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Journal of Small Business & Entrepreneurship
ISSN: 0827-6331 (Print) 2169-2610 (Online) Journal homepage: https://www.tandfonline.com/loi/rsbe20
Assessing the impact of information and
communication technologies on the performance
of microfinance institutions in Niger
Hadizatou Ali, Jean-Pierre Gueyié & Cédric Okou
To cite this article: Hadizatou Ali, Jean-Pierre Gueyié & Cédric Okou (2020): Assessing the impact
of information and communication technologies on the performance of microfinance institutions in
Niger, Journal of Small Business & Entrepreneurship, DOI: 10.1080/08276331.2019.1698222
To link to this article: https://doi.org/10.1080/08276331.2019.1698222
Published online: 06 Mar 2020.
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2. Assessing the impact of information and communication
technologies on the performance of microfinance
institutions in Niger
Hadizatou Ali, Jean-Pierre Gueyi
e and C
edric Okou
School of Management, University of Quebec in Montreal, Montreal, Quebec, Canada
ABSTRACT
This paper assesses the impact of information and communica-
tion technologies (ICTs) on the performance of microfinance
institutions (MFIs) in Niger, West Africa. MFIs play a pivotal role
in improving financial inclusion in Niger because the majority of
the country’s poor live in rural areas, with only limited and
costly access to formal financial services. Using an unbalanced
panel of 23 MFIs spanning 2005–2013, single-step generalized
moments method (GMM) estimations are run to appraise
whether ICT investments improve the financial and the social
performance of MFIs. The results show a positive relationship
between investments in ICTs and MFIs’ financial performance.
Investing more in technologies enables managers to reduce the
frequency of operational errors, increase the speed of task
execution, decrease operating costs, and increase the likelihood
of higher financial profits. The findings also reveal a positive
effect of institutional affiliation on the financial performance of
MFIs. Namely, MFIs affiliated with a network and investing in
ICTs tend to perform better. The impact of ICT investments on
the social performance of MFIs is rather weak. From a policy
perspective, developing ICT infrastructure can yield substantial
performance dividends and should remain a top developmental
priority in Niger.
RÉSUMÉ
Cet article
evalue l’impact des technologies de l’information et de
la communication (TIC) sur la performance des institutions de
microfinance (IMF) au Niger, Afrique de l’Ouest. Les IMF jouent un
r^
ole central dans l’am
elioration de l’inclusion financi
ere au Niger
parce que la majorit
e des pauvres du pays vivent dans des zones
rurales et n’ont qu’un acc
es limit
e et co^
uteux aux services finan-
ciers formels.
A partir d’un panel non
equilibr
e de 23 IMF couv-
rant la p
eriode 2005–2013, des estimations sont r
ealis
ees selon la
m
ethode des moments g
en
eralis
es (MMG) en une seule
etape
pour v
erifier si les investissements dans les TIC am
eliorent la per-
formance financi
ere et sociale des IMF. Les r
esultats montrent un
rapport positif entre les investissements dans les TIC et les per-
formances financi
eres des IMF. Investir davantage dans les tech-
nologies permet aux managers de r
eduire la fr
equence des
ARTICLE HISTORY
Received 13 March 2019
Revised 14 November 2019
Accepted 24 November 2019
KEYWORDS
Microfinance; financial
performance; social
performance; information
and communication
technologies;
network; Niger
MOTS-CLÉS
Microfinance; performance
financi
ere; performance
sociale; technologies de
l’information et de la
communication; Niger
CONTACT Jean-Pierre Gueyi
e gueyie.jean-pierre@uqam.ca School of Management, University of Quebec in
Montreal, 315 Sainte Catherine East, Montreal, Quebec H2X 3X2, Canada
ß 2020 Journal of the Canadian Council for Small Business and Entrepreneurship/Conseil de la PME et de l’entrepreneuriat
JOURNAL OF SMALL BUSINESS ENTREPRENEURSHIP
https://doi.org/10.1080/08276331.2019.1698222

3. erreurs op
erationnelles, d’acc
el
erer l’ex
ecution des t^
aches, de
diminuer les co^
uts d’exploitation et d’augmenter la probabilit
e de
profits financiers plus
elev
es. Les r
esultats r
ev
elent aussi un effet
positif de l’affiliation institutionnelle sur les performances
financi
eres des IMF. En effet, les IMF affili
ees
a un r
eseau et inves-
tissant dans les TIC ont tendance
a ^
etre plus performantes.
L’impact des investissements dans les TIC sur la performance
sociale des IMF est plut^
ot faible. D’un point de vue politique, le
d
eveloppement de l’infrastructure des TIC peut produire des divi-
dendes substantiels en termes de performance et devrait
demeurer une priorit
e absolue du d
eveloppement au Niger.
1. Introduction
Microfinance institutions (MFIs) have been present in Niger since the colonial period.
MFIs provide financial services to poor and vulnerable populations that are excluded
from the formal financial system. Thus, profitable MFIs can offer a powerful funding
lever to catalyze the creation and expansion of microenterprises. By supporting add-
itional income generation, MFIs can help lift low-income borrowers out poverty and
set them on a path toward inclusive prosperity. While financial performance aims at
long-term financial sustainability, it is also crucial that MFIs improve their social per-
formance by expanding financial inclusion. A good proxy for MFIs’ social effective-
ness is their ability to reach the poor and extremely poor and provide quality services
that improve the living standards of their clients—that is, their social responsi-
bility (Hashemi 2007).
As the microfinance industry matures, it faces headwinds pertaining to accessibility
and profitability of services to the very poor. Information and communication tech-
nologies (ICTs) are heralded as important tools that can help MFIs extend their reach
to a less fortunate clientele, while remaining financially viable in an increasingly com-
petitive environment (Binuyo and Aregbeshola 2014). According to Ashrafi and
Murtaza (2008), ‘ICTs refer to the wide range of computerized information and com-
munication technologies. These technologies include products and services such as
desktop computers, laptops, handheld devices, wired or wireless intranet, business
productivity software such as text editor and spreadsheet, enterprise software, data
storage and security, network security and others’. MFIs can leverage ICTs to enhance
their performance via reduced costs, improved product quality, widened spectrum of
products, higher customer satisfaction, and increased productivity. Achieving higher
financial and social performance also requires a larger volume of activities and better
governance (Berger 2003; Ivatury 2006; Visconti and Quirici 2014).
This paper investigates whether investing in ICTs improves the financial and the
social performance of microfinance institutions in Niger. Given the enormous devel-
opmental needs in Niger, this question has far-reaching socio-economic implications.
We hypothesize that investment in ICTs does enhance the financial and the social
performance of MFIs in Niger.
Our findings show a positive impact of ICTs on the financial performance of the
MFIs sampled. Investing in ICTs enables managers to reduce the frequency of
2 H. ALI ET AL.

4. operational errors, increase the speed of task execution, and decrease operating costs.
By contrast, the impact of ICTs on social performance, measured by MFIs’ ability to
reach a large number of clients, is marginal at best. This may reflect the fact that
building financially sustainable MFIs is only a first step toward positive social impact.
The results also reveal the importance of institutional affiliation on the financial per-
formance; MFIs affiliated to a network tend to perform better.
Our study contributes to the literature on the proper assessment of the link
between ICTs and performance in two ways. First, although this topic has been
widely investigated in advanced economies (Hunter and Timme 1986, 1991; Lehr and
Lichtenberg 2003; Yap 1989; Becalli 2007), studies on African countries are scant.
The analysis in this work focuses on Niger to fill this gap.
Niger is a landlocked African country, with substantial development needs: ‘With a
poverty rate of 44.1% and a per capita income of $420, it is one of the world’s poor-
est nations. In 2016, it ranked second to last—187th
out of 188 countries—on the
United Nations Human Development Index’ (World Bank 2019). The bulk of the
country’s poor live in rural areas, which are difficult to access due to the lack of
infrastructures (road, bridges, electricity, etc.). Moreover, there are not enough poten-
tial clients to support the creation of physical branches for MFIs in many rural areas.
Thus, serving these areas entails sizable costs.
Our results suggest that ICTs can help overcome accessibility challenges and cata-
lyze performance gains for MFIs. Second, we use a panel of 23 MFIs observed over a
time window—from 2005 to 2013—long enough to account for several changes
beyond technological innovations. For instance, in 2007, the Central Bank of West
African States introduced a new regulatory framework, which led to important
changes in the microfinance sector. To the best of our knowledge, this study is the
first to gauge the impact of investments in ICTs on the performance of MFIs in
Niger amid major shifts in regulation.
The article proceeds as follows. Section 2 presents the theoretical underpinnings
and reviews the literature linking ICTs to value creation. Section 3 presents the data
and outlines the methodology. Section 4 reports and discusses the results. Section 5
offers recommendations and conclusions.
2. Theoretical foundation and literature review
The business environment is facing constant technological changes. Financial and
non-financial firms have gradually stepped in the digital age, a transition marked by
increasing computerization of tasks and processes, as well as a globalization of com-
munications. The speed of adoption of new technology has reignited the debate
around firms’ absorptive capacity (Ongori and Migiro 2010). An important strand of
the entrepreneurship literature explores the impacts of ICTs on organizational practi-
ces, industrial structures, and various dimensions of performance (Whisler 1970;
Pfeffer and Leblebici 1977; Sarkar, Butler, and Steinfield 1995; White 1998; Bockstedt,
Kauffman, and Riggins 2006; Granados, Kauffman, and King 2008; Kauffman and
Riggins 2012; Riggins and Weber 2016). This section outlines the sources of value
creation from ICTs and synthesizes the literature on ICT-driven performance.
JOURNAL OF SMALL BUSINESS ENTREPRENEURSHIP 3

5. 2.1. Sources of ICT value creation
Revenue growth. Investment in ICTs support revenue growth, through new value
propositions, innovative marketing and sales channels, and enhanced management of
the customer life cycle (Mithas et al. 2012). ICT systems can be used to better under-
stand and meet customers’ needs by developing new offerings and tailored products.
They widen the means of communication with the customer, allowing firms to target
customers through a variety of new information technology (IT)-enabled channels—
e-mail, short messaging systems, websites, and targeted databases—thereby adding to
their revenue stream (Mithas et al. 2012).
People living in rural and remote areas are potential clients for banks and MFIs.
However, serving these people has always been a challenge for the banking system;
they are difficult to access because of the lack of adequate infrastructure, such as
roads and even electrical service. Moreover, many remote areas are sparsely popu-
lated. This increases the cost of deploying branches to those areas. Consequently,
only a few affordable financing choices are available to rural and remote populations.
According to Mas and Kumar (2008), rural populations can either embark on a long
journey and then queue at a remote branch to make their financial transactions or
self-manage their savings as cash holdings or investments in property.
Many studies have claimed that ICTs can play a significant role in improving poor
people’s access to banking services by delivering sustainable financial services to dis-
tant and underserved locations (Stegman, Rocha, and Davis 2005; Claessens 2006).
This can be done through mobile banking or bank-controlled arrangements, often
referred to as a correspondent banking relationship. In such schemes, arrangements
are made with representatives or local social groups—local authorities, small retailers,
technology providers—that serve as a relay for the provision of banking services
(Diniz, Birochi, and Pozzebon 2012). Mobile and point-of-service devices can be used
to reach rural and remote costumers by allowing local traders to carry out cash trans-
actions on formal banks’ behalf (Kota 2007). ICT-enabled financial operations are
much easier and faster than traveling to or from remote areas to traditional bank
branches. Mobile banking also plays an important role in areas where several house-
holds rely on remittances from family members working away from home or abroad
(Kota 2007). To sum up, ICTs foster new and cheaper ways of doing business
(Ivatury and Mas 2008; Molo 2002).
The quality of MFIs’ portfolios determines the supply, rationing and management
of credit (Fall 2011, Mersland and Strøm 2009). In making loan decisions, MFIs face
a problem of asymmetry of information. They need to collect relevant credit informa-
tion about borrowers to minimize default risks. ICT-based tools can be used to build
updated credit registries, monitor associated risks, and allow MFIs to be more effi-
cient in their dealings with potential customers (Kauffman and Riggins 2012).
Cost reduction and production efficiency. ICT systems help firms reduce oper-
ational, general and administrative costs (Mithas et al. 2012). Organizations can use
ICTs to automate tasks and reengineer their structures (Muet 2006). In manufactur-
ing companies, for instance, production processes are now handled by numerically
controlled machines; specialized and customizable software allows for partial or com-
plete automation of the production process. Computerization also facilitates the
4 H. ALI ET AL.

6. automation of transactions (pay, billing, etc.), improvements in inventory, accounting,
and commercial management (Kling and Iacono 1988). This cuts inventory and oper-
ational costs, optimizes the production cycle, and increases productivity. Moreover,
automation reduces the propensity of employees to make mistakes, increases the abil-
ity of managers to deal with difficulties, and improves overall organizational efficiency
(Lehr and Lichtenberg 2003; Abdul-Gader and Kozar 1995; Pfeffer and Leblebici
1977). Ultimately, ICTs increase labor productivity through improvements in both
total factor productivity and capital deepening.
2.2. Literature review
Several studies have analyzed the link between performance and ICT investments,
both for financial and non-financial firms. Early studies in the 1980s found no or lit-
tle empirical evidence that ICT significantly increased productivity (Turner 1983;
Hunter and Timme 1986; Loveman 1993; Weill and Olson 1989; Markus and Soh
1993). Subsequent studies relied on improved estimation strategies to elicit the appar-
ent weak positive correlation between technological innovation and productivity
gains, the so-called ‘productivity paradox’ (Brynjolfsson and Hitt 1995, 1996, 2000;
Quinn and Baily 1994; Harris and Katz 1988; Lichtenberg 1995; Barua, Kriebel, and
Mukhopadhyay 1995; Barua and Lee 1997; Dewan and Min 1997; Lee and Barua
1999; Mithas et al. 2012). There are at least four possible explanations for a weak
empirical link between ICTs and productivity: (1) mismeasurement of outputs and
inputs, (2) delayed effects due to learning and adjustment, (3) redistribution and dis-
sipation of ICT-generated profits, and (4) mismanagement of information and tech-
nology (Brynjolfsson 1993).
A few studies on the phenomenon have focused on African countries. For instance,
using annual data in South Africa from 1990 to 2012, Binuyo and Aregbeshola (2014)
document a positive relationship between the use of ICTs and return on capital as
well as return on asset in the banking industry. The authors show that the proper use
of existing ICTs is more beneficial than the continuous acquisition of unused ICTs.
In Ghana, Leckson-Leckey, Osei, and Harvey (2011) exploit a balance scorecard
framework from 1998 to 2007 and find that ICT investments increase banks’
profitability.
2.3. Complexity in the evaluation of ICTs impacts
Assessing the performance of technology investments is more complex than other
types of investment decisions because the associated costs and benefits are difficult to
identify and quantify (Willcocks and Lester 1997). Intangible factors, such as the ICT
intensity–management nexus, may influence the decision-making process (Powell
1992). Productivity gains and efficiency impacts from ICT investments are contingent
on internal and external factors, including the availability of complementary organiza-
tional resources within the firm and its trading partners, as well as the existence of a
competitive macro-environment (Melville, Kraemer, and Gurbaxani 2004). Becalli’s
(2007) findings suggest that investments in ICT services from external providers—
JOURNAL OF SMALL BUSINESS ENTREPRENEURSHIP 5

7. consulting, implementation, training, and support services—have positive effects on
accounting profits and profit efficiency, whereas the acquisition of hardware and soft-
ware seems to reduce banks’ financial performance. Many firms have low productivity
partly because they do not integrate ICTs with their organizational structure, man-
agerial style, core business, end-user training programs, or monitoring of client satis-
faction (Weill and Olson 1989; Mukhopadhyay, Kekre, and Kalathur 1995; Igbaria
and Tan 1997; Dehning, Richardson, and Zmund 2003).
3. Data and methodology
3.1. Data
As of December 31, 2013, the microfinance sector in Niger had 52 institutions. Our
sample is an unbalanced panel comprising 23 institutions active during the study
period (2005–2013). Our panel data come mainly from two sources: Data on inde-
pendent MFIs are extracted from the database of the Microfinance Sector Regulatory
Agency (ARSM). The ARSM only publishes the global information of networked
MFIs, while our study requires individual data from each organization. Therefore, we
extract individual information—network status, etc.—from financial reports. Grubbs’
(1950) test is applied to correct for the presence of extreme observations, as outliers
can induce significant estimation biases in empirical studies.
3.2. Performance measures
Microfinance institutions pursue dual objectives: To serve the poor and achieve self-
sustainability (Ibtissem and Bouri 2013). We measure MFIs’ performance using both
financial and social indicators. Following Ben Naceur and Kandil (2009), Cull,
Demirguc-Kunt, and Morduch (2011), Guidara et al. (2013), Gueyie, Guidara, and
Lai (2019), we use the return on assets (ROA ¼ net income/assets) as a measure of
financial performance. We also consider the return on equity (ROE ¼ net income/
equity). Social performance is proxied by the natural log of the number of clients of
each microfinance institution (COVERAGE ¼ natural logarithm of number of clients
served by MFIs). We complement this measure of social performance by the natural
log of the share of MFIs’ loans in the gross domestic product (EXTEND). We bear in
mind that a key objective of microfinance is to improve the well-being to the poor by
enabling them to access credit, invest in income-generating activities, and build sav-
ings (Schreiner 2001); thus, the EXTEND ratio captures the ability of microfinance
organizations to provide services to a large proportion of the population.
3.3. Exogenous variables
Here, we list the exogenous variables.
ICTi,t: investments in computer hardware (e.g. computers, printers, scanners),
management software, expenditures related to the use of ICTs (e.g. maintenance,
communication), by MFI i at time t. Brynjolfsson and Hitt (1996), Pfeffer and
Leblebici (1977), and Becalli (2007) use the same measure. By reducing costs and
6 H. ALI ET AL.

8. enhancing the efficiency of operations, ICT investments can improve the profitability
of MFIs; thus, a positive effect of investment in ICTs on MFIs’ performance
is expected.
DEPOSITSi,t: natural log of the total amount of client deposits at MFI i at time t.
Deposits are the source of refinancing for most MFIs (Hartarska and Nadolnyak
2007). MFIs with large deposits base are less exposed to refinancing risk; such MFIs
are better protected against refinancing risk than those that only rely on international
donors (Littlefield and Kneiding 2009). Furthermore, in some MFIs, having savings is
a prerequisite for a client to qualify for a loan (Tchakoute-Tchuigoua 2014). A posi-
tive relationship is expected between deposits and performance.
RISKi,t: ratio of outstanding loans (more than 90 days late) to total outstanding
loans of MFI i in period t; Mersland and Strøm (2009) and Tchakoute-Tchuigoua
(2010) use this indicator as a proxy for financial portfolio risk. A healthy loan port-
folio improves the performance of financial institutions. MFIs with riskier portfolios
are less likely to obtain financing (Fall 2011) or must pay a higher premium. We
expect an increase in MFIs’ portfolio risk to negatively affect their performance.
CAPITALi,t: ratio of equity to the total assets of MFI i at date t. This metric is
used by several others, including Ben Naceur and Kandil (2009) and Hartarska and
Nadolnyak (2007). Capital can have a positive effect on performance, as these funds
are available for MFIs to use as financing. However, from a regulatory perspective,
capital is perceived as a buffer against unexpected losses. Capital may have a negative
impact on performance, as regulations specify the amount of capital that institutions
are required to have on hand to weather adverse shocks (Barth et al. 2013). Simply
put, higher capital may reflect elevated risk and poor performance. Therefore, the
expected effect of CAPITAL on performance is unclear.
AGEi,t: number of years MFI i has been operating as of time t (Hartarska and
Nadolnyak 2007). We expect that employees and managers learn and acquire experi-
ence over time. Therefore, a positive relationship between MFIs’ age and performance
is expected.
SIZEi,t: natural log of the total assets of MFI i at time t. Large size firms can
enhance productivity through the diversification of products and services, economies
of scale, improved managerial skills, increased investment in research and develop-
ment (Athanasoglou, Brissimis, and Delis 2005; Tchakoute-Tchuigoua 2010; Aladwan
2015; Bibi et al. 2018). We expect a positive relation between performance and size.
REGLi,t: binary variable of regulation (REGL ¼ 1 if MFI i is regulated, 0 other-
wise). In Niger, microfinance regulations are set and implemented by authorities to
prevent defaults and protect investors (Black, Miller, and Posner 1978; Arun 2005).
Hartarska (2005) and Mersland and Strøm (2009) also use regulation dummy varia-
bles to test for institutional compliance. All else equal, we expect regulated MFIs
should be less exposed to risk and have better performance.
ZONEi,t: binary variable that takes 1 if MFI i operates in rural or semi-rural areas
and 0 if it operates in urban areas at time t. Operating a financial institution in rural
areas is costly, mainly because of poor quality infrastructures (Wampfler 2004;
Lafourcade 2007). Therefore, a negative association is expected between the social
performance and location variables.
JOURNAL OF SMALL BUSINESS ENTREPRENEURSHIP 7

9. AFFL: binary variable that indicates whether the MFI belongs to a network.
Affiliation is 1 if the MFI belongs to a network, and 0 otherwise. Affiliated institu-
tions share costs and benefit from economies of scale, and thus, are likely more prof-
itable (Lh
eriau 2009). Affiliation is expected to improve the financial and social
performance of MFIs.
AFFLICTi,t: interaction between ICT spending and institutional affiliation
(AFFL). The relationship between this variable and performance should be positive,
as this is the case for the two variables taken individually.
3.4. Econometric specifications
Previous studies on the performance of financial institutions are based on multivari-
ate econometric analyzes. We employ the same methodological framework as Becalli
(2007), Hartarska and Nadolnyak (2007), Tchakoute-Tchuigoua (2010), and
Prhadhan, Arvin, and Norman (2015). Our econometric specifications are:
Financial performance
PERFFit¼ a0þa1ICTit þ a2PERFFit 1 þ a3RISKit þ a4DEPOSITSit þ a5AGEit
þa6REGLit þ a7SIZEit þ a8CAPITALit þ a9AFFL þ a10AFFL ICTit þ eit
(1)
Social performance
PERFSit¼ b0þb1ICTit þ b2ICTit 1 þ b3PERFSit 1 þ b4ROAit þ b5ZONEit
þb6AGEit þ b7REGLit þ b8SIZEit þ b9RISKit þ b10AFFL
þb11AFFL ICTit þ eit (2)
where PERF_Fi,t is a measure of financial performance (e.g. the return on assets,
denoted by ROAi,t) for a given MFI i at time t. Similarly, PERF_Si,t is a measure
of social performance (e.g. the number of clients served, denoted by
COVERAGEi,t) for MFI i at time t. The parameters a0 to a10 and b0 to b11 are
coefficients to be estimated, e is an error term, and all regressors are defined in
Section 3.3. A lag of ICT (ICTi,t-1) is included in the second specification to con-
trol for possible delay between ICT investments and their impacts on MFIs’ per-
formance (Becalli 2007).
Ben Naceur and Kandil (2009) have shown that empirical studies on the determi-
nants of bank performance may suffer from endogeneity biases. The dynamic panel
generalized moments method (GMM) estimation corrects for potential problems of
endogeneity, heteroscedasticity, and autocorrelation (Thao Tran, Lin, and Nguyen
2016). In this work, Equations (1) and (2) are estimated using a single-step GMM
(Blundell and Bond 1998; Roodman 2009).1
8 H. ALI ET AL.

10. 4. Results and discussion
4.1. Descriptive statistics
Grubbs’ (1950) test is applied to control for the distorting effects of extreme values.
The descriptive statistics are presented in Table 1.2
The average return on MFIs’ assets is 1.34%. An average MFI reaches 218,581 cli-
ents, corresponding to nearly 1.25% of the total population in Niger in 2013.3
According to the United Nations World Population Prospects report, the projected
total population in Niger in 2013 was 17,831,000. Moreover, MFIs spend on average
768,044 FCFA on technology. Their average equity to total asset ratio (CAPITAL) is
41%, almost three times higher than the 15% capital requirement ratio imposed by
the regulatory framework of 2007 (BCEAO 2011).
The average amount of deposits collected by MFIs in Niger is 4,626,979,722 FCFA.
MFIs use these deposits to open credit lines for their clients. In order to be qualified
for a loan, clients are required to offer guarantees. Usually, prior cash savings help
build their credibility with the financial institution (Tchakoute-Tchuigoua 2014). The
average proportion of unpaid credit in MFIs’ portfolios is 13%. This highlights the
importance of loan repayment rates as a key determinant of the performance of
MFIs. Figure 1 plots the provisions for loan losses against ICT expenditures. We
observe two groups of microfinance institutions: The first comprises MFIs that hold
high reserves to cover potential loan losses and spend less than 500,000 XOF on
ICTs; the second comprises MFIs that invest more than 500,000 XOF in ICTs and
have low loan-loss provisions.
4.2. Associations
We now turn to Table 2, which presents a comparison between independent and net-
worked MFIs. Independent MFIs (7 of 23 MFIs in our sample) have, on average,
higher ROAs and ROEs (ROA_IND, ROE_IND) than those in a network
(ROA_NET, ROE_NET). Networked MFIs (COVERAGE_AFFL, EXTEND_AFFL) are
on average less able to reach clients than independent MFIs (COVERAGE_IND,
EXTEND_IND). In addition, independent institutions invest more in ICTs
Table 1. Descriptive statistics.
Variable Observations Mean Std. Dev. Min Max
ROA (%) 167 1.3441 6.8084 29.4221 24.4551
ROE (%) 144 10.1494 45.6134 205.1312 196.896
COVERAGE 186 7.6902 1.2689 5.6629 11.2056
EXTEND (%) 206 0.0106 0.0195 0 0.0757
ICT 200 768044.9 1994141 0 1.05e þ 07
RISK 187 13.9249 29.2864 4.8004 214.9919
DEPOSITS 170 17.6542 1.3281 13.3469 20.3310
SIZE 170 20.0808 8.1562 13.4161 33.0902
AGE 215 9.6744 3.4837 1 16
REGL 215 0.9627 0.1897 0 1
CAPITAL 155 41.0943 79.5223 225.0598 784.1225
ZONE 215 0.6697 0.4713 0 1
AFFL 215 0.7069 0.4562 0 1
AFFLICT 200 262632.5 781622.5 0 7480000
JOURNAL OF SMALL BUSINESS ENTREPRENEURSHIP 9

11. (ICT_IND) than networked institutions (ICT_AFFL). A possible explanation is that
networked institutions hold fewer assets and mainly operate in rural areas, while in
contrast, independent MFIs mainly operate in urban areas and hold more assets.
A correlation is used to assess the strength of association among our variables
(Table 3). The correlation between ICTs and return on assets (ROA) is positive but
relatively low (23.08%). By contrast, the association between ICT and the number of
clients reached (COVERAGE) is much higher (59.94%). The correlations between
SIZE and CAPITAL (51.41%) and between SIZE and DEPOSITS (61.59%) are also
high, which may cause multicollinearity. To address this issue, we remove the variable
SIZE from the financial performance estimation, and we drop the variable CAPITAL
from the social performance estimation. The correlation among the other explanatory
variables is low, which limits the effect of multicollinearity.
We also perform partial and semi-partial correlation tests (Table 4). The partial
correlation shows the relation between ROA and investment in ICTs when other
regressors in the model are held constant. The semi-partial correlation test gives the
impact of ROA on ICT when the effects of other explanatory variables are purged
from investment in ICTs. The partial correlation is about 25.46% and significant at
the 5% level, suggesting that ICT is an important correlate of ROA.
The 2007–2010 sub-period was marked by the international financial crisis, which
profoundly affected financial institutions around the world (Littlefield and Kneiding
0
200
400
600
800
0 2000000 4000000 6000000 8000000 10000000
Expenditures in ICT
Figure 1. ICT expenditures and provisions for loan losses.
Table 2. Comparative ICTs expenditures and performance’s measure by type of MFIs.
Variable Observations Mean Std. Dev. Min Max
ROA_IND (%) 59 1.8479 5.3802 20.8521 20.5759
ROA_NET (%) 108 1.2478 7.6798 29.4221 24.4551
ROE_IND (%) 59 13.7814 34.1760 42.0434 187.3875
ROE_NET (%) 85 9.0443 53.4807 205.1312 196.896
COVERAGE_IND (LOG) 53 9.1751 1.1536 6.2065 11.2056
COVERAGE_NET (LOG) 133 7.1029 0.7108 5.6629 8.8529
EXTEND_IND 54 0.0361 0.0238 0 0.0757
EXTEND_NET 152 0.0016 0.0022 0 0.0112
ICT_IND(FCFA) 50 2026828 3406524 0 1.05e þ 07
ICT_AFFL(CFA) 150 347857.7 883550.6 0 7480000
10 H. ALI ET AL.

12. 2009). This may have induced a structural break data for MFIs in Niger (Maddala
and Wu 1999). To check for a possible structural break in the data, we divide our
sample into three sub-periods: 2005–2006 for the pre-crisis period, 2007–2010 for the
crisis period, and 2011–2013 for the post-crisis period. We then perform mean differ-
ence Z and Student’s t tests on selected variables. The null hypothesis is that the dif-
ference in mean between two sub-periods is equal to 0. We find no significant
difference in means between the sub-periods (Table 5).4
The results in Table 6 point to the presence of errors from heteroscedasticity.
Therefore, our GMM estimation procedure computes heteroscedasticity and autocor-
relation-robust estimates.
4.3. Regression results
Our main goal is to assess whether investment in ICTs improves the financial and
social performance of MFIs. To address this question, we use our panel dataset to
estimate Equations 1 and 2 (Table 7).
Table 3. Correlation matrix.
Variables ROA Coverage RISK ICT DEPOSITS SIZE CAPITAL AGE
ROA 1.000
Coverage 0.0490 1.000
RISK 0.4528 0.0524 1.000
ICT 0.2308 0.5994 0.0980 1.000
DEPOSITS 0.0439 0.5694 0.1311 0.3885 1.000
SIZE 0.1131 0.6035 0.0661 0.3448 0.6159 1.000
CAPITAL 0.0381 0.0677 0.1237 0.0756 0.1297 0.5141 1.000
AGE 0.0539 0.0490 0.2134 0.2539 0.0961 0.2166 0.0263 1.000
Table 4. Partial and semi-partial correlations of ROA and regressors.
Variables Partial correlation Semi partial correlation Partial correlation Semi partial correlation Significance value
ICT 0.2546 0.2076 0.0648 0.0431 0.0275
ROAt-1 0.2674 0.2188 0.0715 0.0479 0.0204
RISK 0.1647 0.1317 0.0271 0.0173 0.1579
AGE 0.0941 0.0745 0.0089 0.0056 0.4220
REGL 0.0943 0.0747 0.0089 0.0056 0.4211
DEPOSITS 0.2939 0.2425 0.0864 0.0588 0.0105
CAPITAL 0.0707 0.0559 0.0050 0.0031 0.05465
AFFL 0.4428 0.3894 0.1961 0.1517 0.0001
AFFLICT 0.2285 0.1851 0.0522 0.0343 0.0487
Note: Significant at 10%, Significant at 5%, Significant at 1%.
Table 5. Z and t tests for difference in sample means between sub-periods.
Variables Sub-periods ROA ROE COVERAGE EXTEND ICT DEPOSIT RISK CAPITAL
Z test E1 E2 0.887 0.331 0.283 0.975 0.062 0.922 0.341 0.321
E1 E3 0.512 0.584 0.745 0.296 0.005 0.244 0.724 0.418
E2 E3 0.559 0.191 0.424 0.245 0.077 0.104 0.627 0.159
Student’s t test E1 E2 0.895 0.333 0.285 0.976 0.067 0.919 0.342 0.324
E1 E3 0.515 0.586 0.745 0.287 0.007 0.247 0.725 0.421
E2 E3 0.562 0.194 0.425 0.261 0.079 0.131 0.630 0.177
This table shows p values for difference in means tests between subperiods, based on Z and Student’s t tests. Three
subperiods are considered: E1 for 2005–2006, E2 for 2007–2010, and E3 for 2011–2013.
JOURNAL OF SMALL BUSINESS ENTREPRENEURSHIP 11

13. In columns 1 and 2 of Table 7, investment in ICT as a whole is considered. In col-
umns 3 and 4, ICT investment is decomposed into the HARD (expenditure to
acquire a computer, printer, scanner) and SOFT (investment in software) variables,
and both are included as explanatory variables in the regressions. Estimations in col-
umns 5 and 6, and columns 7 and 8, include the variables HARD and SOFT in turn.
Let us first consider financial performance, proxied by return on assets (ROA).
Prior to the recent digital revolution in the financial sector in Niger, withdrawal and
payment operations, as well as the financial statements were all recorded (in some
cases manually) on paper. The probability of errors and information loss was quite
high in this context. We observe that ICT investments improve the performance of
MFIs, in accordance with our assumption (Table 7, column 1). When the variable
ICT is disentangled into HARD and SOFT, we notice that expenditures on computer
hardware (computers, printers, scanners, etc.) have a positive effect on the ROA
(Table 7, column 5). Technology facilitates the automation of some tasks, improves
product and service quality, and enhances productivity, even in rural areas (Yap
1989). The use of ICTs reduces the propensity of agents to make mistakes, increases
the ability and efficiency of managers to deal with difficulties, enhances product and
service quality, and lifts productivity (Abdul-Gader and Kozar 1995; Bharadwaj,
Bharadwaj, and Konsynski 1999; Lehr and Lichtenberg 2003; Pfeffer and Leblebici
1977; Yap 1989).
The effect of software investments (SOFT) is statistically insignificant (columns 3
and 7). According to Ashta and Patel (2013), it is difficult for some to apply third-
party software in their businesses. In Niger, software packages are developed by exter-
nal institutions, such as the Central Bank and other providers. The coefficient of
lagged return on asset (ROAt-1) is statistically significant and positive, consistent with
the findings in Ben Naceur and Kandil (2009). As expected, the variable RISK is
negatively related to ROA. Managing a low-risk portfolio tends to increase the per-
formance of MFIs. Abdou (2002) shows that low-risk portfolios improve short-term
credit prospects, enhance the investment climate, and strengthen macroeco-
nomic stability.
The variable DEPOSITS emerges as an important driver of MFIs’ profitability.
This is in contrast with the conclusions of Hartarska and Nadolnyak (2007), who
show that MFIs collecting savings show no difference in performance from those that
do not. In Niger, most MFIs use clients’ deposits to provide loans. Savings collection
is an important source of funds for these institutions. Deposits also represent a stable
and cheap source of refinancing for inter-institution lending or central bank refinanc-
ing (Bass and Henderson 2000).
The variable AFFL is positive and statistically significant, suggesting that belonging
to a network improves the financial profitability of MFIs. Indeed, networked MFIs in
Table 6. Heteroscedasticity test.
Variables ROA on ICT (1)
ROA on
HARD (2)
ROA on
SOFT (3)
COVERAGE on
ICT (4)
COVERAGE on
HARD (5)
COVERAGE on
SOFT (6)
v2
2112.70 1880.55 1372.39 2639.78 7043.64 1.0 e þ 05
Prob. v2
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
This table reports tests results for heteroscedastic errors in regressions of ROA and COVERAGE performance measures
on ICT, HARD, and SOFT.
12 H. ALI ET AL.

14. Table 7. Estimated impacts, as per Equations (1) and (2).
Variables ROA (1) COVERAGE (2) ROA (3) COVERAGE (4) ROA (5) COVERAGE (6) ROA (7) COVERAGE (8)
ICTt 8.72E-07
(2.49E-07)
9.36 E-08
(7.20 E-08)
ICTt-1 9.11 E-10
(3.11 E-08)
HARDt 1.04 E-06
(6.42 E-07)
7.34 E-08
(1.85 E-07)
1.09 E-06
(5.45 E-07)
9.14 E-08
(1.45 E-07)
SOFTt 0.00003
(0.00003)
1.18 E-06
(4.18 E-06)
3.31 E-07
(1.80 E-06)
1.75 E-06
(5.22 E-06)
HARDt-1 9.15 E-08
(4.82 E-08)
4.82 E-08
(5.03 E-08)
SOFTt-1 1.28 E-07
(6.21 E-08)
7.11 E-09
(5.64 E-08)
ROAt-1 0.2698
(0.1491)
0.0111
(0.0088)
0.2224
(0.1272)
0.0057
(0.0092)
0.2181
(0.1136)
0.0106
(0.0124)
0.2229
(0.1318)
0.0103
(0.0077)
COVERAGEt-1 0.5052
(0.1207)
0.5549
(0.1221)
0.5253
(0.1091)
0.5381
(0.0931)
ZONE 0.3138
(0.1720)
0.5261
(0.1977)
0.3355
(0.3234)
0.3950
(0.1748)
SIZE 7.34 E-10
(2.81 E-10)
5.23 E-10
(2.42 E-10)
7.02 E-10
(2.05 E-10)
5.67 E-10
(2.02 E-10)
RISK 0.0182
(0.0186)
0.0012
(0.0012)
0.0364
(0.0151)
0.0012
(0.0015)
0.0376
(0.0138)
0.00004
(0.0034)
0.0447
(0.0151)
0.0007
(0.0015)
AGE 0.1131
(0.1827)
0.0142
(0.0279)
0.1087
(0.2121)
0.0372
(0.0282)
0.1016
(0.2031)
0.0053
(0.0357)
0.0437
(0.1773)
0.0106
(0.0277)
REGL 3.2883
(3.2382)
0.3516
(0.5310)
1.6688
(3.1728)
0.9069
(0.3675)
0.9206
(2.9853)
0.7234
(0.2826)
1.8067
(2.3800)
0.8393
(0.2990)
DEPOSITS 1.8377
(0.9841)
1.8390
(0.9161)
1.8689
(0.8597)
2.1525
(0.9873)
CAPITAL 0.0028
(0.0043)
0.0014
(0.0022)
0.0015
(0.0023)
0.0014
(0.0023)
AFFIL 8.3848
(3.2658)
0.8811
(0.4884)
6.6849
(2.6555)
0.9033
(0.5190)
6.7204
(2.2894)
0.8118
(0.2933)
5.6407
(2.1913)
0.6093
(0.4757)
AFFILICT
/HARD/SOFT
2.51E-06
(1.20E-06)
1.50 E-07
(1.11 E-07)
4.47 E-07
(2.14 E-06)
1.72 E-07
(2.32 E-07)
4.16 E-07
(2.04 E-06)
1.84 E-07
(2.03 E-07)
0.00004
(0.00003)
2.69 E-07
(1.57 E-07)
CONSTANT 42.478
(20.7291)
3.0949
(1.3003)
36.4372
(17.5392)
3.3571
(1.1813)
37.6507
(15.9430)
3.5597
(0.9061)
40.1633
(18.7845)
3.8180
(1.0670)
Number of
observations
83 108 79 101 81 104 83 104
Prob(v2
) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
This table presents the estimated impacts of ICT investments on the performance of microfinance institutions, based on annual data from 2005 to 2013. The financial data come from
the microfinance sector regulatory agency of Niger database and financial reports. Generalized Methods of Moments (GMM) estimations are used. Two performance measures are used
as dependent variables: the return on assets (ROA) and the log of the number of clients deserved by IMFs (COVERAGE). The other variables are defined in Table 1. The values in paren-
theses are robust standard deviations. Significant at 10%, Significant at 5%, Significant at 1%.
JOURNAL
OF
SMALL
BUSINESS
ENTREPRENEURSHIP
13

15. Niger share costs, such as office supplies and equipment expenditures. Similar cost-
sharing channels apply for technology acquisition costs and yield performance gains
for affiliated MFIs. However, the variable AFFLICT is negative and statistically sig-
nificant. This may suggest that additional investments in ICTs for networked MFIs
weigh on their financial profitability. Our results show a positive impact of being in a
network (AFFL) on MFIs’ financial and social performance. The future of microfi-
nance lies in innovation through new management styles and new types of contracts,
including Bank–MFI partnerships (Morduch 1999). For example, Guaranteed
Bankansi is the third-largest commercial bank in Turkey in terms of volume of assets.
In 2001, this commercial bank started offering tailored services to a small MFI, Maya
Enterprise for Microfinance. These services included shared access to the Guaranteed
Bankansi branch network and electronic infrastructures for Maya’s customers. This
technical partnership was mutually beneficial for both institutions. It resulted in shar-
ing the knowledge on savings habits of low-income customers (Fall 2009).
Regarding social performance (variable COVERAGE), the results in column 2 of
Table 7 show that the variable ICT has no statistically significant impact. This is also
the case in column 4, where ICT is disentangled into HARD and SOFT. In column 4,
the lagged value of HARD is positive and significant, which signals that the effect of
technologies on the social performance may be delayed (Becalli 2007). The coefficient
of return on assets (ROAt-1) is positive, but not statistically significant. This may sug-
gest that large-scale outreach to the poor and broader financial inclusion are less
likely to be achieved if MFIs are not financially sustainable (Hermes and
Lensink 2011).
MFIs are primarily focused on their financial performance to survive in a competi-
tive environment (Abdulai and Tewari 2017). Many MFIs rely on ICTs to promote
social performance as a secondary goal. Moreover, providing financial services to the
poor in rural areas (ZONE) seems to be disadvantageous for the social performance
of MFIs. In sparsely populated rural as in the northern desertic region, MFIs operat-
ing costs are exorbitant (Wampfler 2004). Niger is a vast, landlocked country cover-
ing more than a million square kilometers, with large swaths of territory in dry areas.
Moreover, the variable AGE is not statistically significant (Table 7, columns 2, 4,
6). This contrasts with Barry and Tacneng (2014) argument that older MFIs may per-
form better because their employees and staff have more experience than newly estab-
lished ones. The variable REGL is negative and statistically significant, consistent with
the findings of Hartarska and Nadolnyak (2007). They show that external regulation
is not an advantage for the social performance of MFIs.
4.4. Robustness tests
4.4.1. Alternative methods of estimation
To check the robustness of the results to alternative estimation methods, Equations
(1) and (2) are also estimated using generalized least squares (GLS) with the inde-
pendent variables ROA and COVERAGE (Table 8, columns 1 and 2).
These estimates show that investment in ICTs improves the ROA of MFIs. Thus,
our results regarding financial performance are robust to alternative estimation
14 H. ALI ET AL.

16. methods (Table 8, column 1). The impact of ICTs on the social performance of MFIs
remain statistically insignificant (Table 8, column 2).
4.4.2. Alternatives performance measures
Robustness tests are performed using two additional measures of performance:
Return on equity (ROE) as a proxy for the financial performance, and the contribu-
tion of MFIs’ loans to gross domestic product (EXTEND). ROE provides a measure,
increasingly examined by managers, of how well a financial institution is managing
the resources invested by shareholders (Becalli 2007). The coefficient of the ICT vari-
able is statistically insignificant for the model with ROE (Table 8, columns 3 and 4).
Thus, the positive link between ICT investments and ROA is robust to alternative
estimation methods (GMM and GLS) but wanes with ROE.
Table 8. Robustness assessment of estimated impacts.
Variable ROA (1) COVERAGE (2) ROE (3) EXTEND (4)
ICTt 6.31 E-07
(2.63 E-07)
7.53 E-08
(4.89 E-08)
2.69 E-06
(1.69 E-06)
7.42 E-09
(7.04 E-08)
ICTt-1 8.39 E-10
(4.08 E-08)
6.03 E-08
(6.65 E-08)
ROEt-1 0.7632
(0.2688)
0.0015
(0.0028)
EXTENDt-1 21.6439
(10.9105)
ROAt-1 0.2052
(0.0811)
0.0117
(0.0095)
COVERAGEt-1 0.5363
(0.0857)
ZONE 0.2457
(0.3276)
0.5436
(0.5724)
RISK 0.0260
(0.0171)
0.0006
(0.0018)
0.2153
(0.1175)
0.0129
(0.0081)
AGE 0.1537
(0.1785)
0.0093
(0.0238)
0.3946
(1.9238)
0.0384
(0.0367)
REGL 1.9811
(2.2965)
0.7179
(0.3711)
42.3287
(45.1567)
0.2919
(0.6867)
DEPOSITS 1.5286
(0.5457)
16.6405
(6.2248)
CAPITAL 0.0032
(0.0050)
0.0544
(0.0211)
AFFLL 7.5796
(1.6847)
0.8303
(0.4379)
54.7340
(14.0679)
2.4392
(0.6788)
AFFLLICT 2.12 E-06
(9.89 E-07)
1.76 E-07
(1.30 E-07)
0.00001
(4.45 E-06)
8.94 E-07
(1.96 E-07)
CONSTANT 31.2978
(10.4267)
3.2541
(0.8899)
364.5715
(132.0141)
4.4490
(0.7256)
Number of
observations
83 108 76 103
Prob
(chi2)
0.0000 0.0000 0.005 0.0000
This table presents the estimated impacts of ICT investments on the performance of microfinance institutions, based
on annual data from 2005 to 2013. The financial data come from the database of the microfinance sector regulatory
agency of Niger and financial reports. Generalized least squares (GLS) estimations are used. Four performance meas-
ures are used as dependent variables: the return on asset (ROA), the return on equity (ROE), natural log of the num-
ber of clients served by MFIs (COVERAGE), and the share of loans in GDP (EXTEND). The values in parentheses are
robust standard deviations. Significant at 10%, Significant at 5%, Significant at 1%.
JOURNAL OF SMALL BUSINESS ENTREPRENEURSHIP 15

17. The estimation also shows no statistically significant coefficient on the relation
between investment in ICTs and the contribution of loans to GDP (EXTEND). Our
previous results for the impact of ICTs on the social performance of MFIs remain
broadly unchanged.
5. Conclusion and recommandations
This article analyzes the ICT-related performance of MFIs. Specifically, it assesses
whether ICTs improve the financial and social performance of MFIs in Niger. Our
panel data runs from 2005 to 2013 and accounts for changes in the microfinance sec-
tor during that period, such as the 2007 change in the regulatory environment.
Our investigation reveals that investing in ICTs is beneficial for the financial per-
formance of MFIs in Niger. Computerization has allowed managers to reduce the
occurrence of operational errors, increase the speed of task execution, and decrease
operating costs. The positive link between ICT investments and return on asset
(ROA) is robust to alternative estimation methods (GMM and GLS) but wanes with
ROE. Investments in computer hardware may have a delayed but positive impact on
social performance.
Our findings also highlight the importance of institutional affiliation for financial
performance. Cost sharing for expenses such as office supplies and equipment sup-
ports the performance of MFIs. In contrast, there is no significant effect of ICT
investments on the social performance of MFIs in Niger. This weak relationship may
reflect that social performance, proxied by the number of clients served
(COVERAGE) and the loan-to-GDP ratio (EXTEND), is a secondary goal relative to
financial performance for many MFIs.
A few policy recommendations emerge from this study. Better cooperation among
MFIs making investments in ICTs can magnify cost-sharing benefits. In a context of
effective coordination, MFIs with limited financial resources can reap the performance
gains of deploying ICT infrastructure at a reduced cost. In addition, the Microfinance
Regulatory Agency (ARSM) in Niger and the Central Bank of West African States
(BCEAO) can subsidize the production of software applications for MFIs. Applications
can be developed from platforms and packages that are readily available on computers
to increase their accessibility. Promoting both internal (e.g. social strategy, social moni-
toring systems) and external (e.g. community outreach, green funding) approaches to
social responsibility can also help reinforce the social performance of MFIs.
Our study focuses on specific ICTs-computer hardware and software. However,
there is a strong uptake of mobile phone-enabled financial services in Africa. Future
research can explore the impact of mobile banking on the operations of MFIs in Niger.
Notes
1. Table 6 reports test results showing that regression errors are heteroscedastic.
2. Data are expressed in BCEAO FCFA francs (XOF).
3. The coverage, expressed in terms of number of clients, is computed as 100(exp (7.6902)-
1) ¼ 218,581.
4. We thank a referee for suggesting this test.
16 H. ALI ET AL.

18. Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Hadizatou Ali is a researcher in Economics and Finance. Her main research interests are
macroeconomic and development economics, in particular the impact of international aid in
developing countries, education, and migration. She also investigates issues on financial inclu-
sion, microfinance, digital technologies, regulation and gender. Her research on the role of
microfinance institutions in the fight against food insecurity contributed to the United Nations
for Development Programme report for Africa. She also teaches management courses. She
benefited from research grants from the African Economics Research Consortium (AERC) and
the Council for the Development of Social Science Research in Africa (CODESRIA).
Jean-Pierre Gueyi
e is a full professor in the School of Management, University of Quebec in
Montreal. His research interests are on financial institutions management (including banks,
financial cooperatives and microfinance institutions), financial risk management, corporate
governance, development economics and alternative investments. He has published several
articles and has edited books in several areas in finance.
C
edric Okou is a financial economist. His research interests include macro-finance, asset and
derivatives pricing, risk management, development economics and econometrics.
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