FACTORS AFFECTING THE ACCEPTANCE OF E-LEARNING FOR USERS WITH RESPECT TO USER TYPE, REGION, CULTURE, WELFARE AND DEVELOPMENT LEVELS

Yıl 2019, Cilt: 8 Sayı: 2, 2214 - 2242, 27.04.2019

Rahmi Baki Adnan Aktepe Burak Birgören

https://doi.org/10.33206/mjss.558331

Cited By: 1

Öz

As a result of the widespread use of internet, advances in information and communication technologies, and economic and educational developments in developing countries, e-learning systems have been used in many different regions and cultures. Users from different regions and cultures may have different needs and expectations, thus they may exhibit different behaviors. Determination of regional and cultural differences which may affect the acceptance of users in elearning systems, and use of these differences in the design of these systems is a strategic element in their success. In this study, the acceptance of e-learning by users is examined based on the Technology Acceptance Model (TAM); 186 studies and 650 hypotheses which were tested in those studies are analyzed. The studies and hypotheses are classified into five categories with respect to user type, geographic region, the level of economic development, the level of educational development, the level of development of information and communication technologies. Thus, this study aims at determining behaviors that vary across different users according to user type, region, culture, welfare level and development level; and providing guidance to e-learning system designers. 

Anahtar Kelimeler

Information Technologies, Regional Diversity, E-learning, Cultural Difference, Technology Acceptance Model

KULLANICI TİPİ, BÖLGE, KÜLTÜR, REFAH VE GELİŞMİŞLİK SEVİYELERİNE GÖRE KULLANICILARIN E-ÖĞRENME KABULÜNÜ ETKİLEYEN FAKTÖRLERİN ANALİZİ

Öz

İnternetin yaygınlaşması, bilgi ve iletişim teknolojilerindeki yenilikler, gelişmekte olan ülkelerde ekonomi ve eğitim alanındaki ilerlemelerin bir sonucu olarak günümüzde e-öğrenme sistemleri pek çok farklı bölge ve kültürde kullanılmaktadır. Farklı bölge ve kültürlerden kullanıcılar, farklı ihtiyaç ve beklentilere sahip olabilir ve bunun sonucunda da farklı davranışlar gösterebilirler. Kullanıcıların e-öğrenme sistem kabulünü etkileyebilecek bölgesel ve kültürel farklılıkların belirlenebilmesi ve bu farklılıkların tasarımda kullanılması sistem başarısında stratejik bir unsurdur. Bu çalışmada kullanıcıların e-öğrenme kabulü, Teknoloji Kabul Modeli (TKM) esas alınarak incelemiş, 186 araştırma ve bu araştırmalarda test edilmiş 650 hipotez analiz edilmiştir. İncelenen araştırma ve hipotezler; kullanıcı tipi, coğrafi bölge, ekonomik gelişme, eğitim bazında insani gelişmişlik, bilgi ve iletişim teknolojileri gelişme seviyesi olmak üzerek beş kategoride sınıflandırılmıştır. Böylece farklı kullanıcı tipi, bölge, kültür, refah ve gelişmişlik seviyelerinde kullanıcılarda farklılık gösteren davranışların belirlenmesi ve bu bilgilerin e-öğrenme sistem tasarımcılarına yol göstermesi amaçlanmaktadır. 

Anahtar Kelimeler

Bilgi Teknolojileri, Bölgesel Farklılık, E-öğrenme, Kültürel Farklılık, Teknoloji Kabul Modeli

Kaynakça

• Abbad, M. M. Morris, D. Nahlik, C.D. (2009). Looking under the bonnet: factors affecting student adoption of eLearning systems in Jordan. International Review of Research in Open and Distance Learning, 10(2), 115. Abbas, T. (2016). Social factors affecting students’ acceptance of e-learning environments in developing and developed countries: A structural equation modeling approach. Journal of Hospitality and Tourism Technology, 7(2), 200-212. Abdel-Wahap, A.G. (2008). Modeling students intention to adopt e-learning a case from Egypt. The Electronical Journal of Information Systems in Developing Countries, 34(1), 1-13. Abdullah, F. Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by Analysing Commonly Used External Factors. Computers in Human Behaviour, 56, 238-256. Abdullah, F. Ward, R. Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of eportfolios. Computers in Human Behavior, 63, 75-90. Abramson, J. Dawson, M. Stevens, J. (2015). An Examination of the prior use of e-Learning within an extended technology acceptance model and the factors that influence the behavioral intention of users to use mLearning. SAGE Open, 5(4), 1-9. Adetimirin, A. (2015). An Empirical study of online discussion forums by library and information science postgraduate students using technology acceptance model 3. Journal of Information Technology Education: Research, 14, 257-269. Agudo-Peregrina, A.F. Hernandez-Garcia, A. Pascual-Miguel, F. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. Al-Adwan, A. Al-Adwan, A. Smedley, J. (2013). Exploring students acceptance of e-learning using technology acceptance model in Jordanian universities. International Journal of Education and Development using Information and Communication Technology, 9(2), 4-18. Al-Alak, B.A. Alnawas, I.A.M. (2011). Measuring the Acceptance and Adoption of E-Learning by Academic Staff. Knowledge Management & E-Learning: An International Journal, 3(2), 201. Al-Ammarı., D.J. Hamad, M.S. (2008). Factors influencing the adoption of e-learning at University of Bahrain. Second International Conference and Exhibition for Zain E-learning Center, 28-30, 2008. Al-Ammary, J.H. Al-Sherooqi, A.H. Al-Sherooqi, H.K. (2014). The acceptance of social networking as a learning tools at university of Bahrain. International Journal of Information and Education Technology, 4(2), 208-214. Al-Aulamie, A. Mansour, A. Daly, H. Adjei, O. (2012). The effect of intrinsic motivation on learners' behavioural intention to use e-learning systems. In International Conference on Information Technology Based Higher Education and Training. 1-4. Al-Azawei, A. Lundqvist, K. (2015). Learner differences in perceived satisfaction of an online learning: An extension to the technology acceptance model in an Arabic sample. The Electronic Journal of eLearning, 13(5), 408-426. Al-Azawei, A. Parslow, P. Lundqvist, K. (2017). Investigating the effect of learning styles in a blended elearning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology. 33(2), 1-23. Alenezi, A. R. (2012). E-learning acceptance: technological key factors for the successful students' engagement in e-learning system. In EEE'12 -The 2012 International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government, 16-19. Alenezi, A. R. Karim, A.M.A. Veloo, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use elearning: A case study from Saudi Arabian Governmental Universities. The Turkish Online Journal of Educational Technology, 9(4), 22-34. Alenezi, A. R. Karim, A. M. A. Veloo, A. (2011). Institutional support and e-learning acceptance: an extension of the technology acceptance model. International Journal of Instructional Technology and Distance Learning, 8(2), 3-16. Al-Gahtani, S.S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12, 27–50. Al-Hawari. M.A. Mouakket, S. (2010). The influence of technology acceptance model (TAM) factors on students’ e-satisfaction and e-retention within the context of UAE e-learning. Education, Business and Society: Contemporary Middle Eastern Issues, 3(4), 299 – 314.
• Ali, H. Ahmed, A. A. Tariq, T. G. Safdar, H. (2013). Second life (SL) in education: The intensions to use at university of Bahrain. In Fourth International Conference on e-Learning Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity, 205-215. Al-Mushasha, N. F. (2013). Determinants of e-learning acceptance in higher education environment based on extended technology acceptance model. In Fourth International Conference on E-learning Best Practices in Management, Design and Development of E-courses: Standards of Excellence and Creativity, 261266. Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human. Behavior, 65–71. Arenas-Gaitán, J. Rondan-Cataluña, F.J. Ramırez-Correa, P.E. (2010). Gender influence in perception and adoption of e-learning platforms. Advances in Data Networks, Communications, Computers, 30-35. Arenas-Gaitán, J. Ramírez-Correa, P.E. Rondán-Cataluña, F.J. (2011). Cross Cultural Analysis of the Use and Perceptions of Web Based Learning Systems. Computers & Education, 57, 1762–1774. Armenteros, M. Liaw, S.S. Fernández, M. Díaz, R.F. Sánchez, R.A. (2013). Surveying FIFA instructors’ behavioral intention toward the Multimedia Teaching Materials. Computers & Education, 61, 91-104. Attis, J. (2014). An investıgatıon of the variables that predict teacher e-learning acceptance (Published PhD thesis). Liberty University, Virginia, U.S.A, 62-65. Aypay, A. Çelik, H. C. Aypay, A. Sever, M. (2012). Technology acceptance in education: A study of pre-service teachers in Turkey. Turkish Online Journal of Educational Technology, 11(4), 264-272. Baharin, A.T. Latehb, H. Nathan, S.S. Nawawi, H.M. (2015). Evaluating effectiveness of IDEWL using Technology Acceptance Model. Procedia - Social and Behavioral Sciences, 171, 897-904. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147. Bao, Y. Xıong, T. Hu, Z. Kıbelloh, M. (2013). Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. J. Educatıonal Computıng Research, 29(1), 111-132. Başoğlu, N. Özdoğan, M.K. (2011). Exploring the major determinants of mobile learning adaption. Boğaziçi University Journal of Education, 28(1), 31-46. Bhatiasevi, V. (2011). Acceptance of e-learning for users in higher education: An extension of the technology acceptance model. The Social Sciences, 6(6), 513-520. Bolliger, D. U. Wasilik, O. (2009). Factors Influencing Faculty Satisfaction with Online Teaching and Learning in Higher Education. Distance Education, 30(1), 103-116. Cabada, R.Z. Estrada, M.L.B. Hernández, F.G. Bustillos, R.O. Reyes-García, C.A. (2018). An affective and Web 3.0-based learning environment for a programming language. Telematics and Informatics, 35: 611-628. Calisir, F. Gumussoy, Ç.A. Bayraktaroglu, A.E. Karaali, D. (2014). Predicting the intention to use a web-based learning system: perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515–531. Capece, G. Campisi, D. (2011). Technological change and innovation behaviour in high level education: An international comparison between Italian and Portuguese samples. Knowledge and Process Management, 18(1), 67-74. Capece, G. Campisi, D. (2013). User satisfaction affecting the acceptance of an e-learning platform as a mean for the development of the human capital. Behaviour & Information Technology, 32(4), 335–343. Chang, C.C. Yan, C.F. Tseng, C.S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809-826. Chang, C.C. Tseng, K.H. Liang, C. Yan, C.F. (2013). The influence of perceived convenience and curiosity on continuance intention in mobile English learning for high school students using PDAs. Technology, Pedagogy and Education, 22(3), 373–386. Chang, T.F. Chao, C.M. Cheng, B.R. (2015). Framework and verification of a blended e-learning system behavioral intention model among clinical nurses. Journal of Baltic Science Education, 14(6), 733-743. Chang, C.T. Hajiyev, J. Su, C.R. (2017). Examining the students’ behavioral intention to use elearning in Azerbaijan? The General Extended Technology Acceptance Model for E-learning approach. Computers & Education, 111, 128-143. Chang, Y.H. Liu, J.C. (2013). Applying an AR technıque to enhance situated heritage learning in a ubiquıious learning environment. The Turkish Online Journal of Educational Technology, 12(3), 21-32. Chang, S.C. Tung, F.C. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83. Chen, Y.C. Lin, Y.C. Yeh, R.C. Lou, S.J. (2013). Examining factors affecting college students' intention to use web-based instruction systems: Towards an integrated model. Turkish Online Journal of Educational Technology-TOJET, 12(2), 111-121.
• Chen, H.R. Tseng, H.F. (2012). Factors that influence acceptance of web-based e-learning systems for the inservice education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35, 398–406. Cheng, Y.M. (2011). Antecedents and consequences of e-learning acceptance. Information Systems Journal, 21, 269–299. Cheng, Y.M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research. 22(3), 361-390. Cheng, Y.M. (2013). Exploring the roles of interaction and flow in explaining nurses' e-learning acceptance. Nurse Education Today, 33, 73-80. Cheng, Y.M. (2014). Roles of interactivity and usage experience in e-learning acceptance: A longitudinal study. International Journal of Web Information Systems, 10(1), 2-23. Cheng, Y.M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20, 109-119. Cheung, R. Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. Cho, V. Cheng, T.C.E. Hung, H. (2009). Continued usage of technology versus situational factors: An empirical analysis. J. Eng. Technol. Manage, 26, 264-284. Cho, V. Cheng, T.C.E. Lai, W.M.J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53, 216–227. Chow, M. Chan, L. Lo, B. Chu, W.P. Chan, T. Lai, Y.M. (2013). Exploring the intention to use a clinical imaging portal for enhancing healthcare education. Nurse Education Today, 33, 655-662. Chow, M. Herold, D.K. Choo, T.M. Chan, H. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59, 11361144, 2012. Çigdem, H. Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52, 22–28. Çoşkunçay, D.F. Özkan, S. (2013). A model for instructors’ adoption of learning management systems: empirical validatıon in higher education context. The Turkish Online Journal of Educational Technology, 12(2), 13-25. Davis, F.D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Result. (PHD Thesis). Massachusetts Institute of Technology, Massachusetts, U.S.A. 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. Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly, 13 (3), 319-340. Davis, F.D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions, and Behavioral Impacts. International Journal of Man Machine Studies, 38, 475-487. De Smet, C. Bourgonjon, J. De Wever, B. Schellens, T. Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers & Education, 58, 688–696. Deshpande, Y. Bhattacharya, S. Yammiyavar, P. (2012). A behavioral approach to modeling ındian children’s ability of adopting to e-learning environment. IEEE Proceedings of 4th International Conference on Intelligent Human Computer Interaction, Kharagpur, 27-29. Escobar-Rodriguez, T. Monge-Lozano, P. (2012). The acceptance of Moodle technology by business administration students. Computers & Education, 58, 1085-093. Fadare, O.G. Babatunde, O.H. Akomolafe, D.T. Lawal, O.O. (2011). Behavioral intention for mobile learning on 3G mobile internet technology in south-west part of Nigeria, World J of Engineering and Pure and Applied Sci, 1(2), 19-28. Fagan, M. Kilmon, C. Pandey, V. (2012). Exploring the adoption of a virtual reality simulation: The role of perceived ease of use, perceived usefulness and personal innovativeness. Campus-Wide Information Systems, 29 (2), 117-127. Farahat, T. (2012). Applying the technology acceptance model to online learning in the Egyptian universities. Procedia - Social and Behavioral Sciences, 64, 95-104. Florenthal, B. (2016). The value of interactive assignments in the online learning environment. Marketing Education Review, 26(3), 154-170. Freitas, A.S.D. Ferreira, J. B. Garcia, R. A. Kurtz, R. (2017). O efeito da ınteratividade e do suporte técnico na ıntenção de uso de um sistema de e-learning. Revista de Ciências da Administração, 19(47), 45. Harmon, D.J. (2015). User acceptance of a novel anatomical sciences mobile app for medical education-An extension of the technology acceptance model. The Ohio State University. (PhD thesis), Ohiao, A.B.D.
• Hashim, J. (2008). Factors influencing the acceptance of web-based training in Malaysia: Applying the technology acceptance model. International Journal of Training and Development, 12(4), 253-264. Heijden, H.V.D. (2003). Factors Influencing the Usage of Websites: The Case of a Generic Portal in The Netherlands. Information & Management, 40, 541–549. Hidayanto, A.N. Febriawan, D. Sucahyo, Y.G. Purwandari, B. (2014). Factors influencing the use of e-class. Journal of Industrial and Intelligent Information, 2(2), 121-125. Ho, C.K.Y. Ke, W. Liu, H. (2015). Choice decision of e-learning system: Implications from construal level theory. Information & Management, 52, 160–169. Hsia, J.W. Chang, C.C. Tseng, A.H. (2014). Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour & Information Technology, 33(1), 5164. 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. In International Conference on Cyberworlds, 39-44. Hsiao, K.L. Chen, C.C. (2015). How do we inspire children to learn with e-readers?, Library Hi Tec, 33(4), 584 596. Hsu, H.H. Chang, Y.Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of Moodle. US-China Education Review, 3(4), 211-218. Hu, P.J. Chau, P.Y.K. Sheng, O.R.L., Tam, K.Y. (1999). Examining The Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. Journal of Management Information Systems, 16 (2), 91–112. Hussein, Z. (2017). Leading to Intention:The role of attitude in relation to technology acceptance model in eLearning. Procedia Computer Science, 105, 159-164. Ibrahim, R. Leng, N.S. Yusoff, R.C.M. Samy, G.N. Masrom, S. Rizman, Z.I. (2017). E-learnıng acceptance based on technology acceptance model (TAM). Journal of Fundamental and Applied Sciences, 9(4S), 871-889. Igbaria, M. Zinatelli, N. Cragg, P. Cavaye, L.M. (1997). Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model. MIS Quarterly, 21(3), 279-306. Indahyanti, U. Sukarjadi. (2015). Applying the technology acceptance model to measure the learning management system acceptance by students of Politeknik Sakti Surabay. Jurnal Teknologi, 72(4), 127– 131. International Telecommunication Union, Measuring the Information Society Report, https://www.itu.int/en/ITUD/Statistics/Documents/publications/mis2014/MIS2014_without_Annex_4.pdf, 2014. Ismail, N.Z. Razak, M.R. Zakariah, Z. Alias, N. Aziz, M.N.A. (2012). E-Learning continuance intention among higher learning institution students’ in Malaysia. Procedia - Social and Behavioral Sciences. 67, 409415. ITU, ICT (Information and Communication Technologies) Development Index 2016, http://www.itu.int/net4/ITU-D/idi/2016/, (27.09.2017). Jan, A.U. Contreras, V. (2011). Technology acceptance model for the use of information technology in universities. Computers in Human Behavior, 27, 845-851. Jung, H.J. (2015). Fostering an English teaching environment: Factors influencing English as a foreign language teachers’ adoption of mobile learning. Informatics in Education, 14(2), 219-241. Joy, S. Kolb, D.A. (2009). Are There Cultural Differences in Learning Style? International Journal of Intercultural Relations, 33, 69-85. Kang, M. Shin, W.S. (2015). Investigation of student acceptance of synchronous e-learning in an online university. Journal of Educational Computing Research, 52(4), 475-495. Karaali, D. Gumussoy, C.A. Calisir, F. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27, 343354. Karahanna, E. Straub, D.W. Chervany, N.L. (1999). Information Technology Adoption Across Time: A CrossSectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Quarterly, 23 (2), 83-213. Kayan, S. Fussell, S.R. Setlock, L.D. (2006). Cultural Differences in the Use of Instant Messaging in Asia and North America. Computer Supported Cooperative Work (CSCW) 2006, Banff, Alberta-Canada, 525528. Khor, E.T. (2014). Student Perceptions of Using a SCORM-Compliant Learning Object (SCLO) for Learning in an ODL Environment. Asian Association of Open Universities Journal, 9(1), 47-56. Kilic, E. Güler, Ç. Çelik, H.E. Tatli, C. (2015). Learning with interactive whiteboards determining the factors on promoting interactive whiteboards to students by technology acceptance model. Interactive Technology and Smart Education, 12(4), 285-297.
• Kim, S.H. Kim, H.C. Han, S.K. (2013). A development of learning widget on m-learning and e-learning environments. Behaviour & Information Technology, 32(2), 190-202. Lai, J.Y. Ulhas, K.R. (2012). Understanding acceptance of dedicated e-textbook applications for learning: Involving Taiwanese university students. The Electronic Library, 30(3), 321-338. Lau, S.H. Woods, P.C. (2008). An empirical study of learning object acceptance in multimedia learning environment. Communications of the IBIMA, 5(1), 1-6. Lau, S.H. Woods, P.C. (2009). Understanding learner acceptance of learning objects: The roles of learning object characteristics and individual differences. British Journal of Educational Technology, 40(6), 1059-1075. Lee, Y.C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50, 1423-1438. Lee, M.C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54, 506–516. Lee, Y.H. Hsieh, Y.C. Ma, C.Y. (2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems, 24, 355–366. Lee, Y.H. Hsieh, Y.C. Hsu, C.N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Educational Technology & Society, 14 (4), 124–137. Lee, Y.H. Hsieh, Y.C. Chen, Y.H. (2013). An investigation of employees’ use of e-learning systems: Applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173–189. Lee, Y.H. Hsiao, C. Purnomo, S.H. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5), 562-579. Lee, D.Y. Lehto, M.R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61, 193–208. Lee, B.C. Yoon, J.O. Lee, I. (2009). Learners’ Acceptance of E-learning in South Korea: Theories and Results. Computers & Education, 53, 1320–1329. Lefievre, V. (2012). Gender differences in acceptance by students of training software for office tools. In Athens: ATINER'S conference paper series, 1-13. Legris, P. Ingham, J. Collerette, P. (2003). Why Do People Use Information Technology? A Critical Review of The Technology Acceptance Model. Information & Management, 40(3), 191-204. Letchumanan, M. Tarmizi, R. (2011). Assessing the intention to use e-book among engineering undergraduates in Universiti Putra Malaysia. Malaysia. Library Hi Tech, 29(3), 512-528. Li, Y. Duan, Y. Fu, Z. Alford, P. (2012). An empirical study on behavioural intention to reuse e-learning systems in rural China. British Journal of Educational Technology, 43(6), 933-948. Lin, Y.C. Chen, Y.C. Yeh, R.C. (2010). Understanding college students’ continuing intentions to use multimedia e learning systems. World Transactions on Engineering and Technology Education, 8(4), 488-493. Lin, H.F. (2013). The effect of absorptive capacity perceptions on the context-aware ubiquitous learning acceptance. Campus-Wide Information Systems, 30(4), 249-265. Lin, S.C. Persada, S.F. Nadlifatin, R. (2014). A study of student behavior in accepting the blackboard learning system: A technology acceptance model (tam) approach. In IEEE 18th international conference on computer supported cooperative work in design, 457-462. Little, P. (2016). An investigation of factors that influence registered nurses’ intentions to use e-learning systems in completing higher degrees in nursing (Published PhD thesis). College of Engineering and Computing Nova Southeastern University, Florida, U.S.A. Liu, S.H. Liao, H.L. Pratt, J.A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52, 599–607. Liu, X. (2010). Empirical testing of a theoretical extension of the technology acceptance model: An exploratory study of educational wikis. Communication Education, 59(1), 52-69. Liu, Y. Li, H. Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55, 1211–1219. Liu, X. Liu, S. Lee, S. Magjuka, R.J. (2010). Cultural Differences in Online Learning: International Student Perceptions. Journal of Educational Technology & Society, 13(3), 177-188. Lo, F.C. Hong, J.C. Lin, M.X. Hsu, C.Y. (2012). Extending the technology acceptance model to investigate impact of embodied games on learning of Xiao-zhuan. Procedia - Social and Behavioral Sciences, 64, 545-554. Lo, H.Y. Liu, G.Z. Wang, T.I. (2014). Learning how to write effectively for academic journals: A case study investigating the design and development of a genre-based writing tutorial system. Computers & Education, 78, 250-267. Loukis, E. Pazalos, K. Salagara, A. (2012). Transforming e-services evaluation data into business analytics using value models. Electronic Commerce Research and Applications, 11, 129–141.
• Lowe, B. D’alessandro, S. Wınzar, H. Laffey, D. Collıer, W. (2013). The use of Web 2.0 technologies in marketing classes: Key drivers of student acceptance. Journal of Consumer Behaviour, 12(1), 412–422. Lubbe, B. Louw, L. (2010). The perceived value of mobile devices to passengers across the airline travel activity chain. Journal of Air Transport Management, 16, 12-15. Ma, C. Chao, C. Cheng, B. (2013). Integrating technology acceptance model and task-technology fit into blended E-learning system. Journal of Applied Sciences, 13(5), 736-742. Macharia, J. Nyakwende, E. (2009). Factors affecting the adoption and diffusion of internet in higher educational institutions in Kenya. Journal of Language. Technology & Entrepreneurship in Africa, 1(2), 6-23. Mafunda, B. Swart, A. Bere, A. (2016). Mobile learning usability evaluation using two adoption models. Thammasat International Journal of Science and Technology, 21(5), 76-81. Martin, R. G. (2012). Factors affecting the usefulness of social networking in elearning at German university of technology in Oman. International Journal of e- Education, e-Business, e Management and e-Learning, 2(6), 498-502. Martinez-Torres, M. R. Marin, S.L.T. Garcia, F. B. Vazquez, S.G. Oliva, M.A. Torres, T. (2008). A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area. Behaviour & Information Technology, 27(6), 495-505. Mathieson, K. (1991). Predicting User Intentions: Comparing The Technology Acceptance Model with The Theory of Planned Behavior. Information Systems Research, 2(3), 173-191. Moghadam, A.H. Bairamzadeh, S. (2009). Extending the technology acceptance model for E-learning: a case study of Iran. In The Sixth International Conference on Information Technology: New Generations, 1659-1660. Mohamed, N. Abdul Karim, S.N. (2012). Open source e-learning anxiety, self-efficacy and acceptance - A partial least square approach. International Journal of Mathematics and Computers in Simulation, 4(6), 361-368. Mohammadi, H. (2015.a). Factors Affecting the E-learning Outcomes: An Integration of TAM and IS Success Model. Telematics and Informatics, 32, 701–719. Mohammadi, H. (2015.b). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. Moore, G.C. Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222. Moreno, V. Cavazotte, F. Alves, I. (2016). Explaining university students’ effective use of e-learning platforms. British Journal of Educational Technology, 48 (4), 995-1009. Motaghian, H. Hassanzadeh, A. Moghadam, D.K. (2013). Factors affecting university instructors’ adoption of web-based learning systems: Case study of Iran. Computers & Education, 61, 158-167. Naidu, S. (2006). E-Learning A Guide of Principles, Procedures and Practices. Commonwealth Educational Media Centre for Asia, New Delhi, India. Ok, K. Gülseçen, S. (2011). Cultural Factors on E-learnıng Systems. 5th International Computer & Instructional Technologies Symposium, Elazığ-Turkey, 541-545. Okazaki, S. Santos, L.M.R.D. (2012). Understanding e-learning adoption in Brazil: Major determinants and gender effects. In The International Review Of Research In Open And Distance Learning, 13(4), 91106. Ouyang, Y. Tang, C. Rong, W. Zhang, L. Yin, C. Xiong, Z. (2017). Task-technology fit aware expectationconfirmation model towards understanding of MOOCs continued usage. Proceedings of the 50th Hawaii International Conference on System Sciences 2017, 174-183. Padilla-Melendez, A. Garrido-Moreno, A. Aguila-Obra, A.R.D. (2008). Factors affecting e-collaboration technology use among management students. Computers & Education, 51, 609–623. Padilla-Meléndez, A. Aguila-Obra, A.D.L. Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306–317. 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(3), 150-162. Park, N. Lee, K.M. Cheong, P.H. (2008). University instructors’ acceptance of electronic courseware: An application of the technology acceptance model. Journal of Computer-Mediated Communication, 13, 163–186. Park, S.Y. Nam, M.W. Cha, S.B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592– 605. Park, Y. Son, H. Kim, C. (2012). Investigating the determinants of construction professionals' acceptance of web-based training: an extension of the technology acceptance model. Automation in Construction, 22, 377-386.
• Pereira, F.A.M. Ramos, A.S.M. Chagas, M.M.D. (2015). Satısfação e contınuıdade de uso em um ambıente vırtual de aprendızagem. Artıgo–Tecnologıa da Informação, 22(1), 133-153. Poelmans, S. Wessa, P. Milis, K. Bloemen, E. Doom, C. (2008). Usability and acceptance of e-learning in statistics education, based on the compendium platform. In International Conference of Education, Research and Innovation (ICERI2008), 1-10. Post, S.W. (2010). Modelıng of stakeholders’ perceptions and beliefs about e-learning technologies in servic elearning practices (Published PhD thesis). TUI University, California, U.S.A. Premchaiswadi, W. Porouhan, P. Premchaiswadi, N. (2012). An empirical study of the key success factors to adopt e-learning in Thailand. In International conference on information society (i-Society 2012), 333338. Punnoose, A.C. (2012). Determinants of Intention to Use eLearning Based on the Technology Acceptance Model. Journal of Information Technology Education: Research, 11(1), 302-337. Purnomo, S.H. Lee, Y.H. (2012). E-learning adoption in the banking workplace in Indonesia: An empirical study. Information Development, 29(2), 138–153. Raaij, E.M.V. Schepers, J.J.L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50, 838–852. Ramayah, T. Lee, J.W.C. (2012). System characteristıcs, satisfaction and e-learning usage: A structural equation model (SEM). The Turkish Online Journal of Educational Technology, 11(2), 196-206. Ramírez-Correa, P.E. Arenas-Gaitán, J. Rondán-Cataluñ, F.J. (2015). Gender and acceptance of e-learning: A multi-group analysis based on a structural equation model among college students in Chile and Spain. Plos One, 1-17. Rejon-Guardia, F. Sanchez-Fernandez, J. Munoz-Leiva, F. (2013). The acceptance of mıcrobloggıng ın the learnıng process: The μbam model. Journal of Technology and Science Education, 3(1), 33-50. Rezaei, M. Mohammadi, H.M. Asadi, A. Kalantary, K. (2008). Predictıng e-learning application in agricultural hıgher education using technology acceptance model. Turkish Online Journal of Distance Education, 98(1), 85-85. Roca, J.C. Gagne, M. (2008). Understanding e-learning continuance intention in the workplace: A selfdetermination theory perspective. Computers in Human Behavior, 24, 1585-1604. Ros, S. Hernández, R. Caminero, A. Robles, A. Barbero, I. Maciá, A. Holgado, F.P. (2014). On the use of extended TAM to assess students’ acceptance and intent to use third-generation learning management systems. British Journal of Educational Technology. 46(6), 1250–1271. Ruiz, J.G. Minzter, MJ. Leipzig, R.M. (2006). The Impact of E-Learning in Medical Education. Acad Med, 81(3), 208-212. Sadeghi, K. Saribagloo, J.A. Aghdam, S.H. Mahmoudi, H. (2014). The impact of İranıan teachers cultural values on computer technology acceptance. The Turkish Online Journal of Educational Technology, 13(4), 124-136. Sanchez-Franco, M.J. (2010). WebCT-The quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers & Education, 54, 37-46. Sanchez, R.A. Hueros, A.D. Ordaz, MG. (2013). E-learning and the University of Huelva: a study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135-160. Seet, B.C. Goh, T.T. (2012). Exploring the affordance and acceptance of an e-reader device as a collaborative learning system. The Electronic Library, 30(4), 516-542. Seif, M.H. Rastegar, A. Ardakani, S.J.H. Saeedikiya, M. (2013). Factors influencing intention to use and application of web-based learning among students of Shiraz Payame Noor University (providing a path analysis model). Journal of Basic and Applied Scientific Research, 3(2), 848-852. Shah, G.U.G. Bhatti, M.N. Iftikhar, M. Qureshi, M.I. Zaman, K. (2013). Implementation of technology acceptance model in e-learning environment in rural and urban areas of Pakistan. World Applied Sciences Journal, 27(11), 1495-1507. Shah, S.A.M. Iqbal, N. Janjua, S.Y. Amjad, S. (2013). Employee behavior towards adoption of e-learning courses: validating technology acceptance model. Mediterranean Journal of Social Sciences, 4(14), 765-774. Shen, C.C. Chuang, H.M. (2010). Exploring users’ attitudes and intentions toward the interactive whiteboard technology environment. International Review on Computers and Software, 5(2), 200-208. Shen, J. Eder, L.B. (2009). Intentions to use virtual worlds for education. Journal of Information Systems Education, 2(2), 225-233. Shih, B.Y. Chen, C.Y. Shih, C.H. Su, W.L. (2012). The control application and simulation–particle swarm optimization exploration of control application for user intention toward mobile Mandarin learning system. Journal of Vibration and Control, 19(13), 2036-2045.
• Shroff, R.H. Deneen, C.C. Ng, E.M.W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an eportfolio system. Australasian Journal of Educational Technology, 27(4), 600-618. Shyu, S.H.P. Huang, J.H. (2011). Elucidating usage of e-government learning: A perspective of the extended technology acceptance model. Government Information Quarterly, 28, 491–502. Smith, J.A. Sivo, S.A. (2012). Predicting continued use of online teacher professional development and the influence of social presence and sociability. British Journal of Educational Technology, 43(6), 871– 882. Song, Y. Kong, S.C. (2017). Investigating students’ acceptance of a statistics learning platform using technology acceptance model. Journal of Educational Computing Research, 55(6), 865-897. Suki, N.M. Suki, N.M. (2011). Users‘ behavıor towards ubıquıtous m-learnıng. Turkish Online Journal of Distance Education. 12(3), 118-129. Sumak, B. Hericko, M. Pušnik, M. (2011). A Meta-Analysis of E-Learning Technology Acceptance: The Role of User Types and E-Learning Technology Types. Computers in Human Behavior, 27, 2067-2077. Tajudeen, S.A. Basha, M.K. Michael, F.O. Mukthar, A.L. (2012). Determinant of mobile devices acceptance for learning among students in developing country. The Malaysian Online Journal of Educational Technology, 1(3), 17-29. Tan, P.J.B. (2015). English e-learning in the virtual classroom and the factors that influence esl (English as a second language): Taiwanese citizens’ acceptance and use of the modular object- oriented dynamic learning environment. Social Science Information, 54(2), 211-228. Tarhini, A. Hone, K. Liu, X. (2013.a). 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. Tarhini, A. Hone, K. Liu, X. (2013.b). Factors affecting students’ acceptance of e-learning environments in developing countries: A structural equation modeling approach. International Journal of Information and Education Technology, 3(1), 54-59. Tarhini, A. Hone, K. Liu, X. (2014). 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. Liu, X. (2015.a). 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, 46(4), 739–755. Tarhini, A. Hassouna, M. Abbasi, M.S. Orozco, J. (2015.b). Towards the acceptance of RSS to support learning: An empirical study to validate the technology acceptance model in Lebanon. Electronic Journal of eLearning Volume, 13(1), 30-41. Tarhini, A. Hone, K. Liu, X. Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of e-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactıve Learnıng Envıronments. 25(3). Taylor, S. Todd, P.A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176. Teo, T. (2011). Modeling the determinants of pre-service teachers' perceived usefulness of e-learning. CampusWide Information Systems, 28(2), 124-140. Teo, T. Luan, W.S. 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). Educational Technology & Society, 11 (4), 265–280. Tobing, V. Hamzah, M. Sura, S. Amin, H. (2008). Assessing the acceptability of adaptive e-learning system. Fifth International Conference on eLearning for Knowledge-Based Society, 11-12. Tran, K.N.N. (2016). The adoption of blended e-learning technology in Vietnam using a revision of the technology acceptance model. Journal of Information Technology Education: Research, 15, 253-282. Trayek, F.A.A. Hassan, S.S.S. (2013). Attıtude towards the use of learnıng management system among unıversıty students: A case study. Turkish Online Journal of Distance Education, 14(3), 91-103. Tselios, N. Daskalakis, S. Papadopoulou, M. (2011). Assessing the acceptance of a blended learning university course. Educational Technology & Society, 14 (2), 224-235. Tseng, A.H. Hsia, J.W. (2008). The impact of internal locus of control on perceived usefulness and perceived ease of use in e-learning: An extension of the technology acceptance model. In International conference on cyberworlds, 815-819. Tung, F.C. Chang, S.C. (2008.a). Nursing students’ behavioral intention to use online courses: A questionnaire survey. International Journal of Nursing Studies, 45, 1299-1309. Tung, F.C. Chang, S.C. (2018.b). A new hybrid model for exploring the adoption of online nursing courses. Nurse Education Today, 28, 293-300. United Nations Development Programme, Education Index, http://hdr.undp.org/en/data (08.08.2017
• Ursavaş, Ö.F. (2015). Öğretmenlerin hazcı ve faydacı motivasyonlarının tablet PC kullanımına yönelik davranışsal niyetleri üzerinde etkisi. Eğitim ve Bilim, 40, 25-43.
• Veloo, R. Masood, M. (2014). Acceptance and ıntention to use the ilearn system in an automotive semiconductor company in the northern region of Malaysia. Procedia - Social and Behavioral Sciences. 116, 13781382.
• Venkatesh, V. Davis, F.D. (2000). A Theoretical Extension of The Technology Acceptance Model: Four Longitudinal Field Studies. Management 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.
• Wang, W.W. Wang, C.C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53,761-774.
• Webster, J. Martocchio, J.J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly. 16 (2), 201-226.
• Williams, M. Williams, J. (2009). Evaluating a model of business school students' acceptance of web-based course management systems. International Journal of Management Education, 8(3), 59-70. World Bank, World Bank List of Economies June 2017, https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lendinggroups (08.08.2017)
• Wu, B. Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
• Wu, B. Zhang, C. (2014). Empirical study on continuance intentions towards e-learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027-1038.
• Wu, C. Kuo, Y. Wu, S. (2013). Investigating the antecedents of university students’ behavioral ıntention to use ipad for learning. International Journal of e-Education, e-Business, e-Management and e-Learning, 3(6), 468-471.
• Yang, S.C. Lin, C.H. (2011). Factors affecting the intention to use Facebook to support problem-based learning among employees in a Taiwanese manufacturing company. African Journal of Business Management, 5(22), 9014-9022.
• Yuen, A.H.K. Ma, W.W.K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229-243.
• Zare, H. Yazdanparast, S. (2013). The causal model of effective factors on intention to use of information technology among payam noor and traditional universities students. Life Science Journal, 10(2), 46-50.
• Zhang, S. Zhao, J. Tan, W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science & Technology, 13(3), 312-317.
• Zhao, J. Tan, W. (2010). E-learning systems adoption across cultures: A comparison study. In E-Product Eservice and E-entertainment, 1-4, 2010.