A Model Proposition for Prescreening Candidates in Recruitment Process Using Fuzzy Vikor Method

(1)

S

U

ME

D

H

A

J

O

U

R

N

A

L

O

F

M

A

N

A

G

EMEN

T

A

p

ri

l-J

u

n

e

2

0

1

9

CMR College of Engineering & Technology

About Sumedha Journal of Management

Submission of Articles

is Sponsored by MGR Educational Society, which has established in 2002. The College is situated on Hyderabad - Nagpur National Highway (Medchal Road), Andhra green and free of pollution. The college endeavors to impart Quality Technical Education and to meet the challenge imposed on, by being in tune with the fast changing Technology & Globalization. Since 2006, Department of Master in Business Administration at CMR has been building individuals with the capability to think act and lead in an increasingly complex business world. CMR Strongly believes in the change that individuals can bring about in the world and in themselves, when empowered with the knowledge to analyze the world the confidence to act on their decisions and perseverance to lead others in pursuit of change. It provides a platform to building a strong society and nation.

aims at promoting and disseminating relevant, high quality research in the field of management. It is a double blind reviewed referred academic quarterly journal focuses on publishing scholarly articles from the areas of management, management principles, recent inventions in management, company management, financial management, human resources, accounting, marketing, operations management, human resource management, statistics, international business, information technology, environment, risk management, globalization and related areas. Asian journal of management research seeks original manuscripts that identify, extend, unify, test or apply scientific and multi-disciplinary knowledge concerned to the management field. The journal endeavors to provide forum for academicians, scholars and practitioners.

The following types of Papers are considered for publication: 1. Original research works in the above-mentioned fields.

2. Surveys, opinions, abstracts and essays related to Operations research.

3. Few review papers will be published if the author had done considerable work in that area. 4. Case studies related to management domain.

Authors are invited to submit their research articles, review papers, case studies in a properly formatted file as per the author guidelines to [email protected] mentioning the name of the journal or through the submission.

V o lu m e 8 N u m b e r 2

Vol. 8 No. 2 April-June 2019

Referred Journal of CMR College of Engineering & Technology

ISSN (Online) : 2322-0449

UGC Approval Journal (Serial No: 46802)

A Model Proposition for Prescreening Candidates in Recruitment Process Using Fuzzy Vikor Method

Murat Bolelli

An UGC Autonomous Institution

(Approved by AICTE, permanently affiliated to JNTU, Hyderabad)

(NAAC Accredited Institution with ‘A’ Grade)

ISSN (PRINT) : 2277-6753

Department of Master of Business Administration

An Integrated Marketing Communications, Media Synergies and its effect on the Consumer Decision Making Process Reshma Nikhat

Influence of Organizational Climate on Employee Turnover Intention in Information Technology Industry in Kerala Jnaneswar. K

Gayathri Ranjit

Impact of Transformational Leadership Style Dimensions on Organizational Performance: An Empirical Analysis Shruti Balhara

Harbhajan Bansal

Impact of Quality of Work Life on Organisational Commitment Indu Bala, Ramandeep Saini, B.B. Goyal

Mediating Role of Personal Accomplishment among Emotional Labour Strategies and Teaching Satisfaction among Professional College Teachers Jitha G. Nair

Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India Tanvi Bhalala

Emerging Distribution Channel Effectiveness in Rural Jharkhand for Consumer Electronics

Punit Kumar Mishra Girish Kumar Srivastava

Changing Role of Learning and Development Methodologies Digital Age - A Comparison between Manufacturing and Service Industry S. Rajeswari, D.Raghunatha Reddy

M.Ramakrishna Reddy

Creativity and Innovation in B-Schools:Potential Areas for Development

K. Renuka Raju, Shakeel Ahmad A. Ramachandra Aryasri

Training Effectiveness on Job Performance - An Analytical Study with Reference to Dairy Industry

Menaka.Bammidi Puppala. Hyndhavi

Performance Appraisal Impact on Employee Job Satisfaction with Reference to TSSPDCL

M. Ramu Mohd. Akbar Ali Khan

Mobile Data Usage Behavior: A Study on Bottom of the Pyramid Market Leena Sharma

(2)

Dr. A Kotishwar

Dr. P. Vijaya Lakshmi, Associate Professor

Editorial Board

GUIDELINES FOR AUTHORS

1. The cover page of the article/research paper should include the title of the paper, Author’s name, Designation, organization with the address, contact number & email address.

2. Abstract of not more than 200 words outlining the purpose of study should be presented on a separate sheet along with 5-6 keyword immediately preceding the text.

3. Name(s) of the author(s) should not appear anywhere in the entire text other than the cover page. 4. All the manuscript will be sent for blind review process, the corresponding author will be informed by

the editor about acceptance or rejection of the manuscript within a period of time. On acceptance of the manuscript, the modifications suggested by the reviewers are to be incorporated by the author with in a period of 10 days and send copy of the revised manuscript again.

5. The editor reserves the right to modify or improve the manuscript as per the standard of journal. 6. The copyright of the research papers published in the journal shall lie with the publisher.

7. The authors whose papers are selected for publication shall make a subscription (Rs. 2500) for annual of the printed edition of the journal per author.

8. Copies of the journal are available by way annual subscription (Rs. 2,500 for annual or Life Time Rs. 10,000).

General Information about submission

1.

• Covering Letter: Title of the Paper, Author’s Name, Designation, Organizations, Official Address, Personal Address, Contact Numbers (Official & Personal) and e-mail address

• - objectives, Research Questions / Hypothesis, Methodology,

findings and 5-6 keywords. Words limit - 250-300 words.

2.

• Format : A4 size, Ms-word

• Word Limit : Not to exceed 5000 words • Font : Times New Roman

• Size : Title - 18, Heading - 14, Text-12 • Line spacing : 1.5

3. Author must declare originality of work. The article should not have been published or be submitted for publication else where.

4. Editorial Board’s decision will be final.

5. The copyright of all accepted papers will vest with dept. of MBA - CMRCET For Submission:

Abstract: should contain

Full Paper

Declaration :

Medchal Road, Hyderabad.

Assistant Managing Editors

Telangana, India. Chairman CMRGI

Vice-Chairman CMRGI Secretary & Correspondent CMRGI Ch. Malla Reddy,

Ch. Narasimha Reddy, Ch. Gopal Reddy,

Director - (Academics), CMRGI Principal - CMRCET Dr. N. Satyanarayana,

Major Dr. V.A. Narayana,

Chief Editor

Professor & HOD, Dep of MBA, CMRCET

Dr. A Kotishwar,

Managing Editors

Professor Dr. P. Hima Bindu,

Dr. P. Alekhya, Associate Professor

S.No Name Address

1 Honorable Former Vice-Chancellor. Telangana University, Nizamabad,

Andhra Pradesh

2 Professor, Department of Commerce , Bangalore University

3 Pro-Vice chancellor, Maulana Azad National Urdu University,

Hyderabad

4 Professor & Head, Department of Commerce, University of Madras, Chennai

5 Head & Associate Professor, AL Buriami University College, Oman

6 Professor & HOD, Department of Banking & Business Economics,

Mohan Lal Sukhadia University, Udaipur, Rajasthan.

7 Director, School of commerce & Management Science, Swami

Ramanand Teerth, Marathwada University, Nandeds

8 Head, Department of Commerce. University of Mumbai

9 Professor, SMS, JNTUH

10 Professor, School of Management Studies, University, Hyderabad

11 Professor Department of Commerce, Dr. Babasaheb Ambedkar

Marathwada University, Aurangabad

12 Dean & Chairperson, Faculty of Commerce& Mgt, Bhagat Phool Singh

Mahila Vishwavidyalaya, Khanpur Kalan, Haryana

13 Associate Professor, Indian Institute of Management Kozhikode, Kerala

14 Professor & Head, School of Management Studies, Punjabi University,

Patiala, Punjab, India

15 Associate Professor, School of Management Studies, University,

Hyderabad

16 Associate Professor, Department of Commerce, Delhi School of

Economics, University of Delhi

17 Professor Finance , Indian institute of management Kozhikode, Kerala

18 Associate Professor, Department of Business Administration,

Annamalai University, Tamil Nadu.

19 Associate Professor Department of Business Administration and

Accounting, Al-Buraimi University College

(Affiliated to California State University, Northridge, USA), Prof. Mohd Akbar Ali Khan

Dr. M Muninarayanappa Dr. Shakeel Ahmad

Dr. S Gurusamy

Dr. Mohammed Jahangir Ali Prof. Renu Jatana

Prof. Vani N Laturkar

Dr. Vivek Deolankar Dr. Sindhu

Dr Mary Jessica

Prof. Walmik K. Sarwade

Prof. (Dr.) Sanket Vij

Dr. Sudershan Kuntluru Prof. G S Batra

Dr. Chetan Srivastava

Dr. Vanita Tripathi

Dr. Rachappa Shette Dr. A.A. Ananth

(3)

ISSN: 2277-6753 (Print) ISSN: 2322-0449 (Online)

SUMEDHA-Journal of Management

Referred Journal of CMR College of Engineering & Technology

April-June2019, Volume 8, No. 2

S. No.

Title Authors Page No.

1. A Model Proposition for Prescreening Candidates in Recruitment Process Using Fuzzy Vikor Method

Murat Bolelli* 1-19

2. An Integrated Marketing Communications, Media Synergies and its effect on the Consumer Decision Making Process

Reshma Nikhat* 20-32

3. Influence of Organizational Climate on Employee Turnover Intention in Information Technology Industry in Kerala

Jnaneswar. K*, Gayathri Ranjit**

33-46

4. Impact of Transformational Leadership Style Dimensions on Organizational Performance: An Empirical Analysis

Shruti Balhara*, Harbhajan Bansal**

47-57

5. Impact of Quality of Work Life on Organisational Commitment

Indu Bala*, Ramandeep Saini**, B.B. Goyal***

58-72

6. Mediating Role of Personal Accomplishment among Emotional Labour Strategies and Teaching Satisfaction among Professional College Teachers

Jitha G. Nair* 73-82

7. Testing of Long-Run Relationship between Gold Prices and Stock Market Return: An Empirical Analysis in India

Tanvi Bhalala* 83-96

8. Emerging Distribution Channel Effectiveness in Rural Jharkhand for Consumer Electronics

Punit Kumar Mishra*, Girish Kumar Srivastava**

97-112

9. Changing Role of Learning and Development Methodologies in Digital Age - A Comparison between Manufacturing and Service Industry

S. Rajeshwari*, D.Raghunatha Reddy**, M.Ramakrishna Reddy***

113-126

10. Creativity and Innovation in B-Schools: Potential Areas for Development

K. Renuka Raju*, Shakeel Ahmad**, A. Ramachandra Aryasri***

127-133

11. Training Effectiveness on Job Performance - An Analytical Study with Reference to Dairy Industry

Menaka.Bammidi*, Puppala. Hyndhavi **

134-147

12. Performance Appraisal Impact on Employee Job Satisfaction With Reference to TSSPDCL

M. Ramu*, Mohd. Akbar Ali Khan**

148-156

13. Mobile Data Usage Behavior:

A Study on Bottom of the Pyramid Market

Leena Sharma* 157-169

14. Cash to Cashless Economy: Challenges and Opportunities

Saneem Fatima*, Shakeel Ahmad**

(4)

As SUMEDHA Journal of Management Thirtieth issue, We look forward to the momentous

growth of our Journal, increasing in their appeal, readership and relevance to the fast-changing

world of Business Management. During these six years journey our journal has been critically

evaluated by various institutions with similar line of interest and faculty fraternity. We have

been consistently seeking advice from experts to continuously improve the quality of the

journal. Our journal has got UGC Approval Journal (Serial No : 46802 ), Indexed in Indian

Citation Index (ICI), PROQUEST Database, Google scholar JUSER Database, MIAR Data

Base, Index Copernicus value 59.11, ISRA Journal Factor 4.018, Sjifactor Impact factor 4.339,

International Scientific Indexing Impact factor 1.332. On behalf of the Management, Editorial

Board and Editorial Team, I express my profound gratitude to all our authors, reviewers,

readers and patrons for offering their overwhelming support and I anticipate a continued

and lively partnership for years to come.

All of us recognize the necessity for change, which results in progress. It gives way to new

ideas and perspectives reflecting the current and emerging environment, which builds on

the solid foundations of the past.

Last but not least valuable would be your response and suggestions on this issue. Kindly

send us your views so that we can keep on upgrading our journal.

Thanking you

(5)

A Model Proposition for Prescreening Candidates in Recruitment Process Using Fuzzy Vikor Method Murat Bolelli* SUMEDHA-Journal of Management

Referred Journal of CMR College of Engineering & Technology April-June 2019, Volume 8, No. 2, pp 1-19

ISSN: 2277-6753 (Print) ISSN: 2322-0449 (Online) http://cmrcetmba.in/sumedha/

A Model Proposition for Prescreening Candidates in

Recruitment Process Using Fuzzy Vikor Method

Murat Bolelli*

*Asst. Prof., Istanbul Okan University, Faculty of Business and Administrative Sciences, Avni Dilligil Sok. No:18 34394 Mecidiyekoy / Istanbul, TURKEY.

Abstract

Purpose of this study wasto test if fuzzy VIKOR method can be used to prescreen job applications and to select candidates to be interviewed as well as to propose a model forthe process.Recruiting qualified employees is a major concern for firms today. One of the important determinants for the success of recruitment process is pre-screening,whichrequire multi criteria decision making containingnoncommensurable and conflicting criteriawhere use of fuzzy techniques are appropriate. To test the use of fuzzy VIKOR method in prescreening,external hiring of a Region Manager to a pharmaceutical company scenario is developed, resumes of individuals working in the industry are found from various career portals, all the information are anonymizedand presented to decision makers as if they are actual job applicants.Following VIKOR methods steps, decision maker group consisting eight recruitment and human resources professionals is gathered. Decision makers determined and weigthed criteria to be used for prescreening (Total Experience, Experience in the Industry, Education Level, Education-Profession Relation, Languages, Age, Remuneration Expectations) and assessed candidates using them. After fuzzification and calculations, Candidate 6 is found to present the best compromise solutionsatisfying maximum group utility of the "majority" and minimum individual regret of the "opponent" conditions. Findingsare indicating that fuzzy VIKOR multicriteria decision making method with pre-defined criteria can effectively be used to prescreen job applications. .

Keywords: Recruitment, Prescreening, Fuzzy Logic, VIKOR, Multi Criteria Decision Making.

JEL classification : M10, M12, M51

PUBLISHING CHRONOLOGY PAPER SUBMISSION DATE :

DECEMBER 3, 2018;

PAPERSENTBACKFOR REVISION :

JANUARY 7, 2019;

PAPER ACCEPTANCE DATE :

FEBRUARY 5, 2019

Reference to this paper should be made as follows:

(6)

Murat Bolelli*

1. I

NTRODUCTION

Human resources departmentsare expected to identify potentialtalent and also conceptualize and implement relevant strategies tocontribute effectively to achieve organizational objectivesin today's fast changing, competitive, global business environment (Yadav & Singh, 2014).Attracting qualified employees, recruiting and retaining them are considered to be critical success factors in achieving competitive advantage.An important step of the recruitment process is prescreening inwhich applications are measured and eliminated against qualifications the job requires. Candidates are generally assessed by usingvarious pre-determined criteria like job requirements,job specifications, competencies etc. (Sadullah, Uyargil, Acar, Ozcelik, Dundar, Ataay, Adal and Tuzuner, 2015). Considering recruitment process isproceedingonly with the applications which remain after pre-screening, it can be said that decisons made in this step are one of the important factors which determines the quality of the overall output.

Assessments made in prescreening involves multi criteria decision making (MCDM) which has some weaknesses. Some criteria used to assess the level of appropriateness can be measured with relativelytangible expressions like exists or non-exists, 1 or 0, Yes or No, meets or does not meet (education level, certificates, language, time in job, time in position etc.) and others cannot be measured with absolute terms or crisp numbers hence should be measured with subjective statements like strongly agree-disagree, agree,neutral, slightly agree-disagree, sometimes-often-never, high-mid-low level etc.Another problem with the process is using different units of measurement that should be taken into accountsimultaneously. Even though thesehardshipsare solved, it is still difficult to find a solution which satisfies all criteria (Yildizand Deveci, 2013).Literature indicates that fuzzy MCDM method is appropriate especially when variables are subjective, conflicting, non-commensurable and vague (Zarghami and Szidarovszky, 2009).Since fuzzy approach and VIKOR methodhas the capability to present compromised solution using qualitative and quantitative criteria, they are observed to be used widelyon human resource processes,specifically on recruitment(Kelemenis and Askounis, 2010).

Purpose of this study is to test if fuzzy VIKOR method can be used to prescreen job applications and to select candidates to be interviewed as well as to propose a model for the process. The paper is organized as follows. Next section briefly reviews the literature of the concepts recruitment, fuzzy logic, and fuzzy VIKOR method. Third chapter presentsa numerical examplewhich is based on the scenario of external hiring a Region Manager to a pharmaceutical company. Fourth chapter draws conclusions and discusses findings.

2. L

ITERATUE

R

EVIEW

2.1. RECRUITMENT

(7)

A Model Proposition for Prescreening Candidates in Recruitment Process Using Fuzzy Vikor Method Murat Bolelli*

finding, selecting and recruiting individuals who has necessary qualifications for the job and consists of main steps given below (Sadullah et. al, 2015):

• Candidate Search • Selection

– Prescreening, – Interviewing,

– Testing (oral and written), Work Sampling, Assessment Center etc. applications. • Hiring Decision

(8)

Murat Bolelli*

Table1. Selected Studies Using MCDM Techniques in the Field of HR

Author(s) Position Method(s)

Chen (2000) System Engineer Fuzzy Logic Saghafi anandHejazi(2005) University Professor Fuzzy Logic Ecer (2006) Sales Person Fuzzy TOPSIS

Özkan (2007) R&D Personnel AHP, ELECTRE, TOPSIS Dereliet al.(2010) Industry Engineer Fuzzy PROMETHEE Afshariet al. (2010) IT Specialist SAW

NasabandRostamy-Malkhalifeh(2010) System Engineer Interval Fuzzy Method KelemenisandAskounis (2010) IT Specialist Fuzzy TOPSIS

DursunandKarsak(2010) Industry Engineer Multicriteria Fuzzy Logic

BaşkayaandÖztürk(2011) Sales Person Fuzzy TOPSIS

Ersoylu(2011) Aviation School Student Candidates

Fuzzy AHPand Fuzzy VIKOR

KabakandKazançoğlu (2012) Military School

Instructors Fuzzy AHP El-Santawy(2012) Personnel Training VIKOR Kabak, Burmaoğluand

Kazançoglu (2012) Professional Sniper

Fuzzy ANP, Fuzzy TOPSIS, Fuzzy ELECTRE

Source: (Yildiz& Deveci, 2013

This study focuses on decisions made in the prescreening phase which takes place after creating pool of candidates, aiming to determine applicationsto be processed in the following steps of the recruitment process. Prescreening is generally conducted by recruitment specialists, managers' etc. whomreviews resumes of the candidates and categorizes applications as fit or not. The nature of this assessment contains subjective elements and use of multiple and usually conflicting criteria. This research intends to suggesta model for prescreening job applications and to select candidates to be interviewed using fuzzy VIKOR method which may reduce subjectivity as well as helping to find the best compromise solution.

2.2. FUZZY LOGIC

(9)

at 1965, suggesting classical system theory and mathematical applications are not sufficient enough when dealing with issues which contain subjectivejudgements. Zadeh proposed fuzzy sets definition in which membership function is usedfor qualification as a solution to the problem (Ozturk, Ertugrul and Karakasoglu, 2008). Later, in 1973 and 1975 studies author applied fuzzy logic to systems which contain uncertainty and concept become widespread. After the second half of 1980's fuzy logic is started to be used not only in theory but in practice and applied widely onJapan production processes. Mamdani (1974, 1976), Mamdani and Assilian (1975) adapted concept to control systems and had an important part in making the field where fuzzy logic is extensively used. Today fuzzy logic has broad applications especially on artificial intelligence, system analyses, decision analyses, data processing, economy, robotics, control systems, detection and identification technologies, optimization areas (Liou, Tsai, Lin and Tzeng, 2010; Changand Hsu, 2009; Sanayei, Mousavi and Yazdankhah, 2010; Chen and Wang, 2009). VIKOR method based on fuzzy MCDM technique is also frequently used in different areas of social sciences namely recruitment, employee training, supplier selection, project selection, performance evaluation of bank branches, supply chain strategy selection etc. (Ersoylu, 2011; El Santawy, 2012; Yildiz and Deveci, 2013; Chen and Wang, 2009; Akyuz, 2012; Yildiz, 2014; Ertugrul and Karakasoglu, 2009; Gorener, 2013). Essential elements of fuzzy logic are fuzzy sets, membership function and fuzzy numbers.

Figure1. Classical Set

Source: (Altaº,1999)

(10)

Murat Bolelli*

the set have 0 membership degree and elements which are in the set have membership degrees between 0 and 1. For example a person who isbelow twenty years old has 0 membership degree in the old category, members in twenties has a little over 0 membership degree and members in seventies has close to 1 membership degree in the fuzzy set.

Figure 2. Fuzzy Set

Source: (Altaº,1999)

Membership functions which represent fuzzy sets may have different forms. Although there are numerous forms namelygaussian, sigmoid, sinusoidal, cauchy, trapezoidal, polynomial, z shape, s shape etc., in practice triangular functions are stated to be used most commonly because of their simplicity and calculation convenience (Yildizand Deveci, 2013; Altas, 1999).Triangular membership function (Formula 1) is used in this study (Triantaphyllou, 2000).

(1)

Source: (Triantaphyllou, 2000)

(11)

Figure3. Triangular Membership Function

Source: (Chen, 2000)

This approach is useful to transform vague linguistic termssuch as very good, good, mediumetc. to fuzzy numbers. For example, variable very good takes 9,10; good 7,9,10; medium good 5,7,9 values. Fuzzy membership functions of linguistic variablesareexhibitedin Figure 4 (BüyüközkanandÇiftçi, 2012).

Figure 4. Linguistic variables with fuzzy membership functions

Source: (BüyüközkanveÇiftçi, 2012)

2.3. VIKOR METHOD

(12)

Murat Bolelli*

The multi criteria measure for compromise ranking is developed from Lpmetric used as an aggregating function in a compromise programming method (Yu, 1973, Zeleny, 1982). Each one of i number of alternatives represented as c₁, c₂, c₃,….c_i is measured against ith criteria and denoted by f_ij.

(2)

Source: (Opricovic&Tzeng,2004)

L_pi measure shows normalized values of the distance between all alternatives and positive ideal solution (Formula 2). In the VIKOR method L_1i, S_i and Li are used to formulate the ranking measure. Min S_i represents maximum group utility (majority rule) andmin R_i represents minimum individual regret of the opponent. Compromise solution is reached by optimizing both. Fuzzy VIKOR method contains following steps (

Yildiz and Deveci, 2013

): Step1: Identifying nnumber of decision makers, m number of alternatives and choosing k number of criteria.

Step 2: Identifying appropriate linguistic varibles and triangular fuzzy numbers to represent alternatives and criteria. The decision makers use variables to evaluate the importance of the criteria and therating of alternatives.

Step 3: Weight of decision criteria and values assigned toalternatives are pulled from decision makers assessments and by using Formula 3 and 4,singular integrated values are calculated (Chen,Ching-Torngand Huang, 2006).

(3)

(4)

Source: (Chen et al., 2006)

Step 4: After obtaining single values for criteria and alternatives, fuzzy decision matrix and subjective weighting matrix is constructed.

(13)

X_ij, shows the fuzzy rating of ith_{alternative regarding j}th_criterion.

Step 5: The best f*_j and and the worst f*_jvalues of all criterion ratings are determined using Formula 6.

(6)

Source: (Opricovic&Tzeng,2004)

Step 6: S_i and R_i values are calculated using Formula 7 and Formula 8.

(7)

(8)

Source: (Opricovic&Tzeng, 2004)

Step 7: S*_i, S–

i and R*i, R

–

i which are the weights for maximum group utility and individual regret are determined using Formula 9 and Formula 10.

(9)

(10)

Step 8: Q_i value is calculated using Formula 11 where both group utility and individual regret taken into consideration. v is introduced as a weight for the strategy of maximum group utility, whereas 1-v is the weight of the individual regret. Although literature is indicating the use of different v values, 0,5 is observed to be the one which is generally accepted (Opricovic and Tzeng, 2007; Opricovic, 2011).

(11)

(14)

Murat Bolelli*

Step 9: S_i, R_i, Q_i which are average values of fuzzy numbers are calculated. Alternatives are ranked, sorted by the values S, R and Q in ascending order. The alternative which has the smallest index value presents the best solution.

Step 10: In order to decide if the best ranked alternative is the compromise solution, two conditions must be satisfied:

• C1. Acceptable advantage: requires existence of a significant difference between best solution and next (Formula 12). A1_{represents the best ranked alternative, A}2_represents second best alternative and m represents the number of alternatives.

(12)

• C2. Acceptable stability in decision making: Alternative A1 must also be the best ranked by S and/or R(OpricovicandTzeng, 2007).

If one of the conditions is not satisfied, then a set of compromise solutions is proposed, which consist of

• Alternatives A1_{and A}2_{if only the conditions C}

2 is not satisfied, or • Alternatives A1_,A2_{, . . .,A}(M)_{if the condition C}

1 is not satisfied; A(M) is determined by the relation Q(A(M)_{) – Q(A}1)_{< DQ for maximum M (the positions of these alternatives} are ''incloseness'').

3. R

ESEARCH

M

ETHODOLOGY

In order to apply proposed model, external hiring of a Region Manager to a pharmaceutical company scenario is developed, resumes of individuals working in the industry are found from various career portals, all the information are anonymized and presented to decision makers as if they are actual job applicants. Following process is conducted using VIKOR methods steps to evaluate applications and to determine candidates to be interviewed.

3.1. STEP 1

(15)

Table2.Decision Criteria

Criteria

C1 C2 C3 C4 C5 C6 C7

Total Experience

Experience in the Industry

Education Level

Education-Profession Relation

Languages Age Remuneration Expectations

Source: Research Data

3.2. STEP 2

Criteria shown in Table 2 are weighted (Table 3), candidates are assessed by decision makers using them. Then linguistic terms (ie. very low, medium, very high, medium high etc.) are converted into triangular fuzzy numbers using Table 4.

Table 3. Importance Weigth of Criteria Assessed By Decision Makers

C1 C2 C3 C4 C5 C6 C7

DM1 High High High High High Medium Medium High

DM2 High Medium

High

Medium Medium Medium High

Medium High

DM3 High Very High High Medium Medium

High

High High

DM4 Medium

High

Medium High High Medium Medium Low

Medium

DM5 High High Medium

High

Medium High

High Very High

DM6 Medium High

Medium High Low High Medium Low

Medium

DM7 Medium High

High High Medium Low

Very High

Medium Medium

DM8 Medium

High

High High High Very High

High Medium High

(16)

Murat Bolelli*

Table4. Linguistic Terms and Corresponding Fuzzy Numbers

C riteria C and id ate R atin g

L in gustic Term s

Fuzzy Num bers

L in gustic Term s

Fuz zy Num bers

Very Low 0,0 0,0 0,1 Very Poor 0 0 1 Low 0,0 0,1 0,3 P oor 0 1 3 Medium Low 0,1 0,3 0,5 Medium P oor 1 3 5 Medium 0,3 0,5 0,7 Medium 3 5 7 Medium High 0,5 0,7 0,9 Medium Good 5 7 9 High 0,7 0,9 1,0 Good 7 9 10 Very High 0,9 1,0 1,0 Very Good 9 10 10

Source: Author's Calculation

3.3. STEP 3

Formula 3 is used to calculate fuzzy numbers for criteria. After that, aggregated fuzzy weights of criteria are computed by averaging decision makers weightings (Table5).

Table5. Aggregated Fuzzy Weights of Criteria

Aggregated Fuzzy Weights Criteria Fuzzy Weights

C1 0,60 0,80 0,95

C2 0,60 0,79 0,91

C3 0,63 0,83 0,95

C4 0,41 0,60 0,76

C5 0,63 0,80 0,93

C6 0,40 0,60 0,76

C7 0,53 0,71 0,86

3.4. STEP 4

(17)

Table 6. Fuzzy Decision Matrix

Alternatives

Criteria A1 A2 A3 A4 A5 A6

C1 8,25 9,63 10,00 7,00 8,50 9,38 5,75 7,63 8,88 3,88 5,75 7,50 7,00 8,63 9,63 6,75 8,50 9,50

C2 8,00 9,50 10,00 7,00 8,63 9,63 6,50 8,25 9,38 3,88 5,75 7,63 6,75 8,50 9,63 6,75 8,50 9,50

C3 4,50 6,50 8,13 5,25 7,25 8,88 4,50 6,50 8,13 2,38 4,25 6,13 4,50 6,50 8,25 7,50 9,00 9,75

C4 2,63 4,50 6,38 6,00 8,00 9,38 3,50 5,50 7,38 1,88 3,75 5,75 3,38 5,13 6,88 7,75 9,13 9,63

C5 3,88 5,75 7,38 4,50 6,38 7,88 3,63 5,50 7,25 2,75 4,75 6,63 2,25 3,75 5,63 5,00 6,38 7,50

C6 3,00 4,75 6,50 3,63 5,50 7,25 4,00 6,00 7,75 4,00 6,00 7,88 3,63 5,50 7,13 4,25 6,25 7,88

C7 5,00 6,88 8,50 3,25 5,25 7,25 5,00 7,00 8,50 4,00 6,00 7,75 2,88 4,75 6,63 4,13 6,00 7,63

3.5. STEP 5

Formula 6 is used to determine the best (f*_j) and the worst (f–

j) values of all criterion ratings (Table 7).

Table7. The Best and The Worst Values of Criteria

The Best and The Worst Values f*_j _f-_j

C1 8,25 9,63 10,00 3,88 5,75 7,50

C2 8,00 9,50 10,00 3,88 5,75 7,63

C3 7,50 9,00 9,75 2,38 4,25 6,13

C4 7,75 9,13 9,63 1,88 3,75 5,75

C5 5,00 6,38 7,50 2,75 4,75 6,63

C6 4,25 6,25 7,88 3,00 4,75 6,50

C7 5,00 7,00 8,50 2,88 4,75 6,63

3.6. STEP 6

(18)

Murat Bolelli*

Table 8. The Values of S_i for each Alternative Table 9. The Values of R_i for eachAlternative

SiValues

Si Avg. Si

A1 1,44 1,90 1,96 1,77

A2 1,49 1,70 1,19 1,46

A3 1,69 2,05 1,87 1,87

A4 3,19 4,23 4,85 4,09

A5 2,52 3,60 4,48 3,53

A6 0,60 0,76 0,78 0,72

Ri Values

Ri Avg. Ri

A1 0,40 0,60 0,76 0,59 A2 0,43 0,55 0,58 0,52 A3 0,38 0,43 0,44 0,42 A4 0,63 0,83 0,95 0,80 A5 0,76 1,29 1,98 1,35 A6 0,22 0,32 0,40 0,31

Source: Author's Calculation Source: Author's Calculation

3.7. STEP 7

In order to compute Q_i which shows the compromise solution which meets the conditions of maximum group utility and minimum individual regret, first the values of S–

i and S*i (Table 10) are determined using Formula 9 and Table 8. After that, R–

i and R*i values shown in Table 11 are determined using Formula 10 and Table 9. Finally Q_i values are calculated using Formula 11 (Table 12).

Table10. The Values of S–

i and S*i Table11. The Values of R –

i and R*i Si min 0,60 0,76 0,78

Si max 3,19 4,23 4,85

Ri min 0,22 0,32 0,40 Rimax 0,76 1,29 1,98

Source: Author's Calculation Source: Author's Calculation

Table 12. The Q_i Values for Each Alternative

Qi Values

Alternatives Qi Avg. Qi

A1 0,329 0,309 0,259 0,299

A2 0,368 0,257 0,104 0,243

A3 0,361 0,246 0,146 0,251

A4 0,873 0,761 0,673 0,769

A5 0,870 0,910 0,955 0,911

A6 0,000 0,000 0,000 0,000

(19)

3.8. STEP 8

Defuzzified S_i, R_i, Q_i values are obtained by averaging fuzzy numbers (Table 13).Alternative 6, which has the smallest index value shows the best solution.

Table 13. Q_i, S_i and R_i Rankings in Ascending Order by Q_i

Alternatives Qi Si Ri

A6 0,000 0,716 0,312

A2 0,243 1,457 0,521

A3 0,251 1,867 0,420

A1 0,299 1,765 0,588

A4 0,769 4,088 0,800

A5 0,911 3,533 1,346

In order to propose a compromise solution following two conditions is tested:

3.8.1. ACCEPTABLE ADVANTAGE

According to the Formula 12, Q(A2_{) – Q(A}1_{) > 0,20 condition should be satisfied. Since the} difference between first and second alternatives is bigger then 0.20 (0,243-0 > 0,20) according to the Table 13, it is concluded that A6 alternative is satisfying acceptable advantage condition.

3.8.2. ACCEPTABLE STABILITY IN DECISION MAKING

Alternative A1 must also be the best ranked by S and/or R (Opricovic and Tzeng, 2007). Table 13 shows that A6 alternative is also best ranked by S_i and R_i index values.

Satisfied by the two conditions, alternative A6 has the best rank and is identified as the best compromise solution.

(20)

Murat Bolelli*

Table 14. Remuneration Expectations of Candidates

Alternative 1 16.850$/net annual + 4.150 $ bonus if targets achieved + company car+ cell phone + private heath insurance.

Alternative 3 12.650 $/net annual + 8.350 $ bonus if targets achieved + company car+ cell phone + private heath insurance.

Source: Research Data

4. C

ONCLUSION

Human capital is a distinctive factor forfirms in obtaining and sustaining competitive advantage. As part of personnel planning which is conducted by taking current and future needs into consideration, securingto have right number of employees with the qualifications needed in the right place and time is critical for the success of the company. Decisions and choices made in every step of this process have a significant effect on the overall quality of human capital. This study focuses on proposing a model for prescreening job applications as well as testing if fuzzy VIKOR method can be used for the process. For this purpose, external hiring of a Region Manager to a pharmaceutical company scenario is developed. After that,a group of eight decision makers consist of human resources managers and recruitment agents is formed. Recruitment scenario is presented to them to determine criteria and weights to be used for prescreening. Total experience, experience in the industry, education level, education-profession relation, languages, age, remuneration expectations are selected as criteria. Using criteria, candidates are assessed,linguistic variables provided by decision makers are transformed to fuzzy numbers and alternatives are evaluated by following VIKOR methodssteps. Candidate 6 is ranked as the best compromise solution which satisfies acceptable advantage and acceptable stability in decision making conditons as well.

(21)

conducted in pharmaceteucal industry which is competitive and result oriented and the rising trend of pay for performance policies.

R

ESEARCH

I

MPLICATIONS

Fuzzy VIKOR method can efficiently be used for prescreening job applications.

L

IMITATIONSOFTHE

S

TUDY

There are two limitations of the study:

1. Scenario developed to test the method comprises of external recruitment of a manager in pharmaceutical industry. In order to generalize the findings of this research, different scenarios for various positions, industries and source of recruitment can be studied. 2. The decision maker group who determined and weighted criteria to be used for

prescreening and assessed candidates is consisted of human resources and recruitment professionals. Model can be studied with different decision makers in future research.

S

COPEFOR

F

UTURE

R

ESEARCH

This study is focused on a specific problemof the recruitment process. It can also be used for other human resources processes which require multi criteria decision making, namely training and development, compensation and benefits, career management, performance management as well. Future studies are suggested to conduct research in different processes of human resources, alternative industries and positionswith samples containing larger data, using different number of decision makers, criteria, weights, v values etc. to examine subject further.

REFERENCES

[1]. Afshari, A., Majid, M. and Rosnah, M.Y. (2010). Simple Additive Weighting Approach to

PersonnelSelection Problem. International Journal of Innovation, Management and Technology, 1(5):511-515.

[2]. Akyüz, G. A. (2012). Bulanik VIKOR yöntemiiletedarikçiseçimi. Atatürk Üniversitesi?ktisadive?dari

Bilimler Dergisi, 26(1). Retrieved from http://dergipark.org.tr/atauniiibd/issue/2704/35554

[3]. Arvey, R.D., Campion, J.E. (1982).The employment interview: A summary and review of recent research,

Personel Psychology, 35(1), 281-322.https://doi.org/10.1111/j.1744-6570.1982.tb02197.x

[4]. Başkaya, Z. veÖztürk, B. (2011).Bulanýk TOPSIS ileSatýþElemanýAdaylarýnýnDeðerlendirilmesi.

Business and Economics Research Journal, 2(2):77-100.

[5]. Büyüközkan, G., &Çiftçi, G. (2012). A Combined Fuzzy AHP and Fuzzy TOPSIS Based StrategicAnalysis

of Electronic Service Quality in Healthcare Industry. Expert Systems with Applications, 39,2341-2354.https://doi.org/10.1016/j.eswa.2011.08.061

[6]. Chang, C.-L., & Hsu, C.-H. (2009). Multi-criteria analysis via the VIKOR method for prioritizing

land-use restraint strategies in the Tseng-Wen reservoir watershed. Journal of Environmental Management, 90, 3226-3230.https://doi.org/10.1016/j.jenvman.2009.04.020

[7]. Chen, C.T. (2000). Extensions of the TOPSIS for Group Decision Making Under Fuzzy Environment.

(22)

Murat Bolelli*

[8]. Chang, T.-H., & Wang, T.-C. (2009). Using the fuzzy multi-criteria decision making approach for

measuring the possibility of successful knowledge management. Information Sciences, 179, 355-370. https://doi.org/10.1016/j.ins.2008.10.012

[9]. Chen, T.C., Ching-Torng L., & Huang, S.F. (2006). A Fuzzy Approach for Supplier Evaluation and

Selection in Supply Chain Management. International Journal of Production Economics, 102, 289-301.https://doi.org/10.1016/j.ijpe.2005.03.009

[10]. Chen, L. Y., & Wang, T. C. (2009). Optimizing partners' choice in IS/IT outsourcing projects: The

strategic decision of fuzzy VIKOR. International Journal of Production Economics, 120(1), 233-242.https://doi.org/10.1016/j.ijpe.2008.07.022

[11]. Da?deviren, M. (2007). Bulanikanalitikhiyerar?iprosesiilepersonelseçimivebiruygulama.

GaziÜniversitesiMühendislik-Mimarl?kFakültesiDergisi, 22(4).Retrieved from http://dergipark.org.tr/ gazimmfd/issue/6674/88430

[12]. Dursun, E.M. veKarsak, E.E. (2010). A Fuzzy MCDM Approach for Personel Selection.Expert Systems

with Applications, 37:4324-4330.https://doi.org/10.1016/j.eswa.2009.11.067

[13]. Ecer, F. (2006).Bulan?kOrtamlardaGrupKararVermeyeYard?mc?BirYöntem: Fuzzy TOPSIS

veBirUygulama.??letmeFakültesiDergisi, 7(2):77-96.Retrieved from http://dergipark.org.tr/ifede/issue/ 4601/62873

[14]. El-Santawy, M. F. (2012). A VIKOR method for solving personnel training selection problem.

International Journal of Computing Science, 1(2), 9-12.

[15]. El-Santawy, M. F. (2012). Personnel Training Selection Problem Based On Modified Topsis. Computing

& Information Systems, 16(1).

[16]. Ersoylu, ?. (2011). Bulan?k VIKOR vebulan?k AHP yöntemleriileperformansölçümü:

Havac?l?kveUzayTeknolojileriEnstitüsü'neö?renciseçimi.Yay?nlanmam??YüksekLisansTezi.

[17]. Gargano, M.L., Marose, R.A., Kleeck, L. (1991). An application of artificial neural Networks and genetic

algorithms to personnel selection in the financial industry, Proceedings of the First International Conference on Artificial Intelligence Applications, 257-262.10.1109/AIAWS.1991.236592

[18]. Hooper, R.S., Galvin, T.P., Kimler, R.A., Liebowitz, J. (1998). Use of an expert system in a personnel

selection process, Expert Systems with Applications, 14(1), 425-432.https://doi.org/10.1016/S0957-4174(98)00002-5

[19]. Kabak, M. veKazanço?lu, Y. (2012).Bulan?kAnalitikHiyerar?iYöntemiyleÖ? retmenSeçimiveBir

Uygulama. AfyonKocatepeÜniversitesi?ktisadive?dariBilimlerFakültesiDergisi, 14(1):95-111.Retrieved from http://dergipark.org.tr/akuiibfd/issue/1621/20319

[20]. Kabak, M., Burmao?lu S. veKazanço?lu, Y. (2012). A Fuzzy Hybrid MCDM Approach for Professional

Selection,Expert Systems with Applications, 39:3516-3525.https://doi.org/10.1016/j.eswa.2011.09.042

[21]. Kelemenis, A. andAskounis D. (2010). A new TOPSIS Based Multicriteria Approach Personnel

Selection,Expert Systems with Applications, 37:4999-5008.https://doi.org/10.1016/j.eswa.2009.12.013

[22]. Liang, S.L., Wang M.J. (1994). Personnel selection using fuzzy MCDM algorithm, European Journal

of Operational Research, 78(2), 22-33.https://doi.org/10.1016/0377-2217(94)90119-8

[23]. Liou, J. J. H., Tsai, C.-Y., Lin, R.-H., &Tzeng, G.-H. (2010). A modified VIKOR multiplecriteria decision

method for improving domestic airlines service quality. Journal of Air Transport Management, 1-5. doi:10.1016/j.jairtraman.2010.03.004.https://doi.org/10.1016/j.jairtraman.2010.03.004

[24]. Mamdani, E.H. (1974). Application of fuzzy algorithms for control of simple dynamic plant.Proc.Iee,

(23)

[25]. Mamdani, E.H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy

logicController. Int. J. Man-Machine Studies7, 1-13.https://doi.org/10.1016/S0020-7373(75)80002-2

[26]. Mamdani, E.H. (1976). Advances in the linguisticsynthesis of fuzzy controllers. Int. J. Man-Machine

Studies, 8,.669-678.https://doi.org/10.1016/S0020-7373(76)80028-4

[27]. Nasab, F.G. veRostamy-Malkhalifeh, M. (2010). Extension of TOPSIS for Group Decision Making

Based on the Type-2 fuzzy Positive and Negative Ideal Solutions,International Industrial Mathematics, 2(3):199-213.

[28]. Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil

Engineering, Belgrade, 2(1), 5-21.

[29]. Opricovic, S. veTzeng, G.H. (2004). Compromise Solution by MCDM Methods: A Comparative Analysis

of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.https://doi.org/ 10.1016/S0377-2217(03)00020-1

[30]. Opricovic, S. veTzeng, G.H. (2007). Extended VIKOR Method in Comparison with Other Outranking

Methods.EuropeanJournal of Operational Research, 178(2), 514-529.https://doi.org/10.1016/ j.ejor.2006.01.020

[31]. Opricovic, S. (2011). Fuzzy VIKOR with an Application to Water Resources Planning. Expert Systems

with Applications, 38, 12983-12990.https://doi.org/10.1016/j.eswa.2011.04.097

[32]. Öztürk A., Ertu?rul, ?. veKaraka?o?lu, N. (2008). NakliyeFirmas?SeçimindeBulan?k AHP veBulan?k

TOPSISYöntemlerininKar??la?t?r?lmas?.Marmara Üni. ??BF Dergisi, 15(2), 785-824.

[33]. Padala, S. R. (2014). Human Resource Practices in Voluntary Organisation (With reference to Integrated

Development through Environmental Awakening (IDEA)). Sumedha Journal of Management, 3(2), 12.

[34]. Sanayei, A., Mousavi, S. F., &Yazdankhah, A. (2010). Group decision making process for supplier

selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37, 24-30. https:/ /doi.org/10.1016/j.eswa.2009.04.063

[35]. Saghafian, S., &Hejazi, S. R. (2005, November). Multi-criteria group decision making using a modified

fuzzy TOPSIS procedure. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) (Vol. 2, pp. 215-221). IEEE. 10.1109/CIMCA.2005.1631471

[36]. Triantaphyllou, E. (2000). Multi-criteria decision making methods. In Multi-criteria decision making methods:

A comparative study. (pp. 5-21). Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3157-6_2

[37]. Y?ld?z, A., &Deveci, M. (2013). Bulan?k VIKOR YöntemineDayal?PersonelSeçimSüreci/Based on Fuzzy

VIKOR Approach to Personnel Selection Process. EgeAkademikBak??, 13(4), 427.

[38]. Yu, P. L. (1973). A Class of Solutions for Group Decision Problems. Management Science, 19 (8),

936-946.https://doi.org/10.1287/mnsc.19.8.936

[39]. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3),

338-353.https://doi.org/10.1016/S0019-9958(65)90241-X

[40]. Zadeh, L.A. (1973). Outline of a new approach to the analysis of complex systems and

decisionProcesses. IEEE Transactions on Systems, man, and Cybernetics, Vol. SMC-3, No. 1,.28-44,January.10.1109/TSMC.1973.5408575

[41]. Zadeh, L.A. (1975). The concept of linguistic variable and its application to approximateReasoning.

Part 1, 2, and 3. Information Sciences, 8: 199-249, 8: 301-357-, 9: 43-80.https://doi.org/10.1016/0020-0255(75)90036-5

[42]. Zarghami, M., &Szidarovszky, F. (2009). Revising the OWA operator for multi criteria decision making