The Determinants of Senior Students' Behavioral Intention on the Blended E-Learning for the Ceramics Teaching Course at the Active Aging University

In this paper, the authors try to investigate the
determinants of behavioral intention of the blended E-learning course
for senior students at the Active Ageing University in Taiwan. Due to
lower proficiency in the use of computers and less experience on
learning styles of the blended E-learning course for senior students
will be expected quite different from those for most young students.
After more than five weeks course for two years the questionnaire
survey is executed to collect data for statistical analysis in order to
understand the determinants of the behavioral intention for senior
students. The object of this study is at one of the Active Ageing
University in Taiwan total of 84 senior students in the blended
E-learning for the ceramics teaching course. The research results show
that only the perceived usefulness of the blended E-learning course has
significant positive relationship with the behavioral intention.

• Horng-Jyh Chen
• Yi-Fang Chen
• Chien-Liang Lin

• Active Aging University
• blended E-learning
• ceramics teaching course
• behavioral intention

[1] OEPD, Executive Yuan.（2010）. Estimation Report of Populations in
Taiwan 2010~2060,Retrieved ,1, 2, 2013, from
http://www.cepd.gov.tw/m1.aspx?sNo=0000455
[2] Y.-S.Kim, & Merriam, S. B.(2010). Situated learning and identity
development in a Korean older adults’ computer classroom. Adult
Education Quarterly, 60(5), 438-455.
[3] C.C.Chang, Yan, C.F., & Tseng, J.S. (2012). Perceived convenience in
anextended technology acceptance model: Mobile technology and
English learningfor college students. Australasian Journal of Educational
Technology, 28(5),809-826.
[4] B.Cheng, Wang, M., & Moormann, J. (2012). The Effects of
Organizational Learning Environment Factors on E-Learning
Acceptance. Computers and Education, 58(3), 885-899.
[5] K.-M. Lin,(2011). e-Learning continuance intention: Moderating effects
of user e-learning experience. Computers and Education, 56, 515-526.
[6] T.Teo, (2009). Modelling technology acceptance in education: A study of
pre-service teachers. Computers and Education, 52(1), 302-312.
[7] Davis, F. D., Bagozzi, R. P., ＆ Warshaw, P. R. (1989). User acceptance
of computer technology: A comparison of two theoretical models.
Management Science, 35(8), 982-1003.
[8] T.Ahn, Ryu, S., ＆ Han, I. (2007).The impact of web quality and
playfulness on user acceptance of online retailing. Information and
Management, 44(3), 263-275.
[9] S.-Y.Park, (2009). An Analysis of the Technology Acceptance Model in
Understanding University Students' Behavioral Intention to Use
e-Learning. Educational Technology and Society, 12(3), 150-162.
[10] Tourangeau,R. Rips, L.J. and Rasinski, K. (2000), "The psychology of
survey response,” CambridgeUniversity Press, Cambridge.
[11] P.M. Podsakoff, MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003),
"Common method biasesin behavioral research: a critical review of the
literature and recommended remedies,”Journal of Applied
Psychology,Vol. 88 No. 5, pp. 879-903.
[12] W.-W. Chin, (1998). Issues and opinion on structural equation modeling.
MIS Quarterly, 22(1), VII-XVI.
[13] D.Gefen, Karahanna, E., ＆ Straub, D.W. (2003). Trust and TAM in
online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.
[14] D.Gefen, Straub, D. W., & Boudreau, M. C. (2000). Structural equation
modeling and regression: Guidelines for research practives.
Communication of the Association for Information Systems, 7(4), 1-79.
[15] W.-W.Chin, Marcolin, B. L., & Newsted, P. R. (2003). A Partial Least
Squares Latent Variable Modeling Approach for Measuring Interaction
Effects: Results from a Monte Carlo Simulation Study and an
Electronic-Mail Emotion/Adoption Study. Information Systems
Research, 14(2), 189-217.