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Kharazmi University
Abstract:   (665 Views)
Objective: The Covid-19 pandemic crisis has highlighted the importance of open and timely access to scientific information. The aim of this study was to explain the world community's efforts to open access in the Covid-19 pandemic crisis.
Methods: This was a review study in terms of approach using documentary research method. In this regard, open access literature was studied using searches on Google search engine, Google Scholar and databases such as PubMed, Web of Science, Scopus and the websites of publishers, journals and scientific institutions, focusing on the Covid-19 pandemic crisis.
Results: In response to the Covid-19 pandemic crisis, a global effort has been made to facilitate open access to Covid-19 research findings. The need for appropriate strategies for the treatment, prevention and control of the disease and the increasing number of Covid-19 research has affected the system of scientific communication and the publication of research findings and the world community, especially reputable publishers and scientific institutions, have made extensive statements, measures and efforts to provide open access to as much scientific information as possible about the disease.
Conclusion: The world community’s efforts demonstrate the growing importance of open access and increasing attention to this issue. It seems that the global response to the Covid-19 pandemic crisis could provide lessons for better preparedness for possible future crises. If open access is considered as a value in the system of publishing and research communications, the development and generalization of this value requires the adoption of policies and requirements of the scientific community in order to develop it as a global value.
     
Type of Study: Research | Subject: Special

References
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28. Uther, M. (2019). Mobile learning-trends and practices. Education Sciences, 9(1), 33. [DOI:10.3390/educsci9010033]
29. Yeap, J. A., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26(4), 323-338. [DOI:10.1007/s12525-015-0214-x]
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31. Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in human behavior, 63, 75-90. [DOI:10.1016/j.chb.2016.05.014]
32. Aburub, F., & Alnawas, I. (2019). A new integrated model to explore factors that influence adoption of mobile learning in higher education: An empirical investigation. Education and Information Technologies, 24(3), 2145-2158. [DOI:10.1007/s10639-019-09862-x]
33. Adel Ali, R., & Rafie Mohd Arshad, M. (2018). Empirical analysis on factors impacting on intention to use m-learning in basic education in Egypt. International Review of Research in Open and Distributed Learning, 19(2). [DOI:10.19173/irrodl.v19i2.3510]
34. Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining factors influencing Jordanian customers' intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125-138. [DOI:10.1016/j.jretconser.2017.08.026]
35. Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human behavior, 56, 93-102. [DOI:10.1016/j.chb.2015.11.033]
36. Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students' acceptance of mobile learning system in higher education. IEEE Access, 7, 174673-174686. [DOI:10.1109/ACCESS.2019.2957206]
37. Arokiasamy, A. R. A. (2017). A qualitative study on the impact of mobile technology among students in private higher education institutions (PHEIs) in Peninsular Malaysia. Journal of Entrepreneurship and Business, 5(2). [DOI:10.17687/JEB.0502.03]
38. Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies-Students' behavior. Computers in human behavior, 72, 612-620. [DOI:10.1016/j.chb.2016.05.027]
39. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & education, 59(3), 1054-1064. [DOI:10.1016/j.compedu.2012.04.015]
40. Crompton, H., & Burke, D. (2018). The use of mobile learning in higher education: A systematic review. Computers & Education, 123, 53-64. [DOI:10.1016/j.compedu.2018.04.007]
41. Dassa, L., & Vaughan, M. (2018). # Class again? How education faculty engage the disengaged college student. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 91(1), 42-45. [DOI:10.1080/00098655.2017.1342434]
42. Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning acceptance among university students: An integrated model of TPB and TAM. International Review of Research in Open and Distributed Learning, 20(3). [DOI:10.19173/irrodl.v20i4.4061]
43. Güler, Ç. (2017). Use of WhatsApp in higher education: What's up with assessing peers anonymously?. Journal of Educational Computing Research, 55(2), 272-289. [DOI:10.1177/0735633116667359]
44. Hameed, F., & Qayyum, A. (2018). Determinants of behavioral intention towards mobile learning in Pakistan: Mediating role of attitude. Business and Economic Review, 10(1), 33-61. [DOI:10.22547/BER/10.1.2]
45. Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International journal of medical informatics, 101, 75-84. [DOI:10.1016/j.ijmedinf.2017.02.002] [PMID]
46. Kim, J., Eys, M., Robertson-Wilson, J., Dunn, E., & Rellinger, K. (2019). Subjective norms matter for physical activity intentions more than previously thought: Reconsidering measurement and analytical approaches. Psychology of Sport and Exercise, 43, 359-367. [DOI:10.1016/j.psychsport.2019.04.013]
47. Koksal, M. H. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal of bank marketing. [DOI:10.1108/IJBM-03-2015-0025]
48. Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access, 8, 208058-208074. [DOI:10.1109/ACCESS.2020.3037925]
49. Naveed, Q. N., Alam, M. M., & Tairan, N. (2020). Structural equation modeling for mobile learning acceptance by university students: An empirical study. Sustainability, 12(20), 8618. [DOI:10.3390/su12208618]
50. Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73. [DOI:10.1016/j.compedu.2017.02.005]
51. O'Dea, S. (2020). Number of smartphone users worldwide from 2016 to 2021. Statista Research Department.
52. Peciuliauskiene, P., Tamoliune, G., & Trepule, E. (2022). Exploring the roles of information search and information evaluation literacy and pre-service teachers' ICT self-efficacy in teaching. International Journal of Educational Technology in Higher Education, 19(1), 1-19. [DOI:10.1186/s41239-022-00339-5] [PMID] []
53. Peteranetz, M. S., Flanigan, A. E., Shell, D. F., & Soh, L. K. (2018). Career aspirations, perceived instrumentality, and achievement in undergraduate computer science courses. Contemporary Educational Psychology, 53, 27-44. [DOI:10.1016/j.cedpsych.2018.01.006]
54. Quan, L., Al-Ansi, A., & Han, H. (2022). Assessing customer financial risk perception and attitude in the hotel industry: Exploring the role of protective measures against COVID-19. International Journal of Hospitality Management, 101, 103123. [DOI:10.1016/j.ijhm.2021.103123] [PMID] []
55. Shamsuddin, A., Wahab, E., Abdullah, N. H., & Suratkon, A. (2018, November). Mobile learning adoption in enhancing learning experience among HEI students. In 2018 IEEE 10th International Conference on Engineering Education (ICEED) (pp. 202-207). IEEE. [DOI:10.1109/ICEED.2018.8626923]
56. Siripipatthanakul, S., Siripipattanakul, S., Limna, P., & Pholphong, L. (2022). Predicting Intention to Choose the Online Degree During the COVID-19 Pandemic: The Mediating Role of Perceived Effectiveness. Asia-Pacific Review of Research in Education, 1(1), 1-19. [DOI:10.2139/ssrn.4046240]
57. Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived ease of use, perceived usefulness, perceived security and intention to use e-filing: The role of technology readiness. The Journal of Asian Finance, Economics and Business, 7(9), 537-547. [DOI:10.13106/jafeb.2020.vol7.no9.537]
58. Uther, M. (2019). Mobile learning-trends and practices. Education Sciences, 9(1), 33. [DOI:10.3390/educsci9010033]
59. Yeap, J. A., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26(4), 323-338. [DOI:10.1007/s12525-015-0214-x------]

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