a multi criteria

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A multi-criteria approach fordetermination of investment

regions: Turkish caseErgun Eraslan and Yusuf Tansel Ic

Department of Industrial Engineering, Baskent University, Ankara, Turkey

Abstract

Purpose The major aim of this research is to determine the socio-economic level of geographicalinvestment regions through fuzzy multi-criteria decision-making (MCDM) method. The resultsobtained from this method are analyzed and compared with the current system and the differences areinterpreted.

Design/methodology/approach A user friendly MCDM method, the fuzzy TOPSIS, was selected

and ten independent criteria out of 53 were used, that have been evaluated by reduction according tothe correlations among them. Therefore, the rankings of the 26 geographical investment regions ofTurkey were calculated based on their criteria.

Findings The examinations of the rankings have shown that only four regions had similarrankings but the rankings of the remaining 22 regions differed according to the authority rankings.Furthermore, significant differences have been observed for eight regions.

Social implications In globalization process, certain issues are of particular importance inshaping the resource allocation policies of countries, through which they adjust their resources formanufacturing and service sectors to the changing competitive conditions and govern the effect ofglobal economics on the human resources of their countries. The allowances taken from social andeconomic criteria have indicated the inter-regional differences in terms of development.

Originality/value From a policy perspective, this study highlighted that a large number of socialand economic criteria failed in identifying homogenous groups of provinces and hence failed in

producing realistic policies. However, the proposed method significantly contributed to obtainingmore accurate rankings by using fuzzy decision-making under multi-criteria.

Keywords Socio-economic level, Fuzzy TOPSIS, Multi-criteria analysis, Spearman correlation test,Turkey, Decision making, Investments

Paper type Research paper

1. IntroductionThe differences in the distribution of natural and social resources cause unbalanceddevelopment courses in the countries. Expressions such as developed, developing,and undeveloped are not only used to indicate the different development levelsbetween the countries but also those different development levels between the regionswithin the same country. Population increase with migration waves to cities make itdifficult to provide municipal facilities such as drinkable water, electricity,accommodation, education, and health services at satisfactory levels and causesmany urban problems such as, traffic congestion, crowd, noise, and environmentalpollution. Those factors prevent the fulfillment of necessary public investments sincethis meant a considerable amount of fiscal expenditure which is many times verydifficult to spare if not impossible for the governments and municipalities. Anotherresult of uneven development between regions reflects the deficit of undeveloped ones.Migration towards developed regions are not only reducing the local population

The current issue and full text archive of this journal is available at

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Received 16 December 2010Revised 28 January 2011Accepted 11 March 2011

Industrial Management & DataSystemsVol. 111 No. 6, 2011pp. 890-909q Emerald Group Publishing Limited0263-5577DOI 10.1108/02635571111144964


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but also reducing the dynamics of local development through loss of young workforceand capital (Knox, 1996).

Strategic planners focused on the determination of development levels viameasurable and comparable social, economic, and cultural indicators. This kind of

research has contributed to both the success of present policies and to the infrastructureof new up-to-date ones. Consequently, sustainable development concepts have emerged.This concept is based on inter-regional consolidation, socio-economic balance,increasing the quality of life, principle of equal opportunity, cultural development,and participation policies. The new approach has broadened the scope of developmentanalyses (Dincer et al., 2003).

The development concept has multi-criteria factors including social, economic,political, cultural, and structural variations throughout a whole country. This conceptutilizes the integrated approach (Cooke and Morgan, 1993). The alternative regionaldevelopment strategies to prevent imbalance and polarization are integrated to publicimplementation. Additionally, geographical promotions and investment incentiveshave become instruments of the balanced localization and development.

The socio-economic development level of the provinces or geographical regions hasbecome the main focus in literature. In recent years, studies have mostly focused onhealth issues. Agirbasli et al. (2008) studied 640 Turkish adolescents living in themiddle socio-economic level and have observed the body mass index, blood pressure,and effects of parental smoking habits on cardiovascular risk factors. Atallah et al.(2006) observed higher prevalence and poorer control of hypertension in lowsocio-economic areas and have demonstrated the complex relationship between thefactors and hypertension. Marmot and Friel (2008) studied the effect of social andenvironmental conditions on health and Boyle et al. (2006) investigated the influence ofeconomic development level on child health in the 42 developing countries. Educationhas also been an important research area in these studies. Lindberg (2007) tried to

constitute the relationship between the type of transition and the socio-economicprestige of employment obtained after graduation through regression analysis.

Additionally, Lamande et al. (2004) underlined the importance level of the economicindicators for establishing international policies, Sun and Zang (2006) stated theimportance of socio-economic level on monetary liquidity and real estate bubbles forforeign-dependent economies. Bonaiuto et al. (1999) investigated the perception ofresidential environment quality and neighborhood attachment in the urban areas.The socio-economic level was also important for waste management system, e.g. Cirelliand Ojeda (2008) studied wastewater management and Li et al. (2008) long-termwaste-management strategies.

In previous public studies on the evaluation of the development level of the provinces,gross domestic product (GDP) have generally been used as a dependent variable and the

economic, the social and the cultural criteria have been used as independent variables,such as in path analysis and principal components analysis (McGranahan et al., 1985;Castells, 1993; Benko and Dunford, 1991). These types of studies have generally restedon pure statistics and on regression analysis. However, this approach seemed to beinaccurate in many cases since it was not clear whether the criteria used had absolutevalues or average values per head and since the intrinsic accuracy of these studiesregarding GDP was ambiguous. This situation is an inherent insufficiency of theseperpetuated studies. Therefore, a multi-criteria approach can be considered as a more

Determinationof investment

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favorable approach to resolve the levels of the criteria and subcriteria. However, thereare limited numbers of studies in this area using this approach.

Although geographic regions with various socio-economic factors are typicallyassessed in an investment decision,the determination of the most appropriate geographic

region or province have been a very difficult decision because of wide diversity ofalternatives, inevitability of conflicting multiple criteria that are both qualitative andquantitative and the lack of standards and benchmarks about performance.

Constructing the hierarchical structure and/or establishing the decision matriceshave provided easier evaluation in this kind of research. This type of studies can beeasily transformed into multi-criteria decision-making (MCDM) problems. Based on theauthors literature survey it can be stated that MCDM is the most appropriate approachapplied to the selection problem. Therefore, there are several MCDM methodspresented in literature. The most common are AHP (Eraslan and Kurt, 2007), TOPSIS(Isiklar and Buyukozkan, 2007; Yurdakul and Ic, 2009b), ELECTRE (Almeida, 2007),and PROMETHEE (Brans and Vincke, 1985) methods. In this study, TOPSIS methodamong the MCDM approaches is selected because of its advantages over others.TOPSIS is a widely used MCDM technique because it has a simple and programmablecomputation procedure (Chakraborty, 2011).

TOPSIS is used to obtain ranking scores and to rank the alternatives accordingly.The main advantage of the TOPSIS approach is its user friendly application whereusers may directly input judgment data without any previous mathematicalcalculations. Besides, it can also be combined with other MCDM approaches such asAHP (Yurdakul and Ic, 2009b; Ertugrul and Karakasoglu, 2009; Secme et al., 2009;Amiri, 2010) and operations research models (Lin et al., 2011) to allow users to structurecomplex problems. The monotonically increasing or decreasing utility of the TOPSISmethod makes it easy to define and locate both the ideal solution and the negative idealsolution. The concept of TOPSIS is that an alternative which is closest to the ideal

solution and farthest from the negative ideal solution in a multi-dimensional space isthe optimal choice (Deng et al., 2000).

In the traditional TOPSIS, human judgments are presented with crisp values.Therefore, in many practical applications such as geographic region selection problem,the human preference is uncertain and decision makers might be unable to assign crispvalues to the comparison of judgments. When the decision-maker faces an uncertainproblem and expresses his/her judgments as uncertain ratios, such as between threeand five times more important, the standard TOPSIS steps, cannot be considered asstraight forward procedures. In order to overcome such short comings, a fuzzy extensionof TOPSIS was developed to solve the fuzzy problems. There are various studies thatincorporated fuzzy numbers into TOPSIS models in the literature (Amiri, 2010;Secme et al., 2009; Ertugrul and Karakasoglu, 2009; Ebrahimnejad et al., 2010;

Chamodrakas et al., 2011). The fuzzy extension of the TOPSIS approach is also used torank the feasible geographic regions in this study to take the advantages provided above.

The primary purpose of this study is to determine the criteria of the socio-economicdevelopment levels of the provinces and rank the development levels of themaccordingly. The secondary purpose is to present the homogenous geographicalinvestment regions by using the same data set.

In the following sections, the socio-economic criterion that is employed in this studyand a brief explanation of the fuzzy TOPSIS (FTOPSIS) method are presented.

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The rankings of the 26 geographical regions are calculated in fuzzy environmentand the comparison study with the current system is performed in the applicationsection. The study finishes with concluding remarks.

2. The Socio-economic criteria for development analysisThe determination of the development level of the provinces or investment areasis required to elaborate the multi-criteria. Therefore, adequate evaluation criteriamust be included in every level of the study. The analysis reflected only thecurrent status in the time windows. There are approximately 100 indicators to measurethe socio-economic level of 81 provinces of Turkey. The indicators measured byvarious state organizations are used in the evaluation process. A total of 53 subcriteriawhich were chosen from State Planning Organization (DPT)s study are used in thisstudy.

The level of development affects the people and the socio-economic level of theseprovinces. In this context, it should be carefully considered that whether the global

indicators should be the average values or per head values. In addition, the effects of thepopulation on the socio-economic levels are not negligible. If the variables are usedper head according to the population, over-crowded provinces are affected negatively.To prevent this disadvantage and increase the sensibility, some of the variables havebeen taken as proportional or absolute values and a balanced analysis has beenperformed.

The authority uses the main criteria in evaluation process under the titles of bothsocial and economic. This distinction has also grounded the actual facts of thecountry and the studies in this area. For example, the socio-economic criteria wereevaluated in this way in the recent studies such as; Mandelik et al. (2010) in reviewingof biodiversity indicators of ecosystems; Xie and Hou (2010) in determination of therelationships with air quality in UK; and Oni et al. (2010) for household food security

and food industry. The social and economic indicators are also discussed separately invarious studies. Challinor et al. (2010) investigated the crop productivity in China usingthe agricultural and prosperity criteria, Ekholm et al. (2010) underlined both health andemployment criteria in their health survey.

The socio-economic indicators were used in several studies about developmentlevels of provinces and management systems. Marques and Monterio (2001) usedthe performance indicators in water utility management in Portugal, Lamande et al.(2004) mentioned about measuring regional economic development in Russia, Turdean(2008) prospected the social indicators for assessing the regional development level inintegration process to involve new institutional and financial models, Firth et al. (2009)examined how the Chinese state-owned banks allocated loans to private firms intheir capital allocation model. Furthermore, Ersoz and Bayrak (2008) measured the

commonalities and disparities of socio-economic indicators between Central Europe andTurkey.

Consequently, the criteria are classified and stated as social and economic in thefirst level. The indicators of measured 53 subcriteria are used for the publicinvestments are shown in Figure 1 (Dincer et al., 2003).

The Figure 1 shows that there are some dependencies among the social andeconomic criteria. It is necessary to supply independency to reduce them. The details ofthe criteria developed in this study are described in Table I.


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Figure 1.

The classification andrelationships of thesocio-economic criteria

Socio-economic criteria

Social criteria Economic criteria

Demographic criteria

Employment criteria

Prosperity criteria

Education criteria

Manufacturing criteria

Infrastructure criteria

Health criteria Financial criteria

Agricultural criteria

Building trade criteria

Criteria Definition

The social criteriaDemographic criteria The criteria containing populations growth rate and spatial variability

and demographic components such as population, urbanization rate,population density, and average households values

Employment criteria The portions of both the gainful occupation and employers ofagricultural, industrial and financial institutions in the employment ofthe provinces

Education criteria The educational indicators such as portions of literature, graduates,and schooling rates

Health criteria The sustainable health services, doctor/patient ratio, child mortality,hospital bed rates, etc.

Infrastructure criteria The benefits from the services and the satisfactory technicalinstruments in selecting the site of establishment such as electricity,fresh water, and public transportation

Prosperity criteria The number of automobiles per head and the rate of electricity andphone usage

The economic criteria

Manufacturing criteria The indicators included in all workplaces of public and private sectorssuch as numbers of employees and employees, electricity consumption,and value added tax per head

Building trade criteria The number of flats and housing zone per head in provincesAgricultural criteria The agricultural production values per rural population and the rate of

this productionFinancial criteria The financial indicators reflected in income levels, capital

accumulation, capital rate of return to investment, and import andexport values in the provinces

Table I.Definitions of socialand economic criteria

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3. The fuzzy TOPSIS methodFTOPSIS approach (Chen and Hwang, 1992) has been frequently used as a rankingmethod in multi-criteria decision analysis in recent decades. In many real lifeapplications, the human preference is uncertain and managers might be unable to

assign crisp values to the comparison judgments. The FTOPSIS approach is proposedin literature for decision problems where uncertainty and imprecision are involved.There are some studies that have incorporated fuzzy numbers into TOPSIS models inrecent years (Yurdakul and Ic, 2009a, b; Amiri, 2010; Ebrahimnejad, 2010; Ic andYurdakul, 2010; Chamodrakas et al., 2011).

In the FTOPSIS method, the criteria weights [ ~wj; j 1,2, . . . , number of criteria(n)]and the values of alternatives [(xij); i 1, 2, . . . , m, j 1, 2, . . . , n ] are inputs and arespecified in matrices as given in Step 1 (Negi, 1989; Chen and Hwang, 1992).

Step 1. Inputs are expressed in matrix form (equation (1)):

D

x11 x2

12 x1n

x21 x

2

22 x2nxm1 xm2 xmn

2664 3775 1The fuzzy numbers used in this study are determined as trapezoidal type by thedecision makers. Ifxij is a trapezoidal fuzzy number as ~xij aij; bij; cij; dij

, the fuzzy

weights can be defined by ~wij aj;bj;gj; dj

.Step 2. The normalized decision matrix is constructed using equations (2) and (3)

(Negi, 1989; Chen and Hwang, 1992). When xij is fuzzy; its corresponding rij must be

fuzzy. Let ~xij aij; bij; cij; dij

and ~xj b2

j ; b2

j ; b2

j ; b2

j

.

~rij ~xij4~x*j

aij

b*j

;bij

b*j

;cij

b*j

;dij

b*j

!; j [ I 2~rij ~x

2

j 4~xij c2j

dij;

c2j

cij;

c2j

bij;

c2j

aij

; j [ I0 3

b2j ; b2

j ; b2

j ; b2

j

imax aij; bij; cij; dij; if j [ I

c2j ; c2

j ; c2

j ; c2

j

imin aij; bij; cij; dij; if j [ I

0

where I and I0 are the set of benefit and cost criteria, respectively.

Step 3. The weighted normalized decision matrix is calculated:

~V ~vij

mxni 1; 2; . . . ; m; j 1; 2; . . . ; n 4

where:~vij ~rij^ ~wj 5

Step 4. Each fuzzy number is defuzzified using equation (6) (Cheng and Lin, 2002). For atrapezoidal fuzzy number ~vij a; b; c; d its defuzzification value is defined as:


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vij a b c d

46

and defuzzified weighted normalized matrix (Chen and Hwang, 1992) determined as

equation (7):V vijmxn; i 1; 2; . . . ; m; j 1; 2; . . . ; n 7

Step 5. The ideal solution, A *, is the best performance scores and the negative-idealsolution, A 2 , is the worst performance scores. They are calculated usingequations (8)-(11). We assume for convenience that I0 is the set of cost type measuresand I is the set of benefit type measures:

A * v*1 ; v*2 ; . . . ; v

*n

8

v*j

i

minXij j [ I0 ; imaxXij j [ Ij ; i 1; 2; . . . ; m& '; j 1; 2; . . . ; n 9

A 2 v21 ; v2

2 ; . . . ; v2

n

10

v2j imaxXij j [ I

0 ;

iminXij j [ Ij ; i 1; 2; . . . ; m

& ';

j 1; 2; . . . ; n

11

Step 6. The distance of an alternative ito the ideal solution (d*i ), and from the negativeideal solution (d*j ) are calculated using equations (12) and (13):

d*

i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXm

j1vij

2v*

j

2vuuti 1; 2; . . .n; j 1; 2; . . . ; m 12

d2i

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXmj1

vij 2 v2

j 2

vuut i 1; 2; . . .n; j 1; 2; . . . ; m 13Step 7. The ranking score (C*i ) is calculated using equation (14). The obtained rankingscores represent the regional heads performance achievement. A higher scorecorresponds to a better performance (Chen and Hwang, 1992):

C*

i d2

i =d2

i d*i ; 0 # C

*i # 1 i 1; 2; . . . ; m 14

4. Determining the development level of provinces with FTOPSIS methodThe analysis of the model undertaken and determination of the weights (developmentindices) are as follows the five steps as follows.

Step 1. Organization of the expert evaluation teamFive-person team comprised of experts and academicians experienced in these studieswas organized so as to get their opinions in every level of the evaluation. The team

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possessed specific experience in determination of the criteria used in theimplementation phase and in criteria weights.

Step 2. Data collection

The data are classified as social and economic as described above. The social data weretaken from governmental institutions such as Turkish Statistical Institute, StatePlanning Organization (DPT), Ministry of National Education, Ministry of Health,General Directorate of Rural Services, General Directorate of Highways and TurkishTelecommunication Administration. For the economic criteria, the data from TurkishStatistical Institute, Undersecretariat of Foreign Trade, and Ministries of Industry,Agriculture, and Finance were used. The most up-to-date data were collected for theevaluation criteria (years 2005-2006) pertaining to 81 provinces of Turkey. The expertteam evaluated the 53 criteria during classification process stated in Section 2.

Step 3. The correlation test with criteria selectionSelection of criteria required application of formal procedures to obtain an independentset of approximately 8 ^ 2 criteria in multi-criteria analysis (Miller et al., 1990;Yurdakul and Ic, 2009b). Correlation tests are commonly used to measure thedependency between two variables. Based on the outcome of the correlation test, if itcan be concluded that there is a relationship between two criteria, one of them will beenough to predict their total behavior, and the other one can be eliminated.

The two hypotheses, namely:

H0. There is no positive relationship between the two criteria.

H1. There is positive relationship between the two criteria.

were tested with the correlation test. In the correlation test, correlation coefficient (r) isused to test the hypothesis and calculated using equation (Montgomery, 1996). In the

equations, n is the number of input value pairs, Xand Yare two selection criteria andX and Yare their average values. The calculated value of rcan range from 2 1 to 1;and it is independent of the units of measurement. A value of r near 0 indicates littlecorrelation between criteria; a value near 1 or 2 1 indicates a high level of correlation(Montgomery, 1996):

r

Xni1

Xi 2X Yi 2 YffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX

n

i1

Xi 2X2

!

Xn

i1

Yi 2 Y2

!vuut

15

In the application of equation (15), (n) corresponds to the number of criteria andXand Yare the criteria specification values for the criteria pair whose correlation coefficient isbeing calculated. The calculated correlation coefficient values for some criteria areprovided in Appendix Table AI. The calculated r values should be compared to apre-selected threshold value to eliminate the dependent criteria. In this study, thethreshold correlation coefficient value is selected as 0.65 and marked italics in AppendixTable AI. The corresponding value of T-statistic of the two-tailed t-distribution


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with 51 degree of freedom is calculated using equation (16) as 6.108. This valuecorresponds to 0.0005 p-value in the t-distribution (p-value , 0.05):

T rffiffiffiffiffiffiffiffiffiffiffiffiffin 2 2

1 2 r2r

16

Any criteria pair whose correlation coefficient value is (p # 0.65 or p $ 2 0.65)considered a correlated (dependent) pair. Using 0.65 as the threshold value, ten criteriaare selected out of 53 criteria. The independent criteria set includes population growth(PG), birth rate (BR), potable water per head (PW), parcels of organized industrial zone(PZ), public investment per head (PI), infant mortality rate (IM), inpatient bedavailability (BA), GDP per head (GP), agricultural production per rural population (AP),and rate of agricultural production (RA) and these factors are given with the names ofgeographical regions in Table II. Among these ten attributes PG, BR, PW, PZ, PI, BA,GP, and RA are beneficial in nature (where higher values are desirable), whereas, IM and

AP are non-beneficial attributes (where lower value is desirable).

Step 4. Evaluation of the selected criteria in fuzzy environmentAfter determination of the criteria for the model, the weights to be used in evaluationprocess were determined by the experts. The experts defined the trapezoidal fuzzy

Regions PG BR PW PZ PI IM BA GP AP RA

Istanbul 33.10 1.97 60.00 625 222.02 39.00 34.14 2,749.71 7.43 215.72Ankara 21.38 1.90 98.54 13,041 425.53 36.00 37.71 2,587.87 872.69 263.96Izmir 22.39 1.75 94.91 1,252 272.28 40.00 29.01 2,696.36 460.97 817.37Bursa 22.61 1.90 94.92 1,797 458.00 40.33 24.57 2,147.74 178.02 764.06

Kocaeli 17.70 2.08 96.54 597 307.45 43.00 19.79 3,248.37 46.86 1,375.98Tekirdag 13.55 1.73 95.17 636 188.99 37.00 22.22 2,321.52 311.50 1,029.73Adana 21.75 2.53 84.71 634 169.05 44.50 20.85 2,065.14 228.56 404.70Aydin 16.26 2.08 82.31 530 322.77 37.67 17.47 2,038.92 72.36 222.87Antalya 31.25 2.03 89.75 330 238.00 32.33 25.17 1,700.30 109.11 75.35Balikesir 9.15 1.82 91.74 517 271.11 37.50 22.95 1,887.75 76.93 278.24Zonguldak 0.10 2.01 65.64 160 152.26 41.67 25.86 1,892.47 11.00 225.47Manisa 9.99 2.33 89.06 1,657 98.92 42.00 21.29 1,578.51 83.17 229.71Konya 21.32 2.89 92.03 1,110 126.56 41.50 14.68 1,447.60 58.30 128.45Gaziantep 20.33 3.68 79.03 893 133.68 44.67 16.95 1,160.76 69.26 191.35Hatay 12.82 3.15 86.73 334 188.15 37.00 10.58 1,311.48 33.68 102.49Kayseri 8.73 2.74 96.93 769 268.58 46.67 22.51 1,152.16 94.96 190.75Kirikkale 10.07 2.63 94.81 832 185.52 41.40 16.70 1,533.92 89.23 416.16

Samsun 5.19 2.65 89.53 793 222.84 47.75 22.03 1,332.77 108.82 1,111.11Trabzon 9.32 2.40 81.35 270 289.33 35.50 21.80 1,160.48 528.66 84.30Malatya 11.06 2.64 81.27 359 490.68 42.50 24.14 1,180.31 59.15 87.21Kastamonu 0.09 2.31 83.21 238 237.05 46.00 29.01 1,275.85 87.05 84.44Erzurum 7.43 3.11 93.92 344 246.13 48.67 27.27 917.43 66.10 26.53Sanliurfa 29.09 4.67 71.02 566 378.38 47.00 14.77 926.77 29.51 16.20Mardin 23.38 5.84 75.80 274 41.01 51.75 6.68 761.51 14.64 28.30Agri 5.05 4.09 69.07 68 150.50 63.00 7.96 621.52 100.84 13.26Van 25.47 5.48 76.57 100 140.26 55.75 11.17 610.53 19.36 13.92

Table II.Decision matrix theindependent criteriaset (Step 1)

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weights to assign each criterion to evaluate the importance of each criterion.The linguistic weighting variables are shown in Table III and the aggregate weights ofeach criterion are given in Table IV.

Thus, the more accurate results are obtained with the selected criteria in the fuzzy

environment. Hence, FTOPSIS application steps are illustrated in Tables V and VI.

Step 5. The comparative analysis with the current modelThe rankings obtained with the ten independent criteria were compared with therankings obtained with the DPT results. To determine the statistical significance of thebenefits achieved by the lower number of independent criteria, Spearmansrank-correlation test was used. Spearmans rank-correlation test, which is a specialform of correlation test, is used when the actual values of paired data are substitutedwith the ranks which the values occupy in the respective samples (Miller et al., 1990).In this study, Spearmans test evaluated the similarity of the results (rankings of themachine tools for various cases). To test the null hypothesis in the Spearmans test,(H0), a test statistic, Z, was calculated using equations (17) and (18) and compared witha pre-determined level of significance a value. In this study, 1.645 was selected as thecritical Z-value at the level of significance ofa 0.05. If the test statistic computed byequation (18) exceeded 1.645, the null hypothesis was rejected and it was concludedthat there is evidence of a positive relationship between the two sets of rankings. In theequations (17) and (18), dj represents the ranking difference of machine tool j, K is thenumber of machine tools to be compared and rs represents the Spearmansrank-correlation coefficient:

Linguistic variables Weights

Very low (VL) 1,2,2,3Low (L) 2,3,3,4Medium low (ML) 4,5,5,6Medium (M) 6,7,7,8Medium high (MH) 7,8,8,9High (H) 8,9,9,10Very high (VH) 9,10,10,10

Table III.Linguistic variables ofthe importance weight

Criteria Weights

PG MH

BR MHPW VLPZ VLPI LIM MLBA MGP VHAP LRA VL

Table IV.Trapezoidal fuzzy

weights of eachevaluation criterion

by three experts


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12/20

rs 1 2

6 XKj1

dj2

K K2 2 1

266664

377775 17

Z rsffiffiffiffiffiffiffiffiffiffiffiffiffiffi

K 2 1p

18

The results are given in the columns under the main heading analysis of differencesin Table VII. The Spearman rank correlation test results show that the differencesbetween rankings are not statistically significant (the Z-values, calculated in the lastrows 4.49, is above 1.645). Figure 2 shows the graphics of the rankings of the regionsutilizing both the two methods.

In Figure 2, the significant differences in rankings are highlighted for eightprovinces. These geographic regions are Adana, Konya, Gaziantep, Hatay, Kayseri,Trabzon, Malatya, and Kastamonu. It is thought that the calculated criteria weightsand balanced criteria usage contributed to these differences.

FTOPSIS resultsRegions d* d2 C* Rank

Istanbul 5.6646 17.9446 0.7601 3Ankara 4.7853 18.0257 0.7902 2Izmir 4.4744 19.1470 0.8106 1Bursa 5.8018 17.0341 0.7459 4Kocaeli 6.8742 16.1830 0.7019 6Tekirdag 6.9904 18.2847 0.7234 5Adana 9.8217 12.1962 0.5539 11Aydin 8.3892 14.4501 0.6327 9Antalya 7.4265 16.1692 0.6853 7Balkesir 8.3802 16.6745 0.6655 8Zonguldak 10.6304 14.4280 0.5758 10Manisa 10.7675 11.9223 0.5254 12Konya 12.6119 9.7326 0.4356 18Gaziantep 15.2636 7.5370 0.3306 22

Hatay 14.5263 7.5590 0.3423 21Kayseri 12.9350 9.5364 0.4244 19Kirikkale 12.2001 10.0177 0.4509 16Samsun 12.7055 9.9958 0.4403 17Trabzon 11.7258 11.3004 0.4908 14Malatya 12.1929 10.4174 0.4607 15Kastamonu 12.1387 12.1244 0.4997 13Erzurum 14.5496 8.2655 0.3623 20Sanliurfa 17.2795 8.1941 0.3217 23Mardin 19.7222 6.1399 0.2374 25Agri 18.3613 4.9593 0.2127 26Van 19.1275 6.8443 0.2635 24

Table VI.Calculation of FTOPSIS

ranking results(Steps 4-7)


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5. Conclusion and discussionIn this study, FTOPSIS method was performed to evaluate the rankings of thesocio-economic development level of the provinces. The data related with the social andeconomic criteria are collected from public enterprises and financial institutions. Theselected 53 criteria were reduced by using inter-correlations among them and then theobtained ten independent criteria were used to provide the independency principle ofthe FTOPSIS method. The main advantage of this method is its simplicity and abilityto yield an indisputable preference order. Additionally, to prevent subjectivity ofdecision makers, the evaluation has been carried out in the fuzzy environment. Human

judgments are presented with crisp values in the traditional TOPSIS. Therefore, thehuman preference is usually uncertain and decision-makers might be unable to assigncrisp values to the comparison judgments in this study. When the decision-maker facesa uncertain situation such as assigning criteria weights, his/her comparison judgmentsas uncertain ratios, such as about two times more important, between two and fourtimes less important, etc. the standard TOPSIS steps cannot be considered as straightforward procedures. In order to overcome this insufficiency, a fuzzy extension ofTOPSIS is developed to solve the provinces ranking.

Analysis of differencesRegions FTOPSIS ranking results DPT results Difference

Istanbul 3 1 2 2Ankara 2 2 0Izmir 1 3 2Bursa 4 4 0Kocaeli 6 5 2 1Tekirdag 5 6 1Adana 11 7 2 4Aydin 9 8 2 1Antalya 7 9 2Balkesir 8 10 2Zonguldak 10 11 1Manisa 12 12 0Konya 18 13 2 5Gaziantep 22 14 2 8

Hatay 21 15 2 6Kayseri 19 16 2 3Kirikkale 16 17 1Samsun 17 18 1Trabzon 14 19 5Malatya 15 20 5Kastamonu 13 21 8Erzurum 20 22 2Sanliurfa 23 23 0Mardin 25 24 2 1Agri 26 25 2 1Van 24 26 2rs

0.899

Z 4.49

Table VII.The comparison of therankings for FTOPSIS

method and DPT results

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The collected data were categorized and classified under the observation of the expert

team and were also weighted accordingly. The five experts described trapezoidal fuzzynumbers in this stage which are more accurate and suitable. The decision matrices wereconstituted and the steps of FTOPSIS method were followed. Thus, the rankings of thedevelopment levels of provinces were obtained in homogenous groups.

The examination of rankings showed that only four regions had similar rankingswhile the rankings of the remaining 22 regions differed according to the authorityrankings. In addition, there were significant differences observed for eight regions.The results of this study and high accuracy garnered in comparison with the currentsystem the previous utilized methods will be presented to the public authorities inTurkey. The diversities demonstrated the sensibility of the fuzzy multi-criteria methodin this study.

In previous studies on this concept, GDP was taken as the best evaluation criteria andthe analysts preferred to investigate the contribution rate of the criteria to the GDP.Thus, the contribution level of all the criteria in the whole was taken into account. Assome of the values used were rate per head but others were absolute values, exact resultcould not be obtained. The calculations are performed only with quantitative evaluation.The contribution of the qualitative criteria to the evaluation system is usually inevitable.

One of the alternative methods in those studies is the clustering analysis. In this typeof analysis, the relevant results are not obtained because of existence of multi-criteria,quite often the subjectivity is obligatory and additionally, the evaluation scale became

Figure 2.The ranking differences

of provinces

28

FTOPSIS DPT

2726

25242322212019181716151413121110

9

876543210

Istanbul

Ankara

Izmir

Bursa

Kocaeli

Tekirdag

Adana

Aydm

Antalya

Balikesir

Zonguldak

Manisa

Konya

Gaziantep

Hatay

Kayseri

Kirikkale

Samsun

Trabzon

Malatya

Kastamonu

Erzurum

Sanliurfa

Mardin

Agri

Van


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empirical after further replications. The results of these analyses were not acceptableaccording to the proof marks.

Most of the methods utilized in the previous studies are not relevant to theseanalyses. This type of studies has generally depended on statistics and regression

analysis, such as path analysis and principal components analysis. This situation isthe deficiency of these perpetuated studies since the criterion used in these studies hasonly quantitative features. Moreover, this kind of research could not be described as amulti-criteria problem. Thus, FTOPSIS method, which is inhold all the specificationsrequired above, is preferred in this problem.

The Helsinki European Council in 1999 decided that Turkey is a candidate countrythat can join the union on the basis of the same criteria as applied to the other candidatecountries. Turkey will benefit from a pre-accession strategy to stimulate and supportits reforms. Two years later, the accession partnership with Turkey was adopted bythe council and the National Program for the Adoption of the Acquis was approved bythe government (TR Prime Ministry State Planning Organization, 2003).

In order to provide the general framework, a medium-term regional developmentstrategy of Turkey for 26 NUTS II regions (nomenclatures of territorial units forstatistics II) is developed. It should be noted that there are significant differencesbetween the economic and social development levels of 26 NUTS II regions in Turkeyas well as between that of Turkey and many European Union countries. Therefore, it isnecessary to ensure convergence in the field of economic and social cohesion toimplement successful regional programs aiming the reduction of regional inequalities.

A human-oriented development approach targeting effective activation of economicand social potentials of regions is adopted in this strategy. On this basis localdevelopment, initiatives will be supported, institutional capacities will be developed,funds will be provided for infrastructure investments and rural development will bepromoted. Thus, the main objective is to reduce economic and social inequalities

between regions by stimulating job creation and increasing competitive powers of lessdeveloped regions. Supporting and strengthening of small- and medium-sizedenterprises, supporting small-scale infrastructure construction, supporting localinitiatives, building, and strengthening institutional capacity are the priority fields ofmedium-term regional development strategy at national level.

The socio-economic evaluation required for the effective management of themedium-term regional development strategy is achieved more precisely throughFTOPSIS approach that employs the information about the geographic regionalfactors. The outcomes of the study are expected to guide the selection of developingprovinces more effectively and to improve the efficiency of public funds.

The evaluation of public fund performance has not been analyzed effectively inapplications. In order to manage these funds, evaluation procedures would be performed

in details and the obtaining feedback information should be provided for selecting thedeveloping provinces for the future works (Moon and Sohn, 2005). In recent years,various kinds of public funds have been devoted to select under developed provinces inTurkey, but their effects have not been evaluated so far. In order to ensure effectivemanagement of such funds, the feedback regarding to the achievements of previousfunds is required in the selection of developing provinces in future.

The socio-economic evaluation based on FTOPSIS approach for the effectivemanagement of the public funds with information about the geographic regional

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factors is eventually obtained. The results of the study are expected to guide theselection of developing provinces and improve the efficiency of public funds.

In order to recommend further approaches that can be used for the performanceevaluation of the funds, the environmental factors in details are needed so as to reflect

the present conditions of them. More specific performance variables should also beknown to appraise the performance of public funds more realistically. This problem isleft for further research.

Consequently, the proposed model is highly flexible for the intended use bychanging the criteria weights. For example, the socio-economic ranking level of theregions can be a reliable reference for loan distribution of the banks to the regionalbranches and can be used as a guide model for both the public and private sectors intheir studies to prevent inter-regional disparity.

This study also examines how the state-banks in Turkey allocate their commercialor private loan budgets to provinces. Basing on these results of the study (ranking ofprovinces) state-banks can extend their loans to developing provinces, which impliesthat they use commercial judgments in this segment of the market.

In addition, FTOPSIS method can also be combined with operations researchmodels. We find also that FTOPSIS results appear to be used in Turkish private bankscommercial loans decision-making processes. For example, an integration of FTOPSISand mathematical programming method is proposed to consider both tangible andintangible factors in choosing the best provinces and in determining the optimumregional credit concentration limits among them.

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Corresponding authorErgun Eraslan can be contacted at: [email protected]

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Appendix

Criteria

1

2

3

4

5

6

7

8

9

.

.

.

5

3

1

1.0

0000

0.6

9090

0.4

3160

0.8

9080

0.51460

2

0.1

9470

2

0.28840

2

0.7

2620

0.6

2330

.

.

.

0.63240

2

0.6

9090

1.00000

0.50160

0.5

0910

0.6

5270

2

0.24890

2

0.39460

2

0.8

7060

0.7

1750

.

.

.

0.53170

3

0.4

3160

0.50160

1.00000

0.3

1470

0.7

5860

0.22150

2

0.0

4390

2

0.4

5800

0.36420

.

.

.

0.28200

4

0.8

9080

0.50910

0.31470

1.0

0000

0.4

1820

2

0.16830

2

0.2

1790

2

0.6

1110

0.60820

.

.

.

0.55430

5

0.51460

0.6

5270

0.7

5860

0.4

1820

1.0

0000

2

0.34270

2

0.5

9030

2

0.7

2680

0.7

1630

.

.

.

0.66790

6

2

0.19470

2

0.24890

0.22150

2

0.16830

2

0.34270

1.00000

0.9

0230

0.4

2420

2

0.52120

.

.

.

20.62360

7

2

0.28840

2

0.3

9460

2

0.0

4390

2

0.21790

2

0.59030

0.9

0230

1.0

0000

0.5

8200

2

0.64050

.

.

.

2

0.76070

8

2

0.7

2620

2

0.8

7060

2

0.4

5800

2

0.61110

2

0.7

2680

0.4

2420

0.58200

1.0

0000

2

0.8

6070

.

.

.

2

0.75460

9

0.62330

0.7

1750

0.3

6420

0.60820

0.7

1630

2

0.5

2120

2

0.6

4050

2

0.8

6070

1.00000

.

.

.

0.70010

. ..

. ..

. ..

. ..

. ..

. ..

. ..

. ..

. ..

. ..

. ..

. ..

53

0.6

3240

0.5

3170

0.2

8200

0.55430

0.6

6790

2

0.6

2360

2

0.7

6070

2

0.7

5460

0.7

0010

.

.

.

1.00000

Table AI.The criteria correlation

matrix


regions

909

a multi criteria

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