Based on 35 empirical studies, this study conducts a meta-analysis of the factors related to users’ adoption of mobile learning to obtain unified findings. The results showed that factors are correlated with adoption intention in the following order, from highest to lowest: performance expectancy, attitude, perceived enjoyment, learning autonomy, facilitating conditions, effort expectancy, self-management, social influence, and personal innovativeness. Cultural differences among countries/regions moderate the relationships between perceived enjoyment and adoption intention and self-management and adoption intention. This study proposes an extended unified theory of acceptance and use of technology (UTAUT) to provide references and ideas on how the use of mobile learning can be improved in the future.
AbdallahNAbdallahOBohraOM (2021) Factors affecting mobile learning acceptance in higher education: An empirical study. International Journal of Advanced Computer Science and Applications12(4): 664–671.
2.
Abu-Al-AishALoveS (2013) Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning14(5): 82–107.
3.
AburubFAlnawasI (2019) A new integrated model to explore factors that influence adoption of mobile learning in higher education: An empirical investigation. Education and Information Technologies24(3): 2145–2158.
4.
Afacan AdanırGMuhametjanovaG (2021) University students’ acceptance of mobile learning: A comparative study in Turkey and Kyrgyzstan. Education and Information Technologies26(5): 6163–6181.
5.
Al-AdwanASAl-MadadhaAZvirzdinaiteZ (2018) Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. International Review of Research in Open and Distributed Learning19(1): 221–241.
6.
AlasmariTZhangK (2019) Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and A mixed-method study. Education and Information Technologies24(3): 2127–2144.
7.
Al-EmranMMezhuyevVKamaludinA (2020) Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technology in Society61: 101247.
8.
AlmaiahMAJalilMAManM (2016) Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education3(4): 453–485.
9.
AlmaiahMAAlamriMMAl-RahmiW (2019) Applying the UTAUT model to explain the Students’ acceptance of mobile learning system in higher education. IEEE Access7: 174673–174686.
10.
AlowayrAAl-AzaweiA (2021) Predicting mobile learning acceptance: An integrated model and empirical study based on the perceptions of higher education students. Australasian Journal of Educational Technology37(3): 38–55.
11.
Al-RahmiAMAl-RahmiWMAlturkiU, et al. (2022) Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies27(6): 7805–7826.
12.
AndersonCAShibuyaAIhoriN, et al. (2010) Violent video game effects on aggression, empathy, and prosocial behavior in eastern and western countries: A meta-analytic review. Psychological Bulletin136(2): 151–173.
13.
ArpaciI (2015) A comparative study of the effects of cultural differences on the adoption of mobile learning. British Journal of Educational Technology46(4): 699–712.
14.
AziziSMKhatonyA (2019) Investigating factors affecting on medical sciences students’ intention to adopt mobile learning. BMC Medical Education19(1): 381.
15.
BaghcheghiNKoohestaniHKarimyM, et al. (2020) Factors affecting mobile learning adoption in healthcare professional students based on technology acceptance model. Acta Facultatis Medicae Naissensis37(2): 191–200.
16.
BatmetanJRPalilinganVR (2017) Higher education students’ behaviour to adopt mobile learning. In: 2nd International Conference on Innovation in Engineering and Vocational Education, Manado, Indonesia.
17.
BaydasOYilmazRM (2018) Pre-service teachers’ intention to adopt mobile learning: A motivational model. British Journal of Educational Technology49(1): 137–152.
18.
BesbesALegohérelPKucukustaD, et al. (2016) A cross-cultural validation of the tourism web acceptance model (T-WAM) in different cultural contexts. Journal of International Consumer Marketing28(3): 1–16.
19.
BöhmSConstantineGP (2016) Impact of contextuality on mobile learning acceptance. Interactive Technology and Smart Education13(2): 107–122.
20.
BoteroGGQuestierFCincinnatoS, et al. (2018) Acceptance and usage of mobile assisted language learning by higher education students. Journal of Computing in Higher Education30(3): 426–451.
21.
ChangCYSheuJPChanTW (2003) Concept and design of ad hoc and mobile classrooms. Journal of Computer Assisted Learning19(3): 336–346.
22.
ChaoC-M (2019) Factors determining the behavioral intention to use Mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology10: 1652.
23.
ChavoshiAHamidiH (2019) Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran. Telematics and Informatics38: 133–165.
24.
ChungH-HChenS-CKuoM-H (2015) A study of EFL college students’ acceptance of mobile learning. Procedia – Social and Behavioral Sciences176: 333–339.
25.
CohenJ (1992) A power primer. Psychological Bulletin112(1): 155–159.
26.
DwivediYKRanaNPJeyarajA, et al. (2019) Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers21(3): 719–734.
27.
FaganMH (2019) Factors influencing student acceptance of mobile learning in higher education. Computers in the Schools36(2): 105–121.
28.
FriedlandJJainT (2022) Reframing the purpose of business education: Crowding-in a culture of moral self-awareness. Journal of Management Inquiry31(1): 15–29.
29.
HabibiAYaakobMFMAl-AdwanAS (2023) m-Learning management system use during COVID-19. Information Development39(1): 123–135.
30.
HamidiHChavoshiA (2018) Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the university of technology. Telematics and Informatics35(4): 1053–1070.
31.
HoiVN (2020) Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education146: 103761.
32.
HoppeHUJoinerRMilradM, et al. (2003) Guest editorial: Wireless and mobile technologies in education. Journal of Computer Assisted Learning19(3): 255–259.
33.
HsiaJ-W (2015) The effects of locus of control on university students’ mobile learning adoption. Journal of Computing in Higher Education28(1): 1–17.
34.
HuanYLiXAydenizM, et al. (2015) Mobile learning adoption: An empirical investigation for engineering education. International Journal of Engineering Education31(4): 1081–1091.
35.
HuangL (2017) Acceptance of mobile learning in classroom instruction among college English teachers in China using an extended TAM. In: 2017 International Conference of Educational Innovation through Technology (EITT), pp.283–287.
36.
HuangTK (2015) Exploring the antecedents of screenshot-based interactions in the context of advanced computer software learning. Computers & Education80: 95–107.
37.
HubalovskySHubalovskaMMusilekM (2019) Assessment of the influence of adaptive E-learning on learning effectiveness of primary school pupils. Computers in Human Behavior92(2): 691–705.
38.
HungW-CJengI (2013) Factors influencing future educational technologists’ intentions to participate in online teaching. British Journal of Educational Technology44(2): 255–272.
39.
JadilYRanaNPDwivediYK (2021) A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. Journal of Business Research132: 354–372.
40.
JaramilloFMulkiJPMarshallGW (2005) A meta-analysis of the relationship between organizational commitment and salesperson job performance: 25 years of research. Journal of Business Research58(6): 705–714.
41.
JoshiARohH (2009) The role of context in work team diversity research: A meta-analytic review. Academy of Management Journal52(3): 599–627.
42.
JungH-J (2015) Fostering an English teaching environment: Factors influencing English as a foreign language teachers’ adoption of mobile learning. Informatics in Education14(2): 219–241.
43.
KangMLiewBYTLimH, et al. (2014) Investigating the determinants of mobile learning acceptance in Korea using UTAUT2. In: International Conference on Smart Learning Environments (ICSLE), Hong Kong, Peoples R China, pp.209–216.
44.
KangMLiewBYTLimH, et al. (2015) Investigating the determinants of mobile learning acceptance in Korea using UTAUT2. In: Emerging Issues in Smart Learning, pp.209–216.
45.
KuciapskiM (2016) Students acceptance of m-learning for higher education – UTAUT model validation. In: Information Systems: Development, Research, Applications, Education, pp.155–166.
46.
KumarBAChandSS (2019) Mobile learning adoption: A systematic review. Education and Information Technologies24(1): 471–487.
47.
KurtOE (2022) Learning with smartphones: the acceptance of m-learning in higher education. Online Information Review. DOI: 10.1108/OIR-10-2021-0516.
48.
LaiC-L (2020) Trends of mobile learning: A review of the top 100 highly cited papers. British Journal of Educational Technology51(3): 721–742.
49.
LiX (2017) Students’ acceptance of mobile learning: An empirical study based on blackboard mobile learn. International Journal of Interdisciplinary Telecommunications and Networking9(1): 52–69.
LuAChenQYanZ, et al. (2016) Investigating the determinants of mobile learning acceptance in higher education based on UTAUT. In: International Computer Symposium (ICS). Chiayi, Taiwan: Natl Chung Cheng Univ, pp.651–655.
52.
LuJYaoJEYuC-S (2005) Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems14(3): 245–268.
53.
Mojarro AliañoÁDuarte HuerosAMGuzmán FrancoMD, et al. (2019) Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research8(1): 7–17.
54.
MutambaraDBayagaA (2020) Rural-based science, technology, engineering and mathematics teachers’ and learners’ acceptance of mobile learning. SA Journal of Information Management22(1): a1200.
55.
MutambaraDBayagaA (2021) Determinants of mobile learning acceptance for STEM education in rural areas. Computers & Education160: 104010.
56.
NaveedQNAlamMMTairanN (2020) Structural equation modeling for mobile learning acceptance by university students: An empirical study. Sustainability12(20): 1–17.
57.
PedersenPELingR (2003) Modifying adoption research for mobile Internet service adoption: Cross-disciplinary interactions. In: 36th Annual Hawaii International Conference on System Sciences.
58.
PigottTD (2006) Methods of meta-analysis: Correcting error and bias in research findings. Evaluation and Program Planning29(3): 236–237.
59.
PoongYSYamaguchiSTakadaJ-i (2016) Investigating the drivers of mobile learning acceptance among young adults in the world heritage town of Luang Prabang, Laos. Information Development33(1): 57–71.
60.
PramanaE (2018) Determinants of the adoption of mobile learning systems among university students in Indonesia. Journal of Information Technology Education: Research17: 365–398.
61.
PratamaAR (2020) Fun first, useful later: Mobile learning acceptance among secondary school students in Indonesia. Education and Information Technologies26(2): 1737–1753.
62.
QashouA (2020) Influencing factors in M-learning adoption in higher education. Education and Information Technologies26(2): 1755–1785.
63.
RazaSAUmerAQaziW, et al. (2017) The effects of attitudinal, normative, and control beliefs on M-learning adoption among the students of higher education in Pakistan. Journal of Educational Computing Research56(4): 563–588.
64.
SarkarSChauhanSKhareA (2020) A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management50: 286–301.
65.
SchaperLKPervanGP (2007) ICT and OTs: A model of information and communication technology acceptance and utilisation by occupational therapists. International Journal of Medical Informatics76(1): S212–S221.
66.
Sitar-TautDA (2021) Mobile learning acceptance in social distancing during the COVID-19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies3(3): 366–378.
67.
Sitar-TautDAMicanD (2021) Mobile learning acceptance and use in higher education during social distancing circumstances: An expansion and customization of UTAUT2. Online Information Review45(5): 1000–1019.
68.
SnyderLLStaffordJMDabeleaD, et al. (2019) Socio-economic, demographic, and clinical correlates of poor glycaemic control within insulin regimens among children with type 1 diabetes: The SEARCH for diabetes in youth study. Diabetic Medicine36(8): 1028–1036.
69.
SophonhiranrakS (2021) Features, barriers, and influencing factors of mobile learning in higher education: A systematic review. Heliyon7(4): e06696.
70.
SungY-TChangK-EYangJ-M (2015) How effective are mobile devices for language learning? A meta-analysis. Educational Research Review16: 68–84.
71.
TaoDWangTWangT, et al. (2020) A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies. Computers in Human Behavior104: 106147.
72.
TliliAPadilla-ZeaNGarzonJ, et al. (2022) The changing landscape of mobile learning pedagogy: A systematic literature review. Interactive Learning Environments. DOI: 10.1080/10494820.2022.2039948.
73.
VenkateshVMorrisMGDavisGB, et al. (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27(3): 425–478.
74.
VenkateshVThongJYLXuX (2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly36(1): 157–178.
75.
VenkateshVThongJYLXuX (2016) Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems17(5): 328–376.
76.
VirvouMAlepisE (2005) Mobile educational features in authoring tools for personalised tutoring. Computers & Education44(1): 53–68.
77.
WangY-SWuM-CWangH-Y (2009) Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology40(1): 92–118.
78.
WelchRAladeTNicholL (2020) Using the unified theory of acceptance and use of technology (utaut) model to determine factors affecting Mobile learning adoption in the workplace: A study of the science museum group. Iadis International Journal on Computer Science and Information Systems15(1): 85–98.
79.
WuW-HWuY-CJChenC-Y, et al. (2012) Review of trends from mobile learning studies: A meta-analysis. Computers & Education59(2): 817–827.
80.
YangHLeeH (2019) Understanding user behavior of virtual personal assistant devices. Information Systems and E-Business Management17(1): 65–87.
81.
YeapJALRamayahTSoto-AcostaP (2016) Factors propelling the adoption of m-learning among students in higher education. Electronic Markets26(4): 323–338.
82.
YuanY-PWei-Han TanGOoiK-B, et al. (2021) Can COVID-19 pandemic influence experience response in mobile learning?Telematics and Informatics64: 101676.
83.
ZhangLZhuJLiuQ (2012) A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior28(5): 1902–1911.
84.
ZhangMZhaoX-Y, XueY-X, et al. (2021) A meta-analysis of how the culture and technical development level influence citizens’ adoption of m-government. International Review of Administrative Sciences. DOI: 10.1177/00208523211057358.
85.
ZhangYWengQZhuN (2018) The relationships between electronic banking adoption and its antecedents: A meta-analytic study of the role of national culture. International Journal of Information Management40: 76–87.
86.
ZhangZLCaoTHShuJB, et al. (2022) Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments. Interactive Learning Environments30(8): 1388–1401.
