Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12673
Full metadata record
DC FieldValueLanguage
dc.rights.licenserestrictedAccess-
dc.contributor.authorPozón-López I.-
dc.contributor.authorKalinić, Zoran-
dc.contributor.authorHigueras-Castillo E.-
dc.contributor.authorLIEBANA-CABANILLAS, FRANCISCO-
dc.date.accessioned2021-04-20T21:26:33Z-
dc.date.available2021-04-20T21:26:33Z-
dc.date.issued2020-
dc.identifier.issn1049-4820-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/12673-
dc.description.abstract© 2019 Informa UK Limited, trading as Taylor & Francis Group. The purpose of this study is to classify the predictors of satisfaction and intention to use in Massive Open Online Courses (MOOC). Informed by a scientific literature review, this work poses a behavioral model to explain intention to use via various constructs. To this end, the authors have carried out a study through an online survey of Spanish Internet users. Two techniques were used: first, structural equation modeling (SEM) was approached to determine which variables had significant influence on MOOC adoption; in a second phase, the neural network model was used to rank the relative influence of significant predictors obtained by SEM. The analysis also shows that perceived satisfaction is affected by the quality of the course, its entertainment value and its usefulness. The latter variable also plays a major role when addressing user emotions. On the other hand, results from the neural network analysis confirmed many SEM findings and also provided a slightly different order of influence of significant predictors. The study provides an original focus in the study of perceived satisfaction and intention to use for MOOCs by extending the models proposed in previous research with regard to this emerging field.-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.sourceInteractive Learning Environments-
dc.titleA multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC)-
dc.typearticle-
dc.identifier.doi10.1080/10494820.2019.1636074-
dc.identifier.scopus2-s2.0-85068793774-
Appears in Collections:Faculty of Economics, Kragujevac

Page views(s)

503

Downloads(s)

18

Files in This Item:
File Description SizeFormat 
PaperMissing.pdf
  Restricted Access
29.86 kBAdobe PDFThumbnail
View/Open


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