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Impact of Digital Literacy on Intention to Use Technology for Online Distribution of Higher Education in Vietnam: A Study of Covid19 Context

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2022, v.20 no.6, pp.75-86
https://doi.org/https://doi.org/10.15722/jds.20.06.202206.75
LE, Thi Lan Huong
HOANG, Vu Hiep
HOANG, Mai Duc Minh
NGUYEN, Hong Phuc
BUI, Xuan Bach
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Abstract

Purpose: This research aims to provide empirical evidence on the impact of digital literacy on behavioural intention regarding using technology for distribution of higher education. Design, Methodology, and Approach: Quantitative analysis was carried out using Covariance-Based Structural Equation Model with data collected from 901 students who fully experienced 2-year study online at different universities in Vietnam. The structural model was built with digital literacy as the primary indicator and other variables were included based on modified version of Unified Theory of Acceptance and Use of Technology (UTAUT2) by adopting performance expectancy, effort expectancy, social influence, habit, and hedonic motivation variables specifically for education sector. Self-efficacy was added to eliminate possible bias in technology acceptance. Results: From the results of model estimation, digital literacy presented positive impact on the online distribution of higher education in Vietnam. The mediating effects of various indicators such as performance expectancy, effort expectancy, social influence, habit, hedonic motivation, and self-efficacy are significantly determined by research model. Conclusion: The higher level of digital literacy of the students, the more likely that they will use technology in higher education study, especially online learning. Additionally, the mediating effects of indicators from the UTAUT2 theoretical model were also evident to be positively significant.

keywords
Digital Literacy, Distribution of Education, Technology Acceptance, Structural Equation Modelling

Reference

1.

Aburub, F., & Alnawas, I. (2019). A new integrated model to explore factors that influence adoption of mobile learning in higher education: An empirical investigation. Education and Information Technologies, 24(3), 2145–2158. https://doi.org/10.1007/s10639-019-09862-x.

2.

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015). Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), 145-157. https://doi.org/10.1057/fsm.2015.5

3.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

4.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice- Hall, Inc.

5.

Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co.

6.

Bawden, D. (2001). Information and digital literacies: a review of concepts. Journal of documentation, 57(2), 218-259.

7.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (136-162). Newbury Park, CA: Sage.

8.

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge/Taylor & Francis Group.

9.

Cooper, J. (2006). The digital divide: the special case of gender. Journal of Computer Assisted Learning, 22(5), 320–334. https://doi.org/10.1111/j.1365-2729.2006.00185.x

10.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

11.

Escobar-Rodríguez, T. and Carvajal-Trujillo, E. (2013). Online drivers of consumer purchase of website airline tickets. Journal of Air Transport Management, 32, 58-64. https://doi.org/10.1016/j.jairtraman.2013.06.018

12.

Eshet-Alkalai, Y. (2004). Digital Literacy: A Conceptual Framework for Survival Skills in the Digital Era. Journal of Educational Multimedia and Hypermedia, 13(1), 93-106.

13.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

14.

Ghalandari, K. (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

15.

Ghavifekr, S., & Rosdy, W. A. W. (2015). Teaching and learning with technology: Effectiveness of ICT integration in schools. International Journal of Research in Education and Science, 1(2), 175–191.

16.

Gilster (1997). Digital Literacy. Wiley: Michigan, United States.

17.

Goncalves, G., Oliveira, T., and Jesus, F. (2018) Understanding individual-level digital divide: Evidence of an African country. Computers in Human Behaviour, 87, 276-291. https://doi.org/10.1016/j.chb.2018.05.039

18.

Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate Data Analysis (5th ed.) Prentice Hall, Upper Saddle River, New Jersey.

19.

Hair, J., Risher, J., Sarstedt, M., & Ringle, C. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

20.

Hasan, B., and Ahmed, M. (2010) A path analysis of the impact of application-specific perceptions of computer self-efficacy and anxiety on technology acceptance. Journal of Organisational and End User Computing, 22(3), 82-95. https://doi.org/10.4018/joeuc.2010070105

21.

Herrero, A., San-Martín, H., and Garcia-De los Salmones, M. (2017). Explaining the adoption of social networks sites for sharing user-generated content: a revision of the UTAUT2. Computers in Human Behavior, 71, 209–217. https://doi.org/10.1016/j.chb.2017.02.007

22.

Hu, L.-t., & Bentler, P. M. (1999). Cutoffcriteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

23.

Huang, J., Baptista, J., and Galliers, R. (2013). Reconceptualizing rhetorical practices in organizations: The impact of social media on internal communications. Information and Management. 50(2-3), 112-124. https://doi.org/10.1016/j.im.2012.11.003

24.

Huang, Y.-M. (2015). Exploring the factors that affect the intention to use collaborative technologies: The differing perspectives of sequential/global learners. Australasian Journal of Educational Technology, 31(3), 278-292. https://doi.org/10.14742/ajet.1868

25.

Huffman, A. H., Whetten, J., & Huffman, W. H. (2013). Using technology in higher education: The influence of gender roles on technology self-efficacy. Computers in Human Behavior, 29(4), 1779–1786. https://doi.org/10.1016/j.chb.2013.02.012.

26.

Jang, M., Aavakare, M., Nikou, S., & Kim, S. (2021). The impact of literacy on intention to use digital technology for learning: A comparative study of Korea and Finland. Telecommunications Policy, 45(7). DOI: https://doi.org/10.1016/j.telpol.2021.102154

27.

Jaradat, M.-I. R. M. (2011). Exploring the factors that affecting intention to use mobile learning. International Journal of Mobile Learning and Organisation, 5(2), 115–130. https://doi.org/10.1504/IJMLO.2011.041564.

28.

Kang, M., Liew, B., Lim, H., Jang, J., & Lee, S. (2015). Investigating the Determinants of Mobile Learning Acceptance in Korea Using UTAUT2. In: Chen, G., Kumar, V., Kinshuk, Huang, R., Kong, S. (eds) Emerging Issues in Smart Learning. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44188-6_29

29.

Kim, S., & Malhotra, N. (2005). A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Post-Adoption Phenomena. Management Science, 51(5), 741-755. https://doi.org/10.1287/mnsc.1040.0326

30.

Kline, R. B. (2011). Principles and practice of structural equation modelling (3rd ed.). Guilford Press.

31.

Knutsson, O., Blåsjö, M., Hållsten, S., & Karlström, P. (2012). Identifying Different Registers of Digital Literacy in Virtual Learning Environments. Internet and Higher Education, 15(4), 237-246. https://doi.org/10.1016/j.iheduc.2011.11.002

32.

Laakso, M. J., Kaila, E., & Rajala, T. (2018). ViLLE – collaborative education tool: Designing and utilizing an exercise-based learning environment. Education and Information Technologies, 23(4), 1655–1676. https://doi.org/10.1007/s10639-017-9659-1

33.

Limayem, M., Hirt, S., and Cheung, C. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 31(4), 705-737. https://doi.org/10.2307/25148817

34.

Lowenthal, J. (2010). Using Mobile Learning: Determinates Impacting Behavioral Intention. American Journal of Distance Education, 24(4), 195-206. https://doi.org/10.1080/08923647.2010.519947

35.

Marks, A., Al-Ali, M., Atassi, R., Abualkishik, A. Z., & Rezgui, Y. (2020). Digital Transformation in Higher Education: A Framework for Maturity Assessment. International Journal of Advanced Computer Science and Applications, 11(12), 504-513.

36.

Martin, A., & Grudziecki, J. (2006). DigEuLit: Concepts and Tools for Digital Literacy Development. Innovation in Teaching and Learning in Information and Computer Sciences, 5(4), 249-267. https://doi.org/10.11120/ital.2006.05040249

37.

Mohammadyari, S., and Singh, H. (2014). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers and Education, 82, 11-25. https://doi.org/10.1016/j.compedu.2014.10.025

38.

Morosan, C. and DeFranco, A. (2016). Co-creating value in hotels using mobile devices: A conceptual model with empirical validation. International Journal of Hospitality Management, 52, 131-142. https://doi.org/10.1016/j.ijhm.2015.10.004

39.

Ng, W. (2012). Can we teach digital natives digital literacy? Computers and Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016.

40.

Nikou, S., & Aavakara, M. (2021). An assessment of the interplay between literacy and digital Technology in Higher Education. Education and Information Technologies, 26(5), 1-23. https://doi.org/10.1007/s10639-021-10451-0.

41.

Nikou, S., Brännback, M., & Widén, G. (2018). The Impact of Multidimensionality of Literacy on the Use of Digital Technology: Digital Immigrants and Digital Natives. Well-Being In The Information Society. Fighting Inequalities, 117-133. https://doi.org/10.1007/978-3-319-97931-1_10

42.

Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self efficacy: Flow-on effects for online learning behavior. Internet and Higher Education, 29, 91–97. https://doi.org/10.1016/j.iheduc.2016.01.001.

43.

San Martin, H., & Herrero, A. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350. https://doi.org/10.1016/j.tourman.2011.04.003

44.

Sharif, A., & Raza, S. (2017). The Influence of Hedonic Motivation, Self-efficacy, Trust and Habit on Adoption of Internet Banking: A Case of Developing Country. International Journal of Electronic Customer Relationship Management, 11(1), 1-22. https://doi.org/10.1504/IJECRM.2017.086750

45.

Sung, H. N., Jeong, D. Y., Jeong, Y. S., & Shin, J. I. (2015). The Relationship among Self-Efficacy, Social Influence, Performance Expectancy, Effort Expectancy, and Behavioral Intention in Mobile Learning Service. International Journal of u- and e- Service, Science and Technology, 8(9), 197-206. http://dx.doi.org/10.14257/ijunesst.2015.8.9.21

46.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144

47.

Thompson, R.L., Higgins, C.A. and Howell, J.M. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly, 15(1), 125-143. http://dx.doi.org/10.2307/249443

48.

Ting, Y. L. (2015). Tapping into students’ digital literacy and designing negotiated learning to promote learner autonomy. Internet and Higher Education, 26, 25–32. https://doi.org/10.1016/j.iheduc.2015.04.004

49.

Venkatesh V, Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412.

50.

Venkatesh, V., 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.

51.

Yamane, T. (1973). Statistics: An Introductory Analysis. 3rd Edition, Harper and Row, New York.

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