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Adoption of Mobile Learning at Universities Using the Extended Technology Acceptance Model
Corresponding Author(s) : Misbahul Aziz
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 7, No. 4, November 2022
Abstract
This study aims to contribute to the proof of factors likely to determine the success of M-learning adoption based on previous research. This is done because there are many different theoretical models proposed. However, there is not yet a model that can be generally accepted as an established theoretical model for the adoption of M-learning in universities. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determinants that influence the adoption of M-learning using the Technology Acceptance Model (TAM). To collect the data, researchers distributed questionnaires to respondents using google forms. Forms are distributed via WhatsApp and Telegram. The data used was 515 M-learning users. Theoretical model research was carried out with Structural Equation Model (SEM) analysis, then SPSS and Amos as support for analysis. There are six factors that determine the results of acceptance of M-leaning adoption in this study, namely Social Influence, Perceived Enjoyment, Facilitating Condition, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. The five factors that show positive and significant relationships are Social Influence, Perceived Enjoyment, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. Perceived Usefulness has the first strongest positive and significant value, and then Social Influence has the second strongest value. Each factor has a medium influence value on Behavioral Intention. That factor is the most influential in the application of M-learning in universities.
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- S. S. Alghazi, S. Y. Wong, A. Kamsin, E. Yadegaridehkordi, and L. Shuib, “Towards sustainable mobile learning: A brief review of the factors influencing acceptance of the use of mobile phones as learning tools,” Sustain., vol. 12, no. 24, pp. 1–19, 2020, doi: 10.3390/su122410527.
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- G. D. Israel, “Determining Sample Size 1 The Level Of Precision,” Univ. Florida, 1992.
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References
S. S. Alghazi, S. Y. Wong, A. Kamsin, E. Yadegaridehkordi, and L. Shuib, “Towards sustainable mobile learning: A brief review of the factors influencing acceptance of the use of mobile phones as learning tools,” Sustain., vol. 12, no. 24, pp. 1–19, 2020, doi: 10.3390/su122410527.
C. Buabeng-Andoh, “Exploring University students’ intention to use mobile learning: A research model approach,” Educ. Inf. Technol., vol. 26, no. 1, pp. 241–256, Jan. 2021, doi: 10.1007/s10639-020-10267-4.
E. Pramana, “Determinants of the adoption of mobile learning systems among university students in Indonesia,” J. Inf. Technol. Educ. Res., vol. 17, pp. 365–398, 2018, doi: 10.28945/4119.
Q. N. Naveed, M. M. Alam, and N. Tairan, “Structural equation modeling for mobile learning acceptance by university students: An empirical study,” Sustain., vol. 12, no. 20, pp. 1–20, Oct. 2020, doi: 10.3390/su12208618.
A. Aytekin, H. Özköse, and A. Ayaz, “Unified theory of acceptance and use of technology (UTAUT) in mobile learning adoption : Systematic literature review and bibliometric analysis,” COLLNET J. Sci. Inf. Manag., vol. 16, no. 1, pp. 75–116, 2022, doi: 10.1080/09737766.2021.2007037.
A. Chelvarayan, J. E. Chee, S. F. Yeo, and H. Hashim, “STUDENT’S PERCEPTION ON MOBILE LEARNING: THE INFLUENCING FACTORS,” Int. J. Educ. Psychol. Couns., vol. 5, no. 37, pp. 01–09, Dec. 2020, doi: 10.35631/ijepc.537001.
A. M. Al‐rahmi, W. M. Al‐rahmi, U. Alturki, A. Aldraiweesh, S. Almutairy, and A. S. Al‐adwan, “Exploring the factors affecting mobile learning for sustainability in higher education,” Sustain., vol. 13, no. 14, Jul. 2021, doi: 10.3390/su13147893.
M. N. Masrek and I. Samadi, “Determinants of mobile learning adoption in higher education setting,” Asian J. Sci. Res., vol. 10, no. 2, pp. 60–69, 2017, doi: 10.3923/ajsr.2017.60.69.
D. Mutambara and A. Bayaga, “Predicting Rural Stem Teachers’ Acceptance of Mobile Learning in the Fourth Industrial Revolution,” J. Constr. Proj. Manag. Innov., vol. 10, no. 2, pp. 14–29, 2020, doi: 10.36615/jcpmi.v10i2.404.
S. Shukla, “M-learning adoption of management students’: A case of India,” Educ. Inf. Technol., vol. 26, no. 1, pp. 279–310, Jan. 2021, doi: 10.1007/s10639-020-10271-8.
G. C. Moore and I. Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation,” Inf. Syst. Res., vol. 2, no. 3, pp. 192–222, 1991.
A. Bandura, “Self-efficacy: Toward a Unifying Theory of Behavioral Change,” Psychol. Rev., vol. 84, no. 2, pp. 191–215, 1977.
D. R. Compeau and C. A. Higgins, “Computer Self-Efficacy: Development of a Measure and Initial Test,” Source MIS Q., vol. 19, no. 2, pp. 189–211, 1995.
Y. U. Huan, X. Li, M. Aydeniz, and T. Wyatt, “Mobile Learning Adoption: An Empirical Investigation for Engineering Education*,” Int. J. Eng. Educ., vol. 31, no. 4, pp. 1081–1091, 2015.
F. D. Davis, “Perceived Usefulness, Perceived Ease Of Use, And User Accep,” Manag. Inf. Syst. Q., vol. 13, no. 3, pp. 319–340, 1989.
V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User Acceptance of Information Technology: Toward a Unified View,” Source MIS Q., vol. 27, no. 3, pp. 425–478, 2003.
Y. S. Wang, M. C. Wu, and H. Y. Wang, “Investigating the determinants and age and gender differences in the acceptance of mobile learning,” Br. J. Educ. Technol., vol. 40, no. 1, pp. 92–118, Jan. 2009, doi: 10.1111/j.1467-8535.2007.00809.x.
I. Ajzen, “The Theory of Planned Behavior,” Organ. Behav. Hum. Decis. Process., vol. 50, no. 2, pp. 179–211, 1991.
G. D. Israel, “Determining Sample Size 1 The Level Of Precision,” Univ. Florida, 1992.
W. L. (William L. Neuman, Social research methods : qualitative and quantitative approaches. 2014.
D. Straub, M.-C. Boudreau, and D. Gefen, “Validation Guidelines for IS Positivist Research,” Commun. Assoc. Inf. Syst., vol. 13, 2004, doi: 10.17705/1cais.01324.
D. George and P. Mallery, “SPSS for Windows Step by Step: A Simple Guide and Reference. 11.0 Update,” Bost. Allyn Bacon, 2003.
R. B. Kline, Principles and Practice of Structural Equation Modeling, 4th edn. 2016.
J. . & C. P. Cohen, “Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences,” 1983.
Neil J Salkind, Encyclopedia of research design, vol. 1–0. 2010.
M. E. Sobel, “Some New Results on Indirect Effects and Their Standard Errors in Covariance Structure Models,” Sociol. Methodol., vol. 16, pp. 159–186, 1986, doi: 10.2307/270922.
Cohen J., Statistical Power Analysis for the Behavioural Science (2nd Edition). 1988.
K. A. Bollen, Structural equations with latent variables. New York: Wiley, 1989.