A Multi-Analytical Approach to Modeling of Customer Satisfaction and Intention to Use in Massive Open Online Courses (MOOC)

Autor/inn/en	Pozón-López, I.; Kalinic, Zoran; Higueras-Castillo, Elena; Liébana-Cabanillas, Francisco
Titel	A Multi-Analytical Approach to Modeling of Customer Satisfaction and Intention to Use in Massive Open Online Courses (MOOC)
Quelle	In: Interactive Learning Environments, 28 (2020) 8, S.1003-1021 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Liébana-Cabanillas, Francisco)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2019.1636074
Schlagwörter	Online Courses; Large Group Instruction; Predictor Variables; Student Satisfaction; Intention; Educational Technology; Technology Uses in Education; Distance Education; Artificial Intelligence; Foreign Countries; Higher Education; Student Attitudes; Program Effectiveness; College Students; Interaction; Spain + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Prädiktor; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Distance study; Distance learning; Fernunterricht; Künstliche Intelligenz; Ausland; Hochschulbildung; Hochschulsystem; Hochschulwesen; Schülerverhalten; Collegestudent; Interaktion; Spanien
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01