Yılmaz, Mustafa Kemal

Profil fotoğrafı
 E-posta Adresi
mustafa.yilmaz@ihu.edu.tr
ORCID Profili
0000-0001-6036-0559
WoS Profili
S-9489-2017
Scopus Profili
57214083720
YÖK Araştırmacı Profili
00E2002187F4AF69
Google Akademik Profili
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TR-Dizin Profili
802607
SOBİAD Profili
268880
Web Sitesi
akademik.ihu.edu.tr/tr/mustafa-yilmaz

Araştırma projeleri

Organizasyon Birimleri

Organizasyon Birimi Logosu
Organizasyon Birimi
Yönetim Bilimleri Fakültesi, İşletme Bölümü
Küresel rekabete ayak uydurmak ve sürdürülebilir olmak isteyen tüm şirketler ve kurumlar, değişimi doğru bir şekilde yönetmek, teknolojinin gerekli kıldığı zihinsel ve operasyonel dönüşümü kurumlarına hızlı bir şekilde adapte etmek zorundadırlar.

Adı Soyadı

Mustafa Kemal Yılmaz

İlgi Alanları

Capital Markets, Derivatives Markets, Risk Yönetimi, Kurumsal Finansman, Sürdürülebilirlik

Kurumdaki Durumu

Aktif Personel

Filtreler

20221
Filtreleri Sıfırla

Ayarlar

WoS Q: Q1 × Konu: Airport Efficiency ×

Arama Sonuçları

Listeleniyor 1 - 1 / 1
  • Küçük Resim
    Yayın
    The evaluation of operational efficiencies of Turkish airports: An integrated spherical fuzzy AHP/DEA approach
    (Elsevier, 2022) Yılmaz, Mustafa Kemal; Kuşakcı, Ali Osman; Aksoy, Mine; Hacıoğlu, Ümit; Hacıoğlu, Ümit; Yılmaz, Mustafa Kemal; Kuşakcı, Ali Osman; Yönetim Bilimleri Fakültesi, İşletme Bölümü
    The demand for air transport services has significantly increased around the globe, which has brought new investments in airports, which, in turn, requires in-depth efficiency analysis of these capital-intensive endeavors. This study examines the operational efficiencies of 46 Turkish civil airports from 2015 to 2018. We employ a novel hybrid methodology that combines Spherical Fuzzy Sets based Analytic Hierarchy Process (SFS-AHP) and Data Envelopment Analysis (DEA), which provides a solid basis for efficiency analysis. To this end, it can handle the hesitancy and uncertainty that the subjective evaluation process of input and output factors possess. Then, we use Self Organizing Maps (SOM), a machine learning method for clustering, to examine the effect of outlier airports on the efficiency scores. Finally, a posthoc analysis is conducted with Tobit regression model to assess the explanatory power of external factors on the efficiency scores, i.e., tourism potential, number of international flights, distance to the city center, population, public/private ownership, and age of airport. The findings show that 67.2% of the Turkish airports operate below the optimal efficiency level, and 93.5% of them should make considerable efforts to refine their operations by implementing managerial and structural changes to reduce input factors. The results also suggest that the airports located in high-density touristic areas achieve higher efficiency levels. Those relatively closer to the city center lead to more airport traffic, generating more revenues. Thus, both factors have a significant impact on efficiency scores. The study provides a novel efficiency analysis framework for airport operators and policy makers that helps them make informed decisions.