




































Indian Journal of Finance and Banking 

 Vol. 5, No. 2; 2021 

                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

 

44 

IMPACT OF INFORMATION TECHNOLOGIES’ 

INVESTMENTS ON THE PROFITABILITY OF TUNISIAN 

BANKS: PANEL DATA ANALYSIS 

 

 

Syrine Ben Romdhane 

Assistant Professor 

High Institute of Management of Tunis 

University of Tunis, Tunisia 

E-mail: syrine_br@yahoo.fr 

 

 

ABSTRACT 

This study examines the relationship between Information Technology investment and the 

profitability of Tunisian banks, via static and dynamic panel regression models. Our study 

focused on 15 Tunisian banks for 19 years (2001-2019). To assess the profitability of these 

banks, three measures were used: two traditional accounting ratios and net interest margin. Our 

research has shown the importance of the role played by IT in Tunisian banks since IT 

investments improve their profitability. This finding contradicts the “Productivity Paradox” that 

high IT investments are not associated with better performance. Indeed, Tunisian banks are 

acting on their size to boost their performance, and the more the banks take the risk by granting 

more loans, the more profitable they are by increasing their Return on Assets. Finally, public 

banks are more profitable than private banks when considering their net interest margin. 

 

Keywords: Information Technology, Profitability, Banks, Statistic Panel, Dynamic Panel. 

 

JEL Classification Codes: B21, C58, G21, G32, O32. 

 

INTRODUCTION 

Today, in banks, different Information Technologies (IT) has become the key to the financial 

engineering process for organizations wishing to survive and continue to thrive in this rapidly 

changing financial environment. The advent of the Internet has revolutionized the world of 

communication, making it possible to optimize the strategy of globalization advocated by the 

contemporary vision of the business world. This technological revolution has led to the 

decompartmentalization of financial markets and necessary deregulation, which has profoundly 

transformed market structures and forms of competition (Hoque et al., 2020). Financial 

institutions have not escaped this upheaval and, under the pressure of new technologies and 

customer expectations, have been forced to change their structure and modify their strategy as 

well as the conditions of performance. IT is an essential tool that banks must manage and master 

to ensure their competitiveness. This gives them a privileged place in the banking production 

process and raises permanent questions about the relevance of their strengthening and the 

methods of their optimization. A new conception of banking performance then emerges and an 

appropriate performance evaluation system must be implemented. 

mailto:syrine_br@yahoo.fr


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The massive use of IT by banks has created a need to assess its impact on bank 

profitability. Profitability assessment is a ubiquitous activity proving even more a necessity with 

any technological change operating within the company (Teru et al., 2017). However, finding 

suitable analytical tools for these technologies is proving difficult. This has resulted in a lack of 

empirical validation on the impact of IT on bank profitability. 

In this research work, we will attempt to study the impact of IT on the profitability of 15 

Tunisian banks for 19 years (from 2001 to 2019). To this end, three parts will be presented: the 

first will introduce a review of both theoretical and empirical literature. The second will present 

the hypotheses and the methodology of the research. Finally, the results of the regressions 

estimated on a static and dynamic panel will be the subject of the third part. 

 

LITERATURE REVIEW 

From the beginning of the 1990s, the various waves of technological innovation have aroused the 

interest of many economists. However, most of these different studies have focused on the 

relationship between investments in IT and the increased productivity of firms. Their main 

hypothesis is as follows: investments in IT provide improvements in productivity, hence 

management efficiency (Landauer, 1995). With the development of IT, the objectives of firms 

continued to vary between two poles: improving efficiency and developing market power 

(Chowdhary, 2017). In the following, we will present a literature review on the effects of IT 

investments on productivity, then on the evolution of bank profitability. 

 

IT Investments and Productivity 
“We can see computers everywhere except in productivity statistics” (Solow, 1987: 36). This 

“Productivity Paradox”, formulated fifteen years ago by Robert Solow during his speech to 

receive the Nobel Prize for economics in Stockholm in 1987, has given rise to countless applied 

studies in the United States as in Europe and has identified the most diverse explanations. What 

has led Solow to say this famous quote is that numerous studies have shown that investing in IT 

has little or no impact on productivity. In other words, the massive IT spending over the past two 

decades did not seem to have any effect on the productivity of its users, either at the micro-level 

or at the macro level.  

Berndt in 1991 was the first theorist to attempt to measure the productivity from IT 

investments. He relied on the assumption that investments contribute positively to the output 

measured by the gross marginal profit. His work was followed by that of Loveman (1994) and 

Morrison and Berndt (1990) who asserted the absence of a link between IT investments and 

improved productivity using a production function of the Cobb-Douglas form.  

Studies recently carried out by several authors have highlighted several factors that have 

contributed to the “Productivity Paradox”. First, and according to Triplett (1997), some IT 

spillovers, especially in the service sector, have not been captured in productivity statistics. 

Second, the implications of using IT could take a considerable amount of time to materialize. 

Finally, and to measure the impact of IT on the performance of firms, several earlier studies 

relied on relatively small samples of firms. This could lead to less robust and statistically less 

significant results. As a result, many studies conducted on this question notably those of 

Brynjolfsson and Hitt (1996), Oliner and Sichel (2000) have qualified Solow's assertion. 

Brynjolfsson (2001) has shown, from the estimation of a Cobb-Douglas production function of a 

sample of 367 firms over the period 1987-1991, that IT investments give firms higher 

productivity than other types of investment.  



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Several explanations have been put forward to explain the poor efficiency of IT in 

improving the performance of banks. Firstly, some researchers have identified errors in 

performance measurement with traditional tools proving to be ineffective in determining 

precisely the costs and performance associated with computerization. The problems encountered 

are at the level of inputs than outputs (Thenet & Guillouzo, 2002). Also, Entorf et al. (1999) add 

that the impact perimeter of an IT investment is difficult to define especially in the banking 

sector with the organization of network branches and the establishment of EDI (electronic data 

exchange) links with client companies. Secondly, the existence of problems in defining general 

concepts such as IT and bank performance is therefore in measuring the impact of the former on 

the latter. The notion of IT is indeed difficult to define: it is not limited to materials and 

equipment, but also includes intangible investments. On the other hand, some studies on the 

“Productivity Paradox” have found a positive relationship between investment in IT and firm 

performance. Industry-level research has yielded several results. This is partly because IT is 

indirectly linked to the performance of the firm (Chen & Zhu, 2004). Indeed, according to these 

authors, the link between IT investment and firm performance is indirect because of the 

mediating and moderating variables.  

While most of the work on the effects of IT investments has focused on evaluating 

productivity, very few economists have tried to study the effects of these investments on the 

evolution of firm profitability. 

 

IT Investments and Profitability 

The profit function offers the advantage of measuring performance through output but also input. 

It is made up of the "income" variable resulting from specialization or diversification and the 

"costs" one resulting from the combination of a certain number of inputs. Controlling a 

company's performance requires mastering managerial practices, but measuring them is a real 

challenge. Also, manufacturing performance appears to be much easier to measure than that of 

services (Akber, 2019). Unlike the traditional economy which often associates the production 

and consumption of commodities with the quantities of output which were considered to be 

performance indicators, the modern economy is characterized by a diversity of products and 

services (Singh & Singh Brar, 2016). In this environment, the traditional tools of productivity are 

no longer appropriate. 

In the case of the banking industry or financial institutions defining performance reverts 

to measure certain indicators such as ROA (Return on Assets) which is an instrument used by 

many authors such as Barua et al. (1991). They found a positive correlation between investment 

in IT and bank performance as measured by ROA and ROE (Return on Equity) which assesses 

the efficiency of firms by the use of their financial capital. This tool has been used in many 

studies. While Alpar and Kim (1990) found that by measuring the impact of IT investments on 

the performance of manufacturing firms reasoning on the value generated by IT can mislead, 

Prasad and Harker (1997) and Brynjolfsson and Hitt (1996) have shown the existence of a 

negative correlation between IT investments and firms' ROE. However, Bakos (1993) showed 

the ambiguity of the relationship between these two variables. In addition to the ROA and ROE, 

some authors have considered the Total Shareholder Return which makes it possible to calculate 

the value created by the firm for the shareholders. This tool was introduced by Dos Santos and 

al. in 1993. These authors have shown the existence of a positive correlation between investment 

in IT and the performance of firms measured by the Total Shareholder Return. Finally, Gayathri 

and Suvitha (2018) tried to measure the impact of IT on the profitability of 21 Indian banks (12 



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47 

public and 9 private banks) over the period 2011-2015. Their results show that IT investments 

have a positive impact on the profitability and performance of banks more than marketing 

expenses, knowing that profitability was measured by profit after tax (PAT). They concluded 

that Indian banks should promote technology in their operations. 

To sum up, we can say that the results of studies on the impact of IT on the profitability 

of banking firms are often contradictory. The various studies on the effects of IT investments on 

the profitability of user firms often show that these investments have no impact on the 

profitability of banking firms. However, several authors claim the opposite, that is, the existence 

of a positive association between these two variables.  

 

METHOD 

For this research, we considered a database made up of variables whose choice was guided by 

recent studies on bank performance.  

 

Research Hypothesis 

The literature review exposed above enabled us to identify a set of variables supposed to explain 

variations in performance levels among banks. We have selected those which seem to better 

characterize the Tunisian banking system and whose data are available throughout the study 

period (2001-2019). Each of these variables is translated by a separate assumption. 

Hypothesis 1: Effect of information technology 

Works studying the effects of IT investments on the profitability of the user companies 

often show that these investments have no impact on the profitability of banking firms. Most of 

these studies have concluded that there is no link between these two variables, notably the work 

of Licht and Moch (1999), Gayathri and Suvitha (2018). In our study, a positive sign of this 

variable is expected since IT has the potential to reduce operating costs. We then state our first 

hypothesis: 

 

H1: Investment in IT has a positive impact on bank profitability. 

 

Hypothesis 2: Effect of the “intermediation” variable 

 

The “intermediation” variable will be measured by the “interest margin/GNP” ratio. A low ratio 

could lead to increased profitability of banks to the extent that they benefit from economies of 

scale. In this context, Ben Naceur (2003) adds that the lower this ratio, the higher the interest and 

profit margins. We then state our second hypothesis: 

 

H 2: The “intermediation” variable has a positive effect on profitability. 

 

Hypothesis 3: Effect of size 

Cook and al. (2000) emphasized that large banks, generally publicly owned, operate on a 

suboptimal scale and tend to grant loans regardless of their profitability to promote primarily 

political priorities. They thus find themselves with a large volume of irrecoverable credits. We 

can therefore expect a negative effect of size on banking performance. 

 

H3: The size variable has a negative effect on bank profitability. 

 



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Hypothesis 4: Effect of credit risk 

According to Yao (2005), the issue of banking risk is one of the recurring actual themes. 

According to Kupper (1998), financial institutions face three main types of risk: credit risk, 

market risk, and operational risk. According to the Basel Committee (2007), the most important 

banking risk is credit risk. The latter is generally measured by the weight of provisions in the 

result of gross receivables. Indeed, the rise in risks can be explained by the allocations to 

provisions made by banks. This increase in provisions affects banks' results. We thus conclude 

the higher this risk, the greater the probability of having bad debts, and therefore lower bank 

profitability. 

 

H4: The risk variable has a negative effect on bank profitability. 

 

Hypothesis 5: Effect of the staff supervision rate 

According to Zaghla and Boujelbene (2008), the more the bank employs high-quality staff the 

more, it manages to control its use of inputs and therefore maximizes its output level. This idea is 

retrieved from the work carried out on the banking industry by Chaffai (1997) who have shown 

that productivity gains expected from an improvement in managerial efficiency are more 

significant than those achieved by size effect. Therefore, the "staff supervision rate" ratio 

positively influences the profitability of banks since it leads to an improvement in agents' 

productivity. 

 

H5: The variable “staff supervision rate” has a positive effect on profitability. 

 

Hypothesis 6: Bank public ownership versus private ownership  

Several studies have shown that a bank's capital ownership can be an important variable in 

explaining bank profitability. According to Bourke (1989), there is a negative relationship 

between the public ownership of a bank and its profitability. This is explained by the fact that the 

objective of public banks is not always profit maximization but rather the financing of strategic 

sectors with a relatively high-level risk. However, Molyneux and Thornton (1992) found that 

there is a positive relationship between bank public ownership and return on equity. State-owned 

banks generate a higher return on equity than their private sector counterparts as the government 

implicitly covers the transactions carried out by the latter by the fact that public banks generally 

maintain a lower capital ratio. We then expose our last hypothesis: 

 

H6: The variable “bank ownership” has a negative effect on profitability. 

 

Sample Presentation 

To determine the impact of IT investment on bank profitability, we will use a sample of 15 

Tunisian banks. The database that we have built for this analysis depends on the availability of 

data in Tunisia. However, the only publicly available individual banking data are those published 

in banks' activity reports and by databases of the Tunisian Professional Association of Banks and 

Financial Institutions (TPABFI), the latter themselves taken from balance sheets and accounts of 

results published by banks. The data are collected over 19 years from 2001 to 2019, that’s to say 

285 observations. 

 

 



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Definition of Variables 

In the model that we will adopt, profitability will be regressed on the use of IT (variable of 

interest) and other appropriate variables (control variables). Table (1) defines the different 

variables retained in our study and specifies the expected signs: 

 

Table 1.  Definition of variables 

 

Variables Definition Sources Expected 

sign 

The dependent variable 

Profitability (P)  

The ROA: Net income/Total assets. It measures the bank's 

ability to convert assets into net income and is therefore 

interested in the overall value of the bank, in other words, 

economic profitability. 

The ROE: Net income/Equity. This ratio makes it possible to 

assess the performance from the point of view of the 

shareholders. 

The NIM: (Sum of interest income - Sum of interest expense) 

/ Total assets. 

 

TPABFI and 

ARB 

 

 

The independent variables 

Variable of interest: 

Investments in IT (IT) 

 

Tangible (material), intangible (software), training, and 

maintenance investments related to the bank's equity 

 

Questionnaire*  

+ 

Control variables: 

 

Intermediation (INT) 

 Size (SIZE) 

Credit risk (CRISQ) 

 

Staff supervision rate 

(SSR) 

Bank Ownership 

(BOWN) 

 

 

 Interest margin/GNP 

 Natural logarithm of total assets (in DT) 

 Litigation rate calculated by overdue debts ratio to total 

credits 

 Share of senior executives in relation to the total workforce. 

 Dummy variable (mute) which takes the value 1 at year t if the 

bank is public, 0 if the bank is private. 

 

 

 

TPABFI 

TPABFI 

CBT 

 

TPABFI 

 

 

+ 

- 

- 

 

+ 

 

- 

 

ARB: Annual reports of banking activities. TPABFI: Tunisian Professional Association of Banks and Financial 

Institutions. CBT: Central Bank of Tunisia. 

* To collect data relating to IT investments from Tunisian banks, we opted for a questionnaire to be sent to 

Management Control Directors or Information System Directors of these banks. This questionnaire aims to find out 

the amounts invested in IT during the period 2001-2019. 

 

Econometric Approach and Model Specification 

We will follow a standard form, adopted by Beccalli (2007), to estimate the relationship between 

IT investment and profitability measurement. Therefore, we decline the IT investment and 

control variables against the measure of profitability. The model we are going to estimate is as 

follows: 

 



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Pit = αi + β1it ITit + β2it SIZEit + β3it INTit + β4it SSRit + β5it CRISQit + β6it BOWNit + εit               (1) 

 

Where Pit measures the profitability of bank "i" at date "t" either through the ROA and 

ROE ratios or through the net interest margin (NIM), ITit is the variable "IT investments". Note 

that in the literature, several ratios have been used to measure IT investments. These ratios refer 

to different size measures such as the number of staff, equity, total cost, and sales. Based on the 

convention established in the literature using IT ratios to test the relationship between IT 

investments and financial performance, in this study we use the IT/Equity ratio (share of IT in 

banks' equity). The current choice of the denominator for IT measurement will not significantly 

affect the results (Beccalli, 2007). SIZEit is the "size" variable measured by the logarithm of the 

active total, INTit is the intermediation variable measured by the ratio of interest margin to GNP, 

CRISQit is the “credit risk” variable calculated by the ratio of overdue debts to total loans, SSRit 

is the "supervision rate" variable measured by the share of senior managers in relation to the total 

workforce, BOWNit is a dummy variable = 1 if a bank "i" at period "t" is public and = 0 if a bank 

"i" at period "t" is private, εit: error term. The estimation of our model will be done using two 

approaches: the first is static and the second is dynamic. 

Model (1) is first estimated using the assumption of uniformity of behavior over time and 

among banks. We estimate the model using the Ordinary Least Squares (OLS) method by 

assuming that the error is the same and follows the normal distribution N(0,ζ). The specification 

of the above model implies that the coefficients obtained are identical for the 15 banks 

considered. However, it is possible to think that there are differences between Tunisian banks in 

their activity and functioning. It is, therefore, appropriate to adopt a specification that highlights 

individual effects. This is why we resume the specification (1) again by introducing 

heterogeneity between the banks. By performing the Fisher test (F-test) and the Breusch and 

Pagan Lagrangian Multiplier (LM) test, we accept the rejection of a perfectly homogeneous 

panel structure and therefore our model is either a fixed individual effects model or a random 

individual effects one. Based on the values of this statistic, we reject the H0 hypothesis (lack of 

autocorrelation). This implies that the fixed effects model is preferable to the random effects 

model in the case where we consider the banks' net interest margin as a variable to be explained. 

The choice then relates to the “Within” estimator. 

The same approach was followed to determine the best estimator of the model where 

performance is measured by the banks' ROE. The results show that the random-effects model is 

preferable to the fixed effects model according to the Hausman test. However, considering the 

ROA as an explanatory variable, the estimated results have shown that there are no individual 

effects specific to each bank. Since the specific deterministic effect symbolized by constant 

values specific to each bank is not proven, it might seem more natural to treat this effect as a 

random and non-deterministic effect. The random-effects model to be estimated is written: 

 

Pit = α + β1it ITit + β2it SIZEit + β3it INTit + β4it SSRit + β5it CRISQit + β6it BOWNit + εit               (2) 

 

εit = µi + ρit 

 

To introduce the specific effect as a random effect into the analysis, we consider that the 

error, or residue εit, is composed of two elements: the first represents the individual effect, 

reflecting the influence on the performance of variables not taken into account, as long as they 

are stable over time; the second represents the influence of the other omitted variables also 



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varying over time from one bank to another. We assume that the εit are identically and 

independently distributed and that the µi are not correlated with the explanatory variables. The 

estimated model for performance equations is usually written in the following form: 

                                                      

         Pit = β ITit + δ X
k

t + + µi + ρit                                                                        (3) 

 

In this specification, the variable Pit represents the performance variable of bank i at time 

t, such as NIM, ROA, and ROE. X corresponds to a vector composed of (k) control variables; µi 

represents the specific effect specific to each bank, which remains invariable over time, while ρit 

is a random perturbation. The index (i) refers to the banks in our sample and "t" is the time 

index. The estimation of this model will be done according to a static approach and a dynamic 

approach. In our study, the partial adjustment approach is applied to our equation. 

Model (3) was estimated on panel data according to two methodologies that take into 

account the characteristics of our sample. Initially, the estimation is carried out by the OLS 

method. Secondly, the announced model is estimated with delays of the endogenous variable. 

Thus, the estimated dynamic equations take the following form: 

                                          

      Pit =   Σ φm Pi,t-1 + β ITit + δ X
k

t +  µi + ρit                                                                 (4) 

 

The vector of control variables X
k

t has the same meaning as before. The variable Pi,t-1 is 

the delayed endogenous variable. We also assume that the absolute value of the sum of 

parameters φm is less than unity and that ρit is the error term with zero expectation and variance E 

(ρit
2
) = ζρ

2
. Moreover, these stochastic disturbances are independent of the specific effects (µi) 

and taken in pairs; they are not correlated. On the other hand, the presence of a lagged variable 

makes the usual estimation techniques on panel data inappropriate. This is due to the correlation 

between the endogenous variable and residuals from the regression. To overcome this problem, 

the method of instrumental variables applied to the first difference model allows endogeneity to 

be taken into account by the use of delayed explanatory variables as instruments. 

Arellano and Bond (1991) proposed a procedure of estimations by the Generalized 

Moments’ Method (GMM) to improve the efficiency of the method proposed by Anderson and 

Hsiao (1981-1982) and which proved to produce consistent estimators that are not necessarily 

effective. This procedure contains two steps. First of all, it is necessary to rewrite the dynamic 

model in first differences to eliminate the specific effects (µi), and in a second step, this equation 

is estimated according to the GMM method by adopting a set of instrumental variables. The 

vector of instrumental variables retained in this analysis is composed of all the delayed values of 

the endogenous variable. 

RESULTS AND DISCUSSION 

Table (2) shows the disparity in the mean values of the explanatory variables, the variables to be 

explained, and their standard deviations for the different banks in the sample.  

 

         Table 2. Descriptive statistics of the variables to be explained and the explanatory variables 

 
 Average Standard 

deviation 

Min Max 

NIM .0256356 .0174386 .0075367 .0763757 

ROA .1174876 6.83454 -74.0456 9.26575 



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ROE 7.834567 80.5646 -428.4564 954.3454 

IT .0114998 .087343 -1.116654 .266536 

INT .652881 .136754 .2154757 .9583346 

SIZE 13.64798 1.28343 10.5267 15.28674 

SSR .2147646 .0856464 0 .4364674 

RISQ .1486752 .1176954 .02 .565 

 

Looking at Table (3), we see that the average net interest margin of Tunisian banks 

decreases slightly from 0.0364 in 2001 to 0.0357 in 2019. On the other hand, the average ROA 

increased by 0.024 in 2001 to reach 3.758 in 2019. The same goes for the ROE which goes on 

average from 0.157 in 2001 to 12.748 in 2019. The average numbers of IT investments clearly 

show that our sample is based on banks installed in a growing country in terms of technology. To 

get around the multicollinearity problem, we were able to select only the best variables (Table 4). 

 

Table 3. Annual descriptive statistics of performance measures and IT investment in Tunisian 

banks 

 

NIM 2001 2004 2007 2010 2013 2016 2019 

Average .036475 .0336467 .0306613 .0267485 .0264566 .0249437 .0357654 

Deviation 

standard 

.008175 .0105987 .013549 .0098103 .0094424 .0070653 .0072169 

Min .0149432 .0196912 .0113846 .0124497 .0076826 .0114803 .0090055 

Max .0673452 .0522107 .0638269 .0428411 .0397337 .035338 .1564324 

ROA 2001 2004 2007 2010 2013 2016 2019 

Average .024563 .820709 -

7.839602 

.8706546 .411611 1.191564 3.75832 

Deviation 

standard 

.0095347 1.179944 21.01162 1.346316 2.591446 1.084216 .8212117 

Min .0054411 .6155183 -

75.19608 

-1.711013 -

8.130311 

0 .0414251 

Max . 1435653 5.016689 1.977789 4.086188 3.931209 3.608938 5.970182 

ROE 2001 2004 2007 2010 2013 2016 2019 

Average .1574643 0.68589 -

29.29276 

4.490838 -

6.716453 

8.801352 12.74893 

Deviation 

standard 

.0614159 9.350515 111.8833 5.113827 47.21951 7.761342 5.522733 

Min .0117211 1.167886 -

429.0495 

-4.225255 -

176.4798 

0 .2059946 

Max .2032746 39.89513 13.0334 14.55628 15.07121 29.76911 29.44269 

IT 2001 2004 2007 2010 2013 2016 2019 

Average .026578 0152642 .0143741 .0149916 .0282695 .0334631 .468532 

Deviation 

standard 

.0204895 .0214314 .0231528 .023959 .0529575 .0784827 .0779477 



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Min .0001479 .000317 .0004975 .0002913 .0009346 .0014959 .0016363 

Max .0748671 .0834234 .0907636 .09402151 .1974172 .3046716 .4953611 

 

Table 4. Matrix of the variables’ correlations 

 

Variables NIM ROA ROE INT SIZE SSR CRISQ IT 

NIM 1.0000        

ROA -0.0576 1.0000       

ROE -0.0734 0.3267 1.0000  

 

    

INT 0.6816 -0.046 -0.1156 1.0000     

SIZE -0.6728 0.1574 0.1465 -0.4564 1.0000    

SSR 0.1276 -0.0564 -0.0463 0.0054 -0.1267 1.000   

CRISQ -0.4536 0.0264 0.2546 -0.2675 0.2564 -0.1683 1.000  

IT -0.0356 0.1258 -0.021 -0.0265 0.0564 -0.0536 0.0375 1.000 

 

We present here the empirical results concerning the determinants of bank profitability as 

measured by the three indicators explained above namely the NIM, the ROE, and the ROA. 

Interpretations of the obtained results will allow us to clarify the sign and extent of the estimated 

relationships. 

 

The Sensitivity of the Net Interest Margin to IT Investment 

To determine the impact of IT investment on the NIM of Tunisian banks, we estimated the 

previous dynamic and static specifications where the endogenous variable to be explained is the 

ratio of the bank interest margin to the total assets of each bank. The results of the estimations 

are displayed in the table (5). 

 

Table 5. Results of the estimation of the net interest margin equation 

 

 Model 1: 

Fixed effects 

Model 2: Random effects 
Dynamic panel 

Variables Coefficient 

(Student's t) 

Coefficient 

(Student's t) 

Coefficient 

(Student's t) 

Coefficient 

(Student's t) 

NIM (-1) 

 

IT 

 

INT 

 

SIZE 

 

SSR 

 

CRISQ 

 

- 

 

0,0045 

(0,34) 

0,0347*** 

(4,23) 

-0,0242*** 

(-8,14) 

0,025*** 

(3,76) 

-0,0165** 

(-2,78) 

- 

 

0,121 

(6,54) 

0,035*** 

(6,21) 

-0,0095*** 

(-5,61) 

0,0081 

(0,83) 

0,0039 

(0,76) 

- 

 

0,0091 

(0,54) 

0,017*** 

(6,43) 

-0,0082*** 

(-4,23) 

0,0275 

(1,83) 

-0,0361 

(-1,62) 

0,0163 

(0,83) 

0,0018*** 

(5,41) 

0,3711*** 

(6,37) 

-0,0281*** 

(-10,62) 

0,0037** 

(2,82) 

-0,5130** 

(-2,46) 



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54 

Constant 

 

BOWN 

0,237*** 

(8,58) 

- 

 

0,182*** 

(5,73) 

- 

0,085*** 

(5,12) 

0,026** 

(2,62) 

0,282* 

(11,41) 

- 

 

R
2
 64,75% 45,82% 43,78%  

*** Significant at the 1% threshold, ** Significant at the 5% threshold, * Significant at the 10% threshold. 

 

Fisher's test indicates that the model is globally significant up to the 1% threshold (Prob> 

F = 0.0000). The goodness of adjustment is acceptable since the model explains 64.75% of the 

total variance. The results show some similarities. The two static models, fixed effects and 

random effects show that IT investments have no impact on the NIM of the banks in the sample. 

This result confirms that found by Prasad and Harker (1997) who concluded in their work that 

there is no link between these two variables. The third model is based on the assumption that all 

the explanatory variables are exogenous as in the static case. The delayed profitability variable 

(in first differences) is instrumented by its own delays at level t-3. For the sake of comparison, 

estimates by the GMM method suggested by Arellano and Bond (1991) applied to the dynamic 

performance equation are also included in Table (5). Although the lagged coefficient is not 

significant, it appears that IT has a positive and significant relationship at the 1% level with the 

NIM of banks. 

Importantly, the size and intermediation parameters remain statistically significant at the 

1% level in all estimates, whether dynamic or static. This result thus observed suggests that these 

two variables, combined with IT, influence the NIM of Tunisian banks. However, this significant 

influence is positive for the "intermediation" variable but negative for the "size" variable. It, 

therefore, appears that the intermediation activity, combined and accentuated by the development 

and use of new technologies, plays an important role for Tunisian banks since it improves their 

NIMs. This result can be explained by the complementarity between bank credit and deposit 

policies and the use of IT. Indeed, the strengthening of the credit policy should be carried out in 

symbiosis with an efficient strategy of draining additional resources and with efficient use of IT. 

This will undoubtedly lead to an increase in the interest margin of the banks. On the other hand, 

and concerning the significant but negative relationship between the size variable and the NIM, it 

appears that the larger the size of the banks the more their interest margin deteriorates. This 

result confirms that found by Hermalin and Wallace (1994), Isik and Hassan (2002), Bakkeri and 

Ali (2020) who found a negative relationship between the size and the profitability of banks. On 

the other hand, this result contradicts the conclusions of Aly and al. (1990), Berger and al. (1993) 

who argue that the larger the size, the more banks have a positive attitude towards the 

development of new technologies, and therefore the more their performance improves. The 

simplest explanation is the existence of substitution between IT and labor to produce more and at 

a lower cost. 

According to the results of the static panel with fixed effects, the managerial capacity of 

staff is significantly and positively linked to the NIM of Tunisian banks at the 1% threshold. This 

result is similar to that found by the dynamic panel with a positive significance at the 5% level. 

It, therefore, appears that a high staffing rate and good training of executives lead to an 

improvement in the productivity of agents and a significant managerial capacity, which has a 

positive influence on the MIN of banks. This result confirms that found by Zaghla and 

Boujelbene (2008) who showed that an increase in the supervisory rate positively affects the 

level of profitability of Tunisian banks and that the latter therefore do not suffer from weak 



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55 

managerial capacity. Ben Naceur (2003) insisted on the major positive impact of managerial 

variables since they positively affect interest margins. 

As for the "risk" variable measured by the ratio of overdue debts to total bank credit, the 

estimation of the model by the static panel with fixed effects and by the dynamic panel reveals 

that this variable significantly and negatively affects the NIM of Tunisian banks at the 5% 

threshold. The policy of compliance with international standards in terms of prevention against 

insolvency risk encouraged Tunisian banks to increase their capital volumes, which deteriorated 

the volume of loans granted to individuals and therefore the interest margin received. 

Finally, concerning the dummy variable, the results of the static random-effects model 

show a positive and significant sign at the 5% level. This result suggests that public banks are 

more profitable than private banks when considering their NIMs. This confirms the results 

previously found by Chaffai and Dietsch (1998), Smida and Ayadi (2006), Anwar and al. (2020). 

 

The Sensitivity of Profitability Measures to IT Investment 

Econometric tests show that our model is globally significant and that the quality of adjustment 

is practically good. This means that there are other variables, in particular economic zones, 

which would explain performance apart from financial, environmental, or managerial factors. 

Table (6) shows that according to the model estimations by the dynamic panel method, the ROE 

is negatively affected by the IT investments of banks. This relationship is significant at the 1% 

level. This finding argues that large IT investments are not associated with high returns. This 

confirms the "Productivity Paradox" and implies that Tunisian banks which devote large budgets 

to IT investment do not improve their ROE. In contrast, the random-effects model presents a 

contradictory result: investment in IT is significantly and positively linked to the ROE of banks 

at the 1% threshold. The results also show that the relationship between IT investment and banks' 

ROA is less clear. The random-effects model showed no link between these two variables. On 

the other hand, according to the results of the dynamic panel, with a significant lagged 

coefficient at the 1% threshold, it appears that IT has a positive relationship with the ROA of 

banks. 

 

Table 6. Results of the estimation of the equation on ROE and ROA 

 

Variables 

Model 2: Random effects 
Dynamic 

panel 
Variables 

Model 2: Random effects 
Dynamic 

panel Coefficient 

(Student's t) 

Coefficient 

(Student's t) 

Coefficient 

(Student's t) 

Coefficient 

(Student's t) 

 

ROE (-1) 

 

IT 

 

INT 

 

SIZE 

 

SSR 

 

CRISQ 

 

 

- 

 

-645,13*** 

(-32,45) 

-24,75 

(-0,65) 

6,82 

(1,37) 

-13,82 

(-0,88) 

121,45** 

(1,72) 

 

- 

 

1,625*** 

(0,08) 

-32,11 

(-0,68) 

6,128 

(0,71) 

-7,295 

(-0,42) 

120,53** 

(1,64) 

 

0,022*** 

(2,54) 

-735,69*** 

(-5,43) 

55,712* 

(1,68) 

79,412*** 

(7,59) 

-54,933* 

(-1,75) 

-21,907 

(-0,61) 

 

ROA (-1) 

 

IT 

 

INT 

 

SIZE 

 

SSR 

 

CRISQ 

 

 

- 

 

7,143 

(1,67) 

0,013 

(0,04) 

0,746** 

(1,72) 

-1,471 

(-0,08) 

-2,114 

(-0,83) 

 

- 

 

8,174 

(6,336) 

0723 

(4,08) 

1,518** 

(0,903) 

-8,621 

(9,68) 

-0,735 

(6,993) 

 

0,074*** 

(4,76) 

4,732*** 

(32,78) 

18,62*** 

(18,72) 

9,75*** 

(2,82) 

0,114 

(0,03) 

-3,896 

(-0,64) 



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56 

Constant 

 

BOWN 

-76,34 

(-1,36) 

- 

-76,13 

(-0,87) 

1,341 

(0,09) 

-45,247*** 

(-7,82) 

- 

Constant 

 

BOWN 

-11,97 

(-1,51) 

- 

-25,72** 

(11,95) 

5,735 

(2,00) 

-

86,45*** 

(-7,72) 

- 

R
2
 47,18% 38,62%  R

2
 37,93% 39,44%  

*** Significant at the 1% threshold, ** Significant at the 5% threshold, * Significant at the 10% threshold. 

 

The lack of a clear relationship between IT investment and measures of bank profitability 

is consistent in early work by Brynjolfsson and Hitt (1996). These authors have found different 

reasons for this result. They referred to the ability of IT to reduce or increase barriers to entry, 

and therefore to intensify or decrease competition. They also cited, as another reason, the effect 

of IT on competitive strategy and the structure of the industry. Also, the work of Omri and 

Hachana (2008) provides a better explanation for this ambiguity in the relationship between IT 

investment and measures of bank profitability. These authors have confirmed in their work one 

of the explanations of the "Productivity Paradox" which is the existence of delay. Indeed, they 

raised, on the one hand, that taking into account the delay between investment in IT and financial 

measures of profitability slightly improves the relationship between these two variables, and that 

efficiency-X explains better than traditional ratios the relationship between IT investment and 

bank profitability on the other hand. 

The results of the dynamic model show that the "intermediation" variable is favorable to 

the profitability of banks. This variable is positively and significantly linked to ROE and ROA at 

the respective thresholds of 10% and 1%. It, therefore, appears that this managerial variable 

constitutes an important source of profits for Tunisian banks. This result seems to indicate that 

the most active banks in the customer lending segment tend to perform better by increasing their 

ROA and ROE. 

As for the "size" variable, empirical results indicate that there is a positive and significant 

relationship between the size of the bank and the return on assets and equity. Indeed, according 

to the results of the dynamic panel, size is positively and significantly related to ROE at the 1% 

threshold. The relationship between the same explanatory variable and the ROA of Tunisian 

banks is positive and significant at the 5% threshold according to the random-effects model, and 

at the 1% threshold according to the results of the dynamic panel. These results suggest that the 

tendency to improve the level of economies of scale generates products and tends to improve 

profits. These econometric results show that Tunisian banks, with efficient use of IT, have the 

managerial capacity necessary to manage a large total of assets. 

According to the results of the dynamic panel, it is surprising to find that the managerial 

capacity of staff is significantly but negatively linked to the return on equity of Tunisian banks. 

The random-effects model shows that there is no positive relationship between this managerial 

variable and the ROA of Tunisian banks. This result suggests that the executive supervision rate 

is not high enough to improve and positively influence the profitability measures of banks. It 

appears that Tunisian banks suffer from low managerial capacity given the low added value of 

senior executives. 

As for the "risk" variable, the estimation of the model reveals that this variable does not 

affect the ROA of Tunisian banks. However, the influence of non-performing loans on the ROE 

of these banks is positive and significant at the 5% threshold according to the results of the 

random-effects model. This result, although it is different from what is expected, corroborates 

the results found by Ndeffo and Ningaye (2007) in their study analyzing the impact of the 

financial reforms implemented in the CEMAC zone since the end of the 1980s on the 



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57 

profitability of the banking system of the countries of this sub-region. It, therefore, appears that 

the more the banks take the risk by granting more loans, the more profitable they are by 

increasing their ROA. Therefore, banks should extend credit to businesses more to address the 

excess liquidity problem that has characterized them for several years. 

Finally, concerning the dummy variable, the results of the estimated models show that the 

ownership of the bank, whether public or private, does not affect the profitability measured by 

the ROA and ROE. 

CONCLUSION 

As part of this research, we examined the relationship between IT investment and the 

profitability of Tunisian banks, via static and dynamic panel regression models. Our study 

focused on 15 Tunisian banks for 19 years (2001-2019). To assess the profitability of these 

banks, three measures were used: traditional accounting ratios, ROA and ROE, and net interest 

margin. Our research has shown the importance of the role played by IT in Tunisian banks since 

IT investments improve their profitability. This finding contradicts the “Productivity Paradox” 

that high IT investments are not associated with better performance. Indeed, an analysis of the 

empirical results of our study shows that IT has a high share of the explanation of bank 

profitability in comparison with other variables. The dynamic panel estimations suggest that IT 

favors bank profitability as measured by ROA and NIM, while it is against profitability 

measured by ROE. It appears that large investments in IT are associated with high returns as 

measured by the bank's ROA and NIM. We confirm then that Tunisian banks have an interest in 

investing more in IT and promoting technology in their operations. 

The results also show that Tunisian banks are acting on their size to boost their 

performance, which explains the continuous expansion of the networks of Tunisian banks, and 

which confirms that the latter have not yet reached a level of the size that will be harmful to their 

profitability, even if a negative correlation between the size variable and profitability measured 

by the NIM is detected during our study. It appears that the large Tunisian banks do not follow 

the concept of economy of scale. Also, the managerial variable "intermediation" is favorable to 

the profitability of banks measured by the ROA, ROE, and NIM. It is thus an important source of 

profits for Tunisian banks. The most active banks in the customer loan segment tend to be more 

efficient by increasing their profitability. The results also showed that Tunisian banks suffer from 

low managerial capacity given the low added value of senior executives and that the more the 

banks take the risk by granting more loans, the more profitable they are by increasing their ROA. 

Therefore, banks should extend credit to businesses more to address the excess liquidity problem 

that has characterized them for several years. Finally, the results of the estimated models show 

that the ownership of the bank, whether public or private, does not affect profitability measured 

by ROA and ROE, but that public banks are more profitable than private banks when considering 

their NIM. 

 

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