The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan

The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan

The utilization of advanced network technologies and modern computer applications in distance learning raises the importance of distance learning system in the delivery of learning materials and resources to remote trainees. This innovation offers the organizations and their employees an opportunity...

Full description

Saved in:
Bibliographic Details
Main Author: Alrawashdeh, Thamer A.
Format: Thesis
Language:eng
eng
Published: 2011
Subjects:
HF5549-5549.5 Personnel Management
Employment
Online Access:https://etd.uum.edu.my/2798/1/Thamer_A._Alrawashdeh.pdf
https://etd.uum.edu.my/2798/2/1.Thamer_A._Alrawashdeh.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uum-etd.2798
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Ibrahim, Huda
Mohd Yusof, Shafiz Affendi
topic HF5549-5549.5 Personnel Management
Employment
spellingShingle HF5549-5549.5 Personnel Management
Employment
Alrawashdeh, Thamer A.
The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan
description The utilization of advanced network technologies and modern computer applications in distance learning raises the importance of distance learning system in the delivery of learning materials and resources to remote trainees. This innovation offers the organizations and their employees an opportunity to solve the problems associated with traditional training methods. In this respect, the acceptance of computer based distance training system (CBDTS) is considered critical in determining the success of its implementation. However, the number of studies that have been conducted to examine the acceptance of distance training system by employees of public sector organizations in the Kingdom of Jordan is very limited. It is also questionable whether the information system acceptance models that have been previously developed can be used to examine the acceptance of CBDTS by public sector employees in Jordan. Questions are also raised to the idea that perhaps there may be other factors that play important roles in this context. The main objectives of this study therefore are to determine the factors that lead to the acceptance of public sector employees on computer-based distance training system and finally to propose a model of technology acceptance of computer-based distance training system by public sector employees. A total of 600 questionnaires were distributed through a survey to public sector employees in Jordan. The study received about 386 responses, which represents 64.3% returned rate. Structural equation model (SEM) was used with AMOS version 16.0 to analyze the data. The findings indicate that six core determinants, namely, performance expectancy, effort expectancy, system flexibility, system enjoyment, social influence, and facilitating conditions significantly influenced employee intention to use distance training system. Five core determinants; system interactivity, system enjoyment, computer anxiety, computer self efficacy, and facilitating conditions significantly determine effort expectancy while only four of them including system interactivity, system enjoyment, computer anxiety, and effort expectancy significantly determine performance expectancy. Consequently, based on these findings, the final research model known as computer-based distance training acceptance model (CBDTAM) is proposed to explain and predict public sector employee’s intention in using computer-based distance training system. A comprehensive understanding of this model will assist decision makers to identify the reasons for the acceptance or resistance of computer based distance training system among public sector employees in the future and finally to support them to enhance the system’s acceptance and usage.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Alrawashdeh, Thamer A.
author_facet Alrawashdeh, Thamer A.
author_sort Alrawashdeh, Thamer A.
title The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan
title_short The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan
title_full The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan
title_fullStr The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan
title_full_unstemmed The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan
title_sort extended utaut acceptance model of computer-based distance training system among public sector's employees in jordan
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2011
url https://etd.uum.edu.my/2798/1/Thamer_A._Alrawashdeh.pdf
https://etd.uum.edu.my/2798/2/1.Thamer_A._Alrawashdeh.pdf
_version_ 1747827431812628480
spelling my-uum-etd.27982022-04-12T00:17:06Z The Extended UTAUT Acceptance Model of Computer-Based Distance Training System Among Public Sector's Employees in Jordan 2011 Alrawashdeh, Thamer A. Ibrahim, Huda Mohd Yusof, Shafiz Affendi Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Sciences HF5549-5549.5 Personnel Management. Employment The utilization of advanced network technologies and modern computer applications in distance learning raises the importance of distance learning system in the delivery of learning materials and resources to remote trainees. This innovation offers the organizations and their employees an opportunity to solve the problems associated with traditional training methods. In this respect, the acceptance of computer based distance training system (CBDTS) is considered critical in determining the success of its implementation. However, the number of studies that have been conducted to examine the acceptance of distance training system by employees of public sector organizations in the Kingdom of Jordan is very limited. It is also questionable whether the information system acceptance models that have been previously developed can be used to examine the acceptance of CBDTS by public sector employees in Jordan. Questions are also raised to the idea that perhaps there may be other factors that play important roles in this context. The main objectives of this study therefore are to determine the factors that lead to the acceptance of public sector employees on computer-based distance training system and finally to propose a model of technology acceptance of computer-based distance training system by public sector employees. A total of 600 questionnaires were distributed through a survey to public sector employees in Jordan. The study received about 386 responses, which represents 64.3% returned rate. Structural equation model (SEM) was used with AMOS version 16.0 to analyze the data. The findings indicate that six core determinants, namely, performance expectancy, effort expectancy, system flexibility, system enjoyment, social influence, and facilitating conditions significantly influenced employee intention to use distance training system. Five core determinants; system interactivity, system enjoyment, computer anxiety, computer self efficacy, and facilitating conditions significantly determine effort expectancy while only four of them including system interactivity, system enjoyment, computer anxiety, and effort expectancy significantly determine performance expectancy. Consequently, based on these findings, the final research model known as computer-based distance training acceptance model (CBDTAM) is proposed to explain and predict public sector employee’s intention in using computer-based distance training system. A comprehensive understanding of this model will assist decision makers to identify the reasons for the acceptance or resistance of computer based distance training system among public sector employees in the future and finally to support them to enhance the system’s acceptance and usage. 2011 Thesis https://etd.uum.edu.my/2798/ https://etd.uum.edu.my/2798/1/Thamer_A._Alrawashdeh.pdf text eng public https://etd.uum.edu.my/2798/2/1.Thamer_A._Alrawashdeh.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Abbad, M. M., Morris, D., & Nahlik, C. (2009). Looking under the bonnet: Factors affecting student adoption of e-learning systems in Jordan. International Review of Research in Open and Distance Learning, 10(2), 1492-383. Advancelearning (2008). ICDL in the Middle East. Retrieved May 20, 2009, from:eu.advancelearning.com/solutions/icdl/middle-east/ Agarwal, R., Sambamurthy, V., & Stair, R. (2000). Research report: The evolving relationship between general and specific computer self-efficacy - An empirical assessment. Information Systems Research, 11(4), 418-430. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human Decision Processes. 50(2), 179-211. Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888- 918. Ajzen, I., & Fishbein, M. A. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley: Addison-Wesley publishing. Al-ammari, J., & Hamad, S. (2008). Factor influencing the adoption of e-learning at UOB. Retrieved on June 20, 2009, fom: http://www.uqu.edu.sa/page/ar/2643#E-Commerce_&_E-Learning. Alavi, M.A., & Yoo, Y. (1997). Using information technology to add value to management education. Academy of Management Journal, 40(6), 1310-1333. Al-Harby, F., Qahwaji, R., & Kamala, M. (2010). Users' acceptance of secure biometrics authentication system: Reliability and validate of an extended UTAUT model. Communications in Computer and Information Science, 87(1), 254-258. Anderson, J. E., & Schwager, P. H. (2004). SME adoption of wireless LAN technology: Applying the UTAUT model. Proceedings of the 7th Annual Conference of the Southern Association for Information Systems. Angel, D., Scott, D., Gail, D., & Olga, N. (2004). Distance learning in postsecondary career and technical education: Comparison of achievement in online VS - On campus CTE courses. National Research Center for Career and Technical Education, USA. Arafeh, S. (2004). The implications of information and communications technologies for distance education: Looking toward the future. American Institutes for Research under Subcontract to SRI International. Arbuckle, J. L. (2005). AMOS 6.0 guide. SPSS: Chicago. Ashby, C. (2002). Distance education, growth in distance education programs and implications for federal education policy. Statement by the director of education, workforce and income security issues to the US senate committee on health, education, labor and pensions. Asif, A. (2004). Multimedia and cooperative learning in signal processing techniques in communications. IEEE Signal Processing Letters, 11(2), 278-28. Barki, H., & Benbasat, I. (1996). Contribution of the theory of reasoned action prove to study of information system foundation, empirical research, and extension. 4th European conference on information system. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182. Behling, K., Orczyk, J., & Jenkins, J. (2007). Live distance learning delivery of Master of Science courses in building construction management. 37th ASEE/IEEE Frontiers in Education Conference. Bill, L. (2002). Growing number of employers jump on e-learning bandwagon. Retrieved on April 10, 2009, from: http://www.allbusiness.com/human-resources/careers-job-training/639116-1.html Bodain, Y., & Robert, J. (2000). Investigating distance learning on the internet. INET 2000 Conference. Bollen, K. A., & Laing, J. (1988). Some properties of Hoelter’s CN. Sociological Methods and Research, 16, 492-503. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit in structural equation models. Sociological Methods and Research, 21(2), 205-229. Bollen, P. M. (1998). Structural equations with latent variables. New York: Wiley. Bouras, C., Destounis, P., Garofalakis, J., Gkamas, A., Sakalis, G., Sakkopoulos, E., Tsaknakis, J. and Tsiatsos, Th. (2000). Efficient web-based open and distance learning services. Telematics and informatics, 17(3), 213-237. Bouras, C., Gkamas, A., Kapoulas, V., Lampsas, P., & Tsiatsos, T. (1998). A platform for the implementation of the services of an educational network. 15th IFIP World Computer Congress TELETEACHING'98 in Vienna and Budapest. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In A. Kenneth, & J. Bollen. Testing structural equation models ( pp.136–162). California: SAGE Publication, Inc. Burgess, J. R. D., & Russell, J.E.A. (2003). The effectiveness of distance learning initiatives in organizations. Journal of Vocational Behavior, 63(2), 289–303. Byrne, B. M. (2006). Structural equation modeling with EQS: Basic concepts, applications and programming. New Jersey: Lawrence Erlbaum. Cairncross, F. (2003). The company of the future: Meeting the management challenges of the communications revolution. London: Profile Books. Campbell, R., Eisenbarth, S., Skurla, C. (2007). Electronic content development for engineering distance learning. 37th ASEE/IEEE Frontiers in Education Conference, 18-22. Castro, M. (1998). Technology innovation and integration in distance education. Electro technical Conference, 1,164-168. Castro, M., Clara, M., & John, S. (2001). Examples of distance learning projects in the european community. IEEE Transactions on Education, 44(4), 406 - 411. Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, E. (2009). Investigating Greek employees’ intention to use web-based training. Computers & Education, 53(3), 877–889. Chau, P. (2001). Influence of computer attitudes and self-efficacy on IT usage behavior. Journal of End User Computing, 13(1), 26-34. Chau, P. Y. K., & Hu, P. J. H. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. Chau, P., & Hu, P. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems, 18, 191-229. CHEA (Council for Higher Education Accreditation) (2002). Accreditation and assuring quality in distance learning. Retrieved on May, 18, 2009, from: http://www.chea.org/pdf/mono_1_accred_distance_02.pdf. Chesney, T. (2008). An acceptance model for useful and fun information system. An Interdisciplinary Journal on Humans in ICT Environments, 2(2), 225–235. Chou, C. (2002). A comparative content analysis of student interaction in synchronous and asynchronous learning networks. Proceedings of the 35th Hawaii International Conference on System Sciences, 5, 134. Christina, K. (2007). Technology acceptance in academic organizations: implementation of virtual learning environments. 14th European Conference on Information Systems. Chute, A., Thompson, M., & Hancock, B. (1999). The McGraw-Hill handbook of distance learning. New York: McGraw-Hill. Clark, T. (2001). Virtual Schools: Trends and issues - A study of virtual schools in the United States. Retrieved on May, 30, 2009, from: http://www.wested.org/online_pubs/virtualschools.pdf. Cleary, Y. (2005). Technical communication at the University of Limerick: Comparison of available data for distance learning and full-time students. 2005 IEEE International Professional Communication Conference Proceedings, 689- 697. Coakes, S. J., & Steed, L. G. (2003). SPSS analysis without anguish. Sydney: John Wiley & Sons. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. Conci, M., Pianesi, F., & Zancanaro, M. (2009). Useful, social and enjoyable: Mobile phone adoption by older people. 12th IFIP TC13 Conference on Human-Computer Interaction, 63-76. CSB (2009). Civil Service Bureau. Retrieved on December, 15, 2009, from: http://www.csb.gov.jo. Dadayan, L., & Ferro, E. (2005). When technology meets the mind: A comparative study of the technology acceptance model. International conference on electronic government, 35 (91), 137-144. Dalziel, C. (2008). Online in the United States. Retrieved on April, 3, 2009 from: www.studyoverseas .com /distance / onlineus .html Dark, M., York, C., Popescu, V., & Nita-Rotaru, C. (2007). Evaluating interactivity and presence in an online distance learning system. 37th ASEE/IEEE Frontiers in Education Conference, 24-29. Davis F., Bagozzi R., & Warshaw P. (1992). Extrinsic and intrinsic motivation to use computer in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance. MIS Quarterly, 13(3), 319-340. Dillon, A., & Morris, M. G. (1996). User acceptance of information technology: Theories and models. Annual Review of Information Science and Technology, 31(1), 3-32. Easton, R., & Stratton, J. (2004). Distance learning: Facts, failures, foibles, and the future. Engineering Education Annual Conference. ECDL Foundation Programmes (2009). ECDL / ICDL. Retrieved on February, 4, 2009 from: http://www.ecdl.com. Elaiza, O. N., & Geri, N. (2008). Easy as e-mail? Probing the slow adoption of an online assignment submission system. Proceedings of the Chais conference on instructional technologies research 2008, 94-101. Elena, A. (2006). Theoretical basics of adult vocational training by means distance learning technologies. International Siberian workshop and tutorial EDM’2006, 201-206. Folorunso, O., Ogunseye, O.S., & Sharma, S.K. (2006). An exploratory study of the critical factors affecting the acceptability of e-learning in Nigerian universities. Information Management and Computer Security, 14(5), 496-505. Fornell, C. & David F.L. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. Friedrich, H.F., & Hron, A. (2010). Factors influencing pupils' acceptance of an e-learning system for secondary schools. Journal of Educational Computing Research, 42, 63-78. Gagne, R., & Rojas, A. (1991). Planning and authoring computer assisted instruction lessons. Educational Technology, 21(9), 17-21. Galvin, T. (2002). 2002 industry report: Training magazine’s 21st annual comprehensive Gefen, D. & Straub, D. W. (1997). Gender difference in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400. Gefen, D., Karahanna, E. & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90 George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference 12.0 update. Needham Heights, MA, USA: Allyn & Bacon, Inc. Gerbing, D. W., & Anderson, J. C. (1988). An update paradigm for scale development incorporation unidimensionality and its assessment. Journal of Marketing Research, 25, 186-192. Geri, N., & Elaiza, O. N. (2008). Beyond adoption: Barriers to an online assignment submission system continued use. Interdisciplinary Journal of E-Learning and Learning Objects, 4(1), 225-241. Ghaemi, P., Swift, J., Sisterb, C., Wilson, J. P., & Wolch, J. (2009). Design and implementation of a web-based platform to support interactive environmental planning. Computers, Environment and Urban Systems, 33(1), 482-491. Gliem, J. A, & Gliem, R.R. (2003). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. 2003 Midwest Research to Practice Conference in Adult, continuing, and community education. Goodwin, C., Graham, M., & Scarborough. (2001). Developing an asynchronous learning network. Educational Technology & Society, 4(4), 1436-4522. Gordon, P., & Yefalvi Z. (2004). Distance learning: How to use this new didactic method in education of electronics engineering. 2004 Electronic Components and Technology Conference, 2(1). Goussal, D. M., & Udrízar Lezcano, M. S. (2003). Synchronous distance learning and virtual classrooms: A case study on student expectations and conditioning factors. Australian Journal of Educational Technology, 19(3), 388- 404. Gracanin, D. (2003). Combining traditional and web-based distance learning in information technology education. Information Technology: Research and Education, 2003. Proceedings. ITRE2003. International Conference on, 595- 599. Grandon, E. E., Alshare, K., & Kwun, O. (2005). Factors influencing student intention to adopt online classes: A cross cultural study. Journal of Computing Science in College, 20(4), 46-56. Grant, R., & Danziger, J. (2005). Exploring the corporate benefits and employee adoption of corporate e learning. Center for Research on Information Technology and Organizations. I.T. in Business. Graziadei, W. D. (1993). Virtual instructional classroom environment in science (VICES) in research. Education, Service & Teaching (REST). Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New Jersey: Pearson Education, Inc. Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). New Jersey: Upper Saddle River. Halawi, L., & McCarthy, R. (2008). Measuring students perceptions of blackboard using the technology acceptance model: A PLS approach. Issues in Information Systems, 9(2), 95-102. Hall, B. (1999). Five common questions and the answers. Inside Technology Training, 3(2), 42-43. Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440-465. Henry, P. D., & Motet, A. (2006). Bridging great divides: Innovation diffusion in Hawaiian distance learning. TCC worldwide online conference 2006 Proceedings. Hermans, C. M., Haytko, D. L., & Stenerson, M.B. (2009) Student satisfaction in web enhanced learning environments. Journal of Instructional Pedagogies, 1, 296-304. Hisham, N. Campton, P., & FitzGerald, F. (2004). A tale of two cities: A study on the satisfaction of asynchronous e learning systems in two Australian universities. Proceedings of the 21st ASCILITE Conference, 395-402. Ho, I., Kiyohara, H., Sugimoto, A., & Yana, K. (2005). Enhancing global and synchronous distance learning and teaching by using instant transcript and translation. Proceedings of the 2005 International Conference, 377, 23-25. Holmes-smith, C. E., & Coote, L. (2006). Structural equation modeling: From the fundamentals to advanced topics, school research, evaluation and measurement services, education and statistics consultancy. Statsline. Holmes-Smith, P. (2000). Introduction to structural equation modeling using AMOS 4.0 and LISREL. Paper presented at the ACSPRI 2000 summer program, Elsternwick. Howard, D. (2002). Enhanced by technology, not diminished: A practical guide to effective distance communication. New York: McGraw-Hill. Hoyle, R. H., Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural Equation Modeling (pp. 158-176). Washington, DC: American Psychological Association. Hsia, J. W., & Tseng, A. H. (2008). An enhanced technology acceptance model for e-learning systems in high-tech companies in Taiwan: Analyzed by structural equation modeling. International Conference on Cyberworlds 2008, 39-44. Hu, P., Chau, P., Liu Sheng, O. R., & Tam, K. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91-112. Huang, Y. H, Wag, Y. S., & Chou, S.C. (2007). User acceptance of e-government services. 11th Pacific-Asia Conference on Information Systems, 97. Hussein, R., Aditiawarman, U., & Mohamed, N. (2007). E-learning acceptance in a developing country: A case of the Indonesian Open University. German e-Science Conference. ICDL US (2009). ICDL foundation. Retrieved on March, 20, 2009, from: http://www.icdlus.com. Igbaria, M. (1992). An examination of microcomputer usage in Taiwan. Information & Management, 22(1), 19-28. IM, I., Hong, S., & Soo, M. K. (2010). An international comparison of technology adoption: Testing the UTAUT model. Information & Management 48(1), 1-8. Instone, K. (2004). An information architecture perspective on personalization. In: Karat, C. (Eds), Designing personalized user experiences in eCommerce (pp. 75-93). Netherlands: Kluwer Academic Publishers. Integrated Technology Group (2005). The ultimate Middle East business resource. Retrieved on May 30, 2009, from http://www.ameinfo.com/news/Company_News/I/Integrated_Technology_Group__ITG_/. Jackson, A. (2002). Learning on-line: A virtual education. Engineering Education Annual Conference . Janz, B. (1999). Self-directed teams in IS: Correlates for improved systems development work outcomes. Information & Management, 35(3), 171-192. Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in I innovation adoption research. Journal of Information Technology, 21(1), 1-23. Jian, G., Lan, J., & Zhuang, X. (2001). Distance learning technologies and an interactive multimedia educational system. Advanced Learning Technologies, 2001. Proceedings. IEEE International Conference, 405-408. John, O. (1999). Students’ perceptions of distance learning, online learning and the traditional classroom. Online Journal of Distance Learning Administration, 2(4), 1-16. Jong, D., & Wang, T.S. (2009). Student acceptance of Web based learning system. 2009 International Symposium on Web information system and application (WISA’09), 533-536. Jordan Time (2002). ICDL in Jordan. Retrieved on March, 12, 2009, from: http://www.writefix.com/ICDL/icdljordan.htm Karadediz, S. (2009). Flexible design for the future of distance learning. Procedia-Social and Behavioral Science, 1, 358-363. Khan, S. (2007). Adoption issues of internet banking in Pakistani’ firms. Unpublished dissertation. Lulea University of technology. Koifman, I., Shimshoni, I., & Tal, A. (2002). MAVIS: A multi-level algorithm visualization system within a collaborative distance learning environment. Journal of Visual Language and Computing, 19(2), 182-202. Kroth, M. (2007). Maslow—Move Aside! A heuristical motivation model for leaders in career and technical education. Journal of Industrial Teacher Education, 44(2), 5-36. Kurilova-Rich, L. V., & Falaleev, A. G. (2003). Russian distance learning projects within Far Eastern National University and beyond. Distance Learning and the Internet Conference 2003. Kwok, R., Ma, J., Vogel, D., & Zhou, D. (2001). Collaborative assessment in education: An application of a fuzzy GSS. Information & Management, 39(3), 243-253. Lai, V., & Li, H. (2005). Technology acceptance model for internet banking: An invariance analysis. Information and Management Archive, 42(2), 373-386. Lee, J. S., Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2003). Technology acceptance and social networking in distance learning. Educational Technology & Society, 6(2), 50-61. Lim, B. C., Kian, H. S., & Kock, T.W. (2008). Acceptance of e-learning among distance learners: A Malaysian perspective. Proceedings ascilite Melbourne 2008, 541-551. Liska, A. E. (1984). A critical examination of the causal structure of the Fishbein/Ajzen attitude model. Social Psychology Quarterly, 4 (1), 61-74. Liu, G. Z., & Hwang, G. J. (2009). A key step to understanding paradigm shifts in e-learning: Towards context-aware ubiquitous learning. British Journal of Educational Technology, 41(2), 1-9. Liu, H., Liao, H., & Pratt, J.A. (2009). Impact of media richness and flow on e-learning technology acceptance. Journal of Computers & Education, 52(3), 599-607. Liu, Y., & Wah, Y. H. (2007). Human-centered multimedia e-learning system for real-time interactive distance education. Multimedia and Expo, 2007 IEEE International Conference on, 2042 – 2045 Mahadeo, D., Munhurrun, R., & Bhiwajee, L. (2007). An evaluation of learners’ perceptions of e-courses. Computer Science and IT Education Conference. Mahony, M. J., & Wozniak, H. (2005). Diffusion of innovation and professional development in eLearning: The CHS eLearning resource case study. Conference on the International Experience in Open, Distance and Flexible Education, 9-11. Adelaide. Malhotra, Y., & Galletta, D.F. (1999). Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation. In proceedings of the thirty- second annual Hawaii international conference on system sciences, 1, 14 Manning, R., Cohen, M., & DeMichiell, R. (2003). Distance learning: Step by step. Journal of Information Technology Education, 2, 115-130. Marakas, G., Yi, M., & Johnson, R. (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. Marchewka, J., Liu, C., & Kostiwa, K. (2007). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2), 93-104. Maria, P. S., & Ataide, G.D. (2007). Theories about technology acceptance: Why the users accept or reject the information technology? Brazilian Journal in Information Science, 1(2), 69-86. Mashhour, A. S. (2007). A distance education model for Jordanian students based on an empirical study. Turkish Online Journal of Distance Education-TOJDE, 8(2), 1302–6488. Masrom, M. (2007). Technology acceptance model and e-learning. International Conference on Education, Sultan Hassanal Bolkiah Institute of Education Universiti Brunei Darussalam 21-24 May 2007. Malaysia. Mathieson, K., Peacock, E., & Chin, W. (2001). Extending the technology acceptance model: The influence of perceived user resources. Database for Advances in Information Systems, 32(3), 86-112. Merchant, S. (2007). Exploring the influence of cultural values on the acceptance of information technology: An application of the technology acceptance model. Issues in Informing Science and Information Technology, 4, 431-443. Midkiff, S., & DaSilva, L. (2000). Leveraging the web for synchronous versus asynchronous distance learning. International Conference on Engineering Education. Miller, M. D., Kelly, R. R., & Ken, J. C. (2003). Predictors of engagement and participation in an on-line course. Online Journal of Distance Learning Administration, 6(1). Retrieved June 3, 2009 from the World Wide Web: http://www.westga.edu/~distance/ojdla/spring61/miller61.htm. Mitchell, T. J. F., Chen, S. Y., & Macredie, R. D. (2005). The relationship between web enjoyment and student perceptions and learning using a web-based tutorial. Learning, Media and Technology, 30(1), 27-40. Mofleh, S., Wanous, M., & Strachan, P. (2008). Developing countries and ICT initiatives: Lessons learning from Jordan’s experience. The Electronic Journal on Information System in the Developing Countries, 34(5), 1-17. Monolescu, D., Schifter, C., & Greenwood, L. (2004). The distance education evolution: Issues and case studies. Turkish Online Journal of Distance Education-TOJDE, 5, 163-184. Moon, J., & Kim, Y. (2001). Extending the technology acceptance model for the World Wide Web context: Playfulness salient belief. Information & Management, 38(4), 217-230. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(2), 192-222. Moore, G. C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. In K. Kautz, & J. Pries-Heje. Diffusion and Adoption of Information Technology (pp. 132-146). London: Chapman and Hall Publishers. Moran, M. (2006). College students’ acceptance of tablet PCs: An application of the UTAUT model. Ph.D. Thesis Papella University. Mennesota, USA. Morcos, M., & Soldan, D. (2001). Supporting distance engineering education is greatly needed. Large Engineering Systems Conference, 69-72. Morley, L., & LaMaster, K. (1999). Using WebCT bulletin board option to extend transitional classroom walls. ERIC Database, accession No. ED440922. Nanayakkara, C. (2005). A model of user acceptance of learning management systems: a study within tertiary institutions in New Zealand. International Journal of Learning, 13(12), 223-232. Nanayakkara, C., & Widdett, D. (2005). A model of user acceptance of e-learning technology: A case study of a polytechnic in New Zealand. The fourth international conference on information system technology and applications. New Zealand, 180-189. Naor, O., & Geri, N. (2008). Easy as e-mail? Probing the slow adoption of an online assignment submission system. Proceedings of the Chais conference on instructional technologies research 2008, 5, 94-101. National Military Family Association (NMFA) (2008). Distance learning. Retrieved on March, 22, 2009, from: http://www.nmfa.org/site/PageServer?pagename=choosing_school. Netemeyer, R. G., Burton, S., & Johnston, M. (1991). A comparison of two models for the prediction of volitional and goal directed behaviors: A confirmatory analysis approach. Social Psychology Quarterly, 54(2), 87-100. Neuman, W. L. (2003). Social research methods: Qualitative and Quantitative approaches. Boston: Pearson Education. Norman, P., & Smith, L. (1995). The theory of planned behavior and exercise: An investigation into the role of prior behavior, behavioral intentions and attitude variability. European Journal of Social Psychology, 25, 403-415. Ok, S., & Shon, J. (2006).The determinant of internet banking usage behavior in Korea: A comparison of two theoretical models. CollECTeR ’06, 9 December. Adelaide. Pahwa, A., Gruenbacher, D., Starrett, S., & Morcos, M. (2005). Distance learning for power professional. IEEE Power and Energy Magazine, 3(1), 53-58. Pallant, J. (2005). SPSS survival manual: A step by step guide to data analysis using SPSS (2nd ed.). Crows Nest: Allen @ Unwin. Pange, J., Leontitsis, A., & Siogka, E. (2004). Are the Greek preschool teachers able to use distance learning technologies? Information Technology Based Higher Education and Training, 2004. Proceedings of the FIfth International Conference on, 672-673. Partridge, H. L. (2007). Redefining the digital divide: attitudes do matter. Proceedings 70th American Society for Information Science and Technology (ASIS&T), 44(1). Milwaukee, Wisconsin, USA. Paul, O., & Hardt, Ed.D. (2008). How do executives really evaluate distance training? ASTD 2008 International Conference & Exposition, pp 1-6. Pauli, K. P., Gilson, R. L., & May,D.R.(2007). Anxiety and avoidance: The mediating effects of computer self-efficacy on computer anxiety and intention to use computers. Review of Business Information Systems, 11, 57-64. Payne, E.A., & Curtis, M. B. (2008). Can the unified theory of acceptance and use of technology help us? Retrieved on June 14, 2009, from: http://aaahq.org/meetings/AUD2009/CanTheUnifiedTheory.pdf Peak Group (2002). Virtual schools across America: Trends in K–12 online education. Software Industry & Information Association. USA Pedersen, P., & Nysveen, H. (2003). Usefulness and self-expressiveness: Extending TAM to explain the Adoption of a mobile parking Services. In the proceeding of the 16th Bled eCommerce Conference, Bled, Slovenia, June 9-11. Peter, J. P. (1979). Reliability: A review of psychometric basics and recent marketing practices. Journal of Marketing Research, 16, 6-7. Poon, W. C., Low, L. T., & Yong, G. F. (2004). A study of Web-based learning (WBL) environment in Malaysia. The International Journal of Educational Management, 18(6), 374-385. Pratikakis, I., Karahaliou, A., Vassiou, K., Virvilis, V., Kosmopoulos, D., & Perantonis, S. (2009). eMedI: Web-Based E-Training for Multimodal Breast Imaging. International Journal of Biological and Medical Sciences, 4(2), 82-88. Quinn, B., Barroca, L., Nuseibeh, B., Fernández, J., Rapanotti, L., Thomas, P., & Wermelinger, M. (2006). Learning software engineering at a distance. IEEE Computer Society, 23(6), 36-43. Raaij, M. R., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computer and Education, 50, 838-852. Ramayah, T., Jantan, M., & Ismail, N. (2003). Impact of intrinsic and extrinsic motivation on internet usage in MALAYSIA. The 12th International Conference on Management of Technology, 13-15 May 2003, Nancy, France. Ramayah, T., Rouibah, K., Gopi, M. & John, G. (2009). A decomposed theory of reasoned action to explain intention to use Internet stock trading among Malaysian investors. Computers in Human Behavior, 25, 1222–1230. Reddy, M. (2004). Jordan learns on Linux. Retrieved on March, 24, 2009, from: http://www.arabianbusiness.com/480789. Rezaei, M., Movahed, H. M., Asadi, A., & Kalantary, K. (2008). Predicting e-learning application in agricultural higher education using technology acceptance model. Turkish Online Journal of Distance Education, 98(1), 85-95. Robinson, L. (2009). A summary of diffusion of innovations. Retrieved on May, 30, 2009, from: www.enablingchange.com.au Rogers, E. (1995). Diffusion of innovations. New York: Free Press. Rogers, E. (2003). Diffusion of innovations (5th ed.). New York: Free Press. Ruhig, R. (2002). Distance learning: A systems view an assessment and review of the literature. Retrieved on May, 10, 2009, from: http://www.rcet.org/research/ATT-OLN/Dumont-finalreport.pdf Ruttenbur, B. W., Spickler, G. C., & Lurie, S. (2000). E-learning the engine of the knowledge economy. Retrieved on July, 20, 2009, from: http://www.fondazionecrui.it/elearning/data/allegati/links/1193/2000%20Morgan%20Keegan.pdf Saade, R. G, & Kira, D. (2006). The emotional state of technology acceptance. Issues in Informing Science and Information Technology, 3(1), 529-540. Saade, R. G., Nebebe, F., & Tan, W. (2007). Viability of the technology acceptance model in multimedia learning environments: A comparative study. Interdisciplinary Journal of Knowledge and Learning Objects, 3(2), 175-184. Sahin, I., & Shelley, M. (2008). Considering students’ perceptions: The distance education student satisfaction model. Educational Technology & Society, 11(3), 216–223. Said, S. A. (2006). Information technology adoption, the roadmap to sustainable development: Examining three models. 18th National Computer Conference 2006. Saudi Arabia. Samuel, M. D., Joly, D. O., Wild, M. A., Wright, S. D., Otis, D. L., Werge, R. W., & Miller, M. W. (2003). Surveillance strategies for detecting chronic wasting disease in free-ranging deer and elk. Result of a CWD surveillance workshop USGS- National WILDLIFE HEALTH CENTER, Madison, Wisconsin. Schape, L., & Pervan, G. (2007). A model of information and communication technology acceptance and utilization by occupational therapists. International Journal of Medical Informatics, 76(1), 212-221. Schott, M., Chernish, W., Dooley, K., & Lindner, J.R. (2003). Innovations in distance learning program development and delivery. Online Journal of Distance Learning Administration, 6(2), 1-27. Sekaran, U. (2000). Research methods for business: a skill-building approach. NYC: John Wiley & Sons, Inc. Sekaran, U. (2003). Research methods for business. United States of America: John Wiley &Sons, Ltd. Sekaran, U. (2007). Research methods for business. New Delhi: Wiley India. Selim, H. M. (2005). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396-413. Sevin, E., & Thalmann, D. (2005). The complexity of testing a motivational model of action selection for virtual humans. Computer Graphical International, 540-543. Seyal, F. H., Rahman, M. A., & Tajuddin, S. T. (2007). Internet users’ behavior model: A structural equation approach. Proceedings of the 13th Asia Pacific Management Conference, 210-221. Melbourne, Australia, 2007. Sharma, S. (1996). Applied multivariate technique. New York: John Wiley& Sons, Inc. Shen, D., Laffey, J., Lin, Y., & Huang, X. (2006). Social influence for perceived usefulness and ease-of-use of course delivery systems. Journal of Interactive Online Learning, 5(3), 163-177. Sheng, Z., Jue, Z., & Weiwei, T. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology, 13(3),312-317. Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). Theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325-343. Smith, T. D., & McMillan, B. F. (2001). A primer of model fit indices in structural equation model. Annual meeting of the southwest educational research association, 1-3. New Orleans. Soh, P., & Subramanian, A. M. (2008). Is usage a missing link in explaining the perceived learning outcome of technology mediated learning? IEEE Transactions on Engineering Management, 55, 393-408. Song, J., & Zahedi, F. (2001). Web design in e-commerce: A theory and empirical analysis. Twenty-Second International Conference on Information Systems, 205-219. New Orleans, LA. Stoyanov, S., Ganchev, I., Popchev, I., & O'Droma, M. (2008). Service-oriented and agent-based approach for the development of InfoStation eLearning intelligent system architectures. Paper presented at the 4th International IEEE Conference "Intelligent Systems". Sumak, B., Hericko, M., Polancic, G., & Pusnik, M. (2010). Investigation of e-learning system acceptance using UTAUT. International Journal of Engineering Education 26(6), 1327-1342. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Allyn and Bacon. Taylor, S., & Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. Thomas, K., & Allen, S. (2006). The learning organization: A meta-analysis of themes in literature. Journal of Learning Organization, 13(2), 123- 139. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15, 124-143. Training Directory. (2007). Face to Face Training. Retrieved on March 5, 2011, from: http://www.cii.co.uk/documents/face-to-face-training.pdf. Tynjala, P., & Hakkinen, P. (2005). E-Learning at work: Theoretical underpinnings and pedagogical challenges. The Journal of Workplace Learning, 17, 318-336. UNSCO (2006). Distance learning: Bureau of Public Information. Retrieved on May, 20, 2009, from: www.unesco.org/bpi/pdf/memobpi38_distancelearning_en.pdf. Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management and Science, 46(2), 186-204. 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. Voinea, L., Dima, G., & Profirescu, M. (2001). Pilot Implementation of a web-based microelectronics laboratory within EDIT distance learning network. 24th International Spring Seminar on Electronics Technology, pp.18 - 19. Volery, T., & Lord, D. (2000). Critical success factors in online education. International Journal of Educational Management, 14(5), 216-223. Wag, H., Lin, Chang, W., & Shih, T. (2005). Applying Petri Nets to model learning sequence with SCORM specification in collaborative learning. Proceedings of the 2005 International Conference, pp.181- 186.Taiwan. Walclzak, S., & Scott, J.E. (2009). Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Information and Management, 46, 221-232. Weihua, L. (1997). The research and development of multimedia software. Proceeding of the EuroChinatel conference (Telematics Multimedia Application, Education and Healthcare), pp. 126–129. Beijing, China. Wolk, M. (2007). Using the technology acceptance model for outcomes assessment in higher education. Information Systems Education Journal, 7(43), 86-112. Wozencroft, S. (2005). Discuss the relative strengths and weaknesses of qualitative and quantitative approaches to the socio-cultural study of sport. Retrieved August 14, 2009, from: www.seanwozencroft.com/oldportfolio/uni/research.doc Xie, K., Debacker, T. K., & Ferguson, C. (2006). Extending the traditional classroom through online discussion: The role of student motivation. Journal of Educational Computing Research, 34(1), 67- 89. Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). New York: Harper and Row. Yayla, A., & Qing, H. (2007). User acceptance of e-commerce technology: A meta-analytic comparison of competing models. Retrieved on June, 2, 2009, from http://is2.lse.ac.uk/asp/aspecis/20070192.pdf Yin, R. K. (1994). Case study research: Design and methods. Thousand Oaks: Sage. Young-Ju Joo, Mimi Bong, & Ha-Jeen Choi. (2000). Self-efficacy for self-regulated learning, academic self efficacy, and Internet self-efficacy in web-based instruction. Educational Technology, Research and Development, 48(2), 5-13. Zachman, J. A. (1999). A framework for information systems architecture. IBM Systems Journal, 38(2/3), 454-470. Zhang, W., Perris, K., & Yeung, L. (2005). Online tutorial support in open and distance learning: Students’ perceptions. British Journal of Educational Technology, 36(5), 789-804. Zhao, X., Zhong, Y., Matsumoto,M. (2006). A real-time interactive shared system for distance learning. Multi Media Modelling Conference Proceedings, pp.6. Zhuravleva, O., Krouk, B., & Solomina, E. (2006). System of corporate distance learning. 7th Annual 2006 International Workshop and Tutorials, Pp.172-176. Erlagol. Zikmund, W. G. (1994). Business research method (4th ed.). Orlando: The Dryden press.

Similar Items