International Association of Educators   |  ISSN: 1309-0682
Akdeniz Eğitim Araştırmaları Dergisi
International Association of Educators

Orjinal Araştırma Makalesi | Akdeniz Eğitim Araştırmaları Dergisi 2019, Cil. 13(30) 114-125

The Usage of Cloud and Web Based Mobile Applications by Students at Higher Education: The Two-Step Cluster Analysis

Farıd Huseynov

ss. 114 - 125   |  DOI: https://doi.org/10.29329/mjer.2019.218.7   |  Makale No: MANU-1909-12-0001.R1

Yayın tarihi: Aralık 24, 2019  |   Okunma Sayısı: 117  |  İndirilme Sayısı: 688

PDF İndir

Özet

It is possible to see the successful implementation of cloud and web based mobile technology solutions in key industries such as finance, retailing, healthcare, manufacturing, etc. Along with these key industries, cloud and web based technologies also have its significant effect in education sector. These technologies are significantly changing the learning and teaching landscape in various types of educational institutions. The way students learn, teachers teach and educational institutions maintain their key functions have been transformed and become more effective and efficient via these technologies. This research focuses on the students’ use of cloud and web based mobile educational tools in higher education. In this research, Two-Step cluster analysis has been conducted in order to identify different student groups with respect to their use of cloud and web based mobile apps in higher education. Cluster analysis has been conducted around seven key attributes. Five of these attributes have been adopted from the “Diffusion of Innovations” theory which is one of the well-known social sciences theories that seeks to explain how, why, and at what rate new technological ideas spread across societies. These factors are relative advantage, compatibility, complexity, trialability, and observability. The other two factors are perceived data security and perceived social pressure. As a result of Two-Step cluster analysis, four different student groups have been identified. Behavioral characteristics of each student group has been discussed with respect to their use of such key technologies in higher education context. Results of this study are expected to guide practitioners and marketers to develop more effective cloud and web based mobile apps and associated marketing strategies to improve the adoption and usage rate of their apps in higher education.

Anahtar Kelimeler: Mobile Learning, Cloud-Based Mobile Apps, Web-Based Mobile Apps, Higher Education Students, Diffusion of Innovation, Two-Step Cluster Analysis

Bu makaleye nasıl atıf yapılır?

APA 6th edition
Huseynov, F. (2019). The Usage of Cloud and Web Based Mobile Applications by Students at Higher Education: The Two-Step Cluster Analysis . Akdeniz Eğitim Araştırmaları Dergisi, 13(30), 114-125. doi: 10.29329/mjer.2019.218.7

Harvard
Huseynov, F. (2019). The Usage of Cloud and Web Based Mobile Applications by Students at Higher Education: The Two-Step Cluster Analysis . Akdeniz Eğitim Araştırmaları Dergisi, 13(30), pp. 114-125.

Chicago 16th edition
Huseynov, Farid (2019). "The Usage of Cloud and Web Based Mobile Applications by Students at Higher Education: The Two-Step Cluster Analysis ". Akdeniz Eğitim Araştırmaları Dergisi 13 (30):114-125. doi:10.29329/mjer.2019.218.7.
Kaynakça
  1. Ariffin, S. K., Mohan, T., & Goh, Y. N. (2018). Influence of consumers’ perceived risk on consumers’ online purchase intention. Journal of Research in Interactive Marketing, 12, 309-327. [Google Scholar]
  2. Changchit, C., Cutshall, R., Lonkani, R., Pholwan, K., & Pongwiritthon, R. (2018). Determinants of online shopping influencing Thai consumer’s buying choices. Journal of Internet Commerce, 18, 1-23. [Google Scholar]
  3. CNET. (2018, March 27). Apps announced at Apple's Chicago education event. Retrieved August 5, 2019, from https://www.cnet.com/pictures/all-the-2018-education-apps-apple-announced/  [Google Scholar]
  4. Çavuş, N., & Uzunboylu, H. (2009). Improving critical thinking skills in mobile learning. Procedia - Social and Behavioral Sciences, 1(1), 434–438.  [Google Scholar]
  5. Flavian, C., & Guinaliu, M. (2006). Consumer trust, perceived security and privacy policy three basic elements of loyalty to a web site. Industrial Management & Data Systems, 106, 601-620. [Google Scholar]
  6. Gonzalez-Martínez, J., Bote-Lorenzo, M., Eduardo, G., & Rafael, C. (2015). Cloud computing and education: A state-of-the-art survey. Computers & Education, 80, 132-151. [Google Scholar]
  7. Huseynov, F., & Yıldırım, S. O. (2016). Internet users’ attitudes toward business-to-consumer online shopping: A survey. Information Development, 32, 452-465. [Google Scholar]
  8. Järveläinen, J. (2007). Online purchase intentions: An empirical testing of a multiple-theory model. Journal of Organizational Computing and Electronic Commerce, 17, 53-74. [Google Scholar]
  9. Jeno, L. M., Grytnes, J. A., & Vandvik, V. (2017). The effect of a mobile-application tool on biology students' motivation and achievement in species identification: A Self-Determination Theory perspective. Computers & Education, 107, 1-12. [Google Scholar]
  10. Köse, U., Koç, D., & Yücesoy, S. A. (2013). An augmented reality based mobile software to support learning experiences in computer science courses. Procedia Computer Science, 25, 370–374.  [Google Scholar]
  11. Kutluk, F. A., & Gülmez, M. (2014). A Research about Mobile Learning Perspectives of University Students who have Accounting Lessons. Procedia - Social and Behavioral Sciences, 116, 291–297. [Google Scholar]
  12. Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76–85.  [Google Scholar]
  13. Meriçelli, M. & Uluyol, Ç. (2016). The Effect of Web Supported and Mobıle Supported Blended Learning Environment on Students Academic Achievement and Motivation. Turkish Studies, 11(9), 879-904. [Google Scholar]
  14. Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2(3), 192–222.  [Google Scholar]
  15. O’Cass, A., & Fenech, T. (2003). Web retailing adoption: Exploring the nature of internet users’ web retailing behavior. Journal of Retailing and Consumer Services, 10, 81-94. [Google Scholar]
  16. Rogers, E. (2003). Diffusion of Innovations, New York, NY: Free Press. [Google Scholar]
  17. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.  [Google Scholar]
  18. Sclater, N. (2010). eLearning in the Cloud. International Journal of Virtual and Personal Learning Environments, 1(1), 10-19. [Google Scholar]
  19. Sırakaya, M., & Sirakaya D. A. (2017). An Examination of Associate Degree Students’ Mobile Learning Attitudes According to Various Variables. Gazi University Journal of Gazi Educational Faculty, 37(3), 1085 – 1114 [Google Scholar]
  20. Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly, 20(2), 167–196. [Google Scholar]
  21. SPSS Inc. (2001). The SPSS TwoStep cluster component. Retrieved August 10, 2019 from http://www.spss.ch/upload/1122644952_The%20SPSS%20TwoStep%20Cluster%20Component.pdf [Google Scholar]
  22. Statista. (2019, August 1). Worldwide mobile app revenues in 2014 to 2023 (in billion U.S. dollars). Retrieved August 1, 2019 from https://www.statista.com/statistics/269025/worldwide-mobile-app-revenue-forecast/ [Google Scholar]
İndir
Metric
Geçmiş
Benzer

Cilt 13 Sayı 30

Aralık 2019

Tüm Makaleler

Akdeniz Eğitim Araştırmaları Dergisi

International Association of Educators.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Gizlilik     |     Kullanım Koşulları     |     Çerez Politikaları
Creative Commons Lisansı
© 2012 - 2024 - THDSoft e-Journal Management System.