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هدف: بحران بی‌سابقه همه‌گیری کووید-19، اهمیت دسترسی آزاد و به‌هنگام به اطلاعات را بیش از پیش برجسته کرده است. پژوهش حاضر با هدف تبیین تلاش‌های جامعه جهانی برای دسترسی آزاد در بحران همه‌گیری کووید-19 صورت گرفته است.
روش: پژوهش حاضر یک مطالعه مروری تحلیلی بوده و با استفاده از روش پژوهش اسنادی صورت گرفته است. در این راستا، ادبیات دسترسی آزاد با تمرکز بر بحران همه‌گیری کووید-19، با استفاده از جست‌و‌جو در موتور جست‌و‌جوی گوگل، گوگل اسکالر، پایگاه‌های اطلاعاتی پابمد، وب آو ساینس، اسکوپوس و نیز وبگاه‌های ناشران، مجلات و نهادهای معتبر علمی، مورد مطالعه قرار گرفته است.
یافته‌ها: در پاسخ به بحران همه‌گیری کووید-19، جریانی جهانی در راستای تسهیل دسترسی آزاد به یافته‌های پژوهشی مرتبط با این بیماری، صورت گرفته است. نیاز به راهکارهای مناسب درمان، پیشگیری و مهار بیماری و افزایش روزافزون پژوهش‌های مرتبط با کووید-19، نظام ارتباطات علمی و نشر یافته‌های پژوهشی را تحت تأثیر قرار داده و بیانیه‌ها، تدابیر و اقدامات گسترده‌ای در راستای دسترسی آزاد و دستیابی هر چه گسترده‌تر به اطلاعات علمی مرتبط با این بیماری از سوی جامعه جهانی به‌ویژه ناشران، مؤسسات و نهادهای معتبر علمی، انجام شده است.
نتیجه‌گیری: تلاش‌های جامعه جهانی، نشان از اهمیت فزاینده دسترسی آزاد و توجه بیش از پیش به این موضوع دارد. به‌نظر می‌رسد پاسخ جهانی به بحران همه‌گیری کووید-19 می‌تواند درس‌هایی برای آمادگی بهتر برای مدیریت نشر و ارتباطات علمی در بحران‌های احتمالی آینده به‌همراه داشته باشد. در صورتی که دسترسی آزاد به‌عنوان یک ارزش در نظام نشر و ارتباطات علمی در نظر گرفته شود، توسعه و عمومی شدن آن، مستلزم اتخاذ خط‌مشی‌ها و الزامات جامعه علمی به‌عنوان یک ارزش جهانی است.

     
نوع مطالعه: پژوهشي | موضوع مقاله: تخصصي

فهرست منابع
1. References 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
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8. 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]
9. 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]
10. 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]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. 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]
17. 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]
18. 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]
19. 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]
20. 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]
21. O'Dea, S. (2020). Number of smartphone users worldwide from 2016 to 2021. Statista Research Department.
22. 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] []
23. 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]
24. 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] []
25. 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]
26. 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]
27. 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]
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]
30. References
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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