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Exploring the Role of Individual Differences on Instructors’ Technology Acceptance in Online Education through a Motivational Perspective

Yıl 2024, Cilt: 9 Sayı: 1, 17 - 31, 05.01.2024
https://doi.org/10.53850/joltida.1219447

Öz

The present study aims to investigate the potential variables that influence the faculty members’ intention to continue using online learning systems during and after the pandemic based on extended Technology Acceptance Model (TAM) and Self Determination Theory (SDT), and to study individual differences between these variables. The methodology of the study was based on survey research and causal comparative methods. Convenience sampling method was used to identify the participants of the study, who are 302 faculty members working at twelve different state universities. Explanatory and confirmatory factor analysis (EFA-CFA) were used to test the factor structure of the data collection tool and to validate the tool through examining the model fit. Descriptive statistics were used to examine the distribution of the dependent variable scores of the participants, and one-way MANOVA was used to compare the variables based on individual differences. The findings indicated that CMP had the highest mean score, followed by the constructs of SDT (competence, autonomy, relatedness). A significant difference for male participants was observed in perceived ease of use and competence variables based on gender. No significant difference was found between the variables based on academic title. The present study established that all variables except relatedness indicated a significant difference that favors instructors with high and medium level online learning experience. It was concluded that the comparison of the motivational variables based on the individual differences of the instructors, which have critical importance in online education as well as in higher education, can contribute to the establishment of effective and sustainable quality learning environments (distance or hybrid) and to the existing literature.

Kaynakça

  • 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), 30–55. https://doi.org/10.1108/17410391111097410.
  • 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 Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
  • 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 e-portfolios. Computers in Human Behavior, 63, 75–90. https://doi.org/10.1016/j.chb.2016.05.014
  • Adele, S., & Brangier, E. (2013). Characteristics and modalities of changes in Human Technology Relationship models. In IADIS International conference ICT, Society and Human Beings 2013 and IADIS International conference e-Commerce 2013 (pp. pp-101). IADIS Press.
  • Al-alak, B. A., & Alnawas, I. A. (2011). Measuring the acceptance and adoption of e-learning by academic staff. Knowledge Management & E-Learning: An International Journal, 3(2), 201-221. https://doi.org/10.34105/j.kmel.2011.03.016
  • Armenteros, M., Liaw, S.-S., Fernandez, M., Diaz, R. F., & Sanchez, R. A. (2013). Surveying FIFA instructors’ behavioral intention toward the multimedia teaching materials. Computers & Education, 61, 91–104. https://doi.org/10.1016/j.compedu.2012.09.010
  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191. https://doi.org/10.1037/0033-295X.84.2.191
  • Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-A study of South Korea. The International Journal of Management Education, 19(2), 100503. https://doi.org/10.1016/j.ijme.2021.100503
  • Baron, N. S., & Hård af Segerstad, Y. (2010). Cross-cultural patterns in mobile-phone use: Public space and reachability in Sweden, the USA and Japan. New Media & Society, 12(1), 13–34. https://doi.org/10.1177/1461444809355111
  • Baydaş, Ö. (2015). Öğretmen adaylarının gelecekteki derslerinde bilişim teknolojilerini kullanma niyetlerini belirlemeye yönelik bir model önerisi [Unpublished doctoral dissertation]. Atatürk Üniversitesi, Erzurum.
  • Baydas, O., & Goktas, Y. (2017). A model for preservice teachers’ intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), 930-945. https://doi.org/10.1080/10494820.2016.1232277
  • Baydas, O., & Yilmaz, R. M. (2018). Pre‐service teachers’ intention to adopt mobile learning: A motivational model. British Journal of Educational Technology, 49(1), 137-152. https://doi.org/10.1111/bjet.12521
  • Bayrak, F, Tıbı, M, & Altun, A. (2020). Development of online course satisfaction scale. Turkish Online Journal of Distance Education, 21(4), 110-123. https://doi.org/10.17718/tojde.803378
  • Berniak-Wozny, J., Rataj, M., & Plebanska, M. (2021). The impact of learning mode on student satisfaction with teaching quality: Evaluation of academic staff teaching before and during Covid-19. European Research Studies Journal, 24(3B), 722-738.
  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  • Buyukozturk, S., Kilic Cakmak, E., Akgun, O.E., Karadeniz, S, & Demirel, F. (2013). Bilimsel araştırma yöntemleri. Ankara: Pegem Yayıncılık.
  • Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010
  • Cheok, M. L., & Wong, S. L. (2015). Predictors of e-learning satisfaction in teaching and learning for school teachers: A literature review. International Journal of Instruction, 8(1), 75-90. https://files.eric.ed.gov/fulltext/EJ1085289.pdf
  • Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674–1684. https://doi.org/10.1016/j.chb.2010.06.016
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008
  • 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. https://doi.org/10.1287/mnsc.35.8.982
  • Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. https://doi.org/10.1207/S15327965PLI1104_01
  • 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(2), 688–696. https://doi.org/10.1016/j.compedu.2011.09.013
  • Dundar, H., & Akcayır, M. (2014). Implementing tablet PCs in schools: Students’ attitudes and opinions. Computers in Human Behavior, 32, 40–46. https://doi.org/10.1016/j.chb.2013.11.020
  • Ebardo, R., & Suarez, M. T. (2023). Do cognitive, affective and social needs influence mobile learning adoption in emergency remote teaching?. Research and Practice in Technology Enhanced Learning, 18, 014-014. https://doi.org/10.58459/rptel.2023.18014
  • El Alfy, S., Gomez, J. M., & Ivanov, D. (2017). Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates. Education and Information Technologies, 22(5), 2605–2627. https://doi.org/10.1007/s10639-016-9562-1
  • Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2), 210–232. https://jolt.merlot.org/Vol11no2/Fathema_0615.pdf
  • Ferrer, J., Ringer, A., Saville, K., Parris, M. A., & Kashi, K. (2022). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83, 317–338. https://doi.org/10.1007/s1073 4-020-00657-5
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–47. https://doi.org/10.1177/002224378101800104 Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (Vol. 7, p. 429). New York: McGraw-hill.
  • Garone, A., Pynoo, B., Tondeur, J., Cocquyt, C., Vanslambrouck, S., Bruggeman, B., & Struyven, K. (2019). Clustering university teaching staff through UTAUT: Implications for the acceptance of a new learning management system. British Journal of Educational Technology, 50(5), 2466–2483. https://doi.org/10.1111/bjet.12867
  • Gonzalez-Gomez, F., Guardiola, J., Rodriguez, O. M., & Alonso, M. A. M. (2012). Gender differences in e-learning satisfaction. Computers & Education, 58(1), 283-290. doi: https://doi.org/10.1016/j.compedu.2011.08.017
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
  • Hair, J. J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). London: SAGE Publications.
  • Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners' intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology, 46(2), 381-390. https://doi.org/10.1111/bjet.12148
  • Harvey, H. L., Parahoo, S., & Santally, M. (2017). Should gender differences be considered when assessing student satisfaction in the online learning environment for millennials?. Higher Education Quarterly, 71(2), 141-158. https://doi.org/10.1111/hequ.12116.
  • Hijazi-Omari, H., & Ribak, R. (2008). Playing with fire: On the domestication of the mobile phone among Palestinian teenage girls in Israel. Information, Communication & Society, 1(2), 149–166. https://doi.org/10.1080/13691180801934099
  • Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, L. T. M., Van Pham, A. T., & Dinh, H. V. (2020). Students' adoption of e-learning in emergency situation: the case of a Vietnamese university during COVID-19. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-08-2020-0164
  • Huang, R. H., Liu, D. J., Guo, J., Yang, J. F., Zhao, J. H., Wei, X. F., Knyazeva, S., Li, M., Zhuang, R. X., Looi, C. K., & Chang, T. W. (2020). Guidance on flexible learning during campus closures: Ensuring course quality of higher education in COVID-19 outbreak. Smart Learning Institute of Beijing Normal University.
  • Huck, S. W. (2012). Reading statistics and research (6th edition). Boston, MA: Pearson Education.
  • İlic, U. (2021). Online course satisfaction in a holistic flipped classroom approach. Journal of Educational Technology and Online Learning, 4(3), 432-447. https://doi.org/10.31681/jetol.93532
  • Jan, S. K. (2015). The relationship between academic self-efficacy, computer self-efficacy, prior experience, and satisfaction with online learning. American Journal of Distance Education, 29(1), 30–40. https://doi.org/10.1080/08923647.2015.994366
  • Jeong, J. S., & Lee, J. H. (2012). Path analysis among perceived autonomy support, self-determination motivation and academic performance in a cyber university. Journal of Korean Association for Educational Information and Media, 18(3), 365–387.
  • Khan, M., Parvaiz, G. S., Bashir, N., Imtiaz, S., & Bae, J. (2022). Students’ key determinant structure towards educational technology acceptance at universities, during COVID 19 lockdown: Pakistani perspective. Cogent Education, 9(1), 2039088. https://doi.org/10.1080/2331186X.2022.2039088
  • Kılıçer, K. & Odabaşı, H. F., (2010). Individual Innovativeness Scale (IS): the study of adaptation to Turkish, validity and reliability. Hacettepe University Journal of Education, 38, 150-164. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003
  • Kovačević, I., Labrović, J. A., Petrović, N., & Kužet, I. (2021). Recognizing predictors of students' emergency remote online learning satisfaction during COVID-19. Education Sciences, 11(11), 693. https://doi.org/10.3390/educs ci11110693
  • Kurudirek, A. M., & Kurudirek, I. M. (2021). individual innovativeness and online learning attitudes of academic staff in institutions providing sports training at the level of bachelor degree. Asian Journal of Education and Training, 7(3), 163-168. https://doi.org/10.20448/journal.522.2021.73.163.168
  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
  • Liu, O. L. (2011). Student evaluation of instruction: In the new paradigm of distance education. Research in Higher Education, 53(4), 471–486. https://doi.org/10.1007/s11162-011-9236-1.
  • Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610. https://doi.org/10.1016/j.compedu.2009.09.009.
  • Lowenthal, P., Borup, J., West, R., & Archambault, L. (2020). Thinking beyond Zoom: Using asynchronous video to maintain connection and engagement during the COVID-19 Pandemic. Journal of Technology and Teacher Education, 28(2), 383–391. Retrieved from https:// www.learntechlib.org/primary/p/216192/
  • Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90, 153-169. https://doi.org/10.1016/j.chb.2018.08.056
  • Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. https://doi.org/10.1007/s10639-021-10557-5
  • Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95. https://doi.org/10.1007/s10209-014-0348-1
  • McKnight-Tutein, G. & Thackaberry, A.S. (2011). Having it all: The hybrid solution for the best of both worlds in women’s postsecondary education. Distance Learning, 8(3), 17-22. https://www.infoagepub.com/dl-issue.html?i=p54c11064c6dfa
  • Navimipour, N. J., & Zareie, B. (2015). A model for assessing the impact of e-learning systems on employees’ satisfaction. Computers in Human Behavior, 53, 475-485. https://doi.org/10.1016/j.chb.2015.07.026 Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
  • Ocak, M. A., & Ünsal, N. Ö. (2021). A content analysis of blended learning studies conducted during Covid-19 Pandemic period. Akademik Açı, 1(2), 175-210.
  • Ong, Ch. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829. https://doi.org/10.1016/j.chb.2004.03.006
  • Padilla-Meléndez, A., del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317. https://doi.org/10.1016/j.compedu.2012.12.014
  • Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS for Windows. (3rd edition). Maidenhead, PA: Open University Press.
  • Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683–696. https://doi.org/10.1016/j.ijhcs.2006.01.003
  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: Free Press.
  • Ryan, R. M., & Deci, E. L. (2000a). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. https://doi.org/10.1006/ceps.1999.1020
  • Ryan, R. M., & Deci, E. L. (2000b). Self-Determination Theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68. https://doi.org/10.1037/0003-066X.55.1.68
  • Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
  • Sánchez-Franco, M. J. (2006). Exploring the influence of gender on the web usage via partial least squares. Behaviour & Information Technology, 25(1), 19–36. https://doi.org/10.1080/01449290500124536
  • Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019). Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158-167. https://doi.org/10.1016/j.chb.2019.01.019
  • Sahin, F., & Sahin, Y. L. (2021). Examining the acceptance of e-learning systems during the pandemic: The role of compatibility, enjoyment and anxiety. International Technology and Education Journal, 5(1), 1-10.
  • Şahin, F. (2021). Öğretmen adaylarının bilişim teknolojileri kullanım niyetlerinde duyguların ve temel psikolojik ihtiyaçların rolü: Teknolojinin kabulüne motivasyonel bir yaklaşım (Unpublished doctoral dissertation). Anadolu University.
  • Şahin, F., Doğan, E., İlic, U., & Şahin, Y. L. (2021). Factors influencing instructors’ intentions to use information technologies in higher education amid the pandemic. Education and Information Technologies, 26(4), 4795-4820. https://doi.org/10.1007/s10639-021-10497-0
  • Şahin, F., Doğan, E., Okur, M. R., & Şahin, Y. L. (2022). Emotional outcomes of e-learning adoption during compulsory online education. Education and Information Technologies, 27, 7827–7849. https://doi.org/10.1007/s10639-022-10930-y
  • Şahin, F., & Şahin, Y. L. (2022). Drivers of technology adoption during the COVID-19 pandemic: The motivational role of psychological needs and emotions for pre-service teachers. Social Psychology of Education, 25, 567-592. https://doi.org/10.1007/s11218-022-09702-w
  • Şahin, F., Doğan, E., Yıldız, G., & Okur, M. R. (2022). University students with special needs: Investigating factors influencing e- learning adoption. Australasian Journal of Educational Technology, 38(5), 146-162. https://doi.org/10.14742/ajet.7454
  • Taghizadeh, S. K., Rahman, S. A., Nikbin, D., Alam, M. M. D., Alexa, L., Ling Suan, C., & Taghizadeh, S. (2021). Factors influencing students’ continuance usage intention with online learning during the pandemic: a cross-country analysis. Behaviour & Information Technology, 1-20. https://doi.org/10.1080/0144929X.2021.1912181
  • Tarhini, A., Hassouna, M., Abbasi, M. S., & Orozco, J. (2015). Towards the acceptance of RSS to support learning: An empirical study to validate the technology acceptance model in Lebanon. Electronic Journal of e-Learning, 13(1), 30–41.
  • Tarhini, A., Hone, K., & Liu, X. (2014). The S-COMPects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153-163. https://doi.org/10.1016/j.chb.2014.09.020
  • Teo, T. (2008). Pre-service teachers' attitudes towards computer use: A Singapore survey. Australasian Journal of Educational Technology, 24(4). 413-424. https://doi.org/10.14742/ajet.1201
  • Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51–66. https://doi.org/10.1080/10494820.2011.641674
  • Teo, T. (2014). Preservice teachers' satisfaction with e-learning. Social Behavior and Personality: An International Journal, 42(1), 3-6. https://doi.org/10.2224/sbp.2014.42.1.3
  • Trust, T., & Whalen, J. (2020). Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 Pandemic. Journal of Technology and Teacher Education, 28(2), 189–199.
  • Tondeur, J., van Braak, J., Siddiq, F., & Scherer, R. (2016). Time for a new approach to prepare future teachers for educational technology use: Its meaning and measurement. Computers & Education, 94, 134–150. https://doi.org/10.1016/j.compedu.2015.11.009
  • Ursavaş, Ö. F. (2014). Öğretmenlerin bilişim teknolojilerini kullanmaya yönelik davranışlarının modellenmesi [Unpublished doctoral dissertation]. Gazi Üniversitesi, Ankara.
  • Ursavaş, Ö., Şahin, S., & McIlroy, D. (2014). Technology acceptance measure for teachers: T-TAM/Öğretmenler için Teknoloji Kabul Ölçeği: Ö-TKÖ. Eğitimde Kuram ve Uygulama, 10(4), 885-917.
  • Vansteenkiste, M., Zhou, M., Lens, W., & Soenens, B. (2005). Experiences of autonomy and control among Chinese learners: Vitalizing or immobilizing? Journal of Educational Psychology, 97(3), 468–483. https://doi.org/10.1037/0022-0663.97.3.468
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 115-139. https://doi.org/10.2307/3250981
  • 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. https://doi.org/10.2307/30036540
  • Vlachopoulos, D., & Makri, A. (2021). Quality teaching in online higher education: The perspectives of 250 online tutors on technology and pedagogy. International Journal of Emerging Technologies in Learning (iJET), 16(6), 40-56.
  • Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40, 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
  • Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.
  • Xu, D., & Wang, H. (2006). Intelligent agent supported personalization for virtual learning environments. Decision Support Systems, 42(2), 825–843. https://doi.org/10.1016/j.dss.2005.05.033
  • Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: S-S-COMPicacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449. https://doi.org/10.1016/S1071-5819(03)00114-9
Yıl 2024, Cilt: 9 Sayı: 1, 17 - 31, 05.01.2024
https://doi.org/10.53850/joltida.1219447

Öz

Kaynakça

  • 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), 30–55. https://doi.org/10.1108/17410391111097410.
  • 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 Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
  • 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 e-portfolios. Computers in Human Behavior, 63, 75–90. https://doi.org/10.1016/j.chb.2016.05.014
  • Adele, S., & Brangier, E. (2013). Characteristics and modalities of changes in Human Technology Relationship models. In IADIS International conference ICT, Society and Human Beings 2013 and IADIS International conference e-Commerce 2013 (pp. pp-101). IADIS Press.
  • Al-alak, B. A., & Alnawas, I. A. (2011). Measuring the acceptance and adoption of e-learning by academic staff. Knowledge Management & E-Learning: An International Journal, 3(2), 201-221. https://doi.org/10.34105/j.kmel.2011.03.016
  • Armenteros, M., Liaw, S.-S., Fernandez, M., Diaz, R. F., & Sanchez, R. A. (2013). Surveying FIFA instructors’ behavioral intention toward the multimedia teaching materials. Computers & Education, 61, 91–104. https://doi.org/10.1016/j.compedu.2012.09.010
  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191. https://doi.org/10.1037/0033-295X.84.2.191
  • Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-A study of South Korea. The International Journal of Management Education, 19(2), 100503. https://doi.org/10.1016/j.ijme.2021.100503
  • Baron, N. S., & Hård af Segerstad, Y. (2010). Cross-cultural patterns in mobile-phone use: Public space and reachability in Sweden, the USA and Japan. New Media & Society, 12(1), 13–34. https://doi.org/10.1177/1461444809355111
  • Baydaş, Ö. (2015). Öğretmen adaylarının gelecekteki derslerinde bilişim teknolojilerini kullanma niyetlerini belirlemeye yönelik bir model önerisi [Unpublished doctoral dissertation]. Atatürk Üniversitesi, Erzurum.
  • Baydas, O., & Goktas, Y. (2017). A model for preservice teachers’ intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), 930-945. https://doi.org/10.1080/10494820.2016.1232277
  • Baydas, O., & Yilmaz, R. M. (2018). Pre‐service teachers’ intention to adopt mobile learning: A motivational model. British Journal of Educational Technology, 49(1), 137-152. https://doi.org/10.1111/bjet.12521
  • Bayrak, F, Tıbı, M, & Altun, A. (2020). Development of online course satisfaction scale. Turkish Online Journal of Distance Education, 21(4), 110-123. https://doi.org/10.17718/tojde.803378
  • Berniak-Wozny, J., Rataj, M., & Plebanska, M. (2021). The impact of learning mode on student satisfaction with teaching quality: Evaluation of academic staff teaching before and during Covid-19. European Research Studies Journal, 24(3B), 722-738.
  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  • Buyukozturk, S., Kilic Cakmak, E., Akgun, O.E., Karadeniz, S, & Demirel, F. (2013). Bilimsel araştırma yöntemleri. Ankara: Pegem Yayıncılık.
  • Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010
  • Cheok, M. L., & Wong, S. L. (2015). Predictors of e-learning satisfaction in teaching and learning for school teachers: A literature review. International Journal of Instruction, 8(1), 75-90. https://files.eric.ed.gov/fulltext/EJ1085289.pdf
  • Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674–1684. https://doi.org/10.1016/j.chb.2010.06.016
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008
  • 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. https://doi.org/10.1287/mnsc.35.8.982
  • Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. https://doi.org/10.1207/S15327965PLI1104_01
  • 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(2), 688–696. https://doi.org/10.1016/j.compedu.2011.09.013
  • Dundar, H., & Akcayır, M. (2014). Implementing tablet PCs in schools: Students’ attitudes and opinions. Computers in Human Behavior, 32, 40–46. https://doi.org/10.1016/j.chb.2013.11.020
  • Ebardo, R., & Suarez, M. T. (2023). Do cognitive, affective and social needs influence mobile learning adoption in emergency remote teaching?. Research and Practice in Technology Enhanced Learning, 18, 014-014. https://doi.org/10.58459/rptel.2023.18014
  • El Alfy, S., Gomez, J. M., & Ivanov, D. (2017). Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates. Education and Information Technologies, 22(5), 2605–2627. https://doi.org/10.1007/s10639-016-9562-1
  • Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2), 210–232. https://jolt.merlot.org/Vol11no2/Fathema_0615.pdf
  • Ferrer, J., Ringer, A., Saville, K., Parris, M. A., & Kashi, K. (2022). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83, 317–338. https://doi.org/10.1007/s1073 4-020-00657-5
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–47. https://doi.org/10.1177/002224378101800104 Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (Vol. 7, p. 429). New York: McGraw-hill.
  • Garone, A., Pynoo, B., Tondeur, J., Cocquyt, C., Vanslambrouck, S., Bruggeman, B., & Struyven, K. (2019). Clustering university teaching staff through UTAUT: Implications for the acceptance of a new learning management system. British Journal of Educational Technology, 50(5), 2466–2483. https://doi.org/10.1111/bjet.12867
  • Gonzalez-Gomez, F., Guardiola, J., Rodriguez, O. M., & Alonso, M. A. M. (2012). Gender differences in e-learning satisfaction. Computers & Education, 58(1), 283-290. doi: https://doi.org/10.1016/j.compedu.2011.08.017
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
  • Hair, J. J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). London: SAGE Publications.
  • Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners' intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology, 46(2), 381-390. https://doi.org/10.1111/bjet.12148
  • Harvey, H. L., Parahoo, S., & Santally, M. (2017). Should gender differences be considered when assessing student satisfaction in the online learning environment for millennials?. Higher Education Quarterly, 71(2), 141-158. https://doi.org/10.1111/hequ.12116.
  • Hijazi-Omari, H., & Ribak, R. (2008). Playing with fire: On the domestication of the mobile phone among Palestinian teenage girls in Israel. Information, Communication & Society, 1(2), 149–166. https://doi.org/10.1080/13691180801934099
  • Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, L. T. M., Van Pham, A. T., & Dinh, H. V. (2020). Students' adoption of e-learning in emergency situation: the case of a Vietnamese university during COVID-19. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-08-2020-0164
  • Huang, R. H., Liu, D. J., Guo, J., Yang, J. F., Zhao, J. H., Wei, X. F., Knyazeva, S., Li, M., Zhuang, R. X., Looi, C. K., & Chang, T. W. (2020). Guidance on flexible learning during campus closures: Ensuring course quality of higher education in COVID-19 outbreak. Smart Learning Institute of Beijing Normal University.
  • Huck, S. W. (2012). Reading statistics and research (6th edition). Boston, MA: Pearson Education.
  • İlic, U. (2021). Online course satisfaction in a holistic flipped classroom approach. Journal of Educational Technology and Online Learning, 4(3), 432-447. https://doi.org/10.31681/jetol.93532
  • Jan, S. K. (2015). The relationship between academic self-efficacy, computer self-efficacy, prior experience, and satisfaction with online learning. American Journal of Distance Education, 29(1), 30–40. https://doi.org/10.1080/08923647.2015.994366
  • Jeong, J. S., & Lee, J. H. (2012). Path analysis among perceived autonomy support, self-determination motivation and academic performance in a cyber university. Journal of Korean Association for Educational Information and Media, 18(3), 365–387.
  • Khan, M., Parvaiz, G. S., Bashir, N., Imtiaz, S., & Bae, J. (2022). Students’ key determinant structure towards educational technology acceptance at universities, during COVID 19 lockdown: Pakistani perspective. Cogent Education, 9(1), 2039088. https://doi.org/10.1080/2331186X.2022.2039088
  • Kılıçer, K. & Odabaşı, H. F., (2010). Individual Innovativeness Scale (IS): the study of adaptation to Turkish, validity and reliability. Hacettepe University Journal of Education, 38, 150-164. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003
  • Kovačević, I., Labrović, J. A., Petrović, N., & Kužet, I. (2021). Recognizing predictors of students' emergency remote online learning satisfaction during COVID-19. Education Sciences, 11(11), 693. https://doi.org/10.3390/educs ci11110693
  • Kurudirek, A. M., & Kurudirek, I. M. (2021). individual innovativeness and online learning attitudes of academic staff in institutions providing sports training at the level of bachelor degree. Asian Journal of Education and Training, 7(3), 163-168. https://doi.org/10.20448/journal.522.2021.73.163.168
  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
  • Liu, O. L. (2011). Student evaluation of instruction: In the new paradigm of distance education. Research in Higher Education, 53(4), 471–486. https://doi.org/10.1007/s11162-011-9236-1.
  • Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610. https://doi.org/10.1016/j.compedu.2009.09.009.
  • Lowenthal, P., Borup, J., West, R., & Archambault, L. (2020). Thinking beyond Zoom: Using asynchronous video to maintain connection and engagement during the COVID-19 Pandemic. Journal of Technology and Teacher Education, 28(2), 383–391. Retrieved from https:// www.learntechlib.org/primary/p/216192/
  • Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90, 153-169. https://doi.org/10.1016/j.chb.2018.08.056
  • Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. https://doi.org/10.1007/s10639-021-10557-5
  • Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95. https://doi.org/10.1007/s10209-014-0348-1
  • McKnight-Tutein, G. & Thackaberry, A.S. (2011). Having it all: The hybrid solution for the best of both worlds in women’s postsecondary education. Distance Learning, 8(3), 17-22. https://www.infoagepub.com/dl-issue.html?i=p54c11064c6dfa
  • Navimipour, N. J., & Zareie, B. (2015). A model for assessing the impact of e-learning systems on employees’ satisfaction. Computers in Human Behavior, 53, 475-485. https://doi.org/10.1016/j.chb.2015.07.026 Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
  • Ocak, M. A., & Ünsal, N. Ö. (2021). A content analysis of blended learning studies conducted during Covid-19 Pandemic period. Akademik Açı, 1(2), 175-210.
  • Ong, Ch. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829. https://doi.org/10.1016/j.chb.2004.03.006
  • Padilla-Meléndez, A., del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317. https://doi.org/10.1016/j.compedu.2012.12.014
  • Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS for Windows. (3rd edition). Maidenhead, PA: Open University Press.
  • Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683–696. https://doi.org/10.1016/j.ijhcs.2006.01.003
  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: Free Press.
  • Ryan, R. M., & Deci, E. L. (2000a). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. https://doi.org/10.1006/ceps.1999.1020
  • Ryan, R. M., & Deci, E. L. (2000b). Self-Determination Theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68. https://doi.org/10.1037/0003-066X.55.1.68
  • Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
  • Sánchez-Franco, M. J. (2006). Exploring the influence of gender on the web usage via partial least squares. Behaviour & Information Technology, 25(1), 19–36. https://doi.org/10.1080/01449290500124536
  • Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019). Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158-167. https://doi.org/10.1016/j.chb.2019.01.019
  • Sahin, F., & Sahin, Y. L. (2021). Examining the acceptance of e-learning systems during the pandemic: The role of compatibility, enjoyment and anxiety. International Technology and Education Journal, 5(1), 1-10.
  • Şahin, F. (2021). Öğretmen adaylarının bilişim teknolojileri kullanım niyetlerinde duyguların ve temel psikolojik ihtiyaçların rolü: Teknolojinin kabulüne motivasyonel bir yaklaşım (Unpublished doctoral dissertation). Anadolu University.
  • Şahin, F., Doğan, E., İlic, U., & Şahin, Y. L. (2021). Factors influencing instructors’ intentions to use information technologies in higher education amid the pandemic. Education and Information Technologies, 26(4), 4795-4820. https://doi.org/10.1007/s10639-021-10497-0
  • Şahin, F., Doğan, E., Okur, M. R., & Şahin, Y. L. (2022). Emotional outcomes of e-learning adoption during compulsory online education. Education and Information Technologies, 27, 7827–7849. https://doi.org/10.1007/s10639-022-10930-y
  • Şahin, F., & Şahin, Y. L. (2022). Drivers of technology adoption during the COVID-19 pandemic: The motivational role of psychological needs and emotions for pre-service teachers. Social Psychology of Education, 25, 567-592. https://doi.org/10.1007/s11218-022-09702-w
  • Şahin, F., Doğan, E., Yıldız, G., & Okur, M. R. (2022). University students with special needs: Investigating factors influencing e- learning adoption. Australasian Journal of Educational Technology, 38(5), 146-162. https://doi.org/10.14742/ajet.7454
  • Taghizadeh, S. K., Rahman, S. A., Nikbin, D., Alam, M. M. D., Alexa, L., Ling Suan, C., & Taghizadeh, S. (2021). Factors influencing students’ continuance usage intention with online learning during the pandemic: a cross-country analysis. Behaviour & Information Technology, 1-20. https://doi.org/10.1080/0144929X.2021.1912181
  • Tarhini, A., Hassouna, M., Abbasi, M. S., & Orozco, J. (2015). Towards the acceptance of RSS to support learning: An empirical study to validate the technology acceptance model in Lebanon. Electronic Journal of e-Learning, 13(1), 30–41.
  • Tarhini, A., Hone, K., & Liu, X. (2014). The S-COMPects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153-163. https://doi.org/10.1016/j.chb.2014.09.020
  • Teo, T. (2008). Pre-service teachers' attitudes towards computer use: A Singapore survey. Australasian Journal of Educational Technology, 24(4). 413-424. https://doi.org/10.14742/ajet.1201
  • Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51–66. https://doi.org/10.1080/10494820.2011.641674
  • Teo, T. (2014). Preservice teachers' satisfaction with e-learning. Social Behavior and Personality: An International Journal, 42(1), 3-6. https://doi.org/10.2224/sbp.2014.42.1.3
  • Trust, T., & Whalen, J. (2020). Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 Pandemic. Journal of Technology and Teacher Education, 28(2), 189–199.
  • Tondeur, J., van Braak, J., Siddiq, F., & Scherer, R. (2016). Time for a new approach to prepare future teachers for educational technology use: Its meaning and measurement. Computers & Education, 94, 134–150. https://doi.org/10.1016/j.compedu.2015.11.009
  • Ursavaş, Ö. F. (2014). Öğretmenlerin bilişim teknolojilerini kullanmaya yönelik davranışlarının modellenmesi [Unpublished doctoral dissertation]. Gazi Üniversitesi, Ankara.
  • Ursavaş, Ö., Şahin, S., & McIlroy, D. (2014). Technology acceptance measure for teachers: T-TAM/Öğretmenler için Teknoloji Kabul Ölçeği: Ö-TKÖ. Eğitimde Kuram ve Uygulama, 10(4), 885-917.
  • Vansteenkiste, M., Zhou, M., Lens, W., & Soenens, B. (2005). Experiences of autonomy and control among Chinese learners: Vitalizing or immobilizing? Journal of Educational Psychology, 97(3), 468–483. https://doi.org/10.1037/0022-0663.97.3.468
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 115-139. https://doi.org/10.2307/3250981
  • 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. https://doi.org/10.2307/30036540
  • Vlachopoulos, D., & Makri, A. (2021). Quality teaching in online higher education: The perspectives of 250 online tutors on technology and pedagogy. International Journal of Emerging Technologies in Learning (iJET), 16(6), 40-56.
  • Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40, 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
  • Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.
  • Xu, D., & Wang, H. (2006). Intelligent agent supported personalization for virtual learning environments. Decision Support Systems, 42(2), 825–843. https://doi.org/10.1016/j.dss.2005.05.033
  • Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: S-S-COMPicacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449. https://doi.org/10.1016/S1071-5819(03)00114-9
Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri
Bölüm Research Article
Yazarlar

Ulaş İlic 0000-0003-4213-8713

Ferhan Şahin 0000-0003-4973-9562

Ezgi Doğan 0000-0001-8011-438X

Yayımlanma Tarihi 5 Ocak 2024
Gönderilme Tarihi 15 Aralık 2022
Yayımlandığı Sayı Yıl 2024 Cilt: 9 Sayı: 1

Kaynak Göster

APA İlic, U., Şahin, F., & Doğan, E. (2024). Exploring the Role of Individual Differences on Instructors’ Technology Acceptance in Online Education through a Motivational Perspective. Journal of Learning and Teaching in Digital Age, 9(1), 17-31. https://doi.org/10.53850/joltida.1219447

Journal of Learning and Teaching in Digital Age 2023. © 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. 19195

Journal of Learning and Teaching in Digital Age. Tüm hakları saklıdır, 2023. ISSN:2458-8350