Öneri Karar Destek Sistemlerinin Karar Süreçlerindeki Etkisinin Değerlendirilmesi

Öz

Bu çalışma, öneri karar destek sistemlerinin bireysel ve kurumsal düzeydeki karar alma süreçleri üzerindeki etkilerini değerlendirmeyi amaçlamaktadır. Teknolojik gelişmeler ve dijital dönüşümün etkisiyle, veri temelli karar alma süreçleri günümüz organizasyonlarının temel dinamiklerinden biri haline gelmiştir. Bu bağlamda, çalışmada içerik tabanlı filtreleme, işbirlikçi filtreleme ve hibrit sistemler gibi öneri sistemlerinin işleyişi ve karar destek sistemleriyle entegrasyonu incelenmiştir. Çalışma, sağlık, finans, e-ticaret ve kamu yönetimi gibi farklı sektörlerden örneklerle desteklenerek, öneri sistemlerinin karar kalitesi, doğruluk ve verimlilik üzerindeki etkilerini somutlaştırmaktadır. Literatür taramasına dayalı bu araştırmada yalnızca algoritmik performans değil, aynı zamanda bu sistemlerin karar verici üzerindeki bilişsel, davranışsal ve etik etkileri de ele alınarak değerlendirilmiştir. Veri bilimi ve makine öğrenmesinin karar süreçlerindeki rolü detaylandırılmış, veri odaklı karar almanın sağladığı avantajların yanında veri güvenliği, algoritmik önyargı ve etik sorunlar gibi zorluklara da değinilmiştir. Netflix, McDonald’s, UBER ve Zara gibi firmalardan elde edilen gerçek hayat uygulamalarıyla, öneri karar destek sistemlerinin işletmelerde stratejik karar mekanizmalarına olan katkıları ortaya konmuştur. Bu yönüyle çalışma, karar destek sistemlerinin teknik altyapısının ötesine geçerek, karar süreçlerindeki çok boyutlu etkilerini bütüncül bir yaklaşımla analiz etmekte, araştırmacılar, iş dünyası profesyonelleri ve politika yapıcılar için değerli bir kaynak sunmaktadır.

Anahtar Kelimeler

• Öneri Sistemleri
• Karar Destek Sistemleri
• Veri Bilimi
• Makine Öğrenmesi
• Karar Kalitesi

Evaluation of the Effect of Proposal Decision Support Systems on Decision Processes

Öz

This study aims to evaluate the impact of recommendation decision support systems on individual and organizational decision-making processes. With the impact of technological advancements and digital transformation, data-driven decision-making processes have become a fundamental dynamic in today's organizations. In this context, the study examines the functioning of recommendation systems such as content-based filtering, collaborative filtering, and hybrid systems, and their integration with decision support systems. Supported by examples from diverse sectors such as healthcare, finance, e-commerce, and public administration, the study demonstrates the impact of recommendation systems on decision quality, accuracy, and efficiency. Based on a literature review, this research examines not only algorithmic performance but also the cognitive, behavioral, and ethical impacts of these systems on decision-makers. The role of data science and machine learning in decision-making is detailed, addressing the advantages of data-driven decision-making, as well as challenges such as data security, algorithmic bias, and ethical issues. Real-life applications from companies such as Netflix, McDonald's, Uber, and Zara demonstrate the contributions of recommendation decision support systems to strategic decision-making in businesses. In this respect, the study goes beyond the technical infrastructure of decision support systems and analyzes their multidimensional effects on decision processes with a holistic approach, providing a valuable resource for researchers, business professionals and policy makers.

Anahtar Kelimeler

• Recommender Systems
• Decision Support Systems
• Data Science
• Machine Learning
• Decision Quality

Kaynakça

1. Adomavicius, G., ve Zhang, J. (2012). Impact of data characteristics on recommender systems performance. ACM Transactions on Management Information Systems (TMIS), 3(1),1–17. DOI: 10.1145/2151163.2151166
2. Alter, S. (1996). Information systems: A management perspective. Benjamin/Cummings Publishing Company.
3. Asharaf, Z. (2025). Big data analytics: Harnessing the power of data science for enhanced decision-making in modern business environments. Erişim adresi: https://www.researchgate.net/publication/388953991_Big_Data_Analytics_Harnessing_the_Power_of_Data_Science_for_Enhanced_Decision-Making_in_Modern_Business_Environments
4. Bhareti, K., Perera, S., Jamal, S., Pallege, M. H., Akash, V. ve Wiieweera, S. (2020). "A Literature Review of Recommendation Systems," 2020 IEEE International Conference for Innovation in Technology (INOCON), Bangluru, India, 2020, 1-7. doi: 10.1109/INOCON50539.2020.9298450
5. Bhargava, H. K., Krishnan, R., ve Muller, R. (1997). Decision support on demand: Emerging electronic markets for decision technologies. Decision Support Systems, 19(3), 193–214. doi: 10.1016/S0167-9236(96)00056-5
6. Bozdemir, E., ve Orhan, M. S. (2011). Üretim maliyetlerinin düşürülmesinde Kaizen maliyetleme yönteminin rolü ve uygulanabilirliğine yönelik bir araştırma. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 15(2), 463–480.
7. Budak, H. ve Gümüştaş, E. (2022). Kişiselleştirilmiş ürün öneri sistemi için kullanıcı bazlı işbirlikçi filtreleme ve kümeleme kullanan hibrit bir yaklaşım. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 21(43), 253-268. doi: 10.46928/iticusbe.1055162
8. Bulut, C. (2025). Sağlık Yönetiminde Yapay Zeka ve Maliyet Azaltma Stratejileri: Fırsatlar ve Sınırlamalar. Acta Infologicala, 9(1), 133-146. doi: 10.26650/acin.1631851

9. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370. doi: 10.1023/A:1021240730564
10. Bobadilla, J., Ortega, F., Hernando, A. ve Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132. doi: doi.org/10.1016/j.knosys.2013.03.012
11. Colombari, R., Geuna, A., Yardımcısı, S., Martins, R., Paolucci, E., Ricci, R., ve Seamans, R. (2023). The interplay between data-driven decision-making and digitalization: A firm-level survey of the Italian and U.S. automotive industries, 255, 108718. doi: 10.1016/j.ijpe.2022.108718
12. Coussement, K., ve Benoit, D. F. (2021). Interpretable data science for decision making. Decision Support Systems, 150, 113561. doi: 10.1016/j.dss.2021.113561
13. Davenport, T. H., ve Harris, J. (2007). Competing on analytics: The new science of winning. Boston, MA: Harvard Business Review Press.
14. Davenport, T. H., ve Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review. Erişim adresi: http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
15. De Caigny, A., Coussement, K., ve De Bock, K. W. (2020). Leveraging fine-grained transaction data for customer life event predictions. Decision Support Systems, 130, 113232. doi: 10.1016/j.dss.2019.113232
16. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73. doi: 10.1145/2500499 Deo, K., Rai, K., ve Tiwari, A. (2025). Leveraging AI and data science in business services: Opportunities and challenges. ResearchGate. doi: 10.13140/RG.2.2.27830.41281
17. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., . . . Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57. doi: 10.1016/j.ijinfomgt.2019.08.002
18. Elden, B. (2015). Yapay Zekâ Tabanlı Karar Destek Sistemleri ile Personel Seçimi. Ahi Evran Akademi (AEA), 7(2), 13-29. Erişim adresi: https://dergipark.org.tr/tr/pub/aea/article/1791449
19. Elgendy, N., Elragal, A., ve Paivärinta, T. (2022). DECAS: a modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 31(4), 337–373. doi: 10.1080/12460125.2021.1894674
20. Elragal, A. ve Klischewski, R. (2017). Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. Journal of Big Data, 4(19), 1-20. doi: 10.1186/s40537-017-0079-2
21. Elragal, A., ve Elgendy, N. (2024). A data-driven decision-making readiness assessment model: The case of a Swedish food manufacturer. Decision Analytics Journal, 10, 100405. doi: 10.1016/j.dajour.2024.100405
22. Molina Fernandez, L. E. (2018). Recommendation system for Netflix. Master of Science in Business Analytics, 6, 1–34. Erişim adresi: https://www.cs.vu.nl/~sbhulai/papers/paper-fernandez.pdf European Council. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council. Erişim adresi: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689
23. Fu, C., Xu, C., Xue, M., Liu, W., ve Yang, S. (2021). Evidence-based reasoning approach and data-driven decision making based on machine learning algorithms. Applied Soft Computing, 111, 107622. doi: 10.1016/j.asoc.2021.107622
24. Gong, Z. (2024). Data science applications in supply chain management decision-making. Advances in Economics, Management and Political Sciences, 89(1), 121–128. doi: 10.54254/2754-1169/89/20241920
25. Gryncewicz, W., ve Sitarska-Buba, M. (2021). Data Science in Decision-Making Processes: A Scientometric Analysis. European Research Studies Journal, 24(3, Part 2), 1061–1074. doi: 10.35808/ersj/2558
26. Gudigantala, N., Madhavaram, S. ve Bicen, P. (2023). An AI decision-making framework for business value maximization. AI Magazine, 44, 67-84. doi: 10.1002/aaai.12076
27. Güler, Ş. (2019). Öneri Sistemleri ve E-Ticarette Öneri Sistemlerinin Kullanımı (Yayımlanmamış Yüksek Lisans tezi). Sakarya Üniversitesi, İstanbul. Erişim adresi: https://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=aEzj_IdWAsjiSAfK3qwrBqN7cUAcd7MrpeuuQevm5wwZG8ALg-fNdKiTuS7NdMi2
28. Henkoğlu, T. (2022). Karar Destek Sistemlerinin Çevrimiçi Öneride Kullanımı ve Algoritma Yanlılığı. T. Benli (Ed.), Sosyal Bilimlerde Güncel Çalışmalar içinde (s. 17-32). Ankara: Gazi Kitabevi.
29. Henkoğlu, T. (2025). Türkiye’nin Dijital Dönüşüm Politikaları ve Stratejik Yaklaşımlar. B. F. Yıldırım ve H. Şerefoğlu Henkoğlu (Ed.), Dijital Dönüşüm: Bilgi Yönetimi Yaklaşımları içinde (Vol. 2, s. 1-40). Ankara: Nobel Akademik Yayıncılık.
30. Hiremath, B., ve Kenchakkanavar, A. Y. (2024). A historical perspective on artificial intelligence: Development, challenges and future directions. Journal of Advances in Library and Information Science, 14(1), 68–75. doi: 10.5281/zenodo.14877653
31. Huang, Y., ve Meng, S. (2019). Automobile insurance classification ratemaking based on telematics driving data. Decision Support Systems, 127, 113156. doi: 10.1016/j.dss.2019.113156
32. Intezari A, Gressel S (2017), "Information and reformation in KM systems: big data and strategic decision-making". Journal of Knowledge Management, Vol. 21 No. 1 pp. 71–91, doi: https://doi.org/10.1108/JKM-07-2015-0293
33. Isinkaye, F. O., Folajimi, Y. O., ve Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261–273. doi: 10.1016/j.eij.2015.06.005
34. Jindal, R., Bharadwaj, M., ve Mishra, V. (2022). Impact of big data on business decisions through the view of data science-based decision making. International Research Journal of Engineering and Technology (IRJET), 9(5). Erişim adresi: https://www.academia.edu/86878098/IMPACT_OF_BIG_DATA_ON_BUSINESS_DECISIONS_THROUGH_THE_VIEW_OF_DATA_SCIENCE_BASED_DECISION_MAKING?nav_from=9f63b0fc-fa7a-4b05-bdb6-4089590aa596
35. Lasisi, M., Kolade, K., ve Rotimi, O. (2025). Data science. In Encyclopedia of Libraries, Librarianship, and Information Science (Cilt. 4, s. 89–96). Elsevier. doi: 10.1016/B978-0-323-95689-5.00026-8
36. Linden, G., Smith, B., ve York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80. doi: 10.1109/MIC.2003.1167344
37. Lu, J., Wu, D., Mao, M., Wang, W. ve Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12-32. doi: doi.org/10.1016/j.dss.2015.03.008
38. Madupati, B. (2022). Data science in public relations software development. Journal of Mathematical & Computer Applications, 2022(1), E118. doi: 10.47363/JMCA/2022(1)E118
39. Matheus, R., Janssen, M., ve Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 37(3), 101284. doi: 10.1016/j.giq.2018.01.006
40. Mishra, R. K., Mishra, D., ve Agarwal, R. (2024). Applications of data science in e-commerce. Journal of Science Research International, 10(2), 66–75. doi: 10.5281/zenodo.14257784
41. Naser, T., Samani, S., Minaei, M. ve Harirchi, F. (2019). An artificial intelligence decision support system for unconventional field development design. SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver, Colorado, USA.
42. Nasir, F., Ahmed, A. A., Kiraz, M. S., Yevseyeva, I., ve Saif, M. (2024). Data-driven decision-making for bank target marketing using supervised learning classifiers on imbalanced big data. Computers, Materials & Continua, 81(1), 1703–1728. doi: 10.32604/cmc.2024.055192
43. Oracle. (2025). Neden Büyük Veri? Erişim adresi: https://www.oracle.com/tr/big-data/what-is-big-data/
44. Özcan, A. (2021). Büyük veri: Fırsatlar ve tehditler. TRT Akademi, 6(11), 12–25. Erişim adresi: https://dergipark.org.tr/en/download/article-file/1371552
45. Papakostas, N., Papachatzakis, P., Xanthakis, V., Mourtzis, D., ve Chryssolouris, G. (2010). An approach to operational aircraft maintenance planning. Decision Support Systems, 48(4), 604–612. doi: 10.1016/j.dss.2009.11.010
46. Papouskova, M., ve Hajek, P. (2019). Two-stage consumer credit risk modelling using heterogeneous ensemble learning. Decision Support Systems, 118, 33–45. doi: 10.1016/j.dss.2019.01.002
47. Patel Hiral, M., Tetarwal, P., Jayasudha, J. ve Patel Umang, B. (2024). Big Data Application in Agriculture. K. Manobharathi, J. Jayasudha, T. Gowthaman, R. Nidhishree ve M. Patel Hiral (Ed.), Beyond the Plow: A Guide to the Latest Trends in Modern Agriculture içinde. New Delhi: AkiNik Publications.
48. Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., ve Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290. doi: 10.1016/j.dss.2020.113290
49. Provost, F., ve Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. doi: 10.1089/big.2013.1508
50. Power, D. J. (2007). A Brief History of Decision Support Systems. Erişim adresi: http://dssresources.com/history/dsshistory.html
51. Power, D. J. (2017). What is a recommender system? Erişim adresi: https://dssresources.com/faq/index.php?action=artikel&id=323
52. Qureshi, A. R. A. (2020). Recommendatıon Systems (YL Seminer Raporu). Gazi University Ankara. Retrieved from https://www.researchgate.net/profile/Abdur-Qureshi/publication/360456896_RECOMMENDATION_SYSTEMS_MASTER_SEMINAR_REPORT_DEPARTMENT_OF_COMPUTER_SCIENCE_INSTITUTE_OF_INFORMATICS_GAZI_UNIVERSITY/links/6277dce03a23744a726ee76c/RECOMMENDATION-SYSTEMS-MASTER-SEMINAR-REPORT-DEPARTMENT-OF-COMPUTER-SCIENCE-INSTITUTE-OF-INFORMATICS-GAZI-UNIVERSITY.pdf
53. Shao, Q. (2024). The frontier exploration and application prospect of data science. Applied and Computational Engineering, 1(1), 1743. doi: 10.54254/2755-2721/116/20251743
54. Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., ve Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126. doi: 10.1016/S0167-9236(01)00139-7
55. Shu, X., ve Ye, Y. (2023). Knowledge discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. doi: 10.1016/j.ssresearch.2022.102817
56. Stobierski, T. (2019). The advantages of data-driven decision-making. Harvard Business School Online. Erişim adresi: https://online.hbs.edu/blog/post/data-driven-decision-making
57. Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M. ve Floridi, L. (2022). The ethics of algorithms: key problems and solutions. AI & Society, 37, 215–230. doi: 10.1007/s00146-021-01154-8
58. Ünal, A. (2003). Çalışanların katılımı ve öneri sistemleri. İşgüç Dergisi, 5(2), 14–36. Erişim adresi: https://www.isguc.org/?p=article&id=171&cilt=5&sayi=2&yil=2003
59. Van Steenbergen, R. M., ve Mes, M. R. K. (2020). Forecasting demand profiles of new products. Decision Support Systems, 139, 113401. doi: 10.1016/j.dss.2020.113401
60. Varley-Winter, O. ve Shah, H. (2016). The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics. Phil. Trans. R. Soc. A 374:20160116. doi: doi.org/10.1098/rsta.2016.0116
61. Wu, Y., Xu, Y., ve Li, J. (2019). Feature construction for fraudulent credit card cash-out detection. Decision Support Systems, 127, 113155. doi: 10.1016/j.dss.2019.113155
62. Zhang, D., Yin, C., Zeng, J., ve Zhang, Y. (2020). Combining structured and unstructured data for predictive models: A deep learning approach. BMC Medical Informatics and Decision Making, 20(1), 280. doi: 10.1186/s12911-020-01297-6
63. Zhu, L. (2024). Study of data science in the managerial decision-making of enterprises. Highlights in Business, Economics and Management, 28, 506–510. doi: 10.54097/5qa0rq15
64. Zoroğlu, B., ve Yıldız, M. S. (2013). Bir otomotiv yan sanayi fabrikasında öneri sistemi ve uygulamalar. In 13. Üretim Araştırmaları Sempozyumu (ÜAS 2013) Bildiriler Kitabı (ss. 176–185).