Analysis of Companies' Intellectual Capital Performance Using the MEREC-Based MARCOS Method: The Case of the BIST Forest, Paper and Printing Index

Yıl 2025, Cilt: 21 Sayı: 2, 205 - 222, 30.12.2025

Nadir Ersen , İlker Akyüz , Kadri Cemil Akyüz

https://doi.org/10.58816/duzceod.1763131

Öz

This study aims to measure and comparatively rank the intellectual capital performance of companies in the BIST Forest, Paper, and Printing Index by developing the MEREC–MARCOS integrated method, which will contribute to the literature in the field of multi-criteria decision making (MCDM).The study used decision matrices generated from financial data for the years 2020–2024. Criteria weights were determined using the MEREC method, and company performance rankings were performed using the MARCOS method. The reliability of the method was tested using comparative analysis using WASPAS, COPRAS, SAW, and MOOSRA methods, and the Spearman rank correlation coefficient. The analyses revealed that the criteria weights change periodically. While the Intangible Assets (K8) criterion was of the highest importance in 2020–2021, the Market Value–Book Value (K5) criterion gained prominence in subsequent years. In the company rankings, ALKA achieved the highest performance between 2022 and 2024, while DGNMO and MNDTR experienced a decline in performance. The Spearman correlation coefficient averaged 0.95, demonstrating high agreement between the methods. The MEREC–MARCOS method is an effective tool for objective, reliable, and comparative assessment of intellectual capital performance. The method can be adapted to different sectors and decision-making problems, and more flexible decision support systems can be developed by integrating it with fuzzy logic or artificial intelligence-based approaches.

Anahtar Kelimeler

BIST , Multi-criteria decision making , Intellectual capital

Şirketlerin Entelektüel Sermaye Performanslarının MEREC Tabanlı MARCOS Yöntemi ile Analizi: BIST Orman, Kâğıt ve Basım Endeksi Örneği

Öz

Bu çalışma, çok kriterli karar verme (ÇKKV) alanında literatüre katkı sağlayacak MEREC–MARCOS bütünleşik yöntemini geliştirerek, BIST Orman, Kâğıt ve Basım Endeksi’ndeki şirketlerin entelektüel sermaye performanslarını ölçmeyi ve karşılaştırmalı olarak sıralamayı amaçlamaktadır. Araştırmada, 2020–2024 yıllarına ait finansal verilerden oluşturulan karar matrisleri kullanılmıştır. MEREC yöntemi ile kriter ağırlıkları belirlenmiş, MARCOS yöntemi ile şirket performans sıralamaları yapılmıştır. Yöntemin güvenilirliği, WASPAS, COPRAS, SAW ve MOOSRA yöntemleri ile karşılaştırmalı analiz ve Spearman sıra korelasyon katsayısı ile test edilmiştir. Analizler, kriter ağırlıklarının dönemsel olarak değiştiğini ortaya koymuştur. 2020–2021’de Maddi Olmayan Duran Varlıklar (K8) kriteri en yüksek öneme sahipken, sonraki yıllarda Piyasa Değeri–Defter Değeri (K5) kriteri öne çıkmıştır. Şirket sıralamalarında ALKA, 2022–2024 yıllarında en yüksek performansa ulaşırken, DGNMO ve MNDTR’nin performansında düşüş gözlenmiştir. Spearman korelasyon katsayısı ortalama 0,95 olup yöntemler arası yüksek uyum elde edilmiştir. MEREC–MARCOS yöntemi, entelektüel sermaye performansının nesnel, güvenilir ve karşılaştırmalı olarak değerlendirilmesinde etkili bir araçtır. Yöntem, farklı sektörler ve karar verme problemlerine uyarlanabilir; fuzzy mantık veya yapay zekâ tabanlı yaklaşımlarla bütünleştirilerek daha esnek karar destek sistemleri geliştirilebilir.

Anahtar Kelimeler

BIST , Çok kriterli karar verme , Entelektüel sermaye

Kaynakça

• Abedi, R. (2022). Application of multi–criteria decision making models to forest fire management. International Journal of Geoheritage and Parks, 10(1), 84–96. https://doi.org/10.1016/j.ijgeop.2022.02.005
• Akay, A. O., & Demir, M. (2022). A scenario–based analysis of forest product transportation using a hybrid fuzzy multi–criteria decision–making method. Forests, 13(5), 730. https://doi.org/10.3390/f13050730
• Akgün, A. İ., & Günay, B. (2021). Use of multiple criteria decision–making models for the prioritization of intellectual capital efficiency: a case of healthcare sector. Sosyoekonomi, 29(47), 337–365. https://doi.org/10.17233/sosyoekonomi.2021.01.17
• Arıkan Kargı, V. S. (2025). Analysis of the performance of companies in the individual pension system using the MEREC–based MARCOS method. Fırat University Journal of Social Sciences, 35(2), 685–702. https://doi.org/10.18069/firatsbed.1588881
• Arslankaya, S., & Göraltay, K. (2019). Current approaches in multi–criteria decision making methods. Iksad Publications.
• Azizi, M., Mohebbi, N., & De Felice, F. (2016). Evaluation of sustainable development of wooden furniture industry using multi criteria decision making method. Agriculture and Agricultural Science Procedia, 8, 387–394. https://doi.org/10.1016/j.aaspro.2016.02.034
• Chang, C. C., Hung, S. W, & Huang, S. Y. (2013). Evaluating the operational performance of knowledge–based industries: the perspective of intellectual capital. Quality & Quantity: International Journal of Methodology, 47, 1367–1383. https://doi.org/10.1007/s11135-011-9595-x
• Chavenetidou, M., Tsiaras, S., Koulelis, P. P., & Raptis, D. I. (2025). Evaluating the wood quality of conifer species in the Greek forest sector using an integrated multi–criteria decision analysis (MCDA) approach. Forests, 16(6), 1028. https://doi.org/10.3390/f16061028
• Chen, I.S., & Chen, J. K. (2010). How to manage knowledge well? Evidence from the life insurance industry. African Journal of Business Management, 4(17), 3605–3617.
• Costa, R. (2012). Assessing intellectual capital efficiency and productivity: an application to the Italian yacht manufacturing sector. Expert Systems with Application, 39(8), 7255–7261. https://doi.org/10.1016/j.eswa.2012.01.099
• Çevik, G., & Arslan, Ö. (2022). Analytic evaluation of intellectual capital for ship management companies under a fuzzy environment. Journal of ETA Maritime Science, 10(3), 185–194. https://doi.org/10.4274/jems.2022.41033
• Daşdemir, İ., & Gençay, E. (2021). Prioritization of forest cadastre commissions by a multi–criteria approach according to their performances. Turkish Journal of Forestry, 22(3), 241–249. https://doi.org/10.18182/tjf.922347
• Deng, D., Ye, C., Tong, K., & Zhang, J. (2023). Evaluation of the sustainable forest management performance in forestry enterprises based on a hybrid multi–criteria decision–making model: a case study in China. Forests, 14(11), 2267. https://doi.org/10.3390/f14112267
• Diker, F. (2025). Evaluation of the forest products and furniture industry in terms of sustainable supply chain using grey relational analysis method. Turkish Studies– Economics, Finance, Politics, 20(2), 673–691 https://doi.org/10.7827/TurkishStudies.77152
• El–Araby, A. (2023). The utilization of MARCOS method for different engineering applications: A comparative study. International Journal of Research in Industrial Engineering, 12(2), 155–164. https://doi.org/10.22105/riej.2023.395104.1379
• El–Araby, A., Sabry, I., & El–Assal, A. (2024). Ranking performance of MARCOS method for location selection problem in the presence of conflicting criteria. Decision Making Advances, 2(1), 148–162. https://doi.org/10.31181/dma21202435
• Elsayed, A. (2024). Comprehensive review MEREC weighting method for smart building selection for new capital using neutrosophic theory. Neutrosophic Sets and Systems, 63, 342–366.
• Ersoy, N. (2022). Measurement of innovation performance of OECD and EU member countries using the MEREC–MARCOS integrated model. Dokuz Eylül University The Journal of Graduate School of Social Sciences, 24(3), 1039–1063. https://doi.org/10.16953/deusosbil.1106249
• Ivanovic, B., Saha, A., Stevic, Z., Puska, A., & Zavadskas, E.K. (2022). Selection of truckmixer concrete pump using novel MEREC DNMARCOS model. Archives of Civil and Mechanical Engineering, 22, 173. https://doi.org/10.1007/s43452–022-00491-9
• Keleş, N. (2023). A multi–criteria decision–making framework based on the MEREC Method for the comprehensive solution of forklift selection problem. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 18(2), 573 –590. https://doi.org/10.17153/oguiibf.1270016
• Keshavarz–Ghorabaee, M., Amiri, M., Zavadskas, E. K., & Turskis, Z. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(3), 1–20. https://doi.org/10.3390/sym13040525
• Kumar, S., Arzaghi, E., Baalisampang, T., Abaei, M. M., Garaniya, V., & Abbassi, R. (2024). A risk–based multi-criteria decision–making framework for offshore green hydrogen system developments: Pathways for utilizing existing and new infrastructure. Sustainable Production and Consumption, 46, 655–678. https://doi.org/10.1016/j.spc.2024.03.020
• Kumar, S., Ahijith Kumar, P. V., Bharati, K. Patnaik, L., Maity, S. R., & Lepicka, M. (2025). Coating material selection for bulk metal forming dies: A MEREC–integrated approach with multiple MCDM methods. International Journal on Interactive Design and Manufacturing, 19, 4055–4070. https://doi.org/10.1007/s12008-024-01983-z
• Kurt, R., İmren, E., & Karayılmazlar, S. (2021). Analysis of financial performance of paper, forest and furniture companies operating under the Turkish forest industry sector by entropy–based PROMETHEE method. Journal of Bartın Faculty of Forestry, 23(2), 545–554. https://doi.org/10.24011/barofd.904299
• Liu, C., Liao, Q., Gao, W., Li, S., Jiang, P., & Li, D. (2024). Intellectual capital evaluation index based on a hybrid multi–criteria decision–making technique. Mathematics, 12(9), 1323. https://doi.org/10.3390/math12091323
• Lu, M., & Wudhikarn, R. (2022). Using the best–worst method to develop intellectual capital indicators in financial service company. In 2022 joint international conference on digital arts, media and technology with ecti northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI DAMT & NCON), (pp. 81–86), https://doi.org/qjgr
• Lu, W. M., Wang, W. K., Tung, W. T., & Lin, F. (2010). Capability and efficiency of intellectual capital: The case of fabless companies in Taiwan. Expert Systems with Applications, 37(1), 546–555. https://doi.org/10.1016/j.eswa.2009.05.031
• Mastilo, Z., Stilic, A., Gligovic, D., Puska, A. (2024). Assessing the banking sector of Bosnia and Herzegovina: an analysis of financial indicators through the MEREC and MARCOS method. Journal of Central Banking Theory and Practice, 1, 167–197. https://doi.org/10.2478/jcbtp-2024-0008
• Mondal, M. K., Mahapatra, B. S., Bera, M. B. & Mahapatra, G. S. (2024). Sustainable forest resources management model through Pythagorean fuzzy MEREC–MARCOS approach. Environment, Development and Sustainability. 1–32. https://doi.org/10.1007/s10668-024-05164-6
• Özdemir, L., & Balkan, O. (2010). Benefits of intellectual capital components to organizations. Journal of Organization and Management Sciences, 2(1), 115–121.
• Özel, H. B., Karayılmazlar, S., & Demirci, A. (2014). Location selection for afforestation activities using mediterranean pine species (Pinus brutia Ten. and Pinus pinea L.) in Bartın watershed by analytical hierarchy process (AHP) method. In II. National Mediterranean Forest and Environment Symposium (pp. 104–110).
• Saeedi, N., Alipour, A., Mirzapour, S. A. R., & Chaboki, M. M. (2012). Ranking the intellectual capital components using fuzzy TOPSIS technique (case study: an Iranian company). Journal of Basic and Applied Scientific Research, 2(10), 10360–10368.
• Saidin, M. S., Lee, L. S., Marjugi, S. M., Ahmad, M. Z., & Seow, H. V. (2023). Fuzzy Method Based on the Removal Effects of Criteria (MEREC) for Determining Objective Weights in Multi–Criteria Decision–Making Problems. Mathematics, 11(6), 1544. https://doi.org/10.3390/math11061544
• Sehgal, K., Kaur, H., Kaur, S., Singh, S., Channi, H. K., & Stevic, Z. (2025). Cost–effective optimization of hybrid renewable energy system for micro, small, and medium enterprises: A decision–making framework integrating MEREC and MARCOS. Opportunities and Challenges in Sustainability, 4(1), 17–32. https://doi.org/10.56578/ocs040102
• Sekhar; C., Patwardhan, M., & Vyas, V. (2015). A Delphi–AHP–TOPSIS based framework for the prioritization of intellectual capital indicators: a SMEs perspective. Procedia – Social and Behavioral Sciences, 189, 275–284. https://doi.org/10.1016/j.sbspro.2015.03.223
• Simic, V., Gokasar, I., Deveci, M., & Svadlenka, L. (2022). Mitigating climate change effects of urban transportation using a Type–2 neutrosophic MEREC–MARCOS model. IEEE Transactions on Engineering Management, 71, 3233–3249. https://doi.org/10.1109/tem.2022.3207375
• Singer, H., & İlçe, A. Ç. (2024). A multicriteria solution approach for material combination selection in furniture production. Gazi University Journal of Science Part C: Design and Technology, 12(1), 117–127. https://doi.org/10.29109/gujsc.1397494
• Soylu, N. (2020). Evaluation of intellectual capital efficiency with data envelopment analysis: A research on BIST technology companies. Journal of Accounting and Finance, 85, 269–286. https://doi.org/10.25095/mufad.673738
• Soylu, N., & Zafari, A. K. (2024). Measuring intellectual capital performance employing CRITIC and Gray Relational Analysis method: the case of metal products sector. Journal of Productivity, 58(2), 247–262. https://doi.org/10.51551/verimlilik.1404849
• Stanković, M., Stević, Ž., Das, D. K., Subotić, M., & Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8, 457. https://doi.org/10.3390/math8030457
• Stevic, Z., Pamucar, D., Puska, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to Compromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231
• Stilic, A., Puska, A., & Bozanic, D. (2024). Ranking European countries using hybrid MEREC– MARCOS MCDA based on travel and tourism development index. Tourism: An International Interdisciplinary Journal, 72(4), 592–608. https://doi.org/10.37741/t.72.4.6
• Sumerli Sarıgül, S., Ünlü, M., & Yaşar, E. (2023b). A new MCDM approach in evaluating airport service quality: MEREC–based MARCOS and CoCoSo methods. International Journal of Management Academy, 6(1), 90–108. https://doi.org/10.33712/mana.1250335
• Tamosiuniene, R., & Sajaviciute, M. (2022). Evaluation of the intellectual capital impact on the company's attractiveness. International Scientific Conference, 151–156, Gabrovo.
• Trung, D. D. (2022a). Multi–criteria decision making under the MARCOS method and the weighting methods: applied to milling, grinding and turning processes. Manufacturing Review, 9(3), 1–13. https://doi.org/10.1051/mfreview/2022003
• Trung, D. D. (2022b). Development of data normalization methods for multi–criteria decision making: Applying for MARCOS method. Manufacturing Review, 9(22), 1–15. https://doi.org/10.1051/mfreview/2022019
• Public Disclosure Platform. (2025, July 25). https://www.kap.org.tr/
• İş Investment. (2025, July 25). https://www.isyatirim.com.tr/
• Urmak, E. D., Çatal, Y., & Karaatlı, M. (2017). Evaluation of the cities of forestry with the AHP based MAUT and SAW methods. Süleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, 22(2), 301–325.
• Wudhikarn, R. (2018). Improving the intellectual capital management approach using the hybrid decision method. Journal of Intellectual Capital, 19(4), 670–691. https://doi.org/10.1108/jic-07-2017-0088
• Yeşilkaya, M. (2018). Selection of paper factory location using multi–criteria decision making methods. Çukurova University Journal of the Faculty of Engineering and Architecture, 33(4), 31–44. https://doi.org/10.21605/cukurovaummfd.521775
• Yeşilkaya, M., Çabuk, Y., & Karayılmazlar, S. (2022). Industrial wood production analysis of provinces in Turkey with TOPSIS–VIKOR methods. Journal of Bartın Faculty of Forestry, 24(3), 476–487. https://doi.org/10.24011/barofd.1137955
• Yeşilkaya, M., Daş, G. S., & Yaşin, M. F. (2023). Evaluation of the Turkish forest products industry in the context of circular economy and industrial symbiosis. Journal of Turkish Operations Management, 7(2), 1701–1723. https://doi.org/10.56554/jtom.1169240
• Yılmaz, E., Kayacan, A., Alkan, S., & Bayir, Y. (2020). Determination of effective mass media announcing forestry activities to the public with Analytic Hierarchy Process (AHP) (the case of Isparta RDF in Turkey). Tree and Forest, 1(2), 1–12. https://doi.org/10.17568/ogmoad.692363
• Zhang, Z., Cheng, Y., & Liu, N. C. (2014). Comparison of the effect of mean–based method and Z–Score for field normalization of citations at the level of web of science subject categories. Scientometrics, 101, 1679–1693. https://doi.org/10.1007/s11192-014-1294-7
• Zor, İ., & Cengiz, S. (2013). The relationship between intellectual capital and firm value: A study in Istanbul Stock Exchange. Çankırı Karatekin University Faculty of Economics and Administrative Sciences Journal, 3(1), 37–56.