Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (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.https://doi.org/10.1016/j.chb. 2013.10.035
Akman, I., & Turhan, C. (2015). User acceptance of social learning systems in higher education: An application of the extended Technology Acceptance Model. Innovations in Education and Teaching International, 54(3), 229–237. https://doi.org/10.1080/14703297.2015.1093426
Alassafi, M. O. (2022). E-learning intention material using TAM: A case study. Materials Today: Proceedings, 61(3), 873–877.
Al-Hattami, H. M. (2023). Understanding perceptions of academics toward technology acceptance in accounting education. Heliyon, 9, Article e13141. https://doi.org/10.1016/j.heliyon.2023.e13141
Alyoussef, I. Y. (2023). Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model. Heliyon, 18, Article e1375. https://doi.org/10.1016/j.heliyon.2023.e1375
Akinwande, M. O., Dikko, H. G., & Samson, A. (2015). Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open Journal of Statistics, 5(7), 754–767. http://doi.org/10.4236/ ojs.2015.57075
Antonowicz, D., Kwiek, M., & Westerheijden, D. F. (2017). The government response to the private sector expansion in Poland. In H. de Boer, J. File, J. Huisman, M. Seeber, M. Vukasovic, & D. F. Westerheijden (Eds.), Policy analysis of structural reforms in higher education. Palgrave Studies in Global Higher Education (pp. 119–138). Palgrave Macmillan. https://doi.org/10.1007/978-3319-42237-4_6
Arak, P., & Miniszewski, M. (2022). Calculating inflation in Poland during the COVID-19 pandemic and aftermath of Russia’s attack on Ukraine using transactional data (Working Paper, No. 4). Polish Economic Institute, Warsaw. Retrieved May 25, 2023, from https://pie.net.pl/wp-content/ uploads/2022/10/WorkingPaper-Inflacja.pdf
Central Statistical Office of Poland (2022, June 15). Szkolnictwo wyższe w roku akademickim 2021/2022 (wyniki wstępne) [Higher education in the academic year 2021/2022 (preliminary data)]. Retrieved October 18, 2022, from https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultaktualnosci/5488/8/8/1/szkolnictwo_wyzsze_w_roku_akademickim_2021-2022_2.pdf
Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: Combining technology acceptance model with external variables. Journal of Computing in Higher Education, 34, 844–867. https://doi.org/10.1007/s12528-022-09327-0
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
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(3), 109–119. https://doi.org/10.1016/ j.apmrv.2014.12.011
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publisher.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). Lawrence Erlbaum Associates.
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
Dobbins, M., & Knill, C. (2009). Higher education policies in Central and Eastern Europe: Convergence toward a common model? Governance, 22(3), 397–430.
Estrada, M. A. R. (2021). How COVID-19 quarantine(s) can generate poverty? Contemporary Economics, 15(3), 332–338.
Eurostat (2023). Digital economy and society statistics – households and individuals. Retrieved May 27, 2023, from https://ec.europa.eu/eurostat/statistics-explained/ index.php?title=Digital_economy_and_society_statistics_-_households_and_ individuals#Internet_access
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
García, M. V., Blasco López, M. F., & Sastre Castillo, M. Á. (2019). Determinants of the acceptance of mobile learning as an element of human capital training in organisations. Technological Forecasting and Social Change, 149, Article 119783. https://doi.org/10.1016/j.techfore.2019.119783
Goh, E., & Wen, J. (2020). Applying the technology acceptance model to understand hospitality management students’ intentions to use electronic discussion boards as a learning tool. Journal of Teaching in Travel & Tourism, 21(2), 142–154. https://doi.org/10.1080/15313220.2020.176862
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.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, L. T. M., Pham, A. T. V., & 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, 18(2), 246–269. https://doi.org/10.1108/itse-08-2020-0164
Huang, F., Teo, T. & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68, 575–591. https://doi.org/10.1007/s11423-019-09695-y
Jiang, M. Y., Jong, M. S., Lau, W. W., Meng, Y., Chai, C., & Chen, M. (2021). Validating the General Extended Technology Acceptance Model for e-learning: Evidence from an online English as a foreign language course amid COVID-19. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.671615
Kapera, I. (2017). Motives and expectations of students from Ukraine with respect to higher education in Poland in the field of tourism. Tourism, 27(1), 17–21. https://doi.org/10.18778/0867-5856.27.1.10
Kaewsaiha, P., & Chanchalor, S. (2021). Factors affecting the usage of learning management systems in higher education. Education and Information Technologies, 26, 2919–2939. https://doi.org/10.1007/s10639-020-10374-2
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.
Kumar, P., Kumar, A., Palvia, S. & Verma, S. (2019). Online business education research: Systematic analysis and a conceptual model. The International Journal of Management Education, 17, 26–35.
Kwiek, N., & Szadkowski, K. (2018). Higher education systems and institutions, Poland. In J. C. Shin & P. N. Teixeira (Eds.), The encyclopedia of international higher education systems and institutions (pp. 1–9). Springer. https://doi. org/10.1007/978-94-017-9553-1_375-1
López, R., Valarezo, Á., & Pérez-Amaral, T. (2023). Unleashing the potential of online learning in Spain: An econometric analysis. Telecommunications Policy, 47(6), Article 102544. https://doi.org/10.1016/j.telpol.2023.102544
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, 7057–7077. https://doi.org/10.1007/s10639-021-10557-5
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044
Muñoz-Carril, P.-C., Hernández-Sellés, N., Fuentes-Abeledo, E.-J., & GonzálezSanmamed, M. (2021). Factors influencing students’ perceived impact of learning and satisfaction in Computer Supported Collaborative Learning. Computers & Education, 174, Article 104310. https://doi.org/10.1016/j.compedu.2021.104310
Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2022). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512–520.
Okano, M. T., dos Santosm H. C. L., Ursini, E. L., Fernandes, M. E., & Gomes, J. G. C. (2023). Open and distance learning (ODL): Traditional or frugal innovation? Contemporary Economics, 17(1), 24–42. https://doi.org/10.5709/ce.1897-9254.497
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.
Paszkowicz, M. A., & Hrynenko, A. (2019). Causes and results of labour migrations from Ukraine to Poland. Studia Oeconomica Posnaniensia, 4, 7–26. https:// doi.org/10.18559/SOEP.2019.4.1
Peng, M. Y. P., Xu, Y., & Xu, C. (2023). Enhancing students’ English language learning via M-learning: Integrating technology acceptance model and S-O-R model. Heliyon, 9, Article e13302. https://doi.org/10.1016/j.heliyon. 2023.e13302
Polish Bank Association. (2022, September). Portfel studenta [Students’ wallets]. Retrieved March 15, 2023, from https://zbp.pl/getmedia/4c6898c6-8989-4af5-9925-2f556272dfb9/Portfel-Studenta_2022_online
Ramírez-Correa, P. E., Arenas-Gaitán, J., & Rondán-Cataluña, 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 10(10), Article e0140460. https://doi.org/10.1371/journal.pone.0140460
Ringle, C. M., Wende, S., & Becker, J. M. (2015). “SmartPLS 3.” Boenningstedt: SmartPLS GmbH. Available at http://www.smartpls.com
Saleh, S. S., Nat, M., & Aqel, M. (2022). Sustainable adoption of e-learning from the TAM perspective. Sustainability, 14. https://doi.org/10.3390/su14063690
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(4), 491–502. https://doi.org/10.1016/j.giq.2011.04.002
Staniszewski, R. M. (2022). Polish economy during the COVID-19 pandemic: slowdown or regression? Political Science Studies, 65, 58–86.
Szopiński, T., & Bachnik, K. (2022). Student evaluation of online learning during the COVID-19 pandemic. Technological Forecasting and Social Change, 174, Article 121203. https://doi.org/10.1016/j.techfore.2021.121203
Szopiński, T. (2023). University students` attitude to e-learning in the postCOVID-19 era. Contemporary Economics. In press.
The World Bank (2023). Individuals using the Internet (% of population). Retrieved October 6, 2022, from https://data.worldbank.org/indicator/IT.NET. USER.ZS?end=2021&start=2016
Yao, Y., Wang, P., Jiang, Y., Li, Q., & Li, Y. (2022). Innovative online learning strategies for the successful construction of student self-awareness during the COVID-19 pandemic: Merging TAM with TPB. Journal of Innovation & Knowledge, 7(4), Article 100252. https://doi.org/10.1016/j.jik.2022.100252
Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology, 13(3), 312–317. https://doi.org/-10.1016/s1007-0214(08)70050-6