and environmental
sustainability, (ii) increasing the access to mobile phones and the use of internet contributes to the development of the financial sector,
(iii) the contribution of financial sector development on the achievement of the SDGs increases with the presence of ICT. Overall, we
show that ICT diffusion strengthens financial sector development for achieving the 17 SDGs in SSA countries.
In addition to these contributions and findings, this study provides some policy and practical implications. Our findings show the
significance of ICT in the process of reaching SDGs. For this reason, more ICT diffusion is required to ensure financial systems in SSA,
hence realizing SDGs. In this direction, the World Bank (2016) has argued that digital adoption is not fruitful unless countries pursue
“analog complements.” However, the internet and mobile phone depend largely on their users’ skills, levels, and quality of education.

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As a result, policymakers in SSA should modernize educational institutions by lowering the cost of the internet and mobile phones, for
example, to enhance ICT education among young people (Adeleye and Eboagu, 2019; Hasbi and Dubus, 2020). Learning in the
technical, scientific, and technological fields may guide and structure the students to better use technologies. SSA countries need to
give more importance to ICT development through subsidies and supplies for IT manufacturers or by strengthening the necessary
infrastructure to develop this sector. To expand the volume of credit, the weight of banking assets, and the size of African financial
markets, technology must be deployed in the financial services sector. Significant investments must be made in the construction of
bank branches and the proliferation of automated teller machines, which may promote the efficiency of the financial system, thereby
improving economic productivity, social welfare, and environmental quality. Hence, financial resources should be allocated efficiently
to encourage green technology innovation projects and friendly technologies, despite their expensive costs for a better future. Finally,
SSA countries should also consider the role of the institutional environment in promoting ICT, as a well-developed legal system and
property rights tend to encourage risk-taking and innovative behavior, and any strategy in this regard can support sustainable
development. Thus, a good political connection can enhance a local government’s ability to motivate companies to innovate for
sustainability (Wu et al., 2022).
Despite these recommendations and findings, our research still suffers from certain limitations that must be considered. Firstly, we
employed mobile phones and internet use as ICT proxies due to data constraints. However, the tremendous acceleration of the ICT
revolution has produced many new technologies, such as the Internet of Things, cloud computing, social media, 5 G, Artificial In­
telligence, and electronic commerce (Vu et al., 2020). Thus, considering these new forms is worthwhile in this field, particularly their
effects on the three dimensions of sustainable development. Secondly, we only conducted our analysis based on the data of SSA
countries and their distribution according to their low and middle-income levels, without highlighting the heterogeneity in sustain­
ability. Future research should look into the role of financial sector growth and ICT in achieving the SDGs, considering the diversity of
SSA countries’ sustainability. Finally, the interaction term in our model pushes the effect of ICT on sustainable development to
monotonically increase (or decrease) with the financial sector development level. Yet, the effectiveness of ICT in financial sector
development may require a certain level to affect sustainable development. Therefore, future studies can apply a regression model
based on the concept of threshold levels to identify the existence of contingency impacts.

CRediT authorship contribution statement

Sabrine Dhahri: Conceptualization, Methodology, Investigation. Anis Omri: Methodology, Data collection and Software.
Nawazish Mirza: Investigation and language editing.

Data Availability

Data will be made available on request.

Appendices

Table A
List of Sub-Saharan African countries by income levels (SSA=48).

Low-income countries (N = 23) Middle-income countries (N = 25)

Burkina Faso Angola

Burundi Benin
Central African Republic Botswana
Chad Cabo Verde
Congo Democratic Republic Cameroon
Eritrea Comoros
Ethiopia Congo Republic
Gambia Cote d’Ivoire
Guinea Equatorial Guinea
Guinea-Bissau Eswatini
Liberia Gabon
Madagascar Ghana
Malawi Kenya
Mali Lesotho
Mozambique Mauritania
Niger Mauritius
Rwanda Namibia
Sierra Leone Nigeria
Somalia São Tomé and Principe
South Sudan Senegal
Sudan Seychelles
South Africa
(continued on next page)

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Table A (continued )
Low-income countries (N = 23) Middle-income countries (N = 25)

Togo Tanzania
Uganda Zambia
Zimbabwe
Note: N: is the number of countries.

Table B
Variables abbreviations, descriptions, and data sources.

Variable name Abbreviation Description Sources

Dependent variables
Sustainable Development SDI the 17 SDGs were calculated by one-third economic dimension, one-third social WDI and SWIID
Index dimension, and one-third environmental dimension. (goal 10)
Economic dimension ECD economic dimension is calculated using two selected PCs (economic1 and economic2). WDI
Social dimension SD social dimension is examined using four selected PCs (social1, social2, social3 and WDI and SWIID
social4)). (goal 10)
Environmental dimension EVD environmental dimension is evaluated using two PCs (envirnmt1 and envirnmt2. WDI
Independent variables
Information communications ICT mobile phone subscriptions for 100 people. WDI
technologies Internet use subscriptions for 100 people.
Financial sector development FS we used eight financial indicators, reflecting the four financial dimensions, i.e., financial GFD
depth, financial inclusion, financial efficiency, and financial stability.
Financial depth DC the domestic credit to the private sector as a share of GDP.
DB deposit bank assets as a share of GDP.
MS money supply as a share of GDP.
LL liquid liabilities as a share of GDP.
Financial efficiency BM the bank’s net interest margin as a percentage.
Financial inclusion AM number of automatic teller machines per 100,000 adults.
Financial stability BZ bank’s Z-score.
BL bank’s ratio of non-performing loans to gross loans in percentage.
Financial sector development FSI 1 The first composite index of financial sector development (FSI 1) represents the financial
index 1 depth, which is composed of the variables DC, DB, MS, and LL.
Financial sector development FSI 2 includes a financial depth indicator DC, a financial inclusion indicator AM, a financial
index 2 efficiency indicator BM and a financial stability indicator BL.
Financial sector development FSI 3 is composed of the bank’s Z-score financial stability indicator, a DC indicator, an AM
index 3 indicator, and a BM indicator.
Control variables
GDP growth GDP measured by annual percentage. WDI
Foreign direct investment FDI measured by net inflows as a share of GDP.
Remittances R are measured as a share of GDP.
Trade openness TO total imports plus exports of goods and services as a share of GDP.
Rule of law RL measure the extent to which agents trust and respect the rules of society, particularly the WGI
quality of contract enforcement, property rights, the police, and courts, or the likelihood of
crime and violence.

Table C
Cross-sectional dependence tests results.

Breusch-Pagan (1980) Friedman (1937) Pesaran (2004)

Statistics 1.04 14.34 24.239

Prob. 0.123 0.344 0.733
Note: Null hypothesis: Cross-sectional independence.

Table D
Panel unit root tests results.

Variables LLC test IPS test

Level Δ Level Δ

SDI 4.75 –21.055 (0.000) –3.291 –19.22

(1.000) (1.000) (0.000)
Economic sus –10.278 –32.519 (0.000) –7.347 –27.198
(0.679) (1.000) (0.000)
(continued on next page)

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Table D (continued )
Variables LLC test IPS test

Level Δ Level Δ

Social sus –5.686 –24.453 (0.000) –2.295 –22.22

(0.973) (0.111) (0.000)
Env sus –0.445 –13.387 (0.000) 3.106 –15.856
(0.328) (0.999) (0.000)
Mobile phone 2.59 –16.098 (0.000) 2.594 –11.019
(0.995) (0.995) (0.000)
Internet use 4.191 –2.4392 (0.007) 1.898 –6.481
(1.000) (0.971) (0.000)
Findex1 0.971 –11.211 (0.000) –4.286 –9.946
(1.000) (1.000) (0.000)
Findex2 –11.353 –20.25 (0.000) –10.367 –18.413
(0.249) (1.000) (0.000)
Findex3 –13.351 –21.189 (0.000) –9.85 –18.091
(1.000) (1.000) (0.000)
DC 1.622 –15.542 (0.000) 0.029 –0.216
(0.948) (0.512) (0.000)
DB 1.544 –4.095 (0.000) –0.488 –0.431
(0.939) (0.313) (0.000)
MS 1.648 –8.423 (0.000) 0.194 –7.105
(0.95) (0.577) (0.000)
LL 2.025 –16.464 (0.000) 0.347 –3.99
(0.979) (0.636) (0.000)
BM 5.237 –15.867 (0.000) –1.254 –19.514
(1.000) (0.105) (0.000)
AM –22.493 –29.234 (0.000) –9.218 –21.947
(0.000) (0.000) (0.000)
BL 4.13 –20.454 (0.000) 0.798 –16.725
(1.000) (0.788) (0.000)
BZ 8.483 –6.218 3.916 –11.053
(1.000) (0.000) (1.000) (0.000)
GDP –14.555 –33.678 (0.000) –11.469 –32.086
(0.361) (0.998) (0.000)
FDI –8.01 –32.898 (0.000) –7.64 –29.49
(0.000) (0.000) (0.000)
R –6.152 –19.364 (0.000) –5.599 –17.703
(0.000) (0.000) (0.000)
TO –3.695 –22.387 (0.000) –1968 –19.232
(0.000) (0.025) (0.000)
RL –4.755 –25.556 (0.000) –3.45 –22.773
(0.000) (0.000) (0.000)
Note: All the variables are expressed in natural logarithms except for the sustainable development index, sustainable devel­
opment dimensions, and financial sector development indexes. Δ represents the first difference operator. The null hypothesis of
non-stationary is examined with LLC and IPS tests. The selection of lag lengths (automatic) is based on Schwarz Information
Criteria (SIC). The P-values are shown in parentheses.

Table E
Results of panel Cointegration tests.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Kao (1999) ADF 1.353 0.983 1.873 0.739 0.698 0.927

Pedroni (1999, 2004) ρ 0.698 0.372 0.124 –0.883 0.983 1.124
Note: Model 1: SDI=f(internet use, FSI 1, GDP, foreign direct investment, remittances, TO, rule of law), Model 2: SDI=f(internet use, FSI 2, GDP,
foreign direct investment, remittances, TO, rule of law), Model 3; SDI=f(internet use, FSI 3, GDP, FDI, remittances, TO, RL), Model 4: SDI=f(mobile
phone, FSI 1, GDP, FDI, R, TO, RL), Model 5: SDI=f(mobile phone, FSI 2, GDP, FDI, R, TO, RL), and Model 6; SDI=f(mobile phone FSI 3, GDP, FDI, R,
TO, RL). H0 represents the case of no-cointegration for the panel cointegration tests.

References

Abbasi, K.R., Lv, K., Radulescu, M., Shaikh, P.A., 2021. Economic complexity, tourism, energy prices, and environmental degradation in the top economic complexity
countries: fresh panel evidence. Environ. Sci. Pollut. Res. 28, 68717–68731.
Abeka, M.J., Andoh, E., Gatsi, J.G., Kawor, S., 2021. Financial development and economic growth nexus in SSA economies: the moderating role of telecommunication
development. Cogent Econ. Financ. 9 (1), 1862395.
Abid, M., 2016. Impact of economic, financial, and institutional factors on CO2 emissions: evidence from sub-Saharan Africa economies. Uti. Policy 41, 85–94.

27
S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Abou-Ali, H., Abdelfattah, Y.M., 2013. Integrated paradigm for sustainable development: a panel data study. Econ. Model 30, 334–342.
Acheampong, A.O., 2019. Modelling for insight: Does financial development improve environmental quality? Energy Econ. 83, 156–179.
Adams, S., Klobodu, E., 2018. Financial development and environmental degradation: does political regime matter? J. Clean. Prod. 197, 1472–1479.
Adams, S., Akobeng, E., 2021. ICT, governance and inequality in Africa. Telecommun. Policy 45 (10), 102198.
Adeleye, N., Eboagu, C., 2019. Evaluation of ICT development and economic growth in Africa. In NETNOMICS: Economic research and electronic networking, 20,
31–53.
Alam, M.S., Islam, M.S., Shahzad, S.J.H., Bilal, S., 2021. Rapid rise of life expectancy in Bangladesh: does financial development matter? Int. J. Financ. Econ. 26 (4),
4918–4931.
Ali, W., Abdullah, A., Azam, M., 2017. Re-visiting the environmental Kuznets curve hypothesis for Malaysia: fresh evidence from ARDL bounds testing approach.
Renew. Sustain. Energy Rev. 77, 990–1000.
Al-Mulali, U., Ozturk, I., Lean, H.H., 2015. The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on
pollution in Europe. Nat. Hazards 79, 621–644.
Alshubiri, F., Jamil, S.A., Elheddad, M., 2019. The impact of ICT on financial development: empirical evidence from the Gulf Cooperation Council countries. Int. J.
Eng. Bus. Manag. 11, 1847979019870670.
Aluko, O.A., Ibrahim, M., 2020. Institutions and the financial development–economic growth nexus in sub-Saharan Africa. Econ. Notes 49 (3), e12163.
Aluko, O.A., Obalade, A.A., 2020. Financial development and environmental quality in Sub-Saharan Africa: is there a technology effect? Sci. Total Environ. 747,
141–515.
Amri, F., Zaied, Y.B., Lahouel, B.B., 2019. ICT, total factor productivity, and carbon dioxide emissions in Tunisia. Technol. Forecast Soc. Chang 146, 212–217.
Appiah-Otoo, I., Song, N., 2021. The impact of ICT on economic growth-Comparing rich and poor countries. Telecommun. Policy 45 (2), 102082.
Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58 (2),
277–297.
Asongu, S.A., Odhiambo, N.M., 2020. Foreign direct investment, information technology and economic growth dynamics in Sub-Saharan Africa. Telecommun. Policy
44 (1), 101838.
Asongu, S.A., Le Roux, S., Biekpe, N., 2018. Enhancing ICT for environmental sustainability in sub-Saharan Africa. Technol. Forecast Soc. Change 127, 209–216.
Asongu, S.A., Adegboye, A., Ejemeyovwi, J., Umukoro, O., 2021. The mobile phone technology, gender inclusive education and public accountability in Sub-Saharan
Africa. Telecommun. Policy 45 (4), 102108.
Asongu, S.A., Nounamo, Y., Njangang, H., Tadadjeu, S., 2021. Gender inclusive intermediary education, financial stability and female employment in the industry in
Sub-Saharan Africa. Financ. Res. Lett. 43, 101968.
Assefa, T.A., Mollick, A.V., 2017. Financial development and economic growth in Africa. J. Afr. Bus. 18 (3), 320–339.
Avom, D., Nkengfack, H., Fotio, H.K., Totouom, A., 2020. ICT and environmental quality in Sub-Saharan Africa: effects and transmission channels. Technol. Forecast.
Soc. Change 155, 120028.
Bali Swain, R., Yang-Wallentin, F., 2020. Achieving sustainable development goals: predicaments and strategies. Int. J. Sustain. Dev. World Ecol 27 (2), 96–106.
Baloch, M.A., Zhang, J., Iqbal, K., Iqbal, Z., 2019. The effect of financial development on ecological footprint in BRI countries: evidence from panel data estimation.
Environ. Sci. Pollut. Res. 26, 6199–6208.
Barbier, E.B., Burgess, J.C., 2019. Sustainable development goal indicators: analyzing trade-offs and complementarities. World Dev. 122, 295–305.
Ben Youssef, A., Boubaker, S., Omri, A., 2018. Entrepreneurship and sustainability: the need for innovative and institutional solutions. Technol. Forecast. Soc. Change
129, 232–241.
Ben Youssef, A., Boubaker, S., Omri, A., 2020. Financial development and macroeconomic sustainability: modeling based on a modified environmental Kuznets curve.
Clim. Change 163 (2), 767–785.
Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87 (1), 115–143.
Boateng, A., Asongu, S.A., Akamavi, R., Tchamyou, V.S., 2018. Information asymmetry and market power in the african banking industry. J. Multinatl. Financ.
Manag. 44, 69–83.
Bolarinwa, S.T., Olaoye, O.O., Ullah, W., Agbi, B., 2022. Does financial development really matter for poverty reduction in Africa?. In: In Forum for Social Economics,
51 Routledge, pp. 415–432.
Bouzguenda, I., Alalouch, C., Fava, N., 2019. Towards smart sustainable cities: a review of the role digital citizen participation could play in advancing social
sustainability. Sustain. Cities Soc. 50, 101627.
Breusch, T.S., Pagan, A.R., 1980. The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 47 (1), 239–253.
Cheng, C.Y., Chien, M.S., Lee, C.C., 2021. ICT diffusion, financial development, and economic growth: an international cross-country analysis. Econ. Model. 94,
662–671.
Chien, M.S., Cheng, C.Y., Kurniawati, M.A., 2020. The non-linear relationship between ICT diffusion and financial development. Telecommun. Policy 44 (9), 102023.
Chinoda, T., Kapingura, F.M., 2023. The impact of digital financial inclusion and bank competition on bank stability in sub-Saharan Africa. Economies 11 (1), 15.
Chireshe, J., Ocran, M.K., 2020. Financial development and health care expenditure in Sub Saharan Africa Countries. Cogent Econ. Financ. 8 (1), 1771878.
Choi, C., Rhee, D., Oh, Y., 2014. Information and capital flows revisited: the internet as a determinant of transactions in financial assets. Econ. Model. 40, 191–198.
Choi, S., Ng, A., 2011. Environmental and economic dimensions of sustainability and price effects on consumer responses. J. Bus. Ethics 104, 269–282.
Cieslik, K., Barford, A., Vira, B., 2022. Young people not in employment, education or training (NEET) in Sub-Saharan Africa: sustainable development target 8.6
missed and reset. J. Youth Stud. 25 (8), 1126–1147.
Claeys, S., Vander Vennet, R., 2008. Determinants of bank interest margins in Central and Eastern Europe: a comparison with the West. Econ. Syst. 32 (2), 197–216.
Costanza, R., Daly, L., Fioramonti, L., Giovannini, E., Kubiszewski, I., Mortensen, L.F., Pickett, K.E., Ragnarsdottir, K.V., De Vogli, R., Wilkinson, R., 2016. Modelling
and measuring sustainable wellbeing in connection with the UN sustainable development goals. Ecol. Econ. 130, 350–355.
Danish, Khan, N., Baloch, M.A., Saud, S., Fatima, T., 2018. The effect of ICT on CO 2 emissions in emerging economies: does the level of income matters? Environ. Sci.
Pollut. Res. 25, 22850–22860.
Das, A., Chowdhury, M., Seaborn, S., 2018. ICT diffusion, financial development and economic growth: new evidence from low and lower middle-income countries.
J. Knowl. Econ. 9, 928–947.
Dhahri, S., Slimani, S., Omri, A., 2021. Behavioral entrepreneurship for achieving the sustainable development goals. Technol. Forecast. Soc. Change 165, 120561.
Donou-Adonsou, F., 2019. Technology, education, and economic growth in Sub-Saharan Africa. Telecommun. Policy 43 (4), 353–360.
Donou-Adonsou, F., Lim, S., Mathey, S.A., 2016. Technological progress and economic growth in Sub-Saharan Africa: evidence from telecommunications
infrastructure. Int. Adv. Econ. Res. 22, 65–75.
Edziah, B.K., Sun, H., Adom, P.K., Wang, F., Agyemang, A.O., 2022. The role of exogenous technological factors and renewable energy in carbon dioxide emission
reduction in Sub-Saharan Africa. Renew. Energy 196, 1418–1428.
Ejemeyovwi, J.O., Osabuohien, E.S., Bowale, E.I., 2021. ICT adoption, innovation and financial development in a digital world: empirical analysis from Africa.
Transnatl. Corp. Rev. 13 (1), 16–31.
Evans, O., 2018. Connecting the poor: the internet, mobile phones and financial inclusion in Africa. Digit. Policy, Regul. Gov. 20 (6), 568–581.
Flick, C., Zamani, E., Carsten, B., Brem, A., 2020. The future of ICT for health and ageing: unveiling ethical and social issues through horizon scanning foresight.
Technol. Forecast. Soc. Change 155, 119995.
Fowowe, B., 2011. The finance-growth nexus in Sub-Saharan Africa: panel cointegration and causality tests. J. Int. Dev. 23, 220–239.
Friedman, M., 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32 (200), 675–701.
Gallego-Losada, M.J., Montero-Navarro, A., García-Abajo, E., Gallego-Losada, R., 2023. Digital financial inclusion. Visualizing the academic literature. Res. Int. Bus.
Financ. 64, 101862.

28
S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Grossman, G.M., Krueger, A.B., 1993. Environmental impacts of the North American free trade agreement’. In: Garber, P. (Ed.), The U.S.–Mexico Free Trade
Agreement. MIT Press, Cambridge, MA, pp. 13–56.
Guptha, K., S, K., Rao, R., P., 2018. The causal relationship between financial development and economic growth: an experience with BRICS economies. J. Soc. Econ.
Dev. 20 (2), 308–326.
Haftu, G.G., 2019. Information communications technology and economic growth in Sub-Saharan Africa: a panel data approach. Telecommun. Policy 43 (1), 88–99.
Haluza, D., Jungwirth, D., 2018. ICT and the future of healthcare: aspects of pervasive health monitoring. Inform. Health Soc. care 43 (1), 1–11.
Hasbi, M., Dubus, A., 2020. Determinants of mobile broadband use in developing economies: evidence from Sub-Saharan Africa. Telecommun. Policy 44 (5), 101944.
Hatemi-J, A., Shamsuddin, M., 2016. The causal interaction between financial development and human development in Bangladesh. Appl. Econ. Lett. 23 (14),
995–998.
Higón, D.A., Gholami, R., Shirazi, F., 2017. ICT and environmental sustainability: a global perspective. Telemat. Inform. 34 (4), 85–95.
Hong, J.P., 2017. Causal relationship between ICT R&D investment and economic growth in Korea. Technol. Forecast. Soc. Change 116, 70–75.
Hunjra, A.I., Azam, M., Bruna, M.G., Taskin, D., 2022. Role of financial development for sustainable economic development in low middle-income countries. Financ.
Res. Lett. 47, 102793.
Hussein, M.A., Ahmed, S.A., Ahmed, H.M.S., 2020. Impact of financial development on economic growth in Sudan: short-run and long-run analysis. Int. J. Manag.
(IJM) 11, 12.
ILO, 2020. World Economic and Social Outlook Trends – 2020. International Labour Office, Geneva.
Im, K.S., Pesaran, M.H., Shin, Y., 2003. Testing for unit roots in heterogeneous panels. J. Econ. 115 (1), 53–74.
International Monetary Fund, 2019. FinTech in SubSaharan African countries: a game changer? Washington. DC: Int. Monet. Fund.
International Monetary Fund., 2021. World Economic Outlook: Managing Divergent Recoveries. Washington, DC, April.
International Telecommunication Union (ITU), 2021. Digital trends in Africa 2021 Information and communication technology trends and developments in the Africa
region 2017–2020. Geneva 2021.
International Union for the Conservation of Nature and Natural Resources (IUCN)., 1980. World Conservation Strategy: Living Resource Conservation for Sustainable
Development (IUCN, Gland, Switzerland).
Kaidi, N., Mensi, S., Ben Amor, M., 2019. Financial development, institutional quality and poverty reduction: worldwide evidence. Soc. Indic. Res. 141, 131–156.
Kaiser, H.F., 1960. The application of electronic computers to factor analysis. Ed. Psychol. Meas. 20, 141–151.
Kao, C., 1999. Spurious regression and residual-based tests for cointegration in panel data. J. Econ. 90 (1), 1–44.
Kardos, M., 2012. The relationship between entrepreneurship, innovation and sustainable development. Research on European Union countries. Procedia Econ.
Financ. 3, 1030–1035.
Karim, Z.A., Nizam, R., Law, S.H., Hassan, M.K., 2022. Does financial inclusiveness affect economic growth? New evidence using a dynamic panel threshold
regression. Financ. Res. Lett. 46, 102364.
Kassi, D.F., Sun, G., Ding, N., 2020. Does governance quality moderate the finance-renewable energy-growth nexus? Evidence from five major regions in the world.
Environ. Sci. Pollut. Res. 27 (11), 12152–12180.
Kelikume, I., 2021. Digital financial inclusion, informal economy and poverty reduction in Africa. J. Enterprising Communities: People Places Glob. Econ. 15 (4),
626–640.
Khan, F.N., Sana, A., Arif, U., 2020. Information and communication technology (ICT) and environmental sustainability: a panel data analysis. Environ. Sci. Pollut.
Res 27, 36718–36731.
Khan, I., Khan, I., Sayal, A.U., Khan, M.Z., 2022. Does financial inclusion induce poverty, income inequality, and financial stability: empirical evidence from the 54
African countries? J. Econ. Stud. 49 (2), 303–314.
Khan, M., Ozturk, I.0, 2021. Examining the direct and indirect effects of financial development on CO2 emissions for 88 developing countries. J. Environ. Manag. 293,
112812.
Kirikkaleli, D., Adebayo, T.S., 2021. Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence.
Sustain. Dev. 29 (4), 583–594.
Leimbach, M., Roming, N., Schultes, A., Schwerhoff, G., 2018. Long-term development perspectives of Sub-Saharan Africa under climate policies. Ecol. Econ. 144,
148–159.
Levin, A., Lin, C.F., Chu, C.S.J., 2002. Unit root tests in panel data: asymptotic and finite-sample properties. J. Econ. 108 (1), 1–24.
Lozano, R., 2008. Envisioning sustainability three-dimensionally. J. Clean. Prod. 16 (17), 1838–1846.
Mahmood, H., Adow, A.H., Abbas, M., Iqbal, A., Murshed, M., Furqan, M., 2022. The fiscal and monetary policies and environment in GCC countries: analysis of
territory and consumption-based CO2 emissions. Sustainability 14 (3), 1225.
McKinnon, R.I., 1973. Money and Capital in Economic Development. Brookings Institution. Washington D.C.
Murshed, M., Ahmed, R., Khudoykulov, K., Kumpamool, C., Alrwashdeh, N.N.F., Mahmood, H., 2023. Can enhancing financial inclusivity lower climate risks by
inhibiting carbon emissions? Contextual evidence from emerging economies. Res. Int. Bus. Financ. 65, 101902.
Nandelenga, M.W., Oduor, J., 2020. Asymmetric analysis of finance-Inequality nexus: evidence from sub-Saharan Africa. J. Econ. Asymmetries 22, e00184.
Nasir, M.A., Huynh, T.L.D., Tram, H.T.X., 2019. Role of financial development, economic growth & foreign direct investment in driving climate change: a case of
emerging ASEAN. J. Environ. Manag 242, 131–141.
Ojo, A., Janowski, T., Awotwi, J., 2013. Enabling development through governance and mobile technology. Gov. Inf. Q. 30, S32–S45.
Omar, M.A., Inaba, K., 2020. Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis. J. Econ. Struct. 9 (1), 37.
Omri, A., Kahia, M., Kahouli, B., 2021. Does good governance moderate the financial development–CO2 emissions relationship? Environ. Sci. Pollut. Res 28,
47503–47516.
Owusu-Agyei, S., Okafor, G., Chijoke-Mgbame, A.M., Ohalehi, P., Hasan, F., 2020. Internet adoption and financial development in sub-Saharan Africa. Technol.
Forecast. Soc. Change 161, 120293.
Ozcan, B., Apergis, N., 2018. The impact of internet use on air pollution: evidence from emerging countries. Environ. Sci. Pollut. Res. 25, 4174–4189.
Ozili, P.K., 2023. Sustainable development and bank non-performing loans: are they correlated? Arab Gulf J. Sci. Res.
Ozturk, I., Acaravci, A., 2013. The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey. Energy Econ.
36, 262–267.
Park, Y., Meng, F., Baloch, M.A., 2018. The effect of ICT, financial development, growth, and trade openness on CO 2 emissions: an empirical analysis. Environ. Sci.
Pollut. Res. 25, 30708–30719.
Pedroni, P., 1999. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxf. Bull. Econ. Stat. 61 (S1), 653–670.
Pedroni, P., 2004. Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econom. Theory
20 (3), 597–625.
Pellegrina, L.D., Frazzoni, S., Rotondi, Z., et al., 2017. Does ICT adoption improve access to credit for small enterprises? Small Bus. Econ. 48, 657–679.
Pesaran, M.H., 2004. General diagnostic tests for cross section dependence in panels. Available at SSRN 572504.
Pradhan, R.P., Arvin, M.B., Norman, N.R., 2015. The dynamics of information and communications technologies infrastructure, economic growth, and financial
development: Evidence from Asian countries. Technol. Soc. 42, 135–149.
Pradhan, R.P., Arvin, M.B., Nair, M.S., Hall, J.H., Bennett, S.E., 2021. Sustainable economic development in India: the dynamics between financial inclusion, ICT
development, and economic growth. Technol. Forecast. Soc. Change 169, 120758.
Purvis, B., Mao, Y., Robinson, D., 2019. Three pillars of sustainability: in search of conceptual origins. Sustain. Sci. 14, 681–695.
Raheem, I.D., Tiwari, A.K., Balsalobre-Lorente, D., 2020. The role of ICT and financial development in CO2 emissions and economic growth. Environ. Sci. Pollut. Res.
27 (2), 1912–1922.
Rashid, A., Intartaglia, M., 2017. Financial development–does it lessen poverty? J. Econ. Stud. 44 (1), 69–86.

29
S. Dhahri et al. Research in International Business and Finance 67 (2024) 102156

Renzhi, N., Baek, Y.J., 2020. Can financial inclusion be an effective mitigation measure? evidence from panel data analysis of the environmental Kuznets curve.
Financ. Res. Lett. 37, 101725.
Rogers, E.M., 1962. Diffusion of innovations, first ed. Free Press, New York.
Ruiz, J.L., 2018. Financial development, institutional investors, and economic growth. Int. Rev. Econ. Financ. 54, 218–224.
Sagar, A.D., Najam, A., 1998. The human development index: a critical review. Ecol. Econ. 25 (3), 249–264.
Saksonova, S., 2014. The role of net interest margin in improving banks’ asset structure and assessing the stability and efficiency of their operations. Procedia-Soc.
Behav. Sci. 150, 132–141.
Sarpong-Kumankoma, E., Abor, J., Aboagye, A.Q.Q., Amidu, M., 2020. Financial freedom, market power and bank margins in sub-Saharan Africa. J. Financ. Regul.
Compliance 28 (2), 283–299.
Schumpeter, J., 1939. Business Cycles: A Theoretical, Historical and Statistical Analysis of the Capitalist. Process. 2 Vols. McGraw-Hill, New York.
Schumpeter, J.A., 1911. The theory of economic development. Mass.: Camb.
Schumpeter, P., 1934. The Theory of Economic Development. Harvard University Press, Cambridge, MA.
Shahbaz, M., Nasir, M.A., Hille, E., Mahalik, M.K., 2020. UK’s net-zero carbon emissions target: Investigating the potential role of economic growth, financial
development, and R&D expenditures based on historical data (1870–2017). Technol. Forecast. Soc. Change 161, 120255.
Shahnazi, R., Shabani, Z.D., 2019. The effects of spatial spillover information and communications technology on carbon dioxide emissions in Iran. Environ. Sci.
Pollut. Res. 26, 24198–24212.
Shamim, F., 2007. The ICT environment, financial sector and economic growth: a cross-country analysis. J. Econ. Stud. 34 (4), 352–370.
Shehzad, K., Xiaoxing, L., Sarfraz, M., Zulfiqar, M., 2020. Signifying the imperative nexus between climate change and information and communication technology
development: a case from Pakistan. Environ. Sci. Pollut. Res. 27, 30502–30517.
Shetewy, N., Shahin, A.I., Omri, A., Dai, K., 2022. Impact of financial development and internet use on export growth: new evidence from machine learning models.
Res. Int. Bus. Financ. 61, 101643.
Spaiser, V., Ranganathan, S., Swain, R.B., Sumpter, D.J., 2017. The sustainable development oxymoron: quantifying and modelling the incompatibility of sustainable
development goals. Int. J. Sustain. Dev. World Ecol. 24 (6), 457–470.
Sumberg, J., Fox, L., Flynn, J., Mader, P., Oosterom, M., 2021. Africa’s “youth employment” crisis is actually a “missing jobs” crisis. Dev. Policy Rev. 39 (4), 621–643.
Taddese Bekele, D., Abebaw Degu, A., 2023. The effect of financial sector development on economic growth of selected sub-Saharan Africa countries. Int. J. Financ.
Econ. 28 (3), 2834–2842.
Taiwo, A., 2021. Financial development, real sector and economic growth in Sub-Saharan Africa: the threshold effect. J. Afr. Bus. 22 (4), 603–626.
Tamazian, A., Chousa, J.P., Vadlamannati, K.C., 2009. Does higher economic and financial development lead to environmental degradation: evidence from BRIC
countries. Energy Policy 37 (1), 246–253.
Tchamyou, V.S., 2020. Education, lifelong learning, inequality and financial access: evidence from African countries. Contemp. Soc. Sci. 15 (1), 7–25.
Tchamyou, V.S., Erreygers, G., Cassimon, D., 2019. Inequality, ICT and financial access in Africa. Technol. Forecast. Soc. Change 139, 169–184.
UNDP, 2008. The state of human development. UNDP, New York (forthcoming in September).
United Nations, 1992. Agenda 21. Technical Report. United Nations.
United Nations General Assembly (UN), 2015. Transforming our world: The 2030 Agenda for sustainable development.
Vo, X.V., Zaman, K., 2020. Relationship between energy demand, financial development, and carbon emissions in a panel of 101 countries: “Go the extra mile” for
sustainable development. Environ. Sci. Pollut. Res. 27, 23356–23363.
Vu, K., Hanafizadeh, P., Bohlin, E., 2020. ICT as a driver of economic growth: a survey of the literature and directions for future research. Telecommun. Policy 44 (2),
101922.
WCED, 1987. Our Common. Future: a Report from the United Nations World Commission on Environment and Development. Oxford University Press, Oxford.
Wen, H., Lee, C.C., Song, Z., 2021. Digitalization and environment: how does ICT affect enterprise environmental performance? Environ. Sci. Pollut. Res. 28 (39),
54826–54841.
World Bank, 2016. World Development Report 2016: Digital Dividends. World Bank, Washington.
World Bank, 2017. ICT in Agriculture (Updated Edition): connecting smallholders to knowledge, networks, and institutions. World Bank, Washington, DC.
World Bank, 2020. World Bank Poverty and Shared Prosperity 2020: Reversals of Fortunes (October.). World Bank, Washington, DC.
World Commission on Environment and Development, 1987. Our Common Future. Oxford University Press, Oxford.
Wu, B., Fang, H., Jacoby, G., Li, G., Wu, Z., 2022. Environmental regulations and innovation for sustainability? Moderating effect of political connections. Emerg.
Mark. Rev. 50, 100835.
Wuelser, G., Pohl, C., Hirsch Hadorn, G., 2012. Structuring complexity for tailoring research contributions to sustainable development. Sustain. Sci. 7, 81–93.
Xu, Z., Baloch, M.A., Danish, Meng, F., Zhang, J., Mahmood, Z., 2018. Nexus between financial development and CO 2 emissions in Saudi Arabia: analyzing the role of
globalization. Environ. Sci. Pollut. Res. 25, 28378–28390.
Zaidi, S.A.H., Wei, Z., Gedikli, A., Zafar, M.W., Hou, F., Iftikhar, Y., 2019. The impact of globalization, natural resources abundance, and human capital on financial
development: evidence from thirty-one OECD countries. Resour. Policy 64, 101476.
Zhang, U., Naceur, S.B., 2019. Financial development, inequality, and poverty: some international evidence. Int. Rev. Econ. Financ. 61, 1–16.

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