G7 GRUBU ÜLKELERİN ORGANİZE SUÇLARLA MÜCADELE PERFORMANSLARININ ANALİZİ: DNMA YÖNTEMİ İLE BİR UYGULAMA

Büyük ekonomilere sahip olan ülkelerin organize suçlarla mücadele performansları küresel anlamda ekonomiyi ve ekonomi ile ilişkili diğer boyutları etkilediğinden dolayı büyük ekonomilerin organize suçlarla mücadele performanslarının analizi büyük önem arz etmektedir. Bu anlamda araştırmada, dünya sermayesinin yarısından fazlasına sahip olan G7 ülkelerinin en güncel nitelikteki 2021 Küresel Organize Suç Endeksi (Global Organized Crime Index-GOCI) bileşen değerleri üzerinden söz konusu ülkelerin organize suçlarla mücadele performansları DNMA çok kriterli karar verme yöntemi ile ölçülmüştür. Bulgulara göre, ülkelerin organize suçlarla performans değerleri Kanada, Japonya, İngiltere, Almanya, İtalya, ABD ve Fransa olarak gözlenmiştir. Bunun dışında, DNMA sonuçlarına istinaden ülkelerin ortalama organize suçlarla mücadele performansları hesaplanarak yalnızca Kanada ve Fransa’nın ilgili ortalama performans değerinden fazla olduğu tespit edilmiştir. Bu sonuca göre Fransa, ABD, İtalya, Almanya ve İngiltere’nin küresel ekonomiye olan katkılarının daha fazla olması için organize suçlarla mücadele performanslarını artırması gerektiği değerlendirilmiştir. Yöntem bakımından ise duyarlılık, ayrım ve korelâsyon analizleri ile ülkelerin organize suç performansları GOCI kapsamında DNMA ile ölçülebileceği sonucuna ulaşılmıştır.

___

  • Abadinsky, H. (2010). Organized Crime. Belmont: Wadsworth/Cengage Learning.
  • Albini, J. (1971). The American Mafia: Genesis of a Legend. New York: Appleton Century Crofts. Blackburn, K., Neanidis, K., & Rana, M. P. (2017). A Theory of Organized Crime, Corruption and Economic Growth. Econ Theory Bull, 5, 227–245.
  • Bonnier, L. (2022). Illicit Trade:A Global Threat to Development, Economic Growth and Security, G7 Executive Talk Series, Branded Story / TRACIT, Erişim Tarihi: 18.03.2023, https://digital.thecatcompanyinc.com/g7magazine/june-2018/illicit-trade-global-threat-development-econ. Brown, R., & Smith, R. G. (2018). Exploring the Relationship between Organised Crime And Volume Crime. Australian Goverment Australian Institute of Criminology(565), 1-14.
  • Burkay, S. (2008). Teorik Çerçevede Suç. ETHOS: Felsefe ve Toplumsal Bilimlerde Diyaloglar(2/4), 1-15.
  • Castle, A. (1997). Transnational Organized Crime and International Security. Institute of International RelationsThe University of British Columbia(Woking Paper 19), 1-12.
  • Corne, L. (2015). Using Basic Neurobiological Measures Incriminological Research. Crime Science, 4(7), s. 1-16.
  • Çınar, M., & Taş, C. (2022). Türkiye’de Bölgesel İşsizlik ve Suç Türleri İlişkisi: Panel Veri Yaklaşımı. Business and Economics Research Journal, 13(2), 179-197. Drakos, I., Kenny, P., Fearn, T., & Speller, R. (2017). Multivariate Analysis of Energy Dispersive X Ray Diffraction Data for The Detection of Illicit Drugs In Border Control. Crime Science, 6(1), 1-10.
  • Ecer, F. (2020). Çok Kriterli Karar Verme. Ankara: Seçkin Yayıncılık.
  • Ecer, F., & Zolfani, S. H. (2022). Evaluating Economic Freedom Via A Multi-Criteria MEREC-DNMA Model-Based Composite System:Case of OPEC Countries. Technological and Economic Development of Economy, 28(4), 1158–1181.
  • EGM (2023), Organize Suç Örgütü Nedir?. Erişim tarihi: 19.04.2023, https://www.egm.gov.tr/kom/organizetanitim
  • Ekblom, P., & Pease, K. (2014). Innovation and Crime Prevention. G. Bruinsma, & D. Weisburd içinde, Encyclopedia of Criminology and Criminal Justice (s. 2523-2531). New York: Springer Science+Business Media. Gelder, J.-L., Otte, M., & Luciano, E. C. (2014). Using Virtual Reality in Criminological Research. Crime Science, 3(10), 1-12.
  • Gerritsen, C. (2015). Agent-based Modelling As A Research Tool for Criminological Research. Crime Science, 4(2), 1-12. Gigovic, L., Pamucar, D., Bajic, Z., & Milicevic, M. (2016). The Combination of Expert Judgment and GIS-MAIRCA Analysis for the Selection of Sites for Ammunition Depots. Sustainability, 8, 1-30.
  • Global Initiative. (2022). Global Organized Crime Index 2021. Geneva: Global Initiative Publisching.
  • Gün, E. (2022). Yaşlılık ve Suç İlişkisi Üzerine Bir Değerlendirme. Sosyoloji Dergisi,(44), 275-294. Hoeben, E. M., Bernasco, W., Weerman, F. N., Pauwels, L., & Halem, S. (2014). The Space-Time Budget Method In Criminological Research. Crime Science, 3(12), 1-15.
  • Hoffman, K. (2009). The Impact of Organized Crime on Democratic Governance-Focus on Latin America and the Caribbean. Berlin: Friedrich-Ebert-Stiftung. Işık, S., Sezgin, B., & Öksüz, M. (2022). Türkiye’de Kamu Eğitim Harcamalarının Suç Oranları Üzerindeki Etkisi. Biga İktisadi ve İdari Bilimler Fakültesi Dergisi, 3(2), 108-118.
  • Klimczuk, A. (2015). Causes of Crime. F. Wherry içinde, The SAGE Encyclopedia of Economics (s. 308-311). Los Angeles : SAGE Publications.
  • Kumar, S. (2013). Crime and Economic Growth: Evidence from India. MPRA(48794), 1-24.
  • Kusuma, H., Hariyani, H. F., & Hidayat, W. (2019). The Relationship Between Crime and Economics Growth in Indonesia. The 2nd International Conference on Islamic Economics, Business, and Philanthropy (ICIEBP) Theme: Sustainability and Socio Economic Growth (s. 1105–1113). Surabaya: KnE Social Science.
  • Laenen, F. V. (2015). Not Just Another Focus Group: Making The Case for The Nominal Group Technique in Criminology. Crime Science, 4(5), 1-12.
  • Lai, H., & Liao, H. (2021). A Multi Criteria Decision Making Method Based on DNMA and CRITIC with Linguistic D Numbers for Blockchain Platform Evaluation. Engineering Applications of Artificial Intelligence, 101, 1-12.
  • Lai, H., Liao, H., Šaparauskas, J., Banaitis, A., Ferreira, F., & Al-Barakati, A. (2020). Sustainability, 12, 1-17.
  • Lampe, K. (2019). Tackling Organized Crime:From Theory to Practice. Crimen, 10(3), 215–224.
  • Lemieux, A. M. (2015). Geotagged Photos: A Useful Tool for Criminological Research? Crime Science , 4(3), 1-12.
  • Levi, M. (2016). The Impacts of Organised Crime in the EU: Some Preliminary Thoughts on Measurement Difficulties. Journal of the Academy of Social Sciences, 11(4), 392-402.
  • Liao, H., & Wu, X. (2019). DNMA: A Double Normalization-Based Multiple Aggregation Method for Multi-Expert Multi-Criteria Decision Making. Omega, s. 2-37. DOI:https://doi.org/10.1016/j.omega.2019.04.001.
  • Liao, H., Long, Y., Tang, M., Streimikiene, D., & Lev, B. (2019). Early Lung Cancer Screening Using Double Normalization Based Multi Aggregation (DNMA) and Delphi Methods with Hesitant Fuzzy Information. Computers & Industrial Engineering, 136, 453-463. Liao, H., Ren, R., Antucheviciene, J., Šaparauskas, J., & Al-Barakati, A. (2020). Sustainable Construction Supplier Selection by A Multiple Criteria Decision-Making Method with Hesitant Linguistic Information. E&M Economics and Management,, 23(4), 119–136.
  • Locke, R. (2012). Organized Crime, Conflict, and Fragility: A New Approach. New York: International Peace Institute. Mishra, A. R., Rani, P., Saha, A., Hezam, I., Cavallaro, F., & Chakrabortty, R. (2023). An Extended Dnma-Based Multi-Criteria Decision-Making Method and its application in the Assessment Of Sustainable Location for A lithium-ion Batteries’ Manufacturing Plant. Heliyon, 9, 1-24.
  • Mulok, D., Kogidb, M., Lilyc, J., & Asid, R. (2016). The Relationship between Crime and Economic Growth in Malaysia: Re Examine Using Bound Test Approach. Malaysian Journal of Business and Economics, 3(1), 15–26. Neanidis, K. C., Rana, M. P., & Blackburn, K. (2017). An Empirical Analysis Of Organized Crime, Corruption and Economic Growth. Ann Finance, 13, 273–298.
  • Neanidis, K., Rana, M. P., & Blackburn, K. (2017). An Empirical Analysis of Organized Crime, Corruption. Ann Finance, 13, 273–298.
  • Perdomo, C., & Burcher, C. U. (2016). Protecting Politics Deterring the Influence of Organized Crime on Local Democracy. Stockholm-Geneva: International Institute for Democracy and Electoral Assistance and Global Initiative against Transnational Organized Crime.
  • Petta, D. (2018). Why there is no real difference between a Terrorist Organization and an Organized Crime Faction, Just A Matter of Interaction towards the State. Contemporary Voices, 1(1), 26-35.
  • Plessis, C. (2000). The Links between Crime Prevention and Sustainable Development. R. Lawrence içinde, Sustaining Human Settlement: A Challenge For The New Millennium (ss. 239-270). North Shields: Urban International Press. Remeikiene, R., Gaspareniene, L., Fedajev, A., Raistenskis, E., & Krivins, A. (2022). Links between Crime and Economic Development: EU Classification. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(4), 909–938.
  • Saha, A., Mishra, A. R., Rani, P., Hezam, I., & Cavallaro, F. (2022). A q-Rung Orthopair Fuzzy FUCOMDouble Normalization-Based Multi-Aggregation Method for HealthcareWaste Treatment Method Selection. Sustainability, 14, 1-28.
  • Sarı, İ. (2015). The Nexus Between Terrorism And Organized Crime; Growing Threat? Uyuşmazlık Mahkemesi Dergisi(6), 463-503.
  • Schultze-Kraft, M. (2016). Organised Crime, Violence and Development: Topic guide. Birmingham: Birmingham University. Selçuk, S. (2014). Suç, Suçun Öz Nitelikleri ve Tanımı. İstanbul: Beta Yayınları.
  • Sezer, S., Kahya, O., & Yıldırım, K. (2022). Eğitim ve Suç Arasındaki İlişkinin Sosyolojik Analizi. MSKU Eğitim Fakültesi Dergisi, 9(2), 441-452.
  • Sowmyya, T. (2014). Crime: A Conceptual Understanding. Forensic Science, 4(3), 196-198.
  • Stevanović, K. (2021). The Connection Between Ideology and Organized Crime. ПОЛИТИЧКА РЕВИЈА бр, 67, 145-164.
  • UNODC. (2018). E4J University Module Series: Organized Crime, Erişim tarihi: 19.03.2023.https://www.unodc.org/documents/e4j/flyers/E4J_Module_Flyer_OC_web_EN_rev.pdf.
  • Vandeviver, C. (2014). Applying Google Maps and Google Street View in Criminological Research. Crime Science, 3(13), 1-16.
  • Wallace, W. C. (2017). An Exploratory Study on the Impact of Organized Crime on Societies in Small Island Developing States:Evidence from Five (5) Caribbean Countries. Washington: National Defense University.
  • Wu, X., & Liao, H. (2019). Comparison Analysis between DNMA Method and Other MCDM Methods. ICSES Transaction on Neural and Fuzzy Computing, 2(1), 4-10.
  • Yang, L., Zou, H., Shang, C., Ye, X., & Rani, P. (2023). Technological Forecasting and Social Change. Adoption of Information and Digital Technologies for Sustainable Smart Manufacturing Systems for Industry 4.0 in Small, Medium, and Micro Enterprises (SMMEs), 188, 1-15.
  • Zhang, H., Liao, H., Wu, X., Zavadskas, E. K., & Al-Barakati, A. (2020). Internet Financial Investment Product Selection with Pythagorean Fuzzy DNMA Method. Inzinerine Ekonomika-Engineering Economics, 31(1), 61–71.