The mediating role of students' ability to adapt to online activities on the relationship between perceived university culture and academic performance

Authors

DOI:

https://doi.org/10.24136/oc.2022.036

Keywords:

online academic activities, engagement, e-learning academic performance, adaptability, PLS-SEM

Abstract

Research background: The COVID-19 pandemic has affected higher education globally and disrupted its usual activities, according to differing perspectives. The ability to adapt to online activities was an important factor for many researchers during the pandemic period.

Purpose of the article: In this article, the authors are studying the ability of the students to adapt to online activities, and also the direct and indirect effect on their academic performances.

Methods: The data was collected with a questionnaire and the respondents are students from Romanian Universities. The analysis was made with an econometric model by using the PLS-SEM methodology. The goal of the paper was to find and analyse the factors used to perform academic online activities during the pandemic period.

Findings & value added: The results of the paper validate the research hypotheses formulated in the introductory part and confirm that the students? academic performances are a direct result of many factors, such as: system parameters, personal demand, personal commitment, and regulatory environment. The identification of the exogenous variables with significant impact on the students? performances through online activities could help the management of the universities to implement the positive aspects and to reward them for their efforts while preventing from resilience to change. The higher education system has to acknowledge that flexible online learning opportunities are needed by students to fit their coursework around their employment and family responsibilities.

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References

Abbad, M. M. M. (2021). Using the UTAUT model to understand students? usage of e-learning systems in developing countries. Education and Information Technologies, 26, 7205?7224. doi: 10.1007/s10639-021-10573-5.

DOI: https://doi.org/10.1007/s10639-021-10573-5
View in Google Scholar

Abumalloh, R. A., Asadi, S., Nilashi, M., Minaei-Bidgoli, B., Nayer, F.K., Samad, S., Mohd, S., & Ibrahim, O. (2021). The impact of coronavirus pandemic
View in Google Scholar

(COVID-19) on education: the role of virtual and remote laboratories in edu-cation. Technology in Society, 67, 101728. doi: 10.1016/j.techsoc.2021.101728.

DOI: https://doi.org/10.1016/j.techsoc.2021.101728
View in Google Scholar

Adnan, M., & Anwar, K. (2020). Online learning amid the COVID-19 pandemic: Students? perspectives. Journal of Pedagogical Sociology and Psychology, 2, 45?51. doi: 10.33902/JPSP. 2020261309.

DOI: https://doi.org/10.33902/JPSP.2020261309
View in Google Scholar

Agasisti, T., & Soncin, M. (2021). Higher education in troubled times: on the impact of COVID-19 in Italy. Studies in Higher Education, 46(1), 86?95. doi: 10.1080/03075079.2020.1859689.

DOI: https://doi.org/10.1080/03075079.2020.1859689
View in Google Scholar

Ahmad, S., Li, K., Amin, A., Anwar, M. S., & Khan, W. (2018). A multilayer prediction approach for the student cognitive skills measurement. IEEE Ac-cess, 6, 57470?57484. doi: 10.1109/ACCESS.2018.2873608.

DOI: https://doi.org/10.1109/ACCESS.2018.2873608
View in Google Scholar

Akram, A., Fu, C., Li, Y., Javed, M.Y., Lin, R., Jiang, Y., & Tang, Y. (2019). Predicting students? academic procrastination in blended learning course us-ing homework submission data. IEEE Access, 7, 102487?102498. doi: 10.1109/AC CESS.2019.2930867.

DOI: https://doi.org/10.1109/ACCESS.2019.2930867
View in Google Scholar

Alghamdi, A., Karpinski, A. C., Lepp, A., & Barkley, J. (2020). Online and face-to-face classroom multitasking and academic performance: moderated media-tion with self-efficacy for self-regulated learning and gender. Computers in Human Behavior, 102, 214?222.

DOI: https://doi.org/10.1016/j.chb.2019.08.018
View in Google Scholar

Alk?ş, N., & Temizel, T.T. (2018). The impact of motivation and personality on academic performance in online and blended learning environments. Journal of Educational Technology & Society, 21(3), 35?47.
View in Google Scholar

Avc?, Ü., & Ergün, E. (2019). Online students? LMS activities and their effect on engagement, information literacy and academic performance. Interactive Learning Environments, 30(1), 71?94. doi: 10.1080/10494820.2019.1636088.

DOI: https://doi.org/10.1080/10494820.2019.1636088
View in Google Scholar

Baber, H. (2020). Determinants of students? perceived learning outcome and satisfaction in online learning during the pandemic of COVID-19. Journal of Education and E-Learning Research, 7(3), 285?292. doi: 10.20448/journal.509 .2020.73.285.292.

DOI: https://doi.org/10.20448/journal.509.2020.73.285.292
View in Google Scholar

Balcerzak, A. P., & Pietrzak, M. B. (2016). Structural Equation Modeling in eval-uation of technological potential of European Union countries in the years 2008-2012. In M. Papież & S. Śmiech (Eds.). The 10th Professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena. Conference proceedings (pp. 9?18). Cracow: Founda-tion of the Cracow University of Economics.
View in Google Scholar

Bolisani, E., Scarso, E., Ipsen, C., Kirchner, K., & Hansen, J.P. (2020). Working from home during COVID-19 pandemic: lessons learned and issues. Management & Marketing. Challenges for the Knowledge Society, 15(1), 458?476. doi: 10.2478/mmcks-2020-0027.

DOI: https://doi.org/10.2478/mmcks-2020-0027
View in Google Scholar

Broadbent, J. (2017). Comparing online and blended learner?s self-regulated learning strategies and academic performance. Internet and Higher Education, 33, 24?32. doi: 10.1016/j.iheduc.2017.01.004.

DOI: https://doi.org/10.1016/j.iheduc.2017.01.004
View in Google Scholar

Cao, Y., Gao, J., Lian, D., Rong, Z., Shi, J., & Wang, Q. (2018). Orderliness pre-dicts academic performance: behavioural analysis on campus lifestyle. Jour-nal of the Royal Society Interface, 15(146), 20180210. doi: 10.1098/rsif.2018.0210.

DOI: https://doi.org/10.1098/rsif.2018.0210
View in Google Scholar

Cataldo, R., Crocetta, C., Grassia, M. G., Lauro, N. C., Marino, M., & Voytsekhovska, V. (2021). Methodological PLS-PM framework for SDGs sys-tem. Social Indicators Research, 156(2), 701?723. doi: 10.1007/s11205-020-02271-5.

DOI: https://doi.org/10.1007/s11205-020-02271-5
View in Google Scholar

Çebi, A., & Güyer, T. (2020). Students? interaction patterns in different online learning activities and their relationship with motivation, self-regulated learn-ing strategy and learning performance. Education and Information Technologies, 25(5), 3975?3993. doi : 10.1007/s10639-020-10151-1.

DOI: https://doi.org/10.1007/s10639-020-10151-1
View in Google Scholar

Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Nú?ez, J. C. (2016). Students? LMS interaction patterns and their relationship with achievement: a case study in higher education. Computers & Education, 96, 42?54. doi: 10.1016 /j.compedu.2016.02.006.

DOI: https://doi.org/10.1016/j.compedu.2016.02.006
View in Google Scholar

Chemers, M. M., Hu, L. T., & Garcia, B. F. (2001). Academic self-efficacy and first year college student performance and adjustment. Journal of Educational psychology, 93(1), 55. doi: 10.1037/0022-0663.93.1.55.

DOI: https://doi.org/10.1037/0022-0663.93.1.55
View in Google Scholar

Cheng, C. K., Pare, D. E., Collimore, L .M., & Joordens, S. (2011). Assessing the effectiveness of a voluntary online discussion forum on improving students? course performance. Computers & Education, 56(1), 253e261. doi: 10.1016/j. compedu.2010.07.024.

DOI: https://doi.org/10.1016/j.compedu.2010.07.024
View in Google Scholar

Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Bur-ton, R., Magni, P., & Lam, S. (2020). COVID-19: 20 countries? higher educa-tion intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1), 1?20. doi: 10.37074/jalt.2020.3.1.7.

DOI: https://doi.org/10.37074/jalt.2020.3.1.7
View in Google Scholar

Dhawan, S. (2020). Online learning: a panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5?22. doi: 10.1177/004723 9520934018.

DOI: https://doi.org/10.1177/0047239520934018
View in Google Scholar

Doan, K. (2022).The differences in the impact of entrepreneurship education on entrepreneurial knowledge: a cross-country analysis. Management & Marketing. Challenges for the Knowledge Society, 17(1), 73?97. doi: 10.2478/ mmcks-2022-0005.

DOI: https://doi.org/10.2478/mmcks-2022-0005
View in Google Scholar

Drennan, J., Dennedy, J., & Pisarski, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. Journal of Educational Research, 98(6), 331?338. doi: 10.3200/JOER.98.6.331-338.

DOI: https://doi.org/10.3200/JOER.98.6.331-338
View in Google Scholar

Edu, T., Negricea, C., Zaharia, R., & Zaharia, R.M. (2021). Factors influencing student transition to online education in the COVID-19 pandemic lockdown: evidence from Romania. Economic Research - Ekonomska Istraživanja, 35(1), 3291?3304. doi: 10.1080/1331677X.2021.1990782.

DOI: https://doi.org/10.1080/1331677X.2021.1990782
View in Google Scholar

Elmer, T., Mepham, K., & Stadtfeld, C. (2020). Students under lockdown: com-parisons of students? social networks and mental health before and during the COVID-19 crisis in Switzerland. Plos One, 15(7), e0236337. doi: 10.1371/jour nal.pone.0236337.

DOI: https://doi.org/10.1371/journal.pone.0236337
View in Google Scholar

El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65, 743?763. doi: 10.1007/s11423-016-9508-8.

DOI: https://doi.org/10.1007/s11423-016-9508-8
View in Google Scholar

Eringfeld, S. (2021). Higher education and its post-coronial future: utopian hopes and dystopian fears at Cambridge University during COVID-19. Studies in Higher Education, 46(1), 146?157. doi: 10.1080/03075079.2020.1859681.

DOI: https://doi.org/10.1080/03075079.2020.1859681
View in Google Scholar

Falk, M., & Miller, A.G. (1992). Infrared spectrum of carbon dioxide in aqueous solution. Vibrational Spectroscopy, 4(1), 105?108. doi: 10.1016/0924-2031(92) 87018-B.

DOI: https://doi.org/10.1016/0924-2031(92)87018-B
View in Google Scholar

Faught, E. L., Gleddie, D., Storey, K. E., Davison, C. M., & Veugelers, P. J. (2017). Healthy lifestyle behaviours are positively and independently associ-ated with academic achievement: An analysis of self-reported data from a na-tionally representative sample of Canadian early adolescents. PLoS ONE, 12(7), e0181938. doi: 10.1371/journal.pone.0181938.

DOI: https://doi.org/10.1371/journal.pone.0181938
View in Google Scholar

Fazil, A., & Rupert, W. (2016). Developing a general extended technology ac-ceptance model for e-learning (GETAMEL) by analysing commonly used ex-ternal factors. Computers in Human Behavior, 56, 238?256. doi: 10.1016/j.ch b.2015.11.036.

DOI: https://doi.org/10.1016/j.chb.2015.11.036
View in Google Scholar

Federmeier, K. D., Jongman, S. R., & Szewczyk, J. M. (2020). Examining the role of general cognitive skills in language processing: a window into complex cognition. Current Directions in Psychological Science, 29(6), 575?582. doi: 10.11 77/0963721420964095.

DOI: https://doi.org/10.1177/0963721420964095
View in Google Scholar

Fredericksen, E., Pickett, A., & Shea, P. (2006). Student satisfaction and per-ceived learning with on-line courses: principles and examples from the SUNY learning network. Journal of Asynchronous Learning Networks, 4(2), 2?31.

DOI: https://doi.org/10.24059/olj.v4i2.1899
View in Google Scholar

Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Re-search, 18(1), 39?50. doi: 10.2307/3151312.

DOI: https://doi.org/10.1177/002224378101800104
View in Google Scholar

Gimeno-Arias, F., & Santos-Jaén, J. M. (2022). Using PLS-SEM for assessing negative impact and cooperation as antecedents of gray market in FMCG sup-ply chains: an analysis on Spanish wholesale distributors. International Journal of Physical Distribution & Logistics Management. Advance online publication. doi: 10.1108/IJPDLM-02-2022-0038.

DOI: https://doi.org/10.1108/IJPDLM-02-2022-0038
View in Google Scholar

Gonzalez, T., de la Rubia, M. A., Hincz, K. P., Comas-Lopez, M., Subirats, L., Fort, S., & Sacha, G. M. (2020). Influence of COVID-19 confinement on stu-dents? performance in higher education. PLoS ONE, 15, e0239490. doi: 10.137 1/journal.pone.0239490.

DOI: https://doi.org/10.1371/journal.pone.0239490
View in Google Scholar

Haider, A. S., & Al-Salman, S. (2020). Dataset of Jordanian university students? psychological health impacted by using e-learning tools during COVID-19. Data in Brief, 32, 106104. doi: 10.1016/j.dib.2020.106104.

DOI: https://doi.org/10.1016/j.dib.2020.106104
View in Google Scholar

Hair, J., F. Jr, Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares Structural Equation Modeling (PLS-SEM): an emerging tool in business research. European Business Review, 26(2), 106?121. doi: 10.1108/E BR-10-2013-0128.

DOI: https://doi.org/10.1108/EBR-10-2013-0128
View in Google Scholar

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1?12. doi: 10.1016/j.lrp.2013.01.001.

DOI: https://doi.org/10.1016/j.lrp.2013.01.001
View in Google Scholar

Hossein, M. (2015). Investigating users' perspectives on e-learning: an integration of TAM and IS success model. Computers in Human Behavior, 45, 359?374. doi: 10.1016/j.chb.2014.07.044.

DOI: https://doi.org/10.1016/j.chb.2014.07.044
View in Google Scholar

Jung, Il, Choi, S., Lim, C., & Leem, J. (2002). Effects of different types of inter-action on learning achievement, satisfaction and participation in web-based instruction. Innovations in Education and Teaching International, 39(2), 153?162. doi: 10.1080/14703290252934603.

DOI: https://doi.org/10.1080/14703290252934603
View in Google Scholar

Jung, J., Horta, H., & Postiglione, G. A. (2021). Living in uncertainty: the COVID-19 pandemic and higher education in Hong Kong. Studies in Higher Education, 46(1), 107?120. doi: 10.1080/03075079.2020.1859685.

DOI: https://doi.org/10.1080/03075079.2020.1859685
View in Google Scholar

Kadam, P., & Bhalerao, S. (2010). Sample size calculation. International Journal of Ayurveda Research, 1(1), 55. doi: 10.4103/0974-7788.59946.

DOI: https://doi.org/10.4103/0974-7788.59946
View in Google Scholar

Kassarnig, V., Mones, E., Bjerre-Nielsen, A., Sapiezynski, P., Lassen, D. D., & Lehmann, S. (2018). Academic performance and behavioral patterns. EPJ Da-ta Science, 7(1), 10. doi: 10.1140/epjds/s13688-018-0138-8.

DOI: https://doi.org/10.1140/epjds/s13688-018-0138-8
View in Google Scholar

Kazancoglu, I., Ozbiltekin-Pala, M., Mangla, S. K., Kazancoglu, Y., & Jabeen, F. (2022). Role of flexibility, agility and responsiveness for sustainable supply chain resilience during COVID-19. Journal of Cleaner Production, 362 132431. doi: 10.1016/j.jclepro.2022.132431.

DOI: https://doi.org/10.1016/j.jclepro.2022.132431
View in Google Scholar

Kim, H. J., Hong, A. J., & Song, H. D. (2019). The roles of academic engagement
View in Google Scholar

and digital readiness in students? achievements in university e-learning envi-ronments. International Journal of Educational Technology in Higher Education, 16(1), 1?18. doi: 10.1186/s41239-019-0152-3.

DOI: https://doi.org/10.1186/s41239-019-0152-3
View in Google Scholar

Kozakowski, W. (2019). Moving the classroom to the computer lab: can online learning with in-person support improve outcomes in community colleges? Economics of Education Review, 70, 159?172. doi: 10.1016/j.econedurev.2019. 03.004.

DOI: https://doi.org/10.1016/j.econedurev.2019.03.004
View in Google Scholar

Laffey, J., Lin, G. Y., & Lin, Y. (2006). Assessing social ability in online learning environments. Journal of Interactive Learning Research, 17(2), 163e177.
View in Google Scholar

Langford, R., Bonell, C. P., Jones, H. E., Pouliou, T., Murphy, S. M., & Waters, E. (2014). The WHO health promoting school framework for improving the health and well-being of students and their academic achievement. Cochrane Database Systematic Review, 4(4), CD008958. doi: 10.1002/14651858.CD008 958.pub2.

DOI: https://doi.org/10.1002/14651858.CD008958.pub2
View in Google Scholar

Li, L. Y., & Tsai, C. C. (2017). Accessing online learning material: quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286?297. doi: 10.1016/j.compedu.2017.07.007.

DOI: https://doi.org/10.1016/j.compedu.2017.07.007
View in Google Scholar

Liu, Z., Zhang, W., Cheng, H. N. H., Sun, J., & Liu, S. (2018). Investigating rela-tionship between discourse behavioral patterns and academic achievements of students in SPOC discussion forum. International Journal of Distance Education Technologies, 16(2), 37?50. doi: 10.4018/ijdet.2018040103.

DOI: https://doi.org/10.4018/IJDET.2018040103
View in Google Scholar

Lu, C., & Cutumisu, M. (2022). Online engagement and performance on forma-tive assessments mediate the relationship between attendance and course per-formance. International Journal of Educational Technology in Higher Educa-tion, 19(1), 1?23. doi: 10.1186/s41239-021-00307-5.

DOI: https://doi.org/10.1186/s41239-021-00307-5
View in Google Scholar

Luo, Y., Lin, J., & Yang, Y. (2021). Students? motivation and continued intention with online self-regulated learning: a self-determination theory perspective. Z Erziehungswiss, 24, 1379?1399. doi: 10.1007/s11618-021-01042-3.

DOI: https://doi.org/10.1007/s11618-021-01042-3
View in Google Scholar

Ma, Y., Friel, C., & Xing, W. (2014). Instructional activities in a discussion board forum of an e-leaning management system. In C. Stephanidis (Ed.). HCI inter-national 2014 - posters? extended abstracts. HCI 2014. Communications in Computer and information science, vol 435. Springer, Cham. doi: 10.1007/978-3-319-07854-0_20.

DOI: https://doi.org/10.1007/978-3-319-07854-0_20
View in Google Scholar

Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students? behavioural intention to use e-learning during the COVID-19 pandemic: an ex-tended TAM model. Education and Information Technologies, 26, 7057?7077. doi: 10.1007/s10639-021-10557-5.

DOI: https://doi.org/10.1007/s10639-021-10557-5
View in Google Scholar

Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The influence of values on e-learning adoption. Computers & Education, 141, 103617. doi: 10.1016/j.compedu.2019.103617.

DOI: https://doi.org/10.1016/j.compedu.2019.103617
View in Google Scholar

Meşe, E., & Sevilen, Ç. (2021). Factors influencing EFL students? motivation in online learning: a qualitative case study. Journal of Educational Technology & Online Learning, 4(1), 11?22. doi: 10.31681/ jetol.817680.
View in Google Scholar

Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K. (2021). Students? per-ception and preference for online education in India during COVID-19 pan-demic. Social Sciences & Humanities Open, 3(1), 100101. doi: 10.1016/j.s sa-ho.2020.100101.

DOI: https://doi.org/10.1016/j.ssaho.2020.100101
View in Google Scholar

Nacaskul, P. (2017). Financial risk management and sustainability. The sufficien-cy economy philosophy nexus. SSRN. doi: 10.2139/ssrn.3057886.

DOI: https://doi.org/10.2139/ssrn.3057886
View in Google Scholar

Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: helping researchers discuss more sophisticated mod-els. Industrial Management & Data Systems, 116(9), 1849?1864. doi: 10.1108/IM DS-07-2015-0302.

DOI: https://doi.org/10.1108/IMDS-07-2015-0302
View in Google Scholar

Owusu, V., Gregar, A., & Ntsiful, A. (2021). Organizational diversity and compe-tency-based performance: the mediating role of employee commitment and job satisfaction. Management & Marketing. Challenges for the Knowledge So-ciety, 16(4), 352?369. doi: 10.2478/mmcks-2021-0021.

DOI: https://doi.org/10.2478/mmcks-2021-0021
View in Google Scholar

Palacios-Manzano, M., León-Gomez, A., & Santos-Jaén, J. M. (2021). Corporate social responsibility as a vehicle for ensuring the survival of construction SMEs. The mediating role of job satisfaction and innovation. IEEE Transactions on Engineering Management. Advance online publication. doi: 10.1109/TEM.2021.3114441.

DOI: https://doi.org/10.1109/TEM.2021.3114441
View in Google Scholar

Pardo, A., Han, F., & Ellis, R. A. (2016). Exploring the relation between self-regulation, online activities, and academic performance: a case study. In Pro-ceedings of the sixth international conference on learning analytics & knowledge (pp. 422?429). ACM Digital Library. doi: 10.1145/2883851.2883 883.

DOI: https://doi.org/10.1145/2883851.2883883
View in Google Scholar

Parmar, V., Channar, Z. A., Ahmed, R. R., Streimikiene, D., Pahi, M. H., & Streimikis, J. (2022). Assessing the organizational commitment, subjective vi-tality and burnout effects on turnover intention in private universities. Oeconomia Copernicana, 13(1), 251?286. doi: 10.24136/oc.2022.008.

DOI: https://doi.org/10.24136/oc.2022.008
View in Google Scholar

Păunescu, C., & Mátyus, E. (2020). Resilience measures to dealing with the
View in Google Scholar

COVID-19 pandemic. Evidence from Romanian micro and small enterprises. Management & Marketing. Challenges for the Knowledge Society, 15(1), 439?457. doi: 10.2478/mmcks-2020-0026.

DOI: https://doi.org/10.2478/mmcks-2020-0026
View in Google Scholar

Philip, B., Shetty, R. L., Thomas, L. P., & Manoj, J. (2021). Virtual learning: a panacea in the phase of covid pandemic and prospect of education. Advances and Applications in Mathematical Sciences, 20(10), 2333?2349.
View in Google Scholar

Pollák, F., Vavrek, R., Váchal, J., Markovič, P., & Konečný, M. (2021). Analysis of digital customer communities in terms of their interactions during the first wave of the COVID-19 pandemic. Management & Marketing. Challenges for the Knowledge Society, 16(2), 134?151. doi: 10.2478/mmcks-2021-0009.

DOI: https://doi.org/10.2478/mmcks-2021-0009
View in Google Scholar

Qu, S., Li, K., Zhang, S., & Wang, Y. (2018). Predicting achievement of students in smart campus. IEEE Access, 6, 60264?60273. doi: 10.1109/ACCESS.2018.2 875742.

DOI: https://doi.org/10.1109/ACCESS.2018.2875742
View in Google Scholar

Ringle, C., Da Silva, D., & Bido, D. (2015). Structural Equation Modeling with the SmartPLS. Revista Brasileira de Marketing, 13(2), 57?73. doi: 10.5585/remar k.v13i2.2717.

DOI: https://doi.org/10.5585/remark.v13i2.2717
View in Google Scholar

Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: guidelines for using partial least squares in information systems re-search. In M. Mora (Ed.). Research methodologies, innovations and philoso-phies in software systems engineering and information systems (pp. 193?221). IGI global.

DOI: https://doi.org/10.4018/978-1-4666-0179-6.ch010
View in Google Scholar

Rosenthal, R. (1994). Parametric measures of effect size. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.). The handbook of research synthesis (pp. 231?244). New York: Russell Sage Foundation.
View in Google Scholar

Sahebi, S., & Brusilovshky, P. (2018). Student performance prediction by discov-ering inter-activity relations. International Conference on Educational Data Mining (EDM), Buffalo, NY, USA. Retrieved from https://files.eric.ed .gov/fulltext/ED593107.pdf.
View in Google Scholar

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2014). PLS-SEM: Looking back and moving forward. Long Range Planning, 47(3), 132?137. doi: 10.1016/j.lrp.2 014.02.008

DOI: https://doi.org/10.1016/j.lrp.2014.02.008
View in Google Scholar

Seok, S. (2007). eTeacher?s role and pedagogical issues in elearning. In C. Mont-gomerie & J. Seale (Eds.). Proceedings of ED-MEDIA 2007--world conference on educational multimedia, hypermedia & telecommunications (pp. 2627?2630). Vancouver, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primar y/p/25742/.
View in Google Scholar

Shaha, S., Glassett, K., Copas, A., & Ellsworth, H. (2015). I schools: the student-based impact of online, on-demand professional development on educators. Contemporary Issues in Education Research, 8(4), 227?234. doi: 10.1016/j.ta te.2009.09.006.

DOI: https://doi.org/10.19030/cier.v8i4.9430
View in Google Scholar

Shukor, N. A., Tasir, Z., Van der Meijden, H., & Harun, J. (2014). Predictive model to evaluate students? cognitive engagement in online learning. Procedia ? Social and Behavioral Sciences, 116, 4844?4853. doi: 10.1016/j.sbspro.2014.0 1.1036.

DOI: https://doi.org/10.1016/j.sbspro.2014.01.1036
View in Google Scholar

Stein, L. (2004). End of the beginning. Nature, 431, 915?916. doi: 10.1038/43 1915a.

DOI: https://doi.org/10.1038/431915a
View in Google Scholar

Sugden, N., Brunton, R., MacDonald, J. B., Yeo, M., & Hicks, B. (2021). Evaluat-ing student engagement and deep learning in interactive online psychology learning activities. Australasian Journal of Educational Technology, 37(2), 45?65. doi: 10.14742/ajet.6632.

DOI: https://doi.org/10.14742/ajet.6632
View in Google Scholar

Szostek, D., Balcerzak, A. P., & Rogalska, E. (2020). The relationship between personality, organizational and interpersonal counterproductive work chal-lenges in industry 4.0. Acta Montanistica Slovaca, 25(4), 577?592. doi: 10.46544 /AMS .v25i4.11.

DOI: https://doi.org/10.46544/AMS.v25i4.11
View in Google Scholar

Szostek, D., Balcerzak, A. P., & Rogalska, E. (2022a). The impact of personality traits on subjective categories of counterproductive work behaviors in Central European environment. Transformations in Business & Economics, 21, 2 (56), 163?180.
View in Google Scholar

Szostek, D., & Balcerzak, A. P., Rogalska, E., N., & MacGregor Pelikánová, R. (2022b). Personality traits and counterproductive work behaviors: the moder-ating role of demographic characteristics. Economics and Sociology, 15(4), 231?263. doi: 10.14254/2071-789X.2022/15-4/12.

DOI: https://doi.org/10.14254/2071-789X.2022/15-4/12
View in Google Scholar

Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path mod-eling. Computational Statistics & Data Analysis, 48(1), 159?205.

DOI: https://doi.org/10.1016/j.csda.2004.03.005
View in Google Scholar

Tsai, C. L., Ku, H. U., & Campbell, A. (2021). Impacts of course activities on student perceptions of engagement and learning online. Distance Education, 42(1), 106?125. doi: 10.1080/01587919.2020.1869525.

DOI: https://doi.org/10.1080/01587919.2020.1869525
View in Google Scholar

Chayomchai, A. (2020). The online technology acceptance model of generation-Z people in Thailand during COVID-19 crisis. Management & Marketing. Chal-lenges for the Knowledge Society, 15, 496?513. doi: 10.2478/mmcks-2020-0029.

DOI: https://doi.org/10.2478/mmcks-2020-0029
View in Google Scholar

Wang, R., Harari, G., Hao, P., Zhou, X., & Campbell, A.T. (2015). SmartGPA: How smartphones can assess and predict academic performance of college students. Proceedings of the ACM international joint conference on pervasive and ubiquitous computing (UbiComp), Osaka, Japan (pp. 295?306). ACM Digital Library. doi: 10.1145/27 50858.2804251.

DOI: https://doi.org/10.1145/2750858.2804251
View in Google Scholar

Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students? course outcomes: evidence from a large community and technical college system. Economics of Education Review, 37, 46?57. doi: 10.1016/j.econedurev.2013. 08.001.

DOI: https://doi.org/10.1016/j.econedurev.2013.08.001
View in Google Scholar

Yao, H., Lian, D., Cao, Y., Wu, Y., & Zhou, T. (2019). Predicting academic per-formance for college students: a campus behavior perspective. ACM Transac-tions on Intelligent Systems and Technology, 1(1), 1?20. doi: 10.48550/arXiv.1 903.06726.
View in Google Scholar

Yao, Y., Wangb, P., Jiangc, Y., Lid, Q., & Lie, Y. (2022). Innovative online learn-ing strategies for the successful construction of student self-awareness during the COVID-19 pandemic: merging TAM with TPB. Journal of Innovation & Knowledge, 7(4), 100252, doi: 10.1016/j.jik.2022.100252.

DOI: https://doi.org/10.1016/j.jik.2022.100252
View in Google Scholar

Zhang, T., Shaikh, Z. A., Yumashev, A. V., & Chłąd, M. (2020). Applied model of E-learning in the framework of education for sustainable development. Sustainability, 12(16), 6420. doi: 10.3390/su12166420.

DOI: https://doi.org/10.3390/su12166420
View in Google Scholar

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2022-12-30

How to Cite

Dima , A. M., Busu, M., & Vargas, V. M. (2022). The mediating role of students’ ability to adapt to online activities on the relationship between perceived university culture and academic performance. Oeconomia Copernicana, 13(4), 1253–1281. https://doi.org/10.24136/oc.2022.036

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