Comparative analysis of multicriteria decision-making methods evaluating the efficiency of technology transfer
Abstract
Purpose – to find appropriate tools to measure the efficiency of the technology transfer process (TTP) in higher education institutions (HEIs). Scientific problem is a lack of methods measuring the efficiency of TTP. The objective – comparative analysis of efficiency evaluation methods.
Research methodology – the research methodology is based on a comparative analysis of the research papers on the advantages and disadvantages of methods suitable to evaluate the efficiency of TTP.
Findings – among some tools, FARE is highlighted for identifying the variables of TTP and assigning their weights, when TOPSIS – to rank the variables and identify the most important. MULTO-MOORA and COPRAS methods with ranking abilities are suitable to select the number of HEIs. DEA method is intended for the economic evaluation of TTP efficiency in HEIs. The social sciences are strengthened by suitable founded tools to measure the efficiency of TTP in HEIs.
Research limitations – this paper is providing all advantages and disadvantages (limitations) of decision-making multicriteria methods.
Practical implications – the original structure of methods enabling stakeholders (HEIs, TTOs and public authorities) for efficient allocation of an organisation’s financial resources, foresee the future goals for improving the efficiency of TTP.
Originality/Value – the original framework of methods incorporated into the one model, enabling related stakeholders (HEIs, TTOs and public authorities) allocate financial resources efficiently.
Keyword : efficiency, evaluation, technology transfer, methods
How to Cite
Kraujalienė, L. (2019). Comparative analysis of multicriteria decision-making methods evaluating the efficiency of technology transfer. Business, Management and Economics Engineering, 17(1), 72-93. https://doi.org/10.3846/bme.2019.11014
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Ghorabaee, M. K., Amiri, M., Sadaghiani, J. S., & Goodarzi, G. H. (2014). Multiple criteria group decision-making for supplier selection based on COPRAS method with interval type-2 fuzzy sets. The International Journal of Advanced Manufacturing Technology, 75(5-8), 1115-1130. https://doi.org/10.1007/s00170-014-6142-7
Ginevičius, R. (2011). A new determining method for the criteria weights in multicriteria evaluation. International Journal of Information Technology & Decision Making, 10(06), 1067-1095. https://doi.org/10.1142/S0219622011004713
Ginevičius, R. (2006). Multicriteria evaluation of the criteria weights based on their interrelationship. Business: Theory and Practice, 7, 3-13.
Ginevičius, R. (2007). A comparative analysis of the criteria weights determined by using multicriteria evaluation methods AHP and FARE. 4th International Scientific Conference “Enterpsire Management: Diagnosis, Strategy, Efficiency” (pp. 16-18). Vilnius: Technika.
Ginevičius, R. (2008). Normalisation of quantities of various dimensions. Journal of Business Economics and Management, (1), 79-86. https://doi.org/10.3846/1611-1699.2008.9.79-86
Ginting, G., Fadlina, M., Siahaan, A. P. U., & Rahim, R. (2017). Technical approach of TOPSIS in decision making. International Journal of Recent Trends in Engineering & Research, 3(8), 58-64. https://doi.org/10.23883/IJRTER.2017.3388.WPYUJ
Hafezalkotob, A., Hafezalkotob, A., & Sayadi, M. K. (2016). Extension of MULTIMOORA method with interval numbers: an application in materials selection. Applied Mathematical Modelling, 40(2), 1372-1386. https://doi.org/10.1016/j.apm.2015.07.019
Hafezalkotob, A., & Hafezalkotob, A. (2015). Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications. Materials & Design, 87, 949-959. https://doi.org/10.1016/j.matdes.2015.08.087
Hashemkhani Zolfani, S., & Bahrami, M. (2014). Investment prioritizing in high tech industries based on SWARA-COPRAS approach. Technological and Economic Development of Economy, 20(3), 534-553. https://doi.org/10.3846/20294913.2014.881435
Kaklauskas, A., Zavadskas, E. K., Naimavicienė, J., Krutinis, M., Plakys, V., & Venskus, D. (2010). Model for a complex analysis of intelligent built environment. Automation in Construction, 19(3), 326-340. https://doi.org/10.1016/j.autcon.2009.12.006
Kaklauskas, A., Zavadskas, E. K., Raslanas, S., Ginevičius, R., Komka, A., & Malinauskas, P. (2006). Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case. Energy and Buildings, 38(5), 454-462. https://doi.org/10.1016/j.enbuild.2005.08.005
Karabasevic, D., Stanujkic, D., Urosevic, S., & Maksimovic, M. (2015). Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods. Acta Montanistica Slovaca, 20(2).
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Kracka, M., Brauers, W. K. M., & Zavadskas, E. K. (2010). Ranking heating losses in a building by applying the MULTIMOORA. Engineering Economics, 21(4), 352-359.
Kildienė, S., Zavadskas, E. K., & Tamošaitienė, J. (2014). Complex assessment model for advanced technology deployment. Journal of Civil Engineering and Management, 20(2), 280-290. https://doi.org/10.3846/13923730.2014.904813
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