Technology Acceptance Model for Smartphone Use in Higher Education
Authors: Mejía-Mancilla Juan , Mejía-Trejo Juan 
Published: May 2024

Context. The technology acceptance model (TAM) is a theoretical framework that consists of perceived usefulness (PUS), perceived ease of use (PEU), attitude toward using (ATT), behavioral intention to use (USI), and actual system use. Here, actual system use is posed by the smartphone use in higher education (SHE) described such as student self-management (MNG), student learning results (LRS), student achievements perceptions (SFB), student cost-benefits perceptions (VCB), and student expectations (EXP) that help to understand and explain how students’ acceptance and adoption of smartphone technology could be better achieved. Nowadays, after the COVID-19 pandemic, student motivation (MTV) and student quality perceptions (SQY) are two factors that reinforce the TAM model.
Problem. The research confronts challenges from the dynamic and rapidly changing technology and education environments. The post-COVID-19 era introduces uncertainties, potentially affecting the TAM-SHE model's long-term sustainability. The fluidity of student preferences and technological advancements obstruct the establishment of a universally applicable framework for smartphone acceptance in education. This raises concerns about the model's adaptability and generalizability across diverse educational settings, emphasizing the careful consideration of evolving factors. Therefore, the following research question is proposed: What is the TAM for SHE empirical framework as an innovative tool?
Purpose. The research aims to explore students' acceptance of smartphone technology in education using the technology acceptance model (TAM), focusing on perceived usefulness, ease of use, attitude, intention, and actual system use within smartphone use in higher education (SHE) in the context of post-COVID-19 era, the study considers student motivation (MTV) and student quality perceptions (SQY) as crucial factors enhancing the TAM-SHE framework.
Methodology. We determined the following steps: Step 1. A qualitative study based on the Delphi Panel-Focus Group and Analytic Hierarchy Process (AHP) to determine the questionnaire TAM-SHE among three specialists: 1 information technology expert, 1 information technology professor, and 1 university student related to TAM for SHE and questioned about the items and factors related to the preliminary questionnaire design.
Step 2. A literature review to explain the items and factors for the questionnaire (ex-ante) proposal involved in the design will be applied to more than 523 Mexican university students in the second semester of 2023.
Step 3. Once all the data in the questionnaires had been collected were probed regarding the Cronbach Alpha reliability. A quantitative study on confirmatory factor analysis based on partial least square structural equation modeling (PLS-SEM) with SMART PLS (4.0.9.8) was used to probe convergent, discriminant, and nomological validity for the final conceptual TAM-SHE framework.
Theoretical and practical findings. We propose a robust empirical TAM-SHE framework able to explain and predict how their factors enhance smartphone use in higher education.
Transdisciplinary and sustainable innovation originality. The utilization of smartphones in higher education contributes to sustainable development by reducing educational disparities between students from different socioeconomic backgrounds. Additionally, mobile learning aligns with the Sustainable Development Goals (SDGs), particularly SDG4, by advancing sustainable quality higher education. Furthermore, it facilitates worldwide access to education, promoting a more inclusive and equitable learning environment
Conclusions and limitations. For the post-COVID pandemic era, more studies are necessary to verify the new student motivations (MTV), student quality perceptions (SQY), and the actual system use factors to facilitate mobile technology in use for higher education through the technology acceptance model (TAM).