Czasopismo
Tytuł artykułu
Warianty tytułu
Języki publikacji
Abstrakty
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. (original abstract)
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. (original abstract)
Słowa kluczowe
Twórcy
autor
- Bucharest University of Economic Studies, Romania
autor
- Bucharest University of Economic Studies, Romania
autor
- Bucharest University of Economic Studies, Romania
Bibliografia
- 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.
- 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 (COVID-19) on education: the role of virtual and remote laboratories in education. Technology in Society, 67, 101728. doi: 10.1016/j.techsoc.2021.101728.
- 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.
- 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.
- Ahmad, S., Li, K., Amin, A., Anwar, M. S., & Khan, W. (2018). A multilayer prediction approach for the student cognitive skills measurement. IEEE Access, 6, 57470-57484. doi: 10.1109/ACCESS.2018.2873608.
- Akram, A., Fu, C., Li, Y., Javed, M.Y., Lin, R., Jiang, Y., & Tang, Y. (2019). Predicting students' academic procrastination in blended learning course using homework submission data. IEEE Access, 7, 102487-102498. doi: 10.1109/ACCESS.2019.2930867.
- Alghamdi, A., Karpinski, A. C., Lepp, A., & Barkley, J. (2020). Online and faceto-face classroom multitasking and academic performance: moderated mediation with self-efficacy for self-regulated learning and gender. Computers in Human Behavior, 102, 214-222.
- Alkış, 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.
- Avcı, 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.
- 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.
- Balcerzak, A. P., & Pietrzak, M. B. (2016). Structural Equation Modeling in evaluation 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: Foundation of the Cracow University of Economics.
- 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.
- 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.
- Cao, Y., Gao, J., Lian, D., Rong, Z., Shi, J., & Wang, Q. (2018). Orderliness predicts academic performance: behavioural analysis on campus lifestyle. Journal of the Royal Society Interface, 15(146), 20180210. doi: 10.1098/rsif.2018.0210.
- Cataldo, R., Crocetta, C., Grassia, M. G., Lauro, N. C., Marino, M., & Voytsekhovska, V. (2021). Methodological PLS-PM framework for SDGs system. Social Indicators Research, 156(2), 701-723. doi: 10.1007/s11205-020-02271-5.
- Çebi, A., & Güyer, T. (2020). Students' interaction patterns in different online learning activities and their relationship with motivation, self-regulated learning strategy and learning performance. Education and Information Technologies, 25(5), 3975-3993. doi : 10.1007/s10639-020-10151-1.
- Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, 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.
- 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.
- 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.
- Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., Magni, P., & Lam, S. (2020). COVID-19: 20 countries' higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1), 1-20. doi: 10.37074/jalt.2020.3.1.7.
- Dhawan, S. (2020). Online learning: a panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22. doi: 10.1177/0047239520934018.
- 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.
- 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.
- 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.
- Elmer, T., Mepham, K., & Stadtfeld, C. (2020). Students under lockdown: comparisons of students' social networks and mental health before and during the COVID-19 crisis in Switzerland. Plos One, 15(7), e0236337. doi: 10.1371/journal.pone.0236337.
- 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.
- 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.
- 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.
- Faught, E. L., Gleddie, D., Storey, K. E., Davison, C. M., & Veugelers, P. J. (2017). Healthy lifestyle behaviours are positively and independently associated with academic achievement: An analysis of self-reported data from a nationally representative sample of Canadian early adolescents. PLoS ONE, 12(7), e0181938. doi: 10.1371/journal.pone.0181938.
- Fazil, A., & Rupert, W. (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. doi: 10.1016/j.chb.2015.11.036.
- 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: 10.1177/0963721420964095.
- Fredericksen, E., Pickett, A., & Shea, P. (2006). Student satisfaction and perceived learning with on-line courses: principles and examples from the SUNY learning network. Journal of Asynchronous Learning Networks, 4(2), 2-31.
- Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi: 10.2307/3151312.
- Gimeno-Arias, F., & Santos-Jaén, J. M. (2022). Using PLS-SEM for assessing negative impact and cooperation as antecedents of gray market in FMCG supply chains: an analysis on Spanish wholesale distributors. International Journal of Physical Distribution & Logistics Management. Advance online publication. doi: 10.1108/IJPDLM-02-2022-0038.
- 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 students' performance in higher education. PLoS ONE, 15, e0239490. doi: 10.1371/journal.pone.0239490.
- 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.
- 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/EBR-10-2013-0128.
- 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.
- 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.
- Jung, Il, Choi, S., Lim, C., & Leem, J. (2002). Effects of different types of interaction on learning achievement, satisfaction and participation in web-based instruction. Innovations in Education and Teaching International, 39(2), 153-162. doi: 10.1080/14703290252934603.
- 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.
- Kadam, P., & Bhalerao, S. (2010). Sample size calculation. International Journal of Ayurveda Research, 1(1), 55. doi: 10.4103/0974-7788.59946.
- Kassarnig, V., Mones, E., Bjerre-Nielsen, A., Sapiezynski, P., Lassen, D. D., & Lehmann, S. (2018). Academic performance and behavioral patterns. EPJ Data Science, 7(1), 10. doi: 10.1140/epjds/s13688-018-0138-8.
- 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.
- Kim, H. J., Hong, A. J., & Song, H. D. (2019). The roles of academic engagement and digital readiness in students' achievements in university e-learning environments. International Journal of Educational Technology in Higher Education, 16(1), 1-18. doi: 10.1186/s41239-019-0152-3.
- 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.
- Laffey, J., Lin, G. Y., & Lin, Y. (2006). Assessing social ability in online learning environments. Journal of Interactive Learning Research, 17(2), 163e177.
- 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.CD008958.pub2.
- 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.
- Liu, Z., Zhang, W., Cheng, H. N. H., Sun, J., & Liu, S. (2018). Investigating relationship 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.
- Lu, C., & Cutumisu, M. (2022). Online engagement and performance on formative assessments mediate the relationship between attendance and course performance. International Journal of Educational Technology in Higher Education, 19(1), 1-23. doi: 10.1186/s41239-021-00307-5.
- 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.
- 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 international 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.
- 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. doi: 10.1007/s10639-021-10557-5.
- 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.
- 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.
- Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K. (2021). Students' perception and preference for online education in India during COVID-19 pandemic. Social Sciences & Humanities Open, 3(1), 100101. doi: 10.1016/j.ssaho.2020.100101.
- Nacaskul, P. (2017). Financial risk management and sustainability. The sufficiency economy philosophy nexus. SSRN. doi: 10.2139/ssrn.3057886.
- Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9), 1849-1864. doi: 10.1108/IMDS-07-2015-0302.
- Owusu, V., Gregar, A., & Ntsiful, A. (2021). Organizational diversity and competency-based performance: the mediating role of employee commitment and job satisfaction. Management & Marketing. Challenges for the Knowledge Society, 16(4), 352-369. doi: 10.2478/mmcks-2021-0021.
- 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.
- Pardo, A., Han, F., & Ellis, R. A. (2016). Exploring the relation between selfregulation, online activities, and academic performance: a case study. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 422-429). ACM Digital Library. doi: 10.1145/2883851.2883883.
- Parmar, V., Channar, Z. A., Ahmed, R. R., Streimikiene, D., Pahi, M. H., & Streimikis, J. (2022). Assessing the organizational commitment, subjective vitality and burnout effects on turnover intention in private universities. Oeconomia Copernicana, 13(1), 251-286. doi: 10.24136/oc.2022.008.
- Păunescu, C., & Mátyus, E. (2020). Resilience measures to dealing with the 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.
- 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.
- 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.
- 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.2018.2875742.
- 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/remark.v13i2.2717.
- Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: guidelines for using partial least squares in information systems research. In M. Mora (Ed.). Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193-221). IGI global.
- 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.
- Sahebi, S., & Brusilovshky, P. (2018). Student performance prediction by discovering inter-activity relations. International Conference on Educational Data Mining (EDM), Buffalo, NY, USA. Retrieved from https://files.eric.ed.gov/fulltext/ED593107.pdf.
- 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.2014.02.008
- Seok, S. (2007). eTeacher's role and pedagogical issues in elearning. In C. Montgomerie & 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/primary/p/25742/.
- Shaha, S., Glassett, K., Copas, A., & Ellsworth, H. (2015). I schools: the studentbased impact of online, on-demand professional development on educators. Contemporary Issues in Education Research, 8(4), 227-234. doi: 10.1016/j.tate.2009.09.006.
- 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.2014.01.1036.
- Stein, L. (2004). End of the beginning. Nature, 431, 915-916. doi: 10.1038/431915a.
- Sugden, N., Brunton, R., MacDonald, J. B., Yeo, M., & Hicks, B. (2021). Evaluating 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.
- Szostek, D., Balcerzak, A. P., & Rogalska, E. (2020). The relationship between personality, organizational and interpersonal counterproductive work challenges in industry 4.0. Acta Montanistica Slovaca, 25(4), 577-592. doi: 10.46544/AMS.v25i4.11.
- 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.
- Szostek, D., & Balcerzak, A. P., Rogalska, E., N., & MacGregor Pelikánová, R. (2022b). Personality traits and counterproductive work behaviors: the moderating role of demographic characteristics. Economics and Sociology, 15(4), 231-263. doi: 10.14254/2071-789X.2022/15-4/12.
- Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- 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.
- Chayomchai, A. (2020). The online technology acceptance model of generation-Z people in Thailand during COVID-19 crisis. Management & Marketing. Challenges for the Knowledge Society, 15, 496-513. doi: 10.2478/mmcks-2020-0029.
- 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.
- 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.
- Yao, H., Lian, D., Cao, Y., Wu, Y., & Zhou, T. (2019). Predicting academic performance for college students: a campus behavior perspective. ACM Transactions on Intelligent Systems and Technology, 1(1), 1-20. doi: 10.48550/arXiv.1903.06726.
- Yao, Y., Wangb, P., Jiangc, Y., Lid, Q., & Lie, 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), 100252, doi: 10.1016/j.jik.2022.100252.
- 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.
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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