Decision Support Systems: A Content Analysis of Graduate Theses in Turkey

Yıl 2021, Cilt: 12 Sayı: 46, 12 - 28, 28.08.2021

Hüseyin Gökal , Volkan Cantemir , Ahmet Adalıer

https://doi.org/10.5824/ajite.2021.03.001.x

Öz

This study aims to conduct a descriptive analysis and evaluation of graduate theses regarding decision support systems carried out between 1989 and 2020 in Turkey. The qualitative research methodology was applied, the theses were analyzed through the descriptive content analysis technique. Forty-eight graduate studies accessible from the national thesis center database of YÖK were included in the analysis. The theses were coded according to the date of publication, university, institute, department, degree level, the academic title of the thesis supervisor, thesis language, research methodology, research sub-areas. Graduate studies on DSS have increased in the last 15 years, and 42 studies have been conducted in the last 15 years. Selçuk University ranks first with the most studies on DSS. Half of the studies were carried out in the Institute of Science, and most of the studies that were produced in the universities were master's theses. Most supervisors were "Prof. Dr." titled faculty members. Most of the theses were written in Turkish, and primarily experimental studies were conducted. The business administration department produced most of the theses on DSS. Business and environment studies were the primary disciplines that produced theses. These were carried out in 7 institutes and 26 different departments. The findings of this study will guide other researchers who are willing to work in the decision support systems field.

Anahtar Kelimeler

Decision Support Systems, Graduate Theses, Descriptive Content Analysis, Technology, Higher Education

Karar Destek Sistemleri: Türkiye’deki Lisansüstü Tezlerin Betimsel İçerik Analizi

Öz

Bu çalışma, Türkiye'de 1989-2020 yılları arasında yürütülen karar destek sistemlerine ilişkin lisansüstü tezlerinin betimsel bir analizini ve değerlendirmesini yapmayı amaçlamaktadır. Araştırmada, nitel araştırma yöntemleri kullanılmış ve tezler betimsel içerik analizi tekniği ile analiz edilmiştir. YÖK ulusal tez merkezi veri tabanında kayıtlı ve erişilme izni olan 48 lisansüstü çalışma incelenmiştir. Tezler yayın tarihi, üniversite, enstitü, bölüm, yüksek lisans//doktora düzeyi, danışman unvanı, dili, araştırma yöntemi, araştırma alt alanına göre kodlanmıştır. Tezler incelendiğinde son 15 yılda bu konu ile ilgili 42 çalışma yapılarak yüksek oranda artış göstermiştir. Karar Destek Sistemleriyle ilgili birçok üniversitede çalışmalar yapılmış olup, Selçuk Üniversitesi lisansüstü çalışmasıyla konuya en fazla katkı sağlayan üniversitedir. Çalışmaların yarısı Fen Bilimleri Enstitüsünde yapılmış ve üniversitelerde üretilen çalışmaların çoğu yüksek lisans tezlerinden oluşmuştur. Tez yöneticilerinin çoğu "Prof. Dr." ünvanlı öğretim üyeleridir. Çalışmaların çoğu Türkçe yazılmış ve en çok deneysel yöntem kullanılmıştır. Tezlerin çoğu işletme bölümündeki çalışmalardan üretilmiştir. Tezler başlıca işletme ve çevre disiplinlerinde yapılmıştır. Tezler 7 enstitü ve 26 farklı bölümde yürütülmüştür. Bu çalışmanın bulguları, karar destek sistemleri alanında çalışmak isteyen diğer araştırmacılara yol gösterecektir.

Anahtar Kelimeler

Karar Destek Sistemleri, Lisansüstü Tezler, Betimsel İçerik Analizi, Teknoloji, Yükseköğretim

Kaynakça

• Abu-Abed, T., & Khabarov, A. (2019). Supplies of Oil and Gas Extracting Industry and Intelligent Decision Support System. Dilemas contemporáneos: Educación, Política y Valores, 6.
• Arnott, D., & Pervan, G. (2016). A critical analysis of decision support systems research revisited: the rise of design science. In Enacting Research Methods in Information Systems (pp. 43-103). Palgrave Macmillan, Cham.
• Belciug, S., & Gorunescu, F. (2020). How can intelligent decision support systems help the medical research? In Intelligent Decision Support Systems—A Journey to Smarter Healthcare. (pp. 71-102). Springer, Cham.
• Biswas, J. (2020). Management information systems. 16th Edition. SAGE:Texts.
• Brusko, G. D., Kolcun, J. P. G., & Wang, M. Y. (2018). Machine-learning models: the future of predictive analytics in neurosurgery. Neurosurgery, 83(1), E3-E4.
• Buchlak, Q. D., Esmaili, N., Leveque, J. C., Farrokhi, F., Bennett, C., Piccardi, M., & Sethi, R. K. (2019). Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurgical review, 1-19.
• Gorry, G.A. and Scott, Morton, M.S. (1971) A Framework for Management Information Systems. Sloan Management Review. 13 (1), 55-70.
• Hafezalkotob, A., Hami-Dindar, A., Rabie, N., & Hafezalkotob, A. (2018). A decision support system for agricultural machines and equipment selection: A case study on olive harvester machines. Computers and Electronics in Agriculture, 148 (2018), 207-216.
• Hozairi, H., & Krisnafi, Y. (2017). Decision support system determination of main work unit in WPP-711 using Fuzzy TOPSIS. Knowledge Engineering and Data Science, 1(1), 8-19.
• Isoda, S., Hidaka, M., Matsuda, Y., Suwa, H., & Yasumoto, K. (2020, November). User decision support system for on-site tourism navigation on smartphone: demo abstract. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems (pp. 641-642).
• Kharbat, F. F., & Sultan, J. A. A. (2017). Environmental decision support systems: a literature review. Empirical Studies on Economics of Innovation, Public Economics and Management, (pp. 211-223). Springer, Cham.
• Lakshmanaprabu, S. K., Mohanty, S. N., Krishnamoorthy, S., Uthayakumar, J., & Shankar, K. (2019). Online clinical decision support system using optimal deep neural networks. Applied Soft Computing, 81(2019):105487.
• Laudon, K. C., and Laudon, J. P. (2018). Essentials of Management information systems. 13th Edition. Pearson.
• Laudon, K. C., and Laudon, J. P. (2020). Management information systems: Managing the digital firm. 16th Edition. Pearson.
• Liang, Z., Zhang, G., Huang, J. X., & Hu, Q. V. (2014, November). Deep learning for healthcare decision making with EMRs. In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 556-559). IEEE.
• Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19(6), 1236-1246.
• Pearlson, K., E., Saunders, C., S. and Galletta, D.,F. (2019). Managing and Using Information Systems: A Strategic Approach. Seventh Edition. USA: Wiley.
• Rainer, K., Prince, B., and Watson, H., J. (2017). Management information systems. 4th Edition. Wiley.
• Sun, F., Dubey, A., White, J., & Gokhale, A. (2019). Transit-hub: A smart public transportation decision support system with multi-timescale analytical services. Cluster Computing, 22(1), 2239-2254.
• Teniwut, W., & Hasyim, C. (2020). Decision support system in supply chain: A systematic literature review. Uncertain Supply Chain Management, 8(1), 131-148.
• Zhu, Y. (2018). A Data Driven Educational Decision Support System. International Journal of Emerging Technologies in Learning (iJET), 13(11), 4-16.