@article{10.11648/j.ajcst.20250804.16,
  author = {Flavia Agatha Ikwap and Samuel Oonge and Calvins Otieno},
  title = {Systematic Review of Models Examining Factors Influencing SaaS Adoption in Higher Education Institutions},
  journal = {American Journal of Computer Science and Technology},
  volume = {8},
  number = {4},
  pages = {228-241},
  doi = {10.11648/j.ajcst.20250804.16},
  url = {https://doi.org/10.11648/j.ajcst.20250804.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20250804.16},
  abstract = {Since the early 2000s, Higher Education Institutions (HEIs) have increasingly adopted Software as a Service (SaaS). Despite benefits such as improved collaboration, efficiency, and reduced costs, adoption remains low in many developing regions, particularly Sub-Saharan Africa. Most research emphasizes organizational-level adoption, especially in East Africa, with limited focus on individual user factors. Using the PRISMA protocol, this review analyzed 34 studies from 2015–2025 sourced from Google Scholar, ScienceDirect, SpringerLink, and IEEE. The review examines factors influencing individual SaaS adoption in HEIs and evaluates three key models: TAM, UTAUT, and TRI, highlighting their strengths and limitations. Results show that UTAUT explains up to 70% of behavioral intention variance, compared to 40–60% for TAM. UTAUT expands TAM with additional constructs, while TRI is most effective when integrated with other frameworks. Key variables influencing user adoption were identified, offering insights to enhance individual uptake of cloud technologies in HEIs. Many studies apply TAM and UTAUT without contextual adaptation. Core constructs such as Perceived Usefulness (PU–39%), Perceived Ease of Use (PEU–94%), Facilitating Conditions (FC–50%), Social Influence (SI–44%), and Effort Expectancy (EE–50%) show higher significance than local/ External factors like skill transfer (6%), transition to e-learning (6%), and institutional policies (6%). Security and price emerge as the most prominent contextual factors that are addressed in roughly 39% and 17% of the studies respectively, this highlights their relative influence on technology adoption. Regionally, 80% of studies originate from Asia, reflecting strong academic interest and rapid digital growth. The Americas (20%) show less focus, possibly due to maturity in cloud adoption. Europe (40%) exhibits moderate engagement, while the Middle East/West Asia and Africa (30% each) show emerging research hindered by infrastructure, trust, and capacity challenges, leading to more emphasis on organizational adoption. The review identifies key variables shaping user adoption, offering insights to strengthen individual uptake of cloud technologies in HEIs across the region.},
 year = {2025}
}

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TY  - JOUR
T1  - Systematic Review of Models Examining Factors Influencing SaaS Adoption in Higher Education Institutions
AU  - Flavia Agatha Ikwap
AU  - Samuel Oonge
AU  - Calvins Otieno
Y1  - 2025/12/19
PY  - 2025
N1  - https://doi.org/10.11648/j.ajcst.20250804.16
DO  - 10.11648/j.ajcst.20250804.16
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 228
EP  - 241
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20250804.16
AB  - Since the early 2000s, Higher Education Institutions (HEIs) have increasingly adopted Software as a Service (SaaS). Despite benefits such as improved collaboration, efficiency, and reduced costs, adoption remains low in many developing regions, particularly Sub-Saharan Africa. Most research emphasizes organizational-level adoption, especially in East Africa, with limited focus on individual user factors. Using the PRISMA protocol, this review analyzed 34 studies from 2015–2025 sourced from Google Scholar, ScienceDirect, SpringerLink, and IEEE. The review examines factors influencing individual SaaS adoption in HEIs and evaluates three key models: TAM, UTAUT, and TRI, highlighting their strengths and limitations. Results show that UTAUT explains up to 70% of behavioral intention variance, compared to 40–60% for TAM. UTAUT expands TAM with additional constructs, while TRI is most effective when integrated with other frameworks. Key variables influencing user adoption were identified, offering insights to enhance individual uptake of cloud technologies in HEIs. Many studies apply TAM and UTAUT without contextual adaptation. Core constructs such as Perceived Usefulness (PU–39%), Perceived Ease of Use (PEU–94%), Facilitating Conditions (FC–50%), Social Influence (SI–44%), and Effort Expectancy (EE–50%) show higher significance than local/ External factors like skill transfer (6%), transition to e-learning (6%), and institutional policies (6%). Security and price emerge as the most prominent contextual factors that are addressed in roughly 39% and 17% of the studies respectively, this highlights their relative influence on technology adoption. Regionally, 80% of studies originate from Asia, reflecting strong academic interest and rapid digital growth. The Americas (20%) show less focus, possibly due to maturity in cloud adoption. Europe (40%) exhibits moderate engagement, while the Middle East/West Asia and Africa (30% each) show emerging research hindered by infrastructure, trust, and capacity challenges, leading to more emphasis on organizational adoption. The review identifies key variables shaping user adoption, offering insights to strengthen individual uptake of cloud technologies in HEIs across the region.
VL  - 8
IS  - 4
ER  - 

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