Date of submission: March 10, 2020; date of acceptance: April 24, 2020.
* Contact information: ghazi.zouari@fsegs.usf.tn, Faculty of Economic Sciences and

Management, University of Sfax, Airport Road, km 4.5, PB: 1088, Sfax 3018, Tunisia,
phone: +216 22 633 500; ORCID ID: https://orcid.org/0000-0002-8168-3266.

** Contact information: imenabdelmalek@fsegs.u-sfax.tn, Faculty of Economic Sci-
ences and Management, University of Sfax, Airport Road, km 4.5, PB: 1088, Sfax 3018,
Tunisia, phone: +216 28 461 817; ORCID ID: https://orcid.org/0000-0003-2453-6312.

Copernican Journal of Finance & Accounting

e-ISSN 2300-3065
p-ISSN 2300-12402020, volume 9, issue 1

Zouari, G., & Abdelmalek, I. (2020). Financial innovation, risk management, and bank perfor-
mance. Copernican Journal of Finance & Accounting, 9(1), 77–100. http://dx.doi.org/10.12775/
CJFA.2020.004

gHazi zouari*
University of Sfax

imen abdelmaleK**
University of Sfax

financial innovation, risK management,
and banK performance

Keywords: financial innovation, risk management, performance of Tunisian banks.

J E L Classification: G21, G32, O30.

Abstract: Several researches conducted from the angle of corporate governance reve-
als that the majority of the work that examines the direct association between financial
innovation and bank performance has displayed mixed results and has ignored the in-
direct relationship between these two variables through the risk management of incur-
red by the bank. In this context, the purpose of this study is to investigate whether or
not there is a mediating effect of risk management on the relationship between finan-
cial innovation and bank performance. Data collected from annual activity reports of
banks listed on the Tunis Stock Exchange is analyzed, and used to validate the theore-
tical and empirical contributions of our article. The empirical study uses the Panel data
extracted from 10 banks, over the period ranging from 2009 to 2017, and the Baron and

Ghazi Zouari, Imen Abdelmalek78

Kenny (1986) mediation approach has been estimated, using specifications of random
effects. Our empirical analysis substantiates that financial innovation has a positive
direct effect on stock market performance, and a non-significative impact on financial
profitability. Additionally, our results show that improving the stock market perfor-
mance of banks by financial innovation depends on the mediator role of the operational
risk management. To remain improving performance, Tunisian banks must pay more
attention on special training of bank managers whose function relate to the choice of fi-
nancial innovations and manage the associated risks.

Introduction

In literature review, the concept of financial innovation is so broad, and evolves
over time. It is defined in different ways (Merton, 1992; Tufano, 2003; Frame
& White, 2002; Llewellyn, 2009; Sokołowska, 2014). According to Tufano
(2003), “financial innovation is the act of creating and then popularizing new
financial instruments as well as new financial technologies, institutions and
markets”. Financial innovation is a process, carried out by any institution, in-
volving the creation, promotion and adoption of new (including both incre-
mental and radical) products, platforms and processes. It is also a technology
catalyst introducing new ways of or changes in conducting a financial activity
(Khraisha & Arthur, 2018; Sokołowska, 2014). We act in accordance with Ne-
jad’s (2016) concept of financial innovation, consisting in “the development, in-
troduction, and management of a product, service, a business model, or a pro-
cess that is developed to directly serve the financial industry”.

The development of financial innovation contributes to the wealth of share-
holders. However, the crisis that affected global financial stability and the econ-
omy in 2007/2008, the financial innovation has become risky (Arthur, 2017),
and a source of bank fragility (Beck, Chen, Lin & Song, 2016). Researchers have
revealed that the leading cause of crisis is the lack of how to govern innovation,
specifically the management risk of innovation. Consequently, the crisis has re-
inforced the need to rethink some of the approaches adopted by the financial
community in assessing bank performance. To this end, it is important to ob-
tain a comprehensive view of the key factors that may inf luence banks’ perfor-
mance, including the adequacy of financial innovations choices in relation to
risk management, and the question of how this adequacy is handled through
banking governance.

finanCial innovation, risk managEmEnt, and Bank PErformanCE 79

Like the innovation in the industrial sector, the financial innovation is char-
acterized by high risk, costly investment, specific asset to the firm, and long-
horizon return (Zouari & Zouari-Hadiji, 2014a; 2014b). These characteristics
represent the factors bringing about agency conf licts between shareholders
and stakeholders, on the one hand, and the opportunistic behavior of manag-
ers for the purpose of maximizing their wealth at the expense of stakeholders,
on the other hand. According to the agency theory, managers are risk averse
and innovation activity (Minetti, Murro & Paiella, 2015) whereas the share-
holders facilitate the innovation activity in order to increase company stabil-
ity, and to generate value (Asensio-López, Cabeza-García & González-Álvarez,
2019). Owing to the interest conf lict and information asymmetry problems be-
tween managers, shareholders and stakeholders, managers will be encouraged
to take advantage of an opportunistic behavior. Thereby, the manager’s behav-
ior toward risk and corporate governance can affect the choice of innovation
and risk management activities.

Moreover, depending on the transaction cost theory, Williamson (1985) ad-
vocates that investing in specialized assets increases the transaction costs via
providing concerns about exchange problems such as potential bargaining and
opportunism. Thus, innovations spur on risk-taking, generating costs. Hence,
risk management is necessary to reduce these costs and control risks.

For the reasons mentioned above, our study aims to scrutinize the effect of
financial innovation on risk management and bank performance.

The rest of the paper is structured as follows. The next section deals with
a literature review and hypothesis development. The subsequent section cov-
ers the methodology while the fourth one presents the analyses. The fifth sec-
tion is about results and discussions, and the last one concludes, presents the
limitations, and outlines possible future research.

Related literature and hypothesis development

The research methodology and the course of the research process

A conceptual model for this study is indicated in figure 1. Our research asserts
that financial innovation will have an inf luence on bank performance. The risk
management is shown as a prominent mediator between financial innovation
and bank performance.

Ghazi Zouari, Imen Abdelmalek80

Financial innovation and bank performance

The financial innovation allows banks to create a competitive advantage
through cost reduction with the aim of improving their financial performance
via mitigating risk. Innovation is so substantial that it contributes to the eco-
nomic growth and stability of financial system (Lerner & Tufano, 2011). Spe-
cifically, banks improve their quality, and enhance their performance by devel-
oping a financial innovation. For instance, they innovate new forms of financial
securities, new forms of mortgage lending, new means of processing transac-
tions, or new organizational forms such as Internet banks. Studying the diffu-
sion of financial innovation has been linked to the speed and pattern. The dif-
fusion of financial innovation is of great value for ensuring corporate return on
investment from innovation (Khraisha & Arthur, 2018). De Young, Lang and No-
lle (2007) suggest that Internet adoption makes the profitability of American
community banks better. Ciciretti, Hasanand and Zazzara (2009) provide evi-
dence of a significant link between Internet banking service and bank perfor-
mance. In addition, Mabrouk and Mamoghli (2010) point out that the primary
mover of product innovation improves the bank profitability, and the primary
mover in process innovation has a positive effect on bank profitability and ef-
ficiency. Mabrouk, Dhouibi and Rouetbi (2016) conclude that financial innova-
tion is a value creation instrument for Tunisian banks. Several studies demon-
strate as well that bank performance increase after the adoption of innovation
(Mustapha, 2018; Chipeta & Muthinja, 2018; Lotto, 2019). Hence, this study hy-
pothesizes the following:

H1. The financial innovation has a positive impact on the bank performance.

Financial innovation and risk management

Financial innovation is associated with risks (Baiquan & Kebao, 2010) leading
to bank fragility (Beck et al., 2016). These risks include market risk, credit risk
and operational risk (Baiquan & Kebao, 2010). Investment in financial inno-
vation reinforces the financial risk management. The risk management is an
essential part of the innovation lifecycle, and it helps address ambiguity and
uncertainty. However, a few empirical studies have tested the relationship of
financial innovation and risk management (Hu, Li & Deng, 2009; Baiquan & Ke-

finanCial innovation, risk managEmEnt, and Bank PErformanCE 81

bao, 2010; González, Rodríguez Gil, Cunill & Lindahl, 2016). Philippas and Sirio-
poulos (2009) analyze the relationship between financial innovation (process
/ organizational) and operational risk. Their results indicate that the speed
of diffusion of financial innovation increases operational risk. González et al.
(2016) add that securitization increases the overall default risk of European fi-
nancial institutions. The results for study of Zia, Muhammad, Sarwar and Asif
Raz (2019) identified four areas of impact on credit risk management: corpo-
rate governance exerts the greatest impact, followed by diversification or in-
novation, which plays a significant role, hedging and, finally, the bank’s Capital
Adequacy Ratio. This study highlights these four risk management strategies,
which are critical for commercial banks to resolve their credit risk. Mabrouk
et al. (2016) show that there is a significant direct effect of each innovation
type (process / product) on credit risk, adopting that product innovation has
increased the volume of non-performing loans of Tunisian Banks. Therefore,
we posit the hypothesis as follows:

H2. The financial innovation has a positive impact on the risk management.

Risk management and bank performance

The traditional role of banking intermediation is changing as the bank is oper-
ating in an uncertain financial environment. Risk management becomes a rele-
vant function reducing the costs and preventing distortions in investment pol-
icy (Stulz, 2016; Ellul, 2015).

“The risk management as an active, strategic, and integrated process that
encompasses both the measurement and the mitigation of risk, with the ulti-
mate goal of maximizing the value of a bank” (COSO, 2004). In the institutional
theory framework, Stulz and Smith (1985) are the first introducing the rele-
vant role of risk management at the firm, enhancing the firm value under ineffi-
ciency of market conditions. The risk management decreases the agency cost of
equity or debt, and cost transaction. Additionally, the bank uses the risk man-
agement to reduce the cost of financial distress by decreasing the default prob-
ability, and to increase the value of firms and shareholders.

In the empirical studies, the relationship between risk management and
bank performance is not usually positive. This is related to different determi-
nants like country of studies. In fact, the risk management reduces the cost,

Ghazi Zouari, Imen Abdelmalek82

but does not guarantee increase in the return on equity (Olamide, Uwalomwa
& Ranti, 2015; Mendoza & Rivera, 2017). In recent studies, Zgarni and Hassou-
na (2018) have perceived that prudential rules measured by the solvency ratio
and liquidity ratio improve the accounting performance of Tunisian banks. On
the other hand, recent empirical contributions have given proof that the bank
with Chief Risk Officer (CRO) maintains a positive relationship with bank per-
formance and the value of shareholders (McShane, Nair & Rustambekov, 2011;
Battaglia, Fiordelisi & Ricci, 2017). In the same context, Fatemi and Fooladi
(2006) note that effective risk management leads to more balanced trade-off
between risk and reward, to realize a better position in the future. The studies
of Hosna, Manzura and Juanjuan (2009), Toutou and Xiaodong (2011) and Zeze
(2012) found that have a positive correlation between the financial risk man-
agement practices to the financial performance of commercial banks.

In this respect, Wanjohi, Wanjohi and Ndambiri (2017) have discovered that
an efficient risk management can help banks attenuate their exposure to risk
and enhance their market competitiveness. Moreover, the study on Malaysian
banks by Trofimov, Aris and Ying Ying (2018) also shows that the continuing
need to manage credit risk is among the most important factors that explain
high bank profitability. For Paroush and Schreiber (2019), there are significant
relationship between profitability, capital, and risk of US commercial. Further-
more, Lotto (2019) revealed that capital adequacy has a positive relationship
with bank operating efficiency, and reduce the risk of moral hazard between
shareholders and dets-holders.

Findings of Kiambati (2020) and Sathyamoorthi, Mogotsinyana, Mphoeng
and Mashoko (2020) suggest that commercial banks should strike a proper bal-
ance between financial risk management practices and financial performance
by engaging in appropriate market, credit, and liquidity risk management prac-
tices that will ensure safety for their banks and yield positive profits. Conse-
quently, we present the following hypothesis:

H3. The risk management has a positive impact on the bank performance.

finanCial innovation, risk managEmEnt, and Bank PErformanCE 83

The risk management as a mediator between financial innovation
and bank performance

The risk management is defined from different angles as institutional, regula-
tory or functional. From the regulatory angle, the risk management is deter-
mined by the prudential rules defined by the Basel Committee. We will focus
on Basel II/III agreements approved by Tunisian banks. The complexity and dy-
namic of financial innovation have raised an uncertain environment. This un-
certainty is at the origin of the bank’s lack of understanding of the new finan-
cial products, obsolete regulations and excessive risk-taking.

Several research studies show that the financial innovation is associated
with high risk and contributed to the fragility of banks. According to the study
of Philippas and Siriopoulos (2009), the diffusion of financial innovation raises
the operational risk. Hu and Xie (2016) evince that innovation positively affects
bank risk-taking. In line with these ideas, Wang (2014) argues that the finan-
cial innovation decreases the bank value. Similarly, Chen and Peng (2019) ar-
gue that financial innovation increase banks’ liquidity risk. They conclude that
banks should ensure an appropriate management of financial innovation to im-
prove their performance.

In other words, the bank should monitor these risks correlated with the
conf licts of interest between shareholders and stakeholders, and the oppor-
tunistic behavior of agents that could generate agency and transaction costs,
and information asymmetry. These costs inf luence the profitability of the
shareholders as well as the firm value, especially in the short term and in the
long run, without managing the internal and external risks throughout the in-
novation process.

In the governance framework, the financial institutions use risk manage-
ment processes so as to minimize costs and control risks. The risk manage-
ment allows to reduce the cost of investment innovation incurred due to agen-
cy conf lict. Some studies have tested the mediating effect of risk management
on the relationship between innovation and project management in non-finan-
cial companies (Jordan, Jorgensen & Mitterhoferh, 2013). However, Guermazi
(2017) has studied the mediating role of risk management in financial corpo-
rations, and has realised that the improvement in insurance and bank quality
throughout innovation depends on the mediating role of risk management.

Ghazi Zouari, Imen Abdelmalek84

In our paper, we will look for the existence of a mediating effect of risk man-
agement between innovation and the bank performance. Accordingly, we de-
duce the following hypothesis:

H4. Risk management mediates the relationship between financial innova-
tion and bank performance.

Figure 1. Research Model

mediating effect of risk management on the relationship between innovation and project

management in non-financial companies (Jordan, Jorgensen & Mitterhoferh, 2013).

However, Guermazi (2017) has studied the mediating role of risk management in financial

corporations, and has realised that the improvement in insurance and bank quality

throughout innovation depends on the mediating role of risk management.

In our paper, we will look for the existence of a mediating effect of risk management

between innovation and the bank performance. Accordingly, we deduce the following

hypothesis:

H4. Risk management mediates the relationship between financial innovation and bank

performance.

Figure 1. Research Model

Source: author's modeling, 2019.

RESEARCH METHODOLOGY
This section aims to test the impact of the characteristics of financial innovation on the

bank performance. This effect is mediated by risk management. To do this, we will present

our sample, the response and explanatory variables, and the regression analysis.

Presentation of data and variable measurements
The data is collected from 10 Tunisian listed commercial banks1 listed on the Tunis Stock

Exchange. All the relevant data is obtained from the bank annual reports, and reference

documents of Financial Market Council in the period spanning from 2009 through 2017.

Our choice of this period is based on the availability of data and it represents a post-

1 ATB : Arab Tunisian Bank, BIAT : Banque Internationale Arabe de Tunisie, AB :Amen Bank, UBCI :Union

Bancaire pour le Commerce et l’Industrie, ATTIJARI :Attijari Bank, UIB :Union Internationale de Banque,
BT :Banque de Tunisie, BNA :Banque Nationale Agricole, STB :Société Tunisienne de Banque, BH :Banque de
l’Habitat.

Risk management

Financial
Innovation Bank performance

Control variables

S o u r c e : author’s modeling, 2019.

Research methodology

This section aims to test the impact of the characteristics of financial innova-
tion on the bank performance. This effect is mediated by risk management. To
do this, we will present our sample, the response and explanatory variables,
and the regression analysis.

Presentation of data and variable measurements

The data is collected from 10 Tunisian listed commercial banks1 listed on the
Tunis Stock Exchange. All the relevant data is obtained from the bank annu-

1 ATB: Arab Tunisian Bank, BIAT: Banque Internationale Arabe de Tunisie, AB:
Amen Bank, UBCI: Union Bancaire pour le Commerce et l’Industrie, ATTIJARI: Attijari
Bank, UIB: Union Internationale de Banque, BT: Banque de Tunisie, BNA: Banque Natio-
nale Agricole, STB: Société Tunisienne de Banque, BH: Banque de l’Habitat.

finanCial innovation, risk managEmEnt, and Bank PErformanCE 85

al reports, and reference documents of Financial Market Council in the peri-
od spanning from 2009 through 2017. Our choice of this period is based on
the availability of data and it represents a post-financial crisis period from
2007/2008 so as not to bias the results by the effect of this crisis on banking
performance. In sum, the total number of Tunisian banks selected for statis-
tical testing amounts to 10 banks, making up a total number of observations
equal to 90.

Dependent variables

According to the European Central Bank (ECB, 2010), the bank performance is
defined as the “capacity to generate sustainable profitability”. Therefore, and
as in previous studies, we use two measures of bank performance. Following
Mabrouk and Mamoghli (2010), Zouari and Zouari-Hadiji (2014a; 2014b)2 and
Wanjohi et al. (2017), the first one is the return on equity as a traditional meas-
ure of performance, ref lecting the ability of a bank to generate profits through
investment (Return On Assets “ROE” = operating income before depreciation
and R&D / total equity). The second one is the market to book value as market-
based measures of performance, relating the market value of stockholders’ eq-
uity to its book value (Market to Book “MTB” = market capitalization / book
value of equity).

Independent variables

Through empirical studies, financial innovation is assessed, using two differ-
ent types of input and output ratios (Stone, Shipp & Leader, 2008), and it is
measured by three different ways. The first measurement indicator is the in-
novation index (Heffernan, Fu & Fu, 2008) that has been converted into per-
centages to facilitate exposure, and can vary from 0 % to 100%. The second
measurement indicator is banking channels such as ATM’s, mobile payment,
call center, mobile banking, Internet banking, POS (point of sale) terminals and
branch (De Young, Lang & Nolle, 2007; Ciciretti, Hasanand & Zazzara, 2009;

2 This measurement of the accounting performance has the advantage of eliminat-
ing the effect of accounting choices related to the treatment of R & D in the financial
statements largely subject to the opportunism of managers.

Ghazi Zouari, Imen Abdelmalek86

Usman, 2016; Tunay, Tunay & Akhisar, 2015). The last measurement one is the
financial R&D intensity (value added) and the financial R&D intensity (cost)
(Beck et al., 2016).

Based on indicators of Oslo Manuel via the OECD/Eurostat (2005), we use
input ratios for the purpose of measuring the financial innovation, ref lecting
the acquisition of capital goods related to innovation activities. These activities
include, on the one hand, the acquisition of external knowledge and technology
(patents, licenses, software, trademarks…) and, on the other hand, the acqui-
sition of machinery, equipment, and other capital goods. FinInn is the ratio of
the sum of tangible and intangible assets related to innovation to total assets
(OECD, 2005; Beck et al., 2016).

Mediating variable

The stability of the bank is linked to an effective risk management through ap-
plying regulatory conditions (Halim, Mustika, Sari, Anugerah & Mohd-Sanusi,
2017). Risk management is assessed in two different ways: prudential and op-
erational. We use the prudential proxy on the basis of the measure applied on
the second Basel Accords by Basel Committee (El Attar & Atmani, 2015). We
take on capital adequacy ratio (CAR), credit risk-weighted asset ratio (CRW)
and operational risk-weighted asset ratio (ORW) so that we can measure the
bank risk management.

CAR is a measure of the amount of bank’s core capital expressed as a per-
centage of its risk-weighted asset, determining the bank’s capacity to meet li-
abilities and other risks such as credit and operational risks. CRW is the ratio of
credit risk-weighted asset to total risk-weighted asset. ORW is the ratio of op-
erational risk-weighted asset to total risk-weighted asset.

Control variables

Control variables are specific to banks which are: non-performing loans (NPLs)
as the ratio of non-performing loans to total loans, liquidity coverage ratio
(LCR) as liquid assets to liquid liabilities, operational efficiency (OE) as the ra-
tio of operating incomes to operating expenses (Malhotra & Singh, 2009; Arn-
aboldi & Rossignoli, 2015; Piyananda, Chandrasena & Fernando, 2015).

finanCial innovation, risk managEmEnt, and Bank PErformanCE 87

Regression model specifications

The empirical study, as undertaken in our work, is based on using hierarchi-
cal regression models3 for the purpose of testing the research of advanced hy-
potheses. For the assumptions made to be assessed via a procedure achievable
through the construction of four models, it seems necessary to test the exist-
ence of a mediating effect. Baron and Kenny (1986) consider four conditions for
a complete mediating effect to be checked in terms of CAR, CRW, ORW of the Fi-
nInn- ROA, MTB relationship. These conditions are the following:
(1) The independent variable (FinInn) has a significant impact on the bank

performance (ROA, MTB).
(2) The FinInn significantly inf luences the mediator variable (CAR, CRW,

ORW).
(3) When the inf luence of CAR, CRW, ORW on the bank performance is ta-

ken into account, the FinInn will have no significant effect on perfor-
mance.

(4) The direct effect of FinInn on performance should be null, or reduced
by the insertion of the mediator variable in order to deduce its media-
ting impact within the relationship.

Econometrically, we first estimate Model 1 with the aim of testing the di-
rect effect of FinInn on bank performance (ROA, MTB), and validating hypoth-
esis (H1).

3 In this work, the treatment of mediating variables should follow the approach de-
vised by Baron and Kenny (1986). This framework, which aims at testing the mediating
effect, is implemented via a multiple-hierarchical regression. This analysis consists in
assessing the total effect (cumulative) of the explanatory variables on a certain criteri-
on. The method can be performed on the basis of several steps. Firstly, it undertakes to
test the predictor effect (independent variable) firstly on the criterion (dependent vari-
able), and secondly on the mediator using partial and simple regressions. Then, the oth-
er relationship has to be tested (predictor and mediator on the criterion). In this case,
a multiple-hierarchical regression has to be applied. It consists in gradually introduc-
ing certain independent variables into the regression equation: starting with the pre-
dictor and control variables (Step 1), and then the mediating variable (Step 2). Upon
reaching an increase in the adjusted R² after inserting the mediator, it will be possible
to assume the mediator effect on the relationship between the predictor and the crite-
rion, Zouari and Zouari-Hadiji (2014a; 2014b).

Ghazi Zouari, Imen Abdelmalek88

assumptions made to be assessed via a procedure achievable through the construction of

four models, it seems necessary to test the existence of a mediating effect. Baron and

Kenny (1986) consider four conditions for a complete mediating effect to be checked in

terms of CAR, CRW, ORW of the FinInn- ROA, MTB relationship. These conditions are

the following:
(1) The independent variable (FinInn) has a significant impact on the bank performance

(ROA, MTB).

(2) The FinInn significantly influences the mediator variable (CAR, CRW, ORW).

(3) When the influence of CAR, CRW, ORW on the bank performance is taken into

account, the FinInn will have no significant effect on performance.

(4) The direct effect of FinInn on performance should be null, or reduced by the

insertion of the mediator variable in order to deduce its mediating impact within the

relationship.

Econometrically, we first estimate Model 1 with the aim of testing the direct effect of

FinInn on bank performance (ROA, MTB), and validating hypothesis (H1).

𝑃𝑃�� = 𝛼𝛼 𝛼 𝛼𝛼��𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹�� 𝛼 𝛿𝛿�𝑍𝑍�� 𝛼 𝜀𝜀��
Second, we estimate the relationship between FinInn and risk management (CAR,

CRW, ORW) so as to validate hypothesis (H2)

𝑅𝑅𝑅𝑅�� = 𝛼𝛼 𝛼 𝛼𝛼��𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹�� 𝛼 𝛿𝛿�𝑍𝑍�� 𝛼 𝜀𝜀��
Afterwards, we estimate the relationship between risk management (CAR, CRW,

ORW) and bank performance (ROA, MTB) for validating hypothesis (H3)

𝑃𝑃�� = 𝛼𝛼 𝛼 𝛼𝛼��𝑅𝑅𝑅𝑅�� 𝛼 𝛿𝛿�𝑍𝑍�� 𝛼 𝜀𝜀��
Eventually, we examine the indirect relationship between FinInn and bank performance

(ROE, MTB), using the effect of risk management (CAR, CRW, ORW)

𝑃𝑃�� = 𝛼𝛼 𝛼 𝛼𝛼��𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹�� 𝛼 𝛾𝛾��𝑅𝑅𝑅𝑅�� 𝛼 𝛿𝛿�𝑍𝑍�� 𝛼 𝜀𝜀�� ( 𝐻𝐻𝐻𝐻
Pit = Bank performance measured by ROAit and MTBit ratios

FinInnit = Financial Innovation measured by the ratio = sum of tangible and intangible

assetit / innovation to total assetit )

RMit = Risk management measured by CARit ,CRWit and ORWit

Zit = Control variables measured by NPLit,LCRit and OEit

  and , : Parameters to be estimated

 iε : The random error

Second, we estimate the relationship between FinInn and risk management
(CAR, CRW, ORW) so as to validate hypothesis (H2)

assumptions made to be assessed via a procedure achievable through the construction of

four models, it seems necessary to test the existence of a mediating effect. Baron and

Kenny (1986) consider four conditions for a complete mediating effect to be checked in

terms of CAR, CRW, ORW of the FinInn- ROA, MTB relationship. These conditions are

the following:
(1) The independent variable (FinInn) has a significant impact on the bank performance

(ROA, MTB).

(2) The FinInn significantly influences the mediator variable (CAR, CRW, ORW).

(3) When the influence of CAR, CRW, ORW on the bank performance is taken into

account, the FinInn will have no significant effect on performance.

(4) The direct effect of FinInn on performance should be null, or reduced by the

insertion of the mediator variable in order to deduce its mediating impact within the

relationship.

Econometrically, we first estimate Model 1 with the aim of testing the direct effect of

FinInn on bank performance (ROA, MTB), and validating hypothesis (H1).

CRW, ORW) so as to validate hypothesis (H2)

ORW) and bank performance (ROA, MTB) for validating hypothesis (H3)

(ROE, MTB), using the effect of risk management (CAR, CRW, ORW)

FinInnit = Financial Innovation measured by the ratio = sum of tangible and intangible

assetit / innovation to total assetit )

RMit = Risk management measured by CARit ,CRWit and ORWit

Zit = Control variables measured by NPLit,LCRit and OEit

  and , : Parameters to be estimated

 iε : The random error

Afterwards, we estimate the relationship between risk management (CAR,
CRW, ORW) and bank performance (ROA, MTB) for validating hypothesis (H3)

assumptions made to be assessed via a procedure achievable through the construction of

four models, it seems necessary to test the existence of a mediating effect. Baron and

Kenny (1986) consider four conditions for a complete mediating effect to be checked in

terms of CAR, CRW, ORW of the FinInn- ROA, MTB relationship. These conditions are

the following:
(1) The independent variable (FinInn) has a significant impact on the bank performance

(ROA, MTB).

(2) The FinInn significantly influences the mediator variable (CAR, CRW, ORW).

(3) When the influence of CAR, CRW, ORW on the bank performance is taken into

account, the FinInn will have no significant effect on performance.

(4) The direct effect of FinInn on performance should be null, or reduced by the

insertion of the mediator variable in order to deduce its mediating impact within the

relationship.

Econometrically, we first estimate Model 1 with the aim of testing the direct effect of

FinInn on bank performance (ROA, MTB), and validating hypothesis (H1).

CRW, ORW) so as to validate hypothesis (H2)

ORW) and bank performance (ROA, MTB) for validating hypothesis (H3)

(ROE, MTB), using the effect of risk management (CAR, CRW, ORW)

FinInnit = Financial Innovation measured by the ratio = sum of tangible and intangible

assetit / innovation to total assetit )

RMit = Risk management measured by CARit ,CRWit and ORWit

Zit = Control variables measured by NPLit,LCRit and OEit

  and , : Parameters to be estimated

 iε : The random error

Eventually, we examine the indirect relationship between FinInn and bank per-
formance (ROE, MTB), using the effect of risk management (CAR, CRW, ORW)

assumptions made to be assessed via a procedure achievable through the construction of

four models, it seems necessary to test the existence of a mediating effect. Baron and

Kenny (1986) consider four conditions for a complete mediating effect to be checked in

terms of CAR, CRW, ORW of the FinInn- ROA, MTB relationship. These conditions are

the following:
(1) The independent variable (FinInn) has a significant impact on the bank performance

(ROA, MTB).

(2) The FinInn significantly influences the mediator variable (CAR, CRW, ORW).

(3) When the influence of CAR, CRW, ORW on the bank performance is taken into

account, the FinInn will have no significant effect on performance.

(4) The direct effect of FinInn on performance should be null, or reduced by the

insertion of the mediator variable in order to deduce its mediating impact within the

relationship.

Econometrically, we first estimate Model 1 with the aim of testing the direct effect of

FinInn on bank performance (ROA, MTB), and validating hypothesis (H1).

CRW, ORW) so as to validate hypothesis (H2)

ORW) and bank performance (ROA, MTB) for validating hypothesis (H3)

(ROE, MTB), using the effect of risk management (CAR, CRW, ORW)

FinInnit = Financial Innovation measured by the ratio = sum of tangible and intangible

assetit / innovation to total assetit )

RMit = Risk management measured by CARit ,CRWit and ORWit

Zit = Control variables measured by NPLit,LCRit and OEit

  and , : Parameters to be estimated

 iε : The random error

Pit = Bank performance measured by ROAit and MTBit ratios

FinInnit = Financial Innovation measured by the ratio = sum of tangible and in-
tangible assetit / innovation to total assetit )

RMit = Risk management measured by CARit ,CRWit and ORWit
Zit = Control variables measured by NPLit,LCRit and OEit

■ β, γ and α : Parameters to be estimated
■ ε1 : The random error

Analyses

In order to test the hypothetical relationships, our research has followed the
commonly established two-stage procedure. The first stage is the descriptive
statistics and correlation results. Indeed, the normality of the variables is sup-
posed to be checked because the number of observations is greater than 30.

finanCial innovation, risk managEmEnt, and Bank PErformanCE 89

According to the Pearson correlation, as shown in table 1, there are no corre-
lations exceeding 0.5 between our explanatory variables, which means the ab-
sence of multi-collinearity problems between the independent variables.

Table 1. Descriptive statistics and Pearson correlation matrix

Mean (s.d)a 1 2 3 4 5 6 7

ROE 0.12 0.11 - - - - - - -

MTB 1.51 0.97 - - - - - - -

1 FinInn 0.09 0.11 1

2 CAR 0.11 0.03 -0.103 1

3 CRW 0.89 0.11 0.032 -0.0003 1

4 ORW 0.09 0.02 0.1427 0.151 -0.184 1

5 NPL 0.13 0.09 -0.196 -0.221 0.025 -0.463 1

6 LCR 1.08 0.43 -0.0111 0.184 0.020 0.284 -0.119 1

7 OE 3.28 5.17 -0.122 -0.249 -0.018 -0.291 0.291 -0.167 1

N o t e : aStandard deviation. All correlations between variables are significantly smaller than 0.6
(threshold at which we begin to experience serious problems of multi-colinearity). In the Pearson
test and the index of conditioning, we have found that these variables are distinct from each oth-
er and are not significant (correlation thresholds are above 10% ,and the packaging is less than
1000).

S o u r c e : author’s computation, 2019.

In the second stage, the relationship between the variables are analysed by us-
ing Panel data, following the method of Baron and Kenny (1986). To start with,
the test of homogeneity is conducted to validate the existence of individual ef-
fects in models. Referring to table 2, the results show that the p-values associ-
ated with the F-statistic calculated for each model are less than 1% and 10%,
except for models 4 and 10 which do not require specific effects. These two
models will be estimated with pooled OLS regression.

The second specification test determines the nature of the specific effects
to be either of random or fixed type. The Hausman test is the most appropri-
ate one to specify the preferred model. The null hypotheses of models 2 and 11
which are rejected (p-values < 1%) are estimated by the fixed effects. Given
these results, the final model to be estimated will be a heterogeneous panel of
random effects for 1,3,5,6,7,8,9 models.

Ghazi Zouari, Imen Abdelmalek90

Aiming to achieve better results, we need to check the problem of heter-
oscedasticity and autocorrelation of errors. The Breush-Pagan test accepts
the null hypothesis for models 2,3,4,5,9,10 whereas for models 1,6,7,8,11, the
test detects heteroskedasticity problems (p-values are less than 5%).The in-
tra-individual autocorrelation test by Wooldrigde (2002) spots an autocorre-
lation problem for models 2,5,7,8,9,11, and no serial autocorrelation for models
1,3,4,6,10.

Eventually, the Panel of models 4 and 10 are evaluated by pooled OLS re-
gression. The Panel models 3,2,5,9 are homoscedastic and uncorrelated, and
are estimated by fixed effects / random effects with an autocorrelation correc-
tion if necessary for models 2,5 and 9. Models 1 and 6 are characterized by the
presence of a heteroskedasticity problem and lack of autocorrelation. We use
the model of random effects in order to correct the heteroskedasticity prob-
lem, and to correct the standard deviations by the Eicker-White method with
the “robust” option. Models 7,8 and 11 present a problem of heteroscedasticity
and autocorrelation of the residues. In this regression, the method of clustered
standard errors is applied for correcting standard deviations of heteroscedas-
ticity and autocorrelation.

Results and discussions

With the intention of testing the research hypothesis, multiple regression anal-
ysis is performed, and the outcomes are displayed in table 2. In model 1, the
study estimates the effect of financial innovation on the bank performance to
assess H1. The regression analysis in model 1 provides different results across
the samples. The overall quality of the model is significantly acceptable. Howev-
er, the findings show that there is a non-significant relationship between finan-
cial innovation and ROE while there is a positive relationship between financial
innovation and MTB (ẞ= 1.48, p < 0.01). The first hypothesis applying that fi-
nancial innovation positively inf luences the MTB is supported. These findings
confirm the previous studies such as of Mabrouk and Mamoghli (2010), Xiang-
ying, Yueyan and Xianhua (2015) and Aayale (2017). The positive effect thus
found reinforces the assertions of Mustapha (2018) that investment in innova-
tion enhances bank performance. However, there is no impact of financial inno-
vation on ROE. These outcomes are inconsistent with prior results (De Young,
Lang & Nolle, 2007; Mabrouk & Mamoghli, 2010; Chipeta & Muthinja, 2018).

finanCial innovation, risk managEmEnt, and Bank PErformanCE 91

In model 2, the study estimates the effect of financial innovation on risk
management to evaluate H2. The overall quality of model 2 is significantly ac-
ceptable (chi2=43.10, F=2.52, chi2=252.59, p<1%), but the F-statistic is insig-
nificant for the credit risk management variable (F=2.52, p >10%).

In condition 2, the analysis records negative and significant beta coeffi-
cients across CAR and ORW, which is not verified for credit risk management.
As a matter of fact, this result partially validates H2, and confirms the findings
in the previous empirical research stating that innovation inf luences risk man-
agement (Philippas & Siriopoulos, 2009; Mabrouk et al., 2016; Zia et al., 2019).

Our research seeks to assess the mediating impact of risk management on
the relationship between financial innovation and bank performance in H2
and H3. The overall quality of models 3, 4 and 5 is significantly acceptable, but
only for model 3, the relationship between risk management and bank perfor-
mance is insignificant (t=0.701, t=0.45, p>10%). At the detailed level, the im-
pact of risk management on bank performance shows a positive relationship
between financial risk management and bank performance. The findings pro-
vide evidence that the assertion made by Fatemi and Fooladi (2006), Hosna et
al. (2009), Toutou and Xiaodong (2011), Zeze (2012), Trofimov et al. (2018) and
Zgarni and Hassouna (2018) sets out that risk management improves the bank
performance. Similarly, the research of Kiambati (2020) and Sathyamoorthi et
al. (2020) demonstrated that there is a relationship between credit risk and
shareholder market value among the commercial banks.

But there is an insignificant relationship between CAR and bank perfor-
mance, these results are consistent with the findings of Mendoza and Rivera
(2017) who found that capital adequacy has no significant effect of bank prof-
itability while, they findings contradict the empirical studies of Wang (2014)
and Lotto (2019).

Considering the fact that the first and the second conditions are met, the
study performs the mediating effect in models 3, 4 and 5. If a non-significant
coefficient is found for the dependent variable (bank performance) as the me-
diator (risk management) is introduced into the equation, full mediation will
occur. If the coefficient of the dependent variable remains significant in the
presence of the mediator variable in models 3, 4 and 5, partial mediation will
take place. Models 3 and 4 test the mediating effect of credit risk management,
and total risk management shows there is a direct relationship between fi-
nancial innovation and MTB, but a lack of relationship between financial in-
novation and ROE. Model 5 assesses the mediating impact of operational risk

Ghazi Zouari, Imen Abdelmalek92

management, and proves that operational risk management partially mediates
the relationship between financial innovation and stock market performance.
However, there is no effect with the ROE. These results are consistent with the
findings of Guermazi (2017) who finds out that risk management mediates in-
novation, and service quality of banks and insurance.

Our paper has shown and corroborated that the more the bank innovates,
the greater it improves its market value. Moreover, the research in the banking
sector is usually interested in financial risk even though the operational risk is
becoming relevant in an uncertain environment. Our article accounts for the
impact of operational risk management on market performance, which relates
to the credibility of information and the reputation of banks.

Banks in Tunisia should focus more attention on capacity building and spe-
cial training of bank managers whose function relate to financial innovations,
credit and loans to serve as a conduit of giving them sufficient knowledge on
how to deal with innovation issues and mitigate risks faced by these banks in
order to improve their performance.

In addition, banks should do more efforts for contribute to revitalize the
growth of the economy by investing in research and development and manag-
ing risks effectively in order to increase the performance.

Conclusions

Our paper reports the results of an empirical model including financial innova-
tion, risk management and bank performance of 10 Tunisian listed commercial
banks. A theoretical framework has been empirically tested, and the main pur-
pose of our study is to investigate the mediating role of risk management be-
tween financial innovation and bank performance.

Our findings support the four hypotheses specified, and indicate a signifi-
cant implication for the theory and empirical research. The initial hypothesis
confirms a positive and significant relationship between financial innovation
and market performance. On the other hand, the result shows there is a non-sig-
nificant relationship between financial innovation and financial performance,
which contradicts previous empirical research. In hypothesis 2, there is a posi-
tive and significant relationship between Financial innovation, operational risk
management and capital adequacy ratio. We also find a non-significant rela-
tionship between financial innovation and credit risk management. In hypoth-

finanCial innovation, risk managEmEnt, and Bank PErformanCE 93

esis 3, we prove that the relationship is positive and significant between opera-
tional risk management and bank performance as well as between credit risk
management and financial performance. This hypothesis is not supported for
global risk management. In hypothesis 4, we have provided evidence that there
is a partial mediating effect of operational risk management between financial
innovation and bank performance. In addition, we also find out an indirect rela-
tionship between financial innovation and market performance through credit
risk management.

The findings of the study are significant as Tunisian commercial banks will
understand the effectiveness of various risk management strategies (especial-
ly, innovation) and may apply them for minimizing risks incurred and enhance
their market competitiveness.

Like any other research, this study has some limitations, and presents some
new opportunities for future studies. Our findings can be generalized to other
countries. Future studies may seek an alternative dataset such as a survey, can
use an analysis method like a structural equation modeling and might apply
our research in Islamic banks. Eventually, a theoretical model can also be ex-
tended by integrating a different typology of financial innovation such as prod-
uct innovation, process innovation and organizational innovation.

Ghazi Zouari, Imen Abdelmalek94
T

ab
le

2
. R

eg
re

ss
io

n
re

su
lt

V
ar

ia
bl

Co
nd

it
io

n
1

Co
nd

it
io

n
2

Co
nd

it
io

n
3

M
od

el
1

:
di

re
ct

e
ff

ec
t

M
od

el
2

:
M

ed
ia

to
rs

M
od

el
3

C
A

R
m

ed
ia

ti
on

M
od

el
4

C
R

W
m

ed
ia

ti
on

M
od

el
5

O
R

W
m

ed
ia

ti
on

R
O

E
M

TB
CA

R
CR

W
O

R
W

R
O

E
M

TB
R

O
E

M
TB

R
O

E
M

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

IN
N

0.
04

6
(0

.9
6)

1.
48

0*
**

(3
.6

7)
-0

.0
28

**
(-

2.
58

)
0.

04
3

(0
.8

9)
-0

.0
3*

**
(-

3.
35

)
0.

06
(0

.6
7)

0.
94

8*
*

(2
.5

2)
0.

04
7*

(1
.7

0)
1.

79
7*

*
(2

.5
1)

0.
01

5
(0

.2
5)

1.
60

7*
**

(3
.0

C
A

R
-

-
-

0.
70

1
(0

.4
5)

2.
94

4
(1

.1
6)

-
-

CR
W

-
-

-
-0

.1
34

(0
.6

7)
0.

33
4*

**
(2

.6
5)

-
-

O
RW

-
-

-
1.

98
6

(1
.4

7)
8.

96
4*

*
(2

.0
9)

N
PL

-0
.1

31
(-

0.
58

)
1.

69
2

(0
.1

54
)

-0
.0

05
(-

0.
24

)
0.

11
9

(1
.6

9)
-0

.0
52

(-1
.2

9)
-0

.1
02

(-
0.

53
)

0.
2

(0
.6

4)
-0

.5
21

**
*

(-
4.

49
)

0.
36

2
(0

.0
6)

-0
.6

71
(-1

.6
0)

-5
.5

34
**

*
(-

2.
89

)

LC
R

0.
02

9*
*

(2
.1

7)
0.

28
3

(1
.2

2)
0.

00
6

(0
.7

0)
0.

00
6

(0
.3

4)
-0

.0
00

7
(-

0.
29

)
0.

02
3*

(1
.7

2)
0.

36
4*

**
(2

.9
7)

0.
02

6*
**

(3
.2

2)
0.

30
7

(1
.1

4)
0.

00
5

(0
.2

3)
0.

09
8

(0
.6

O
E

-0
.0

02
(-1

.0
7)

0.
03

1
(0

.9
6)

-0
.0

00
5*

(-
01

.7
2)

-0
.0

01
(-

0.
57

)
-0

.0
00

2
(-

0.
80

)
-0

.0
02

(-
0.

57
)

0.
02

(0
.2

3)
-0

.0
03

(-1
.4

5)
0.

10
1*

(1
.9

1)
0.

00
4

(0
.5

5)
0.

02
7

(0
.6

Co
ns

ta
nt

0.
11

3*
*

(2
.3

5)
0.

63
9*

*
(2

.4
7)

0.
10

1*
**

(7
.4

8)
0.

87
1*

**
(2

3.
20

)
0.

10
2*

**
(9

.9
8)

0.
34

(0
.1

6)
0.

53
5

(0
.4

4)
0.

05
1

(0
.2

8)
0.

38
1

(0
.6

2)
0.

00
9

(0
.0

8)
0.

97
3*

(1
.8

Fi
sh

er
5.

09
**

2.
52

*
8.

73
**

finanCial innovation, risk managEmEnt, and Bank PErformanCE 95

V
ar

ia
bl

Co
nd

it
io

n
1

Co
nd

it
io

n
2

Co
nd

it
io

n
3

M
od

el
1

:
di

re
ct

e
ff

ec
t

M
od

el
2

:
M

ed
ia

to
rs

M
od

el
3

C
A

R
m

ed
ia

ti
on

M
od

el
4

C
R

W
m

ed
ia

ti
on

M
od

el
5

O
R

W
m

ed
ia

ti
on

R
O

E
M

TB
CA

R
CR

W
O

R
W

R
O

E
M

TB
R

O
E

M
TB

R
O

E
M

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

Co
ef

.
(P

-v
al

ue
)

W
al

d
Ch

i2
-t

es
t

29
.2

8*
**

43
.1

0*
**

25
2.

59
**

*
43

.2
1*

**
15

.7
9*

**
77

.8
7*

**
16

7.
63

**
*

45
.8

6*
**

H
om

og
en

ei
ty

st
1.

94
*

7.
23

**
*

10
.4

5*
**

0.
94

16
.1

3*
**

1.
89

*
6.

91
**

*
1.

74
*

6.
00

**
*

1.
39

4.
57

**
*

H
au

sm
an

t
es

t
2.

37
23

.4
4*

**
3.

86
4.

80
1.

56
6.

2.
56

7.
54

14
.3

9*
*

M
od

RE
FE

RE
Po

ol
ed

RE
RE

RE
Po

ol
ed

H
et

er
os

ce
da

st
i-

ci
ty

t
es

t
3.

07
*

2.
34

1.
40

6.
36

**
0.

22
43

.1
3*

**
26

.7
2*

**
4.

86
**

1.
27

29
.7

1*
**

4.
84

W
oo

lr
id

ge
T

es
t

0.
60

24
.0

9*
**

2.
29

29
.8

82
**

*
8.

34
**

0.
73

40
.7

3*
**

12
.9

0*
**

17
.9

6*
**

0.
06

4
17

.3
5*

R-
sq

ua
re

d
0.

07
62

0.
13

07
0.

11
34

0.
00

49
0.

22
59

0.
09

18
0.

15
88

0.
17

24
0.

23
23

0.
24

05
0.

33
39

A
dj

R
-s

qu
ar

ed
0.

02
34

0.
08

10
0.

06
27

-0
.0

63
7

0.
17

25
0.

02
59

0.
09

78
0.

09
98

0.
16

50
0.

17
39

0.
27

R
o

b
u

s
t

s
ta

n
d

a
r

d
e

r
r

o
r

s
i

n
p

a
r

e
n

th
e

s
e

s
: *

**
p<

0.
01

, *
*p

<
0.

05
, *

p<
0.

1.
F

E:
fi

xe
d

ef
fe

ct
; R

E:
r

an
do

m
e

ff
ec

t;
P

oo
le

d
O

LS
: o

rd
in

ar
y

le
as

t
sq

ua
re

. N
um

be
r

of
o

bs
er

va
ti

on
s

of
f a

ll
m

od
el

s
=

63
.

S
o

u
r

c
e

: a
ut

ho
r’

s
co

m
pu

ta
ti

on
, 2

01
9.

T
ab

le
2

. R
eg

re
ss

io
n…

Ghazi Zouari, Imen Abdelmalek96

References
Aayale, J. (2017). The impact of financial innovations on the financial industry’s per-

formance: a study of BRICS and G6 nations. International Journal of Economics, Com-
merce and Management, 7(11), 138-152.

Arnaboldi, F., & Rossignoli, B. (2015). Financial innovation in banking. In E. Beccal-
li, F. Poli (Eds.). Bank Risk, Governance and Regulation. Palgrave Macmillan Studies
in Banking and Financial Institutions. London: Palgrave Macmillan. https://dx.doi.
org/10.1057/9781137530943_5.

Arthur, K.N.A (2017). The Emerging financial innovation and its governance: a histori-
cal literature review. Journal of Innovation Management, 5(4), 48-73. https://dx.doi.
org/10.24840/2183-0606_005.004_0005.

Asensio-López, D., Cabeza-García, L., & González-Álvarez, N. (2019). Corporate govern-
ance and innovation: a theoretical review. European Journal of Management and Busi-
ness Economics, 28(3), 266-284. https://dx.doi.org/10.1108/EJMBE-05-2018-0056.

Baiquan, S., & Kebao, W. (2010). Managing the Risks of Financial Innovations in Bank-
ing. In 2010 International Conference on Management and Service Science. Wuhan:
Institute of Electrical and Electronics Engineers. https://dx.doi.org/10.1109/IC-
MSS.2010.5577186.

Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in
social psychological research: Conceptual, strategic and statistical considera-
tions. Journal of Personality and Social Psychology, 51(6), 1173-1182. https://dx.doi.
org/10.1037//0022-3514.51.6.1173.

Battaglia, F., Fiordelisi, F., & Ricci, O. (2017). Enterprise Risk Management and Bank
Performance: Evidence from Eastern Europe during the Financial Crisis. In S. Bou-
baker (Ed.). Risk Management in Emerging Markets, Issues, Framework, and Modeling.
Bingley: Emerald Group Publishing Limited.

Beck, T., Chen, T., Lin, C., & Song, F.M. (2016). Financial innovation: The bright and the
dark sides. Journal of Banking and Finance, 72, 25-51. https://dx.doi.org/10.1016/j.
jbankfin.2016.06.012.

Chen, T.H., & Peng, J.L. (2019). Statistical and bibliometric analysis of financial in-
novation. Library Hi Tech, Vol. ahead-of-print No. ahead-of-print. https://dx.doi.
org/10.1108/LHT-09-2018-0140.

Chipeta, C., & Muthinja, M. (2018). Financial innovations and bank performance in Ken-
ya: Evidence from branchless banking models. South African Journal of Economic and
Management Sciences, 21(1), 1-11. http://dx.doi.org/10.4102/sajems.v21i1.1681.

Ciciretti, R., Hasan, I., & Zazzara, C. (2009). Do Internet Activities Add Value? Evidence
from the Traditional Banks. Journal of Financial Services Research, 35(1), 81-98.
https://dx.doi.org/10.1007/s10693-008-0039-2.

COSO - Executive Summary (2004). Enterprise Risk Management — Integrated Frame-
work, New York, 3, 1-16. https://www.coso.org/Documents/COSO-ERM-Executive-
Summary.pdf.

finanCial innovation, risk managEmEnt, and Bank PErformanCE 97

De Young, R., Lang, W.W., & Nolle, D.L. (2007). How the Internet affects output and per-
formance at community banks. Journal of Banking and Finance, 31(4), 1033-1060.
https://dx.doi.org/10.1016/j.jbankfin.2006.10.003.

El Attar, A., & Atmani, M. (2015). La gestion des risques des produits financiers islam-
iques: essai de modélisation. Dossiers de Recherches en Economie et Gestion, 2(4),
2-29. http://dx.doi.org/10.12816/0030990.

European Central Bank (2010). Beyond roe – how to measure bank performance, Eu-
ropean Central Bank, Appendix to the report on EU banking structures, https://
www.ecb.europa.eu/pub/pdf/other/beyondroehowtomeasurebankperformance
201009en.pdf.

Fatemi, A., & Fooladi, I. (2006). Credit Risk Management: A Survey of Practices. Manage-
rial Finance, 32(3), 227-233. https://dx.doi.org/10.1108/03074350610646735.

Frame, W.S., & White, L.J. (2002). Empirical Studies of Financial Innovation: Lots of talk,
Little Action. FRB of Atlanta Working Paper, 2002-12. http://dx.doi.org/10.2139/
ssrn.325800.

González, L.O., Rodríguez Gil, L.I., Cunill, O.M., & Lindahl, J.M. (2016). The effect of finan-
cial innovation on European banks’ risk. Journal of Business Research, 69(11), 4781-
-4786. https://dx.doi.org/10.1016/j.jbusres.2016.04.030.

Guermazi, H.H. (2017). Effet médiateur de la gestion de risque et du style de direc-
tion sur la relation Innovation – Qualité du service: étude des assurances et des
banques en Tunisie. Revue Questions de Management, 18(3), 113-124. https://dx.doi.
org/10.3917/qdm.173.0113.

Halim, E.H., Mustika, G., Sari, R.N., Anugerah, R., & Mohd-Sanusi, Z. (2017). Corporate
governance practices and financial performance: The mediating effect of risk man-
agement committee at manufacturing firms. Journal of International Studies, 10(4),
272-289. http://dx.doi.org/10.14254/2071-8330.2017/10-4/21.

Heffernan, S., Fu, X., & Fu, M. (2008). Financial Innovation in the UK. Faculty of Finance
Working Paper, 4. http://dx.doi.org/10.2139/ssrn.1266125.

Hosna, A., Manzura, B., & Juanjuan, S. (2009). Credit Risk Management and Profitability in
Commercial Banks in Sweden. Gothenburg: University of Gothenburg.

Hu, D.-L., Li J.-Q., & Deng, Y.H. (2009). Notice of Retraction: Insighting the Risk Manage-
ment of Financial Innovation from the Viewpoint of Subprime Mortgage Crisis. In In-
ternational Conference on Management and Service Science Wuhan: Institute of Elec-
trical and Electronics Engineers. http://dx.doi.org/10.1109/ICMSS.2009.5301359.

Hu, T., & Xie, C. (2016). Competition, Innovation, Risk-Taking, and Profitability in
the Chinese Banking Sector: An Empirical Analysis Based on Structural Equa-
tion Modeling. Discrete Dynamics in Nature and Society. 6, 1-10. http://dx.doi.
org/10.1155/2016/3695379.

Jordan, S., Jorgensen, L., & Mitterhoferh, H. (2013). Performing risk and the project: Risk
maps as mediating instruments. Management Accounting Research, 24(2), 156-174.
https://dx.doi.org/10.1016/j.mar.2013.04.009.

Khraisha, T., & Arthur, K. (2018). Can we have a general theory of financial innova-
tion processes? A conceptual review. Financial Innovation, 4(4), 1-27. https://dx.doi.
org/10.1186/s40854-018-0088-y.

Ghazi Zouari, Imen Abdelmalek98

Kiambati, K. (2020). Inf luence of credit risk on shareholder market value of commer-
cial banks listed in Nairobi Securities Exchange. International Journal of Research in
Business and Social Science (2147-4478), 9(2), 107-117. http://dx.doi.org/10.20525/
ijrbs.v9i2.613.

Lerner, J., & Tufano, P. (2011). The Consequences of Financial Innovation: A Counterfac-
tual Research Agenda. Annual Review of Financial Economics, 3(1), 41-85. https://
dx.doi.org/10.1146/annurev.financial.050808.114326.

Llewellyn, D.T. (2009). Financial Innovation and the Economics of Banking and the Fi-
nancial System. In L. Anderloni, D.T. Llewellyn, R.H. Schmidt (Eds.). Financial Innova-
tion in Retail and Corporate Banking. Cheltenham: Edward Elgar Publishing. https://
doi.org/10.4337/9781848447189.00007.

Lotto, J. (2019). Evaluation of factors inf luencing bank operating efficiency in Tanzani-
an banking sector. Cogent Economics & Finance, 7(1). https://dx.doi.org/10.1080/23
322039.2019.1664192.

Mabrouk, A., & Mamoghli, C. (2010). Dynamic of financial innovation and performance
of banking firms: context of an emerging banking industry. International research
Journal of Finance and Economics, 51, 17-36.

Mabrouk, A., Dhouibi, R., & Rouetbi, E. (2016). Interaction effect between product and
process innovation: the case of Tunisian banks. Banks and Bank Systems, 11(1),
60-70. http://dx.doi.org/10.21511/bbs.11(1).2016.07.

Malhotra, P., & Singh, B. (2009). The Impact of Internet Banking on Bank Performance
and Risk: The Indian Experience. Eurasian Journal of Business and Economics, 2(4),
43-62.

McShane, M.K., Nair, A., & Rustambekov, E. (2011). Does Enterprise Risk Management
Increase Firm Value. Journal of Accounting, Auditing & Finance, 26(4), 641-658.
https://dx.doi.org/10.1177/0148558X11409160.

Mendoza, R., & Rivera, J.P.R. (2017). The effect of credit risk and capital adequacy on
the profitability of rural banks in the Philippines. Scientific Annals of Economics and
Business, 64(1), 83-96. https://dx.doi.org/ 10.1515/saeb-2017-0006.

Merton, R.C. (1992). Financial Innovation and Economic Performance. Journal of Ap-
plied Corporate Finance, 4(4), 12-22. https://dx.doi.org/10.1111/j.1745-6622.1992.
tb00214.x .

Minetti, R., Murro, P., & Paiella, M. (2015). Ownership Structure, Governance and Inno-
vation. European Economic Review, 80(C), 165-193. https://dx.doi.org/10.1016/j.eu-
roecorev.2015.09.007.

Mustapha, S.A. (2018). E-Payment technology effect on bank performance in emerging
economies-evidence from Nigeria. Journal of Open Innovation: Technology, Market,
and Complexity, 4(4), 43. https://dx.doi.org/10.3390/joitmc4040043.

Nejad, M.G. (2016). Research on Financial Services Innovations: A Quantitative Review
and Future Research Directions. International Journal of Bank Marketing, 34(7),
1042-1068. http://dx.doi.org/10.1108/IJBM-08-2015-0129.

OECD (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data.
3rd Edition. Paris: OECD Publishing. https://www.oecd-ilibrary.org/docserver/9789

finanCial innovation, risk managEmEnt, and Bank PErformanCE 99

264013100-en.pdf ?expires=1587684547&id=id&accname=guest&checksum=53DB
6ACD0B7BAECC6A8C10FB54DEFC19.

Olamide, O., Uwalomwa, U., & Ranti, U.O (2015). The Effect of Risk Management on
Bank’s Financial Performance in Nigeria. Journal of Accounting and Auditing: Re-
search & Practice, 2015, 1-7. https://dx.doi.org/10.5171/2015.239854.

Paroush, J., & Schreiber, B.-Z. (2019). Profitability, capital, and risk in US commercial and
savings banks: Re-examination of estimation methods. The Quarterly Review of Eco-
nomics and Finance, 74(C), 148-162. https://dx.doi.org/10.1016/j.qref.2018.12.007.

Philippas, D.T., & Siriopoulos, C. (2009). Inf luence of financial innovation to the val-
idation of operational risk. Managerial Finance, 35(11), 940-947. https://dx.doi.
org/10.2139/ssrn.1851633.

Piyananda, S.D.P, Chandrasena, S.M., & Fernando, J.M (2015). Bank-Specific Determi-
nants of Risk Management Efficiency: Evidence from Listed Commercial Banks in Sri
Lanka. In 6th International Conference on Business and Information (ICBI). Sri Lanka:
Faculty of Commerce and Management Studies, University of Kelaniya, 78-89. htt-
ps://pdfs.semanticscholar.org/ec76/285d5c88496b014dfe466c614d493bcdb866.
pdf ?_ga=2.266247759.1732821706.1587683938-52817079.1587683938.

Sathyamoorthi, C.R., Mogotsinyana, M., Mphoeng, M., & Mashoko, D. (2020). Impact of
Financial Risk Management Practices on Financial Performance: Evidence from
Commercial Banks in Botswana. Applied Finance and Accounting, 6(1), 25-39. htt-
ps://dx.doi.org/10.11114/afa.v6i1.4650.

Sokołowska, E. (2014). Alternative Investments in Wealth Management. Switzerland:
Springer. http://dx.doi.org/10.1007/978-3-319-08075-8.

Stone, A., Shipp, S., & Leader, P. (2008). Measuring Innovation and Intangibles: A Busi-
ness Perspective. Washington: Science & Technology Policy Institute. IDA Document
D-3704. https://www.ida.org/-/media/feature/publications/m/me/measuring-in-
novation-and-intangibles-a-business-perspective/ida-d-3704.ashx.

Stulz, R.M. (2016). Risk Management, Governance, Culture, and Risk Taking in Banks.
Economic Policy Review, 22(1), 43-60.

Stulz, R.M., & Smith, C.W. (1985). The Determinants of Firm’s Hedging Policies. Journal
of Financial and Quantitative Analysis, 20(4), 391-406. https://dx.doi.org/10.2307/
2330757.

Toutou, J., & Xiaodong X. (2011). The Relationship between Liquidity Risk and Perfor-
mance, an Empirical Study of Banks in Europe 2005-2010. Umeå: Umea School of Busi-
ness. https://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Aumu%3Adiva-54456.

Trofimov, I.D., Aris, N.M., & Ying Ying, J.K. (2018). Determinants of commercial banks’
profitability in Malaysia. Management and Economics Review, 3(1), 85-101. http://
dx.doi.org/10.24818/mer/2018.06-07.

Tufano, P. (2003). Financial Innovation. In G.M. Constantinides, M. Harris, R.M. Stulz
(Eds.). Handbook of the Economics of Finance. Amsterdam: Elsevier. https://dx.doi.
org/10.1016/S1574-0102(03)01010-0.

Tunay, K.B., Tunay, N., & Akhisar, I. (2015). The Effects of Innovations on Bank Perfor-
mance: The Case of Electronic Banking Services. Procedia - Social and Behavioral Sci-
ences, 195, 369-375. https://dx.doi.org/10.1016/j.sbspro.2015.06.336.

Ghazi Zouari, Imen Abdelmalek100

Usman, M. (2016). Bank Performance, Risk and Economic Growth: Role of Finan-
cial Innovation. Journal on Innovation and Sustainability, 7(3), 3-16. https://dx.doi.
org/10.24212/2179-3565.2016v7i3p3-22.

Wang, M.S. (2014). Financial Innovation, Basel Accord III, and Bank Value. Emerging
Markets Finance and Trade, 50(2), 23-42. https://dx.doi.org/10.2753/REE1540-
496X5002S202.

Wanjohi, S.M., Wanjohi, J.G., & Ndambiri, J.M. (2017). The Effect of Financial Risk Man-
agement on the Financial Performance of Commercial Banks in Kenya. International
Journal of Finance and Banking Research, 3(5), 70-81. http://dx.doi.org/10.11648/j.
ijf br.20170305.

Williamson, O.E. (1985). The Economic Institutions of Capitalism: Firms, Markets and Ver-
tical Contracting. New York: Free Press. Traduction française, Les institutions de
l’économie, 1994, InetrEditions.

Xiangying, M., Yueyan, Z., & Xianhua, W.E.I. (2015). Market value of Innovation: An em-
pirical analysis on China’ s stock market. Procedia Computer Science, 55, 1275-1284.
https://dx.doi.org/10.1016/j.procs.2015.07.138.

Zeze, O.S. (2012). The Relationship between Credit Risk Management Practices and Fi-
nancial Performance of Commercial Banks in Liberia. MBA Unpublished Research
Project. Nairobi: University of Nairobi. http://erepository.uonbi.ac.ke/han-
dle/11295/13262.

Zgarni, A., & Hassouna, F. (2018). Regulation and Bank Performance. The Journal of
American Business Review, 6(2), 15-22.

Zia, U.-R., Muhammad, N., Sarwar, B., & Asif Raz, M. (2019). Impact of risk management
strategies on the credit risk faced by commercial banks of Balochistan. Financial In-
novation, 5(44), 1-13. https://dx.doi.org/10.1186/s40854-019-0159-8.

Zouari, G., & Zouari-Hadiji, R. (2014a). Directors’ Board, R&D Investment and the Firm’s
Performance: Evidence from the French Case. Corporate Board: Role, Duties & Com-
position, 10(2), 85-101. http://dx.doi.org/10.22495/cbv10i2art7.

Zouari, G., & Zouari-Hadiji, R. (2014b). The Indirect Impact of the Board of Directors’
Composition on the Firm’s Performance: An International Comparison. Asian Jour-
nal of Empirical Research, 4(10), 468-487.