Determinants of the Behavioural Intention of Open Distance Learning Students to Use Digital Tools and Resources for Learning in Nigeria

Abstract

This study investigated determinants of the behavioural intention of Open Distance Learning (ODL) students to use digital tools and resources for learning in Nigeria. The study utilized an online questionnaire to gather data from 522 ODL students from an ODL institution in Nigeria. The Unified Theory of Acceptance and Use of Technology (UTAUT) was the main framework used for the analysis in which attitude was included as an additional variable to seek out a much better model capable of improving the understanding of ODL students’ behavioural intention to use digital tools and resources for learning. Thus, an extended UTAUT model was developed and tested in this study. The model consisted of six constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, attitude and behavioural intention. A Partial Least Square-Structural Equation Modelling (PLS-SEM) was used for data analysis. Results revealed that the proposed model successfully explained critical factors that determine ODL students’ behavioural intention to use digital tools and resources for learning. The study suggested that attitude, performance expectancy and facilitating conditions are the major determinants of behavioural intention to use digital tools and resources but attitude is the most prominent factor. Thus, implications and suggestions for further studies were highlighted.

Keywords

digital tools and resources Behavioural intention ODL students UTUAT Nigeria

Get full access to this article

View all access options for this article.

References

Abbad

M. M. M.

(2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https://doi.org/10.1007/s10639-021-10573-5

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-t

Ajzen

Fishbein

(1972). Attitudes and normative beliefs as factors influencing behavioral intentions. Journal of Personality and Social Psychology, 21(1), 1–9. https://doi.org/10.1037/h0031930

Arthur-Nyarko

Agyei

D. D.

Armah

J. K.

(2020). Digitizing distance learning materials: Measuring students’ readiness and intended challenges. Education and Information Technologies, 25(5), 2987–3002. https://doi.org/10.1007/s10639-019-10060-y

Attuquayefio

Addo

(2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development Using Information and Communication Technology (IJEDICT), 10(3), 75–86. https://files.eric.ed.gov/fulltext/EJ1059042.pdf

Bawack

R. E.

Kala Kamdjoug

J. R.

(2018). Adequacy of UTAUT in clinician adoption of health information systems in developing countries: The case of Cameroon. International Journal of Medical Informatics, 109, 15–22. https://doi.org/10.1016/j.ijmedinf.2017.10.016

Berry

(2017). NSUWorks behavioral intention and use behavior of social networking websites among senior adults. https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=2023&context=gscis_etd

Bruess

(2003). University ESL instructors’ perceptions and use of computer technology in teaching. https://scholarworks.uno.edu/td/7/

Chang

(2020). Factors that determine behavioural intention: A Review of Literature from 2015 to 2019. International Journal of Innovation, Creativity and Changeijicc, 14(4), 2020. https://www.ijicc.net/images/Vol_14/Iss_4/14450_Chang_2020_E_R.pdf

10.

Chua

P. Y.

Rezaei

M.-L.

Jambulingam

(2018). Elucidating social networking apps decisions. Nankai Business Review International, 9(2), 118–142. https://doi.org/10.1108/nbri-01-2017-0003

11.

Dwivedi

Y. K.

Rana

N. P.

Jeyaraj

Clement

Williams

M. D.

(2017). Re-Examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y

12.

Genova

M. M.

(2019). 21st century Language classroom with digital tools and Resources. International Scientific Journal “Industry, 44(03), 142–145. https://stumejournals.com/journals/i4/2019/3/142.full.pdf

13.

Ghalandari

(2012). The effect of performance expectancy, effort expectancy, social influence and facilitating conditions on acceptance of E-banking services in Iran: The moderating Role of age and gender. Middle-East Journal of Scientific Research, 12(6), 801–807. https://doi.org/10.5829/idosi.mejsr.2012.12.6.2536

14.

Hair

J. F.

Sarstedt

Hopkins

Kuppelwieser

G.V.

(2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106-121. https://doi.org/10.1108/ebr-10-2013-0128.

15.

Humida

Al Mamun

M. H.

Keikhosrokiani

(2021). Predicting behavioral intention to use e-learning system: A case-study in begum rokeya university, rangpur, Bangladesh. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10707-9

16.

Hussein

(2017). Leading to intention: The Role of attitude in Relation to technology acceptance model in E-learning. Procedia Computer Science, 105, 159–164. https://doi.org/10.1016/j.procs.2017.01.196

17.

Ibrahim

J. S.

Taib

C. A.

Shahzad

(2016). The behavioral intention of citizen of Nigeria on E-government service by utilization of the unified theory of acceptance and use of technology (UTAUT). Sains Humanika, 8(4-2). https://doi.org/10.11113/sh.v8n4-2.1057

18.

Hong

Kang

M. S.

(2011). An international comparison of technology adoption. Information & Management, 48(1), 1–8. https://doi.org/10.1016/j.im.2010.09.001

19.

Isaias

Reis

Coutinho

Lencastre

J. A.

(2017). Empathic technologies for distance/mobile learning. Interactive Technology and Smart Education, 14(2), 159–180. https://doi.org/10.1108/itse-02-2017-0014

20.

Itasanmi

S. A.

Ekpenyong

V. O.

Akintolu

Ajani

O. A.

(2022a). A predicting analysis of academic staff’s motivation to teach online in a Nigerian university. Electronic Journal of E-Learning, 20(3), pp284–295. https://doi.org/10.34190/ejel.20.3.2123

21.

Itasanmi

S. A.

Oni

M. T.

(2021, February 16). Determinants of learners’ satisfaction in open distance learning programmes in Nigeria. Pakistan Journal of Distance and Online Learning, 6(2), 1226. https://pjdol.aiou.edu.pk/?p=1226

22.

Itasanmi

S. A.

Oni

M. T.

Adelore

O. O.

(2020). Students’ assessment of open distance learning programmes and services in Nigeria: A comparative Description of three selected distance learning institutions. IJORER: International Journal of Recent Educational Research, 1(3), 191–208. https://doi.org/10.46245/ijorer.v1i3.64

23.

Itasanmi

S. A.

Oni

M. T.

Ekpenyong

V. O.

Ajani

O. A.

Omorinkoba

O. G.

(2022b). Academic staff’s motivation for online teaching in Nigerian universities: Empirical evidence from the university of Ibadan. International Journal of Learning, Teaching and Educational Research, 21(7), 345–365. https://doi.org/10.26803/ijlter.21.7.18

24.

Izuma

(2017). The Neural bases of social influence on Valuation and behavior. In Decision Neuroscience (pp. 199–209), Elsevier Academic Press. https://doi.org/10.1016/b978-0-12-805308-9.00016-6

25.

Jairak

Praneetpolgrang

Mekhabunchakij

(2009). An acceptance of mobile learning for higher education students in Thailand. The Sixth International Conference on ELearning for Knowledge-Based Society, 36(1), 17–18. http://www.cmruir.cmru.ac.th/bitstream/123456789/416/1/Full_KallayaJairak.pdf

26.

Jere

J. N.

(2020). Investigating university academics behavioural intention in the adoption of e-learning in a time of COVID-19. SA Journal of Information Management, 22(1), 1280. https://doi.org/10.4102/sajim.v22i1.1280

27.

Kaba

Touré

(2014). Understanding information and communication technology behavioral intention to use: Applying the UTAUT model to social networking site adoption by young people in a least developed country. Journal of the Association for Information Science and Technology, 65(8), 1662–1674. https://doi.org/10.1002/asi.23069

28.

Kotzé

D. A.

(2021). Theoretical framework for open distance learning: A South African case study. The Independent Journal of Teaching and Learning, 16(1), 10–23. https://journals.co.za/doi/pdf/10.10520/ejc-jitl1-v16-n1-a4

29.

Krismadinata

Jalinus

Pitra Rosmena

Yahfizham

(2019). Understanding behavioral intention in Implementation of the ICTs based on UTAUT model. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 10(2), 96. https://doi.org/10.24843/lkjiti.2019.v10.i02.p04

30.

Lakhal

Khechine

Pascot

(2013). Student behavioural intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computing in Higher Education, 25(2), 93–121. https://doi.org/10.1007/s12528-013-9069-3

31.

Liebenberg

Benade

Ellis

(2018). Part of the higher education commons, and the Management information systems. The African Journal of Information Systems, 10(3), 1. https://digitalcommons.kennesaw.edu/cgi/viewcontent.cgi?article=1499&context=ajis

32.

Lim

F.-W.

Ahmad

Talib

A. N. B. A.

(2019). Behavioural intention towards using Electronic Wallet: A conceptual framework in the Light of the unified theory of acceptance and use of technology (UTAUT). Imperial Journal of Interdisciplinary Research (IJIR), 5(1), 79–86. https://www.researchgate.net/publication/340090603_Behavioural_Intention_towards_Using_Electronic_Wallet_A_Conceptual_Framework_in_the_Light_of_the_Unified_Theory_of_Acceptance_and_Use_of_Technology_UTAUT

33.

Lutfi

(2022). Factors influencing the continuance intention to use accounting information system in Jordanian SMEs from the Perspectives of UTAUT: Top Management support and self-Efficacy as predictor factors. Economies, 10(4), 75. https://doi.org/10.3390/economies10040075

34.

Madzamba

Govender

D. W.

(2021). Investigating determinants of behavioural intention to accept technology of Zimbabwean teacher education colleges Lecturers. Universal Journal of Educational Research, 9(7), 1369–1374. https://doi.org/10.13189/ujer.2021.090703

35.

Magsamen-Conrad

(2015). Bridging the Divide: Using UT Bridging the Divide: Using UTAUT to predict multigener edict multigenerational ational tablet adoption practices. https://scholarworks.bgsu.edu/cgi/viewcontent.cgi?article=1049&context=smc_pub

36.

Mailizar

Burg

Maulina

(2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(2), 7057–7077. https://doi.org/10.1007/s10639-021-10557-5

37.

Mtebe

Raisamo

(2014). Investigating students’ behavioural intention to adopt and use mobile learning in higher education in East Africa. International Journal of Education and Development Using Information and Communication Technology (IJEDICT), 10(3), 4–20. https://files.eric.ed.gov/fulltext/EJ1059061.pdf

38.

Naranjo-Zolotov

Oliveira

Casteleyn

(2018). Citizens’ intention to use and recommend e-participation. Information Technology & People, 32(2), 364–386. https://doi.org/10.1108/itp-08-2017-0257

39.

Nassuora

(2013). Students acceptance of mobile learning for higher education in Saudi arabia. International Journal of Learning Management Systems, 1(1), 1–9. https://doi.org/10.12785/ijlms/010101

40.

Ozdamar-Keskin

Ozata

F. Z.

Banar

Royle

(2015). Examining digital Literacy competences and learning Habits of open and distance learners. Contemporary Educational Technology, 6(1), 6140. https://doi.org/10.30935/cedtech/6140

41.

Pangaribuan

Wulandar

(2019). A crowdfunding platform user acceptance: An empirical examination of performance expectancy, effort expectancy, social factors, facilitating condition, attitude, and behavioral intention. In Proceedings of the 1st Sampoerna University-AFBE International Conference, SU-AFBE 2018, Jakarta Indonesia, 6-7 December 2018. https://doi.org/10.4108/eai.6-12-2018.2286301

42.

Patel

(2018). The impact of digital technologies on society. Gbim.com. https://www.gbim.com/the-impact-of-digital-technologies-on-society/

43.

Purwanto

Loisa

(2020). The intention and use behaviour of the mobile banking system in Indonesia: UTAUT model. Technology Reports of Kansai University, 62(6), 2757-2767.https://www.researchgate.net/profile/Edi-Purwanto-3/publication/343230847_The_Intention_and_Use_Behaviour_of_the_Mobile_Banking_System_in_indonesia_UTAUT_Model/links/5f1ea826a6fdcc9626b68a3c/The-Intention-and-Use-Behaviour-of-the-Mobile-Banking-System-in-indonesia-UTAUT-Model.pdf

44.

Ramllah Nurkhin

(2020). Analysis of factors affecting behavioral intention to use E-learning uses the unified theory of acceptance and use of technology approach. KnE Social Sciences. https://doi.org/10.18502/kss.v4i6.6658

45.

Ryu

J. S.

Fortenberry

(2021). Performance Expectancy and Effort Expectancy in Omnichannel Retailing. Journal of Industrial Distribution & Business, 12(4), 27-34. https://doi.org/10.13106/jidb.2021.vol12.no4.27.

46.

Säljö

(2010). Digital tools and challenges to institutional traditions of learning: Technologies, social memory and the performative nature of learning. Journal of Computer Assisted Learning, 26(1), 53–64. https://doi.org/10.1111/j.1365-2729.2009.00341.x

47.

Sample

Warland

(1973). Attitude and prediction of behavior. Social Forces, 51(3), 292–304. https://doi.org/10.1093/sf/51.3.292

48.

Suki

N. M.

Suki

N. M.

(2017). Determining students’ behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528–538. https://doi.org/10.1016/j.ijme.2017.10.002

49.

Teo

(2011). Technology acceptance Research in education. In Technology Acceptance in Education (pp. 1–5), Springer. https://doi.org/10.1007/978-94-6091-487-4_1

50.

Tey

T. C. Y.

Moses

(2018). UTAUT: Integrating achievement goals and learning styles for undergraduates’ behavioural intention to use technology. EAI Endorsed Transactions on E-Learning, 5(17), 155573. https://doi.org/10.4108/eai.25-9-2018.155573

51.

Thomas

T. D.

Singh

Gaffar

(2013). The utility of the UTAUT model in explaining mobile learning adoption inhigher education in Guyana. International Journal of Education and Development Using Information and Communication Technology(IJEDICT), 9(3), 71-89. https://www.researchgate.net/publication/259479186_The_Utility_of_the_UTAUT_Model_in_Explaining_Mobile_Learning_Adoption_in_Higher_Education_in_Guyana.

52.

UNESCO . (2020, March 5). Distance learning solutions. UNESCO. https://en.unesco.org/covid19/educationresponse/solutions

53.

United Nations . (2020). The impact of digital technologies. United Nations. Www.un.org. https://www.un.org/en/un75/impact-digital-technologies

54.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

55.

Venkatesh

Thong

(2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428

56.

Venkatesh

Thong

J. Y. L.

(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412

57.

Wang

Y.-S.

Shih

Y.-W.

(2009). Why do people use information kiosks? A validation of the unified theory of acceptance and use of technology. Government Information Quarterly, 26(1), 158–165. https://doi.org/10.1016/j.giq.2008.07.001

58.

Wangpipatwong

(2008, June 30). Factors influencing the intention to use E-learning: A Case Study of Bangkok University. Www.learntechlib.org; Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/29227/

59.

Zhang

Kim

Xing

(2020). Image congruence between sports event and host city and its impact on attitude and behavior intention. International Journal of Sports Marketing and Sponsorship, 22(1), 67–86. https://doi.org/10.1108/ijsms-03-2020-0040

60.

Zuiderwijk

Janssen

Dwivedi

Y. K.

(2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly, 32(4), 429–440. https://doi.org/10.1016/j.giq.2015.09.005