Person: Akinet, Mert
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Akinet
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Mert
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Mert AKINET
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Publication Türkiye’de Pazarlama Alanında Yapılan Lisansüstü Tezlerin Analitik İncelemesi (2013-2018)(Finans Ekonomi ve Sosyal Arastirmalar Dergisi, 2019-09-30) Elif Tuğba ŞAHİN; Karaaslanoğlu, Funda; Akinet, Mert; Şahin, Elif TuğbaBu çalışmada, 2013-2018 yılları arasında Yükseköğretim Kurulu Başkanlığı Ulusal Tez Merkezi’nde kayıtlı ve pazarlama alanında hazırlanmış olan lisansüstü tezler içerik analizi yöntemi ile çok boyutlu olarak incelenecektir. Çalışma kapsamına 236 doktora, 914 yüksek lisans tezi olmak üzere toplamda 1150 adet lisansüstü tez dâhil edilecektir. Bu çalışma neticesinde, Yükseköğretim Kurulu Başkanlığı Ulusal Tez Merkezi’nde yer alan ve pazarlama alanında hazırlanmış olan lisansüstü tezler; konu dağılımı, analiz türü, çalışma yöntemi, üniversite, şehir ve bölge dağılımı, tez yayım dili ve kaynakça ağırlığı gibi çeşitli boyutlarda incelenerek mevcut durumun ortaya konulması, var olan boşluk ve eksikliklerin belirlenmesi ve gelecekteki çalışmalara yol gösterici olması hedeflenmektedir. Çalışma neticesinde; yüksek lisans tez çalışmalarının doktora tez çalışmalarına oranla daha fazla olduğu; devlet üniversitelerinde daha fazla tez çalışması yapıldığı; tezlerin en çok İstanbul’da, Marmara Bölgesi’nde, en az ise Güneydoğu Anadolu Bölgesi’nde hazırlandığı; lisansüstü tezlerin daha fazla nicel yöntemlerle çalışıldığı ve en çok tüketici davranışı, bütünleşik pazarlama iletişimi ve yeşil pazarlama konularının çalışıldığı belirlenmiştir.Publication Evaluation of the Airline Business Strategic Marketing Performance: The Asia-Pacific Region Case(Journal of Aviation, 2022-07-24) Niyazi Cem GÜRSOY; Furkan KARAMAN; Mert AKINET; Gürsoy, Niyazi Cem; Karaman, Furkan; Akinet, MertBusinesses provide various marketing strategies in order to gain a competitive advantage and achieve sustainable profitability in today's globally competitive environment. While some of these strategies are realized through traditional marketing methods, some of them are implemented through digital marketing applications. The continuous and rapid change in information and communication technologies has made it obligatory for businesses to reconsider their marketing strategies and activities. In the literature, there are various studies conducted with multi-criteria decision-making methods in order to measure the marketing performance of businesses. However, there is no study conducted with these criteria specific to airline companies’ marketing performance. The criteria determined as a result of the literature review were analyzed using the fuzzy-AHP and Fuzzy-BWM methods for weight determination, and the TOPSIS method for alternative selection which are among the multi-criteria decision-making techniques. As a result of the study, net profitability, load rate, and total passenger number criteria came to the fore among other criteria, evaluations were made for the 6 airline companies examined, and the best and the worst alternative airline companies were determined, and evaluations were made in terms of marketing strategies. As a result, an exemplary application was introduced to airline companies in order to improve their marketing strategies and performances, and inferences that could contribute to future studies were made in the literature.Publication Flight Delay Prediction with Airport Traffic Density Data from an Aviation Risk Management Perspective(2025-06-28) Altunoğlu; Altunoğlu, Burcu; Akinet, MertFlight delays are significantly important in risk management for the aviation industry, impacting airline operations, passenger satisfaction, and air traffic management. While existing studies primarily focus on weather-related factors in flight delay prediction, this study explores the influence of airport traffic density on delays from an aviation risk management perspective. Using data mining techniques, the study integrates airport traffic and en-route delay datasets from EUROCONTROL to develop predictive models for delay estimation. The methodology follows a structured approach, including data preprocessing, feature engineering, clustering, and predictive modeling using the Random Forest algorithm. The findings indicate that airport traffic density is a critical predictor of delays, alongside seasonal and regional factors. Regression analysis highlights a strong correlation between congestion levels and delay severity, particularly in peak travel periods. The clustering results reveal four distinct delay patterns, reflecting variations in operational disruptions due to equipment failures and adverse weather conditions. The Random Forest model demonstrates high predictive accuracy, with low error rates confirming its robustness for delay estimation. This study contributes to aviation risk management by providing data-driven insights into flight delays and offering strategic decision-making tools for airline and airport operators. The results emphasize the need for proactive delay mitigation strategies, such as improved airspace allocation and enhanced maintenance processes. Future research could extend this approach by incorporating additional delay factors, such as incident-related disruptions, to further enhance predictive capabilities. By integrating operational data and advanced analytics, this study presents a novel framework for improving delay forecasting and optimizing flight operations.