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COVID-19 sürecinde uzaktan eğitime yönelik akademisyenlerin kullanıcı dirençlerinin teknoloji kabul modeli ile analiz edilmesi

Yıl 2022, Cilt: 15 Sayı: 2, 373 - 392, 31.12.2022
https://doi.org/10.17218/hititsbd.1166639

Öz

Bu çalışmanın amacı COVID-19 sürecinde akademisyenlerin uzaktan eğitime karşı kullanıcı dirençlerini, uzaktan eğitim sistemlerine ilişkin algılarını, eğitim sisteminde meydana gelen değişikliklere yönelik algılarını etkileyen faktörlerin belirlenmesidir. Araştırmanın örneklemini Türkiye’de 43 farklı üniversitede görev yapan 440 akademisyen oluşturmaktadır. Araştırmanın örneklemi kolayda ve kartopu örneklem yöntemleriyle belirlenmiştir. Veriler Google Forms’da oluşturulan çevrimiçi anket formu kullanılarak elde edilmiştir. Veriler üzerinde geçerlilik ve güvenirlik analizleri yapıldıktan sonra yapısal eşitlik modeli kullanılarak 8 farklı hipotez test edilmiştir. Analiz sonucunda çalışma kapsamında önerilen sekiz hipotezin tümü kabul edilmiştir. Yürütülen çalışma kapsamında, değişime karşı direncin uzaktan eğitim sistemi kullanıcılarını önemli ölçüde olumsuz bir şekilde etkilediği bulgulanmıştır. Bununla birlikte, algılanan kullanım kolaylılığı ve faydanın ise tutum ve davranışlar üzerinde olumlu bir etkisi bulunmaktadır

Destekleyen Kurum

yok

Proje Numarası

yok

Kaynakça

  • Aguilera-Hermida, A.P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open 1, (100011), 1-8. doi:10.1016/j.ijedro.2020.100011
  • Aguilera-Hermida, A.P., Quiroga-Garza, A., Gómez-Mendoza, S., Villanueva, C.A.D.R., Alecchi, B.A., and Avci, D. (2021). Comparison of students’ use and acceptance of emergency online learning due to Covid-19 in the USA, Mexico, Peru, and Turkey. Education and Information Technologies, 1-23. doi:10.1007/s10639-021-10473-8
  • Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., and Salloum, S. (2021). Using machine learning algorithms to predict people’s intentiont to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach. Jmir Medical Education 7(1), 1-17, E24032. doi: 10.2196/24032
  • Alam, M. (2020). Organisational processes and COVID-19 pandemic: implications for job design. Journal Of Accounting & Organizational Change, 16(4), 599-606. doi:10.1108/JAOC-08-2020-0121
  • Alexandrakis, D., Chorianopoulos, K., and Tselios, N. (2020). Older adults and web 2.0 storytelling technologies: probing the technology acceptance model through an age-related perspective. International Journal of Human–Computer Interaction, 36, 1623-1635. doi:10.1080/10447318.2020.1768673
  • Alfadda, H.A. and Mahdi, H.S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50, 883-900. doi:10.1007/s10936-020-09752-1
  • Alhumaid, K., Ali, S., Waheed, A., Zahid, E., and Habes, M. (2020). COVID-19 and E-learning: perceptions and attitudes of teachers towards e-learning acceptance in the developing countries. Multicultural Education, 6(2), 100-115. doi: 10.5281/zenodo.4060121
  • Almaiah, M.A., Al-Khasawneh, A., and Althunibat, A. (2020). Exploring the critical challenges and factors influencing the e-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280. doi:10.1007/s10639-020-10219-y
  • Alshurafat, H., Al Shbail, M.O., Masadeh, W.M., Dahmash, F., and Al-Msiedeen, J.M., (2021). Factors affecting online accounting education during the COVID-19 pandemic: An integrated perspective of social capital theory, the theory of reasoned action and the technology acceptance model. Education and Information Technologies, 1-19. doi:10.1007/s10639-021-10550-y
  • Ambarwati, M.F.L. (2021). Technology use analysis for administrative assistants by using the theory of technology acceptance model. Jurnal Administrasi Dan Kesekretarisan, 6(1), 78-90. doi:10.36914/jak.v6i1.565
  • Amoako-Gyampah, K. and Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. doi:10.1016/j.im.2003.08.010
  • Aryana, B. and Clemmensen, T. (2013). Mobile Usability: Experiences from Iran and Turkey. International Journal of Human-Computer Interaction, 29, 220-242. doi:10.1080/10447318.2013.765760
  • Asan, O. and Carayon, P. (2017). Human factors of health information technology—challenges and opportunities. International Journal of Human–Computer Interaction, 33(4), 255–257. doi:10.1080/10447318.2017.128275
  • Asghar, M.Z., Barberà, E., and Younas, I. (2021). Mobile learning technology readiness and acceptance among pre-service teachers in Pakistan during the COVID-19 pandemic. Knowledge Management & E-Learning: An International Journal, 13(1), 83-101. doi:10.34105/j.kmel.2021.13.005
  • Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-a study of South Korea. The International Journal of Management Education, 19, 1-15, 100503. doi:10.1016/j.ijme.2021.100503
  • Basyal, D.K. and Seo, J.-W. (2017). Employees’ resistance to change and technology acceptance in Nepal. The Journal of University Grants Commission, 6, 1-15. Retrieved from: http://journals.pu.edu.pk/journals/index.php/IJSAS/article/view/3114
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  • Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J.A., and 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
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Analysing user resistance to distance learning systems by academics within the Covid-19 pandemic using the technology acceptance model

Yıl 2022, Cilt: 15 Sayı: 2, 373 - 392, 31.12.2022
https://doi.org/10.17218/hititsbd.1166639

Öz

The aim of this study is to determine academics’ user resistance to distance learning, their perceptions of the distance learning systems and factors affecting their perceptions of the changes in the education system during the COVID-19 pandemic. The study’s population consists of 440 academics working in 43 different universities in Turkey. The research sample were determined through convenience and snowball sampling methods. The data were collected using an online questionnaire form created within Google Forms. After validity and reliability analyses on the data, eight different hypotheses were tested using structural equation analysis. All eight hypotheses proposed within the study were accepted after receiving the results of this analysis. The results of the study show that user resistance has a significantly negative effect on the users that utilise distance learning. However, the perceived ease of use and usefulness have a significantly positive effect on attitude and behavior.

Proje Numarası

yok

Kaynakça

  • Aguilera-Hermida, A.P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open 1, (100011), 1-8. doi:10.1016/j.ijedro.2020.100011
  • Aguilera-Hermida, A.P., Quiroga-Garza, A., Gómez-Mendoza, S., Villanueva, C.A.D.R., Alecchi, B.A., and Avci, D. (2021). Comparison of students’ use and acceptance of emergency online learning due to Covid-19 in the USA, Mexico, Peru, and Turkey. Education and Information Technologies, 1-23. doi:10.1007/s10639-021-10473-8
  • Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., and Salloum, S. (2021). Using machine learning algorithms to predict people’s intentiont to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach. Jmir Medical Education 7(1), 1-17, E24032. doi: 10.2196/24032
  • Alam, M. (2020). Organisational processes and COVID-19 pandemic: implications for job design. Journal Of Accounting & Organizational Change, 16(4), 599-606. doi:10.1108/JAOC-08-2020-0121
  • Alexandrakis, D., Chorianopoulos, K., and Tselios, N. (2020). Older adults and web 2.0 storytelling technologies: probing the technology acceptance model through an age-related perspective. International Journal of Human–Computer Interaction, 36, 1623-1635. doi:10.1080/10447318.2020.1768673
  • Alfadda, H.A. and Mahdi, H.S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50, 883-900. doi:10.1007/s10936-020-09752-1
  • Alhumaid, K., Ali, S., Waheed, A., Zahid, E., and Habes, M. (2020). COVID-19 and E-learning: perceptions and attitudes of teachers towards e-learning acceptance in the developing countries. Multicultural Education, 6(2), 100-115. doi: 10.5281/zenodo.4060121
  • Almaiah, M.A., Al-Khasawneh, A., and Althunibat, A. (2020). Exploring the critical challenges and factors influencing the e-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280. doi:10.1007/s10639-020-10219-y
  • Alshurafat, H., Al Shbail, M.O., Masadeh, W.M., Dahmash, F., and Al-Msiedeen, J.M., (2021). Factors affecting online accounting education during the COVID-19 pandemic: An integrated perspective of social capital theory, the theory of reasoned action and the technology acceptance model. Education and Information Technologies, 1-19. doi:10.1007/s10639-021-10550-y
  • Ambarwati, M.F.L. (2021). Technology use analysis for administrative assistants by using the theory of technology acceptance model. Jurnal Administrasi Dan Kesekretarisan, 6(1), 78-90. doi:10.36914/jak.v6i1.565
  • Amoako-Gyampah, K. and Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. doi:10.1016/j.im.2003.08.010
  • Aryana, B. and Clemmensen, T. (2013). Mobile Usability: Experiences from Iran and Turkey. International Journal of Human-Computer Interaction, 29, 220-242. doi:10.1080/10447318.2013.765760
  • Asan, O. and Carayon, P. (2017). Human factors of health information technology—challenges and opportunities. International Journal of Human–Computer Interaction, 33(4), 255–257. doi:10.1080/10447318.2017.128275
  • Asghar, M.Z., Barberà, E., and Younas, I. (2021). Mobile learning technology readiness and acceptance among pre-service teachers in Pakistan during the COVID-19 pandemic. Knowledge Management & E-Learning: An International Journal, 13(1), 83-101. doi:10.34105/j.kmel.2021.13.005
  • Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-a study of South Korea. The International Journal of Management Education, 19, 1-15, 100503. doi:10.1016/j.ijme.2021.100503
  • Basyal, D.K. and Seo, J.-W. (2017). Employees’ resistance to change and technology acceptance in Nepal. The Journal of University Grants Commission, 6, 1-15. Retrieved from: http://journals.pu.edu.pk/journals/index.php/IJSAS/article/view/3114
  • Baş, T. (2008). Anket nasıl hazırlanır nasıl uygulanır nasıl değerlendirilir? Ankara: Seçkin Press.
  • Bozpolat, C. and Seyhan, H. (2020). Mobil ödeme teknolojisi kabulünün teknoloji kabul modeli ile incelenmesi: Ampirik bir araştırma. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(1), 119-145. doi:10.18074/ckuiibfd.619852
  • Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J.A., and 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
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York: Guilford Publications.
  • Büyüköztürk, Ş. (2002). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, 32(32), 470-483. Retrieved from: https://dergipark.org.tr/en/pub/kuey/issue/10365/126871
  • Chayomchai, A. (2020). The online technology acceptance model of generation-z people in thailand during COVID-19 crisis. Management & Marketing, 1, 496-513. doi:10.2478/mmcks-2020-0029
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  • Hamutoğlu, N. B. (2018). Bulut bilişim teknolojileri kabul modeli 3: ölçek uyarlama çalışması. Sakarya University Journal of Education, 8(2), 8-25. doi: 10.19126/suje.297586
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  • Harrington, D. 2009. Confirmatory factor analysis. USA: Oxford University Press.
  • Hegner, S.M., Beldad, A.D., and Brunswick, G.J., (2019). In automatic we trust: Investigating the impact of trust, control, personality characteristics, and extrinsic and intrinsic motivations on the acceptance of autonomous vehicles. International Journal of Human–Computer Interaction, 35, 1769-1780. doi:10.1080/10447318.2019.1572353
  • Ji, Y.G., Park, J.H., Lee, C., and Yun, M.H. (2006). A usability checklist for the usability evaluation of mobile phone user interface. International Journal of Human-Computer Interaction, 20, 207- 231. doi:10.1207/s15327590ijhc2003_3
  • Jin, B.S., Yoon, S.H., and Ji, Y.G. (2013). Development of a continuous usage model for the adoption and continuous usage of a smartphone. International Journal of Human-Computer Interaction, 29, 563-581. doi:10.1080/10447318.2012.729997
  • Kalaycı, Ş. (2010). SPSS uygulamalı çok değişkenli istatistik teknikleri. Ankara: Asil Yayın Dağıtım.
  • Kamal, S.A., Shafiq, M., and Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society. 60, 1-10. 101212. doi:10.1016/j.techsoc.2019.101212
  • Khan, S. A., Zainuddin, M., Mahi, M., and Arif, I. (2020, December). Behavioral intention to use online learning during COVID-19: An analysis of the technology acceptance model. In International Conference on Innovative Methods of Teaching and Technological Advancements in Higher Education (IMTTAHE). Tbilisi: Georgia.
  • Kim, J., Merrill, K., Xu, K., and Sellnow, D.D. (2020). My teacher is a machine: Understanding students’ perceptions of ai teaching assistants in online education. International Journal Of Human–Computer Interaction 36, 1902-1911. doi:10.1080/10447318.2020.1801227
  • Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press.
  • Kurt, Ö. E. (2015). Üniversite öğrencilerinin uzaktan eğitime bakış açılarının teknoloji kabul modeli ve bilgi sistemleri başarı modeli entegrasyonu ile belirlenmesi. Uluslararası Alanya İşletme Fakültesi Dergisi, 7(3). 223-234. Retrieved from: https://dergipark.org.tr/en/pub/uaifd/issue/21604/232056
  • Kusumadewi, A.N., Lubis, N.A., Prastiyo, R., and Tamara, D. (2021). Technology acceptance model (tam) in the use of online learning applications during the Covid-19 pandemic for parents of elementary school students. Edunesia. Jurnal Ilmiah Pendidikan, 2, 272-292. doi:10.51276/edu.v2i1.120
  • Lazim, C., Ismail, N.D.B., and Tazilah, M. (2021). Application of technology acceptance model (tam) towards online learning during covid-19 pandemic: Accounting students perspective. International Journal of Business, Economics and Law, 24(1), 13-20. Retrieved from: https://www.ijbel.com/wp-content/uploads/2021/02/IJBEL24_507.pdf
  • Lin, P.-H. and Yeh, S.-C. (2019). How Motion-control influences a vr-supported technology for mental rotation learning: from the perspectives of playfulness, gender difference and technology acceptance model. International Journal of Human–Computer Interaction, 35, 1736-1746. doi:10.1080/10447318.2019.1571784
  • Menzi, N., Nezih, Ö., and Çalışkan, E. (2012). Mobil teknolojilerin eğitim amaçlı kullanımına yönelik akademisyen görüşlerinin teknoloji kabul modeli çerçevesinde incelenmesi. Ege Eğitim Dergisi, 13(1), 39-55. Retrieved from: https://dergipark.org.tr/en/pub/egeefd/issue/4904/67213
  • Nagel, L. (2020). The influence of the COVID-19 pandemic on the digital transformation of work. International Journal of Sociology and Social Policy.1-15. Retrieved from: https://www.emerald.com/insight/content/doi/10.1108/IJSSP-07-2020-0323/full/html?c
  • Nam, C. S., Bahn, S., and Lee, R. (2013). Acceptance of assistive technology by special education teachers: A structural equation model approach. International Journal of Human-Computer Interaction, 29(5), 365-377. doi:10.1080/10447318.2012.711990.
  • Ngabiyanto, Nurkhin, A., Widiyanto, Saputro, I. H., and Kholid, A. M. (2021). Teacher’s intention to use online learning; an extended technology acceptance model (TAM) investigation. In Journal of Physics: Conference Series, 1783 (1), 1-6. doi:10.1088/1742-6596/1783/1/012123.
  • Nov, O. and Ye, C. (2008). Personality and technology acceptance: personal innovativeness in it, openness and resistance to change, Proceedings of the 41st Annual Hawaii International Conference on System Sciences, 1-9.
  • Pal, D. and Vanijja, V. (2020). Perceived Usability evaluation of microsoft teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in india. Children and Youth Services Review, 119, 105535, 1-12. doi:10.1016/j.childyouth.2020.105535
  • Raza, S.A., Qazi, W., Khan, K.A. and Salam, J. (2021). Social isolation and acceptance of the learning management system (lms) in the time of COVID -19 pandemic: An expansion of the utaut model. Journal of Educational Computing Research, 59, 183-208. doi:10.1177/0735633120960421
  • Razif, M., Miraja, B.A., Persada, S.F., Nadlifatin, R., Belgiawan, P.F., Redi, A.A.N.P., and Shu-Chiang, L. (2020). Investigating the role of environmental concern and the unified theory of acceptance and use of technology on working from home technologies adoption during COVID-19. Entrepreneurship and Sustainability Issues, 8(1), 795-808. doi:10.9770/jesi.2020.8.1(53)
  • Republic of Turkey Ministry of Health. (2020, July 20). COVID-19 nedir? Retrieved from: saglik.gov.tr
  • Sagnier, C., Loup-Escande, E., Lourdeaux, D., Thouvenin, I., and Valléry, G. (2020). User acceptance of virtual reality: an extended technology acceptance model. International Journal of Human–Computer Interaction, 36, 993-1007. doi:10.1080/10447318.2019.1708612
  • Seyhun, S. and Kurtuldu, G. (2020). Genişletilmiş Teknoloji Kabul Modeli Bağlaminda Mobil Alişveriş Uygulamalarinin Benimsenmesini Etkileyen Faktörler. Trakya Üniversitesi Sosyal Bilimler Dergisi, 22(1), 599-627. Retrieved from: https://dergipark.org.tr/en/pub/trakyasobed/issue/52498/617630
  • Siegel, D., Acharya, P., and Sivo, S. (2017). Extending the technology acceptance model to improve usage and decrease resistance toward a new technology by faculty in higher education. Journal of Technology Studies, 43, 58-69. Retrieved from: https://www.jstor.org/stable/90023142#metadata_info_tab_contents
  • Su, C.-Y. and Chiu, C.-H. (2020). Perceived enjoyment and attractiveness influence Taiwanese elementary school students’ intention to use interactive video learning. International Journal of Human-Computer Interaction, 37(6), 1–10. doi:10.1080/10447318.2020.1841423
  • Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F.A., and Hakim, H. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during Covid-19:Indonesian sport science education context. Heliyon, 6, 1-9.E05410. doi:10.1016/j.heliyon.2020.e05410
  • Tandon, U. (2020). Factors Influencing adoption of online teaching by school teachers: A study during COVID‐19 pandemic. Journal of Public Affairs, 1-11, E2503. doi:10.1002/pa.2503.
  • Teo, T. (2012). Examining the intention to use technology among pre service teachers: An integration of the technology acceptance model and theory of planned behavior, Interactive Learning Environments, 20(1), 3-18. doi:10.1080/1049482100371463
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  • Türker, A. and Türker, Ö. G. (2013). Turistik ürün satın alma davranışının teknoloji kabul modeli ile incelenmesi. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 15(2), 281-312. Retrieved from: https://dergipark.org.tr/en/pub/deusosbil/issue/4633/63156
  • Vassli, L.T. and Farshchian, B.A. (2018). Acceptance of health-related ict among elderly people living in the community: A systematic review of qualitative evidence. International Journal of Human–Computer Interaction, 34, 99-116. doi:10.1080/10447318.2017.1328024
  • Vladova, G., Ullrich, A., Bender, B., and Gronau, N. (2021). Students’ acceptance of technology-mediated teaching–how it was influenced during the COVID-19 Pandemic in 2020: A study from Germany. Frontiers in Psychology. 12, 1-15. doi:10.3389/fpsyg.2021.636086
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  • Yang, Y., Liu, H., and Chen, X. (2020). COVID-19 and restaurant demand: Early effects of the pandemic and stay-at-home orders. International Journal of Contemporary Hospitality Management, 32(12), 3809-3834. doi:10.1108/IJCHM-06-2020-0504
  • Yüksek Öğretim Bilgi Yönetim Sistemi (2021, September 4). Özet Öğretim Elemanı Sayıları Raporu. Retrieved From: https://istatistik.yok.gov.tr/
  • Zaharias, P. and Poylymenakou, A. (2009). Developing a usability evaluation method for e-learning applications: beyond functional usability. Intl. Journal of Human–Computer Interaction, 25, 75-98. doi:10.1080/10447310802546716
  • Zhang, D. and Adipat, B. (2005). Challenges, methodologies, and issues in the usability testing of mobile applications. International Journal of Human-Computer Interaction, 18, 293-308. https://doi.org/10.1207/s15327590ijhc1803_3
  • Zhou, T. and Lu, Y. (2011a). The effects of personality traits on user acceptance of mobile commerce. Intl. Journal of Human–Computer Interaction, 27, 545-561. doi: 10.1080/10447318.2011.555298
  • Zhou, T. and Lu, Y. (2011b). Examining postadoption usage of mobile services from a dual perspective of enablers and inhibitors. International Journal of Human-Computer Interaction, 27, 1177-1191. doi: 10.1080/10447318.2011.565717.
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Fevziye Bekar 0000-0003-1692-4294

Handan Çam 0000-0003-0982-2919

Proje Numarası yok
Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 25 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 15 Sayı: 2

Kaynak Göster

APA Bekar, F., & Çam, H. (2022). Analysing user resistance to distance learning systems by academics within the Covid-19 pandemic using the technology acceptance model. Hitit Sosyal Bilimler Dergisi, 15(2), 373-392. https://doi.org/10.17218/hititsbd.1166639
                                                     Hitit Sosyal Bilimler Dergisi  Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.