Technology Acceptance Model for Smartphone Use in Higher Education




technology acceptance model, smartphone, university, higher education, ahp, pls-sem


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 ( 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).


Download data is not yet available.

Author Biographies

Juan Mejía-Mancilla, Estudiante de Doctorado en Academia Mexicana de Investigación y Docencia en Innovación (AMIDI)

Estudiante de Doctorado en Academia Mexicana de Investigación y Docencia en Innovación (AMIDI)

Juan Mejía-Trejo, Profesor Investigador en el Centro Universitario de Ciencias Econbómico-Administrativas (CUCEA), Universidad de Guadalajara Guadalajara, Jalisco, México

Dr. Juan Mejía Trejo
He is born in 1964 in CDMX, México.
As professional experience:
1986-1987. Quality Department Control in KOKAI Electrónica S.A.
1987-2008. Former Internal Plant Exploitation Manager at Teléfonos de México S.A.B. Western Division.
As academic experience :
1987. He earned his degree in Communications and Electronics Engineering from the Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional (ESIME at the IPN)
2004. He earned his master’s in Telecommunications Business Administration from INTTELMEX and France Telecom.
2010. He earned his doctorate in Administrative Sciences from the Escuela Superior de Comercio y Administración (ESCA at the IPN)
2011.He is a member of the Sistema Nacional de Investigadores (SNI) Level I of the Consejo Nacional de Ciencia y Tecnología (CONACYT) , México.
2010 to the present, he is Titular Research Professor B at the Department of Marketing and International Business at the Universidad de Guadalajara, México.
2015-2022.He earned the Coordination of the Doctorate in Management Sciences at the Universidad de Guadalajara.
2018-2020. He earned his master’s in Valuing Business in the Centro de Valores S.C. México.
2019.He earned Level II of the SNI/CONACYT.
2019. He is the Founder, the main Sponsor and Director of the AMIDI (Academia Mexicana de Investigacion y Docencia en Innovación SC) (
2021. He is the Founder, the main Sponsor and Editor-in-Chief of the Scientific Journal Scientia et PRAXIS (https://scientiaetpraxis
2023. He is the Founder, the main Sponsor and Editor-in-Chief of the Digital Repository AMIDI.Biblioteca
2024.He earned Level III of the SNI/CONAHCYT.

Currently, his line of research is Innovation Management, publishing articles and books that can be found on the Internet.
His ORCID is on
ResearcherID: O-8416-2017
ResearcherID: HMW-2043-2023


Abu-Talib, M., Bettayeb, A.M. & Omer, R.I. Analytical study on the impact of technology in

higher education during the age of COVID-19: Systematic literature review. Education and Information Technologies, 26, 6719–6746.

Al-Debei, M.M. (2014). The quality and acceptance of websites: an empirical investigation in the

context of higher education. International Journal of Business Information Systems , 15 (2)

Alam, G.M.; Forhad, M.A.R. (2023). The Impact of Accessing Education via Smartphone

Technology on Education Disparity—A Sustainable Education Perspective. Sustainability 15, 10979.

Alkhawaja, M.I., Halim, M.S.A., Abumandil, M.S.S., Al-Adwan A.,S. (2022).System Quality and

Student’s Acceptance of the E-learning System: The Serial Mediation of Perceived

Usefulness and Intention to Use. Contemporary Educational Technology, 14 (2).ep350.

Anderson, J.C. & Gerbing, D.W. (1988). Structural Equation Modeling in Practice: A Review

and Recommended Two-Step Approach. Psychological Bulletin, 103 (3), 411-423.

Asadullah, Md., Yeasmin, M., Alam, A.F., Alsolami, A, Ahmad , N., & Auoum, I. (2023).

Towards a Sustainable Future: A Systematic Review of Mobile Learning and Studies in Higher Education. Sustainability 15 (17)

Badwelan, A., Bahaddad, A.A. (2021).Functional Requirements to Increase Acceptance of

MLearning Applications among University Students in the Kingdom of Saudi Arabia (KSA). International Journal of Computer Science and Network Security, 21 (2).

Baiyun, Ch., Denoyelles, A., Brown, T., & Seilhamer, R.(2023). The Evolving Landscape of

Students' Mobile Learning Practices in Higher Education. Retrieved Dec-23-2023, form:

Becker, J.M., Cheah, J.H., Gholamzade, R., Ringle, C.M. & Sarstedt, M. (2023). PLS-SEM’s

most wanted guidance. International Journal of Contemporary Hospitality Management 35 (1), 321-346.

Belsley, D.A. (1991) A Guide to using the collinearity diagnostics. Computer Science in

Economics and Management 4, 33–50.

Baker-Eveleth,L.& Stone, R.W. (2020). User's perceptions of perceived usefulness, satisfaction,

and intentions of mobile application. International Journal of Mobile Communications, 18 (1),

Bentler, P.M. & Chou, C. (1987) Practical Issues in Structural Modeling. Sociological Methods

and Research, 16, 78- 117.

Boomsma, A., & Hoogland, J. J. (2001). The Robustness of LISREL Modeling Revisited. In R.

Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural Equation Models: Present and Future. A Festschrift in Honor of Karl Jöreskog (pp. 139-168). Lincolnwood, IL: Scientific Software International.

Camilleri, M., & Camilleri, A. (2019). The students' readiness to engage with mobile learning apps.

Interactive Technology and Smart Education,7 (1), 28-38

Combination Calculator (CombCal, 2023). Section 7. Combinations of m elements taken from n

to n. Retrieved 2-Dec-2023, from:

Chans, G.,M., Orona-Navar, A., Orona-Navar, C.,& Sánchez-Rodríguez, E.P. (2023). Higher

Education in Mexico: The Effects and Consequences of the COVID-19 Pandemic.

Sustainability, 15 (12).

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge.

Dafonte-Gómez, A., Maina, M.F., García-Crespo, O. (2021). Smartphone use in university

students: An opportunity for learning. Pixel-Bit-Revista de Medios y Educación, 60, 211-

Darko-Adjei, Noah (2019). The use and effect of smartphones in students’ learning activities:

Evidence from the University of Ghana, Legon. Library Philosophy and Practice (e-

Journal), 2851.

de Koff, J.P. (2020). Utilizing teaching technologies for higher education in a post-COVID-19

environment. Natural Science Education, 50 (1), e20032.

Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of Estimation Methods, Number of

Indicators per Factor, and Improper Solutions on Structural Equation Modeling Fit Indices. Structural Equation Modeling: A Multidisciplinary Journal, 2, 119-143.

Dijkstra, T.K., Henseler, J. (2015). Consistent and asymptotically normal PLS-PM

estimators for linear structural equations. Computational Statistics & Data Analysis. 81, 10–23.

Drolet, A.L., & Morrison, D.G., (2001). Do we really need multiple-item measures in service

research? Journal of Service Research 3 (3), 196–204.

Dzamesi, J., Y.,W.,Akyia, K.O., Manu, J., & Danso, E. (2019). Perceived Effects of Smartphone

Usage on Students’ Attitude Towards Learning in a Health Institution. Journal of Education and Practice, 10 (2), 71-81-

Estriegana, R., Medina-Merodio, J.-A., Robina-Ramírez, R., Barchino, R., & De-Pablos-Heredero,

C. (2023). E-learning Acceptance in Face-to-Face Universities due to COVID-19. SAGE Open, 13(4).

Fornell, C.L, & Larcker, D. F. (1981). Evaluating structural equation models with unobservable

variables and measurement error. Journal of Marketing Research 18(1), 39-50.

Franke,G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a

comparison of four procedures. Internet Research 29 (3), 430–447.

Feng,Y.J., Worrachanun, I.L.,& Lai I.K.W. (2015). Students' Preferences and Intention on Using

Smartphone Education Applications. nternational Symposium on Educational Technology (ISET), Wuhan, China, 109-112,

Fook, C. Y., Selamat, N., Narusaman, S. & Muthukrishnan, P. (2022). The Mediating Effect of

Academic Behaviour towards Mobile Phone Use and Intention for Mobile Learning among University Students. International Conference on Engineering and Emerging Technologies

(ICEET), Kuala Lumpur, Malaysia, 1-5.

Fuchs, K. (2020). Using an extended technology acceptance model to determine students’

behavioral intentions toward smartphone technology in the classroom. Frontiers in

Education, 7.

George-Reyes, C.E.; Glasserman-Morales, L.D.; Rocha-Estrada, F.J.; Ruíz-Ramírez, J.A. Study

Habits Developed by Mexican Higher Education Students during the Complexity of the COVID-19 Pandemic. Education Sciences 13, 563.

Gyamfi, S.A.(2021). Influencing Factors of Students’ Smartphones Use for Academic Purposes: A

Developing Country’s Perspective. International Journal of Emerging Technologies in Learning, 16 (23).

Hair, J.F., Babin, B.J., Anderson, R.E., Black, W.C. (2019). Multivariate Data Analysis.8th


Hair,J.F., Sarstedt, M. Ringle, C.M., and Gudergan, S.P. (2023). Advanced issues in partial least

squares structural equation modeling. Sage.

Hameed, F., Qayyum, A. & Khan, F.A. (2022). A new trend of learning and teaching: Behavioral

intention towards mobile learning. Journal of Computers in Education.

Hamzah, W. M. A. F., Yusoff, M. H., Ismail, I., & Yacob, A. (2020). The Behavioural

Intentions of Secondary School Students to Use Tablet as a Mobile Learning Device. International Journal of Interactive Mobile Technologies, 14(13), 161–171.

Henseler, J., Dijkstra, T. K., Sarstedt, M.; Ringle, Ch. M.; Diamantopoulos, A.; Straub,

D.W.; Ketchen, D. J.; Hair, J. F.; Hult, G. T. M. (2014). Common Beliefs and Reality About PLS. Organizational Research Methods. 17 (2), 182–209.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant

validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43, 115–135.

Hoogland, J. J., & Boomsma, A. (1998). Robustness Studies in Covariance Structure Modeling:

An Overview and a Meta-Analysis. Sociological Methods & Research, 26, 329-367.

Humida, T., Al-Mamun M.H., & Keikhosrokiani, P. (2022) Predicting behavioral intention to use

e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh. Education and Inforation Technologies, 27(2), 2241-2265.

Huey, M., & Giguere D. (2023). The Impact of Smartphone Use on Course Comprehension and

Psychological Well-Being in the College Classroom. Innovative Higher Education, 48


Iqbal, S. & Bhatti, Z.A. (2015). An Investigation of University Student Readiness towards M-

learning using Technology Acceptance Model. International Review of Research in Open

and Distributed Learning, 16 (4), 83-103.

James, G. Witten, D., Hastie,T., and Tibshirani, R. (2013). An introduction to statistical learning.


Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford


Kraemer H.C. , Morgan, G.A., Leech, N.L., Glinner, J.A., Vaske, & Harmon, R.J. (2003).

Measures of clinical significance. Journal of the American Academy of Child & Adolescent Psychiatry 42 (12), 1524–1529.

Lang, V. & Šorgo, A. (2024). Views of Students, Parents, and Teachers on Smartphones and

Tablets in the Development of 21st-Century Skills as a Prerequisite for a Sustainable

Future. Sustainability 16, 3004.

Larmuseau, Ch., Desmiet, p., & Depapepe, F. (2018)Perceptions of instructional quality: impact

on acceptance and use of an online learning environment. Interactive Learning

Environments, 27 (7), 953-964.

Lin, C.W., Lin, Y.S., Lia, C.C., Chen, C.C. (2021).Utilizing Technology Acceptance Model

for Influences of Smartphone Addiction on Behavioural Intention.Matematical

Problems in Engeenering 2021

Lohmoller, J.B. (1989). Latent Variable Path Modeling with Partial Least Squares. Heidelberg:


Maketo, L., Tomayess, I., ISSA, T. & Nau, S.Z. (2023). M-Learning adoption in higher education

towards SDG4. Future Generation Computer Systems 147, pp. 3014-315.

Masadeh, T.S.Y.(2021). Smartphone use in Learning as Perceived by University Undergraduates:

Benefits and Barriers. International Journal of Research-Granthaalatah. A Knowledge

Repository, 9(3),

Matyokurehwa, K., Rudhumbu, N., Mlambo, Ch.P. (2020). Intentions of First Year University

Business Students to use Smartphones as learning tools in Botswana: Issues and challenges

International Journal of Education and Development using Information and Communication Technology,16(1), 27-43

Matzavela, V., & Alepis, E. (2021). M-learning in the COVID-19 era: physical vs. digital class.

Education and Information Technologies, 26, 7183–7203.

Mejía-Trejo (2017). Las ciencias de la administración y el análisis multivariante: Proyectos de

investigación, análisis y discusión de los resultados Tomo II Las técnicas interdependientes. CUCEA-UdeG. Ditribuído por AMIDI.Biblioteca una dividión de la AMIDI. Academia Mexicana de Investigación y Docencia en Innovación.

Mejía-Trejo (2018). Creación de Escalas en las Ciencia de la Administración. CUCEA-UdeG.

Ditribuído por AMIDI.Biblioteca una división de la AMIDI. Academia Mexicana de Investigación y Docencia en Innovación.

Mejía-Trejo (2021). NOMOFOMO in the health of the Smartphone User for the New Normal: a

contribution to the Social Media Health Interaction Theory. Scientia et PRAXIS, 01 (02),


Mella-Norambuena, J., Cobo-Rendon, R.; Lobos, K., Sáez-Delgado, F. & Maldonado-Trapp,

A. (2021). Smartphone Use among Undergraduate STEM Students during COVID-19: An Opportunity for Higher Education? Education Sciences 11, 417.

Methodspace (2023). Partial Least Squares Structural Equation Modeling: An Emerging Tool in

Research. Retrieved 04-Nov-2023, from:

Mina, J.R,A., & Lashayo, D.M.(2023). Direct and indirect effects of smartphone use on academic

performance of undergraduate students in Tanzania. International Journal of Mobile

Learning and Organization, 17 (3).

Morales-Rodríguez, F.M., Giménez-lozanbo, J.M., Linares-Mingorance, P.,& Pérez-Mármol, J.M.

(2020). Influence of Smartphone Use on Emotional, Cognitive and Educational Dimensions

in University Students.Sustainability 12,6646

Mostafa, L. (2023). Student Intention Behavior to use Smart Campus in Egyptian University.

Computer Science, Education, Engineering, 2 (14), 486-507

Mtebe, J.S. & Raisam, R. (2014). Investigating Perceived Barriers to the Use of

Open Educational Resources in Higher Education in Tanzania. The International Review

of Research in Open and Distance Learning, 43- 66

Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size

and determine power. Structural Equation Modeling, 9(4), 599–620.

Naciri, A., Baba, M. A., Achbani, A., & Kharbach, A. (2020). Mobile Learning in Higher

Education: Unavoidable Alternative during COVID-19. Aquademia, 4(1).

Nes A.A.G., Fossum, M., Steindal, S.A., Solberg, M.T., Strandell-Laine, C.,¿ Zlamal, J., &.

Gjevjon, E.L.R. (2020). Research protocol: Technology-supported guidance to increase

flexibility, quality and efficiency in the clinical practicum of nursing education. International Journal of Educational Research, 103.

Okpanum, I., & McElhinney, S. (2022). Disruptive Innovation in Teaching and Learning: The Post

Covid-19 Era in China. International Journal of Education, Psychology and Counseling, 7

(47), 01-09.

Organisation for Economic Co-operation and Development (OECD, 2023). Digital equity

and inclusion in education: An overview of practice and policy in OECD.

Özbek,V, Alnıaçık, V., Koc, F., Akkılıç, M.E., & Kaş, E. (2014). The Impact of Personality on

Technology Acceptance: A Study on Smart Phone Users. Procedia-Social and Behavioral Sciences, 150, 541-551.

Parveen, N. & Zamir, S. (2020). Factors Affecting Behavioural Intentions in the Use of Mobile

Learning in Higher Education. International Journal of Distance Education and E- Learning 6 (1), 198-216.

Peteros, E. D., de Vera, J. V., Laguna, C. G.; Lapatha, V. Ch. B., Mamites, I. O.; Astillero, J.C.

(2022). Effects of Smartphone Utilization on Junior High School Students' Mathematics

Performance. World Journal on Educational Technology: Current Issues, 14 (2), 401-413

Rigdon, E. E., Sarstedt, M., Ringle, M. (2017). On Comparing Results from CB-SEM and PLS-

SEM: Five Perspectives and Five Recommendations. Marketing ZFP. 39 (3), 4–16.


Rodríguez, M.L., Pulido-Montes, C (2022) Use of Digital Resources in Higher Education during

COVID-19: A Literature Review. Education Sciences, 12 (612).

Rojas-Osorio ,M., &Alvarez-Risco (2019). Intention to Use Smartphones among Peruvian

University Students. Retrieved Dec-22-2023, from:

Rosli, M.S., Saleh, N.S. (2023) Technology enhanced learning acceptance among

university students during Covid-19: Integrating the full spectrum of Self-Determination Theory and self-efficacy into the Technology Acceptance Model. Current Psychology. 42:18212–18231

Saaty.L., (2008). Decision making with the analytic hierarchy process. International Journal

Services Sciences 1 (1), 83–98.

Sambo, A. , Umar A.M. & Noma A.M. (2022). Determinants of University Students’ Behavioural

Intention to Use Smartphone for Academic Learning in Nigeria. International Academic

Journal of Management and Marketing, 7 (1), 104-118.

Sarstedt, M., Hair, J.F., Ringle, C.M., Thiele, K.O. & Gudergan, S.P. (2016). Estimation issues

with PLS and CBSEM: Where the bias lies!. Journal of Business Research 69 (10), 3998–

Shanmugapriya K, Seethalakshmi A, Zayabalaradjane Z, Rani NRV. Mobile technology

acceptance among undergraduate nursing students instructed by blended learning at selected educational institutions in South India. Journal of Education Health Promotion, 12 (45).

Shmueli, G., Ray, S., Velasquez Estrada, J., Chatla, S.B. (2016) The Elephant in the Room:

Evaluating the Predictive Performance of PLS Models. Journal of Business Research 69, 4552-4564.

Siew F.N., Nor S. I. Ch. H., Nor H. M.N., Nur, A. A. M. (2017). The Relationship Between

Smartphone Use and Academic Performance: A Case of Students in a Malaysian Tertiary Institution. Malaysian OnLine Journal of Educational Technology, 5(4).

Statista (2023). Number of mobile phone users in Mexico from 2009 to 2022. Retrieved Dec-15-

, from:

Sun, Q., Norman, T.J., & Abdourazakou, Y.(2018). Perceived value of interactive digital textbook

and adaptive learning: Implications on student learning effectiveness. Journal of EduCation for Business,93 (7)

Sun, Y. & Gao, F. (2019). An Investigation of the Influence of Intrinsic Motivation on Students’

Intention to Use Mobile Devices in Language Learning. Visual Communications and

Technology Education Faculty Publications. 51.

Sung Y. P., Min-Woo, .N., Seung-Bong, Ch. (2011). University students' behavioral intention to

use mobile learning: Evaluating the technology acceptance model. British Journal of

Educational Technology, 4 (34), 592-605.

Sunyoung, H. & Yong, J.Y. (2019.)How does the smartphone usage of college students affect

academic performance. Journal of Computer Assisted Learning 35 (1), 13-22.

Straub D., Boudreau,M., and Gefen, D. (2004). Validation guidelines for IS positivist research.

Communications of the Association for Information Systems 13(1) 24.

Tabachnick, B.G. & Fidell, L.S. (2001) Using Multivariate Statistics. 4th Edition. Allyn and Bacon,


Tang, K.Y, Hsiao, Ch., Tu, Y.F., Hwang, G.J., Wang, Y. (2021).Factors influencing university

teachers' use of a mobile technology-enhanced teaching (MTT) platform. Education

Technology Research Development, 69(5), 2705-2728.

Tejedor, S., Cervi, L., Pérez-Escoda, A, Tusa, F. (2020). Smartphone usage among students during

COVID-19 pandemic in Spain, Italy and Ecuador. Proceedings Eight International

Conference on Technological Ecosystems for Enhancing Multiculturality, 571-576.

Tinsley, H. E., & Tinsley, D. J. (1987). Uses of factor analysis in counseling psychology research.

Journal of Counseling Psychology, 34(4), 414–424.

Tossell,Ch.C., Kortum, P., Shepard, C., Rahmati, A., & Zhong, L. (2015). You can lead a horse to

water but you cannot make him learn: Smartphone use in higher education. British Journal

of Educational Technology, 46 (4).

United Nations Educational, Scientific and Cultural Organization (UNESCO, 2012). Turning on

Mobile Learning in Latin America. UNESCO working Paper on mobile learning

Vega, J. , Marentes, F. , Chávez, G. and Paredes, M. (2022) Distance Education:

Technology and Connectivity as Preventive Resources in the COVID-19 Pandemic at Public Higher Teacher Training Schools in Baja California Sur, Mexico. Creative Education, 13, 2597-2611.

Wang, J.C., Hsieh, CY. & Kung, SH. (2023).The impact of smartphone use on learning

effectiveness: A case study of primary school students. Education and Information Technologiues 28, 6287–6320

Wismantoro, Y., ; Himawan, H.,Widiyatmoko, K. (2020). Measuring the Interest of Smartphone

Usage by Using Technology Acceptance Model Approach. Journal of Asian Finance Economics and Business, 7 (9), 613-620


Wold, H.O.A. (1982) Soft Modeling: The Basic Design and Some Extensions. In: Joreskog, K.G.

and Wold, H.O.A., Eds., Systems under Indirect Observations: Part II, North-Holland, Amsterdam, 1-54.

Yu, T.K., & Chao, C.M. (2023) Encouraging teacher participation in Professional Learning

Communities: exploring the Facilitating or restricting factors that Influence collaborative

activities. Education and information Technologies, 28 (5), 5779-5804

Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to Explore

Students’ Intention to Use an Online Education Platform at a University in China. SAGE Open, 12(1).

Zapata-Garibay, R., González-Fagoaga, J.E., Meza-Rodríguez, E.B., Salazar-Ramírez, E., &

Plascencia-López, I. (2021). Mexico’s Higher Education Students’ Experience During the

Lockdown due to the COVID-19 Pandemic. Frontiers Education 6

Zogheib, B. & Daniela, L. (2022). Students’ Perception of Cell Phones Effect on their Academic

Performance: A Latvian and a Middle Eastern University Cases. Technology, Knowledge and Learning, 27(4), 1115–311.




How to Cite

Mejía-Mancilla, J., & Mejía-Trejo, J. (2024). Technology Acceptance Model for Smartphone Use in Higher Education. Scientia Et PRAXIS, 4(07), 113–158.



Scientific Articles

Similar Articles

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 > >>