Copyright © 2019 The Author(s). Published by VGTU Press

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.
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and source are credited.

*Corresponding author. E-mail: lidija.kraujaliene@vgtu.lt

Business, Management and Education
ISSN 2029-7491 / eISSN 2029-6169

2019 Volume 17: 72–93

https://doi.org/10.3846/bme.2019.11014

Introduction 

During the tracking of HEIs research and innovation activities, management issues, efficiency 
evaluation tools, the most important factors, which can influence the results of technology 
transfer (TT), the concept of efficiency evaluation can be arranged in the context of HEIs. 

Successful TT is a sensitive issue in the context of HEIs. Usually, researchers like more 
concentrating on their exciting topics of the research forgetting about the research priorities 

COMPARATIVE ANALYSIS OF MULTICRITERIA  
DECISION-MAKING METHODS EVALUATING THE EFFICIENCY 

OF TECHNOLOGY TRANSFER 

Lidija KRAUJALIENĖ *

Department of Financial Engineering, Faculty of Business Management,  
Vilnius Gediminas Technical University, Vilnius, Lithuania 

Received 4 June 2019; accepted 31 July 2019

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 mea-
suring 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 assign-
ing 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 de-
cision-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. 

Keywords: efficiency, evaluation, technology transfer, methods.

JEL Classification: C44, O32, O34.



Business, Management and Education, 2019, 17: 72–93 73

of HEIs, and the government. Researchers searching for recognition are are interested in 
sharing their inventions, findings with society through research papers. Hence, they have 
little time, which is oriented for science-business collaboration activities in a bilateral project 
or contract works. To overcome this gap, TT activities are coming to push research results 
to be search for perspective ideas, protect them (if needed), contact with industry seeking 
of commercialization finalisation and economic benefit for HEIs. University-business activi-
ties, promoted by TTOs, brings economic utility, thus necessary for HEIs wellbeing. Many 
countries are investing in research and development (R&D) activities, but it is challenging 
to evaluate TTP without appropriate tools. The information about suitable tools valuing the 
TTP in HEIs is quite limited. Therefore, it is relevant to search for and analyse existing mul-
ticriteria decision-making methods appropriate for the evaluation of the efficiency of TTP. 
Thus, the objective of the research is a comparative analysis of efficiency evaluation methods. 
After efficiency evaluation results and the current situation’s map, relevant changings could 
be implemented to improve TT performance in HEIs. 

Among different HEI’s performance variables, calculation methods as well as deliver-
ing the results outside to society (e.g. presenting results in the annual HEIs’ reports), such 
problems exist as converging performance results to the one platform and also finding the 
suitable method for facilitation and evaluation of TTP performance in HEIs. Moreover, there 
is one more issue of determining the variables and improving them by raising the level of 
HEIs’ economic results of TTP.

The methodology of this paper is intended to find a suitable approach to measure the 
efficiency of TTP performance in HEIs. 

Decision-making methods serve to analyse the performance of universities. A number 
of research papers have been concluded that government investments are relating to the TT 
results implemented in HEIs. R&D and innovation activities in HEIs are significant. Different 
countries have identified strategic priority areas and governments are investing in these areas 
to develop and strengthen activities in specific fields to reach economic benefit.

The concept of evaluation of the TT results is formed based on the analysis of literature. 
Taking into account the aspect of Lithuanian culture, searching for performance variables to 
evaluate the TTP, data-gathering aspects, appropriate matchmaking methods is leading to the 
creation of the framework to evaluate HEIs’ TTP economic results. European TT models are 
more close to Lithuanian culture than American. Next, some examples are provided.

The cases of Germany and Belgium are presented in Kurgonaitė’s (2015) work on the 
analysis of good foreign experience in TT and commercialisation activities. The most devel-
oped countries have applied such TT model, when TTO is established outside universities, 
including the best specialists in TT, intellectual property (IP), commercialisation activities, 
what have the positive effect on economic results of HEIs. TTO serves for a few most promi-
nent universities and hospitals in the countries. To ensure the connection with scientists, 
TTO specialists have planned periodically meetings with the researchers, at least one or two 
times per month. Strong TTO team is required, motivated researchers, pleasant entrepre-
neurial atmosphere, perspective sector, high level of the market abilities and well-developed 
funding possibilities (Kurgonaitė, 2015). In Lithuania, the government allows HEIs to create 
their roles relating to TT and commercialization activities. Since 2009 December, when the 



74 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

order of the Minister of education and science takes effect, HEIs has been promoted to imple-
ment TT and commercialization activities (Order of the Ministry of Education and Science 
of the Republic of Lithuania, 2009).

A similar situation is in Massachusetts Institute of Technology (MIT) (in the Boston, 
Cambridge), working mostly in the biotechnology sector, and being successful in TT and 
commercialisation. MIT has a high concentration of the most prominent and leading re-
search institutions – hospitals and universities. However here TTO is situated outside HEIs/
research organisations, and the culture developed here is positive in cooperation channel 
between TTO specialists and researchers. Around 40% of the newly started spin-off com-
panies are formed by MIT’s alumni. MIT’s culture leads others to think in the way of “I can 
do it too”, that ensures many opportunities (e.g. competitions of a business plan) to seek 
strategies and get advice. Dozens of MIT’s students achieve venture capital funding. Thus, 
MIT TT model depends on surrounding nature and having an entrepreneurial community 
surrounding HEIs. TT is successful with a legal and relatively non-bureaucratic procedure, 
sufficient funds for IP protection and filing patents of HEIs. The formation of spin-offs (based 
on HEIs IP) and development of clusters requires talented staff: world-class scientists and 
researchers; TT professionals; entrepreneurial founders of start-ups or spin-offs and the work 
teams involving managers and scientists; knowledgeable investors not only for funding, but 
also for advising and guiding the company, etc. (Nelsen, 2005). 

Every process is measuring by relevant indicators. Respondents could be included in the 
research helping to identify performance variables of the particular process, assigning the 
weights (if relevant).

1. Comparative analysis of multicriteria decision-making methods

During valuing the TTP, first of all, appropriate methods should be selected to find the 
framework to measure the efficiency of TTP. Therefore, there is the need to value multicri-
teria decision-making methods with its advantages and disadvantages in applying them for 
evaluation of the efficiency of TTP in HEIs. For that purpose, the comparative analysis of 
the most popular decision-making methods were performed (see Table 1). It is worth noting 
that there are some limitations in the data collecting (as the lack of data), in designing the 
database for implementation of research, as well as in applying certain tools due to restric-
tions of particular methods.

A brief discussion and comparative analysis of the presented methods is provided be-
low.

Every multicriteria method has its advantages and disadvantages. To avoid disadvantages, 
and to avail advantages of methods, the simultaneous use of several methods would deliver 
the benefit. To solve the issue of evaluating the TTP, the concept of the framework and in-
volved methods able to evaluate the efficiency of TTP is presented below. 

There are three steps identified to start measuring the efficiency of TTP.
The first step is identifying the variables suitable to measure the efficiency of TTP in HEIs. 

The Factor Relationship (FARE) method is suitable to realize this goal and set weights for 
variables. It serves in the case of some various variables when the weights of their importance 



Business, Management and Education, 2019, 17: 72–93 75

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76 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

on the TTP are unknown. The FARE method requires specialists (respondents), in this case 
from the sphere of TT, commercialisation and innovation management. Specialists help to 
estimate the importance of suggested criteria to select the most important one, as well as to 
measure the distances of the most crucial variable following all other criteria. This framework 
of the research is able to identify variables by the impact influencing the TTP (Ginevičius, 
2006, 2007), while Technique for Order of Preference by Similarity to Ideal Solution (TOP-
SIS) method helps to rank variables by importance of TTP For the second step, HEIs should 
be selected to include them to the research sample. Some considerations exist in choosing 
the HEIs with TT and commercialisation performance results. Therefore, the ranking tools 
serve to select the research sample. For that goal, such ranking multicriteria tools as Multi-
Objective Optimization by Ratio Analysis (MULTIMOORA) and Complex Proportional As-
sessment (COPRAS) are identified to rank the HEIs (A. Hafezalkotob & A. Hafezalkotob, 
2015; Chatterjee, Mondal, Boral, Banerjee, & Chakraborty, 2017; Chatterjee, Athawale, & 
Chakraborty, 2011). When the variables and research sample are known, the final third step 
is intended to find the tool for the efficiency evaluation of the TTP in HEIs. Thus, the Data 
Envelopment Analysis (DEA) tool is identified to calculate the economic efficiency of TTP 
in universities (Palecková, 2016; Cook, Tone, & Zhu, 2014). All mentioned methods were 
selected and involved in the framework of efficiency evaluation of TTP.

Next, decision-making methods in Table 1 are discussed to understand their abilities and 
contribution to the efficiency evaluation process. Selected tools for Evaluation of TTP are 
comparing with other decision-making methods. 

The FARE method serves to evaluate the TTP performance of HEIs in case of multicri-
teria decision-making system. Ginevičius (2006) has been developed the tool of the FARE 
to help of estimation of multicriteria weights (only one method with this possibility, see 
Table 1). It helps to assess the importance of variables analysed. The latter tool helps to pro-
vide the consistency of formed decision matrix. The central aspect of the FARE method is a 
superiority comparison having performed (one from all variables in the research), which is 
addressing for creation of decision-making system. While decision-making matrix has been 
already created, the most important variable has been selected among all other variables. The 
variable, which has the highest superiority total values are highlighted as the most critical 
variable since the superior level of the essential variable is equal to one or over than one in 
comparison with other variables (Chatterjee et al., 2017; Kazan, Özçelik, & Hobikoğlu, 2015).

The FARE tool is selected in the first research step based on the situation with a minor 
volume of initial data when the estimation of relationships is required (Ginevičius, 2006, 
2007). In comparison with other multicriteria decision-making tools, the Simple Multi-At-
tribute Rating Technique (SMART) method is not suitable for the reason for its ability to 
convert weights to real factual numbers. Moreover, the overall framework of the method’s 
implementation is quite complicated (Velasquez & Hester, 2013). In turn, the Analytic Hier-
archy Process (AHP) is not suitable for the identification of variables because the principle is 
based on the pair-wise comparisons (Velasquez & Hester, 2013). The Case-Based Reasoning 
(CBR) tool is not suitable for identification of the variables because for applying this method 
we should have an existing database of various cases when the tool is proposing the solu-
tion of similar cases (Velasquez & Hester, 2013). Another tool of the Multi-Attribute Utility 



Business, Management and Education, 2019, 17: 72–93 77

Theory (MAUT) is also not applicable for implementation of efficiency evaluation of TTP 
because this is the method of expected utility theory measuring the best possible benefit, 
instead of a selection of the variables by their importance level (Velasquez & Hester, 2013). 
The Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) 
is the method, which is not providing a definite possibility to assign weights of variables 
(Velasquez & Hester, 2013). The VlseKriterijumslca Optimizacija I Kompromisno Resenje 
(VIKOR) method needs initial weights in advance; however, when you have only the names 
of variables, it is not possible to apply this method (Liu & Wang, 2011).

In turn, the TOPSIS method serves in determining the best and the worst alternative 
values for the variables of TTP. The TOPSIS is the technique suitable to select the best alter-
native from a system of other alternatives in the research sample. The most important ad-
vantage of the TOPSIS method is inability, when the best alternative, which was selected, has 
not only the smallest distance from the ideal solution but also the longest distance situating 
from the ideal worst solution. The TOPSIS final calculation results supply interested person 
with information helping to make some decisions, on the one hand, close to the best pos-
sible, when from another – far from the worst. These possibilities ensuring decision-makers 
(e.g. the head of HEI) to make decisions on the best alternative, and finally selecting one of 
the right decision for the organisation (Ginting, Fadlina, Siahaan, & Rahim, 2017; Džunić, 
Stanković, & Janković-Milić, 2018; Ding & Zeng, 2015). 

The TOPSIS method was included in the TT efficiency evaluation model for simplicity 
of application framework, because it is programmable, and providing the most stable perfor-
mance results in the case when input data is oscillating. The proposed efficiency evaluation 
model of TTP is input-oriented; therefore the TOPSIS method is ideally appropriate for eval-
uating every alternative, and its deviation magnitude is able to assess alternatives from the 
best and the worst concerning the average attained (Choudhury, 2015; Ding & Zeng, 2015).

The MULTIMOORA is proposed as a non-subjective and more robust tool in compari-
son with other methods using subjective estimation framework. This method is enabling to 
maximize and minimize the variables’ values, similar to the COPRAS method. The MULTI-
MOORA method is based on quantitative numbers; therefore, it fits for the research. Besides, 
the latter tool has one limitation − the data incorporated to the research should be positive 
(Altuntas, Dereli, & Yilmaz, 2015; Karabasevic, Stanujkic, Urosevic, & Maksimovic, 2015).

The COPRAS method allows comparing the data and ranking it. Since 1994, the research-
ers from Vilnius Gediminas Technical University (VGTU) as Zavadskas, Kaklauskas and 
Sarka have been introduced the complex proportional multicriteria evaluation tool, named 
as the COPRAS. This method is appropriate for quantitative multicriteria evaluation of maxi-
mising and minimising the number of different variables. 

The tool to measure efficiency is named the DEA. This method is involved in the efficien-
cy evaluation model of TTP in HEIs. On the other hand, efficiency could also be evaluated by 
applying the DEA complex proportional assessment method (Nazarko & Šaparauskas, 2014; 
Stefano, Casarotto Filho, Vergara, & da Rocha, 2015). The DEA method is intended for the 
relative evaluation of individual efficiency or evaluation of the performance of a DMU (deci-
sion-making unit) within the target group of specific interest. The DEA is acting in a particu-
lar field of activity like health care, banking, agricultural sphere, the sector of education (incl. 



78 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

higher education), other. DMU means the production of HEIs. The DEA is the tool which 
is applying to identify sources of inefficiency, management level (to compare manufacturing 
and service operations), rank universities, evaluate the efficiency of programmes/policies, 
quantitative evaluation of resources that help to reallocate them, evaluate the efficiency of 
emissions or energy efficiency, etc. (Liu, L. Y. Lu, W. M. Lu, & Lin, 2013; Wang, Wei, & Zhang, 
2013; Zhang & Choi, 2013a; Zhang, Zhou, & Choi, 2013b). 

The COPRAS quantitative multicriteria tool is applied with maximisation and minimi-
sation of variables’ values. It allows the user to compare and check calculated results easily. 
Going more deep into the comparative analysis of the COPRAS, it can be less stable in 
comparison with the SAW or the TOPSIS tools on the case of variation of data; thus the 
COPRAS is used separately from other methods. The COPRAS tool is suitable to compare 
and evaluate the variables, describing hierarchically structured complex dimensions, being 
on the same level of the hierarchy, and therefore, it is appropriate for efficiency evaluation of 
HEIs (A. Hafezalkotob & A. Hafezalkotob, 2015; Chatterjee et al., 2017).

The DEA is suggested for the efficiency evaluation of DMUs acting a convenient method, 
employing an input−output oriented model, which is minimising input and maximising out-
put variables. It is available in the case of a mixture of ratios, percentiles, and raw data. The 
efficiency with the DEA method can be easily analysed and quantified, which is essential at 
the end of the study. The DEA method fits to evaluate the TTP in HEIs (Cook et al., 2014; 
Feruś, 2008).

There are some other general efficiency evaluation tools of economic performance; how-
ever, the practice of their use in the case of evaluation of TTO in HEIs have not been used 
before. In general, the goal of economic analyses is oriented for optimization of prevention, 
control, or monitoring of investments, and also to minimize the total expenditures. The 
choices of detection, control, and prevention are interdependent. The managers firstly should 
evaluate the efficiency of alternatives’ costs on each step, before developing of new strategies 
or policies to improve HEIs’ activities (Epanchin-Niell, 2017). 

There are some frameworks, which are able to evaluate the efficiency of one or another 
activity. For instance, the potential approaches are suitable for cost-effective management 
or identifying an efficient allocation of resources (Shen, Han, Price, Lu, & Liu, 2017). One 
of the useful approaches is a cost-benefit analysis, which is appropriate to measure the ef-
ficiency of the cost of the project in order to determine the relation with investments. The 
model of cost-benefit analysis determines the case of whether benefits are higher than costs. 
Another approach of return on investment analysis is prioritizing the allocation of finan-
cial resources across some independent, discrete projects (e.g. cost efficiency ranking of the 
projects). The methodology of the last approach is able to identify the projects in decreasing 
order when the formula’s sense lying on the ratio of benefits divided by costs. The third ap-
proach is named as “optimization”, which is measuring the efficiency level of investments’ (by 
maximising the utility of net) and creating management approaches to reach the best objec-
tive. Moving forward, the methodology to implement the optimisation model is dynamic 
optimization and optimal control, etc. within a bioeconomic system of modelling. Another 
efficiency evaluation framework is the optimal design of activity, which is measuring the 
optimal parameters to change behaviour or private decision-making to achieve management 



Business, Management and Education, 2019, 17: 72–93 79

goals. Some methods are proposed to apply the latter approach: dynamic optimization, or 
optimal control, etc., which composed for private decision-making (Epanchin-Niell, 2017; 
Beikler & Flemmig, 2015).

There are many analytical models for evaluating the economic status of health-care inter-
ventions, for instance. The Consolidated Health Economic Evaluation Reporting Standards 
(CHEERS) Statement means the evaluation of cost-consequence analyses and economic util-
ity of interventions, and leaves for the user the space for interpretation of information. Cost-
minimisation analyses (CMA) is suitable for the comparison of the costs of interventions 
with equivalent outcomes (focus on the costs and excluding outcomes). The cost-effectiveness 
analyses (CEA) model relates the measures of outcome with costs, which advantage is in 
informing of the additional outcome improvement between some interventions. The CEA 
measure is usually introduced in terms of incremental cost-effectiveness ratios ICERs, which 
are relating the difference in efficiency to the difference in the costs between various alterna-
tive interventions from 0 till 1 (Beikler & Flemmig, 2015). 

The economic evaluation methods presented in this comparative research do not solve the 
issue of TTP evaluation tool, but this paper has proposed the framework of suitable meth-
ods appropriate to measure the efficiency of TTP in HEIs by incorporating them in the one 
evaluation model. The model of suggested efficiency evaluation framework consists of such 
selected methods as the FARE, TOPSIS, MULTIMOORA/COPRAS, and DEA. The reasons 
for a selection of such tools are more deeply analysed in Section 3, where the advantages and 
disadvantages of tools are provided.

2. Formulas to implement the efficiency evaluation of technology transfer 
process in higher education institutions

The formulas of applying the FARE tool is presented in a number of research works 
(Ginevičius, 2006, 2007, 2008, 2011), as well as the formulas of the TOPSIS (Zavadskas et 
al., 2016; Choudhury, 2015; Ding & Zeng, 2015; Song & Zheng, 2015; Behzadian, Otaghsara, 
Yazdani, & Ignatius, 2012). The MULTIMOORA method’s formulas are described in many 
other research papers (A. Hafezalkotob & A. Hafezalkotob, 2015; A. Hafezalkotob, A. Hafe-
zalkotob, & Sayadi, 2016; Akkaya, Turanoğlu, & Öztaş, 2015; Altuntas et al., 2015; Karabase-
vic et al., 2015; Lazauskas, Zavadskas, & Saparauskas, 2015a; Lazauskas, Kutut, & Zavadskas, 
2015b; Obayiuwana & Falowo, 2015; Brauers & Zavadskas, 2010; Stanujkic, 2015a; Stanujkic, 
Zavadskas, Brauers, & Karabasevic, 2015b; Stanujkic, 2016; Liu, Fan, Li, & Chen, 2014; Liu, 
You, Lu, & Chen, 2015; Kildienė, Zavadskas, & Tamošaitienė, 2014), as the formulas of the 
COPRAS (Chatterjee et al., 2017, 2011; Mousavi-Nasab & Sotoudeh-Anvari, 2017; Rezaza-
deh, Sancholi, Rad, Feyzabadi, & Kadkhodaei, 2017; Rivera, Fajardo, A. J. Ávila, C. F. Ávila, 
& Martinez-Gómez, 2017; Zolfani et al. 2018; Liou et al., 2016; Mulliner, Malys, & Maliene, 
2016; Xue, You, Zhao, & Liu, 2016; Bausys, Zavadskas, & Kaklauskas, 2015; Nguyen, Dawal, 
Nukman, Aoyama, & Case, 2015; Ghorabaee, Amiri, Sadaghiani, & Goodarzi, 2014; Hash-
emkhani Zolfani & Bahrami, 2014; Pitchipoo, Vincent, Rajini, & Rajakarunakaran, 2014; 
Zavadskas, Turskis, & Kildienė, 2014; Aghdaie, Zolfani, & Zavadskas, 2013; Tavana, Mo-
meni, Rezaeiniya, Mirhedayatian, & Rezaeiniya, 2013; Ginevičius, 2008; Kracka et al., 2010; 



80 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

Tupenaite et al., 2010; Kaklauskas et al., 2006, 2010; Turskis, Zavadskas, & Peldschus, 2009). 
The DEA method’s approach and formulas are discussed in other research projects (Paleck-
ová, 2016; Cook et al., 2014; Feruś, 2008; Simar & Wilson, 2007).

The formulas should be analysed before their’s implementation taking into account the 
advantages and disadvantages of precise method. Proposed methods as the FARE, TOPSIS, 
MULTIMOORA, COPRAS, and DEA are quite easy in use and understandable in the appli-
cation. Section 3 is presenting the comparative analysis on the advantages and disadvantages 
of efficiency evaluation methods.

3. Comparative analysis of advantages and disadvantages of efficiency 
evaluation methods 

This section is analysing the advantages and disadvantages of a number of the most popular 
decision-making methods in the economic arena.

This research paper is intended to search for suitable tools to evaluate the efficiency of 
TTP in such organizations as HEIs.

After a brief analysis of decision-making methods we see, that the most suitable tools to 
measure the efficiency of TTP are the FARE, TOPSIS, MULTIMOORA/COPRAS, and DEA. 
However, there are many other multicriteria tools discussed in this paper.

The COPRAS (Complex Proportional Assessment) method’s advantages and disadvan-
tages are presented in Table 2.

The COPRAS tool is suitable to measure the variables of the multicriteria system while 
maximising and minimising the values, compare the variables, what is needed to identify 
the research sample of HEIs. Also, it is convenient, that is not requiring minimisation of the 

Table  2. Advantages and disadvantages of the COPRAS decision-making method (compiled by 
author, based on A. Podviezko & V. Podvezko, 2014; Podvezko, 2011)

The COPRAS method

No Advantages Disadvantages

1 The method is used for evaluation of the 
multicriteria system of variables for maximising 
and minimising the values

COPRAS may be less stable in comparison 
with SAW or TOPSIS methods in data 
variation case

2 The method allows to compare and also check 
the final results of measuring easily

The results may be sensitive to a slight 
variation of data, and the ranks devoted 
may differ from ones obtained with other 
methods

3 The typical properties of the tool allow being 
used to implement the comparison and 
evaluation of variables describing hierarchically 
structured complex magnitudes, positioning on 
the same hierarchical level

4 This tool is not requiring such transformation 
as minimising the variables; therefore the 
transformation of the data is not distorted; this 
tool is appropriate to evaluate a single alternative



Business, Management and Education, 2019, 17: 72–93 81

variables, and the transformation of the data is not strained. Attention should be paid to the 
data variation because the COPRAS could be less stable than the SAW or TOPSIS, and the 
calculation results may be sensitive relating to data variation. Nevertheless, based on several 
advantages, the COPRAS was included in the framework of efficiency evaluation of TTP.

Table 3. Advantages and disadvantages of the MULTIMOORA decision-making method (compiled 
by author, based on Brauers & Zavadskas, 2010)

The MULTIMOORA method

No Advantages Disadvantages

1 Comparing the MULTIMOORA method with other tools, it is more 
robust and involving all related stakeholders (including sovereignty 
of the consumer), interested in a particular issue like an advantage.

The MULTIMOORA 
has one disadvantage 
in the data of 
objectives used in the 
database, when the 
data cannot be equal 
to the zero or dealing 
with the negative 
numbers.

2 The MULTIMOORA method with all non-correlated goals is more 
robust in comparison with a limited number of goals.

3 The MULTIMOORA tool is more robust, when all objectives’ 
and alternatives’ interrelations are taken into account, and 
simultaneously in comparison with interrelations when only 
investigated two by two.

4 The MULTIMOORA method is non-subjective from one side, and 
more robust comparing with tools applying subjective estimations 
to implement the choice for importance and normalisation of the 
objectives.

5 The choice to set objectives. A system of robust objectives would be 
identified after the session of brainstorming technique with all the 
stakeholders or representative experts.
Normalisation. The MILTIMOORA is the tool that does not need 
external normalisation and more robust in comparison with such 
based on the subjective external normalisation. This multiple 
objectives’ method is lying on dimensionless non-subjective 
measures without normalisation, in that way become more robust 
compared with methods, which are using subjective non-additive 
values or subjective weights.
Giving the importance of the objective. Together with the scores 
and weights, the importance of some objectives is mixed with 
normalisation.

6 The MULTIMOORA method is based on quantitative numbers, and 
it is more robust than other tools based on ordinal measures.

7 The MULTIMOORA method with the available data is the base for 
more robust studies than based on earlier available data.

8 The application of two methods of multi-criteria objective’s 
optimisation is more robust in comparison with applying a single 
one; when the application of three tools is more robust than 
applying two tools, etc.



82 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

We see in Table 3 that the MULTIMOORA method has many advantages and only one 
disadvantage. This method is more robust and involving all related stakeholders, interested 
in certain economic problem-solving. All interrelations of objectives and alternatives have 
been taken into account, and the MULTIMOORA does not need external normalisation. For 
the reason of several advantages and abilities, in case of a multicriteria system of TTP, this 
method is a suitable tool, involved in the efficiency evaluation framework of TTP in HEIs.

The DEA tool input-output oriented method is presented in the research papers as a 
convenient programmable method to evaluate the efficiency of the decision-making unit 
working in different spheres (see Table 4). The efficiency could be easily analysed and quanti-
fied. A mixture of raw data, ratios, and percentiles is available for calculations, what has an 

Table 4. Advantages and disadvantages of the DEA decision-making method (compiled by author, based 
on Cook et al., 2014; Velasquez & Hester, 2013; Banker, Charnes, Cooper, Swarts, & Thomas, 1989)

The DEA method

No Advantages Disadvantages

1 Efficiency evaluation method, input-output 
oriented method, which is maximising 
output and minimising input variables; the 
tool is based on proportional reduction.

The number of alternatives analysed is that 
the sample should be at least twice lower than 
the number of inputs measures and outputs 
combined. When Banker (1989) has been 
stated, that the number of variables should be 
at least three times higher than the number of 
outputs and inputs.

2 This method is able to handle multiple 
outputs and inputs.

Potential issues are existing during the 
selection of the variables for the DEA tool 
when the raw data (e.g. revenues, the number 
of employees, assets, profits, etc.) and the 
ratios (e.g. returns on investment) would not 
be incorporated in the one model.

3 The tool is suitable to measure the efficiency 
that can be analysed and quantified.

The method does not deal with an inaccurate 
number of data and suppose that all output 
and input measures are known. However, in 
real life, this assumption would not be true.

4 The method is able to uncover relationships, 
which may be in hidden under other 
methods.

The method does not deal with an inaccurate 
number of data and suppose that all output 
and input measures are known. However, in 
real life, this assumption would not be valid.

5 A mixture of ratios, raw data and percentiles 
are permissible in one calculation of 
efficiency with the DEA method.

The measurement results could be sensitive 
depending on the identified outputs and 
inputs.

6 The DEA method is widely used in 
economic, road safety, medical, utilities, 
retail, business and agriculture problem-
solving. These categories have precise data 
using for the input, which deficiencies avoid 
one of the significant tool’s.

7 The tool is programmable and quite 
effortless in use.



Business, Management and Education, 2019, 17: 72–93 83

advantage in case of missing data from one source, or similar. This tool fits to measure the 
efficiency however has some disadvantages, like the number of units in the samples should be 
at least twice higher than the number of alternatives, and others. Therefore, due to the DEA 
advantages, this tool is selected and involved in the frameworks to measure the efficiency of 
TTP in HEIs.

The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) tool 
(Table  5) is based on that principle: the optimal dote should have the farthest point in the 
distance from the negative ideal solution point and the shortest line from the positive ideal 
solution (Liu & Wang, 2011).

The TOPSIS tool serves the function of ranking identifying the variables (with the FARE 
method), which will be involved in the evaluation of the efficiency of TTP. The TOPSIS is 
easy to use, not requiring minimisation of variables, and applicable in many different areas. 
Therefore, the TOPSIS method was selected for the framework of efficiency evaluation of 
TTP in HEIs.

Table  5. Advantages and disadvantages of the TOPSIS decision-making method (compiled by 
author, based on A. Podviezko & V. Podvezko, 2014; Velasquez & Hester, 2013)

The TOPSIS method

No Advantages Disadvantages

1 This absolute evaluation tool, which is not 
requiring transformation to minimize the 
variables; the data transformation is not 
perverted.

The application of Euclidean Distance 
does not look to the correlation of the 
attributes.

2 The TOPSIS method is allowing to interpret 
the absolute evaluation of certain alternative, 
its deviation magnitude assessing the results 
starting from the best and the worst average 
alternatives.

In this tool is quite difficult to weight and 
also keep the consistency of judgment, 
particularly with additional attributes.

3 This tool is providing the possibility of the most 
stable performance results in case the input data 
is varying.

4 The research of developing hypothetical worst 
and best objects is suitable for certain tasks 
are worth to be started in many areas, where 
quantitative evaluation is needed.

5 The TOPSIS is based on the simple process; it is 
programmable and easy to apply.

6 The TOPSIS method is easy in terms of 
maintaining the same number of steps in regard 
to the size of the problem.

7 The TOPSIS tool is widely in use for areas 
like logistics, manufacturing systems and 
engineering, environmental management, 
marketing management, design, business, water 
and human resources management.



84 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

Table 6. Advantages and disadvantages of the SAW decision-making method (compiled by author, 
based on Podvezko, 2011; A. Podviezko & V. Podvezko, 2014)

The SAW method

No Advantages Disadvantages

1
This tool is able to compensate among 
variables

The SAW method may be applied if all the 
variables are maximising (and transformed 
into maximising variables) before analysis.

2
Intuitive method for decision-makers; the way 
of measuring is quite simple and does not 
require several computer programs or tools.

All the values of the variables should be positive. 
The calculation is depending on the type of 
transformation converting to positive dimensions.

3
This tool integrates the values of variables 
and weights into a single one magnitude.

The largest dimension of the variable of 
the SAW tool maybe about unity, while the 
smallest dimension may reach the 0.

4

The calculation algorithm of this method is 
not complicated and can be implemented 
without the computer tools or by using a 
simple computer program.

The SAW method’s estimates yielded do not 
always reflect the real status. The result may 
not be in terms with logic, with the measures 
of one particular variable widely differing 
from once of other variables.

5
Normalised values of the evaluation help 
visually calculate the differences between the 
alternatives.

The SAW tool is based on normalisation, with 
minimising the variables, converting to the 
maximising.

6 This tool is suitable to evaluate a singlealternative.
Result gathered may not be logical.

The SAW method (Table 6) would fit to value the efficiency of TTP in HEIs, but we see 
many disadvantages of carrying out this tool: the method may be applied in case when all 
variables are maximising and positive, the result may not be in terms with logic. Therefore, 
this tool, despite that fact that it also has advantages, was not selected to the framework for 
evaluation model of efficiency of TTP in HEIs.

Table  7. Advantages and disadvantages of the PROMETHEE decision-making method (compiled 
by author, based on A. Podviezko & V. Podvezko, 2014; Velasquez & Hester, 2013)

The PROMETHEE method

No Advantages Disadvantages

1 This tool is not needed the transformation for 
minimising the variables, and the data transfor-
mation in case this method is not distorted.

This tool does not provide a clear frame-
work for assigning the weights.

2 The PROMETHEE is easy in application. This tool is requiring the assignment of mea-
sures, although it does not provide an under-
standable framework to assign the values.

3 It does not require the criteria to be proportionate.
4 Widely used in such area as financial management, 

environmental, business management, manage-
ment in general, water management and hydrology, 
chemistry, manufacturing, assembly, agriculture, 
transportation, logistics, energy management.

5 This tool needs normalisation.



Business, Management and Education, 2019, 17: 72–93 85

The PROMETHEE tool in Table  7 does not provide a logical and accessible framework 
to assign values and weights. Therefore this method was not involved in the framework of 
efficiency evaluation of TTP in HEIs.

Table 8. Advantages and disadvantages of the VIKOR decision-making method (compiled by au-
thor, based on J. K. Chen & I. Chen, 2008; Velasquez & Hester, 2013)

The VIKOR method

No Advantages Disadvantages

1 The method is based on the principle of multi-
criteria decision making (MCDM) system’s 
compromise programming.

The ranking needs can be performed with 
different values of variables’ weights.

2 This method is supporting multicriteria decision-
maker in such cases, when he is unstable, or 
when there is no idea to express one’s preference, 
e.g. at the beginning of creating the system.

The analysis of the impact is applied from 
the side of all weights of variables on a 
suggested compromise solution.

3 A compromise solution is applicable based on 
the maximum group utility, and also on an 
individual regret’s minimum.

This tool needs initial weights.

4 The result of ranking is the list of alternatives 
after special compromise ranking and the 
solution with an advantage rate.

Suitable in such cases when the 
information is in numerical values.

5 The VIKOR method is determining the stability 
intervals in weights.

6 The compromise solution in the VIKOR tool 
will be replaced if the measure of weight does 
not fit in the stability interval.

7 Single variable analysis of weight’s stability 
intervals is used for all variables functions, with 
initial measures of weights. The stability of an 
acquired compromise solution could be analysed 
with the VIKOR electronic program.

8 This tool needs normalisation.

This tool (Table  8) means the optimisation and compromise solution of multi-criteria 
system (Liu & Wang, 2011). 

The VIKOR method requires initial weights because it is determining the stability inter-
vals in weights. When we do not have weights in advance, this tool is not suitable. For that 
reason, this method is not involved in the suggested framework to evaluate the efficiency of 
TTP in HEIs.

The MAUT (Multi-Attribute Utility Theory) means an expected utility theory; it can con-
clude on the best action for a given issue and measure the best possible benefit (Velasquez 
& Hester, 2013).

The MAUT tool is not suitable in case of TT (Table  9), because it is requiring a huge 
number of inputs on every step when HEIs do not gather vast number of information relat-
ing TT. Therefore this tool is exceptionally intensive in data. Usually, indicators in Strategic 
plans value the activities of HEIs. There are not many indicators relating TT, so HEIs do not 
have huge information on that purpose. The MAUT method is not suitable for valuing the 
efficiency of TTP in HEIs.



86 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

Table  9. Advantages and disadvantages of the MAUT decision-making method (compiled by au-
thor, based on Velasquez & Hester, 2013)

The MAUT method

No Advantages Disadvantages

1 The MAUT method is taking uncertainty into 
account.

The considerable volume of input is required 
at every step in order to record preferences of 
the decision-maker, and making this method 
extremely intensive in data.

2 It is comprehensive; besides, it can evaluate 
the preferences of every consequence in all 
calculation steps of the tool.

The level of input measures and the 
massive number of data may not be 
available for a particular decision-making 
problem.

3 The MAUT tool is widely applicable in 
economic, water management, financial, 
actuarial, agricultural, and energy management 
problem-solving. All mentioned types of issues 
have significant amounts and uncertainty of 
available data, which should be enough to make 
the MAUT method a proper technique for 
decision-making.

The precise preferences of the decision-
makers should be done.

4 This tool needs normalisation in order to 
eliminate the influence of various physical 
values on decision-making.

Stronger assumptions are requiring on 
every level. Therefore, it would be relatively 
subjective and challenging to apply.

Table 10. Advantages and disadvantages of the AHP decision-making method (compiled by author, 
based on Velasquez & Hester, 2013)

The AHP method
No Advantages Disadvantages
1 This tool is easy to apply. Interdependence between variables and 

alternatives.
2 A scalable tool that easily adjusts in size to 

application in solving decision-making problems 
according to their hierarchical structure.

Due to the tool of pairwise comparisons, 
the AHP is able to be the subject to 
inconsistencies during ranking and 
judgment variables.

3 The AHP method is intensively comparing 
to the MAUT method. Although, the AHP 
requires a quite significant amount of the data 
to implement pairwise comparisons suitably.

The AHP method does not allow the user 
grading one instrument separately, however 
only in comparison with the all rest, 
without finding the strengths or weaknesses.

4 The AHP tool is widely used in performance-
type problem-solving, corporate strategy and 
policy, public policy, resource management, 
political strategy and planning. Resource 
management issues solve the limitation of 
rank reversal based on the limited number of 
alternatives. The AHP tool is appropriate to 
handle more significant problems making them 
perfect to handle issues, which are comparing 
the results between alternatives.

The overall form of the AHP method 
is susceptible to reversal of the 
ranking function. Due to the specific 
of comparisons, the supplement of 
alternatives at the end of the measurement 
process could lead to the reverse of the 
final results of rankings.

5 Hierarchy structure can easily be adjusted to fit 
a lot of sized issues.



Business, Management and Education, 2019, 17: 72–93 87

The method of AHP (Analytic Hierarchy Process) was analysed. The primary characteristic 
of this tool is pairwise comparisons, which are appropriate to compare many alternatives in the 
cases of different variables, serving for estimating the weights of the variables. This method is 
relying on the judgments of selected specialists-experts to derive the priority scales.

The AHP (Table  10) is the tool of pairwise comparisons, requiring the big amount of the 
data, therefore this tool is not suggested for the efficiency evaluation framework of TTP in HEIs.

Table 11. Advantages and disadvantages of the CBR decision-making method (compiled by author, 
based on Velasquez & Hester, 2013)

The CBR method

No Advantages Disadvantages

1 This method is requiring a little effort for 
gaining the process of additional data

The sensitivity to inconsistency in different 
data.

2 The CBR tool is not data intensive. It requires many cases.
3 Minimum expenditure on maintenance of 

the data-system is needed, requiring little 
funding for maintenance.

The CBR is implemented in such industries 
in a substantial number of existing previous 
cases (medicine, engineering designs, 
comparisons of businesses, vehicle insurance).

4 This tool can improve its ability over time 
when more and more cases are included in 
the maintaining data-system.

5 The CBR method can adapt to changes 
in the surrounding environment with its 
created and used database of a big number 
of cases.

Moving forward, the CBR (Case-Based Reasoning) method is analysed in Table 12. The 
CBR method is the tool retrieving the cases very similar to an issue from an existing data-
system of various variants. The CBR is proposing a solution for the decision-maker based 
on similar cases in the existing database (Velasquez & Hester, 2013).

The CBR method (Table 11) is not data-intensive, but it requires many cases and sensi-
tive to inconsistency in different data. In the case of HEIs TT activities, there are different 
data in number, ratios, dimensions. Therefore this CBR method is not suggested for an 
efficiency evaluation framework. 

The SMART (Simple Multi-Attribute Rating Technique) is one of the simplest forms 
of the MAUT method, conveniently converting weights to the actual numbers (Velasquez 
& Hester, 2013).

The SMART tool (Table 12), analysed in Table 12, due to it is a complicated framework, 
and only open, accessible data suitable for the tool, it is not suggested for the efficiency 
evaluation framework of TTP in HEIs. 

After analysing several presented multicriteria decision-making methods, the frame-
work to evaluate the efficiency of TTP in HEIs is proposed. Several methods are not 
suitable due to its disadvantages or application conditions, mentioned above after every 
method’s analysis. The framework of efficiency evaluation of TTP in HEIs is proposed next: 
FARE method firstly integrated to identify the variables and their importance of TTP in 



88 L. Kraujalienė. Comparative analysis of multicriteria decision-making methods evaluating...

HEIs, when the TOPSIS method is proposed to rank the variables by their importance on 
the process. The MULTIMOORA or the COPRAS multicriteria decision-making methods 
are suggested to rank HEIs and to select the research sample. The DEA method is selected 
to calculate the efficiency of decision-making units (HEIs).

Conclusions

This research is suggesting the framework of evaluating the efficiency of decision-making 
units, in this case – HEIs, to value the performance of the technology transfer process. This 
is leading with searching for appropriate methods (tools) to measure the efficiency of tech-
nology transfer in HEIs. The higher education organisations are particular, and the activity 
of TT and commercialisation. HEIs in Lithuania is on the way of developing the policy for 
initiation, protection and commercialisation of HEIs intellectual property in comparison 
with other countries abroad.

After the implementation of the comparative analysis of economic decision-making tools, 
it is possible to propose the model to evaluate the efficiency of TTP in HEIs. This model 
is constructed from the complex of decision-making tools suitable to perform a particular 
function. Thus, FARE method is suggested to select the variables, estimate their weights and 
evaluate the importance on the TTP in HEIs. The TOPSIS method is allowing to rank the 
variables and select the most critical variable for the research. The MULTMOORA and the 
COPRAS tools are suitable for ranking function in estimating the number of research sample 
composing from HEIs, which have valuable results in implementing the intellectual prop-
erty management and commercialisation activities. The DEA tool is entirely suitable for the 

Table  12. Advantages and disadvantages of the SMART decision-making method (compiled by 
author, based on Velasquez & Hester, 2013)

The SMART method

No Advantages Disadvantages

1 The MAUT method, all advantages is 
adapted.

The procedure for determining work is quite 
difficult and not very friendly, considering the 
complicated framework for the user.

2 This method is allowing assignment 
techniques (absolute, relative, etc.) for any 
type of weight.

The SMART method is easy to use when 
there is a fair amount of information, and it is 
openly accessible, available for the decision-
maker.

3 It requires less effort for users in comparison 
with the MAUT.

4 It also handles the informative data well 
under every variable.

5 This tool is usually solving issues in such 
spheres as transportation and logistics, 
military, environmental, manufacturing, 
construction, assembly.

6 It is an accessible and understandable tool.



Business, Management and Education, 2019, 17: 72–93 89

evaluation of the efficiency of TTP in HEIs. Due to different applicability and nuances, other
presented methods in this research work as the SMART, CBR, AHP, MAUT, PROMETHEE,
VIKOR, and SAW are suitable in measuring other processes, but not TTP in HEIs.

For further research would be useful to analyse the impact of new strategic initiatives on
the TTP relating to economic benefit.

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