Determinants of E-Learning Adoption among Moroccan University Educators: A Literature Review and an Extended UTAUT2 Perspective
This literature review examines the determinants of e-learning adoption by university instructors in Moroccan higher education through an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. In response to rapid digital transformation and increasing instructional demands, the study synthesizes research on personal, institutional, and socio-technical factors shaping instructors’ engagement with digital learning environments. Following PRISMA 2020 guidelines, peer-reviewed studies (2010–2025) were systematically screened from Scopus, Web of Science, and Google Scholar. The analysis integrates socio-technical perspectives emphasizing learner diversity (Saykili, 2018), change management approaches supporting sustainable innovation (DeRouin et al., 2005), and critiques of pedagogical limitations in current e-learning solutions (Pange & Pange, 2011). While UTAUT2 provides a robust structure for explaining technology adoption, it does not explicitly theorize professional aspiration as a determinant of sustained integration. The framework is therefore extended through three constructs: Learning Value, Aspiration, and D-Fit Choice. Aspiration is conceptualized as a higher-order construct composed of Motivation and Expectation, reflecting instructors’ long-term professional orientation and anticipated academic outcomes. Findings suggest that adoption extends beyond expectancy-based determinants to include professional aspiration, socio-technical alignment, and pedagogical relevance as critical drivers of sustained digital implementation. The study contributes to a context-sensitive understanding of digital transformation and informs strategic institutional decision-making.
Authors
- Hassan El Idrissi Bouzidi
- Kabaili Hind
Institutions
- Casablanca (Brazil) (BR)
- Institut Supérieur de Commerce et d'Administration des Entreprises (MA)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-03-12
- DOI
- https://doi.org/10.5281/zenodo.18985197
- Primary Topic
- Online and Blended Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00