The current study examined factors affecting higher education students’ use of a mobile-based Learning Management System (m-LMS) for distance education during the Corona Virus Disease 2019 (Covid-19). A survey instrument based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) was established and validated. The primary data were gathered from 1032 responses. The data were computed in SmartPLS 3.2. Partial Least Square Structural Equation Modeling (PLS-SEM) procedures were conducted to assess the measurement and structural models. The proposed model is valid and reliable. Findings of the study show that relationships emerge in seven out of ten hypotheses. The strongest relationship is between Perceived Usefulness and Attitude while the weakest exists between Perceived Ease of Use and Attitude. This study emphasizes awareness about the use of m-LMS in education, especially during distance education due to pandemics like Covid-19. The model is expected to provide a useful base for future academicians interested in conducting similar topics of research.
AjzenI (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes50(2): 179–211. DOI: 10.1016/0749-5978(91)90020-T.
2.
Al-AzaweiAAlowayrA (2020) Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society62. DOI: 10.1016/j.techsoc.2020.101325.
3.
Al-EmranMMezhuyevVKamaludinA (2018) Technology Acceptance Model in M-learning context: A systematic review. Computers and Education125: 389–412. DOI: 10.1016/j.compedu.2018.06.008.
4.
Al-EmranMArpaciISalloumSA (2020) An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies25(4): 2899–2918. DOI: 10.1007/s10639-019-10094-2.
5.
Al-ShihiHSharmaSKSarrabM (2018) Neural network approach to predict mobile learning acceptance. Education and Information Technologies23(5): 1805–1824. DOI: 10.1007/s10639-018-9691-9.
6.
Badan Nasional Penanggulangan Bencana (BNPB) (2020) Peta sebaran COVID-19 [Spread mapping of Covid-19]. Available at: https://covid19.go.id/peta-sebaran.
7.
ChengEWL (2019) Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development67(1): 21–37. DOI: 10.1007/s11423-018-9598-6.
8.
CheonJLeeSCrooksSM, et al. (2012) An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers and Education59(3): 1054–1064. DOI: 10.1016/j.compedu.2012.04.015.
Dalvi-EsfahaniMWai LeongLIbrahimO, et al. (2020) Explaining Students’ Continuance Intention to Use Mobile Web 2.0 Learning and Their Perceived Learning: An Integrated Approach. Journal of Educational Computing Research57(8): 1956–2005. DOI: 10.1177/0735633118805211.
12.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems13(3): 319–339. DOI: 10.2307/249008.
13.
ErdfelderEFAulFBuchnerA, et al. (2009) Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods41(4): 1149–1160. DOI: 10.3758/BRM.41.4.1149.
14.
FornellCLarckerDF (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research18(1): 39. DOI: 10.2307/3151312.
15.
Hair JosephF.SarstedtMRingleCM (2019) Rethinking some of the rethinking of partial least squares. European Journal of Marketing53(4): 566–584. DOI: 10.1108/EJM-10-2018-0665.
16.
Hair JosephFRisherJJSarstedtM, et al. (2019) When to use and how to report the results of PLS-SEM. European Business Review. DOI: 10.1108/EBR-11-2018-0203.
17.
HuangRTHsiaoCHTangTW, et al. (2014) Exploring the moderating role of perceived flexibility advantages in mobile learning continuance intention (MLCI). International Review of Research in Open and Distance Learning15(3): 140–157. DOI: 10.19173/irrodl.v15i3.1722.
18.
IqbalSBhattiZA (2017) What drives m-learning? An empirical investigation of university student perceptions in Pakistan. Higher Education Research and Development36(4): 730–746. DOI: 10.1080/07294360.2016.1236782.
19.
IqbalSQureshiIA (2012) M-learning adoption: A perspective from a developing country. International Review of Research in Open and Distance Learning13(3): 147–164. DOI: 10.19173/irrodl.v13i3.1152.
20.
JooYJKimNKimNH (2016) Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development64(4): 611–630. DOI: 10.1007/s11423-016-9436-7.
21.
KhanHMalikA (2021) Academic use of smartphones among medical students in Pakistan. Information Development. doi:10.1177/0266666921993518
22.
KimJBelzerA (2021) Return on Investment for Adult Basic Education: Existing Evidence and Future Directions. Adult Education Quarterly: 074171362110021. DOI: 10.1177/07417136211002155.
23.
KumarBAChandSS (2019) Mobile learning adoption: A systematic review. Education and Information Technologies. DOI: 10.1007/s10639-018-9783-6.
24.
LarssonHTegernMMonnierA, et al. (2015) Content validity index and intra- and inter- rater reliability of a new muscle strength/ endurance test battery for Swedish soldiers. PLoS ONE10(7). DOI: 10.1371/journal.pone.0132185.
25.
LinLHuangZOthmanB, et al. (2020) Let’s make it better: An updated model interpreting international student satisfaction in China based on PLS-SEM approach. PLoS ONE15(7 July). DOI: 10.1371/journal.pone.0233546.
26.
LynnMR (1986) Determination and quantification of content validity. Nursing Research35(6): 382–386. DOI: 10.1097/00006199-198611000-00017.
27.
MartinFErtzbergerJ (2013) Here and now mobile learning: An experimental study on the use of mobile technology. Computers and Education68: 76–85. DOI: 10.1016/j.compedu.2013.04.021.
MohammadiH (2015) Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior49: 191–207. DOI: 10.1016/j.chb.2015.03.006.
30.
MurilloGGNovoa-HernándezPRodríguezRS (2020) Technology Acceptance Model and Moodle: A systematic mapping study. Information Development. doi:10.1177/0266666920959
31.
ParkSYNamMWChaSB (2012) University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 2012, pp. 592–605. DOI: 10.1111/j.1467-8535.2011.01229.x.
32.
RefatNKassimHRahmanMA, et al. (2020) Measuring student motivation on the use of a mobile assisted grammar learning tool. PLoS ONE15(8 August). DOI: 10.1371/journal.pone.0236862.
33.
Romo-GonzálezJRTarangoJMachin-MastromatteoJD (2018) PLS SEM, a quantitative methodology to test theoretical models from library and information science. Information Development, 34(5): 526–531. doi: 10.1177/0266666918795025.
34.
SabahNM (2016) Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior65: 522–533. DOI: 10.1016/j.chb.2016.09.009.
35.
SaroiaAIGaoS (2019) Investigating university students’ intention to use mobile learning management systems in Sweden. Innovations in Education and Teaching International56(5): 569–580. DOI: 10.1080/14703297.2018.1557068.
36.
SarrabMAl-ShihiHAl-ManthariB, et al. (2018) Toward Educational Requirements Model for Mobile Learning Development and Adoption in Higher Education. TechTrends62(6): 635–646. DOI: 10.1007/s11528-018-0331-4.
37.
TanGWHOoiKBSimJJ, et al. (2012) Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems. DOI: 10.1080/08874417.2012.11645561.
38.
ThongsriNShenLBaoY, et al. (2018) Integrating UTAUT and UGT to explain behavioural intention to use M-learning: A developing country’s perspective. Journal of Systems and Information Technology20(3): 278–297. DOI: 10.1108/JSIT-11-2017-0107.
YeapJALRamayahTSoto-AcostaP (2016) Factors propelling the adoption of m-learning among students in higher education. Electronic Markets26(4): 323–338. DOI: 10.1007/s12525-015-0214-x.
