Influencing Factors in MOOCs Adoption in Higher Education: A Meta-Analytic Path Analysis

Zeinab Zaremohzzabieh, Samsilah Roslan, Zulkifli Mohamad, Ismi Arif Ismail, Habibah Ab Jalil and Seyedali Ahrari
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Zeinab Zaremohzzabieh: Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan 43400, Selangor, Malaysia
Samsilah Roslan: Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan 43400, Selangor, Malaysia
Zulkifli Mohamad: Pusat Pengajian Citra Universiti, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
Ismi Arif Ismail: Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan 43400, Selangor, Malaysia
Habibah Ab Jalil: Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan 43400, Selangor, Malaysia
Seyedali Ahrari: Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan 43400, Selangor, Malaysia

Sustainability, 2022, vol. 14, issue 14, 1-21

Abstract: (1) Background: Due to the rapid growth of Massive Online Open Courses (MOOCs), higher educational institutions across the world are investing heavily in MOOCs to support their traditional teaching, their students’ learning experience, and their performance. However, the success of MOOCs highly depends on several factors that influence their success in higher education. Prior studies have attempted to investigate and predict user acceptance of MOOCs in higher education by using a variety of theoretical viewpoints. Nonetheless, these studies have yielded conflicting findings and are inconclusive. (2) Purpose: This study aims to develop a model that integrates the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), as well as the Task-Technology Fit (TTF) to explore the factors that influence the acceptance and use of MOOCs in higher education institutions, while synthesizing previous empirical findings in the field. (3) Methods: The model was tested using Meta-analytic Structural Equation Modelling (MASEM) based on the data gathered from 43 studies (k = 45 samples, n = 16,774). (4) Results: Effort expectancy (EE), attitude (ATT), performance expectancy (PE), and TTF—determined by several task and technology characteristics—were identified as the direct predictors of behavioral intention (BI) to continue using MOOCs. (5) Conclusions: This model provides a cohesive view of MOOCs’ acceptance in higher educational institutions, and it helps to identify potential research opportunities in this area. (6) Implications: Results from MASEM offer managerial guidance for the effective implementation of MOOCs and provide directions for further research, to augment current knowledge of MOOCs’ adoption, by higher education institutions.

Keywords: higher education; MOOCs adoption; Task-Technology Fit; Theory of Planned Behavior; Unified Theory of Acceptance and Use of Technology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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