Vol. 9 No. 2 (2021): Business & Management Studies: An International Journal
Articles

Financial performance analysis of electricity generation companies with multi-criteria decision making: Entropy-based Cocoso method

PDF (Türkçe)
  • Ayşe Topal
Ayşe Topal
Asst. Prof. Dr., Niğde Ömer Halisdemir University
DOI: https://doi.org/10.15295/bmij.v9i2.1794

Published 2021-06-25

Keywords

  • ÇKKV, CoCoSo, Elektrik Üretim Şirketleri
  • MCDM, CoCoSo, Electricity Generation Companies

How to Cite

Topal, A. (2021). Financial performance analysis of electricity generation companies with multi-criteria decision making: Entropy-based Cocoso method. Business & Management Studies: An International Journal, 9(2), 532–546. https://doi.org/10.15295/bmij.v9i2.1794

Copyright (c) 2021 Ayşe Topal

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Energy consumption is increasing rapidly nowadays due to the increasing population and industrialization. To meet these growing energy needs efficiently, Turkey's electricity sector has begun to transform in the early 2000s, and the electricity generation segment has been opened to privatization. With the privatization, many private enterprises entered the electricity sector. Electricity generation companies need to improve their financial performance to adapt to competition in the electricity market. The primary purpose of this study is to apply the multi-criteria decision-making model based on Entropy and CoCoSo methods to evaluate the financial performance of electricity generation companies. For this purpose, the financial performances of 10 electricity generation companies which are the first 40 in terms of installed capacity in Turkey and are included in the Forbes 500 list, are analyzed with Entropy and CoCoSo methods, which are the multi-criteria decision-making techniques. Criterion weights were found by Entropy. The ranking of the electricity generation companies financial performances were made by the CoCoSo method. In this study, net sales, net sales change, profit before interest/tax, change in profit before interest/tax, total assets, equity, exports and number of employees are used. Actual data of 10 energy companies in 2019 taken from the Forbes 500 were used. According to the results, the electricity generation company with the highest financial performance is Enka, and the lowest performance belongs to the Gama Energy company. Various studies in the literature evaluate the financial performance of electricity generation companies using multi-criteria decision-making methods. However, unlike other studies, in this study, the financial performance of electricity generation companies was evaluated using Entropy and CoCoSo methods. In addition, there is no study examining the financial performance of the electricity generation companies from Turkey in the Fortune 500 list in the literature.

PDF (Türkçe)

Downloads

Download data is not yet available.

References

  1. Ağ, A., & Kuloğlu, E. (2020). İşletmelerin finansal performansının Veri Zarflama Analizi yöntemiyle tespit edilmesi: Borsa İstanbul’da işlem gören enerji işletmelerine yönelik bir uygulama. OPUS Uluslararası Toplum Araştırmaları Dergisi, 16(29 Ekim Özel Sayısı), 3756-3772.
  2. Akçakaya, O., & Akçakaya, E. D. U. (2019). Türkiye’deki büyükşehirlerin çevresel performanslarının Entropi temelli COPRAS ve ARAS yöntemleri ile değerlendirilmesi. OPUS Uluslararası Toplum Araştırmaları Dergisi, 11(18), 1437-1473.
  3. Akın, N. G. (2019). Makine seçimi probleminde Entropi-ROV ve CRITIC-ROV yöntemlerinin karşılaştırılması. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (62), 20-39.
  4. Bağcı, H., & Yiğiter, Ş. Y. (2019). BİST'te yer alan enerji şirketlerinin finansal performansının SD ve WASPAS yöntemleriyle ölçülmesi. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(18), 877-898.
  5. Barua, A., Jeet, S., Bagal, D. K., Satapathy, P., & Agrawal, P. K. (2019). Evaluation of mechanical behavior of hybrid natural fiber reinforced nano sic particles composite using hybrid Taguchi-CoCoSo method. International Journal of Innovative Technology Exploring Engineering, 8, 3341-5.
  6. Biswas, T. K., Stević, Ž., Chatterjee, P., & Yazdani, M. (2019). An integrated methodology for evaluation of electric vehicles under sustainable automotive environment. In Advanced multi-criteria decision making for addressing complex sustainability issues (pp. 41-62). IGI Global.
  7. Chen, C. H. (2020). A novel multi-criteria decision-making model for building material supplier selection based on entropy-AHP weighted TOPSIS. Entropy, 22(2), 259.
  8. Canakcıoglu, M. (2019). Evaluation of banking performance of the Balkan countries with an integrated MCDM approach consist of Entropy and OCRA techniques. Economy & Business Journal, 13(1), 341-366.
  9. Çiftçi, H. N., & Yıldırım, B. F. (2020). BİST enerji sektöründe faaliyet gösteren işletmelerin finansal performanslarının incelenmesi: Gri Sayılara dayalı Zaman Kesiti örneği. Muhasebe Bilim Dünyası Dergisi, 22(3), 384-404.
  10. Cui, Y., Feng, P., Jin, J., & Liu, L. (2018). Water resources carrying capacity evaluation and diagnosis based on set pair analysis and improved the entropy weight method. Entropy, 20(5), 359.
  11. Eyüboglu, K., & Çelik, P. (2016). Financial performance evaluation of Turkish energy companies with fuzzy AHP and fuzzy TOPSIS methods. Business and economics research Journal, 7(3), 21.
  12. Ece, N. (2019). Holding şirketlerinin finansal performans sıralamasının Entropi tabanlı TOPSIS yöntemleri ile incelenmesi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 4(1), 63-73.
  13. Işık, A. T., & Adalı, E. A. (2017). The decision-making approach based on the combination of entropy and ROV methods for the apple selection problem. European Journal of Interdisciplinary Studies, 3(3), 80-86.
  14. Işık, Ö., & Koşaroğlu, Ş. M. (2020). Analysis of the financial performance of Turkish listed oil companies through the application of SD and MAUT methods. Third Sector Social Economic Review, 55(3), 1395-1411.
  15. İlkuçar, M., & Çifci, A. (2016). Performance evaluation of electricity generation companies traded on BIST according to the financial parameters through the application of TOPSIS method. International Journal of Social Sciences and Education Research, 2(3), 1010-1021.
  16. Kara, O., & Uslu, M. (2020). Elektrik dağıtım şirketlerinin etkinliği: Bootstrap tahminli iki aşamalı DEA analizi. Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 2(2), 189-206.
  17. Karaatlı, M. (2016). Entropi-Gri İlişkisel Analiz Yöntemleri ile Bütünleşik Bir Yaklaşım: Turizm Sektöründe Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), 63-77.
  18. Karakul, A. K., & Özaydin, G. (2019). TOPSIS ve VİKOR yöntemleri ile finansal performans değerlendirmesi: XELKT üzerinde bir uygulama. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 60, 68-86.
  19. Karasan, A., & Bolturk, E. (2019). Solid waste disposal site selection by using Neutrosophic Combined Compromise Solution method. 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), 9-13 Eylül, Prag, Çek Cumhuriyeti.
  20. Karcıoğlu, R., Yalçın, S., & Gültekin, Ö. F. (2020). Sezgisel Bulanık Mantık ve Entropi tabanlı çok kriterli karar verme yöntemiyle finansal performans analizi: BİST’de işlem gören enerji şirketleri üzerine bir uygulama. MANAS Sosyal Araştırmalar Dergisi, 9(1), 360-372.
  21. Khan, S., & Haleem, A. (2021). Investigation of circular economy practices in the context of emerging economies: a CoCoSo approach. International Journal of Sustainable Engineering, 1-11.
  22. Li, H., Wang, W., Fan, L., Li, Q., & Chen, X. (2020). A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Applied Soft Computing, 91, 106207.
  23. Liu, P., Rani, P., & Mishra, A. R. (2021). A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology. Journal of Cleaner Production, 292, 126047.
  24. Mercan, Y., & Çetin, O. (2020). COPRAS ve VIKOR yöntemleri ile BIST elektrik endeksindeki firmalarinin finansal performans analizi. Uluslararası Afro-Avrasya Araştırmaları Dergisi, 5(9), 123-139.
  25. Kuvat, Ö., & Güler, G. (2020). Energy sector analysis with Fuzzy TOPSIS. International Scientific and Vocational Studies Journal, 4(1), 37-48.
  26. Metin, S., Yaman, S., & Korkmaz, T. (2017). Finansal performansın TOPSIS ve MOORA yöntemleri ile belirlenmesi: BİST enerji firmaları üzerine karşılaştırmalı bir uygulama. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 14(2), 371-394.
  27. Orçun, Ç. (2019). Enerji sektöründe WASPAS yöntemiyle performans analizi. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(2), 439-453.
  28. Özaydın, G., & Karakul, A. K. (2021). Entropi tabanlı MAUT, SAW ve EDAS yöntemleri ile finansal performans değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(1), 13-29.
  29. Özdağoğlu, A., & Keleş, M. K. (2019). Spor yönetimi açısından Gri Entropi tabanlı ROV yöntemi ile 4 büyük futbol kulübünün finansal performans analizi. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (35), 107-123.
  30. Özdağoğlu, A., Ulutaş, A., & Keleş, M. K. (2020). The Ranking of Turkish universities with CoCoSo and MARCOS. Economics Business and Organization Research, 374-392.
  31. Öztel, A., Aydin, B., & Köse, M. S. (2018). Entropi tabanlı TOPSIS yöntemi İle enerji sektöründe kurumsal sürdürülebilirlik performansının ölçümü: Akenerji örneği. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 9(24), 1-24.
  32. Peng, X., & Smarandache, F. (2020). A decision-making framework for China’s rare earth industry security evaluation by neutrosophic soft CoCoSo method. Journal of Intelligent & Fuzzy Systems, 39(5) 7571-7585.
  33. Peng, X., Zhang, X., & Luo, Z., (2020). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53, 3813–3847.
  34. Perçin, S., & Sönmez, Ö. (2018). Bütünleşik Entropi Ağırlık ve TOPSIS yöntemleri kullanılarak Türk Sigorta şirketlerinin performansının ölçülmesi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 18, 565-582.
  35. Rani, P., Mishra, A. R., Pardasani, K. R., Mardani, A., Liao, H., & Streimikiene, D. (2019). A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. Journal of Cleaner Production, 238, 117936.
  36. Salehi, V., Zarei, H., Shirali, G. A., & Hajizadeh, K. (2020). An entropy-based TOPSIS approach for analyzing and assessing crisis management systems in petrochemical industries. Journal of Loss Prevention in the Process Industries, 67, 104241.
  37. Shannon C. E. (1948). A mathematical theory of communications. Bell Systems Technical Journal, 27(3), 379-423.
  38. Siew, L. W., Fai, L. K., & Hoe, L. W. (2021). Performance evaluation of construction companies in Malaysia with Entropy-VIKOR model. Engineering Journal, 25(1), 297-305.
  39. TEİAŞ (2020). Türkiye elektrik üretim-iletim istatistikleri. Erişim tarihi: 02.12.2020, https://www.teias.gov.tr/tr-TR/turkiye-elektrik-uretim-iletim-istatistikleri
  40. Torkayesh, S. E., Amiri, A., Iranizad, A., & Torkayesh, A. E. (2020). Entropy based EDAS decision making model for neighborhood selection: A case study in istanbul. Journal of Industrial Engineering and Decision Making, 1(1), 1-11.
  41. Ulutaş, A. (2018). Entropi temelli ROV yöntemi ile esnek üretim sistemi seçimi. Business and Economics Research Journal, 9(1), 187-194.
  42. Ulutaş, A., Karakuş, C. B., & Topal, A. (2020). Location selection for logistics center with fuzzy SWARA and CoCoSo methods. Journal of Intelligent & Fuzzy Systems, 38(4), 4693-4709.
  43. Ünal, E. A. (2019). Bütünleşik Entropi ve Edas Yöntemleri Kullanılarak BİST Sigorta Şirketlerinin Performansının Ölçülmesi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 4(4), 555-566.
  44. Wen, Z., Liao, H., Zavadskas, E. K., & Al-Barakati, A. (2019a). Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Economic research-Ekonomska istraživanja, 32(1), 4033-4058.
  45. Wen, Z., Liao, H., Ren, R., Bai, C., Zavadskas, E. K., Antucheviciene, J., & Al-Barakati, A. (2019b). Cold chain logistics management of medicine with an integrated multi-criteria decision-making method. International journal of environmental research and public health, 16(23), 4843.
  46. Wu, J., Sun, J., Liang, L., & Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165.
  47. Wu, X., Zhang, C., & Yang, L. (2021). Evaluation and selection of transportation service provider by TOPSIS method with entropy weight. Thermal Science, (Pre-print).
  48. Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019a). A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. Journal of Civil Engineering and Management, 25(8), 858-874.
  49. Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2019b). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519.
  50. Yazdani, M., Torkayesh, A. E., Santibanez-Gonzalez, E. D., & Otaghsara, S. K. (2020a). Evaluation of renewable energy resources using integrated Shannon Entropy–EDAS model. Sustainable Operations and Computers, 1, 35-42.
  51. Yazdani, M., Chatterjee, P., Pamucar, D., & Chakraborty, S. (2020b). Development of an integrated decision making model for location selection of logistics centers in the Spanish autonomous communities. Expert Systems with Applications, 148, 113208.
  52. Yenioğlu, Z. A., & Toklu, B. (2021). Stokastik veri zarflama analizi ile etkinlik ölçümü: Türkiye elektrik dağıtım şirketlerinin karşılaştırmalı analizi. Politeknik Dergisi, 24(1), 87-101.
  53. Zhang, X., Wang, C., Li, E. & Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on improved Entropy weight method. The Scientific World Journal, 2014, 797814, 1-7.
  54. Zolfani, S. H., Chatterjee, P., & Yazdani, M. (2019). A structured framework for sustainable supplier selection using a combined BWM-COCOSO model. Contemporary Issues in Business, Management and Economics Engineering’2019, 9–10 May, Vilnius, Lithuania, https://doi.org/10.3846/cibmee.2019.081.
  55. Zolfani, S. H., Yazdani, M., Ebadi Torkayesh, A., & Derakhti, A. (2020). Application of a gray-based decision support framework for location selection of a temporary hospital during COVID-19 pandemic. Symmetry, 12(6), 886.
  56. https://www.enerjiatlasi.com/firma/