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BİLGİ TEKNOLOJİLERİNİ KULLANIMLARINA GÖRE HANEHALKLARININ KÜMELENMESİ: TÜRKİYE ÜZERİNE BİR İNCELEME

Year 2023, Volume: 6 Issue: 2, 75 - 86, 13.10.2023

Abstract

Bilgi ve iletişim teknolojilerindeki gelişmelerle birlikte teknolojiye erişim giderek daha kolay hale gelmektedir. Ancak artık çoğu kullanıcının günlük görevleri cep telefonu üzerinden gerçekleştirdiğine inanılıyor. Literatürde öğrencilerin, öğretmenlerin ve çalışanların bilgi ve iletişim teknolojileri (BİT) okuryazarlığı veya kullanım durumlarının incelenmesine odaklanılmıştır. Ancak evdeki BİT cihazlarının kullanımına ilişkin araştırmalar daha az olmuştur. Günümüzde akıllı binalar ve şehirler yaratmak için hanelerin BİT adaptasyonu ve kullanım düzeyleri büyük önem taşımaktadır. Bu açıdan çalışma Türkiye'de akıllı şehir planlayan kişi ve kurumlara fikir vermektedir.
Çalışma, evdeki BİT cihazlarını kullanımlarına göre kümelere ayırıyor ve kullanım kalıplarındaki farklılıkları inceliyor. 2021 yılı verileri TÜİK'in Türkiye'de gerçekleştirdiği hane halkı bilgi ve iletişim teknolojileri araştırmasından elde edildi. Katılımcılar iki gruba ayrıldı: ileri düzey kullanıcılar ve temel kullanıcılar. Bölgesel farklılıklara bakıldığında, Türkiye'nin batısındaki şehirlerde ileri düzey kullanıcı oranının diğer bölgelere göre daha yüksek olduğu sonucuna varılmıştır. Araştırmaya göre insanların çoğunlukla mobil cihazları günlük kullanım için kullandıkları tespit edildi. Gruplar interneti ne kadar süre kullandıkları, e-ticaret alışkanlıkları ve web sitesi satın alma sorunlarıyla karşılaştıklarında gösterdikleri davranışlar açısından farklılık göstermektedir.

References

  • Baym, N. K. (2010). Personal connections in the digital age. John Wiley & Sons.
  • Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223-252.
  • Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160115.
  • Rogers, E. M. (1962). Diffusion of innovations. Free Press.
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  • Warschauer, M. (2003). Technology and social inclusion: Rethinking the digital divide. The MIT Press.
  • Berg, M. (2022). Digital technography: A methodology for interrogating emerging digital technologies and their futures. Qualitative Inquiry, 28(7), 827-836.
  • Perera, R. H. A. T., & Abeysekera, N. (2022). Factors affecting learners’ perception of e-learning during the COVID-19 pandemic. Asian association of open universities journal, 17(1), 84-100.
  • Russell, G., Finger, G., & Russell, N. (2000). Information technology skills of Australian teachers: Implications for teacher education. Journal of Information Technology for Teacher Education, 9(2), 149-166.
  • Hollman, A. K., Hollman, T. J., Shimerdla, F., Bice, M. R., & Adkins, M. (2019). Information technology pathways in education: Interventions with middle school students. Computers & Education, 135, 49-60.
  • Marler, J. H., Liang, X., & Dulebohn, J. H. (2006). Training and effective employee information technology use. Journal of Management, 32(5), 721-743.
  • Buhalis, D. (1998). Strategic use of information technologies in the tourism industry. Tourism Management, 19(5), 409-421.
  • Drias, H., Cherif, N. F., & Kechid, A. (2016). K-MM: A hybrid clustering algorithm based on k-means and k-medoids. In Advances in Nature and Biologically Inspired Computing: Proceedings of the 7th World Congress on Nature and Biologically Inspired Computing (NaBIC2015) in Pietermaritzburg, South Africa, held December 01-03, 2015 (pp. 37-48). Springer International Publishing.
  • Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive advanced user clustering algorithm. Procedia Computer Science, 54, 764-771. Kuo, R. J., Ho, L. M., & Hu, C. M. (2002). Integration of self-organizing feature map and K-means algorithm for market segmentation. Computers & Operations Research, 29(11), 1475-1493.
  • Ng, H. P., Ong, S. H., Foong, K. W. C., Goh, P. S., & Nowinski, W. L. (2006, March). Medical image segmentation using k-means clustering and improved watershed algorithm. In 2006 IEEE southwest symposium on Image Analysis and interpretation (pp. 61–65). IEEE.
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CLUSTERING OF HOUSEHOLDS BASED ON THEIR USE OF INFORMATION TECHNOLOGIES: A REVIEW OF TURKIYE

Year 2023, Volume: 6 Issue: 2, 75 - 86, 13.10.2023

Abstract

Access to technology is becoming increasingly available with the advancements in information and communication technologies. However, it is believed that most users now perform daily tasks on their mobile phones. In the literature, there has been a focus on examining the information and communication technology (ICT) literacy or usage status of students, teachers, and employees. However, there has been less exploration regarding the use of household ICT devices, which is a topic of interest. Today, households' ICT adaptation and usage levels are essential for creating smart buildings and cities. In this respect, the study provides an idea for people and institutions planning smart cities in Turkey.
The study categorizes household ICT devices into clusters based on their usage and examines the differences in usage patterns. The data for the year 2021 was obtained from the household information and communication technologies survey conducted by TURKSTAT in Turkey. The participants were categorized into two groups: advanced and basic users. Regarding regional differences, it is concluded that the rate of advanced users is higher in cities in western Turkey than in other regions. Based on the study, it was found that people mostly use mobile devices for daily usage. The groups differ in terms of how long they use the internet, their e-commerce habits, and their behaviors when encountering website purchasing problems.

References

  • Baym, N. K. (2010). Personal connections in the digital age. John Wiley & Sons.
  • Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223-252.
  • Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160115.
  • Rogers, E. M. (1962). Diffusion of innovations. Free Press.
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
  • Vorderer, P., Klimmt, C., & Ritterfeld, U. (2012). Enjoyment: At the heart of media entertainment. Media Psychology, 14(4), 303-310.
  • Warschauer, M. (2003). Technology and social inclusion: Rethinking the digital divide. The MIT Press.
  • Berg, M. (2022). Digital technography: A methodology for interrogating emerging digital technologies and their futures. Qualitative Inquiry, 28(7), 827-836.
  • Perera, R. H. A. T., & Abeysekera, N. (2022). Factors affecting learners’ perception of e-learning during the COVID-19 pandemic. Asian association of open universities journal, 17(1), 84-100.
  • Russell, G., Finger, G., & Russell, N. (2000). Information technology skills of Australian teachers: Implications for teacher education. Journal of Information Technology for Teacher Education, 9(2), 149-166.
  • Hollman, A. K., Hollman, T. J., Shimerdla, F., Bice, M. R., & Adkins, M. (2019). Information technology pathways in education: Interventions with middle school students. Computers & Education, 135, 49-60.
  • Marler, J. H., Liang, X., & Dulebohn, J. H. (2006). Training and effective employee information technology use. Journal of Management, 32(5), 721-743.
  • Buhalis, D. (1998). Strategic use of information technologies in the tourism industry. Tourism Management, 19(5), 409-421.
  • Drias, H., Cherif, N. F., & Kechid, A. (2016). K-MM: A hybrid clustering algorithm based on k-means and k-medoids. In Advances in Nature and Biologically Inspired Computing: Proceedings of the 7th World Congress on Nature and Biologically Inspired Computing (NaBIC2015) in Pietermaritzburg, South Africa, held December 01-03, 2015 (pp. 37-48). Springer International Publishing.
  • Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive advanced user clustering algorithm. Procedia Computer Science, 54, 764-771. Kuo, R. J., Ho, L. M., & Hu, C. M. (2002). Integration of self-organizing feature map and K-means algorithm for market segmentation. Computers & Operations Research, 29(11), 1475-1493.
  • Ng, H. P., Ong, S. H., Foong, K. W. C., Goh, P. S., & Nowinski, W. L. (2006, March). Medical image segmentation using k-means clustering and improved watershed algorithm. In 2006 IEEE southwest symposium on Image Analysis and interpretation (pp. 61–65). IEEE.
  • Teknomo, K. (2006). K-means clustering tutorial. Medicine, 100(4), 3.
There are 17 citations in total.

Details

Primary Language English
Subjects Policy and Administration (Other)
Journal Section Articles
Authors

Ömer Faruk Rençber 0000-0001-8020-2750

Eda Dalbudak Zorkirişçi 0000-0003-2858-5648

Mehmet Aytekin 0000-0001-5464-0677

Publication Date October 13, 2023
Submission Date September 7, 2023
Published in Issue Year 2023 Volume: 6 Issue: 2

Cite

APA Rençber, Ö. F., Dalbudak Zorkirişçi, E., & Aytekin, M. (2023). CLUSTERING OF HOUSEHOLDS BASED ON THEIR USE OF INFORMATION TECHNOLOGIES: A REVIEW OF TURKIYE. Journal of Political Administrative and Local Studies, 6(2), 75-86.