Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets

Fuzzy conjoint method (FCM) is one of the available methods suggested for job satisfaction evaluation. The main feature of job satisfaction evaluation is the use of rating of agreement to indicate employee feelings and beliefs about their job. Currently the linguistic terms used for rating of agreem...

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Published in:Indonesian Journal of Electrical Engineering and Computer Science
Main Author: Shahari N.; Rasmani K.A.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083100282&doi=10.11591%2fijeecs.v19.i1.pp363-370&partnerID=40&md5=0185d84741cfc8e74bd39ea33205e5e3
id 2-s2.0-85083100282
spelling 2-s2.0-85083100282
Shahari N.; Rasmani K.A.
Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
2020
Indonesian Journal of Electrical Engineering and Computer Science
19
1
10.11591/ijeecs.v19.i1.pp363-370
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083100282&doi=10.11591%2fijeecs.v19.i1.pp363-370&partnerID=40&md5=0185d84741cfc8e74bd39ea33205e5e3
Fuzzy conjoint method (FCM) is one of the available methods suggested for job satisfaction evaluation. The main feature of job satisfaction evaluation is the use of rating of agreement to indicate employee feelings and beliefs about their job. Currently the linguistic terms used for rating of agreement in FCM are represented in the form of discrete fuzzy sets. This paper investigates the potential use of continuous fuzzy sets to represent linguistic terms used in the FCM process. To investigate the consistency of the decision outcomes produced by the proposed approach, four different types of fuzzy similarity measures were used: similarity based on Matching Function, similarity based on Euclidean Distance, similarity based on Set-Theoretic and similarity based on vector. These classification outcomes are also compared with classification drawn on the basis of statistical inference. The finding of this study shows that both discrete fuzzy sets and continuous fuzzy sets produce consistent results regardless of whether the fuzzy similarity measure was used. Hence, the inclusion of other methods in FCM is particularly very useful for calculating the closeness coefficients and specifically addresses the shortcoming in FCM for job satisfaction evaluation. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.
Institute of Advanced Engineering and Science
25024752
English
Article
All Open Access; Gold Open Access; Green Open Access
author Shahari N.; Rasmani K.A.
spellingShingle Shahari N.; Rasmani K.A.
Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
author_facet Shahari N.; Rasmani K.A.
author_sort Shahari N.; Rasmani K.A.
title Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
title_short Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
title_full Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
title_fullStr Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
title_full_unstemmed Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
title_sort Job satisfaction evaluation based on fuzzy conjoint method with continuous fuzzy sets
publishDate 2020
container_title Indonesian Journal of Electrical Engineering and Computer Science
container_volume 19
container_issue 1
doi_str_mv 10.11591/ijeecs.v19.i1.pp363-370
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083100282&doi=10.11591%2fijeecs.v19.i1.pp363-370&partnerID=40&md5=0185d84741cfc8e74bd39ea33205e5e3
description Fuzzy conjoint method (FCM) is one of the available methods suggested for job satisfaction evaluation. The main feature of job satisfaction evaluation is the use of rating of agreement to indicate employee feelings and beliefs about their job. Currently the linguistic terms used for rating of agreement in FCM are represented in the form of discrete fuzzy sets. This paper investigates the potential use of continuous fuzzy sets to represent linguistic terms used in the FCM process. To investigate the consistency of the decision outcomes produced by the proposed approach, four different types of fuzzy similarity measures were used: similarity based on Matching Function, similarity based on Euclidean Distance, similarity based on Set-Theoretic and similarity based on vector. These classification outcomes are also compared with classification drawn on the basis of statistical inference. The finding of this study shows that both discrete fuzzy sets and continuous fuzzy sets produce consistent results regardless of whether the fuzzy similarity measure was used. Hence, the inclusion of other methods in FCM is particularly very useful for calculating the closeness coefficients and specifically addresses the shortcoming in FCM for job satisfaction evaluation. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.
publisher Institute of Advanced Engineering and Science
issn 25024752
language English
format Article
accesstype All Open Access; Gold Open Access; Green Open Access
record_format scopus
collection Scopus
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