Dynamic Linkage(s) between Financial Innovation and Efficiency of

Deposit Money Banks in Nigeria

MUSA ABDULMALIK, HAUWA ABUBAKAR LAMINO, FAIZA MAITALA,

MURITALA TAIWO.

Department of Business Administration,

Nile University of Nigeria, Abuja,

NIGERIA

Abstract: The emergence of new technologies, changing customer expectations, and regulatory imperatives, among

others, have driven the financial industry into an era where "digital innovations" thrive, culminating in the

emergence and growth of innovative products such as agency banking, mobile/internet banking, mobile money,

unstructured supplementary service data (USSD), just to name a few. There is increased customers' reliance on e-

channels. This has further established the need for banks to engage in financial innovation for relevance,

competitiveness, efficiency and growth. This study, therefore, examines the dynamic linkage(s) between financial

innovation and the efficiency of deposit money banks (DMBs) in Nigeria. The population of the study comprises

the 13 listed DMBs in Nigeria as of 31 December 2021, and these serve as the sample size. The period covered is

48 months -2016 and 2019. Data were collected from the bank's annual reports and Apex bank's statistical bulletin.

Descriptive statistics, correlation and autoregressive distributed lag (ARDL) cointegration techniques were used for

data analyses. The efficiency of DMBs was estimated using data envelopment analysis (DEA). Findings reveal that

financial innovation has forward and backwards dynamic linkages with the efficiency of DMBs in Nigeria.

Key-Words: - Financial innovation; Agency Banking; USSD; ARDL; and DEA.

Received: July 9, 2022. Revised: October 23, 2022. Accepted: November 22, 2022. Published: December 21, 2022.

1 Introduction

Over the years the global economy has been greatly

interconnected and interwoven. In the same vein

financial services have been deeply integrated. In

recent times, the financial landscape has been riding

upon waves of technological disruptions, [1]. The

emergence of new technologies, changing customer

expectations, and regulatory imperatives, among

others, have driven the financial industry into an era

where "digital innovations" thrive, culminating in the

emergence and growth of innovative products such as

agency banking, mobile/internet banking,

unstructured supplementary service data (USSD),

"virtual banking", and many more, [2].

The Nigerian financial industry is without a

doubt one of the country's most digitally-driven

industries. Over the years, DMBs in Nigeria have

gradually subscribed to and adopted financial

innovation products such as agency banking,

mobile/electronic banking, (USSD), etc., as selling

points and operational strategies to ensure better

service provision, improved customer satisfaction,

and enhance organizational performance in terms of

efficiency. They use certain inputs to optimize

output. They make an effort to use low input to

generate high output and thus achieve a significant

reduction in costs and wastes, to realize optimum

results, [3], [4]. All of these are a result of advances

and improvements in ICT, digitization, improved

regulation and heightened customer expectation,

among others, [1], [2]. The reality, therefore, is to

determine if there is a corresponding effect on

organizational performance in terms of the efficiency

of the DMBs. As input changes through improved

technology and innovation, is there a linkage

between output (improved performance/efficiency)?

What is the effect of “process and product

improvement” on the performance and efficiency of

DMBs? Is there any significant effect of digitization

(financial innovation) on performance and

efficiency? Naturally, the expectation would be that

changes and embrace(s) of digital know-how

(financial innovation) by banks would lead to

increased performance and efficiency. This is a

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Musa Abdulmalik,

Hauwa Abubakar Lamino,

Faiza Maitala, Muritala Taiwo

E-ISSN: 2224-2899

1979

Volume 19, 2022

research question this study is poised to find the

answer to.

Financial innovation was one of the inevitable

phenomena that resulted from the liberalization of the

Nigerian financial sector. While studies exist that

investigate the effect of financial innovation on

DMBs in Nigeria, only a few have researched its

effect on their performance in terms of efficiency.

Among them, just a handful have researched agency

banking; and none has researched USSD as one of

the financial innovation variables because it is new.

Besides, most of the studies employ the traditional

metrics of performance measurement such as RoI,

RoE, RoA, Total Asset, Income and Cost Analyses,

(not DEA) in assessing the effect of financial

innovation on the performance of DMBs in Nigeria.

Data envelopment analysis (DEA) is a nonparametric

scientific technique that is used to measure

efficiency. It helps to overcome the challenges of

using accounting ratios such as RoI, RoA, etc.

Besides, this is one study that examines the dynamic

linkages between financial innovation and the

efficiency of DMBs in Nigeria using bank size as a

control variable. The dearth of empirical studies,

therefore, mandates this study, which fills this gap in

the literature.

Different researchers such as [1], [5], [6], [7],

[8], [9], [10], [11], [12], [13], [14], [15], [16], [17],

[18], [19], [20] researched financial innovation using

different financial innovation variables. None has

researched financial innovation using USSD. None of

these studies measured performance using efficiency,

or the DEA technique. Also, none of the studies used

bank size as a control variable. This study is

therefore unique as it helps to fill the gap thus

identified.

Other studies that investigate the dynamic

linkage(s) between financial innovation and the

efficiency of banks are as follows: [1], [7], [17], [18],

[20] assess the effect of financial innovation on the

financial performance and found that financial

innovation has a positive and significant effect on the

efficiency of banks globally. Studies with negative

findings include the study of [12], [13], [18], [19],

which concludes that digital financial services

offered by fintech have a negative effect on the

performance of commercial banks in Kenya, Nigeria

and Ghana.

Therefore, the general objective of this study is

to examine the effect of financial innovation on the

efficiency of DMBs; while the specific objective of

the study is to examine the dynamic linkage(s)

between financial innovation and the efficiency of

DMBs in Nigeria. Financial innovation in this study

does not cover digital currency such as eNaira or

cryptocurrency. The period of this study covers 4

years, i.e., 48 months from 2016 to 2019. The years

and months are chosen because unlike other financial

innovations such as ATM and PoS, agency banking

and USSD are very current as they were introduced

in Nigeria in 2016 and data are only obtained for this

period. The study used 48 months and used agency

banking and USSD as a proxy for financial

innovation while organizational performance is

measured with efficiency. The study measured

efficiency using DEA which involves using the

banks' input and output variables. Descriptive and

inferential statistics were used, while correlation and

autoregressive distributed lag (ARDL) cointegration

techniques were used for data analysis. Findings

reveal that financial innovation has forward and

backwards dynamic linkages with the efficiency of

DMBs in Nigeria. The hypotheses for the study are

therefore stated as follows: H1: Financial innovation

has no significant effect on the efficiency of deposit

money banks in Nigeria; H2: Bank size has no

significant effect on the efficiency of deposit money

banks in Nigeria; H3: There is no short run, long

run dynamic effect of financial innovation on the

efficiency of deposit money banks in Nigeria.

2 Conceptual Framework

The conceptual framework highlights the connection

between the dependent and independent variables as

well as the control variable. Figure 1 below clearly

depicts this connection.

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Musa Abdulmalik,

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Faiza Maitala, Muritala Taiwo

E-ISSN: 2224-2899

1980

Volume 19, 2022

Figure 1: Conceptual Framework

Independent variable

Dependent variable

Financial Innovation

Efficiency

Control variable

Bank Size

(Log of Total Assets)

Agency Banking

Unstructured Supplementary

Service Data (USSD): m-Cash

Fig. 1: Conceptual Framework

Source: Researcher’s Model of Financial Innovation and Performance (Efficiency)

Financial Innovation: Financial innovation is

generally marked by the introduction of a new product

or a new process in the financial system, [21], [22],

[23]. Financial innovation may also involve

modifying an existing idea as either a product or a

process, [10], [24].

Branchless (Direct) Banking: Banking outside the

conventional banking system, or using platforms other

than the traditional bank branches is often referred to

as branchless banking [9]. It comprises the delivery of

financial services using retail agents or other third-

party intermediaries. It also involves the use of digital

platforms by DMBs (such as mobile banking) as the

key point of contact with customers, [9], [19].

Agency Banking: Agency banking (otherwise referred

to as agent banking) is the delivery of financial

services to customers via a third party (agent) on

behalf of an authorized deposit-taking financial

institution or mobile money operator, [19]. According

to [6], agency banking is a form of branchless

banking that enables traditional banks to increase their

network of branches and services (cost-effectively and

efficiently, through authorized agents) into areas,

where traditional banking services are difficult to

reach, [6].

Unstructured Supplementary Service Data (USSD):

USSD is a global system for mobile communications

(GSM) protocol that is used for mobile money

services, prepaid services, menu-based information

services and location-based content services (CBN

Bulletin, 2018). With USSD, users interact directly

from their mobile phones by making selections from

various menus. Unlike an SMS message, during a

USSD session, a USSD message creates a real-time

connection. This means USSD enables two-way

communication of information, [25].

Performance: Performance (in the context of an

organization) is the practice of measuring the results

of a firm’s strategies, policies and operations in

monetary terms (Bonn et al., 2004). Performance is in

two forms: financial performance and non-financial

performance, [26]. Performance is a general structure

that refers to the enterprise's operations, [3].

Performance can also be seen as a reflection of the

productivity and efficiency of members of an

enterprise and the enterprise itself, measured in terms

of revenue, profit, growth, competitiveness,

development, and expansion of the organization, [3].

Some organizations measure performance using

market share, growth metrics, expansion, efficiency,

survival, number of employees, quality of employees,

employee turnover, competitiveness, and capital

employed, [26], just to mention a few. In this study,

performance is measured using efficiency.

Efficiency: Generally speaking, efficiency involves a

process that helps to optimize the use of resources

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(Zala, 2020). We can say there is efficiency where the

least amount of resources (e.g. time and money) is

used to achieve the best (most) possible outcomes

[10]. Efficiency is defined as a level of performance

that describes a process that uses the least amount of

inputs to make the foremost significant amount of

outputs, [10]. Efficiency relates to any or

all inputs employed in producing any given output,

including personal time and energy, [27].

Bank Size: According to [25] two measures of bank

size are mostly found in the literature. They are

systemic and absolute bank size. Systemic bank size is

measured either as the ratio of gross turnover to total

assets, [28], or bank assets as a percentage of the

statement of financial position. This is then compared

to the industry standard, [29]. A bank’s absolute size

may be defined as the log of total assets, [28], [29], or

the ratio of the bank's total assets to total financial

position.

2.1 Theoretical Framework

In the course of this study, several theories were

reviewed. Kane's theory of regulatory dialectics and

the branchless banking theory were found to underpin

the study:

Kane’s Theory of Regulatory Dialectics: Kane

developed this theory in 1984, and he sees financial

innovation as an institutional response to financial

costs created by changes in technology, market needs

and political forces, particularly laws and regulations,

[30]. Kane describes the interactive regulatory process

that follows institutional avoidance and innovation as

a dialectical process. Financial innovation in Nigeria

is largely driven by the CBN cashless policy

instrument encapsulated in its FSS 2020 policy

document. This theory is relevant to this study as it

highlights some of the reasons behind banks’ use of

innovation to address requirements of the regulation,

ICT, competition, and market forces for efficient

performance.

Branchless Banking: The branchless banking theories

are divided into three groups: bank-led, non-bank-led,

and bank-focused theories, [31]. These theories are

very significant and relevant to this study because

many financial innovation products ride on the

principle of branchless (Direct) banking. The theories

mainly seek to explain how financial innovation has

given rise to the notion of branchless banking. They

explain how branchless banking is conducted, and

thus contribute to this study's independent variable

[9], [31], [32]. A regulated financial institution

delivers financial services and products through a

retail agent in the bank-led theory of branchless

banking. This is also known as agency banking. The

bank generates financial services and products and

then distributes them to customers through retail

agents. Retail agents deal with customers face-to-face

and execute cash-in/cash-out activities in the same

way that a teller at a bank's branch would take

deposits and process withdrawals [9]. The bank does

not need to set up a branch at the agents' location.

3 Methodology

This study uses the ex-post facto research design. This

is because the study tries to find out the cause-and-

effect relationship between the variables whose

occurrence has already taken place. The data has

already manifested and cannot be manipulated.

According to [31], [32] Ex-post facto research design

is a systematic empirical inquiry in which the

researcher does not directly control the variables

because their manifestations have already occurred

and they are inherently not manipulated. The

population of the study comprises the 13 DMBs listed

on the Nigeria Exchange Group (NGX) as of

December 2021, and they also serve as the sample

size. Data were analyzed using descriptive and

inferential statistics, as well as the ARDL

cointegration technique. Input (total asset) and output

(turnover) data were extracted for banks' Annual

Reports for the period. DEA was used to determine

banks' efficiency.

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Table 1. Measurement of the Variables

Variables

Measures

Authors

Bank Size

The natural logarithm of total assets is used as a

proxy for bank size (ln SIZE), in line with Salehi et

al. (2012)

[25], [27], [28], [29]

Financial

innovation

Agency Banking (volume and value); and USSD

(volume and value)

[5] and [17]

Performance

Efficiency

[26]

Efficiency

Input and Output

[13], [18], [22]

Input (4)

Total assets (current and non-current assets)

Self-Measurement

Output (4)

Turnover (gross)

Self-Measurement

Source: Author’ Compilation (2022)

The study adopted a pre-test analysis of correlation

and unit root test using the Augmented Dickey-Fuller

(ADF) test. After analyzing the unit root test, a

decision to use ARDL was determined.

3.1 Model Specification

The model specification follows the established

theoretical framework. It aids to establish the dynamic

linkage (s) between financial innovation and

performance in terms of the efficiency of DMBs in

Nigeria. The model used is represented and

summarized below:

Model:

EFF=β0 +β1VAUSSDi,t+β2VAAGBAi,t+β3BSi,t+ϵi,t

… 1

Where:

EFF = Efficiency

VAUSSD = Value of USSD (m-cash)

VAAGBA = Value of agency banking

BS = Bank size

β0 = Constant

β1-β3 = Beta coefficient that

measures the sensitivity of independent variables to

changes in the dependent variable

ϵ = Error Term

4 Results and Discussion

Descriptive Statistics and Test Results

Table 2 shows a summary of the statistical methods

employed in this empirical study. The mean value of

efficiency (EFF) has the lowest mean value of 1.2774,

while the mean value of (USSD) has the greatest

mean value of 620277, as shown in the table. The

value of USSD (VAUSSD) and Bank size (BS) have

mean values of 8.94 and 2.516 respectively. The

table's values for skewness and kurtosis were also

used to help with the analysis. The skewness of the

histogram is used to determine symmetry, while the

tail shape of the histogram is used to determine

kurtosis. As a result, all of the variables are positively

biased except efficiency. Kurtosis, on the other hand,

comes in three varieties: mesokurtic, platykurtic, and

leptokurtic. As can be seen from the table, all of the

variables in the distribution have a positive kurtosis

value, indicating that the distribution is leptokurtic.

See Table below.

Table 2. Descriptive Statistics Results

EFF

VAUSSD

VAAGBA

Mean

1.277423

62027752

8.94E+11

2.516592

Std. Dev.

0.493452

50750315

8.49E+11

0.342307

Skewness

-0.047235

1.207014

0.682392

2.338479

Kurtosis

1.965503

4.103824

1.912541

8.701889

Observations

Source: Author’s Computation, (2022)

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Table 3. Unit Root Test

Variables

Level first difference

Order in integration

Augmented

Dickey-Fuller

MacKinnon

Critical Value

Augmented

Dickey-Fuller

MacKinnon

Critical Value

EFF

-6.3183***

-3.5777***

1(0)

VAUSSD

-3.16298***

-3.57772***

1(0)

VAAGBA

-8.66108***

-3.58115***

1(1)

-7.42405***

-3.57772***

1(0)

Source: Author’s Computation, (2022)

Result of Unit Root Test: Augmented Dickey-Fuller

unit root are respectively reported in Table 3.

From the Augmented Dickey-Fuller unit root table

above (Table 3), it could be seen that the value

efficiency (EFF), the value of USSD (VAUSSD), and

the value of bank size (BS) are all non-stationary

series in level form except the value of agency

banking (VAAGBA). Meaning all the variables are of

order 0 except the value of agency banking which is

of order 1. This justifies the reason to embark on the

ARDL estimation.

Estimation of the ARDL Model for EFF: This

hypothesis is rejected because the result shows the

existence of both short-run and long-run dynamic

effects of financial innovation on the efficiency of

DMBs in Nigeria. The result of the hypothesis is

presented in the Figure below (Figure 2) showing the

model criteria selection graph and Table 4 showing

the bound test, after which the estimation was broken

down into its short-run and long-run components as

well as the speed of adjustment to equilibrium in the

case of disequilibrium.

Model Selection – Criteria Graph for EFF Model:

The best 20 models, among which the overall best is

automatically chosen for the estimation of the ARDL

EFF Model is exhibited in Figure 2 below.

1.02

1.03

1.04

1.05

1.06

1.07

1.08

1.09

1.10

ARDL(4, 3, 4, 3, 4, 2, 4, 3)

ARDL(4, 3, 4, 3, 4, 2, 4, 4)

ARDL(4, 3, 4, 3, 4, 4, 4, 3)

ARDL(4, 3, 4, 3, 2, 2, 4, 4)

ARDL(4, 3, 4, 3, 4, 3, 4, 3)

ARDL(4, 4, 4, 3, 2, 1, 4, 4)

ARDL(4, 3, 4, 3, 2, 2, 4, 3)

ARDL(4, 4, 4, 3, 4, 2, 4, 3)

ARDL(4, 4, 4, 3, 2, 2, 4, 4)

ARDL(4, 3, 4, 4, 4, 4, 4, 3)

ARDL(4, 3, 4, 4, 4, 2, 4, 3)

ARDL(4, 3, 4, 3, 2, 3, 4, 4)

ARDL(4, 4, 4, 3, 4, 2, 4, 4)

ARDL(4, 3, 4, 4, 4, 2, 4, 4)

ARDL(4, 3, 4, 3, 4, 3, 4, 4)

ARDL(4, 4, 4, 3, 4, 4, 4, 3)

ARDL(4, 3, 4, 3, 4, 4, 4, 4)

ARDL(4, 3, 4, 4, 2, 2, 4, 4)

ARDL(4, 3, 4, 3, 2, 4, 4, 4)

ARDL(4, 3, 4, 3, 3, 3, 4, 4)

Akaike Information Criteria (top 20 models)

Fig. 2: Model Selection – Criteria Graph for Model for EFF

Source: Computed by the Researcher, (2022)

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Figure 2 above gives the values of the Akaike

information criterion for the estimated ARDL model.

The purpose is to see clearly that the model that

minimizes the AIC is chosen given the maximum lag

selected.

Cointegrating Bound Testing for ARDL EFF Model:

Table 4 below, highlights the bound testing for

cointegration for EFF. Test statistics (K), F-statistics

and the Critical values for EFF are all depicted in the

table.

As presented in Table 4, the parameter k simply

equals total variables minus one which is 7.

Cointegration is tested on EFF Model using the

measure of organizational performance proxy by

efficiency as the dependent variable. The findings

reveal that the F-statistic is higher than both the lower

and upper bound critical value at 1%, 2.5%, 5% and

10%, levels of significance using restricted intercept

and no trend in all the specifications. The findings,

therefore, suggest the presence of cointegration

among the measure of organizational performance

proxy by efficiency (EFF), the value of USSD

(VAUSSD), the value of Agency Banking

(VAAGBA) and bank size (BS). Based on the results,

the null hypothesis of no cointegration is rejected.

Therefore, this implies that the measure of

organizational performance proxy by efficiency

(EFF), the value of USSD (VAUSSD), the value of

Agency Banking (VAAGBA) and bank size (BS) are

all bound by a long-run relationship in Nigeria. The

study, therefore, moves on to the estimation of the

short-run and long-run situations as presented by the

tables to follow (Tables 5 and 6).

Table 4: The Bound Test for Co Integration

EFF

Test Statistics (K)

F-Statistics

3.727665

Critical Value

Bounds

I(0) Bound

I (1) Bound

10%

2.03

3.13

2.32

3.5

2.5%

2.6

3.54

2.96

3.26

Source: Authors’ Computation, (2022).

ARDL Short run Estimates for Model EFF

Table 5. Summary of Estimation for Short run (Speed of Adjustment) ARDL Model for EFF

Models

Variable

Coefficient

Std. Error

t-Statistic

Prob.

EFF Model

CointEq(-1)

-0.295177

0.675095

-0.437238

0.0007

Source: Authors’ Computation, (2022).

Table 6. Summary of the Estimation of the Long run ARDL Model for EFF

Variables

Coefficient

Std. Error

t-Statistic

VAUSSD

0.435198

0.562011

0.774358***

VAAGBA

0.234176

0.982340

0.238385***

0.657376

0.244063

2.693468***

2.988700

0.719680

4.152819***

Source: Authors’ Computation, (2022).

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The short-run cointegrating form of the model is

presented in Table 5, in which the coefficient of the

error correction model for all eight specifications is

presented. The coefficient of the Error Correction

Mechanism (ECM-speed of adjustment) is negative as

expected and significant at a 5% level. The

coefficients suggest that over 29% of the short-run

disequilibrium is corrected in the long-run equilibrium

in each of the eight specifications.

ARDL Long-run Estimates for Model EFF: Table 6

below shows the details of the long-run estimates for

the model.

Table 6 presents the long-run coefficients of the three

specifications estimated using the ARDL approach.

The findings for EFF model specification give the

long-run impact of financial innovation on the

organizational performance of DMBs using efficiency

proxy measures. From the table, the study found the

coefficient of the value of USSD (VAUSSD), the

value of Agency Banking (VAAGBA) and bank size

(BS) are positively significant with organizational

performance proxy by efficiency.

Model Diagnostic Result

In other to test for the diagnostic test in the study, the

result can be obtained from table 7 below:

Table 7. Residual Diagnostic Test and Stability Diagnostic Test Result

Residual Diagnostic Test Result

Tests

F-statistic

Probability

EFF Model

Breusch-Godfrey Serial Correlation LM Test:

1.759643

0.2404

Heteroskedasticity Test: ARCH

0.934348

0.5912

Stability Diagnostic Test Result

Tests

F-statistic Values

Probability

FDCPS Model 1

Ramsey RESET Test

3.141618

0.1143

Source: Authors Computation (2022).

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From the table above, the Breusch-Godfrey Serial

Correlation LM test for the model reveals that there is

no presence of serial correlation judging from the F-

Statistics and the probability values which are greater

than 0.05. Also, the heteroskedasticity ARCH LM test

for the model reveals that there is no

heteroscedasticity problem judging from the F-

Statistics and the probability values which are greater

than 0.05. However, the stability test result using the

Ramsey RESET test shows that the model was very

stable considering the probability value that was

greater than 5%.

4.2 Discussion of Findings

Empirical findings from the bound test confirm the

existence of a long-run cointegration relationship

between the variables. The result of the long-run

ARDL estimates revealed that the value of

transactions on agency banking and USSD exert a

positive and significant impact on the efficiency of

DMBs in Nigeria in the long run. It also reveals a

dynamic short-run positive linkage between financial

innovation and the efficiency of DMBs in Nigeria.

The study also reveals that bank size has a dynamic

linkage with efficiency. The findings of this study are

in line with the findings of [1] whose study reveals

that financial innovation influenced financial

performance positively and it is significant at 5 per

cent. Also, the findings of this study are in agreement

with those of [17], [18] which reveals that financial

innovation (mobile money) has forward and backward

linkages with banks’ performance in Nigeria. Besides

the findings of this study are in line with those of

Jingquin et al (2019) who found a positive and

significant effect of financial innovation on the

performance of commercial banks in Africa.

However, this study is not in line with the findings of

[6], [14], [31] who found that there is a negative and

insignificant effect of financial innovation on

performance in terms of efficiency of DMBs in

Nigeria.

5 Conclusion

Based on the empirical results and findings, this study

concludes that there is a causal relationship between

financial innovation (agency banking and USSD) and

banks' efficiency. Hence the study concludes that

financial innovation has forward and backward

linkages with the efficiency of DMBs in Nigeria. This

implies that DMBs should invest in financial

innovation strategies and ICT in their quest to

improve performance. The value of the transaction has

a dynamic short-run and long-run positive and

significant effect on efficiency. Bank size also has a

positive and significant effect on the efficiency of

DMBs in Nigeria.

5.1 Recommendation

The study recommends that DMBs should seek

collaboration with relevant stakeholders to promote

awareness, acceptance, usage and more investment in

financial innovation (especially agency banking and

USSD) to increase efficiency. They should continue to

increase the value of transactions since it is positive

and can increase efficiency. There should be

continuous and sustained investment in ICT and

human capital development to further deepen and

cement the dynamic linkages between financial

innovation and efficiency. DMBs should seek

collaboration and partnership with relevant

stakeholders to achieve positive increases in this

regard. They should harness opportunities and

synergies around the issue of financial innovation,

drive customer satisfaction, and embrace more

branchless (Direct/Virtual) banking and regulatory

dialectics.

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Authors Declaration

-Availability of Data and Materials

All data generated and analyzed during this study are

included in this article. The datasets generated and

analysed during the current study are not publicly

available due to privacy clauses from the Central

Bank of Nigeria (CBN) but the companies' Annual

reports are available from the banks' websites. Data

sharing does not apply to this article as no datasets

were generated. Some of the data that support the

findings of this study are available from Google

scholar but restrictions apply to the availability of

these data, which were used under license, and so are

not publicly available. Data are however available

from the authors upon reasonable request and

subscription to the journal.

Competing Interests

The authors declare that they have no competing

interests.

Contribution of Individual Authors to the Creation

of a Scientific Article (Ghostwriting Policy)

MA is responsible for the main work; Prof HA is the

course supervisor and reviewer of the articles; while

DR MT analyses, interpreted data and performed

major critique. All authors read and approved the final

manuscript.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

Funding for this article is solely a personal effort of

the corresponding author for the award of PhD

management finance from the Nile University of

Nigeria.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en_

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.177

Musa Abdulmalik,

Hauwa Abubakar Lamino,

Faiza Maitala, Muritala Taiwo

E-ISSN: 2224-2899

1989

Volume 19, 2022