SavedAdd your journal

Journals / Yönetim Bilimleri Dergisi / 2020 / Cilt: 18 Sayı: 38

An Alternative Scale Proposal for Project Evaluation using Best Worst Method: The Case of KOSGEB

Güçlü Zayıf Yöntemi İle Proje Değerlendirme İçin Alternatif Bir Ölçek Önerisi: KOSGEB Örneği

Bahadır Fatih Yıldırım(İSTANBUL ÜNİVERSİTESİ)0000-0002-0475-741X
Pages
747–765
Publication Date
2020-10-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Using the obtained weight values, a new scoring scale was proposed and compared with the current score table.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, both public and private sectors provide funds to external stakeholders through project calls in order to achieve their prior plans and objectives.
📊
Key Findings & Data:In order to ensure that the funds provided to these projects, whose costs have been calculated and budgeted, are produced in line with the objectives, the institutions and organizations must operate the project evaluation processes correctly.
💡
Conclusion & Impact:Using the obtained weight values, a new scoring scale was proposed and compared with the current score table.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Güçlü Zayıf Yöntemi · Proje Değerlendirme · Çok Kriterli Karar Verme

Abstract

Nowadays, both public and private sectors provide funds to external stakeholders through project calls in order to achieve their prior plans and objectives. In order to ensure that the funds provided to these projects, whose costs have been calculated and budgeted, are produced in line with the objectives, the institutions and organizations must operate the project evaluation processes correctly. In this study, the support programs provided by KOSGEB was examined and the scoring table of a support program was weighted by taking expert opinion by using Best Worst Method. As a result of the study, the most important criterion in the project evaluation process is the Evaluation of the Economic Impact of the Investment Project and the Physical and Structure Adequacy of the Investment Place as the weakest criterion. Using the obtained weight values, a new scoring scale was proposed and compared with the current score table.

Özet

Günümüzde gerek kamu gerekse özel sektör, öncelikli plan ve hedeflerine ulaşmak için proje çağrıları yoluyla dış paydaşlara fon sağlamaktadır. Maliyetleri hesaplanmış ve bütçelenmiş bu projelere sağlanan fonların amaçlara uygun olarak sonuçlanacağından emin olmak için kurum ve kuruluşların proje değerlendirme süreçlerini doğru bir şekilde yürütmeleri gerekmektedir. Bu çalışmada KOSGEB tarafından sağlanan destek programları incelenmiş ve Güçlü Zayıf Yöntemi kullanılarak bir destek programının puanlama tablosu uzman görüşü alınarak ağırlıklandırılmıştır. Çalışma sonucunda, proje değerlendirme sürecindeki en önemli kriterin Yatırım Projesinin Ekonomik Etkilerinin Değerlendirilmesi kriteri ve Yatırım Alanının Fiziksel ve Yapı Yeterliliğinin en zayıf kriter olarak belirlenmiştir. Elde edilen ağırlık değerleri kullanılarak yeni bir puanlama ölçeği önerilmiş ve mevcut puan tablosu ile karşılaştırılıştır.

🔖

Cite This Article

Yıldırım, B. F. (2020). An Alternative Scale Proposal for Project Evaluation using Best Worst Method: The Case of KOSGEB. Yönetim Bilimleri Dergisi, Cilt: 18 Sayı: 38, 747–765. https://doi.org/10.35408/comuybd.589622

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (38)

  1. Altun, A., & Demir, Y. (2015). Analitik Hiyerarşi Prosesi Yöntemi İle Tarımsal Araştırma Projelerinin Değerlendirilmesi ve Seçimi. Toprak Su Dergisi, 4(2), 41-48.
  2. Amiri, M. P. (2010). Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(9), 6218-6224.
  3. Arıbaş, M., & Özcan, U. (2016). Akademik Araştırma Projelerinin AHP ve TOPSIS Yöntemleri Kullanılarak Değerlendirilmesi. Politeknik Dergisi, 19(2), 163-173.
  4. Atıcı, K. B., & Ulucan, A. (2009). Enerji Projelerinin Değerlendirilmesi Sürecinde Çok Kriterli Karar Verme Yaklaşımları ve Türkiye Uygulamaları. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 27(1), 161-186.
  5. Badri Ahmadi, H., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126(May), 99–106. https://doi.org/10.1016/j.resconrec.2017.07.020
  6. Bakshi, T., & Sarkar, B. (2011). MCA based performance evaluation of project selection. International Journal of Software Engineering & Applications, 2(2), 14-22.
  7. Beemsterboer, D. J. C., Hendrix, E. M. T., & Claassen, G. D. H. (2018). On solving the Best-Worst Method in multi-criteria decision-making⁎. IFAC-PapersOnLine, 51(11), 1660–1665. https://doi.org/10.1016/j.ifacol.2018.08.218
  8. Çakır, E. ve Özdemir, M. (2016). Bulanık Çok Kriterli Karar Verme Yöntemlerinin Altı Sigma Projeleri Seçiminde Uygulanması. Business and Economics Research Journal, 7(2), 167-201.
  9. Er Kara, M., & Firat, S. Ümit O. (2018). Supplier risk assessment based on best-worst method and k-means clustering: A case study. Sustainability (Switzerland), 10(4), 1–25. https://doi.org/10.3390/su10041066
  10. Ghorabaee, M. K., Amiri, M., Sadaghiani, J. S., & Zavadskas, E. K. (2015). Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets. International Journal of Information Technology & Decision Making, 14(05), 993-1016.
  11. 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
  12. Gupta, H. (2018). Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS. Journal of Environmental Management, 226(August), 201–216. https://doi.org/10.1016/j.jenvman.2018.08.005
  13. Gupta, H. (2018). Evaluating service quality of airline industry using hybrid best worst method and VIKOR. Journal of Air Transport Management, 68, 35–47. https://doi.org/10.1016/j.jairtraman.2017.06.001
  14. Gupta, H., & Barua, M. K. (2016). Identifying enablers of technological innovation for Indian MSMEs using best-worst multi criteria decision making method. Technological Forecasting and Social Change, 107, 69–79. https://doi.org/10.1016/j.techfore.2016.03.028
  15. Gupta, H., & Barua, M. K. (2017). Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 152, 242–258. https://doi.org/10.1016/j.jclepro.2017.03.125
  16. Gupta, P., Anand, S., & Gupta, H. (2017). Developing a roadmap to overcome barriers to energy efficiency in buildings using best worst method. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2017.02.005
  17. Hafezalkotob, A., & Hafezalkotob, A. (2017). A novel approach for combination of individual and group decisions based on fuzzy best-worst method. Applied Soft Computing Journal, 59, 316–325. https://doi.org/10.1016/j.asoc.2017.05.036
  18. Hamurcu, M., & Eren, T. (2017). Raylı Sistem Projeleri Kararında AHS-HP ve AAS-HP Kombinasyonu. Gazi Mühendislik Bilimleri Dergisi (GMBD), 3(3), 1-13.
  19. Hamurcu, M., & Eren, T. (2018). Kent İçi Ulaşım İçin Bulanık Ahp Tabanlı VIKOR Yöntemi İle Proje Seçimi. Engineering Sciences, 13(3), 217-228.
  20. Mou, Q., Xu, Z., & Liao, H. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences. https://doi.org/10.1016/j.ins.2016.08.074
  21. Nawaz, F., Asadabadi, M. R., Janjua, N. K., Hussain, O. K., Chang, E., & Saberi, M. (2018). An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowledge-Based Systems, 159(November 2017), 120–131. https://doi.org/10.1016/j.knosys.2018.06.010
  22. Omrani, H., Alizadeh, A., & Emrouznejad, A. (2018). Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method. Journal of Cleaner Production, 203, 210–223. https://doi.org/10.1016/j.jclepro.2018.08.238
  23. Palaz, H., & Kovancı, A. (2008). Türk Deniz Kuvvetleri Denizaltılarının Seçilimin AHP İle Değerlendirilmesi. Journal of Aeronautics and Space Technologies, 3(3), 53-60.
  24. Pamučar, D., Petrović, I., & Ćirović, G. (2018). Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert Systems with Applications, 91, 89–106. https://doi.org/10.1016/j.eswa.2017.08.042
  25. Popović, G., Stanujkić, D., & Stojanović, S. (2012). Investment project selection by applying copras method and imprecise data. Serbian Journal of Management, 7(2), 257-269.
  26. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  27. Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega (United Kingdom), 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
  28. Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2016.06.125
  29. Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68(December 2017), 158–169. https://doi.org/10.1016/j.tranpol.2018.05.007
  30. Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2015.07.073
  31. Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), 9-26.
  32. Salimi, N., & Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and Program Planning. https://doi.org/10.1016/j.evalprogplan.2017.10.002
  33. San Cristóbal, J. R. (2011). Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method. Renewable energy, 36(2), 498-502.
  34. Shojaei, P., Seyed Haeri, S. A., & Mohammadi, S. (2018). Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. Journal of Air Transport Management, 68, 4–13. https://doi.org/10.1016/j.jairtraman.2017.05.006
  35. Tian, Z. peng, Wang, J. qiang, & Zhang, H. yu. (2018). An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Applied Soft Computing Journal, 72, 636–646. https://doi.org/10.1016/j.asoc.2018.03.037
  36. van Roekel, W. S. (2017). Improving International Logistics Performance Measuring. Delft University of Technology.
  37. Wan Ahmad, W. N. K., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production, 153, 242–252. https://doi.org/10.1016/j.jclepro.2017.03.166
  38. Yıldız, A. (2014). Bulanık VIKOR Yöntemini Kullanarak Proje Seçim Sürecinin İncelenmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 14(1), 115-128.