Purpose – The purpose of this paper is to provide a framework for evaluating the overallperformance of bank branches in terms of profitability efficiency and effectiveness.Design/methodology/approach – Applying a two-stage DEA model to a sample of bank branchesof a large commercial bank in Greece this study disaggregates overall performance into profitabilityefficiency and effectiveness.
22
International Journal of Productivity and Performance Management Modeling bank branch profitability and effectiveness by means of DEA Ioannis E. Tsolas Article information: To cite this document: Ioannis E. Tsolas, (2010),"Modeling bank branch profitability and effectiveness by means of DEA", International Journal of Productivity and Performance Management, Vol. 59 Iss 5 pp. 432 - 451 Permanent link to this document: http://dx.doi.org/10.1108/17410401011052878 Downloaded on: 29 November 2014, At: 00:47 (PT) References: this document contains references to 61 other documents. To copy this document: [email protected]The fulltext of this document has been downloaded 1737 times since 2010* Users who downloaded this article also downloaded: Fadzlan Sufian, (2007),"The efficiency of Islamic banking industry in Malaysia: Foreign vs domestic banks", Humanomics, Vol. 23 Iss 3 pp. 174-192 http://dx.doi.org/10.1108/08288660710779399 Ioanna Keramidou, Angelos Mimis, Aikaterini Fotinopoulou, Chrisanthos D. Tassis, (2013),"Exploring the relationship between efficiency and profitability", Benchmarking: An International Journal, Vol. 20 Iss 5 pp. 647-660 http://dx.doi.org/10.1108/BIJ-12-2011-0090 Cengiz Erol, Hasan F. Baklaci, Berna Aydo#an, Gökçe Tunç, (2014),"Performance comparison of Islamic (participation) banks and commercial banks in Turkish banking sector", EuroMed Journal of Business, Vol. 9 Iss 2 pp. 114-128 http://dx.doi.org/10.1108/EMJB-05-2013-0024 Access to this document was granted through an Emerald subscription provided by 502910 [] For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download. Downloaded by Independent University At 00:47 29 November 2014 (PT)
Transcript
International Journal of Productivity and Performance ManagementModeling bank branch profitability and effectiveness by means of DEAIoannis E. Tsolas
Article information:To cite this document:Ioannis E. Tsolas, (2010),"Modeling bank branch profitability and effectiveness by means of DEA",International Journal of Productivity and Performance Management, Vol. 59 Iss 5 pp. 432 - 451Permanent link to this document:http://dx.doi.org/10.1108/17410401011052878
Downloaded on: 29 November 2014, At: 00:47 (PT)References: this document contains references to 61 other documents.To copy this document: [email protected] fulltext of this document has been downloaded 1737 times since 2010*
Users who downloaded this article also downloaded:Fadzlan Sufian, (2007),"The efficiency of Islamic banking industry in Malaysia: Foreign vs domestic banks",Humanomics, Vol. 23 Iss 3 pp. 174-192 http://dx.doi.org/10.1108/08288660710779399Ioanna Keramidou, Angelos Mimis, Aikaterini Fotinopoulou, Chrisanthos D. Tassis, (2013),"Exploring therelationship between efficiency and profitability", Benchmarking: An International Journal, Vol. 20 Iss 5 pp.647-660 http://dx.doi.org/10.1108/BIJ-12-2011-0090Cengiz Erol, Hasan F. Baklaci, Berna Aydo#an, Gökçe Tunç, (2014),"Performance comparison of Islamic(participation) banks and commercial banks in Turkish banking sector", EuroMed Journal of Business, Vol.9 Iss 2 pp. 114-128 http://dx.doi.org/10.1108/EMJB-05-2013-0024
Access to this document was granted through an Emerald subscription provided by 502910 []
For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald forAuthors service information about how to choose which publication to write for and submission guidelinesare available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The companymanages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well asproviding an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committeeon Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archivepreservation.
*Related content and download information correct at time of download.
Modeling bank branchprofitability and effectiveness
by means of DEAIoannis E. Tsolas
National Technical University of Athens, Athens, Greece
Abstract
Purpose – The purpose of this paper is to provide a framework for evaluating the overallperformance of bank branches in terms of profitability efficiency and effectiveness.
Design/methodology/approach – Applying a two-stage DEA model to a sample of bank branchesof a large commercial bank in Greece this study disaggregates overall performance into profitabilityefficiency and effectiveness.
Findings – The results indicate that superior insights can be obtained by employing the proposedtwo-stage DEA model compared to the outcomes from the analysis based on selected key performanceindicators (KPIs). Some relations between profitability efficiency and effectiveness are alsoinvestigated.
Originality/value – The study highlights the importance of encouraging increased profitability andefficiency throughout the branch network of the commercial bank under study.
Keywords Banking, Banks, Process efficiency, Organizational effectiveness, Data analysis, Greece
Paper type Research paper
1. IntroductionTo maintain viability and to improve competitiveness, commercial banks in Greece arecurrently restructuring the operation of branch networks. Branches are a majordelivery vehicle of business volume in banking and the performance of the branchnetwork is bound to have a significant impact on the bank performance as a whole.
The Greek financial system is dominated by banking institutions. In the end of2006, domestic commercial banks controlled approximately 86.5 per cent of the totalasset pool, followed by foreign owned banks operating in Greece (10.1 per cent),cooperative banks (0.8 per cent) and special credit organizations (2.6 per cent) (Bank ofGreece, 2007). The banking sector has significant growth potential since Greek bankshave a lower branch density relative to population compared to the Eurozone average(3.2 versus 5.4 branches per 10,000 inhabitants). As they conduct a relatively smallerbusiness volume from their branch networks, profitability and size can be improved bya concerted effort to expand their network and increase their services penetration in theeconomy.
The problems that Greek banks face are related to the declining income to cost ratio,the low turnover of the retail network, the high percentage of non-performing loans, thelengthy legal bankruptcy procedures that lower the value of collateral in case ofdefault, and the high cost of capital. They also face competition from foreign banksthat have a significant edge in terms of banking know-how and technology(Hardouvelis et al., 2006).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1741-0401.htm
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Received December 2008Revised November 2009Accepted November 2009
International Journal of Productivityand Performance ManagementVol. 59 No. 5, 2010pp. 432-451q Emerald Group Publishing Limited1741-0401DOI 10.1108/17410401011052878
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The banks are forced to re-evaluate their branch networks in order to identify thebusiness drivers and improve the performance of their branches. The availability ofperformance analysis tools of bank networks can contribute positively in this effort.Among them a linear programming-based benchmarking technique, called dataenvelopment analysis (DEA), has been gaining increasing popularity as a viabletechnique for the analysis of branch performance. The employment of such a techniquecan assist in restructuring branch networks by gaining insight into the operation of thebranches so that managerial actions can be taken to improve their performance (Bergerand Humphrey, 1997; Oral and Yolalan, 1990; Sherman and Gold, 1985; see alsoManandhar and Tang, 2002).
DEA has successfully been used to provide bank branch benchmarks, whenmultiple outputs and multiple inputs are considered. However, even though some DEAmodels appeared in the literature address issues of profitability and effectiveness (Hoand Zhu, 2004), most DEA models developed to assess bank branch performance donot assess both profitability and effectiveness as different dimensions of performance.
This paper aims to provide a framework for evaluating the overall performance ofbank branches in terms of both profitability efficiency and effectiveness. The casestudy presented here concerns a sample of branches of a large commercial Greek bank(henceforth simply “The Bank”). We focus on the branch network, since “The Bank”that provided the data set used in this study was interested in gaining insights into theperformance of its network of branches as a first step in comparing the results of aDEA assessment with the results of an in-house performance measurement model(referred to as “PMM” henceforth).
The current study deviates from previous studies in several respects. First, we focusseparately on both profitability efficiency and effectiveness, as components of a newoverall performance metric concept. This is the overall performance(profitability-effectiveness measure) extracted from the DEA-profitability efficiencyand DEA-effectiveness measures. Our findings provide direct guidance for the optimaldeployment of cost input categories and the scale of outcomes produced in terms ofincome categories and net income. In particular, we aim at answering the followingthree questions:
(1) What is the most efficient level of cost categories in generating income?
(2) What is the most efficient level of income (interest and non-interest) ingenerating profits?
(3) Is there a correlation between bank branch profitability efficiency andeffectiveness?
Second, the research is designed to measure the branch-level profitability efficiencyand effectiveness of The Bank compared to the in-house PMM by answering one morequestion:
(4) Is there a correlation between DEA measures and PMM outcomes?
In Section 2 we include a brief review of the relevant literature. Section 3 provides theconceptual framework and briefly outlines the DEA methodology and the proposedmodels. The data sources along with identification of inputs and outputs for the casestudy are reported in Section 4. Section 5 discusses the findings from the empiricalanalysis. Section 6 concludes the paper.
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2. Survey of branch efficiency evaluationDEA models that have been used in branch banking performance have approached thesubject from a production, intermediation and profitability perspectives (Paradi et al.,2004; Giokas, 2008a).
In the production approach branches are considered as units that deliver services totheir clients in the form of transactions or number of accounts produced. Production(operating) efficiency evaluates the branch operation in the use of resources (e.g. labor,capital, space) in service production (e.g. number of transactions); see, e.g. Al-Faraj et al.(1993), Drake and Howcroft (1994), Parkan (1987), Sherman and Gold (1985).
The intermediation approach considers various types of costs as the inputs that arecombined to support the income generating accounts. In this case, branches play anintermediate role and the idea behind the intermediary model is to examine the bank’sability to take in deposits and sell the money in the form of loans and otherincome-earning activities.
In the profitability approach it is examined how well different branches combinetheir resources (i.e. expenses) to produce revenues. Profitability efficiency evaluates theability of branches to minimize the cost of resources for the level of revenue generatedfrom different activities (e.g. Athanassopoulos, 1997; Oral et al., 1992; Oral and Yolalan,1990; Soteriou and Zenios, 1999; Manandhar and Tang, 2002). In the profitabilityefficiency assessment the objective function of DEA models is the ratio of the weightedsum of revenues to the weighted sum of expenses, i.e. an indicator of profitability (Oraland Yolalan, 1990; Athanassopoulos, 1997; Giokas, 2008a).
Published DEA studies of branch banking are numerous, see Paradi et al. (2004) fora recent survey, but Greek studies are relatively few, see Vassiloglou and Giokas(1990); Giokas (1991, 2008a, b); Athanassopoulos (1997); Athanassopoulos and Giokas(2000). Studies of DEA applied to branch banking in Greece with their specific featuresare presented in Table I.
DEA studies that analyze bank branch efficiency in other countries are those ofSherman and Gold (1985), Tulkens (1993), Drake and Howcroft (1994), Haag and Jaska(1995), Sherman and Ladino (1995), Athanassopoulos (1998), Berger et al. (1997), Lovelland Pastor (1997), Camanho and Dyson (1999, 2005), Zenios et al. (1999), Schaffnit et al.(1997), Golany and Storbeck (1999), Avkiran (1999), Kantor and Maital (1999), Soteriouet al. (1999), Cook et al. (2000), Cook and Hababou (2001), Dekker and Post (2001),Hartman et al. (2001), Bala and Cook (2003), Portela et al. (2003, 2004), Paradi andSchaffnit (2004) and Portela and Thanassoulis (2005, 2007).
Our work differs from previous studies by focusing on a two-stage DEA modelformulation, based on DEA-profitability efficiency and DEA-effectiveness measures,keeping each measure independent from each another.
The two-stage concept in DEA dates back to the work by Schinnar et al. (1990) tomeasure the performance of mental health care programs, see Kao and Hwang (2008)for a recent survey. The two-stage DEA method keeping each stage in the two-stageproduction process independent from one another (i.e. the second stage uses theoutputs of the first stage as its inputs) was applied by Wang et al. (1997) to assessinformation technology impact on firm performance, see also Rho and An (2007) for asurvey. Other works appeared in the banking literature are those by Seiford and Zhu(1999) to divide the production process of a commercial bank into marketability andprofitability, Chen (2002) to analyze banking operations, Luo (2003) to evaluate theprofitability and marketability efficiency of large banks and Ho and Zhu (2004) to
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Table I.Studies of DEA applied tobranch banking in Greece
with their specificfeatures
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measure the performance of Taiwan’s commercial banks. Related studies are those byZhu (2000) to analyze the financial efficiency of firms, Sexton and Lewis (2003) tomeasure the efficiency of the American Major Baseball League, Chen and Zhu (2004) tomeasure information technology’s indirect impact on firm performance, Kao andHwang (2008) to decompose the efficiency of non-life insurance companies in Taiwaninto the product of the efficiencies of the two sub-processes, and Chen et al. (2009) toexamine relations and equivalence between the approaches of Chen and Zhu (2004) andKao and Hwang (2008).
3. The conceptual frameworkThe proposed framework, which is referred to as the “overall performance” model,encompasses two performance dimensions as depicted in Figure 1. The modelspecifically incorporates a direct measure of profitability efficiency and its integrationwith an effectiveness model; see also Ho and Zhu (2004).
The performance metric Net income/Total cost ratio (overall performance) assessesthe level of total cost to achieve the expected level of net income generation, and couldbe treated as overall performance in this study. It can be disaggregated intoprofitability and effectiveness as follows:
Net income=Total cost ratio ðoverall performanceÞ ¼
Net income=Total income ðeffectivenessÞ £
Total income= Total cost ðprofitabilityÞ
ð1Þ
Net income/Total income (effectiveness) could be treated as a metric of effectiveness inthis study and is defined as the ability to achieve the expected level of income generation.
Total income/Total cost (profitability) assesses the ability of the branch to generateincome with the available resources expressed in monetary values and it is an index ofprofitability.
DEA can be applied to revenue-producing organizations by converting financialperformance indicators to their efficiency and effectiveness equivalents, see also Hoand Zhu (2004). This decomposition facilitates the examination of Net income/Totalcost ratio (overall performance) in terms of a measure of profitability (efficiency), levelof costs required to generate income and a measure of effectiveness, level of totalincome to achieve the goal of net income generation. As such, Net income/Total costratio encompasses measures of Net income, Total income and Total cost.
The overall efficiency model assesses performance of bank branches in terms ofprofitability and effectiveness. That is, we suppose an efficient and effective bankbranch as using a minimum of resources (i.e. expenses) to generate total income and a
Figure 1.Profitability efficiency(stage 1) and effectiveness(stage 2)
minimum of total income to generate net income. The above analysis shows thatoverall performance is disaggregated into profitability efficiency and effectiveness.This approach accounts for the possible trade-off between profitability efficiency andeffectiveness in bank branch operation.
The operationalization of overall performance model, i.e. the specification of inputsand outputs for each individual model, and their integration to reflect the causalrelationship among the performance dimensions is shown in Figure 1.
The causal relationships are operationalized by specifying the outputs of onemodel (stage 1) as inputs to another (stage 2). The output of profitability efficiencymodel, which is specified as the aggregate measure of total income to total cost ratio, isan input to the effectiveness model. The outputs of profitability efficiency modelpositively influence effectiveness and hence they are inputs to effectiveness model.
Our sample as described in section 4 includes branches of various sizes, hence, thevariable returns to scale (VRS) model, accounting for possible scale effects, is a naturalchoice (see also Paradi and Schaffnit, 2004). Since the branches typically have little or nodirect control over the demand for services required by their customers, input-orientationwas chosen for the first (profitability efficiency) model presented in this study. For theeffectiveness approach we retain the same model orientation in order to investigatewhether the branches use the efficient level of income (interest and non-interest) ingenerating profits.
For the identification of best-practice branches for each dimension the inputminimization BCC (Banker et al., 1984) model (2) is used.
Given a set of n decision making units (DMUs), i.e. bank branches, j ¼ 1, . . . , n, utilizingquantities of inputs X [ (mþ to produce quantities of outputs Y [ (kþ , we can denote xij andyrj the amount of the ith input and rth output respectively used by the jth DMU.
The following VRS input oriented value-based model (Thanassoulis, 2001) can beused to assess efficiencies.
Max h ¼Xk
r¼1
mryrj0 þ v ð2Þ
s.t.
Xm
i¼1
vixij0 ¼ 1
Xk
r¼1
mryrj 2Xm
i¼1
vixij þ v # 0
j ¼ 1; 2; :::; j0; :::; n
mr $ 1
r ¼ 1; 2; :::; k
vi $ 1
i ¼ 1; 2; :::m
v free on sign
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where:
1 . 0 ¼ a convenient small positive number (non-Archimedean), see also Charneset al. (1994).
mr ¼ output weights estimated by the model.
vi ¼ input weights estimated by the model.
We can use the model (2) to identify the nature of returns to scale holding locally atDMU jo in case that jo is Pareto-efficient:
. If v , 0 in all optimal solutions to model (2) then locally at DMU jo decreasingreturns to scale hold.
. If v ¼ 0 in some optimal solutions to model (2) then locally constant returns toscale hold where DMU jo lies or is projected on the efficient frontier.
. If v . 0 in all optimal solutions to model (2) then locally at DMU jo increasingreturns to scale hold.
The radial efficiency h derived from model (2) shows the rate of reduction to the inputlevels of branch under evaluation. The dual of model (2) provides the slack values ofinputs (see Thanassoulis, 2001).
We use the inputs and outputs in stage 1 and stage 2 to characterize the profitabilityefficiency and effectiveness, respectively (Figure 1). The overall efficiency is calculatedas the product of profitability efficiency and effectiveness, see also Ho and Zhu (2004).
4. Data sources and identification of inputs and outputsData on branches of The Bank for the year 2006 were collected as part of the diplomathesis of I. Lamprinidis (2008). The Bank is one of the major commercial banksoperating in Greece and it has already developed an “in-house” performance model thatis based on its management information system (MIS) for assessing themultidimensional performance of its branch network.
These data are likely to be considerably cleaner than standard banking data sets.Our information is retrieved from the MIS of The Bank (i.e. a single bank) and refers toa sample of its bank branches operating all over Greece. Moreover, the same data feedthe PMM, a model already in use in The Bank and the models used here.
DEA models are most meaningful when they are applied to observation sets of bankbranches providing similar services and using similar resources. The Bank uses itsPMM to classify the various branches according to their net interest income taking intoaccount the type and operations performed at each branch. To maintain homogeneity,only the 50 best-in-class branches in selling loans according to PMM classificationwere selected to form the observation set for this study.
As mentioned earlier we aim not only to analyze the profitability of bank branchesbut also to assess their effectiveness. Therefore, two sets of inputs and outputs wereneeded; one set for profitability analysis, and one for effectiveness assessment.
A slight deviation is introduced here in the specification of the output set of theprofitability efficiency model. Instead of using gross interest income with grossnon-interest income in the output side of DEA to assess the efficiency of resource use indelivering income, as done through profitability efficiency model by Oral and Yolalan
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(1990), origination income (net interest income), non-interest (gross) income(commissions and other non-interest income), and the outcome of a predeterminedfunction mapping the performance of the bank branch in giving loans to the clients areconsidered as outputs of our profitability efficiency model.
The input set for profitability analysis consisted of four elements:
x1 ¼ personnel expenses.
x2 ¼ rental expenses[1].
x3 ¼ other operational expenses (i.e. administrative expenses) excluding interestexpenses.
x4 ¼ depreciation.
On the output side of profitability analysis a set of four outputs was considered;namely:
y1 ¼ origination income generated from selling Bank’s assets (used as a proxy forinterest income).
y2 ¼ the outcome of a predetermined function mapping the performance of thebank branch in giving loans to the clients.
y3 ¼ commissions.
y4 ¼ other non-interest income.
The assessment of the profitability efficiency of the bank branches is based on theirability to generate short and long-term profits. By short-term profitability we indicatethe income from commissions that branches generate and by long-term profitability weindicate the income from the lending activity of the branch (see also Giokas, 2008a).
Note that the inputs above correspond to major cost items of bank operations. Theoutput set of profitability assessment, on the other hand, included only two itemswhich accounted for a sufficiently large part of total income of a bank branch; interestsearned on loans (net interest income), and non-interest (gross) income (commissionsand other non-interest income). Another output, the outcome of a predeterminedfunction mapping the performance of the bank branch in giving loans to the clients isconsidered in line with the PMM, a model already in use in The Bank.
With the above sets of inputs and outputs for profitability assessment, it is clearthat the ratio appearing in the objective functions of DEA models is nothing but theratio of weighted sum of incomes to weighted sum of expenses, hence an index ofprofitability.
The input set for effectiveness assessment consisted of four elements:
x1 ¼ origination income generated from selling Bank’s assets (used as a proxy forinterest income).
x2 ¼ the outcome of a predetermined function mapping the performance of thebank branch in giving loans to the clients.
x3 ¼ commissions.
x4 ¼ other non-interest income.
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The output set of effectiveness assessment, on the other hand, included only one itemwhich accounted for the net income of a bank branch:
y1 ¼ net income.
With the above sets of inputs and outputs for effectiveness assessment, it is clear thatthe ratio appearing in the objective functions of DEA models is nothing but the ratio ofweighted net income to weighted sum of income categories, hence an index ofeffectiveness.
Descriptive statistics of inputs-outputs used in the assessment are presented inTable II.
5. Results5.1 Profitability efficiencyThe profitability efficiency of the bank branches is assessed in the light of contrastingtheir operating cost with the monetary outcomes (i.e. incomes) that are generated bythe branches. Such assessment is achieved by creating an input-output frameworkexplained in the previous section. Results concerning the distribution of theprofitability efficiency of the bank branches are presented in Figure 2. The medianefficiency is of the order of 92 per cent. Noteworthy are also the extreme values and inparticular the minimum efficiency value below the 60 per cent (Table III). The resultsindicate that there is scope for efficiency improvement in profitability efficiency byminimizing costs of about 12.4 per cent.
Out of the 50 branches 19 (38 per cent) were found relatively efficient. The modelsuggests that most of the technically efficient branches (74 per cent) are operatingunder constant returns to scale (CRS), three branches (15 per cent) under localdecreasing returns to scale (DRS) and the rest of the branches (11 per cent) under localincreasing returns to scale (IRS). For branches not operating on the frontier, theirreturns to scale determined after elimination of pure technical inefficiency through theprojection towards the VRS frontier. Most of the branches (48 per cent) of the inefficientbranches seem to operate under IRS, eight branches (26 per cent) under DRS and theremaining (26 per cent) under CRS. The results indicate that a possible increase in thescale size of operations for 34 per cent of the branches will lead to increased levels ofmonetary outcomes with a rate higher than that used to increase the input level(expenses). In other words there is potential in the branch network to accommodate andmanage higher levels of business volume (Table IV).
5.2 EffectivenessThe overall results concerning the effectiveness are presented in Figure 2. As canbe seen from the results’ distribution the median efficiency is of the order of 98 percent. Out of the 50 branches 19 (38 per cent) were found to be relatively efficient(Table III).
The results indicate that most (58 per cent) of the efficient branches operate underCRS, four branches (21 per cent) operate under local IRS and the rest of the efficientbranches (21 per cent) operate under local DRS. In the same way as before, the modelsuggests also that most of the branches (68 per cent) of the inefficient branches areoperating under IRS, eight branches (26 per cent) under DRS and the rest of thebranches (6 per cent) under CRS. The results indicate that a possible increase in the
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Per
son
nel
exp
ense
sR
enta
lex
pen
ses
Oth
erop
erat
ion
alex
pen
ses
Dep
reci
atio
nO
rig
inat
ion
inco
me
Loa
ns
sell
ing
bra
nch
per
form
ance
Com
mis
sion
sO
ther
non
-in
tere
stin
com
eN
etin
com
e
Mea
n0.
991
0.13
70.
112
0.05
41.
817
0.03
01.
063
0.01
31.
629
SD
0.63
90.
131
0.06
90.
026
1.16
20.
021
0.88
40.
054
1.32
0M
edia
n0.
861
0.08
30.
096
0.05
41.
403
0.02
40.
808
0.00
41.
210
Q1
0.64
30.
051
0.07
60.
038
1.26
50.
018
0.51
30.
002
1.02
7Q
31.
048
0.16
20.
125
0.06
11.
724
0.03
21.
300
0.00
81.
631
Min
0.25
40.
021
0.04
10.
011
0.99
30.
009
0.18
00.
001
0.51
0M
ax3.
653
0.51
10.
390
0.13
87.
270
0.12
44.
550
0.38
57.
406
Notes:
Q1=
firs
tq
uar
tile
,Q
3=th
ird
qu
arti
le
Table II.Descriptive statistics ofinputs-outputs used in
the assessment(mil euros)
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scale size of operations for 50 per cent of the branches will lead to increased levels ofnet profits with a rate higher than that used to increase the input level (incomecategories) (Table IV). The results indicate that there is scope for efficiencyimprovement in effectiveness by minimizing the income categories’ values of about 9.3per cent.
5.3. Profitability efficiency versus effectivenessTo further illustrate the important difference between profitability efficiency andeffectiveness, a cross-tabulation is presented in Figure 3 and moreover, DEA scoresobtained from the profitability and effectiveness assessments are plotted in Figure 4.Branches fall into four quadrants: stars, sleepers, dogs, and question marks similar tothe classifications done in the efficiency-profitability matrix proposed by Dyson et al.(1990) and Boussofiane et al. (1991); see also Luo (2003), Portela and Thanassoulis(2007), and Giokas (2008a). Splitting half by the median was used to create high-lowgroups profitability efficiency and effectiveness (based on Model (2) results), see alsoLuo (2003). As a result, a total of four groups (2x2 high versus low groups) is created,each representing one of the four quadrants. It should be noted that these thresholdsare arbitrary since the managerial implications of drawing such a matrix do not reallydepend on the chosen thresholds, but on the number of units close to the idealperformance (1,1). It should be noted that different thresholds might be justified bydifferent magnitudes of efficiency scores.
Bank branches that achieve both higher level of profitability efficiency andeffectiveness can be classified as “stars”. “Star” branches (n ¼ 21, or 42 per cent of thetotal sample) represent benchmarks to be matched by inefficient branches.
“Problem” branches are the branches that are in the bottom-left quadrant, withinferior performance both in profitability efficiency and effectiveness. The results
Figure 2.Distribution of efficiencymeasures of bankbranches
Max values of efficiencymeasures, number andpercentage of efficient
branches
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Effi
cien
tP
roje
cted
Tot
al
RT
SN
um
ber
ofb
ran
ches
Per
cen
tag
eof
bra
nch
esN
um
ber
ofb
ran
ches
Per
cen
tag
eof
bra
nch
esN
um
ber
ofb
ran
ches
Per
cen
tag
eof
bra
nch
es
PanelA:profitabilityefficiency
IRS
211
1548
1734
CR
S14
748
2622
44D
RS
315
826
1122
Tot
al19
100
3110
050
100
PanelB:effectiveness
IRS
421
2168
2550
CR
S11
582
613
26D
RS
421
826
1224
Tot
al19
100
3110
050
100
Notes:
IRS
:in
crea
sin
gre
turn
sto
scal
e,C
RS
:co
nst
ant
retu
rns
tosc
ale,
DR
S:
dec
reas
ing
retu
rns
tosc
ale
Table IV.Returns to scale (RTS)classification
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indicate that there are 21 “problem” branches, consisting 42 per cent of the sampledbranches and special attention should be paid to these branches as well as action isneeded to diagnose their problems and to improve their performance.
“Sleepers” experience higher level of effectiveness, but lower profitability efficiency.There are four branches (8 per cent of the total sample) falling into the sleepers’quadrant.
Finally, “dogs” are those branches that earn higher profitability efficiency, butlower effectiveness. Thus, these branches (n ¼ 21, or 42 per cent of the total sample)should place more emphasis on generating profits.
The Pearson’s correlation coefficient between profitability efficiency andeffectiveness is 0.725 ( p value ¼ 0.000), a high correlation coefficient that it isstatistically significant at the 0.01 level (two-tailed). Moreover, Kendall’s andSpearman’s correlation coefficients are 0.575 ( p value ¼ 0.000), and 0.715 ( pvalue ¼ 0.000), respectively. This means that, higher profitability efficiency tends tobe related with higher effectiveness as can be seen in Figure 4.
5.4. Overall efficiencyThe median efficiency is of the order of 83 per cent. Noteworthy are also the extremevalues and in particular the low efficiency values below the 40 per cent.
The Pearson’s correlation coefficient between overall efficiency and its componentsis greater (0.948; p value ¼ 0.000) for profitability efficiency compared to effectiveness(0.903; p value ¼ 0.000). Moreover, Kendall’s and Spearman’s correlation coefficientsare statistically significant and higher for profitability efficiency (0.825 and 0.939,respectively) compared to effectiveness (0.769 and 0.886, respectively).
These results indicate that the overall efficiency level is governed primarily by thebranches’ ability to generate income using their resources, and therefore higher overallefficiency tends to be related with higher profitability efficiency.
5.5. PMM key performance indicator and efficiency measuresThe Bank uses the net income value, the net income per employee and the total cost tototal income ratio as key performance indicators (KPI) for assessing the branchperformance. We prefer to use the net income to personnel expenses ratio and thereciprocal of the total cost to total income ratio (i.e. total income to total cost) as KPIsand compare them with our efficiency measures.
The Pearson’s correlation and Kendall’s and Spearman’s coefficients of KPIs andefficiency measures indicate that the total income to total cost ratio is highlycorrelated to the overall efficiency measure (Pearson’s correlation coefficient ¼ 0.790,p value ¼ 0.000; Kendall’s rank correlation coefficient ¼ 0.656, p value ¼ 0.000;Spearman’s rank correlation coefficient ¼ 0.840, p value ¼ 0.000).
Of the three KPIs used by the bank, the reciprocal of total cost to total income ratio(i.e. total income to total cost ratio) is closest to DEA context, but it contains no directinformation about branch network best practices. Therefore, DEA which provides twoindividual metrics (profitability efficiency and effectiveness) and a summary measure(overall performance) can be used as complement to the “PMM” for the evaluation ofbranch network. Our results indicate that superior insights can be obtained byemploying the proposed two-stage DEA (profitability efficiency and effectiveness)model compared to the outcomes from the analysis based on selected key performanceindicators (KPIs).
6. ConclusionsWe have a presented a general framework for modeling the profitability andeffectiveness in bank branch networks by means of DEA. In particular, we examinedthe performance for a sample of The Bank’s branches and addressed four relatedquestions:
(1) What is the most efficient level of cost categories in generating income?
(2) What is the most efficient level of income (interest and non-interest) ingenerating profits?
(3) Is there a correlation between bank branch profitability efficiency andeffectiveness?
(4) Is there a correlation between DEA measures and “PMM” outcomes?
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Two different input-output models are proposed for assessing the performance interms of profitability efficiency and effectiveness, respectively. The profitabilityefficiency is assessed based on four inputs (cost categories) and four outputs (incomecategories). The effectiveness efficiency is modeled by using the net income as outputand the income categories as inputs.
In both cases, the empirical results, providing answers to questions (1) and (2),indicate that there is scope for substantial efficiency improvements; there is scope forefficiency improvement in profitability and in effectiveness by minimizing costs ofabout 12.4 per cent and income categories’ values of about 9.3 per cent, respectively. Inparticular, we found that the real problem of bank branch inefficiency is due toprofitability inefficiency rather than ineffectiveness. Thus, the branches that acquirehigher effectiveness, but lower profitability performance should place more emphasison income generating activities by minimizing costs.
The efficiency improvement at the worst-performing branches can generate asubstantial increase in profit for The Bank. From the analysis of the “efficiencyprofitability-effectiveness matrix” it becomes evident that branches can still increasetheir profit through efficiency improvements. In addition, we had the opportunity todiscriminate among bank branches that have excelled in all performance dimensionsand therefore could be proposed as benchmark branches across the network.
Results, providing answers to questions (3) and (4), point out for positive linksbetween DEA individual measures as well as between DEA-overall efficiency measureand one of the PMM outcomes, respectively; higher significant correlations areobserved between DEA-profitability efficiency and effectiveness, and between totalincome to total cost ratio, a selected KPI, and DEA-overall efficiency measure. Anotherstriking result of the analysis is that the overall efficiency level is governed primarilyby profitability efficiency level, and therefore higher overall efficiency tends to berelated with higher profitability efficiency.
Future research regarding the performance of Bank branches should no doubtconsider as impressive the incorporation of environmental factors into the proposedDEA models. These factors were excluded from our DEA models due to non-provisionfrom The Bank of branch-specific data like location, investment portfolio risk etc.
Note
1. Rental expenses are used as a proxy to quantify premises usage (Paradi and Schaffnit, 2004).
References
Al-Faraj, T.N., Alidi, A.S. and Bu-Bshait, K.A. (1993), “Evaluation of bank branches by means ofData Envelopment Analysis”, International Journal of Operations & ProductionManagement, Vol. 13 No. 9, pp. 45-53.
Athanassopoulos, A.D. (1997), “Service quality and operating efficiency synergies formanagement control in the provision of financial services: evidence from Greek bankbranches”, European Journal of Operational Research, Vol. 98, pp. 300-13.
Athanassopoulos, A.D. (1998), “Nonparametric frontier models for assessing the market and costefficiency of large-scale bank branch networks”, Journal of Money and Credit Banking,Vol. 30, pp. 172-92.
Athanassopoulos, A.D. and Giokas, D. (2000), “The use of data envelopment analysis in bankinginstitutions: evidence from the Commercial Bank of Greece”, Interfaces, Vol. 30 No. 2,pp. 81-95.
Avkiran, N.K. (1999), “An application reference for data envelopment analysis in branchbanking: helping the novice researcher”, International Journal of Bank Marketing, Vol. 17No. 5, pp. 206-20.
Bala, K. and Cook, W.D. (2003), “Performance measurement with classification information:an enhanced additive DEA model”, Omega – International Journal of ManagementScience, Vol. 31 No. 6, pp. 439-50.
Bank of Greece (2007), Annual Report 2006, Bank of Greece, Athens.
Banker, R.D., Charnes, A. and Cooper, W.W. (1984), “Models for estimating technical and scaleefficiencies in data envelopment analysis”, Management Science, Vol. 30 No. 9, pp. 1078-92.
Berger, A.N. and Humphrey, D.B. (1997), “Efficiency of financial institutions: Inter-nationalsurvey and directions for future research”, European Journal of Operational Research,Vol. 98, pp. 175-212.
Berger, A.N., Leusner, J. and Mingo, J. (1997), “The efficiency of bank branches”, Journal ofMonetary Economics, Vol. 40, pp. 141-62.
Boussofiane, A., Dyson, R.G. and Thanassoulis, E. (1991), “Applied data envelopment analysis”,European Journal of Operational Research, Vol. 52, pp. 1-15.
Camanho, A.S. and Dyson, R.G. (1999), “Efficiency, size, benchmarks and targets for bankbranches: an application of data envelopment analysis”, Journal of the OperationalResearch Society, Vol. 50, pp. 903-15.
Camanho, A.S. and Dyson, R.G. (2005), “Cost efficiency measurement with price uncertainty:a DEA application to bank branch assessments”, European Journal of OperationalResearch, Vol. 161 No. 2, pp. 432-46.
Charnes, A., Cooper, W., Lewin, A. and Seiford, L. (1994), Data Envelopment Analysis: Theory,Methodology, and Application, Kluwer Academic Publishers, Dordrecht.
Charnes, A., Cooper, W.W. and Rhodes, E. (1978), “Measuring the efficiency of decision makingunits”, European Journal of Operational Research, Vol. 2, pp. 429-44.
Chen, T.Y. (2002), “Measuring operation, market and financial efficiency in the management ofTaiwan’s banks”, Services Marketing Quarterly, Vol. 24 No. 2, pp. 15-28.
Chen, Y. and Zhu, J. (2004), “Measuring information technology’s indirect impact on firmperformance”, Information Technology & Management Journal, Vol. 5 Nos 1-2, pp. 9-22.
Chen, Y., Liang, L. and Zhu, J. (2009), “Equivalence in two-stage DEA approaches”, EuropeanJournal of Operational Research, Vol. 193 No. 2, pp. 600-4.
Cook, W.D. and Hababou, M. (2001), “Sales performance measurement in bank branches”, Omega– International Journal of Management Science, Vol. 29, pp. 299-307.
Cook, W.D., Hababou, M. and Tuenter, H.J. (2000), “Multicomponent efficiency measurement andshared inputs in data envelopment analysis: an application to sales and serviceperformance in bank branches”, Journal of Productivity Analysis, Vol. 14, pp. 209-24.
Dekker, D. and Post, T. (2001), “A quasi-concave DEA model with an application for bank branchperformance evaluation”, European Journal of Operational Research, Vol. 132 No. 2,pp. 296-311.
Drake, L. and Howcroft, B. (1994), “Relative efficiency in the branch network of a UK bank:an empirical study”, Omega – International Journal of Management Science, Vol. 22 No. 1,pp. 83-90.
Dyson, R.G., Thanassoulis, E. and Boussofiane, A. (1990), “Data envelopment analysis”,in Henry, L.C. and Eglese, R. (Eds), Operational Research Tutorial Papers 1990,Operational Research Society, Birmingham, pp. 13-28.
Giokas, D. (1991), “Bank branch operating efficiency: a comparative application of DEA and theloglinear model”, Omega – International Journal of Management Science, Vol. 19 No. 6,pp. 549-57.
Giokas, D.I. (2008a), “Assessing the efficiency in operations of a large Greek bank branchnetwork adopting different economic behaviors”, Economic Modelling, Vol. 25, pp. 559-74.
Giokas, D.I. (2008b), “Cost efficiency impact of bank branch characteristics and location.An illustrative application to Greek bank branches”, Managerial Finance, Vol. 34 No. 3,pp. 172-85.
Golany, B. and Storbeck, J.E. (1999), “A data envelopment analysis of the operational efficiency ofbank branches”, Interfaces, Vol. 29 No. 3, pp. 14-26.
Haag, S.E. and Jaska, P.V. (1995), “Interpreting inefficiency ratings: an application of bankbranch operating efficiencies”, Managerial and Decision Economics, Vol. 16 No. 1, pp. 7-15.
Hardouvelis, G., Lekkos, I. and Simintzi, E. (2006), The Greek Banking System in 2006:Comparative Performance, Greek Banking Review, Annual Report on the Greek BankingSector, Division of Research and Forecasting, Eurobank Research, Eurobank FCG,November.
Hartman, T.E., Storbeck, J.E. and Byrnes, P. (2001), “Allocative efficiency in branch banking”,European Journal of Operational Research, Vol. 134, pp. 232-42.
Ho, C.T. and Zhu, D.S. (2004), “Performance measurement of Taiwan’s commercial banks”,International Journal of Productivity and Performance Management, Vol. 53 No. 5,pp. 425-34.
Kantor, J. and Maital, S. (1999), “Measuring efficiency by product group: integrating DEA withactivity-based accounting in a large mideast bank”, Interfaces, Vol. 29 No. 3, pp. 27-36.
Kao, C. and Hwang, S.N. (2008), “Efficiency decomposition in two-stage data envelopmentanalysis: an application to non-life insurance companies in Taiwan”, European Journal ofOperational Research, Vol. 185 No. 1, pp. 418-29.
Lamprinidis, I. (2008), “Performance measurement of bank branches. A DEA application”,diploma thesis, School of Applied Mathematics and Physics, National TechnicalUniversity of Athens (in Greek).
Lovell, C.A.K. and Pastor, J.T. (1997), “Target setting: An application to a bank branch network”,European Journal of Operational Research, Vol. 98, pp. 290-9.
Luo, X. (2003), “Evaluating the profitability and marketability efficiency of large banks.An application of data envelopment analysis”, Journal of Business Research, Vol. 56,pp. 627-35.
Manandhar, R. and Tang, J.C.S. (2002), “The evaluation of bank branch performance using dataenvelopment analysis. A framework”, Journal of High Technology Management Research,Vol. 13, pp. 1-17.
Oral, M. and Yolalan, R. (1990), “An empirical study on measuring operating efficiency andprofitability of bank branches”, European Journal of Operational Research, Vol. 46,pp. 282-94.
Oral, M., Kettani, O. and Yolalan, R. (1992), “An empirical study of analyzing the productivity ofbank branches”, IIE Transactions, Vol. 24, pp. 166-76.
Paradi, C.J. and Schaffnit, C. (2004), “Commercial branch performance evaluation and resultscommunication in a Canadian bank – a DEA application”, European Journal ofOperational Research, Vol. 156, pp. 719-35.
Paradi, J.C., Vela, S. and Yang, Z. (2004), “Assessing bank and bank branch performance –modeling considerations and approaches”, in Cooper, W.W., Seiford, L.M. and Zhu, J. (Eds),Handbook on Data Envelopment Analysis, Kluwer Academic Publishers, Dordrecht,pp. 349-400.
Parkan, C. (1987), “Measuring the efficiency of service operations: an application to bankbranches”, Engineering Costs and Production Economics, Vol. 12, pp. 237-42.
Portela, M.C.A.S. and Thanassoulis, E. (2005), “Profitability of a sample of Portuguese bankbranches and its decomposition into technical and allocative components”, EuropeanJournal of Operational Research, Vol. 162 No. 3, pp. 850-66.
Portela, M.C.A.S. and Thanassoulis, E. (2007), “Comparative efficiency analysis of Portuguesebank branches”, European Journal of Operational Research, Vol. 177, pp. 1275-88.
Portela, M.C.A.S., Borges, P.C. and Thanassoulis, E. (2003), “Finding closest targets innon-oriented DEA models: the case of convex and non-convex technologies”, Journal ofProductivity Analysis, Vol. 19 Nos 2-3, pp. 251-69.
Portela, M.C.A.S., Thanassoulis, E. and Simpson, G.P.M. (2004), “Negative data in DEA:a directional distance approach applied to bank branches”, Journal of the OperationalResearch Society, Vol. 55, pp. 1111-21.
Rho, S. and An, J. (2007), “Evaluating the efficiency of a two-stage production process using dataenvelopment analysis”, International Transactions in Operations Research, Vol. 14,pp. 395-410.
Schaffnit, C., Rosen, D. and Paradi, J.C. (1997), “Best practice analysis of bank branches:an application of DEA in a large Canadian bank”, European Journal of OperationalResearch, Vol. 98, pp. 269-89.
Schinnar, A.P., Kamis-Gould, E., Delucia, N. and Rothbard, A.B. (1990), “Organizationaldeterminants of efficiency and effectiveness in mental health partial care programs”,Health Services Research, Vol. 25, pp. 387-420.
Seiford, L.M. and Zhu, J. (1999), “Profitability and marketability of the top 55 US commercialbanks”, Management Science, Vol. 45 No. 9, pp. 1270-88.
Sexton, T.R. and Lewis, H.F. (2003), “Two-stage DEA: an application to major league baseball”,Journal of Productivity Analysis, Vol. 19, pp. 227-49.
Sherman, H.D. and Gold, F. (1985), “Bank branch operating efficiency: evaluation with dataenvelopment analysis”, Journal of Banking and Finance, Vol. 9, pp. 297-315.
Sherman, H.D. and Ladino, G. (1995), “Managing bank productivity using data envelopmentanalysis”, Interfaces, Vol. 25 No. 2, pp. 60-73.
Soteriou, A. and Zenios, S. (1999), “Using data envelopment analysis for costing bank products”,European Journal of Operational Research, Vol. 114, pp. 234-48.
Soteriou, A.C., Zenios, C.V., Agathocleous, K. and Zenios, S.A. (1999), “Benchmarks of theefficiency of bank branches”, Interfaces, Vol. 29 No. 3, pp. 37-51.
Thanassoulis, E. (2001), Introduction to the Theory and Application of Data EnvelopmentAnalysis: A Foundation Text with Integrated Software, Kluwer Academic Publishers,Dordrecht.
Tulkens, H. (1993), “On FDH efficiency analysis: some methodological issues and applications toretail banking, courts, and urban transit”, Journal of Productivity Analysis, Vol. 4,pp. 183-210.
Vassiloglou, M. and Giokas, D. (1990), “A study of the relative efficiency of bank branches:an application of data envelopment analysis”, Journal of Operational Research Society,Vol. 41 No. 7, pp. 591-7.
Wang, C.H., Gopal, R.D. and Zionts, S. (1997), “Use of data envelopment analysis in assessinginformation technology impact on firm performance”, Annals of Operations Research,Vol. 73, pp. 191-213.
Zenios, C.V., Zenios, S.A., Agathocleous, K. and Soteriou, A.C. (1999), “Benchmarks of theefficiency of bank branches”, Interfaces, Vol. 29 No. 3, pp. 37-51.
Zhu, J. (2000), “Multi-factor performance measure model with an application to Fortune 500companies”, European Journal of Operational Research, Vol. 123 No. 1, pp. 105-24.
About the authorIoannis E. Tsolas is lecturer in economic analysis at National Technical University of Athens,School of Applied Mathematics and Physics, Greece. He earned his PhD from the NationalTechnical University of Athens. His teaching and research interests are in economic analysis(microeconomics, macroeconomics) and business economics. Ioannis E. Tsolas can be contactedat: [email protected]
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6. Hung-Jen Tu. 2012. Performance implications of internet channels in financial services: A comprehensiveperspective. Electronic Markets 22:4, 243-254. [CrossRef]
7. Rashmi Malhotra, Raymond R. Poteau, D.K. MalhotraA DEA-Based Multidimensional Framework forAnalyzing Indian Commercial Banks 61-85. [Abstract] [Full Text] [PDF] [PDF]
8. Yossi Hadad, Baruch Keren, Ofer Barkai. 2011. A wage incentive plan for branch managers using theDEA methodology. International Journal of Productivity and Performance Management 60:4, 326-338.[Abstract] [Full Text] [PDF]
