DC FieldValueLanguage
dc.contributor.authorLoukeris, Nikolaos-
dc.date.accessioned2024-04-22T13:48:52Z-
dc.date.available2024-04-22T13:48:52Z-
dc.date.issued2009-
dc.identifiergoogle_scholar-XlKYe28AAAAJ:IjCSPb-OGe4C-
dc.identifier.issn1998-4308-
dc.identifier.otherXlKYe28AAAAJ:IjCSPb-OGe4C-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2215-
dc.description.abstractStrategic Planning is formed considering customers satisfaction to maximise the market share. In shipping companies, the identification of satisfaction within clients is very difficult, thus satisfaction’s prediction provides valuable information. Previous research in the field used techniques of multicriteria analysis, data mining and analytical-synthetical preference models. This research paper aims to define the most effective method to predict satisfaction, between techniques of data mining, rough sets, neural networks and multcriteria decision analysis.en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Computersen_US
dc.sourceInternal Journal of Computers 3 (4), 349-356, 2009-
dc.subjectShippingen_US
dc.subjectNeural networksen_US
dc.subjectMulticriteria analysisen_US
dc.subjectData miningen_US
dc.subjectRough setsen_US
dc.titleCustomers satisfaction in shipping companies under artificial intelligence and multicriteria decision analysisen_US
dc.typeArticleen_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume3en_US
dc.relation.issue4en_US
dc.identifier.spage349en_US
dc.identifier.epage356en_US
dc.linkhttps://www.naun.org/main/NAUN/computers/19-093.pdfen_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-1891-8245-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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