An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning

Many universities implement e-learning for various reasons. It is obvious that the number of e-learning opportunities provided by higher educational institutes continues to grow in Korea. Yet little research has been done to verify the process of how university students adopt and use e-learning. A sample of 628 university students took part in the research. The structural equation modeling (SEM) technique was employed with the LISREL program to explain the adoption process. The general structural model, which included e-learning selfefficacy, subjective norm, system accessibility, perceived usefulness, perceived ease of use, attitude, and behavioral intention to use e-learning, was developed based on the technology acceptance model (TAM). The result proved TAM to be a good theoretical tool to understand users’ acceptance of e-learning. E-learning selfefficacy was the most important construct, followed by subjective norm in explicating the causal process in the model.

[1]  N. Hara.  STUDENT DISTRESS IN A WEB-BASED DISTANCE EDUCATION COURSE , 2000 .

[2]  Judy Chuan-Chuan Lin,et al.  Towards an understanding of the behavioural intention to use a web site , 2000, Int. J. Inf. Manag..

[3]  Hyun-Jeong Lee,et al.  International Review: Creating a Knowledge-Based Society Through E-Learning in Korea , 2006 .

[4]  Keenan A. Pituch,et al.  The influence of system characteristics on e-learning use , 2006, Comput. Educ..

[5]  Raafat George Saadé,et al.  Viability of the "Technology Acceptance Model" in Multimedia Learning Environments: A Comparative Study , 2007 .

[6]  Mark Campbell,et al.  What campus-based students think about the quality and benefits of e-learning , 2005, Br. J. Educ. Technol..

[7]  R. P. McDonald,et al.  Goodness-of-fit indexes in confirmatory factor analysis : The effect of sample size , 1988 .

[8]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[9]  Venkateshviswanath,et al.  A Theoretical Extension of the Technology Acceptance Model , 2000 .

[10]  Charles D. Barrett.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[11]  John Ingham,et al.  Why do people use information technology? A critical review of the technology acceptance model , 2003, Inf. Manag..

[12]  Su-Houn Liu,et al.  APPLYING THE TECHNOLOGY ACCEPTANCE MODEL AND FLOW THEORY TO ONLINE E-LEARNING USERS' ACCEPTANCE BEHAVIOR , 2005 .

[13]  Karl G. Jöreskog,et al.  LISREL 7: A guide to the program and applications , 1988 .

[14]  Byung-Ro Lim,et al.  The Current Status of E-learning and Strategies to Enhance Educational Competitiveness in Korean Higher Education , 2007 .

[15]  Hassan M. Selim,et al.  An empirical investigation of student acceptance of course websites , 2003, Comput. Educ..

[16]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[17]  Daniel W. Surry,et al.  A model for integrating instructional technology into higher education , 2005, Br. J. Educ. Technol..

[18]  Khaled A. Alshare,et al.  Factors influencing student intention to adopt online classes: a cross-cultural study , 2005 .

[19]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[20]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[21]  D. Schunk,et al.  Self-Efficacy and Academic Motivation , 1991 .

[22]  Kenneth A. Bollen,et al.  Overall Fit in Covariance Structure Models: Two Types of Sample Size Effects , 1990 .

[23]  Raafat George Saadé,et al.  Web-Based Educational Information System for Enhanced Learning, EISEL: Student Assessment , 2003, J. Inf. Technol. Educ..

[24]  Irwin G. Sarason,et al.  Social support: An interactional view. , 1990 .

[25]  Alex Koohang,et al.  Learners' Perceptions toward the Web-based Distance Learning Activities/Assignments Portion of an Undergraduate Hybrid Instructional Model , 2003, J. Inf. Technol. Educ..

[26]  Nelson Oly Ndubisi,et al.  Factors of Online Learning Adoption: A Comparative Juxtaposition of the Theory of Planned Behaviour and the Technology Acceptance Model. , 2006 .

[27]  Matthew K. O. Lee,et al.  Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation , 2005, Inf. Manag..

[28]  Kar Yan Tam,et al.  Understanding User Acceptance of Digital Libraries: What are the Roles of Interface Characteristics, Organizational Context, and Individual Differences? , 2002, Int. J. Hum. Comput. Stud..

[29]  Mun Y. Yi,et al.  Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model , 2003, Int. J. Hum. Comput. Stud..

[30]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[31]  J. Hoelter.  The Analysis of Covariance Structures , 1983 .

[32]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[33]  I. Ajzen.  The theory of planned behavior , 1991 .

[34]  Rowena Santiago,et al.  Construction of E-learning environments in Korea , 2005 .

[35]  Terry Anderson,et al.  The Theory and Practice of Online Learning , 2009 .

[36]  Fred D. Davis,et al.  A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .

[37]  Thomas G. Reio,et al.  If E-Learners Get There, Will They Stay? The Role of E-Learning Self-Efficacy. , 2005 .

[38]  A. Bandura.  Self-efficacy. I: V. S. Ramachandran (red.), Encyclopedia of human behavior (vol. s. ). New York: Academic Press. , 1994 .

[39]  Terri Gullickson,et al.  Encyclopedia of human behavior , 1995 .

[40]  Fred D. Davis.  A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[41]  M. Newcomb,et al.  What structural equation modeling can tell us about social support. , 1990 .

[42]  Dennis F. Galletta,et al.  Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[43]  M. Sobel.  Direct and Indirect Effects in Linear Structural Equation Models , 1987 .