The Impact of IS Investment on Bank’s Performance based on
MCDM Techniques
Ansar Daghouri, Khalifa Mansouri and Mohammed Qbadou
Laboratory: Signals, Distributed Systems and Artificial Intelligence (SSDIA)
ENSET of Mohammedia, University HASSAN II Casablanca
Keywords: Banks, information system, investment, multi-criteria, performance.
Abstract: While banks are investing heavily in information system (IS), the results of studies of the relation between
those investments and superior performance of the firm are mixed. Many studies have analysed the impact
of IS investment on firm performance, without taking into account the non-financial firm’s performance,
This paper proposes a framework to evaluate the non-financial bank’s performance based on an approach
combining two most used MCDM methods and the impact of IS investment on this performance. The
results of this study that dealt with fifty banks show that IS investment does not insure superior
performance.
1 INTRODUCTION
The relationship between information system (IS)
and firm performance is among the topics that are
worrying researchers as well as leaders who invest
heavily in IS, and want to discover if those
investments are rewarded by the improved firm
performance.
However, results from research that have study
this relation are contradictory; some authors have
confirmed the positive impact of IS investment on
firm performance (Barua et al., 1995) (Rai et al.,
1997) (Dedrick et al., 2003) (Ada et al., 2012) (Lim
& Trim, n.d.). While other found no significatif
impact of IS investment on firm performance
(Koski, 1999) (Strassman, 1990) (Ho et al., 2011).
The mixed results can be explained by firm’s
sector, work methodology and the choice of research
model’s variables (Kleis, 2012) (Liao et al., 2015)
(Saunders & Brynjolfsson, 2016).
The majority of literature’s studies deal with
financial firm’s performance forgetting the non-
financial aspect of the performance.
This paper investigates the impact of IS
investment on non-financial performance of banks
using actual data from fifty banks. Besides, this
study proposes a combined approach of mutli
criteria decision-making methods (MCDM) to
evaluate the non-financial bank’s performance.
The structure of this paper is as follows: section
2 and 3 present respectively an overview of works
related with non-financial performance, IS
investment and its impact on firm performance and
the most used MCDM methods. Next section
exposes work methodology and main results. At the
end, we present concluding remarks.
2 LITERATURE REVIEW
2.1 Non-financial Performance
The evaluation of firm’s performance has long been
based on financial results through financial
indicators (Gijsel, 2012), but this purely financial
vision has been strongly criticized, in this way we do
not assess the true and global firm’s performance.
The overall performance sought at the firm level
need to be assessed on the basis of financial and
non-financial indicators (Bogieevie et al., 2016).
Performance measurement system is a group of
techniques implemented by leaders to evaluate the
performance of firm’s activities (Neely et al., 2000).
Authors have fixed the examples of the most popular
techniques for proposing a set of performance
measures such as: balanced scorecard (Kaplan &
Norton, 2005)and performance hierarchies (Lynch &
Cross, 1991).
86
Daghouri, A., Mansouri, K. and Qbadou, M.
The Impact of IS Investment on Bank’s Performance based on MCDM Techniques.
DOI: 10.5220/0009773900860093
In Proceedings of the 1st International Conference of Computer Science and Renewable Energies (ICCSRE 2018), pages 86-93
ISBN: 978-989-758-431-2
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Nevertheless, the choice of appropriate
indicators to evaluate performance is one of the most
critical projects due to the multidimensional aspect
of performance and yet the indicators are not an
independent process that can be applied to all types
of firms.
The performance measures based on non-
financial indicators have been widely applied by
researchers (Drury & Tayles, 1995) (Gomes et al.,
2004) (Imsail & King, 2007) (Ibrahim & Lloyd,
2011).
With the multitude of non-financial performance
indicators, in this work, we choose to use the most
used indicators (Milan & Aluç, 2017) (Zhelyuk &
Popa, 2009) (Strandberg, 2014) such as: customer
satisfaction, market share, employee feedback, and
human resource…
2.2 IS Investment and Firm
Performance
Since 1980, the authors began to study the impact of
investment in information system on firm
performance (Solow, 1987). The finding of previous
studies can be grouping to three possibilities. Studies
confirming the positive impact of IS investment on
firm performance (Kwon, 2007), in 2005, the results
of study (Lee & Kim, 2006) has showed that IS
investments cause economic performance, other
studies have confirmed the positive impact between
the IS investment and performance but taking in
each time specific variables to the study and based
on different theories. According to studies based on
the IT productivity paradox and RBV theory (Jung,
2009) (Anderson et al., 2003) (Huang et al., 2006)
(Otim et al., 2012), they confirm the negative impact
of IS investment on firm’s performance. Finally,
some studies (Ho et al., 2011) (Motiwalla et al.,
2005) showed that IS investment does not impact
firm performance.
3 MCDM METHODS
3.1 Analytical Hierarchy Process
The analytical hierarchy process (AHP) is a
powerful tool that can be used to analyse decision
(Saaty, 1970).
It can be used when multiple or conflicting
criteria are present also when the process of making
decision is based on both qualitative and quantitative
decisions.
The AHP method takes into account a set of
evaluation criteria and alternatives to choose later
the best decision among others based on the criteria
of the study. The AHP implementation consists of
three main steps (Saaty & Penivati, 2008).
An AHP analysis uses pairwise matrix A {m*n}
to measure the item’s impact on one level of the
AHP hierarchy on the next higher level.
Each entry a
ij
of A represents the importance of
criterion i relative to criterion j (with; a
ij
a
ji
=1):
 If aij > 1: i is more important than j;
 If aij < 1: i is less important than j;
 If aij =1: same importance.
The normalized decision matrix A
norm
is derived
by A using Eq (1):
C
ij
=a
ij
/
∑




/ i, j= 1,2…n
(1)
Finally, the weighted normalized decision matrix is
built using Eq (2) under the form (3):
W
i
=
∑




/ n= 1,2…n
(2)
W=




⋯



(3)
3.2 Topsis Method
TOPSIS (Technique for Order Preference by
Similarity to Ideal Solution) method developed in
1981 (Hwang & Yoon, 1981)is one of the most used
MCDM methods that depend on distance to positive
ideal solution and negative ideal solution.
The positive ideal solution (Wang & Wu, 2012)
is composed of all the good values of criteria, wile
the negative ideal solution include all worst values
of criteria.
TOPSIS method procedure steps (Roszkowska,
2011) as follows:
 Construction of normalized decision matrix:
r
ij
=



∑





/ j= 1,2…J; i=1,2…n
(4)
Where x
ij
and r
ij
are original and normalized score of
decision matrix.
 Construction of weighted normalized decision
matrix:




∗

/ j= 1,2…J; i=1,2…n
(5)
The Impact of IS Investment on Bank’s Performance based on MCDM Techniques
87

 Determination of positive ideal solution and
negative ideal solution:


=



|

,



|




,







,


…



(6)


=



|

,



|




,







,


…



(7)
J and J’ represent respectively maximization and
minimization values.
 Calculation the separation measures of each
alternative from positive ideal solution and
negative ideal solution:









∗




(8)














(9)
 Calculation the relative closeness coefficient
to the ideal solution:


∗









∗
Where 0

∗
1
(10)
 Closeness of the alternatives to the ideal
solution is ranked according to the value C
i
*
the best alternative is that having the highest
value.
4 IMPLEMENTATION
4.1 Work Methodology
Existing works on firm’s performance and IS
investment has looked at this subject from only the
financial aspect of performance and all most the
studies use only data collection and meta-analysis.
This study is focus on two main axes; it offers a
framework to evaluate the non-financial bank’s
performance based on two famous MCDM methods
namely AHP and TOPSIS, thereafter, it investigates
the impact of IS investments on banks performance.
This work uses actual data providing from fifty
banks, the choice of banking sector was made on the
basis of its large consumption and investments on
information systems.
To obtain our results, we implement a work
methodology by following the steps shown in Figure
1.
Figure 1: Research Procedure
The study begins with a literature review to get
an overview of works related with non-financial
performance and the impact of IS investments on
this performance. Then, we formulate the work’s
problem to identify the inputs and outputs, in our
case, we work on the bank’s data to analyse the
impact of IS investment on bank’s performance;
that’s why we passed to data collection. In the step
of data analysis, we used AHP method to calculate
the weights of criteria and sub-criteria (Table 1)
used to evaluate the non-financial performance;
those criteria were taken from previous works. The
weight’s criteria are used next by TOPSIS method to
evaluate bank’s performance and to rank the
different alternatives. In the step of data collection,
we collect also data in relation with the percentage
of IS investment to analyse afterwards the impact
and to conclude with remarks.
Table 1: Hierarchical Representation of Criteria.
Main Criteria Sub Criteria
Customer (C
1
)
CustomerNumber (C
11
),
CustomerSatisfaction (C
12
)
and ComplaintsNumber (C
13
)
Expansion and
Market Share (C
2
)
BrancheNumber (C
21
),
NewProducts (C
22
) and
NewService (C
23
)
Employees (C
3
)
Headcount (C
31
),
AverageAge (C
32
),
Satisfaction (C
33
) and
TrainingInvestment (C
34
)
Service Quality (C
4
)
OnTimeDelivery (C
41
),
CommunicationCapability
(C
42
), RateDelay (C
43
),
Availability (C
44
) and Access
(C
45
)
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88

Environment (C
5
)
TotalPaperConsumption
(C
51
) and EnergyUse (C
52
)
Security (C
6
)
RiskRate (C
61
) and
Breakdown (C
62
)
5 ANALYSIS RESULTS
In the previous section, we have presented the
conceptual model adopted to evaluate the bank’s
non-financial performance. Figure 2 is a
visualization of Table 1 representing six main
criteria chosen to evaluate bank’s performance
(customer, expansion and market share, employees,
service quality, environment and security) and sub-
criteria (three sub-criteria to evaluate customer, three
sub-criteria to evaluate expansion and market share,
four sub-criteria to evaluate employees, five sub-
criteria to evaluate service quality, two sub-criteria
to evaluate environment and two sub-criteria to
evaluate security).
To calculate the weights of criteria and sub-
criteria, we implemented Eq (1)-(2)-(3). As can be
seen from Figure 3, customer (w
1
=0,36) is the most
important non-financial criterion followed by
expansion and market share (w
2
=0,20), employees
(w
3
=0,17), service quality (w
4
=0,16), environment
(w
5
=0,07) and security (w
6
=0,03) is the least
important non-financial criterion.
We can conclude that customer is the most
influent criterion (Bolton, 1998) (Bolton et al., 2004)
on firm’s non-financial performance which is a
logical result given the importance of the customers
who are the mark of a good firm’s image and who
insure the others criteria especially market share and
service quality.
Based on these results, we have implemented
TOPSIS method to rank the fifty banks on terms of
non-financial performance as shown in Table 2. In
the stage of data collection, we worked by the
value's conversion to facilitate data entry.
Figure 2: Hierarchical Structure
Figure 3: Banks Weight Criteria
The Impact of IS Investment on Bank’s Performance based on MCDM Techniques
89

Table 2: Decision Matrix of 50 Banks
C
1
C
2
C
3
C
4
C
5
C
6
N 1 2 3 1 2 3 1 2 3 4 1 2 3 4 5 1 2 1 2
1 A AB D A AA D B AA E G C AC D I L B AA GA HB
2 A AC D A AA D B AB F G D AB D I L B AB GA HB
3 B AC D B AB D A AA F G D AB D I L B AB FA HB
4 C AC D C AB D B AA E G D AB D I J B AB GA HB
5 B AC E B AB E C AA E G D AC D H L A AB EA IB
6 A AC D C AA D A AB E H D AB D I K B AA GA HB
7 B AC D B AA E A AB E G D AB D I L B AA GA HB
8 A AB E B AA D B AB E G D AB D I J B AA FA HB
9 A AB E B AA E A AB E G D AC D H L C AA FA HB
10 D AB E C AC F C AB F G D AC E I L C AC FA IB
11 B AC D B AC F B AB F G C AB D I J B AA GA HB
12 B AC D B AA E B AB F G C AB D I L A AA GA HB
13 C AB D A AA D A AB F H C AB D H K B AB GA IB
14 C AB E C AB D A AB F G D AC D H K B AB EA HB
15 C AB E C AB D A AB E G D AA D I L B AB GA JB
16 A AB E C AB D C AB F G D AB E I J C AB GA HB
17 C AB E B AB E B AB E G D AB D I L C AA GA IB
18 B AB D B AA E B AB E G D AC D H L C AA GA HB
19 C AC D B AB E C AB F H C AC D H K C AA GA HB
20 C AB F C AC E B AB F G C AC D H K B AA GA HB
21 C AB D A AA E B AB E G C AC D I K B AB GA HB
22 C AC E B AA D B AB F G D AC E I L B AB FA IB
23 B AB D C AB D A AB E G D AB D I L C AB FA HB
24 B AC D B AA F B AB F G D AB D H J B AB GA HB
25 A AB E A AC D A AB F G D AB D H L B AB GA HB
26 A AB E B AB E C AB E H C AB D I K A AA GA IB
27 A AB E C AB D B AB E G C AA D I K C AA GA HB
28 A AB E B AB D B AB E G C AB D I L C AC GA HB
29 B AC E B AA D B AB F G C AB E I J C AA EA JB
30 C AC D C AB E B AB E G D AC D H L B AB GA HB
31 A AC D A AA E B AB E H C AC D H L B AB GA HB
32 B AC D B AC E C AB E G D AC D I K A AB GA HB
33 B AC D C AA D B AB E G C AC D I K B AB GA IB
34 B AB F B AA D B AB E G D AC E H K C AA GA HB
35 C AB D B AB F A AB E G C AC D H L B AA GA HB
36 C AC E C AB D A AB F G D AC D H L B AB EA HB
37 A AB F A AB E A AB E G C AB D H L A AB GA HB
38 A AC E A AC E A AB E H D AB D I L B AB FA HB
39 A AB E B AB F B AB F G D AB E I L C AA GA IB
40 A AC E C AB D C AB E G D AB D I J B AB GA HB
41 B AB E B AB E B AB F G C AC D I L B AB GA HB
42 B AC E B AB E B AB E G C AC D I L A AB EA HB
43 C AC F B AA F A AB E G C AC E H L B AA GA JB
44 C AC F C AA D B AB F G C AC D H L C AB GA HB
45 A AB D C AC D B AB E H C AC D H L B AA FA HB
46 A AB E B AB E C AB F G D AB D I K B AB FA IB
47 B AB E A AB F B AB F G C AB E I K B AA FA HB
48 B AC E B AC D B AB E G C AC D I K B AA GA HB
49 A AB D C AA E B AB E G D AC D I L B AB GA HB
50 A AB D B AA D B AB E G D AC D I L C AA GA HB
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90

Figure 4: Sub-Criteria Weight
In this section, we evaluate the non-financial
bank’s performance based on the decision matrix
(Table 2) and the weight’s sub-criteria (Figure 4)
using in order Eq (4) (5) (6) (7) (8) (9)(10) to obtain
the rank of each alternative (bank).
Subsequently, we studied the correlation between
the two variables (IS investment and non-financial
performance). In general way, firms invest on IS to
achieve better competitive advantages through
reducing costs. Given the number of alternative, ten
banks were selected based on the results of the non-
financial performance evaluation (the first three, the
four averages and the last three).
The financial sector is considered as the biggest
investor in the IS. The figure below (Figure 5)
shows the results of the IS investment percentage
compared to the bank’s turnover. It can be
concluded that more than 80% of financial firms
invest between 21 and 60% of their turnover in
information systems; which is a huge investment
given the large turnover of banks.
Figure 5: Bank's Investment Percentage
The curve shows the ranking of the bank's non-
financial performance according to the IS
investment (Figure 6), we find that the impact of IS
investment does not always ensure the performance
of the company, as shown concretely the example of
the B
3
bank which is ranked third performance
rating but in return invests only a percentage
between 1-20%. Unlike the B
9
bank which invests
61-80% of its turnover but is ranked among the last
three banks in terms of performance. These two
contradictory examples lead us to believe that there
are other factors that influence the relationship
between IS investment and non-financial bank
performance.
Figure 6: Impact of IS investment on Banks Performance
6 CONCLUSIONS
Evaluating the non-financial bank performance is
crucial for the competitors and managers. Customer,
expansion and market share, employee service
quality, environment and security affect this type of
performance. The use of several criteria and sub-
criteria for bank evaluation makes the process of
evaluating and ranking bank more difficult. In this
study, we present a framework using the analytic
hierarchy process (AHP) with TOPSIS method for
evaluating the non-financial banking performance
and supporting bank selection decision. The weights
of different criteria and sub-criteria are calculated
using the AHP method, and for ranking banks, one
of the most popular MCDM namely TOPSIS has
been used. Furthermore, this paper investigates the
correlation between IS investment and non-financial
bank’s performance; more than 80% of bank’s invest
between 21 and 60% of their turnover in information
system which is a huge investment unfortunately
those investments are not rewarded by the improved
bank performance, since we have bank who invest
heavily in IS and are ranked at last among the others
on term of non-financial performance. In the future,
we will work in other sector to discover the way to
evaluating their performance and ranking companies
of the studied sector.
The Impact of IS Investment on Bank’s Performance based on MCDM Techniques
91

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