Technology Acceptance Model for Smartphone Use in Higher Education
DOI:
https://doi.org/10.55965/setp.4.07.a5Keywords:
technology acceptance model, smartphone, university, higher education, ahp, pls-semAbstract
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).
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References
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.
https://link.springer.com/article/10.1007/s10639-021-10507-1#citeas
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)
https://www.inderscience.com/offers.php?id=59252
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.
https://doi.org/10.3390/su151410979
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.
https://pdfs.semanticscholar.org/3505/f1d8d5fe63ed984ce0ac5b8774591cac12dd.pdf
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.
https://www3.nd.edu/~kyuan/courses/sem/readpapers/ANDERSON.pdf
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)
https://www.mdpi.com/2071-1050/15/17/12847
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).
http://paper.ijcsns.org/07_book/202102/20210204.pdf
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.
https://doi.org/10.1108/IJCHM-04-2022-0474
Belsley, D.A. (1991) A Guide to using the collinearity diagnostics. Computer Science in
Economics and Management 4, 33–50.
https://doi.org/10.1007/BF00426854
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), https://www.inderscience.com/offers.php?id=104431
Bentler, P.M. & Chou, C. (1987) Practical Issues in Structural Modeling. Sociological Methods
and Research, 16, 78- 117.
http://dx.doi.org/10.1177/0049124187016001004
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
https://www.emerald.com/insight/content/doi/10.1108/ITSE-06-2019-0027/full/html
Combination Calculator (CombCal, 2023). Section 7. Combinations of m elements taken from n
to n. Retrieved 2-Dec-2023, from:
https://www.estadisticaparatodos.es/software/misjavascript/javascript_combinatorio2.html
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).
https://www.mdpi.com/2071-1050/15/12/9476
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge.
https://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf
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-
https://institucional.us.es/revistas/PixelBit/60/76861.pdf
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.
https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=6260&context=libphilprac
de Koff, J.P. (2020). Utilizing teaching technologies for higher education in a post-COVID-19
environment. Natural Science Education, 50 (1), e20032.
https://acsess.onlinelibrary.wiley.com/doi/10.1002/nse2.20032
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.
https://doi.org/10.1080/10705519509540000
Dijkstra, T.K., Henseler, J. (2015). Consistent and asymptotically normal PLS-PM
estimators for linear structural equations. Computational Statistics & Data Analysis. 81, 10–23.
https://www.sciencedirect.com/science/article/pii/S0167947314002126?via%3Dihub
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.
https://journals.sagepub.com/doi/10.1177/109467050133001
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-
https://core.ac.uk/reader/234642350
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). https://doi.org/10.1177/21582440231214873
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.
https://www.jstor.org/stable/3151312
Franke,G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a
comparison of four procedures. Internet Research 29 (3), 430–447.
https://www.emerald.com/insight/content/doi/10.1108/IntR-12-2017-0515/full/html
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,
https://ieeexplore.ieee.org/document/7439646/authors#authors
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.
https://ieeexplore.ieee.org/document/10007290
Fuchs, K. (2020). Using an extended technology acceptance model to determine students’
behavioral intentions toward smartphone technology in the classroom. Frontiers in
Education, 7.
https://www.frontiersin.org/articles/10.3389/feduc.2022.972338/full
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. https://doi.org/10.3390/educsci13060563
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).
https://online-journals.org/index.php/i-jet/article/view/26675
Hair, J.F., Babin, B.J., Anderson, R.E., Black, W.C. (2019). Multivariate Data Analysis.8th
Edition.Cengage.
https://www.amazon.com/Multivariate-Analysis-Joseph-Anderson-William/dp/9353501350
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. https://doi.org/10.1007/s40692-022-00252-w
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.
https://online-journals.org/index.php/i-jim/article/view/13027
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.
https://journals.sagepub.com/doi/10.1177/1094428114526928
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.
https://link.springer.com/article/10.1007/s11747-014-0403-8
Hoogland, J. J., & Boomsma, A. (1998). Robustness Studies in Covariance Structure Modeling:
An Overview and a Meta-Analysis. Sociological Methods & Research, 26, 329-367.
https://doi.org/10.1177/0049124198026003003
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.
https://link.springer.com/article/10.1007/s10639-021-10707-9
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
(3),527-537.
https://link.springer.com/article/10.1007/s10755-022-09638-1
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.
https://files.eric.ed.gov/fulltext/EJ1082185.pdf
James, G. Witten, D., Hastie,T., and Tibshirani, R. (2013). An introduction to statistical learning.
Springer.
https://link.springer.com/book/10.1007/978-1-4614-7138-7
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.
https://www.jaacap.org/article/S0890-8567(09)62138-9/fulltext
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.
https://doi.org/10.3390/su16073004
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.
https://www.tandfonline.com/doi/full/10.1080/10494820.2018.1509874
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
https://www.hindawi.com/journals/mpe/2021/5592187/
Lohmoller, J.B. (1989). Latent Variable Path Modeling with Partial Least Squares. Heidelberg:
Physica.
https://doi.org/10.1007/978-3-642-52512-4
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
https://files.eric.ed.gov/fulltext/EJ1254827.pdf
Matzavela, V., & Alepis, E. (2021). M-learning in the COVID-19 era: physical vs. digital class.
Education and Information Technologies, 26, 7183–7203.
https://link.springer.com/article/10.1007/s10639-021-10572-6
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.
https://www.amidibiblioteca.amidi.mx/index.php/AB/catalog/book/21
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.
https://www.amidibiblioteca.amidi.mx/index.php/AB/catalog/book/18
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),
-82.
https://scientiaetpraxis.amidi.mx/index.php/sp/article/view/40/44
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.
https://doi.org/10.3390/educsci11080417
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).
https://www.inderscience.com/offers.php?id=131843
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
https://www.semanticscholar.org/reader/afa0576dbcb67784e1586c7f1521235f297e1ece
Mostafa, L. (2023). Student Intention Behavior to use Smart Campus in Egyptian University.
Computer Science, Education, Engineering, 2 (14), 486-507
https://jces.journals.ekb.eg/article_304147.html
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
https://files.eric.ed.gov/fulltext/EJ1030130.pdf
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. https://doi.org/10.1207/S15328007SEM0904_8
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.
http://www.ijepc.com/PDF/IJEPC-2022-47-09-01.pdf
Organisation for Economic Co-operation and Development (OECD, 2023). Digital equity
and inclusion in education: An overview of practice and policy in OECD.
https://one.oecd.org/document/EDU/WKP(2023)14/en/pdf
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.
https://www.sciencedirect.com/science/article/pii/S1877042814051222?via%3Dihub
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.
https://www.semanticscholar.org/reader/7cc3b5e7e83a9ce91ba96f4591f8c9794dd007f5
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
https://eric.ed.gov/?id=EJ1345146
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.
doi:10.15358/0344-1369-2017-3-4.
Rodríguez, M.L., Pulido-Montes, C (2022) Use of Digital Resources in Higher Education during
COVID-19: A Literature Review. Education Sciences, 12 (612). https://doi.org/10.3390/educsci12090612
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
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953966/
Saaty.L., (2008). Decision making with the analytic hierarchy process. International Journal
Services Sciences 1 (1), 83–98.
https://www.rafikulislam.com/uploads/resourses/197245512559a37aadea6d.pdf
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.
https://www.arcnjournals.org/images/NRDA-IAJMM-7-1-7.pdf
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–
https://www.sciencedirect.com/science/article/pii/S0148296316304404?via%3Dihub
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).
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10127508/
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.
https://doi.org/10.1016/j.jbusres.2016.03.049
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).
https://files.eric.ed.gov/fulltext/EJ1156718.pdf
Statista (2023). Number of mobile phone users in Mexico from 2009 to 2022. Retrieved Dec-15-
, from:
https://www.statista.com/statistics/731346/number-of-mobile-phone-users-mexico/
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)
https://www.tandfonline.com/doi/full/10.1080/08832323.2018.1493422
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.
https://scholarworks.bgsu.edu/vcte_pub/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.
https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/j.1467-8535.2011.01229.x
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.
https://onlinelibrary.wiley.com/doi/10.1111/jcal.12306
Straub D., Boudreau,M., and Gefen, D. (2004). Validation guidelines for IS positivist research.
Communications of the Association for Information Systems 13(1) 24.
https://aisel.aisnet.org/cais/vol13/iss1/24/
Tabachnick, B.G. & Fidell, L.S. (2001) Using Multivariate Statistics. 4th Edition. Allyn and Bacon,
Boston.
http://bayes.acs.unt.edu:8083/BayesContent/class/Jon/ResourcesWkshp/2001_TabachnickFidell_Ch4.pdf
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.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327896/
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.
https://dl.acm.org/doi/10.1145/3434780.3436587
Tinsley, H. E., & Tinsley, D. J. (1987). Uses of factor analysis in counseling psychology research.
Journal of Counseling Psychology, 34(4), 414–424.
https://doi.org/10.1037/0022-0167.34.4.414
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).
https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.12176
United Nations Educational, Scientific and Cultural Organization (UNESCO, 2012). Turning on
Mobile Learning in Latin America. UNESCO working Paper on mobile learning
https://unesdoc.unesco.org/ark:/48223/pf0000216080
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.
https://www.scirp.org/journal/paperinformation?paperid=119466
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 https://doi.org/10.1007/s10639-022-11430-9
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
DOI10.13106/jafeb.2020.vol7.no9.613
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
https://link-springer-com.wdg.biblio.udg.mx:8443/article/10.1007/s10639-022-11376-y
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).
https://doi.org/10.1177/21582440221085259
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
https://www.frontiersin.org/articles/10.3389/feduc.2021.683222/full
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.
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