College Learning From Classrooms to the Internet: Adoption of the YouTube as Supplementary Tool in COVID-19 Pandemic Environment

Abstract

To prevent the further spread of COVID-19, many teachings were transferred to the Internet. This study unearths the motivations to participate in YouTube as a supplementary learning resource among college students to fight COVID-19. Using an integration and development of TAM and TTF model with 302 registered YouTube students completed an online survey that helped understanding user behavior. Employing a structural equation model on the sample indicated that behavioral intention was significantly influenced by perceived ease of use, perceived usefulness, user satisfaction, and YouTube self-efficacy. However, task technology fit and content richness were not significantly predictive of behavioral intention. Moreover, perceived ease of use and YouTube self-efficacy also was not significantly predictive perceived usefulness. The implications of integrating and development of TAM and TTF with content richness and YouTube self-efficacy model are discussed.

Keywords

computer-mediated communication interactive learning environments learning communities COVID-19

Get full access to this article

View all access options for this article.

References

Agarwal

Prasad

(1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.

Asanov

Flores

McKenzie

Mensmann

Schulte

(2021). Remote-learning, time-use, and mental health of Ecuadorian high-school students during the COVID-19 quarantine. World Development, 138, 105225. https://doi.org/10.1016/j.worlddev.2020.105225

Bandura

(1986). Social foundations of thought and action: A social cognitive theory. Journal of Applied Psychology, 12(1), 169.

Bandura

Freeman

W. H.

Lightsey

(1999). Self-efficacy: The exercise of control. Journal of Cognitive Psychotherapy, 13(2), 158–166.

Bhattacherjee

(2001). Understanding information systems continuance: An expectation-confirmation model. Society for Information Management and The Management Information Systems Research Center.

Chen

H. L.

Gilchrist

S. B.

(2013). Online access to higher education on YouTubeEDU. New Library World, 114(3/4), 99–109.

Chintalapati

Daruri

V. S. K.

(2017). Examining the use of YouTube as a learning resource in higher education: Scale development and validation of TAM model. Telematics and Informatics, 34, 853–860.

Cho

(2013). YouTube and academic libraries: Building a digital collection. Journal of Electronic Resources Librarianship, 25(1), 39–50.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

10.

De Wulf

Schillewaert

Muylle

Rangarajan

. (2006). The role of pleasure in web site success. Information Management, 43(4), 434–446.

11.

Dishaw

M. T.

Strong

D. M.

(1999). Extending the technology acceptance model with task–technology fit constructs. Information Management, 36(1), 9–21.

12.

Doll

W. J.

Torkzadeh

(1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259–274.

13.

Everson

Gundlach

Miller

(2013). Social media and the introductory statistics course. Computers in Human Behavior, 29(5), A69–A81.

14.

Fishbein

Ajzen

(1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2), 130–132.

15.

Gefen

Karahanna

Straub

D. W.

(2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.

16.

Goodhue

D. L.

(1995). Understanding user evaluations of information systems. Management Science, 41, 1827–1844.

17.

Goodhue

D. L.

Thompson

R. L.

(1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

18.

Hrastinski

Aghaee

N. M.

(2012). How are campus students using social media to support their studies? An explorative interview study. Education and Information Technologies, 17(4), 451–464.

19.

Hsu

C. L.

Lin

J. C. C.

(2015). What drives purchase intention for paid mobile apps? – An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1), 46–57.

20.

Hui

P. J. H.

Clark

T. H.

Tam

K. Y.

Milton

(2008). Technology-assisted learning: A longitudinal field study of knowledge category, learning effectiveness and satisfaction in language learning. Journal of Computer Assisted Learning, 24(3), 245–259.

21.

Igbaria

Iivari

. (2009). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605.

22.

Johnson

S. D.

Aragon

S. R.

Shaik

Palma-Rivas

(2000). Comparative analysis of learner satisfaction and learning outcomes in online and face-to-face learning environments. Journal of Interactive Learning Research, 11(1), 29–49.

23.

Jung

Perez-Mira

Wiley-Patton

(2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123–129.

24.

Junglas

Abraham

Watson

R. T.

(2008). Task-technology fit for mobile locatable information systems. Decision Support Systems, 45(4), 1046–1057.

25.

Kang

Y. S.

Hong

Lee

(2009). Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in Human Behavior, 25(1), 111–122.

26.

Khan

M. L.

(2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236–247.

27.

Lane

A. M.

Kyprianou

(2004). Self-Efficacy, self-esteem and their impact on academic performance. Social Behavior & Personality: An International Journal, 32(3), 247–256.

28.

Lee

D. Y.

Lehto

M. R.

(2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61(1), 193–208.

29.

Lee

M. C.

(2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141.

30.

Lee

Y. C.

(2015). An empirical investigation into factors influencing the adoption of an e-learning system. Bioinformatics, 31(11), 1701–1707.

31.

Lee

Y.-H.

Hsieh

Y.-C.

Chen

Y.-H.

(2013). An investigation of employees’ use of e-learning systems: Applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173–189.

32.

Liao

C. H.

Huang

W. L.

(2009). Community adaptability, computer and internet self-efficacy, and intention of blended e-learning. International Journal of Society Systems Science, 1(3), 209–226.

33.

Liaw

S. S.

(2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the blackboard system. Computers & Education, 51(2), 864–873.

34.

Limayem

Hirt

S. G.

Chin

W. W.

(2001). Intention does not always matter: The contingent role of habit on IT usage behavior. ECIS 2001 Proceedings, 56.

35.

Lin

T. C.

Huang

C. C.

(2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information Management, 45(6), 410–417.

36.

Lowe

Laffey

(2011). Is Twitter for the birds? Using Twitter to enhance student learning in a marketing course. Journal of Marketing Education, 33(2), 183–192.

37.

Marakas

G. M.

M. Y.

Johnson

R. D.

(1998). The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information Systems Research, 9(2), 126–163.

38.

Moos

D. C.

Azevedo

(2009). Learning with computer-based learning environments: A literature review of computer self-efficacy. Review of Educational Research, 79(2), 576–600.

39.

Morris

Anderson

(2015). ‘Charlie is so cool like’: Authenticity, popularity and inclusive masculinity on YouTube. Sociology, 49(6), 1200–1217.

40.

Ong

C. S.

Lai

J. Y.

Wang

Y. S.

(2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information Management, 41(6), 795–804.

41.

Oum

Han

(2011). An empirical study of the determinants of the intention to participate in user-created contents (UCC) services. Expert Systems With Applications, 38(12), 15110–15121.

42.

Park

Roman

Lee

Chung

J. E.

(2009). User acceptance of a digital library system in developing countries: An application of the technology acceptance model. International Journal of Information Management, 29(3), 196–209.

43.

Rawstorne

Jayasuriya

Caputi

(1998). An integrative model of information systems use in mandatory environments [Paper presentation]. International Conference on Information Systems, Helsinki, Finland.

44.

Ryu

M. H.

Kim

Lee

(2009). Understanding the factors affecting online elderly user’s participation in video UCC services. Computers in Human Behavior, 25(3), 619–632.

45.

Schunk

D. H.

(1983). Reward contingencies and the development of children’s skills and self-efficacy. Journal of Education & Psychology, 75(4), 511–518.

46.

Schunk

D. H.

Pintrich

P. R.

Meece

J. L.

(1995). Motivation in education: Theory, research, and applications. Routledge.

47.

Shang

R. A.

Chen

Y. C.

Chen

C. M.

(2007). Why people blog? An empirical investigations of the task technology fit model. PACIS 2007 Proceedings, 5.

48.

Shee

D. Y.

Wang

Y. S.

(2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education, 50(3), 894–905.

49.

Sun

P. C.

Tsai

R. J.

Finger

Chen

Y. Y.

Yeh

(2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202.

50.

Swanson

E. B.

(1988). Information system implementation: Bridging the gap between design and utilization. McGraw-Hill/Irwin.

51.

Szymanski

D. M.

Hise

R. T.

(2000). E-satisfaction: An initial examination. Journal of Retailing, 76(3), 309–322.

52.

Topps

Helmer

Ellaway

(2013). YouTube as a platform for publishing clinical skills training videos. Academic Medicine: Journal of the Association of American Medical Colleges, 88(2), 192–197.

53.

Vila

Kuster

(2011). Consumer feelings and behaviours towards well designed websites. Information Management, 48(4–5), 166–177.

54.

Wang

Butler

J. E.

(2007). Effects of adoption determinants in voluntary contexts on IS mandated usage. Journal of Information Science & Technology, 3(3), 5–23.

55.

Wang

Gray

P. H.

Meister

D. B.

(2014). Task-driven learning: The antecedents and outcomes of internal and external knowledge sourcing. Information Management, 51(8), 939–951.

56.

J. H.

Tennyson

R. D.

Hsia

T. L.

(2010). A study of student satisfaction in a blended e-learning system environment. Elsevier Science Ltd.

57.

Yang

Zeng

Wang

Wong

S.-S.

Liang

Zanin

Liu

Cao

Gao

Mai

Liang

Liu

Guan

Yang

Luo

, . . . He

. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165–174. https://doi.org/10.21037/jtd.2020.02.64

58.

Zhao

Huang

Wang

(2010). Internet inequality: The relationship between high school students’ Internet use in different locations and their Internet self-efficacy. Computers & Education, 55(4), 1405–1423.

59.

Zhou

Wang

(2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767.

60.

Zhu

Y. Q.

Chen

L. Y.

Chen

H. G.

Chern

C. C.

(2011). How does Internet information seeking help academic performance? – The moderating and mediating roles of academic self-efficacy. Computers & Education, 57(4), 2476–2484.

61.

Zimmerman

(1995). Self-efficacy and educational development. In Bandura

(Ed.), Self-efficacy in changing societies (pp. 202–231). Cambridge University Press.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.16 MB