LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Tarhini, A,; Hassouna, M.; Abbasi, M. S.; Orozco, J. (2015)
Publisher: Academic Publishing Ltd.
Languages: English
Types: Article
Subjects: L1, LB
Simpler is better. There are a lot of "needs" in e-Learning, and there's often a limit to the time, talent, and money that can be thrown at them individually. Contemporary pedagogy in technology and engineering disciplines, within the higher education context, champion instructional designs that emphasize peer instruction and rich formative feedback. However, it can be challenging to maintain student engagement outside the traditional classroom environment and ensure that students receive feedback in time to help them with ongoing assignments. The use of virtual learning platforms, such as Blackboard Learn, and web feed syndication, using technology such as Rich Site Summaries (RSS), can help overcome such challenges. However, during an initial pilot at an institution in Lebanon, only 21% of students reported making use of both these facilities. In this study, the Technology Acceptance Model (TAM) was used to guide the development of a scale to be used to investigate antecedents to the use of web feeds. The proposed scale was reviewed by 4 experts and piloted with 235 students. The collected data were analysed using structural equation modeling (SEM) technique based on AMOS methods. The results revealed adequate face, content, and construct validity. However, perceived ease of use was not a significant predictor of attitude towards use. Overall, the proposed model achieves acceptable fit and explains for 38% of its variance of which is lower than that of the original TAM. This suggests that aspects of the model may lack criterion validity in the Lebanese context. Consequently, it may be necessary to extend the scale by capturing additional moderators and predictors, such as cultural values and subjective norms. We concluded that the existence of RSS feeds in education improves significantly the content presented by the instructors to the e-learning user decreasing at the same time the size and access cost.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Abbasi, M. S., Chandio, F. H., Soomro, A. F., & Shah, F. (2011). Social influence, voluntariness, experience and the internet acceptance: An extension of technology acceptance model within a south-Asian country context. Journal of Enterprise Information Management, 24(1), 30-52.
    • Agarwal, R. and Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research 9, 204-215.
    • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179-211.
    • Ajzen, I. and Fishbein, M. (1980) Understanding attitudes and predicting social behavior (278). Prentice-Hall.
    • Arbuckle, J. (2009) Amos 18 User's Guide. SPSS Incorporated.
    • Arenas-Gaitán, J., Ramírez-Correa, P. E. and Javier Rondán-Cataluña, F. (2011). Cross cultural analysis of the use and perceptions of web Based learning systems. Computers & Education 57, 1762-1774.
    • Asmus, J., Bonner, C., Esterhay, D., Lechner, A. and Rentfrow, C. (2005). Instructional design technology trend analysis. Retrieved April 20, 2009.
    • Bagozzi, R. P. (2007). The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift. Journal of the Association for Information Systems 8, 3.
    • Baroud, F. and Abouchedid, k. (2010) eLEARNING IN LEBANON: Patterns of E-learning Development in Lebanon's Mosaic Educational Context. In Demiray, U. (Editor) e-learning practices: cases on challenges facing e-learning and national development, institutional studies and practices (pp. 409-424). Eskisehir-Turkey, Anadolu University.
    • Bollen, K. A. (1989) Structural equations with latent variables. MA: Wiley.
    • Carmines, E. G. and McIver, J. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. Social measurement: Current issues, 65-115.
    • Chang, S. C. and Tung, F. C. (2008). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology 39, 71-83.
    • Chau, P. Y. K. and Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of management information systems 18, 191-230.
    • Cold, S. J. (2006). Using Really Simple Syndication (RSS) to enhance student research. ACM SIGITE Newsletter 3, 6-9.
    • D'Souza, Q. (2006) RSS ideas for educators [WWW document]|. http://www.teachinghacks.com/wpcontent/uploads/2006/01/RSS%20Ideas%20for%20Educators111.pdf URL|
    • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
    • Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 982-1003.
    • Duffy, P. D. and Bruns, A. (2006). The use of blogs, wikis and RSS in education: A conversation of possibilities.
    • Fernandez, V., Simo, P. and Sallan, J. M. (2009). Podcasting: A new technological tool to facilitate good practice in higher education. Computers & Education 53, 385-392.
    • Fornell, C. and Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
    • Fornell, C., Tellis, G. J. and Zinkhan, G. M. (1982). Validity assessment: A structural equations approach using partial least squares. AMA Chicago, 405-409.
    • Gefen, D. and Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS quarterly, 389-400.
    • Gefen, D., Straub, D. W. and Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice.
    • Hair, J. F. J., Black, W. C., Babin, B. J., Anderson, R. E. and Tatham, R. L. (2010) Multivariate data analysis. New Jersey: Prentice-Hall.
    • Hrastinski, S. (2008). Asynchronous and synchronous e-learning. Educause quarterly 31, 51-55.
    • Hu, L. and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6, 1-55.
    • Kanthawongs, P. and Kanthawongs, P. (2013). Individual and Social Factors Affecting Student's Usage Intention in Using Learning Management System. Procedia-Social and Behavioral Sciences 88, 89-95.
    • Kiraz, E. and Ozdemir, D. (2006). The relationship between educational ideologies and technology acceptance in pre-service teachers. Journal Of Educational Technology And Society 9, 152.
    • Kline, R. B. (2010) Principles and practice of structural equation modeling. The Guilford Press.
    • Kung-Teck, W., Osman, R. and Rahmat, M. K. (2013). Understanding Student Teachers' Behavioural Intention to Use Technology: Technology Acceptance Model (TAM) Validation and Testing. International Journal of Instruction 6.
    • Lan, Y.-F. and Sie, Y.-S. (2010). Using RSS to support mobile learning based on media richness theory. Computers & Education 55, 723-732.
    • Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D. and Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education 54, 600-610.
    • Liu, S.-H., Liao, H.-L. and Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education 52, 599-607.
    • MacCallum, R. C., Browne, M. W. and Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods 1, 130.
    • Markett, C., Sánchez, I. A., Weber, S. and Tangney, B. (2006). Using short message service to encourage interactivity in the classroom. Computers & Education 46, 280-293.
    • McCoy, S., Everard, A. and Jones, B. (2005). An examination of the technology acceptance model in Uruguay and the U.S.: a focus on culture. Journal of Global Information Technology 8, 27-45.
    • McCoy, S., Galletta, D. F. and King, W. R. (2007). Applying TAM across cultures: the need for caution. European Journal of Information Systems 16, 81-90.
    • Meurant, R. C. (2007) A Preliminary Survey of the Use of Cell Phones, Electronic Dictionaries, SMS, Email, Computers and the Internet by Korean College EFL Students with Respect to Patterns of L1: L2 Language Use and the Associated Language Learning Strategies Used in Acce. Multimedia and Ubiquitous Engineering, 2007. MUE'07. International Conference on. (pp. 767-772). IEEE.
    • Nasser, R., Abouchedid, K. (2000). Attitudes and concerns towards distance education: the case of Lebanon. Online Journal of Distance Learning Administration 3, 1-10.
    • Nasser, R. N., Khoury, B. and Abouchedid, K. (2008). University students' knowledge of services and programs in relation to satisfaction: a case study of a private university in Lebanon. Quality Assurance in Education 16, 80-97.
    • Ngai, E. W. T., Poon, J. K. L. and Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education 48, 250-267.
    • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society 12, 150-162.
    • Pituch, K. A. and Lee, Y.-k. (2006). The influence of system characteristics on e-learning use. Computers & Education 47, 222-244.
    • Prabowo, R., Thelwall, M. and Alexandrov, M. (2007). Generating overview timelines for major events in an RSS corpus. Journal of Informetrics 1, 131-144.
    • Richardson, W. (2005) RSS Quick start guide for educators [WWW document]|. http://weblogg-ed.com/wpcontent/uploads/2006/05/RSSFAQ4.pdf URL|
    • Rodriguez, T. and Lozano, P. (2011). The acceptance of Moodle technology by business administration students. Computers & Education.
    • Rogers, E. M. (1995) Diffusion of innovations. New York: Simon and Schuster.
    • Rose, G. and Straub, D. (1998). Predicting general IT use: Applying TAM to the Arabic world. Journal of Global Information Management (JGIM) 6, 39-46.
    • Schumacker, R. E. and Lomax, R. G. (2010) A beginner's guide to structural equation modeling. York: Routledge: Lawrence Erlbaum.
    • Shim, J. and Guo, C. (2009). Weblog Technology for Instruction, Learning, and Information Delivery*. Decision Sciences Journal of Innovative Education 7, 171-193.
    • Srite, M. and Karahanna, E. (2006). The role of espoused national cultural values in technology acceptance. MIS quarterly 30, 679-704.
    • Straub, D., Keil, M. and Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management 33, 1-11.
    • Sun, P.-C. and Cheng, H. K. (2007). The design of instructional multimedia in e-Learning: A Media Richness Theory-based approach. Computers & Education 49, 662-676.
    • Tarhini, A., Scott, M., Sharma, K.S., & Abbasi, M.S. (2015a). Differences in Students' Intention to Use RSS Feeds Between Lebanese and British students: A Multi-Group Invariance Analysis Based on the Technology Acceptance Model. Electronic Journal of e-Learning, 13(1 ), 14-29
    • Tarhini, A., Teo, T, & Tarhini, T. (2015b). A cross-cultural validity of the E-learning Acceptance Measure (ElAM) in Lebanon and England: A Confirmatory Factor Analysis, Education and Information Technologies, DOI: 10.1007/s10639-015- 9381-9
    • Tarhini, A., Hone, K. and Liu, X. (2014a). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology.
    • Tarhini, A., Hone, K. and Liu, X. (2014b). The effects of individual differences on e-learning users' behaviour in developing countries: A structural equation model. Computers in Human Behavior 41, 153-163.
    • Tarhini, A., Hone, K. and Liu, X. (2014c). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural Equation Modelling Approach of an extended Technology Acceptance Model. Journal of Educational Computing Research 51(2), 163-184.
    • Tarhini, A., Hone, K. and Liu, X. (2013a) Extending the TAM model to empirically investigate the students' behavioural intention to use e-learning in developing countries. Science and Information Conference (SAI), 2013. (pp. 732- 737). IEEE.
    • Tarhini, A., Hone, K. and Liu, X. (2013b). Factors Affecting Students' Acceptance of e-Learning Environments in Developing Countries: A Structural Equation Modeling Approach. International Journal of Information and Education Technology 3, 54-59.
    • Tarhini, A., Hone, K. and Liu, X. (2013c). User Acceptance Towards Web-based Learning Systems: Investigating the Role of Social, Organizational and Individual Factors in European Higher Education. Procedia Computer Science 17, 189- 197.
    • Teo, T. (2009a). The Impact of Subjective Norm and Facilitating Conditions on Pre-Service Teachers' Attitude toward Computer Use: A Structural Equation Modeling of an Extended Technology Acceptance Model. Journal of Educational Computing Research 40, 89-109.
    • Teo, T. (2009b). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education 52, 302-312.
    • Teo, T. (2010a). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: a structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review 11, 253-262.
    • Teo, T. (2010b). A structural equation modelling of factors influencing student teachers' satisfaction with e-learning. British Journal of Educational Technology 41, E150-E152.
    • Teo, T. and Lee, C. B. (2008). Attitudes towards computers among students in higher education: A case study in Singapore. British Journal of Educational Technology 39, 160.
    • Teo, T., Luan, W. S. and Sing, C. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: an application of the Technology Acceptance Model (TAM). Journal of Educational Technology and Society 11, 265-280.
    • Teo, T. and Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education 57, 1645-1653.
    • Venkatesh, V. and Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 39, 273-315.
    • Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
    • Venkatesh, V. and Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of global information technology management 13, 5-27.
    • West, R. E., Wright, G., Gabbitas, B. and Graham, C. R. (2006). Reflections from the introduction of blogs and RSS feeds into a preservice instructional technology course. TechTrends 50, 54-60.
    • Yousafzai, S. Y., Foxall, G. R. and Pallister, J. G. (2007). Technology acceptance: a meta-analysis of the TAM: Part 2. Journal of Modelling in Management 2, 281-304.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article