DC FieldValueLanguage
dc.contributor.authorSalmon, Ioannis-
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorPsarras, Alkinoos-
dc.contributor.authorPsycharis, Panagiotis Nikolaos-
dc.contributor.authorTagkouta, Eleni-
dc.date.accessioned2024-03-26T08:07:57Z-
dc.date.available2024-03-26T08:07:57Z-
dc.date.issued2023-01-01-
dc.identifierscopus-85166520323-
dc.identifier.issn22242899-
dc.identifier.issn11099526-
dc.identifier.other85166520323-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/1576-
dc.description.abstractMachine Learning (ML) can be proved as an important tool in planning better business strategies. For the purposes of the present study, the prospect for the development of an electronic platform by a technology firm providing financial services is explored. The purpose of this article is to demonstrate the ways in which a start-up can predict the success of an online platform prior to its market launch. The prediction is achieved by applying Artificial Intelligence (AI) on Key Performance Indicators (KPIs) derived from the customers’ perspective, as shown in the Balanced Scorecard (BSC). The research methodology was quantitative and online questionnaires were used to collect empirical quantitative data related to bank loans. Subsequently, KPIs were created based on the collected data, to measure and assess the success of the platform. The effectiveness of the model was calculated up to 91.89%, and thus, it is estimated that the online platform will be of great success with 91.89% validity. In conclusion, prediction was found to be crucial for businesses to prevent a dire economic situation. Finally, the necessity for businesses to keep up with technological advances is highlighted.en_US
dc.language.isoenen_US
dc.relation.ispartofWSEAS Transactions on Business and Economicsen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectBalanced Scorecarden_US
dc.subjectBusiness planen_US
dc.subjectBusiness strategyen_US
dc.subjectChange managementen_US
dc.subjectE-Businessen_US
dc.subjectMachine Learningen_US
dc.subjectProduct Successen_US
dc.subjectStart-upsen_US
dc.titlePredicting Success for Web Product through Key Performance Indicators based on Balanced Scorecard with the Use of Machine Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.37394/23207.2023.20.59en_US
dc.identifier.scopus2-s2.0-85166520323-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume20en_US
dc.identifier.spage646en_US
dc.identifier.epage656en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.languageiso639-1en-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0009-0006-9089-8898-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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