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Factors influencing instructors’ intentions to use information technologies in higher education amid the pandemic

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Abstract

In today's world where digital transformation is taking place very strongly with the effect of the pandemic, there has been a transition from face-to-face education to online education. In this respect, examining variables that affect the instructors' intention to use these technologies during the pandemic has a critical role in terms of the quality of education both during and after the pandemic. The purpose of the study is to determine the variables that affect the instructors’ intentions to use ITs by extending TAM and to examine the roles of individual differences (moderators) in the proposed model. Data were collected online from 321 faculty members working at various universities in fall semester 2020. PLS-SEM technique and multi-group analysis were used in data analysis. The proposed model explains 75.3% of the intention. The results showed that self-efficacy, perceived enjoyment, compatibility and facilitating conditions affect the intention to use IT. The most influential construct among these was compatibility. In addition, contrary to expectations, perceived usefulness and perceived ease of use, which are expressed as the most critical determinants, and openness and resistance to change, which are important personality traits, did not affect intention. The results provide valuable information about education during the pandemic, which can contribute to improving the quality of education during and after the pandemic. Multi-group analysis revealed that all of the moderators (gender, age, and experience) had an influence on the various relationships. Accordingly, implications for research and practice are discussed.

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References

  • 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

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.

    Google Scholar 

  • Alalak, B.A., Alnawas, A.I. (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.2011.03.0162011.03.016.

  • Alavi, M., & Gallupe, R. B. (2003). Using information technology in learning: Case studies in business and management education programs. Academy of Management Learning & Education, 2(2), 139–153. https://doi.org/10.5465/amle.2003.9901667

    Article  Google Scholar 

  • Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389–412.

    Article  Google Scholar 

  • Anderson, J. E., Schwager, P. H., & Kerns, R. L. (2006). The drivers for acceptance of tablet PCs by faculty in a college of business. Journal of Information Systems Education, 17(4), 429.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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.

  • Baydaş, 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

    Article  Google Scholar 

  • Baylor, A. L., & Ritchie, D. (2002). What factors facilitate teacher skill, teacher morale, and perceived student learning in technology-using classrooms? Computers & Education, 39(4), 395–414. https://doi.org/10.1016/S0360-1315(02)00075-1

    Article  Google Scholar 

  • Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 689-710. https://doi.org/10.2307/25750701

  • Bhattacherjee, A., & Hikmet, N. (2007). Physicians’ resistance toward healthcare information technology: A theoretical model and empirical test. European Journal of Information Systems, 16(6), 725–737. https://doi.org/10.1057/palgrave.ejis.3000717

    Article  Google Scholar 

  • Bin, E., Islam, A. A., Gu, X., Spector, J. M., & Wang, F. (2020). A study of Chinese technical and vocational college teachers’ adoption and gratification in new technologies. British Journal of Educational Technology, 51(6), 2359–2375. https://doi.org/10.1111/bjet.12915

    Article  Google Scholar 

  • Buchanan, T., Sainter, P., & Saunders, G. (2013). Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computing in Higher education, 25(1), 1–11.

    Article  Google Scholar 

  • Chaffin, A. J., & Harlow, S. D. (2005). Cognitive learning applied to older adult learners and technology. Educational Gerontology, 31(4), 301–329. https://doi.org/10.1080/03601270590916803

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Chen, J. L. (2011). The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education, 57(2), 1501–1511. https://doi.org/10.1016/j.compedu.2011.02.009

    Article  Google Scholar 

  • Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information and Management, 39(8), 705–719. https://doi.org/10.1016/S0378-7206(01)00127-6

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.compedu.2012.12.003

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dündar, H., & Akçayır, 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

    Article  Google Scholar 

  • El Alfy, S., Gómez, 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

    Article  Google Scholar 

  • Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2015). The acceptance of e-learning as a tool for teaching and learning in Libyan higher education. International Journal of Information Technology, 3(4), 1–11.

    Google Scholar 

  • 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.

    Google Scholar 

  • Fearnley, M. R., & Amora, J. T. (2020). Learning Management System adoption in higher education using the Extended Technology Acceptance Model. IAFOR Journal of Education, 8(2), 89–106.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

  • 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

    Article  Google Scholar 

  • Garson, G. D. (2016). Partial least squares: Regression & structural equation models. Statistical Publishing Associates.

    Google Scholar 

  • Guo, X., Sun, Y., Wang, N., Peng, Z., & Yan, Z. (2013). The dark side of elderly acceptance of preventive mobile health services in China. Electronic Markets, 23(1), 49–61.

    Article  Google Scholar 

  • Hair, J. F., Black, W., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Pearson.

    Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151. https://doi.org/10.2753/MTP1069-6679190202

    Article  Google Scholar 

  • 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). SAGE Publications.

    MATH  Google Scholar 

  • Hair, J. R., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson-Prentice Hall.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431. https://doi.org/10.1108/IMR-09-2014-0304

    Article  Google Scholar 

  • Hofstede, G. H., & Hofstede, G. J. (2005). Cultures and organizations: Software of the mind (Vol. 2). Mcgraw-hill.

    Google Scholar 

  • Huang, F., Teo, T., & Zhou, M. (2019). Factors affecting Chinese English as a foreign language teachers’ technology acceptance: A qualitative study. Journal of Educational Computing Research, 57(1), 83–105.

  • John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research, 2(1999), 102–138.

    Google Scholar 

  • Kılıçer, K., Odabaşı, H. (2010). Bireysel Yenilikçilik Ölçeği (BYÖ): Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 38 (38), 150–164. Retrieved May 12, 2020, from https://dergipark.org.tr/en/pub/hunefd/issue/7798/102155.

  • 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

    Article  Google Scholar 

  • Korukonda, A. R. (2007). Differences that do matter: A dialectic analysis of individual characteristics and personality dimensions contributing to computer anxiety. Computers in Human Behavior, 23(4), 1921–1942. https://doi.org/10.1016/j.chb.2006.02.003

    Article  Google Scholar 

  • Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong. Computers & Education, 59(2), 569–579. https://doi.org/10.1016/j.compedu.2012.03.006

    Article  Google Scholar 

  • Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 50.

  • Liao, H. L., & Lu, H. P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405–1416. https://doi.org/10.1016/j.compedu.2007.11.006

    Article  MathSciNet  Google Scholar 

  • Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49(4), 1066–1080. https://doi.org/10.1016/j.compedu.2006.01.001

    Article  Google Scholar 

  • Liu, H., Wang, L., & Koehler, M. J. (2019). Exploring the intention‐behavior gap in the technology acceptance model: A mixed‐methods study in the context of foreign‐language teaching in China. British Journal of Educational Technology, 50(5), 2536–2556.

  • 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

    Article  Google Scholar 

  • McGill, T., Klobas, J., & Renzi, S. (2011). LMS use and instructor performance: The role of task-technology fit. International Journal on E-Learning, 10(1), 43–62.

    Google Scholar 

  • McLoughlin, J. A., Wang, L. C. C., & Beasley, W. A. (2008). Transforming the college through technology: A change of culture. Innov High Educ, 33, 99–109. https://doi.org/10.1007/s10755-008-9065-0

  • Minton, H. L., Schneider, F. W., & Wrightsman, L. S. (1980). Differential psychology. Brooks/Cole Publishing Company.

  • Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375–403.

  • Muilenburg, L., & Berge, Z. L. (2001). Barriers to distance education: A factor-analytic study. American Journal of Distance Education, 15(2), 7–22. https://doi.org/10.1080/08923640109527081

    Article  Google Scholar 

  • Mutambara, D., & Bayaga, A. (2020). Predicting rural stem teachers’ acceptance of mobile learning in the fourth industrial revolution. Journal of Construction Project Management and Innovation, 10(2), 14–29.

  • Nam, C. S., Bahn, S., & Lee, R. (2013). Acceptance of assistive technology by special education teachers: A structural equation model approach. International Journal of Human-Computer Interaction, 29(5), 365–377. https://doi.org/10.1080/10447318.2012.711990

    Article  Google Scholar 

  • Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267. https://doi.org/10.1016/j.compedu.2004.11.007

    Article  Google Scholar 

  • Nistor, N., Baltes, B., & Schustek, M. (2012). Knowledge sharing and educational technology acceptance in online academic communities of practice. Campus-Wide Information Systems. https://doi.org/10.1108/10650741211212377

    Article  Google Scholar 

  • Nistor, N., Göğüş, A., & Lerche, T. (2013). Educational technology acceptance across national and professional cultures: A European study. Educational Technology Research and Development, 61(4), 733–749.

    Article  Google Scholar 

  • Nov, O., & Ye, C. (2008). Users’ personality and perceived ease of use of digital libraries: The case for resistance to change. Journal of the American Society for Information Science and Technology, 59(5), 845–851. https://doi.org/10.1002/asi.20800

    Article  Google Scholar 

  • Oreg, S. (2003). Resistance to change: Developing an individual differences measure. Journal of Applied Psychology, 88(4), 680. https://doi.org/10.1037/0021-9010.88.4.680

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.autcon.2011.09.016

    Article  Google Scholar 

  • Rahimi, M., & Yadollahi, S. (2010). ICT integration into English classes: Investigating EFL teachers' personal characteristics and technology-related variables. In The Second International Conference on E-Learning and E-Teaching (ICELET 2010) (pp. 27–32). IEEE.

  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.

    Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020

    Article  Google Scholar 

  • Şahin, F. (2016). Öğretmen adaylarının bilişim teknolojileri kabul düzeyleri ile bireysel yenilikçilik düzeyleri arasındaki ilişkinin incelenmesi (Unpublished master's thesis). Anadolu Üniversitesi, Eskişehir.

  • 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

    Article  Google Scholar 

  • Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. https://doi.org/10.1016/j.chb.2016.09.061

    Article  Google Scholar 

  • Saunders, E. J. (2004). Maximizing computer use among the elderly in rural senior centers. Educational Gerontology, 30(7), 573–585.

    Article  Google Scholar 

  • Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009

    Article  Google Scholar 

  • Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers & Education, 71, 247–256. https://doi.org/10.1016/j.compedu.2013.09.016

    Article  Google Scholar 

  • Siyam, N. (2019). Factors impacting special education teachers’ acceptance and actual use of technology. Education and Information Technologies, 24(3), 2035–2057. https://doi.org/10.1007/s10639-018-09859-y

    Article  Google Scholar 

  • Stewart, C., Bachman, C., & Johnson, R. (2010). Predictors of faculty acceptance of online education. MERLOT Journal of Online Learning and Teaching, 6(3), 597–616.

    Google Scholar 

  • Tarhini, A. (2013). The effects of individual-level culture and demographic characteristics on e-learning acceptance in Lebanon and England: A structural equation modelling approach (Unpublished doctoral dissertation) Brunel University, London.

  • 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. https://doi.org/10.1016/j.chb.2014.09.020

    Article  Google Scholar 

  • Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144

    Article  Google Scholar 

  • Teo, T. (2009). 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(1), 89–109. https://doi.org/10.2190/EC.40.1.d

    Article  Google Scholar 

  • Teo, T. (2010). 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(2), 253–262. https://doi.org/10.1007/s12564-009-9066-4

    Article  MathSciNet  Google Scholar 

  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008

    Article  Google Scholar 

  • Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128–143. https://doi.org/10.1111/j.1365-2729.2007.00247.x

    Article  Google Scholar 

  • Teo, T., & 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(2), 1645–1653.

  • 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

    Article  Google Scholar 

  • Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530–546. https://doi.org/10.1080/10494820.2018.1489290

    Article  Google Scholar 

  • Teo, T., Ursavaş, Ö. F., & Bahçekapili, E. (2012). An assessment of pre-service teachers’ technology acceptance in Turkey: A structural equation modeling approach. Asia-Pacific Education Researcher, 21(1), 191–202.

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Toquero, C. M. (2020). Challenges and opportunities for higher education amid the COVID-19 Pandemic: The Philippine context. Pedagogical Research, 5(4). em0063. https://doi.org/10.29333/pr/7947.

  • 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.

    Google Scholar 

  • Tschannen-Moran, M., & Hoy, A. W. (2007). The differential antecedents of self-efficacy beliefs of novice and experienced teachers. Teaching and Teacher Education, 23(6), 944–956. https://doi.org/10.1016/j.tate.2006.05.003

    Article  Google Scholar 

  • 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. Retrieved from https://dergipark.org.tr/en/pub/eku/issue/5462/74152

  • Ursavaş, Ö. F., Yalçın, Y., & Bakır, E. (2019). The effect of subjective norms on preservice and in-service teachers’ behavioural intentions to use technology: A multigroup multimodel study. British Journal of Educational Technology, 50(5), 2501–2519. https://doi.org/10.1111/bjet.12834

    Article  Google Scholar 

  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.

  • 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

    Article  Google Scholar 

  • Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015) The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28 (3):443–488.

  • Wong, G. K. (2015). Understanding technology acceptance in pre-service teachers of primary mathematics in Hong Kong. Australasian Journal of Educational Technology, 31(6). https://doi.org/10.14742/ajet.1890

  • 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. https://doi.org/10.1016/j.chb.2016.10.028

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: S-efficacy, 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

    Article  Google Scholar 

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Acknowledgements

The authors thank the Editor and anonymous reviewers for their outstanding feedback on the previous versions of the manuscript. The authors also thank Prof. Dr. Yavuz Akbulut for his suggestions.

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Authors and Affiliations

  1. Department of Computer Education & Instructional Technology, Faculty of Education, Anadolu University, Eskişehir, Turkey

    Ferhan Şahin, Ezgi Doğan & Yusuf Levent Şahin

  2. Department of Computer Education & Instructional Technology, Faculty of Education, Pamukkale University, Denizli, Turkey

    Ulaş İlic

Authors
  1. Ferhan Şahin
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  2. Ezgi Doğan
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  3. Ulaş İlic
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  4. Yusuf Levent Şahin
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Correspondence to Ferhan Şahin.

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Şahin, F., Doğan, E., İlic, U. et al. Factors influencing instructors’ intentions to use information technologies in higher education amid the pandemic. Educ Inf Technol 26, 4795–4820 (2021). https://doi.org/10.1007/s10639-021-10497-0

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  • DOI: https://doi.org/10.1007/s10639-021-10497-0

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