The Impact of Using the Internet for Learning for Students with Technology Acceptance Model (TAM)
The development of the internet can provide convenience to humans in general, and students as one of the educational academics can provide changes in their life processes. The technique used in this study is Generalized Least Squares (GLS), which is proxied by using asymptotic covariance matrix data. The purpose of the study is to analyze the impact of the use of technology, in this case, is the internet in the learning process carried out by students. There are four constructs used in the TAM research, namely Perceived Ease of Use, Perceived Usefulness, Attitude Toward Using, and Actual Technology Usage. The research has three indicators for each latent variable. In this case, the latent variables are four. Then it can conclude that the number of samples used is 120. The sampling technique in this study uses Nonprobability sampling. The research hypothesis will test by analysis of SEM (Structural Equation Model) with the IBM AMOS (Analysis of Moment Structure) Program. The technique for using Generalized Least Squares (GLS), which is proxied by using the asymptotic covariance matrix data. From the results of the study, it found that this model has shown an overview of the aspects of the behavior of internet users that use for practical learning, where many users can efficiently operate the internet because it fits with what they need. Technology Acceptance Model (TAM) provides a reliable and straightforward explanation in accepting the technology and behavior of its users.
 S. Y. Park, “An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning.,” Educ. Technol. Soc., vol. 12, no. 3, pp. 150–162, 2009.
 M. Mistler‐Jackson and N. Butler Songer, “Student motivation and Internet technology: Are students empowered to learn science?,” J. Res. Sci. Teach. Off. J. Natl. Assoc. Res. Sci. Teach., vol. 37, no. 5, pp. 459–479, 2000.
 E. K. Bailey and M. Cotlar, “Teaching via the Internet,” Commun. Educ., vol. 43, no. 2, pp. 184–193, 1994.
 W. Aspray and P. E. Ceruzzi, The internet and American business. The MIT Press, 2010.
 D. E. Comer and R. E. Droms, Computer networks and internets. Prentice-Hall, Inc., 2003.
 S. Keshav and S. Kesahv, An engineering approach to computer networking: ATM networks, the Internet, and the telephone network, vol. 1. Addison-Wesley Reading, 1997.
 G. R. Cobine, Studying with the Computer. ERIC Clearinghouse on Reading, English, and Communication, Indiana University, 1997.
 J. M. Roschelle, R. D. Pea, C. M. Hoadley, D. N. Gordin, and B. M. Means, “Changing how and what children learn in school with computer-based technologies,” Futur. Child., pp. 76–101, 2000.
 D. N. Gordin, L. M. Gomez, R. D. Pea, and B. J. Fishman, “Using the World Wide Web to build learning communities in K-12,” J. Comput. Commun., vol. 2, no. 3, p. JCMC233, 1996.
 F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Q., pp. 319–340, 1989.
 J. Y. H. Lee and N. Panteli, “Business strategic conflict in computer-mediated communication,” Eur. J. Inf. Syst., vol. 19, no. 2, pp. 196–208, 2010.
 V. Venkatesh and F. D. Davis, “A theoretical extension of the technology acceptance model: Four longitudinal field studies,” Manage. Sci., vol. 46, no. 2, pp. 186–204, 2000.
 G. D. Garson, Structural Equation Modeling, Blue Book. Asheboro, North Corolina: Statistical Associates Publishing, 2012.
 J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. Harlow, England: Pearson New International Edition, 2014.
 R. L. Scheaffer and N. Yes, “Categorical data analysis,” NCSSM Stat. Leadersh. Inst., 1999.
 P. A. Glasow, “Fundamentals of survey research methodology,” Retrieved January, vol. 18, p. 2013, 2005.
 J. R. Fraenkel and N. E. Wallen, How to Design and Evaluate Research in Education, 7th ed. New York: McGraw-Hill Higher Education, 2009.
 K. G. Jöreskog and D. Sörbom, LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International, 1993.
 K. G. Jöreskog and D. Sörbom, LISREL 8: User’s reference guide. Scientific Software International, 1996.
 K. G. Jöreskog, “Censored variables and censored regression,” 2002.
 K. G. Jöreskog, “Testing structural equation models,” Sage Focus Ed., vol. 154, p. 294, 1993.
 R. E. Schumacher and R. G. Lomax, A Beginner’s Guide to Structural Equation Modeling: Third Edition, 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates, 2010.
 F. Chen, P. J. Curran, K. A. Bollen, J. Kirby, and P. Paxton, “An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models,” Sociol. Methods Res., vol. 36, no. 4, pp. 462–494, 2008.
 J. S. Tanaka and G. J. Huba, “A general coefficient of determination for covariance structure models under arbitrary GLS estimation,” Br. J. Math. Stat. Psychol., vol. 42, no. 2, pp. 233–239, 1989.
 L. R. Tucker and C. Lewis, “A reliability coefficient for maximum likelihood factor analysis,” Psychometrika, vol. 38, no. 1, pp. 1–10, 1973.
 P. M. Bentler and L. T. Hu, “Evaluating model fit,” in Structural equation modeling: Concepts, issues, and applications, Thousand Oaks, CA: SAGE Publications, 1995, pp. 76–99.
 C. C. DiClemente and J. O. Prochaska, “Self-change and therapy change of smoking behavior: A comparison of processes of change in cessation and maintenance,” Addict. Behav., vol. 7, no. 2, pp. 133–142, 1982.
 S. A. Mulaik, L. R. James, J. Van Alstine, N. Bennett, S. Lind, and C. D. Stilwell, “Evaluation of goodness-of-fit indices for structural equation models.,” Psychol. Bull., vol. 105, no. 4, pp. 430–445, 1989.
 S. Sharma, Applied Multivariate Techniques. New York, USA: John Wiley & Sons, Inc., 1996.
 E. G. Carmines, “Analyzing models with unobserved variables,” Soc. Meas. Curr. issues, vol. 80, 1981.
 B. Wheaton, B. Muthen, D. F. Alwin, and G. F. Summers, “Assessing reliability and stability in panel models,” Sociol. Methodol., vol. 8, pp. 84–136, 1977.
 K. Mathieson, “Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior,” Inf. Syst. Res., vol. 2, no. 3, pp. 173–191, 1991.
 F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User acceptance of computer technology: a comparison of two theoretical models,” Manage. Sci., vol. 35, no. 8, pp. 982–1003, 1989.
 D. A. Adams, R. R. Nelson, and P. A. Todd, “Perceived usefulness, ease of use, and usage of information technology: A replication,” MIS Q., pp. 227–247, 1992.
 G. Rigopoulos and D. Askounis, “A TAM Framework to Evaluate User’s Perception towards Online Electronic Payments,” J. Internet Bank. Commer., vol. 12, no. 3, pp. 1–6, 1970.
 D. Gefen, E. Karahanna, and D. W. Straub, “Trust and TAM in online shopping: an integrated model,” MIS Q., vol. 27, no. 1, pp. 51–90, 2003.
 Y. M. Yusoff, Z. Muhammad, M. S. M. Zahari, E. S. Pasah, and E. Robert, “Individual differences, perceived ease of use, and perceived usefulness in the e-Library usage,” Comput. Inf. Sci., vol. 2, no. 1, pp. 76–83, 2009.
 N. Yahyapour, “Determining factors affecting intention to adopt banking recommender system: case of Iran.” 2008.
 K. Eriksson, K. Kerem, and D. Nilsson, “Customer acceptance of internet banking in Estonia,” Int. J. bank Mark., vol. 23, no. 2, pp. 200–216, 2005.
 N. Jahangir and N. Begum, “The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking,” African J. Bus. Manag., vol. 2, no. 2, pp. 32–40, 2008.
 B. Rajeev, J. G. Myers, and D. A. Aaker, Advertising Management. New Jersey, 2008.
 J.-W. Lee, “Online support service quality, online learning acceptance, and student satisfaction,” Internet High. Educ., vol. 13, no. 4, pp. 277–283, 2010.
 K. Davis and J. W. Newstrom, Human behavior at work: Organizational behavior. McGraw-Hill, 1989.
 C.-T. Lu, S.-Y. Huang, and P.-Y. Lo, “An empirical study of on-line tax filing acceptance model: Integrating TAM and TPB,” African J. Bus. Manag., vol. 4, no. 5, pp. 800–810, 2010.
 I. Ajzen, “The theory of planned behavior,” Orgnizational Behav. Hum. Decis. Process., vol. 50, no. 2, pp. 179–211, 1991.
 M. Thomas, Pedagogical Considerations and Opportunities for Teaching and Learning on the Web. IGI Global, 2013.
 W. H. Delone and E. R. McLean, “The DeLone and McLean model of information systems success: a ten-year update,” J. Manag. Inf. Syst., vol. 19, no. 4, pp. 9–30, 2003.
 H. C. Lucas Jr and V. Spitler, “Implementation in a world of workstations and networks,” Inf. Manag., vol. 38, no. 2, pp. 119–128, 2000.
 A. L. Lederer, D. J. Maupin, M. P. Sena, and Y. Zhuang, “The technology acceptance model and the World Wide Web,” Decis. Support Syst., vol. 29, no. 3, pp. 269–282, 2000.
 C. Dowling, Writing and Learning with Computers. ERIC, 1999.
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