Determination of individuals
’ life
satisfaction levels living in
Turkey by FMCDM methods
Nimet Yap
ıcı Pehlivan
Department of Statistics, Selcuk Universitesi, Konya, Turkey, and
Zeynep Gürsoy
Turkiye Istatistik Kurumu, Ankara, Turkey
Abstract
Purpose–This study aims to determine the ranking of the 81 provinces at the NUTS-3 level in Turkey with respect to the personal satisfaction and public services satisfaction by applying Fuzzy Multi-Criteria Decision-Making methods to the Life Satisfaction Survey Results.
Design/methodology/approach – Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS are implemented to assess life satisfaction of the individuals who lived in provinces, based on Life Satisfaction Survey 2013 for Turkey’s national comparison. In the solution process, 14 indicators for personal satisfaction and 38 indicators for public services satisfaction were considered.
Findings– The results showed that personal health satisfaction, earnings from work satisfaction and monthly income satisfaction are the most important criteria in terms of personal satisfaction. Also, healthcare services satisfaction, judicial services satisfaction and education services satisfaction have the highest importance in terms of public services satisfaction. Thefinal ranking of the 81 provinces is obtained by considered methods. According to the ranking results, there is no significant difference between the east and the west part of Turkey in terms of personal satisfaction, whereas there is a distinct difference between them in terms of satisfaction with public services.
Originality/value–This study is thefirst research for evaluating the ranking of the provinces at the NUTS-3 level in Turkey according to the Life Satisfaction Survey 2013 results considering 14 indicators for personal satisfaction and 38 indicators for public services satisfaction by using FMCDM approaches that have not been applied before.
Keywords Decision-making, Optimization techniques, Operational research, Knowledge management, Fuzzy logic, Fuzzy multi-criteria-decision-making Paper type Research paper
1. Introduction
Life satisfaction is defined as enduring satisfaction with people’s lives as a whole and commonly referred to as happiness. Happiness can be defined as the overall appreciation of one’s life as a whole. When used in a broad sense, the word ‘happiness’ is synonymous with quality of life or well-being. Happiness in this sense is a state of mind, which cannot be assessed objectively in the same way as weight or blood pressure. Happiness cannot be measured with access to merit goods, as the effect of such life chances depends on life abilities (Veenhoven, 2004). Happiness has divided into three factors by some psychologists: subjective well-being, life satisfaction and depression or lack of anxiety. Subjective well-being is defined as the moods or feelings which people have of joy or gladness. Life satisfaction refers to qualities or living conditions that can lead to satisfaction or dissatisfaction with respect to personal wealth, family relationships, community involvement, employment, target achievement, etc. The absence of
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Received 17 April 2018 Revised 10 September 2018 Accepted 17 October 2018 Kybernetes Vol. 48 No. 8, 2019 pp. 1871-1893© Emerald Publishing Limited 0368-492X DOI10.1108/K-04-2018-0184
The current issue and full text archive of this journal is available on Emerald Insight at:
depression, anxiety, insecurity, etc., does not form happiness or well-being, but it is still an important prerequisite (Duncan, 2010). Today, governments around the world have begun to initiate policies for enhancing individual happiness levels (Gökdemir, 2015#1).
Multiple criteria making (MCDM) is the most well-known branch of decision-making. MCDM problem is to select an appropriate alternative among afinite number of feasible alternatives in the presence of multiple, generally conflicting criteria. MCDM problems are classified into two categories: multi-objective decision-making (MODM) and multi-attribute decision-making (MADM), depends on the domain of the alternatives, that is, continuous or discrete (Brauers and Zavadskas, 2006;Rao, 2007;Brauers and Zavadskas, 2010). Because the judgments and preferences of decision-makers are affected by uncertainty, the use of definite and precise numbers in linguistic judgments is unreasonable (Calabrese et al., 2013). Therefore, various fuzzy MCDM methods have been proposed by several authors for the selection, ordering and classification of the alternatives considering the fuzzy set theory.
There are a number of data sets containing subjective well-being measures which cover a large number of countries. Gallup World Survey (GWS) and the World Values Survey (WVS) which are the two largest data sets, includes comparable subjective well-being measures across countries. For completing WVS and GWS, many supplementary surveys exist in various forms. Among them, the European Social Survey ensures knowledge about well-being aspects for European countries. Besides, the European Quality of Life Survey includes comprehensive knowledge about subjective well-being. Eurobarometer is an opinion survey containing relatively limited questions on subjective well-being which is regularly performed on European Union nations. Similar questions are also included in the Latinobarometro for Latin America countries. In addition to these cross-sectional surveys, various panel surveys such as the German Socio-Economic Panel and the British Household Panel Study are available for analyzing the subjective well-being (OECD, 2013). Further, the OECD Better Life Index (BLI) is used for comparing well-being across countries according to the weights given to the dimensions. The BLI is composed of essential indicators of well-being which are housing, income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety and work-life balance to measure overall satisfaction with life for OECD countries. According to the report, general life satisfaction level which is scaled between 0 and 10, of OECD countries is 6.5, while Turkey has a relatively low level with 5.5 (OECD, 2016;Lorenz et al., 2017). In Turkey, Life Satisfaction Surveys have been applied to the individuals in terms of the personal satisfaction and public services satisfaction from the year 2003.
To the best of our knowledge, there are no studies on evaluation of the personal satisfaction and public services satisfaction which take into account the various criteria using FMCDM methods. The proposed methods are thefirst to use them in the assessment of the personal satisfaction and public services satisfaction of provinces at the NUTS-3 level in Turkey based on Life Satisfaction Survey 2013.
The remainder of the paper is organized as follows: The next section briely reviews the literature on life satisfaction and multi-criteria decision-making, while Section 3 explains Life Satisfaction Survey of Turkey. Section 4 provides brief information on three FMCDM methods, Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS. In Section 5, the ranking results of the proposed FMCDM methods are presented. Finally, summary and conclusion of the study are drawn in the last section.
2. Literature review
This section presents a brief review on Life Satisfaction and Life Satisfaction Survey of Turkey, followed by the Fuzzy Multi-Criteria Decision-Making methods.
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Several studies have been carried out on life satisfaction, happiness and subjective well-being by various authors.Diener et al. (1985)reported the developments and validation of a scale to measure global life satisfaction called as Satisfaction with Life Scale (SWLS), among the various components of subjective well-being.Myers and Diener (1995) presented the elements of an appraisal-based theory of happiness that recognizes the importance of adaptation, cultural worldview and personal goals.Veenhoven (1996)reviewed the literature on life satisfaction by considering six questions at the individual level as well as the societal level.Kousha and Mohseni (1997)explored life satisfaction among married and unmarried Iranian women in urban areas.Diener (2000)reviewed and described selectedfindings on Subjective Well-Being.McConatha et al. (2004)compared the life satisfaction of adults from the USA, Turkey and Germany, and they determined the significant mean differences between the countries.Peterson et al. (2005)investigated different orientations to happiness and their association with life satisfaction by considering 845 adults responding to internet surveys.Steger and Kashdan (2007)evaluated the longitudinal stability of the Meaning in Life Questionnaire (MLQ) and SWLS which are two well-being measures.Dumludag et al. (2015)analyzed the effects of social comparison on a variety of reference groups on the life satisfaction of Turkish immigrants living in The Netherlands.Thieme and Dittrich (2015)
analyzed age heterogeneity with regard to current life satisfaction and life satisfaction domains which measured as satisfaction with work, income, family and health.Eksi and Kaya (2017)investigated the comparison of people’s well-being taking into account of the well-being of their home country folks as well as other country citizens by using life satisfaction data obtained from the World Database of Happiness.
There are many studies on life satisfaction, happiness and subjective well-being deal with Turkey. Among them,Gitmez and Morcöl (1994)discussed the results of a survey performed in Turkey to find the impacts of socio-economic status on satisfaction with variousfields of life by using univariate, bivariate analyses and the multiple discriminant analysis.Selim (2008)investigated life satisfaction and happiness in Turkey and extended the previous studies on subjective well-being (SWB) for Turkey taking into consideration happiness and life satisfaction. Toker (2012) examined the life satisfaction levels of academicians in Turkey and investigated the effects of demographics on life satisfaction among them. Dumludag (2013) evaluated the relative impact of internal and external comparisons on subjective well-being in Turkey.Ekici and Koydemir (2014)explored the relationship between a variety of social capital indicators, satisfaction with government and democracy and subjective well-being in Turkey. In the study, happiness and life satisfaction were used as outcome measures of subjective well-being and a comparison of European Values Surveys in 1999 and 2008 was given.Gökdemir (2015)appraised the impacts of seven different components of consumption expenditures, on subjective well-being in Turkey based on Life in Transition Survey which was conducted by the European Bank for Reconstruction and Development and the World Bank.Sandikci et al. (2016)offered a socio-temporally situated understanding of QoL in a developing country setting and investigate the effects of macrostructures on consumer well-being under the context of Turkey.
On the other hand, there are quite a few studies based on the results of the Life Satisfaction Survey for Turkey. Dumludag et al. (2016) examined the impact of income comparisons on life satisfaction in Turkey using ordered logit estimations based on the results of the LSS for 2011.Caner (2015)investigated the determinants of happiness with an emphasis on comparison effects and expectations about own future income. To perform regression analyses, nationally representative cross-sectional data are collected by LSS of Turkey in years 2003-2011.Eren and Asici (2016)analyzed the determinants of happiness in
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Turkey by applying an ordered logit model based on the results of LSS during the period between 2004 and 2013.
There are numerous numbers of studies on MCDM and FMCDM. Some of the recent articles are given as follows:Merigo (2010)proposed a decision-making model by using immediate probabilities and information. He also suggested an aggregation operator called as the Immediate Probability-Fuzzy OWA (IP-FOWA) operator using the OWA operator, fuzzy numbers and probabilistic information.Ülker (2015)proposed a FMCDM algorithm to determine the best remotely operated vehicle (ROV) design alternative. Beheshtinia and Omidi (2017)proposed a hybrid MCDM technique for performance evaluation of banks. Performance evaluation criteria of the banks considering the balanced scorecard (BSC) methodology and corporate social responsibility (CSR) were determined by AHP and modified digital logic (MDL) techniques. Then, the banks were ranked by using fuzzy TOPSIS and fuzzy VIKOR. Cortez Alejandro (2017) aimed to analyse the differences in financial performance portfolios between sustainable and non-sustainable firms thanks to the use of portfolio theory and OptQuest algorithms from 2007 to 2013.Durmusoglu (2018)
proposed an approach for assessing the sustainability aspect of facility layouts. Alternatives of activity relationship chart which is a widely used assistive tool for facility layout design assessing closeness requirement among all pairs are evaluated with respect to environmental aspect, safety concerns and manufacturing efficiency factors by using TOPSIS method.
Among the various multi-criteria decision-making problems, TOPSIS and its fuzzy extension have been attracted the great interest of researchers. Fuzzy TOPSIS method has been applied to many different research areas by various researchers, such as supplier/green supplier evaluation and selection, project evaluation and selection, location selection, site selection, etc. For example,Chen et al. (2006),Wang and Elhag (2006),Önüt et al. (2010),Celik et al. (2009), Kahraman et al. (2009),Torfi et al. (2010), Rostamzadeh and Sofian (2011),
Rasoul and Mohammad (2013), Kaya and Kahraman (2014), Lima Junior et al. (2014),
Roszkowska and Wachowicz (2015)and ahin and Yapici Pehlivan (2017)S .
MOORA method wasfirst introduced byBrauers and Zavadskas (2006)and applied on privatization in transition economies.Brauers and Zavadskas (2010) proposed a project management for a transition problem by applying MOORA and MULTIMOORA methods which are supported by Ameliorated Nominal Group and Delphi Techniques. Kildiene (2013)aimed to rank European Union (EU) member states according to business conditions and measure their potential for development of the construction sector Small and Medium Enterprises by using MULTIMOORA. Brauers et al. (2011) introduced fuzzy MULTIMOORA to rank the EU Member States in three groups based on the cited domination principles and according to their performance in reaching the goals of the Lisbon Strategy 2000-2008.Balešentis et al. (2011)proposed MULTIMOORA method for international comparison of the well-being in the EU member states.Baležentis et al. (2012a)
introduced the fuzzy MULTIMOORA for group decision-making (MULTIMOORA–FG) to determine the best candidate for a personnel selection problem. Zavadskas et al. (2010)
applied ARAS method for the selection process of the foundation installment by taking into consideration several criteria.Turskis and Zavadskas (2010)proposed fuzzy ARAS method to determine the most suitable site for the logistic center.Baležentis et al. (2012b)proposed a procedure based on financial ratios and fuzzy VIKOR, fuzzy TOPSIS and fuzzy ARAS methods for integrated assessment and comparison of economic sectors in Lithuania.
Keršulien_e and Turskis (2014)introduced a FMCDM algorithm which consists of the fuzzy ARAS and the AHP methods tofind and to promote the most adequate chief accountant.
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3. Life satisfaction survey of Turkey
Life satisfaction is focused on how individuals belonging to certain communities experience their lives as worthwhile, enjoyable and fulfilling (McConatha et al., 2004). Life Satisfaction Survey (LSS) in Turkey has been conducted by the Turkish Statistical Institute (TurkStat) as an official statistical study. LSS is aimed to measure the general satisfaction and public services satisfaction of individuals in the areas of happiness, hopes and basic life, such as health, social security, formal education, work life, personal security and judicial services, personal development, etc. It is thefirst survey of TurkStat in terms of including social contents and subjective elements.
LSS wasfirst implemented in 2003 as an additional module of the Household Budget Survey. It has been conducted as an independent survey every year since 2004. The survey consists of two parts: the household part includes questions about household level variables. The individual part contains questions about happiness, expectations, current or past situation and demographic characteristics at the individual level (Caner, 2015).
The sampling framework of the LSS is based on the records of the National Address Database and the Address-Based Population Registration System. Sampling units are selected from the household and individuals by using a two-stage stratified cluster sampling method. Demographic information, such as age, gender, education status, household relationship, etc., about all individuals of the household, are collected. The coverage of LSS includes household living conditions, individual happiness and satisfaction, satisfaction with public services, expectations, personal development and prospects, values, EU membership perspective, etc. The survey is carried out every November and the results of the survey are published according to the rural and urban details in Turkey. The results of the LSS have been presented in press releases, publications, microdata, statistical tables and databases on the country level and the province level as well as the urban and rural emplacement level. LSS covers all private households who are aged 18 years and over citizens of the Republic of Turkey and also foreign nationals, except institutional population such as dormitories, rest homes for the elderly, private hospitals and military barracks (OfficialStatistics, 2014;TurkStat,2014a, 2014b). LSS was conducted by the TurkStat for the first time in 2013 at the NUTS-3 level, that is, province level and is designed to be implemented triennial. Within the scope of LSS-2013, 125.720 households were visited and face-to-face interviews were made with 196.203 people. The satisfaction of municipal services under general public services includes the satisfaction of all municipal services within the provincial borders (TurkStat,2014a, 2014b).
It should be noted that there is no overall index for evaluation of the personal satisfaction and public services satisfaction in Turkey which take into account all criteria. Therefore, ranking results of all provinces have been given as percentages according to each criterion individually in the press releases related to Life Satisfaction Survey published by TurkStat. The superiority of our study is that thefirst to use FMCDM methods in the evaluation of the personal satisfaction and public services satisfaction of provinces considering all related criteria at NUTS-3 level in Turkey based on Life Satisfaction Survey 2013.
4. Theoretical foundations
Fuzzy set theory (FST) was introduced byZadeh (1965)in a pioneer work. In the study, a fuzzy set is characterized by a membership function which assigns to each object a membership degree ranging between 0 and 1. The notions of inclusion, union, intersection, complement, relation, convexity, etc., were given and various properties of these notions were set (Zadeh, 1965). After that, FST has been applied to many differentfields such as operation research, decision-making, engineering, expert systems, etc. FST has been also
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widely used for multiple criteria decision-making problems (MCDM) based on imprecise and vague information represented by linguistic variables in the opinion of the decision-makers’ opinion.
MCDM is one of the most widely used decision-making methodologies including multiple and usually conflicting quantitative and qualitative criteria. In recent years, to deal with the uncertainty and vagueness from the subjective perception and the experience of people in the decision-making process, various Fuzzy Multiple criteria decision-making (FMCDM) techniques have been proposed by considering the fuzzy set theory. Some of them are Fuzzy Analytic Hierarchy Process, Fuzzy Simple Additive Weighting, Fuzzy Technique for Order Preference by Similarity to Ideal Solution, Fuzzy Multi-Objective Optimization by Ratio Analysis, Fuzzy Additive Assesment Ratio, etc. Both MCDM and FMCDM techniques are applied in various fields such as engineering, technology, science, business and management, energy and environment, economy, production and so on (Mardani et al., 2015a,2015b).
Several authors investigated researches on fuzzy set theory and fuzzy multi-criteria decision-making.Merigo et al. (2015)made a general representation on fuzzy research from 1965 using bibliometric techniques. They aimed to demonstrate the most efficient and effective research in the scientific community with respect to the information in the Web of Science (WoS) which is classified by articles, authors, journals, institutions and countries.
Mardani et al. (2015a)reviewed the literature on MCDM techniques and applications from 2000 to 2014. Mardani et al. (2015b) analyzed the studies on FMCDM techniques and applications between 1994 and 2014.Blanco-Mesa et al. (2017)presented an overview of the main research in the fuzzy decision-makingfield by using a bibliometric approach. Various bibliometric indicators including the citations and the h-index were used according to the information obtained from the WoS. Also, VOS viewer software was used to illustrate the main trends in the fuzzy decision-making area.
To evaluate the criteria and the alternatives, linguistic variables are used by decision-makers (or experts) and converted to corresponding fuzzy numbers for FMCDM methods. Thus,~wkj describes the fuzzy weight of the criterion j (j = 1,2,. . .,n), given by the decision maker k which are identified by using appropriate linguistic variables or alternatively obtained by any FMCDM methods. Similarly,~xkijdescribes the fuzzy rating of the alternative i (i = 1,2,. . .,m), with respect to criterion j, given by the decision-maker k (Chen et al., 2006;
Lima Junior et al., 2014;Önüt et al., 2010;Roszkowska and Wachowicz, 2015). Some of the advantages of FMCDM methods are simplicity, rationality, comprehensibility, computational easiness and efficiency and the ability to measure the relative performance for each alternative in a simple mathematical form (Yeh, 2002).
Linguistic variables and their corresponding triangular fuzzy numbers (TFNs) to evaluate the criteria and the alternatives for FMCDM methods are shown inTables IandII, respectively (Kahraman et al., 2007).
In this study, three FMCDM methods, Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS, were considered to evaluate the life satisfaction of the individuals lived in provinces, based on LSS 2013 for Turkey. The computation process of these methods is very similar to each other and simple. These FMCDM methods were chosen because they can easily be applied to our large-scale multi-criteria decision-making problem with 81 provinces, 14 indicators for personal satisfaction and 38 indicators for public service satisfaction.
4.1 Fuzzy TOPSIS method
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the most well-known MCDM techniques developed byHwang and Yoon (1981). In the method, a
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closeness coefficient is defined to determine the ranking order of all alternatives by calculating the distances to both the positive-ideal solution (PIS) and negative-ideal solution (NIS) simultaneously. The Fuzzy TOPSIS method proposed byChen (2000)have been used for solving the MCDM problems under fuzzy environment and explained as follows:
Step 1: The appropriate linguistic variables for the evaluation of criteria and
alternatives are determined by decision-makers (or experts) and converted to corresponding TFNs.
Step 2: The fuzzy weights of criteria and the fuzzy ratings of alternatives given by k
decision-makers are aggregated as:
~wj¼1k ~w1j ~w 2 j . . . ~w k j h i (1) ~xij¼1k ~x1ij ~w 2 ij . . . ~x k ij h i (2) where;~wj¼ a j; bj; djand~xij¼ aij; bij; cij are TFNs.
Step 3: The fuzzy decision matrix -~D¼ ~x ij and the fuzzy weight vector- ~W ¼ ~w j
are constructed as follows:
~D ¼ C1 C2 Cn A1 A2 ... Am ~x11 ~x12 ~x1n ~x21 ~x22 ~x2n ... ... ... ... ~xm1 ~xm2 ~xmn 2 6 6 6 4 3 7 7 7 5; ~ W ¼ ~w½ 1; ~w2; . . . ; ~wn
Step 4: The normalized fuzzy decision matrix- ~N¼ ~r ij by using linear scale
transformation is given as:
Table I. Linguistic variables for the evaluation of criteria
Linguistic variable Triangular fuzzy no.
Very low (VL) (0, 0, 0.2) Low (L) (0, 0.2, 0.4) Medium (M) (0.3, 0.5, 0.7) High (H) (0.8, 0.8, 1) Very High (VH) (0.8, 1, 1) Table II. Linguistic variables for the evaluation of alternatives with respect to the criteria
Linguistic variable Triangular fuzzy no.
Very Low (VL) (0, 0, 20) Low (L) (0, 20, 40) Medium (M) (30, 50, 70) High (H) (80, 80, 100) Very High (VH) (80, 100, 100)
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~rij¼ aij c* j ;bij c* j ;cij c* j ! ; c*
j ¼ maxicij; j 2 B Benefit criteriað Þ aj cij ; aj bij; aj aij ; a
j ¼ miniaij; j 2 C Cost criteriað Þ 8 > > > > < > > > > : (3)
Step 5: The weighted normalized fuzzy decision matrix- ~V ¼ ~vij is computed
by:
~vij¼ ~rij ~wj (4)
Step 6: The Fuzzy Positive Ideal Solution (A) and the Fuzzy Negative Ideal Solution
(A–) are defined as A*¼ ~v*1; ~v*2; . . . ~v* n h i and A¼ ~v 1; ~v2; . . . ~vn, respectively. Where; v* j ¼ 1; 1; 1ð Þ and vj ¼ 0; 0; 0ð Þ.
Step 7: The distances d*
i and di of each alternative from fuzzy positive ideal
solution and fuzzy negative ideal solution are calculated byEquations (5)and(6),
respectively: di*¼X n j¼1 d ~vij; ~v*j ; i ¼ 1; 2; . . . ; m (5) di¼X n j¼1 d ~vij; ~vj ; i ¼ 1; 2; . . . ; m (6) where; d ~A; ~Bindicates the distance between two fuzzy numbers and computed by the Vertex Method.
Step 8: The closeness coefficients of each alternative are calculated as follows:
CCi¼ di diþ d*
i
(7)
The alternatives are ranked in descending order of the CCiindex (Celik et al., 2009;Chen,
2000).
4.2 Fuzzy MULTIMOORA method
MOORA introduced byBrauers and Zavadskas (2006) consists of two parts: the ratio system and the reference point approach. Then, MULTIMOORA (Multiplicative form of MOORA) method was developed byBrauers and Zavadskas (2010). Fuzzy MULTIMOORA method based on MULTIMOORA with fuzzy numbers is proposed byBrauers et al. (2011)
and summarized as follows:
Step 1-3: Same as in Fuzzy TOPSIS algorithm.
Step 4: The fuzzy normalized decision matrix ~N ¼ ~rij is constructed by using
Equation (8):
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~rij¼ xL ij ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xm i¼1 xLij 2 s ; x M ij ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xm i¼1 xMij 2 s ; x U ij ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xm i¼1 xUij 2 s 0 B B @ 1 C C A (8)
Step 5: The normalized ratios (~y*i) of each alternative with respect to all criteria are
computed as: ~yi¼ X j2B ~rijH X j2C ~rij; i ¼ 1; 2; . . . ; m (9)
Step 6: The each normalized ratio (~yi) is defuzzifed by Center of Area (CoA) method.
Alternatives are ranked in decreasing order of yi according to the Fuzzy Ratio
System approach.
4.3 Fuzzy ARAS method
Additive Ratio Assesment (ARAS) method introduced by (Zavadskas et al., 2010) is based on a utility function value determining the complex relative efficiency of a reasonable alternative which is directly proportional to the relative effect of values and weights of the main criteria.Turskis and Zavadskas (2010)developed Fuzzy ARAS method which is the fuzzy version of ARAS method, to solve different problems in transport, construction, economics, technology and sustainable development:
Step 1-2: Same as in FTOPSIS algorithm.
Step 3: The fuzzy decision matrix ~D¼ ~x ij ; i ¼ 0; 1; . . . ; m; j ¼ 1; 2; . . . ; n is
constructed.
where:~x0jrepresents optimal fuzzy performance score of criterion j. If the optimal value of criterion j is unknown, then~x0j¼ max
i ~xij; j 2 B ; max i ~xij; j 2 C .
Step 4: The normalized fuzzy decision matrix ~N ¼ ~r ij is computed by using
Equation (10): ~rij¼ Xm~xij i¼1 ~xij ; j 2 B; ~xij Xm i¼1 ~xij 0 B B @ 1 C C A 1 ; j 2 C 8 > > > < > > > : (10)
Step 5: The fuzzy weighted normalized decision matrix ~V¼ ~vij is calculated as:
~vij¼ ~rij ~wj (11)
Step 6: The fuzzy optimality function is computed byEquation (12):
~Si¼ Xn
j¼1
~vij; i ¼ 1; 2; . . . ; m (12)
Step 7: ~Si’s are defuzzifed by Center of Area (CoA) method and alternatives are
ranked with respect to the Siin decreasing order.
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Step 8: Alternatives are ranked in increasing order of Ki ¼SS0iwhich indicates utility
degree (Turskis and Zavadskas, 2010).
5. Numerical example
This section presents three FMCDM methods, Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS, for ranking the 81 provinces at the NUTS-3 level in Turkey based on micro data of Life Satisfaction Survey 2013 in terms of personal satisfaction and public services
Figure 1. Provincial map of Turkey belonging to seven regions
Table III.
The fuzzy weights of decision criteria for the personal satisfaction
Criteria Criteria description Fuzzy weights
KI_1 Personal health satisfaction (0.72, 0.92, 1)
KI_2 Marriage satisfaction (0.62, 0.82, 0.94)
KI_3 Education satisfaction (0.62, 0.82, 0.94)
KI_4 Dwelling satisfaction (0.50, 0.70, 0.82)
KI_5 District of residence or neighborhood satisfaction (0.50, 0.70, 0.82)
KI_6 Work satisfaction (0.68, 0.88, 1)
KI_7 Earnings from work satisfaction (0.72, 0.92, 1)
KI_8 Monthly income satisfaction (0.72, 0.92, 1)
KI_10 Personal care satisfaction (0.58, 0.78, 0.94)
KI_11 Spent time going to work satisfaction (0.56, 0.76, 0.88) KI_12 Relationships with relatives satisfaction (0.56, 0.76, 0.88) KI_13 Relationships with friends satisfaction (0.46, 0.66, 0.82) KI_14 Relationships with neighbors satisfaction (0.52, 0.72, 0.88)
KI_15 Business contacts satisfaction (0.62, 0.82, 0.94)
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satisfaction. In the solution process, 14 decision criteria that define personal satisfaction and 38 indicators that identify public services satisfaction are considered. The importance of our study is that it is thefirst study to rank the provinces taking into account all the criteria that determine both personal satisfaction and public services satisfaction. Detailed explanations are given inGürsoy (2016).
Table IV. The fuzzy weights of decision criteria for the public services satisfaction
Criteria Criteria description Fuzzy weights
KA_1 Health care services satisfaction (0.76, 0.96, 1)
KA_2 Public security services satisfaction (0.66, 0.86, 0.94)
KA_3 Judicial services satisfaction (0.70, 0.90, 0.94)
KA_4 Education services satisfaction (0.70, 0.90, 0.94)
KA_5 Social Security Institution (SGK) services satisfaction (0.66, 0.86, 0.94)
KA_6 Transportation services satisfaction (0.66, 0.86, 0.94)
KA_8 Public services provided in electronic form satisfaction (0.28, 0.44, 0.60) KA_10 Municipial garbage and environmental waste collection services
satisfaction
(0.60, 0.80, 0.88) KA_11 Municipial sewerage services satisfaction (0.62, 0.82, 0.94) KA_12 Municipial tap water services satisfaction (0.60, 0.80, 0.88) KA_13 Municipial public transport services satisfaction (0.56, 0.76, 0.88) KA_14 Municipial constabulary services satisfaction (0.56, 0.76, 0.88) KA_15 Municipial road and pavement construction services satisfaction (0.50, 0.70, 0.82) KA_16 Amount of Municipal green area satisfaction (0.60, 0.80, 0.88) KA_17 Municipial struggle against air pollution satisfaction (0.54, 0.74, 0.82) KA_18 Municipial health and sport centers satisfaction (0.38, 0.54, 0.66) KA_19 Municipal process of reconstruction/settlement/license satisfaction (0.38, 0.54, 0.66) KA_20 Municipial disability-oriented regulation satisfaction (0.54, 0.74, 0.82) KA_21 Municipial help services for sick and poor satisfaction (0.60, 0.80, 0.88) KA_22 Municipal activities of exhibitions, festivals, fairs and concerts
satisfaction
(0.50, 0.70, 0.82) KA_23 Municipal courses of getting the profession and developing the hand
skills satisfaction
(0.44, 0.60, 0.72) KA_24 Municipality’s lighting service satisfaction (0.44, 0.64, 0.76) KA_25 Municipial cleaning service satisfaction (0.50, 0.70, 0.82)
KA_26 Municipialfire service satisfaction (0.54, 0.74, 0.82)
KA_28 Municipial funeral services satisfaction (0.38, 0.58, 0.70) KA_29 Municipial street signs and outer door numbering services satisfaction (0.60, 0.80, 0.88) KA_30 Municipial food facility services satisfaction (0.50, 0.70, 0.82) KA_31 Special Provincial Administration’s sewerage services satisfaction (0.54, 0.74, 0.82) KA_32 Special Provincial Administration’s tap water services satisfaction (0.54, 0.74, 0.82) KA_33 Special Provincial Administration’s road and pavement construction
services satisfaction
(0.38, 0.58, 0.70) KA_34 Special Provincial Administration’s process of reconstruction/
settlement/license satisfaction
(0.38, 0.58, 0.70) KA_35 Special Provincial Administration’s disability-oriented regulation
satisfaction
(0.54, 0.74, 0.82) KA_36 Special Provincial Administration’s help services for sick and poor
satisfaction
(0.50, 0.66, 0.78) KA_37 Special Provincial Administration’s activities of exhibitions, festivals,
fairs and concerts satisfaction
(0.44, 0.64, 0.76) KA_38 Special Provincial Administration’s courses of getting the profession
and developing the hand skills satisfaction
(0.44, 0.60, 0.72) KA_39 Special Provincial Administration’s lighting service satisfaction (0.54, 0.74, 0.82) KA_40 Special Provincial Administration’s cleaning service satisfaction (0.38, 0.54, 0.66)
Individuals
’
life satisfaction
levels
Turkey is divided into seven regions as Marmara, Aegean, Mediterranean, Black Sea, Central Anatolia, Eastern Anatolia and Southeast Anatolia, at The 1st Geography Congress held in Ankara between 6 and 21 June 1941. These geographical regions were determined according to their climate, location,flora and fauna, human habitat, agricultural diversities, transportation, topography, etc. Provincial map of Turkey belonging to the seven regions given in different color is illustrated inFigure 1.
At first, the importance weights of decision criteria which are related to personal satisfaction and public service satisfaction of individuals were determined via a questionnaire. This questionnaire was applied tofive experts working in the Department of Gender where performed LSSs in TurkStat. Linguistic variables are used for assessment of the criteria byfive experts and converted to triangular fuzzy numbers given inTable I. The fuzzy weights of all the criteria for personal satisfaction and public service satisfaction were obtained by collecting experts’ evaluations as shown inTables IIIandIV, respectively.
After the determination of fuzzy weights for all the decision criteria, fuzzy decision matrices are established for personal and public services satisfaction. For this aim, each province was evaluated in terms of all the criteria using linguistic variables given in
Table II, by the individuals who lived in the mentioned province and participated in the LSS. Then 81 provinces at the NUTS-3 level in Turkey are ranked by Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS. For simplicity, the calculation steps of the proposed methods are not presented. However, they are available upon request.
Afterwards, thefinal rankings of the best and the worst 10 provinces are presented in
Table Vfor personal satisfaction. Among the three methods, the province ranked as the best in terms of personal satisfaction is Kastamonu, whereas Kilis ranked as the worst. Personal
Table V. The best and the worst 10 provinces according to the personal satisfaction based on proposed fuzzy MCDM methods Rank
Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS
Province Score Province Score Province Score
The best 10 Provinces
1 Kastamonu 0.621 Kastamonu 1.774 Kastamonu 0.990
2 Usak 0.604 Usak 1.692 Usak 0.965
3 Afyonkarahisar 0.600 Afyonkarahisar 1.676 Isparta 0.960
4 Isparta 0.599 Isparta 1,667 Afyonkarahisar 0.958
5 Kütahya 0.597 Kütahya 1.665 Kütahya 0.954
6 Balıkesir 0.593 Balıkesir 1.647 Balıkesir 0.949
7 Zonguldak 0.590 Zonguldak 1.646 Bolu 0.933
8 Bolu 0.588 Bolu 1.632 Giresun 0.933
9 Giresun 0.588 Giresun 1.630 Zonguldak 0.932
10 Sinop 0.584 Sinop 1.628 Çankırı 0.927
The worst 10 Provinces
72 Malatya 0.545 Hatay 1.472 Malatya 0.846
73 Mus 0.540 Hakkari 1.459 Siirt 0.844
74 Siirt 0.540 Mus 1.459 Adana 0.841
75 Hakkari 0.539 Siirt 1.452 Mus 0.838
76 Adana 0.539 Adana 1.449 Hakkari 0.831
77 Diyarbakır 0.535 Diyarbakır 1.436 Diyarbakır 0.830
78 anlıurfaS 0.530 anlıurfaS 1.424 Batman 0.821
79 Batman 0.528 Batman 1.414 S anlıurfa 0.817
80 Van 0.523 Van 1.390 Van 0.811
81 Kilis 0.512 Kilis 1.357 Kilis 0.787
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satisfaction levels obtained from each Fuzzy MCDM method are also illustrated inFigure 2. Detailed calculations and ranking results of all provinces are presented inGürsoy (2016).
WhenFigure 2is examined, the provinces located in Marmara region (excluding _Istanbul and Kocaeli), Aegean region, Central Anatolia region (except for Ankara), Black Sea region (apart from Düzce and Tokat) generally have medium, high and very high personal
Figure 2. Map of personal satisfaction level by Provinces at NUTS-3 level with methods of
Individuals
’
life satisfaction
levels
satisfaction levels. Unlike, the provinces in Mediterranean region (except for Kahramanmaras), Eastern Anatolia Region (excluding Bitlis, Ardahan and Erzurum) and Southeast Anatolia Region generally have low and very low personal satisfaction levels.
Similarly, thefinal rankings (the best and the worst 10 provinces) through the proposed FMCDM methods are given inTable VIfor public services satisfaction. According to each method, Zonguldak was the best with regard to public services satisfaction, while Van was the worst. Public services satisfaction levels obtained from Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS methods are also illustrated inFigure 3 and detailed ranking results of all provinces are given inGürsoy (2016).
It can be seen from Figure 3that the provinces in Marmara region, Aegean region, Central Anatolia region (except for Aksaray), Black Sea region (excluding Düzce and Giresun) generally have medium, high and very high personal satisfaction levels. Conversely, the provinces located in Mediterranean region (except for Antalya, Burdur, Mersin and Osmaniye), Eastern Anatolia Region (apart from Ardahan and Erzurum) and Southeast Anatolia Region (except for Adıyaman) have low and very low personal satisfaction levels in general.
Overall ranking results for the provinces in Turkey with respect to the personal and public services satisfaction are shown in Table A1 in Appendix. The ranking results obtained from Fuzzy TOPSIS, Fuzzy MultiMOORA and Fuzzy ARAS methods were found to be very similar. Spearman’s rho rank correlations between the ranking results obtained from proposed FMCDM approaches were reported inTables VII andVIII for personal satisfaction and public services satisfaction, respectively. It was seen that correlations between the Fuzzy TOPSIS and Fuzzy MultiMOORA is 0.999 (0.998), correlations between the Fuzzy TOPSIS and Fuzzy ARAS is 0.996 (0.999) and correlations between the Fuzzy
Table VI. The best and the worst 10 provinces according to the public services satisfaction based on proposed fuzzy MCDM methods Rank
Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS
Province Score Province Score Province Score
The Best 10 Provinces
1 Zonguldak 0.551 Zonguldak 4.951 Zonguldak 0.971
2 Çankırı 0.537 Kastamonu 4.782 Çankırı 0.947
3 Usak 0.537 Usak 4.770 Usak 0.947
4 Manisa 0.535 Çankırı 4.753 Manisa 0.945
5 Kastamonu 0.534 Manisa 4.739 Karaman 0.939
6 Bolu 0.532 Karaman 4.705 Bolu 0.938
7 Karaman 0.532 Bolu 4.705 Kastamonu 0.935
8 Afyonkarahisar 0.530 Afyonkarahisar 4.677 Afyonkarahisar 0.934
9 Ordu 0.529 Ordu 4.669 Ordu 0.933
10 Sinop 0.499 Konya 4.659 Konya 0.930
The Worst 10 Provinces
72 S anlıurfa 0.422 anlıurfaS 3.473 S anlıurfa 0.717
73 Mardin 0.421 Erzincan 3.440 Mardin 0.716
74 Erzincan 0.417 Mardin 3.428 Erzincan 0.710
75 Düzce 0.411 Düzce 3.370 Düzce 0.703
76 Gaziantep 0.410 Gaziantep 3.365 Gaziantep 0.702
77 Siirt 0.400 Siirt 3.216 Siirt 0.679
78 Batman 0.398 Batman 3.171 Batman 0.672
79 Hakkari 0.385 Hakkari 3.104 Hakkari 0.649
80 Bitlis 0.348 Bitlis 2.720 Bitlis 0.584
81 Van 0.345 Van 2.634 Van 0.575
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Figure 3. Map of public service satisfaction level by Provinces at NUTS-3 level with methods of
Individuals
’
life satisfaction
levels
MultiMOORA and Fuzzy ARAS is 0.993 (0.997) for personal satisfaction (for public services satisfaction) are very high and statistically significant.
Furthermore, the Wilcoxon signed-ranks test was used to determine whether there is a difference between rankings of the provinces according to FMCDM methods in terms of personal satisfaction and public service satisfaction. To the analysis results of Wilcoxon signed-ranks test, there are no significant differences between personal satisfaction and public service satisfaction of the provinces for the methods of Fuzzy TOPSIS (Sig. = 0.679>a = 0.05), Fuzzy MULTIMOORA (Sig. = 0.714>a = 0.05) and Fuzzy ARAS (Sig. = 0.730>a = 0.05). We also computed Spearman’s rho rank correlations between the personal and public services satisfaction with respect to these FMCDM methods. Spearman’s rho correlations between personal and public service satisfaction which were calculated as 0.719 with fuzzy TOPSIS, 0.711 with Fuzzy MULTIMOORA and 0.732 with Fuzzy ARAS, have been shown to be high and statistically significant.
6. Conclusions
This research investigates the determination of life satisfaction level of individuals living in Turkey in terms of personal satisfaction and public services satisfaction considering all the criteria. By the way, three FMCDM methods, Fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS are applied to the Life Satisfaction Survey 2013 micro data for ranking the 81 provinces at the NUTS-3 level in Turkey. It was revealed that Kastamonu achieved the highest level of personal satisfaction and Zonguldak achieved the highest level of public services satisfaction. On the contrary, Kilis has the lowest personal satisfaction and Van has the lowest public services satisfaction.
The ranking results of the FMCDM methods are similar except for some provinces in terms of personal satisfaction and public service satisfaction. For example, the satisfaction level of
Table VIII. Spearman’s rho rank correlations between the FMCDM ranking results for public services satisfaction
Methods Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS
Fuzzy TOPSIS 1.000 – (Sig. = 0.000)0.998** 0.999** (Sig. = 0.000) Fuzzy MULTIMOORA 0.998** (Sig. = 0.000) 1.000 – (Sig. = 0.000)0.997** Fuzzy ARAS 0.999** (Sig. = 0.000) 0.997** (Sig. = 0.000) 1.000 – Note: **Correlation is significant at the 0.01 level (two-tailed)
Table VII. Spearman’s rho correlations between the FMCDM ranking results for personal satisfaction
Methods Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS
Fuzzy TOPSIS 1.000 – (Sig. = 0.000)0.999** 0.996** (Sig. = 0.000) Fuzzy MULTIMOORA 0.999** (Sig. = 0.000) 1.000 – (Sig. = 0.000)0.993** Fuzzy ARAS 0.996** (Sig. = 0.000) 0.993** (Sig. = 0.000) 1.000 – Note: **Correlation is significant at the 0.01 level (two-tailed)
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public services is low, while personal satisfaction is high, for Kırıkkale and Giresun. The satisfaction level of public services is high and personal satisfaction is low, for _Istanbul. On the other hand, some provinces such as Tunceli, Van, Mus, Hakkari, Kilis, Sanlıurfa, Batman and Siirt have the lowest rankings, while Afyonkarahisar, Usak, Bolu, Zonguldak and Kastamonu have the highest rankings with regard to both personal and public services satisfaction. The results of Wilcoxon signed-ranks test supported these situations, because there are no significant differences between the methods of fuzzy TOPSIS, Fuzzy MULTIMOORA and Fuzzy ARAS, in terms of personal satisfaction and public service satisfaction. Also, Sperman’s rho correlations between personal and public service satisfaction calculated by utilizing the rankings of mentioned methods showed that rankings obtained from the three FMCDM methods are highly correlated and statistically significant.
In general, people think that there is an adverse relationship between population density and satisfaction level, that is, satisfaction decreases as population density increases. The personal satisfaction is low (or very low) in metropolises with high population density such as Istanbul, Ankara, Antalya, Adana, Gaziantep, Sanliurfa, Mersin, Kocaeli, Diyarbakir, Hatay and Van and also in cities with low population density like Düzce, Siirt, Hakkari and Kilis. Also, the public services satisfaction is low (or very low) in metropolitans such as Istanbul, Ankara, Antalya, Adana, Gaziantep, S
anlıurfa, Mersin, Kocaeli, Diyarbakır, Hatay and Van which have high population density, unlike Düzce, Siirt, Hakkari and Kilis which have low population density.
Although there is no significant difference between the east and the west of Turkey in terms of personal satisfaction, it is seen that there is a distinct difference between them in terms of satisfaction with public services. The provinces of western Turkey have a higher level of public services satisfaction, while the provinces located in the east of Turkey such as Batman, Bitlis, Hakkari, Siirt and Van have very low satisfaction.
As a consequence, this study is thefirst research for ranking the provinces at the NUTS-3 level in Turkey taking into consideration all the criteria that determine both personal satisfaction and public services satisfaction.
It may be suggested that municipalities, special provincial administration’s and governments should improve or increase public services in the direction of the demands and recommendations of the individuals lived in the provinces.
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Appendix
NUTS-3
Code Province
Personal satisfaction Public services satisfaction Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS TRA11 ERZURUM 23 22 25 28 23 28 TRA12 ERZ_INCAN 58 56 60 74 73 74 TRA13 BAYBURT 33 32 34 62 61 61 TRA21 AĞRI 51 52 51 68 69 68 TRA22 KARS 57 57 58 66 66 66 TRA23 IĞDIR 52 51 54 64 62 64 TRA24 ARDAHAN 15 15 13 11 13 11 TRB11 MALATYA 72 71 72 59 59 59 TRB12 ELAZIĞ 60 61 61 54 54 55 TRB13 B_INGÖL 67 67 69 65 65 65 TRB14 TUNCEL_I 69 68 70 71 71 71 TRB21 VAN 80 80 80 81 81 81 TRB22 MUS 73 74 75 70 70 70 TRB23 B_ITL_IS 41 38 48 80 80 80 TRB24 HAKKAR_I 75 73 76 79 79 79 TRC11 GAZ_IANTEP 61 63 62 76 76 76 TRC12 ADIYAMAN 65 66 64 30 33 32 TRC13 K_IL_IS 81 81 81 63 63 62 TRC21 S ANLIURFA 78 78 79 72 72 72 TRC22 D_IYARBAKIR 77 77 77 61 64 63 TRC31 MARD_IN 66 65 65 73 74 73 TRC32 BATMAN 79 79 78 78 78 78 TRC33 S IRNAK 55 53 56 69 68 69 TRC34 S_I_IRT 74 75 73 77 77 77 TR100 _ISTANBUL 54 55 52 19 17 18 TR211 TEK_IRDAĞ 21 19 21 36 34 36 TR212 ED_IRNE 24 24 28 13 11 12 TR213 KIRKLAREL_I 30 30 33 20 18 20 TR221 BALIKES_IR 6 6 6 25 29 24 TR222 ÇANAKKALE 18 18 15 37 40 37 TR310 _IZM_IR 50 49 50 48 48 48 TR321 AYDIN 35 33 36 16 15 16 TR322 DEN_IZL_I 27 26 26 12 14 14 TR323 MUĞLA 42 42 41 29 26 29 TR331 MAN_ISA 14 14 11 4 5 4 TR332 AFYONKARAH_ISAR 3 3 4 8 8 8 TR333 KÜTAHYA 5 5 5 31 30 30 TR334 US AK 2 2 2 3 3 3 TR411 BURSA 40 41 40 14 12 13 TR412 ESK_IS EH_IR 44 47 44 49 49 50 TR413 B_ILEC_IK 20 21 19 15 16 15 TR421 KOCAEL_I 53 54 53 35 32 33 TR422 SAKARYA 34 35 35 33 35 31 TR423 DÜZCE 56 58 55 75 75 75 TR424 BOLU 8 8 7 6 7 6 TR425 YALOVA 37 36 37 40 42 41 TR510 ANKARA 68 69 66 41 39 39 (continued ) Table AI. Overall ranking results for the provinces according to the personal and public services satisfaction
Individuals
’
life satisfaction
levels
1891
NUTS-3
Code Province
Personal satisfaction Public services satisfaction Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS Fuzzy TOPSIS Fuzzy MULTIMOORA Fuzzy ARAS TR521 KONYA 26 27 24 10 10 10 TR522 KARAMAN 19 20 17 7 6 5 TR611 ANTALYA 59 60 57 53 53 53 TR612 ISPARTA 4 4 3 17 20 17 TR613 BURDUR 29 29 29 39 41 40 TR621 ADANA 76 76 74 60 60 60 TR622 MERS_IN 70 70 68 50 50 49 TR631 HATAY 71 72 71 58 58 58 TR632 KAHRAMANMARAS 36 37 32 55 55 54 TR633 OSMAN_IYE 62 59 63 32 31 35 TR711 KIRIKKALE 12 12 12 51 52 52 TR712 AKSARAY 25 25 23 56 57 57 TR713 N_IĞDE 63 64 59 46 45 46 TR714 NEVS EH_IR 28 28 27 45 46 45 TR715 KIRS EH_IR 38 39 38 43 43 42 TR721 KAYSER_I 46 46 45 24 24 25 TR722 S_IVAS 47 44 47 34 36 34 TR723 YOZGAT 13 13 18 27 27 27 TR811 ZONGULDAK 7 7 9 1 1 1 TR812 KARABÜK 48 48 46 52 51 51 TR813 BARTIN 31 31 30 18 19 19 TR821 KASTAMONU 1 1 1 5 2 7 TR822 ÇANKIRI 11 11 10 2 4 2 TR823 S_INOP 10 10 20 42 37 43 TR831 SAMSUN 22 23 22 38 38 38 TR832 TOKAT 64 62 67 47 47 47 TR833 ÇORUM 17 17 14 23 25 23 TR834 AMASYA 39 40 39 21 22 21 TR901 TRABZON 45 45 43 57 56 56 TR902 ORDU 16 16 16 9 9 9 TR903 G_IRESUN 9 9 8 67 67 67 TR904 R_IZE 43 43 42 22 21 22 TR905 ARTV_IN 32 34 31 26 28 26 TR906 GÜMÜS HANE 49 50 49 44 44 44 Table AI.
K
48,8
1892
About the authors
Nimet Yapıcı Pehlivan is a full Professor at Department of Statistics of Selçuk University. She graduated from Hacettepe University, Department of Statistics in 1997. She received her Master’s degree in Statistics from the Selçuk University, Institute of Science and Technology in 2000 and Ph.D. degree in Mathematics from the Selçuk University, Institute of Science and Technology in 2005. She has published several international conference papers and journal papers. Her current research interests include Operations Research, Optimization, Applied Statistics, Fuzzy Set Theory, and Multi-Criteria Decision-Making. Nimet Yapıcı Pehlivan is the corresponding author and can be contacted at:nimet@selcuk.edu.tr
Zeynep Gürsoy is currently Expert in Labour Force and Living Conditions Department in Turkish Statistical Institute (TURKSTAT). She graduated from Ankara University, Department of Statistics in 2001. She received her Master’s degree in Statistics from the Ankara University, Graduate School of Natural Science in 2005 and PhD degree in Statistics from the Selçuk University, Graduate School of Natural Science in 2016.
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