DC FieldValueLanguage
dc.contributor.authorAsonitou, Sofia-
dc.contributor.authorNtanos, Stamatios-
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorGkika, Eleni-
dc.contributor.authorKyriakopoulos, Grigorios-
dc.date.accessioned2024-04-09T11:25:12Z-
dc.date.available2024-04-09T11:25:12Z-
dc.date.issued2020-04-20-
dc.identifierscopus-85085770245-
dc.identifier.issn2071-1050-
dc.identifier.other85085770245-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/1880-
dc.description.abstractWillingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused on the respondents' perceptions and offered benefits for wind energy, solar photovoltaics (PVs), small hydro parks and biomass investments. The classification algorithms of Bayesian Network classifier, Logistic Regression, Support Vector Machine (SVM), C4.5, k-Nearest Neighbors (k-NN) and Long Short Term Memory (LSTM) were used. The Bayesian Network classifier was the best method, with a prediction accuracy of 0.7942. The most important variables for the prediction of willingness to invest were the level of information, the level of acceptance and the contribution to sustainable development. Future studies should include data on state incentives and their impact on willingness to invest.en_US
dc.language.isoenen_US
dc.relation.ispartofSustainabilityen_US
dc.subjectBusiness investmenten_US
dc.subjectData miningen_US
dc.subjectIntelligent predictive analyticsen_US
dc.subjectRenewable energy sourcesen_US
dc.subjectSustainable managementen_US
dc.titleIntelligent predictive analytics for sustainable business investment in renewable energy sourcesen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/su12072817en_US
dc.identifier.scopus2-s2.0-85085770245-
dcterms.accessRights1en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume12en_US
dc.relation.issue7en_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.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.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.orcid0000-0002-5861-9514-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Articles / Άρθρα
CORE Recommender
Show simple item record

SCOPUSTM   
Citations

19
checked on Jul 12, 2024

Page view(s)

13
checked on Jul 14, 2024

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.