TY - JOUR
T1 - Influence of Technology Acceptance Model (TAM) constructs on the effectiveness of mobile learning applications among university students
AU - Churampi-Cangalaya, Roberto Lider
AU - Inga-Ávila, Miguel Fernando
AU - Ninahuaman, Jesus Ulloa
AU - Caceres, Moises Jesus Calle
AU - Robles, Zenon Manuel Lopez
AU - Camargo, Luis Antonio Visurraga
AU - Caballero, Enrique Mendoza
AU - Mustto, Victor Oscar Moyano
N1 - Publisher Copyright:
© 2026 by the authors; licensee Growing Science, Canada.
PY - 2026
Y1 - 2026
N2 - The research aimed to analyze the influence of the Technology Acceptance Model (TAM) on the learning effectiveness of higher education students in Tarma during the year 2025. It was conducted using a quantitative approach, explanatory level, non-experimental design, and cross-sectional study. The sample consisted of 253 students selected through stratified sampling. Data collection was carried out using Likert-type questionnaires, and the analysis was performed using SmartPLS 4.0 with structural equation modeling (SEM) and bootstrapping with 5,000 subsamples. The results of the confirmatory factor analysis showed adequate levels of reliability and validity for the instrument, registering Cronbach's alpha and composite reliability coefficients greater than 0.90 and AVE values greater than 0.50. The structural model confirmed that perceived usefulness significantly influences learning effectiveness (β = 0.65; p < 0.001), followed by intention to use (β = 0.58; p < 0.001) and ease of use (β = 0.42; p < 0.010). The model achieved an explanatory power of R2 = 0.74. It is concluded that technological acceptance is a determining factor in improving educational outcomes through mobile applications.
AB - The research aimed to analyze the influence of the Technology Acceptance Model (TAM) on the learning effectiveness of higher education students in Tarma during the year 2025. It was conducted using a quantitative approach, explanatory level, non-experimental design, and cross-sectional study. The sample consisted of 253 students selected through stratified sampling. Data collection was carried out using Likert-type questionnaires, and the analysis was performed using SmartPLS 4.0 with structural equation modeling (SEM) and bootstrapping with 5,000 subsamples. The results of the confirmatory factor analysis showed adequate levels of reliability and validity for the instrument, registering Cronbach's alpha and composite reliability coefficients greater than 0.90 and AVE values greater than 0.50. The structural model confirmed that perceived usefulness significantly influences learning effectiveness (β = 0.65; p < 0.001), followed by intention to use (β = 0.58; p < 0.001) and ease of use (β = 0.42; p < 0.010). The model achieved an explanatory power of R2 = 0.74. It is concluded that technological acceptance is a determining factor in improving educational outcomes through mobile applications.
KW - Higher education
KW - Learning effectiveness
KW - Mobile applications
KW - Technology acceptance model
UR - https://www.scopus.com/pages/publications/105050783310
U2 - 10.5267/j.ijdns.2026.5.019
DO - 10.5267/j.ijdns.2026.5.019
M3 - Artículo
AN - SCOPUS:105050783310
SN - 2561-8148
VL - 10
SP - 1829
EP - 1838
JO - International Journal of Data and Network Science
JF - International Journal of Data and Network Science
IS - 4
ER -