Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12673
Title: A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC)
Authors: Pozón-López I.
Kalinić, Zoran
Higueras-Castillo E.
LIEBANA-CABANILLAS, FRANCISCO
Journal: Interactive Learning Environments
Issue Date: 16-Nov-2020
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.
URI: https://scidar.kg.ac.rs/handle/123456789/12673
Type: article
DOI: 10.1080/10494820.2019.1636074
ISSN: 10494820
SCOPUS: 85068793774
Appears in Collections:Faculty of Economics, Kragujevac

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