This study examines the adoption of educational technology (EdTech) platforms for nonformal education in Indonesia, focusing on students from kindergarten to high school, using an extended UTAUT2 model. Data were collected via a questionnaire from 279 students and analyzed using PLS-SEM. A key addition to this research is the investigation of the new national curriculum in Indonesia, i.e., Kurikulum Merdeka (Independent Curriculum) and its influence on students’ behavioral intention to use EdTech platforms. The findings reveal that performance expectancy, Independent Curriculum, and habit shape behavioral intention, while facilitating conditions, habit, and behavioral intention significantly impact use behavior. This study fills a crucial research gap by analyzing EdTech adoption in younger students, offering valuable insights for EdTech companies to expand market reach and improve retention, particularly for elementary and middle school students. Additionally, it extends the UTAUT2 model with a new construct and provides sociocultural insights for policymakers to support digital learning. These contributions are vital for shaping the future of EdTech as a nonformal education platform in Indonesia.
AbbadMM (2021) Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies26(6): 7205–7224.
2.
AinNKaurKWaheedM (2015) The influence of learning value on learning management system use. Information Development32(5): 1306–1321.
3.
AjzenI (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes50(2): 179–211.
4.
Al-AdwanASAl-DebeiMM (2024) The determinants of Gen Z’s metaverse adoption decisions in higher education: Integrating UTAUT2 with personal innovativeness in IT. Education and Information Technologies29: 7413–7445.
5.
Al-AdwanASYaseenHAlsoudA, et al. (2022) Novel extension of the UTAUT model to understand continued usage intention of learning management systems: The role of learning tradition. Education and Information Technologies27: 3567–3593.
6.
Al MulhemA (2020) Exploring the Key Factors in the Use of an E-Learning System Among Students at King Faisal University, Saudi Arabia. International Journal of Interactive Mobile Technologies (iJIM)14(3): 19–37.
7.
AshrafSKhanTRehmanI (2016) E-learning for secondary and higher education sectors: A survey. International Journal of Advanced Computer Science and Applications7(9): 275–283.
8.
Asosiasi Penyelenggara Jasa Internet Indonesia (2022) Profil Internet Indonesia 2022. Available at https://survei.apjii.or.id/
9.
Asosiasi Penyelenggara Jasa Internet Indonesia (2024) Survey Penetrasi dan Perilaku Internet 2024.Available at https://survei.apjii.or.id/
10.
AsvialMMayangsariJYudistriansyahA (2021) Behavioral intention of e-learning: A case study of distance learning at a junior high school in Indonesia due to the COVID-19 pandemic. International Journal of Technology12(1): 54–64.
11.
AziziSMRoozbahaniNKhatonyA (2020) Factors affecting the acceptance of blended learning in medical education: Application of UTAUT2 model. BMC Medical Education20: 1–9.
12.
BarclayDHigginsCThompsonR (1995) The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology Studies2: 285–309.
13.
BaronRMKennyDA (1986) The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology51(6): 1173.
14.
BharatiVJSrikanthR (2018) Modified UTAUT2 model for m-learning among students in India. International Journal of Learning and Change10(1): 5.
15.
BhardwajRYarrowNBCaliM (2020) EdTech in Indonesia: Ready for Take-off (English). Washington, D.C.: World Bank Group.
16.
BorgersNDe LeeuwEHoxJ (2000) Children as respondents in survey research: Cognitive development and response quality. Bulletin of Sociological Methodology/Bulletin de méthodologie sociologique66(1): 60–75.
17.
CamaioniLCastelliM CLongobardiE, et al. (1991) A parent report instrument for early language assessment. First Language11(33): 345–358.
18.
ChinWW (1998) The partial least squares approach to structural equation modeling. Modern Methods for Business Research2: 295–336.
19.
DalePS (1991) The validity of a parent report measure of vocabulary and syntax at 24 months. Journal of Speech, Language, and Hearing Research34(3): 565–571.
20.
Data Indonesia (2023) Ada 44,19 Juta Murid di Indonesia pada 2022/2023. https://dataindonesia.id/ragam/detail/ada-4419-juta-murid-di-indonesia-pada-20222023 (accessed 19 September 2023)
21.
DavisFD (1986) A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral Dissertation. Sloan School of Management, Massachusetts Institute of Technology.
22.
De Los ReyesAKazdinAE (2005) Informant discrepancies in the assessment of childhood psychopathology: A critical review, theoretical framework, and recommendations for further study. Psychological Bulletin131(4): 483.
23.
DengPChenBWangL (2023) Predicting students' continued intention to use E-learning platform for college English study: The mediating effect of E-satisfaction and habit. Front. Psychol14: 1182980.
24.
DwivediYKRanaNPJeyarajA, et al. (2019) Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information systems frontiers21: 719–734.
25.
El GayarOFMoranM (2007) Examining students’ acceptance of tablet PC using TAM. Issues in Information Systems8(1): 167–172.
26.
El MasriMTarhiniA (2017) Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development65: 743–763.
27.
FacerKSelwynN (2021) Digital technology and the future of education, toward non-stupid optimism. Paris, France: UNESCO. Available at https://unesdoc.unesco.org/ark:/48223/pf0000377071
28.
FahleviMR (2022) Kajian project based blended learning sebagai model pembelajaran pasca pandemi dan bentuk implementasi Kurikulum Merdeka. Sustainable Jurnal Kajian Mutu Pendidikan5(2): 230–249.
29.
FeldmanHMDalePSCampbellTF, et al. (2005) Concurrent and predictive validity of parent reports of child language at ages 2 and 3 years. Child development76(4): 856–868.
30.
FishbeinMAjzenI (1975) Belief, attitude, intention, and behavior: An introduction to theory and research. Reading: Addison-Wesley.
31.
Giraldo-HuertasJSchaferG (2021) Agreement and reliability of parental reports and direct screening of developmental outcomes in toddlers at risk. Frontiers in Psychology12: 725146.
HairJFBlackWCBabinBJ, et al. (2009) Multivariate Data Analysis 7th ed. New Jersey: Pearson Education Inc.
34.
HairJFRingleCMSarstedtM (2011) PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice19(2): 139–152.
35.
HairJFRisherJJSarstedtM, et al. (2019) When to use and how to report the results of PLS-SEM. European Business Review31(1): 2–24.
36.
HarisCASoedijonoWABNasiriA (2020) Evaluasi aplikasi Ruangguru menggunakan model UTAUT2 dan model kesuksesan DeLone and McLean. J. Tek. dan Inov4(1): 1–9.
37.
HasanahRLWatiFFRianaD (2019) TAM Analysis on the factors affecting admission of students for Ruangguru application. Jurnal Sistem Informasi15(2): 1–14.
38.
HeckmanJ JMasterovD V (2007) The Productivity Argument for Investing in Young Children. Review of Agricultural Economics29(3): 446–493.
39.
HenselerJRingleCMSinkovicsRR (2009) The use of partial least squares path modeling in international marketing. In New Challenges to International Marketing20: 277–319. Emerald Group Publishing Limited.
40.
HidayatiVNDaniFRWatiMS, et al. (2022) Pengaruh pelaksanaan kurikulum merdeka belajar terhadap motivasi siswa kelas X di SMAN 1 Payung Sekaki. Jurnal Eduscience9(3): 707–716.
41.
HsuHH (2012) The acceptance of Moodle: An empirical study based on UTAUT. Creative Education3: 44–46.
42.
JambulingamM (2013) Behavioural intention to adopt mobile technology among tertiary students. World Applied Sciences Journal22(9): 1262–1271.
43.
JameelASAbdallaSNKaremMA (2020) Behavioural intention to use e-learning from student's perspective during COVID-19 pandemic. In: In 2020 2nd Annual International Conference on Information and Sciences (AiCIS), pp.165–171: IEEE.
44.
JameelASKaremMAAlheetyAS (2022) Behavioral intention and actual use of mobile learning during the COVID-19 pandemic in the higher education system. In: In International Conference on Emerging Technologies and Intelligent Systems, pp.155–167: Cham: Springer International Publishing.
45.
JongDWangTS (2009) Student acceptance of web-based learning system. In: In Proceedings. The 2009 International Symposium on Web Information Systems and Applications (WISA 2009), pp.533. Academy Publisher.
46.
Kemendikbud (2023) Kurikulum Merdeka untuk keleluasaan pendidik dan pembelajaran berkualitas. https://kurikulum.kemdikbud.go.id/tentang
47.
KockN (2015) Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration11(4): 1–10.
48.
LiaoQShimJPLuoX (2004) Student acceptance of web-based learning environment: An empirical investigation of an undergraduate IS course. In: AMCIS 2004 Proceedings, pp.377.
49.
LimayemMHirtS GCheungС M К (2007) How Habit Limits the Predictive Power of Intentions: The Case of IS Continuance. MIS Quarterly31(4): 70.
50.
LinceL (2022) Implementasi kurikulum merdeka untuk meningkatkan motivasi belajar pada sekolah menengah kejuruan pusat keunggulan. In Prosiding Seminar Nasional Fakultas Tarbiyah Dan Ilmu Keguruan IAIM Sinjai1: 38–49.
51.
MeetRKKalaDAl-AdwanAS (2022) Exploring factors affecting the adoption of MOOC in generation Z using extended UTAUT2 model. Education and Information Technologies27(7): 10261–10283.
52.
MemonMATingHCheahJH, et al. (2020) Sample size for survey research: Review and recommendations”. Journal of Applied Structural Equation Modeling4(2): i–xx.
53.
MillerJFSedeyALMioloG (1995) Validity of parent report measures of vocabulary development for children with down syndrome. Journal of Speech Language and Hearing Research38(5): 1037.
54.
MuangmeeCKotSMeekaewkunchornN, et al. (2021) Students’ use behavior towards E-learning tools during COVID-19 pandemics: Case study of higher educational institutions of Thailand. International Journal of Evaluation and Research in Education10(4): 1166–1175.
55.
NikolopoulouKGialamasVLavidasK (2021) Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open2: 100041.
56.
PatilimaS (2022) Sekolah penggerak sebagai upaya peningkatan kualitas pendidikan. Prosiding Seminar Nasional Pendidikan Dasar (25 November 2021): 228–236.
57.
PodsakoffPMMacKenzieSBLeeJ-Y, et al. (2003) Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
58.
RingleCMWendeSBeckerJM (2022). SmartPLS 4. Oststeinbek: SmartPLS GmbH. Available at: http://www.smartpls.com.
59.
RogersEM (1983) Diffusion of innovations. 3rd ed.New York: Free Press.
60.
SaputraRIsnainiNAdhyS, et al. (2020) Factors influencing student’s adoption of e-learning in Indonesian secondary schools. In: In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), pp.1–6: IEEE.
61.
SEAMEO Regional Open Learning Center (2023) Technology in education: a case study on Indonesia.Paper commissioned for the 2023 Global Education Monitoring Report, Southeast Asia - Technology in education.
62.
SharifAAfshanSQureshiMA (2019) Acceptance of learning management system in university students: An integrating framework of modified UTAUT2 and TTF theories. International Journal of Technology Enhanced Learning11(2): 201–229.
63.
ShmueliGSarstedtMHairJF, et al. (2019) Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing53(11): 2322–2347.
TarhiniAMasa’dehREAl-BusaidiKA, et al. (2017) Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business10(2): 164–182.
TsengTHLinSWangYS, et al. (2019) Investigating teachers’ adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments30(4): 635–650.
68.
UNICEF (2021) Situational analysis on digital learning landscape in Indonesia. United Nations Children’s Fund. https://www.unicef.org/indonesia/media/8766/file/Digita.
69.
VarniJWLimbersCABurwinkleTM (2007) Parent proxy-report of their children's health-related quality of life: an analysis of 13,878 parents' reliability and validity across age subgroups using the PedsQL™ 4.0 Generic Core Scales. Health and Quality of Life Outcomes5: 1–10.
70.
VenkateshVDavisF D (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science46(2): 186–204.
71.
VenkateshVMorrisMGDavisGB, et al. (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27(3): 425–478.
72.
VenkateshVThongJYXuX (2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly36(1): 157–178.
73.
WahabRAARHandayaniPW (2021) Analysis of user acceptance for Rumah Belajar mobile application. In: In 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), pp.232–238: IEEE.
WidjajaHAESantosoSWPetrusS (2019) The enhancement of learning management system in teaching learning process with the UTAUT2 and trust model. In: In 2019 International Conference on Information Management and Technology (ICIMTech) 1, pp.309–313: IEEE.
76.
ZwainAAA (2019) Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2. Interactive Technology and Smart Education16(3): 239–254.
