Öneri Karar Destek Sistemlerinin Karar Süreçlerindeki Etkisinin Değerlendirilmesi

Abstract

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.

Keywords

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

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

Abstract

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.

Keywords

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

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