Analysis of Companies' Intellectual Capital Performance Using the MEREC-Based MARCOS Method: The Case of the BIST Forest, Paper and Printing Index
Abstract
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.
Keywords
BIST , Multi-criteria decision making , Intellectual capital
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