UYGULAMALI GİRİŞİMCİLİK EĞİTİMLERİNİN İL, DÜZEY-I VE DÜZEY-II BÖLGELERİ BAZINDA BULANIK BWM VE AROMAN YÖNTEMLERİYLE DEĞERLENDİRİLMESİ

Abstract

Bir ülkenin girişimcilik ekosistemin seviyesi, o ülkenin ekonomik kalkınmasına hizmet eden önemli bir göstergedir. Günümüz dünyasında ise hükümetler, girişimcilik altyapısını güçlendirmek amacıyla çeşitli enstrümanlara başvurmaktadır. Türkiye’de ise bu konudaki faaliyetlerin önemli bir kısmı KOSGEB tarafından yürütülmektedir. 2010 yılından itibaren uygulanmaya başlanan Uygulamalı Girişimcilik Eğitimleri, bu amaca hizmet eden uygulamalardan birisidir. Bu araştırmada, Türkiye’deki her bir ilin, Düzey-I bölgelerinin ve Düzey-II bölgelerinin girişimcilik desteklerinden yararlanma açısından performansları incelenmiştir. Değerlendirme esnasında kullanılan kriterler Bulanık BWM yöntemiyle ağırlıklandırılmış olup en önemli ölçüt, “eğitim sonrası desteklenen işletme sayısı” olarak belirlenmiştir. AROMAN yöntemiyle gerçekleştirilen performans sıralamasında ise TR6, TR3 ve TR4 en başarılı Düzey-I bölgeleri olarak ilk üç sırada yer almıştır. Ayrıca, benzer bir değerlendirme il ve Düzey-II bölgeleri açısından da gerçekleştirilmiştir.

Keywords

• Girişimcilik
• KOSGEB
• ÇKKV
• Bulanık BWM
• AROMAN

EVALUATION OF HANDS-ON ENTREPRENEURSHIP TRAININGS ON THE BASIS OF PROVINCES, NUTS-I AND NUTS-II REGIONS BY FUZZY BWM AND AROMAN METHODS

Abstract

The level of a country's entrepreneurship ecosystem is an important indicator that serves the economic development of that country. In today's world, governments resort to various instruments to strengthen the entrepreneurship infrastructure. In Türkiye, a significant part of these activities is implemented by KOSGEB. Since 2010, Hands-On Entrepreneurship Trainings are one of the initiatives that contribute to this objective. In this study, the performances of each province, NUTS-I regions and NUTS-II regions in Türkiye in terms of benefiting from entrepreneurship supports are analysed. The criteria used during the evaluation are weighted by Fuzzy BWM method where the most important criterion is determined as “number of enterprises supported following the trainings". In the performance ranking performed by AROMAN method TR6, TR3 and TR4 regions are ranked in the first three order as the most successful NUTS-I regions. In addition, a similar evaluation is carried out in terms of provinces and NUTS-II regions.

Keywords

• Entrepreneurship
• KOSGEB
• MCDM
• Fuzzy BWM
• AROMAN

References

1. Aksoy, B., Koçancı, M., & Namal, M. K. (2019). Girişimcilik Motivasyonu: Uygulamalı Girişimcilik Eğitimi Katılımcıları Örneği. Yönetim ve Ekonomi Dergisi, 26(1), 109–129. https://doi.org/10.18657/yonveek.526604
2. Altunay, K. (2020). Yeni Girişimci Desteği Nihai Değerlendirme Raporu (2020/02; p. 123). KOSGEB. https://webdosya.kosgeb.gov.tr/Content/Upload/Dosya/Kurumsal/Raporlar/Giri%C5%9Fimcilik_Destek_Program%C4%B1_Yeni_Giri%C5%9Fimci_Deste%C4%9Fi_Nihai_De%C4%9Ferlendirme_....pdf
3. Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2021). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development & World Ecology, 28(2), 125–142. https://doi.org/10.1080/13504509.2020.1793424
4. Amoozad Mahdiraji, H., Arzaghi, S., Stauskis, G., & Zavadskas, E. K. (2018). A Hybrid Fuzzy BWM-COPRAS Method for Analyzing Key Factors of Sustainable Architecture. Sustainability, 10(5). https://doi.org/10.3390/su10051626
5. Badi, I., Pamučar, D., Stević, Ž., & Muhammad, L. J. (2023). Wind farm site selection using BWM-AHP-MARCOS method: A case study of Libya. Scientific African, 19, e01511. https://doi.org/10.1016/j.sciaf.2022.e01511 Bakanlıklara Bağlı, İlgili, İlişkili Kurum ve Kuruluşlar ile Diğer Kurum ve Kuruluşların Teşkilatı Hakkında Cumhurbaşkanlığı Kararnamesi, 4 (2018). https://webdosya.kosgeb.gov.tr/Content/Upload/Dosya/Mevzuat/cbaskanligi_4_kararname.pdf
6. Biswas, T., Chatterjee, P., & Choudhuri, B. (2020). Selection of commercially available alternative passenger vehicle in automotive environment. Operational Research in Engineering Sciences: Theory and Applications, 3, 2620–1747. https://doi.org/10.31181/oresta200113b
7. Boldureanu, G., Ionescu, A. M., Bercu, A.-M., Bedrule-Grigoruță, M. V., & Boldureanu, D. (2020). Entrepreneurship Education through Successful Entrepreneurial Models in Higher Education Institutions. Sustainability, 12(3). https://doi.org/10.3390/su12031267
8. Bonab, S. R., Ghoushchi, S. J., Deveci, M., & Haseli, G. (2023). Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet Integral approach. Expert Systems with Applications, 214, 119205. https://doi.org/10.1016/j.eswa.2022.119205

9. 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
10. Bouraima, M. B., Tengecha, N. A., Stević, Ž., Simić, V., & Qiu, Y. (2023). An integrated fuzzy MCDM model for prioritizing strategies for successful implementation and operation of the bus rapid transit system. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05183-y
11. Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, 121981. https://doi.org/10.1016/j.jclepro.2020.121981
12. 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
13. Ecer, F., Murat, T., Dinçer, H., & Yüksel, S. (2024). A fuzzy BWM and MARCOS integrated framework with Heronian function for evaluating cryptocurrency exchanges: A case study of Türkiye. Financial Innovation, 10(1), 31. https://doi.org/10.1186/s40854-023-00543-w
14. Ferrandiz, J., Fidel, P., & Conchado, A. (2018). Promoting entrepreneurial intention through a higher education program integrated in an entrepreneurship ecosystem. International Journal of Innovation Science, 10(1), 6–21. https://doi.org/10.1108/IJIS-09-2017-0089
15. Frederick, H., O’Connor, A., & Kuratko, D. D. (2019). Entrepreneurship: Theory/Process/Practice (5th ed.). Cengage Learning Inc.
16. Gan, J., Zhong, S., Liu, S., & Yang, D. (2019). Resilient Supplier Selection Based on Fuzzy BWM and GMo-RTOPSIS under Supply Chain Environment. Discrete Dynamics in Nature and Society, 2019, 2456260. https://doi.org/10.1155/2019/2456260
17. Ghorabaee, M. K., Amiri, M., Hashemi-Tabatabaei, M., & Ghahremanloo, M. (2021). Sustainable Public Transportation Evaluation using a Novel Hybrid Method Based on Fuzzy BWM and MABAC. The Open Transportation Journal, 15, 31–44. https://doi.org/10.2174/1874447802115010031
18. Ghoushchi, S. J., Yousefi, S., & Khazaeili, M. (2019). An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied Soft Computing, 81, 105505. https://doi.org/10.1016/j.asoc.2019.105505
19. Görçün, Ö. F., & Doğan, G. (2023). Mobile crane selection in project logistics operations using Best and Worst Method (BWM) and fuzzy Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS). Automation in Construction, 147, 104729. https://doi.org/10.1016/j.autcon.2022.104729
20. Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010
21. Kara, K., Yalçın, G. C., Acar, A. Z., Simic, V., Konya, S., & Pamucar, D. (2024). The MEREC-AROMAN method for determining sustainable competitiveness levels: A case study for Turkey. Socio-Economic Planning Sciences, 91, 101762. https://doi.org/10.1016/j.seps.2023.101762
22. Khan, S., Haleem, A., & Khan, M. I. (2021). Assessment of risk in the management of Halal supply chain using fuzzy BWM method. Supply Chain Forum: An International Journal, 22(1), 57–73. https://doi.org/10.1080/16258312.2020.1788905
23. Küçük ve Orta Ölçekli İşletmeleri Geliştirme ve Destekleme İdaresi Başkanlığı Teşvik ve Muafiyetleri Kanunu, 3624 (1990). https://webdosya.kosgeb.gov.tr/Content/Upload/Dosya/Mevzuat/2019_KOSGEB_Kanunu.pdf
24. Kumar, S., Maity, S. R., & Patnaik, L. (2023). A novel BWM integrated MABAC decision-making approach to optimize the wear parameter of CrN/TiAlSiN coating. Journal of Industrial and Management Optimization, 19(4), 2676–2703. https://doi.org/10.3934/jimo.2022061
25. Kurniawan, V. R. B., & Puspitasari, F. H. (2021). A Fuzzy BWM Method for Evaluating Supplier Selection Factors in a SME Paper Manufacturer. IOP Conference Series: Materials Science and Engineering, 1071, 012004. https://doi.org/10.1088/1757-899X/1071/1/012004
26. Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM Solutions for Evaluation of the Logistics Performance Index of the Western Balkan Countries. ECONOMICS, 10(1), 13–34. https://doi.org/10.2478/eoik-2022-0004
27. Mwatsika, C. (2016). Measuring the Number of Jobs Created through Entrepreneurship Training. International Journal of Academic Research in Business and Social Sciences, 6(7), 223–240. https://doi.org/10.6007/IJARBSS/v6-i7/2243
28. Puška, A., Božanić, D., Nedeljković, M., & Janošević, M. (2022). Green Supplier Selection in an Uncertain Environment in Agriculture Using a Hybrid MCDM Model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS Model. Axioms, 11(9). https://doi.org/10.3390/axioms11090427
29. Qahtan, S., Alsattar, H. A., Zaidan, A. A., Deveci, M., Pamucar, D., Delen, D., & Pedrycz, W. (2023). Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model. Applied Soft Computing, 138, 110170. https://doi.org/10.1016/j.asoc.2023.110170
30. Qian, S., Brannon, D. L., & Tabak, F. (2022). Exploring Mechanisms in the Entrepreneurial Passion–Entrepreneurial Behavior Relationship: Mediating Role of Growth-Oriented Intentions. Journal of Career Development, 49(4), 922–933. https://doi.org/10.1177/08948453211005848
31. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
32. Ringo Ho, M.-H., Uy, M. Y., Kang, B., & Chan, K.-Y. (2018). Impact of Entrepreneurship Training on Entrepreneurial Efficacy and Alertness among Adolescent Youth. Frontiers in Education, 3(13), 1–10. https://doi.org/10.3389/feduc.2018.00013
33. Roy, P., & Shaw, K. (2022). An integrated fuzzy credit rating model using fuzzy-BWM and new fuzzy-TOPSIS-Sort-C. Complex & Intelligent Systems, 9(4). https://doi.org/10.1007/s40747-022-00823-5
34. Schumpeter, J. A. (1951). Change and the Entrepreneur. In Essays of J.A. Schumpeter (1st ed., p. 325). Addison-Wesley Press, Inc.
35. Şimşek, A. (2013). Kalkınma Ajanslarının Performans Ölçümü [Uzmanlık Tezi, T.C. Kalkınma Bakanlığı Bölgesel Gelişme ve Yapısal Uyum Genel Müdürlüğü]. https://www.academia.edu/5451657/Kalkinma_Ajanslarinda_Performans_olcumu_UZMANLIK_TEZ
36. Taşdoğan, C., Şekeroğlu, S., & Zengin, B. (2023). Üniversite öğrencilerinin girişimci kişilik ve eğilimini etkileyen ‘girişimcilik eğitimi’ yeterli mi? Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(2), 322–342. https://doi.org/10.25287/ohuiibf.1160048
37. Tu, Y., Chen, K., Wang, H., & Li, Z. (2020). Regional Water Resources Security Evaluation Based on a Hybrid Fuzzy BWM-TOPSIS Method. International Journal of Environmental Research and Public Health, 17(14). https://doi.org/10.3390/ijerph17144987
38. Ünüvar, İ., & Darıcı, B. (2021). KOSGEB Desteklerine Girişimcilik Destekleri Bağlamında Eleştirel Bir Yaklaşım. In A. Ateş (Ed.), Sosyal, Beşeri ve İdari Bilimler Alanında Uluslararası Araştırmalar VIII (1st ed., p. 533). Eğitim Yayınevi.
39. Wei, Q., & Zhou, C. (2023). A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China. Environmental Science and Pollution Research, 30(4), 10540–10559. https://doi.org/10.1007/s11356-022-22783-6
40. Yaran Ögel, İ., Aygün Özgöz, A., & Ecer, F. (2023). Prioritizing causes and drivers of retail food waste through a fuzzy Dombi-Bonferroni operators-based best–worst approach: An emerging economy perspective. Environmental Science and Pollution Research, 30(2), 4899–4916. https://doi.org/10.1007/s11356-022-22553-4
41. Yazdani, M., Chatterjee, P., Pamucar, D., & Doval, M. (2019). A risk-based integrated decision-making model for green supplier selection: A case study of a construction company in Spain. Kybernetes, 49(4), 1229–1252. https://doi.org/10.1108/K-09-2018-0509