LOPCOW ve Entropi Temelli ALWAS Modeli ile Dijital Platform Ekonomileri Açısından Ülkelerin Analizi

Yıl 2025, Cilt: 27 Sayı: 2, 386 - 419, 29.12.2025

Pembe Güçlü

Öz

Dijitalleşme, özellikle gelişmekte olan ülkelerde ekonomik büyümeyi teşvik etmekte, tedarik zincirlerini daha verimli hale getirmekte ve iş süreçlerini dönüştürmektedir. Ancak, dijital girişimcilik ekosistemlerinin ölçümü, henüz yeterince olgunlaşmamıştır. Çalışma, dijital platformların güçlenmesiyle ortaya çıkan yeni iş modelleri ve rekabet ortamlarını değerlendirirken, dijital girişimcilik ve platform ekonomisi arasındaki etkileşimi incelemektedir. Bu çalışmanın amacı, dijital platform ekonomisinin (DPE) performansını değerlendiren mevcut indeks hesaplaması yerine, daha sağlam sonuçlar elde etmeyi hedefleyen bir hibrit ağırlıklandırma yöntemi kullanan çok kriterli karar verme yöntemi önermektir. Çalışmada dijital platform ekonomisi performansını analiz etmek için, literatürde daha önceleri geliştirilmiş olan DPE indeksinde ele alınan kriterler LOPCOW ve Entropi yöntemleri ile ağırlıklandırılmış, elde edilen ağırlıklar dual ürün operatörü kullanılarak toplulaştırılmıştır. Bulgular, farklı ağırlıklandırma yöntemleriyle elde edilen sonuçların benzer eğilimler gösterdiğini ortaya koymuştur. Ağırlıklandırma sonucu teknoloji emilimi ve ağ kurma en önemli kriterler olarak bulunmuştur. Robust ağırlıklandırmanın ardından 116 ülke ALWAS yöntemi kullanılarak 116 ülke sıralanmıştır. Önerilen yöntem ile DPEI sıralamaları arasında yüksek bir korelasyon bulunduğu tespit edilmiştir. Dijital platform ekonomileri içinde ABD ilk sırayı, Etiyopya ise son sırayı almıştır.

Anahtar Kelimeler

LOPCOW, , Entropi , ALWAS , Dijital Platform Ekonomisi

Analysis of Countries in terms of Digital Platform Economies with LOPCOW and Entropy-Based ALWAS Model

Öz

Digitalization drives economic growth, making supply chains more efficient and transforming business processes, especially in developing countries. However, the measurement of digital entrepreneurship ecosystems is not yet mature enough. The study examines the interplay between digital entrepreneurship and the platform economy, assessing the new business models and competitive environments that have emerged with the empowerment of digital platforms. This study proposes a multi-criteria decision-making methodology that uses a hybrid weighting method to evaluate the performance of the digital platform economy (DPE) instead of the existing index calculation, aiming to achieve more robust results. To analyze the performance of the digital platform economy, the criteria considered in the DPE index, which has been previously developed in the literature, are weighted by the LOPCOW and Entropy methods, and the obtained weights are aggregated using the dual product operator. The findings reveal that the results obtained with different weighting methods show similar trends. As a result of the weighting, technology absorption and networking were found to be the most important criteria. After robust weighting, 116 countries were ranked using the ALWAS method. It is found that there is a high correlation between the proposed method and DPEI rankings. Among the digital platform economies, the United States ranked first, while Ethiopia ranked last.

Anahtar Kelimeler

LOPCOW , Entropi , ALWAS , Digital Platform Economy

Kaynakça

• Acs, Z. J. (2023). The Global Digital Platform Economy and the Region. The Annals of Regional Science, 70(1), 101-133. https://doi.org/10.1007/s00168-022-01154-6
• Acs, Z.J., Song, A.K., Szerb, L., Audretsch, D. B. & Komlósi, E. (2021). The Evolution of the Global Digital Platform Economy: 1971–2021. Small Bus Econ, 57, 1629–1659. https://doi.org/10.1007/s11187-021-00561-x
• Acs, Z. J., Szerb, L., Song, A., Komlosi, E. & Lafuente, E. (2021). The Digital Platform Economy Index 2020. Erişim Adresi: www.thegedi.org
• Aggarwal, G. & Kashiramka, S. (2025). Unveiling the Dynamics of Entrepreneurial Ecosystems in an Emerging Economy: A Delphi and MCDM Approach. Thunderbird International Business Review. 0, 1-17. https://doi.org/10.1002/tie.22432
• Arcesati, R., Holzmann, A., Mao, Y., Nyamdorj, M., Shi-Kupfer, K., von Carnap, K. & Wessling, C. (2020). China’s Digital Platform Economy: Assessing Developments Towards Industry 4.0. Challenges and Opportunities for German Actors. Mercator Institute for China Studies. Erişim Adresi: https://merics. org/sites/defau lt/files/2020-06/MERIC
• Ayçin, E. (2019). Çok Kriterli Karar Verme Bilgisayar Uygulamalı Çözümler. Ankara: Nobel.
• Ayçin, E. & Güçlü, P. (2020). BIST Ticaret Endeksinde Yer Alan İşletmelerin Finansal Performanslarının Entropi ve MAIRCA Yöntemleri ile Değerlendirilmesi. Muhasebe ve Finansman Dergisi, (85), 287-312. https://doi.org/10.25095/mufad.673739.
• Bektaş, S. (2022). Evaluating the Performance of the Turkish Insurance Sector for the Period 2002-2021 with MEREC, LOPCOW, COCOSO, EDAS MCDM Methods. Journal of BRSA Banking and Financial Markets, 16(2), 247-283.
• Beliakov G., Pradera, A. & Calvo T. (2007). Aggregation Functions: A Guide for Practitioners (221). Springer- Heidelberg.
• Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simić, V. & Bacanin, N. (2023). An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)—A Case Study of the Electric Vehicle Selection Problem. IEEE Access, 11, 39496-39507. https://doi.org/10.1109/ACCESS.2023.3265818
• Brans, J. P., & Vincke, Ph. (1985). A Preference Ranking Organisation Method: The PROMETHEE Method for Multiple Criteria Decision-Making. Management Science, 31(6), 647–656.
• Campanella, G. & Ribeiro, RA. (2011). A Framework for Dynamic Multiple Criteria Decision Making. Decision Support Systems, 52(1), 52–60. http://dx.doi.org/10.1016/j.dss.2011.05.003
• Deveci, M., Gokasar, I., Pamucar, D., Zaidan, A. A., Wei, W. & Pedrycz, W. (2024). Advantage Prioritization of Digital Carbon Footprint Awareness in Optimized Urban Mobility Using Fuzzy Aczel Alsina Based Decision Making. Applied Soft Computing, 151, 111-136. https://doi.org/10.1016/j.asoc.2023.111136
• Deveci, M., Pamucar, D., Gokasar, I., Zaidan, B. B., Martinez, L. & Pedrycz, W. (2023). Assessing Alternatives of Including Social Robots in Urban Transport Using Fuzzy Trigonometric Operators Based Decision-Making Model. Technological Forecasting and Social Change, 194, 122743. https://doi.org/10.1016/j.techfore.2023.122743
• Dhruva, S., Krishankumar, R., Zavadskas, E. K., Ravichandran, K. S. & Gandomi, A. H. (2024). Selection of Suitable Cloud Vendors for Health Centre: a Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo. Informatica, 35(1), 65-98. 10.15388/23-INFOR537
• Dorjnyambuu, B. (2023). Estonian digital Entrepreneurship Ecosystem Based on Digital Platform Economy Index 2020. The Journal of Entrepreneurship, 32(2), 347-375.
• Ecer, F., Küçükönder, H., Kaya, S.K. & Görçün, Ö.F. (2023). Sustainability Performance Analysis of Micro-Mobility Solutions in Urban Transportation with a Novel IVFNN-Delphi-LOPCOW-CoCoSo Framework. Transportation Research Part a: Policy and Practice, 172(103667). https://doi.org/10.1016/j.tra.2023.103667
• Ecer, F. & Pamucar, D. (2022). A Novel LOPCOW‐DOBI Multi‐Criteria Sustainability Performance Assessment Methodology: An Application in Developing Country Banking Sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
• El-Araby, A., Sabry, I. & El-Assal, A. (2022). A Comparative Study of Using MCDM Methods Integrated with Entropy Weight Method for Evaluating Facility Location Problem. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 121-138. https://doi.org/10.31181/oresta250322151a
• Elia, G., Margherita, A. & Passiante, G. (2020). Digital Entrepreneurship Ecosystem: How Digital Technologies and Collective Intelligence Are Reshaping the Entrepreneurial Process. Technological Forecasting and Social Change, 150(119791). https://doi.org/10.1016/j.techfore.2019.119791
• Ersoy, A. & Ersoy, N. (2024). Personnel Selection in Hospitality Industry with the Integrated Entropy-RAPS Model. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(29), 76-96. https://doi.org/10.36543/kauiibfd.2024.004
• European Comission (2020). Digital Economy and Society Index Luxembourg. Erişim Adresi: https://digital-strategy.ec.europa.eu/en/policies/desi-luxembourg
• Fidan, Ü. (2023). Assessment of Sustainability Compliance Performance of Information Technology Companies. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(28), 1031-1050. https://doi.org/10.36543/kauiibfd.2023.041
• Fors, A. C. (2013). The Ontology of the Subject in Digitalization. İçinde: P. A. Hershey (Ed.), Handbook of Research on Technoself: Identity in a Technological Society; Information Science Reference (s. 45-63). PA: Hershey
• French, S. (2023). Reflections on 50 Years of MCDM: Issues and Future Research Needs. EURO Journal on Decision Processes, 11, 100030. https://doi.org/10.1016/j.ejdp.2023.100030
• GEDI Institute. (2020). The Digital Platform Economy Index 2020. The Global Entrepreneurship and Development Institute. Erişim Adresi: https://thegedi.org/wp-content/uploads/2020/12/DPE-2020-Report-Final.pdf
• Gokasar, I., Pamucar, D., Deveci, M., Gupta, B. B., Martinez, L. & Castillo, O. (2023). Metaverse Integration Alternatives of Connected Autonomous Vehicles with Self-Powered Sensors Using Fuzzy Decision Making Model. Information Sciences, 642, 119192. https://doi.org/10.1016/j.ins.2023.119192
• Güçlü, P. (2024). A Dynamic Model Proposal for the Solution of a Multi-Period Multi-Criteria DecisionMaking Problem: An Application with LOPCOW-MCRAT Integrated Method. V. International Applied Statistics Congress, İstanbul/ Türkiye, May 21-23, 2024. https://www.uyik.org/uploads/uyik-2024-proceeding-book.pdf
• Huang, J. Y. (2014). Intuitionistic Fuzzy Hamacher Aggregation Operators and Their Application to Multiple Attribute Decision Making. Journal of Intelligent & Fuzzy Systems, 27(1), 505-513. https://doi.org/10.3233/IFS-131019
• Huang, P. H., & Moh, T. T. (2017). A Non-Linear Non-Weight Method for Multi-Criteria Decision Making. Annals of Operations Research, 248(1), 239-251. https://doi.org/10.1007/s10479-016-2208-2
• Huawei. (2024). Global Digitalization Index (GDI) 2024. Huawei. Erişim Adresi: https://www.huawei.com/en/gdi
• Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications — A State-of-the-Art Survey. Springer-Verlag, Berlin
• IMD. (2024). World Digital Competitiveness Ranking 2024. IMD World Competitiveness Center. Erişim Adresi: https://www.imd.org/centers/wcc/world-competitiveness-center/rankings/world-digital-competitiveness-ranking/
• Jassbi, J.J., Ribeiro, R.A. & Varela, L.R. (2014). Dynamic MCDM with Future Knowledge for Supplier Selection. Journal of Decision Systems, 23(3), 232-248. https://doi.org/10.1080/12460125.2014.886850
• Jiang, H. & Murmann, J. P. (2022). The Rise of China’s Digital Economy: An Overview. Management and Organization Review; 18(4):790-802. https://doi.org/10.1017/mor.2022.32
• Juhász, R., Sakabe, S. & Weinstein, D. (2024). Codification, Technology Absorption, and the Globalization of the Industrial Revolution (No. w32667). National Bureau of Economic Research.
• Kizielewicz, B., Tomczyk, T., Gandor, M. & Sałabun, W. (2024). Subjective Weight Determination Methods in Multi-Criteria Decision-Making: A Systematic Review. Procedia Computer Science, 246, 5396-5407. 10.1016/j.procs.2024.09.67
• Klement, E. P., Mesiar, R. & Pap, E. (2005). Triangular Norms: Basic Notions and Properties. İçinde: E. P. Klement & R. Mesiar (Eds.), Logical, Algebraic, Analytic and Probabilistic Aspects of Triangular Norms (s. 17-60). Elsevier Science BV. https://doi.org/10.1016/B978-044451814-9/50002-1
• Lafuente, E., Ács, Z. J. & Szerb, L. (2024) Analysis of the Digital Platform Economy Around the World: A Network DEA Model for Identifying Policy Priorities, Journal of Small Business Management, 62(2), 847-891, https://doi.org/10.1080/00472778.2022.2100895
• Li, W., Badr, Y. & Biennier, F. (2012). Digital Ecosystems: Challenges and Prospects. Proceedings of the International Conference on Management of Emergent Digital EcoSystems, Addis Ababa, Ethiopia, 117–122, ACM: New York.
• Li, J., Butler-Purry, K. L., Benner, C. L. & Russell, B. D. (2004). Selecting a Fuzzy Aggregation Operator for Multicriteria Fault Location Problem. IEEE PES Power Systems Conference and Exposition, 1476-1482.
• Mohd, W. R. W. & Abdullah, L. (2017). Aggregation Methods in Group Decision Making: A Decade Survey. Informatica, 41(1). https://informatica.si/index.php/informatica/article/view/1321/972
• Musik, C. & Bogner, A. (2019). Digitalization & Society. Österreich Z Soziol, 44(1), 1–14. https://doi.org/10.1007/s11614-019-00344-5
• Myovella, G., Karacuka, M. & Haucap, J. (2020). Digitalization and economic Growth: A Comparative Analysis of Sub-Saharan Africa and OECD Economies. Telecommunications Policy, 44(2), 101856. https://doi.org/10.1016/j.telpol.2019.101856
• Orhan, M. (2019). Türkiye ile Avrupa Birliği Ülkelerinin Lojistik Performanslarının Entropi Ağırlıklı EDAS Yöntemiyle Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, 17, 1222-1238. https://doi.org/10.31590/ejosat.657693
• Opricovic, S. (1998) Multicriteria Optimization of Civil Engineering Systems. (Doktora Tezi) Faculty of Civil Engineering, Belgrade.
• Öztaş, T. & Öztaş, G. Z. (2024). Innovation Performance Analysis of G20 Countries: A Novel Integrated LOPCOW-MAIRCA MCDM Approach Including the COVID-19 Period. Verimlilik Dergisi, 1-20. https://doi.org/10.51551/verimlilik.1320794
• Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M. & Deveci, M. (2023). A Novel WENSLO and ALWAS Multicriteria Methodology and Its Application to Green Growth Performance Evaluation. IEEE Transactions on Engineering Management, 71, 9510-9525. https://doi.org/10.1109/TEM.2023.3321697
• Popovic, G., Fedajev, A., Mitic, P. & Meidute-Kavaliauskiene, I. (2024). An ADAM-Based Approach to Unveiling Entrepreneurial Ecosystems in Selected European Countries. Management Decision, 63(4), 1262–1291. https://doi.org/10.1108/MD-12-2023-2420
• Rong, Y., Yu, L., Liu, Y., Simic, V. & Garg, H. (2024). The FMEA Model Based on LOPCOW-ARAS Methods with İnterval-Valued Fermatean Fuzzy Information for Risk Assessment of R&D Projects in Industrial Robot Offline Programming Systems. Computational and Applied Mathematics, 43(1), 25. https://doi.org/10.1007/s40314-023-02532-2
• Saaty, T. L. (1977). A Scaling Method for Priorities in Hierarchical Structures. Journal of Mathematical Psychology, 15(3), 234-281.
• Saaty, T. L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications: Pittsburgh.
• Sahut, J. M., Iandoli, L. & Teulon, F. (2021). The Age of Digital Entrepreneurship. Small Business Economics, 56(3), 1159-1169. https://doi.org/10.1007/s11187-019-00260-8
• Sasikumar, S. K. & Sersia, K. (2020). Digital Platform Economy: Overview, Emerging Trends and Policy Perspectives. Productivity, 61(3), 336-347. https://doi.org/10.32381/PROD.2020.61.03.8
• Senapati, T., Chen, G. & Yager, R. R. (2022). Aczel–Alsina Aggregation Operators and Their Application to Intuitionistic Fuzzy Multiple Attribute Decision Making. International Journal of Intelligent Systems, 37(2), 1529-1551. https://doi.org/10.1002/int.22684
• Sitaridis, I. & Kitsios, F. (2020). Competitiveness Analysis and Evaluation of Entrepreneurial Ecosystems: A Multi-Criteria Approach. Annals of Operations Research, 294(1), 377-399. doi: https://doi.org/10.1007/s10479-019-03404-x
• Song, A. K. (2019). The Digital Entrepreneurial Ecosystem—A Critique and Reconfiguration. Small Business Economics, 53(3), 569–590. https://doi.org/10.1007/s11187-019-00232-y
• Sussan, F. & Acs, Z. J. (2017). The Digital Entrepreneurial Ecosystem. Small Business Economics, 49(1), 55–73. https://doi.org/10.1007/s11187-017-9867-5
• Szerb, L., Komlosi, E., Acs, Z., Lafuente, E. & Song, A. (2022). The Digital Platform Economy Index 2020. Springer. https://doi.org/10.1007/978-3-030-89651-5
• Taherdoost, H. & Madanchian, M. (2023). Multi-Criteria Decision Making (MCDM) Methods and Concepts. Encyclopedia, 3(1), 77-87. https://doi.org/10.3390/encyclopedia3010006
• Tesfachew, T. (2022). Ethiopia’s Drive to Advance Digital Transformation. United Nations Conference on Trade and Development. https://unctad. org/system/files/information-document/BRI-Project_policy-brief-02_en. pdf.
• Torkayesh, A. E., Alizadeh, R. & Soltanisehat, L. (2024). Assessing Entrepreneurial Performance of G7 Countries Under Context of Social Sustainable Development Goals: A Multicriteria Decision Analysis Approach. IEEE Transactions on Engineering Management, 71, 4918-4929. https://doi.org/10.1109/TEM.2022.3227240
• Ulutaş, A., Topal, A., Görçün, Ö. F. & Ecer, F. (2024). Evaluation of Third-Party Logistics Service Providers for Car Manufacturing Firms Using a Novel Integrated Grey LOPCOW-PSI-MACONT Model. Expert Systems with Applications, 241, 122680. https://doi.org/10.1016/j.eswa.2023.122680
• Van Dua, T., Van Duc, D. & Bao, N. C. (2024). Integration of Objective Weighting Methods for Criteria and MCDM Methods: Application in Material Selection. EUREKA: Physics and Engineering, (2), 131-148.
• Veit, D., Clemons, E., Benlian, A., Buxmann, P., Hess, T., Spann, M., Kundisch, D., Leimeister, J. M. & Loos, P. (2014). Business Models - An Information Systems Research Agenda. Business & Information Systems Engineering, 6, 45–53. https://doi.org/10.1007/s12599-013-0308-y
• Wen, Z., Liao, H. & Zavadskas, E. K. (2020). MACONT: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis. Informatica, 31(4), 857-880. https://doi.org/10.15388/20-INFOR417
• Woetzel, J., Seong, J., Wang, K. W., Manyika, J., Chui, M. & Wong, W. (2017). China’s Digital Economy A Leading Global Force. Discussion Paper, McKinsey Global Institute.
• Yalcin, A. S., Kilic, H. S. & Delen, D. (2022). The Use of Multi-Criteria Decision-Making Methods in Business Analytics: A Comprehensive Literature Review. Technological Forecasting and Social Change, 174, 121193. https://doi.org/10.1016/j.techfore.2021.121193
• Yaşar, E. & Ünlü, M. (2023). Üniversitelerde Sürdürülebilirliğin İncelenmesi: LOPCOW ve MEREC Tabanlı CoCoSo Yöntemleriyle Çevreci Üniversitelerin Analizi. İşletme Akademisi Dergisi, 4(2), 125-142. https://doi.org/10.26677/TR1010.2023.1246
• Yoo, Y., Henfridsson, O. & Lyytinen, K. (2010). The New Organizing Logic of Digital Innovation: An Agenda for Information Systems Research. Information Systems Research, 21, 724–735. https://doi.org/10.1287/isre.1100.0322
• Zafar, S., Alamgir, Z. & Rehman, M. H. (2021). An Effective Blockchain Evaluation System Based on Entropy-CRITIC Weight Method and MCDM Techniques. Peer-to-Peer Networking and Applications, 14(5), 3110-3123. https://doi.org/10.1007/s12083-021-01173-8
• Zavadskas, E. K., Turskis, Z. & Kildienė, S. (2014). State of Art Surveys of Overviews on MCDM/MADM Methods. Technological and Economic Development of Economy, 20(1), 165–179. https://doi.org/10.3846/20294913.2014.892037
• Zhang, M. L. & Chen, M. S. (2019). China’s Digital Economy: Opportunities and Risks. (IMF Working Papers No. 016). https://doi.org/10.5089/9781484389706.001
• Zhao, X. & Wei, G. (2013). Some Intuitionistic Fuzzy Einstein Hybrid Aggregation Operators and Their Application to Multiple Attribute Decision Making. Knowledge-Based Systems, 37, 472-479. https://doi.org/10.1016/j.knosys.2012.09.006
• Zhu, Y., Tian, D. & Yan, F. (2020). Effectiveness of Entropy Weight Method in Decision‐Making. Mathematical Problems in Engineering, 2020(1), 3564835. https://doi.org/10.1155/2020/3564835