Abstract
The furniture sector has a strategic importance in Turkey. That sector contributes greatly to the national economy, provides high levels of employment, is rapidly growing, and does not have a foreign trade deficit. In this paper, a hybrid multi-criteria decision-making method was used to determine the contribution of some furniture products to Turkey’s economy. First, the entropy method was used to determine the weight values of the criteria, and then the ELimination Et Choix Traduisant la REalité (ELimination Et Choice Translating REality) (ELECTRE) method was used to compare and rank the alternatives. As a result of this analysis, the furniture products that contributed the most to the national economy were “seating furniture and parts thereof, other wooden furniture”, “wooden bedroom furniture”, “other metal furniture”, and “parts of furniture other than metal and wood”. The least contributing products were “furniture used in medicine, surgical, dental, veterinary, and barber chairs etc.”, “plastic furniture”, and “furniture other than metal, wood, and plastic”.
Download PDF
Full Article
Analysis of Furniture Products’ Contribution to Turkey’s Economy with a Hybrid Multi-criteria Decision Making Method
Nadir Ersen a,*
The furniture sector has a strategic importance in Turkey. That sector contributes greatly to the national economy, provides high levels of employment, is rapidly growing, and does not have a foreign trade deficit. In this paper, a hybrid multi-criteria decision-making method was used to determine the contribution of some furniture products to Turkey’s economy. First, the entropy method was used to determine the weight values of the criteria, and then the ELimination Et Choix Traduisant la REalité (ELimination Et Choice Translating REality) (ELECTRE) method was used to compare and rank the alternatives. As a result of this analysis, the furniture products that contributed the most to the national economy were “seating furniture and parts thereof, other wooden furniture”, “wooden bedroom furniture”, “other metal furniture”, and “parts of furniture other than metal and wood”. The least contributing products were “furniture used in medicine, surgical, dental, veterinary, and barber chairs etc.”, “plastic furniture”, and “furniture other than metal, wood, and plastic”.
Keywords: Furniture products; Entropy; ELECTRE; Hybrid decision making model
Contact information: Department of Forestry, Artvin Çoruh University, Artvin 08000 Turkey;
* Corresponding author: nadirersen20@artvin.edu.tr
INTRODUCTION
Items of furniture are objects created in different materials or forms that have a role in human life by helping ensure the well-being of the individual or society. They additionally serve social and cultural needs and directly affect people’s quality of life. Demand for furniture has continually increased due to factors such as population growth, increasing living standards, and an increase in the sector’s export value, which directly affects the furniture sector (Şahin 2016; TOBB 2018).
The furniture industry can be regarded as a process that transforms materials such as wood, metal, marble, and plastic into intermediate products. Economically, the furniture sector provides significant employment opportunities to the country and contributes to the country’s economy with its high added value (Ceylan 2009; Çolak 2009).
Turkey is one of the quickly growing countries in the furniture sector compared to other countries. In Turkey, this sector’s history dates to the 19th century. Furniture was first produced by furniture masters in small workshops. Significant increases have been observed in the number of medium and large enterprises since the 1990s. However, Turkey’s furniture industry is mainly composed of small- and medium-sized firms. The Turkish furniture sector achieved the fastest growth in 2005 with 8% growth (Çolak 2009; Köksal 2017).
Examining the production and foreign trade performance of furniture in the world reveals that the furniture industry reached a volume of approximately $240 billion worldwide in 2018 (West Mediterranean Exporters Association 2020). While the center of furniture production in the world was previously in Europe, recently the weight of furniture production has shifted to the east, being mainly based in China. China is the leader in furniture production, followed by the US, Italy, and Germany. Turkey’s furniture production was approximately 25 billion Turkish liras, according to 2018 data (Taşçı and Çanakçı 2016; Çoşkun 2019; TSI 2020a). Total furniture exports in the world were approximately $194 billion in 2018. China is the leading country in world furniture exports with a 33% share. China is followed by Germany, Poland, Italy and the USA. These five countries make up approximately 58% of the world furniture exports. Turkey with $2.687 billion ranks 14th in the world in terms of furniture exports. Total furniture imports in the world were approximately $193 billion in 2018. The largest furniture importer country in the world is the USA. The USA imported $59.132 billion worth of furniture in 2018. Other large importing countries are Germany, United Kingdom, France, Canada, Japan, Netherlands, and Spain. Turkey had $592.8 million in import volume in 2018 (Republic of Turkey Ministry of Trade 2020).
The furniture sector is one of the leading sectors in the Turkish economy. This is because the furniture industry provides high employment opportunities, has a high added value, and is one of the industries with the least foreign dependency. As a result, this sector has the potential to reduce Turkey’s dependence on foreign entities. For this, how much furniture products contribute to the country’s economy and the export potential of the products must be known. However, this is a difficult problem that must be solved by decision makers. For this reason, determining the economic contribution of furniture products to country has been chosen as the research subject of this study.
With multi-criteria decision-making (MCDM) techniques, decision makers can make more effective and easier decisions. The MCDM techniques are selective techniques used to evaluate, rank, and select the most suitable alternative among various alternatives (Azadfallah 2017). There are various MCDM techniques used in the literature. Some techniques include analytical hierarchy process (AHP), entropy, ELimination Et Choix Traduisant la REalité (ELimination Et Choice Translating REality) (ELECTRE), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIseKriterijumsa Optimizacija I Kompromisno Resenje (VIKOR), The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), etc. In some studies, the MCDM techniques have been used alone or by combining two techniques (Halicka 2020; Singh et al. 2020). In this research, a two-step hybrid MDCM technique was used, which was created by combining the entropy and ELECTRE methods. In the first step of this hybrid method, the weights of the criteria were determined via the entropy method. The ELECTRE method was used to rank furniture products against each other in terms of their economic contribution.
Many methods are used for weighting the criteria; however, in practice, one of the most used is the AHP method. The AHP method is influenced by subjective preferences. Objective weighting methods, such as entropy, can be used to prevent subjective preference assessment (Chen 2019; Yuan et al. 2019). In this method, the weight of each criterion is calculated based on observation values. The low entropy value of the criterion indicates that the criterion is important and that the weight value is large (Chen et al. 2015a; Chen 2020).
The ELECTRE method is a technique that represents the dominance of relations between different alternatives by outranking relations (Pang et al. 2011). This method may be more suitable for problems with many alternatives and few criteria because it has a clearer view of alternatives by eliminating the less favorable ones (Pohekar and Ramachandran 2004). It has some advantages: it is easy to understand, there is a clear logic relationship, the calculation can be made with a program such as Microsoft Excel, all the data in the decision matrix can be used, and it can process heterogeneous scale criteria by not changing the actual value of the alternatives on each criterion without need for normalization or prediction techniques. The main disadvantage of the ELECTRE method is that it may not be suitable when calculating the total score for each alternative because it supposes that all criteria are at a similar generalization level (Safitri and İbrahim 2019; Zhang and Xue 2019).
Examining the literature shows that the two-step hybrid (entropy-ELECTRE) method has been used in a variety of research areas. Singh et al. (2020) used the entropy-ELECTRE method to select the best brake friction composite composition from a range of natural fiber reinforced composites in their research. Lee and Chang (2018) evaluated renewable energy sources for electricity generation in Taiwan; they used the entropy method to evaluate the criteria and the weighted sum method (WSM), VIKOR, TOPSIS, and ELECTRE methods to rank the alternatives. Wang and Liu (2016) evaluated the energy saving and emission reduction in a thermal power plant using the entropy-ELECTRE hybrid method. Khosheghbal et al. (2014) investigated factors to improve the performance of BIK port, one of the important ports of Iran, using ELECTRE and entropy methods.
In this paper, an attempt is made to determine the furniture products that contribute most to Turkey’s economy using a hybrid MCDM method. For this purpose, firstly, the criteria and alternatives were determined. Then, the weight values of the criteria were determined using the entropy method. The superiority of each alternative over each other and the ranking of the alternatives were made using the ELECTRE method.
This ranking was done to specify the products that will be able to contribute the most to the country, economically. As a result of this, helping decision-makers in the creation of strategies and plans to reduce the trade deficit in country. Furthermore, this work can contribute to literature from a different aspect such as the decision-making applications in the furniture industry and economic contribution to the country.
EXPERIMENTAL
Materials
The data used were sourced from the Turkey Statistical Institute, and the research covers years 2009 to 2018. The Harmonized Commodity Description and Coding System (HS) was used for the export and import data, whereas the European Community Industrial Product List was used for the production data. In addition, the main product groups were obtained by bringing together the sub-groups of each product (Table 1). The codes 31.00, 31.01, 31.02, 31.03, and 32.50.30 are the production data and the codes 94.01, 94.02, and 94.03 are the export and import data.
Methods
Entropy method
Proposed by Shannon and Weaver (1949), entropy can be used to calculate the weights of the criteria. This method is defined as the measure of uncertainty in information formulated using probability theory (Shemshadi et al. 2011). The entropy method consists of four steps, which are listed below (Li et al. 2011; Lee et al. 2012; Chen et al. 2015b).
Table 1. Production and Foreign Trade Codes of Furniture Products Used in the Study
The decision matrix consists of the alternatives and the evaluation criteria.Step 1: Creating the decision matrix
Step 2: Normalization of the decision matrix
The data was subjected to normalization using Eqs. 1, 2, and 3,
where 
is the performance value of the i alternative in j criteria, 
is the benefit value, and pij is the normalized value.
Step 3: Calculating the entropy value
After normalization, the entropy value of each criterion was calculated. The k value was the inverse of the natural logarithm of the total number of alternatives (k =1 / ln(m)). The ej value takes a value between 0 and 1. The entropy value was calculated according to Eq. 4,
(4)
where ej is the entropy value and k is the entropy coefficient.
Step 4: Calculation of weight value
The entropy value of each criterion was used when calculating the weight values. The sum of the weight values calculated for each criterion should be 1. The weight value is indicated by “wj” and the formula of wj is given in Eq. 5,
(5)
where wj is weight value.
ELECTRE method
The ELECTRE method was introduced by Bernard Roy in 1968 and has been widely researched since then (Roy 1991). This method starts with the creation of the decision matrix and consists of seven steps. The stage of forming the decision matrix was described in the steps of the entropy method, and the other steps were listed as follows (Pang et al. 2011).
Step 2: Normalization of the decision matrix
The decision matrix was normalized using Eq. 6,
(6)
where 
is normalized value.
Step 3: Weighting of normalized decision matrix
The weighting normalized decision matrix was created by multiplying the normalized values with the weight values obtained by the entropy method, as shown in Eq. 7,
(7)
where 
is weighted normalized value.
Step 4: Determination of concordance and discordance interval sets
The weighted normalized decision matrix was used to determine the concordance and discordance interval sets. The concordance and discordance sets were created for each via Eqs. 8 and 9, respectively:
(8)
(9)
Step 5: Calculation of the concordance and discordance indices
Through using the concordance and discordance sets, the concordance and discordance matrix was created. Eq. 10 was used for the concordance matrix, and Eq. 11 was used for the discordance matrix:
Step 6: Superiority comparison
The averages for the concordance 
and discordance 
values were determined via Eqs. 12 and 13:
If 
in the concordance matrix, it was stated as “YES”. If 
in the discordance matrix, it was stated as “YES”.
Step 7: Calculation of net concordance and discordance matrix
The net concordance and discordance values were used to rank alternatives. These values were calculated via Eqs. 14 and 15:
RESULTS AND DISCUSSION
The decision matrix consists of the alternatives and the evaluation criteria. There were 11 alternatives and 3 criteria in this research. The alternatives were as follows: seating furniture and parts thereof (SF), furniture used in medicine, surgical, dental, veterinary, and barber chairs, etc. (FM), metal furniture used in offices (MFO), wooden furniture used in offices (WFO), kitchen furniture (KF), wooden bedroom furniture (WBF), plastic furniture (PF), furniture other than metal, wood, and plastic (FOWP), other wooden furniture (OWF), parts of furniture other than metal and wood (PFO), and other metal furniture (OMF). The criteria researched for this paper were the production value, export value, and import value. Attention has been paid to make the data available in the selection of criteria and alternatives. Furthermore, the ELECTRE method is more appropriate to use in decision-making problems where several criteria are compared and there are many alternatives (Pohekar and Ramachandran 2004). The decision matrix (Table 2) was created by taking an arithmetic average of the ten-year data of alternatives and criteria (Turkish Statistical Institute 2020a; 2020b).
Table 2. Decision Matrix (TSI 2020a; 2020b)
After the decision matrix was created, the data were normalized with Eqs. 1, 2, and 3, and the normalized data are shown in Table 3. After normalization, the entropy value of each criterion and the weight value of each criterion by using Eqs. 4 and 5 were determined, respectively. Results regarding entropy and weight values are shown in Table 4. According to the Table 4, the weight values of the criteria determined by the entropy method were the export value (0.38), production value (0.357), and import value (0.263), as ranked from high to low. This study represented that the export value had more importance when ranking the contribution of furniture products to Turkey’s economy. The export value was followed by the production and import values, respectively. Exports are important for the growth and development of Turkey’s economy (Bedük and Ince 2005). In Turkey, various steps have been taken to increase these exports, such as creating regional export associations, informing the sectors by generating export-oriented reports through Chambers of Commerce and Industry, and providing incentives to businesses engaged in export, etc. (Çoşkun 2019). Different products must be exported, and the products must be exported to different countries to ensure stability and increase in exports (Acaravcı and Kargı 2015). An effort to overcome the reduction in domestic demand resulting from the economic crisis was attempted via export-orientation of Turkish companies. Therefore, today exporting has become a necessity, not only to get work done, but also as a means to grow companies and countries (Republic of Turkey Ministry of Trade 2020). Although there has not been much research on which products contribute more to the national economy, Bayram (2020) researched forest products using the entropy-TOPSIS method. In the study related to the contribution of forest products to Turkey’s economy, the export value criterion was found to have the lowest weight value in between value related criteria. According to the results of this study, the reason for the opposite result Bayram’s study may be that the weight value of the export differs on a product basis.
Table 3. Normalized Data
Table 4. Entropy and Weight Values
After determining the importance levels of the criteria with the entropy method, the ranking of the alternatives was determined using the ELECTRE method. In the ELECTRE method, the decision matrix (Table 2) used in determining the weights of the criteria was used. Firstly, the normalization of the data in the decision matrix was done. The decision matrix was normalized using Eq. 6 and the results were given in Table 5. Then, the Vij matrices (weighting normalized decision matrix) were obtained. To get this matrix, the normalized data were multiplied by the weight values of the criteria. The Vij matrices are shown in Table 6. In this paper, 110 concordance and discordance sets were created for each via Eq. 8 and Eq. 9, respectively. Through using the concordance and discordance sets, the concordance and discordance matrix was created (Table 7 and 8). Equation 10 was used for the concordance matrix, and Eq. 11 was used for the discordance matrix. The averages for the concordance () and discordance () values were found as 0.5 and 0.58, respectively. With and values, 43 out of 110 superiority comparisons had superiority relations. Comparison of the alternatives with the superiority relationship was shown in Table 9. Finally, the net concordance (C) and discordance (D) values were calculated using Eqs. 14 and 15, respectively, and the alternatives based on C and D values were ranked.
Table 5. The Normalized Decision Matrix
Table 6. Weighting of Normalized Decision Matrix
According to Table 10, the ranking was as follows: seating furniture and parts thereof, other wooden furniture, wooden bedroom furniture, other metal furniture, parts of furniture other than metal and wood, kitchen furniture, metal furniture used in offices, wooden furniture used in offices, furniture used in medicine, surgical, dental, veterinary, and barber chairs, etc., plastic furniture, and furniture other than metal, wood, and plastic. Güleç and Adıgüzel (2016) and Şahin (2016) determined that Turkey has the high comparative advantage for the “seating furniture and parts thereof” product group, whilst it has not a comparative advantage for “furniture used in medicine, surgical, dental, veterinary, and barber chairs, etc.” product group. Additionally, other studies have shown that the Turkish furniture industry has increased its competitive position over time (Altay and Gürpınar 2008; Bashimov 2017).
Table 7. The Concordance Index Matrix
Table 8. The Discordance Index Matrix
Table 9. Comparisons
Table 10. Ranking Alternatives with C and D Values
When the decision matrix was examined, the export-import balance of all furniture product groups was positive, i.e., the Turkish furniture sector was one of the sectors that had low levels of foreign dependency. In addition, furniture product groups are generally produced for the domestic market in Turkey.
The production, export, and import values of the seating furniture product group were higher than the other furniture products examined in this study. The wooden bedroom furniture product group was second in production and export value, while the other wooden furniture product group was second in import value. In terms of its contribution to the country’s economy, the other wooden furniture product group was ranked higher than the wooden bedroom furniture product group. The reason for such a result may be due to the high import value of the other wooden furniture product groups. Although the production value of the metal furniture product group used in offices was high, it lagged due to the low export and import values. The production value of the kitchen furniture product group was high, but other metal furniture and other furniture parts other than wood and metal were ranked higher than the kitchen furniture product group because the export values of these two product groups were high.
CONCLUSIONS
The Turkish furniture sector has importance due to its number of workplaces and level of employment. Although the furniture sector is a sector with a foreign trade surplus, high competitiveness, and is manufacturing-based, it remains low in the share of Turkey’s total exports. In this paper, the contribution of the furniture industry to the national economy, which is one of the most important sectors in Turkey, was aimed to analyze in terms of sub-product group. To achieve this purpose, a hybrid (entropy-ELECTRE) technique was used. The following results were obtained:
- Using the entropy method, the export value was the criterion with the highest importance, while the import value was the criterion with the lowest importance.
- Among the furniture products, the five products that make the most contribution to the national economy were determined as follows: seating furniture, other wooden furniture, wooden bedroom furniture, other metal furniture, and parts of furniture other than metal and wood.
- The production value of the seating furniture and other wooden furniture product groups was high. However, it was a remarkable result that the import values were quite high.
- It is noteworthy that the export values of the products, such as seating furniture, metal and wooden furniture used in offices, kitchen furniture, and wooden bedroom furniture, were low compared to the production values. When evaluated, Turkey’s furniture sector does not have the desired success in terms of exports. To increase exports, higher quality products can be produced, and more attention can be paid to research, development, and branding.
- Through determining which furniture products contribute more to the country’s economy, countries and businesses can determine strategies accordingly. In this study, only three criteria were addressed (i.e., production value, export value, and import value). This situation is a limitation of the study. More criteria can be added to this study. In addition, different multi-criteria decision-making methods can be applied and compared.
- Studies on decision making applications in the furniture industry are very limited. Research on the furniture sector is generally related to production, trade, competitiveness, and development, and it does not cover the contribution of furniture products to the country’s economy. This study aims to fill a gap in the literature. In this respect, this study is one of the pioneering works in the field of furniture as far as we know for today.
REFERENCES CITED
Acaravcı, A., and Kargı, G. (2015). “Export diversification and economic growth in Turkey,” International Journal of Economics and Innovation 1(1), 1-16.
Altay, B., and Gürpınar, B. (2008). “Revealed comparative advantages and some competitiveness indices: An application on the Turkish furniture sector,” Afyon Kocatepe University J. of Economics and Administrative Sciences 10(1), 257-274.
Azadfallah, M. (2017). “Comparative analysis of different measurement scale and normalization method performances in ELECTRE method,” National Journal of System and Information Technology 10(2), 127-138.
Bashimov, G. (2017). “The furniture industry: Turkey’s comparative advantage in the global market,” Journal of Economic Innovation 4(2), 20-29.
Bayram, B. Ç. (2020). “Evaluation of forest products trade economic contribution by entropy-TOPSIS: Case study of Turkey,” BioResources 15(1), 1419-1429.DOI: 10.15376/biores.15.1.1419-1429
Bedük, B., and İnce, M. (2005). “The importance of sectoral foreign trade companies in development of exportation in Turkey,” Journal of Öneri 6(23), 169-179.
Ceylan, A. (2009). Supply Chain Management in Furniture Sector and an Application, Master’s Thesis, Yıldız Technical University, Istanbul, Turkey.
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), 259-281. DOI: 10.3390/e22020259
Chen, J., Zihang, Y., Chen, Z., and Nie, Z. (2015b). “Improving assessment of groundwater sustainability with analytic hierarchy process and information entropy method: A case study of the Hohhot Plain, China,” Environmental Earth Sciences 73, 2353-2363. DOI: 10.1007/s12665-014-3583-0
Chen, P. (2019). “On the diversity-based weighting method for risk assessment and decision-making about natural hazards,” Entropy 21(3), 269-281. DOI: 10.3390/e21030269
Chen, W., Feng, D., and Chu, X. (2015a). “Study of poverty alleviation effects for Chinese fourteen contiguous destitute areas based on entropy method,” International Journal of Economics and Finance 7(4), 89-98. DOI: 10.5539/ijef.v7n4p89
Çolak, M. (2009). Determination of the Factors Effecting Profitability in Furniture Sector: Comparative Logistics Regression and UTADIS Analysis, Ph.D. Thesis, Hacettepe University, Ankara, Turkey.
Çoşkun, S. (2019). The Improving of Export Potential in Furniture Sector: Instance of İnegöl, Master’s Thesis, Uludağ University, Bursa, Turkey.
Güleç, E., and Adıgüzel, M. (2016). Examining the International Competitiveness of Turkey’s Furniture Sector, Working Paper Series, Istanbul Commerce University Foreign Trade Institute, Istanbul, Turkey.
Halicka, K. (2020). “Technology selection using the TOPSIS method,” Foresight and STI Governance 14(1), 85-96.
Khosheghbal, B., Saeidi, N., Kaabi, A., and Hallafi, H. R. (2014). “Studying the impact of effective factors on grain terminal’s performance in BIK using MCDM methods,” Kuwait Chapter of Arabian Journal of Business and Management Review 3(11), 243-253. DOI: 10.12816/0018710
Köksal, B. (2017). Development of Furniture Sector in Turkey: Example of Kayseri, Master’s Thesis, Maltepe University, Istanbul, Turkey.
Lee, H.-C., and Chang, C.-T. (2018). “Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan,” Renewable and Sustainable Energy Reviews 92, 883-896. DOI: 10.1016/j.rser.2018.05.007
Lee, P. T.-W., Lin, C.-W., and Shin, S.-H. (2012). “A comparative study on financial positions of shipping companies in Taiwan and Korea using entropy and grey relation analysis,” Expert Systems with Applications 39(5), 5649-5657. DOI: 10.1016/j.eswa.2011.11.052
Li, X., Wang, K., Liu, L., Xin, J., Yang, H., and Gao, C. (2011). “Application of the entropy weight and TOPSIS method in safety evaluation of coal mines,” Procedia Engineering 26, 2085-2091. DOI: 10.016/j.proeng.2011.11.2410
Pang, J., Zhang, G., and Chen, G. (2011). “ELECTRE I decision model of reliability design scheme for computer numerical control machine,” Journal of Software 6(5), 894-900. DOI: 10.4304/jsw.6.5.894-900
Pohekar, S. D., and Ramachandran, M. (2004). “Application of multi-criteria decision making to sustainable energy planning – A review,” Renewable and Sustainable Energy Reviews 8(4), 365-381. DOI: 10.1016/j.rser.2003.12.007
Republic of Turkey Ministry of Trade (2020). Furniture Sector Report, “(https://ticaret.gov.tr/data/5b87000813b8761450e18d7b/Mobilya%20Sekt%C3%B6r%20Raporu%202020.pdf), Accessed 06 May 2020.
Roy, B. (1968). “Classement et choix en présence de points de vue multiples (la méthode ELECTRE) [Ranking and choice in the presence of multiple points of view (the ELECTRE method)],” Operations Research 8, 57-75.
Roy, B. (1991). “The outranking approach and the foundations of ELECTRE methods,” Theory and Decision 31, 49-73. DOI: 10.1007/BF00134132
Safitri, V. T., and İbrahim, A. (2019). “Decision support system for performance appraisal using elimination method and translation reality choices (ELECTRE),” in: Proceedings of the Sriwijaya International Conference on Information Technology and its Applications(SICONIAN 2019), Palembang, Indonesia, pp. 405-411.
Shannon, C. E., and Weaver, W. (1949). The Mathematical Theory of Communication, University of Illinois Press, Urbana, IL, USA.
Shemshadi, A., Shirazi, H., Toreihi, M., and Tarokh, M. J. (2011). “A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting,” Expert Systems with Applications 38(10), 12160-12167. DOI: 10.1016/j.eswa.2011.03.027
Singh, T., Pattnaik, P., Pruncu, C.I., Tiwari, A., and Fekete, G. (2020). “Selection of natural fibers based brake friction composites using hybrid ELECTRE-entropy optimization technique,” Polymer Testing 89, 106614-106624. DOI:10.1016/j.polymertesting.2020.106614
Şahin, D. (2016). “Analysis of foreign trade structure of Turkey’s furniture industry,” Journal of Life Economics 3(3), 7-26. DOI: 10.15637/jlecon.135
Taşçı, K., and Çanakçı, T. (2016). TR72 Furniture Sector Report, Central Anatolia Development Agency, Turkey.
The Union of Chambers and Commodity Exchanges of Turkey (TOBB) (2018). Assembly of Furniture Products Sector in Turkey: 2017, “(https://www.tobb.org.tr/Documents/yayinlar/2018/MobilyaSektorleriMeclisi.PDF), Accessed 06 May 2020.
Turkish Statistical Institute (TSI) (2020a). “Annual industry product statistics,” (http:// www.tuik.gov.tr/VeriTabanlari.do?vt_id=65&ust_id=111), Accessed 06 May 2020.
Turkish Statistical Institute (TSI) (2020b). “Foreign trade statistics,” (http://www.tuik.gov.tr/PreTablo.do?alt_id=1046), Accessed 06 May 2020.
Wang, P., and Liu, C. (2016). “The power plant’s comprehensive evaluation of energy saving based on entropy and ELECTRE method,” in: Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics, Zhengzhou, China.
West Mediterranean Exporters Association (BAIB) (2020). “Furniture Industry Analysis in Growing Turkey,” ( https://baib.gov.tr/files/downloads/PageFiles/%7B305D133A-A258-E911-86D1-001A64655414%7D/Files/%C4%B0hracat%20D%C3%BCnyas%C4%B1%20Say%C4%B1_%201%20-%20Mart%202019.pdf), Accessed 06 June 2020.
Yuan, J., Li, X., Xu, C., Zhao, C., and Liu, Y. (2019). “Investment risk assessment of coal-fired power plants in countries along the Belt and Road initiative based on ANP-Entropy-TODIM method,” Energy 176, 623-640. DOI: 10.1016/j.energy.2019.04.038
Zhang, X., and Xue, X. (2019). “Application research of ELECTRE method in ecological environment assessment,” Journal of Applied Science and Engineering Innovation 6(3), 135-138.
Article submitted: September 2, 2020; Peer review completed: November 7, 2020; Revised version received and accepted: November 13, 2020; Published: November 18, 2020.
DOI: 10.15376/biores.16.1.339-354